Coronary artery disease risk prediction by combined stratification of low-density lipoprotein-cholesterol and high-density lipoprotein-cholesterol levels across different glucose statuses.

AIM: To investigate the association between low-density lipoprotein-cholesterol (LDL-C) levels and coronary artery disease (CAD) incidence based on combining high-density lipoprotein-cholesterol (HDL-C) levels and glucose status. MATERIALS AND METHODS: In this retrospective cohort study, we used data from a nationwide claims database (1,524,289 individuals without a history of CAD or familial hypercholesterolaemia; 2008-2019). Cox proportional hazards modelling identified the risk of incident CAD by a novel combination of four HDL-C levels, seven LDL-C levels and glucose status. RESULTS: During the follow-up period (mean: 5.5 years), 8301 (0.99/1000 person-years) events occurred. The risk of CAD increased from lower LDL-C levels accompanied by lower HDL-C levels regardless of the glucose status. Using the most favourable levels of HDL-C and LDL-C (i.e. 60-99 mg/dL and <80 mg/dL, respectively) as references, the hazard ratios (95% confidence interval) for the group with HDL-C levels <40 mg/dL and LDL-C levels <80 mg/dL were 2.74 (1.47-5.11), 2.52 (1.30-4.91) and 2.85 (1.68-4.84) for normoglycaemia, borderline glycaemia and diabetes, respectively. Comparison of the most favourable levels of HDL-C and LDL-C with their least favourable levels (i.e. <40 mg/dL and 180-199 mg/dL, respectively) revealed that the risk of new-onset CAD exhibited a 19-, nine- and seven-fold increase in individuals with normoglycaemia, borderline glycaemia and diabetes, respectively. CONCLUSIONS: To prevent CAD, LDL-C levels should be strictly controlled in patients with low HDL-C levels regardless of glucose tolerance. Individualized treatment, which involves setting target LDL-C levels based on glucose tolerance and HDL-C values, is required.

Achieving normoglycaemia with tirzepatide: Post hoc exploratory analysis of the SURPASS J-mono and J-combo studies.

AIM: Normoglycaemia was achieved in a significant proportion of Japanese participants with type 2 diabetes in two phase 3 studies of tirzepatide. This post hoc exploratory analysis aimed to identify predictive factors associated with normoglycaemia achievement. MATERIALS AND METHODS: SURPASS J-mono and SURPASS J-combo study data were pooled for this analysis. Characteristics of participants in whom normoglycaemia [glycated haemoglobin (HbA1c) <5.7%] was achieved were summarized. Logistic regression analyses were performed with HbA1c <5.7% achievement as the target variable. RESULTS: Of 912 participants, normoglycaemia was achieved in 553 (60.6%) following 52 weeks of tirzepatide treatment. Overall, the mean (SD) age was 56.7 (10.6) years and mean diabetes duration was 7.7 (6.0) years, and 76% of participants were men. Mean (SD) change from baseline in HbA1c and bodyweight was -2.87% (0.95) versus -2.47% (1.1) and -10.30 (5.8) kg versus -3.75 (4.3) kg for participants in whom normoglycaemia was and was not reached, respectively. Multivariate regression analyses showed that lower baseline body mass index, shorter disease duration and lower baseline HbA1c were significantly associated with higher rates of normoglycaemia achievement (p = 0.009, p = 0.008, p < 0.001, respectively) as was a tirzepatide dose of 10 or 15 mg compared with 5 mg (p < 0.001). The highest percentage of participants in whom normoglycaemia (94%) was achieved were those with lower baseline HbA1c (<8%) and the greatest weight reduction (≥15%). CONCLUSIONS: Baseline HbA1c and body mass index, disease duration and the tirzepatide treatment group were shown to be predictive factors for achieving normoglycaemia. A lower baseline HbA1c was most strongly associated with normoglycaemia achievement.

Risk stratification for cardiovascular disease based on prior coronary artery disease, cerebrovascular disease and type 2 diabetes mellitus.

