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Introduction: Carbohydrate intake and physical activity are related to glucose homeostasis, both being influenced by individual genetic makeup. However, the interactions between these 2 factors, as affected by genetics, on glycaemia have been scarcely reported. Objective: We focused on analysing the interplay between carbohydrate intake and physical activity levels on blood glucose, taking into account a genetic risk score (GRS), based on SNPs related to glucose/energy metabolism. Methods: A total of 1,271 individuals from the Food4Me cohort, who completed the nutritional intervention, were evaluated at baseline. We collected dietary information by using an online-validated food frequency questionnaire, a questionnaire on physical activity, blood biochemistry by analysis of dried blood spots, and by analysis of selected SNPs. Fifteen out of 31 SNPs, with recognized participation in carbohydrate/energy metabolism, were included in the component analyses. The GRS included risk alleles involved in the control of glycaemia or energy-yielding processes. Results: Data concerning anthropometric, clinical, metabolic, dietary intake, physical activity, and genetics related to blood glucose levels showed expected trends in European individuals of comparable sex and age, being categorized by lifestyle, BMI, and energy/carbohydrate intakes, in this Food4Me population. Blood glucose was inversely associated with physical activity level (β = −0.041, p = 0.013) and positively correlated with the GRS values (β = 0.015, p = 0.047). Interestingly, an interaction affecting glycaemia, concerning physical activity level with carbohydrate intake, was found (β = −0.060, p = 0.033), which also significantly depended on the genetic background (GRS). Conclusions: The relationships of carbohydrate intake and physical activity are important in understanding glucose homeostasis, where a role for the genetic background should be ascribed.

Background: Recent evidence indicates that insulin resistance (IR) in obesity may develop independently in different organs, representing different etiologies toward type 2 diabetes and other cardiometabolic diseases. The aim of this study was to investigate whether IR in the liver and IR in skeletal muscle are associated with distinct metabolic profiles. Methods: This study includes baseline data from 634 adults with overweight or obesity (BMI ≥ 27 kg/m 2) (≤65 years; 63% women) without diabetes of the European Diogenes Study. Hepatic insulin resistance index (HIRI) and muscle insulin sensitivity index (MISI), were derived from a five-point OGTT. At baseline 17 serum metabolites were identified and quantified by nuclear-magnetic-resonance spectroscopy. Linear mixed model analyses (adjusting for center, sex, body mass index (BMI), waist-to-hip ratio) were used to associate HIRI and MISI with these metabolites. In an independent sample of 540 participants without diabetes (BMI ≥ 27 kg/m 2; 40–65 years; 46% women) of the Maastricht Study, an observational prospective population-based cohort study, 11 plasma metabolites and a seven-point OGTT were available for validation. Results: Both HIRI and MISI were associated with higher levels of valine, isoleucine, oxo-isovaleric acid, alanine, lactate, and triglycerides, and lower levels of glycine (all p < 0.05). HIRI was also associated with higher levels of leucine, hydroxyisobutyrate, tyrosine, proline, creatine, and n-acetyl and lower levels of acetoacetate and 3-OH-butyrate (all p < 0.05). Except for valine, these results were replicated for all available metabolites in the Maastricht Study. Conclusions: In persons with obesity without diabetes, both liver and muscle IR show a circulating metabolic profile of elevated (branched-chain) amino acids, lactate, and triglycerides, and lower glycine levels, but only liver IR associates with lower ketone body levels and elevated ketogenic amino acids in circulation, suggestive of decreased ketogenesis. This knowledge might enhance developments of more targeted tissue-specific interventions to prevent progression to more severe disease stages.

Purpose High-fat and low-fibre discretionary food intake and FTO genotype are each associated independently with higher risk of obesity. However, few studies have investigated links between obesity and dietary patterns based on discretionary food intake, and the interaction effect of FTO genotype are unknown. Thus, this study aimed to derive dietary patterns based on intake of discretionary foods, saturated fatty acids (SFA) and fibre, and examine cross-sectional associations with BMI and waist circumference (WC), and interaction effects of FTO genotype. Methods Baseline data on 1280 adults from seven European countries were included (the Food4Me study). Dietary intake was estimated from a Food Frequency Questionnaire. Reduced rank regression was used to derive three dietary patterns using response variables of discretionary foods, SFA and fibre density. DNA was extracted from buccal swabs. Anthropometrics were self-measured. Linear regression analyses were used to examine associations between dietary patterns and BMI and WC, with an interaction for FTO genotype. Results Dietary pattern 1 (positively correlated with discretionary foods and SFA, and inversely correlated with fibre) was associated with higher BMI (β:0.64; 95% CI 0.44, 0.84) and WC (β:1.58; 95% CI 1.08, 2.07). There was limited evidence dietary pattern 2 (positively correlated with discretionary foods and SFA) and dietary pattern 3 (positively correlated with SFA and fibre) were associated with anthropometrics. FTO risk genotype was associated with higher BMI and WC, with no evidence of a dietary interaction. Conclusions Consuming a dietary pattern low in discretionary foods and high-SFA and low-fibre foods is likely to be important for maintaining a healthy weight, regardless of FTO predisposition to obesity. Trial registration Clinicaltrials.gov NCT01530139. Registered 9 February 2012 https://clinicaltrials.gov/ct2/show/NCT01530139

Obesity-related insulin resistance (IR) may develop in multiple organs, representing various etiologies for cardiometabolic diseases. We identified abdominal subcutaneous adipose tissue (ScAT) transcriptome profiles in liver or muscle IR by means of RNA sequencing in overweight or obese participants of the Diet, Obesity, and Genes (DiOGenes) (NCT00390637, ClinicalTrials.gov) cohort (n = 368). Tissue-specific IR phenotypes were derived from a 5-point oral glucose tolerance test. Hepatic and muscle IR were characterized by distinct abdominal ScAT transcriptome profiles. Genes related to extracellular remodeling were upregulated in individuals with primarily hepatic IR, while genes related to inflammation were upregulated in individuals with primarily muscle IR. In line with this, in two independent cohorts, the Cohort on Diabetes and Atherosclerosis Maastricht (CODAM) (n = 325) and the Maastricht Study (n = 685), an increased systemic low-grade inflammation profile was specifically related to muscle IR but not to liver IR. We propose that increased ScAT inflammatory gene expression may translate into an increased systemic inflammatory profile, linking ScAT inflammation to the muscle IR phenotype. These distinct IR phenotypes may provide leads for more personalized prevention of cardiometabolic diseases.

