Poxleitner et al.
Journal of Neuroinflammation(2024) 21:129
https://doi.org/10.1186/s12974-024-03080-0

Journal of Neuroinflammation

Open Access

RESEARCH

Western diet increases brain metabolism
and adaptive immune responses in a mousemodel of amyloidosis
Marilena Poxleitner1, Sabrina H. L. Hoffmann1, Georgy Berezhnoy1, Tudor M. Ionescu1,
Irene Gonzalez‑Menendez2,4, Florian C. Maier1, Dominik Seyfried1, Walter Ehrlichmann1,
Leticia Quintanilla‑Martinez2,4, Andreas M. Schmid1,4, Gerald Reischl1,4, Christoph Trautwein1,4,
Andreas Maurer1,4, Bernd J. Pichler1,4, Kristina Herfert1* and Nicolas Beziere1,3*

Abstract
Diet-induced increase in body weight is a growing health concern worldwide. Often accompanied by a low-grade
metabolic inflammation that changes systemic functions, diet-induced alterations may contribute to neurodegenera‑tive disorder progression as well. This study aims to non-invasively investigate diet-induced metabolic and inflam‑
matory effects in the brain of an APPPS1 mouse model of Alzheimer’s disease. [­ 18F]FDG, ­[18F]FTHA, and ­[18F]GE-180
were used for in vivo PET imaging in wild-type and APPPS1 mice. Ex vivo flow cytometry and histology in brains
complemented the in vivo findings. 1H- magnetic resonance spectroscopy in the liver, plasma metabolomics and flow
cytometry of the white adipose tissue were used to confirm metaflammatory condition in the periphery. We founddisrupted glucose and fatty acid metabolism after Western diet consumption, with only small regional changes
in glial-dependent neuroinflammation in the brains of APPPS1 mice. Further ex vivo investigations revealed cyto‑
toxic T cell involvement in the brains of Western diet-fed mice and a disrupted plasma metabolome. 1H-magentic
resonance spectroscopy and immunological results revealed diet-dependent inflammatory-like misbalance in livers
and fatty tissue. Our multimodal imaging study highlights the role of the brain-liver-fat axis and the adaptive immune
system in the disruption of brain homeostasis in amyloid models of Alzheimer’s disease.
Keywords Western diet, Alzheimer’s disease, PET imaging, [18F]FDG, [18F]FTHA, [18F]GE-180, APPPS1, 1H spectroscopy,
Metabolomics, Flow cytometry*Correspondence:
Kristina Herfert
kristina.herfert@med.uni-tuebingen.deNicolas Beziere
Nicolas.beziere@med.uni-tuebingen.de1
Werner Siemens Imaging Center, Department of Preclinical Imaging
and Radiopharmacy, Eberhard Karls University Tübingen, Tübingen,Germany2
Department of Pathology and Neuropathology, University Hospital
Tübingen, Eberhard Karls University, Tübingen, Germany
3
Cluster of Excellence CMFI (EXC 2124) “Controlling Microbes to Fight
Infections”, Eberhard Karls University, Tübingen, Germany
4
Cluster of Excellence iFIT (EXC 2180) “Image Guided and Functionally
Instructed Tumor Therapies”, Eberhard Karls University, Tübingen,Germany© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Poxleitner et al. Journal of Neuroinflammation(2024) 21:129

Introduction
Overweight and obesity are serious health problems
with an increasing prevalence worldwide [1]. In longitudinal studies mid-life obesity in overweight and obese
individuals has been linked to changing lifestyles, characterized by reduced physical activity and poor dietary
choices. These alterations result in metabolic disorders
such as type 2 diabetes, and cardiovascular disease havebeen identified as risk factors for developing dementia and cognitive decline decades later [2–5]. Numerous
investigations described the systemic alterations throughhigh-caloric diets like Western diets (WDs) [6–8]. In
response to obesity and associated chronic oversupply of
fatty acids and sugar, a low-grade chronic inflammation
develops, which if persisting over time, leads to a constant release of inflammatory effectors into the periphery[9–11]. Adipose and hepatic tissues are the main drivers behind this mechanism and diet-induced severe fatty
liver disease could be seen in rodent and human subjects[6, 7, 12–14]. Therefore, advancements in understanding the implications of diet-induced obesity for the wholebody are an important factor in health research.
In light of the ongoing debate regarding the interplay
between dietary habits, obesity and neurodegenerative diseases, our study aims to shed light on the intricate mechanisms underpinning cognitive decline andAlzheimer´s disease. While numerous studies haveestablished a link between high-energy diets and adverse
neurological outcomes including increased oligomeric
Amyloid β (Aβ) levels, Aβ plaque and neuroinflammation [15–18], the role of diet-induced obesity in triggering neuroinflammatory processes remains contentious.
Notably, dietary components such as fatty acids (FAs)and sugars have increasingly been recognized for theirpotential to modulate central metabolism and, by extension, the susceptibility to dementia [19–23]. The prevailing hypothesis suggests that metabolic and inflammatory
processes initiated by the proliferation of macrophagesin adipose tissue and subsequent release of pro-inflammatory cytokines may activate immunomodulatory cascades, leading to neuroinflammation [15, 24, 25]. This
perspective is supported by substantial evidence of a
rapid and transient microglia activation and proinflammatory cytokine upregulation in the hypothalamus, acritical center for energy homeostasis [26, 27] and hippocampus, implicating neuroinflammation in theseregions [15, 28]. However, emerging research challenges
this view, documenting cognitive impairments in theabsence of significant neuroinflammation in hippocampus and hypothalamus, suggesting that such inflammatory responses may be transient [29, 30]. By integratingthese findings, our work aims to elucidate the complexities of diet-induced obesity and its neurological impact,

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contributing valuable insights to a field marked by divergent evidence.
To date, the molecular mechanisms that connect obesity, glial activity and AD are not fully discovered [31].
Several ways exist to ensure communication between the
CNS and periphery, which allow the CNS to adapt andrespond to peripheral cues [32, 33]. In neurodegenerative diseases like Parkinson´s disease and AD, emerging
evidence indicates that neuroinflammation does not only
rely on glial activation, but innate and adaptive immune
cells can modulate inflammatory processes in the brain as
well [34–36]. In human brain samples and animal models of AD, immunohistochemical experiments revealed
substantial involvement of peripheral innate and adaptive immune system components in the pathogenesis. For
example in multiple sclerosis mouse models, self-antigen-recognizing T cells have been identified in brains to
act as primary drivers of the autoimmune response [37,38]. Furthermore, infiltration of bone marrow-derived
monocytes into CNS was triggered by a high-fat diet[39]. Study results on the impact of peripheral immune
cells on AD pathology are however still not in agreement[40–44].
In this study, we show through a multimodal and multiparametric approach that the consumption of a palatable, high-caloric WD during early to mid-life can cause
several systemic effects affecting the peripheral andcentral metabolism. Using the APPPS1 mouse model,
a mouse model of early accelerated amyloidosis [45],we identified changes in brain metabolism after diseasedevelopment using different positron emission tomography (PET) tracers and employed 1H- magnetic resonancespectroscopy (1H-MRS) and metabolomics to investigate systemic alterations. We first investigated changes
in cerebral glucose metabolism, using 2-deoxy-2-[18F]
fluoro-D-glucose ­([18F]FDG), a well-established PET
marker, widely used to investigate cerebral abnormalities.
Second, the diet-induced changes in fatty acid metabolism were analyzed using the long-chain fatty acid surrogate 14(R,S)-[18F]fluoro-6-thia-heptadecanoic acid (­[18F]
FTHA). Third, to assess diet-induced neuroinflammatorychanges in the brain we used the translocator protein(TSPO) tracer [­18F]GE-180, which is a surrogate marker
for neuroinflammation found mainly on activated glia
cells after neuronal damage and inflammation [46–48].
In addition, we performed flow cytometric and metabolic analyses ex vivo to investigate the metaflammation profile of the animals in-depth during WD feeding.
With PET, we obtained complementary results showing that diet-induced obesity (DIO) and AD had alteredbrain glucose and fatty acid metabolism throughout thebrain, which are independent of the Aβ pathology andelevated glial reactivity pattern. Moreover, we identified

Poxleitner et al. Journal of Neuroinflammation(2024) 21:129

T cells as an additive factor in the interplay of AD pathology and metaflammation and provide further insight
into the multifaceted dynamic of glial contribution toneuroinflammation in a model of overnutrition and AD.
The imbalance of key plasma metabolites and liver lipids
in the periphery, along with the disruption of glucose
and fatty acid metabolism in the brain, underscores theimportance of a healthy lifestyle and provides further
insight into the complex interplay of the brain-liver-fat
axis.

Materials and methods
Animals

This study was performed in double transgenic
APPPS1-21
mice(B6.Cg-Tg(Thy1-APPSw,Thy1PSEN1*L166P)21Jckr; APPPS1, n = 21) that co-expressed
the human Swedish double mutation APP KM670/671NL
and the L166P mutated human PS1 under the controlof neuron-specific Thy-1 promotor. This model shows
accelerated amyloid deposition at six weeks of age,accompanied in further age by microglial reactivity torising pathology [45]. As controls, wild-type C57BL/6J
mice (WT, n = 23) were used. In transgenic and wild-typegroups, male and female mice were investigated. APPPS1
breeding pairs were kindly provided by Prof. Mathias
Jucker and bred at the animal facility of the University
Hospital Tübingen (Germany). WT and APPPPS1 werehoused with litter mates of the same sex, thus housing
was genotype mixed with up to five mice per cage. Animals housed in genotype mixed groups in individuallyventilated cages with food and water ad libitum in a 12-h
light/dark cycle. All procedures, including humane endpoints (set on a combination of factors: (1) occurrenceof behavioural changes (e.g. apathy); (2) breathing difficulties or a sharp increase in the respiratory rate; or (3) aweight loss of at least 20% compared to control animalsof the same age, sex and diet), were performed in accordance with German federal regulations on the use andcare of experimental animals and approved by the local
authorities [Regierungspräsidium Tübingen (R06/21 G)].
Diet and study design

Mice were fed a normal rodent diet (V1534-000, ssniff,
Soest, Germany) until start of the feeding period. Starting at the age of 2.1 ± 0.1 months, animals were either
fed (WD (E15721-347, ssniff, Soest, Germany) or
ND (V1534-000, ssniff, Soest, Germany) for a total of
6 months (Fig. 1a). Compared to ND, WD contains
higher percentages of fat, sugar, and protein which wereaccompanied by a shifted balance of fatty acids, minerals
and trace elements (Additional file 1: Table S1). To ensure
the stability of the dietary constituents, WD was entirely
replaced once per week. For ND-fed animals, weight wasPage 3 of 29

registered starting at 2.9 ± 0.5 months (n = 20; APPPS1 = 9
and WT = 11), whereas for WD-fed animals, registration started at 2.1 ± 0.1 months (n = 24, APPPS1 = 12
and WT = 12). The weighting of animals was conducted
weekly. At 7.7 ± 0.4 months, the animals underwent
in vivo PET and MR imaging over one month and weresacrificed for further flow cytometric, metabolic, and histological analyses (Fig. 1a). Notably in vivo and ex vivo
results include individual dropouts of experimental animals due to e.g., technical problems during procedure
and/or analyses. Animal numbers per experiment andgroup are shown in Additional file 1: Table S2. For PET
following exclusions were made: for ­[18F]FDG WT-ND
group in one animal tracer injection was para; for ­[18F]
GE-180 a number of scans had to be excluded due toattenuation correction problems on a scanner (incl. one
WT-ND; three WT-WD; two APPPS1-WD). For othermissing data either measurement or tracer production
failed. Re-measurements were not manageable due toanimal welfare and the availability of scanners and tracers. For ex vivo flow cytometry/metabolomics: APPPS1ND one animal died, WT-WD one animal died, one did
not undergo experiments, APPPS1-WS one animal died,and two did not undergo experiments. Only flow cytometry: two WAT probes were contaminated during preprocessing. In all experiments, mice underwent imaging
in alternating sequence and ex vivo experiments in mixed
groups of wild-types and transgenics as well as sex.
Radiotracer synthesis

