Objective To investigate the effect of urate-lowering therapy on renal function in chronic kidney disease (CKD) stages 2-5 patients with hyperuricemia (HUA).Methods A total of 132 patients of CKD stages 2-5 with HUA between July 2016 and December 2017 in Department of Nephrology of the Second Affiliated Hospital of Anhui Medical University were prospectively and self-controlled analyzed.Serum uric acid (SUA),estimated glomerular filtration rate (eGFR) and other clinical parameters were measured at baseline and after 1-6 months treatment.The patients were divided into group A (CKD stages 2-3a) and group B (CKD stages 3b-5) on the baseline value of eGFR.The changes of SUA and eGFR before and after treatment were compared.According to the level of SUA after 6 months treatment,patients were divided into attainment group (SUA ＜ 360 μmol/L) and nonattainment group (SUA ≥ 360 μmol/L).The difference of renal function in pre-treatment and post-treatment was compared.Multiple stepwise linear regression was used to analyze the relationship among the change of eGFR after receiving 6 months' treatment (deGFR) and SUA level,baseline eGFR and other indexes.Results After 1,3,6 months treatment,the average levels of SUA,Scr and urea nitrogen of all patients were decreased significantly while eGFR value was increased significantly (all P ＜ 0.050) than those in pre-treatment period.After six-month-therapy,proteinuria and hematuria were improved significantly in all patients (P ＜ 0.001,P=0.001).Compared with pre-treatment period,both the SUA levels of group A and group B were declined significantly while eGFR had a significant rise after treatment (P ＜ 0.001).The change of eGFR post-treatment in group A was significantly higher than that of group B [(13.64±15.35) vs (8.97±9.79) ml· min-1· (1.73 m2)-1,P=0.044].At 6 months after treatment,the eGFR value increased markedly in both attainment group and nonattainment group compared with pre-treatment period (P ＜ 0.001).After six-month-therapy,the eGFR value in attainment group was increased more obviously than that of nonattainment group [(13.96 ± 14.64) vs (8.03±9.69) ml· min-1· (1.73 m2)-1,P=0.021].Multiple stepwise linear regression analysis showed that the baseline eGFR value was an influencing factor of deGFR (b=0.161,P=0.020).Conclusions The renal function of CKD stages 2-5 patients with HUA can be significantly improved by urate-lowering therapy,which can effectively reduce proteinuria and hematuria.

Purpose: This retrospective study aimed to evaluate the distribution pattern and prognostic value of 21-gene recurrence score (RS) in Chinese patients with mucinous breast cancer (MC) and compared with infiltrating ductal carcinoma (IDC). Materials and Methods: Patients diagnosed with MC or IDC from January 2010 to January 2017 were retrospectively recruited. Reverse transcriptase–polymerase chain reaction assay of 21 genes was conducted to calculate the RS. Univariate and multivariate analyses were performed to assess the association between RS and clinicopathological factors. Survival outcomes including disease-free survival (DFS) and overall survival (OS) were estimated by Kaplan-Meier method and compared by log-rank test. Results: The MC cohort included 128 patients and the IDC cohort included 707 patients. The proportions of patients with a low (RS < 18), intermediate (18-30), or high risk (RS > 30) were 32.0%, 48.4%, and 19.5% in MC cohort, and 26.9%, 46.8% and 26.3% in IDC cohort. The distribution of RS varied significantly according to different Ki-67 index and molecular subtype in both cohorts. Moreover, the receipt of chemotherapy was associated with RS in both cohorts. Among patients with MC, tumor stage was related to the DFS (p=0.040). No significant differences in DFS and OS were found among MC patients in different RS risk groups (OS, p=0.695; DFS, p=0.926). Conclusion RS was significantly related to Ki-67 index and molecular subtypes in MC patients, which is similar in IDC patients. However, RS was not able to predict DFS and OS in patients with MC.

OBJECTIVE: To analyze the spatial distribution of the prevalence of cognitive dysfunction and its risk factors in Chinese population aged 45 years and above to provide evidence for formulating regional prevention and control strategies. METHODS: The study subjects with complete cognitive function data were selected from the follow-up data of the China Health and Retirement Longitudinal Study (CHARLS) Phase IV. ArcGis 10.4 software was used for spatial analysis of the prevalence of cognitive dysfunction in the population aged 45 years and above for each province based on the geographic information system (GIS) technology. RESULTS: In 2018, the overall prevalence of cognitive dysfunction was 33.59% (5951/17716) in individuals aged 45 and above in China. Global spatial autocorrelation analysis indicated a spatial clustering and a positive autocorrelation (P < 0.001) of the prevalence of cognitive dysfunction in the study subjects, with a Moran's I value of 0.333085. The results of local spatial autocorrelation analysis showed that the southwestern region of China was the main aggregation area of patients with cognitive dysfunction. Geographically weighted regression analysis suggested that a male gender, an advanced age, and illiteracy were the major risk factors for cognitive dysfunction (P < 0.05). These 3 risk factors showed a spatial distribution heterogeneity with greater impact in the northern, western, and northwestern regions of China, respectively. CONCLUSION The prevalence of cognitive dysfunction is relatively high in individuals aged 45 years and above in China. A male gender, an advanced age, and illiteracy are the major risk factors for cognitive dysfunction and show different spatial distribution patterns, with the northern, western and northwestern regions of China as the key areas for prevention and control, where the prevention and control measures should be designed based on local conditions.
Humans ; Male ; China/epidemiology* ; Cluster Analysis ; Cognitive Dysfunction/epidemiology* ; East Asian People ; Longitudinal Studies ; Risk Factors ; Middle Aged

OBJECTIVE: To investigate the incidence trend and spatial clustering characteristics of scarlet fever in China from 2016 to 2020 to provide evidence for development of regional disease prevention and control strategies. METHODS: The incidence data of scarlet fever in 31 provinces and municipalities in mainland China from 2016 to 2020 were obtained from the Chinese Health Statistics Yearbook and the Public Health Science Data Center led by the Chinese Center for Disease Control and Prevention.The three-dimensional spatial trend map of scarlet fever incidence in China was drawn using ArcGIS to determine the regional trend of scarlet fever incidence.GeoDa spatial autocorrelation analysis was used to explore the spatial aggregation of scarlet fever in China in recent years. RESULTS: From 2016 to 2020, a total of 310 816 cases of scarlet fever were reported in 31 provinces, municipalities directly under the central government and autonomous regions, with an average annual incidence of 4.48/100 000.The reported incidence decreased from 4.32/100 000 in 2016 to 1.18/100 000 in 2020(Z=103.47, P < 0.001).The incidence of scarlet fever in China showed an obvious regional clustering from 2016 to 2019(Moran's I>0, P < 0.05), but was randomly distributed in 2020(Moran's I>0, P=0.16).The incidence of scarlet fever showed a U-shaped distribution in eastern and western regions of China, and increased gradually from the southern to northern regions.Inner Mongolia Autonomous Region and Hebei and Gansu provinces had the High-high (H-H) clusters of scarlet fever in China. CONCLUSION Scarlet fever still has a high incidence in China with an obvious spatial clustering.For the northern regions of China with H-H clusters of scarlet fever, the allocation of health resources and public health education dynamics should be strengthened, and local scarlet fever prevention and control policies should be made to contain the hotspots of scarlet fever.
Humans ; Incidence ; Scarlet Fever/epidemiology* ; China/epidemiology* ; Spatial Analysis ; Cluster Analysis ; Spatio-Temporal Analysis