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Heart fatty acid binding protein and Aβ-associated Alzheimer’s neurodegeneration

Molecular Neurodegeneration volume 8, Article number: 39 (2013) Cite this article

Abstract

Background

Epidemiological and molecular findings suggest a relationship between Alzheimer’s disease (AD) and dyslipidemia, although the nature of this association is not well understood.

Results

Using linear mixed effects models, we investigated the relationship between CSF levels of heart fatty acid binding protein (HFABP), a lipid binding protein involved with fatty acid metabolism and lipid transport, amyloid-β (Aβ), phospho-tau, and longitudinal MRI-based measures of brain atrophy among 295 non-demented and demented older individuals. Across all participants, we found a significant association of CSF HFABP with longitudinal atrophy of the entorhinal cortex and other AD-vulnerable neuroanatomic regions. However, we found that the relationship between CSF HABP and brain atrophy was significant only among those with low CSF Aβ1–42 and occurred irrespective of phospho-tau181p status.

Conclusions

Our findings indicate that Aβ-associated volume loss occurs in the presence of elevated HFABP irrespective of phospho-tau. This implicates a potentially important role for fatty acid binding proteins in Alzheimer’s disease neurodegeneration.

Background

A growing number of epidemiological and experimental studies suggest an association between Alzheimer’s disease (AD) and dyslipidemia. In observational studies, high serum cholesterol levels have been associated with increased risk of AD [1, 2]. Genetic linkage and genome-wide association studies have identified a number of genes involved with cholesterol metabolism and transport as AD susceptibility loci [3, 4] and cellular and molecular biology research has indicated a critical role for neuronal membrane phospholipids ('lipid rafts’) in modulating AD-associated pathogenesis [5]. However, in animal models, apolipoprotein E modulates the relationship between low-density lipoproteins and amyloid-β (Aβ) deposition [6, 7] suggesting an indirect effect of intra-cranial cholesterol on Alzheimer’s pathology. Furthermore, conflicting evidence from epidemiological studies shows an unclear association between elevated cholesterol levels and AD risk [8, 9], protein levels of fatty acid binding proteins have been noted to be decreased in brains of AD patients [10], and randomized clinical trials have not shown a clear benefit of lipid-lowering therapy on AD onset [2]. Thus there is a need for additional research evaluating the relationship between lipid biology and neurodegeneration in individuals at risk for AD.

In humans, structural MRI and CSF biomarkers allow for the indirect assessment of the cellular changes underlying AD in vivo. Structural MRI provides measures of brain atrophy, which reflect loss of dendrites, synapses [11] and neurons [12]. Low CSF levels of Aβ strongly correlate with intracranial amyloid plaques and high concentrations of CSF phospho-tau (p-tau) correlate with tau-associated neurofibrillary tangles [13, 14]. Recent work suggests that CSF levels of heart fatty acid binding protein (HFABP or FABP3), a lipid binding protein involved with fatty acid metabolism and lipid transport [15] may have diagnostic and prognostic value in the earliest stages of AD [16–20]. Here, among non-demented older individuals at risk for AD and demented participants diagnosed with probable AD, we investigated whether CSF HFABP is associated with brain atrophy over time and whether interactions between high CSF HFABP and low CSF Aβ1–42 and high CSF HFABP and high CSF p-tau181p are associated with brain atrophy over time. We also evaluated the relationship between CSF HFABP and other lipid binding proteins including Apolipoprotein (Apo) C III, Apo D, and Apo E.

Results

CSF HFABP and brain atrophy rates

In our initial analyses, we used linear mixed effects models, co-varying for baseline age, sex, presence ("carriers") or absence ("non-carriers") of at least one ϵ4 allele of apolipoprotein E (APOE ϵ4), diagnostic status (AD vs mild cognitive impairment (MCI) vs healthy elderly controls (HC)), and disease severity (CDR-Sum of Boxes score at baseline) to examine whether CSF HFABP levels are associated with longitudinal atrophy of the entorhinal cortex and other AD-vulnerable regions of interest ('AD vulnerable ROI’ – for additional details see Methods) (Figure 1). We found a significant relationship between CSF HFABP, time, and atrophy rate of the entorhinal cortex (β-coefficient = -0.007, standard error (SE) = 0.003, p-value = 0.013) and the AD-vulnerable ROI (β-coefficient = -0.005, SE = 0.002, p-value = 0.017), indicating increased volume loss with elevated CSF HFABP levels.

