Dement Geriatr Cogn Disord 2014;37:113–124 DOI: 10.1159/000354955 Accepted: July 16, 2013 Published online: October 10, 2013
© 2013 S. Karger AG, Basel 1420–8008/14/0372–0113$39.50/0 www.karger.com/dem
Original Research Article
Mild Cognitive Impairment: Clinical and Imaging Profile in a Memory Clinic Setting in India Suvarna Alladi Mekala Shailaja Kandadai Rukmini Mridula Chowdary Arikaudi Haritha Nallapareddy Kavitha Shujath Ali Khan Gollahalli Divyaraj Subhash Kaul Department of Neurology, Nizam’s Institute of Medical Sciences, Hyderabad, India
Key Words Mild cognitive impairment · Clinical profile · Petersen’s criteria · Revised NIA-AA criteria · Diagnosis · India Abstract Background: Despite the increasing burden of dementia in developing countries, mild cognitive impairment (MCI) continues to be underexplored. MCI has conventionally been identified based on clinical profile, but recently, biomarkers suggestive of Alzheimer’s disease pathology have been included in the revised National Institute on Aging and the Alzheimer’s Association (NIA-AA) criteria. In this study, we evaluated the profile of MCI in a memory clinic in India and explored the applicability of the revised NIA-AA criteria in a limited resource setting. Methods: Consecutive subjects evaluated at the memory clinic for mild memory complaints were included and underwent clinical and neuropsychological examination as well as standard brain imaging. A subset of patients was subjected to imaging biomarker studies as a part of routine clinical practice. Results: Among the 1,190 patients evaluated during the study period, 226 (19.0%) presented with mild memory complaints. Cerebrovascular disease was a common secondary cause. Nearly half of the patients (109 of 226) had MCI according to the modified Petersen criteria. All MCI subjects were educated and the majority were male. A total of 12% of the cohort was classified by imaging biomarkers as having MCI with intermediate likelihood of AD according to the NIA-AA criteria. Conclusion: In the setting of urban India, MCI is an emerging problem; therefore, it was feasible to operationalise the revised NIA-AA criteria in © 2013 S. Karger AG, Basel identifying subjects with MCI with intermediate likelihood of AD.
This work was conducted at the Nizam’s Institute of Medical Sciences, Hyderabad, India. Suvarna Alladi, DM Department of Neurology Nizam’s Institute of Medical Sciences Panjagutta, Hyderabad 500082 (India) E-Mail alladisuvarna @ hotmail.com
114
Dement Geriatr Cogn Disord 2014;37:113–124 DOI: 10.1159/000354955
© 2013 S. Karger AG, Basel www.karger.com/dem
Alladi et al.: Mild Cognitive Impairment: Clinical and Imaging Profile in a Memory Clinic Setting in India
Introduction
The concept of mild cognitive impairment (MCI) has been proposed to identify an early but abnormal state of cognitive impairment and is regarded to be a cognitive continuum between normal aging and early Alzheimer’s disease (AD) [1]. This entity is considered to be useful in identifying subjects with mild memory complaints who are at a high risk of developing AD [2–4]. Most of the literature regarding MCI has emerged from developed countries that have a high level of awareness about cognitive disorders [1–11]. However, there is limited literature from developing countries about MCI. The burden of dementia is increasing in India and other developing countries [12], and it is likely that the preclinical state of MCI is concomitantly increasing. Recent epidemiological and hospital-based studies from India, China, South Korea, Mexico, Nigeria and the cross-national 10/66 population-based study suggest that MCI is emerging as a problem in these countries [13–18]. Epidemiological studies conducted in these countries report the prevalence of MCI to range from 0.6 to 12.7% [13–16]. Furthermore, a study from Brazil reported an incidence of 13.2 per 1,000 persons per year for MCI [19]. Reasons for the smaller number of clinic-based studies include a low awareness about cognitive disorders and few specialised centres available for their diagnosis and management [14, 17, 20]. In the Indian context, the diagnosis of MCI is made further challenging due to the limited number of culture fair, locally validated neuropsychological tools available for assessment of mild cognitive