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Protein profiling reveals consequences of lifestyle choices on predicted biological aging.

Enroth S, Enroth SB, Johansson Å, Gyllensten U - Sci Rep (2015)

Bottom Line: Lifestyle factors such as smoking or stress can impact some of these molecular processes and thereby affect the ageing of an individual.Here we demonstrate by analysis of 77 plasma proteins in 976 individuals, that the abundance of circulating proteins accurately predicts chronological age, as well as anthropometrical measurements such as weight, height and hip circumference.We found smoking, high BMI and consumption of sugar-sweetened beverages to increase the predicted chronological age by 2-6 years, while consumption of fatty fish, drinking moderate amounts of coffee and exercising reduced the predicted age by approximately the same amount.

View Article: PubMed Central - PubMed

Affiliation: Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, SE-75108 Uppsala, Sweden.

ABSTRACT
Ageing is linked to a number of changes in how the body and its organs function. On a molecular level, ageing is associated with a reduction of telomere length, changes in metabolic and gene-transcription profiles and an altered DNA-methylation pattern. Lifestyle factors such as smoking or stress can impact some of these molecular processes and thereby affect the ageing of an individual. Here we demonstrate by analysis of 77 plasma proteins in 976 individuals, that the abundance of circulating proteins accurately predicts chronological age, as well as anthropometrical measurements such as weight, height and hip circumference. The plasma protein profile can also be used to identify lifestyle factors that accelerate and decelerate ageing. We found smoking, high BMI and consumption of sugar-sweetened beverages to increase the predicted chronological age by 2-6 years, while consumption of fatty fish, drinking moderate amounts of coffee and exercising reduced the predicted age by approximately the same amount. This method can be applied to dried blood spots and may thus be useful in forensic medicine to provide basic anthropometrical measures for an individual based on a biological evidence sample.

No MeSH data available.


Related in: MedlinePlus

Effect of single proteins on predicted age in smokers.The age predicting model was trained on non-smokers and applied to smokers. The Y-axis shows the contribution of each protein to the total age-difference between predicted and chronological age in smokers, based on the change in protein levels between the two groups. Red (blue) colour corresponds to a positive (negative) contribution to the age in smokers compared to non-smokers. The X-axis depicts the statistical significance of that contribution for each protein (two-sided Wilcoxon Ranked Sum test, −log10(p)).
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f4: Effect of single proteins on predicted age in smokers.The age predicting model was trained on non-smokers and applied to smokers. The Y-axis shows the contribution of each protein to the total age-difference between predicted and chronological age in smokers, based on the change in protein levels between the two groups. Red (blue) colour corresponds to a positive (negative) contribution to the age in smokers compared to non-smokers. The X-axis depicts the statistical significance of that contribution for each protein (two-sided Wilcoxon Ranked Sum test, −log10(p)).

Mentions: The contribution of an individual protein to the age model and the difference between groups (e.g. smokers vs. non-smokers) was in most cases modest, and an increase in protein abundance was shown to have either an additive or subtractive effect on the predicted age (Supplementary Tables 1–6). This is illustrated using the effect of individual proteins on predicted age of smokers versus non-smokers. The majority of proteins contributed a small positive or negative effect on the predicted age (Fig. 4). Some proteins however, such as the cytokines CXCL9 and CXCL10, mediated relatively large effects (on average +0.27 years in smokers compared to non-smokers, p < 5.6 × 10−7 and −0.77 years, p < 5.6 × 10−2 respectively). Both CXCL9 and CXCL10 have previously been shown to be down-regulated in response to cigarette smoke extract compared to control samples in human monocyte-derived macrophages28. In our age prediction model, the coefficient (β) for CXCL9 was positive while negative for CXCL10 and both abundance levels were found to be higher in non-smokers compared to smokers. Therefore, the contribution from CXCL9 to the predicted age was lower in smokers compared to non-smokers, while higher in smokers compared to non-smokers for CXCL10. Notably, IL-12 was found to contribute the largest effect (on average, +0.82 years in smokers compared to non-smokers, p < 7.6 × 10−14, Wilcoxon Ranked Sum test). For IL-12 the sign of the coefficient (β) in the age prediction model was negative, meaning that smokers have lower levels of IL-12, which in turn contributes to a higher predicted age compared to non-smokers.


