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  • What Exactly is an Abstract?
  • How Do I Make Sure I Understand an Assignment?
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  • How Can I Write More Descriptively?
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What Exactly is an Abstract, and How Do I Write One?

An abstract is a short summary of your completed research. It is intended to describe your work without going into great detail. Abstracts should be self-contained and concise, explaining your work as briefly and clearly as possible. Different disciplines call for slightly different approaches to abstracts, as will be illustrated by the examples below, so it would be wise to study some abstracts from your own field before you begin to write one.

General Considerations

Probably the most important function of an abstract is to help a reader decide if he or she is interested in reading your entire publication. For instance, imagine that you’re an undergraduate student sitting in the library late on a Friday night. You’re tired, bored, and sick of looking up articles about the history of celery. The last thing you want to do is reading an entire article only to discover it contributes nothing to your argument. A good abstract can solve this problem by indicating to the reader if the work is likely to be meaningful to his or her particular research project. Additionally, abstracts are used to help libraries catalogue publications based on the keywords that appear in them.

An effective abstract will contain several key features:

  • Motivation/problem statement: Why is your research/argument important? What practical, scientific, theoretical or artistic gap is your project filling?
  • Methods/procedure/approach: What did you actually do to get your results? (e.g. analyzed 3 novels, completed a series of 5 oil paintings, interviewed 17 students)
  • Results/findings/product: As a result of completing the above procedure, what did you learn/invent/create?
  • Conclusion/implications: What are the larger implications of your findings, especially for the problem/gap identified previously? Why is this research valuable?

In Practice

Let’s take a look at some sample abstracts, and see where these components show up. To give you an idea of how the author meets these “requirements” of abstract writing, the various features have been color-coded to correspond with the numbers listed above. The general format of an abstract is largely predictable, with some discipline-based differences. One type of abstract not discussed here is the “Descriptive Abstract,” which only summarizes and explains existing research, rather than informing the reader of a new perspective. As you can imagine, such an abstract would omit certain components of our four-colored model.

SAMPLE ABSTRACTS

ABSTRACT #1: History / Social Science

"Their War": The Perspective of the South Vietnamese Military in Their Own Words Author: Julie Pham

Despite the vast research by Americans on the Vietnam War, little is known about the perspective of South Vietnamese military, officially called the Republic of Vietnam Armed Forces (RVNAF). The overall image that emerges from the literature is negative: lazy, corrupt, unpatriotic, apathetic soldiers with poor fighting spirits. This study recovers some of the South Vietnamese military perspective for an American audience through qualititative interviews with 40 RVNAF veterans now living in San José, Sacramento, and Seattle, home to three of the top five largest Vietnamese American communities in the nation. An analysis of these interviews yields the veterans' own explanations that complicate and sometimes even challenge three widely held assumptions about the South Vietnamese military: 1) the RVNAF was rife with corruption at the top ranks, hurting the morale of the lower ranks; 2) racial relations between the South Vietnamese military and the Americans were tense and hostile; and 3) the RVNAF was apathetic in defending South Vietnam from communism. The stories add nuance to our understanding of who the South Vietnamese were in the Vietnam War. This study is part of a growing body of research on non-American perspectives of the war. In using a largely untapped source of Vietnamese history—oral histories with Vietnamese immigrants—this project will contribute to future research on similar topics.

That was a fairly basic abstract that allows us to examine its individual parts more thoroughly.

Motivation/problem statement: The author identifies that previous research has been done about the Vietnam War, but that it has failed to address the specific topic of South Vietnam’s military. This is good because it shows how the author’s research fits into the bigger picture. It isn’t a bad thing to be critical of other research, but be respectful from an academic standpoint (i.e. “Previous researchers are stupid and don’t know what they’re talking about” sounds kind of unprofessional).

Methods/procedure/approach: The author does a good job of explaining how she performed her research, without giving unnecessary detail. Noting that she conducted qualitative interviews with 40 subjects is significant, but she wisely does not explicitly state the kinds of questions asked during the interview, which would be excessive.

Results/findings/product: The results make good use of numbering to clearly indicate what was ascertained from the research—particularly useful, as people often just scan abstracts for the results of an experiment.

Conclusion/implications: Since this paper is historical in nature, its findings may be hard to extrapolate to modern-day phenomena, but the author identifies the importance of her work as part of a growing body of research, which merits further investigation. This strategy functions to encourage future research on the topic.

ABSTRACT #2: Natural Science “A Lysimeter Study of Grass Cover and Water Table Depth Effects on Pesticide Residues in Drainage Water” Authors: A. Liaghat, S.O. Prasher

A study was undertaken to investigate the effect of soil and grass cover, when integrated with water table management (subsurface drainage and controlled drainage), in reducing herbicide residues in agricultural drainage water. Twelve PVC lysimeters, 1 m long and 450 mm diameter, were packed with a sandy soil and used to study the following four treatments: subsurface drainage, controlled drainage, grass (sod) cover, and bare soil. Contaminated water containing atrazine, metolachlor, and metribuzin residues was applied to the lysimeters and samples of drain effluent were collected. Significant reductions in pesticide concentrations were found in all treatments. In the first year, herbicide levels were reduced significantly (1% level), from an average of 250 mg/L to less than 10 mg/L . In the second year, polluted water of 50 mg/L, which is considered more realistic and reasonable in natural drainage waters, was applied to the lysimeters and herbicide residues in the drainage waters were reduced to less than 1 mg/L. The subsurface drainage lysimeters covered with grass proved to be the most effective treatment system.

Motivation/problem statement: Once again, we see that the problem—more like subject of study —is stated first in the abstract. This is normal for abstracts, in that you want to include the most important information first. The results may seem like the most important part of the abstract, but without mentioning the subject, the results won’t make much sense to readers. Notice that the abstract makes no references to other research, which is fine. It is not obligatory to cite other publications in an abstract, and in fact, doing so might distract your reader from YOUR experiment. Either way, it is likely that other sources will surface in your paper’s discussion/conclusion.

Methods/procedure/approach: Notice that the authors include pertinent numbers and figures in describing their methods. An extended description of the methods would probably include a long list of numerical values and conditions for each experimental trial, so it is important to include only the most important values in your abstract—ones that might make your study unique. Additionally, we see that a methodological description appears in two different parts of the abstract. This is fine. It may work better to explain your experiment by more closely connecting each method to its result. One last point: the author doesn’t take time to define—or give any background information about—“atrazine,” “metalachlor,” “lysimeter,” or “metribuzin.” This may be because other ecologists know what these are, but even if that’s not the case, you shouldn’t take time to define terms in your abstract.

Results/findings/product: Similar to the methods component of the abstract, you want to condense your findings to include only the major result of the experiment. Again, this study focused on two major trials, so both trials and both major results are listed. A particularly important word to consider when sharing results in an abstract is “significant.” In statistics, “significant” means roughly that your results were not due to chance. In your paper, your results may be hundreds of words long, and involve dozens of tables and graphs, but ultimately, your reader only wants to know: “What was the main result, and was that result significant?” So, try to answer both these questions in the abstract.

Conclusion/implications: This abstract’s conclusion sounds more like a result: “…lysimeters covered with grass were found to be the most effective treatment system.” This may seem incomplete, since it does not explain how this system could/should/would be applied to other situations, but that’s okay. There is plenty of space for addressing those issues in the body of the paper.

ABSTRACT #3: Philosophy / Literature [Note: Many papers don’t precisely follow the previous format, since they do not involve an experiment and its methods. Nonetheless, they typically rely on a similar structure.]

“Participatory Legitimation: A Reply to Arash Abizadeh” Author: Eric Schmidt, Louisiana State University, 2011

Arash Abizadeh’s argument against unilateral border control relies on his unbounded demos thesis, which is supported negatively by arguing that the ‘bounded demos thesis’ is incoherent. The incoherency arises for two reasons: (1) Democratic principles cannot be brought to bear on matters (border control) logically prior to the constitution of a group, and (2), the civic definition of citizens and non-citizens creates an ‘externality problem’ because the act of definition is an exercise of coercive power over all persons. The bounded demos thesis is rejected because the “will of the people” fails to legitimate democratic political order because there can be no pre-political political will of the people. However, I argue that “the will of the people” can be made manifest under a robust understanding of participatory legitimation, which exists concurrently with the political state, and thus defines both its borders and citizens as bounded , rescuing the bounded demos thesis and compromising the rest of Abizadeh’s article.

This paper may not make any sense to someone not studying philosophy, or not having read the text being critiqued. However, we can still see where the author separates the different components of the abstract, even if we don’t understand the terminology used.

Motivation/problem statement: The problem is not really a problem, but rather another person’s belief on a subject matter. For that reason, the author takes time to carefully explain the exact theory that he will be arguing against.

Methods/procedure/approach: [Note that there is no traditional “Methods” component of this abstract.] Reviews like this are purely critical and don’t necessarily involve performing experiments as in the other abstracts we have seen. Still, a paper like this may incorporate ideas from other sources, much like our traditional definition of experimental research.

Results/findings/product: In a paper like this, the “findings” tend to resemble what you have concluded about something, which will largely be based on your own opinion, supported by various examples. For that reason, the finding of this paper is: “The ‘will of the people,’ actually corresponds to a ‘bounded demos thesis.’” Even though we aren’t sure what the terms mean, we can plainly see that the finding (argument) is in support of “bounded,” rather than “unbounded.”

Conclusion/implications: If our finding is that “bounded” is correct, then what should we conclude? [In this case, the conclusion is simply that the initial author, A.A., is wrong.] Some critical papers attempt to broaden the conclusion to show something outside the scope of the paper. For example, if A.A. believes his “unbounded demos thesis” to be correct (when he is actually mistaken), what does this say about him? About his philosophy? About society as a whole? Maybe people who agree with him are more likely to vote Democrat, more likely to approve of certain immigration policies, more likely to own Labrador retrievers as pets, etc.

Applying These Skills

Now that you know the general layout of an abstract, here are some tips to keep in mind as you write your own:

1. The abstract stands alone

  • An abstract shouldn’t be considered “part” of a paper—it should be able to stand independently and still tell the reader something significant.

2. Keep it short

  • A general rule of abstract length is 200-300 words, or about 1/10th of the entire paper.

3. Don’t add new information

  • If something doesn’t appear in your actual paper, then don’t put it in the abstract.

4. Be consistent with voice, tone, and style

  • Try to write the abstract in the same style as your paper (i.e. If you’re not using contractions in your paper, the do not use them in your abstract).

5. Be concise

  • Try to shorten your sentences as often as possible. If you can say something clearly in five words rather than ten, then do it.

6. Break up its components

  • If allowed, subdivide the components of your abstract with bolded headings for “Background,” “Methods,” etc.

7. The abstract should be part of your writing process

  • Consider writing your abstract after you finish your entire paper.
  • There’s nothing wrong with copying and pasting important sentences and phrases from your paper … provided that they’re your own words.
  • Write multiple drafts, and keep revising. An abstract is very important to your publication (or assignment) and should be treated as such.

"Abstracts." The Writing Center. The University of North Carolina, n.d. Web. 1 Jun 2011. http://www.unc.edu/depts/wcweb/handouts/abstracts.html "Abstracts." The Writing Center. Rensselaer Polytechnic Institute, n.d. Web. 1 Jun 2011. http://www.rpi.edu/web/writingcenter/abstracts.html

Last updated August 2013

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How to Write an Abstract (With Examples)

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Sarah Oakley

how to write an abstract

Table of Contents

What is an abstract in a paper, how long should an abstract be, 5 steps for writing an abstract, examples of an abstract, how prowritingaid can help you write an abstract.

If you are writing a scientific research paper or a book proposal, you need to know how to write an abstract, which summarizes the contents of the paper or book.

When researchers are looking for peer-reviewed papers to use in their studies, the first place they will check is the abstract to see if it applies to their work. Therefore, your abstract is one of the most important parts of your entire paper.

In this article, we’ll explain what an abstract is, what it should include, and how to write one.

An abstract is a concise summary of the details within a report. Some abstracts give more details than others, but the main things you’ll be talking about are why you conducted the research, what you did, and what the results show.

When a reader is deciding whether to read your paper completely, they will first look at the abstract. You need to be concise in your abstract and give the reader the most important information so they can determine if they want to read the whole paper.

Remember that an abstract is the last thing you’ll want to write for the research paper because it directly references parts of the report. If you haven’t written the report, you won’t know what to include in your abstract.

If you are writing a paper for a journal or an assignment, the publication or academic institution might have specific formatting rules for how long your abstract should be. However, if they don’t, most abstracts are between 150 and 300 words long.

A short word count means your writing has to be precise and without filler words or phrases. Once you’ve written a first draft, you can always use an editing tool, such as ProWritingAid, to identify areas where you can reduce words and increase readability.

If your abstract is over the word limit, and you’ve edited it but still can’t figure out how to reduce it further, your abstract might include some things that aren’t needed. Here’s a list of three elements you can remove from your abstract:

Discussion : You don’t need to go into detail about the findings of your research because your reader will find your discussion within the paper.

Definition of terms : Your readers are interested the field you are writing about, so they are likely to understand the terms you are using. If not, they can always look them up. Your readers do not expect you to give a definition of terms in your abstract.

References and citations : You can mention there have been studies that support or have inspired your research, but you do not need to give details as the reader will find them in your bibliography.

what is abstract in article writing

Good writing = better grades

ProWritingAid will help you improve the style, strength, and clarity of all your assignments.

If you’ve never written an abstract before, and you’re wondering how to write an abstract, we’ve got some steps for you to follow. It’s best to start with planning your abstract, so we’ve outlined the details you need to include in your plan before you write.

Remember to consider your audience when you’re planning and writing your abstract. They are likely to skim read your abstract, so you want to be sure your abstract delivers all the information they’re expecting to see at key points.

1. What Should an Abstract Include?

Abstracts have a lot of information to cover in a short number of words, so it’s important to know what to include. There are three elements that need to be present in your abstract:

Your context is the background for where your research sits within your field of study. You should briefly mention any previous scientific papers or experiments that have led to your hypothesis and how research develops in those studies.

Your hypothesis is your prediction of what your study will show. As you are writing your abstract after you have conducted your research, you should still include your hypothesis in your abstract because it shows the motivation for your paper.

Throughout your abstract, you also need to include keywords and phrases that will help researchers to find your article in the databases they’re searching. Make sure the keywords are specific to your field of study and the subject you’re reporting on, otherwise your article might not reach the relevant audience.

2. Can You Use First Person in an Abstract?

You might think that first person is too informal for a research paper, but it’s not. Historically, writers of academic reports avoided writing in first person to uphold the formality standards of the time. However, first person is more accepted in research papers in modern times.

