Sleep disturbances and circadian misalignment (social jet lag, late chronotype, or shift work) have been associated with worse glycemic control in type 2 diabetes (T2D). Whether these findings apply to adults with prediabetes is yet unexplored. We hypothesized that self-reported short sleep, poor sleep quality, and/or circadian misalignment are associated with higher glycemia, BMI, and blood pressure (BP) in adults with prediabetes or recently diagnosed, untreated T2D. RESEARCH DESIGN AND METHODSOur cohort included 962 overweight/obese adults ages 20-65 years with prediabetes or recently diagnosed, untreated T2D who completed a 2-h oral glucose tolerance test and validated sleep questionnaires. Independent associations of sleep and circadian variables with glycemia, BMI, and BP were evaluated with regression models. RESULTSThe multiethnic cohort was 55% men, with mean 6 SD age 52.2 6 9.5 years and BMI 34.7 6 5.5 kg/m 2 . Mean sleep duration was 6.6 6 1.3 h. Poor sleep quality was reported by 54% and high risk for obstructive sleep apnea by 64%. HbA 1c was significantly higher in those reporting <5 or >8 h sleep per night. Sleep duration >8 h was also associated with higher fasting glucose and <6 h with higher BMI. Shift work was also associated with higher BMI. Social jet lag and delayed chronotype were associated with higher BP. CONCLUSIONSIn our cohort, self-reported short and long sleep were both associated with adverse measures of glycemia, and short sleep and shift work were associated with higher BMI. Further research using objective measures of sleep is needed to better delineate the relationship between sleep and glycemia in adults with prediabetes or T2D.The obesity epidemic has led to an increase in type 2 diabetes (T2D) (1). There are ;30 million individuals with T2D and nearly 90 million with prediabetes in the U.S. (2). In parallel, there has been an increase in the prevalence of sleep disturbances (3). Chronic partial sleep loss due to bedtime restriction is increasingly prevalent in our

The Restoring Insulin Secretion (RISE) Adult Medication Study compared pharmacological approaches targeted to improve b-cell function in individuals with impaired glucose tolerance (IGT) or treatment-naive type 2 diabetes of <12 months duration. RESEARCH DESIGN AND METHODS A total of 267 adults with IGT (n = 197, 74%) or recently diagnosed type 2 diabetes (n = 70, 26%) were studied. Participants were randomized to receive 12 months of metformin alone, 3 months of insulin glargine with a target fasting glucose <5 mmol/L followed by 9 months of metformin, 12 months of liraglutide combined with metformin, or 12 months of placebo. b-Cell function was assessed using hyperglycemic clamps at baseline, 12 months (on treatment), and 15 months (3 months off treatment). The primary outcome was b-cell function at 15 months compared with baseline. RESULTS All three active treatments produced on-treatment reductions in weight and improvements in HbA 1c compared with placebo; the greatest reductions were seen in the liraglutide plus metformin group. At 12 months, glucose-stimulated C-peptide responses improved in the three active treatment groups and were greatest in the liraglutide plus metformin group, but the arginine-stimulated incremental C-peptide response was reduced in the liraglutide plus metformin group. Despite on-treatment benefits, 3 months after treatment withdrawal there were no sustained improvements in b-cell function in any treatment group. CONCLUSIONS In adults with IGT or recently diagnosed type 2 diabetes, interventions that improved b-cell function during active treatment failed to produce persistent benefits after treatment withdrawal. These observations suggest that continued intervention may be required to alter the progressive b-cell dysfunction in IGT or early type 2 diabetes.

