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Background: Obesity is a complex, diverse and multifactorial disease that has become a major public health concern in the last decades. The current classification systems relies on anthropometric measurements, such as BMI, that are unable to capture the physiopathological diversity of this disease. The aim of this study was to redefine the classification of obesity based on the different H-NMR metabolomics profiles found in individuals with obesity to better assess the risk of future development of cardiometabolic disease. Materials and methods: Serum samples of a subset of the Di@bet.es cohort consisting of 1387 individuals with obesity were analyzed by H-NMR. A K-means algorithm was deployed to define different H-NMR metabolomics-based clusters. Then, the association of these clusters with future development of cardiometabolic disease was evaluated using different univariate and multivariate statistical approaches. Moreover, machine learning-based models were built to predict the development of future cardiometabolic disease using BMI and waist-to-hip circumference ratio measures in combination with H-NMR metabolomics. Results: Three clusters with no differences in BMI nor in waist-to-hip circumference ratio but with very different metabolomics profiles were obtained. The first cluster showed a metabolically healthy profile, whereas atherogenic dyslipidemia and hypercholesterolemia were predominant in the second and third clusters, respectively. Individuals within the cluster of atherogenic dyslipidemia were found to be at a higher risk of developing type 2 DM in a 8 years follow-up. On the other hand, individuals within the cluster of hypercholesterolemia showed a higher risk of suffering a cardiovascular event in the follow-up. The individuals with a metabolically healthy profile displayed a lower association with future cardiometabolic disease, even though some association with future development of type 2 DM was still observed. In addition, H-NMR metabolomics improved the prediction of future cardiometabolic disease in comparison with models relying on just anthropometric measures. Conclusions: This study demonstrated the benefits of using precision techniques like H-NMR to better assess the risk of obesity-derived cardiometabolic disease. [ABSTRACT FROM AUTHOR]

The burden of cardiovascular disease is particularly high among individuals with diabetes, even when LDL cholesterol is normal or within the therapeutic target. Despite this, cholesterol accumulates in their arteries, in part, due to persistent atherogenic dyslipidaemia characterized by elevated triglycerides, remnant cholesterol, smaller LDL particles and reduced HDL cholesterol. The causal link between dyslipidaemia and atherosclerosis in T2DM is complex, and our contention is that a deeper understanding of lipoprotein composition and functionality, the vehicle that delivers cholesterol to the artery, will provide insight for improving our understanding of the hidden cardiovascular risk of diabetes. This narrative review covers three levels of complexity in lipoprotein characterization: 1—the information provided by routine clinical biochemistry, 2—advanced nuclear magnetic resonance (NMR)‐based lipoprotein profiling and 3—the identification of minor components or physical properties of lipoproteins that can help explain arterial accumulation in individuals with normal LDLc levels, which is typically the case in individuals with T2DM. This document highlights the importance of incorporating these three layers of lipoprotein‐related information into population‐based studies on ASCVD in T2DM. Such an attempt should inevitably run in parallel with biotechnological solutions that allow large‐scale determination of these sets of methodologically diverse parameters. [ABSTRACT FROM AUTHOR]

Glycosylation of ApoC-III modulates its function in TG metabolism, with some variants being associated with a more atherogenic lipid profile. These associations have been studied in whole plasma but rarely in individual lipoprotein fractions. In this study, we aimed to measure the relative content of ApoC-III glycoforms in each lipoprotein fraction as a potential biomarker for TG metabolism and cardiovascular risk. Lipoprotein fractions were separated by differential ultracentrifugation of plasma samples from healthy subjects. Relative concentrations of seven ApoC-III variants were measured by MSIA. ApoC-III 1 , ApoC-III 0b and ApoC-III 2 were the most abundant glycoforms. There was high interindividual variability in the distribution of glycoforms across the study population but a uniform proportion in all lipoprotein fractions of each given subject. Two ApoC-III variants, ApoC-III 0b and ApoC-III 1d , negatively correlated with plasma and VLDL triglycerides irrespectively of VLDL size and were associated with increased LDL size when transported in LDL particles. ApoC-III 0b also showed a negative correlation with lipoprotein-insulin resistance score. We have been able to measure seven ApoC-III glycoforms in each lipoprotein fraction, setting the basis for future studies exploring their role on cardiovascular risk. Some glycoforms suggest a less proatherogenic role on TG and lipoprotein metabolism. Apo CIII has an important role on plasma TG metabolism through different mechanisms and it is also involved in type 1 and type 2 Diabetes Mellitus. Different glycosylated forms of Apo CIII exist and they show different roles. For this reason, this protein has gained interest in the las years and the relationship between ApoC-III glycoforms and lipids, lipoproteins and metabolic disorders has been increasingly studied in the last years. Apo CIII glycoforms have been previously analysed in plasma, and the function of the main four glycoforms has been assessed in a variety of cohorts; but in the present study, ApoC-III glycoforms are measured in each lipoprotein fraction, which may be of clinical interest. [Display omitted] • 7 ApoC-III glycoforms can be measured in lipoprotein fractions of healthy subjects. • The proportion of each ApoC-III glycoform is constant among lipoprotein fractions. • The distribution of ApoC-III glycoforms shows high variability between volunteers. • ApoC-III 0b and ApoC-II 1d are associated with lower TG and LDL size. • ApoC-III 0b is also associated with decreased VLDL particles and a lower LPIR score. [ABSTRACT FROM AUTHOR]

• Glucose-matched individuals showed a less advanced state of insulin resistance. • Individuals with incident type 2 diabetes had smaller LDL and HDL particles. • Creatinine and creatine suggested a key role for muscle in type 2 diabetes development. • A differential glycosylation pattern was observed in type 2 diabetes mellitus. The aim of this study was to combine nuclear magnetic resonance-based metabolomics and machine learning to find a glucose-independent molecular signature associated with future type 2 diabetes mellitus development in a subgroup of individuals from the Di@bet.es study. The study group included 145 individuals developing type 2 diabetes mellitus during the 8-year follow-up, 145 individuals matched by age, sex and BMI who did not develop diabetes during the follow-up but had equal glucose concentrations to those who did and 145 controls matched by age and sex. A metabolomic analysis of serum was performed to obtain the lipoprotein and glycoprotein profiles and 15 low molecular weight metabolites. Several machine learning-based models were trained. Logistic regression performed the best classification between individuals developing type 2 diabetes during the follow-up and glucose-matched individuals. The area under the curve was 0.628, and its 95% confidence interval was 0.510–0.746. Glycoprotein-related variables, creatinine, creatine, small HDL particles and the Johnson-Neyman intervals of the interaction of Glyc A and Glyc B were statistically significant. The model highlighted a relevant contribution of inflammation (glycosylation pattern and HDL) and muscle (creatinine and creatine) in the development of type 2 diabetes as independent factors of hyperglycemia. [ABSTRACT FROM AUTHOR]