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Di D, Zhou H, Cui Z, Zhang J, Liu Q, Yuan T, Zhou T, Luo X, Ling D, Wang Q. Frailty phenotype as mediator between systemic inflammation and osteoporosis and fracture risks: A prospective study. J Cachexia Sarcopenia Muscle 2024. [PMID: 38468152 DOI: 10.1002/jcsm.13447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/17/2023] [Accepted: 02/08/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Systemic inflammation and frailty have been implicated in osteoporosis (OP) and fracture risks; however, existing evidence remains limited and inconclusive. This study aimed to assess the associations of systemic inflammation and frailty phenotype with incident OP and fracture and to evaluate the mediating role of frailty phenotype. METHODS The present study analysed data from the UK Biobank, a comprehensive and representative dataset encompassing over 500 000 individuals from the general population. Baseline peripheral blood cell counts were employed to calculate the systemic inflammation markers, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and systemic immune-inflammation index (SII). Frailty phenotype was assessed using five criteria, defined as frail (≥3 items met), pre-frail (1-2 items met) and non-frail (0 items met). OP and fracture events were confirmed through participants' health-related records. Multivariable linear and Cox regression models were utilized, along with mediation analysis. RESULTS Increased systemic inflammation was associated with increased risks of OP and fracture. The corresponding hazard ratios and 95% confidence intervals (CIs) for OP risk per standard deviation increase in the log-transformed NLR, PLR and SII were 1.113 (1.093-1.132), 1.098 (1.079-1.118) and 1.092 (1.073-1.111), and for fracture risk, they were 1.066 (1.051-1.082), 1.059 (1.044-1.075) and 1.073 (1.058-1.089), respectively. Compared with the non-frail individuals, the pre-frail and frail ones showed an elevated OP risk by 21.2% (95% CI: 16.5-26.2%) and 111.0% (95% CI: 98.1-124.8%), respectively, and an elevated fracture risk by 6.1% (95% CI: 2.8-9.5%) and 38.2% (95% CI: 30.7-46.2%), respectively. The systemic inflammation level demonstrated a positive association with frailty, with β (95% CI) of 0.034 (0.031-0.037), 0.026 (0.023-0.029) and 0.008 (0.005-0.011) in response to per standard deviation increment in log-transformed SII, NLR and PLR, respectively. The frailty phenotype mediated the association between systemic inflammation and OP/fracture risk. Subgroup and sensitivity analyses confirmed the robustness of these findings. CONCLUSIONS Systemic inflammation and frailty phenotype are independently linked to increased risks of OP and fracture. The frailty phenotype partially mediates the association between systemic inflammation and osteoporotic traits. These results highlight the significance of interventions targeting systemic inflammation and frailty in OP and fracture prevention and management.
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Affiliation(s)
- Dongsheng Di
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haolong Zhou
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhangbo Cui
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jianli Zhang
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Liu
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tingting Yuan
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tingting Zhou
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao Luo
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Danyang Ling
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wang
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Di D, Zhou H, Cui Z, Zhang J, Liu Q, Yuan T, Zhou T, Luo X, Ling D, Wang Q. Early-life tobacco smoke elevating later-life osteoporosis risk: Mediated by telomere length and interplayed with genetic predisposition. J Adv Res 2024:S2090-1232(24)00083-3. [PMID: 38431123 DOI: 10.1016/j.jare.2024.02.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/11/2024] [Accepted: 02/28/2024] [Indexed: 03/05/2024] Open
Abstract
INTRODUCTION The growing prevalence of osteoporosis (OP) in an aging global population presents a significant public health concern. Tobacco smoke negatively affects bone turnover, leading to reduced bone mass and heightened OP and fracture risk. However, the impact of early-life tobacco smoke exposure on later-life OP risk remains unclear. OBJECTIVES This study was to explore the effects of early-life tobacco smoke exposure on incident OP risk in later life. The mediating role of telomere length (TL) and the interaction with genetic predisposition were also studied. METHODS Data on in utero tobacco smoke exposure (IUTSE) status and age of tobacco use initiation from the UK Biobank were used to estimate early-life tobacco smoke exposure. Incident OP cases were identified according to health-related records. Linear, Cox, and Laplace regression models were mainly used for data analysis. RESULTS Individuals with IUTSE showed a higher OP risk [hazard ratio (HR): 1.06, 95 % confidence interval (CI): 1.01, 1.11] and experienced earlier OP onset by 0.30 years [50th percentile difference = -0.30, 95 % CI: -0.51, -0.09] compared to those without. Participants initiating tobacco smoke in childhood, adolescence, and adulthood had 1.41 times (95 % CI: 1.23, 1.61), 1.17 times (95 % CI:1.10, 1.24), and 1.14 times (95 % CI: 1.07, 1.20) the risk of OP, respectively, compared to never smokers. They also experienced earlier OP onset by 2.16, 0.95, and 0.71 years, sequentially. The TL significantly mediated the early-life tobacco exposure and OP association. Significant joint and interactive effects were detected between early-life tobacco smoke exposure and genetic elements. CONCLUSIONS Our findings implicate that early-life tobacco smoke exposure elevates the later-life OP risk, mediated by telomere length and interplayed with genetic predisposition. These findings highlight the importance of early-life intervention against tobacco smoke exposure and ageing status for precise OP prevention, especially in individuals with a high genetic risk.
