Cerebral glucose metabolism in adults with early treated classic phenylketonuria

Molecular Genetics and Metabolism 87 (2006) 272–277 www.elsevier.com/locate/ymgme

Brief communication

Cerebral glucose metabolism in adults with early treated classic phenylketonuria M.P. Wasserstein a,¤, S.E. Snyderman a, C. Sansaricq a, M.S. Buchsbaum b a

b

Departments of Human Genetics and Pediatrics, New York, NY 10029, USA Department of Psychiatry, Mount Sinai School of Medicine, New York, NY 10029, USA

Received 17 December 2004; received in revised form 20 June 2005; accepted 21 June 2005 Available online 15 December 2005

Abstract Classic phenylketonuria (PKU) is characterized by severe mental retardation in untreated individuals and mild neurocognitive abnormalities in some early treated adults. The exact biochemical mechanisms underlying this neurotoxicity remain undetermined. Several theories implicate abnormal cerebral energy utilization and alterations in biochemical pathways that involve glucose metabolism. This pilot study was undertaken to investigate whether 18F-deoxyglucose positron emission tomography (PET) is an eVective tool to study cerebral glucose metabolism in early treated PKU. After PET coregistration with SPGR MRI, relative glucose metabolic rates (rGMR) at the center of standard atlas positions was determined. Repeated measures MANOVA was used to assess regional metabolic diVerences, which were then correlated with age-speciWc and day-of-scan plasma phenylalanine and age. Patients with PKU in comparison to controls had decreased rGMR in cortical regions including the prefrontal, somatosensory, and visual cortices, and increased activity in subcortical regions including the striatum and limbic system. Day-of-scan phenylalanine correlated with abnormal activity in subcortical structures, and older age was associated with decreased activity in the prefrontal and visual cortices. The clinical signiWcance of these abnormalities of glucose metabolism in speciWc areas of the brain remains unknown. © 2005 Elsevier Inc. All rights reserved. Keywords: PKU; PET scan; Glucose metabolism; Brain

Introduction Classic phenylketonuria (PKU) is a panethnic, autosomal recessive disorder resulting from mutations in the gene encoding phenylalanine hydroxylase (EC 1.14.16.1) [1]. Untreated, PKU causes severe mental retardation that is preventable by the institution of a phenylalaninerestricted diet in early infancy. Although signiWcant advances have been made in understanding the genetic and enzymatic determinants of disease activity and anticipated treatment outcomes, there is still inadequate information about how excess phenylalanine causes brain damage.

*

Corresponding author. Fax: +1 212 860 3316. E-mail address: [email protected] (M.P. Wasserstein).

1096-7192/$ - see front matter © 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.ymgme.2005.06.010

A number of hypotheses have been proposed, including interference with protein synthesis, decreased cerebral concentrations of amino acids that share a transporter with phenylalanine, tyrosine deWciency with subsequent decreased synthesis of dopamine, and altered energy production in the brain. This last hypothesis has been explored in in vitro and in animal studies, which showed that phenylalanine has widespread eVects on glucose metabolism. Phenylalanine and its metabolites have been demonstrated to inhibit glucose uptake in the rat brain, inhibit the glycosylation of cytoskeletal proteins [2], inhibit pyruvate kinase, [3] and decrease Xux through the glycolytic pathyway [4]. In vivo studies with magnetic resonance spectroscopy have demonstrated phenylalanine-responsive abnormalities in cerebral energy metabolism [5]. To investigate whether altered glycolytic Xux and subsequent depressed energy metabolism play a role in

M.P. Wasserstein et al. / Molecular Genetics and Metabolism 87 (2006) 272–277

the neurocognitive abnormalities in PKU, in vivo cerebral glucose metabolism should be studied. Positron emission tomography scan using Xuorodeoxyglucose (FDG-PET) is a non-invasive method that can be used to measure regional glucose metabolic rate and has the advantage of high resolution and absolute quantitation. The study of in vivo cerebral glucose metabolism in patients with PKU has been limited to single case reports [6] or the investigation of white matter abnormalities in small numbers of patients [7]. The aim of this pilot study was to determine if FDG-PET can be used to detect abnormalities in brain glucose metabolism in the gray matter of early treated adults with classic PKU.

