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Purpose of Review Body fat distribution plays a significant role in the cardiometabolic consequences of obesity. We review the impact of visceral and hepatic fat and highlight important interventions. Recent Findings Several epidemiologic studies have established a clear association between visceral fat and cardiovascular disease. The association between hepatic fat and cardiovascular disease is less clear with discordant results. Novel evidence demonstrates sodium glucose co-transporter-2 (SGLT2) inhibitors facilitate modest weight loss and reductions in ectopic fat depots in patient with type 2 diabetes. Glucagon-like peptide-1 (GLP-1) receptor agonists have been associated with decreased visceral/hepatic fat and reductions in MACE in populations with type 2 diabetes and with overweight/obesity. Summary Clear associations between visceral fat and cardiometabolic outcomes have been established, whereas the impact of hepatic fat remains less clear. Lifestyle modification and pharmacologic interventions remain the initial therapies, while surgical intervention is associated with improved long-term outcomes. Emerging therapies have demonstrated a profound impact on body fat distribution and cardiometabolic risk.

Purpose of Review Obesity, generally defined by body mass index (BMI), is an established risk factor for the development of cardiovascular disease (CVD), while cardiorespiratory fitness (CRF) decreases risk. In chronic CVD, an obesity survival paradox in which higher BMI is associated with improved prognosis has been reported. This paper will examine the effect of obesity on CVD risk, explore obesity as a risk factor in patients with established CVD, and investigate the relationship between CRF, obesity, and CVD. Recent Findings Through metabolic and hemodynamic changes, obesity increases the risk for CVD and contributes to the development of other cardiovascular risk factors such as diabetes, dyslipidemia, and hypertension. Obesity is associated with metabolic, hormonal, and inflammatory changes that leads to atherosclerosis increasing the risk for coronary artery disease, and myocardial remodeling increasing the risk for heart failure. However, it has also been observed that overweight/obese patients with established CVD have a better prognosis when compared to non-obese individuals termed the obesity paradox. CRF is a vital component of health associated with improved cardiovascular outcomes and furthermore has been shown to markedly attenuate or nullify the relationship between obesity and CVD risk/prognosis. Summary Increasing CRF mitigates CVD risk factors and improves overall prognosis in CVD regardless of obesity status.

The Cardiometabolic Index (CMI) is a well-recognized risk factor for a range of cardiovascular diseases and diabetes mellitus. However, the population-level characteristics of CMI and its potential association with mortality risk among individuals over 40 years of age have not been investigated. This study aims to assess the association between CMI and both all-cause and cardiovascular mortality among the middle-aged and elderly population. This cohort study utilized data from 3752 American adults extracted from the Sleep Heart Health Study (SHHS) conducted from 1995 to 2011. The CMI was calculated using the waist-to-height ratio, triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C). The primary outcomes were all-cause mortality and cardiovascular mortality, with mortality data sourced from the SHHS Linked Mortality File. Kaplan-Meier survival curves and Cox regression models were employed to assess the prognostic value of the CMI. Among the 3752 American adults, the mean (SD) age was 65.9 (10.1) years, and 1969 (52.5%) were women. The mean (SD) CMI was 0.914 ± 0.939. Over an average follow-up period of 10.7 years, there were 926 all-cause deaths and 289 cardiovascular deaths. Participants were categorized into three groups based on their CMI levels: tertile (T) 1: 0.315 ± 0.0994; T2: 0.680 ± 0.128; T3: 1.75 ± 1.23. Multivariate Cox proportional hazards analysis showed that elevated CMI was significantly associated with all-cause mortality (HR 1.215, 95% CI 1.032–1.43 for T2; HR 1.309, 95% CI 1.115–1.537 for T3) and cardiovascular mortality (HR 1.305, 95% CI 0.971–1.755 for T2; HR 1.457, 95% CI 1.091–1.947 for T3). After adjusting for confounders, elevated CMI remained significantly associated with all-cause mortality (HR 1.315, 95% CI 1.098–1.575 for T3) and cardiovascular mortality (HR 1.562, 95% CI 1.124–2.17 for T3). Kaplan-Meier survival curves indicated significantly worse outcomes for participants in the higher CMI tertiles for both all-cause mortality (log-rank p  = 0.0035) and cardiovascular mortality (log-rank p  = 0.035). This national cohort study found that CMI is significantly associated with both all-cause and cardiovascular mortality among American adults aged over 40. These findings suggest that CMI could be a valuable tool for identifying high-risk individuals, thereby aiding in the implementation of targeted preventive strategies.