problems, a wide heterogeneity in educational and sociocultural backgrounds that makes it difficult to determine premorbid functioning successfully, and few trained personnel to identify the subtle deficits encountered in MCI subjects. Due to increasing numbers of subjects with MCI, determining the clinical and aetiological profile of patients seeking help from memory clinics is essential to plan and develop meaningful health care programs [21]. There has been a recent thrust on the role of biomarkers to identify MCI and demarcate it from normal aging as well as early AD. Various criteria defining MCI using clinical features and neuropsychological profile have been proposed over the past decade [5–7]. Petersen’s criteria are most widely used and depend on clinical and neuropsychological features in defining MCI and its subtypes [1]. Studies have also used measures of functional activities, such as the Functional Activities Questionnaire (FAQ), to assess Instrumental Activities of Daily Living (ADL) to aid in the diagnosis of MCI [11]. The criteria for MCI have been recently revised by the National Institute on Aging and the Alzheimer’s Association (NIA-AA) and recommend the use of biomarkers to improve the diagnostic confidence in identifying subjects with mild cognitive complaints whose underlying pathophysiological process could be AD [8]. Standardization of these biomarkers in detecting preclinical AD among subjects with MCI is currently an area of active study [9, 10]. However, availability and access to advanced neuroimaging techniques and studies on CSF biomarker are limited [22]. Therefore, there is a need to examine the feasibility of operationalising the revised criteria to diagnose MCI in this setting. The current hospital-based MCI study was initiated to evaluate and follow-up a cohort of people with mild memory complaints presenting to a memory clinic in a developing country. The purpose of this study was to examine the demographic and clinical profile of MCI, and since a subset of subjects underwent biomarker studies as a part of routine evaluation, we aimed to assess the applicability of the recently recommended NIA-AA criteria for diagnosing MCI due to AD in this limited resource setting.
115
Dement Geriatr Cogn Disord 2014;37:113–124 DOI: 10.1159/000354955
© 2013 S. Karger AG, Basel www.karger.com/dem
Alladi et al.: Mild Cognitive Impairment: Clinical and Imaging Profile in a Memory Clinic Setting in India
Methods Consecutive subjects aged 50 years and above, with mild memory disturbances as their primary complaint and without other significant neurological symptoms, who attended the memory clinic between January 2006 and March 2011, were included in this study. All participants were prospectively enrolled as a part of an ongoing longitudinal project that aims to evaluate subjects with cognitive impairment with detailed demographic, clinical, aetiological, imaging, and follow-up studies. Patients were referred to the memory clinic by general practitioners and neurologists, and the patient profile is representative of the pattern seen at a tertiary neurology service in an Indian city. The study was approved by the local Institutional Ethics Committee, and written informed consent was obtained from the patients and their caregivers. A detailed demographic and medical history was taken from each patient and attendant, and a complete general physical and neurological examination was performed by a neurologist. Neuropsychological testing was conducted by trained psychologists. All subjects had an informant, typically a spouse or one of their children. Patient age, gender, educational status in years, occupational status as classified by the National Classification of Occupations – 2004 [23], history of stroke and the presence of vascular risk factors were recorded for all subjects according to a structured protocol [20]. All participants were evaluated using the Mini-Mental State Examination (MMSE) [24], Addenbrooke’s Cognitive Examination-Revised (ACE-R), adapted and translated into Telugu and Hindi [20, 25], and the Clinical Dementia Rating Scale (CDR) [26]. Depression and anxiety were scored using the Hospital Anxiety and Depression Scale (HADS) [27]. A standard set of screening blood tests, including thyroid function tests