Assessment of intellectual impairment, health-related quality of life, and behavioral phenotype in patients with neurotransmitter related disorders: Data from the iNTD registry

ORIGINAL ARTICLE Assessment of intellectual impairment, health-related quality of life, and behavioral phenotype in patients with neurotransmitter related disorders: Data from the iNTD registry Mareike Keller 1 | Heiko Brennenstuhl 1 | Oya Kuseyri Hübschmann 1 | Filippo Manti 2 | Natalia Alexandra Julia Palacios 3 | Jennifer Friedman 4 | Yılmaz Yıldız 5 | Jeanette Aimee Koht 6 |Suet-NaWong 7 | Dimitrios I. Zafeiriou 8 | Eduardo L opez-Laso 9 | Roser Pons 10 | Jan Kulh anek 11 | Kathrin Jeltsch 1 | Jesus Serrano-Lomelin 12 | Sven F. Garbade 1,13 | Thomas Opladen 1 | Helly Goez 14 | International Working Group on Neurotransmitter related Disorders (iNTD) | Alberto Burlina 15 | Elisenda Cortès-Saladelafont 3,16 | Joaquín Alejandro Fern andez Ramos 9 | Angeles García-Cazorla 3 | Georg F. Hoffmann 1 | Stacey Tay Kiat Hong 17 | Tom ašHonzík 11 | Ivana Kavecan 18 | Manju A. Kurian 19 | Vincenzo Leuzzi 2 | Thomas Lücke 20 | Francesca Manzoni 15 | Mario Mastrangelo 2 | Saadet Mercimek-Andrews 21,22 | Pablo Mir 23 | Mari Oppebøen 24 | Toni S. Pearson 25 | H. Serap Sivri 5 | Dora Steel 19 | Galina Stevanovi c 26 | Cheuk-Wing Fung 7 1 Division of Child Neurology and Metabolic Medicine, University Children's Hospital Heidelberg, Heidelberg, Germany 2 Department of Human Neuroscience, Unit of Child Neurology and Psychiatry, Università degli Studi di Roma La Sapienza, Rome, Italy 3 Inborn errors of metabolism Unit, Department of Neurology, Institut de Recerca Sant Joan de Déu and CIBERER-ISCIII, Barcelona, Spain 4 UCSD Departments of Neuroscience and Pediatrics; Rady Children's Hospital Division of Neurology, Rady Children's Institute for Genomic Medicine, San Diego, California, USA 5 Hacettepe University, Faculty of Medicine, Department of Pediatrics, Section of Pediatric Metabolism, Ankara, Turkey 6 Department of Neurology, Oslo University Hospital, Oslo, Norway 7 Department of Pediatrics and Adolescent Medicine, The Hong Kong Children's Hospital, Hong Kong, Hong Kong 8 First Department of Pediatrics Aristotle University of Thessaloniki, Thessaloniki, Greece 9 Pediatric Neurology Unit, Department of Pediatrics, University Hospital Reina Sofía, IMIBIC and CIBERER, C ordoba, Spain 10 First Department of Pediatrics of the University of Athens, Aghia Sofia Hospital, Athens, Greece 11 Department of Pediatrics and Inherited Metabolic Disorders, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic Mareike Keller, Heiko Brennenstuhl, Thomas Opladen, and Helly Goez contributed equally to this study. Received: 21 May 2021 Revised: 30 June 2021 Accepted: 7 July 2021 DOI: 10.1002/jimd.12416 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2021 The Authors. Journal of Inherited Metabolic Disease published by John Wiley & Sons Ltd on behalf of SSIEM. J Inherit Metab Dis. 2021;44:1489–1502. wileyonlinelibrary.com/journal/jimd 1489 12 Women and Children's Health Research Institute, University of Alberta, Edmonton, Alberta, Canada 13 Dietmar-Hopp Metabolic Center, University Children's Hospital Heidelberg, Heidelberg, Germany 14 Department of Pediatrics, University of Alberta, Glenrose Rehabilitation Hospital, Edmonton, Alberta, Canada 15 U.O.C. Malattie Metaboliche Ereditarie, Dipartimento della Salute della Donna e del Bambino, Azienda Ospedaliera Universitaria di Padova – Campus Biomedico Pietro d'Abano, Padova, Italy 16 Inborn Errors of Metabolism and Child Neurology Unit, Department of Pediatrics, Hospital Germans Trias i Pujol, Badalona and Faculty of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain 17 KTP-National University Children's Medical Institute, National University Health System, Singapore, Singapore 18 Faculty of Medicine, University of Novi Sad, Institute for Children and Youth Health Care of Vojvodina, Novi Sad, Serbia 19 Developmental Neurosciences, UCL Great Ormond Street-Institute of Child Health and Department of Neurology, Great Ormond Street Hospital, London, UK 