Individual and additive effects of vitamin D, omega-3 and exercise on DNA methylation clocks of biological aging in older adults from the DO-HEALTH trial

Heike A. Bischoff-Ferrari, Stephanie Gängler, Maud Wieczorek, Daniel W. Belsky, Joanne Ryan, Reto W. Kressig, Hannes B. Stähelin, Robert Theiler, Bess Dawson-Hughes, René Rizzoli, Bruno Vellas, Laure Rouch, Sophie Guyonnet, Andreas Egli, E. John Orav, Walter Willett, Steve Horvath
Nature Aging
University of Zurich

Table of Contents

Overall Summary

Study Background and Main Findings

This post-hoc analysis of the DO-HEALTH trial investigated the effects of vitamin D, omega-3, and exercise on biological aging, measured by four DNAm clocks. Omega-3 supplementation was associated with statistically significant reductions in age-acceleration or pace-of-aging values for PhenoAge (d = -0.16, 95% CI: -0.30 to -0.02), GrimAge2 (d = -0.32, 95% CI: -0.59 to -0.06), and DunedinPACE (d = -0.17, 95% CI: -0.31 to -0.04). Additive effects of all three interventions were observed on PhenoAge. Vitamin D and exercise alone showed no significant effects on the DNAm clocks.

Research Impact and Future Directions

The study provides evidence for a correlation between omega-3 supplementation and a slowing of biological aging, as measured by DNAm clocks. However, it is crucial to note that this is a post-hoc analysis of a subgroup within a larger trial, and the observed effects, while statistically significant, are relatively small. The study does not establish a causal relationship between the interventions and changes in DNAm clocks. Other unmeasured factors could contribute to the observed associations.

The practical utility of the findings is that they suggest a potential benefit of omega-3 supplementation, and possibly combined interventions, on biological aging. This aligns with previous research indicating the health benefits of omega-3s. However, the magnitude of the effect on DNAm clocks (equivalent to a few months over three years) needs to be considered in the context of overall health and aging. The findings are placed within the context of existing research, notably the CALERIE trial, highlighting both similarities and differences.

While the study suggests that omega-3 supplementation may be a beneficial strategy for promoting healthy aging, it is important to acknowledge the uncertainties. The long-term effects of these interventions on health outcomes remain to be determined. The study also emphasizes the potential for personalized approaches, suggesting that individuals with lower baseline omega-3 levels may benefit more from supplementation. This guidance is tentative and requires further investigation.

Critical unanswered questions include the precise mechanisms by which omega-3 influences DNAm clocks, the long-term clinical significance of the observed changes, and whether these findings are generalizable to other populations. The methodological limitation of using a healthier and more active subgroup may affect the generalizability of the conclusions. While the study's rigorous design (randomized, double-blind, placebo-controlled) strengthens the internal validity, the post-hoc nature of the analysis and the focus on a specific subgroup limit the extent to which these findings can be definitively interpreted. Further research is needed to confirm these findings and address these open questions.

Critical Analysis and Recommendations

Omega-3 Effects on DNAm Clocks (written-content)
Omega-3 supplementation was associated with statistically significant reductions in age-acceleration or pace-of-aging values for PhenoAge, GrimAge2, and DunedinPACE. This suggests a potential benefit of omega-3s on biological aging, a finding with implications for preventative health strategies.
Section: Results
Additive Effects of Interventions (written-content)
Additive effects of omega-3, vitamin D, and exercise were observed on PhenoAge. This suggests a potential synergistic effect of combined interventions, which could inform more effective healthy aging strategies.
Section: Results
Robust Study Design (written-content)
The study utilized a randomized, double-blind, placebo-controlled design with a 2x2x2 factorial structure. This robust design allows for the evaluation of individual and combined effects of the interventions, strengthening causal inference.
Section: Methods
Acknowledged Limitations (written-content)
The study acknowledges limitations, including the lack of a gold standard for biological aging and the use of a specific subgroup. This transparency is crucial for a balanced interpretation of the findings and highlights areas for future research.
Section: Discussion
Limited Generalizability (written-content)
The analysis was conducted on a subgroup of Swiss participants, who were healthier and more active than the overall DO-HEALTH population. This limits the generalizability of the findings to other populations with different health characteristics.
Section: Introduction
Lack of Mechanistic Explanation (written-content)
The study did not elaborate on the potential mechanisms by which omega-3 supplementation might influence DNAm clocks. A more in-depth discussion of potential pathways would strengthen the interpretation of the findings.
Section: Discussion
Clear Visualization of Treatment Effects (graphical-figure)
Figure 2 effectively visualizes the treatment effects and confidence intervals for each DNAm clock. This allows for a clear comparison of the interventions' impact on different measures of biological aging.
Section: Results
Missing Clinical Significance in Abstract (written-content)
The abstract does not explicitly state the clinical significance of the findings. Adding a sentence highlighting the potential implications for healthy aging strategies would improve the abstract's impact.
Section: Abstract

