Comparison of gross body fat-water magnetic resonance imaging at 3 Tesla to dual-energy X-ray absorptiometry in obese women

Poorly controlled glycemia and worse beta cell function associate with higher resting and total energy expenditure in adults with obesity and type 2 diabetes: A doubly labeled water study

BACKGROUND

Some studies comparing persons with and without type 2 diabetes (T2DM) show no difference in resting energy expenditure (REE). However, the degree of glycemic control may be a crucial factor in determining energy requirements. Few studies have employed the doubly labeled water (DLW) method in persons with T2DM to objectively measure daily energy expenditure.

AIMS

To determine relationships between glycemia, body composition, and energy expenditure in adults with obesity and T2DM. We hypothesized that worse hyperglycemia, insulin resistance, and beta cell function would associate with higher resting and total energy expenditure (TEE).

METHODS

Two cohorts age 31-50 years were included: 78 with obesity and T2DM, 19 with normal weight and no chronic disease. Baseline data from clinical biomarkers, intravenous glucose tolerance tests, DXA and MRI for body composition, and dietary intakes were used in multivariable regression models to predict REE and TEE. Additionally, comparisons were made by categorizing participants as having controlled or uncontrolled glycemia based on glucose levels ≥175 mg/dL.

RESULTS

REE was higher in participants with T2DM by 534.08 ± 74.35 kcal/d (p < 0.001). Higher fasting glucose and HbA1C levels associated with higher TEE. Abdominal SAT and VAT were also predictors in regression models accounting for 76 % of the variance in REE and 89 % of TEE. Participants with uncontrolled glycemia had 22 % higher adipose/lean ratio, two-fold higher VAT/SAT ratio, 21 % higher HOMA-IR score, and worse beta cell function (mean difference in HOMA2-%β of 74.09 ± 14.01, p < 0.001) than those with controlled glycemia. Both REE and TEE were significantly higher in uncontrolled glycemia, difference in REE of 154.17 ± 96.28 kcals/day (p = 0.04) and difference in TEE of 480.64 ± 215.45 kcals/day (p = 0.03).

CONCLUSIONS

Poor beta cell function and uncontrolled glycemia associate with higher REE and TEE in persons with obesity and T2DM. This study is registered with clinicaltrials.gov identifier: NCT01239550.

Factors that predict weight loss success differ by diet intervention type

Background

Many types of diet intervention can achieve negative energy balance and successful weight loss in persons with obesity. However, within any dietary strategy, there is large inter-individual variation in the weight loss response. The aim of this study is to determine factors that predict weight loss success for diet interventions that vary by macronutrient and caloric composition.

Methods

Participants with BMI 30.0 to 49.9 kg/m2 self-selected one of three diet intervention trials for weight loss: low carbohydrate (LOW CHO), low fat (LOW FAT), or low calorie (LOW KCAL). Multivariable regression models were developed to determine the significance of predictor demographic, body composition, metabolic, clinical, and dietary variables for each diet type.

Results

Weight loss over 12-16 weeks averaging -5.1 ± 4.0 kg from baseline weight, p < 0.001, was not significantly different among diet types. Several different factors were identified that account for the inter-individual variance in weight loss success. Regardless of diet type, the most robust predictor of weight loss success was completion of the intervention, accounting for 20-30% of the variance. Factors predicting diet intervention completion were age, physical activity level, blood leptin level, blood pressure, and the amount of weight loss occurring. Differences by diet type in cardiometabolic risk factor reduction were identified with LOW CHO decreasing glycemia/insulinemia factors, LOW FAT decreasing lipidemia factors, and LOW KCAL decreasing inflammatory factors.

Conclusion

These data provide evidence to inform more precise and personalized approaches to diet intervention for weight loss and cardiometabolic health.

Semiautomated segmentation of lower extremity MRI reveals distinctive subcutaneous adipose tissue in lipedema: a pilot study

Purpose

Lipedema is a painful subcutaneous adipose tissue (SAT) disease involving disproportionate SAT accumulation in the lower extremities that is frequently misdiagnosed as obesity. We developed a semiautomatic segmentation pipeline to quantify the unique lower-extremity SAT quantity in lipedema from multislice chemical-shift-encoded (CSE) magnetic resonance imaging (MRI).

Approach

Patients with lipedema (n = 15 ) and controls (n = 13 ) matched for age and body mass index (BMI) underwent CSE-MRI acquired from the thighs to ankles. Images were segmented to partition SAT and skeletal muscle with a semiautomated algorithm incorporating classical image processing techniques (thresholding, active contours, Boolean operations, and morphological operations). The Dice similarity coefficient (DSC) was computed for SAT and muscle automated versus ground truth segmentations in the calf and thigh. SAT and muscle volumes and the SAT-to-muscle volume ratio were calculated across slices for decades containing 10% of total slices per participant. The effect size was calculated, and Mann-Whitney U test applied to compare metrics in each decade between groups (significance: two-sided P < 0.05 ).

Results

Mean DSC for SAT segmentations was 0.96 in the calf and 0.98 in the thigh, and for muscle was 0.97 in the calf and 0.97 in the thigh. In all decades, mean SAT volume was significantly elevated in participants with versus without lipedema (P < 0.01 ), whereas muscle volume did not differ. Mean SAT-to-muscle volume ratio was significantly elevated (P < 0.001 ) in all decades, where the greatest effect size for distinguishing lipedema was in the seventh decade approximately midthigh (r = 0.76 ).

Conclusions

The semiautomated segmentation of lower-extremity SAT and muscle from CSE-MRI could enable fast multislice analysis of SAT deposition throughout the legs relevant to distinguishing patients with lipedema from females with similar BMI but without SAT disease.

