Effects of a personalized nutrition program on cardiometabolic health: a randomized controlled trial.

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 .

Snack quality and snack timing are associated with cardiometabolic blood markers: the ZOE PREDICT study.

BACKGROUND: Snacking is a common diet behaviour which accounts for a large proportion of daily energy intake, making it a key determinant of diet quality. However, the relationship between snacking frequency, quality and timing with cardiometabolic health remains unclear. DESIGN: Demography, diet, health (fasting and postprandial cardiometabolic blood and anthropometrics markers) and stool metagenomics data were assessed in the UK PREDICT 1 cohort (N = 1002) (NCT03479866). Snacks (foods or drinks consumed between main meals) were self-reported (weighed records) across 2-4 days. Average snacking frequency and quality [snack diet index (SDI)] were determined (N = 854 after exclusions). Associations between snacking frequency, quality and timing with cardiometabolic blood and anthropometric markers were assessed using regression models (adjusted for age, sex, BMI, education, physical activity level and main meal quality). RESULTS: Participants were aged (mean, SD) 46.1 ± 11.9 years, had a mean BMI of 25.6 ± 4.88 kg/m2 and were predominantly female (73%). 95% of participants were snackers (≥ 1 snack/day; n = 813); mean daily snack intake was 2.28 snacks/day (24 ± 16% of daily calories; 203 ± 170 kcal); and 44% of participants were discordant for meal and snack quality. In snackers, overall snacking frequency and quantity of snack energy were not associated with cardiometabolic risk markers. However, lower snack quality (SDI range 1-11) was associated with higher blood markers, including elevated fasting triglycerides (TG (mmol/L) ß; - 0.02, P = 0.02), postprandial TGs (6hiAUC (mmol/L.s); ß; - 400, P = 0.01), fasting insulin (mIU/L) (ß; - 0.15, P = 0.04), insulin resistance (HOMA-IR; ß; - 0.04, P = 0.04) and hunger (scale 0-100) (ß; - 0.52, P = 0.02) (P values non-significant after multiple testing adjustments). Late-evening snacking (≥ 9 pm; 31%) was associated with lower blood markers (HbA1c; 5.54 ± 0.42% vs 5.46 ± 0.28%, glucose 2hiAUC; 8212 ± 5559 vs 7321 ± 4928 mmol/L.s, P = 0.01 and TG 6hiAUC; 11,638 ± 8166 vs 9781 ± 6997 mmol/L.s, P = 0.01) compared to all other snacking times (HbA1c remained significant after multiple testing). CONCLUSION: Snack quality and timing of consumption are simple diet features which may be targeted to improve diet quality, with potential health benefits. CLINICAL TRIAL REGISTRY NUMBER AND WEBSITE: NCT03479866, https://clinicaltrials.gov/ct2/show/NCT03479866?term=NCT03479866&draw=2&rank=1.

Glycaemic variability, assessed with continuous glucose monitors, is associated with diet, lifestyle and health in people without diabetes.

Background: Continuous glucose monitors (CGMs) provide high-frequency information regarding daily glucose variation and are recognised as effective for improving glycaemic control in individuals living with diabetes. Despite increased use in individuals with non-diabetic blood glucose concentrations (euglycemia), their utility as a health tool in this population remains unclear. Objectives: To characterise variation in time in range (TIR) and glycaemic variability in large populations without diabetes or impaired glucose tolerance; describe associations between CGM-derived glycaemic metrics and metabolic and cardiometabolic health traits; identify key diet and lifestyle factors associated with TIR and glycaemic variability. Design: Glycaemic variability (coefficient of variation) and time spent in both the ADA secondary target range (TIRADA; 3.9-7.8 mmol/L) and a more stringent range (TIR3.9-5.6; 3.9-5.6 mmol/L) were calculated during free-living in PREDICT 1, PREDICT 2, and PREDICT 3 euglycaemic community-based volunteer cohorts. Associations between CGM derived glycaemic metrics, markers of cardiometabolic health, diet (food frequency questionnaire and logged diet records), diet-habits, and lifestyle were explored. Results: Data from N=4135 participants (Mean SD; Age: 47 12 y; Sex: 83% Female, BMI: 27 6 kg/m2). Median glycaemic variability was 14.8% (IQR 12.6-17.6%), median TIRADA was 95.8% (IQR 89.6-98.6%) and TIR3.9-5.6 was 75.0% (IQR 64.6-82.8%). Greater TIR3.9-5.6 was associated with lower HbA1c, ASCVD 10y risk and HOMA-IR (all p < 0.05). Lower glycaemic variability was associated with lower % energy derived from carbohydrate (rs: 0.17, p < 0.01), ultra-processed foods (NOVA 4, % EI; rs: 0.12, p = 0.01) and a longer overnight fasting duration (rs: -0.10, p = 0.01). Conclusions: A stringent TIR target provides sensitivity to detect changes in HOMA-IR, ASCVD 10 y risk and HbA1c that were not detected using ADA secondary targets. Associations among TIR, glycaemic variability, dietary intake (e.g. carbohydrate and protein) and habits (e.g. nocturnal fasting duration) highlight potential strategic targets to improve glycaemic metrics derived from continuous glucose monitors.

