Abstract
ObjectivesPrecision 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. MethodsIn 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. ResultsArmy 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. ConclusionsDigital 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 SourcesThe TriService Nursing Research Program
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