INTRODUCTION:Greater awareness of the relationship between co-morbidities and fracture risk may improve fracture-prediction algorithms such as FRAX. MATERIALS AND METHODS:We used a large, multinational cohort study (GLOW) to investigate the effect of co-morbidities on fracture risk. Women completed a baseline questionnaire detailing past medical history, including co-morbidity history and fracture. They were re-contacted annually to determine incident clinical fractures. A co-morbidity index, defined as number of baseline co-morbidities, was derived. The effect of adding the co-morbidity index to FRAX risk factors on fracture prevention was examined using chi-squared tests, the May-Hosmer test, c index and comparison of predicted versus observed fracture rates. RESULTS:Of 52,960 women with follow-up data, enrolled between October 2006 and February 2008, 3224 (6.1%) sustained an incident fracture over 2 years. All recorded co-morbidities were significantly associated with fracture, except for high cholesterol, hypertension, celiac disease, and cancer. The strongest association was seen with Parkinson's disease (age-adjusted hazard ratio [HR]: 2.2; 95% CI: 1.6-3.1; P<0.001). Co-morbidities that contributed most to fracture prediction in a Cox regression model with FRAX risk factors as additional predictors were: Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, osteoarthritis, and heart disease. CONCLUSION:Co-morbidities, as captured in a co-morbidity index, contributed significantly to fracture risk in this study population. Parkinson's disease carried a particularly high risk of fracture; and increasing co-morbidity index was associated with increasing fracture risk. Addition of co-morbidity index to FRAX risk factors improved fracture prediction.

Gene-lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13-0.31], P = 1.63 × 10(-6)). All SNPs were associated with 2-h glucose (β = 0.06-0.12 mmol/allele, P ≤ 1.53 × 10(-7)), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene-lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions.

Objective

Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology.

Research design and methods

We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates.

Results

Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10(-8)). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10(-4)), improved β-cell function (P = 1.1 × 10(-5)), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10(-6)). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets.

Conclusions

We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis.