Alzheimer’s & Dementia - (2013) 1–9
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The prevalence of mild cognitive impairment and its etiological subtypes in elderly Chinese Jianping Jiaa,*, Aihong Zhoua, Cuibai Weia, Xiangfei Jiab, Fen Wanga, Fang Lic, Xiaoguang Wud, Vincent Moke, Serge Gauthierf, Muni Tangg, Lan Chuh, Youlong Zhoui, Chunkui Zhouj, Yong Cuik, Qi Wanga, Weishan Wangl, Peng Yinm, Nan Hum, Xiumei Zuoa, Haiqing Songa, Wei Qina, Liyong Wua, Dan Lia, Longfei Jian, Juexian Songa, Ying Hana, Yi Xinga, Peijie Yanga, Yuemei Lia, Yuchen Qiaoa, Yi Tanga, Jihui Lvl, Xiumin Donga a
Department of Neurology, Xuan Wu Hospital, Capital Medical University, Beijing, People’s Republic of China b Department of Computer Science, University of Otago, Dunedin, New Zealand c Department of Neurology, Fu Xing Hospital, Capital Medical University, Beijing, People’s Republic of China d Evidence-Based Medicine Center, Xuan Wu Hospital, Capital Medical University, Beijing, People’s Republic of China e Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China f McGill Center for Studies in Aging, McGill University, Montreal, Quebec, Canada g Department of Geriatrics, Guangzhou Brain Hospital, Affiliated Hospital of Guangzhou Medical College, Guangzhou, Guangdong Province, People’s Republic of China h Department of Neurology, Affiliated Hospital of Guiyang Medical College, Guiyang, Guizhou Province, People’s Republic of China i Department of Neurology, Third Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan Province, People’s Republic of China j Department of Neurology, First Hospital of Jilin University, Changchun, Jinlin Province, People’s Republic of China k Department of Neurology, Fourth Hospital of Jilin University, Changchun, Jinlin Province, People’s Republic of China l Department of Neurology, Beijing Geriatric Hospital, Beijing, People’s Republic of China m National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing, People’s Republic of China n Department of Neurology, Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China
Abstract
Background: Epidemiologic studies on mild cognitive impairment (MCI) are limited in China. Methods: Using a multistage cluster sampling design, a total of 10,276 community residents (6096 urban, 4180 rural) aged 65 years or older were evaluated and diagnosed with normal cognition, MCI, or dementia. MCI was further categorized by imaging into MCI caused by prodromal Alzheimer’s disease (MCI-A), MCI resulting from cerebrovascular disease (MCI-CVD), MCI with vascular risk factors (MCI-VRF), and MCI caused by other diseases (MCI-O). Results: The prevalences of overall MCI, MCI-A, MCI-CVD, MCI-VRF, and MCI-O were 20.8% (95% confidence interval [CI] 5 20.0–21.6%), 6.1% (95% CI 5 5.7–6.6%), 3.8% (95% CI 5 3.4–4.2%), 4.9% (95% CI 5 4.5–5.4%), and 5.9% (95% CI 5 5.5–6.4%) respectively. The rural population had a higher prevalence of overall MCI (23.4% vs 16.8%, P , .001). Conclusions: The prevalence of MCI in elderly Chinese is higher in rural than in urban areas. Vascular-related MCI (MCI-CVD and MCI-VRF) was most common. Ó 2013 The Alzheimer’s Association. All rights reserved.
Keywords:
Mild cognitive impairment; Etiological subtypes; Prevalence; Risk factors
1. Introduction
*Corresponding author. Tel.: 0086-10-83198730; Fax: 0086-10-83171070. E-mail address: [email protected]
Mild cognitive impairment (MCI) constitutes an intermediate stage between normal aging and dementia [1,2]. As a key interventional target for dementia, MCI has been an important
1552-5260/$ - see front matter Ó 2013 The Alzheimer’s Association. All rights reserved. http://dx.doi.org/10.1016/j.jalz.2013.09.008
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research topic during the past several decades. Initially, Petersen and colleagues characterized MCI as memory impairment, referred to as amnestic MCI in subsequent years [3]. Thereafter, the concept of MCI has been broadened to include patients with cognitive impairments in the nonmemory domain or multiple domains [1,2]. Currently, epidemiological studies usually subcategorize MCI according to the impaired cognitive domains [4–8], which is less helpful for etiological intervention. MCI demonstrates considerable heterogeneity regarding its etiology [1,2]. MCI has been attributed to numerous etiologies, such as Alzheimer’s pathology, ischemia, trauma, psychiatric disorders, and others [1,2]. Understanding the etiologies of MCI is imperative in terms of establishing precise prevention and treatment strategies. Some longitudinal studies have already addressed various MCI etiologies [9]. However, only two epidemiological studies investigated the etiology of “cognitive impairment, no dementia” (CIND), a concept similar to the broadened concept of MCI [10,11]. A U.S. survey used 12 subcategories, including prodromal Alzheimer’s disease (AD), amnestic MCI, vascular cognitive impairment without dementia, stroke, medical conditions or sensory impairment, and other origins. These classifications are based not only on etiologies but also on cognitive domains, and they overlap one another to some degree [10]. Another study conducted in Portugal subcategorized MCI as general vascular disorders, depression, cerebrovascular disorders, alcohol or drug abuse, traumatic brain injury, and other neurological conditions, but it provided no definitive criteria [11]. MCI due to Alzheimer’s pathology, the first identified cause of dementia, was also neglected in that study [11]. Furthermore, the diagnostic subtypes in these two studies were loosely defined, relied substantially on clinical judgment, and lacked the support of brain imaging findings, which are necessary for an etiological diagnosis [10,11]. Few surveys on MCI have been conducted in China [12–17]. Furthermore, most of these studies were regional and recruited only individuals from urban populations. The nationwide prevalence of MCI in rural and urban populations of China is currently unavailable. In the present study, using a more precise etiological classification based on brain scans, we estimated the prevalence of MCI and its etiological subtypes in rural and urban populations of community-dwelling elderly Chinese individuals.
2. Methods 2.1. Study design and samples A multistage, cluster sampling design was used. First, we chose five representative regional centers across China (Changchun for Northeast China, Beijing for North China, Zhengzhou for Central China, Guangzhou for South China,
and Guiyang for Southwest China). Using random-number tables, 10 urban districts and 12 rural counties were randomly selected from these centers. Finally, within the selected districts and counties, 30 urban communities and 45 rural villages were randomly sampled. The investigation was conducted from October 2008 through October 2009. The inclusion criteria were being aged 65 years and older, Han Chinese, and listed in the census of the community registry office as well as living in the target community for at least 1 year preceding the survey date. Institutionalized people were not included. Two eligible populations were drawn from urban (n 5 8414) and rural (n 5 5392) areas. Subjects who were untraceable or deceased, who refused to participate, or who had incomplete data for diagnostic purposes were excluded. Finally, a total of 10,276 residents (6096 urban and 4180 rural) participated in the survey (Figure 1). The participation rate among rural residents was higher than that among urban residents (77.5% vs 72.5%, P , .001). Participation rates did not differ significantly among centers. No significant differences in age, sex, or education distribution were found between participants and nonparticipants in the urban or the rural population. The medical ethics committee at each center approved the study. Informed consent was directly obtained from each subject or was indirectly obtained from his or her guardian. 2.2. Assessment and diagnosis procedure Eight to 10 pairs of interviewers were recruited in each region. Most were junior neurologists or senior graduate students specializing in neurology. Furthermore, we set up a regional expert panel that included two neurologists and two neuropsychologists with special expertise in cognitive impairment disorders. All interviewers and experts received uniform training on neuropsychological assessment and diagnosis for 1 week and participated in a retraining course every 3 months thereafter. The interrater reliability for cognitive tests and diagnoses, which relied on videotaped interviews, was required to exceed 0.90. A two-step door-to-door diagnostic procedure was used. First, each specially trained interviewer pair conducted a semistructured interview with participants and their close informants at their residence. The interview lasted approximately 2 hours. Sociodemographic characteristics were collected. A questionnaire on cigarette smoking and alcohol consumption was administered. A self-report medical history questionnaire was also administered. Participants responded “yes” or “no” to ever being diagnosed with hypertension, diabetes, hypercholesterolemia, coronary artery disease, stroke, or other neurologic or systemic diseases. Medical records were examined as far back as possible. Thereafter, extended neuropsychological tests examining four cognitive domains were administered to
J. Jia et al. / Alzheimer’s & Dementia - (2013) 1–9
Eligible urban population: 8414
3
Eligible rural population: 5392
Non-respondents: 1676
Non-respondents: 772
1.
Refused: 1378
1.
2.
Untraceable: 185
2.
Refused: 625 Untraceable: 82
3.
Life-threatening illness: 92
3.
Life-threatening illness: 52
4.
Deceased: 21
4.
Deceased: 13
Respondents: 4620
Respondents: 6738 Excluded: 642
Excluded: 440
1.
Incomplete data: 497
1.
Incomplete data: 334
2.
Repeated or doubtful data: 62
2.
