Evidence mapPaperPMID 40689343Full record

ArticleCurrent developments in nutrition2025

Computationally Modeling the Physiologic Impact of the Ratio of Fats to Carbohydrates in the Diet on Intake Among Metabolically Healthy Adults.

Marie F Martinez, Jessie Heneghan, Colleen Weatherwax, Timothy H Moran, Britt Burton-Freeman, Kavya Velmurugan, José M Ordovás, Sarah M Bartsch, Tej D Shah, Jennifer Lee and 7 more

Abstract read
In one paragraph

Article in Current developments in nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Marie F MartinezPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, United States.
Jessie HeneghanPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, United States.
Colleen WeatherwaxPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, United States.
Timothy H MoranDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, United States.
Britt Burton-FreemanDepartment of Food Science and Nutrition, Illinois Institute of Technology, Chicago, IL, United States.
Kavya VelmuruganPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, United States.
José M OrdovásJean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, United States.
Sarah M BartschPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, United States.
Tej D ShahPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, United States.
Jennifer LeeJean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, United States.
Sarah L BoothJean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, United States.
Samantha KleinbergDepartment of Computer Science, Stevens Institute of Technology, Hoboken, NJ, United States.
Kevin L ChinPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, United States.
Kayla de la HayeCenter for Economic and Social Research, University of Southern California, Los Angeles, CA, United States.
Alexis DibbsPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, United States.
Sheryl A ScannellPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, United States.
Bruce Y LeePublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: When it comes to how effectively a diet can help reduce or maintain body weight, a key question is how that diet affects a person's hunger, satiety, and subsequent eating. Objectives: This study aimed to analyze modeling, from a physiologic perspective, how varying the ratio of fats to carbohydrates in a diet impacts hunger, satiety, and subsequent eating among metabolically healthy adults. Methods: We developed a model representing an adult, their dietary intake, gastrointestinal tract, hunger/satiety levels, and meal consumption. We simulated agents eating fixed ratios of macronutrients and measured their subsequent eating over 24 h driven by physiologic responses. Results: When increasing the proportion of energy from fats relative to carbohydrates, daily calories decrease by on mean 149 and 110 calories per 10% increase in fats for males and females, respectively. Additionally, a simulated diet with a relative ratio of energy from fats:carbohydrates of 20%:80% results in individuals snacking after 21:00 for ∼93% of days in both sexes, whereas a relative fat:carbohydrate ratio of 80%:20% results in late-night snacking ∼55% and ∼60% of days for males and females, respectively. Agents consuming at least a 40%:60% relative ratio of energy from fat:carbohydrate ratio can achieve the largest reductions in total calories consumed and late-night snacking compared with consuming higher relative proportions of carbohydrates. Conclusions: Eating a diet with ≥40% of its energy from fats relative to carbohydrates can achieve the largest reductions in total calories consumed and late-night snacking each day than consuming higher proportions of carbohydrates, with even further reductions as more fat is added to the diet, when considering the physiologic responses to dietary intake alone. Future research should layer in other strong contributing factors to eating such as stress, social context, palatability, physical activity, and types of macronutrients, and also represent other metabolic profiles and ages.

Indexed as

computational modelingdietary intakehungerprecision nutritionsatietysystems science

Identifiers

PMID40689343
PMCPMC12272436

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.