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.

Analysis on the glucose and lipid metabolism levels of different sleep duration residents in a community in Urumqi / 中华内分泌代谢杂志

Objective:To investigate different sleep duration and glucose and lipid metabolism levels in residents of a community in Urumqi.Methods:Using the 2 049 residents′ data of chronic metabolic disease in a community of Urumqi collected in May 2017, 1 822 subjects aged between 19-80 years with complete information were enrolled, their blood pressure, waist circumference, height, weight, body mass index were measured and recorded. Using oral glucose tolerance test to measure fasting and 2 h after meal plasma glucose, uric acid, HbA 1C, total cholesterol, triglyceride, low density lipoprotein-cholesterol (LDL-C), high density lipoprotein-cholesterol (HDL-C) levels were all tested. Results:(1)There were 363 (19.9%), 1 349 (74.0%), and 110 (6.1%) respondents with sleep time≤6.0, 6.1 to 8.0, and>8.0 h/d, respectively. There were statistically significant differences in age, education, and family income in groups with different sleep time ( P<0.05), while their gender, smoking status, and exercise status were not statistically significant ( P>0.05). The rates of overweight, obesity, abdominal obesity, high uric acid, and hypertension in people with different sleep durations were statistically different ( P<0.01). The rates of the above indicators were higher in the group of sleep time≤6.0 h/d than the other two groups. (2) Differences in diastolic blood pressure, systolic blood pressure, body mass index, abdominal circumference, total cholesterol, and LDL-C levels were statistically significant among different sleep duration groups ( P<0.05). Further comparisons of the above indicators among three groups with different sleep durations were performed ( P<0.05). The levels of the above indicators in the sleep time≤6.0 h/d group were higher than those in the other two groups. There were no significant differences in fasting blood glucose, glycated hemoglobin, uric acid, triglyceride, and HDL-C among the three groups. (3) Multivariate logistic regression analysis showed that groups whether or not adjusted of age, family income, and education level, sleep time≤6.0 h/d was related to abdominal obesity, and sleep time≤6.0 h/d was be a risk factor for abdominal obesity [Unadjusted: OR=1.48(95% CI1.04-2.08); Adjusted: OR=1.65(95% CI1.18-2.32; P<0.05]. Conclusion:Sleep time ≤6.0 h/d is associated with abdominal obesity, and sleep time≤6.0 h/d may be a risk factor for abdominal obesity.

Correlation between rs231775 polymorphism of CTLA4 gene and autoimmune thyroid disease of Uygur in Xinjiang Uygur Autonomous Region / 中华地方病学杂志

Objective:To explore the correlation between rs231775 polymorphism of cytotoxic T lymphocyte-associated antigen 4 (CTLA4) gene and autoimmune thyroid disease (AITD) of Uygur in Xinjiang Uygur Autonomous Region.Methods:A total of 382 Uygur patients with AITD [including 328 Hashimoto's thyroiditis (HT) patients and 54 Graves' disease (GD) patients] diagnosed in the People's Hospital of Xinjiang Uygur Autonomous Region from January 2017 to December 2018 were selected as the case group, and 383 Uygur health physical examiners in the same period were selected as the control group. The whole blood genomic DNA of the study subjects was extracted, and the Sequenom-mass spectrometry analysis platform was used to determine the genotyping of CTLA4 gene single nucleotide polymorphism (SNP) locus rs231775 and analyze the genetic model, and the correlation between rs231775 polymorphism and AITD under different genetic models was compared. The logistic regression analysis model was used to analyze the influencing factors of AITD. And the thyroid function index of different genotype population was compared.Results:In the case group and the control group, the differences of CTLA4 gene rs231775 alleles (A: 41.88%, 49.35%; G: 58.12%, 50.65%) and genotype frequencies (AA: 17.80%, 23.24%; AG: 48.17%, 52.22%; GG: 34.03%, 24.54%) were statistically significant (χ 2=8.586, 9.260, P < 0.05). Compared with the control group, the genotype frequency of rs231775 in HT group, the alleles and genotype frequencies of rs231775 in GD group were significantly different (χ 2=5.997, 11.130, 10.210, P < 0.05). Under the additive and dominant models, the CTLA4 gene rs231775 was correlated with AITD [odds ratio ( OR)=0.67, 0.55, 0.63] and HT ( OR=0.69, 0.62, 0.67, P < 0.05); and correlated with GD under the additive, dominant and recessive genetic models ( OR=0.53, 0.23, 0.44, 0.34, P < 0.05). The logistic regression analysis showed that genotype, gender, age, thyroid stimulating hormone (TSH) and free thyroxine (FT 4) were independent influencing factors of AITD ( P < 0.05). Among all the subjects, the level of thyroglobulin antibody(TgAb) in the population with the recessive genotype (GG) at the rs231775 of the CTLA4 gene was higher than that in the dominant genotype (AA+AG) population ( P < 0.05). Conclusion:The CTLA4 gene rs231775 polymorphism is significantly related to AITD of Uygur in Xinjiang Uygur Autonomous Region, and the level of TgAb in GG genotype is higher than that in other genotypes.

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.