Plasma proteomic signatures predict dementia and cognitive impairment

Prospective Investigation Unravels Plasma Proteomic Links to Dementia

Investigating plasma proteomic signatures of dementia offers insights into its pathology, aids biomarker discovery, supports disease monitoring, and informs drug development. Here, we analyzed data from 48,367 UK Biobank participants with proteomic profiling. Using Cox and generalized linear models, we examined the longitudinal associations between proteomic signatures and dementia-related phenotypes. Mendelian randomization analysis was employed to identify causal associations, and machine learning algorithms were applied to develop protein-based models for dementia prediction. We identified 74 proteins significantly associated with the risk of various types of dementia and cognitive functions after Bonferroni correction. Among these, strong associations were observed for growth/differentiation factor 15 (GDF15), glial fibrillary acidic protein (GFAP), and neurofilament light polypeptide (NEFL), across all types of dementia. Additionally, 15 proteins demonstrated significant associations with neuroimaging-defined dementia endophenotypes. Two-sample Mendelian randomization analyses further substantiated causal relationships between dementia-associated proteins and Alzheimer's disease, particularly involving GDF15, proto-oncogene tyrosine-protein kinase receptor Ret (RET), and GFAP. Moreover, we identified three protein modules associated with dementia, primarily linked to immune system processes, angiogenesis, and energy metabolism, providing insights into potential biological pathways underlying the disease. Furthermore, we proposed a ten-protein panel capable of forecasting dementia over a median follow-up period of 8.6 years, achieving an area under the curve (AUC) of 0.857 (95% confidence interval (CI), 0.837-0.876). Our results revealed dementia-associated plasma proteomic signatures, and their causal relationships, notably GDF15-RET signaling with Alzheimer's disease, and proposed a promising protein panel for high-risk dementia screening.

Alzheimer's Disease polygenic risk, the plasma proteome, and dementia incidence among UK older adults

Alzheimer's Disease (AD) is a complex polygenic neurodegenerative disorder. Its genetic risk's relationship with all-cause dementia may be influenced by the plasma proteome. Up to 40,139 UK Biobank participants aged ≥ 50y at baseline assessment (2006-2010) were followed-up for ≤ 15 y for dementia incidence. Plasma proteomics were performed on a sub-sample of UK Biobank participants (k = 1,463 plasma proteins). AD polygenic risk scores (PRS) were used as the primary exposure and Cox proportional hazards models were conducted to examine the AD PRS-dementia relationship. A four-way decomposition model then partitioned the total effect (TE) of AD PRS on dementia into an effect due to mediation only, an effect due to interaction only, neither or both. The study found that AD PRS tertiles significantly increased the risk for all-cause dementia, particularly among women. The study specifically found that AD PRS was associated with a 79% higher risk for all-cause dementia for each unit increase (HR = 1.79, 95% CI: 1.70-1.87, P < 0.001). Eighty-six plasma proteins were significantly predicted by AD PRS, including a positive association with PLA2G7, BRK1, the glial acidic fibrillary protein (GFAP), neurofilament light chain (NfL), and negative with TREM2. Both GFAP and NfL significantly interacted synergistically with AD PRS to increase all-dementia risk (> 10% of TE is pure interaction), while GFAP was also an important consistent mediator in the AD PRS-dementia relationship. In summary, we detected significant interactions of NfL and GFAP with AD PRS, in relation to dementia incidence, suggesting potential for personalized dementia prevention and management.

Differential gene expression patterns in Niemann-Pick Type C and Tay-Sachs diseases: Implications for neurodegenerative mechanisms

Lysosomal storage disorders (LSDs) are a group of rare genetic conditions characterized by the impaired function of enzymes responsible for lipid digestion. Among these LSDs, Tay-Sachs disease (TSD) and Niemann-Pick type C (NPC) may share a common gene expression profile. In this study, we conducted a bioinformatics analysis to explore the gene expression profile overlap between TSD and NPC. Analyses were performed on RNA-seq datasets for both TSD and NPC from the Gene Expression Omnibus (GEO) database. Datasets were subjected to differential gene expression analysis utilizing the DESeq2 package in the R programming language. A total of 147 differentially expressed genes (DEG) were found to be shared between the TSD and NPC datasets. Enrichment analysis was then performed on the DEGs. We found that the common DEGs are predominantly associated with processes such as cell adhesion mediated by integrin, cell-substrate adhesion, and urogenital system development. Furthermore, construction of protein-protein interaction (PPI) networks using the Cytoscape software led to the identification of four hub genes: APOE, CD44, SNCA, and ITGB5. Those hub genes not only can unravel the pathogenesis of related neurologic diseases with common impaired pathways, but also may pave the way towards targeted gene therapy of LSDs.In addition, they serve as the potential biomarkers for related neurodegenerative diseases warranting further investigations.

Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer's disease biomarkers

PURPOSE

This study explores the application of machine learning to high-dimensional proteomics datasets for identifying Alzheimer's disease (AD) biomarkers. AD, a neurodegenerative disorder affecting millions worldwide, necessitates early and accurate diagnosis for effective management.

METHODS

We leverage Tandem Mass Tag (TMT) proteomics data from the cerebrospinal fluid (CSF) samples from the frontal cortex of patients with idiopathic normal pressure hydrocephalus (iNPH), a condition often comorbid with AD, with rare access to both lumbar and ventricular samples. Our methodology includes extensive data preprocessing to address batch effects and missing values, followed by the use of the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation to overcome the small sample size. We apply linear, and non-linear machine learning models, and ensemble methods, to compare iNPH patients with and without biomarker evidence of AD pathology ( A β - T - or A β + T + ) in a classification task.

