Assessment of the effects of glutamic acid decarboxylase antibodies and trace elements on cognitive performance in older adults
Authors Alghadir A, Gabr S, S. Al-Eisa E
Received 8 September 2015
Accepted for publication 23 October 2015
Published 3 December 2015 Volume 2015:10 Pages 1901—1907
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Richard Walker
Ahmad H Alghadir,1 Sami A Gabr,1,2 Einas Al-Eisa1
1Department of Rehabilitation Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia; 2Department of Anatomy, Faculty of Medicine, Mansoura University, Mansoura, Egypt
Background: Homeostatic imbalance of trace elements such as iron (Fe), copper (Cu), and zinc (Zn) demonstrated adverse effects on brain function among older adults.
Objective: The present study aimed to investigate the effects of trace elements and the presence of anti-glutamic acid decarboxylase antibodies (GADAs) in human cognitive abilities among healthy older adults.
Methods: A total of 100 healthy subjects (65 males, 35 females; age range; 64–96 years) were recruited for this study. Based on Loewenstein Occupational Therapy Cognitive Assessment (LOTCA) score, the participants were classified according to cognitive performance into normal (n=45), moderate (n=30), and severe (n=25). Cognitive functioning, leisure-time physical activity (LTPA), serum trace elements – Fe, Cu, Zn, Zn/Cu, and GADAs were assessed using LOTCA battery, pre-validated physical activity (PA) questionnaire, atomic absorption, and immunoassay techniques, respectively.
Results: Approximately 45% of the study population (n=45) had normal distribution of cognitive function and 55% of the study population (n=55) had abnormal cognitive function; they were classified into moderate (score 62–92) and severe (score 31–62). There was a significant reduction in the level of Zn and Zn/Cu ratio along with an increase in the level of Fe, Cu, and anti-GADAs in subjects of severe (P=0.01) and moderate (P=0.01) cognitive performance. LOTCA-cognitive scores correlated positively with sex, HbA1c, Fe, Cu, Zn, and Zn/Cu ratio, and negatively with age, PA, body mass index, and anti-GADAs. Significant inter-correlation was reported between serum trace element concentrations and anti-GADAs which suggest producing a cognitive decline via oxidative and neural damage mechanism.
Conclusion: This study found significant associations among trace elements, anti-GADAs, and cognitive function in older adults. The homeostatic balance of trace elements should be recommended among older adults for better cognitive performance.
Keywords: LOTCA, trace elements, anti-GADAs, cognitive performance, older adults
Certain cognitive domains and physical inactivity were significantly associated with human aging.1,2 A number of cognitive processes, including attention, learning and memory, and executive control, were changed among older ages.3,4 A decline in cognitive abilities was shown to produce more drastic problems for older adults to perform their daily life activities.5
The severity and prevalence rate of cognitive decline depend mainly on various biological, social, and physiological factors such as lifestyle,6 social network,7,8 and various biomarkers,9 including oxidative stress and free radical damage.10,11 Among these potential markers, trace elements such as iron (Fe), copper (Cu), and zinc (Zn) are biologically essential metals, normally present in the brain.12,13 It was reported that less or excess accumulation of these metals in the brain may cause neurodegenerative disease and cognitive impairment.14–16
In older ages, the decline in brain function pathologically attributed with tissue damage, neural cell death,17,18 and significant changes in neural and enzymatic biomarkers such as glutamic acid decarboxylase (GAD),19 which modulates and synchronizes neural network activity in the central nervous system via impairment in the synthesis of γ-aminobutyric acid (GABA). Most research works suggested the association of glutamic acid decarboxylase antibodies (GADAs) in various neurological disorders including cognitive function by affecting the GABAergic system.20,21
Previously, it was reported that cognitive problems may be associated with dysfunction of the GABAergic system.22–24 However, it is not known whether cognitive decline appears as a result of neurological changes in the central nervous system associated with anti-GAD antibodies and/or trace elements. So, to study the role of anti-GAD antibodies and trace elements Fe, Cu, and Zn as risk factors in human cognitive abilities, we assessed these parameters and its association with cognitive function in healthy older adults.
