Growth Trajectory and Adult Height in Children with Nonclassical Congenital Adrenal Hyperplasia

Background: Children with nonclassical congenital adrenal hyperplasia (NCCAH) often present increased growth velocity secondary to elevation of adrenal androgens that accelerates bone maturation and might compromise adult height (AH). Objective: The aim of the study was to analyze prognostic factors affecting growth trajectory (GT) and AH in children with NCCAH. Methods: The study was a retrospective, multicentric study. The study population consisted of 192 children with a confirmed molecular diagnosis of NCCAH, followed by pediatric endocrinology centers from diagnosis up to AH. Clinical records were collected and analyzed. AH (standard deviation score; SDS), pubertal growth (PG) (cm), GT from diagnosis to AH (SDS), and AH adjusted to target height (TH) (AH-TH SDS) were evaluated as outcome indicators using stepwise linear regression models. Results: The stepwise linear regression analysis showed that AH and AH-TH were significantly related to chronological age (CA) (p = 0.008 and 0.016), bone age (BA)/CA ratio (p = 0.004 and 0.001), height (H) (p < 0.001 for both parameters) at NCCAH diagnosis, and TH (p = 0.013 and <0.001). PG was higher in males than in females (22.59 ± 5.74 vs. 20.72 ± 17.4 cm, p = 0.002), as physiologically observed, and was positively related to height (p = 0.027), negatively to BMI (p = 0.001) and BA/CA ratio (p = 0.001) at NCCAH diagnosis. Gender, genotype, biochemical data, and hydrocortisone treatment did not significantly impair height outcomes of these NCCAH children. Conclusions: The results of this study suggest that AH and GT of NCCAH patients are mainly affected by the severity of phenotype (CA, BA/CA ratio, and H) at the time of diagnosis. © 2020 S. Karger AG. All rights reserved

Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes.

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

Mining the human phenome using allelic scores that index biological intermediates

J. Kaprio ja M-L. Lokki työryhmien jäseniä.It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.Peer reviewe