Bo Zhang

Jul 4, 2024 | nature.com | Yangyang Mei |Yangmeina Li |Xingliang Feng |Bo Zhang |Renfang Xu

The C-reactive protein-triglyceride glucose index (CTI) is emerging as a novel indicator for comprehensively assessing the severity of both inflammation and insulin resistance. However, the association between CTI and erectile dysfunction (ED) remains largely unexplored. Participant data for this study were sourced from NHANES 2001–2004, with exclusion criteria applied to those lacking information on clinical variables. The CTI was defined as 0.412*Ln (CRP) + ln [T.G. (mg/dL) × FPG (mg/dL)/2]. Weighted univariable and multivariable logistic regression models were utilized to examine the correlation between the CTI and ED, assessing the CTI as both a continuous and categorical variable (quartile). Moreover, subgroup analyses were conducted to pinpoint sensitive populations, and interaction analysis was performed to validate the findings. A total of 1502 participants were included in the final analysis, encompassing 302 with ED and 1200 without ED. After adjusting for potential confounders, the CTI was positively associated with ED incidence (OR = 1.56, 95% CI: 1.27–1.90, P = 0.002). The fourth quartile of the CTI significantly increased the incidence of ED (OR = 2.69, 95% CI: 1.07–6.74, P = 0.04), and the lowest quartile of CTI was used as the reference. The dose-response curve revealed a positive linear relationship between the CTI and the incidence of ED. Subgroup analysis confirmed the consistent positive relationship between the CTI and ED. The interaction test indicated no significant impact on this association. Finally, a sensitivity analysis was performed to verify the significant positive correlation between the CTI and severe ED (OR = 1.44, 95% CI: 1.19–1.76, P = 0.004). Our national data indicate that a greater CTI is positively linked to an increased risk of ED in US men, suggesting its potential for use in clinical practice for ED prevention or early intervention. Additional large-scale prospective studies are warranted to substantiate the causative relationship between CTI and ED.

Apr 25, 2024 | nature.com | Bo Zhang |Xun Xu |Jinliuxing Yang |Yuan Liu |Zhuoting Zhu |Jun Chen | +8 more

To develop and validate a machine learning based algorithm to estimate physical activity (PA) intensity using the smartwatch with the capacity to record PA and determine outdoor state. Two groups of participants, including 24 adults (13 males) and 18 children (9 boys), completed a sequential activity trial. During each trial, participants wore a smartwatch, and energy expenditure was measured using indirect calorimetry as gold standard. The support vector machine algorithm and the least squares regression model were applied for the metabolic equivalent (MET) estimation using raw data derived from the smartwatch. Exercise intensity was categorized based on MET values into sedentary activity (SED), light activity (LPA), moderate activity (MPA), and vigorous activity (VPA). The classification accuracy was evaluated using area under the ROC curve (AUC). The METs estimation accuracy were assessed via the mean absolute error (MAE), the correlation coefficient, Bland–Altman plots, and intraclass correlation (ICC). A total of 24 adults aged 21–34 years and 18 children aged 9–13 years participated in the study, yielding 1790 and 1246 data points for adults and children respectively for model building and validation. For adults, the AUC for classifying SED, MVPA, and VPA were 0.96, 0.88, and 0.86, respectively. The MAE between true METs and estimated METs was 0.75 METs. The correlation coefficient and ICC were 0.87 (p < 0.001) and 0.89, respectively. For children, comparable levels of accuracy were demonstrated, with the AUC for SED, MVPA, and VPA being 0.98, 0.89, and 0.85, respectively. The MAE between true METs and estimated METs was 0.80 METs. The correlation coefficient and ICC were 0.79 (p < 0.001) and 0.84, respectively. The developed model successfully estimated PA intensity with high accuracy in both adults and children. The application of this model enables independent investigation of PA intensity, facilitating research in health monitoring and potentially in areas such as myopia prevention and control.