Plasma glial fibrillary acidic protein in autosomal dominant Alzheimer’s disease: associations with β-amyloid-PET, neurodegeneration and cognition

doi:10.21203/rs.3.rs-1752559/v1

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Plasma glial fibrillary acidic protein in autosomal dominant Alzheimer’s disease: associations with β-amyloid-PET, neurodegeneration and cognition

https://doi.org/10.21203/rs.3.rs-1752559/v1

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published 27 Dec, 2022

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Glial fibrillary acidic protein (GFAP) is a promising candidate blood-based biomarker for Alzheimer’s disease (AD) diagnosis and prognostication. The timing of its disease-associated changes, its clinical correlates, and biofluid-type dependency will influence its clinical utility. We evaluated plasma, serum, and CSF GFAP in families with autosomal dominant AD (ADAD), leveraging the predictable age at symptom onset to determine changes by stage of disease. Plasma GFAP elevations appear a decade before expected symptom onset, after β-amyloid accumulation and prior to neurodegeneration and cognitive decline. Plasma GFAP distinguished β-amyloid-positive from β-amyloid-negative ADAD participants and showed a stronger relationship with β-amyloid load in asymptomatic than symptomatic ADAD. Higher plasma GFAP was associated with the degree and rate of neurodegeneration and cognitive impairment. Serum GFAP showed similar relationships, but these were less pronounced for CSF GFAP. Our findings support a role for plasma GFAP as a clinical biomarker for β-amyloid-associated cognitive deterioration in AD.

The use of cerebrospinal fluid (CSF) biomarkers and positron emission tomography (PET) tracers for β-amyloid (Aβ) and tau has helped overcome the need for histopathological confirmation of the core Alzheimer’s disease (AD) hallmarks, namely Aβ plaques and neurofibrillary tangles, enabling reliable ante-mortem diagnosis ¹. However, the invasiveness and cost associated with CSF and PET biomarkers limit their use – especially in resource poor settings. Blood-based biomarkers are less invasive, lower in cost and have wide applicability. Plasma glial fibrillary acidic protein (GFAP) has potential clinical utility for diagnosis and prognostication in AD ^2–4.

GFAP contributes to cell structure maintenance as one of the cytoskeletal proteins in astrocytes and is upregulated in reactive astrocytes ⁵. Reactive astrocytes have been observed surrounding Aβ plaques and can drive neurodegeneration in AD ^5–8. GFAP expression in brain tissue is associated with Aβ plaque density ^9–11. In addition, evidence of reactive astrocytes using monoamine oxidase B PET tracers has been reported in preclinical and early symptomatic AD ^10,12,13. It has been posited that reactive astrocytes release GFAP, either directly or through perivascular glymphatics, into blood via astrocyte end-feet encompassing brain capillaries ^14–16. Indeed, circulating levels of plasma and serum GFAP are elevated in sporadic preclinical AD and AD dementia ^16–20. Further, associations of plasma or serum GFAP levels with brain Aβ load, cerebral atrophy, and cognitive function have been reported in different stages along the AD continuum ^16–20. Together, these studies suggest that plasma GFAP is a promising biomarker in AD that is related to brain Aβ load and to disease progression. However, to determine the clinical utility of GFAP in AD, the timing of GFAP elevation across the disease trajectory, its clinical correlates, and its matrix-dependency must be understood.

The study of GFAP in autosomal dominant AD (ADAD) is particularly suited for this purpose. Previous biomarker studies in ADAD have provided important insights into the sequence of biomarker changes and clinical events in AD ^21,22. Due to the heritable age at symptom onset, it is possible to determine an ADAD mutation carrier’s estimated time (years) to symptom onset (EYO). Employing EYO will provide insight into the trajectory of GFAP from the earliest stage of the disease and will aid investigate the temporal order of GFAP changes with respect to brain Aβ load, neurodegeneration, and cognitive decline. In addition, the typically young age at onset of ADAD means relatively little confounding age-related co-pathology compared with sporadic AD, providing an assessment of the biomarker changes that are specifically related to AD pathogenesis.

