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Plasma protein patterns as comprehensive indicators of health

Abstract

Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3,4,5,6,7,8,9,10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12,13,14,15,16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.

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Fig. 1: Model outputs compared to the truth standards against which they were derived.

Data availability

Pre-existing data access policies for each of the five parent cohort studies specify that research data requests can be submitted to each steering committee; these will be promptly reviewed for confidentiality or intellectual property restrictions and will not unreasonably be refused. Individual-level patient or protein data may further be restricted by consent, confidentiality or privacy laws/considerations. These policies apply to both clinical and proteomic data.

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Acknowledgements

The Whitehall II study is supported by the UK Medical Research Council UK (no. MR/R024227/1, to M.K.), the US National Institutes on Aging (NIH, nos. US R01AG056477, R01AG062553) to M.K. and the British Heart Foundation (no. RG/16/11/32334) to M.J.S. A.D.H. is a NIHR Senior Investigator and was also supported, in part, by the National Institute for Health Research University College London Hospitals Biomedical Research Centre and the UCL BHF Research Accelerator (AA/18/6/34223). FENLAND (the Fenland study, no. 10.22025/2017.10.101.00001) is funded by UK Medical Research Council (no. MC_UU_12015/1), and N.W. is a NIHR senior investigator. We also thank the Fenland Study Investigators, Fenland Study Co-ordination team and the Epidemiology Field, Data and Laboratory teams. HUNT3 is funded by the Norwegian Ministry of Health, Norwegian University of Science and Technology and Norwegian Research Council, Central Norway Regional Health Authority, the Nord-Trondelag County Council and the Norwegian Institute of Public Health. The HERITAGE Family study was funded by the US National Heart, Lung and Blood Institute grants (NIH/NHLBI, no. R01HL146462 to M.A.S.) and no. HL45670 (HERITAGE, to C.B.). All authors are grateful to all volunteers/participants in all of the cohorts, and to the general practitioners, other physicans and practice staff for assistance with recruitment. SomaScan assays and the Covance study were funded by SomaLogic, Inc. The authors also thank A. Lowell (leader of the SomaLogic assay team), D. Perry for the bioinformatics of quality control, J. Williams for the agreements with the study institutions and J. Zach for clinical data organization and management.

Author information

Affiliations

Authors

Contributions

In an academic–industry partnership, SomaLogic, Inc. and the academic collaborators worked together on study design, interpretation of the data and preparation of the manuscript. S.A.W., P.G. and N.W. were responsible for designing, writing and final editing of the manuscript and responses to reviewer comments. In addition to all authors being generally involved in the program, specific contributions were as follows: M.K. and M.J.S. were accountable for the data from the Whitehall II study and advised on the study design for the CV and diabetes models. C.L. and N.W. were accountable for the data from the Fenland study and advising on diabetes risk and behavioral models. C.B. and M.A.S. were accountable for the data from the Heritage Family study. C.J. was accountable for the data from the HUNT3 study. R.O. was accountable for the data from the Covance study. L.A., G.D., R.K.D., Y.H., M.H. and S.W. designed and executed the machine learning tactics and developed the models. R.O., J.A., T.B., J.C. and S.A.W. were responsible for the design and integration of the program across studies. A.D.H. and J.P.C. were particularly involved in the design, execution and interpretation of the CV risk evaluations.

Corresponding author

Correspondence to Stephen A. Williams.

Ethics declarations

Competing interests

The SomaLogic co-authors (S.W., L.A., J.A., T.B., J.C., G.D., R.K.D., Y.H., M.H., R.O. and S.W.) were/are all employees of SomaLogic, Inc., which has a commercial interest in the results. N.W. and C.L. declared that SomaLogic, Inc. has given a grant to the University of Cambridge. P.G. is a member of the SomaLogic Medical Advisory board, for which he receives no remuneration of any kind. The remaining authors (M.K., A.H., J.P.C., C.B., C.J., M.S. and M.S.) have no competing interests.

Additional information

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 Descriptors of parent studies and fractions used for model derivation and validation.

Solid black arrows designate how fractions of samples and clinical data were utilized independently; blue dashed arrows designate the validation of finalized models either in new fractions of the same dataset or in independent datasets. eGFR = estimated glomerular filtration rate; VO2max. = maximum rate of oxygen consumption; kg. = kilograms. *For Fenland, the precise numbers available for 70%/15%/15% fractions depended on the numbers of participants with data for each endpoint as follows: n=9654 for self-reported alcohol units, n = 11,471 with DEXA scans for body composition, n=10,077 with ultrasound for liver fat, n=11,695 with individually calibrated heart rate and movement sensing for caloric expenditure due to physical activity. **For HERITAGE the model was trained on the pre-training time point from half the 523 participants and the post training time point from the other half of the participants. The model was tested on samples with the opposite time points in the same participants and finally replicated in the 10% fraction not used for training.

Extended Data Fig. 2

Details of the 5 parent cohort studies.

Extended Data Fig. 3

Participant characteristics for current health state models.

Extended Data Fig. 4

Participant characteristics for current state body composition models.

Extended Data Fig. 5

Participant characteristics for modifiable behavioral factors models.

Extended Data Fig. 6

Participant characteristics for future metabolic health risks models.

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–6.

Source data

Source Data Fig. 1

Statistical Source Data for 12 individual panels in Fig. 1

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Williams, S.A., Kivimaki, M., Langenberg, C. et al. Plasma protein patterns as comprehensive indicators of health. Nat Med 25, 1851–1857 (2019). https://doi.org/10.1038/s41591-019-0665-2

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