Exploring the relationship between social jetlag with gut microbial composition, diet and cardiometabolic health, in the ZOE PREDICT 1 cohort.

PURPOSE: In this study, we explore the relationship between social jetlag (SJL), a parameter of circadian misalignment, and gut microbial composition, diet and cardiometabolic health in the ZOE PREDICT 1 cohort (NCT03479866). METHODS: We assessed demographic, diet, cardiometabolic, stool metagenomics and postprandial metabolic measures (n = 1002). We used self-reported habitual sleep (n = 934) to calculate SJL (difference in mid-sleep time point of ≥ 1.5 h on week versus weekend days). We tested group differences (SJL vs no-SJL) in cardiometabolic markers and diet (ANCOVA) adjusting for sex, age, BMI, ethnicity, and socio-economic status. We performed comparisons of gut microbial composition using machine learning and association analyses on the species level genome bins present in at least 20% of the samples. RESULTS: The SJL group (16%, n = 145) had a greater proportion of males (39% vs 25%), shorter sleepers (average sleep < 7 h; 5% vs 3%), and were younger (38.4 ± 11.3y vs 46.8 ± 11.7y) compared to the no-SJL group. SJL was associated with a higher relative abundance of 9 gut bacteria and lower abundance of 8 gut bacteria (q < 0.2 and absolute Cohen's effect size > 0.2), in part mediated by diet. SJL was associated with unfavourable diet quality (less healthful Plant-based Diet Index), higher intakes of potatoes and sugar-sweetened beverages, and lower intakes of fruits, and nuts, and slightly higher markers of inflammation (GlycA and IL-6) compared with no-SJL (P < 0.05 adjusted for covariates); rendered non-significant after multiple testing adjustments. CONCLUSIONS: Novel associations between SJL and a more disadvantageous gut microbiome in a cohort of predominantly adequate sleepers highlight the potential implications of SJL for health.

Characterisation of Fasting and Postprandial NMR Metabolites: Insights from the ZOE PREDICT 1 Study.

BACKGROUND: Postprandial metabolomic profiles and their inter-individual variability are not well characterised. Here, we describe postprandial metabolite changes, their correlations with fasting values and their inter- and intra-individual variability, following a standardised meal in the ZOE PREDICT 1 cohort. METHODS: In the ZOE PREDICT 1 study (n = 1002 (NCT03479866)), 250 metabolites, mainly lipids, were measured by a Nightingale NMR panel in fasting and postprandial (4 and 6 h after a 3.7 MJ mixed nutrient meal, with a second 2.2 MJ mixed nutrient meal at 4 h) serum samples. For each metabolite, inter- and intra-individual variability over time was evaluated using linear mixed modelling and intraclass correlation coefficients (ICC) were calculated. RESULTS: Postprandially, 85% (of 250 metabolites) significantly changed from fasting at 6 h (47% increased, 53% decreased; Kruskal-Wallis), with 37 measures increasing by >25% and 14 increasing by >50%. The largest changes were observed in very large lipoprotein particles and ketone bodies. Seventy-one percent of circulating metabolites were strongly correlated (Spearman's rho >0.80) between fasting and postprandial timepoints, and 5% were weakly correlated (rho <0.50). The median ICC of the 250 metabolites was 0.91 (range 0.08-0.99). The lowest ICCs (ICC <0.40, 4% of measures) were found for glucose, pyruvate, ketone bodies (ß-hydroxybutyrate, acetoacetate, acetate) and lactate. CONCLUSIONS: In this large-scale postprandial metabolomic study, circulating metabolites were highly variable between individuals following sequential mixed meals. Findings suggest that a meal challenge may yield postprandial responses divergent from fasting measures, specifically for glycolysis, essential amino acid, ketone body and lipoprotein size metabolites.

