Screening Model for Estimating Undiagnosed Diabetes among People with a Family History of Diabetes Mellitus: A KNHANES-Based Study

Impact of Education as a Social Determinant on the Risk of Type 2 Diabetes Mellitus in Korean Adults

Education is correlated with health literacy, which is a combination of reading and listening skills, data analysis, and decision-making during the necessary health situations. This study aims to evaluate the effect of education on the risk of type 2 diabetes mellitus (T2DM). This is a population-based cross-sectional study using the 2019 nationwide survey data in Korea. There were 3951 study subjects, after excluding participants with missing data for key exposures and outcome variables. Descriptive statistics, χ2 (chi-square) test, and logistic regression were performed to analyze the data. The prevalence of T2DM was associated with educational attainment, sex, age, smoking status, physical activity, carbohydrate intake, and obesity. In the logistic regression model, the odds ratio (OR) of having T2DM was much lower among people educated in college or higher (OR = 0.49, 95% confidence interval [95% CI] = 0.34-0.64) than those with only or without primary education after adjusting for biological factors (sex, age) and health behaviors (smoking status, physical activity, carbohydrate intake, and obesity). This study shows that educational attainment is a significant social determinant influencing health outcomes both directly and indirectly. Therefore, it is necessary to develop policies to reduce the health inequity of T2DM caused by differences in educational attainment.

Analysis of risk factors and clinical implications for diabetes in first-degree relatives in the northeastern region of China

Background

The prevalence of diabetes has risen fast with a considerable weighted prevalence of undiagnosed diabetes or uncontrolled diabetes. Then it becomes more necessary to timely screen out and monitor high-risk populations who are likely to be ignored during the COVID-19 pandemic. To classify and find the common risks of undiagnosed diabetes and uncontrolled diabetes, it's beneficial to put specific risk control measures into effect for comprehensive primary care. Especially, there is a need for accurate yet accessible prediction models.

Objective

Based on a cross-sectional study and secondary analysis on the health examination held in Changchun City (2016), we aimed to evaluate the factors associated with hyperglycemia, analyze the management status of T2DM, and determine the best cutoff value of incidence of diabetes in the first-degree relatives to suggest the necessity of early diagnosis of diabetes after first screening.

Results

A total of 5658 volunteers were analyzed. Prevalence of T2DM and impaired fasting glucose were 8.4% (n=477) and 11.5% (n=648), respectively. There were 925 participants (16.3%) with a family history of T2DM in their first-degree relatives. Multivariable analysis demonstrated that family history was associated with hyperglycemia. Among the 477 patients with T2DM, 40.9% had not been previously diagnosed. The predictive equation was calculated with the following logistic regression parameters with 0.71 (95% CI: 0.67-0.76) of the area under the ROC curve, 64.0% of sensitivity and 29% of specificity (P < 0.001): P = \frac{1}{1 + e^{-z}}, where z = -3.08 + [0.89 (Family history-group) + 0.69 (age-group)+ 0.25 (BMI-group)]. Positive family history was associated with the diagnosis of T2DM, but not glucose level in the diagnosed patients. The best cutoff value of incidence of diabetes in the first-degree relatives was 9.55% (P < 0.001).

Conclusions

Family history of diabetes was independently associated with glucose dysfunction. Classification by the first-degree relatives with diabetes is prominent for targeting high-risk population. Meanwhile, positive family history of diabetes was associated with diabetes being diagnosed rather than the glycemic control in patients who had been diagnosed. It's necessary to emphasize the linkage between early diagnosis and positive family history for high proportions of undiagnosed T2DM.

Pulse pressure and the risk of renal hyperfiltration in young adults: Results from Korea National Health and Nutrition Examination Survey (2010–2019)

Background

High pulse pressure (PP) is associated with increased risk of decline of kidney function. However, little is known about the association between PP and RHF in young adults. This study aimed to evaluate the association between PP and RHF in healthy young adults.

Methods

Data were retrieved from the Korea National Health and Nutrition Examination Survey from 2010 to 2019. A total of 10,365 participants aged 19–39 years with no hypertension and normal kidney function were analyzed. RHF was defined as logarithm transformed estimated glomerular filtration rate (eGFR) with residuals >90th percentile after adjustment for sex, logarithm transformed age, weight, and height. Participants were divided into tertile based on PP levels.

