Obesity and White Matter Neuroinflammation Related Edema in Alzheimer’s Disease Dementia Biomarker Negative Cognitively Normal Individuals

Abstract

Background:

Obesity is related to quantitative neuroimaging abnormalities including reduced gray matter volumes and impaired white matter microstructural integrity, although the underlying mechanisms are not well understood.

Objective:

We assessed influence of obesity on neuroinflammation imaging that may mediate brain morphometric changes. Establishing the role of neuroinflammation in obesity will enhance understanding of this modifiable disorder as a risk factor for Alzheimer’s disease (AD) dementia.

Methods:

We analyzed brain MRIs from 104 cognitively normal participants (CDR = 0) and biomarker negativity for CSF amyloid or tau. We classified body mass index (BMI) as normal (BMI <25, N = 62) or overweight and obese (BMI ≥25, N = 42). Blood pressure was measured. BMI and blood pressure classifications were related to neuroinflammation imaging (NII) derived edema fraction in 17 white matter tracts. This metric was also correlated to hippocampal volumes and CSF biomarkers of inflammation and neurodegeneration: YKL-40, SNAP25, VILIP, tau, and NFL.

Results:

Participants with BMI <25 had lower NII-derived edema fraction, with protective effects of normal blood pressure. Statistically significant white matter tracts included the internal capsule, external capsule, and corona radiata, FDR correc-ted for multiple comparisons to alpha = 0.05. Higher NII-derived edema fractions in the internal capsule, corpus callosum, gyrus, and superior fronto-occipital fasciculus were related with smaller hippocampal volumes only in individuals with BMI ≥25. There were no statistically significant correlations between NII-derived edema fraction and CSF biomarkers.

Conclusion:

We demonstrate statistically significant relationships between neuroinflammation, elevated BMI, and hippocampal volume, raising implications for neuroinflammation mechanisms of obesity-related brain dysfunction in cognitively normal elderly.

Keywords

Alzheimer’s disease neuroinflammation imaging obesity

INTRODUCTION

There is a growing need to understand modifiable risk factors for Alzheimer's disease (AD) dementia, the most common dementia. As the total number of individuals with dementia is projected to reach 81 million individuals worldwide by 2040 [1], coupled with a continuing lack of effective drug therapy development [2, 3], understanding modifiable risk factors for AD in persons that are cognitively normal may create opportunities to intervene in the 20-year latency period between amyloid deposition and the onset of cognitive decline [4]. Prior literature has suggested multiple risk factors that contribute to at least 50%of AD risk [5], and at least one randomized clinical trial has concluded that reducing the burden of these factors attenuates cognitive decline [6].

Chief among modifiable risk factors is having excess body tissue adiposity characterized as being overweight and obese, an increasingly pervasive public health problem projected to affect 85%of the U.S. and 58%of the global population by 2030 [7]. The elderly population is not excluded from these overall trends, with as many as 35%of individuals who are 65 years and older being overweight or obese [8]. It is therefore not surprising that obesity is independently related to cognitive decline [9, 10]. A recent Lancet Commission Article again emphasized the importance of obesity as a risk factor for dementia [11]. While some data suggest that loss of weight may precede AD itself in the so-called “obesity paradox” [12], this is thought to be an artifact of the disease itself manifesting as an apparent reverse causation of weight loss and improved brain structure [13]. However, individuals with earlier symptoms may also eat less for reasons related to subtle cognitive impairment leading to weight loss, and persons who are underweight/normal weight may be more vulnerable to the disease than higher BMI counterparts. Such results and related questions further emphasize the need to study obesity-related brain effects in cognitively normal individuals particularly those who lack AD dementia biomarkers such as amyloid and tau.

Yet, despite work delineating the relationship bet-ween increasing body mass index (BMI) and decreasing brain volumes in multiple cohorts [14 –16], little is known about the actual mechanisms underlying the potential deleterious influences of ob-esity on the brain. A recent paper showing an inverse relationship between BMI and amyloid even in cog-nitively normal individuals suggests that obesity-related brain atrophy is unlikely to be mediated by AD biomarkers such as amyloid [17]. In addition, whereas the majority of prior investigations of obesity and brain structure have focused largely on gray matter volume and cortical thickness, comparatively few have examined white matter.

