Genetic Polymorphisms Related to Delirium Tremens: A Systematic Review
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SOURCE, OR PART OF THE FOLLOWING SOURCE: Type Dissertation Title Pathophysiological studies in delirium : a focus on genetics Author B.C. van Munster Faculty Faculty of Medicine Year 2009 Pages 149
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Pathophysiological studies in delirium, a focus on genetics
Pathophysiological studies in delirium, a focus on genetics. PhD thesis, University of Amsterdam, The Netherlands © B.C. van Munster, Amsterdam, The Netherlands 2009 All rights reserved. No part of this thesis may be reproduced or transmitted in any form or by any means without prior permission of the author. A digital version of this thesis can be found at www.dare.uva.nl Lay‐out: J.W. Broek Cover: Professor V. Cejka Printed by: Buijten & Schipperheijn The printing of this thesis is financially supported by: Alzheimer Nederland, Internationale Stichting Alzheimer Onderzoek, Stichting tot bevordering van de Klinische Epidemiologie, Eli Lilly Nederland, and Boehringer Ingelheim bv.
Pathophysiological studies in delirium, a focus on genetics ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus prof. dr. D.C. van den Boom ten overstaan van een door het college voor promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel op vrijdag 15 mei 2009, te 12.00 uur door Barbara Charlotte van Munster geboren te Amstelveen
Promotiecommissie Promotores: Co‐promotores: Overige leden:
Prof. dr. M.M. Levi Prof. dr. A.H. Zwinderman
Dr. J.C. Korevaar Dr. S.E.J.A. de Rooij
Prof. dr. F. Baas Prof. dr. W.A. van Gool Prof. dr. R.J. de Haan Prof. dr. E. de Jonge Prof. dr. R.C. van der Mast Prof. dr. R.G.J. Westendorp
Faculteit der Geneeskunde
CONTENTS Chapter 1 Chapter 2
Chapter 3
Chapter 4 Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10 Chapter 11 Chapter 12
General introduction Proteomic profiling of plasma and serum of elderly patients with delirium Markers of cerebral damage during delirium in elderly patients with hip fracture Serum S100B in elderly medical patients with and without delirium Time‐course of cytokines during delirium in elderly patients with hip fracture Genetic polymorphisms related to delirium tremens: a systematic review Genetic polymorphisms in the DRD2, DRD3 and SLC6A3 gene in elderly patients with delirium The association between delirium and the apolipoprotein E epsilon 4 allele in the elderly The association between delirium and the APOE epsilon 4 allele, new study results and a meta‐analysis General discussion and conclusions Summary Summary in Dutch (Samenvatting) References Publications Acknowledgements (Dankwoord)
7 13 27 37 47 59 71 87 99 109 119 125 131 145 147
General introduction
Chapter 1
Delirium Delirium is an important neuropsychiatric syndrome with frequencies in the elderly population of up to 56% during hospital admission125. The prevalence is approximately 0.4% in the total population and 1.1% in persons above 54 years52. It is defined by a fluctuating consciousness and an acute change in cognition or a perceptual derangement7. Three clinical subtypes of delirium (hyperactive, hypoactive, and mixed subtype) are distinguished based on the symptoms present124. Delirium can be precipitated by any somatic factor, which includes a variety of different illnesses, surgery, or substance (medication) intoxication or withdrawal. Predisposing factors are higher age and cognitive and functional impairment, among others98. Though patients usually recover after treatment of the precipitating factor, delirium is independently associated with an increase in mortality, impaired physical and cognitive recovery, and increased hospital costs, estimated at $2,500 per patient76. Since the number of elderly people with greater risk for delirium continues to grow, the absolute number of patients with delirium and associated problems can be expected to rise as well. Delirium recognition rates are low (12–43%), and its management remains consequently inadequate in up to 80% of the patients125. This is the main reason the Dutch Health Inspection has decided in 2006 to make the proper management of delirium one of the indicators for the quality of care in Dutch hospitals193. The diagnosis of delirium has to be made by an experienced clinician with use of a classification scale like the Diagnostic and Statistical Manual of Mental Disorders7 or the International Classification of Diseases123. Missing the diagnosis is often related to the manifestation of the syndrome125. First, symptoms develop acutely, and the duration of delirium is limited to a few days if the precipitating factor is adequately treated. Therefore, the time frame for making an adequate diagnosis is short195. Second, symptoms of delirium fluctuate over time; because symptoms are mostly present during the night, the patient has to be closely observed for 24 hours. Finally, the hypoactive subtype, with its absence of overt distress or disturbance, is especially likely to be overlooked79. Apart from failure to recognize the symptoms, misdiagnosis is a considerable problem since depression and dementia are important differential diagnoses for hypoactive delirium. Hyperactive and mixed subtype of delirium may falsely be diagnosed as functional psychosis, dementia, (hypo)mania, anxiety disorders, or akathisia124.
