Continuous positive airway pressure treatment reduces renal tubular damage in patients with obstructive sleep apnea: A retrospective single-center cohort study.

BACKGROUND: Chronic intermittent hypoxia (IH) plays a significant role in the pathogenesis of obstructive sleep apnea (OSA) comorbidities. The prevalence of chronic kidney disease is higher in patients with OSA than the general population, and renal function decline is well correlated with renal tubular injury. However, little is known about the impact of OSA-induced chronic IH on the renal tubules. METHODS: We conducted a retrospective survey of clinical records performing multiple regression analysis and cluster analysis with particular attention to the 3% oxygen desaturation index (ODI) and urinary N-acetyl-ß-d-glucosaminidase (NAG). RESULTS: In patients with suspicion of OSA, urinary NAG creatinine ratio (UNCR) was elevated as their 3% ODI increased (n = 197, p < 0.001), and the elevated UNCR decreased following CPAP treatment in patients with OSA (n = 46, p = 0.014). Multiple regression analysis showed that 3% ODI was associated with UNCR. Cluster analysis identified three clusters of patients with OSA, including two younger age clusters, one of which was characterized by high BMI, high 3% ODI, and high prevalence of major comorbidities. In a comparative analysis of younger age cases (age ≤ 55, n = 82), the UNCR level was higher in patients with severe 3% ODI (3% ODI > 40 events/h, n = 24) (p = 0.014). CONCLUSIONS: Our results indicate that even at younger ages, OSA patients with severe chronic IH and major comorbidities are susceptible to renal tubular damage. Early treatment with CPAP may attenuate renal tubular injury and progression toward end-stage renal disease.

The relationship between adherence to continuous positive airway pressure and nasal resistance measured by rhinomanometry in patients with obstructive sleep apnea syndrome.

Nasal breathing disorders are associated with obstructive sleep apnea (OSA) syndrome and influence the availability of continuous positive airway pressure (CPAP) therapy. However, information is scarce about the impact of nasal resistance assessed by rhinomanometry on CPAP therapy. This study aimed to examine the relationship between CPAP adherence and nasal resistance evaluated by rhinomanometry, and to identify clinical findings that can affect adherence to CPAP therapy for patients with OSA. This study included 260 patients (199 men, 61 women; age 58 [interquartile ranges (IQR) 50-66] years) with a new diagnosis of OSA who underwent rhinomanometry (before, and 1 and 3 months after CPAP introduction) between January 2011 and December 2018. CPAP use was recorded, and the good and poor CPAP adherence groups at the time of patient registration were compared. Furthermore, those with improved and unimproved pre-CPAP high rhinomanometry values were also compared. Their apnea-hypopnea index (AHI) by polysomnography at diagnosis was 45.6 (IQR 33.7-61.6)/hour, but the residual respiratory event (estimated AHI) at enrollment was 2.5 (IQR 1.4-3.9)/hour and the usage time was 318 (IQR 226-397) minutes, indicating that CPAP was effective and adherence was good. CPAP adherence was negatively correlated with nasal resistance (r = -0.188, p = 0.002). The participants were divided into good (n = 153) and poor (n = 107) CPAP adherence groups. In the poor adherence group, rhinomanometry values before CPAP introduction were worse (inspiration, p = 0.003; expiration, p = 0.006). There was no significant difference in patient background when comparing those with improved (n = 16) and unimproved (n = 12) pre-CPAP high rhinomanometry values. However, CPAP usage time was significantly longer in the improved group 1 month (p = 0.002) and 3 months (p = 0.026) after CPAP introduction. The results suggest that nasal resistance evaluated by rhinomanometry is a useful predictor of CPAP adherence, and that improved rhinomanometry values may contribute to extending the duration of CPAP use.

Weight cycling and the subsequent onset of type 2 diabetes mellitus: 10-year cohort studies in urban and rural Japan.

OBJECTIVE: To investigate how weight cycling (gaining and losing weight) affects the risk of diabetes. DESIGN: Cohort studies. SETTING: Primary healthcare in urban and rural Japan. PARTICIPANTS: 20 708 urban and 9670 rural residents. PRIMARY OUTCOME MEASURES: ORs for diabetes in those with weight loss, weight loss-gain, stable weight, weight gain-loss and weight gain over 10 years. Weight gain and loss were defined as a change of more than ±4% from baseline weight. RESULTS: In the urban region, the ORs relative to the stable group for the loss-gain and gain-loss groups were 0.63 (95% CI 0.45 to 0.89) and 0.51 (95% CI 0.32 to 0.82) for men and 0.72 (95% CI 0.39 to 1.34) and 1.05 (95% CI 0.57 to 1.95) for women. In the rural region, they were 1.58 (95% CI 0.78 to 3.17) and 0.44 (95% CI 0.15 to 1.29) in men and 0.41 (95% CI 0.12 to 1.44) and 0.77 (95% CI 0.28 to 2.14) in women. The ORs for an increase in weight between 5 and 10 kg from the age of 20 years were 1.54 (95% CI 1.03 to 2.30) in men and 0.96 (95% CI 0.55 to 1.65) in women. CONCLUSIONS: In Japan, weight cycling was associated with a significant reduction in the risk of diabetes for men from urban regions. The associations were unclear for women from urban regions and both men and women from rural regions. These results differ from those in Western studies, probably because of differences in diet, insulin secretion and sensitivity and weight-consciousness.

Do body mass index trajectories affect the risk of type 2 diabetes? A case-control study.

BACKGROUND: Although obesity is a well-studied risk factor for diabetes, there remains an interest in whether "increasing body mass index (BMI)," "high BMI per se," or both are the actual risk factors for diabetes. The present study aimed to retrospectively compare BMI trajectories of individuals with and without diabetes in a case-control design and to assess whether increasing BMI alone would be a risk factor. METHODS: Using comprehensive health check-up data measured over ten years, we conducted a case-control study and graphically drew the trajectories of BMIs among diabetic patients and healthy subjects, based on coefficients in fitted linear mixed-effects models. Patient group was matched with healthy control group at the onset of diabetes with an optimal matching method in a 1:10 ratio. Simple fixed-effects models assessed the differences in increasing BMIs over 10 years between patient and control groups. RESULTS: At the time of matching, the mean ages in male patients and controls were 59.3 years [standard deviation (SD) = 9.2] and 57.7 years (SD = 11.2), whereas the mean BMIs were 25.0 kg/m(2) (SD = 3.1) and 25.2 kg/m(2) (SD = 2.9), respectively. In female patients and controls, the mean ages were 61.4 years (SD = 7.9) and 60.1 years (SD = 9.6), whereas the mean BMIs were 24.8 kg/m(2) (SD = 3.5) and 24.9 kg/m(2) (SD = 3.4), respectively. The simple fixed-effects models detected no statistical significance for the differences of increasing BMIs between patient and control groups in males (P = 0.19) and females (P = 0.67). Sudden increases in BMI were observed in both male and female patients when compared with BMIs 1 year prior to diabetes onset. CONCLUSIONS: The present study suggested that the pace of increasing BMIs is similar between Japanese diabetic patients and healthy individuals. The increasing BMI was not detected to independently affect the onset of type 2 diabetes.