Global Warming Research Paper Abstract About Alzheimers Disease


Regions of the temporal and parietal lobes are particularly damaged in Alzheimer's disease (AD), and this leads to a predictable pattern of brain atrophy. In vivo quantification of subregional atrophy, such as changes in cortical thickness or structure volume, could lead to improved diagnosis and better assessment of the neuroprotective effects of a therapy. Toward this end, we have developed a fast and robust method for accurately quantifying cerebral structural changes in several cortical and subcortical regions using serial MRI scans. In 169 healthy controls, 299 subjects with mild cognitive impairment (MCI), and 129 subjects with AD, we measured rates of subregional cerebral volume change for each cohort and performed power calculations to identify regions that would provide the most sensitive outcome measures in clinical trials of disease-modifying agents. Consistent with regional specificity of AD, temporal-lobe cortical regions showed the greatest disease-related changes and significantly outperformed any of the clinical or cognitive measures examined for both AD and MCI. Global measures of change in brain structure, including whole-brain and ventricular volumes, were also elevated in AD and MCI, but were less salient when compared to changes in normal subjects. Therefore, these biomarkers are less powerful for quantifying disease-modifying effects of compounds that target AD pathology. The findings indicate that regional temporal lobe cortical changes would have great utility as outcome measures in clinical trials and may also have utility in clinical practice for aiding early diagnosis of neurodegenerative disease.

The healthy adult brain is remarkably stable structurally but undergoes gradual changes with normal aging. Structural change is accelerated in neurodegenerative disease, including Alzheimer's disease (AD). The atrophy in AD arises from neuron and synapse loss that begins in the entorhinal cortex. The pathology then spreads throughout the limbic regions of the temporal lobe, including the hippocampal formation. Subsequently, neuron loss and atrophy is observed throughout neocortical association areas in temporal, parietal, and frontal lobes (1).

The fact that atrophy associated with AD can be detected in vivo using MRI has long been known (2). Hippocampal volume loss is a consistent finding (3) and is predictive of clinical decline (4–7). However, hippocampal atrophy is not specific to AD, as it is seen in a number of psychiatric and neurodegenerative diseases (8–10). Recently, it has been shown that cortical atrophy measured on MRI parallels the spread of AD pathology (11–13). Accurate measurement of cortical thickness and subcortical volumes across multiple regions may provide a signature of the disease specific enough to be useful for early diagnosis of AD (14).

In recent studies, measures of progressive AD-related atrophy detected from serial MRI scans show promise as biomarkers in evaluating the effectiveness of disease-modifying agents. So far, these studies have focused on relatively global measures, such as whole-brain and ventricular volume change (15, 16), although some have also looked at hippocampal volume change (17, 18). In these studies, despite the known regional specificity of AD-related volumetric changes, global measures have shown greater sensitivity than local measurements, possibly because of the difficulty in obtaining accurate measurement of local brain structure change using existing methods (17). Nevertheless, these global measures of brain structure change are highly correlated with gold-standard clinical outcome measures, such as the Clinical Dementia Rating Scale Sum of Boxes and Mini Mental State Examination scores (15, 19).

The use of longitudinal anatomical quantification in multicenter clinical trials presents a number of challenges, including differences in MRI pulse sequences across scanner manufacturers, scanner-specific spatial distortions, and changes in scanner hardware and software over time that can affect image properties. In view of this, the Alzheimer's Disease Neuroimaging Initiative (ADNI) was designed to validate and compare imaging and biofluid markers of disease progression in a realistic multicenter clinical trial setting (20). The large, publicly available ADNI database thus provides a realistic setting in which to validate imaging methods aimed at assessing AD pathology. To this database, we applied a recently developed method for obtaining precise measures of interval change in cortical and subcortical regions, based on structural MRI, and determined the relative statistical power to discriminate pathology afforded by different regional measures.


We examined two models of treatable effects for power calculations. The first, Model T (for “total”), assumes that the study drug modifies both disease- and aging-related changes; the second, Model D (for “disease-specific”), assumes that the study drug modifies AD- or mild cognitive impairment- (MCI) related changes but has no effect on aging-related changes. We found that multiple regional volume changes, including those of whole brain, ventricle, hippocampus, entorhinal, fusiform, inferior temporal and middle temporal cortices, provided powerful outcome measures, with several measures requiring fewer than 100 subjects per arm to detect a 25% reduction in the rate of total change in AD, with 80% power at the P < 0.05 significance level (see Methods for a description of the power calculations). Power calculations using ventricle and whole-brain volume change as outcome measures were particularly sensitive to the choice of treatable-effect model, especially in the case of MCI, where Model D required as much as six times the number of subjects per arm as Model T. When Model D was used for MCI, the best cognitive measure was as good as or outperformed these measures of more global structural change in the brain. For AD, regional cortical-volume change provided consistently superior power compared to cognitive measures regardless of choice of treatable effects model. The results indicate that volume loss in entorhinal, fusiform, inferior temporal and middle temporal cortices would serve as superior outcome measures for study drugs specifically targeting AD pathology in patients with MCI or AD.

Estimated changes across the brain at 6 and 12 months, along with cortical and subcortical tissue segmentation, are shown in Fig. 1 for an individual from the MCI cohort. Fig. 2 shows the results of power calculations for imaging measures of regional change, along with the best clinical cognitive-outcome measure, based on AD subjects and healthy controls. Results for Model T are in blue, and results for Model D are in red; numerical values are in Table 1(see Fig. S1 and Table S1 for sample size estimates not incorporating random rates of change).

Fig. 1.

Tissue segmentation, with 6- and 12-month volume change fields for an MCI subject. (A) Segmentation of the baseline MRI scan, with different brain structures represented in different colors. (B) Corresponding coronal slice overlain with a heat map representation of the voxelwise estimates of volumetric change at 6 months and (C) 12 months. (D) Left hemisphere cortical parcellation of the baseline MRI scan. (E) Cortical surface overlain with a heat map representation of the estimates of cortical volumetric change at 6 months and (F) 12 months. Region-specific estimates were obtained by averaging the voxelwise change within each region of interest. In this subject, the left middle-temporal gyrus has decreased in volume by 4.7% at 6 months and by 8.2% at 12 months; the left temporal-horn lateral ventricle has increased by 17.4% at 6 months and by 35.3% at 12 months.

Fig. 2.