RESULTS

We present a machine learning workflow for working with high-dimensional TMT proteomics data that addresses their inherent data characteristics. Our results demonstrate that batch effect correction has no or minor impact on the models' performance and robust feature selection is critical for model stability and performance, especially in the high-dimensional proteomics data setting for AD diagnostics. The results further indicated that removing features with missing values produced stronger models than imputing them, and the batch effect had minimal impact on the models Our best-performing disease-progression detection model, a random forest, achieves an AUC of 0.84 (± 0.03).

CONCLUSION

We identify several novel protein biomarkers candidates, such as FABP3 and GOT1, with potential diagnostic value for AD pathology detection, suggesting the necessity of different biomarkers for AD diagnoses for patients with iNPH, and considering different biomarkers for ventricular and lumbar CSF samples. This work underscores the importance of a meticulous machine learning process in enhancing biomarker discovery. Our study also provides insights in translating biomarkers from other central nervous system diseases like iNPH, and both ventricular and lumbar CSF samples for biomarker discovery, providing a foundation for future research and clinical applications.

Insulin-Like Growth Factor Signaling in Alzheimer's Disease: Pathophysiology and Therapeutic Strategies

Alzheimer's disease (AD) is the leading cause of dementia among the elderly population, posing a significant public health challenge due to limited therapeutic options that merely delay cognitive decline. AD is associated with impaired energy metabolism and reduced neurotrophic signaling. The insulin-like growth factor (IGF) signaling pathway, crucial for central nervous system (CNS) development, metabolism, repair, cognition, and emotion regulation, includes IGF-1, IGF-2, IGF-1R, IGF-2R, insulin receptor (IR), and six insulin-like growth factor binding proteins (IGFBPs). Research has identified abnormalities in IGF signaling in individuals with AD and AD models. Dysregulated expression of IGFs, receptors, IGFBPs, and disruptions in downstream phosphoinositide 3-kinase-protein kinase B (PI3K/AKT) and mitogen-activated protein kinase (MAPK) pathways collectively increase AD susceptibility. Studies suggest modulating the IGF pathway may ameliorate AD pathology and cognitive decline. This review explores the CNS pathophysiology of IGF signaling in AD progression and assesses the potential of targeting the IGF system as a novel therapeutic strategy. Further research is essential to elucidate how aberrant IGF signaling contributes to AD development, understand underlying molecular mechanisms, and evaluate the safety and efficacy of IGF-based treatments.

Plasma proteomics for cognitive decline and dementia-A Southeast Asian cohort study

INTRODUCTION

The prognostic utility of plasma proteomics for cognitive decline and dementia in a Southeast Asian population characterized by high cerebrovascular disease (CeVD) burden is underexplored.

METHODS

We examined this in a Singaporean memory clinic cohort of 528 subjects (n = 300, CeVD; n = 167, incident cognitive decline) followed-up for 4 years.

RESULTS

Of 1441 plasma proteins surveyed, a 12-protein signature significantly predicted cognitive decline (q-value < .05). Sixteen diverse biological processes were implicated in cognitive decline. Ten proteins independently predicted incident dementia (q-value < .05). A unified prediction model combining plasma proteins with clinical risk factors increased the area under the curve for outcome prediction from 0.62 to 0.85. External validation in the cerebrospinal fluid proteome of an independent Caucasian cohort replicated four of the significantly predictive plasma markers for cognitive decline namely: GFAP, NEFL, AREG, and PPY.

DISCUSSION

The prognostic proteins prioritized in our study provide robust signals in two different biological matrices, representing potential mechanistic targets for dementia and cognitive decline.

HIGHLIGHTS

A total of 1441 plasma proteins were profiled in a Singaporean memory clinic cohort. We report prognostic plasma protein signatures for cognitive decline and dementia. External validation was performed in the cerebrospinal fluid proteome of a Caucasian cohort. A concordant proteomic signature was identified across both biofluids and cohorts. Further studies are needed to explore the therapeutic implications of these proteins for dementia.

Mediating and moderating effects of plasma proteomic biomarkers on the association between poor oral health problems and brain white matter microstructural integrity: the UK Biobank study

The plasma proteome can mediate associations between periodontal disease (Pd) and brain white matter integrity (WMI). We screened 5089 UK Biobank participants aged 40-70 years for poor oral health problems (POHP). We examined the association between POHP and WMI (fractional anisotropy (FA), mean diffusivity (MD), Intracellular Volume Fraction (ICVF), Isotropic Volume Fraction (ISOVF) and Orientation Diffusion (OD)), decomposing the total effect through the plasma proteome of 1463 proteins into pure mediation, pure interaction, neither, while adjusting for socio-demographic and cardiovascular health factors. Similarly, structural equations modeling (SEM) was conducted. POHP was more prevalent among men (12.3% vs. 9.6%), and was associated with lower WMI on most metrics, in a sex-specific manner. Of 15 proteins strongly associated with POHP, growth differentiation factor 15 (GDF15) and WAP four-disulfide core domain 2 (WFDC2; also known as human epididymis protein 4; HE4) were consistent mediators. Both proteins mediated 7-8% of total POHP effect on FAmean. SEM yielded significant total effects for FAmean, MDmean and ISOVFmean in full models, with %mediated by common latent factor (GDF15 and WFDC2) ranging between 13% (FAmean) and 19% (ISOVFmean). For FA, mediation by this common factor was found for 16 of 49 tract-specific and global mean metrics. Protein metabolism, immune system, and signal transduction were the most common pathways for mediational effects. POHP was associated with poorer WMI, which was partially mediated by GDF15 and WFDC2.

