Benefits of Iterative Searches of Large Databases to Interpret Large Human Gut Metaproteomic Data Sets.

The gut microbiota are increasingly considered as a main partner of human health. Metaproteomics enables us to move from the functional potential revealed by metagenomics to the functions actually operating in the microbiome. However, metaproteome deciphering remains challenging. In particular, confident interpretation of a myriad of MS/MS spectra can only be pursued with smart database searches. Here, we compare the interpretation of MS/MS data sets from 48 individual human gut microbiomes using three interrogation strategies of the dedicated Integrated nonredundant Gene Catalog (IGC 9.9 million genes from 1267 individual fecal samples) together with the Homo sapiens database: the classical single-step interrogation strategy and two iterative strategies (in either two or three steps) aimed at preselecting a reduced-sized, more targeted search space for the final peptide spectrum matching. Both iterative searches outperformed the single-step classical search in terms of the number of peptides and protein clusters identified and the depth of taxonomic and functional knowledge, and this was the most convincing with the three-step approach. However, iterative searches do not help in reducing variability of repeated analyses, which is inherent to the traditional data-dependent acquisition mode, but this variability did not affect the hierarchical relationship between replicates and all other samples.

Dietary Fibre Consensus from the International Carbohydrate Quality Consortium (ICQC).

Dietary fibre is a generic term describing non-absorbed plant carbohydrates and small amounts of associated non-carbohydrate components. The main contributors of fibre to the diet are the cell walls of plant tissues, which are supramolecular polymer networks containing variable proportions of cellulose, hemicelluloses, pectic substances, and non-carbohydrate components, such as lignin. Other contributors of fibre are the intracellular storage oligosaccharides, such as fructans. A distinction needs to be made between intrinsic sources of dietary fibre and purified forms of fibre, given that the three-dimensional matrix of the plant cell wall confers benefits beyond fibre isolates. Movement through the digestive tract modifies the cell wall structure and may affect the interactions with the colonic microbes (e.g., small intestinally non-absorbed carbohydrates are broken down by bacteria to short-chain fatty acids, absorbed by colonocytes). These aspects, combined with the fibre associated components (e.g., micronutrients, polyphenols, phytosterols, and phytoestrogens), may contribute to the health outcomes seen with the consumption of dietary fibre. Therefore, where possible, processing should minimise the degradation of the plant cell wall structures to preserve some of its benefits. Food labelling should include dietary fibre values and distinguish between intrinsic and added fibre. Labelling may also help achieve the recommended intake of 14 g/1000 kcal/day.

Elevated serum ceramides are linked with obesity-associated gut dysbiosis and impaired glucose metabolism.

INTRODUCTION: Low gut microbiome richness is associated with dyslipidemia and insulin resistance, and ceramides and other sphingolipids are implicated in the development of diabetes. OBJECTIVES: Determine whether circulating sphingolipids, particularly ceramides, are associated with alterations in the gut microbiome among obese patients with increased diabetes risk. METHODS: This was a cross-sectional and longitudinal retrospective analysis of a dietary/weight loss intervention. Fasted serum was collected from 49 participants (41 women) and analyzed by HPLC-MS/MS to quantify 45 sphingolipids. Shotgun metagenomic sequencing of stool was performed to profile the gut microbiome. RESULTS: Confirming the link to deteriorated glucose homeostasis, serum ceramides were positively correlated with fasting glucose, but inversely correlated with fasting and OGTT-derived measures of insulin sensitivity and ß-cell function. Significant associations with gut dysbiosis were demonstrated, with SM and ceramides being inversely correlated with gene richness. Ceramides with fatty acid chain lengths of 20-24 carbons were the most associated with low richness. Diet-induced weight loss, which improved gene richness, decreased most sphingolipids. Thirty-one MGS, mostly corresponding to unidentified bacteria species, were inversely correlated with ceramides, including a number of Bifidobacterium and Methanobrevibacter smithii. Higher ceramide levels were also associated with increased metagenomic modules for lipopolysaccharide synthesis and flagellan synthesis, two pathogen-associated molecular patterns, and decreased enrichment of genes involved in methanogenesis and bile acid metabolism. CONCLUSION: This study identifies an association between gut microbiota richness, ceramides, and diabetes risk in overweight/obese humans, and suggests that the gut microbiota may contribute to dysregulation of lipid metabolism in metabolic disorders.

