BACKGROUND:Excess adiposity is a major risk factor for insulin resistance, prediabetes, and Type 2 diabetes and increases the risk for sarcopenia and osteosarcopenia later in life. It has been proposed that altered metabolic function and musculoskeletal status in people with obesity are directly linked, presumably because they share common pathophysiological mechanisms. However, the effect of metabolic dysfunction, independent of adiposity, on musculoskeletal status is unknown. METHODS:We performed a comprehensive assessment of musculoskeletal status in people with overweight/obesity and prediabetes (n = 12; 72% women; age: 67 ± 6 years; weight: 81 ± 11 kg; mean ± SD) and a control group of sex-, age- and adiposity-matched participants with normoglycaemia (n = 18; 67% women; age: 65 ± 6 years; weight: 81 ± 12 kg). RESULTS:Appendicular muscle mass expressed relative to the sarcopenia threshold (-5.6% ± 2.5% vs. 1.8% ± 2.0%; mean ± SEM) and the bone mineral density T-score (-0.22 ± 0.41 vs. 0.82 ± 0.33) were lower (p < 0.05) in the prediabetic group than the control group. Additionally, the prediabetic group had ~25% smaller (by cross-sectional area) myofibres and ~40% fewer muscle Type 2 macrophages (all p < 0.05), whereas intramyocellular lipid content was more than 50% higher (p < 0.05) in the prediabetic than the control group. Maximal muscle strength was not different between the two groups, but muscle strength during repeated maximum voluntary contractions declined more (p < 0.05) in the prediabetic group. CONCLUSION:In people with overweight/obesity, metabolic dysfunction associates with musculoskeletal dysfunction independent of adiposity.

BackgroundMidlife obesity is considered one of the top modifiable risk factors for dementia and Alzheimer's disease (AD). However, body mass index (BMI) on its own does not fully represent obesity-associated risks and it is crucial to disentangle the role of body adiposity and its localization.ObjectiveTo investigate the relationship of MRI-derived body adiposity metrics with AD-related pathology at midlife.MethodsNinety-seven cognitively normal midlife individuals underwent brain amyloid and tau PET, body MRI, and metabolic and cognitive assessments. Key measures included hepatic fat fraction, visceral (VAT) and subcutaneous adipose tissue (SAT) volumes, and thigh muscle and adiposity. The correlation between adiposity/metabolic measurements and amyloid/tau pathologies was investigated.ResultsThe average age of participants was 49.8 years, 65.3% were female and 53.6% had obesity. Amyloid PET burden in Centiloids correlated with VAT (rho = 0.36, p = 0.002), BMI (rho = 0.33, p = 0.002), SAT (rho = 0.33, p = 0.002), and insulin resistance (IR) (rho = 0.34, p = 0.003) in females and Whites, lower high-density lipoprotein (HDL) cholesterol (rho = -0.36, p = 0.002) irrespective of sex and race, and lower MMSE scores (rho = -0.57, p = 0.043) in only in African-Americans, after correction for age, sex, and education. There was no evidence that HDL nor IR mediated VAT-related amyloid. VAT/SAT ratio was significantly associated with mean cortical tau SUVR (β = 0.138, p = 0.030) after adjustment for age, sex, education, and amyloid.ConclusionsAmong fat depots in our study, visceral fat was more strongly correlated to amyloid pathology, and this association is present even independent from BMI. Also, higher visceral compared to subcutaneous fat is related to higher tau pathology.

Obesity in midlife, defined as body mass index (BMI) of 30 kg/m 2 or higher in those between 40-60 years, is related to higher Alzheimer’s disease (AD) later in life. Non-alcoholic fatty liver disease, as a complication of obesity is associated with impaired cognitive function. We investigated the relationship between hepatic fat quantification by use of MRI-derived Positron Density Fat Fraction (PDFF) and brain cortical thickness in cognitively normal midlife individuals. Overall, 63 cognitively normal middle-aged participants (Age: 50.46±6.19 years, female: 22 (71%), obesity: 32 (50.28 %), BMI: 36.46±4.9 kg/m 2 ) underwent brain and abdominal 3T MRI. PDFF values were calculated using a trained U-Net convolutional neural network (CNN) model to infer the hepatic PDFF maps from conventional T1-weighted images. The CNN included the Adam optimizer for training with a learning rate of 1e-4. Visceral and subcutaneous adipose tissue (VAT, SAT) were automatically segmented using VOXel Analysis Suite (Voxa). FreeSurfer 7.1.1 was used for automatic segmentation of cortical and subcortical brain regions using a probabilistic atlas, followed by visual inspection and if needed, manual editing. A multivariable linear regression analysis was carried out to test the association of PDFF, VAT, SAT, and BMI with brain cortical thickness, with age and sex as covariates. We observed a statistically significant association between PDFF and VAT (p=3e-7), BMI (p=2e-4), and insulin resistance (1e-4). Among late-onset AD (LOAD) cortical regions, there was a significant inverse correlation (Adjusted R²=0.19, p<0.001) between left temporal pole thickness and PDFF. Similarly, higher VAT was associated with thinning in left temporal pole (Adjusted R²=0.22, p<0.001) and middle temporal lobe (Adjusted R²=0.12, p=0.014). Overall, our data suggest a potential role of hepatic fat fraction in promoting neurodegeneration in cognitively normal midlife individuals. These findings lend insight into a pathway that can be utilized for future AD risk reduction.