Convergent effects of synthetic glucocorticoid dexamethasone and amyloid beta in human olfactory neurosphere-derived cells

Stressful life events and glucocorticoid (stress) hormones appear to increase the risk of Alzheimer's disease and hasten its progression, but the reasons for this remain unclear. One potential explanation is that when amyloid β (Aβ) pathology is accumulating in the preclinical disease stage, glucocorticoid receptor signalling during stressful events exacerbates cellular dysfunction caused by Aβ. Alternatively, Aβ may disrupt glucocorticoid receptor signalling. To explore these possibilities, we investigated whether the synthetic glucocorticoid dexamethasone and Aβ have overlapping effects on the cellular proteome and whether Aβ influences canonical glucocorticoid receptor function. Human olfactory neurosphere-derived (ONS) cells, collected from the olfactory mucosa of six adult donors, were treated with soluble Aβ40 or Aβ42 followed by dexamethasone. Proteins were quantified by mass spectrometry. After 32 h treatment, Aβ40 and Aβ42 both induced profound changes in innate immunity-related proteins. After 72 h, Aβ42 formed widespread aggregates and induced few proteomic changes, whereas Aβ40 remained soluble and altered expression of mitochondrial and innate immunity-related proteins. ONS cells revealed overlapping impacts of Aβ40 and dexamethasone, with 23 proteins altered by both treatments. For 16 proteins (including eight mitochondrial proteins) dexamethasone counteracted the effects of Aβ40. For example, caspase 4 and methylmalonate-semialdehyde dehydrogenase were increased by Aβ40 and decreased by dexamethasone. Consistent with this finding, Aβ40 increased, but dexamethasone decreased, ONS cell proliferation. For seven proteins, including superoxide dismutase [Mn] mitochondrial, dexamethasone exacerbated the effects of Aβ40. For some proteins, including complement C3, the effects of dexamethasone differed depending on whether Aβ40 was present or absent. Neither Aβ species influenced glucocorticoid receptor nuclear translocation. Overall, this study revealed that glucocorticoid receptor signalling modifies the intracellular effects of Aß40, counteracting some effects and exacerbating others. It suggests that cellular mechanisms through which glucocorticoid receptor signalling influences Alzheimer's disease risk/progression are complex and determined by the balance of beneficial and detrimental glucocorticoid effects.

Plasma proteomic biomarkers as mediators or moderators for the association between poor cardiovascular health and white matter microstructural integrity: The UK Biobank study

INTRODUCTION

The plasma proteome's mediating or moderating roles in the association between poor cardiovascular health (CVH) and brain white matter (WM) microstructural integrity are largely unknown.

METHODS

Data from 3953 UK Biobank participants were used (40-70 years, 2006-2010), with a neuroimaging visit between 2014 and 2021. Poor CVH was determined using Life's Essential 8 (LE8) and reversing standardized z-scores (LE8z _rev). The plasma proteome was examined as a potential mediator or moderator of LE8z _rev's effects on quantitative diffusion-weighted magnetic resonance imaging (dMRI) metrics.

RESULTS

LE8z_rev was significantly associated with deteriorated WM microstructural integrity, as reflected by lower tract-averaged fractional anisotropy (dMRI-FAmean), (β ± standared error (SE): -0.00152 ± 0.0003, p < 0.001) and higher tract-averaged orientation dispersion (dMRI-ODmean), (β ± SE:+0.00081 ± 0.00017, p < 0.001). Ten strongly mediating plasma proteins of 1463 were identified, with leptin as the principal driver.

DISCUSSION

Poor CVH is linked to poor WM microstructural integrity measures (lower FAmean and higher ODmean), mostly mediated through leptin.

HIGHLIGHTS

Up to 3953 UK Biobank participants were selected for this study. Poor cardiovascular health (CVH) was determined using Life's Essential 8. The plasma proteome was examined as a potential mediator or moderator of poor CVH's effect on dMRI metrics. Ten plasma proteins were identified with strong mediating effects, with leptin being the principal driver.

Large-scale proteomic analyses of incident Alzheimer's disease reveal new pathophysiological insights and potential therapeutic targets

Pathophysiological evolutions in early-stage Alzheimer's disease (AD) are not well understood. We used data of 2923 Olink plasma proteins from 51,296 non-demented middle-aged adults. During a follow-up of 15 years, 689 incident AD cases occurred. Cox-proportional hazard models were applied to identify AD-associated proteins in different time intervals. Through linking to protein categories, changing sequences of protein z-scores can reflect pathophysiological evolutions. Mendelian randomization using blood protein quantitative loci data provided causal evidence for potentially druggable proteins. We identified 48 AD-related proteins, with CEND1, GFAP, NEFL, and SYT1 being top hits in both near-term (HR:1.15-1.77; P:9.11 × 10-65-2.78 × 10-6) and long-term AD risk (HR:1.20-1.54; P:2.43 × 10-21-3.95 × 10-6). These four proteins increased 15 years before AD diagnosis and progressively escalated, indicating early and sustained dysfunction in synapse and neurons. Proteins related to extracellular matrix organization, apoptosis, innate immunity, coagulation, and lipid homeostasis showed early disturbances, followed by malfunctions in metabolism, adaptive immunity, and final synaptic and neuronal loss. Combining CEND1, GFAP, NEFL, and SYT1 with demographics generated desirable predictions for 10-year (AUC = 0.901) and over-10-year AD (AUC = 0.864), comparable to full model. Mendelian randomization supports potential genetic link between CEND1, SYT1, and AD as outcome. Our findings highlight the importance of exploring the pathophysiological evolutions in early stages of AD, which is essential for the development of early biomarkers and precision therapeutics.

Addressing statistical challenges in the analysis of proteomics data with extremely small sample size: a simulation study

BACKGROUND

One of the most promising approaches for early and more precise disease prediction and diagnosis is through the inclusion of proteomics data augmented with clinical data. Clinical proteomics data is often characterized by its high dimensionality and extremely limited sample size, posing a significant challenge when employing machine learning techniques for extracting only the most relevant information. Although there is a wide array of statistical techniques and numerous analysis pipelines employed in proteomics data analysis, it is unclear which of these methods produce the most efficient, reproducible, and clinically meaningful results.

