Potential prognostic value of CSF-targeted proteomics across the Alzheimer's disease continuum.

BACKGROUND: Core biomarkers for Alzheimer's disease (AD), such as Aß42 and tau, have demonstrated high prognostic accuracy but do not fully capture the complex pathophysiology of AD. In this study, our objective was to identify novel cerebrospinal fluid (CSF) biomarkers using proteomics across the entire AD continuum to predict conversion to AD and explore their involvement in AD pathogenesis. METHODS: A cohort of 186 cognitively normal (CN), 127 subjective memory complaint (SMC), 79 early mild cognitive impairment (EMCI), 249 late MCI (LMCI), and 132 AD individuals was analyzed, with a follow-up period of over 3 years for non-AD participants. CSF 65 peptides, as well as hippocampal and entorhinal volumes were analyzed, and cognitive function was evaluated using the 13-item cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-Cog 13). Cox proportional hazards models and mediation analysis were performed to investigate associations and causal relationships. RESULTS: During the follow-up, approximately one-fourth (146/580) of the non-AD participants progressed to AD. After adjusting for baseline diagnosis (CN to LMCI) and other variables, multivariable Cox regression analysis identified three peptides (VAELEDEK, VSFELFADK, and VVSSIEQK) as significant predictors of conversion to AD. Incorporating these three peptides into the initial model significantly improved the C-statistic from 0.82 to 0.85 for predicting AD conversion, surpassing the predictive ability of Aß42 and P-tau. Moreover, hippocampal and entorhinal volumes mediated 30.3-53.8% of the association between the three peptides and ADAS-Cog 13 scores. CONCLUSIONS: These findings underscore the potential of these three peptides as robust prognostic biomarker candidates for AD conversion across the entire AD continuum, with a mechanism involving the mediation of hippocampal and entorhinal volumes.

Risk factors, prognostic factors, and nomograms for synchronous brain metastases of solid tumors: a population-based study.

In previous literatures, we found that similar studies on the short-term prognosis of synchronous brain metastases (S-BM) from other systems are rare. Our aim was to evaluate the early mortality rate of patients with S-BM from the Surveillance, Epidemiology, and End Result (SEER) database and explore the risk factors for early mortality (≤ 1 year). We used Kaplan-Meier (KM) curves to evaluate early mortality in patients with S-BM from the SEER database. Logistic regression analyses were used to identify significant independent prognostic factors in patients with a follow-up time > 12 months. And the meaningful factors were used to construct a nomogram of overall early death. The receiver operating characteristic (ROC) curve was used to test the predictive ability of the model, while the decision curve analysis (DCA) curve was used to validate the clinical application ability of the model. A total of 47,284 patients were used for univariate and multivariate logistic regression analysis to screen variables to constructing a nomogram. In the all-cause early mortality specific model, the area under the ROC (AUC) curve of the training set was 0.764 (95% confidence interval (CI): 0.758-0.769), and the AUC of the validation set was 0.761 (95% CI: 0.752-0.770). The DCA calibration curves of the training set and validation set indicate that the 1-year early mortality rate predicted by this model is consistent with the actual situation. We found that the 1-year early mortality rate was 76.4%. We constructed a validated nomogram using these covariates to effectively predict 1-year early mortality in patients with S-BM. This nomogram can help clinical workers screen high-risk patients to develop more reasonable treatment plans.