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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

Effects of lifestyle factors on predicted age.(A) Smoking. Age predicting model trained on non-smokers and applied to smokers. (B) Snus. Snus is Swedish wet tobacco. Age predicting model trained on non-snus-users and applied to snus-users. (C) Fatty fish. Age prediction model trained on individuals with the most common consumption of fatty fish (Salmon, Whitefish and Herring) applied to groups with other levels of fatty fish consumption. Analysis restricted to individuals between 20 and 50 years of age. (D) Significant correlations between Soda consumption and other phenotypic traits in the study cohort, with red colour indicating positive and blue negative correlations. (E) BMI. Model trained on individuals with normal BMI (18.5–24.9) and applied to individuals with higher BMI. Analysis restricted to individuals over 20 years of age. (F) Soda. Age prediction model trained on individuals that do not drink soda and applied to groups with different levels of soda consumption. Analysis restricted to individuals between 20 and 50 years of age. (G) Coffee. Age prediction model trained on non-coffee drinkers and applied to groups with different levels of coffee consumption. Analysis restricted to individuals between 20 and 50 years of age. (H) Exercise. Model trained on individuals reporting that they are as active on their free-time as other individuals in their age-group, and applied to individuals that reporting to be much less, less, more or much more active than individuals in their age-group. (A–C,E–H). Specifically written out predicted phenotype differences imply a statistically significant (p < 0.05, two-sided Wilcoxon Ranked Sum test) change compared to the control group (coloured black). All other differences have a p > 0.05. All actual phenotype differences have a p > 0.05.
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f3: Effects of lifestyle factors on predicted age.(A) Smoking. Age predicting model trained on non-smokers and applied to smokers. (B) Snus. Snus is Swedish wet tobacco. Age predicting model trained on non-snus-users and applied to snus-users. (C) Fatty fish. Age prediction model trained on individuals with the most common consumption of fatty fish (Salmon, Whitefish and Herring) applied to groups with other levels of fatty fish consumption. Analysis restricted to individuals between 20 and 50 years of age. (D) Significant correlations between Soda consumption and other phenotypic traits in the study cohort, with red colour indicating positive and blue negative correlations. (E) BMI. Model trained on individuals with normal BMI (18.5–24.9) and applied to individuals with higher BMI. Analysis restricted to individuals over 20 years of age. (F) Soda. Age prediction model trained on individuals that do not drink soda and applied to groups with different levels of soda consumption. Analysis restricted to individuals between 20 and 50 years of age. (G) Coffee. Age prediction model trained on non-coffee drinkers and applied to groups with different levels of coffee consumption. Analysis restricted to individuals between 20 and 50 years of age. (H) Exercise. Model trained on individuals reporting that they are as active on their free-time as other individuals in their age-group, and applied to individuals that reporting to be much less, less, more or much more active than individuals in their age-group. (A–C,E–H). Specifically written out predicted phenotype differences imply a statistically significant (p < 0.05, two-sided Wilcoxon Ranked Sum test) change compared to the control group (coloured black). All other differences have a p > 0.05. All actual phenotype differences have a p > 0.05.

Mentions: The ability to use the plasma protein profile to accurately predict age allowed us to examine the effect of lifestyle choices on the predicted phenotype (age). We first studied smoking by comparing data on 115 individuals in the study cohort who self-reported as smokers with 860 individuals that reported as non-smokers. Smoking status was used to split the cohort into training and test sets, and an age-prediction model was built using the non-smokers. This model predicted smokers to be on average 2.3 years older (Fig. 3A, p < 1.8 × 10−4, Wilcoxon Ranked Sum test) than their chronological age, even though the two groups do not differ in chronological age (p > 0.9, Wilcoxon Ranked Sum test). Usage of the Swedish wet tobacco product “snus” did not alter the predicted age (Fig. 3B, p > 0.5, Wilcoxon Ranked Sum test).