Sample size estimates for AD from a linear mixed-effects model with random slopes. The bars, with 95% confidence intervals, indicate the expected number of subjects needed per arm to detect a 25% reduction in rate of change at the P < 0.05 level with 80% power, assuming a 24-month trial with scans every 6 months. Results for Model T are in blue and results for Model D are in red; numerical values are shown in Table 1.

Imaging measures generally outperformed the best cognitive measure, regardless of model choice. While power estimates for cognitive measures were relatively unaffected by model choice, the power estimates for the imaging measures were strongly dependent on the treatment model used. Subregional cortical measures outperformed global imaging measures and were less dependent on choice of treatment model.

For MCI, as shown in Fig. 3 and Table 2, the dependence on model choice is even more salient than for AD. Notably, for ventricular volume, the sample size calculated using Model D is six times higher than that calculated using Model T, and exceeds that calculated for the best clinical or cognitive measure. Similar to what was found in AD, the regional temporal lobe cortical measures afforded the smallest sample sizes, regardless of model choice (see Fig. S2 and Table S2 for sample size estimates not incorporating random rates of change).

Fig. 3.

Sample size estimates for MCI (see Fig. 2 for description). Numerical values are in Table 2. Note that the Model D (red) upper bound on the 95% confidence interval for ventricles is 2,421.

Table 1.

Sample size estimates (N) and annualized percent change for AD

Table 2.

Sample size estimates (N) and annualized percent change for MCI


The findings demonstrate that longitudinal volumetric change provides powerful outcome measures with which to examine putative disease-modifying medications for AD and MCI. Whole brain, ventricle, hippocampus, and cortical volumes of the entorhinal, fusiform, inferior temporal and middle temporal gyri undergo high rates of change in AD and MCI, which are quantifiable using serial MRI and the nonlinear registration procedures used here. A comparison of the current method with a standard method for quantifying global change is provided in the SI, where the analysis was restricted to a common data set of serial scans at 0, 6, and 12 months (Figs. S3 and S4 and Tables S3 and S4).

For clinical trial power calculations using longitudinal volumetric change as an outcome measure, choice of treatable-effect model influences which brain regions would be most sensitive to detect a drug effect, especially in MCI. If the drug is presumed to slow both age- and AD-related brain atrophy, then global and subregional medial temporal lobe (MTL) and cortical measures provide excellent statistical power to detect treatment effects. However, if the study drug is presumed to specifically slow AD-related brain atrophy, then subregional cortical measures provide superior power. For MCI, entorhinal cortex provided the most powerful outcome measure, which is consistent with findings suggesting that atrophy in this region is a sensitive marker of prodromal AD (11, 21).

Choice of treatment model differentially affects cognitive and MRI variables; cognitive measures often show improvements over time in healthy controls because of practice effects (22), but deterioration over time in patients. Therefore, for cognitive measures, Model T can provide more conservative power estimates and is the most commonly used model in powering clinical trials. In contrast, both normal aging and disease are associated with atrophic changes over time. Thus, Model D generally provides more conservative power estimates for imaging measures. For this reason, it is important to consider both models when comparing across cognitive and imaging measures.

One of the primary motivations for using brain volumetric changes as outcome measures in clinical trials has been the evidence for greater statistical power afforded by such measures relative to clinical and cognitive measures (23). The present results, however, demonstrate that the most commonly used global imaging measures may be less powerful than the best clinical and cognitive measures, when the more conservative, and perhaps more realistic, disease-specific model is used. These effects are magnified in the MCI cohort, which is a patient population of particular interest for drug development (24, 25). Because of the overlap in behavioral features between MCI and healthy elderly controls, MCI trials would require particularly large subject numbers when using behavioral outcome measures alone.

Another motivation for using regional volumetric changes as outcome measures in clinical trials is the desire to more directly examine the effects of therapy on the brain's AD pathology. Because AD pathology is known to be concentrated in particular cortical and subcortical gray-matter regions, it would be desirable to measure change in the specific regions where neuronal dystrophy leads to pronounced atrophy. By itself, the halting of such neuronal dystrophy would lead to a stabilization of volume loss, but other drug effects, perhaps unrelated to therapeutic effect, may also be at play. For example, a recent active immunization trial against amyloid showed greater overall brain-volume loss in subjects who generated an immune response when compared to those who did not. In this case, global volume loss was attributed to possible changes in brain hydration state related to therapy. A trial of passive immunization against amyloid showed an association between higher doses of the medication and vasogenic edema. Thus, a short-term effect of the drug might be an increase in global brain volume that could be mistaken for a neuroprotection. Further study is needed to determine whether these processes are even more salient in regions enriched for amyloid and also whether such processes eventually reach a steady state upon which a drug's neuroprotective effect may still be evaluated. Nevertheless, regional measures of volumetric change offer a finer-grained examination of these processes and the effects of a therapy on the brain, and might be proportionally less affected by global effects unrelated to regional AD pathology (17, 26).

Although not a direct measure of the molecular pathology in AD, subregional brain structural changes are a direct measure of the neurodegeneration associated with the disease, and are more directly associated with progression of clinical symptoms than are measures of amyloid (27). Imaging of amyloid protein provides a direct measure of one of the components of AD, but the sensitivity and specificity of this measure as a biomarker of AD remains an open question. There is growing evidence that amyloid protein may be elevated in some subjects who remain cognitively normal during the period of follow-up. Functional imaging measures (28–30) also show great promise as biomarkers in AD clinical trials and may be sensitive to pathology, at even earlier stages of disease.

An essential characteristic of an AD therapeutic is that it results in clinical or cognitive improvement. This improvement may be achieved through symptomatic modification (31, 32) or, preferably, through disease modification (33–36). Assessment of disease modification therefore relies on detecting a slowing of clinical decline. Clinical decline in AD occurs slowly over years, and so detecting a halt to this decline would be aided through complementary and sensitive measures. Although cognitive outcome is central to assessing therapeutic efficacy, cognitive decline is in fact a secondary effect of neuronal damage from the disease, partially reflected in regional atrophy. MRI longitudinal measures of regional volumetric change provide a valuable complement to cognitive measures in that they are not influenced by temporary symptomatic improvements, and they provide an early index of the drug's ability to reach the target organ and have an effect on AD-related atrophy.

