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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.

Ultra-processed foods (UPF) are energy-dense, nutritionally unbalanced products, low in fiber but high in saturated fat, salt, and sugar. Recently, UPF consumption has increased likewise the incidence of obesity and cardiometabolic diseases. To highlight a possible relationship, we conducted a systematic review of prospective studies from PubMed and Web of Science investigating the association between UPF consumption and the incidence of obesity and cardiometabolic risk factors. Seventeen studies were selected. Eight evaluated the incidence of general and abdominal obesity, one the incidence of impaired fasting blood glucose, four the incidence of diabetes, two the incidence of dyslipidemia, and only one the incidence of metabolic syndrome. Studies’ quality was assessed according to the Critical Appraisal Checklist for cohort studies proposed by the Joanna Briggs Institute. Substantial agreement emerged among the studies in defining UPF consumption as being associated with the incident risk of general and abdominal obesity. More limited was the evidence on cardiometabolic risk. Nevertheless, most studies reported that UPF consumption as being associated with an increased risk of hypertension, diabetes, and dyslipidemia. In conclusion, evidence supports the existence of a relationship between UPF consumption and the incidence of obesity and cardiometabolic risk. However, further longitudinal studies considering diet quality and changes over time are needed.

Aims/hypothesis The associations of sitting, standing, physical activity and sleep with cardiometabolic health and glycaemic control markers are interrelated. We aimed to identify 24 h time-use compositions associated with optimal metabolic and glycaemic control and determine whether these varied by diabetes status. Methods Thigh-worn activPAL data from 2388 participants aged 40–75 years (48.7% female; mean age 60.1 [SD = 8.1] years; n =684 with type 2 diabetes) in The Maastricht Study were examined. Compositional isometric log ratios were generated from mean 24 h time use (sitting, standing, light-intensity physical activity [LPA], moderate-to-vigorous physical activity [MVPA] and sleeping) and regressed with outcomes of waist circumference, fasting plasma glucose (FPG), 2 h plasma glucose, HbA 1c , the Matsuda index expressed as z scores, and with a clustered cardiometabolic risk score. Overall analyses were adjusted for demographics, smoking, dietary intake and diabetes status, and interaction by diabetes status was examined separately. The estimated difference when substituting 30 min of one behaviour with another was determined with isotemporal substitution. To identify optimal time use, all combinations of 24 h compositions possible within the study footprint (1st–99th percentile of each behaviour) were investigated to determine those cross-sectionally associated with the most-optimal outcome (top 5%) for each outcome measure. Results Compositions lower in sitting time and with greater standing time, physical activity and sleeping had the most beneficial associations with outcomes. Associations were stronger in participants with type 2 diabetes ( p <0.05 for interactions), with larger estimated benefits for waist circumference, FPG and HbA 1c when sitting was replaced by LPA or MVPA in those with type 2 diabetes vs the overall sample. The mean (range) optimal compositions of 24 h time use, considering all outcomes, were 6 h (range 5 h 40 min–7 h 10 min) for sitting, 5 h 10 min (4 h 10 min–6 h 10 min) for standing, 2 h 10 min (2 h–2 h 20 min) for LPA, 2 h 10 min (1 h 40 min–2 h 20 min) for MVPA and 8 h 20 min (7 h 30 min–9 h) for sleeping. Conclusions/interpretation Shorter sitting time and more time spent standing, undergoing physical activity and sleeping are associated with preferable cardiometabolic health. The substitutions of behavioural time use were significantly stronger in their associations with glycaemic control in those with type 2 diabetes compared with those with normoglycaemic metabolism, especially when sitting time was balanced with greater physical activity. Graphical Abstract

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.

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.

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.

