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Uncovering the risk-transmitting path within economic sectors in China is crucial for understanding the stability of the Chinese economic system, especially under the current situation of the China–US trade conflicts. In this paper, we aim to uncover the risk spreading channels by means of volatility spillovers within the Chinese sectors using stock market data. By applying the generalized variance decomposition framework based on the VAR model and the rolling window approach, a set of connectedness matrices is obtained to reveal the overall and dynamic spillovers within sectors. We find that 17 sectors (mechanical equipment, electrical equipment, utilities, and so on) are risk transmitters and 11 sectors (national defense, bank, non-bank finance, and so on) are risk takers during the whole period. Under the extreme risk events (i.e., the global financial crisis, the Chinese interbank liquidity crisis, the Chinese stock market crash, and the China–US trade war), the connectedness measures significantly increase and the financial sectors play a buffer role in stabilizing the economic system. Our results are robust to changes of the model parameters. Our study not only uncovers the spillover effects within the Chinese sectors, but also highlights the deep understanding of the risk contagion patterns in the Chinese stock markets. [ABSTRACT FROM AUTHOR]

Cerebrospinal fluid (CSF) biomarkers are essential for the accurate diagnosis of Alzheimer's disease (AD), yet their measurement levels vary widely across centers and regions, leaving no uniform cutoff values to date. Diagnostic cutoff values of CSF biomarkers for AD are lacking for the Chinese population. As a member of the Alzheimer's Association Quality Control program for CSF biomarkers, we aimed to establish diagnostic models based on CSF biomarkers and risk factors for AD in a Chinese cohort. A total of 64 AD dementia patients and 105 age- and sex-matched cognitively normal (CN) controls from the Chongqing Ageing & Dementia Study cohort were included. CSF Aβ42, P-tau181, and T-tau levels were measured by ELISA. Combined biomarker models and integrative models with demographic characteristics were established by logistic regression. The cutoff values to distinguish AD from CN were 933 pg/mL for Aβ42, 48.7 pg/mL for P-tau181 and 313 pg/mL for T-tau. The AN model, including Aβ42 and T-tau, had a higher diagnostic accuracy of 89.9%. Integrating age and APOE ε4 status to AN model (the ANA'E model) increased the diagnostic accuracy to 90.5% and improved the model performance. This study established cutoff values of CSF biomarkers and optimal combined models for AD diagnosis in a Chinese cohort., (© 2022. The Author(s).)

To evaluate whether the polygenic profile modifies the development of sporadic Alzheimer's disease (sAD) and pathological biomarkers in cerebrospinal fluid (CSF), 462 sAD patients and 463 age-matched cognitively normal (CN) controls were genotyped for 35 single-nucleotide polymorphisms (SNPs) that are significantly associated with sAD. Then, the alleles found to be associated with sAD were used to build polygenic risk score (PRS) models to represent the genetic risk. Receiver operating characteristic (ROC) analyses and the Cox proportional hazards model were used to evaluate the predictive value of PRS for the sAD risk and age at onset. We measured the CSF levels of Aβ42, Aβ42/Aβ40, total tau (T-tau), and phosphorylated tau (P-tau) in a subgroup (60 sAD and 200 CN participants), and analyzed their relationships with the PRSs. We found that 14 SNPs, including SNPs in the APOE, BIN1, CD33, EPHA1, SORL1, and TOMM40 genes, were associated with sAD risk in our cohort. The PRS models built with these SNPs showed potential for discriminating sAD patients from CN controls, and were able to predict the incidence rate of sAD and age at onset. Furthermore, the PRSs were correlated with the CSF levels of Aβ42, Aβ42/Aβ40, T-tau, and P-tau. Our study suggests that PRS models hold promise for assessing the genetic risk and development of AD. As genetic risk profiles vary among populations, large-scale genome-wide sequencing studies are urgently needed to identify the genetic risk loci of sAD in Chinese populations to build accurate PRS models for clinical practice.