Fasted Sprint Interval Training Results in Some Beneficial Skeletal Muscle Metabolic, but Similar Metabolomic and Performance Adaptations Compared With Carbohydrate-Fed Training in Recreationally Active Male.

Endurance training in fasted conditions (FAST) induces favorable skeletal muscle metabolic adaptations compared with carbohydrate feeding (CHO), manifesting in improved exercise performance over time. Sprint interval training (SIT) is a potent metabolic stimulus, however nutritional strategies to optimize adaptations to SIT are poorly characterized. Here we investigated the efficacy of FAST versus CHO SIT (4-6 × 30-s Wingate sprints interspersed with 4-min rest) on muscle metabolic, serum metabolome and exercise performance adaptations in a double-blind parallel group design in recreationally active males. Following acute SIT, we observed exercise-induced increases in pan-acetylation and several genes associated with mitochondrial biogenesis, fatty acid oxidation, and NAD+-biosynthesis, along with favorable regulation of PDK4 (p = .004), NAMPT (p = .0013), and NNMT (p = .001) in FAST. Following 3 weeks of SIT, NRF2 (p = .029) was favorably regulated in FAST, with augmented pan-acetylation in CHO but not FAST (p = .033). SIT induced increases in maximal citrate synthase activity were evident with no effect of nutrition, while 3-hydroxyacyl-CoA dehydrogenase activity did not change. Despite no difference in the overall serum metabolome, training-induced changes in C3:1 (p = .013) and C4:1 (p = .010) which increased in FAST, and C16:1 (p = .046) and glutamine (p = .021) which increased in CHO, were different between groups. Training-induced increases in anaerobic (p = .898) and aerobic power (p = .249) were not influenced by nutrition. These findings suggest some beneficial muscle metabolic adaptations are evident in FAST versus CHO SIT following acute exercise and 3 weeks of SIT. However, this stimulus did not manifest in differential exercise performance adaptations.

Menopause is associated with postprandial metabolism, metabolic health and lifestyle: The ZOE PREDICT study.

BACKGROUND: The menopause transition is associated with unfavourable alterations in health. However, postprandial metabolic changes and their mediating factors are poorly understood. METHODS: The PREDICT 1 UK cohort (n=1002; pre- n=366, peri- n=55, and post-menopausal females n=206) assessed phenotypic characteristics, anthropometric, diet and gut microbiome data, and fasting and postprandial (0-6 h) cardiometabolic blood measurements, including continuous glucose monitoring (CGM) data. Differences between menopausal groups were assessed in the cohort and in an age-matched subgroup, adjusting for age, BMI, menopausal hormone therapy (MHT) use, and smoking status. FINDINGS: Post-menopausal females had higher fasting blood measures (glucose, HbA1c and inflammation (GlycA), 6%, 5% and 4% respectively), sugar intakes (12%) and poorer sleep (12%) compared with pre-menopausal females (p<0.05 for all). Postprandial metabolic responses for glucose2hiauc and insulin2hiauc were higher (42% and 4% respectively) and CGM measures (glycaemic variability and time in range) were unfavourable post- versus pre-menopause (p<0.05 for all). In age-matched subgroups (n=150), postprandial glucose responses remained higher post-menopause (peak0-2h 4%). MHT was associated with favourable visceral fat, fasting (glucose and insulin) and postprandial (triglyceride6hiauc) measures. Mediation analysis showed that associations between menopause and metabolic health indicators (visceral fat, GlycA360mins and glycaemia (peak0-2h)) were in part mediated by diet and gut bacterial species. INTERPRETATION: Findings from this large scale, in-depth nutrition metabolic study of menopause, support the importance of monitoring risk factors for type-2 diabetes and cardiovascular disease in mid-life to older women to reduce morbidity and mortality associated with oestrogen decline. FUNDING: Zoe Ltd.

Validity of continuous glucose monitoring for categorizing glycemic responses to diet: implications for use in personalized nutrition.

