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Ageing is seen as one of the greatest challenges of the coming decades, as it leads to enormous financial and social burden (Prince et al., 2015). Globally, the gap in lifespan and health span between high and low socioeconomic classes has been growing for the last decades (Partridge et al., 2018). The main differences between these classes are found in lifestyle and environment, strengthening the observation that genetics only explain a minor proportion of lifespan variation (Fong et al., 2021; Kaplanis et al., 2018). The heterogeneity in the ageing process (and consequential susceptibility to diseases) between individuals renders chronological age unequipped to accurately represent the ageing process (Levine et al., 2018). As determining causes of accelerated or decelerated ageing rates has potential social, economic, and public health benefits, researchers have been studying alternative methods that can more accurately represent the ageing process. To date, the most accurate biomarker of human ageing are so called epigenetic clocks (Levine et al., 2018), which represents one’s biological age. Epigenetic clocks estimate a person’s biological age by analyzing the degree of methylation on a set of CpG sites determined by machine learning (Horvath & Raj, 2018). Briefly, DNA methylation is the process that involves the transfer of a methyl-group onto the C5 position of cytosine, forming 5-methylcytosine (5mC). This process, like other epigenetic modifications, regulates gene expression without changing the DNA sequence (Abdul et al., 2017). Interestingly, epigenetic clocks show excellent correlations with chronological age, are able to predict illness and death and can be ‘rewound’ by healthy lifestyle interventions, such as a Mediterranean diet (Kresovich et al., 2022; Lu et al., 2019). Additionally, a study from Quach et al. (2017) investigated the relationships between epigenetic age acceleration in blood and dietary variables, education, alcohol, and exercise by using data from older women within the Women's Health Initiative (WHI) from two large epidemiological cohorts. The main findings showed a relatively strong correlation between higher intake of fish, vegetables, fruits and poultry and decelerated epigenetic ageing, reflecting a potential health benefit by specific dietary elements (Quach et al., 2017). These results are only the first examples of an emerging new field in human ageing research: making use of epigenetic clocks to identify dietary habits that protect against age-acceleration. A limitation of the study method of Quach et al. (2017) and other studies investigating epigenetic ageing, is the hypothesis-driven approach, since it limits the amount of nutrients that can be identified to play a role in biological ageing. With an entirely data-driven approach in a suitable dataset, the importance of specific dietary habits in light of ageing can be identified in a more objective and open-minded fashion. After all, although various studies have shown the causal relationship between a healthy lifestyle and slowing down of DNA methylation-based biomarkers of biological ageing (Fiorito et al., 2021; Fitzgerald et al., 2021), the effects of specific nutrients that could slow down ageing are still unclear. With a data-driven approach, which considers the full range of nutrients, new doors in the field of nutrition and ageing research can be opened. These leads can be further unraveled in hypothesis-driven, observational or interventional study designs. For optimal data-driven analyses, the data should be of high quality, with epigenetic age data of many participants, and with extensive information on diet, physical activity, smoking and drinking habits, as well as general covariates. Therefore, using the extensive data of the WHI in which these high-quality data are available in a large sample of older women would be of great value for our research. We