BMI and coronary heart disease risk among low-income and underinsured diabetic patients

Genetically predicted lifestyle factors, socioeconomic status and risk of coronary artery disease in individuals with diabetes: a Mendelian randomization study

Background

This study explores the causal links between genetically predicted lifestyle factors, socioeconomic status, and coronary artery disease (CAD) risk in individuals with diabetes using a bidirectional Mendelian-randomization approach.

Methods

This study explored the potential causal relationships of lifestyle factors and socioeconomic status with the risk of CAD in diabetes patients by a bidirectional, two-sample Mendelian-randomization (MR) analysis.

Results

Genetically predicted smoking initiation (p = 0.005, 95% CI: 1.08-1.55) and insomnia (p = 0.001, 95% CI: 1.06-1.29) were associated with a higher risk of CAD in individuals with diabetes, whereas educational attainment (p = 0.0001, 95% CI: 0.47-0.78) was associated with a lower risk of CAD. The lifetime smoking index (p = 0.016, 95% CI: 1.12-3.03) was suggestively associated with a higher risk of CAD, while household income before taxes (p = 0.048, 95% CI: 0.41-1.00) was suggestively associated with a lower risk of CAD. In addition, we observed a suggestive negative association between the genetically predicted risk of CAD and the lifetime smoking index (p = 0.016, 95% CI: 0.98-0.99) and a significant causal relationship between the risk of CAD and household income before taxes (p = 0.006, 95% CI: 0.97-0.99).

Conclusion

The results of this study provide evidence that smoking initiation, lifetime smoking index and insomnia are associated with an increased risk of CAD in individuals with diabetes, educational attainment and household income before taxes are associated with a reduced risk of CAD in individuals with diabetes, and the possible role of lifetime smoking index and household income before taxes on the risk of CAD in individuals with diabetes. It provides an opportunity for the prevention and management of CAD in individuals with diabetes.

Framingham Risk Score Assessment in Subjects with Pre-diabetes and Diabetes: A Cross-Sectional Study in Korea

Background

This study aimed to evaluate cardiovascular risk in subjects with pre-diabetes and diabetes in Korea.

Methods

In this pan-Korean, non-interventional, cross-sectional study, data were collected from medical records of 10 hospitals between November 2013 and June 2014. Subjects (aged ≥40 years) with medical records of dysglycemia and documentation of total cholesterol level, high-density lipoprotein cholesterol level, systolic blood pressure, and smoking status in the past 6 months were included. The primary endpoint was to determine the Framingham risk score (FRS). The relationships between FRS and cardiovascular risk factors, glycated hemoglobin, and insulin usage were determined by multiple linear regression analyses.

Results

Data from 1,537 subjects with pre-diabetes (n=1,025) and diabetes (n=512) were analyzed. The mean FRS (mean±standard deviation) in subjects with pre-diabetes/diabetes was 13.72±8.77. FRS was higher in subjects with diabetes than pre-diabetes (P<0.001). FRS in men with pre-diabetes was comparable to that in women with diabetes (13.80±7.37 vs. 13.35±7.13). FRS was elevated in subjects who consumed alcohol (2.66, P=0.033) and with obesity-class II (6.10, P=0.015) among subjects with diabetes (n=199), and was elevated in patients with left ventricular hypertrophy (11.10, P=0.005), those who consumed alcohol (3.06, P=0.000), were pre-obese (3.21, P=0.002), or were obesity-class I (2.89, P=0.002) among subjects with pre-diabetes (n=306) in comparison to subjects without these coexisting risk factors.

Conclusion

Overall, Korean subjects with pre-diabetes and diabetes have an increased cardiovascular risk, which is significantly higher in those subjects with diabetes than with pre-diabetes. The present data can be used to develop measures to prevent and manage cardiovascular complications in Koreans with impaired glucose metabolism.

Practical use of electronic health records among patients with diabetes in scientific research

Electronic health (medical) records, which are also considered as patients’ information that are routinely collected, provide a great chance for researchers to develop an epidemiological understanding of disease. Electronic health records systems cannot develop without the advance of computer industries. While conducting clinical trials that are always costly, feasible and reasonable analysis of routine patients’ information is more cost-effective and reflective of clinical practice, which is also called real world study. Real world studies can be well supported by big data in healthcare industry. Real world studies become more and more focused and important with the development of evidence-based medicine. These big data will definitely help in making decisions, making policies and guidelines, monitoring of effectiveness and safety on new drugs or technologies. Extracting, cleaning, and analyzing such big data will be a great challenge for clinical researchers. Successful applications and developments of electronic health record in western countries (eg, disease registries, health insurance claims, etc) have provided a clear direction for Chinese researchers. However, it is still at primary stages in China. This review tries to provide a full perspective on how to translate the electronic health records into scientific achievements, for example, among patients with diabetes. As a summary in the end, resource sharing and collaborations are highly recommended among hospitals and healthcare groups.

