Proteomics and Epidemiological Models of Human Aging

Ubaida‐Mohien, Ceereena; Moaddel, Ruin; Moore, Ann Zenobia; Kuo, Pei‐Lun; Faghri, Faraz; Tharakan, Ravi; Tanaka, Toshiko; Nalls, Mike A.; Ferrucci, Luigi

doi:10.3389/fphys.2021.674013

Cited by 14 publications

(9 citation statements)

References 91 publications

(122 reference statements)

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“…Over the past decade, several studies have provided evidence that biological aging by itself is profoundly involved in the pathogenesis of many diseases and age-related health conditions 29 . On the background of genetic predispositions, nongenetic stressors 1b for more details.Source data (environmental, behavioral and social) show substantial stochastic variability, suggesting that they are potentially modifiable by preventive interventions 5,8,30 . Nevertheless, chronological age remains the most used proxy for aging in both clinical and research settings.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, there is substantial heterogeneity in the accumulation of health and functional problems over the lifespan, and such heterogeneity is due to environmental and genetic differences that modulate the rate of aging. Studies in model organisms demonstrate that the rate of biological aging can be tweaked not only by genetic manipulation but also by behavioral and pharmacological interventions [5][6][7][8][9] . To this end, a robust measure that defines the rate of phenotypic aging independent of chronological age would be an extremely useful benchmark to identify intrinsic mechanisms of biological aging.…”

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confidence: 99%

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Longitudinal phenotypic aging metrics in the Baltimore Longitudinal Study of Aging

et al. 2022

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To define metrics of phenotypic aging, it is essential to identify biological and environmental factors that influence the pace of aging. Previous attempts to develop aging metrics were hampered by cross-sectional designs and/or focused on younger populations. In the Baltimore Longitudinal Study of Aging (BLSA), we collected longitudinally across the adult age range a comprehensive list of phenotypes within four domains (body composition, energetics, homeostatic mechanisms and neurodegeneration/neuroplasticity) and functional outcomes. We integrated individual deviations from population trajectories into a global longitudinal phenotypic metric of aging and demonstrate that accelerated longitudinal phenotypic aging is associated with faster physical and cognitive decline, faster accumulation of multimorbidity and shorter survival. These associations are more robust compared with the use of phenotypic and epigenetic measurements at a single time point. Estimation of these metrics required repeated measures of multiple phenotypes over time but may uniquely facilitate the identification of mechanisms driving phenotypic aging and subsequent age-related functional decline.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Longitudinal phenotypic aging metrics in the Baltimore Longitudinal Study of Aging

et al. 2022

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“…1 Therefore, the composition of extracellular proteins represents a valuable source for capturing dynamic biological phenomena occurring in the body. 1–3 Recent advances in affinity proteomics technology have enabled the easy measurement of thousands of proteins simultaneously. 4–6 Importantly, the reliability of this measurement method has been demonstrated to be high (intra- and inter-plate median coefficient of variation < 5%), 4, 5 which permits the capturing of changes in the proteome with unprecedented precision.…”

Section: Introductionmentioning

confidence: 99%

Changes in the cerebrospinal fluid proteome precede and stratify the course of Parkinson’s Disease