RESULTS

In this study, we compared 9 unique analysis schemes comprised of different machine learning and dimensionality reduction methods for the analysis of simulated proteomics data consisting of 1317 proteins measured in 26 subjects (i.e., 13 controls and 13 cases). In scenarios where the sample size is extremely small (i.e., n < 30), all schemes resulted in an exceptionally high level of performance metrics, indicating potential overfitting. While performance metrics did not exhibit significant differences across schemes, the set of proteins selected to be discriminatory between groups demonstrated a substantial level of heterogeneity. However, despite heterogeneity in the selected proteins, their biological pathways and genetic diseases exhibited similarities. A sensitivity analysis conducted using varying sample sizes indicated that the stability of a set of selected biomarkers improves with larger sample sizes within a scheme.

CONCLUSIONS

When the aim of the study is to identify a statistical model that best distinguishes between cohort groups using proteomics data and to uncover the biological pathways and disorders common among the selected proteins, the majority of widely used analysis pipelines perform similarly. However, if the main objective is to pinpoint a set of selected proteins that wield significant influence in discriminating cohort groups and utilize them for subsequent investigations, meticulous consideration is necessary when opting for statistical models, due to the possibility of heterogeneity in the sets of selected proteins.

Proximity Elongation Assay and ELISA for the Identification of Serum Diagnostic Biomarkers in Parkinson's Disease and Progressive Supranuclear Palsy

Clinical differentiation of progressive supranuclear palsy (PSP) from Parkinson's disease (PD) is challenging due to overlapping phenotypes and late onset of PSP specific symptoms, highlighting the need for easily assessable biomarkers. We used proximity elongation assay (PEA) to analyze 460 proteins in serum samples from 46 PD, 30 PSP patients, and 24 healthy controls. ANCOVA was used to identify the most promising proteins and machine learning (ML) XGBoost and random forest algorithms to assess their classification performance. Promising proteins were also quantified by ELISA. Moreover, correlations between serum biomarkers and biological and clinical features were investigated. We identified five proteins (TFF3, CPB1, OPG, CNTN1, TIMP4) showing different levels between PSP and PD, which achieved good performance (AUC: 0.892) when combined by ML. On the other hand, when the three most significant biomarkers (TFF3, CPB1 and OPG) were analyzed by ELISA, there was no difference between groups. Serum levels of TFF3 positively correlated with age in all subjects' groups, while for OPG and CPB1 such a correlation occurred in PSP patients only. Moreover, CPB1 positively correlated with disease severity in PD, while no correlations were observed in the PSP group. Overall, we identified CPB1 correlating with PD severity, which may support clinical staging of PD. In addition, our results showing discrepancy between PEA and ELISA technology suggest that caution should be used when translating proteomic findings into clinical practice.

Mediating and moderating effects of plasma proteomic biomarkers on the association between poor oral health problems and incident dementia: The UK Biobank study

The plasma proteome can mediate poor oral health problems (POHP)'s link to incident dementia. We screened 37,269 UK Biobank participants 50-74 years old (2006-2010) for prevalent POHP, further tested against 1463 plasma proteins and incident dementia over up to 15 years of follow-up. Total effect (TE) of POHP-dementia through plasma proteomic markers was decomposed into pure indirect effect (PIE), interaction referent (INTREF), controlled direct effect (CDE), or mediated interaction (INTMED). POHP increased the risk of all-cause dementia by 17% (P < 0.05). Growth differentiation factor 15 (GDF15) exhibited the strongest mediating effects (PIE > 0, P < 0.001), explaining 28% the total effect of POHP on dementia, as a pure indirect effect. A first principal component encompassing top 4 mediators (GDF15, IL19, MMP12, and ACVRL1), explained 11% of the POHP-dementia effect as a pure indirect effect. Pathway analysis including all mediators (k = 173 plasma proteins) revealed the involvement of the immune system, signal transduction, metabolism, disease, and gene expression, while STRING analysis indicated that top mediators within the first principal component were also represented in the two largest proteomic clusters. The dominant biological GO pathway for the GDF15 cluster was GO:0007169 labeled as "transmembrane receptor protein tyrosine kinase signaling pathway." Dementia is linked to POHP mediated by GDF15 among several proteomic markers.

Acute exercise alters brain glucose metabolism in aging and Alzheimer's disease

There is evidence that aerobic exercise improves brain health. Benefits may be modulated by acute physiological responses to exercise, but this has not been well characterized in older or cognitively impaired adults. The randomized controlled trial 'AEROBIC' (NCT04299308) enrolled 60 older adults who were cognitively healthy (n = 30) or cognitively impaired (n = 30) to characterize the acute brain responses to moderate [45-55% heart rate reserve (HRR)] and higher (65-75% HRR) intensity acute exercise. Each participant received two fluorodeoxyglucose positron emission tomography (FDG-PET) scans, one at rest and one following acute exercise. Change in cerebral glucose metabolism from rest to exercise was the primary outcome. Blood biomarker responses were also characterized as secondary outcomes. Whole grey matter FDG-PET standardized uptake value ratio (SUVR) differed between exercise (1.045 ± 0.082) and rest (0.985 ± 0.077) across subjects [Diff = -0.060, t(58) = 13.8, P < 0.001] regardless of diagnosis. Exercise increased lactate area under the curve (AUC) [F(1,56) = 161.99, P < 0.001] more in the higher intensity group [mean difference (MD) = 97.0 ± 50.8] than the moderate intensity group (MD = 40.3 ± 27.5; t = -5.252, P < 0.001). Change in lactate AUC and FDG-PET SUVR correlated significantly (R2 = 0.179, P < 0.001). Acute exercise decreased whole grey matter cerebral glucose metabolism. This effect tracked with the systemic lactate response, suggesting that lactate may serve as a key brain fuel during exercise. Direct measurements of brain lactate metabolism in response to exercise are warranted. KEY POINTS: Acute exercise is associated with a drop in global brain glucose metabolism in both cognitively healthy older adults and those with Alzheimer's disease. Blood lactate levels increase following acute exercise. Change in brain metabolism tracks with blood lactate, suggesting it may be an important brain fuel. Acute exercise stimulates changes in brain-derived neurotrophic factor and other blood biomarkers.