Dietary Glycemic Index and Load and the Risk of Type 2 Diabetes: Assessment of Causal Relations.

While dietary factors are important modifiable risk factors for type 2 diabetes (T2D), the causal role of carbohydrate quality in nutrition remains controversial. Dietary glycemic index (GI) and glycemic load (GL) have been examined in relation to the risk of T2D in multiple prospective cohort studies. Previous meta-analyses indicate significant relations but consideration of causality has been minimal. Here, the results of our recent meta-analyses of prospective cohort studies of 4 to 26-y follow-up are interpreted in the context of the nine Bradford-Hill criteria for causality, that is: (1) Strength of Association, (2) Consistency, (3) Specificity, (4) Temporality, (5) Biological Gradient, (6) Plausibility, (7) Experimental evidence, (8) Analogy, and (9) Coherence. These criteria necessitated referral to a body of literature wider than prospective cohort studies alone, especially in criteria 6 to 9. In this analysis, all nine of the Hill's criteria were met for GI and GL indicating that we can be confident of a role for GI and GL as causal factors contributing to incident T2D. In addition, neither dietary fiber nor cereal fiber nor wholegrain were found to be reliable or effective surrogate measures of GI or GL. Finally, our cost-benefit analysis suggests food and nutrition advice favors lower GI or GL and would produce significant potential cost savings in national healthcare budgets. The high confidence in causal associations for incident T2D is sufficient to consider inclusion of GI and GL in food and nutrient-based recommendations.

Dietary Glycemic Index and Load and the Risk of Type 2 Diabetes: A Systematic Review and Updated Meta-Analyses of Prospective Cohort Studies.

Published meta-analyses indicate significant but inconsistent incident type-2 diabetes(T2D)-dietary glycemic index (GI) and glycemic load (GL) risk ratios or risk relations (RR). It is nowover a decade ago that a published meta-analysis used a predefined standard to identify validstudies. Considering valid studies only, and using random effects dose-response meta-analysis(DRM) while withdrawing spurious results (p < 0.05), we ascertained whether these relationswould support nutrition guidance, specifically for an RR > 1.20 with a lower 95% confidence limit>1.10 across typical intakes (approximately 10th to 90th percentiles of population intakes). Thecombined T2D-GI RR was 1.27 (1.15-1.40) (p < 0.001, n = 10 studies) per 10 units GI, while that forthe T2D-GL RR was 1.26 (1.15-1.37) (p < 0.001, n = 15) per 80 g/d GL in a 2000 kcal (8400 kJ) diet.The corresponding global DRM using restricted cubic splines were 1.87 (1.56-2.25) (p < 0.001, n =10) and 1.89 (1.66-2.16) (p < 0.001, n = 15) from 47.6 to 76.1 units GI and 73 to 257 g/d GL in a 2000kcal diet, respectively. In conclusion, among adults initially in good health, diets higher in GI or GLwere robustly associated with incident T2D. Together with mechanistic and other data, thissupports that consideration should be given to these dietary risk factors in nutrition advice.Concerning the public health relevance at the global level, our evidence indicates that GI and GLare substantial food markers predicting the development of T2D worldwide, for persons ofEuropean ancestry and of East Asian ancestry.

Impact of bacterial probiotics on obesity, diabetes and non-alcoholic fatty liver disease related variables: a systematic review and meta-analysis of randomised controlled trials.