Repeated or doubtful data: 36
3.
Hearing or vision deficit: 53
3.
Hearing or vision deficit: 48
4.
Other reasons: 30
4.
Other reasons: 22
Participants: 6096
Normal: 4740
MCI-A: 261
MCI: 1089
MCI-CVD: 243
MCI-VRF: 302
Participants: 4180
Dementia: 267
MCI-O: 283
Normal: 2871
MCI-A: 369
MCI: 1048
MCI-CVD: 149
Dementia: 261
MCI-VRF: 205
MCI-O: 325
Fig. 1. Study flow chart. Abbreviations: MCI, mild cognitive impairment; MCI-A, MCI prodromal Alzheimer disease; MCI-CVD, MCI resulting from cerebrovascular disease; MCI-VRF, MCI with vascular risk factors; MCI-O, MCI caused by other factors.
participants: (1) memory—the World Health OrganizationUniversity of California–Los Angeles Auditory Verbal Learning Test (WHO-UCLA AVLT), including immediate recall, short-delay free recall (3 minutes), long-delay free recall (30 minutes), and long-delay recognition [18]; (2) executive function—Trail Making Test B [19]; (3) language— semantic verbal fluency test (category, animals) [20]; and (4) visuoconstructive skill—clock-drawing test (CDT) [21]. In addition, the Mini-Mental State Examination (MMSE) [22] and Montreal Cognitive Assessment (MoCA) [23] were performed to assess global cognition. The Functional Activities Questionnaire (FAQ) [24] was administered for social functioning, the Center for Epidemiologic Studies Depression Scale (CESD) [25] was adopted for mood, and the Hachinski Ischemic Index (HIS) [26] was used to differentiate between degenerative and vascular etiologies. The Clinical Dementia Rating Scale (CDR) [27] was administered to assess cognitive level. Finally, standardized general and neurological examinations were performed. At the end of each workday, the expert panel and interviewers reviewed all data and assigned final cognitive diagnoses. When a consensus could not be reached, an expert returned to the residence the following day for further evaluation. Participants were divided into the following categories according to cognitive level: cognitively normal (CN), MCI, or dementia. CN was assigned when participants achieved a normal score in all four cognitive domains and scored 0 on the CDR. Dementia was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria for dementia [28]. Participants who were neither normal nor demented
were classified as having MCI [1,2]. The criteria included the following elements: cognitive impairment in one or more domains (scored at least 1.5 standard deviations below the norm in memory, executive function, language, or visuoconstructive skill), global CDR score of 0.5 or less, preserved ability to perform daily activities and social functions, and absence of dementia. Once the MCI diagnosis was made, brain scans were evaluated for etiological diagnosis. For economic reasons, computed tomography (CT) scans were performed first, followed by magnetic resonance imaging (MRI) if the etiology could not be determined based on the CT images. Brain scans were not necessary when the diagnosis could be made based on clinical data (e.g., patients with an identified stroke history and temporal relationship between the stroke and cognitive impairment, which strongly supports a diagnosis of MCI resulting from cerebrovascular disease [CVD]) or when patients had undergone a brain scan within 3 months before the cognitive assessments. Ultimately, 1676 (78.43%) patients with MCI underwent brain imaging, including 475 who underwent MRI scans. We divided MCI into four etiological subtypes: MCI prodromal AD (MCI-A), MCI resulting from CVD (MCI-CVD), MCI with vascular risk factors (MCI-VRF), and MCI caused by other diseases (MCI-O). The diagnoses of etiological subtypes were made by consensus, taking into account all collected data (clinical characteristics, neuropsychological profile, brain images, medical history, and lifestyle). Patients with all of the following elements were diagnosed with MCI-A [3]: (1) memory impairment
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J. Jia et al. / Alzheimer’s & Dementia - (2013) 1–9
with an insidious onset and gradual progression, (2) neuropsychological profile supporting prodromal AD (scores at least 1.5 standard deviations below the age- and educationadjusted norms on the WHO-UCLA AVLT long-delay free recall), (3) neuroimaging features consistent with incipient AD (i.e., hippocampal and entorhinal cortex atrophy) and no other diseases, and (4) no other medical or neuropsychiatric conditions that could account for the cognitive impairment. MCI-CVD was diagnosed according to one of the following criteria [29,30]: (1) cognitive impairment caused by strategic infarcts or multiple infarcts, as indicated by a sudden onset or stepwise progression, and temporal relationship between cognitive decline and infarcts confirmed by MRI or CT scan; (2) cognitive impairment due to subcortical small-vessel disease, as suggested by minor neurological signs, moderate white matter changes (at least one region score of 2 according to the Wahlund rating scale) [31], and/or multiple lacunar infarcts (2) on brain imaging; and (3) cognitive impairment resulting from hemorrhagic stroke as supported by the temporal relationship between cognitive deficits and cerebral hemorrhage confirmed by CT scans. The detailed criteria for MCI-VRF were (1) patients with long-term (5 years) vascular risk factors (such as hypertension, diabetes, high cholesterol, cardiac disease, etc.), (2) neuroimaging scans showing neither evidence for AD nor features of cerebral vascular lesions, and (3) no other medical or neuropsychiatric conditions that could explain the cognitive impairment. Patients with cognitive impairment attributed to conditions other than MCI-A, MCI-CVD, and MCI-VRF were classified as MCI-O. In such cases, several potential etiologies, such as Parkinson’s disease, alcohol and drug abuse, depression, and psychiatric illness may be involved. Patients with no identified diseases contributing to the cognitive impairment were also classified as MCI-O. 3. Statistical analysis We conducted all analyses using the Statistical Package for the Social Sciences version 16.5 (SPSS Inc., Chicago, IL). Descriptive statistics (sociodemographic characteristics and comorbidities) of the study populations were calculated by percentages. The c2 test was used to assess group differences between urban and rural populations. The prevalence of overall MCI and that of its subtypes were then calculated with 95% confidence intervals (CIs) in the total, rural, and urban populations. The crude prevalence was directly calculated by dividing the number of patients with MCI by the corresponding population. Standardized prevalence rates were estimated using the direct standardization method adjusted by age and sex to the total Chinese population (according to the census conducted in 2005). The prevalence ratios (PRs) between urban and rural populations were calculated, and differences were analyzed using c2 tests. The age- and sex-specific prevalences were then calculated. Finally, using sex, age, education, occupa-
tion, smoking, alcohol consumption, and comorbidities as the independent variables and diagnosis as the dependent variable, binary logistic regression analyses with the forward conditional method were performed to examine the potential risk factors for each subtype. The normal cognitive population (those with neither MCI nor dementia) was used as the reference group for all regression models. Odds ratios (ORs) were calculated for each variable, and a significance level of P , .05 was required for variable retention in the model. 4. Results Table 1 details the major characteristics of the study populations. Notably, very large differences in education and occupation were found between the urban and rural participants (P , .001 for both). Most participants (86.4%) living in rural areas were farm laborers, and nearly half (48.2%) were illiterate. Urban participants were better educated and held more diverse occupations. Table 2 shows the crude and standardized prevalences of MCI and its etiological subtypes in the total, urban, and rural populations. MCI was high in rural (25.1%, Table 1 Characteristics of the urban and rural sample populations Characteristics
Urban, n (%*) Rural, n (%*) Px
Overall Male Age, years 65–69 70–74 75–79 80 Education, years ,1 1–6 7–9 10 Occupation Farm laborer Nonfarm laborer Civil servants and professional Others Cigarette smokingy Alcohol consumptionz Comorbidity Hypertension Hyperlipidemia Diabetes mellitus Heart disease Stroke
6096 (100) 2633 (43.2)
4180 (100) 1746 (41.8)
1767 (29.0) 2088 (34.3) 1399 (22.9) 842 (13.8)
1450 (34.7) 1141 (27.3) 950 (22.7) 639 (15.3)
,.001
1076 (17.7) 1993 (32.7) 1161 (19.0) 1822 (29.9)
2015 (48.2) 1621 (38.8) 409 (9.8) 129 (3.1)
,.001
400 (6.6) 3195 (52.4) 2103 (34.5) 67 (1.1) 1617 (26.5) 524 (8.6)
3613 (86.4) 280 (6.7) 218 (5.2) 0 (0.0) 1524 (36.5) 717 (17.2)
,.001
2870 (47.1) 1454 (23.9) 964 (15.8) 1599 (26.2) 979 (16.1)
1431 (34.2) 464 (11.1) 829 (19.8) 437 (10.5) 434 (10.4)
,.001 ,.001 ,.001 ,.001 ,.001
.152
,.001 ,.001
*The characteristics were calculated by percentages (in parentheses). Totals do not all add up to 100% because of missing data. y Cigarette smoking was defined as having smoked at least 100 cigarettes in one’s lifetime. z Alcohol drinking was defined as consumption of at least 0.1 drink/day for 1 year and 1 drink is equal to 10 g of pure alcohol. x 2 c tests were used to assess group differences between urban and rural populations.
