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Background: People age at different rates. Biological age is a risk factor for many chronic diseases independent of chronological age. A good lifestyle is known to improve overall health, but its association with biological age is unclear. Methods: This study included participants from the UK Biobank who had undergone 12-lead resting electrocardiography (ECG). Biological age was estimated by a deep learning model (defined as ECG-age), and the difference between ECG-age and chronological age was defined as Delta age. Participants were further categorized into an ideal (score 4), intermediate (scores 2 and 3) or unfavorable lifestyle (score 0 or 1). Four lifestyle factors were investigated, including diet, alcohol consumption, physical activity, and smoking. Linear regression models were used to examine the association between lifestyle factors and Delta age, and the models were adjusted for sex and chronological age. Results: This study included 44,094 individuals (mean age 64 +/- 8, 51.4% females). A significant correlation was observed between predicted biological age and chronological age (correlation coefficient = 0.54, P < 0.001) and the mean Delta age (absolute error of biological age and chronological age) was 9.8 +/- 7.4 years. Delta age was significantly associated with all of the four lifestyle factors, with the effect size ranging from 0.41 +/- 0.11 for the healthy diet to 2.37 +/- 0.30 for non-smoking. Compared with an ideal lifestyle, an unfavorable lifestyle was associated with an average of 2.50 +/- 0.29 years of older predicted ECG-age. Conclusion: In this large contemporary population, a strong association was observed between all four studied healthy lifestyle factors and deaccelerated aging. Our study underscores the importance of a healthy lifestyle to reduce the burden of aging-related diseases.

Background: People age at different rates. Biological age is a risk factor for many chronic diseases independent of chronological age. A good lifestyle is known to improve overall health, but its association with biological age is unclear. Methods: This study included participants from the UK Biobank who had undergone 12-lead resting electrocardiography (ECG). Biological age was estimated by a deep learning model (defined as ECG-age), and the difference between ECG-age and chronological age was defined as Delta age. Participants were further categorized into an ideal (score 4), intermediate (scores 2 and 3) or unfavorable lifestyle (score 0 or 1). Four lifestyle factors were investigated, including diet, alcohol consumption, physical activity, and smoking. Linear regression models were used to examine the association between lifestyle factors and Delta age, and the models were adjusted for sex and chronological age. Results: This study included 44,094 individuals (mean age 64 +/- 8, 51.4% females). A significant correlation was observed between predicted biological age and chronological age (correlation coefficient = 0.54, P < 0.001) and the mean Delta age (absolute error of biological age and chronological age) was 9.8 +/- 7.4 years. Delta age was significantly associated with all of the four lifestyle factors, with the effect size ranging from 0.41 +/- 0.11 for the healthy diet to 2.37 +/- 0.30 for non-smoking. Compared with an ideal lifestyle, an unfavorable lifestyle was associated with an average of 2.50 +/- 0.29 years of older predicted ECG-age. Conclusion: In this large contemporary population, a strong association was observed between all four studied healthy lifestyle factors and deaccelerated aging. Our study underscores the importance of a healthy lifestyle to reduce the burden of aging-related diseases.

Background: People age at different rates. Biological age is a risk factor for many chronic diseases independent of chronological age. A good lifestyle is known to improve overall health, but its association with biological age is unclear. Methods: This study included participants from the UK Biobank who had undergone 12-lead resting electrocardiography (ECG). Biological age was estimated by a deep learning model (defined as ECG-age), and the difference between ECG-age and chronological age was defined as Delta age. Participants were further categorized into an ideal (score 4), intermediate (scores 2 and 3) or unfavorable lifestyle (score 0 or 1). Four lifestyle factors were investigated, including diet, alcohol consumption, physical activity, and smoking. Linear regression models were used to examine the association between lifestyle factors and Delta age, and the models were adjusted for sex and chronological age. Results: This study included 44,094 individuals (mean age 64 +/- 8, 51.4% females). A significant correlation was observed between predicted biological age and chronological age (correlation coefficient = 0.54, P < 0.001) and the mean Delta age (absolute error of biological age and chronological age) was 9.8 +/- 7.4 years. Delta age was significantly associated with all of the four lifestyle factors, with the effect size ranging from 0.41 +/- 0.11 for the healthy diet to 2.37 +/- 0.30 for non-smoking. Compared with an ideal lifestyle, an unfavorable lifestyle was associated with an average of 2.50 +/- 0.29 years of older predicted ECG-age. Conclusion: In this large contemporary population, a strong association was observed between all four studied healthy lifestyle factors and deaccelerated aging. Our study underscores the importance of a healthy lifestyle to reduce the burden of aging-related diseases.

