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Experimental Group in Psychology: Definition, Purpose, and Examples

• Psycholinguistics
• NeuroLaunch editorial team
• September 15, 2024
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Experimental groups, the backbone of psychological research, hold the key to unraveling the complexities of the human mind and behavior. As we delve into the fascinating world of psychological experiments, we’ll discover how these carefully crafted groups help researchers unlock the secrets of our thoughts, emotions, and actions.

Picture yourself in a dimly lit laboratory, surrounded by an array of mysterious equipment and eager participants. This is where the magic of experimental psychology unfolds. But what exactly are experimental groups, and why are they so crucial to our understanding of the human psyche?

At its core, experimental research in psychology is all about uncovering cause-and-effect relationships. It’s like being a detective, but instead of solving crimes, you’re solving the mysteries of the mind. Experimental groups are the protagonists in this scientific story, playing a vital role in helping researchers test their hypotheses and draw meaningful conclusions.

The Building Blocks of Psychological Experiments

Before we dive deeper into the nitty-gritty of experimental groups, let’s take a moment to appreciate the key components that make up a psychological experiment. It’s like assembling a puzzle, where each piece is essential for creating a complete picture.

First, we have the researcher – the mastermind behind the experiment. They’re the ones who come up with the brilliant (or sometimes not-so-brilliant) ideas that drive scientific progress. Next, we have the participants, the brave souls who volunteer to be part of these studies. Without them, we’d be left with nothing but theories and hunches.

Then there’s the independent variable – the factor that the researcher manipulates to see its effect on behavior. Think of it as the “cause” in our cause-and-effect equation. On the flip side, we have the dependent variable, which is the behavior or outcome that the researcher measures. It’s the “effect” we’re looking for.

Last but not least, we have our star players: the experimental and control groups. These groups are like two sides of the same coin, each serving a unique purpose in the grand scheme of things.

Experimental Group: The Scientific Spotlight

Now, let’s shine a spotlight on the experimental group. In the world of psychology, an experimental group is a group of participants who receive the treatment or manipulation that the researcher is interested in studying. It’s like being the lead actor in a play – all eyes are on you!

The AP Psychology definition of an experimental group is pretty straightforward: it’s the group that is exposed to the independent variable. But don’t let this simplicity fool you – experimental groups are the workhorses of psychological research, carrying the weight of scientific inquiry on their shoulders.

What sets experimental groups apart from their control group counterparts? Well, it’s all about that special treatment. While the control group goes about its business as usual, the experimental group gets to experience something new and potentially exciting (or, let’s be honest, sometimes boring or even mildly unpleasant – all in the name of science, of course!).

The role of experimental groups in hypothesis testing is crucial. They’re like the guinea pigs of the research world, helping scientists determine whether their predictions about human behavior hold water. Without experimental groups, we’d be left with a lot of questions and very few answers.

Control Groups: The Unsung Heroes

While experimental groups bask in the limelight, let’s not forget about their equally important counterparts – the control groups. These unsung heroes of psychological research deserve some recognition too!

So, what exactly is a control group in psychology? Simply put, it’s a group of participants who do not receive the treatment or manipulation being studied. They serve as a baseline, allowing researchers to compare the results of the experimental group against a standard.

The purpose of control groups is twofold. First, they help researchers determine whether any changes in the dependent variable are actually due to the independent variable, rather than some other factor. Second, they provide a point of comparison, making it possible to measure the effect of the treatment.

There are different types of control groups, each with its own flavor:

1. Placebo groups: These participants receive a fake treatment that looks and feels like the real deal but has no active ingredients. It’s like giving someone a sugar pill and telling them it’s a powerful medication.

2. No-treatment groups: As the name suggests, these folks don’t receive any treatment at all. They’re the “business as usual” crowd.

3. Wait-list groups: These participants are told they’ll receive the treatment later, after the study is complete. It’s like being in line for a rollercoaster – you know the excitement is coming, but you have to wait your turn.

Control group psychology examples are everywhere. For instance, in a study on the effectiveness of a new therapy for depression, the control group might receive standard talk therapy while the experimental group tries out the new approach. Or in a study on the effects of caffeine on memory, the control group might drink decaf coffee while the experimental group gets the real deal.

Designing Experiments: A Delicate Balance

Creating experimental and control groups isn’t just a matter of flipping a coin and dividing people up. It’s a delicate process that requires careful planning and execution. Let’s walk through the steps, shall we?

1. Define your research question: What burning question about human behavior are you dying to answer?

2. Identify your variables: What will you manipulate (independent variable) and what will you measure (dependent variable)?

3. Choose your participants: Who will be the stars of your research show?

4. Randomly assign participants: This is where the magic of random assignment comes in. It’s like a scientific lottery, ensuring that each participant has an equal chance of ending up in either the experimental or control group.

5. Control for confounding variables: These sneaky factors can mess up your results if you’re not careful. It’s like trying to bake a cake while someone keeps adding random ingredients when you’re not looking.

Random assignment is the secret sauce that gives experiments their power. By randomly assigning participants to groups, researchers can be more confident that any differences between the groups are due to the treatment and not some pre-existing characteristic of the participants.

Ensuring internal and external validity is another crucial aspect of experimental design. Internal validity is all about making sure your experiment actually measures what you think it’s measuring. External validity, on the other hand, is about how well your results can be generalized to the real world. It’s a balancing act that keeps researchers on their toes!

Experimental Groups in Action: Real-World Applications

Now that we’ve covered the basics, let’s see how experimental groups are put to work in various areas of psychology. It’s like watching a Swiss Army knife in action – versatile and always ready for the job at hand!

In clinical psychology, experimental groups help researchers test the effectiveness of new treatments for mental health disorders. For example, a study might use an experimental group to try out a new form of cognitive-behavioral therapy for anxiety, while the control group receives standard treatment.

Social psychology research often uses experimental groups to explore how people interact with each other. The famous Minimal Group Paradigm studies, for instance, use experimental groups to investigate how easily people form in-groups and out-groups, even based on arbitrary criteria.

Cognitive psychology studies frequently employ experimental groups to investigate mental processes like attention, memory, and decision-making. A researcher might use an experimental group to test the effects of different study techniques on memory retention, while the control group uses their usual study methods.

In developmental psychology, experimental groups help us understand how children grow and change over time. A study might use an experimental group to test the impact of a new educational program on children’s language development, while the control group follows the standard curriculum.

The Flip Side: Limitations and Ethical Considerations

As much as we love experimental groups, it’s important to acknowledge that they’re not perfect. Like any scientific tool, they come with their own set of limitations and ethical considerations.

One potential issue is bias. Despite our best efforts, sometimes researchers or participants can inadvertently influence the results. It’s like trying to be completely objective while judging a bake-off – your personal preferences might sneak in without you even realizing it.

Ethical concerns are another big deal in human subject research. We can’t just go around manipulating people’s behavior willy-nilly! Researchers have to carefully consider the potential risks and benefits of their studies, ensuring that participants are protected and informed.

Balancing scientific rigor with participant well-being is a constant challenge. It’s like walking a tightrope – lean too far in either direction, and you risk compromising either the validity of your research or the welfare of your participants.

Sometimes, traditional experimental designs just aren’t feasible or ethical. That’s where alternatives like quasi-experimental designs come in handy. These approaches allow researchers to study real-world phenomena without the strict control of a true experiment. It’s like studying animals in their natural habitat instead of a zoo – you might sacrifice some control, but you gain ecological validity.

Wrapping It Up: The Power of Experimental Groups

As we come to the end of our journey through the world of experimental groups in psychology, let’s take a moment to reflect on what we’ve learned. Experimental groups are the workhorses of psychological research, helping us uncover the mysteries of the human mind and behavior.

From testing new therapies for mental health disorders to exploring the intricacies of social interaction, experimental groups are at the forefront of psychological discovery. They’re like the explorers of the scientific world, venturing into uncharted territory and bringing back valuable insights.

Understanding experimental and control groups is crucial for anyone interested in psychology, whether you’re a student, a professional, or just a curious mind. It’s like having a backstage pass to the scientific process – you get to see how knowledge is created and tested.

As we look to the future, experimental psychology research continues to evolve. New technologies and methodologies are opening up exciting possibilities for studying the mind in ways we never thought possible. Who knows what groundbreaking discoveries are just around the corner?

So the next time you hear about a psychological study, remember the unsung heroes behind the scenes – the experimental groups that make it all possible. They’re the true stars of the show, helping us understand ourselves and the world around us, one experiment at a time.

References:

1. Coolican, H. (2018). Research Methods and Statistics in Psychology. Routledge.

2. Goodwin, C. J., & Goodwin, K. A. (2016). Research in Psychology: Methods and Design. John Wiley & Sons.

3. Kazdin, A. E. (2017). Research Design in Clinical Psychology. Pearson.

4. Leary, M. R. (2011). Introduction to Behavioral Research Methods. Pearson.

5. Shaughnessy, J. J., Zechmeister, E. B., & Zechmeister, J. S. (2015). Research Methods in Psychology. McGraw-Hill Education.

6. Smith, R. A., & Davis, S. F. (2013). The Psychologist as Detective: An Introduction to Conducting Research in Psychology. Pearson.

7. Stangor, C. (2014). Research Methods for the Behavioral Sciences. Cengage Learning.

8. Weathington, B. L., Cunningham, C. J., & Pittenger, D. J. (2012). Understanding Business Research. John Wiley & Sons.

9. American Psychological Association. (2017). Ethical Principles of Psychologists and Code of Conduct. https://www.apa.org/ethics/code/

10. National Institutes of Health. (2018). The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research. https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html

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• Mental Health

Experimental Design: Types, Examples & Methods

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

Educational resources and simple solutions for your research journey

What are Experimental Groups in Research

The experimental group plays a key role in scientific studies, especially in psychology, medicine, and social sciences. In experimental research, this group helps in exploring cause-and-effect relationships and evaluating interventions or treatments. Understanding the importance, characteristics, and factors related to the experimental group is essential for accurate and reliable scientific studies.  

Table of Contents

What is an Experimental Group in Research ? (1)  

In research, an experimental group is a particular group of participants exposed to a treatment. Researchers use this group to observe and analyze the effects of the changed variable, called the independent variable.  

Control Group vs Experimental Group (2)  

Unlike the experimental group, the control group acts as a starting point. It experiences conditions similar to the experimental group but without exposure to the independent variable. Comparing these groups helps researchers understand the impact of the independent variable and establish cause-and-effect relationships in their studies.  

Example of Experimental Group (4)  

For instance, in a drug trial, the group trying the new medicine is the experimental group, and the group getting a fake treatment is the control group. In an educational study comparing teaching methods, the experimental group tries the new approach, while the control group sticks to the usual teaching method.  

Consider an experiment examining the impact of temperature on plant growth. The experimental group would be subjected to increased temperatures, while the control group experiences normal temperature conditions. This example illustrates the versatility of experimental groups across various research domains.  