The pathophysiology of delirium Despite growing interest in delirium in elderly patients, relatively little studies have attempted to elucidate the pathophysiology of delirium. This may be partly attributable to specific methodological issues related to these types of studies. However, pathophysiological studies are urgently required to make any further progress in finding markers for early recognition and to improve treatment. A number of hypotheses have been put forward in an attempt to explain the pathophysiological processes leading to the development of delirium118‐120,186. Several
8
General introduction
theories describe involvement of different systems in the brain, such as the (1) ‘neurotransmitter’, (2) ‘inflammatory’, (3) ‘physiological stress’, (4) ‘cellular‐signaling’, (5) ‘oxygen supply’, or (6) ‘sleep‐wake cycle’ system. The burden of proof for the diverse hypotheses varies from almost hypothetical (the role of melatonin) till fairly proven (dysbalance between dopamine and acetylcholine activity in the brain). 1) The most widely propagated theory centers on the neurotransmitter system. This theory states that relative acetylcholine deficiency and dopamine excess could mediate the characteristic symptoms of delirium186. This is supported on the one hand by that fact that delirium can be evoked by dopamine agonists and anticholinergic medication and on the other hand because delirium can be successfully treated with dopamine receptor antagonists and probably also by cholinesterase inhibitors5. 2) Proinflammatory cytokines are known to contribute to the development of sickness behavior. This syndrome is characterized by symptoms overlapping with delirium and can be induced by a wide variety of clinical conditions just like delirium152. 3) There is some evidence that dysregulation of the limbic–hypothalamic–pituitary– adrenal axis, with pathologically sustained high levels of cortisol occurring with acute stress, can precipitate and/or sustain delirium118. 4) The “cellular‐signaling hypothesis” suggests that more fundamental processes like intraneuronal signal transduction may be disturbed, thereby affecting neurotransmitter synthesis and release119. 5) The “oxygen deprivation hypothesis” proposes that decreased oxidative metabolism in the brain causes cerebral dysfunction because of abnormalities in various neurotransmitter systems119. 6) Disruption of the sleep–wake cycle is an important characteristic of delirium. Melatonin, a hormone involved in the circadian rhythm, could be responsible for the disturbance in this system110. Several case studies have shown a difference in melatonin secretion in patients with delirium compared to patients without delirium110,176. Since many of the above systems interact, these theories are probably not mutually exclusive. On the contrary, since the syndrome of delirium is the result of a wide variety of combinations of predisposing and precipitating factors, the concept of a final common pathway seems to be the most plausible186. The candidate system for this final common pathway, most widely supported by the existing evidence, is the neurotransmitter system. The suggested pathophysiological mechanisms may differ within the diverse subtypes of delirium.
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Chapter 1
Genetics in delirium Genetics, the science of heredity and variation, is a promising field of research for delirium. Genetic research offers new possibilities in unraveling the pathophysiological mechanisms lacking in conventional research methods. Genetic markers can be easily determined in DNA obtained from somatic cells, e.g. white blood cells. The second opportunity of genetic research is to identify patients potentially at high risk for delirium. The identification of genetic risk factors for delirium would permit individual patients prone to develop delirium to be identified in advance. Preventive geriatric interventions could then be undertaken. Additionally, delirium would be missed less frequently and patients could be treated at an early stage. Moreover, the possible identification of genetic variations also provides an approach for adjusting pharmacotherapy at the individual level. Candidate gene association studies are best equipped to study genetics in delirium. This type of studies can test the effect of genetic variants of a potential contributing gene (the candidate gene) in unrelated cases and controls. Most genes contain many known DNA sequence variations called single nucleotides polymorphisms (SNPs). The most interesting variations for association studies are functional SNPs, which can influence the trait of interest by producing proteins with an altered structure, function, or concentration.