Methods Subjects Ten patients with classic PKU (three women, seven men; age range: 24–34 years) participated in this study. Twenty normal, age- and gender-matched subjects served as controls. The protocol was approved by the Mount Sinai School of Medicine Institutional Review Board. Voluntary, written informed consent was obtained from all participants prior to any study-related procedures. All PKU patients were diagnosed and placed on a phenylalanine-restricted diet within the Wrst 2 weeks of life after conWrmation of positive results from the New York State Newborn Screening Program. Patient 1 had seizures of unclear etiology as an infant, but had been seizure-free without medication since early childhood. All PKU patients remained on therapy without interruption and received routine clinical assessments and frequent measurements of plasma phenylalanine levels. Although the patients had continued on therapy, there were diVerences in the degree of control. Several had phenylalanine levels that exceeded the recommended therapeutic limits on a number of occasions. Lifelong phenylalanine levels were available for nine study participants (Table 1). Age at scan, intelligence quotients, and day-of-scan plasma phenylalanine levels are also shown in the Table 1.

273

Measurement of plasma phenylalanine Plasma phenylalanine was determined by ion exchange column chromatography on a Beckman System 6300 amino acid analyzer (single-column ion exchange chromatograph) [8]. PET methods FDG (185 MBq) was injected intravenously.During the 35-min FDG update period, participants were read standard instruction about a task analogous to the California verbal learning test (CVLT), which assesses frontal lobe function and memory [9], and is described in detail elsewhere [10]. PET scans were obtained using a GE 2048 head-dedicated scanner with measured resolution of 4.5 mm in plane and 5.0 mm axially. Fifteen slices of 1.5–3 m counts at 6.5 mm intervals were obtained in two sets of 15 slices each (axial Weld of view 105 mm) to cover the entire brain. Scans were reconstructed with a blank and a transmission scan using the Hann Wlter to a resolution of 3.15 mm, full width, half maximum (FWHM), to yield a matrix size of 256 £ 256, in plane Weld of view 230 mm, pixel size 0.898 mm. Uniform scanning parameters were maintained over the course of the study. A structural MRI using a GE signa horizon 1.5 T v 5.5 MRI scanner was obtained for image coregistration and morphometry. MRI were placed in standard Talairach and Tournoux [11] position using Matlab and the SPM package [41] and then the PET was coregistered to the MRI using our adaptation of the Woods algorithm [12]. An individually molded thermoplastic face mask was used for each scan to keep the head stationary during image acquisition and to assist in PET/MRI image coregistration. PET images were obtained in nanocuries/ pixel and standardized as rGMR by dividing each pixel by the mean value across the entire brain (deWned by axial brain edges from coregistered MRI and extending from the most dorsal circular slice to the most ventral slice which formed an approximate oval). While this limits interpretations of single structure absolute activity, this method has been used to investigate metabolic rates

Table 1 Individualized patient data Patient demographics

Patient ID 1

2

3

4

5

6

7

8

9

10

Age at scan (years) IQ Day-of-scan Phe Mean plasma Phe Birth to 12 years of age Over 12 years of age

26 82 17.1

23 84 17.6

27 119 17.5

30 97 25.8

30 79 19.2

29 124 6.8

35 74 18.1

24 105 22.0

33 109 26.4

31 115 19.0

6.2 15

5.6 11.8

6.3 9.6

6.5 7.8

9.6 19.5

8.5 12.1

9.5 16.8

7.7 NA

4.7 13.4

7.6 14.2

Phenylalanine (Phe) expressed in mg/dl. Normal phenylalanine levels in patients without PKU range from 0.5 to 1.4 mg/dl. NA: data not available.