Finally, regional volumetric measures of change show promise for eventual use in clinical practice to assist risk stratification and differential diagnosis at the earliest stages of neurodegenerative disease. These measures may be particularly powerful when combined with baseline volumetry (14) and other diagnostics, such as cerebrospinal fluid biomarkers, nuclear medicine ligands, neuropsychological tests, and genetics. The present results suggest that change in MTL cortical regions, in particular the entorhinal cortex, would provide the most sensitive and specific volumetric imaging measures early in the disease. Changes in regions such as the hippocampus, ventricles, and whole brain provide sensitive indices of disease progression but are also seen in healthy aging adults, thus reducing their specificity for the detection of AD.



Data used in the preparation of this article were obtained from the ADNI database ( ADNI was launched in 2003 by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, the Food and Drug Administration, private pharmaceutical companies, and nonprofit organizations as a $60 million, 5-year public-private partnership. ADNI's goal is to test whether serial MRI, PET, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as lessen the time and cost of clinical trials.

ADNI is the result of efforts of many coinvestigators from a broad range of academic institutions and private corporations. Subjects have been recruited from over 50 sites across the United States and Canada. ADNI's goal was to recruit 800 adults, ages 55 to 90, to participate in the research: ≈200 cognitively normal individuals to be followed for 3 years, 400 people with MCI to be followed for 3 years, and 200 people with early AD to be followed for 2 years (see The research protocol was approved by each local institutional review board and written informed consent is obtained from each participant.


The ADNI general eligibility criteria are described in the ADNI Protocol Summary page of the ADNI-Info Web site at for 2009. Briefly, subjects are not depressed, have a modified Hachinski score of 4 or less, and have a study partner able to provide an independent evaluation of functioning. Healthy control subjects have a Clinical Dementia Rating (37) of 0. Subjects with AD have a Clinical Dementia Rating of 0.5 or 1.0 and meet National Institute of Neurological Disorders and Stroke and Alzheimer's Disease and Related Disorders Association criteria for probable AD (38).

In this study, we used baseline and follow-up data collected before August 27, 2009 from the ADNI database. Group clinical and demographic baseline data for the 169 healthy control, 299 MCI subjects, and 129 AD subjects in this study are presented in Table 3.

Table 3.

Group demographics at baseline

Data Acquisition and Preparation.

Raw Digital Imaging and Communications in Medicine MRI scans, including two three-dimensional T1-weighted volumes per subject per visit, were downloaded from the public ADNI site ( These data were collected across a variety of scanners with protocols individualized for each scanner, as defined at In our laboratory, MRI data were reviewed for quality and automatically corrected for spatial distortion caused by gradient nonlinearity (39). For each subject at each visit, the two three-dimensional T1-weighted images were rigid-body aligned to each other, averaged to improve signal-to-noise ratio, and resampled to isotropic 1-mm voxels. Baseline volumetric segmentation (40, 41) and cortical surface reconstruction (42–45) and pacrellation (46, 47) were performed using a data analysis pipeline based on the FreeSurfer software package and customized Matlab code, optimized for use on large multisite data sets. The automated whole-brain segmentation procedure uses a probabilistic atlas and applies a Bayesian classification rule to assign a neuroanatomic label to each voxel. The atlas consists of a manually derived training set created by the Center for Morphometric Analysis (Massachusetts General Hospital, Harvard Medical School) from 40 non-ADNI subjects across the adult age range, including individuals with AD. Automated volumetric segmentation required only qualitative review to ensure that there was no technical failure of the application.

The cortical surface was reconstructed to measure thickness at each surface location, or vertex, to allow visualization of group differences at each vertex. The surface was parceled into distinct regions of interest (ROIs). The cortical-surface model was manually reviewed and edited for accuracy. Minimal editing was performed according to standard, objective rules, including correction of errors in removal of nonbrain areas and inclusion of white-matter areas of hypointensity adjacent to the cortical ribbon. Qualitative review and editing were performed, with blinding to the diagnostic status, by one of three technicians trained and supervised by an expert neuroanatomist with more than 10 years of experience (C.F.-N.). The technicians had a minimum of 4 months of experience reviewing brain MR images before their involvement in this project.

Qualitative review and editing required ≈45 min per subject. Baseline image construction was carried out on a Linux cluster composed of dual quad-core 2.5 GHz CPUs (Xeon E5420; Intel) with 16 GB RAM; each image reconstruction was run as an independent process and took ≈24 h of computational time.

Estimation of ROI Volumentric Interval Change.

For each subject, follow-up images were fully affine-registered to the baseline image, and their intensities brought into local agreement (i.e., corrected for relative B1-induced intensity distortion). Nonlinear registration of the images was then performed, where voxel centers are moved about until a good match between the images is made. Several methods exist for causing this to happen, including fluid deformation (48–50) and tensor-based morphometry (51). For the results presented here, however, we developed and applied a method (52) based on linear elasticity and closer in spirit to tensor-based morphometry. This method proceeds essentially as follows. The images are heavily blurred (smoothed), making them almost identical, and a merit or potential function calculated. This merit function expresses the intensity difference between the images at each voxel, and depends on the displacement field for the voxel centers of the image being transformed; it is also regularized to keep the displacement field spatially smooth. The merit function by design will have a minimum when the displacement field induces a good match between the images. The displacement field in general will turn cubic voxels into displaced, irregular hexahedra whose volumes (53) give the volume-change field. The merit function is minimized efficiently using standard numerical methods. Having found a displacement field for the heavily blurred pair of images, the blurring is reduced and the procedure repeated, thus iteratively building up a better displacement field. Two important additions to this are: (i) applying the final displacement field to the image being transformed, then nonlinearly registering the resultant image to the same target, and finally tracing back through the displacement fields, thus calculated to find the net displacement field; and (ii) restricting to ROIs and zooming when tissue structures are separated by only a voxel or two. These additional features enable very precise registration involving large or subtle deformations, even at small spatial scales with low boundary contrast.

