1. Epidemiology and Global Health

Lifestyles and their relative contribution to biological aging across multiple-organ systems: Change analysis from the China Multi-Ethnic Cohort study

  1. Yuan Zhang
  2. Dan Tang
  3. Ning Zhang
  4. Yi Xiang
  5. Yifan Hu
  6. Wen Qian
  7. Yangji Baima
  8. Xianbin Ding
  9. Ziyun Wang
  10. Jianzhong Yin  Is a corresponding author
  11. Xiong Xiao  Is a corresponding author
  12. Xing Zhao
  1. West China School of Public Health and West China Fourth Hospital, Sichuan University, China
  2. Xiamen Center for Disease Control and Prevention, China
  3. Chengdu Center for Disease Control and Prevention, China
  4. School of Medicine, Tibet University, China
  5. Chongqing Municipal Centre for Disease Control and Prevention, China
  6. School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, China
  7. School of Public Health, Kunming Medical University, China
https://doi.org/10.7554/eLife.99924.3
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Cite this article
  1. Yuan Zhang
  2. Dan Tang
  3. Ning Zhang
  4. Yi Xiang
  5. Yifan Hu
  6. Wen Qian
  7. Yangji Baima
  8. Xianbin Ding
  9. Ziyun Wang
  10. Jianzhong Yin
  11. Xiong Xiao
  12. Xing Zhao
(2025)
Lifestyles and their relative contribution to biological aging across multiple-organ systems: Change analysis from the China Multi-Ethnic Cohort study
eLife 13:RP99924.
https://doi.org/10.7554/eLife.99924.3
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4 figures, 4 tables and 2 additional files

Figures

Figure 1
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Flowchart of the study.
Figure 2
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Changes in lifestyle factors from baseline to repeated survey.

The blue bars represent the percentage of individuals who have transitioned to healthier behaviors or improved their healthy lifestyle indicator (HLI) score; the gray bars represent the percentage of individuals whose behaviors or HLI score have not changed; the orange bars represent the percentage of individuals who have transitioned to unhealthier behaviors or whose HLI score has decreased.

Figure 3 with 6 supplements
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Relative contributions of five healthy lifestyle components to biological age (BA) acceleration.

Panels: results of the comprehensive BA acceleration, the cardiopulmonary BA acceleration, the metabolic BA acceleration, and the liver BA acceleration (a–d). ΔSmoking, change in smoking status between the baseline and repeated survey; ΔAlcohol, change in alcohol consumption between the baseline and repeated survey; ΔDiet, change in dietary quality between the baseline and repeated survey; ΔExercise, change in exercise between the baseline and repeated survey; ΔSleep, change in sleep between the baseline and repeated survey. Estimates were obtained using quantile G-computation (QGC), which treated the BA accelerations as the dependent variables and five individual lifestyle factors as the independent variables. Models were adjusted for age, occupation, marital status, total energy intake, depression symptoms, anxiety symptoms, menopausal status in women, beverage intake, dietary supplement intake, diabetes, cardiovascular disease, cancer, sex, ethnicity, urbanicity, education, and the participants' age at baseline. The blue bars represent results that are statistically significant in the fixed effects model (FEM) analysis, while the gray bars represent results in the FEM analysis that were not found to be statistically significant and positive weights were not shown.

Figure 3—figure supplement 1
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Relative contributions of five healthy lifestyle components to biological age (BA) acceleration with adjustment for time-varying covariate.

