By
Makoto Ishii, MD, PhD
Assistant Professor of Neuroscience and Neurology, Feil Family Brain and Mind Research Institute, Department
of Neurology, Weill Cornell Medical College
Dr. Ishii reports no financial relationships relevant to this field of study.
SYNOPSIS: In this population-based study, researchers used imaging biomarkers of amyloid and neuronal injury to estimate an absolute risk of mild cognitive impairment in the elderly.
SOURCE: Petersen RC, Lundt ES, Therneau TM, et al. Predicting progression to mild cognitive impairment. Ann Neurol 2018; Dec. 6. doi.org/10.1002/ana.25388. [Epub ahead of print].
Alzheimer’s disease (AD) is recognized to be a continuum that begins years to decades before the mild cognitive impairment (MCI) with a preclinical stage, where amyloid-β and tau pathologically accumulate prior to any significant cognitive decline. Importantly, it is now possible to identify individuals in vivo with preclinical AD using biomarkers of amyloid-β (A) by cerebrospinal fluid (CSF) or positron emission tomography (PET) and measures of neuronal injury (N) by CSF, 18F-fluorodeoxyglucose (FDG) PET, or MRI. This has led to an AD biomarker-based staging of cognitively unimpaired individuals based on normal (-) or abnormal (+) levels of A and N, with the A+N+ group having an elevated risk of progression to cognitive impairment. Despite the advances in AD biomarker-based staging, the absolute risk of cognitive impairment for the elderly patient with these biomarkers is not clear, which makes it difficult to use these biomarkers in clinical practice. Therefore, Petersen et al set out to determine the role of imaging biomarkers in predicting progression to cognitive impairment.
Study participants were part of the Mayo Clinic Study of Aging, a longitudinal, population-based study of residents of Olmsted County, Minnesota. All participants were cognitively unimpaired and 70 years of age or older at the baseline visit. Each participant was evaluated clinically approximately every 15 months, with the clinical diagnosis of cognitively unimpaired, MCI, or dementia determined by consensus based on previously established criteria. Amyloid PET imaging was performed using Pittsburgh Compound-B. 3T MRI was performed, and a composite AD-characteristic cortical thickness measure averaging entorhinal, inferior temporal, middle temporal, and fusiform gyri thickness was obtained. Cutoffs for each imaging biomarker were determined based on recent analysis, and individuals were placed into one of four groups: A-N-, A+N-, A-N+, and A+N+.
Out of 763 cognitively unimpaired participants at baseline, 26% were A-N-, 15% were A+N-, 30% were A-N+, and 28% were A+N+. Both A+ and N+ were associated with older age, and men were more likely to be N+. Over a median follow-up of four years, 159 (22%) individuals progressed to MCI (n = 152) or dementia (n = 7). Overall progression rates (events per 100-person years with 95% confidence interval [CI]) were 2.4 (95% CI, 1.8-3.2) at age 75 and 6.5 (95% CI, 5.5-7.6) at age 85. Based on biomarker status, the progression rates at age 75 years were highest for the A+N+ group at 3.9 (95% CI, 2.7-5.7), intermediate for A+N- at 2.3 (95% CI, 1.4-3.7) and A-N+ at 2.3 (95% CI, 1.5-3.4), and lowest for A-N- 1.1 (95% CI, 0.7-1.9). At age 85, the progression rates increased for all groups, with the highest rate in the A+N+ group at 8.9 (95% CI, 6.8-11.5) followed by intermediate rates for both the A+N- and A-N+ groups, and lowest for A-N- group at 2.6 (95% CI, 1.5-4.3). Using the A-N- group as a reference, the relative rate for A+N+ was 3.5 (95% CI, 2.1-6.2), A+N- was 2.0 (95% CI, 1.1-3.9), and A-N+ was 2.0 (95% CI, 1.2-3.6). Male sex and having a high school education or less increased the relative risk (RR, 1.2; 95% CI, 0.9-1.7 for male sex and RR, 1.3; 95% CI, 1.0-1.9 for high school education). Using biomarker status, age, sex, and education level, the investigators calculated an estimated risk of progression to MCI/dementia in five and 10 years. For example, a cognitively unimpaired 75-year-old A-N- woman with some college education was estimated to have a 6% chance of becoming cognitively impaired within five years; however, this increased to 19% if she was A+N+.
COMMENTARY
The findings of this study are of great interest, as it is becoming clear that for an AD intervention to be most effective it will need to be implemented as early as possible. As expected, A+N+ individuals had the highest risk of progression. Interestingly, participants with only one positive biomarker (A+N- or A-N+) yielded a similar intermediate risk for developing cognitive impairment. This result differs from other studies that found no significant risk for A-N+. Any discrepancy could be caused by differences in the biomarkers used for neuronal injury. Although amyloid biomarkers are relatively well-established, the best biomarker for neuronal injury is less clear with a range of modalities. Even when one uses the same modality such as structural MRI, the analyses often differ as one study may focus on hippocampal volumes while others, including this study, may use a composite of cortical thickness from various brain regions. Therefore, a uniformed “best” approach to measuring neuronal injury needs to be established.
A major strength of this study is the relatively large sample size and population-based design, which minimizes any potential referral bias. However, a limitation of the study is that all participants were from one county in Minnesota. It is not clear if the results from this study can be extrapolated to other communities because of differences in race/ethnicity, local environmental effects, or other unforeseen factors. Therefore, similarly designed population studies are needed in other communities to verify and validate these findings. Furthermore, future studies using additional factors strongly associated with AD, including genetic (APOE genotype) and vascular risk factors, would further help stratify the risk of developing cognitive impairment. Despite any limitations, this important study brings us closer to being able to predict the risk of cognitive impairment for any given patient in the clinic.
