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

Exploring the relationship between fat mass index and metabolic syndrome among cancer patients in the U.S: An NHANES analysis

Data regarding the connection between Fat Mass Index (FMI) and Metabolic Syndrome (MetS) in cancer survivors remain limited. This study aimed to assess the association between FMI and the likelihood of MetS among cancer survivors by conducting a population-based cross-sectional study. This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) to examine a sample of 799 adult cancer survivors, aged over 20 years old, spanning the years 1999-2006 and 2011-2018. MetS was defined according to the criteria established by the Adult Treatment Panel III of the National Cholesterol Education Program (NCEP). To explore the association between fat mass index FMI and the prevalence of MetS among cancer survivors, multivariate logistic regression analyses were conducted. Additionally, restricted cubic splines were used to assess both linear and nonlinear relationships between FMI and MetS. After adjusting for potential confounders, the logistic regression analysis of multistage weighted complex sampling data demonstrated that a higher FMI significantly increased the odds of developing MetS (odds ratio [OR] = 1.33, 95% confidence interval [CI]: 1.09-1.61, p = 0.01). This association remained robust when FMI was categorized into tertiles. Specifically, the adjusted ORs for MetS in the second and third tertiles were 4.80 (95% CI: 1.95-11.79) and 8.95 (95% CI: 2.51-31.94), respectively (p for trend = 0.001). Furthermore, our analysis indicated a significant nonlinear relationship between FMI and the likelihood of MetS (p < 0.0001). In this research, we discovered that an elevated FMI is significantly associated with a higher prevalence of MetS among cancer survivors in the U.S. adult population. These findings underscore the importance of managing body fat to prevent MetS in this group.

The Impact of Physical Activity on Weight Loss in Relation to the Pillars of Lifestyle Medicine-A Narrative Review

Currently, overweight and obesity are key problems globally. Several modifiable factors influence weight management. The number of obese and overweight people has significantly increased over the past few decades. Therefore, it is crucial to find effective and tailored strategies for weight management in public health and medicine. It has become necessary to take a comprehensive look at the problem of obesity and the process of weight loss, taking into account various aspects of lifestyle. To date, the effectiveness of dietary interventions, training interventions, or a combination of both has been repeatedly studied, with varying results, but a combination of properly selected diet and physical activity is considered the most effective therapy. Physical activity is one of the main tools in the treatment of obesity, in part due to its direct effect on body weight by increasing energy expenditure, especially when paired with other elements of lifestyle. The effect of physical activity is broad, and to properly implement it in obesity therapy, it is necessary to understand its impact on aspects such as body composition, food intake, sleep, alcohol use, and mental state. The primary aim of this review is to present the influence of physical activity on weight loss in combination with the influence of physical activity on other pillars of lifestyle medicine in adults. The secondary aim is to present various dietary, exercise, and combined interventions on weight loss with their efficacies.

Physical Activity before and After Bariatric Surgery

Lifestyle changes including reduced calorie intake and increased physical activity (PA) improve the prognosis associated with bariatric surgery (BS) and metabolic indices. Early implementation of exercise leads to improved physical performance, better glycemic control and lipid profile, reduces the risks associated with anesthesia, and accelerates recovery from surgery. Undertaking systematic exercise after BS is associated with a better quality of life, improves insulin sensitivity, results in additional weight loss, reduces adverse effects on bone mass, and results in better body composition. The aim of this review was to summarize recommendations for physical activity in patients undergoing BS and to highlight the key role of physical activity in this patient group.

Fat-Free Mass: Friend or Foe to Metabolic Health?

BACKGROUND

Fat mass (FM) and fat-free mass (FFM) are body composition estimates commonly reported in research studies and clinical settings. Recently, fat-free mass indexed to height (fat-free mass index; FFMI) has been shown to be positively associated with impaired insulin sensitivity or insulin resistance. Consequently, hypertrophic resistance training which can increase FFM was also questioned. This paper sets out to evaluate these propositions.

METHODS

In this narrative review, we discuss possible reasons that link FFMI to adverse metabolic health outcomes including the limitations of the body composition model that utilizes FFM. The safety of resistance training is also briefly discussed.

