Decreased glomerular filtration rate (GFR) and albuminuria are used in combination to define chronic kidney disease, but their separate and combined effects on cardiovascular and all-cause mortality have not been studied in the general population. The linked mortality file of the Third National Health and Nutrition Examination Survey includes data from 13 years of follow-up (1988–2000) for 14,586 US adults. The authors estimated GFR from standardized serum creatinine levels. Albuminuria was defined by the urinary albumin:creatinine ratio. Incidence rate ratios (IRRs) were adjusted for major cardiovascular disease risk factors and C-reactive protein. Lower estimated GFR was associated with higher risks of cardiovascular and all-cause mortality overall and within every albuminuria category. Likewise, increasing albuminuria was associated with higher risk of estimated GFR overall and within every category. When estimated GFR and albuminuria were examined simultaneously, a 10-ml/minute/1.73 m² lower estimated GFR (among persons with estimated GFR <60 ml/minute/1.73 m²) was associated with an IRR of 1.29 (95% confidence interval: 1.06, 1.55) for cardiovascular mortality and a doubling of albuminuria was associated with an IRR of 1.06 (95% confidence interval: 1.04, 1.08) for cardiovascular mortality. The authors conclude that moderately decreased estimated GFR and albuminuria independently predict cardiovascular and all-cause mortality in the general population. These data support recent recommendations defining chronic kidney disease and stratifying subsequent risks based on both decreased GFR and albuminuria.

Several studies have examined associations between genetic variants and the risk of gestational diabetes mellitus (GDM). However, inferences from these studies were often hindered by limited statistical power and conflicting results. We aimed to systematically review and quantitatively summarize the association of commonly studied single nucleotide polymorphisms (SNPs) with GDM risk and to identify important gaps that remain for consideration in future studies. Genetic association studies of GDM published through 1 October 2012 were searched using the HuGE Navigator and PubMed databases. A SNP was included if the SNP-GDM associations were assessed in three or more independent studies. Two reviewers independently evaluated the eligibility for inclusion and extracted the data. The allele-specific odds ratios (ORs) and 95% confidence intervals (CIs) were pooled using random effects models accounting for heterogeneity. Overall, 29 eligible articles capturing associations of 12 SNPs from 10 genes were included for the systematic review. The minor alleles of rs7903146 (TCF7L2), rs12255372 (TCF7L2), rs1799884 (-30G/A, GCK), rs5219 (E23K, KCNJ11), rs7754840 (CDKAL1), rs4402960 (IGF2BP2), rs10830963 (MTNR1B), rs1387153 (MTNR1B) and rs1801278 (Gly972Arg, IRS1) were significantly associated with a higher risk of GDM. Among them, genetic variants in TCF7L2 showed the strongest association with GDM risk, with ORs (95% CIs) of 1.44 (1.29-1.60, P < 0.001) per T allele of rs7903146 and 1.46 (1.15-1.84, P = 0.002) per T allele of rs12255372. In this systematic review, we found significant associations of GDM risk with nine SNPs in seven genes, most of which have been related to the regulation of insulin secretion.

Context: Conflicting findings have been reported regarding the effect of menstrual cycle phase and sex hormones on insulin sensitivity. Objective: The aim was to determine the pattern of insulin resistance over the menstrual cycle and whether variations in sex hormones explain these patterns. Design: The BioCycle study is a longitudinal study that measured hormones at different phases of the menstrual cycle. Participants had up to eight visits per cycle; each visit was timed using fertility monitors to capture sensitive windows of hormonal changes. Setting: The study was conducted in the general community of the University at Buffalo (Buffalo, NY). Participants: A total of 257 healthy, premenopausal women (age, 27 ± 8 yr; body mass index, 24 ± 4 kg/m²) participated in the study. Main Outcome Measures: We measured fasting insulin, glucose, and insulin resistance by the homeostasis model of insulin resistance (HOMA-IR). Results: Significant changes in HOMA-IR were observed over the menstrual cycle; from a midfollicular phase level of 1.35, levels rose to 1.59 during the early luteal phase and decreased to 1.55 in the late-luteal phase. HOMA-IR levels primarily reflected changes in insulin and not glucose. After adjustment for age, race, cycle, and other sex hormones, HOMA-IR was positively associated with estradiol (β = 0.082; P < 0.001) and progesterone (β = 0.025; P < 0.001), and inversely associated with FSH (adjusted β = −0.040; P < 0.001) and SHBG (β = −0.085; P < 0.001). LH was not associated with HOMA-IR. Further adjustment for BMI weakened the association with SHBG (β = −0.057; P = 0.06) but did not affect other associations. Conclusion: Insulin exhibited minor menstrual cycle variability. Estradiol and progesterone were positively associated with insulin resistance and should be considered in studies of insulin resistance among premenopausal women.