and vitamin B12 levels, and brain imaging with CT scan or MRI were performed in the majority of patients. Subjects with dementia, clinical stroke, significant head injury, seizures, psychiatric diseases, including depression (clinical judgement or HADS >14), other neurological or medical disorders that could account for cognitive impairment were excluded. Patients with mild memory problems as their primary complaint and in whom a definite cause could not be identified were subjected to further neuropsychological evaluation. Neuropsychological Testing Subjects underwent neuropsychological testing according to a structured neuropsychological test battery to assess a range of cognitive domains including memory, attention/executive function, language and visuospatial functions. Episodic memory was tested using the Rey Auditory Verbal Learning Test (RAVLT), and attention/executive function was assessed by the Trail Making Test (TMT A and B). Language was evaluated with Category and P letter fluency, naming of twelve pictures, comprehension and repetition of ACE-R to obtain a 26-point language subscore. Visuospatial functions were assessed using a copy of the Rey-Osterrieth complex figure (ROCF). These tests are sensitive to early deficits in cognitive domains and are widely used in routine neuropsychological practice. Furthermore, they have been validated in the Indian context and normative data are available. Control data for the RAVLT and ROCF tests and TMT A and B were taken from age-, sex- and education-matched normative data from the NIMHANS neuropsychological battery derived from an urban population aged between 51 and 65 years in Bangalore, India [28]. For subjects older than 65 years, control data for RAVLT, ROCF and the TMT were obtained from 60 healthy age-, sex- and education-matched subjects who were volunteers evaluated in the memory clinic. Control data for ACE-R were derived from 109 age-, sex- and education-matched volunteers evaluated in the clinic. To assess each individual subject’s performance on each test, we calculated the z-scores. Subjects were considered to be impaired on a test if their scores were 1.5 SD below the mean for age-, sex- and educationmatched normative data. Imaging A CT scan or a 1.5 T MRI of the brain was performed when feasible. The presence of white matter hyperintensities, cerebral infarcts and diffuse cerebral atrophy was noted. Only few patients underwent advanced neuroimaging techniques. These include MRI of the brain for evidence of medial temporal lobe atrophy and 18F-fluorodeoxyglucose (FDG)-PET for bilateral temporal and parietal hypometabolism [29–31]. Medial temporal lobe atrophy was rated using a visual medial temporal atrophy (MTA) scale based on a T1-weighted coronal section of the brain. The MTA score ranged from 0 (no atrophy) to 4 (severe atrophy) which took into account the width of the choroid fissure, the height of the hippocampus, and the width of the temporal horn [32]. Patients who had medial temporal lobe atrophy with an MTA score of more than 1 were considered likely to have AD pathology [33]. FDG-PET/CT of the brain was performed using Siemens Biograph 16 with
116
Dement Geriatr Cogn Disord 2014;37:113–124 DOI: 10.1159/000354955
© 2013 S. Karger AG, Basel www.karger.com/dem
Alladi et al.: Mild Cognitive Impairment: Clinical and Imaging Profile in a Memory Clinic Setting in India
a dose of 10 mci of FDG and image acquisition 45 min after injection. Images were interpreted on Bronson’s colour scale. Patients were diagnosed as having FDG-PET findings suggestive of AD in the presence of areas of decreased metabolism involving the temporoparietal cortex bilaterally [34]. Hypometabolism in the posterior cingulate gyrus and preserved metabolism in the sensorimotor strip, cerebellum and basal ganglia were considered to be supportive of the AD pattern. Diagnosis Based on the above clinical, neuropsychological, neuroimaging and laboratory data, MCI was diagnosed by a neurologist with experience in evaluating cognitive disorders, according to the modified Petersen criteria [1] that have been widely used by the majority of previous studies. Subjects fulfilling criteria for MCI based on Petersen’s criteria, i.e. subjective memory complaints, objective memory loss as determined by neuropsychological