Causal association between telomere length and female reproductive endocrine diseases: a univariable and multivariable Mendelian randomization analysis.

BACKGROUND: The relationship between leukocyte telomere length (LTL) and female reproductive endocrine diseases has gained significant attention and research interest in recent years. However, there is still limited understanding of the exact impacts of LTL on these diseases. Therefore, the primary objective of this study was to investigate the genetic causal association between LTL and female reproductive endocrine diseases by employing Mendelian randomization (MR) analysis. METHODS: Instruments for assessing genetic variation associated with exposure and outcome were derived from summary data of published genome-wide association studies (GWAS). Inverse-variance weighted (IVW) was utilized as the main analysis method to investigate the causal relationship between LTL and female reproductive endocrine diseases. The exposure data were obtained from the UK Biobanks GWAS dataset, comprising 472,174 participants of European ancestry. The outcome data were acquired from the FinnGen consortium, including abnormal uterine bleeding (menorrhagia and oligomenorrhea), endometriosis (ovarian endometrioma and adenomyosis), infertility, polycystic ovary syndrome (PCOS), premature ovarian insufficiency (POI) and premenstrual syndrome (PMS). Furthermore, to account for potential confounding factors such as smoking, alcohol consumption, insomnia, body mass index (BMI) and a history of pelvic inflammatory disease (PID), multivariable MR (MVMR) analysis was also conducted. Lastly, a series of pleiotropy tests and sensitivity analyses were performed to ensure the reliability and robustness of our findings. P < 0.0063 was considered to indicate statistically significant causality following Bonferroni correction. RESULTS: Our univariable MR analysis demonstrated that longer LTL was causally associated with an increased risk of menorrhagia (IVW: odds ratio [OR]: 1.1803; 95% confidence interval [CI]: 1.0880-1.2804; P = 0.0001) and ovarian endometrioma (IVW: OR: 1.2946; 95%CI: 1.0970-1.5278; P = 0.0022) at the Bonferroni significance level. However, no significant correlation was observed between LTL and oligomenorrhea (IVW: OR: 1.0124; 95%CI: 0.7350-1.3946; P = 0.9398), adenomyosis (IVW: OR: 1.1978; 95%CI: 0.9983-1.4372; P = 0.0522), infertility (IVW: OR: 1.0735; 95%CI: 0.9671-1.1915; P = 0.1828), PCOS (IVW: OR: 1.0633; 95%CI: 0.7919-1.4278; P = 0.6829), POI (IVW: OR: 0.8971; 95%CI: 0.5644-1.4257; P = 0.6459) or PMS (IVW: OR: 0.7749; 95%CI: 0.4137-1.4513; P = 0.4256). Reverse MR analysis indicated that female reproductive endocrine diseases have no causal effect on LTL. MVMR analysis suggested that the causal effect of LTL on menorrhagia and ovarian endometrioma remained significant after accounting for smoking, alcohol consumption, insomnia, BMI and a history of PID. Pleiotropic and sensitivity analyses also showed robustness of our results. CONCLUSION: The results of our bidirectional two-sample MR analysis revealed that genetically predicted longer LTL significantly increased the risk of menorrhagia and ovarian endometrioma, which is consistent with the findings from MVMR studies. However, we did not notice any significant effects of LTL on oligomenorrhea, adenomyosis, infertility, PCOS, POI or PMS. Additionally, reproductive endocrine disorders were found to have no impact on LTL. To enhance our understanding of the effect and underlying mechanism of LTL on female reproductive endocrine diseases, further large-scale studies are warranted in the future.

Artificial intelligence can accurately distinguish IgA nephropathy from diabetic nephropathy under Masson staining and becomes an important assistant for renal pathologists.

