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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.

Background The cline of human genetic diversity observable across Europe is recapitulated at a micro-geographic scale by variation within the Italian population. Besides resulting from extensive gene flow, this might be ascribable also to local adaptations to diverse ecological contexts evolved by people who anciently spread along the Italian Peninsula. Dissecting the evolutionary history of the ancestors of present-day Italians may thus improve the understanding of demographic and biological processes that contributed to shape the gene pool of European populations. However, previous SNP array-based studies failed to investigate the full spectrum of Italian variation, generally neglecting low-frequency genetic variants and examining a limited set of small effect size alleles, which may represent important determinants of population structure and complex adaptive traits. To overcome these issues, we analyzed 38 high-coverage whole-genome sequences representative of population clusters at the opposite ends of the cline of Italian variation, along with a large panel of modern and ancient Euro-Mediterranean genomes. Results We provided evidence for the early divergence of Italian groups dating back to the Late Glacial and for Neolithic and distinct Bronze Age migrations having further differentiated their gene pools. We inferred adaptive evolution at insulin-related loci in people from Italian regions with a temperate climate, while possible adaptations to pathogens and ultraviolet radiation were observed in Mediterranean Italians. Some of these adaptive events may also have secondarily modulated population disease or longevity predisposition. Conclusions We disentangled the contribution of multiple migratory and adaptive events in shaping the heterogeneous Italian genomic background, which exemplify population dynamics and gene-environment interactions that played significant roles also in the formation of the Continental and Southern European genomic landscapes.

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.

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 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.

Aims To identify genetic variants that have a regulatory impact on circulating microRNAs (miRNAs) and to connect genetic risk to blood traits/biomarkers through the circulating miRNAs. Methods and results Leveraging miRNA-Seq data and the 1000 Genomes imputed genotypes, we carried out genome-wide association analysis for SNPs that regulate the expression of circulating miRNAs in a sample of 710 unrelated subjects of European ancestry. Wherever possible, we used data from the Framingham and the Geuvadis studies to replicate our findings. We found at least one genome-wide significant (P Conclusions This study identifies SNPs that have a regulatory impact on circulating miRNAs, and underlines the role of several circulating miRNAs in mediating the effect of a number of GWAS loci on cardiometabolic phenotypes.

Background: Classical risk factors, such as fasting cholesterol, blood pressure (BP), and diabetes status are used today to predict the risk of developing cardiovascular disease (CVD). However, accurate prediction remains limited, particularly in low-risk groups such as women and younger individuals. Growing evidence suggests that biomarker concentrations following consumption of a meal challenge are better and earlier predictors of disease development than biomarker concentrations. Objective: To test the hypothesis that postprandial responses of circulating biomarkers differ between healthy subjects with and without subclinical atherosclerosis (SA) in an Asian population at low risk of coronary artery disease (CAD). Methods: One hundred healthy Chinese subjects (46 women, 54 men) completed the study. Subjects consumed a mixed-meal test and 164 blood biomarkers were analyzed over 6 h by using a combination of chemical and NMR techniques. Models were trained using different methodologies (including logistic regression, elastic net, random forest, sparse partial least square) on a random 75% subset of the data, and their performance was evaluated on the remaining 25%. Results: We found that models based on baseline clinical parameters or fasting biomarkers could not reliably predict SA. By contrast, an omics model based on magnitude and timing of postprandial biomarkers achieved high performance [receiving operating characteristic (ROC) AUC: 91%; 95% CI: 77, 100). Investigation of key features of this model enabled derivation of a considerably simpler model, solely based on postprandial BP and age, with excellent performance (AUC: 91%; 95% CI: 78, 100). Conclusion: We report a novel model to detect SA based on postprandial BP and age in a population of Asian subjects at low risk of CAD. The use of this model in large-scale CVD prevention programs should be explored. Agency for Science, Technology and Research (A*STAR) Supported financially by Société des Produits Nestlé SA, Lausanne, Switzerland and the Agency for Science, Technology and Research (A∗STAR).

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.

Extreme longevity is the paradigm of healthy aging as individuals who reached the extreme decades of human life avoided or largely postponed all major age-related diseases. In this study, we sequenced at high coverage (90X) the whole genome of 81 semi-supercentenarians and supercentenarians [105+/110+] (mean age: 106.6 ± 1.6) and of 36 healthy unrelated geographically matched controls (mean age 68.0 ± 5.9) recruited in Italy. The results showed that 105+/110+ are characterized by a peculiar genetic background associated with efficient DNA repair mechanisms, as evidenced by both germline data (common and rare variants) and somatic mutations patterns (lower mutation load if compared to younger healthy controls). Results were replicated in a second independent cohort of 333 Italian centenarians and 358 geographically matched controls. The genetics of 105+/110+ identified DNA repair and clonal haematopoiesis as crucial players for healthy aging and for the protection from cardiovascular events.

