SPON1 Is Associated with Amyloid-β and APOE ε4-Related Cognitive Decline in Cognitively Normal Adults

Cerebrospinal fluid proteomics define the natural history of autosomal dominant Alzheimer's disease

Alzheimer's disease (AD) pathology develops many years before the onset of cognitive symptoms. Two pathological processes-aggregation of the amyloid-β (Aβ) peptide into plaques and the microtubule protein tau into neurofibrillary tangles (NFTs)-are hallmarks of the disease. However, other pathological brain processes are thought to be key disease mediators of Aβ plaque and NFT pathology. How these additional pathologies evolve over the course of the disease is currently unknown. Here we show that proteomic measurements in autosomal dominant AD cerebrospinal fluid (CSF) linked to brain protein coexpression can be used to characterize the evolution of AD pathology over a timescale spanning six decades. SMOC1 and SPON1 proteins associated with Aβ plaques were elevated in AD CSF nearly 30 years before the onset of symptoms, followed by changes in synaptic proteins, metabolic proteins, axonal proteins, inflammatory proteins and finally decreases in neurosecretory proteins. The proteome discriminated mutation carriers from noncarriers before symptom onset as well or better than Aβ and tau measures. Our results highlight the multifaceted landscape of AD pathophysiology and its temporal evolution. Such knowledge will be critical for developing precision therapeutic interventions and biomarkers for AD beyond those associated with Aβ and tau.

Identification of pyroptosis-related genes and potential drugs in diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN) is one of the serious microvascular complications of diabetes mellitus (DM). A growing body of research has demonstrated that the inflammatory state plays a critical role in the incidence and development of DN. Pyroptosis is a new way of programmed cell death, which has the particularity of natural immune inflammation. The inhibition of inflammatory cytokine expression and regulation of pathways related to pyroptosis may be a novel strategy for DN treatment. The aim of this study is to identify pyroptosis-related genes and potential drugs for DN.

METHODS

DN differentially expressed pyroptosis-related genes were identified via bioinformatic analysis Gene Expression Omnibus (GEO) dataset GSE96804. Dataset GSE30528 and GSE142025 were downloaded to verify pyroptosis-related differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a pyroptosis-related gene predictive model. A consensus clustering analysis was performed to identify pyroptosis-related DN subtypes. Subsequently, Gene Set Variation Analysis (GSVA), Gene Ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to explore the differences between DN clusters. A protein-protein interaction (PPI) network was used to select hub genes and DGIdb database was utilized to screen potential therapeutic drugs/compounds targeting hub genes.

RESULTS

A total of 24 differentially expressed pyroptosis-related genes were identified in DN. A 16 gene predictive model was conducted via LASSO regression analysis. According to the expression level of these 16 genes, DN cases were divided into two subtypes, and the subtypes are mainly associated with inflammation, activation of immune response and cell metabolism. In addition, we identified 10 hub genes among these subtypes, and predicted 65 potential DN therapeutics that target key genes.

CONCLUSION

We identified two pyroptosis-related DN clusters and 65 potential therapeutical agents/compounds for DN, which might shed a light on the treatment of DN.

Fine or Gross Motor Index as a Simple Tool for Predicting Cognitive Impairment in Elderly People: Findings from The Irish Longitudinal Study on Ageing (TILDA)

BACKGROUND

Several kinds of motor dysfunction can predict future cognitive impairment in elderly individuals. However, the ability of the fine motor index (FINEA) and gross motor index (GROSSA) to predict the risk of cognitive impairment has not been assessed.

OBJECTIVE

We investigated the associations between FINEA/GROSSA and cognitive impairment.

METHODS

The data of 4,745 participants from The Irish Longitudinal Study on Ageing (TILDA) were analyzed. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). We first assessed the correlation between the FINEA/GROSSA and MMSE in a cross-sectional study. Then, we further investigated the predictive role of the incidence of cognitive impairment in a prospective cohort study.

RESULTS

We found that both FINEA and GROSSA were negatively correlated with MMSE in both the unadjusted (FINEA: B = -1.00, 95%confidence intervals (CI): -1.17, -0.83, t = -11.53, p < 0.001; GROSSA: B = -0.85, 95%CI: -0.94, -0.76, t = -18.29, p < 0.001) and adjusted (FINEA: B = -0.63, 95%CI: -0.79, -0.47, t = -7.77, p < 0.001; GROSSA: B = -0.57, 95%CI: -0.66, -0.48, t = -12.61, p < 0.001) analyses in a cross-sectional study. In a prospective cohort study, both high FINEA and high GROSSA were associated with an increased incidence of cognitive function impairment (FINEA: adjusted odds ratios (OR) = 2.35, 95%CI: 1.05, 5.23, p = 0.036; GROSSA adjusted OR = 3.00, 95%CI: 1.49, 6.03, p = 0.002) after 2 years of follow-up.

CONCLUSION

Higher FINEA and GROSSA scores were both associated with an increased incidence of cognitive impairment. FINEA or GROSSA might be a simple tool for identifying patients with cognitive impairment.