BACKGROUND: Continuous glucose monitor (CGM) devices enable characterization of individuals' glycemic variation. However, there are concerns about their reliability for categorizing glycemic responses to foods that would limit their potential application in personalized nutrition recommendations. OBJECTIVES: We aimed to evaluate the concordance of 2 simultaneously worn CGM devices in measuring postprandial glycemic responses. METHODS: Within ZOE PREDICT (Personalised Responses to Dietary Composition Trial) 1, 394 participants wore 2 CGM devices simultaneously [n = 360 participants with 2 Abbott Freestyle Libre Pro (FSL) devices; n = 34 participants with both FSL and Dexcom G6] for ≤14 d while consuming standardized (n = 4457) and ad libitum (n = 5738) meals. We examined the CV and correlation of the incremental area under the glucose curve at 2 h (glucoseiAUC0-2 h). Within-subject meal ranking was assessed using Kendall τ rank correlation. Concordance between paired devices in time in range according to the American Diabetes Association cutoffs (TIRADA) and glucose variability (glucose CV) was also investigated. RESULTS: The CV of glucoseiAUC0-2 h for standardized meals was 3.7% (IQR: 1.7%-7.1%) for intrabrand device and 12.5% (IQR: 5.1%-24.8%) for interbrand device comparisons. Similar estimates were observed for ad libitum meals, with intrabrand and interbrand device CVs of glucoseiAUC0-2 h of 4.1% (IQR: 1.8%-7.1%) and 16.6% (IQR: 5.5%-30.7%), respectively. Kendall τ rank correlation showed glucoseiAUC0-2h-derived meal rankings were agreeable between paired CGM devices (intrabrand: 0.9; IQR: 0.8-0.9; interbrand: 0.7; IQR: 0.5-0.8). Paired CGMs also showed strong concordance for TIRADA with a intrabrand device CV of 4.8% (IQR: 1.9%-9.8%) and an interbrand device CV of 3.2% (IQR: 1.1%-6.2%). CONCLUSIONS: Our data demonstrate strong concordance of CGM devices in monitoring glycemic responses and suggest their potential use in personalized nutrition.This trial was registered at clinicaltrials.gov as NCT03479866.

Short sleep and social jetlag are associated with higher intakes of non-milk extrinsic sugars, and social jetlag is associated with lower fibre intakes in those with adequate sleep duration: a cross-sectional analysis from the National Diet and Nutrition Survey Rolling Programme (Years 1-9).

OBJECTIVE: To investigate associations and interactions between sleep duration and social jetlag status with nutrient intake, nutrient status, body composition and cardio-metabolic risk factors in a nationally representative UK adult population. DESIGN: A cross-sectional study using 4-d food diary and self-reported sleep data from the UK National Diet and Nutrition Survey Rolling Programme 2008-2017. SETTING: UK free-living population. SUBJECTS: Totally, 5015 adults aged 19-64 years. RESULTS: Thirty-four per cent were short sleepers (< 7 h); 7 % slept ≥ 9 h; 14 % had > 2 h difference in average sleep duration between weeknights and weekend nights (social jetlag). Compared to those reporting optimal sleep duration (≥ 7-< 9 h), short sleep was associated with higher intakes of non-milk extrinsic sugars (NMES) (0·9 % energy, 95 % CI: 0·4, 1·4), total carbohydrate (0·8 % energy, 95 % CI: 0·2, 1·4) and a lower non-starch polysaccharides fibre intake (-0·5 g/d, 95 % CI -0·8, -0·2). There was a significant interaction between short sleep and social jetlag for fibre intakes, where adequate sleepers with social jetlag as well as all short sleepers (regardless of social jetlag) had lower fibre intakes than adequate sleepers with no social jetlag. Short sleep, but not social jetlag, was associated with greater adiposity, but there were no differences in other markers of cardiometabolic disease risk. CONCLUSIONS: The present study reports that both short sleep and social jetlag are associated with higher intakes of NMES, but only sleep duration is associated with markers of adiposity. Social jetlag was associated with lower fibre intakes even in individuals with adequate weekly sleep duration, suggesting catch-up sleep does not prevent the adverse impact of irregular sleep habits on food choices.

Divergent serum metabolomic, skeletal muscle signaling, transcriptomic, and performance adaptations to fasted versus whey protein-fed sprint interval training.