274

M.P. Wasserstein et al. / Molecular Genetics and Metabolism 87 (2006) 272–277

in areas of the brain [13] in sleep [14], Parkinson’s disease [15], Alzheimer’s disease, [16] psychoactive drug response [17], and analyses of O15 blood Xow in behavioral studies. Similar to these studies, a 35 min uptake period was used. Relative glucose metabolism in the prefrontal cortex, limbic system, frontal and temporal lobes, striatum, and thalamus was studied in PKU patients and compared to that of normal, age- and gender-matched controls. Regions of interest were selected from the standard atlas of Talairach and Tournoux [11]. For Brodmann areas (BA), position of BA numerals or halfway between duplicate numerals, 5 mm from the cortical edge were used on z-levels in the center of each BA. The square region of interest (ROI) (3 £ 3 pixels) was centered on that coordinate in the MRI and the coordinate used to assess metabolic rate in the FDG-PET image using our application of the proportional system of the Talairach and Tournoux atlas [11]. An adjustment was made so that ROIs were moved closer to the centroid of the slice if the area fell partly outside the coregistered brain outline, which is possible in brains that are especially narrow in the x direction at 45° and 135°. This method is described in more detail in our earlier publications [13,18].

tions involving slice level, replicated or ROI adjacent in position were not followed up as they were not part of our hypothesis or were neuroanatomically not important. The reverse Talairach hypothesis-driven strategy was used for three reasons: (1) to minimize Type I statistical errors in evaluating large numbers of ROIs in both hemispheres through the use of multiway repeated measures ANOVA and a single F ratio test indicating the hypothesized diagnostic Group £ Region interaction; (2) to minimize Type II errors resulting from assessing small individual, potentially noisy ROIs; and (3) to provide standard and known brain atlas locations for replication. We also controlled Type I error by not discussing either main eVects or interactions that are not interpretable (e.g., main eVect of slice level across caudate and putamen measured at two multiple axial slice levels and for normals and PKU considered together) or peripheral to our interest. Our analysis was limited in power by the sample size (n D 30, 20 normals and 10 patients) and, therefore, it was especially important to test coordinates chosen in advance.

Statistical design

Memory performance

We chose brain regions symmetrically in the right and left hemispheres, and grouped in the frontal lobe, central gray (caudate, putamen, and thalamus), and limbic stystem (cingulate, hippocampus). This set of regions of interest was congruent with a multivariate analysis of variance with independent groups (normal, PKU) and repeated measures for hemisphere (right, left) and structure (e.g., caudate, putamen). In a typical analysis, a two group (normal, PKU) £ 2 region (e.g., putamen, thalamus) £ 2 hemisphere (right, left) repeated measures ANOVA design was applied to relative glucose metabolic rate (rGMR) data obtained from frontal, cingulate, striatal, and other regions. The striatal analysis consisted of two slice levels (z D +12, 4), three regions (caudate, putamen, and thalamus) and two hemispheres (right, left). This allows testing the hypothesis that the striatum diVered between normals and patients with a single test (main eVect of group). All statistical analyses involving repeated measures with more than two levels used Greenhouse–Geisser epsilon corrections to adjust probabilities for repeated measures F values where there were up to two-way interactions; our program yielded only Rao’s R for higher-order interactions. Uncorrected, corrected, and MANOVA degrees of freedom are reported. To detect the source of signiWcant interactions between group and hypothesized Brodmann area, we carried out ANOVA on each Brodmann area separately. Interac-

To validate the performance of our patients with that of PKU patients reported in published studies, CVLT results were compared to normal age- and sex-matched controls. Similar to an earlier study [19], PKU patients remembered fewer words correctly than normal volunteers (11.02 vs. 13.73; t D 3.5, p D 0.002) and depended less on semantic clues (6.11 vs. 8.65; t D 2.6, p D 0.02).

