Date Published: October 10, 2017
Publisher: BioMed Central
Author(s): Lutz Frölich, Oliver Peters, Piotr Lewczuk, Oliver Gruber, Stefan J. Teipel, Hermann J. Gertz, Holger Jahn, Frank Jessen, Alexander Kurz, Christian Luckhaus, Michael Hüll, Johannes Pantel, Friedel M. Reischies, Johannes Schröder, Michael Wagner, Otto Rienhoff, Stefanie Wolf, Chris Bauer, Johannes Schuchhardt, Isabella Heuser, Eckart Rüther, Fritz Henn, Wolfgang Maier, Jens Wiltfang, Johannes Kornhuber.
The progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) dementia can be predicted by cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers. Since most biomarkers reveal complementary information, a combination of biomarkers may increase the predictive power. We investigated which combination of the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR)-sum-of-boxes, the word list delayed free recall from the Consortium to Establish a Registry of Dementia (CERAD) test battery, hippocampal volume (HCV), amyloid-beta1–42 (Aβ42), amyloid-beta1–40 (Aβ40) levels, the ratio of Aβ42/Aβ40, phosphorylated tau, and total tau (t-Tau) levels in the CSF best predicted a short-term conversion from MCI to AD dementia.
We used 115 complete datasets from MCI patients of the “Dementia Competence Network”, a German multicenter cohort study with annual follow-up up to 3 years. MCI was broadly defined to include amnestic and nonamnestic syndromes. Variables known to predict progression in MCI patients were selected a priori. Nine individual predictors were compared by receiver operating characteristic (ROC) curve analysis. ROC curves of the five best two-, three-, and four-parameter combinations were analyzed for significant superiority by a bootstrapping wrapper around a support vector machine with linear kernel. The incremental value of combinations was tested for statistical significance by comparing the specificities of the different classifiers at a given sensitivity of 85%.
Out of 115 subjects, 28 (24.3%) with MCI progressed to AD dementia within a mean follow-up period of 25.5 months. At baseline, MCI-AD patients were no different from stable MCI in age and gender distribution, but had lower educational attainment. All single biomarkers were significantly different between the two groups at baseline. ROC curves of the individual predictors gave areas under the curve (AUC) between 0.66 and 0.77, and all single predictors were statistically superior to Aβ40. The AUC of the two-parameter combinations ranged from 0.77 to 0.81. The three-parameter combinations ranged from AUC 0.80–0.83, and the four-parameter combination from AUC 0.81–0.82. None of the predictor combinations was significantly superior to the two best single predictors (HCV and t-Tau). When maximizing the AUC differences by fixing sensitivity at 85%, the two- to four-parameter combinations were superior to HCV alone.
A combination of two biomarkers of neurodegeneration (e.g., HCV and t-Tau) is not superior over the single parameters in identifying patients with MCI who are most likely to progress to AD dementia, although there is a gradual increase in the statistical measures across increasing biomarker combinations. This may have implications for clinical diagnosis and for selecting subjects for participation in clinical trials.
Slowly progressive mild cognitive impairment (MCI) with insidious onset often results in neurodegenerative dementia, e.g., dementia due to Alzheimer’s disease (AD). A current plausible model for the development of AD suggests a temporal order of pathological brain changes; amyloid deposition occurs early in the disease, but may not directly cause clinical symptoms and is believed to trigger neuronal injury and loss [1, 2]. Neuronal and synaptic losses are key determinants of cognitive impairment, which are accompanied by brain atrophy on magnetic resonance imaging (MRI) [3, 4]. Thus, the pathological cascade in AD is regarded as a two-stage, slowly progressive process in which amyloidosis and neuronal injury (tauopathy and neurodegeneration) are largely sequential rather than simultaneous processes [1, 2].
