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.
Study Type
OBSERVATIONAL
Enrollment
90
University Hospital Inselspital, Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism (UDEM)
Bern, Switzerland
RECRUITINGBrain Age Gap
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.
Time frame: Time Point 2 (5-year follow-up)
Sustained Attention
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.
Time frame: Time Point 2 (5-year follow-up)
Cognitive flexibility
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.
Time frame: Time Point 2 (5-year follow-up)
Plasma concentration of Phe
Plasma Phenylalanine (Phe) concentrations are measured in patients with PKU
Time frame: Time Point 2 (5-year follow-up)
Diffusion tensor imaging (DTI)
DTI is used to assess white matter integrity in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
Arterial Spin Labeling (ASL)
ASL is used to assess cerebral blood flow in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
Resting-state fMRI
Resting-state fMRI will be used to assess functional connectivity in certain brain regions in patients with PKU and healthy controls
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
FLAIR-sequence
The T2-fluid-attenuated inversion recovery (Flair) will be used to measure white matter lesions in patients with PKU and healthy controls.
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
MPRAGE
A Magnetization Prepared-RApid Gradient Echo (MPRAGE) will be used to obtain high-resolution structural T1-weighted images in patients with PKU and healthy controls.
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
General intelligence
General intelligence will be evaluated using four subtests (vocabulary, arithmetics, symbol search, and matrix reasoning) of the Wechsler Adult Intelligence Scale Fourth Edition (WAIS-IV) in patients with PKU and healthy controls. Average IQ ranges from 85 - 115. Scores above indicate above average IQ, scores below indicate below average IQ.
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Processing speed
Processing speed will be assessed using the first and second condition of the Stroop test (naming and reading speed; D-KEFS) in patients with PKU and healthy controls. Time is measured in seconds with higher completion time indication worse performance in cognitive flexibility.
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Working memory
Working memory will be evaluated using the subtest n-back of the computerized test of attentional performance (TAP) in patients with PKU and healthy controls.
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
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Inhibition
Inhibition will be measured using the third condition of the Stroop test of the D-KEFS in patients with PKU and healthy controls.
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Design fluency
Design fluency, the initiation and fluency of generating visual patterns, will be examined using the subtest "design fluency" of the D-KEFS in patients with PKU and healthy controls.
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Motor control and speed
Fine motor control and fine motor speed will be evaluated using the Purdue Pegboard, which will help to determine manual dexterity and bimanual coordination in patients with PKU and healthy controls.
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Verbal fluency
Verbal fluency, including letter fluency, will be assessed using the respective subtest of the D-KEFS in patients with PKU and healthy controls.
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Body fat
Body fat, including total fat content and visceral fat, will be measured using the Lunar iDXA system (GE Medical Systems, Madison, USA) and Encore software version 18 in patients with PKU and healthy controls. Percentages of fat will be calculated and fat content will be transferred into z-scores. Visceral fat will be measured in gram. A fat mass index will also be calculated.
Time frame: Time Point 2 (5-year follow-up)
Bone density
Bone density for whole-body, femur, and spine densitometry, will be measured using the Lunar iDXA system (GE Medical Systems, Madison, USA) and Encore software version 18 in patients with PKU and healthy controls. Measurements will all be transferred into t-scores and z-scores.
Time frame: Time Point 2 (5-year follow-up)
Body Mass Index
To asses cardiovascular risk factors, the Body Mass Index (BMI) will be measured in kg/m² in patients with PKU and healthy controls.
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Tyrosine
To asses the amino acid profile, plasma concentrations of tyrosine (Tyr) will be measured through a blood sample following an 8-12 hour overnight fast in patients with PKU and healthy controls. High-performance ion-exchange chromatography (HPLC) coupled with post-column photometric detection of ninhydrin-derivatized amino acids will be employed for amino acid quantification.