Introduction: Hyperplasia of the mesangial area is common in IgA nephropathy (IgAN) and diabetic nephropathy (DN), and it is often difficult to distinguish them by light microscopy alone, especially in the absence of clinical data. At present, artificial intelligence (AI) is widely used in pathological diagnosis, but mainly in tumor pathology. The application of AI in renal pathological is still in its infancy. Methods: Patients diagnosed as IgAN or DN by renal biopsy in First Affiliated Hospital of Zhejiang Chinese Medicine University from September 1, 2020 to April 30, 2022 were selected as the training set, and patients who diagnosed from May 1, 2022 to June 30, 2022 were selected as the test set. We focused on the glomerulus and captured the field of the glomerulus in Masson staining WSI at 200x magnification, all in 1,000 × 1,000 pixels JPEG format. We augmented the data from training set through minor affine transformation, and then randomly split the training set into training and adjustment data according to 8:2. The training data and the Yolov5 6.1 algorithm were used to train the AI model with constant adjustment of parameters according to the adjusted data. Finally, we obtained the optimal model, tested this model with test set and compared it with renal pathologists. Results: AI can accurately detect the glomeruli. The overall accuracy of AI glomerulus detection was 98.67% and the omission rate was only 1.30%. No Intact glomerulus was missed. The overall accuracy of AI reached 73.24%, among which the accuracy of IgAN reached 77.27% and DN reached 69.59%. The AUC of IgAN was 0.733 and that of DN was 0.627. In addition, compared with renal pathologists, AI can distinguish IgAN from DN more quickly and accurately, and has higher consistency. Discussion: We constructed an AI model based on Masson staining images of renal tissue to distinguish IgAN from DN. This model has also been successfully deployed in the work of renal pathologists to assist them in their daily diagnosis and teaching work.

Network Pharmacology and Experimental Validation to Reveal the Pharmacological Mechanisms of Chongcaoyishen Decoction Against Chronic Kidney Disease.

Objective: To explore the pharmacological mechanisms of Chongcaoyishen decoction (CCYSD) against chronic kidney disease (CKD) via network pharmacology analysis combined with experimental validation. Methods: The bioactive components and potential regulatory targets of CCYSD were extracted from the TCMSP database, and the putative CKD-related target proteins were collected from the GeneCards and OMIM database. We matched the active ingredients with gene targets and conducted regulatory networks through Perl5 and R 3.6.1. The network visualization analysis was performed by Cytoscape 3.7.1, which contains ClueGO plug-in for GO and KEGG analysis. In vivo experiments were performed on 40 male SD rats, which were randomly divided into the control group (n = 10), sham group (n = 10), UUO group (n = 10), and CCYSD group (n = 10). A tubulointerstitial fibrosis model was constructed by unilateral ureteral obstruction through surgery and treated for seven consecutive days with CCYSD (0.00657 g/g/d). At the end of treatment, the rats were euthanized and the serum and kidney were collected for further detection. Results: In total, 53 chemical compounds from CCYSD were identified and 12,348 CKD-related targets were collected from the OMIM and GeneCards. A total of 130 shared targets of CCYSD and CKD were acquired by Venn diagram analysis. Functional enrichment analysis suggested that CCYSD might exert its pharmacological effects in multiple biological processes, including oxidative stress, apoptosis, inflammatory response, autophagy, and fiber synthesis, and the potential targets might be associated with JAK-STAT and PI3K-AKT, as well as other signaling pathways. The results of the experiments revealed that the oxidative stress in the UUO group was significantly higher than that in normal state and was accompanied by severe tubulointerstitial fibrosis (TIF), which could be effectively reversed by CCYSD (p < 0.05). Meanwhile, aggravated mitochondrial injury and autophagy was observed in the epithelial cells of the renal tubule in the UUO group, compared to the normal ones (p < 0.05), while the intervention of CCYSD could further activate the autophagy and reduce the mitochondrial injury (p < 0.05). Conclusion: We provide an integrative network pharmacology approach combined with in vivo experiments to explore the underlying mechanisms governing the CCYSD treatment of CKD, which indicates that the relationship between CCYSD and CKD is related to its activation of autophagy, promotion of mitochondrial degradation, and reduction of tissue oxidative stress injury, promoting the explanation and understanding of the biological mechanism of CCYSD in the treatment of CKD.