In the current study, we compared GFAP concentrations between ADAD mutation carriers and non-carrier siblings and determined when disease-related changes in GFAP occurred across the disease trajectory utilising EYO. We investigated whether GFAP was also associated with brain Aβ load, neurodegeneration and cognitive and functional performance in ADAD. Lastly, comparisons between plasma, serum and CSF, were investigated.

We utilised samples from the global multi-site Dominantly Inherited Alzheimer Network (DIAN) cohort comprising adults at risk of, or having, symptomatic AD due to a confirmed ADAD mutation in their family. The participants comprise mutation carriers and non-carriers. In total, 184 plasma GFAP levels were analysed, as well as 60 paired serum and CSF samples (including 12 matched to plasma samples). Participant demographics, imaging measures, clinical assessments, and plasma, serum and CSF GFAP concentrations are presented in Table 1. Mean (± standard deviation) EYO and age of participants were − 11 (± 12) and 37 (± 12) years, respectively, for plasma samples. Mean EYO and age of participants was − 7 (± 13) and 40 (± 12) years, respectively, for serum and CSF samples. All characteristics were significantly different among the non-carrier, asymptomatic mutation carrier and symptomatic mutation carrier groups except for the Apolipoprotein E (APOE) ε4 carrier frequency. No differences in plasma GFAP levels among APP, PSEN1 and PSEN2 mutation carriers were observed (Supplementary Fig. 1).

Table 1

Demographic, neuroimaging, cognition and GFAP levels in A.) plasma and B.) matched serum and cerebrospinal fluid in ADAD mutation carriers and non-carriers.
A.	N	Mutation non-carriers	Mutation carriers		P
		(n = 86)	Asymptomatic (n = 66)	Symptomatic (n = 32)
Age (years)	184	36 ± 12	32 ± 8	47 ± 11	< .0001
Female (n (%))	184	58 (67)	36 (54)	14 (44)	.046
Education (years)	184	15 ± 3	15 ± 3	13 ± 4	.020
Apolipoprotein-E ε4 (n (%))	184	28 (33)	19 (29)	13 (41)	.55
EYO (years)	184	-12 ± 12	-16 ± 7	2 ± 6	< .0001
Cortical PiB-PET SUVR	147 (73, 56, 18)	1.06 ± 0.07	1.45 ± 0.64	2.41 ± 0.89	< .0001
PiB+ (cut-off = 1.25; n (%))	147 (73, 56, 18)	1 (1)	23 (41)	16 (89)	< .0001
Hippocampal volume (cm³)	168 (81, 62, 25)	8.74 ± 0.66	8.90 ± 0.66	7.28 ± 1.20	< .0001
Precuneus thickness (mm)	168 (81, 62, 25)	2.39 ± 0.14	2.39 ± 0.15	2.16 ± 0.20	< .0001
Precuneus FDG-PET SUVR	159 (75, 60, 24)	1.91 ± 0.16	1.93 ± 0.19	1.75 ± 0.23	< .0001
Cognitive composite score	176 (84, 65, 27)	0.24 ± 0.51	0.24 ± 0.52	-1.10 ± 0.75	< .0001
MMSE score	182 (84, 66, 31)	29 ± 1	29 ± 1	23 ± 7	< .0001
CDR-SOB score	184	0.1 ± 0.3	0.0 ± 0.1	4.4 ± 4.8	< .0001
Plasma GFAP (pg/mL)	184	77 ± 36	86 ± 52	211 ± 113	< .0001