We analyzed 115 patients with complete datasets at baseline and clinical follow-up. The datasets analyzed in the current study are available from the corresponding author on reasonable request. They were a subset from 1071 MCI subjects in whom baseline demographics and neuropsychological test results were available. Due to various missing data, 956 subjects could not be analyzed (see Fig. 1 for exact patient loss). Table 1 compares the demographic characteristics, cognitive and psychometric test scores, hippocampal volume measures, and cerebrospinal fluid biomarkers at baseline between the maximal MCI sample, in those for which the respective measures were available, and the final analysis set (115 patients, 12% of the MCI cohort) which was used for predictor analysis. There was no significant difference between the groups on any parameter (with the exception of CDR-sb which was not clinically relevant). All p values were based on pairwise comparisons, uncorrected for multiple testing. Table 2 summarizes the demographic characteristics, cognitive test scores, hippocampal volume measures and cerebrospinal fluid biomarkers at baseline and follow-up for the groups used for the prediction analysis (final analysis set, MCI-AD, MCI-stable) and the statistical differences between the MCI-AD and MCI-stable groups. Of the 115 MCI patients, 28 patients (24.3%) progressed to AD dementia (MCI-AD) after a mean follow-up of 26.2 months corresponding to an annual conversion rate of 11.2%; 87 patients did not progress to AD (MCI-stable), and their mean follow-up was 25.2 months which was not significantly different from the MCI-AD follow-up (Wilcoxon test, p > 0.1). In addition, 17 MCI patients progressed to non-AD dementia. Because potential misclassification between clinical diagnosis and actual pathology may decrease the predictive power of the biomarkers to be analyzed, we decided to exclude MCI subjects with clinical progression to non-AD dementias from our analysis. At the first follow-up (year 1), 21 out of 28 MCI subjects had progressed to AD dementia, 5 out of the remaining 7 MCI subjects had progressed to AD dementia at the second follow-up (year 2), and the final 2 MCI subjects had progressed to AD dementia at the third follow-up (year 3).Fig. 1Patient loss due to missing data. The absolute sample size is given in the rectangles; the loss of sample size due to missing data of the respective measures is given in the diamonds. AD Alzheimer’s disease, CSF cerebrospinal fluid, MCI mild cognitive impairmentTable 1Sociodemographic, clinical, and biomarker variables in the final analysis set (n = 115) and in the respective comparison group (max. n = 956; actual group size for each variable is the maximal N available for the comparison group)VariableFinal analysis sample (n =115) MCI-stable + MCI-ADComparison group (n = 956)P valueGroup size (n)All other MCI with data availableAge65.7 ± 9.095667.1 ± 8.6nsSex (male = 1)1.4 ± 0.59561.5 ± 0.5nseducation9.5 ± 1.99569.5 ± 1.9nsMMSE27.0 ± 2.195627.2 ± 2.2nsMADRS7.4 ± 5.69087.8 ± 6.2nsB-ADL2.5 ± 1.59032.3 ± 1.4nsCDR-sb1.8 ± 1.19561.5 ± 1.00.01CERAD-DR-WL4.8 ± 2.39565.0 ± 2.3nsHippocampal volume4450 ± 6723684511 ± 653nsAβ42749 ± 300292751 ± 353nsAβ409654 ± 27322689684 ± 3030nsAβ ratio0.08 ± 0.032660.08 ± 0.04nsTotal tau411 ± 251284446 ± 304nsPhosphorylated tau61 ± 3028967 ± 36nsData are given as mean ± standard deviationThere is no significant difference between the groups with the exception of CDR-sb which is not clinically relevantP values were uncorrected for multiple comparisonsAβ40 amyloid-beta1–40, Aβ42 amyloid-beta1–42, AD Alzheimer’s disease, B-ADL Bayer activities of daily living, CDR-sb Clinical Dementia Rating–sum-of-boxes, CERAD-DR-WL Consortium to Establish a Registry of Dementia–delayed recall word list, MADRS Montgomery-Asberg Depression Rating Scale, MCI cognitive impairment, MMSE Mini-Mental State Examination, ns not significantTable 2Demographic characteristics, cognitive test scores, APO E allele distribution, brain volumetric measures, and cerebrospinal fluid biomarkers at baseline and follow-up