Figure 1
figure 1

Three-dimensional representations of the neuroanatomic regions examined in the current study (only one hemisphere is shown). All of the examined neocortical regions are illustrated in the lateral and medial views of the gray matter surface (top row). The two non-neocortical regions (i.e., the hippocampus and amygdala) are illustrated in the coronal view of a T1-weighted MRI image (bottom row). Regions illustrated in red constitute the 'AD-vulnerable ROI’ (for further details please see manuscript text).

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CSF HFABP, CSF Aβ1–42, CSF p-tau181p, and brain atrophy rates

Next, we asked whether statistical interactions between CSF HFABP and CSF Aβ1–42 and between CSF HFABP and CSF p-tau181p are associated with brain atrophy over time. These linear mixed effects models included the main and interactive effects of CSF HFABP, CSF Aβ1–42, status and CSF p-tau181 status, and co-varied for the demographic and clinical variables mentioned previously (see Methods for further details on the model). Key results from these models are shown in Table 1.

Table 1 Linear mixed effects model results for analyses involving all 295 participants
Full size table

As illustrated in Table 1, with the interaction terms of the three CSF biomarkers in the model, the association of CSF HFABP with brain atrophy over time was not significant. However, there were significant interactive effects between CSF HFABP x CSF Aβ1–42 status x time and CSF HFABP x CSF p-tau181p status x time on atrophy of both the entorhinal cortex and the AD-vulnerable ROI. The only CSF biomarker to show a significant association with atrophy over time in the entorhinal cortex and AD-vulnerable ROI was CSF p-tau181p. Of the co-variates, the interaction of time by diagnostic status and APOE ϵ4 carrier status were significant for both entorhinal cortex atrophy (diagnostic status x time: β-coefficient = -0.007, SE = 0.002, p-value = 0.0005; APOE ϵ4 carrier status x time (β-coefficient = -0.004, SE = 0.002, p-value = 0.03) and AD-vulnerable ROI atrophy (diagnostic status x time: β-coefficient = -0.006, SE = 0.002, p-value = 0.0003; APOE ϵ4 carrier status x time (β-coefficient = -0.003, SE = 0.001, p-value = 0.02). None of the other variables showed significant main or interactive effects.

To further investigate the three-way interactions, we performed follow-up analyses after stratifying on the basis of CSF Aβ1–42 status (i.e. low and high status) and CSF p-tau181p status (i.e. high and low status). We found a significant CSF HFABP by time interaction on entorhinal cortex and AD-vulnerable ROI atrophy rate only among individuals with low CSF Aβ1–42 levels (Table 1, Figure 2A) Among individuals with high CSF Aβ1–42 levels and high or low CSF p-tau181p levels, we found no effect of CSF HFABP by time on brain atrophy (Table 1, Figure 2A, 2B). This indicates elevated volume loss with elevated CSF HFABP and low CSF Aβ1–42 irrespective of CSF p-tau181p status.

Figure 2
figure 2

(A) Spaghetti plots illustrating atrophy of the entorhinal cortex among all participants classified as low Aβ 1–42 and high HFABP (based on median value of FABP) (top left panel), low Aβ 1–42 and low HFABP (top right panel), high Aβ 1–42 and high FABP (bottom left panel), and high Aβ 1–42 and low FABP (bottom right panel). The red line indicates the mean atrophy rate for the four respective groups (i.e. low Aβ1–42 and high FABP, low Aβ1–42 and low FABP, high Aβ1–42 and high FABP and high Aβ1–42 and low FABP). As illustrated, the slopes of the red lines are significantly different depending on CSF Aβ1–42 status (please see text for further details). (B) Spaghetti plots illustrating atrophy of the entorhinal cortex among all participants classified as high p-tau181p and high HFABP (based on median value of FABP) (top left panel), high p-tau181p and low HFABP (top right panel), low p-tau181p and high FABP (bottom left panel), and low p-tau181p and low FABP (bottom right panel). The red line indicates the mean atrophy rate for the four respective groups (i.e. high p-tau181p and high FABP, high p-tau181p and low FABP, low p-tau181p and high FABP and low p-tau181p and low FABP). As illustrated, the slopes of the red lines are not significantly different depending on CSF p-tau181p status (please see text for further details).

Full size image

We also evaluated whether interactions between high CSF HFABP and low CSF Aβ1–42 and high CSF HFABP and high CSF p-tau181p are associated with longitudinal clinical decline as assessed with the Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog). In these analyses, neither the interaction between CSF HFABP, CSF Aβ1–42 status and time nor the interaction between CSF HFABP, CSF p-tau181p status and time was significantly associated with longitudinal change in ADAS-cog (see Additional file 1).

