Independent Predictors of Cognitive Decline in Healthy Elderly Persons FREE

ALZHEIMER DISEASE (AD) is a formidable public health issue, affecting an estimated 12% of the population over the age of 65 years in the United States.¹ Approaches that allow better anticipation of progression to dementia in healthy individuals would enhance current efforts aimed at preventing or slowing AD onset.²

Several risk factors have been identified that predict the onset of AD and cognitive impairment in elderly populations. The strongest include age, sex, educational level,³ genetic makeup (family history of dementia or allele producing the ϵ4 type of apolipoprotein E [APOE ϵ4]),⁴ mild cognitive impairment (MCI) (especially memory loss),^2,5- 6 parkinsonism,⁷ gait impairment,⁸ and hippocampal or medial temporal volume measures.^9- 13

The present study was aimed at integrating several individually validated predictors of cognitive decline to determine if they independently predict decline in a group of initially healthy elderly persons. We examined clinical measures of memory,⁶ timed gait,⁸ and hippocampal volume.¹² We hypothesized that models combining these measures could refine our ability to forecast the likelihood of progression to questionable dementia (QD) or persistent cognitive impairment (PCI), harbingers of AD, and candidates for clinical trials aimed at preventing the onset of AD.

The Oregon Brain Aging Study is a prospective study of neurologic function in the optimally healthy elderly population. Subjects were 65 years or older at the initial assessment, without comorbid conditions, mentally healthy (normal) by mental status examination, and without memory impairment by self-report or proxy.¹⁴ None of the cohort had had a history of significant head trauma, risk factors for vascular disease, or abuse of alcohol or other substances based on medical history or medical record review. Clinical examination findings and laboratory study results that included a complete blood cell count, chemistry profile, vitamin B₁₂ and folate levels, chest x-ray film, and electrocardiogram were additionally used to exclude covert medical conditions that might contribute to cognitive impairment. At the time of recruitment, participants were taking only vitamins, hormone replacement therapy, and/or nonsteroidal anti-inflammatory drugs. Use of drugs that might affect cognitive function, including ascorbic acid and vitamin E, coenzyme Q, nonsteroidal anti-inflammatory agents, and gingko biloba was recorded. Each participant underwent evaluation at 6-month intervals using standardized assessment tools and annual neurologic and neuropsychological assessments, and magnetic resonance imaging using previously described protocols.^6,12 Participants who had at least one volumetrically analyzed magnetic resonance imaging scan at the time of enrollment were entered into the current analysis.

The standardized neurologic assessment included a patient interview, mental status evaluation, and standardized neurologic examination. Dementia status was graded using the Clinical Dementia Rating Scale (CDR),¹⁵ based on subject or informant report of cognitive or functional decline and confirmed by cognitive testing using the Mini-Mental State Examination (score <24)¹⁶ and the Neurobehavioral Cognitive Status Examinations,¹⁷ as previously described.³ Depressive symptoms were rated using the Geriatric Depression Scale.¹⁸ Socioeconomic status was assessed at baseline using the Hollingshead scale.¹⁹ Other indicators of cognitive performance were part of a standardized neuropsychological battery (including parts of the Wechsler Memory Scale–Revised and the Wechsler Adult Intelligence Scale–Revised) that was administered annually but was not used to determine dementia status.¹⁴ The logical memory (LM) test from the Wechsler Memory Scale–Revised, in particular, included a brief story that was read to the subject and then scored for recall both immediately (LM I) and again 25 to 30 minutes later (LM II). Gait was evaluated annually by a neurologist (J.A.K., R.C., or colleagues) as part of a comprehensive neurologic examination and was quantified by having the participant walk at a self-selected pace 15 ft (4.5 m) out to a marker on the ground, turn, and walk back to the starting point. The time (seconds) and the number of steps (excluding steps taken to turn) were recorded.²⁰

Magnetic resonance imaging scans were obtained near the time of enrollment using a 1.5-T scanner (GE Medical Systems, Milwaukee, Wis), with the following image parameters: multiecho multiplanar, 4-mm coronal slices; field of view, 24 cm²; acquisition matrix, 256 × 256 pixels; number of excitations, 0.5; repetition time, 3000 milliseconds; and echo times, 30 and 80 milliseconds. Image analysis was performed by semiautomated recursive segmentation using the program REGION (Oregon Aging and Alzheimer Disease Center, Portland) and by manual tracing using the National Institutes of Health Image (Version 1.5; National Institutes of Health, Bethesda, Md) on Macintosh computers (Apple Computers, Cupertino, Calif).¹² Total pixel counts for each region (intracranial and hippocampal areas) were summed for each slice and multiplied by the slice thickness to convert areas to volumes. Hippocampal volumes were measured by manually tracing the area between the red nucleus and the superior colliculus on serial slices using the National Institutes of Health Image. These analysis techniques have been previously shown to be reliable: for hippocampal volume, intraclass correlation = 0.90; for the measurement of intracranial volume, intraclass correlation = 0.98.¹²

