Background: Glucose and lipid metabolic disorders are involved in the impairment of cognitive function. However, it remains unclear the link between a new indicator of glucose-lipid metabolism index (GLMI) and cognitive impairment. Objective: This study investigates the relationship between GLMI and multidimensional cognitive function in adults aged ≥ 60 years. Methods: GLMI was derived from glucose and lipid metabolism parameters. Consortium to Establish a Registry for Alzheimer's Disease (CERAD) Word Learning test, Animal Fluency Test (AFT) and Digit Symbol Substitution Test (DSST) were used to evaluate the cognitive abilities of participants. Linear regression, subgroup analyses, multivariable adjustments, and restricted cubic spline (RCS) models were employed to evaluate GLMI-cognition associations. Results: Among 787 participants from NHANES 2011–2014, diabetic individuals exhibited higher GLMI levels and more severe cognitive impairment than non-diabetic counterparts. In non-diabetic adults, GLMI showed linear correlations with CERAD and DSST assessed cognitive deficits. Subgroup analyses confirmed high GLMI as an independent risk factor for cognitive dysfunction in CERAD and DSST assessment. Multivariable regression revealed increased GLMI significantly elevated cognitive decline risk. ROC analysis identified 581.41 as the optimal GLMI cutoff (specificity: 86.0%) for predicting DSST impairment, outperforming traditional indices (TyG/HOMA-IR). RCS models demonstrated nonlinear GLMI-cognition associations, with a dose-dependent risk curve (200–800 range) and critical threshold at 545. Conclusions: This study establishes that a high score of GLMI is associated with great severity of cognitive impairment in non-diabetic population.

The ε4 allele of apolipoprotein E (APOE4) and aging are the major risk factors for Alzheimer’s disease (AD). SUMOylation is intimately linked to the development of AD and the aging process. However, the SUMOylation status in APOE4 mice has not been uncovered. In this study, we investigated SENP1 and SUMOylation changes in the brains of aged APOE3 and APOE4 mice, aiming to understand their potential impact on mitochondrial metabolism and their contribution to cellular senescence in APOE4 mice. Concurrently, SUMO1-conjugated protein levels decreased, while SUMO2/3-conjugated protein levels increased relatively with the aging of APOE4 mice. This suggests that the equilibrium between the SUMOylation and deSUMOylation processes may be associated with senescence and longevity. Our findings highlight the significant roles of SENP1 and SUMOylation changes in APOE4-driven pathology and the aging process.

Background/Objectives: Affinity proteomics of amyloid precursor protein (APP) is complicated by proteolytic processing, dynamic trafficking, and purification background. We generated a C-terminal 3×FLAG knock-in at the endogenous App locus to establish a cortical affinity-proteomic resource and prioritize APP-associated candidates for further study. Methods: Eight-week-old male homozygous APP-3×FLAG mice on a C57BL/6J background and wild-type littermates were characterized by biochemical and behavioral assays (n = 6 and n = 7 per genotype, respectively). Cortical material from three APP-3×FLAG males was pooled for anti-FLAG affinity purification and liquid chromatography–tandem mass spectrometry. Protein accessions were filtered by positive Area, unique-peptide support, and control recovery, with Gene Ontology (GO) analyses conducted under alternative backgrounds. Selected candidates underwent targeted co-immunoprecipitation and immunoblotting. Results: Baseline APP, amyloid-β, and behavioral measurements showed no statistically significant genotype-associated differences. Five shared high-confidence APP regions showed local Cα RMSD values below 0.3 Å in structural-model comparisons. Across APP-IP and control datasets, 1874 accessions were identified. Evidence filtering retained 990 non-bait candidate accessions representing 967 APP-IP protein groups. Two de novo-only spectra supported recovery of the engineered APP–3×FLAG junction peptide. GO analysis identified 21 significant cellular-component terms under the observed-union background, with none under the evidence-eligible background. Targeted anti-FLAG co-immunoprecipitation and immunoblotting supported ANXA2 and RACK1 co-recovery with APP-3×FLAG. Conclusions: The endogenous tagging model, evidence-filtered proteomic dataset, and targeted biochemical findings establish a candidate resource for investigating APP-associated assemblies and prioritizing proteins for studies of trafficking and proteostasis.

DNA methylation age prediction from incomplete profiles requires evaluation under empirical and random missingness. We evaluated whole‐blood chronological‐age prediction using 524 development and 132 held‐out GSE40279 samples and an external GSE73103 cohort ( n = 355). Feature‐stability analysis across 750 prespecified perturbation conditions identified a 209‐CpG primary panel and a 52‐CpG stringent sensitivity subset. The primary predictor combined five‐nearest‐neighbor reconstruction (KNN5) with Ridge regression. In the internal holdout, intact‐profile prediction achieved a mean absolute error of 4.24 years, a root‐mean‐square error (RMSE) of 5.38 years, and Pearson r = 0.934. Under masks derived from 42 empirical templates of formalin‐fixed paraffin‐embedded detection failure, KNN5 reduced RMSE by 0.365 years relative to development‐set mean replacement (95% bootstrap interval for the difference, −0.546 to −0.205), with larger reductions at higher missingness burdens. The reconstruction advantage persisted in complementary development analyses after randomization preserving both sample‐level and CpG‐level failure counts. External predictions remained associated with chronological age ( r = 0.835), despite age‐dependent calibration bias. Mixture analyses showed that background accuracy was central to target recovery as mixture asymmetry increased. These findings support local reconstruction within a stability‐selected CpG panel for chronological‐age prediction under the evaluated empirical and random missingness conditions.