Sprint interval training (SIT) is a time-efficient alternative to endurance exercise, conferring beneficial skeletal muscle metabolic adaptations. Current literature has investigated the nutritional regulation of acute and chronic exercise-induced metabolic adaptations in muscle following endurance exercise, principally comparing the impact of training in fasted and carbohydrate-fed (CHO) conditions. Alternative strategies such as exercising in low CHO, protein-fed conditions remain poorly characterized, specifically pertaining to adaptations associated with SIT. Thus, this study aimed to compare the metabolic and performance adaptations to acute and short-term SIT in the fasted state with preexercise hydrolyzed (WPH) or concentrated (WPC) whey protein supplementation. In healthy males, preexercise protein ingestion did not alter exercise-induced increases in PGC-1α, PDK4, SIRT1, and PPAR-δ mRNA expression following acute SIT. However, supplementation of WPH beneficially altered acute exercise-induced CD36 mRNA expression. Preexercise protein ingestion attenuated acute exercise-induced increases in muscle pan-acetylation and PARP1 protein content compared with fasted SIT. Acute serum metabolomic differences confirmed greater preexercise amino acid delivery in protein-fed compared with fasted conditions. Following 3 wk of SIT, training-induced increases in mitochondrial enzymatic activity and exercise performance were similar across nutritional groups. Interestingly, resting muscle acetylation status was downregulated in WPH conditions following training. Such findings suggest preexercise WPC and WPH ingestion positively influences metabolic adaptations to SIT compared with fasted training, resulting in either similar or enhanced performance adaptations. Future studies investigating nutritional modulation of metabolic adaptations to exercise are warranted to build upon these novel findings.NEW & NOTEWORTHY These are the first data to show the influence of preexercise protein on serum and skeletal muscle metabolic adaptations to acute and short-term sprint interval training (SIT). Preexercise whey protein concentrate (WPC) or hydrolysate (WPH) feeding acutely affected the serum metabolome, which differentially influenced acute and chronic changes in mitochondrial gene expression, intracellular signaling (acetylation and PARylation) resulting in either similar or enhanced performance outcomes when compared with fasted training.

Genetic and Environmental Contributions to Variation in the Stable Urinary NMR Metabolome over Time: A Classic Twin Study.

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.

Genetic and environmental influences on covariation in reproducible diet-metabolite associations.

BACKGROUND: Early applications of metabolomics in nutrition and health research identified associations between dietary patterns and metabolomic profiles. Twin studies show that diet-related phenotypes and diet-associated metabolites are influenced by genes. However, studies have not examined whether diet-metabolite associations are explained by genetic or environmental factors and whether these associations are reproducible over multiple time points. OBJECTIVE: This research aims to examine the genetic and environmental factors influencing covariation in diet-metabolite associations that are reproducible over time in healthy twins. METHODS: The UCD Twin Study is a semi-longitudinal classic twin study that collected repeated dietary, anthropometric, and urinary data over 2 months. Correlation analysis identified associations between diet quality measured using the Healthy Eating Index (HEI) and urinary metabolomic profiles at 3 time points. Diet-associated metabolites were examined using linear regression to identify those significantly influenced by familial factors between twins and those significantly influenced by unique factors. Cholesky decomposition modeling quantified the genetic and environmental path coefficients through associated dietary components onto the metabolites. RESULTS: The HEI was associated with 14 urinary metabolites across 3 metabolomic profiles (r: ±0.15-0.49). For 8 diet-metabolite associations, genetic or shared environmental factors influencing HEI component scores significantly influenced variation in metabolites (ß: 0.40-0.52). A significant relation was observed between dietary intakes of whole grain and acetoacetate (ß: -0.50, P < 0.001) and ß-hydroxybutyrate (ß: -0.46, P < 0.001), as well as intakes of saturated fat and acetoacetate (ß: 0.47, P < 0.001) and ß-hydroxybutyrate (ß: 0.52, P < 0.001). For these diet-metabolite associations a common shared environmental factor explained 66-69% of variance in the metabolites. CONCLUSIONS: This study shows that diet-metabolite associations are reproducible in 3 urinary metabolomic profiles. Components of the HEI covary with metabolites, and covariation is largely due to the shared environment.

Exploring Covariation between Traditional Markers of Metabolic Health and the Plasma Metabolomic Profile: A Classic Twin Design.

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.