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Large variability exists in people’s responses to foods. However, the efficacy of personalized dietary advice for health remains understudied. We compared a personalized dietary program (PDP) versus general advice (control) on cardiometabolic health using a randomized clinical trial. The PDP used food characteristics, individual postprandial glucose and triglyceride (TG) responses to foods, microbiomes and health history, to produce personalized food scores in an 18-week app-based program. The control group received standard care dietary advice (US Department of Agriculture Guidelines for Americans, 2020–2025) using online resources, check-ins, video lessons and a leaflet. Primary outcomes were serum low-density lipoprotein cholesterol and TG concentrations at baseline and at 18 weeks. Participants (n= 347), aged 41–70 years and generally representative of the average US population, were randomized to the PDP (n= 177) or control (n= 170). Intention-to-treat analysis (n= 347) between groups showed significant reduction in TGs (mean difference = −0.13 mmol l−1; log-transformed 95% confidence interval = −0.07 to −0.01, P= 0.016). Changes in low-density lipoprotein cholesterol were not significant. There were improvements in secondary outcomes, including body weight, waist circumference, HbA1c, diet quality and microbiome (beta-diversity) (P< 0.05), particularly in highly adherent PDP participants. However, blood pressure, insulin, glucose, C-peptide, apolipoprotein A1 and B, and postprandial TGs did not differ between groups. No serious intervention-related adverse events were reported. Following a personalized diet led to some improvements in cardiometabolic health compared to standard dietary advice. ClinicalTrials.gov registration: NCT05273268.

Genes, sex, age, diet, lifestyle, gut microbiome, and multiple other factors affect human metabolomic profiles. Understanding metabolomic variation is critical in human nutrition research as metabolites that are sensitive to change versus those that are more stable might be more informative for a particular study design. This study aims to identify stable metabolomic regions and determine the genetic and environmental contributions to stability. Using a classic twin design, 1H nuclear magnetic resonance (NMR) urinary metabolomic profiles were measured in 128 twins at baseline, 1 month, and 2 months. Multivariate mixed models identified stable urinary metabolites with intraclass correlation coefficients ≥0.51. Longitudinal twin modeling measured the contribution of genetic and environmental influences to variation in the stable urinary NMR metabolome, comprising stable metabolites. The conservation of an individual’s stable urinary NMR metabolome over time was assessed by calculating conservation indices. In this study, 20% of the urinary NMR metabolome is stable over 2 months (intraclass correlation (ICC) 0.51–0.65). Common genetic and shared environmental factors contributed to variance in the stable urinary NMR metabolome over time. Using the stable metabolome, 91% of individuals had good metabolomic conservation indices ≥0.70. To conclude, this research identifies 20% of the urinary NMR metabolome as stable, improves our knowledge of the sources of metabolomic variation over time, and demonstrates the conservation of an individual’s urinary NMR metabolome.

Novel metabolomic profiling techniques combined with traditional biomarkers provide knowledge of mechanisms underlying metabolic health. Twin studies describe the impact of genes and environment on variation in traits. This study aims to identify relationships between traditional markers of metabolic health and the plasma metabolomic profile using a twin modeling approach and determine whether covariation is caused by shared genetic and environmental factors. Using a classic twin design, this study examined covariation between anthropometric, clinical chemistry, and metabolomic profiles. Cholesky decomposition modeling was used to determine the genetic and environmental path coefficients through successive anthropometric and clinical chemistry traits onto metabolomic derived metabolites. This study shows that WC, TAG, and a metabolomic signature composed of 7 metabolites are inter-related, and that covariation can be attributed to common genetic, shared and unique environmental factors as well as unique environmental factors specific to the metabolite. This quantitative modeling connecting the traditional anthropometry and clinical chemistry traits with the more recent and potentially more sensitive metabolomic profile approach may provide further insight on the pleiotropic genes or modifiable environmental factors influencing variation in metabolic health.