Additional file 1 of Associations between air pollutants and acute exacerbation of drug-resistant tuberculosis: evidence from a prospective cohort study

Supplementary Material 1: Table S1 RR (95% CIs) for the association between first-time outpatient visits for acute exacerbations of DR-TB and air pollutants concentrations with each IQR increase based on single-pollutant models. Table S2 Single-lag RR (95% CIs) for the association between first-time outpatient visits for acute exacerbations of DR-TB and air pollutants concentrations with each IQR increase based on two-pollutants models. Table S3 Cumulative RR (95% CIs) for the association between first-time outpatient visits for acute exacerbations of DR-TB and air pollutants concentrations with each IQR increase based on two-pollutants models. Figure S1. RR (95% CIs) for the association between first-time outpatient visits for acute exacerbations of DR-TB and air pollutants concentrations with each IQR increase based on single-pollutant models stratified by gender. Figure S2. RR (95% CIs) for the association between first-time outpatient visits for acute exacerbations of DR-TB and air pollutants concentrations with each IQR increase based on single-pollutant models stratified by age. Figure S3. RR (95% CIs) for the association between first-time outpatient visits for acute exacerbations of DR-TB and air pollutants concentrations with each IQR increase based on single-pollutant models stratified by occupation. Figure S4. RR (95% CIs) for the association between first-time outpatient visits for DR-TB and air pollutants concentrations with each IQR increase based on single-pollutant models stratified by high-risk subgroup. Figure S5. RR (95% CIs) for the association between first-time outpatient visits for DR-TB and air pollutants concentrations with each IQR increase based on single-pollutant models stratified by history of treatment. Figure S6. RR (95% CIs) for the association between first-time outpatient visits for DR-TB and air pollutants concentrations with each IQR increase based on single-pollutant models stratified by seaso

A flow diagram for process for categorising events and assigning causality of adverse events following immunization in Anhui of China.

<p>A flow diagram for process for categorising events and assigning causality of adverse events following immunization in Anhui of China.</p

The reported rates of adverse events after immunization with measles containing vaccines in Anhui, China, 2009–2014.

<p>The reported rates of adverse events after immunization with measles containing vaccines in Anhui, China, 2009–2014.</p

The adverse events following measles containing vaccines (MCVs) in Anhui Province 2009–2014.

<p>The adverse events following measles containing vaccines (MCVs) in Anhui Province 2009–2014.</p

Serious AEFI in Anhui from 2009 through 2014.

<p>Serious AEFI in Anhui from 2009 through 2014.</p

Data_Sheet_1_Causal effect of polyunsaturated fatty acids on bone mineral density and fracture.docx

BackgroundPolyunsaturated fatty acids (PUFAs) are closely related to osteoporosis. To test their causal relationship, we conducted a Mendelian randomization (MR) analysis.MethodsWe analyzed the causal relationship between four PUFAs measures, n-3 PUFAs (n-3), n-6 PUFAs (n-6), the ratio of n-3 PUFAs to total fatty acids (n-3 pct), and the ratio of n-6 PUFAs to n-3 PUFAs (n-6 to n-3), and five measures of osteoporosis, including estimated bone mineral density (eBMD), forearm (FA) BMD, femoral neck (FN) BMD, lumbar spine (LS) BMD, and fracture, using two-sample MR analysis. In order to verify the direct effect between PUFAs and BMD, we chose interleukin-6 (IL-6), tumor necrosis factor-β (TNF-β), and bone morphogenetic proteins 7 (BMP-7), three markers or cytokines strongly related to BMD, as possible confounding factors, and analyzed the possible causal relationships between them and PUFAs or BMD by MR. Inverse variance weighting (IVW), MR-Egger, weighted and weighted median were conducted. MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) and MR-Egger regression methods were used to evaluate the potential pleiotropy of instrumental variables (IVs) and outliers were identified by MR-PRESSO. Cochran’s Q statistic was used to detect the heterogeneity among IVs. Leave-one-out sensitivity analysis was used to find SNPs that have a significant impact on the results. All results were corrected by the Bonferroni correction.ResultsThe IVW results showed that n-3 PUFAs (OR = 1.030, 95% CI: 1.013, 1.047, P = 0.001) and n-6 PUFAs (OR = 1.053, 95% CI: 1.034, 1.072, P 0.0025). None of IL-6, TNF-β, and BMP-7 had a causal effect on PUFA and BMD simultaneously (all P > 0.05).ConclusionEvidence from this MR study supports the genetically predicted causal effects of n-3, n-6, n-3 pct, and n-6 to n-3 on eBMD. In addition, n-3 not only associate with FA BMD and LS BMD through its own level and n-6 to n-3, but also link to fracture through n-3 pct.</p

Data_Sheet_2_Causal effect of polyunsaturated fatty acids on bone mineral density and fracture.docx

