Evidence map›Paper›PMID 40467897›Full record

ArticleNature medicine2025

Individual variations in glycemic responses to carbohydrates and underlying metabolic physiology.

Yue Wu, Ben Ehlert, Ahmed A Metwally, Dalia Perelman, Heyjun Park, Andrew Wallace Brooks, Fahim Abbasi, Basil Michael, Alessandra Celli, Caroline Bejikian and 19 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03919877 (Precision Diets for Diabetes Prevention), which is not on this map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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.

NCT03919877 nacompletednot on this map

Precision Diets for Diabetes Prevention

TypeinterventionalSponsorStanford UniversityRan2018 to 2023Enrolled115ConditionsPre Diabetes, Insulin Resistance, Diabetes Mellitus, Type 2ArmsDietary, Oral Food Challege
3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Article
  4. Review
  5. Review
  6. Human-Centered Innovation: Precision Nutrition and the Future of Food.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Review
  14. Advancing Precision Nutrition Through Multimodal Data and Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  15. Article
  16. Review
  17. Article
  18. Article
  19. Review
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

29 authors.

Yue Wu *Department of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-7170-0053
Ben Ehlert *Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-4537-1051
Ahmed A Metwally *Department of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-0155-7412
Dalia PerelmanDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-3335-1950
Heyjun ParkDepartment of Genetics, Stanford University, Stanford, CA, USA.
Andrew Wallace BrooksDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-9237-5349
Fahim AbbasiDepartment of Medicine, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-3932-8375
Basil MichaelDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-4009-0912
Alessandra CelliDepartment of Medicine, Stanford University, Stanford, CA, USA.
Caroline BejikianDepartment of Medicine, Stanford University, Stanford, CA, USA.
Ekrem AyhanDepartment of Medicine, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0009-0001-6923-3449
Yingzhou LuDepartment of Pathology, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0009-0008-7774-6018
Samuel M LancasterDepartment of Genetics, Stanford University, Stanford, CA, USA.
Daniel HornburgDepartment of Genetics, Stanford University, Stanford, CA, USA.
Lucia RamirezDepartment of Genetics, Stanford University, Stanford, CA, USA.
David BogumilUltima Genomics, Newark, CA, USA.
Sarah PollockUltima Genomics, Newark, CA, USA.
Frank WongDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0009-0001-7640-2693
Denver BradleyDepartment of Genetics, Stanford University, Stanford, CA, USA.
Georg GutjahrAmrita School of Medicine, Amrita Vishwa Vidyapeetham (University), Kochi, India.ORCID http://orcid.org/0000-0002-1925-8349
Ekanath Srihari RanganDepartment of Medicine, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-5168-3508
Tao WangDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-0865-0062
Lettie McGuireDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-0899-3521
P Venkat RanganAmrita Vishwa Vidyapeetham (University), Kochi, India.
Helge RæderDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-9465-8580
Zohar ShiponyUltima Genomics, Newark, CA, USA.
Doron LipsonUltima Genomics, Newark, CA, USA.
Tracey McLaughlinDepartment of Medicine, Stanford University, Stanford, CA, USA. tmclaugh@stanford.edu.ORCID http://orcid.org/0000-0002-4829-8649
Michael P SnyderDepartment of Genetics, Stanford University, Stanford, CA, USA. mpsnyder@stanford.edu.ORCID http://orcid.org/0000-0003-0784-7987

Funding

Stanford Center for Clinical and Translational Research and EducationUM1TR004921 · NCATS · STANFORD UNIVERSITY · PI MANISHA DESAI, DEAN W FELSHER · 2024 to 2026
$30.1M
POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI HASSAN CHAIB · 2017 to 2026
$19.5M
Proteomic determinants of direct measures of insulin sensitivityR01DK114183 · NIDDK · STANFORD UNIVERSITY · PI ASSIMES, THEMISTOCLES LEONARD · 2018 to 2022
$3.5M
Longitudinal Multi-Omic Profiles to Reveal Mechanisms of Obesity-Mediated Insulin ResistanceR01DK110186 · NIDDK · STANFORD UNIVERSITY · PI MCLAUGHLIN, TRACEY, SNYDER, MICHAEL P. · 2017 to 2021
$3.2M
Gut Bacteriophage Correspondence with Inflammation and Clinical Dietary InterventionsF32DK126287 · NIDDK · STANFORD UNIVERSITY · PI BROOKS, ANDREW WALLACE · 2021 to 2023
$205k
American Diabetes Association (ADA) 11-23-PDF-76NCATS NIH HHS UM1 TR004921NIDDK NIH HHS F32 DK126287NIDDK NIH HHS P30 DK116074NIDDK NIH HHS R01 DK110186NIDDK NIH HHS R01 DK114183NLM NIH HHS T15 LM007033U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) F32DK126287U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) P30DK116074U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) R01 DK110186-01U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (NLM) 2T15LM007033
6 · The paper itself

Abstract

Elevated postprandial glycemic responses (PPGRs) are associated with type 2 diabetes and cardiovascular disease. PPGRs to the same foods have been shown to vary between individuals, but systematic characterization of the underlying physiologic and molecular basis is lacking. We measured PPGRs using continuous glucose monitoring in 55 well-phenotyped participants challenged with seven different standard carbohydrate meals administered in replicate. We also examined whether preloading a rice meal with fiber, protein or fat ('mitigators') altered PPGRs. We performed gold-standard metabolic tests and multi-omics profiling to examine the physiologic and molecular basis for interindividual PPGR differences. Overall, rice was the most glucose-elevating carbohydrate meal, but there was considerable interindividual variability. Individuals with the highest PPGR to potatoes (potato-spikers) were more insulin resistant and had lower beta cell function, whereas grape-spikers were more insulin sensitive. Rice-spikers were more likely to be Asian individuals, and bread-spikers had higher blood pressure. Mitigators were less effective in reducing PPGRs in insulin-resistant as compared to insulin-sensitive participants. Multi-omics signatures of PPGR and metabolic phenotypes were discovered, including insulin-resistance-associated triglycerides, hypertension-associated metabolites and PPGR-associated microbiome pathways. These results demonstrate interindividual variability in PPGRs to carbohydrate meals and mitigators and their association with metabolic and molecular profiles.

Indexed as

Blood GlucoseDiabetes Mellitus, Type 2Dietary CarbohydratesAdultAgedFemaleGlycemic IndexHumansInsulinInsulin ResistanceMaleMiddle AgedOryzaPostprandial PeriodSolanum tuberosumBlood GlucoseDietary CarbohydratesInsulin

Identifiers

PMID40467897
PMCPMC12283382

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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.