Brain imaging measurements can provide evidence of possible preclinical Alzheimer’s disease (AD). Their ability to predict individual imminent clinical conversion remains unclear.
Objective:
To investigate the ability of pre-specified volumetric magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) measurements to predict which cognitively unimpaired older participants would subsequently progress to amnestic mild cognitive impairment (aMCI) within 2 years.
Methods:
From an apolipoprotein E4 (APOE4) enriched prospective cohort study, 18 participants subsequently progressed to the clinical diagnosis of aMCI or probable AD dementia within 1.8±0.8 years (progressors); 20 participants matched for sex, age, education, and APOE allele dose remained cognitively unimpaired for at least 4 years (nonprogressors). A complementary control group not matched for APOE allele dose included 35 nonprogressors. Groups were compared on baseline FDG-PET and MRI measures known to be preferentially affected in the preclinical and clinical stages of AD and by voxel-wise differences in regional gray matter volume and glucose metabolism. Receiver Operating Characteristic, binary logistic regression, and leave-one-out procedures were used to predict clinical outcome for the a priori measures.
Results:
Compared to non-progressors and regardless of APOE-matching, progressors had significantly reduced baseline MRI and PET measurements in brain regions preferentially affected by AD and reduced hippocampal volume was the strongest predictor of an individual’s imminent progression to clinically significant memory decline (79% sensitivity/78% specificity among APOE-matched cohorts).
Conclusion:
Regional MRI and FDG-PET measurements may be useful in predicting imminent progression to clinically significant memory decline.
SperlingRA, AisenPS, BeckettLA, BennettDA, CraftS, FaganAM, IwatsuboT, JackCRJr, KayeJ, MontineTJ, ParkDC, ReimanEM, RoweCC, SiemersE, SternY, YaffeK, CarrilloMC, ThiesB, Morrison-BogoradM, WagsterMV, PhelpsCH (2011) Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement7, 280–292.
2.
PetersenRC (2004) Mild cognitive impairment as a diagnostic entity. J Intern Med256, 183–194.
3.
JackCRJr, HoltzmanDM (2013) Biomarker modeling of Alzheimer’s disease. Neuron80, 1347–1358.
4.
CarlsonNE, MooreMM, DameA, HowiesonD, SilbertLC, QuinnJF, KayeJA (2008) Trajectories of brain loss in aging and the development of cognitive impairment. Neurology70, 828–833.
5.
de Toledo-MorrellL, GoncharovaI, DickersonB, WilsonRS, BennettDA (2000) From healthy aging to early Alzheimer’s disease: In vivo detection of entorhinal cortex atrophy. Ann N Y Acad Sci911, 240–253.
6.
LongX, ChenL, JiangC, ZhangL (2017) Prediction and classification of Alzheimer disease based on quantification of MRI deformation. PLoS One12, e0173372.
7.
TondelliM, WilcockGK, NichelliP, De JagerCA, JenkinsonM, ZamboniG (2012) Structural MRI changes detectable up to ten years before clinical Alzheimer’s disease. Neurobiol Aging33, 825e825–836>.
8.
Rizk-JacksonA, InselP, PetersenR, AisenP, JackC, WeinerM (2013) Early indications of future cognitive decline: Stable versus declining controls. PLoS One8, e74062.
9.
de LeonMJ, ConvitA, WolfOT, TarshishCY, DeSantiS, RusinekH, TsuiW, KandilE, SchererAJ, RocheA, ImossiA, ThornE, BobinskiM, CaraosC, LesbreP, SchlyerD, PoirierJ, ReisbergB, FowlerJ (2001) Prediction of cognitive decline in normal elderly subjects with 2-[(18)F]fluoro-2-deoxy-D-glucose/poitron-emission tomography (FDG/PET). Proc Natl Acad Sci U S A98, 10966–10971.
10.
MosconiL, De SantiS, LiJ, TsuiWH, LiY, BoppanaM, LaskaE, RusinekH, de LeonMJ (2008) Hippocampal hypometabolism predicts cognitive decline from normal aging. Neurobiol Aging29, 676–692.
11.
EwersM, BrendelM, Rizk-JacksonA, RomingerA, BartensteinP, SchuffN, WeinerMW (2014) Reduced FDG-PET brain metabolism and executive function predict clinical progression in elderly healthy subjects. Neuroimage Clin4, 45–52.