Obesity in midlife is a risk factor for developing Alzheimer disease later in life. However, the metabolic and inflammatory effects of body fat varies based on its anatomical localization. In this study, we aimed to investigate the association of MRI-derived abdominal visceral and subcutaneous adipose tissue (VAT and SAT), liver proton-density fat fraction (PDFF), thigh fat-to-muscle ratio (FMR), and insulin resistance with whole-brain amyloid burden in cognitively normal midlife individuals. A total of 49 cognitively normal midlife individuals (Age: 50.65±5.77 years, 61.2% female, BMI: 32.0±67.34, 57.1% obese) underwent brain PET scan, body MRI, and metabolic assessment. Homeostatic Model Assessment for Insulin Resistance (HOMAIR) was used for measuring insulin resistance. Dynamic amyloid imaging was performed with a bolus injection of ∼15mCi [11C]PiB, followed by a 60-min scan. Data from the 30–60 minute post-injection window was used for calculating whole-brain amyloid centiloid. VAT and SAT were semi-automatically segmented using an in-house MATLAB-based software. A trained U-Net convolutional neural network (CNN) model was used to calculate the hepatic PDFF maps from abdominal T1-weighted images. After preprocessing and N4ITK bias correction on mid-thigh slices between the ischial ramus and the medial knee condyle, a MATLAB program was used for segmenting thigh total fat including subcutaneous, inter-, and intra-muscular fat, and muscle volumes. Total thigh fat-to-muscle ratio (FMR) was calculated. Using Spearman correlation test, the association between whole-brain amyloid centiloid and BMI, HOMAIR, VAT, SAT, PDFF, and FMR was assessed, with age and sex as covariates. Obese individuals had a higher amyloid burden compared to the non-obese (p=0.011). Whole-brain amyloid centiloid values were significantly associated with VAT (rho=0.62, p<0.0001) and HOMAIR (rho=0.56, p=0.013), but not other fat metrics. A mediation analysis showed significant direct effect of VAT (p=0.02) on amyloid burden, while the indirect VAT effects mediated by HOMAIR were non-significant (p=0.28). Obesity, higher visceral fat and insulin resistance, but not BMI, subcutaneous abdominal fat, liver fat, or thigh fat, are associated with higher whole-brain amyloid burden in midlife. This highlights the importance of anatomical characterization of body fat for Alzheimer disease risk, where visceral fat shows a strong relationship with amyloid pathology.

Emerging research underscores the significance of midlife obesity, defined by a BMI of 30 kg/m 2 or higher in persons age 40-60 years, as a risk factor for Alzheimer's disease (AD) in later life. Due to the various properties of each body component, it is important to characterize the neurodegenerative effects of fat within the muscle, known as a predictor of metabolic health and cognition. We investigated the relationships between thigh total fat-to-muscle ratio (FMR) and brain cortical thickness in cognitively normal midlife individuals. Our study focused on a sample of 35 cognitively normal midlife participants (age: 51.59±5.72 years; 42.9% male; 60% obese; average BMI: 32.04±6.98 kg/m 2 ). The brain and thigh scans were obtained from Siemens 3T MR scanners. On thigh MRI nine mid-thigh slices located between Ischial Ramus and the medial knee condyle were identified and preprocessed. N4ITK Bias correction was performed with 3D Slicer to correct inhomogeneities. An in-house MATLAB program measured total fat (subcutaneous, inter-, and intra-muscular fat) and muscle volumes derived from the summation of compartments from both thighs to calculate total FMR. FreeSurfer 7.1.1 was utilized for automated segmentation of both cortical and subcortical brain regions using a probabilistic atlas followed by a visual review, and where necessary, adjustments were made manually. The association between FMR and cortical thickness in 22 Late-onset Alzheimer disease (LOAD) brain regions was examined using Spearman rank test, adjusted for age and sex and multiple comparisons. FMR in the thigh is highly correlated to BMI, but not insulin resistance. There was a negative association between both FMR and InterMAT/muscle ratio and cortical thickness in multiple LOAD regions. Out of 22 LOAD regions, FMR showed negative correlation with cortical thickness in right superior and inferior parietal, inferior temporal, supramarginal, entorhinal, and precuneus cortex, as well as left middle and superior temporal lobe, and temporal pole. Thigh fat/muscle ratio is highly associated with BMI and negatively correlated with cortical thickness across various AD-related brain regions. This may suggest that interventions that improve muscle quality, by reducing fat surrounding and inside the muscle, may be preventive for Alzheimer disease risk.