B.	N	Mutation non-carriers	Mutation carriers		P
		(n = 30)	Asymptomatic (n = 22)	Symptomatic (n = 8)
Age (years)	60	45 ± 13	33 ± 8	43 ± 9	< .0001
Female (n (%))	60	18 (60)	6 (27)	5 (62)	.042
Education (years)	60	15 ± 3	16 ± 3	12 ± 2	.005
Apolipoprotein-E ε4 (n (%))	60	14 (47)	5 (23)	4 (50)	.153
EYO (years)	60	-3 ± 14	-16 ± 9	2 ± 3	.0001
Cortical PiB-PET SUVR	52 (26, 21,5)	1.04 ± 0.08	1.59 ± 0.74	2.80 ± 1.02	< .0001
PiB+ (cut-off = 1.25; n (%))	52 (26, 21,5)	0 (0)	11 (52)	5 (100)	-
Hippocampal volume (cm³)	53 (27, 21,5)	8.85 ± 0.71	9.33 ± 0.99	7.22 ± 1.81	.0008
Precuneus thickness (mm)	53 (27, 21,5)	2.35 ± 0.17	2.39 ± 0.11	2.1 ± 0.3	.023
Precuneus FDG-PET SUVR	53 (27, 21,5)	1.94 ± 0.18	1.86 ± 0.13	1.60 ± 0.32	.005
Cognitive composite score	60	0.28 ± 0.47	0.21 ± 0.41	-1.62 ± 1.01	< .0001
MMSE score	60	29 ± 1	29 ± 1	20 ± 8	< .0001
CDR-SOB score	60	0.0 ± 0.1	0.0 ± 0.0	5.0 ± 3.8	< .0001
Serum GFAP (pg/mL)	60	65 ± 38	69 ± 34	217 ± 163	.0001
CSF GFAP (pg/mL)	60	7750 ± 3585	8002 ± 4202	11523 ± 4542	.057
Among non-carriers, asymptomatic mutation carriers and symptomatic mutation carriers, the significance of the characteristic difference was calculated using linear mixed-effects models (for continuous outcomes) and generalized linear mixed-effects models with a logistic link (for categorical outcomes). All mixed models included a random family effect to account for the associations on the outcome measures between participants within the same family. Continuous measures are presented as mean ± SD. N: total number of participants (with numbers of mutation non-carriers, asymptomatic mutation carriers and symptomatic mutation carriers, respectively). EYO: estimated years to symptom onset; PiB: ¹¹C-Pittsburgh compound B; PET: Positron Emission Tomography; FDG: ¹⁸F-fluorodeoxyglucose; SUVR: Standard Uptake Value Ratio; MMSE: Mini-Mental State Examination; CDR-SOB: Clinical Dementia Rating – Sum of Boxes; GFAP: glial fibrillary acidic protein.

Plasma GFAP between mutation carriers and non-carriers

Plasma GFAP levels were higher in symptomatic mutation carriers compared with non-carriers (mean difference (95% Confidence Interval (CI)): 125 pg/mL (116–134); P < .0001) and asymptomatic mutation carriers (mean difference (95% CI): 107 pg/mL (100–114); P < .0001; Supplementary Fig. 2). Plasma GFAP levels were higher in asymptomatic mutation carriers compared with non-carriers (mean difference (95% CI): 18 pg/mL (16–19); P = .035).

When stratifying the asymptomatic mutation carriers by Aβ-/+ status, plasma GFAP was higher in symptomatic mutation carriers compared with Aβ + asymptomatic mutation carriers (mean difference (95% CI): 80 pg/mL (78–83); P = .002) and Aβ- asymptomatic mutation carriers (mean difference (95% CI): 124 pg/mL (123–126); p < .0001). Plasma GFAP was higher in Aβ + asymptomatic mutation carriers compared with non-carriers (mean difference (95% CI): 45 pg/mL (33–56); P = .0003) and compared with Aβ- asymptomatic mutation carriers (mean difference (95% CI): 44 (40–48); P = .002), however, no significant difference was observed between Aβ- asymptomatic mutation carriers and non-carriers (Fig. 1a).

An EYO was calculated for each participant based on the difference between each participant’s age and the average age of symptom onset for the specific mutation in that family for both mutation carriers and non-carriers ²³. When investigating plasma GFAP as a function of EYO in mutation carriers versus non-carriers, plasma GFAP was significantly higher in mutation carriers compared with non-carriers at -10.0 EYO (Fig. 1b, Supplementary Fig. 3). Using the same methods to estimate the sequence of events, we found that the divergence of plasma GFAP between mutation carriers and non-carriers lies between aberrant Aβ accumulation (EYO − 18.4) and cognitive decline and structural neurodegeneration (EYO − 7.9 to -4.2, Fig. 1c).

