The transition from genomics to phenomics in personalized population health.

Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.

Multiomic signatures of body mass index identify heterogeneous health phenotypes and responses to a lifestyle intervention.

Multiomic profiling can reveal population heterogeneity for both health and disease states. Obesity drives a myriad of metabolic perturbations and is a risk factor for multiple chronic diseases. Here we report an atlas of cross-sectional and longitudinal changes in 1,111 blood analytes associated with variation in body mass index (BMI), as well as multiomic associations with host polygenic risk scores and gut microbiome composition, from a cohort of 1,277 individuals enrolled in a wellness program (Arivale). Machine learning model predictions of BMI from blood multiomics captured heterogeneous phenotypic states of host metabolism and gut microbiome composition better than BMI, which was also validated in an external cohort (TwinsUK). Moreover, longitudinal analyses identified variable BMI trajectories for different omics measures in response to a healthy lifestyle intervention; metabolomics-inferred BMI decreased to a greater extent than actual BMI, whereas proteomics-inferred BMI exhibited greater resistance to change. Our analyses further identified blood analyte-analyte associations that were modified by metabolomics-inferred BMI and partially reversed in individuals with metabolic obesity during the intervention. Taken together, our findings provide a blood atlas of the molecular perturbations associated with changes in obesity status, serving as a resource to quantify metabolic health for predictive and preventive medicine.

The Coaching for Cognition in Alzheimer's (COCOA) trial: Study design.

Comprehensive treatment of Alzheimer's disease (AD) requires not only pharmacologic treatment but also management of existing medical conditions and lifestyle modifications including diet, cognitive training, and exercise. We present the design and methodology for the Coaching for Cognition in Alzheimer's (COCOA) trial. AD and other dementias result from the interplay of multiple interacting dysfunctional biological systems. Monotherapies have had limited success. More interventional studies are needed to test the effectiveness of multimodal multi-domain therapies for dementia prevention and treatment. Multimodal therapies use multiple interventions to address multiple systemic causes and potentiators of cognitive decline and functional loss; they can be personalized, as different sets of etiologies and systems responsive to therapy may be present in different individuals. COCOA is designed to test the hypothesis that coached multimodal interventions beneficially alter the trajectory of cognitive decline for individuals on the spectrum of AD and related dementias (ADRD). COCOA is a two-arm prospective randomized controlled trial (RCT). COCOA collects psychometric, clinical, lifestyle, genomic, proteomic, metabolomic, and microbiome data at multiple timepoints across 2 years for each participant. These data enable systems biology analyses. One arm receives standard of care and generic healthy aging recommendations. The other arm receives standard of care and personalized data-driven remote coaching. The primary outcome measure is the Memory Performance Index (MPI), a measure of cognition. The MPI is a summary statistic of the MCI Screen (MCIS). Secondary outcome measures include the Functional Assessment Staging Test (FAST), a measure of function. COCOA began enrollment in January 2018. We hypothesize that multimodal interventions will ameliorate cognitive decline and that data-driven health coaching will increase compliance, assist in personalizing multimodal interventions, and improve outcomes for patients, particularly for those in the early stages of the AD spectrum. Highlights: The Coaching for Cognition in Alzheimer's (COCOA) trial tests personalized multimodal lifestyle interventions for Alzheimer's disease and related dementias.Dense longitudinal molecular data will be useful for future studies.Increased use of Hill's criteria in analyses may advance knowledge generation.Remote coaching may be an effective intervention.Because lifestyle interventions are inexpensive, they may be particularly valuable in reducing global socioeconomic disparities in dementia care.

Heterogeneity in statin responses explained by variation in the human gut microbiome.

