Background: Immune cells play a pivotal role in maintaining ovarian function. However, the specific contributions of different immune cell phenotypes to the pathogenesis of specific ovarian-related diseases remain poorly understood. The authors aim to investigate the correlation between 731 immunophenotypes and ovarian-related diseases. Materials and methods: Utilizing publicly available genetic data, the authors undertook a series of quality control measures to identify instrumental variables associated with exposure. Subsequently, we conducted two-sample Mendelian randomization (MR) using inverse variance weighting to explore the causal relationships between 731 immune cell features and six ovarian-related diseases: ovarian cysts, ovarian dysfunction, premature ovarian failure (POF), polycystic ovary syndrome (PCOS), benign neoplasm of ovary, and malignant neoplasm of ovary at the genetic level. Sensitivity analyses, including leave-one-out and other MR analysis models, were performed. Finally, Bayesian colocalization (COLOC) analysis was employed to identify specific co-localized genes, thereby validating the MR results. Results: At the significance level corrected by Bonferroni, four immune phenotypes, including CD25 on IgD- CD38- B cells, were associated with ovarian cysts; four immune phenotypes, including CD39+ CD4+ T cell Absolute Count, were associated with ovarian dysfunction; eight immune phenotypes, including SSC-A on HLA DR+ CD8+ T cells, were associated with POF; five immune phenotypes, including CD20- CD38- B cell Absolute Count, were associated with PCOS; five immune phenotypes, including CD4+ CD8dim T cell Absolute Count, were associated with benign ovarian tumors; and three immune phenotypes, including BAFF-R on IgD- CD38+ B cells, were associated with malignant ovarian tumors. Sensitivity analysis indicated robust results. COLOC analysis identified four immune cell co-localized variants (rs150386792, rs117936291, rs75926368, and rs575687159) with ovarian diseases. Conclusion: Our study elucidates the close genetic associations between immune cells and six ovarian-related diseases, thereby providing valuable insights for future research endeavors and clinical applications.

Background: Osteomyelitis (OM) is a severe bone infection with rising incidence rates, particularly among elderly and diabetic patients. The immune–inflammatory axis is implicated in OM pathogenesis, but its complex interplay remains poorly defined. This study aims to explore the causal relationships between immunophenotypes, plasma inflammatory proteins, and OM using Mendelian randomization (MR) and bioinformatics. Methods: Utilizing publicly available genetic data, we undertook a series of quality control measures to identify instrumental variables (IVs) associated with exposure. Subsequently, we conducted MR using inverse variance weighting to explore the causal relationships between immunophenotypes, plasma inflammatory proteins, and OM. Bioinformatics tools were applied to explore the functional enrichment and protein–protein interaction networks of the implicated genes. Results: The MR analysis identified 13 immune cell phenotypes and 2 plasma inflammatory proteins associated with risk of OM. Notably, higher levels of HLA DR on plasmacytoid dendritic cells, memory B cell absolute counts, and CD8dim T cell percentages were associated with increased OM risk. Additionally, elevated levels of CD6 and IL‐12 subunit B were correlated with OM risk. Bioinformatics analysis revealed the enrichment of related genes in immune‐related pathways and highlighted the complex interaction networks of the implicated proteins. Conclusions: This study provides novel insights into the immune–inflammatory axis in OM and identifies potential biomarkers for risk assessment. The findings warrant further validation in diverse populations and pave the way for developing targeted preventive and therapeutic strategies for OM.

Rationale: 17α-hydroxylase/17,20 lyase deficiency (17OHD) is a rare autosomal recessive disorder that accounts for only 1% of all congenital adrenal hyperplasia. It has a high rate of misdiagnosis and mistreatment. However, the cases of repeated misdiagnosis and 3 times of mistreatment are extremely rare, which has important significance in medical education. Patient concerns: In this report, a 54-year-old female patient was transferred to the renal department due to proteinuria on physical examination. Enhanced adrenal computed tomography showed right adrenal hyperplasia with multiple nodules, likely adenoma. Blood tests showed low levels of renin, cortisol, estradiol, and androgens, and elevated levels of adrenocorticotropin. Uterine ultrasound indicated a rudimentary uterus. Genetic testing identified heterozygous variants of C.118A>T and C.316T>C in the CYP17A1 gene. Diagnoses: After revising the diagnosis of primary amenorrhea, adrenal adenoma, and chronic nephritis, 17OHD was finally confirmed. Interventions: The patient received maintenance treatment with hydrocortisone (10 mg at 6 am and 10 mg at 2 pm). Outcomes: Postreatment, blood pressure, potassium levels, and urine protein normalized, with stable cortisol levels. Lessons: This case illustrates the importance of early and correct diagnosis of 17OHD through the patient’s tortuous medical experience, and all treatments should be very cautious before the accurate diagnosis of 17OHD. The possibility of 17OHD should be considered in patients with hypertension, hypokalemia, and insufficient puberty. When adult 17OHD patients cannot tolerate long-acting glucocorticoids, they can be replaced with hydrocortisone therapy.

Background: Osteoporosis (OP) is characterized by reduced bone mineral density and bone structural deterioration, with a growing global prevalence. This study aims to explore the relationship between systemic inflammatory markers (SII, SIRI, AISI) and OP risk, and to assess their causal effects using genetic methods. Methods: A cross‐sectional analysis was conducted using data from 8,070 eligible participants. The relationships between SII, SIRI, AISI, bone mineral density, and OP risk were assessed through weighted multivariable regression and smooth curve fitting. Subgroup analyses examined the moderating effects of factors such as BMI and alcohol consumption. Mendelian randomization analysis was also performed using GWAS data to explore the causal relationship between blood cell counts and OP. Results: Elevated levels of SII, SIRI, and AISI were significantly associated with decreased bone mineral density and increased OP risk, with these associations remaining significant after adjusting for various confounders. Subgroup analyses revealed that the association between SII and OP was more pronounced in individuals with low BMI and those who consumed alcohol. Genetic analysis further provided evidence supporting that higher levels of specific blood cells, such as platelets and eosinophils, serve as causal factors contributing to increased OP risk. Conclusions: This study demonstrates that SII, SIRI, and AISI, as reliable indicators of systemic inflammation, can effectively predict OP risk, particularly in populations with low BMI and those who consume alcohol. These inflammatory markers may serve as tools for early OP screening and offer new insights for personalized prevention and intervention strategies.