Briefly, using the 18O(p,n)18F nuclear reaction fluorine-18
was produced as [­18F]fluoride by proton irradiation of­[18O]H2O (Rotem, Leipzig, Germany) at the Tübingen
PETtrace cyclotron (GE Healthcare, Uppsala, Sweden).
[18F]FDG was synthesized in a TRACERlab M­ XFDG
synthesizer (GE Healthcare, Liège, Belgium) as described
previously, using mannose triflate (ABX, Radeberg, Germany) as a precursor [49]. Quality control was performed
according to Ph. Eur. guidelines. Particularly, radiochemical purity, as determined by thin-layer chromatography(TLC), was > 95%. Molar radioactivities were > 50 GBq/µmol at the end of synthesis.
[18F]FTHA was synthesized using the method from
DeGrado [50] with modifications on a modified TRACERlab ­FXF-N synthesizer (GE Healthcare, Münster,Germany). Briefly, 2 µL of the precursor benzyl-14-(R,S)tosyloxy-6-thiaheptadecanoate (ABX, Germany) in 1 mLof acetonitrile were reacted with a mixture of aceotropically dried ­[18F]fluoride, 15 mg of Kryptofix 2.2.2. and3.5 mg ­K2CO3 at 110 °C for 5 min. After hydrolysis with
350 µL of 0.14 N KOH (110 °C, 5 min) 0.3 mL of 6.5% sulfuric acid was added for neutralization. The product was
purified using HPLC (Supelcosil ABZ + ; 10 × 250 mm;

Poxleitner et al. Journal of Neuroinflammation(2024) 21:129

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Fig. 1 Study design and weight. a General study design of in vivo and ex vivo experiments. Western diet (WD) or the normal rodent diet (ND)
feeding period started at 2 months of age and continued over 24 weeks. At ~ 8 months, imaging (PET, MRI), flow cytometry, metabolomics,
and histology were performed. b Mean weight gain ± SD between ND-fed (black) and WD-fed (blue) animals over 24 weeks starting on the dayof the diet change. c Mean weight gain between females and males fed an ND (blank white, blue) or WD (striped grey, blue). ***p < 0.001. Weightgain over time using student’s t-test. Comparison of sex and diet with two-way ANOVA, post hoc Tukey corrected for multiple comparisons.
ND, normal rodent diet; WD, western diet; WD (n = 24, male = 12, female = 12), ND (n = 20, male = 11, female = 9)

­H2O/MeOH 80/20 with 1% H
­ 3PO4; 5 ml/min; detection:
UV 216 nm and NaI(Tl)). The product was obtained in
uncorrected yields of 15 ± 5% (n = 13), corresponding
to 9.3 ± 3.3 GBq of isolated ­[18F]FTHA, after irradiations using 35 to 60 µA for 40 to 60 min. Radiochemical
purity as determined by TLC was > 90%. Molar activities
were > 50 GBq/µmol at the end of synthesis.
[18F]GE-180 was synthesized according to Wickstrøm
et al. [51] using a FASTlab synthesizer with single-use
disposable cassettes (GE Healthcare, Germany) according to the manufacturer’s instructions. Quality control
was performed via HPLC, yielding the product in chemical purity above 90% and high molar radioactivity above
600 GBq/µmol at the end of the synthesis.
PET imaging

PET studies were performed in C57BL/6J and APPPS1
littermates of each group (ND and WD) over 4 weeks.
Animals were anesthetized by using isoflurane (carriergas 100% oxygen at 1 L/min, 5% for induction, 1.2–1.5%
maintenance) and body temperature was maintained at
37 °C throughout the studies using mouse beds with temperature feedback control (Medres, Cologne, Germany

and Jomatik, Tuebingen, Germany). All PET scans were
performed using an Inveon dedicated small-animal
microPET scanner (Siemens Healthcare, Knoxville (TN),USA), and scans were acquired dynamically for 60 min,immediately followed by a 14 min [57] Co transmission scan as well as correction of dead time, random
and scatter events. Mice were positioned in the centerof the field of view and injected intravenously (i.v.) intoa lateral tail vein with 12.0 ± 0.3 MBq ­[18F]FDG (WTND n = 10; APPPS1-ND n = 7; WT-WD n = 7; APPPS1WD n = 8), 14.4 ± 2.3 MBq ­[18F]FTHA (WT-ND n = 8;
APPPS1-ND n = 8; WT-WD n = 8; APPPS1-WD n = 7)
and 13.5 ± 2.5 MBq ­[18F]GE-180 (WT-ND n = 8; APPPS1ND n = 7; WT-WD n = 9; APPPS1-WD n = 10) on consecutive days with at least one day of recovery. Mice were
measured with the PET tracers in different order depending on tracer availability. The mice recovered after eachscan on a heating pad in an empty cage, and their healthwas monitored by the researcher.
PET image reconstruction and data analysis

List-mode data for all scans were histogramed in 23
frames (8 × 30 s, 6 × 60 s, 7 × 300 s, and 2 × 450 s) andPoxleitner et al. Journal of Neuroinflammation(2024) 21:129

reconstructed with a two-dimensional ordered subsets
expectation maximization (OSEM2D) algorithm with
an image zoom of 2 and a 256 × 256 matrix using Inveon
Acquisition Workplace (Siemens Healthcare, USA). Volume-of-interest (VOI) and voxel-wise analyses were performed on reconstructed images using PMOD softwarev3.2 (PMOD Technologies, Zürich, Switzerland) andstatistical parametric mapping SPM 12 (Wellcome TrustCenter for Neuroimaging, University College London,
United Kingdom). Individual PET images were co-registered to a predefined mouse brain template MouseMirrione-T2 (PMOD technologies), and a whole-brain
VOI as well as a brain-region specific atlas were applied[52, 53]. The anterior prefrontal cortex area was removed
from the cortex VOI to avoid spill-over effects from theharderian glands. The following brain areas were analyzed: cortex (CTX), hippocampus (HIP), cerebellum(CB), and hypothalamus (HYP). Time activity curves(TACS) were extracted and standardized uptake values(SUVs) for each animal were calculated. For comparisonof uptake in all four groups, the mean SUV was evaluatedbetween 30- and 60-min post injection (p.i.).
For voxel-wise analysis, PET images were automatically overlaid to the Mouse-Mirrione-T2 atlas as reference (PMOD technologies). Differences between groups
for each PET tracer were identified using a general linear model (GLM) available in SPM 12. After estimating GLM, statistical parameter maps were generated byinterrogating the outcome using contrast vectors. A oneway ANOVA without post hoc correction was applied.
Contrasts were compared between groups using nofurther masking or determined voxel clusters. The significance threshold was set for the tracers individually.
Images were prepared using dedicated software (MRIcron [54]).
1

H‑ magnetic resonance spectroscopy (1H‑MRS) of the liver

For 1H-MRS on a 7 T BioSpec 70/30 MR scanner (Bruker
BioSpin GmbH, Ettlingen, Germany) equipped with agradient insert, animals were anesthetized using isoflurane (carrier gas oxygen 100% at 1 L/min, 5% for induction, 1.2–1.5% maintenance). Animal body temperature
was maintained by placing mice on an MR-compatible
water-warmed mouse bed (Jomatik, Tuebingen, Germany). During the whole acquisition, breathing wasmonitored using a specialized MR breathing pad. Mice
were positioned in the center of a 1H volume coil with an
inner diameter of 86 mm. For correct positioning of theliver voxel, an anatomical T2-weighted TurboRARE protocol (TR = 800 ms; TE = 37.63 ms; FOV = 74 × 32 × 18;
image size = 296 × 128 × 72) was acquired. Next, ­B0 map(TR = 30 ms; FOV = 60 × 60 × 60 ­mm3, Averages = 1) was
acquired. After placing the voxel (3 × 3 × 3 ­mm3), avoiding

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major hepatic blood vessels, the localized shim was
acquired resulting in mean shim values of 52.4 ± 13.3 Hz.
For spectral acquisition, a stimulated echo acquisitionmode (STEAM; TR = 1500 ms; TE = 3 ms; averages = 512)with and without water suppression (VAPOR) was used.
All sequences were acquired using Paravision softwarev6.0.1 (Bruker, Ettlingen, Germany).
Spectral analysis was performed using LC Model analysis software v6.3-1L (Stephen Provencher, Oakville, ON,
Canada) [55]. Subsequently, lipid peaks were evaluatedaccording to Ye et al. 2012 [56], and the following lipids
were extracted: Lip09, Lip13, Lip16, Lip21, Lip23, Lip28,
Lip41, Lip43, Lip52, and Lip53. Lipids with a standard
deviation (SD) > 20 were excluded; thus, animal numbers
differ between lipids [57] (Additional file 1: Table S2).
Liver fat composition, including lipid mass (LM); fractional lipid mass (fLM), saturated lipid component (SL),fraction of unsaturated lipids (fUL), fraction of saturated
lipids (fSL), fraction of polyunsaturated lipid (fPUL),
fraction of monounsaturated lipids (fMUL), and mean
chain length (MCL) was calculated as described previously [56]. For LM and fLM lipid peaks Lip13 + Lip16 andLip21 + Lip23 + Lip28 were united to reduce SD below 20
and hence include all animals into the calculation.
Ex vivo experiments

Following the completion of in vivo measurements, themice were anesthetized by using isoflurane (carrier gas
oxygen), and blood was retro-orbitally taken. Then, themice were sacrificed through asphyxiation with ­CO2 andperfused through the left ventricle with 20 mL of cold
phosphate-buffered saline (PBS), and brain and white
adipose tissue (WAT) were removed for ex vivo analysis.
Brain tissue was then either paraffin-embedded for histological analysis or further worked-up for cytometricanalyses.
Flow cytometry

