Objective:To explore the correlation between hyperuricemia (HUA) and chronic kidney disease (CKD) in the individuals undergoing physical examinations.Methods:It was a retrospective cohort study. The study selected 6 910 individuals who received health check-ups at the Xiangya Hospital Health Management Center of Central South University in 2012 and 2022, with none of them having developed CKD in 2012. Using the presence of HUA in 2012 as the independent variable and the occurrence of CKD in 2022 as the outcome variable, four Cox proportional hazards regression models were constructed, with baseline age, gender, body mass index, waist circumference, glomerular filtration rate, presence of hypertension, presence of diabetes, presence of dyslipidemia, white blood cell count, hemoglobin level, direct bilirubin level, alanine aminotransferase level, and blood uric acid level in 2013 as confounding variables. These models were used to analyze the correlation between HUA and CKD, and sensitivity analyses were conducted. The percentile bootstrap method was employed to conduct mediation effect testing, analyzing the intermediary risk factors that influence the correlation between HUA and CKD.Results:Among the 6 910 participants included in the study, the overall baseline detection rate of HUA was 8.78% (607/6 910). In 2022, the incidence of CKD was 7.2% (498/6 910). Cox regression analysis showed a positive correlation between HUA and the occurrence of CKD in the overall population ( HR=1.586, 95% CI: 1.224-2.055). However, after gradually adjusting for confounding factors, the correlation between HUA and CKD was not statistically significant. Stratified by gender, the occurrence of HUA was positively correlated with the incidence of CKD in women ( HR=2.599, 95% CI: 1.069-6.316), but the correlation became non-significant after adjusting for confounding factors. In contrast, there was no significant correlation between HUA and CKD in men. In sensitivity analysis, When uric acid levels were analyzed by grouping participants into two categories based on thresholds of>420 μmol/L for men and>360 μmol/L for women, or as a continuous variable, the results showed a positive correlation between HUA and CKD in the overall population and in women, the HR (95% CI) value was 1.627 (1.282-2.064), 2.465 (1.552-3.914), 1.004 (1.003-1.005) and 1.006 (1.004-1.008), respectively. However, after adjusting for confounding factors, the correlation between HUA and CKD became non-significant in both cases. In the males, there was no correlation between uric acid and the occurrence of CKD, regardless of whether uric acid was treated as a categorical or continuous variable. Mediation analysis revealed that diabetes and hypertension were full mediators between HUA/blood uric acid levels and CKD in the overall population. Among males, diabetes and hypertension were full mediators between blood uric acid levels and CKD. In females, hypertension was a full mediator between HUA/blood uric acid levels and CKD, with an effect proportion of 100%. Conclusion:HUA is positively correlated with the risk of CKD, particularly in females, but HUA is not an independent predictor of CKD. HUA influences the occurrence of CKD through conditions such as diabetes and hypertension.

Objective:To explore the correlation between hyperuricemia (HUA) and chronic kidney disease (CKD) in the individuals undergoing physical examinations.Methods:It was a retrospective cohort study. The study selected 6 910 individuals who received health check-ups at the Xiangya Hospital Health Management Center of Central South University in 2012 and 2022, with none of them having developed CKD in 2012. Using the presence of HUA in 2012 as the independent variable and the occurrence of CKD in 2022 as the outcome variable, four Cox proportional hazards regression models were constructed, with baseline age, gender, body mass index, waist circumference, glomerular filtration rate, presence of hypertension, presence of diabetes, presence of dyslipidemia, white blood cell count, hemoglobin level, direct bilirubin level, alanine aminotransferase level, and blood uric acid level in 2013 as confounding variables. These models were used to analyze the correlation between HUA and CKD, and sensitivity analyses were conducted. The percentile bootstrap method was employed to conduct mediation effect testing, analyzing the intermediary risk factors that influence the correlation between HUA and CKD.Results:Among the 6 910 participants included in the study, the