UNASSIGNED: Recent studies suggest that gut microbiota composition, abundance and diversity can influence many chronic diseases such as type 2 diabetes. Modulating gut microbiota through targeted nutrition can provide beneficial effects leading to the concept of personalized nutrition for health improvement. In this prospective clinical trial, we evaluated the impact of a microbiome-based targeted personalized diet on hyperglycaemic and hyperlipidaemic individuals. Specifically, BugSpeaks®-a microbiome profile test that profiles microbiota using next generation sequencing and provides personalized nutritional recommendation based on the individual microbiota profile was evaluated.
UNASSIGNED: A total of 30 participants with type 2 diabetes and hyperlipidaemia were recruited for this study. The microbiome profile of the 15 participants (test arm) was evaluated using whole genome shotgun metagenomics and personalized nutritional recommendations based on their microbiota profile were provided. The remaining 15 participants (control arm) were provided with diabetic nutritional guidance for 3 months. Clinical and anthropometric parameters such as HbA1c, systolic/diastolic pressure, c-reactive protein levels and microbiota composition were measured and compared during the study.
UNASSIGNED: The test arm (microbiome-based nutrition) showed a statistically significant decrease in HbA1c level from 8.30 (95% confidence interval (CI), [7.74-8.85]) to 6.67 (95% CI [6.2-7.05]), p < 0.001 after 90 days. The test arm also showed a 5% decline in the systolic pressure whereas the control arm showed a 7% increase. Incidentally, a sub-cohort of the test arm of patients with >130 mm Hg systolic pressure showed a statistically significant decrease of systolic pressure by 14%. Interestingly, CRP level was also found to drop by 19.5%. Alpha diversity measures showed a significant increase in Shannon diversity measure (p < 0.05), after the microbiome-based personalized dietary intervention. The intervention led to a minimum two-fold (Log2 fold change increase in species like Phascolarctobacterium succinatutens, Bifidobacterium angulatum, and Levilactobacillus brevis which might have a beneficial role in the current context and a similar decrease in species like Alistipes finegoldii, and Sutterella faecalis which have been earlier shown to have some negative effects in the host. Overall, the study indicated a net positive impact of the microbiota based personalized dietary regime on the gut microbiome and correlated clinical parameters.

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Many physical, social, and psychological changes occur during aging that raise the risk of developing chronic diseases, frailty, and dependency. These changes adversely affect the gut microbiota, a phenomenon known as microbe-aging. Those microbiota alterations are, in turn, associated with the development of age-related diseases. The gut microbiota is highly responsive to lifestyle and dietary changes, displaying a flexibility that also provides anactionable tool by which healthy aging can be promoted. This review covers, firstly, the main lifestyle and socioeconomic factors that modify the gut microbiota composition and function during healthy or unhealthy aging and, secondly, the advances being made in defining and promoting healthy aging, including microbiome-informed artificial intelligence tools, personalized dietary patterns, and food probiotic systems.

UNASSIGNED: Appetitive traits are influenced by the interplay between genetic and environmental factors. This study aimed to explore the relationship between gene polymorphisms involved in the regulation of energy balance and food reward and appetitive traits in young Mexican subjects.
UNASSIGNED: This cross-sectional study involved 118 university freshman undergraduates who completed the Adult Eating Behaviour Questionnaire for Spanish speakers (AEBQ-Esp) to assess their appetitive traits. A real-time PCR system was employed to determine gene polymorphisms involved in energy balance (LEP rs7799039, MC4R rs17782313, FTO rs9939609, GHRL rs696217), and reward system (DRD2/ANKK1 Taq1A rs1800497 and COMT rs4680).
UNASSIGNED: The mean age of participants was 20.14 ± 3.95 years, 71.2% were women and their mean BMI was 23.52 ± 4.05 kg/m2. COMT Met allele carriers presented a significantly higher \"Emotional overeating\" mean score than Val allele carriers (2.63 ± 0.70 vs. 2.23 ± 0.70, p = 0.028). The MC4R CC + CT genotype correlated positively with \"Emotional overeating\" (Phi = 0.308, p = 0.01). The COMT MetMet+MetVal genotype correlated with higher \"Emotional overeating\" (r = 0.257, p = 0.028; Phi = 0.249, p = 0.033). The protective genotype FTO TT correlated positively with \"Emotional undereating\" (Phi = 0.298, p = 0.012). Carriers of the risk genotype MC4R CC + CT presented a higher risk of \"Emotional overeating\" than TT carriers (OR = 2.4, 95% CI 1.3-4.8, p = 0.034). Carriers of the risk genotype COMT MetMet+MetVal (OR = 3.4, 95% CI 1.1-10.3, p = 0.033), were associated with a higher risk of \"Emotional overeating\" than ValVal carriers. The protective FTO genotype TT was associated with \"Emotional undereating\" (OR = 1.8, 95% CI 1.1-9.1, p = 0.014).
UNASSIGNED: The study found a relationship between the protective genotypes of FTO TT and \"Emotional undereating\" and risk genotypes of COMT Met/Met+Met/Val and MC4R CC + CT with \"Emotional overeating.\" These genetic factors may increase weight gain by enhancing hedonic food consumption and reducing satiety control. Future studies should focus on replication studies in ethnically diverse young adults and life stages to explore the relationship between polymorphisms and appetitive traits and weight. This will help tailor personalized nutrigenetic strategies to counteract disordered eating patterns leading to obesity and associated co-morbidities.