20 University Children's Hospital, St. Josef-Hospital, Ruhr-University Bochum, Bochum, Germany 21 Division of Clinical and Metabolic Genetics, Department of Pediatrics, University of Toronto, The Hospital for Sick Children, Toronto, Ontario, Canada 22 Department of Medical Genetics, University of Alberta, Women and Children's Health Research Institute, Stollery Children's Hospital, Edmonton, Alberta, Canada 23 Unidad de Trastornos del Movimiento Servicio de Neurología y Neurofisiología Clínica Unidad de Gesti on Clínica de Neurociencias Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío, Sevilla, Spain 24 Children's Department Division of Child Neurology Oslo University Hospital Rikshospitalet, Oslo, Norway 25 Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA 26 Clinic of Neurology and Psychiatry for Children and Youth, School of Medicine, University of Belgrade, Belgrade, Serbia Correspondence Mareike Keller, Department of General Pediatrics, Division of Neuropediatrics and Metabolic Medicine, University Children's Hospital Heidelberg, 69120 Heidelberg, Germany. Email: [email protected] heidelberg.de Funding information Dietmar Hopp Stiftung (DE); Medical Faculty of the University of Heidelberg Communicating Editor: Avihu Boneh Abstract Inherited disorders of neurotransmitter metabolism are a group of rare diseases, which are caused by impaired synthesis, transport, or degradation of neurotransmitters or cofactors and result in various degrees of delayed or impaired psychomotor development. To assess the effect of neurotransmitter deficiencies on intelligence, quality of life, and behavior, the data of 148 patients in the registry of the International Working Group on Neurotransmitter Related Disorders (iNTD) was evaluated using results from standardized age-adjusted tests and questionnaires. Patients with a primary disorder of monoamine metabolism had lower IQ scores (mean IQ 58, range 40-100) within the range of cognitive impairment (<70) compared to patients with a BH 4 deficiency (mean IQ 84, range 40-129). Short attention span and distractibility were most frequently mentioned by parents, while patients reported most frequently anxiety and distractibility when asked for behavioral traits. In individuals with succinic semialdehyde dehydrogenase deficiency, selfstimulatory behaviors were commonly reported by parents, whereas in patients with dopamine transporter deficiency, DNAJC12 deficiency, and monoamine oxidase A deficiency, self-injurious or mutilating behaviors have commonly been observed. Phobic fears were increased in patients with 6-pyruvoyltetrahydropterin synthase deficiency, while individuals with sepiapterin reductase deficiency frequently experienced communication and sleep difficulties. Patients with BH 4 deficiencies achieved significantly higher quality of life as compared to other groups. This analysis of the iNTD registry data highlights: (a) difference in IQ and subdomains of quality of life between BH 4 deficiencies and primary neurotransmitter-related disorders and (b) previously underreported behavioral traits. KEYWORDS behavioral phenotype, cognitive impairment, iNTD, intelligence, neurotransmitter deficiencies, quality of life 1490 KELLER ET AL. 1|INTRODUCTION Neurotransmitter deficiencies (NTDs) are a group of rare inherited metabolic diseases. The group consists of disorders directly affecting the synthesis, transport or degradation of monoamine neurotransmitters, and disorders affecting cofactor metabolism. Neurotransmitter metabolites include biogenic amines (catecholamines norepinephrine, epinephrine, and dopamine and serotonin) and amino acids (glycine, glutamate and γ-aminobutyric acid [GABA]). Tetrahydrobiopterin (BH 4 ) is an essential cofactor for enzymes involved in biogenic amine synthesis. A lack of BH 4 can, therefore, mimic the clinical and biochemical profile of monoamine NTDs. 1-3 Approximately 1500 cases of NTDs are reported in the literature; the prevalence of individual diseases is difficult to estimate. 