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Metabolome-wide association identifies ferredoxin-1 (FDX1) as a determinant of cholesterol metabolism and cardiovascular risk in Asian populations

Nature Cardiovascular Research volume 4, pages 567–583 (2025)Cite this article

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Abstract

The burden of cardiovascular disease is rising in the Asia-Pacific region, in contrast to falling cardiovascular disease mortality rates in Europe and North America. Here we perform quantification of 883 metabolites by untargeted mass spectroscopy in 8,124 Asian adults and investigate their relationships with carotid intima media thickness, a marker of atherosclerosis. Plasma concentrations of 3beta-hydroxy-5-cholestenoate (3BH5C), a cholesterol metabolite, were inversely associated with carotid intima media thickness, and Mendelian randomization studies supported a causal relationship between 3BH5C and coronary artery disease. The observed effect size was 5- to 6-fold higher in Asians than Europeans. Colocalization analyses indicated the presence of a shared causal variant between 3BH5C plasma levels and messenger RNA and protein expression of ferredoxin-1 (FDX1), a protein that is essential for sterol and bile acid synthesis. We validated FDX1 as a regulator of 3BH5C synthesis in hepatocytes and macrophages and demonstrated its role in cholesterol efflux in macrophages and aortic smooth muscle cells, using knockout and overexpression models.

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Fig. 1: Summary of study design.
Fig. 2: Summary of results from MR studies evaluating effect of exposure 3BH5C levels on outcome CAD risk.
Fig. 3: Association of rs10488763 with 3BH5C and CAD in different populations, and its global variation in allele frequency.
Fig. 4: Colocalization of genetic variants associated with plasma levels of 3BH5C and expression of FDX1.
Fig. 5: Functional validation of FDX1 as a key regulator of 3BH5C synthesis.

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Data availability

Researchers may apply for access to individual-level data for the HELIOS study through the study Data Access Committee (email, [email protected]). All summary data have been made available in the supplementary tables. Full GWAS summary statistics of metabolites are available through the GWAS catalog (https://www.ebi.ac.uk/gwas/). GWAS summary statistics for van der Harst et al.22 (accession codes GCST005194–005195), Yeung et al.17 (accession codes GCST90100572–90100582) and Chen et al.20 (accession codes GCST90199621–90201020) are available through the GWAS catalog (https://www.ebi.ac.uk/gwas/), for Nikpay et al.23 at the CARDIoGRAMplusC4D Consortium website (http://www.cardiogramplusc4d.org/data-downloads/), for Koyama et al.18 at the National Bioscience Database Center (https://biosciencedbc.jp/en; ID hum0014), and for Yin et al.21 at the METSIM Metabolomics PheWeb (https://pheweb.org/metsim-metab/pheno/C100006370).

Code availability

Genetic analysis was performed using GCTA v.1.93 (https://yanglab.westlake.edu.cn/software/gcta/#Overview) and PLINK v.1.90 and v.2.0 (https://www.cog-genomics.org/plink/). FUMA annotation was performed using the webtool v.1.4.1 (https://fuma.ctglab.nl/). Fine-mapping was performed using R package susieR (v.0.12.27) (https://cran.r-project.org/web/packages/susieR/vignettes/finemapping_summary_statistics.html). MR was performed using GSMR in GCTA v.1.93 (https://yanglab.westlake.edu.cn/software/gcta/#MendelianRandomisation) and R package TwoSampleMR (v.0.5.6) (https://mrcieu.github.io/TwoSampleMR/articles/introduction.html). Colocalization analysis was performed using SMR v.1.3.1 for Linux (https://yanglab.westlake.edu.cn/software/smr/#Overview) and R package coloc (v.5.2.1) (https://cran.r-project.org/web/packages/coloc/vignettes/a01_intro.html). Regional plots were generated using LocusZoom (https://my.locuszoom.org/). Other analyses and plotting were performed using the following R packages: data.table (v.1.14.2), dplyr (v.1.0.9), forestplot (v.3.1.1), ggplot2 (v.3.4.2), ggrepel (v.0.9.1), MatrixEQTL (v.2.3), metafor (v.4.6-0), ppcor (v.1.1), randomForest (v.4.7-1.1), RcppEigen (v.0.3.4.0.0), stringr (v.1.4.0), tibble (v.3.1.7) and tidyr (v.1.2.0). R v.4.2.1 or v.4.2.2 was used for all data analysis in R. Analyses and plotting scripts are available via GitHub at https://github.com/nsadhu/metabolomics_atherosclerosis.

