Diabetes leading to heart failure and heart failure leading to diabetes: epidemiological and clinical evidence

Machine-learning clustering analysis identifies novel phenogroups in patients with ST-elevation acute myocardial infarction

BACKGROUND

Machine learning clustering of patients with ST-elevation acute myocardial infarction (STEMI) may provide important insights into their risk profile, management and prognosis.

METHODS

All adult discharges for STEMI in the National Inpatient Sample (October 2015 to December 2019) were included, excluding patients with prior myocardial infarction. Machine-learning clustering analysis was used to define clusters based on 21 clinical attributes of interest. Main outcomes of the study were cluster-based comparison of risk profile, in-hospital clinical outcomes and utilization of invasive management. Binomial hierarchical multivariable logistic regression with adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) was used to detect the between-cluster differences.

RESULTS

Out of overall 470,960 STEMI cases, the machine-learning analysis revealed 4 different clusters with 205,640 (cluster 0: 'behavioural risk cluster'), 146,400 (cluster 1: 'least comorbidity cluster'), 45,100 (cluster 2: 'diabetes with end-organ damage cluster') and 73,820 (cluster 3: 'cardiometabolic cluster') cases. Attributes with the highest importance for clustering were hypertension and diabetes. After multivariable adjustment, patients from 'diabetes with end-organ damage cluster' exhibited the worst mortality, MACCE and ischemic stroke (p < 0.001 for all), as well as the lowest utilization of invasive management (p < 0.001 for all), in comparison to other clusters. Patients from 'behavioural risk cluster' exhibited the best in-hospital prognosis and the highest utilization of invasive management, compared to other clusters (p < 0.001 for all).

CONCLUSIONS

Machine learning driven clustering of inpatients with STEMI reveals important population subgroups with distinct prevalence, risk profile, prognosis and management. Data driven approaches may identify high risk phenogroups and warrants further study.

Single-cell insights: pioneering an integrated atlas of chromatin accessibility and transcriptomic landscapes in diabetic cardiomyopathy

BACKGROUND

Diabetic cardiomyopathy (DCM) poses a growing health threat, elevating heart failure risk in diabetic individuals. Understanding DCM is crucial, with fibroblasts and endothelial cells playing pivotal roles in driving myocardial fibrosis and contributing to cardiac dysfunction. Advances in Multimodal single-cell profiling, such as scRNA-seq and scATAC-seq, provide deeper insights into DCM's unique cell states and molecular landscape for targeted therapeutic interventions.

METHODS

Single-cell RNA and ATAC data from 10x Multiome libraries were processed using Cell Ranger ARC v2.0.1. Gene expression and ATAC data underwent Seurat and Signac filtration. Differential gene expression and accessible chromatin regions were identified. Transcription factor activity was estimated with chromVAR, and Cis-coaccessibility networks were calculated using Cicero. Coaccessibility connections were compared to the GeneHancer database. Gene Ontology analysis, biological process scoring, cell-cell communication analysis, and gene-motif correlation was performed to reveal intricate molecular changes. Immunofluorescent staining utilized various antibodies on paraffin-embedded tissues to verify the findings.

RESULTS

This study integrated scRNA-seq and scATAC-seq data obtained from hearts of WT and DCM mice, elucidating molecular changes at the single-cell level throughout the diabetic cardiomyopathy progression. Robust and accurate clustering analysis of the integrated data revealed altered cell proportions, showcasing decreased endothelial cells and macrophages, coupled with increased fibroblasts and myocardial cells in the DCM group, indicating enhanced fibrosis and endothelial damage. Chromatin accessibility analysis unveiled unique patterns in cell types, with heightened transcriptional activity in myocardial cells. Subpopulation analysis highlighted distinct changes in cardiomyocytes and fibroblasts, emphasizing pathways related to fatty acid metabolism and cardiac contraction. Fibroblast-centered communication analysis identified interactions with endothelial cells, implicating VEGF receptors. Endothelial cell subpopulations exhibited altered gene expressions, emphasizing contraction and growth-related pathways. Candidate regulators, including Tcf21, Arnt, Stat5a, and Stat5b, were identified, suggesting their pivotal roles in DCM development. Immunofluorescence staining validated marker genes of cell subpopulations, confirming PDK4, PPARγ and Tpm1 as markers for metabolic pattern-altered cardiomyocytes, activated fibroblasts and endothelial cells with compromised proliferation.

CONCLUSION

Our integrated scRNA-seq and scATAC-seq analysis unveils intricate cell states and molecular alterations in diabetic cardiomyopathy. Identified cell type-specific changes, transcription factors, and marker genes offer valuable insights. The study sheds light on potential therapeutic targets for DCM.

Development and validation of a prediction model based on machine learning algorithms for predicting the risk of heart failure in middle-aged and older US people with prediabetes or diabetes

BACKGROUND

The purpose of this study was to develop and validate a machine learning (ML) based prediction model for the risk of heart failure (HF) in patients with prediabetes or diabetes.