CSF HFABP and other lipid binding proteins

We next used generalized linear models to investigate the relationship between CSF HFABP and CSF p-tau181p, and levels of other lipid binding proteins including Apolipoprotein (Apo) C III, Apo D, and Apo E at baseline. The relation of each of these apolipoproteins with CSF HFABP was assessed in separate models; all models controlled for age, sex, APOE ϵ4 carrier status, diagnostic status, and CDR-Sum of Boxes score.

We found significant associations between CSF HFABP and CSF p-tau181p (β-coefficient = 0.008, SE = 0.001, p-value = < 2.0 x 10-16), CSF levels of Apo C III (β-coefficient = 0.29, SE = 0.07, p-value = 4.2 x 10-5), ApoD (β-coefficient = 0.35, SE = 0.09, p-value = 2.8 x 10-4), and ApoE (β-coefficient = 0.86, SE = 0.12, p-value = 2.9 x 10-16) (Figure 3).

Figure 3
figure 3

Scatter plots demonstrating the relationship between baseline CSF levels of HFABP (quality-controlled, transformed values as described in reference 18) CSF p-tau 181p (top left), CSF ApoC III (top right), CSF ApoD (bottom left) and CSF ApoE (bottom right). The black line represents the best-fit regression line.

Full size image

Using the same linear mixed effects framework described above, we additionally evaluated whether statistical interactions between CSF HFABP and the other lipid binding proteins are associated with brain atrophy over time. We found a significant interaction only between CSF HFABP and CSF Apo C III on entorhinal cortex atrophy rate (β-coefficient = 0.02, SE = 0.009, p-value = 0.02). None of the other lipid binding proteins demonstrated a significant interaction with CSF HFABP on atrophy rates of the entorhinal cortex and the AD-vulnerable ROI (p-values > 0.2).

Discussion

Here, we demonstrate that in non-demented older individuals at risk for AD and in mild AD participants, HFABP is associated with volume loss in brain areas selectively affected in the earliest stages of AD. However, we found that the relationship between HFABP and brain atrophy is present only among individuals with low CSF Aβ1–42, and occurs irrespective of phospho-tau levels. Considered together, these results suggest a potentially important role for fatty acid binding proteins in early Alzheimer’s disease pathobiology.

Our findings also indicate that rather than simply representing a generalized marker of neuronal degeneration, elevated CSF levels of HFABP may reflect central nervous system lipid dyshomeostasis. Consistent with prior studies [16, 18], we found a significant association between HFABP and phospho-tau. However, we also found a strong relationship between HFABP and a number of apolipoproteins including ApoE, as well as an interaction between HFABP and ApoC III on volume loss. Together, these findings support the hypothesis that intra-cranial lipid biology may influence Alzheimer’s neurodegeneration [5].

Our results suggest that the relationship between neuronal lipid biology and neurodegeneration may be influenced by amyloid pathology even after controlling for the effects of APOE ϵ4. An important aspect of our findings is the specific relationship between HFABP and Aβ deposition, where volume loss occurs only in the presence of elevated HFABP and decreased Aβ (i.e. increased Aβ deposition in the brain). A growing body of experimental evidence from various model systems indicates that phospholipids play an integral role in regulating amyloidogenesis. Enriched cholesterol and lipid microenvironments ('lipid rafts’) within the plasma membrane and the mitochondria-associated endoplasmic reticulum membrane promote γ-secretase activity resulting in increased Aβ production [5, 21]. In comparison, outside lipid rafts, amyloid-precursor protein (APP) processing occurs predominantly via the non-amyloidogenic α-secretase pathway [22]. Membrane phospholipids also influence Aβ aggregation and clearance [5] and through release of arachidonic acid via the phospholipase 2 pathway, may additionally serve as critical mediators in Aβ-induced synaptoxicity, leading to learning, memory, and behavioral impairments in mouse models of AD [23]. Our results are consistent with these observations and suggest that phospholipids and lipid binding proteins may affect Alzheimer’s neurodegeneration primarily via Aβ-associated mechanisms.