Plasma proteomic biomarkers and the association between poor cardiovascular health and incident dementia: The UK Biobank study

BACKGROUND

The study examined how plasma proteome indicators may explain the link between poor cardiovascular health (CVH) and dementia risk.

METHODS

The present study involved 28,974 UK Biobank participants aged 50-74y at baseline (2006-2010) who were followed-up for ≤ 15 y for incidence of dementia. CVH was calculated using Life's Essential 8 (LE8) total scores. The scores were standardized and reverse coded to reflect poor CVH (LE8z_rev). OLINK proteomics was available on this sample (k = 1,463 plasma proteins). The study primarily tested the mediating effects of the plasma proteome in LE8z_rev-dementia effect. The total effect was decomposed into "mediation only" or pure indirect effect (PIE), "interaction only" or interaction referent (INTREF), "neither mediation nor interaction" or controlled direct effect (CDE), and "both mediation and interaction" or mediated interaction (INTMED).

RESULTS

The study found poorer CVH assessed by LE8z_rev increased the risk of all-cause dementia by 11 % [per 1 SD, hazard ratio, (HR) = 1.11, 95 % CI: 1.03-1.20, p = 0.005). The study identified 11 plasma proteins with strong mediating effects, with GDF15 having the strongest association with dementia risk (per 1 SD, HR = 1.24, 95 % CI: 1.16, 1.33, P < 0.001 when LE8z_rev is set at its mean value) and the largest proportion mediated combining PIE and INTMED (62.6 %; 48 % of TE is PIE), followed by adrenomedullin or ADM. A first principal component with 10 top mediators (TNFRSF1A, GDF15, FSTL3, COL6A3, PLAUR, ADM, GFRAL, ACVRL1, TNFRSF6B, TGFA) mediated 53.6 % of the LE8z_rev-dementia effect. Using all the significant PIE (k = 526) proteins, we used OLINK Insight pathway analysis to identify key pathways, which revealed the involvement of the immune system, signal transduction, metabolism, disease, protein metabolism, hemostasis, membrane trafficking, extracellular matrix organization, developmental biology, and gene expression among others. STRING analysis revealed that five top consistent proteomic mediators were represented in two larger clusters reflecting numerous interconnected biological gene ontology pathways, most notably cytokine-mediated signaling pathway for GDF15 cluster (GO:0019221) and regulation of peptidyl-tyrosine phosphorylation for the ADM cluster (GO:0050730).

CONCLUSION

Dementia is linked to poor CVH mediated by GDF15 and ADM among several key proteomic markers which collectively explained ∼ 54 % of the total effect.

Histological assessment of cardiac amyloidosis in patients undergoing transcatheter aortic valve replacement

AIMS

Studies have reported a strongly varying co-prevalence of aortic stenosis (AS) and cardiac amyloidosis (CA). We sought to histologically determine the co-prevalence of AS and CA in patients undergoing transcatheter aortic valve replacement (TAVR). Consequently, we aimed to derive an algorithm to identify cases in which to suspect the co-prevalence of AS and CA.

METHODS AND RESULTS

In this prospective, monocentric study, endomyocardial biopsies of 162 patients undergoing TAVR between January 2017 and March 2021 at the University Medical Centre Göttingen were analysed by one pathologist blinded to clinical data using haematoxylin-eosin staining, Elastica van Gieson staining, and Congo red staining of endomyocardial biopsies. CA was identified in only eight patients (4.9%). CA patients had significantly higher N-terminal pro-brain natriuretic peptide (NT-proBNP) levels (4356.20 vs. 1938.00 ng/L, P = 0.034), a lower voltage-to-mass ratio (0.73 vs. 1.46 × 10-2 mVm2/g, P = 0.022), and lower transaortic gradients (Pmean 17.5 vs. 38.0 mmHg, P = 0.004) than AS patients. Concomitant CA was associated with a higher prevalence of post-procedural acute kidney injury (50.0% vs. 13.1%, P = 0.018) and sudden cardiac death [SCD; P (log-rank test) = 0.017]. Following propensity score matching, 184 proteins were analysed to identify serum biomarkers of concomitant CA. CA patients expressed lower levels of chymotrypsin (P = 0.018) and carboxypeptidase 1 (P = 0.027). We propose an algorithm using commonly documented parameters-stroke volume index, ejection fraction, NT-proBNP levels, posterior wall thickness, and QRS voltage-to-mass ratio-to screen for CA in AS patients, reaching a sensitivity of 66.6% with a specificity of 98.1%.

CONCLUSIONS

The co-prevalence of AS and CA was lower than expected, at 4.9%. Despite excellent 1 year mortality, AS + CA patients died significantly more often from SCD. We propose a multimodal algorithm to facilitate more effective screening for CA containing parameters commonly documented during clinical routine. Proteomic biomarkers may yield additional information in the future.

Plasma proteomic profiles predict future dementia in healthy adults

The advent of proteomics offers an unprecedented opportunity to predict dementia onset. We examined this in data from 52,645 adults without dementia in the UK Biobank, with 1,417 incident cases and a follow-up time of 14.1 years. Of 1,463 plasma proteins, GFAP, NEFL, GDF15 and LTBP2 consistently associated most with incident all-cause dementia (ACD), Alzheimer's disease (AD) and vascular dementia (VaD), and ranked high in protein importance ordering. Combining GFAP (or GDF15) with demographics produced desirable predictions for ACD (area under the curve (AUC) = 0.891) and AD (AUC = 0.872) (or VaD (AUC = 0.912)). This was also true when predicting over 10-year ACD, AD and VaD. Individuals with higher GFAP levels were 2.32 times more likely to develop dementia. Notably, GFAP and LTBP2 were highly specific for dementia prediction. GFAP and NEFL began to change at least 10 years before dementia diagnosis. Our findings strongly highlight GFAP as an optimal biomarker for dementia prediction, even more than 10 years before the diagnosis, with implications for screening people at high risk for dementia and for early intervention.