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Affiliation(s)
- Dongsheng Di
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Haolong Zhou
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Zhangbo Cui
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Jianli Zhang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Qian Liu
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Tingting Yuan
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Tingting Zhou
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Xiao Luo
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Danyang Ling
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Qi Wang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Zhang J, Mai Q, Di D, Zhou H, Zhang R, Wang Q. Potential roles of gut microbiota in metal mixture and bone mineral density and osteoporosis risk association: an epidemiologic study in Wuhan. Environ Sci Pollut Res Int 2023; 30:117201-117213. [PMID: 37864687 DOI: 10.1007/s11356-023-30388-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 10/06/2023] [Indexed: 10/23/2023]
Abstract
Few studies have focused on the effects of multiple metal mixtures on bone health and the underlying mechanisms related to alterations in the gut microbiota. This study aimed to examine the potential roles of gut microbiota alterations in metal mixtures and their association with osteoporosis traits. Adults aged ≥ 55 years were recruited from two community healthcare centers in Wuhan City during 2016-2019. The plasma concentrations of six metals (zinc, iron, selenium, lead, cadmium, and arsenic) were measured using an inductively coupled plasma mass spectrometer. The k-means clustering method was employed to explore the exposure profiles of metal mixtures for all participants. 16S rRNA gene sequencing was used to profile the gut microbiota of participants. Combining these results with those of our previous study, we identified overlapping taxa and evaluated their potential roles. A total of 806 participants (516 females), with an average age of 67.36 years were included. The participants were grouped into three clusters using k-means clustering: Cluster 1 (n = 458), Cluster 2 (n = 199), and Cluster 3 (n = 149). The high-exposure group for iron, zinc, lead, and cadmium (Cluster 3) showed a negative association with lumbar spine 1-4 bone mineral density (BMD). A total of 201 individuals (121 females) underwent sequencing of the gut microbiota. Both alpha and beta diversities were statistically different among the three groups. Bacteroidaceae, Lachnospiraceae, Bifidobacteriaceae, Bacteroides, and Lachnospiraceae_incertae_sedis were identified as overlapping taxa associated with the metal mixtures and BMD. Interaction analysis revealed that Cluster 3 interacted with Bacteroidaceae/Bacteroides, resulting in a positive effect on LS1-4 BMD (β = 0.358 g/cm2, 95% CI: 0.047 to 0.669, P = 0.025). Our findings indicate associations between multiple metal mixtures and BMD as well as gut microbiota alterations. Exploring the interaction between metal mixtures and the gut microbiota provides new perspectives for the precise prevention and treatment of osteoporosis.
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Affiliation(s)
- Jianli Zhang
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Mai
- Wuhan Center for Disease Control and Prevention, Wuhan, China
| | - Dongsheng Di
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haolong Zhou
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ruyi Zhang
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wang
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Di D, Zhang J, Zhou H, Cui Z, Zhang R, Liu Q, Yuan T, Zhou T, Luo X, Ling D, Wang Q. Mediating role of host metabolites in strontium's effect on osteoporosis among older individuals: Findings from Wuhan, China. Bone 2023; 175:116858. [PMID: 37487859 DOI: 10.1016/j.bone.2023.116858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/14/2023] [Accepted: 07/20/2023] [Indexed: 07/26/2023]
Abstract
Strontium is receiving widespread attention due to its remarkable biological qualities in preventing bone resorption and fostering osteogenesis. However, the chemical processes behind strontium's dual activities on bone cells are not yet fully understood. This study used the metabolomic technique to identify and examine potential biomarkers between strontium exposure and osteoporosis (OP) risk. A total of 806 participants were recruited for the detection of plasma strontium content via inductively coupled plasma-mass spectrometry. Plasma metabolites were profiled in 254 participants through an untargeted metabolomics technique. Generalized linear models were primarily used to analyze associations among plasma strontium, metabolites, and OP. The mediating effects of metabolites on the strontium-OP association were further investigated. A total of 31 differential metabolites were observed, 10 of which were upregulated and 21 were downregulated in the OP group compared with the non-OP group. Five metabolites (3-phenoxybenzoic acid, Cer (t18:0/16:1), HexCer(t16:1/12:1(2OH)), HexCer(t14:2/18:1(2OH)), and TG(16:0-18:1-24:4)) were selected as potential mediators based on their significant association with OP risk and with femoral neck and lumbar spine bone mineral density (BMD). Moreover, all except TG(16:0-18:1-24:4) were involved in the OP discrimination model with excellent power combined with several traditional variables. 3-Phenoxybenzoic acid and Cer(t18:0/16:1) had significant indirect effects on the strontium-OP association. The five candidate metabolites mediated 83.79 % of the strontium-OP association. Plasma strontium level was associated with reduced OP risk in the Han population in Wuhan. Thus, plasma metabolite profiling revealed five BMD/OP-associated metabolites that acted as mediators in the strontium-OP association. Our findings provided evidence of the mediating role of host plasma metabolites in strontium's effect on OP pathology.
