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Body mass index stratification optimizes polygenic prediction of type 2 diabetes in cross-biobank analyses

Nature Genetics volume 56, pages 1100–1109 (2024)Cite this article

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Abstract

Type 2 diabetes (T2D) shows heterogeneous body mass index (BMI) sensitivity. Here, we performed stratification based on BMI to optimize predictions for BMI-related diseases. We obtained BMI-stratified datasets using data from more than 195,000 individuals (nT2D = 55,284) from BioBank Japan (BBJ) and UK Biobank. T2D heritability in the low-BMI group was greater than that in the high-BMI group. Polygenic predictions of T2D toward low-BMI targets had pseudo-R2 values that were more than 22% higher than BMI-unstratified targets. Polygenic risk scores (PRSs) from low-BMI discovery outperformed PRSs from high BMI, while PRSs from BMI-unstratified discovery performed best. Pathway-specific PRSs demonstrated the biological contributions of pathogenic pathways. Low-BMI T2D cases showed higher rates of neuropathy and retinopathy. Combining BMI stratification and a method integrating cross-population effects, T2D predictions showed greater than 37% improvements over unstratified-matched-population prediction. We replicated findings in the Tohoku Medical Megabank (n = 26,000) and the second BBJ cohort (n = 33,096). Our findings suggest that target stratification based on existing traits can improve the polygenic prediction of heterogeneous diseases.

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Fig. 1: Overview of the study design.
Fig. 2: Variation in heritability based on BMI-stratified groups.
Fig. 3: BMI-stratified PRS heatmaps for T2D within each biobank.
Fig. 4: BMI-stratified PRS heatmaps for CAD within each biobank.
Fig. 5: Odds ratio curve of T2D prevalence for each BMI-stratified PRS.
Fig. 6: BMI-stratified PRS heatmaps for T2D across biobanks.

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Data availability

The genotype data of BBJ are available at the NBDC Human Database (research IDs hum0014 and hum0311). The UKBB analysis was conducted under application number 47821 (https://www.ukbiobank.ac.uk). Individual genotyping results and other cohort data used for the polygenic prediction are stored in Tohoku Medical Megabank Organization (ToMMo). In response to reasonable requests for these data (contact us at dist@megabank.tohoku.ac.jp), we will share the stored data after assembling the dataset following approval of the Ethics Committee and Materials and Information Distribution Review Committee of ToMMo. The PRS weights developed for TMM and BBJ-2nd have been released through the PGS Catalog (https://www.pgscatalog.org) with publication ID PGP000593 and score IDs PGS004615-PGS004620. The PRS weights can also be accessed through application to the NBDC Human Database with accession code hum0197 (https://humandbs.dbcls.jp/en/hum0197-latest).

Code availability

We used publicly available software for the analysis. The software used is described in the Methods section. The codes used in this study are available at https://github.com/takafumiojima/BMI_stratified_T2D_PRS and https://doi.org/10.5281/zenodo.11057931 (ref. 77).

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Acknowledgements

We acknowledge the participants and investigators of BioBank Japan, UK Biobank and Tohoku Medical Megabank. T.O. was supported by JST SPRING (JPMJSP2138) and the Osaka University Transdisciplinary Program for Biomedical Entrepreneurship and Innovation (WISE program). S.N. and K. Sonehara were Supported by Takeda Science Foundation. Y.O. was supported by JSPS KAKENHI (22H00476), and AMED (JP23km0405211, JP23km0405217, JP23ek0109594, JP23ek0410113, JP23kk0305022, JP223fa627002, JP223fa627010, JP233fa627011, JP23zf0127008, JP23tm0524002), JST Moonshot R&D (JPMJMS2021, JPMJMS2024), Takeda Science Foundation, Bioinformatics Initiative of Osaka University Graduate School of Medicine, Institute for Open and Transdisciplinary Research Initiatives and Center for Infectious Disease Education and Research (CiDER), and Center for Advanced Modality and DDS (CAMaD), Osaka University.

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  1. A full list of members and their affiliations appears in the Supplementary Information.

Authors and Affiliations

  1. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan

    Takafumi Ojima, Shinichi Namba, Ken Suzuki, Kenichi Yamamoto, Kyuto Sonehara & Yukinori Okada

  2. Graduate School of Medicine, Tohoku University, Sendai, Japan

    Takafumi Ojima, Gen Tamiya & Masayuki Yamamoto

  3. Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Takafumi Ojima, Kyuto Sonehara & Yukinori Okada

  4. Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan

    Takafumi Ojima & Gen Tamiya

  5. Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Shinichi Namba, Kyuto Sonehara & Yukinori Okada

  6. Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Ken Suzuki & Toshimasa Yamauchi

  7. Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan

    Kenichi Yamamoto

  8. Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan

    Kenichi Yamamoto & Yukinori Okada

  9. Laboratory of Children’s Health and Genetics, Division of Health Science, Osaka University Graduate School of Medicine, Osaka, Japan