J. Jia et al. / Alzheimer’s & Dementia - (2013) 1–9
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Table 2 Prevalence (95% CI) of MCI and MCI subtypes in the total, urban, and rural populations Crude*
PR (rural/urban)
Standardized*
Diagnosis
Total
Urban
Rural
Total
Urban
Rural
MCI MCI-A MCI-CVD MCI-VRF MCI-O
20.8 (20.0–21.6) 6.1 (5.7–6.6) 3.8 (3.4–4.2) 4.9 (4.5–5.4) 5.9 (5.5–6.4)
17.9 (16.9–18.8) 4.3 (3.8–4.8) 4.0 (3.5–4.5) 5.0 (4.4–5.5) 4.6 (4.1–5.2)
25.1 (23.8–26.4) 8.8 (8.0–9.7) 3.6 (3.0–4.3) 4.0 (4.3–5.6) 7.8 (7.0–8.6)
19.5 (18.8–20.3) 5.6 (5.2–6.0) 3.4(3.3–4.1) 4.6 (4.2–5.0) 5.6(5.2–6.1)
16.8 (15.9–17.8) 3.9 (3.4–4.4) 3.9 (3.4–4.4) 4.7 (4.1–5.2) 4.4(3.9–4.9)
23.4 (22.1–24.7) 8.1 (7.3–8.9) 3.5 (2.9–4.0) 4.5 (3.9–5.2) 7.3 (6.5–8.1)
Py 1.5 (1.4–1.7) 2.2 (1.8–2.6) 0.9 (0.7–1.1) 1.0 (0.8–1.2) 1.7 (1.4–2.0)
,.001 ,.001 .2719 .7756 ,.001
Abbreviations: CI, confidence intervals; MCI, mild cognitive impairment; MCI-A, MCI prodromal Alzheimer disease; MCI-CVD, MCI resulting from cerebrovascular disease; MCI-VRF, MCI with vascular risk factors; MCI-O, MCI caused by other factors; PR, prevalence ratio. *Prevalence (%) and 95% CIs (in parentheses) provided. y Comparison between urban and rural populations after age and sex standardization using c2 tests.
95% CI 5 23.8–26.4%) and urban (17.9%, 95% CI 5 16.9–18.8%) populations for an overall prevalence of 20.8% (95% CI 5 20.0–21.6%). The rural population had higher prevalences of MCI, MCI-A, and MCI-O
than did the urban population (P , .001). No significant differences in MCI-CVD (P 5 .2719) or MCI-VRF (P 5 .7756) were found between the rural and urban populations. The frequencies of the four etiological subtypes
Fig. 2. Age- and sex- specific prevalence of MCI and its etiological subtypes in the total population. The overall MCI and the MCI-A increased sharply by 5-year age intervals in both sexes. MCI-O and MCI-VRF also showed a relatively modest age trend in both sexes. MCI-CVD decreased after the age of 80 years in both sexes (A, B, C). Abbreviations: MCI, mild cognitive impairment; MCI-A, MCI prodromal Alzheimer disease; MCI-CVD, MCI resulting from cerebrovascular disease; MCI-VRF, MCI with vascular risk factors; MCI-O, MCI caused by other factors.
J. Jia et al. / Alzheimer’s & Dementia - (2013) 1–9
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of MCI were estimated further. The percentages of MCIA, MCI-CVD, MCI-VRF, and MCI-O were 29.5%, 18.3%, 23.7%, and 28.5%, respectively, among all of the patients. The vascular-related MCI (including MCI-CVD and MCI-VRF) constituted the most frequent subcategory (42.0%). The age- and gender-specific prevalences of MCI and its subtypes in the total population are shown in Figure 2. The prevalences of overall MCI, MCI-A, MCI-VRF, and MCI-O increased with age in both sexes. Notably, the prevalence of the overall MCI and MCI-A increased sharply by 5-year age intervals. The increase in the other subtypes was relatively modest. MCI-CVD differed in that it decreased after 80 years of age in both sexes. A similar age pattern was identified in the rural and urban populations. Binary logistic regression analysis found that older age was significantly associated with greater odds of developing MCI and all MCI subtypes, whereas higher education was identified as a protective factor for all MCI groups. Compared with farm laborers, other occupations provided a protective effect for total MCI, MCI-AD, and MCI-O (Table 3). Compared with women, men had a greater risk of developing MCI-CVD. Stroke increased the risk of overall MCI and MCI-CVD. Hypertension, dia-
betes mellitus, and heart disease were associated with an increased risk of developing MCI-VRF. Hypertension and diabetes mellitus also tended to increase the risk of MCI-CVD. Hyperlipidemia, smoking, and alcohol intake were not associated with overall MCI or any of its subtypes. 5. Discussion The current study is the first to report the prevalence of MCI and its etiological subtypes in multiregional centers of rural and urban populations in China. We found an overall MCI prevalence of 20.8% (95% CI 5 20.0–21.6%), indicating that approximately 23.86 million individuals aged 65 years or older suffer from MCI in China. Further study of the etiological subtypes identified that vascularrelated MCI subtypes (MCI-CVD and MCI-VRF) are most frequent (42.0% vs 29.5% for MCI-A and 28.5% for MCI-O), indicating that interventions for stroke and its risk factors are highly important for MCI prevention. The findings that poor education is the major factor associated with the higher prevalence of MCI in rural compared with urban populations reveals the importance of establishing appropriate educational programs for rural populations.
Table 3 Logistic regression models for MCI and the main subtypes
Characteristics Sex Female Male Age, years 65–69 70–74 75–79 80 Education, years ,1 1-6 7-9 10 Occupation Farm laborer Nonfarm laborer Official and professional Comorbidity Hypertension Diabetes mellitus Heart disease Stroke
Total MCI
MCI-A
MCI-CVD
MCI-VRF
MCI-O
OR (95% CI)
OR (95% CI)
OR (95% CI)
OR (95% CI)
OR (95% CI)
Ref.
Ref.
Ref. 1.79 (1.52–2.09) 2.39 (2.03–2.83) 3.56 (2.96–4.27)
Ref. 1.61 (1.18–2.18) 2.73 (2.02–3.69) 4.37 (3.19–5.99)
Ref. 0.53 (0.46–0.60) 0.36 (0.29–0.44) 0.31 (0.24–0.39) Ref. 0.75 (0.65–0.85) 0.63 (0.50–0.79)
2.21 (1.90–2.56)
Ref. 1.36 (1.035–1.780)
Ref.
Ref.
Ref. 1.52 (1.07–2.16) 1.84 (1.28–2.67) 2.06 (1.34–3.15)
Ref. 1.79 (1.34–2.38) 2.06 (1.52–2.80) 3.10 (2.23–4.30)
Ref. 1.88 (1.43–2.48) 2.24 (1.67–3.01) 3.68 (2.68–5.05)
Ref. 0.39 (0.31–0.50) 0.17 (0.11–0.28) 0.12 (0.07–0.23)
Ref. 0.56 (0.41–0.78) 0.31 (0.20–0.48) 0.22 (0.15–0.33)
Ref. 0.53 (0.42–0.68) 0.429 (0.30–0.59) 0.35 (0.25–0.47)
Ref. 0.57 (0.45–0.72) 0.40 (0.27–0.60) 0.42 (0.26–0.68)
Ref. 0.59 (0.46–0.75) 0.68 (0.43–1.09)
Ref.
Ref.
Ref. 0.74 (0.59–0.93) 0.50 (0.32–0.78)
1.32 (1.00–1.75) 1.38 (1.02–1.86)
3.05 (2.44–3.82) 1.48 (1.16–1.87) 1.96 (1.57–2.43)
47.53 (33.9–66.59)
Abbreviations: MCI, mild cognitive impairment; MCI-A, MCI prodromal Alzheimer disease; MCI-CVD, MCI resulting from cerebrovascular disease; MCI-VRF, MCI with vascular risk factors; MCI-O, MCI caused by other factors; OR, odds ratio; CI, confidence interval; Ref., reference. NOTE. Where numbers are missing are variables that were not significant and therefore not included in the models. Binary logistic regression analyses with the forward conditional method were used with sex, age, education, occupation, smoking, alcohol consumption, and comorbidities (hypertension, high cholesterol, diabetes mellitus, heart disease, and stroke) as the independent variables and diagnosis as the dependent variable. OR and 95% CIs (in parentheses) provided.
