Valiauga et al. Journal of Neuroinflammation
https://doi.org/10.1186/s12974-024-03140-5(2024) 21:151

Journal of Neuroinflammation

Open Access

RESEARCH

Sex‑dependent effects of carbohydrate
source and quantity on caspase‑1 activity
in the mouse central nervous system
Rasa Valiauga1, Sarah Talley2, Mark Khemmani2, Melline Fontes Noronha2, Rocco Gogliotti3,4, Alan J. Wolfe2 andEdward Campbell1,2*

Abstract
Background Mounting evidence links glucose intolerance and diabetes as aspects of metabolic dysregulationthat are associated with an increased risk of developing dementia. Inflammation and inflammasome activation haveemerged as a potential link between these disparate pathologies. As diet is a key factor in both the developmentof metabolic disorders and inflammation, we hypothesize that long term changes in dietary factors can influence
nervous system function by regulating inflammasome activity and that this phenotype would be sex-dependent,
as sex hormones are known to regulate metabolism and immune processes.
Methods 5-week-old male and female transgenic mice expressing a caspase-1 bioluminescent reporter underwent
cranial window surgeries and were fed control (65% complex carbohydrates, 15% fat), high glycemic index (65%
carbohydrates from sucrose, 15% fat), or ketogenic (1% complex carbohydrates, 79% fat) diet from 6 to 26 weeks
of age. Glucose regulation was assessed with a glucose tolerance test following a 4-h morning fast. Bioluminescencein the brain was quantified using IVIS in vivo imaging. Blood cytokine levels were measured using cytokine bead array.
16S ribosomal RNA gene amplicon sequencing of mouse feces was performed to assess alterations in the gut microbiome. Behavior associated with these dietary changes was also evaluated.
Results The ketogenic diet caused weight gain and glucose intolerance in both male and female mice. In male mice,the high glycemic diet led to increased caspase-1 biosensor activation over the course of the study, while in females
the ketogenic diet drove an increase in biosensor activation compared to their respective controls. These changes
correlated with an increase in inflammatory cytokines present in the serum of test mice and the emergence of anxiety-like behavior. The microbiome composition differed significantly between diets; however no significant link
between diet, glucose tolerance, or caspase-1 signal was established.
Conclusions Our findings suggest that diet composition, specifically the source and quantity of carbohydrates,
has sex-specific effects on inflammasome activation in the central nervous system and behavior. This phenotype
manifested as increased anxiety in male mice, and future studies are needed to determine if this phenotype is linked
to alterations in microbiome composition.
Keywords High glycemic index diet, Ketogenic diet, Glucose intolerance, Inflammation, Inflammasome,
Neuroinflammation, Caspase-1, Sex, Microbiome, Behavior*Correspondence:
Edward Campbell
ecampbell@luc.edu
Full list of author information is available at the end of the article© 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
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Valiauga et al. Journal of Neuroinflammation(2024) 21:151

BackgroundAn estimated 55 million people worldwide suffer fromdementia, with the prevalence expected to drasticallyincrease in the coming years as the population ages [1].
The term dementia describes a range of cognitive disorders characterized by progressively declining memory,
executive function, and ability to carry out activities
of daily living due to the underlying neuronal degeneration. The most common form of senile dementia is
Alzheimer’s disease, and a combination of genetic, environmental, and lifestyle factors likely contribute to thepathophysiology [2–4]. Dementia is one of the leading
causes of morbidity and mortality worldwide, affecting
not only individual and caregiver quality of life but theeconomy, with financial cost associated with dementia
constituting about 1% of the global gross product [5].
As current treatments are largely ineffective, elucidating the roles of potential players in the pathogenesis andprogression of this debilitating disease can lead to novel
therapeutic targets.
Mounting evidence suggests metabolic dysregulation
may be a key factor in the onset and progression of neurodegenerative disorders, with type 2 diabetes mellitus
conferring a nearly two-fold greater risk of developing
dementia [6–11]. The primary characteristic of diabetes
mellitus is hyperglycemia, which is the driver of microand macro-vascular complications of diabetes such as
retinopathy, neuropathy, and cerebrovascular disease.
Glucose intolerance is the inability to regulate blood
sugar in the body effectively and over time, persistent
glucose intolerance can contribute to the development ofhyperglycemia and diabetes. Sustained elevation in blood
glucose also contributes to chronic low-grade inflammation, as glucose induces oxidative stress, activates
immune cells, promotes transcription of proinflammatory genes, and increases proinflammatory cytokines inthe blood [12–14].
Inflammation is a hallmark feature of both metabolicand neurodegenerative diseases, and in recent years the
inflammasome has emerged as a potential link between
these pathologies [15, 16]. Inflammasomes are cytosolic
multiprotein complexes that activate innate inflammatory responses, primarily through promoting the maturation and release of proinflammatory cytokines [17].
Although they play a critical role in host defense against
infections, inflammasome dysregulation has been implicated in numerous pathologies, including metabolicand neurodegenerative diseases [18–20]. While several inflammasomes with unique structures and functions have been identified (reviewed in [21–25]), they
all share some common features. The key components
of an inflammasome are (1) a sensor protein that recognizes pathogen-associated molecular patterns (PAMPs)