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Affiliation(s)
- Dongsheng Di
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jianli Zhang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haolong Zhou
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhangbo Cui
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ruyi Zhang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Liu
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tingting Yuan
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tingting Zhou
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao Luo
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Danyang Ling
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Liu Q, Tooki T, Di D, Zhou H, Cui Z, Zhang R, Zhang J, Yuan T, Zhou T, Luo X, Ling D, Wang Q. Role of lifestyle factors in mediating the effect of educational attainment on bone mineral density: a Mendelian randomization study. Arch Osteoporos 2023; 18:120. [PMID: 37723362 DOI: 10.1007/s11657-023-01329-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 09/07/2023] [Indexed: 09/20/2023]
Abstract
We performed two-step multivariable Mendelian randomization analysis to explore the mediating role of lifestyle factors in educational attainment (EA) and bone mineral density (BMD). Summary statistics from genome-wide association studies of European lineages were used. Coffee intake and processed-meat intake mediated the association between EA and BMD. PURPOSE This study aimed to explore the causal relationship between educational attainment (EA) and bone mineral density (BMD), as well as the potential mediating roles of lifestyle factors in the expected EA-BMD relationship. By identifying modifiable lifestyle factors, we hope to provide relevant information to prevent osteoporosis or low BMD in the less educated population. METHODS Using summary statistics from genome-wide association studies (GWAS) of major European lineages, one- and two-sample Mendelian randomization (MR) analyses were performed to estimate the association between EA (in the social sciences genetic association consortium (SSGAC) involving 766,345 individuals and in the UK Biobank (UKB) involving 293,723 individuals) and BMD (in the Genetic Factors for Osteoporosis Consortium involving 426,824 individuals selected from the UKB). The EA variable in both consortia were expressed by years of schooling completed. Two-step multivariable MR was used to assess the mediating roles of eight lifestyle-related factors (moderate-to-vigorous physical activity, watching television, computer using, smoking initiation, coffee intake, alcohol intake frequency, tea intake, and processed-meat intake) in the EA and BMD association, and the corresponding mediating proportion was calculated. Meta-analysis was used to present a pooled estimate. RESULTS A total of 317 and 73 independent single-nucleotide polymorphisms (SNPs) of GWAS significance (P < 5.0 × 10-8) were selected as instrumental variables (IVs) for EA in the SSGAC and UKB, respectively. A total of 513 SNPs were selected as IVs for the BMD. The results of one- and two-sample MR revealed that the genetically predicted BMD increased by 0.094 and 0.047 g/cm2, respectively, in response to each SD increment of genetically predicted schooling years. Among the eight candidate mediators, coffee intake and processed-meat intake were potential mediators revealed by the two-step multivariable MR analysis, mediating 26.87% and 23.92% of EA's effect on BMD, respectively. Meta-analysis showed consistent findings. Results of sensitivity analysis indicated the robustness of our findings. CONCLUSION We elucidated the causal protective effect of EA on BMD and the mediating roles of coffee intake and processed-meat intake. Intervening with these factors can potentially reduce the burden of bone density loss or osteoporotic fractures among the less educated population.
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Affiliation(s)
- Qian Liu
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tiaeki Tooki
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dongsheng Di
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haolong Zhou
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhangbo Cui
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ruyi Zhang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jianli Zhang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tingting Yuan
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tingting Zhou
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao Luo
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Danyang Ling
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Di D, Tooki T, Zhou H, Cui Z, Zhang R, Zhang JL, Yuan T, Liu Q, Zhou T, Luo X, Ling D, Wang Q. Metal mixture and osteoporosis risk: Insights from plasma metabolite profiling. Ecotoxicol Environ Saf 2023; 263:115256. [PMID: 37454484 DOI: 10.1016/j.ecoenv.2023.115256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/02/2023] [Accepted: 07/12/2023] [Indexed: 07/18/2023]
Abstract
The pathophysiology of osteoporosis (OP) is influenced by exposure to nonessential harmful metals and insufficient or excessive intake of necessary metals. Investigating multiple plasma metals, metabolites, and OP risk among older adults may reveal novel clues of underlying mechanisms for metal toxicity on bone mass. A total of 294 adults ≥ 55 years from Wuhan communities were included. Plasma concentrations of 23 metals and metabolites were measured via inductively coupled plasma-mass spectrometry and global metabolite detection. To investigate the relationships between plasma metals, OP risk, and OP-related metabolites, three different statistical techniques were used: generalized linear regression model, two-way orthogonal partial least-squares analysis (O2PLS), and weighted quantile sum (WQS). The mean ages were 66.82 and 66.21 years in OP (n = 115) and non-OP (n = 179) groups, respectively. Of all 2999 metabolites detected, 111 differential between-group members were observed. The OP risk decreased by 58.5% (OR=0.415, 95% CI: 0.237, 0.727) per quartile increment in the WQS index indicative of metal mixture exposure. Consistency remained for bone mineral density (BMD) measurements. The O2PLS model identified the top five OP-related metabolites, namely, DG(18:2_22:6), 3-phenoxybenzoic acid, TG(16:1_16:1_22:6), TG(16:0_16:0_20:4), and TG(14:1_18:2_18:3), contributing most to the joint covariation between the metal mixture and metabolites. Significant correlations between each of them and the metal mixture were found using WQS regression. Furthermore, the five metabolites mediated the associations of the metal mixtures, BMD, and OP risk. Our findings shed additional light on the mediation functions of plasma metabolites in the connection between multiple metal co-exposure and OP pathogenesis and offer new insights into the probable mechanisms underpinning the bone effects of the metal mixture.
