In the United States and beyond, dementia cases are increasing in epidemic proportions. In 2017, it was estimated that approximately 50 million people have dementia worldwide, and 10 million new cases are expected to occur every year (World Health Organization [WHO], 2017). Alzheimer's disease (AD) dementia is an irreversible, progressive neurodegenerative disorder that accounts for 60% to 80% of all dementias. In 2017, it was estimated that approximately 5.5 million Americans had AD dementia and, by 2050, an AD dementia diagnosis will occur every 33 seconds (Alzheimer's Association, 2017). However, curative treatments for AD dementia have not been established (Alzheimer's Association, 2017; WHO, 2017).
Mild cognitive impairment (MCI) is a prodromal stage of dementia, representing a transitional state between normal cognition and dementia. The etiologies and causes of MCI subtypes are different. Clinical subtypes of MCI include amnestic or non-amnestic (aMCI or non-aMCI). These profiles may be related to different patho-physiological processes. The aMCI subtype is characterized by a primary decline in memory function either alone (single-domain) or in conjunction with other cognitive domains (multiple-domain), but shows insufficient severity to constitute dementia. On the other hand, non-aMCI is diagnosed when individuals present impairments in non-memory cognitive domains (e.g., language, attention, executive function) (Petersen, 2004). The aMCI subtype would presumably represent a prodromal stage of AD dementia, whereas the non-aMCI subtype seems to progress to non-AD dementias, such as frontotemporal, Lewy body, or vascular dementia. The rate of progression to clinically diagnosable AD dementia was 10% to 15% per year among persons with aMCI; however, the rate was 1% to 2% per year among older adults with normal cognition (Petersen et al., 1999). Moreover, it has been reported that approximately 80% of aMCI can progress to AD dementia within 7 years (National Institute on Aging, 2017; Petersen et al., 1999).
Given that there is no current disease-modifying treatment for AD dementia, prevention is of major importance. Delaying onset by 5 years is estimated to reduce the number of American older adults with AD dementia by approximately one half by 2050 (Alzheimer's Association, 2015). Although the optimal time window for interventions to delay the onset of dementias is uncertain, administering preventive efforts during cognitively normal states (the preclinical phase) is likely to be more effective than starting when certain cognitive impairment (the prodromal phase: MCI) is already established (Karlawish et al., 2017; Winblad et al., 2016). Therefore, it is critical to understand risk factors for the onset of either aMCI or AD dementia to intervene where possible in the preclinical phase.
Subjective cognitive decline (SCD) refers to individuals' concerns about self-perceived cognitive decline compared to their previous cognitive abilities. However, persons with SCD are found to have clinically normal neuropsychological functions (Molinuevo et al., 2017), and although they are in the cognitively normal category, the presence of SCD may imply risk of future cognitive deterioration. A meta-analysis indicated that approximately 6.6% and 2.3% of older adults with SCD were diagnosed with MCI and dementia after 1 year, respectively, and having SCD doubled individuals' risk of developing dementia (Mitchell et al., 2014).
Previous studies have suggested that vascular and/or neuropsychiatric risk factors could affect the development of aMCI and AD dementia. Vascular risk factors include diabetes mellitus (DM), hypercholesterolemia, hypertension, overweight/obesity, and cigarette smoking history. Anxiety and depressive symptoms were introduced as neuropsychiatric risk factors. Most of these risk factors are modifiable with the appropriate management or therapy. A meta-analysis of longitudinal studies indicated that individuals with type 2 DM had 50% higher risk of AD dementia than those without diabetes (Cheng et al., 2012). Observational studies indicated that type 2 DM, hypercholesterolemia, and hypertension multiply risk of aMCI or AD dementia by 1.1 to 2.4 times in older adults (Casado Naranjo et al., 2015; Li et al., 2011; Winkler et al., 2014). A meta-analysis of cohort studies showed that DM (adjusted combined odds ratio [COR] = 1.40), hypercholesterolemia (COR = 1.72), hypertension (COR = 1.31), and midlife obesity (COR = 1.88) were associated with increased risk of incident AD dementia later in life (Meng et al., 2014). It has also been suggested that increased body mass index (BMI) in midlife can be a risk factor of AD dementia. One-unit increases in midlife BMI corresponded with 6.7 months earlier onset of AD dementia (Chuang et al., 2016). In addition, compared to individuals with normal weight, being overweight or obese was associated with 70% higher risk of aMCI in individuals aged ≥60 years (Chuang et al., 2016; Wang et al., 2017).
Previous observational studies indicated that current smoking or cumulative cigarette exposure was associated with 1.7 to 3.4 times higher risk of aMCI or AD dementia compared to non-smoking in older adults (Aggarwal et al., 2006; de Bruijn et al., 2014; Xue et al., 2017). Individuals with anxiety are 1.3 to 1.8 times more likely to get AD dementia than those without anxiety in older adults aged >75 years (Mah et al., 2015; Palmer et al., 2007). In addition, individuals with depression had >50% higher risk of developing AD dementia compared to those without depression (Diniz et al., 2013). The presence of depression was also associated with 1.7 to 7.6 times higher risk of development of aMCI in individuals aged >70 years (Casado Naranjo et al., 2015; Geda et al., 2014; Sundermann et al., 2017).