Association of plasma GFAP with Aβ-PET and clinical progression in ADAD

In the mutation carriers, we observed a significant association between plasma GFAP and brain Aβ load, assessed by PET using the tracer Pittsburgh Compound B (PiB-PET) (β = 0.66, P < .0001). Upon stratifying mutation carriers by absence/presence of symptoms, the association of plasma GFAP levels with brain Aβ load was highly significant in the asymptomatic mutation carriers (β = 0.57, P < .0001) but not in the symptomatic mutation carriers (Supplementary Table 1A, Fig. 2). Additional adjustment for age did not have a major effect on these associations (Supplementary Table 1B). When stratifying this progression purely based upon cortical PiB-PET quartiles (Q1 ≤ 1.072, 1.072 < Q2 ≤ 1.264, 1.264 < Q3 ≤ 2.105) in mutation carriers, GFAP levels in PiB-PET quartiles 3 and 4 were significantly higher than GFAP levels in PiB-PET quartiles 1 and 2 (Fig. 3a). This could be attributed to all participants in Q3 and Q4 meeting the Aβ + threshold.

Using ROC curves, plasma GFAP levels classified absence/presence of aberrant brain Aβ load (Aβ-/+, PiB-PET SUVR ≥ 1.25 ²²) within the entire mutation carrier group with an area under the curve (AUC) = 84% (95% CI:74%-93%) (Fig. 3b). In the asymptomatic mutation carrier subset, GFAP was observed to have an AUC = 77% (95% CI:63%-91%) for distinguishing between Aβ-/+ status (Fig. 3c), similar to sporadic preclinical AD ²⁰. Sensitivity and specificity along with model diagnostics (including optimal cut-off, accuracy, negative predictive value, and positive predictive value) are provided in Supplementary Table 2.

Additionally, we investigated plasma GFAP levels across clinical progression in mutation carriers, spanning the Aβ- cognitively normal status, Aβ + cognitively normal status and clinical dementia rating scale (CDR) > 0 symptomatic stages. Plasma GFAP levels increased with the onset of Aβ pathology in the asymptomatic stage and these levels increased further with disease severity (Fig. 3d).

Cross-sectional association of plasma GFAP with neurodegeneration, cerebral glucose metabolism, cognitive and functional performance in ADAD

Associations of plasma GFAP with hippocampal volume (β=-0.47, P < .0001), precuneus thickness (β=-0.47, P < .0001), precuneus ¹⁸F-fluorodeoxyglucose (FDG)-PET (β=-0.22, P = .004), a cognitive composite (β=-0.53, P < .0001), Mini-mental State Examination (MMSE, β=-0.46, P < .0001) and CDR-Sum of Boxes (CDR-SOB, β = 0.55, P < .0001) were observed after adjusting for sex (and education for cognition; Fig. 2, Supplementary Table 1). Within the mutation carriers, similar associations were observed (β between 0.27 and 0.66), while in the non-carriers no significant relationships were observed between GFAP and these markers (β between 0 and 0.12). As a sensitivity analysis, we adjusted the models for age. When adjusting for age, plasma GFAP levels and FDG-PET SUVR were no longer associated; all other associations persisted (Supplementary Table 1).

Association of plasma biomarkers with subsequent neurodegeneration, cerebral glucose hypometabolism, and cognitive and functional decline in ADAD

Prospective analyses were performed to investigate whether plasma GFAP was associated with subsequent neurodegeneration, cerebral glucose hypometabolism, cognitive and functional decline in mutation carriers with longitudinal MR (n = 36), FDG-PET (n = 36), cognitive composite (n = 36), MMSE (n = 38) and CDR-SOB (n = 38) data available. We observed that GFAP was predictive of future hippocampal atrophy (Unstandardised beta (B) in all mutation carriers = -0.20, P = .013), cortical thinning (B= -0.04, P = .001) and cognitive and functional decline based on performance on the MMSE (B= -0.72, P = .041) and CDR-SOB (B = 0.57, P = .013) adjusting for age and sex (and education for cognition) (Supplementary Table 3, Fig. 4). Plasma GFAP was not significantly associated with subsequent glucose hypometabolism represented by FDG-PET. Similar analyses were not performed for changes in brain Aβ load due to limited data.

