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Metabolic variation reflects dietary exposure in a multi-ethnic Asian population

Nature Metabolism volume 7, pages 1939–1954 (2025)Cite this article

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Abstract

Understanding how diet shapes metabolism across diverse populations is essential to improving nutrition and health. Biomarkers reflecting diet are explored largely in European and American populations, but the food metabolome is highly complex and varies across region and culture. We assessed 1,055 plasma metabolites and 169 foods/beverages in 8,391 multi-ethnic Asian individuals and carried out diet–metabolite association analyses. Using machine learning, we developed multi-biomarker panels and composite scores for key foods, beverages and overall diet quality. Here we show these biomarker panels can be used to objectively assess dietary intakes in the Asian multi-ethnic population and can explain variances in intake prediction models better than single biomarkers. The identified diet–metabolite relationships are reproducible over time and improve prediction of clinical outcomes (insulin resistance, diabetes, body mass index, carotid intima-media thickness and hypertension), compared to self-reports. Our findings show insights into multi-ethnic diet-related metabolic variations and an opportunity to link exposure to population health outcomes.

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Fig. 1: Overview of study design.
Fig. 2: Metabolome-wide association heat map (non-lipid metabolites).
Fig. 3: Plasma metabolome-wide association (lipid metabolites).
Fig. 4: Summary of dietary metabolite panels for foods and beverages and diet quality.
Fig. 5: Assessment of dietary intakes and quality from dietary metabolite panels.
Fig. 6: Reproducibility of diet–metabolite relationships across visits.
Fig. 7: Associations between dietary intakes, diet quality and clinical phenotypes.

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Data availability

The HELIOS study individual-level datasets are not publicly available due to data privacy regulations. Researchers may apply for access through HELIOS Data Access Committee (helios_science@ntu.edu.sg). Summary-level datasets are available as source data including food–metabolite and metabolite–metabolite partial correlation coefficients, correlation coefficients between self-report against estimated intakes, ICCs from the reproducibility study, and regression coefficients relating self-reports and metabolite scores with clinical phenotypes. Source data are provided with this paper.

Code availability

Data processing, analysis and visualization were performed using R (v4.5.0) and the following packages: tidyverse (v1.3.2), readxl (v1.4.1), glue (v1.6.2), pcor (v1.1), glmnet (v4.1-7), stats (v4.2.1), irr (v0.84.1), ggplot2 (v3.4.2) and gplots (v3.1.3). Codes for analyses and visualization are available via GitHub at https://github.com/HELIOS-SG100K-LKC/Metabolic-variation-reflects-dietary-intake-in-a-multi-ethnic-Asian-population/.

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Acknowledgements

We thank the participants of the HELIOS study and the HELIOS operation team for recruitment, organization and data/sample collection. This study (NTU IRB: 2016-11-030) is supported by Singapore Ministry of Health’s (MOH) National Medical Research Council (NMRC) under its OF-LCG funding scheme (MOH-000271) and CS-IRG funding scheme (MOH-001708), NMRC through National Cohorts Office (P2022-01-03) and National Research Foundation, Singapore, through NMRC and the Precision Health Research, Singapore (PRECISE) under the National Precision Medicine programme (NMRC/PRECISE/2020) and intramural funding from Nanyang Technological University, Lee Kong Chian School of Medicine and National Healthcare Group.

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Authors and Affiliations

  1. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

    Dorrain Y. Low, Theresia H. Mina, Nilanjana Sadhu, Pritesh Rajesh Jain, Rinkoo Dalan, Hong Kiat Ng, Wubin Xie, Benjamin Lam, Darwin Tay, Xiaoyan Wang, Yik Weng Yew, James D. Best, Paul Elliott, Elio Riboli, Jimmy Lee, Eng Sing Lee, Joanne Ngeow, Xue Li Guan, Marie Loh & John C. Chambers

  2. Metabolon, Morrisville, NC, USA

    Kari E. Wong, Rangaprasad Sarangarajan, Patricia A. Sheridan & Gregory A. Michelotti

  3. Department of Endocrinology, Tan Tock Seng Hospital, Singapore, Singapore

    Rinkoo Dalan

  4. Integrated Care for Obesity & Diabetes, Khoo Teck Puat Hospital, Singapore, Singapore

    Benjamin Lam

  5. National Skin Centre, Research Division, Singapore, Singapore

    Yik Weng Yew & Marie Loh

  6. Department of Epidemiology and Biostatistics, Imperial College London, London, UK

    Paul Elliott, Elio Riboli & John C. Chambers

  7. North Region, Institute of Mental Health, Singapore, Singapore

    Jimmy Lee

  8. Clinical Research Unit, National Healthcare Group Polyclinic, Singapore, Singapore

    Eng Sing Lee

  9. Division of Medical Oncology, National Cancer Centre, Singapore, Singapore

    Joanne Ngeow

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  1. Dorrain Y. Low
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  2. Theresia H. Mina
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  22. Gregory A. Michelotti
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Contributions