Section Analysis

Abstract

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Key Aspects

Strengths

Suggestions for Improvement

Methods

Key Aspects

Strengths

Suggestions for Improvement

Results

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Table 1 | Baseline characteristics of the study population overall and by...
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Table 1 | Baseline characteristics of the study population overall and by treatment group

Figure/Table Image (Page 2)
Table 1 | Baseline characteristics of the study population overall and by treatment group
First Reference in Text
The participant characteristics and allocation across treatment arms in DO-HEALTH are presented in Table 1 and Fig. 1.
Description
  • Overview of baseline characteristics: Table 1 presents the baseline characteristics of the study participants in the DO-HEALTH trial, both overall and broken down by treatment group. The treatment groups are: Vitamin D, No Vitamin D, Omega-3, No Omega-3, SHEP (simple home exercise program), and No SHEP. It includes demographic information like chronological age (average of around 75 years) and sex (roughly 60% female). It also shows health-related characteristics such as BMI (Body Mass Index, a measure of body fat based on height and weight, averaging around 25.7 kg/m²), years of education (averaging about 13.5 years), and whether they were considered a 'healthy ager' according to Nurses' Health Study criteria (around 52%).
  • Health-related characteristics: The table provides data on co-existing illnesses using the Sangha comorbidity score, where higher scores indicate more illnesses (average around 2.66). It also shows the percentage of participants with low Vitamin D levels (25(OH)D <20 ng/mL, around 34%) and average blood omega-3 levels (DHA+EPA, around 94 ng/mL).
  • Physical activity levels and statistical comparisons: Finally, the table describes the physical activity levels of the participants, categorized as inactive, 1-3 times per week, or >3 times per week, with most participants reporting activity more than three times per week (around 59%). The table also includes p-values for comparisons between different subgroups of DO-HEALTH participants. A p-value is used to determine the statistical significance. In this context, it tests the hypothesis that there is no difference between the groups being compared. A small p-value (typically less than 0.05) suggests that the observed data is inconsistent with this hypothesis, and thus, there is a statistically significant difference between the groups.
Scientific Validity
  • Adherence to scientific standards: The table adheres to scientific standards by clearly defining each characteristic and providing appropriate descriptive statistics. The use of standard deviations alongside means provides a measure of data variability.
  • Addressing selection bias: The inclusion of p-values for comparisons between the Swiss DNAm subgroup and other DO-HEALTH participants, and between the Swiss DNAm subgroup and Swiss participants without DNAm, strengthens the validity of the analysis by addressing potential selection bias.
  • Consideration of imbalances: While the table presents a comprehensive overview of baseline characteristics, it would benefit from a discussion of the potential implications of any observed imbalances between treatment groups on the study's outcomes. This could involve a sensitivity analysis to assess the robustness of the findings to these imbalances.
Communication
  • Clear organization and descriptive title: The table is well-organized, presenting baseline characteristics clearly. The use of distinct columns for each treatment group allows for easy comparison. The table's title is descriptive and accurately reflects its content.
  • Appropriate summary statistics: The use of standard deviations (s.d.) for continuous variables and percentages for categorical variables is appropriate for summarizing the data. However, providing medians and interquartile ranges for skewed continuous variables might offer a more complete picture.
  • Helpful inclusion of p-values: The inclusion of p-values assessing differences between the Swiss DNAm subgroup and other DO-HEALTH participants, and between the Swiss DNAm subgroup and Swiss participants without DNAm, is helpful for contextualizing the representativeness of the study sample.
Fig. 1 | Flowchart of the DO-HEALTH Bio-Age trial in the Swiss subset of...
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Fig. 1 | Flowchart of the DO-HEALTH Bio-Age trial in the Swiss subset of DO-HEALTH. The flowchart shows the allocation of participants across the eight treatment arms.

