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.

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 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.

The effect of tobacco cessation on weight gain, obesity, and diabetes risk.

OBJECTIVE: Most smokers gain weight after quitting, and some develop new onset obesity and type 2 diabetes. The purpose of this paper is to synthesize the current science investigating the consequences of tobacco cessation on body weight and diabetes, as well as intervention strategies that minimize or prevent weight gain while still allowing for successful tobacco cessation. METHODS: Systematic reviews and relevant studies that were published since prior reviews were selected. RESULTS: Smoking cessation can cause excessive weight gain in some individuals and can be associated with clinically significant outcomes such as diabetes or obesity onset. Interventions that combine smoking cessation and weight control can be effective for improving cessation and minimizing weight gain but need to be tested in specific populations. CONCLUSIONS: Despite the health benefits of quitting tobacco, post-cessation weight gain and new onset obesity and diabetes are a significant concern. Promising interventions may need to be more widely applied to reduce the consequences of both obesity and tobacco use.

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.

Outcomes and utilization of a low intensity workplace weight loss program.

Obesity is related to high health care costs and lost productivity in the workplace. Employers are increasingly sponsoring weight loss and wellness programs to ameliorate these costs. We evaluated weight loss outcomes, treatment utilization, and health behavior change in a low intensity phone- and web-based, employer-sponsored weight loss program. The intervention included three proactive counseling phone calls with a registered dietician and a behavioral health coach as well as a comprehensive website. At six months, one third of those who responded to the follow-up survey had lost a clinically significant amount of weight (≥5% of body weight). Clinically significant weight loss was predicted by the use of both the counseling calls and the website. When examining specific features of the web site, the weight tracking tool was the most predictive of weight loss. Health behavior changes such as eating more fruits and vegetables, increasing physical activity, and reducing stress were all predictive of clinically significant weight loss. Although limited by the low follow-up rate, this evaluation suggests that even low intensity weight loss programs can lead to clinical weight loss for a significant number of participants.

Dietary intake in the diabetes prevention program cohort: baseline and 1-year post randomization.

PURPOSE: To describe usual dietary intake assessment at baseline and 1-year post-randomization in the ethnically diverse Diabetes Prevention Program cohort. METHODS: Participants were randomized to Lifestyle Modification, Metformin, or Placebo. Usual diet was assessed by a modified, previously validated food frequency interview. RESULTS: Complete data were available for 2934 subjects (90.7% of those randomized). Baseline median estimated energy intake was 7676 kJ/d (1828 kcal/d) and 8585 kJ/d (2044 kcal/d) for women and men, respectively. The median percent of energy from fat ranged from 30.6% for Asian American men to 37.5% for American Indian men and women. After 1 year among the Lifestyle group, the median change in total energy and percent energy from fat was -1897 kJ/d (-452 kcal/d) and -6.6%, respectively. For the Metformin and Placebo groups, change in median total energy was -1235 kJ/d (-294 kcal/d) and-1051 kJ/d (-250 kcal/d), respectively, and change in median percent energy from fat was -0.8% and-0.8%, respectively (p < 0.001 for differences between groups, adjusted for gender and ethnicity). CONCLUSIONS: One-year post-randomization, significant differences in dietary intake were observed in the Lifestyle compared with the Metformin or Placebo group, and these were consistent with the general intent of the DPP lifestyle modification intervention.