In analyzing the responses of 100 predominantly white, well educated and happily married couples to a self-report questionnaire, this study examined the frequency of sexual problems experienced and the relations of those problems to sexual satisfaction. Although over 80 per cent of the couples reported that their marital and sexual relations were happy and satisfying, 40 per cent of the men reported erectile or ejaculatory dysfunction, and 63 per cent of the women reported arousal or orgasmic dysfunction. In addition, 50 per cent of the men and 77 per cent of the women reported difficulty that was not dysfunctional in nature (e.g., lack of interest or inability to relax). The number of "difficulties" reported was more strongly and consistently related to overall sexual dissatisfaction than the number of "dysfunctions."

We examined the effects of metformin on diabetes prevention and the subgroups that benefited most over 15 years in the Diabetes Prevention Program (DPP) and its follow-up, the Diabetes Prevention Program Outcomes Study (DPPOS). RESEARCH DESIGN AND METHODS During the DPP (1996-2001), adults at high risk of developing diabetes were randomly assigned to masked placebo (n = 1,082) or metformin 850 mg twice daily (n = 1,073). Participants originally assigned to metformin continued to receive metformin, unmasked, in the DPPOS (2002-present). Ascertainment of diabetes development was based on fasting or 2-h glucose levels after an oral glucose tolerance test or on HbA 1c. Reduction in diabetes incidence with metformin was compared with placebo in subgroups by hazard ratio (HR) and rate differences (RDs). RESULTS During 15 years of postrandomization follow-up, metformin reduced the incidence (by HR) of diabetes compared to placebo by 17% or 36% based on glucose or HbA 1c levels, respectively. Metformin's effect on the development of glucose-defined diabetes was greater for women with a history of prior gestational diabetes mellitus (GDM) (HR 0.59, RD 24.57 cases/100 person-years) compared with parous women without GDM (HR 0.94, RD 20.38 cases/100 person-years [interaction P = 0.03 for HR, P = 0.01 for RD]). Metformin also had greater effects, by HR and RD, at higher baseline fasting glucose levels. With diabetes development based on HbA 1c , metformin was more effective in subjects with higher baseline HbA 1c by RD, with metformin RD 21.03 cases/100 person-years with baseline HbA 1c <6.0% (42 mmol/mol) and 23.88 cases/100 person-years with 6.0-6.4% (P = 0.0001). CONCLUSIONS Metformin reduces the development of diabetes over 15 years. The subsets that benefitted the most include subjects with higher baseline fasting glucose or HbA 1c and women with a history of GDM.

Background: Many CpGs become hyper or hypo-methylated with age. Multiple methods have been developed by Horvath et al. to estimate DNA methylation (DNAm) age including Pan-tissue, Skin & Blood, PhenoAge, and GrimAge. Pan-tissue and Skin & Blood try to estimate chronological age in the normal population whereas PhenoAge and GrimAge use surrogate markers associated with mortality to estimate biological age and its departure from chronological age. Here, we applied Horvath's four methods to calculate and compare DNAm age in 499 subjects with type 1 diabetes (T1D) from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study using DNAm data measured by Illumina EPIC array in the whole blood. Association of the four DNAm ages with development of diabetic complications including cardiovascular diseases (CVD), nephropathy, retinopathy, and neuropathy, and their risk factors were investigated. Results: Pan-tissue and GrimAge were higher whereas Skin & Blood and PhenoAge were lower than chronological age (p < 0.0001). DNAm age was not associated with the risk of CVD or retinopathy over 18-20 years after DNAm measurement. However, higher PhenoAge (β = 0.023, p = 0.007) and GrimAge (β = 0.029, p = 0.002) were associated with higher albumin excretion rate (AER), an indicator of diabetic renal disease, measured over time. GrimAge was also associated with development of both diabetic peripheral neuropathy (OR = 1.07, p = 9.24E−3) and cardiovascular autonomic neuropathy (OR = 1.06, p = 0.011). Both HbA1c (β = 0.38, p = 0.026) and T1D duration (β = 0.01, p = 0.043) were associated with higher PhenoAge. Employment (β = − 1.99, p = 0.045) and leisure time (β = − 0.81, p = 0.022) physical activity were associated with lower Pan-tissue and Skin & Blood, respectively. BMI (β = 0.09, p = 0.048) and current smoking (β = 7.13, p = 9.03E−50) were positively associated with Skin & Blood and GrimAge, respectively. Blood pressure, lipid levels, pulse rate, and alcohol consumption were not associated with DNAm age regardless of the method used. Conclusions: Various methods of measuring DNAm age are sub-optimal in detecting people at higher risk of developing diabetic complications although some work better than the others.

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.

Refractive error (RE) is a complex, multifactorial disorder characterized by a mismatch between the optical power of the eye and its axial length that causes object images to be focused off the retina. The two major subtypes of RE are myopia (nearsightedness) and hyperopia (farsightedness), which represent opposite ends of the distribution of the quantitative measure of spherical refraction. We performed a fixed effects meta-analysis of genome-wide association results of myopia and hyperopia from 9 studies of European-derived populations: AREDS, KORA, FES, OGP-Talana, MESA, RSI, RSII, RSIII and ERF. One genome-wide significant region was observed for myopia, corresponding to a previously identified myopia locus on 8q12 (p = 1.25×10−8), which has been reported by Kiefer et al. as significantly associated with myopia age at onset and Verhoeven et al. as significantly associated to mean spherical-equivalent (MSE) refractive error. We observed two genome-wide significant associations with hyperopia. These regions overlapped with loci on 15q14 (minimum p value = 9.11×10−11) and 8q12 (minimum p value 1.82×10−11) previously reported for MSE and myopia age at onset. We also used an intermarker linkage- disequilibrium-based method for calculating the effective number of tests in targeted regional replication analyses. We analyzed myopia (which represents the closest phenotype in our data to the one used by Kiefer et al.) and showed replication of 10 additional loci associated with myopia previously reported by Kiefer et al. This is the first replication of these loci using myopia as the trait under analysis. “Replication-level” association was also seen between hyperopia and 12 of Kiefer et al.'s published loci. For the loci that show evidence of association to both myopia and hyperopia, the estimated effect of the risk alleles were in opposite directions for the two traits. This suggests that these loci are important contributors to variation of refractive error across the distribution.