OBJECTIVE: To systematically review the effect of oral intake of bacterial probiotics on 15 variables related to obesity, diabetes and non-alcoholic fatty liver disease. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Medline, EMBASE and COCHRANE from 1990 to June 2018. ELIGIBILITY CRITERIA: Randomised controlled trials (≥14 days) excluding hypercholesterolaemia, alcoholic liver disease, polycystic ovary syndrome and children <3 years. RESULTS: One hundred and five articles met inclusion criteria, representing 6826 subjects. In overweight but not obese subjects, probiotics induced improvements in: body weight (k=25 trials, d=-0.94 kg mean difference, 95% CI -1.17 to -0.70, I²=0.0%), body mass index (k=32, d=-0.55 kg/m², 95% CI -0.86 to -0.23, I²=91.9%), waist circumference (k=13, d=-1.31 cm, 95% CI -1.79 to -0.83, I²=14.5%), body fat mass (k=11, d=-0.96 kg, 95% CI -1.21 to -0.71, I²=0.0%) and visceral adipose tissue mass (k=5, d=-6.30 cm², 95% CI -9.05 to -3.56, I²=0.0%). In type 2 diabetics, probiotics reduced fasting glucose (k=19, d=-0.66 mmol/L, 95% CI -1.00 to -0.31, I²=27.7%), glycated haemoglobin (k=13, d=-0.28 pp, 95% CI -0.46 to -0.11, I²=54.1%), insulin (k=13, d=-1.66 mU/L, 95% CI -2.70 to -0.61, I²=37.8%) and homeostatic model of insulin resistance (k=10, d=-1.05 pp, 95% CI -1.48 to -0.61, I²=18.2%). In subjects with fatty liver diseases, probiotics reduced alanine (k=12, d=-10.2 U/L, 95% CI -14.3 to -6.0, I²=93.50%) and aspartate aminotransferases (k=10, d=-9.9 U/L, 95% CI -14.1 to -5.8, I²=96.1%). These improvements were mostly observed with bifidobacteria (Bifidobacterium breve, B. longum), Streptococcus salivarius subsp. thermophilus and lactobacilli (Lactobacillus acidophilus, L. casei, L. delbrueckii) containing mixtures and influenced by trials conducted in one country. CONCLUSIONS: The intake of probiotics resulted in minor but consistent improvements in several metabolic risk factors in subjects with metabolic diseases. TRIAL REGISTRATION NUMBER: CRD42016033273.

Phosphatidylglycerols are induced by gut dysbiosis and inflammation, and favorably modulate adipose tissue remodeling in obesity.

Lipidomic techniques can improve our understanding of complex lipid interactions that regulate metabolic diseases. Here, a serum phospholipidomics analysis identified associations between phosphatidylglycerols (PGs) and gut microbiota dysbiosis. Compared with the other phospholipids, serum PGs were the most elevated in patients with low microbiota gene richness, which were normalized after a dietary intervention that restored gut microbial diversity. Serum PG levels were positively correlated with metagenomic functional capacities for bacterial LPS synthesis and host markers of low-grade inflammation; transcriptome databases identified PG synthase, the first committed enzyme in PG synthesis, as a potential mediator. Experiments in mice and cultured human-derived macrophages demonstrated that LPS induces PG release. Acute PG treatment in mice altered adipose tissue gene expression toward remodeling and inhibited ex vivo lipolysis in adipose tissue, suggesting that PGs favor lipid storage. Indeed, several PG species were associated with the severity of obesity in mice and humans. Finally, despite enrichment in PGs in bacterial membranes, experiments employing gnotobiotic mice colonized with recombinant PG overproducing Lactococcus lactis showed limited direct contribution of microbial PGs to the host. In summary, PGs are inflammation-responsive lipids indirectly regulated by the gut microbiota via endotoxins and regulate adipose tissue homeostasis in obesity.-Kayser, B. D., Lhomme, M., Prifti, E., Da Cunha, C., Marquet, F., Chain, F., Naas, I., Pelloux, V., Dao, M.-C., Kontush, A., Rizkalla, S. W., Aron-Wisnewsky, J., Bermúdez-Humarán, L. G., Oakley, F., Langella, P., Clément, K., Dugail, I. Phosphatidylglycerols are induced by gut dysbiosis and inflammation, and favorably modulate adipose tissue remodeling in obesity.

