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Diabetes mellitus (DM) is a highly prevalent disease worldwide, estimated to affect 1 in every 11 adults; among them, 90–95% of cases are type 2 diabetes mellitus. This is partly attributed to the surge in the prevalence of obesity, which has reached epidemic proportions since 2008. In these patients, cardiovascular (CV) risk stands as the primary cause of morbidity and mortality, placing a substantial burden on healthcare systems due to the potential for macrovascular and microvascular complications. In this context, leptin, an adipocyte-derived hormone, plays a fundamental role. This hormone is essential for regulating the cellular metabolism and energy balance, controlling inflammatory responses, and maintaining CV system homeostasis. Thus, leptin resistance not only contributes to weight gain but may also lead to increased cardiac inflammation, greater fibrosis, hypertension, and impairment of the cardiac metabolism. Understanding the relationship between leptin resistance and CV risk in obese individuals with type 2 DM (T2DM) could improve the management and prevention of this complication. Therefore, in this narrative review, we will discuss the evidence linking leptin with the presence, severity, and/or prognosis of obesity and T2DM regarding CV disease, aiming to shed light on the potential implications for better management and preventive strategies.

Background: Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) share metabolic alterations such as abnormal insulin and lipid metabolism and have some common genetic factors such as APOE genotype. Taking this into account, we hypothesized that we could identify common genetic factors involved in the development of diabetes and cardiovascular diseases. Methodology: We first genotyped 48 single nucleotide polymorphisms (SNPs) previously associated with AD in a cohort composed of 330 patients with cognitive impairment (CI) to assess their association with plasma lipids. Second, we conducted pleiotropy-informed conjunctional false discovery rate (FDR) analysis designed to identify shared variants between AD and plasma lipid levels. Finally, we used the SNPs to be found associated with lipid parameters and AD to search for associations with lipoprotein parameters in 281 patients with cardiometabolic risk. Results: Five SNPs were significantly associated with lower levels of cholesterol transported in remnant lipoprotein particles (RLPc) in subjects with CI; among these SNPs was the rs73572039 variant in PVRL2. Stratified QQ-plots were conducted on GWAS designed for AD and triglycerides (TG). The cross-trait analysis resulted in a total of 22 independent genomic loci associated with both AD and TG levels with a conjFDR < 0.05. Among these loci, two pleiotropic variants were located in PVRL2 (rs12978931 and rs11667640). The three SNPs in PVRL2 were significantly associated with RLPc, TG, and number of circulating VLDL and HDL particles in subjects with cardiometabolic risk. Conclusions: We have identified three variants in PVRL2 that predispose individuals to AD that also influence the lipid profile that confers cardiovascular risk in T2DM subjects. PVRL2 is a potential new modulating factor of atherogenic dyslipidemia.

Minimally invasive prognostic markers of inflammation and dyslipidemia in individuals with a risk of psychosis, also called “at-risk mental state” (ARMS), or in the first episode of psychosis (FEP) are of utmost clinical importance to prevent cardiovascular disorders. We analyzed the plasma concentration of inflammation-linked glycoproteins (Glycs) and lipoprotein subclasses by proton nuclear magnetic resonance (1H NMR) in a single acquisition. Study participants were healthy controls (HCs, N = 67) and patients with ARMS (N = 58), FEP (N = 110), or early psychosis diagnosis with ≥2 episodes (critical period (CP), N = 53). Clinical biomarkers such as high-sensitivity C-reactive protein, interleukin 6, fibrinogen, insulin, and lipoproteins were also measured. Although all participants had normal lipoprotein profiles and no inflammation according to conventional biomarkers, a gradual increase in the Glyc 1H NMR levels was observed from HCs to CP patients; this increase was statistically significant for GlycA (CP vs HC). In parallel, a progressive and significant proatherogenic 1H NMR lipoprotein profile was also identified across stages of psychosis (ARMS and CP vs HC). These findings highlight the potential of using 1H NMR Glyc and lipoprotein profiling to identify blood changes in individuals with ARMS or FEP and pave the way for applications using this technology to monitor metabolic and cardiovascular risks in clinical psychiatry.

