ScopeMicronutrients are in small amounts in foods, act in concert, and require variable amounts of time to see changes in health and risk for disease. These first principles are incorporated into an intervention study designed to develop new experimental strategies for setting target recommendations for food bioactives for populations and individuals.Methods and resultsA 6‐week multivitamin/mineral intervention is conducted in 9–13 year olds. Participants (136) are (i) their own control (n‐of‐1); (ii) monitored for compliance; (iii) measured for 36 circulating vitamin forms, 30 clinical, anthropometric, and food intake parameters at baseline, post intervention, and following a 6‐week washout; and (iv) had their ancestry accounted for as modifier of vitamin baseline or response. The same intervention is repeated the following year (135 participants). Most vitamins respond positively and many clinical parameters change in directions consistent with improved metabolic health to the intervention. Baseline levels of any metabolite predict its own response to the intervention. Elastic net penalized regression models are identified, and significantly predict response to intervention on the basis of multiple vitamin/clinical baseline measures.ConclusionsThe study design, computational methods, and results are a step toward developing recommendations for optimizing vitamin levels and health parameters for individuals.

Background Increased protein intake has been suggested to improve gains in muscle mass and strength in adults. Furthermore, the timing of protein intake has been discussed as a margin of opportunity for improved prevention measures. Objective This systematic review investigated the effect of protein supplementation on body composition and muscle function (strength and synthesis) in healthy adults, with an emphasis on the timing of protein intake. Methods Randomized controlled trials were identified using PubMed, Web of Science, CINAHL, and Embase, up to March 2019. For meta-analyses, data on lean body mass (LBM), handgrip strength, and leg press strength were pooled by age group (mean age 18–55 or >55 y) and timing of protein intake. The quality of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations approach. Results Data from 65 studies with 2907 participants (1514 men and 1380 women, 13 unknown sex) were included in the review. Twenty-six, 8, and 24 studies were used for meta-analysis on LBM, handgrip strength, and leg press strength, respectively. The protein supplementation was effective in improving (mean difference; 95% CI) LBM in adults (0.62 kg; 0.36, 0.88) and older adults (0.46 kg; 0.23, 0.70), but not handgrip strength (older adults: 0.26 kg; −0.51, 1.04) and leg press strength (adults: 5.80 kg; −0.33, 11.93; older adults: 1.97 kg; −2.78, 6.72). Sensitivity analyses removing studies without exercise training had no impact on the outcomes. Data regarding muscle synthesis were scarce and inconclusive. Subgroup analyses showed no beneficial effect of a specific timing of protein intake on LBM, handgrip strength, and leg press strength. Conclusion Overall, the results support the positive impact of protein supplementation on LBM of adults and older adults, independently of intake timing. Effects on muscle strength and synthesis are less clear and need further investigation. This systematic review was registered on PROSPERO as CRD42019126742.

Context Dementia is the fifth leading cause of death in the world. Animal studies indicate that in addition to the aging process, intestinal microbiota may play an important role in the neurodegeneration process through the modulation of the gut-brain axis. Objective A systematic review and meta-analysis was conducted to determine the effectiveness of probiotic and synbiotic supplementation on the cognitive function of individuals with dementia. Data Sources MEDLINE, BVS, SciELO, CENTRAL, Embase, and grey literature were searched from their inception to January 2019. Study Selection We included data from randomized clinical trials (RCTs) that addressed dementias and assessed the following outcomes: cognitive function; inflammatory, oxidative stress, and metabolic markers; nutritional status; and intestinal microbiota composition. Data Extraction Data searches, article selection, data extraction, and risk-of-bias assessments were performed according to the Cochrane guidelines. Data were pooled by inverse-variance random-effects meta-analyses. GRADE (Grading of Recommendations Assessment, Development, and Evaluations) was used to assess the quality of evidence. Results Data from 3 RCTs involving 161 individuals with Alzheimer’s disease receiving Lactobacillus and Bifidobacterium strains showed no beneficial effect of probiotic supplementation on cognitive function (standardized mean difference, 0.56; 95%CI: −0.06 to 1.18), with very low certainty of evidence. However, probiotic supplementation improved plasma triglycerides, very-low-density lipoprotein cholesterol, insulin resistance, and plasma malondialdehyde. No RCTs included synbiotic supplementation or assessed microbiota composition. Conclusion Current evidence regarding the use of probiotics and synbiotics for individuals with dementia is insufficient to support their clinical application. Systematic Review Registration PROSPERO registration no: CRD42018116148.

Background Targeted nutrition is defined as dietary advice tailored at a group level. Groups known as metabotypes can be identified based on individual metabolic profiles. Metabotypes have been associated with differential responses to diet, which support their use to deliver dietary advice. We aimed to optimise a metabotype approach to deliver targeted dietary advice by encompassing more specific recommendations on nutrient and food intakes and dietary behaviours. Methods Participants (n = 207) were classified into three metabotypes based on four biomarkers (triacylglycerol, total cholesterol, HDL-cholesterol and glucose) and using a k-means cluster model. Participants in metabotype-1 had the highest average HDL-cholesterol, in metabotype-2 the lowest triacylglycerol and total cholesterol, and in metabotype-3 the highest triacylglycerol and total cholesterol. For each participant, dietary advice was assigned using decision trees for both metabotype (group level) and personalised (individual level) approaches. Agreement between methods was compared at the message level and the metabotype approach was optimised to incorporate messages exclusively assigned by the personalised approach and current dietary guidelines. The optimised metabotype approach was subsequently compared with individualised advice manually compiled. Results The metabotype approach comprised advice for improving the intake of saturated fat (69% of participants), fibre (66%) and salt (18%), while the personalised approach assigned advice for improving the intake of folate (63%), fibre (63%), saturated fat (61%), calcium (34%), monounsaturated fat (24%) and salt (14%). Following the optimisation of the metabotype approach, the most frequent messages assigned to address intake of key nutrients were to increase the intake of fruit and vegetables, beans and pulses, dark green vegetables, and oily fish, to limit processed meats and high-fat food products and to choose fibre-rich carbohydrates, low-fat dairy and lean meats (60–69%). An average agreement of 82.8% between metabotype and manual approaches was revealed, with excellent agreements in metabotype-1 (94.4%) and metabotype-3 (92.3%). Conclusions The optimised metabotype approach proved capable of delivering targeted dietary advice for healthy adults, being highly comparable with individualised advice. The next step is to ascertain whether the optimised metabotype approach is effective in changing diet quality.

Personalised nutrition is at its simplest form the delivery of dietary advice at an individual level. Incorporating response to different diets has resulted in the concept of precision nutrition. Harnessing the metabolic phenotype to identify subgroups of individuals that respond differentially to dietary interventions is becoming a reality. More specifically, the classification of individuals in subgroups according to their metabolic profile is defined as metabotyping and this approach has been employed to successfully identify differential response to dietary interventions. Furthermore, the approach has been expanded to develop a framework for the delivery of targeted nutrition. The present review examines the application of the metabotype approach in nutrition research with a focus on developing personalised nutrition. Application of metabotyping in longitudinal studies demonstrates that metabotypes can be associated with cardiometabolic risk factors and diet-related diseases while application in interventions can identify metabotypes with differential responses. In general, there is strong evidence that metabolic phenotyping is a promising strategy to identify groups at risk and to potentially improve health promotion at a population level. Future work should verify if targeted nutrition can change behaviours and have an impact on health outcomes.