In particular, white matter vasogenic edema is a promising marker of neuroinflammation that may offer a mechanism for the effects of obesity on brain structure [18,19, 18,19]. Such insight would add to current understanding of the systemic pro-inflammatory eff-ects of obesity and long-term risks of inflammation and dementia [20, 21]. Obesity, particularly in relation to blood pressure, is thought to also increase white matter edema as reflected by both focal and confluent white matter hyperintense lesions [22].

Recent work has demonstrated an increased burden of neuroinflammation in the white matter of cognit-ively normal young individuals who are obese, evaluated using a newer MR-based form of neuroinfl-ammation imaging (NII) to obtain white matter extracellular water fraction as a measure of edema [23]. However, there is limited replication of these results at present and further validation of the NII approach with diffusion basis spectrum imaging (DBSI), especially with postmortem studies, is ongoing. It remains unknown if elevated neuroinflammation as a function of higher body tissue adiposity exists in the white matter for an older, cognitively normal population.

Therefore, the purpose of this study was to quantify patterns of neuroinflammation within cognitively normal, middle- to older-aged individuals as a function of elevated BMI. We therefore evaluated whether NII derived edema fraction in these white matter tracts varied in individuals as a function of normal or increased BMI, and whether blood pressure mediated this relationship. We then related white matter tract neuroinflammatory edema to hippocampal volumes, as well as CSF biomarkers of neurodegeneration and neuroinflammation.

METHODS

Participants

The participants in this current study represent a subset of those enrolled in ongoing longitudinal clinical and biomarker studies of memory and aging at the Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC) at Washington University in St. Louis. Recruitment and assessment protocols for the parent cohort have been published in prior work [24]. These studies have been approved by the Washington University in St. Louis Institutional Review Board (IRB), and all participants provided written consent.

The inclusion criteria for this current study were normal cognition based on evaluations of dementia status (CDR = 0), Mini-Mental Status Examination (MMSE), and AD biomarker negativity within 24 months of the MRI scan session (pTau/CSF Aβ₄₂ <0.0198) [25]. The exclusion criteria were diagnosis of dementia or other disorders that contribute to cognitive decline aside from obesity and hypertension, contraindications to MRI, contraindications to CSF collection, and AD biomarker positivity. Blo-od pressure, as well as BMI and CDR were measured at the baseline clinical visit at the time of study entry with MR measurements taking place three to six months later. These criteria resulted in a cohort of 42 normal weight controls (BMI <25, mean BMI = 22.7±1.9, mean age = 63.0±7.7 years, mean SBP = 121.6±15.2, mean DBP = 70.5±9.4) and 62 overweight or obese individuals (BMI≥25, mean BMI = 29.9±3.5, mean age = 61.3±7.9 years, mean SBP = 129.9±17.8, mean DBP = 76.1±9.8) for a total sample size of 104 (Table 1).

Table 1

Demographic and clinical information

	Normal weight (N = 42)	Overweight and obese (N = 62)	p
Sex (%female)	69.0	61.3	χ²(1)=0.657, p = 0.4175
Education (y)	16.0±2.5	15.8±2.3	t(102)=0.502, p = 0.6162
Age (y)	63.0±7.7	61.3±7.9	t(102)=1.082, p = 0.2819
MMSE	29.6±0.83	29.3±0.94	t(102)=1.525, p = 0.1304
BMI	22.7±1.9	29.9±3.5	t(102)=–12.21, p < 0.0001
%normal BP	57.1	33.9	χ²(1)=5.524, p = 0.0188
SBP	121.6±15.2	129.9±17.8	t(102)=–2.480, p = 0.0148
DBP	70.5±9.4	76.1±9.8	t(102)=–2.966, p = 0.0039
pTau/Aβ₄₂ ratio	0.0097±0.0026	0.0102±0.0024	t(102)=–1.082, p = 0.2817

Summary of participant demographic variables and clinical features. Values are listed as mean±standard deviation. MMSE, Mini-Mental State Examination; BMI, body mass index; Unhealthy BP, nominal, systolic blood pressure > 120, diastolic blood pressure >80.