BackgroundPolyunsaturated fatty acids (PUFAs) are closely related to osteoporosis. To test their causal relationship, we conducted a Mendelian randomization (MR) analysis.MethodsWe analyzed the causal relationship between four PUFAs measures, n-3 PUFAs (n-3), n-6 PUFAs (n-6), the ratio of n-3 PUFAs to total fatty acids (n-3 pct), and the ratio of n-6 PUFAs to n-3 PUFAs (n-6 to n-3), and five measures of osteoporosis, including estimated bone mineral density (eBMD), forearm (FA) BMD, femoral neck (FN) BMD, lumbar spine (LS) BMD, and fracture, using two-sample MR analysis. In order to verify the direct effect between PUFAs and BMD, we chose interleukin-6 (IL-6), tumor necrosis factor-β (TNF-β), and bone morphogenetic proteins 7 (BMP-7), three markers or cytokines strongly related to BMD, as possible confounding factors, and analyzed the possible causal relationships between them and PUFAs or BMD by MR. Inverse variance weighting (IVW), MR-Egger, weighted and weighted median were conducted. MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) and MR-Egger regression methods were used to evaluate the potential pleiotropy of instrumental variables (IVs) and outliers were identified by MR-PRESSO. Cochran’s Q statistic was used to detect the heterogeneity among IVs. Leave-one-out sensitivity analysis was used to find SNPs that have a significant impact on the results. All results were corrected by the Bonferroni correction.ResultsThe IVW results showed that n-3 PUFAs (OR = 1.030, 95% CI: 1.013, 1.047, P = 0.001) and n-6 PUFAs (OR = 1.053, 95% CI: 1.034, 1.072, P 0.0025). None of IL-6, TNF-β, and BMP-7 had a causal effect on PUFA and BMD simultaneously (all P > 0.05).ConclusionEvidence from this MR study supports the genetically predicted causal effects of n-3, n-6, n-3 pct, and n-6 to n-3 on eBMD. In addition, n-3 not only associate with FA BMD and LS BMD through its own level and n-6 to n-3, but also link to fracture through n-3 pct.</p

Supplementary material for genetic links between metabolic syndrome and osteoarthritis: insights from cross-trait analysis

Background: Previous observational studies have indicated a bidirectional association between metabolic syndrome (MetS) and osteoarthritis (OA). However, it remains unclear whether these bidirectional associations reflect causal relationships or shared genetic factors, and the underlying biological mechanisms of this association are not fully understood. Methods: Leveraging summary statistics from genome-wide association studies (GWASs) conducted by the UK Biobank and the Glucose and Insulin-related Traits Consortium (MAGIC), we performed global genetic correlation analyses, genome-wide cross-trait meta-analyses, and a bidirectional two-sample Mendelian randomization analyses using summary statistics from GWASs to comprehensively assess the relationship of MetS and OA.Results: We first detected an extensive genetic correlation between MetS and OA (rg=0.393, P=1.52×10-18), which was consistent in four MetS components, including waist circumference, triglycerides, hypertension and high-density lipoprotein cholesterol and OA with rg ranging from -0.229 to 0.490. We then discovered 32 variants jointly associated with MetS and OA through multi-trait Analysis of GWAS. Co-localization analysis founded 12 genes shared between MetS and OA, with functional implications in several biological pathways. Finally, MR analysis suggested genetic liability to MetS significantly increased the risk of OA, but no reverse causality was found.Conclusion: Our results illustrate a common genetic architecture, pleiotropic loci, as well as causality between MetS and OA, potentially enhancing our knowledge of high comorbidity and genetic processes that overlap between the two disorders.</p

Additional file 1 of Changes in frailty and depressive symptoms among middle-aged and older Chinese people: a nationwide cohort study

Supplementary Material 1: Supplementary Table 1. Items and assigned value of frailty index. Supplementary Figure 1. Spearman correlation between frailty and depressive symptoms at T1, T2, T3 and T4 in persons aged 45 - 59 years. Supplementary Figure 2. Spearman correlation between frailty and depressive symptoms at T1, T2, T3 and T4 in persons aged ≥ 60 years. Supplementary Figure 3. Spearman correlation between frailty and depressive symptoms at T1, T2, T3 and T4 in Male. Supplementary Figure 4. Spearman correlation between frailty and depressive symptoms at T1, T2, T3 and T4 in Female. Supplementary Figure 5. A parallel latent growth model for depressive symptoms on frailty in persons aged 45 - 59 years. Supplementary Figure 6. A parallel latent growth model for frailty on depressive symptoms in persons aged 45 - 59 years. Supplementary Figure 7. A parallel latent growth model for depressive symptoms on frailty in persons aged ≥ 60 years. Supplementary Figure 8. A parallel latent growth model for frailty on depressive symptoms in persons aged ≥ 60 years. Supplementary Figure 9. A parallel latent growth model for depressive symptoms on frailty in Male. Supplementary Figure 10. A parallel latent growth model for frailty on depressive symptoms in Male. Supplementary Figure 11. A parallel latent growth model for depressive symptoms on frailty in Female. Supplementary Figure 12. A parallel latent growth model for frailty on depressive symptoms in Female. Supplementary Table 2. Parallel latent growth model adjusted covariate parameters. Supplementary Figure 13. Cross-lagged Model for Frailty and Depressive Symptoms in persons aged 45 - 59 years. Supplementary Figure 14. Cross-lagged Model for Frailty and Depressive Symptoms in persons aged ≥ 60 years. Supplementary Figure 15. Cross-lagged Model for Frailty and Depressive Symptoms in Male. Supplementary Figure 16. Cross-lagged Model for Frailty and Depressive Symptoms in Female

Supplemental Material - <b>Associations of RPEL1 and miR-1307 gene polymorphisms with disease susceptibility, glucocorticoid efficacy, anxiety, depression, and health-related quality of life in Chinese systemic lupus erythematosus patients</b>

Supplemental Material for Associations of RPEL1 and miR-1307 gene polymorphisms with disease susceptibility, glucocorticoid efficacy, anxiety, depression, and health-related quality of life in Chinese systemic lupus erythematosus patients by Zi-Ye Yan, Wan-Qin Hu, Qi-Qun Zong, Guang-Hui Yu, Chun-Xia Zhai, Lin-Lin Wang, Yu-Hua Wang, Ting-Yu Zhang, Zhen Li, Ying Teng, Jing Cai, Yang-Fan Chen, Mu Li, Zhou-Zhou Xu, Fa-Ming Pan, Hai-Feng Pan, Hong Su, and Yan-Feng Zou in Lupus.</p