overall baseline detection rate of HUA was 8.78% (607/6 910). In 2022, the incidence of CKD was 7.2% (498/6 910). Cox regression analysis showed a positive correlation between HUA and the occurrence of CKD in the overall population ( HR=1.586, 95% CI: 1.224-2.055). However, after gradually adjusting for confounding factors, the correlation between HUA and CKD was not statistically significant. Stratified by gender, the occurrence of HUA was positively correlated with the incidence of CKD in women ( HR=2.599, 95% CI: 1.069-6.316), but the correlation became non-significant after adjusting for confounding factors. In contrast, there was no significant correlation between HUA and CKD in men. In sensitivity analysis, When uric acid levels were analyzed by grouping participants into two categories based on thresholds of>420 μmol/L for men and>360 μmol/L for women, or as a continuous variable, the results showed a positive correlation between HUA and CKD in the overall population and in women, the HR (95% CI) value was 1.627 (1.282-2.064), 2.465 (1.552-3.914), 1.004 (1.003-1.005) and 1.006 (1.004-1.008), respectively. However, after adjusting for confounding factors, the correlation between HUA and CKD became non-significant in both cases. In the males, there was no correlation between uric acid and the occurrence of CKD, regardless of whether uric acid was treated as a categorical or continuous variable. Mediation analysis revealed that diabetes and hypertension were full mediators between HUA/blood uric acid levels and CKD in the overall population. Among males, diabetes and hypertension were full mediators between blood uric acid levels and CKD. In females, hypertension was a full mediator between HUA/blood uric acid levels and CKD, with an effect proportion of 100%. Conclusion:HUA is positively correlated with the risk of CKD, particularly in females, but HUA is not an independent predictor of CKD. HUA influences the occurrence of CKD through conditions such as diabetes and hypertension.

Objective:To understand the relationship between lipid accumulation product (LAP) and hyperuricemia in physical examination population.Methods:This was a cross-sectional study. The analysis was based on baseline data from a retrospective cohort study. Total of 44 294 people who received physical examination in the Health Management Center of Xiangya Hospital of Central South University from January to December 2012 were selected as subjects with whole-group sampling method. All the subjects aged ≥18 years with complete study variables. The minimum waist circumference of the subjects was calculated to determine the criteria for calculating LAP in those population. With LAP as the observed variable and hyperuricemia as the outcome variable, LAP was divided into four groups according to the interquartile interval (Q 1-Q 4 groups): group Q 1<10.56 cm·mmol/L, 10.56 cm·mmol/L≤Q 2<20.79 cm·mmol/L, 20.79 cm·mmol/L≤Q 3<38.94 cm·mmol/L, Q 4≥38.94 cm·mmol/L. Five models were constructed with logistic regression analysis. No confounding factors was adjusted in Model 1, model 2 was adjusted for age and gender; and model 3 was further adjusted for body mass index, hypertension, hyperlipidemia, creatinine and glomerular filtration rate; model 4 was further adjusted education level, occupation, health insurance, smoking, drinking, diet scores and physical exercise; model 5 was further adjusted the family history of gout, diabetes and hypertension. And the relationship between different LAP levels and hyperuricemia was analyzed. Results:In this study, the minimum waist circumference in the physical examination population was 58 cm and 53 cm for men and women, respectively. The total incidence of hyperuricemia was 13.4% in this population, 5.94% for women and 19.40% for men. When the confounding factors were not adjusted (model 1), the risk of hyperuricemia in women′s LAP Q 2 to Q 4 groups was 1.76 times (95% CI: 1.42-2.17), 5.08 times (95% CI: 4.20-6.14) and 12.58 times (95% CI: 10.43-15.18), and it was 1.68 times (95% CI: 1.43-1.96), 2.74 times (95% CI: 2.36-3.18), and 5.32 times (95% CI: 4.62-6.14) in men, respectively. After gender stratification and adjustment for confounding factors (model 5), the risk still existed, compared with that in Q 1 group of LAP, the risk of hyperuricemia in women in Q 4 group was 8.28 times higher (95% CI: 2.50-27.38) and 3.31 times higher in men (95% CI: 1.57-6.95). Conclusion:The risk of hyperuricemia in health examination population increases with LAP, especially in women.