AIMS/INTRODUCTION: History of coronary artery disease (CAD), cerebrovascular disease (CeVD), type 2 diabetes and their combined effect on cardiovascular disease are essential for cardiovascular risk management. We investigated the association of prior CAD, prior CeVD, type 2 diabetes and their combination with the risk of cardiovascular disease. MATERIALS AND METHODS: This is a historical cohort study including 342,033 participants (aged 18-72 years) followed up for ≥5 years between 2008 and 2016. Participants were classified into eight groups (with or without prior CAD, prior CeVD and type 2 diabetes). Type 2 Diabetes was defined by fasting plasma glucose and glycated hemoglobin levels, and antidiabetic drug prescription. Prior and subsequent CAD and CeVD were identified according to claims using International Classification of Diseases 10th Revision codes, medical procedures and questionnaires. Cox regression models were used to evaluate the risk of cardiovascular events. RESULTS: The median follow-up period was 6.4 years. The incidence of composite cardiovascular events of CAD and CeVD in the CAD-/CeVD-, CAD+/CeVD-, CAD-/CeVD+ and CAD+/CeVD+ groups were 1.92 and 6.94, 25.14 and 31.98 per 1,000 person-years in non-diabetes participants, and 8.66, 18.04, 39.98 and 60.72 in type 2 diabetes patients, respectively. Hazard ratios of cardiovascular events compared with CAD-/CeVD-/non-diabetes were 1.66 (95% confidence interval 1.55-1.78) in CAD-/CeVD-/type 2 diabetes and 1.84 (1.56-2.18) in CAD+/CeVD-/non-diabetes. CeVD+ was linked to a 4-7-fold increase in the risk of cardiovascular events regardless of CAD+ or type 2 diabetes. CONCLUSIONS: Type 2 diabetes increased the risk of cardiovascular disease as high as a history of CAD, whereas prior CeVD alone increased the risk of future CeVD without additional effects by type 2 diabetes.

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.

Lifestyle Intervention With Smartphone App and isCGM for People at High Risk of Type 2 Diabetes: Randomized Trial.

AIMS: Although conventional interventions for people at high risk of developing type 2 diabetes are usually conducted face-to-face, such interventions are burdensome for health care providers. We developed a lifestyle intervention program combining lifestyle coaching via a smartphone application augmented by intermittently scanned continuous glucose monitoring without burdening health care providers. Its effectiveness for glycemic control and body weight reduction in people at risk of type 2 diabetes was investigated. MATERIALS AND METHODS: For this 12-week randomized unblinded trial with offline recruitment, participants with a hemoglobin A1c level of 5.6% to 6.4% or a fasting blood glucose of 110 to 125 mg/dL and body mass index (BMI) >23 kg/m2 but <40 kg/m2 were randomly assigned to the intervention group (App) and control group (C). The primary endpoint was the difference in time in range of blood glucose between 70 and 140 mg/dL (3.9-7.8 mmol/L) before and after the study period between the 2 groups. RESULTS: Among 168 patients (mean age, 48.1 years; mean BMI, 26.6 kg/m2; and male, 80.4%), 82 and 86 were assigned to the App group and C group, respectively. After 12 weeks, time in range of blood glucose at 70 to 140 mg/dL significantly improved in the App group compared with the C group (-2.6 minutes/day vs +31.5 minutes/day, P = .03). Changes in time above range did not differ, whereas time below range (blood glucose <70 mg/dL; +23.5 minutes/day vs -8.9 minutes/day, P = .02) improved in the App group. BMI (-0.26 vs -0.59, P = .017) was reduced in the App group compared with the C group. CONCLUSION: Intervention with a smartphone app and intermittently scanned continuous glucose monitoring increased glycemic control accompanied by decreased carbohydrate intake and weight loss. Further trials are needed to confirm whether these interventions can reduce incident type 2 diabetes.

Utility of a Physical Fitness Score in Screening for Chronic Diseases.