Key Characteristics of Effective Experimental Groups in Research (1)  

• Random Assignment: Random assignment is when participants are chosen for the experimental or control group purely by chance. Random assignment makes the study more reliable by minimizing the impact of individual differences and enhancing the credibility of the research findings.  
• Homogeneity: Homogeneity ensures that people in the experimental group are similar to each other. When everyone in the experimental group is alike, it’s easier to link any changes to the treatment or intervention being studied, making the research results more reliable.  
• Isolation of Variables: In a scientific study, researchers change only one thing on purpose—the independent variable. This focused approach helps researchers connect any observed changes directly to that specific factor.   
• Replicability: A good experimental group is designed in a way that other researchers can do the same experiment to check or question the original findings. This ensures that the methods used in the study are clear and can be followed by others.   
• Data Collection: Information is gathered from the experimental group to understand specific outcomes or responses. It can involve different methods like surveys, observations, tests, or measurements, depending on the study’s design and goals. The collected data forms the basis for analysis and helps draw meaningful conclusions from the study.  

Advantages and Disadvantages of Experimental Groups in Research (1)(3)  

Advantages  

• Control Over Variables: Control over variables means that scientists deliberately focus on changing only one thing while keeping everything else constant. It ensures that changes in outcomes are likely due to the intentional changes made during the experiment.
• Precision: Precision means measuring the effects of an intervention very carefully, giving a clearer understanding of how it affects the study outcomes.  
• Comparative analysis: Comparative analysis involves comparing the group receiving an intervention (experimental group) with the group that doesn’t (control group). This comparison helps scientists see if and how the intervention is making a difference by carefully looking at the outcomes of both groups. Comparative analysis involves comparing the group receiving an intervention (experimental group) with the group that doesn’t (control group). This comparison helps scientists see if and how the intervention is making a difference by carefully looking at the outcomes of both groups.  

Disadvantages  

• Ethical Concerns: Ethical Concerns: Some experiments could be ethically problematic, especially when changing variables might harm those in the experimental group. Researchers must prioritize the well-being of participants in such cases.  
• External Validity: Findings from experimental groups might not apply to real-world situations, making the study less broadly applicable.  
• Time intensive: Engaging in experimental research demands a considerable investment of time, effort, and resources. This includes various activities such as recruiting participants, putting interventions into action, and gathering as well as analyzing data.  

Experimental groups in research are indispensable tools in science, providing a structured framework for investigating the impact of independent variables. By understanding the definition, examples, and key characteristics of experimental groups, researchers can conduct experiments that yield valuable insights. However, it is essential to acknowledge the advantages and disadvantages inherent in the use of experimental groups, ensuring a balanced and ethical approach to scientific inquiry.  

References:  

• The Role of Experimental Groups in Research – Mind the Graph Blog  
• The Difference Between Control Group and Experimental Group – ThoughtCo  
• Experimental & Control Group – Study.com  
• Experimental Group (Treatment Group): Definition, Examples – Statistics How To  

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• Clinical Research Explained

Experimental Group

• 8. February 2024

Definition of Experimental Group

Role of the experimental group, formation of the experimental group, importance of the experimental group in clinical research, experimental group and the scientific method, experimental group and statistical analysis, challenges and considerations in forming an experimental group, randomization, examples of experimental groups in clinical research, drug trials, behavioral intervention studies.

The term ‘Experimental Group’ is a fundamental concept in the realm of Clinical Research. It refers to the group of subjects in a research study who are exposed to the variable under investigation. This group is contrasted with a ‘Control Group’, which is not exposed to the experimental variable. The comparison between these two groups allows researchers to draw conclusions about the effect of the variable being studied.

Understanding the role of the Experimental Group is crucial for anyone involved in or studying Clinical Research. It is the cornerstone of experimental design , and its proper use ensures the validity and reliability of the results obtained. This article will delve into the intricacies of the Experimental Group, exploring its definition, purpose, formation, and role in Clinical Research.

The Experimental Group, also known as the treatment group, is the group of subjects in a research study who receive the experimental treatment or intervention. This treatment or intervention is the variable that the researcher is interested in studying. The outcomes or effects observed in the Experimental Group are then compared with those in a Control Group, which does not receive the experimental treatment.

The Experimental Group is thus the group that is exposed to the variable of interest. The changes that occur in this group as a result of exposure to the variable provide the data that the researcher uses to draw conclusions about the effect of the variable.

The Experimental Group plays a critical role in Clinical Research. It is the group that provides the data that the researcher uses to draw conclusions about the effect of the variable being studied. Without an Experimental Group, it would be impossible to determine whether the variable has any effect at all.

The Experimental Group is also crucial for establishing causality. By comparing the outcomes in the Experimental Group with those in the Control Group, the researcher can determine whether the variable causes any changes in the outcome. If the outcomes are different in the two groups, then it can be concluded that the variable has an effect.

The formation of the Experimental Group is a critical step in the design of a research study. The subjects in the Experimental Group must be selected in a way that ensures that they are representative of the population that the researcher is interested in studying. This is typically achieved through random selection and assignment, which helps to ensure that the Experimental Group is similar to the Control Group in all respects except for the variable being studied.

Once the subjects have been selected, they are assigned to the Experimental Group and exposed to the variable. The researcher then observes the effects of the variable on the subjects in the Experimental Group and compares these effects with those observed in the Control Group.

The Experimental Group is of paramount importance in Clinical Research. It is the group that provides the data that the researcher uses to draw conclusions about the effect of the variable being studied. Without an Experimental Group, it would be impossible to determine whether the variable has any effect at all.

Moreover, the Experimental Group is crucial for establishing causality. By comparing the outcomes in the Experimental Group with those in the Control Group, the researcher can determine whether the variable causes any changes in the outcome. If the outcomes are different in the two groups, then it can be concluded that the variable has an effect.

The Experimental Group is a key component of the scientific method, which is the process that scientists use to investigate phenomena, acquire new knowledge, or correct and integrate previous knowledge. The scientific method involves formulating hypotheses, conducting experiments to test these hypotheses, and analyzing the results to draw conclusions.

In the context of the scientific method, the Experimental Group is the group that is exposed to the experimental variable, or the factor that the researcher is testing. The results obtained from the Experimental Group are then compared with those from the Control Group, which is not exposed to the experimental variable. This comparison allows the researcher to determine whether the experimental variable has an effect, and if so, what that effect is.

The data obtained from the Experimental Group is typically subjected to statistical analysis to determine whether the observed effects are statistically significant. Statistical significance is a measure of the likelihood that the observed effects are due to the experimental variable, rather than chance.

Statistical analysis involves comparing the means of the Experimental Group and the Control Group, and calculating a p-value. If the p-value is less than a predetermined threshold (usually 0.05), then the observed effects are considered statistically significant, and it can be concluded that the experimental variable has an effect.

Forming an Experimental Group is not without its challenges. One of the main challenges is ensuring that the Experimental Group is representative of the population that the researcher is interested in studying. This is typically achieved through random selection and assignment, but this can be difficult to achieve in practice.

Another challenge is ensuring that the Experimental Group and the Control Group are similar in all respects except for the experimental variable. This is crucial for ensuring that any observed effects are due to the experimental variable, and not other factors. However, it can be difficult to control for all potential confounding variables, especially in complex research studies.

Randomization is a technique used in research to ensure that the Experimental Group and the Control Group are similar in all respects except for the experimental variable. It involves randomly assigning subjects to the Experimental Group or the Control Group, which helps to ensure that the two groups are similar in terms of age, gender, health status, and other factors that could potentially influence the outcome of the study.

However, randomization is not always possible or practical. For example, in some research studies, it may not be ethical to randomly assign subjects to the Experimental Group or the Control Group. In such cases, other techniques, such as matching or stratification, may be used to ensure that the two groups are similar.

Blinding is another technique used in research to minimize bias and ensure the validity of the study results. It involves keeping the subjects and/or the researchers unaware of which group (Experimental or Control) the subjects have been assigned to. This helps to prevent the subjects’ and researchers’ expectations from influencing the outcome of the study.

Blinding can be single-blind, where the subjects do not know which group they have been assigned to, or double-blind, where both the subjects and the researchers do not know which group the subjects have been assigned to. Double-blind studies are considered the gold standard in research, as they minimize both subject and researcher bias.

Experimental Groups are used in a wide variety of clinical research studies. For example, in a drug trial, the Experimental Group would be the group of subjects who receive the drug being tested. The effects of the drug on these subjects would then be compared with those in a Control Group, who receive a placebo or a different drug.

In a behavioral intervention study, the Experimental Group might be the group of subjects who receive the intervention, such as a new therapy or counseling technique. The effects of the intervention on these subjects would then be compared with those in a Control Group, who receive standard care or a different intervention.

Drug trials are a common type of clinical research study that use Experimental Groups. In a drug trial, the Experimental Group is the group of subjects who receive the drug being tested. The effects of the drug on these subjects are then compared with those in a Control Group, who receive a placebo or a different drug.

The purpose of a drug trial is to determine whether the drug is safe and effective for treating a particular condition. The data obtained from the Experimental Group is crucial for making this determination. If the drug is found to be safe and effective, it may be approved for use in the general population.

Behavioral intervention studies are another type of clinical research study that use Experimental Groups. In a behavioral intervention study, the Experimental Group is the group of subjects who receive the intervention, such as a new therapy or counseling technique. The effects of the intervention on these subjects are then compared with those in a Control Group, who receive standard care or a different intervention.

The purpose of a behavioral intervention study is to determine whether the intervention is effective for changing behavior or improving health outcomes. The data obtained from the Experimental Group is crucial for making this determination. If the intervention is found to be effective, it may be implemented in clinical practice or public health programs.

In conclusion, the Experimental Group is a fundamental concept in Clinical Research. It is the group of subjects who are exposed to the variable under investigation, and it provides the data that the researcher uses to draw conclusions about the effect of the variable. Understanding the role of the Experimental Group is crucial for anyone involved in or studying Clinical Research.

Despite the challenges involved in forming an Experimental Group, it is a critical component of the scientific method and is essential for establishing causality. By comparing the outcomes in the Experimental Group with those in the Control Group, researchers can determine whether the variable under investigation has an effect, and if so, what that effect is.

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Experimental Group

• Reference work entry
• First Online: 01 January 2020
• pp 1491–1493
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• Sven Hilbert 3 , 4 , 5  

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In an experimental treatment study, the experimental group is the group that receives the treatment.

Introduction

Experimental treatment studies are designed to estimate the effect of a particular treatment on one or more variables. Typically, the variables of interest are observed before and after treatment to detect changes that occurred in between. The two observations of the variables are called pretest and posttest to indicate their temporal position before and after the treatment. However, any differences between pre- and posttest need not be caused by the treatment. Therefore, experimental treatment studies use at least two groups: the experimental group receives the treatment, while the control group does not. The effect of the treatment can be estimated by comparing the change observed in the treatment group with the change observed in the control group.

Treatment Groups as Independent Variables in an Experiment

In an experimental treatment study, the variables of...

