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Plasma metabolites to profile pathways in noncommunicable disease multimorbidity

Nature Medicine volume 27, pages 471–479 (2021)Cite this article

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Abstract

Multimorbidity, the simultaneous presence of multiple chronic conditions, is an increasing global health problem and research into its determinants is of high priority. We used baseline untargeted plasma metabolomics profiling covering >1,000 metabolites as a comprehensive readout of human physiology to characterize pathways associated with and across 27 incident noncommunicable diseases (NCDs) assessed using electronic health record hospitalization and cancer registry data from over 11,000 participants (219,415 person years). We identified 420 metabolites shared between at least 2 NCDs, representing 65.5% of all 640 significant metabolite–disease associations. We integrated baseline data on over 50 diverse clinical risk factors and characteristics to identify actionable shared pathways represented by those metabolites. Our study highlights liver and kidney function, lipid and glucose metabolism, low-grade inflammation, surrogates of gut microbial diversity and specific health-related behaviors as antecedents of common NCD multimorbidity with potential for early prevention. We integrated results into an open-access webserver (https://omicscience.org/apps/mwasdisease/) to facilitate future research and meta-analyses.

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Fig. 1: Connectivity between incident diseases established based on associated metabolites.
Fig. 2: Brick plot showing the ranking of metabolites based on the number of associated incident end points.
Fig. 3: Summary of mediation analysis.
Fig. 4: Percentage of each disease acquired during follow-up.
Fig. 5: Metabolites associated with multimorbidity.
Fig. 6: Variance explained in the plasma levels of selected metabolites associated with multimorbidity.

Data availability

We have provided open access to all summary statistics for academic use through an interactive web server. The EPIC-Norfolk data can be requested by bona fide researchers for specified scientific purposes via the study website (https://www.mrc-epid.cam.ac.uk/research/studies/epic-norfolk/). Data will either be shared through an institutional data sharing agreement or arrangements will be made for analyses to be conducted remotely without the need for data transfer.

Code availability

Any code used in the present analysis is freely available to academic researchers upon request from the corresponding author.

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Acknowledgements

We thank all the participants who have been part of the project and the many members of the study teams at the University of Cambridge who enabled this research. The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) has received funding from the Medical Research Council (nos. MR/N003284/1 and MC-UU_12015/1) and Cancer Research UK (no. C864/A14136). Metabolite measurements in the EPIC-Norfolk study were supported by the MRC Cambridge Initiative in Metabolic Science (no. MR/L00002/1) and the Innovative Medicines Initiative Joint Undertaking under European Medical Information Framework grant agreement no. 115372. M.P. was supported by a fellowship of the German Research Foundation (no. 1446/2-1). J.R. is supported by the German Federal Ministry of Education and Research within the framework of the e:Med research and funding concept (grant no. 01ZX1912D). G.K. is supported by grants from the National Institute on Aging (NIA): R01 AG057452, RF1 AG058942, RF1 AG059093, U01 AG061359 and U19 AG063744 and by a grant from the German Federal Ministry of Education and Research (BMBF): 01GM1906C.

Author information

Authors and Affiliations

  1. MRC Epidemiology Unit, University of Cambridge, Cambridge, UK

    Maik Pietzner, Isobel D. Stewart, Gabi Kastenmüller, Nicholas J. Wareham & Claudia Langenberg

  2. Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany

    Johannes Raffler & Gabi Kastenmüller

  3. Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Kay-Tee Khaw

  4. Metabolon, Inc., Durham, NC, USA

    Gregory A. Michelotti

  5. Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Cambridge, UK

    Claudia Langenberg

  6. Computational Medicine, Berlin Institute of Health, Charité University Medicine, Berlin, Germany

    Claudia Langenberg

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Contributions

M.P. and C.L. designed the analysis and drafted the manuscript. M.P. and I.D.S. analyzed the data. J.R. and G.K. designed and implemented the web server. K.-T.K. and N.J.W. are principal investigators of the EPIC-Norfolk cohort. G.A.M. advised on metabolite mapping across batches and provided annotations for retired unknown compounds. All authors contributed to the interpretation of the results and critically reviewed the manuscript.

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Correspondence to Claudia Langenberg.

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Competing interests

G.A.M. is an employee of Metabolon. All other authors declare no competing interests.

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Peer review information Jennifer Sargent was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Summary of event distribution during follow-up.

Occurrence of events during follow-up. Each line indicates an event. The pin plot on the right gives the total number of cases for each disease. COPD=chronic obstructive pulmonary disease.

Extended Data Fig. 2 Manhattan-like plot summarizing results from Cox proportional hazard models.