Materials and methods
A total of 350 healthy subjects were subjected to randomized electoral roll selection. Out of them, only 100 healthy subjects (65 males, 35 females), aged 64–96 years with a mean age of 65.2±3.6 years, randomly participated in this study (Table 1). Subjects with endocrine, immune, psychiatric illness, eating disorders, and taking glucocorticoid medication that could interfere with cognitive ability measurements were excluded from this study. Based on the Loewenstein Occupational Therapy Cognitive Assessment (LOTCA) score, participants were classified according to their cognitive performance into normal (93–123; n=45), moderate (62–92; n=30), and severe (31–62; n=25). Standardized measures of weight and height were taken in light clothing and body mass index (BMI; in kg/m2) was calculated. All study participants gave informed consent prior to inclusion. This study was approved by ethical committee of Rehabilitation Research Chair of King Saud University, under file number (RRC-2014-016).
Assessment of cognitive abilities
Trained research assistants assessed the cognitive abilities of older adult’s pre- and post-supervised aerobic exercise using the LOTCA battery. Assessments required between 45 and 90 minutes. The LOTCA consists of seven major domains divided into 26 subtests, with each subtest scored on a 4- or 5-point Likert scale. The assessment of LOTCA test was performed according to instruction manuals as reported in the literature.25
Results are presented as a profile along all subtests. A composite score for each domain was calculated by summing the scores of the relevant subtests. The LOTCA score was calculated by summing the score of all subtests. The maximum score on the test is 123 and the minimum score is 27. A higher score indicates a better cognitive performance. Based on the LOTCA score, the cognitive performance of participants was classified into nrmal (93–123; n=45), moderate (62–92; n=30), and severe (31–62; n=25).
LOTCA test validity
The test has excellent intra-rater reliability (100%), and good inter-rater reliability (86%) as well as criterion validity (78%).26 This LOTCA test was chosen because of its psychometric properties and primarily non-verbal nature, making it potentially more suitable for evaluating the cognitive abilities of individuals from non-Western and non-English-speaking cultures. Several studies have been conducted using this instrument in both Western26 and Arab populations.2
Assessment of physical activity
Physical activity of the participants was assessed in relation to the time spent in performing moderate and intense exercise programs. The activity denoted as leisure-time physical activity (LTPA) was measured by metabolic equivalents as previously reported.27,28
Assessment of anti-GADAs
Serum anti-GAD antibody titers were measured using a commercially available ELISA kit (RSR cat GDE96, RSR Limited, Cardiff, UK), which provides a specific and sensitive method for evaluating GAD antibodies.
Assessment of serum trace elements
Levels of Fe, Cu, and Zn in the sera were determined using an atomic absorption spectrophotometer device (Varian AA240FS Model, Varian Inc., Belrose, Australia). The measurements were conducted twice for each sample, using light at 2,139 wavelength according to a flame atomization method. Levels of serum Fe, Cu, and Zn were determined as μg/dL.
Descriptive statistics were expressed as mean and standard deviation. Student’s t-test was used to compare groups. Spearman’s correlations were used to assess correlations between cognitive performance score and related biochemical and demographic factors. P-values <0.05 were considered to be significant. Statistical analysis was performed using SPSS version 17.
A total of 100 healthy subjects aged 64–96 years were subjected for cognitive ability measurements and estimation of biochemical related factors. Most of subjects (80%) participated in these study had attended some college or more. Statistical significant analysis of other demographic and cognitive function parameters is shown in Table 1. According to cognitive ability measurements, approximately 45% of the study population (n=45) had normal distribution of cognitive function with 111.2 mean LOTCA-7 score, and 55% of the study population (n=55) had abnormal cognitive function; they were classified into moderate (score 62–92), and severe (score 31–62) (Table 2).
Table 2 Cognitive performance in the studies population based on LOTCA-7 scores (n=100)
As shown, a significant increase in BMI (P=0.05), waist to hip ratio (WHR) (P=0.05), and glycemic control parameter; HbA1c (P=0.01) was reported in participants with abnormally distributed cognitive function as was the lack of physical fitness measured by leisure-time physical activity (LTPA) (P=0.01) as shown in Table 3.
The results showed a statistically significant difference in the level of serum concentrations of trace elements, anti-GADAs, and HbA1c%. There was a significant reduction in the level of Zn and Zn/Cu ratio along with an increase in the level of Fe, Cu, anti-GADAs, and HbA1c% in subjects of severe (P=0.01) and moderate (P=0.01) cognitive performance compared to normal control group (Table 3).
Based on the presence of anti-GADAs, the data obtained showed a significant reduction (P=0.01) in serum concentrations of Zn and Zn/Cu ratio along with a significant increase in the level of serum Cu, Fe, and HbA1c% in subjects with positive anti-GADAs compared to control negative cases as shown in Table 4.
