Night-shift work is associated with poorer glycaemic control in patients with type 2 diabetes.

The circadian system plays a role in regulating metabolism. Night-shift work, a form of circadian misalignment, is associated with increased type 2 diabetes risk. This study aimed to determine if night-shift workers with type 2 diabetes experience poorer glycaemic control than non-shift workers. Patients with type 2 diabetes (104 unemployed, 85 day workers and 60 night-shift workers) participated. Sleep duration, sleep quality, morningness-eveningness preference, depressive symptoms and dietary intake were assessed using standardized questionnaires. Haemoglobin A1c levels were measured. Night-shift workers had significantly higher haemoglobin A1c levels compared with others, while there were no differences between day workers and unemployed participants (median 7.86% versus 7.24% versus 7.09%, respectively). Additionally, night-shift workers were younger, had a higher body mass index, and consumed more daily calories than others. Among night-shift workers, there were no significant differences in haemoglobin A1c levels between those performing rotating versus non-rotating shifts (P = 0.856), or those with clockwise versus counterclockwise shift rotation (P = 0.833). After adjusting for age, body mass index, insulin use, sleep duration, morningness-eveningness preference and percentage of daily intake from carbohydrates, night-shift work, compared with day work, was associated with significantly higher haemoglobin A1c (B = 0.059, P = 0.044), while there were no differences between unemployed participants and day workers (B = 0.016, P = 0.572). In summary, night-shift work is associated with poorer glycaemic control in patients with type 2 diabetes.

Evaluation of a Community-Based Diabetes Prevention Program in Thailand: A Cluster Randomized Controlled Trial.

OBJECTIVES: Lifestyle interventions have been shown to effectively reduce the incidence of diabetes, but evidence from middle-income countries is scarce. We evaluated the effectiveness of a lifestyle program to prevent diabetes in primary-care settings in Thailand. METHODS: A matched-pair cluster randomized controlled trial was conducted in 68 primary care units in 8 provinces. The primary care units were randomly assigned to intervention or control arms. Individuals aged 30 to 65 years with impaired oral glucose tolerance were recruited and followed up for 2 years. The intervention included periodic group-based activities on healthy lifestyle behaviors; the control group received a one-time education program. The primary outcome was the incidence rate of type 2 diabetes at 24 months after the intervention. RESULTS: A total of 1903 individuals participated (873 in the control group and 1030 in the intervention group). At baseline, participants' characteristics did not differ between groups. After 24 months, the incidence rates (per 100 person-year) of diabetes was 12.1% (95% CI 10.7% to 13.8%) in the intervention group, and 16.6% (95% CI 14.6 to 18.8%) in the control group ( P < .001). Overall, the adjusted hazard ratio for diabetes incidence was 0.72 (95% CI 0.60 to 0.86). A mean body weight reduction of 1.5 kg was observed in the intervention group, whereas, an increase of 0.4 kg was observed in the control group ( P < .001). CONCLUSION: A community-based lifestyle modification through participatory group activities can prevent or delay the incidence of diabetes among Thai populations with impaired glucose tolerance.

Detecting Prediabetes and Diabetes: Agreement between Fasting Plasma Glucose and Oral Glucose Tolerance Test in Thai Adults.

AIM: To evaluate an agreement in identifying dysglycemia between fasting plasma glucose (FPG) and the 2 hr postprandial glucose tolerance test (OGTT) in a population with high risk of diabetes. METHODS: A total of 6,884 individuals aged 35-65 years recruited for a community-based diabetes prevention program were tested for prediabetes including impaired fasting glucose (IFG) or impaired glucose tolerance (IGT), and diabetes. The agreement was assessed by Kappa statistics. Logistic regression was used to examine factors associated with missed prediabetes and diabetes by FPG. RESULTS: A total of 2671 (38.8%) individuals with prediabetes were identified. The prevalence of prediabetes identified by FPG and OGTT was 32.2% and 22.3%, respectively. The proportions of diabetes classified by OGTT were two times higher than those identified by FPG (11.0% versus 5.4%, resp.). The Kappa statistics for agreement of both tests was 0.55. Overall, FPG missed 46.3% of all prediabetes and 54.7% of all diabetes cases. Prediabetes was more likely to be missed by FPG among female, people aged <45 yrs, and those without family history of diabetes. CONCLUSION: The detection of prediabetes and diabetes using FPG only may miss half of the cases. Benefit of adding OGTT to FPG in some specific groups should be confirmed.