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and damage-associated molecular patterns (DAMPs),(2) an adaptor protein containing a caspase-recruitment
domain, and (3) the effector protein, which is an inflammatory caspase (caspase-1, -4, -5, or -11).
The most widely studied inflammasome is the nucleotide-binding oligomerization domain family pyrin
domain containing 3 (NLRP3) inflammasome. The
NLRP3 inflammasome is composed of three proteins:
NLRP3, apoptosis-associated speck-like protein containing a caspase activating recruitment domain (ASC),and caspase-1. Canonical NLRP3 inflammasome activation is a two-step process, with priming triggered by pattern recognition receptors (PRRs) at the transcriptional/translational level and assembly of the proteins activated
by PAMPs and DAMPs. Upon assembly, active caspase-1 cleaves pro-IL-1β, pro-IL-18, and gasdermin-D(GSDMD) into their active forms, leading to the releaseof proinflammatory interleukins IL-1β and IL-18 andinduction of pyroptosis by GSDMD. Diet plays a significant role in regulating the activity of the inflammasome,with several dietary factors identified as potential modulators [26–30].
Diet is also known to influence the development andprogression of metabolic disorders such as diabetes,
and there is literature to suggest that dietary modifications are beneficial in dementia [31, 32]. Diets with ahigh glycemic index, which contain carbohydrates thatare quickly metabolized and cause a rapid increase inblood sugar, have been shown to cause inflammation andbehavioral deficits in rodents [33–35]. In contrast, studies examining low carbohydrate, high fat ketogenic diets
have demonstrated improved cognitive function in mice,in part through modulating neuroinflammatory processes [36–39].
However, few animal studies examining the connection between diet and neuroinflammatory outcomes haveincorporated sex as a consideration, despite knowledge
that metabolism, immune responses, and risk of dementia are highly influenced by sex hormones [40–42]. Many
researchers choose to exclusively use male mice to avoid
the potential physiological variability associated with the
estrous cycle, although recent findings suggest that the
estrous cycle does not influence female mouse behavior[43]. Additionally, investigators prefer to use male micein metabolic studies since males are more responsive to
dietary interventions [44–46]. This sex bias has led toa limited understanding of the female brain and poortranslation to human studies, which is why our study
incorporates both male and female mice.
Here we used a high glycemic index diet and a
ketogenic diet to establish whether the source and quantity of carbohydrates in the diet can influence systemic
inflammation and CNS inflammasome activation, andValiauga et al. Journal of Neuroinflammation(2024) 21:151

determine if sex specific sensitivities exist for any ofthese variables. We found that the ketogenic diet caused
metabolic derangements in both male and female mice,but that the high glycemic index diet in males and the
ketogenic diet in females led to increased central nervous system inflammasome activation. These findings
correlated with elevated IL-1β and TNF levels in theserum and the emergence of anxiety-like phenotypes. We
observed that although high glycemic index diet and the
ketogenic diet induced significant changes in the fecal
microbiomes, these changes were not significantly linked
with changes in caspase-1 activity or glucose tolerance.

Results
Ketogenic diet leads to weight gain and glucose
intolerance in male and female miceAt 6 weeks of age, mice were started on a control (CD),high glycemic (HGD), or ketogenic (KD) diet, which wasmaintained until 26 weeks. The HGD is identical to the
CD in macronutrient composition but all the carbohydrates in the HGD are derived from sucrose. Conversely,
the KD is nearly devoid of carbohydrates and is composed primarily of plant-based fats (Additional file 1).
To assess the metabolic effects of each diet, we measured body weight weekly and tested glucose tolerance
monthly. To confirm the KD induced ketosis, we performed a β-hydroxybutyrate assay to assess the level of
ketone bodies and found significant elevations of this
ketone in both male and female mice at 26 weeks (Additional file 2). Male and female mice on the KD gained
significantly more weight relative to mice on the CD(Fig. 1A). Fasting blood glucose is a simple and common method of assessing glucose regulation and is often
used as a screening tool for prediabetes and diabetes in
the clinic. In rodents, the most common assessment of
glucose homeostasis is the glucose tolerance test (GTT),
which evaluates the ability of the body to move sugarfrom the blood into tissues. Abnormal glucose tolerance
generally precedes impaired fasting glucose. At 26 weeks,male mice fed the KD had higher fasting blood glucose
relative to sex matched controls, while female fasting glucose was unaffected (Fig. 1B). Between the two test diets,
a GTT revealed that only the KD led to impaired glucose homeostasis, which was observed in both male andfemale mice (Fig. 1C).
Impaired glucose tolerance was first observed at
10 weeks in males (Fig. 1D) and at 14 weeks in females(Fig. 1E), 4 weeks and 8 weeks after diet initiation,
respectively. Glucose intolerance is often associated withobesity, but we found that the intolerance in males preceded any substantial weight gain, which was not statistically significant until 14 weeks and remained modest
at that time (Fig. 1D). In females, glucose intolerance

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corresponded to a mild but significant increased bodyweight at 14 weeks (Fig. 1E). Together, these results
suggest that the KD, high in plant-based fat and nearly
devoid of carbohydrates, causes metabolic dysregulationin male and female mice with a C57BL/6 background.
Diet composition influences caspase‑1 activationin the brains of mice in a sex‑dependent manner

To evaluate how changes in diet influence inflammasome
activation in the central nervous system (CNS), we utilized mice expressing a caspase-1 biosensor that allows
caspase-1 activation to be monitored in vivo [47–50].
These mice express a circularly permuted luciferase thatis inactive prior to activation of caspase-1. The cleavageof caspase-1 causes the two terminal ends of luciferase
to come together and generates an enzymatically active
luciferase molecule, allowing caspase-1 activation to bemonitored in vivo and ex vivo [47]. To directly monitor
biosensor activation in the CNS in vivo, a modified cranial window technique was performed on 5-week-old
mice, wherein the skull was thinned, and a layer of transparent cyanoacrylate glue was applied to protect the cranium [51].
We observe that this procedure induces transient caspase-1 activation at the procedure site, which resolves in
3 to 5 weeks (Additional file 3). We therefore recorded
biosensor activation at 10 weeks, when biosensor activation induced by the surgery had subsided, and at
26 weeks of age. We found that the HGD in males andthe KD in females caused a significant increase in caspase-1 activation over the course of the study whenweek 10 and week 26 time points for individual animals
were compared (Fig. 2A, B). At 26 weeks, male mice fed
the HGD displayed significantly more caspase-1 activation than males on the CD (Fig. 2C, top panel), consistent with the changes observed over time, while atrend towards an increase was observed in female micereceiving the KD, although this difference was not statistically significant (Fig. 2C, bottom panel). A two-way
ANOVA revelated that there was a significant interaction
between the effects of diet and sex on caspase-1 activation (F(2,38) = 9.046, p = 0.0006). These data suggest that
inflammasome activity in the brain is sensitive to dietchanges and the carbohydrate load and type affects males
and females differently.
IL‑1β and TNF in the serum correlate with caspase‑1
activation in the CNS

To define the systemic changes in inflammation inducedby HGD and KD, we quantified the presence of proinflammatory cytokines and chemokines in the serum
collected from mice at 26 weeks using a cytokine beadarray (CBA). Only IL-1α in male mice fed the KD wasValiauga et al. Journal of Neuroinflammation(2024) 21:151