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.

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.

Supplementation with the NAD+ precursor nicotinamide riboside (NR) ameliorates and prevents a broad array of metabolic and aging disorders in mice. However, little is known about the physiological role of endogenous NR metabolism. We have previously shown that NR kinase 1 (NRK1) is rate-limiting and essential for NR-induced NAD+ synthesis in hepatic cells. To understand the relevance of hepatic NR metabolism, we generated whole body and liver-specific NRK1 knockout mice. Here, we show that NRK1 deficiency leads to decreased gluconeogenic potential and impaired mitochondrial function. Upon high-fat feeding, NRK1 deficient mice develop glucose intolerance, insulin resistance and hepatosteatosis. Furthermore, they are more susceptible to diet-induced liver DNA damage, due to compromised PARP1 activity. Our results demonstrate that endogenous NR metabolism is critical to sustain hepatic NAD+ levels and hinder diet-induced metabolic damage, highlighting the relevance of NRK1 as a therapeutic target for metabolic disorders., Nicotinamide adenine dinucleotide (NAD+) sustains cellular energy metabolism, functions as a substrate of Sirt and PARP enzymes, and its supplementation is explored therapeutically in aging and other contexts. Here the authors provide insight into the role of endogenous NAD+ metabolism by studying nicotinamide riboside kinase 1 (NRK1) deficient mice.

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.This study includes baseline data from 634 adults with overweight or obesity (BMI ≥ 27 kg/mBoth 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.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.

Holistic human proteome maps are expected to complement comprehensive profile assessment of health and disease phenotypes. However, methodologies to analyze proteomes in human tissue or body fluid samples at relevant scale and performance are still limited in clinical research. Their deployment and demonstration in large enough human populations are even sparser. In the present study, we have characterized and compared the plasma proteomes of two large independent cohorts of obese and overweight individuals using shotgun mass spectrometry (MS)-based proteomics. Herein, we showed, in both populations from different continents of about 500 individuals each, the concordance of plasma protein MS measurements in terms of variability, gender-specificity, and age-relationship. Additionally, we replicated several known and new associations between proteins, clinical and molecular variables, such as insulin and glucose concentrations. In conclusion, our MS-based analyses of plasma samples from independent human cohorts proved the practical feasibility and efficiency of a large and unified discovery/replication approach in proteomics, which was also recently coined “rectangular” design.

Background Constitutional thinness (CT) is a natural state of underweight (13–17.5 kg/m2) without the presence of any eating disorders and abnormal hormonal profile, and with preserved menses in women. We previously conducted a four-week fat overfeeding study showing weight gain resistance in CT women and one of our main results was the identification of an energy gap: a positive energy balance (higher energy intake than energy expenditure). Objective This new overfeeding study is designed to confirm the energy gap and propose mechanistic hypothesis. Design A 2-week overfeeding (daily consumption of one bottle of Renutryl® Booster (600 kcal, 30 g protein, 72 g carbohydrate, 21 g fat) on top of the dietary intake) is performed to compare 15 women and men in each CT group (Body Mass Index [BMI] Results and conclusions Data inter-linking will be able to be established with results of this study. The findings could possibly open to therapeutic approaches to help CT patients to gain weight as well as provide a better understanding of energy regulation with regard to treat obesity (resistance to weight loss), a mirror image of CT (resistance to weight gain).

