Affiliations: [a] Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN, USA | [b] Geriatric Research, Education and Clinical Center, Minneapolis Veterans Affairs Medical Center, Minneapolis, MN, USA | [c] Departments of Neurology and Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA | [d] School of Nursing & Center on Aging, University of Minnesota, Minneapolis, MN, USA
Correspondence:
[*]
Correspondence to: Karen M. Kuntz, 420 Delaware St. S.E., MMC 729, Minneapolis, MN 55455, USA. Tel.: +1 612 625 9333; Fax: +1 612 624 2196; [email protected]
Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writingof this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Abstract:
Background: Cerebrospinal fluid (CSF) biomarkers can distinguish Alzheimer’s disease (AD) patients from normal controls; however, their interpretation and potential for use in patients with mild cognitive impairment (MCI) remains unclear.
Objective: To examine whether biomarker levels allow for risk stratification among MCI patients who are at increased risk to develop AD, thus allowing for improved targeting of early interventions for those whose risk are higher.
Methods: We analyzed data from the Alzheimer’s Disease Neuroimaging Initiative on MCI patients (n = 195) to estimate their risk of developing AD for up to 6 years on the basis of baseline CSF biomarkers. We used time-dependent receiver operating characteristic analysis to identify the best combination of biomarkers to discriminate those who converted to AD from those who remained stable. We used these data to construct a multi-biomarker score and estimated the risk of progression to AD for each quintile of the multi-biomarker score.
Results: We found that Aβ
1-42 and P-tau181p were the best combination among CSF biomarkers to predict the overall risk of developing AD among MCI patients (area under the curve = 0.77). The hazard ratio of developing AD among MCI patients with high-risk (3rd–5th quintiles) biomarker levels was about 4 times greater than MCI patients with low-risk (1st quintile) levels (95% confidence interval, 1.93–7.26).
Conclusion: Our study identifies MCI patients at increased risk of developing AD by applying a multi-biomarker score using CSF biomarker results. Our findings may be of value to MCI patients and their clinicians for planning purposes and early intervention as well as for future clinical trials.