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Fig. 1 Ketogenic diet leads to weight gain and glucose intolerance in male and female mice. A Body weight was measured prior to dietintervention and weekly thereafter until the end of the experiment, at 26 weeks. Difference in body weight was assessed using a mixed effects
model for high glycemic index (HGD) and ketogenic (KD) diets relative to control diet (CD). B Fasting glucose was measured following a 4-hmorning fast at 26 weeks. C Mice were injected with a glucose solution and blood glucose was measured at 15-, 30-, 60-, and 120-min
post injection. Area under the curve (AUC) for the glucose tolerance test (GTT) was calculated as an index of whole glucose excursion. D Male miceon the KD first displayed impaired glucose tolerance at 10 weeks, which did not correspond to weight gain, while E female mice on the KD first
showed impaired glucose at 14 weeks, coinciding with significant weight gain. All data are shown as mean ± SEM and n = 5–11 per group. Statisticaldifferences for weight over time were calculated with a mixed effects model. One-way ANOVA followed a Dunnett’s test was used to compare
multiple groups (A–C) and a Student’s t test was used to compare two groups (D, E). For all statistical tests, *, **, ***, ****p < 0.05, 0.01, 0.001,
and 0.0001, respectively

significantly elevated (Additional file 4). However, we
did note that, although statistically insignificant, IL-1β
and TNF levels showed a pattern consistent with biosensor activation that was elevated with the HGD in
males (Fig. 3A, B) and the KD in females (Fig. 3E, F).

We assessed the correlation of these cytokines with thebrain caspase-1 activation and found a significant association between IL-1β (R = 0.5435, P = 0.0041 for males;
R = 0.5557, P = 0.0254 for females) and TNF (R = 0.7207,
P < 0.0001 for males; R = 0.6116, P = 0.0118 for females) inValiauga et al. Journal of Neuroinflammation(2024) 21:151

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Fig. 2 High glycemic index diet (HGD) in males and ketogenic diet (KD) in females causes increased caspase-1 activation in the brain. A
Representative images of caspase-1 biosensor mice with cranial windows at 26-weeks. B Cleaved caspase-1 signal was measured in miceat 10- and 26-weeks of age and the difference between the two time points was calculated for each animal. C Caspase-1 signal at 26 weeks in males
and females. All data are shown as mean ± SEM and n = 4–11 per group. Statistical differences were determined using one-way ANOVA followed
by a Dunnett’s test, comparing the HGD or KD to their sex-matched CD, where *, **p < 0.05 and 0.01 respectively

Fig. 3 IL-1β and TNF in the serum correlate with caspase-1 activation in the brain. Cytokine levels in serum collected from 26-week-old mice were
quantified using a CBA. A IL1-β and B TNF levels were measured in the serum of male mice. Correlation between C IL-1β and D TNF in the serumand caspase-1 activation in the CNS was assessed using simple linear regression in males. E IL-1β and F TNF levels were measured in the serumof female mice. Correlation between G IL-1β and H TNF in the serum and caspase-1 activation in the CNS was assessed using simple linear
regression in females. All data are shown as mean ± SEM and n = 4–10 per group. Statistical differences were determined using one-way ANOVA
followed by a Dunnett’s test, comparing the HGD or KD to their sex-matched CD. Correlation analysis of serum cytokines and caspase-1 activation
were performed using a simple linear regression

the serum and biosensor signal in the brain in both males(Fig. 3C, D) and females (Fig. 3G, H). These data revealthat significant increases in CNS inflammation may not
be reflected in significant changes in serum cytokines,
as measured by CBA. However, the strong correlation
between biosensor activation in the CNS and serum
cytokine levels further demonstrate that the differencesin biosensor activation due to dietary changes underlie
differences in diet-driven inflammatory responses.

Diet composition influences the fecal microbiome

Given the substantial body of evidence suggesting the
gut microbiome regulates glucose metabolism (reviewed
in [52]) and modulates neuroinflammation (reviewed in[53]), we performed 16S ribosomal RNA gene sequencing of mouse feces collected at 26 weeks on the same day
the bioluminescence brain measurements were taken andserum was collected for cytokine analysis. We focusedon the male microbiome given the statistically significant

Valiauga et al. Journal of Neuroinflammation(2024) 21:151

increase in CNS caspase-1 activation observed in theseanimals at 26 weeks. A total of 26 samples (10 CD, 8
HGD, 8 KD) were analyzed; 1 CD sample (sample 4) was
excluded from further analysis because it differed substantially from all other samples (Additional file 5), andthus skewed downstream analyses.
Raw measurements of area under the curve for the glucose tolerance test and total flux for caspase-1 activation
were each binned into either normal or high (at least one
standard deviation outside of the mean of CD measurement). Our analyses revealed no significant link between
diet and glucose tolerance (Additional file 6), or diet andcaspase-1 signal (Additional file 7). However, we didobserve significant differences in the composition of themouse fecal microbiome between diets.
The Bray–Curtis Index identified clusters of samples
that were similar in composition. Whereas the genus
Faecalibaculum predominated in all fecal samples, thesubgroups clustered as a function of their non-Faecalibaculum composition (Fig. 4A). These clusters generally
related to diet. For example, in mice fed the KD relative
to mice on the CD, alpha (within sample) diversity analysis revealed a significant increase in 3 different measures(Observed, Chao1, and ACE) of richness (number of
unique taxa) and 1 measure of overall diversity (Shannon) (Fig. 4B). Beta diversity (between sample) analysis
revealed significant differences in microbial composition between diets (Fig. 4C). These differences resulted
primarily from the relative abundance of the genera Lactobacillus, Bifidobacterium, and Lactococcus (Fig. 5A).
In the CD samples, the greatest relative abundance was
Faecalibaculum, followed by Bifidobacterium and Lactobacillus. In the HGD samples, the greatest relative abundance was Faecalibaculum, followed by Lactococcus and
Lactobacillus. In the KD samples, the greatest abundance
was once again Faecalibaculum, followed by Lactobacillus. Finally, we used DEseq analysis to more deeply compare compositions and identify several additional genera
significantly enriched in the experimental diets comparedto each other and to the control (Fig. 5B). Together, thesedata indicate that diet composition significantly alters the
microbiome but that changes in glucose intolerance andcaspase-1 activation in the brain are not associated withspecific changes in the fecal microbiome.
Carbohydrate source and quantity alters mouse anxiety
behavior, but does not impact motor function or learningand memory