Results

Regional brain metabolic rate MANOVA of the prefrontal region and limbic system revealed that patients with PKU had lower metabolic rates than controls in the prefrontal cortex (BA 10, BA 44, and BA 47) and higher metabolic rates in the limbic areas [BA 24, BA 30, hippocampus, inferior cingulate (see Fig. 1)]; this diVerence was greater in frontal than limbic cortex (Group £ frontal–limbic system interaction, F D 12.75, df D 1,30, p D 0.0013; Group £ frontal– limbic £ region £ hemisphere interaction, F D 6.86, df D 2, 60, p D 0.0021; Huynh–Feldt, df D 1.69, 50.8, p D 0.0037). Substituting BA 11 (orbital Xoor) for BA 25 yielded a slightly greater eVect (F D 17.5, df D 1,30, p D 0.00022). Follow-up post hoc t tests indicated signiWcantly greater values for normals than patients for BA 10 (right hemisphere in normals: 0.96, sd D 0.21 vs. patients: 0.76, sd D 0.20, t D 3.69, p D 0.00088; left hemisphere in normals: 0.99, sd D 0.20, vs. patients: 0.80, sd D 0.18, t D 2.53, p D 0.016), and lower values for nor-

M.P. Wasserstein et al. / Molecular Genetics and Metabolism 87 (2006) 272–277

275

Day-of-scan versus lifelong phenylalanine Higher phenylalanine level on the day of the PET scan correlated with lower activity in the left thalamus (r D ¡0.68, p D 0.031), and higher activity in the globus pallidus (r D 0.6764, p D 0.032) and secondary motor cortex (r D 0.6698, p D 0.034). Higher lifelong phenylalanine correlated with lower activity in the prefrontal cortex (BA 10, ¡0.7892, p D 0.02; BA 45, ¡0.7319, p D 0.039), the secondary visual system (BA 18, ¡0.8058, p D 0.016), the somatosensory cortex (BA 39, ¡0.7447, p D 0.034) and the limbic system (BA 21, ¡0.7476, p D0.33). Higher lifelong phenylalanine correlated with higher activity in the thalamus (r D 0.7105, p D 0.048) and the globus pallidus (0.8461, p D 0.008). InXuence of age on regional brain metabolic rate Fig. 1. Representative FDG PET in patient and normal control. Note relatively more active cingulate gyrus that dorsolateral prefrontal cortex in patient and more active frontal cortex than cingulate in typical normal.

mals than patients in the right hippocampus (0.72, sd D .0.09 vs. 0.82, t D 2.29, p D 0.029). MANOVA of the frontal and temporal lobe demonstrated that patients with PKU had lower relative metabolic rates than controls in the dorsolateral area of the prefrontal cortex (BA 10, BA 47) and lateral temporal lobe (BA 21,BA 22) (main eVect of group; F D 7.15, df D 1,30, p D 0.012) and this diVerence was more marked for the left frontal lobe BA 47 but unapparent for the left temporal lobe BA 22 (group £ lobe £ BA £ hemisphere interaction, F D 10.6, df D 1,30, p D 0.0028). MANOVA of the striatum and thalamus with group (patient, normal) and repeated measures for anatomical region (caudate, putamen, and thalamus), dorsoventral slice levels (dorsal and ventral) and hemisphere (right, left), showed that patients with PKU had higher relative metabolic rate bilaterally in the putamen, the right thalamus, and the left caudate, and lower rates in the right caudate versus controls (group by region by hemisphere interaction, F (2, 0.60) D 6.43, p D 0.0029; MANOVA Wilks, df D 2,29, p D 0.0071). The use of the memory task prompted examination of the hippocampus. The right hippocampus was signiWcantly lower in normals than patients (0.72, sd D 0.094 vs. 0.81, sd D 0.13, t D 2.28, p D 0.022) but the diVerence in the left hippocampus was not signiWcant (0.68, sd D 0.13, vs. 0.75, sd D 0.13, t D 1.54, p D 0.13). The hippocampus and amygdala examined together showed asymmetrical change with the right side higher in normals than patients in the amygdala and the reverse on the left side (group £ structure by hemisphere interaction, F D 4.94, df D 1,30, p D 0.034); the main eVect of subject group was not signiWcant (F D 2.13, df D 1,30, p D 0.15) and the group £ structure interaction was only a trend (F D 3.99, df D 1,30, p D 0.054).