    Kenichi Yamamoto

  10. Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan

    Akira Narita, Gen Tamiya & Masayuki Yamamoto

  11. Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan

    Yoichiro Kamatani

  12. Toranomon Hospital, Tokyo, Japan

    Takashi Kadowaki

  13. Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Osaka, Japan

    Yukinori Okada

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  1. Takafumi Ojima
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Consortia

the Tohoku Medical Megabank Project Study Group

  • Masayuki Yamamoto
  • , Gen Tamiya
  •  & Akira Narita

the Biobank Japan Project

  • Yukinori Okada
  •  & Yoichiro Kamatani

Contributions

T.O. and Y.O. conceived and designed the study. T.O. and Y.O. wrote the manuscript with critical input from S.N., K. Suzuki, K.Y., K. Sonehara and A.N. T.O., S.N., K. Suzuki and A.N. conducted the data analysis. T.O., S.N., K. Suzuki, K.Y., K. Sonehara, A.N., Y.K., M.Y., G.T., T.Y., T.K., Y.O. and the members of the Tohoku Medical Megabank Project Study Group and the BioBank Japan Project contributed to the collection of samples and management of genotype data and clinical information. G.T., T.Y., T.K. and Y.O. supervised the study. All authors contributed to the article and approved the submitted version.

Corresponding author

Correspondence to Yukinori Okada.

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Extended data

Extended Data Fig. 1 Performances of BMI-stratified polygenic prediction of T2D.

Schematic representation of the relative levels of the performances of BMI-stratified polygenic prediction of T2D based on the analysis results. Each block in the heatmaps above corresponds to the bar graph below. Red, high polygenic prediction accuracy; white, low polygenic prediction accuracy. All, group consisting of all samples obtained by stratified random sampling; High, high BMI group; Mid, middle BMI group; Low, low BMI group.

Extended Data Fig. 2 BMI-stratified PRS heatmaps for T2D evaluated using AUC in BBJ.

a,b, The values in the PRS heatmaps were calculated by AUC in two ways. The AUC calculated with the model with full covariates was used in the upper heatmap (a), and the AUC calculated with the model with no covariates was used in the lower heatmap (b).

Extended Data Fig. 3 BMI-stratified PRS heatmaps for T2D evaluated using AUC in UKBB.

a,b, The values in the PRS heatmap were calculated by AUC in two ways. The AUC calculated with the model with full covariates was used in the upper heatmap (a), and the AUC calculated with the model with no covariates was used in the lower heatmap (b).

Extended Data Fig. 4 BMI-stratified PRS heatmaps for T2D with BMI adjustment.

PRS heatmaps with and without BMI adjustment as covariates can be compared vertically. The values in PRS heatmaps are Fisher’s z-transformation averages of pseudo-R2 calculated by the LOGO-PRS method. Red, high polygenic prediction accuracy; white, low polygenic prediction accuracy. The upper description of the heatmap represents the BMI group of the discovery population, and the left side represents that of the target population. a, PRS heatmaps in BBJ. b, PRS heatmaps in UKBB.

Extended Data Fig. 5 BMI-stratified PRS heatmaps for T2D with BMI adjustment evaluated using AUC.

PRS heatmaps with and without BMI adjustment as covariates can be compared vertically. The values in PRS heatmaps are calculated by simple model AUC. Red, high polygenic prediction accuracy; white, low polygenic prediction accuracy. The upper description of the heatmap represents the BMI group of the discovery population, and the left side represents that of the target population. a, PRS heatmaps in BBJ. b, PRS heatmaps in UKBB.

Extended Data Fig. 6 BMI-stratified PRS heatmaps for T2D across biobanks targeting BBJ evaluated using AUC.

PRS heatmaps for the same target (BBJ) with different discovery populations can be compared vertically. a,b, The values in the PRS heatmaps were calculated by AUC in two ways. The AUC calculated with the model with full covariates was used in the upper heatmap (a), and the AUC calculated with the model with no covariates was used in the lower heatmap (b).

Extended Data Fig. 7 BMI-stratified PRS heatmaps for T2D across biobanks targeting UKBB evaluated using AUC.

PRS heatmaps for the same target (UKBB) with different discovery populations can be compared vertically. a,b, The values in the PRS heatmaps were calculated by AUC in two ways. The AUC calculated with the model with full covariates was used in the upper heatmap (a), and the AUC calculated with the model with no covariates was used in the lower heatmap (b).

Extended Data Fig. 8 Odds ratio curve of T2D prevalence for each BMI-stratified PRS across biobanks.

Odds ratio curves for each BMI-stratified PRS in each box are locally estimated scatterplot smoothing (LOESS) curves. On the left, the comparison is made with combinations of discovery and target populations; in the middle, two BMI-stratified groups in the meta-populational method; on the right, three BMI-stratified groups in the meta-populational method. a, Odds ratio curves for T2D with BBJ as target population. b, Odds ratio curves for T2D with UKBB as target population. High BMI, high BMI group; Mid BMI, middle BMI group; Low BMI, low BMI group; Total, total dataset including both male and female samples.

Supplementary information

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Ojima, T., Namba, S., Suzuki, K. et al. Body mass index stratification optimizes polygenic prediction of type 2 diabetes in cross-biobank analyses. Nat Genet 56, 1100–1109 (2024). https://doi.org/10.1038/s41588-024-01782-y

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