Cholesterol has been associated as a risk factor for cardiovascular disease. Recently, however, there is growing evidence about crucial requirement of neuron membrane cholesterol in the organization and function of the 5-HT1A serotonin receptor. For this, low cholesterol level has been reported to be associated with depression and suicidality. However there have been inconsistent reports about this finding and the exact relationship between these factors remains controversial. Therefore, we investigated the link between serum cholesterol and its fractions with depression disorder and suicide attempt in 467 adult subjects in Mexican mestizo population. Plasma levels of total cholesterol, triglycerides, and high-density lipoprotein cholesterol (HDL-c) and low density lipoprotein cholesterol (LDL-c) were determined in 261 MDD patients meeting the DSM-5 criteria for major depressive disorder (MDD), 59 of whom had undergone an episode of suicide attempt, and 206 healthy controls. A significant decrease in total cholesterol, LDL-cholesterol, VLDL-cholesterol and triglyceride serum levels was observed in the groups of MDD patients and suicide attempt compared to those without suicidal behavior (p < 0.05). After adjusting for covariates, lower cholesterol levels were significantly associated with MDD (OR 4.229 CI 95% 2.555 – 7.000, p<.001) and suicide attempt (OR 5.540 CI 95% 2.825 – 10.866, p<.001) These results support the hypothesis that lower levels of cholesterol are associated with mood disorders like MDD and suicidal behavior. More mechanistic studies are needed to further explain this association.

Background: Blood metabolites have emerged as promising candidates in the search for biomarkers for Alzheimer's disease (AD), as evidence shows that various metabolic derangements contribute to neurodegeneration in AD. Objective: We aim to identify metabolic biomarkers for AD diagnosis. Methods: We conducted an in-depth analysis of the serum metabolome of AD patients and age, sex-matched cognitively unimpaired older adults using ultra-high-performance liquid chromatography-high resolution mass spectrometry. The biomarkers associated with AD were identified using machine learning algorithms. Results: Using the discovery dataset and support vector machine (SVM) algorithm, we identified a panel of 14 metabolites predicting AD with a 1.00 area under the curve (AUC) of receiver operating characteristic (ROC). The SVM model was tested against the verification dataset using an independent cohort and retained high predictive accuracy with a 0.97 AUC. Using the random forest (RF) algorithm, we identified a panel of 13 metabolites that predicted AD with a 0.96 AUC when tested against the verification dataset. Conclusions: These findings pave the way for an efficient, blood-based diagnostic test for AD, holding promise for clinical screenings and diagnostic procedures.

Background: Alterations in peripheral red blood cell (RBC) indices have been proposed as potential biomarkers for Parkinson’s disease (PD), but their diagnostic utility and relation to clinical features remain uncertain. Methods: We conducted a pilot case–control study involving 70 PD patients and 122 controls from two neurology centers in Mexico. Standardized hematology analyses provided RBC indices, and neuropsychiatric assessments included the Hamilton Depression Rating Scale (HAM-D) and Mini-Mental State Examination (MMSE). Associations between RBC indices and PD were tested using multivariable logistic regression adjusted for age, sex, and smoking. Diagnostic performance was evaluated by receiver operating characteristic (ROC) curve analysis. Subgroup analyses stratified PD patients by age at onset, disease duration, and Hoehn and Yahr (HY) stage. Results: PD patients exhibited significantly higher mean corpuscular hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC) than controls. Elevated MCHC was independently associated with PD (OR = 1.68, 95% CI 1.35–2.09; p < 0.001). Sex-stratified models confirmed consistent associations in women (OR = 1.57) and men (OR = 1.79). ROC analysis demonstrated fair diagnostic accuracy for MCHC (AUC 0.72, 95% CI 0.65–0.80; cutoff 33.9 g/dL, sensitivity 62.9%, specificity 72.1%). Sex-specific thresholds improved sensitivity in women (90.6%) and specificity in men (74.6%). Within the PD group, MCHC did not differ by HY stage or disease duration, and showed no correlation with UPDRS, HAM-D, or MMSE scores. Early-onset cases (<50 years) showed numerically higher MCHC, though numbers were limited. Conclusions: This pilot study confirms that an elevated MCHC is independently associated with PD, a finding consistent across both sexes and independent of disease severity. MCHC demonstrates fair diagnostic performance, supporting its potential as a low-cost, accessible biomarker. Larger longitudinal studies integrating RBC indices with inflammatory and iron-regulatory markers are warranted to establish their role in the diagnosis and differential diagnosis of PD. Elevated MCHC was associated with PD, and an MCHC-based index (cutoff 33.9 g/dL; AUC 0.72, sensitivity 62.9%, specificity 72.1%) showed potential as a simple diagnostic marker.

Tau protein aggregates adopt distinct conformations across tauopathies, yet the protein interactions engaged by disease-specific polymorphs remain poorly characterized. Here, we demonstrate that conformationally distinct tau polymorphs associate with disease-specific interaction networks across Alzheimer’s disease (AD), progressive supranuclear palsy (PSP), and dementia with Lewy bodies (DLB). Interactome profiling of tau aggregates from PBS- and sarkosyl-soluble brain fractions identified 493 high-confidence interactors exhibiting remarkable disease specificity. As an exploratory feature discovery machine learning classification discriminated against diseases using as few as four to six protein features. AD tau selectively engaged glycolytic enzymes, TCA cycle components, and glutamate/GABA cycling machinery, with the astrocytic transporter SLC1A2 showing 27-fold enrichment. PSP tau exhibited extensive interactor depletion alongside selective proteasome enrichment, whereas DLB tau associated with neurogenesis modulators while depleting neuroinflammatory mediators. Interaction patterns were corroborated by parallel reaction monitoring mass spectrometry and proximity ligation assays and corresponded to disease-specific post-translational modification profiles. These findings show that tau polymorph conformations are associated with disease-specific interaction networks, providing molecular insight into tauopathy heterogeneity.