Panels: results of the comprehensive BA acceleration, the cardiopulmonary BA acceleration, the metabolic BA acceleration, and the liver BA acceleration (a–d). ΔSmoking, change in smoking status between the baseline and repeated survey; ΔAlcohol, change in alcohol consumption between the baseline and repeated survey; ΔDiet, change in dietary quality between the baseline and repeated survey; ΔExercise, change in exercise between the baseline and repeated survey; ΔSleep, change in sleep between the baseline and repeated survey. Estimates were obtained using quantile G-computation (QGC) treating the BA accelerations as the dependent variables and five individual lifestyle factors as the independent variables. Models were adjusted for age, occupation, marital status, total energy intake, depression symptoms, anxiety symptoms, menopausal status in women, beverage intake, dietary supplement intake, diabetes, cardiovascular disease, and cancer. The blue bars represent results that are statistically significant in the fixed effects model (FEM) analysis, while the gray bars represent results in the FEM analysis that were not found to be statistically significant and positive weights were not shown.

Figure 3—figure supplement 2
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The relative contributions of five healthy lifestyle components to the comprehensive biological age (BA) acceleration with altered healthy lifestyle criteria.

Panels: results of the comprehensive BA acceleration when definitions of smoking, alcohol, diet, exercise, and sleep are changed separately (a–e). ΔSmoking, change in smoking status between the baseline and repeated survey; ΔAlcohol, change in alcohol consumption between the baseline and repeated survey; ΔDiet, change in dietary quality between the baseline and repeated survey; ΔExercise, change in exercise between the baseline and repeated survey; ΔSleep, change in sleep between the baseline and repeated survey. Estimates were obtained using quantile G-computation (QGC) treating the comprehensive BA acceleration as the dependent variables and five individual lifestyle factors as the independent variables. All models were adjusted for age, occupation, marital status, total energy intake, depression symptoms, anxiety symptoms, menopausal status in women, beverage intake, dietary supplement intake, diabetes, cardiovascular disease, cancer, sex, ethnicity, urbanicity, education, and the participants' age at baseline. The blue bars represent results that are statistically significant in the fixed effects model (FEM) analysis, while the gray bars represent results in the FEM analysis that were not found to be statistically significant and positive weights were not shown.

Figure 3—figure supplement 3
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The relative contributions of five healthy lifestyle components to the cardiopulmonary biological age (BA) acceleration with altered healthy lifestyle criteria.

Panels: results of the cardiopulmonary BA acceleration when definitions of smoking, alcohol, diet, exercise, and sleep are changed separately (a–e). ΔSmoking, change in smoking status between the baseline and repeated survey; ΔAlcohol, change in alcohol consumption between the baseline and repeated survey; ΔDiet, change in dietary quality between the baseline and repeated survey; ΔExercise, change in exercise between the baseline and repeated survey; ΔSleep, change in sleep between the baseline and repeated survey. Estimates were obtained using quantile G-computation (QGC) treating the cardiopulmonary BA acceleration as the dependent variables and five individual lifestyle factors as the independent variables. All models were adjusted for age, occupation, marital status, total energy intake, depression symptoms, anxiety symptoms, menopausal status in women, beverage intake, dietary supplement intake, diabetes, cardiovascular disease, cancer, sex, ethnicity, urbanicity, education, and the participants' age at baseline. The blue bars represent results that are statistically significant in the fixed effects model (FEM) analysis, while the gray bars represent results in the FEM analysis that were not found to be statistically significant and positive weights were not shown.

Figure 3—figure supplement 4
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The relative contributions of five healthy lifestyle components to the metabolic biological age (BA) acceleration with altered healthy lifestyle criteria.

Panels: results of the metabolic BA acceleration when definitions of smoking, alcohol, diet, exercise, and sleep are changed separately (a–e). ΔSmoking, change in smoking status between the baseline and repeated survey; ΔAlcohol, change in alcohol consumption between the baseline and repeated survey; ΔDiet, change in dietary quality between the baseline and repeated survey; ΔExercise, change in exercise between the baseline and repeated survey; ΔSleep, change in sleep between the baseline and repeated survey. Estimates were obtained using quantile G-computation (QGC) treating the metabolic BA acceleration as the dependent variables and five individual lifestyle factors as the independent variables. All models were adjusted for age, occupation, marital status, total energy intake, depression symptoms, anxiety symptoms, menopausal status in women, beverage intake, dietary supplement intake, diabetes, cardiovascular disease, cancer, sex, ethnicity, urbanicity, education, and the participants' age at baseline. The blue bars represent results that are statistically significant in the fixed effects model (FEM) analysis, while the gray bars represent results in the FEM analysis that were not found to be statistically significant and positive weights were not shown.