We next sought to evaluate whether the changes in neuroinflammation have an impact on mouse behavior
using accelerated rotarod (motor coordination and learning), zero maze (anxiety), novel object recognition (spatial memory), and fear conditioning (associative memory)

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at 26 weeks. The tests were executed from least invasive(zero maze) to most invasive (fear conditioning) and only
one test was performed per day.
In the rotarod test, we observed no meaningful differences between the diets in both males and females(Fig. 6A), suggesting that our experimental paradigm
did not alter gross motor function or coordination.
Additionally, we assessed if diet composition impacts
memory using novel object recognition and fear conditioning tests. We found no differences in spatial learning
via novel object recognition or in associative memory
via cued fear conditioning, independent of sex or diet(Fig. 6B, C).
Since neuroinflammation is linked to changes in anxiety behaviors, we tested mice in an elevated zero maze(EZM), which incorporates open space and elevation
as anxiogenic factors [54, 55]. We found that male miceon the HGD have an increased anxiety-like phenotype,
manifesting as significantly decreased total distancetraveled (Fig. 6F). Females on the same diet also showed
increased anxiety-like behaviors in the form of significantly less entries made to the open zones (Fig. 6E). Neither sex had significant deficits in the time spent in open
zones of the EZM on the KD (Fig. 6D). Females on the
KD made significantly fewer entries into the open zones(Fig. 6E), which correlated with significantly decreased
distance explored (Fig. 6F). A two-way ANOVA revealed
that there was not a statistically significant interaction
between the effects of diet and sex on number of entries
made into the open zones (F = (2,36) = 0.9493, p = 0.3965)
or distance traveled (F = (2,45) = 1.5711, p = 0.2191).
Together, our phenotypic analysis points to a potential
diet-driven link between inflammasome activation andthe emergence of anxiety like phenotypes.

Discussion
Metabolic dysfunction linked to poor dietary choices isincreasingly recognized as a contributor to cognitive
decline [56–59]. While Mediterranean-style diets haverepeatedly been found to improve overall health and prevent cognitive decline, the Western diet has been linked
to increased risk of dementia and Alzheimer’s disease[31, 60–63]. The Western diet is high in fat and refinedcarbohydrates, and there has been research addressing
both aspects. Numerous studies have investigated theimpact of high fat diet on cognition, with mixed results[64–74]. However, fewer animal studies have focused onhigh glycemic index diets, composed of refined food thatcauses rapid spikes in blood glucose, despite clinical findings suggesting that this type of diet causes metabolic
impairments, cognitive decline, and increased amyloid
load [75–79].

Valiauga et al. Journal of Neuroinflammation(2024) 21:151

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Fig. 4 Diet alters the male mouse microbiome. DNA was extracted from 26-week-old male mice and 16S ribosomal RNA gene ampliconsequencing performed. A Bray–Curtis Dissimilarity Index identified clusters of samples that were similar in composition. B Alpha diversity analysis
was performed by calculating richness (ace, chao1, and observed richness), evenness (Pielou) and overall diversity (Shannon and Simpson).
C Beta diversity was assessed by PCoA using the Bray–Curtis Dissimilarity Index at the ASV level and resultant values tested for significancewith the experimental covariates using the PERMANOVA test. N = 8–10 per group. Statistical comparisons of bacterial abundance wereconducted using the Phyloseq package and involved pairwise Chi-Squared Tests, and results were filtered based on a significance thresholdof Benjamini-Hochberg adjusted p-value < 0.01

In finding dietary interventions that may counteract
neurodegenerative processes, several human studies
have assessed the impact of the ketogenic diet on cognitive health [80–83]. Ketogenic diets are high fat, low carbohydrate diets that have shown promising outcomes inpatients suffering from refractory epilepsy and promote
quick weight loss, which is why they are being exploredas a treatment for dementia. However, the clinical trials thus far are small and inconclusive, necessitating theneed for further animal studies.

Valiauga et al. Journal of Neuroinflammation(2024) 21:151

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Fig. 5 Specific genera of bacteria are enriched in the high glycemic index and ketogenic diets. DNA was extracted from 26-week-old male miceand 16S ribosomal RNA gene amplicon sequencing performed. A The top 30 genera displayed as a relative abundance heatmap. B DESeq analysis
of bacterial abundance between diets, where individual dots represent different amplicon sequence variants within the genus. N = 8–10 per group.
Statistical comparisons of bacterial abundance were conducted using the DESeq package and involved pairwise Chi-Squared Tests, and results were
filtered based on a significance threshold of Benjamini-Hochberg adjusted p-value < 0.01

In the current study, we chose to investigate how theeffects of high glycemic index and ketogenic diets on
glucose regulation influence the neuroinflammatory
response. As this pathology is important in disorders likedementia, we posit that linking diet to CNS inflammasome activation would be a salient finding. Importantly,
we included both male and female mice because there
is a sex bias in dementia (females are more affected) andmetabolism of macronutrients is different in males andfemales. The mice were fed a control (CD), high glycemic
index (HGD), or ketogenic (KD) diet from 6 to 26 weeks
of age, with continuous monitoring of weight and glucose
tolerance.
We found that both male and female mice on the KD
gained more weight (Fig. 1A). Many studies have shownthat mice fed a KD maintain or lose weight, but mostof these investigations are short in duration or restrict
food intake [84–87]. In contrast, our study involved

Valiauga et al. Journal of Neuroinflammation(2024) 21:151

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Fig. 6 Carbohydrate source and quantity alters mouse anxiety behavior without impacting motor function or learning and memory. C57BL/6 mice
were randomly assigned to control (CD), high glycemic index (HGD), or ketogenic (KD) diet at 6 weeks of age and put through a battery of behavior
tests at 26 weeks. A Accelerated rotarod was used to evaluate motor coordination and learning by recording latency to fall from the rod. B Percent
freezing in cued fear conditioning was used as a measure of associative memory. C Novel object recognition assessed spatial learning. Preference
index was defined ­Timenovel/(Timenovel + ­Timefamiliar). Elevated zero maze evaluated anxiety-like behavior via D percent time spent in open zone, E
number of open zone entries, and F total distance traveled. All data are shown as mean ± SEM and n = 6–11 per group. One-way ANOVA followed
by a Dunnett’s test was used to assess statistical significance, comparing the HGD or KD to their sex-matched CD, wherein *, **, ***p < 0.05, 0.01,
and 0.001, respectively

