Platelet Biomarkers For Cognitive Impairment In Different Types Of Elderly

Background: Type 2 diabetes mellitus(T2DM)and aging are independent risk factors for Alzheimer’s disease.Gradual loss of memory is the most common clinical feature of AD,with 10% to 15% of patients with mild cognitive impairment(MCI)developing AD each year.With the increasing incidence of T2 DM and the increasing aging of the population,the prevalence of AD also showed a trend of sharp increase.Therefore,finding peripheral biomarkers that can objectively predict which T2 DM patients or elderly people are likely to develop AD in the early stage is of great significance for early intervention and ultimately to reduce the prevalence of AD.Platelets have many biological similarities with neurons,making them an ideal resource for exploring peripheral markers of neurological diseases such as AD.Currently,the molecular link between platelets and brain has not been reported.Objective: Searching for the platelet biomarkers of elderly people with cognitive decline and establish an early diagnosis model of AD.Integrating brain and platelet omics reveal their common altered and driven molecules in AD.Methods: Based on the cognitive cohort established by our laboratory relying on communities and hospitals,the elderly was divided into those with type 2 diabetes and those without type 2 diabetes.Based on the mini mental state examination(MMSE),we classified patients with type 2 diabetes into T2DM-n MCI(MMSE = 28-30)and T2DM-MCI patients(MMSE = 18-24).For elderly without type 2 diabetes,we divided them into the populations with mild cognitive impairment(MCI,MMSE = 18-23),severe cognitive impairments(AD,MMSE = 2-17)and the age-/sex-matched normal cognition controls(MMSE = 29-30)according to MMSE score.Then,the platelet proteomics was characterized by TMT marker combined with mass spectrometry.Bioinformatics was used to analyze the biological functions of the differentially expressed proteins.Western Blots and ELISA kits were used to detect the expression levels of related proteins.Partial least squares discriminant analysis(PLS-DA),logistic regression and machine learning were used to construct cognitive impairment discrimination model.Finally,we combined platelet proteomics with the AD brain database to analyze the molecular association between platelet and brain in the cognitive decline process.Results:1.Platelet biomarkers identifying mild cognitive impairment in elderly people with type 2 diabetes mellitus: By using TMT-LC-MS/MS proteomics,a total of 2994 platelet proteins were captured,of which 46 differentially expressed proteins(DEPs)were identified in T2DM-MCI vs.T2DM-n MCI(P <0.05).Pearson analysis of the omics data with MMSE(mini-mental state examination),Aβ1-42/Aβ1-40(β-amyloid),and r GSK-3β(T/S9)(total to Serine-9-phosphorylated glycogen synthase kinase-3β)revealed that mitophagy/autophagy-,insulin signaling-,and glycolysis/gluconeogenesis pathways-related proteins were most significantly involved.Among them,only the increase of optineurin,an autophagy-related protein,was simultaneously correlated with the reduced MMSE score,and the increased Aβ1-42/Aβ1-40 and r GSK-3β(T/S9),and the optineurin alone could discriminate T2 DMMCI from T2DM-n MCI.Combination of the elevated platelet optineurin and r GSK-3β(T/S9)enhanced the MCI-discriminating efficiency with AUC of 0.927,specificity of86.7%,sensitivity of 85.3%,and accuracy of 0.859,which is promising for predicting cognitive decline in T2 DM patients.2.Platelet biomarkers for a descending cognitive function in elderly people without type 2 diabetes mellitus: A total of 360 differential proteins were detected in MCI and AD patients compared with the controls.These differential proteins were involved in multiple KEGG pathways,including AD,AMP-activated protein kinase(AMPK)pathway,telomerase RNA localization,platelet activation,and complement activation.By correlation analysis with MMSE score,three positively correlated pathways and two negatively correlated pathways were identified to be closely related to cognitive decline in MCI and AD patients.Partial least squares discriminant analysis(PLS-DA)showed that changes of nine proteins,including PHB,UQCRH,CD63,GP1 BA,FINC,RAP1 A,ITPR1/2 and ADAM10,could effectively distinguish the cognitively impaired patients from the controls.Further machine learning analysis revealed that a combination of four decreased platelet proteins,i.e.PHB,UQCRH,GP1 BA and FINC,were most promising for predicting cognitive decline in MCI and AD patients.3.Integrated analyses of brain and platelet omics in Alzheimer’s disease:By integrating the gene and protein expression profiles from 269 AD patients,we deduced 239 differentially expressed proteins(DEPs)appeared in both brain and the platelet,and 70.3%of them were consistent changes.Further analysis demonstrated that the altered brain and peripheral regulations were pinpointed into ten imbalanced pathways.The changes of IDH3 B and RTN1 had a potential diagnostic value for cognitive impairment analyzed by machine learning.Finally,we identified that HMOX2 and SERPINA3 could serve as driving molecules in AD pathology,and they were respectively decreased and increased in AD patients.Conclusion: Such in-depth and comprehensive analysis of peripheral platelet protein expression profiles of MCI and AD patients revealed that the linkage effect between peripheral and AD reflected by platelet omics involved platelet activation,complement pathway activation,mitochondrial dysfunction,calcium ion imbalance,and APP metabolic abnormality.Machine learning identified two distinctive cognitive impairment-platelet combination biomarkers(PHB,UQCRH,GP1 BA,FINC;IDH3B,RTN1).Furthermore,we determined that platelet OPTN combined with r GSK-3β(T/S9)significantly improved the efficiency of distinguishing T2DM-MCI from T2DM-n MCI.Altogether,the exploration of platelet proteomics is novel and a great supplement to understanding the peripheral changes of AD,while the integrated brain and platelet omics provides a valuable resource for establishing efficient peripheral diagnostic biomarkers and potential therapeutic targets for AD,which further reflects the huge application potential of proteomics-driven precision medicine(PDPM)in AD.