Parkinson's disease (PD) is a common neurodegenerative disease characterized by neuronal loss in the substantia nigra. The p75 neurotrophin receptor (p75NTR, encoded by NGFR) was found to play an important role in the selective neuronal death of dopamine neurons in the substantia nigra, as well as the pathogenesis and development of PD. To assess the association between NGFR gene polymorphism and the susceptibility of PD, this case-control study consisting of 414 PD patients and 623 age- and sex-matched controls in a Chinese Han cohort was conducted. Twelve tag-single nucleotide polymorphisms (tag-SNPs) were selected from the NGFR gene through the construction of linkage disequilibrium blocks. One tag-SNP from the ADAM17 gene was also selected owing to its function of encoding tumor necrosis factor α-converting enzyme, which is responsible for the shedding of the extracellular domain of p75NTR. A multiplex polymerase chain reaction-ligase detection reaction (PCR-LDR) method was applied for genotyping. The associations between tag-SNPs and the risk of PD with the adjustment for age and sex were analyzed by unconditional logistic regression, and five genetic models including codominant, dominant, recessive, over-dominant, and additive models were applied. The results showed that among the 13 tag-SNPs, rs741073 was associated with a reduced risk of PD in the codominant (OR = 0.71, 95% CI = 0.54-0.93, P = 0.037), dominant (OR = 0.76, 95% CI = 0.58-0.98, P = 0.033), and over-dominant models (OR = 0.71, 95% CI = 0.54-0.92, P = 0.010), and rs1804011 was also associated with a reduced risk of PD in the codominant (OR = 0.69, 95% CI = 0.50-0.95, P = 0.049), dominant (OR = 0.69, 95% CI = 0.50-0.93, P = 0.014), over-dominant (OR = 0.70, 95% CI = 0.51-0.96, P = 0.025), and additive models (OR = 0.72, 95% CI = 0.54-0.94, P = 0.016). However, these associations did not retain after Bonferroni correction. Conclusively, our study failed to reveal the association between the selected tag-SNPs within NGFR, ADAM17, and the susceptibility of PD. The role of p75NTR and its gene polymorphisms in the pathogenesis of PD needs to be further studied.

Parkinson's disease (PD) is attributed to interactions among genes and environmental and lifestyle factors, but the genetic architecture of PD is complex and not completely understood. To evaluate whether the genetic profile modifies PD development and cerebrospinal fluid (CSF) pathological biomarkers, we enrolled 418 PD patients and 426 age- and sex-matched normal controls. Forty-six single nucleotide polymorphisms (SNPs) that were reported to be significantly associated with PD in large-scale genome-wide association studies (GWASs) were genotyped and analysed. The alleles associated with PD were used to build polygenic risk score (PRS) models to represent polygenic risk. The Cox proportional hazards model and receiver operating characteristic (ROC) analyses were used to evaluate the prediction value of the PRS for PD risk and age at onset. The CSF α-synuclein levels were measured in a subgroup of control subjects (n = 262), and its relationship with the PRS was analysed. We found that some SNPs identified from other populations had significant correlations with PD in our Chinese cohort. The PRS we built had prediction value for PD risk and age at onset. The CSF α-synuclein level had no correlation with the PRS in normal subjects.

To understand the risks associated with aplastic anemia (AA) in 4 cities of Zhejiang Province, China, with special focus on the joint contributions of multiple risks.Based on an Electronic Data Capture (EDC), a case control study was carried out. Data regarding socio-demographic, diseases history, living habits, and exposures to toxic substances, etc., were collected through survey questionnaires. t Test, chi-square test, or non-parametric rank sum test, and univariate and multivariate Logistic regression analysis were conducted to analyze data.The univariate logistic regression analysis results indicated that among all study participants (n = 1802), AA was associated with over 30 risks, in terms of their individual behaviors, daily and environmental exposures, diseases history, and family history. Multivariate logistic regression analysis further confirmed that the independent risks related to AA included presence of chemical factory within 3 km of living residence (odds ratio [OR] = 8.73, 95% CI: 1.42-53.74, P = .019), living in a newly decorated house/apartment (OR = 25.37, 95% CI: 4.44-144.81, P < .001), vegetarian diet (OR = 131.60, 95% CI: 3.45-5020.16, P = .009), preference of sugar (OR = 89.38, 95% CI: 7.22-1106.44, P < .001), preference of oily food (OR = 55.68, 95% CI: 5.12-605.26, P = .001), drinking lake water or pond water (OR = 58.05, 95% CI: 3.21-1049.81, P < .001), habit of staying up late (OR = 11.87, 95% CI: 3.43-41.02, P < .001), infection history (OR = 10.08, 95% CI: 2.75-36.93, P < .001). Result of receiver operating characteristic curve (ROC) analysis on the joint contribution of multiple risks indicated that AA was 13.835 times likely to occur when exposed to ≥1 risks than those exposed to 0 risks (95% CI: 9.995-19.149).Our study results demonstrated a comprehensive epidemiological pattern, in which the joint contributions of individual inherited health status, environment exposure, and individual behaviors lead to the occurrence of AA.