J. Jia et al. / Alzheimer’s & Dementia - (2013) 1–9
5.1. High MCI prevalence in China To date, only a few surveys on MCI have been conducted in China [12–17]. However, most of these studies were regional, small, and restricted to urban residents. The nationwide prevalence of MCI in urban and rural populations remains unknown. The prevalence reported by these previous surveys ranges from 0.4% to 12.7% [12–17], which is significantly lower than our estimate. The disparity may be explained by the use of different diagnostic criteria. Different regions such as rural or urban areas and age intervals of the participants may also affect the final data. Most previous studies in China were conducted among urban residents, used the amnestic MCI criteria, and recruited subjects aged 55 or 60 years (and few older subjects). As a result, a relatively low prevalence has been reported (6.1–12.3% even after adjustment for individuals older than 65 years) [15–17]. In contrast, our survey used broader criteria (various presentations with different causes) [1,2], which are more suitable for the early detection of MCI resulting from different causes. Moreover, our study recruited individuals aged 65 years and older, a standard that is adopted worldwide. Furthermore, we included more elderly people from rural areas (4180) than did previous studies. These factors may account for the higher estimated prevalences found in the present study. A high prevalence of MCI has also been reported in other countries. Surveys in the United States reported that 16.0–22.2% of elderly subjects met the broadened MCI criteria [4,10,32]. A nationwide survey in South Korea found an MCI prevalence of 24.1% [33]. In Japan, 18.9% of participants were diagnosed with MCI [6]. The present study shows that the prevalence of MCI in community elders in China is higher than expected, but it is similar to that in some Western and other Asian countries. 5.2. Higher MCI prevalence in rural than in urban populations We found a higher prevalence of MCI in the rural than in the urban population. An epidemiological survey conducted in Portugal supported this finding [11]. The less-advanced conditions (illiteracy and low-skill occupations) in rural areas may explain the high prevalence of MCI. Nearly 70% of participants in our study were more than 70 years of age. These people were born during World War II and were deprived of educational opportunities. This situation was more pronounced in rural areas because of poverty at that time. As a result, 48.2% of the rural participants were illiterate, which is 2.7 times the rate for the urban elders. A low educational level has been consistently found to be an independent risk factor for cognitive impairment [10,11], and the present study revealed a steep increase in the MCI prevalence as the educational level declined. Exceedingly poor literacy
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levels may be the primary reason for the high prevalence of MCI in rural areas. Occupation may be another important contributing factor. China is traditionally an agricultural country, and most elderly people in rural areas are farmers. Working in low-skill occupations has been repeatedly identified as a risk factor for dementia [34]. In our study, the regression analysis also revealed that working as a farm laborer was associated with a greater risk of developing MCI. The focus on appropriate programs should be shifted to rural populations to enhance the educational level, which may reduce the incidence of MCI in the future. 5.3. Rationale for subclassification of MCI with vascular risk factors On the basis of clinical experience and an extensive literature review, the current study proposed a concise etiological subcategory of MCI including a vascular risk factor-related subtype with no clear CVD or visible radiologic evidence on conventional imaging (MCIVRF). It is necessary to elaborate the application of the MCI-VRF subtype, a subtype with preventive potential that is frequently seen in clinical practice. A long duration of vascular risk factors is known to increase the risk of MCI through different pathways. Diabetes mellitus may increase the risk of cognitive impairment mediated by hyperglycemia toxicity, insulin resistance, and oxidative stress [35–37]. Hypertension may result in loss of brain volume [38], leading to a decline in global cognition [39]. Moreover, diabetes and hypertension might evoke microinfarcts and microbleeds [40,41], which typically go undetected on conventional structural MRI [42]. Increasing evidence has suggested that microinfarcts and microbleeds are associated with a greater risk of developing cognitive impairment, even after controlling for macroscopic infarcts and other pathological covariates [43]. Thus, studies conducted from various viewpoints have verified that vascular risk factors may act on cognitive decline in the absence of visible cerebral vascular lesions on conventional imaging. Therefore, MCI-VRF was introduced in the current study as a specific MCI subtype. MCI-VRF may be an earlier stage before MCI-CVD and of greater importance for early intervention. 5.4. Higher prevalence of vascular-related MCI subtypes The present study estimated the prevalence of four etiological subtypes of MCI. The results showed that vascular-related MCI subtypes, including MCI-CVD and MCI-VRF, were the most common subtypes (42.0%). This may reflect an unexpected constituent ratio for the MCI pattern. In fact, China has a high incidence of cerebral vascular disease because of increased rates of hypertension, diabetes, and high cholesterol in recent years [44]. Stroke directly injures the brain and affects
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cognition. The frequency of cognitive impairment after stroke reached 37.1% in one Chinese survey [45]. Moreover, several studies have shown that hypertension and diabetes are associated with cognitive decline independent of identified strokes [46,47]. The high prevalence of cerebral vascular disease and its risk factors in China may underlie the high prevalence of MCI with vascular components. This result is not unique to China. A study in Portugal, a country in which stroke is prevalent [48], revealed that CIND associated either with CVD or vascular risk factors was more frequent (39.7%) than CIND of any other etiology [11]. In the United States, CIND with a vascular origin accounted for 25.6% of the total cases, second only to the prodromal AD subtype (34.2%) [10]. These results emphasize the importance of vascular-related MCI and call for more resources and greater effort toward addressing these relatively neglected MCI subtypes. Several limitations need to be discussed. We subcategorized MCI into four subtypes according to potential etiologies in the current survey. Although the MCI-VRF subtype is of great importance in terms of identifying individuals in whom prevention will be effective and thus reduce the prevalence of MCI, this concept was proposed for the first time here and requires validation in longitudinal follow-up. The current study assessed four cognitive domains (memory, executive function, language, and visuoconstructive skill) in all participants. However, only one test was adopted for each cognitive domain, which may have increased the occurrence of false-positive or false-negative results and thus have led to misdiagnosis in some cases. Epidemiological studies usually adopt a screening procedure and may miss some real patients. However, in the current study, the results of a detailed clinical history, standardized neuropsychological tests, and systematic general and neurologic examinations were collected for all participants. Misdiagnosis was expected to be relatively low by this single-stage assessment. The high prevalence of MCI in Chinese elders imposes a heavy burden that must be met with appropriate public policies. The higher MCI prevalence in rural than in urban areas indicates that special attention must be given to implementing new strategies for these areas. The constituent ratio of MCI indicates that vascular-related subtypes were more common than any other, suggesting that more work should be done on the prevention of stroke and intervention in its risk factors. Follow-up studies are necessary to refine our MCI subtype classifications in future. Acknowledgements All authors report no conflicts of interest. This work was supported by the National Key Technology R&D Program in the Eleventh Five-Year Plan Period (2006BAI02B01),
the Major Program of Science and Technology Plan of Beijing (D111107003111009), and the Medical Elite Foundation from the Peking Public Health Bureau (2011-3-089).
RESEARCH IN CONTEXT
1. Systematic review: We searched the MEDLINE and China National Knowledge Infrastructure (CNKI) databases for the past 10 years, focusing on population-based studies on the prevalence of MCI. There have been no nationwide epidemiologic studies on MCI so far, in particularly on its etiological classification by imaging, in rural and urban populations in China. 2. Interpretation: In the present study, we found a high MCI prevalence (20.8%) in community Chinese elders and a higher prevalence in rural (25.1%) than urban populations. By etiological classification, vascular-related MCI is identified as the most common subtype (42.0%). The results call for a special policy to improve conditions in rural areas of China and a useful strategy for early intervention in patients with vascular-related MCI. 3. Future directions: Larger samples are necessary to refine our MCI subtype classifications, and an interventional clinical trial might be needed to further validate their clinical relevance.