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Department of Psychology, Psychological Methods and Assessment, Münich, Germany

Sven Hilbert

Faculty of Psychology, Educational Science, and Sport Science, University of Regensburg, Regensburg, Germany

Psychological Methods and Assessment, LMU Munich, Munich, Germany

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Oakland University, Rochester, MI, USA

Virgil Zeigler-Hill

Todd K. Shackelford

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Humboldt University, Germany, Berlin, Germany

Matthias Ziegler

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Hilbert, S. (2020). Experimental Group. In: Zeigler-Hill, V., Shackelford, T.K. (eds) Encyclopedia of Personality and Individual Differences. Springer, Cham. https://doi.org/10.1007/978-3-319-24612-3_1301

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That actually explain what's on your next test, experimental group, from class:, advanced communication research methods.

An experimental group is a set of subjects or participants in an experiment that receives the treatment or intervention being tested, allowing researchers to observe the effects of that treatment. This group is compared against a control group, which does not receive the treatment, enabling scientists to determine the effectiveness of the intervention and establish cause-and-effect relationships.

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5 Must Know Facts For Your Next Test

• The experimental group is critical for establishing causal relationships between variables by allowing researchers to see how changes affect outcomes.
• In well-designed experiments, participants are randomly assigned to either the experimental group or the control group to reduce bias and ensure that results are valid.
• The size of the experimental group can impact the statistical power of an experiment; larger groups typically provide more reliable results.
• Researchers must carefully consider ethical implications when creating experimental groups, ensuring that participants are treated fairly and given informed consent.
• In some studies, multiple experimental groups may be used to test different levels or types of treatment simultaneously.

Review Questions

• The presence of an experimental group enhances the validity of research findings by allowing for a direct comparison between those who receive a treatment and those who do not. This comparison helps researchers isolate the effects of the treatment from other variables, ensuring that observed changes can be attributed specifically to the intervention. By controlling for other factors through random assignment and proper design, researchers can confidently conclude whether their treatment is effective.
• Random assignment impacts the integrity of an experimental group's results by minimizing selection bias and ensuring that each participant has an equal chance of being assigned to either the experimental or control group. This randomness helps balance out individual differences among participants, making it more likely that any observed differences in outcomes can be attributed solely to the treatment itself. As a result, this increases the generalizability and reliability of the findings.
• Using multiple experimental groups allows researchers to evaluate different levels or variations of a treatment within a single study, providing a more nuanced understanding of how each condition affects outcomes. This approach can reveal dose-response relationships or identify which specific aspects of a treatment are most effective. However, it also complicates data analysis and interpretation, as researchers must ensure that any differences between groups are valid and meaningful, thereby reinforcing or challenging existing conclusions about treatment efficacy.

Related terms

control group : A control group is a baseline group in an experiment that does not receive the experimental treatment, serving as a comparison to the experimental group.

independent variable : An independent variable is the factor that is manipulated or changed by researchers in an experiment to observe its effect on the dependent variable.

dependent variable : A dependent variable is the outcome measured in an experiment, which is expected to change in response to manipulations of the independent variable.

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Study Design 101: Randomized Controlled Trial

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A study design that randomly assigns participants into an experimental group or a control group. As the study is conducted, the only expected difference between the control and experimental groups in a randomized controlled trial (RCT) is the outcome variable being studied.

• Good randomization will "wash out" any population bias
• Easier to blind/mask than observational studies
• Results can be analyzed with well known statistical tools
• Populations of participating individuals are clearly identified

Disadvantages

• Expensive in terms of time and money
• Volunteer biases: the population that participates may not be representative of the whole
• Loss to follow-up attributed to treatment

Design pitfalls to look out for

An RCT should be a study of one population only.

Was the randomization actually "random", or are there really two populations being studied?

The variables being studied should be the only variables between the experimental group and the control group.

Are there any confounding variables between the groups?

Fictitious Example

To determine how a new type of short wave UVA-blocking sunscreen affects the general health of skin in comparison to a regular long wave UVA-blocking sunscreen, 40 trial participants were randomly separated into equal groups of 20: an experimental group and a control group. All participants' skin health was then initially evaluated. The experimental group wore the short wave UVA-blocking sunscreen daily, and the control group wore the long wave UVA-blocking sunscreen daily.

After one year, the general health of the skin was measured in both groups and statistically analyzed. In the control group, wearing long wave UVA-blocking sunscreen daily led to improvements in general skin health for 60% of the participants. In the experimental group, wearing short wave UVA-blocking sunscreen daily led to improvements in general skin health for 75% of the participants.

Real-life Examples

van Der Horst, N., Smits, D., Petersen, J., Goedhart, E., & Backx, F. (2015). The preventive effect of the nordic hamstring exercise on hamstring injuries in amateur soccer players: a randomized controlled trial. The American Journal of Sports Medicine, 43 (6), 1316-1323. https://doi.org/10.1177/0363546515574057

This article reports on the research investigating whether the Nordic Hamstring Exercise is effective in preventing both the incidence and severity of hamstring injuries in male amateur soccer players. Over the course of a year, there was a statistically significant reduction in the incidence of hamstring injuries in players performing the NHE, but for those injured, there was no difference in severity of injury. There was also a high level of compliance in performing the NHE in that group of players.

Natour, J., Cazotti, L., Ribeiro, L., Baptista, A., & Jones, A. (2015). Pilates improves pain, function and quality of life in patients with chronic low back pain: a randomized controlled trial. Clinical Rehabilitation, 29 (1), 59-68. https://doi.org/10.1177/0269215514538981

This study assessed the effect of adding pilates to a treatment regimen of NSAID use for individuals with chronic low back pain. Individuals who included the pilates method in their therapy took fewer NSAIDs and experienced statistically significant improvements in pain, function, and quality of life.

Related Formulas

• Relative Risk

Related Terms

Blinding/Masking

When the groups that have been randomly selected from a population do not know whether they are in the control group or the experimental group.

Being able to show that an independent variable directly causes the dependent variable. This is generally very difficult to demonstrate in most study designs.

Confounding Variables

Variables that cause/prevent an outcome from occurring outside of or along with the variable being studied. These variables render it difficult or impossible to distinguish the relationship between the variable and outcome being studied).

Correlation

A relationship between two variables, but not necessarily a causation relationship.

Double Blinding/Masking

When the researchers conducting a blinded study do not know which participants are in the control group of the experimental group.

Null Hypothesis

That the relationship between the independent and dependent variables the researchers believe they will prove through conducting a study does not exist. To "reject the null hypothesis" is to say that there is a relationship between the variables.

Population/Cohort

A group that shares the same characteristics among its members (population).

Population Bias/Volunteer Bias

A sample may be skewed by those who are selected or self-selected into a study. If only certain portions of a population are considered in the selection process, the results of a study may have poor validity.

Randomization

Any of a number of mechanisms used to assign participants into different groups with the expectation that these groups will not differ in any significant way other than treatment and outcome.

Research (alternative) Hypothesis

The relationship between the independent and dependent variables that researchers believe they will prove through conducting a study.

Sensitivity

The relationship between what is considered a symptom of an outcome and the outcome itself; or the percent chance of not getting a false positive (see formulas).

Specificity

The relationship between not having a symptom of an outcome and not having the outcome itself; or the percent chance of not getting a false negative (see formulas).

Type 1 error

Rejecting a null hypothesis when it is in fact true. This is also known as an error of commission.

Type 2 error

The failure to reject a null hypothesis when it is in fact false. This is also known as an error of omission.

Now test yourself!

1. Having a volunteer bias in the population group is a good thing because it means the study participants are eager and make the study even stronger.

a) True b) False

2. Why is randomization important to assignment in an RCT?

a) It enables blinding/masking b) So causation may be extrapolated from results c) It balances out individual characteristics between groups. d) a and c e) b and c

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What Is a Control Group?

Control Groups vs. Experimental Groups in Psychology Research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

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Control Group vs. Experimental Group

Types of control groups.

In simple terms, the control group comprises participants who do not receive the experimental treatment. When conducting an experiment, these people are randomly assigned to this group. They also closely resemble the participants who are in the experimental group or the individuals who receive the treatment.

Experimenters utilize variables to make comparisons between an experimental group and a control group. A variable is something that researchers can manipulate, measure, and control in an experiment. The independent variable is the aspect of the experiment that the researchers manipulate (or the treatment). The dependent variable is what the researchers measure to see if the independent variable had an effect.

While they do not receive the treatment, the control group does play a vital role in the research process. Experimenters compare the experimental group to the control group to determine if the treatment had an effect.

By serving as a comparison group, researchers can isolate the independent variable and look at the impact it had.

The simplest way to determine the difference between a control group and an experimental group is to determine which group receives the treatment and which does not. To ensure that the results can then be compared accurately, the two groups should be otherwise identical.

Not exposed to the treatment (the independent variable)

Used to provide a baseline to compare results against

May receive a placebo treatment

Exposed to the treatment

Used to measure the effects of the independent variable

Identical to the control group aside from their exposure to the treatment

Why a Control Group Is Important

While the control group does not receive treatment, it does play a critical role in the experimental process. This group serves as a benchmark, allowing researchers to compare the experimental group to the control group to see what sort of impact changes to the independent variable produced.  

Because participants have been randomly assigned to either the control group or the experimental group, it can be assumed that the groups are comparable.

Any differences between the two groups are, therefore, the result of the manipulations of the independent variable. The experimenters carry out the exact same procedures with both groups with the exception of the manipulation of the independent variable in the experimental group.

There are a number of different types of control groups that might be utilized in psychology research. Some of these include:

• Positive control groups : In this case, researchers already know that a treatment is effective but want to learn more about the impact of variations of the treatment. In this case, the control group receives the treatment that is known to work, while the experimental group receives the variation so that researchers can learn more about how it performs and compares to the control.
• Negative control group : In this type of control group, the participants are not given a treatment. The experimental group can then be compared to the group that did not experience any change or results.
• Placebo control group : This type of control group receives a placebo treatment that they believe will have an effect. This control group allows researchers to examine the impact of the placebo effect and how the experimental treatment compared to the placebo treatment.
• Randomized control group : This type of control group involves using random selection to help ensure that the participants in the control group accurately reflect the demographics of the larger population.
• Natural control group : This type of control group is naturally selected, often by situational factors. For example, researchers might compare people who have experienced trauma due to war to people who have not experienced war. The people who have not experienced war-related trauma would be the control group.

Examples of Control Groups

Control groups can be used in a variety of situations. For example, imagine a study in which researchers example how distractions during an exam influence test results. The control group would take an exam in a setting with no distractions, while the experimental groups would be exposed to different distractions. The results of the exam would then be compared to see the effects that distractions had on test scores.

Experiments that look at the effects of medications on certain conditions are also examples of how a control group can be used in research. For example, researchers looking at the effectiveness of a new antidepressant might use a control group that receives a placebo and an experimental group that receives the new medication. At the end of the study, researchers would compare measures of depression for both groups to determine what impact the new medication had.

