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 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.

Longitudinal analysis reveals transition barriers between dominant ecological states in the gut microbiome.

The Pioneer 100 Wellness Project involved quantitatively profiling 108 participants' molecular physiology over time, including genomes, gut microbiomes, blood metabolomes, blood proteomes, clinical chemistries, and data from wearable devices. Here, we present a longitudinal analysis focused specifically around the Pioneer 100 gut microbiomes. We distinguished a subpopulation of individuals with reduced gut diversity, elevated relative abundance of the genus Prevotella, and reduced levels of the genus Bacteroides We found that the relative abundances of Bacteroides and Prevotella were significantly correlated with certain serum metabolites, including omega-6 fatty acids. Primary dimensions in distance-based redundancy analysis of clinical chemistries explained 18.5% of the variance in bacterial community composition, and revealed a Bacteroides/Prevotella dichotomy aligned with inflammation and dietary markers. Finally, longitudinal analysis of gut microbiome dynamics within individuals showed that direct transitions between Bacteroides-dominated and Prevotella-dominated communities were rare, suggesting the presence of a barrier between these states. One implication is that interventions seeking to transition between Bacteroides- and Prevotella-dominated communities will need to identify permissible paths through ecological state-space that circumvent this apparent barrier.

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.

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.

Simultaneous vs. sequential treatment for smoking and weight management in tobacco quitlines: 6 and 12 month outcomes from a randomized trial.

BACKGROUND: Smoking cessation often results in weight gain which discourages many smokers from quitting and can increase health risks. Treatments to reduce cessation-related weight gain have been tested in highly controlled trials of in-person treatment, but have never been tested in a real-world setting, which has inhibited dissemination. METHODS: The Best Quit Study (BQS) is a replication and "real world" translation using telephone delivery of a prior in-person efficacy trial. DESIGN: randomized control trial in a quitline setting. Eligible smokers (n = 2540) were randomized to the standard 5-call quitline intervention or quitline plus simultaneous or sequential weight management. Regression analyses tested effectiveness of treatments on self-reported smoking abstinence and weight change at 6 and 12 months. RESULTS: Study enrollees were from 10 commercial employer groups and three state quitlines. Participants were between ages 18-72, 65.8% female, 68.2% white; 23.0% Medicaid-insured, and 76.3% overweight/obese. The follow-up response rate was lower in the simultaneous group than the control group at 6 months (p = 0.01). While a completers analysis of 30-day point prevalence abstinence detected no differences among groups at 6 or 12 months, multiply imputed abstinence showed quit rate differences at 6 months for:simultaneous (40.3%) vs. sequential (48.3%), p = 0.034 and simultaneous vs. control (44.9%), p = 0.043. At 12 months, multiply imputed abstinence, was significantly lower for the simultaneous group (40.7%) vs. control (46.0%), p < 0.05 and vs. sequential (46.3%), p < 0.05. Weight gain at 6 and 12 months was minimal and not different among treatment groups. The sequential group completed fewer total calls (3.75) vs. control (4.16) and vs. simultaneous group (3.83), p = 0.01, and fewer weight calls (0.94) than simultaneous (2.33), p < 0.0001. The number of calls completed predicted 30-day abstinence, p < 0.001, but not weight outcomes. DISCUSSION: This study offers a model for evaluating population-level public health interventions conducted in partnership with tobacco quitlines. CONCLUSIONS: Simultaneous (vs. sequential) delivery of phone/web weight management with cessation treatment in the quitline setting may adversely affect quit rate. Neither a simultaneous nor sequential approach to addressing weight produced any benefit on suppressing weight gain. This study highlights the need and the challenges of testing intensive interventions in real-world settings. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01867983 . Registered: May 30, 2013.

Comparative effectiveness of adding weight control simultaneously or sequentially to smoking cessation quitlines: study protocol of a randomized controlled trial.

BACKGROUND: Prevalence of multiple health risk behaviors is growing, and obesity and smoking are costly. Weight gain associated with quitting smoking is common and can interfere with quit success. Efficacy of adding weight management to tobacco cessation treatment has been tested with women in group sessions over an extended period of time, but has never been tested in real-world settings with men and women seeking help to quit. This paper describes the Best Quit study which tests the effectiveness of delivering tobacco and weight control interventions via existing quitline infrastructures. METHODS: Eligible and consenting smokers (n = 2550) who call a telephone quitline will be randomized to one of three groups; the standard quitline or standard quitline plus a weight management program added either simultaneously or sequentially to the tobacco program. The study aims to test: 1) the effectiveness of the combined intervention on smoking cessation and weight, 2) the cost-effectiveness of the combined intervention on cessation and weight and 3) theoretically pre-specified mediators of treatment effects on cessation: reduced weight concerns, increased outcome expectancies about quitting and improved self-efficacy about quitting without weight gain. Baseline, 6 month and 12 month data will be analyzed using multivariate statistical analyses and groups will be compared on treatment adherence, quit rates and change in weight among abstinent participants. To determine if the association between group assignment and primary outcomes (30-day abstinence and change in weight at 6 months) is moderated by pre-determined baseline and process measures, interaction terms will be included in the regression models and their significance assessed. DISCUSSION: This study will generate information to inform whether adding weight management to a tobacco cessation intervention delivered by phone, mail and web for smokers seeking help to quit will help or harm quit rates and whether a simultaneous or sequential approach is better at increasing abstinence and reducing weight gain post quit. If proven effective, the combined intervention could be disseminated across the U.S. through quitlines and could encourage additional smokers who have not sought cessation treatment for fear of gaining weight to make quit attempts. TRIAL REGISTRATION: Clinicaltrials.gov NCT01867983 . Registered: May 30, 2013.

