Age affects the association between socioeconomic status and infertility: a cross-sectional study.

BACKGROUND: Previous studies have shown the interaction between age and socioeconomic status (SES) on the risk of infertility in the UK, but the association is still unclear in the United States. Therefore, the present study investigated the effect of age on the relationship between SES and the risk of infertility in American women. METHODS: The study included adults who participated in the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2018. The poverty income ratio (PIR) was used to represent the SES of the population. With participants stratified according to age category (< 35 years; ≥ 35 years), we further assessed differences in the relationship between PIR and infertility risk among participants of different age groups using multivariate logistic regression and interaction tests. RESULTS: Approximately 3,273 participants were enrolled in the study. There were 399 cases of infertility and 2,874 cases without infertility. In women ≥ 35 years of age, PIR levels were significantly higher in infertile participants than in non-infertile participants, but no such difference was found in those < 35 years of age. The association of PIR with the risk of infertility appeared to differ between age < 35 years and age ≥ 35 years (OR: 0.99, 95%Cl: 0.86-1.13 vs. OR: 1.24, 95%Cl: 1.12-1.39) in a fully adjusted model. Furthermore, an interaction between age and PIR increased the risk of infertility (p-value for interaction < 0.001). CONCLUSION: Our study found that age may influence the association between PIR and infertility. It is imperative to perform further studies to provide more evidence.

Implications of serum uric acid for female infertility: results from the national health and nutrition examination survey, 2013-2020.

BACKGROUND: There is limited concrete evidence connecting serum uric acid levels to female infertility. Therefore, this study aimed to find out if serum uric acid levels are independently related to female infertility. METHODS: From the National Health and Nutrition Examination Survey (NHANES) 2013-2020, a total sample of 5872 chosen female participants between the ages of 18 and 49 were identified for this cross-sectional study. The serum uric acid levels (mg/dL) of each participant were tested, and the reproductive health questionnaire was used to evaluate each subject's reproductive status. Both in the analyses of the full sample and each subgroup, logistic regression models were used to evaluate the relationship between the two variables. A stratified multivariate logistic regression model was used to perform the subgroup analysis based on serum uric acid levels. RESULTS: Infertility was found in 649 (11.1%) of the 5,872 female adults in this study, with greater mean serum uric acid levels (4.7 mg/dL vs. 4.5 mg/dL). Serum uric acid levels were associated with infertility in both the initial and adjusted models. According to multivariate logistic regression, the odds of female infertility were found to be significantly higher with rising serum uric acid levels (Q4 [≥ 5.2 mg/dL] vs. Q1 [≤ 3.6 mg/dL]), adjusted odds ratio [aOR] = 1.59, p = 0.002]. The data suggests that there is a dose-response relationship between the two. CONCLUSIONS: The results from this nationally representative sample from the United States confirmed the idea that there is a link between increased serum uric acid levels and female infertility. Future research is necessary to evaluate the relationship between serum uric acid levels and female infertility and explicate the underlying mechanisms of this relationship.

Association of Serum Triglyceride and Stress Urinary Incontinence in Women From the National Health and Nutrition Examination Survey: A Cross-Sectional Study.

OBJECTIVE: To investigate the relationship between serum triglyceride levels and stress urinary incontinence (SUI) in women from the National Health and Nutrition Examination Survey. METHODS: Adults who participated in the National Health and Nutrition Examination Survey from 2005 to 2018 were included in the study. Univariate and multivariate logistic regressions were used to assess the relationship between serum triglyceride levels and the incidence and severity of SUI. RESULTS: Approximately 7973 participants (mean, 49.9 years of age) were enrolled in the study. Of those, 3367 had SUI, and 4606 did not have SUI. An adjusted multivariate logistic regression analysis demonstrated a positive correlation between serum triglyceride levels and the incidence of SUI (ORs, 1.05; 95% CI, 1-1.11, P = .045). Besides, subgroup analyses indicated that the results were robust among women with different characteristics. Additionally, serum triglyceride levels were positively associated with the severity of SUI. CONCLUSION: Serum triglyceride levels were closely related to the incidence and severity of SUI. Based on our findings, we suggest that serum triglycerides can be included as a risk indicator for screening high-risk groups of SUI.

Characteristic gene prognostic model of type 1 diabetes mellitus via machine learning strategy.

The present study was designed to detect possible biomarkers associated with Type 1 diabetes mellitus (T1DM) incidence in an effort to develop novel treatments for this condition. Three mRNA expression datasets of peripheral blood mononuclear cells (PBMCs) were obtained from the GEO database. Differentially expressed genes (DEGs) between T1DM patients and healthy controls were identified by Limma package in R, and using the DEGs to conduct GO and DO pathway enrichment. The LASSO-SVM were used to screen the hub genes. We performed immune correlation analysis of hub genes and established a T1DM prognosis model. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. The correlation of the hub genes and immune cells was analyzed by Spearman. ROC curves were used to assess the diagnostic value of genes in T1DM. A total of 60 immune related DEGs were obtained from the T1DM and normal samples. Then, DEGs were further screened to obtain 3 hub genes, ANP32A-IT1, ESCO2 and NBPF1. CIBERSORT analysis revealed the percentage of immune cells in each sample, indicating that there was significant difference in monocytes, T cells CD8+, gamma delta T cells, naive CD4+ T cells and activated memory CD4+ T cells between T1DM and normal samples. The area under curve (AUC) of ESCO2, ANP32A-IT1 and NBPF1 were all greater than 0.8, indicating that these three genes have high diagnostic value for T1DM. Together, the findings of these bioinformatics analyses thus identified key hub genes associated with T1DM development.

Development and validation of a nomogram to predict the risk of peripheral artery disease in patients with type 2 diabetes mellitus.

Objective: To develop and validate a nomogram for predicting the risk of peripheral artery disease (PAD) in patients with type 2 diabetes mellitus (T2DM) and assess its clinical application value. Methods: Clinical data were retrospectively collected from 474 patients with T2DM at the Air Force Medical Center between January 2019 and April 2022. The patients were divided into training and validation sets using the random number table method in a ratio of 7:3. Multivariate logistic regression analysis was performed to identify the independent risk factors for PAD in patients with T2DM. A nomogram prediction model was developed based on the independent risk factors. The predictive efficacy of the prediction model was evaluated using the consistency index (C-index), area under the curve (AUC), receiver operating characteristic (ROC) curve, Hosmer-Lemeshow (HL) test, and calibration curve analysis. Additionally, decision curve analysis (DCA) was performed to evaluate the prediction model's performance during clinical application. Results: Age, disease duration, blood urea nitrogen (BUN), and hemoglobin (P<0.05) were observed as independent risk factors for PAD in patients with T2DM. The C-index and the AUC were 0.765 (95% CI: 0.711-0.819) and 0.716 (95% CI: 0.619-0.813) for the training and validation sets, respectively, indicating that the model had good discriminatory power. The calibration curves showed good agreement between the predicted and actual probabilities for both the training and validation sets. In addition, the P-values of the HL test for the training and validation sets were 0.205 and 0.414, respectively, indicating that the model was well-calibrated. Finally, the DCA curve indicated that the model had good clinical utility. Conclusion: A simple nomogram based on three independent factors-duration of diabetes, BUN, and hemoglobin levels-may help clinicians predict the risk of developing PAD in patients with T2DM.