French Recommendations for Sugar Intake in Adults: A Novel Approach Chosen by ANSES.

This article presents a systematic review of the scientific evidence linking sugar consumption and health in the adult population performed by a group of experts, mandated by the French Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement, et du travail (ANSES). A literature search was performed by crossing search terms for overweight/obesity, diabetes/insulin resistance, dyslipidemia/cardiovascular diseases, non-alcoholic fatty liver diseases (NAFLD), and uric acid concentrations on one hand and for intake of sugars on the other. Controlled mechanistic studies, prospective cohort studies, and randomized clinical trials were extracted and assessed. A literature analysis supported links between sugar intake and both total energy intake and body weight gain, and between sugar intake and blood triglycerides independently of total energy intake. The effects of sugar on blood triglycerides were shown to be mediated by the fructose component of sucrose and were observed with an intake of fructose >50 g/day. In addition, prospective cohort studies showed associations between sugar intake and the risk of diabetes/insulin resistance, cardiovascular diseases, NAFLD, and hyperuricemia. Based on these observations, ANSES proposed to set a maximum limit to the intake of total sugars containing fructose (sucrose, glucose⁻fructose syrups, honey or other syrups, and natural concentrates, etc.) of 100 g/day.

A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity.

Background: The mechanisms responsible for calorie restriction (CR)-induced improvement in insulin sensitivity (IS) have not been fully elucidated. Greater insight can be achieved through deep biological phenotyping of subjects undergoing CR, and integration of big data. Materials and Methods: An integrative approach was applied to investigate associations between change in IS and factors from host, microbiota, and lifestyle after a 6-week CR period in 27 overweight or obese adults (ClinicalTrials.gov: NCT01314690). Partial least squares regression was used to determine associations of change (week 6 - baseline) between IS markers and lifestyle factors (diet and physical activity), subcutaneous adipose tissue (sAT) gene expression, metabolomics of serum, urine and feces, and gut microbiota composition. ScaleNet, a network learning approach based on spectral consensus strategy (SCS, developed by us) was used for reconstruction of biological networks. Results: A spectrum of variables from lifestyle factors (10 nutrients), gut microbiota (10 metagenomics species), and host multi-omics (metabolic features: 84 from serum, 73 from urine, and 131 from feces; and 257 sAT gene probes) most associated with IS were identified. Biological network reconstruction using SCS, highlighted links between changes in IS, serum branched chain amino acids, sAT genes involved in endoplasmic reticulum stress and ubiquitination, and gut metagenomic species (MGS). Linear regression analysis to model how changes of select variables over the CR period contribute to changes in IS, showed greatest contributions from gut MGS and fiber intake. Conclusion: This work has enhanced previous knowledge on links between host glucose homeostasis, lifestyle factors and the gut microbiota, and has identified potential biomarkers that may be used in future studies to predict and improve individual response to weight-loss interventions. Furthermore, this is the first study showing integration of the wide range of data presented herein, identifying 115 variables of interest with respect to IS from the initial input, consisting of 9,986 variables. Clinical Trial Registration: clinicaltrials.gov (NCT01314690).

Increased Basement Membrane Components in Adipose Tissue During Obesity: Links With TGFß and Metabolic Phenotypes.