Background An aim of weight loss is to reduce the risk of type 2 diabetes (T2D) in obese subjects. However, the relation with long-term glycemic improvement remains unknown. Objective We evaluated the changes in lipid composition during weight loss and their association with long-term glycemic improvement. Design We investigated the plasma lipidome of 383 obese, nondiabetic patients within a randomized, controlled dietary intervention in 8 European countries at baseline, after an 8-wk low-caloric diet (LCD) (800-1000 kcal/d), and after 6 mo of weight maintenance. Results After weight loss, a lipid signature identified 2 groups of patients who were comparable at baseline but who differed in their capacities to lose weight and improve glycemic control. Six months after the LCD, one group had significant glycemic improvement [homeostasis model assessment of insulin resistance (HOMA-IR) mean change: -0.92; 95% CI: -1.17, -0.67)]. The other group showed no improvement in glycemic control (HOMA-IR mean change: -0.26; 95% CI: -0.64, 0.13). These differences were sustained for ≥1 y after the LCD. The same conclusions were obtained with other endpoints (Matsuda index and fasting insulin and glucose concentrations). Significant differences between the 2 groups were shown in leptin gene expression in adipose tissue biopsies. Significant differences were also observed in weight-related endpoints (body mass index, weight, and fat mass). The lipid signature allowed prediction of which subjects would be considered to be insulin resistant after 6 mo of weight maintenance [validation's receiver operating characteristic (ROC) area under the curve (AUC): 71%; 95% CI: 62%, 81%]. This model outperformed a clinical data-only model (validation's ROC AUC: 61%; 95% CI: 50%, 71%; P = 0.01). Conclusions In this study, we report a lipid signature of LCD success (for weight and glycemic outcome) in obese, nondiabetic patients. Lipid changes during an 8-wk LCD allowed us to predict insulin-resistant patients after 6 mo of weight maintenance. The determination of the lipid composition during an LCD enables the identification of nonresponders and may help clinicians manage metabolic outcomes with further intervention, thereby improving the long-term outcome and preventing T2D. This trial was registered at clinicaltrials.gov as NCT00390637.

The overall impact of proteomics on clinical research and its translation has lagged behind expectations. One recognized caveat is the limited size (subject numbers) of (pre-)clinical studies performed at the discovery stage, the findings of which fail to be replicated in larger verification/validation trials. Compromised study designs and insufficient statistical power are consequences of the to-date still limited capacity of mass spectrometry (MS)-based workflows to handle large numbers of samples in a realistic time frame, while delivering comprehensive proteome coverages. We developed a highly automated proteomic biomarker discovery workflow. Herein, we have applied this approach to analyze 1'000 plasma samples from the multi-centered human dietary intervention study "DiOGenes". Study design, sample randomization, tracking, and logistics were the foundations of our large-scale study. We checked the quality of the MS data and provided descriptive statistics. The dataset was interrogated for proteins with most stable expression levels in that set of plasma samples. We evaluated standard clinical variables that typically impact forthcoming results and assessed body mass index-associated and gender-specific proteins at 2 time points. We demonstrate that analyzing a large number of human plasma samples for biomarker discovery with MS using isobaric tagging is feasible, providing robust and consistent biological results.

Background: Several studies recently reported contradicting results regarding the link between amylase 1 (AMY1) copy numbers (CNs), obesity, and type 2 diabetes.Objective: The aim of this study was to assess the impact of AMY1 CN on anthropometrics and glycemic outcomes in obese individuals following a 2-phase dietary weight loss intervention.Methods: Using the paralog ratio test, AMY1 CNs were accurately measured in 761 obese individuals from the DiOGenes study. Subjects first underwent an 8-wk low-calorie diet (LCD, at 800 kcal/d) and then were randomly assigned to a 6-mo weight maintenance dietary (WMD) intervention with arms having different glycemic loads.Results: At baseline, a modest association between AMY1 CN and BMI (P = 0.04) was observed. AMY1 CN was not associated with baseline glycemic variables. In addition, AMY1 CN was not associated with anthropometric or glycemic outcomes following either LCD or WMD. Interaction analyses between AMY1 CN and nutrient intake did not reveal any significant association with clinical parameters (at baseline and following LCD or WMD) or when testing gene x WMD interactions during the WMD phase.Conclusion: In the absence of association with weight trajectories or glycemic improvements, the AMY1 CN cannot be considered as an important biomarker for response to a clinical weight loss and weight maintenance programs in overweight/obese subjects. This trial was registered at www.clinicaltrials.gov as NCT00390637.

Differences between genomes can be due to single nucleotide variants (SNPs), translocations, inversions and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 250 kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease or phenotypic traits.While the link between SNPs and disease susceptibility has been well studied, to date there are still very few published CNV genome-wide association studies; probably owing to the fact that CNV analysis remains a slightly more complex task than SNP analysis (both in term of bioinformatics workflow and uncertainty in the CNV calling leading to high false positive rates and unknown false negative rates). This chapter aims at explaining computational methods for the analysis of CNVs, ranging from study design, data processing and quality control, up to genome-wide association study with clinical traits.

