The odds ratio (OR) of impaired glucose metabolism (n = 119) according to tertile categories of each fatty acid intake (% energy).

1<p>Adjusted for age (y) and sex.</p>2<p>Adjusted for age (y) sex, BMI (kg/m²), shiftwork (yes or no), leisure time physical activity (0, 1<–<3, 3–<10, ≥10 Mets-h/w), work-related physical activity (<3, 3–<7, 7–<20, ≥20 Mets-h/w), smoking status (never and past, current and <20 cigarette/d, or current and ≥20 cigarette/d), alcohol consumption (nondrinker and 1–3 d/m, <23 g ethanol/d, 23–<46 g ethanol/d, or ≥46 g ethanol/d), hypertension (yes or no), hyperlipidemia (yes or no), parental history of diabetes (yes, no or unknown), log transformed total energy intake (kcal/d), and protein intake (% energy).</p>3<p>Based on multiple linear regression analysis, assigning ordinal numbers 0−2 to tertile categories of each fatty acid intake.</p

Factor loading matrix for the major fatty acid patterns identified by principal component analysis.

<p>Factor loading matrix for the major fatty acid patterns identified by principal component analysis.</p

The odds ratio (OR) of impaired glucose metabolism (n = 119) according to tertile categories of fatty acid pattern score.

1<p>Adjusted for age (y) and sex.</p>2<p>Adjusted for age (y) sex, BMI (kg/m²), shiftwork (yes or no), leisure time physical activity (0, 1<–<3, 3–<10, ≥10 Mets-h/w), work-related physical activity (<3, 3–<7, 7–<20, ≥20 Mets-h/w), smoking status (never and past, current and <20 cigarette/d, or current and ≥20 cigarette/d), alcohol consumption (nondrinker and 1–3 d/m, <23 g ethanol/d, 23–<46 g ethanol/d, or ≥46 g ethanol/d), hypertension (yes or no), hyperlipidemia (yes or no), parental history of diabetes (yes, no or unknown), log transformed total energy intake (kcal/d), and protein intake (% energy).</p>3<p>Number of cases of tertile 1 to tertile 3 was 27, 35, and 57 for factor 1, 40, 45, and 34 for factor 2, and 44, 35, and 40 for factor 3, respectively.</p>4<p>Based on multiple linear regression analysis, assigning ordinal numbers 0−2 to tertile categories of each fatty acid intake.</p>5<p>Number of cases of tertile 1 to tertile 3 was 17, 18, and 26 for factor 1, 16, 27, and 18 for factor 2, and 24, 15, and 22 for factor 3, respectively.</p>6<p>Number of cases of tertile 1 to tertile 3 was 10, 16, and 32 for factor 1, 25, 17, and 16 for factor 2, and 20, 20, and 18 for factor 3, respectively.</p

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

Hba1c, Blood Pressure, and Lipid Control in People with Diabetes: Japan Epidemiology Collaboration on Occupational Health Study

<div><p>Aims</p><p>The control of blood glucose levels, blood pressure (BP), and low-density lipoprotein cholesterol (LDL-C) levels reduces the risk of diabetes complications; however, data are scarce on control status of these factors among workers with diabetes. The present study aimed to estimate the prevalence of participants with diabetes who meet glycated hemoglobin (HbA1c), BP, and LDL-C recommendations, and to investigate correlates of poor glycemic control in a large working population in Japan.</p><p>Methods</p><p>The Japan Epidemiology Collaboration on Occupational Health (J-ECOH) Study is an ongoing cohort investigation, consisting mainly of employees in large manufacturing companies. We conducted a cross-sectional analysis of 3,070 employees with diabetes (2,854 men and 216 women) aged 20–69 years who attended periodic health examinations. BP was measured and recorded using different company protocols. Risk factor targets were defined using both American Diabetes Association (ADA) guidelines (HbA1c < 7.0%, BP < 140/90 mmHg, and LDL-C < 100 mg/dL) and Japan Diabetes Society (JDS) guidelines (HbA1c < 7.0%, BP < 130/80 mmHg, and LDL-C < 120 mg/dL). Logistic regression models were used to explore correlates of poor glycemic control (defined as HbA1c ≥ 8.0%).</p><p>Results</p><p>The percentages of participants who met ADA (and JDS) targets were 44.9% (44.9%) for HbA1c, 76.6% (36.3%) for BP, 27.1% (56.2%) for LDL-C, and 11.2% (10.8%) for simultaneous control of all three risk factors. Younger age, obesity, smoking, and uncontrolled dyslipidemia were associated with poor glycemic control. The adjusted odds ratio of poor glycemic control was 0.58 (95% confidence interval, 0.46–0.73) for participants with treated but uncontrolled hypertension, and 0.47 (0.33–0.66) for participants with treated and controlled hypertension, as compared with participants without hypertension. There was no significant difference in HbA1c levels between participants with treated but uncontrolled hypertension and those with treated and controlled hypertension.</p><p>Conclusion</p><p>Data from a large working population, predominantly composed of men, suggest that achievement of HbA1c, BP, and LDL-C targets was less than optimal, especially in younger participants. Uncontrolled dyslipidemia was associated with poor glycemic control. Participants not receiving antihypertensive treatment had higher HbA1c levels.</p></div

Factors related with poor glycemic control (HbA1c ≥ 8.0%).

<p>Factors related with poor glycemic control (HbA1c ≥ 8.0%).</p

Additional file 1: of Optimal waist circumference cut-off points and ability of different metabolic syndrome criteria for predicting diabetes in Japanese men and women: Japan Epidemiology Collaboration on Occupational Health Study

Table S1. Baseline characteristics of participants with no history of diabetes stratified by metabolic syndrome and sex. (DOCX 16 kb

Prevalence of meeting risk factor targets in participants with diabetes.

<p>Prevalence of meeting risk factor targets in participants with diabetes.</p