test scores and absence of dementia as indicated by CDR, were interviewed by a clinician experienced in the diagnosis of dementia. According to the imaging findings, patients detected to have strategically located infarcts in areas known to affect cognition were excluded. In addition, subjects with extensive white matter changes (grade 2 and 3) on Fazekas scale were also excluded because they were considered to have vascular MCI [35]. Following neuropsychological testing, MCI patients were classified as having amnestic and non-amnestic MCI. Amnestic MCI patients were further classified into single-domain (memory only) and multi-domain MCI (memory plus other cognitive domains). Patients with impairment in non-memory domains were separately designated as non-amnestic MCI. Subjects were termed as having subjective memory impairment (SMI) when they presented with memory complaints but performed normally on the neuropsychological tests. We also studied the applicability of the NIA-AA MCI criteria [8] in the same cohort by re-evaluating clinical details, neuropsychological and brain imaging data. The NIA-AA criteria incorporate evidence from biomarkers to support the diagnosis of MCI due to AD as a part of a research. Two classes of biomarkers are identified: CSF Aβ levels and PET imaging of amyloid deposition using specific ligands (PET amyloid imaging) that are grouped as biomarkers reflecting Aβ deposition. Further biomarkers reflecting downstream neuronal injury are: CSF tau levels, various structural and functional imaging measures like hippocampal volume or medial temporal atrophy by volumetric measures or visual rating, rate of brain atrophy on MRI of the brain, FDG-PET imaging, and SPECT perfusion imaging. Based on evidence from biomarker studies, patients with MCI are classified as having MCI due to AD at four levels of certainty. We compared the characteristics of the MCI cohort derived from applying the modified Petersen criteria and the NIA-AA MCI criteria. In addition, we compared the demographic characteristics and cognitive status of subjects with MCI and those with AD evaluated in the memory clinic during the same period. Diagnosis of AD was established by NINDS-ADRDA criteria [36]. Statistics Subjects who scored less than 1.5 SD (less than 7th percentile on z-scores) of control mean scores were considered to be abnormal. Group means were compared using independent samples t tests. Differences in frequency of deficits between groups were analysed using the χ2 test.
Results
Clinical and Cognitive Profile of the Study Cohort Of the 1,190 patients evaluated in the memory clinic during the study period, 226 (19%) presented with mild memory problems as their primary complaint. Forty-four were detected to have an underlying cause that could explain their memory complaints: 15 subjects were detected to have vascular aetiology, 12 were significantly depressed, 5 had anxiety, 10 had vitamin B12 deficiency, 1 patient was subsequently detected to have vasculitis of the central nervous system, and 1 had a frontal lobe tumour. Thirty-one subjects performed normally on the neuropsychological tests and were categorised as having SMI (tables 1, 2). Forty-two subjects with mild memory complaints performed normally on the screening cognitive tests, but sufficient neuropsychological data were not available to classify them more accurately, and hence they were also excluded (fig. 1a).
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Dement Geriatr Cogn Disord 2014;37:113–124 © 2013 S. Karger AG, Basel www.karger.com/dem
DOI: 10.1159/000354955
Alladi et al.: Mild Cognitive Impairment: Clinical and Imaging Profile in a Memory Clinic Setting in India
Table 1. Demographic details of
the MCI, AD and SMI groups Age*** Male gender* Literates* Years of education** Monolinguals** Multilinguals** MMSE*** ACE-R***
MCI (n = 109)
SMI (n = 31)
AD (n = 172)
64.7 ± 9.4 90 (84.1) 109 (100) 13.8 ± 3.8 25 (22.9) 84 (77.1) 27.7 (1.6) 86.8 (6.4)
59.6 ± 11.1 22 (71.0) 29 (93.5) 13.0 ± 5.5 8 (25.8) 23 (74.2) 29.5 (0.8) 93.3 (2.5)
69.8 ± 9.2 97 (56.4) 152 (88.4) 10.5 ± 5.8 75 (43.6) 97 (66.4) 18.6 (8.2) 54.2 (25.2)
Values are mean ± SD or n (%). * p < 0.05, MCI vs. AD. ** p < 0.05, MCI vs. AD, and SMI vs. AD. *** p < 0.0001, MCI vs. SMI, MCI vs. AD, and AD vs. SMI.