Aim of Study: The use of weighed food diaries in nutritional studies provides a powerful method to quantify food and nutrient intakes. Yet, mapping these records onto food composition tables (FCTs) is a challenging, time-consuming and error-prone process. Experts make this effort manually and no automation has been previously proposed. Our study aimed to assess automated approaches to map food items onto FCTs.Methods: We used food diaries (~170,000 records pertaining to 4,200 unique food items) from the DiOGenes randomized clinical trial. We attempted to map these items onto six FCTs available from the EuroFIR resource. Two approaches were tested: the first was based solely on food name similarity (fuzzy matching). The second used a machine learning approach (C5.0 classifier) combining both fuzzy matching and food energy. We tested mapping food items using their original names and also an English-translation. Top matching pairs were reviewed manually to derive performance metrics: precision (the percentage of correctly mapped items) and recall (percentage of mapped items).Results: The simpler approach: fuzzy matching, provided very good performance. Under a relaxed threshold (score > 50%), this approach enabled to remap 99.49% of the items with a precision of 88.75%. With a slightly more stringent threshold (score > 63%), the precision could be significantly improved to 96.81% while keeping a recall rate > 95% (i.e., only 5% of the queried items would not be mapped). The machine learning approach did not lead to any improvements compared to the fuzzy matching. However, it could increase substantially the recall rate for food items without any clear equivalent in the FCTs (+7 and +20% when mapping items using their original or English-translated names). Our approaches have been implemented as R packages and are freely available from GitHub.Conclusion: This study is the first to provide automated approaches for large-scale food item mapping onto FCTs. We demonstrate that both high precision and recall can be achieved. Our solutions can be used with any FCT and do not require any programming background. These methodologies and findings are useful to any small or large nutritional study (observational as well as interventional).

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.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.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 explained7%. Receiver operating characteristic (ROC) curves indicated that rs174537 genotype can be distinguished with SAT fatty acids (AUC = 0.842), but not gene expression (AUC = 0.627). No differences in SAT inflammatory gene expression were observed between rs174537 genotypes. SAT 20:3n-6 levels were positively correlated with the expression of several inflammatory genes, and inversely correlated with FADS1 expression.This study showed that FADS1 genotype is distinguished by SAT fatty acid profiles, but not inflammatory gene expression.

Background: Weight loss in obese individuals aims to reduce the risk of type 2 diabetes by improving glycemic control. Yet, significant intersubject variability is observed and the outcomes remain poorly predictable.Objective: The aim of the study was to predict whether an individual will show improvements in insulin sensitivity above or below the median population change at 6 mo after a low-calorie-diet (LCD) intervention.Design: With the use of plasma lipidomics and metabolomics for 433 subjects from the Diet, Obesity, and Genes (DiOGenes) Study, we attempted to predict good or poor Matsuda index improvements 6 mo after an 8-wk LCD intervention (800 kcal/d). Three independent analysis groups were defined: "training" (n = 119) for model construction, "testing" (n = 162) for model comparison, and "validation" (n = 152) to validate the final model.Results: Initial modeling with baseline clinical variables (body mass index, Matsuda index, total lipid concentrations, sex, age) showed limited performance [area under the curve (AUC) on the "testing dataset" = 0.69; 95% CI: 0.61, 0.77]. Significantly better performance was achieved with an omics model based on 27 variables (AUC = 0.77; 95% CI: 0.70, 0.85; P = 0.0297). This model could be greatly simplified while keeping the same performance. The simplified model relied on baseline Matsuda index, proline, and phosphatidylcholine 0-34:1. It successfully replicated on the validation set (AUC = 0.75; 95% CI: 0.67, 0.83) with the following characteristics: specificity = 0.73, sensitivity = 0.68, negative predictive value = 0.60, and positive predictive value = 0.80. Marginally lower performance was obtained when replacing the Matsuda index with homeostasis model assessment of insulin resistance (AUC = 0.72; 95% CI: 0.64, 0.80; P = 0.08).Conclusions: Our study proposes a model to predict insulin sensitivity improvements, 6 mo after LCD completion in a large population of overweight or obese nondiabetic subjects. It relies on baseline information from 3 variables, accessible from blood samples. This model may help clinicians assessing the large variability in dietary interventions and predict outcomes before an intervention. This trial was registered at www.clinicaltrials.gov as NCT00390637.

Differences between genomes can be due to single nucleotide variants (SNPs), translocations, inversions and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 250 kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease or phenotypic traits.While the link between SNPs and disease susceptibility has been well studied, to date there are still very few published CNV genome-wide association studies; probably owing to the fact that CNV analysis remains a slightly more complex task than SNP analysis (both in term of bioinformatics workflow and uncertainty in the CNV calling leading to high false positive rates and unknown false negative rates). This chapter aims at explaining computational methods for the analysis of CNVs, ranging from study design, data processing and quality control, up to genome-wide association study with clinical traits.