Obesity in midlife, body mass index (BMI) of 30 kg/m 2 or higher, is recognized as a contributor to Alzheimer disease (AD) later in life. Adiposity in visceral tissues such as liver is associated with increased systemic inflammation and impaired cognition. In this study, we aimed to investigate the relationship between MRI-derived Positron Density Fat Fraction (PDFF) and brain histology and neuroinflammation using Diffusion Basis Spectrum Imaging (DBSI) in cognitively normal midlife individuals. In total, 67 cognitively normal middle-aged participants (Age: 50.02±6.00 years, female: 65.7%, obesity: 53.7%, BMI: 31.72±6.81 kg/m 2 ) underwent brain and abdominal 3T MRI and metabolic assessment. Homeostatic Model Assessment for Insulin Resistance (HOMAIR) was used as for measuring insulin resistance. Using a trained U-Net convolutional neural network (CNN) model, hepatic PDFF maps were calculated from conventional T1-weighted images. The CNN included the Adam optimizer for training. A DBSI scheme with a total of 98 diffusion samplings was acquired. After eddy current and movement correction and removing non-brain tissue, DBSI maps including fractional anisotropy (FA, overall integrity), axial diffusivity (AD, axonal injury), radial diffusivity (RD, myelin loss), restricted fraction (RF, inflammation cellularity), hindered fraction (HF, extracellular edema), and fiber fraction (FF, axonal density) maps were calculated using in-house software scripted in MATLAB and Statistics Toolbox Release. DBSI-derived maps were processed using a tract-based spatial statistics (TBSS) pipeline to allow for whole-brain white matter voxel-wise analyses. Using the Randomize tool from FSL, the association of PDFF and HOMAIR with DBSI-derived white matter skeleton, with age and sex as covariates, and a threshold of 0.05 for false-discovery rate. There was a significant association between PDFF and HOMAIR (p<0.001), and obese individuals showed higher PDFF (p=0.02) but no difference in HOMAIR (p=0.12). We observed a significant positive association between PDFF and restricted fraction in widespread white matter tracts. There was no significant association between HOMAIR and DBSI measures. Our results indicate the role of hepatic fat in increased inflammation-related cellularity in the brain in cognitively normal midlife individuals. These findings suggest that excess hepatic fat can potentially increase the risk for Alzheimer disease and cognitive impairment at least partly through promoting neuroinflammation.

Obesity is a risk factor for dementia, creating a chronic inflammatory state that results in white matter (WM) injury. Edge density imaging (EDI) is a novel technique that has demonstrated reliability in quantifying WM changes. Thirty obese and 20 non-obese cognitively normal adults underwent structural and diffusion-weighted magnetic resonance imaging. Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were quantified via VOXel Analysis Suite by separating signal intensities of adipose and non-adipose tissue. Scans were processed by a pipeline (MaPPeRTrac) to generate EDI. Among obese participants, there was a negative association between the VAT/SAT ratio and EDI, which was not seen among non-obese participants. Additionally, males had decreased EDI compared to females. The results of this study suggest that obesity, through WM damage, may confer increased risk of dementia, with sex as a potential differential factor. EDI demonstrates promise in delineating the neuropathology of obesity and dementia.

Within the research field of neurodegenerative disorders, unbiased analysis of body fat composition, particularly muscle mass, is gaining attention as a potential biological marker for refining Alzheimer’s disease risk. The objective of this study was to employ a deep learning model for fully automated and accurate segmentation of thigh tissues, potentially contributing to early Alzheimer's diagnostics. In an IRB-approved study, 49 participants underwent thigh Dixon MRI scans with a TR=9.99s, TE=2.46s, flip angle=10°, and slice thickness= 5mm. The Dixon Fat/Water images were semi-automatically segmented by an expert operator in all available slices to obtain the bone, intermuscular fat (InterFat), intramuscular fat (IntraFat), Subcutaneous Adipose Tissue (SAT), Muscle, and Gluteus. We trained and compared the performance of baseline and state-of-the-art deep neural networks, namely, UNet, VNet, and two vision transformers (ViTs): UNETR and SwinUNETR. The performance of the trained models was tested on all data sets using a 3-fold cross-validation scheme. We found SwinUNETR outperformed the others with a mean dice similarity coefficient 96.20 (± 0.51), 80.91 (± 0.55), 50.56 (± 1.43), 95.26 (± 0.80), 98.70), 86.72 (± 1.12) in Bone, InterFat, IntraFat, SAT, Muscle, and Gluteus, respectively. Bland–Altman analysis and scatter plot (Figure 1) indicated that the differences between manual annotations and predictions by the SwinUNETR model were relatively minor for Bone volume, Intramuscular Fat volume, Muscle volume, and Gluteus volume classes. The overall mean difference is -88.8cm 3 with a 95% confidence interval (CI) of [-159.53, -18.13]. Biases [95% CI] for each tissue class were 5.44cm 3 [−8.61, 19.50] for Bone volume, 51.74cm 3 [−22.72, 126.20] for InterFat volume, 11.15cm 3 [2.76, 19.53] for IntraFat volume, -191.09cm 3 [−309.54, -72.64] for SAT volume, 15.16cm 3 [−6.37, 36.70] for Muscle volume, and 18.76cm 3 [2.20, 35.32] for Gluteus volume. This study highlighted the use of ViTs for the automated segmentation of thigh tissues in MR which may allow for the detection of subtle changes in muscle mass and fat composition, that are of increasing interests in their associations with the neurodegenerative processes in Alzheimer's disease.