Protein profiling reveals consequences of lifestyle choices on predicted biological aging.

Enroth S, Enroth SB, Johansson Å, Gyllensten U - Sci Rep (2015)

Effect of single proteins on predicted age in smokers.The age predicting model was trained on non-smokers and applied to smokers. The Y-axis shows the contribution of each protein to the total age-difference between predicted and chronological age in smokers, based on the change in protein levels between the two groups. Red (blue) colour corresponds to a positive (negative) contribution to the age in smokers compared to non-smokers. The X-axis depicts the statistical significance of that contribution for each protein (two-sided Wilcoxon Ranked Sum test, −log10(p)).
© Copyright Policy - open-access
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4664859&req=5

f4: Effect of single proteins on predicted age in smokers.The age predicting model was trained on non-smokers and applied to smokers. The Y-axis shows the contribution of each protein to the total age-difference between predicted and chronological age in smokers, based on the change in protein levels between the two groups. Red (blue) colour corresponds to a positive (negative) contribution to the age in smokers compared to non-smokers. The X-axis depicts the statistical significance of that contribution for each protein (two-sided Wilcoxon Ranked Sum test, −log10(p)).
Mentions: The contribution of an individual protein to the age model and the difference between groups (e.g. smokers vs. non-smokers) was in most cases modest, and an increase in protein abundance was shown to have either an additive or subtractive effect on the predicted age (Supplementary Tables 1–6). This is illustrated using the effect of individual proteins on predicted age of smokers versus non-smokers. The majority of proteins contributed a small positive or negative effect on the predicted age (Fig. 4). Some proteins however, such as the cytokines CXCL9 and CXCL10, mediated relatively large effects (on average +0.27 years in smokers compared to non-smokers, p < 5.6 × 10−7 and −0.77 years, p < 5.6 × 10−2 respectively). Both CXCL9 and CXCL10 have previously been shown to be down-regulated in response to cigarette smoke extract compared to control samples in human monocyte-derived macrophages28. In our age prediction model, the coefficient (β) for CXCL9 was positive while negative for CXCL10 and both abundance levels were found to be higher in non-smokers compared to smokers. Therefore, the contribution from CXCL9 to the predicted age was lower in smokers compared to non-smokers, while higher in smokers compared to non-smokers for CXCL10. Notably, IL-12 was found to contribute the largest effect (on average, +0.82 years in smokers compared to non-smokers, p < 7.6 × 10−14, Wilcoxon Ranked Sum test). For IL-12 the sign of the coefficient (β) in the age prediction model was negative, meaning that smokers have lower levels of IL-12, which in turn contributes to a higher predicted age compared to non-smokers.

Bottom Line: Lifestyle factors such as smoking or stress can impact some of these molecular processes and thereby affect the ageing of an individual.Here we demonstrate by analysis of 77 plasma proteins in 976 individuals, that the abundance of circulating proteins accurately predicts chronological age, as well as anthropometrical measurements such as weight, height and hip circumference.We found smoking, high BMI and consumption of sugar-sweetened beverages to increase the predicted chronological age by 2-6 years, while consumption of fatty fish, drinking moderate amounts of coffee and exercising reduced the predicted age by approximately the same amount.

View Article: PubMed Central - PubMed

Affiliation: Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, SE-75108 Uppsala, Sweden.

ABSTRACT
Ageing is linked to a number of changes in how the body and its organs function. On a molecular level, ageing is associated with a reduction of telomere length, changes in metabolic and gene-transcription profiles and an altered DNA-methylation pattern. Lifestyle factors such as smoking or stress can impact some of these molecular processes and thereby affect the ageing of an individual. Here we demonstrate by analysis of 77 plasma proteins in 976 individuals, that the abundance of circulating proteins accurately predicts chronological age, as well as anthropometrical measurements such as weight, height and hip circumference. The plasma protein profile can also be used to identify lifestyle factors that accelerate and decelerate ageing. We found smoking, high BMI and consumption of sugar-sweetened beverages to increase the predicted chronological age by 2-6 years, while consumption of fatty fish, drinking moderate amounts of coffee and exercising reduced the predicted age by approximately the same amount. This method can be applied to dried blood spots and may thus be useful in forensic medicine to provide basic anthropometrical measures for an individual based on a biological evidence sample.

No MeSH data available.


Related in: MedlinePlus