FAT-FREE MASS, METABOLICALLY HEALTHY OBESITY, AND TYPE 2 DIABETES IN SEVERELY OBESE ASIAN ADULTS

A cohort study on the predictive capability of body composition for diabetes mellitus using machine learning

Purpose

We applied machine learning to study associations between regional body fat distribution and diabetes mellitus in a population of community adults in order to investigate the predictive capability. We retrospectively analyzed a subset of data from the published Fasa cohort study using individual standard classifiers as well as ensemble learning algorithms.

Methods

We measured segmental body composition using the Tanita Analyzer BC-418 MA (Tanita Corp, Japan). The following features were input to our machine learning model: fat-free mass, fat percentage, basal metabolic rate, total body water, right arm fat-free mass, right leg fat-free mass, trunk fat-free mass, trunk fat percentage, sex, age, right leg fat percentage, and right arm fat percentage. We performed classification into diabetes vs. no diabetes classes using linear support vector machine, decision tree, stochastic gradient descent, logistic regression, Gaussian naïve Bayes, k-nearest neighbors (k = 3 and k = 4), and multi-layer perceptron, as well as ensemble learning using random forest, gradient boosting, adaptive boosting, XGBoost, and ensemble voting classifiers with Top3 and Top4 algorithms. 4661 subjects (mean age 47.64 ± 9.37 years, range 35 to 70 years; 2155 male, 2506 female) were analyzed and stratified into 571 and 4090 subjects with and without a self-declared history of diabetes, respectively.

Results

Age, fat mass, and fat percentages in the legs, arms, and trunk were positively associated with diabetes; fat-free mass in the legs, arms, and trunk, were negatively associated. Using XGBoost, our model attained the best excellent accuracy, precision, recall, and F1-score of 89.96%, 90.20%, 89.65%, and 89.91%, respectively.

Conclusions

Our machine learning model showed that regional body fat compositions were predictive of diabetes status.

Comparing Effects of Bariatric Surgery on Body Composition Changes in Metabolically Healthy and Metabolically Unhealthy Severely Obese Patients: Tehran Obesity Treatment Study (TOTS)

BACKGROUND

Among two popular obesity phenotypes, metabolically healthy severely obese (MHSO) and metabolically unhealthy severely obese (MUSO), it is important to clarify whether or not those with MHSO phenotype would benefit from bariatric surgery in terms of an improvement in body composition parameters.

METHODS

A prospective cohort was conducted on a total of 4028 participants (1404 MHSO and 2624 MUSO) who underwent bariatric surgery; MHSO was defined as having abnormalities in none or one of these four parameters: systolic blood pressure and/or diastolic blood pressure, triglycerides, fasting plasma glucose, and high-density lipoprotein. Otherwise, the definition of MUSO was met. Body composition analysis was performed at the baseline and 6-, 12-, 24-, and 36-month post-surgery using bioelectrical impedance analyzer.

RESULTS

Both phenotypes showed a significant decrease in fat mass (FM) and fat-free mass (FFM) and a significant increase in EWL% and TWL% (P_trend < 0.05). FFM, FM%, and excess weight loss (EWL%) were significantly different between the two phenotypes (P_between < 0.05) during the follow-up. Multivariate linear regression demonstrated that compared to MUSO patients, MHSO individuals experienced a greater increase in total weight loss (TWL%) and EWL% at 12- and 24-month and in EWL% at 36-month post-surgery and also a lower decrease in the FFML/WL% after 12 months.

CONCLUSION

Despite a lower decrease of FFML/WL% and a greater increase in TWL and EWL in MHSO phenotype at some time points, there were no clinically significant differences between the study groups in terms of body composition changes throughout the follow-up period.

Obesity Metabolic Phenotype, Changes in Time and Risk of Diabetes Mellitus in an Observational Prospective Study on General Population

Objectives: To evaluate the distribution and changes in different obesity metabolic phenotypes, as well as their impact on the incidence of type 2 diabetes mellitus (T2DM) in a northwest Chinese population sample.

Methods: Data comes from prospective cohort study (n = 1,393, mean follow up = 9.46 years). Participants were classified into four groups through a combination of the Chinese Diabetes Society (CDS) diagnostic criteria for metabolic syndrome with anthropometric measurements: metabolically healthy normal weight (MHNW), metabolically healthy overweight/obese (MHO), metabolically unhealthy normal weight (MUNW), and metabolically unhealthy overweight/obese (MUO). Cox regression models with time-dependent covariates were used to evaluate changes in obesity metabolic phenotypes and risk of T2DM.