12.
JackCRJr, PetersenRC, XuY, O’BrienPC, SmithGE, IvnikRJ, BoeveBF, TangalosEG, KokmenE (2000) Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology55, 484–489.
13.
RusinekH, De SantiS, FridD, TsuiWH, TarshishCY, ConvitA, de LeonMJ (2003) Regional brain atrophy rate predicts future cognitive decline: 6-year longitudinal MR imaging study of normal aging. Radiology229, 691–696.
14.
PachecoJ, GohJO, KrautMA, FerrucciL, ResnickSM (2015) Greater cortical thinning in normal older adults predicts later cognitive impairment. Neurobiol Aging36, 903–908.
15.
JackCRJr, ShiungMM, GunterJL, O’BrienPC, WeigandSD, KnopmanDS, BoeveBF, IvnikRJ, SmithGE, ChaRH, TangalosEG, PetersenRC (2004) Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology62, 591–600.
16.
CaselliRJ, DueckAC, OsborneD, SabbaghMN, ConnorDJ, AhernGL, BaxterLC, RapcsakSZ, ShiJ, WoodruffBK, LockeDE, SnyderCH, AlexanderGE, RademakersR, ReimanEM (2009) Longitudinal modeling of age-related memory decline and the APOE epsilon4 effect. N Engl J Med361, 255–263.
17.
CaselliRJ, ReimanEM, LockeDE, HuttonML, HentzJG, Hoffman-SnyderC, WoodruffBK, AlexanderGE, OsborneD (2007) Cognitive domain decline in healthy apolipoprotein E epsilon4 homozygotes before the diagnosis of mild cognitive impairment. Arch Neurol64, 1306–1311.
18.
CaselliRJ, ReimanEM, OsborneD, HentzJG, BaxterLC, HernandezJL, AlexanderGG (2004) Longitudinal changes in cognition and behavior in asymptomatic carriers of the APOE e4 allele. Neurology62, 1990–1995.
19.
FolsteinMF, FolsteinSE, McHughPR (1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res12, 189–198.
20.
PfefferRI, KurosakiTT, HarrahCHJr, ChanceJM, FilosS (1982) Measurement of functional activities in older adults in the community. J Gerontol37, 323–329.
21.
LawtonMP, BrodyEM (1969) Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist9, 179–186.
22.
AlbertMS, DeKoskyST, DicksonD, DuboisB, FeldmanHH, FoxNC, GamstA, HoltzmanDM, JagustWJ, PetersenRC, SnyderPJ, CarrilloMC, ThiesB, PhelpsCH (2011) The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement7, 270–279.
23.
McKhannGM, KnopmanDS, ChertkowH, HymanBT, JackCRJr, KawasCH, KlunkWE, KoroshetzWJ, ManlyJJ, MayeuxR, MohsRC, MorrisJC, RossorMN, ScheltensP, CarrilloMC, ThiesB, WeintraubS, PhelpsCH (2011) The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement7, 263–269.
24.
ReimanEM, CaselliRJ, YunLS, ChenK, BandyD, MinoshimaS, ThibodeauSN, OsborneD (1996) Preclinical evidence of Alzheimer’s disease in persons homozygous for the epsilon 4 allele for apolipoprotein E. N Engl J Med334, 752–758.
25.
ReimanEM, ChenK, AlexanderGE, CaselliRJ, BandyD, OsborneD, SaundersAM, HardyJ (2005) Correlations between apolipoprotein E epsilon4 gene dose and brain-imaging measurements of regional hypometabolism. Proc Natl Acad Sci U S A102, 8299–8302.
26.
AlexanderGE, ChenK, PietriniP, RapoportSI, ReimanEM (2002) Longitudinal PET Evaluation of Cerebral metabolic decline in dementia: A potential outcome measure in Alzheimer’s disease treatment studies. Am J Psychiatry159, 738–745.
27.
LangbaumJB, ChenK, LeeW, ReschkeC, BandyD, FleisherAS, AlexanderGE, FosterNL, WeinerMW, KoeppeRA, JagustWJ, ReimanEM (2009) Categorical and correlational analyses of baseline fluorodeoxyglucose positron emission tomography images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Neuroimage45, 1107–1116.