One potential concern is that our current findings do not explain the previously noted relationships observed between HFABP and non-AD neurodegenerative diseases such as vascular dementia, Creutzfeldt-Jakob disease, Parkinson’s disease, and dementia with Lewy bodies [18, 24–27]. Though we did not explicitly evaluate the role of HFABP in other neurodegenerative conditions or as a differential disease marker of AD, our findings may help provide insights into common mechanisms underlying an array of protein misfolding neurologic disorders. For example, lipid metabolism may play an important role in synaptic degeneration and regeneration [28] and as such, may be involved in a number of neurodegenerative diseases. Another intriguing notion is that in addition to APP and Aβ, phospholipid rich lipid rafts may play an important role in mediating pathogenesis associated with a number of proteins including α-synuclein [29, 30] and prions [31, 32] thus raising the possibility that lipid dyshomeostasis may represent an early trigger for a number of protein misfolding neurodegenerative disorders.

A limitation of our study is its observational nature, which precludes conclusions regarding causation. Our results cannot differentiate whether elevated HFABP causes, results from, or is simply correlated with amyloid deposition and volume loss. Additionally, our findings require further validation on a larger, independent population-based cohort.

Conclusions

In summary, our results suggest that CSF HFABP reflects intra-cranial lipid biology and associates with Aβ-associated neurodegeneration irrespective of tau. Clinically, our findings suggest that HFABP may represent an important modifier of progression from amyloid deposition to neurodegeneration. Considered together with our prior findings [33, 34], this suggests that in addition to phospho-tau, the HFABP/Aβ/neurodegeneration axis may represent an important area for further investigation. Using experimental models, it would be helpful to better understand the precise relationship between HFABP and Aβ dyshomeostasis and whether proteins influence or modulate this association. The results of these studies could provide insights into whether fatty acids and lipids represent a viable therapeutic target for individuals in the presymptomatic and very mildly symptomatic phase of the disease process.

Methods

We evaluated cognitively normal older adults (HC = 90), individuals diagnosed with amnestic MCI (n = 139), and probable AD (n = 66) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI; see Additional file 1 for further details). From a total of 415 demented and non-demented older individuals who underwent longitudinal MR imaging and CSF lumbar puncture, we restricted our analyses to those participants with quality-assured baseline and at least one follow-up MRI scan (Table 2).

Table 2 Demographic, clinical, and imaging data for all participants in this study
Full size table

We evaluated baseline CSF HFABP levels analyzed using a multiplex-based immunoassay panel. This immunoassay panel, based on Luminex xMAP immunoassay technology and developed by Rules Based Medicine (MyriadMBM), measures a range of lipid, inflammatory, metabolic, and other AD-relevant indices (for further details, please see reference 18). We also examined baseline CSF Aβ1–42 and CSF p-tau181p levels, determined using the AlzBio3 Luminex xMAP immunoassay (Innogenetics, Ghent, Belgium). Using previously proposed CSF cutoffs 19 we classified participants based on low (<192 pg/ml) and high (>192 pg/ml) Aβ1–42 levels and high (>23 pg/ml) and low (<23 pg/ml) p-tau181p levels.

We examined 1205 T1-weighted MRI scans. We performed quantitative volume and surface-based analysis of all baseline MRI scans using automated region-of-interest (ROI) labeling techniques [35, 36], primarily focusing on the entorhinal cortex, a medial temporal lobe region that is selectively affected in the earliest stages of AD [37] (Figure 1). To additionally investigate neuroanatomic regions that are involved in the later stages of the disease process [37, 38], and to minimize multiple comparisons, we averaged longitudinal volume change in the temporal pole, parahippocampal gyrus, inferior temporal gyrus, banks of the superior temporal sulcus, inferior parietal lobule, amygdala, and hippocampus to create an 'AD-vulnerable’ ROI [33, 34] (Figure 1). Using an image-analysis method developed within our laboratory [39], we assessed longitudinal sub-regional change in gray matter volume (atrophy) on serial MRI scans (see Additional file 1 for additional details).

For the primary analyses, we used linear mixed effects models to examine the associations of CSF HFABP, CSF Aβ1–42 status, and CSF p-tau181p status on atrophy rate of the entorhinal cortex, and of the AD-vulnerable ROI. All analyses co-varied for the effects of the baseline age, sex, presence ("carriers") or absence ("non-carriers") of at least one ϵ4 allele of apolipoprotein E (APOE ϵ4), diagnostic status (AD vs MCI vs HC), and disease severity (CDR-Sum of Boxes score at baseline).

