RESUMEN

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet its current treatments are limited to stopping disease progression. Moreover, the effectiveness of these treatments remains uncertain due to the heterogeneity of the disease. Therefore, it is essential to identify disease subtypes at a very early stage. Current data-driven approaches can be used to classify subtypes during later stages of AD or related disorders, but making predictions in the asymptomatic or prodromal stage is challenging. Furthermore, the classifications of most existing models lack explainability, and these models rely solely on a single modality for assessment, limiting the scope of their analysis. Thus, we propose a multimodal framework that utilizes early-stage indicators, including imaging, genetics, and clinical assessments, to classify AD patients into progression-specific subtypes at an early stage. In our framework, we introduce a tri-modal co-attention mechanism (Tri-COAT) to explicitly capture cross-modal feature associations. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (slow progressing = 177, intermediate = 302, and fast = 15) were used to train and evaluate Tri-COAT using a 10-fold stratified cross-testing approach. Our proposed model outperforms baseline models and sheds light on essential associations across multimodal features supported by known biological mechanisms. The multimodal design behind Tri-COAT allows it to achieve the highest classification area under the receiver operating characteristic curve while simultaneously providing interpretability to the model predictions through the co-attention mechanism.

RESUMEN

BACKGROUND: Biomarkers for suicidal behavior in patients with acute coronary syndrome (ACS) have yet to be elucidated. This study aimed to identify a panel of serum biomarkers associated with suicidal ideation (SI) in patients with ACS. METHODS: The study evaluated 969 patients within 2 weeks of ACS (acute phase) and 711 patients 12 months later (chronic phase). The evaluation included 14 serum biomarkers covering 7 functional systems, socio-demographic/clinical characteristics, and SI assessed by the "suicidal thoughts" item of the Montgomery-Åsberg Depression Rating Scale. Logistic regression models were used to analyze the data. The results showed that 195 patients (20.1 %) had SI in the acute phase, and 87 patients (12.2 %) had SI in the chronic phase. RESULTS: A combination of five serum biomarkers (tumor necrosis factor-α, interleukin-1ß, folate, troponin I, and creatine kinase-MB) was significantly associated with SI in the acute phase, and a combination of three serum biomarkers (tumor necrosis factor-α, interleukin-1ß, and folate) was significantly associated with SI in the chronic phase in a clear dose-dependent manner (all P-values < 0.001) after adjustment for relevant covariates. DISCUSSION: These findings suggest that the application of a combination of multiple serum biomarkers could improve the predictability of SI in patients with ACS at both acute and chronic phases.

Asunto(s)

RESUMEN

Increased mitotic activity is associated with the genesis and aggressiveness of many cancers. To assess the clinical value of mitotic activity as prognostic biomarker, we performed a pan-cancer study on the mitotic network activity index (MNAI) constructed based on 54-gene mitotic apparatus network. Our pan-cancer assessment on TCGA (33 tumor types, 10,061 patients) and validation on other publicly available cohorts (23 tumor types, 9,209 patients) confirmed the significant association of MNAI with overall survival, progression-free survival, and other prognostic endpoints in multiple cancer types, including lower-grade gliomas (LGG), breast invasive carcinoma (BRCA), as well as many others. We also showed significant association between MNAI and genetic instability, which provides a biological explanation of its prognostic impact at pan-cancer landscape. Our association analysis revealed that patients with high MNAI benefitted more from anti-PD-1 and Anti-CTLA-4 treatment. In addition, we demonstrated that multimodal integration of MNAI and the AI-empowered Cellular Morphometric Subtypes (CMS) significantly improved the predictive power of prognosis compared to using MNAI and CMS alone. Our results suggest that MNAI can be used as a potential prognostic biomarker for different tumor types toward different clinical endpoints, and multimodal integration of MNAI and CMS exceeds individual biomarker for precision prognosis.

RESUMEN

INTRODUCTION: This study employed an integrative system and causal inference approach to explore molecular signatures in blood and CSF, the amyloid/tau/neurodegeneration [AT(N)] framework, mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD), and genetic risk for AD. METHODS: Using the European Medical Information Framework (EMIF)-AD cohort, we measured 696 proteins in cerebrospinal fluid (n = 371), 4001 proteins in plasma (n = 972), 611 metabolites in plasma (n = 696), and genotyped whole-blood (7,778,465 autosomal single nucleotide epolymorphisms, n = 936). We investigated associations: molecular modules to AT(N), module hubs with AD Polygenic Risk scores and APOE4 genotypes, molecular hubs to MCI conversion and probed for causality with AD using Mendelian randomization (MR). RESULTS: AT(N) framework associated with protein and lipid hubs. In plasma, Proprotein Convertase Subtilisin/Kexin Type 7 showed evidence for causal associations with AD. AD was causally associated with Reticulocalbin 2 and sphingomyelins, an association driven by the APOE isoform. DISCUSSION: This study reveals multi-omics networks associated with AT(N) and causal AD molecular candidates.

