To develop an equation that can estimate the 24-h urinary Na excretion by using casual spot urine specimen for older hypertensive participants in rural Ningxia and further to compare with the INTERSALT method, Kawasaki method and Tanaka method.

Older hypertensive participants in rural Ningxia provided their casual spot urine samples and 24-h urine samples between January 2015 and February 2017. Sex-specific equation was developed using linear forward stepwise regression analysis. Model fit was assessed using adjusted R2. Approximately half of all participants were randomly selected to validate the equation. Mean differences, intraclass correlation coefficients and Bland–Altman plots were used to evaluate the performance of all methods.

Pingluo County and Qingtongxia County in Ningxia Hui Autonomous Region, China.

Older hypertensive participants in rural Ningxia.

Totally, 807 of 1120 invited participants provided qualified 24-h urine samples and spot urine samples. There was no statistical difference comparing the laboratory-based method against the new method and the INTERSALT method, while Kawasaki method had the largest bias with a mean difference of 40·81 g/d (95 % CI 39·27, 42·35 g/d). Bland–Altman plots showed similar pattern of the results.

The INTERSALT method and the new equation have the potential to estimate the 24-h urinary Na excretion in this study population. However, the extrapolation of the results to other population needs to be careful. Future research is required to establish a more reliable method to estimate 24-h urinary Na excretion.

Cognitive decline in advanced age is closely related to dementia. The trajectory of cognitive function in older Chinese is yet to be fully investigated. We aimed to investigate the trajectories of cognitive function in a nationally representative sample of older people living in China and to explore the potential determinants of these trajectories.

This study included 2,038 cognitively healthy persons aged 65–104 years at their first observation in the Chinese Longitudinal Healthy Longevity Survey from 2002 to 2014. Cognitive function was measured using the Chinese version of the Mini-Mental State Examination (MMSE). Group-based trajectory modeling was used to identify potential heterogeneity of longitudinal changes over the 12 years and to investigate associations between baseline predictors of group membership and these trajectories.

Three trajectories were identified according to the following types of changes in MMSE scores: slow decline (14.0%), rapid decline (4.5%), and stable function (81.5%). Older age, female gender, having no schooling, a low frequency of leisure activity, and a low baseline MMSE score were associated with the slow decline trajectory. Older age, body mass index (BMI) less than 18.5 kg/m2, and having more than one cardiovascular disease (CVD) were associated with the rapid decline trajectory.

Three trajectories of cognitive function were identified in the older Chinese population. The identified determinants of these trajectories could be targeted for developing prevention and intervention strategies for dementia.

Several genome-wide linkage scans have reported that chromosome 22q11-13 might contain susceptibility loci for both schizophrenia and mood disorder.

We genotyped 44 Chinese Han family trios with mixed family history of schizophrenia and mood disorder with 11 DNA microsatellite markers on chromosome 22q11-13. These markers spanned 56.55 cM on 22q11-13 with mean intervals of 5.66 cM and average heterozygosity 0.71.The transmission disequilibrium test (TDT) was used to search for susceptibility loci to schizophrenia and mood disorder.

Including all family trios regardless of proband diagnosis, we found six markers associated with susceptibility to psychotic disorders., including D22S420 (χ2=4.76, df=1, P=0.029)%3001D22S277 (χ2=5.44, df=1, P=0.020)%3001D22s315 (allele 5, χ2=7.00, df=1, P=0.008; allele 7, χ2=-4.83, df=1, P=0.028; allele 11, χ2=4.00, df=1, P=0.046)%3001D22S274 (allele 7, χ2=-5.40, df=1, P=0.020; allele 10, χ2=6.23 df=1, P=0.013)%3001D22S1160 (χ2=-4, df=1, P=0.046) and D22S1161 (χ2=5.14, df=1, P=0.023). When grouped separately into schizophrenia and mood disorder according to proband diagnosis, four markers D22S420(χ2=7.36, df=1, P=0.007) %3001D22S315 (allele 5, χ2=4., df=1, P=0.046; allele 7, χ2=-8.89, df=1, P=0.003)%3001D22S1161(χ2=6.23, df=1, P=0.013) and D22S280 (χ2=4, df=1, P=0.046) were significantly associated with schizophrenia, but were not significantly associated with mood disorder, D22S274(allele 7, χ2=5., df=1, P=0.025; allele 10, χ2=6, df=1, P=0.014) were significantly associated with mood disorder only, and D22S277(χ2=4, df=1, P=0.046) was associated with both schizophrenia and mood disorder.

These results indicate that chromosome 22q11-13 contains the susceptibility loci to schizophrenia and mood disorder, and that overlapping regions may be shared by these disorders.

The purpose of this study was to investigated the prevalence child depression in primary schools.

3685 students from Grade 3 to Grade 5 were selected from 7 primary schools of Pudong district in Shanghai by random and cluster sampling. The study design consisted of a screening stage in which the Center for Epidemiological Studies Depression Scale for Children(CES-DC) were used, and a clinical interview stage in which the K-SADS-present state version (K-SADS) and DSM-IV were used. The diagnoses of depressive disorder were made according the DSM-IV criteria.

