Heart failure with preserved ejection fraction (HFpEF) represents a significant and growing clinical challenge. Initially, for an extended period, HFpEF was simply considered as a subset of heart failure, manifesting as haemodynamic disorders such as hypertension, myocardial hypertrophy, and diastolic dysfunction. However, the rising prevalence of obesity and diabetes has reshaped the HFpEF phenotype, with nearly 45% of cases coexisting with diabetes. Currently, it is recognized as a multi-system disorder that involves the heart, liver, kidneys, skeletal muscle, adipose tissue, along with immune and inflammatory signaling pathways. In this review, we summarize the landscape of metabolic rewiring and the crosstalk between the heart and other organs/systems (e.g., adipose, gut, liver and hematopoiesis system) in diabetic HFpEF for the first instance. A diverse array of metabolites and cytokines play pivotal roles in this intricate crosstalk process, with metabolic rewiring, chronic inflammatory responses, immune dysregulation, endothelial dysfunction, and myocardial fibrosis identified as the central mechanisms at the heart of this complex interplay. The liver-heart axis links nonalcoholic steatohepatitis and HFpEF through shared lipid accumulation, inflammation, and fibrosis pathways, while the gut-heart axis involves dysbiosis-driven metabolites (e.g., trimethylamine N-oxide, indole-3-propionic acid and short-chain fatty acids) impacting cardiac function and inflammation. Adipose-heart crosstalk highlights epicardial adipose tissue as a source of local inflammation and mechanical stress, whereas the hematopoietic system contributes via immune cell activation and cytokine release. We contend that, based on the viewpoints expounded in this review, breaking this inter-organ/system vicious cycle is the linchpin of treating diabetic HFpEF.
Background: This study sought to examine the associations between cardiometabolic indices and the onset of metabolic dysfunction-associated steatotic liver disease (MASLD) as well as its progression to liver fibrosis.
Methods: This study comprised 25,366 subjects aged 18 years and older, free of MASLD at baseline, from the Dalian Health Management Cohort (DHMC). Cardiometabolic indices include cardiometabolic index (CMI), atherogenic index of plasma (AIP), triglyceride glucose (TyG), triglyceride glucose-body mass index (TyG-BMI), triglyceride glucose-waist circumference (TyG-WC) and triglyceride glucose-waist height ratio (TyG-WHtR). All participants were categorized into quartile groups based on cardiometabolic indices. Cox proportional hazards regression models and restricted cubic splines were employed to examine the relationship between cardiometabolic indices and the incidence of MASLD as well as its progression to liver fibrosis, and analyses were performed between different subgroups. Mediation analysis was employed to explore how obesity and inflammation serve as mediators in the connection between cardiometabolic indices and MASLD. To evaluate the predictive ability of cardiometabolic indices for the onset of MASLD, the time-dependent receiver operating characteristic (ROC) curve was utilized.
Results: A total of 5378 (21.2%) individuals developed MASLD during the follow-up period of 82,445 person-years. Multivariates Cox regression analyses showed that participants in the highest quartile of cardiometabolic indices had greater risk of MASLD than those in the lowest quartile (CMI: HR = 6.11, 95% CI 5.45-6.86; AIP: HR = 4.58, 95% CI 4.11-5.10; TyG: HR = 3.55, 95% CI 3.21-3.92; TyG-BMI: HR = 13.55, 95% CI 11.80-15.57; TyG-WC: HR = 12.52, 95% CI 10.93-14.34; TyG-WHtR: HR = 11.37, 95% CI 9.96-12.98). TyG-BMI (HR = 1.36, 95% CI 1.18-1.57), but not other cardiometabolic indices, was associated with liver fibrosis. Mediation analysis indicated that BMI mediated 40.4%, 33.2%, 36.5%, - 10.4%, 37.4%, 48.5% of the associations between CMI, AIP, TyG, TyG-BMI, TyG-WC, TyG-WHtR and MASLD. Time-dependent ROC curves demonstrated that TyG-BMI had a superior predictive ability for MASLD onset compared to other indicators.