CONTEXT: Collagen accumulation around adipocytes and vessels (ie, pericellular fibrosis) is a hallmark of obese adipose tissue associated with altered metabolism. OBJECTIVE: Our objective was to evaluate components of basement membrane (BM) in adipose tissue, including collagen IV, a major BM component, and its relationships with metabolic parameters and TGFß isoforms. DESIGN AND SETTING: We used immuno-techniques and gene expression approaches to detect BM components in subcutaneous and visceral adipose tissue samples. Adipocytes and endothelial cells were isolated from lean and obese adipose tissue. We also focused on the expression of COL4A1 correlated to metabolic variables in moderate obesity and, in severe obesity before and after bariatric surgery. Using in vitro analysis, we explored the impact of TGFß isoforms on the expression of inflammatory and extracellular matrix genes in adipocytes and endothelial cells. RESULTS: BM components were detected around adipocytes and endothelial cells, and were increased in obese adipocytes. COL4A1 expression was positively correlated with insulin-resistance indices in obese subjects and showed less reduction in severely obese subjects with poorer insulin-resistance outcomes 6 months after gastric bypass. COL4A1 expression also correlated with TGFß1 and TGFß3 gene expressions in subcutaneous adipose tissue. Stimulating isolated adipocytes and endothelial cells in vitro with these TGFß isoforms showed an inflammatory and pro-fibrotic phenotype. However, TGFß1 and TGFß3 exposure only provoked COL4A1 overexpression in endothelial cells and not in adipocytes. CONCLUSION: The disorganization of several BM components, including collagen IV, could contribute to pathological alterations of obese adipose tissue and cells.

Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology.

OBJECTIVE: Individuals with obesity and type 2 diabetes differ from lean and healthy individuals in their abundance of certain gut microbial species and microbial gene richness. Abundance of Akkermansia muciniphila, a mucin-degrading bacterium, has been inversely associated with body fat mass and glucose intolerance in mice, but more evidence is needed in humans. The impact of diet and weight loss on this bacterial species is unknown. Our objective was to evaluate the association between faecal A. muciniphila abundance, faecal microbiome gene richness, diet, host characteristics, and their changes after calorie restriction (CR). DESIGN: The intervention consisted of a 6-week CR period followed by a 6-week weight stabilisation diet in overweight and obese adults (N=49, including 41 women). Faecal A. muciniphila abundance, faecal microbial gene richness, diet and bioclinical parameters were measured at baseline and after CR and weight stabilisation. RESULTS: At baseline A. muciniphila was inversely related to fasting glucose, waist-to-hip ratio and subcutaneous adipocyte diameter. Subjects with higher gene richness and A. muciniphila abundance exhibited the healthiest metabolic status, particularly in fasting plasma glucose, plasma triglycerides and body fat distribution. Individuals with higher baseline A. muciniphila displayed greater improvement in insulin sensitivity markers and other clinical parameters after CR. These participants also experienced a reduction in A. muciniphila abundance, but it remained significantly higher than in individuals with lower baseline abundance. A. muciniphila was associated with microbial species known to be related to health. CONCLUSIONS: A. muciniphila is associated with a healthier metabolic status and better clinical outcomes after CR in overweight/obese adults. The interaction between gut microbiota ecology and A. muciniphila warrants further investigation. TRIAL REGISTRATION NUMBER: NCT01314690.

A Dietary Supplement Containing Cinnamon, Chromium and Carnosine Decreases Fasting Plasma Glucose and Increases Lean Mass in Overweight or Obese Pre-Diabetic Subjects: A Randomized, Placebo-Controlled Trial.

