Phenylketonuria (PKU)NCT06969209
Brain Aging in Phenylketonuria
An observational study of Phenylketonuria (PKU), run by Insel Gruppe AG, University Hospital Bern at 1 site in Switzerland.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Phenylketonuria (PKU)
- How many people
- 90 participants
- Where
- 1 siteSwitzerland
- Study dates
- 13 Jan 2025to 30 Aug 2030
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients with PKU were recruited in the framework of the PICO study (2019-2022) and will be recontacted in respect to study participation to the PICO-5-study.
Who may be able to join
- 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
- Age ≥18 years
- Written informed consent
Who cannot join
- 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
Show all 10Show fewer
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Switzerland1 site, 1 recruiting
- Recruiting
University Hospital Inselspital, Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism (UDEM)
Bern, 3010
Regula Everts, Prof. Dr. phil., +41 31 63 2 84 97, regula.everts@insel.ch
About this study
Official title: Brain Aging in Patients With Phenylketonuria
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.
Read the detailed description
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:
- 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.
- The Brain Age Gap relates to cognitive performance, blood-Phe levels, and other metabolic parameters in patients with PKU.
- 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.
- Patients' cognitive performance decreases more strongly over the 5-year follow-up period in sustained attention and cognitive flexibility than controls' cognitive performance.
- 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.
- Conditions
- Phenylketonuria (PKU)
- Keywords
- Phenylketonuria (PKU), Brain aging, Brain age gap, Cognition, Magnetic resonance imaging, Metabolic control, Rare disease, Longitudinal study
- Medical subject headings
- Phenylketonurias, Rare Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Patients | Adult patients with Phenylketonuria | |
| Controls | Healthy controls |
What is being measured
The primary outcome is the main question the study is designed to answer.
Brain 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.
Measured: 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.
Measured: 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.
Measured: Time Point 2 (5-year follow-up)
Plasma concentration of Phe
Plasma Phenylalanine (Phe) concentrations are measured in patients with PKU
Measured: 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.
Measured: 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.
Measured: Time Point 2 (5-year follow-up)
Secondary outcomes (35)
Resting-state fMRI
Resting-state fMRI will be used to assess functional connectivity in certain brain regions in patients with PKU and healthy controls
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Inhibition
Inhibition will be measured using the third condition of the Stroop test of the D-KEFS in patients with PKU and healthy controls.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Time Point 2 (5-year follow-up)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 May 2025
- Study start13 Jan 2025
- Primary completion30 May 2030 (estimated)
- Study completion30 Aug 2030 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Phenylketonuria (PKU)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Other
- Enrolment
- 90 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Insel Gruppe AG, University Hospital Bern
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Regula Everts, Prof. Dr. phil., Principal investigator, Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06969209
- Sponsor's protocol ID
- PICO-5
- Status last verified
- May 2025
- First submitted
- 10 Mar 2025
- Last updated
- 13 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Upon agreement among project partners, MRI data of healthy participants might be used for control comparisons in upcoming projects at the Institute for Diagnostic and Interventional Neuroradiology (NRAD), Inselspital, University Hospital Bern. Healthy controls are explicitly asked for the further use of their MRI data in the written consent. Demographic, cognitive, and behavioral outcome measures are stored in REDCap, neuroimaging data in K-PACS. Anonymised demographic, cognitive and behavioral data will be shared on a FAIR repository (www.datadryad.org).
Publications
Trepp R, Muri R, Maissen-Abgottspon S, Haynes AG, Hochuli M, Everts R. Cognition after a 4-week high phenylalanine intake in adults with phenylketonuria - a randomized controlled trial. Am J Clin Nutr. 2024 Apr;119(4):908-916. doi: 10.1016/j.ajcnut.2023.11.007. Epub 2024 Feb 9.
PubMed 38569786Steiner L, Muri R, Wijesinghe D, Jann K, Maissen-Abgottspon S, Radojewski P, Pospieszny K, Kreis R, Kiefer C, Hochuli M, Trepp R, Everts R. Cerebral blood flow and white matter alterations in adults with phenylketonuria. Neuroimage Clin. 2024;41:103550. doi: 10.1016/j.nicl.2023.103550. Epub 2023 Dec 9.
PubMed 38091797Trepp R, Muri R, Abgottspon S, Bosanska L, Hochuli M, Slotboom J, Rummel C, Kreis R, Everts R. Impact of phenylalanine on cognitive, cerebral, and neurometabolic parameters in adult patients with phenylketonuria (the PICO study): a randomized, placebo-controlled, crossover, noninferiority trial. Trials. 2020 Feb 13;21(1):178. doi: 10.1186/s13063-019-4022-z.
