Evidence map›Paper›PMID 42316028›Full record

ArticleGenes & nutrition2026

Incorporating dietary information to enhance polygenic prediction models with applications to body mass index and type 2 diabetes.

Eunice Y Lee, Bryan L Dinh, Ji Tang, Samantha A Streicher, Xinran Wang, Subarna Biswas, He Tian, Xian Yu, Kekoa Taparra, Take Naseri and 9 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Genes & nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Eunice Y LeeCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
Bryan L DinhCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
Ji TangCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
Samantha A StreicherPopulation Sciences in the Pacific Program, Cancer Center, University of Hawai'i, Honolulu, HI, USA.
Xinran WangCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
Subarna BiswasCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
He TianCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
Xian YuCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
Kekoa TaparraDepartment of Radiation Oncology at the David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
Take NaseriMinistry of Health, Apia, Samoa.
Satupa'itea VialiOceania University of Medicine, Apia, Samoa.
Daniel E WeeksDepartment of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA.
Jenna C CarlsonDepartment of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA.
Christopher A HaimanCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
Loïc Le MarchandPopulation Sciences in the Pacific Program, Cancer Center, University of Hawai'i, Honolulu, HI, USA.
Gertraud MaskarinecPopulation Sciences in the Pacific Program, Cancer Center, University of Hawai'i, Honolulu, HI, USA.
Lynne R WilkensPopulation Sciences in the Pacific Program, Cancer Center, University of Hawai'i, Honolulu, HI, USA.
Song-Yi ParkPopulation Sciences in the Pacific Program, Cancer Center, University of Hawai'i, Honolulu, HI, USA.
Charleston W K ChiangCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA. charleston.chiang@med.usc.edu.

Funding

Leveraging the Evolutionary History to Improve Identification of Trait-Associated Alleles and Risk Stratification Models in Native HawaiiansR01HG011646 · NHGRI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Charleston Chiang · 2022 to 2026
$4.0M
NHGRI NIH HHS R01 HG011646NIH HHS R01HG011646
6 · The paper itself

Abstract

backgroundPolygenic predictors can enhance screening for metabolism-related traits such as body mass index (BMI) and type 2 diabetes (T2D). However, these predictors explain limited phenotypic variance and face implementation challenges in non-European populations. Dietary patterns are well-established metabolic risk factors that remain under-investigated in quantitative risk stratification models.

methodsWe developed and evaluated risk stratification models combining polygenic predictors and data-driven dietary scores (DDS) for BMI and T2D in 14,346 Native Hawaiians from the Multiethnic Cohort (MEC-NH), a population with high prevalence of obesity and T2D. Using 5,374 participants with genetic data, we integrated publicly available large-scale GWAS summary statistics to develop cross-ancestry polygenic score (PGS) models using PRS-CSx. We developed DDS using machine learning algorithms on 520 dietary variables and evaluated model performance in held-out test sets.

resultsTrans-ancestry PGS achieved better prediction accuracy than single-ancestry models for both phenotypes (partial R² [SE] = 0.12 [0.04] vs. 0.03-0.09 for BMI; liability R² [SE] = 0.09 [0.04] vs. 0.01-0.07 for T2D). The best-performing DDS was based on a Random Forest model and substantially explained BMI variation (partial R² [SE] = 0.12 [0.01]), comparable to genetic prediction. Combined models integrating PGS and DDS significantly outperformed single-predictor models for BMI (adjusted R² = 0.29 vs. 0.21, P < 10⁻¹¹⁷). For T2D, combined models showed marginal but significant improvement over PGS alone. The BMI dietary score additionally associated with multiple chronic diseases, with effects partially mediated through inflammatory and lipid pathways.

conclusionTrans-ancestry polygenic scores and data-driven dietary scores provide complementary information for metabolic trait prediction. Combined genetic-dietary models significantly outperform single-predictor approaches, with improvement being most pronounced for BMI. In Native Hawaiians, systematic integration of dietary information substantially improved BMI prediction, demonstrating the value of incorporating modifiable environmental factors alongside genetic information.

Indexed as

BMIDiet scorePGSRisk prediction modelT2D

Identifiers

PMID42316028
PMCPMC13523231

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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 OpenQuestion graph.