BACKGROUND: Statins remain one of the most prescribed medications worldwide. While effective in decreasing atherosclerotic cardiovascular disease risk, statin use is associated with adverse effects for a subset of patients, including disrupted metabolic control and increased risk of type 2 diabetes. METHODS: We investigated the potential role of the gut microbiome in modifying patient responses to statin therapy across two independent cohorts (discovery n = 1,848, validation n = 991). Microbiome composition was assessed in these cohorts using stool 16S rRNA amplicon and shotgun metagenomic sequencing, respectively. Microbiome associations with markers of statin on-target and adverse effects were tested via a covariate-adjusted interaction analysis framework, utilizing blood metabolomics, clinical laboratory tests, genomics, and demographics data. FINDINGS: The hydrolyzed substrate for 3-hydroxy-3-methylglutarate-coenzyme-A (HMG-CoA) reductase, HMG, emerged as a promising marker for statin on-target effects in cross-sectional cohorts. Plasma HMG levels reflected both statin therapy intensity and known genetic markers for variable statin responses. Through exploring gut microbiome associations between blood-derived measures of statin effectiveness and adverse metabolic effects of statins, we find that heterogeneity in statin responses was consistently associated with variation in the gut microbiome across two independent cohorts. A Bacteroides-enriched and diversity-depleted gut microbiome was associated with more intense statin responses, both in terms of on-target and adverse effects. CONCLUSIONS: With further study and refinement, gut microbiome monitoring may help inform precision statin treatment. FUNDING: This research was supported by the M.J. Murdock Charitable Trust, WRF, NAM Catalyst Award, and NIH grant U19AG023122 awarded by the NIA.

Manifestations of Alzheimer's disease genetic risk in the blood are evident in a multiomic analysis in healthy adults aged 18 to 90.

Genetics play an important role in late-onset Alzheimer's Disease (AD) etiology and dozens of genetic variants have been implicated in AD risk through large-scale GWAS meta-analyses. However, the precise mechanistic effects of most of these variants have yet to be determined. Deeply phenotyped cohort data can reveal physiological changes associated with genetic risk for AD across an age spectrum that may provide clues to the biology of the disease. We utilized over 2000 high-quality quantitative measurements obtained from blood of 2831 cognitively normal adult clients of a consumer-based scientific wellness company, each with CLIA-certified whole-genome sequencing data. Measurements included: clinical laboratory blood tests, targeted chip-based proteomics, and metabolomics. We performed a phenome-wide association study utilizing this diverse blood marker data and 25 known AD genetic variants and an AD-specific polygenic risk score (PGRS), adjusting for sex, age, vendor (for clinical labs), and the first four genetic principal components; sex-SNP interactions were also assessed. We observed statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE), with effects detectable from early adulthood. The ABCA7 SNP and the APOE2 and APOE4 encoding alleles were associated with lipid variability, as seen in previous studies; in addition, six novel proteins were associated with the e2 allele. The most statistically significant finding was between the NYAP1 variant and PILRA and PILRB protein levels, supporting previous functional genomic studies in the identification of a putative causal variant within the PILRA gene. We did not observe associations between the PGRS and any analyte. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. In post-hoc analysis, sex-stratified GWAS results from an independent AD case-control meta-analysis supported sex-specific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. Known AD genetic variation influenced lipid metabolism and immune response systems in a population of non-AD individuals, with associations observed from early adulthood onward. Further research is needed to determine whether and how these effects are implicated in early-stage biological pathways to AD. These analyses aim to complement ongoing work on the functional interpretation of AD-associated genetic variants.

Baseline Gut Metagenomic Functional Gene Signature Associated with Variable Weight Loss Responses following a Healthy Lifestyle Intervention in Humans.

