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Frailty is an age-associated syndrome and can be described as a state of increased vulnerability to adverse health outcomes when exposed to stressors (e.g. an infection), caused by the cumulative decline in one or more domains of functioning. To prevent frailty, we need to better understand the underlying mechanisms and identify at an early stage people at risk of becoming frail. The aim of this thesis is therefore to identify (bio)markers (based on questionnaire data, anthropometric measurements, biochemical markers, and genetic data) of frailty, which could be indicative of the underlying processes that cause frailty and/or could help to detect people at risk. In this thesis, frailty is defined using a multi-domain approach based on the Tilburg Frailty Indicator (TFI) which allowed us to study (bio)markers for the physical, cognitive, psychological, and social frailty domains. For this research, data of two cohort studies was used, the Doetinchem Cohort Study (DCS) and the European study to establish biomarkers of human Ageing (MARK-AGE). In addition, multiple research designs and various analysis techniques were used. The research shows that physical and cognitive frailty is more common in older age, while psychological frailty is equally common at all ages. Further, the four frailty domains consist of different groups of frail people with similar characteristics. Therefore, prevention strategies should target multiple frailty domains (e.g. physical, cognitive, psychological, and social frailty) rather than one domain only. Biomarkers for early detection of frailty and that can be used to better target preventive measures have not been found. With the development of new assays and laboratory techniques in the field of omics (metabolomics, transcriptomics and proteomics) one might be able to identify frail groups at an early stage and gain more insight in the near future in the underlying processes that cause frailty. The biomarker results in this thesis do suggest that inflammation and the possible involvement of the immune system may play a role in the development of physical and cognitive frailty. Finally, people who are physically active have a lower risk of being frail. The findings of this thesis underline the importance of a healthy lifestyle for healthy ageing.

Frailty is an age-associated syndrome and can be described as a state of increased vulnerability to adverse health outcomes when exposed to stressors (e.g. an infection), caused by the cumulative decline in one or more domains of functioning. To prevent frailty, we need to better understand the underlying mechanisms and identify at an early stage people at risk of becoming frail. The aim of this thesis is therefore to identify (bio)markers (based on questionnaire data, anthropometric measurements, biochemical markers, and genetic data) of frailty, which could be indicative of the underlying processes that cause frailty and/or could help to detect people at risk. In this thesis, frailty is defined using a multi-domain approach based on the Tilburg Frailty Indicator (TFI) which allowed us to study (bio)markers for the physical, cognitive, psychological, and social frailty domains. For this research, data of two cohort studies was used, the Doetinchem Cohort Study (DCS) and the European study to establish biomarkers of human Ageing (MARK-AGE). In addition, multiple research designs and various analysis techniques were used. The research shows that physical and cognitive frailty is more common in older age, while psychological frailty is equally common at all ages. Further, the four frailty domains consist of different groups of frail people with similar characteristics. Therefore, prevention strategies should target multiple frailty domains (e.g. physical, cognitive, psychological, and social frailty) rather than one domain only. Biomarkers for early detection of frailty and that can be used to better target preventive measures have not been found. With the development of new assays and laboratory techniques in the field of omics (metabolomics, transcriptomics and proteomics) one might be able to identify frail groups at an early stage and gain more insight in the near future in the underlying processes that cause frailty. The biomarker results in this thesis do suggest that inflammation and the possible involvement of the immune system may play a role in the development of physical and cognitive frailty. Finally, people who are physically active have a lower risk of being frail. The findings of this thesis underline the importance of a healthy lifestyle for healthy ageing.

Many candidate biomarkers of human ageing have been proposed in the scientific literature but in all cases their variability in cross-sectional studies is considerable, and therefore no single measurement has proven to serve a useful marker to determine, on its own, biological age. A plausible reason for this is the intrinsic multi-causal and multi-system nature of the ageing process. The recently completed MARK-AGE study was a large-scale integrated project supported by the European Commission. The major aim of this project was to conduct a population study comprising about 3200 subjects in order to identify a set of biomarkers of ageing which, as a combination of parameters with appropriate weighting, would measure biological age better than any marker in isolation. (C) 2015 The Authors. Published by Elsevier Ireland Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

MARK-AGE aims at the identification of biomarkers of human aging capable of discriminating between the chronological age and the effective functional status of the organism. To achieve this, given the structure of the collected data, a proper statistical analysis has to be performed, as the structure of the data are non trivial and the number of features under study is near to the number of subjects used, requiring special care to avoid overfitting. Here we described some of the possible strategies suitable for this analysis. We also include a description of the main techniques used, to explain and justify the selected strategies. Among other possibilities, we suggest to model and analyze the data with a three step strategy