Summary: We present an approach for evaluating abdominal computed tomography (CT) scans that generates reproducible measures relevant to donor site morbidity after abdominally based breast reconstruction. Seventeen preoperative CT metrics were measured in 20 patients with software: interanterior superior iliac spine distance; abdominal wall protrusion; interrectus distance; rectus abdominis width, thickness, and width-to-thickness ratio; abdominal wall thickness; subcutaneous fat volume; visceral fat volume; right/left psoas volumes and densities; and right/left rectus abdominis volumes and densities. Two operators performed measures to determine interrater reliability (n = 10). Interclass coefficients (ICCs) were calculated, and Bland–Altman plots were fashioned. Intrarater reliability was excellent (ICC > 0.9, 0.958–1) for 15 measures, and good (0.75 < ICC < 0.9, 0.815–0.853) for 2 measures. Interrater reliability was excellent (ICC > 0.9, 0.912–0.995) for 12 measures and good (0.75 < ICC < 0.9, 0.78–0.896) for 5 measures. Bland–Altman plots confirmed intra/interrater agreement. Our study meets its objective of establishing a protocol for obtaining abdominal CT measurements with high reproducibility and intrarater and interrater reliability. Although this study is not meant to weigh the particular influences of various CT measurements on clinical outcomes, we are now actively studying this with the intention of reporting our findings in the near future. Larger patient cohorts must be leveraged to determine correlations between abdominal CT scan findings and donor site outcomes using machine learning algorithms that generate models for predicting abdominal donor site complications.

Obesity and higher adiposity in midlife are recognized as contributors to Alzheimer disease (AD). Neurodegeneration in AD is at least partly mediated by vascular compromise and brain hypoperfusion. In this study, we aimed to investigate the associations between BMI and abdominal visceral and subcutaneous adipose tissue (VAT, SAT) and brain cerebral blood flow (CBF) in cognitively normal midlife individuals. A total of 66 middle-aged cognitively normal adults (age: 49.86±5.99 years, females: 66.7%, obesity (BMI of 30 kg/m2 or higher): 51.5 %, BMI: 31.72±6.96 kg/m2) underwent abdominal and brain MRI. Using an in-house Matlab program, abdominal VAT and SAT were automatically segmented followed by manual editing. A 3D Pseudo-Continuous Arterial Spin Labeling (pCASL) sequence, with a single post-labeling delay of 2.025 s, was used for assessing CBF. SPM12 was used to generate ASL absolute CBF (aCBF) maps with a single compartment model, co-registered to the gray matter segmentations, and normalized to MNI space, followed by spatial smoothing. Using AAL3 atlas and Matlab, region of interest masks were created for amygdala, hippocampus, posterior cingulate, precuneus, parahippocampal, medial orbitofrontal, and middle temporal cortices and applied to absolute CBF (aCBF) maps. The aCBF differences between the obese vs. non-obese, high-VAT vs. low-VAT, and high-SAT vs. low-SAT was assessed, with age and sex as covariates. Also, BMI, VAT, and SAT as separate predictor variables, with age and sex as covariates, were used for voxel-wise analysis. There was a lower whole-brain aCBF in the high-VAT (p=0.004) group and obese (p=0.005) individuals, more prominently in the left middle temporal lobe (p=0.002). No significant difference was observed in global and regional aCBF in the high-SAT vs. low-SAT groups. Voxel-wise analyses showed significantly lower aCBF in association with BMI in temporal, occipital, and frontal lobe clusters after false discovery rate correction. Obesity and increased visceral abdominal fat are associated with a lower cerebral blood flow, with a more prominent decrease in the middle temporal cortex, as an AD-signature area, in cognitively normal midlife individuals. These findings highlight the role of obesity, especially visceral obesity, in brain hypoperfusion and potentially Alzheimer disease risk, as early as midlife.