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Affiliation(s)
- Dongsheng Di
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tiaeki Tooki
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haolong Zhou
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhangbo Cui
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ruyi Zhang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jian-Li Zhang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tingting Yuan
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Liu
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tingting Zhou
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao Luo
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Danyang Ling
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Di D, Zhang R, Zhou H, Wei M, Cui Y, Zhang J, Yuan T, Liu Q, Zhou T, Liu J, Wang Q. Exposure to phenols, chlorophenol pesticides, phthalate and PAHs and mortality risk: A prospective study based on 6 rounds of NHANES. Chemosphere 2023; 329:138650. [PMID: 37037349 DOI: 10.1016/j.chemosphere.2023.138650] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 03/27/2023] [Accepted: 04/07/2023] [Indexed: 05/03/2023]
Abstract
OBJECTIVES Human exposure to various endocrine disrupting chemicals (EDCs) is widespread and long-lasting. The primary objective of this study was to prospectively evaluate the association of combined exposure of phenols, chlorophenol pesticides, phthalate and polycyclic aromatic hydrocarbons (PAHs) and mortality risk in a representative US population. METHODS The data on urinary levels of phenols, chlorophenol pesticides, phthalates, and PAH metabolites, were collected from participants aged ≥20 years in six rounds of the National Health and Nutrition Examination Survey (NHANES) (2003-2014). NHANES-linked death records up to December 31, 2015 were used to ascertain mortality status and cause of death. Cox proportional hazards and competing risk models were mainly used for chemical and mortality risk association analysis. The weighted quantile sum (WQS) regression and the least absolute shrinkage and selection operator regression were employed to estimate the association between EDC co-exposure and mortality risk. RESULTS High levels of mono-n-butyl phthalate, monobenzyl phthalate, and 1-napthol were significantly associated with increased risk of all cause, cardiovascular disease (CVD) and cancer mortality among all participants. WQS index was associated with the risks of all-cause (hazard ratio [HR] = 1.389, 95%CI: 1.155-1.669) and CVD mortality (HR = 1.925, 95%CI: 1.152-3.216). High co-exposure scores were associated with elevated all-cause (HR = 2.842, 95% CI: 1.2.094-3.858), CVD (HR = 1.855, 95% CI: 1.525-2.255), and cancer mortality risks (HR = 2.961, 95% CI: 1.468-5.972). The results of subgroup analysis, competing risk model, and sensitivity analysis were generally consistent with the findings from the main analyses, indicating the robustness of our findings. CONCLUSIONS This study provided the first epidemiological evidence that co-exposure to EDC at fairly low levels contributed to elevated mortality risk among US adults. The underlying mechanisms for the effects of EDC co-exposure on human health are worthy of future exploration.
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Affiliation(s)
- Dongsheng Di
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Ruyi Zhang
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Haolong Zhou
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Muhong Wei
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Yuan Cui
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Jianli Zhang
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Tingting Yuan
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Qian Liu
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Tingting Zhou
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Junan Liu
- Department of Social Medicine and Health Management, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Qi Wang
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
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8
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Di D, Zhang R, Zhou H, Wei M, Cui Y, Zhang J, Yuan T, Liu Q, Zhou T, Wang Q. Joint effects of phenol, chlorophenol pesticide, phthalate, and polycyclic aromatic hydrocarbon on bone mineral density: comparison of four statistical models. Environ Sci Pollut Res Int 2023; 30:80001-80013. [PMID: 37289393 DOI: 10.1007/s11356-023-28065-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 05/30/2023] [Indexed: 06/09/2023]
Abstract
Exposure to phenols, phthalates, pesticides, and polycyclic aromatic hydrocarbons (PAHs) can harm the skeleton. However, data about the joint effects of these chemicals' mixture on bone health are limited. The final analysis involved 6766 participants aged over 20 years recruited from the National Health and Nutrition Examination Survey. Generalized linear regression, weighted quantile sum (WQS) regression, Bayesian kernel machine regression (BKMR), and quantile g-computation (qgcomp) were performed to investigate the association of the urinary levels of chemicals (three phenols, two chlorophenol pesticides, nine phthalates, and six polycyclic aromatic hydrocarbon [PAH] metabolites) with bone mineral density (BMD) measurements and osteoporosis (OP) risk. Generalized linear regression identified that benzophenone-3, 2,4-dichlorophenol, mono-n-butyl phthalate, 1-napthol, 3-fluorene, 2-fluorene, and 1-phenanthrene were significantly associated with lower BMD and increased OP risk. The WQS index was negatively associated with total femur, femoral neck, and lumbar spine vertebra 1 (L1) BMD among all the participants, with corresponding β (95% confidence interval) values of -0.028 g/cm2 (-0.040, -0.017), -0.015 g/cm2 (-0.025, -0.004), and -0.018 g/cm2 (-0.033, -0.003). In the BKMR analysis, the overall effect of the mixture was significantly associated with femoral neck BMD among males and OP risk among females. The qgcomp model found a significant association between co-exposure and L1 BMD among all the participants and among males. Our study presents compelling epidemiological evidence that co-exposure to phenols, chlorophenol pesticides, phthalates, and PAHs is associated with reduced BMD and elevated OP risk. It provides epidemiologic evidence for the detrimental effects of these chemicals on bone health.
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Affiliation(s)
- Dongsheng Di
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Ruyi Zhang
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Haolong Zhou
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Muhong Wei
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Yuan Cui
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Jianli Zhang
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Tingting Yuan
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Qian Liu
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Tingting Zhou
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Qi Wang
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
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9
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Zhang R, Ni Z, Wei M, Cui Y, Zhou H, Di D, Wang Q. Composite dietary antioxidant intake and osteoporosis likelihood in premenopausal and postmenopausal women: a population-based study in the United States. Menopause 2023; 30:529-538. [PMID: 36944153 DOI: 10.1097/gme.0000000000002173] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
OBJECTIVE Osteoporosis is a skeletal disease characterized by low bone mass, reduced bone strength, and increased fracture risk. We aimed to investigate the association between combined dietary antioxidant intake and the likelihood of osteoporosis in premenopausal and postmenopausal women, based on data from the National Health and Nutrition Examination Survey. METHODS Nutrient intake data were obtained using two 24-hour recalls. Composite dietary antioxidant index (CDAI), which refers to the intake amounts of β-carotene, vitamin A, vitamin C, vitamin E, selenium, zinc, copper, and iron, was then constructed. Prevalent osteoporosis was defined according to bone mineral density T scores of ≤ -2.5 and self-reports. Multiple logistic and Poisson regression models were used for association analyses. RESULTS A total of 3,418 participants (1,157 premenopausal and 2,261 postmenopausal women) 40 years or older were included, 776 (22.70%) of whom had prevalent osteoporosis. In terms of individual nutrients, postmenopausal women in the highest CDAI quartiles for dietary β-carotene, vitamin A, vitamin C, and iron intakes had a low likelihood of osteoporosis. Regarding the CDAI-osteoporosis association, postmenopausal women in the highest quartile were less likely to have osteoporosis (OR Q3 vs Q1 , 0.64; 95% CI, 0.43-0.96; OR Q4 vs Q1 , 0.56; 95% CI, 0.35-0.89; P for trend = 0.013), after controlling for covariates. CONCLUSIONS CDAI was negatively associated with the likelihood of osteoporosis in postmenopausal women. Our findings suggest that the combined intake of antioxidant nutrients can help reduce the likelihood of osteoporosis in women.