We developed a new Physical Score (PS) consisting of comprehensive physical fitness indicators and elucidated the association between the resultant PS and metabolic diseases, i.e., diabetes, hypertension, dyslipidemia, fatty liver, and metabolic syndrome (MetS), among Japanese. Analyzed were 49,850 persons (30,039 men) aged 30 to 69 y who underwent physical fitness tests. Principal component analysis was performed on the correlation matrix of the physical fitness test results (relative grip strength, single-leg balance with eyes closed, and forward bending) according to sex and age. We defined the PS as the first principal component score. A formula was developed for various age groups comprised of men and women from 30 to 69 years of age from which the PS for each age and sex was calculated. The PS for both men and women was normally distributed with a value of 0 ± 1.15-1.16. Multivariate logistic regression analysis showed that the risk of metabolic diseases increased approximately 1.1-1.6 times per each 1-point reduction in the PS. The association between PS and MetS was particularly strong in that a 1-point reduction in the PS increased the risk of MetS by 1.54 times (95% confidence interval 1.46 to 1.62) in men and by 1.21 times (1.15 to 1.28) in women. The association between a lower PS and disease risk was stronger in younger men for fatty liver and in older men for MetS. Conversely, in women, the association between a lower PS and disease risk was stronger in older women for fatty liver and in younger women for MetS. For diabetes, hypertension, and dyslipidemia, the change in the impact of PS reductions across age groups was small. The PS is a useful and simple non-invasive tool for screening Japanese people for metabolic diseases.

Combined effects of blood pressure and glycemic status on risk of heart failure: a population-based study.

AIMS: To investigate the combined effects of blood pressure (BP) and glycemic status on the risk of heart failure. METHODS: Examined was a Japanese claims database from 2008 to 2019 on 589 621 individuals. Cox proportional hazards model identified the incidence of heart failure among five levels of SBP/DBP according to glucose status. RESULTS: Mean follow-up period was 5.6 years. The incidence of heart failure per 1000 person-years in the normoglycemia, borderline glycemia, and diabetes groups were 0.10, 0.18, and 0.80, respectively. In normoglycemia, a linear trend was observed between both SBP and DBP categories and hazard ratios for heart failure ( P for linearity <0.001). In borderline glycemia, J-shaped association was observed between DBP categories and hazard ratios, although the liner trend was significant ( P  < 0.001). In diabetes, the linear trend for the relationship between DBP categories and hazard ratios was not significant ( P  = 0.09) and the J-shaped association in relation to the hazard ratios was observed between SBP categories and heart failure risk. In the lowest SBP category (i.e. SBP < 120 mmHg), patients with diabetes had more than five-fold heart failure risk [hazard ratio (95% confidence interval), 5.10 (3.19-8.15)], compared with those with normoglycemia and SBP less than 120 mmHg. CONCLUSION: The association between SBP/DBP and heart failure risk weakened with worsening of glucose metabolism, suggesting strict BP control accompanied by excessively lowered DBP should be cautious in prevent heart failure in abnormal glycemic status. Particularly in diabetes, comprehensive management of risk factors other than BP may be essential to prevent heart failure. Further trials are needed to support these suggestions and apply them to clinical practice.

Association of the estimated glomerular filtration rate (eGFR) and/or proteinuria to predict the risk of initiation of dialysis in people with and without diabetes.

Aims: To determine the associations between combined urinary protein (UP) and a reduced estimated glomerular filtration rate (eGFR) and the risk of starting dialysis with or without diabetes mellitus (DM). Methods: A nationwide database with claims data on 335,778 people with and without DM aged 19-72 years in Japan was used to elucidate the impact of the severities of UP and eGFR on starting dialysis. Initiation of dialysis was determined from claims using ICD-10 codes and medical procedures. Using multivariate Cox modeling, we investigated the severities of UP and eGFR to predict the initiation of dialysis with and without DM. Results: Both eGFR < 60 and UP(+) were independent predictors for starting dialysis with and without DM, and their values exhibited a synergistic risk of dialysis. eGFR < 60 presented a nearly twofold risk for starting dialysis compared to UP(+) regardless of DM. Risk of starting dialysis was increased with UP(+) and eGFR ≥ 60 accompanied by DM although this association was not observed without DM. Those who had UP(-) and eGFR < 60 had a high risk of starting dialysis regardless of DM. Compared with DM(-)UP(-)eGFR ≥ 60, HRs for starting dialysis for DM(+)UP(+)eGFR ≥ 60, DM(+)UP(-)eGFR < 60 and DM(+)UP(+)eGFR < 60 significantly increased 17.7 (10.6-29.7), 25.5 (13.8-47.1) and 358.1 (239.1-536.5) times, respectively. Conclusions: eGFR < 60 and UP(+) together presented an extremely high risk of dialysis especially with DM. UP( +) increased the risk of starting dialysis regardless of the eGFR with DM. Both patient education and a treatment strategy by physicians might be helpful to avoid the progression of renal failure.