Conclusions: The risk of developing MASLD increases as the level of cardiometabolic indices increases. Obesity may serve as a mediating factor in the aforementioned association. TyG-BMI showed the strongest association with the onset of MASLD and its progression to liver fibrosis, proved to be outperformed other cardiometabolic indicators, and could be the best clinical non-invasive biomarker for early screening of MASLD and liver fibrosis.
Background and aims: The hemoglobin glycation index (HGI) has been linked to cardiovascular disease in diabetic patients. However, it remains unclear whether an elevated HGI similarly affects the cardiovascular system in individuals with normal glucose tolerance or prediabetes. In this cross-sectional study, we aimed to determine whether increased HGI levels are associated with a reduction in myocardial mechano-energetic efficiency (MEE), a key predictor of cardiovascular events and heart failure, in non-diabetic subjects.
Methods: Myocardial MEE per gram of left ventricular mass (MEEi) was assessed via echocardiography in a cohort of 1,074 adults with different glucose tolerance statuses, enrolled in the CATAnzaro MEtabolic RIsk factors (CATAMERI) study. HGI was defined as the difference between the measured HbA1c and the predicted HbA1c, the latter calculated from the linear association between HbA1c and fasting plasma glucose levels.
Results: Subjects in the highest HGI quartile exhibited significantly elevated myocardial oxygen consumption and a marked reduction in MEEi compared to those in the lowest quartile. A significant inverse correlation was observed between HGI and MEEi (r = - 0.210, P < 0.001). A multivariate linear regression analysis confirmed the strong relationship between higher HGI levels and lower MEEi, even after adjusting for several potential confounders, including sex, age, body mass index, waist circumference, smoking status, triglycerides, HDL cholesterol, 2-hour post-load glucose, glucose tolerance status, fasting insulin, HOMA-IR, hs-CRP, antihypertensive therapy, and lipid-lowering therapy.
Conclusions: These findings support the hypothesis that higher HGI values may affect myocardial mechano-energetic efficiency in non-diabetic individuals.
Background: Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality globally. Traditional risk models, primarily based on established risk factors, often lack the precision needed to accurately predict new-onset major adverse cardiovascular events (MACE). This study aimed to improve prediction and risk stratification by integrating traditional risk factors with biochemical and metabolomic biomarkers.
Methods: We analyzed data from 229,352 participants in the UK Biobank (median age 58.0 years; 45.4% male) who were free of baseline MACE. Biomarker selection was conducted using area under the curve (AUC), minimal joint mutual information maximization (JMIM), and correlation analyses, while Cox proportional hazards models were employed to evaluate the predictive performance of combined traditional risk factors and biomarkers. Optimal binary thresholds were determined utilizing CatBoost and SHAP, leading to the calculation of a Biomarker Risk Score (BRS) for each participant. Multivariable Cox models were conducted to assess the associations of each concerned biomarker and BRS with new-onset endpoints.
Results: The combination of PANEL + All Biochemistry + Cor0.95 of Nonov Met predictors demonstrated significantly improved discriminative performance compared to traditional models, such as Age + Sex and ASCVD, across all endpoints. Although the prediction for hemorrhagic stroke was suboptimal (C-index = 0.699), C-index values for other outcomes surpassed 0.75, with the highest value (0.822) recorded for CVD-related mortality. Key predictors of new-onset MACE included cystatin C, HbA1c, GlycA, and GGT, while IGF-1 and DHA exhibited potential protective effects. The BRS stratified individuals into low-, intermediate-, and high-risk groups, with the strongest effect observed for CVD death, where the high-risk group had a relative risk of 2.76 (95% CI 2.48-3.07) compared to the low-risk group.
Conclusion: Integrating traditional risk factors and biomarkers improves prediction and risk stratification of new-onset MACE. The BRS shows promise as a tool for identifying high-risk individuals, with the potential to support personalized CVD prevention and management strategies.
Background: The triglyceride-glucose (TyG) index is recognized as an indicator of insulin resistance and is linked to cardiovascular disease (CVD) in patients with type 2 diabetes. However, its utility in patients with Type 1 diabetes (T1DM) has not been studied.