Integrating big data and actionable health coaching to optimize wellness.

The Hundred Person Wellness Project (HPWP) is a 10-month pilot study of 100 'well' individuals where integrated data from whole-genome sequencing, gut microbiome, clinical laboratory tests and quantified self measures from each individual are used to provide actionable results for health coaching with the goal of optimizing wellness and minimizing disease. In a commentary in BMC Medicine, Diamandis argues that HPWP and similar projects will likely result in 'unnecessary and potential harmful over-testing'. We argue that this new approach will ultimately lead to lower costs, better healthcare, innovation and economic growth. The central points of the HPWP are: 1) it is focused on optimizing wellness through longitudinal data collection, integration and mining of individual data clouds, enabling development of predictive models of wellness and disease that will reveal actionable possibilities; and 2) by extending this study to 100,000 well people, we will establish multiparameter, quantifiable wellness metrics and identify markers for wellness to early disease transitions for most common diseases, which will ultimately allow earlier disease intervention, eventually transitioning the individual early on from a disease back to a wellness trajectory.

A Remotely Coached Multimodal Lifestyle Intervention for Alzheimer's Disease Ameliorates Functional and Cognitive Outcomes.

BACKGROUND: Comprehensive treatment of Alzheimer's disease and related dementias (ADRD) requires not only pharmacologic treatment but also management of existing medical conditions and lifestyle modifications including diet, cognitive training, and exercise. Personalized, multimodal therapies are needed to best prevent and treat Alzheimer's disease (AD). OBJECTIVE: The Coaching for Cognition in Alzheimer's (COCOA) trial was a prospective randomized controlled trial to test the hypothesis that a remotely coached multimodal lifestyle intervention would improve early-stage AD. METHODS: Participants with early-stage AD were randomized into two arms. Arm 1 (N = 24) received standard of care. Arm 2 (N = 31) additionally received telephonic personalized coaching for multiple lifestyle interventions. The primary outcome was a test of the hypothesis that the Memory Performance Index (MPI) change over time would be better in the intervention arm than in the control arm. The Functional Assessment Staging Test was assessed for a secondary outcome. COCOA collected psychometric, clinical, lifestyle, genomic, proteomic, metabolomic, and microbiome data at multiple timepoints (dynamic dense data) across two years for each participant. RESULTS: The intervention arm ameliorated 2.1 [1.0] MPI points (mean [SD], p = 0.016) compared to the control over the two-year intervention. No important adverse events or side effects were observed. CONCLUSION: Multimodal lifestyle interventions are effective for ameliorating cognitive decline and have a larger effect size than pharmacological interventions. Dietary changes and exercise are likely to be beneficial components of multimodal interventions in many individuals. Remote coaching is an effective intervention for early stage ADRD. Remote interventions were effective during the COVID pandemic.

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.

Psychological effects of Yi Ren Medical Qigong and progressive resistance training in adults with type 2 diabetes mellitus: a randomized controlled pilot study.

BACKGROUND: Previous studies suggest that qigong therapy has physiological benefits for adults with type 2 diabetes; however, information about the psychological benefits of qigong therapy in this population is limited. OBJECTIVE: The objective of this research project was to identify psychological responses to qigong vs control interventions in adults with type 2 diabetes. DESIGN: The research team designed a randomized, controlled, three-arm clinical trial comparing 12 weeks of Yi Ren Medical Qigong (YRMQ), progressive resistance training (PRT), and standard care. SETTING: The study was performed at Bastyr University Research Institute, Kenmore, Washington. PARTICIPANTS: Participants were 13 men and 19 women (N=32) with diagnosed type 2 diabetes, a mean age of 56.3 ± 8.1 (standard deviation) years, glycated hemoglobin > 7.5%, and fasting blood glucose > 7 mmol/dL (126 mg/dL). INTERVENTION: For 12 weeks, participants in the YRMQ and PRT group attended a 1-hour weekly group session that a certified instructor led and were instructed to practice at least twice a week for 30 minutes. PRIMARY OUTCOME MEASURES: The research team used the Perceived Stress Scale and the Beck Depression Inventory scores to analyze the data. RESULTS: YRMQ decreased perceived-stress scores by 29.3% (P < .05) and depression scores by 14.3% (not significant [NS]). The active control group, PRT, also decreased stress scores by 18.6% (NS) and decreased depression scores by 50% (P < .03). Stress and depression measures remained unchanged in the standard care group. CONCLUSION: YRMQ and PRT may be beneficial in reducing perceived stress and improving depression in patients with type 2 diabetes, although verification of the clinical significance of these findings requires a longer study with a larger sample size.

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.

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.

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.

The geometry of clinical labs and wellness states from deeply phenotyped humans.

Longitudinal multi-omics measurements are highly valuable in studying heterogeneity in health and disease phenotypes. For thousands of people, we have collected longitudinal multi-omics data. To analyze, interpret and visualize this extremely high-dimensional data, we use the Pareto Task Inference (ParTI) method. We find that the clinical labs data fall within a tetrahedron. We then use all other data types to characterize the four archetypes. We find that the tetrahedron comprises three wellness states, defining a wellness triangular plane, and one aberrant health state that captures aspects of commonality in movement away from wellness. We reveal the tradeoffs that shape the data and their hierarchy, and use longitudinal data to observe individual trajectories. We then demonstrate how the movement on the tetrahedron can be used for detecting unexpected trajectories, which might indicate transitions from health to disease and reveal abnormal conditions, even when all individual blood measurements are in the norm.

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.