In vivo and neuropathology data support locus coeruleus integrity as indicator of Alzheimer's disease pathology and cognitive decline

Abstract

Several autopsy studies recognize the locus coeruleus (LC) as the initial site of hyperphosphorylated TAU aggregation, and as the number of LC neurons harboring TAU increases, TAU pathology emerges throughout the cortex. By conjointly using dedicated MRI measures of LC integrity and TAU and amyloid PET imaging, we aimed to address the question whether in vivo LC measures relate to initial cortical patterns of Alzheimer’s disease (AD) fibrillar proteinopathies or cognitive dysfunction in 174 cognitively unimpaired and impaired older individuals with longitudinal cognitive measures. To guide our interpretations, we verified these associations in autopsy data from 1524 Religious Orders Study and Rush Memory and Aging Project and 2145 National Alzheimer’s Coordinating Center cases providing three different LC measures (pigmentation, tangle density, and neuronal density), Braak staging, β-amyloid, and longitudinal cognitive measures. Lower LC integrity was associated with elevated TAU deposition in the entorhinal cortex among unimpaired individuals consistent with postmortem correlations between LC tangle density and successive Braak staging. LC pigmentation ratings correlated with LC neuronal density but not with LC tangle density and were particularly worse at advanced Braak stages. In the context of elevated β-amyloid, lower LC integrity and greater cortical tangle density were associated with greater TAU burden beyond the medial temporal lobe and retrospective memory decline. These findings support neuropathologic data in which early LC TAU accumulation relates to disease progression and identify LC integrity as a promising indicator of initial AD-related processes and subtle changes in cognitive trajectories of preclinical AD.

Fig. 1:

Relationships between LC measures and age, CDR score / diagnosis or APOE-status. A) Group differences in LC intensity_r when selecting 5 contiguous voxels with highest intensities (n=221, ANOVA, p<0.001, results for 1, 8 or 12 voxels are in Figure S3). B) Group differences in LC tangle density in MAP (n=160, ANOVA, p=0.001). Group comparisons for neuronal density and the adjusted tangle density are in Figure S3. C) Proportion of cases with LC hypopigmentation across the diagnostic groups (n=1524, χ², p<0.001) in ROSMAP. D) Proportion of cases with LC hypopigmentation at different CDR global scores in NACC (n=2145, χ², p<0.001). E) LC intensity_r in individuals carrying at least one ε4 allele and in those not carrying an ε4-allele (n=169, Wilcoxon Test, p=0.067). F) LC tangle density in cases with at least one ε4 allele and in those with no ε4 allele in MAP (n=160, Wilcoxon Test, p=0.078). G) LC hypopigmentation among ROSMAP individuals with at least one ε4 allele and in those not carrying an ε4-allele (n=1524, ordinal regression, p=0.054). H) LC hypopigmentation in individuals with 1 or 2 ε4 alleles or no ε4 allele in NACC (n=1853, ordinal regression, p<0.001). I) LC neuronal density across LC hypopigmentation rating scores in MAP (n=160, ANOVA, p<0.01; Fig S3 for tangle density results). J) Association between age and LC intensity_r (n=185, only CU individuals, robust regression, p<0.001). The intensities of the CI group (n=36, boxplot) were included as comparison. K,L) Effect of PiB or entorhinal TAU on the association between age and LC intensity_r in CU older individuals (n=152, robust regression, PiB: p=0.031 and entorhinal TAU: p=0.027). Individuals with elevated pathology are indicated in orange, those with lower pathology in blue (continuous interactions are reported in the text). Box plots show median and interquartile range overlaid with individual data points.

Fig.2:

Associations between cortical TAU and LC measures A) Cortical vertex-wise associations between FTP-PET binding and LC intensity_r (adjusted for age and sex, n=174, robust regressions). B) For each vertex, the association between TAU and LC intensity_r was adjusted for the cortical thickness value at that vertex, C) or was adjusted for neocortical PiB. D) Vertex-wise associations between FTP-PET binding and LC intensity_r in CU individuals (n=152),. Threshold in surface maps was set at p<0.01 cluster-wise corrected (expressed in −log(10), min. cluster size of 88.9mm²). PET surface data was adjusted for partial volume effects using the extended Müller-Gartner method. The scale bars reflect the magnitude of the negative associations (yellow: greater; blue: smaller). Abbreviations: L=left; R=right. The plot in (E) shows the relationship between LC intensity_r and left entorhinal FTP (SUVr, PVC) indicated in A (arrow). F) Correlations between LC tangle density and cortical tangle density (partial Spearman correlation, p<0.001) and G) between LC tangle density and Braak stages in MAP (n=160, partial Spearman correlation, p<0.001). Correlations between LC neuron density and cortical tangle density or Braak stage are shown in Fig. S4B–C. H) Comparison of cortical tangle density across LC hypopigmentation ratings in ROSMAP (n=1524, partial Spearman correlation, p<0.001) I) The proportion of LC hypopigmentation scores across different Braak stages in ROSMAP (n=1524, χ^2, p=0.003). J) The proportion of LC hypopigmentation across different Braak stages in NACC (n=2145, χ², p<0.001). Box plots show median and interquartile range overlaid with individual data points.

