The study of circulating biomarkers and their association with disease outcomes has become progressively complex due to advances in the measurement of these biomarkers through multiplex technologies. The Least Absolute Shrinkage and Selection Operator (LASSO) is a data analysis method that may be utilized for biomarker selection in these high dimensional data. However, it is unclear which LASSO-type method is preferable when considering data scenarios that may be present in serum biomarker research, such as high correlation between biomarkers, weak associations with the outcome, and sparse number of true signals. The goal of this study was to compare the LASSO to five LASSO-type methods given these scenarios. A simulation study was performed to compare the LASSO, Adaptive LASSO, Elastic Net, Iterated LASSO, Bootstrap-Enhanced LASSO, and Weighted Fusion for the binary logistic regression model. The simulation study was designed to reflect the data structure of the population-based Tucson Epidemiological Study of Airway Obstructive Disease (TESAOD), specifically the sample size (N = 1000 for total population, 500 for sub-analyses), correlation of biomarkers (0.20, 0.50, 0.80), prevalence of overweight (40%) and obese (12%) outcomes, and the association of outcomes with standardized serum biomarker concentrations (log-odds ratio = 0.05–1.75). Each LASSO-type method was then applied to the TESAOD data of 306 overweight, 66 obese, and 463 normal-weight subjects with a panel of 86 serum biomarkers. Based on the simulation study, no method had an overall superior performance. The Weighted Fusion correctly identified more true signals, but incorrectly included more noise variables. The LASSO and Elastic Net correctly identified many true signals and excluded more noise variables. In the application study, biomarkers of overweight and obesity selected by all methods were Adiponectin, Apolipoprotein H, Calcitonin, CD14, Complement 3, C-reactive protein, Ferritin, Growth Hormone, Immunoglobulin M, Interleukin-18, Leptin, Monocyte Chemotactic Protein-1, Myoglobin, Sex Hormone Binding Globulin, Surfactant Protein D, and YKL-40. For the data scenarios examined, choice of optimal LASSO-type method was data structure dependent and should be guided by the research objective. The LASSO-type methods identified biomarkers that have known associations with obesity and obesity related conditions.

Two-thirds of U.S. adult women are overweight or obese. High body mass index (BMI) and adult weight gain are risk factors for a number of chronic diseases, including postmenopausal breast cancer. The higher postmenopausal breast cancer risk in women with elevated BMI is likely to be attributable to related metabolic disturbances including altered circulating sex steroid hormones and adipokines, elevated pro-inflammatory cytokines, and insulin resistance. Metformin is a widely used antidiabetic drug that has demonstrated favorable effects on metabolic disturbances and as such may lead to lower breast cancer risk in obese women. Further, the anti-proliferative effects of metformin suggest it may decrease breast density, an accepted biomarker of breast cancer risk. This is a Phase II randomized, double-blind, placebo-controlled trial of metformin in overweight/obese premenopausal women who have elements of metabolic syndrome. Eligible participants will be randomized to receive metformin 850 mg BID (n = 75) or placebo (n = 75) for 12 months. The primary endpoint is change in breast density, based on magnetic resonance imaging (MRI) acquired fat-water features. Secondary outcomes include changes in serum insulin levels, serum insulin-like growth factor (IGF)-1 to insulin-like growth factor binding protein (IGFBP)-3 ratio, serum IGF-2 levels, serum testosterone levels, serum leptin to adiponectin ratio, body weight, and waist circumference. Exploratory outcomes include changes in metabolomic profiles in plasma and nipple aspirate fluid. Changes in tissue architecture as well as cellular and molecular targets in breast tissue collected in a subgroup of participants will also be explored. The study will evaluate whether metformin can result in favorable changes in breast density, select proteins and hormones, products of body metabolism, and body weight and composition. The study should help determine the potential breast cancer preventive activity of metformin in a growing population at risk for multiple diseases. ClinicalTrials.gov Identifier: NCT02028221 . Registered on January 2, 2014. Grant #: 1R01CA172444-01A1 awarded on Sept 11, 2013.

It is well established that behavioral lifestyle interventions resulting in modest weight reduction in adults can prevent or delay type 2 diabetes mellitus; however in children, successful weight management interventions are rarely found outside of controlled clinical settings. The lack of effective community-based programs is a barrier to reducing obesity prevalence and diabetes risk in children. The objective of our study is to develop and test a group-randomized family-centered community-based type 2 diabetes prevention intervention targeting at-risk children, 9- to 12-years-old. Using participatory methods, the adult-focused YMCA Diabetes Prevention Program was adapted for families, creating a novel lifestyle behavior change program focused on healthy eating, physical activity, and a supportive home environment. The program will be tested in sixty 9- to 12-year-old children at risk of diabetes and sixty parents over 12 consecutive weeks with two intervention formats randomized by location: a face-to-face instructor-led program, or a hybrid program with alternating face-to-face and mobile technology-delivered content. Anthropometric, behavioral, psychosocial and physiological outcomes will be assessed at baseline, post-intervention (12 weeks), and follow-up (24 weeks). Secondary outcomes are participant acceptability, feasibility, and adherence. The RE-AIM framework (reach, efficacy, adoption, implementation, and maintenance) will guide intervention implementation and evaluation. Changes at 12 weeks will be assessed using a paired t-test combining both delivery formats. Exploratory models using linear regression analysis will estimate the magnitude of the difference between the face-to-face and hybrid format. The sample size of 60 children, informed by a previous YMCA intervention in which −4.3 % change in overweight (SE = 1.1) was observed over 6 months, will give us 80 % power to detect an effect size of this magnitude, assuming a one-sided test at alpha = 0.05. The proposed study capitalizes on a partnership with the YMCA, a popular and widespread community organization, and uses mobile technologies to extend program reach while potentially reducing burden associated with weekly attendance. The long-term goal is to create a scalable, replicable, and sustainable pediatric “diabesity” prevention program that overcomes existing barriers to the translation of efficacious interventions into effective community programs. ClinicalTrials.gov, NCT02421198 on April 15, 2015