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Affiliation(s)
- Ruyi Zhang
- From the MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zemin Ni
- Women and Children Medical Center for Jiang-an District, Wuhan, China
| | - Muhong Wei
- From the MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuan Cui
- From the MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haolong Zhou
- From the MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dongsheng Di
- From the MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wang
- From the MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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10
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Zhang R, Huang Q, Su G, Wei M, Cui Y, Zhou H, Song W, Di D, Liu J, Wang Q. Association between multiple vitamins and bone mineral density: a cross-sectional and population-based study in the NHANES from 2005 to 2006. BMC Musculoskelet Disord 2023; 24:113. [PMID: 36765290 PMCID: PMC9912521 DOI: 10.1186/s12891-023-06202-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 01/27/2023] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND Bone mineral density (BMD) alterations in response to multivitamin exposure were rarely studied. Our study assessed the association of coexposure to six types of vitamins (i.e., vitamins B12, B9, C, D, A and E) with BMD measurements in adults in the US. METHODS Data were collected from participants aged ≥ 20 years (n = 2757) in the U.S. National Health and Nutrition Examination Surveys (NHANES) from 2005 to 2006. Multiple linear regression, restricted cubic splines, principal component analysis (PCA) and weighted quantile sum (WQS) regression were performed for statistical analysis. RESULTS The circulating levels of vitamins B12 and C were positively associated with BMDs, and an inverted L-shaped exposure relationship was observed between serum vitamin C and BMDs. PCA identified two principal components: one for 'water-soluble vitamins', including vitamins B12, B9 and C, and one for 'fat-soluble vitamins', including vitamins A, D and E. The former was positively associated with total femur (β = 0.009, 95%CI: 0.004, 0.015) and femoral neck (β = 0.007, 95%CI: 0.002, 0.013) BMDs, and the latter was negatively associated with BMDs with non-statistical significance. The WQS index constructed for the six vitamins was significantly related to total femur (β = 0.010, 95%CI: 0.001, 0.018) and femoral neck (β = 0.008, 95%CI: 0.001, 0.015) BMDs, and vitamins B12 and C weighted the most. The WQS index was inversely related to BMDs with non-statistical significance, and vitamins E and A weighted the most. CONCLUSION Our findings suggested a positive association between water-soluble vitamin coexposure and BMD, and the association was mainly driven by vitamins B12 and C. Negative association between fat-soluble vitamin coexposure and BMD was indicated, mainly driven by vitamins E and A. An inverted L-shaped exposure relationship was found between vitamin C and BMD.
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Affiliation(s)
- Ruyi Zhang
- grid.33199.310000 0004 0368 7223MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China
| | - Qin Huang
- grid.33199.310000 0004 0368 7223Department of Rehabilitation Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China
| | - Guanhua Su
- grid.33199.310000 0004 0368 7223Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China
| | - Muhong Wei
- grid.33199.310000 0004 0368 7223MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China
| | - Yuan Cui
- grid.33199.310000 0004 0368 7223MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China
| | - Haolong Zhou
- grid.33199.310000 0004 0368 7223MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China
| | - Wenjing Song
- grid.33199.310000 0004 0368 7223MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China
| | - Dongsheng Di
- grid.33199.310000 0004 0368 7223MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China
| | - Junan Liu
- grid.33199.310000 0004 0368 7223Department of Social Medicine and Health Management, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China
| | - Qi Wang
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
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11
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Wang H, Gan M, Wu B, Zeng R, Wang Z, Xu J, Li J, Zhang Y, Cao J, Chen L, Di D, Peng S, Lei J, Zhao Y, Song X, Yuan T, Zhou T, Liu Q, Yi J, Wang X, Cai H, Lei Y, Wen Y, Li W, Chen Q, Wang Y, Long P, Yuan Y, Wang C, Pan A, Wang Q, Gong R, Fan X, Wu T, Liu L. Humoral and cellular immunity of two-dose inactivated COVID-19 vaccination in Chinese children: A prospective cohort study. J Med Virol 2023; 95:e28380. [PMID: 36478357 PMCID: PMC9877748 DOI: 10.1002/jmv.28380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/18/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022]
Abstract
Children are the high-risk group for COVID-19, and in need of vaccination. However, humoral and cellular immune responses of COVID-19 vaccine remain unclear in vaccinated children. To establish the rational immunization strategy of inactivated COVID-19 vaccine for children, the immunogenicity of either one dose or two doses of the vaccine in children was evaluated. A prospective cohort study of 322 children receiving inactivated COVID-19 vaccine was established in China. The baseline was conducted after 28 days of the first dose, and the follow-up was conducted after 28 days of the second dose. The median titers of receptor binding domain (RBD)-IgG, and neutralizing antibody (NAb) against prototype strain and Omicron variant after the second dose increased significantly compared to those after the first dose (first dose: 70.0, [interquartile range, 30.0-151.0] vs. second dose: 1261.0 [636.0-2060.0] for RBD-IgG; 2.5 [2.5-18.6] vs. 252.0 [138.6-462.1] for NAb against prototype strain; 2.5 [2.5-2.5] vs. 15.0 [7.8-26.5] for NAb against Omicron variant, all p < 0.05). The flow cytometry results showed that the first dose elicited SARS-CoV-2 specific cellular immunity, while the second dose strengthened SARS-CoV-2 specific IL-2+ or TNF-α+ monofunctional, IFN-γ+ TNF-α+ bifunctional, and IFN-γ- IL-2+ TNF-α+ multifunctional CD4+ T cell responses (p < 0.05). Moreover, SARS-CoV-2 specific memory T cells were generated after the first vaccination, including the central memory T cells and effector memory T cells. The present findings provide scientific evidence for the vaccination strategy of the inactive vaccines among children against COVID-19 pandemic.