Table 2. Performance of the MCI
and SMI groups on the neuropsychological test battery MMSE (max. 30) ACE-R (max. 100) RAVLT-total (max. 75) RAVLT-IR (max. 15) RAVLT-DR (max. 15) ROCF-copy (max. 36) ROCF-delayed recall (max. 36) TMT A, s TMT B, s Letter fluency Category fluency
MCI (n = 109)
SMI (n = 321)
p value
27.7 (1.6) 86.8 (6.4) 32.2 (7.2) 6.1 (2.3) 4.9 (2.6) 30.5 (5.4)
29.5 (0.8) 93.3 (2.5) 44.6 (3.4) 9.2 (1.8) 9.6 (3.4) 34.2 (1.8)
© 2013 S. Karger AG, Basel 1420–8008/14/0372–0113$39.50/0 www.karger.com/dem
Original Research Article
Mild Cognitive Impairment: Clinical and Imaging Profile in a Memory Clinic Setting in India Suvarna Alladi Mekala Shailaja Kandadai Rukmini Mridula Chowdary Arikaudi Haritha Nallapareddy Kavitha Shujath Ali Khan Gollahalli Divyaraj Subhash Kaul Department of Neurology, Nizam’s Institute of Medical Sciences, Hyderabad, India
Key Words Mild cognitive impairment · Clinical profile · Petersen’s criteria · Revised NIA-AA criteria · Diagnosis · India Abstract Background: Despite the increasing burden of dementia in developing countries, mild cognitive impairment (MCI) continues to be underexplored. MCI has conventionally been identified based on clinical profile, but recently, biomarkers suggestive of Alzheimer’s disease pathology have been included in the revised National Institute on Aging and the Alzheimer’s Association (NIA-AA) criteria. In this study, we evaluated the profile of MCI in a memory clinic in India and explored the applicability of the revised NIA-AA criteria in a limited resource setting. Methods: Consecutive subjects evaluated at the memory clinic for mild memory complaints were included and underwent clinical and neuropsychological examination as well as standard brain imaging. A subset of patients was subjected to imaging biomarker studies as a part of routine clinical practice. Results: Among the 1,190 patients evaluated during the study period, 226 (19.0%) presented with mild memory complaints. Cerebrovascular disease was a common secondary cause. Nearly half of the patients (109 of 226) had MCI according to the modified Petersen criteria. All MCI subjects were educated and the majority were male. A total of 12% of the cohort was classified by imaging biomarkers as having MCI with intermediate likelihood of AD according to the NIA-AA criteria. Conclusion: In the setting of urban India, MCI is an emerging problem; therefore, it was feasible to operationalise the revised NIA-AA criteria in © 2013 S. Karger AG, Basel identifying subjects with MCI with intermediate likelihood of AD.
This work was conducted at the Nizam’s Institute of Medical Sciences, Hyderabad, India. Suvarna Alladi, DM Department of Neurology Nizam’s Institute of Medical Sciences Panjagutta, Hyderabad 500082 (India) E-Mail alladisuvarna @ hotmail.com
114
Dement Geriatr Cogn Disord 2014;37:113–124 DOI: 10.1159/000354955
© 2013 S. Karger AG, Basel www.karger.com/dem
Alladi et al.: Mild Cognitive Impairment: Clinical and Imaging Profile in a Memory Clinic Setting in India
Introduction
The concept of mild cognitive impairment (MCI) has been proposed to identify an early but abnormal state of cognitive impairment and is regarded to be a cognitive continuum between normal aging and early Alzheimer’s disease (AD) [1]. This entity is considered to be useful in identifying subjects with mild memory complaints who are at a high risk of developing AD [2–4]. Most of the literature regarding MCI has emerged from developed countries that have a high level of awareness about cognitive disorders [1–11]. However, there is limited literature from developing countries about MCI. The burden of dementia is increasing in India and other developing countries [12], and it is likely that the preclinical state of MCI is concomitantly increasing. Recent epidemiological and hospital-based studies from India, China, South Korea, Mexico, Nigeria and the cross-national 10/66 population-based study suggest that MCI is emerging as a problem in these countries [13–18]. Epidemiological studies conducted in these countries report the prevalence of MCI to range from 0.6 to 12.7% [13–16]. Furthermore, a study from Brazil reported an incidence of 13.2 per 1,000 persons per year for MCI [19]. Reasons for the smaller number of clinic-based studies include a low awareness about cognitive disorders and few specialised centres available for their diagnosis and management [14, 17, 20]. In the Indian context, the diagnosis of MCI is made further