CSF collection and analysis

Within 24 months of the MRI scan session, 20–30 mL of CSF was collected by lumbar puncture with a 22-gauge atraumatic Sprotte spinal needle after overnight fasting, as detailed in prior work [26]. The samples were gently inverted to prevent possible gradient effects, then centrifuged at low speed, separated into 0.5 mL aliquots in polypropylene tubes, and frozen at –84°C. Samples were analyzed with ELISA for the following biomarkers: AD pathology in the CSF was evaluated using Aβ₄₂ and t-tau. Neuroinflammation biomarkers were evaluated using YKL40 and TREM2. Synaptic integrity was evaluated using SNAP25, VLIP, and Ng.

Brain MRI acquisition parameters

As detailed in prior work [27], brain MRI data was acquired on a 3T TIM Siemens Trio scanner with a 12-channel head coil with parallel imaging capacity located at Washington University in St. Louis. High resolution structural T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) sequences were collected with TR = 2400 ms, TE = 3.16 ms, TI = 1000 ms, resolution: 1×1×1 mm³. T2-weighted fast spin echo (T2w-FSE) sequences were collected with TR = 3200 mx, TE = 455 ms, resolution: 1×1×1 mm³. Diffusion-weighted (DW) single-shot echo planar imaging (EPI) sequences were collected with TR = 14500 ms, TE = 122 ms, directions = 25 (24 diffusion, 1 B₀), maximal b value = 1400 s/mm², resolution = 2×2×2 mm³.

Image processing

Motion correction and registration of the diffusion-weighted images involved the following steps: 1) Rigid body transformation was utilized to register the diffusion-weighted images to the b₀ image in order to correct for eddy current distortions, 2) 9-parametric affine transformation was then applied to register the b₀ image to the T2w-FSE in order to correct for stretch along the phase code direction, 3) the T2w-FSE was registered to the T1w MPRAGE and then aligned with the atlas image. Freesurfer version 5.03 was used to perform volumetric segmentation [28]. We focused on hippocampal volume (mean of left and right) in our analysis given its involvement in both AD and obesity, particularly recent studies of obesity with large population samples [15]. We included the following regional volumes in our analysis: bilateral hippocampal and entorhinal cortex volumes given the involvement of these structures in prior studies of obesity and brain structure particularly in cognitively normal persons [14].

Quantification of the edema fraction in white matter tracts has been detailed in previously published work [27, 29]. Briefly, we utilized an in-house multi-tensor model analysis package developed in MATLAB version 2019a (Mathworks; Natick, Massachusetts: The MathWorks Inc.) to solve the NII model. The details of this model are fully described in previously published work [27]. Briefly, this model incorporates voxel-wise diffusion tensors representing a range of potential pathological components such as extracellular water, that is a considered marker of edema related to neuroinflammation [29, 30]. White matter tracts were defined according to the JHU-ICBM-DTI-81 white matter labels atlas and white matter regions of interest were used for further analyses from a tract based spatial statistics analysis [31]. These tracts were combined across left and right and anterior and posterior regions as we have no a priori expectation of either laterality or anterior-posterior influences on our results.

Statistical analysis

Prior to statistical analysis using JMP Pro 14.1.0 (SAS Institute Inc., 2018), we assessed data distributions for normality and tested for outliers. Variables that deviated from Gaussian distribution were transformed through log transformation. Outliers were removed [32] when the absolute studentized residual [33] exceeded 3 [34]. This resulted in the removal of 17 measurements of NII-derived edema fraction across all 48 original tracts and all 104 individuals that were attribute to either artifacts of imaging or statistical outliers.

The demographic and clinical characteristics were compared using Student’s independent sample t-tests in continuous variables, and χ² tests for categorical variables.

BMI was transformed into a nominal variable at a cut-off value of 25. As the most commonly used diagnostic metric for characterizing obesity, the BMI value of 25 is widely accepted and defined by the World Health Organization as a threshold for abnormal or excessive fat accumulation [35]. Although as a measure, BMI does not distinguish between lean and fat body mass, nor between visceral and subcutaneous adiposity, this threshold has previously been noted to be significantly correlated with increased health risks such as late-life dementia and is widely accepted in the medical field.