Artificial Neural Network Models for Accurate Predictions of Fat-Free and Fat Masses, Using Easy-to-Measure Anthropometric Parameters

Abdominal fat and fat-free masses report a close association with cardiometabolic risks, therefore this specific body compartment presents more interest than whole-body masses. This research aimed to develop accurate algorithms that predict body masses and specifically trunk fat and fat-free masses from easy to measure parameters in any setting. The study included 104 apparently healthy subjects, but with a higher-than-normal percent of adiposity or waist circumference. Multiple linear regression (MLR) and artificial neural network (ANN) models were built for predicting abdominal fat and fat-free masses in patients with relatively low cardiometabolic risks. The data were divided into training, validation and test sets, and this process was repeated 20 times per each model to reduce the bias of data division on model accuracy. The best performance models used a maximum number of five anthropometric inputs, with higher R² values for ANN models than for MLR models (R² = 0.96-0.98 vs. R² = 0.80-0.94, p = 0.006). The root mean square error (RMSE) for all predicted parameters was significantly lower for ANN models than for MLR models, suggesting a higher accuracy for ANN models. From all body masses predicted, trunk fat mass and fat-free mass registered the best performance with ANN, allowing a possible error of 1.84 kg for predicting the correct trunk fat mass and 1.48 kg for predicting the correct trunk fat-free mass. The developed algorithms represent cost-effective prediction tools for the most relevant adipose and lean tissues involved in the physiopathology of cardiometabolic risks.

Noninvasive monitoring of muscle atrophy and bone metabolic disorders using dual-energy X-ray absorptiometry in diabetic mice

Tracking metabolic changes in skeletal muscle and bone using animal models of diabetes mellitus (DM) provides important insights for the management of DM complications. In this study, we aimed to establish a method for monitoring changes in body composition characteristics, such as fat mass, skeletal muscle mass (lean mass), bone mineral density, and bone mineral content, during DM progression using a dual-energy X-ray absorptiometry (DXA) system in a mouse model of streptozotocin (STZ)-induced type 1 DM. In the DM model, STZ administration resulted in increased blood glucose levels, increased water and food intake, and decreased body weight. Serum insulin levels were significantly decreased on day 30 of STZ administration. The DXA analysis revealed significant and persistent decreases in fat mass, lower limb skeletal muscle mass, and bone mineral content in DM mice. We measured tibialis anterior (TA) muscle weight and performed a quantitative analysis of tibial microstructure by micro-computed tomography imaging in DM mice. The TA muscle weight of DM mice was significantly lower than that of control mice. In addition, the trabecular bone volume fraction, trabecular thickness, trabecular number, and cortical thickness were significantly decreased in DM mice. Pearson's product-moment correlation coefficient analysis showed a high correlation between the DXA-measured and actual body composition. In conclusion, longitudinal measurement of body composition changes using a DXA system may be useful for monitoring abnormalities in muscle and bone metabolism in animal models of metabolic diseases such as DM mice.

TG/HDL Ratio Is an Independent Predictor for Estimating Resting Energy Expenditure in Adults with Normal Weight, Overweight, and Obesity

Factors that determine resting energy expenditure (REE) remain under investigation, particularly in persons with a high body mass index (BMI). The accurate estimation of energy expenditure is essential for conducting comprehensive nutrition assessments, planning menus and meals, prescribing weight and chronic disease interventions, and the prevention of malnutrition. This study aimed to: (a) determine the contribution of cardiometabolic biomarkers to the inter-individual variation in REE in persons categorized by BMI; and (b) assess the contribution of these biomarkers in the prediction of REE when persons of varying BMI status were categorized by their glycemic and metabolic syndrome status. Baseline data from 645 adults enrolled in diet intervention trials included REE measured by indirect calorimetry, body composition by dual energy X-ray absorptiometry, anthropometrics, and cardiometabolic biomarkers. Multivariate linear regression modeling was conducted to determine the most parsimonious model that significantly predicted REE by BMI category, metabolic syndrome status, and glycemic status. Modeling with the traditional predictors (age, sex, height, weight) accounted for 58-63% of the inter-individual variance in REE. When including age, sex, height, weight and fat-free mass as covariates, adding TG/HDL to regression modeling accounted for 71-87% of the variance in REE. The finding that TG/HDL is an independent predictor in estimating REE was further confirmed when participants were categorized by metabolic syndrome status and by glycemic status. The clinical utility of calculating the TG/HDL ratio not only aids health care providers in identifying patients with impaired lipid metabolism but can optimize the estimation of REE to better meet therapeutic goals for weight and disease management.

Seeking an Initial-Weight-Independent Metric in a Mediterranean Cohort of Gastric Bypass Patients: the %AWL Revisited

BACKGROUND

Most relative weight-loss metrics follow the formula "Weight loss(%) = 100 · (Initial BMI - Final BMI) / (Initial BMI-a)," where a is the reference point that defines the metric. The percentage of total weight loss (%TWL, a = 0) and percentage of excess weight loss (%EWL, a = 25) are influenced by a patient's initial weight. Recently, the percentage of alterable weight loss metric (%AWL, a = 13) has been reported to produce initial-weight-independent outcomes.

OBJECTIVES

This study aimed to replicate the methodology used for %AWL determination in a Mediterranean cohort of bariatric patients.

SETTINGS

Multicenter study in 10 large hospitals in Spain.

METHODS

Two large prospective databases were retrospectively searched for all primary laparoscopic gastric bypass patients with 2 years of follow-up. Outcomes at nadir were expressed and analyzed with 26 different metrics (a from 0 to 25), looking for the metric whose outcomes produced (1) the lowest coefficient of variation, (2) no differences between initially lighter and heavier patients, and (3) no correlation with patients' initial BMI.

RESULTS

A cohort of 1793 patients was stratified into 4 gender-age groups: younger women (YW, n = 733), older women (OW, n = 674), younger men (YM, n = 197), and older men (OM, n = 189). The calculations suggested an optimal reference point of 18 kg/m², defining a new metric (percentage of Mediterranean alterable weight loss, %MAWL). When %TWL, %EWL, %AWL, and %MAWL were tested on the whole sample, only %MAWL produced initial-weight-independent results.

CONCLUSIONS

In our Mediterranean cohort of patients, a reference point of 18 (and not 13) yielded initial-weight-independent outcomes.

Measuring Muscle Mass and Strength in Obesity: a Review of Various Methods

Lower muscle mass in populations with obesity is associated obesity-related diseases like hypertension and type 2 diabetes mellitus. Bariatric surgery leads to sustained weight loss. During the weight reduction, loss of muscle should be minimized. Thus reliable quantification of muscle mass is much needed and therefore the also the need for validated methods. Imaging methods, magnetic resonance imaging and computed tomography scan, have been the gold standard for many years. However, these methods are costly and have limitations such as the maximum weight. Dual-energy X-ray absorptiometry is currently the most used alternative. Other, less expensive methods are very limited in their validation in populations with morbid obesity. This narrative review summarizes the current knowledge regarding measuring muscle mass and strength in obesity.

Quantification of Human Central Adipose Tissue Depots: An Anatomically Matched Comparison Between DXA and MRI

Excess visceral adipose tissue (VAT) and VAT volume relative to subcutaneous adipose tissue (SAT) are associated with elevated health risks. This study compares fat measurements by dual-energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI). In total, 21 control subjects (Control) and 16 individuals with metabolic syndrome (MetSyn) were scanned by DXA and MRI. The region measured by MRI was matched to the android region defined by DXA, and MRI reproducibility was also evaluated. In addition, liver fat fraction was quantified via MRI and whole-body fat by DXA. VAT measurements are interchangeable between DXA and MRI in the Control (R = 0.946), MetSyn (R = 0.968), and combined cohort (R = 0.983). VAT/SAT ratio did not differ in the Control group (P = .10), but VAT/SAT ratio measured by DXA was significantly higher in the MetSyn group (P < .01) and the combined (P = .03) cohort. Intraobserver (ICC = 0.998) and interobserver (ICC = 0.977) reproducibility of MRI VAT measurements was excellent. Liver fat fraction by MRI was higher (P = .001) in MetSyn (12.4% ± 7.6%) than in controls (2.6% ± 2.2%), as was whole-body fat percentage by DXA (P = .001) between the MetSyn (42.0% ± 8.1%) and Control groups (26.7% ± 6.9%). DXA and MRI VAT are interchangeable when measured over an anatomically matched region of the abdomen, while SAT and VAT/SAT ratio differ between the 2 modalities.

Association between carbohydrate intake and body composition: The Korean National Health and Nutrition Examination Survey

OBJECTIVE

Several studies have investigated the effects of dietary carbohydrate intake on body composition. However, the results are controversial and few studies have been conducted on an Asian population. The aim of this study was to investigate whether dietary carbohydrate intake is associated with body composition in Korean adults.

METHODS

The present study used data from the Korean National Health and Nutrition Examination Survey, a cross-sectional survey of Korean civilians, conducted from 2008 to 2011. The study analyzed 9594 participants. Carbohydrate intake was defined as the proportion of energy consumed from carbohydrate. Waist circumference, body mass index, and lean and fat mass using a whole-body dual-energy x-ray absorptiometry scanner were measured as body composition parameters.

RESULTS

After adjusting for age, household income, smoking, alcohol consumption, physical activity, history of diabetes, hypertension, dyslipidemia, and intake of energy and fiber per day, the proportion of carbohydrate intake was positively correlated with total limb lean mass in men (β = 0.141, P = 0.046), and in women, the proportion of carbohydrate intake was positively associated with appendicular skeletal muscle mass index (β = 0804, P = 0.003) but negatively associated with trunk fat percentage (β = -0.075, P = 0.026). Total limb lean mass and appendicular skeletal muscle mass index in women showed an increasing trend as the proportion of carbohydrate intake increased.

CONCLUSIONS

No positive association was found between the proportion of carbohydrate intake and any measure of obesity or body fat mass in either men or women. Further studies are needed to evaluate the effects of quantity and quality of carbohydrate intake on body composition.

3D Chemical Shift-Encoded MRI for Volume and Composition Quantification of Abdominal Adipose Tissue During an Overfeeding Protocol in Healthy Volunteers

BACKGROUND

Overweight and obesity are major worldwide health concerns characterized by an abnormal accumulation of fat in adipose tissue (AT) and liver.

PURPOSE

To evaluate the volume and the fatty acid (FA) composition of the subcutaneous adipose tissue (SAT) and the visceral adipose tissue (VAT) and the fat content in the liver from 3D chemical-shift-encoded (CSE)-MRI acquisition, before and after a 31-day overfeeding protocol.

STUDY TYPE

Prospective and longitudinal study.

SUBJECTS

Twenty-one nonobese healthy male volunteers.

FIELD STRENGTH/SEQUENCE

A 3D spoiled-gradient multiple echo sequence and STEAM sequence were performed at 3T.

ASSESSMENT

AT volume was automatically segmented on CSE-MRI between L2 to L4 lumbar vertebrae and compared to the dual-energy X-ray absorptiometry (DEXA) measurement. CSE-MRI and MR spectroscopy (MRS) data were analyzed to assess the proton density fat fraction (PDFF) in the liver and the FA composition in SAT and VAT. Gas chromatography-mass spectrometry (GC-MS) analyses were performed on 13 SAT samples as a FA composition countermeasure.

STATISTICAL TESTS

Paired t-test, Pearson's correlation coefficient, and Bland-Altman plots were used to compare measurements.