Baseline characteristics were compared using t or χ² tests. Survival analyses were done using the Cox proportional hazards model to determine if individual traits at study baseline could predict the earliest signs of cognitive decline or persistent impairment. Age, sex, educational level, depressive symptoms, APOE status (any ϵ4 allele), neuropsychological measures of memory and hippocampal volume, and gait measures were considered as potential explanatory variables. Length of follow-up was determined for all participants and each individual was classified as having attained 1 of the 3 study end points—QD, PCI, or censored status:

Two regression analyses were run to model QD and PCI. The effects of age and sex were controlled for by entering them into the model at the first step. Hippocampal volumetric measures, memory scores, APOE ϵ4 level status, depressive symptoms, and motor measures were added by forward stepwise regression. With the exception of sex and age, variables are only reported if the survival model–calculated P values were less than .05 in 1 of the models examined. The interdependence of the predictor variables (age, LM II test score, time to walk 30 ft [9 m], and hippocampal volume) was further examined by calculating the partial correlation coefficients among these 4 variables.

Logical memory II test scores were determined for the time of the latest available examination or after achieving QD or PCI status. The proportion of participants achieving an LM II test score of less than 5 recalled items at follow-up (corresponding to 1.5 SDs below the normative value for persons older than 70 years) was also examined to determine the relationship among QD, PCI, and MCI.^2,21

Outcomes of 108 participants (68 women and 40 men) followed up for a mean of 6 years from the Oregon Brain Aging Study were examined. Ninety-seven percent were white, 2% Hispanic, and 1% Native American. At the time of the analysis, 20 of the 108 subjects were dead and 2 had withdrawn from the study. As given in Table 1, 48 subjects had a rating of QD, while 38 had subsequently developed PCI. Patients with QD and PCI were older than those whose cognition remained intact. There was no difference between groups in the use of drugs that might affect cognitive function (P>.10 for all drugs), including ascorbic acid, vitamin E, coenzyme Q10, nonsteroidal anti-inflammatory agents, and gingko biloba. The Mini-Mental State Examination and Neurobehavioral Cognitive Status Examinations scores were higher at baseline in those whose cognition remained intact. Sex, educational level, vocabulary, socioeconomic class, APOE status, Geriatric Depression Scale score, and duration of follow-up did not differ between those whose cognition became impaired and those whose cognition remained intact.

Participants whose cognition became impaired had poorer recall on the LM II test, smaller hippocampal volumes, and took longer to walk 30 ft. Participants whose cognition became impaired (data not available for 3 QD and 3 PCI participants) with follow-up declined on the LM II test, while those whose cognition remained intact improved (data not available for 1 participant). The proportion of participants remaining intact who achieved a score of less than 5 (1.5 SDs) on the LM II test was 3 of 59, while 18 of the 45 participants with QD and 18 of the 35 participants with PCI showed this degree of impairment. Total brain, but not total intracranial volumes, were smaller in participants who became impaired (Table 2); however, these were not entered into the models.

Coefficients in the multivariable Cox models are listed in Table 3. Older age, a worse LM II test score, and decreased hippocampal volume were significant predictors of QD. A 1-year increase in age yielded an increased risk (hazard ratio) of 1.07 times, while a 1-point increase in LM II test score yielded a decreased risk of 0.90, and a 1-cm³ increase in hippocampal volume yielded a decreased risk of 0.027. The LM II test score, hippocampal volume, and time to walk 30 ft were significant in the model for the development of PCI. A 1-point increase in the LM II test score yielded a decreased risk of 0.85 and a 1-cm³ increase in the hippocampal volume yielded a decreased risk of 0.036. A 1-second increase in time to walk 30 ft yielded an increased risk of 1.14 times of developing PCI. Age only approached statistical significance in this model and sex was not a significant predictor in either model.

Age was negatively correlated with hippocampal volume (partial r = −0.44) and LM II test score (partial r = −0.23) and positively correlated with time to walk 30 ft (partial r = 0.25). The correlation between the other variables was weak (partial r<0.1), except for a negative correlation between hippocampal volume and time to walk 30 ft (r = −0.12).

Our study combined memory performance, neuroimaging, and physical findings in one predictive model. Logical memory II test score, hippocampal volume, and time to walk 30 ft forecast cognitive decline, independent of age and sex, in this cohort of initially healthy elderly participants. Age was a significant risk for the development of QD only. Time to walk 30 ft only entered the model for the development of PCI.

A previous population-based study found that global cognitive function, age, and family report of memory loss were associated with subsequent cognitive decline.²² In another study, neuropsychological variables did not contribute to the prediction of dementia in subjects with reported memory losses.²³ Memory measures combined with medial temporal volumes presaged dementia in elderly persons younger than 85 years.²⁴ That memory performance and hippocampal volume independently contributed to the model in our study supports the observation that hippocampal atrophy does not serve as a surrogate for memory loss.²⁵ This proposal is supported by the weak correlation between the predictor variables. In a study of younger elderly persons, memory measures and APOE genotype did not predict the development of AD, whereas the fusiform, middle, and inferior temporal gyral volumes had a sensitivity of 93%.²⁶ Progressive brain atrophy,^27- 28 or functional imaging have also been examined in other studies.²⁹

