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Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case.

The proper functioning of adipose tissue is one of the factors in maintaining energy homeostasis. Adipocytes not only store lipids but also produce active molecules such as adipokines and adipocytokines, which are involved in many functions of adipose tissue, including the secretion of hormones that regulate energy and lipid metabolism. Inflammation has been shown to underlie the deregulation of adipose tissue function. Bradykinin belongs to a family of pro-inflammatory kinin peptides that are abundant in most tissues and biological fluids. This study aimed to determine the ability to produce kinin peptides and characterize the effect of bradykinin on pro-inflammatory responses in adipocytes. The Chub-S7 human preadipocyte line was differentiated to show specific properties for adipose tissue cells. The differentiated cells expressed genes that encode proteins such as kininogen, kallikrein, and prolylcarboxypeptidase that are involved in the production of kinins and also showed the expression of kinin receptors. The response of adipocytes to bradykinin was examined in relation to kinin concentration and the presence of kininase inhibitors. The high concentration of bradykinin induced a moderate increase in lipid accumulation, increased release of pro-inflammatory cytokines, and altered gene expression of molecules involved in adipocyte function, such as adiponectin, lipoprotein lipase, and other transcription factors. This study suggests an important role for kinin peptides in inducing inflammatory responses in adipocytes, which can modify the function of adipose tissue and ultimately lead to diseases related to disturbance of energy homeostasis. The results obtained may enrich our understanding of the mechanisms underlying obesity-related disorders.

International audience; Objective: Obesity is a key factor in the development of the metabolic syndrome (MetS), which is associated with increased cardiometabolic risk. We investigated whether obesity classification by BMI and body fat percentage (BF%) influences cardiometabolic profile and dietary responsiveness in 486 MetS subjects (LIPGENE dietary intervention study). Design and Methods: Anthropometric measures, markers of inflammation and glucose metabolism, lipid profiles, adhesion molecules, and hemostatic factors were determined at baseline and after 12 weeks of four dietary interventions (high saturated fat (SFA), high monounsaturated fat (MUFA), and two low fat high complex carbohydrate (LFHCC) diets, one supplemented with long chain n-3 polyunsaturated fatty acids (LC n-3 PUFAs)). Results: About 39 and 87% of subjects classified as normal and overweight by BMI were obese according to their BF%. Individuals classified as obese by BMI (>= 30 kg/m(2)) and BF% (>= 25% (men) and >= 35% (women)) (OO, n = 284) had larger waist and hip measurements, higher BMI and were heavier (P < 0.001) than those classified as nonobese by BMI but obese by BF% (NOO, n = 92). OO individuals displayed a more proinflammatory (higher C reactive protein (CRP) and leptin), prothrombotic (higher plasminogen activator inhibitor-1 (PAI-1)), proatherogenic (higher leptin/adiponectin ratio) and more insulin resistant (higher HOMA-IR) metabolic profile relative to the NOO group (P < 0.001). Interestingly, tumor necrosis factor-alpha (TNF-alpha) concentrations were lower post-intervention in NOO individuals compared with OO subjects (P < 0.001). Conclusions: In conclusion, assessing BF% and BMI as part of a metabotype may help to identify individuals at greater cardiometabolic risk than BMI alone.

Obesity is a key factor in the development of the metabolic syndrome (MetS), which is associated with increased cardiometabolic risk. We investigated whether obesity classification by BMI and body fat percentage (BF%) influences cardiometabolic profile and dietary responsiveness in 486 MetS subjects (LIPGENE dietary intervention study).Anthropometric measures, markers of inflammation and glucose metabolism, lipid profiles, adhesion molecules, and hemostatic factors were determined at baseline and after 12 weeks of four dietary interventions (high saturated fat (SFA), high monounsaturated fat (MUFA), and two low fat high complex carbohydrate (LFHCC) diets, one supplemented with long chain n-3 polyunsaturated fatty acids (LC n-3 PUFAs)).About 39 and 87% of subjects classified as normal and overweight by BMI were obese according to their BF%. Individuals classified as obese by BMI (≥ 30 kg/m(2)) and BF% (≥ 25% (men) and ≥ 35% (women)) (OO, n = 284) had larger waist and hip measurements, higher BMI and were heavier (P0.001) than those classified as nonobese by BMI but obese by BF% (NOO, n = 92). OO individuals displayed a more proinflammatory (higher C reactive protein (CRP) and leptin), prothrombotic (higher plasminogen activator inhibitor-1 (PAI-1)), proatherogenic (higher leptin/adiponectin ratio) and more insulin resistant (higher HOMA-IR) metabolic profile relative to the NOO group (P0.001). Interestingly, tumor necrosis factor-α (TNF-α) concentrations were lower post-intervention in NOO individuals compared with OO subjects (P0.001).In conclusion, assessing BF% and BMI as part of a metabotype may help to identify individuals at greater cardiometabolic risk than BMI alone.

Several association studies of type 2 diabetes mellitus (T2DM) and adiponectin gene polymorphisms have been reported with conflicting results. Our aim was to search for the association of three polymorphisms (-11.391GA, +45TG, and +276GT) in the adiponectin gene with T2DM and prediabetic quantitative traits in Polish Caucasians. The study groups comprised 495 unrelated T2DM cases and 435 controls. We compared the distribution of genotypes between study groups. In addition, genotype-quantitative trait analyses were also done in the controls. The study subjects were genotyped using the restriction fragment length polymorphism technique. The frequencies of the minor alleles were as follows: 10.6 versus 8.2% for -11.391GA (p = 0.0722), 7.0 versus 8.0% for +45TG (p = 0.48), and 15.5% in T2DM versus 19.8% in controls (p = 0.0145) for +276GT, respectively. The difference for genotype distribution between the groups was statistically significant (p = 0.0247) for the +276GT variant: 71.31 versus 62.99%, 26.46 versus 34.48% and 2.22 versus 2.53%, respectively, for GG, GT and TT. In quantitative traits analysis, the T allele of +276GT was associated (p0.05) with lower insulin resistance (HOMA-IR, fasting insulin) among controls. Additionally, the A allele at position -11.391 was associated (p0.05) with higher insulin resistance (HOMA-IR, fasting insulin). In multiple regression analysis, all identified association remained significant after the inclusion in the model of gender, BMI and age. In addition, in this model, -11.391GA and +276GT were independently associated with T2DM. Finally, we conclude that the adiponectin gene polymorphisms are associated with T2DM and prediabetic quantitative traits in Polish Caucasians.

Supplementary material 2 (DOCX 19Â kb)

Supplementary material 2 (DOCX 19Â kb)