Network meta-analysis of glucose-lowering drug treatment regimens with the potential risk of hypoglycemia in patients with type 2 diabetes mellitus in terms of glycemic control and severe hypoglycemia.

Insulin and its secretagogues are essential for some patients with type 2 diabetes (T2D) to maintain good glycemic control (GC), but severe hypoglycemia (SH) is a concern. This network meta-analysis aimed to find optimal glucose-lowering drug treatment regimens in terms of GC and SH in T2D patients. MEDLINE and EMBASE were used to identify trials that compared two or more treatments including insulins and/or sulfonylurea or glinides and that examined both GC and SH. Treatment hierarchy was expressed as the surface under the cumulative ranking curve (SUCRA) probabilities. We identified 137 eligible trials comprising 42 treatments. The use of insulins and non-insulin glucose-lowering agents except for sulfonylurea or glinide had a higher SUCRA than insulins only for hemoglobin A1c (A1C) (p = 0.01) changes and achievement of A1C < 7.0% (p = 0.02) or A1C ≤ 6.5% (p = 0.002). The use of sulfonylurea or glinide and other non-insulin glucose-lowering agents resulted in a lower SUCRA for SH than insulins only when trials were analyzed for A1C change (p = 0.06) and achievement of A1C < 7.0% (p = 0.004) or A1C ≤ 6.5% (p = 0.004). Cluster analysis indicated that premixed insulin plus glucagon-like peptide-1 receptor agonist (Mix-ins + GLP1) belonged to the high-efficacy category for GC and glinide plus thiazolidinedione (glinide + TZD) belonged to the relatively high-efficacy category for GC among several high-safety categories regarding SH. In T2D patients, clinicians should consider appropriate combinations of non-insulin glucose-lowering agents (especially glinide + TZD) for reducing SH risk before switching to insulin therapies. If switching, they should be willing to add non-insulin glucose-lowering agents (especially, Mix-ins + GLP1) to insulins to further improve GC.

Fast walking and physical activity are independent contributors to improved physical function.

BACKGROUND AND OBJECTIVES: To clarify whether the presence or absence of fast walking and habitual physical activity are independently associated with the incidence of functional disability. METHODS: This historical cohort study was comprised of 9,652 (4,412 men, mean age 65 years) individuals aged 39-98 years without functional disability at baseline. Functional disability was determined based on the Japanese long-term care insurance system, which specified requirements for assistance in the activities of daily living. The impact of fast walking and habitual physical activity on the incidence of functional disability was analysed by Cox proportional hazards models. RESULTS: The follow-up period was a median of 3.7 years during which 165 patients were newly certified as having functional disability. In the multivariate analysis, baseline age in 5-year increments (hazard ratio 2.42 [95% confidence interval 2.18-2.69]), no habitual physical activity (1.56 [1.07-2.27]), and not fast walking (1.89 [1.32-2.69]) significantly increased the risk of functional disability after adjustment for covariates. The stratified analysis showed that compared with physical activity (+), the impact of physical activity (-) on the incidence of functional disability was observed in those aged ≥75 years regardless of fast walking (+). Fast walking (-) significantly increased the risk of disability compared with fast walking (+) in those aged <75 years regardless of a physical activity habit. CONCLUSION: In Japanese, slow walking speed and lack of a physical activity habit were shown to be independent risk factors for incident functional disability, with their impact differing according to age.

Impact of metabolic syndrome and metabolic dysfunction-associated fatty liver disease on cardiovascular risk by the presence or absence of type 2 diabetes and according to sex.

