Methods to explore risk factors in complex health conditions

Understanding how risk factors relate to complex health outcomes is a key goal in health research. This thesis investigates two such conditions: Irritable Bowel Syndrome (IBS) and frailty. Both conditions are marked by multifactorial origins, inconsistent definitions, and diverse behavioural and biological contributors. Demographics and geography add further layers of complexity.
Despite their differences, IBS and frailty are examined using a shared analytical framework and population-based datasets. IBS, a Disorder of Gut-Brain Interaction, affects gastrointestinal function and presents symptoms like pain, bloating, constipation, and diarrhoea. Its variability and sensitivity to dietary triggers make it analytically challenging. Frailty, defined as heightened vulnerability to adverse health outcomes, lacks a standard definition and is often assessed using biomarkers. These biomarkers, however, show high variability, raising concerns about their reliability.
To navigate these complexities, the thesis applies advanced modelling techniques: regression, Principal Component Analysis (PCA), Latent Class Analysis (LCA), and Model-Based Recursive Partitioning (MOB).
Results show that LCA improves interpretability in IBS research by grouping dietary patterns in symptom-relevant ways. MOB further identifies subtle variations in diet-IBS links. In contrast, frailty’s association with biomarkers yields less clarity, suggesting that biological data alone cannot capture its multidimensional nature. Yet, MOB proves useful again, revealing nuanced relationships across frailty’s biological, functional, and social domains.