All available healthy controls, MCI subjects, and subjects with AD who passed the qualitative baseline review described above were thus registered. From the deformation field, a volume-change field was calculated; an example is shown in Fig. 1. For each subject, the volume-change field was averaged over each ROI, including those of the cortical surface (change in cortical volume to first-order results from change in thickness), to give the percentage change from baseline. Further visual quality control, blind to diagnosis, was carried out by a technician on the volume-change field to exclude cases where there was significant degradation in meaningful registration for at least one ROI because of artifacts or major changes in scanner hardware between visits (e.g., change of scanner model or type of RF coil). The most common form of artifact, affecting approximately half of the rejected scans, was caused by within-scan subject motion. In future clinical trials, the loss of scans caused by motion artifacts may be greatly reduced by using real-time motion-correction procedures (54, 55). Artifacts resulting from change in scanner models between visits typically include differential contrast or spatial blurring, mostly affecting the fine-scale estimates of change (e.g., within the cortical ROIs). Artifacts resulting from change in RF coil, specifically from a traditional quadrature head coil to a phased-array coil, primarily resulted in dramatic changes in blood inflow effects, which in turn predominantly affected MTL measures. The combination of artifacts affecting the volume change field reduced the number of healthy control follow-up scans by 14.2%, the number of MCI follow-up scans by 14.5%, and the number of AD follow-up scans by 15.8%.

For a subject to be included in our statistical analyses, several criteria needed to be satisfied: the baseline cortical parcellation and subcortical segmentation had to pass review, as described above; for a tight comparison between cognitive and volumetric measures, a subject's follow-up was eliminated unless both volumetric and cognitive data, including a clinical diagnosis, existed for that follow-up; there was at least one good follow-up, along with the good baseline; a healthy control needed to remain such at all follow-ups; and finally, the volume-change field had to pass review. Quality control on the volume-change field reduced the number of healthy controls by 8.6% to 169, the number of MCI subjects by 8.5% to 299, and the number of AD subjects by 12.2% to 129.

Power Calculations.

We examined two models of treatable effects for power calculations: Model T assumes that the study drug modifies both disease- and aging-related changes; Model D assumes that the study drug modifies only AD- or MCI-related changes.

Power calculations were performed using a mixed-effects regression model for the outcome variable (absolute cognitive measure or subregional percent-volume change) as a linear function of time, with random (individual-specific) slope or trend term and, for the cognitive measures, random intercept (baseline value). Sample sizes per arm were estimated based on a z-test (56) for absolute mean slopes for AD and MCI subjects (Model T), and the difference in mean slopes for AD and MCI subjects from healthy controls (Model D). The sample size required to detect 25% slowing in mean rate of decline for a hypothetical disease-modifying treatment versus placebo was estimated for a 24-month, two-arm, equal-allocation trial, with a 6-month assessment interval. Power calculations were performed with the requirement that the trial have 80% power to detect the treatment effect using a two-sided significance level of 5%. The sample size per arm scales with the variance of the within-group rate of change (slope), which has both between-subject and within-subject (residual error variance of the mixed-effects model) components. Thus, for Model T, the treatment-effect size of interest was 25% of the rate of change in the patient population (MCI or AD), and for Model D it was 25% of the difference between the rates of change in the patient and normal populations. Confidence intervals of 95% for sample sizes were based on 95% confidence intervals for the treatment-effect size of interest. Power calculations were implemented in Matlab version 2008b, using the nlme function in the Statistics Toolbox. Sample size estimates based on a linear random-effects model ignoring between-subject variance in the rate of change (i.e., taking the group-specific rate of change as a fixed effect) are provided in Figs. S1 and S2, and Tables S1 and S2.


Thanks to Alan Koyama, Robin Jennings, and Chris Pung for downloading and processing the Alzheimer's Disease Neuroimaging Initiative MRI data. This research was supported by National Institute of Health Grants R01AG031224, R01AG22381, U54NS056883, P50NS22343, and P50MH081755 (to A.M.D.). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institute of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Pfizer Inc., Wyeth Research, Bristol-Myers Squibb, Eli Lilly and Company, GlaxoSmithKline, Merck and Co. Inc., AstraZeneca AB, Novartis Pharmaceuticals Corporation, Alzheimer's Association, Eisai Global Clinical Development, Elan Corporation plc, Forest Laboratories, and the Institute for the Study of Aging, with participation from the U.S. Food and Drug Administration. Industry partnerships are coordinated through the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory of Neuro Imaging at the University of California, Los Angeles.


  • 1To whom correspondence should be addressed. Multimodal Imaging Laboratory, Suite C101; 8950 Villa La Jolla Drive, La Jolla, CA 92037. E-mail: dominic.holland{at}
  • Author contributions: D.H. and A.M.D. designed research; D.H., J.B.B., D.J.H., and A.M.D. performed research; D.H. and A.M.D. contributed new reagents/analytic tools; D.H., J.B.B., D.J.H., C.F.-N., and A.M.D. analyzed data; and D.H., J.B.B., and A.M.D. wrote the paper.

  • 2Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database ( As such, the investigators within the ADNI contributed to the design and implementation of ADNI and provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators is available at

  • Conflict of interest statement: A.M.D. is a founder and holds equity in CorTechs Labs, Inc, and also serves on the Scientific Advisory Board. The terms of this arrangement have been reviewed and approved by the University of California at San Diego in accordance with its conflict of interest policies.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at



The development of Alzheimer's disease (AD) later in life may be reflective of environmental factors operating over the course of a lifetime. Educational and occupational attainments have been found to be protective against the development of the disease but participation in activities has received little attention. In a case-control study, we collected questionnaire data about 26 nonoccupational activities from ages 20 to 60. Participants included 193 people with probable or possible AD and 358 healthy control-group members. Activity patterns for intellectual, passive, and physical activities were classified by using an adaptation of a published scale in terms of “diversity” (total number of activities), “intensity” (hours per month), and “percentage intensity” (percentage of total activity hours devoted to each activity category). The control group was more active during midlife than the case group was for all three activity categories, even after controlling for age, gender, income adequacy, and education. The odds ratio for AD in those performing less than the mean value of activities was 3.85 (95% confidence interval: 2.65–5.58, P < 0.001). The increase in time devoted to intellectual activities from early adulthood (20–39) to middle adulthood (40–60) was associated with a significant decrease in the probability of membership in the case group. We conclude that diversity of activities and intensity of intellectual activities were reduced in patients with AD as compared with the control group. These findings may be because inactivity is a risk factor for the disease or because inactivity is a reflection of very early subclinical effects of the disease, or both.