Obesity and abdominal adiposity in midlife are shown to increase the risk of Alzheimer disease. However, it is not clear whether midlife adiposity is associated with increased neuroinflammation. We aimed to investigate the associations of obesity, BMI of 30 kg/m 2 or higher, and abdominal visceral and subcutaneous adipose tissue (VAT and SAT) with brain histology, using diffusion basis spectrum imaging (DBSI) analysis; In total, 54 cognitively normal middle-aged subjects (50.46±6.19 years, male: 21 (38.9%), obesity: 32 (59.3 %), BMI: 32.18±6.99 kg/m 2 ) underwent brain and abdominal 3T MRI. Abdominal VAT and SAT were semi-automatically segmented using VOXel Analysis Suite (Voxa). A DBSI scheme with a total of 98 diffusion samplings was acquired followed by movement and eddy current correction and brain tissue extraction in FSL. DBSI maps including fractional anisotropy (FA, overall integrity), axial diffusivity (AD, axonal injury), radial diffusivity (RD, myelin loss), restricted fraction (RF, inflammation cellularity), hindered fraction (HF, extracellular edema), and fiber fraction (FF, axonal density) were generated using in-house software scripted in MATLAB. DBSI-derived maps were processed using a tract-based spatial statistics (TBSS) pipeline for whole-brain white matter voxel-wise analyses. Using the Randomize tool from FSL, the difference between obese vs. non-obese, high-VAT vs. low-VAT, and high-SAT vs. low-SAT groups were investigated for each DBSI-derived skeleton, with age and sex as covariates, and a threshold of 0.05 for false-discovery rate. The sex differences were further investigated; Lower FF and AD and higher RF in widespread white matter areas were observed in the obese vs. non-obese group, and in the high-SAT vs. low-SAT group. Lower AD and higher RF were observed for the high-VAT vs. low-VAT group. All of these differences were only significant in females, not males, but higher RF in obese vs. non-obese was observed both in males and females; Our data support lower axonal density and fiber integrity, as well as higher inflammation cellularity in cognitively normal, middle-aged obese individuals, and those with high VAT and high SAT, especially in females. Overall, our data suggest the differential role of visceral and subcutaneous abdominal fat in promoting neuroinflammation and axonal damage.

Beta amyloid (Aβ) PET results are quantified in centiloids to standardize cerebral Aβ burden, an established Alzheimer's disease (AD) biomarker. Spectral domain optical coherence tomography (SD-OCT) imaging studies of the retina support retinal layer thinning as a biomarker of AD. Research examining the relationships between cerebral Aβ burden (in centiloids) and retinal layer thickness in preclinical AD remains understudied. This study aims to (1) examine the relationship between retinal layer thicknesses and PET centiloid values and (2) identify which retinal layers may be biomarkers of preclinical AD. Heidelberg SPECTRALIS captured SD-OCT images from 40 cognitively unimpaired older adults (ages 65-80; mean=67.8). HEYEX software computed the ETDRS thickness maps for each retinal layer (mRNFL, GCL, IPL, INL, OPL, ONL, IRL, ORL, RPE). In this analysis, retinal structure measurements (thickness, volume) were averaged for each layer (i.e., full layer, central quadrant, inner ring, outer ring). PET scans using florbetaben were conducted within six weeks of retinal imaging. PET results were quantified via centiloid scale combined with visual reads to determine PET status: positive ( n  = 10) or negative ( n  = 30). Linear regressions, controlling for age, examined whether retinal thickness averages predicted centiloid values. Logistic regressions, controlling for age, examined whether retinal thicknesses predicted binary PET results. Retinal mRNFL outer thickness significantly predicted PET centiloid value ( p  = 0.0206). Trends were seen in the relationship between mRNFL total layer thickness and PET centiloid ( p  = 0.0921) as well as mRNFL total layer volume and PET centiloid ( p  = 0.0679). The relationship between RPE outer thickness and PET status ( p  = 0.0757) and OPL total layer volume and PET status ( p  = 0.0572) was trending toward significance. The significant relationship between mRNFL and centiloid values aligned with previous work showing mRNFL thinning in MCI and AD. This is the first study to examine this relationship in a preclinical population. Retinal OPL findings replicate prior research by our group which demonstrated a relationship between OPL thickness and plasma ptau217, an indicator of cerebral amyloidosis. Further studies of longitudinal changes are needed to validate retinal biomarkers in preclinical AD.