20 weeks of ad libitum feeding and our results are similar to the findings of Goldberg et al., which indicatethat while short-term KD feeding improves metabolic
parameters, long-term continuous KD feeding resultsin weight gain, glucose intolerance, and immune dysfunction [88]. We also noted that at 26 weeks, malemice on the KD had higher fasting blood glucose(Fig. 1B). However, only the KD led to impaired glucose tolerance in both sexes (Fig. 1C), which preceded
significant weight gain in males at 10 weeks and coincided with weight gain in females at 14 weeks (Fig. 1D,E). These findings are similar to those noted in a recent
study using male mice, where the authors observed
a ketogenic diet caused glucose intolerance [89]. The
delayed glucose intolerance in females is not surprising,as female mice are known to display improved glucose
tolerance and increased insulin sensitivity, in part due
to the action of sex hormone regulation of metabolism[90–92]. These results suggest that the high-fat, lowcarbohydrate composition of the KD causes glucose
metabolism dysregulation independent of weight gainin males and in the context of excess body weight infemales.
To assess how changes in glucose homeostasis impact
neuroinflammation, our study utilized mice expressing
a caspase-1 biosensor to monitor inflammasome activation in the CNS [49]. We employed a modified cranial
window technique on 5-week-old mice to allow visualization of biosensor activation in the brain [51]. When wecompared bioluminescence at 10 and 26 weeks, the data
revealed significantly increased caspase-1 activationin males on the HGD and females on the KD over thecourse of the study (Fig. 2A). The observations associated with the KD differ from a recent investigation where
the authors showed the ketogenic diet with a similar proportion of macronutrients as ours reduced neuroinflammation and improved cognitive function [36]. However,their study used 7-month-old 5xFAD male mice, whichhave a much different metabolic profile and CNS environment. Our data suggests that carbohydrate sourceand quantity affect inflammasome activity in the brain
differently in non-diseased males and females.

Valiauga et al. Journal of Neuroinflammation(2024) 21:151

The sex-specific differences in CNS caspase-1 activation in response to diet is a novel finding, but there
is research to suggest that the inflammasome is regulated differently in male and female mice. For example,
Posillico et al. showed that central administration of
the immunostimulant polyinosinic:polycytidylic acidinduces greater elevation in hippocampal cytokine andchemokine levels in male compared to female mice [93].
In contrast, Chen et al. found that the NLRP3 inflammasome exerts a more significant influence on high fat dietinduced atherosclerosis in female than male mice [94].
This suggests sex-dependent variations in the inflammasome’s response to diverse inflammatory stimuli, largely
due to the regulatory effects of sex hormones [95–98].
Considering that inflammasome signaling proteins caspase-1, ASC, and IL-1β are increased in the cortex ofaged female mice compared to aged male mice [99], further studies would benefit from a longer timeline or theuse of ovariectomized female mice to test whether our
phenotype is estrogen-dependent.
We hypothesized that increased inflammasome activation in the CNS may be a result of systemic inflammation, as peripheral inflammation has been shown by ourgroup and others to modulate inflammasome activity inthe brain [48, 100–102]. Consistent with this, we foundthat there was significant correlation between IL-1β and
TNF levels in the serum and biosensor signal in the brain
in both males and females (Fig. 3C and 3D). Notably, we
did not observe a statistically significant increase in theseserum cytokines induced by HGD or KD in either males
or females, but observed clear, statistically significant
changes in biosensor activation over time in animals, aswell as a significant correlation between serum cytokines
and inflammasome activation in the CNS, as measured
by biosensor activation. This underscores the limitationsof using systemic cytokines as a measure for tracking
minor changes in inflammation caused by diet or otherstimuli. It also highlights that alterations in tissue caspase-1 activation can reveal substantial and biologically
relevant changes that go beyond what can be observed
solely by monitoring serum cytokine levels.
Given substantial evidence that the gut microbiome
affects brain health, we performed 16S ribosomal RNA
gene amplicon sequencing on the stool isolated fromthe 26-week-old male mice used in the metabolism andinflammation experiments to explore if the changes inmetabolism and neuroinflammation were due to alterations in the gut microbiota. Although the fecal microbiome did not fully explain the glucose intolerance andincreased caspase-1 signal in the brain (Fig. S4 and S5),
we found significant differences in the composition of
microbes of the experimental diets compared to control
and each other (Figs. 4, 5), an outcome that has been

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previously documented in studies using other diet paradigms [103–107].
The link between microbiome composition and inflammasome activity is a complex, bidirectional relationshipthat has garnered significant attention in recent years in
the pathophysiology of numerous diseases. Several studies have demonstrated that certain bacteria can influence
inflammasome activation and downstream inflammatory
processes by production of metabolites that are sensed by
the inflammasome (reviewed in [108–111]). For example,
acetate is a short-chain fatty acid produced by commensal gut microbiota inhibits the NLRP3 inflammasome ina calcium dependent manner [112]. Conversely, lipopolysaccharide produced by gram negative in the gut is aknown endotoxin and activator of the inflammasome[113–116]. Therefore, we explored whether the changes
in CNS caspase-1 activation and glucose intolerance
were byproducts of diet-associated changes in the microbiome. Although we did not find a statistically significant
link, this is likely due to our relatively small sample sizeand stringent statistical cutoffs for significance in theanalysis. Nonetheless, it is important to frame the study
in the context of the microbiome as it is well known thatmurine gut microbiota differ significantly between institutions and can affect experimental outcomes [117–121].
Lastly, we evaluated whether our test diets influencemouse behavior due to the established link between neuroinflammation, neurodegeneration, and cognitive deficits. We observed no significant differences in males or
females with respect to motor coordination and motor
learning (Fig. 6A), spatial memory (Fig. 6B), or associative memory (Fig. 6C) at 26 weeks. However, we foundthat male mice on the HGD had significantly increased
anxiety, traveling less distance within the maze (Fig. 6E),
compared to male mice fed the CD. Female mice on
the KD also showed increased anxiety-like behaviors,with less entries made into the open zones (Fig. 6E) anddecreased distance explored (Fig. 6E). These results demonstrate that diets with a high glycemic index or thoseextremely high in fat influence the anxiety phenotype inrodents in a sex-dependent manner and manifest prior
to memory impairment, a mental health issue known
to occur in the early stages of Alzheimer’s disease in
humans (reviewed in [122, 123]).
One major strength of our investigation is that weinvestigated two diets that are in direct opposition withrespect to the carbohydrate source and quantity within
a single study that followed the mice over a period of
approximately 5 months in both males and females. To
our knowledge, ours is the first study to examine sexspecific differences in diet-induced CNS inflammasome
activation. Another strength of our study is the use of a
caspase-1 biosensor mouse model, which allowed us toValiauga et al. Journal of Neuroinflammation(2024) 21:151

