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.

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.

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.

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.

Skipping breakfast, late-night eating and current smoking are associated with medication adherence in Japanese patients with diabetes.

AIMS: Little is known about the relationship between medication adherence for oral hypoglycemic agents (OHAs) and glycemic control after adjusting healthy adherer effect in large scale study. Thus, adjusting for health-related behaviors, we investigated the clinical variables associated with medication adherence and the relationship between medication adherence and glycemic control using a large claims database. METHODS: Analyzed were 8805 patients with diabetes whose medication records for OHA were available for at least 1year. Medication adherence was evaluated by the proportion of days covered (PDC). Multivariate logistic regression model was used to identify clinical variables significantly associated with non-adherence. Multiple regression analysis evaluated the relationship between PDC and HbA1c after adjusting for health-related behaviors. RESULTS: Mean PDC was 80.1% and 32.8% of patients were non-adherence. Logistic analysis indicated that older age and taking concomitant medications were significantly associated with adherence while skipping breakfast (odds ratio 0.66 [95% CI 0.57-0.76]), late-night eating (0.86 [0.75-0.98]), and current smoking (0.89 [0.80-0.99]) were significantly associated with non-adherence. CONCLUSIONS: Skipping breakfast, late-night eating and current smoking were significantly associated with medication adherence, suggesting that clinicians pay attention to those health-related behaviors to achieve good medication adherence.

Risk of coronary artery disease according to glucose abnormality status and prior coronary artery disease in Japanese men.

OBJECTIVE: Although glucose abnormality status (GAS), prior coronary artery disease (CAD), and other traditional risk factors affect the incidence of subsequent CAD, their impact in the same cohort has been scantly studied. RESEARCH DESIGN AND METHODS: We analyzed data from a nationwide claims database in Japan that was accumulated during 2008-2016 involving 138,162 men aged 18-72 years. Participants were classified as having normoglycemia, borderline glycemia, or diabetes mellitus (DM) with prior CAD (CAD+) or without prior CAD (CAD-). Cox regression model identified variables related to the incidence of CAD. RESULTS: Among CAD-, management of traditional risks differed from those with and without subsequent CAD events. On the other hand, such differences were weaker in borderline glycemia and DM CAD+, and the influence of traditional risk factors on subsequent CAD was not observed. Cox regression model showed that borderline glycemia and DM confer approximately 1.2- and 2.8-fold excess risks of CAD, respectively, compared with CAD- with normoglycemia. CAD+ confers approximately a 5- to 8-fold increased risk. The impacts of DM and prior CAD additively reached a hazard ratio (HR) of 15.74 (95% confidence interval [CI]: 11.82-21.00). However, the HR in those with borderline glycemia and CAD+ was 7.20 (95% CI: 5.01-10.34), which was not different from those with normoglycemia and CAD+. CONCLUSION: Control status of traditional risk factors and impact on subsequent CAD differ among categories of glycemic status with and without prior CAD. Individualizing treatment strategies is needed in consideration of risk factors, such as GAS and CAD+.