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Acknowledgements

We are very grateful for the outstanding support of past and present members of the HELIOS study steering committee, operational study team, and administrative staff for driving the study and assisting in the data collection. We also express our gratitude to the Nightingale Health Biobank Collaborative Group for providing GWAS summary statistics of NMR data from the UKBB Study. This work was supported by intramural funding from Nanyang Technological University, Lee Kong Chian School of Medicine, and the National Healthcare Group. J.C.C. is supported by the Singapore Ministry of Health and National Medical Research Council STaR funding scheme (NMRC/StaR/0028/2017), Large Collaborative Grant funding (MOH-000271), Phase II National Precision Medicine Programme (Research Platform and Data Enablers) (NMRC/PRECISE/2020), the Singapore Agency for Science Technology and Research Industry Alignment Fund - Pre-positioning Programme (IAF-PP) National Precision Medicine Program Phase 1A (A Population Level Genomic Infrastructure) (H17/01/a0/007), and the IAF-PP Asian Skin Microbiome Programme (H18/01/a0/016). R.S.Y.F. is supported by grants from the Singapore Ministry of Health’s National Medical Research Council under the Clinician Scientist-Individual Research Grant (MOH-001480-00), Open Fund - Large Collaborative Grant (MOH-001226-00), Ministry of Education (MOH-000333-00), and the Biomedical Research Council, Agency for Science, Technology and Research. C.J.M.L. is supported by grants from the Singapore Ministry of Health’s National Medical Research Council under the Young Investigator Research Grant (MOH-001712-01). Haojie Yu is supported by grants from the Singapore Ministry of Health’s National Medical Research Council Open Fund - Individual Research Grant (MOH-000896-00) and the Ministry of Education (MOE-000322-00). C.C. is supported by a grant from the Singapore Ministry of Education Academic Research Fund (MOE-T2EP30122-0018). N.S. and J.C.C. had full access to all the data in this study and take responsibility for the integrity of the data and the accuracy of the data analysis. Computational work for this study was partially performed on resources of the National Supercomputing Centre, Singapore (https://www.nscc.sg).

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Authors and Affiliations

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

    Nilanjana Sadhu, Rinkoo Dalan, Pritesh R. Jain, Kai Yi Tay, Theresia H. Mina, Dorrain Low, Darwin Tay, Hong Kiat Ng, Xiaoyan Wang, Max Lam, Xue Li Guan, James Best, Paul Elliott, Elio Riboli, Jimmy Lee, Eng Sing Lee, Joanne Ngeow, Christine Cheung, Marie Loh & John C. Chambers

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

    Rinkoo Dalan

  3. Cardiovascular Research Institute, National University Health System, Singapore, Singapore

    Chang Jie Mick Lee, Matthew Ackers-Johnson, Vicky Lai & Roger SY Foo

  4. Cardiovascular Metabolic Disease Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Chang Jie Mick Lee, Matthew Ackers-Johnson, Vicky Lai, Roger SY Foo & Haojie Yu

  5. Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Leroy Sivappiragasam Pakkiri & Chester Lee Drum

  6. Precision Medicine Translational Research Programme, and Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Yilin Min, Thi Tun Thi, Yu Shi, Yan Liu, Yang Yang & Haojie Yu

  7. Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Vishnu Goutham Kota, Hanry Yu & Roger SY Foo

  8. Metabolon Inc, Durham, NC, USA

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

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

    Max Lam & Jimmy Lee

  10. Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore

    Nicolas Bertin, Eleanor Wong & Patrick Tan

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

    Paul Elliott, Elio Riboli, Marie Loh & John C. Chambers

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

    Eng Sing Lee

  13. Cancer Genetics Service, National Cancer Centre, Singapore, Singapore

    Joanne Ngeow

  14. Precision Health Research, Singapore, Singapore

    Patrick Tan & John C. Chambers

  15. Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore

    Christine Cheung

  16. National Skin Centre, Singapore, Singapore

    Marie Loh

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  1. Nilanjana Sadhu
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Contributions

J.C.C., P.E., E.R., J.N., E.S.L., J.L., J.B. and T.H.M. conceived and designed the HELIOS study. R.D. and T.H.M. collected phenotype data. K.E.W., P.A.S., G.A.M. and R.S. carried out metabolite quantification. N.S. and P.R.J. performed the data analyses. C.J.M.L., L.S.P., Y.M., M.A.-J., T.T.T., V.G.K., Y.S., Y.L., Hanry Yu, V.L., Y.Y., Haojie Yu, C.L.D., R.S.Y.F., K.Y.T. and C.C. designed and carried out the experimental studies. D.L., M. Lam, M. Loh, H.K.N., T.H.M., D.T., X.W., X.L.G., N.B., E.W. and P.T. provided critical feedback on the manuscript and interpretation of results. N.S., J.C.C., P.R.J., C.J.M.L., Haojie Yu, L.S.P., K.Y.T., T.H.M. and P.A.S. wrote the manuscript. All authors reviewed and contributed to the revision of the submitted manuscript.