RESULTS

SAT and VAT volumes significantly increased (P < 0.001). CSE-MRI and DEXA measurements were strongly correlated (r = 0.98, P < 0.001). PDFF significantly increased in the liver (+1.35, P = 0.002 for CSE-MRI, + 1.74, P = 0.002 for MRS). FA composition of SAT and VAT appeared to be consistent between localized-MRS and CSE-MRI (on whole segmented volume) measurements. A significant difference between SAT and VAT FA composition was found (P < 0.001 for CSE-MRI, P = 0.001 for MRS). MRS and CSE-MRI measurements of the FA composition were correlated with the GC-MS results (for ndb: r_MRS/GC-MS  = 0.83 P < 0.001, r_{CSE-MRI/GC-MS}  = 0.84, P = 0.001; for nmidb: r_MRS/GC-MS  = 0.74, P = 0.006, r_{CSE-MRI/GC-MS}  = 0.66, P = 0.020) DATA CONCLUSION: The follow-up of liver PDFF, volume, and FA composition of AT during an overfeeding diet was demonstrated through different methods. The CSE-MRI sequence associated with a dedicated postprocessing was found reliable for such quantification.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1587-1599.

MRI adipose tissue and muscle composition analysis-a review of automation techniques

MRI is becoming more frequently used in studies involving measurements of adipose tissue and volume and composition of skeletal muscles. The large amount of data generated by MRI calls for automated analysis methods. This review article presents a summary of automated and semi-automated techniques published between 2013 and 2017. Technical aspects and clinical applications for MRI-based adipose tissue and muscle composition analysis are discussed based on recently published studies. The conclusion is that very few clinical studies have used highly automated analysis methods, despite the rapidly increasing use of MRI for body composition analysis. Possible reasons for this are that the availability of highly automated methods has been limited for non-imaging experts, and also that there is a limited number of studies investigating the reproducibility of automated methods for MRI-based body composition analysis.

DXA-measured visceral fat mass and lean body mass reflect abnormal metabolic phenotypes among some obese and nonobese Chinese children and adolescents

BACKGROUND AND AIM

The exact constellation of body composition characteristics among metabolically unhealthy obese (MUO) and nonobese (MUNO) children and adolescents remains unclear. The purpose of this study was to identify the major body composition determinants of metabolically unhealthy phenotypes among Chinese children and adolescents.

METHODS AND RESULTS

We used data from a cross-sectional survey in 2015 that included 1983 children and adolescents aged 6-18 years. Subjects were classified into two phenotypes based on a combination of body mass index (BMI) and metabolic syndrome components. Body composition was measured by dual-energy X-ray absorptiometry (DXA). Among all boys and among adolescent boys, those with MUNO phenotypes displayed significantly higher indices of body composition except for fat mass (FM) percentage and trunk-to-legs FM ratio compared with the metabolically healthy nonobese phenotype (all P < 0.05). MUO individuals had higher arm FM, lean body mass (LBM), and trunk lean mass compared to metabolically healthy obese individuals (all P < 0.05). Visceral fat mass (VFM) and BMI were the major independent determinants of MUNO (VFM, 6- to 9-year-old boys, OR = 1.02, 95% CI = 1.00-1.03, P = 0.021; BMI, 6- to 9-year-old girls, OR = 1.90, 95% CI = 1.31-2.84, P = 0.001; and adolescent boys, OR = 1.34, 95% CI = 1.23-1.44, P < 0.001). LBM was the major independent predictor of MUO among adolescent boys (OR = 1.90, 95% CI = 1.03-1.17, P = 0.003).

CONCLUSIONS

Among children and adolescents, the metabolically unhealthy phenotype was associated with excess of body composition, but with significant differences observed based on age and sex. VFM and LBM derived by DXA can predict the metabolically unhealthy phenotype effectively in specific sex and age groups.

Advanced body composition assessment: from body mass index to body composition profiling

This paper gives a brief overview of common non-invasive techniques for body composition analysis and a more in-depth review of a body composition assessment method based on fat-referenced quantitative MRI. Earlier published studies of this method are summarized, and a previously unpublished validation study, based on 4753 subjects from the UK Biobank imaging cohort, comparing the quantitative MRI method with dual-energy X-ray absorptiometry (DXA) is presented. For whole-body measurements of adipose tissue (AT) or fat and lean tissue (LT), DXA and quantitative MRIs show excellent agreement with linear correlation of 0.99 and 0.97, and coefficient of variation (CV) of 4.5 and 4.6 per cent for fat (computed from AT) and LT, respectively, but the agreement was found significantly lower for visceral adipose tissue, with a CV of >20 per cent. The additional ability of MRI to also measure muscle volumes, muscle AT infiltration and ectopic fat, in combination with rapid scanning protocols and efficient image analysis tools, makes quantitative MRI a powerful tool for advanced body composition assessment.

Balanced high fat diet reduces cardiovascular risk in obese women although changes in adipose tissue, lipoproteins, and insulin resistance differ by race

BACKGROUND

We previously reported that consuming a balanced high fat diet (BHFD) wherein total saturated fat was reduced and total unsaturated fat increased by proportionately balancing the type of fat (1/3 saturated, 1/3 monounsaturated, 1/3 polyunsaturated) led to significant improvements in inflammatory burden, blood pressure, and vascular function in obese premenopausal European American (EA) and African American (AA) women.

OBJECTIVE

Here we compared changes in adipose tissue, lipoproteins, insulin resistance, and cardiovascular risk between EA and AA women.

METHODS

Dietary intakes, plasma fatty acids, lipids, apolipoproteins, lipoproteins, HOMA-IR and ASCVD risk was measured in 144 women who consumed BHFD for 16 weeks. Generalized linear modeling was performed while controlling for change in body weight.