Blood pressure was similarly transformed into a nominal variable of normal or elevated (pre-hypert-ensive or hypertensive) BP based on established cri-teria [36] of healthy blood pressure being systolic <120 mmHg and diastolic <80 mmHg, with unhea-lthy BP being higher values.

Statistical relationships between NII-derived ed-ema fraction with BMI and blood pressure were assessed with a multivariable linear model featuring the following factors: age (continuous), sex (male or female), BMI (<25 or ≥25), BP (healthy or unhealthy), and BMI-BP interactions. The reference groups for BP and BMI were unhealthy BP and BMI ≥25. This was performed for each structure’s tract aggregate separately. All other interaction effects we-re non-significant and not included in the final model.

Stratified groups of BMI <25 and ≥25 were also correlated against hippocampal volumes (mean of left and right). These volumes were not adjusted for total intracranial volume as the relationship between hippocampal volume and cognition does not vary as a function of ICV [37]. Similar analyses were performed for the CSF markers previously detailed.

To account for multiple comparisons, FDR corrections at levels of alpha = 0.05 and alpha = 0.10 were applied with surviving significant results after corrections indicated at each level.

RESULTS

The demographic and clinical characteristics of the study participants are displayed in Table 1. BMI <25 and ≥25 groups did not differ on age, sex, education, MMSE performance, or pTau/Aβ₄₂ ratio. Individuals with BMI≥25 had significantly higher BMI [t(102)=–12.21, p < 0.0001] and significantly higher prevalence of hypertension [χ² (1)=5.524, p = 0.0188] as compared to the BMI <25 group.

Main analysis

We first modeled the association between age, BP, BMI, sex, and the BP*BMI interaction with 17 separate NII-derived edema fraction regions as these variables are commonly related to brain structure and obesity in prior work [14 , 39]. The results (effect p-values, coefficients, and effect sizes) of these multivariable linear regression models are shown in Fig. 1.

Fig. 1

Partial correlation matrix of age, sex, blood pressure, and BMI against NII edema fraction. This figure is a matrix of partial correlations between age, sex, healthy blood pressure (BP, defined as systolic blood pressure <120 and diastolic blood pressure <80), BMI <25, and the interaction between BMI <25 and healthy BP against NII-derived edema fraction. Male sex was correlated with a lower level of NII edema fraction. There was no statistically significant relationship between BP alone and edema fraction. BMI <25 related to lower levels of NII edema fraction. The interaction between healthy BP and BMI <25 also showed a similar relationship with lower NII edema fraction. ^**indicates significance after FDR correction with alpha = 0.05. ^*indicates significance after FDR correction with alpha = 0.10.

There were no significant relationships between age and NII-derived edema fraction for any of the regions assessed. There were negative relationships between male sex and white matter edema in the internal capsule and cingulum as well as between BMI <25 and white matter edema in the internal and external capsules. There were no significant relationships between BP alone and NII-derived edema fraction. However, there were significant negative interaction effects between BMI <25 and healthy BP on white matter edema in the cerebral peduncle, internal and external capsules, corona radiata, and uncinate fasciculus. This interaction suggests that individuals with both BMI <25 and healthy BP demonstrated lower white matter edema compared to individuals with BMI <25 but unhealthy BP. Effect sizes of significant associations were evaluated to be of medium size using Cohen’s f.

Hippocampal volume analysis

There were no significant relationships between hippocampal volume and NII-derived edema fraction regions in individuals with BMI <25. However, for individuals with BMI ≥25, increased white matter edema in the corpus callosum, internal capsule, cingulate gyrus, and superior fronto-occipital fasciculus was related to decreased hippocampal volume. These results are shown in Table 2. Scatterplots of significant associations are shown in Fig. 2. The relationship between NII and hippocampal volume did not vary as a function of blood pressure in a statistically significant result.