If you’re still unsure whether to write in first person for your abstract, refer to any style guide rules imposed by the journal you’re writing for or your teachers if you are writing an assignment.

3. Abstract Structure

Some scientific journals have strict rules on how to structure an abstract, so it’s best to check those first. If you don’t have any style rules to follow, try using the IMRaD structure, which stands for Introduction, Methodology, Results, and Discussion.

how to structure an abstract

Following the IMRaD structure, start with an introduction. The amount of background information you should include depends on your specific research area. Adding a broad overview gives you less room to include other details. Remember to include your hypothesis in this section.

The next part of your abstract should cover your methodology. Try to include the following details if they apply to your study:

What type of research was conducted?

How were the test subjects sampled?

What were the sample sizes?

What was done to each group?

How long was the experiment?

How was data recorded and interpreted?

Following the methodology, include a sentence or two about the results, which is where your reader will determine if your research supports or contradicts their own investigations.

The results are also where most people will want to find out what your outcomes were, even if they are just mildly interested in your research area. You should be specific about all the details but as concise as possible.

The last few sentences are your conclusion. It needs to explain how your findings affect the context and whether your hypothesis was correct. Include the primary take-home message, additional findings of importance, and perspective. Also explain whether there is scope for further research into the subject of your report.

Your conclusion should be honest and give the reader the ultimate message that your research shows. Readers trust the conclusion, so make sure you’re not fabricating the results of your research. Some readers won’t read your entire paper, but this section will tell them if it’s worth them referencing it in their own study.

4. How to Start an Abstract

The first line of your abstract should give your reader the context of your report by providing background information. You can use this sentence to imply the motivation for your research.

You don’t need to use a hook phrase or device in your first sentence to grab the reader’s attention. Your reader will look to establish relevance quickly, so readability and clarity are more important than trying to persuade the reader to read on.

5. How to Format an Abstract

Most abstracts use the same formatting rules, which help the reader identify the abstract so they know where to look for it.

Here’s a list of formatting guidelines for writing an abstract:

Stick to one paragraph

Use block formatting with no indentation at the beginning

Put your abstract straight after the title and acknowledgements pages

Use present or past tense, not future tense

There are two primary types of abstract you could write for your paper—descriptive and informative.

An informative abstract is the most common, and they follow the structure mentioned previously. They are longer than descriptive abstracts because they cover more details.

Descriptive abstracts differ from informative abstracts, as they don’t include as much discussion or detail. The word count for a descriptive abstract is between 50 and 150 words.

Here is an example of an informative abstract:

A growing trend exists for authors to employ a more informal writing style that uses “we” in academic writing to acknowledge one’s stance and engagement. However, few studies have compared the ways in which the first-person pronoun “we” is used in the abstracts and conclusions of empirical papers. To address this lacuna in the literature, this study conducted a systematic corpus analysis of the use of “we” in the abstracts and conclusions of 400 articles collected from eight leading electrical and electronic (EE) engineering journals. The abstracts and conclusions were extracted to form two subcorpora, and an integrated framework was applied to analyze and seek to explain how we-clusters and we-collocations were employed. Results revealed whether authors’ use of first-person pronouns partially depends on a journal policy. The trend of using “we” showed that a yearly increase occurred in the frequency of “we” in EE journal papers, as well as the existence of three “we-use” types in the article conclusions and abstracts: exclusive, inclusive, and ambiguous. Other possible “we-use” alternatives such as “I” and other personal pronouns were used very rarely—if at all—in either section. These findings also suggest that the present tense was used more in article abstracts, but the present perfect tense was the most preferred tense in article conclusions. Both research and pedagogical implications are proffered and critically discussed.

Wang, S., Tseng, W.-T., & Johanson, R. (2021). To We or Not to We: Corpus-Based Research on First-Person Pronoun Use in Abstracts and Conclusions. SAGE Open, 11(2).

Here is an example of a descriptive abstract:

From the 1850s to the present, considerable criminological attention has focused on the development of theoretically-significant systems for classifying crime. This article reviews and attempts to evaluate a number of these efforts, and we conclude that further work on this basic task is needed. The latter part of the article explicates a conceptual foundation for a crime pattern classification system, and offers a preliminary taxonomy of crime.

Farr, K. A., & Gibbons, D. C. (1990). Observations on the Development of Crime Categories. International Journal of Offender Therapy and Comparative Criminology, 34(3), 223–237.

If you want to ensure your abstract is grammatically correct and easy to read, you can use ProWritingAid to edit it. The software integrates with Microsoft Word, Google Docs, and most web browsers, so you can make the most of it wherever you’re writing your paper.

academic document type

Before you edit with ProWritingAid, make sure the suggestions you are seeing are relevant for your document by changing the document type to “Abstract” within the Academic writing style section.

You can use the Readability report to check your abstract for places to improve the clarity of your writing. Some suggestions might show you where to remove words, which is great if you’re over your word count.

We hope the five steps and examples we’ve provided help you write a great abstract for your research paper.

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Abstract Writing: A Step-by-Step Guide With Tips & Examples

Sumalatha G

Table of Contents

step-by-step-guide-to-abstract-writing

Introduction

Abstracts of research papers have always played an essential role in describing your research concisely and clearly to researchers and editors of journals, enticing them to continue reading. However, with the widespread availability of scientific databases, the need to write a convincing abstract is more crucial now than during the time of paper-bound manuscripts.

Abstracts serve to "sell" your research and can be compared with your "executive outline" of a resume or, rather, a formal summary of the critical aspects of your work. Also, it can be the "gist" of your study. Since most educational research is done online, it's a sign that you have a shorter time for impressing your readers, and have more competition from other abstracts that are available to be read.

The APCI (Academic Publishing and Conferences International) articulates 12 issues or points considered during the final approval process for conferences & journals and emphasises the importance of writing an abstract that checks all these boxes (12 points). Since it's the only opportunity you have to captivate your readers, you must invest time and effort in creating an abstract that accurately reflects the critical points of your research.

With that in mind, let’s head over to understand and discover the core concept and guidelines to create a substantial abstract. Also, learn how to organise the ideas or plots into an effective abstract that will be awe-inspiring to the readers you want to reach.

What is Abstract? Definition and Overview

The word "Abstract' is derived from Latin abstractus meaning "drawn off." This etymological meaning also applies to art movements as well as music, like abstract expressionism. In this context, it refers to the revealing of the artist's intention.

Based on this, you can determine the meaning of an abstract: A condensed research summary. It must be self-contained and independent of the body of the research. However, it should outline the subject, the strategies used to study the problem, and the methods implemented to attain the outcomes. The specific elements of the study differ based on the area of study; however, together, it must be a succinct summary of the entire research paper.

Abstracts are typically written at the end of the paper, even though it serves as a prologue. In general, the abstract must be in a position to:

  • Describe the paper.
  • Identify the problem or the issue at hand.
  • Explain to the reader the research process, the results you came up with, and what conclusion you've reached using these results.
  • Include keywords to guide your strategy and the content.

Furthermore, the abstract you submit should not reflect upon any of  the following elements:

  • Examine, analyse or defend the paper or your opinion.
  • What you want to study, achieve or discover.
  • Be redundant or irrelevant.

After reading an abstract, your audience should understand the reason - what the research was about in the first place, what the study has revealed and how it can be utilised or can be used to benefit others. You can understand the importance of abstract by knowing the fact that the abstract is the most frequently read portion of any research paper. In simpler terms, it should contain all the main points of the research paper.

purpose-of-abstract-writing

What is the Purpose of an Abstract?

Abstracts are typically an essential requirement for research papers; however, it's not an obligation to preserve traditional reasons without any purpose. Abstracts allow readers to scan the text to determine whether it is relevant to their research or studies. The abstract allows other researchers to decide if your research paper can provide them with some additional information. A good abstract paves the interest of the audience to pore through your entire paper to find the content or context they're searching for.

Abstract writing is essential for indexing, as well. The Digital Repository of academic papers makes use of abstracts to index the entire content of academic research papers. Like meta descriptions in the regular Google outcomes, abstracts must include keywords that help researchers locate what they seek.

Types of Abstract

Informative and Descriptive are two kinds of abstracts often used in scientific writing.

A descriptive abstract gives readers an outline of the author's main points in their study. The reader can determine if they want to stick to the research work, based on their interest in the topic. An abstract that is descriptive is similar to the contents table of books, however, the format of an abstract depicts complete sentences encapsulated in one paragraph. It is unfortunate that the abstract can't be used as a substitute for reading a piece of writing because it's just an overview, which omits readers from getting an entire view. Also, it cannot be a way to fill in the gaps the reader may have after reading this kind of abstract since it does not contain crucial information needed to evaluate the article.

To conclude, a descriptive abstract is:

  • A simple summary of the task, just summarises the work, but some researchers think it is much more of an outline
  • Typically, the length is approximately 100 words. It is too short when compared to an informative abstract.
  • A brief explanation but doesn't provide the reader with the complete information they need;
  • An overview that omits conclusions and results

An informative abstract is a comprehensive outline of the research. There are times when people rely on the abstract as an information source. And the reason is why it is crucial to provide entire data of particular research. A well-written, informative abstract could be a good substitute for the remainder of the paper on its own.

A well-written abstract typically follows a particular style. The author begins by providing the identifying information, backed by citations and other identifiers of the papers. Then, the major elements are summarised to make the reader aware of the study. It is followed by the methodology and all-important findings from the study. The conclusion then presents study results and ends the abstract with a comprehensive summary.

In a nutshell, an informative abstract:

  • Has a length that can vary, based on the subject, but is not longer than 300 words.
  • Contains all the content-like methods and intentions
  • Offers evidence and possible recommendations.

Informative Abstracts are more frequent than descriptive abstracts because of their extensive content and linkage to the topic specifically. You should select different types of abstracts to papers based on their length: informative abstracts for extended and more complex abstracts and descriptive ones for simpler and shorter research papers.

What are the Characteristics of a Good Abstract?

  • A good abstract clearly defines the goals and purposes of the study.
  • It should clearly describe the research methodology with a primary focus on data gathering, processing, and subsequent analysis.
  • A good abstract should provide specific research findings.
  • It presents the principal conclusions of the systematic study.
  • It should be concise, clear, and relevant to the field of study.
  • A well-designed abstract should be unifying and coherent.
  • It is easy to grasp and free of technical jargon.
  • It is written impartially and objectively.

the-various-sections-of-abstract-writing

What are the various sections of an ideal Abstract?

By now, you must have gained some concrete idea of the essential elements that your abstract needs to convey . Accordingly, the information is broken down into six key sections of the abstract, which include:

An Introduction or Background

Research methodology, objectives and goals, limitations.

Let's go over them in detail.

The introduction, also known as background, is the most concise part of your abstract. Ideally, it comprises a couple of sentences. Some researchers only write one sentence to introduce their abstract. The idea behind this is to guide readers through the key factors that led to your study.

It's understandable that this information might seem difficult to explain in a couple of sentences. For example, think about the following two questions like the background of your study:

  • What is currently available about the subject with respect to the paper being discussed?
  • What isn't understood about this issue? (This is the subject of your research)

While writing the abstract’s introduction, make sure that it is not lengthy. Because if it crosses the word limit, it may eat up the words meant to be used for providing other key information.

Research methodology is where you describe the theories and techniques you used in your research. It is recommended that you describe what you have done and the method you used to get your thorough investigation results. Certainly, it is the second-longest paragraph in the abstract.

In the research methodology section, it is essential to mention the kind of research you conducted; for instance, qualitative research or quantitative research (this will guide your research methodology too) . If you've conducted quantitative research, your abstract should contain information like the sample size, data collection method, sampling techniques, and duration of the study. Likewise, your abstract should reflect observational data, opinions, questionnaires (especially the non-numerical data) if you work on qualitative research.

The research objectives and goals speak about what you intend to accomplish with your research. The majority of research projects focus on the long-term effects of a project, and the goals focus on the immediate, short-term outcomes of the research. It is possible to summarise both in just multiple sentences.

In stating your objectives and goals, you give readers a picture of the scope of the study, its depth and the direction your research ultimately follows. Your readers can evaluate the results of your research against the goals and stated objectives to determine if you have achieved the goal of your research.

In the end, your readers are more attracted by the results you've obtained through your study. Therefore, you must take the time to explain each relevant result and explain how they impact your research. The results section exists as the longest in your abstract, and nothing should diminish its reach or quality.

One of the most important things you should adhere to is to spell out details and figures on the results of your research.

Instead of making a vague assertion such as, "We noticed that response rates varied greatly between respondents with high incomes and those with low incomes", Try these: "The response rate was higher for high-income respondents than those with lower incomes (59 30 percent vs. 30 percent in both cases; P<0.01)."

You're likely to encounter certain obstacles during your research. It could have been during data collection or even during conducting the sample . Whatever the issue, it's essential to inform your readers about them and their effects on the research.

Research limitations offer an opportunity to suggest further and deep research. If, for instance, you were forced to change for convenient sampling and snowball samples because of difficulties in reaching well-suited research participants, then you should mention this reason when you write your research abstract. In addition, a lack of prior studies on the subject could hinder your research.

Your conclusion should include the same number of sentences to wrap the abstract as the introduction. The majority of researchers offer an idea of the consequences of their research in this case.

Your conclusion should include three essential components:

  • A significant take-home message.
  • Corresponding important findings.
  • The Interpretation.

Even though the conclusion of your abstract needs to be brief, it can have an enormous influence on the way that readers view your research. Therefore, make use of this section to reinforce the central message from your research. Be sure that your statements reflect the actual results and the methods you used to conduct your research.

examples-of-good-abstract-writing

Good Abstract Examples

Abstract example #1.

Children’s consumption behavior in response to food product placements in movies.

The abstract:

"Almost all research into the effects of brand placements on children has focused on the brand's attitudes or behavior intentions. Based on the significant differences between attitudes and behavioral intentions on one hand and actual behavior on the other hand, this study examines the impact of placements by brands on children's eating habits. Children aged 6-14 years old were shown an excerpt from the popular film Alvin and the Chipmunks and were shown places for the item Cheese Balls. Three different versions were developed with no placements, one with moderately frequent placements and the third with the highest frequency of placement. The results revealed that exposure to high-frequency places had a profound effect on snack consumption, however, there was no impact on consumer attitudes towards brands or products. The effects were not dependent on the age of the children. These findings are of major importance to researchers studying consumer behavior as well as nutrition experts as well as policy regulators."