4 NTDs can either present with an acute onset epileptic encephalopathy after birth and/or chronic metabolic disturbances resulting in a wide variety of clinical symptoms, including motor dysfunction, hypotonia, and developmental delay. Since the clinical phenotype overlaps with more frequently occurring neurological entities, inherited NTDs are underrecognized and often misdiagnosed. 1,3 Neuropsychological symptoms have been described in all NTDs, however, a global structured analysis of data on cognitive aspects, neurobehavioral attributes, or quality of life (QoL) and also the differences between subgroups on patients with NTDs is missing so far. 5 We, therefore, gathered and statistically analyzed data from patients registered to the patient registry of the International Working Group on Neurotransmitter Related Disorders (iNTD; www.intd-registy.org). 4 Our main goal was to improve our understanding of NTDs by covering the following objectives: (a) assessing IQ outcomes in individuals with NTDs; (b) identifying behavioral traits and problems associated with NTDs; and (c) exploring potential differences in QoL in the domains of physical wellbeing, psychological health, social relationships, and environment. The results from this research will help clinicians to include NTDs in their differential diagnosis, enable the development of targeted psychosocial support programs and inform future research. 2|MATERIALS AND METHODS 2.1 |Database characteristics The iNTD is a worldwide consortium of clinicians, laboratory, and basic scientists. 4 The iNTD patient registry is an important cornerstone within the network activities. Currently, 42 health-care providers from 26 countries contribute patients to the registry. The database query for this study was completed on 1 August 2020. Patients missing a definitive diagnosis of a neurotransmitterrelated disease and infants with a birth weight less than 1500 g were excluded from the analyses. After applying the selection criteria, n =148 individuals remained for whom information on at least one quality: IQ, QoL, or behavior was available. If measurement results at multiple time points for individual patients were stored in the registry database, only the most recent ones were evaluated. Detailed characteristics of the subanalysis datasets are discussed in the following sections. Table 1 provides an overview of the distribution of patients and their diagnoses. 2.2 |IQ analysis Each center had the freedom to choose a standardized test to perform IQ assessments. All tests were ageadjusted and included: The Wechsler Preschool and Primary Scale for Intelligence (WPPSI-III), Wechsler Intelligence Scale for Children (WISC-IV), and the Wechsler Adult Intelligence Scale (WAIS-IV). Individuals with scores below 70 were interpreted as intellectually disabled according to the standard definition of the tests. Scores between 85 and 114 are considered as average range. 6 Individuals that were evaluated with the Denver II Score, the Bayley Scales of Infant and Toddler Development (BDSI)-II and -III, and Griffiths Developmental Scale were excluded from the analysis. 2.3 |Behavior and emotional problems Behavior and emotional traits were assessed using a self-report (for patients above the age of 13 years) or a parental report form. Thirty-three items were selected from the symptom list of the Mannheimer Eltern Interview by Esser et al 7 and covered four main categories: (a) neurotic and emotional disorders, (b) anti-social and delinquent behavior, (c) hyperkinetic syndrome, and (d) additional specific disorders. The items covered the features of extraversive, introversive, autistic, attention deficit hyperactivity disorder (ADHD), and psychosomatic spectra behaviors (aggressiveness, anorexia, anxiety, complaining of pain, compulsive behavior, difficulties falling asleep, distractibility, encopresis, enuresis, hyperactivity, impulsiveness, lying, mood swings, mutism, overeating, phobias, pica, problems communicating wishes, problems understanding other people's feelings, being sad or unhappy, self-endangering behavior, self-mutilation, self-stimulation, inappropriate sexual behavior, short