Hong Qiao | PubMed Health

PubMed2026

Age-stratified associations of glycemia, blood pressure, and cholesterol with mortality in diabetes: A prospective cohort study.

BACKGROUND: Optimization of HbA1c, blood pressure and cholesterol, referred to as the "ABCs", is central to the management of diabetes. However, the age-specific associations of these factors with mortality in patients with diabetes remains unclear. METHODS: In this prospective cohort study, 43,732 Chinese adults aged ≥ 40 years with diabetes were included from the China Cardiometabolic Disease and Cancer Cohort (4C) Study. Participants were stratified by age (< 55, 55-<65, 65-<75, ≥ 75 years). Cox proportional hazards regression and Fine-Gray competing risk models were employed to estimate the associations of HbA1c, systolic blood pressure (SBP), and low-density lipoprotein cholesterol (LDL-C) with all-cause, cardiovascular, and non-cardiovascular mortality across age groups. Relative importance and population attributable fractions (PAFs) were computed for each metabolic factor. RESULTS: During a median follow-up of 10.1 years, 3,975 deaths were documented. Age significantly modified the associations of HbA1c, SBP, and LDL-C with all mortality outcomes (all P for interaction < 0.05). Among participants aged < 75 years, HbA1c showed graded positive associations with all-cause, cardiovascular, and non-cardiovascular mortality. The SBP thresholds associated with increased mortality risk were 140 mmHg in those aged < 65 years and 160 mmHg in those aged 65-<75 years. Among those aged ≥ 75 years, however, the patterns of these associations differed markedly. Elevated mortality risk was observed only at HbA1c ≥ 9%, with a hazard ratio (HR) of 1.51 (95% confidence interval [CI]: 1.19-1.91) for all-cause mortality and a subdistribution hazard ratio (SHR) of 1.70 (95% CI: 1.23-2.36) for cardiovascular mortality, while SBP showed no significant association with any mortality outcome in this age group. Moreover, LDL-C emerged as a significant risk factor for cardiovascular mortality. Compared with participants with LDL-C < 1.8 mmol/L, those with LDL-C of 1.8-<2.6 mmol/L exhibited a significantly higher risk (SHR: 1.86; 95% CI: 1.11-3.11). Additionally, LDL-C had the largest PAF for cardiovascular mortality (9.6%) within this age group. CONCLUSIONS: The impacts of ABC factors on mortality risk vary substantially by age among adults with diabetes. In patients aged ≥ 75 years, less stringent glycemic and blood pressure targets may be appropriate, whereas lipid management remains critically important for reducing cardiovascular mortality.

Humans↗

PubMed2025

Development and Validation an Integrated Deep Learning Model to Assist Eosinophilic Chronic Rhinosinusitis Diagnosis: A Multicenter Study.

BACKGROUND: The assessment of eosinophilic chronic rhinosinusitis (eCRS) lacks accurate non-invasive preoperative prediction methods, relying primarily on invasive histopathological sections. This study aims to use computed tomography (CT) images and clinical parameters to develop an integrated deep learning model for the preoperative identification of eCRS and further explore the biological basis of its predictions. METHODS: A total of 1098 patients with sinus CT images were included from two hospitals and were divided into training, internal, and external test sets. The region of interest of sinus lesions was manually outlined by an experienced radiologist. We utilized three deep learning models (3D-ResNet, 3D-Xception, and HR-Net) to extract features from CT images and calculate deep learning scores. The clinical signature and deep learning score were inputted into a support vector machine for classification. The receiver operating characteristic curve, sensitivity, specificity, and accuracy were used to evaluate the integrated deep learning model. Additionally, proteomic analysis was performed on 34 patients to explore the biological basis of the model's predictions. RESULTS: The area under the curve of the integrated deep learning model to predict eCRS was 0.851 (95% confidence interval [CI]: 0.77-0.93) and 0.821 (95% CI: 0.78-0.86) in the internal and external test sets. Proteomic analysis revealed that in patients predicted to be eCRS, 594 genes were dysregulated, and some of them were associated with pathways and biological processes such as chemokine signaling pathway. CONCLUSIONS: The proposed integrated deep learning model could effectively predict eCRS patients. This study provided a non-invasive way of identifying eCRS to facilitate personalized therapy, which will pave the way toward precision medicine for CRS.

Humans↗