Results: Participants in MUO state had the highest risk of developing T2DM, the incidence density was 12.10/1,000 person-year. The MHO and MUO groups showed an increased risk of incident diabetes based on body mass index (BMI) (HR, 1.29; 95% CI, 1.03–1.61; p = 0.026 and HR, 1.20; 95% CI, 1.02–1.40; p = 0.024 respectively.) Besides, the MHO group had an increased risk of incident diabetes based on waist circumference (WC) (HR, 1.41; 95% CI, 1.10–1.80; p = 0.006).

Conclusion: Diabetes is more frequent in the MHO and MUO groups and co-occurrence of obesity and metabolic abnormalities (MA) contributes to the development of T2DM.

Increased odds of having the metabolic syndrome with greater fat-free mass: counterintuitive results from the National Health and Nutrition Examination Survey database

BACKGROUND

It is well established that body composition influences metabolic health, but emerging data are conflicting with the largely purported idea that a large fat-free mass (FFM) has a protective effect on health. A potential explanation for these discrepancies is the way FFM is represented. The first objective is to determine the association between the metabolic syndrome (MetS) and FFM when the latter was represented in three different ways: 1-absolute FFM; 2-relative to squared height (FFMi); and 3-relative to body weight (FFM%). The second objective is to assess the impact of FFM on the relative risk of having the MetS after taking fat mass, physical activity, and sociodemographic variables into account.

METHODS

A total of 5274 individuals from the National Health and Nutrition Examination Survey database were studied. Age-specific and sex-specific quartiles of the three representations of FFM were defined, and the prevalence of MetS was determined in each of them. Quartiles of FFMi (kg/m² ) were used to calculate the odds ratios of having the MetS independently of FM, physical activity levels, and sociodemographic variables.

RESULTS

The prevalence of MetS decreased with increasing quartiles of whole-body FFM% (Q1: 40%; Q4: 10%) but grew with increasing quartiles of absolute FFM (Q1: 13%; Q4: 40%) and FFMi (Q1: 10%; Q4: 44%). Similar results were observed for appendicular and truncal FFM. The odds ratios of having the MetS, independently of fat mass, physical activity, and sociodemographic variables, were significantly greater in the fourth quartile of FFMi when compared with the first quartiles of each specific subgroup [Q4 vs. Q1: younger men: 4.16 (1.99-8.68); younger women: 5.74 (2.46-13.39); older men: 1.98 (1.22-3.22); older women: 2.88 (1.69-4.90); all P ≤ 0.01].

CONCLUSIONS

These results support the notion that the representation of FFM significantly influences its association with MetS and that a larger FFM, whether absolute or relative to height, is associated with alterations in cardiometabolic health.

Gender Differences in Body Composition in Pre-Frail Older Adults With Diabetes Mellitus

BACKGROUND & AIMS

Ageing is a risk factor for diabetes mellitus (DM) and frailty. It is associated with body composition changes including increase in fat mass (FM), central fat distribution, decrease in fat free mass (FFM) and skeletal muscle which are risk factors for DM. This study aims to evaluate gender differences in body composition in pre-frail diabetics and association with physical performance, cognitive function and perceived health. In addition, we aim to explore the association of obesity, sarcopenia, sarcopenic obesity, and body composition in pre-frail older adults to DM status.

METHODS

Cross-sectional study of 192 pre-frail community dwelling older adults (≥ 65 years). Data was collected on demographics, physical function, cognition, frailty, sarcopenia, perceived health and body composition using the InBody S10. Univariate and multivariate logistic regression were undertaken to explore the association of sarcopenic obesity, obesity, sarcopenia and body composition measures to DM status.

RESULTS

There were insignificant within-gender differences for physical function, cognition and body composition, except for a higher prevalence of obesity defined by body mass index (BMI) and body fat percentage (BF%), increased fat mass index(FMI) and fat free mass index(FFMI) in females with DM. There were significant between-gender differences for those with DM where females overall had lower education levels, lower perceived health, higher prevalence of depression and low mental vitality, lower overall physical function (low short physical performance battery scores, low gait speed and hand grip strength), lower cognitive scores, lower muscle mass and muscle quality with higher FMI, FM/FFM and visceral fat area(VFA). BMI, VFA>100 cm², FMI and FFMI were found to be independently associated with DM status after multivariable adjustment.

CONCLUSION

Within pre-frail DM vs non-DM, there were insignificant differences in body composition, physical function, cognition and perceived health within gender except for FMI, BF% and FFMI in females. There were significant differences between gender in pre-frail DM in muscle mass, quality, functional, cognitive and mental status. Further longitudinal studies are required to understand the pathogenesis, trajectory of DM and protective role of oral hypoglycemics in pre-frail older adults.