attention, being shy or timid, social withdrawal, stealing, substance abuse, temper tantrums, tics, waking up at night, and yelling). KELLER ET AL.1491 TABLE 1 Groups, diseases, and number of patients included in this study Group Affected enzyme (OMIM disease code) (n =148, percent of total) Age at diagnosis (in months), range IQ analysis Behavior analysis (n =128) PedsQL (n =38) WHOQoL (n =29) Professional assessment (n =39) Parental reports (n =86) Patient reports (n =42) Parental reports (n =15) Patient reports (n =23) Patient reports (n =26) BH 4 deficiencies arGTPCH (#233910) 6 (4%) 50.7, 6.9-167.9 2 (5%) 2 (2%) 2 (5%) 1 (7%) 1 (4%) 2 (8%) adGTPCH (#233910) 24 (16%) 173.8, 27.6-623.6 5 (13%) 3 (3%) 15 (36%) 6 (40%) 10 (43%) 8 (31%) PTPS (#261640) 32 (21%) 23.2, 0.1-395.7 14 (36%) 19 (22%) 10 (24%) 4 (27%) 3 (13%) 7 (27%) DHPR (#261630) 13 (9%) 9.6, 0.4-23.7 3 (8%) 8 (9%) 4 (10%) ——— SR (#612716) 11 (7%) 120.8, 6.9-311.8 2 (5%) 7 (8%) 2 (5%) —1 (4%) 4 (15%) Primary disorders of monoamine metabolism - defects in biosynthesis and catabolism AADC (#608643) 25 (17%) 61.5, 4.6-383.7 8 (21%) 18 (21%) 7 (17%) 2 (13%) 2 (9%) 3 (12%) TH (#605407) 13 (9%) 63, 4.4-203.9 2 (5%) 7 (8%) 1 (2%) 2 (13%) 6 (26%) 1 (4%) MAOA (309850) 4 (3%) 174.9, 6.4-431.7 1 (3%) 4 (5%) —— — 1 (4%) Chaperon deficiency DNAJC12 (#617384) 2 (1%) 143.9, 119.9-167.9 —2 (2%) —— —— Dopamine transporter deficiency DAT (#613135) 1 (1%) 8.9, 8.9-8.9 —1 (1%) —— —— Other amino acid metabolism disorders SSADH (#271980) 16 (11%) 55.7, 0.5-191.9 2 (5%) 14 (16%) 1 (2%) ——— NKH (#605899) 1 (1%) 3.9, 3.9-3.9 —1 (1%) —— —— Abbreviations: AADC, aromatic L-amino acid decarboxylase; adGTPCH, autosomal dominant GTP cyclohydrolase I; arGTPCH, autosomal recessive GTP cyclohydrolase I; DAT, dopamine transporter; DHPR, dihydropteridine reductase; MAOA, monoamine oxidase A; NKH, nonketotic hyperglycinemia; PTPS, 6-pyruvoyltetrahydropterin synthase; SR, sepiapterin reductase; SSADH, succinic semialdehyde dehydrogenase; TH, tyrosine hydroxylase. 1492 KELLER ET AL. 2.4 |Health-related QoL QoL was measured using the Pediatric Quality of Life Inventory Version 4 (PedsQL 4.0) for patients between the age of 2 and 18 years, 8,9 and the World Health Organization Quality of Life Project assessment tool WHOQoLBREF (Skevington et al 10 ) for patients age 18 and older. Both tests measure QoL as a multidimensional construct. The PedsQL consists of 23 items on a five-step Likert-scale and measures functioning and adaption in physical, emotional, social, and academic domains over the last month. The subscales can be grouped into the dimensions of physical health (8 items) and psychosocial health (15 items). The PedsQL is available as a self-report (used for age ≥18 years) and parent proxy report forms (used for ages from 8 to 18 years). The WHOQoL-BREF is a short form of the original WHOQoL-100. 11 It consists of 26 items on a five-step Likert-scale and measures QoL over the following dimensions: physical well-being, psychological health, social relationships, and environment over the 4 weeks preceding the completion of the questionnaire. 2.5 |Statistical analysis Descriptive statistics for IQ (mean and standard deviation [SD]) are reported for BH 4 deficiencies and primary NTDs. Analysis of variance (ANOVA) was used to estimate differences in a continuous variable (IQ and WHOQoL-BREF score) among NTD groups. ANOVA post hoc comparisons were computed using Tukey HSD contrasts. Two groups were compared with ttest with Welch correction when the response variable was continuous. A classification and regression tree (CART) analysis was used to perform supervised clustering of IQ values across all diagnoses. 12 This method uses a binary algorithm that divides patients over all diagnosis groups according to differences in IQ. The term binary refers to the fact that two groups at a time can arise from a subdivision. Count data from frequency tables were analyzed with log-linear models, and likelihood ratio test was used to assess statistical significance. Deviation between observed and expected frequency were interpreted by means of Pearson residuals. Resulting Pvalues are reported as *P≤.05, **P≤.01, ***P≤.001. 