Table 2

Correlation between NII-derived edema fraction and mean hippocampal volume in individuals with BMI≥25

	coefficient	std err	t-ratio	p	Pearson’s r
corpus callosum	–0.00066	0.00021	–3.18	0.0024^**	–0.3795
corticospinal tract	–0.00019	0.00017	–1.13	0.2626	–0.1445
medial lemniscus	0.00019	0.00023	0.83	0.4078	–0.107
cerebellar peduncle	–0.00003	0.00020	–0.14	0.8901	–0.0179
cerebral peduncle	–0.00014	0.00025	–0.56	0.5776	–0.0721
internal capsule	–0.00046	0.00016	–2.94	0.0046^**	–0.3549
corona radiata	–0.00032	0.00015	–2.11	0.0388	–0.2631
post. thalamic radiation	–0.00043	0.00021	–2.06	0.0436	–0.2572
sagittal stratum	–0.00034	0.00023	–1.46	0.1491	–0.1854
external capsule	–0.00043	0.00024	–1.81	0.0755	–0.2274
cingulate gyrus	–0.00078	0.00025	–3.12	0.0028^**	–0.3731
cingulum (hippocampus)	0.00019	0.00023	0.84	0.4059	–0.1074
fornix/stria terminalis	–0.00032	0.00022	–1.48	0.1446	–0.1875
sup. fronto–occ. fasc.	–0.00064	0.00021	–3.01	0.0039^**	–0.3617
sup. long. fasc.	–0.00051	0.00025	–2.05	0.0448	–0.2558
uncinate fasc.	–0.00005	0.00022	–0.23	0.8198	–0.0295
tapetum	–0.00008	0.00024	–0.34	0.7382	–0.0433

There were no significant relationships between NII-edema fraction and mean hippocampal volume for participants with BMI <25. ^**indicates significance after FDR correction with alpha = 0.05. post. thalamic radiation, posterior thalamic radiation; sup. fronto-occ. fasc., superior fronto-occipital fasciculus; sup. long. fasc., superior longitudinal fasciculus; uncinate fasc., uncinate fasciculus; std err, standard error.

Fig. 2

Scatterplots of statistically significant relationships between total hippocampal volume and regional NII edema fraction. Scatterplots of significant associations between NII-derived edema fraction for different white matter tracts and hippocampal volumes are shown for individuals with BMI≥25. Linear regression lines are plotted as well in red.

CSF marker analysis

NII-derived edema fraction in white matter tracts did not show any significant relationships with any of the CSF markers analyzed.

DISCUSSION

In this work, we have demonstrated relationships between, sex, BMI, blood pressure, NII-derived edema fraction, and hippocampal volume. In particular, within multiple white matter tracts, BMI <25 was associated with lower edema fraction, as was male sex. Additionally, an interaction effect was noted between BMI <25 and normal blood pressure in association with lower NII-derived edema fraction. The results carry implications for how obesity relates to neuroinflammation in the brain, particularly given the potential risk for future AD.

The concept that obesity can relate to higher neuroinflammation in the brain, in our work represented by NII-derived edema fraction, is possible given the proinflammatory nature of excess body adipose tissue, including as mediated by inflammatory cytokines such as IL-5, IL-10, IL-12, IL-13, IFN-γ, and TNF-α [40]. The level of underlying inflammation in obesity may also vary as a function of related metabolic derangements in so called “metabolically abnormal” versus “metabolically normal” overweight and obese individuals [41]. For example, higher hemoglobin A1c was related to increased levels of systemic inflammatory biomarkers, C-reactive protein, and polymorphonuclear monocytes, even in non-diabetic adults [42]. Interestingly, the Cardiovascular Health Study found a relationship between higher physical activity and lower systemic inflammation and this was speculated to be mediated at least in part by BMI [43]. The relationship between obesity and possible treatments for AD dementia are increasingly investigated as receptors for soluble TNF-α are elevated in obesity [44] and therapeutic target in AD [45].

