Background: The early detection of benign and malignant lung tumors enabled patients to diagnose lesions and implement appropriate health measures earlier, dramatically improving lung cancer patients’ quality of living. Machine learning methods performed admirably when recognizing small benign and malignant lung nodules. However, exploration and investigation are required to fully leverage the potential of machine learning in distinguishing between benign and malignant small lung nodules. Objective: The aim of this study was to develop and evaluate the ResNet50-Ensemble Voting model for detecting the benign and malignant nature of small pulmonary nodules (<20 mm) based on CT images. Methods: In this study, 834 CT imaging data from 396 patients with small pulmonary nodules were gathered and randomly assigned to the training and validation sets in an 8:2 ratio. ResNet50 and VGG16 algorithms were utilized to extract CT image features, followed by XGBoost, SVM, and Ensemble Voting techniques for classification, for a total of ten different classes of machine learning combinatorial classifiers. Indicators such as accuracy, sensitivity, and specificity were used to assess the models. The collected features are also shown to investigate the contrasts between them. Results: The algorithm we presented, ResNet50-Ensemble Voting, performed best in the test set, with an accuracy of 0.943 (0.938, 0.948) and sensitivity and specificity of 0.964 and 0.911, respectively. VGG16-Ensemble Voting had an accuracy of 0.887 (0.880, 0.894), with a sensitivity and specificity of 0.952 and 0.784, respectively. Conclusion: Machine learning models that were implemented and integrated ResNet50-Ensemble Voting performed exceptionally well in identifying benign and malignant small pulmonary nodules (<20 mm) from various sites, which might help doctors in accurately diagnosing the nature of early-stage lung nodules in clinical practice.

Objectives: To investigate the association between visceral fat accumulation and the risk of cardiometabolic multimorbidity (CMM) and the potential roles of accelerated biological aging in this relationship. Methods: Using data from the UK Biobank, a nationwide cohort study was conducted using the available baseline body roundness index (BRI) measurement. Biological aging was assessed using the Klemera–Doubal method for biological age and the phenotypic age algorithms. The association between the BRI and CMM was estimated using the Cox proportional hazards regression model, while the roles of biological aging were examined through interaction and mediation analyses. Results: During a median follow-up of 14.52 years, 6156 cases of CMM were identified. A significant association was observed between the BRI and CMM. The hazard ratio (HR) for CMM was 3.72 (95% confidence interval [CI]: 3.35–4.13) for individuals in the highest quartile compared with those in the lowest quartile of the BRI. More importantly, the BRI (AUC, 0.701; 95% CI, 0.694–0.707) demonstrated superior predictive performance relative to body mass index (AUC, 0.657; 95% CI, 0.650–0.664). Furthermore, the BRI exhibited additive interactions with accelerated biological aging on the risk of CMM, and accelerated biological aging partially mediated the association between the BRI and CMM. Conclusions: These findings provide evidence for the application of the BRI as a novel and readily accessible screening tool associated with CMM, suggesting that the effective management of visceral fat and biological aging deceleration may hold promise for reducing CMM risk.

Objectives: Previous studies have confirmed that biological age (BA) acceleration is associated with single cardio-renal–metabolic diseases (CRMDs), typically including type 2 diabetes mellitus, cardiovascular disease, and chronic kidney disease. However, its association with progression to cardio-renal–metabolic multimorbidity (CRMM, coexistence of ≥2 CRMDs) and subsequent mortality remains unexplored. Methods: Using the multi-state model, we analyzed 278,927 UK Biobank participants free of CRMDs at baseline to investigate the association between BA acceleration—measured by phenotypic age (PhenoAge) and Klemera–Doubal method age (KDMAge)—and CRMM progression trajectory, from health to the first CRMD and then to CRMM and death. BA acceleration was the residual from regressing BA on chronological age; positive values indicated a biologically older individual. Results: PhenoAge acceleration showed stronger associations than KDMAge acceleration. Per the 1-SD increase in PhenoAge acceleration; HRs (95% CIs) were observed at 1.18 (1.17–1.19) for baseline to first CRMD; 1.24 (1.22–1.26) for first CRMD to CRMM; 1.25 (1.22–1.27) for baseline to death; 1.13 (1.11–1.15) for first CRMD to death; and 1.09 (1.06–1.12) for CRMM to death. Biologically older individuals by PhenoAge acceleration showed greater reductions in CRMD-free and total life expectancy than those by KDMAge acceleration. Age, socioeconomic status, education, smoking status, alcohol consumption, physical activity, and diet-modified risks for specific transitions. Conclusions: BA acceleration, particularly PhenoAge acceleration, relates to higher CRMM progression risk and shorter life expectancy. Combining BA acceleration with sociodemographic or lifestyle factors improves risk identification for specific transitions. BA acceleration offers the potential to guide CRMM prevention across its entire progression.

Background: Prior studies have reported the cross‐sectional relationship between lung function and arterial stiffness, while the longitudinal association remains unclear to date. This study aimed to investigate whether abnormal lung function and its subtypes at baseline are associated with increased arterial stiffness using a cohort. Methods and Results: This was a secondary analysis extracting 2461 participants from Beijing Health Management Cohort as baseline and annually followed for development of arterial stiffness. Abnormal lung function was defined by forced expiratory volume in 1s <80% of the predicted value, forced vital capacity of the predicted value, or forced expiratory volume in 1s/forced vital capacity ratio <70%. Increased arterial stiffness was determined by brachial‐ankle pulse wave velocity ≥1400 cm/s. Cox proportional hazards model was used to calculate the hazard ratio and population attributable fraction. The mean age was 42.8±8.1 years, and 444 (18.0%) cases developed increased arterial stiffness during a median follow‐up of 3.0 years. The adjusted hazard ratio (95% CI) of arterial stiffness was 1.47 (95% CI, 1.10–1.96) for abnormal lung function, with a population attributable fraction of 3.9% (95% CI, 0.8–7.1). Of subtypes, only obstructive ventilatory dysfunction was significantly associated with arterial stiffness (adjusted hazard ratio, 2.06 [95% CI, 1.27–3.36]), not restricted ventilatory dysfunction (adjusted hazard ratio, 0.95 [95% CI, 0.54–1.65]). Consistent results were observed on multiple sensitivity analyses. Conclusions: Our study indicated a longitudinal association of abnormal lung function with increased arterial stiffness using a large cohort, especially for the obstructive ventilatory dysfunction.