Abstract Example #2

Social comparisons on social media: The impact of Facebook on young women’s body image concerns and mood. The abstract:

"The research conducted in this study investigated the effects of Facebook use on women's moods and body image if the effects are different from an internet-based fashion journal and if the appearance comparison tendencies moderate one or more of these effects. Participants who were female ( N = 112) were randomly allocated to spend 10 minutes exploring their Facebook account or a magazine's website or an appearance neutral control website prior to completing state assessments of body dissatisfaction, mood, and differences in appearance (weight-related and facial hair, face, and skin). Participants also completed a test of the tendency to compare appearances. The participants who used Facebook were reported to be more depressed than those who stayed on the control site. In addition, women who have the tendency to compare appearances reported more facial, hair and skin-related issues following Facebook exposure than when they were exposed to the control site. Due to its popularity it is imperative to conduct more research to understand the effect that Facebook affects the way people view themselves."

Abstract Example #3

The Relationship Between Cell Phone Use and Academic Performance in a Sample of U.S. College Students

"The cellphone is always present on campuses of colleges and is often utilised in situations in which learning takes place. The study examined the connection between the use of cell phones and the actual grades point average (GPA) after adjusting for predictors that are known to be a factor. In the end 536 students in the undergraduate program from 82 self-reported majors of an enormous, public institution were studied. Hierarchical analysis ( R 2 = .449) showed that use of mobile phones is significantly ( p < .001) and negative (b equal to -.164) connected to the actual college GPA, after taking into account factors such as demographics, self-efficacy in self-regulated learning, self-efficacy to improve academic performance, and the actual high school GPA that were all important predictors ( p < .05). Therefore, after adjusting for other known predictors increasing cell phone usage was associated with lower academic performance. While more research is required to determine the mechanisms behind these results, they suggest the need to educate teachers and students to the possible academic risks that are associated with high-frequency mobile phone usage."

quick-tips-on-writing-a-good-abstract

Quick tips on writing a good abstract

There exists a common dilemma among early age researchers whether to write the abstract at first or last? However, it's recommended to compose your abstract when you've completed the research since you'll have all the information to give to your readers. You can, however, write a draft at the beginning of your research and add in any gaps later.

If you find abstract writing a herculean task, here are the few tips to help you with it:

1. Always develop a framework to support your abstract

Before writing, ensure you create a clear outline for your abstract. Divide it into sections and draw the primary and supporting elements in each one. You can include keywords and a few sentences that convey the essence of your message.

2. Review Other Abstracts

Abstracts are among the most frequently used research documents, and thousands of them were written in the past. Therefore, prior to writing yours, take a look at some examples from other abstracts. There are plenty of examples of abstracts for dissertations in the dissertation and thesis databases.

3. Avoid Jargon To the Maximum

When you write your abstract, focus on simplicity over formality. You should  write in simple language, and avoid excessive filler words or ambiguous sentences. Keep in mind that your abstract must be readable to those who aren't acquainted with your subject.

4. Focus on Your Research

It's a given fact that the abstract you write should be about your research and the findings you've made. It is not the right time to mention secondary and primary data sources unless it's absolutely required.

Conclusion: How to Structure an Interesting Abstract?

Abstracts are a short outline of your essay. However, it's among the most important, if not the most important. The process of writing an abstract is not straightforward. A few early-age researchers tend to begin by writing it, thinking they are doing it to "tease" the next step (the document itself). However, it is better to treat it as a spoiler.

The simple, concise style of the abstract lends itself to a well-written and well-investigated study. If your research paper doesn't provide definitive results, or the goal of your research is questioned, so will the abstract. Thus, only write your abstract after witnessing your findings and put your findings in the context of a larger scenario.

The process of writing an abstract can be daunting, but with these guidelines, you will succeed. The most efficient method of writing an excellent abstract is to centre the primary points of your abstract, including the research question and goals methods, as well as key results.

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  • How to Write an Abstract

Abstract

Expedite peer review, increase search-ability, and set the tone for your study

The abstract is your chance to let your readers know what they can expect from your article. Learn how to write a clear, and concise abstract that will keep your audience reading.

How your abstract impacts editorial evaluation and future readership

After the title , the abstract is the second-most-read part of your article. A good abstract can help to expedite peer review and, if your article is accepted for publication, it’s an important tool for readers to find and evaluate your work. Editors use your abstract when they first assess your article. Prospective reviewers see it when they decide whether to accept an invitation to review. Once published, the abstract gets indexed in PubMed and Google Scholar , as well as library systems and other popular databases. Like the title, your abstract influences keyword search results. Readers will use it to decide whether to read the rest of your article. Other researchers will use it to evaluate your work for inclusion in systematic reviews and meta-analysis. It should be a concise standalone piece that accurately represents your research. 

what is abstract in article writing

What to include in an abstract

The main challenge you’ll face when writing your abstract is keeping it concise AND fitting in all the information you need. Depending on your subject area the journal may require a structured abstract following specific headings. A structured abstract helps your readers understand your study more easily. If your journal doesn’t require a structured abstract it’s still a good idea to follow a similar format, just present the abstract as one paragraph without headings. 

Background or Introduction – What is currently known? Start with a brief, 2 or 3 sentence, introduction to the research area. 

Objectives or Aims – What is the study and why did you do it? Clearly state the research question you’re trying to answer.

Methods – What did you do? Explain what you did and how you did it. Include important information about your methods, but avoid the low-level specifics. Some disciplines have specific requirements for abstract methods. 

  • CONSORT for randomized trials.
  • STROBE for observational studies
  • PRISMA for systematic reviews and meta-analyses

Results – What did you find? Briefly give the key findings of your study. Include key numeric data (including confidence intervals or p values), where possible.

Conclusions – What did you conclude? Tell the reader why your findings matter, and what this could mean for the ‘bigger picture’ of this area of research. 

Writing tips

The main challenge you may find when writing your abstract is keeping it concise AND convering all the information you need to.

what is abstract in article writing

  • Keep it concise and to the point. Most journals have a maximum word count, so check guidelines before you write the abstract to save time editing it later.
  • Write for your audience. Are they specialists in your specific field? Are they cross-disciplinary? Are they non-specialists? If you’re writing for a general audience, or your research could be of interest to the public keep your language as straightforward as possible. If you’re writing in English, do remember that not all of your readers will necessarily be native English speakers.
  • Focus on key results, conclusions and take home messages.
  • Write your paper first, then create the abstract as a summary.
  • Check the journal requirements before you write your abstract, eg. required subheadings.
  • Include keywords or phrases to help readers search for your work in indexing databases like PubMed or Google Scholar.
  • Double and triple check your abstract for spelling and grammar errors. These kind of errors can give potential reviewers the impression that your research isn’t sound, and can make it easier to find reviewers who accept the invitation to review your manuscript. Your abstract should be a taste of what is to come in the rest of your article.

what is abstract in article writing

Don’t

  • Sensationalize your research.
  • Speculate about where this research might lead in the future.
  • Use abbreviations or acronyms (unless absolutely necessary or unless they’re widely known, eg. DNA).
  • Repeat yourself unnecessarily, eg. “Methods: We used X technique. Results: Using X technique, we found…”
  • Contradict anything in the rest of your manuscript.
  • Include content that isn’t also covered in the main manuscript.
  • Include citations or references.

Tip: How to edit your work

Editing is challenging, especially if you are acting as both a writer and an editor. Read our guidelines for advice on how to refine your work, including useful tips for setting your intentions, re-review, and consultation with colleagues.

  • How to Write a Great Title
  • How to Write Your Methods
  • How to Report Statistics
  • How to Write Discussions and Conclusions
  • How to Edit Your Work

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The contents of the Writing Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

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How to write an abstract

what is abstract in article writing

What is an abstract?

General format of an abstract, the content of an abstract, abstract example, abstract style guides, frequently asked questions about writing an abstract, related articles.

An abstract is a summary of the main contents of a paper.

The abstract is the first glimpse that readers get of the content of a research paper. It can influence the popularity of a paper, as a well-written one will attract readers, and a poorly-written one will drive them away.

➡️ Different types of papers may require distinct abstract styles. Visit our guide on the different types of research papers to learn more.

Tip: Always wait until you’ve written your entire paper before you write the abstract.

Before you actually start writing an abstract, make sure to follow these steps:

  • Read other papers : find papers with similar topics, or similar methodologies, simply to have an idea of how others have written their abstracts. Notice which points they decided to include, and how in depth they described them.
  • Double check the journal requirements : always make sure to review the journal guidelines to format your paper accordingly. Usually, they also specify abstract's formats.
  • Write the abstract after you finish writing the paper : you can only write an abstract once you finish writing the whole paper. This way you can include all important aspects, such as scope, methodology, and conclusion.

➡️ Read more about  what is a research methodology?

The general format of an abstract includes the following features:

  • Between 150-300 words .
  • An independent page , after the title page and before the table of contents.
  • Concise summary including the aim of the research, methodology , and conclusion .
  • Keywords describing the content.

As mentioned before, an abstract is a text that summarizes the main points of a research. Here is a break down of each element that should be included in an abstract:

  • Purpose : every abstract should start by describing the main purpose or aim of the research.
  • Methods : as a second point, the methodology carried out should be explained.
  • Results : then, a concise summary of the results should be included.
  • Conclusion : finally, a short outline of the general outcome of the research should be given.
  • Keywords : along with the abstract, specific words and phrases related to the topics discussed in the research should be added. These words are usually around five, but the number can vary depending on the journal's guidelines.

This abstract, taken from ScienceDirect , illustrates the ideal structure of an abstract. It has 155 words, it's concise, and it clearly shows the division of elements necessary to write a successful abstract.

This paper explores the implicit assumption in the growing body of literature that social media usage is fundamentally different in business-to-business (B2B) companies than in the extant business-to-consumer (B2C) literature. Sashi's (2012) customer engagement cycle is utilized to compare organizational practices in relation to social media marketing in B2B, B2C, Mixed B2B/B2C and B2B2C business models. Utilizing 449 responses to an exploratory panel based survey instrument, we clearly identify differences in social media usage and its perceived importance as a communications channel. In particular we identify distinct differences in the relationship between social media importance and the perceived effectiveness of social media marketing across business models. Our results indicate that B2B social media usage is distinct from B2C, Mixed and B2B2C business model approaches. Specifically B2B organizational members perceive social media to have a lower overall effectiveness as a channel and identify it as less important for relationship oriented usage than other business models.

The exact format of an abstract depends on the citation style you implement. Whether it’s a well-known style (like APA, IEEE, etc.) or a journal's style, each format has its own guidelines, so make sure you know which style you are using before writing your abstract.

APA is one of the most commonly used styles to format an abstract. Therefore, we created a guide with exact instructions on how to write an abstract in APA style, and a template to download:

📕 APA abstract page: format and template

Additionally, you will find below an IEEE and ASA abstract guide by Purdue Online Writing Lab :

📗 IEEE General Format - Abstract

📘 ASA Manuscript Formatting - Abstract

No. You should always write an abstract once you finish writing the whole paper. This way you can include all important aspects of the paper, such as scope, methodology, and conclusion.

The length of an abstract depends on the formatting style of the paper. For example, APA style calls for 150 to 250 words. Generally, you need between 150-300 words.

No. An abstract has an independent section after the title page and before the table of contents, and should not be included in the table of contents.

Take a look at APA abstract page: format and template for exact details on how to format an abstract in APA style.

You can access any paper through Google Scholar or any other search engine; pick a paper and read the abstract. Abstracts are always freely available to read.

How to give a good scientific presentation

Writing Center Home Page

OASIS: Writing Center

Writing for publication: abstracts.

An abstract is "a brief, comprehensive summary of the contents of the paper" (American Psychological Association [APA], 2020, p. 38). This summary is intended to share the topic, argument, and conclusions of a research study or course paper, similar to the text on the back cover of a book. When submitting your work for publication, an abstract is often the first piece of your writing a reviewer will encounter. An abstract may not be required for course papers.

Read on for more tips on making a good first impression with a successful abstract.

An abstract is a single paragraph preceded by the heading " Abstract ," centered and in bold font. The abstract does not begin with an indented line. APA (2020) recommends that abstracts should generally be less than 250 words, though many journals have their own word limits; it is always a good idea to check journal-specific requirements before submitting. The Writing Center's APA templates are great resources for visual examples of abstracts.

Abstracts use the present tense to describe currently applicable results (e.g., "Results indicate...") and the past tense to describe research steps (e.g., "The survey measured..."), and they do not typically include citations.

Key terms are sometimes included at the end of the abstract and should be chosen by considering the words or phrases that a reader might use to search for your article.

An abstract should include information such as

  • The problem or central argument of your article
  • A brief exposition of research design, methods, and procedures.
  • A brief summary of your findings
  • A brief summary of the implications of the research on practice and theory

It is also appropriate, depending on the type of article you are writing, to include information such as:

  • Participant number and type
  • Study eligibility criteria
  • Limitations of your study
  • Implications of your study's conclusions or areas for additional research

Your abstract should avoid unnecessary wordiness and focus on quickly and concisely summarizing the major points of your work. An abstract is not an introduction; you are not trying to capture the reader's attention with timeliness or to orient the reader to the entire background of your study. When readers finish reading your abstract, they should have a strong sense of your article's purpose, approach, and conclusions. The Walden Office of Research and Doctoral Services has additional  tutorial material on abstracts .

Clinical or Empirical Study Abstract Exemplar

In the following abstract, the article's problem is stated in red , the approach and design are in blue , and the results are in green .

End-stage renal disease (ESRD) patients have a high cardiovascular mortality rate. Precise estimates of the prevalence, risk factors and prognosis of different manifestations of cardiac disease are unavailable. In this study a prospective cohort of 433 ESRD patients was followed from the start of ESRD therapy for a mean of 41 months. Baseline clinical assessment and echocardiography were performed on all patients.  The major outcome measure was death while on dialysis therapy. Clinical manifestations of cardiovascular disease were highly prevalent at the start of ESRD therapy: 14% had coronary artery disease, 19% angina pectoris, 31% cardiac failure, 7% dysrhythmia and 8% peripheral vascular disease. On echocardiography 15% had systolic dysfunction, 32% left ventricular dilatation and 74% left ventricular hypertrophy. The overall median survival time was 50 months. Age, diabetes mellitus, cardiac failure, peripheral vascular disease and systolic dysfunction independently predicted death in all time frames. Coronary artery disease was associated with a worse prognosis in patients with cardiac failure at baseline. High left ventricular cavity volume and mass index were independently associated with death after two years. The independent associations of the different echocardiographic abnormalities were: systolic dysfunction--older age and coronary artery disease; left ventricular dilatation--male gender, anemia, hypocalcemia and hyperphosphatemia; left ventricular hypertrophy--older age, female gender, wide arterial pulse pressure, low blood urea and hypoalbuminemia. We conclude that clinical and echocardiographic cardiovascular disease are already present in a very high proportion of patients starting ESRD therapy and are independent mortality factors.

