Plasma Proteomic Biomarkers Relating to Alzheimer’s Disease: A Meta-Analysis Based on Our Own Studies

Shi, Liu; Buckley, Noel J.; Bos, Isabelle; Engelborghs, Sebastiaan; Sleegers, Kristel; Frisoni, Giovanni B.; Wallin, Anders; Lleó, Alberto; Popp, Julius; Martínez-Lage, Pablo; Legido‐Quigley, Cristina; Barkhof, Frederik; Zetterberg, Henrik; Visser, Pieter Jelle; Bertram, Lars; Lovestone, Simon; Nevado‐Holgado, Alejo J.

doi:10.3389/fnagi.2021.712545

Cited by 19 publications

(12 citation statements)

References 51 publications

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“…[25,26] Insulin resistance plays an important role in the progression of T2DM, which is observed in approximately 80% of the patients with AD. [27] Insulin signaling pathways, including altered levels of insulin and insulin-like growth factor (IGF), are impaired in the brains of AD patients. [28,29] Insulin-related signaling pathways play a key role in energy homeostasis, neuronal survival, and memory processes, all of which are critical for learning and cognitive functions in the cortical and hippocampal areas.…”

Section: Neuronmentioning

confidence: 99%

Aberrant energy metabolism in Alzheimer’s disease

Jin

et al. 2022

Journal of Translational Internal Medicine

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To maintain energy supply to the brain, a direct energy source called adenosine triphosphate (ATP) is produced by oxidative phosphorylation and aerobic glycolysis of glucose in the mitochondria and cytoplasm. Brain glucose metabolism is reduced in many neurodegenerative diseases, including Alzheimer’s disease (AD), where it appears presymptomatically in a progressive and region-specific manner. Following dysregulation of energy metabolism in AD, many cellular repair/regenerative processes are activated to conserve the energy required for cell viability. Glucose metabolism plays an important role in the pathology of AD and is closely associated with the tricarboxylic acid cycle, type 2 diabetes mellitus, and insulin resistance. The glucose intake in neurons is from endothelial cells, astrocytes, and microglia. Damage to neurocentric glucose also damages the energy transport systems in AD. Gut microbiota is necessary to modulate bidirectional communication between the gastrointestinal tract and brain. Gut microbiota may influence the process of AD by regulating the immune system and maintaining the integrity of the intestinal barrier. Furthermore, some therapeutic strategies have shown promising therapeutic effects in the treatment of AD at different stages, including the use of antidiabetic drugs, rescuing mitochondrial dysfunction, and epigenetic and dietary intervention. This review discusses the underlying mechanisms of alterations in energy metabolism in AD and provides potential therapeutic strategies in the treatment of AD.

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Section: Neuronmentioning

confidence: 99%

Aberrant energy metabolism in Alzheimer’s disease

Jin

et al. 2022

Journal of Translational Internal Medicine

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“…It is associated to amyloid deposition [ 41 ] and brain atrophy [ 42 ]. The lower levels of this protein in AD compared to FTD and controls [ 43 ] indicate that this marker is specifically associated to AD pathogenesis. Experimental and neuropathological studies indeed suggest that this protein may contribute to AD by altering thrombosis and fibrinolysis [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Plasma proteome profiling identifies changes associated to AD but not to FTD

