Deniz Yagmur Urey

Abstract Animal studies show aging varies between individuals as well as between organs within an individual 1–4 , but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20–50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer’s disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. 5 ), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.

Rates of cognitive decline in Alzheimer’s disease (AD) are extremely heterogeneous. Although biomarkers for amyloid-beta (Aβ) and tau proteins, the hallmark AD pathologies, have improved pathology-based diagnosis, they explain only 20–40% of the variance in AD-related cognitive impairment (CI). To discover novel biomarkers of CI in AD, we performed cerebrospinal fluid (CSF) proteomics on 3,397 individuals from six major prospective AD case–control cohorts. Synapse proteins emerged as the strongest correlates of CI, independent of Aβ and tau. Using machine learning, we derived the CSF YWHAG:NPTX2 synapse protein ratio, which explained 27% of the variance in CI beyond CSF pTau181:Aβ42, 11% beyond tau positron emission tomography, and 28% beyond CSF neurofilament, growth-associated protein 43 and neurogranin in Aβ+ and phosphorylated tau+ (A+T1+) individuals. CSF YWHAG:NPTX2 also increased with normal aging and 20 years before estimated symptom onset in carriers of autosomal dominant AD mutations. Regarding cognitive prognosis, CSF YWHAG:NPTX2 predicted conversion from A+T1+ cognitively normal to mild cognitive impairment (standard deviation increase hazard ratio = 3.0, P = 7.0 × 10–4) and A+T1+ mild cognitive impairment to dementia (standard deviation increase hazard ratio = 2.2, P = 8.2 × 10–16) over a 15-year follow-up, adjusting for CSF pTau181:Aβ42, CSF neurofilament, CSF neurogranin, CSF growth-associated protein 43, age, APOE4 and sex. We also developed a plasma proteomic signature of CI, which we evaluated in 13,401 samples, which partly recapitulated CSF YWHAG:NPTX2. Overall, our findings underscore CSF YWHAG:NPTX2 as a robust prognostic biomarker for cognitive resilience versus AD onset and progression, highlight the potential of plasma proteomics in replacing CSF measurement and further implicate synapse dysfunction as a core driver of AD dementia. The ratio between the levels of two synaptic proteins in cerebrospinal fluid predicts future cognitive resilience versus decline among presymptomatic individuals and individuals with early Alzheimer’s disease harboring amyloid and tau pathology.

Plasma proteins derived from specific organs can estimate organ age and mortality, but their sensitivity to environmental factors and their robustness in forecasting onset of organ diseases and mortality remain unclear. To address this gap, we estimate the biological age of 11 organs using plasma proteomics data (2,916 proteins) from 44,498 individuals in the UK Biobank. Organ age estimates were sensitive to lifestyle factors and medications and were associated with future onset (within 17 years' follow-up) of a range of diseases, including heart failure, chronic obstructive pulmonary disease, type 2 diabetes and Alzheimer's disease. Notably, having an especially aged brain posed a risk of Alzheimer's disease (hazard ratio (HR) = 3.1) that was similar to carrying one copy of APOE4, the strongest genetic risk factor for sporadic Alzheimer's disease, whereas a youthful brain (HR = 0.26) provided protection that was similar to carrying two copies of APOE2, independent of APOE genotype. Accrual of aged organs progressively increased mortality risk (2-4 aged organs, HR = 2.3; 5-7 aged organs, HR = 4.5; 8+ aged organs, HR = 8.3), whereas youthful brains and immune systems were uniquely associated with longevity (youthful brain, HR = 0.60 for mortality risk; youthful immune system, HR = 0.58; youthful both, HR = 0.44). Altogether, these findings support the use of plasma proteins for monitoring of organ health and point to the brain and immune systems as key targets for longevity interventions.

Pneumonia has been one of the fatal diseases and has the potential to result in severe consequences within a short period of time, due to the flow of fluid in lungs, which leads to drowning. If not acted upon by drugs at the right time, pneumonia may result in death of individuals. Therefore, the early diagnosis is a key factor along the progress of the disease. This paper focuses on the biological progress of pneumonia and its detection by x-ray imaging, overviews the studies conducted on enhancing the level of diagnosis, and presents the methodology and results of an automation of xray images based on various parameters in order to detect the disease at very early stages. In this study we propose our deep learning architecture for the classification task, which is trained with modified images, through multiple steps of preprocessing. Our classification method uses convolutional neural networks and residual network architecture for classifying the images. Our findings yield an accuracy of 78.73%, surpassing the previously top scoring accuracy of 76.8%.

Organ-derived plasma protein signatures derived from aptamer protein arrays track organ-specific aging, disease, and mortality in humans, but the robustness and clinical utility of these models and their biological underpinnings remain unknown. Here, we estimate biological age of 11 organs from 44,526 individuals in the UK Biobank using an antibody-based proteomics platform to model disease and mortality risk. Organ age estimates are associated with future onset of heart failure (heart age HR=1.83), chronic obstructive pulmonary disease (lung age HR=1.39), type II diabetes (kidney age HR=1.58), and Alzheimer's disease (brain age HR=1.81) and sensitive to lifestyle factors such as smoking and exercise, hormone replacement therapy, or supplements. Remarkably, the accrual of aged organs progressively increases mortality risk while a youthful brain and immune system are uniquely associated with disease-free longevity. These findings support the use of plasma proteins for monitoring organ health and the efficacy of drugs targeting organ aging disease.