Taken together, vascular and neuropsychiatric risk factors appear to be associated with the development of either aMCI or AD dementia. However, given that previous studies have rarely differentiated between SCD and non-SCD, there has been limited empirical consideration of the association between those risk factors and clinical progression (i.e., conversion to either aMCI or AD dementia) in persons with SCD. Therefore, the purpose of the current study was to identify modifiable risk factors contributing to the development of aMCI or AD dementia in individuals with SCD.
Study Design and Population
The current study adopted a longitudinal cohort design. Data were drawn from the National Alzheimer's Coordinating Center's Uniform Data Set (NACC-UDS). Alzheimer's Disease Centers (ADCs) previously or currently funded by the National Institute on Aging (NIA) evaluate individuals approximately annually with the same standardized measures and provide data for research through the NACC, located at the University of Washington (Seattle, WA). As a result, the NACC-UDS has systematic de-identified information on characteristics of individuals. Because each ADC uses its own recruitment methods and study protocols approved by its institutional review board (IRB), research using the NACC-UDS is not considered a population-based study (Beekly et al., 2007). The current study was not defined as human research, so it was exempt from IRB review and approval.
The presence of SCD was identified when individuals reported a decline in memory relative to previous abilities in a semi-structured interview with a trained clinician at their first visit (baseline). SCD was not subsequently assessed in the current study. The current study included individuals who were aged ≥60 years at baseline, given that the onset of SCD in adults older than 60 years is more closely linked to the Alzheimer's pathway (Jessen et al., 2014). Individuals with SCD were classified as cognitively normal based on the judgment of a trained clinician, having a Mini Mental State Examination (MMSE) score of ≥27, and a Clinical Dementia Rating Dementia Staging Instrument (CDR) global score of 0 at baseline. The MMSE and CDR are standardized tools to comprehensively measure the presence and severity of cognitive impairment (Folstein et al., 1975; Hughes et al., 1982). The MMSE assesses seven cognitive domains: visual construction, language, recall, attention/calculation, registration, orientation to place, and orientation to time. Scores range from 0 to 30, with higher scores indicating better global cognition. The CDR assesses six cognitive domains: memory, orientation, judgment/problem solving, community affairs, home/hobbies, and personal care. A global CDR rating indicates a certain cognitive status: 0 (no impairment), 0.5 (questionable impairment), 1 (mild impairment), 2 (moderate impairment), and 3 (severe impairment). The sample was further restricted to individuals who had follow-up evaluations. Individuals were not diagnosed with any type of MCI or dementia at baseline. The current analyses included 1,525 individuals from whom data had been collected between September 2005 and August 2018.
As independent variables, vascular risk factors included DM, hypercholesterolemia, hypertension, overweight/obesity, and cigarette smoking history at baseline. Anxiety and depressive symptoms were assessed as neuropsychiatric risk factors at baseline. Information on DM, hypercholesterolemia, and hypertension was obtained from clinician assessment of medical history (yes/no). Medical history was determined based on self-report, informant-report, and medical records. Overweight/obesity was determined by BMI. The categorization of BMI followed the WHO criteria (weight in pounds × 703)/(height in inches2): under-weight (<18.5 kg/m2), normal weight (≥18.5 kg/m2 and <25 kg/m2), over-weight (≥25 kg/m2 and <30 kg/m2), and obese (≥30 kg/m2). Individuals who reported they never smoked cigarettes during their lifetime were assigned to the non-smoker group and those who reported any history of cigarette smoking but did not smoke currently were designated as former smokers. If individuals reported they currently smoke, they were assigned to current smokers.
Anxiety or depressive symptoms were measured by the Neuropsychiatric Inventory Questionnaire (NPI-Q), an informant-based report of symptoms that have occurred over the past 1 month. The NPI-Q is a structured interview with established reliability and validity (Kaufer et al., 2000). The presence of symptoms was reported as no (no symptoms reported) or yes (mild/moderate/severe symptoms). As a dependent variable, clinical progression was defined as conversion to either aMCI or AD dementia during follow-up visits among individuals with SCD at baseline. For diagnosis of cognitive state, trained clinicians make determinations in accordance with published research diagnostic criteria. The diagnosis of aMCI included either single- or multiple-domain aMCI by the Petersen (2004) criteria. The confirmation of AD dementia was determined using either the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association criteria (McKhann et al., 1984) or the National Institute on Aging-Alzheimer's Association criteria (McKhann et al., 2011).
Baseline demographics, medical conditions that could affect cognition, and ADCs were included as covariates. Demographics included age (years), sex (male/female), education (years), race (White/Other), ethnicity (Hispanic/Other), and marital status (married/non-married [widowed, divorced, separated, or never married]). Medical conditions were measured by clinician assessment of individuals' medical history (yes/no), including Parkinson's disease, seizures, traumatic brain injury, stroke, vitamin B12 deficiency, alcohol or other substance use, atrial fibrillation, congestive heart failure, and thyroid disease. ADC was a categorical variable with de-identified numbers representing each of the 35 ADCs.