Serum and CSF GFAP in ADAD

Serum and plasma GFAP collected from twelve overlapping participants yielded very similar levels, that were strongly correlated between these blood matrices (Spearman’s Rho = 0.902, P < .0001; Fig. 5a). CSF GFAP levels showed moderate correlations with the blood matrices (Spearman’s Rho = 0.64–0.65, P < .005; Fig. 5b-c). In line with the plasma results, serum GFAP was higher in symptomatic (mean difference (95% CI): 81 pg/mL (73–88)) and asymptomatic mutation carriers (mean difference (95% CI): 26 pg/mL (25–27)) compared with non-carriers (Fig. 5d). CSF GFAP was only higher in symptomatic mutation carriers (mean difference (95% CI): 3845 pg/mL (2811–4880)) compared with non-carriers (Fig. 5e). Serum, but not CSF, GFAP trajectory diverged between mutation carriers and non-carriers at EYO − 10.2 (Supplementary Fig. 3).

Serum GFAP (β = 0.38, P = .002), but not CSF GFAP (β = 0.15, P = .27), was associated with brain Aβ load, however, after stratifying for mutation carrier status, did not reach statistical significance thresholds (β = 0.32, P = .079). Serum GFAP was more strongly associated with neurodegeneration (hippocampal volume β=-0.46, P = .001; precuneus thickness β=-0.36, P = .009); cerebral glucose metabolism (β=-0.35, P = .008); cognition (cognitive composite β=-0.59, P < .001; MMSE β=-0.45, P < .001) and functional (CDR-SOB β = 0.46, P < .001) performance in all participants than CSF GFAP (hippocampal volume β=-0.39, P = .002; precuneus thickness β=-0.28, P = .040; cognitive composite β=-0.34, P = .006; MMSE β=-0.29, P = .019; CDR-SOB β = 0.28, P = .021, Supplementary Tables 4 and 5). Similar observations were found in the mutation carrier subset. Associations between serum, but not CSF, GFAP and hippocampal volume (β=-0.50, P = .006), cognitive composite (β=-0.37, P = .004), and CDR-SOB (β = 0.35, P = .029) persisted after correcting for age in mutation carriers.

The main findings from the current study are that plasma GFAP levels (i) were elevated in ADAD mutation carriers relative to non-carriers 10 years prior to symptom onset, which was after aberrant Aβ accumulation but prior to cognitive decline and structural neurodegeneration; (ii) predicted cortical Aβ PET positivity in all mutation carriers and in the asymptomatic mutation carrier subset with AUCs of 84% and 77% respectively; (iii) were associated with brain Aβ load more strongly in the asymptomatic stage than in the symptomatic stage; (iv) were associated with cerebral atrophy and worse cognition in mutation carriers; (v) were associated with subsequent hippocampal atrophy, cortical thinning and cognitive decline in mutation carriers; and (vi) had a similar AD-related elevation pattern to serum GFAP but was much more pronounced compared to CSF GFAP.

This is the first study to evaluate plasma GFAP levels across the ADAD trajectory. Elevated plasma GFAP in mutation carriers compared with non-carriers was observed around 10 years prior to expected symptom onset and is consistent with studies reporting higher plasma GFAP in Aβ PET defined preclinical sporadic AD ^{18,20,24−26}. Elevation in plasma GFAP relative to other biomarker and clinical events was observed after aberrant Aβ accumulation and before neurodegeneration and cognitive decline. This is in line with studies in transgenic AD mouse models showing that astrocyte reactivity in the presence of Aβ pathology may drive disease progression in AD ^8,11,27,28. In addition, reports on the high discriminative performance of plasma GFAP for Aβ PET -/+ individuals with AUCs ranging between 76–81% within the sporadic AD continuum ^{18,20,24−26} corroborate our observations in ADAD (AUCs 77%-84%), highlighting the commonalities between sporadic AD and ADAD, and the utility of ADAD as a model to explore biomarker trajectories in sporadic AD ²⁹.