Methods: In this nationwide cohort study, we enrolled 14,543 patients with T1DM between 2009 and 2015, with a median follow-up duration of 7.52 years. The primary outcome was the incidence of CVD, including myocardial infarction, ischemic stroke, and heart failure. The secondary outcome was the all-cause mortality. The risk of CVD across the TyG index quartiles was compared using the Cox proportional hazards model.
Results: The cut-off points for the TyG quartiles were 8.46, 9.03, and 9.60. Patients in the highest TyG quartile exhibited a higher burden of cardiometabolic risk factors, including obesity, hypertension, dyslipidemia, and lower HDL cholesterol levels. Compared to the lowest quartile, the highest TyG quartile group showed a significantly increased risk of CVD (Composite CVD: adjusted hazard ratio [aHR] = 1.80; 95% confidence interval [CI] = 1.62-2.00, myocardial infarction: aHR = 1.70;95% CI = 1.38-2.10, ischemic stroke: aHR = 2.11; 95% CI = 1.78-2.50, heart failure: aHR = 1.65, 95% CI = 1.45-1.88) and all-cause mortality (aHR = 1.60, 95% CI = 1.41-1.81).
Conclusions: A higher TyG index was significantly associated with an increased risk of CVD and all-cause mortality in patients with T1DM.
Research insights: What is currently known about this topic? 1. The TyG index is associated with insulin resistance and cardiovascular disease in both patients with type 2 diabetes and the general population. What is the key research question? 1. Could the TyG index also be utilized to assess insulin resistance and cardiovascular disease risk in patients with type 1 diabetes? What is new? 1. In patients with type 1 diabetes, those in the higher TyG quartile showed a higher prevalence of metabolic dysfunction such as obesity, hypertension and dyslipidemia. 2. A higher TyG index in patients with type 1 diabetes was associated with an increased risk of all-cause mortality and cardiovascular disease including myocardial infarction, heart failure and stroke. How might this study influence clinical practice? 1. The TyG index, a simple and non-invasive marker composed of triglycerides and fasting glucose, could be used to identify patients with type 1 diabetes who have high insulin resistance and cardiovascular disease risk.
Background: Disruption of lipid metabolism contributes to increased cardiovascular risk in diabetes.
Methods: We evaluated the associations between serum lipidomic profile and subclinical carotid atherosclerosis (SCA) in type 1 (T1D) and type 2 (T2D) diabetes, and in subjects without diabetes (controls) in a cross-sectional study. All subjects underwent a lipidomic analysis using ultra-high performance liquid chromatography-electrospray ionization tandem mass spectrometry, carotid ultrasound (mode B) to assess SCA, and clinical assessment. Multiple linear regression models were used to assess the association between features and the presence and burden of SCA in subjects with T1D, T2D, and controls separately. Additionally, multiple linear regression models with interaction terms were employed to determine features significantly associated with SCA within risk groups, including smoking habit, hypertension, dyslipidaemia, antiplatelet use and sex. Depending on the population under study, different confounding factors were considered and adjusted for, including sample origin, sex, age, hypertension, dyslipidaemia, body mass index, waist circumference, glycated haemoglobin, glucose levels, smoking habit, diabetes duration, antiplatelet use, and alanine aminotransferase levels.
Results: A total of 513 subjects (151 T1D, 155 T2D, and 207 non-diabetic control) were included, in whom the percentage with SCA was 48.3%, 49.7%, and 46.9%, respectively. A total of 27 unique lipid species were associated with SCA in subjects with T2D, in former/current smokers with T2D, and in individuals with T2D without dyslipidaemia. Phosphatidylcholines and diacylglycerols were the main SCA-associated lipidic classes. Ten different species of phosphatidylcholines were up-regulated, while 4 phosphatidylcholines containing polyunsaturated fatty acids were down-regulated. One diacylglycerol was down-regulated, while the other 3 were positively associated with SCA in individuals with T2D without dyslipidaemia. We discovered several features significantly associated with SCA in individuals with T1D, but only one sterol could be partially annotated.
Conclusions: We revealed a significant disruption of lipid metabolism associated with SCA in subjects with T2D, and a larger SCA-associated disruption in former/current smokers with T2D and individuals with T2D who do not undergo lipid-lowering treatment.