RESULTS

EA women had greater reductions in visceral adipose tissue. Only EA women had significant reductions in fasting insulin levels (↓24.8%) and HOMA-IR (↓29%) scores. In EA women, the most significant improvements occurred in VLDL particle size (↑), apolipoprotein B levels (↑), serum TG (↓), number of plasma LDL particles (↓), and serum LDL-cholesterol (↓). In AA women, significant improvements occurred in HDL particle size (↑), number of large HDL particles (↑), and apolipoprotein AI levels (↑). Consequently, both groups had improved ASCVD risk scores (↓5.5%).

CONCLUSIONS

Consuming the balanced high fat diet led to significant reduction in cardiovascular risk factors in both groups. However, the pattern of response to BHFD differed with EA women responding more in components of the apolipoprotein B pathway versus AA women responding more in components of the apolipoprotein AI pathway.

Expression of Metabolic Syndrome in Women with Severe Obesity

BACKGROUND

The prevalence of metabolic syndrome (MetS) generally rises with increasing adiposity, but tends to plateau at the highest levels of body mass index (BMI) with some individuals, even with severe obesity, expressing few or no components of MetS. We examined factors associated with the expression of MetS in severely obese women participating in a large observational study.

METHODS

Anthropometrics, including Heath equation-adjusted bioimpedance-determined fat-free mass (FFM) and fat mass (FM), lipids and related laboratory measurements, resting energy expenditure (REE), and respiratory quotient (RQ), were studied in 949 women with severe obesity.

RESULTS

Even though the mean BMI was 45.7 kg/m² and all participants met MetS criteria for increased waist circumference, 30% of subjects did not have MetS. Unadjusted FM (P = 0.0011), FFM (P < 0.0001), and REE (P < 0.0001) were greater in the women with MetS. Surprisingly, in multivariate logistic regression FFM was positively associated with MetS (P = 0.0002), while FM was not (P = 0.89). Moreover, FFM, not FM, was significantly associated with all five components of MetS except for triglyceride levels. REE and RQ were higher in those with MetS, and REE was strongly associated with multiple components of MetS.

CONCLUSIONS

In women with severe obesity, higher FFM and REE were paradoxically associated with increased rather than decreased risk of MetS, while FFM-adjusted FM was unrelated to MetS.

Whole-body adipose tissue and lean muscle volumes and their distribution across gender and age: MR-derived normative values in a normal-weight Swiss population

PURPOSE

To determine age- and gender-dependent whole-body adipose tissue and muscle volumes in healthy Swiss volunteers in Dixon MRI in comparison with anthropometric and bioelectrical impedance (BIA) measurements.

METHODS

Fat-water-separated whole-body 3 Tesla MRI of 80 healthy volunteers (ages 20 to 62 years) with a body mass index (BMI) of 17.5 to 26.2 kg/m² (10 men, 10 women per decade). Age and gender-dependent volumes of total adipose tissue (TAT), visceral adipose tissue (VAT), total abdominal subcutaneous adipose tissue (ASAT) and total abdominal adipose tissue (TAAT), and the total lean muscle tissue (TLMT) normalized for body height were determined by semi-automatic segmentation, and correlated with anthropometric and BIA measurements as well as lifestyle parameters.

RESULTS

The TAT, ASAT, VAT, and TLMT indexes (TATi, ASATi, VATi, and TLMTi, respectively) (L/m²  ± standard deviation) for women/men were 6.4 ± 1.8/5.3 ± 1.7, 1.6 ± 0.7/1.2 ± 0.5, 0.4 ± 0.2/0.8 ± 0.5, and 5.6 ± 0.6/7.1 ± 0.7, respectively. The TATi correlated strongly with ASATi (r > 0.93), VATi, BMI and BIA (r > 0.70), and TAATi (r > 0.96), and weak with TLMTi for both genders (r > -0.34). The VAT was the only parameter showing an age dependency (r > 0.32). The BMI and BIA showed strong correlation with all MR-derived adipose tissue volumes. The TAT mass was estimated significantly lower from BIA than from MRI (both genders P < .001; mean bias -5 kg).

CONCLUSIONS

The reported gender-specific MRI-based adipose tissue and muscle volumes might serve as normative values. The estimation of adipose tissue volumes was significantly lower from anthropometric and BIA measurements than from MRI. Magn Reson Med 79:449-458, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Fat-water MRI of a diet-induced obesity mouse model at 15.2T

Quantitative fat-water MRI (FWMRI) methods provide valuable information about the distribution, volume, and composition of adipose tissue (AT). Ultra high field FWMRI of animal models may have the potential to provide insights into the progression of obesity and its comorbidities. Here, we present quantitative FWMRI with all known confounder corrections on a 15.2T preclinical scanner for noninvasive in vivo monitoring of an established diet-induced obesity mouse model. Male C57BL/6J mice were placed on a low-fat (LFD) or a high-fat diet (HFD). Three-dimensional (3-D) multiple gradient echo MRI at 15.2T was performed at baseline, 4, 8, 12, and 16 weeks after diet onset. A 3-D fat-water separation algorithm and additional processing were used to generate proton-density fat fraction (PDFF), local magnetic field offset, and [Formula: see text] maps. We examined these parameters in perirenal AT ROIs from LFD and HFD mice. The data suggest that PDFF, local field offset, and [Formula: see text] have different time course behaviors between LFD and HFD mice over 16 weeks. This work suggests FWMRI at 15.2T may be a useful tool for longitudinal studies of adiposity due to the advantages of ultra high field although further investigation is needed to understand the observed time course behavior.

