Association between screen time, including that for smartphones, and overweight/obesity among children in Japan: NICE EVIDENCE Study 4.

The association between screen time (ST), including that for smartphones, and overweight/obesity in children was examined separately for boys and girls, considering the influence of lifestyle factors. A cross-sectional study was conducted in 2,242 Japanese children (1,278 girls) aged 10-14 years. Overweight/obesity was defined by the International Obesity Task Force. Logistic regression analysis showed that only for girls, total ST (≥4 h), smartphone ST (≥3 h), and non-smartphone ST (≥2 h) were all independently and significantly associated with overweight/obesity compared to <2 h total ST, non-use of smartphones, and <1 h non-smartphone ST. Thus, smartphone ST ≥3 h and non-smartphone ST ≥2 h were additively associated with overweight/obesity in girls only. Girls having smartphone ST ≥3 h and non-smartphone ST ≥2 h were 6.79 times (95% CI: 3.11-14.81) more likely to have overweight/obesity than girls with less usage of both. In girls, when total ST was ≥4 < 5 h or smartphone ST was ≥2 h, the significant association with overweight/obesity disappeared when physical activity was ≥60 min/day and sleep time was ≥8.5 h. In addition, none of these associations was significant in boys. In Japanese girls, smartphone ST, non-smartphone ST, and total ST were all significantly associated with overweight/obesity. To avoid overweight/obesity, it is suggested to keep smartphone ST, non-smartphone ST, and total ST to <3 h, <2 h, and <4 h, respectively, and to engage in sufficient physical activity and sleep time.

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.

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.

Retrospective cohort study to examine the association between serum amylase and the incidence of type 2 diabetes mellitus, Toranomon Hospital Health Management Center Study 23 (TOPICS 23).

INTRODUCTION: Low serum amylase values are cross-sectionally associated with the prevalence of type 2 diabetes mellitus (T2DM) but have not been shown to be longitudinally associated with its incidence. This retrospective cohort (ie, historical cohort) study aimed to examine the association of previously lowered levels of serum amylase with incident T2DM. RESEARCH DESIGN AND METHODS: Examined were 8316 individuals who had annual health examinations for 6 years (ie, 7 times) at the Toranomon Hospital Health Management Center. The trajectory of serum amylase as the study exposure was classified into two elements: (1) serum amylase level at entry and (2) change in serum amylase, which was expressed as the annual change rate. The annual change rate was calculated by dividing the change in the amylase values according to follow-up periods. Regression analyses were performed to examine the association between low and decreased levels of serum amylase and the incidence of T2DM. RESULTS: Analyzed were 6917 individuals who had not developed T2DM within 1 year after cohort entry. T2DM thereafter occurred in 1021 patients. Cox regression indicated that the adjusted HR (95% CI) for incident T2DM for amylase ≤57 IU/L (quintile (Q) 1) was 0.97 (0.84 to 1.13) compared with amylase ≥58 IU/L (Q2-Q5). Logistic regression indicated that the adjusted OR (95% CI) for an annual change rate of amylase ≤-2.0% (Q1) vs ≥-1.9% (Q2-Q5) was 3.53 (3.00 to 4.16). The adjusted ORs were consistently significant throughout sensitivity analyses according to baseline amylase and the combination of age, body mass index, and hemoglobin A1c. CONCLUSIONS: Results showed that not low but previously decreased serum amylase was a risk factor for T2DM, suggesting the significance of periodic examinations of serum amylase values to detect individuals at high risk of T2DM.

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.

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.

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.

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.

Leisure-time physical activity and incidence of objectively assessed hearing loss: The Niigata Wellness Study.