Immune cell isolation was performed as described in
Hoffmann et al. 2019 [58]. Briefly, brains and WAT of theabdominal cavity were isolated and chopped into small
pieces. Tissue was digested for 45 min in 1 mg/mL Collagenase IV (Sigma Aldrich, St. Louis, Missouri, USA) in
DMEM supplemented with 5% FCS and 10 mM HEPES
at 37 °C. Then, the digested tissue was washed througha 70 µm mesh cell strainer with 1% FCS in PBS. Brainhomogenates were centrifuged for 5 min (4 °C, 400 rcf )and resuspended in 70% percoll (in PBS; GE Healthcare, CA, Illinois, USA) layered under 37% percoll solution topped by a 30% percoll solution in a 15 mL Falcon
tube and immediately centrifuged 30 min (4 °C, 800 rcf,
acceleration of 2 and deceleration of 1). The immune
cells, located between percoll layers 70% and 37% afterPoxleitner et al. Journal of Neuroinflammation(2024) 21:129

centrifugation, were isolated and centrifuged for 5 min(4 °C, 400 rcf ). The remaining erythrocytes were lysedwith 3 mL ACK lysing buffer (Lonza, Basel, Switzerland)for 5 min at room temperature. Following washing, thecell suspension was then pipetted into a 5 mL polystyrene tube via a 40 µm cell strainer snap cap (Corning
Inc., Corning, New York, USA). Afterward, isolated
cells from WAT were counted using cell counting chambers (one-way Neubauer counting chambers, C-Chip,
Merck, Darmstadt, Germany). Single-cell suspensions
were first stained with viability stain (Zombie NIR fixable viability kit, BioLegend, San Diego, California,
USA) followed by either BV510-αCD45 (clone: 30-F11),
AF700-αB220 (clone: RA3-6B2), BV605-αCD11b (clone:
M1/70), BV711-αLy6G (clone: 1A8), BV785-αCD11c(clone: N418), PE/Cy7-αI-A/I-E (clone: M5/114.15.2)
and PE-αF4/80 (clone: BM8) or with PE-αCD45.2 (clone104), FITC-αCD3 (clone 500A2), AF700-αCD8 (clone
53–6.7), BV521-αCD25 (clone PC61), BV510-αCD44(clone IM7), PE/Cy7-αCD62L (clone MEL-14), BV650αCD69 (clone H1.2F3), BV785-αCD127 (clone A7R34),
BV711-αPD-1 (clone 29F.1A12). All antibodies were purchased from BioLegend (San Diego, CA, USA). Fc block
reagent (CD16/32, clone 93, BioLegend) was included inboth antibody panels. The staining took 30 min at 4 °C,
and cells were afterward washed three times with PBS(centrifugation 4 °C, 5 min, 400 rcf ), fixed in 0.5% formalin, and analyzed on the BD LSRFortessa flow cytometer (BD Biosciences, Franklin Lakes, New Jersey, USA).
Analysis was performed with FlowJo software v10.0.7(BD Biosciences, USA). The gating strategy for both
antibody panels is shown in Additional file 1: Fig. S1, S2.
Animal numbers for the organs were for brain (WT-ND
n = 11; APPPS1-ND n = 8, WT-WD n = 10, APPPS1-WD
n = 9) and for WAT (WT-ND n = 11, APPPS1-ND n = 8,
WT-WD n = 10, APPPS1-WD n = 7).
Metabolomics

For plasma metabolome analysis, blood was collected in
an EDTA tube and centrifuged at 4 °C (10 min 2200 rpm)
to separate the blood plasma, and aliquots werequenched and snap-frozen with liquid N­ 2. A two-phaseextraction protocol (polar and lipophilic phases) wasapplied according to Eggers and Schwudke[59]. In brief:
Blood plasma was transferred to 2 mL AFA glass tubes(Covaris Inc, Woburn, Massachusetts, USA) and mixed
with ultra-pure water, tert-butyl methyl ether (MTBE,
CAS: 1634–04-4, Sigma-Aldrich Chemie, Taufkirchen,
Germany) and methanol. Plasma metabolites wereextracted using focused ultrasonication (Covaris Inc,
USA) applying the following setup: two treatment cycles,
1st: 30 s, Peak Power 125.0, Duty Factor 32.0, Cycles/
Burst 400, Avg. Power 40.0. 2nd: 30 s, Peak Power 100.0,

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Duty Factor 30.0, Cycles/Burst 800, Avg. Power 30.0.
Temperature range 5.0 to 15.0 °C. Each cycle repeatedfive times per sample, the total run time per sample
was 5 min. Afterwards, the mixture was centrifuged for
5 min, then the polar (water and methanol) phase wasdecanted. The resulting solution was evaporated to dryness in three hours with a vacuum concentrator (SpeedVac: Preset 2, Thermo Fischer Scientific Inc., Waltham,
Massachusetts, USA). Dried pellets of the polar metabolites were resuspended in deuterated phosphate buffer(75 mM ­Na2HPO4, 4% ­NaN3, pH = 7.40) with internal
standard 3-(trimethylsilyl) propionic-2,2,3,3-d4 acidsodium salt (TSP, CAS: 24,493-21-8). For maximum
dissolution, the Eppendorf cups containing solutions
were sonicated and then centrifuged for 5 min aiming
to remove any solid residue. The supernatant was transferred into 1.7 mm NMR tubes, then centrifuged for 30 s
and subsequently placed in a 96-well rack. Samples were
kept cooled (6° C) in the NMR automatic sample handling robot unit—SampleJet (Bruker BioSpin, Karlsruhe,
Germany) until the measurement. NMR spectra wererecorded by a 14.10 Tesla (600 MHz for proton channel)
ultra-shielded NMR spectrometer Avance III HD (Bruker
BioSpin, Karlsruhe, Germany) with installed 1.7 mm TXI
triple resonance microprobe. NMR measurement routine
was performed via a 1D CPMG (Carr-Purcell-MeiboomGill) experiment to suppress residual background signals from remaining macromolecules like peptides (timedomain = 64 k points, sweep width = 20 ppm, 512 scans,
1 h long, temperature 298 K). The recorded free induction decays (FIDs) were Fourier-transformed (FT), andspectra were phase and baseline-corrected.
Bruker TopSpin 3.6.1 software was used for spectra
acquisition and processing (offset correction, baseline,
and phase correction). ChenomX NMR Suite 8.5 Professional (Chenomx Inc., Edmonton, Canada) was used formetabolite annotation and concentration calculation,
additionally, internal ChenomX library was included fora resonance frequency of 600 MHz. The annotated andquantified metabolites of each sample were exported intoa spreadsheet file, containing all molar concentrations(before normalization prior statistical analysis) of the fullcohort and then further processed with statistical tools.
Histology and immunohistochemical reactions

Brains were fixed in 4% formalin for 24 h, dehydrated
and paraffin-embedded following a standard protocol:
70% Ethanol, 80% Ethanol, 95% Ethanol, 100% Ethanol, Xylene, paraffin wax (58‐60 ºC), and finally, embedded into paraffin blocks. The tissue was then sectioned
coronally with a microtome (Microm HS355S Therm
Scientific) and the region of interest demarcated using
the Mouse Brain Atlas. For each group three randomlyPoxleitner et al. Journal of Neuroinflammation(2024) 21:129

chosen specimens underwent histological validation.
For histology, 3–5 µm sections were cut and stainedwith hematoxylin and eosin (H&E). Immunohistochemistry was performed on an automated immunostainer(Ventana Medical Systems, Inc., Oro Valley, Arizona,USA) according to the company’s protocols for open
procedures with slight modifications. In short, sections
were deparaffinized (Xylene, ethanol 100%–95%–80%)
for 8 min and washed. Remaining paraffin was removed
by incubation in methanol peroxide (Sigma-Aldrich,
St Louis, MO, USA) for ten minutes. The sections werethen heated for 20 to 30 min in a 0.01 M citrate-buffered
solution and incubated for two hours in Tris buffer (5%FCS). The sections were then incubated with the primary
antibodies for two hours and washed 10 times in Tris
buffer. Sections were stained with the antibodies CD3(1:50, Clone SP7, DCS Innovative Diagnostik-Systeme
GmbH u. Co. KG, Hamburg, Germany), Iba1 (1:3000,
Clone EPR15688. Abcam, Cambridge, UK), beta-amyloid(1:400, Clone Abeta 42, Synaptic Systems, Goettingen,Germany), NeuN XP (1:400, Clone D4G40, Cell Signaling
Technology, Danvers, Massachusetts, USA) and GFAP
(1:1000, Clone 6F2, Dako, Agilent, Santa Clara, CA,
USA). Labelling with the secondary antibody and development was automated using the BasicDAB or iVIEW
DAB detection kit. Appropriate positive and negative
controls were used to confirm the adequacy of the staining. All samples were scanned with the Ventana DP200(Roche, Basel, Switzerland) and processed with the Image
Viewer MFC Application v.2.3.0. 200 × snapshots weretaken in all samples in the cortex, hippocampus, thalamus, choroid plexus, and hypothalamus. The positive
cells per field for β-amyloid plaques, CD3, GFAP, andIba1 positive cells were determined in three snapshots
per area of interest. The NeuN quantification was determined for two fields (right and left; 600x) in each of thethree cortex cells. NeuN was given as positive cells/total
cells. Final image preparation was performed with Adobe
Photoshop CS6 (final n number: WT-ND n = 2; APPPS1ND n = 3; WT-WD n = 3; APPPS1-WD n = 3).
Statistical analyses

In our study, results for weight, 1H-MRS lipids, blood
glucose, PET tracers, immunological analyses and histologic analyses are presented as mean ± SD. Using Data
were analyzed using GraphPad Prism 9.5.1 (GraphPad
Software LLC, San Diego, USA) to assess normal distribution and to perform various statistical analyses. To
determine statistical significance two-way ANOVA using
multiple comparisons with post hoc Holm-Sidak correction (p-value threshold set to α = 0.05) was applied formean weight gain (sex x diet), metabolomics pyruvate
results (genotype x diet), blood glucose (genotype x diet),

Page 7 of 29

PET tracers (genotype x diet), immunological analyses(genotype x diet). Pearson´s correlation test was utilized
to explore the relationship between cortical uptake of
PET tracers (SUV) and immune cell populations (percentage of viable cells).
For the analysis of 1H-MRS lipids, multiple unpaired
t-tests with post hoc multiple comparison corrections
using Holm-Sidak method (p-value threshold set toα = 0.05) were conducted. SPM12 was used to apply aone-way ANOVA without post hoc correction for voxelwise data analysis.
For metabolomics, MetaboAnalyst 5.0 web server(R-based online analysis tool, www.​metab​oanal​yst.​ca)
was used for metabolite statistical analysis [60]. Additional checks were performed via MetaboAnalyst 6.0 web
server (www.​metab​oanal​yst.​ca). To do so, the full cohort
spreadsheet with all raw metabolite concentrations was
uploaded to the web server. Missing values of metabolites that were below the SNR in one or the other samples
were replaced by a small value (20% of the minimum positive value in the original data). Data was further normalized using probabilistic quotient normalization (PQN)[61] in order to account for dilution effects. Next, Pareto
scaling was applied for variance stabilization (meancentered and divided by the square root of the standard
deviation of each variable). The data was then analyzed
using statistical approaches: one-way ANOVA (analysis
of variance), partial least squares discriminant analysis(PLS-DA) and t-testing. Box plot graphical design (anda two-way ANOVA run) was performed in GraphPad
Prism 9.5.1 (GraphPad Software LLC).
Results were considered statistically significant with a pvalue < 0.05.