Descriptive statistics were means (standard deviations) and numbers (proportions), which were compared using analysis of variance (ANOVA) for continuous variables and chi-square tests or Fisher's exact tests for categorical variables for baseline characteristics by follow-up diagnosis between converters (i.e., conversion from SCD to either aMCI or AD dementia at follow-up visits) and non-converters. Univariate and multivariate Cox proportional hazard regression analyses were used to estimate hazard ratio (HR) with 95% confidence intervals (CI) of clinical progression with vascular and neuropsychiatric risk factors as primary predictors. Covariates controlled for confounders. Stepwise analyses were performed: univariate-unadjusted analyses in the first model. The second model adjusted for all covariates in univariate analyses. Independent variables were selected for further multivariate analyses if they revealed a significant p value in the second model. Multivariate analyses were performed in the third model adjusting mutually for selected independent variables and for all covariates. All analyses were conducted using IBM SPSS version 25.
Study Population Characteristics
Of 3,211 individuals, 1,686 were excluded based on age, MMSE score, CDR score, and number of evaluation criteria (i.e., no follow-up data). Thus, a total of 1,525 individuals with SCD were included in the current study (mean age = 73.8 [SD = 8.1, range = 60 to 99] years, mean education = 15.9 [SD = 2.8] years, 1,023 [67.1%] female, 81 [5.3%] Hispanic, 1,287 [84.4%] White, 840 [55.1%] married). The mean follow-up duration was 4.7 [SD = 2.9] years. At baseline, DM, hypercholesterolemia, and hypertension were present in 167 (11%), 789 (51.7%), and 775 (50.8%) individuals, respectively. Based on BMI, 595 (39%), 24 (1.6%), 569 (37.3%), and 337 (22.1%) were normal weight, under-weight, overweight, and obese, respectively. Smoking history included no lifetime smoking (n = 772, 50.6%), former smoking (n = 701, 46%), and current smoking (n = 52, 3.4%). Anxiety and depressive symptoms were present in 164 (10.8%; mild = 124, moderate = 35, severe = 5) and 239 (15.7%; mild = 179, moderate = 54, severe = 6) individuals, respectively.
There were 386 individuals who progressed to either aMCI (n = 335) or AD dementia (n = 51). Table 1 compares baseline characteristics between non-converters (n = 1,139) and converters (n = 386). Converters were older at baseline (mean age = 72.7 [SD = 7.8] vs. 77 [SD = 8.3] years, p < 0.001), were less likely to be Hispanic (6% vs. 3.4%, p < 0.05), married (56.5% vs. 50.8%, p < 0.05), and abuse alcohol (4.6% vs. 2.1%, p < 0.05). Converters also tended to be less obese (24.2% vs. 15.8%, p = 0.001).
Baseline Characteristics of Study Population by Follow-Up Status (N = 1,525)
Clinical Progression Hazard Models
Table 2 shows Cox proportional hazard models for clinical progression (i.e., conversion to either aMCI or AD dementia). In the univariate-unadjusted model (Model 1), obese status was associated with less risk of clinical progression compared to normal weight status (HR = 0.59; 95% CI [0.44, 0.78]). In the univariate-adjusted model (Model 2), obese status (HR = 0.64; 95% CI [0.47, 0.88]), current smoking (HR = 2.02; 95% CI [1.18, 3.44], reference: no smoking), and depressive symptoms (HR = 1.35; 95% CI [1.03, 1.77]) were significant predictors of clinical progression after adjusting for all covariates (i.e., demographics, medical conditions, and indicators for ADCs). These significant independent variables were selected and entered into Model 3, which was adjusted mutually for selected predictors and for all covariates. As a result, the multivariate-adjusted Cox proportional hazard model demonstrated that obese status (HR = 0.64; 95% CI [0.47, 0.89]), current smoking (HR = 2.04; 95% CI [1.20, 3.50]), and depressive symptoms (HR = 1.36; 95% CI [1.03, 1.79]) remained significant predictors of clinical progression. Therefore, current smoking and depressive symptoms were risk factors of conversion; however, obese status was a protective factor of clinical progression in individuals with SCD.
Cox Proportional Hazard Models for Clinical Progression (N = 1,525)
The goal of the current study was to identify risk factors associated with clinical progression in persons with SCD who underwent standardized clinical follow up with a mean duration of approximately 5 years. Leveraging a well-characterized national sample of cognitively normal older adults with SCD, obesity, current smoking, and depressive symptoms were found to be associated with clinical progression after adjusting for demographics and medical conditions that would affect cognition and ADC sites. Current smoking and depressive symptoms conferred an increased risk of clinical progression, but obesity was predictive of less clinical progression relative to normal weight. As few studies have investigated the prediction of clinical progression by modifiable risk factors in SCD, this study may present promising avenues for reducing risk of future cognitive impairment.