Table 4 Serum trace elements concentrations in studied population based on anti-GADAs titers (n=100)
Correlation coefficients of the studied independent factors were estimated using a stepwise regression analysis. There was significant correlation among serum concentrations of trace elements, BMI, PA status, HbA1c, anti-GADAs, and LOTCA-cognitive score analyses in older adults. LOTCA-cognitive scores correlated positively with sex, HbA1c, trace elements; Fe, Cu, Zn, Zn/Cu ratio and negatively with age, PA, BMI, and anti-GADAs as shown in Tables 5 and 6.
More than 20% of cognitive decline was reported in older adults.29,30 Many research studies supported that age-related cognitive impairment may be associated with many demographic, physiological, and modifiable risk factors.8,9,31,32
The present study aimed to investigate the effects of trace elements and the presence of anti-GADAs in human recognition among healthy older adults.
In the present study, approximately 45% of participants had a normal distribution of cognitive function with 111.2 mean LOTCA-7 score and 55% of participants had abnormal scores of cognitive performance; they were classified into moderate (score 62–92; 30%), and severe (score 31–62; 25%).
There was statistical significant variability in the studied cognitive parameters such as motor praxis, vasomotor organization, thinking operations, attention, and concentration in older adults with moderate or severe cognitive impairment. Also, there was significant correlation among age, sex, HbA1c, BMI, PA status, and LOTCA-7 score in all stages of cognitive performance. LOTCA-7 cognitive score variables correlated positively with sex, HbA1c and negatively with age, BMI, WHR, and PA in participants with moderate and severe cognitive disorders compared with the control group. Previous research reports suggested the influence of life style and demographic parameters on cognitive function in older adults, whereas more than 20% of adult populations were suffering from cognitive disorder.30,33,34 Also, several research studies reported positive effects of physical activity on cognitive performance scores among older adults.2,35 In a study, Dunton et al36 reported improvement of cognitive function in older adults following physical activity. In the same manner, our results were inconsistent with other studies showing that the beneficial effects of PA in reducing the risks of cognitive impairment,37,38 as well as better processing speed, memory, and executive function.38 In agreement with other studies, our results showed that obesity-related variables and higher level of HbA1c were associated with a significant degree of cognitive decline among older adults.39–41
In the present study, there was a significant increase in the level of anti-GADAs in participants with a moderate and severe decline of cognitive function compared to healthy group. The results showed that LOTCA-cognitive scores correlated negatively with anti-GADAs in the participants with abnormal cognitive function.
The results of our study matched with other studies that reported the association of GADAs with various neurological disorders including cognitive function by affecting the GABAergic system.20,21 Similarly, Saidha et al23 and Black et al24 reported that cognitive problems may be associated with dysfunction of the GABAergic system.
Recently, it was reported that higher anti-GAD antibody levels are significantly related to more neurological disorders via stimulating anti-GAD autoimmunity which considers the potential cause of the cerebral involvement and cognitive decline.20,47
There was a significant association between trace elements and cognitive function in older adults of the present study. Participants with moderate and severe cognitive disorders showed significantly different concentrations of Fe, Cu, and Zn and different patterns of associations between these minerals and cognitive function scores. Analyses using trace minerals as continuous variables showed that higher concentrations of Fe, Cu, and lower concentrations of Zn and Zn/Cu ratio were greatly associated with poorer cognitive performance on tests of long-term memory, motor praxis, and vasomotor organization, thinking operations, attention and concentration compared to intermediate concentrations.
In previous study, positive relationship between cognitive function and dietary intake of Zn and Fe was reported among healthy elderly adults.48 In addition, copper intake was found to be a risk factor for cognitive decline in persons with high-saturated and trans-fat levels.49
Previous finding supported the association of plasma copper and cognitive score among elderly people of our study.50 The severity of copper on cognition depends on oxidative stress and neurotoxicity mechanism,51 especially in individuals with high-saturated or trans-fat levels.48,52
The homeostatic balance of Fe, Cu, and Zn is essential for optimized brain function.53,54 Whereas, the lower change in the levels of these mineral effects on cognitive abilities via poor neurotransmission,55,56 and impair neuropsychological function,57 and that higher levels produce impairment of brain function via oxidative stress and neuro-degeneration processes.53,58
In addition, Faber et al59 reported that the change in Zn and Cu is inversely related and the normal Zn-to-Cu ratio in children and adults is close to 1:1. In our study, a significant decrease in the Zn-to-Cu ratio was reported in participants with poorer cognitive performance. The lower Zn/Cu ratios may reflect total body Zn deficiency or accumulation of Zn-antagonistic toxic metals which effects on the metallothionein system.60
In the present study, there was a significant correlation between Fe, Cu, Zn concentrations and the level of anti-GADAs in participants with poorer cognitive performance. The increase in Cu and Fe levels is attributed with a significant increase in anti-GADAs which reflects the inhibition rate of GAD enzyme via oxidative stress mechanism including hydroxyl radicals.61 Also, it was reported that copper plays a significant role in oxidative attack on GAD enzyme through Fenton-like reactions.62,63
This study found significant associations among trace elements, anti-GADAs, and cognitive function in older adults. The homeostatic balance of trace elements should be recommended among older adults for better cognitive performance.