Work in North America, Europe, Asia, and the Middle East has shown that the incidence and prevalence of Alzheimer's disease (AD) is lower in subjects with relatively higher levels of education (1–4). According to the East Boston study, each year of education reduced the risk of AD by 17% (3). Although the protection against development of AD provided by education could be an artifact produced by the ability of more highly educated persons to perform better on cognitive tests (4–7), many studies have used functional rather than psychometric measures for diagnosis and have documented the protective effect of education (1, 2, 4). Although the mechanisms of education protection remain unknown, Katzman (1) has proposed that the protective effects of education are related to neuronal reserve; individuals with higher levels of education are more resistant to the effects of the disease on cognition because of enhanced synaptic complexity. Occupational attainment also has been demonstrated to be protective against the disease (3, 8).

Educational protection also may be induced by lifelong patterns of neuronal activation associated with exposure to education (9–12). But education and occupation are not the only reflection of these lifelong patterns; recreational activities are also indications of the ways in which cognitive and other skills are used in daily life (13, 14). We have hypothesized that recreational tasks, in addition to education and occupation, are protective against the development of AD (10, 11). Leisure endeavors are reflective of the intrinsic value of an activity for an individual (14)—they may be more reflective of neurological factors than education or occupation, which are strongly influenced by socioeconomic determinants, especially in the earlier years of this century when economic, social, and military factors often determined who went to school and for how long. Recreational activities provide a reflection of neuronal reserve and activation that may be relatively independent of these economic, social, and military factors.

The pathological features of AD are most profound in the limbic system and temporal, frontal and association neocortices, and basal forebrain areas involved in learning, memory, emotion, judgement, abstraction, language, and executive functions (15). We therefore hypothesized that intellectual activities involving learning and memory would be most protective against the development of the disease.

Hultsch et al. (16) have reported that “favorable life experiences or conditions may forestall or attenuate the declines typically seen in a variety of cognitive processes in later adulthood.” Similarly, Schooler (17) has found that “environmental complexity” is associated with enhanced cognitive function throughout life. Because of the very chronic nature of AD (18) and its strong relation to age, it is likely that interactions between “favorable life experiences” (which may be associated with education and occupation) and cognitive decline will be operative for both healthy aging as well as neurodegenerative disorders such as AD.

We have evaluated the relationships between nonoccupational activities and AD in a case-control study. Activities from the ages of 20 to 60 years were studied. Information about activities after age 60 or 5 years before disease onset was not collected, because it is clear that the disease itself is associated with reduced activities (19, 20), a reduction that could very well occur in the premorbid period before the patient or family is aware of the onset of dementia.



Subjects were participants in the Alzheimer's Disease Case-Control Study at Case Western Reserve University, University Hospitals of Cleveland, which was initiated in 1991. This project was approved by the Institutional Review Board of University Hospitals of Cleveland (09–92-210). Patients (N = 193) were recruited from clinical settings and the community and all were enrolled in the Research Registry of the University Alzheimer Center, University Hospitals of Cleveland. Patients were evaluated by neuropsychological, laboratory, and neurological exams and all had x-ray computed tomography or MRI scans of the brain. In all cases, patients had a probable (79%) or a possible AD (21%) diagnosis that was reached by consensus conference by using National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association criteria (21). Case-group patients were required to have had an onset of symptoms within 5 years of evaluation at the University Alzheimer Center, to minimize the contribution of premorbid features. The control-group members (N = 358) were the friends or neighbors of the case-group members or were members of the same organizations to which the case-group members belonged. Surrogates for case-group members were asked to identify friends, neighbors, or organizations to which the case-group members belonged. The control-group members were acquired by using frequency matching for age and gender. Spouse control-group members were not used to avoid overmatching. All case-group members had surrogates available who had known the case-group member for at least the last 10 years, and who had a close personal relationship with the case-group member. Surrogates were 62% spouses, 28% children, and 10% siblings or friends. Refusal rates for participation as control-group members were 65/346 (19%) for males and 106/537 (20%) for females (some subjects who agreed may have been refused participation because of exclusion criteria). Refusers were no different in comparison to responders in regard to gender, age, geographical location, or education. Control-group members were examined in the same way as case-group members and were determined to be free of neurological, psychiatric, or medical diseases affecting cognition. We compared control-group members obtained as friends or neighbors of case-group members with those acquired from organizations and found no differences in the two types of control-group members in regard to demographic variables, cognitive performance, or personality (neurotocism, extraversion, openness personality inventory; ref. 41). Subjects with a history of alcoholism, drug abuse, major head trauma, cancer, or other illnesses likely to impair cognition were not accepted into either the case or control groups. Control-group members were paid $30 for their participation in the study. After a complete description of the study was given to the subjects and their families, written informed consent was obtained.


We studied 26 different types of activities, asking three questions about each one: (i) Did subjects participate in the activity at least once per month? If yes, (ii) how many hours per month in their 20s and 30s (i.e., early adulthood); and (iii) how many hours per month in their 40s and 50s (i.e., middle adulthood)? These data were referred to as the “ever/never” data, the “20s and 30s hours” data, and the “40s and 50s hours” data, respectively. We inquired about activities in the teen years but found these data to be unreliable because of missing data, as few appropriate informants could be found. We did not acquire data about the period following age 60, or less than 5 years before disease onset in case-group members, because of the confounding effect of the premorbid and morbid effects of illness on participation in activities. Questionnaire data included other possible risk or protective factors, including education, family history, medication use, medical history, diet, and smoking habits (22). Questionnaires were completed in the home by the control-group members themselves and by a surrogate for case-group members and mailed to the Alzheimer Center. Ninety-nine percent of questionnaires were returned. The activity questionnaire used is available from the authors upon request.

Data from the 26 activities were grouped into three general activity categories (passive, intellectual, and physical) adapted from empirical-theoretical work by Hultsch et al. (23). The 26 activities for the three activity categories also are available from the authors upon request. These activity types and categories were used to develop three major measures: diversity, intensity, and percentage intensity.


Diversity was defined as the sum of the total number of activities participated in at least once per month per category, divided by the total number of activities making up an activity category. (For example, for subjects who reported doing five physical activities, diversity scores equaled 0.56, because there were 9 possible physical activities variables and 5/9 = 0.56.)


Passive, intellectual, and physical intensity were defined as the sum of the total hours per month devoted to each activity type. For example, physical intensity was calculated by summing the hours per month devoted to baseball, football, basketball, soccer, hockey, working out in a gym, racquet sports, bike riding, golf, bowling, gardening, ice skating, roller skating, jogging, swimming, and walking for exercise. Separate intensity scores were calculated for early (ages 20 to 39) and middle adulthood (40 to 59).

Percentage intensity.