Aim Studies on genetics in delirium in elderly patients have been scarce until now and first results were just published in 2007, while the field of genetics had already acquired a prominent place at that time in pathophysiological research in other psychiatric disorders like dementia, schizophrenia, and depression. Using new high‐throughput technology, genetic research has identified several common variations in the human genome as being associated with these psychiatric disorders212. The aim of the research in this thesis was to explore several pathophysiological factors of delirium in elderly patients and especially the role of genetic factors.
Study cohort In 2002 the geriatric team in the Academic Medical Center in Amsterdam was founded. The most important focus of the team was recognizing the frail elderly patients at risk for complications out of all patients aged 65 years and older to provide extra geriatric care to these patients. Since delirium is often a sign of frailty, the geriatric consultation team decided to start with the early recognition of delirium in a cohort study at the medical departments and combining care for these patients with collection of data for research. From all patients aged 65 years and older, demographical, medical and biochemical data were collected during admission as well as information about their physical and cognitive functionality at 3 and 12 months after hospital admission. In 2004 the cohort study was extended to patients with a hip fracture, acutely admitted to the department of traumatology or orthopedics of the Academic Medical Center. In 2003 in the medical patients and in 2005 in the hip fracture patients, DNA collection of patients was started to
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General introduction
perform genetic research. Moreover, in hip fracture patients repeated blood samples were drawn for proteomics research and several pathophysiological parameters. Data from both study cohorts provide the basis for the majority of the studies in this thesis.
Outline The thesis consists of two types of research in delirium, Chapters 2 to 5 focuses on pathophysiological markers in blood and Chapters 6 to 9 focuses on genetic markers. In Chapter 2, a proteomics study is described that explored the entire human proteome in plasma and serum of patients with and without delirium to identify proteins that are differentially expressed in patients with delirium. Proteomics research provides the unique opportunity to generate new hypotheses about the pathophysiological mechanisms and to discover new biomarkers for identification of delirium. In the next chapters we report about the association of two markers of cerebral damage, S100B (Chapter 3 and Chapter 4) and neuron specific Enolase (Chapter 3) with delirium. Possibly, the high frequency of dementia after delirium reflects irreversible brain damage caused by the detrimental effects of the pathophysiological mechanisms of delirium on the brain. One of the mechanisms leading to possible cerebral damage may be the inflammatory process, described in Chapter 5. We study the time‐course of cytokines before, during and after delirium and compare levels of cytokines between the different subtypes of delirium. We started our genetic research, with a systematic review of candidate genes that were already known to be associated with delirium. Because no studies in this subject in the elderly were performed, Chapter 6 is a review about all genetic polymorphisms related to alcohol withdrawal delirium. Despite the fact that the symptoms of alcohol withdrawal delirium are similar, it is unknown if the pathophysiological mechanisms are the same as in delirium in the elderly. On the basis of this review and the treatment of delirium with a dopamine receptor 2 antagonist, three genes involved in dopamine metabolism were chosen as candidate genes for the study described in Chapter 7. The apolipoprotein E (APOE) ε4‐allele is strongly associated with Alzheimer’s dementia, which is a major risk factor for delirium. In Chapter 8, the association between delirium and the APOE ε4‐allele in medical patients is described in the first hundreds included patients. In 2007 more small studies about this subject were published. In the mean time our study group had included a larger sample, so we repeated the analysis in this larger population. Chapter 9 describes these new results and a meta‐analysis of the association between delirium and the APOE ε4‐allele. The general discussion in Chapter 10 elaborates on the observed results and discusses a number of methodological issues. In addition we offer directions for future research.
