Reproductive system diseases are among the primary contributors to the decline in social fertility rates and the intensification of aging, posing significant threats to both physical and mental health, as well as quality of life. Recent research has revealed the substantial potential of the gut microbiota in improving reproductive system diseases. Under healthy conditions, the gut microbiota maintains a dynamic balance, whereas dysfunction can trigger immune-inflammatory responses, metabolic disorders, and other issues, subsequently leading to reproductive system diseases through the gut-gonadal axis. Reproductive diseases, in turn, can exacerbate gut microbiota imbalance. This article reviews the impact of the gut microbiota and its metabolites on both male and female reproductive systems, analyzing changes in typical gut microorganisms and their metabolites related to reproductive function. The composition, diversity, and metabolites of gut bacteria, such as Bacteroides, Prevotella, and Firmicutes, including short-chain fatty acids, 5-hydroxytryptamine, γ-aminobutyric acid, and bile acids, are closely linked to reproductive function. As reproductive diseases develop, intestinal immune function typically undergoes changes, and the expression levels of immune-related factors, such as Toll-like receptors and inflammatory cytokines (including IL-6, TNF-α, and TGF-β), also vary. The gut microbiota and its metabolites influence reproductive hormones such as estrogen, luteinizing hormone, and testosterone, thereby affecting folliculogenesis and spermatogenesis. Additionally, the metabolism and absorption of vitamins can also impact spermatogenesis through the gut-testis axis. As the relationship between the gut microbiota and reproductive diseases becomes clearer, targeted regulation of the gut microbiota can be employed to address reproductive system issues in both humans and animals. This article discusses the regulation of the gut microbiota and intestinal immune function through microecological preparations, fecal microbiota transplantation, and drug therapy to treat reproductive diseases. Microbial preparations and drug therapy can help maintain the intestinal barrier and reduce chronic inflammation. Fecal microbiota transplantation involves transferring feces from healthy individuals into the recipient’s intestine, enhancing mucosal integrity and increasing microbial diversity. This article also delves into the underlying mechanisms by which the gut microbiota influences reproductive capacity through the gut-gonadal axis and explores the latest research in diagnosing and treating reproductive diseases using gut microbiota. The goal is to restore reproductive capacity by targeting the regulation of the gut microbiota. While the gut microbiota holds promise as a therapeutic target for reproductive diseases, several challenges remain. First, research on the association between gut microbiota and reproductive diseases is insufficient to establish a clear causal relationship, which is essential for proposing effective therapeutic methods targeting the gut microbiota. Second, although gut microbiota metabolites can influence lipid, glucose, and hormone synthesis and metabolism via various signaling pathways—thereby indirectly affecting ovarian and testicular function—more in-depth research is required to understand the direct effects of these metabolites on germ cells or granulosa cells. Lastly, the specific efficacy of gut microbiota in treating reproductive diseases is influenced by multiple factors, necessitating further mechanistic research and clinical studies to validate and optimize treatment regimens.