BACKGROUND: To determine the impact of metabolic syndrome (MetS) and/or metabolic dysfunction-associated fatty liver disease (MAFLD), which are pathophysiologically similar and include insulin resistance, on the development of new-onset cardiovascular disease with and without type 2 diabetes and according to sex. METHODS: This study included 570,426 individuals without a history of cardiovascular disease who were enrolled in a nationwide claims database from 2008 to 2016 and were classified by the presence or absence of MetS and/or MAFLD stratified by the presence or absence of type 2 diabetes and sex. The fatty liver index was used to determine the presence or absence of fatty liver that required a diagnosis of MAFLD. Risks of developing coronary artery disease (CAD) and cerebrovascular disease (CVD) in each category were analyzed using a multivariate Cox proportional hazard model. RESULTS: During a median follow-up of 5.2 years, 2252 CAD and 3128 CVD events occurred. Without type 2 diabetes the hazard ratio (HR) (95% CI) for CAD/CVD compared with neither MAFLD nor MetS was 1.32 (1.17-1.50)/1.41(1.28-1.57) for MAFLD only (without MetS), 1.78 (1.22-2.58)/1.66 (1.34-2.06) for MetS only (without MAFLD), and 2.10 (1.84-2.39)/1.73 (1.54-1.95) for MAFLD + MetS. For those with type 2 diabetes, the HR for CAD for MAFLD only (compared with neither MAFLD nor MetS) was 1.29 (1.06-1.58), for MetS only 1.34 (0.84-2.13), and for MAFLD + MetS 1.22 (1.02-1.47). For CVD, there was a significant increase in HR only in MAFLD + MetS [1.44 (1.18-1.76)]. The results of the analysis stratified by sex showed that MAFLD had a greater impact in men, and MetS had a greater impact in women regarding the development of CAD. CONCLUSIONS: Distinguishing between MetS and/or MAFLD in the presence or absence of type 2 diabetes and according to sex may aid in accurately identifying patients at high risk of cardiovascular disease.

Differences in occupational stress by smoking intensity and gender in cross-sectional study of 59 355 Japanese employees using the Brief Job Stress Questionnaire (BJSQ): the Niigata Wellness Study.

OBJECTIVES: It has been hypothesised that smoking intensity may be related to occupational stress. This study aimed to investigate whether stress, including problems with superiors or co-workers, is a driver of smoking. DESIGN: Cross-sectional study. SETTING AND PARTICIPANTS: 59 355 employees (34 865 men and 24 490 women) across multiple occupations who completed a self-reported questionnaire-based occupational stress survey between April 2016 and March 2017 in Niigata Prefecture. MAIN OUTCOME MEASURES: Stress scores for the Brief Job Stress Questionnaire subscales summed up after assigning high points for high stress and converted to Z-scores based on the mean of all participants. Heavy smokers (HS) smoked ≥15 cigarettes/day and light smokers (LS) smoked <15 cigarettes/day and were compared with non-smokers (NS) by gender. RESULTS: The main subscale items that were significantly associated with smoking status in both genders included 'physical burden', 'irritation' and 'physical symptoms'. In the analysis that included smoking intensity, the stress score for 'co-workers' support' was significantly lower for LS men than NS men (NS 0.091±0.98, LS -0.027±1.00, HS 0.033±0.99), and was significantly higher for HS women than NS women (NS -0.091±1.00, LS -0.080±1.05, HS 0.079±1.03). However, the stress score for 'co-workers' support' was low among LS women aged ≤39 years in the manufacturing industry. CONCLUSIONS: It was speculated that LS men and some LS women gained 'co-workers' support' using smoking as a communication tool while reducing the degree of smoking. The existence of such 'social smokers' suggested that to promote smoking cessation, measures are essential to improve the communication between workers in addition to implementing smoking restrictions in the workplace.

Impact of Medication Adherence and Glycemic Control on the Risk of Micro- and Macrovascular Diseases in Patients with Diabetes.

