Longitudinal association of inflammatory markers with markers of glycaemia and insulin resistance in European children

Association between serum leptin concentrations and homeostasis model assessment-insulin resistance of 2.5 and higher in normal weight Japanese women

Normal weight insulin resistant phenotype was characterized in 251 Japanese female university students using homeostasis model assessment-insulin resistance. Birth weight, body composition at age 20, cardiometabolic traits and dietary intake were compared cross-sectionally between insulin sensitive (< 1.6, n = 194) and insulin resistant (2.5 and higher, n = 16) women. BMI averaged < 21 kg/m² and waist < 72 cm and did not differ between two groups. The percentage of macrosomia and serum absolute and fat-mass corrected leptin concentrations were higher in insulin resistant women although there was no difference in birth weight, fat mass index, trunk/leg fat ratio and serum adiponectin. In addition, resting pulse rate, serum concentrations of free fatty acids, triglycerides and remnant-like particle cholesterol were higher in insulin resistant women although HDL cholesterol and blood pressure did not differ. In multivariate logistic regression analyses, serum leptin (odds ratio:1.68, 95% confidential interval:1.08-2.63, p = 0.02) was associated with normal weight insulin resistance independently of macrosomia, free fatty acids, triglycerides, remnant-like particle cholesterol and resting pulse rate. In conclusion, normal weight IR phenotype may be associated with increased plasma leptin concentrations and leptin to fat mass ratio in young Japanese women, suggesting higher leptin production by body fat unit.

Omics biomarkers and an approach for their practical implementation to delineate health status for personalized nutrition strategies

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.

Association between Urinary Advanced Glycation End Products and Subclinical Inflammation in Children and Adolescents: Results from the Italian I.Family Cohort

Advanced Glycation End Products (AGEs) have been positively correlated with inflammation in adults, while inconsistent evidence is available in children. We evaluated the association between urinary AGEs, measured by fluorescence spectroscopy, and biomarkers of subclinical inflammation in 676 healthy children/adolescents (age 11.8 ± 1.6 years, M ± SD) from the Italian cohort of the I.Family project. Urinary fluorescent AGEs were used as independent variable and high-sensitivity C-reactive protein (hs-CRP) was the primary outcome, while other biomarkers of inflammation were investigated as secondary outcomes. Participants with urinary AGEs above the median of the study population showed statistically significantly higher hs-CRP levels as compared to those below the median (hs-CRP 0.44 ± 1.1 vs. 0.24 ± 0.6 mg/dL, M ± SD p = 0.002). We found significant positive correlations between urinary AGEs and hs-CRP (p = 0.0001), IL-15 (p = 0.001), IP-10 (p = 0.006), and IL-1Ra (p = 0.001). At multiple regression analysis, urinary AGEs, age, and BMI Z-score were independent variables predicting hs-CRP levels. We demonstrated for the first time, in a large cohort of children and adolescents, that the measurement of fluorescent urinary AGEs may represent a simple, noninvasive, and rapid technique to evaluate the association between AGEs and biomarkers of inflammation. Our data support a role of AGEs as biomarkers of subclinical inflammation in otherwise healthy children and adolescents.