RESULTS

Approximately 50% of FFM is comprised of skeletal muscle (SM), with the other 50% being viscera, skin, and bone; FFM and SM cannot be conflated. FFM and fat mass (FM) can both rise with increasing body weight and adiposity, indicating a positive correlation between the two compartments. Risk assessment models not adequately adjusting for this correlation may cause erroneous conclusions, however which way FM and FFM are indexed. Adipose tissue accumulation with weight gain, measured by dual-energy X-ray absorptiometry or bioelectrical impedance, can inflate FFM estimates owing to increased connective tissue. Increased adiposity can also result in fat deposition within skeletal muscle disrupting metabolic health. Importantly, non-skeletal muscle components of the FFM, i.e., the liver and pancreas, both critical in metabolic health, can also be negatively affected by the same lifestyle factors that impact SM. The most frequently used body composition techniques used to estimate FM and FFM cannot detect muscle, liver or pancreas fat infiltration. Prospective evidence demonstrates that resistance training is a safe and effective exercise modality across all ages, especially in older adults experiencing age- or disease-related declines in muscle health.

CONCLUSIONS

The association between FFM and insulin resistance is largely an artefact driven by inadequate assessment of skeletal muscle. If FM and FFM are used, at the minimum, they need to be evaluated in context with one another. Body composition methods, such as magnetic resonance imaging, which measures skeletal muscle rather than fat-free mass, and adipose tissue as well as muscle ectopic fat, are preferred methods. Resistance training is important in achieving and maintaining good health across the lifespan. While strength and power are critical components of resistance training, the reduction of skeletal mass through ageing or disease may require hypertrophic training to mitigate and slow down the progression of this often-inevitable process.

The lean body mass to visceral fat mass ratio is negatively associated with cardiometabolic disorders: a cross-sectional study

The literature has documented conflicting and inconsistent associations between muscle-to-fat ratios and metabolic diseases. Additionally, different adipose tissues can have contrasting effects, with visceral adipose tissue being identified as particularly harmful. This study aimed to explore the relationship between the ratio of the lean mass index (LMI) to the visceral fat mass index (VFMI) and cardiometabolic disorders, including dyslipidemia, hypertension, and diabetes, as previous research on this topic is lacking. This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) conducted in the United States and included 10,867 individuals. Logistic regression was employed to explore the association between LMI/VFMI and cardiometabolic disorders. Generalized additive models were utilized to examine the nonlinear relationships between variables. Data analysis revealed a consistent inverse association between ln(LMI/VFMI) and dyslipidemia, hypertension, and diabetes. Each 2.7-fold increase in LMI/VFMI (one unit of ln[LMI/VFMI]) was associated with lower odds ratios (ORs) for these conditions. In men, the ORs were 0.21 (95% CI 0.17-0.25) for dyslipidemia, 0.37 (95% CI 0.30-0.45) for hypertension, and 0.16 (95% CI 0.10-0.23) for diabetes. Similarly, in women, the ORs were 0.22 (95% CI 0.19-0.26), 0.51 (95% CI 0.42-0.61), and 0.19 (95% CI 0.13-0.27). Quartile analysis showed that participants in the highest quartile (Q4) had significantly lower ORs compared to those in the lowest quartile (Q1). In men, Q4 ORs were 0.18 (95% CI 0.14-0.23) for dyslipidemia, 0.30 (95% CI 0.23-0.39) for hypertension, and 0.11 (95% CI 0.06-0.20) for diabetes. In women, Q4 ORs were 0.12 (95% CI 0.10-0.15), 0.39 (95% CI 0.29-0.52), and 0.12 (95% CI 0.06-0.25), respectively. Dyslipidemia and diabetes demonstrated nonlinear patterns, while a linear association was found for hypertension. Subgroup analyses across various characteristics confirmed these findings with no substantial directional changes. Maintaining an appropriate ratio of LMI to VFMI may be associated with favorable metabolic health.

Agreement between equation-derived body fat estimator and bioelectrical impedance analysis for body fat measurement in middle-aged southern Indians

Excess body fat (BF) contributes to metabolic syndrome (MetS). The Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) is an equation-derived body fat estimator proposed to assess BF. However, its efficiency compared to the standard method is unknown. We aimed to compare the efficacy of CUN-BAE with the standard method in estimating BF in southern Indians. We included 351 subjects, with 166 MetS patients and 185 non-MetS subjects. BF was obtained from the standard bioelectrical impedance analysis (BIA) method and measured by CUN-BAE in the same subjects. We compared the efficacy of CUN-BAE in estimating BF with that of BIA via Bland-Altman plots, intraclass correlation coefficients, concordance correlation coefficients and the kappa index. The mean body fat percentage (BF%) values measured by BIA and CUN-BAE in all the subjects were 28.91 ± 8.94 and 29.22 ± 8.63, respectively. We observed significant absolute agreement between CUN-BAE and BIA for BF%. BIA and CUN-BAE showed good reproducibility for BF%. CUN-BAE had accuracy comparable to BIA for detecting MetS using BF%. Our findings indicate that CUN-BAE provides precise BF estimates similar to the BIA method, making it suitable for routine clinical practice when access to BF measurement devices is limited.