28.
GotoM, AbeO, AokiS, HayashiN, MiyatiT, TakaoH, MatsudaH, YamashitaF, IwatsuboT, MoriH, KunimatsuA, InoK, YanoK, OhtomoK (2015) Influence of parameter settings in voxel-based morphometry 8. Using DARTEL and region-of-interest on reproducibility in gray matter volumetry. Methods Inf Med54, 171–178.
29.
TailarachJ, TournouxP (1988) Co-planar stereotaxic atlas of the human brain. Thieme, New York.
30.
FjellAM, WestlyeLT, AmlienI, EspesethT, ReinvangI, RazN, AgartzI, SalatDH, GreveDN, FischlB, DaleAM, WalhovdKB (2009) High consistency of regional cortical thinning in aging across multiple samples. Cereb Cortex19, 2001–2012.
31.
HanX, JovicichJ, SalatD, van der KouweA, QuinnB, CzannerS, BusaE, PachecoJ, AlbertM, KillianyR, MaguireP, RosasD, MakrisN, DaleA, DickersonB, FischlB (2006) Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer. Neuroimage32, 180–194.
32.
Tzourio-MazoyerN, LandeauB, PapathanassiouD, CrivelloF, EtardO, DelcroixN, MazoyerB, JoliotM (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage15, 273–289.
SchramlF, ChenK, AyutyanontN, AuttawutR, LangbaumJB, LeeW, LiuX, BandyD, ReederSQ, AlexanderGE, CaselliRJ, FleisherAS, ReimanEM (2013) Association between an Alzheimer’s disease-related index and gene dose. PLoS One8, e67163.
35.
DeLongER, DeLongDM, Clarke-PearsonDL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics44, 837–845.
36.
MasonSJ, GrahamNE (2002) Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation. Q J R Meteorol Soc128, 2145–2166.
37.
Jimenez-ValverdeA, LoboJM (2007) Threshold criteria for conversion of probability of species presence to either-or presence-absence. Acta Oecol (Montrouge)31, 361–369.
38.
BrooksBL, IversonGL, HoldnackJA, FeldmanHH (2008) Potential for misclassification of mild cognitive impairment: A study of memory scores on the Wechsler Memory Scale-III in healthy older adults. J Int Neuropsychol Soc14, 463–478.
39.
SchretlenDJ, MunroCA, AnthonyJC, PearlsonGD (2003) Examining the range of normal intraindividual variability in neuropsychological test performance. J Int Neuropsychol Soc9, 864–870.
ProtasHD, ChenK, LangbaumJB, FleisherAS, AlexanderGE, LeeW, BandyD, de LeonMJ, MosconiL, BuckleyS, Truran-SacreyD, SchuffN, WeinerMW, CaselliRJ, ReimanEM (2013) Posterior cingulate glucose metabolism, hippocampal glucose metabolism, and hippocampal volume in cognitively normal, late-middle-aged persons at 3 levels of genetic risk for Alzheimer disease. JAMA Neurol70, 320–325.
44.
ReimanEM, UeckerA, CaselliRJ, LewisS, BandyD, de LeonMJ, De SantiS, ConvitA, OsborneD, WeaverA, ThibodeauSN (1998) Hippocampal volumes in cognitively normal persons at genetic risk for Alzheimer’s disease. Ann Neurol44, 288–291.
45.
LiB, ShiJ, GutmanBA, BaxterLC, ThompsonPM, CaselliRJ, WangY (2016) Influence of APOE genotype on hippocampal atrophy over time - an N=1925 surface-based ADNI study. PLoS One11, e0152901.
BrueggenK, DyrbaM, BarkhofF, HausnerL, FilippiM, NestorPJ, HauensteinK, KloppelS, GrotheMJ, KasperE, TeipelSJ (2015) Basal forebrain and hippocampus as predictors of conversion to Alzheimer’s disease in patients with mild cognitive impairment - a multicenter DTI and volumetry study. J Alzheimers Dis48, 197–204.
ReimanEM, ChenK, AlexanderGE, CaselliRJ, BandyD, OsborneD, SaundersAM, HardyJ (2004) Functional brain abnormalities in young adults at genetic risk for late-onset Alzheimer’s dementia. Proc Natl Acad Sci U S A101, 284–289.
