BackgroundTo determine whether the TyG index, a product of the levels of triglycerides and fasting plasma glucose (FPG) might be a valuable marker for predicting future diabetes.MethodsA total of 5,354 nondiabetic subjects who had completed their follow-up visit for evaluating diabetes status were selected from a large cohort of middle-aged Koreans in the Chungju Metabolic Disease Cohort study. The risk of diabetes was assessed according to the baseline TyG index, calculated as ln[fasting triglycerides (mg/dL) × FPG (mg/dL)/2]. The median follow-up period was 4.6 years.ResultsDuring the follow-up period, 420 subjects (7.8%) developed diabetes. The baseline values of the TyG index were significantly higher in these subjects compared with nondiabetic subjects (8.9±0.6 vs. 8.6±0.6; P<0.0001) and the incidence of diabetes increased in proportion to TyG index quartiles. After adjusting for age, gender, body mass index, waist circumference, systolic blood pressure, high-density lipoprotein (HDL)-cholesterol level, a family history of diabetes, smoking, alcohol drinking, education level and serum insulin level, the risk of diabetes onset was more than fourfold higher in the highest vs. the lowest quartile of the TyG index (relative risk, 4.095; 95% CI, 2.701–6.207). The predictive power of the TyG index was better than the triglyceride/HDL-cholesterol ratio or the homeostasis model assessment of insulin resistance.ConclusionsThe TyG index, a simple measure reflecting insulin resistance, might be useful in identifying individuals at high risk of developing diabetes.

BMI, metabolic health status, and their interactions should be considered for estimating mortality risk; however, the data are controversial and unknown in Asians. We aimed to investigate this issue in Korean population. Total 323175 adults were followed-up for 96 (60–120) (median [5–95%]) months in a nationwide population-based cohort study. Participants were classified as “obese” (O) or “non-obese” (NO) using a BMI cut-off of 25 kg/m2. People who developed ≥1 metabolic disease component (hypertension, diabetes, dyslipidaemia) in the index year were considered “metabolically unhealthy” (MU), while those with none were considered “metabolically healthy” (MH). The MUNO group had a significantly higher risk of all-cause (hazard ratio, 1.28 [95% CI, 1.21–1.35]) and cardiovascular (1.88 [1.63–2.16]) mortality, whereas the MHO group had a lower mortality risk (all-cause: 0.81 [0.74–0.88]), cardiovascular: 0.73 [0.57–0.95]), compared to the MHNO group. A similar pattern was noted for cancer and other-cause mortality. Metabolically unhealthy status was associated with higher risk of all-cause and cardiovascular mortality regardless of BMI levels, and there was a dose-response relationship between the number of incident metabolic diseases and mortality risk. In conclusion, poor metabolic health status contributed more to mortality than high BMI did, in Korean adults.

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More than 10% of normal-weight subjects were classed as MONW in this cohort. Identification of these subjects based on lipid profiles could aid in the early detection of a high risk group of developing cardiometabolic diseases.

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Objective Pressure injuries are common and serious complications for hospitalized patients. The pressure injury rate is an important patient safety metric and an indicator of the quality of nursing care. Timely and accurate prediction of pressure injury risk can significantly facilitate early prevention and treatment and avoid adverse outcomes. While many pressure injury risk assessment tools exist, most were developed before there was access to large clinical datasets and advanced statistical methods, limiting their accuracy. In this paper, we describe the development of machine learning-based predictive models, using phenotypes derived from nurse-entered direct patient assessment data. Methods We utilized rich electronic health record data, including full assessment records entered by nurses, from 5 different hospitals affiliated with a large integrated healthcare organization to develop machine learning-based prediction models for pressure injury. Five-fold cross-validation was conducted to evaluate model performance. Results Two pressure injury phenotypes were defined for model development: nonhospital acquired pressure injury (N = 4398) and hospital acquired pressure injury (N = 1767), representing 2 distinct clinical scenarios. A total of 28 clinical features were extracted and multiple machine learning predictive models were developed for both pressure injury phenotypes. The random forest model performed best and achieved an AUC of 0.92 and 0.94 in 2 test sets, respectively. The Glasgow coma scale, a nurse-entered level of consciousness measurement, was the most important feature for both groups. Conclusions This model accurately predicts pressure injury development and, if validated externally, may be helpful in widespread pressure injury prevention.

Background/AimsGastrointestinal (GI) symptoms are common among patients with non-insulin dependent diabetes mellitus (NIDDM). Our aim was to investigate the frequency of chronic GI symptoms in Korean patients with NIDDM.MethodsA cross-sectional survey, using a reliable and valid questionnaire, was performed in diabetes clinics from seven hospitals of the Catholic University of Korea.ResultsA total of 608 patients (249 males and 359 females, mean age 53.7±10.9 years) were investigated. The frequencies of weekly heartburn and acid regurgitation (esophageal symptoms) were 7.1% (95% confidence interval [CI], 5.0 to 9.2) and 4.4% (95% CI, 2.8 to 6.1), respectively. The frequency of dyspepsia was 13.2% (95% CI, 10.5 to 15.8). The frequencies of constipation and diarrhea were 15.0% (95% CI, 12.2 to 18.0) and 5.3% (95% CI, 3.5 to 7.1), respectively. Nausea and the use of manual maneuvers to facilitate defecation were more prevalent in women than in men. Constipation and fecal incontinence were more common in diabetes patients with long duration (>10 years). Fecal incontinence and using laxatives were more frequent in the complicated diabetes group. Using laxatives was more frequent in the uncontrolled diabetes group.ConclusionsTwo-thirds of diabetic patients experienced GI symptoms. The prevalence of GI symptoms was more common in patients who had diabetic complications and a long duration of diabetes.

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