Recent human feeding studies have shown how the baseline taxonomic composition of the gut microbiome can determine responses to weight loss interventions. However, the functional determinants underlying this phenomenon remain unclear. We report a weight loss response analysis on a cohort of 105 individuals selected from a larger population enrolled in a commercial wellness program, which included healthy lifestyle coaching. Each individual in the cohort had baseline blood metabolomics, blood proteomics, clinical labs, dietary questionnaires, stool 16S rRNA gene sequencing data, and follow-up data on weight change. We generated additional targeted proteomics data on obesity-associated proteins in blood before and after intervention, along with baseline stool metagenomic data, for a subset of 25 individuals who showed the most extreme weight change phenotypes. We built regression models to identify baseline blood, stool, and dietary features associated with weight loss, independent of age, sex, and baseline body mass index (BMI). Many features were independently associated with baseline BMI, but few were independently associated with weight loss. Baseline diet was not associated with weight loss, and only one blood analyte was associated with changes in weight. However, 31 baseline stool metagenomic functional features, including complex polysaccharide and protein degradation genes, stress-response genes, respiration-related genes, and cell wall synthesis genes, along with gut bacterial replication rates, were associated with weight loss responses after controlling for age, sex, and baseline BMI. Together, these results provide a set of compelling hypotheses for how commensal gut microbiota influence weight loss outcomes in humans. IMPORTANCE Recent human feeding studies have shown how the baseline taxonomic composition of the gut microbiome can determine responses to dietary interventions, but the exact functional determinants underlying this phenomenon remain unclear. In this study, we set out to better understand interactions between baseline BMI, metabolic health, diet, gut microbiome functional profiles, and subsequent weight changes in a human cohort that underwent a healthy lifestyle intervention. Overall, our results suggest that the microbiota may influence host weight loss responses through variable bacterial growth rates, dietary energy harvest efficiency, and immunomodulation.

Gut microbiome pattern reflects healthy ageing and predicts survival in humans.

The gut microbiome has important effects on human health, yet its importance in human ageing remains unclear. In the present study, we demonstrate that, starting in mid-to-late adulthood, gut microbiomes become increasingly unique to individuals with age. We leverage three independent cohorts comprising over 9,000 individuals and find that compositional uniqueness is strongly associated with microbially produced amino acid derivatives circulating in the bloodstream. In older age (over ~80 years), healthy individuals show continued microbial drift towards a unique compositional state, whereas this drift is absent in less healthy individuals. The identified microbiome pattern of healthy ageing is characterized by a depletion of core genera found across most humans, primarily Bacteroides. Retaining a high Bacteroides dominance into older age, or having a low gut microbiome uniqueness measure, predicts decreased survival in a 4-year follow-up. Our analysis identifies increasing compositional uniqueness of the gut microbiome as a component of healthy ageing, which is characterized by distinct microbial metabolic outputs in the blood.

Race differences in predictors of weight gain among a community sample of smokers enrolled in a randomized controlled trial of a multiple behavior change intervention.

African Americans have disproportionate rates of post-cessation weight gain compared to non-Hispanic whites, but few studies have examined this weight gain in a multiracial sample of smokers receiving evidence-based treatment in a community setting. We examined race differences in short-term weight gain during an intervention to foster smoking cessation plus weight management. Data were drawn from the Best Quit Study, a randomized controlled trial conducted via telephone quitlines across the U.S. from 2013 to 2017. The trial tested the effects on cessation and weight gain prevention of adding a weight control intervention either simultaneously with or sequentially after smoking cessation treatment. African Americans (n = 665) and whites (n = 1723) self-reported smoking status and weight during ten intervention calls. Random effects longitudinal modeling was used to examine predictors of weight change over the intervention period (average 16 weeks). There was a significant race × treatment effect; in the simultaneous group, weight increased for African Americans at a faster rate compared to whites (b = 0.302, SE = 0.129, p < 0.05), independent of smoking status, age, baseline obesity, and education. After stratifying the sample, the effect of treatment group differed by race. Education level attenuated the rate of weight gain for African Americans in the simultaneous group, but not for whites. African Americans receiving smoking and weight content simultaneously gained weight faster than whites in the same group; however, the weight gain was slower for African Americans with higher educational attainment. Future studies are needed to understand social factors associated with treatment receptivity that may influence weight among African American smokers.

Untargeted longitudinal analysis of a wellness cohort identifies markers of metastatic cancer years prior to diagnosis.