Corresponding authors

Correspondence to Nilanjana Sadhu or John C. Chambers.

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Competing interests

K.E.W. and G.A.M. are employees of Metabolon, and P.A.S. and R.S. were employees of Metabolon. The authors do not hold stocks in Metabolon and declare no competing interests.

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Nature Cardiovascular Research thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1

Distribution of mean cIMT in the study population (N = 8,124).

Extended Data Fig. 2

Distribution of quantified metabolites across different metabolite categories.

Extended Data Fig. 3 MR estimates from inverse variance weighted leave-one-out sensitivity analysis of 3BH5C (exposure) on CAD (outcome).

Data are presented as beta estimates +/- 95% confidence interval.

Extended Data Fig. 4 Genome-wide association analysis of metabolite 3BH5C GWAS in the HELIOS cohort (N = 1,876).

(a) Manhattan plot of 3BH5C GWAS where x-axis is displaying position of genetic variants on chromosome (hg38), and y-axis is displaying the strength of association of genetic variants with mean-cIMT. Horizontal red line indicates a GWAS threshold of P = 5×10-8. The top variant is rs2051466 (P = 7.9x10-43). (b) Quantile-Quantile plot of 3BH5C GWAS (λGC = 1.002) where x- and y-axes indicate the expected P-values under a null distribution and the observed P-values, respectively.

Extended Data Fig. 5 Phenome-wide association of rs10488763 in the Biobank Japan Project.

Volcano plot displaying the association of rs10488763 effect allele T with 259 traits listed in the Biobank Japan PheWeb (https://pheweb.jp/). Vertical grey line indicates no effect. Associations highlighted in green are significant at a P-value threshold of P = 2x10-4 after correcting for 259 tests.

Source data

Extended Data Fig. 6 Correlation of metabolite 3BH5C with traditional vascular risk factors.

Bar plot displaying Pearson correlation coefficient estimates adjusted for age, sex, and ethnicity. Bars are coloured by risk factor categories that include (1) BMI: Body Mass Index [red], (2) SBP: Systolic Blood Pressure [green], (3) DBP: Diastolic Blood Pressure [green], (4) TC: Total Cholesterol [blue], (5) HDL-C: High Density Lipoprotein Cholesterol [blue], (6)LDL-C: Low Density Lipoprotein Cholesterol [blue], (7) TG: Triglyceride[blue], (8) FGlucose, fasting plasma glucose [purple], (9) HbA1C: Glycated hemoglobin [purple]. For all correlations, Bonferroni-corrected P-value < 0.05.

Source data

Extended Data Fig. 7

Illustration of the acidic pathway of cholesterol metabolism.

Extended Data Fig. 8 Supporting data for experimental studies.

(a) FDX1 protein expression in FDX1 knock-out (FDX1 KO) and FDX1 over-expression (FDX1 OE) human hepatoma Huh7 cells, FDX1 KO mouse hepatocyte-derived AML12 cells, and FDX1 KO human monocyte-derived THP1 cells, compared to scrambled controls (SC). (b) FDX1 protein expression in FDX1 WT and KO, followed by over-expression (OE) and rescue, show robust and stable transgene expression following macrophage differentiation. (c) Sequencing validation with two independent FDX1 knock-out (KO) human Embryonic Stem Cell (hESC) clones using two independent sgRNAs targeting Exon 1 splice junction, and a 16-bp deletion on Exon 1 respectively. (d) Schematic illustration of the human FDX1 transgene payload, over-expression (OE) and rescue by knocking into the safe-harbour Pansio-1 locus in H1-hESCs Pansio-1 Safe Harbour line. (e) Representative flow cytometry profile of FDX1 WT and KO cells in the presence and absence of 200 ng/ul HDL (inducer of cholesterol efflux), with its corresponding median fluorescence intensity (MFI). (f) Quantitative RT-PCR of FDX1 expression in FDX1 knock-down (KD) human aortic smooth muscle cells compared to scrambled controls (SC).

Source data

Supplementary information

Reporting Summary

Supplementary Tables 1–15

Excel file with 15 worksheets, one for each table.

Source data

Source Data Fig. 3

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 8a

Unprocessed blots.

Source Data Extended Data Fig. 8b

Unprocessed blots.

Source Data Extended Data Fig. 8f

Statistical source data.

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Sadhu, N., Dalan, R., Jain, P.R. et al. Metabolome-wide association identifies ferredoxin-1 (FDX1) as a determinant of cholesterol metabolism and cardiovascular risk in Asian populations. Nat Cardiovasc Res 4, 567–583 (2025). https://doi.org/10.1038/s44161-025-00638-w

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