BACKGROUND: We previously showed that dietary intervention effects on cardiometabolic health were driven by tissue-specific insulin resistance (IR) phenotype: individuals with predominant muscle IR (MIR) benefited more from a low-fat, high-protein, and high-fiber (LFHP) diet, whereas individuals with predominant liver insulin resistance (LIR) benefited more from a high-monounsaturated fatty acid (HMUFA) diet.
OBJECTIVE: To further characterize the effects of LFHP and HMUFA diets and their interaction with tissue-specific IR, we investigated dietary intervention effects on fasting and postprandial plasma metabolite profile.
METHODS: Adults with MIR or LIR (40-75 y, BMI 25-40 kg/m2) were randomly assigned to a 12-wk HMUFA or LFHP diet (n = 242). After the exclusion of statin use, 214 participants were included in this prespecified secondary analysis. Plasma samples were collected before (T = 0) and after (T = 30, 60, 120, and 240 min) a high-fat mixed meal for quantification of 247 metabolite measures using nuclear magnetic resonance spectroscopy.
RESULTS: A larger reduction in fasting VLDL-triacylglycerol (TAG) and VLDL particle size was observed in individuals with MIR following the LFHP diet and those with LIR following the HMUFA diet, although no longer statistically significant after false discovery rate (FDR) adjustment. No IR phenotype-by-diet interactions were found for postprandial plasma metabolites assessed as total area under the curve (tAUC). Irrespective of IR phenotype, the LFHP diet induced greater reductions in postprandial plasma tAUC of the larger VLDL particles and small HDL particles, and TAG content in most VLDL subclasses and the smaller LDL and HDL subclasses (for example, VLDL-TAG tAUC standardized mean change [95% CI] LFHP = -0.29 [-0.43, -0.16] compared with HMUFA = -0.04 [-0.16, 0.09]; FDR-adjusted P for diet × time = 0.041).
CONCLUSIONS: Diet effects on plasma metabolite profiles were more pronounced than phenotype-by-diet interactions. An LFHP diet may be more effective than an HMUFA diet for reducing cardiometabolic risk in individuals with tissue-specific IR, irrespective of IR phenotype. Am J Clin Nutr 20xx;x:xx. This trial was registered at the clinicaltrials.gov registration (https://clinicaltrials.gov/study/NCT03708419?term=NCT03708419&rank=1) as NCT03708419 and CCMO registration (https://www.toetsingonline.nl/to/ccmo_search.nsf/fABRpop?readform&unids=3969AABCD9BA27FEC12587F1001BCC65) as NL63768.068.17.

Glucose meal response information collected via Continuous Glucose Monitoring (CGM) is relevant to the assessment of individual metabolic status and the support of personalized diet prescriptions. However, the complexity of the data produced by CGM monitors pushes the limits of existing analytic methods. CGM data often exhibits substantial within-person variability and has a natural multilevel structure. This research is motivated by the analysis of CGM data from individuals without diabetes in the AEGIS study. The dataset includes detailed information on meal timing and nutrition for each individual over different days. The primary focus of this study is to examine CGM glucose responses following patients\' meals and explore the time-dependent associations with dietary and patient characteristics. Motivated by this problem, we propose a new analytical framework based on multilevel functional models, including a new functional mixed R-square coefficient. The use of these models illustrates 3 key points: (i) The importance of analyzing glucose responses across the entire functional domain when making diet recommendations; (ii) The differential metabolic responses between normoglycemic and prediabetic patients, particularly with regards to lipid intake; (iii) The importance of including random, person-level effects when modelling this scientific problem.