Findings are in line with previous studies that associate cigarette smoking with risk of clinical progression. Current smoking was predictive of AD dementia in older adults with an increased risk ranging from 1.7 to 3.4 times higher than no smoking (Aggarwal et al., 2006; Xue et al., 2017). Although the exact mechanisms by which smoking can exert its detrimental effects on MCI/dementia remain elusive, it has been hypothesized that risk may markedly increase with greater cumulative cigarette exposure (Durazzo et al., 2014). A lifetime smoking habit was associated with a higher risk of aMCI (odds ratio [OR] = 2.45) and cerebral oxidative stress (effect size = 0.6) in older adults with normal cognition. Chronic cerebral oxidative stress was associated with at least 70% greater risk of AD dementia (de Bruijn et al., 2014; Durazzo et al., 2014; Durazzo et al., 2016). Further studies are necessary to elucidate mechanisms that would explain the association of smoking and clinical progression in individuals with SCD.
Findings related to depressive symptoms are also consistent with previous studies. Older adults with depressive symptoms (mean age = 79 years) had an increased risk of AD dementia (HR = 1.76) in the Framingham Heart Study (Saczynski et al., 2010). Depressive symptoms were also proposed as a risk factor for the incidence of aMCI with HR ranging from 1.74 to 2.22 in community-dwelling older adults aged >70 years (Geda et al., 2014; Sundermann et al., 2017). Given that 75% (179/239) of symptoms were mild in the current study, managing even minor depressive symptoms may be important to delay objective cognitive decline in SCD.
Obesity acted as a protective factor of clinical progression in the current study. Generally, individuals who experience weight gain may be at risk of the presence of AD dementia and/or AD neuropathology due to deleterious effects of increased adiposity. Previous studies indicated that midlife obesity is predictive of AD dementia in later life and 1-unit increases in midlife BMI affected earlier onset of AD dementia by 6.7 months (Chuang et al., 2016; Meng et al., 2014). However, the relationship between BMI and risk of onset of MCI or AD dementia becomes equivocal with increasing age. A meta-analysis showed that being obese at age <65 years has a positive association with incident dementia (risk ratio = 1.41), but being obese at age >65 years has a negative association with incident dementia (risk ratio = 0.83) (Pedditzi et al., 2016).
There are a few reasons that could explain this “obesity paradox” in older adults. First, the AD-related neurodegenerative brain changes may increase energy expenditure, which may obscure the relationship between later life obesity and risk of AD dementia. Second, weight loss is primarily due to sarcopenia and not a loss of fat in older adults. Therefore, total body weight and BMI may not be valid surrogate measures of fat accumulation in older adults (Alhurani et al., 2016; Müller et al., 2017; Pedditzi et al., 2016). Other body composition metrics are likely to be more accurate measures of adiposity for older adults. For example, using the waist-to-hip ratio as a measure of central obesity reveals that abdominal obesity was associated with the development of AD dementia (HR = 2.5), whereas BMI in the same older cohort was not predictive of AD dementia. In addition, central obesity showed a significant association with aMCI (OR = 1.73) in community-dwelling older adults with a mean age of 66 years who did not report SCD (Feng et al., 2013; Luchsinger et al., 2012). Future research for older adults with SCD needs to use a measure of abdominal fat given that central obesity may be a more reliable vascular risk factor to predict clinical progression than global obesity as measured with BMI.
Lack of significant findings for other vascular risk factors in the current study may result from chronicity, which could not be exactly measured in the dataset. A meta-analysis of cohort studies for midlife vascular risk factors indicated that hypertension (COR = 1.31), hypercholesterolemia (COR = 1.72), and DM (COR = 1.40) in midlife were associated with higher risk of incident AD dementia in later life (Meng et al., 2014). Some time lag may exist between exposure to these risk factors and clinical progression. Meanwhile, baseline anxiety was not predictive of aMCI but predicted non-aMCI (HR = 2.74), whereas depression predicted aMCI (HR = 1.74) but not non-aMCI in older adults with a median age of 79 years (Geda et al., 2014). Anxiety and depressive symptoms may affect clinical progression differently; however, further studies are necessary to draw conclusions.
Although the standardized NACC-UDS data are reliable, findings of the current study should be interpreted cautiously as the sample from the NACC database contained a disproportionate representation of female gender, White race, and high educational attainment compared to the general population. Thus, the findings from this study may reflect sample characteristics specific to the NACC cohort. Additional studies are required to determine the extent to which the results of this study may be generalized to population-based samples with SCD. In addition, although the statistical models contained a variety of covariates that would be related to cognition, the covariates were not all-inclusive. For example, the study did not include the use of anticholinergics as well as lifestyle levels, such as physical activity and diet patterns, as covariates that may affect cognition due to availability.
Our findings have several clinical implications. First, health care providers (e.g., clinicians, nurses) need to invite older adults to discuss the presence of SCD and assess SCD early. According to the Centers for Disease Control and Prevention, in 2018, only 45.3% of persons with SCD aged ≥45 years discussed their symptoms (i.e., SCD) with a health care provider (48.8% in persons aged 45 to 64 years and 39.8% in persons age ≥65 years). To assess the number of individuals with SCD accurately, health care providers should encourage older adults to discuss any cognitive concerns at medical appointments. Second, health care providers need to educate older adults with SCD about manageable risk factors. Health care providers can also increase awareness of the importance of managing cigarette smoking and depressive symptoms for older adults with SCD as initial care plans. Third, for older adults with SCD, in the context of current smoking and/or depressive symptoms, health care providers may consider appropriate interventions, such as a smoking cessation program and cognitive-behavioral therapy to manage risk factors. Last, the impact of interventions should be evaluated at routine follow ups by seeing whether the programs would delay the progression to aMCI or AD dementia among older adults with SCD.