Non-linear interaction between physical activity and polygenic risk score of body mass index in Danish and Russian populations

Body mass index (BMI) is a highly heritable polygenic trait. It is also affected by various environmental and behavioral risk factors. We used a BMI polygenic risk score (PRS) to study the interplay between the genetic and environmental factors defining BMI. First, we generated a BMI PRS that explained more variance than a BMI genetic risk score (GRS), which was using only genome-wide significant BMI-associated variants (R² = 13.1% compared to 6.1%). Second, we analyzed interactions between BMI PRS and seven environmental factors. We found a significant interaction between physical activity and BMI PRS, even when the well-known effect of the FTO region was excluded from the PRS, using a small dataset of 6,179 samples. Third, we stratified the study population into two risk groups using BMI PRS. The top 22% of the studied populations were included in a high PRS risk group. Engagement in self-reported physical activity was associated with a 1.66 kg/m² decrease in BMI in this group, compared to a 0.84 kg/m² decrease in BMI in the rest of the population. Our results (i) confirm that genetic background strongly affects adult BMI in the general population, (ii) show a non-linear interaction between BMI genetics and physical activity, and (iii) provide a standardized framework for future gene-environment interaction analyses.

Body Composition Methods in Adults with Type 2 Diabetes or at Risk for T2D: a Clinical Review

PURPOSE OF REVIEW

The aim of this study is to summarize anthropometric and advanced methods used to assess body composition in adults diagnosed with type 2 diabetes (T2D) or at risk for T2D that provide clinically relevant information about T2D disease-related complications or risk factors.

RECENT FINDINGS

Anthropometry is commonly used in clinical settings; however, provides unreliable estimates of fat mass, fat-free mass, and body fat distribution for metabolic health assessments compared to advanced techniques such as bioelectrical impedance analysis (BIA), dual-energy x-ray absorptiometry (DXA), computerized tomography (CT), and magnetic resonance imaging (MRI). Few studies report the clinical use of anthropometric and advanced body composition methods that identify T2D disease-related complications or T2D risk factors. Anthropometry, BIA, DXA, CT, and MRI were used to estimate body adiposity and distribution, visceral and subcutaneous adipose tissue depots, and skeletal muscle mass. Review findings indicate that these methods were capable of identifying clinically relevant T2D disease-related complications such as sarcopenia and T2D risk factors such as obesity or regional adiposity. However, estimates were often sex and race/ethnicity specific warranting cross-validation of these methods in broader populations with T2D or risk for T2D prior to clinical implementation.

Relationship of Fat Mass Index and Fat Free Mass Index With Body Mass Index and Association With Function, Cognition and Sarcopenia in Pre-Frail Older Adults

BACKGROUND

Body mass index (BMI) is an inadequate marker of obesity, and cannot distinguish between fat mass, fat free mass and distribution of adipose tissue. The purpose of this study was twofold. First, to assess cross-sectional relationship of BMI with fat mass index (FMI), fat free mass index (FFMI) and ratio of fat mass to fat free mass (FM/FFM). Second, to study the association of FMI, FFMI and FM/FFM with physical function including sarcopenia, and cognition in pre-frail older adults.

METHODS

Cross-sectional study of 191 pre-frail participants ≥ 65 years, 57.1% females. Data was collected on demographics, cognition [Montreal Cognitive Assessment (MoCA)], function, frailty, calf circumference, handgrip strength (HGS), short physical performance battery (SPPB) and gait speed. Body composition was measured using InBody S10. FMI, FFMI and FM/FFM were classified into tertiles (T1, T2, T3) with T1 classified as lowest and T3 highest tertile respectively and stratified by BMI.

RESULTS

Higher FFMI and lower FM/FFM in the high BMI group were associated with better functional outcomes. Prevalence of low muscle mass was higher in the normal BMI group. FMI and FM/FFM were significantly higher in females and FFMI in males with significant gender differences except for FFMI in ≥ 80 years old. Small calf circumference was significantly less prevalent in the highest tertile of FMI, FM/FMI and FFMI. Prevalence of sarcopenic obesity and low physical function (HGS, gait speed and SPPB scores) were significantly higher in the highest FMI and FM/FFM tertile. Highest FFMI tertile group had higher physical function, higher MoCA scores, lower prevalence of sarcopenic obesity and sarcopenia, After adjustment, highest tertile of FFMI was associated with lower odds of sarcopenia especially in the high BMI group. Highest tertile of FM/FFM was associated with higher odds of sarcopenia. Higher BMI was associated with lower odds of sarcopenia.

CONCLUSION

FFMI and FM/FFM may be a better predictor of functional outcomes in pre-frail older adults than BMI. Cut-off values for healthy BMI values and role of calf circumference as a screening tool for sarcopenia need to be validated in larger population. Health promotion intervention should focus on FFMI increment.