Background: The association between sleep-related disorders and inflammation has been demonstrated in previous studies. The systemic immune-inflammation index (SII) is a novel inflammatory index based on leukocytes, but its relationship with sleep-related disorder is unclear. We aimed to investigate the relationship between sleep-related disorder and SII in a nationally representative nonhospitalized sample. Methods: Data were obtained from the 2005–2008 National Health and Nutrition Examination Survey (NHANES). Exposure variables included self-reported sleep-related disorders, such as sleep duration, sleep problems, high risk of OSA, and daytime sleepiness. SII and other traditional markers of inflammation were considered as outcome variables, including platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR). Multiple linear regression models were employed to examine the correlation between sleep-related disorders and inflammatory markers. Subgroup interactions were analyzed using likelihood ratio tests, and nonlinear relationships were explored by fitting restricted cubic splines. Results: A total of 8,505 participants were enrolled in this study. Overall, sleep-related disorders were found to have a stronger association with SII compared to the PLR and NLR. The results of multiple linear regression analysis revealed that participants who experienced sleep problems (β: 21.421; 95% CI 1.484, 41.358), had symptoms of OSA (β: 23.088; 95% CI 0.441, 45.735), and reported daytime sleepiness (β: 30.320; 95% CI 5.851, 54.789) exhibited a positive association with higher SII. For the analysis of other inflammatory markers, we only found that daytime sleepiness was associated with increased NLR levels (β: 0.081; 95% CI 0.002, 0.159). Conclusion: Sleep problems, symptoms of OSA, and daytime sleepiness were found to have a positive association with the SII in US adults. However, further prospective studies are necessary to establish whether there is a causal relationship between these factors.

Objective To investigate the association between red cell distribution width (RDW) and the RDW to platelet count ratio (RPR) and cardiovascular diseases (CVDs) and to further investigate whether the association involves population differences and dose–response relationships. Design Cross-sectional population-based study. Setting The National Health and Nutrition Examination Survey (1999–2020). Participants A total of 48 283 participants aged 20 years or older (CVD, n=4593; non-CVD, n=43 690) were included in this study. Primary and secondary outcome measures The primary outcome was the presence of CVD, while the secondary outcome was the presence of specific CVDs. Multivariable logistic regression analysis was performed to determine the relationship between RDW or the RPR and CVD. Subgroup analyses were performed to test the interactions between demographics variables and their associations with disease prevalence. Results A logistic regression model was fully adjusted for potential confounders; the ORs with 95% CIs for CVD across the second to fourth quartiles were 1.03 (0.91 to 1.18), 1.19 (1.04 to 1.37) and 1.49 (1.29 to 1.72) for RDW (p for trend <0.0001) compared with the lowest quartile. The ORs with 95% CIs for CVD across the second to fourth quartiles were 1.04 (0.92 to 1.17), 1.22 (1.05 to 1.42) and 1.64 (1.43 to 1.87) for the RPR compared with the lowest quartile (p for trend <0.0001). The association of RDW with CVD prevalence was more pronounced in females and smokers (all p for interaction <0.05). The association of the RPR with CVD prevalence was more pronounced in the group younger than 60 years (p for interaction=0.022). The restricted cubic spline also suggested a linear association between RDW and CVD and a non-linear association between the RPR and CVD (p for non-linear <0.05). Conclusion There are statistical heterogeneities in the association between RWD, RPR distributions and the CVD prevalence, across sex, smoking status and age groups.

PURPOSE: Robotic-assisted proctectomy (RAP) has emerged as the predominant surgical approach for patients with rectal cancer in recent years; although good postoperative patient recovery with accurate prediction is a guarantee of adaptive surveillance management, there is still a lack of easy-to-use prognostic tools and risk scores designed specifically for those patients undergoing RAP. METHODS: This study used the electronic health records of 506 RAP participants, including a National Specialist Center for da Vinci Robotic Colorectal Surgery (NSCVRCS) meta cohort, and an independent external validation Sun Yat-sen Memorial Hospital cohort. In the NSCVRCS meta cohort, patients were divided into a discovery cohort (70%, n = 268), where the best-fit model was applied to model our prediction system, RAP-AIscore. Subsequently, an internal validation process for RAP-AIscore was conducted using a replication cohort (30%, n = 116). The study designed and implemented a large-scale artificial intelligence (AI) hybrid framework to identify the best strategy for building a survival assessment system, the RAP-AIscore, from 132 potential modeling scenarios through a combination of iterative cross-validation, Monte Carlo cross-validation, and bootstrap resampling. The 10 variables most relevant to clinical interpretability were identified on the basis of the AI hybrid optimal model values, which helps provide reliable prognostic survival guidance for new patients. RESULTS: The consistent evaluation of discrimination, calibration, generalization, and prognostic value across cohorts reaffirmed the accuracy and robust extrapolation capability of this system. The 10 feature variables most associated with clinical interpretability on the basis of Shapley values were identified, facilitating reliable prognostic survival guidance for new patients. CONCLUSION: This study introduces a promising and informative tool, the RAP-AIscore, which can be explained through nomograms for interpreting clinical outcomes. It facilitates postoperative risk stratification management and enhances clinical management of prognosis for RAP patients.