Results
Body weight

To assess the effect of long-term consumption of a WD,
WT and APPPS1, animals were fed either ND or a WD
starting at the age of 2 months over 24 weeks (Fig. 1a).
In vivo imaging took place over four weeks and was complemented via flow cytometry, histology and metabolomic experiments (Fig. 1a). Weight monitoring showeda significantly faster weight gain in WD-fed mice compared to ND-fed animals over time (Fig. 1b) with a 2.5fold higher mean weight gain in the WD group (start toend: 8.7 ± 3.8 g) compared to the ND group (start to end:
3.3 ± 1.4 g). Mean weight gain between males and females
did not differ but confirmed dietary effects (Fig. 1c,
Df = 1, F = 54.7, ­pANOVA < 0.001).
Liver fat composition and metabolomics

By using 1H- magnetic resonance spectroscopy (1HMRS), we next aimed to assess liver fat composition

Poxleitner et al. Journal of Neuroinflammation(2024) 21:129

non-invasively by spectral analysis (Additional file 1:
Fig. S3). 1H-MRS analyses revealed higher lipid fractions with different chain lengths in WD-fed animals(Fig. 2b, absolute values presented in Additional file 1:
Table S3). We observed a tenfold higher calculated lipid
mass (ND: 0.36 ± 0.15; WD: 3.35 ± 2.00) and fractional
lipid mass (ND: 0.48 ± 0.19; WD: 0.88 ± 0.09) in WDtreated mice (Fig. 2c, Additional file 1: Table S3). Interestingly, among the unsaturated lipid components, only
the calculated fraction of polyunsaturated lipids (fPUL)
was significantly smaller in WD livers (ND: 0.29 ± 0.10;
WD: 0.12 ± 0.10), whereas the saturated lipids (SL andfSL) did not differ between both diets (Additional file 1:
Table S3). Thus, WD leads to a higher accumulation ofhepatic lipids. Overall, MR images indicated higher body
fat accumulation including abdominal and subcutaneousfat (Fig. 2d).
Plasma metabolite profiles were examined by NMR
metabolomics. In total, 24 serum (timepoint 8-months)
metabolites from various metabolite classes, such asamino acids, ketone body—3-hydroxybutyrate (3-HB),
energy metabolites, and short-chain fatty acids wereidentified (all metabolomics NMR based summary isreported in the Additional file 1: Table S4; with fitting
signals characteristics; chemical shifts (ppm), and normalized (concentrations) averages with corresponding
SDs (standard deviations)). We observed a substantialchange for pyruvate in APPPS1-WD animals (Fig. 2d;
VIP score = 1.2), with a strong diet effect (Df = 1, F = 34.2,­pANOVA < 0.001), small genotype effect (Df = 1, F = 4.57,­pANOVA = 0.04) and interaction between the two factors (Df = 1, F = 9.70, ­pANOVA = 0.004). All groups, aside
from the ND-WT group, developed significantly higher
amounts of 3-HB and isoleucine (Additional file 1:
Table S5 group comparisons A–C). Further, in the PLSDA analysis and t-test, histidine was drastically loweredin the plasma of the APPPS1-WD group (Additional
file 1: Table S5 group comparison C, VIP score = 0.7).
Moreover, considerable changes were identified for glucose (Additional file 1: Table S5 group comparison B,
VIP score = 4.6). Consistently with the higher accumulation of liver fat, animals showed a peripheral misbalancecaused by overnutrition. Regression model analysis identified more changes within transgenic and wild-type micegroup comparison (Additional file 1: Table S5 groups
comparisons A, C). Here, we found lower levels of citrateand succinate—important TCA cycle metabolites, theamino acids phenylalanine and tyrosine, and creatine intransgenic animals.
Cerebral glucose metabolism

To examine changes in cerebral glucose metabolism, weused ­[18F]FDG-PET. Mean blood glucose values were

Page 8 of 29

measured before the imaging and did not differ between
groups (Additional file 1: Fig. S4). Representative imagesof axially positioned brains of each group showed higher­[18F]FDG uptake in the APPPS1-WD group. In contrast,
no changes between the other groups were observed
visually (Fig. 3a). Mean SUV of [­18F]FDG in the wholebrain displayed an effect between genotypes and the diets(Table 1a) with pronounced values in WD-fed APPPS1
mice compared to the other conditions (Fig. 3b). No
significant interaction in [­18F]FDG SUV between theother groups could be seen. Further region-based quantification highlighted similar effects for the cortex andhippocampus (Table 1a) with differences between the
APPPS1-WD group and the other groups, underlining an
overall brain effect. Other analyzed regions showed dieteffects only (Table 1a). Voxel-wise analysis confirmed awhole-brain effect in the APPPS1-WD group (Fig. 3c).
A minor difference in ­[18F]FDG accumulation between
WT-ND and WT-WD could be seen in anterior areas,
which could not be identified in the prior quantification
procedure, indicating a minor effect of WD on [­ 18F]FDG
in the anterior region of healthy brains. Together with the
VOI-based results, ­[18F]FDG showed high uptake in the
WD-fed amyloid mouse model.
Cerebral fatty acid metabolism

Next, we investigated the impact of WD on fatty acidmetabolism in vivo using the long-chain fatty acid analog­[18F]FTHA. Axial brain images representing ­[18F]FTHA
uptake in all groups displayed a higher brain uptake
in WD-fed groups (Fig. 4a). VOI-based whole-brain
mean SUVs confirmed a significantly higher [­18F]FTHA
uptake in WD-fed animals, both in WT and APPPS1
animals with no differences between genotypes (Fig. 4b,
Table 1b). Segmentation of the brain regions could nothighlight any region-specific statistical difference in [­ 18F]
FTHA accumulation, suggesting a whole-brain effect.
Overall, the higher uptake of ­[18F]FTHA in WD-fed
mice was independent of the genotype in all observed
brain regions. Further data analysis on voxel level showed
higher signals in WD-fed animals over ND-fed animals inall observed regions in the brain but did not support theidentification of more prominent areas (Fig. 4c).
Neuroinflammation

Next, we aimed to investigate the influence of WD on
neuroglial reactivity in WT and APPPS1 mice using
the TSPO-PET tracer ­[18F]GE-180. Whole-brain SUV
analysis showed that ­[18F]GE-180 accumulation was
significantly elevated in APPPS1 brains, with small
effects of the diet (Fig. 5a, Table 1c), pointing to a genotype-dependent effect. Further region-specific analysis pointed at significant radiotracer accumulation

Poxleitner et al. Journal of Neuroinflammation(2024) 21:129

Page 9 of 29

Fig. 2 MR-based lipid analysis and metabolomics. 1H MRS of hepatic lipid composition and metabolomic results between ND and WD-fed
animals. a single lipids are depicted according to their chemical shift, indicating changes between ND (white, circles)- and WD-fed animals (blue,
triangles). b Calculated lipid compositions using the single lipid peaks. c Exemplary contrast-normalized T2-weighted images illustrating fat depots
in ND- and WD-fed mice. Subcutaneous fat is marked with yellow arrows; abdominal fat is marked with green arrows. d Box plot of pyruvatechanges between the four mice groups. where the following components are defined: center line, median; box limits, 25–75 percentiles; whiskers,
minimum to maximum; and all points are shown. WT-ND n = 11 (male = 7, female = 4), APPPS1-ND n = 8 (male = 4, female = 4), WT-WD n = 10
(male = 4, female = 6), APPPS1-WD n = 9 (male = 4, female = 5). 1H MRS results were analyzed using multiple unpaired t-tests with post hoc multiple
comparison corrections using the Holm-Sidak method (p-value threshold set to α = 0.05). Metabolomics with two-way ANOVA, post hoc Holm-Sidak
correction for multiple comparisons. fLM, fractional lipid mass; SL, saturated lipid component; fUL, fraction of unsaturated lipids; fSL, fractionof saturated lipids; fPUL, fraction of polyunsaturated lipids; fMUL, fraction of monounsaturated lipids; L, liver; Statistical significance: *p < 0.05,**p < 0.01, ***p < 0.001

Poxleitner et al. Journal of Neuroinflammation(2024) 21:129

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Fig. 3 [18F]FDG-PET imaging. a Comparison of axial brain images of ­[18F]FDG uptake in all four groups indicates a higher uptake in APPPS1-WD
mice. Regions are indicated as follows: green = WB; red = CTX; yellow = HIP; blue = HYP. b mean SUV (30–60 min p.i.) in WB, CTX, HIP, CB, and HYP
between all groups. c T-maps comparing SUVs are shown with threshold p < 0.01. Individual data points are shown and displayed with mean
and SD. WT-ND n = 10 (male = 6, female = 4), APPPS1-ND n = 7 (male = 3, female = 4), WT-WD n = 7 (male = 4, female = 3), APPPS1-WD n = 8(male = 5, female = 3). *p < 0.05, **p < 0.01, ***p < 0.001, post hoc Holm-Sidak corrected for multiple comparisons; WB, whole brain; CTX, cortex;
HIP, hippocampus; CB, cerebellum; HYP, hypothalamus

Table 1 Two-way ANOVA results representing degree of freedom (Df ), F-value (F), and p-value for PET results divided into factors‘genotype’ and ‘diet’DfF

p-valuea ­[18F]FDG
WB

Df

F

p-valueb ­[18F]FTHA

Df

F

p-value< 0.001***

C ­[18F]GE-180

Genotype1

5.289

0.029*

1

0.453

0.507

1

25.420Diet

112.6500.001**

1

28.850< 0.001***

1

3.510

0.071

CTX

Genotype1

9.291

0.005**

1

1.383

0.250

1

47.440< 0.001***

Diet

1

15.630< 0.001***1

23.040< 0.001***

1

5.237

0.029*

HIP

Genotype1

6.770

0.015*

1

0.657

0.425

1

33.150< 0.001***

Diet

1

12.3600.002**

1

25.790< 0.001***

1

3.942

0.056

CB

Genotype

1

3.042

0.092

1

1.441

0.240

1

5.541

0.025*

Diet

1

11.8700.002**

1

27.610< 0.001***

1

3.013

0.093

HYP

Genotype

1

3.410

0.075

1

0.086

0.772

1

3.486

0.072

0.018*

1

25.970< 0.001***1

13.300< 0.001***

Diet

1
18

6.365
18

18

Results are shown for a ­[ F]FDG, b [­ F]FTHA, and c [­ F]GE-180
WB, whole brain; CTX, cortex; HIP, hippocampus; CB, cerebellum; HYP, hypothalamus*p < 0.05, **p < 0.01, ***p < 0.001

Poxleitner et al. Journal of Neuroinflammation(2024) 21:129

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Fig. 4 [18F]FTHA-PET imaging. a Exemplary axial brain images of ­[18F]FTHA uptake display higher uptake in WD-fed mice irrespective of genotype.
Regions are indicated as follows: green = WB; red = CTX; yellow = HIP; blue = HYP. b Mean SUVs (30–60 min p.i.) in WB, CTX, HIP, CB, and HYPfor ­[18F]FTHA. c Comparison of voxel-wise analysis. The threshold was set to p < 0.05. Individual data points are shown and displayed with mean
and SD. WT-ND n = 8 (male = 5, female = 3), APPPS1-ND n = 8 (male = 4, female = 4), WT-WD n = 8 (male = 5, female = 3), APPPS1-WD n = 7(male = 4, female = 3). *p < 0.05, **p < 0.01, ***p < 0.001, post hoc Holm-Sidak corrected for multiple comparisons; WB, whole brain; CTX, cortex;
HIP, hippocampus; CB, cerebellum; HYP , hypothalamus

differences in the CTX and HIP, regions typicallythe most affected by amyloidosis (Fig. 5b, Table 1c).
Regions with less to no amyloid burden, however,showed small genotype effects and none to very highdiet effects (Table 1c). Tracer uptake over time revealed
a higher injection peak in transgenic animals comparedto wild-type animals for cortex, whereas for cerebellum
peaks did not differ (Fig. 5d, e). Ratios between cortex
and cerebellum as well as hippocampus and cerebellum
showed significantly higher values in APPPS1 brains

than in WT (Fig. 5f ). However, no differences between
diets were observed. Further voxel-wise comparison
highlighted the main ­[18F]GE-180 accumulation differences between WT and APPPS1 brains to be located in
CTX and HIP primarily (Fig. 5c). Pearsons’s correlation
have shown that there is no correlation between thewhole brain tracer uptake except for ­[18F]FDG and [­ 18F]
FTHA in APPPS1-ND group.