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[27] Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993;43:2412–4. [28] American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 4th ed. Washington, DC: American Psychiatric Association; 1994. [29] Ingles JL, Wentzel C, Fisk JD, Rockwood K. Neuropsychological predictors of incident dementia in patients with vascular cognitive impairment, without dementia. Stroke 2002;33:1999–2002. [30] Garrett KD, Browndyke JN, Whelihan W, Paul RH, DiCarlo M, Moser DJ, et al. The neuropsychological profile of vascular cognitive impairment–no dementia: comparisons to patients at risk for cerebrovascular disease and vascular dementia. Arch Clin Neuropsychol 2004;19:745–57. [31] Wahlund LO, Barkhof F, Fazekas F, Bronge L, Augustin M, Sjogren M, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke 2001;32:1318–22. [32] Petersen RC, Roberts RO, Knopman DS, Geda YE, Cha RH, Pankratz VS, et al. Prevalence of mild cognitive impairment is higher in men. The Mayo Clinic Study of Aging. Neurology 2010;75:889–97. [33] Kim KW, Park JH, Kim MH, Kim MD, Kim BJ, Kim SK, et al. A nationwide survey on the prevalence of dementia and mild cognitive impairment in South Korea. J Alzheimers Dis 2011;23:281–91. [34] Scazufca M, Almeida OP, Menezes PR. The role of literacy, occupation and income in dementia prevention: the Sao Paulo Ageing & Health Study (SPAH). Int Psychogeriatr 2010;22:1209–15. [35] Strachan MW, Frier BM, Deary IJ. Type 2 diabetes and cognitive impairment. Diabet Med 2003;20:1–2. [36] Logroscino G, Kang JH, Grodstein F. Prospective study of type 2 diabetes and cognitive decline in women aged 70-81 years. BMJ 2004;328:548. [37] Whitmer RA. Type 2 diabetes and risk of cognitive impairment and dementia. Curr Neurol Neurosci Rep 2007;7:373–80. [38] Gorelick PB. Risk factors for vascular dementia and Alzheimer disease. Stroke 2004;35:2620–2. [39] Luck T, Luppa M, Briel S, Riedel-Heller SG. Incidence of mild cognitive impairment: a systematic review. Dement Geriatr Cogn Disord 2010;29:164–75. [40] Biessels GJ, Staekenborg S, Brunner E, Brayne C, Scheltens P. Risk of dementia in diabetes mellitus: a systematic review. Lancet Neurol 2006;5:64–74. [41] Wang LY, Larson EB, Sonnen JA, Shofer JB, McCormick W, Bowen JD, et al. Blood pressure and brain injury in older adults: findings from a community-based autopsy study. J Am Geriatr Soc 2009; 57:1975–81. [42] Smith EE, Schneider JA, Wardlaw JM, Greenberg SM. Cerebral microinfarcts: the invisible lesions. Lancet Neurol 2012;11:272–82. [43] Arvanitakis Z, Leurgans SE, Barnes LL, Bennett DA, Schneider JA. Microinfarct pathology, dementia, and cognitive systems. Stroke 2011;42:722–7. [44] Yang ZJ, Liu J, Ge JP, Chen L, Zhao ZG, Yang WY. Prevalence of cardiovascular disease risk factor in the Chinese population: the 20072008 China National Diabetes and Metabolic Disorders Study. Eur Heart J 2012;33:213–20. [45] Zhou DH, Wang JY, Li J, Deng J, Gao C, Chen M. Frequency and risk factors of vascular cognitive impairment three months after ischemic stroke in china: the Chongqing stroke study. Neuroepidemiology 2005;24:87–95. [46] Posner HB, Tang MX, Luchsinger J, Lantigua R, Stern Y, Mayeux R. The relationship of hypertension in the elderly to AD, vascular dementia, and cognitive function. Neurology 2002;58:1175–81. [47] Fontbonne A, Berr C, Ducimetiere P, Alperovitch A. Changes in cognitive abilities over a 4-year period are unfavorably affected in elderly diabetic subjects: results of the Epidemiology of Vascular Aging Study. Diabetes Care 2001;24:366–70. [48] Correia M, Silva MR, Matos I, Magalhaes R, Lopes JC, Ferro JM, et al. Prospective community-based study of stroke in Northern Portugal: incidence and case fatality in rural and urban populations. Stroke 2004;35:2048–53.
Featured Articles
The prevalence of mild cognitive impairment and its etiological subtypes in elderly Chinese Jianping Jiaa,*, Aihong Zhoua, Cuibai Weia, Xiangfei Jiab, Fen Wanga, Fang Lic, Xiaoguang Wud, Vincent Moke, Serge Gauthierf, Muni Tangg, Lan Chuh, Youlong Zhoui, Chunkui Zhouj, Yong Cuik, Qi Wanga, Weishan Wangl, Peng Yinm, Nan Hum, Xiumei Zuoa, Haiqing Songa, Wei Qina, Liyong Wua, Dan Lia, Longfei Jian, Juexian Songa, Ying Hana, Yi Xinga, Peijie Yanga, Yuemei Lia, Yuchen Qiaoa, Yi Tanga, Jihui Lvl, Xiumin Donga a
Department of Neurology, Xuan Wu Hospital, Capital Medical University, Beijing, People’s Republic of China b Department of Computer Science, University of Otago, Dunedin, New Zealand c Department of Neurology, Fu Xing Hospital, Capital Medical University, Beijing, People’s Republic of China d Evidence-Based Medicine Center, Xuan Wu Hospital, Capital Medical University, Beijing, People’s Republic of China e Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China f McGill Center for Studies in Aging, McGill University, Montreal, Quebec, Canada g Department of Geriatrics, Guangzhou Brain Hospital, Affiliated Hospital of Guangzhou Medical College, Guangzhou, Guangdong Province, People’s Republic of China h Department of Neurology, Affiliated Hospital of Guiyang Medical College, Guiyang, Guizhou Province, People’s Republic of China i Department of Neurology, Third Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan Province, People’s Republic of China j Department of Neurology, First Hospital of Jilin University, Changchun, Jinlin Province, People’s Republic of China k Department of Neurology, Fourth Hospital of Jilin University, Changchun, Jinlin Province, People’s Republic of China l Department of Neurology, Beijing Geriatric Hospital, Beijing, People’s Republic of China m National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing, People’s Republic of China n Department of Neurology, Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China
Abstract
Background: Epidemiologic studies on mild cognitive impairment (MCI) are limited in China. Methods: Using a multistage cluster sampling design, a total of 10,276 community residents (6096 urban, 4180 rural) aged 65 years or older were evaluated and diagnosed with normal cognition, MCI, or dementia. MCI was further categorized by imaging into MCI caused by prodromal Alzheimer’s disease (MCI-A), MCI resulting from cerebrovascular disease (MCI-CVD), MCI with vascular risk factors (MCI-VRF), and MCI caused by other diseases (MCI-O). Results: The prevalences of overall MCI, MCI-A, MCI-CVD, MCI-VRF, and MCI-O were 20.8% (95% confidence interval [CI] 5 20.0–21.6%), 6.1% (95% CI 5 5.7–6.6%), 3.8% (95% CI 5 3.4–4.2%), 4.9% (95% CI 5 4.5–5.4%), and 5.9% (95% CI 5 5.5–6.4%) respectively. The rural population had a higher prevalence of overall MCI (23.4% vs 16.8%, P , .001). Conclusions: The prevalence of MCI in elderly Chinese is higher in rural than in urban areas. Vascular-related MCI (MCI-CVD and MCI-VRF) was most common. Ó 2013 The Alzheimer’s Association. All rights reserved.
Keywords:
Mild cognitive impairment; Etiological subtypes; Prevalence; Risk factors
1. Introduction
*Corresponding author. Tel.: 0086-10-83198730; Fax: 0086-10-83171070. E-mail address: [email protected]
Mild cognitive impairment (MCI) constitutes an intermediate stage between normal aging and dementia [1,2]. As a key interventional target for dementia, MCI has been an important
1552-5260/$ - see front matter Ó 2013 The Alzheimer’s Association. All rights reserved. http://dx.doi.org/10.1016/j.jalz.2013.09.008
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research topic during the past several decades. Initially, Petersen and colleagues characterized MCI as memory impairment, referred to as amnestic MCI in subsequent years [3]. Thereafter, the concept of MCI has been broadened to include patients with cognitive impairments in the nonmemory domain or multiple domains [1,2]. Currently, epidemiological studies usually subcategorize MCI according to the impaired cognitive domains [4–8], which is less helpful for etiological intervention. MCI demonstrates considerable heterogeneity regarding its etiology [1,2]. MCI has been attributed to numerous etiologies, such as Alzheimer’s pathology, ischemia, trauma, psychiatric disorders, and others [1,2]. Understanding the etiologies of MCI is imperative in terms of establishing precise prevention and treatment strategies. Some longitudinal studies have already addressed various MCI etiologies [9]. However, only two epidemiological studies investigated the etiology of “cognitive impairment, no dementia” (CIND), a concept similar to the broadened concept of MCI [10,11]. A U.S. survey used 12 subcategories, including prodromal Alzheimer’s disease (AD), amnestic MCI, vascular cognitive impairment without dementia, stroke, medical conditions or sensory impairment, and other origins. These classifications are based not only on etiologies but also on cognitive domains, and they overlap one another to some degree [10]. Another study conducted in Portugal subcategorized MCI as general vascular disorders, depression, cerebrovascular disorders, alcohol or drug abuse, traumatic brain injury, and other neurological conditions, but it provided no definitive criteria [11]. MCI due to Alzheimer’s pathology, the first identified cause of dementia, was also neglected in that study [11]. Furthermore, the diagnostic subtypes in these two studies were loosely defined, relied substantially on clinical judgment, and lacked the support of brain imaging findings, which are necessary for an etiological diagnosis [10,11]. Few surveys on MCI have been conducted in China [12–17]. Furthermore, most of these studies were regional and recruited only individuals from urban populations. The nationwide prevalence of MCI in rural and urban populations of China is currently unavailable. In the present study, using a more precise etiological classification based on brain scans, we estimated the prevalence of MCI and its etiological subtypes in rural and urban populations of community-dwelling elderly Chinese individuals.