We analyzed 1196 proteins in longitudinal plasma samples from participants in a commercial wellness program, including samples collected pre-diagnosis from ten cancer patients and 69 controls. For three individuals ultimately diagnosed with metastatic breast, lung, or pancreatic cancer, CEACAM5 was a persistent longitudinal outlier as early as 26.5 months pre-diagnosis. CALCA, a biomarker for medullary thyroid cancer, was hypersecreted in metastatic pancreatic cancer at least 16.5 months pre-diagnosis. ERBB2 levels spiked in metastatic breast cancer between 10.0 and 4.0 months pre-diagnosis. Our results support the value of deep phenotyping seemingly healthy individuals in prospectively inferring disease transitions.

Health and disease markers correlate with gut microbiome composition across thousands of people.

Variation in the human gut microbiome can reflect host lifestyle and behaviors and influence disease biomarker levels in the blood. Understanding the relationships between gut microbes and host phenotypes are critical for understanding wellness and disease. Here, we examine associations between the gut microbiota and ~150 host phenotypic features across ~3,400 individuals. We identify major axes of taxonomic variance in the gut and a putative diversity maximum along the Firmicutes-to-Bacteroidetes axis. Our analyses reveal both known and unknown associations between microbiome composition and host clinical markers and lifestyle factors, including host-microbe associations that are composition-specific. These results suggest potential opportunities for targeted interventions that alter the composition of the microbiome to improve host health. By uncovering the interrelationships between host diet and lifestyle factors, clinical blood markers, and the human gut microbiome at the population-scale, our results serve as a roadmap for future studies on host-microbe interactions and interventions.

Multiomic blood correlates of genetic risk identify presymptomatic disease alterations.

Transitions from health to disease are characterized by dysregulation of biological networks under the influence of genetic and environmental factors, often over the course of years to decades before clinical symptoms appear. Understanding these dynamics has important implications for preventive medicine. However, progress has been hindered both by the difficulty of identifying individuals who will eventually go on to develop a particular disease and by the inaccessibility of most disease-relevant tissues in living individuals. Here we developed an alternative approach using polygenic risk scores (PRSs) based on genome-wide association studies (GWAS) for 54 diseases and complex traits coupled with multiomic profiling and found that these PRSs were associated with 766 detectable alterations in proteomic, metabolomic, and standard clinical laboratory measurements (clinical labs) from blood plasma across several thousand mostly healthy individuals. We recapitulated a variety of known relationships (e.g., glutamatergic neurotransmission and inflammation with depression, IL-33 with asthma) and found associations directly suggesting therapeutic strategies (e.g., Ω-6 supplementation and IL-13 inhibition for amyotrophic lateral sclerosis) and influences on longevity (leukemia inhibitory factor, ceramides). Analytes altered in high-genetic-risk individuals showed concordant changes in disease cases, supporting the notion that PRS-associated analytes represent presymptomatic disease alterations. Our results provide insights into the molecular pathophysiology of a range of traits and suggest avenues for the prevention of health-to-disease transitions.

Racial differences in body composition and cardiometabolic risk during the menopause transition: a prospective, observational cohort study.