(See figure on next page.)
Fig. 5 [18F]GE-180-PET imaging. a Higher uptake of ­[18F]GE-180 in APPPS1 mice compared to WT shown in representative axial brain images.
Colored outlines illustrate the analyzed brain regions green = WB; red = CTX; yellow = HIP; blue = HYP. b Mean SUVs (30–60 min p.i.) in WB, CTX,
HIP, CB, and HYP for ­[18F]GE-180 in all groups. c Representative images of voxel-wise analyzed SUVs are shown with threshold p < 0.01. Individual
data points are shown and displayed with mean and SD. WT-ND n = 8 (male = 5, female = 3), APPPS1-ND n = 7 (male = 3, female = 4), WT-WD n = 9(male = 5, female = 4), APPPS1-WD n = 10 (male = 5, female = 5). *p < 0.05, **p < 0.01, ***p < 0.001; post hoc Holm-Sidak corrected for multiple
comparisons; WB, whole brain; CTX, cortex; HIP, hippocampus; CB, cerebellum; HYP, hypothalamus

Poxleitner et al. Journal of Neuroinflammation

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Poxleitner et al. Journal of Neuroinflammation(2024) 21:129

Immune cell presence in the brain

In order to uncover the potential immune changes
underlying the brain metabolism and to go beyond glial
activation as a marker for brain inflammation as seen
by in vivo PET, we investigated changes in immune cell
infiltration in mice brains. Leukocytes were extracted
from brains and sorted between innate and adaptive
immune cells by two individual antibody panels, making
it possible to check for subtypes. Brains of the APPPS1WD group had less ­CD11b+Ly6G+ neutrophils (Fig. 6a;
WT-ND vs. APPPSS1-WD p = 0.02; APPPS1-ND vs.
APPPS1-WD p = 0.02) than ND brains showing a strong
diet effect in this group (Table 2a). No differences could
be detected for ­CD11b+ myeloid cells, ­CD11b+F4/80+
macrophages, and dendritic cells (­ CD11c+MHCII+ DCs)
between the groups, however, ANOVA revealed a dieteffect in DCs (Table 2a). Similar results were observed
for the WT-WD group (­CD11b+Ly6G+ neutrophils:
APPPS1-ND vs. WT-WD p = 0.05). When investigating T
cell infiltration (Fig. 6b), the APPPS1-WD group showeda tendency of increased number of C
­ D3+ cells compared to WT-ND controls (p = 0.0524) and showed a dieteffect (Table 2b). Further discrimination between ­CD8+
cytotoxic T cells and C­ D8− T cells revealed elevated+­CD8 T cells in APPPS1-WD brains compared to WTgroups (APPPS1-WD vs. WT-ND p = 0.01; vs. WT-WD
p = 0.02). No difference within the groups was observedfor ­CD25+CD127− regulatory T cells (Tregs), but tendencies towards diet involvement (Table 2b). B cell populations did not differ either (Fig. 6g). To determine the Tcells’ possible function in the brain, we next investigatedthe T cell subtypes. Here, WD-fed animals had a higher­CD8− T cell effector memory ­(TEM) phenotype, whereas
central memory (­TCM) and naive T cell populations did
not change (Fig. 6c, Table 2c). Moreover, these groups
had a higher proportion of C
­ D69+ lymphocytes, indicating activation, compared to WT-ND controls (Table 2c).
In comparison, ­CD8+ T cell subpopulations were elevated only in the APPPS1-WD group (Fig. 6d). Here,a higher percentage of effector memory T cells (­TEM)

Page 13 of 29

compared to the other groups was detected (Fig. 6d;
WT-ND versus APPPS1-WD; p = 0.02; APPPS1-ND versus APPPS1-WD p = 0.04; WT-WD versus APPPS1-WD
p = 0.009) and a trend towards higher ­CD69+CD44+ activated effector population compared to the WT groups
emerged (Fig. 6d, WT-ND versus APPPS1-WD p = 0.05;
WT-WD versus APPPS1-WD p = 0.04). The immune
checkpoint ­PD1+ revealed no differences in C
­ D8− profile(Fig. 6c, Table 2c), but a genotype effect in C
­ D8+ profile(Fig. 6d, Table 2d). While we detected no pronounced
infiltration of innate immune cells, these results indicatethat WD initiated T cell involvement which displayed aneffector state. Correlations between PET tracers and significant immune cell populations in brain revealed distinct positive association between and CD8-TEMs and­[18F]FDG cortices ­(R2 = 0.611; p = 0.007) and ­[18F]GE-180
cortices ­(R2 = 0.611; p = 0.026), but no overall correlation
pattern between tracers and other immune cell populations (Additional file 1: Table S6).
Immune cell population in white adipose tissue (WAT)

One major hallmark of obesity-induced inflammation isthe accumulation and activation of macrophages in adipose tissue [12]. Therefore, we next investigated changes
in T cells and myeloid cells in WAT by flow cytometry(Fig. 6e and f ). T cell populations in WAT between
groups were not different, but showed a diet effect in­CD8− populations and Tregs (Fig. 6e, Table 2e). Additionally, B cell populations were significantly higher in
WAT of obese compared to lean animals irrespective of
their genotype (Fig. 6h).A substantial elevation of macrophage marker F4/80+ was detected in both WD-fed
groups (Fig. 6f ). Further examination of the pro-inflammatory macrophage M1 phenotype using ­CD11c+ [62]displayed higher populations in WD groups. The ratioof M1 to M2 F4/80+ macrophages was shifted towards
a higher M1 portion in WD-fed animals (Fig. 6i). To
ensure that we see dendritic cells (DCs) and not M1 macrophages as all APCs express MHCII and CD11c in WAT,
the DC population was additionally gated negative for(See figure on next page.)
Fig. 6 Immune cell analysis of brain and WAT. Brain and WAT immune cell population displayed as the % of viable cells. a Brain myeloid cells show
only minor changes. b ­CD3+ T cells and ­CD8+ T cells are significantly higher in APPPS1-WD mice compared to non-treated WT animals. c ­CD8− Tcell subpopulations reveal higher effector memory T cells ­( TEM) and higher activated effector T cells in WD groups. d ­CD8+ T cell populations show
higher effector memory and activated effector T cell phenotype, but only in APPPS-WD animals. e WAT myeloid cell population displays significantly
higher ­CD11b+F4/80+ macrophages, inflammatory M1 macrophages ­(CD11b+F4/80+CD11c+), and ­CD11c+MHCII+ DCs in WD-fed groups. f T
cell populations in WAT reveal no differences between groups. g Flow cytometry results show no changed B cell populations in the brain, but hsignificantly elevated in WAT of WD-fed mice. M1/M2 ratio is higher in WD-WAT (i). Results in mean ± SD; *p < 0.05, **p < 0.01, ***p < 0.001, post hoc
Holm-Sidak corrected for multiple comparisons; Brain (a-d; g): WT-ND n = 11 (male = 7, female = 4), APPPS1-ND n = 8 (male = 4, female = 4), WT-WD
n = 10 (male = 6, female = 4), APPPS1-WD n = 9 (male = 5, female = 4). WAT (e; f; i): WT-ND n = 11 (male = 7, female = 4), APPPS1-ND n = 8 (male = 4,
female = 4), WT-WD n = 10 (male = 6, female = 4), APPPS1-WD n = 7 (male = 3, female = 4). *p < 0.05, **p < 0.01, ***p < 0.001. DC, dendritic cells;­TCM, central memory T cells; ­TEM = effector memory T cells; Tregs = regulatory T cells

Poxleitner et al. Journal of Neuroinflammation

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Poxleitner et al. Journal of Neuroinflammation(2024) 21:129

Page 15 of 29

Table 2 Two-way ANOVA results showing degree of freedom (Df ), F value (F), and significance (p-value) for the flow cytometry data
divided into factors ‘genotype’ and ‘diet’
FactorDf

Fp-valueaBrain myeloid cells

CD11b+cells

Genotype

1

3.095

0.088

Diet

1

0.435

0.514

CD1b+F4/80+cells

Genotype

1

1.526

0.225

Diet

1

0.882

0.354

CD11b+Ly6G+cells

Genotype

1

0.016

0.898

Diet

1

16.710< 0.001***

CD11c+MHCII+DCs

Genotype

1

0.355

0.555

DIET

1

5.853

0.021*c

Brain CD8− T cell

CD62L+CD44+TCM

Genotype

1

0.025

0.876

Diet

1

5.279

0.028*

CD62L−CD44+TEM

Genotype

1

0.225

0.639

Diet

1

55.500< 0.001***

CD62L+CD44−naive

Genotype1

4.987

0.032*

Diet

1

2.337

0.136

CD69+CD44+effector

Genotype

1

0.007

0.935

Diet

1

14.400< 0.001***

PD1+

Genotype

1

0.036

0.851

Diet

1

2.939

0.096

e

WAT T cells

CD3+cells

Genotype

1

0.067

0.798

Diet

1

4.090

0.052#

CD8−cells

Genotype

1

0.136

0.715

Diet

1

8.452

0.007**

CD8+cells

Genotype

1

0.005

0.944

Diet

1

0.031

0.861

Genotype

1

0.00

0.990

Diet

1

9.480

0.004**

Tregs

factorDf

Fp-valuebBrain T cells

CD3+cells

Genotype

1

2.304

Diet

1

5.279

0.028*

CD8−cells

Genotype

1

0.478

0.494

Diet

1

6.554

0.015*

CD8+cells

Genotype1

6.360

0.017*

Diet

1

3.735

0.062#

Tregs

Genotype

1

0.915

0.346

Diet

1

3.640

0.065#dBrain CD8+ T cell

CD62L+CD44+TCM

Genotype

1

1.569

0.219

Diet

1

1.647

0.208

CD62L−CD44+TEM

Genotype1

5.910

0.021*

Diet

1

3.071

0.089

CD62L+CD44−naive

Genotype1

4.387

0.044*

Diet

1

0.079

0.781

CD69+CD44+effector

Genotype1

5.622

0.024*

Diet

1

1.922

0.175

PD1+

Genotype1

4.426

0.043*

Diet

1

0.320

0.575

f

WAT myeloid cells

CD11b+cells

Genotype

1

1.944

0.173

diet

1

7.339

0.011*

CD11b+F4/80+cells

Genotype

1

1.003

0.324

Diet

1

36.400< 0.001***

M1 macrophages

Genotype

1

0.993

0.326

Diet

1

52.810< 0.001***

Genotype

1

2.505

0.123

Diet

1

13.370< 0.001***

CD11c+MHCII+F4/80−DCs

0.138

The results are presented: (a) brain myeloid cells, (b) brain T cells, (c) brain CD8- T cell subpopulations, (d) brain CD8 + T cell subpopulations, (e) WAT T cells and (f ) WAT
myeloid cells
DC, dendritic cells; ­TCM, central memory T cells; ­TEM, effector memory T cells; Tregs, regulatory T cells*p < 0.05, **p < 0.01, ***p < 0.001