Across the Diabetes Prevention Program (DPP) follow-up, cumulative diabetes incidence remained lower in the lifestyle compared with the placebo and metformin randomized groups and could not be explained by weight. Collection of self-reported physical activity (PA) (yearly) with cross-sectional objective PA (in follow-up) allowed for examination of PA and its long-term impact on diabetes prevention. RESEARCH DESIGN AND METHODS Yearly self-reported PA and diabetes assessment and oral glucose tolerance test results (fasting glucose semiannually) were collected for 3,232 participants with one accelerometry assessment 11-13 years after randomization (n 5 1,793). Mixed models determined PA differences across treatment groups. The association between PA and diabetes incidence was examined using Cox proportional hazards models. RESULTS There was a 6% decrease (Cox proportional hazard ratio 0.94 [95% CI 0.92, 0.96]; P < 0.001) in diabetes incidence per 6 MET-h/week increase in time-dependent PA for the entire cohort over an average of 12 years (controlled for age, sex, baseline PA, and weight). The effect of PA was greater (12% decrease) among participants less active at baseline (<7.5 MET-h/week) (n 5 1,338) (0.88 [0.83, 0.93]; P < 0.0001), with stronger findings for lifestyle participants. Lifestyle had higher cumulative PA compared with metformin or placebo (P < 0.0001) and higher accelerometry total minutes per day measured during follow-up (P 5 0.001 and 0.047). All associations remained significant with the addition of weight in the models. CONCLUSIONS PA was inversely related to incident diabetes in the entire cohort across the study, with cross-sectional accelerometry results supporting these findings. This highlights the importance of PA within lifestyle intervention efforts designed to prevent diabetes and urges health care providers to consider both PA and weight when counseling high-risk patients.

Weight loss outcomes among young adults in technology-based programs have been equivocal. The purpose of this study was to deliver digital weight loss treatments to young adults and examine the 6, 12, and 18 month effects on weight loss. Young adults with overweight/obesity (N = 459; 23.3 ± 4.4 years) were recruited from two university sites and randomly assigned to receive through Facebook and text messaging either personalized (TAILORED; n = 150) or generic (TARGETED; n = 152) weight loss information, messages, and feedback or general healthy body content (e.g., body image, sleep; CONTROL; n = 157). The study was powered to detect a 2.1-kg difference at all time points with the primary outcome being 18 months. There was no overall effect of treatment group on 6, 12, or 18 month weight loss (ps = NS). However, at 6 months, those in TAILORED who were highly engaged (completing &gt;66%) lost more weight compared to CONTROL (−2.32 kg [95% confidence intervals: −3.90, −0.74]; p = .004), with the trend continuing at 12 months. A significant baseline body mass index (BMI) by treatment group interaction (p = .004) was observed at 6 months. Among participants in the lowest baseline BMI category (25–27.5 kg/m2), those in TAILORED lost 2.27 kg (−3.86, −0.68) more, and those in TARGETED lost 1.72 kg (−3.16, −0.29) more than CONTROL after adjusting for covariates. Among participants with a BMI between 27.5 and 30 kg/m2, those in TAILORED lost 2.20 kg (−3.90, −0.51) more than participants in TARGETED. Results did not persist over time with no treatment interaction at 12 or 18 months. Initial body weight should be considered when recommending weight loss treatments for young adults. More intensive interventions or stepped care approaches may be needed for young adults with obesity.

Background We evaluated whether diet quality is a predictor of weight loss and reduced diabetes risk, independent of caloric intake in the Diabetes Prevention Program (DPP) cohort, a randomized clinical trial of adults at risk for diabetes. Methods This secondary analysis included 2914 participants with available data (964 intensive lifestyle (ILS), 977 metformin, 973 placebo). Dietary intake was assessed using a 117-item food frequency questionnaire. Diet quality was quantified using the Alternative Healthy Eating Index 2010 (AHEI). AHEI ranges from 0 to 110, with higher scores corresponding to higher quality diets. ILS participants had greater improvement (p < 0.001) in AHEI over 1-year (4.2 ± 9.0) compared to metformin (1.2 ± 8.5) and placebo (1.4 ± 8.4). We examined the association between AHEI change and weight change from baseline to 1-year using linear regression, and that between 1-year AHEI change and incident diabetes, using hazard models over an average 3 years follow-up. Models were evaluated within treatment group and adjusted for relevant characteristics including caloric intake, physical activity, BMI and AHEI. Models testing incident diabetes were further adjusted for baseline fasting and 2 h glucose. Results An increase in AHEI score was associated with weight loss in ILS [β per 10-point increase (SE) -1.2 kg (0.3, p < 0.001)], metformin [− 0. 90 kg (0.2, p < 0.001)] and placebo [− 0.55 kg (0.2, p = 0.01)]. However, AHEI change was not associated with incident diabetes in any group before or after adjustment for weight change. Conclusions Controlling for weight, diet quality was not associated with diabetes incidence but helps achieve weight loss, an important factor in diabetes prevention.