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Affiliation(s)
- Hao Wang
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Mengze Gan
- Department of Pathogen Biology, School of Basic Medicine, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Bihao Wu
- CAS Key Laboratory of Special Pathogens and Biosafety, Wuhan Institute of Virology, Center for Biosafety Mega‐ScienceChinese Academy of SciencesWuhanChina
| | - Rui Zeng
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Zhen Wang
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Jun Xu
- Qichun Center for Disease Control and PreventionHuanggangChina
| | - Jia Li
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Yandi Zhang
- Department of Pathogen Biology, School of Basic Medicine, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Jinge Cao
- Department of Pathogen Biology, School of Basic Medicine, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Li Chen
- CAS Key Laboratory of Special Pathogens and Biosafety, Wuhan Institute of Virology, Center for Biosafety Mega‐ScienceChinese Academy of SciencesWuhanChina
| | - Dongsheng Di
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Siyuan Peng
- Qichun Center for Disease Control and PreventionHuanggangChina
| | - Jinfeng Lei
- Qichun Center for Disease Control and PreventionHuanggangChina
| | - Yingying Zhao
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Xuemei Song
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Tingting Yuan
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Tingting Zhou
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Qian Liu
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Jing Yi
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Xi Wang
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Hao Cai
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Yanshou Lei
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Yuying Wen
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Wenhui Li
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Qinlin Chen
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Yufei Wang
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Pinpin Long
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Yu Yuan
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Chaolong Wang
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - An Pan
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Qi Wang
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Rui Gong
- CAS Key Laboratory of Special Pathogens and Biosafety, Wuhan Institute of Virology, Center for Biosafety Mega‐ScienceChinese Academy of SciencesWuhanChina
| | - Xionglin Fan
- Department of Pathogen Biology, School of Basic Medicine, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Tangchun Wu
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
| | - Li Liu
- Department of Occupational and Environmental Health and Department of Epidemiology and Biostatistics, Ministry of Education and State Key Laboratory of Environmental HealthHuazhong University of Science and TechnologyWuhanChina
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12
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Wei M, Huang Q, Dai Y, Zhou H, Cui Y, Song W, Di D, Zhang R, Li C, Wang Q, Jing T. Manganese, iron, copper, and selenium co-exposure and osteoporosis risk in Chinese adults. J Trace Elem Med Biol 2022; 72:126989. [PMID: 35512597 DOI: 10.1016/j.jtemb.2022.126989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 04/18/2022] [Accepted: 04/26/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND & AIMS Previous experimental studies demonstrated that either deficient or excessive trace elements, such as manganese (Mn), iron (Fe), copper (Cu) and selenium (Se), are detrimental to bone health. Epidemiologic evidence for the effect of the four trace elements on osteoporosis (OP) risk remains inadequate. This cross-sectional study aimed to examine their associations with the OP risk among Chinese adults. METHODS Concentrations of Mn, Fe, Cu, and Se were measured in plasma using an inductively coupled plasma mass spectrometer among 627 Chinese adults aged ≥ 50 years. Individual effect of the four elements on OP risk was analyzed by logistic regression and Bayesian Kernel Machine Regression (BKMR) models. The latter model was also adopted to examine the exposure-response relationships and joint effects of the four elements on OP risk. RESULTS The median Mn, Fe, Cu, and Se levels were 4.78, 1026.63, 904.55, and 105.39 μg/L, respectively, in all participants. Inverse associations of Fe and Se levels with OP risk were observed in the logistic regression model. BKMR analysis revealed a U-shape pattern for the Fe-OP association, and a reduced OP risk in response to co-exposure of the four elements above the 50th percentiles but an elevated one in response to that below the 50th percentiles. Sex discrepancy existed in the findings. No interactions were found for the four elements affecting OP risk. CONCLUSIONS Co-exposure to Mn, Fe, Cu, and Se was associated with improved bone density, where Fe contributed most to the beneficial effect. Further studies are needed to verify these findings and explore the underlying biological mechanism.