Foley, R. N., Parfrey, P. S., Harnett, J. D., Kent, G. M., Martin, C. J., Murray, D. C., & Barre, P. E. (1995). Clinical and echocardiographic disease in patients starting end-stage renal disease therapy. Kidney International , 47 , 186–192. https://doi.org/10.1038/ki.1995.22

Literature Review Abstract Exemplar

In the following abstract, the purpose and scope of the literature review are in red , the specific span of topics is in blue , and the implications for further research are in green .

This paper provides a review of research into the relationships between psychological types, as measured by the Myers-Briggs Type Indicator (MBTI), and managerial attributes, behaviors and effectiveness. The literature review includes an examination of the psychometric properties of the MBTI and the contributions and limitations of research on psychological types. Next, key findings are discussed and used to advance propositions that relate psychological type to diverse topics such as risk tolerance, problem solving, information systems design, conflict management and leadership. We conclude with a research agenda that advocates: (a) the exploration of potential psychometric refinements of the MBTI, (b) more rigorous research designs, and (c) a broadening of the scope of managerial research into type.

Gardner, W. L., & Martinko, M. J. (1996). Using the Myers-Briggs Type Indicator to study managers: A literature review and research agenda. Journal of Management, 22 (1), 45–83. https://doi.org/10.1177/014920639602200103

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Writing an abstract

Although it is usually brief (typically 150-300 words), an abstract is an important part of journal article writing (as well as for your thesis and for conferences). Done well, the abstract should create enough reader interest that readers will want to read more!

Whereas the purpose of an introduction is to broadly introduce your topic and your key message, the purpose of an abstract is to give an overview of your entire project, in particular its findings and contribution to the field. An abstract should be a standalone summary of your paper, which readers can use to decide whether it's relevant to them before they dive in to read the paper.

Usually an abstract includes the following.

  • A brief introduction to the topic that you're investigating.
  • Explanation of why the topic is important in your field/s.
  • Statement about what the gap is in the research.
  • Your research question/s / aim/s.
  • An indication of your research methods and approach.
  • Your key message.
  • A summary of your key findings.
  • An explanation of why your findings and key message contribute to the field/s.

In other words, an abstract includes points covering these questions.

  • What is your paper about?
  • Why is it important?
  • How did you do it?
  • What did you find?
  • Why are your findings important?

To see the specific conventions in your field/s, have a look at the structure of a variety of abstracts from relevant journal articles. Do they include the same kinds of information as listed above? What structure do they follow? You can model your own abstract on these conventions.

Dealing with feedback >>

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Turning a chapter into an article

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How to Write an Abstract

Definition and Tips

  • An Introduction to Punctuation
  • Ph.D., Rhetoric and English, University of Georgia
  • M.A., Modern English and American Literature, University of Leicester
  • B.A., English, State University of New York

An abstract is a brief overview of the key points of an article , report , thesis, or proposal . Positioned at the head of a paper, the abstract is usually "the first thing that individuals read and, as such, decide whether to continue reading" the article or report, wrote Dan W. Butin in his book "The Education Dissertation." "It is also what is most accessed by search engines and researchers conducting their own literature reviews " (2010). The abstract is also called a synopsis or an executive summary (especially in business writing).

What a Good Abstract Contains

An abstract serves the purpose of summarizing your research or making your case for a project (or grant funding) to be awarded to you. It should encapsulate the most important information that the paper or proposal will present. In the case of obtaining grants or bids, that could include why your firm or organization is the best for the job or award. Present your company as the solution to the problem.

If you're summarizing research, you'll want to mention your methodology behind how you tackled the question or problem and your basic conclusion. It's not like writing a news lead—you don't want to tease your readers with unanswered questions to get them to read the article. You want to hit the high points so that readers will know that your in-depth research is just what they are seeking out, without reading the whole piece at that moment.

Tips on Writing an Abstract

The abstract may not be what you write first, as it might be easiest to summarize your whole paper after it's been completed. You could draft it from your outline, but you'll want to double-check later that you have included the most important points from your article and that there's nothing in the abstract that you decided not to include in your report.

The abstract is a summary and shouldn't have anything in it that's not in the paper itself. Neither is it the same as the introduction to your report, which sets out your thesis and your aims. The abstract also contains information about your conclusion.

There are two types of abstracts, descriptive or informative. "The Handbook of Technical Writing" explains it this way:

Abstract Length

An abstract is not overly long. Mikael Berndtsson and colleagues advise, "A typical [informative] abstract is about 250-500 words. This is not more than 10-20 sentences, so you will obviously have to choose your words very carefully to cover so much information in such a condensed format." (Mikael Berndtsson, et al., "Thesis Projects: A Guide for Students in Computer Science and Information Systems," 2nd ed. Springer-Verlag, 2008.)

If you can hit all the high points in fewer words—if you're just writing a descriptive abstract—don't add extra just to reach 250 words, of course. Unnecessary detail doesn't do you or your reviewers any favors. Also, the proposal requirements or the journal that you wish to be published in may have length requirements. Always follow guidelines you've received, as even minor errors can cause your paper or grant request to be rejected.

  • Jennifer Evans, " Your Psychology Project: The Essential Guide ." Sage, 2007.
  • David Gilborn, quoted by Pat Thomson and Barbara Kamler in " Writing for Peer-Reviewed Journals: Strategies for Getting Published ." Routledge, 2013.
  • Sharon J. Gerson and Steven M. Gerson, " Technical Writing: Process and Product ." Pearson, 2003
  • Gerald J. Alred, Charles T. Brusaw, and Walter E. Oliu, " Handbook of Technical Writing ." Bedford/St. Martin's, 2006
  • Robert Day and Barbara Gastel, " How to Write and Publish a Scientific Paper ," 7th ed. Cambridge University Press, 2012.
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How to Write an Abstract?

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An abstract is a crisp, short, powerful, and self-contained summary of a research manuscript used to help the reader swiftly determine the paper’s purpose. Although the abstract is the first paragraph of the manuscript it should be written last when all the other sections have been addressed.

Research is formalized curiosity. It is poking and prying with a purpose. — Zora Neale Hurston, American Author, Anthropologist and Filmmaker (1891–1960)

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what is abstract in article writing

Writing the Abstract

what is abstract in article writing

Abstract and Keywords

what is abstract in article writing

Additional Commentaries

1 what is an abstract.

An abstract is usually a standalone document that informs the reader about the details of the manuscript to follow. It is like a trailer to a movie, if the trailer is good, it stimulates the audience to watch the movie. The abstract should be written from scratch and not ‘cut –and-pasted’ [ 1 ].

2 What is the History of the Abstract?

An abstract, in the form of a single paragraph, was first published in the Canadian Medical Association Journal in 1960 with the idea that the readers may not have enough time to go through the whole paper, and the first abstract with a defined structure was published in 1991 [ 2 ]. The idea sold and now most original articles and reviews are required to have a structured abstract. The abstract attracts the reader to read the full manuscript [ 3 ].

3 What are the Qualities of a Good Abstract?

The quality of information in an abstract can be summarized by four ‘C’s. It should be:

C: Condensed

C: Critical

4 What are the Types of Abstract?

Before writing the abstract, you need to check with the journal website about which type of abstract it requires, with its length and style in the ‘Instructions to Authors’ section.

The abstract types can be divided into:

Descriptive: Usually written for psychology, social science, and humanities papers. It is about 50–100 words long. No conclusions can be drawn from this abstract as it describes the major points in the paper.

Informative: The majority of abstracts for science-related manuscripts are informative and are surrogates for the research done. They are single paragraphs that provide the reader an overview of the research paper and are about 100–150 words in length. Conclusions can be drawn from the abstracts and in the recommendations written in the last line.

Critical: This type of abstract is lengthy and about 400–500 words. In this, the authors’ own research is discussed for reliability, judgement, and validation. A comparison is also made with similar studies done earlier.

Highlighting: This is rarely used in scientific writing. The style of the abstract is to attract more readers. It is not a balanced or complete overview of the article with which it is published.

Structured: A structured abstract contains information under subheadings like background, aims, material and methods, results, conclusion, and recommendations (Fig. 15.1 ). Most leading journals now carry these.

figure 1

Example of a structured abstract (with permission editor CMRP)

5 What is the Purpose of an Abstract?

An abstract is written to educate the reader about the study that follows and provide an overview of the science behind it. If written well it also attracts more readers to the article. It also helps the article getting indexed. The fate of a paper both before and after publication often depends upon its abstract. Most readers decide if a paper is worth reading on the basis of the abstract. Additionally, the selection of papers in systematic reviews is often dependent upon the abstract.

6 What are the Steps of Writing an Abstract?

An abstract should be written last after all the other sections of an article have been addressed. A poor abstract may turn off the reader and they may cause indexing errors as well. The abstract should state the purpose of the study, the methodology used, and summarize the results and important conclusions. It is usually written in the IMRAD format and is called a structured abstract [ 4 , 5 ].

I: The introduction in the opening line should state the problem you are addressing.

M: Methodology—what method was chosen to finish the experiment?

R: Results—state the important findings of your study.

D: Discussion—discuss why your study is important.

Mention the following information:

Important results with the statistical information ( p values, confidence intervals, standard/mean deviation).

Arrange all information in a chronological order.

Do not repeat any information.

The last line should state the recommendations from your study.

The abstract should be written in the past tense.

7 What are the Things to Be Avoided While Writing an Abstract?

Cut and paste information from the main text

Hold back important information

Use abbreviations

Tables or Figures

Generalized statements

Arguments about the study

figure a

8 What are Key Words?

These are important words that are repeated throughout the manuscript and which help in the indexing of a paper. Depending upon the journal 3–10 key words may be required which are indexed with the help of MESH (Medical Subject Heading).

9 How is an Abstract Written for a Conference Different from a Journal Paper?

The basic concept for writing abstracts is the same. However, in a conference abstract occasionally a table or figure is allowed. A word limit is important in both of them. Many of the abstracts which are presented in conferences are never published in fact one study found that only 27% of the abstracts presented in conferences were published in the next five years [ 6 ].

Table 15.1 gives a template for writing an abstract.

10 What are the Important Recommendations of the International Committees of Medical Journal of Editors?

The recommendations are [ 7 ]:

An abstract is required for original articles, metanalysis, and systematic reviews.

A structured abstract is preferred.

The abstract should mention the purpose of the scientific study, how the procedure was carried out, the analysis used, and principal conclusion.

Clinical trials should be reported according to the CONSORT guidelines.

The trials should also mention the funding and the trial number.

The abstract should be accurate as many readers have access only to the abstract.

11 Conclusions

An Abstract should be written last after all the other sections of the manuscript have been completed and with due care and attention to the details.

It should be structured and written in the IMRAD format.

For many readers, the abstract attracts them to go through the complete content of the article.

The abstract is usually followed by key words that help to index the paper.

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Preparing a manuscript for submission to a medical journal. Available on http://www.icmje.org/recommendations/browse/manuscript-preparation/preparing-for-submission.html . Accessed 10 May 2020.

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Nundy, S., Kakar, A., Bhutta, Z.A. (2022). How to Write an Abstract?. In: How to Practice Academic Medicine and Publish from Developing Countries?. Springer, Singapore. https://doi.org/10.1007/978-981-16-5248-6_15

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4 Examples of Academic Writing

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Written by  Scribendi

The best way to understand what effective academic writing looks like is to review academic writing examples.

Let's begin with four of the most common types of academic writing: research proposals, dissertations, abstracts, and academic articles. We'll be examining each type of writing and providing academic writing samples of each. 

Whether you aim to earn funding for a passion project or are stymied by how to format an abstract, these academic writing examples will help you nail your next undertaking.

Academic Writing Example 1: Research Proposals

A research proposal is an outline of the proposed research of a PhD candidate, a private researcher, or someone hoping to obtain a research grant . 

Your proposal should put your best foot forward: It details your intended research question and how it relates to existing research, makes an argument for why your research should be chosen for advancement or funding, and explains the deliverables you hope to achieve with your research. 

A more detailed look at what proposal writing is and what goes into a research proposal may also be beneficial. Every proposal is different because every project is different. Proposal requirements also differ according to the university or funding agency that reviews the proposal. 

Research Proposal Structure

A cover letter summarizing your proposal and showcasing why you should be chosen

An introduction or abstract

An explanation of the background, purpose, and significance of your research

A research plan or methodology that includes a timeline (a Gantt chart may be beneficial)

A projected budget, if applicable

Academic Writing Sample: Research Proposal Excerpt

Building on the work of the three foundational sociological theorists—Marx, Weber, and Durkheim—and Mark Traugott's theory of the "insurgent barricade," this proposed research will analyze the appearance, use, and disappearance of barricade warfare as an effective battle strategy. 

Focusing on these three theorists, this research will determine which theory or theories best explain the life cycle of barricade warfare, focusing in particular on its disappearance. A brief but comprehensive history of barricade warfare will be provided in addition to the theoretical explanations of barricade warfare's utility.

Research Proposal Writing Tips

Before you format your proposal, contact your targeted university, private organization, or funding agency to confirm what they require for proposals. Then, try to follow this format as closely as possible.

Be detailed when outlining your goals and your funding needs. Connect the objectives of the research to the resources you're requesting.

Be realistic in what you ask for as far as resources—don't ask for more or less than you need, and show evidence to justify your choices.

Don't dedicate too much text in your proposal to describing past research. A summary of key points, arguments, theories, and how your research will build on them should suffice.

Remember that no matter how good your proposal is, it might be rejected. You're likely up against dozens or even hundreds of other candidates who have equally sound proposals. Don't be discouraged if this happens. See it as a learning opportunity for your next proposal.

Academic Writing Example 2: Dissertations

A dissertation is a body of writing that represents original research and is generally written as part of a PhD or master's program. 

Typically, it builds on previous research in the field to make a significant contribution or advancement. You may benefit from more detailed information on what a dissertation is , how to write a dissertation , and how to edit a dissertation .

Dissertation Structure

Introduction/background and the significance of the study

Literature review

Methodology

Results/findings

Conclusion/contribution to the body of research

Academic Writing Sample: Dissertation Excerpt

There are two options for choosing a unit of analysis for this phenomenon: the social artifact (erected barricades) or the social interaction (the collaboration of insurgents engaged in barricade warfare). The best choice is social interaction. 