OBJECTIVE: To investigate the effects of astragaloside IV (AS-IV) on podocyte injury of diabetic nephropathy (DN) and reveal its potential mechanism. METHODS: In in vitro experiment, podocytes were divided into 4 groups, normal, high glucose (HG), inositol-requiring enzyme 1 (IRE-1) α activator (HG+thapsigargin 1 µmol/L), and IRE-1α inhibitor (HG+STF-083010, 20 µmol/L) groups. Additionally, podocytes were divided into 4 groups, including normal, HG, AS-IV (HG+AS-IV 20 µmol/L), and IRE-1α inhibitor (HG+STF-083010, 20 µmol/L) groups, respectively. After 24 h treatment, the morphology of podocytes and endoplasmic reticulum (ER) was observed by electron microscopy. The expressions of glucose-regulated protein 78 (GRP78) and IRE-1α were detected by cellular immunofluorescence. In in vivo experiment, DN rat model was established via a consecutive 3-day intraperitoneal streptozotocin (STZ) injections. A total of 40 rats were assigned into the normal, DN, AS-IV [AS-IV 40 mg/(kg·d)], and IRE-1α inhibitor [STF-083010, 10 mg/(kg·d)] groups (n=10), respectively. The general condition, 24-h urine volume, random blood glucose, urinary protein excretion rate (UAER), urea nitrogen (BUN), and serum creatinine (SCr) levels of rats were measured after 8 weeks of intervention. Pathological changes in the renal tissue were observed by hematoxylin and eosin (HE) staining. Quantitative reverse transcription-polymerase chain reaction (RT-PCR) and Western blot were used to detect the expressions of GRP78, IRE-1α, nuclear factor kappa Bp65 (NF-κBp65), interleukin (IL)-1β, NLR family pyrin domain containing 3 (NLRP3), caspase-1, gasdermin D-N (GSDMD-N), and nephrin at the mRNA and protein levels in vivo and in vitro, respectively. RESULTS: Cytoplasmic vacuolation and ER swelling were observed in the HG and IRE-1α activator groups. Podocyte morphology and ER expansion were improved in AS-IV and IRE-1α inhibitor groups compared with HG group. Cellular immunofluorescence showed that compared with the normal group, the fluorescence intensity of GRP78 and IRE-1α in the HG and IRE-1α activator groups were significantly increased whereas decreased in AS-IV and IRE-1α inhibitor groups (P<0.05). Compared with the normal group, the mRNA and protein expressions of GRP78, IRE-1α, NF-κ Bp65, IL-1β, NLRP3, caspase-1 and GSDMD-N in the HG group was increased (P<0.05). Compared with HG group, the expression of above indices was decreased in the AS-IV and IRE-1α inhibitor groups, and the expression in the IRE-1α activator group was increased (P<0.05). The expression of nephrin was decreased in the HG group, and increased in AS-IV and IRE-1α inhibitor groups (P<0.05). The in vivo experiment results revealed that compared to the normal group, the levels of blood glucose, triglyceride, total cholesterol, BUN, blood creatinine and urinary protein in the DN group were higher (P<0.05). Compared with DN group, the above indices in AS-IV and IRE-1α inhibitor groups were decreased (P<0.05). HE staining revealed glomerular hypertrophy, mesangial widening and mesangial cell proliferation in the renal tissue of the DN group. Compared with the DN group, the above pathological changes in renal tissue of AS-IV and IRE-1α inhibitor groups were alleviated. Quantitative RT-PCR and Western blot results of GRP78, IRE-1α, NF-κ Bp65, IL-1β, NLRP3, caspase-1 and GSDMD-N were consistent with immunofluorescence analysis. CONCLUSION AS-IV could reduce ERS and inflammation, improve podocyte pyroptosis, thus exerting a podocyte-protective effect in DN, through regulating IRE-1α/NF-κ B/NLRP3 signaling pathway.
Podocytes/metabolism* ; Animals ; Diabetic Nephropathies/metabolism* ; Saponins/therapeutic use* ; Triterpenes/therapeutic use* ; Signal Transduction/drug effects* ; NF-kappa B/metabolism* ; Protein Serine-Threonine Kinases/metabolism* ; Male ; Rats, Sprague-Dawley ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Endoribonucleases/metabolism* ; Endoplasmic Reticulum Chaperone BiP ; Rats ; Diabetes Mellitus, Experimental/complications* ; Endoplasmic Reticulum/metabolism* ; Multienzyme Complexes

OBJECTIVE: To explore the neuroprotective effects of Xuefu Zhuyu Decoction (XFZYD) based on in vivo and metabolomics experiments. METHODS: Traumatic brain injury (TBI) was induced via a controlled cortical impact (CCI) method. Thirty rats were randomly divided into 3 groups (10 for each): sham, CCI and XFZYD groups (9 g/kg). The administration was performed by intragastric administration for 3 days. Neurological functions tests, histology staining, coagulation and haemorheology assays, and Western blot were examined. Untargeted metabolomics was employed to identify metabolites. The key metabolite was validated by enzyme-linked immunosorbent assay and immunofluorescence. RESULTS: XFZYD significantly alleviated neurological dysfunction in CCI model rats (P<0.01) but had no impact on coagulation function. As evidenced by Evans blue and IgG staining, XFZYD effectively prevented blood-brain barrier (BBB) disruption (P<0.05, P<0.01). Moreover, XFZYD not only increased the expression of collagen IV, occludin and zona occludens 1 but also decreased matrix metalloproteinase-9 (MMP-9) and cyclooxygenase-2 (COX-2), which protected BBB integrity (all P<0.05). Nine potential metabolites were identified, and all of them were reversed by XFZYD. Adenosine was the most significantly altered metabolite related to BBB repair. XFZYD significantly reduced the level of equilibrative nucleoside transporter 2 (ENT2) and increased adenosine (P<0.01), which may improve BBB integrity. CONCLUSIONS XFZYD ameliorates BBB disruption after TBI by decreasing the levels of MMP-9 and COX-2. Through further exploration via metabolomics, we found that XFZYD may exert a protective effect on BBB by regulating adenosine metabolism via ENT2.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Blood-Brain Barrier/metabolism* ; Brain Injuries, Traumatic/metabolism* ; Adenosine/metabolism* ; Male ; Rats, Sprague-Dawley ; Rats