Computer-based magnetic resonance imaging as a tool in clinical diagnosis in neurodegenerative diseases

Magnetic resonance imaging (MRI) is one of the core elements within the differential diagnostic work-up of patients with neurodegenerative diseases such as dementia syndromes, Parkinsonian syndromes, and motor neuron diseases. Currently, computerized MRI analyses are not routinely used for individual diagnosis; however, they have improved the anatomical understanding of pathomorphological alterations in various neurodegenerative diseases by quantitative comparisons between patients and controls at the group level. For multiparametric MRI protocols, including T1-weighted MRI, diffusion-weighted imaging, and intrinsic functional connectivity MRI, the potential as a surrogate marker is a subject of investigation. The additional value of MRI with respect to diagnosis at the individual level and for future disease-modifying multicentre trials remains to be defined. Here, we give an overview of recent applications of multiparametric MRI to patients with various neurodegenerative diseases. Starting from applications at the group level, continuous progress of a transfer to individual diagnostic classification is ongoing.

Magnetic resonance imaging of skeletal muscle disease

Neuromuscular diseases often exhibit a temporally varying, spatially heterogeneous, and multifaceted pathology. The goals of this chapter are to describe and evaluate the use of quantitative magnetic resonance imaging (MRI) methods to characterize muscle pathology. The following criteria are used for this evaluation: objective measurement of continuously distributed variables; clear and well-understood relationship to the pathology of interest; sensitivity to improvement or worsening of clinical status; and the measurement properties of accuracy and precision. Two major classes of MRI methods meet all of these criteria: (1) MRI methods for measuring muscle contractile volume or cross-sectional area by combining structural MRI and quantitative fat-water MRI; and (2) an MRI method for characterizing the edema caused by inflammation, the measurement of the transverse relaxation time constant (T2). These methods are evaluated with respect to the four criteria listed above and examples from neuromuscular disorders are provided. Finally, these methods are summarized and synthesized and recommendations for additional quantitative MRI developments are made.

Segmentation and quantification of adipose tissue by magnetic resonance imaging

In this brief review, introductory concepts in animal and human adipose tissue segmentation using proton magnetic resonance imaging (MRI) and computed tomography are summarized in the context of obesity research. Adipose tissue segmentation and quantification using spin relaxation-based (e.g., T1-weighted, T2-weighted), relaxometry-based (e.g., T1-, T2-, T2*-mapping), chemical-shift selective, and chemical-shift encoded water-fat MRI pulse sequences are briefly discussed. The continuing interest to classify subcutaneous and visceral adipose tissue depots into smaller sub-depot compartments is mentioned. The use of a single slice, a stack of slices across a limited anatomical region, or a whole body protocol is considered. Common image post-processing steps and emerging atlas-based automated segmentation techniques are noted. Finally, the article identifies some directions of future research, including a discussion on the growing topic of brown adipose tissue and related segmentation considerations.

Imaging methods for analyzing body composition in human obesity and cardiometabolic disease

Advances in the technological qualities of imaging modalities for assessing human body composition have been stimulated by accumulating evidence that individual components of body composition have significant influences on chronic disease onset, disease progression, treatment response, and health outcomes. Importantly, imaging modalities have provided a systematic method for differentiating phenotypes of body composition that diverge from what is considered normal, that is, having low bone mass (osteopenia/osteoporosis), low muscle mass (sarcopenia), high fat mass (obesity), or high fat with low muscle mass (sarcopenic obesity). Moreover, advances over the past three decades in the sensitivity and quality of imaging not just to discern the amount and distribution of adipose and lean tissue but also to differentiate layers or depots within tissues and cells is enhancing our understanding of distinct mechanistic, metabolic, and functional roles of body composition within human phenotypes. In this review, we focus on advances in imaging technologies that show great promise for future investigation of human body composition and how they are being used to address the pandemic of obesity, metabolic syndrome, and diabetes.

Correlation of the controlled attenuation parameter with indices of liver steatosis in overweight or obese individuals: a pilot study

OBJECTIVE

The aim of this study was to assess the clinical relevance of the controlled attenuation parameter (CAP) by analyzing the correlations between CAP and indirect indices of liver steatosis in obese or overweight individuals.

METHODS

Consecutive participants were prospectively enrolled. BMI, waist circumference, hepatic steatosis index, fatty liver index, percent fat mass and regional fat masses as assessed by dual-energy X-ray absorptiometry (DXA), fat signal fraction as assessed by MRI, and CAP were obtained. Pearson's r coefficient was used to test the correlation between two study variables.

RESULTS

A total of 88 individuals were studied. They included 31 men [age, 50.4 years (12.9 years); BMI, 30.7 kg/m (4.8 kg/m)] and 57 women [age, 49.0 years (12.6 years); BMI, 31.4 kg/m (5.6 kg/m)]. DXA, anthropometric parameters, and fatty liver index were moderately correlated with CAP in men. In women, there was a moderate correlation of CAP with the hepatic steatosis index and anthropometric parameters and only a slight or fair correlation of CAP with DXA parameters. CAP and fat signal fraction showed a good correlation (r=0.65 in men, P=0.002; r=0.68 in women, P=0.0009).

CONCLUSION

Measurement of CAP is a reliable method for noninvasive assessment of liver steatosis, showing a correlation with other indirect markers of central obesity and a good correlation with MRI results.

DXA-measured visceral adipose tissue predicts impaired glucose tolerance and metabolic syndrome in obese Caucasian and African-American women

BACKGROUND/OBJECTIVES

New methods to measure visceral adipose tissue (VAT) by dual-energy X-ray absorptiometry (DXA) may help discern sex, race and phenotype differences in the role of VAT in cardiometabolic risk. This study was designed (1) to compare relationships of DXA-VAT, anthropometric and body composition variables with cardiometabolic risk factors in obese women; (2) to determine which variables most robustly predict impaired glucose tolerance (IGT) and metabolic syndrome (MetSx); and (3) to determine thresholds for DXA-VAT by race.

SUBJECTS/METHODS

VAT mass (g) and volume (cm(3)) were measured in 229 obese (body mass index (BMI), 30-49.9) women aged 21-69 years of European-American (EA=123) and African-American (AA=106) descent using the CoreScan algorithm on a Lunar iDXA scanner. Linear regression modeling and areas under the curve (AUC of ROC (receiver operating characteristic) curves) compared relationships with cardiometabolic risk. Bootstrapping with LASSO (least absolute shrinkage and selection operator) regression modeling determined thresholds and predictors of IGT and MetSx.