BACKGROUND: Preventing or slowing the progression of prediabetes to diabetes is a major therapeutic issue. OBJECTIVES: Our aim was to evaluate the effects of 4-month treatment with a dietary supplement containing cinnamon, chromium and carnosine in moderately obese or overweight pre-diabetic subjects, the primary outcome being change in fasting plasma glucose (FPG) level. Other parameters of plasma glucose homeostasis, lipid profile, adiposity and inflammatory markers were also assessed. METHODS: In a randomized, double-blind, placebo-controlled study, 62 subjects with a FPG level ranging from 5.55 to 7 mmol/L and a body mass index ≥ 25 kg/m(2), unwilling to change their dietary and physical activity habits, were allocated to receive a 4-month treatment with either 1.2 g/day of the dietary supplement or placebo. Patients were followed up until 6 months post-randomization. RESULTS: Four-month treatment with the dietary supplement decreased FPG compared to placebo (-0.24 ± 0.50 vs +0.12 ± 0.59 mmol/L, respectively, p = 0.02), without detectable significant changes in HbA1c. Insulin sensitivity markers, plasma insulin, plasma lipids and inflammatory markers did not differ between the treatment groups. Although there were no significant differences in changes in body weight and energy or macronutrient intakes between the two groups, fat-free mass (%) increased with the dietary supplement compared to placebo (p = 0.02). Subjects with a higher FPG level and a milder inflammatory state at baseline benefited most from the dietary supplement. CONCLUSIONS: Four-month treatment with a dietary supplement containing cinnamon, chromium and carnosine decreased FPG and increased fat-free mass in overweight or obese pre-diabetic subjects. These beneficial effects might open up new avenues in the prevention of diabetes. TRIAL REGISTRATION: ClinicalTrials.gov NCT01530685.

Sparse Zero-Sum Games as Stable Functional Feature Selection.

In large-scale systems biology applications, features are structured in hidden functional categories whose predictive power is identical. Feature selection, therefore, can lead not only to a problem with a reduced dimensionality, but also reveal some knowledge on functional classes of variables. In this contribution, we propose a framework based on a sparse zero-sum game which performs a stable functional feature selection. In particular, the approach is based on feature subsets ranking by a thresholding stochastic bandit. We provide a theoretical analysis of the introduced algorithm. We illustrate by experiments on both synthetic and real complex data that the proposed method is competitive from the predictive and stability viewpoints.

Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome.

The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.

Dietary patterns differently associate with inflammation and gut microbiota in overweight and obese subjects.

BACKGROUND: Associations between dietary patterns, metabolic and inflammatory markers and gut microbiota are yet to be elucidated. OBJECTIVES: We aimed to characterize dietary patterns in overweight and obese subjects and evaluate the different dietary patterns in relation to metabolic and inflammatory variables as well as gut microbiota. DESIGN: Dietary patterns, plasma and adipose tissue markers, and gut microbiota were evaluated in a group of 45 overweight and obese subjects (6 men and 39 women). A group of 14 lean subjects were also evaluated as a reference group. RESULTS: Three clusters of dietary patterns were identified in overweight/obese subjects. Cluster 1 had the least healthy eating behavior (highest consumption of potatoes, confectionary and sugary drinks, and the lowest consumption of fruits that was associated also with low consumption of yogurt, and water). This dietary pattern was associated with the highest LDL cholesterol, plasma soluble CD14 (p = 0.01) a marker of systemic inflammation but the lowest accumulation of CD163+ macrophages with anti-inflammatory profile in adipose tissue (p = 0.05). Cluster 3 had the healthiest eating behavior (lower consumption of confectionary and sugary drinks, and highest consumption of fruits but also yogurts and soups). Subjects in this Cluster had the lowest inflammatory markers (sCD14) and the highest anti-inflammatory adipose tissue CD163+ macrophages. Dietary intakes, insulin sensitivity and some inflammatory markers (plasma IL6) in Cluster 3 were close to those of lean subjects. Cluster 2 was in-between clusters 1 and 3 in terms of healthfulness. The 7 gut microbiota groups measured by qPCR were similar across the clusters. However, the healthiest dietary cluster had the highest microbial gene richness, as evaluated by quantitative metagenomics. CONCLUSION: A healthier dietary pattern was associated with lower inflammatory markers as well as greater gut microbiota richness in overweight and obese subjects. TRIAL REGISTRATION: ClinicalTrials.gov NCT01314690.

