Impact of prior cerebrovascular disease and glucose status on incident cerebrovascular disease in Japanese.

BACKGROUND: Although both a history of cerebrovascular disease (CVD) and glucose abnormality are risk factors for CVD, few large studies have examined their association with subsequent CVD in the same cohort. Thus, we compared the impact of prior CVD, glucose status, and their combinations on subsequent CVD using real-world data. METHODS: This is a retrospective cohort study including 363,627 men aged 18-72 years followed for ≥ 3 years between 2008 and 2016. Participants were classified as normoglycemia, borderline glycemia, or diabetes defined by fasting plasma glucose, HbA1c, and antidiabetic drug prescription. Prior and subsequent CVD (i.e. ischemic stroke, transient ischemic attack, and non-traumatic intracerebral hemorrhage) were identified according to claims using ICD-10 codes, medical procedures, and questionnaires. RESULTS: Participants' mean age was 46.1 ± 9.3, and median follow up was 5.2 (4.2, 6.7) years. Cox regression analysis showed that prior CVD + conferred excess risk for CVD regardless of glucose status (normoglycemia: hazard ratio (HR), 8.77; 95% CI 6.96-11.05; borderline glycemia: HR, 7.40, 95% CI 5.97-9.17; diabetes: HR, 5.73, 95% CI 4.52-7.25). Compared with normoglycemia, borderline glycemia did not influence risk of CVD, whereas diabetes affected subsequent CVD in those with CVD- (HR, 1.50, 95% CI 1.34-1.68). In CVD-/diabetes, age, current smoking, systolic blood pressure, high-density lipoprotein cholesterol, and HbA1c were associated with risk of CVD, but only systolic blood pressure was related to CVD risk in CVD + /diabetes. CONCLUSIONS: Prior CVD had a greater impact on the risk of CVD than glucose tolerance and glycemic control. In participants with diabetes and prior CVD, systolic blood pressure was a stronger risk factor than HbA1c. Individualized treatment strategies should consider glucose tolerance status and prior CVD.

Usefulness of New Criteria for Metabolic Syndrome Optimized for Prediction of Cardiovascular Diseases in Japanese.

AIMS: We attempted to clarify whether the multiple criteria for metabolic syndrome (MetS) can sufficiently predict cardiovascular disease, whether waist circumference (WC) should be required, and whether sex-specific thresholds for each component are necessary. Only a few large-scale studies among East Asians have addressed the ability of MetS to predict cardiovascular disease. METHODS: We analyzed the data of 330,051 men and 235,028 women aged 18-74 years with no history of coronary artery disease (CAD) or cerebrovascular disease (CVD) from a nationwide Japanese claims database accumulated during 2008-2016. The association of each MetS component with CAD or CVD (CAD/CVD), MetS associated with CAD/CVD according to various criteria, and utility of modified criteria with more specific optimal values for each component were examined using multivariate Cox regression and receiver operating characteristic (ROC) analysis. RESULTS: During the study, 3,934 men (1.19%) and 893 women (0.38%) developed CAD/CVD. For each current MetS criteria, there was a 1.3- to 2.9-fold increased risk of CAD/CVD. Optimal thresholds for predicting CAD/CVD were WCs of 83 and 77 cm, triglycerides levels of 130 and 90 mg/dl, high-density lipoprotein cholesterol levels of 50 and 65 mg/dl, blood pressures of 130/80 and 120/80 mmHg, and fasting plasma glucose levels of 100 and 90 mg/dl for men and women, respectively. The existing MetS criteria and modified criteria were not significantly different in predicting CAD/CVD, but using the modified criteria markedly increased the prevalence of MetS and percentage of people with MetS developing CAD/CVD. CONCLUSIONS: Although various criteria for MetS similarly predicted CAD/CVD, the new criteria greatly reduced the number of high-risk individuals, especially women, overlooked by the current criteria.

Impact of health practice index and cardiovascular health metrics on incident cardiovascular disease according to glucose tolerance status.

Aims: To evaluate and compare the association of incident cardiovascular disease (CVD) with the Health Practice Index (HPI) reflecting only lifestyle habits and Ideal Cardiovascular Health Metrics (ICVHMs) consisting of lifestyle habits and factors targeted for control in the same population according to glucose status. Methods: This retrospective cohort study included 1,28,162 participants aged 18-72 years with no history of CVD followed for ≥ 3 years between 2008 and 2016. Participants were classified according to normal glucose tolerance (86,174), prediabetes (36,096), or diabetes (5892). HPI and ICVHMs scores were classified into three groups (high/medium/low). Multivariate Cox regression hazard analysis examined CVD risk. Results: During a mean follow-up of 5.2 years, 1057 CVD events occurred. In prediabetes, CVD risk was significantly higher in groups with both medium and low HPI scores and ICVHMs scores compared to high scores for normal glucose tolerance (hazard ratios [HRs] for high/medium/low HPI scores were 0.95 [0.78-1.17], 1.56 [1.29-1.89], and 2.41 [1.74-3.34] and for ICVHMs scores were 0.74 [0.50-1.11], 1.58 [1.26-1.98], and 2.63 [2.10-3.31], respectively). Regarding diabetes, compared with high HPI/ICVHMs scores in the normal glucose tolerance group, a significantly increased CVD risk was observed in the high-score HPI group, but not in the high-score ICVHMs group (HPI high/medium/low HR, 1.63 [1.22-2.18], 2.19 [1.69-2.83], and 2.26 [1.34 -3.83]; ICVHMs high/medium/low HR, 1.14 [0.47-2.81], 2.38 [1.75-3.23], and 3.31 [2.50-4.38], respectively). Conclusions: In diabetes, ideal lifestyle practices alone were insufficient for primary prevention of CVD but had a greater impact on primary prevention of CVD in prediabetes. Supplementary Information: The online version contains supplementary material available at 10.1007/s13340-024-00708-7.