Cardiometabolic risk factors such as obesity, raised blood pressure, high blood glucose and dyslipidemia are emerging health concerns worldwide. Therefore, the aim of this study was to estimate the combined association between physical activity and depressive symptoms with cardiometabolic risk factors in Chilean adults. Data was obtained from the National Health Survey of Chile 2016–2017, with a sample of 5995 adult participants. Assessment of Physical activity and depressive symptoms were done using the Global Physical Activity Questionnaire (GPAQ) and the CIDI ShortForm (CIDI-SF), respectively. Multivariable logistic regression was performed to estimate the combined association of physical activity and depressive symptoms with cardiometabolic risk factors. Participants in the category ≥ 150 min/Depressive symptoms had the highest prevalence of overweight (OR: 1.55, 95% CI: 1.17–2.05), obesity (OR: 1.97, 95% CI: 1.49–2.59) and high waist circumference (OR: 1.63, 95% CI: 1.39–1.92). Participants in the < 150 min/No depressive symptoms category had a lower prevalence of overweight/obesity (OR: 0.68, 95% CI: 0.60–0.78) and a 25% reduced high triglycerides prevalence, in comparison with the active category with no depressive symptoms. There is a positive association between depressive symptoms and overweight, obesity and waist circumference among subjects that complete physical activity recommendations but have depressive symptoms.

Patients with schizophrenia spectrum disorders (SSD) have worse physical health and reduced life expectancy compared to the general population. In 2009, the European Psychiatric Association, the European Society of Cardiology and the European Association for the Study of Diabetes published a position paper aimed to improve cardiovascular and diabetes care in patients with severe mental illnesses. However, the initiative did not produce the expected results. Experts in SSD or in cardiovascular and metabolic diseases convened to identify main issues relevant to management of cardiometabolic risk factors in schizophrenia patients and to seek consensus through the Delphi method. The steering committee identified four topics: 1) cardiometabolic risk factors in schizophrenia patients; 2) cardiometabolic risk factors related to antipsychotic treatment; 3) differences in antipsychotic cardiometabolic profiles; 4) management of cardiometabolic risk. Twelve key statements were included in a Delphi questionnaire delivered to a panel of expert European psychiatrists. Consensus was reached for all statements with positive agreement higher than 85% in the first round. European psychiatrists agreed on: 1) high cardiometabolic risk in patients with SSD, 2) importance of correct risk management of cardiometabolic diseases, from lifestyle modification to treatment of risk factors, including the choice of antipsychotic drugs with a favourable cardiometabolic profile. The expert panel identified the psychiatrist as the central coordinating figure of management, possibly assisted by other specialists and general practitioners. This study demonstrates high level of agreement among European psychiatrists regarding the importance of cardiovascular risk assessment and management in subjects with SSD.

The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs) are associated with dementia and other brain disorders. In this mixed cross‐sectional and longitudinal study (interval mean = 19.7 months), including 790 healthy individuals (mean age = 46.7 years, 53% women), we investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI‐based morphometry and diffusion tensor imaging (DTI). We performed tissue specific brain age prediction using machine learning and performed Bayesian multilevel modeling to assess changes in each CMR over time, their respective association with brain age gap (BAG), and their interaction effects with time and age on the tissue‐specific BAGs. The results showed credible associations between DTI‐based BAG and blood levels of phosphate and mean cell volume (MCV), and between T1‐based BAG and systolic blood pressure, smoking, pulse, and C‐reactive protein (CRP), indicating older‐appearing brains in people with higher cardiometabolic risk (smoking, higher blood pressure and pulse, low‐grade inflammation). Longitudinal evidence supported interactions between both BAGs and waist‐to‐hip ratio (WHR), and between DTI‐based BAG and systolic blood pressure and smoking, indicating accelerated ageing in people with higher cardiometabolic risk (smoking, higher blood pressure, and WHR). The results demonstrate that cardiometabolic risk factors are associated with brain ageing. While randomized controlled trials are needed to establish causality, our results indicate that public health initiatives and treatment strategies targeting modifiable cardiometabolic risk factors may also improve risk trajectories and delay brain ageing. The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs). We investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI‐based morphometry and diffusion tensor imaging (DTI). Tissue‐specific brain age prediction using machine learning revealed older‐appearing brains and accelerated ageing in people with higher cardiometabolic risk.

Background This study sought to elucidate the associations of cardiometabolic index (CMI), as a metabolism-related index, with all-cause and cardiovascular mortality among the older population. Utilizing data from the National Health and Nutrition Examination Survey (NHANES), we further explored the potential mediating effect of inflammation within these associations. Methods A cohort of 3029 participants aged over 65 years old, spanning six NHANES cycles from 2005 to 2016, was enrolled and assessed. The primary endpoints of the study included all-cause mortality and cardiovascular mortality utilizing data from National Center for Health Statistics (NCHS). Cox regression model and subgroup analysis were conducted to assess the associations of CMI with all-cause and cardiovascular mortality. The mediating effect of inflammation-related indicators including leukocyte, neutrophil, lymphocyte, systemic immune-inflammation index (SII), neutrophil to lymphocyte ratio (NLR) were evaluated to investigate the potential mechanism of the associations between CMI and mortality through mediation package in R 4.2.2. Results The mean CMI among the enrolled participants was 0.74±0.66, with an average age of 73.28±5.50 years. After an average follow-up period of 89.20 months, there were 1,015 instances of all-cause deaths and 348 cardiovascular deaths documented. In the multivariable-adjusted model, CMI was positively related to all-cause mortality (Hazard Ratio (HR)=1.11, 95% CI=1.01-1.21). Mediation analysis indicated that leukocytes and neutrophils mediated 6.6% and 13.9% of the association of CMI with all-cause mortality. Conclusion Elevated CMI is positively associated with all-cause mortality in the older adults. The association appeared to be partially mediated through inflammatory pathways, indicating that CMI may serve as a valuable indicator for poor prognosis among the older population.