Most individual occurrences of barricade warfare involve the construction of more than one barricade, and the number of barricades is not necessarily a valid indicator of the sociological magnitude of an insurgence. The most relevant choice is an insurgence, the event of a conflict involving barricade warfare.

Dissertation Writing Tips

Remember to bear in mind the significance of your study. It doesn't have to be paradigm shifting, but you want to infuse the dissertation with reminders of why your research is important.

Don't get bogged down in trying to show that your research is one of a kind or uniquely contributive to the body of research. It likely isn't, and it's more effective to show how you are building on previous research .

Remember to check with your college or university to ensure that you're formatting your dissertation according to the school's expectations.

Ask your advisor questions when you need to.

Be prepared to make alterations to your dissertation according to your thesis committee's suggestions. This doesn't mean you did a bad job—it just means there's room for improvement.

Academic Writing Example 3: Abstracts

The abstract is actually a component of other forms of academic writing, such as scholarly articles and dissertations. The abstract acts as a comprehensive outline of your paper in paragraph form. 

Abstract Structure

Results 

You may want to read more about what abstracts are and why they are important in preparing yourself for writing one.

Academic Writing Sample: Abstract

Barricade warfare has occurred across several spectra, but most notably, it occurred almost exclusively in a 300-year period between the 16th and 19th centuries. Each instance had an inciting incident, but a common thread was the culture of revolution: a revolutionary tradition based on the belief that injustice was being carried out and that, in this case, barricade insurgence was the way to resolve it. 

This study uses the theories of Karl Marx and Emile Durkheim to analyze barricade warfare, its appearance, and its disappearance. Ultimately, neither theory can independently explain this phenomenon. 

Marx offers a reasonable explanation for why barricade warfare may have died, but his theory is difficult to test empirically and fails to explain the absence of recurrences. Conversely, Durkheim's theory is much easier to observe and can explain why barricade warfare has not experienced a renaissance. However, he offered no reason as to why it died in the first place. 

These two theoretical orientations complement each other nicely and, ultimately, neither can stand alone.

Notice that this abstract comes in at under 200 words (a common limit) but nevertheless covers the background of the study, how it was approached, and the results and conclusions of the research. 

If you are struggling to meet a word count, check out 10 Academic Phrases Your Writing Doesn't Need .

Abstract Writing Tips

Be conscious of your word count. Stay under the limit.

Check with your school or target journal to make sure special formatting is not required.

Don't use abbreviations or citations in the abstract.

Don't simply restate your thesis or copy your introduction. Neither of these is an abstract.

Remember that your abstract often gives readers their first impressions of your work. Despite its short length, it deserves a lot of attention. 

Academic Writing Example 4: Articles

Academic articles are pieces of writing intended for publication in academic journals or other scholarly sources. They may be original research studies, literature analyses, critiques , or other forms of scholarly writing.

Article Structure

Abstract and keywords

Introduction

Materials and methods

References and appendices

Academic Writing Sample: Article Excerpt

"Those great revolutionary barricades were places where heroes came together" (Hugo, 2008). This description by Victor Hugo of the 1832 June Rebellion in Paris comes from his seminal work of fiction, Les Miserables. 

Although the account is fictionalized, it is deeply representative of what historian Mark Traugott (2010, p. 225) terms the "culture of revolution." This spirit of heroic response to social injustice swept across Europe during the second half of the millennium and was characterized in part by barricade warfare. 

The phenomenon of the insurgent barricade has essentially disappeared, however, leaving no trace of its short-lived but intense epoch, and the question of why this happened remains a mystery. The theories of Karl Marx and Emile Durkheim, when taken together, provide a compelling explanation for the disappearance of barricade warfare, and the tenets of each theory will be examined to explain this phenomenon.

Article Writing Tips

Follow these detailed steps for writing an article and publishing it in a journal .

Make sure that you follow all of your target journal's guidelines.

Have a second set of educated eyes look over your article to correct typos, confusing language, and unclear arguments.

Don't be discouraged if your article is not chosen for publication. As with proposal writing, you are up against countless others with equally compelling research.

Don't be discouraged if the journal asks you to make changes to your article. This is common. It means they see value in your article, as well as room for improvement.

Whether you're applying for funding, earning an advanced degree, aiming to publish in a journal, or just trying to cram your 4,000-word study into a 150-word abstract, hopefully these academic writing examples have helped get your creative juices flowing. 

Go out there and write! With these academic writing samples at your side, you are sure to model your academic writing appropriately.

Achieve Your Academic Goals

Hire an expert academic editor , or get a free sample, about the author.

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Scribendi's in-house editors work with writers from all over the globe to perfect their writing. They know that no piece of writing is complete without a professional edit, and they love to see a good piece of writing transformed into a great one. Scribendi's in-house editors are unrivaled in both experience and education, having collectively edited millions of words and obtained numerous degrees. They love consuming caffeinated beverages, reading books of various genres, and relaxing in quiet, dimly lit spaces.

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Auriemma CL , O’Donnell H , Klaiman T, et al. How Traditional Advance Directives Undermine Advance Care Planning : If You Have It in Writing, You Do Not Have to Worry About It . JAMA Intern Med. 2022;182(6):682–684. doi:10.1001/jamainternmed.2022.1180

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How Traditional Advance Directives Undermine Advance Care Planning : If You Have It in Writing, You Do Not Have to Worry About It

  • 1 Palliative and Advanced Illness Research Center (PAIR), University of Pennsylvania, Philadelphia
  • 2 Department of Medicine, University of Pennsylvania, Philadelphia
  • 3 Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
  • 4 Perelman School of Medicine, University of Pennsylvania, Philadelphia,
  • 5 College of Arts and Sciences, University of Pennsylvania, Philadelphia,
  • 6 Mixed Methods Research Laboratory, University of Pennsylvania, Philadelphia,

Advance directives (ADs) are typically construed as legal documents in which individuals may identify a health care proxy and denote future care preferences. Some scholars view ADs as an important component of advance care planning (ACP), a process by which patients come to understand and share values, life goals, and preferences for future care. 1 Advance directives and ACP are even viewed as interchangeable constructs, such as in literature reviews that suggest these approaches are not associated with improved delivery of goal-concordant care, reduced aggressive end-of-life care, or higher quality of life for patients and families. 2 By contrast, it has been suggested that traditional ADs might actually undermine robust ACP. Because clinicians and researchers have recently called for complete abandonment of efforts to promote or even improve on ACP, 3 it is essential to better define the relationship between AD completion and the elicitation, clarification, and documentation of the patient’s goals and values. Therefore, we explored the perspectives of seriously ill patients on ADs and ACP.

We conducted a qualitative study using semistructured interviews with community-dwelling adults after being discharged to home from 1 of 2 urban, academic hospitals from July 31, 2020, to September 30, 2020. Participants were English-speaking, 65 years of age or older, and had been admitted for a chronic medical illness, such as cancer, heart failure, or pulmonary disease. A multidisciplinary team developed and piloted the interview guide. Telephone interviews were recorded and transcribed. Participants self-reported demographic information. An initial codebook was developed inductively, and transcripts were analyzed using content analysis with constant comparison techniques. 4 Two coders established strong interrater reliability (κ = 0.83), with 6 (21%) interviews double-coded. The institutional review board at the University of Pennsylvania approved this research, and participants provided verbal informed consent. Additional details are available in the eMethods in the Supplement ).

Thematic saturation was achieved after 29 interviews. Participants’ median (interquartile range) age was 72 (69-75) years. Approximately half (52%) of the participants were women; almost half (48%) did not identify as White; and 20 (69%) reported having completed an AD.

From among the 20 participants who had completed ADs, several key themes emerged. First, participants described completing generic forms that captured few details, often with the assistance of a lawyer rather than a clinician or family member. Second, participants were unable to recall the details of their completed ADs and did not describe revisiting the content of their ADs after the original completion ( Box 1 ). Third, the existence of an AD, regardless of the setting in which it was completed or its content, was commonly cited as a reason not to have or need a goals-of-care conversation with clinicians or family ( Box 2 ).

Advance Directives Were Not Based on Profound Beliefs

Themes and representative statements, a generalized form.

I don’t have it handy, but it was pretty much a stereotype form…fairly general.

Well, I put it off for years…You don’t want to be faced with that so you avoid it. But when I came up to the surgery, what went out for me was that I didn’t want my son and daughter to have to be put in that position, so I filled it out. I didn’t know what to put down sometimes. I just hadn’t thought about it as I had no experience with it in my life otherwise…Nobody talked to me about it or asked me anything. I got to the point where I did it like a day or 2 before the surgery...And I just was so motivated to get it done that it’s possible I skipped through some things and didn’t spend a lot of time dwelling on one question over another. My goal was to fill the damn thing and hope for the best.

Cannot recall the content

I don’t remember that now, it was a couple of years ago. I forget what we went through. I don’t remember exactly, no.

We have a Living Will…an advance directive. I remember doing it, I just don’t remember what it says.

Well, I have a living will. [pause] Yeah. I don’t know exactly what it says, but pretty much it’s like do what you can for an acceptable length of time.

Advance Directives as a Reason to Avoid Discussing Health Goals and Preferences With the Medical Team and Family

Representative statements.

I went through that part of the [living] will and all that. Maybe we didn’t talk about it as much as we should have, but they do know that if I was seriously ill and something were to happen, I have the living will…So we didn’t go in depth about it.

I am at a stage where I just need to make things less complicated for my family members. If you have it in writing, this that and the other, you don’t have to worry about it or discuss it. They don’t have to make any decisions.

We felt it was our responsibility, we didn’t want to leave that to our kids to decide. It’s too much pressure, so we decided to put it on the will to make everything easy for them…and, for [my wife], too. It’s not something my wife should have to figure out on her own without knowing what I wanted. So, no responsibility for her and for our kids. So, if I made those decisions for them, I think it’s a lot easier.

It [the advance directive] is for them [the medical team] to make the right decision. They almost have to make the right decision with your family, and your family already should know what you want. Mine do, pretty much. Although we never went into [it] in depth.

He [my doctor] is just dealing with the cancer. He’s just focused on getting this trial done and see how I do. He’s not interested in anything else right now…I don’t think it [my advance directive] would influence him at all. I would have to deal with that with a different kind of doctor, not my oncologist. He knows what I want. But I certainly would have to share that with, like, an internist or someone else, because he’s just a cancer doctor.

I don’t want anything heroic done if I really get sick and my heart stops, depending on the reason. Like if it’s something like pneumonia or an infection, that’s one thing, but if it’s just my body’s tired and there is no real point or quality of life, I don’t want anything heroic. You know we did not talk about under what circumstances, we certainly did not. That’s something maybe we should talk about, but we did not talk about it.

The findings of this study suggest that AD completion should not be viewed as the equivalent to engaging in ACP; indeed, the former may obstruct the goals of the latter. The participants’ statements suggest that the content of an AD is typically not based on deeply held values or nuanced beliefs. Furthermore, participants demonstrated a “set it and forget it” mentality toward ADs, revealing that AD completion often lessened the patient’s willingness to engage in more nuanced conversations about their current clinical status. These findings help to explain why structured interventions, including written ADs 5 and videos, 6 have conferred no benefits in carefully designed randomized clinical trials. These findings also demonstrate the importance of distinguishing between AD completion and robust goals-of-care conversations when interpreting calls to “end ACP.” 3

By enrolling older English-speaking patients from a single health system, these study findings may not be generalizable to younger populations or different cultural backgrounds and geographic areas. Although we used data saturation to define the sample size in this study, the relatively small sample may be another limitation.

This study provides important evidence to help reconcile differing perspectives on the value of ACP. Specifically, the perspectives of these 20 patients suggest that research sponsors and policy makers should continue to support efforts to improve the quality and frequency of goals-of-care conversations. Conversely, efforts to promote AD completion or to facilitate the AD process without prioritizing goals-of-care conversations should be halted.

Accepted for Publication: March 2, 2022.

Published Online: April 25, 2022. doi:10.1001/jamainternmed.2022.1180

Corresponding Author: Catherine L. Auriemma, MD, MS, PAIR Center, University of Pennsylvania, 300 Blockley Hall, 423 Guardian Dr, Philadelphia, PA 19104-6021 ( [email protected] ).

Author Contributions : Dr Auriemma had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Auriemma, O’Donnell, Klaiman, Halpern.

Acquisition, analysis, or interpretation of data: Auriemma, O’Donnell, Klaiman, Jones, Barbati, Akpek.

Drafting of the manuscript: Auriemma, O’Donnell, Klaiman, Jones, Barbati, Halpern.

Critical revision of the manuscript for important intellectual content: Auriemma, O’Donnell, Klaiman, Jones, Barbati, Akpek.

Statistical analysis: O’Donnell.

Obtained funding: Auriemma.

Administrative, technical, or material support: O’Donnell, Klaiman, Barbati, Halpern.

Supervision: Auriemma, Klaiman, Akpek.

Other—transcribing data and offering feedback during focus groups: Jones.

Conflict of Interest Disclosures: None reported.

Funding: Financial support for this study was provided through a Quartet Pilot Research Award funded by the Center for Health Incentives and Behavioral Economics and the PAIR Center at the University of Pennsylvania. Dr Auriemma is supported by a US National Institutes of Health’s (NIH) National Heart, Lung, and Blood Institute training grant (No. T32HL098054) and an NIH Loan Repayment Program Award (No. L30HL154185).

Role of the Funder: The PAIR Center, NIH, and University of Pennsylvania had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the University of Pennsylvania or the NIH.