Fig. 3:

Vertex-wise associations between Aβ and LC measures A) Cortical vertex-wise PiB associations with LC intensity_r (adjusted for age and sex, n=174, robust regressions). The scale bar reflects the magnitude of the positive associations (yellow: greater; blue: smaller). Threshold was set at a cluster-corrected threshold of p<0.01 (expressed as −log10, min. cluster size of 88.9mm²). Abbreviations: L=left; R=right. The scatter plot in B) shows the association between LC intensity_r and left precuneus PiB (DVR) (left arrow and circle in A). C) Association between LC tangle density and cortical Aβ density in MAP (n=160, partial Spearman correlation, p<0.001). Association between LC neuron density and cortical Aβ density is shown in Fig. S6A. D) Comparison of cortical Aβ density across different LC hypopigmentation ratings in ROSMAP, (n=1524, ANOVA, p=0.003). Box plot shows median and interquartile range overlaid with individual data points. E) Proportion of LC hypopigmentation across Thal phases in NACC (n = 2145, partial Spearman correlation, p<0.001).

Fig. 4:

Interactive effects among Aβ, TAU burden and LC measures A) Vertex-wise FTP analyses examining the interaction between neocortical PiB and LC intensity_r (adjusted for age and sex, n=174, robust regressions). B) The line plot depicts the interaction between LC intensity_r and neocortical PiB (DVR, PVC) on left entorhinal-inferior temporal FTP (SUVr, PVC). For visualization, the estimated marginal means of LC intensity_r is shown at mean, + and −1 standard deviation, but analyses were done continuously. Shaded regions show 95% confidence interval. The scale bar reflects the magnitude of the positive associations (yellow: greater; red: smaller). Threshold was set at a cluster-corrected threshold of p<0.01 (expressed as −log10). Abbreviations: L=left; R=right. C) Comparison of LC tangle density across groups of increasing likelihood of a NIA-Reagan AD-diagnosis (for LC neuron density: Fig. S6F) in MAP (n=160, ANOVA, p<0.001). Box plot shows median and interquartile range overlaid with individual data points. D) Proportion of LC hypopigmentation across NIA-Reagan AD likelihood diagnostic groups in ROSMAP (n=1524, partial Spearman correlation, p=0.004). E) Proportion of LC hypopigmentation across groups based on the likelihood of AD neuropathologic change in NACC (n = 2145, partial Spearman correlation, p<0.001).

Fig. 5:

Associations between cross-sectional cognitive domains and LC measures A) Radar chart showing the magnitude of the relationship between LC intensity_r and cognitive measures in the PET-sample (n=165, Table S7). The green line in the radar chart indicates the effect sizes of composite measures and the blue line the effect sizes of subtests. Effect size are expressed in t-values (robust regression); red dotted line indicates t-value=1.96 (p<0.05), the outer line indicates t-value=3.50 (p<0.0005). TMT scores were inversed to facilitate comparison. B) The scatterplot illustrates the association between LC intensity_r and memory composite (z-score) for the PET-sample (n=165, robust regression, p=0.003). C) Effect of LC intensity_r on the association between PiB and memory performance (n=165, robust regression, p<0.001). D) Radar chart showing the magnitude of the relationship between LC tangle density and cognitive measures in MAP (n=160, Table S11). The outer line indicates t-value=−8.00 (p< 2.43e⁻¹³). E) The association between LC tangle density and memory in MAP (n=160, robust regression, p<0.001). F) Effect modification of LC tangle density on the association between Aβ density values and memory performance in MAP (n=160, robust regression, p=0.016). The grey area represents the range of PiB values (C) or Aβ density values (F) where the interaction was significant. For visualization purposes, LC intensity_r or LC tangle density is shown at mean, + and −1 standard deviation, but analyses were done continuously. Abbreviations: PACC=Preclinical Alzheimer’s disease Cognitive Composite; CAT= Categorical Fluency; FAS=phonological fluency; FcSRT= free and cued selective reminding test; SRT=selective reminding test (DR=delayed recall and TR=total recall); LM=Logical memory (IR=immediate recall); TMT=Trail Making Test; DSST=Digit Symbol Substitution Test.

Fig. 6:

Vertex-wise mediation by TAU on the relationship between LC measures and memory A) vertex-wise Quasi-Bayesian Monte Carlo Mediation analyses showing in which regions, TAU (FTP SUVr, PVC) mediated the relationship between LC intensity_r and memory (n=174). For orientation purposes, the entorhinal cortex is marked in the black outline (see magnification). The blue spot indicates the location of the mediation effect in CU individuals (n=152). Analyses were corrected for multiple comparisons using a cluster-wise correction at p<0.01. The scale bar reflects the magnitude of the probability of the indirect (mediation) effect (expressed in −log10(p-value); yellow: greater; red: smaller). B) ROI-based analyses for entorhinal FTP (SUVr, PVC) as mediator of the relationship between LC intensity_r and memory (z-score, n=174, p=0.003). C) Analyses for likelihood of Braak stage 2 or higher as mediator of the relationship between LC tangle density and memory (n=160, p=0.033). Abbreviations: EC= entorhinal, L=left, R=right

Fig. 7.

Associations between LC measures and retrospective cognitive decline A) Linear mixed effect analyses examining the relationship between LC intensity_r and retrospective memory decline (n=165, 867 observations, p<0.001). B) Effect of LC intensity_r on the association between neocortical PiB binding and retrospective memory decline (z-score) in the PET-sample (n=165, linear mixed effect analyses, p<0.001). The vertical solid line represents the range of PiB values at which the association is significant in the entire sample, and the dashed line for the CU individuals (n=152, floodlight analyses, estimated at p_fdr<0.05). C) Linear mixed effects model in MAP showing the associations between LC tangle density and memory decline. (n=160, 857 observations, p<0.001). D) Effect of LC tangle density on the association between Aβ density and retrospective memory decline (n=160, 857 observations, linear mixed effect analyses, p=0.009). For visualization, LC intensity_r or LC tangle density are shown at simple slopes estimated at mean, + and −1 standard deviation, but analyses were done continuously. Shaded regions show the 95% confidence interval.