Influencing factors of coronary heart disease in type 2 diabetes
In this study, the data analysis results clearly indicate that hypertension, smoking, neuropathy, vascular complications, cerebral infarction, bilateral lower extremity arteriosclerosis, elevated microalbuminuria, and uric acid levels are significant risk factors for coronary heart disease (CHD) in patients with type 2 diabetes (T2DM). These findings align closely with those reported in the existing literature9. For instance, our study identified an odds ratio (OR) of 3.613 for hypertension, consistent with the mechanism by which hypertension promotes CHD through accelerated atherosclerosis. Similarly, the OR for smoking was 2.5, which corresponds with the detrimental effects of smoking on vascular endothelial damage and inflammatory responses. The significance of microalbuminuria and uric acid levels further validates the impact of diabetes-related metabolic disorders on the cardiovascular system, particularly in terms of microvascular and macrovascular damage.
These significant risk factors and their common threshold values are crucial for assessing and predicting the risk of coronary heart disease (CHD) in type 2 diabetes patients. Hypertension is a key factor, with a threshold of systolic pressure ≥ 140 mmHg or diastolic pressure ≥ 90 mmHg, indicating both hypertension and an increased risk of CHD. Smoking, particularly long-term smoking (≥ 20 pack-years), significantly raises CHD risk, and smokers should regularly undergo CHD screening and receive smoking cessation support.
Neuropathy, especially diabetic peripheral neuropathy (DPN), is common in diabetes and often correlates with higher CHD risk. Vascular complications such as arteriosclerosis and peripheral artery disease also serve as important indicators of CHD risk. A history of stroke, especially in diabetic patients, further increases CHD risk, necessitating close cardiovascular monitoring. Additionally, lower limb arteriosclerosis, detected via ultrasound, can identify potential CHD risks. Urinary microalbumin (ACR ≥ 30 mg/g) and elevated uric acid (≥ 7.0 mg/dL) are markers of higher CHD risk in diabetic patients. These threshold values help clinicians identify high-risk individuals for early intervention, reducing the occurrence of CHD.
These data support our hypothesis regarding the key risk factors and further emphasize the importance of early intervention to address these risk factors. For example, the OR for neuropathy was 2.5, indicating that changes in autonomic nervous function may significantly increase the risk of CHD by exacerbating arrhythmias and reducing cardiac ischemic tolerance. By verifying the specific impact of these risk factors on the patient population, our study not only supports the existing theoretical framework but also provides a data-driven predictive model that enables more accurate CHD risk assessment and guides personalized interventions.
Impact of hypertension on coronary heart disease
Hypertension is widely recognized as a significant independent risk factor for coronary heart disease (CHD)10. Numerous studies indicate that hypertension significantly increases the risk of CHD through mechanisms such as endothelial damage and accelerated arteriosclerosis11. Epidemiological studies and extensive prospective studies have shown that even mild hypertension can substantially raise the risk of CHD12. Multiple meta-analyses further suggest that the coexistence of hypertension with other cardiovascular risk factors, such as diabetes and hyperlipidemia, exacerbates the risk of CHD13. Moreover, extensive randomized controlled trials, such as the ALLHAT study, have confirmed that antihypertensive treatments (e.g., ACE inhibitors, ARBs, and calcium channel blockers) can significantly reduce the incidence of CHD in hypertensive patients14. These findings underscore the importance of early identification and intervention of hypertension and support the selection of optimal antihypertensive medications based on individual cases to prevent CHD effectively.
Impact of smoking on coronary heart disease in type 2 diabetes
Smoking significantly increases the risk of coronary heart disease (CHD) in patients with type 2 diabetes (T2DM)15. It does so through various mechanisms, including endothelial damage to coronary arteries, increased oxidative stress and inflammation, and accelerated atherosclerosis13. Studies have shown that the risk of CHD is approximately 50–80% higher in smoking T2DM patients compared to non-smokers16. Additionally, smoking exacerbates T2DM-related metabolic abnormalities, such as insulin resistance and dyslipidemia, further worsening cardiovascular health17. Extensive epidemiological studies indicate that the incidence of CHD is significantly higher in smokers compared to non-smokers, with greater risk associated with higher daily smoking quantities18. Notably, quitting smoking can dramatically reduce the risk of CHD in T2DM patients, although it may take several years to reach levels comparable to non-smokers19. These findings emphasize the widespread and significant negative impact of smoking on the risk of CHD in T2DM patients and further support smoking cessation as an essential intervention in diabetes management to reduce CHD incidence and improve cardiovascular outcomes effectively.