High-fat and low-fibre discretionary food intake and FTO genotype are each associated independently with higher risk of obesity. However, few studies have investigated links between obesity and dietary patterns based on discretionary food intake, and the interaction effect of FTO genotype are unknown. Thus, this study aimed to derive dietary patterns based on intake of discretionary foods, saturated fatty acids (SFA) and fibre, and examine cross-sectional associations with BMI and waist circumference (WC), and interaction effects of FTO genotype.Baseline data on 1280 adults from seven European countries were included (the Food4Me study). Dietary intake was estimated from a Food Frequency Questionnaire. Reduced rank regression was used to derive three dietary patterns using response variables of discretionary foods, SFA and fibre density. DNA was extracted from buccal swabs. Anthropometrics were self-measured. Linear regression analyses were used to examine associations between dietary patterns and BMI and WC, with an interaction for FTO genotype.Dietary pattern 1 (positively correlated with discretionary foods and SFA, and inversely correlated with fibre) was associated with higher BMI (β:0.64; 95% CI 0.44, 0.84) and WC (β:1.58; 95% CI 1.08, 2.07). There was limited evidence dietary pattern 2 (positively correlated with discretionary foods and SFA) and dietary pattern 3 (positively correlated with SFA and fibre) were associated with anthropometrics. FTO risk genotype was associated with higher BMI and WC, with no evidence of a dietary interaction.Consuming a dietary pattern low in discretionary foods and high-SFA and low-fibre foods is likely to be important for maintaining a healthy weight, regardless of FTO predisposition to obesity.Clinicaltrials.gov NCT01530139. Registered 9 February 2012 https://clinicaltrials.gov/ct2/show/NCT01530139.

Background The effect of personalised nutrition advice on discretionary foods intake is unknown. To date, two national classifications for discretionary foods have been derived. This study examined changes in intake of discretionary foods and beverages following a personalised nutrition intervention using these two classifications. Methods Participants were recruited into a 6-month RCT across seven European countries (Food4Me) and were randomised to receive generalised dietary advice (control) or one of three levels of personalised nutrition advice (based on diet [L1], phenotype [L2] and genotype [L3]). Dietary intake was derived from an FFQ. An analysis of covariance was used to determine intervention effects at month 6 between personalised nutrition (overall and by levels) and control on i) percentage energy from discretionary items and ii) percentage contribution of total fat, SFA, total sugars and salt to discretionary intake, defined by Food Standards Scotland (FSS) and Australian Dietary Guidelines (ADG) classifications. Results Of the 1607 adults at baseline, n = 1270 (57% female) completed the intervention. Percentage sugars from FSS discretionary items was lower in personalised nutrition vs control (19.0 ± 0.37 vs 21.1 ± 0.65; P = 0.005). Percentage energy (31.2 ± 0.59 vs 32.7 ± 0.59; P = 0.031), percentage total fat (31.5 ± 0.37 vs 33.3 ± 0.65; P = 0.021), SFA (36.0 ± 0.43 vs 37.8 ± 0.75; P = 0.034) and sugars (31.7 ± 0.44 vs 34.7 ± 0.78; P Conclusions Compared with generalised dietary advice, personalised nutrition advice achieved greater reductions in discretionary foods intake when the classification included all foods high in fat, added sugars and salt. Future personalised nutrition approaches may be used to target intake of discretionary foods. Trial registration Clinicaltrials.gov NCT01530139. Registered 9 February 2012.

In this secondary analysis of the DiOGenes study, we investigated whether physical activity (PA) contributes to diet-induced weight loss and helps to reduce subsequent regain. We also studied the associations of PA with changes in cardiometabolic variables. Adults with overweight were included and followed an 8-week low-calorie diet (LCD). When successful (>8% weight loss), participants were randomized to different ad libitum diet groups and were advised to maintain their weight loss over the 6-month intervention period. Body weight (BW), body composition, cardiometabolic variables and subjectively-assessed PA were measured at baseline, at the end of weight loss and at the end of the intervention. BW was reduced by the LCD (from 99.8 ± 16.7 to 88.4 ± 14.9 kg; P < 0.001). This reduction was maintained during the weight maintenance period (89.2 ± 16.0 kg). Total PA (sum score of the three subscales of the Baecke questionnaire) increased during the weight loss period (from 8.16 ± 0.83 to 8.39 ± 0.78; P < 0.001) and this increase was subsequently maintained (8.42 ± 0.90). We found no evidence that baseline PA predicted weight loss. However, a higher level of baseline PA predicted a larger weight-loss-induced improvement in total cholesterol, triglycerides, glucose and CRP, and in post-prandial insulin sensitivity (Matsuda index). Subsequent weight and fat mass maintenance were predicted by the post-weight loss level of PA and associated with changes in PA during the weight maintenance phase. In conclusion, despite the fact that higher baseline levels of PA did not predict more weight loss during the LCD, nor that an increase in PA during the LCD was associated with more weight loss, higher PA levels were associated with more improvements in several cardiometabolic variables. The positive effect of higher PA on weight loss maintenance seems in contrast to randomized controlled trials that have not been able to confirm a positive effect of exercise training programmes on weight loss maintenance. This analysis supports the notion that higher self-imposed levels of PA may improve the cardiometabolic risk profile during weight loss and help to maintain weight loss afterwards.