Percentage intensity was defined as the percent of total activity hours per month devoted to each activity category (passive, intellectual, and physical). Percentage intensity in early and middle adulthood was calculated by dividing intensity scores by the total number of hours devoted to all three activity categories. The result was then multiplied by 100. Separate percentage intensity scores were calculated for early and middle adulthood. (Because the percentage intensities of passive, intellectual, and physical activities were percent scores, by definition they always summed to 100%). Thus, for example, to calculate the percentage intellectual intensity in early adulthood, we summed the total hours devoted to “intellectual activities” in early adulthood (i.e., intellectual intensity), then divided by the sum of the hours devoted to all three activity categories in early adulthood, and then multiplied the result by 100.

Treatment of Missing Data.

Missing data were imputed in a two-step sequence. In the first step, missing values for the “ever/never” variables were imputed by using “hot-deck” procedures available in the SOLAS MISSING DATA ANALYSIS 1.0 statistical software (Statistical Solutions, Saugus, MA.). “Hot-deck imputation” sorts respondents and nonrespondents into imputation classes according to a user-specified set of auxiliary variables. Missing values are replaced with values taken from matching respondents (i.e., respondents similar with respect to the auxiliary variables). In the current study, sorting variables included: (i) year of birth, (ii) gender, and (iii) years of education. Imputed values were selected randomly from the imputation classes created by using these variables. The percentages of missing data across the 26 “ever/never” variables before imputation ranged from 0.5% to 3.8%.

In the second step, we imputed missing data for the “hours” variables. For subjects who reported that they had never completed an activity during their lives (as indicated by the “ever/never” variables), the corresponding missing “hours” data were coded automatically as 0. After this adjustment, missing data for the “hours” variables were imputed by using the “hot-deck” methods described above. Year of birth, gender, and years of education again were used as sorting variables. The percentages of missing data across the “hours” variables before imputation ranged from 0.5% to 11.6% in early adulthood and from 0.2% to 6.4% in middle adulthood.

Data Analysis.

Data analysis was accomplished in five steps:

1. Sociodemographic characteristics. Case- and control-group members were compared on the basis of basic sociodemographic characteristics by using t tests, χ2 tests, and the Wilcoxon sign-rank test, where appropriate.

2. Activity count. A t test was used to compare case- and control-group members on the basis of the overall raw count of activities in which subjects ever participated. All subjects were divided into two groups and were defined by the mean raw count of activities in which subjects ever participated, and the odds ratio for disease status in those having less than the mean raw count of activities was calculated.

3. Diversity. Separate one-way between-subjects ANOVAs were completed with case/control status as the independent variable and passive, intellectual, and physical “diversity” scores as dependent measures. To decrease the variance associated with sociodemographic characteristics, a series of one-way between-subjects analyses of covariance (ANCOVA) also were run, with year of birth, gender, years of education, and income adequacy as covariates. All subjects then were divided into two groups, defined by the mean diversity score for each diversity-dependent variable, and the odds ratio for disease status in those having less than the mean diversity scores was calculated.

4. Intensity. Case- and control-group members were compared in separate one-way ANOVAs on “intensity” scores in early adulthood and in middle adulthood. Again, to decrease variance associated with sociodemographic characteristics, a series of one-way ANCOVAs were run, with year of birth, gender, education, and income adequacy as covariates. All subjects were then divided into two groups, defined by the mean intensity score for each intensity variable, and the odds ratios for disease status in those having less than the mean intensity scores were calculated.

5. Percentage intensity. In a final set of analyses, the percentages of total hours per month devoted to intellectual, passive, and physical activities in early and middle adulthood were calculated. The percentages of intellectual and physical variables then were included as predictors in a logistic regression model, along with sociodemographic characteristics (year of birth, gender, years of education, and income adequacy) to predict membership in the case vs. control group.

Surrogate Substudy.

We examined the issue of the bias that may be introduced by the use of the surrogates caused by systematic under- or over-reporting types or hours of activities. The first 50 cognitively intact individuals who entered the case-control study to serve as control-group members were asked to fill out the Life History Questionnaire and also to have it filled out by a person whom they felt was well acquainted with their past and present activities. Because it would not have been possible to dictate the type of relationship between the case-group members with AD and their surrogate respondents, no attempt was made to influence choices regarding the relationship between the control-group members and their surrogates. By using the Hultsch classification scheme, intensity scores for passive, intellectual, and physical activities in the 20s and 30s and in the 40s and 50s were computed for the self and the surrogate responses. Paired t tests were run to test the null hypotheses of zero mean difference between self and surrogate responses.


Sociodemographic Characteristics.

The sociodemographic characteristics of our case- and control-group members are shown in Table 1. Case-group members had significantly lower levels of education than did control-group members, and the case group's median year of birth was slightly earlier than the control group's median year of birth.

Table 1

Sociodemographic characteristics of case- and control-group members (total n = 551)

Activity Count.

Among the 26 activities we studied, control-group members reported performing more activities (mean = 16.0, SD = 3.4) than case-group members (mean = 12.9, SD = 4.1; P < 0.001). This result remained significant after controlling for the potential confounders, year of birth, sex, education, and income adequacy (P < .001). When all subjects were divided into two groups, defined by the mean of the raw count of activities, the odds ratio for disease status in those having less than the mean value of activities was 3.85 (95% confidence interval: 2.65–5.58, P < 0.001).


Passive, intellectual, and physical diversity scores were submitted to separate univariate ANOVAs and ANCOVAs. Results of diversity score ANOVAs and ANCOVAs are presented in Table 2. Control-group members participated in a greater diversity of passive, intellectual, and physical activities than did case-group members [all p values <0.001]. Results remained significant after controlling for covariates [all p values <0.001]. Thus, case- and control-group members differed in terms of the diversity of activities reported across the lifespan, with control-group members participating in a greater diversity of each class of activities than case-group members. The odds ratio for low passive diversity was 2.51 [95% confidence interval (CI): 1.75–3.59, P < 0.001], the ratio for low intellectual diversity was 2.43 (95% CI: 1.66–3.54, P < 0.001), and the ratio for low physical diversity was 2.67 (95% CI: 1.85–3.85, P < 0.001)

Table 2

Comparisons between case- and control-group members on diversity scores


Passive, intellectual, and physical “intensity” scores in early and middle adulthood were submitted to separate univariate ANOVAs. Results are presented in Table 3. The data show that control-group members participated in a higher mean total hours per month of passive and intellectual activities in early adulthood than did case-group members. Control-group members also participated in a higher mean total hours per month of intellectual activities in late adulthood than did case-group members. However, after controlling for covariates, the “passive hour” difference in early adulthood was no longer statistically significant. Thus, in both early and middle adulthood, control-group members participated in a higher mean total hours per month of intellectual activities than did case-group members.