The associations between sexual dysfunction (SD) and sleep disorders, sleep quality, and sleep duration remain unclear. To assess the relationship between sleep and SD through a literature review and meta-analysis. The PubMed, Scopus, Embase, Ovid MEDLINE, and Cochrane Library databases were systematically searched from inception to November 10, 2022. Pooled relative risks and 95% CIs were used to examine the association of sleep disorders with SD in longitudinal studies. Pooled odds ratios (ORs) and 95% CIs were used to examine the associations between SD and sleep disorders, sleep quality, and sleep duration in cross-sectional studies. Forty-three articles, including 11 longitudinal studies and 32 cross-sectional studies, were included in the quantitative analysis. The pooled relative risk of SD in patients with sleep disorders was 1.97 in longitudinal studies (95% CI, 1.46-2.67, P < .001; heterogeneity: I² = 95.0%, P < .001), while the pooled OR of SD in patients with sleep disorders was 2.05 in cross-sectional studies (95% CI, 1.76-2.39, P < .001; heterogeneity: I² = 91.4%, P < .001). When compared with controls, subjects with poor sleep quality had a 1.49-fold increased risk of SD (OR, 1.49; 95% CI, 1.31-1.71, P < .001; heterogeneity: I² = 73.4%, P < .001). In addition, short sleep duration was associated with the risk of SD (OR, 1.14; 95% CI, 1.06-1.22, P < .001; heterogeneity: I² = 0.0%, P = .849). The risk of SD is significantly increased in patients with sleep disorders and poor sleep quality, indicating that clinicians should monitor sleep among patients with SD. This study is the most comprehensive meta-analysis of the association between sleep and SD to date. However, different sleep disorders may have varying associations with sleep duration and sleep quality; thus, we could not identify the independent effects across the studies. Our systematic review and meta-analysis results suggest that sleep disorders, especially obstructive sleep apnea, increase the risk of SD in men and women. Poor sleep quality is significantly associated with SD. Short sleep duration is associated with an increased risk of SD.

Hyperuricemia (HUA) and gout have been demonstrated as independent risk factors for cardiovascular disease. The relationship between the recently updated Life’s Essentials 8 (LE8), which measures ideal cardiovascular health (CVH), and HUA and gout remains unclear. The aim of this study was to explore the relationship between CVH and the prevalence of HUA and gout among a nationally representative sample of US adults. This study utilized cross-sectional analysis of data from the National Health and Nutrition Examination Survey (NHANES) for the years 2007 to 2018. The CVH scores and their corresponding components were defined according to the guidelines established by the American Heart Association. The association between the LE8 score and both HUA and gout was assessed using weighted multivariable logistic and restricted cubic spline (RCS) models. Among the 21,155 participants aged 20 years and older, the prevalence of HUA was 17.20% (95% CI, 16.05–18.36%), and the prevalence of gout was 3.58% (95% CI, 3.13–4.02%). After adjusting for potential confounders, compared to participants exhibiting low CVH, the multivariable adjusted odds ratio (OR) for HUA was 0.65 (95% CI, 0.56–0.75) in those with moderate CVH, and 0.25 (95% CI, 0.20–0.31) in those with high CVH. Additionally, compared to participants with low CVH, the multivariable adjusted OR for gout was 0.66 (95% CI, 0.53–0.81) in those with moderate CVH and 0.32 (95% CI, 0.20–0.50) in those with high CVH. The LE8 score exhibited a significant nonlinear negative association with HUA and linear negative correlation with gout. In subgroup analyses focusing on HUA, significant interactions were observed between LE8 score and age, sex, and CKD (P for interaction < 0.05). For gout, only a significant interaction between LE8 score and sex was observed (P for interaction < 0.05). Among adults, there was a significant negative correlation between LE8 score and the prevalence of HUA and gout. Maintaining an ideal CVH may be beneficial in reducing the burden of HUA and gout.

Objective We aimed to explore the association between periodontitis and abdominal aortic calcification (AAC) among a nationally representative sample of US adults. Design Cross- sectional study. Setting The National Health and Nutrition Examination Survey (2013–2014). Participants A total of 2149 participants aged 40 years or older who have complete information for periodontitis and AAC assessment test were included in this study. Primary and secondary outcome measures AAC scores can be accurately identified on lateral spine images obtained by dual-energy X-ray absorptiometry, and both the AAC-24 and AAC-8 semiquantitative scoring tools were used for AAC evaluation. Linear regression analysis was used to investigate the relationship between periodontitis and the AAC-8 and AAC-24 scores. Multivariate logistic regression models and reported ORs were used to examine the relationship between periodontitis and AAC. Results The prevalence of severe periodontitis combined with severe AAC was 8.49%–8.54%. According to the AAC-8 and AAC-24 score classifications, patients with severe periodontitis had higher odds of severe AAC (AAC-8 score ≥3: (OR: 2.53; 95% CI 1.04 to 6.17) and AAC-24 score >6: (OR: 3.60; 95% CI 1.48 to 8.78)). A positive association between mild–moderate periodontitis and severe AAC was found only when the AAC-24 score was applied (OR: 2.25; 95% CI 1.24 to 4.06). In the subgroup analyses, the likelihood ratio test showed no multiplicative interaction (all p value for interaction >0.05). Conclusions The findings showed that periodontitis is associated with an increased risk of severe AAC in the US population aged 40 years and older; this requires further large-scale prospective studies for confirmation.