Background: In phenylketonuria (PKU), treatment monitoring is based on frequent blood phenylalanine (Phe) measurements, as this is the predictor of neurocognitive and behavioural outcome by... Show moreBackground: In phenylketonuria (PKU), treatment monitoring is based on frequent blood phenylalanine (Phe) measurements, as this is the predictor of neurocognitive and behavioural outcome by reflecting brain Phe con-centrations and brain biochemical changes. Despite clinical studies describing the relevance of blood Phe to out-come in PKU patients, blood Phe does not explain the variance in neurocognitive and behavioural outcome completely. Methods: In a PKU mouse model we investigated 1) the relationship between plasma Phe and brain biochemistry (Brain Phe and monoaminergic neurotransmitter concentrations), and 2) whether blood non-Phe Large Neutral Amino Acids (LNAA) would be of additional value to blood Phe concentrations to explain brain biochemistry. To this purpose, we assessed blood amino acid concentrations and brain Phe as well as monoaminergic neuro -transmitter levels in in 114 Pah-Enu2 mice on both B6 and BTBR backgrounds using (multiple) linear regression analyses. Results: Plasma Phe concentrations were strongly correlated to brain Phe concentrations, significantly negatively correlated to brain serotonin and norepinephrine concentrations and only weakly correlated to brain dopamine concentrations. From all blood markers, Phe showed the strongest correlation to brain biochemistry in PKU mice. Including non-Phe LNAA concentrations to the multiple regression model, in addition to plasma Phe, did not help explain brain biochemistry. Conclusion: This study showed that blood Phe is still the best amino acid predictor of brain biochemistry in PKU. Nevertheless, neurocognitive and behavioural outcome cannot fully be explained by blood or brain Phe concen-trations, necessitating a search for other additional parameters. Take-home message: Blood Phe is still the best amino acid predictor of brain biochemistry in PKU. Nevertheless, neurocognitive and behavioural outcome cannot fully be explained by blood or brain Phe concentrations, neces-sitating a search for other additional parameters. (c) 2021 Published by Elsevier Inc. Show less
Uleman, J.F.; Melis, R.J.F.; Quax, R.; Zee, E.A. van der; Thijssen, D.; Dresler, M.; ... ; Rikkert, M.G.M.O. 2020
Alzheimer's disease (AD) is a complex, multicausal disorder involving several spatiotemporal scales and scientific domains. While many studies focus on specific parts of this system, the complexity... Show moreAlzheimer's disease (AD) is a complex, multicausal disorder involving several spatiotemporal scales and scientific domains. While many studies focus on specific parts of this system, the complexity of AD is rarely studied as a whole. In this work, we apply systems thinking to map out known causal mechanisms and risk factors ranging from intracellular to psychosocial scales in sporadic AD. We report on the first systemic causal loop diagram (CLD) for AD, which is the result of an interdisciplinary group model building (GMB) process. The GMB was based on the input of experts from multiple domains and all proposed mechanisms were supported by scientific literature. The CLD elucidates interaction and feedback mechanisms that contribute to cognitive decline from midlife onward as described by the experts. As an immediate outcome, we observed several non-trivial reinforcing feedback loops involving factors at multiple spatial scales, which are rarely considered within the same theoretical framework. We also observed high centrality for modifiable risk factors such as social relationships and physical activity, which suggests they may be promising leverage points for interventions. This illustrates how a CLD from an interdisciplinary GMB process may lead to novel insights into complex disorders. Furthermore, the CLD is the first step in the development of a computational model for simulating the effects of risk factors on AD. Show less