The prevalence of children depression was 1.60% (95%CI = 1.19%∼2.00%). The prevalence rate of male(2.08%) was significant higher than that of female (1.09%)(X2=5.40, P = 0.02). The rate of depressive disorder increased with age from 0.57% (8 years old) to 2.47% (12 years old). The prevalence of depression was no significant difference between ages from 8 to 12 years old (X2 = 4.49, P = 0.34).

The prevalence rate of children depression in Shanghai is low. The prevalence of depression among boys is much higher than that of girls.It shows the prevalence of depression is no significant difference between ages from 8 to 12 years old.

rs10761482 in ANK3 gene showed a significant association with schizophrenia in a genome-wide association study (GWAS). Another marker rs10994336 in ANK3 with the risk of bipolar disorder (BD) which might have more genetic overlap with schizophrenia, had been reported in two meta-analyses of GWAS. In this study, we investigated the association between ANK3 polymorphisms and the susceptibility of schizophrenia in Chinese Han population.

Population-based (schizophrenia patients = 516 and controls = 400) and family based (trios of early onset schizophrenia= 81) study was performed through genotyping the most promising makers rs10761482, rs10994336, and two missenses rs3808942 and rs3808943 near promoter of ANK3. Particularly, we conducted an association analysis for the combined case-control and family samples.

Our population-based study replicated the association between rs10761482 (P = 0.0268 with C allele) and schizophrenia, and detected a novel association with rs10994336 (P = 4.0 × 10−4 with T allele). Haplotype analysis revealed the higher frequencies of C-T, and T-C (rs10761482–10994336) in the cases than controls (P = 0.0032 and P = 0.0012, respectively). In the family study, the C allele of rs10761482 (P = 0.0940) and T allele of rs10994336 (P = 0.0832) were slightly over-transmitted, and T-C was significantly associated with schizophrenia (P = 0.0304). The results from the combined samples analysis were consistent with independent analysis. rs10761482, rs10994336, C-T, and T-C were significantly associated with schizophrenia (P = 3.3 × 10−6∼3.9 × 10−5), whilst rs3808942 and rs3808943 did not reach normal significance.

Our data strongly support ANK3 gene is a schizophrenia susceptibility gene, and also provide further evidence for the shared susceptibility loci between schizophrenia and BD.

The present study compared the expression profile and made the classification with the leukocytes by using whole-genome cRNA microarrays among patients with SSD, major depressive disorder (MDD) and healthy controls.

Gene expression profiling was conducted in peripheral blood leucocytes from drug-free first-episode subjects with SSD, MDD, and matched controls (8 subjects in each group) using global mRNA expression arrays. Support vector machines (SVMs) were utilized for training and testing on candidate signature expression profiles from signature selection step.

We identified SSD and MDD gene signatures from blood-based gene expression profile and build a SSD- MDD disorder model with higher predictive power. Firstly, we identified 63 differentially expressed SSD signatures in contrast to control (P <= 5.0E-4) and 30 differentially expressed MDD signatures in contrast to control, respectively. Then, 123 gene signatures were identified with significantly differential expression level between SSD and MDD. Secondly, in order to conduct priority selection for biomarkers for SSD and MDD together, we selected top gene signatures from each group of pair-wise comparison results, and merged the signatures together to generate better profiles used for clearly classify SSD and MDD sets in the same time. In details, we tried different combination of signatures from the three pair-wise compartmental results and finally determined 48 gene expression signatures with 100% accuracy.

Blood cell-derived RNA may have significant value for performing diagnostic functions and identifying disease biomarkers in SSD and MDD. These 48 gene model could classify SSD, MDD, and healthy controls.

We aimed to investigate the associations between school-level characteristics and obesity among Chinese primary school children with consideration of individual-level characteristics.

This cross-sectional study was conducted in 2015/2016. School-level characteristics were assessed using an interviewer-administered school questionnaire, and a ‘school-based obesity prevention index’ was further developed. Individual-level characteristics were collected by self-administered questionnaires. Objectively measured height and weight of students were collected, and obesity status was classified according to the International Obesity Task Force criteria for Asian children. Generalised linear mixed models were used to estimate the associations among the school- and individual-level characteristics and obesity of students.

Thirty-seven primary schools from an urban and a rural district of Beijing, China.

School staffs, 2201 students and their parents.

The school-based obesity prevention index involved the number of health professionals, availability of students’ health records, monitoring students’ nutrition status, frequency of health education activities, reporting achievements of obesity prevention activities to parents, duration of physical activity during school time and availability of playground equipment. The prevalence of obesity was lower in schools with the higher index value compared with that in schools with the lower index value (OR 0·56; 95 % CI 0·40, 0·79). Some individual-level characteristics were negatively associated with childhood obesity: liking sports, duration of screen time ≤2 h/d, perceived lower eating speed, parental non-overweight/obesity.

Irrespective of individual-level characteristics, the specific school-level characteristics had a cumulative effect on obesity among Chinese primary school children. Further school-based obesity intervention should consider these characteristics simultaneously.