Impact of neuropathy on coronary heart disease in type 2 diabetes
Neuropathy significantly increases the risk of coronary heart disease (CHD) in patients with type 2 diabetes (T2DM). Studies have shown that neuropathy, especially autonomic neuropathy, exacerbates the progression of CHD by affecting cardiovascular function and increasing the risk of arrhythmias20. Additionally, neuropathy may lead to reduced heart rate variability, decreasing the heart’s tolerance to ischemia and thereby increasing the risk of myocardial infarction21. Peripheral neuropathy also worsens T2DM-related metabolic abnormalities, such as reduced physical activity, obesity, and poor glycemic control, further increasing the risk of CHD22. Therefore, early identification and management of neuropathy are crucial for reducing the risk of CHD in T2DM patients.
Impact of vascular complications on CHD
Vascular complications are a vital factor in the development of CHD in T2DM patients. Vascular complications, such as atherosclerosis and endothelial dysfunction, significantly increase the risk of CHD by causing narrowing and obstruction of coronary arteries23. Multiple studies have indicated a significant association between vascular complications and the incidence of CHD in diabetes patients14. Research has found that vascular complications can increase the risk of CHD by 50–70% in diabetic patients24. These results are consistent with other literature and highlight the importance of managing vascular complications to prevent CHD in diabetes patients.
Impact of cerebral infarction on CHD
Cerebral infarction, a common complication in diabetic patients, is also closely linked to coronary heart disease (CHD). Studies have shown that cerebral infarction and CHD often coexist, with a significant increase in the risk of CHD in patients with cerebral infarction25. Research indicates that the risk of CHD in patients with cerebral infarction is about 60% higher than in diabetic patients without cerebral infarction26. This may be related to systemic vascular damage and chronic inflammation induced by cerebral infarction27. The presence of cerebral infarction exacerbates cardiovascular risk in diabetes patients, suggesting a need for comprehensive management of both conditions to reduce cardiovascular events.
Impact of bilateral lower extremity arteriosclerosis on CHD
Bilateral lower extremity arteriosclerosis is a common vascular complication in diabetic patients and has also been studied about CHD. Literature shows a significant association between bilateral lower extremity arteriosclerosis and CHD28. For example, studies have demonstrated that diabetic patients with bilateral lower extremity arteriosclerosis have a CHD incidence risk twice that of patients without arteriosclerosis29. This phenomenon may be related to systemic vascular damage and circulatory impairment caused by arteriosclerosis30. Bilateral lower extremity arteriosclerosis is essential to predict CHD in diabetes patients.
Impact of microalbuminuria on CHD
Microalbuminuria (MAU) is one of the early markers of vascular damage in diabetic patients and is widely studied about CHD. Literature indicates that elevated microalbumin levels are an essential predictor of CHD31. Studies have found a significant association between microalbuminuria and the incidence of CHD, with high levels of microalbuminuria associated with an increased risk of CHD31. This consistency with other literature emphasizes the importance of early detection and management of microalbuminuria to reduce the risk of CHD32.
Impact of uric acid levels on CHD
Elevated uric acid levels are significantly associated with the incidence of CHD in diabetic patients. Research has shown that high uric acid levels increase the risk of CHD in diabetes patients33. One study found that for every 1 mg/dL increase in uric acid levels, the risk of CHD in diabetes patients increased by approximately 10%34. Uric acid may exacerbate atherosclerosis by promoting oxidative stress and inflammatory responses, affecting cardiovascular health35. The impact of uric acid levels on CHD is relatively consistent across different studies, suggesting a need for further research into its mechanisms and clinical intervention strategies.
Construction of a prediction model for coronary heart disease in type 2 diabetes
The nomogram we developed provides an easy-to-use and personalized model for predicting coronary heart disease (CHD) in patients with type 2 diabetes (T2DM) and hypertension, which aids in optimizing clinical management. Hypertension, smoking, neuropathy, vascular complications, cerebral infarction, bilateral lower extremity arteriosclerosis, microalbuminuria, and uric acid levels are identified as independent risk factors for hypertension in T2DM. The prediction model demonstrates vital predictive accuracy and discriminatory ability. Unfortunately, our model has not yet been validated in external populations, so the external validity of the model may need further confirmation.
Compared to some existing CHD prediction models (such as the Framingham Risk Score, QRISK, and diabetes-specific models), the model developed in this study takes into account more diabetes-specific risk factors, such as microalbuminuria and uric acid levels, which are highly relevant in patients with type 2 diabetes. Therefore, although our model shows promising performance with an AUC of 0.83, it still requires validation in larger, multicenter datasets to further assess its applicability and accuracy across different populations.