Background: People age at different rates. Biological age is a risk factor for many chronic diseases independent of chronological age. A good lifestyle is known to improve overall health, but its association with biological age is unclear. Methods: This study included participants from the UK Biobank who had undergone 12-lead resting electrocardiography (ECG). Biological age was estimated by a deep learning model (defined as ECG-age), and the difference between ECG-age and chronological age was defined as Delta age. Participants were further categorized into an ideal (score 4), intermediate (scores 2 and 3) or unfavorable lifestyle (score 0 or 1). Four lifestyle factors were investigated, including diet, alcohol consumption, physical activity, and smoking. Linear regression models were used to examine the association between lifestyle factors and Delta age, and the models were adjusted for sex and chronological age. Results: This study included 44,094 individuals (mean age 64 +/- 8, 51.4% females). A significant correlation was observed between predicted biological age and chronological age (correlation coefficient = 0.54, P < 0.001) and the mean Delta age (absolute error of biological age and chronological age) was 9.8 +/- 7.4 years. Delta age was significantly associated with all of the four lifestyle factors, with the effect size ranging from 0.41 +/- 0.11 for the healthy diet to 2.37 +/- 0.30 for non-smoking. Compared with an ideal lifestyle, an unfavorable lifestyle was associated with an average of 2.50 +/- 0.29 years of older predicted ECG-age. Conclusion: In this large contemporary population, a strong association was observed between all four studied healthy lifestyle factors and deaccelerated aging. Our study underscores the importance of a healthy lifestyle to reduce the burden of aging-related diseases.

Background: People age at different rates. Biological age is a risk factor for many chronic diseases independent of chronological age. A good lifestyle is known to improve overall health, but its association with biological age is unclear. Methods: This study included participants from the UK Biobank who had undergone 12-lead resting electrocardiography (ECG). Biological age was estimated by a deep learning model (defined as ECG-age), and the difference between ECG-age and chronological age was defined as Delta age. Participants were further categorized into an ideal (score 4), intermediate (scores 2 and 3) or unfavorable lifestyle (score 0 or 1). Four lifestyle factors were investigated, including diet, alcohol consumption, physical activity, and smoking. Linear regression models were used to examine the association between lifestyle factors and Delta age, and the models were adjusted for sex and chronological age. Results: This study included 44,094 individuals (mean age 64 +/- 8, 51.4% females). A significant correlation was observed between predicted biological age and chronological age (correlation coefficient = 0.54, P < 0.001) and the mean Delta age (absolute error of biological age and chronological age) was 9.8 +/- 7.4 years. Delta age was significantly associated with all of the four lifestyle factors, with the effect size ranging from 0.41 +/- 0.11 for the healthy diet to 2.37 +/- 0.30 for non-smoking. Compared with an ideal lifestyle, an unfavorable lifestyle was associated with an average of 2.50 +/- 0.29 years of older predicted ECG-age. Conclusion: In this large contemporary population, a strong association was observed between all four studied healthy lifestyle factors and deaccelerated aging. Our study underscores the importance of a healthy lifestyle to reduce the burden of aging-related diseases.

Although hepatocellular cancer (HCC) usually occurs in the setting of liver fibrosis, the causal relationship between liver fibrosis and HCC is unclear. in vivo and in vitro models of HCC involving Colr/r mice (that produce a collagenase-resistant type I collagen) or wild-type (WT) mice were used to assess the relationship between type I collagen, liver fibrosis, and experimental HCC. HCC was either chemically induced in WT and Colr/r mice or Hepa 1-6 cells were engrafted into WT and Colr/r livers. The effect of hepatic stellate cells (HSCs) from WT and Colr/r mice on the growth of Hepa 1-6 cells was studied by using multicellular tumor spheroids and xenografts. Collagen type I deposition and fibrosis were increased in Colr/r mice, but they developed fewer and smaller tumors. Hepa 1-6 cells had reduced tumor growth in the livers of Colr/r mice. Although Colr/r HSCs exhibited a more activated phenotype, Hepa 1-6 growth and malignancy were suppressed in multicellular tumor spheroids and in xenografts containing Colr/r HSCs. Treatment with vitronectin, which mimics the presence of degraded collagen fragments, converted the Colr/r phenotype into a WT phenotype. Although Colr/r mice have increased liver fibrosis, they exhibited decreased HCC in several models. Thus, increased liver type I collagen does not produce increased experimental HCC.