propose to use Copula Graphical Modeling (CGM), which is a network analysis method that copes with non-gaussian, ordinal and count data, as well as with complex inter-nutrient relationships. This flexibility makes CGM highly applicable in nutritional epidemiology. CGM allows to identify direct relationships between dietary factors and biological ageing, while adjusting for every other variable present in the dataset (Lauritzen, 1996; Behrouzi et al., 2020), thereby preventing subjective confounder selection while allowing us to include all measured nutrient intake in the networks. In previous unpublished work, we successfully carried out CGM analyses on a small dataset with lifestyle factors and biological age. For this study, we aim to repeat the methodology in a large subsample of the WHI, to discover nutritional and lifestyle factors that influence the rate of epigenetic ageing. References . Abdul, Q. A., et al. (2017). Epigenetic modifications of gene expression by lifestyle and environment. Archives of Pharmacal Research 40(11): 1219-1237. https://doi.org/10.1007/s12272-017-0973-3 Behrouzi, P., Grootswagers, P., Keizer, P. L. C., Smeets, E. T. H. C., Feskens, E. J. M., de Groot, L. C. P. G. M., van Eeuwijk, F. A. (2020). Dietary Intakes of Vegetable Protein, Folate, and Vitamins B-6 and B-12 Are Partially Correlated with Physical Functioning of Dutch Older Adults Using Copula Graphical Models. Journal of Nutrition, 150(3). https://doi.org/10.1093/jn/nxz269 Bell CG, Lowe R, Adams PD, Baccarelli AA, Beck S, Bell JT, et al. DNA methylation aging clocks: challenges and recommendations. Genome Biology. 2019;20(1):249. Fiorito, G., et al. (2021). "DNA methylation-based biomarkers of aging were slowed down in a two-year diet and physical activity intervention trial: the DAMA study." Aging Cell 20(10): e13439. https://doi.org/10.1111/acel.13439 Fong, B. Y. F., et al. (2021). A Review Study of a Green Diet and Healthy Ageing. International Journal of Environmental Research and Public Health 18(15): 8024. https://doi.org/10.3390/ijerph18158024 Horvath, S. and K. Raj (2018). DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics 19(6): 371-384. https://doi.org/10.1038/s41576-018-0004-3 Levine, M. E., et al. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY) 10(4): 573-591. http://doi.org/10.18632/aging.101414 Kresovich, J. K., et al. (2022). Healthy eating patterns and epigenetic measures of biological age. Am J Clin Nutr 115(1): 171-179. http://doi.org/10.1093/ajcn/nqab307 Kaplanis, J., et al. (2018). Quantitative analysis of population-scale family trees with millions of relatives. Science 360(6385): 171-175. https://www.science.org/doi/abs/10.1126/science.aam9309 Lauritzen, S. L. (1996). Graphical models. Claren- don Press. Levine, M. E., Lu, A. T., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., Hou, L., Baccarelli, A. A., Stewart, J. D., Li, Y., Whitsel, E. A., Wilson, J. G., Reiner, A. P., Aviv, A., Lohman, K., Liu, Y., Ferrucci, L., & Horvath, S. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging, 10(4), 573–591. https://doi.org/10.18632/aging.101414 Lu, A. T., et al. (2019). DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY) 11(2): 303-327. http://doi.org/10.18632/aging.101684 Partridge, L., et al. (2018). Facing up to the global challenges of ageing. Nature 561(7721): 45-56. https://doi.org/10.1038/s41586-018-0457-8 Prince, M. J., et al. (2015). The burden of disease in older people and implications for health policy and practice. The Lancet 385(9967): 549-562. https://doi.org/10.1016/S0140-6736(14)61347-7 Quach A, Levine ME, Tanaka T, Lu AT, Chen BH, Ferrucci L, Ritz B, Bandinelli S, Neuhouser ML, Beasley JM, Snetselaar L, Wallace RB, Tsao PS, Absher D, Assimes TL, Stewart JD, Li Y, Hou L, Baccarelli AA, Whitsel EA, Horvath S. Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging (Albany NY). 2017 Feb 14;9(2):419-446. doi: 10.18632/aging.101168. PMID: 28198702; PMCID: PMC5361673.

