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首页> 外文期刊>Journal of medical Internet research >Comparison of Nutrigenomics Technology Interface Tools for Consumers and Health Professionals: A Sequential Explanatory Mixed Methods Investigation
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Comparison of Nutrigenomics Technology Interface Tools for Consumers and Health Professionals: A Sequential Explanatory Mixed Methods Investigation

机译:消费者和卫生专业人员的营养基因组学技术接口工具比较:序贯解释性混合方法调查

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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, F _(1,53)=11.68; P =.001; eta-squared [η2]=0.183) and saturated fat (MD=?1.7%; λ=0.816; F _(1,53)=11.71; P =.001; η2=0.18) as well as HRQoL scores (MD=8.1 points; λ=0.914; F _(1,53)=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; F _(1,53)=9.47; P =.003; η2=0.15) and HEI-C scores (λ=0.660; F _(1,53)=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; F _(1,51)=11.36; P =.001; η2=0.19) and saturated fat (MD=?1.3%; λ=0.822; F _(1,51)=10.86; P =.002; η2=0.18) intakes. Time-by-group interactions were found for sodium (λ=0.844; F _(3,51)=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
机译:背景营养基因组学通过结合生命阶段,当前健康状况和基因组信息来定制个人的饮食计划,从而构成个性化营养的基础。基于营养的多基因测试组中包含的一些常见基因包括CYP1A2(咖啡因分解率),MTHFR(叶酸使用量),NOS3(与omega-3脂肪摄入量有关的甘油三酸酯水平升高的风险)和ACE(血压)反应与钠摄入量有关)。基于基因测试的个性化营养的复杂性为其实施提供了障碍。目的本研究旨在比较基于基因测试的自我驱动方法进行的营养教育与从业人员协助的综合方法,以帮助开发针对个性化营养实践的改进界面工具。方法对55名健康成年人(35至55岁)进行了一项顺序性,解释性混合方法研究,其中包括(1)一项为期9周的随机对照试验,其中参与者随机分配以接受基于营养的标准基因测试报告(对照; n = 19)或从业人员协助的个性化营养干预(干预; n = 36);以及(2)焦点小组访谈数据的解释性主题分析。结果指标包括饮食质量得分的差异(加拿大健康饮食指数[HEI-C];总脂肪,饱和脂肪和糖中卡路里的比例[%];ω3脂肪酸摄入量[克];钠摄入量[毫克] ]);以及与健康相关的生活质量(HRQoL)量表得分。结果在完成研究的55名参与者(55/58名,参与率95%)中,大多数年龄在40至51岁之间(n = 37,67%),女性(n = 41,75%),并且获得了高家庭收入(n = 32,58%)。与基线测量值相比,发现总脂肪中卡路里百分比的组差异(平均值差异[MD] =?5.1%; Wilks lambda(λ)= 0.817,F _(1,53)= 11.68; P = .001) ;η平方[η2] = 0.183)和饱和脂肪(MD =?1.7%;λ= 0.816; F _(1,53)= 11.71; P = .001;η2= 0.18)以及HRQoL得分(MD与干预后第9周的测量值相比,= 8.1分;λ= 0.914; F_(1,53)= 4.92; P = .03;η2= 0.086)。发现钠摄入量(λ= 0.846; F _(1,53)= 9.47; P = .003;η2= 0.15)和HEI-C评分(λ= 0.660; F _( 1,53)= 27.43; P <.001;η2= 0.35)。通过分组分配对表型和基因型信息进行分析,发现总脂肪(MD =?5%;λ= 0.815; F _(1,51)= 11.36; P = .001;η2= 0.19)和饱和脂肪(MD = ≤1.3%;λ= 0.822; F _(1,51)= 10.86; P = .002;η2= 0.18)。发现钠的时间-基团相互作用(λ= 0.844; F _(3,51)= 3.09; P = .04;η2= 0.16);和n = 1。事后分析显示干预组的干预前后差异(干预前平均3611 mg,95%CI 3039-4182;干预后平均2135 mg,95%CI 1564-2705)并且没有基因风险变异(干预前平均3722 mg,95%CI 2949-4496;干预后平均2071 mg,95%CI 1299-2843)。与饮食参考摄入量相关的前后差异显示,在可接受的脂肪大量营养素分布范围内,干预参与者的比例有所增加(前后平均差异= 41.2%; P = .02)。文本数据分析揭示了三类反馈:(1)将营养相关基因测试信息转化为作用; (2)促进饮食行为的改变,尤其是对于大量营养素和钠的摄取; (3)未来个性化营养实践的指令。结论尽管两组均观察到改善,但健康成年人似乎从从业者主导的个性化营养干预中获得了更多的健康益处。需要做进一步的工作以更好地促进微量营养素摄入量的积极变化。试验注册ClinicalTrials.gov NCT03310814; http://clinicaltrials.gov/ct2/show/NCT03310814国际注册报告标识符(IRRID)RR2-10.2196 / resprot.9846

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