Cardiovascular disease (CVD) is the most common cause of morbidity and mortality in developed countries. The prevalence of CVD is much higher in patients with type 2 diabetes mellitus (T2DM), who may benefit from lifestyle changes, which include adapted diets. In this review, we provide the role of different groups of nutrients in patients with T2DM and CVD, as well as dietary approaches that have been associated with better and worse outcomes in those patients. Many different diets and supplements have proved to be beneficial in T2DM and CVD, but further studies, guidelines, and dietary recommendations are particularly required for patients with both diseases.

Altres ajuts: CERCA Programme/Generalitat de Catalunya, Altres ajuts: This study was funded by Hospital Universitari de Bellvitge, part of the Institut Català de la Salut (ICS) and Department of Biochemistry, Molecular Biology and Biomedicine, Autonomous University of Barcelona (UAB), Barcelona, Spain, Background. LDL-C lowering is the main measure in cardiovascular disease prevention but a residual risk of ischemic events still remains. Alterations of lipoproteins, specially, increase in small dense LDL (sdLDL) particles are related to this risk. Objective. To investigate the potential use of sdLDL cholesterol concentration (sdLDL-C) isolated by an easy precipitation method and to assess the impact of a set of clinical and biochemical variables determined by NMR on sdLDL concentration. Methods. sdLDL-C and NMR lipid profile were performed in 85 men samples. Association among them was evaluated using Pearson coefficients (rxy). A multivariate regression was performed to identify the influence of NMR variables on sdLDL-C. Results. A strong association between sdLDL-C and LDLLDL-P (rxy = 0.687) and with LDL-Z (rxy = −0.603) was found. The multivariate regression explained a 56.8% in sdLDL-C variation (P = 8.77.10-12). BMI, ApoB, triglycerides, FFA, and LDL-Z showed a significant contribution. The most important ones were ApoB and LDL-Z; a 1nm increase (LDL-Z) leads to decrease 126 nmol/L in sdLDL-C. Conclusion. The association between sdLDL-C, LDL-Z, and LDL-P is clear. From a large number of variables, especially LDL-Z and apoB influence on sdLDL-C. Results show that the smaller the LDL size, the higher their cholesterol concentration. Therefore, sdLDL-C determination by using this easy method would be useful to risk stratification and to uncover cardiovascular residual risk.

Background LDL-C lowering is the main measure in cardiovascular disease prevention but a residual risk of ischemic events still remains. Alterations of lipoproteins, specially, increase in small dense LDL (sdLDL) particles are related to this risk. Objective To investigate the potential use of sdLDL cholesterol concentration (sdLDL-C) isolated by an easy precipitation method and to assess the impact of a set of clinical and biochemical variables determined by NMR on sdLDL concentration. Methods sdLDL-C and NMR lipid profile were performed in 85 men samples. Association among them was evaluated using Pearson coefficients (r(xy)). A multivariate regression was performed to identify the influence of NMR variables on sdLDL-C. Results A strong association between sdLDL-C and LDLLDL-P (r(xy) = 0.687) and with LDL-Z (r(xy) = -0.603) was found. The multivariate regression explained a 56.8% in sdLDL-C variation (P = 8.77.10-12). BMI, ApoB, triglycerides, FFA, and LDL-Z showed a significant contribution. The most important ones were ApoB and LDL-Z; a 1nm increase (LDL-Z) leads to decrease 126 nmol/L in sdLDL-C. Conclusion The association between sdLDL-C, LDL-Z, and LDL-P is clear. From a large number of variables, especially LDL-Z and apoB influence on sdLDL-C. Results show that the smaller the LDL size, the higher their cholesterol concentration. Therefore, sdLDL-C determination by using this easy method would be useful to risk stratification and to uncover cardiovascular residual risk.