Tuesday, March 5, 2024
HomeMen HealthIs your body out of sync? Study finds organs age at various...

Is your body out of sync? Study finds organs age at various rates

In a recent study published within the journal Nature, researchers used cutting-edge blood plasma proteomics to analyze if human organs age at different rates. They analyzed 11 organs in almost 5,700 adults of various ages and located that almost 20% of study participants experienced accelerated organ aging in at the least one organ. Alarmingly, 1.7% of participants depicted accelerated aging in multiple organs. They followed these findings with an estimation of the potential increases in risk of age-related diseases. Their results present that accelerated organ aging is related to a 250% increased risk of cardiac failure and a heightened risk of Alzheimer’s disease.



Study: Organ aging signatures within the plasma proteome track health and disease. Image Credit: Icruci / Shutterstock

Is age really only a number?

Aging is a universally detrimental process leading to the deterioration of the structure and performance of somatic tissues. Since natural selection is blind to all non-reproductive-success-related diseases, aging beyond reproductive age is related to a drastic increase in non-communicable conditions, including cardiovascular diseases (CVDs), cognitive impairment (resembling Alzheimer’s disease), and cancers.

Extensive studies on animal systems, especially murine models, have revealed molecular changes across multiple mouse organs, which, in turn, have been found to end in brain, heart, and kidney diseases. Unexpectedly, these studies revealed discordance between animal (mouse) age and organ age, with the identical mouse presenting differences in aging-associated biomarkers across its organs.

Studies on human aging, while available, are scarce and share a typical demerit of using magnetic resonance imaging (MRI)-based analyses. Unfortunately, MRI systems are restricted to being able to measuring only brain volume and functional connectivity; they fail to determine the molecular underpinnings of observed results. Clinical chemistry approaches have attempted to bridge the MRI-associated knowledge gap, however the biomarkers used herein depict low organ specificity and hard susceptibility to bias and error.

Lately, blood plasma biomarkers have increasingly provided a really perfect technique of accurate molecular aging of mice models, but this approach has yet to be applied to human subjects.

“A molecular understanding of human organ aging is of critical importance to deal with the large global disease burden of aging and will revolutionize patient care, preventative medicine and drug development.”

Concerning the study

In the current study, researchers used blood plasma from 5,676 participants across five distinct study cohorts to find and map a human organ-specific plasma proteome. They identified and measured 4,979 proteins, which were then used to develop and train models of organ aging. Organ-enriched proteins are characterised by having 4 times or greater protein abundance compared to other organs. Of the 4.969 proteins analyzed, 19% (893) proteins were found to be enriched and were used for modeling and analyses.

A bagged ensemble of least absolute shrinkage and selection operator (LASSO) machine learning (ML) model was trained to discover organ-specific aging. The model was optimized to guage the age of 11 major organ types: adipose tissue, brain, artery, heart, immune tissue, kidney, intestine, lung, liver, pancreas, and muscles. These organs were chosen as a consequence of previous research which has associated these systems with age-related mortality and morbidity. Moreover, 3,907 non-enriched proteins were used to coach an ‘organismal’ ML model, and all 4,979 proteins were used to elucidate the worldwide effects of organ aging.

Data from two of the five cohorts was used to analyze the association between organ age and disease risk. Hazard ratios (HRs) for mortality and morbidity were computed. Finally, a separate ‘second-generation brain aging model’ termed CognitionBrain was developed using only the brain-associated enriched proteins to elucidate the impacts of brain aging on future cognitive performance.

Study findings

This study presents the primary investigation to find out human organ-specific aging using molecular slightly than conventional MRI approaches. Proteomic analyses using next-generation sequencing revealed greater than 4700 proteins related to organ-specific aging, 18% of which were enriched only in a single organ, thereby highlighting their potential as future organ-age biomarkers.