PURPOSE: Our purpose in the research was to clarify the impact of medication adherence to oral hypoglycemic agents during a 1-year period and subsequent glycemic control on the risk of micro- and macrovascular diseases. METHODS: Examined was a nationwide claims database on 13,256 individuals with diabetic eye disease without requiring prior treatment, 7,862 without prior initiation of dialysis, 15,556 without prior coronary artery disease, 16,243 without prior cerebrovascular disease, and 19,386 without prior heart failure from 2008 to 2016 in Japan. Medication adherence was evaluated by the proportion of days covered. Patients were considered to have poor adherence if the proportion of days covered was <80%. Multivariate Cox regression model identified risks of micro- and macrovascular diseases. RESULTS: In each group, mean age was 53 to 54 years, HbA1c was 7.1% to 7.2%, and median follow-up period was 4.6 to 5.1 years, and the percentage of poor adherence was approximately 30%. During the study period, 532 treatment-requiring diabetic eye disease, 75 dialysis, 389 coronary artery disease, 316 cerebrovascular disease, and 144 heart failure events occurred. Multivariate Cox regression model revealed that the hazard ratio (95% confidence interval) of dialysis in the poor adherence group was 2.04 (1.27-3.30) compared with the good adherence group. The hazard ratios in the poor adherence/poor glycemic control group were 3.34 (2.63-4.24) for treatment-requiring diabetic eye disease, 4.23 (2.17-8.26) for dialysis, 1.69 (1.23-2.31) for coronary artery disease, and 2.08 (1.25-3.48) for heart failure compared with the good adherence/good glycemic control group. CONCLUSIONS: Poor medication adherence was an independent risk factor for the initiation of dialysis, suggesting that clinicians must pay close attention to these patients.

Predictive ability of current machine learning algorithms for type 2 diabetes mellitus: A meta-analysis.

AIMS/INTRODUCTION: Recently, an increasing number of cohort studies have suggested using machine learning (ML) to predict type 2 diabetes mellitus. However, its predictive ability remains inconclusive. This meta-analysis evaluated the current ability of ML algorithms for predicting incident type 2 diabetes mellitus. MATERIALS AND METHODS: We systematically searched longitudinal studies published from 1 January 1950 to 17 May 2020 using MEDLINE and EMBASE. Included studies had to compare ML's classification with the actual incidence of type 2 diabetes mellitus, and present data on the number of true positives, false positives, true negatives and false negatives. The dataset for these four values was pooled with a hierarchical summary receiver operating characteristic and a bivariate random effects model. RESULTS: There were 12 eligible studies. The pooled sensitivity, specificity, positive likelihood ratio and negative likelihood ratio were 0.81 (95% confidence interval [CI] 0.67-0.90), 0.82 [95% CI 0.74-0.88], 4.55 [95% CI 3.07-6.75] and 0.23 [95% CI 0.13-0.42], respectively. The area under the summarized receiver operating characteristic curve was 0.88 (95% CI 0.85-0.91). CONCLUSIONS: Current ML algorithms have sufficient ability to help clinicians determine whether individuals will develop type 2 diabetes mellitus in the future. However, persons should be cautious before changing their attitude toward future diabetes risk after learning the result of the diabetes prediction test using ML algorithms.

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.

Associations of Systolic Blood Pressure and Diastolic Blood Pressure With the Incidence of Coronary Artery Disease or Cerebrovascular Disease According to Glucose Status.