After the experiment is complete, researchers can then look at the test results and start making comparisons between the control group and the experimental group.

Uses for Control Groups

Researchers utilize control groups to conduct research in a range of different fields. Some common uses include:

• Psychology : Researchers utilize control groups to learn more about mental health, behaviors, and treatments.
• Medicine : Control groups can be used to learn more about certain health conditions, assess how well medications work to treat these conditions, and assess potential side effects that may result.
• Education : Educational researchers utilize control groups to learn more about how different curriculums, programs, or instructional methods impact student outcomes.
• Marketing : Researchers utilize control groups to learn more about how consumers respond to advertising and marketing efforts.

Malay S, Chung KC. The choice of controls for providing validity and evidence in clinical research . Plast Reconstr Surg. 2012 Oct;130(4):959-965. doi:10.1097/PRS.0b013e318262f4c8

National Cancer Institute. Control group.

Pithon MM. Importance of the control group in scientific research . Dental Press J Orthod. 2013;18(6):13-14. doi:10.1590/s2176-94512013000600003

Karlsson P, Bergmark A. Compared with what? An analysis of control-group types in Cochrane and Campbell reviews of psychosocial treatment efficacy with substance use disorders . Addiction . 2015;110(3):420-8. doi:10.1111/add.12799

Myers A, Hansen C. Experimental Psychology . Belmont, CA: Cengage Learning; 2012.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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What are Controlled Experiments?

Determining Cause and Effect

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A controlled experiment is a highly focused way of collecting data and is especially useful for determining patterns of cause and effect. This type of experiment is used in a wide variety of fields, including medical, psychological, and sociological research. Below, we’ll define what controlled experiments are and provide some examples.

Key Takeaways: Controlled Experiments

• A controlled experiment is a research study in which participants are randomly assigned to experimental and control groups.
• A controlled experiment allows researchers to determine cause and effect between variables.
• One drawback of controlled experiments is that they lack external validity (which means their results may not generalize to real-world settings).

Experimental and Control Groups

To conduct a controlled experiment , two groups are needed: an experimental group and a control group . The experimental group is a group of individuals that are exposed to the factor being examined. The control group, on the other hand, is not exposed to the factor. It is imperative that all other external influences are held constant . That is, every other factor or influence in the situation needs to remain exactly the same between the experimental group and the control group. The only thing that is different between the two groups is the factor being researched.

For example, if you were studying the effects of taking naps on test performance, you could assign participants to two groups: participants in one group would be asked to take a nap before their test, and those in the other group would be asked to stay awake. You would want to ensure that everything else about the groups (the demeanor of the study staff, the environment of the testing room, etc.) would be equivalent for each group. Researchers can also develop more complex study designs with more than two groups. For example, they might compare test performance among participants who had a 2-hour nap, participants who had a 20-minute nap, and participants who didn’t nap.

Assigning Participants to Groups

In controlled experiments, researchers use  random assignment (i.e. participants are randomly assigned to be in the experimental group or the control group) in order to minimize potential confounding variables in the study. For example, imagine a study of a new drug in which all of the female participants were assigned to the experimental group and all of the male participants were assigned to the control group. In this case, the researchers couldn’t be sure if the study results were due to the drug being effective or due to gender—in this case, gender would be a confounding variable.

Random assignment is done in order to ensure that participants are not assigned to experimental groups in a way that could bias the study results. A study that compares two groups but does not randomly assign participants to the groups is referred to as quasi-experimental, rather than a true experiment.

Blind and Double-Blind Studies

In a blind experiment, participants don’t know whether they are in the experimental or control group. For example, in a study of a new experimental drug, participants in the control group may be given a pill (known as a placebo ) that has no active ingredients but looks just like the experimental drug. In a double-blind study , neither the participants nor the experimenter knows which group the participant is in (instead, someone else on the research staff is responsible for keeping track of group assignments). Double-blind studies prevent the researcher from inadvertently introducing sources of bias into the data collected.

Example of a Controlled Experiment

If you were interested in studying whether or not violent television programming causes aggressive behavior in children, you could conduct a controlled experiment to investigate. In such a study, the dependent variable would be the children’s behavior, while the independent variable would be exposure to violent programming. To conduct the experiment, you would expose an experimental group of children to a movie containing a lot of violence, such as martial arts or gun fighting. The control group, on the other hand, would watch a movie that contained no violence.

To test the aggressiveness of the children, you would take two measurements : one pre-test measurement made before the movies are shown, and one post-test measurement made after the movies are watched. Pre-test and post-test measurements should be taken of both the control group and the experimental group. You would then use statistical techniques to determine whether the experimental group showed a significantly greater increase in aggression, compared to participants in the control group.

Studies of this sort have been done many times and they usually find that children who watch a violent movie are more aggressive afterward than those who watch a movie containing no violence.

Strengths and Weaknesses

Controlled experiments have both strengths and weaknesses. Among the strengths is the fact that results can establish causation. That is, they can determine cause and effect between variables. In the above example, one could conclude that being exposed to representations of violence causes an increase in aggressive behavior. This kind of experiment can also zero-in on a single independent variable, since all other factors in the experiment are held constant.

On the downside, controlled experiments can be artificial. That is, they are done, for the most part, in a manufactured laboratory setting and therefore tend to eliminate many real-life effects. As a result, analysis of a controlled experiment must include judgments about how much the artificial setting has affected the results. Results from the example given might be different if, say, the children studied had a conversation about the violence they watched with a respected adult authority figure, like a parent or teacher, before their behavior was measured. Because of this, controlled experiments can sometimes have lower external validity (that is, their results might not generalize to real-world settings).

Updated  by Nicki Lisa Cole, Ph.D.

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• Open access
• Published: 13 October 2024

Effects of monoglyceride blend on systemic and intestinal immune responses, and gut health of weaned pigs experimentally infected with a pathogenic Escherichia coli

• Sangwoo Park 1 ,
• Shuhan Sun 1 ,
• Lauren Kovanda 1 ,
• Adebayo O. Sokale 2 ,
• Adriana Barri 3 ,
• Kwangwook Kim 4 ,
• Xunde Li 5 &
• Yanhong Liu   ORCID: orcid.org/0000-0001-7727-4796 1 , 2  

Journal of Animal Science and Biotechnology volume  15 , Article number:  141 ( 2024 ) Cite this article

444 Accesses

Metrics details

Monoglycerides have emerged as a promising alternative to conventional practices due to their biological activities, including antimicrobial properties. However, few studies have assessed the efficacy of monoglyceride blend on weaned pigs and their impacts on performance, immune response, and gut health using a disease challenge model. Therefore, this study aimed to investigate the effects of dietary monoglycerides of short- and medium-chain fatty acids on the immunity and gut health of weaned pigs experimentally infected with an enterotoxigenic Escherichia coli F18.

Pigs supplemented with high-dose zinc oxide (ZNO) had greater ( P  < 0.05) growth performance than other treatments, but no difference was observed in average daily feed intake between ZNO and monoglycerides groups during the post-challenge period. Pigs in ZNO and antibiotic groups had lower ( P  < 0.05) severity of diarrhea than control, but the severity of diarrhea was not different between antibiotic and monoglycerides groups. Pigs fed with monoglycerides or ZNO had lower ( P  < 0.05) serum haptoglobin on d 2 or 5 post-inoculation than control. Pigs in ZNO had greater ( P  < 0.05) goblet cell numbers per villus, villus area and height, and villus height:crypt depth ratio (VH:CD) in duodenum on d 5 post-inoculation than pigs in other treatments. Pigs supplemented with monoglycerides, ZNO, or antibiotics had reduced ( P  < 0.05) ileal crypt depth compared with control on d 5 post-inoculation, contributing to the increase ( P  = 0.06) in VH:CD. Consistently, pigs in ZNO expressed the lowest ( P  < 0.05) TNFa , IL6 , IL10 , IL12 , IL1A , IL1B , and PTGS2 in ileal mucosa on d 5 post-inoculation, and no difference was observed in the expression of those genes between ZNO and monoglycerides. Supplementation of ZNO and antibiotic had significant impacts on metabolic pathways in the serum compared with control, particularly on carbohydrate and amino acid metabolism, while limited impacts on serum metabolites were observed in monoglycerides group when compared with control.

Conclusions

The results suggest that supplementation of monoglyceride blend may enhance disease resistance of weaned pigs by alleviating the severity of diarrhea and mitigating intestinal and systemic inflammation, although the effectiveness may not be comparable to high-dose zinc oxide.

Weaning piglets, the process of separating them from their mother, exposes them to nutritional, physiological, and environmental challenges [ 1 , 2 , 3 ]. These weaning stressors impair intestinal barrier function and induce intestinal and systemic inflammation, in addition to the typically occurring decrease in feed intake [ 4 , 5 ]. The compromised intestinal barrier increases the risk of external factors (e.g., toxins, antigens, and pathogens) entering the body, making piglets vulnerable to enteric diseases [ 6 , 7 ]. Post-weaning diarrhea, caused by the infection of enterotoxigenic Escherichia coli (ETEC) F18, is one of the common problems in young pigs [ 8 , 9 ]. This disease is characterized by watery diarrhea and deterioration of intestinal health, causing tremendous economic losses in swine production due to growth lag, morbidity, cost of medication, and mortality [ 10 , 11 , 12 , 13 ]. In-feed antibiotics or pharmacological doses of zinc oxide (2,000–3,000 mg/kg) have been widely applied to nursery diets for controlling post-weaning diarrhea and promoting animal health and growth [ 14 , 15 , 16 ]. However, along with the increased public health concern regarding antimicrobial resistance [ 17 , 18 , 19 , 20 , 21 , 22 ], the use of antibiotics for growth promoting purposes in animal production has been restricted since 2017 in the United States [ 23 ]. Furthermore, considering sustainable animal agriculture, it is noteworthy that Europe not only banned the use of pharmacological doses of zinc oxide but also limited dietary zinc oxide supplementation to 150 mg/kg [ 24 , 25 , 26 ]. Hence, alternative practices, including animal management and nutrition interventions, are needed to promote animal health and welfare, as increased morbidity and economic losses due to the constraints of conventional practices are inevitable.