In future research, we plan to conduct a more in-depth comparative analysis of this model with existing prediction models to explore its relative advantages and disadvantages in different settings. Additionally, we aim to optimize and refine the model to further enhance its clinical utility and practical value.
In this study, the coronary heart disease (CHD) prediction model was primarily developed using data from patients with type 2 diabetes (T2DM) and hypertension. However, the heterogeneity of patient populations (such as age differences, severity of diabetes, and comorbidities) may significantly impact the model’s predictive performance. Therefore, we believe it is important to further explore the effectiveness of the model in different patient groups, particularly in elderly patients and those with severe T2DM.
Elderly Patient Group: As patients age, their cardiovascular system typically undergoes physiological decline and may be accompanied by multiple chronic diseases and reduced physiological function. Therefore, the risk of CHD in elderly patients is influenced by various factors, such as aggravated arteriosclerosis due to aging and differences in drug metabolism, which may result in variations in the model’s predictive performance for this group. Notably, elderly patients are more likely to experience drug side effects and may undergo polypharmacy, which complicates the effects on cardiovascular health and potentially impacts the model’s accuracy.
Severe Type 2 Diabetes Patient Group: In patients with severe T2DM, long-term hyperglycemia often leads to multiple complications, such as diabetic nephropathy, retinopathy, and neuropathy, which make their vascular health and cardiovascular risk more complex. These patients’ clinical characteristics may differ from those with mild or moderate diabetes, including longer disease duration, poorer glycemic control, and more frequent comorbidities. Therefore, we hypothesize that the model’s predictive performance might differ in these patients, and adjustments to the weight of certain predictive factors may be necessary to better fit the specific circumstances of this group.
To validate the model’s effectiveness across different patient groups, future research will aim to expand the sample size, especially through multi-center studies that include more patients from different age groups and with varying severities of diabetes, in order to assess the model’s accuracy and applicability in these populations. Additionally, future studies will consider optimizing the model by adjusting the weights of predictive factors for specific populations to enhance the model’s utility and precision in different clinical contexts.
Integration and application prospects of the model in existing healthcare systems
The coronary heart disease (CHD) prediction model developed in this study holds significant potential for clinical practice and can be integrated into existing healthcare systems in the following ways. Firstly, the model can be embedded into electronic health record (EHR) systems to enable automated and dynamic CHD risk assessments for diabetes patients, assisting clinicians in formulating more precise, individualized prevention and treatment plans. Secondly, by utilizing risk scores generated by the model, healthcare systems can establish stratified management mechanisms, prioritizing high-risk patients for intensive management or intervention programs, thereby optimizing the allocation of healthcare resources. Additionally, the model can be incorporated into mobile health platforms (e.g., patient-facing applications) to provide patient education and real-time risk feedback, encouraging self-management behaviors such as lifestyle improvements, smoking cessation, and blood pressure control.
However, further validation and optimization of the model are necessary before practical application, particularly through external validation across multiple centers and diverse populations to ensure its generalizability and robustness.
In future research, we plan to incorporate additional variables into the model. For example, research on genetic markers is still in an evolving stage, and although existing genetic data is not yet fully mature, we expect to gradually integrate relevant genetic information into the model as genomics advances. At the same time, lifestyle factors such as dietary habits and physical activity will also be considered in future studies by collecting more comprehensive data to optimize the model’s predictive power. We plan to conduct multi-center, large-scale studies to further validate the impact of these variables on the model’s performance and explore how to adjust the model to suit different populations and clinical settings.
We plan to conduct longitudinal studies to further address causality issues. By performing long-term follow-up on diabetic patients, we can explore the pathways through which different risk factors affect coronary heart disease (CHD) and assess how changes in these factors over time are closely related to the occurrence of CHD. Additionally, longitudinal studies will help us identify potential time-dependent or stage-specific risk factors in clinical practice, further optimizing existing predictive models and improving their accuracy.
We will collect a broader range of longitudinal data through multi-center studies to ensure diversity across different populations, while analyzing how risk factors change over time and their specific relationship with the development of CHD.
Additionally, we also aim to explore how integrating other clinical indicators could further enhance the model’s overall predictive capability. For example, combining factors such as patients’ psychological status, medication history, and lipid levels may help provide a more comprehensive assessment of cardiovascular risk in diabetes patients. We believe that these efforts will make the prediction model more accurate and personalized, ultimately offering better support for clinical practice.In the future, integrating the model with existing telemedicine and health management tools could enhance its usability and provide more comprehensive support for chronic disease management within healthcare systems.
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