Specifically, our initial model was:

Δv = b 0 + b 1 Δt + b 2 CSF _ HFABP × Δt + covariates × Δt + e
(1)

Here, Δv is the entorhinal cortex or AD-vulnerable ROI thickness change from baseline (millimeters3) and Δt is the change in time from baseline MRI scan (years).

Next, investigated whether interactions of CSF HFABP with CSF Aβ1–42 status, and CSF p-tau181p status were significantly associated with atrophy over time. Specifically:

Δv = β 0 + β 1 Δt + β 2 CSF _ HFABP × Δt + β 3 CSF _ Ab 1 – 42 _ status × Δt + β 4 CSF _ p ‒ tau 181 p _ status × Δt + β 5 CSF _ HFABP × CSF _ Ab 1 – 42 _ status × Δt + β 6 CSF _ HFABP × CSFp ‒ tau 181 p _ status × Δt + covariates × Δt + e
(2)

In both models, the main effects of all variables were also included. For brevity, we focus above on the effects of interest.

Abbreviations

HFABP:

Heart fatty acid binding protein

Aβ:

Amyloid-β

p-tau181p:

Phospho-tau

AD:

Alzheimer’s disease

MCI:

Mild cognitive impairment

HC:

Healthy older controls

AD-vulnerable ROI:

Neocortical and subcortical regions affected in the later stages of Alzheimer’s disease.

References

  1. Shepardson NE, Shankar GM, Selkoe DJ: Cholesterol level and statin use in Alzheimer disease: I. Review of epidemiological and preclinical studies. Arch Neurol. 2011, 68: 1239-1244. 10.1001/archneurol.2011.203.

    Article  PubMed Central  PubMed  Google Scholar 

  2. Shepardson NE, Shankar GM, Selkoe DJ: Cholesterol level and statin use in Alzheimer disease: II. Review of human trials and recommendations. Arch Neurol. 2011, 68: 1385-1392. 10.1001/archneurol.2011.242.

    Article  PubMed Central  PubMed  Google Scholar 

  3. Jones L, Holmans PA, Hamshere ML, Harold D, Moskvina V, Ivanov D, Pocklington A, Abraham R, Hollingworth P, Sims R, Gerrish A, Pahwa JS, Jones N, Stretton A, Morgan AR, Lovestone S, Powell J, Proitsi P, Lupton MK, Brayne C, Rubinsztein DC, Gill M, Lawlor B, Lynch A, Morgan K, Brown KS, Passmore PA, Craig D, McGuinness B, Todd S, Holmes C, Mann D, Smith AD, Love S, Kehoe PG, Mead S, Fox N, Rossor M, Collinge J, Maier W, Jessen F, Schürmann B, Heun R, Kölsch H, van den Bussche H, Heuser I, Peters O, Kornhuber J, Wiltfang J, Dichgans M, Frölich L, Hampel H, Hüll M, Rujescu D, Goate AM, Kauwe JS, Cruchaga C, Nowotny P, Morris JC, Mayo K, Livingston G, Bass NJ, Gurling H, McQuillin A, Gwilliam R, Deloukas P, Al-Chalabi A, Shaw CE, Singleton AB, Guerreiro R, Mühleisen TW, Nöthen MM, Moebus S, Jöckel KH, Klopp N, Wichmann HE, Rüther E, Carrasquillo MM, Pankratz VS, Younkin SG, Hardy J, O’Donovan MC, Owen MJ, Williams J: Genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer’s disease. PLoS One. 2010, 5: e13950-10.1371/journal.pone.0013950.

    Article  PubMed Central  PubMed  Google Scholar 

  4. Jones L, Harold D, Williams J: Genetic evidence for the involvement of lipid metabolism in Alzheimer’s disease. Biochim Biophys Acta. 1801, 2010: 754-761.

    Google Scholar 

  5. Di Paolo G, Kim TW: Linking lipids to Alzheimer’s disease: cholesterol and beyond. Nat Rev Neurosci. 2011, 12: 284-296. 10.1038/nrn3012.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  6. Wahrle SE, Jiang H, Parsadanian M, Kim J, Li A, Knoten A, Jain S, Hirsch-Reinshagen V, Wellington CL, Bales KR, Paul SM, Holtzman DM: Overexpression of ABCA1 reduces amyloid deposition in the PDAPP mouse model of Alzheimer disease. J Clin Invest. 2008, 118: 671-682.