METHODS

We used 3527 subjects aged 40 years and older with a prior diagnosis of prediabetes or diabetes from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. The search for independent risk variables linked to HF was conducted using univariate and multivariable logistic regression analysis. The 3527 subjects were randomly divided into training set and validation set in a 7:3 ratio. Five ML models were built on the training set using five ML algorithms, including random forest (RF), and then validated on the validation set. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis and Bootstrap resampling method were used to measure the predictive performance of the five ML models.

RESULTS

Multivariate logistic regression analysis showed that age, poverty-to-income ratio, myocardial infarction condition, coronary heart disease condition, chest pain condition, and glucose-lowering medication use were independent predictors of HF. By comparing the performance of the five ML models, the RF model (AUC = 0.978) was the best prediction model.

CONCLUSIONS

The risk of HF in middle-aged and elderly patients with prediabetes or diabetes can be accurately predicted using ML models. The best prediction performance is presented by RF model, which can assist doctors in making clinical decisions.

Serum sclerostin level is positively associated with endothelial dysfunction measured by digital thermal monitoring in patients with type 2 diabetes: A prospective cross-sectional study

Sclerostin and dickkopf-1 (DKK1), extracellular inhibitors of the canonical Wnt/β-catenin signaling pathway, have been associated with vascular aging and atherosclerosis. This study aimed to assess the correlation of sclerostin and DKK1 concentrations with endothelial function measured using vascular reactivity index (VRI) in patients with type 2 diabetes mellitus (T2DM). Fasting blood samples were collected from 100 patients with T2DM. Endothelial function and VRI were measured using digital thermal monitoring and circulating sclerostin and DKK1 levels by enzyme-linked immunosorbent assays. VRI values < 1.0, 1.0-1.9, and > 2.0 indicated poor, intermediate, and good vascular reactivity, respectively. Overall, 30, 38, and 32 patients had poor, intermediate, and good vascular reactivity, respectively. Older age, higher serum glycated hemoglobulin, urinary albumin-to-creatinine ratio, and sclerostin as well as lower hypertension prevalence, systolic blood pressure, and diastolic blood pressure (DBP) were associated with poor VRI. Multivariable forward stepwise linear regression analysis showed that DBP (β = 0.294, adjusted R2 change = 0.098, P < .001), log-glycated hemoglobin (β = -0.235, adjusted R2 change = 0.050, P = .002), log-urine albumin-to-creatinine ratio (β = -0.342, adjusted R2 change = 0.227, P < .001), and log-sclerostin level (β = -0.327, adjusted R2 change = 0.101, P < .001) were independently associated with VRI. Serum sclerostin, along with glycated hemoglobin and albumin-to-creatinine ratio, exhibited a negative correlation with VRI, while DBP showed a positive correlation with VRI. These factors can independently predict endothelial dysfunction in patients with T2DM.

The cardio-renal-metabolic connection: a review of the evidence

Type 2 diabetes (T2D), cardiovascular disease (CVD) and chronic kidney disease (CKD), are recognized among the most disruptive public health issues of the current century. A large body of evidence from epidemiological and clinical research supports the existence of a strong interconnection between these conditions, such that the unifying term cardio-metabolic-renal (CMR) disease has been defined. This coexistence has remarkable epidemiological, pathophysiologic, and prognostic implications. The mechanisms of hyperglycemia-induced damage to the cardio-renal system are well validated, as are those that tie cardiac and renal disease together. Yet, it remains controversial how and to what extent CVD and CKD can promote metabolic dysregulation. The aim of this review is to recapitulate the epidemiology of the CMR connections; to discuss the well-established, as well as the putative and emerging mechanisms implicated in the interplay among these three entities; and to provide a pathophysiological background for an integrated therapeutic intervention aiming at interrupting this vicious crosstalks.

Biomarkers for the Diagnosis of Heart Failure in People with Diabetes: A Consensus Report from Diabetes Technology Society

Diabetes Technology Society assembled a panel of clinician experts in diabetology, cardiology, clinical chemistry, nephrology, and primary care to review the current evidence on biomarker screening of people with diabetes (PWD) for heart failure (HF), who are, by definition, at risk for HF (Stage A HF). This consensus report reviews features of HF in PWD from the perspectives of 1) epidemiology, 2) classification of stages, 3) pathophysiology, 4) biomarkers for diagnosing, 5) biomarker assays, 6) diagnostic accuracy of biomarkers, 7) benefits of biomarker screening, 8) consensus recommendations for biomarker screening, 9) stratification of Stage B HF, 10) echocardiographic screening, 11) management of Stage A and Stage B HF, and 12) future directions. The Diabetes Technology Society panel recommends 1) biomarker screening with one of two circulating natriuretic peptides (B-type natriuretic peptide or N-terminal prohormone of B-type natriuretic peptide), 2) beginning screening five years following diagnosis of type 1 diabetes (T1D) and at the diagnosis of type 2 diabetes (T2D), 3) beginning routine screening no earlier than at age 30 years for T1D (irrespective of age of diagnosis) and at any age for T2D, 4) screening annually, and 5) testing any time of day. The panel also recommends that an abnormal biomarker test defines asymptomatic preclinical HF (Stage B HF). This diagnosis requires follow-up using transthoracic echocardiography for classification into one of four subcategories of Stage B HF, corresponding to risk of progression to symptomatic clinical HF (Stage C HF). These recommendations will allow identification and management of Stage A and Stage B HF in PWD to prevent progression to Stage C HF or advanced HF (Stage D HF).