challenging due to the limited number of culture fair, locally validated neuropsychological tools available for assessment of mild cognitive problems, a wide heterogeneity in educational and sociocultural backgrounds that makes it difficult to determine premorbid functioning successfully, and few trained personnel to identify the subtle deficits encountered in MCI subjects. Due to increasing numbers of subjects with MCI, determining the clinical and aetiological profile of patients seeking help from memory clinics is essential to plan and develop meaningful health care programs [21]. There has been a recent thrust on the role of biomarkers to identify MCI and demarcate it from normal aging as well as early AD. Various criteria defining MCI using clinical features and neuropsychological profile have been proposed over the past decade [5–7]. Petersen’s criteria are most widely used and depend on clinical and neuropsychological features in defining MCI and its subtypes [1]. Studies have also used measures of functional activities, such as the Functional Activities Questionnaire (FAQ), to assess Instrumental Activities of Daily Living (ADL) to aid in the diagnosis of MCI [11]. The criteria for MCI have been recently revised by the National Institute on Aging and the Alzheimer’s Association (NIA-AA) and recommend the use of biomarkers to improve the diagnostic confidence in identifying subjects with mild cognitive complaints whose underlying pathophysiological process could be AD [8]. Standardization of these biomarkers in detecting preclinical AD among subjects with MCI is currently an area of active study [9, 10]. However, availability and access to advanced neuroimaging techniques and studies on CSF biomarker are limited [22]. Therefore, there is a need to examine the feasibility of operationalising the revised criteria to diagnose MCI in this setting. The current hospital-based MCI study was initiated to evaluate and follow-up a cohort of people with mild memory complaints presenting to a memory clinic in a developing country. The purpose of this study was to examine the demographic and clinical profile of MCI, and since a subset of subjects underwent biomarker studies as a part of routine evaluation, we aimed to assess the applicability of the recently recommended NIA-AA criteria for diagnosing MCI due to AD in this limited resource setting.
115
Dement Geriatr Cogn Disord 2014;37:113–124 DOI: 10.1159/000354955
© 2013 S. Karger AG, Basel www.karger.com/dem
Alladi et al.: Mild Cognitive Impairment: Clinical and Imaging Profile in a Memory Clinic Setting in India
Methods Consecutive subjects aged 50 years and above, with mild memory disturbances as their primary complaint and without other significant neurological symptoms, who attended the memory clinic between January 2006 and March 2011, were included in this study. All participants were prospectively enrolled as a part of an ongoing longitudinal project that aims to evaluate subjects with cognitive impairment with detailed demographic, clinical, aetiological, imaging, and follow-up studies. Patients were referred to the memory clinic by general practitioners and neurologists, and the patient profile is representative of the pattern seen at a tertiary neurology service in an Indian city. The study was approved by the local Institutional Ethics Committee, and written informed consent was obtained from the patients and their caregivers. A detailed demographic and medical history was taken from each patient and attendant, and a complete general physical and neurological examination was performed by a neurologist. Neuropsychological testing was conducted by trained psychologists. All subjects had an informant, typically a spouse or one of their children. Patient age, gender, educational status in years, occupational status as classified by the National Classification of Occupations – 2004 [23], history of stroke and the presence of vascular risk factors were recorded for all subjects according to a structured protocol [20]. All participants were evaluated using the Mini-Mental State Examination (MMSE) [24], Addenbrooke’s Cognitive Examination-Revised (ACE-R), adapted and translated into Telugu and Hindi [20, 25], and the Clinical Dementia Rating Scale (CDR) [26]. Depression and anxiety were scored using the Hospital Anxiety and Depression Scale (HADS) [27]. A standard set of screening blood tests, including thyroid function tests and vitamin B12 levels, and brain imaging