F4/80. DC populations were elevated in WD groups, inwhich WT-WD showed the greatest differences (Fig. 6f ).
Interestingly, all examined populations had a strong dieteffect in ANOVA analyses (Table 2f ).
Immunohistochemistry (IHC)

For all analyzed brains, H&E staining revealed no morphological differences between groups (Figs. 7–11). To
clearly differentiate between changes between neurons
and glial cells, and further examine morphological alterations, NeuN staining was performed. The NeuN IHC
revealed mild differences in the APPPS1 retrosplenial
areas with a smaller percentage of N
­ euN+ cells without
an influence of the diet (Figs. 7–11b). No differences weredetected between groups in the primary somatosensory
and dorsal auditory areas (Additional file 1: Fig. S5).
Amyloid plaque load in transgenic animals was high,mainly in the CTX (Fig. 7c) and THA (Fig. 8c), fewer wereidentified in the HIP (Fig. 9c), only a few animals showedplaques in HYP (Fig. 10c) and none were detected in choroid plexus (CP, Fig. 11c). In WD-fed groups, we could
not observe that the diet increased plaque load in theinvestigated brain regions CTX, THA, HIP and HYP.
To clarify possible differences in glial reactivity patterns
upon diet and validate our in vivo results, brains wereanalyzed for Iba-1, a microglia marker (Figs. 7–11d and
GFAP, an astrocyte marker (Figs. 7–11e). In transgenic
animals, the morphology of microglia changed from aPoxleitner et al. Journal of Neuroinflammation(2024) 21:129

thin and ramified structure of spines to an amoeboid
structure, confirming a classical activated-defined phenotype of microglia with higher cell numbers in CTX(Fig. 7), THA (Fig. 8), HIP (Fig. 9), and HYP (Fig. 10)
for transgenic animals. No differences were observed
between the diets for the investigated regions, consistent with the observed in vivo results. Iba-1 microglia
were highly reactive in regions of high Aβ positive plaqueload (Figs. 7–10c, d), whereas they were less activatedin regions with few to no plaques (Fig. 8, red box). The
GFAP IHC revealed that no G
­ FAP+ cells were present in
the CTX (Fig. 7e), THA (Fig. 8e), and HYP (Fig. 10e) of
the WT animals, independently of the diet received. In
contrast, the APPPS1 animals showed G­ FAP+ cells in
the above-mentioned areas, without a strong influenceof the diet. Interestingly, the diet did affect the numberof ­GFAP+ cells in the HIP (Fig. 9e). The WT animals fed
with ND showed few positive cells, while more positive
cells were detected in the WT and APPPS1 animals fed
with the WD, but also APPPS1 animals fed a ND. Additionally, in transgenic animals G­ FAP+ cells showed areactive phenotype with thicker and shorter dendrites(Fig. 7e). To confirm flow cytometric T cell infiltration in the brain parenchyma, we stained for infiltrating­CD3+ T cells and found more T cells in APPPS1-WD
brains compared to the other groups in CTX (Fig. 7f ) as
well HIP (Fig. 9f ) and HYP (Fig. 10f ). Furthermore, forsome animals we found a high number near the choroid
plexus, the main entry site of peripheral T cells and B
cells (Fig. 11), however intragroup variability was high. In
wild-type animals, isolated T cells were observed independent of the diet.

Discussion
The aging of the general population in Western countries is accompanied by an increase in the prevalenceof dementia, and it is suspected that the increase inoverweight and obesity exacerbates this challenge inpublic health. The suspected underlying link involves
a general chronic inflammatory state of the patient
called metaflammation, but further work is required

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to understand the intricacies governing cerebral metabolic disruption and unbalanced diets. In this work, weinvestigated such potential interaction first by using noninvasive imaging techniques to identify molecular andmetabolic dysfunctions in different organs in vivo. In
our experiments involving wild-type mice and a murine
amyloidosis model, a surrogate for AD progression, mice
were fed a WD upon manifestation of amyloid pathology,
covering the preclinical early to mid-life period when
pathophysiological changes can already be detected [63].
We, therefore, used an amyloidosis mouse model which
is well described [45, 64] and represents an un-modifiable
AD risk factor. Importantly, our model does not include
age-related effects on the brain and periphery induced by
the so-called inflammaging, a phenomenon proposed to
be a low-grade systemic inflammatory process [65] alsofavoring age-related diseases like AD [66]. We chose forthis study WD which is known to mimic the nutritionof Western countries with high fat and high sugar content together with simple carbohydrates and a shifted fat
composition towards saturated fatty acids [67]. This led
to a significant weight gain increase when fed over six
months in both, male and female mice.
As the liver is one of the organs heavily affected by ahigh-caloric diet leading to systemic disruption andmetabolic imbalance, we wanted to monitor the gradeof fatty liver syndrome in WD-fed animals. Via noninvasive proton magnet resonance spectroscopy (1HMRS), we detected markedly elevated lipid levels in thelivers of WD-exposed mice. Similar results could also be
detected in the livers of patients already after a 2-week
HFD [68]. 1H-MRS revealed significantly higher lipid
mass and fractional lipid mass together with lower levelsof polyunsaturated lipids in WD compared to ND livers,
which mirrors the evolution of liver composition in rats
fed with a high-fat diet [69]. In another study, comparing leptin-deficient ob/ob mice to controls, a decrease inpolyunsaturated lipids and an increase in saturated lipids
were measured in this obesity-only model excluding
dietary impact [56]. However, we could not detect differences in saturated lipids between the diets. Even though(See figure on next page.)
Fig. 7 Histological analysis—cortex. Histological staining results are depicted in representative images per group per staining staining for corticesof all groups. a H&E staining between groups as overview (Scale bar 3 mm) and magnification (Scale bar 150 µm). The black rectangular depicts
magnification area. b NeuN staining reveals no differences between groups. c Amyloid plaques stained specifically with Aβ1-42 antibody were visible
in APPPS1 animals (Scale bar 150 µm). d Microglia staining using Iba-1 as a marker shows a ramified/resting phenotype in WT brains, whereas
activated amoeboid phenotype of microglia in transgenic AD animals was observed (Scale bar 150 µm). e GFAP staining shows reactive astrocytes
in APPPS1 animals in CTX. No GFAP + cells were found in WT animals (Scale bar 150 µm). f ­CD3+ staining revealed T cells in APPPS1 cortices (Scalebar 100 µm). g Mean ± SD Aβ1-42 positive plaques in cortices show plaque load in transgenic mice but no difference between diets. h Mean ± SD
Iba-1+ cells are elevated in APPPS1 cortices independent of the diet. i No mean ± SD ­GFAP+ cells in WT mice, but elevated diet-independently
in APPPS1 mice. j Higher mean ± SD CD3 positive T cells in APPPS1-WD group compared to the other groups. CTX = cortex; HIP = hippocampus;
HYP = hypothalamus. WT-ND n = 2; APPPS1-ND n = 3; WT-WD n = 3; APPPS1-WD n = 3

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some assume a homogenous hepatic fat distribution [70],others report heterogeneous hepatic fat distribution following HFD [71, 72], making it likely that the spectra of
the positioned single voxel do not reflect the whole livercondition. The changes in single hepatic lipid peaks andlipid fractions after WD endorse the impact of the diet onliver fat accumulation, even after 6 months of WD.
Metabolite analysis of plasma revealed significant differences for pyruvate, 3-hydroxybutyrate (3-HB), histidine, and isoleucine. 3-Hydroxybutyrate has been shown
to function in rodents as an anti-AD drug [73] and neuroprotective agent [74]. In our study, the levels were
found to be highest in ND-fed transgenic animals, but wefound high levels in WD-fed animals too. Moreover, levels decline in WD-fed APPPS1 animals, which we foundto have high brain glucose uptake. Together with a contradicting study, which showed 3-HB to be high in 3xTg
animals but low in HFD-fed animals and was associatedwith glucose metabolism compensation [75], 3-HB might
be an important plasma marker to indicate brain glucose
metabolism changes. Administration of histidine to micebrains has been postulated to alleviate chronic effects ofhypoperfusion by, among other, improving BBB integrity[76]. Even further, histidine application is associated witha neuroprotective role in AD [77]. Here, the lower levels
only in APPPS1-WD animals might indicate the acceleration of detrimental processes in this group and combination with other plasma markers (3-HB, pyruvate) could
be used to specify central metabolic disruptions further.
In our in vivo imaging approach, we investigated dietinduced effects on brain metabolism. By using glucose
and long-chain fatty acid surrogates, we could monitor
brain metabolism alterations induced by diet in wildtypes and APPPS1 mice. ­[18F]FDG brain uptake washigher in WD-fed APPPS1 mice compared to the otherconditions, indicating that WD may lead to higher glucose transport activity in AD transgenic animals. Thisregional uptake of [­ 18F]FDG across the brain is a measureof glucose transport into cells and has to be distinguishedfrom glucose oxidation, which refers to the metabolic
processing of glucose once it is inside the cell. ­[18F]
FDG is a glucose analog actively transported into cells