3|RESULTS 3.1 |Overall dataset metrics The study sample consists of a total of n =148 patients from the iNTD patient registry, in whom information about the IQ, QoL, or behavioral and emotional traits was entered. An overview of the diseases included in the data set of this study and the abbreviations used for them throughout the manuscript can be found in Table 1. We divided disorders according to their biochemical background or the main pathophysiological principle in five groups. The group of BH 4 deficiencies included ar/adGTPCH, PTPS, DHPR, and SR deficiency and the group of primary disorders of monoamine metabolism included AADC, tyrosine hydroxylase (TH), and MAOA deficiency. The remaining diseases are distributed into the groups of chaperone deficiencies (DNAJC12 deficiency), transportopathies (DAT deficiency), and amino acid disorders (nonketotic hyperglycinemia [NKH] and succinic semialdehyde dehydrogenase [SSADH] deficiency, see Table 1). Patients included in this study were primarily from Germany (n =30), Italy (n =24), Spain (n =20), the United States (n =13), and Turkey (n =12). A world map with the distribution of patients included in the analysis can be found in Figure 1. 3.2 |General IQ analysis clustering of IQ data (CART analysis) Data on the intellectual performance of 39 patients were available. A total of n =26 (67%) individuals were diagnosed with a BH 4 deficiency, whereas n =11 (28%) patients were diagnosed with a primary monoamine NTD, and one patient suffered from SSADH deficiency (3%) (Table 2). The IQ recorded at the last visit (mean age 16.5 years, range 3-42 years) was determined using WISC-IV IQ tests (n =16), WISC-V tests (n =2), WAISIV IQ tests (n =14), and WPPSI-III IQ tests (n =7). Sex was equally distributed (51.3% [n =20] female, 48.7% [n =19] male). With regard to sex, no significant difference in total IQ score (P=.51) between male (mean 73, SD ± 27, range 33-129) and female (66, SD ± 33, range 32-129) individuals was detected. The mean overall IQ was 70 and showed a wide dispersion across all disease groups (SD ± 29, range 32-129). There was no statistically significant difference in IQ score when performing ANOVA comparison over individual diseases. It was found that a higher age at diagnosis was associated with a slight trend toward higher IQ scores. However, when looking at the individual disorders, it is striking that a trend toward higher IQs at later diagnosis time points is only observable for AADC and TH deficiency, as well as for adGTPCH deficiency. Individuals with PTPS deficiency revealed a trend toward lower IQ scores at later age of diagnosis, whereas a large variability of IQ values was observed in the group of early diagnosed PTPS patients (Figure 2A,C). Across all diseases, a longer KELLER ET AL.1493 diagnostic delay was associated with a trend toward lower IQ scores, however, the length of diagnostic delay did not significantly affect the IQ score in our cohort (P=.78). Again, when looking at the individual diseases, this trend predominantly applied to individuals with AADC and arGTPCH deficiency. In contrast, a slight positive correlation between diagnostic delay and IQ was shown for PTPS deficiency, although in this case the diagnostic delay reached negative values due to possible identification of patients in newborn screening (Figure 2B,D). A decision tree model was used to explore the IQ data and find disease groups that differ considering IQ. We detected a first order binary separation of IQ values (Figure 3) distinguishing primary monoamine NTDs and BH 4 deficiencies. The mean full IQ score was lower in the group of primary monoamine NTDs (n =11; mean IQ 58, SD ± 20, range 40-100) compared to the group of BH 4 deficiencies (n =7; mean IQ 84, SD ± 29, range 40-129), with the exception of PTPS deficiency (n =14; mean IQ 65, SD ± 30, range 32-124). Patients with adGTPCH deficiency (n =5) had an overall clinically very mild course with predominantly motor involvement and a mean IQ of 99 (SD ± 30, range 48-129). To prevent any distortion of the data toward higher IQ scores in the group of BH 4 deficiencies, we decided to exclude patients with adGTPCH deficiency from the CART analysis. 