Eating garlic, berries, and tea may support younger, healthier cells

By Tarun Sai LomteReviewed by Susha Cheriyedath, M.Sc.May 21 2025

New research suggests that eating more foods like turmeric, green tea, and berries may help slow your body’s aging clock, offering fresh hope for diet-driven healthy aging.

Study: Dietary associations with reduced epigenetic age: a secondary data analysis of the methylation diet and lifestyle study. Image Credit: Bojsha / Shutterstock

In a recent study published in the journal Aging, researchers found that higher intake of methyl adaptogen foods was associated with reductions in epigenetic age (EA), but did not establish a causal effect.

Chronological age is a leading risk factor for non-communicable diseases. Alterations in cellular function regulating energy production, cell cycle, inflammation, detoxification, and oxidative stress remediation are mechanistically linked to chronic diseases of aging. EA measures have been proposed as markers of biological aging.

The Methylation Diet and Lifestyle Study (MDLS), a pilot randomized controlled trial, evaluated changes in EA to assess the effectiveness of a lifestyle and diet intervention in middle-aged males. Although a significant reduction in EA was observed after treatment, there was substantial variability in EA changes in both intervention and control groups.

About the study

In the present study, researchers investigated diet as a potential contributor to the variability in EA changes observed in the MDLS. MDLS recruited healthy adult males aged 50–72 and assessed EA before and after the intervention. The intervention encompassed a specialized diet to modulate DNA methylation, moderate-to-vigorous physical activity, meditation, and seven hours of sleep per night. Controls followed their regular eating patterns.

Data were collected at baseline, week 5, and week 9. Dietary data from week 9 were used for analyses. Dietary guidelines included daily intake of dark leafy greens, colorful or cruciferous vegetables, beets, lean meat, sunflower or pumpkin seeds, low glycemic fruits, and methyl adaptogen foods (that contain polyphenols or vitamins and modulate the methylome, such as garlic, berries, turmeric, rosemary, and oolong or green tea).

Participants were allowed to choose one or more methyl adaptogen foods every day. The primary outcome (of the MDLS) was the change in EA from baseline to week 9 per Horvath’s clock. Participants completed a food frequency questionnaire (FFQ) at the specified time points, and dietary variables were generated using FFQ data.

Methyltetrahydrofolate (MF) blood levels were used as a marker for folate-rich food intake; while the intervention group consumed more of these foods, their MF levels actually decreased slightly over the study period. Weight change and baseline EA acceleration (EAA), which is defined as the difference between an individual's epigenetic age and their chronological age at baseline, were selected as factors contributing to the variability of EA change.

Independent sample T-tests analyzed group differences in the mean weight change between week 9 and baseline visits, baseline EAA, and dietary variables between groups. The correlation between EA change, weight change, baseline EAA, and use of restricted and recommended foods was assessed using Spearman’s rank correlation. After adjusting for weight change, significantly correlated variables were selected for a hierarchical linear regression analysis.

Findings

Overall, 43 males were recruited, of whom 38 completed the study. Most subjects were White and highly educated. The most frequently consumed foods were fruits and colorful vegetables. The intervention group consumed fruits and colorful vegetables at an average of 93.5 and 125 times in the last month, whereas controls consumed these foods at an average of 67.7 and 65.2 times, respectively.

As anticipated, controls consumed little or none of the intervention-specific foods. There were significant differences in the average weight change, baseline EAA, and intake of most dietary variables between groups. At baseline, the intervention group was on average 5.26 years epigenetically younger than their chronological age, and the control group was 0.81 years older, prior to the intervention.

Further, the researchers found significant negative correlations between EA change and eggs, beets, cruciferous vegetables, colorful vegetables, methyl adaptogens, and baseline EA.

A positive correlation was observed between weight change and EA change. Dietary variables were generally highly correlated with each other. After adjusting for weight change, only methyl adaptogens and baseline EAA remained significantly negatively correlated with changes in EA, and thus selected for inclusion in hierarchical linear regression.

Methyl adaptogens showed significant linear associations with changes in EA, with higher intake predicting EA reductions. This association remained significant after adjusting for weight change and baseline EAA. Baseline EAA was also significantly associated with changes in EA, with higher baseline EAA predicting a decline in EA.

Conclusions

In sum, higher intake of methyl adaptogens was robustly associated with EA reductions, after adjusting for baseline EAA and weight changes, consistent with evidence of the protective role of polyphenols in biological aging. Notably, the regression model accounted for 44% of the variability in EA change noted in the parent study. The intervention diet excluded certain foods, such as whole grains, legumes, and dairy, to minimize potential short-term gastrointestinal side effects and focus on the specific mechanistic dietary targets. The findings are based on a small, homogeneous sample of healthy, middle-aged men and may not be generalizable to women, other age groups, or diverse populations. The study's small sample size and reliance on self-reported dietary data may limit the strength of the conclusions. These results should be considered hypothesis-generating ,and further studies in more diverse and larger populations are needed to determine clinical relevance and generalizability.

Future research should include more extensive and diverse populations to examine the intervention's relevance in mitigating morbidity and mortality.