Results

The prevalence of RHF was higher in higher PP tertile group (6.6, 10.5, and 12.7% in T1, T2, and T3; P for trend < 0.001). In multivariable logistic regression analyses, the risk for RHF was increased in higher PP tertiles compared to the lowest tertile [odds ratio (OR), 1.42; 95% confidence interval (CI), 1.19–1.69 in T2; OR, 1.44; 95% CI, 1.20–1.73 in T3]. When PP levels were treated as continuous variable, the risk of RHF was increased 2.36 per 1.0 increase of PP (P < 0.001). In subgroup analyses stratified sex, histories of diabetes or dyslipidemia, and isolated systolic hypertension or isolated diastolic hypertension, there were no significant interactions with PP for the risk for RHF, suggesting that high PP was associated with increased risk of RHF regardless of subgroups. However, the subgroup with BMI showed significant interaction with PP for the risk of RHF, indicating that participants with BMI ≥ 25 kg/m² were at higher risk of RHF with increasing PP levels than those with BMI < 25 kg/m² (OR, 1.89; 95% CI, 1.25–2.87 in BMI < 25 kg/m²; OR, 3.16; 95% CI, 1.74–5.73 in BMI ≥ 25 kg/m²; P for interaction = 0.01).

Conclusion

High PP is associated with an increased risk of RHF in healthy young adults and this association is prominent in obese young adults. The assessment of PP and associated RHF may give benefit to early detect the potential risk of CKD development in young adults.

Statins and the risk of gastric, colorectal, and esophageal cancer incidence and mortality: a cohort study based on data from the Korean national health insurance claims database

BACKGROUND

This study investigated the association between the use of statins, the incidence of gastric, colorectal, and esophageal cancers, and mortality between January 2005 and June 2013 in South Korea.

METHODS

We compared patients aged 45-70 years statin users for at least 6 months to non-statin users matched by age and sex, from 2004 to June 2013 using the National Health Insurance database. Main outcomes were gastric, colorectal, and esophageal cancer incidence and mortality. Cox proportional hazard regression was used to calculate the adjusted hazard ratios (aHRs) and 95% confidence intervals (95% CIs) among overall cohort and matched cohort after propensity score matching with a 1:1 ratio.

RESULTS

Out of 1,008,101 people, 20,473 incident cancers, 3938 cancer deaths occurred and 7669 incident cancer, 1438 cancer death in matched cohort. The aHRs for the association between the risk of cancers and statin use were 0.7 (95% CI 0.65-0.74) for gastric cancer, 0.73 (95% CI 0.69-0.78) for colorectal cancer, and 0.55 (95% CI 0.43-0.71) for esophageal cancer. There were associations between statin use and decreased gastric cancer mortality (HR 0.46, 95% CI 0.52-0.57), colorectal cancer mortality (HR 0.43, 95% CI 0.36-0.51), and esophageal cancer mortality (HR 0.41, 95% CI 0.27-0.50) in the overall cohort and this pattern was similar in the matched cohort.

DISCUSSION

Statin use for at least 6 months was significantly associated with a lower risk of stomach, colorectal, and esophageal cancer incidence as well as cancer mortality after a diagnosis.

Machine learning-based diagnosis and risk factor analysis of cardiocerebrovascular disease based on KNHANES

The prevalence of cardiocerebrovascular disease (CVD) is continuously increasing, and it is the leading cause of human death. Since it is difficult for physicians to screen thousands of people, high-accuracy and interpretable methods need to be presented. We developed four machine learning-based CVD classifiers (i.e., multi-layer perceptron, support vector machine, random forest, and light gradient boosting) based on the Korea National Health and Nutrition Examination Survey. We resampled and rebalanced KNHANES data using complex sampling weights such that the rebalanced dataset mimics a uniformly sampled dataset from overall population. For clear risk factor analysis, we removed multicollinearity and CVD-irrelevant variables using VIF-based filtering and the Boruta algorithm. We applied synthetic minority oversampling technique and random undersampling before ML training. We demonstrated that the proposed classifiers achieved excellent performance with AUCs over 0.853. Using Shapley value-based risk factor analysis, we identified that the most significant risk factors of CVD were age, sex, and the prevalence of hypertension. Additionally, we identified that age, hypertension, and BMI were positively correlated with CVD prevalence, while sex (female), alcohol consumption and, monthly income were negative. The results showed that the feature selection and the class balancing technique effectively improve the interpretability of models.