Previous cohort study reported that high physical activity was associated with a low risk of self-reported hearing loss in women. However, no studies have examined the association between physical activity and the development of hearing loss as measured using an objective assessment of hearing loss in men and women. Here, we used cohort data to examine the association between leisure-time physical activity and incidence of objectively assessed hearing loss in men and women. Participants included 27 537 Japanese adults aged 20-80 years without hearing loss, who completed a self-administered physical activity questionnaire between April 2001 and March 2002. The participants were followed up for the development of hearing loss as measured by audiometry between April 2002 and March 2008. During follow-up, 3691 participants developed hearing loss. Compared with the none physical activity group, multivariable adjusted hazard ratios (HRs) for developing hearing loss were 0.93 (95% confidence interval (CI), 0.86-1.01) and 0.87 (0.81-0.95) for the medium (<525 MET-min/week) and high (≥525 MET-min/week) physical activity groups, respectively (p for trend = 0.001). The magnitude of risk reduction was slightly greater in vigorous-intensity activity than in moderate-intensity activity (p for interaction = 0.01). Analysis by sound frequency showed that the amount of physical activity was inversely associated with high frequency hearing loss development (p for trend <0.001), but not with low frequency hearing loss development (p for trend = 0.19). Higher level of leisure-time physical activity was associated with lower incidence of hearing loss, particularly for vigorous-intensity activities and high sound frequencies.

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.

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%.

Association of estimated plasma volume and weight loss after long-term administration and subsequent discontinuation of the sodium-glucose cotransporter-2 inhibitor tofogliflozin.

Sodium-glucose cotransporter-2 inhibitors (SGLT2) are drugs that have been reported to have several effects through the regulation of plasma volume, for example, antihypertensive effects. This study aimed to clarify the impact of long-term administration and subsequent discontinuation of the SGLT2 inhibitor tofogliflozin on estimated plasma volume (ePV), brain natriuretic peptide (BNP) and the relationship between changes in ePV, BNP and body weight (BW). Data from 157 participants with type 2 diabetes receiving tofogliflozin monotherapy in a phase 3 study were analysed. Changes in variables or correlations among them during a 52-week administration and a 2-week post-treatment period were investigated. Percent change in ePV was calculated using the Strauss formula. Significant decreases in BW, ePV and ln-transformed BNP (ln-BNP) were noted by week 52. %ΔBW was not significantly correlated with %ΔePV and Δln-BNP, while %ΔePV was significantly correlated with Δln-BNP. Two weeks after discontinuation of tofogliflozin, BW, ePV and ln-BNP were significantly increased. %ΔBW was significantly correlated with %ΔePV and Δln-BNP. Furthermore, ePV and BNP were significantly higher than baseline levels.

Dipeptidyl peptidase-4 inhibitor, anagliptin, alters hepatic insulin clearance in relation to the glycemic status in Japanese individuals with type 2 diabetes.

AIMS/INTRODUCTION: This study investigated the impact of the dipeptidyl peptidase-4 inhibitor, anagliptin, on hepatic insulin clearance (HIC) in Japanese type 2 diabetes patients and explored its relationship to glycemic status. MATERIALS AND METHODS: Data on 765 participants in anagliptin phase 2 and 3 studies were analyzed. Adjusted changes in variables during 12 weeks of anagliptin therapy were compared with a placebo. HIC was calculated as the ratio, C-peptide area under the curve 0-120 min to insulin area under the curve 0-120 min, after a meal tolerance test. To explore the effects of baseline HIC levels on variables, participants receiving anagliptin were divided according to quartiles of baseline HIC. Furthermore, multivariate analysis investigated the association between baseline HIC levels and glycemic status. RESULTS: Anagliptin significantly reduced glycosylated hemoglobin levels (P < 0.001 vs placebo) and HIC levels (P < 0.01). Longer duration of diabetes, lower body mass index, higher glycosylated hemoglobin and lower insulin secretion capacity were observed with increases in baseline HIC levels. Improvements in glycosylated hemoglobin, glycoalbumin and 1,5-anhydroglucitol levels were greater in the relatively higher HIC group (baseline HIC levels ≥median) than in the lower HIC group (

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.

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.

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.