    PubMed Central  CAS  PubMed  Google Scholar 

  7. Castellano JM, Deane R, Gottesdiener AJ, Verghese PB, Stewart FR, West T, Paoletti AC, Kasper TR, DeMattos RB, Zlokovic BV, Holtzman DM: Low-density lipoprotein receptor overexpression enhances the rate of brain-to-blood Aβ clearance in a mouse model of β-amyloidosis. Proc Natl Acad Sci USA. 2012, 109: 15502-15507. 10.1073/pnas.1206446109.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Reitz C, Luchsinger J, Tang MX, Manly J, Mayeux R: Impact of plasma lipids and time on memory performance in healthy elderly without dementia. Neurology. 2005, 64: 1378-1383. 10.1212/01.WNL.0000158274.31318.3C.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Reitz C, Tang MX, Manly J, Schupf N, Mayeux R, Luchsinger JA: Plasma lipid levels in the elderly are not associated with the risk of mild cognitive impairment. Dement Geriatr Cogn Disord. 2008, 25: 232-237. 10.1159/000115847.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  10. Cheon MS, Kim SH, Fountoulakis M, Lubec G: Heart type fatty acid binding protein (H-FABP) is decreased in brains of patients with Down syndrome and Alzheimer’s disease. J Neural Transm Suppl. 2003, 67: 225-34. 10.1007/978-3-7091-6721-2_20.

    Article  CAS  PubMed  Google Scholar 

  11. Freeman SH, Kandel R, Cruz L, Rozkalne A, Newell K, Frosch MP, Hedley-Whyte ET, Locascio JJ, Lipsitz LA, Hyman BT: Preservation of neuronal number despite age-related cortical brain atrophy in elderly subjects without Alzheimer disease. J Neuropathol Exp Neurol. 2008, 67: 1205-1212. 10.1097/NEN.0b013e31818fc72f.

    Article  PubMed Central  PubMed  Google Scholar 

  12. Bobinski M, de Leon MJ, Wegiel J, Desanti S, Convit A, Saint Louis LA, Rusinek H, Wisniewski HM: The histological validation of post mortem magnetic resonance imaging-determined hippocampal volume in Alzheimer’s disease. Neuroscience. 2000, 95: 721-725.

    Article  CAS  PubMed  Google Scholar 

  13. Blennow K, Hampel H, Weiner M, Zetterberg H: Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol. 2010, 6: 131-144. 10.1038/nrneurol.2010.4.

    Article  CAS  PubMed  Google Scholar 

  14. Fagan AM, Holtzman DM: Cerebrospinal fluid biomarkers of Alzheimer’s disease. Biomark Med. 2010, 4: 51-63. 10.2217/bmm.09.83.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  15. Moullé VS, Cansell C, Luquet S, Cruciani-Guglielmacci C: The multiple roles of fatty acid handling proteins in brain. Front Physiol. 2012, 3: 385-

    Article  PubMed Central  PubMed  Google Scholar 

  16. Chiasserini D, Parnetti L, Andreasson U, Zetterberg H, Giannandrea D, Calabresi P, Blennow K: CSF levels of heart fatty acid binding protein are altered during early phases of Alzheimer’s disease. J Alzheimers Dis. 2010, 22: 1281-1288.

    CAS  PubMed  Google Scholar 

  17. Rosén C, Mattsson N, Johansson PM, Andreasson U, Wallin A, Hansson O, Johansson JO, Lamont J, Svensson J, Blennow K, Zetterberg H: Discriminatory Analysis of Biochip-Derived Protein Patterns in CSF and Plasma in Neurodegenerative Diseases. Front Aging Neurosci. 2011, 3: 1-

    Article  PubMed Central  PubMed  Google Scholar 

  18. Olsson B, Hertze J, Ohlsson M, Nägga K, Höglund K, Basun H, Annas P, Lannfelt L, Andreasen N, Minthon L, Zetterberg H, Blennow K, Hansson O: Cerebrospinal Fluid Levels of Heart Fatty Acid Binding Protein are Elevated Prodromally in Alzheimer’s Disease and Vascular Dementia. J Alzheimers Dis. 2012, Dec 19

    Google Scholar 

  19. Olsson B, Hertze J, Ohlsson M, Nägga K, Höglund K, Basun H, Annas P, Lannfelt L, Andreasen N, Minthon L, Zetterberg H, Blennow K, Hansson O: Cerebrospinal fluid levels of heart fatty acid binding protein are elevated prodromally in Alzheimer's disease and vascular dementia. J Alzheimers Dis. 2013, 34 (3): 673-9.