In the current study, we observed higher plasma GFAP levels with advancing clinical progression (plasma GFAP in MC with CDR ≥ 1 > MC with CDR = 0.5 > Aβ + MC with CDR = 0 > Aβ- MC with CDR = 0). This is in line with a previous study on plasma GFAP in the AD continuum, wherein median levels were higher in individuals with a more advanced clinical diagnosis ¹⁶. Similar to plasma/serum GFAP levels, CSF GFAP was also observed to be elevated in the mutation carriers; however, in line with previous reports, this relationship was specific to the dementia stage of AD, ^30,31 or when both Aβ and tau PET biomarkers were positive ^16,32. Taken together, these findings suggest that plasma GFAP (but not CSF GFAP) may serve as an early biomarker of AD associated neuropathological changes. It could therefore be speculated that plasma GFAP is more closely associated with astrocyte reactivity because of Aβ accumulation, whilst CSF GFAP is more reflective of reactive astrogliosis due to advanced neurodegeneration ¹¹. Further, given that astrocyte end feet encompass brain capillaries, it could be posited that blood matrices are more sensitive to astrocyte reaction in AD than the CSF ²⁷. Future studies employing stable isotope labelling kinetics, animal and/or cell models are required to explore the release and turnover of GFAP in different matrices in AD.

In line with our observations, plasma GFAP levels have been reported to be positively associated with cognitive dysfunction and cerebral atrophy ^18,33,34. These clinical observations fit well with pathology studies that show a gradual increase of GFAP levels in the brain in relation to AD severity ^35,36. Plasma GFAP levels have also been reported to associate with longitudinal cognitive decline and cerebral atrophy, which is confirmed in our study, ^37–40 and higher incident dementia risk ^26,33. Thus, familial, clinical, and population-based studies suggest that increased plasma or serum GFAP levels not only associate with, but also predict, disease progression in AD.

A limitation of this study involves the inclusion of a modest Aβ-PET imaged sample size subset, particularly among the symptomatic mutation carriers. For CSF samples, due toa modest sample size of mutation carriers (n = 30), subtle effects could have been missed. However, the sample size was identical to that of serum, which showed similar relations as plasma. GFAP levels have been shown to be sensitive to freeze-thaw cycles in CSF but not in plasma/serum ^41,42. However, this is unlikely to affect our results, because both, mutation carrier and non-carrier samples, underwent the same freeze-thaw cycles, and stronger blood-matrix than CSF effects with respect to Aβ pathology is supported by earlier work ¹⁶. It is also important to note that the analysis of plasma biomarker changes as a function of EYO may be influenced by sample size. Therefore, interpretation of the EYO at which plasma GFAP changes were observed in this study should be considered relative to other markers in the sequence of events of ADAD. Future studies with longitudinal GFAP measures are required to (i) calculate the rate of change of GFAP and how early its levels begin to differ between mutation carriers and non-carriers, (ii) investigate whether the rate of change of GFAP improves the predictive value for future decline and, (iii) investigate true longitudinal changes within individuals.

To conclude, the current study is the first of its kind, wherein plasma, serum and CSF GFAP levels were investigated in ADAD. Our findings of elevated GFAP levels in ADAD mutation carriers support a role for GFAP as a blood-based biomarker for Aβ associated cognitive deterioration in AD.

Participants

Participants were from the Dominantly Inherited Alzheimer Network (DIAN) cohort ²¹ that comprises biological offspring of individuals carrying an ADAD mutation (in the amyloid precursor protein (APP), presenilin 1 (PSEN1) or presenilin 2 (PSEN2) genes) thus having 50% chance of inheriting the mutation. The presence or absence of an ADAD mutation was confirmed using PCR-based amplification of the relevant exon, followed by Sanger sequencing. Given the complete penetrance of ADAD mutations and the relatively consistent age at symptom onset for mutation carriers within each family, an estimated time to symptom onset in years (EYO) was calculated for each participant (mutation carriers and their non-carrier siblings) based on the difference between each participant’s age and the average age of onset for the specific mutation ²³. EYO for symptomatic participants (Clinical Dementia Rating; CDR > 0) was defined as the difference between the age at clinical assessment and reported age of actual symptom onset. Participants underwent comprehensive clinical assessments, neuroimaging, and blood and CSF collection after fasting overnight, however, at each visit, each participant may not have completed all procedures. Participants with the Dutch mutation (APP Glu693Gln mutation) were excluded from the study because they manifest an atypical clinical syndrome ⁴³. Plasma samples were available from 86 mutation non-carriers and 98 mutation carriers (DIAN data freeze 13, DIAN request T1605), and serum and CSF samples were available from 30 mutation non-carriers and 30 mutation carriers (DIAN data freeze 15, DIAN request T2010). Twelve overlapping samples were available from DIAN request T1605 (plasma) and DIAN request T2010 (serum/CSF). Samples utilised were based on availability from the DIAN biobank. Serum and CSF samples were paired. The study was approved by the Human Research Ethics Committees (HREC) of Macquarie University, Edith Cowan University, and Ramsay Health Care WA|SA HREC in Australia, and Washington University in St. Louis, USA. All participants provided written informed consent.