The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for funding this research through the research group No RGP-VPP-209.
The authors report no conflicts of interest in this work.
Middleton LE, Barnes DE, Lui L, Yaffe K. Physical activity over the life course and its association with cognitive performance and impairment in old age. J Am Geriatr Soc. 2010;58(7):1322–1326.
Josman N, Abdallah TM, Engel-Yeger B. Using the LOTCA to measure cultural and sociodemographic effects on cognitive skills in two groups of children. Am J Occup Ther. 2011;65:29–37
Grady C. The cognitive neuroscience of ageing. Nat Rev Neurosci. 2012;13(7):491–505.
Albert MS, DeKosky ST, Dickson D, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the national institute on aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 2011;7(3):270–279.
Royall DR, Palmer R, Chiodo LK, Polk MJ. Normal rates of cognitive change in successful aging: the freedom house study. J Int Neuropsychol Soc. 2005;11:899–909.
Stine-Morrow EAL, Parisi JM, Morrow DG, Park DC. The effects of an engaged lifestyle on cognitive vitality: a field experiment. Psychol Aging. 2008;23(4):778–786.
Atti AR, Forlani C, De Ronchi D, Palmer K, Casadio P, Dalmonte E. Cognitive impairment after age 60: clinical and social correlates in the “Faenza Project”. J Alzheimer’s Dis. 2010;21(4):1325–1334.
Whitson HE, Ansah D, Whitaker D, et al. Prevalence and patterns of comorbid cognitive impairment in low vision rehabilitation for macular disease. Arch Gerontol Geriatr. 2010;50(2):209–212.
Song F, Poljak A, Smythe GA, Sachdev P. Plasma biomarkers for mild cognitive impairment and Alzheimer’s disease. Brain Res Rev. 2009;61(2):69–80.
Lovell MA, Markesbery WR. Oxidative DNA damage in mild cognitive impairment and late-stage Alzheimer’s disease. Nucleic Acids Res. 2007;35(22):7497–504.
Butterfield DA, Sultana R. Redox proteomics identification of oxidatively modified brain proteins in Alzheimer’s disease and mild cognitive impairment: insights into the progression of this dementing disorder. J Alzheimer’s Dis. 2007;12(1):61–72.
Pratico D. Alzheimer’s disease and oxygen radicals: new insights. Biochem Pharmacol. 2002;63:563–567.
Dosunmu R, Wu J, Basha MR, Zawia NH. Environmental and dietary risk factors in Alzheimer’s disease. Expert Rev Neurotherap. 2007;7(7):887–900.
Silvestri L, Camaschella C. Apotential pathogenetic role of iron in Alzeimer’s Disease. J Cell Mol Med. 2008 May 1.
Lau FC, Shukitt-Hale B, Joseph JA. Nutritional intervention in brain aging: reducing the effects of inflammation and oxidative stress. Subcell Biochem. 2007;42:299–318.
Cole GM, Lim GP, Yang F, et al. Prevention of Alzheimer’s disease: Omega-3 fatty acid and phenolic anti-oxidant interventions. Neurobiol Aging. 2005;26(Suppl 1):133–136.
Garde E, Mortensen EL, Krabbe K, Rostrup E, Larsson HBW. Relation between age-related decline in intelligence and cerebral white-matter hyperintensities in healthy octogenarians: a longitudinal study. Lancet. 2000;356:628–634.
De Groot JC, De Leeuw FE, Oudkerk M, Gijn JV, Hofman A, Jolles J. Periventricular cerebral white matter lesions predict rate of cognitive decline. Ann Neurol. 2002;52:335–341.
Cobb SR, Buhl EH, Halasy K, Paulsen O, Somogyi P. Synchronization of neuronal activity in hippocampus by individual GABAergic interneurons. Nature. 1995;378:75–78.