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Fig. 1 | Flowchart of the DO-HEALTH Bio-Age trial in the Swiss subset of DO-HEALTH. The flowchart shows the allocation of participants across the eight treatment arms.
First Reference in Text
The participant characteristics and allocation across treatment arms in DO-HEALTH are presented in Table 1 and Fig. 1.
Description
  • Overview of participant selection: Figure 1 is a flowchart that visually summarizes how participants were selected and assigned to different treatment groups in the DO-HEALTH Bio-Age trial, focusing on the Swiss participants. Starting with the initial 1,006 Swiss participants in the DO-HEALTH trial, the flowchart shows that epigenetic analysis (which involves studying changes in gene expression) was available for 790 participants. This indicates some participants were excluded from this type of analysis.
  • Reasons for exclusion and treatment arm allocation: The flowchart then shows reasons for excluding further participants from the epigenetic analysis: 216 had no follow-up data or didn't approve genetic analysis, 6 had low-quality DNA, and 3 had sex mismatches (where predicted sex from DNA didn't match reported sex). This led to a final sample of 777 participants used in the analysis. These participants were then divided into eight treatment arms: Placebo (95 participants), Vitamin D (101), Omega-3 (98), SHEP (92), Vitamin D + Omega-3 (95), Vitamin D + SHEP (104), Omega-3 + SHEP (95), and All treatments (97).
  • Explanation of treatment arms: The treatment arms represent the different combinations of interventions being tested: vitamin D supplementation, omega-3 fatty acid supplementation, and a simple home exercise program (SHEP). A placebo is a substance or intervention that has no therapeutic effect and is used as a control in experiments. The numbers in parentheses indicate the number of participants in each treatment arm, showing how the participants were distributed across the different intervention groups.
Scientific Validity
  • Accurate representation of participant flow: The flowchart accurately reflects the participant flow in the study and provides essential information regarding inclusion and exclusion criteria. This transparency is crucial for assessing the potential for selection bias and the generalizability of the findings.
  • Systematic data management: The clear delineation of exclusion criteria enhances the rigor of the study by demonstrating a systematic approach to data management and analysis. However, a more detailed explanation of the criteria used to assess DNA quality would further strengthen the validity of the flowchart.
  • Facilitates assessment of randomization: By outlining the allocation of participants to each treatment arm, the flowchart facilitates an assessment of the balance achieved through randomization. This is important for ensuring that any observed treatment effects are not attributable to pre-existing differences between the groups.
Communication
  • Clear visual representation: The flowchart provides a clear and concise visual representation of the participant flow throughout the study. The use of boxes and arrows effectively illustrates the different stages of the trial, from initial enrollment to the final analysis.
  • Quantification of participant flow: The inclusion of participant numbers at each stage of the flowchart allows the reader to quickly understand the attrition rate and the number of participants included in the final analysis. However, the reasons for exclusion at each stage could be briefly mentioned for clarity.
  • Easy-to-follow design: The labeling of each treatment arm is clear and consistent, facilitating easy identification of the different intervention groups. The overall design is easy to follow and understand, even for readers unfamiliar with clinical trial methodology.
Fig. 2 | Treatment effects of vitamin D, omega-3 and SHEP individually and in...
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Fig. 2 | Treatment effects of vitamin D, omega-3 and SHEP individually and in combination on changes in DNAm measures from baseline to year 3. a-d, Treatment effects are expressed as standardized estimates of the change in DNAm measures from baseline to year 3 at the respective 95% CI.

Figure/Table Image (Page 4)
Fig. 2 | Treatment effects of vitamin D, omega-3 and SHEP individually and in combination on changes in DNAm measures from baseline to year 3. a-d, Treatment effects are expressed as standardized estimates of the change in DNAm measures from baseline to year 3 at the respective 95% CI.
First Reference in Text
Treatment effects are shown in Fig. 2 and numerically in Extended Data Table 3.
Description
  • Overview of treatment effects: Figure 2 presents the treatment effects of vitamin D, omega-3, and SHEP (simple home exercise program) on changes in DNA methylation (DNAm) measures related to biological aging. It shows the effects of these treatments, both individually and in combination, on four different DNAm measures: PhenoAge, GrimAge, GrimAge2, and DunedinPACE. These measures are used to estimate biological age or the pace of aging based on patterns of DNA methylation, a process that modifies DNA without changing the underlying genetic code and can affect gene expression.
  • Forest plot representation: The figure consists of four forest plots (a-d), one for each DNAm measure. Each plot displays the treatment effect as a point estimate with a 95% confidence interval (CI). A point estimate is the best estimate of the treatment effect, while the CI provides a range of values within which the true effect is likely to lie. If the CI includes zero, it suggests that the treatment effect is not statistically significant at the 0.05 significance level.
  • Standardized estimates and treatment comparisons: The treatment effects are expressed as standardized estimates, which means they have been adjusted to have a mean of zero and a standard deviation of one. This allows for comparison of the effects across different DNAm measures, as they may have different units or scales. The treatments include Vitamin D (vs. no vitamin D), Omega-3 (vs. no omega-3), SHEP (vs. no SHEP), and combinations of these treatments. For example, 'Vitamin D (vs. no vitamin D)' compares individuals who received vitamin D to those who did not.
Scientific Validity
  • Appropriate visualization method: The use of forest plots is appropriate for visualizing treatment effects and their associated uncertainty. The inclusion of 95% confidence intervals allows for an assessment of statistical significance.
  • Standardized estimates: Expressing the treatment effects as standardized estimates facilitates comparison across different DNAm measures. However, it is important to consider whether standardization may obscure clinically meaningful differences in the original scales.
  • Adjustment for confounders: The figure caption clearly states that the treatment effects are adjusted for potential confounders. However, specifying the covariates included in the analysis (e.g., age, sex, BMI) would further enhance the transparency and rigor of the study.
Communication
  • Effective visualization of treatment effects: The forest plots effectively visualize the treatment effects and their associated uncertainty (confidence intervals). The use of standardized estimates allows for comparison across different DNAm measures, which may have different scales.
  • Clear panel separation: The separate panels for each DNAm measure (PhenoAge, GrimAge, GrimAge2, and DunedinPACE) facilitate a clear understanding of the intervention's impact on each aging clock. However, it would be helpful to include a vertical line at zero to visually indicate the null effect.
  • Clear treatment effect labeling: The labeling of each treatment effect (e.g., Vitamin D (vs. no vitamin D)) is clear. However, a brief explanation of what 'vs. no vitamin D' means (i.e., comparing all individuals who received vitamin D to those who did not) in the caption or within the figure would improve clarity for readers unfamiliar with factorial trial designs.
Fig. 3 | Treatment effects of vitamin D, omega-3 and SHEP individually and in...
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Fig. 3 | Treatment effects of vitamin D, omega-3 and SHEP individually and in combination on changes in DNAm-based surrogate biomarkers of plasma proteins based on GrimAge. a-g, For the seven DNAm-based surrogate markers of plasma proteins underlying GrimAge, we analyzed versions constructed from DNAm PCs.

