Background and Objective: In half to two thirds of patients who are diagnosed with mild cognitive impairment (MCI), the diagnosis neither converts to dementia nor reverts to normal cognition; however, little is known about predictors of MCI stability. Our study aimed to identify those predictors.
Methods: We obtained 3-year longitudinal data from the National Alzheimer’s Coordinating Center Uniform Data Set for patients with a baseline diagnosis of MCI. To predict MCI stability, we used the patients’ baseline data to conduct three logistic regression models: demographics, global function, and neuropsychological performance.
Results: Our final sample had 1059 patients. At the end of 3 years, 596 still had MCI and 463 had converted to dementia. The most reliable predictors of stable MCI were higher baseline scores on delayed recall, processing speed, and global function; younger age; and absence of apolipoprotein E4 alleles.
Conclusions: Not all patients with MCI progress to dementia. Of the protective factors that we identified from demographic, functional, and cognitive data, the absence of apolipoprotein E4 alleles best predicted MCI stability. Our predictors may help clinicians better evaluate and treat patients, and may help researchers recruit more homogeneous samples for clinical trials.
Departments of *Psychiatry
§Clinical Sciences, and
∥Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas
†Veterans Affairs North Texas Health Care System, Dallas, Texas
¶Seton Brain & Spine Institute, Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas
‡Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida
Supported in part by the Friends of the UT Southwestern Alzheimer’s Disease Center.
The authors obtained the data for this study from the National Alzheimer’s Coordinating Center (NACC) database. The database is funded by National Institute on Aging/National Institutes of Health (NIA/NIH) Grant U01 AG016976. NACC data are contributed by the NIA-funded Alzheimer’s Disease Centers: P30 AG019610 (Principal Investigator [PI] Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Steven Ferris, PhD), P30 AG013854 (PI M. Marsel Mesulam, MD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG016570 (PI David Teplow, PhD), P50 AG005131 (PI Douglas Galasko, MD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG028383 (PI Linda Van Eldik, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P50 AG005136 (PI Thomas Montine, MD, PhD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), and P50 AG005681 (PI John Morris, MD).
The authors declare no conflicts of interest.
Reprints: Matthew A. Clem, MEd, 2201 Inwood Road, Dallas, Texas 75390 (e-mail: Maclem@UTexas.edu).
Received March 24, 2016
Accepted December 8, 2016
