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We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032 distinct features. Multiomics factor analysis revealed distinct and independent molecular factors linked to obesity, diabetes, liver function, cardiovascular disease, inflammation, immunity, exercise, diet, and hormonal effects. For example, ethinyl estradiol, a common oral contraceptive, produced characteristic molecular and physiological effects, including increased levels of inflammation and impact on thyroid, cortisol levels, and pulse, that were distinct from other sources of variability observed in our study. In total, this work illustrates the value of combining deep molecular and digital monitoring of human health. A record of this paper's transparent peer review process is included in the supplemental information.

We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032 distinct features. Multiomics factor analysis revealed distinct and independent molecular factors linked to obesity, diabetes, liver function, cardiovascular disease, inflammation, immunity, exercise, diet, and hormonal effects. For example, ethinyl estradiol, a common oral contraceptive, produced characteristic molecular and physiological effects, including increased levels of inflammation and impact on thyroid, cortisol levels, and pulse, that were distinct from other sources of variability observed in our study. In total, this work illustrates the value of combining deep molecular and digital monitoring of human health. A record of this paper's transparent peer review process is included in the supplemental information., QC 20220407

Background: Genetic factors modify serum 25-hydroxyvitamin D [25(OH)D] concentration and can affect the optimal intake of vitamin D. Objectives: We aimed to personalize vitamin D supplementation by applying knowledge of genetic factors affecting serum 25(OH)D concentration. Methods: We performed a genome-wide association study of serum 25(OH)D concentration in the Finnish Health 2011 cohort (n = 3339) using linear regression and applied the results to develop a population-matched genetic risk score (GRS) for serum 25(OH)D. This GRS was used to tailor vitamin D supplementation for 96 participants of a longitudinal Digital Health Revolution (DHR) Study. The GRS, serum 25(OH)D concentrations, and personalized supplementation and dietary advice were electronically returned to participants. Serum 25(OH)D concentrations were assessed using immunoassays and vitamin D intake using FFQs. In data analyses, cross-sectional and repeated-measures statistical tests and models were applied as described in detail elsewhere. Results: GC vitamin D-binding protein and cytochrome P450 family 2 subfamily R polypeptide 1 genes showed genome-wide significant associations with serum 25(OH)D concentration. One single nucleotide polymorphism from each locus (rs4588 and rs10741657) was used to develop the GRS. After returning data to the DHR Study participants, daily vitamin D supplement users increased from 32.6% to 60.2% (P = 6.5 x 10(-6)) and serum 25(OH)D concentration from 64.4 +/- 20.9 nmol/L to 68.5 +/- 19.2 nmol/L (P = 0.006) between August and November. Notably, the difference in serum 25(OH)D concentrations between participants with no risk alleles and those with 3 or 4 risk alleles decreased from 20.7 nmol/L to 8.0 nmol/L (P = 0.0063). Conclusions: We developed and applied a population-matched GRS to identify individuals genetically predisposed to low serum 25(OH)D concentration. We show how the electronic return of individual genetic risk, serum 25(OH)D concentrations, and factors