The current study findings have several implications for future research. SCD was measured at only one time point to select the study sample. It is possible that the trajectory of SCD may be an important indicator for later conversion to aMCI or AD dementia. For example, older adults with persistent SCD over time may be more susceptible to conversion to aMCI/AD dementia than those who only reported SCD one time. Future research is also needed to examine if modifiable vascular and/or neuropsychiatric risk factors are differently associated with clinical progression between individuals with persistent and intermittent SCD during the specific time frame. Furthermore, the combined effects of modifiable vascular and/or neuropsychiatric risk factors and SCD on clinical progression need to be studied, particularly in comparison to a cohort without SCD at baseline.
The results of the current study demonstrate that current cigarette smoking and depressive symptoms were predictive of clinical progression, as opposed to obesity, which was associated with a reduced risk of conversion to either aMCI or AD dementia in individuals with SCD. These results underscore the need for further research to elucidate associations between obesity and clinical progression using other body composition metrics than BMI in older adults with SCD.
- Aggarwal, N. T., Bienias, J. L., Bennett, D. A., Wilson, R. S., Morris, M. C., Schneider, J. A., Shah, R. C. & Evans, D. A. (2006). The relation of cigarette smoking to incident Alzheimer's disease in a biracial urban community population. Neuroepidemiology, 26(3), 140–146 doi:10.1159/000091654 [CrossRef] PMID:16493200
- Alhurani, R. E., Vassilaki, M., Aakre, J. A., Mielke, M. M., Kremers, W. K., Machulda, M. M., Geda, Y. E., Knopman, D. S., Petersen, R. C. & Roberts, R. O. (2016). Decline in weight and incident mild cognitive impairment: Mayo Clinic study of aging. JAMA Neurology, 73(4), 439–446 doi:10.1001/jamaneurol.2015.4756 [CrossRef] PMID:26831542
- Alzheimer's Association. (2015). Changing the trajectory of Alzheimer's disease: How a treatment by 2025 saves lives and dollars. https://www.alz.org/media/documents/changing-the-trajectory-r.pdf
- Alzheimer's Association. (2017). 2017 Alzheimer's disease facts and figures. Alzheimer's & Dementia, 13(4), 325–373 doi:10.1016/j.jalz.2017.02.001 [CrossRef]
- Beekly, D. L., Ramos, E. M., Lee, W. W., Deitrich, W. D., Jacka, M. E., Wu, J., Hubbard, J. L., Koepsell, T. D., Morris, J. C. & Kukull, W. A.the NIA Alzheimer's Disease Centers. (2007). The National Alzheimer's Coordinating Center (NACC) database: The uniform data set. Alzheimer Disease and Associated Disorders, 21(3), 249–258 doi:10.1097/WAD.0b013e318142774e [CrossRef] PMID:17804958
- Casado Naranjo, I., Portilla Cuenca, J. C., Duque de San Juan, B., García, A. F., Sevilla, R. R., Serrano Cabrera, A., Hijón, C. C., Romero Chala, S., Fuentes, J. M. & Ramírez Moreno, J. M. (2015). Association of vascular factors and amnestic mild cognitive impairment: A comprehensive approach. Journal of Alzheimer's Disease, 44(2), 695–704 doi:10.3233/JAD-141770 [CrossRef] PMID:25362037
- Centers for Disease Control and Prevention. (2018). Subjective cognitive decline – A public health issue. https://www.cdc.gov/aging/agingdata/docs/subjective-cognitive-decline-508.pdf
- Cheng, G., Huang, C., Deng, H. & Wang, H. (2012). Diabetes as a risk factor for dementia and mild cognitive impairment: A meta-analysis of longitudinal studies. Internal Medicine Journal, 42(5), 484–491 doi:10.1111/j.1445-5994.2012.02758.x [CrossRef] PMID:22372522
- Chuang, Y. F., An, Y., Bilgel, M., Wong, D. F., Troncoso, J. C., O'Brien, R. J., Breitner, J. C., Ferruci, L., Resnick, S. M. & Thambisetty, M. (2016). Midlife adiposity predicts earlier onset of Alzheimer's dementia, neuropathology and presymptomatic cerebral amyloid accumulation. Molecular Psychiatry, 21(7), 910–915 doi:10.1038/mp.2015.129 [CrossRef] PMID:26324099