SigniWcant correlations between age and regional brain metabolic rate were restricted to the patient group. Older age was associated with decreased relative metabolism in the prefrontal cortex (patients: BA 10, r D ¡0.704 vs. r D ¡0.08 in normals; patients: BA 47, r D ¡0.675 vs. r D 0.09 in normals), temporal lobe (patients: BA 21, r D ¡0.652 vs. r D 0.53 in normals) primary and secondary visual cortices (patients: BA 19, r D ¡0.652 vs. r D 0.09 in normals), and increased activity in the limbic system (patients: BA 24, r D 0.642 vs. r D ¡13 in normals).

Discussion FDG-PET demonstrated that regional gray matter glucose metabolism in adults with early treated PKU was signiWcantly diVerent from healthy controls. In general, hypometabolism was found in the cortical regions, including the prefrontal, somatosensory, and visual cortices. Hypermetabolism was found in dopaminergic and subcortical regions including the striatum and limbic system. These changes correlated more with lifelong phenylalanine level than with day-of-scan phenylalanine. The NIH consensus statement on the management of patients with PKU recommends maintaining phenylalanine levels between 2 and 6 mg/dl from birth to 12 years of age, and from 2 to 15 mg/dl after 12 years of age, with 2–10 mg/dl being ideal after 12 years of age [20]. Half of the study subjects maintained the NIH recommended levels during the Wrst 12 years, while eight had recommended values after 12 years. These are average values which obscure the variations that can occur. This is apparent in the day-of-scan levels, when only one patient had a phenylalanine level in the acceptable range. The phenylalanine restricted diet is very diYcult to maintain, and many adolescents and adult patients either liberalize or discontinue it [21,22].

276

M.P. Wasserstein et al. / Molecular Genetics and Metabolism 87 (2006) 272–277

Neurological abnormalities have been described in early treated adult PKU patients, including impaired prefrontal-dependent tasks [23–27], motor abnormalities [28–31], abnormal visual evoked potentials [32,33], and emotional disturbances [34–37]. It is possible to hypothesize correlations between these neurological abnormalities and the regional glucose metabolic abnormalities found in the prefrontal cortex, striatum and somatosensory cortex, visual cortices, and limbic system, respectively. However, such correlations are premature. As this was a pilot study to assess whether PET could detect gray matter abnormalities in PKU patients, psychological and neurological evaluations were not performed. The data from these studies are necessary before any conclusions about the clinical signiWcance of these Wndings can be drawn. Older patients with PKU were more likely than younger PKU patients to have abnormal glucose metabolism in the prefrontal cortex, visual cortex, and limbic system. Aging in normal adults may be associated with relative frontal hypometabolism [38]; however, recent studies have suggested that this is due to brain atrophy rather than decreased metabolism [39]. Any signiWcant age-related decline in function or volume would not be expected in the age range (23–35 years) of the PKU patients. However, the PKU patients, unlike the controls, showed the signiWcant frontal and temporal decreases observed in normal subjects over much greater age ranges [40]. This suggest that the PKU brain may be more vulnerable to the eVects of aging than the normal brain. Based on these preliminary results, the eVect of aging on cerebral glucose metabolism in patients with PKU requires further study. In summary, we report regional cerebral glucose abnormalities in adult patients with early treated PKU. DeWning the signiWcance of these abnormalities may be helpful in instituting more precise dietary recommendations, especially for older patients.