Numerous nutritional interventions (e.g., exogenous enzymes, bioactive compounds derived from animals or plants, microbiome modulators) have been investigated and adopted in the swine industry to address the emergence of the post-antibiotic era [ 27 , 28 ]. One promising alternative is a group of products based on organic acids, specifically short-chain fatty acids (SCFA; less than 6 carbons) or medium-chain fatty acids (MCFA; 6–12 carbons). Research has shown that SCFA and MCFA have strong antibacterial activity [ 29 , 30 , 31 ]. In addition, they also exhibit various biological activities in pigs [ 32 , 33 , 34 ], including beneficial effects on growth performance, intestinal physiology, and immunity, making them more than just an energy source. However, the effectiveness of supplementing organic fatty acids is often hindered by limiting factors such as unpalatable flavor and losses prior to reaching the lower gastrointestinal tract [ 35 , 36 ]. In this respect, monoglycerides, composed of fatty acid esterified to glycerol, may address the limitations due to the two criteria: (1) they are relatively easy to handle; and (2) they allow active substances to be gradually released throughout the intestine [ 37 ]. Moreover, in vitro antimicrobial activity against a wide range of pathogenic bacteria was observed in glycerol esters derived from SCFA and MCFA [ 30 , 38 , 39 , 40 , 41 ]. There is growing interest in monoglycerides as antibacterial lipids in nutrition and health. Their physiological activities have been extensively studied in poultry [ 42 , 43 , 44 ], however, limited research has been reported on the efficacy of monoglycerides in weaned pigs using disease models. Therefore, the objective of this study was to investigate the influence of dietary supplementation of a monoglyceride blend on growth performance, intestinal health, and systemic immunity of weaned pigs experimentally infected with ETEC F18.

Materials and methods

Animals, housing, experimental design, and diet.

Sixty weaned pigs with 28 barrows and 32 gilts (average body weight [BW] = 6.49 ± 0.74 kg; around 21 to 24 d old) were obtained from the Swine Teaching and Research Center at the University of California, Davis, USA. The sows and piglets used in this experiment did not receive Escherichia coli vaccines, antibiotic injections, or antibiotics in creep feed. Before weaning, feces were collected from sows and all their piglets destined for this study to verify the absence of β-hemolytic Escherichia coli . The ETEC F18 receptor status was also tested by polymerase chain reaction (PCR)-restriction fragment length polymorphism [ 45 ], and piglets susceptible to ETEC F18 were selected for this study. After weaning, all pigs were randomly assigned to one of the four dietary treatments (15 replicates/treatment) in a randomized complete block design with BW within sex and litter as the block and pig as the experimental unit. Pigs were housed in individual pens (0.61 m × 1.22 m) for 28 d, including 7 d before and 21 d after the first ETEC challenge. All piglets had free access to feed and water. The light was on at 07:30 h and off at 19:30 h daily in the environmental control unit.

The four dietary treatments included: (1) a corn-soybean meal-based basal diet (control); (2) the basal diet with 0.3% monoglyceride blend (BalanGut™ LS L; BASF SE, Ludwigshafen, Germany) of butyric, caprylic, and capric acids; (3) the basal diet with 3,000 mg/kg of zinc oxide (ZNO); (4) the basal diet with 50 mg/kg of carbadox (antibiotic). A 2-phase feeding program was used with the first two weeks as phase 1 and the last two weeks as phase 2 (Table  1 ). Spray-dried plasma, antibiotics, and high levels of zinc oxide exceeding recommendation and normal practice were not included in basal diet. All diets were formulated to meet pig nutritional requirements [ 46 ] and provided as mash form throughout the experiment.

After 7 days of adaptation, all pigs were orally inoculated with 3 mL of ETEC F18 for three consecutive days from d 0 post-inoculation (PI). The ETEC F18 was originally isolated from a field disease outbreak by the University of Montreal (isolate number: ECL22131). The ETEC F18 expresses heat-labile toxin and heat-stable toxins a and b. The inoculums were prepared at 10 10 colony-forming units per 3 mL dose in phosphate buffered saline. This dose caused mild diarrhea in the current study, consistent with our previously published research [ 47 , 48 , 49 ].

Clinical observations and sample collections

The procedures of this experiment were adapted from previous research [ 47 , 50 , 51 , 52 ]. Clinical observations (fecal score and alertness score) were recorded twice daily throughout the study. The fecal score of each pig was assessed each day visually by two independent evaluators, with the score ranging from 1 to 5 (1 = normal feces, 2 = moist feces, 3 = mild diarrhea, 4 = severe diarrhea, and 5 = watery diarrhea). The frequency of diarrhea was calculated as the percentage of the pig days with fecal score of 3 or greater, as well as calculated as the percentage of the pig days with fecal score of 4 or greater. Alertness was scored from 1 to 3 (1 = normal, 2 = slightly depressed or listless, and 3 = severely depressed or recumbent). Scores for alertness did not exceed two throughout the experiment (data not shown).

Pigs were weighed on weaning day (d −7; initial BW), d 0 (before first inoculation), 5, 14, and 21 PI. Feed intake was recorded throughout the study. Average daily gain (ADG), average daily feed intake (ADFI), and feed efficiency (gain:feed ratio) were calculated for each period. Fecal samples were collected from the rectum of all pigs throughout the experiment using a cotton swab on d −7, 2, 5, 7, 10, 14, and 21 PI to test β-hemolytic coliforms and the percentage of β-hemolytic coliforms to total coliforms [ 47 , 50 , 51 , 52 ]. Blood samples were collected from the jugular vein of all pigs before ETEC challenge (d 0), and on d 2, 5, 14, and 21 PI to collect serum samples, which were stored at − 80°C until further analysis.

Twenty-four pigs (6 pigs/treatment, 3 barrows and 3 gilts) were euthanized on d 5 PI near the peak of ETEC infection, and the remaining pigs were euthanized at the end of the experiment (d 21 PI). Before euthanization, pigs were anesthetized with 1 mL mixture of 100 mg Telazol, 50 mg ketamine, and 50 mg xylazine (2:1:1) by intramuscular injection. After anesthesia, intracardiac injection with 78 mg Fatal-Plus solution (sodium pentobarbital, MWI Animal Health, Visalia, CA, USA) per 1 kg of BW was used to euthanize each pig. Intestinal mucosa samples were collected from jejunum and ileum, snap-frozen in liquid nitrogen, and then stored at −80 °C for gene expression analysis. Three 4-cm segments from the duodenum, the middle of the jejunum, and the ileum (10 cm close to the ileocecal junction) were collected and fixed in 10% neutral buffered formalin for intestinal morphology analysis.

Detection of β-hemolytic coliforms

Briefly, fecal samples were plated on Columbia Blood Agar with 5% sheep blood to identify hemolytic coliforms, which can lyse red blood cells surrounding the colony. Fecal samples were also plated on MacConkey agar to enumerate total coliforms. Hemolytic colonies from the blood agar were sub-cultured on MacConkey agar to confirm that they were lactose-fermenting bacteria and flat pink colonies. All plates were incubated at 37 °C for 24 h in an air incubator. Populations of both total coliforms and β-hemolytic coliforms on blood agar were visually scored from 0 to 8 (0 = no bacterial growth, 8 = very heavy bacterial growth). The ratio of scores of β-hemolytic coliforms to total coliforms was calculated.

Measurements of serum cytokine and acute phase proteins

Serum samples were analyzed for tumor necrosis factor-α (TNF-α; R&D Systems Inc., Minneapolis, MN, USA), C-reactive protein (CRP; R&D Systems Inc., Minneapolis, MN, USA), and haptoglobin (Aviva Systems Biology, San Diego, CA, USA) using porcine-specific enzyme-linked immunosorbent assay kits following the manufacturer’s procedures. All samples, including standards, were analyzed in duplicate. The intensity of the color was measured at 450 nm with the correction wavelength set at 530 nm using a plate reader (BioTek Instruments, Inc., Winooski, VT, USA). The intra-assay coefficients of variation for TNF-α, CRP, and haptoglobin were less than 7%. The inter-assay coefficients of variation for TNF-α, CRP, and haptoglobin were less than 10%. The concentrations of each analyte in the tested samples were calculated based on a standard curve.

Intestinal morphology

Fixed intestinal tissues were embedded in paraffin, sectioned at 5 μm, and stained with hematoxylin and eosin. The slides were photographed by an Olympus BX51 microscope at 10× magnification, and all measurements were conducted in the image processing and analysis software (Image J, NIH). Ten straight and integrated villi and their associated crypts and surrounding areas were selected to analyze villus height (VH), area, and width; crypt depth (CD) and width; and goblet cell number per villus as described in previous studies [ 52 , 53 ].

Immunohistochemistry

The immunohistochemistry procedures were based on previous research [ 47 , 54 ]. Briefly, the embedded ileal tissues were sectioned at 5 μm and placed on the microslides. The slides were incubated with 5 µg/mL porcine neutrophil-specific antibody PM1 (BMA Biomedicals, Augst, Switzerland) or 0.4 µg/mL porcine macrophage-specific antibody MAC387 (Thermo Scientific, Waltham, MA, USA). Antibody binding was visualized by using the avidin-biotin complex, and the diaminobenzidine chromogen (Vector Laboratories, Inc., CA, USA). Hematoxylin was applied as a counter stain. Slides incubated without the primary antibodies but with PBS were used as negative controls. Images were captured by an Olympus BX51 microscope at 10× magnification, and all measurements were analyzed by Image J software. Eight straight and integrated ileal villi were selected for measurement. The unit was the number of cells/mm 2 .

Intestinal barrier and innate immunity

Jejunal and ileal mucosa samples were analyzed for gene expression by quantitative real-time PCR (qRT-PCR). Briefly, approximately 100 mg of mucosa sample was homogenized using TRIzol reagent (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA). Then, total ribonucleic acid (RNA) was extracted following RNA extraction procedural guidelines provided by the reagent manufacturer. The quality and quantity of RNA were evaluated using a Thermo Scientific NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific, Inc., Waltham, MA, USA). The complementary DNA (cDNA) was produced from 1 µg of total RNA per sample using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems; Thermo Fisher Scientific, Inc., Waltham, MA, USA) in a total volume of 20 µL. The mRNA expression of Mucin 2 ( MUC2 ), Claudin-1 ( CLDN1 ), Zonula occludens-1 ( ZO-1 ), and Occludin ( OCLN ) in jejunal mucosa and the mRNA expression of Tumor necrosis factor-alpha ( TNFa ), Interleukin 6 ( IL6 ), Interleukin 7 ( IL7 ), Interleukin 10 ( IL10 ), Interleukin 12 ( IL12 ), Interleukin-1 alpha ( IL1A ), Interleukin-1 beta ( IL1B ), MUC2 , and Prostaglandin-endoperoxide synthase 2 ( PTGS2 ) in ileal mucosa were analyzed. Data normalization was accomplished using 18S ribosomal RNA as a housekeeping gene. Primers were designed based on published literature and commercially synthesized by Integrated DNA Technologies, Coralville, IA, USA. All primers were verified prior to qRT-PCR (Table S1 ). The qRT-PCR reaction conditions followed the published research [ 55 ]. The 2 −ΔΔCT method was used to analyze the relative expression of genes compared to control [ 56 ].