Mirrored Manhattan-like plot showing the p-values from Cox proportional hazard models using metabolite levels as exposure and disease onset as outcome adjusting for age and sex. Colours indicate metabolite classes (see Fig. 1 in main text for a legend) and numbers on top indicate number of significantly associated metabolites (p < 4.93 × 10−5). Grey dots indicate associations not reaching significance. Positive associations are displayed in the upper panel and inverse associations in the lower. COPD=chronic obstructive pulmonary disease.

Extended Data Fig. 3 Top five associated metabolites with each outcome.

For each incident disease under investigation hazard ratios with 95%-confidence intervals for the five metabolites with the lowest p-value are shown. Cox models with age as underlying scale were adjusted for sex. T2D=type 2 diabetes mellitus; CHD=coronary heart disease.

Extended Data Fig. 4 Relation between cases numbers and associated metabolites.

Number of cases against number of significantly associated metabolites for all incident diseases and all-cause mortality, for associations with nominal significance (left) and Bonferroni corrected significance (right panel). The black line indicates a linear fit between both.

Extended Data Fig. 5 Summary of sensitivity analysis.

A Left panel opposes effect estimates from Cox proportional hazard models (x-axis) with those from logistic regression models (y-axis) using binary event data only. Points are coloured by incident endpoints as labelled on the right and larger points indicate metabolite—disease pairs with p < 0.001. The right panel shows correlation coefficients for effect estimates across all metabolites for a given incident endpoint. B Left panel opposes effect estimates from Cox proportional hazard models (x-axis) including the whole study population with exclusion criteria applied as mentioned in the main text with those from further excluding 469 participants who have died within the first five years after baseline examinations (y-axis). Points are coloured by incident endpoints as labelled on the right and larger points indicate metabolite—disease pairs with p < 0.001. The right panel shows correlation coefficients for effect estimates across all metabolites for a given incident endpoint. C Pearson (left) and Spearman (right) correlation coefficients of effect estimates from Cox proportional hazard models comparing initial results as described in the main text with successive exclusion of participants experiencing any event (excluding all-cause mortality) within the first five years of follow-up.

Extended Data Fig. 6 Results testing for a modulating effect of sex.

Colour coded heatmap of β-estimates for a sex-metabolite interaction term in Cox proportional hazard models. Cox models were run with the metabolite, sex, and a sex-metabolite interaction term as exposure and disease onset as outcome with age as the underlying time scale. Red shades indicate a stronger effect among women, whereas blue indicates the opposite. Rectangles surrounded with a black frame indicate a p-value<0.001 correcting for 28 outcomes tested for each metabolite.

Extended Data Fig. 7 Amount of variance explained in plasma levels of metabolites by different risk factors at baseline.

A Results from variance decomposition analysis of plasma metabolites levels using information on 50 baseline characteristics. Each trait was treated separately to avoid collinearity in a model comprising age, sex, blood sampling time, and fasting duration. B Mirrored Manhattan-like plot showing the p-values from linear regression models using one of the traits on the x-axis as exposure and metabolite levels as outcome adjusting for age and sex. Colours indicate metabolite classes and numbers on top indicate number of significantly associated metabolites (p < 4.93 × 10−5). Grey dots indicate associations not reaching significance. Positive associations are displayed in the upper panel and inverse associations in the lower. Labels are explained in Supplementary Table 2.

Extended Data Fig. 8 Results from Cox models testing risk factors and outcomes.

Colour coded heatmap of β-estimates from Cox proportional hazard models. Results are restricted to significantly associated (p < 0.01; black frames) cross-sectional traits of at least one incident disease. Positive associations are indicated in red, whereas inverse associations are blue. Diseases and cross-sectional traits were ordered using hierarchical clustering with absolute correlations as distance. All abbreviations are listed in Supplementary Table 2.

Extended Data Fig. 9 Amount of effect mediated from a risk factor onto an outcome through a specific metabolite.

Heatmap of risk factor—metabolite pairs with a significant indirect effect of the risk factor on at least one of the diseases under investigation. Colouring was done based on the median proportion mediated by a metabolite across all diseases. The proportion mediated was derived as quotient of the main effect from two different Cox proportional hazed models, one with and one without adjusting for the metabolite and both including the risk factor as main exposure. Boxes indicate corrected statistical significance with at least one disease (p < 0.05/6,364) and grey shades indicate not tested due to missing requirements for mediation analysis.

Extended Data Fig. 10 Pairwise correlation heatmap of multimorbidity candidate metabolites.

Pairwise correlation matrix of plasma metabolites significantly associated with the incidence of NCD multimorbidity. Colours indicate positive (red) or inverse (blue) correlations and black frames indicate statistical significance after correction for multiple testing. Metabolites were clustered based on correlation profiles using hierarchical clustering.

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Pietzner, M., Stewart, I.D., Raffler, J. et al. Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med 27, 471–479 (2021). https://doi.org/10.1038/s41591-021-01266-0

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