Machine Learning Approach to Decision Making for Insulin Initiation in Japanese Patients With Type 2 Diabetes (JDDM 58): Model Development and Validation Study.

BACKGROUND: Applications of machine learning for the early detection of diseases for which a clear-cut diagnostic gold standard exists have been evaluated. However, little is known about the usefulness of machine learning approaches in the decision-making process for decisions such as insulin initiation by diabetes specialists for which no absolute standards exist in clinical settings. OBJECTIVE: The objectives of this study were to examine the ability of machine learning models to predict insulin initiation by specialists and whether the machine learning approach could support decision making by general physicians for insulin initiation in patients with type 2 diabetes. METHODS: Data from patients prescribed hypoglycemic agents from December 2009 to March 2015 were extracted from diabetes specialists' registries, resulting in a sample size of 4860 patients who had received initial monotherapy with either insulin (n=293) or noninsulin (n=4567). Neural network output was insulin initiation ranging from 0 to 1 with a cutoff of >0.5 for the dichotomous classification. Accuracy, recall, and area under the receiver operating characteristic curve (AUC) were calculated to compare the ability of machine learning models to make decisions regarding insulin initiation to the decision-making ability of logistic regression and general physicians. By comparing the decision-making ability of machine learning and logistic regression to that of general physicians, 7 cases were chosen based on patient information as the gold standard based on the agreement of 8 of the 9 specialists. RESULTS: The AUCs, accuracy, and recall of logistic regression were higher than those of machine learning (AUCs of 0.89-0.90 for logistic regression versus 0.67-0.74 for machine learning). When the examination was limited to cases receiving insulin, discrimination by machine learning was similar to that of logistic regression analysis (recall of 0.05-0.68 for logistic regression versus 0.11-0.52 for machine learning). Accuracies of logistic regression, a machine learning model (downsampling ratio of 1:8), and general physicians were 0.80, 0.70, and 0.66, respectively, for 43 randomly selected cases. For the 7 gold standard cases, the accuracies of logistic regression and the machine learning model were 1.00 and 0.86, respectively, with a downsampling ratio of 1:8, which were higher than the accuracy of general physicians (ie, 0.43). CONCLUSIONS: Although we found no superior performance of machine learning over logistic regression, machine learning had higher accuracy in prediction of insulin initiation than general physicians, defined by diabetes specialists' choice of the gold standard. Further study is needed before the use of machine learning-based decision support systems for insulin initiation can be incorporated into clinical practice.

Severity of hypertension as a predictor of initiation of dialysis among study participants with and without diabetes mellitus.

To determine associations between severity of hypertension and risk of starting dialysis in the presence or absence of diabetes mellitus (DM). A nationwide database with claims data on 258 874 people with and without DM aged 19-72 years in Japan was used to elucidate the impact of severity of hypertension on starting dialysis. Initiation of dialysis was determined from claims using International Classification of Diseases-10 codes and medical procedures. Using multivariate Cox modeling, we investigated the severity of hypertension to predict the initiation of dialysis with and without DM. Hypertension was significantly associated with the initiation of dialysis regardless of DM. The incidence of starting dialysis in those with systolic blood pressure (SBP) ≤119 mm Hg and DM (DM+) was almost the same as in those with SBP ≥150 mm Hg and absence of DM (DM-). In comparison with SBP ≤119 mm Hg, SBP ≥150 mm Hg significantly increased the risk of the initiation of dialysis about 2.5 times regardless of DM+ or DM-. Compared with DM- and SBP ≤119 mm Hg, the HR for DM+ and SBP ≥150 mm Hg was 6.88 (95% CI 3.66 to 12.9). Although the risks of hypertension differed only slightly regardless of the presence or absence of DM, risks for starting dialysis with DM+ and SBP ≤119 mm Hg were equivalent to DM- and SBP ≥150 mm Hg, indicating more strict blood pressure interventions in DM+ are needed to avoid dialysis. Future studies are required to clarify the cut-off SBP level to avoid initiation of dialysis considering the risks of strict control of blood pressure.

Combination of diabetes mellitus and lack of habitual physical activity is a risk factor for functional disability in Japanese.

OBJECTIVE: Declining healthy life expectancy due to functional disability is relevant and urgent because of its association with decreased quality of life and also for its enormous socioeconomic impact. The aim of this study is to examine the impact of diabetes, hypertension, dyslipidemia and physical activity habits on functional disability among community-dwelling Japanese adults. RESEARCH DESIGN AND METHODS: This is a population-based retrospective cohort study including 9673 people aged 39-98 years in Japan (4420, men). Functional disability was defined as a condition meeting Japan's new long-term care insurance certification requirements for the need of assistance in the activities of daily living whether by caregivers or assistive devices. Cox proportional-hazards regression model identified variables related to functional disability. RESULTS: Median follow-up was 3.7 years. During the study period, 165 disabilities occurred in the overall study population. Multivariate analysis showed that diabetes (HR 1.74 (95% CI 1.12 to 2.68)) and no physical activity habit (HR 1.83 (1.27 to 2.65)) presented increased risks for disability. HR for disability increased with the number of risk factors (HR of individuals with four conditions, 3.96 (1.59 to 9.99) vs individuals with none of those conditions as a reference). HR for disability among patients with diabetes with and without a physical activity habit was 1.68 (0.70 to 4.04) and 3.19 (1.79 to 5.70), respectively, compared with individuals without diabetes with a physical activity habit. CONCLUSIONS: The combination of diabetes and lack of habitual physical activity is predictive of functional disability in Japanese. Habitual physical activity attenuates the risk of functional disability in patients with diabetes.