2. Methods 2.1. Study design and samples A multistage, cluster sampling design was used. First, we chose five representative regional centers across China (Changchun for Northeast China, Beijing for North China, Zhengzhou for Central China, Guangzhou for South China,
and Guiyang for Southwest China). Using random-number tables, 10 urban districts and 12 rural counties were randomly selected from these centers. Finally, within the selected districts and counties, 30 urban communities and 45 rural villages were randomly sampled. The investigation was conducted from October 2008 through October 2009. The inclusion criteria were being aged 65 years and older, Han Chinese, and listed in the census of the community registry office as well as living in the target community for at least 1 year preceding the survey date. Institutionalized people were not included. Two eligible populations were drawn from urban (n 5 8414) and rural (n 5 5392) areas. Subjects who were untraceable or deceased, who refused to participate, or who had incomplete data for diagnostic purposes were excluded. Finally, a total of 10,276 residents (6096 urban and 4180 rural) participated in the survey (Figure 1). The participation rate among rural residents was higher than that among urban residents (77.5% vs 72.5%, P , .001). Participation rates did not differ significantly among centers. No significant differences in age, sex, or education distribution were found between participants and nonparticipants in the urban or the rural population. The medical ethics committee at each center approved the study. Informed consent was directly obtained from each subject or was indirectly obtained from his or her guardian. 2.2. Assessment and diagnosis procedure Eight to 10 pairs of interviewers were recruited in each region. Most were junior neurologists or senior graduate students specializing in neurology. Furthermore, we set up a regional expert panel that included two neurologists and two neuropsychologists with special expertise in cognitive impairment disorders. All interviewers and experts received uniform training on neuropsychological assessment and diagnosis for 1 week and participated in a retraining course every 3 months thereafter. The interrater reliability for cognitive tests and diagnoses, which relied on videotaped interviews, was required to exceed 0.90. A two-step door-to-door diagnostic procedure was used. First, each specially trained interviewer pair conducted a semistructured interview with participants and their close informants at their residence. The interview lasted approximately 2 hours. Sociodemographic characteristics were collected. A questionnaire on cigarette smoking and alcohol consumption was administered. A self-report medical history questionnaire was also administered. Participants responded “yes” or “no” to ever being diagnosed with hypertension, diabetes, hypercholesterolemia, coronary artery disease, stroke, or other neurologic or systemic diseases. Medical records were examined as far back as possible. Thereafter, extended neuropsychological tests examining four cognitive domains were administered to
J. Jia et al. / Alzheimer’s & Dementia - (2013) 1–9
Eligible urban population: 8414
3
Eligible rural population: 5392
Non-respondents: 1676
Non-respondents: 772
1.
Refused: 1378
1.
2.
Untraceable: 185
2.
Refused: 625 Untraceable: 82
3.
Life-threatening illness: 92
3.
Life-threatening illness: 52
4.
Deceased: 21
4.
Deceased: 13
Respondents: 4620
Respondents: 6738 Excluded: 642
Excluded: 440
1.
Incomplete data: 497
1.
Incomplete data: 334
2.
Repeated or doubtful data: 62
2.
Repeated or doubtful data: 36
3.
Hearing or vision deficit: 53
3.
Hearing or vision deficit: 48
4.
Other reasons: 30
4.
Other reasons: 22
Participants: 6096
Normal: 4740
MCI-A: 261
MCI: 1089
MCI-CVD: 243
MCI-VRF: 302
Participants: 4180
Dementia: 267
MCI-O: 283
Normal: 2871
MCI-A: 369
MCI: 1048
MCI-CVD: 149
Dementia: 261
MCI-VRF: 205
MCI-O: 325
Fig. 1. Study flow chart. Abbreviations: MCI, mild cognitive impairment; MCI-A, MCI prodromal Alzheimer disease; MCI-CVD, MCI resulting from cerebrovascular disease; MCI-VRF, MCI with vascular risk factors; MCI-O, MCI caused by other factors.
participants: (1) memory—the World Health OrganizationUniversity of California–Los Angeles Auditory Verbal Learning Test (WHO-UCLA AVLT), including immediate recall, short-delay free recall (3 minutes), long-delay free recall (30 minutes), and long-delay recognition [18]; (2) executive function—Trail Making Test B [19]; (3) language— semantic verbal fluency test (category, animals) [20]; and (4) visuoconstructive skill—clock-drawing test (CDT) [21]. In addition, the Mini-Mental State Examination (MMSE) [22] and Montreal Cognitive Assessment (MoCA) [23] were performed to assess global cognition. The Functional Activities Questionnaire (FAQ) [24] was administered for social functioning, the Center for Epidemiologic Studies Depression Scale (CESD) [25] was adopted for mood, and the Hachinski Ischemic Index (HIS) [26] was used to differentiate between degenerative and vascular etiologies. The Clinical Dementia Rating Scale (CDR) [27] was administered to assess cognitive level. Finally, standardized general and neurological examinations were performed. At the end of each workday, the expert panel and interviewers reviewed all data and assigned final cognitive diagnoses. When a consensus could not be reached, an expert returned to the residence the following day for further evaluation. Participants were divided into the following categories according to cognitive level: cognitively normal (CN), MCI, or dementia. CN was assigned when participants achieved a normal score in all four cognitive domains and scored 0 on the CDR. Dementia was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria for dementia [28]. Participants who were neither normal nor demented
were classified as having MCI [1,2]. The criteria included the following elements: cognitive impairment in one or more domains (scored at least 1.5 standard deviations below the norm in memory, executive function, language, or visuoconstructive skill), global CDR score of 0.5 or less, preserved ability to perform daily activities and social functions, and absence of dementia. Once the MCI diagnosis was made, brain scans were evaluated for etiological diagnosis. For economic reasons, computed tomography (CT) scans were performed first, followed by magnetic resonance imaging (MRI) if the etiology could not be determined based on the CT images. Brain scans were not necessary when the diagnosis could be made based on clinical data (e.g., patients with an identified stroke history and temporal relationship between the stroke and cognitive impairment, which strongly supports a diagnosis of MCI resulting from cerebrovascular disease [CVD]) or when patients had undergone a brain scan within 3 months before the cognitive assessments. Ultimately, 1676 (78.43%) patients with MCI underwent brain imaging, including 475 who underwent MRI scans. We divided MCI into four etiological subtypes: MCI prodromal AD (MCI-A), MCI resulting from CVD (MCI-CVD), MCI with vascular risk factors (MCI-VRF), and MCI caused by other diseases (MCI-O). The diagnoses of etiological subtypes were made by consensus, taking into account all collected data (clinical characteristics, neuropsychological profile, brain images, medical history, and lifestyle). Patients with all of the following elements were diagnosed with MCI-A [3]: (1) memory impairment
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with an insidious onset and gradual progression, (2) neuropsychological profile supporting prodromal AD (scores at least 1.5 standard deviations below the age- and educationadjusted norms on the WHO-UCLA AVLT long-delay free recall), (3) neuroimaging features consistent with incipient AD (i.e., hippocampal and entorhinal cortex atrophy) and no other diseases, and (4) no other medical or neuropsychiatric conditions that could account for the cognitive impairment. MCI-CVD was diagnosed according to one of the following criteria [29,30]: (1) cognitive impairment caused by strategic infarcts or multiple infarcts, as indicated by a sudden onset or stepwise progression, and temporal relationship between cognitive decline and infarcts confirmed by MRI or CT scan; (2) cognitive impairment due to subcortical small-vessel disease, as suggested by minor neurological signs, moderate white matter changes (at least one region score of 2 according to the Wahlund rating scale) [31], and/or multiple lacunar infarcts (2) on brain imaging; and (3) cognitive impairment resulting from hemorrhagic stroke as supported by the temporal relationship between cognitive deficits and cerebral hemorrhage confirmed by CT scans. The detailed criteria for MCI-VRF were (1) patients with long-term (5 years) vascular risk factors (such as hypertension, diabetes, high cholesterol, cardiac disease, etc.), (2) neuroimaging scans showing neither evidence for AD nor features of cerebral vascular lesions, and (3) no other medical or neuropsychiatric conditions that could explain the cognitive impairment. Patients with cognitive impairment attributed to conditions other than MCI-A, MCI-CVD, and MCI-VRF were classified as MCI-O. In such cases, several potential etiologies, such as Parkinson’s disease, alcohol and drug abuse, depression, and psychiatric illness may be involved. Patients with no identified diseases contributing to the cognitive impairment were also classified as MCI-O. 3. Statistical analysis We conducted all analyses using the Statistical Package for the Social Sciences version 16.5 (SPSS Inc., Chicago, IL). Descriptive statistics (sociodemographic characteristics and comorbidities) of the study populations were calculated by percentages. The c2 test was used to assess group differences between urban and rural populations. The prevalence of overall MCI and that of its subtypes were then calculated with 95% confidence intervals (CIs) in the total, rural, and urban populations. The crude prevalence was directly calculated by dividing the number of patients with MCI by the corresponding population. Standardized prevalence rates were estimated using the direct standardization method adjusted by age and sex to the total Chinese population (according to the census conducted in 2005). The prevalence ratios (PRs) between urban and rural populations were calculated, and differences were analyzed using c2 tests. The age- and sex-specific prevalences were then calculated. Finally, using sex, age, education, occupa-
tion, smoking, alcohol consumption, and comorbidities as the independent variables and diagnosis as the dependent variable, binary logistic regression analyses with the forward conditional method were performed to examine the potential risk factors for each subtype. The normal cognitive population (those with neither MCI nor dementia) was used as the reference group for all regression models. Odds ratios (ORs) were calculated for each variable, and a significance level of P , .05 was required for variable retention in the model. 4. Results Table 1 details the major characteristics of the study populations. Notably, very large differences in education and occupation were found between the urban and rural participants (P , .001 for both). Most participants (86.4%) living in rural areas were farm laborers, and nearly half (48.2%) were illiterate. Urban participants were better educated and held more diverse occupations. Table 2 shows the crude and standardized prevalences of MCI and its etiological subtypes in the total, urban, and rural populations. MCI was high in rural (25.1%, Table 1 Characteristics of the urban and rural sample populations Characteristics
Urban, n (%*) Rural, n (%*) Px
Overall Male Age, years 65–69 70–74 75–79 80 Education, years ,1 1–6 7–9 10 Occupation Farm laborer Nonfarm laborer Civil servants and professional Others Cigarette smokingy Alcohol consumptionz Comorbidity Hypertension Hyperlipidemia Diabetes mellitus Heart disease Stroke
6096 (100) 2633 (43.2)
4180 (100) 1746 (41.8)
1767 (29.0) 2088 (34.3) 1399 (22.9) 842 (13.8)
1450 (34.7) 1141 (27.3) 950 (22.7) 639 (15.3)
,.001
1076 (17.7) 1993 (32.7) 1161 (19.0) 1822 (29.9)
2015 (48.2) 1621 (38.8) 409 (9.8) 129 (3.1)
,.001
400 (6.6) 3195 (52.4) 2103 (34.5) 67 (1.1) 1617 (26.5) 524 (8.6)
3613 (86.4) 280 (6.7) 218 (5.2) 0 (0.0) 1524 (36.5) 717 (17.2)
,.001
2870 (47.1) 1454 (23.9) 964 (15.8) 1599 (26.2) 979 (16.1)
1431 (34.2) 464 (11.1) 829 (19.8) 437 (10.5) 434 (10.4)
,.001 ,.001 ,.001 ,.001 ,.001
.152
,.001 ,.001
*The characteristics were calculated by percentages (in parentheses). Totals do not all add up to 100% because of missing data. y Cigarette smoking was defined as having smoked at least 100 cigarettes in one’s lifetime. z Alcohol drinking was defined as consumption of at least 0.1 drink/day for 1 year and 1 drink is equal to 10 g of pure alcohol. x 2 c tests were used to assess group differences between urban and rural populations.