Table 3

Comparisons between case- and control-group members on “intensity” scores

Percentage Intensity and Logistic Regression Results.

The percentage of total hours per month devoted to passive, intellectual, and physical activities in early and in middle adulthood were calculated. Means and standard deviations, stratified by group and gender, are shown in Table 4. Changes (i.e., difference scores) from early to middle adulthood in the percentage of total hours per month devoted to passive, intellectual, and physical activities were also calculated. The data show that many of the subjects in our sample neither increased nor decreased the time devoted to passive, intellectual, or physical activities from early to middle adulthood, with modal scores of 0 for each of the three difference scores (data not shown). However, there were some subjects who increased their percent passive, intellectual, or physical activities from early to middle adulthood, whereas others decreased their percentage of activities in one or more categories. On average, for both case and control groups, passive activities increased, whereas intellectual and physical activities decreased with age.

Next, the intellectual and physical percentages for intensity variables in early and middle adulthood and the sociodemographic variables were included as predictors in a logistic regression model with case vs. control status as the dependent measure. Preliminary analyses, which included interaction variables with gender as predictors, did not reveal significant effects [P values >0.05]. Therefore, interaction terms with gender were not included in the equation.

Results from a logistic regression model indicate that, when holding constant sociodemographic characteristics, percentage intensity of intellectual and physical activities in early adulthood, and physical activities in middle adulthood, the percentage of intellectual intensity during middle adulthood was a significant predictor of membership in the case vs. the control group (P < .05). As a graphical presentation of these results, we plotted separately for men and women the probability of membership in the case vs. the control group as a function of change in the percentage of total hours per month devoted to intellectual activities in middle adulthood and the means of the other independent variables (Fig. 1). These results mean that increases from early to middle adulthood in the percentage of intensity of intellectual activities is associated with a statistically significant decrease in the probability of membership in the case vs. the control group when also controlling for covariates. The model fit was good, with a Nagelkerke pseudo R2 of 0.26 (24). In this analysis, we held constant the early-adulthood activities measures so that an increase in the middle-adulthood activities measures can be interpreted as a change from early to middle adulthood.

Figure 1

Probability of membership in the case group for men and women as a function of changes from early to middle adulthood in percentage of total hours per month devoted to intellectual activities (means of other independent variables included in the logistic regression model).

Fig. 1 represents predicted case- vs. control-group membership results for the “average” male and for the “average” female in our sample in terms of sociodemographic features—passive, intellectual, and physical activities in early adulthood and passive and physical activities in middle adulthood. The graph shows that, assuming these average values, for those subjects who increased their percentages of intellectual activities from early to middle adulthood (and thus decreased their passive activities by a corresponding amount), the probability of membership in the case group decreased. However, for those subjects who decreased their percentages of intellectual activities from early to middle adulthood (and thus increased their passive activities by a corresponding amount), the probability of membership in the case group increased.

Table 4

Mean percent total hours per month devoted to passive, intellectual, and physical activities, by group and gender

Surrogate Substudy.

Forty-nine pairs were available for analysis. The null hypotheses of zero mean differences were not rejected. The test values obtained were as follows: passive intensity 20s/30s: t(29) = 1.271, P = 0.214; passive intensity: 40s/50s t(39) = 0.736, P = 0.466; intellectual intensity 20s/30s: t(29) = 0.232, P = 0.818; intellectual intensity 40s/50s: t(38) = 0.408, P = 0.686; physical intensity 20s/30s: t(26) = 0.418, P = 0.679; and physical intensity 40s/50s: t(35) = −1.125, P = 0.268. Thus, on average, surrogates do not appear to under- or over-report events. The different degrees of freedom of the test statistics reflect missing data, largely from the surrogate respondents. As expected, this occurs more frequently when recalling the earlier periods of life.


Our results indicate that patients with AD are less active in midlife (early and middle adulthood) in terms of intellectual, passive, and physical activities than control-group members. The lower premorbid activity levels in patients with AD persisted in measures of intellectual, passive, and physical activities, calculated by using an independently developed scale following statistical correction for year of birth, sex, education, and income adequacy. These differences were not explained by differing educational levels in the two groups. We minimized the influence of early disease on participation in activities by collecting data only concerning the period of midlife ending at age 60 or ending 5 years before disease onset (whichever was earlier). Our results indicate that low participation in activities in midlife (in addition to low levels of educational and occupational achievement) is a risk factor for the disease.

We found that diversity of intellectual, passive, and physical activities were all protective against the development of AD. Our hypothesis that intellectual activities were protective was confirmed, but the effects were seen for passive and physical activities as well. However, the differences between case- and control-group members were greatest in regard to intellectual activities. Odds ratios showed that people who were relatively inactive (for intellectual, passive, or physical activities) had about a 250% increased risk of developing AD. Physical exercise has been reported by our group to be protective against development of the disease.** There are many beneficial effects of physical activity that may be related to reduced risk of AD: lower body weight, improved diet (including increased consumption of antioxidants and lower fat intake), improved blood pressure and cardiovascular health, as well as beneficial effects on blood clotting (25).

We cannot exclude the possibility that our data reflect the very early effects of the disease, several decades before symptom onset. Snowdon and colleagues (18) in the Nun Study have demonstrated a remarkable relationship between early-life linguistic abilities and the late risk of developing AD, suggesting that the disease may have early effects on performance several decades before symptom onset. Positron emission tomographic (PET) studies have also shown that the metabolic effects of AD in apolipoprotein (apo) E-ɛ4 homozygotes may begin 10–20 years before diagnosis (26, 27). Also, ApoE-ɛ4 homozygotes have been reported to have preclinical memory decline in immediate and delayed recall (28). A prospective study of the Framingham cohort also has shown preclinical decline in verbal memory in people who were to eventually develop AD (29). Pathological studies of Down's syndrome brain have shown that diffuse plaques of β-amyloid 1–42 develop as early as 12 years of age (30), even though loss of function is not seen until after age 35. It is possible that the presymptomatic cognitive effects demonstrated in the Nun Study (18) and the early metabolic changes documented with PET (26, 27) also represent risk factors for AD. Both explanations may very well be concurrently correct. It may be possible to analyze the relative contributions of early disease and risk/protective effects in longitudinal studies of transgenic mice having AD genes.

