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.

Protective effect of unsaturated fatty acids on cognitive impairment in CKD patients: Results from the National Health and Nutrition Examination Survey (2011-2014).

BACKGROUND: It is still unknown whether unsaturated fatty acids (UFA) have the same effect on preventing cognitive impairment in chronic kidney disease (CKD) patients as in healthy people. OBJECTIVE: To investigate the protective effect of dietary UFA intake and proportion on cognitive impairment in patients with CKD. METHODS: We extracted data from the National Health and Nutrition Examination Survey (NHANES, 2011-2014) on participants with a previous diagnosis of CKD and at least one complete cognitive assessment (Consortium to Establish a Registry for Alzheimer's Disease test, Animal Fluency Test and Digit Symbol Substitution Test). We used the lower quartile of the total scores of these three tests as the cut-off point, and divided the participants into two groups of normal cognitive performance and low cognitive performance to extract participants' intake of various UFA from the NHANES dietary module.

Association between depression and infertility risk among American women aged 18-45 years: the mediating effect of the NHHR.

BACKGROUND/OBJECTIVE: Depression and infertility are major medical and social problems. The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) serves as an innovative and reliable lipid marker for cardiovascular disease risk assessment. Previous research has indicated a potential correlation among lipid metabolism, depression, and infertility. Nonetheless, the exact involvement of lipid metabolism in modulating the pathological mechanisms associated with depression-induced infertility remains to be fully elucidated. The aim of this study was to explore the connection between depression and infertility and to assess whether the NHHR mediates this association. METHODS: A cross-sectional analysis was performed utilizing data from there cycles (2013-2018) of the National Health and Nutrition Examination Survey (NHANES) database. Female infertility was assessed according to the responses to the RHQ074 question in the reproductive health questionnaire module. Depression states were evaluated using the Patient Health Questionnaire-9 and classified into three grades based on the total scores: no depression (0-4 points), minimal-to-mild depression (5-9 points) and moderate-to-severe depression (10 or more points). The NHHR was calculated from laboratory cholesterol test results. Baseline population characteristics were compared, and subgroup analyses were carried out based on the stratification of age and body mass index (BMI). Weighted multivariable logistic regression and linear regression models, with adjustments for various covariables, were employed to examine the associations among depression, infertility and the NHHR. Finally, mediation analysis was utilized to explore the NHHR's potential mediating role in depression states and female infertility. RESULTS: Within this cross-sectional study, 2,668 women aged 18 to 45 years residing in the United States were recruited, 305 (11.43%) of whom experienced infertility. The study revealed a markedly higher prevalence of depression (P = 0.040) and elevated NHHR (P < 0.001) among infertile women compared to the control cohort. Furthermore, moderate-to-severe depression states independently correlated with increased infertility risk, irrespective of adjustments for various covariables. Subgroup analysis indicated a positive association between depression and infertility risk within certain age categories, although no such relationship was observed within subgroups stratified by BMI. The findings from the weighted logistic regression analysis demonstrated that the elevated NHHR is positively associated with heightened infertility risk. Additionally, the weighted linear regression analysis indicated that moderate-to-severe depression is positively linked to the NHHR levels as well. Finally, the association between depression states and female infertility was partially mediated by the NHHR, with the mediation proportion estimated at 6.57%. CONCLUSION: In the United States, depression is strongly correlated with an increased likelihood of infertility among women of childbearing age, with evidence suggesting that this relationship is mediated by the NHHR. Subsequent research efforts should further explore the underlying mechanisms connecting depression and infertility.

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.

Construct a classification decision tree model to select the optimal equation for estimating glomerular filtration rate and estimate it more accurately.

Chronic kidney disease (CKD) has become a worldwide public health problem and accurate assessment of renal function in CKD patients is important for the treatment. Although the glomerular filtration rate (GFR) can accurately evaluate the renal function, the procedure of measurement is complicated. Therefore, endogenous markers are often chosen to estimate GFR indirectly. However, the accuracy of the equations for estimating GFR is not optimistic. To estimate GFR more precisely, we constructed a classification decision tree model to select the most befitting GFR estimation equation for CKD patients. By searching the HIS system of the First Affiliated Hospital of Zhejiang Chinese Medicine University for all CKD patients who visited the hospital from December 1, 2018 to December 1, 2021 and underwent Gate's method of 99mTc-DTPA renal dynamic imaging to detect GFR, we eventually collected 518 eligible subjects, who were randomly divided into a training set (70%, 362) and a test set (30%, 156). Then, we used the training set data to build a classification decision tree model that would choose the most accurate equation from the four equations of BIS-2, CKD-EPI(CysC), CKD-EPI(Cr-CysC) and Ruijin, and the equation was selected by the model to estimate GFR. Next, we utilized the test set data to verify our tree model, and compared the GFR estimated by the tree model with other 13 equations. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Bland-Altman plot were used to evaluate the accuracy of the estimates by different methods. A classification decision tree model, including BSA, BMI, 24-hour Urine protein quantity, diabetic nephropathy, age and RASi, was eventually retrieved. In the test set, the RMSE and MAE of GFR estimated by the classification decision tree model were 12.2 and 8.5 respectively, which were lower than other GFR estimation equations. According to Bland-Altman plot of patients in the test set, the eGFR was calculated based on this model and had the smallest degree of variation. We applied the classification decision tree model to select an appropriate GFR estimation equation for CKD patients, and the final GFR estimation was based on the model selection results, which provided us with greater accuracy in GFR estimation.

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.

Alcohol Consumption Can be a "Double-Edged Sword" for Chronic Kidney Disease Patients.

Excessive drinking of alcohol is becoming a worldwide problem, and people have recognized that there exists a close relationship between chronic kidney disease (CKD) and alcohol consumption. However, there are many inconsistencies between experimental and clinical studies on alcohol consumption and kidney damage. The possible reason for this contradictory conclusion is the complex drinking pattern of humans and some bioactivators in wine. In addition, the design itself of the clinical studies can also produce conflicting interpretations of the results. Considering the benefits of light-to-moderate alcohol consumption, we recommend that CKD patients continue light-to-moderate drinking, which is beneficial to them. Because alcohol consumption can lead to adverse events, we do not advise non-drinkers to start to drink. Although light-to-moderate alcohol consumption may not pose a risk to patients with CKD, the patients' condition needs to be considered. Consumption of even small amounts of alcohol can be associated with increased death risk. Additional clinical and experimental studies are needed to clarify the effect of alcohol on the kidneys and alcohol consumption on CKD patients.