ClinicalTrials.gov record NCT06969209
Brain Aging in Phenylketonuria
Brain Aging in Patients With Phenylketonuria
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AI-written from the registry record, not the study's own wording. May contain errors or omissions. Check the registry text on this page and the original record ↗ before acting on it.
This is a longitudinal observational study comparing adults with phenylketonuria (PKU) and healthy controls. Participants repeat the same assessments done at baseline about five years apart, including MRI, cognitive testing, and blood measures. The study seeks links between MRI-derived "brain age," cognition, and metabolic control.
What it is studying. The study tests whether the trajectory of MRI-derived brain age differs between adults with PKU and matched healthy controls over five years, and whether brain age relates to cognitive performance, plasma phenylalanine (Phe) and other metabolic and cardiometabolic markers.
What the researchers hope to learn. To describe brain-aging trajectories in adults with PKU using the Brain Age Gap metric; to test associations between brain age, cognitive performance, plasma Phe and other metabolic and cardiometabolic risk factors; and to identify patterns of brain structural and functional change over a 5-year period.
What this study is about
The study's own summary, as published on ClinicalTrials.gov. Underlined terms have a short explanation.
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
The criteria exactly as listed on ClinicalTrials.gov. Only the study team can say whether they apply to you.
Inclusion criteria
- Patients with PKU
- 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
- Age ≥18 years
- Written informed consent
- 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
Study description and what participation involves
The study's detailed description, as published on ClinicalTrials.gov.
Show the detailed description from the registry
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.
AI summaryWhat participation may involve, in plain languageShow ▾Hide ▴
AI-written from the registry record, not the study's own wording. May contain errors or omissions. Check the registry text on this page and the original record ↗ before acting on it.
Participants will repeat the baseline assessments at a single follow-up visit five years later. Visits begin after an overnight fast with an early-morning blood draw, a DXA body scan, a roughly 1-hour MRI, and a break (including a low‑protein snack) followed by about a 2-hour neuropsychological testing session and questionnaires on mood and quality of life. Patients with PKU will also provide dry blood spot samples twice weekly during the month before the visit (7 filter cards total) and have fasting plasma Phe and other blood biomarkers measured. Body composition, bone density, blood pressure and cardiometabolic markers will be measured and the same imaging and cognitive tests are repeated to compare changes over five years.
How the study is structured. Observational, case-control, longitudinal design. Adult patients with PKU and healthy control participants undergo the same assessments at two time points about five years apart (baseline and 5-year follow-up). Each visit includes a fasting blood draw, DXA body scan, a roughly 1-hour MRI, and a 2-hour neuropsychological assessment, with questionnaires about mood and quality of life.
Questions you could ask
These questions are suggestions for a conversation with the study team or your own clinician, not advice.
- This is an observational study, so the study itself gives no treatment. What would taking part involve for me, and would it change my usual care?
- The listed sites are in Switzerland. Is any of them reachable for me, and does the study help with travel?
- Which of the eligibility criteria would need to be checked with tests before I could join?
More on phenylketonuria
Important dates
- Study start
- Jan 13, 2025
- Primary completion
- May 30, 2030 (estimated)
- Study completion
- Aug 30, 2030 (estimated)
- Target enrollment
- 90 participants (estimated)
- Last updated on registry
- May 13, 2025
- Data verified
- Oct 10, 2026
Sponsor and research team
The lead sponsor funds and oversees the trial. A pharmaceutical or biotech company sponsor typically means the study is testing that company's drug or device; a hospital, university, or government agency usually indicates investigator-initiated or publicly funded research.
- Lead sponsor
- Insel Gruppe AG, University Hospital Bern
Central study contact
If you're interested in participating, the central contact can answer general questions about the study — including eligibility requirements and how to find a recruiting site near you. Actual eligibility is determined by the site's medical team.
- Regula Everts, Prof. Dr. phil. 📞 +41 31 63 2 84 97 ✉ regula.everts@insel.ch
Participating sites (1)
- University Hospital Inselspital, Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism (UDEM)Recruiting
Bern, 3010 · Switzerland
- Regula Everts, Prof. Dr. phil. · +41 31 63 2 84 97 · regula.everts@insel.ch
Terms used on this page · explanations by MedicalUpdates.ai
- MRI
- A scan that uses magnets and radio waves, not X-rays, to make detailed pictures of the inside of the body. Some implants and metal objects mean a person cannot have one.
- baseline
- Measurements taken at the start of the study, before any study treatment, so that later results can be compared with them.
- Written informed consent
- The process of explaining a study, including its risks and what participation involves, so that a person can decide freely whether to take part. Consent is usually documented in writing, and a participant can choose to leave the study later.
- Inclusion Criteria
- The conditions a person must meet to be considered for the study, for example a confirmed diagnosis, an age range or a test result.
- Exclusion Criteria
- The conditions that rule a person out even if they meet the inclusion criteria, for example other illnesses, certain medicines or pregnancy.
- follow-up
- The period after treatment or enrolment during which participants are observed or contacted, for example to record side effects or outcomes.
- Interventional
- A study in which researchers assign participants to one or more interventions, such as a drug, procedure, device, behavioural programme or other approach, to measure their effects.
The study record on this page is reproduced from ClinicalTrials.gov, a database of the U.S. National Library of Medicine, where it was submitted by the study sponsor. MedicalUpdates.ai does not own this content, is not affiliated with or endorsed by ClinicalTrials.gov, NIH, or the U.S. Government, and does not verify the record. Plain-language notes, the glossary and the questions above are added by this site; text in the boxes marked "AI summary" was written by an AI model and may contain errors. Registry records change over time. Check the original record for the current version and discuss eligibility or participation with a qualified healthcare professional.
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