OBJECTIVE: To characterise clusters of individuals based on adherence to dietary recommendations and to determine whether changes in Healthy Eating Index (HEI) scores in response to a personalised nutrition (PN) intervention varied between clusters. DESIGN: Food4Me study participants were clustered according to whether their baseline dietary intakes met European dietary recommendations. Changes in HEI scores between baseline and month 6 were compared between clusters and stratified by whether individuals received generalised or PN advice. SETTING: Pan-European, Internet-based, 6-month randomised controlled trial. SUBJECTS: Adults aged 18–79 years (n 1480). RESULTS: Individuals in cluster 1 (C1) met all recommended intakes except for red meat, those in cluster 2 (C2) met two recommendations, and those in cluster 3 (C3) and cluster 4 (C4) met one recommendation each. C1 had higher intakes of white fish, beans and lentils and low-fat dairy products and lower percentage energy intake from SFA (P

Background Single nucleotide polymorphisms (SNPs) in FADS1/FADS2 genes are associated with changes in serum and tissue polyunsaturated fatty acid (PUFA) content. PUFA regulate inflammatory signaling pathways in adipose tissue; however, the effect of SNPs in FADS1/FADS2 on adipose tissue inflammation is equivocal. The present study examined if SNPs in FADS1/FADS2 modify human subcutaneous adipose tissue (SAT) fatty acid profiles and the expression of genes associated with inflammation/immune function, lipid metabolism, and cellular differentiation. Methods SAT fatty acids and the expression of 117 genes were measured in 174 men and women from the DiOGenes Study using gas chromatography and qRT-PCR, respectively. Associations between fatty acids, gene expression, and SNPs in FADS1/FADS2 were investigated by linear regression and multivariate analysis. Results Four SNPs (rs174537, rs174546, rs174556, rs174601) in FADS1/FADS2 were significantly associated with SAT fatty acids. All SNPs were in high linkage disequilibrium with the commonly reported rs174537 SNP in FADS1. Minor allele carriers for rs174537 (GT+TT) had reduced 20:4n-6 (p = 1.74E-5), lower delta-5 desaturase enzyme activity (p = 2.09E-9), and lower FADS1 gene expression (p = 0.03) compared to major GG carriers. Multivariate analysis revealed that 20:4n-6 and 20:3n-6 explained ~19% of the variance between rs174537 genotypes, while gene expression explained Conclusion This study showed that FADS1 genotype is distinguished by SAT fatty acid profiles, but not inflammatory gene expression.

We established a robust capillary-flow data-independent acquisition MS platform capable of measuring 31 plasma proteomes per day without the need of repeated acquisition of the same sample. We acquired 1508 samples of the DiOGenes study (multicentered, Europa-wide caloric restriction weight loss and maintenance study of overweight and obese, non-diabetic participants). This was achieved using a single analytical column. Comprehensive biological reactions to weight loss and maintenance were observed., Graphical Abstract Highlights Robust capillary-flow DIA was established at 31 clinical samples per day. 1508 samples of dietary intervention study DiOGenes were measured on a single column. Comprehensive biological reactions to weight loss and maintenance were observed. Comparison to independent studies shows high reproducibility of potential biomarkers., Comprehensive, high throughput analysis of the plasma proteome has the potential to enable holistic analysis of the health state of an individual. Based on our own experience and the evaluation of recent large-scale plasma mass spectrometry (MS) based proteomic studies, we identified two outstanding challenges: slow and delicate nano-flow liquid chromatography (LC) and irreproducibility of identification of data-dependent acquisition (DDA). We determined an optimal solution reducing these limitations with robust capillary-flow data-independent acquisition (DIA) MS. This platform can measure 31 plasma proteomes per day. Using this setup, we acquired a large-scale plasma study of the diet, obesity and genes dietary (DiOGenes) comprising 1508 samples. Proving the robustness, the complete acquisition was achieved on a single analytical column. Totally, 565 proteins (459 identified with two or more peptide sequences) were profiled with 74% data set completeness. On average 408 proteins (5246 peptides) were identified per acquisition (319 proteins in 90% of all acquisitions). The workflow reproducibility was assessed using 34 quality control pools acquired at regular intervals, resulting in 92% data set completeness with CVs for protein measurements of 10.9%. The profiles of 20 apolipoproteins could be profiled revealing distinct changes. The weight loss and weight maintenance resulted in sustained effects on low-grade inflammation, as well as steroid hormone and lipid metabolism, indicating beneficial effects. Comparison to other large-scale plasma weight loss studies demonstrated high robustness and quality of biomarker candidates identified. Tracking of nonenzymatic glycation indicated a delayed, slight reduction of glycation in the weight maintenance phase. Using stable-isotope-references, we could directly and absolutely quantify 60 proteins in the DIA. In conclusion, we present herein the first large-scale plasma DIA study and one of the largest clinical research proteomic studies to date. Application of this fast and robust workflow has great potential to advance biomarker discovery in plasma.

