Christos S. Mantzoros’s research while affiliated with Beth Israel Deaconess Medical Center and other places

Background and Aims Adequate lipid control has emerged as a key factor in the prevention and management of chronic kidney disease (CKD). Remnant cholesterol (RC), a lipoprotein with an established association with cardiovascular risk, has been investigated in the context of CKD. Given the conflicting results from recent studies, we performed this meta-analysis to summarize the existing evidence on the association between RC and CKD. Methods Medline, Cochrane Library and Scopus were searched until 16 September 2024. Double-independent study selection, data extraction and quality assessment were performed. Evidence was pooled using random-effects meta-analyses. We set as primary end-point of interest the association between RC and CKD. Results Twelve studies (4 139 674 participants) were included. Participants with RC values in the highest quantile had significantly greater odds of CKD compared to those in the lowest quantile (Odds Ratio [OR] = 1.46, 95% confidence interval [CI] = 1.26–1.68). In a sensitivity analysis confined to subjects with type 2 diabetes (T2D), those in the higher RC quantile also exhibited significantly increased odds of CKD compared to those in the lowest quantile (OR = 1.46, 95% CI = 1.20–1.78). A significant inverse association was observed between RC and estimated glomerular filtration rate (Mean Difference [MD] = −1.43 mL/min/1.73 m2 for each 1 mmol/L increase in RC, 95% CI = [−2.67, −0.19]). Additionally, individuals with T2D-related CKD had a 24% increased risk of progression to end-stage renal disease for each 1 standard deviation increase in RC (Hazard Ratio [HR] = 1.24, 95% CI = 1.04–1.47). Conclusions RC is directly associated with higher risk for CKD. Beyond traditional lipid markers, greater emphasis should be placed on RC levels in individuals with or at risk for CKD.

Background Medications targeting the leptin and Apolipoprotein CIII (APOC3) pathways are currently under development for the treatment of hypertriglyceridaemia. Given that both pathways are implicated in triglyceride regulation, it is unknown whether they function independently or interact under physiological conditions and under acute or long‐term energy deficiency. Methods APOC3 levels and their association with circulating lipids and lipoproteins were evaluated in the context of two randomised controlled studies. In Study‐1, 15 healthy individuals were examined under three distinct conditions, each lasting 72 h: isocaloric feeding, fasting with placebo administration and fasting with leptin administered at replacement doses. In Study‐2, 20 females with hypoleptinemia due to relative energy deficiency in sport (REDs) for a minimum of 6 months were treated with either leptin or a placebo for 36 weeks. Results In Study‐1, APOC3 levels remained stable across all arms and were unaffected by leptin administration. In the fed state, APOC3 levels presented positive correlations with various VLDL, IDL, LDL and HDL sizes, and free fatty acids (FFA), most of which were not replicated in fasting. During complete energy deprivation, APOC3 was correlated with HDL molecules, glutamine and FFA, whereas its levels were positively associated only with FFA under leptin treatment. In Study‐2, APOC3 levels were lower in the leptin group, but this was not a leptin‐dependent effect. A positive correlation between APOC3 levels and HDL was observed in the leptin group. Conclusions These results contribute towards our better understanding of the intricate nature of lipid regulation under energy deficiency, suggesting that medications targeting the leptin and APOC3 pathways act through different metabolic pathways and thus may have independent effects from each other in regulating triglycerides.

OBJECTIVE Progression of prediabetes to type 2 diabetes has been associated with β-cell dysfunction, whereas its remission to normoglycemia has been related to improvement of insulin sensitivity. To understand the mechanisms and identify potential biomarkers related to prediabetes trajectories, we compared the proteomics and metabolomics profile of people with prediabetes progressing to diabetes or reversing to normoglycemia within 1 year. RESEARCH DESIGN AND METHODS The fasting plasma concentrations of 1,389 proteins and the fasting, 30-min, and 120-min post–oral glucose tolerance test (OGTT) plasma concentrations of 152 metabolites were measured in up to 134 individuals with new-onset diabetes, prediabetes, or normal glucose tolerance. For 108 participants, the analysis was repeated with samples from 1 year before, when all had prediabetes. RESULTS The plasma concentrations of 14 proteins were higher in diabetes compared with normoglycemia in a population with prediabetes 1 year before, and they correlated with indices of insulin sensitivity. Higher levels of dicarbonyl/L-xylulose reductase and glutathione S-transferase A3 in the prediabetic state were associated with an increased risk of diabetes 1 year later. Pathway analysis pointed toward differences in immune response between diabetes and normoglycemia that were already recognizable in the prediabetic state 1 year prior at baseline. The area under the curve during OGTT of the concentrations of IDL particles, IDL apolipoprotein B, and IDL cholesterol was higher in new-onset diabetes compared with normoglycemia. The concentration of glutamate increased in prediabetes progressing to diabetes. CONCLUSIONS We identify new candidates associated with the progression of prediabetes to diabetes or its remission to normoglycemia. Pathways regulating the immune response are related to prediabetes trajectories.

Summary Background Coronary artery disease (CAD) comprises one of the leading causes of morbidity and mortality both in the European population and globally. All established clinical risk stratification scores and models require blood lipids and physical measurements. The latest reports of the European Commission suggest that attracting health professionals to collect these data can be challenging, both from a logistic and cost perspective, which limits the usefulness of established models and makes them unsuitable for population-wide screening in resource-limited settings, i.e., rural areas. Therefore, the aim of this study was to develop and externally validate a questionnaire-based risk stratification model on a population scale at minimal cost, i.e., the Questionnaire-Based Evaluation for Estimating Coronary Artery Disease (QUES-CAD) to stratify the 10-year incidence of coronary artery disease. Methods Cox proportional hazards (CoxPH) and Cox gradient boosting (CoxGBT) models were trained with 10-fold cross-validation using combinations of ten questionnaire variables on the White population of the UK Biobank (n = 448,818) and internally validated the models in all ethnic minorities (n = 27,433). The Lifelines cohort was employed as an independent external validation population (n = 97,770). Additionally, we compared QUES-CAD’s performance, containing only questionnaire variables, to clinically established risk prediction tools, i.e., Framingham Coronary Heart Disease Risk Score, American College of Cardiology/American Heart Association pooled cohort equation, World Health Organization cardiovascular disease risk charts, and Systematic Coronary Risk Estimation 2 (SCORE2). We conducted partial log-likelihood ratio (PLR) tests and C-index comparisons between QUES-CAD and established clinical prediction models. Findings In the external validation set, QUES-CAD exhibited C-index values of CoxPH: 0.692 (95% Confidence Interval [CI]: 0.673–0.71) and CoxGBT: 0.699 (95% CI: 0.681–0.717) for the male population and CoxPH: 0.771 (95% CI: 0.748–0.794) and CoxGBT: 0.759 (95% CI: 0.736–0.783) for the female population. The addition of measurement-based variables and variables that require a prior medical examination (i.e., insulin use, number of treatments/medications taken, prevalent cardiovascular disease [other than CAD, and stroke diagnosed by a doctor]) and the further addition of biomarkers/other measurements (i.e., high-density lipoprotein [HDL] cholesterol, total cholesterol, and glycated haemoglobin) did not significantly improve QUES-CAD’s performance in most instances. C-index comparisons and PLR tests showed that QUES-CAD performs and fits the data at least as well as the clinical prediction models. Interpretation QUES-CAD performs comparably to established clinical prediction models and enables a population-wide identification of high-risk individuals for CAD. The model developed and validated herein relies solely on ten questionnaire variables, overcoming the limitations of existing models that depend on physical measurements or biomarkers. Funding University Medical Center Groningen.