TiFN PhD fellow Roland Hangelbroek: “A PhD kick-starts your career”

13 February 2018 – 

A curiosity about the scientific and societal impact of the research motivated Roland Hangelbroek to (kick-)start his career with a PhD at TiFN. The nutritionist defended his thesis – on the molecular assessment of muscle health and function at Wageningen University.

“Working towards my PhD gave me substantial knowledge on programming and machine learning”, says Hangelbroek, who graduated with an MSc in Molecular Nutrition & Toxicology at Wageningen University, before joining TiFN. “It also challenged me to improve my soft skills, for example in project management, presenting and writing”, he continues. “TiFN provided the perfect environment to practice and evolve, especially via the regular expert meetings and opportunities to follow a variety of courses.”

Hangelbroek and his colleagues are the first scientists to make a detailed investigation of the metabolome and gene expressions in human muscle tissue. “The aim of my PhD project was, by leveraging the sensitivity and comprehensiveness of transcriptomics and metabolomics, to reveal the mechanisms and processes that contribute to the effects of age, frailty and physical activity” he explains. Data were collected among young and, both healthy and frail, older volunteers.

Personalized nutrition

The PhD candidate applied modelling and machine learning techniques to develop a predictive model. “The gene expression data gave an excellent impression of lifestyle factors, for example whether someone consumes enough protein, or how much physical activity one does”, says Hangelbroek. “This has provided promising leads for the development of personalized nutrition.”

Significant differences between frail and healthy older adults were seen in both gene expressions and the metabolome. “The differences were similar to the differences between healthy young men and healthy older adults, suggesting that frailty presents itself as a more pronounced form of aging, somewhat independent of chronological age”, says Hangelbroek. The age and frailty related differences in the transcriptome were partially reversed by resistance-type exercise training.

Defining the key message

The PhD candidate valued the industrial and societal relevance of his project. He also felt inspired by the expert meetings with TiFN partners. “Discussing my research approach and results with people from different backgrounds, with such a depth of experience and expertise, helped me to define and sharpen the key messages of my work.”

Hangelbroek is applying his TiFN ‘learning’ every day in his new role as a data scientist at the Dutch water company Vitens. “I investigate how to detect and identify substances in water that originate from humans, such as drug metabolites and pesticides. I also look at how to detect leaks and contaminations in our water-supply network; research that greatly benefits from modelling and machine learning.“

Want to read more about Hangelbroek’s research? Click here