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primarily via glucose transporters such as GLUT1 andGLUT3, abundantly expressed at the BBB, astroglia andneurons. Therefore, ­[18F]FDG uptake as measured in ourstudy serves as an indicator of glucose transport activity rather than direct glucose metabolism or oxidation
within brain tissues. Analysis of the voxel level confirmed
a whole-brain effect in APPPS1-WD animals. As studies
have already shown a positive correlation between the­[18F]FDG and [­ 18F]GE-180 signal in aging wild-type mice
assuming higher glucose demand due to higher glial reactivity [78], a comparison of both tracers revealed in ourstudy no such correlations in all groups. This was complemented by Iba-1 staining in brain tissue. Althoughobtained using a different diet and model, these results
can be compared to studies done using mice infusedwith human Aβ42 while fed an HFD over 3 months [79].
The authors could show a ­[18F]FDG hypermetabolism
when diet and Aβ were combined and saw no association between TSPO signal and glucose uptake, assumingthat gliosis is not the only player in diet-induced neuroinflammation. Longitudinal assessment of [­18F]FDG brain
uptake in the same transgenic model has been shownto not differ from controls in mid-age, similar to our
results, but decrease with advanced age [80], assumingthat the consumption of a WD can initiate hypermetabolism in this amyloid model. Interestingly, Ashraf et al.
claimed that the hypermetabolic phase they observed inpatients with mild cognitive impairment (MCI) mightreflect a compensatory neuroplastic mechanism of neurons, which, when overstimulated, could exhaust thereby
accelerating the degenerative process [81]. Thus, the high­[18F]FDG uptake that we see in the APPPS1-WD group
may represent a transient process of a neuronal compensatory response that eventually leads to neuronal deathand cognitive decline as a consequence of diet-induced
obesity (DIO) and/or dietary components. Even further,immunohistochemistry indicated higher numbers of
GFAP + cells in the hippocampi of APPPS1-WD animals
compared to the other groups assuming that the abovementioned transient process might not be limited toneurons but could be supported by higher glucose consumption of astrocytes in this region [82]. Furthermore,(See figure on next page.)
Fig. 8 Histological analysis—Thalamus. Histological staining results are depicted in representative images per group per staining for the thalamus(THA) in all groups. a H&E staining between groups as overview (Scale bar 3 mm) and magnification (Scale bar 150 µm). The black rectangular
depicts magnification area. b No difference in ­NeuN+ cells between groups. c Aβ1-42 positive plaques were visible in APPPS1 animals (Scalebar 150 µm). d Microglia staining using Iba-1. Ramified/resting phenotype in WT brains and activated amoeboid phenotype of microglia
in transgenic AD animals was observed. (Scale bar 150 µm). e ­GFAP+ cells in APPPS1 animals in THA. No GFAP + cells were found in WT animals.
(Scale bar 150 µm). f ­CD3+ T cells observed in WT-ND and APPPS1 ND and WD (Scale bar 100 µm). g Mean ± SD Aβ1-42 positive plaques load in THA
in transgenic mice. h Mean ± SD Iba-1+ cells are elevated in APPPS1 animals. i No mean ± SD ­GFAP+ cells in WT mice, but elevated in APPPS1 mice. j
Similar mean ± SD CD3 positive T cells in WT-ND, APPPS1-ND and APPPS1-WD group. THA = thalamus. WT-ND n = 2; APPPS1-ND n = 3; WT-WD n = 3;
APPPS1-WD n = 3

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metabolomic analysis revealed elevated pyruvate plasma
levels in the WD-fed transgenic animals complementing the PET results. In addition, metabolomic results
revealed a higher VIP (Variable Importance in Projection) score for glucose; however not significant in statistical analysis.
Many studies could show that direct or indirect (viadiet) supplementation of peripheral fatty acids can activate inflammatory cascades in the brain e.g., via TLRs,
and therefore induce inflammatory processes [82]. We
used the long-chain fatty acid tracer [­ 18F]FTHA to determine fatty acid metabolism when a continuous deliveryof fatty acids is given. Brain uptake revealed indeed adiet-dependent higher fatty acid metabolism, which was
independent of the genotype. In human and pig brains,­[18F]FTHA has been shown to cross the BBB and to represent central fatty acid oxidation [83, 84]. Moreover,in patients with metabolic syndrome, [­18F]FTHA brain
uptake was higher, similar to our results. Studies proposethat high levels of saturated FA could lead to a state of
microglial and astrocytic reactivity [85, 86], so we compared ­[18F]GE-180 signals to the ­[18F]FTHA signals, but
could not detect correlations in VOI-based or overlapping regions in voxel-wise analyses. However, no further
discrimination between normal and diseased brains wasfound in this model using ­[18F]FTHA.
Neuroinflammation was assessed by imaging using
the TSPO tracer ­[18F]GE-180which, despite its limitations (notably non-specific binding and susceptibility togenetic variations in the TSPO gene [87]) revealed higher
uptake in pathology-rich regions of the transgenic brain.
This increased uptake correlates with results obtained
from other studies using other AD models [48, 88, 89]. In
contrast with other studies showing higher glial activity
after energy-rich diets (for a comprehensive overview see[90]), we could demonstrate small diet-dependent variations of G
­ FAP+ cells in the hippocampus in this model.
A recently published study in wild-type female mice
exposed to a long-term HFD found no diet-induced ­[18F]
GE-180 effects in brains supporting the obtained resultsin this study [91]. It is, however, also possible that thefeeding duration or composition of our WD might not

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initiate higher glial inflammation. For instance, in a study
that used two high-caloric diets in the same experimental
set-up, only the diet with high lard content (60% fat) led
to increased neuroinflammation, whereas the WD (40%fat) did not [92]. Furthermore, different durations of HFDseem to employ region-specific inflammatory processes
in the cortex and the cerebellum of mice [93]. It is important to note that the TSPO tracer, ­[18F]GE-180 has been
the subject of an extensive debate as several studies haveshown that the tracer only has notable uptake when the
integrity of the BBB is sufficiently affected by the pathology [94]. Our results show higher brain uptake of ­[18F]
GE-180 in transgenic mice compared to wild-types forthe pathology-rich cortex, which could be related to analtered BBB in WD-fed APPPS1 mice. In DIO rats fed a
WD, an elevated BBB permeability was not observed earlier than 90 days, suggesting a gradual BBB breakdown[95]. Other studies report no increased permeability [93,96]. However, in our study, the Western diet might act as
an additive factor for BBB permeability in amyloid-prone
animals by disturbing brain metabolic balance. Further
investigations need to validate this hypothesis. Systemic
and central alterations caused by the chronic consumption of a high-caloric diet or the AD pathology per se
might affect tracer uptake into the brain. By determining
regional cerebral blood flow (rCBF) no changes in perfusion were reported for our AD model [97] and in mice fed
a WD for 12 weeks [98], even after HFD for six months,
lower perfusion was measured [96]. However, other studies report no increased permeability [93]. In DIO rats fed
a WD, an elevated BBB permeability was not observed
earlier than 90 days, suggesting a gradual BBB breakdown[95]. In our study, WD might act as an additive factor for
BBB permeability in amyloid-prone animals by disturbing brain metabolic balance. However, further investigations need to clarify this hypothesis.
The neuroinflammatory concept is constantly underrevision and extensive work in this field is ongoing[99], supporting evidence in addition to the initial
amyloid cascade hypothesis, that systemic alterations
act as neuroinflammatory drivers by activating inflammatory processes e.g. by immune cell infiltration and(See figure on next page.)
Fig. 9 Histological analysis—Hippocampus. Histological staining results are depicted in representative images per group per staining
for the hippocampus (HIP) in all groups. a H&E staining between groups as overview (Scale bar 3 mm) and magnification (Scale bar 150 µm). The
black rectangular depicts magnification area. b No difference in ­NeuN+ cells between groups. c Aβ1-42 positive plaques were visible in APPPS1
animals (Scale bar 150 µm). d Microglia staining using Iba-1. Ramified/resting phenotype in WT brains and activated amoeboid phenotype
of microglia in transgenic AD animals was observed. (Scale bar 150 µm). e ­GFAP+ cells in HIP of APPPS1 animals and WT-WD. (Scale bar 150 µm).
f ­CD3+ T cells observed mainly in APPPS1 hippocampi (Scale bar 100 µm). g Mean ± SD Aβ1-42 positive plaques load in HIP in transgenic mice. hMean ± SD Iba-1+ cells were elevated in APPPS1 animals. i Mean ± SD ­GFAP+ cells elevated in WT-WD and in APPPS1 mice. j S Higher mean ± SD CD3
positive T cells in APPPS1-WD group compared to the other groups. HIP = hippocampus. WT-ND n = 2; APPPS1-ND n = 3; WT-WD n = 3; APPPS1-WD
n=3

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activation. To determine immune cell involvement in
the brain, we examined infiltration of innate immune
cells as they have been shown to invade brains after
HFD treatment [39] as well as in AD-prone mice models [42]. Except for lower ­Ly6G+ populations, no higher
infiltration of innate immune cells was detected, but
we could find a higher number of ­C D3+ cells in thegroup of APPPS1-WD whose infiltration we validated
via histological staining. Further discrimination into
­C D3+CD8− T helper cells and C­ D3+CD8+ cytotoxic T
cells revealed significantly higher cytotoxic T cells in
the WD-fed transgenic animals compared to the wildtypes. In patients of advanced stages of AD, a study
could also find a higher ­C D3+ T cell population inbrains, which were CD8 positive [100]. A recent study
in male rats reported higher C­ D8+ T cell infiltrationin aged rats fed a three-day HFD but no higher number of C
­ D4+ T cells [101]. We could not identify T cells
near plaques in our model, which is in line with otherreports that have shown T cells to be present in mousemodels of AD, but could not see interaction with theplaques or tau pathology [102, 103]. The role T cells
play in neurodegenerative disease and which mechanisms the infiltrating T cells initiate once they residein the parenchyma is still discussed. Several studies
point towards a neuro-protective role in AD mousemodels [36, 44], while others report detrimental effects[43]. Further discrimination of T cell subtypes in ourstudy, could show a polarization towards an effector memory or activated effector phenotype of both
­C D8− and C­ D8+ T cells in the WD group. The chronic
metabolic inflammation in organs like adipose tissue,
which display a constant pro-inflammatory burden forthe body, might facilitate the activation of naïve T cells
in immune compartments of the periphery before they
enter the CNS [104, 105]. As we could detect positive
associations only between distinct immune cells populations and PET tracers, further studies are needed to
draw conclusive relationships and clarify whether the Tcells are polarized by signals from the periphery or via
CNS internal signals and to which extent they disturb