Thousands of genetic variants have been associated with complex traits through genome-wide association studies. However, the functional variants or mechanistic consequences remain elusive. Intermediate traits such as gene expression or protein levels are good proxies of the metabolic state of an organism. Proteome analysis especially can provide new insights into the molecular mechanisms of complex traits like obesity. The role of genetic variation in determining protein level variation has not been assessed in obesity. To address this, we design a large-scale protein quantitative trait locus (pQTL) analysis based on a set of 1129 proteins from 494 obese subjects before and after a weight loss intervention. This reveals 55 BMI-associated cis-pQTLs and trans-pQTLs at baseline and 3 trans-pQTLs after the intervention. We provide evidence for distinct genetic mechanisms regulating BMI-associated proteins before and after weight loss. Finally, by functional analysis, we identify and validate FAM46A as a trans regulator for leptin., Although many genetic variants are known for obesity, their function remains largely unknown. Here, in a weight-loss intervention cohort, the authors identify protein quantitative trait loci associated with BMI at baseline and after weight loss and find FAM46A to be a regulator of leptin in adipocytes.

Validated protein biomarkers are needed for assessing health trajectories, predicting and subclassifying disease, and optimizing diagnostic and therapeutic clinical decision-making. The sensitivity, specificity, accuracy, and precision of single or combinations of protein biomarkers may be altered by differences in physiological states limiting the ability to translate research results to clinically useful diagnostic tests. Aptamer based affinity assays were used to test whether low abundant serum proteins differed based on age, sex, and fat mass in a healthy population of 94 males and 102 females from the MECHE cohort. The findings were replicated in 217 healthy male and 377 healthy female participants in the DiOGenes consortium. Of the 1129 proteins in the panel, 141, 51, and 112 proteins (adjusted p < 0.1) were identified in the MECHE cohort and significantly replicated in DiOGenes for sexual dimorphism, age, and fat mass, respectively. Pathway analysis classified a subset of proteins from the 3 phenotypes to the complement and coagulation cascades pathways and to immune and coagulation processes. These results demonstrated that specific proteins were statistically associated with dichotomous (male vs female) and continuous phenotypes (age, fat mass), which may influence the identification and use of biomarkers of clinical utility for health diagnosis and therapeutic strategies. Department of Agriculture, Food and the Marine European Commission Food for Health Ireland

Background/Objectives: Inter-individual variability in weight loss during obesity treatment is complex and poorly understood. Here we use whole body and tissue approaches to investigate fuel oxidation characteristics in skeletal muscle fibers, cells and distinct circulating protein biomarkers before and after a high fat meal (HFM) challenge in those who lost the most (obese diet-sensitive; ODS) vs the least (obese diet-resistant; ODR) amount of weight in a highly controlled weight management program. Subjects/Methods: In 20 weight stable-matched ODS and ODR women who previously completed a standardized clinical weight loss program, we analyzed whole-body energetics and metabolic parameters in vastus lateralis biopsies and plasma samples that were obtained in the fasting state and 6 h after a defined HFM, equivalent to 35% of total daily energy requirements. Results: At baseline (fasting) and post-HFM, muscle fatty acid oxidation and maximal oxidative phosphorylation were significantly greater in ODS vs ODR, as was reactive oxygen species emission. Plasma proteomics of 1130 proteins pre and 1, 2, 5 and 6 h after the HFM demonstrated distinct group and interaction differences. Group differences identified S-formyl glutathione hydratase, heat shock 70 kDA protein 1A/B (HSP72), and eukaryotic translation initiation factor 5 (eIF5) to be higher in ODS vs ODR. Group-time differences included aryl hydrocarbon interacting protein (AIP), peptidylpropyl isomerase D (PPID) and tyrosine protein-kinase Fgr, which increased in ODR vs ODS over time. HSP72 levels correlated with muscle oxidation and citrate synthase activity. These proteins circulate in exosomes; exosomes isolated from ODS plasma increased resting, leak and maximal respiration rates in C2C12 myotubes by 58%, 21% and 51%, respectively, vs those isolated from ODR plasma. Conclusions: Findings demonstrate distinct muscle metabolism and plasma proteomics in fasting and post-HFM states corresponding in diet-sensitive vs diet-resistant obese women.