Risk of Typical Diabetes-Associated Complications in Different Clusters of Diabetic Patients: Analysis of Nine Risk Factors

Objectives: Diabetic patients are often diagnosed with several comorbidities. The aim of the present study was to investigate the relationship between different combinations of risk factors and complications in diabetic patients. Research design and methods: We used a longitudinal, population-wide dataset of patients with hospital diagnoses and identified all patients (n = 195,575) receiving a diagnosis of diabetes in the observation period from 2003–2014. We defined nine ICD-10-codes as risk factors and 16 ICD-10 codes as complications. Using a computational algorithm, cohort patients were assigned to clusters based on the risk factors they were diagnosed with. The clusters were defined so that the patients assigned to them developed similar complications. Complication risk was quantified in terms of relative risk (RR) compared with healthy control patients. Results: We identified five clusters associated with an increased risk of complications. A combined diagnosis of arterial hypertension (aHTN) and dyslipidemia was shared by all clusters and expressed a baseline of increased risk. Additional diagnosis of (1) smoking, (2) depression, (3) liver disease, or (4) obesity made up the other four clusters and further increased the risk of complications. Cluster 9 (aHTN, dyslipidemia and depression) represented diabetic patients at high risk of angina pectoris “AP” (RR: 7.35, CI: 6.74–8.01), kidney disease (RR: 3.18, CI: 3.04–3.32), polyneuropathy (RR: 4.80, CI: 4.23–5.45), and stroke (RR: 4.32, CI: 3.95–4.71), whereas cluster 10 (aHTN, dyslipidemia and smoking) identified patients with the highest risk of AP (RR: 10.10, CI: 9.28–10.98), atherosclerosis (RR: 4.07, CI: 3.84–4.31), and loss of extremities (RR: 4.21, CI: 1.5–11.84) compared to the controls. Conclusions: A comorbidity of aHTN and dyslipidemia was shown to be associated with diabetic complications across all risk-clusters. This effect was amplified by a combination with either depression, smoking, obesity, or non-specific liver disease.

Combinatorial K-Means Clustering as a Machine Learning Tool Applied to Diabetes Mellitus Type 2

A new original procedure based on k-means clustering is designed to find the most appropriate clinical variables able to efficiently separate into groups similar patients diagnosed with diabetes mellitus type 2 (DMT2) and underlying diseases (arterial hypertonia (AH), ischemic heart disease (CHD), diabetic polyneuropathy (DPNP), and diabetic microangiopathy (DMA)). Clustering is a machine learning tool for discovering structures in datasets. Clustering has been proven to be efficient for pattern recognition based on clinical records. The considered combinatorial k-means procedure explores all possible k-means clustering with a determined number of descriptors and groups. The predetermined conditions for the partitioning were as follows: every single group of patients included patients with DMT2 and one of the underlying diseases; each subgroup formed in such a way was subject to partitioning into three patterns (good health status, medium health status, and degenerated health status); optimal descriptors for each disease and groups. The selection of the best clustering is obtained through the parameter called global variance, defined as the sum of all variance values of all clinical variables of all the clusters. The best clinical parameters are found by minimizing this global variance. This methodology has to identify a set of variables that are assumed to separate each underlying disease efficiently in three different subgroups of patients. The hierarchical clustering obtained for these four underlying diseases could be used to build groups of patients with correlated clinical data. The proposed methodology gives surmised results from complex data based on a relationship with the health status of the group and draws a picture of the prediction rate of the ongoing health status.

Risk Factors of Undiagnosed Diabetes Mellitus among Korean Adults: A National Cross-Sectional Study Using the KNHANES Data

In this cross-sectional study, we investigated the baseline risk factors of diabetes mellitus (DM) in patients with undiagnosed DM (UDM). We utilized the Korean National Health and Nutrition Examination Survey (KNHANES) 2010–2017 data. Data regarding the participants’ demographic characteristics, health status, health determinants, healthcare accessibility, and laboratory tests were gathered to explore the differences between the DM, UDM, and without-DM groups. Among the 64,759 individuals who participated in the KNHANES 2010–2017, 32,611 individuals aged ≥20 years with fasting plasma glucose levels of <100 or ≥126 mg/dL were selected. The odds ratios (ORs) regarding family history of diabetes and the performance of national health and cancer screening tests were lower in the UDM group than in the DM group (adjusted OR: 0.54; 95% confidence interval (CI): 0.43, 0.66; adjusted OR: 0.74; 95% CI: 0.62, 0.89; adjusted OR: 0.71; 95% CI: 0.60, 0.85). The ORs of hypertension and obesity were higher in the UDM group than in the DM group (adjusted OR: 1.32; 95% CI: 1.06, 1.64; adjusted OR: 1.80; 95% CI: 1.37, 2.36, respectively). Patients with UDM were more likely to be exposed to DM-related risk factors than those with and without DM. Public health interventions to prevent UDM development are necessary.