BackgroundDecreased muscle volume and increased muscle-associated adipose tissue (MAAT, sum of intra and inter-muscular adipose tissue) of the foot intrinsic muscle compartment are associated with deformity, decreased function, and increased risk of ulceration and amputation in those with diabetic peripheral neuropathy (DPN).Research questionWhat is the muscle quality (normal, abnormal muscle, and adipose volumes) of the DPN foot intrinsic compartment, how does it change over time, and is muscle quality related to gait and foot function?MethodsComputed tomography was performed on the intrinsic foot muscle compartment of 45 subjects with DPN (mean age: 67.2 ± 6.4 years) at baseline and 3.6 years. Images were processed to obtain volumes of MAAT, highly abnormal, mildly abnormal, and normal muscle. For each category, annual rates of change were calculated. Paired t-tests compared baseline and follow-up. Foot function during gait was assessed using 3D motion analysis and the Foot and Ankle Ability Measure. Correlations between muscle compartment and foot function during gait were analyzed using Pearson’s correlations.ResultsTotal muscle volume decreased, driven by a loss of normal muscle and mildly abnormal muscle (p<0.05). MAAT and the adipose-muscle ratio increased. At baseline, 51.5% of the compartment was abnormal muscle or MAAT, increasing to 55.0% at follow-up. Decreased total muscle volume correlated with greater midfoot collapse during gait (r = -0.40, p = 0.02). Greater volumes of highly abnormal muscle correlated with a lower FAAM score (r = -0.33, p = 0.03).SignificanceMuscle volume loss may progress in parallel with MAAT accumulation, impacting contractile performance in individuals with DPN. Only 48.5% of the DPN intrinsic foot muscle compartment consists of normal muscle and greater abnormal muscle is associated with worse foot function. These changes identify an important target for rehabilitative intervention to slow or prevent muscle deterioration and poor foot outcomes.

Obesity and excess adiposity at midlife are risk factors for Alzheimer disease (AD). Visceral fat is known to be associated with insulin resistance and a pro-inflammatory state, the two mechanisms involved in AD pathology. We assessed the association of obesity, MRI-determined abdominal adipose tissue volumes, and insulin resistance with PET-determined amyloid and tau uptake in default mode network areas, and MRI-determined brain volume and cortical thickness in AD cortical signature in the cognitively normal midlife population. Thirty-two middle-aged (age: 51.27±6.12 years, 15 males, body mass index (BMI): 32.28±6.39 kg/m2) cognitively normal participants, underwent bloodwork, brain and abdominal MRI, and amyloid and tau PET scan. Visceral and subcutaneous adipose tissue (VAT, SAT) were semi-automatically segmented using VOXel Analysis Suite (Voxa). FreeSurfer was used to automatically segment brain regions using a probabilistic atlas. PET scans were acquired using [11C]PiB and AV-1451 tracers and were analyzed using PET unified pipeline. The association of brain volumes, cortical thicknesses, and PiB and AV-1451 standardized uptake value ratios (SUVRs) with BMI, VAT/SAT ratio, and insulin resistance were assessed using Spearman's partial correlation. VAT/SAT ratio was associated significantly with PiB SUVRs in the right precuneus cortex (p=0.034) overall, controlling for sex. This association was significant only in males (p=0.044), not females (p=0.166). Higher VAT/SAT ratio and PiB SUVRs in the right precuneus cortex were associated with lower cortical thickness in AD-signature areas predominantly including bilateral temporal cortices, parahippocampal, medial orbitofrontal, and cingulate cortices, with age and sex as covariates. Also, higher BMI and insulin resistance were associated with lower cortical thickness in bilateral temporal poles. In midlife cognitively normal adults, we demonstrated higher amyloid pathology in the right precuneus cortex in individuals with a higher VAT/SAT ratio, a marker of visceral obesity, along with a lower cortical thickness in AD-signature areas associated with higher visceral obesity, insulin resistance, and amyloid pathology.

OBJECTIVE:This study investigated how obesity, BMI ≥ 30 kg/m2, abdominal adiposity, and systemic inflammation relate to neuroinflammation using diffusion basis spectrum imaging. METHODS:We analyzed data from 98 cognitively normal midlife participants (mean age: 49.4 [SD 6.2] years; 34 males [34.7%]; 56 with obesity [57.1%]). Participants underwent brain and abdominal magnetic resonance imaging (MRI), blood tests, and amyloid positron emission tomography (PET) imaging. Abdominal visceral and subcutaneous adipose tissue (VAT and SAT, respectively) was segmented, and Centiloids were calculated. Diffusion basis spectrum imaging parameter maps were created using an in-house script, and tract-based spatial statistics assessed white matter differences in high versus low BMI values, VAT, SAT, insulin resistance, systemic inflammation, and Centiloids, with age and sex as covariates. RESULTS:Obesity, high VAT, and high SAT were linked to lower axial diffusivity, reduced fiber fraction, and increased restricted fraction in white matter. Obesity was additionally associated with higher hindered fraction and lower fractional anisotropy. Also, individuals with high C-reactive protein showed lower axial diffusivity. Higher restricted fraction correlated with continuous BMI and SAT particularly in male individuals, whereas VAT effects were similar in male and female individuals. CONCLUSIONS:The findings suggest that, at midlife, obesity and abdominal fat are associated with reduced brain axonal density and increased inflammation, with visceral fat playing a significant role in both sexes.