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  • The role of COVID-19 vaccines in preventing post-COVID-19 thromboembolic and cardiovascular complications
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  • Núria Mercadé-Besora 1 , 2 , 3 ,
  • Xintong Li 1 ,
  • Raivo Kolde 4 ,
  • Nhung TH Trinh 5 ,
  • Maria T Sanchez-Santos 1 ,
  • Wai Yi Man 1 ,
  • Elena Roel 3 ,
  • Carlen Reyes 3 ,
  • http://orcid.org/0000-0003-0388-3403 Antonella Delmestri 1 ,
  • Hedvig M E Nordeng 6 , 7 ,
  • http://orcid.org/0000-0002-4036-3856 Anneli Uusküla 8 ,
  • http://orcid.org/0000-0002-8274-0357 Talita Duarte-Salles 3 , 9 ,
  • Clara Prats 2 ,
  • http://orcid.org/0000-0002-3950-6346 Daniel Prieto-Alhambra 1 , 9 ,
  • http://orcid.org/0000-0002-0000-0110 Annika M Jödicke 1 ,
  • Martí Català 1
  • 1 Pharmaco- and Device Epidemiology Group, Health Data Sciences, Botnar Research Centre, NDORMS , University of Oxford , Oxford , UK
  • 2 Department of Physics , Universitat Politècnica de Catalunya , Barcelona , Spain
  • 3 Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol) , IDIAP Jordi Gol , Barcelona , Catalunya , Spain
  • 4 Institute of Computer Science , University of Tartu , Tartu , Estonia
  • 5 Pharmacoepidemiology and Drug Safety Research Group, Department of Pharmacy, Faculty of Mathematics and Natural Sciences , University of Oslo , Oslo , Norway
  • 6 School of Pharmacy , University of Oslo , Oslo , Norway
  • 7 Division of Mental Health , Norwegian Institute of Public Health , Oslo , Norway
  • 8 Department of Family Medicine and Public Health , University of Tartu , Tartu , Estonia
  • 9 Department of Medical Informatics, Erasmus University Medical Center , Erasmus University Rotterdam , Rotterdam , Zuid-Holland , Netherlands
  • Correspondence to Prof Daniel Prieto-Alhambra, Pharmaco- and Device Epidemiology Group, Health Data Sciences, Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK; daniel.prietoalhambra{at}ndorms.ox.ac.uk

Objective To study the association between COVID-19 vaccination and the risk of post-COVID-19 cardiac and thromboembolic complications.

Methods We conducted a staggered cohort study based on national vaccination campaigns using electronic health records from the UK, Spain and Estonia. Vaccine rollout was grouped into four stages with predefined enrolment periods. Each stage included all individuals eligible for vaccination, with no previous SARS-CoV-2 infection or COVID-19 vaccine at the start date. Vaccination status was used as a time-varying exposure. Outcomes included heart failure (HF), venous thromboembolism (VTE) and arterial thrombosis/thromboembolism (ATE) recorded in four time windows after SARS-CoV-2 infection: 0–30, 31–90, 91–180 and 181–365 days. Propensity score overlap weighting and empirical calibration were used to minimise observed and unobserved confounding, respectively.

Fine-Gray models estimated subdistribution hazard ratios (sHR). Random effect meta-analyses were conducted across staggered cohorts and databases.

Results The study included 10.17 million vaccinated and 10.39 million unvaccinated people. Vaccination was associated with reduced risks of acute (30-day) and post-acute COVID-19 VTE, ATE and HF: for example, meta-analytic sHR of 0.22 (95% CI 0.17 to 0.29), 0.53 (0.44 to 0.63) and 0.45 (0.38 to 0.53), respectively, for 0–30 days after SARS-CoV-2 infection, while in the 91–180 days sHR were 0.53 (0.40 to 0.70), 0.72 (0.58 to 0.88) and 0.61 (0.51 to 0.73), respectively.

Conclusions COVID-19 vaccination reduced the risk of post-COVID-19 cardiac and thromboembolic outcomes. These effects were more pronounced for acute COVID-19 outcomes, consistent with known reductions in disease severity following breakthrough versus unvaccinated SARS-CoV-2 infection.

  • Epidemiology
  • PUBLIC HEALTH
  • Electronic Health Records

Data availability statement

Data may be obtained from a third party and are not publicly available. CPRD: CPRD data were obtained under the CPRD multi-study license held by the University of Oxford after Research Data Governance (RDG) approval. Direct data sharing is not allowed. SIDIAP: In accordance with current European and national law, the data used in this study is only available for the researchers participating in this study. Thus, we are not allowed to distribute or make publicly available the data to other parties. However, researchers from public institutions can request data from SIDIAP if they comply with certain requirements. Further information is available online ( https://www.sidiap.org/index.php/menu-solicitudesen/application-proccedure ) or by contacting SIDIAP ([email protected]). CORIVA: CORIVA data were obtained under the approval of Research Ethics Committee of the University of Tartu and the patient level data sharing is not allowed. All analyses in this study were conducted in a federated manner, where analytical code and aggregated (anonymised) results were shared, but no patient-level data was transferred across the collaborating institutions.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/heartjnl-2023-323483

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WHAT IS ALREADY KNOWN ON THIS TOPIC

COVID-19 vaccines proved to be highly effective in reducing the severity of acute SARS-CoV-2 infection.

While COVID-19 vaccines were associated with increased risk for cardiac and thromboembolic events, such as myocarditis and thrombosis, the risk of complications was substantially higher due to SARS-CoV-2 infection.

WHAT THIS STUDY ADDS

COVID-19 vaccination reduced the risk of heart failure, venous thromboembolism and arterial thrombosis/thromboembolism in the acute (30 days) and post-acute (31 to 365 days) phase following SARS-CoV-2 infection. This effect was stronger in the acute phase.

The overall additive effect of vaccination on the risk of post-vaccine and/or post-COVID thromboembolic and cardiac events needs further research.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

COVID-19 vaccines proved to be highly effective in reducing the risk of post-COVID cardiovascular and thromboembolic complications.

Introduction

COVID-19 vaccines were approved under emergency authorisation in December 2020 and showed high effectiveness against SARS-CoV-2 infection, COVID-19-related hospitalisation and death. 1 2 However, concerns were raised after spontaneous reports of unusual thromboembolic events following adenovirus-based COVID-19 vaccines, an association that was further assessed in observational studies. 3 4 More recently, mRNA-based vaccines were found to be associated with a risk of rare myocarditis events. 5 6

On the other hand, SARS-CoV-2 infection can trigger cardiac and thromboembolic complications. 7 8 Previous studies showed that, while slowly decreasing over time, the risk for serious complications remain high for up to a year after infection. 9 10 Although acute and post-acute cardiac and thromboembolic complications following COVID-19 are rare, they present a substantial burden to the affected patients, and the absolute number of cases globally could become substantial.

Recent studies suggest that COVID-19 vaccination could protect against cardiac and thromboembolic complications attributable to COVID-19. 11 12 However, most studies did not include long-term complications and were conducted among specific populations.

Evidence is still scarce as to whether the combined effects of COVID-19 vaccines protecting against SARS-CoV-2 infection and reducing post-COVID-19 cardiac and thromboembolic outcomes, outweigh any risks of these complications potentially associated with vaccination.

We therefore used large, representative data sources from three European countries to assess the overall effect of COVID-19 vaccines on the risk of acute and post-acute COVID-19 complications including venous thromboembolism (VTE), arterial thrombosis/thromboembolism (ATE) and other cardiac events. Additionally, we studied the comparative effects of ChAdOx1 versus BNT162b2 on the risk of these same outcomes.

Data sources

We used four routinely collected population-based healthcare datasets from three European countries: the UK, Spain and Estonia.

For the UK, we used data from two primary care databases—namely, Clinical Practice Research Datalink, CPRD Aurum 13 and CPRD Gold. 14 CPRD Aurum currently covers 13 million people from predominantly English practices, while CPRD Gold comprises 3.1 million active participants mostly from GP practices in Wales and Scotland. Spanish data were provided by the Information System for the Development of Research in Primary Care (SIDIAP), 15 which encompasses primary care records from 6 million active patients (around 75% of the population in the region of Catalonia) linked to hospital admissions data (Conjunt Mínim Bàsic de Dades d’Alta Hospitalària). Finally, the CORIVA dataset based on national health claims data from Estonia was used. It contains all COVID-19 cases from the first year of the pandemic and ~440 000 randomly selected controls. CORIVA was linked to the death registry and all COVID-19 testing from the national health information system.

Databases included sociodemographic information, diagnoses, measurements, prescriptions and secondary care referrals and were linked to vaccine registries, including records of all administered vaccines from all healthcare settings. Data availability for CPRD Gold ended in December 2021, CPRD Aurum in January 2022, SIDIAP in June 2022 and CORIVA in December 2022.

All databases were mapped to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) 16 to facilitate federated analytics.

Multinational network staggered cohort study: study design and participants

The study design has been published in detail elsewhere. 17 Briefly, we used a staggered cohort design considering vaccination as a time-varying exposure. Four staggered cohorts were designed with each cohort representing a country-specific vaccination rollout phase (eg, dates when people became eligible for vaccination, and eligibility criteria).

The source population comprised all adults registered in the respective database for at least 180 days at the start of the study (4 January 2021 for CPRD Gold and Aurum, 20 February 2021 for SIDIAP and 28 January 2021 for CORIVA). Subsequently, each staggered cohort corresponded to an enrolment period: all people eligible for vaccination during this time were included in the cohort and people with a history of SARS-CoV-2 infection or COVID-19 vaccination before the start of the enrolment period were excluded. Across countries, cohort 1 comprised older age groups, whereas cohort 2 comprised individuals at risk for severe COVID-19. Cohort 3 included people aged ≥40 and cohort 4 enrolled people aged ≥18.

In each cohort, people receiving a first vaccine dose during the enrolment period were allocated to the vaccinated group, with their index date being the date of vaccination. Individuals who did not receive a vaccine dose comprised the unvaccinated group and their index date was assigned within the enrolment period, based on the distribution of index dates in the vaccinated group. People with COVID-19 before the index date were excluded.

Follow-up started from the index date until the earliest of end of available data, death, change in exposure status (first vaccine dose for those unvaccinated) or outcome of interest.

COVID-19 vaccination

All vaccines approved within the study period from January 2021 to July 2021—namely, ChAdOx1 (Oxford/AstraZeneca), BNT162b2 (BioNTech/Pfizer]) Ad26.COV2.S (Janssen) and mRNA-1273 (Moderna), were included for this study.

Post-COVID-19 outcomes of interest

Outcomes of interest were defined as SARS-CoV-2 infection followed by a predefined thromboembolic or cardiac event of interest within a year after infection, and with no record of the same clinical event in the 6 months before COVID-19. Outcome date was set as the corresponding SARS-CoV-2 infection date.

COVID-19 was identified from either a positive SARS-CoV-2 test (polymerase chain reaction (PCR) or antigen), or a clinical COVID-19 diagnosis, with no record of COVID-19 in the previous 6 weeks. This wash-out period was imposed to exclude re-recordings of the same COVID-19 episode.

Post-COVID-19 outcome events were selected based on previous studies. 11–13 Events comprised ischaemic stroke (IS), haemorrhagic stroke (HS), transient ischaemic attack (TIA), ventricular arrhythmia/cardiac arrest (VACA), myocarditis/pericarditis (MP), myocardial infarction (MI), heart failure (HF), pulmonary embolism (PE) and deep vein thrombosis (DVT). We used two composite outcomes: (1) VTE, as an aggregate of PE and DVT and (2) ATE, as a composite of IS, TIA and MI. To avoid re-recording of the same complication we imposed a wash-out period of 90 days between records. Phenotypes for these complications were based on previously published studies. 3 4 8 18

All outcomes were ascertained in four different time periods following SARS-CoV-2 infection: the first period described the acute infection phase—that is, 0–30 days after COVID-19, whereas the later periods - which are 31–90 days, 91–180 days and 181–365 days, illustrate the post-acute phase ( figure 1 ).

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Study outcome design. Study outcomes of interest are defined as a COVID-19 infection followed by one of the complications in the figure, within a year after infection. Outcomes were ascertained in four different time windows after SARS-CoV-2 infection: 0–30 days (namely the acute phase), 31–90 days, 91–180 days and 181–365 days (these last three comprise the post-acute phase).

Negative control outcomes

Negative control outcomes (NCOs) were used to detect residual confounding. NCOs are outcomes which are not believed to be causally associated with the exposure, but share the same bias structure with the exposure and outcome of interest. Therefore, no significant association between exposure and NCO is to be expected. Our study used 43 different NCOs from previous work assessing vaccine effectiveness. 19

Statistical analysis

Federated network analyses.

A template for an analytical script was developed and subsequently tailored to include the country-specific aspects (eg, dates, priority groups) for the vaccination rollout. Analyses were conducted locally for each database. Only aggregated data were shared and person counts <5 were clouded.

Propensity score weighting

Large-scale propensity scores (PS) were calculated to estimate the likelihood of a person receiving the vaccine based on their demographic and health-related characteristics (eg, conditions, medications) prior to the index date. PS were then used to minimise observed confounding by creating a weighted population (overlap weighting 20 ), in which individuals contributed with a different weight based on their PS and vaccination status.

Prespecified key variables included in the PS comprised age, sex, location, index date, prior observation time in the database, number of previous outpatient visits and previous SARS-CoV-2 PCR/antigen tests. Regional vaccination, testing and COVID-19 incidence rates were also forced into the PS equation for the UK databases 21 and SIDIAP. 22 In addition, least absolute shrinkage and selection operator (LASSO) regression, a technique for variable selection, was used to identify additional variables from all recorded conditions and prescriptions within 0–30 days, 31–180 days and 181-any time (conditions only) before the index date that had a prevalence of >0.5% in the study population.

PS were then separately estimated for each staggered cohort and analysis. We considered covariate balance to be achieved if absolute standardised mean differences (ASMDs) were ≤0.1 after weighting. Baseline characteristics such as demographics and comorbidities were reported.

Effect estimation

To account for the competing risk of death associated with COVID-19, Fine-and-Grey models 23 were used to calculate subdistribution hazard ratios (sHRs). Subsequently, sHRs and confidence intervals were empirically calibrated from NCO estimates 24 to account for unmeasured confounding. To calibrate the estimates, the empirical null distribution was derived from NCO estimates and was used to compute calibrated confidence intervals. For each outcome, sHRs from the four staggered cohorts were pooled using random-effect meta-analysis, both separately for each database and across all four databases.

Sensitivity analysis

Sensitivity analyses comprised 1) censoring follow-up for vaccinated people at the time when they received their second vaccine dose and 2) considering only the first post-COVID-19 outcome within the year after infection ( online supplemental figure S1 ). In addition, comparative effectiveness analyses were conducted for BNT162b2 versus ChAdOx1.

Supplemental material

Data and code availability.

All analytic code for the study is available in GitHub ( https://github.com/oxford-pharmacoepi/vaccineEffectOnPostCovidCardiacThromboembolicEvents ), including code lists for vaccines, COVID-19 tests and diagnoses, cardiac and thromboembolic events, NCO and health conditions to prioritise patients for vaccination in each country. We used R version 4.2.3 and statistical packages survival (3.5–3), Empirical Calibration (3.1.1), glmnet (4.1-7), and Hmisc (5.0–1).

Patient and public involvement

Owing to the nature of the study and the limitations regarding data privacy, the study design, analysis, interpretation of data and revision of the manuscript did not involve any patients or members of the public.

All aggregated results are available in a web application ( https://dpa-pde-oxford.shinyapps.io/PostCovidComplications/ ).