- de Bruijn, R. F., Akoudad, S., Cremers, L. G., Hofman, A., Niessen, W. J., van der Lugt, A., Koudstaal, P. J., Vernooij, M. W. & Ikram, M. A. (2014). Determinants, MRI correlates, and prognosis of mild cognitive impairment: The Rotterdam study. Journal of Alzheimer's Disease, 42(Suppl. 3), S239–S249 doi:10.3233/JAD-132558 [CrossRef] PMID:24825566
- Diniz, B. S., Butters, M. A., Albert, S. M., Dew, M. A. & Reynolds, C. F. III. . (2013). Late-life depression and risk of vascular dementia and Alzheimer's disease: Systematic review and meta-analysis of community-based cohort studies. The British Journal of Psychiatry, 202(5), 329–335 doi:10.1192/bjp.bp.112.118307 [CrossRef] PMID:23637108
- Durazzo, T. C., Korecka, M., Trojanowski, J. Q., Weiner, M. W., O'Hara, R., Ashford, J. W. & Shaw, L. M.the Alzheimer's Disease Neuroimaging Initiative. (2016). Active cigarette smoking in cognitively-normal elders and probable Alzheimer's disease is associated with elevated cerebro-spinal fluid oxidative stress biomarkers. Journal of Alzheimer's Disease, 54(1), 99–107 doi:10.3233/JAD-160413 [CrossRef] PMID:27472882
- Durazzo, T. C., Mattsson, N. & Weiner, M. W.the Alzheimer's Disease Neuro-imaging Initiative. (2014). Smoking and increased Alzheimer's disease risk: A review of potential mechanisms. Alzheimer's & Dementia, 10(Suppl. 3), S122–S145 doi:10.1016/j.jalz.2014.04.009 [CrossRef] PMID:24924665
- Feng, L., Chong, M. S., Lim, W. S., Lee, T. S., Collinson, S. L., Yap, P. & Ng, T. P. (2013). Metabolic syndrome and amnestic mild cognitive impairment: Singapore Longitudinal Ageing Study-2 findings. Journal of Alzheimer's Disease, 34(3), 649–657 doi:10.3233/JAD-121885 [CrossRef] PMID:23246920
- Folstein, M. F., Folstein, S. E. & McHugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198 doi:10.1016/0022-3956(75)90026-6 [CrossRef] PMID:1202204
- Geda, Y. E., Roberts, R. O., Mielke, M. M., Knopman, D. S., Christianson, T. J., Pankratz, V. S., Boeve, B. F., Sochor, O., Tangalos, E. G., Petersen, R. C. & Rocca, W. A. (2014). Baseline neuropsychiatric symptoms and the risk of incident mild cognitive impairment: A population-based study. The American Journal of Psychiatry, 171(5), 572–581 doi:10.1176/appi.ajp.2014.13060821 [CrossRef] PMID:24700290
- Hughes, C. P., Berg, L., Danziger, W. L., Coben, L. A. & Martin, R. L. (1982). A new clinical scale for the staging of dementia. The British Journal of Psychiatry, 140, 566–572 doi:10.1192/bjp.140.6.566 [CrossRef] PMID:7104545
- Jessen, F., Amariglio, R. E., van Boxtel, M., Breteler, M., Ceccaldi, M., Chételat, G., Dubois, B., Dufouil, C., Ellis, K. A., van der Flier, W. M., Glodzik, L., van Harten, A. C., de Leon, M. J., McHugh, P., Mielke, M. M., Molinuevo, J. L., Mosconi, L., Osorio, R. S., Perrotin, A. & Wagner, M.the Subjective Cognitive Decline Initiative (SCD-I) Working Group. (2014). A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease. Alzheimer's & Dementia, 10(6), 844–852 doi:10.1016/j.jalz.2014.01.001 [CrossRef] PMID:24798886
- Karlawish, J., Jack, C. R. Jr.. , Rocca, W. A., Snyder, H. M. & Carrillo, M. C. (2017). Alzheimer's disease: The next frontier–Special Report 2017. Alzheimer's & Dementia, 13(4), 374–380 doi:10.1016/j.jalz.2017.02.006 [CrossRef] PMID:28314660
- Kaufer, D. I., Cummings, J. L., Ketchel, P., Smith, V., MacMillan, A., Shelley, T., Lopez, O. L. & DeKosky, S. T. (2000). Validation of the NPI-Q, a brief clinical form of the Neuropsychiatric Inventory. The Journal of Neuropsychiatry and Clinical Neurosciences, 12(2), 233–239 doi:10.1176/jnp.12.2.233 [CrossRef] PMID:11001602
- Li, J., Wang, Y. J., Zhang, M., Xu, Z. Q., Gao, C. Y., Fang, C. Q., Yan, J. C. & Zhou, H. D.the Chongqing Ageing Study Group. (2011). Vascular risk factors promote conversion from mild cognitive impairment to Alzheimer disease. Neurology, 76(17), 1485–1491 doi:10.1212/WNL.0b013e318217e7a4 [CrossRef] PMID:21490316
- Luchsinger, J. A., Cheng, D., Tang, M. X., Schupf, N. & Mayeux, R. (2012). Central obesity in the elderly is related to late-onset Alzheimer disease. Alzheimer Disease and Associated Disorders, 26(2), 101–105 doi:10.1097/WAD.0b013e318222f0d4 [CrossRef] PMID:21666429