Saiz A, Blanco Y, Sabater L, et al. Spectrum of neurological syndromes associated with glutamic acid decarboxylase antibodies: diagnostic clues for this association. Brain. 2008;131:2553–2563.
Ali F, Rowley M, Jayakrishnan B, Teuber S, Gershwin ME, Mackay IR. Stiff-person syndrome (SPS) and anti-GAD-related CNS degenerations: protean additions to the autoimmune central neuropathies. J Autoimmun. 2011;37:79–87.
Tinsley JA, Barth EM, Black JL, Williams DE. Psychiatric consultations in stiff-man syndrome. J Clin Psychiatry. 1997;58:444–449.
Saidha S, Murphy S, Ronayne A, McCarthy P, Hennessy MJ, Counihan T. Treatment of anti-glutamic acid decarboxylase antibody-associated limbic encephalitis with mycophenolate mofetil. J Neurol. 2010;257:1035–1038.
Black JL, Barth EM, Williams DE, Tinsley JA. Stiff-man syndrome. Results of interviews and psychologic testing. Psychosomatics. 1998;39:38–44.
Almomani F, Josman N, Almomani MO, et al. Factors related to cognitive function among elementary school children. Scand J Occup Ther. 2014;21:191–198.
Itzkovich M, Elazar B, Averbuch S. Loewenstein Occupational Therapy Cognitive Assessment (LOTCA) manual. 2nd ed. Printed in USA, Occupational Therapy Department, Loewenstein Rehabilitation Hospital. Israel: Maddac; 2000.
Bull FC, Maslin TS, Armstrong T. Global physical activity questionnaire (GPAQ): nine country reliability and validity study. J Phys Act Health. 2009;6:790–804.
Trinh OTH, Nguyen ND, van der Ploeg HP, DibleyMJ, Bauman A. Test-retest repeatability and relative validity of the Global Physical Activity Questionnaire in a developing country context. J Phys Act Health. 2009;6:S46–S53.
Schroder J, Kratz B, Pantel J, Minnemann E, Lehr U, Sauer H. Prevalence of mild cognitive impairment in an elderly community sample. J Neural Transm Suppl. 1998;54:51–59.
Unverzagt FW, Gao S, Baiyewu O, et al. Prevalence of cognitive impairment: data from the Indianapolis Study of Health and Aging. Neurology. 2001;57:1655–1662.
Vellas B, Lauque S, Andrieu S, et al. Nutrition assessment in the elderly. Curr Opin Clin Nutr Metab Care. 2001;4:5–8.
Ekmekcioglu C. The role of trace elements for the health of elderly individuals. Nahrung. 2001;45:309–316.
Glymour MM, Manly JJ. Lifecourse social conditions and racial and ethnic patterns of cognitive aging. Neuropsychol Rev. 2008;18:223–254. doi:10.1007/s11065-008-9064-z.
Zeki Al Hazzouri A, Haan MN, Osypuk T, Abdou C, Hinton L, Aiello AE. Neighborhood socioeconomic context and cognitive decline among older Mexican Americans: results from the Sacramento Area Latino Study on Aging. Am J Epidemiol. 2011;174:423–431.
Archer T, Garcia D. Physical exercise influences academic performance and well-being in children and adolescents. Int J School Cogn Psychol. 2014;1:e102.
Dunton GF, Huh J, Leventhal AM, et al. Momentary assessment of affect, physical feeling states, and physical activity in children. Health Psychol. 2014;33(3):255–263.
Woodard JL, Sugarman MA, Nielson KA, et al. Lifestyle and genetic contributions to cognitive decline and hippocampal structure and function in healthy aging. Curr Alzheimer Res. 2012;9(4):436–446.
Chang YK, Nien YH, Tasi CL, Etnier JL. Physical activity and cognition in older adults: the potential of Tai Chi Chuan. J Aging Phys Activity. 2010;18:451–472.
Jacobson AM, Musen G, Ryan CM, et al. Long-term effect of diabetes and its treatment on cognitive function. New Engl J Med. 2007;356(18):1842–1852.
Cukierman-Yaffe T, Gerstein HC, Williamson JD, et al. Relationship between baseline glycemic control and cognitive function in individuals with type 2 diabetes and other cardiovascular risk factors. Diabetes Care. 2009;32(2):221–226.
Reijmer YD, van den Berg E, Ruis C, Kappelle LJ, Biessels GJ. Cognitive dysfunction in patients with type 2 diabetes. Diabetes/Metab Res Rev. 2010;26(7):507–519.
