Table_1_The impact of intermittent fasting on gut microbiota: a systematic review of human studies.docx

BackgroundIntermittent fasting (IF) has gained popularity in interventions targeting overweight, obesity and metabolic syndrome. IF may affect the gut microbiome composition and therefore have various effects on gut microbiome mediated functions in humans. Research on the effects of IF on human gut microbiome is limited. Therefore, the objective of this systematic review was to determine how different types of IF affect the human gut microbiome.MethodsA literature search was conducted for studies investigating the association of different types of IF and gut microbiota richness, alpha and beta diversity, and composition in human subjects. Databases included Cochrane Library (RRID:SCR_013000), PubMed (RRID:SCR_004846), Scopus (RRID:SCR_022559) and Web of Science (RRID:SCR_022706). A total of 1,332 studies were retrieved, of which 940 remained after removing duplicates. Ultimately, a total of 8 studies were included in the review. The included studies were randomized controlled trials, quasi-experimental studies and pilot studies implementing an IF intervention (time-restricted eating, alternate day fasting or 5:2 diet) in healthy subjects or subjects with any disease.ResultsMost studies found an association between IF and gut microbiota richness, diversity and compositional changes. There was heterogeneity in the results, and bacteria which were found to be statistically significantly affected by IF varied widely depending on the study.ConclusionThe findings in this systematic review suggest that IF influences gut microbiota. It seems possible that IF can improve richness and alpha diversity. Due to the substantial heterogeneity of the results, more research is required to validate these findings and clarify whether the compositional changes might be beneficial to human health.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42021241619.</p

Table_1_Cross-sectional associations between cardiorespiratory fitness and NMR-derived metabolic biomarkers in children – the PANIC study.docx

ObjectiveCardiorespiratory fitness has been inversely associated with cardiovascular risk across the lifespan. Some studies in adults suggest that higher cardiorespiratory fitness is associated with cardioprotective metabolite profile, but the evidence in children is lacking. Therefore, we investigated the cross-sectional association of cardiorespiratory fitness with serum nuclear magnetic resonance derived metabolic biomarkers in children.MethodsA population sample of 450 children aged 6–8 years was examined. Cardiorespiratory fitness was assessed by a maximal exercise test on a cycle ergometer and quantified as maximal power output normalised for lean body mass assessed by dual-energy X-ray absorbtiometry. Serum metabolites were assessed using a high throughput nuclear magnetic resonance platform. The data were analysed using linear regression analyses adjusted for age and sex and subsequently for body fat percentage (BF%) assessed by DXA.ResultsCardiorespiratory fitness was directly associated with high density lipoprotein (HDL) cholesterol (β=0.138, 95% CI=0.042 to 0.135, p=0.005), average HDL particle diameter (β=0.102, 95% CI=0.004 to 0.199, p=0.041), and the concentrations of extra-large HDL particles (β=0.103, 95% CI=0.006 to 0.201, p=0.038), large HDL particles (β=0.122, 95% CI=0.025 to 0.220, p=0.014), and medium HDL particles (β=0.143, 95% CI=0.047 to 0.239, p=0.004) after adjustment for age and sex. Higher cardiorespiratory fitness was also associated with higher concentrations of ApoA1 (β=0.145, 95% CI=0.047 to 0.242, p=0.003), glutamine (β=0.161, 95% CI=0.064 to 0.257, p=0.001), and phenylalanine (β=0.187, 95% CI=0.091 to 0.283, pConclusionsHigher cardiorespiratory fitness was associated with a cardioprotective biomarker profile in children. Most associations were independent of BF% suggesting that the differences in serum metabolites between children are driven by cardiorespiratory fitness and not adiposity.</p

CMPF concentrations before and after the intervention (0wk and 12 wk) in the Healthy Diet, WGED and Control groups.

<p>Changes between the groups were tested using ANOVA and Bonferroni’s post-hoc tests.</p

Baseline correlations and associations between absolute changes in CMPF and glucose parameters adjusted for effect of intervention group (n = 106).

<p>¹ Area under the curve in 2-hour oral glucose tolerance test</p><p>² Homeostasis model of insulin resistance, HOMA IR = (fasting glucose mmol/l x fasting insulin mU/l) / 22.5</p><p>³ Insulinogenic index, IGI = (insulin 30 min—insulin 0 min, pmol/l) / (glucose 30 min – glucose 0 min, mmol/l)</p><p>⁴ Quantitative insulin sensitivity check index, Quicky = 1 / (lg10(insulin 0 min, mU/l) + lg10(glucose 0 min, mg/dl))</p><p>⁵ DI = IGI x Quicky</p><p>Baseline correlations and associations between absolute changes in CMPF and glucose parameters adjusted for effect of intervention group (n = 106).</p

Genotype information and quality control statistics for the eight childhood obesity-susceptibility SNPs.

<p>Abbreviations: bp, base pairs; HWE, Hardy-Weinberg equilibrium; MAF, minor allele frequency; SNP, single nucleotide polymorphism.</p

Interaction between genetic risk score (GRS) and meal patterns on BMI.

<p>Mean BMI values with 95% confidence interval error bars are presented.</p

Interaction between <i>FTO</i> rs1421085 genotypes (additive model) and meal patterns on BMI.

<p>Mean BMI values with 95% confidence interval error bars are presented.</p

Distributions of eight single nucleotide polymorphisms, genetic predisposition score, Tanner stages of puberty, meal patterns and body mass index among adolescents in the Northern Finland Birth Cohort 1986.

<p>Data presented as mean values (95% CIs) and percentages.</p><p>Abbreviations: BMI, body mass index; CI, confidence interval.</p

Absolute changes in glucose parameters according to quartiles¹ based on absolute changes in CMPF (n = 106).

<p>¹ Average changes in quartiles: quartile 1: -7.6 μmol/l; quartile 2: -2.2 μmol/l; quartile 3: 0.89 μmol/l; quartile 4: 7.7 μmol/l</p><p>² Area under the curve in 2-hour oral glucose tolerance test</p><p>³ Homeostasis model of insulin resistance, HOMA IR = (fasting glucose mmol/l x fasting insulin mU/l) / 22.5</p><p>⁴ Insulinogenic index, IGI = (insulin 30 min—insulin 0 min, pmol/l) / (glucose 30 min – glucose 0 min, mmol/l)</p><p>⁵ Quantitative insulin sensitivity check index, Quicky = 1 / (lg10(insulin 0 min, mU/l) + lg10(glucose 0 min, mg/dl))</p><p>⁶ IGI x Quicky</p><p>Absolute changes in glucose parameters according to quartiles<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124379#t002fn001" target="_blank">¹</a> based on absolute changes in CMPF (n = 106).</p

Interaction between <i>MC4R</i> rs17782313 genotypes (additive model) and meal patterns on BMI.

<p>Mean BMI values with 95% confidence interval error bars are presented.</p