- Mah, L., Binns, M. A. & Steffens, D. C.the Alzheimer's Disease Neuroimaging Initiative. (2015). Anxiety symptoms in amnestic mild cognitive impairment are associated with medial temporal atrophy and predict conversion to Alzheimer disease. The American Journal of Geriatric Psychiatry, 23(5), 466–476 doi:10.1016/j.jagp.2014.10.005 [CrossRef] PMID:25500120
- McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D. & Stadlan, E. M. (1984). Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology, 34(7), 939–944 doi:10.1212/WNL.34.7.939 [CrossRef] PMID:6610841
- McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R. Jr.. , Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., Mohs, R. C., Morris, J. C., Rossor, M. N., Scheltens, P., Carrillo, M. C., Thies, B., Weintraub, S. & Phelps, C. H. (2011). The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's & Dementia, 7(3), 263–269 doi:10.1016/j.jalz.2011.03.005 [CrossRef] PMID:21514250
- Meng, X.-F., Yu, J.-T., Wang, H.-F., Tan, M.-S., Wang, C., Tan, C.-C. & Tan, L. (2014). Midlife vascular risk factors and the risk of Alzheimer's disease: A systematic review and meta-analysis. Journal of Alzheimer's Disease, 42(4), 1295–1310 doi:10.3233/JAD-140954 [CrossRef] PMID:25024338
- Mitchell, A. J., Beaumont, H., Ferguson, D., Yadegarfar, M. & Stubbs, B. (2014). Risk of dementia and mild cognitive impairment in older people with subjective memory complaints: Meta-analysis. Acta Psychiatrica Scandinavica, 130(6), 439–451 doi:10.1111/acps.12336 [CrossRef] PMID:25219393
- Molinuevo, J. L., Rabin, L. A., Amariglio, R., Buckley, R., Dubois, B., Ellis, K. A., Ewers, M., Hampel, H., Klöppel, S., Rami, L., Reisberg, B., Saykin, A. J., Sikkes, S., Smart, C. M., Snitz, B. E., Sperling, R., van der Flier, W. M., Wagner, M. & Jessen, F.the Subjective Cognitive Decline Initiative (SCD-I) Working Group. (2017). Implementation of subjective cognitive decline criteria in research studies. Alzheimer's & Dementia, 13(3), 296–311 doi:10.1016/j.jalz.2016.09.012 [CrossRef] PMID:27825022
- Müller, S., Preische, O., Sohrabi, H. R., Gräber, S., Jucker, M., Dietzsch, J., Ringman, J. M., Martins, R. N., McDade, E., Schofield, P. R., Ghetti, B., Rossor, M., Graff-Radford, N. R., Levin, J., Galasko, D., Quaid, K. A., Salloway, S., Xiong, C., Benzinger, T. & Laske, C. (2017). Decreased body mass index in the preclinical stage of autosomal dominant Alzheimer's disease. Scientific Reports, 7(1), 1225 doi:10.1038/s41598-017-01327-w [CrossRef] PMID:28450713
- National Institute on Aging. (2017). Symptoms and diagnosis of Alzheimer's disease: What is mild cognitive impairment?http://www.nia.nih.gov/health/what-mild-cognitive-impairment
- Palmer, K., Berger, A. K., Monastero, R., Winblad, B., Bäckman, L. & Fratiglioni, L. (2007). Predictors of progression from mild cognitive impairment to Alzheimer disease. Neurology, 68(19), 1596–1602 doi:10.1212/01.wnl.0000260968.92345.3f [CrossRef] PMID:17485646
- Pedditzi, E., Peters, R. & Beckett, N. (2016). The risk of overweight/obesity in mid-life and late life for the development of dementia: A systematic review and meta-analysis of longitudinal studies. Age and Ageing, 45(1), 14–21 doi:10.1093/ageing/afv151 [CrossRef] PMID:26764391
- Petersen, R. C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of Internal Medicine, 256(3), 183–194 doi:10.1111/j.1365-2796.2004.01388.x [CrossRef] PMID:15324362
- Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G. & Kokmen, E. (1999). Mild cognitive impairment: Clinical characterization and outcome. Archives of Neurology, 56(3), 303–308 doi:10.1001/archneur.56.3.303 [CrossRef] PMID:10190820
- Saczynski, J. S., Beiser, A., Seshadri, S., Auerbach, S., Wolf, P. A. & Au, R. (2010). Depressive symptoms and risk of dementia: The Framingham Heart Study. Neurology, 75(1), 35–41 doi:10.1212/WNL.0b013e3181e62138 [CrossRef] PMID:20603483
- Sundermann, E. E., Katz, M. J. & Lipton, R. B. (2017). Sex differences in the relationship between depressive symptoms and risk of amnestic mild cognitive impairment. The American Journal of Geriatric Psychiatry, 25(1), 13–22 doi:10.1016/j.jagp.2016.08.022 [CrossRef] PMID:27986237
