Pulmonary Function and Sleep Breathing: Two New Targets for Type 2 Diabetes Care.

Population-based studies showing the negative impact of type 2 diabetes (T2D) on lung function are overviewed. Among the well-recognized pathophysiological mechanisms, the metabolic pathways related to insulin resistance (IR), low-grade chronic inflammation, leptin resistance, microvascular damage, and autonomic neuropathy are emphasized. Histopathological changes are exposed, and findings reported from experimental models are clearly differentiated from those described in humans. The accelerated decline in pulmonary function that appears in patients with cystic fibrosis (CF) with related abnormalities of glucose tolerance and diabetes is considered as an example to further investigate the relationship between T2D and the lung. Furthermore, a possible causal link between antihyperglycemic therapies and pulmonary function is examined. T2D similarly affects breathing during sleep, becoming an independent risk factor for higher rates of sleep apnea, leading to nocturnal hypoxemia and daytime sleepiness. Therefore, the impact of T2D on sleep breathing and its influence on sleep architecture is analyzed. Finally, the effect of improving some pathophysiological mechanisms, primarily IR and inflammation, as well as the optimization of blood glucose control on sleep breathing is evaluated. In summary, the lung should be considered by those providing care for people with diabetes and raise the central issue of whether the normalization of glucose levels can improve pulmonary function and ameliorate sleep-disordered breathing. Therefore, patients with T2D should be considered a vulnerable group for pulmonary dysfunction. However, further research aimed at elucidating how to screen for the lung impairment in the population with diabetes in a cost-effective manner is needed.

Predicting non-diabetic renal disease in type 2 diabetic adults: The value of glycated hemoglobin.

AIMS: The indications for renal biopsy in type 2 diabetes mellitus (T2D) are not well established. We investigated the prevalence, spectrum, and predictors of biopsy-proven non-diabetic renal disease (NDRD) in T2D. METHODS: An observational, single-center, retrospective study of T2D adults who underwent renal biopsies (N = 51) over 10 years for nephrotic-range proteinuria, microscopic hematuria, or rapidly declining renal function. RESULTS: Thirty-five (68.6%) biopsies were diagnostic of NDRD, and 16 (31.4%) revealed isolated diabetic nephropathy. The most common NDRDs were interstitial nephritis (20%), progressive crescentic glomerulonephritis (14%), membranous nephropathy (11%), and focal segmental glomerulosclerosis (11%). The odds for NDRD declined by 97% in the presence of diabetic retinopathy (P < 0.001). The deterioration of HbA1c during the year before biopsy predicted NDRD even after adjusting for diabetic retinopathy (OR, 7.65; 95% CI, 1.36-123.04; P = 0.003). A model based on the interaction between the HbA1c values 12 months before biopsy and the absolute change in these values during the preceding year predicted NDRD with 73.7% sensitivity and 75% specificity (AUC, 0.77; 95% CI, 0.59-0.94). CONCLUSIONS: This study demonstrated a considerably high prevalence of NDRD in T2D adults undergoing renal biopsy. The absence of diabetic retinopathy, lower HbA1c values 12 months before biopsy and greater deterioration in HbA1c prior to biopsy predicted NDRD in T2D. Further studies are needed to validate the findings.

Sleep apnea determines soluble TNF-α receptor 2 response to massive weight loss.