OBJECTIVE: To explore the mechanism of colon dialysis with Yishen Decoction (YS) in improving the autophagy disorder of intestinal epithelial cells in chronic renal failure (CRF) in vivo and in vitro. METHODS: Thirty male SD rats were randomly divided into normal, CRF, and colonic dialysis with YS groups by a random number table method (n=10). The CRF model was established by orally gavage of adenine 200 mg/(kg•d) for 4 weeks. CRF rats in the YS group were treated with colonic dialysis using YS 20 g/(kg•d) for 14 consecutive days. The serum creatinine (SCr) and urea nitrogen (BUN) levels were detected by enzyme-linked immunosorbent assay. Pathological changes of kidney and colon tissues were observed by hematoxylin and eosin staining. Autophagosome changes in colonic epithelial cells was observed with electron microscopy. In vitro experiments, human colon cancer epithelial cells (T84) were cultured and divided into normal, urea model (74U), YS colon dialysis, autophagy activator rapamycin (Ra), autophagy inhibitor 3-methyladenine (3-MA), and SIRT1 activator resveratrol (Re) groups. RT-PCR and Western blot were used to detect the mRNA and protein expressions of zonula occludens-1 (ZO-1), Claudin-1, silent information regulator sirtuin 1 (SIRT1), LC3, and Beclin-1 both in vitro and in vivo. RESULTS: Colonic dialysis with YS decreased SCr and BUN levels in CRF rats (P<0.05), and alleviated the pathological changes of renal and colon tissues. Expressions of SIRT1, ZO-1, Claudin-1, Beclin-1, and LC3II/I were increased in the YS group compared with the CRF group in vivo (P<0.05). In in vitro study, compared with normal group, the expressions of SIRT1, ZO-1, and Claudin-1 were decreased, and expressions of Beclin-1, and LC3II/I were increased in the 74U group (P<0.05). Compared with the 74U group, expressions of SIRT1, ZO-1, and Claudin-1 were increased, whereas Beclin-1, and LC3II/I were decreased in the YS group (P<0.05). The treatment of 3-MA and rapamycin regulated autophagy and the expression of SIRT1. SIRT1 activator intervention up-regulated autophagy as well as the expressions of ZO-1 and Claudin-1 compared with the 74U group (P<0.05). CONCLUSION Colonic dialysis with YS could improve autophagy disorder and repair CRF intestinal mucosal barrier injury by regulating SIRT1 expression in intestinal epithelial cells.
Animals ; Sirtuin 1/metabolism* ; Drugs, Chinese Herbal/therapeutic use* ; Autophagy/drug effects* ; Male ; Intestinal Mucosa/drug effects* ; Rats, Sprague-Dawley ; Epithelial Cells/metabolism* ; Colon/drug effects* ; Humans ; Kidney Failure, Chronic/drug therapy* ; Signal Transduction/drug effects* ; Renal Dialysis ; Rats ; Kidney/drug effects*

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional

OBJECTIVE: This study examines utilizes the advantages of machine learning algorithms to discern key determinants in prognosticate postoperative circulatory complications (PCCs) for older patients. METHODS: This secondary analysis of data from a randomized controlled trial involved 1,720 elderly participants in five tertiary hospitals in Beijing, China. Participants aged 60-90 years undergoing major non-cardiac surgery under general anesthesia. The primary outcome metric of the study was the occurrence of PCCs, according to the European Society of Cardiology and the European Society of Anaesthesiology diagnostic criteria. The analysis metrics contained 67 candidate variables, including baseline characteristics, laboratory tests, and scale assessments. RESULTS: Our feature selection process identified key variables that significantly impact patient outcomes, including the duration of ICU stay, surgery, and anesthesia; APACHE-II score; intraoperative average heart rate and blood loss; cumulative opioid use during surgery; patient age; VAS-Move-Median score on the 1st to 3rd day; Charlson comorbidity score; volumes of intraoperative plasma, crystalloid, and colloid fluids; cumulative red blood cell transfusion during surgery; and endotracheal intubation duration. Notably, our Random Forest model demonstrated exceptional performance with an accuracy of 0.9872. CONCLUSION We have developed and validated an algorithm for predicting PCCs in elderly patients by identifying key risk factors.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Cardiovascular Diseases/etiology* ; Machine Learning ; Postoperative Complications/etiology* ; Risk Factors ; Randomized Controlled Trials as Topic ; Secondary Data Analysis