PubMed 32054509Muri R, Rummel C, McKinley R, Rebsamen M, Maissen-Abgottspon S, Kreis R, Radojewski P, Pospieszny K, Hochuli M, Wiest R, Trepp R, Everts R. Transient brain structure changes after high phenylalanine exposure in adults with phenylketonuria. Brain. 2024 Nov 4;147(11):3863-3873. doi: 10.1093/brain/awae139.
PubMed 38723047Muri R, Maissen-Abgottspon S, Reed MB, Kreis R, Hoefemann M, Radojewski P, Pospieszny K, Hochuli M, Wiest R, Lanzenberger R, Trepp R, Everts R. Compromised white matter is related to lower cognitive performance in adults with phenylketonuria. Brain Commun. 2023 May 15;5(3):fcad155. doi: 10.1093/braincomms/fcad155. eCollection 2023.
PubMed 37265600Muri R, Maissen-Abgottspon S, Rummel C, Rebsamen M, Wiest R, Hochuli M, Jansma BM, Trepp R, Everts R. Cortical thickness and its relationship to cognitive performance and metabolic control in adults with phenylketonuria. J Inherit Metab Dis. 2022 Nov;45(6):1082-1093. doi: 10.1002/jimd.12561. Epub 2022 Sep 27.
PubMed 36117142
Study at a glance
From the registry record, last updated by the sponsor on 13 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Brain Aging in Patients With Phenylketonuria
- ClinicalTrials.gov ID
- NCT06969209
- Acronym / sponsor ID
- PICO-5
- Conditions
- Phenylketonuria (PKU)
- Keywords
- Phenylketonuria (PKU), Brain aging, Brain age gap, Cognition, Magnetic resonance imaging, Metabolic control, Rare disease, Longitudinal study
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 90 participants (estimated)
- Lead sponsor
- Insel Gruppe AG, University Hospital BernUniversity, hospital or other
- Study start
- 13 Jan 2025 (actual)
- Primary completion
- 30 May 2030 (estimated)
- Study completion
- 30 Aug 2030 (estimated)
- First posted
- 13 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 description
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:
- 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.
- The Brain Age Gap relates to cognitive performance, blood-Phe levels, and other metabolic parameters in patients with PKU.
- 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.
- Patients' cognitive performance decreases more strongly over the 5-year follow-up period in sustained attention and cognitive flexibility than controls' cognitive performance.
- 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 design
- Observational model
- Case-control
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Patients | – | –Adult patients with Phenylketonuria |
| Controls | – | –Healthy controls |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with PKU were recruited in the framework of the PICO study (2019-2022) and will be recontacted in respect to study participation to the PICO-5-study.
Key inclusion criteria (7 listed)
- 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
Key exclusion criteria (10 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Brain Age Gap
Time frame: 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 frame: 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 frame: 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 frame: Time Point 2 (5-year follow-up)
Plasma Phenylalanine (Phe) concentrations are measured in patients with PKU
Diffusion tensor imaging (DTI)
Time frame: 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 frame: Time Point 2 (5-year follow-up)
ASL is used to assess cerebral blood flow in patients with PKU and healthy controls.
35 secondary outcomes
Resting-state fMRI
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
FLAIR-sequence
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
MPRAGE
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
General intelligence
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Processing speed
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Working memory
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Inhibition
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Design fluency
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Motor control and speed
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Verbal fluency
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Body fat
Time frame: Time Point 2 (5-year follow-up)
Bone density
Time frame: Time Point 2 (5-year follow-up)
Body Mass Index
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Tyrosine
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Tryptophan
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Dry blood samples
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
Total cholesterol
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
LDL cholesterol
Time frame: Time Point 1 (Baseline) and Time Point 2 (5-year follow-up)
HDL cholesterol
Time frame: Time Point 2 (5-year follow-up)
Triglycerides
Time frame: Time Point 2 (5-year follow-up)
Apolipoprotein B (ApoB)
Time frame: Time Point 2 (5-year follow-up)
Lipoprotein (a)
Time frame: Time Point 2 (5-year follow-up)
Fasting Glucose
Time frame: Time Point 2 (5-year follow-up)
HbA1c
Time frame: Time Point 2 (5-year follow-up)
High-sensitivity C-reactive protein
Time frame: Time Point 2 (5-year follow-up)
Blood pressure
Time frame: Time Point 2 (5-year follow-up)
Heart rate
Time frame: Time Point 2 (5-year follow-up)
Body Mass Index
Time frame: Time Point 2 (5-year follow-up)
Total tau
Time frame: Time Point 2 (5-year follow-up)
Total tau and phosphorylated tau
Time frame: Time Point 2 (5-year follow-up)
Neurofilament light chain
Time frame: Time Point 2 (5-year follow-up)
Myeloid Cells 2
Time frame: Time Point 2 (5-year follow-up)
Glial fibrillary acidic
Time frame: Time Point 2 (5-year follow-up)
CC-chemokine ligand 11 and C-C Motif Chemokine Ligand 2
Time frame: Time Point 2 (5-year follow-up)
Albumin
Time frame: Time Point 2 (5-year follow-up)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 May 2025.