Fig. 1 demonstrates that for the average subject, reduction in intellectual pursuits over the four decades from early to middle adulthood increases the probability of AD. This relationship between disease and intellectual activity may be interpreted as evidence of the progressive effects of the disease on participation or may also represent a protective effect of intellectual activities.

We used an “asymmetrical” method of data collection, obtaining information from surrogates for case-group members and obtaining information from control-group members from themselves, because case-group members cannot self report and control-group members have the most accurate information about their own lives. We did not use surrogates for control-group members because that would not have created a genuinely “symmetrical” method, as the caregiving relationship between case-group members and their surrogates is not the same as the noncaregiving relationship between control-group members and their surrogates in that the surrogates of control-group members are not as well informed. Our surrogate substudy demonstrates that the use of surrogate responders for control-group members would have added imprecision to the data without altering average values, and that there was no systematic under- or over-reporting introduced by the use of control-group members responding for themselves. Our choice of respondents is also supported by a study of alcohol consumption in which primary and proxy respondents have been found to provide similar information (31). Kondo et al. (32) also compared direct and indirect answers in a study on AD, and found that all responses agreed 70% or more (κ = 1, P < 0.05). Proxy respondents have also been found to be a reliable source of information for dementia studies in observations from the MIRAGE project (42).

Previous reports of relationships between activities and AD did not account for the effects of early disease in the several years preceding symptoms. Certainly premorbid decline can cause reduction in activities in years preceding onset of clinical dementia, as demonstrated by Fabrigoule et al. (20) in an incidence study of dementia in subjects who were followed for 3 years. This premorbid effect could influence relationships with physical (33), mental (32, 34), and social activities (19). A study by Zabar and colleagues‡‡ found no difference in activities between case- and highly selected control-group members participating in the Baltimore Longitudinal Study of aging. Again, premorbid activities were not addressed.

Our study has important limitations. Case-group members were recruited from our Alzheimer Center and, along with our control-group members, were not population-based. However, the demographic features of our case- and control-group members (Table 1) are similar to other studies on AD in this country. The use of friends as control-group members and the use of control-group members from the same organizations as case-group members is likely to produce some overmatching, suggesting that our main effects may have been stronger if population-based control-group members were used. Activity participation reflects a complex constellation of economic, occupational, and other factors, and hours of participation do not necessarily reflect quality of participation (i.e., some people may do a task more effectively and spend less time doing it). We have been able to record only whether an activity was done and for how long. We were able to statistically account for some possible confounders (age, sex, education, income adequacy) but did not consider others, such as apoE genotype. Also deserving consideration are possible confounders, which may be independently related to both AD risk and activity levels, including early-life environment, level of medical care, heart disease, personality, stress, occupation, and socioeconomic status (9, 40). Another important confounder, education, was controlled for in the analysis. The classification of activities as intellectual, passive, or physical is arbitrary but supported by the work of Hultsch et al. (23).

Retrospective assessment of participation in activities is likely to contain inaccuracies. However, the only other way to obtain the data required is with a very long-term prospective study. Data from short-term (3–5 years) prospective studies may be influenced by the premorbid and morbid effects of disease—“high-ability individuals lead intellectually active lives until cognitive decline in old age limits their activities” (16).

Donald Hebb had predicted that use contributes to the establishment and maintenance of synapses (12). It may also be that neuronal activation, associated with functional activity, spares the brain from the Alzheimer process through beneficial effects on membranes and amyloid β protein production, degradation, and aggregation (9, 10, 15).

Our results are in accord with those of Hultsch et al. (16), who reported recently that “intellectually engaging activities buffer against longitudinally measured cognitive decline” in a study of 214 persons aged 55 to 86 at time of first assessment. Wilson and colleagues (36) evaluated 6,162 persons in a geographically defined biracial population and also found that cognitive function was related to “composite measures of the frequency and intensity of cognitive activity.” Our results are compatible also with the view that environmental complexity is associated with enhanced cognitive functioning (17) and that underactivity is a risk factor for the development of AD, as proposed in Swaab's (12, 37) “use it or lose it” scenario.

Humans remain genetically equipped for life as Paleolithic hunter–gatherers (35). Activity levels consistent with human survival were certainly higher for all of human history than they are now in the 21st century. A protective relationship between high activity levels and AD may explain partially the low prevalence of the disease in rural India (38) or urban Nigeria [despite high apoE-e4 allele frequency (39)]. Activity levels in developing countries are certainly very high. Relationships between recreational activities and the development of neurodegenerative disorders has received relatively little attention. We believe that the interactions reported here are important because of their significance for public policy.


We are grateful for the early guidance of A. R. Feinstein, A. A. Rimm, and J. Guralnik. The contributions of M. McClendon and C. Esteban-Santillian are acknowledged gratefully. This work was supported in part by grants from the National Institute on Aging (PO 263-MO-818915 and VO1 AG1713-01A1), the Alzheimer's Disease Research Center Program (P50 AG 08012), the Mandel Foundation, the Nickman family, the Institute for the Study of Aging (New York), and Philip Morris USA. The sponsors had no role in the design, conduct, interpretation, or analysis of the study. Preliminary accounts of this work have been presented (11).


  • ↵† To whom reprint requests should be addressed at: Case Western Reserve University, School of Medicine, Department of Neurology, 10900 Euclid Avenue, Cleveland, OH 44106. E-mail: rpf2{at}

  • ↵** Smith, A. L., Cole, R., Smyth, K. A., Koss, E., Lerner, A. J., Rowland, D. Y., Debanne, S. M., Petot, G. J., Teel, W. B. & Friedland, R. P. (1998) Neurology50, A90 (abstr.).

  • ‖ Present address: National Institute on Aging/National Institutes of Health, Bethesda, MD 20892.

  • ↵‡‡ Zabar, Y. U., Corrada, M., Fozard, J., Cosa, P. & Kawas, C. (1996) Neurology 46, A435 (abstr.).


Alzheimer's disease;
analyses of covariance
  • Received February 17, 2000.
  • Accepted January 2, 2001.
  • Copyright © 2001, The National Academy of Sciences


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