BACKGROUND: Obesity disproportionately affects more women than men. The loss of ovarian function during the menopause transition coincides with weight gain, increases in abdominal adiposity, and impaired metabolic health. Racial differences in obesity prevalence that results from the menopause transition are not well understood. OBJECTIVE: The purpose of the study was to assess longitudinal changes in body composition and cardiometabolic risk among black and white women during the menopausal transition. STUDY DESIGN: In a secondary analysis of a prospective, observational cohort study (the Healthy Transitions study), 161 women ≥43 years old with a body mass index of 20-40 kg/m2 and who had not yet transitioned through menopause were enrolled at Pennington Biomedical Research Center. Women were seen annually for body composition by dual-energy X-ray absorptiometry, for abdominal adipose tissue distribution by computed tomography, for sex steroid hormones, and for cardiometabolic risk factors that include fasting glucose, insulin, and lipids. Surrogate measures of insulin sensitivity were also calculated. RESULTS: Ninety-four women (25 black, 69 white) transitioned through menopause and were included within the analyses. At menopause onset, black women weighed more (77.8±3.0 vs 70.8±1.8 kg) and had a higher systolic (125±16 vs 118±14 mm Hg) and diastolic (80±8 vs 74±7 mm Hg) blood pressure compared with white women (all P≤.05). No other differences in body composition, sex steroid hormones, or cardiometabolic risk factors were observed at menopause onset. Before menopause, white women gained significant weight (3 kg), total body adiposity (6% percent body fat, 9% fat mass, 12% trunk fat mass) and abdominal adipose tissue (19% subcutaneous fat, 15% visceral fat, 19% total adipose tissue), which coincided with significant decreases in estradiol, sex hormone-binding globulin, and estrone sulfate and increases in follicle-stimulating hormone, total cholesterol, and low-density lipoprotein cholesterol. Conversely, black women had more abdominal adipose tissue before menopause, which was maintained across the menopause transition. Black women also had significant decreases in estrone sulfate and total testosterone and increases in follicle-stimulating hormone before menopause. In the postmenopausal years, abdominal subcutaneous adipose tissue, total adipose tissue, follicle-stimulating hormone, total cholesterol, and low-density and high-density lipoprotein cholesterol increased only in white women. CONCLUSION: White women gained more abdominal adiposity during the menopause transition compared with black women, which, in part, may be due to differences in the pattern of sex steroid hormone changes between women of different racial backgrounds. The gains in abdominal adiposity in white women were observed in tandem with increased cardiometabolic risk factors. Future studies should consider comprehensive lifestyle approaches to target these increased gains in abdominal adiposity (ie, nutrition and physical activity coaching), while taking into account the potential interactions of race, body adiposity, sex steroid hormones, and their influence on cardiometabolic risk.

Genetic Predisposition Impacts Clinical Changes in a Lifestyle Coaching Program.

Both genetic and lifestyle factors contribute to an individual's disease risk, suggesting a multi-omic approach is essential for personalized prevention. Studies have examined the effectiveness of lifestyle coaching on clinical outcomes, however, little is known about the impact of genetic predisposition on the response to lifestyle coaching. Here we report on the results of a real-world observational study in 2531 participants enrolled in a commercial "Scientific Wellness" program, which combines multi-omic data with personalized, telephonic lifestyle coaching. Specifically, we examined: 1) the impact of this program on 55 clinical markers and 2) the effect of genetic predisposition on these clinical changes. We identified sustained improvements in clinical markers related to cardiometabolic risk, inflammation, nutrition, and anthropometrics. Notably, improvements in HbA1c were akin to those observed in landmark trials. Furthermore, genetic markers were associated with longitudinal changes in clinical markers. For example, individuals with genetic predisposition for higher LDL-C had a lesser decrease in LDL-C on average than those with genetic predisposition for average LDL-C. Overall, these results suggest that a program combining multi-omic data with lifestyle coaching produces clinically meaningful improvements, and that genetic predisposition impacts clinical responses to lifestyle change.

Six Month Abstinence Heterogeneity in the Best Quit Study.