Impaired gait independent of significant medical, orthopedic, or rheumatologic disease was a significant predictor only for the development of PCI, a persistent state of cognitive decline, which may be closer to dementia than QD. Another study has shown that a change in brain magnetic resonance imaging and APOEϵ4 both predicted cognitive and lower extremity functional decline in an elderly male cohort.³⁰ The presence of the APOEϵ4 allele did not contribute significantly to our multivariable model, possibly reflecting the older age of the participants.³¹ That depressive symptoms were not significant predictors in our cohort (data not shown) might be related to the exclusion of participants with severe depressive symptoms at baseline, or to other selection criteria.³²

The identification of QD in this cohort puts individuals at risk for subsequent evolution to PCI at a rate consistent with the conversion to dementia reported in other studies.³³ With longer follow-up, these individuals may progress to dementia.^34- 35 Questionable dementia represents a clinically definable end point that is independent of psychometric testing, unlike MCI. Both are precursors to dementia, but MCI was not examined in the current study since LM II test performance was examined as a predictor. It would be inappropriate to use the same psychological predictor as an end point since poor performance on memory testing at baseline would be expected to lead to poor subsequent memory performance.

Only a small portion (5%) of intact elderly persons achieved impairment on the LM II test of 1.5 SDs below norms for that age group, corresponding to MCI, with longitudinal follow-up. A higher proportion of QD (40%) and PCI (51%) showed this degree of impairment. These proportions are conservative because appropriate norms for longitudinally presented memory tests in this highly educated, very elderly population are unavailable. Nevertheless, these findings highlight the incomplete overlap between categories of cognitive impairment in elderly persons and point to the need to better define their significance in longitudinal cohort studies.³⁶Gait impairment, which was a predictor of PCI, may indicate more widespread pathologic change, or the coexistence of vascular disease, either of which might accelerate the development of a more persistent state of cognitive impairment.³⁷ We believe that the latter category merited definition as an irreversible state. Motor impairment has previously been described in association with cognitive impairment, but our data suggest that it can be observed prior to the development of cognitive decline in the oldest old.³⁸By focusing on exceptionally healthy, very elderly volunteers, we were able to identify intrinsic characteristics associated with dementia risk without confounding medical conditions. The participants in our study represent the upper 1% to 3% of the elderly population in terms of health and, hence, our results cannot be generalized.³⁹ Among the strengths of our study are its prospective design with biannual clinical assessments, and a small dropout rate, that allowed impairment to be reliably identified at an early stage. Despite optimal health, our participants exhibited cognitive decline and decreased survival at rates similar to previous studies.^40- 43 Nevertheless, a broader age range of participants with representative medical conditions (eg, hypertension, diabetes mellitus) should be examined in future investigations to confirm our findings. Another strength is that most of our participants have agreed to brain autopsy, which will ultimately clarify the cause(s) of cognitive impairment. To date, patients with cognitive impairment (both QD and PCI) have met criteria for AD at autopsy, consistent with the experience of other investigators,^44- 45 and suggesting that these end points are clinically meaningful.

Logical memory II test scores at baseline, in our study, were well above published criteria for MCI suggesting that our subjects were in a presymptomatic state rather than at an early phase of dementia.² Others have provided evidence for a long preclinical phase for dementia.^46- 47 Future studies will examine the transition between QD to PCI and progression to subsequent dementia.

Assessment of risk factors for the development of cognitive impairment in elderly persons will need to include cognitive and motor measures as well as neuroimaging. A combined approach will be useful for studies targeting preventive therapies for those at highest risk prior to the onset of AD.

Accepted for publication December 19, 2001.

Author contributions: Study concept and design (Mr Marquis and Drs Howieson, Kaye, and Camicioli); acquisition of data (Ms Moore and Drs Howieson, Payami, Kaye, and Camicioli); analysis and interpretation of data (Mr Marquis, Ms Moore, and Drs Sexton and Camicioli); drafting of the manuscript (Mr Marquis and Drs Kaye and Camicioli); critical revision of the manuscript for important intellectual content (Mr Marquis, Ms Moore, and Drs Sexton, Payami, Kaye, Howieson, and Camicioli); statistical expertise (Mr Marquis, Ms Moore, and Drs Sexton, Payami, and Kaye); obtained funding (Drs Payami, Kaye, and Camicioli); administrative, technical, and material support (Dr Kaye); study supervision (Drs Kaye and Camicioli).

The Oregon Brain Bank and the Oregon Aging and Alzheimer Disease Center are supported by grants AG08017 and 5M01RR0034 from the National Institutes of Health; the Alzheimer Research Alliance of Oregon, Portland; the Medical Research Foundation of Oregon, Portland; and donations from individuals. The Oregon Brain Aging Study is additionally supported by a Merit Review Grant from the Department of Veterans Affairs, Washington, DC (Dr Kaye).

We thank the staff of the Oregon Aging and Alzheimer Disease Center and the Oregon Brain Aging Study.

Corresponding author: Richard Camicioli, MD, Department of Medicine (Neurology), University of Alberta, 2E3.08, 8440 112th St, Edmonton, Alberta, Canada T6G 2B7 (e-mail: richard.camicioli@ualberta.ca).