BACKGROUND AND PURPOSE: CT imaging exposes patients to ionizing radiation. MR imaging is radiation free but previously has not been able to produce diagnostic-quality images of bone on a timeline suitable for clinical use. We developed automated motion correction and use deep learning to generate pseudo-CT images from MR images. We aim to evaluate whether motion-corrected pseudo-CT produces cranial images that have potential to be acceptable for clinical use. MATERIALS AND METHODS: Patients younger than age 18 who underwent CT imaging of the head for either trauma or evaluation of cranial suture patency were recruited. Subjects underwent a 5-minute golden-angle stack-of-stars radial volumetric interpolated breath-hold MR image. Motion correction was applied to the MR imaging followed by a deep learning-based method to generate pseudo-CT images. CT and pseudo-CT images were evaluated and, based on indication for imaging, either presence of skull fracture or cranial suture patency was first recorded while viewing the MR imaging-based pseudo-CT and then recorded while viewing the clinical CT. RESULTS: A total of 12 patients underwent CT and MR imaging to evaluate suture patency, and 60 patients underwent CT and MR imaging for evaluation of head trauma. For cranial suture patency, pseudo-CT had 100% specificity and 100% sensitivity for the identification of suture closure. For identification of skull fractures, pseudo-CT had 100% specificity and 90% sensitivity. CONCLUSIONS: Our early results show that automated motion-corrected and deep learning-generated pseudo-CT images of the pediatric skull have potential for clinical use and offer a high level of diagnostic accuracy when compared with standard CT scans.

Background Myocardial fibrosis, as diagnosed on cardiac magnetic resonance imaging (cMRI) by late gadolinium enhancement (LGE), is associated with adverse outcomes in adults with hypertrophic cardiomyopathy (HCM), but its prevalence and magnitude in children with HCM have not been established. We investigated: (1) the prevalence and extent of myocardial fibrosis as detected by LGE cMRI; (2) the agreement between echocardiographic and cMRI measurements of cardiac structure; and (3) whether serum concentrations of N-terminal pro hormone B-type natriuretic peptide (NT-proBNP) and cardiac troponin-T are associated with cMRI measurements. Methods A cross-section of children with HCM from 9 tertiary-care pediatric heart centers in the U.S. and Canada were enrolled in this prospective NHLBI study of cardiac biomarkers in pediatric cardiomyopathy (ClinicalTrials.gov Identifier: NCT01873976). The median age of the 67 participants was 13.8 years (range 1-18 years). Core laboratories analyzed echocardiographic and cMRI measurements, and serum biomarker concentrations. Results In 52 children with non-obstructive HCM undergoing cMRI, overall low levels of myocardial fibrosis with LGE > 2% of left ventricular (LV) mass were detected in 37 (71%) (median %LGE, 9.0%; IQR: 6.0%, 13.0%; range, 0% to 57%). Echocardiographic and cMRI measurements of LV dimensions, LV mass, and interventricular septal thickness showed good agreement using the Bland-Altman method. NT-proBNP concentrations were strongly and positively associated with LV mass and interventricular septal thickness ( P < .001), but not LGE. Conclusions Low levels of myocardial fibrosis are common in pediatric patients with HCM seen at referral centers. Longitudinal studies of myocardial fibrosis and serum biomarkers are warranted to determine their predictive value for adverse outcomes in pediatric patients with HCM. (Am Heart J 2023;264:153-162.)