OBJECTIVE: To determine associations of systolic blood pressure (SBP) and diastolic blood pressure (DBP) with new-onset coronary artery disease (CAD) or cerebrovascular disease (CVD) according to glucose status. RESEARCH DESIGN AND METHODS: Examined was a nationwide claims database from 2008 to 2016 on 593,196 individuals. A Cox proportional hazards model identified risks of CAD and CVD events among five levels of SBP and DBP. RESULTS: During the study period 2,240 CAD and 3,207 CVD events occurred. Compared with SBP ≤119 mmHg, which was the lowest quintile of SBP, hazard ratios (95% CI) for CAD/CVD in the 4 higher quintiles (120-129, 130-139, 140-149, ≥150 mmHg) gradually increased from 2.10 (1.73-2.56)/1.46 (1.27-1.68) in quintile 2 to 3.21 (2.37-4.34)/4.76 (3.94-5.75) in quintile 5 for normoglycemia, from 1.39 (1.14-1.69)/1.70 (1.44-2.01) in quintile 2 to 2.52 (1.95-3.26)/4.12 (3.38-5.02) in quintile 5 for borderline glycemia, and from 1.50 (1.19-1.90)/1.72 (1.31-2.26) in quintile 2 to 2.52 (1.95-3.26)/3.54 (2.66-4.70) in quintile 5 for diabetes. A similar trend was observed for DBP across 4 quintiles (75-79, 80-84, 85-89, and ≥90 mmHg) compared with ≥74 mmHg, which was the lowest quintile. CONCLUSIONS: Results indicated that cardiovascular risks gradually increased with increases in SBP and DBP regardless of the presence of and degree of a glucose abnormality. Further interventional trials are required to apply findings from this cohort study to clinical practice.

Rates and risk factors for amputation in people with diabetes in Japan: a historical cohort study using a nationwide claims database.

BACKGROUND: The prevalence of diabetes is rising, and diabetes develops at a younger age in East Asia. Although lower limb amputation negatively affects quality of life and increases the risk of cardiovascular events, little is known about the rates and predictors of amputation among persons with diabetes from young adults to those in the "young-old" category (50-72 y). METHODS: We analyzed data from a nationwide claims database in Japan accumulated from 2008 to 2016 involving 17,288 people with diabetes aged 18-72 y (mean age 50.2 y, HbA1c 7.2%). Amputation occurrence was determined according to information from the claims database. Cox regression model identified variables related to lower limb amputation. RESULTS: The mean follow-up time was 5.3 years, during which time 16 amputations occurred (0.17/1000 person-years). Multivariate Cox regression analysis showed that age (hazard ratio [HR] 1.09 [95% confidence intervals] 1.02-1.16, p = 0.01) and HbA1c (HR 1.46 [1.17-1.81], p < 0.01) were independently associated with amputations. Compared with those aged < 60 years with HbA1c < 8.0%, the HR for amputation was 27.81 (6.54-118.23) in those aged ≥60 years and HbA1c ≥8.0%. CONCLUSIONS: Age and HbA1c were associated with amputations among diabetic individuals, and the rates of amputation were significantly greater in those ≥60 years old and with HbA1c ≥8.0%.

Accuracy of Japanese claims data in identifying diabetes-related complications.

PURPOSE: To evaluate the accuracy of various claims-based definitions of diabetes-related complications (coronary artery disease [CAD], heart failure, cerebrovascular disease and dialysis). METHODS: We evaluated data on 1379 inpatients who received care at the Niigata University Medical & Dental Hospital in September 2018. Manual electronic medical chart reviews were conducted for all patients with regard to diabetes-related complications and were used as the gold standard. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of each claims-based definition associated with diabetes-related complications based on Diagnosis Procedure Combination (DPC), International Classification of Diseases, Tenth Revision (ICD-10) codes, procedure codes and medication codes were calculated. RESULTS: DPC-based definitions had higher sensitivity, specificity, and PPV than ICD-10 code definitions for CAD and cerebrovascular disease, with sensitivity of 0.963-1.000 and 0.905-0.952, specificity of 1.000 and 1.000, and PPV of 1.000 and 1.000, respectively. Sensitivity, specificity, and PPV were high using procedure codes for CAD and dialysis, with sensitivity of 0.963 and 1.000, specificity of 1.000 and 1.000, and PPV of 1.000 and 1.000, respectively. DPC and/or ICD-10 codes + medication were better for heart failure than the ICD-10 code definition, with sensitivity of 0.933, specificity of 1.000, and PPV of 1.000. The PPVs were lower than 60% for all diabetes-related complications using ICD-10 codes only. CONCLUSION: The DPC-based definitions for CAD and cerebrovascular disease, procedure codes for CAD and dialysis, and DPC or ICD-10 codes with medication codes for heart failure could accurately identify these diabetes-related complications from claims databases.