Figure/Table Image (Page 5)
Fig. 3 | Treatment effects of vitamin D, omega-3 and SHEP individually and in combination on changes in DNAm-based surrogate biomarkers of plasma proteins based on GrimAge. a-g, For the seven DNAm-based surrogate markers of plasma proteins underlying GrimAge, we analyzed versions constructed from DNAm PCs.
First Reference in Text
Treatment effects are shown in Fig. 3 and numerically in Extended Data Table 4.
Description
  • Overview of the figure: Figure 3 presents the effects of vitamin D, omega-3, and SHEP (simple home exercise program) on DNA methylation (DNAm)-based surrogate biomarkers of plasma proteins, which are related to GrimAge. GrimAge is an estimate of biological age derived from DNA methylation patterns and includes information about plasma proteins, which are proteins found in the blood.
  • Specific plasma proteins: The figure displays the treatment effects on seven specific plasma proteins: GDF-15 (growth differentiation factor 15), PAI-1 (plasminogen activator inhibitor 1), TIMP-1 (tissue inhibitor metalloproteinase 1), B2M (beta-2 microglobulin), ADM (adrenomedullin), leptin, and cystatin C. These proteins are estimated from DNAm data, meaning that instead of directly measuring the proteins in the blood, the researchers used DNA methylation patterns to infer their levels. Each protein has different functions in the body and is associated with various age-related processes.
  • Forest plots and DNAm PCs: The figure consists of seven forest plots (a-g), one for each protein. Each plot shows the treatment effect as a point estimate with a 95% confidence interval (CI). The treatments include Vitamin D (vs. no vitamin D), Omega-3 (vs. no omega-3), SHEP (vs. no SHEP), and all possible combinations of these treatments. The phrase 'DNAm PCs' means that instead of directly using the DNA methylation values at specific locations in the genome, the researchers used principal components (PCs) derived from these values. Principal component analysis (PCA) is a technique used to reduce the dimensionality of data by identifying underlying patterns and creating new variables (PCs) that capture most of the variance in the original data.
Scientific Validity
  • DNAm-based surrogate markers: The use of DNAm-based surrogate markers is a valid approach for estimating plasma protein levels, given the known associations between DNA methylation and gene expression. However, it is important to acknowledge the limitations of this approach, as DNA methylation only explains a portion of the variance in protein levels.
  • DNAm PCs: The construction of the surrogate markers from DNAm PCs is a reasonable strategy for reducing noise and improving the reliability of the estimates. However, it is crucial to ensure that the PCs capture the relevant biological information and are not driven by technical artifacts.
  • Multiple comparisons: The figure presents the treatment effects on seven different proteins, but it does not address the issue of multiple comparisons. Given the number of tests performed, it is important to consider the potential for false-positive findings and to adjust the p-values accordingly.
Communication
  • Effective visualization: The forest plots effectively visualize the treatment effects on the DNAm-based surrogate markers. The consistent format across the seven sub-figures (a-g) aids in comparison.
  • Clear context: The figure caption clearly indicates that the surrogate markers are based on GrimAge, which provides context for their relevance to aging. However, a brief explanation of what these specific proteins represent in the context of aging biology would enhance understanding.
  • Potential for information overload: While the consistent format is helpful, the sheer number of plots (seven) might make it challenging for readers to identify key patterns. Highlighting the most significant effects or providing a summary statement would improve communication.

Discussion

Key Aspects

Strengths

Suggestions for Improvement

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