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Tryptophan
To asses the amino acid profile, plasma concentrations of tryptophan (Trp) will be measured through a blood sample following an 8-12 hour overnight fast in patients with PKU and healthy controls. High-performance ion-exchange chromatography (HPLC) coupled with post-column photometric detection of ninhydrin-derivatized amino acids will be employed for amino acid quantification.
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Dry blood samples
To asses the amino acid profile, dry blood samples will be collected twice weekly during the month before (7 dry blood filter cards in total) and at the 5-year follow-up in patients with PKU.
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Total cholesterol
To asses cardiometabolic risk factors associated with the Brain Age Gap and accelerated brain aging, total cholesterol will be measured through a blood sample in patients with PKU and healthy controls.
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
LDL cholesterol
To asses cardiometabolic risk factors associated with the Brain Age Gap and accelerated brain aging, LDL (low-density lipoprotein) cholesterol will be measured through a blood sample in patients with PKU and healthy controls.
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
HDL cholesterol
To asses cardiometabolic risk factors associated with the Brain Age Gap and accelerated brain aging, HDL (high-density lipopro-tein) cholesterol will be measured through a blood sample in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
Triglycerides
To asses cardiometabolic risk factors associated with the Brain Age Gap and accelerated brain aging, Triglycerides will be measured through a blood sample in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
Apolipoprotein B (ApoB)
To asses cardiometabolic risk factors associated with the Brain Age Gap and accelerated brain aging, Apolipoprotein B (ApoB) will be measured through a blood sample in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
Lipoprotein (a)
To asses cardiometabolic risk factors associated with the Brain Age Gap and accelerated brain aging, Lipoprotein (a) (Lp (a)) will be measured through a blood sample in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
Fasting Glucose
To asses cardiometabolic risk factors associated with the Brain Age Gap and accelerated brain aging, Fasting Glucose will be measured through a blood sample in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
HbA1c
To asses cardiometabolic risk factors associated with the Brain Age Gap and accelerated brain aging, HbA1c will be measured through a blood sample in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
High-sensitivity C-reactive protein
To asses cardiometabolic risk factors associated with the Brain Age Gap and accelerated brain aging, high-sensitivity C-reactive protein (hs-CRP) will be measured through a blood sample in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
Blood pressure
To asses cardiovascular risk factors associated with the Brain Age Gap and accelerated brain aging , systolic and diastolic blood pressure will be measured in mmHg in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
Heart rate
To asses cardiovascular risk factors associated with the Brain Age Gap and accelerated brain aging heart rate will be measured in bpm in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
Body Mass Index
To asses cardiovascular risk factors associated with the Brain Age Gap and accelerated brain aging Body Mass Index (BMI) will be measured in kg/m2 in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
Total tau
To assess brain age related biomarkers, total tau (t-tau) will be measured through a blood sample in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
Total tau and phosphorylated tau
To investigate blood markers shown to reflect brain aging, total tau (t-tau) and phosphorylated tau (p-tau) will be measured through a blood sample in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
Neurofilament light chain
To investigate blood markers shown to reflect brain aging, neurofilament light chain (NfL) will be measured through a blood sample in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
Myeloid Cells 2
To investigate blood markers shown to reflect brain aging, Myeloid Cells 2 (TREM2) will be measured through a blood sample in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
Glial fibrillary acidic
To investigate blood markers shown to reflect brain aging, glial fibrillary acidic Protein (GFAP) will be measured through a blood sample in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
CC-chemokine ligand 11 and C-C Motif Chemokine Ligand 2
To investigate blood markers shown to reflect brain aging, CC-chemokine ligand 11 (CCL11) and C-C Motif Chemokine Ligand 2 (CCL2) will be measured through a blood sample in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)
Albumin
To investigate blood markers shown to reflect brain aging, Albumin in plasma (BCP-method) will be measured through a blood sample in plasma in patients with PKU and healthy controls.
Time frame: Time Point 2 (5-year follow-up)