- Background: PubMed 38569786Trepp R, Muri R, Maissen-Abgottspon S, Haynes AG, Hochuli M, Everts R. Cognition after a 4-week high phenylalanine intake in adults with phenylketonuria - a randomized controlled trial. Am J Clin Nutr. 2024 Apr;119(4):908-916. doi: 10.1016/j.ajcnut.2023.11.007. Epub 2024 Feb 9.
- Background: PubMed 38091797Steiner L, Muri R, Wijesinghe D, Jann K, Maissen-Abgottspon S, Radojewski P, Pospieszny K, Kreis R, Kiefer C, Hochuli M, Trepp R, Everts R. Cerebral blood flow and white matter alterations in adults with phenylketonuria. Neuroimage Clin. 2024;41:103550. doi: 10.1016/j.nicl.2023.103550. Epub 2023 Dec 9.
- Background: PubMed 32054509Trepp R, Muri R, Abgottspon S, Bosanska L, Hochuli M, Slotboom J, Rummel C, Kreis R, Everts R. Impact of phenylalanine on cognitive, cerebral, and neurometabolic parameters in adult patients with phenylketonuria (the PICO study): a randomized, placebo-controlled, crossover, noninferiority trial. Trials. 2020 Feb 13;21(1):178. doi: 10.1186/s13063-019-4022-z.
- Background: PubMed 38723047Muri R, Rummel C, McKinley R, Rebsamen M, Maissen-Abgottspon S, Kreis R, Radojewski P, Pospieszny K, Hochuli M, Wiest R, Trepp R, Everts R. Transient brain structure changes after high phenylalanine exposure in adults with phenylketonuria. Brain. 2024 Nov 4;147(11):3863-3873. doi: 10.1093/brain/awae139.
- Background: PubMed 37265600Muri R, Maissen-Abgottspon S, Reed MB, Kreis R, Hoefemann M, Radojewski P, Pospieszny K, Hochuli M, Wiest R, Lanzenberger R, Trepp R, Everts R. Compromised white matter is related to lower cognitive performance in adults with phenylketonuria. Brain Commun. 2023 May 15;5(3):fcad155. doi: 10.1093/braincomms/fcad155. eCollection 2023.
- Background: PubMed 36117142Muri R, Maissen-Abgottspon S, Rummel C, Rebsamen M, Wiest R, Hochuli M, Jansma BM, Trepp R, Everts R. Cortical thickness and its relationship to cognitive performance and metabolic control in adults with phenylketonuria. J Inherit Metab Dis. 2022 Nov;45(6):1082-1093. doi: 10.1002/jimd.12561. Epub 2022 Sep 27.
- The official website provides a brief overview of the PICO-5 study and its main focus on cognitive and cerebral aging in patients with phenylketonuria. Additionally, it includes information on financial support, collaborators, and principal investigatorsLink provided by the sponsor.
- The SNSF data portal provides an overview of the funding and includes a brief summary and scientific abstract of the PICO-5 study.Link provided by the sponsor.
- Individual participant dataSharing planned: Yes. Upon agreement among project partners, MRI data of healthy participants might be used for control comparisons in upcoming projects at the Institute for Diagnostic and Interventional Neuroradiology (NRAD), Inselspital, University Hospital Bern. Healthy controls are explicitly asked for the further use of their MRI data in the written consent. Demographic, cognitive, and behavioral outcome measures are stored in REDCap, neuroimaging data in K-PACS. Anonymised demographic, cognitive and behavioral data will be shared on a FAIR repository (www.datadryad.org).
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Regula Everts, Prof. Dr. phil. principal investigator
Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Phenylketonuria (PKU), run by Insel Gruppe AG, University Hospital Bern at 1 site in Switzerland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 10 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 13 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Regula Everts, Prof. Dr. phil. (+41 31 63 2 84 97, regula.everts@insel.ch), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Brain Age Gap (time frame: Time Point 2 (5-year follow-up)). The study lists 6 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06969209, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 May 2025; AzTrials last refreshed its copy on 26 Oct 2026.