[3]

[4]

[5]

[6]

[7]

[8] [9] [10]

[11] [12] [13]

[14]

[15]

Acknowledgments

[16]

These studies were supported by a Grant (5 MO1 RR00071) for the Mount Sinai General Clinical Research Center from the National Center for Research Resources. M.P.W. was supported in part by Mount Sinai Child Health Research Grant 5 P30 HD 28822. MSB was supported in part by MH 60023.

[17]

[18]

References [1] C.R. Scriver, S. Kaufman, Hyperphenylalanimemia: phenylalanine hydroxylase deWciency, in: 8th ed., C.R. Scriver, A.L. Beaudet, W.S. Sly, D. Valle (Eds.), The Metabolic and Molecular Bases of Inherited Disease, McGraw-Hill, New York, 2001, pp. 1667–1724. [2] R.I. Glazer, G. Weber, The eVects of phenylpyruvate and hyperphenylalaninemia on incorporation of [6-3H]glucose into macro-

[19]

molecules of slices of rat cerebral cortex, J. Neurochem. 18 (1971) 2371–2382. G. Weber, Inhibition of human brain pyruvate kinase and hexokinase by phenylalanine and phenylpyruvate: possible relevance to phenylketonuric brain damage, Proc. Natl. Acad. Sci. USA 63 (1969) 1365–1369. R.I. Glazer, G. Weber, The eVects of L-phenylalanine and phenylpyruvate on glycolysis in rat cerebral cortex, Brain Res. 33 (1971) 439–450. J. Pietz, A. Rupp, F. Ebinger, D. Rating, E. Mayatpek, C. Boesch, R. Kreis, Cerebral energy metabolism in phenylketonuria: Wndings by quantitative in vivo 31P MR spectroscopy, Pediatr. Res. 53 (2003) 654–662. K. Yanai, K. Iinuma, T. Matsuzawa, M. Ito, S. Miyabayashi, K. Narisawa, T. Ido, K. Yamada, K. Tada, Cerebral glucose utilization in pediatric neurological disorders determined by positron emission tomography, Eur. J. Nucl. Med. 13 (1987) 292–296. S. Hasselbalch, G.M. Knudsen, P.B. Toft, P. Høgh, E. Tedeschi, S. Holm, C. Videbæk, O. Henriksen, H.C. Lou, O. Paulson, Cerebral glucose metabolism is decreased in white matter changes in patients with phenylketonuria, Pediatr. Res. 40 (1996) 21–24. K.A. Piez, L. Morris, A modiWed procedure for the automatic analysis of amino acids, Anal. Biochem. 1 (1960) 187–201. D. Delis, J. Kramer, E. Kaplan, B. Ober, The California Verbal Learning Test, Psychological Corporation, San Antonio, 1987. E. Hazlett, M. Buchsbaum, R. Mohs, J. Spiegel-Cohen, T.C. Wei, R. Azueta, M.M. Haznedar, M.B. Singer, L. Shihabuddin, C. LuuHsia, P.D. Harvey, Age-related shifts in brain region activity during successful memory performance, Neurobiol. Aging 19 (1998) 437–445. J. Talairach, P. Tournoux, Co-planar Stereotaxic Atlas of The Human Brain, Stuttgart, Thieme, 1988. R.P. Woods, J.C. Mazziotta, S.R. Cherry, MRI-PET registration with automated algorithm, J. Comput. Assist. Tomogr. 17 (1993) 536–546. M.H. Tabert, J.C. Borod, C.Y. Tang, G. Lange, T.C. Wei, R. Johnson, A.O. Nusbaum, M.S. Buchsbaum, DiVerential amygdala activation during emotional decision and recognition memory tasks using unpleasant words: an fMRI study, Neuropsychologia 39 (2001) 556–573. A. Germain, D.J. Buysse, A. Wood, E. Nofzinger, Functional neuroanatomical correlates of eye movements during rapid eye movement sleep in depressed patients, Psychiatry Res. 130 (2004) 259–268. A. Nagano-Saito, Y. Washimi, Y. Arahata, K. Iwai, S. Kawatsu, K. Ito, A. Nakamura, Y. Abe, T. Yamada, T. Kato, T. Kachi, Visual hallucination in Parkinson’s disease with FDG PET, Mov. Disord. 19 (2004) 801–806. S.J. Teipel, F. Willoch, K. Ishii, K. Burger, A. Drzezga, R. Engel, P. Bartenstein, H.J. Moller, M. Schwaiger, H. Hampel, Resting state glucose utilization and the CERAD cognitive battery in patients with Alzheimer’s disease, Neurobiol. Aging 2005 May 20; [Epub ahead of print]. A.S. New, M.S. Buchsbaum, E.A. Hazlett, M. Goodman, H.W. Koenigsberg, J. Lo, L. Iskander, R. Newmark, J. Brand, K. O’Flynn, L.J. Siever, Fluoxetine increases relative metabolic rate in prefrontal cortex in impulsive aggression, Psychopharmacology (Berl.) 176 (2004) 451–458. A.S. New, E.A. Hazlett, M.S. Buchsbaum, M. Goodman, D. Reynolds, V. Mitropoulou, L. Sprung, R.B. Shaw Jr., H. Koenigsberg, J. Platholi, J. Silverman, L.J. Siever, Blunted prefrontal cortical 18 Xuorodeoxyglucose positron emission tomography response to meta-chlorophenylpiperazine in impulsive aggression, Arch. Gen. Psychiatry 59 (2002) 621–629. D.A. White, M.J. Nortz, T. Mandernach, K. Huntington, R.D. Steiner, DeWcits in memory strategy use related to prefrontal dysfunction during early development: evidence from children with phenylketonuria, Neuropsychology 15 (2001) 221–229.