The relationship between obesity and white matter abnormalities is most commonly represented by white matter hyperintensities. One study in 1,825 participants in the LIFE study found obesity predicted a higher burden of white matter hyperintensities and that this influence was partially mediated by systemic levels of IL-6 [46]. Another cross-sectional study of 112 individuals in the California Central Valley showed that a 1 standard deviation increase in waist-to-hip ratio translated to a 27%increase in semi-quantitative rating scales of white matter hyperintensities [47]. Interestingly enough, another found that the functional connectivity of the orbital frontal cortex was reduced in obese individuals with a high white matter hyperintensity burden compared to groups with either factor [48]. This finding is relevant to the overall hypothesized neurophysiology of obesity in which increased cortisol is speculated to related in lower orbital frontal cortex activity and disinhibition of feeding behaviors [49]. Work related to microstructural changes on diffusion MRI are comparatively limited, such as the previously mentioned UK Biobank study. Another study of 268 individuals (52 obese, 96 overweight, and 120 normal weight) found decreased fractional anisotropy in multiple white matter tracts such as the callosal body and cingulum [50] in individuals with higher BMI. Our work extends understanding of white matter influences of obesity by not only examining diffusion MRI but examining a specific biomarker, in this case edema fraction, of neuroinflammation. These findings are also distinguished from prior work on neuroinflammation and obesity by establishing a link to NII-derived edema fraction, a finding noted in one cohort analyzed on prior work [23]. Differences in these findings could be due to the larger sample size of our cohort as well as its older average age whereas that study had participants with average age of 20 s and 30 s.

However, when examining differences in the brain between obese and non-obese individuals the vast majority of studies have focused on gray matter structures in which the frontal lobes are atrophic across all ages with temporal and parietal lobe volume loss in older age [51]. Within the Cardiovascular Health Study cohort multiple studies have shown the inverse relationship between increasing BMI and lower gray matter as well as white matter volumes with tensor based morphometry [14, 52]. These findings were additionally replicated in ADNI [16] and separately linked with the FTO obesity gene [53]. Larger studies, such as the UK Biobank have also replicated these findings, particularly the hippocampus [15]. A separate ADNI with tensor based morphometry focusing only in AD participants also found hippocampal volume loss with higher BMI [54]. The fact that obesity related increased edema fraction was in turn predictive of lower hippocampal volumes suggests that the link between neuroinflammation in white matter and atrophy in gray matter are concurrent processes. However, future work will need to be performed with increasingly granular metrics of hippocampal structure such as subfields and their relationship with body tissue adiposity.

The relationship between blood pressure, BMI, and NII-derived edema fraction was complex. An unexpected aspect to the finding was the additional protective interaction effect of normal BP and BMI <25 together on decreasing NII-derived edema fraction. This effect suggests that individuals with normal blood pressure and BMI were further protected against neuroinflammation than individuals with unhealthy measurements in either domain. Not only does this finding suggest interdependence between BMI and BP when affecting neuroinflammation, it also corroborates previous findings that multidomain modifiable risk factor interventions were more effective in maintaining or improving cognitive function in elderly individuals. In addition, normal BP alone did not have a significant relationship with NII-derived edema fraction. However, it is important to note that hypertension and obesity are commonly interlinked as a subset of the metabolic syndrome presentation, which also entails additional measures outside the scope of this study. Therefore, while this isolated result should not detract from the significance of addressing hypertension, previous work has also suggested that obesity may have a role in its etiology. As such, an isolated therapeutic approach of targeting hypertension alone may not be ideal in lowering neuroinflammation.

With BMI, lower neuroinflammatory white matter edema in individuals with both healthy BMI and healthy BP compared to individuals with healthy BMI and unhealthy BP. This finding could be due to one of two reasons. In individuals with a maintai-ned perfusion pressure, particularly in the setting of hypertension, this finding may be compensatory in sustaining cerebral perfusion and blunting the micro-structural edema in our neuroinflammatory imaging. This may also be an artifact of anti-hypertensive drugs that can increase cerebral perfusion [55], thus lowering neuroinflammation. However, this assertion remains speculative given a lack of information about hypertensive medication use in our particular study. It may also be possible that we could be observing a survivor effect in older individuals with both hypertension and higher BMI [56].