track changes in vivo in the same mouse, permitting fora more direct and accurate comparison of changes andreducing the number of animals needed for research.
However, there are several limitations and technical considerations that necessitate discussion. The main
pitfall of our study is the relatively small sample size,
which possibly led us to missing important findings. It isalso important to consider the effect of the cranial window surgery on the neuroinflammatory response. While
we find that the acute induction in caspase-1 activation
resolves within a month, it is impossible to know whether
the baseline is altered. Whereas this may not be an issue
when exploring changes in inflammasome activity withstrong insults such as lipopolysaccharide or traumaticbrain injury models, this could diminish our ability todetect small differences due to dietary modifications.
Additionally, given that this is a skull-thinning rather
than the traditional cranial window procedure, the thickness of the window is potentially not uniform acrossthe skull or identical from mouse to mouse. Given theblinded nature of the surgery (i.e., the future diet assignment was not known at the time of the surgery), it isunlikely that this caveat biased our results, but it is possible that this introduced additional variability. Regarding the utility of our caspase-1 biosensor mouse, we andothers have validated this tool in various disease models,
including in the CNS [47–51]. However, the sensitivity ofin vivo brain imaging has yet to be determined and it ispossible that our diets induced meaningful, yet not statistically significant, caspase-1 cleavage. Nonetheless, thissystem is the most direct way to measure inflammasome
activation that is currently available and should be considered in future studies examining inflammation in the
CNS.
Lastly, is important to address that age as a major
limitation, our investigation only followed mice into
adulthood. Dysregulation of inflammasome signaling
is implicated in the pathogenesis metabolic dysfunction and dementia, both of which are characterized bychronic inflammation, impaired glucose metabolism, anddisrupted insulin signaling. The most significant risk factor for metabolic and neurodegenerative diseases is aging
and there is strong evidence to support inflammasomes
are critical in driving the chronic low-grade inflammatory state associated with aging, termed “inflammaging”[124–128]. Nonetheless, our findings are important as
researchers and clinicians consider using dietary interventions for preventative purposes. Overall, our results
suggest that the type and amount of carbohydrates infood can influence neuroinflammation and behavior differently in male and female mice. Future studies are necessary to define the underling mechanisms responsible
our findings.

Page 11 of 17

Conclusions
We observed sex specific differences in inflammatory
responses in the CNS to HGD and KD, with a HGD promoting CNS inflammasome activation in males and a KD
promoting CNS inflammasome activation in females.
This phenotype was associated with behavior alterations, specifically increased anxiety. This emphasizes theimportance of sex as a biological variable in dietary studies. Additionally, we found that utilization of a caspase-1
biosensor to monitor inflammasome activation over timein living animals can detect mild inflammatory responses
in the CNS induced by dietary changes that cannot be
reliably detected by monitoring serum cytokine levels.
Methods
Animals and experimental design

Caspase-1 biosensor or C57BL/6 mice were bred inhouse and maintained in pathogen free conditions ateither Loyola University Chicago or Edward Hines, Jr.
VA Hospital. At 5 weeks of age, caspase-1 biosensor mice
were fitted with cranial windows. At 6 weeks, mice wererandomly assigned to a control, high glycemic index, or
ketogenic diet using blocked randomization. For behavioral studies, C57BL/6 mice were randomly assigned to
one of the diets at 6 weeks using blocked randomization.
Mice were fed ad libitum for the duration of the study.
Investigators were not blinded to diet assignment moving forward. All mice were housed in a standard animal
facility with a 12-h alternating light/dark cycle. All experiments were performed according to protocols approved
by Loyola University Chicago Institutional Animal Careand Use Committee.
Diet formulations

Three purified rodent diets from Research Diets, Inc.
were used and contained the following macronutrients
as %kcal: control diet (65% carbohydrates, 20% protein,
15% fat), high glycemic diet (65% carbohydrates, 20%protein, 15% fat), and ketogenic diet (1% carbohydrates,
20% protein, 79% fat). It should be noted that the composition of the control and high glycemic diets is the same
regarding percentages from macronutrients, but all the
carbohydrates in the high glycemic diet are derived fromsucrose. To avoid any confounding results, the control
and ketogenic diets are devoid of any sucrose or dextrose.
Complete formulations are found in the supplemental
material (Additional file 1).
Cranial window procedure

A thinned-skull cranial window technique was employed
to visualize the brain, as described previously [51]. Mice
were anesthetized with intraperitoneal injections of

Valiauga et al. Journal of Neuroinflammation(2024) 21:151

tribromoethanol and placed on a heating pad to maintain body temperature during surgery. Once appropriate
depth of anesthesia was achieved as determined by reflex
testing of the footpad, the mouse head was stabilized ona stereotaxic frame to maintain head position and ophthalmic ointment applied to the eyes. The head was sterilized using 70% ethanol and Betadine. The epidermis andmuscle layers were excised to expose the skull by making
a sagittal incision through the scalp, cutting as needed to
fully expose the skull. The skull was cleaned of any bloodand connective tissue attached to the skull was scraped
off with a surgical blade. A micro-drill was used to thin
the skull, moving quadrant to quadrant and making sure
not to thin in any one area for an extended amount oftime to avoid friction induced overheating. Saline solution was applied intermittently to dissipate any heat andwash away debris. Once a thickness of ~ 20 µm has beenachieved, the skull was thoroughly cleaned and dried,cyanoacrylate glue (PELCO Pro C1000) applied over theentire area, and a few of drops of Insta-Set CA Accelerator (Bob Smith Industries, Inc) were added to rapidly
cure the glue. Mice were given long-acting buprenorphine post-op to minimize any discomfort.
Bioluminescence measurements