Objective: Congenital hypogonadotropic hypogonadism (CHH) is a rare congenital gonadal dysplasia caused by defects in the synthesis, secretion or signal transduction of hypothalamic gonadotropin releasing hormone. The main manifestations of CHH are delayed or lack puberty, low levels of sex hormones and gonadotropins, and may be accompanied with other clinical phenotypes. Some patients with CHH are also accompanied with anosmia or hyposmia, which is called Kalman syndrome (KS). ANOS1, located on X chromosome, is the first gene associated with CHH in an X-linked recessive manner. This study aims to provide a basis for the genetic diagnosis of CHH by analyzing the gene variant spectrum of ANOS1 in CHH and the relationship between clinical phenotype and genotype. Methods: In this study, whole exome sequencing (WES) was used to screen rare sequencing variants (RSVs) of ANOS1 in a Chinese cohort of 165 male CHH patients. Four commonly used in silico tools were used to predict the function of the identified RSVs in coding region, including Polyphen2, Mutation Taster, SIFT, and Combined Annotation Dependent Depletion (CADD). Splice Site Prediction by Neural Network (NNSPLICE) was employed to predict possibilities of intronic RSVs to disrupt splicing. American College of Medical Genetics and Genomics (ACMG) guidelines was used to assess the pathogenicity of the detected RSVs. The ANOS1 genetic variant spectrum of CHH patients in Chinese population was established. The relationship between clinical phenotype and genotype was analyzed by collecting detailed clinical data. Results:Through WES analysis for 165 CHH patients, ANOS1 RSVs were detected in 17 of them, with the frequency of 10.3％. A total of 13 RSVs were detected in the 17 probands, including 5 nonsense variants (p. T76X, p. R191X, p. W257X, p. R262X, and p. W589X), 2 splicing site variants (c. 318+3A>C, c. 1063-1G>C), and 6 missense variants (p. N402S, p. N155D, p. P504L, p. C157R, p. Q635P, and p. V560I). In these 17 CHH probands with ANOS1 RSVs, many were accompanied with other clinical phenotypes. The most common associated phenotype was cryptorchidism (10/17), followed by unilateral renal agenesis (3/17), dental agenesis (3/17), and synkinesia (3/17). Eight RSVs, including p. T76X, p. R191X, p. W257X, p. R262X, p. W589X, c. 318+3A>C, c. 1063-1G>C, and p. C157R, were predicted to be pathogenic or likely pathogenic ANOS1 RSVs by ACMG. Eight CHH patients with pathogenic or likely pathogenic ANOS1 variants had additional features. In contrast, only one out of nine CHH patients with non-pathogenic (likely benign or uncertain of significance) ANOS1 variants according to ACMG exhibited additional features. And function of the non-pathogenic ANOS1 variants accompanied with other CHH-associated RSVs. Conclusion: The ANOS1 genetic spectrum of CHH patients in Chinese population is established. Some of the correlations between clinical phenotype and genotype are also established. Our study indicates that CHH patients with pathogenic or likely pathogenic ANOS1 RSVs tend to exhibit additional phenotypes. Although non-pathogenic ANOS1 variants only may not be sufficient to cause CHH, they may function together with other CHH-associated RSVs to cause the disease.

To find a rapid and accurate genotyping method for specific non-syndromic hearing loss (NSHL)-causing gene mutations for disease diagnosis in different ethnic populations.Methods We performed a novel multiplex primer extension (PE) reaction in combination with denaturing high-performance liquid chromatography (DHPLC) to simultaneously detect and genotype the 6 most common mutations in 180 patients with NSHL (GJB2-235delC,GJB2-299delAT,PDS-A2168G,PDS IVS7-2A ＞ G,mtDNA-A1555G,and mtDNA-C1494T) in Chinese population.This method involved the amplification of the target sequence,followed by a purification step,a multiplex PE reaction,and DHPLC analysis performed on the Transgenomic Wave DNA fragment analysis system under fully-denaturing conditions.Results In a blind analysis,this technique successfully and accurately genotyped 100％ of the samples simultaneously characterized by direct sequencing.Conclusion Combination of PE and DHPLC is simple,rapid,accurate,and cost-effective for genotyping common disease-causing mutations,including substitutions,insertions,and deletions in NSHL,and may be successfully used in other genetic diseases.