The anatomical specificity or our findings also warrants further discussion. A growing literature sug-gests that the internal capsule, particularly the anterior limb functions in cognition [57]. Specifically, the anterior limb of the internal capsule has been found to mediate reward guided learning by conducting fibers from the prefrontal cortex to the thalamus and brainstem [58]. Strategic infarcts to the genu of the internal capsule predict post-stroke memory loss, thought to arise from thalamocortical disconnection [59]. With the influence of chronic high body tissue adiposity on these tracts, it is possible that the lower concentration of aquaporins and vascularization makes this area vulnerable to impairments in the glymphatic system that can in turn be impaired by obesity related conditions such as type II diabetes mellitus [60]. Prior work has also revealed that the external capsule may influence cognition, specifically executive function, though conduction of cholinergic fibers from the nucleus basalis of Meynert [61, 62]. Interestingly, high body tissue adiposity is related to impairments in cholinergic synaptic transmission [63]. Abnormalities in the cortical spinal tract have been linked with impaired psychomotor speed [64]. Our findings of greater neuroinflammation in long fiber tracts such as the frontal-occipital fasciculus have also been noted with greater inflammation in the setting of schizophrenia [65]. The corpus callosum is also a common target of neuroinflammatory conditions, mainly multiple sclerosis [66].

There were no associations between NII-derived edema fraction and CSF markers of neurodegeneration, inflammation, and AD pathology. This was likely due to the lack of specificity of CSF biomarkers for neuroinflammation derived from imaging techniques. In addition, the lack of associations with AD pathology suggests that the influence of BMI elevation on inflammation may occur independent of typical AD pathways such as Aβ and tau, while also contributing to disease vulnerability in other aspects. Further work is necessary to investigate these directions, and future studies may take advantage of blood-based biomarkers to assess their relationships to morphometric brain changes and neuroinflammatory imaging findings. Also, additional studies of CSF biomarkers of AD and obesity related NII-derived edema fraction will require individuals with clinical AD that was beyond the scope of this study.

The sex differences highlighted in this study are also preliminary and have multiple possible mechanisms. One recent study from the Cardiovascular Health Study showed that history of estrogen therapy related to higher brain volumes including in the frontal lobes where the same work also showed decreased volumes from higher BMI [67]. Another study has also related estrogen differences in brain structure in women as a function of menopause [68]. However, as our sample size was relatively small future work with larger samples and AD dementia biomarker positive individuals may reveal additional sex differences not encompassed in this current work.

The strengths of this study include its well characterized cohort of cognitively normal individuals and the availability of multi-parametric MR imaging accompanied by a comparatively newer method of evaluating neuroinflammation related edema. An additional feature of this study that precludes conclusions regarding causality are its cross-sectional design. Longitudinal studies will be required to better establish such causal relationships. Our focus on NII-derived edema fraction was intentional as edema is a classically recognized feature of neuroinflammation. However, future studies of neuroinflammation imaging can examine other potential biomarkers of neuroinflammation such as decreased fiber fraction and cellular infiltration. Additional work can also focus on the link between NII-derived edema fraction and extra-hippocampal regions. The hippocampus was our main focus with respect to its relationship to obesity related neuroinflammatory edema not only because of its crucial role in memory and learning but also because of its frequent association with BMI in the prior studies discussed in this work.

Overall, this study builds upon a growing understanding of obesity brain dysfunction by establishing a relationship between white matter extracellular water as a proxy of neuroinflammation and obesity as measured by BMI. Additionally, this work also links obesity related white matter edema with volume loss in the hippocampus. This work contributes to our understanding of obesity, brain dysfunction, and the possible role of neuroinflammation in underlying these relationships.

Footnotes

ACKNOWLEDGMENTS

This work was funded by grants from the National Institutes of Health (P50AG005681, P01AG026276, and P01AG003991). Dr. Raji is supported in his research by grants from the WUSTL NIH KL2 Grant (KL2 TR000450 –ICTS Multidisciplinary Clinical Research Career Development Program), the Radiological Society of North America Research Scholar Grant and the Foundation of the American Society of Neuroradiology Boerger Research Fund for Alzheimer’s Disease and Neurocognitive Disorders).

Authors’ disclosures available online ().

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