RESULTS

DXA-VAT explained more of the variance in triglycerides, blood pressure, glucose and homeostatic model assessment-insulin resistance (HOMA-IR) compared with anthropometric and other body composition variables. DXA-VAT also had the highest AUC for IGT (0.767) and MetSx (0.749). Including race as a variable and the interaction between VAT and race in modeling did not significantly change the results. Thresholds at which the probability of developing IGT or MetSx was⩾50% were determined separately for AA women (IGT: 2120 cm(3); MetSx: 1320 cm(3)) and EA women (IGT: 2550 cm(3); MetSx: 1713 cm(3)). The odds for IGT or MetSx were fourfold greater with each standard deviation increase in DXA-VAT.

CONCLUSIONS

DXA-VAT provides robust clinical information regarding cardiometabolic risk in AA and EA obese women and offers potential utility in the risk reduction interventions.

Body mass index in dementia

This review comprehensively examines the current knowledge on the relationship between body mass index (BMI) and dementia. The association between BMI and cognition is complex: in younger adults, higher BMIs are associated with impaired cognition. Overweight and obesity in middle age are linked to increased future dementia risk in old age. However, when examined in old age, higher BMIs are associated with better cognition and decreased mortality. Little is known about the optimal BMI for well-being and survival in populations already suffering from dementia. Lifetime trends in weight, rather than single measures, might predict prognosis better and help untangle these apparent contradictions. Thus, the need arises to properly monitor BMI trends in affected dementia patients. Registries can include BMI, improving the management of dementia patients throughout the whole course of the disease. The role of central obesity and systemic inflammation on brain pathology and cognitive decline are discussed in this review. Understanding the life-course changes in BMI and their influence on dementia risk, cognitive prognosis and mortality after diagnosis may provide new insights into the underlying pathophysiology of dementia and shape possible intervention and treatment strategies.

Multi-parametric MRI characterization of healthy human thigh muscles at 3.0 T - relaxation, magnetization transfer, fat/water, and diffusion tensor imaging

Muscle diseases commonly have clinical presentations of inflammation, fat infiltration, fibrosis, and atrophy. However, the results of existing laboratory tests and clinical presentations are not well correlated. Advanced quantitative MRI techniques may allow the assessment of myo-pathological changes in a sensitive and objective manner. To progress towards this goal, an array of quantitative MRI protocols was implemented for human thigh muscles; their reproducibility was assessed; and the statistical relationships among parameters were determined. These quantitative methods included fat/water imaging, multiple spin-echo T2 imaging (with and without fat signal suppression, FS), selective inversion recovery for T1 and quantitative magnetization transfer (qMT) imaging (with and without FS), and diffusion tensor imaging. Data were acquired at 3.0 T from nine healthy subjects. To assess the repeatability of each method, the subjects were re-imaged an average of 35 days later. Pre-testing lifestyle restrictions were applied to standardize physiological conditions across scans. Strong between-day intra-class correlations were observed in all quantitative indices except for the macromolecular-to-free water pool size ratio (PSR) with FS, a metric derived from qMT data. Two-way analysis of variance revealed no significant between-day differences in the mean values for any parameter estimate. The repeatability was further assessed with Bland-Altman plots, and low repeatability coefficients were obtained for all parameters. Among-muscle differences in the quantitative MRI indices and inter-class correlations among the parameters were identified. There were inverse relationships between fractional anisotropy (FA) and the second eigenvalue, the third eigenvalue, and the standard deviation of the first eigenvector. The FA was positively related to the PSR, while the other diffusion indices were inversely related to the PSR. These findings support the use of these T1 , T2 , fat/water, and DTI protocols for characterizing skeletal muscle using MRI. Moreover, the data support the existence of a common biophysical mechanism, water content, as a source of variation in these parameters.

Evaluation of growth patterns and body composition in C57Bl/6J mice using dual energy X-ray absorptiometry

The normal growth pattern of female C57BL/6J mice, from 5 to 30 weeks of age, has been investigated in a longitudinal study. Weight, body surface area (BS), and body mass index (BMI) were evaluated in forty mice. Lean mass and fat mass, bone mineral content (BMC), and bone mineral density (BMD) were monitored by dual energy X-ray absorptiometry (DEXA). Weight and BS increased linearly (16.15 ± 0.64-27.64 ± 1.42 g; 51.13 ± 0.74-79.57 ± 2.15 cm(2), P < 0.01), more markedly from 5 to 9 weeks of age (P < 0.001). BMD showed a peak at 17 weeks (0.0548 ± 0.0011 g/cm(2) ∗ m, P < 0.01). Lean mass showed an evident gain at 9 (15.8 ± 0.8 g, P < 0.001) and 25 weeks (20.5 ± 0.3 g, P < 0.01), like fat mass from 13 to 17 weeks (2.0 ± 0.4-3.6 ± 0.7 g, P < 0.01). BMI and lean mass index (LMI) reached the highest value at 21 weeks (3.57 ± 0.02-0.284 ± 0.010 g/cm(2), resp.), like fat mass index (FMI) at 17 weeks (0.057 ± 0.009 g/cm(2)) (P < 0.01). BMI, weight, and BS showed a moderate positive correlation (0.45-0.85) with lean mass from 5 to 21 weeks. Mixed linear models provided a good prediction for lean mass, fat mass, and BMD. This study may represent a baseline reference for a future comparison of wild-type C57BL/6J mice with models of altered growth.

