Clinical values of serum C5a in Alzheimer's disease patients with different dementia stages.

Alzheimer's disease (AD) is characterized by abnormal inflammatory responses, and complement C5a (C5a) is known to initiate inflammation. This study aimed to investigate the associations between serum C5a, inflammatory responses, and cognitive function in AD patients. A total of 242 AD patients and 132 age-matched controls were included. Enzyme-linked immunosorbent assay revealed increased levels of C5a, interleukin (IL)-4, IL-6, IL-10, IL-1ß, and tumor necrosis factor (TNF)-α with advancing stages of AD. Pearson correlation coefficient and receiver operating characteristic curve revealed positive correlations between serum C5a levels, inflammatory cytokine levels, Neuropsychiatric Inventory (NPI) and Activities of Daily Living (ADL) scores, and negative correlations with Mini-mental State Examination (MMSE) and Montreal cognitive assessment (MoCA) scores. Serum C5a above 68.68 pg/mL could aid in the diagnosis of AD. Multivariable logistic analysis revealed that serum C5a was an independent risk factor for IL-1ß/IL-6/IL-10/TNF-α and an independent protective factor for IL-4. Higher serum C5a levels were associated with lower MMSE and MoCA scores. In conclusion, elevated serum C5a levels were beneficial for AD diagnosis and predictive of inflammation and cognitive dysfunction.

Development and validation of a nomogram model for mortality prediction in stable chronic obstructive pulmonary disease patients: A prospective observational study in the RealDTC cohort.

Background: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. There is no nomogram model available for mortality prediction of stable COPD. We intended to develop and validate a nomogram model to predict mortality risk in stable COPD patients for personalised prognostic assessment. Methods: A prospective observational study was made of COPD outpatients registered in the RealDTC study between December 2016 and December 2019. Patients were randomly assigned to the training cohort and validation cohort in a ratio of 7:3. We used Lasso regression to screen predicted variables. Further, we evaluated the prognostic performance using the area under the time-dependent receiver operating characteristic curve (AUC) and calibration curve. We used the AUC, concordance index, and decision curve analysis to evaluate the net benefits and utility of the nomogram compared with three earlier prediction models. Results: Of 2499 patients, the median follow-up was 38 months. The characteristics of the patients between the training cohort (n = 1743) and the validation cohort (n = 756) were similar. ABEODS nomogram model, combining age, body mass index, educational level, airflow obstruction, dyspnoea, and severe exacerbation in the first year, was constructed to predict mortality in stable COPD patients. In the integrative analysis of training and validation cohorts of the nomogram model, the three-year mortality prediction achieved AUC = 0.84; 95% confidence interval (CI) = 0.81, 0.88 and AUC = 0.80; 95% CI = 0.74, 0.86, respectively. The ABEODS nomogram model preserved excellent calibration in both the training cohort and validation cohort. The time-dependent AUC, concordance index, and net benefit of the nomogram model were higher than those of BODEx, updated ADO, and DOSE, respectively. Conclusions: We developed and validated a prognostic nomogram model that accurately predicts mortality across the COPD severity spectrum. The proposed ABEODS nomogram model performed better than earlier models, including BODEx, updated ADO, and DOSE in Chinese patients with COPD. Registration: ChiCTR-POC-17010431.