Figure 3—figure supplement 5
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The relative contributions of five healthy lifestyle components to the liver biological age (BA) acceleration with altered healthy lifestyle criteria.

Panels: results of the liver BA acceleration when definitions of smoking, alcohol, diet, exercise, and sleep are changed separately (a–e). ΔSmoking, change in smoking status between the baseline and repeated survey; ΔAlcohol, change in alcohol consumption between the baseline and repeated survey; ΔDiet, change in dietary quality between the baseline and repeated survey; ΔExercise, change in exercise between the baseline and repeated survey; ΔSleep, change in sleep between the baseline and repeated survey. Estimates were obtained using quantile G-computation (QGC) treating the liver BA acceleration as the dependent variables and five individual lifestyle factors as the independent variables. All models were adjusted for age, occupation, marital status, total energy intake, depression symptoms, anxiety symptoms, menopausal status in women, beverage intake, dietary supplement intake, diabetes, cardiovascular disease, cancer, sex, ethnicity, urbanicity, education, and the participants' age at baseline. The blue bars represent results that are statistically significant in the fixed effects model (FEM) analysis, while the gray bars represent results in the FEM analysis that were not found to be statistically significant and positive weights were not shown.

Figure 3—figure supplement 6
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Relative contributions of five healthy lifestyle components to the comprehensive and the metabolic biological age (BA) acceleration with additionally adjusted for body mass index (BMI).

Panels: results of the comprehensive BA acceleration, the cardiopulmonary BA acceleration, the metabolic BA acceleration, and the liver BA acceleration (a–d). ΔSmoking, change in smoking status between the baseline and repeated survey; ΔAlcohol, change in alcohol consumption between the baseline and repeated survey; ΔDiet, change in dietary quality between the baseline and repeated survey; ΔExercise, change in exercise between the baseline and repeated survey; ΔSleep, change in sleep between the baseline and repeated survey. Estimates were obtained using quantile G-computation (QGC) treating the BA accelerations as the dependent variables and five individual lifestyle factors as the independent variables. Models were adjusted for age, occupation, marital status, total energy intake, depression symptoms, anxiety symptoms, menopausal status in women, beverage intake, dietary supplement intake, diabetes, cardiovascular disease, cancer, sex, ethnicity, urbanicity, education, and the participants' age at baseline. The blue bars represent results that are statistically significant in the fixed effects model (FEM) analysis, while the gray bars represent results in the FEM analysis that were not found to be statistically significant and positive weights were not shown.

Figure 4 with 3 supplements
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Stratified analysis of estimated associations between the healthy lifestyle index (HLI) and the comprehensive biological age (BA) acceleration.

All models were adjusted for age, occupation, marital status, total energy intake, depression symptoms, anxiety symptoms, menopausal status in women, beverage intake, dietary supplement intake, diabetes, cardiovascular disease, cancer, sex, ethnicity, urbanicity, education, and the participants' age at baseline, with exclusion of the stratified variable as appropriate. The boxes represent point estimations. Horizontal lines represent 95% CI.

Figure 4—figure supplement 1
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Stratified analysis of estimated associations between the healthy lifestyle index (HLI) and the cardiopulmonary biological age (BA) acceleration.

All models were adjusted for age, occupation, marital status, total energy intake, depression symptoms, anxiety symptoms, menopausal status in women, beverage intake, dietary supplement intake, diabetes, cardiovascular disease, cancer, sex, ethnicity, urbanicity, education, and the participants' age at baseline, with the exclusion of the stratified variable as appropriate. The boxes represent point estimations. Horizontal lines represent 95% CI.

