Brain Aging in Phenylketonuria

Condition(s) studied

Study summary

Background: Historically, the primary goal in managing phenylketonuria (PKU) has been to prevent severe and irreversible intellectual disability, as well as to address nutritional deficiencies that could lead to growth impairments or intellectual decline. Since the introduction of neonatal PKU screening in the mid-1960s, early treatment during childhood with a low phenylalanine diet or pharmacological interventions have been effective and prevent severe long-term sequelae. However, concerns persist that insufficient treatment during adulthood may cause subtle and, over time, possibly increasing cognitive and brain alterations. Recently, the first generation of early-treated patients has reached mid-adulthood. Hence, there is an urgent need to understand how PKU and metabolic control impact cognitive and brain aging and vice versa. The investigators preliminary cross-sectional findings suggest that brain aging trajectories may diverge significantly between patients with PKU and healthy controls in mid-adulthood. Until now, no comprehensive research has longitudinally tracked brain aging in patients with PKU through MRI markers and their correlation with cognition, metabolic control, and cardiometabolic risk factors. The "brain age" approach enables the identification of individual health characteristics and risk patterns for age-related changes. The evaluation of brain age in addition to the chronological age allows for the development and monitoring of personalized neuroprotective treatments and interventions. Advancing the investigators understanding of disease progression during aging in patients with PKU and identifying strategies for preventing potential harm later in life is of utmost importance for patients' well-being and clinical practice and, through this, follows the WHO's brain health plan.

Study aims: This longitudinal study will, for the first time, investigate the trajectory of brain aging relative to chronological aging across early and middle adulthood in individuals with PKU compared to healthy controls. Data collected in the investigators previous SNSF study (Nr 192706; 184453) will serve as baseline data and allow the examination of brain health by means of brain age modeling. The association between brain age trajectories and cognitive performance, metabolic control, and cardiometabolic risk factors will be studied to disentangle risk patterns of accelerated brain aging in patients with a rare disease.

Relevance of the study: This study will show whether and how the brain aging trajectory is accelerated in patients with PKU and will determine the functional relevance of brain aging with respect to cognitive performance and metabolic control (i.e., phenylalanine levels). This is one of the first studies to closely examine long-term brain and cognitive changes in PKU during early and mid-adulthood. Its findings could provide valuable insights into the long-term effects of PKU on brain structure and aging processes. Furthermore, the results may support the development of future treatment strategies and improve the quality of life for adults with PKU.

Eligibility

Primary outcome measure(s)

• Brain Age Gap — Time Point 2 (5-year follow-up)
  Defines the biological brain age relative to the chronological age across different brain regions. Machine learning models will be used to estimate brain age based on MRI-derived measures. For each participant, an estimate of the Brain Age Gap (predicted brain age minus chronological age, indicating the degree of brain maintenance) will be calculated using XGBoost. XGBoost uses gradient tree boosting based on 1118 features to predict the Brain Age Gap. These features are extracted using Freesurfer. The features consist of thickness, area, and volume measurements from a multimodal parcellation of the cerebral cortex, cerebellum, and subcortex. Possible changes in the Brain age gap will be evaluated by comparing the baseline measurement with the 5 year follow up.
• Sustained Attention — Time Point 2 (5-year follow-up)
  Changes in sustained attention over 5-years are assessed with the respective subtest "sustained attention" of the Test of Attentional Performance (TAP) in patients with PKU and healthy controls. In this subtest, stimuli with varying features (color, shape, size, filling) appear on a monitor. A target stimulus matches the previous one in one of two predefined dimensions (same shape or same color). Sustained attention is measured in milliseconds, with higher values showing slower reaction time to target stimulus.
• Cognitive flexibility — Time Point 2 (5-year follow-up)
  Changes in cognitive flexibility over 5-years are assessed using the fourth condition "inhibition/switching" of the color-word interference test of the Delis-Kaplan Executive Function System (D-KEFS) in patients with PKU and healthy controls. Time is measured in seconds with higher completion time indication worse performance in cognitive flexibility.
• Plasma concentration of Phe — Time Point 2 (5-year follow-up)
  Plasma Phenylalanine (Phe) concentrations are measured in patients with PKU
• Diffusion tensor imaging (DTI) — Time Point 2 (5-year follow-up)
  DTI is used to assess white matter integrity in patients with PKU and healthy controls.
• Arterial Spin Labeling (ASL) — Time Point 2 (5-year follow-up)
  ASL is used to assess cerebral blood flow in patients with PKU and healthy controls.

Trial sites (1)

More Insel Gruppe AG, University Hospital Bern trials in Switzerland

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