Cardiovascular risk factors associated with acute myocardial infarction and stroke in the MADIABETES cohort

Influencing factors of stroke in patients with type 2 diabetes: A systematic review and meta-analysis

BACKGROUND

Stroke stands as a significant macrovascular complication among individuals with Type 2 diabetes mellitus (T2DM), often resulting in the primary cause of mortality and disability within this patient demographic. Presently, numerous studies have been conducted to investigate the underlying causes of stroke in individuals with T2DM, yet the findings exhibit inconsistencies.

OBJECTIVE

This paper aims to consolidate and summarize the available evidence concerning the influential factors contributing to stroke among patients diagnosed with T2DM.

METHODS

We conducted a comprehensive search across multiple databases, including Cochrane Library, PubMed, Web Of Science, Embase, China Biology Medicine (CBM), China National Knowledge Infrastructure (CNKI), Wanfang and Weipu up to August 2023. Google Scholar was also searched to retrieve gray literature. We calculated odds ratios (OR) and 95% confidence intervals (CI) using Stata software.

RESULTS

Our analysis encompassed 43 observational studies, exploring factors across sociodemographic, biochemical, complications, and hypoglycemic agent categories. The findings identified several risk factors for stroke in patients with T2DM: age, gender, T2DM duration, hypertension, body-mass index (BMI), smoking, Glycated hemoglobin (HbA1c), estimated Glomerular Filtration Rate (eGFR), albuminuria, Triglycerides (TG), Low density lipoprotein cholesterol (LDL-C), Coronary heart disease (CHD), Atrial fibrillation (AF), diabetic retinopathy (DR), Peripheral vascular disease (PVD), and carotid plaque. Conversely, exercise, High density lipoprotein cholesterol (HDL-C), metformin (MET), pioglitazone, and metformin combination therapy emerged as protective factors.

CONCLUSION

This study underscores the multitude of influencing factors contributing to stroke in people with T2DM patients, among which the microvascular complications of T2DM play an most important role. Therefore, we emphasize the importance of screening for microvascular complications in patients with T2DM. However, due to limitations arising from the number of articles reviewed, there remain areas where clarity is lacking. Further research efforts are warranted to expand upon and reinforce our current findings.

Methylome-wide studies of six metabolic traits

Exploring the molecular correlates of metabolic health measures may identify the shared and unique biological processes and pathways that they track. Here, we performed epigenome-wide association studies (EWASs) of six metabolic traits: body mass index (BMI), body fat percentage, waist-hip ratio (WHR), and blood-based measures of glucose, high-density lipoprotein (HDL) cholesterol, and total cholesterol. We considered blood-based DNA methylation (DNAm) from >750,000 CpG sites in over 17,000 volunteers from the Generation Scotland (GS) cohort. Linear regression analyses identified between 304 and 11,815 significant CpGs per trait at P<3.6×10-8, with 37 significant CpG sites across all six traits. Further, we performed a Bayesian EWAS that jointly models all CpGs simultaneously and conditionally on each other, as opposed to the marginal linear regression analyses. This identified between 3 and 27 CpGs with a posterior inclusion probability ≥ 0.95 across the six traits. Next, we used elastic net penalised regression to train epigenetic scores (EpiScores) of each trait in GS, which were then tested in the Lothian Birth Cohort 1936 (LBC1936; European ancestry) and Health for Life in Singapore (HELIOS; Indian-, Malay- and Chinese-ancestries). A maximum of 27.1% of the variance in BMI was explained by the BMI EpiScore in the subset of Malay-ancestry Singaporeans. Four metabolic EpiScores were associated with general cognitive function in LBC1936 in models adjusted for vascular risk factors (Standardised βrange: 0.08 - 0.12, PFDR < 0.05). EpiScores of metabolic health are applicable across ancestries and can reflect differences in brain health.

Association of Nilotinib With Cardiovascular Diseases in Patients With Chronic Myelogenous Leukemia: A National Population-Based Cohort Study

BACKGROUND

Tyrosine kinase inhibitor (TKI) treatment has been identified to be a risk factor for metabolic syndrome and cardiovascular diseases (CVDs) in patients diagnosed with chronic myeloid leukemia (CML). However, the specific contribution of post-TKI metabolic syndrome and the individual TKIs, including imatinib, nilotinib, and dasatinib, contribute to the development of CVDs remains unclear.

METHODS

We conducted a nationwide database to investigate the incidence of post-TKI metabolic syndrome, including diabetes, hyperlipidemia, and hypertension, as well as their association with CVDs. To compare the risk of post-TKI comorbidities and CVDs among TKIs, we utilized the incidence rate ratio (IRR), and subdistribution hazard ratio (SHR) calculated from multiple Fine-Gray models.

RESULTS

A total of 1211 patients without diabetes, 1235 patients without hyperlipidemia, and 1074 patients without hypertension were enrolled in the study. The incidence rate of post-TKI diabetes and hyperlipidemia was the highest in patients treated with nilotinib compared to imatinib and dasatinib (IRRs ≥ 3.15, Ps ≤ .047). After adjusting for confounders, nilotinib remained a significant risk factor for post-TKI diabetes and hyperlipidemia at an SHR of 3.83 (P < .001) and 5.15 (P < .001), respectively. Regarding the occurrence of CVDs, patients treated with nilotinib were more likely to develop CVDs than those treated with imatinib in non-hyperlipidemic group (IRR = 3.21, P = .020). Pre-existing and post-TKI hyperlipidemia were found to have a stronger association with CVDs, with SHR values of 5.81 (P = .034) and 13.21 (P = .001), respectively.

