Non-Classical 21-Hydroxylase Deficiency: Analysis of a Mutant Gene in a Uyghur Family and Literature Review.

OBJECTIVE: The gene mutation and clinical characteristics of a patient with non-classical 21-hydroxylase deficiency and his family were analyzed. METHODS: A patient was diagnosed with non-classical 21-hydroxylase deficiency in the Department of Endocrinology of People's Hospital of Xinjiang Uygur Autonomous Region in December 2016. The clinical data and related gene-sequencing results were analyzed. The detected mutations were verified in nine members of the family. RESULTS: Gene-sequencing results revealed that the proband and the other three members of the family (proband, proband's mother's younger brother and the proband's mother's younger brother's younger daughter, and proband's second elder sister) shared the following mutations: Ile173Asn, Ile237Asn, Val238Glu, Met240Lys, Val282Leu, Leu308Phefs*6, Gln319Ter, Arg357Trp, and Arg484Profs. The Val282Leu mutation was heterozygous in the proband's mother's younger brother's younger daughter, but homozygous in the other three individuals. The father of the proband, the elder brother of the father of the proband, the third younger brother of the father of the proband, and the elder sister of the proband all carried only the Val282Leu mutation. CONCLUSION: Val282Leu is the gene responsible for non-classical 21-hydroxylase deficiency. Screening for this gene in the offspring of patients with non-classical 21-hydroxylase deficiency may help to identify cases early.

Non-lab and semi-lab algorithms for screening undiagnosed diabetes: A cross-sectional study.

BACKGROUND: The terrifying undiagnosed rate and high prevalence of diabetes have become a public emergency. A high efficiency and cost-effective early recognition method is urgently needed. We aimed to generate innovative, user-friendly nomograms that can be applied for diabetes screening in different ethnic groups in China using the non-lab or noninvasive semi-lab data. METHODS: This multicenter, multi-ethnic, population-based, cross-sectional study was conducted in eight sites in China by enrolling subjects aged 20-70. Sociodemographic and anthropometric characteristics were collected. Blood and urine samples were obtained 2 h following a standard 75 g glucose solution. In the final analysis, 10,794 participants were included and randomized into model development (n = 8096) and model validation (n = 2698) group with a ratio of 3:1. Nomograms were developed by the stepwise binary logistic regression. The nomograms were validated internally by a bootstrap sampling method in the model development set and externally in the model validation set. The area under the receiver operating characteristic curve (AUC) was used to assess the screening performance of the nomograms. Decision curve analysis was applied to calculate the net benefit of the screening model. RESULTS: The overall prevalence of undiagnosed diabetes was 9.8% (1059/10794) according to ADA criteria. The non-lab model revealed that gender, age, body mass index, waist circumference, hypertension, ethnicities, vegetable daily consumption and family history of diabetes were independent risk factors for diabetes. By adding 2 h post meal glycosuria qualitative to the non-lab model, the semi-lab model showed an improved Akaike information criterion (AIC: 4506 to 3580). The AUC of the semi-lab model was statistically larger than the non-lab model (0.868 vs 0.763, P < 0.001). The optimal cutoff probability in semi-lab and non-lab nomograms were 0.088 and 0.098, respectively. The sensitivity and specificity were 76.3% and 81.6%, respectively in semi-lab nomogram, and 72.1% and 67.3% in non-lab nomogram at the optimal cut off point. The decision curve analysis also revealed a bigger decrease of avoidable OGTT test (52 per 100 subjects) in the semi-lab model compared to the non-lab model (36 per 100 subjects) and the existed New Chinese Diabetes Risk Score (NCDRS, 35 per 100 subjects). CONCLUSION: The non-lab and semi-lab nomograms appear to be reliable tools for diabetes screening, especially in developing countries. However, the semi-lab model outperformed the non-lab model and NCDRS prediction systems and might be worth being adopted as decision support in diabetes screening in China.