    CAS  PubMed  Google Scholar 

  20. Shaw LM, Vanderstichele H, Knapik-Czajka M, Clark CM, Aisen PS, Petersen RC, Blennow K, Soares H, Simon A, Lewczuk P, Dean R, Siemers E, Potter W, Lee VM, Trojanowski JQ: Alzheimer’s Disease Neuroimaging Initiative. Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol. 2009, 65: 403-13. 10.1002/ana.21610.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. Schon EA, Area-Gomez E: Mitochondria-associated ER membranes in Alzheimer disease. Mol Cell Neurosci. 2013, 55: 26-36.

    Article  CAS  PubMed  Google Scholar 

  22. Vetrivel KS, Thinakaran G: Membrane rafts in Alzheimer’s disease beta-amyloid production. Biochim Biophys Acta. 1801, 2010: 860-7.

    Google Scholar 

  23. Sanchez-Mejia RO, Mucke L: Phospholipase A2 and arachidonic acid in Alzheimer’s disease. Biochim Biophys Acta. 1801, 2010: 784-90.

    Google Scholar 

  24. Steinacker P, Mollenhauer B, Bibl M, Cepek L, Esselmann H, Brechlin P, Lewczuk P, Poser S, Kretzschmar HA, Wiltfang J, Trenkwalder C, Otto M: Heart fatty acid binding protein as a potential diagnostic marker for neurodegenerative diseases. Neurosci Lett. 2004, 370: 36-9. 10.1016/j.neulet.2004.07.061.

    Article  CAS  PubMed  Google Scholar 

  25. Mollenhauer B, Steinacker P, Bahn E, Bibl M, Brechlin P, Schlossmacher MG, Locascio JJ, Wiltfang J, Kretzschmar HA, Poser S, Trenkwalder C, Otto M: Serum heart-type fatty acid-binding protein and cerebrospinal fluid tau: marker candidates for dementia with Lewy bodies. Neurodegener Dis. 2007, 4: 366-75. 10.1159/000105157.

    Article  CAS  PubMed  Google Scholar 

  26. Wada-Isoe K, Imamura K, Kitamaya M, Kowa H, Nakashima K: Serum heart-fatty acid binding protein levels in patients with Lewy body disease. J Neurol Sci. 2008, 266: 20-24. 10.1016/j.jns.2007.08.018.

    Article  CAS  PubMed  Google Scholar 

  27. Ohrfelt A, Andreasson U, Simon A, Zetterberg H, Edman A, Potter W, Holder D, Devanarayan V, Seeburger J, Smith AD, Blennow K, Wallin A: Screening for new biomarkers for subcortical vascular dementia and Alzheimer’s disease. Dement Geriatr Cogn Dis Extra. 2011, 1: 31-42. 10.1159/000323417.

    Article  PubMed Central  PubMed  Google Scholar 

  28. Mauch DH, Nägler K, Schumacher S, Göritz C, Müller EC, Otto A, Pfrieger FW: CNS synaptogenesis promoted by glia-derived cholesterol. Science. 2001, 294: 1354-7. 10.1126/science.294.5545.1354.

    Article  CAS  PubMed  Google Scholar 

  29. Fortin DL, Troyer MD, Nakamura K, Kubo S, Anthony MD, Edwards RH: Lipid rafts mediate the synaptic localization of alpha-synuclein. J Neurosci. 2004, 24: 6715-23. 10.1523/JNEUROSCI.1594-04.2004.

    Article  CAS  PubMed  Google Scholar 

  30. Büttner S, Delay C, Franssens V, Bammens T, Ruli D, Zaunschirm S, de Oliveira RM, Outeiro TF, Madeo F, Buée L, Galas MC, Winderickx J: Synphilin-1 enhances α-synuclein aggregation in yeast and contributes to cellular stress and cell death in a Sir2-dependent manner. PLoS One. 2010, 5: e13700-10.1371/journal.pone.0013700.