with CT scan or MRI were performed in the majority of patients. Subjects with dementia, clinical stroke, significant head injury, seizures, psychiatric diseases, including depression (clinical judgement or HADS >14), other neurological or medical disorders that could account for cognitive impairment were excluded. Patients with mild memory problems as their primary complaint and in whom a definite cause could not be identified were subjected to further neuropsychological evaluation. Neuropsychological Testing Subjects underwent neuropsychological testing according to a structured neuropsychological test battery to assess a range of cognitive domains including memory, attention/executive function, language and visuospatial functions. Episodic memory was tested using the Rey Auditory Verbal Learning Test (RAVLT), and attention/executive function was assessed by the Trail Making Test (TMT A and B). Language was evaluated with Category and P letter fluency, naming of twelve pictures, comprehension and repetition of ACE-R to obtain a 26-point language subscore. Visuospatial functions were assessed using a copy of the Rey-Osterrieth complex figure (ROCF). These tests are sensitive to early deficits in cognitive domains and are widely used in routine neuropsychological practice. Furthermore, they have been validated in the Indian context and normative data are available. Control data for the RAVLT and ROCF tests and TMT A and B were taken from age-, sex- and education-matched normative data from the NIMHANS neuropsychological battery derived from an urban population aged between 51 and 65 years in Bangalore, India [28]. For subjects older than 65 years, control data for RAVLT, ROCF and the TMT were obtained from 60 healthy age-, sex- and education-matched subjects who were volunteers evaluated in the memory clinic. Control data for ACE-R were derived from 109 age-, sex- and education-matched volunteers evaluated in the clinic. To assess each individual subject’s performance on each test, we calculated the z-scores. Subjects were considered to be impaired on a test if their scores were 1.5 SD below the mean for age-, sex- and educationmatched normative data. Imaging A CT scan or a 1.5 T MRI of the brain was performed when feasible. The presence of white matter hyperintensities, cerebral infarcts and diffuse cerebral atrophy was noted. Only few patients underwent advanced neuroimaging techniques. These include MRI of the brain for evidence of medial temporal lobe atrophy and 18F-fluorodeoxyglucose (FDG)-PET for bilateral temporal and parietal hypometabolism [29–31]. Medial temporal lobe atrophy was rated using a visual medial temporal atrophy (MTA) scale based on a T1-weighted coronal section of the brain. The MTA score ranged from 0 (no atrophy) to 4 (severe atrophy) which took into account the width of the choroid fissure, the height of the hippocampus, and the width of the temporal horn [32]. Patients who had medial temporal lobe atrophy with an MTA score of more than 1 were considered likely to have AD pathology [33]. FDG-PET/CT of the brain was performed using Siemens Biograph 16 with
116
Dement Geriatr Cogn Disord 2014;37:113–124 DOI: 10.1159/000354955
© 2013 S. Karger AG, Basel www.karger.com/dem
Alladi et al.: Mild Cognitive Impairment: Clinical and Imaging Profile in a Memory Clinic Setting in India
a dose of 10 mci of FDG and image acquisition 45 min after injection. Images were interpreted on Bronson’s colour scale. Patients were diagnosed as having FDG-PET findings suggestive of AD in the presence of areas of decreased metabolism involving the temporoparietal cortex bilaterally [34]. Hypometabolism in the posterior cingulate gyrus and preserved metabolism in the sensorimotor strip, cerebellum and basal ganglia were considered to be supportive of the AD pattern. Diagnosis Based on the above clinical, neuropsychological, neuroimaging and laboratory data, MCI was diagnosed by a neurologist with experience in evaluating cognitive disorders, according to the modified Petersen criteria [1] that have been widely used by the majority of previous studies. Subjects fulfilling criteria for MCI based on Petersen’s criteria, i.e. subjective memory complaints, objective memory loss as determined by neuropsychological test scores and absence of dementia as indicated by CDR, were interviewed by a clinician experienced in the diagnosis of dementia. According to the imaging findings, patients detected to have strategically