Background: The adipose tissue (AT) is a secretory organ producing a wide variety of factors that participate in the genesis of metabolic disorders linked to excess fat mass. Weight loss improves obesity-related disorders.Objectives: Transcriptomic studies on human AT, and a combination of analyses of transcriptome and proteome profiling of conditioned media from adipocytes and stromal cells isolated from human AT, have led to the identification of apolipoprotein M (apoM) as a putative adipokine. We aimed to validate apoM as novel adipokine, investigate the relation of AT APOM expression with metabolic syndrome and insulin sensitivity, and study the regulation of its expression in AT and secretion during calorie restriction-induced weight loss.Methods: We examined APOM mRNA level and secretion in AT from 485 individuals enrolled in 5 independent clinical trials, and in vitro in human multipotent adipose-derived stem cell adipocytes. APOM expression and secretion were measured during dieting.Results: APOM was expressed in human subcutaneous and visceral AT, mainly by adipocytes. ApoM was released into circulation from AT, and plasma apoM concentrations correlate with AT APOM mRNA levels. In AT, APOM expression inversely correlated with adipocyte size, was lower in obese compared to lean individuals, and reduced in subjects with metabolic syndrome and type 2 diabetes. Regardless of fat depot, there was a positive relation between AT APOM expression and systemic insulin sensitivity, independently of fat mass and plasma HDL cholesterol. In human multipotent adipose-derived stem cell adipocytes, APOM expression was enhanced by insulin-sensitizing peroxisome proliferator-activated receptor agonists and inhibited by tumor necrosis factor a, a cytokine that causes insulin resistance. In obese individuals, calorie restriction increased AT APOM expression and secretion.Conclusions: ApoM is a novel adipokine, the expression of which is a hallmark of healthy AT and is upregulated by calorie restriction. AT apoM deserves further investigation as a potential biomarker of risk for diabetes and cardiovascular diseases.

Hundreds of genetic variants have been associated with Body Mass Index (BMI) through genome-wide association studies (GWAS) using observational cohorts. However, the genetic contribution to efficient weight loss in response to dietary intervention remains unknown. We perform a GWAS in two large low-caloric diet intervention cohorts of obese participants. Two loci close to NKX6.3/MIR486 and RBSG4 are identified in the Canadian discovery cohort (n = 1166) and replicated in the DiOGenes cohort (n = 789). Modulation of HGTX (NKX6.3 ortholog) levels in Drosophila melanogaster leads to significantly altered triglyceride levels. Additional tissue-specific experiments demonstrate an action through the oenocytes, fly hepatocyte-like cells that regulate lipid metabolism. Our results identify genetic variants associated with the efficacy of weight loss in obese subjects and identify a role for NKX6.3 in lipid metabolism, and thereby possibly weight control., Individuals show large variability in their capacity to lose weight and maintain this weight. Here, the authors perform GWAS in two weight loss intervention cohorts and identify two genetic loci associated with weight loss that are taken forward for Bayesian fine-mapping and functional assessment in flies.

Selecting effective dietary strategies for professional football players requires comprehensive information on their energy expenditure (EE) and dietary intake. This observational study aimed to assess EE and dietary intake over a 14-day period in a representative group (n = 41) of professional football players playing in the Dutch Premier League (Eredivisie). Daily EE, as assessed by doubly labelled water, was 13.8 ± 1.5 MJ/day, representing a physical activity level (PAL) of 1.75 ± 0.13. Weighted mean energy intake (EI), as assessed by three face-to-face 24-h recalls, was 11.1 ± 2.9 MJ/day, indicating 18 ± 15% underreporting of EI. Daily EI was higher on match days (13.1 ± 4.1 MJ) compared with training (11.1 ± 3.4 MJ; P < 0.01) and rest days (10.5 ± 3.1 MJ; P < 0.001). Daily carbohydrate intake was significantly higher during match days (5.1 ± 1.7 g/kg body mass (BM)) compared with training (3.9 ± 1.5 g/kg BM; P < 0.001) and rest days (3.7 ± 1.4 g/kg BM; P < 0.001). Weighted mean protein intake was 1.7 ± 0.5 g/kg BM. Daytime distribution of protein intake was skewed, with lowest intakes at breakfast and highest at dinner. In conclusion, daily EE and PAL of professional football players are modest. Daily carbohydrate intake should be increased to maximize performance and recovery. Daily protein intake seems more than adequate, but could be distributed more evenly throughout the day.

Context Adipose tissue (AT) transcriptome studies provide holistic pictures of adaptation to weight and related bioclinical settings changes. Objective To implement AT gene expression profiling and investigate the link between changes in bioclinical parameters and AT gene expression during 3 steps of a 2-phase dietary intervention (DI). Methods AT transcriptome profiling was obtained from sequencing 1051 samples, corresponding to 556 distinct individuals enrolled in a weight loss intervention (8-week low-calorie diet (LCD) at 800 kcal/day) followed with a 6-month ad libitum randomized DI. Transcriptome profiles obtained with QuantSeq sequencing were benchmarked against Illumina RNAseq. Reverse transcription quantitative polymerase chain reaction was used to further confirm associations. Cell specificity was assessed using freshly isolated cells and THP-1 cell line. Results During LCD, 5 modules were found, of which 3 included at least 1 bioclinical variable. Change in body mass index (BMI) connected with changes in mRNA level of genes with inflammatory response signature. In this module, change in BMI was negatively associated with changes in expression of genes encoding secreted protein (GDF15, CCL3, and SPP1). Through all phases of the DI, change in GDF15 was connected to changes in SPP1, CCL3, LIPA and CD68. Further characterization showed that these genes were specific to macrophages (with LIPA, CD68 and GDF15 expressed in anti-inflammatory macrophages) and GDF15 also expressed in preadipocytes. Conclusion Network analyses identified a novel AT feature with GDF15 upregulated with calorie restriction induced weight loss, concomitantly to macrophage markers. In AT, GDF15 was expressed in preadipocytes and macrophages where it was a hallmark of anti-inflammatory cells.

