ABSTRACT
Background: Food energy under-reporting is differentially distributed among populations. Currently, little is known about how mental health state may affect energy-adjusted nutrient intakes among food energy under-reporters. Methods: Stratified analysis of energy-adjusted nutrient intake by mental health (poor vs. good) and age/sex was conducted using data from Canadian Community Health Survey (CCHS) respondents (14-70 years; n = 8,233) who were deemed as under-reporters based on Goldberg's cutoffs. Results: Most were experiencing good mental health (95.2%). Among those reporting poor mental health, significantly lower energy-adjusted nutrient intakes tended to be found for fiber, protein, vitamins A, B2, B3, B6, B9, B12, C, and D, and calcium, potassium, and zinc (probability measures (p) < 0.05). For women (51-70 years), all micronutrient intakes, except iron, were significantly lower among those reporting poor mental health (p < 0.05). For men (31-50 years), B vitamin and most mineral intakes, except sodium, were significantly lower among those reporting poor mental health (p < 0.05). Among women (31-50 years) who reported poor mental health, higher energy-adjusted intakes were reported for vitamin B9 and phosphorus (p < 0.05). Conclusions: Among food energy under-reporters, poor mental health tends to lower the report of specific energy-adjusted nutrient intakes that include ones critical for mental health. Future research is needed to discern if these differences may be attributed to deviations in the accurate reports of food intakes, measurement errors, or mental health states.
ABSTRACT
BACKGROUND: Nutrigenomics forms the basis of personalized nutrition by customizing an individual's dietary plan based on the integration of life stage, current health status, and genome information. Some common genes that are included in nutrition-based multigene test panels include CYP1A2 (rate of caffeine break down), MTHFR (folate usage), NOS3 (risk of elevated triglyceride levels related to omega-3 fat intake), and ACE (blood pressure response in related to sodium intake). The complexity of gene test-based personalized nutrition presents barriers to its implementation. OBJECTIVE: This study aimed to compare a self-driven approach to gene test-based nutrition education versus an integrated practitioner-facilitated method to help develop improved interface tools for personalized nutrition practice. METHODS: A sequential, explanatory mixed methods investigation of 55 healthy adults (35 to 55 years) was conducted that included (1) a 9-week randomized controlled trial where participants were randomized to receive a standard nutrition-based gene test report (control; n=19) or a practitioner-facilitated personalized nutrition intervention (intervention; n=36) and (2) an interpretative thematic analysis of focus group interview data. Outcome measures included differences in the diet quality score (Healthy Eating Index-Canadian [HEI-C]; proportion [%] of calories from total fat, saturated fat, and sugar; omega 3 fatty acid intake [grams]; sodium intake [milligrams]); as well as health-related quality of life (HRQoL) scale score. RESULTS: Of the 55 (55/58 enrolled, 95%) participants who completed the study, most were aged between 40 and 51 years (n=37, 67%), were female (n=41, 75%), and earned a high household income (n=32, 58%). Compared with baseline measures, group differences were found for the percentage of calories from total fat (mean difference [MD]=-5.1%; Wilks lambda (λ)=0.817, F1,53=11.68; P=.001; eta-squared [η²]=0.183) and saturated fat (MD=-1.7%; λ=0.816; F1,53=11.71; P=.001; η²=0.18) as well as HRQoL scores (MD=8.1 points; λ=0.914; F1,53=4.92; P=.03; η²=0.086) compared with week 9 postintervention measures. Interactions of time-by-group assignment were found for sodium intakes (λ=0.846; F1,53=9.47; P=.003; η²=0.15) and HEI-C scores (λ=0.660; F1,53=27.43; P<.001; η²=0.35). An analysis of phenotypic and genotypic information by group assignment found improved total fat (MD=-5%; λ=0.815; F1,51=11.36; P=.001; η²=0.19) and saturated fat (MD=-1.3%; λ=0.822; F1,51=10.86; P=.002; η²=0.18) intakes. Time-by-group interactions were found for sodium (λ=0.844; F3,51=3.09; P=.04; η²=0.16); a post hoc analysis showed pre/post differences for those in the intervention group that did (preintervention mean 3611 mg, 95% CI 3039-4182; postintervention mean 2135 mg, 95% CI 1564-2705) and did not have the gene risk variant (preintervention mean 3722 mg, 95% CI 2949-4496; postintervention mean 2071 mg, 95% CI 1299-2843). Pre- and postdifferences related to the Dietary Reference Intakes showed increases in the proportion of intervention participants within the acceptable macronutrient distribution ranges for fat (pre/post mean difference=41.2%; P=.02). Analysis of textual data revealed 3 categories of feedback: (1) translation of nutrition-related gene test information to action; (2) facilitation of eating behavior change, particularly for the macronutrients and sodium; and (3) directives for future personalized nutrition practice. CONCLUSIONS: Although improvements were observed in both groups, healthy adults appear to derive more health benefits from practitioner-led personalized nutrition interventions. Further work is needed to better facilitate positive changes in micronutrient intakes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03310814; http://clinicaltrials.gov/ct2/show/NCT03310814. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.9846.