brain aging in phenylketonuria

Conditions: Phenylketonuria (PKU)

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.

detailed research plan: the investigators' preliminary findings suggest that patients with pku might show altered aging trajectories compared to controls. the present study will investigate the aging trajectory in patients with pku and its association with cognitive and metabolic aging over a 5-year time period. the investigators will use the well-established "brain age gap" metric, which defines the biological brain age relative to the chronological age across different brain regions. based on the investigators' preliminary and published results the following hypotheses are postulated: a) there is accelerated brain aging in certain brain regions (as measured with an increasing brain age gap) over a 5-year follow-up period in patients with pku. b) the brain age gap relates to cognitive performance, blood-phe levels, and other metabolic parameters in patients with pku. c) in patients, age-related changes in gray matter metrics (prefrontal cortical thickness), white matter microstructure, and cerebral blood flow will be more pronounced over the 5-year follow-up period than in controls. d) patients' cognitive performance decreases more strongly over the 5-year follow-up period in sustained attention and cognitive flexibility than controls' cognitive performance. e) in patients, there is a relationship between changes in structural and functional brain characteristics and changes in cognitive performance and metabolic parameters. study procedure: the study procedure will mimic the baseline assessment as closely as possible. all patients will be asked again to take part in this longitudinal study. participants will therefore be the same as at time point 1 (tp1) which was performed between 2019 and 2022, involving 30 early-treated adult patients with pku (13 females, median age = 35.5 years, iqr = 12.3, age range = 19-48 years) and 59 healthy age-, sex-, and iq-matched controls (33 males, 26 females, median age = 30.0 years, iqr = 11.0, age range = 18-53 years). tp2 (time point 2, 5-year follow-up) will take place between 2024 and 2027, with the same assessments and methods. all participants will undergo identical assessments five years apart to evaluate cognitive function, mood, quality of life, metabolic parameters, and brain structure and function using mri. patients with pku and healthy controls will undergo the same study procedure: after an overnight fasting period, a blood sample will be drawn early in the morning (6-8 am) followed by a dxa (dual energy x-ray absorptiometry). after this, the 1-hour mri will be performed under the guidance of the team from the institute of diagnostic and interventional neuroradiology. after a break, which includes a low-protein snack, a 2-hour neuropsychological assessment will be performed by a neuropsychologist. all assessments will take place at the university hospital inselspital bern. brain age gap: a well-established technique used in different clinical samples will be employed to estimate biological brain age relative to chronological age, the so called "brain age gap". additionally, regional changes in gray matter, brain connectivity and cerebral blood flow will be assessed longitudinally to depict cerebral aging trajectories across mri sequences and brain regions. advanced statistical analyses will associate the brain age gap relative to cognition and metabolic control. 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 the open-source software freesurfer. the features consist of thickness, area, and volume measurements from a multimodal parcellation of the cerebral cortex, cerebellum, and subcortex. statistical analyses: changes in global and regional brain age gaps between baseline (tp1) and the 5-year follow-up (tp2) in patients and controls will be evaluated with linear mixed models using restricted maximum likelihood (reml) estimation (hypothesis a). these models will include global and regional brain age gaps as dependent variables, time, group, and the interaction between time and group as a fixed effect, while age and sex will be incorporated as covariates. participant id will be modeled as a random effect (intercept) to account for within-subject variance. the linear mixed modeling approach will also be applied to the cognitive and metabolic data. to assess the associations between brain age gap estimates, cognitive performance, and metabolic parameters, linear models and raw values, again with bag as dependent variable and cognition and metabolic parameters as independent variables will be calculated (hypothesis b). age-related changes in cerebral markers (structural gray and white matter metrics, cerebral blood flow) in patients and controls will be assessed with the same linear mixed model approach used for hypothesis a, replacing brain age gaps with these cerebral markers as dependent variables (hypothesis c). likewise, changes in cognitive performance in patients and controls will be evaluated with linear mixed models (hypothesis d). finally, the relationship between changes in cerebral markers, cognitive performance, and metabolic data will be investigated using the same model approach as in hypothesis b, with changes in cerebral markers serving as dependent variable and cognition and metabolic parameters as independent variables (hypothesis e). statistical significance will be determined at a threshold of p \< .05, with corrections for multiple comparisons applied via the false discovery rate (fdr) procedure.

Eligibility criteria:
patients with pku inclusion criteria: * participation in pico-study and/or: * pku diagnosed after a positive newborn screening * treatment with phe-restricted diet starting within the first 30 days of life * age ≥18 years * written informed consent exclusion criteria: * patients with pku not following a phe-restricted diet within 6 months before the study * phe concentration above 1600 µmol/l within 6 months before the study * concomitant disease states suspected to significantly affect primary or secondary outcomes * women who are pregnant or who are breast feeding * conditions interfering with mri such as magnetic (metallic) particles in the skull or brain, cardiac pacemaker, deep brain stimulators, cochlear implant, braces or permanent retainers healthy controls inclusion criteria: * age ≥18 years * written informed consent exclusion criteria: * concomitant disease states suspected to significantly affect primary or secondary outcomes * women who are pregnant or who are breast feeding * inability to follow the procedures of the study, e. g. due to language problems (lack of fluency in german or french), psychological disorders, dementia, etc. of the participant * conditions interfering with mri such as magnetic (metallic) particles in the skull or brain, cardiac pacemaker, deep brain stimulators, cochlear implant, braces or permanent retainers

Keywords: phenylketonuria (pku) brain aging brain age gap cognition magnetic resonance imaging metabolic control rare disease longitudinal study