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Affiliation(s)
- Muhong Wei
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Qin Huang
- Department of Rehabilitation Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yu Dai
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Haolong Zhou
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yuan Cui
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wenjing Song
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Dongsheng Di
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ruyi Zhang
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Can Li
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Qi Wang
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Tao Jing
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
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Zhou HL, Wei MH, Di DS, Zhang RY, Zhang JL, Yuan TT, Liu Q, Zhou TT, Huang Q, Wang Q. Association between SEMA3A signaling pathway genes and BMD/OP risk: An epidemiological and experimental study. Front Endocrinol (Lausanne) 2022; 13:1014431. [PMID: 36425469 PMCID: PMC9679019 DOI: 10.3389/fendo.2022.1014431] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/24/2022] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE This study aimed to explore the associations of genetic variants in the semaphorin 3A (SEMA3A) signaling pathway genes, including SEMA3A, NRP1, PLXNA1, PLXNA2 and PLXNA3 with osteoporosis (OP) risk and bone mineral density (BMD) in a Chinese Han older adult population. STUDY DESIGN AND METHOD A two-stage design was adopted. Total of 47.8kb regions in the 5 genes were sequenced using targeted next-generation sequencing (NGS) technology in the discovery stage, and the discovered OP-related single nucleotide polymorphisms (SNPs) were further genotyped using improved multiple linkage detection reaction technique in the validation stage. Methods of ALP/TRAP staining, real-time fluorescent quantitative PCR, and cell proliferation and apoptosis assays were performed with MC3T3-E1 and RAW 264.7 cell lines to clarify biological effects of observed functional variants in cell lines responsible for bone mass remodeling. RESULTS Total of 400 postmenopausal women (211 OP cases) were involved in the discovery stage, where 6 common and 4 rare genetic variants were found to be associated with OP risk. In the validation stage among another 859 participants (417 women, 270 OP cases), the PLXNA2 rs2274446 T allele was associated with reduced OP risk and increased femoral neck (FN) BMD compared to the C allele. Moreover, significant associations of NRP1 rs2070296 with FN BMD/OP risk and of NRP1 rs180868035 with lumbar spine and FN BMDs were also observed in the combination dataset analysis. Compared to the osteoblasts/osteoclasts transfected with the wild-type NRP1 rs180868035, those transfected with the mutant-type had reduced mRNA expression of osteoblastic genes (i.e., ALP, RUNX2, SP7 and OCN), while elevated mRNA expression of osteoclastic genes (i.e., TRAP, NFATc1 and CTSK). Furthermore, mutant NRP1 rs180868035 transfection inhibited osteoblast proliferation and osteoclast apoptosis, while promoted osteoclast proliferation and osteoblast apoptosis in corresponding cell lines. CONCLUSION Genetic variants located in NRP1 and PLXNA2 genes were associated with OP risk and BMD. The NRP1 rs180868035 affects bone metabolism by influencing osteoblasts and osteoclasts differentiation, proliferation and apoptosis.
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Affiliation(s)
- Hao-long Zhou
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mu-hong Wei
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dong-sheng Di
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ru-yi Zhang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jian-li Zhang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ting-ting Yuan
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Liu
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ting-ting Zhou
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qin Huang
- Department of Rehabilitation Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Qin Huang, ; Qi Wang,
| | - Qi Wang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Qin Huang, ; Qi Wang,
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Di D, Ye Q, Wu X, Zhang L, Wang X, Liu R, Huang Q, Ni J, Leng R. Polymorphisms of BLK are associated with renal disorder in patients with systemic lupus erythematosus. J Hum Genet 2020; 65:675-681. [PMID: 32313195 DOI: 10.1038/s10038-020-0756-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 03/23/2020] [Accepted: 03/25/2020] [Indexed: 12/28/2022]
Abstract
Previous studies are inconclusive on the relationships between BLK gene polymorphisms and clinical features of systemic lupus erythematosus (SLE). The present study aimed to estimate association between BLK loci and SLE clinical features in Chinese population. Associations between BLK single-nucleotide polymorphisms (SNPs) and susceptibility to SLE in this study were estimated using data of 1205 health controls previously reported in the same population. And a total of 814 SLE patients recruited from two different sources according with ACR criteria were analyzed for genotype-phenotype associations. A meta-analysis was conducted of the associations between BLK loci and renal disorder in SLE. The expression quantitative trait locus (eQTL) data were also extracted from the public databases for the selected SNPs. Significant associations were observed between these SNPs and susceptibility to SLE. In addition, the data showed that rs2618479 and rs7812879 were associated with renal disorder [OR = 1.51 (95% CI: 1.15, 1.99) and 1.61 (95% CI: 1.21, 2.14), Pcorr = 0.033 and 0.011, respectively] and proteinuria [OR = 1.47 (95% CI: 1.12, 1.95) and 1.52 (95% CI: 1.14, 2.03), Pcorr = 0.048 and 0.040, respectively]. The consistent associations were observed in two independent centers as well as new cases group. The result of meta-analysis for rs2618479 was also significant [OR = 1.35 (95% CI: 1.12, 1.62)]. In addition, bioinformatics analysis demonstrated that the two SNPs were significantly associated with the expression of BLK in whole blood and several immune cells. Our data support that variant loci of BLK are associated with presence of renal disorder in patients with SLE.
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Affiliation(s)
- Dongsheng Di
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.,Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China
| | - Qianling Ye
- Department of Hematology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xiaoxiao Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.,Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China
| | - Linlin Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.,Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China
| | - Xufan Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.,Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China
| | - Ruishan Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.,Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China
| | - Qian Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.,Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China
| | - Jing Ni
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ruixue Leng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China. .,Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China.
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15
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Li S, Di D, Wu X, Zhang L, Liu R, Huang Q, Pan H, Ye D, Leng R. Association study between X-linked susceptibility genes and clinical features in Chinese female patients with systemic lupus erythematosus. Autoimmunity 2019; 52:289-293. [PMID: 31707854 DOI: 10.1080/08916934.2019.1688792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Recent large-scale genetic association studies have identified that several single nucleotide polymorphisms (SNPs) on X chromosome are correlated with risk of systemic lupus erythematosus (SLE) in Chinese population. The aim of this study was to estimate association between these loci and clinical features in female patients with SLE. Six SNPs identified in previous studies were genotyped. Odds ratio (OR) was calculated with adjusting for potential confounding factors. A total of 772 SLE patients were included in the final analysis. The data showed that 3 SNPs (rs5914778, rs3853839 and rs1059702) were marginally associated with several clinical subphenotypes. Furthermore, consistent associations were also found in two independent cohorts. However, the cumulative genetic risk score (GRS) was not associated with clinical manifestations as well as the disease activity index at disease diagnosis. In summary, genetic variants in X-linked genes may be potentially associated with presence of specific clinical feature. Further studies in distinct ethnical populations are required to clarify the association between these loci and presence of SLE clinical features.