Figure 4—figure supplement 2
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Stratified analysis of estimated associations between the healthy lifestyle index (HLI) and the metabolic biological age (BA) acceleration.

All models were adjusted for age, occupation, marital status, total energy intake, depression symptoms, anxiety symptoms, menopausal status in women, beverage intake, dietary supplement intake, diabetes, cardiovascular disease, cancer, sex, ethnicity, urbanicity, education, and the participants' age at baseline, with the exclusion of the stratified variable as appropriate. The boxes represent point estimations. Horizontal lines represent 95% CI.

Figure 4—figure supplement 3
Download asset Open asset
Stratified analysis of estimated associations between the healthy lifestyle index (HLI) and the liver biological age (BA) acceleration.

All models were adjusted for age, occupation, marital status, total energy intake, depression symptoms, anxiety symptoms, menopausal status in women, beverage intake, dietary supplement intake, diabetes, cardiovascular disease, cancer, sex, ethnicity, urbanicity, education, and the participants' age at baseline, with the exclusion of the stratified variable as appropriate. The boxes represent point estimations. Horizontal lines represent 95% CI.

Tables

Table 1
Characteristics of participants in baseline and repeated surveys, based on the categorized HLI*.
CharacteristicBaseline HLI (category)Follow up HLI (category)
UnfavorableN=4935HealthyN=3461UnfavorableN=5121HealthyN=3275
Age (years)51.83 [44.97, 60.44]49.19 [42.51, 56.66]53.75 [47.01, 62.24]50.86 [44.21, 59.14]
Female (%)2511 (50.9)2666 (77.0)2685 (52.4)2492 (76.1)
Urbanicity (%)1472 (29.8)1525 (44.1)1527 (29.8)1470 (44.9)
Majority (%)2665 (54.0)2427 (70.1)2763 (54.0)2329 (71.1)
Education (%)
 No schooling1506 (30.5)653 (18.9)1554 (30.3)605 (18.5)
 Primary school1305 (26.4)721 (20.8)1377 (26.9)649 (19.8)
 Middle/high school1764 (35.7)1525 (44.1)1780 (34.8)1509 (46.1)
 College/university360 (7.3)562 (16.2)410 (8.0)512 (15.6)
Occupation (%)
 Primary industry practitioner1993 (40.4)820 (23.7)2093 (40.9)826 (25.2)
 Secondary industry practitioner335 (6.8)197 (5.7)396 (7.7)174 (5.3)
 Tertiary industry practitioner1699 (34.4)1671 (48.3)1736 (33.9)1462 (44.6)
 Unemployed908 (18.4)773 (22.3)896 (17.5)813 (24.8)
Married (%)4396 (89.1)3111 (89.9)4595 (89.7)2966 (90.6)
Total energy intake (kcal/day)1796.35 [1394.49, 2293.37]1759.71 [1404.92, 2156.58]1510.72 [1129.17, 1994.64]1482.29 [1183.94, 1879.82]
BMI (kg/m2)24.29 [21.94, 26.79]24.12 [22.04, 26.47]24.61 [22.23, 27.05]24.34 [22.31, 26.73]
Depression symptom (%)289 (5.9)117 (3.4)203 (4.0)86 (2.6)
Anxiety symptom (%)335 (6.8)146 (4.2)212 (4.1)71 (2.2)
Menopausal status in women (%)
 Premenopausal1042 (41.5)1336 (50.1)914 (34.0)1099 (44.1)
 Perimenopausal170 (6.8)189 (7.1)133 (5.0)109 (4.4)
 Postmenopausal1299 (51.7)1141 (42.8)1638 (61.0)1284 (51.5)
Beverage (%)
 Never4564 (92.5)3292 (95.1)4746 (92.7)3079 (94.0)
 Former consumer23 (0.5)16 (0.5)27 (0.5)14 (0.4)
 Currently consuming348 (7.1)153 (4.4)348 (6.8)182 (5.6)
Dietary supplement intake (%)711 (14.4)722 (20.9)724 (14.1)712 (21.7)
Major diseases
 Diabetes (%)245 (5.0)161 (4.7)275 (5.4)159 (4.9)
 CVD (%)974 (19.7)628 (18.1)1054 (20.6)602 (18.4)
 Cancer (%)36 (0.7)31 (0.9)39 (0.8)32 (1.0)
Biological ages†
 Comprehensive BA52.35 [43.90, 61.07]49.15 [40.19, 58.61]54.78 [46.48, 63.45]51.67 [42.74, 61.31]
 Comprehensive BA acceleration0.00 [–2.89, 3.00]–0.70 [–3.79, 2.73]0.40 [–2.66, 3.48]–0.13 [–3.21, 3.25]
 Cardiopulmonary BA51.86 [45.05, 60.40]49.10 [42.48, 56.89]53.79 [47.07, 62.14]50.82 [43.97, 59.18]
 Cardiopulmonary BA acceleration0.08 [–0.47, 0.59]–0.03 [–0.65, 0.55]0.04 [–0.56, 0.62]–0.08 [–0.71, 0.56]
 Metabolic BA52.87 [43.86, 61.78]49.42 [40.53, 59.01]55.14 [46.10, 64.29]52.29 [42.89, 61.77]
 Metabolic BA acceleration0.55 [–3.86, 4.85]–0.37 [–4.41, 3.86]0.46 [–3.87, 4.94]0.32 [–3.93, 4.46]
 Liver BA52.42 [41.08, 63.96]49.40 [37.83, 60.43]57.61 [46.15, 68.52]54.13 [42.31, 64.32]
 Liver BA acceleration0.15 [–8.16, 8.46]–0.88 [–8.50, 6.73]2.88 [–5.31, 11.58]1.11 [–6.02, 9.08]

  1. BA, biological age; BMI, body mass index; CVD, cardiovascular disease; HLI, healthy lifestyle indicator.

  2. *

    Data are presented as median (25th, 75th percentile) for continuous variables and count (percentage) for categorical variables. For HLI (category), ‘healthy’ corresponds to a score of 4–5, while ‘unfavorable’ corresponds to a score of 0–3.

  3. †

    Information on each validated BA has been reported. BA acceleration is the difference between each BA and CA in the same survey.

Table 2
Associations of healthy lifestyle factors and HLI with the BA acceleration of validated BAs.
VariablesComprehensiveBA accelerationβ (95% CI)CardiopulmonaryBA accelerationβ (95% CI)MetabolicBA accelerationβ (95% CI)LiverBA accelerationβ (95% CI)
HLI (range)
Per 1-point increase–0.11 (–0.19, –0.03)–0.02 (–0.04, –0.01)–0.12 (–0.22, –0.02)–0.23 (–0.47, 0.00)
HLI (category)
To have a healthy lifestyle–0.19 (–0.34, –0.03)–0.03 (–0.07, 0.00)–0.16 (–0.36, 0.03)–0.19 (–0.66, 0.27)
Healthy lifestyle factors
 ΔSmoking–0.13 (–0.47, 0.21)–0.03 (–0.10, 0.04)–0.54 (–0.97, –0.11)–0.57 (–1.58, 0.44)
 ΔAlcohol–0.17 (-0.47, 0.13)–0.02 (–0.09, 0.04)–0.18 (–0.56, 0.21)–0.59 (–1.50, 0.31)
 ΔDiet–0.15 (–0.29, –0.00)–0.01 (–0.04, 0.02)–0.18 (–0.36, 0.00)–0.10 (–0.53, 0.33)
 ΔExercise–0.16 (–0.32, 0.00)–0.03 (–0.07, 0.00)–0.09 (–0.30, 0.11)–0.05 (–0.53, 0.44)
 ΔSleep–0.02 (–0.16, 0.12)–0.02 (–0.05, 0.01)0.01 (–0.17, 0.19)–0.36 (–0.78, 0.06)

  1. Estimates were obtained using FEMs treating the BA accelerations as the dependent variables and HLI (as either continuous or as categorized) or five individual lifestyle factors as the independent variables. Models were adjusted for age, occupation, marital status, total energy intake, depression symptoms, anxiety symptoms, menopausal status in women, beverage intake, dietary supplement intake, diabetes, cardiovascular disease, cancer, sex, ethnicity, urbanicity, education, and the participants' age at baseline.

  2. BA, biological age; HLI, healthy lifestyle indicator. ΔSmoking, change in smoking status between the baseline and repeated survey; ΔAlcohol, change in alcohol consumption between the baseline and repeated survey; ΔDiet, change in dietary quality between the baseline and repeated survey; ΔExercise, change in exercise between the baseline and repeated survey; ΔSleep, change in sleep between the baseline and repeated survey.

Author response table 1
Baseline characteristics of participants included and not included in the study.
CharacteristicOverall N=99,556Population included N=8396Population not included N=91.160
Age (years)50.48 [43.12, 60.27]50.64 [43.91, 59.26]50.46 [43.04, 60.38]
Female (%)59762 (60.0)5177 (61.7)54585 (59.9)
Urbanicity (%)33578 (34.1)2997 (35.7)30581 (33.9)
Majority (%)55420 (56.2)5092 (60.6)50328 (55.8)
Education(%)
 No schooling27028 (27.4)2159 (25.7)24869 (27.6)
 Primary school25136 (25.5)2026 (24.1)23110 (25.6)
 Middle/high school35850 (36.4)3289 (39.2)32561 (36.1)
 College'university10536 (10.7)922 (11.0)9614 (10.7)
Occupation(%)
 Primary industry practitioner33842 (34.4)2813 (33.5)31029 (34.4)
 Secondary industry practitioner7002 (7.1)532 (6.3)6470 (7.2)
 Tertiary industry practitioner36792 (37.4)3370 (40.1)33422 (37.1)
 Unemployed20838 (21.2)1681 (20.0)19157 (21.3)
Married (%)87420 (88.7)7507 (89.4)79913 (88.6)
Total energy intake (kcal/day)1737.19[1354.75 2201.42]1778.33 [1400.29, 2234.39]1733.40[1350.15 2198.22]
BMI (kg/m2)24.06 [21.78, 26.32]24.22 [21.97, 26.67]24.04 [21.76, 26.31]
Depression symptom (%)4990 (5.1)406 (4.8)4584 (5.1)
Anxiety symptom (%)5833 (5.9)481 (5.7)5352 (6.0)
Menopausal status in women (%)
 Premenopausal26630 (45.0)2378 (45.9)24252 (44.9)
 Perimenopausal3994 (6.8)359 (6.9)3635 (6.7)
 Postmenopausal28544 (48.2)2440 (47.1)26104 (48.3)
Beverage (%)
 Never90684 (92.1)7856 (93.6)82828 (91.9)
 Former consumer429 (0.4)39 (0.5)390 (0.4)
 Currently consuming7400 (7.5)501 (6.0)6899 (7.7)
Dietary supplement intake (%)15679 (16.0)1433 (17.1)14246 (15.8)
HLI (healthy) (%)*37104 (37.9)3461 (41.2)33643 (37.5)
Major diseases
Diabetes (%)4587 (4.7)406 (4.8)4181 (4.6)
CVD (%)18226 (18.5)1602 (19.1)16624 (18.4)
Cancer (%)782 (0.8)67 (0.8)715 (0.8)
Biological ages†
Comprehensive BA51.24 [42.20, 60.97]51.08 [42.28, 60.03]51.26 [42.19, 61.09]
Comprehensive BA acceleration-0.15 [-3.15, 3.03]-0.23[-3.28,2.88]-0.14 [-3.14, 3.05]
Cardiopulmonary BA50.76 [43.46, 60.34]50.66 [43.87, 59.10]50.77 [43.41, 60.45]
Cardiopulmonary BA acceleration0.02 [-0.57, 0.57]0.03 [-0.54, 0.58]0.02 [-0.57, 0.57]
Metabolic BA51.41 [41.91, 61.32]51.57 [42.37, 60.89]51.39 [41.86, 61.37]
Metabolic BA acceleration-0.11 [-4.31, 4.14]0.22 [-4.08, 4.44]-0.14 [-4.33, 4.11]
Liver BA51.88 [39.54, 63.59]51.30 [39.49, 62.68]51.94 [39.55, 63.69]
Liver BA acceleration-0.12[-8.05, 7.94]-0.37[-8.36, 7.73]-0.10 [-8.03, 7.96]

  1. BA, biological age; BMI, body mass index; CVD, cardiovascular disease; HLI, healthy lifestyle indicator.

  2. Data are presented as median (25th, 75th percentile) for continuous variables and count (percentage) for categorical variables.

  3. *

    For HLI, "healthy" corresponds to a score of 4-5.

  4. †

    Information on each validated BA has been reported. BA acceleration is the difference between each BA and CA in the same survey.

Author response table 2
Description of BA and BA acceleration.
BAComprehensive BACardiopulmonary BAMetabolic BALiver BARenal BAImmune BA
KDM-BA
Baseline51.66 (12.45)51.66 (11.50)51.66 (13.31)51.66 (17.35)51.66 (15.24)51.66 (14.00)
Follow-up53.85 (12.04)53.43 (10.84)53.94 (13.01)56.58 (17.59)53.59 (14.67)54.24 (13.51)
Change2.49 (4.09)2.00 (0.94)2.32 (5.26)5.09 (12.57)2.31 (9.11)2.68 (5.07)
KDM-BA acceleration
Baseline0.00 (4.83)0.00 (0.91)–0.00 (6.76)–0.00 (13.02)0.00 (10.03)0.00 (8.04)
Follow-up0.39 (4.89)–0.04 (0.94)0.48 (6.78)3.11 (13.98)0.13 (9.85)0.77 (7.95)
Change0.44 (4.09)–0.05 (0.86)0.27 (5.25)3.05 (12.55)0.26 (9.14)0.63 (5.06)

  1. BA, biological age

  2. Data are presented as mean (standard deviation).

Additional files

Supplementary file 1

Supplementary methods, descriptive analysis of biological age, and results of sensitivity analysis.

(a) Detailed definitions of lifestyle factors in the main analysis and sensitivity analysis. (b) Candidate indicators used to construct the comprehensive and multi-organ systems of biological age. (c) Description of BA and BA acceleration. (d) Associations of the comprehensive and multi-organ systems BA acceleration with organ-specific diseases. (e) Associations of healthy lifestyle factors and HLI with the BA accelerations with adjustment for time-varying covariates. (f) Associations of healthy lifestyle factors and HLI with the BA accelerations altered healthy lifestyle criteria. (g) Associations of healthy lifestyle factors and HLI with the BA accelerations with additionally adjusted for BMI.

https://cdn.elifesciences.org/articles/99924/elife-99924-supp1-v1.docx
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https://cdn.elifesciences.org/articles/99924/elife-99924-mdarchecklist1-v1.docx

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  1. Yuan Zhang
  2. Dan Tang
  3. Ning Zhang
  4. Yi Xiang
  5. Yifan Hu
  6. Wen Qian
  7. Yangji Baima
  8. Xianbin Ding
  9. Ziyun Wang
  10. Jianzhong Yin
  11. Xiong Xiao
  12. Xing Zhao
(2025)
Lifestyles and their relative contribution to biological aging across multiple-organ systems: Change analysis from the China Multi-Ethnic Cohort study
eLife 13:RP99924.
https://doi.org/10.7554/eLife.99924.3