for the final analysis set (115 MCI subjects) and the two groups MCI-stable and MCI-ADAll (n = 115)MCI-stable (n = 87)MCI-AD (n = 28)Standard mean difference*P value**Age (years)65.7 ± 9.03(36–89)66.5 ± 8.95(51–80)65.4 ± 9.37(36–89)0.12nsEducation (years schooling)9.50 ± 1.91(7–13)9.75 ± 1.95(7–13)8.75 ± 1.58(7–13)–0.52<0.05Gender (female = 1, male = 2)Male = 67;female = 48Male = 52;female = 35Male = 15;female = 130.13nsBayer-ADL scale (score: 1–10)2.47 ± 1.48(1–4)2.41 ± 1.52(1–4)2.67 ± 1.34(1–4)0.18nsMADRS (score: 0–60)7.41 ± 5.75(0–13)7.67 ± 5.96(0–13)6.62 ± 5.08(0–11)–0.18nsMMSE27.0 ± 2.12(20–30)27.5 ± 1.87(22–30)25.8 ± 2.34(20–29)–0.81<0.001CDR-sb1.80 ± 1.06(0.5–4.5)1.59 ± 1.00(0.5–4.5)2.45 ± 0.98(0.5–4)0.81<0.001CERAD-DR-WL4.82 ± 2.33(0–10)5.25 ± 2.23(0–10)3.46 ± 2.15(0–8)–0.770.001Amnestic deficit: CERAD-DR-WL below cut-off (<7 correct responses), n (% present)88 (77%)62 (71%)26 (93%)ApoE4 alleles (homo- or heterozygotes), n/sample size (% present)41/103 (40%)32/78 (41%)9/25 (36%)nsHippocampal volume (mm3)4450 ± 672(2509–5996)4585 ± 649(3036–5996)4031 ± 570(2509–5235)–0.82<0.0001Total tau in CSF (pg/ml)411 ± 252(112–1169)351 ± 205(112–1158)596 ± 294(156–1169)0.97<0.0001Total tau in CSF below cut-off (>300 pg/ml), n (% abnormal)65 (57%)41 (47%)24 (86%)Phosphorylated tau in CSF (pg/ml)61.3 ± 30.5(19.7–157)55.6 ± 26.3(19.7–130)78.8 ± 36.1(27.3–157)0.76<0.01Phosphorylated tau in CSF below cut-off (>60 pg/ml), n (% abnormal)44 (38%)27 (31%)17 (61%)Aβ42 in CSF (pg/ml)749 ± 300(245–1792)794 ± 309(276–1792)611 ± 223(245–1134)–0.61<0.001Aβ42 in CSF below cut-off (<600 pg/ml), n (% abnormal)39 (34%)22 (25%)17 (61%)Aβ40 in CSF (pg/ml)9654 ± 2731(2604–16320)9601 ± 2561(4175–16320)9817 ± 3251(2604–15210)0.08nsAβ40 in CSF below cut-off, n (% abnormal)n/an/an/aFollow-up time (months)25.5 ± 9.8(12–36)26.1 ± 8.0(12–36)25.2 ± 8.9(12–36)nsValues are given as means ± SD (range) unless otherwise stated*Standardized (mean values in MCI-AD patients – mean values in MCI-stable patients)/standard deviation in the group of all patients**P values refer to differences between MCI-stable and MCI-ADAβ40 amyloid-beta1–40, Aβ42 amyloid-beta1–42, AD Alzheimer’s disease, B-ADL Bayer activities of daily living, CDR-sb Clinical Dementia Rating–sum-of-boxes, CERAD-DR-WL Consortium to Establish a Registry of Dementia–delayed recall word list, CSF cerebrospinal fluid, MADRS Montgomery-Asberg Depression Rating Scale, MCI cognitive impairment, MMSE Mini-Mental State Examination, n/a not available, ns not significant The power of hippocampal volume, CSF Alzheimer biomarkers, and neuropsychological measures for predicting progression from MCI to AD dementia was analyzed in a relatively large multicentre memory clinic cohort from the German Dementia Competence Network (DCN). A combination of two biomarkers of neurodegeneration (e.g., HCV and t-Tau) did not predict AD dementia in MCI significantly better than any parameter alone, and none of the possible three- to four-parameter combinations improved the predictive power. Our study is unique in applying advanced statistical methods for testing different biomarker combinations for superiority over each other. Our results show that a combination of two biomarkers of neurodegeneration (e.g., HCV and t-Tau) is not superior over the single parameters alone in identifying patients with MCI who are most likely to progress to AD dementia within a relatively short time period. However, there is a gradual increase in the statistical measures across increasing biomarker combinations (always involving t-Tau and HCV as the best parameters). From our data it is not possible to deduct recommendations on how to optimize the predictive diagnosis in individual patients with MCI in clinical practice. For enrichment strategies of MCI patients progressing to AD dementia for clinical trials, a combination of two neurodegeneration parameters (HCV and t-Tau) in addition to clinical measures such as CDR-sb may maximize progression rates in order to minimize false negative results of intervention studies. Source: http://doi.org/10.1186/s13195-017-0301-7