J. Jia et al. / Alzheimer’s & Dementia - (2013) 1–9
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Table 2 Prevalence (95% CI) of MCI and MCI subtypes in the total, urban, and rural populations Crude*
PR (rural/urban)
Standardized*
Diagnosis
Total
Urban
Rural
Total
Urban
Rural
MCI MCI-A MCI-CVD MCI-VRF MCI-O
20.8 (20.0–21.6) 6.1 (5.7–6.6) 3.8 (3.4–4.2) 4.9 (4.5–5.4) 5.9 (5.5–6.4)
17.9 (16.9–18.8) 4.3 (3.8–4.8) 4.0 (3.5–4.5) 5.0 (4.4–5.5) 4.6 (4.1–5.2)
25.1 (23.8–26.4) 8.8 (8.0–9.7) 3.6 (3.0–4.3) 4.0 (4.3–5.6) 7.8 (7.0–8.6)
19.5 (18.8–20.3) 5.6 (5.2–6.0) 3.4(3.3–4.1) 4.6 (4.2–5.0) 5.6(5.2–6.1)
16.8 (15.9–17.8) 3.9 (3.4–4.4) 3.9 (3.4–4.4) 4.7 (4.1–5.2) 4.4(3.9–4.9)
23.4 (22.1–24.7) 8.1 (7.3–8.9) 3.5 (2.9–4.0) 4.5 (3.9–5.2) 7.3 (6.5–8.1)
Py 1.5 (1.4–1.7) 2.2 (1.8–2.6) 0.9 (0.7–1.1) 1.0 (0.8–1.2) 1.7 (1.4–2.0)
,.001 ,.001 .2719 .7756 ,.001
Abbreviations: CI, confidence intervals; MCI, mild cognitive impairment; MCI-A, MCI prodromal Alzheimer disease; MCI-CVD, MCI resulting from cerebrovascular disease; MCI-VRF, MCI with vascular risk factors; MCI-O, MCI caused by other factors; PR, prevalence ratio. *Prevalence (%) and 95% CIs (in parentheses) provided. y Comparison between urban and rural populations after age and sex standardization using c2 tests.
95% CI 5 23.8–26.4%) and urban (17.9%, 95% CI 5 16.9–18.8%) populations for an overall prevalence of 20.8% (95% CI 5 20.0–21.6%). The rural population had higher prevalences of MCI, MCI-A, and MCI-O
than did the urban population (P , .001). No significant differences in MCI-CVD (P 5 .2719) or MCI-VRF (P 5 .7756) were found between the rural and urban populations. The frequencies of the four etiological subtypes
Fig. 2. Age- and sex- specific prevalence of MCI and its etiological subtypes in the total population. The overall MCI and the MCI-A increased sharply by 5-year age intervals in both sexes. MCI-O and MCI-VRF also showed a relatively modest age trend in both sexes. MCI-CVD decreased after the age of 80 years in both sexes (A, B, C). Abbreviations: MCI, mild cognitive impairment; MCI-A, MCI prodromal Alzheimer disease; MCI-CVD, MCI resulting from cerebrovascular disease; MCI-VRF, MCI with vascular risk factors; MCI-O, MCI caused by other factors.
J. Jia et al. / Alzheimer’s & Dementia - (2013) 1–9
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of MCI were estimated further. The percentages of MCIA, MCI-CVD, MCI-VRF, and MCI-O were 29.5%, 18.3%, 23.7%, and 28.5%, respectively, among all of the patients. The vascular-related MCI (including MCI-CVD and MCI-VRF) constituted the most frequent subcategory (42.0%). The age- and gender-specific prevalences of MCI and its subtypes in the total population are shown in Figure 2. The prevalences of overall MCI, MCI-A, MCI-VRF, and MCI-O increased with age in both sexes. Notably, the prevalence of the overall MCI and MCI-A increased sharply by 5-year age intervals. The increase in the other subtypes was relatively modest. MCI-CVD differed in that it decreased after 80 years of age in both sexes. A similar age pattern was identified in the rural and urban populations. Binary logistic regression analysis found that older age was significantly associated with greater odds of developing MCI and all MCI subtypes, whereas higher education was identified as a protective factor for all MCI groups. Compared with farm laborers, other occupations provided a protective effect for total MCI, MCI-AD, and MCI-O (Table 3). Compared with women, men had a greater risk of developing MCI-CVD. Stroke increased the risk of overall MCI and MCI-CVD. Hypertension, dia-
betes mellitus, and heart disease were associated with an increased risk of developing MCI-VRF. Hypertension and diabetes mellitus also tended to increase the risk of MCI-CVD. Hyperlipidemia, smoking, and alcohol intake were not associated with overall MCI or any of its subtypes. 5. Discussion The current study is the first to report the prevalence of MCI and its etiological subtypes in multiregional centers of rural and urban populations in China. We found an overall MCI prevalence of 20.8% (95% CI 5 20.0–21.6%), indicating that approximately 23.86 million individuals aged 65 years or older suffer from MCI in China. Further study of the etiological subtypes identified that vascularrelated MCI subtypes (MCI-CVD and MCI-VRF) are most frequent (42.0% vs 29.5% for MCI-A and 28.5% for MCI-O), indicating that interventions for stroke and its risk factors are highly important for MCI prevention. The findings that poor education is the major factor associated with the higher prevalence of MCI in rural compared with urban populations reveals the importance of establishing appropriate educational programs for rural populations.
Table 3 Logistic regression models for MCI and the main subtypes
Characteristics Sex Female Male Age, years 65–69 70–74 75–79 80 Education, years ,1 1-6 7-9 10 Occupation Farm laborer Nonfarm laborer Official and professional Comorbidity Hypertension Diabetes mellitus Heart disease Stroke
Total MCI
MCI-A
MCI-CVD
MCI-VRF
MCI-O
OR (95% CI)
OR (95% CI)
OR (95% CI)
OR (95% CI)
OR (95% CI)
Ref.
Ref.
Ref. 1.79 (1.52–2.09) 2.39 (2.03–2.83) 3.56 (2.96–4.27)
Ref. 1.61 (1.18–2.18) 2.73 (2.02–3.69) 4.37 (3.19–5.99)
Ref. 0.53 (0.46–0.60) 0.36 (0.29–0.44) 0.31 (0.24–0.39) Ref. 0.75 (0.65–0.85) 0.63 (0.50–0.79)
2.21 (1.90–2.56)
Ref. 1.36 (1.035–1.780)
Ref.
Ref.
Ref. 1.52 (1.07–2.16) 1.84 (1.28–2.67) 2.06 (1.34–3.15)
Ref. 1.79 (1.34–2.38) 2.06 (1.52–2.80) 3.10 (2.23–4.30)
Ref. 1.88 (1.43–2.48) 2.24 (1.67–3.01) 3.68 (2.68–5.05)
Ref. 0.39 (0.31–0.50) 0.17 (0.11–0.28) 0.12 (0.07–0.23)
Ref. 0.56 (0.41–0.78) 0.31 (0.20–0.48) 0.22 (0.15–0.33)
Ref. 0.53 (0.42–0.68) 0.429 (0.30–0.59) 0.35 (0.25–0.47)
Ref. 0.57 (0.45–0.72) 0.40 (0.27–0.60) 0.42 (0.26–0.68)
Ref. 0.59 (0.46–0.75) 0.68 (0.43–1.09)
Ref.