A novel numerical model of combination levels of C-peptide and insulin in coronary artery disease risk prediction

OBJECTIVE

Insulin resistance is a major risk factor for coronary artery disease (CAD). The C-peptide-to-insulin ratio (C/I) is associated with hepatic insulin clearance and insulin resistance. The current study was designed to establish a novel C/I index (CPIRI) model and provide early risk assessment of CAD.

METHODS

A total of 865 adults diagnosed with new-onset diabetes mellitus (DM) within one year and 54 healthy controls (HC) were recruited to develop a CPIRI model. The CPIRI model was established with fasting C/I as the independent variable and homeostasis model assessment of insulin resistance (HOMA-IR) as the dependent variable. Associations between the CPIRI model and the severity of CAD events were also assessed in 45 hyperglycemic patients with CAD documented via coronary arteriography (CAG) and whom underwent stress echocardiography (SE) and exercise electrocardiography test (EET).

RESULTS

Fasting C-peptide/insulin and HOMA-IR were hyperbolically correlated in DM patients and HC, and log(C/I) and log(HOMA-IR) were linearly and negatively correlated. The respective correlational coefficients were -0.83 (p < 0.001) and -0.76 (p < 0.001). The equations CPIRI(DM) = 670/(C/I)2.24 + 0.25 and CPIRI(HC) = 670/(C/I)2.24 - 1 (F = 1904.39, p < 0.001) were obtained. Patients with insulin resistance exhibited severe coronary artery impairment and myocardial ischemia. In CAD patients there was no significant correlation between insulin resistance and the number of vessels involved.

CONCLUSIONS

CPIRI can be used to effectively evaluate insulin resistance, and the combination of CPIRI and non-invasive cardiovascular examination is of great clinical value in the assessment of CAD.

A prospective study of waist circumference trajectories and incident cardiovascular disease in China: the Kailuan Cohort Study

Background

Single measurements of waist circumference (WC) predict the incident cardiovascular disease (CVD); however, long-term patterns of WC and their association with the incidence of CVD are poorly characterized.

Objective

We aimed to identify WC trajectories and determine their association with incident CVD (stroke and myocardial infarction) and examine whether the association persisted among individuals without obesity.

Methods

We included 75,535 participants from a community-based cohort in China who were aged &gt;18 y and free of stroke, coronary artery disease, and cancer in 2010 (the baseline). WC and other covariates were repeatedly measured in 2006, 2008, and 2010. WC trajectories were identified by latent mixture modeling. A Cox proportional hazards model was used to examine the association between WC trajectories and incident CVD, after adjustment for age, sex, income, education, systolic blood pressure, lipid profiles, plasma concentrations of glucose, C-reactive protein, smoking, and alcohol drinking.

Results

We identified 4 WC trajectories based on 2006 WC measurement and change patterns during 2006–2010: low stable (n = 12,072; mean WC 74.1–75.1 cm), moderate stable (n = 41,750; mean WC 85.1–86.6 cm), moderate-high stable (n = 19,914; mean WC 95.6–97.2 cm), and high stable (n = 1,799; mean WC 106.3–110.9 cm). During 2010–2016, we documented 2819 incident CVD events. Compared with the low-stable group, groups with elevated WC trajectories had a higher risk of CVD events during 6 y of follow-up (2010–2016). Adjusted HRs (95% CIs) were 1.49 (1.21, 1.83) for the moderate stable group, 1.71(1.38, 2.12) for the moderate-high stable group, and 1.45 (1.06, 2.00) for the high stable group. After further adjusting for BMI or excluding obese participants, we observed similar results. The positive association between WC and incident CVD was higher in individuals who were aged &lt;60 y (P-interaction &lt; 0.0001).

Conclusions

WC trajectory patterns were associated with altered risk of CVD among Chinese adults, even among people without BMI-defined obesity. When stratifying by age, the association was observed to be higher in younger adults.

A potent risk model for predicting new-onset acute coronary syndrome in patients with type 2 diabetes mellitus in Northwest China

AIMS

Type 2 diabetes mellitus (T2DM) is now very prevalent in China. Due to the lower rate of controlled diabetes in China compared to that in developed countries, there is a higher incidence of serious cardiovascular complications, especially acute coronary syndrome (ACS). The aim of this study was to establish a potent risk predictive model in the economically disadvantaged northwest region of China, which could predict the probability of new-onset ACS in patients with T2DM.