Mice were anesthetized with isoflurane, injected intraperitoneally with 150 mg/kg VivoGlo Luciferin and skulls
imaged 10 min later using the IVIS 100 Imaging System(Xenogen). Regions of interest were drawn around thetissue, and bioluminescence was quantified within eachregion using Living Image software.
Validation of ketogenic diet intervention

Serum was isolated from whole blood collected from thesubmandibular at 26 weeks. A β-hydroxybutyrate Colorimetric Assay Kit (Cayman Chemical) was used to assess
levels of ketone bodies.
Glucose tolerance test

Following a 4-h morning fast, a 12.5% dextrose solution
was injected intraperitoneally at 1 g/kg body weight andblood glucose measurements were taken at baseline, 15-,30-, 60-, and 120-min post injection. Glucose levels wereassessed via a tail blood sample using the AlphaTRAK2
glucometer and appropriate testing strips (Zoetis US,
Parsippany-Troy Hills, NJ).
Cytokine and chemokine measurements

Serum was isolated from whole blood collectedfrom the submandibular. Levels of proinflammatory cytokines and chemokines were measured using
cytokine bead array (BD Biosciences), according to
the manufacturer’s protocol. Samples were incubated

Page 12 of 17

with capture and detection beads for a selected panelof cytokines and chemokines: IL-1α, IL-1β, IL-4, IL-6,
TNF, KC, MCP-1, MIP-1α and MIP-1β. Samples were
measured on an LSRFortessa (BD Biosciences) and analyzed using FCAP Array software v3.0 (BD Biosciences).
Microbiome analysis

16S ribosomal RNA gene amplicon sequencing was
performed on male mouse feces collected at 6 months,
simultaneously with the bioluminescence brain measurements and serum collection for cytokine analysis.
We focused on the male microbiome due to the larger
sample size and because differences in caspase-1 activation at 26 weeks were only significant in male mice.
Genomic DNA was extracted from feces as follows:
the fecal pellet was resuspended in 1 mL of sterile
PBS and 100 μL of the suspension was transferred todeep-well plates. Enzymatic lysis solution consisting of
4 mg Lysozyme, 3kU Mutanolysin, 4 mg Lysostaphin,and 200U Achromopeptidase in buffer (20 mM Tris,
2 mM EDTA, 1.2% Triton, pH 8.0) was added to eachsample and incubated at 37 ˚C for 5 h, shaking intermittently at 1000 rpm (Eppendorf Thermomixer with
SmartBlock DWP1000). Samples were then processedusing DNeasy 96 Blood & Tissue Kit (Qiagen, Inc) per
the manufacturer’s instructions. To assess potential
DNA contamination, an extraction negative control (nofeces) was processed with samples and sequenced. Specifically, the V4 region of the 16S rRNA gene was amplified using the 515f and 806r primers. The sequencing
was carried out by the University of Illinois at Chicago
Genome Research Core (UIC GRC), where a twostage PCR amplification protocol was used to generate
amplicons containing Illumina sequencing adapters, asample-specific barcode sequence, and the amplification target. These amplicons were purified and pooled,then sequenced with paired-end 150 base reads using
an Illumina MiSeq instrument.
Basic processing of microbiome data

Raw reads were quality filtered using FASTQC (v0.11.9)
and cutadapt tools (Version 4.0), with reads shorter than
100 bp discarded. Following this, paired-end FASTQ
reads were processed through the DADA2 pipeline using
default parameters. Taxonomic annotation of the resultant Amplicon Sequence Variants (ASVs) was performedby the BLCA tool and the NCBI 16S Microbial database version 2023 [129]. A total of 26 samples (10 CD, 8
HGD, 8 KD) were analyzed; 1 CD sample (sample 4) was
excluded from further analysis because it was substantially different from all other samples (Additional file 4).

Valiauga et al. Journal of Neuroinflammation(2024) 21:151

Downstream microbiome analyses

The alpha diversity analysis was performed by calculating richness (ace, chao1 and observed richness),diversity (Shannon and Simpson) and evenness (Pielou) indices in R using the phyloseq and vegan packages [130, 131]. The Bray–Curtis Index was used inbeta diversity analysis to identify clusters of samples
that are similar in composition. Principle coordinate
analysis (PCoA) was calculated using the Bray–Curtis
Dissimilarity Index at the ASV level. The resulting values were tested for significance with the experimental
covariates using the PERMANOVA test. The top 30
genera are shown as a relative abundance heatmap foreach sample. Statistical comparisons of bacterial abundance among different Diet covariates were conductedusing the Phyloseq and DESeq2 packages [132]. Statistical analyses involved pairwise Chi-Squared Tests, andresults were filtered based on a significance thresholdof Benjamini-Hochberg adjusted p-value < 0.01.
Rotarod

Accelerated Rotarod was used as a general assessmentof neuromuscular function and motor learning. Mice
were placed on a rotating rod with increasing speed
from 4 to 40 rpm over 600 s and latency to fall wasrecorded. The test was performed three times per day
for three consecutive days, with 1 h between each trial.
Novel object recognition

6-month-old mice were placed inside a chamber with
two identical objects (either a beaker or a T75 culture
flask) and allowed to explore the chamber for 10 min.
The mice were returned to their home cage for 1 h. Following the rest period mice were returned to the chamber, where one of the objects was replaced by a novel
object, and again allowed to explore for 10 min. The
familiar and novel object were assigned at random andalternated to control for potential inherent preference.
The time spent directly sniffing each object was tracked
and scored by Noldus Software. The preference index
was defined as ­Timenovel/(Timenovel + ­Timefamiliar).
Cued fear conditioning

Training for cued fear conditioning was performed
using a 10% vanilla odor cue and two 1-s, 0.7 mA footshocks spaced 30 s apart that were preceded by a tone.
Associative learning was quantified 24 h later as thepercent time spent freezing in a novel chamber containing a 10% vanilla odor cue in response to presentation

Page 13 of 17

of the tone. Freezing over the test period was quantified
using Video Freeze software.
Elevated zero maze (EZM)

