Interplay of cardiovascular health and diabetes: Insights into weight management and risk reduction
Journal
Journal of diabetes investigation
Date Issued
2024-02-22
Author(s)
Hsu, Chih-Yao
Abstract
Cardiovascular health and diabetes mellitus are interrelated health challenges that persist globally1. Meanwhile, excess body weight stands as a major risk factor for both cardiovascular disease and diabetes. In recent years, many research studies have been performed to unravel the complex interplay among cardiovascular disease, diabetes, and obesity, in order to identify novel risk factors and to develop potential prevention and therapeutic strategies. In this article, we present recent findings that expand the understanding in this area. The intrauterine environment has been proposed to have an impact on cardiovascular and metabolic diseases in childhood and adulthood. A large population cohort study has revealed that maternal obesity during the first trimester of pregnancy was associated with an increased risk of congenital heart defects in infants, and this connection was mediated, at least in part, by maternal pregestational diabetes2. One possible mechanism underlying the association is that in early pregnancy insulin resistance, abnormal glucose control, vascular dysfunction, and abnormal placental function are involved in the development of fetal malformations3. Besides, a longitudinal cohort study investigating the association between the maternal pre-pregnancy body mass index (BMI) and offspring cardiometabolic risk factors at 4–6 years of age revealed a significant positive correlation between maternal BMI and both elevated blood pressure and increased retinal vascular tortuosity in the children, independent of the child's own BMI4. In addition, a large Swedish study analyzing data from more than 2 million births found that a higher BMI in early pregnancy increased the risk of cardiovascular diseases in childhood and early adulthood in offspring, particularly cerebrovascular diseases5. These results support the potential of implementing specific interventions to combat maternal obesity before pregnancy as a means of improving the cardiovascular health of future generations, both early and later in life, which should be investigated in the future. In obese people with diabetes, achieving greater and sustained weight loss has been shown to result in a reduced risk of cardiovascular events. In the Look Action for Health in Diabetes (Look AHEAD) trial, while an intensive weight-loss program for overweight or obese adults with type 2 diabetes improved some cardiovascular risk factors, it did not show a significant effect on the risk of cardiovascular events during a median follow-up of 9.6 years6. However, in further examinations of the entire cohort, those who lost at least 10% of their body weight in the first year had a significant reduction (21%) in the incidence of cardiovascular disease, suggesting a connection between greater weight loss and lower cardiovascular disease risk7. Consistent with the results of the Look AHEAD trial, another study conducted in a Japanese cohort of 1,753 individuals with type 2 diabetes and obesity found a significant association between weight loss and improvement in cardiovascular disease risk factors8, providing evidence in an Asian population. Interestingly, recent evidence has demonstrated that fluctuations in body weight, often accompanied by periods of weight loss, were associated with an increased risk of cardiovascular events9. In a recent post-hoc analysis of the Look AHEAD trial, researchers examined the effect of weight fluctuation on major adverse cardiovascular events (MACE)10. They found that increasing weight variability was linked to a higher risk of MACE in those who received standard diabetes support and education. However, there was no association between weight variability and the risk of MACE in people who underwent an intensive lifestyle intervention to promote weight loss. Although the exact mechanisms by which an intensive lifestyle intervention program protects against adverse cardiovascular outcomes associated with weight fluctuations are unclear, exercise may play a key role in mitigating this risk, possibly by reducing abdominal adiposity11. The promotion of cardiovascular health and the reduction of the disease burden could be achieved by the control of cardiovascular risk factors, including smoking, poor dietary habit, physical inactivity, obesity, high blood pressure, dyslipidemia, and hyperglycemia. However, the relationship between these modifiable risk factors and the likelihood of developing prediabetes or diabetes remains uncertain in the Asian population. To fill this knowledge gap, a community-based prospective study from Iran found that ideal cardiovascular health metrics, particularly normal blood pressure and normal body weight, were associated with a lower risk of developing type 2 diabetes12. Another analysis of a nationwide claims database in Japan showed that having more non-ideal cardiovascular health metrics was associated with a higher risk of developing prediabetes or diabetes, with non-ideal BMI showing the strongest association13. In addition, in a research study of over 100,000 Chinese adults, individuals classified as obese were found to have an increased risk of prediabetes in a positive dose–response relationship, contradicting the pattern observed in the obesity paradox14. These findings emphasize the importance of obesity as a contributing factor to the development of abnormal glucose metabolism among the various cardiovascular health metrics examined. Aside from the weight management strategies mentioned above, medical treatments such as sodium-glucose cotransporter 2 (SGLT2) inhibitors have shown efficacy in improving