Association between Asthma and Depressive Symptoms: Evidence from The China Health and Retirement Longitudinal Survey (CHARLS)
Authors:   The Second Deparment of Endocrinology and Metabolism, Cangzhou Central Hospital, China, Yin Xiuping , Li Chunyan ,   Department of Clinical Laboratory, Beijing Jishuitan Hospital, Capital Medical University, China, Sun Xiao ,   The Third Department of Tuberculosis, Cangzhou third Hospital, China, Deng Wei ,   Department of Endocrinology, Beijing Jishuitan Hospital, Capital Medical University, China, Li Xiaopei ,   Hebei Medical University, Hebei Province, China, Wang Guangya ,   The Second Deparment of Endocrinology and Metabolism, Cangzhou Central Hospital, China
Published: November 2024

Osteoporosis, a systemic bone disorder, is characterized by diminished bone mass and disruption of bone tissue microarchitecture, leading to heightened bone fragility and vulnerability to fractures [1]. Disturbingly, recent findings indicate a significant surge in osteoporosis prevalence among postmenopausal women and individuals aged over 70 [2,3]. Clinically, postmenopausal osteoporosis (PMOP) escalates the risk of both asymptomatic vertebral compression fractures and symptomatic fractures, encompassing the wrist or hip [4]. Although Dual-energy X-ray Absorptiometry (DXA) currently stands as the gold standard for osteoporosis diagnosis [5], its high costs and radiation exposure pose notable drawbacks. In light of this, our study harnesses Olink proteomics to identify potential biomarkers for the diagnosis, early prevention, and treatment of PMOP. Employing the diagnostic criteria outlined in the 2022 edition of the Guidelines for Diagnosis and Treatment of Primary Osteoporosis [6] and the 2018 edition of the Chinese Quantitative CT (QCT) Diagnostic Guidelines for Osteoporosis [7], Postmenopausal women were classified into three groups based on dual-energy X-ray absorptiometry and Quantitative Computed Tomography (QCT) results: the osteoporosis group (T value=-2.5) consisting of 24 cases, the bone mass loss group (-2.5<-1.0) with 20 cases, and the control group (T value=-1.0) comprising 16 cases. Fasting serum samples were collected and subjected to biochemical analysis. Using Olink proteomics technology, based on its core protein detection patent - Proximity Extension Assay (PEA), multiple protein expression values were accurately detected from each sample. The protein expression values were standardized using IPC (Inter-plate Control) Normalization. Samples and proteins were grouped and classified based on similarity to generate a clustering heatmap. Hierarchical clustering analysis of the heatmap (depicted in Figure 1A-C) revealed a consis tent trend in the expression of differentially expressed proteins within each group. However, there were significant variations in the expression of these proteins among the different groups. Illustrated in the volcano plot of differentially expressed proteins among the groups (shown in Figure 2A-C), To validate the proteomic data, six differentially expressed proteins exhibiting substantial alterations were selected, and box plots were generated (Figure 3A-F).Further, subjects from the osteopenia and osteoporosis groups were combined (Group 2-3) and compared with the control group (Group 1) characterized by normal bone mass. Notably, in Group 2-3, the expression of ADAM-TS13, ANGPT1, and CTRC increased, while the expression of IL6, IDUA, and TRAIL-R2 decreased. Their trends of variation were consistent with the outcomes of proteomic analysis. To identify proteins that have similar or related functions, we employed the STRING online analysis tool (http://www.string-db.org/) to construct an intricate Protein-Protein Interaction (PPI) network. We then computed the Pearson correlation coefficient for pairs of such proteins and created the clustering heat map shown in Figure 4 to analyze the correlations between the different proteins. Our investigation revealed 61 differentially expressed proteins among the study samples. Differential protein Venn analysis was conducted for the comparisons between osteopenia (M) and normal bone mass (C) groups (Figure 5A), osteoporosis (S) and normal bone mass (C) groups (Figure 5B), as well as osteoporosis (S) and osteopenia (M) groups (Figure 5C). The outcomes, illustrated in Figure 5. Through ROC curve analysis, we identified 12 candidate molecules with potential relevance to PMOP: CD40-L, PGF, IL-1ra, PRSS27, TF, SCF, KIM1, PRELP, HO-1, GDF-2, MMP12, and CD4. Correspondingly, ROC curves were generated for these twelve distinct proteins. As depicted in Figure B, the AUC was 1, indicating a comprehensive discriminatory ability. Our findings underscore the unique protein profiles and their potential diagnostic significance in distinguishing different osteoporosis groups. Concurrent assessment of plasma biomarkers shows promise in facilitating early detection and prognosis of PMOP. Data availability statement The data that support the findings of this study are available from the corresponding author upon reasonable. Funding statement The study was funded by Beijing Jishuitan Hospital Nova Program XKXX202212; This study was supported by the Beijing Municipal Health Commission (BJRITO-RDP-2023). Conflict of Interest The authors declare that they have no conflict of interest.