Background and aims: Body mass index (BMI) and waist circumference (WC) are commonly used markers of cardiometabolic risk. However, sagittal abdominal diameter (SAD) has been proposed as a possibly more sensitive marker of intra-abdominal obesity. We investigated differences in how SAD, WC, and BMI were correlated with cardiometabolic risk markers.Methods and results: This cross-sectional study investigated anthropometric and metabolic baseline measurements of individuals from six trials. Multiple linear regression and (partial) correlation coefficients were used to investigate associations between SAD, WC, and BMI and cardiometabolic risk markers, including components of the metabolic syndrome as well as insulin resistance, blood lipids, and low-grade inflammation.In total 1516 mostly overweight or obese individuals were included in the study. SAD was significantly more correlated with TG than WC for all studies, and overall increase in correlation was 0.05 (95% CI (0.02; 0.08). SAD was significantly more correlated with the markers TG and DBP 0.11 (95% CI (0.08, 0.14)) and 0.04 (95% CI (0.006, 0.07), respectively compared to BMI across all or most studies.Conclusion: This study showed that no single anthropometric indicator was consistently more strongly correlated across all markers of cardiometabolic risk. However, SAD was significantly more strongly correlated with TG than WC and significantly more strongly correlated with DBP and TG than BMI. (C) 2020 Published by Elsevier B.V. on behalf of The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University.

Objective . The strongest locus which associated with type 2 diabetes (T2D) by the common variant rs7903146 is the transcription factor 7-like 2 gene ( TCF7L2 ). We aimed to quantify the interaction of diet/lifestyle interventions and the genetic effect of TCF7L2 rs7903146 on glycemic traits, body weight, or waist circumference in overweight or obese adults in several randomized controlled trials (RCTs). Methods . From October 2016 to May 2018, a large collaborative analysis was performed by pooling individual-participant data from 7 RCTs. These RCTs reported changes in glycemic control and adiposity of the variant rs7903146 after dietary/lifestyle-related interventions in overweight or obese adults. Gene treatment interaction models which used the genetic effect encoded by the allele dose and common covariates were applicable to individual participant data in all studies. Results . In the joint analysis, a total of 7 eligible RCTs were included ( n = 4,114 ). Importantly, we observed a significant effect modification of diet/lifestyle-related interventions on the TCF7L2 variant rs7903146 and changes in fasting glucose. Compared with the control group, diet/lifestyle interventions were related to lower fasting glucose by -3.06 (95% CI, -5.77 to -0.36) mg/dL (test for heterogeneity and overall effect: I 2 = 45.1 % , p < 0.05 ; z = 2.20 , p = 0.028 ) per one copy of the TCF7L2 T risk allele. Furthermore, regardless of genetic risk, diet/lifestyle interventions were associated with lower waist circumference. However, there was no significant change for diet/lifestyle interventions in other glycemic control and adiposity traits per one copy of TCF7L2 risk allele. Conclusions . Our findings suggest that carrying the TCF7L2 T risk allele may have a modestly greater benefit for specific diet/lifestyle interventions to improve the control of fasting glucose in overweight or obese adults.

Context Mitochondria are essential for cellular energy homeostasis, yet their role in subcutaneous adipose tissue (SAT) during different types of weight-loss interventions remains unknown. Objective To investigate how SAT mitochondria change following diet-induced and bariatric surgery–induced weight-loss interventions in 4 independent weight-loss studies. Methods The DiOGenes study is a European multicenter dietary intervention with an 8-week low caloric diet (LCD; 800 kcal/d; n = 261) and 6-month weight-maintenance (n = 121) period. The Kuopio Obesity Surgery study (KOBS) is a Roux-en-Y gastric bypass (RYGB) surgery study (n = 172) with a 1-year follow-up. We associated weight-loss percentage with global and 2210 mitochondria-related RNA transcripts in linear regression analysis adjusted for age and sex. We repeated these analyses in 2 studies. The Finnish CRYO study has a 6-week LCD (800-1000 kcal/d; n = 19) and a 10.5-month follow-up. The Swedish DEOSH study is a RYGB surgery study with a 2-year (n = 49) and 5-year (n = 37) follow-up. Results Diet-induced weight loss led to a significant transcriptional downregulation of oxidative phosphorylation (DiOGenes; ingenuity pathway analysis [IPA] z-scores: −8.7 following LCD, −4.4 following weight maintenance; CRYO: IPA z-score: −5.6, all P < 0.001), while upregulation followed surgery-induced weight loss (KOBS: IPA z-score: 1.8, P < 0.001; in DEOSH: IPA z-scores: 4.0 following 2 years, 0.0 following 5 years). We confirmed an upregulated oxidative phosphorylation at the proteomics level following surgery (IPA z-score: 3.2, P < 0.001). Conclusions Differentially regulated SAT mitochondria-related gene expressions suggest qualitative alterations between weight-loss interventions, providing insights into the potential molecular mechanistic targets for weight-loss success.

Little is known about who would benefit from internet-based personalised nutrition (PN) interventions. This study aimed to evaluate the characteristics of participants who achieved greatest improvements (i.e. benefit) in diet, adiposity and biomarkers following an internet-based PN intervention. Adults (n=1607) from seven European countries were recruited into a 6-month, randomized controlled trial (Food4Me) and randomized to receive conventional dietary advice (control) or PN advice. Information on dietary intake, adiposity, physical activity, blood biomarkers and participant characteristics was collected at baseline and month 6. Benefit from the intervention was defined as ≥5% change in the primary outcome (Healthy Eating Index) and secondary outcomes (waist circumference and BMI, physical activity, sedentary time and plasma concentrations of cholesterol, carotenoids and omega-3 index) at month 6. For our primary outcome, benefit from the intervention was greater in older participants and women. Benefit was greater for individuals reporting greater self-efficacy for “sticking to healthful foods” and who “felt weird if [they] didn’t eat healthily”. Participants benefited more if they reported wanting to improve their health and wellbeing. The characteristics of individuals benefiting did not differ by other demographic, health-related, anthropometric or genotypic characteristics. Findings were similar for secondary outcomes. Older individuals, women and individuals with less healthy diets at baseline benefitted more from PN advice. The odds of benefiting did not differ by weight status, genetic risk or socio-economic position. These findings have implications for the design of more effective future PN intervention studies and for tailored nutritional advice in public health and clinical settings.