Ref.
Ref. 0.74 (0.59–0.93) 0.50 (0.32–0.78)
1.32 (1.00–1.75) 1.38 (1.02–1.86)
3.05 (2.44–3.82) 1.48 (1.16–1.87) 1.96 (1.57–2.43)
47.53 (33.9–66.59)
Abbreviations: MCI, mild cognitive impairment; MCI-A, MCI prodromal Alzheimer disease; MCI-CVD, MCI resulting from cerebrovascular disease; MCI-VRF, MCI with vascular risk factors; MCI-O, MCI caused by other factors; OR, odds ratio; CI, confidence interval; Ref., reference. NOTE. Where numbers are missing are variables that were not significant and therefore not included in the models. Binary logistic regression analyses with the forward conditional method were used with sex, age, education, occupation, smoking, alcohol consumption, and comorbidities (hypertension, high cholesterol, diabetes mellitus, heart disease, and stroke) as the independent variables and diagnosis as the dependent variable. OR and 95% CIs (in parentheses) provided.
J. Jia et al. / Alzheimer’s & Dementia - (2013) 1–9
5.1. High MCI prevalence in China To date, only a few surveys on MCI have been conducted in China [12–17]. However, most of these studies were regional, small, and restricted to urban residents. The nationwide prevalence of MCI in urban and rural populations remains unknown. The prevalence reported by these previous surveys ranges from 0.4% to 12.7% [12–17], which is significantly lower than our estimate. The disparity may be explained by the use of different diagnostic criteria. Different regions such as rural or urban areas and age intervals of the participants may also affect the final data. Most previous studies in China were conducted among urban residents, used the amnestic MCI criteria, and recruited subjects aged 55 or 60 years (and few older subjects). As a result, a relatively low prevalence has been reported (6.1–12.3% even after adjustment for individuals older than 65 years) [15–17]. In contrast, our survey used broader criteria (various presentations with different causes) [1,2], which are more suitable for the early detection of MCI resulting from different causes. Moreover, our study recruited individuals aged 65 years and older, a standard that is adopted worldwide. Furthermore, we included more elderly people from rural areas (4180) than did previous studies. These factors may account for the higher estimated prevalences found in the present study. A high prevalence of MCI has also been reported in other countries. Surveys in the United States reported that 16.0–22.2% of elderly subjects met the broadened MCI criteria [4,10,32]. A nationwide survey in South Korea found an MCI prevalence of 24.1% [33]. In Japan, 18.9% of participants were diagnosed with MCI [6]. The present study shows that the prevalence of MCI in community elders in China is higher than expected, but it is similar to that in some Western and other Asian countries. 5.2. Higher MCI prevalence in rural than in urban populations We found a higher prevalence of MCI in the rural than in the urban population. An epidemiological survey conducted in Portugal supported this finding [11]. The less-advanced conditions (illiteracy and low-skill occupations) in rural areas may explain the high prevalence of MCI. Nearly 70% of participants in our study were more than 70 years of age. These people were born during World War II and were deprived of educational opportunities. This situation was more pronounced in rural areas because of poverty at that time. As a result, 48.2% of the rural participants were illiterate, which is 2.7 times the rate for the urban elders. A low educational level has been consistently found to be an independent risk factor for cognitive impairment [10,11], and the present study revealed a steep increase in the MCI prevalence as the educational level declined. Exceedingly poor literacy
7
levels may be the primary reason for the high prevalence of MCI in rural areas. Occupation may be another important contributing factor. China is traditionally an agricultural country, and most elderly people in rural areas are farmers. Working in low-skill occupations has been repeatedly identified as a risk factor for dementia [34]. In our study, the regression analysis also revealed that working as a farm laborer was associated with a greater risk of developing MCI. The focus on appropriate programs should be shifted to rural populations to enhance the educational level, which may reduce the incidence of MCI in the future. 5.3. Rationale for subclassification of MCI with vascular risk factors On the basis of clinical experience and an extensive literature review, the current study proposed a concise etiological subcategory of MCI including a vascular risk factor-related subtype with no clear CVD or visible radiologic evidence on conventional imaging (MCIVRF). It is necessary to elaborate the application of the MCI-VRF subtype, a subtype with preventive potential that is frequently seen in clinical practice. A long duration of vascular risk factors is known to increase the risk of MCI through different pathways. Diabetes mellitus may increase the risk of cognitive impairment mediated by hyperglycemia toxicity, insulin resistance, and oxidative stress [35–37]. Hypertension may result in loss of brain volume [38], leading to a decline in global cognition [39]. Moreover, diabetes and hypertension might evoke microinfarcts and microbleeds [40,41], which typically go undetected on conventional structural MRI [42]. Increasing evidence has suggested that microinfarcts and microbleeds are associated with a greater risk of developing cognitive impairment, even after controlling for macroscopic infarcts and other pathological covariates [43]. Thus, studies conducted from various viewpoints have verified that vascular risk factors may act on cognitive decline in the absence of visible cerebral vascular lesions on conventional imaging. Therefore, MCI-VRF was introduced in the current study as a specific MCI subtype. MCI-VRF may be an earlier stage before MCI-CVD and of greater importance for early intervention. 5.4. Higher prevalence of vascular-related MCI subtypes The present study estimated the prevalence of four etiological subtypes of MCI. The results showed that vascular-related MCI subtypes, including MCI-CVD and MCI-VRF, were the most common subtypes (42.0%). This may reflect an unexpected constituent ratio for the MCI pattern. In fact, China has a high incidence of cerebral vascular disease because of increased rates of hypertension, diabetes, and high cholesterol in recent years [44]. Stroke directly injures the brain and affects
8
J. Jia et al. / Alzheimer’s & Dementia - (2013) 1–9
cognition. The frequency of cognitive impairment after stroke reached 37.1% in one Chinese survey [45]. Moreover, several studies have shown that hypertension and diabetes are associated with cognitive decline independent of identified strokes [46,47]. The high prevalence of cerebral vascular disease and its risk factors in China may underlie the high prevalence of MCI with vascular components. This result is not unique to China. A study in Portugal, a country in which stroke is prevalent [48], revealed that CIND associated either with CVD or vascular risk factors was more frequent (39.7%) than CIND of any other etiology [11]. In the United States, CIND with a vascular origin accounted for 25.6% of the total cases, second only to the prodromal AD subtype (34.2%) [10]. These results emphasize the importance of vascular-related MCI and call for more resources and greater effort toward addressing these relatively neglected MCI subtypes. Several limitations need to be discussed. We subcategorized MCI into four subtypes according to potential etiologies in the current survey. Although the MCI-VRF subtype is of great importance in terms of identifying individuals in whom prevention will be effective and thus reduce the prevalence of MCI, this concept was proposed for the first time here and requires validation in longitudinal follow-up. The current study assessed four cognitive domains (memory, executive function, language, and visuoconstructive skill) in all participants. However, only one test was adopted for each cognitive domain, which may have increased the occurrence of false-positive or false-negative results and thus have led to misdiagnosis in some cases. Epidemiological studies usually adopt a screening procedure and may miss some real patients. However, in the current study, the results of a detailed clinical history, standardized neuropsychological tests, and systematic general and neurologic examinations were collected for all participants. Misdiagnosis was expected to be relatively low by this single-stage assessment. The high prevalence of MCI in Chinese elders imposes a heavy burden that must be met with appropriate public policies. The higher MCI prevalence in rural than in urban areas indicates that special attention must be given to implementing new strategies for these areas. The constituent ratio of MCI indicates that vascular-related subtypes were more common than any other, suggesting that more work should be done on the prevention of stroke and intervention in its risk factors. Follow-up studies are necessary to refine our MCI subtype classifications in future. Acknowledgements All authors report no conflicts of interest. This work was supported by the National Key Technology R&D Program in the Eleventh Five-Year Plan Period (2006BAI02B01),
the Major Program of Science and Technology Plan of Beijing (D111107003111009), and the Medical Elite Foundation from the Peking Public Health Bureau (2011-3-089).
RESEARCH IN CONTEXT
1. Systematic review: We searched the MEDLINE and China National Knowledge Infrastructure (CNKI) databases for the past 10 years, focusing on population-based studies on the prevalence of MCI. There have been no nationwide epidemiologic studies on MCI so far, in particularly on its etiological classification by imaging, in rural and urban populations in China. 2. Interpretation: In the present study, we found a high MCI prevalence (20.8%) in community Chinese elders and a higher prevalence in rural (25.1%) than urban populations. By etiological classification, vascular-related MCI is identified as the most common subtype (42.0%). The results call for a special policy to improve conditions in rural areas of China and a useful strategy for early intervention in patients with vascular-related MCI. 3. Future directions: Larger samples are necessary to refine our MCI subtype classifications, and an interventional clinical trial might be needed to further validate their clinical relevance.
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