BACKGROUND: Understanding the characteristics of smokers who are successful in quitting may help to increase smoking cessation rates. PURPOSE: To examine heterogeneity in cessation outcome at 6 months following smoking cessation behavioral counseling with or without weight management counseling. METHODS: 2,540 smokers were recruited from a large quitline provider and then randomized to receive proactive smoking cessation behavioral counseling without or with two versions of weight management counseling. A Classification and Regression Tree (CART) analysis was conducted to identify the individual pretreatment and treatment characteristics of groups of smokers with different quitting success (as measured by point prevalence of self-reported smoking of any amount at 6 months). RESULTS: CART analysis identified 10 subgroups ranging from 25.5% to 70.2% abstinent. The splits in the CART tree involved: the total number of counseling and control calls received, whether a smoking cessation pharmacotherapy was used, and baseline measures of cigarettes per day, confidence in quitting, expectation that the study would help the participant quit smoking, the motivation to quit, exercise minutes per week, anxiety, and lack of interest or pleasure in doing things. Costs per quitter ranged from a low of $US270 to a high of $US630. Specific treatment recommendations are made for each group. CONCLUSIONS: These results indicate the presence of a substantial variation in abstinence following treatment, and that the total extent of contact via counseling calls of any type and baseline characteristics, rather than assigned treatment, were most important to subgroup membership and abstinence. Tailored treatments to subgroups who are at high risk for smoking following a quit attempt could increase successful smoking cessation.

A Randomized Pilot Study of a Phone-Based Mindfulness and Weight Loss Program.

This study evaluated the feasibility and efficacy of integrating mindfulness training into a phone-based weight loss program to improve outcomes in those with high levels of emotional eating. Participants were 75 enrollees into an employer-sponsored weight loss program who reported high levels of overeating in response to thoughts and feelings. Seventy-five overweight and obese participants (92% female, 65% Caucasian, aged 26 to 68 years) were randomized to the new mindfulness weight loss program (n = 50) or the standard behavioral weight loss program (n = 25). Both programs consisted of 11 coaching calls with health coaches and registered dietitians with supplemental online materials. Satisfaction, engagement, and percent weight lost did not significantly differ for intervention vs. control at six months. Intervention participants had significantly better scores at six-month follow-up on mindful eating, binge eating, experiential avoidance, and one mindfulness subscale. Exploratory analyses showed that improvements on several measures predicted more weight loss in the intervention group. This pilot study found that integrating mindfulness into a brief phone-based behavioral weight loss program was feasible and acceptable to participants, but did not produce greater weight loss on average, despite hypothesized changes in mindful eating. Only one third of intervention participants reported participating in mindfulness exercises regularly. Mechanisms of change observed within the intervention group suggest that for adults with high levels of emotional eating those who embrace mindful eating and meditation may lose more weight with a mindfulness intervention.

A Multi-omic Association Study of Trimethylamine N-Oxide.

Trimethylamine N-oxide (TMAO) is a circulating metabolite that has been implicated in the development of atherosclerosis and cardiovascular disease (CVD). In this paper, we identify blood markers, metabolites, proteins, gut microbiota patterns, and diets that are significantly associated with levels of plasma TMAO. We find that kidney markers are strongly associated with TMAO and identify CVD-related proteins that are positively correlated with TMAO. We show that metabolites derived by the gut microbiota are strongly correlated with TMAO and that the magnitude of this correlation varies with kidney function. Moreover, we identify diet-associated patterns in the microbiome that are correlated with TMAO. These findings suggest that both the process of TMAO accumulation and the mechanism by which TMAO promotes atherosclerosis are a complex interplay between diet and the microbiome on one hand and other system-level factors such as circulating proteins, metabolites, and kidney function.

A wellness study of 108 individuals using personal, dense, dynamic data clouds.

Personal data for 108 individuals were collected during a 9-month period, including whole genome sequences; clinical tests, metabolomes, proteomes, and microbiomes at three time points; and daily activity tracking. Using all of these data, we generated a correlation network that revealed communities of related analytes associated with physiology and disease. Connectivity within analyte communities enabled the identification of known and candidate biomarkers (e.g., gamma-glutamyltyrosine was densely interconnected with clinical analytes for cardiometabolic disease). We calculated polygenic scores from genome-wide association studies (GWAS) for 127 traits and diseases, and used these to discover molecular correlates of polygenic risk (e.g., genetic risk for inflammatory bowel disease was negatively correlated with plasma cystine). Finally, behavioral coaching informed by personal data helped participants to improve clinical biomarkers. Our results show that measurement of personal data clouds over time can improve our understanding of health and disease, including early transitions to disease states.