- Wang, F., Zhao, M., Han, Z., Li, D., Zhang, S., Zhang, Y., Kong, X., Sun, N., Zhang, Q. & Lei, P. (2017). Association of body mass index with amnestic and non-amnestic mild cognitive impairment risk in elderly. BMC Psychiatry, 17(1), 334 doi:10.1186/s12888-017-1493-x [CrossRef] PMID:28915800
- Winblad, B., Amouyel, P., Andrieu, S., Ballard, C., Brayne, C., Brodaty, H., Cedazo-Minguez, A., Dubois, B., Edvardsson, D., Feldman, H., Fratiglioni, L., Frisoni, G. B., Gauthier, S., Georges, J., Graff, C., Iqbal, K., Jessen, F., Johansson, G., Jönsson, L. & Zetterberg, H. (2016). Defeating Alzheimer's disease and other dementias: A priority for European science and society. Lancet Neurology, 15(5), 455–532 doi:10.1016/S1474-4422(16)00062-4 [CrossRef] PMID:26987701
- Winkler, A., Dlugaj, M., Weimar, C., Jöckel, K. H., Erbel, R., Dragano, N. & Moebus, S. (2014). Association of diabetes mellitus and mild cognitive impairment in middle-aged men and women. Journal of Alzheimer's Disease, 42(4), 1269–1277 doi:10.3233/JAD-140696 [CrossRef] PMID:25024326
- World Health Organization. (2017). Dementia. http://www.who.int/news-room/fact-sheets/detail/dementia
- Xue, H., Sun, Q., Liu, L., Zhou, L., Liang, R., He, R. & Yu, H. (2017). Risk factors of transition from mild cognitive impairment to Alzheimer's disease and death: A cohort study. Comprehensive Psychiatry, 78, 91–97 doi:10.1016/j.comppsych.2017.07.003 [CrossRef] PMID:28806610
Baseline Characteristics of Study Population by Follow-Up Status (N = 1,525)
|Characteristic||n (%)||p Value|
|Non-Converters (n = 1,139)||Converters (n = 386)|
|Female||762 (66.9)||261 (67.6)||0.796|
|White||954 (83.8)||333 (86.3)||0.240|
|Hispanic||68 (6)||13 (3.4)||0.049*|
|Married||644 (56.5)||196 (50.8)||0.049*|
|Parkinson's disease||18 (1.6)||2 (0.5)||0.113|
|Seizures||24 (2.1)||7 (1.8)||0.724|
|Traumatic brain injury||130 (11.4)||32 (8.3)||0.085|
|Stroke||31 (2.7)||18 (4.7)||0.062|
|Vitamin B12 deficiency||55 (4.8)||21 (5.4)||0.633|
|Alcohol use||52 (4.6)||8 (2.1)||0.029*|
|Substance usea||18 (1.6)||1 (0.3)||0.059|
|Atrial fibrillation||81 (7.1)||29 (7.5)||0.792|
|Congestive heart failure||29 (2.5)||6 (1.6)||0.261|
|Thyroid disease||239 (21)||92 (23.8)||0.240|
|Diabetes||124 (10.9)||43 (11.1)||0.891|
|Hypercholesterolemia||600 (52.7)||189 (49)||0.207|
|Hypertension||578 (50.7)||197 (51)||0.922|
|Body mass index||0.001**|
| Normal||425 (37.3)||170 (44)|
| Underweight||14 (1.2)||10 (2.6)|
| Overweight||424 (37.2)||145 (37.6)|
| Obese||276 (24.2)||61 (15.8)|
| No smoking||572 (50.2)||200 (51.8)|
| Former smoking||531 (46.6)||170 (44)|
| Current smoking||36 (3.2)||16 (4.1)|
|Anxiety||121 (10.6)||43 (11.1)||0.777|
|Depressive symptoms||170 (14.9)||69 (17.9)||0.168|
|Mean (SD) (Range)|
|Age (years)||72.7 (7.8) (60 to 99)||77 (8.3) (60 to 99)||<0.001***|
|Education (years)||15.9 (2.8) (2 to 24)||15.8 (2.9) (4 to 23)||0.468|
Cox Proportional Hazard Models for Clinical Progression (N = 1,525)
|Characteristic||Model 1||Model 2||Model 3|
|HR (CI)||p Value||HR (CI)||p Value||HR (CI)||p Value|
|Diabetes||1.08 [0.79, 1.48]||0.634||1.08 [0.77, 1.52]||0.656||—||—|
|Hypercholesterolemia||0.98 [0.80, 1.20]||0.828||1.02 [0.83, 1.26]||0.843||—||—|
|Hypertension||1.12 [0.91, 1.36]||0.285||0.93 [0.75, 1.15]||0.476||—||—|
|Body mass index|
| Underweight||1.46 [0.77, 2.76]||0.247||1.46 [0.75, 2.83]||0.263||1.34 [0.69, 2.62]||0.393|
| Overweight||0.82 [0.66, 1.02]||0.076||0.94 [0.74, 1.18]||0.592||0.95 [0.76, 1.20]||0.684|
| Obese||0.59 [0.44, 0.78]||<0.001***||0.64 [0.47, 0.88]||0.006**||0.64 [0.47, 0.89]||0.007**|
| Former smoking||0.97 [0.79, 1.19]||0.755||0.94 [0.76, 1.17]||0.591||0.95 [0.77, 1.18]||0.631|
| Current smoking||1.49 [0.89, 2.48]||0.127||2.02 [1.18, 3.44]||0.010*||2.04 [1.20, 3.50]||0.009**|
|Anxiety||1.11 [0.81, 1.52]||0.529||1.15 [0.82, 1.61]||0.409||—||—|
|Depressive symptoms||1.22 [0.94, 1.59]||0.129||1.35 [1.03, 1.77]||0.033*||1.36 [1.03, 1.79]||0.028*|