CONCLUSION

The findings of this study indicate that nilotinib treatment is associated with increased risks of diabetes and hyperlipidemia, with hyperlipidemia being the most significant risk for CVDs. Therefore, we recommend that CML patients receiving nilotinib should undergo screening for diabetes and hyperlipidemia prior to initiating TKI treatment. Additionally, regular monitoring of lipid profiles during TKI therapy and implementing effective management strategies to control hyperlipidemia are crucial.

A biomarker discovery of acute myocardial infarction using feature selection and machine learning

Acute myocardial infarction (AMI) or heart attack is a significant global health threat and one of the leading causes of death. The evolution of machine learning has greatly revamped the risk stratification and death prediction of AMI. In this study, an integrated feature selection and machine learning approach was used to identify potential biomarkers for early detection and treatment of AMI. First, feature selection was conducted and evaluated before all classification tasks with machine learning. Full classification models (using all 62 features) and reduced classification models (using various feature selection methods ranging from 5 to 30 features) were built and evaluated using six machine learning classification algorithms. The results showed that the reduced models performed generally better (mean AUPRC via random forest (RF) algorithm for recursive feature elimination (RFE) method ranges from 0.8048 to 0.8260, while for random forest importance (RFI) method, it ranges from 0.8301 to 0.8505) than the full models (mean AUPRC via RF: 0.8044). The most notable finding of this study was the identification of a five-feature model that included cardiac troponin I, HDL cholesterol, HbA1c, anion gap, and albumin, which had achieved comparable results (mean AUPRC via RF: 0.8462) as to the models that containing more features. These five features were proven by the previous studies as significant risk factors for AMI or cardiovascular disease and could be used as potential biomarkers to predict the prognosis of AMI patients. From the medical point of view, fewer features for diagnosis or prognosis could reduce the cost and time of a patient as lesser clinical and pathological tests are needed.

Analysis of the Association between Retinal Artery Occlusion and Acute Ischaemic Stroke/ST-Elevation Myocardial Infarction and Risk Factors in Hungarian Patients

Background and Objectives: We aimed to analyse data on retinal artery occlusion (RAO) patients to explore correlations with acute ischaemic stroke (AIS), ST-elevation myocardial infarction (STEMI), and cardio/cerebrovascular comorbidities. Patients and Methods: Our retrospective cohort study included 169 RAO and 169 age- and gender-matched control patients. We examined the association of AIS, STEMI, and related comorbidities such as hypertension (HT), type 1 and type 2 diabetes (T1DM and T2DM, respectively), hyperlipidaemia, and ischaemic heart disease (IHD) with RAO. We also recorded atrial fibrillation in our RAO patients. Results: Our results demonstrated that RAO patients developed both AIS and STEMI at a significantly higher rate compared to controls (p < 0.001 for both). We also found that RAO patients had a significantly higher prevalence of HT and hyperlipidaemia (p1 = 0.005, p2 < 0.001) compared to controls. Multiple risk factors together significantly increased the odds of developing AIS and STEMI. Conclusions: Our results suggest that through identifying and treating the risk factors for RAO patients, we can reduce the risk of AIS, STEMI, and RAO of the fellow eye. Considering that ophthalmologists are often the first detectors of these cardiovascularly burdened patients, collaboration with colleagues from internal medicine, cardiology, and neurology is essential to achieve secondary prevention.

Characteristics and outcomes of patients with type 1 diabetes admitted with acute coronary syndromes

AIMS

This study explored characteristics and outcomes of patients with type 1 diabetes mellitus (T1DM) and acute coronary syndromes (ACS).

METHODS

A retrospective analysis of patients with T1DM admitted with ACS to an Australian hospital was conducted. Risk factor targets were defined by 2021 European Society of Cardiology Guidelines. Outcomes were defined as an adverse cardiovascular event (ACS, unplanned revascularisation, heart failure, stroke, or cardiovascular death) or all-cause mortality within six-months after discharge.

RESULTS

61 patients were included [age 58.5 ± 12.8 years, 39 % female]. Dyslipidaemia (85 %), hypertension (75 %), smoking (28 %), prior coronary artery disease (CAD) (44 %), and microvascular complications (62 %) were common. HbA1c, low-density lipoprotein cholesterol, and blood pressure targets were attained in 12 %, 36 % and 47 %, respectively. ST-elevation myocardial infarction (65 % versus 7 %, p < 0.001) and revascularisation (77 % versus 41 %, p = 0.008) were more common in those without prior CAD. Peak inpatient blood glucose correlated directly with peak troponin (p = 0.011) and inversely with left ventricular ejection fraction (p = 0.027). Nineteen patients experienced an adverse six-month outcome, with peripheral neuropathy (p = 0.039) and in-hospital hypoglycaemia (p = 0.012) being independent predictors.

CONCLUSIONS

Patients with T1DM and ACS often do not meet guideline targets for cardiovascular risk factors, and frequently present with transmural infarctions. Dysglycemia and microvascular complications predict poorer outcomes.