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Aim: To analyze the risk factors of proximal junctional kyphosis (PJK) after correction surgery in patients with adolescent idiopathic scoliosis (AIS). Methods: PubMed, Medline, Embase, Cochrane Library, Web of Science, CNKI, and EMCC databases were searched for retrospective studies utilizing all AIS patients with PJK after corrective surgery to collect preoperative, postoperative, and follow-up imaging parameters, including thoracic kyphosis (TK), lumbar lordosis (LL), proximal junctional angle (PJA), the sagittal vertical axis (SVA), pelvic incidence (PI), pelvic tilt (PT), pelvic incidence–lumbar lordosis (PI–LL), sacral slope (SS), rod contour angle (RCA) and upper instrumented vertebra (UIV). Results: Nineteen retrospective studies were included in this meta-analysis, including 550 patients in the intervention group and 3456 patients in the control group. Overall, sex (OR 1.40, 95% CI (1.08, 1.83), P = 0.01), larger preoperative TK (WMD 6.82, 95% CI (5.48, 8.16), P < 0.00001), larger follow-up TK (WMD 8.96, 95% CI (5.62, 12.30), P < 0.00001), larger postoperative LL (WMD 2.31, 95% CI (0.91, 3.71), P = 0.001), larger follow-up LL (WMD 2.51, 95% CI (1.19, 3.84), P = 0.0002), great change in LL (WMD − 2.72, 95% CI (− 4.69, − 0.76), P = 0.006), larger postoperative PJA (WMD 4.94, 95% CI (3.62, 6.26), P < 0.00001), larger follow-up PJA (WMD 13.39, 95% CI (11.09, 15.69), P < 0.00001), larger postoperative PI–LL (WMD − 9.57, 95% CI (− 17.42, − 1.71), P = 0.02), larger follow-up PI–LL (WMD − 12.62, 95% CI (− 17.62, − 7.62), P < 0.00001), larger preoperative SVA (WMD 0.73, 95% CI (0.26, 1.19), P = 0.002), larger preoperative SS (WMD − 3.43, 95% CI (− 4.71, − 2.14), P < 0.00001), RCA (WMD 1.66, 95% CI (0.48, 2.84), P = 0.006) were identified as risk factors for PJK in patients with AIS. For patients with Lenke 5 AIS, larger preoperative TK (WMD 7.85, 95% CI (5.69, 10.00), P < 0.00001), larger postoperative TK (WMD 9.66, 95% CI (1.06, 18.26), P = 0.03, larger follow-up TK (WMD 11.92, 95% CI (6.99, 16.86), P < 0.00001, larger preoperative PJA (WMD 0.72, 95% CI (0.03, 1.41), P = 0.04, larger postoperative PJA (WMD 5.54, 95% CI (3.57, 7.52), P < 0.00001), larger follow-up PJA (WMD 12.42, 95% CI 9.24, 15.60), P < 0.00001, larger follow-up SVA (WMD 0.07, 95% CI (− 0.46, 0.60), P = 0.04), larger preoperative PT (WMD − 3.04, 95% CI (− 5.27, − 0.81), P = 0.008, larger follow-up PT (WMD − 3.69, 95% CI (− 6.66, − 0.72), P = 0.02) were identified as risk factors for PJK. Conclusion: Following corrective surgery, 19% of AIS patients experienced PJK, with Lenke 5 contributing to 25%. Prior and post-op measurements play significant roles in predicting PJK occurrence; thus, meticulous, personalized preoperative planning is crucial. This includes considering individualized treatments based on the Lenke classification as our future evaluation standard. [ABSTRACT FROM AUTHOR]

Background: Low back pain (LBP) has drawn much widespread attention and is a major global health concern. In this field, intervertebral disc degeneration (IVDD) is frequently the focus of classic studies. However, the mechanistic foundation of IVDD is unclear and has led to conflicting outcomes. Methods: Gene expression profiles (GSE34095, GSE147383) of IVDD patients alongside control groups were analyzed to identify differentially expressed genes (DEGs) in the GEO database. GSE23130 and GSE70362 were applied to validate the yielded key genes from DEGs by means of a best subset selection regression. Four machine-learning models were established to assess their predictive ability. Single-sample gene set enrichment analysis (ssGSEA) was used to profile the correlation between overall immune infiltration levels with Thompson grades and key genes. The upstream targeting miRNAs of key genes (GSE63492) were also analyzed. A single-cell transcriptome sequencing data (GSE160756) was used to define several cell clusters of nucleus pulposus (NP), annulus fibrosus (AF), and cartilaginous endplate (CEP) of human intervertebral discs and the distribution of key genes in different cell clusters was yielded. Results: By developing appropriate p-values and logFC values, a total of 6 DEGs was obtained. 3 key genes (LRPPRC, GREM1, and SLC39A4) were validated by an externally validated predictive modeling method. The ssGSEA results indicated that key genes were correlated with the infiltration abundance of multiple immune cells, such as dendritic cells and macrophages. Accordingly, these 4 key miRNAs (miR-103a-3p, miR-484, miR-665, miR-107) were identified as upstream regulators targeting key genes using the miRNet database and external GEO datasets. Finally, the spatial distribution of key genes in AF, CEP, and NP was plotted. Pseudo-time series and GSEA analysis indicated that the expression level of GREM1 and the differentiation trajectory of NP chondrocytes are generally consistent. GREM1 may mainly exacerbate the degeneration of NP cells in IVDD. Conclusions: Our study gives a novel perspective for identifying reliable and effective gene therapy targets in IVDD. [ABSTRACT FROM AUTHOR]