    Article  PubMed Central  PubMed  Google Scholar 

  31. Taylor DR, Hooper NM: The prion protein and lipid rafts. Mol Membr Biol. 2006, 23: 89-99. 10.1080/09687860500449994.

    Article  CAS  PubMed  Google Scholar 

  32. Westaway D, Jhamandas JH: The P’s and Q’s of cellular PrP-Aβ interactions. Prion. 2012, 6: 359-63. 10.4161/pri.20675.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  33. Desikan RS, McEvoy LK, Thompson WK, Holland D, Roddey JC, Blennow K, Aisen PS, Brewer JB, Hyman BT, Dale AM: Alzheimer’s Disease Neuroimaging Initiative. Amyloid-β associated volume loss occurs only in the presence of phospho-tau. Ann Neurol. 2011, 70: 657-61. 10.1002/ana.22509.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  34. Desikan RS, McEvoy LK, Holland D, Thompson WK, Brewer JB, Aisen PS, Andreassen OA, Hyman BT, Sperling RA, Dale AM: For the Alzheimer’s Disease Neuroimaging Initiative. Apolipoprotein E {varepsilon}4 Does Not Modulate Amyloid-β-Associated Neurodegeneration in Preclinical Alzheimer Disease. AJNR Am J Neuroradiol. 2013, 34: 505-10. 10.3174/ajnr.A3267.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  35. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002, 33: 341-55. 10.1016/S0896-6273(02)00569-X.

    Article  CAS  PubMed  Google Scholar 

  36. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006, 31: 968-80. 10.1016/j.neuroimage.2006.01.021.

    Article  PubMed  Google Scholar 

  37. Braak H, Braak E: Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991, 82: 239-59. 10.1007/BF00308809.

    Article  CAS  PubMed  Google Scholar 

  38. Arnold SE, Hyman BT, Flory J, Damasio AR, Van Hoesen GW: The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer’s disease. Cereb Cortex. 1991, 1: 103-16. 10.1093/cercor/1.1.103.

    Article  CAS  PubMed  Google Scholar 

  39. Holland D, Dale AM: Nonlinear registration of longitudinal images and measurement of change in regions of interest. Med Image Anal. 2011, 15: 489-97. 10.1016/j.media.2011.02.005.

    Article  PubMed Central  PubMed  Google Scholar 

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Acknowledgements

This research was supported by grants from the National Institutes of Health (R01AG031224; K01AG029218; K02 NS067427; T32 EB005970), the Research Council of Norway (183782/V50) and the South East Norway Health Authority (2010–074). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following:

Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30

AG010129 and K01 AG030514.

*Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.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.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

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    Authors and Affiliations

    1. Department of Radiology, University of California, San Diego, 8950 Villa La Jolla Drive, Suite C101, La Jolla, CA, 92037-0841, USA

      Rahul S Desikan, James B Brewer, Linda K McEvoy & Anders M Dale

    2. Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA

      Wesley K Thompson, Ole A Andreassen & Anders M Dale

    3. Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA

      Dominic Holland & James B Brewer

    4. Neuroradiology Section, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA

      Christopher P Hess

    5. Clinical Neurochemistry Laboratory, The Sahlgrenska Academy at Göteburg University, Gotheburg, Mölndal, 40530, Sweden

      Henrik Zetterberg & Kaj Blennow

    6. UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK

      Henrik Zetterberg

    7. Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway

      Ole A Andreassen

    8. Department of Neurology, Massachusetts General Hospital, Boston, MA, USA

      Bradley T Hyman

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    1. Rahul S Desikan
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    Correspondence to Rahul S Desikan.

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    Competing interests

    Dr. Anders M. Dale is a founder and holds equity in CorTechs Labs, Inc, and also serves on the Scientific Advisory Board. The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies.

    Dr. Linda K. McEvoy’s has stock options in CorTechs Labs, Inc.

    Dr. James B. Brewer holds stock options in CorTechs Labs, Inc and serves on the advisory board and receives financial support from the Eli Lilly Biomarker Unit (Amyvid). Dr. Brewer also receives research support from General Electric and Janssen Alzheimer Immunotherapy.

    Dr. Kaj Blennow has served on the advisory boards for Innogenetics, Lilly, Pfizer and Roche.

    Authors’ contributions

    Drs. D, McE, H and D were involved with conception, design, and interpretation of data. Drs. D, McE, H and D provided general overall supervision of the study. Drs. D and T were involved with data analysis. Drs. D, B, McE, A, and D acquired funding. All authors contributed to drafting and critical revision of the manuscript and have given final approval of the version to be published.

    Bradley T Hyman and Anders M Dale contributed equally to this work.

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    Desikan, R.S., Thompson, W.K., Holland, D. et al. Heart fatty acid binding protein and Aβ-associated Alzheimer’s neurodegeneration. Mol Neurodegeneration 8, 39 (2013). https://0-doi-org.brum.beds.ac.uk/10.1186/1750-1326-8-39

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    Keywords

    Molecular Neurodegeneration

    ISSN: 1750-1326