SUMMARYThe 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.

This study explored the relationship between obstructive sleep apnea (OSA) and the presence of any diabetes-related complications in type 2 diabetes and whether this was mediated by hypertension. Secondly, the relationship between OSA severity and estimated glomerular filtration rate (eGFR) was investigated. A total of 131 patients participated. OSA was diagnosed using a home monitor, and severity was measured by apnea-hypopnea index (AHI) and oxygen desaturation index (ODI). OSA was found in 75.6% of the participants, 40.5% with moderate-to-severe degree. Any diabetes-related complications (retinopathy, neuropathy, nephropathy, or coronary artery disease) were present in 55.5%, and 70.2% of the participants had hypertension. Mediation analysis indicated that, compared to those with mild or no OSA, those with moderate-to-severe OSA were 3.05 times more likely to have any diabetes-related complications and that this relationship was mediated by the presence of hypertension. After adjusting for confounders, ODI (B = −0.036, p = 0.041), but not AHI, was significantly associated with lower eGFR. In conclusion, moderate-to-severe OSA was related to the presence of any diabetes-related complications in type 2 diabetes, and the relationship was mediated by hypertension. The severity of intermittent hypoxia was associated with lower eGFR. Whether OSA treatment will delay or reduce diabetes-related complications should be investigated.

Eveningness is independently associated with greater depressive symptoms in T2D in two different ethnic cohorts. The results support the association between individual differences in circadian rhythms and psychological functioning in T2D.

ObjectiveWe analyzed two cohorts of people with type 2 diabetes to evaluate the relationships between depression, sleep quality, and history of hypoglycemia.Research design and methodsTwo adult cohorts from Chicago (n = 193) and Bangkok, Thailand (n = 282) with type 2 diabetes completed questionnaires to assess sleep quality, depressive symptoms, and hypoglycemia frequency. Proportional odds logistic regression models for each cohort adjusted for duration of therapy, insulin and sulfonylurea management, and other factors.ResultsThose with hypoglycemia in both cohorts had a longer duration of diabetes, greater use of insulin, and worse sleep quality. The Chicago cohort used less sulfonylureas but had higher depressive symptom scores. The Thailand cohort had greater sulfonylurea use. In the final Thailand regression model, depressive symptoms were independently associated with hypoglycemia frequency. In both final Chicago and Thailand models, sleep quality was not associated with hypoglycemia frequency.ConclusionsIn the Thailand cohort, depressive symptoms were associated with hypoglycemia frequency.

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Sleep-disordered breathing (SDB) is common in type 2 diabetes (T2D). This study aimed to develop a screening tool to detect SDB in T2D. Method: SDB was screened in 95 patients by an overnight monitor and diagnosed if apnea-hypopnea index ≥ 5. Anthropometric and HbA1c data, and SDB risk by questionnaire were obtained. Quantitative NMR experiment was performed on a Bruker AVANCE 400 Ascend NMR spectrometer. Carr−Purcell−Meiboom−Gill (CPMG) pulse sequence at 310 K was applied to serum samples to enhance signal-to-noise ratio and hence detect low-molecular weight metabolites. Models based on six machine learning methods (e.g., learned vector quantization, stochastic gradient boosting model, support vector machines, decision tree, partial least squares, generalized linear model, and logistic regression) were constructed to predict SDB, tuned with the CARET R package, and evaluated using three repeats of 10-fold cross validation. Results: Seventy-six patients (80.0%) had SDB. Compared with those without, those with SDB were more likely to be male, have SDB risk, higher BMI and neck circumferences, longer diabetes duration, and higher HbA1c. NMR-based metabolic profiling identified significantly lower levels of branched-chain amino acids (BCAAs) (e.g., L-leucine and L-isoleucine), lactate and threonine metabolites in those with SDB, while level of D-glucose-6 phosphatase was higher than those without SDB. Accuracies for predicting SBD using clinical data alone were ∼80% for each machine learning model. L-leucine and D-glucose-6-phosphatase were identified as being independently associated with SDB using a logistic regression, in addition to clinical factors, with an improved accuracy of 86.3%, and a Cohen’s kappa of 0.429. Conclusions: Due to its high prevalence, clinical factors alone were not sufficient in predicting SDB in T2D. The discovery of metabolic profiles, including BCAAs and those involved in glucose metabolism, could facilitate a more precise and specific SDB prediction. Disclosure K. Lertdetkajorn: None. P. Poungsombat: None. S. Khoomrung: None. J. Phetcharaburanin: None. S. Hongthong: None. C. Kuhakarn: None. N. Siwasaranond: None. A. Manodpitipong: None. H. Ninitphong: Speaker’s Bureau; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novo Nordisk Inc., Takeda Pharmaceutical Company Limited. S. Saetung: None. B. Ongphiphadhanakul: None. V. Reutrakul: None. S. Reutrakul: None. Funding Endocrine Society of Thailand; Ramathibodi Hospital; Mahidol University; Thailand Center of Excellence for Innovation in Chemistry