Affiliations: [a] Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| [b] F. Hoffman-La Roche, Basel, Switzerland
| [c] Department of Neurology, Oregon Health and Science University and Portland VA Medical Center, Portland, OR, USA
Correspondence:
[*]
Correspondence to: Sheng Luo, PhD, Department of Biostatistics, School of Public Health, The University of Texas Health Science Center at Houston, 1200 Herman Pressler Dr, Rm E815, Houston, TX 77030, USA. Tel.: +1 713 500 9554; E-mail: sheng.t.luo@uth.tmc.edu.
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 writing of 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: Identifying predictors of conversion to Alzheimer’s disease (AD) is critically important for AD prevention and targeted treatment. Objective: To compare various clinical and biomarker trajectories for tracking progression and predicting conversion from amnestic mild cognitive impairment to probable AD. Methods: Participants were from the ADNI-1 study. We assessed the ability of 33 longitudinal biomarkers to predict time to AD conversion, accounting for demographic and genetic factors. We used joint modelling of longitudinal and survival data to examine the association between changes of measures and disease progression. We also employed time-dependent receiver operating characteristic method to assess the discriminating capability of the measures. Results: 23 of 33 longitudinal clinical and imaging measures are significant predictors of AD conversion beyond demographic and genetic factors. The strong phenotypic and biological predictors are in the cognitive domain (ADAS-Cog; RAVLT), functional domain (FAQ), and neuroimaging domain (middle temporal gyrus and hippocampal volume). The strongest predictor is ADAS-Cog 13 with an increase of one SD in ADAS-Cog 13 increased the risk of AD conversion by 2.92 times. Conclusion: Prediction of AD conversion can be improved by incorporating longitudinal change information, in addition to baseline characteristics. Cognitive measures are consistently significant and generally stronger predictors than imaging measures.