Background The association of evening chronotype with cardiometabolic disease has been well established. However, the extent to which circadian rhythm disturbances independently result in risk remains unclear. This study aimed to investigate the cross-sectional and prospective longitudinal associations between chronotype and cardiometabolic risk among Chinese young adults. Methods From April to May 2019, a total of 1 135 young adults were selected to complete the self-administered questionnaire, and 744 fasting blood samples were collected to quantify cardiometabolic parameters. From April to May 2021, 340 fasting blood samples were collected to quantify cardiometabolic parameters. The Morning and Evening Questionnaire 5 (MEQ-5) was used to assess chronotype. The cardiometabolic (CM)-risk score was the sum of standardized Z scores based on gender for the 5 indicators: waist circumference (WC), mean arterial pressure (MAP), triglyceride (TG), homeostasis model assessment for insulin resistance (HOMA-IR), and high-density lipoprotein cholesterol (HDL-C), where the HDL-C is multiplied by-1. The generalized linear model was used to determine the cross-sectional and prospective longitudinal associations between chronotype and each cardiometabolic parameter. Results Cross-sectional association analysis showed that lower MEQ-5 scores were correlated with higher fasting insulin ( β =-1.420, 95% CI : -2.386~-0.453), higher HOMA-IR ( β =-0.301, 95% CI : -0.507~-0.095), and higher CM risk score ( β =-0.063, 95% CI : -0.122~-0.003), even after adjustment for covariates. Prospective longitudinal association analysis also showed that lower MEQ-5 scores were associated with 2 years later higher fasting glucose ( β =-0.018, 95% CI : -0.034~-0.003), higher fasting insulin ( β =-0.384, 95% CI : -0.766~-0.003), higher HOMA-IR ( β =-0.089, 95% CI : -0.176~-0.002), and higher CM-risk score ( β =-0.109, 95% CI : -0.214~-0.003) after adjustment for covariates. Conclusions Evening chronotype was significantly correlated with higher CM risk among young adults. Our findings suggest that biologically and socially affected sleep timing misalignment is a contributing factor to cardiovascular disease risk.

The effect of different coffee and tea consumption on postprandial glucose and lipid metabolism has never been reported previously. The aim of the present study was to investigate the effect of different coffee or tea consumption at breakfast on postprandial cardiometabolic risk factors in healthy individuals. Eighteen healthy young subjects completed the trial. After 8-hour overnight fast, volunteers either ingested water, freeze-dried coffee, spray-dried coffee, green tea, black tea or oolong tea together with a breakfast consisting of an egg and 180g deep-fried dough sticks. Blood was drawn at 0h, 0.5h, 1h, 2h, and 3h. The differences in triglyceride (TG) values relative to the baseline levels at 2h and 3h of green tea was significantly decreased compared with black tea and oolong tea (p<0.05). Compared with black tea, green tea and oolong tea significantly reduced postprandial total cholesterol (TC) levels (p<0.05, p<0.01), respectively. Furthermore, the serum concentrations of high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were substantially decreased after oolong tea consumption compared with black tea (p<0.05, p<0.01). Green tea ingestion can lower the elevation of serum TG and TC levels after high-fat or high-cholesterol diets. Our findings have far-reaching implications given the widespread use of coffee and tea and the current concern over cardiometabolic risk factors.

Aims/hypothesisThe aim of the study was to examine the association of retinal vessel morphometry with BP, body composition and biochemistry, and to determine whether these associations differ by diabetes status.MethodsThe UK Biobank ocular assessment included 68,550 participants aged 40-70 years who underwent non-mydriatic retinal photography, BP and body composition measurements, and haematological analysis. A fully automated image analysis program provided measurements of retinal vessel diameter and tortuosity. The associations between retinal vessel morphology and cardiometabolic risk factors by diabetes status were examined using multilevel linear regression, to provide absolute differences in vessel diameter and percentage differences in tortuosity (allowing for within-person clustering).ResultsA total of 50,233 participants (a reduction from 68,550) were included in these analyses. Overall, those with diabetes had significantly more tortuous venules and wider arteriolar diameters compared with those without. Associations between venular tortuosity and cardiometabolic risk factors differed according to diabetes status (p interaction <0.01) for total fat mass index, HbA1c, C-reactive protein, white cell count and granulocyte count. For example, a unit rise in white cell count was associated with a 0.18% increase (95% CI 0.05, 0.32%) in venular tortuosity for those without diabetes and a 1.48% increase (95% CI 0.90, 2.07%) among those with diabetes. For arteriolar diameter, significant interactions were evident for systolic BP, diastolic BP, mean arterial pressure (MAP) and LDL-cholesterol. For example, a 10 mmHg rise in systolic BP was associated with a −0.92 μm difference (95% CI −0.96 to −0.88 μm) in arteriolar diameter for those without diabetes, and a −0.58 μm difference (95% CI −0.76 to −0.41 μm) among those with diabetes. No interactions were observed for arteriolar tortuosity or venular diameters.Conclusions/interpretationWe provide clear evidence of the modifying effect of diabetes on cardiometabolic risk factor associations with retinal microvascular architecture. These observations suggest the occurrence of preclinical disease processes, and may be a sign of impaired autoregulation due to hyperglycaemia, which has been suggested to play a pivotal role in the development of diabetes-related microvascular complications.Data AvailabilityThe data supporting the results reported here are available through the UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access).