Pizzi C, Manzoli L, Mancini S, Costa GM. Analysis of potential predictors of depression among coronary heart disease risk factors including heart rate variability, markers of inflammation, and endothelial function. Eur Heart J. 2008;29(9):1110–1117.
Sweileh WM, Abu-Hadeed HM, Al-Jabi SW, Zyoud SH. Prevalence of depression among people with type 2 diabetes mellitus: a cross sectional study in Palestine. BMC Public Health. 2014;14(1):163.
Maylor EA, Reimers S, Choi J, Collaer ML, Peters M, Silverman I. Gender and sexual orientation differences in cognition across adulthood: age is kinder to women than to men regardless of sexual orientation. Arch Sex Behav. 2007;36(2):235–249.
Tripathi R, Kumar K, Bharath S, Marimuthu P, Varghese M. Age, education and gender effects on neuropsychological functions in healthy Indian older adults. Dement Neuropsychol. 2014;8(2):148–154.
Petersen RC, Roberts RO, Knopman DS, et al. Prevalence of mild cognitive impairment is higher in men. The Mayo Clinic Study of Aging. Neurology. 2010;75(10):889–897.
Dalakas MC, Li M, Fujii M, Jacobowitz DM. Stiff person syndrome: quantification, specificity, and intrathecal synthesis of GAD65 antibodies. Neurology. 2001;57:780–784.
Ortega RM, Requejo AM, Andres P, et al. Dietary intake and cognitive function in a group of elderly people. Am J Clin Nutr. 1997;66:803–809.
Morris MC, Evans DA, Tangney CC, et al. Dietary copper and high saturated and trans fat intakes associated with cognitive decline. Arch Neurol. 2006;63:1085–1088.
Squitti R, Barbati G, Rossi L, et al. Excess of non ceruloplasmin serum copper in AD correlates with MMSE, CSF (beta)-amyloid, and h-tau. Neurology. 2006;67:76–82.
Huang X, Cuajungco MP, Atwood CS, et al. Cu(II) potentiation of Alzheimer abeta neurotoxicity. Correlation with cell-free hydrogen peroxide production and metal reduction. J Biol Chem. 1999;274:37111–37116.
Squitti R, Rossini PM, Cassetta E, et al. D-penicillamine reduces serum oxidative stress in Alzheimer’s disease patients. Eur J Clin Invest. 2002;32:51–59.
Cuajungco MP, Faget KY. Zinc takes the center stage: its paradoxical role in Alzheimer’s disease. Brain Res Brain Res Rev. 2003;41:44–56.
Thompson KJ, Shoham S, Connor JR. Iron and neurodegenerative disorders. Brain Res Bull. 2001;55:155–164.
Youdim MB, Ben Shachar D, Riederer P. Iron in brain function and dysfunction with emphasis on Parkinson’s disease. Eur Neurol. 1991;31(Suppl 1):34–40.
Turnlund JR. Copper. In: Shils M et al., editors. Modern Nutrition in Health and Disease. Baltimore: Williams & Wilkins; 1999:241–252.
Sandstead HH, Frederickson CJ, Penland JG. History of zinc as related to brain function. J Nutr. 2000;130:496S–502S.
Squitti R, Lupoi D, Pasqualetti P, et al. Elevation of serum copper levels in Alzheimer’s disease. Neurology. 2002;59:1153–1161.
Faber S, Zinn GM, Kern JC 2nd, Kingston HM. The plasma zinc/serum copper ratio as a biomarker in children with autism spectrum disorders. Biomarkers. 2009;14:171–180.
Aschner M, Syversen T, Souza DO, Rocha JBT. Metallothioneins: mercury species-specific induction and their potential role in attenuating neurotoxicity. Exp Biol Med. 2006;231:1468–1473.
Khan MW, Sherwani S, Khan WA, Ali R. Characterization of hydroxyl radical modified GAD65: a potential autoantigen in type 1 diabetes. Autoimmunity. 2009;42:150–158.
Li Q, Guo M, Xu X, et al. Rapid decrease of GAD 67 content before the convulsion induced by hyperbaric oxygen exposure. Neurochem Res. 2008;33:185–193.
Trigwell SM, Radford PM, Page SR, et al. Islet glutamic acid decarboxylase modified by reactive oxygen species is recognized by antibodies from patients with type 1 diabetes mellitus. Clin Exp Immunol. 2001;126:242–249.
This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.Download Article [PDF]