a, Study design to estimate organ-specific biological age. A gene was called organ-specific if its expression was four-fold higher in one organ compared to any other organ in GTEX bulk organ RNA-seq. This annotation was then mapped to the plasma proteome. Mutually exclusive organ-specific protein sets were used to train bagged LASSO chronological age predictors with data from 1,398 healthy individuals in the Knight-ADRC cohort. An ‘organismal’ model, which used the nonorgan-specific (organ shared) proteins, and a ‘conventional’ model, which used all proteins regardless of specificity, were also trained. Models were tested in four independent cohorts: Covance (n = 1,029), LonGenity (n = 962), SAMS (n = 192) and Stanford-ADRC (n = 420); models were also tested in the AD patients in the Knight-ADRC cohort (n = 1,677). To test the validity of organ aging models, the age gap was associated with multiple measures of health and disease. An example age prediction (predicted versus chronological age) and an example age gap versus phenotype association (age gap versus phenotype, standard boxplot) are shown. b, Individuals (ID) with the same conventional age gap can have different organ age gap profiles. Three example participants are shown. Bar represents mean age gap across n = 13 age gaps. c, Pairwise correlation of organ age gaps from n = 3,774 healthy participants across all cohorts. Distribution of all pairwise correlations is shown in inset histogram, with dotted line median correlation. The control age gap was highly correlated with the organismal age gap (r = 0.98), the sole outlier in the inset distribution plot. d, Identification of extreme agers, defined by a two standard deviation increase or decrease in at least one age gap. A representative kidney ager, heart ager and multi-organ ager are shown. e, All extreme agers were identified (23% of all n = 5,676 individuals) and clustered after setting age gaps below an absolute z-score of 2 to 0. The mean age gaps for all organs in the kidney agers, heart agers and multi-organ agers clusters are shown.a, Study design to estimate organ-specific biological age. A gene was called organ-specific if its expression was four-fold higher in a single organ in comparison with every other organ in GTEX bulk organ RNA-seq. This annotation was then mapped to the plasma proteome. Mutually exclusive organ-specific protein sets were used to coach bagged LASSO chronological age predictors with data from 1,398 healthy individuals within the Knight-ADRC cohort. An ‘organismal’ model, which used the nonorgan-specific (organ shared) proteins, and a ‘conventional’ model, which used all proteins no matter specificity, were also trained. Models were tested in 4 independent cohorts: Covance (n = 1,029), LonGenity (n = 962), SAMS (n = 192) and Stanford-ADRC (n = 420); models were also tested within the AD patients within the Knight-ADRC cohort (n = 1,677). To check the validity of organ aging models, the age gap was related to multiple measures of health and disease. An example age prediction (predicted versus chronological age) and an example age gap versus phenotype association (age gap versus phenotype, standard boxplot) are shown. b, Individuals (ID) with the identical conventional age gap can have different organ age gap profiles. Three example participants are shown. Bar represents mean age gap across n = 13 age gaps. c, Pairwise correlation of organ age gaps from n = 3,774 healthy participants across all cohorts. Distribution of all pairwise correlations is shown in inset histogram, with dotted line median correlation. The control age gap was highly correlated with the organismal age gap (r = 0.98), the only real outlier within the inset distribution plot. d, Identification of maximum agers, defined by a two standard deviation increase or decrease in at the least one age gap. A representative kidney ager, heart ager and multi-organ ager are shown. e, All extreme agers were identified (23% of all n = 5,676 individuals) and clustered after setting age gaps below an absolute z-score of two to 0. The mean age gaps for all organs within the kidney agers, heart agers and multi-organ agers clusters are shown.

Results of three separate ML algorithms reveal that organ-specific aging was prevalent in 20% of the nearly 6,000 individuals sampled. The aging of specific organs, most notably the kidneys and heart, was related to a significantly (~250%) increased risk of future comorbidities. Analyses of brain aging revealed substantial cognitive reduction (immediate) together with a significantly higher risk of developing memory and mental disorders, including Alzheimer’s disease, in participants with accelerated brain growth.

“There are a lot of future directions for this work. While we’ve got shown that plasma proteomic organ aging models are distinct from previous proteomics models, clinical chemistry-based models and imaging-based models, future studies should assess how proteomic organ aging pertains to other molecular measures of aging and disease resembling methylation aging clocks and disease-specific prediction models.”

Conclusions

In the primary study of its kind conducted on humans, researchers used blood plasma proteomics to elucidate the protein biomarkers and molecular basis of organ-specific aging. Their analyses of just about 6,000 participants across five distinct study cohorts revealed over 4,700 proteins related to early organ aging, of which 18% were organ-specific and may very well be used each for ML model training and as future diagnostic biomarkers.

Their findings revealed that a staggering 20% of participants experienced early aging in at the least one organ, with almost 2% presenting multiple organ age acceleration. Organ aging was found to significantly increase mortality and cognitive risk, with the kidneys, CVD, and brain displaying essentially the most detrimental effects.

“…we show that large-scale plasma proteomics and machine learning may be leveraged to noninvasively measure organ health and aging in living people. We show that biologically motivated modelling, by which we use sets of organ-specific proteins and the FIBA algorithm to further subset to physiological age-related proteins, enables deconvolution of the various rates of aging inside a person and measurement of aging at organ-level resolution.”

- Advertisement -spot_img
- Advertisement -spot_img
Must Read
- Advertisement -spot_img
- Advertisement -spot_img
Related News
- Advertisement -spot_img

LEAVE A REPLY

Please enter your comment!
Please enter your name here