Mofrad

Campo

Peeters

et al. 2022

acta neuropathol commun

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Background Frontotemporal dementia (FTD) is caused by frontotemporal lobar degeneration (FTLD), characterized mainly by inclusions of Tau (FTLD-Tau) or TAR DNA binding43 (FTLD-TDP) proteins. Plasma biomarkers are strongly needed for specific diagnosis and potential treatment monitoring of FTD. We aimed to identify specific FTD plasma biomarker profiles discriminating FTD from AD and controls, and between FTD pathological subtypes. In addition, we compared plasma results with results in post-mortem frontal cortex of FTD cases to understand the underlying process. Methods Plasma proteins (n = 1303) from pathologically and/or genetically confirmed FTD patients (n = 56; FTLD-Tau n = 16; age = 58.2 ± 6.2; 44% female, FTLD-TDP n = 40; age = 59.8 ± 7.9; 45% female), AD patients (n = 57; age = 65.5 ± 8.0; 39% female), and non-demented controls (n = 148; 61.3 ± 7.9; 41% female) were measured using an aptamer-based proteomic technology (SomaScan). In addition, exploratory analysis in post-mortem frontal brain cortex of FTD (n = 10; FTLD-Tau n = 5; age = 56.2 ± 6.9, 60% female, and FTLD-TDP n = 5; age = 64.0 ± 7.7, 60% female) and non-demented controls (n = 4; age = 61.3 ± 8.1; 75% female) were also performed. Differentially regulated plasma and tissue proteins were identified by global testing adjusting for demographic variables and multiple testing. Logistic lasso regression was used to identify plasma protein panels discriminating FTD from non-demented controls and AD, or FTLD-Tau from FTLD-TDP. Performance of the discriminatory plasma protein panels was based on predictions obtained from bootstrapping with 1000 resampled analysis. Results Overall plasma protein expression profiles differed between FTD, AD and controls (6 proteins; p = 0.005), but none of the plasma proteins was specifically associated to FTD. The overall tissue protein expression profile differed between FTD and controls (7-proteins; p = 0.003). There was no difference in overall plasma or tissue expression profile between FTD subtypes. Regression analysis revealed a panel of 12-plasma proteins discriminating FTD from AD with high accuracy (AUC: 0.99). No plasma protein panels discriminating FTD from controls or FTD pathological subtypes were identified. Conclusions We identified a promising plasma protein panel as a minimally-invasive tool to aid in the differential diagnosis of FTD from AD, which was primarily associated to AD pathophysiology. The lack of plasma profiles specifically associated to FTD or its pathological subtypes might be explained by FTD heterogeneity, calling for FTD studies using large and well-characterize cohorts.

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“…Several studies showed that defining blood component changes, including altered circulating proteins, provides fundamental insights into numerous diseases (Luczak et al, 2015;Cao et al, 2020;Harbaum et al, 2022) and helps to identify clinical biomarkers and potential therapeutic targets (Hoogeveen et al, 2020;Shi et al, 2021). Using human plasma proteomic data, statistical models termed 'clocks' have been developed which accurately predict chronological age (Tanaka et al, 2018;Lehallier et al, 2019).…”

Section: Figurementioning

confidence: 99%

Markers of aging: Unsupervised integrated analyses of the human plasma proteome

Coenen

Lehallier²,

Vries

et al. 2023

Front. Aging

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Aging associates with an increased susceptibility for disease and decreased quality of life. To date, processes underlying aging are still not well understood, leading to limited interventions with unknown mechanisms to promote healthy aging. Previous research suggests that changes in the blood proteome are reflective of age-associated phenotypes such as frailty. Moreover, experimentally induced changes in the blood proteome composition can accelerate or decelerate underlying aging processes. The aim of this study is to identify a set of proteins in the human plasma associated with aging by integration of the data of four independent, large-scaled datasets using the aptamer-based SomaScan platform on the human aging plasma proteome. Using this approach, we identified a set of 273 plasma proteins significantly associated with aging (aging proteins, APs) across these cohorts consisting of healthy individuals and individuals with comorbidities and highlight their biological functions. We validated the age-associated effects in an independent study using a centenarian population, showing highly concordant effects. Our results suggest that APs are more associated to diseases than other plasma proteins. Plasma levels of APs can predict chronological age, and a reduced selection of 15 APs can still predict individuals’ age accurately, highlighting their potential as biomarkers of aging processes. Furthermore, we show that individuals presenting accelerated or decelerated aging based on their plasma proteome, respectively have a more aged or younger systemic environment. These results provide novel insights in the understanding of the aging process and its underlying mechanisms and highlight potential modulators contributing to healthy aging.

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Plasma Proteomic Biomarkers Relating to Alzheimer’s Disease: A Meta-Analysis Based on Our Own Studies

Cited by 19 publications

References 51 publications

Aberrant energy metabolism in Alzheimer’s disease

Aberrant energy metabolism in Alzheimer’s disease

Plasma proteome profiling identifies changes associated to AD but not to FTD

Markers of aging: Unsupervised integrated analyses of the human plasma proteome

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