For EZM, mice were placed on an elevated circular
platform, with two closed sections comprising 50% of
the apparatus, and two open sections, comprising theremaining 50%. The time spent exploring open or closed
regions over a 5-min testing period served as a read outof anxiety behavior. The assay was conducted underfull light conditions (~ 400 lx) in the open sections andthe time spent exploring was quantified by ANY-mazesoftware.
Statistical analysis

All data are expressed as mean + SEM. Grubbs’ test with
α = 0.05 was used to identify and remove outliers. Mixedeffects model was used to analyze changes in body weightover time. For comparison of two groups, a Student’s ttest was used. For comparison of three groups, one-way
ANOVA followed by post-hoc Dunnett’s test was used.
Correlation analysis of serum cytokines and caspase-1
activation were performed using a simple linear regression. Statistical significance was set at p < 0.05. Graphs
were created and calculations performed using GraphPad
Prism 10 software (GraphPad Software, Inc.).
AbbreviationsASC	Apoptosis-associated speck-like protein containing a caspase activating recruitment domain
ASV	Amplicon sequence variant
CD	Control diet
CNS	Central nervous system
DAMP	Damage-associated molecular pattern
EZM	Elevated zero maze
GSDMD	Gasdermin-D
GTT​	Glucose tolerance test
HGD	High glycemic index dietKD	Ketogenic dietNLRP3	Nucleotide-binding oligomerization domain family pyrin domain
containing 3
PAMP	Pathogen-associated molecular pattern
PCoA	Principle coordinate analysis
PRR	Pattern recognition receptors

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12974-​024-​03140-5.
Additional file 1. Diet formulations. Experimental and control diets werepurchased from Research Diets, Inc.
Additional file 2. Mice on the Ketogenic Diet Exhibit Ketosis. Serum was
isolated from 26-week-old mice and β-hydroxybutyrate levels assessedusing a commercially available kit. All data are shown as mean ± SEM andn = 5–9 per group. A Student’s t test was used to compare the KD to thesex-matched CD, where *p < 0.05 and ***p < 0.001.
Additional file 3. Cleaved caspase-1 signal was measured in mice at 6-,8-, 10- and 12-weeks of age. Data are shown as mean ± SEM and n = 49.

Valiauga et al. Journal of Neuroinflammation(2024) 21:151

Statistical differences were determined using Student’s t test, comparing
individual time points, where ****p < 0.0001.
Additional file 4. Inflammatory cytokine/chemokine levels in serum collected from 26-week-old mice. Mice were started on a control, high glycemic index, or ketogenic diet at 6 weeks of age and serum was isolated
at 26 weeks. Cytokine/chemokine were measured by CBA in male andfemale mice. All data are shown as mean ± SEM and n = 3–10 per group.
Statistical differences were determined using one-way ANOVA followed
by a Dunnett’s test, comparing the HGD or KD to their sex-matched CD.
*p < 0.05.
Additional file 5. Relative abundance clustered by the Bray-Curtis Dissimilarity Index for all samples. DNA was extracted from 26-week-old malemice and 16S ribosomal RNA gene amplicon sequencing performed. The
Bray-Curtis Dissimilarity Index identified 1 control diet sample (sample
4) that was substantially different and therefore excluded from further
analysis. N = 8–10 per group.
Additional file 6. Microbiome composition does not fully explain glucose
intolerance phenotype. DNA was extracted from 26-week-old male miceand 16S ribosomal RNA gene amplicon sequencing performed. Raw
measurements of area under the curve for the glucose tolerance test werebinned into either normal or high and used as metadata in analyses. (A)Alpha diversity analysis was performed by calculating richness (ace, chao1,
and observed richness), evenness (Pielou) and overall diversity (Shannon
and Simpson). (B) Beta diversity was assessed by PCoA using the BrayCurtis Dissimilarity Index at the ASV level and the resultant values tested
for significance with the experimental covariates using the PERMANOVA
test. N = 8–10 per group. Statistical comparisons of bacterial relative abundance were conducted using the Phyloseq package and involved pairwise
Chi-Squared Tests, and results were filtered based on a significance threshold of Benjamini-Hochberg adjusted p-value < 0.01.
Additional file 7. Microbiome composition does not fully explain caspase-1 activation in the CNS. DNA was extracted from 26-week-old malemice and 16S ribosomal RNA gene amplicon sequencing performed.
Raw measurements of total flux for caspase-1 activation were binned
into either normal or high and used as metadata in analyses. (A) Alpha
diversity analysis was performed by calculating richness (ace, chao1, andobserved richness), diversity (Shannon and Simpson), and evenness (Pielou). (B) Beta diversity was assessed by PCoA using Bray-Curtis dissimilarities at the ASV level and dissimilarity indices were tested for significancewith the experimental covariates using the PERMANOVA test. N = 8–10 pergroup. Statistical comparisons of bacterial abundance were conductedusing the Phyloseq package and involved pairwise Chi-Squared Tests,
and results were filtered based on a significance threshold of BenjaminiHochberg adjusted p-value < 0.01.
Author contributions
RV conceptualized the study and RV, ST, RG, and EC designed experiments. RVand ST carried out experiments. RV, MK, and MFN analyzed the data. RV wrote
the manuscript, with guidance from EC, ST, AJW, and RG. All authors read andapproved the final manuscript.
Funding
This study is supported by funding from the Shearer Family Trust and theArthur J. Schmitt Fellowship at Loyola University – Chicago.
Availability of data and materials
Sequencing data that support the findings of this study have been depositedin the National Center for Biotechnology Information (NCBI) Archive with theprimary accession code PRJNA1067688.

Declarations
Ethics approval and consent to participate
All animal work was performed in compliance with the Institutional Animal
Care and Use Committee (IACUC) at Loyola University – Chicago (protocol
approval numbers 2021003 and 2022006).

Page 14 of 17

Competing interests
The authors declare that they have no competing interests.
Author details
1
Stritch School of Medicine, Loyola University Chicago, Maywood, IL, USA.
2
Department of Microbiology and Immunology, Loyola University Chicago,
Maywood, IL, USA. 3 Department of Molecular Pharmacology and Neuroscience, Loyola University Chicago, Maywood, IL 60153, USA. 4 Edward Hines Jr.
VA Hospital, Hines, IL 60141, USA.
Received: 23 January 2024 Accepted: 23 May 2024

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