Untargeted metabolomics analysis

The untargeted metabolomics analysis was performed by the NIH West Coast Metabolomics Center at the University of California, Davis, using gas chromatography (Agilent 6890 gas chromatograph controlled using Leco ChromaTOF software version 2.32, Agilent, Santa Clara, CA, USA) coupled with time-of-flight mass spectrometry (GC/TOF-MS) (Leco Pegasus IV time-of-flight mass spectrometer controlled using Leco ChromaTOF software version 2.32, Leco, St. Joseph, MI, USA). Metabolite extraction was performed following procedures previously described by Fiehn et al. [ 57 ]. Briefly, frozen serum samples (approximately 30 µL) were homogenized using a Retsch ball mill (Retsch, Newtown, PA, USA) for 30 s at 25 times/s. After homogenization, a prechilled (−20 °C) extraction solution (isopropanol/acetonitrile/water at the volume ratio 3:3:2, degassed with liquid nitrogen) was added at a volume of 1 mL/20 mg of sample. Samples were then vortexed and shaken for metabolite extraction. After centrifugation at 12,800 × g for 2 min, the supernatant was collected and divided into two equal aliquots and concentrated at room temperature for 4 h in a cold-trap vacuum concentrator (Labconco Centrivap, Kansas City, MO, USA). To separate complex lipids and waxes, the residue was re-suspended in 500 µL of 50% aqueous acetonitrile and centrifuged at 12,800 × g for 2 min. The resultant supernatant was collected and concentrated in the vacuum concentrator. Dried sample extracts were derivatized and mixed with internal retention index markers (fatty acid methyl esters with the chain length of C8 to C30). The samples were injected for GC/TOF analysis, and all samples were analyzed in a single batch. Data acquisition by mass spectrometry and mass calibration using FC43 (perfluorotributylamine) before starting analysis sequences. Metabolite identifications were performed based on the two parameters: (1) Retention index window ± 2,000 U (around ± 2 s retention time deviation), and (2) Mass spectral similarity plus additional confidence criteria as detailed below. A detailed methodology for data acquisition and metabolite identification was described in a previously published article by Fiehn et al. [ 57 ].

Statistical analysis

The normality of data was verified and outliers were identified using the UNIVARIATE procedure (SAS Institute Inc., Cary, NC, USA). Outliers were identified and removed as values that deviated from the treatment mean by more than 3 times the interquartile range. All data except frequency of diarrhea and metabolomics were analyzed by ANOVA using the PROC MIXED of SAS (SAS Institute Inc., Cary, NC, USA) in a randomized complete block design with the pig as the experimental unit. The statistical model included diet as the main effect and block as random effect. Treatment means were separated by using the LSMEANS statement and the PDIFF option of PROC MIXED. The Chi-square test was used for analyzing the frequency of diarrhea. Statistical significance and tendency were considered at P  < 0.05 and 0.05 ≤  P  < 0.10, respectively.

The metabolomics data were analyzed using different modules of a web-based platform, MetaboAnalyst 5.0 ( https://www.metaboanalyst.ca ) [ 58 ]. Data were filtered for peaks with detection rates less than 30% of missing abundances and normalized using logarithmic transformation and auto-scaling. Mass univariate analysis was performed using one-way ANOVA followed by Fisher’s least significant difference test (adjusted P  ≤ 0.05). Fold change analysis and t -tests were also conducted to determine the fold change and significance of each identified metabolite. Statistical significance was declared at a false discovery rate (FDR, Benjamini and Hochberg correction; q) < 0.2 and fold change > 2.0. Partial least squares discriminant analysis (PLS-DA) was carried out to further identify discriminative variables (metabolites) among the treatment groups. Pathway analysis and metabolite set enrichment analysis were performed on identified metabolites that had a Variable Importance in Projection (VIP) score > 1. The pathway with a P -value less than 0.05, as well as an impact value greater than 0.1, was defined as a significant impact pathway.

Growth performance, diarrhea, β-hemolytic coliforms

There were no significant differences in the initial (d −7) and d 0 BW of pigs among dietary treatments (Table  2 ). In comparison to control and antibiotic groups, supplementation of monoglycerides did not affect BW, ADG, and ADFI throughout the experiment. Pigs supplemented with ZNO had greater ( P  < 0.05) BW on d 5, 14, and 21 PI, increased ( P  < 0.05) ADG from d 0 to 5 PI, d 0 to 14 PI, and d 0 to 21 PI, and enhanced ( P  < 0.05) ADFI from d 0 to 14 PI and d 0 to 21 PI than the other treatments. However, the ADFI from d 0 to 21 PI was not different between ZNO and monoglycerides groups. Pigs supplemented with ZNO had greater ( P  < 0.01) gain:feed ratio from d 0 to 5 PI compared with the other treatments, but the difference did not persist throughout the post-challenge period. The gain:feed ratio on d 0 to 21 PI was lower ( P  < 0.05) in monoglycerides than in control and antibiotic groups, but did not differ from ZNO group.

Pigs in the ZNO group had the lowest ( P  < 0.05) fecal score from d 1 to 10 PI among dietary treatments (Fig.  1 ). The incidence of diarrhea was 32.09% in control, 30.41% in monoglycerides, 4.01% in ZNO, and 22.53% in antibiotic, while the severity of diarrhea was 19.26% in control, 16.22% in monoglycerides, 0.31% in ZNO, and 12.35% in antibiotic, respectively (Fig.  2 ). The incidence of diarrhea (fecal score ≥ 3) was lower ( P  < 0.05) in ZNO and antibiotic groups than control and monoglycerides groups. The severity of diarrhea (fecal score ≥ 4) in ZNO and antibiotic groups was also lower than that in control, but there was no difference observed in the severity of diarrhea between monoglycerides and antibiotic groups. The ZNO group had the lowest incidence and severity of diarrhea throughout the experimental period.

Daily fecal score of enterotoxigenic Escherichia coli F18-challenged weaned pigs fed diets supplemented with monoglycerides, high-dose zinc oxide (ZNO), or antibiotic. Fecal score = 1, normal feces; 2, moist feces; 3, mild diarrhea; 4, severe diarrhea; 5, watery diarrhea. * P  < 0.05, indicating fecal scores were significantly different among treatments. # P  < 0.10, indicating fecal scores tended to different among treatments. Each least squares mean represents 14–15 observations before d 5 post-inoculation (PI) and each least squares mean represents 8–9 observations after d 5 PI

Frequency of diarrhea (overall period) of enterotoxigenic Escherichia coli F18-challenged weaned pigs fed diets supplemented with monoglycerides, high-dose zinc oxide (ZNO), or antibiotic. Frequency of diarrhea was calculated as the percentage of pig days with fecal score ≥ 3 or 4 in the total of pig days. a–c Means without a common superscript are different ( P  < 0.05) in frequency of diarrhea ≥ 3. A–C Means without a common superscript are different ( P  < 0.05) in frequency of diarrhea ≥ 4

No β-hemolytic coliforms were identified in fecal samples of pigs in all groups prior to ETEC inoculation. Βeta-hemolytic coliforms were identified in all pigs’ feces on d 2 PI. Pigs in ZNO group had lower ( P  < 0.05) percentage of β-hemolytic coliforms in feces on d 5 PI than pigs in control, while no difference was observed among monoglycerides, ZNO, and antibiotic groups (Fig.  3 ). No difference was observed in the percentage of β-hemolytic coliforms in feces among all dietary treatments on d 7, 10, 14, and 21 PI.

The percentage (%) of β-hemolytic coliforms in fecal samples of enterotoxigenic Escherichia coli F18-challenged pigs fed diets supplemented with monoglycerides, high-dose zinc oxide (ZNO), or antibiotic. No β-hemolytic coliforms were observed in the fecal samples of pigs before Escherichia coli challenge. β-Hemolytic coliforms were only observed in control pigs on d 21 post-inoculation (PI). Each least squares mean represents 14–15 observations on d 2 and 5 PI and each least squares mean represents 8–9 observations on d 7, 10, 14, and 21 PI. a,b Means without a common superscript are different ( P  < 0.05)

• Systemic immunity

No difference was observed in serum TNF-α concentrations among all treatments at d 0 before ETEC inoculation, and at d 2, 5, and 21 PI (Table  3 ). Dietary supplements tended ( P  = 0.07) to impact serum TNF-α on d 14 PI, pigs fed with ZNO had the lowest TNF-α and pigs fed with control had the highest level of TNF-α among all treatments. Pigs in monoglycerides group had lower ( P  < 0.05) serum CRP than pigs in the antibiotic group on d 0 before ETEC inoculation. Supplementation of ZNO reduced ( P  < 0.10 and P  < 0.05) serum CRP on d 14 and 21 PI, tended ( P  = 0.06) to reduce serum haptoglobin on d 0, and reduced ( P  < 0.05) serum haptoglobin on d 2 and 5 PI. Pigs fed with monoglycerides also had lower ( P  < 0.05) serum haptoglobin on d 5 PI, compared with control pigs.

On d 5 PI, pigs in ZNO had more ( P  < 0.05) goblet cell numbers per villus, greater ( P  < 0.05) villus area and VH, and higher ( P  < 0.05) VH:CD in duodenum than pigs in other treatments (Table  4 ). Supplementation of monoglycerides, ZNO, or antibiotic reduced ( P  < 0.05) ileal CD compared with control. Consistently, pigs in ZNO group tended ( P  = 0.06) to have the biggest VH:CD in the ileum, followed by pigs in monoglycerides and antibiotic groups. On d 21 PI, pigs supplemented with ZNO tended ( P  = 0.07) to have more goblet cells per villus, and had largest ( P  < 0.05) villus area and highest ( P  < 0.05) VH in the duodenum, when compared with other treatments.

Supplementation of ZNO or antibiotic reduced ( P  < 0.05) neutrophil counts in ileal villi on d 5 PI compared with control (Table  5 ). However, no significant differences in neutrophil counts were observed among monoglycerides, ZNO, and antibiotic groups. Pigs supplemented with ZNO had the lowest ( P  < 0.05) number of macrophages in ileal villi among all treatments on d 5 PI. Pigs fed with antibiotic also had significantly lower ( P  < 0.05) recruitment of macrophages in ileal villi than control group, but comparable to that in pigs fed with monoglycerides.

No differences were observed in the mRNA expression of MUC2 , CLDN1 , ZO-1 , and OCLN in jejunal mucosa of weaned pigs among different treatments on d 5 and 21 PI (Fig.  4 ). On d 5 PI, pigs fed with ZNO had lower ( P  < 0.05) mRNA expression of TNFa , IL6 , IL10 , IL12 , IL1A , IL1B , and PTGS2 in ileal mucosa, compared with other treatments (Fig.  5 ). However, no difference in the expression of listed genes was observed between pigs supplemented with monoglycerides or ZNO. Pigs supplemented with monoglycerides expressed lowest ( P  < 0.05) PTGS2 in ileal mucosa compared with other treatments on d 21 PI.