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CNS homeostasis and accelerate inflammatory processes needs to be further clarified.
The investigation of the metaflammatory condition inwhite adipose tissue (WAT) in the periphery has provento correspond to studies describing macrophage recruitment and polarization towards pro-inflammatory status
in inflamed WAT [12]. Although we found higher B cell
populations in WD-WAT, which contribute to systemic
inflammation by modulating T cells and macrophages[106–108], no significant changes in T cell populations
were detected in our model. Overall, results clearly show
disruption of innate immune cell infiltrates in WAT of
WD-fed animals with minor changes in T cell populations. To our knowledge, we were the first to compare
WAT immune cells of APPPS1 and wild-types, whichshowed no differences for both diets. Further experiments to distinguish T cell phenotypes would be helpful
to examine the impact of the WD on the T cells in WAT.
Imaging methods prove indispensable for AD diagnosis, being generally non-invasive and relatively welltolerated by patients. In AD patients, ­[18F]FDG is used
as a reliable marker to assess cognitive impairment anddifferentiating dementia types [109, 110]. ­[18F]FDG PET
can be used to detect regions which will possibly develop
AD in non-demented individuals [111, 112], an advantage to amyloid tracers which detect pathology but cannot assess cognitive decline [110, 113]. In view of thefact that not only the number of Alzheimer’s patients
will increase [114], but also the number of people suffering from obesity [1], the differentiated consideration of
established methods is becoming increasingly important.
Our results offer a new perspective to clinical studies
interpreting ­[18F]FDG uptake in people potentially at riskof Alzheimer’s disease due to Aβ depositions but not cognitively impaired. In overweight individuals, [­18F]FDG
uptake may differ from current state of studies, especially in the case of pre-existing pathology. As discussed
above, our results may indicate an early stage of preclinical AD, demonstrating compensatory mechanisms that
may be followed by cognitive impairments [81]. Therefore, more frequent monitoring may be necessary during
clinical observations. Interestingly, with view of the other(See figure on next page.)
Fig. 10 Histological analysis – Hypothalamus. Histological staining results are depicted in representative images per group per staining
for the hypothalamus (HYP) in all groups. a H&E staining between groups as overview (Scale bar 3 mm) and magnification (Scale bar 150 µm). The
black rectangular depicts magnification area. b No difference in ­NeuN+ cells between groups. c Only a few Aβ1-42 positive plaques were visible
in APPPS1 animals with high variance between specimens (Scale bar 150 µm). d Microglia staining using Iba-1. Ramified/resting phenotype
in WT brains and activated amoeboid phenotype of microglia in transgenic AD animals was observed. (Scale bar 150 µm) e ­GFAP+ cells in HYPof APPPS1 animals and WT-WD. (Scale bar 150 µm). f ­CD3+ T cells observed mainly in APPPS1-WD (Scale bar 100 µm). (g) Mean ± SD Aβ1-42 positiveplaques load in HYP in transgenic mice. (h) Mean ± SD Iba-1+ cells were elevated in APPPS1 animals. i Mean ± SD ­GFAP+ cells elevated in WT-WD
and in APPPS1 mice, however with high variance between animals. j S Higher mean ± SD CD3 positive T cells in APPPS1-WD group comparedto the other groups. HYP = hypothalamus. WT-ND n = 2; APPPS1-ND n = 3; WT-WD n = 3; APPPS1-WD n = 3

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markers used in this study, neither ­[18F]FTHA nor ­[18F]
GE-180 have been correlated to the glucose uptake seenin the APPPS1-WD group. A recently published clinical
PET study using second-generation TSPO tracer [­11C]
PBR28 and amyloid tracer [­11C]PiB could not show any
correlation between amyloid burden and neuroinflammation similar to our findings. Moreover, they foundno association between midlife insulin resistance ormetaflammation and neuroinflammation but positive
association between metabolic risk factors (high BMI
and insulin resistance) and TSPO in brain regions first
affected by Aβ accumulation in AD assuming dynamic
neuroinflammatory processes in the course of AD andlifestyle.
We are aware that our study includes limitations,which are discussed in the following paragraph. For thein vivo studies, we chose to compare the SUV to correct
for the significant weight changes between diet groups.
We chose to not compare SUV ratios due to the lack ofan adequate reference region in our project. Mostly the
cerebellum is used as a pseudo-reference region, but its
uptake changed significantly between WT under thecontrol diet and AD mice under WD for [­18F]FDG and­[18F]FTHA. That the cerebellum is affected by the diethas already been observed in another study [93]. Wheninterpreting ­[18F]FDG results from different studies,
several factors should be considered. The chosen AD
models seem to have a significant impact on the outcome of studies looking at the brain metabolism with adecreased [80, 115], increased [88, 116], or no different
brain ­[18F]FDG uptake [117]. Our results show higher
brain uptake of ­[18F]GE-180 in transgenic mice comparedto wild-types for the pathology-rich cortex, which could
be related to an altered BBB in WD-fed APPPS1 miceas discussed previously. While ­[18F]GE-180 is a promising high-affinity TSPO PET tracer, potentially offering
improved sensitivity in detecting neuroinflammation[118, 119], TSPO imaging is still challenging. Nonspecific binding and susceptibility to genetic variations in
the TSPO gene still impact [­18F]GE-180 and similar
tracer’s reliability [94, 120]. Ongoing research aims tooptimize TSPO tracers enhancing the credibility of neuroinflammation assessments through PET imaging but

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the development of alternative inflammatory tracers isof enormous importance for future studies in neurological disorders [121]. The low power for sex differences ofdiet-induced effects in the AD model did not allow us toobtain conclusive results, but should be considered for
future experiments as it has very recently been shownthat higher body-mass-index is associated with elevatedmicroglial activation in female patients, but not in male[122] and that also in human AD patients, metabolomics
analysis results can be triggered by sex differences [123].
In this study, we propose that in AD-prone brains,
further mechanisms are triggered by a WD beyond theclassical microglial neuroinflammation. Moreover, we
encourage further studies to examine the relation of T
cells and brain glucose metabolism in AD, as both were
elevated in the amyloidosis model after the WD.

(See figure on next page.)
Fig. 11 Histological analysis—Choroid plexus. Histological staining results are depicted in representative images per group per staining
for the choroid plexus (CP) in all groups. a H&E staining between groups as overview (Scale bar 3 mm) and magnification (Scale bar 150 µm).
The black rectangular depicts magnification area. b No ­NeuN+ cells were observed. c No Aβ1-42 positive plaques were visible in all animals (Scalebar 150 µm). d Microglia staining using Iba-1 (Scale bar 150 µm). e ­GFAP+ cells in CP (Scale bar 150 µm). f ­CD3+ T cells observed in all groups (Scalebar 100 µm). g No Aβ1-42 positive plaques were detected in CP. h Mean ± SD Iba-1+ cells were elevated in APPPS1 animals. i Mean ± SD ­GFAP+ cells
elevated in ND groups. j Tendencies of higher mean ± SD CD3 positive T cells in WD group compared to the other groups. CP = choroid plexus.
WT-ND n = 2; APPPS1-ND n = 3; WT-WD n = 3; APPPS1-WD n = 3

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Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12974-​024-​03080-0.
Additional file 1: Table S1. Comparison between the content of thenormal diet and the Western diet. Changes indicate higher or lower
portions in WD compared to ND for each compound. Table S2. Group
distribution of experimental animals (n) per sex for weight, experiments
and per group. Weight measurement started for ND-group at various
time points (see comment). Table S3. Lipids of different chain lengths
and lipid compositions are listed. Mean values, standard deviation (SD),and animal numbers (n) for ND and WD are depicted. *According to Ye
et al. [56]. Figure S1. Gating strategy for myeloid flow cytometry antibody
panel. For each immune cell population the respective antibodies are
displayed on the Y- and X-axis. Gating is shown as a black outline within
the plots. Figure S2. Gating strategy for T cell flow cytometry antibody
panel. For each immune cell population the respective antibodies are
displayed on the Y- and X-axis. Gating is shown as a black outline within
the plots. Gating for Tregs applies only to C
­ D8- T cell populations. Figure
S3. Typical MRS spectrum of the liver fat composition highlighting themain lipid components of interest shown in Fig. 2 and Table S3. Figure
S4 Mean ± SD blood glucose levels measured before [­ 18F]FDG imaging
for all 4 groups did not differ. WT-ND n = 11, APPPS1-ND n = 7, WT-WD n= 7, APPPS1-WD n= 8, post hoc Holm-Sidak corrected for multiple com‑
parisons. Table S4. Overview table with results from NMR raw spectral
analysis: annotated metabolite names, their corresponding signals in the1
H spectrum (indicative multiplicity and experimentally monitored chemi‑
cal shift), and also a report of averaged (means) normalized concentra‑
tions per metabolite per group with a given standard deviation (SD). S,
singlet; D, doublet, D–D, doublet of doublets, D–D–D, doublet of doubletsof doublets; T, triplet; Q, quartet; M, multiplet. A.u. der. mM, arbitrary units
derived from millimolar concentration values. Negative values are a resultof pareto scaling. Table S5. Metabolomics data changes overview in three
mice group comparisons (A APPPS1-ND vs. WT-ND; B WT-WD vs. WT-ND;
C APPPS1-WD vs. WT-ND) checked against the control ND-WT mice. VIP(Variable Importance in Projection) scores are depicted for each group.
Statistical significance: *p < 0.05, **p < 0.01, ***p < 0.001. Non-significant
changes (by t-test) were not labeled. Table S6. Pearson’s correlations ofwhole brain uptake (SUV) of PET tracers ­[18F]FDG, ­[18F]FTHA, ­[18F]GE-180
and relevant immune cells in brains (percentage of viable cells) for all
experimental groups. For each comparison the correlation coefficient r, rsquared ­R2, p-value and number of compared values n are shown. Statisti‑
cal significance: *p < 0.05, **p < 0.01, ***p < 0.001. Figure S5. Percentage
of positive NeuN cells in cortex (CTX) areas: retrosplenial area, primarysomatosensory area and dorsal auditory area. WT-ND n = 2; APPPS1-ND
n = 3; WT-WD n = 3; APPPS1-WD n = 3.
Acknowledgements
We thank Phillip Knopf, Vera Jörke, Simon Freisinger, Marta Vuozzo, Laura
Kübler, Carsten Calaminus, Max Zimmermann, and Sabrina Buss for theirvaluable support in this study. Mathias Jucker and Synovo GmbH for providing
the APPPS1-21 transgenic model. Funda Cay, Natalie Hermann, Maren Harant,
Linda Schramm, Dennis Haupt, Sandro Aidone, and Miriam Owzcorz for
excellent technical support. Barbara Schörg, Dominik Sonanini, and Manfred
Kneilling for provision of the flow cytometry T cell antibody panel. Bruker
BioSpin GmbH for the support in the NMR-related part of the study. The Facs
Core Facility of the Medical Clinic in Tuebingen for the support of the flow
cytometry measurements.
Author contributions
M.P. wrote the manuscript and performed in vivo imaging, ex vivo flow cytom‑
etry, and respective analyses. S.L.H. contributed to in vivo, ex vivo experiments
and analyses. G. B and C.T. performed metabolomics measurements andanalyses. T.M.I. wrote the code for the voxel-wise analysis and performed
pre-processing. I.G-M. and L.Q-F. performed histological staining and analyses.
F.C.M. developed the concept. D.S., W.E., G.R., and A.M. did radiotracer synthe‑
sis and validation. A.M.S. contributed to the 1H spectroscopy experiments andanalyses. B.J.P. supervised and provided laboratory and equipment. K.H. andPage 26 of 29

N.B. supervised, revised, and edited the manuscript. All authors revised themanuscript and all agree with its content.
Funding
Open Access funding enabled and organized by Projekt DEAL. This study was
supported by the Werner Siemens Foundation to B.J.P. and the intramuralfortüne funding scheme from the University of Tübingen to F.C.M.
Availability of data and materials
The datasets and material generated during and/or analyzed during thecurrent study are available from the corresponding author upon reasonable
request.

Declarations
Ethics approval and consent to participate
All experiments involving animals have been approved by the local authorities(Regierungspräsidium Tübingen, Germany), reference R06/21 G.
Consent for publication
Not applicable.
Competing interests
C.T. and G. B. report a research grant by Bruker BioSpin GmbH, Ettlingen,Germany.
Received: 18 January 2024 Accepted: 29 March 2024

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