J.C.C., J.D.B., P.E., E.R., J.L., E.S.L. and J.N. acquired funding for the HELIOS study. J.C.C., K.E.W., P.A.S., R.S. and G.A.M. acquired funding for metabolomics analysis. J.C.C. and D.Y.L. conceptualized the study. D.Y.L., T.H.M., N.S., P.R.J., R.D., K.E.W., P.A.S., R.S. and G.A.M. generated the data. D.Y.L., T.H.M., N.S. and P.R.J. analysed the data. J.C.C. and D.Y.L. wrote the initial manuscript draft. M.L., T.H.M., N.S., P.R.J. and P.A.S. provided critical feedback to the manuscript draft. All authors reviewed and contributed to revision of the manuscript.

Corresponding authors

Correspondence to Dorrain Y. Low or John C. Chambers.

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Competing interests

K.E.W. and G.A.M. are employees of Metabolon. The other authors declare no competing interests.

Peer review

Peer review information

Nature Metabolism thanks Rui Wang-Sattler and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Yanina-Yasmin Pesch, in collaboration with the Nature Metabolism team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Indices of diet quality components within the HELIOS study population.

a) Sankey illustration of diet quality components. b) Radar plots showing distribution of individual components across diet quality indices. c) Density plots of diet quality scores across age and ethnicity. MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids; SFA, saturated fatty acids; bev, beverage; proc, processed; HF, high fat; LF, low fat.

Source data

Extended Data Fig. 2 Model prediction performance of dietary intakes and diet quality.

a) Percent (%) error observed between self-reported and estimated intakes (log-transformed) of foods and beverages in respective test datasets (n = 2,336-2,513; refer to Supplementary Table 9 for exact sample sizes of test datasets). b) Density distribution of prediction error % of foods and beverages in respective test datasets with dotted line indicating median error and text insert indicating median prediction accuracy (formula: 1 - error %).

Source data

Extended Data Fig. 3 Summary of coffee and tea intakes.

a) Percent (%) of variations of total daily coffee and tea intakes recorded via FFQ in the study population. b) Proportion of kilocalories across the top 5 most consumed variations of coffee or tea.

Source data

Extended Data Fig. 4 Associations between environmental contaminants and clinical phenotypes.

Effect sizes of environmental contaminants (that comprise various dietary biomarker panels), adjusted for age, sex, ethnicity and batch. Estimates were obtained from independent linear regression (for HOMA-IR, FMI, cIMT) or logistic regression (for T2D, hypertension), based on the following sample sizes: T2D (n = 1,040 T2D, n = 7,351 non-T2D), hypertension (n = 2,411 with hypertension, n = 5,980 without hypertension). Association strength represents log-transformed P values. Circle border represents significant associations (P < 0.05). HOMA-IR, homeostatic model assessment for insulin resistance; T2D, type 2 diabetes; FMI, fat mass index; cIMT, carotid intima-media thickness.

Source data

Extended Data Table 1 Characteristics of HELIOS study population at baseline visit
Full size table
Extended Data Table 2 Characteristics of subset of HELIOS study population at baseline and repeat visits
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Extended Data Table 3 Description of 42 food and beverage groups, where multi-biomarker panels were proposed for 20 key foods/beverages1 for in the HELIOS population study
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Supplementary Table 1: List of 1,055 metabolites in the study population. Supplementary Table 2: Partial correlation coefficients of FFQ food/beverage items and metabolites (r ≥ 0.15 and FDR P < 0.05), adjusted for age, sex, ethnicity and batch. Supplementary Table 3: Effect sizes of metabolites (within the multi-biomarker panels) from associations with key foods and beverages, in regression models adjusted for age, sex, batch and ethnicity or genetic ancestry. Supplementary Table 4: Summary table of metabolite and genetic correlations for annotation of unknowns in the dietary biomarker panels. Supplementary Table 5: Summary of metabolic pathways enriched in dietary quality indices. Supplementary Table 6: Effect sizes of nutrients (% kcal) from associations with dietary quality indices. Supplementary Table 7: Effect sizes of dietary intakes (derived from self-report or metabolite score) from associations with clinical phenotypes. Supplementary Table 8: Effect sizes of diet quality (derived from self-report or metabolite score) from associations with clinical phenotypes. Supplementary Table 9: Sample sizes for training and test datasets of each food and beverage dataset. Supplementary Table 10. Effect sizes of independent metabolite–SNP associations above genome-wide significance (P < 5 × 10−8) and genes/loci in the top associations.

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Low, D.Y., Mina, T.H., Sadhu, N. et al. Metabolic variation reflects dietary exposure in a multi-ethnic Asian population. Nat Metab 7, 1939–1954 (2025). https://doi.org/10.1038/s42255-025-01359-x

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