A 52-week randomized controlled trial of ipragliflozin or sitagliptin in type 2 diabetes combined with metformin: The N-ISM study.

AIM: To compare the long-term efficacy of sodium-glucose co-transporter-2 inhibitors and dipeptidyl peptidase-4 inhibitors as second-line drugs after metformin for patients not at high risk of atherosclerotic cardiovascular disease (ASCVD). MATERIALS AND METHODS: In a 52-week randomized open-label trial, we compared ipragliflozin and sitagliptin in Japanese patients diagnosed with type 2 diabetes, without prior ASCVD and treated with metformin. The primary endpoint was a glycated haemoglobin (HbA1c) reduction of ≥0.5% (5.5 mmol/mol) without weight gain at 52 weeks. RESULTS: Of a total of 111 patients (mean age 59.2 years, mean body mass index [BMI] 26.6 kg/m2 , 61.3% men), 54 patients received ipragliflozin and 57 received sitagliptin. After 52 weeks, achievement of the primary endpoint was not significantly different (37.0% and 40.3%; P = 0.72). HbA1c reduction rate at 24 weeks was greater for sitagliptin (56.1%) than for ipragliflozin (31.5%; P = 0.01). From 24 to 52 weeks, the HbA1c reduction with sitagliptin was attenuated, with no significant difference in HbA1c reduction after 52 weeks between sitagliptin (54.4%) and ipragliflozin (38.9%; P = 0.10). Improvements in BMI, C-peptide and high-density lipoprotein cholesterol were greater with ipragliflozin than with sitagliptin. Adverse events occurred in 17 patients with ipragliflozin and in 10 patients with sitagliptin (P = 0.11). CONCLUSION: The HbA1c-lowering effect at 24 weeks was greater with sitagliptin than with ipragliflozin, but with no difference in efficacy related to HbA1c and body weight at 52 weeks. However, some ASCVD risk factors improved with ipragliflozin.

Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis.

BACKGROUND: Machine learning (ML) algorithms have been widely introduced to diabetes research including those for the identification of hypoglycemia. OBJECTIVE: The objective of this meta-analysis is to assess the current ability of ML algorithms to detect hypoglycemia (ie, alert to hypoglycemia coinciding with its symptoms) or predict hypoglycemia (ie, alert to hypoglycemia before its symptoms have occurred). METHODS: Electronic literature searches (from January 1, 1950, to September 14, 2020) were conducted using the Dialog platform that covers 96 databases of peer-reviewed literature. Included studies had to train the ML algorithm in order to build a model to detect or predict hypoglycemia and test its performance. The set of 2 × 2 data (ie, number of true positives, false positives, true negatives, and false negatives) was pooled with a hierarchical summary receiver operating characteristic model. RESULTS: A total of 33 studies (14 studies for detecting hypoglycemia and 19 studies for predicting hypoglycemia) were eligible. For detection of hypoglycemia, pooled estimates (95% CI) of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 0.79 (0.75-0.83), 0.80 (0.64-0.91), 8.05 (4.79-13.51), and 0.18 (0.12-0.27), respectively. For prediction of hypoglycemia, pooled estimates (95% CI) were 0.80 (0.72-0.86) for sensitivity, 0.92 (0.87-0.96) for specificity, 10.42 (5.82-18.65) for PLR, and 0.22 (0.15-0.31) for NLR. CONCLUSIONS: Current ML algorithms have insufficient ability to detect ongoing hypoglycemia and considerate ability to predict impeding hypoglycemia in patients with diabetes mellitus using hypoglycemic drugs with regard to diagnostic tests in accordance with the Users' Guide to Medical Literature (PLR should be ≥5 and NLR should be ≤0.2 for moderate reliability). However, it should be emphasized that the clinical applicability of these ML algorithms should be evaluated according to patients' risk profiles such as for hypoglycemia and its associated complications (eg, arrhythmia, neuroglycopenia) as well as the average ability of the ML algorithms. Continued research is required to develop more accurate ML algorithms than those that currently exist and to enhance the feasibility of applying ML in clinical settings. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020163682; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020163682.