Clinical assessments

Participants underwent standardised comprehensive clinical assessments. The Clinical Dementia Rating (CDR®) scale was used to determine the dementia stage wherein CDR = 0 was rated as cognitively normal, CDR = 0.5 as very mild dementia, CDR = 1 as mild dementia and CDR = 2 as moderate dementia ⁴⁴. The primary measures used to examine global cognitive abilities were the Mini-Mental State Examination (MMSE; range between 0 to 30 indicating severe impairment to no impairment) ⁴⁵ and CDR-Sum of Boxes (CDR-SOB; range between 0 to 18 indicating no dementia to severe dementia). Participants also underwent a comprehensive battery of neuropsychological tests assessing general cognition, as well as specific cognitive domains such as memory, executive function, language and attention ⁴⁶. A global cognitive composite was generated from the average of the z-scores of the Logical Memory delayed recall, word list learning delayed recall, Digit Symbol and MMSE ⁴⁷.

Neuroimaging

Cortical Aβ deposition, glucose metabolism and thickness/hippocampal volume were assessed using ¹¹C PiB-PET, ¹⁸F FDG-PET, and T1-weighted MRI scans, respectively, and consistency between all DIAN sites was maintained using standard procedures ⁴⁸. Briefly, the ¹¹C PiB-PET imaging was conducted over as a 70 min dynamic scan, following ~ 13 mCi of PiB intravenous bolus. The ¹⁸F FDG-PET imaging was conducted over 30 min, acquired after 30 min of ~ 5 mCi of FDG intravenous bolus. The T1 magnetic resonance sequence was acquired on 3T scanners with repetition time = 23,000, echo time = 2.95 and resolution = 1.0×1.0×1.2 mm³. The ¹¹C PiB-PET and ¹⁸F FDG-PET standard uptake value ratios (SUVRs) were obtained using FreeSurfer software (http://surfer.nmr.mgh.harvard.edu/). Region of interest data was corrected for partial volume effects using a geometric transfer matrix ⁴⁹, and the total cerebellum gray matter was used as the reference region to calculate SUVR. The hippocampal volume was averaged between left and right hemispheres, and normalized utilising intracranial volume.

Measurement of plasma, serum, and cerebrospinal fluid GFAP

EDTA plasma, serum and CSF GFAP concentrations were measured employing the ultra-sensitive single-molecule array (Simoa) platform utilising the Neurology 4-Plex E kit (QTX-103670, Quanterix, Billerica, MA) wherein calibrators were run in duplicates and samples were run in singlicates. CV% of GFAP for quality control samples at both sites were < 5%. The analytical lowest limit of quantification was 11.6 pg/ml for GFAP.

Statistical analyses

Participant characteristics were presented as mean ± SD for continuous variables and n (%) for categorical variables. P values for comparing the differences among non-carriers, asymptomatic mutation carriers and symptomatic mutation carriers were obtained using linear mixed-effects models (LMEMs) for continuous variables and generalized LMEMs with a logistic link for categorical variables. These models included a random intercept for participants from the same family. A cortical ¹¹C PiB-PET SUVR = 1.25 was utilised as the cut-off for presence of aberrant cortical Aβ load (Aβ+) ²².

