Precision Nutrition Assessment of Risk for Metabolic Syndrome in Active Duty Service Members

Abstract Objectives Precision nutrition leverages the specificity of molecular and phenotypic differences in personalizing diet and lifestyle interventions.Objective for this phase of the study: 1) examine the effectiveness of gene-based nutrition counseling on behavior change measured by weight, body mass index (BMI), blood glucose, lipids, 25-hydroxyvitamin (OH) D, %body fat (BF), waist circumference, and blood pressure. Methods In this prospective multisite RCT, a baseline genomic profile from 70 diet-responsive genes/80 variants, is augmented by biomarkers specific to metabolic syndrome (MetS) risk for each subject. Treatment group (TG) receives gene-based nutrition counseling for six weekly sessions; Control group (CG) receives evidence-based nutrition content in pamphlets directed at preventing metabolic syndrome. A digital app provides real-time health data capture with continuous feedback and is validated by in-person interviews. Primary outcome is weight loss at 12 weeks. Results Army NW cohort has enrolled 90 subjects to date; 49 are assigned to the TG. Sample demographics: males (70%), mean age 32 yrs, 58% married, 75% Caucasian, non-Hispanic, and 78% report some college education. In females, mean BMI 28.3, %BF 34.2, waist circumference 32.2 in; males mean BMI 30.1, %BF 29, waist circumference 40 in. For MetS components, 27/90 subjects show no abnormal components yet 41/90 have 2 or 3 alterations. Elevations noted as follows: fasting blood glucose in 47%, blood pressure in 38%, and waist circumference in 30%. High variant presence is noteworthy for genes with a role in obesity and hypertension. In 71% of subjects baseline 25(OH) D ≤ 30 ng/mL. Preliminary results for primary outcome of weight loss at 12 weeks reveal no change in TG (n = 21), an average loss of 5 lbs in CG (n = 21) within groups, and a significant difference between groups; TG 197.4 (39.3) vs CG 192.6 (40.3), p < .001. Conclusions Digital health integration, along with genomic data and family history, can reveal early signals of risk in a young, generally healthy, military population. Health promotion efforts must drive behavior change at both the individual and population level. Funding Sources The TriService Nursing Research Program