PURPOSE OF REVIEW: The prevalence of obesity continues to rise steadily. While obesity management typically relies on dietary and lifestyle modifications, individual responses to these interventions vary widely. Clinical guidelines for overweight and obesity stress the importance of personalized approaches to care. This review aims to underscore the role of precision nutrition in delivering tailored interventions for obesity management. RECENT FINDINGS: Recent technological strides have expanded our ability to detect obesity-related genetic polymorphisms, with machine learning algorithms proving pivotal in analyzing intricate genomic data. Machine learning algorithms can also predict postprandial glucose, triglyceride, and insulin levels, facilitating customized dietary interventions and ultimately leading to successful weight loss. Additionally, given that adherence to dietary recommendations is one of the key predictors of weight loss success, employing more objective methods for dietary assessment and monitoring can enhance sustained long-term compliance. Biomarkers of food intake hold promise for a more objective dietary assessment. Acknowledging the multifaceted nature of obesity, precision nutrition stands poised to transform obesity management by tailoring dietary interventions to individuals\' genetic backgrounds, gut microbiota, metabolic profiles, and behavioral patterns. However, there is insufficient evidence demonstrating the superiority of precision nutrition over traditional dietary recommendations. The integration of precision nutrition into routine clinical practice requires further validation through randomized controlled trials and the accumulation of a larger body of evidence to strengthen its foundation.

BACKGROUND: Prebiotic fibers are non-digestible substrates that modulate the gut microbiome by promoting expansion of microbes having the genetic and physiological potential to utilize those molecules. Although several prebiotic substrates have been consistently shown to provide health benefits in human clinical trials, responder and non-responder phenotypes are often reported. These observations had led to interest in identifying, a priori, prebiotic responders and non-responders as a basis for personalized nutrition. In this study, we conducted in vitro fecal enrichments and applied shotgun metagenomics and machine learning tools to identify microbial gene signatures from adult subjects that could be used to predict prebiotic responders and non-responders.
RESULTS: Using short chain fatty acids as a targeted response, we identified genetic features, consisting of carbohydrate active enzymes, transcription factors and sugar transporters, from metagenomic sequencing of in vitro fermentations for three prebiotic substrates: xylooligosacharides, fructooligosacharides, and inulin. A machine learning approach was then used to select substrate-specific gene signatures as predictive features. These features were found to be predictive for XOS responders with respect to SCFA production in an in vivo trial.
CONCLUSIONS: Our results confirm the bifidogenic effect of commonly used prebiotic substrates along with inter-individual microbial responses towards these substrates. We successfully trained classifiers for the prediction of prebiotic responders towards XOS and inulin with robust accuracy (≥ AUC 0.9) and demonstrated its utility in a human feeding trial. Overall, the findings from this study highlight the practical implementation of pre-intervention targeted profiling of individual microbiomes to stratify responders and non-responders.

This review aimed to synthesise existing literature on the efficacy of personalised or precision nutrition (PPN) interventions, including medical nutrition therapy (MNT), in improving outcomes related to glycaemic control (HbA1c, post-prandial glucose [PPG], and fasting blood glucose), anthropometry (weight, BMI, and waist circumference [WC]), blood lipids, blood pressure (BP), and dietary intake among adults with prediabetes or metabolic syndrome (MetS). Six databases were systematically searched (Scopus, Medline, Embase, CINAHL, PsycINFO, and Cochrane) for randomised controlled trials (RCTs) published from January 2000 to 16 April 2023. The Academy of Nutrition and Dietetics Quality Criteria were used to assess the risk of bias. Seven RCTs (n = 873), comprising five PPN and two MNT interventions, lasting 3-24 months were included. Consistent and significant improvements favouring PPN and MNT interventions were reported across studies that examined outcomes like HbA1c, PPG, and waist circumference. Results for other measures, including fasting blood glucose, HOMA-IR, blood lipids, BP, and diet, were inconsistent. Longer, more frequent interventions yielded greater improvements, especially for HbA1c and WC. However, more research in studies with larger sample sizes and standardised PPN definitions is needed. Future studies should also investigate combining MNT with contemporary PPN factors, including genetic, epigenetic, metabolomic, and metagenomic data.

Certain micronutrients exhibit immunomodulatory effects. However, no intervention has yet investigated the effect of individualized supplementation on the severity of upper respiratory tract infections (URIs). Therefore, we investigated whether a personalized supplementation moderates the incidence and severity of URI. Selenium, zinc, and vitamin D were measured in dried blood spots from 59 healthy participants. Accordingly, a personalized supplement was provided with or without the respective micronutrients. We used WURSS-21 questionnaires to assess the disease status. The blood values converged during the intervention and micronutrients no longer differed between treated and untreated volunteers at the end of the intervention period. The incidence and severity of the illness did not significantly differ between the groups. However, when analyzing the WURSS-21 scores by the intention to treat, the initially randomized treatment arm revealed a significantly higher score than the placebo arm. Upon acute administration, individualized combinations of selenium, zinc and vitamin D do not reduce the number, or contribute to a milder course of URIs. Therefore, supplementation in acute infectious situations seems questionable. Further studies must address the habitual diet in more detail, to better understand the impact of individual micronutrient status on the prevention of URI.