METHODS

Of 456 patients with T2DM admitted to the First Affiliated Hospital of Xi'an Jiaotong University from January 2018 to January 2019 and included in this study, 270 had no ACS, while 186 had newly diagnosed ACS. Overall, 32 demographic characteristics and serum biomarkers of the study patients were analysed. The least absolute shrinkage and selection operator regression was used to select variables, while the multivariate logistic regression was used to establish the predictive model that was presented using a nomogram. The area under the receiver operating characteristics curve (AUC) was used to evaluate the discriminatory capacity of the model. A calibration plot and Hosmer-Lemeshow test were used for the calibration of the predictive model, while the decision curve analysis (DCA) was used to evaluate its clinical validity.

RESULTS

After random sampling, 319 and 137 T2DM patients were included in the training and validation sets, respectively. The predictive model included age, body mass index, diabetes duration, systolic blood pressure (SBP), diastolic blood pressure (DBP), low-density lipoprotein cholesterol, serum uric acid, lipoprotein(a), hypertension history and alcohol drinking status as predictors. The AUC of the predictive model and that of the internal validation set was 0.830 [95% confidence interval (CI) 0.786-0.874] and 0.827 (95% CI 0.756-0.899), respectively. The predictive model showed very good fitting degree, and DCA demonstrated a clinically effective predictive model.

CONCLUSIONS

A potent risk predictive model was established, which is of great value for the secondary prevention of diabetes. Weight loss, lowering of SBP and blood uric acid levels and appropriate control for DBP may significantly reduce the risk of new-onset ACS in T2DM patients in Northwest China.

Development of a nomogram that predicts the risk for coronary atherosclerotic heart disease

Studies seldom combine biological, behavioral and psychological factors to estimate coronary atherosclerotic heart disease (CHD) risk. Here, we evaluated the associations between these factors and CHD to develop a predictive nomogram to identify those at high risk of CHD. This case-control study included 4392 participants (1578 CHD cases and 2814 controls) in southeast China. Thirty-three biological, behavioral and psychological variables were evaluated. Following multivariate logistic regression analysis, which revealed eight risk factors associated with CHD, a predictive nomogram was developed based on a final model that included the three non-modifiable (sex, age and family history of CHD) and five modifiable (hypertension, hyperlipidemia, diabetes, recent experience of a major traumatic event, and anxiety) variables. The higher total nomogram score, the greater the CHD risk. Final model accuracy (as estimated from the area under the receiver operating characteristic curve) was 0.726 (95% confidence interval: 0.709-0.747). Validation analysis confirmed the high accuracy of the nomogram. High risk of CHD was associated with several biological, behavioral and psychological factors. We have thus developed an intuitive nomogram that could facilitate development of preliminary prevention strategies for CHD.

C-Peptide Is Independently Associated with an Increased Risk of Coronary Artery Disease in T2DM Subjects: A Cross-Sectional Study

OBJECTIVE

C-peptide has been reported to be a marker of subclinical atherosclerosis in type 2 diabetes mellitus (T2DM) patients, whereas its role in coronary artery disease (CAD) has not been clarified, especially in diabetics with differing body mass indices (BMIs).

DESIGN AND METHODS

This cross-sectional study included 501 patients with T2DM. First, all subjects were divided into the following two groups: CAD and non-CAD. Then, binary logistic regression was used to determine the risk factors for CAD for all patients. To clarify the role of obesity, we re-divided all subjects into two additional groups (obese and non-obese) based on BMI. Finally, binary logistic regression was used to determine the risk factors for CAD for each weight group.

RESULTS

The patients with CAD showed a higher BMI and fasting C-peptide level in addition to an increased prevalence of traditional risk factors for CAD, such as hypertension, insulin resistance, higher cholesterol, cysteine-C (Cys-C) and lower estimated glomerular filtration rate (eGFR). Logistic regression analysis showed that fasting C-peptide (OR=1.513, p=0.005), insulin treatment (OR=1.832, p=0.027) hypertension (OR=1.987, p=0.016) and hyperlipidemia (OR=4.159, p<0.001) significantly increased the risk of clinical CAD in the T2DM patients independent of age, gender, diabetes duration, smoking and alcohol statuses, fasting insulin and glucose, hypoglycemic episodes, UA and eGFR. Additionally, in both of the obese (OR=1.488, p=0.049) and non-obese (OR=1.686, p=0.037) DM groups, C-peptide was associated with an increased risk of CAD after multiple adjustments.

CONCLUSIONS

C-peptide is associated with an increased CAD risk in T2DM patients, no matter whether they are obese or not.