11
Proteomic profiling of plasma and serum of elderly patients with delirium The Journal of Neuropsychiatry and Clinical Neurosciences (accepted).
Barbara C. van Munster1,2, Marielle J. van Breemen3, Perry D. Moerland1, Dave Speijer3, Sophia E. de Rooij2, Christel J. Pfrommer4, Marcel Levi2, Markus W. Hollmann4, Johannes M. Aerts3, Aeilko H. Zwinderman1, Johanna C. Korevaar1 1. Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, Amsterdam, the Netherlands 2. Department of Internal Medicine, Academic Medical Center, Amsterdam, the Netherlands 3. Department of Medical Biochemistry, Academic Medical Center, Amsterdam, the Netherlands 4. Department of Anesthesiology, Academic Medical Center, Amsterdam, the Netherlands
Chapter 2
Abstract The aim of this study was to compare plasma and serum protein profiles in elderly acute hip fracture patients with and without delirium. SELDI‐TOF spectra of 16 patients without and 16 patients with delirium scored by the Confusion Assessment Method were compared. The most discriminating peak of 15.9 kDa in plasma in a testing group of eight patients with delirium compared to eight patients without delirium was confirmed in an independent validation group. Taking both groups together, three discriminating peaks of 7.97, 15.9, and 16.0 kDa were found in delirious patients. These peaks presumably correspond to hemoglobin‐β, its doubly charged ion and its glycosylated form.
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Proteomics profiling of patients with delirium
Introduction Delirium is a neuropsychiatric syndrome characterized by the rapid onset of fluctuating changes in consciousness and attention, caused by physiologic consequences of a medical condition7. A hip fracture and orthopaedic surgery are both important etiologic factors for developing delirium19. The frequency of postoperative delirium following orthopaedic surgery for hip fracture varies between 16% and 62%, with a mean duration of 3 days19. Delirium is often unrecognized or misdiagnosed by physicians caring for elderly patients77. Though patients usually recover after treating the provocative factor, having delirium is associated with a three‐times increased mortality risk, higher morbidity risk, and increased health care costs55,76. The pathophysiology of delirium is still poorly understood although several mechanisms have been proposed51. Some studies looked at individual proteins in relation to delirium to unravel the pathophysiology120. These candidates were studied based on a priori models of pathophysiology, thus running the risk of missing possible alternative mechanisms. Proteomics, the large‐scale study of proteins, provides the opportunity to identify proteins potentially involved in the pathophysiological mechanism for example by comparing protein expression profiles210. Differences in protein profiles in brain tissue have been found in a rat model of hyperactive delirium57. In blood plasma, proteomic techniques have revealed specific oxidized proteins associated with Alzheimer disease109. We hypothesize that differences in protein profiles also occur in patients who develop delirium. Since orthopaedic surgery after a hip fracture is a time‐defined trigger for post‐operative delirium with in‐hospital recovery, this setting provides a good opportunity to study protein expression during the development of delirium in elderly patients. The aim of the current study was to compare the protein profiles found in plasma and serum in patients during a postoperative delirium with the profiles of patients without postoperative delirium and to identify protein(s) corresponding to possible observed discriminating peaks.
Materials and methods Patients All consecutive patients aged 65 years or more suffering from an acute hip fracture and scheduled for operation at the Department of Orthopedic Surgery or Traumatology of the Academic Medical Centre, Amsterdam, were invited to participate in this cohort study from May 2005 till September 2006. We obtained informed consent from the patient or from the substitute decision‐maker in case of cognitive impairment. Patients were excluded if they were unable to speak or understand Dutch or English. The Institutional Medical Ethics Committee approved the study.