Aging is seen as one of the greatest challenges of the coming decades, as it leads to enormous financial and social burden (Prince et al., 2015). Globally, the gap in lifespan and health span between high and low socioeconomic classes has been growing for the last decades (Partridge et al., 2018). The main differences between these classes are found in lifestyle and environment, strengthening the observation that genetics only explain a minor proportion of lifespan variation (Fong et al., 2021; Kaplanis et al., 2018). The heterogeneity in the aging process (and consequential susceptibility to diseases) between individuals renders chronological age unequipped to accurately represent the aging process (Levine et al., 2018). As determining causes of accelerated or decelerated aging rates has potential social, economic, and public health benefits, researchers have been studying alternative methods that can more accurately represent the aging process. To date, the most accurate biomarker of human aging are so called epigenetic clocks (Levine et al., 2018), which represents one’s biological age. Epigenetic clocks estimate a person’s biological age by analyzing the degree of methylation on a set of CpG sites determined by machine learning (Horvath & Raj, 2018). Briefly, DNA methylation is the process that involves the transfer of a methyl-group onto the C5 position of cytosine, forming 5-methylcytosine (5mC). This process, like other epigenetic modifications, regulates gene expression without changing the DNA sequence (Abdul et al., 2017). Interestingly, epigenetic clocks show excellent correlations with chronological age, are able to predict illness and death and can be ‘rewound’ by healthy lifestyle interventions, such as a Mediterranean diet (Kresovich et al., 2022; Lu et al., 2019). Additionally, a study from Quach et al. (2017) investigated the relationships between epigenetic age acceleration in blood and dietary variables, education, alcohol, and exercise by using data from older women within the Women's Health Initiative (WHI) from two large epidemiological cohorts. The main findings showed a relatively strong correlation between higher intake of fish, vegetables, fruits and poultry and decelerated epigenetic ageing, reflecting a potential health benefit by specific dietary elements (Quach et al., 2017). These results are only the first examples of an emerging new field in human aging research: making use of epigenetic clocks to identify dietary habits that protect against age-acceleration. A limitation of the study method of Quach et al. (2017) and other studies investigating epigenetic ageing, is the hypothesis-driven approach, since it limits the amount of nutrients and food groups that can be identified to play a role in biological ageing. With an entirely data-driven approach in a suitable dataset, the importance of specific dietary habits in light of ageing can be identified in a more objective and open-minded fashion. After all, although various studies have shown the causal relationship between a healthy lifestyle and slowing down of DNA methylation-based biomarkers of biological aging (Fiorito et al., 2021; Fitzgerald et al., 2021), the effects of specific nutrients and foods that could slow down aging are still unclear. In addition, as nutrient databases often provide limited information on bioactive compounds, food matrices, and potential other interactions among substances within foods, it is valuable to also explore whether specific food groups relate to the pace of biological aging. With a data-driven approach, which considers the full range of nutrients and food groups, new doors in the field of nutrition and ageing research can be opened. These leads can be further unraveled in hypothesis-driven, observational or interventional study designs. For optimal data-driven analyses, the data should be of high quality, with epigenetic age data of many participants, and with extensive information on diet, physical activity, smoking and drinking habits, as well as general covariates. Therefore, using the extensive data of the WHI in which these high-quality data are available in a large sample of older women would be of great value for our research. We propose to use Copula Graphical Modeling (CGM), which is a network analysis method that copes with non-gaussian, ordinal and count data, as well as with complex inter-nutrient relationships. This flexibility makes CGM highly applicable in nutritional epidemiology. CGM allows to identify direct relationships between dietary factors and biological aging, while adjusting for every other variable present in the dataset (Lauritzen, 1996; Behrouzi et al., 2020), thereby preventing subjective confounder selection while allowing us to include all measured nutrient and food group intake in the networks. In previous unpublished work, we successfully carried out CGM analyses on a small dataset with lifestyle factors and biological age. For this study, we aim to repeat the methodology in a large subsample of the WHI, to discover nutritional and lifestyle factors that influence the rate of epigenetic aging. References . Abdul, Q. A., et al. (2017). Epigenetic modifications of gene expression by lifestyle and environment. Archives of Pharmacal Research 40(11): 1219-1237. https://doi.org/10.1007/s12272-017-0973-3 Behrouzi, P., Grootswagers, P., Keizer, P. L. C., Smeets, E. T. H. C., Feskens, E. J. M., de Groot, L. C. P. G. M., van Eeuwijk, F. A. (2020). Dietary Intakes of Vegetable Protein, Folate, and Vitamins B-6 and B-12 Are Partially Correlated with Physical Functioning of Dutch Older Adults Using Copula Graphical Models. Journal of Nutrition, 150(3). https://doi.org/10.1093/jn/nxz269 Bell CG, Lowe R, Adams PD, Baccarelli AA, Beck S, Bell JT, et al. DNA methylation aging clocks: challenges and recommendations. Genome Biology. 