Weight loss aims to improve glycemic control in obese but strong variability is observed. Using a multi-omics approach, we investigated differences between 174 responders and 201 non-responders, that had lost >8% body weight following a low-caloric diet (LCD, 800 kcal/d for 8 weeks). The two groups were comparable at baseline for body composition, glycemic control, adipose tissue transcriptomics and plasma ketone bodies. But they differed significantly in their response to LCD, including improvements in visceral fat, overall insulin resistance (IR) and tissue-specific IR. Transcriptomics analyses found down-regulation in key lipogenic genes (e.g. SCD, ELOVL5) in responders relative to non-responders; metabolomics showed increase in ketone bodies; while proteomics revealed differences in lipoproteins. Findings were consistent between genders; with women displaying smaller improvements owing to a better baseline metabolic condition. Integrative analyses identified a plasma omics model that was able to predict non-responders with strong performance (on a testing dataset, the Receiving Operating Curve Area Under the Curve (ROC AUC) was 75% with 95% Confidence Intervals (CI) [67%, 83%]). This model was based on baseline parameters without the need for intrusive measurements and outperformed clinical models (p = 0.00075, with a +14% difference on the ROC AUCs). Our approach document differences between responders and non-responders, with strong contributions from liver and adipose tissues. Differences may be due to de novo lipogenesis, keto-metabolism and lipoprotein metabolism. These findings are useful for clinical practice to better characterize non-responders both prior and during weight loss.

Molecular quantitative trait locus (QTL) analyses are increasingly popular to explore the genetic architecture of complex traits, but existing studies do not leverage shared regulatory patterns and suffer from a large multiplicity burden, which hampers the detection of weak signals such as trans associations. Here, we present a fully multivariate proteomic QTL (pQTL) analysis performed with our recently proposed Bayesian method LOCUS on data from two clinical cohorts, with plasma protein levels quantified by mass-spectrometry and aptamer-based assays. Our two-stage study identifies 136 pQTL associations in the first cohort, of which >80% replicate in the second independent cohort and have significant enrichment with functional genomic elements and disease risk loci. Moreover, 78% of the pQTLs whose protein abundance was quantified by both proteomic techniques are confirmed across assays. Our thorough comparisons with standard univariate QTL mapping on (1) these data and (2) synthetic data emulating the real data show how LOCUS borrows strength across correlated protein levels and markers on a genome-wide scale to effectively increase statistical power. Notably, 15% of the pQTLs uncovered by LOCUS would be missed by the univariate approach, including several trans and pleiotropic hits with successful independent validation. Finally, the analysis of extensive clinical data from the two cohorts indicates that the genetically-driven proteins identified by LOCUS are enriched in associations with low-grade inflammation, insulin resistance and dyslipidemia and might therefore act as endophenotypes for metabolic diseases. While considerations on the clinical role of the pQTLs are beyond the scope of our work, these findings generate useful hypotheses to be explored in future research; all results are accessible online from our searchable database. Thanks to its efficient variational Bayes implementation, LOCUS can analyze jointly thousands of traits and millions of markers. Its applicability goes beyond pQTL studies, opening new perspectives for large-scale genome-wide association and QTL analyses. Diet, Obesity and Genes (DiOGenes) trial registration number: NCT00390637., Author summary Exploring the functional mechanisms between the genotype and disease endpoints in view of identifying innovative therapeutic targets has prompted molecular quantitative trait locus studies, which assess how genetic variants (single nucleotide polymorphisms, SNPs) affect intermediate gene (eQTL), protein (pQTL) or metabolite (mQTL) levels. However, conventional univariate screening approaches do not account for local dependencies and association structures shared by multiple molecular levels and markers. Conversely, the current joint modelling approaches are restricted to small datasets by computational constraints. We illustrate and exploit the advantages of our recently introduced Bayesian framework LOCUS in a fully multivariate pQTL study, with ≈300K tag SNPs (capturing information from 4M markers) and 100 − 1, 000 plasma protein levels measured by two distinct technologies. LOCUS identifies novel pQTLs that replicate in an independent cohort, confirms signals documented in studies 2 − 18 times larger, and detects more pQTLs than a conventional two-stage univariate analysis of our datasets. Moreover, some of these pQTLs might be of biomedical relevance and would therefore deserve dedicated investigation. Our extensive numerical experiments on these data and on simulated data demonstrate that the increased statistical power of LOCUS over standard approaches is largely attributable to its ability to exploit shared information across outcomes while efficiently accounting for the genetic correlation structures at a genome-wide level.

Microbially-produced acetate has been reported to beneficially affect metabolic health through effects on satiety, energy expenditure, insulin sensitivity, and substrate utilization. Here, we investigate the association between sex-specific concentrations of acetate and insulin sensitivity/resistance indices (Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), circulating insulin and Matsuda Index) in the Diet, Obesity and Genes (DiOGenes) Dietary study at baseline and after a low-calorie diet (LCD, 800 kcal/d). In this analysis, 692 subjects (Body Mass Index &gt, 27 kg/m2) were included, who underwent an LCD for 8 weeks. Linear mixed models were performed, which were adjusted for mean acetate concentration, center (random factor), age, weight loss, and fat-free mass (FFM). At baseline, no associations between plasma acetate and insulin sensitivity/resistance indices were found. We found a slight positive association between changes in acetate and changes in HOMA-IR (std 0.130, p = 0.033) in women, but not in men (std -0.072, p = 0.310) independently of age, weight loss and FFM. We were not able to confirm previously reported associations between acetate and insulin sensitivity in this large European cohort. The mechanisms behind the sex-specific relationship between LCD-induced changes in acetate and insulin sensitivity require further study.