Biological and Physical Performance Markers for Early Detection of Cognitive Impairment in Older Adults

Dementia is a major cause of poor quality of life, disability, and mortality in old age. According to the geroscience paradigm, the mechanisms that drive the aging process are also involved in the pathogenesis of chronic degenerative diseases, including dementia. The dissection of such mechanisms is therefore instrumental in providing biological targets for interventions and new sources for biomarkers. Within the geroscience paradigm, several biomarkers have been discovered that can be measured in blood and that allow early identification of individuals at risk of cognitive impairment. Examples of such markers include inflammatory biomolecules, markers of neuroaxonal damage, extracellular vesicles, and DNA methylation. Furthermore, gait speed, measured at a usual and fast pace and as part of a dual task, has been shown to detect individuals at risk of future dementia. Here, we provide an overview of available biomarkers that may be used to gauge the risk of cognitive impairment in apparently healthy older adults. Further research should establish which combination of biomarkers possesses the highest predictive accuracy toward incident dementia. The implementation of currently available markers may allow the identification of a large share of at-risk individuals in whom preventive interventions should be implemented to maintain or increase cognitive reserves, thereby reducing the risk of progression to dementia.

Serum Proteomic Signatures of Common Health Outcomes among Older Adults

INTRODUCTION

In aging populations, the coexistence of multiple health comorbidities represents a significant challenge for clinicians and researchers. Leveraging advances in omics techniques to characterize these health conditions may provide insight into disease pathogenesis as well as reveal biomarkers for monitoring, prognostication, and diagnosis. Researchers have previously established the utility of big data approaches with respect to comprehensive health outcome measurements in younger populations, identifying protein markers that may provide significant health information with a single blood sample.

METHODS

Here, we employed a similar approach in two cohorts of older adults, the Baltimore Longitudinal Study of Aging (mean age = 76.12 years) and InCHIANTI Study (mean age = 66.05 years), examining the relationship between levels of serum proteins and 5 key health outcomes: kidney function, fasting glucose, physical activity, lean body mass, and percent body fat.

RESULTS

Correlations between proteins and health outcomes were primarily shared across both older adult cohorts. We further identified that most proteins associated with health outcomes in the older adult cohorts were not associated with the same outcomes in a prior study of a younger population. A subset of proteins, adiponectin, MIC-1, and NCAM-120, were associated with at least three health outcomes in both older adult cohorts but not in the previously published younger cohort, suggesting that they may represent plausible markers of general health in older adult populations.

CONCLUSION

Taken together, these findings suggest that comprehensive protein health markers have utility in aging populations and are distinct from those identified in younger adults, indicating unique mechanisms of disease with aging.

Plasma proteomic profiles predict individual future health risk

Developing a single-domain assay to identify individuals at high risk of future events is a priority for multi-disease and mortality prevention. By training a neural network, we developed a disease/mortality-specific proteomic risk score (ProRS) based on 1461 Olink plasma proteins measured in 52,006 UK Biobank participants. This integrative score markedly stratified the risk for 45 common conditions, including infectious, hematological, endocrine, psychiatric, neurological, sensory, circulatory, respiratory, digestive, cutaneous, musculoskeletal, and genitourinary diseases, cancers, and mortality. The discriminations witnessed high accuracies achieved by ProRS for 10 endpoints (e.g., cancer, dementia, and death), with C-indexes exceeding 0.80. Notably, ProRS produced much better or equivalent predictive performance than established clinical indicators for almost all endpoints. Incorporating clinical predictors with ProRS enhanced predictive power for most endpoints, but this combination only exhibited limited improvement when compared to ProRS alone. Some proteins, e.g., GDF15, exhibited important discriminative values for various diseases. We also showed that the good discriminative performance observed could be largely translated into practical clinical utility. Taken together, proteomic profiles may serve as a replacement for complex laboratory tests or clinical measures to refine the comprehensive risk assessments of multiple diseases and mortalities simultaneously. Our models were internally validated in the UK Biobank; thus, further independent external validations are necessary to confirm our findings before application in clinical settings.

Low-intensity pulsed ultrasound mitigates cognitive impairment by inhibiting muscle atrophy in hindlimb unloaded mice

Microgravity leads to muscle loss, usually accompanied by cognitive impairment. Muscle reduction was associated with the decline of cognitive ability. Our previous studies showed that low-intensity pulsed ultrasound (LIPUS) promoted muscle hypertrophy and prevented muscle atrophy. This study aims to verify whether LIPUS can improve cognitive impairment by preventing muscle atrophy in hindlimb unloaded mice. In this study, mice were randomly divided into normal control (NC), hindlimb unloading (HU), hindlimb unloading + LIPUS (HU+LIPUS) groups. The mice in the HU+LIPUS group received a 30 mW/cm2 LIPUS irradiation on gastrocnemius for 20 min/d. After 21 days, LIPUS significantly prevented the decrease in muscle mass and strength caused by tail suspension. The HU+LIPUS mice showed an enhanced desire to explore unfamiliar environments and their spatial learning and memory abilities, enabling them to quickly identify differences between different objects, as well as their social discrimination abilities. MSTN is a negative regulator of muscle growth and also plays a role in regulating cognition. LIPUS significantly inhibited MSTN expression in skeletal muscle and serum and its receptor ActRIIB expression in brain, upregulated AKT and BDNF expression in brain. Taken together, LIPUS may improve the cognitive dysfunction in hindlimb unloaded rats by inhibiting muscle atrophy through MSTN/AKT/BDNF pathway.

Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease

Perivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in up to 40,095 participants (18 population-based cohorts, 66.3 ± 8.6 yr, 96.9% European ancestry) revealed 24 genome-wide significant PVS risk loci, mainly in the white matter. These were associated with white matter PVS already in young adults (N = 1,748; 22.1 ± 2.3 yr) and were enriched in early-onset leukodystrophy genes and genes expressed in fetal brain endothelial cells, suggesting early-life mechanisms. In total, 53% of white matter PVS risk loci showed nominally significant associations (27% after multiple-testing correction) in a Japanese population-based cohort (N = 2,862; 68.3 ± 5.3 yr). Mendelian randomization supported causal associations of high blood pressure with basal ganglia and hippocampal PVS, and of basal ganglia PVS and hippocampal PVS with stroke, accounting for blood pressure. Our findings provide insight into the biology of PVS and cerebral small vessel disease, pointing to pathways involving extracellular matrix, membrane transport and developmental processes, and the potential for genetically informed prioritization of drug targets.

Proteome Network Analysis Identifies Potential Biomarkers for Brain Aging

BACKGROUND

Alzheimer's disease and related dementias (ADRD) involve biological processes that begin years to decades before onset of clinical symptoms. The plasma proteome can offer insight into brain aging and risk of incident dementia among cognitively healthy adults.

OBJECTIVE

To identify biomarkers and biological pathways associated with neuroimaging measures and incident dementia in two large community-based cohorts by applying a correlation-based network analysis to the plasma proteome.

METHODS

Weighted co-expression network analysis of 1,305 plasma proteins identified four modules of co-expressed proteins, which were related to MRI brain volumes and risk of incident dementia over a median 20-year follow-up in Framingham Heart Study (FHS) Offspring cohort participants (n = 1,861). Analyses were replicated in the Cardiovascular Health Study (CHS) (n = 2,117, mean 6-year follow-up).

RESULTS

Two proteomic modules, one related to protein clearance and synaptic maintenance (M2) and a second to inflammation (M4), were associated with total brain volume in FHS (M2: p = 0.014; M4: p = 4.2×10-5). These modules were not significantly associated with hippocampal volume, white matter hyperintensities, or incident all-cause or AD dementia. Associations with TCBV did not replicate in CHS, an older cohort with a greater burden of comorbidities.

CONCLUSIONS

Proteome networks implicate an early role for biological pathways involving inflammation and synaptic function in preclinical brain atrophy, with implications for clinical dementia.

Genome- and epigenome-wide studies of plasma protein biomarkers for Alzheimer's disease implicate TBCA and TREM2 in disease risk

Introduction

The levels of many blood proteins are associated with Alzheimer's disease (AD) or its pathological hallmarks. Elucidating the molecular factors that control circulating levels of these proteins may help to identify proteins associated with disease risk mechanisms.

Methods

Genome-wide and epigenome-wide studies (nindividuals ≤1064) were performed on plasma levels of 282 AD-associated proteins, identified by a structured literature review. Bayesian penalized regression estimated contributions of genetic and epigenetic variation toward inter-individual differences in plasma protein levels. Mendelian randomization (MR) and co-localization tested associations between proteins and disease-related phenotypes.

Results

Sixty-four independent genetic and 26 epigenetic loci were associated with 45 proteins. Novel findings included an association between plasma triggering receptor expressed on myeloid cells 2 (TREM2) levels and a polymorphism and cytosine-phosphate-guanine (CpG) site within the MS4A4A locus. Higher plasma tubulin-specific chaperone A (TBCA) and TREM2 levels were significantly associated with lower AD risk.

Discussion

Our data inform the regulation of biomarker levels and their relationships with AD.

Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer's disease

BACKGROUND

In Alzheimer's disease, amyloid- β (A β) peptides aggregate in the lowering CSF amyloid levels - a key pathological hallmark of the disease. However, lowered CSF amyloid levels may also be present in cognitively unimpaired elderly individuals. Therefore, it is of great value to explain the variance in disease progression among patients with A β pathology.

METHODS

A cohort of n=2293 participants, of whom n=749 were A β positive, was selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to study heterogeneity in disease progression for individuals with A β pathology. The analysis used baseline clinical variables including demographics, genetic markers, and neuropsychological data to predict how the cognitive ability and AD diagnosis of subjects progressed using statistical models and machine learning. Due to the relatively low prevalence of A β pathology, models fit only to A β-positive subjects were compared to models fit to an extended cohort including subjects without established A β pathology, adjusting for covariate differences between the cohorts.

RESULTS

A β pathology status was determined based on the A β₄₂/A β₄₀ ratio. The best predictive model of change in cognitive test scores for A β-positive subjects at the 2-year follow-up achieved an R² score of 0.388 while the best model predicting adverse changes in diagnosis achieved a weighted F₁ score of 0.791. A β-positive subjects declined faster on average than those without A β pathology, but the specific level of CSF A β was not predictive of progression rate. When predicting cognitive score change 4 years after baseline, the best model achieved an R² score of 0.325 and it was found that fitting models to the extended cohort improved performance. Moreover, using all clinical variables outperformed the best model based only on a suite of cognitive test scores which achieved an R² score of 0.228.

CONCLUSION

Our analysis shows that CSF levels of A β are not strong predictors of the rate of cognitive decline in A β-positive subjects when adjusting for other variables. Baseline assessments of cognitive function accounts for the majority of variance explained in the prediction of 2-year decline but is insufficient for achieving optimal results in longer-term predictions. Predicting changes both in cognitive test scores and in diagnosis provides multiple perspectives of the progression of potential AD subjects.