Consuming a balanced high fat diet for 16 weeks improves body composition, inflammation and vascular function parameters in obese premenopausal women

OBJECTIVE

Inflammation, insulin resistance and vascular dysfunction characterize obesity and predict development of cardiovascular disease (CVD). Although women experience CVD events at an older age, vascular dysfunction is evident 10years prior to coronary artery disease. Questions remain whether replacing SFA entirely with MUFA or PUFA is the optimal approach for cardiometabolic benefits. This study tested the hypotheses that: a) body composition, inflammation and vascular function would improve with a high fat diet (HFD) when type of fat is balanced as 1/3 SFA, 1/3 MUFA and 1/3 PUFA; and b) body composition, inflammation and vascular function would improve more when balanced HFD is supplemented with 18C fatty acids, in proportion to the degree of 18C unsaturation.

METHODS

Obese premenopausal women were stabilized on balanced HFD and randomized to consume 9g/d of encapsulated stearate (18:0), oleate (18:1), linoleate (18:2) or placebo.

RESULTS

Significant improvements occurred in fat oxidation rate (↑6%), body composition (%fat: ↓2.5±2.1%; %lean: ↑2.5±2.1%), inflammation (↓ IL-1α, IL-1β, 1L-12, Il-17, IFNγ, TNFα, TNFβ) and vascular function (↓BP, ↓PAI-1, ↑tPA activity). When compared to HFD+placebo, HFD+stearate had the greatest effect on reducing IFNγ (↓74%) and HFD+linoleate had the greatest effect on reducing PAI-1 (↓31%).

CONCLUSIONS

Balancing the type of dietary fat consumed (SFA/MUFA/PUFA) is a feasible strategy to positively affect markers of CVD risk. Moreover, reductions in inflammatory molecules involved in vascular function might be enhanced when intake of certain 18C fatty acids is supplemented. Long term effects need to be determined for this approach.

Quantitative proton MR techniques for measuring fat

Accurate, precise and reliable techniques for the quantification of body and organ fat distributions are important tools in physiology research. They are critically needed in studies of obesity and diseases involving excess fat accumulation. Proton MR methods address this need by providing an array of relaxometry-based (T1, T2) and chemical shift-based approaches. These techniques can generate informative visualizations of regional and whole-body fat distributions, yield measurements of fat volumes within specific body depots and quantify fat accumulation in abdominal organs and muscles. MR methods are commonly used to investigate the role of fat in nutrition and metabolism, to measure the efficacy of short- and long-term dietary and exercise interventions, to study the implications of fat in organ steatosis and muscular dystrophies and to elucidate pathophysiological mechanisms in the context of obesity and its comorbidities. The purpose of this review is to provide a summary of mainstream MR strategies for fat quantification. The article succinctly describes the principles that differentiate water and fat proton signals, summarizes the advantages and limitations of various techniques and offers a few illustrative examples. The article also highlights recent efforts in the MR of brown adipose tissue and concludes by briefly discussing some future research directions.

Calibration of a semi-automated segmenting method for quantification of adipose tissue compartments from magnetic resonance images of mice

OBJECTIVE

To use an automated water-suppressed magnetic resonance imaging (MRI) method to objectively assess adipose tissue (AT) volumes in whole body and specific regional body components (subcutaneous, thoracic and peritoneal) of obese and lean mice.

MATERIALS/METHODS

Water-suppressed MR images were obtained on a 7T, horizontal-bore MRI system in whole bodies (excluding head) of 26 week old male C57BL6J mice fed a control (10% kcal fat) or high-fat diet (60% kcal fat) for 20 weeks. Manual (outlined regions) versus automated (Gaussian fitting applied to threshold-weighted images) segmentation procedures were compared for whole body AT and regional AT volumes (i.e., subcutaneous, thoracic, and peritoneal). The AT automated segmentation method was compared to dual-energy X-ray (DXA) analysis.

RESULTS

The average AT volumes for whole body and individual compartments correlated well between the manual outlining and the automated methods (R2>0.77, p<0.05). Subcutaneous, peritoneal, and total body AT volumes were increased 2-3 fold and thoracic AT volume increased more than 5-fold in diet-induced obese mice versus controls (p<0.05). MRI and DXA-based method comparisons were highly correlative (R2=0.94, p<0.0001).

CONCLUSIONS

Automated AT segmentation of water-suppressed MRI data using a global Gaussian filtering algorithm resulted in a fairly accurate assessment of total and regional AT volumes in a pre-clinical mouse model of obesity.

Canine body composition quantification using 3 tesla fat-water MRI

PURPOSE

To test the hypothesis that a whole-body fat-water MRI (FWMRI) protocol acquired at 3 Tesla combined with semi-automated image analysis techniques enables precise volume and mass quantification of adipose, lean, and bone tissue depots that agree with static scale mass and scale mass changes in the context of a longitudinal study of large-breed dogs placed on an obesogenic high-fat, high-fructose diet.

MATERIALS AND METHODS

Six healthy adult male dogs were scanned twice, at weeks 0 (baseline) and 4, of the dietary regiment. FWMRI-derived volumes of adipose tissue (total, visceral, and subcutaneous), lean tissue, and cortical bone were quantified using a semi-automated approach. Volumes were converted to masses using published tissue densities.

RESULTS

FWMRI-derived total mass corresponds with scale mass with a concordance correlation coefficient of 0.931 (95% confidence interval = [0.813, 0.975]), and slope and intercept values of 1.12 and -2.23 kg, respectively. Visceral, subcutaneous and total adipose tissue masses increased significantly from weeks 0 to 4, while neither cortical bone nor lean tissue masses changed significantly. This is evidenced by a mean percent change of 70.2% for visceral, 67.0% for subcutaneous, and 67.1% for total adipose tissue.

CONCLUSION

FWMRI can precisely quantify and map body composition with respect to adipose, lean, and bone tissue depots. The described approach provides a valuable tool to examine the role of distinct tissue depots in an established animal model of human metabolic disease.