Relative mRNA abundance of genes in jejunal mucosa of enterotoxigenic Escherichia coli F18-challenged weaned pigs fed diets supplemented with monoglycerides, high-dose zinc oxide, or antibiotic. Each least squares mean represents 6–9 observations. PI, Post-inoculation; MUC2 , Mucin 2; CLDN1 , Claudin-1;  ZO-1 , Zonula occludens-1; OCLN , Occludin

Relative mRNA abundance of genes in ileal mucosa of enterotoxigenic Escherichia coli F18-challenged pigs supplemented with monoglycerides, high-dose zinc oxide, or antibiotic on d 5 ( A ) and 21 PI ( B ). a,b Means without a common superscript are different ( P  < 0.05). Each least squares mean represents 6–9 observations. PI, Post-inoculation; TNFa , Tumor necrosis factor-alpha; IL6 , Interleukin 6; IL7 , Interleukin 7; IL10 , Interleukin 10; IL12 , Interleukin 12; IL1A , Interleukin-1 alpha, IL1B , Interleukin-1 beta; MUC2 , Mucin 2, and  PTGS2 , Prostaglandin-endoperoxide synthase 2

Metabolite profiles in serum

A total of 483 (165 identified and 318 unidentified) metabolites were detected in serum samples. Based on statistical threshold and VIP scores, pantothenic acid and fructose were up-regulated by ZNO, compared with the pigs in control group on d 5 PI (Table  6 ). Supplementation of monoglycerides changed the relative abundances of 14 metabolites (7 up-regulated and 7 down-regulated) compared with ZNO, and upregulated lactose and cellobiose compared with antibiotics on d 5 PI. On d 14 PI, supplementation of ZNO changed abundances of 10 metabolites (7 up-regulated and 3 down-regulated) compared with control. Supplementation of monoglycerides up-regulated 2 metabolites (hippuric acid and indole-3-propionic acid) and down-regulated 8 metabolites (including glutaric acid, serotonin, mannose, etc.) compared with pigs in the ZNO. Pigs fed with antibiotics had greater abundance of hippuric acid and indole-3-propionic acid, but had lower thymine, pantothenic acid, glycerol, and piperidone compared with the pigs in the ZNO group. Limited differential metabolites were identified when comparing control vs. monoglycerides, and control vs. antibiotic throughout the experiment (data not shown).

Based on the identified metabolites and VIP scores, a PLS-DA score with 95% confidence ranges (shaded areas) showed a clear separation between control and ZNO, between monoglycerides and ZNO, between monoglycerides and antibiotic, and between ZNO and antibiotic groups on d 5 PI (Fig.  6 A) and/or d 14 PI (Fig.  6 B). To further explore the metabolic profile differences among dietary treatments, PLS-DA was performed for the following comparisons: (1) control vs. ZNO, (2) monoglycerides vs. ZNO, (3) monoglycerides vs. antibiotic, and (4) ZNO vs. antibiotic on d 5 and 14 PI. The score plot again distinguished control from ZNO (Fig. S1 A and B), monoglycerides from ZNO (Fig. S1 C and D), monoglycerides from antibiotic (Fig. S2 A and B), and ZNO from antibiotic (Fig. S2 C and D).

Partial Least Squares Discriminant Analysis (PLS-DA) 2D score plot of the metabolites in serum showed separated clusters between the CON and ZNO, MG and ZNO, MG and AB, and ZNO and AB groups on d 5 ( A ) and/or d 14 ( B ) post-inoculation, respectively. CON = Control; MG = Monoglycerides; ZNO = High-dose zinc oxide; AB = Antibiotic. Shaded areas in different colors represent in 95% confidence interval

Pathway analysis and metabolite set enrichment analysis were performed on the identified metabolites in serum with VIP > 1 (Table  7 ). On d 5 PI, taurine and hypotaurine metabolism and phenylalanine metabolism were the most affected metabolic pathways in a comparison of control vs. monoglycerides (Fig. S3 A and B). Arginine biosynthesis, β-alanine metabolism, arginine and proline metabolism, pyruvate metabolism, citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolism, and glycolysis/gluconeogenesis were the most affected metabolic pathways when comparing control with ZNO (Fig. S4 A and B). Citrate cycle, taurine and hypotaurine metabolism, and β-alanine metabolism were the most affected metabolic pathways when monoglyceride blend was compared with ZNO (Fig. S5 A and B). Taurine and hypotaurine metabolism, nicotinate and nicotinamide metabolism, and β-alanine metabolism were the most affected metabolic pathways in a comparison of monoglycerides vs. antibiotic (Fig. S6 A and B). β-Alanine metabolism and citrate cycle were the most affected metabolic pathways when comparing ZNO with antibiotic (Fig. S7 A and B). On d 14 PI, glyoxylate and dicarboxylate metabolism and taurine and hypotaurine metabolism were the most affected metabolic pathways in a comparison of control vs. monoglycerides (Fig. S3 C and D). Alanine, aspartate and glutamate metabolism, citrate cycle, glyoxylate and dicarboxylate metabolism, and pyrimidine metabolism were the most affected metabolic pathways when comparing control with ZNO (Fig. S4C and D). Citrate cycle, glyoxylate and dicarboxylate metabolism, alanine, aspartate and glutamate metabolism, and pyrimidine metabolism were the most affected metabolic pathways when monoglyceride blend was compared with ZNO (Fig. S5 C and D), while citrate cycle was the most affected metabolic pathway in comparison of monoglycerides vs. antibiotic (Fig. S6 C and D). Alanine, aspartate and glutamate metabolism, glyoxylate and dicarboxylate metabolism, citrate cycle, D-glutamine and D-glutamate metabolism, pyrimidine metabolism, arginine biosynthesis, and β-alanine metabolism were the most affected metabolic pathways when comparing ZNO with antibiotic (Fig. S7 C and D).

The present study investigated the potential of a monoglyceride blend containing butyric, caprylic, and capric acids in mitigating the adverse effects of ETEC F18 infection on systemic and intestinal immune responses, as well as intestinal health in weaning pigs. Additionally, the study identified metabolic changes resulting from monoglycerides supplementation, shedding light on potential mechanisms underlying the observed physiological responses.

Post-weaning diarrhea, a prevalent gastrointestinal disease occurring shortly after weaning, is often attributed to the adhesion and proliferation of ETEC F18 or F4 in the small intestine. Clinical signs typically include watery diarrhea, dirty appearance, stunted growth, dehydration, and lethargy [ 51 , 59 ]. In this study, successful ETEC F18 infection was confirmed through fecal shedding of β-hemolytic coliforms and the manifestation of typical infection symptoms, including growth retardation and severe diarrhea. These observations are consistent with our previous research [ 50 , 52 ]. The observed pattern of gradual recovery after the peak of infection (d 3 to 5 PI) also aligns with our previous studies using the same ETEC F18 strain [ 47 , 52 , 60 ]. The results of fecal score and the frequency of diarrhea indicated that supplementation of high-dose zinc oxide or antibiotics significantly reduces both the incidence and severity of diarrhea in weaned pigs infected with ETEC F18. However, the impact of dietary monoglycerides on diarrhea was limited.

ETEC toxins can disrupt the regulation of intestinal ion transporters, leading to fluid and electrolyte imbalances [ 61 , 62 ]. Although the percentage of β-hemolytic coliforms in feces was similar across treatments post-infection, supplementation of high-dose zinc oxide notably reduced the β-hemolytic coliforms on d 5 PI, which may be attributed to zinc oxide’s antimicrobial properties and its ability to support intestinal barrier function and epithelial tissue regeneration [ 26 , 28 , 63 ]. Similarly, both monoglycerides and antibiotics showed comparable reductions in ETEC shedding, likely due to their antibacterial activity [ 37 , 64 ]. This reduction corresponded with a decreased incidence of diarrhea across all supplemented groups.

It is well known that ETEC infection can disrupt essential intestinal functions, such as nutrient transport, epithelial barrier integrity, and immune function [ 13 , 65 ]. All of these result in reduced digestive and absorptive capacity, and increased resource expenditure for maintaining intestinal homeostasis, ultimately leading to compromised performance in infected animals [ 51 , 66 , 67 ]. The beneficial effects of high-dose zinc oxide on intestinal morphology were significant, and supplementation with monoglycerides improved CD and VH:CD in the ileum of ETEC-infected pigs on d 5 PI, comparable to high-dose zinc oxide. However, there were limited changes in intestinal morphology on d 21 PI, likely due to the pigs’ recovery from ETEC infection. Consistent with our observations, previous studies have reported the positive effects of pharmacological doses of zinc oxide in managing post-weaning diarrhea caused by ETEC and have summarized its beneficial effects on growth performance, gastrointestinal tract health, and immunity [ 26 ]. Although the exact modes of action of carbadox are unclear, the observed changes in serum inflammatory markers and ileal morphology may be due to their ability to compete for sites important for nutrient absorption and ETEC colonization, thereby reducing resource costs and improving nutrient availability. Intestinal morphology results are also consistent with findings reported by Hung et al. [ 68 ], who observed that carbadox in the diet decreased CD and increased VH:CD in the small intestine of weaned pigs.

In addition to changes in intestinal morphology, high-dose zinc oxide and carbadox supplementation showed a mitigating effect on the recruitment of neutrophils and macrophages in the ileal villi. Supplementation with high-dose zinc oxide also reduced the relative gene expression of inflammatory cytokines ( TNFa , IL6 , IL10 , IL12 , IL1A , IL1B , and PTGS2 ) in ileal mucosa, indicating a moderating effect on the intestinal immune response. Although monoglycerides supplementation partially attenuated intestinal inflammation, its efficacy was not comparable to that of high-dose zinc oxide. The observed changes in the supplementation of monoglycerides suggest reduced intestinal epithelial cell renewal and attenuated inflammatory responses, indicating reduced energy and nutritional costs similar to conventional practices [ 68 ]. These findings also suggest that supplementing monoglycerides may overcome primary obstacles associated with the use of organic acids as feed additives, including undesirable losses in the upper intestine and unfavorable taste and aroma. The antibacterial effects of organic acids and their monoglycerides against Escherichia coli have been verified through numerous in vitro studies [ 30 , 38 , 41 , 69 ]. The biological activity of butyric acid, which constitutes a major portion of our glyceride blend (~ 60%), has been well documented, including its modulation of various cellular responses via histone deacetylase inhibition and G-protein-coupled receptor activation in various cell types [ 36 , 37 , 70 , 71 ], further supporting our findings.

Moreover, local inflammation can influence systemic immunity, and immune activation by external factors can exacerbate the performance status during the weaning period due to metabolic changes [ 72 , 73 , 74 ]. For instance, ETEC infection activates immune cells and increases the secretion of pro-inflammatory cytokines [ 47 , 52 , 75 ], leading to alterations in the absorption and utilization of nutrients or energy, including anorexia, decreased gut motility, and increased hepatic acute-phase protein synthesis [ 73 , 76 , 77 ]. Supplementation with high-dose zinc oxide was associated with a significant reduction in inflammatory biomarkers throughout the experiment, and an anti-inflammatory effect of monoglycerides was also observed during peak infection. This finding is supported by observations reported by Tian et al. [ 78 ], where inclusion of glycerol butyrate in pig diet reduced pro-inflammatory factors ( TNFa , IL6 , and  IL1B) in jejunum and ileum to ETEC infection by inhibiting the NF-κB/MAPK pathway.