We included over 10.17 million vaccinated individuals (1 618 395 from CPRD Gold; 5 729 800 from CPRD Aurum; 2 744 821 from SIDIAP and 77 603 from CORIVA) and 10.39 million unvaccinated individuals (1 640 371; 5 860 564; 2 588 518 and 302 267, respectively). Online supplemental figures S2-5 illustrate study inclusion for each database.

Adequate covariate balance was achieved after PS weighting in most studies: CORIVA (all cohorts) and SIDIAP (cohorts 1 and 4) did not contribute to ChAdOx1 subanalyses owing to sample size and covariate imbalance. ASMD results are accessible in the web application.

NCO analyses suggested residual bias after PS weighting, with a majority of NCOs associated positively with vaccination. Therefore, calibrated estimates are reported in this manuscript. Uncalibrated effect estimates and NCO analyses are available in the web interface.

Population characteristics

Table 1 presents baseline characteristics for the weighted populations in CPRD Aurum, for illustrative purposes. Online supplemental tables S1-25 summarise baseline characteristics for weighted and unweighted populations for each database and comparison. Across databases and cohorts, populations followed similar patterns: cohort 1 represented an older subpopulation (around 80 years old) with a high proportion of women (57%). Median age was lowest in cohort 4 ranging between 30 and 40 years.

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Characteristics of weighted populations in CPRD Aurum database, stratified by staggered cohort and exposure status. Exposure is any COVID-19 vaccine

COVID-19 vaccination and post-COVID-19 complications

Table 2 shows the incidence of post-COVID-19 VTE, ATE and HF, the three most common post-COVID-19 conditions among the studied outcomes. Outcome counts are presented separately for 0–30, 31–90, 91–180 and 181–365 days after SARS-CoV-2 infection. Online supplemental tables S26-36 include all studied complications, also for the sensitivity and subanalyses. Similar pattern for incidences were observed across all databases: higher outcome rates in the older populations (cohort 1) and decreasing frequency with increasing time after infection in all cohorts.

Number of records (and risk per 10 000 individuals) for acute and post-acute COVID-19 cardiac and thromboembolic complications, across cohorts and databases for any COVID-19 vaccination

Forest plots for the effect of COVID-19 vaccines on post-COVID-19 cardiac and thromboembolic complications; meta-analysis across cohorts and databases. Dashed line represents a level of heterogeneity I 2 >0.4. ATE, arterial thrombosis/thromboembolism; CD+HS, cardiac diseases and haemorrhagic stroke; VTE, venous thromboembolism.

Results from calibrated estimates pooled in meta-analysis across cohorts and databases are shown in figure 2 .

Reduced risk associated with vaccination is observed for acute and post-acute VTE, DVT, and PE: acute meta-analytic sHR are 0.22 (95% CI, 0.17–0.29); 0.36 (0.28–0.45); and 0.19 (0.15–0.25), respectively. For VTE in the post-acute phase, sHR estimates are 0.43 (0.34–0.53), 0.53 (0.40–0.70) and 0.50 (0.36–0.70) for 31–90, 91–180, and 181–365 days post COVID-19, respectively. Reduced risk of VTE outcomes was observed in vaccinated across databases and cohorts, see online supplemental figures S14–22 .

Similarly, the risk of ATE, IS and MI in the acute phase after infection was reduced for the vaccinated group, sHR of 0.53 (0.44–0.63), 0.55 (0.43–0.70) and 0.49 (0.38–0.62), respectively. Reduced risk associated with vaccination persisted for post-acute ATE, with sHR of 0.74 (0.60–0.92), 0.72 (0.58–0.88) and 0.62 (0.48–0.80) for 31–90, 91–180 and 181–365 days post-COVID-19, respectively. Risk of post-acute MI remained lower for vaccinated in the 31–90 and 91–180 days after COVID-19, with sHR of 0.64 (0.46–0.87) and 0.64 (0.45–0.90), respectively. Vaccination effect on post-COVID-19 TIA was seen only in the 181–365 days, with sHR of 0.51 (0.31–0.82). Online supplemental figures S23-31 show database-specific and cohort-specific estimates for ATE-related complications.

Risk of post-COVID-19 cardiac complications was reduced in vaccinated individuals. Meta-analytic estimates in the acute phase showed sHR of 0.45 (0.38–0.53) for HF, 0.41 (0.26–0.66) for MP and 0.41 (0.27–0.63) for VACA. Reduced risk persisted for post-acute COVID-19 HF: sHR 0.61 (0.51–0.73) for 31–90 days, 0.61 (0.51–0.73) for 91–180 days and 0.52 (0.43–0.63) for 181–365 days. For post-acute MP, risk was only lowered in the first post-acute window (31–90 days), with sHR of 0.43 (0.21–0.85). Vaccination showed no association with post-COVID-19 HS. Database-specific and cohort-specific results for these cardiac diseases are shown in online supplemental figures S32-40 .

Stratified analyses by vaccine showed similar associations, except for ChAdOx1 which was not associated with reduced VTE and ATE risk in the last post-acute window. Sensitivity analyses were consistent with main results ( online supplemental figures S6-13 ).

Figure 3 shows the results of comparative effects of BNT162b2 versus ChAdOx1, based on UK data. Meta-analytic estimates favoured BNT162b2 (sHR of 0.66 (0.46–0.93)) for VTE in the 0–30 days after infection, but no differences were seen for post-acute VTE or for any of the other outcomes. Results from sensitivity analyses, database-specific and cohort-specific estimates were in line with the main findings ( online supplemental figures S41-51 ).

Forest plots for comparative vaccine effect (BNT162b2 vs ChAdOx1); meta-analysis across cohorts and databases. ATE, arterial thrombosis/thromboembolism; CD+HS, cardiac diseases and haemorrhagic stroke; VTE, venous thromboembolism.

Key findings

Our analyses showed a substantial reduction of risk (45–81%) for thromboembolic and cardiac events in the acute phase of COVID-19 associated with vaccination. This finding was consistent across four databases and three different European countries. Risks for post-acute COVID-19 VTE, ATE and HF were reduced to a lesser extent (24–58%), whereas a reduced risk for post-COVID-19 MP and VACA in vaccinated people was seen only in the acute phase.

Results in context

The relationship between SARS-CoV-2 infection, COVID-19 vaccines and thromboembolic and/or cardiac complications is tangled. Some large studies report an increased risk of VTE and ATE following both ChAdOx1 and BNT162b2 vaccination, 7 whereas other studies have not identified such a risk. 25 Elevated risk of VTE has also been reported among patients with COVID-19 and its occurrence can lead to poor prognosis and mortality. 26 27 Similarly, several observational studies have found an association between COVID-19 mRNA vaccination and a short-term increased risk of myocarditis, particularly among younger male individuals. 5 6 For instance, a self-controlled case series study conducted in England revealed about 30% increased risk of hospital admission due to myocarditis within 28 days following both ChAdOx1 and BNT162b2 vaccines. However, this same study also found a ninefold higher risk for myocarditis following a positive SARS-CoV-2 test, clearly offsetting the observed post-vaccine risk.

COVID-19 vaccines have demonstrated high efficacy and effectiveness in preventing infection and reducing the severity of acute-phase infection. However, with the emergence of newer variants of the virus, such as omicron, and the waning protective effect of the vaccine over time, there is a growing interest in understanding whether the vaccine can also reduce the risk of complications after breakthrough infections. Recent studies suggested that COVID-19 vaccination could potentially protect against acute post-COVID-19 cardiac and thromboembolic events. 11 12 A large prospective cohort study 11 reports risk of VTE after SARS-CoV-2 infection to be substantially reduced in fully vaccinated ambulatory patients. Likewise, Al-Aly et al 12 suggest a reduced risk for post-acute COVID-19 conditions in breakthrough infection versus SARS-CoV-2 infection without prior vaccination. However, the populations were limited to SARS-CoV-2 infected individuals and estimates did not include the effect of the vaccine to prevent COVID-19 in the first place. Other studies on post-acute COVID-19 conditions and symptoms have been conducted, 28 29 but there has been limited reporting on the condition-specific risks associated with COVID-19, even though the prognosis for different complications can vary significantly.

In line with previous studies, our findings suggest a potential benefit of vaccination in reducing the risk of post-COVID-19 thromboembolic and cardiac complications. We included broader populations, estimated the risk in both acute and post-acute infection phases and replicated these using four large independent observational databases. By pooling results across different settings, we provided the most up-to-date and robust evidence on this topic.

Strengths and limitations

The study has several strengths. Our multinational study covering different healthcare systems and settings showed consistent results across all databases, which highlights the robustness and replicability of our findings. All databases had complete recordings of vaccination status (date and vaccine) and are representative of the respective general population. Algorithms to identify study outcomes were used in previous published network studies, including regulatory-funded research. 3 4 8 18 Other strengths are the staggered cohort design which minimises confounding by indication and immortal time bias. PS overlap weighting and NCO empirical calibration have been shown to adequately minimise bias in vaccine effectiveness studies. 19 Furthermore, our estimates include the vaccine effectiveness against COVID-19, which is crucial in the pathway to experience post-COVID-19 complications.

Our study has some limitations. The use of real-world data comes with inherent limitations including data quality concerns and risk of confounding. To deal with these limitations, we employed state-of-the-art methods, including large-scale propensity score weighting and calibration of effect estimates using NCO. 19 24 A recent study 30 has demonstrated that methodologically sound observational studies based on routinely collected data can produce results similar to those of clinical trials. We acknowledge that results from NCO were positively associated with vaccination, and estimates might still be influenced by residual bias despite using calibration. Another limitation is potential under-reporting of post-COVID-19 complications: some asymptomatic and mild COVID-19 infections might have not been recorded. Additionally, post-COVID-19 outcomes of interest might be under-recorded in primary care databases (CPRD Aurum and Gold) without hospital linkage, which represent a large proportion of the data in the study. However, results in SIDIAP and CORIVA, which include secondary care data, were similar. Also, our study included a small number of young men and male teenagers, who were the main population concerned with increased risks of myocarditis/pericarditis following vaccination.

Conclusions

Vaccination against SARS-CoV-2 substantially reduced the risk of acute post-COVID-19 thromboembolic and cardiac complications, probably through a reduction in the risk of SARS-CoV-2 infection and the severity of COVID-19 disease due to vaccine-induced immunity. Reduced risk in vaccinated people lasted for up to 1 year for post-COVID-19 VTE, ATE and HF, but not clearly for other complications. Findings from this study highlight yet another benefit of COVID-19 vaccination. However, further research is needed on the possible waning of the risk reduction over time and on the impact of booster vaccination.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

The study was approved by the CPRD’s Research Data Governance Process, Protocol No 21_000557 and the Clinical Research Ethics committee of Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol) (approval number 4R22/133) and the Research Ethics Committee of the University of Tartu (approval No. 330/T-10).

Acknowledgments

This study is based in part on data from the Clinical Practice Research Datalink (CPRD) obtained under licence from the UK Medicines and Healthcare products Regulatory Agency. We thank the patients who provided these data, and the NHS who collected the data as part of their care and support. All interpretations, conclusions and views expressed in this publication are those of the authors alone and not necessarily those of CPRD. We would also like to thank the healthcare professionals in the Catalan healthcare system involved in the management of COVID-19 during these challenging times, from primary care to intensive care units; the Institut de Català de la Salut and the Program d’Analítica de Dades per a la Recerca i la Innovació en Salut for providing access to the different data sources accessible through The System for the Development of Research in Primary Care (SIDIAP).

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Supplementary materials

Supplementary data.

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AMJ and MC are joint senior authors.

Contributors DPA and AMJ led the conceptualisation of the study with contributions from MC and NM-B. AMJ, TD-S, ER, AU and NTHT adapted the study design with respect to the local vaccine rollouts. AD and WYM mapped and curated CPRD data. MC and NM-B developed code with methodological contributions advice from MTS-S and CP. DPA, MC, NTHT, TD-S, HMEN, XL, CR and AMJ clinically interpreted the results. NM-B, XL, AMJ and DPA wrote the first draft of the manuscript, and all authors read, revised and approved the final version. DPA and AMJ obtained the funding for this research. DPA is responsible for the overall content as guarantor: he accepts full responsibility for the work and the conduct of the study, had access to the data, and controlled the decision to publish.

Funding The research was supported by the National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (BRC). DPA is funded through a NIHR Senior Research Fellowship (Grant number SRF-2018–11-ST2-004). Funding to perform the study in the SIDIAP database was provided by the Real World Epidemiology (RWEpi) research group at IDIAPJGol. Costs of databases mapping to OMOP CDM were covered by the European Health Data and Evidence Network (EHDEN).

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Editorial article, editorial: disciplinary aesthetics: the role of taste and affect for teaching and learning specific school subjects.

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  • 1 Department of Teaching and Learning, Stockholm University, Stockholm, Sweden
  • 2 Education and Administration, Stockholm, Sweden
  • 3 School of Teacher Education and Professional Development, Manchester Metropolitan University, Manchester, United Kingdom

Editorial on the Research Topic Disciplinary aesthetics: the role of taste and affect for teaching and learning specific school subjects

Aesthetics concerns, on the one hand, people's feelings of pleasure and displeasure, and, on the other hand, the objects these feelings are directed to, that is, what people find beautiful or ugly ( Wickman, 2006 ). Traditionally aesthetics and affect have been treated as separate from cognition and only rarely has it been studied how they are intertwined when learning a specific content ( Wickman et al., 2021 ). However, recent situated and socio-culturally oriented research has begun to elucidate how aesthetics plays a key role for selection of content, what route learning takes in the classroom and for students' opportunities to develop an interest or taste for a specific school subject (e.g., Sinclair, 2006 ; Ainsworth and Bell, 2020 ; Wickman et al., 2021 ). This Research Topic compiles contributions from researchers examining these topics further.

Aesthetic judgments are not just reports of inner feelings but also concern outer objects and so constitutes an evaluation of what is the case ( Dewey, 1934 ). What beauty there is in educational settings such as mathematical inquiry ( Sinclair, 2009 ), data modeling ( Ferguson et al., 2021 ), writing a literary text ( Gilbert, 2016 ), learning grammar ( Ainsworth and Bell, 2020 ), cooking ( Berg et al., 2019 ), a ball game ( Maivorsdotter and Lundvall, 2009 ), or when art meets science ( Hannigan et al., 2021 ) is a question of taste and is socially constituted, negotiated, and learnt ( Bourdieu, 1984 ). Distinctions of taste make evident preferences of language and representations, procedures and actions, and ways-to-be as a person. Aesthetics is a question of what and whose content is included and excluded from a school subject ( Anderhag et al., 2015 ). The goal of this Research Topic is to explore these little examined topics extensively and to widen the understanding of what may characterize a school-subject-specific aesthetics and what role it may have when teaching and learning different school subjects, separately or as integrated.