2019;20(1):249. Fiorito, G., et al. (2021). "DNA methylation-based biomarkers of aging were slowed down in a two-year diet and physical activity intervention trial: the DAMA study." Aging Cell 20(10): e13439. https://doi.org/10.1111/acel.13439 Fong, B. Y. F., et al. (2021). A Review Study of a Green Diet and Healthy Ageing. International Journal of Environmental Research and Public Health 18(15): 8024. https://doi.org/10.3390/ijerph18158024 Horvath, S. and K. Raj (2018). DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics 19(6): 371-384. https://doi.org/10.1038/s41576-018-0004-3 Levine, M. E., et al. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY) 10(4): 573-591. http://doi.org/10.18632/aging.101414 Kresovich, J. K., et al. (2022). Healthy eating patterns and epigenetic measures of biological age. Am J Clin Nutr 115(1): 171-179. http://doi.org/10.1093/ajcn/nqab307 Kaplanis, J., et al. (2018). Quantitative analysis of population-scale family trees with millions of relatives. Science 360(6385): 171-175. https://www.science.org/doi/abs/10.1126/science.aam9309 Lauritzen, S. L. (1996). Graphical models. Claren- don Press. Levine, M. E., Lu, A. T., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., Hou, L., Baccarelli, A. A., Stewart, J. D., Li, Y., Whitsel, E. A., Wilson, J. G., Reiner, A. P., Aviv, A., Lohman, K., Liu, Y., Ferrucci, L., & Horvath, S. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging, 10(4), 573–591. https://doi.org/10.18632/aging.101414 Lu, A. T., et al. (2019). DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY) 11(2): 303-327. http://doi.org/10.18632/aging.101684 Partridge, L., et al. (2018). Facing up to the global challenges of ageing. Nature 561(7721): 45-56. https://doi.org/10.1038/s41586-018-0457-8 Prince, M. J., et al. (2015). The burden of disease in older people and implications for health policy and practice. The Lancet 385(9967): 549-562. https://doi.org/10.1016/S0140-6736(14)61347-7 Quach A, Levine ME, Tanaka T, Lu AT, Chen BH, Ferrucci L, Ritz B, Bandinelli S, Neuhouser ML, Beasley JM, Snetselaar L, Wallace RB, Tsao PS, Absher D, Assimes TL, Stewart JD, Li Y, Hou L, Baccarelli AA, Whitsel EA, Horvath S. Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging (Albany NY). 2017 Feb 14;9(2):419-446. doi: 10.18632/aging.101168. PMID: 28198702; PMCID: PMC5361673.

Increased lifespan is a great achievement of humankind, and opens new opportunities for the life of ageing human beings, in part conditional on health and independence. Health and independence are related to physical performance which is often compromised in older adults, to some extent due to age-related decreases in muscle mass and muscle strength decrease. During ageing, muscle strength decreases at a faster rate than muscle mass, indicating a loss in muscle quality. Muscle quality is defined as muscle strength per unit of muscle mass and is determined by factors such as specific muscle fibre atrophy, mitochondrial functioning, neuromuscular functioning and degree of fat infiltration. Targeting the quality of muscle tissue is a promising strategy to improve muscle quality during ageing.An active lifestyle seems to prevent losses in muscle quality to some extent, whereby exercise programs can improve markers of muscle quality. Not many intervention trials have aimed to improve muscle quality via nutrition. However, metabolic aspects play a role in muscle quality, and many nutrients act on pathways that are related to physiological determinants of muscle quality. Together, this gives reasons to pursue the identification of nutritional strategies to target muscle quality. Therefore, in this thesis, we aimed to identify novel nutritional strategies to improve muscle quality and physical performance in older adults.In Chapter 2, we investigated the reliability and validity of handheld dynamometry (HHD) for the assessment of leg strength. Leg strength assessment is proposed to be a better alternative to the often used handgrip strength measurement. However, the current advised equipment to measure leg strength is static and costly, while handgrip strength can be measured with an inexpensive and small device. The results of Chapter 2 show that the HHD is a valid and reliable tool to assess changes in leg strength in older adults or to rank older adults on strength level, indicating that HHD can be used as an alternative to handgrip strength assessment. The results of Chapter 2 are important for the development of leg strength measurements for strength monitoring in older adults.Chapter 3 and Chapter 4 describe the results of two clinical trials aimed at improving physical performance in older adults. In Chapter 3, an intensive exercise and nutrition intervention