Obesity and adiposity at midlife, evidenced by high body mass index (BMI), are increasingly understood as a risk factor for Alzheimer’s disease (AD). Importantly, visceral fat is known to be associated with insulin resistance and proinflammatory state, the mechanisms involved in AD pathology. Herein, we aimed to assess the association between brain MRI volumes as well as amyloid and tau uptake with obesity, insulin resistance, and abdominal adipose tissue in cognitively normal midlife population. A total of 34 middle-aged (age: 51.27 ± 6.12 years, BMI: 32.28 ± 6.39 kg/m2), cognitively normal participants, underwent bloodwork, brain and abdominal MRI, as well as amyloid and tau PET scan. Homeostatic Model Assessment for Insulin Resistance (HOMAIR) > 1.9 was used as a measure of insulin resistance. Visceral and subcutaneous adipose tissue (VAT, SAT) were semi-automatically segmented using VOXel Analysis Suite (Voxa). FreeSurfer 7.1.1 was used for automatic segmentation of cortical and subcortical brain regions using a probabilistic atlas. Dynamic amyloid imaging was performed with a bolus injection of ∼15 mCi of [11C]PiB, followed by a 60-min scan. A single intravenous bolus of between 7.2-10.8 mCi of AV-1451 was administered. Data from the 30-60 minute, and 80-100 minute post-injection window for PiB and AV-1451 were used for the analysis, respectively. The association of brain volumes and PiB and AV-1451 SUVRs within the default mode network areas with BMI and VAT/SAT ratio were assessed using linear regression models. We observed lower right entorhinal white matter volumes in obese participants with insulin resistance compared to metabolically normal non-obese group (p = 0.004), without any significant difference in PiB or AV-1451 SUVRs. Regression models with sex, age and education as covariates showed a significant positive association between VAT/SAT ratio and left precuneus white matter PiB SUVRs (R2 = 0.31, p = 0.005), but no significant associations with AV-1451 SUVRs. In our midlife obese sample with insulin resistance, there were lower right entorhinal white matter volume, which is involved in relaying information to the hippocampus. We also demonstrate higher early amyloid pathology in AD-signature areas such as the precuneus in mid-life persons with high VAT/SAT ratio, a marker of visceral obesity.

Midlife obesity, as evidenced by high body mass index (BMI), is increasingly understood as a risk factor for development of Alzheimer’s disease. Importantly, visceral fat, by producing adipokines, contributes to inflammation as well as insulin resistance, mechanisms involved in development of Alzheimer’s disease. Herein, we aimed to assess association of white matter microstructure, measured by Diffusion tensor imaging (DTI), with obesity and abdominal adipose tissue composition at midlife. A total of 34 middle-aged (age: 51.27 ± 6.12 years, BMI: 32.28 ± 6.39 kg/m2), cognitively normal participants, underwent bloodwork, brain and abdominal MRI. Insulin resistance status was assessed using Homeostatic Model Assessment for Insulin Resistance. Abdominal visceral and subcutaneous adipose tissue (VAT, SAT) were automatically segmented using VOXel Analysis Suite (Voxa) and VAT/SAT ratio was calculated. A diffusion spectrum imaging scheme with a total of 98 diffusion samplings and a voxel size of 2 mm was acquired. Using DSI studio (https://dsi-studio.labsolver.org/), the diffusion data were reconstructed using generalized q-sampling imaging and the tensor metrics were calculated. Fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD, RD, respectively) for white matter tracts were compared across study groups using one-way ANOVA. Multiple regression models with sex, age, and education as covariates were used to assess the association of BMI, VAT/SAT ratio, and HOMA-IR with DTI metrics. Our data showed that BMI was negatively associated with AD values in right inferior fronto-occipital fasciculus, tapetum and forceps major of Corpus Callosum (CC), superior and middle cerebellar peduncle, and bilateral reticular tract. The negative association of BMI with AD values in tapetum of CC was significant only in women. Right reticular tract MD and superior cerebellar peduncle MD and RD values were also significantly associated with BMI. There were no significant associations between neither of diffusion metrics and VAT/SAT ratio or HOMA-IR. Association of midlife obesity with lower axonal diffusivity, reflective of lower white matter integrity, with differential patterns in men and women especially in CC, is in line with previous findings in healthy individuals. Reduced axonal diffusivity in reticular and brain stem pathways could be indicative of early dysregulations observed in Alzheimer’s disease.

In people with diabetes (DM) and peripheral neuropathy (PN), loss of bone mineral density (BMD) in the tarsals and metatarsals contribute to foot complications; however, changes in BMD of the calcaneal bone is most commonly reported. This study reports rate of change in BMD of all the individual bones in the foot, in participants with DM and PN. Our aim was to investigate whether the rate of BMD change is similar across all the bones of the foot. Participants with DM and PN (n = 60) were included in this longitudinal cohort study. Rate of BMD change of individual bones was monitored using computed tomography at baseline and 6 months, 18 months, and 3–4 years from baseline. Personal factors (age, sex, medication use, step count, sedentary time, and PN severity) were assessed. A random coefficient model estimated rate of change of BMD in all bones and Pearson correlation tested relationships between personal factor variables and rate of BMD change. Mean and calcaneal BMD decreased over the study period (p < 0.05). Individual tarsal and metatarsal bones present a range of rate of BMD change (-0.3 to -0.9%/year) but were not significantly different than calcaneal BMD change. Only age showed significant correlation with BMD and rate of BMD change. The rate of BMD change did not significantly differ across different foot bones at the group level in people with DM and PN without foot deformity. Asymmetric BMD loss between individual bones of the foot and aging may be indicators of pathologic changes and require further investigation. Metatarsal Phalangeal Joint Deformity Progression—R01. Registered 25 November 2015, https://clinicaltrials.gov/ct2/show/NCT02616263