Plasma GFAP levels were estimated as a function of EYO using non-linear mixed effect models. Potential non-linear effects were accounted for by modelling EYO as a restricted cubic spline with knots at the 0.10, 0.50, and 0.90 quantiles ⁴⁸. The non-linear mixed effect models for the plasma biomarkers included as fixed effects the mutation status and the linear EYO component; the cubic EYO component; the linear EYO by mutation status interaction; the cubic EYO by mutation status interaction; and a random intercept for family effect. All models were estimated using an opensource package for Hamiltonian Markov chain Monte Carlo analyses, using R, as used previously ^50,51. From this approach a distribution of parameter estimates across iterations is achieved that allows for the estimation of the credible intervals of the model fits at every EYO for non-carriers and mutation carriers, and the distribution of the 99% difference between non-carriers and mutation carriers. The first EYO point where the 99% credible intervals of the difference curve between non-carriers and mutation carriers did not include zero was considered to be the EYO at which the non-carriers and mutation carriers differed. Analyses for brain Aβ load, hippocampal volume, precuneus thickness and cognitive measures as well as for serum and cerebrospinal GFAP were carried out utilising the same method.

Kruskal-Wallis tests followed by pairwise comparisons were used to compare plasma GFAP by Aβ PiB-PET quartile and across clinical progression in mutation carriers. Logistic regression using absence or presence of aberrant brain Aβ load as response (Aβ-/+) was used to evaluate classification models, and receiver operating characteristic (ROC) curves were constructed from the logistic scores within mutation carriers.

The cross-sectional relationships of GFAP with brain Aβ load, hippocampal volume, precuneus thickness, precuneus glucose metabolism, global cognitive composite, MMSE and CDR-SOB were evaluated using LMEMs adjusting for sex (and education for cognition) with family as a random effect. For sensitivity analyses, we tested the effect of additional age-adjustment in the models for the cross-model relationships. Age-adjustment is notoriously difficult to interpret in the context of ADAD, because age is highly correlated with disease stage. Therefore age-adjustment can within the mutation carriers be an unintended adjustment for disease stage instead of for general aging effects, due to the correlation of EYO and age in the mutation carriers. Within the mutation carriers, associations between plasma GFAP levels and subsequent neurodegeneration, cerebral glucose metabolism, cognitive and functional performance (as continuous variables) were evaluated using LMEMsadjusting for age and sex (and education for cognition) with family as a random effect and individual random slopes. For the visualization in Fig. 4, two groups within the mutation carriers were created based on the Youden index cut-off for amyloid positivity. Assumptions of LMEMs were checked, and plasma/serum/CSF GFAP was natural log transformed. Spearman’s correlation coefficient was used to investigate correlations between serum and CSF GFAP (n = 60), plasma and serum GFAP (n = 12) and plasma and CSF GFAP (n = 12). Analyses were conducted using R (v4.0.4) and IBM SPSS (v27). P < .05 was considered to be statistically significant; all statistical tests were two-tailed.

Acknowledgements

Data collection and sharing for this project was supported by the Dominantly Inherited Alzheimer Network (DIAN, U19AG032438) funded by the NIA, the National Health and Medical Research Council, Australia (NHMRC, APP1129627), the German Center for Neurodegenerative Diseases (DZNE), and partial support by the Research and Development, grants for Dementia from Japan Agency for Medical Research and Development. This work was additionally supported by R01 AG071865 (JPC) and by an Alzheimer’s Association Grant (AARF-21-846786; SAS). The study also received support from the Lions Alzheimer's Foundation and Lions Club International for their generous donations that allowed the purchase of the Simoa-HD-X instrument used in this study. We thank Philip Scheltens for his early stage feedback, Alzheimer Nederland (Fellowship 2018) and Stichting Dioraphte (Netherlands Neurdegeneration in Families project). This manuscript has been reviewed by DIAN Study investigators for scientific content and consistency of data interpretation with previous DIAN Study publications. The authors gratefully acknowledge the altruism of the participants and their families and contributions of the DIAN research and support staff at each of the participating sites for their contributions to this study.

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Plasma glial fibrillary acidic protein in autosomal dominant Alzheimer’s disease: associations with β-amyloid-PET, neurodegeneration and cognition

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Abstract

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Introduction

Results

Plasma GFAP between mutation carriers and non-carriers

Association of plasma GFAP with Aβ-PET and clinical progression in ADAD

Serum and CSF GFAP in ADAD

Discussion

Methods

Participants

Clinical assessments

Neuroimaging

Measurement of plasma, serum, and cerebrospinal fluid GFAP

Statistical analyses

Declarations

Acknowledgements

References

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