The Research Topic is grounded in the notion of disciplinary aesthetics ( Wickman et al., 2021 ), that is, school-subject-specific aesthetics. It focuses on the overarching questions of what may characterize such an aesthetics and what role this may have for teaching and learning in different school subjects. Contributions to the field do not merely examine specific school subjects, such as mathematics and history, but also studies on intersections between school subjects, as for example art and science ( Caiman and Jakobson, 2019 ).

Ainsworth and Bell suggest that explicit grammar learning may evoke aesthetic experience as existing tacit knowledge of language is transformed into declarative knowledge, generating aesthetic-epistemic feelings of fittingness. Albuquerque and Moore suggest that additional language teaching and learning might be enhanced by framing it as “coartistry,” a site for “aesthetic, plurilingual/pluriliterate action, and interaction.” Andrée et al. explore learning in programming, demonstrating the importance of aesthetic judgments for orienting student learning toward the movement of the programmed object and the ways to be as a programmer. Berg et al. show how aesthetic values in teaching home and consumer studies play a key role and are constituted as culinary, production, and bodily aesthetics, relating to, for example, presentation of meals, preprocessing of food and bodily consequences of eating, respectively. Ferguson and White draw on a socio-semiotic pragmatist perspective to explore the synergy between science education aesthetics and climate change aesthetics, advocating for a transformative aesthetics of climate change education. Gåfvels explores the aesthetics involved in teaching and learning floristry, providing examples of aesthetic judgements being constructed in interaction, informed by sensory knowing and communicated through embodied actions. Hannigan et al. present a mixed methodology approach to examine the role of aesthetic experiences and art for learning in marine science when children engage in a series of fieldtrips, workshops and lessons on a marine environment. Karavakou et al. present a theoretical model for analyzing students' aesthetically driven mathematical meaning making, using empirical findings to discuss the prospect of an aesthetically oriented curriculum reform. Nemirovsky et al. draw on Rancière's approach to aesthetics and politics and a case study of a conversation between weavers, anthropologists, and mathematics educators on the nature of knots to discuss the implications of aesthetical entanglements for mathematics learning. Prain et al. adopt Peirce's semiotic theory of signs examining disciplinary aesthetics as enjoyment and appreciation learning within and across the two subjects drama and science. They show how students' taste for both subjects is constituted through signs and signs systems.

The contributions within this Research Topic make both empirical and theoretical contributions to the emerging field of disciplinary aesthetics. Together they provide exploratory responses to the hitherto understudied questions:

• What are the objects (language, procedures, and persons) that are aesthetically included or excluded as part of teaching and learning the subject?

• How can such distinctions be seen to be taught and learned as content of the subject?

Substantively, the studies provide an exploration of how the aesthetic dimensions of each of the academic subjects might be characterized, exemplifying the aesthetic experiences that may arise from engaging with particular kinds of subject content in particular ways. Methodologically, the contributions showcase a range of methods that might be used to capture such aesthetic experiences and the particular aspects of knowledge and/or learning that have the potential to evoke them. These articles provide a flexible suite of methodological and theoretical tools, which might be used in future research to broaden the field of disciplinary aesthetics to include further academic disciplines. They also yield a number of important implications for educators, including suggestions for how teachers might harness the aesthetic dimensions of particular subjects to maximize learning and engagement in the classroom. More broadly, the Research Topic argues for the role of aesthetics in education to be taken seriously, and signposts potentially fruitful avenues for future research.

Author contributions

PA: Writing – original draft, Writing – review & editing. CC: Writing – original draft, Writing – review & editing. P-OW: Writing – original draft, Writing – review & editing. SA: Writing – original draft, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Ainsworth, S., and Bell, H. (2020). Affective knowledge versus affective pedagogy: the cases of native grammar learning. Cambr. J. Educ . 50, 597–614. doi: 10.1080/0305764X.2020.1751072

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Berg, G., Elmståhl, H., Mattsson Sydner, Y., and Lundqvist, E. (2019). Aesthetic judgments and meaning-making during cooking in Home and Consumer Studies. Educare 2, 30–57. doi: 10.24834/educare.2019.2.3

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Maivorsdotter, N., and Lundvall, S. (2009). Aesthetic experience as an aspect of embodied learning: stories from physical education student teachers. Sport Educ. Soc . 14, 265–279. doi: 10.1080/13573320903037622

Sinclair, N. (2006). Mathematics and Beauty: Aesthetics Approaches to Teaching Children . New York, NY: Teachers College Press.

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Wickman, P. O. (2006). Aesthetic Experience in Science Education: Learning and Meaning-Making as Situated Talk and Action . Mahwah, NJ: Lawrence Erlbaum Associates.

Wickman, P. O., Prain, V., and Tytler, R. (2021). Aesthetics, affect, and making meaning in science education: an introduction. Int. J. Sci. Educ . 2021:1912434. doi: 10.1080/09500693.2021.1912434

Keywords: methodology, interest, aesthetics, learning, school subjects

Citation: Anderhag P, Caiman C, Wickman P-O and Ainsworth S (2024) Editorial: Disciplinary aesthetics: the role of taste and affect for teaching and learning specific school subjects. Front. Educ. 9:1396318. doi: 10.3389/feduc.2024.1396318

Received: 05 March 2024; Accepted: 12 March 2024; Published: 27 March 2024.

Edited and reviewed by: Stefinee Pinnegar , Brigham Young University, United States

Copyright © 2024 Anderhag, Caiman, Wickman and Ainsworth. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Per Anderhag, per.anderhag@su.se

This article is part of the Research Topic

Disciplinary Aesthetics: the Role of Taste and Affect for Teaching and Learning Specific School Subjects

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  • Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

  • Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
  • Supinya Piampongsant 1 , 2 , 3   na1 ,
  • Miguel Roncoroni   ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3   na1 ,
  • Lloyd Cool   ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
  • Beatriz Herrera-Malaver   ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
  • Christophe Vanderaa   ORCID: orcid.org/0000-0001-7443-5427 4 ,
  • Florian A. Theßeling 1 , 2 , 3 ,
  • Łukasz Kreft   ORCID: orcid.org/0000-0001-7620-4657 5 ,
  • Alexander Botzki   ORCID: orcid.org/0000-0001-6691-4233 5 ,
  • Philippe Malcorps 6 ,
  • Luk Daenen 6 ,
  • Tom Wenseleers   ORCID: orcid.org/0000-0002-1434-861X 4 &
  • Kevin J. Verstrepen   ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3  

Nature Communications volume  15 , Article number:  2368 ( 2024 ) Cite this article

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  • Chemical engineering
  • Gas chromatography
  • Machine learning
  • Metabolomics
  • Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig.  S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table  S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig.  1 , upper panel, Supplementary Data  1 and 2 , and Supplementary Fig.  S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig.  1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

figure 1

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data  1 , correlations between all chemical compounds are depicted in Supplementary Fig.  S2 and correlation values can be found in Supplementary Data  2 . See Supplementary Data  4 for sensory panel assessments and Supplementary Data  5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig.  S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data  3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p  > 0.05), indicating good panel consistency (Supplementary Table  S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig.  1 , bottom left panel and Supplementary Data  4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig.  S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig.  S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig.  2 , Supplementary Fig.  S5 , Supplementary Data  6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

figure 2

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data  6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig.  S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig.  3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig.  3 and below).

figure 3

RateBeer text mining results can be found in Supplementary Data  7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p  < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data  7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig.  3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table  1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2  = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table  S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table  S3 and Supplementary Table  S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table  S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig.  4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig.  4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

figure 4

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig.  4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig.  S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig.  4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig.  S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig.  S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig.  S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig.  S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2  = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig.  S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig.  S9 , Supplementary Table  S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data  1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig.  5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig.  5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

figure 5

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n  = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data  1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig.  S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table  S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table  S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table  S7 and Supplementary Table  S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data  3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table  S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table  S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p  <  0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA).  Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

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VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium

Philippe Malcorps & Luk Daenen

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Contributions

S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

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Correspondence to Kevin J. Verstrepen .

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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A la derecha, _Naranja y amarillo_ de Mark Rothko. A la izquierda, vestido de día en crepé de lana naranja, de Cristóbal Balenciaga, 1967.

Balenciaga and the influence of abstract art

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In January, the TV series Cristóbal Balenciaga premiered, a story inspired by the life of the Spanish designer during his time in Paris, beginning when he arrived in 1937. The plot seeks to explore his personality and what drove him, highlighting key moments in his personal and professional life, such as his relationship with other illustrious designers, the creation of the gazar fabric, the design of Queen Fabiola’s wedding dress , and the creation of Air France’s stewardess uniforms .

Though fashion is present throughout the show’s six episodes, the couturiers’s creations are placed in the background, focusing instead on personal experiences with family, friends, colleagues and employees.

However, several scenes in the first episode present the influences that would come to mark his work. Balenciaga is seen consulting José Ortiz-Echagüe’s book España. Tipos y Trajes (“Spain: people and clothes”) , which details the country’s popular regional dress and costumes . According to written works on the Basque designer, much of his inspiration is derived from Spanish culture, painting and tradition.

For example, his 1947 bolero in blue velvet with black felt decoration and beadwork is an interpretation of bullfighters’ extravagant clothing (known in Spanish as traje de luces , literally “suit of lights”), characterised by chromatic contrast and rich embroidery and trimmings. In a similar vein, his 1949 dress with black stripes on a red background bears a striking resemblance to the traditional women’s clothing of the Pas valley , in Cantabria.

These influences were also reflected in the 2019 exhibition “Balenciaga and Spanish painting” , where pieces by the Basque designer were presented alongside a selection of works by Spanish painters such as Velázquez, Murillo, El Greco and Francisco de Goya.

These included a silk shantung wedding dress embroidered with silver thread (1957) accompanied by the painting “Isabel de Borbón, wife of Felipe IV” by Rodrigo de Villandrando (circa 1620). Also present was an evening ensemble of cotton tulle dress, metal thread embroidery on rayon satin, and silk taffeta overskirt (circa 1951), presented alongside the painting “St. Elizabeth of Portugal” by Francisco de Zurbarán (circa 1635).

Evening ensemble of cotton tulle dress, metal thread embroidery on rayon satin, and silk taffeta overskirt, designed by Balenciaga, and _St. Isabella of Portugal_ by Francisco de Zurbarán.

The subtle similarities between Balenciaga’s innovative garments and their historical influences showcase a non-literal interpretation of traditional forms. This re-imagination would not have been possible without the influence of the nascent abstract art movement that grew alongside Balenciaga’s career.

New artistic directions

In the early to mid 1900s, abstract currents were emerging in painting, moving the seat of art from its traditional home of Paris to New York. This was not reflected in the fashion world, as the French capital remained firmly established as the epicentre of haute couture.

In contrast to the avant-garde movements of the early 20th century, which expressed new values for a new world, the abstract currents of 1940s and 1950s art opened the doors to new forms of individual expression.

The chromatic compositions of artists like Ad Reinhardt and Mark Rohtko offered a whole new field of experimentation, with results that were highly relevant to fashion .

In Rothko’s case, his apparently simple works achieved complexity through the superimposition of colour fields. They are known for their careful compositional balance, achieving visual harmony with a select palette of colours, such as intense reds and deep blues, occasionally offset by softer tones.

Reinhardt, on the other hand, was known for his extreme abstraction and minimalist approach. He did away with all non-essential elements, and his work’s subtle impact was rooted in an extremely limited colour palette: black and dark tones predominate, allowing his geometric and rigorous compositions to generate a sense of order and structure.

On the left, model 125 from Balenciaga's 1965 summer collection. On the right, Abstract Painting, Blue, by Ad Reinhardt, 1953..

Abstract art’s influence on Balenciaga

Some of these characteristics of colour, composition, precision and formal synthesis can be seen in Balenciaga’s work. Examples include the orange wool crepe day dress (1968), the black wool sack dress (autumn-winter collection, 1957) or model 125 from the 1965 summer collection , made at the Maison Balenciaga in Paris.

Abstract art also profoundly impacted mid-twentieth-century architecture, creating a new visual language of sculptural form. This is evident in the works of architects like Frank Lloyd Wright, Oscar Niemeyer and Félix Candela, among many others .

These works were characterised by an honest, unadorned use of materials that showed off their natural texture and colour. At the same time, they featured solid, smooth geometric forms. These clear lines and defined volumes dispensed with the ornamentation and compositions of past constructions, opting instead for a sculptural aesthetic rooted in abstract forms.

Architecture undoubtedly influenced Balenciaga’s innovative use of shape and volume. During the second half of the 1960s, several evening and bridal dresses designed by Balenciaga featured the warped geometric shapes and sculptural lines typical of the period’s buildings . Other dresses, such as the balloon dress (1958) and the summer collection dress (1959), were characterised by generous volumes and clean lines.

On the left, the Solomon R. Guggenheim Museum designed by Frank Lloyd Wright. On the right, an image of a model wearing a dress from the 1958 collection.

An all-encompassing work of art

After the second world war, Balenciaga’s output was marked by a significant shift, as he offered an image for the woman of the time that was far removed from traditional aesthetics. This ran contrary to Christian Dior’s 1947 New Look , which placed a renewed focus on the feminine silhouette as an antidote to wartime austerity.

In the wake of the war, the Basque couturier began to rewrite the rules of fashion with his innovative silhouettes – the “barrel line” , babydoll , sack , balloon and peacock tail dresses, to name a few. These creations stemmed from a commanding control of geometry. They stood out for their formal purity, and were backed by profoundly technical craftsmanship. The result was an extraordinary sculptural sensibility built on a foundation of abstract art.

Alberta Tiburzi wearing the 'envelope' dress by Cristóbal Balenciaga, Harper's Bazaar, June 1967.

Therefore, it could be said that Balenciaga not only offered an interpretation of the past, but also a look at tradition through contemporary eyes. Abstraction is used in his work as both a lens for reinterpreting Spanish culture, and as an artistic language shared with the art and the architecture of the time.

While this artistic context is often overlooked in writings and research on the designer, it is essential to understanding his excellence , not only as a fashion designer, but also as a true artistic genius of his time.

Perhaps nobody expressed this better than Balenciaga himself : “A couturier must be an architect for plans, a sculptor for shapes, an artist for colour, a musician for harmony and a philosopher for the sense of proportion”.

This article was originally published in Spanish

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