Proteomic Analysis of Hydromethylthionine in the Line 66 Model of Frontotemporal Dementia Demonstrates Actions on Tau-Dependent and Tau-Independent Networks

Abnormal aggregation of tau is the pathological hallmark of tauopathies including frontotemporal dementia (FTD). We have generated tau-transgenic mice that express the aggregation-prone P301S human tau (line 66). These mice present with early-onset, high tau load in brain and FTD-like behavioural deficiencies. Several of these behavioural phenotypes and tau pathology are reversed by treatment with hydromethylthionine but key pathways underlying these corrections remain elusive. In two proteomic experiments, line 66 mice were compared with wild-type mice and then vehicle and hydromethylthionine treatments of line 66 mice were compared. The brain proteome was investigated using two-dimensional electrophoresis and mass spectrometry to identify protein networks and pathways that were altered due to tau overexpression or modified by hydromethylthionine treatment. Overexpression of mutant tau induced metabolic/mitochondrial dysfunction, changes in synaptic transmission and in stress responses, and these functions were recovered by hydromethylthionine. Other pathways, such as NRF2, oxidative phosphorylation and protein ubiquitination were activated by hydromethylthionine, presumably independent of its function as a tau aggregation inhibitor. Our results suggest that hydromethylthionine recovers cellular activity in both a tau-dependent and a tau-independent fashion that could lead to a wide-spread improvement of homeostatic function in the FTD brain.

Aging biomarkers and the brain

Quantifying biological aging is critical for understanding why aging is the primary driver of morbidity and mortality and for assessing novel therapies to counter pathological aging. In the past decade, many biomarkers relevant to brain aging have been developed using various data types and modeling techniques. Aging involves numerous interconnected processes, and thus many complementary biomarkers are needed, each capturing a different slice of aging biology. Here we present a hierarchical framework highlighting how these biomarkers are related to each other and the underlying biological processes. We review those measures most studied in the context of brain aging: epigenetic clocks, proteomic clocks, and neuroimaging age predictors. Many studies have linked these biomarkers to cognition, mental health, brain structure, and pathology during aging. We also delve into the challenges and complexities in interpreting these biomarkers and suggest areas for further innovation. Ultimately, a robust mechanistic understanding of these biomarkers will be needed to effectively intervene in the aging process to prevent and treat age-related disease.

Proteomics and Epidemiological Models of Human Aging

Human aging is associated with a decline of physical and cognitive function and high susceptibility to chronic diseases, which is influenced by genetics, epigenetics, environmental, and socio-economic status. In order to identify the factors that modulate the aging process, established measures of aging mechanisms are required, that are both robust and feasible in humans. It is also necessary to connect these measures to the phenotypes of aging and their functional consequences. In this review, we focus on how this has been addressed from an epidemiologic perspective using proteomics. The key aspects of epidemiological models of aging can be incorporated into proteomics and other omics which can provide critical detailed information on the molecular and biological processes that change with age, thus unveiling underlying mechanisms that drive multiple chronic conditions and frailty, and ideally facilitating the identification of new effective approaches for prevention and treatment.

MethylDetectR: a software for methylation-based health profiling

DNA methylation is an important biological process that involves the reversible addition of chemical tags called methyl groups to DNA and affects whether genes are active or inactive. Individual methylation profiles are determined by both genetic and environmental influences. Inter-individual variation in DNA methylation profiles can be exploited to estimate or predict a wide variety of human characteristics and disease risk profiles. Indeed, a number of methylation-based predictors of human traits have been developed and linked to important health outcomes. However, there is an unmet need to communicate the applicability and limitations of state-of-the-art methylation-based predictors to the wider community. To address this need, we have created a secure, web-based interactive platform called 'MethylDetectR' which automates the calculation of estimated values or scores for a variety of human traits using blood methylation data. These traits include age, lifestyle traits and high-density lipoprotein cholesterol. Methylation-based predictors often return scores on arbitrary scales. To provide meaning to these scores, users can interactively view how estimated trait scores for a given individual compare against other individuals in the sample. Users can optionally upload binary phenotypes and investigate how estimated traits vary according to case vs. control status for these phenotypes. Users can also view how different methylation-based predictors correlate with one another, and with phenotypic values for corresponding traits in a large reference sample (n = 4,450; Generation Scotland). The 'MethylDetectR' platform allows for the fast and secure calculation of DNA methylation-derived estimates for several human traits. This platform also helps to show the correlations between methylation-based scores and corresponding traits at the level of a sample, report estimated health profiles at an individual level, demonstrate how scores relate to important binary outcomes of interest and highlight the current limitations of molecular health predictors.

MethylDetectR: a software for methylation-based health profiling

DNA methylation is an important biological process which involves the reversible addition of chemical tags called methyl groups to DNA and affects whether genes are active or inactive. Individual methylation profiles are determined by both genetic and environmental influences. Inter-individual variation in DNA methylation profiles can be exploited to estimate or predict a wide variety of human characteristics and disease risk profiles. Indeed, a number of methylation-based predictors of human traits have been developed and linked to important health outcomes. However, there is an unmet need to communicate the applicability and limitations of state-of-the-art methylation-based predictors to the wider community. To address this, we created a secure, web-based interactive platform called 'MethylDetectR' which calculates estimated values or scores for a variety of human traits using blood methylation data. These traits include age, lifestyle traits, high-density lipoprotein cholesterol and the levels of 27 blood proteins related to inflammatory and neurological processes and disease. Methylation-based predictors often return scores on arbitrary scales. To provide meaning to these scores, users can interactively view how estimated trait scores for a given individual compare against other individuals in the sample. Users can optionally upload binary phenotypes and investigate how estimated traits vary according to case vs. control status for these phenotypes. Users can also view how different methylation-based predictors correlate with one another, and with phenotypic values for corresponding traits in a large reference sample (n = 4,450; Generation Scotland). The 'MethylDetectR' platform allows for the fast and secure calculation of DNA methylation-derived estimates for many human traits. This platform also helps to show the correlations between methylation-based scores and corresponding traits at the level of a sample, report estimated health profiles at an individual level, demonstrate how scores relate to important binary outcomes of interest and highlight the current limitations of molecular health predictors.