BACKGROUND: The effects of surgical weight loss (WL) on inflammatory biomarkers associated with sleep apnea remain unknown. We sought to determine if any biomarkers can predict amelioration of sleep apnea achieved by bariatric surgery. We hypothesized that surgical WL would substantially reduce severity of sleep apnea and levels of proinflammatory cytokines. METHODS: Twenty-three morbidly obese adults underwent anthropometric measurements, polysomnography, and serum biomarker profiling prior to and 1 year following bariatric surgery. We examined the effect of WL and amelioration of sleep apnea on metabolic and inflammatory markers. RESULTS: Surgical WL resulted in significant decreases in BMI (16.7 ± 5.97 kg/m(2)/median 365 days), apnea-hypopnea index (AHI), CRP, IL-6, sTNFαR1, sTNFαR2, and leptin levels, while ghrelin, adiponectin, and soluble leptin receptor concentrations increased significantly. Utilizing an AHI cutoff of 15 events/h, we found significantly elevated levels of baseline sTNFαR2 and greater post-WL sTNFαR2 decreases in subjects with baseline AHI ≥15 events/h compared to those with AHI <15 events/h despite no significant differences in baseline BMI, age, and ΔBMI. In a multivariable linear regression model adjusting for sex, age, impaired glucose metabolism, ΔBMI, and follow-up period, the post-WL decreases in AHI were an independent predictor of the decreases in sTNFαR2 and altogether accounted for 46% of the variance of ΔsTNFαR2 (P = 0.011) in the entire cohort. CONCLUSIONS: Of all the biomarkers, the decrease in sTNFαR2 was independently determined by the amelioration of sleep apnea achieved by bariatric surgery. The results suggest that sTNFαR2 may be a specific sleep apnea biomarker across a wide range of body weight.

Do differences in sleep architecture exist between persons with type 2 diabetes and nondiabetic controls?

BACKGROUND: It has been shown previously that the suppression of slow-wave sleep (SWS) markedly reduced insulin sensitivity and led to an impairment of glucose tolerance. We hypothesized that a decreased amount of SWS is a feature peculiar to subjects with type 2 diabetes. METHOD: A retrospective case-control study analyzed polysomnographic recordings and covariate data of 22 type 2 diabetic and 22 nondiabetic subjects [n = 44; 8 women, 36 men, aged 57.5 +/- 5.5 years, body mass index (BMI) 33.8 +/- 5.9 kg/m(2), apnea-hypopnea index (AHI) 29.6 +/- 22.2 episodes/hr] matched individually for sex, race, age, BMI, and severity of sleep-related breathing disorders (SRBD). We assessed differences in sleep architecture between the study group and the control group. Primary end points included the percentage of total sleep time spent in each sleep stage. RESULTS: Despite similar age and severity of SRBD, subjects with type 2 diabetes demonstrated a significantly decreased amount of SWS (3.9 +/- 5.95% vs 8.4 +/- 4.57%; p = 0.012), increased percentage time in rapid eye movement sleep (24.1 +/- 12.14% vs 13.8 +/- 6.96%; p = 0.005), and higher arousal index (44.3 +/- 19.53/hr vs 35.7 +/- 12.67/hr; p = 0.037) compared to nondiabetic controls. After adjustment for sex, BMI, AHI, and smoking, age and presence of type 2 diabetes were independent predictors of the decreased SWS percentage (p = 0.001). Variables in this model accounted for 34% of the variance in the SWS percentage in our cohort. CONCLUSIONS: Results demonstrated distinct differences in sleep architecture in our cohort with decreased amounts of SWS in type 2 diabetes. These findings suggest that polysomnographic recognition of altered sleep architecture may be partially implicated in the early detection of persons with type 2 diabetes.

Evaluation of the new software program DegifXL4 in the determination of the glycaemic indices of foodstuffs.

AIMS: There is no standardized protocol for measuring glycemic index (GI) that takes time-of-day effects into account. The software DegifXL2 and Medtronic-Minimed's CGMS and Solutions, makes the GI calculation at breakfast and dinner time possible. The aim of this study was to assess the enhanced data processing software (DegifXL4) enabling the GI calculation at breakfast, lunch, afternoon snack and dinner times. METHODS: The glucose levels of 20 healthy volunteers were monitored after they consumed either 50 g of glucose or one of ten alternative foodstuffs either for breakfast and dinner or for lunch or snack. Within the 9-day test period, 10 such meals were monitored in 3 replicates for each volunteer. Specifically, CGMS was used to monitor plasma glucose levels at 5-minute intervals for a period of 120 min following the foodstuff ingestion. RESULTS: Using the enhanced spreadsheed DegifXL 4, a total of 640 profiles were obtained and 491 (77 %) accomplished the criteria for further processing. The percentage of successful tests in each foodstuff varied from 57 to 87 %. CONCLUSIONS: The use of the new software DegifXL4 offers accurate GI estimates for foodstuffs eaten for breakfast, lunch, snacks and dinners in three replicates. In combination with the CGMS Solutions Software is DegifXL4 an enhanced efficient and comfortable way to routinely measure GI values.