Metabolic syndrome (MetS) and its associated cardiometabolic phenotypes significantly contribute to the global burden of cardiovascular disease (CVD), especially in individuals with type 2 diabetes mellitus (T2DM) and prediabetes. This study aimed to explore the association between cardiometabolic phenotypes—specifically, metabolically unhealthy normal weight (MUHNW) and metabolically unhealthy obese (MUHO)—and various cardiovascular risk indices including the triglyceride-glucose (TyG) index and its derivatives, the atherogenic index of plasma (AIP), the cardiometabolic index (CMI), and the cardiac risk ratio (CRR). A total of 300 participants were evaluated (100 with prediabetes and 200 with T2DM). Anthropometric, biochemical, and lifestyle parameters were assessed and stratified across phenotypes. The results demonstrated that cardiovascular risk indices were significantly elevated in the MUHO compared to MUHNW phenotypes, with T2DM patients consistently exhibiting higher risk profiles than their prediabetic counterparts. TyG-derived indices showed strong correlations with BMI, waist–hip ratio (WHR), waist–height ratio (WHtR), and body fat percentage (%BF). The findings suggest that cardiometabolic phenotypes are more strongly associated with elevated cardiometabolic risk indices than body weight alone. These indices may enhance early risk stratification and intervention efforts. The study investigates the association of cardiometabolic phenotypes with surrogate cardiovascular risk indices, not direct CVD outcomes, However, the cross-sectional design and population homogeneity limit the generalizability of the results and preclude causal inference.

The Cardiometabolic Index (CMI), which combines abdominal obesity and lipid levels, has been shown to be associated with non-alcoholic fatty liver disease (NAFLD). NAFLD, through various mechanisms, can lead to cardiometabolic multimorbidity (CMM). Therefore, the aim of this study was to investigate the relationship between CMI and the occurrence of CMM in individuals with NAFLD. This cross-sectional study included 5,993 individuals with NAFLD from the National Health and Nutrition Examination Survey (NHANES) cycles between 1999 and 2018. Weighted multivariable analysis was used to assess the association between CMI and CMM, with stratified and Restricted Cubic Spline (RCS) analyses. The findings revealed a statistically significant positive correlation between CMI levels and CMM risk in NAFLD patients across all three models. Furthermore, stratified analysis indicated that this relationship was more pronounced in females. RCS analysis revealed a nonlinear relationship between CMI and CMM. Our analysis demonstrates a clear positive association between CMI and CMM in U.S. adults with NAFLD, particularly pronounced in females. These findings suggest that CMI may serve as a valuable indicator for assessing the risk of CMM in U.S. adults with NAFLD, providing critical insights for the development of more effective intervention strategies.

Composed of obesity and lipid parameters, the cardiometabolic index (CMI) has emerged as a novel diagnostic tool. Originally developed for diabetes diagnosis, its application has expanded to identifying patients with cardiovascular diseases, such as atherosclerosis and hypertension. However, the relationship between CMI and non-alcoholic fatty liver disease (NAFLD) and liver fibrosis in the US population remains unclear. This cross-sectional study analyzed data from the National Health and Nutrition Examination Survey (NHANES) spanning 2017–2020, involving 2996 participants aged 20 years or older. Vibration controlled transient elastography using a FibroScan® system (model 502, V2 Touch) with controlled attenuation parameter measurements identified NAFLD at a threshold of ≥ 274 dB/m, while liver stiffness measurement (LSM) results (median, ≥ 8.2 kPa) indicated fibrosis. A multifactorial logistic regression model explored the relationship between CMI and NAFLD and fibrosis. The effectiveness of CMI in detecting NAFLD and liver fibrosis was assessed through receiver operating characteristic curve analysis. Controlling for potential confounders, CMI showed a significant positive association with NAFLD (adjusted OR = 1.44, 95% CI 1.44–1.45) and liver fibrosis (adjusted OR = 1.84, 95% CI 1.84–1.85). The Areas Under the Curve for predicting NAFLD and fibrosis were 0.762 (95% CI 0.745 ~ 0.779) and 0.664(95% CI 0.633 ~ 0.696), respectively, with optimal cut-off values of 0.462 and 0.527. There is a positive correlation between CMI and NAFLD and fibrosis, which is a suitable and simple predictor of NAFLD and fibrosis.