Exploring sex disparities in cardiovascular disease risk factors using principal component analysis and latent class analysis techniques

Gender Disparity in Hemodialysis Practices and Mortality: A Nationwide Cross-Sectional Observational Study

Background

Disparities between genders are well documented in incidence, progression, and outcomes of chronic kidney disease (CKD). This study aimed to describe demographic characteristics, clinical and socio-economic factors among males and females on maintenance hemodialysis and to determine any association with mortality among males and females.

Materials and Methods

A nationwide cross-sectional study was conducted in a hemodialysis network in India. All adult (>18 years) patients who died while receiving maintenance hemodialysis and an equal number of surviving control patients on maintenance hemodialysis (MHD) between January 1, 2021 to March 31, 2021 were included in the study. The demographic, socioeconomic, and hemodialysis factors were compared between both the genders.

Results

A total of 1177 patients who died during the study period were included. The majority were males (824, 70.01%). Males were more educated than females (P < 0.001). The proportion of female patients dialysed with temporary catheters where more than males, who had definite vascular access such as AV fistula or AV graft (P < 0.001). More female patients required out-of-pocket expenditure (P = 0.005). Multivariate logistic regression demonstrated that lower educational status, hypoalbuminemia, previous history of hospitalization, and dialysis in centres run by Public Private Partnership (PPP) were associated with mortality in males. Lower educational status, heart failure and previous history of hospitalization were the factors associated with mortality in females.

Conclusion

Males predominated on hemodialysis. Females were less educated and were less likely to be covered under public or private health insurance compared to males.

Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices

Cardiovascular disease remains a leading cause of morbidity and mortality in the United States (US). Although high-quality data are accessible in the US for cardiovascular research, digital literacy (DL) has not been explored as a potential factor influencing cardiovascular mortality, although the Social Vulnerability Index (SVI) has been used previously as a variable in predictive modeling. Utilizing a large language model, ChatGPT4, we investigated the variability in CVD-specific mortality that could be explained by DL and SVI using regression modeling. We fitted two models to calculate the crude and adjusted CVD mortality rates. Mortality data using ICD-10 codes were retrieved from CDC WONDER, and the geographic level data was retrieved from the US Department of Agriculture. Both datasets were merged using the Federal Information Processing Standards code. The initial exploration involved data from 1999 through 2020 (n = 65,791; 99.98% complete for all US Counties) for crude cardiovascular mortality (CCM). Age-adjusted cardiovascular mortality (ACM) had data for 2020 (n = 3118 rows; 99% complete for all US Counties), with the inclusion of SVI and DL in the model (a composite of literacy and internet access). By leveraging on the advanced capabilities of ChatGPT4 and linear regression, we successfully highlighted the importance of incorporating the SVI and DL in predicting adjusted cardiovascular mortality. Our findings imply that just incorporating internet availability in the regression model may not be sufficient without incorporating significant variables, such as DL and SVI, to predict ACM. Further, our approach could enable future researchers to consider DL and SVI as key variables to study other health outcomes of public-health importance, which could inform future clinical practices and policies.

Cuproptosis-related lncRNA SNHG16 as a biomarker for the diagnosis and prognosis of head and neck squamous cell carcinoma

Background

We aim to investigate the potential value of cuproptosis-related lncRNA signaling in predicting clinical prognosis and immunotherapy and its relationship with drug sensitivity in head and neck squamous cell carcinoma (HNSCC).

Methods

We first identified the lncRNAs associated with cuproptosis genes in HNSCC and then conducted a series of analytical studies to investigate the expression and prognostic significance of these lncRNAs. Finally, we used RT-qPCR to validate our findings in a laryngeal squamous cell carcinoma cell line and 12 pairs of laryngeal squamous cell carcinoma and adjacent normal tissues.

Results

We identified 11 differentially expressed lncRNAs that were associated with cuproptosis genes in HNSCC and also served as prognostic markers for this cancer. Enrichment analysis revealed that these lncRNAs were related to immune-related functions that were suppressed in patients with oncogene mutations in the high-risk group. The patients with a high tumor mutation burden exhibited poor overall survival (OS). We used the tumor immune dysfunction and exclusion model to show that the patients in the high-risk group had great potential for immune evasion and less effective immunotherapy. We also identified several drugs that could be effective in treating HNSCC. Experimental validation showed that AC090587.1 and AC012184.3 exhibited differential expression between the TU686 and HBE cell lines, and SNHG16 showed differential expression among the TU686, TU212, and control HBE cells. Among the 12 pairs of cancer and adjacent tissues collected in the clinic, only SNHG16 showed differential expression. Targeted therapy against SNHG16 holds promise as a prospective novel strategy for the clinical management of HNSCC.