3.3 |Behavior In total, data from n =129 behavioral questionnaires were analyzed, of which n =87 were completed by parents and n =42 by patients themselves. The disease distribution of the participants can be found in Table 1. FIGURE 1 Distribution pattern of patients with neurotransmitter related disease in this study. Number of identified affected individuals per country in this study, frequency is coded as a continuous variable in blue scales. The majority of patients originated from European countries TABLE 2 IQ over disease groups and individual diseases Group Affected enzyme (n) Mean IQ (±SD, range) BH 4 deficiencies arGTPCH (n =2) 93 (±25, 75-110) adGTPCH (n =5) 99 (±31, 48-129) PTPS (n =14) 65 (±30, 32-124) DHPR (n =3) 81 (±45, 40-129) SR (n =2) 79 (±9, 72-85) Primary disorders of monoamine metabolism AADC (n =8) 63 (±21, 40-100) TH (n =2) 42 (±2, 40-43) MAOA (n =1) 54 (NA) Other amino acid metabolism disorders SSADH (n =2) 42 (±13, 32-51) Abbreviations: AADC, aromatic L-amino acid decarboxylase deficiency; adGTPCH, autosomal dominant GTP cyclohydrolase I deficiency; arGTPCH, autosomal recessive GTP cyclohydrolase I deficiency; DHPR, dihydropteridine reductase deficiency; MAOA, monoamine oxidase A deficiency; PTPS, 6-pyruvoyltetrahydropterin synthase deficiency; SSADH, succinic semialdehyde dehydrogenase deficiency; SR, sepiapterin reductase deficiency; TH, tyrosine hydroxylase deficiency. 1494 KELLER ET AL. The specified behaviors were analyzed independently of their occurrence time as a symptom report. The behavioral trait most frequently mentioned by parents when reporting on their children across all disease groups was short attention (63.86%), followed by distractibility (59.52%). In the patient self-reports, anxiety was mentioned as the most common emotional problem (46.34%). Distractibility was likewise the second most frequently stated (40.0%). None of the patients reported pica, substance abuse, exaggerated sexual behavior, or stealing as typical behaviors in their self-report. The behavior documented by parents and self-reports are shown in Figure 4A,B. To obtain a more fine-grained representation of the data in relation to the underlying disease of the individual patients, we performed a contingency table analysis. Thus, we found that in parent reports, self-stimulatory behavior was observed with higher frequency in patients with SSADH deficiency and nonketotic hyperglycinemia (SSADH 9/17; NKH 2/2; likelihood ratio test P< .01). In the patient groups with DAT, DNAJC12, and MAOA deficiency we observed an increase in self-injurious or FIGURE 2 Influence of age at diagnosis and diagnostic delay on IQ. Summary of age at diagnosis (in months) (A) and diagnostic delay (in months) (B) on IQ for all diseases displayed individually in (C) and (D) respectively KELLER ET AL.1495 mutilating behaviors (DAT 1/1; DNAJC12 1/2; MAOA 2/4; likelihood ratio test P< .01). In patients with an arGTPCH or DAT deficiency we observed difficulties with food intake (arGTPCH 1/1; DAT 1/1; likelihood ratio test P=.03). Although statistical relevance of these results was detectable in the overall group comparison, FIGURE 3 CART analysis with response IQ and predictor disease. Unsupervised machine learning approach to cluster disease groups by IQ. Boxplots show lower and upper quartile with median marked as a horizontal line and whiskers showing the 1.5 times deviation of the upper and lower quantiles. Outliers are depicted as dots. Group 1 contains only primary NTDs, whereas group 3 non-PTPS-BH 4 deficiencies. Group 2 contains exclusively patients with PTPS deficiency and shows a heterogeneous distribution pattern of IQ values FIGURE 4 Behavioral traits in parental reports and patient self-reports. Percentage distribution of behavioral traits in parental reports (A) and patient self-reports (B) in neurotransmitter-related disorders as listed in Table 1 1496 KELLER ET AL. the very small number of patients must be considered when interpreting these results. In self-report analysis, patients with PTPS deficiency reported phobic fears with high frequency (6/11; likelihood ratio test P< .05). Patients with SR deficiency frequently suffered from communication problems (2/3; likelihood ratio test P< .01) and problems with the initiation of sleep (3/3; likelihood ratio test P< .05). 