Micronutrients are essential to health at every life stage and their deficiencies are associated with increased incidence of various pathophysiological states and poor quality of life. Efficient methods are therefore needed to monitor micronutrient status of individuals and to improve evidenced-based recommendations for populations. This review (i) reports current approaches to assess the vitamin and mineral status in humans, (ii) summarizes current analytical advantages and shortcomings and (iii) provides practical information for both nutrition research and nutritional status diagnostics. Future analytical perspectives are discussed in relation to micronutrient profiling, analytical sensitivity, and miniaturized technologies. (C) 2018 Elsevier B.V. All rights reserved.

Background/objectives To identify predictors of obesity in adults and investigate to what extent these predictors are independent of other major confounding factors.Subjects/methods Data collected at baseline from 1441 participants from the Food4Me study conducted in seven European countries were included in this study. A food frequency questionnaire was used to measure dietary intake. Accelerometers were used to assess physical activity levels (PA), whereas participants self-reported their body weight, height and waist circumference via the internet.Results The main factors associated (p Conclusions These findings are important for public health and suggest that promotion of increased PA, reducing sedentary behaviours and improving the overall quality of dietary patterns are important strategies for addressing the existing obesity epidemic and associated disease burden.

OBJECTIVES: This study aimed to examine changes in intake of discretionary foods and beverages following a personalized nutrition intervention using two national classifications for discretionary foods. METHODS: Participants were recruited into a 6-month RCT across seven European countries (Food4Me) and were randomized to receive generalized dietary advice (Control) or one of three levels of personalized nutrition advice (based on dietary, phenotypic and genotypic information). Dietary intake from a FFQ was used to determine change between baseline and month 6 in (i) % energy, % contribution to total fat, SFA, total sugars and salt and (ii) contribution (%) made by sweets and snacks to intake of total fat, SFA, sugars and salt from discretionary foods and beverages, defined by Food Standards Scotland (FSS) and the Australian Dietary Guidelines (ADG). RESULTS: A total of 1270 adults (40.9 (SD 13.0) years; 57% female) completed the intervention. At month 6, percentage sugars from FSS discretionary items was lower in personalized nutrition vs control (19.0 ± 0.37 vs 21.1 ± 0.65; P = 0.005). Percentage energy (31.2 ± 0.59 vs 32.7 ± 0.59; P = 0.031), % total fat (31.5 ± 0.37 vs 33.3 ± 0.65; P = 0.021), SFA (36.0 ± 0.43 vs 37.8 ± 0.75; P = 0.034) and sugars (31.7 ± 0.44 vs 34.7 ± 0.78; P

Traditionally, personalised nutrition was delivered at an individual level. However, the concept of delivering tailored dietary advice at a group level through the identification of metabotypes or groups of metabolically similar individuals has emerged. Although this approach to personalised nutrition looks promising, further work is needed to examine this concept across a wider population group. Therefore, the objectives of this study are to: (1) identify metabotypes in a European population and (2) develop targeted dietary advice solutions for these metabotypes. Using data from the Food4Me study (n 1607), k-means cluster analysis revealed the presence of three metabolically distinct clusters based on twenty-seven metabolic markers including cholesterol, individual fatty acids and carotenoids. Cluster 2 was identified as a metabolically healthy metabotype as these individuals had the highest Omega-3 Index (6·56 (sd 1·29) %), carotenoids (2·15 (sd 0·71) µm) and lowest total saturated fat levels. On the basis of its fatty acid profile, cluster 1 was characterised as a metabolically unhealthy cluster. Targeted dietary advice solutions were developed per cluster using a decision tree approach. Testing of the approach was performed by comparison with the personalised dietary advice, delivered by nutritionists to Food4Me study participants (n 180). Excellent agreement was observed between the targeted and individualised approaches with an average match of 82 % at the level of delivery of the same dietary message. Future work should ascertain whether this proposed method could be utilised in a healthcare setting, for the rapid and efficient delivery of tailored dietary advice solutions.

Background: A low-calorie diet (LCD) reduces fat mass excess, improves insulin sensitivity, and alters adipose tissue (AT) gene expression, yet the relation with clinical outcomes remains unclear.Objective: We evaluated AT transcriptome alterations during an LCD and the association with weight and glycemic outcomes both at LCD termination and 6 mo after the LCD.Design: Using RNA sequencing (RNAseq), we analyzed transcriptome changes in AT from 191 obese, nondiabetic patients within a multicenter, controlled dietary intervention. Expression changes were associated with outcomes after an 8-wk LCD (800-1000 kcal/d) and 6 mo after the LCD. Results were validated by using quantitative reverse transcriptase-polymerase chain reaction in 350 subjects from the same cohort. Statistical models were constructed to classify weight maintainers or glycemic improvers.Results: With RNAseq analyses, we identified 1173 genes that were differentially expressed after the LCD, of which 350 and 33 were associated with changes in body mass index (BMI; in kg/m(2)) and Matsuda index values, respectively, whereas 29 genes were associated with both endpoints. Pathway analyses highlighted enrichment in lipid and glucose metabolism. Classification models were constructed to identify weight maintainers. A model based on clinical baseline variables could not achieve any classification (validation AUC: 0.50; 95% CI: 0.36, 0.64). However, clinical changes during the LCD yielded better performance of the model (AUC: 0.73; 95% CI: 0.60, 0.87]). Adding baseline expression to this model improved the performance significantly (AUC: 0.87; 95% CI: 0.77, 0.96; Delong's P = 0.012). Similar analyses were performed to classify subjects with good glycemic improvements. Baseline- and LCD-based clinical models yielded similar performance (best AUC: 0.73; 95% CI: 0.60, 0.86). The addition of expression changes during the LCD improved the performance substantially (AUC: 0.80; 95% CI: 0.69, 0.92; P = 0.058).Conclusions: This study investigated AT transcriptome alterations after an LCD in a large cohort of obese, nondiabetic patients. Gene expression combined with clinical variables enabled us to distinguish weight and glycemic responders from nonresponders. These potential biomarkers may help clinicians understand intersubject variability and better predict the success of dietary interventions. This trial was registered at clinicaltrials.gov as NCT00390637.