Safety of new algorithms for premeal insulin boluses in high glycaemic index meals in persons with type 1 diabetes mellitus using insulin pumps.

AIMS: Consumption of glucose or foodstuffs with high glycaemic index (GI) in persons with type 1 diabetes mellitus (PWD1) is a hot topic in present diabetology. The aim of our pilot prospective study was to assess the efficiency of empirically suggested simple algorithms for premeal boluses in PWD1 using insulin pumps and continuous glucose monitoring (CGM). METHODS: Six PWD1 (aged 46.2+/-15.09 y, diabetes duration 14.5+/-9.65 y, HbA1c/IFCC 6.3+/-1.59%, BMI 23.6+/-1.67 kg/m(2), mean+/-SD) on insulin pumps Paradigm 522/722 with RT-CGMS sensors (Medtronic MiniMed, Northridge, CA) underwent a 12-week CGM. In one week, subjects consumed 50 g of carbohydrates in eleven alternative meals (rice squares, dark chocolate, white bread, honey, glucose, ravioli with meat and Eidam cheese, mashed potatoes with fish fingers, apricot dumplings with butter, spa waffles, spalta squares, and tomato soup with pasta) eaten for breakfasts, lunches, snacks and dinners in order to calculate their GI. The insulin boluses were adjusted according to empirically defined algorithms. Average glucose levels and daily insulin doses over three one-week periods (before testing, testing and after testing) were compared. RESULTS: During the observational period, the weekly averages of glucose levels (9.1+/-2.33 mmol/l vs. 9.2+/-2.30 mmol/l vs. 9.0+/-2.43 mmol/l, respectively) and daily insulin doses (39.1+/- 8.14 IU/d vs. 39.7+/-10.7 IU/d vs. 38.6+/-9.97 IU/d, respectively) were similar. One-week consumption of high GI foodstuffs had only a negligeable effect on average glucose levels. CONCLUSION: The suggested algorithms for premeal insulin boluses appear to limit the risk of potential hyperglycaemia resulting from intake of high GI foodstuffs.

Glycaemic index of selected foodstuffs in healthy persons.

AIMS: The aim of this study was to determine glycaemic index (GI) of 10 popular foodstuffs/mixed meals in healthy persons. METHODS: Ten tested foodstuffs and glucose standard were consumed in three replicates in the course of a defined 9-day meal plan: puffed rice squares with chocolate, dark chocolate, white bread, honey and glucose for breakfast (at 7 a.m.) and dinner (at 8 p.m.); pasta with meat, fried fish with mashed potatoes, and buttered apricot dumplings for lunch (at 12 a.m.); wafers, puffed spelt squares with chocolate, and tomato soup for snack (at 4 p.m.). Each portion contained 50 g of carbohydrates and was consumed within 30 minutes. Glucose concentrations were measured by means of the Continous Glucose Monitoring System (CGMS, Medtronic Minimed, Northridge, CA, USA). The results were processed by Solutions Software (Medtronic Minimed, Northridge, CA, USA) and DegifXL4 software, Palacký University, Olomouc, CZ. Twenty healthy persons aged 21.9 +/- 1.39 y (mean +/- SE), BMI 23.6 +/- 0.63 kg/m(2) completed the study. RESULTS: GI of tested foodstuffs ranged from 34.7 % (chocolate) to 105.3 % (puffed rice squares with chocolate). There were more than tenfold differences between minimal and maximal values of the GI for some foodstuffs. Significant interindividual differences were found between GIs of foodstuffs. CONCLUSIONS: In twenty healthy persons the glycaemic indexes of ten popular foodstuffs were determined, to be added to the nutritional labels in order to facilitate the optimum meal planning.