cardiovascular outcomes in addition to glycemic control. While the potential of SGLT2 inhibitors for long-term diabetes control and cardiometabolic benefits depends on real-world adherence, these crucial data in Asian patients are surprisingly limited. Compared with dipeptidyl peptidase-4 (DPP-4) inhibitors, a Japanese study has demonstrated that SGLT2 inhibitors provided greater cardiometabolic risk reduction and treatment adherence15. Results from the Empagliflozin Comparative Effectiveness and Safety (EMPRISE) East Asia study, conducted in Japan, South Korea, and Taiwan, showed that initiation of empagliflozin treatment in real-world clinical settings was associated with a reduced risk of each of the outcomes of hospitalization for heart failure and all-cause mortality in comparison with DPP-4 inhibitors16. Upon revisiting the EMPRISE East Asia study and using different composite outcomes, it was observed that people with type 2 diabetes who started on empagliflozin and other SGLT2 inhibitors had a lower risk of combined hospitalization for heart failure and all-cause mortality, as well as the composite outcome comprising myocardial infarction, stroke, and all-cause mortality17. Another retrospective cohort study using Japanese hospital administrative data also indicated that SGLT2 inhibitors were more effective than DPP-4 inhibitors in reducing hospitalization for heart failure and all-cause mortality in patients with type 2 diabetes, particularly in those without a history of cardiovascular disease18. These results reaffirm the importance of SGLT2 inhibitors as a valuable medical intervention in line with recent diabetes treatment recommendations to reduce cardiovascular risk in Asia19, 20. The development of predictive models for disease and health outcomes has long been a major focus of research. In the era of artificial intelligence and big data, several novel approaches have been attempted. In a recent study, machine learning models were constructed using clinical data and laboratory test results from 4,722 Chinese individuals21. The aim was to employ these models for predicting cardiovascular risk in people with type 2 diabetes. Each of the five different models demonstrated excellent performance, and the area under the receiver operating characteristic curve (AUC) for these models all exceeded 0.8. Among these, the random forest model stood out with the highest AUC in both the test set and the external validation set. Notably, the top five key features identified by the random forest model as having the greatest impact on cardiovascular risk prediction were age, duration of diabetes, history of diabetic peripheral vascular disease, total cholesterol levels, and triglyceride levels. It appears that while machine learning models can identify some novel risk factors and improve predictive accuracy, the most influential factors in these models are traditional risk factors that are already determined by classic statistical methods. Indeed, similar to the findings of this study, other research using machine learning to develop predictive models for cardiovascular complications in people with diabetes often identifies traditional risk factors such as age, BMI, diabetes duration, diabetes-related complications, lipid profiles, glucose markers, and treatment regimens as influential predictors22. In addition to machine learning, there is a growing recognition of novel biomarkers that have been discovered to predict cardiovascular events. For example, elevated serum C1q tumor necrosis factor-related protein 4, which is involved in inflammation and glucose metabolism, has been observed to be positively associated with both the incidence and severity of coronary artery disease23. Besides, metabolomics and metabolic profiling are becoming increasingly popular in a wide range of research projects and offer the potential to discover valuable new biomarkers24. In a study conducted in Hong Kong, a machine learning classifier was built using non-targeted metabolomic analysis of plasma samples from 392 Chinese adults to distinguish incident type 2 diabetes from diabetes-free individuals over a median follow-up of 16 years25. The study found that the addition of 10 diabetes-associated metabolites to traditional risk factors significantly improved the ability to predict future diabetes. In the future, it would be promising to develop different machine learning models to predict the risk of cardiovascular events or diabetes, and the models may involve the integration of demographic information, clinical factors, electronic health record, genomic data, transcriptomic data, and metabolomics data. To summarize, the interconnected challenges of cardiovascular health and diabetes, often exacerbated by obesity, have prompted extensive research (Table 1). Recent research highlights the impact of maternal obesity on the cardiovascular health of offspring. Additionally, the importance of addressing obesity as a contributor to abnormal glucose metabolism and its broader implications on cardiovascular health, the benefits of sustained weight loss in people with diabetes, the effectiveness of treatments such as SGLT2 inhibitors, and the development of predictive models for cardiovascular risk all align with evolving diabetes management. These collective findings underscore the need for a multifaceted approach to reduce the global burden of cardiovascular disease and diabetes. Hung-Yuan Li is an editorial board member of Journal of Diabetes Investigation and a co-author of this article. To minimize bias, he was excluded from all editorial decision-making related to the acceptance of this article for publication. The authors declare no conflict of interest.
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Type
journal article