Background: Circulating branched-chain amino acids (BCAAs) and aromatic amino acids (AAAs) have been shown to be associated with insulin resistance and diabetes risk. The common rs1440581 T allele in the protein phosphatase Mg2+/Mn2+dependent 1K (PPM1K) gene has been related to elevated BCAA concentrations and risk of type 2 diabetes.Objective: In the present study, we tested whether dietary fat and carbohydrate intakes influenced the association between the rs1440581 PPM1K genetic variant and glucose-metabolism traits during weight loss.Design: The rs1440581 PPM1K genetic variant was genotyped in a total of 757 nondiabetic individuals who were randomly assigned to 1 of 2 energy-restricted diets that differed in macronutrient composition (low-fat diet: 20-25% fat, 15% protein, and 60-65% carbohydrate; high-fat diet: 40-45% fat, 15% protein, and 40-45% carbohydrate). The changes in fasting glucose, fasting insulin, insulin resistance (homeostasis model assessment of insulin resistance) and homeostasis model assessment of beta cell function (HOMA-B) were measured after a mean +/- SD weight loss of 6.8 +/- 3.4 kg over 10 wk and analyzed according to the presence of the T allele of rs1440581.Results: The rs1440581 T allele was associated with a smaller improvement in glucose concentrations after the 10-wk dietary intervention (beta +/- SE: 0.05 +/-.02 mg/dL; P = 0.03). In addition, significant gene-diet interactions were shown for the rs1440581 PPM1K genetic variant in relation to changes in insulin and HOMA-B (P-interaction = 0.006 and 0.002, respectively). In response to the high-fat diet, the T allele was associated with a higher reduction of insulin (beta +/- SE: -0.77 +/- 0.40 mu U/mL; P = 0.04) and HOMA-B (beta +/- SE: -13.2 +/- 3.81; P = 0.003). An opposite effect was observed in the low-fat diet group, although in this group the T allele was marginally (P = 0.10) and not significantly (P = 0.24) associated with insulin and HOMA-B, respectively.Conclusion: PPM1K rs1440581 may affect changes in glucose metabolism during weight loss, and this effect is dependent on dietary fat and carbohydrate intakes. This trial was registered at controlled-trials. com as ISRCTN25867281.

Background: There has been limited evidence about whether genotype-tailored advice provides extra benefits in reducing obesityrelated traits compared with the benefits of conventional one-size-fitsall advice. Objective: We determined whether the disclosure of information on fat-mass and obesity-associated (FTO) genotype risk had a greater effect on a reduction of obesity-related traits in risk carriers than in nonrisk carriers across different levels of personalized nutrition. Design: A total of 683 participants (women: 51%; age range: 18'73 y) from the Food4Me randomized controlled trial were included in this analysis. Participants were randomly assigned to 4 intervention arms as follows: level 0, control group; level 1, dietary group; level 2, phenotype group; and level 3, genetic group. FTO (single nucleotide polymorphism rs9939609) was genotyped at baseline in all participants, but only subjects who were randomly assigned to level 3 were informed about their genotypes. Level 3 participants were stratified into risk carriers (AA/AT) and nonrisk carriers (TT) of the FTO gene for analyses. Height, weight, and waist circumference (WC) were self-measured and reported at baseline and months 3 and 6. Results: Changes in adiposity markers were greater in participants who were informed that they carried the FTO risk allele (level 3 AT/AA carriers) than in the nonpersonalized group (level 0) but not in the other personalized groups (level 1 and 2). Mean reductions in weight andWC at month 6 were greater for FTO risk carriers than for noncarriers in the level 3 group [-2.28 kg (95% CI: -3.06, -1.48 kg) compared with -1.99 kg (-2.19, -0.19 kg), respectively (P = 0.037); and -4.34 cm(-5.63, -3.08 cm) compared with -1.99 cm (-4.04, -0.05 cm), respectively, (P = 0.048)]. Conclusions: There are greater body weight and WC reductions in risk carriers than in nonrisk carriers of the FTO gene. This trial was registered at clinicaltrials.gov as NCT01530139. © 2017 American Society for Nutrition.

Individual response to dietary interventions can be highly variable. The phenotypic characteristics of those who will respond positively to personalised dietary advice are largely unknown. The objective of this study was to compare the phenotypic profiles of differential responders to personalised dietary intervention, with a focus on total circulating cholesterol. Subjects from the Food4Me multi-centre study were classified as responders or non-responders to dietary advice on the basis of the change in cholesterol level from baseline to month 6, with lower and upper quartiles defined as responder and non-responder groups, respectively. There were no significant differences between demographic and anthropometric profiles of the groups. Furthermore, with the exception of alcohol, there was no significant difference in reported dietary intake, at baseline. However, there were marked differences in baseline fatty acid profiles. The responder group had significantly higher levels of stearic acid (18 : 0,P=0·034) and lower levels of palmitic acid (16 : 0,P=0·009). Total MUFA (P=0·016) and total PUFA (P=0·008) also differed between the groups. In a step-wise logistic regression model, age, baseline total cholesterol, glucose, five fatty acids and alcohol intakes were selected as factors that successfully discriminated responders from non-responders, with sensitivity of 82 % and specificity of 83 %. The successful delivery of personalised dietary advice may depend on our ability to identify phenotypes that are responsive. The results demonstrate the potential use of metabolic profiles in identifying response to an intervention and could play an important role in the development of precision nutrition.