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Metallothionein (MT) family are cysteine-rich proteins that regulate zinc (Zn) homeostasis and protect against oxidative damage. Studies in transgenic mice have shown that MT favorably influence longevity, although their role in human aging is not completely understood. Within the European multicenter study MARK-AGE, we analyzed MT induction after Zn treatment in peripheral blood mononuclear cells (PBMCs) and its relation with redox biomarkers in 2,936 age-stratified subjects (35-75 years) including the general population (RASIG), centenarian offspring (GO), and their spouses (SGO).

Frailty among elderly people leads to an increased risk for negative health outcomes. To prevent frailty, we need a better understanding of the underlying mechanisms and early detection of individuals at risk. Both may be served by identifying candidate (bio)markers, i.e. biomarkers and markers, for the physical, cognitive, and psychological frailty domains. We used univariate (Rank-ANOVA) and multivariate (elastic net) approaches on the RASIG study population (age range: 35-74 years, n = 2220) of the MARK-AGE study to study up to 331 (bio)markers between individuals with and without frailty for each domain. Biomarkers and markers identified by both approaches were studied further regarding their association with frailty using logistic regression. Univariately, we found lower levels of antioxidants, including β-cryptoxanthin and zeaxanthin, in those who were physically, cognitively or psychologically frail. Additionally, self-reported health was worse in these three frail groups. Multivariately, we observed lower levels of β-cryptoxanthin and zeaxanthin in the cognitively frail. Levels of these carotenoids were inversely associated with the risk of being cognitively frail after adjusting for confounders. Antioxidants and self-reported health are potential (bio)markers to detect persons at risk of becoming frail. The biomarkers identified may indicate the involvement of inflammation in frailty, especially for physical and cognitive frailty.

The use of yeasts enriched with distinct micronutrients as supplement in human nutrition increased over the last years. A special plasmolysed herbal yeast was investigated to demonstrate its beneficial effect on the bioavailability of important micronutrients for human nutrition such as iron, zinc, magnesium and vitamin B1. The well-established Caco-2 model was applied for this investigation. The plasmolysed herbal yeast underwent an in vitro simulation of the gastro-intestinal digest process before applied to the Caco-2 model. The results demonstrate an improved bioavailability of the micronutrients iron, zinc, magnesium and vitamin B1 in the presence of the plasmolysed herbal yeast.

Exhaustive exercise causes muscle damage accompanied by oxidative stress and inflammation leading to muscle fatigue and muscle soreness. Lemon verbena leaves, commonly used as tea and refreshing beverage, demonstrated antioxidant and anti-inflammatory properties. The aim of this study was to investigate the effects of a proprietary lemon verbena extract (Recoverben®) on muscle strength and recovery after exhaustive exercise in comparison to a placebo product.

We modeled red blood cell (RBC)-folate response to supplementation and developed personalized folate supplementation concepts.

A.B.O.S. (A Better Omic System; version 1.1.0; Ars Nova AG, Esslingen, Germany) a simple, interactive software for the analysis of omics data has recently been developed to classify and evaluate biological properties or processes in complex datasets. The tool utilises self-learning algorithms that exploit group-specific properties from large datasets and applies a combination of multivariate analysis techniques such as principal component analysis (PCA), weighting the different variables/parameters according to their discriminatory power. Unlike PCA, however, the program can handle data that are not normally distributed and accounts for the presence of outliers and missing data. The software carries out predictive identifications based on pre-assigned learning groups that can be either detected automatically or defined manually. By combining all parameters shared by the members of each learning groups, the software calculates two ideal reference groups and classifies unknown elements based on their relative distance to these groups. Along with the proposed classification of samples, it also identifies the most important parameters that allow differentiating between classes.