Glycemic index: is it a predictor of metabolic and vascular disorders?

PURPOSE OF REVIEW: The role of glycemic index on metabolic and cardiovascular risk factors received considerable attention in light of the current increase in cardiometabolic disorders. We aimed to deal and identify the recently published prospective epidemiological studies as well as randomized controlled studies investigating the associations of metabolic and cardiovascular risk markers with dietary intake of carbohydrates and with measures of the induced glycemic index. The main prospective studies and meta-analysis grouping the recent prospective and clinical interventions are discussed. RECENT FINDINGS: Recently, during the last few years, evidence exists that high glycemic index/glycemic load diets contribute to risk of type 2 diabetes and cardiovascular disease. Additionally, low glycemic index/glycemic load diets were found to be effective in the treatment of cardiometabolic disorders. SUMMARY: The use of the low glycemic index notion in the dietary recommendations for children, adolescents and adults might play a role in the prevention, and or treatment, of metabolic diseases and their cardiovascular complications.

Insulin resistance and inflammation predict kinetic body weight changes in response to dietary weight loss and maintenance in overweight and obese subjects by using a Bayesian network approach.

BACKGROUND: The ability to identify obese subjects who will lose weight in response to energy restriction is an important strategy in obesity treatment. OBJECTIVE: We aimed to identify obese subjects who would lose weight and maintain weight loss through 6 wk of energy restriction and 6 wk of weight maintenance. DESIGN: Fifty obese or overweight subjects underwent a 6-wk energy-restricted, high-protein diet followed by another 6 wk of weight maintenance. Network modeling by using combined biological, gut microbiota, and environmental factors was performed to identify predictors of weight trajectories. RESULTS: On the basis of body weight trajectories, 3 subject clusters were identified. Clusters A and B lost more weight during energy restriction. During the stabilization phase, cluster A continued to lose weight, whereas cluster B remained stable. Cluster C lost less and rapidly regained weight during the stabilization period. At baseline, cluster C had the highest plasma insulin, interleukin (IL)-6, adipose tissue inflammation (HAM56+ cells), and Lactobacillus/Leuconostoc/Pediococcus numbers in fecal samples. Weight regain after energy restriction correlated positively with insulin resistance (homeostasis model assessment of insulin resistance: r = 0.5, P = 0.0002) and inflammatory markers (IL-6; r = 0.43, P = 0.002) at baseline. The Bayesian network identified plasma insulin, IL-6, leukocyte number, and adipose tissue (HAM56) at baseline as predictors that were sufficient to characterize the 3 clusters. The prediction accuracy reached 75.5%. CONCLUSION: The resistance to weight loss and proneness to weight regain could be predicted by the combination of high plasma insulin and inflammatory markers before dietary intervention.

Dietary intervention impact on gut microbial gene richness.

Complex gene-environment interactions are considered important in the development of obesity. The composition of the gut microbiota can determine the efficacy of energy harvest from food and changes in dietary composition have been associated with changes in the composition of gut microbial populations. The capacity to explore microbiota composition was markedly improved by the development of metagenomic approaches, which have already allowed production of the first human gut microbial gene catalogue and stratifying individuals by their gut genomic profile into different enterotypes, but the analyses were carried out mainly in non-intervention settings. To investigate the temporal relationships between food intake, gut microbiota and metabolic and inflammatory phenotypes, we conducted diet-induced weight-loss and weight-stabilization interventions in a study sample of 38 obese and 11 overweight individuals. Here we report that individuals with reduced microbial gene richness (40%) present more pronounced dys-metabolism and low-grade inflammation, as observed concomitantly in the accompanying paper. Dietary intervention improves low gene richness and clinical phenotypes, but seems to be less efficient for inflammation variables in individuals with lower gene richness. Low gene richness may therefore have predictive potential for the efficacy of intervention.