M.P. Wasserstein et al. / Molecular Genetics and Metabolism 87 (2006) 272–277 [20] Phenylketonuria: screening and management, NIH Consensus Statement Online 2000 October 16–18; 17(3): 1–27. [21] R. Koch, B. Burton, G. Hoganson, R. Peterson, W. Rhead, B. Rouse, R. Scott, J. WolV, A.M. Stern, F. Guttler, M. Nelson, F. de la Cruz, J. Coldwell, R. Erbe, M.T. Geraghty, C. Shear, J. Thomas, C. Azen, Phenylketonuria in adulthood: a collaborative study, J. Inherit. Metab. Dis. 25 (2002) 333–346. [22] H. Walter, F.J. White, S.K. Hall, A. MacDonald, G. Rylance, A. Boneh, D.E. Francis, G.J. Shortland, M. Schmidt, A. Vail, How practical are recommendations for dietary control in phenylketonuria, Lancet 360 (2002) 55–57. [23] L. Brunner, M.K. Jordan, H.K. Berry, Early treated phenylketonuria: neuropsychologic consequences, J. Pediatr. 102 (1983) 831–835. [24] B.F. Pennington, W.J. Van Doorninck, L.L. McCabe, E.R. McCabe, Neuropsychological deWcits in early treated phenylketonuric children, Am. J. Ment. DeWc. 89 (1985) 467–474. [25] M.C. Welsh, B.F. Pennington, S. OzonoV, B. Rouse, E.R. McCabe, Neuropsychology of early treated phenylketonuria: speciWc executive function deWcits, Child. Dev. 61 (1990) 1697–1713. [26] M.C. Welsh, A prefrontal dysfunction model of early-treated phenylketonuria, Eur. J. Pediatr. 55 (Suppl. 1) (1996) S87–S89. [27] A. Diamond, M.B. Prevor, G. Callender, D.P. Druin, Prefrontal cortex cognitive deWcits in children treated early and continuously for PKU, Monogr. Soc. Res. Child. Dev. 62 (1997) 4. [28] G.V. McDonnell, T.F. Esmonde, D.R. Hadden, J.I. Morrow, A neurological evaluation of adult phenylketonuria in Northern Ireland, Eur. Neurol. 39 (1998) 38–43. [29] J. Pietz, R. Dunckelmann, A. Rupp, D. Rating, H.M. Meinck, H. Schmidt, H.J. Bremer, Neurological outcome in adult patients with early-treated phenylketonuria, Eur. J. Pediatr. 157 (1998) 824–830. [30] A.J. Thompson, I. Smith, D. Brenton, B.D. Youl, G. Rylance, D.C. Davidson, B. Kendall, A.J. Lees, Neurological deterioration in young adults with phenylketonuria, Lancet 335 (1990) 602–605. [31] D. Villasana, I.F. Butler, J.C. Williams, S.M. Roontga, Neurological deterioration in adult phenylketonuria, J. Inherit. Metab. Dis. 12 (1989) 451–457.