Tsukita

Sakamaki‐Tsukita

Kaiser

et al. 2022

Preprint

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The proteome encodes for various information. By quantifying 4071 human proteins in the cerebrospinal fluid using high-throughput affinity proteomics, this study aimed to extract proteomic signatures associated with Parkinson's disease using unbiased machine-learning and to examine their impact on Parkinson's disease course. From the Parkinson's Progression Markers Initiative, we first included 312 drug-naive patients with Parkinson's disease without GBA1, LRRK2, and SNCA mutations (non-genetic Parkinson's disease) and 161 healthy controls. Differentially expressed protein analysis identified 149 proteins that were likely to be differentially expressed with a false discovery rate of < 0.2. Subsequently, a logistic regression analysis with the least absolute shrinkage and selection operator created a 55-protein-based model that quantified the degree to which non-genetic Parkinson's disease-associated cerebrospinal fluid proteomic signatures were present in each participant in the form of a score named a "non-genetic Parkinson's disease-associated proteomic score." This score accurately distinguished non-genetic Parkinson's disease from healthy controls in both a derivation cohort (area under the curve = 0.91 [95% confidence interval, 0.89-0.94]) and an independent validation cohort comprising 38 drug-naive patients with non-genetic Parkinson's disease and 15 healthy controls (area under the curve = 0.87 [95% confidence interval, 0.76-0.99]). Notably, this score was also significantly increased in patients with Parkinson's disease harboring GBA1, LRRK2, or SNCA mutations (n = 258), albeit to varying degrees depending on the type of mutation. Furthermore, the score of genetic prodromal individuals (n = 365) was intermediate between that of healthy controls and patients with Parkinson's disease. Next, cross-sectional correlation analyses revealed that regardless of the presence or absence of genetic mutations, this score was significantly correlated with several baseline clinical parameters and biomarkers. Furthermore, longitudinal survival analyses revealed that even after adjustment of baseline characteristics, this score significantly predicted progression to important clinical milestones including mild cognitive impairment, dementia, and Hoehn and Yahr stage IV. Finally, longitudinal analyses using linear-mixed effects models confirmed that even after adjustment of baseline characteristics, the score was significantly associated with subsequent motor and cognitive decline. Collectively, our study demonstrated that cerebrospinal fluid proteomic signatures associated with non-genetic Parkinson's disease could be quantified using a novel non-genetic Parkinson's disease-associated proteomic score. Furthermore, we identified the presence of these signatures in genetic Parkinson's disease to varying degrees depending on the type of mutation. Moreover, these signatures appeared from the prodromal stage and were robustly linked to both subsequent motor and cognitive decline in early Parkinson's disease, indicating that the cerebrospinal fluid proteome encodes important information for both the onset and progression of Parkinson's disease.

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“…Senescence is a protective mechanism that prevents unauthorized proliferation or differentiation in the immune system. The state of senescence is triggered by sensory mechanisms of damage to DNA or cellular stress and accompanied by epigenetic, transcriptomic, and proteomic alterations in the cell [ 187 , 244 , 254 , 255 ]. One such cytosolic DNA sensor that triggers the aging program is cGMP-AMP synthase (cGAS), which activates innate immunity in response to viral damage to cells.…”

Section: Hscs Senility and Immune Aging As A Risk Factor For Pathologiesmentioning

confidence: 99%

Hematopoietic Stem Cells and the Immune System in Development and Aging

Shevyrev

Tereshchenko

Berezina

et al. 2023

IJMS

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Hematopoietic stem cells (HSCs) support haematopoiesis throughout life and give rise to the whole variety of cells of the immune system. Developing in the early embryo, passing through the precursor stage, and maturing into the first HSCs, they undergo a fairly large number of divisions while maintaining a high regenerative potential due to high repair activity. This potential is greatly reduced in adult HSCs. They go into a state of dormancy and anaerobic metabolism to maintain their stemness throughout life. However, with age, changes occur in the pool of HSCs that negatively affect haematopoiesis and the effectiveness of immunity. Niche aging and accumulation of mutations with age reduces the ability of HSCs to self-renew and changes their differentiation potential. This is accompanied by a decrease in clonal diversity and a disturbance of lymphopoiesis (decrease in the formation of naive T- and B-cells) and the predominance of myeloid haematopoiesis. Aging also affects mature cells, regardless of HSC, therefore, phagocytic activity and the intensity of the oxidative burst decrease, and the efficiency of processing and presentation of antigens by myeloid cells is impaired. Aging cells of innate and adaptive immunity produce factors that form a chronic inflammatory background. All these processes have a serious negative impact on the protective properties of the immune system, increasing inflammation, the risk of developing autoimmune, oncological, and cardiovascular diseases with age. Understanding the mechanisms of reducing the regenerative potential in a comparative analysis of embryonic and aging HSCs, the features of inflammatory aging will allow us to get closer to deciphering the programs for the development, aging, regeneration and rejuvenation of HSCs and the immune system.

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Proteomics and Epidemiological Models of Human Aging

Cited by 14 publications

References 91 publications

Longitudinal phenotypic aging metrics in the Baltimore Longitudinal Study of Aging

Longitudinal phenotypic aging metrics in the Baltimore Longitudinal Study of Aging

Changes in the cerebrospinal fluid proteome precede and stratify the course of Parkinson’s Disease

Hematopoietic Stem Cells and the Immune System in Development and Aging

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