Background: The triglyceride-glucose (TyG) index and stress hyperglycemia ratio (SHR) have been linked to the cardiovascular risks in critical ill patients. However, little is known about the predictive power of the TyG index, SHR and their combination on the incidence and mortality risks of new-onset atrial fibrillation (NOAF) in patients with sepsis.
Method: This retrospective study included patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Primary outcomes were defined as the incidence and 360-day mortality of in-hospital NOAF among patients with sepsis. Logistic model, Cox proportional hazard model, Kaplan-Meier analysis and receiver-operating characteristic (ROC) were performed to explore the association between the indices and clinical outcomes. Machine learning approach also was constructed to evaluate and compare the indices in predicting mortality risks.
Results: 4276 patients meeting the inclusion criteria were enrolled and 764 individuals developed NOAF during hospitalization. The multivariable adjusted odds ratios (95%, CI) of incidence of NOAF in patients with sepsis in the highest group versus the lowest group were 1.36 (1.10-1.69), 1.35 (1.09-1.67) and 1.58 (1.23-2.02), respectively, for the TyG index, SHR and the TyG index-SHR combination. However, the predictive powers of these indices were relatively low. Among septic patients who developed in-hospital NOAF, those in the highest TyG index group and the highest SHR group exhibited an increased risk of 360-day mortality compared with those with the lowest TyG index and the lowest SHR (the TyG index: hazard ratio [HR] 1.59, 95% CI 1.00-2.62; SHR: HR 1.67, 95% CI 1.03-2.70). Patients with both the highest the TyG index and the highest SHR demonstrated the highest risk of 360-day mortality (HR 1.72, 95% CI 1.08-2.72). The ROC also confirmed the TyG index-SHR combination had more robust predictive power for 360-day mortality among septic patients with NOAF than the TyG index and SHR itself (p < 0.05). The random forest model validated that the predictive capability was significantly enhanced with the integration of the TyG index and SHR.
Conclusion: The TyG index and SHR were associated with the incidence of in-hospital NOAF during sepsis, although their predictive powers were limited. In septic patients with in-hospital NOAF, high levels of the TyG index and SHR were significantly associated with increased 360-day mortality risks, with their combination demonstrating superior predictive power. Joint assessments of the TyG index and SHR could help identify individuals at high risks of mortality post-discharge, enabling clinicians to prioritize follow-up care and improve patient management.
Background: The Cardiovascular-Kidney-Metabolic (CKM) syndrome underscores the complex interactions among metabolic disorders, kidney disease, and cardiovascular conditions. Insulin resistance (IR) and inflammation are crucial in CKM syndrome development, but their combined effect in stages 0-3 remains unclear.
Methods: Using data from the National Health and Nutrition Examination Survey (NHANES), we included 18,295 participants with CKM syndrome stages 0-3 from 10 cycles between 1999 and 2018. IR was assessed using the estimated glucose disposal rate (eGDR), and systemic inflammation was evaluated using the Systemic Inflammation Response Index (SIRI). The primary endpoint was all-cause mortality, and the secondary endpoint was cardiovascular disease (CVD) mortality.
Results: Over an average follow-up period of 121 months, we recorded 1,998 all-cause deaths and 539 CVD deaths. Both eGDR and SIRI were independent risk factors for mortality. The hazard ratios (HR) for eGDR were 0.90 (0.86, 0.94) for all-cause mortality and 0.85 (0.78, 0.93) for CVD mortality, per unit increase in eGDR. For SIRI, the HRs were 1.16 (1.11, 1.21) for all-cause mortality and 1.33 (1.19, 1.46) for CVD mortality, per unit increase in SIRI. Compared to individuals with high eGDR and low SIRI levels, those with low eGDR and high SIRI levels exhibited significantly higher mortality risks, with HRs of 1.97 (1.58, 2.44) for all-cause mortality and 2.35 (1.48, 3.73) for CVD mortality. Subgroup analysis revealed that the combined impact of eGDR and SIRI was particularly significant in patients under 60 years old.
Conclusion: In CKM syndrome stages 0-3, eGDR and SIRI have joint effect on mortality. Combining these markers can help identify high-risk individuals early, enabling timely monitoring and intervention to improve outcomes.
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