277

[32] V. Leuzzi, S. Rinalduzzi, F. Chiarotti, P. Garzia, G. Trasimeni, N. Accornero, Subclinical visual impairment in phenylketonuria. A neurophysiological study (VEP-P) with clinical, biochemical, and neuroradiological (MRI) correlations, J. Inherit. Metab. Dis. 21 (1998) 351–364. [33] S. Beblo, B.S. Reinhardt, A.C. Muntau, W. Mueller-Felber, A.A. Roscher, B. Koletzko, Fish oil supplementation improves visual evoked potentials in children with phenylketonuria, Neurology 57 (2001) 1488–1491. [34] J. Pietz, M.A. Fatkenheuer, P. Burgard, M. Armbruster, G. Esser, H. Schmidt, Psychiatric disorders in adult patients with early treated phenylketonuria, Pediatrics 99 (1997) 345–350. [35] S.E. Waisbren, J. ZaV, Personality disorder on young women with treated phenylketonuria, J. Inherit. Metab. Dis. 17 (1994) 584–592. [36] J. Pietz, C. Benninger, H. Schmidt, D. ScheVner, H. Bickel, Long term development of intelligence (IQ) and EEG in 34 children with phenylketonuria treated early, Eur. J. Pediatr. 147 (1988) 361–367. [37] R. Koch, B. Burton, G. Hoganson, R. Peterson, W. Rhead, B. Rouse, R. Scott, J. WolV, A.M. Steren, F. Guttler, M. Nelson, F. delaCruz, J. Coldwell, R. Erbe, M.T. Geraghty, C. Shear, J. Thomas, C. Azen, Phenylketonuria in adulthood: a collaborative study, J. Inher. Metab. Dis. 25 (2002) 333–346. [38] J.R. Moeller, T. Ishikawa, V. Dhawan, P. Spetseris, F. Mandel, G.E. Alexander, C. Grady, P. Pietrini, D. Eidelerg, The metabolic topography of normal aging, J. Cereb. Blood Flow Metab. 16 (1996) 385–398. [39] V. Ibanez, P. Pietrini, M.L. Furey, G.E. Alexander, P. Millet, A.L. Bokde, D. Teichberg, M.B. Schapiro, B. Horwitz, S.I. Rapoport, Resting state brain glucose metabolism is not reduced in normotensive healthy men during aging, after correction for brain atrophy, Brain Res. Bull. 63 (2004) 147–154. [40] M.W. Willis, T.A. Ketter, T.A. Kimbrell, M.S. George, P. Herscovitch, A.L. Danielson, B.E. Benson, R.M. Post, Age, sex and laterality eVects on cerebral glucose metabolism in healthy adults, Psychiatry Res. 114 (2002) 23–37. [41] K.J. Friston, J. Ashburner, C.D. Frith, J.B. Poline, J.D. Heather, R.S.J. Frackowiak, Spatial registration and normalization of images, Hum. Brain. Mapp. 2 (1995) 165–189.