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Chapter 2
For the current pilot‐study, we selected a random group of eight patients with delirium and eight patients without delirium as a testing group. We selected the patients without delirium to resemble the delirious patients as much as possible with respect to type of anesthesia (spinal or general), sex and postoperative day. In case this testing group would result in significant differences in protein profile between patients with and without a delirium, a second random group consisting of eight patients with and eight without delirium (validation group) would be selected. If the validation group would confirm the findings from the testing group, both groups would be combined in the subsequent analysis which then would consist of 16 patients with delirium and 16 patients without delirium. Procedures Two geriatric physicians, a fellow in geriatric medicine, and a team of research nurses trained in geriatric medicine collected demographic and clinical data from all study participants. The presence or absence of delirium was scored during weekdays separately by a physician and a nurse using the Confusion Assessment Method (CAM)80. The CAM has an overall sensitivity of 94% (95% confidence interval (CI)=91‐97%) and specificity of 89% (95% CI=85‐94%) for diagnosing delirium202. We based our clinical judgment on psychiatric examination of the patient, medical and nursing records, including the Delirium Observation Screening Scale (DOSS)174, and information given by relatives. When there was disagreement about the diagnosis between nurse and geriatrician, the patient was discussed in the geriatric consultation team to gain consensus. Possible confounding factors were registered for all patients; e.g. fracture characteristics, type of anesthesia, type of surgery, time between hip fracture and surgery, blood transfusions, demography, number of medications taken before admission, cognitive impairment and functionality. Anaesthetic medication was limited to medication of a protocol for spinal anaesthesia and a protocol for general anesthesia. Cognitive functioning was scored by medical history and ‘Informant Questionnaire on COgnitive DEcline’ (IQCODE). The IQCODE assesses the possible presence of global cognitive decline before admission based on the response of an informant who had known the patient for at least 10 years84. The informant was asked to recollect the situation 2 weeks before the hip fracture and to compare it with the situation 10 years before. Patients with a mean score of 3.9 or more were considered to have pre‐existent cognitive impairment32. To measure functionality we asked the informant to complete the 15‐item Katz ADL scale based on the situation two weeks before the hip fracture203. For all patients several blood samples, serum and ethylenediamine tetra‐acetic acid (EDTA) plasma, were collected under similar strict conditions around 11.00 am. Blood samples were taken on average two days after surgery for both patients with and without delirium. For the patients with delirium we used a sample taken during the delirious episode and an additional blood sample after the delirious episode on day six on average. Blood was collected in tubes containing anticoagulants and in tubes without
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Proteomics profiling of patients with delirium
anticoagulants and kept on ice. Serum was obtained after allowing blood samples to clot at room temperature for 30 minutes. After centrifugation for 15 minutes at 4000 RPM (1780g) at 4C the aliquots were stored at ‐80oC. Proteomic analysis Protein profiles of serum and EDTA plasma samples were generated using anionic surfaces of CM10 ProteinChip® Arrays, and cationic surfaces of Q10 ProteinChip® Arrays (Ciphergen Biosystems Inc., Fremont, CA, USA). Experiments were blinded for sample type and samples were applied in random order. ProteinChip Arrays were analyzed using a PBSIIc ProteinChip Reader (Ciphergen Biosystems Inc., Fremont, CA, USA), a linear laser desorption/ionization time‐of‐flight mass spectrometer equipped with time‐lag focusing. This resulted in mass spectra composed of mass to charge ratios (m/z values) and intensities of the desorbed (poly)peptide ions. All spectra were acquired in positive‐ion mode. A detailed description of pre‐processing steps can be found in the Supplementary Information (page 24). Pre‐processing of SELDI‐TOF MS data for further analysis Data were pre‐processed using Ciphergen ProteinChip® Software 3.1.1. Spectra were calibrated against a mixture of known peptides. Spectra from validation group samples were calibrated with testing group calibration coefficients. Further pre‐processsing steps were spot‐to‐spot calibration, baseline subtraction, and normalization to the average total ion current. A detailed description of pre‐processing steps can be found in the Supplementary Information. Peaks in the mass range from 1.7 to 50 kDa were detected using Biomarker Wizard™ (page 24). Resulting peak intensities were log2‐transformed in order to stabilize their variance. Data analysis We tested for differences in demographic and clinical characteristics in patients with and without delirium using t‐tests, Mann‐Whitney tests and Chi‐squared tests. A two‐tailed p‐ value of
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