located infarcts in areas known to affect cognition were excluded. In addition, subjects with extensive white matter changes (grade 2 and 3) on Fazekas scale were also excluded because they were considered to have vascular MCI [35]. Following neuropsychological testing, MCI patients were classified as having amnestic and non-amnestic MCI. Amnestic MCI patients were further classified into single-domain (memory only) and multi-domain MCI (memory plus other cognitive domains). Patients with impairment in non-memory domains were separately designated as non-amnestic MCI. Subjects were termed as having subjective memory impairment (SMI) when they presented with memory complaints but performed normally on the neuropsychological tests. We also studied the applicability of the NIA-AA MCI criteria [8] in the same cohort by re-evaluating clinical details, neuropsychological and brain imaging data. The NIA-AA criteria incorporate evidence from biomarkers to support the diagnosis of MCI due to AD as a part of a research. Two classes of biomarkers are identified: CSF Aβ levels and PET imaging of amyloid deposition using specific ligands (PET amyloid imaging) that are grouped as biomarkers reflecting Aβ deposition. Further biomarkers reflecting downstream neuronal injury are: CSF tau levels, various structural and functional imaging measures like hippocampal volume or medial temporal atrophy by volumetric measures or visual rating, rate of brain atrophy on MRI of the brain, FDG-PET imaging, and SPECT perfusion imaging. Based on evidence from biomarker studies, patients with MCI are classified as having MCI due to AD at four levels of certainty. We compared the characteristics of the MCI cohort derived from applying the modified Petersen criteria and the NIA-AA MCI criteria. In addition, we compared the demographic characteristics and cognitive status of subjects with MCI and those with AD evaluated in the memory clinic during the same period. Diagnosis of AD was established by NINDS-ADRDA criteria [36]. Statistics Subjects who scored less than 1.5 SD (less than 7th percentile on z-scores) of control mean scores were considered to be abnormal. Group means were compared using independent samples t tests. Differences in frequency of deficits between groups were analysed using the χ2 test.
Results
Clinical and Cognitive Profile of the Study Cohort Of the 1,190 patients evaluated in the memory clinic during the study period, 226 (19%) presented with mild memory problems as their primary complaint. Forty-four were detected to have an underlying cause that could explain their memory complaints: 15 subjects were detected to have vascular aetiology, 12 were significantly depressed, 5 had anxiety, 10 had vitamin B12 deficiency, 1 patient was subsequently detected to have vasculitis of the central nervous system, and 1 had a frontal lobe tumour. Thirty-one subjects performed normally on the neuropsychological tests and were categorised as having SMI (tables 1, 2). Forty-two subjects with mild memory complaints performed normally on the screening cognitive tests, but sufficient neuropsychological data were not available to classify them more accurately, and hence they were also excluded (fig. 1a).
117
Dement Geriatr Cogn Disord 2014;37:113–124 © 2013 S. Karger AG, Basel www.karger.com/dem
DOI: 10.1159/000354955
Alladi et al.: Mild Cognitive Impairment: Clinical and Imaging Profile in a Memory Clinic Setting in India
Table 1. Demographic details of
the MCI, AD and SMI groups Age*** Male gender* Literates* Years of education** Monolinguals** Multilinguals** MMSE*** ACE-R***
MCI (n = 109)
SMI (n = 31)
AD (n = 172)
64.7 ± 9.4 90 (84.1) 109 (100) 13.8 ± 3.8 25 (22.9) 84 (77.1) 27.7 (1.6) 86.8 (6.4)
59.6 ± 11.1 22 (71.0) 29 (93.5) 13.0 ± 5.5 8 (25.8) 23 (74.2) 29.5 (0.8) 93.3 (2.5)
69.8 ± 9.2 97 (56.4) 152 (88.4) 10.5 ± 5.8 75 (43.6) 97 (66.4) 18.6 (8.2) 54.2 (25.2)
Values are mean ± SD or n (%). * p < 0.05, MCI vs. AD. ** p < 0.05, MCI vs. AD, and SMI vs. AD. *** p < 0.0001, MCI vs. SMI, MCI vs. AD, and AD vs. SMI.
Table 2. Performance of the MCI
and SMI groups on the neuropsychological test battery MMSE (max. 30) ACE-R (max. 100) RAVLT-total (max. 75) RAVLT-IR (max. 15) RAVLT-DR (max. 15) ROCF-copy (max. 36) ROCF-delayed recall (max. 36) TMT A, s TMT B, s Letter fluency Category fluency
MCI (n = 109)
SMI (n = 321)
p value
27.7 (1.6) 86.8 (6.4) 32.2 (7.2) 6.1 (2.3) 4.9 (2.6) 30.5 (5.4)
29.5 (0.8) 93.3 (2.5) 44.6 (3.4) 9.2 (1.8) 9.6 (3.4) 34.2 (1.8)