Given the biological effects of high-dose zinc oxide discussed earlier and the observed changes in diarrhea, intestinal morphology, and intestinal and serum inflammatory markers, it is not surprising that the pigs fed with high-dose zinc oxide had the greatest growth performance throughout the experimental period among all treatments. On the other hand, carbadox supplementation reduced feed intake compared to high-dose zinc oxide, but feed efficiency was higher than that of monoglycerides throughout the post-challenge period. These results reflect the multifactorial nature of animal growth and suggest that high-dose zinc oxide and antibiotics are likely to exert their beneficial effects through different mechanisms [ 68 ]. In the present study, the monoglyceride blend had limited effects on the growth performance of weaned pigs infected with ETEC F18. This finding aligns with other research showing that dietary supplementation of SCFA or MCFA monoglycerides did not affect the performance of weaned pigs [ 79 , 80 , 81 , 82 ]. Recent studies in poultry also confirmed that dietary supplementation of monoglyceride blend (butyric, caprylic, and capric acids) did not affect the growth performance of early growth stage in broilers infected with necrotic enteritis [ 43 , 83 ]. In this study, supplementation of monoglyceride blend reduced gain:feed ratio of ETEC-infected pigs. However, it is noteworthy that this change was the result of increased feed intake. The observed improvement in feed intake in pigs fed with monoglycerides is further supported by the previously discussed anti-inflammatory effects of monoglycerides. Weaning stress is associated with reduced nutrient and energy intake, which may not recover even two weeks after weaning [ 84 , 85 ]. Thus, the potential impacts of the monoglyceride blend on the feed intake of newly weaned pigs need to be further investigated in a performance trial with a larger number of animals.

The physiological changes caused by external factors, such as nutritional interventions or disease, can be comprehensively evaluated through a metabolomics analysis, providing valuable insights into the underlying mechanisms [ 86 , 87 ]. In this study, pigs supplemented with high-dose zinc oxide exhibited significant alterations in serum metabolites primarily associated with carbohydrate and amino acid metabolism, compared to pigs in the control and monoglycerides groups. These changes are consistent with the mechanistic measurement results discussed earlier, and are also in line with the inferred effects suggested by other research related to nutrient and energy availability [ 68 ]. For example, the citrate cycle is a major metabolic pathway regulated to meet diverse cellular metabolic needs, including playing an important role in energy production and providing intermediates required for biosynthesis [ 88 ]. Recent studies have shown that these intermediates are also involved in cell signaling and have diverse functions, such as the regulation of chromatin modification and DNA methylation, as well as immunomodulation [ 86 , 89 ].

Interestingly, monoglycerides supplementation had limited effects on serum metabolites compared to the control; however, significant pathway alterations were observed in serum metabolites when pigs were supplemented with monoglycerides. Specifically, taurine and hypotaurine metabolism was one of the metabolic pathways significantly affected by the supplementation of monoglycerides during the peak of ETEC infection. Taurine and hypotaurine are known to play crucial roles in cellular homeostasis and antioxidant responses [ 90 , 91 ]. Similar to high-dose zinc oxide, carbadox supplementation had impacts on carbohydrate and amino acid metabolism in serum metabolites compared to control or monoglycerides. These changes include alterations in the citrate cycle and β-alanine metabolism. β-Alanine is a naturally occurring amino acid involved in the synthesis of carnosine, which exhibits beneficial biological activity, including antioxidant and anti-inflammatory properties [ 92 , 93 , 94 ]. Additionally, it has been reported that Mas-related G protein-coupled receptors, specifically responsive to β-alanine, may have beneficial effects on immune stress and homeostasis [ 95 , 96 ].

In conclusion, the findings of this study suggest that supplementation of monoglyceride blend including C4, C8, and C10 saturated fatty acids may enhance disease resistance by mitigating intestinal and systemic inflammation in weaned pigs challenged with enterotoxigenic Escherichia coli F18. Although the effects on performance and disease resistance were not comparable to that of high-dose zinc oxide, the efficacy was similar to the supplementation of carbadox. Additional research is needed to further evaluate the effects of monoglycerides supplementation on growth performance of weaned pigs under various external challenges in commercial conditions. Another area of research may be to explore combinations of monoglycerides with other acids, such as formic acid, as a potential alternative to conventional practices.

Availability of data and materials

All data generated or analyzed during this study are available from the corresponding author upon reasonable request.

Abbreviations

Average daily feed intake

Average daily gain

Body weight

Crypt depth

Complementary DNA

C-reactive protein

• Enterotoxigenic Escherichia coli

False discovery rate

Interleukin 6

Interleukin 7

Interleukin 10

Interleukin 12

Interleukin-1 alpha

Interleukin-1 beta

Medium-chain fatty acids

Polymerase chain reaction

Post-inoculation

Partial least squares discriminant analysis

Prostaglandin-endoperoxide synthase 2

Quantitative real-time PCR

Ribonucleic acid

Short-chain fatty acids

Tumor necrosis factor-alpha

Villus height

Variable importance in projection

High-dose zinc oxide

Zonula occludens-1

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Acknowledgements

We gratefully acknowledge financial support from BASF Corporation and the Jastro Award, granted by the University of California, Davis Animal Biology Graduate Group.

BASF Corporation/SE funded this research.

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Authors and affiliations.

Department of Animal Science, University of California, Davis, CA, 95616, USA

Sangwoo Park, Shuhan Sun, Lauren Kovanda & Yanhong Liu

BASF Corporation, Florham Park, 07932, USA

Adebayo O. Sokale & Yanhong Liu

BASF SE, Lampertheim, Germany

Adriana Barri

Department of Animal Science, Michigan State University, East Lansing, MI, 48824, USA

Kwangwook Kim

School of Veterinary Medicine, University of California, Davis, CA, 95616, USA

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Contributions

The contributions of the authors were as follows: SP conducted the animal work and most of the laboratory work and wrote most of the manuscript. SS, LK, and KK assisted in conducting the animal trial and part of the laboratory work. XL provided enterotoxigenic Escherichia coli F18 inoculum and helped to revise the manuscript. AOS and AB provided suggestions on experimental design and revised the manuscript. YL was the principal investigator. She oversaw the development of the study and the manuscript writing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yanhong Liu .

Ethics declarations

Ethics approval and consent to participate.

The protocol for this study was reviewed and approved by the Institutional Animal Care and Use Committee at the University of California, Davis (UC Davis, IACUC# 21875). The study was conducted at the Cole facility at UC Davis.

Consent for publication

Not applicable.

Competing interests

Adebayo Sokale is an employee of BASF Corporation (Florham Park, NJ, USA) and Adriana Barri is an employee of BASF SE (Ludwigshafen am Rhein, Germany). No other authors have conflicts of interest to declare.

Supplementary Information

Additional file 1: table s1.

Gene-specific primer sequences and polymerase chain reaction conditions.

Additional file 2:

Fig. S1 Partial Least Squares Discriminant Analysis (PLS-DA) 2D score plot of the metabolites in serum showed separated clusters between the CON and ZNO ( A and B ), MG and ZNO ( C and D ) on d 5 ( A and C ) and d 14 ( B and D ) post-inoculation, respectively. CON, Control; MG, Monoglycerides; ZNO, High-dose zinc oxide. Shaded areas in different colors represent in 95% confidence interval.  Fig. S2 Partial Least Squares Discriminant Analysis (PLS-DA) 2D score plot of the metabolites in serum showed separated clusters between the MG and AB ( A and B ), ZNO and AB ( C and D ) on d 5 ( A and C ) and d 14 ( B and D ) post-inoculation, respectively. MG, Monoglycerides; ZNO, High-dose zinc oxide; AB, Antibiotic. Shaded areas in different colors represent in 95% confidence interval.

Additional file 3:

Fig. S3 Significantly changed pathways in serum between the control and monoglycerides groups on d 5 ( A ) and d 14 ( C ) post-inoculation, respectively. The x -axis represents the pathway impact values and the y -axis represents the −log( P ) values from the pathway enrichment analysis. Metabolite set enrichment analysis shows the metabolic pathways were enriched in control compared with monoglycerides on d 5 ( B ) and d 14 ( D ) post-inoculation, respectively. Both pathway analysis and metabolite set enrichment analysis were performed using identified metabolites with VIP > 1. Fig. S4 Significantly changed pathways in serum between the control and high-dose zinc oxide (ZNO) groups on d 5 ( A ) and d 14 ( C ) post-inoculation, respectively. The x -axis represents the pathway impact values and the y -axis represents the −log( P ) values from the pathway enrichment analysis. Metabolite set enrichment analysis shows the metabolic pathways were enriched in control compared with ZNO on d 5 ( B ) and d 14 ( D ) post-inoculation, respectively. Both pathway analysis and metabolite set enrichment analysis were performed using identified metabolites with VIP > 1. Fig. S5 Significantly changed pathways in serum between the monoglycerides and high-dose zinc oxide (ZNO) groups on d 5 ( A ) and d 14 ( C ) post-inoculation, respectively. The x -axis represents the pathway impact values and the y -axis represents the −log(P) values from the pathway enrichment analysis. Metabolite set enrichment analysis shows the metabolic pathways were enriched in monoglycerides compared with ZNO on d 5 ( B ) and d 14 ( D ) post-inoculation, respectively. Both pathway analysis and metabolite set enrichment analysis were performed using identified metabolites with VIP > 1. Fig. S6 Significantly changed pathways in serum between the monoglycerides and antibiotic groups on d 5 ( A ) and d 14 ( C ) post-inoculation, respectively. The x -axis represents the pathway impact values and the y -axis represents the −log( P ) values from the pathway enrichment analysis. Metabolite set enrichment analysis shows the metabolic pathways were enriched in monoglycerides compared with antibiotic on d 5 ( B ) and d 14 ( D ) post-inoculation, respectively. Both pathway analysis and metabolite set enrichment analysis were performed using identified metabolites with VIP > 1. Fig. S7 Significantly changed pathways in serum between the high-dose zinc oxide (ZNO) and antibiotic groups on d 5 ( A ) and d 14 ( C ) post-inoculation, respectively. The x -axis represents the pathway impact values and the y -axis represents the −log( P ) values from the pathway enrichment analysis. Metabolite set enrichment analysis shows the metabolic pathways were enriched in ZNO compared with antibiotic on d 5 ( B ) and d 14 ( D ) post-inoculation, respectively. Both pathway analysis and metabolite set enrichment analysis were performed using identified metabolites with VIP > 1.

Additional file 4:

Fig. S8 Intestinal morphology of enterotoxigenic Escherichia coli F18-challenged weaned pigs fed experimental diets on d 5 post-inoculation.

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Park, S., Sun, S., Kovanda, L. et al. Effects of monoglyceride blend on systemic and intestinal immune responses, and gut health of weaned pigs experimentally infected with a pathogenic Escherichia coli . J Animal Sci Biotechnol 15 , 141 (2024). https://doi.org/10.1186/s40104-024-01103-7

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Received : 03 June 2024

Accepted : 09 September 2024

Published : 13 October 2024

DOI : https://doi.org/10.1186/s40104-024-01103-7

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