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

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

Effects of lifestyle factors on predicted age.(A) Smoking. Age predicting model trained on non-smokers and applied to smokers. (B) Snus. Snus is Swedish wet tobacco. Age predicting model trained on non-snus-users and applied to snus-users. (C) Fatty fish. Age prediction model trained on individuals with the most common consumption of fatty fish (Salmon, Whitefish and Herring) applied to groups with other levels of fatty fish consumption. Analysis restricted to individuals between 20 and 50 years of age. (D) Significant correlations between Soda consumption and other phenotypic traits in the study cohort, with red colour indicating positive and blue negative correlations. (E) BMI. Model trained on individuals with normal BMI (18.5–24.9) and applied to individuals with higher BMI. Analysis restricted to individuals over 20 years of age. (F) Soda. Age prediction model trained on individuals that do not drink soda and applied to groups with different levels of soda consumption. Analysis restricted to individuals between 20 and 50 years of age. (G) Coffee. Age prediction model trained on non-coffee drinkers and applied to groups with different levels of coffee consumption. Analysis restricted to individuals between 20 and 50 years of age. (H) Exercise. Model trained on individuals reporting that they are as active on their free-time as other individuals in their age-group, and applied to individuals that reporting to be much less, less, more or much more active than individuals in their age-group. (A–C,E–H). Specifically written out predicted phenotype differences imply a statistically significant (p < 0.05, two-sided Wilcoxon Ranked Sum test) change compared to the control group (coloured black). All other differences have a p > 0.05. All actual phenotype differences have a p > 0.05.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4664859&req=5

f3: Effects of lifestyle factors on predicted age.(A) Smoking. Age predicting model trained on non-smokers and applied to smokers. (B) Snus. Snus is Swedish wet tobacco. Age predicting model trained on non-snus-users and applied to snus-users. (C) Fatty fish. Age prediction model trained on individuals with the most common consumption of fatty fish (Salmon, Whitefish and Herring) applied to groups with other levels of fatty fish consumption. Analysis restricted to individuals between 20 and 50 years of age. (D) Significant correlations between Soda consumption and other phenotypic traits in the study cohort, with red colour indicating positive and blue negative correlations. (E) BMI. Model trained on individuals with normal BMI (18.5–24.9) and applied to individuals with higher BMI. Analysis restricted to individuals over 20 years of age. (F) Soda. Age prediction model trained on individuals that do not drink soda and applied to groups with different levels of soda consumption. Analysis restricted to individuals between 20 and 50 years of age. (G) Coffee. Age prediction model trained on non-coffee drinkers and applied to groups with different levels of coffee consumption. Analysis restricted to individuals between 20 and 50 years of age. (H) Exercise. Model trained on individuals reporting that they are as active on their free-time as other individuals in their age-group, and applied to individuals that reporting to be much less, less, more or much more active than individuals in their age-group. (A–C,E–H). Specifically written out predicted phenotype differences imply a statistically significant (p < 0.05, two-sided Wilcoxon Ranked Sum test) change compared to the control group (coloured black). All other differences have a p > 0.05. All actual phenotype differences have a p > 0.05.
Mentions: The ability to use the plasma protein profile to accurately predict age allowed us to examine the effect of lifestyle choices on the predicted phenotype (age). We first studied smoking by comparing data on 115 individuals in the study cohort who self-reported as smokers with 860 individuals that reported as non-smokers. Smoking status was used to split the cohort into training and test sets, and an age-prediction model was built using the non-smokers. This model predicted smokers to be on average 2.3 years older (Fig. 3A, p < 1.8 × 10−4, Wilcoxon Ranked Sum test) than their chronological age, even though the two groups do not differ in chronological age (p > 0.9, Wilcoxon Ranked Sum test). Usage of the Swedish wet tobacco product “snus” did not alter the predicted age (Fig. 3B, p > 0.5, Wilcoxon Ranked Sum test).

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