Ultra-Processed Foods and Inflammation Analysis

ISEF Category: Biomedical and Health Sciences

Subcategory: Nutrition and Natural Products  ·  Difficulty: Advanced  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

Some food choices may act like a slow burn in your body. You cannot see that effect in a mirror, but you can measure it in blood data. That makes this topic powerful for a science fair project, because you can study diet and inflammation without running a wet lab.

What Is It?

This project asks whether certain ultra-processed food groups line up with higher inflammation markers in NHANES, a large U.S. health survey. Ultra-processed foods are products made with lots of industrial ingredients and additives, like packaged snacks, sweet drinks, and ready-to-eat meals. Inflammation markers such as CRP, which stands for C-reactive protein, and WBC, which means white blood cell count, give you a rough read on how activated the immune system is.

Think of your body like a building with an alarm system. A little alarm can help when something is wrong, but a loud alarm that stays on too long can signal trouble. Your job is to see whether specific food subgroups are linked to that louder alarm, while adjusting for other factors like income, education, age, and sex that can also affect health.

Why This Is a Good Topic

This is a strong science fair topic because it uses real public health data, a clear outcome, and a question you can test with statistics. It connects directly to diet, chronic disease risk, and health equity, so the project has real-world meaning. You can learn survey analysis, confounder control, and causal thinking without needing a lab bench.

Research Questions

• How does intake of sugar-sweetened ultra-processed drinks relate to CRP levels in NHANES after adjusting for age, sex, and income? ?
• What is the effect of ready-to-eat ultra-processed meals on WBC count after controlling for body mass index and smoking status? ?
• Does the association between ultra-processed snack foods and CRP differ by household income group? ?
• To what extent do specific NOVA ultra-processed subgroups predict inflammation markers better than total ultra-processed food intake? ?
• Which socioeconomic factors change the estimated link between ultra-processed food intake and CRP the most? ?
• How does the association between ultra-processed breakfast foods and inflammatory biomarkers vary across age groups? ?

Basic Materials

• Computer with internet access.
• Free NHANES data files and codebooks from CDC.
• Spreadsheet software for cleaning a small dataset.
• R or Python installed on your computer.
• Notes document for tracking variable definitions and survey filters.
• Headphones or a quiet workspace for long data sessions.

Advanced Materials

• Computer with enough memory to handle NHANES microdata.
• R with survey, MatchIt, cobalt, and tableone packages.
• Python with pandas, statsmodels, and scikit-learn.
• Access to PubMed for background review articles.
• A data dictionary for NOVA food subgroup coding.
• Statistical plotting software for adjusted effect plots and diagnostics.
• Version control with Git for reproducible analysis.

Software & Tools

• R: Runs survey-weighted models and doubly-robust analyses for NHANES data.
• Python: Cleans data, builds plots, and checks model outputs.
• RStudio: Gives you a simple interface for managing scripts and results.
• Jupyter Notebook: Keeps code, notes, and charts together in one place.
• PubMed: Helps you find review articles on ultra-processed foods and inflammation.

Experiment Steps

1. Define the exact food subgroups you will test and decide how you will map them to NOVA codes.
2. Choose one inflammation outcome first, then add the second outcome only after the main model works.
3. Build a clean analysis dataset by matching dietary intake files, biomarker files, and socioeconomic variables.
4. Set up a comparison plan that separates raw associations from adjusted associations.
5. Plan the confounders you will control for so you do not mix diet effects with income or health differences.
6. Predefine how you will judge whether the result is meaningful, including effect size, uncertainty, and sensitivity checks.

Common Pitfalls

• Mixing up total ultra-processed intake with specific NOVA subgroups, which can blur the whole question.
• Ignoring survey weights, which can make NHANES results look more precise than they really are.
• Treating CRP values with extreme outliers as normal, which can distort the model.
• Adjusting for too many variables at once, which can hide the diet signal you are trying to measure.
• Using food categories that are too broad, which can make every subgroup look similar even when it is not.

What Makes This Competitive

A stronger version of this project does more than report a simple correlation. It compares several ultra-processed subgroups, uses survey-aware causal methods, and checks whether the pattern holds across income, age, and sex groups. A sharp analysis plan, careful handling of confounders, and clear sensitivity checks can make the work feel much closer to real research.

Project Variations

• Test whether ultra-processed food subgroups relate to hs-CRP instead of total CRP, which gives a more sensitive inflammation signal.
• Compare adolescents and adults to see whether the diet-inflammation link looks different across age groups.
• Swap in another biomarker, such as neutrophil count or ferritin, to see whether the pattern holds beyond CRP and WBC.

Learn More

• NHANES Documentation: Find survey codebooks, dietary files, and biomarker documentation on the CDC National Center for Health Statistics site.
• PubMed: Search review articles on ultra-processed foods, inflammation, CRP, and NHANES.
• NIH Office of Dietary Supplements: Read background pages on inflammation-related biomarkers and nutrient effects.
• USDA FoodData Central: Look up nutrient profiles if you extend the project into food pattern analysis.
• Public Health Nutrition: Search for NOVA classification papers and studies on ultra-processed foods.
• CDC NHANES Tutorials: Use the CDC site for guides on downloading and merging NHANES files.