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Affiliation(s)
- Shen Li
- Department of Medical Services, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Dongsheng Di
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.,Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China
| | - Xiaoxiao Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.,Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China
| | - Linlin Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.,Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China
| | - Ruishan Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.,Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China
| | - Qian Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.,Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China
| | - Haifeng Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.,Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China
| | - Dongqing Ye
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.,Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China
| | - Ruixue Leng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.,Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China
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16
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Di D, Zhang L, Wu X, Leng R. Long-term exposure to outdoor air pollution and the risk of development of rheumatoid arthritis: A systematic review and meta-analysis. Semin Arthritis Rheum 2019; 50:266-275. [PMID: 31761290 DOI: 10.1016/j.semarthrit.2019.10.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Revised: 09/18/2019] [Accepted: 10/04/2019] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Air pollution ranks high among risk factors for the global burden of disease. The associations of air pollution and rheumatoid arthritis (RA) are controversial. This systematic review and meta-analyses has analyzed the association between outdoor air pollution and the development of RA. METHODS PubMed, Embase and Web of science (last search, May 21, 2019) were searched. A meta-analysis was performed with a random-effects model, and summarized syntheses effects were expressed as relative risks (RRs). RESULTS Eight studies were identified from among 1296 articles. The pooled RR for the association between ozone (O3) exposure and RA was 1.16 (95% CI: 1.15, 1.18). The pooled RR for the association of RA risk with proximity to traffic road was 1.34 (95% CI: 1.11, 1.62) for residence ≤ 50 m from a traffic road compared with residence more far away. In contrast, there was an inverse effect between PM2.5 exposure and incident RA, and similar result of PM10 was found by subgroup analysis in seropositive RA. In addition, there was no clear evidence between exposing to PM10, CO, NO2 and NO2 (tenth year prior) and RA risk. CONCLUSION Existing evidence indicated significant associations between some markers (ozone, proximity to traffic road and PM2.5) of air pollution and RA. For generalizability of evidence, that research should be extended to developing countries where air pollution (including indoor) is high may provide more complete insight into risk factors for RA.
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Affiliation(s)
- Dongsheng Di
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, China; Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui 230032, China
| | - Linlin Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, China; Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui 230032, China
| | - Xiaoxiao Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, China; Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui 230032, China
| | - Ruixue Leng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, China; Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui 230032, China.
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Di D, Yuan H, Zhang L, Wu X, Pan H, Ye D, Leng R. X chromosome and female bias in systemic lupus erythematosus: Focus on population-based evidence. Autoimmun Rev 2018; 18:109-111. [PMID: 30408589 DOI: 10.1016/j.autrev.2018.08.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 08/19/2018] [Indexed: 02/07/2023]
Affiliation(s)
- Dongsheng Di
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Hui Yuan
- School of Public Health, Wannan Medical College, Wuhu, Anhui, China
| | - Linlin Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Xiaoxiao Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Haifeng Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China
| | - Dongqing Ye
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China; Clinic Medical College of Anhui Medical University, Hefei, Anhui, China.
| | - Ruixue Leng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Anhui Province Key Laboratory of Major Autoimmune Diseases, Hefei, Anhui, China.
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Zhang C, Ding R, Xiao C, Xu Y, Cheng H, Zhu F, Lei R, Di D, Zhao Q, Cao J. Association between air pollution and cardiovascular mortality in Hefei, China: A time-series analysis. Environ Pollut 2017; 229:790-797. [PMID: 28797522 DOI: 10.1016/j.envpol.2017.06.022] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 06/08/2017] [Accepted: 06/09/2017] [Indexed: 05/05/2023]
Abstract
In recent years, air pollution has become an alarming problem in China. However, evidence on the effects of air pollution on cardiovascular mortality is still not conclusive to date. This research aimed to assess the short-term effects of air pollution on cardiovascular morbidity in Hefei, China. Data of air pollution, cardiovascular mortality, and meteorological characteristics in Hefei between 2010 and 2015 were collected. Time-series analysis in generalized additive model was applied to evaluate the association between air pollution and daily cardiovascular mortality. During the study period, the annual average concentration of PM10, SO2, and NO2 was 105.91, 20.58, and 30.93 μg/m3, respectively. 21,816 people (including 11,876 man, and 14,494 people over 75 years of age) died of cardiovascular diseases. In single pollutant model, the effects of multi-day exposure were greater than single-day exposure of the air pollution. For every increase of 10 μg/m3 in SO2, NO2, and PM10 levels, CVD mortality increased by 5.26% (95%CI: 3.31%-7.23%), 2.71% (95%CI: 1.23%-4.22%), and 0.68% (95%CI: 0.33%-1.04%) at a lag03, respectively. The multi-pollutant models showed that PM10 and SO2 remained associated with CVD mortality, although the effect estimates attenuated. However, the effect of NO2 on CVD mortality decreased to statistically insignificant. Subgroup analyses further showed that women were more vulnerable than man upon air pollution exposure. These findings showed that air pollution could significantly increase the CVD mortality.
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Affiliation(s)
- Chao Zhang
- Department of Occupational Health and Environmental Heath, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230032, China
| | - Rui Ding
- Department of Occupational Health and Environmental Heath, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230032, China
| | - Changchun Xiao
- Hefei Centre for Disease Control and Prevention, Hefei, Anhui, China
| | - Yachun Xu
- Department of Occupational Health and Environmental Heath, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230032, China
| | - Han Cheng
- Department of Occupational Health and Environmental Heath, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230032, China
| | - Furong Zhu
- Department of Occupational Health and Environmental Heath, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230032, China
| | - Ruoqian Lei
- Department of Occupational Health and Environmental Heath, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230032, China
| | - Dongsheng Di
- Department of Preventive Medicine, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230032, China
| | - Qihong Zhao
- Department of Nutrition and Food Hygiene, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230032, China
| | - Jiyu Cao
- Department of Occupational Health and Environmental Heath, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230032, China.
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