Asunto(s)

RESUMEN

BACKGROUND: Predictive values of multiple serum biomarkers for suicidal behaviours (SBs) have rarely been tested. This study sought to evaluate and develop a panel of multiple serum biomarkers for predicting SBs in outpatients receiving a 12-month pharmacotherapy programme for depressive disorders. METHODS: At baseline, 14 serum biomarkers and socio-demographic/clinical characteristics including previous suicidal attempt and present suicidal severity were evaluated in 1094 patients with depressive disorders without a bipolar diagnosis. Of these, 884 were followed for increased suicidal severity and fatal/non-fatal suicide attempt outcomes over a 12-month treatment period. Individual and combined effects of serum biomarkers on these two prospective SBs were estimated using logistic regression analysis after adjustment for relevant covariates. RESULTS: Increased suicidal severity and fatal/non-fatal suicide attempt during the 12-month pharmacotherapy were present in 155 (17.5%) and 38 (4.3%) participants, respectively. Combined cortisol, total cholesterol, and folate serum biomarkers predicted fatal/non-fatal suicide attempt, and these with interleukin-1 beta and homocysteine additionally predicted increased suicidal severity, with clear gradients robust to adjustment (p values < 0.001). CONCLUSIONS: Application of multiple serum biomarkers could considerably improve the predictability of SBs during the outpatient treatment of depressive disorders, potentially highlighting the need for more frequent monitoring and risk appraisal.

Asunto(s)

RESUMEN

Prognostic biomarkers for depression treatment outcomes have yet to be elucidated. This study sought to evaluate whether a multi-modal serum biomarker panel was prospectively associated with 12-week and 12-month remission in outpatients with depressive disorders receiving stepwise psychopharmacotherapy. At baseline, 14 serum biomarkers and socio-demographic/clinical characteristics were evaluated in 1094 patients. They received initial antidepressant monotherapy followed, as required by a protocol of successive alternative pharmacological strategies administered in 3-week steps during the acute (3-12 week) phase (N = 1086), and in 3-month steps during the continuation (6-12 month) phase (N = 884). Remission was defined as a Hamilton Depression Rating Scale score of ≤ 7. Remission was achieved in 490 (45.1%) over the 12-week, and in 625 (70.7%) over the 12-month, treatment periods. Combination scores of four serum biomarkers (high-sensitivity C-reactive protein, interleukin-1 beta, interleukin-6, and leptin) were prospectively associated with 12-week remission; and four (high-sensitivity C-reactive protein, tumor necrosis factor-alpha, interleukin-1 beta, and brain-derived neurotrophic factor) were prospectively associated with 12-month remission in a clear gradient manner (P-values < 0.001) and after adjustment for relevant covariates. These associations were evident after the Step 1 treatment monotherapy but weakened with increasing treatment steps, falling below statistical significance after 4 + treatment steps. Application of combined multiple serum biomarkers, particularly on inflammatory markers, could improve predictability of remission at acute and continuation treatment phases for depressive disorders. Patients with unfavourable biomarkers might require alternative treatment regimes for better outcomes.

Asunto(s)

RESUMEN

BACKGROUND: This study investigates the prediction of mild cognitive impairment-to-Alzheimer's disease (MCI-to-AD) conversion based on extensive multimodal data with varying degrees of missing values. METHODS: Based on Alzheimer's Disease Neuroimaging Initiative data from MCI-patients including all available modalities, we predicted the conversion to AD within 3 years. Different ways of replacing missing data in combination with different classification algorithms are compared. The performance was evaluated on features prioritized by experts and automatically selected features. RESULTS: The conversion to AD could be predicted with a maximal accuracy of 73% using support vector machines and features chosen by experts. Among data modalities, neuropsychological, magnetic resonance imaging, and positron emission tomography data were most informative. The best single feature was the functional activities questionnaire. CONCLUSION: Extensive multimodal and incomplete data can be adequately handled by a combination of missing data substitution, feature selection, and classification.

RESUMEN

Early diagnoses of Alzheimer's disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different ([Formula: see text]). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones.