TABLE 2.
Factors associated with a slower aging clock in humans
| Factor(s) | Aging clock(s) used | Cohort size | Age information (years) | Tissue/data analyzed | Study reference |
|---|---|---|---|---|---|
| Fatty fish consumption, coffee consumption, exercise | Enroth et al. (2015) | 976 | 14–94 | Plasma | Enroth et al. (2015) |
| Smoking cessation | Horvath (2013) and Hannum et al. (2013) | 22 | 46.77 ± 6.99 | Blood | Lei et al. (2017) |
| Poultry intake, fish intake, markers of vegetable/fruit consumption, education, income, exercise, alcohol consumption | Horvath (2013) and Hannum et al. (2013) | 4575 | 30–100 | Blood | Quach et al. (2017) |
| Markers of vegetable/fruit consumption, nut consumption, education, income, exercise, alcohol consumption | PhenoAge (M. E. Levine et al., 2018) | 4207 | 50–79 | Blood | M. E. Levine et al. (2018) |
| Omega‐3 supplementation, carbohydrate intake, dairy intake, whole grain intake, markers of vegetable/fruit consumption, education, income, exercise, alcohol consumption | GrimAge (A. T. Lu, Quach, et al., 2019) | 2174 | 59–73a | Blood | A. T. Lu, Quach, et al. (2019) |
| Aerobic exercise | Lehallier (Lehallier et al., 2020) | 47 | 19–77 | Plasma | Lehallier et al. (2020) |
| Calcium alpha‐ketoglutarate | TruAge (Demidenko et al., 2021) | 42 | 43–72 | Saliva | Demidenko et al. (2021) |
| Leisure‐time physical activity | GrimAge (A. T. Lu, Quach, et al., 2019) | 1040 | 21–74 | Blood | Kankaanpää et al. (2021) |
| Doxazosin, fiber intake, magnesium intake, vitamin E intake | MoveAge (McIntyre et al., 2021) | 5139 | 18–85+ | Accelerometer data | McIntyre et al. (2021) |
| Lifestyle factors, including physical activity, intake of vegetables and fruits, and moderate drinking | Li (J. Li et al., 2018) | 286 | 48.9 ± 10.6 | Blood | Peng et al. (2021) |
| Cardiovascular health factors, including diet, smoking status, and physical activity | Horvath (Horvath, 2013) and Hannum (Hannum et al., 2013) | 2170 | 64.19 ± 7.06 | Blood | Pottinger et al. (2021) |
| Mediterranean diet, Dietary Approaches to Stop Hypertension diet | Esposito (Esposito et al., 2022) | 4510 | ≥ 35 | Blood | Esposito et al. (2022) |
| Sleep quality | Klemera‐Doubal Method (Klemera & Doubal, 2006) and PhenoAge (M. E. Levine et al., 2018) | 363,886 | 56.5 ± 8.1 | Blood | Gao et al. (2022) |
| Higher diet quality | DunedinPoAm (Belsky et al., 2020), GrimAge (A. T. Lu, Quach, et al., 2019), and PhenoAge (M. E. Levine et al., 2018) | 1995 | 67 ± 9 | Blood | Y. Kim et al. (2022) |
| Higher diet quality | Hannum (Hannum et al., 2013), PhenoAge (M. E. Levine et al., 2018), and GrimAge (A. T. Lu, Quach, et al., 2019) | 2694 | 56 ± 9 | Blood | Kresovich et al. (2022) |
| Light alcohol consumption | MonoDNAmAge (Liang et al., 2022), Horvath (Horvath, 2013), Hannum (Hannum et al., 2013), PhenoAge (M. E. Levine et al., 2018), and GrimAge (A. T. Lu, Quach, et al., 2019) | 2242 | 18–83 | Monocytes, blood, and peripheral blood mononuclear cells | Liang et al. (2022) |
| Serum zinc levels | Horvath (2013) | 10 | 37.83 ± 12.05 | Blood leukocytes | Noronha et al. (2022) |
| Vitamin D supplementation | Horvath (2013) and Vetter et al. (2019) | 1036 | 68.28 ± 3.49 | Blood | Vetter et al. (2022) |
a
Self‐reported omega‐3 intake data was available for 2174 members of a larger cohort composed of 2356 people. The age range provided is for the full cohort (n = 2356).