3.4 |Quality of life Data about QoL were available for n =23 patients and n=15 parents completing the PedsQL and n =26 patients completing the WHOQoL-BREF questionnaire. For some subcategories of the PedsQL, incomplete answer sheets were available from the participants, so that we refrained from forming sum scores during the evaluation. Due to inherent differences in theoretical concepts, we did separate analyses for the two instruments. Patients' assessment of their own QoL differs from that of their parents. Results from the PedsQL questionnaire showed that patients rated their QoL an average of 10 points higher compared to parental rating. Further analyses according to disease subgroups or an overarching biochemical clustering did not yield statistically significant differences. Analysis of the WHOQoL-BREF revealed significant differences in QoL in the three domains psychological well-being, social relationships, and environment between patients with a BH 4 deficiency and those suffering from a primary NTD. In all three domains, patients with a BH 4 deficiency reported significantly higher QoL. Only in the domain of physical health, no significant difference between the two patient groups was found. Table 3 presents the WHOQoL-BREF results on the four subdomains broken down by primary NTDs and BH 4 deficiencies. 4|DISCUSSION Our analysis of 148 patients of the iNTD registry explores neurotransmitter-related disorders through the dimensions of intelligence, behavioral phenotype, and QoL. Such analyses on large cohorts are particularly important in rare diseases to better understand the various aspects of these diseases, recognize them timely, and improve patient-care approaches. 4.1 |Intellectual functioning Neurotransmitters, in particular dopamine and serotonin, are significantly involved in tasks attributed to cognitive function and neurodevelopment. These include verbal and operational skills, working memory, and logical thinking, beyond the link between neurotransmitters and intelligence in general. 13-16 To date, only few studies on the cognitive abilities of patients with neurotransmitter diseases have been published, most of which are based on the analysis of singular diseases and rather small case series. 17-20 By analyzing the comprehensive iNTD registry data set, we were able to review a larger cohort of patients with a variety of underlying diseases. In alignment with previous studies, it was shown that the variability of intelligence within specific groups of conditions and individual diseases was high. 21,22 While the attempt to group diseases according to their biochemical background did not add further clarity to the picture, using the CART algorithm helped in revealing patterns that showed a difference in the mean values between a group of predominantly primary monoamine NTDs and BH 4 deficiencies. Patients with primary monoamine NTDs, which included TH, MAOA, and AADC deficiency showed lower IQ values when compared to patients with BH 4 metabolism defects when excluding adGPTCH and with the exception of PTPS deficiency. In the latter group, TABLE 3 WHOQOL-BREF results on four domains of BH 4 deficiencies and disorders of monoamine metabolism Domain Group n Mean WHOQOL-BREF score SD Range Pvalue Psychological BH 4 21 75.20 ±12.44 45-91.67 .03* Primary monoamine NTD 4 60.42 ±8.67 50-70.83 Social relationships BH 4 20 73.12 ±21.65 0-100 .04* Primary NTD 4 40.62 ±47.19 0-87.50 Environment BH 4 21 79.59 ±13.22 46.88-100 .01* Primary NTD 4 60.16 ±14.29 40.62-75.00 Physical health BH 4 21 72.90 ±12.29 46.43-89.29 .06 (n.s.) Primary NTD 4 59.67 ±11.61 46.43-70.83 Note: The significance of * is p < 0.5. KELLER ET AL.1497