augmented muscle mass (+5%), strength (+13%), and physical performance (10-20%) of older adults already after four weeks. The findings are relevant and important because short-term interventions like these are urgently needed for the future of prehabilitation. Prehabilitation is a discipline aimed at training patients before planned surgery so that patients lose less muscle mass and strength during hospitalisation.In Chapter 4, a novel medical nutrition product was tested against standard medical nutrition in older adults with (or at risk of) undernutrition. Without a concurrent exercise regimen, this novel medical nutrition treatment appeared to be able to improve walking performance and mitochondrial functioning in this vulnerable group. The improvement in walking performance is of clinical relevance, as it relates to lower chances of future falls, hospitalisation and mortality. Being able to improve physical performance without a concurrent exercise program is an important finding for the field of nutrition and ageing, suggesting that besides exercise interventions, there is a potential for nutritional interventions as well.In Chapter 5 and Chapter 6, we aimed to identify new nutritional targets to improve muscle quality in older adults. Chapter 5 was a hypothesis-driven approach in which we tested the associations between dietary intake of vitamin B3, B6, B12, folate and measures of physical performance in healthy older adults. Chapter 6 was a data-driven approach, in which we tested for associations between all nutrients and measures of physical performance in healthy, frail or malnourished older adults. Chapter 5 identified an association between the intake of vitamin B6 and chair rise test in the full population (3% improvement per mg of vitamin B6) and an association between intake of vitamin B6 and handgrip strength in participants with low physical activity (also a 3% improvement per mg of vitamin B6), but not in those with normal or high physical activity. Chapter 6 also identified an association between vitamin B6 and chair rise test and total physical performance score. Chapter 6 additionally identified associations between intakes of vegetable protein, vitamin B12 and folate with measures of physical performance. Chapter 6 made use of Copula Graphical Modelling, a new methodology with many potential applications for the field of nutritional epidemiology.This thesis adds important knowledge on how to measure muscle quality, which strategies are effective and which nutritional strategies could be further explored in the future. Advancing the integration of muscle quality measurements in the field of nutrition and ageing research can lead to important advances in the quest to find strategies that improve physical performance in older adults. With improved physical performance, older adults are more likely to stay mentally, socially, and physically active. This in turn helps them to age healthily and happily.

International audience; ABSTRACT Background Maintenance of high physical performance during aging might be supported by an adequate dietary intake of niacin, vitamins B-6 and B-12, and folate because these B vitamins are involved in multiple processes related to muscle functioning. However, not much is known about the association between dietary intake of these B vitamins and physical performance. Objectives The objectives of this study were to investigate the association between dietary intake of niacin, vitamins B-6 and B-12, and folate and physical performance in older adults and to explore mediation by niacin status and homocysteine concentrations. Methods We used baseline data from the New Dietary Strategies Addressing the Specific Needs of the Elderly Population for Healthy Aging in Europe (NU-AGE) trial, which included n = 1249 healthy older adults (aged 65–79 y) with complete data on dietary intake measured with 7-d food records and questionnaires on vitamin supplement use and physical performance measured with the short physical performance battery and handgrip dynamometry. Associations were assessed by adjusted linear mixed models. Results Intake of vitamin B-6 was related to lower chair rise test time [β: –0.033 ± 0.016 s (log); P = 0.043]. Vitamin B-6 intake was also significantly associated with handgrip strength, but for this association, a significant interaction effect between vitamin B-6 intake and physical activity level was found. In participants with the lowest level of physical activity, higher intake of vitamin B-6 tended to be associated with greater handgrip strength (β: 1.5 ± 0.8 kg; P = 0.051), whereas in participants in the highest quartile of physical activity, higher intake was associated with lower handgrip strength (β: –1.4 ± 0.7 kg; P = 0.041). No evidence was found for an association between intake of niacin, vitamin B-12, or folate and physical performance or for mediation by niacin status or homocysteine concentrations. Conclusions Vitamin B-6 intake was associated with better chair rise test time in a population of European healthy older adults and also with greater handgrip strength in participants with low physical activity only. Homocysteine concentrations did not mediate these associations. The NU-AGE trial was registered at clinicaltrials.gov as NCT01754012.