OBJECTIVE: To identify the key features of facial and tongue images associated with anemia in female populations, establish anemia risk-screening models, and evaluate their performance. METHODS: A total of 533 female participants (anemic and healthy) were recruited from Shuguang Hospital. Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument. Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System (FDAS) and Tongue Diagnosis Analysis System version 2.0 (TDAS v2.0). Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection. Ten machine learning models and one deep learning model (ResNet50V2 + Conv1D) were developed and evaluated. RESULTS: Anemic women showed lower a-values, higher L- and b-values across all age groups. Texture features analysis showed that women aged 30-39 with anemia had higher angular second moment (ASM)and lower entropy (ENT) values in facial images, while those aged 40-49 had lower contrast (CON), ENT, and MEAN values in tongue images but higher ASM. Anemic women exhibited age-related trends similar to healthy women, with decreasing L-values and increasing a-, b-, and ASM-values. LASSO identified 19 key features from 62. Among classifiers, the Artificial Neural Network (ANN) model achieved the best performance [area under the curve (AUC): 0.849, accuracy: 0.781]. The ResNet50V2 model achieved comparable results [AUC: 0.846, accuracy: 0.818]. CONCLUSION Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.
Humans ; Female ; Tongue/diagnostic imaging* ; Adult ; Anemia/diagnosis* ; Middle Aged ; Face/diagnostic imaging* ; Young Adult ; Machine Learning

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

Alzheimer's disease(AD)is a common neurodegenerative disease in the elderly.Increasing evidence shows that peripheral immune cells are involved in the occurrence and development of AD.Both gut microbiota dysbiosis and abnormalities of peripheral immune cells occur in the early stage of AD.The gut microbiota can influence the central nervous system through mechanisms such as the gut-brain axis and the regulatory effects on peripheral immune cells.AD treatment methods targeting the gut microbiota and immune cells have been receiving increasing attention.This review summarizes the regulatory effects and mechanisms of the gut microbiota on peripheral immune cells,as well as the research progress on the gut microbiota's roles in the occurrence and development of AD,aiming to provide new ideas for relevant research on the prevention,diagnosis,and treatment of AD.

Objective To investigate the epidemiological characteristics of knee osteoarthritis(KOA)among military personnel in plateau regions and to explore its risk factors.Methods From July 2023 to July 2024,a multi-stage stratified cluster random sampling method was employed to survey the prevalence of KOA and related risk factors among military personnel in the northwest plateau regions of China,covering different altitudes(1500-4500 m)and geographical areas(Gansu,Qinghai,Tibet,and Xinjiang).All study subjects were divided into KOA and non-KOA groups based on the presence or absence of KOA.Variables including age,gender,body mass index(BMI),education level,smoking status,military rank,military branch,service duration,regional altitude,annual average temperature,training duration,perceived training intensity,and history of knee injury were selected for univariate analyses between groups.Variables with P<0.05 in the univariate analyses were included in the binary multifactor logistic regression to identify risk factors for KOA.Results A total of 3000 questionnaires were distributed,and 2854 valid questionnaires were collected,with a response rate of 95.13%.The sample included 2584 males and 270 females,with 510 cases of KOA,resulting in a prevalence rate of 17.9%.Univariate analysis showed that there were statistically significant differences between KOA and non-KOA groups in terms of age,BMI,smoking status,military rank,military branch,service duration,regional altitude,annual average temperature,training duration,perceived training intensity,and history of knee injury(P<0.05).However,no significant differences were found in gender and education level(P>0.05).Binary multivariate logistic regression analysis revealed that older age(OR=1.382,P=0.017),higher BMI(P<0.01),smoking(OR=1.929,P<0.01),higher military rank(OR=1.485,P=0.007),being a member of the Armed Police(P<0.01),longer service duration(P<0.01),higher regional altitude(OR=1.459,P<0.01),lower annual average temperature(OR=1.188,P=0.001),longer training duration(P<0.01),higher perceived training intensity(OR=2.450,P<0.01),and history of knee injury(OR=2.768,P=0.002)were independent risk factors for KOA.Conclusions Older age,overweight/obesity,smoking,higher military rank,being a member of the Armed Police,longer service duration,higher altitude,cold climate,longer training duration,higher training intensity,and history of knee injury are independent risk factors for KOA among military personnel in the northwest plateau regions of China.