Overtime work and prevalence of diabetes in Japanese employees: Japan epidemiology collaboration on occupational health study.

OBJECTIVE: Epidemiologic evidence on long working hour and diabetes has been conflicting. We examined the association between overtime work and prevalence of diabetes among Japanese workers. METHODS: The subjects were 40,861 employees (35,170 men and 5,691 women), aged 16 to 83 years, of 4 companies in Japan. Hours of overtime were assessed using self-reported questionnaires. Diabetes was defined as a fasting plasma glucose ≥126 mg/dl (7.0 mmol/l), hemoglobin A1c ≥6.5% (48 mmol/mol), or current use of anti-diabetic drug. Multiple logistic regression analysis was used to calculate odds ratio of diabetes for each category of overtime. RESULTS: After adjustment for age, sex, company, smoking, and BMI, there was a suggestion of U-shaped relationship between overtime work and prevalence of diabetes (P for quadratic trend = 0.07). Compared with those who worked <45 hours of overtime per month, the adjusted odds ratios (95% confidence interval) of diabetes were 0.86 (0.77-0.94), 0.69 (0.53-0.89), and 1.03 (0.72-1.46) for those who worked 45-79, 80-99, and ≥100 hours of overtime per month, respectively. In one company (n = 33,807), where other potential confounders including shift work, job position, type of department, alcohol consumption, sleep duration, leisure time physical activity, and family history of diabetes was additionally adjusted for, similar result was obtained (P for quadratic trend = 0.05). CONCLUSIONS: Long hours of overtime work may not be associated with increased prevalence of diabetes among Japanese workers

Subject characteristics according to overtime work hours.

<p>Data are adjusted for age and sex, and presented as mean ± standard error unless otherwise specified.</p><p>*P for trend was obtained from linear regression for continuous variables, or from logistic regression for categorical variables. by assigning 23, 62, 90, and 100 to categories of overtime work.</p>†<p>n = 33,807 in one company.</p>‡<p>Defined as ≥150 min per week.</p

Odds ratio (OR) and 95% confidence interval of diabetes^* according to overtime work hours.

<p>Abbreviations: OR, odds ratio; Ref, reference.</p><p>*Defined as fasting glucose ≥126 mg/dL (7.0 mmol/l), HbA1c ≥6.5% (48 mmol/mol), or current use of anti-diabetic drug.</p>†<p><i>P</i> for quadratic trend obtained from multiple logistic regression analysis by assigning 23, 62, 90, and 100 to categories of overtime work.</p>‡<p>Model 1 adjusted for age (continuous), sex, and company in 4 companies (n = 40,861).</p>§<p>Model 2 adjusted for factors in model 1 and smoking status (never, past, or current) in 4 companies (n = 40,861).</p>||<p>Model 3 adjusted for factors in model 2 and body mass index (kg/m², continuous) in 4 companies (n = 40,861).</p>¶<p>Model 1 adjusted for age (continuous) and sex in 1 company (n = 33,807).</p><p>**Model 2 adjusted for factors in model 1 plus smoking status (never, past, or current), body mass index (kg/m², continuous), alcohol use (non-drinker, drinker consuming >0 to <23 g, 23 to <46 g, or ≥46 g of ethanol per day), family history of diabetes (yes or no), shift work (yes or no), department (field work or non-field work), and job position (high or low) in 1 company (n = 33,807).</p>††<p>Model 3 adjusted for factors in model 2 and sleep duration (<6 hours, 6 to <7 hours, or ≥7 hours per day) in 1 company (n = 33,807).</p>‡‡<p>Model 4 adjusted for factors in model 3 and leisure time physical activity (<150 min or ≥150 min per week) in 1 company (n = 33,807).</p

Odds ratio with 95% confidence interval of diabetes according to overtime work hours stratified by participant characteristics.

<p>Abbreviations: BMI, body mass index; Ref, reference.</p><p>*<i>P</i> for trend obtained from multiple logistic regression analysis by assigning 23, 62, 90, and 100 to categories of overtime work.</p>†<p>Adjusted for age (continuous), sex, company, smoking status (never, past, or current), and BMI (kg/m², continuous) in 4 companies (n = 41,081).</p>‡<p>48 women in 1 company were excluded in this analysis due to no diabetic patients.</p>§<p>Adjusted for age (continuous), sex, company, smoking status (never, past, or current), BMI (kg/m², continuous), alcohol use (non-drinker, drinker consuming >0 to <23 g, 23 to <46 g, or ≥46 g of ethanol per day), sleep duration (<6 hours, 6 to <7 hours, or ≥7 hours per day), physical activity (<150 min or ≥150 min per week), family history of diabetes (yes or no), shift work (yes or no), department (field work or non-field work), and job position (high or low) in 1 company (n = 33,807).</p

Long-term safety and efficacy of alogliptin, a DPP-4 inhibitor, in patients with type 2 diabetes: a 3-year prospective, controlled, observational study (J-BRAND Registry)

Introduction Given an increasing use of dipeptidyl peptidase-4 (DPP-4) inhibitors to treat patients with type 2 diabetes mellitus in the real-world setting, we conducted a prospective observational study (Japan-based Clinical Research Network for Diabetes Registry: J-BRAND Registry) to elucidate the safety and efficacy profile of long-term usage of alogliptin.Research design and methods We registered 5969 patients from April 2012 through September 2014, who started receiving alogliptin (group A) or other classes of oral hypoglycemic agents (OHAs; group B), and were followed for 3 years at 239 sites nationwide. Safety was the primary outcome. Symptomatic hypoglycemia, pancreatitis, skin disorders of non-extrinsic origin, severe infections, and cancer were collected as major adverse events (AEs). Efficacy assessment was the secondary outcome and included changes in hemoglobin A1c (HbA1c), fasting blood glucose, fasting insulin and urinary albumin.Results Of the registered, 5150 (group A: 3395 and group B: 1755) and 5096 (3358 and 1738) were included for safety and efficacy analysis, respectively. Group A patients mostly (&gt;90%) continued to use alogliptin. In group B, biguanides were the primary agents, while DPP-4 inhibitors were added in up to ~36% of patients. The overall incidence of AEs was similar between the two groups (42.7% vs 42.2%). Kaplan-Meier analysis revealed the incidence of cancer was significantly higher in group A than in group B (7.4% vs 4.8%, p=0.040), while no significant incidence difference was observed in the individual cancer. Multivariate Cox regression analysis revealed that the imbalanced patient distribution (more elderly patients in group A than in group B), but not alogliptin usage per se, contributed to cancer development. The incidence of other major AE categories was with no between-group difference. Between-group difference was not detected, either, in the incidence of microvascular and macrovascular complications. HbA1c and fasting glucose decreased significantly at the 0.5-year visit and nearly plateaued thereafter in both groups.Conclusions Alogliptin as a representative of DPP-4 inhibitors was safe and durably efficacious when used alone or with other OHAs for patients with type 2 diabetes in the real world setting