Cardiorenal syndrome and diabetes: an evil pairing

Cardiorenal syndrome (CRS) is a pathology where the heart and kidney are involved, and the deterioration of one of them leads to the malfunction of the other. Diabetes mellitus (DM) carries a higher risk of HF and a worse prognosis. Furthermore, almost half of people with DM will have chronic kidney disease (CKD), which means that DM is the main cause of kidney failure. The triad of cardiorenal syndrome and diabetes is known to be associated with increased risk of hospitalization and mortality. Cardiorenal units, with a multidisciplinary team (cardiologist, nephrologist, nursing), multiple tools for diagnosis, as well as new treatments that help to better control cardio-renal-metabolic patients, offer holistic management of patients with CRS. In recent years, the appearance of drugs such as sodium-glucose cotransporter type 2 inhibitors, have shown cardiovascular benefits, initially in patients with type 2 DM and later in CKD and heart failure with and without DM2, offering a new therapeutic opportunity, especially for cardiorenal patients. In addition, glucagon-like peptide-1 receptor agonists have shown CV benefits in patients with DM and CV disease in addition to a reduced risk of CKD progression.

Streptozotocin-Induced Type 1 and 2 Diabetes Mellitus Mouse Models Show Different Functional, Cellular and Molecular Patterns of Diabetic Cardiomyopathy

The main cause of morbidity and mortality in diabetes mellitus (DM) is cardiovascular complications. Diabetic cardiomyopathy (DCM) remains incompletely understood. Animal models have been crucial in exploring DCM pathophysiology while identifying potential therapeutic targets. Streptozotocin (STZ) has been widely used to produce experimental models of both type 1 and type 2 DM (T1DM and T2DM). Here, we compared these two models for their effects on cardiac structure, function and transcriptome. Different doses of STZ and diet chows were used to generate T1DM and T2DM in C57BL/6J mice. Normal euglycemic and nonobese sex- and age-matched mice served as controls (CTRL). Immunohistochemistry, RT-PCR and RNA-seq were employed to compare hearts from the three animal groups. STZ-induced T1DM and T2DM affected left ventricular function and myocardial performance differently. T1DM displayed exaggerated apoptotic cardiomyocyte (CM) death and reactive hypertrophy and fibrosis, along with increased cardiac oxidative stress, CM DNA damage and senescence, when compared to T2DM in mice. T1DM and T2DM affected the whole cardiac transcriptome differently. In conclusion, the STZ-induced T1DM and T2DM mouse models showed significant differences in cardiac remodeling, function and the whole transcriptome. These differences could be of key relevance when choosing an animal model to study specific features of DCM.

High Glucose-Induced Cardiomyocyte Damage Involves Interplay between Endothelin ET-1/ETA/ETB Receptor and mTOR Pathway

Patients with type two diabetes mellitus (T2DM) are at increased risk for cardiovascular diseases. Impairments of endothelin-1 (ET-1) signaling and mTOR pathway have been implicated in diabetic cardiomyopathies. However, the molecular interplay between the ET-1 and mTOR pathway under high glucose (HG) conditions in H9c2 cardiomyoblasts has not been investigated. We employed MTT assay, qPCR, western blotting, fluorescence assays, and confocal microscopy to assess the oxidative stress and mitochondrial damage under hyperglycemic conditions in H9c2 cells. Our results showed that HG-induced cellular stress leads to a significant decline in cell survival and an impairment in the activation of ETA-R/ETB-R and the mTOR main components, Raptor and Rictor. These changes induced by HG were accompanied by a reactive oxygen species (ROS) level increase and mitochondrial membrane potential (MMP) loss. In addition, the fragmentation of mitochondria and a decrease in mitochondrial size were observed. However, the inhibition of either ETA-R alone by ambrisentan or ETA-R/ETB-R by bosentan or the partial blockage of the mTOR function by silencing Raptor or Rictor counteracted those adverse effects on the cellular function. Altogether, our findings prove that ET-1 signaling under HG conditions leads to a significant mitochondrial dysfunction involving contributions from the mTOR pathway.

Inflammatory Mechanisms of Diabetes and Its Vascular Complications

The main cause of death in patients with type 2 DM is cardiovascular complications resulting from the progression of atherosclerosis. The pathophysiology of the association between diabetes and its vascular complications is complex and multifactorial and closely related to the toxic effects of hyperglycemia that causes increased generation of reactive oxygen species and promotes the secretion of pro-inflammatory cytokines. Subsequent oxidative stress and inflammation are major factors of the progression of type 2 DM and its vascular complications. Data on the pathogenesis of the development of type 2 DM and associated cardiovascular diseases, in particular atherosclerosis, open up broad prospects for the further development of new diagnostic and therapeutic approaches.