Evidence map›Paper›PMID 40500312›Full record

ArticleNPJ digital medicine2025

High-resolution lifestyle profiling and metabolic subphenotypes of type 2 diabetes.

Heyjun Park, Ahmed A Metwally, Alireza Delfarah, Yue Wu, Dalia Perelman, Caleb Mayer, Curtis McGinity, Majid Rodgar, Alessandra Celli, Tracey McLaughlin and 2 more

Registry-linked trialAbstract read
In one paragraph

Article in NPJ digital 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 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
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

12 authors.

Heyjun Park *Department of Genetics, Stanford University, Stanford, CA, USA.
Ahmed A Metwally *Department of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-0155-7412
Alireza DelfarahDepartment of Genetics, Stanford University, Stanford, CA, USA.
Yue WuDepartment of Genetics, Stanford University, Stanford, CA, USA.
Dalia PerelmanDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-3335-1950
Caleb MayerDepartment of Genetics, Stanford University, Stanford, CA, USA.
Curtis McGinityDepartment of Genetics, Stanford University, Stanford, CA, USA.
Majid RodgarDepartment of Genetics, Stanford University, Stanford, CA, USA.
Alessandra CelliDepartment of Genetics, Stanford University, Stanford, CA, USA.
Tracey McLaughlinDepartment of Medicine, Stanford University, Stanford, CA, USA.
Emmanuel MignotCenter for Sleep Sciences and Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.ORCID http://orcid.org/0000-0002-6928-5310
Michael SnyderDepartment of Genetics, Stanford University, Stanford, CA, USA. mpsnyder@stanford.edu.ORCID http://orcid.org/0000-0003-0784-7987

Funding

Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
Postgraduate Training Program in Epithelial BiologyT32AR007422 · NIAMS · STANFORD UNIVERSITY · PI PAUL KHAVARI · 1986 to 2026
$5.0M
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
Heterogeneity of Diabetes: Integrated Muli-Omics to Identify Physiologic Subphenotypes and Evaluate Targeted PreventionR01DK139472 · NIDDK · STANFORD UNIVERSITY · PI TRACEY MCLAUGHLIN, MICHAEL P. SNYDER · 2025 to 2026
$1.4M
NIAMS NIH HHS T32 AR007422NIDDK NIH HHS P30 DK116074NIDDK NIH HHS R01 DK110186NIDDK NIH HHS R01 DK139472U.S. Department of Health & Human Services | National Institutes of Health (NIH) 2T32HL09804911U.S. Department of Health & Human Services | National Institutes of Health (NIH) NIDDK1R01 DK110186-01U.S. Department of Health & Human Services | National Institutes of Health (NIH) Stanford Diabetes Research Center (P30DK116074)U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) NIDDK1R01 DK110186-01
6 · The paper itself

Abstract

Distinct metabolic susceptibilities (beta-cell dysfunction, insulin resistance (IR), and impaired incretin response) underlie type 2 diabetes (T2D). However, their relationships with habitual lifestyle behaviors are underexplored. This study integrated high-resolution lifestyle data from wearable devices, continuous glucose monitoring, and smartphone-based food logs with gold-standard physiological tests in 36 individuals at risk for T2D (ClinicalTrials.Gov; NCT03919877; 2019-04-18). Over 6400 timestamped records of diet, sleep, and physical activity were analyzed with in participants with measures of beta-cell function, tissue-specific IR (muscle, hepatic, adipose), and incretin response. We found that lifestyle timing and variability were strongly associated with metabolic subphenotypes: (1) eating timing was associated with muscle IR and incretin function; (2) irregular sleep correlated to IR and incretin function; and (3) Time-of-day effects of physical activity varied by subphenotype. These findings were validated in an independent cohort. Our results highlight novel physiological links between daily behaviors and metabolic risk, informing potential lifestyle modifications for T2D prevention.

Identifiers

PMID40500312
PMCPMC12159136

What OpenQuestion holds

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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 OpenQuestion graph.