Correlation between tooth decay and insulin resistance in normal weight males prompts a role for myo-inositol as a regenerative factor in dentistry and oral surgery: a feasibility study

Background

In an era of precision and stratified medicine, homogeneity in population-based cohorts, stringent causative entry, and pattern analysis of datasets are key elements to investigate medical treatments. Adhering to these principles, we collected in vivo and in vitro data pointing to an insulin-sensitizing/insulin-mimetic effect of myo-inositol (MYO) relevant to cell regeneration in dentistry and oral surgery. Confirmation of this possibility was obtained by in silico analysis of the relation between in vivo and in vitro results (the so-called bed-to-benchside reverse translational approach).

Results

Fourteen subjects over the 266 screened were young adult, normal weight, euglycemic, sedentary males having normal appetite, free diet, with a regular three-times-a-day eating schedule, standard dental hygiene, and negligible malocclusion/enamel defects. Occlusal caries were detected by fluorescence videoscanning, whereas body composition and energy balance were estimated with plicometry, predictive equations, and handgrip. Statistically significant correlations (Pearson r coefficient) were found between the number of occlusal caries and anthropometric indexes predicting insulin resistance (IR) in relation to the abdominal/visceral fat mass, fat-free mass, muscular strength, and energy expenditure adjusted to the fat and muscle stores. This indicated a role for IR in affecting dentin reparative processes. Consistently, in vitro administration of MYO to HUVEC and Swiss NIH3T3 cells in concentrations corresponding to those administered in vivo to reduce IR resulted in statistically significant cell replication (ANOVA/Turkey tests), suggesting that MYO has the potential to counteract inhibitory effects of IR on dental vascular and stromal cells turnover. Finally, in in silico experiments, quantitative evaluation (WOE and information value) of a bioinformatic Clinical Outcome Pathway confirmed that in vitro trophic effects of MYO could be transferred in vivo with high predictability, providing robust credence of its efficacy for oral health.

Conclusion

Our reverse bed-to-benchside data indicate that MYO might antagonize the detrimental effects of IR on tooth decay. This provides feasibility for clinical studies on MYO as a regenerative factor in dentistry and oral surgery, including dysmetabolic/aging conditions, bone reconstruction in oral destructive/necrotic disorders, dental implants, and for empowering the efficacy of a number of tissue engineering methodologies in dentistry and oral surgery.

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.

Fat-free mass index is a feasible predictor of insulin resistance in women with polycystic ovary syndrome: Evidence from a cross-sectional study

BACKGROUND

Insulin resistance (IR) and adipose tissue amplify the metabolic and reproductive outcomes in women with polycystic ovary syndrome (PCOS). It has been widely discussed that body composition influences metabolic health. Still, limited studies were focused on the role of the fat-free mass index (FFMI) in assessing IR in PCOS women.

AIMS

We aimed to explore the associations between FFMI/fat mass index (FMI) and IR in women with PCOS and assess the role of FFMI in predicting IR in women with PCOS.

METHODS

In the current cross-sectional study, women with PCOS aged between 18 and 40 years were enrolled from October 2018 to July 2022. Baseline demographic information was obtained using standardized self-administered questionnaires. Anthropometric, biochemical, and hormonal information was measured and recorded by investigators. Pearson's correlation and multivariable logistical regression were used to analyze the associations of FFMI/FMI and IR. In addition, receiver operating characteristic (ROC) curves were implied to measure the predictive role of FFMI/FMI for IR in women with PCOS.

RESULTS

A total of 371 women with PCOS, reproductive age (27.58 ± 4.89) were enrolled. PCOS women with IR have higher levels of triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), homeostatic model assessment of insulin resistance (HOMA-IR), FMI, and FFMI than that without IR. FMI (r = 0.492, p < 0.001) and FFMI (r = 0.527, p < 0.001) were positively associated with IR. After adjusting for potential confounders, FMI and FFMI were significantly associated with IR in PCOS women, and the OR was 1.385 (95%CI: 1.212-1.583) and 2.306 (95%CI: 1.675-3.174), respectively. Additionally, the FFMI (0.847, 95%CI: 0.784-0.888) has a larger area of ROC (AUC) than the FMI (0.836, 95%CI: 0.799-0.896), while there is no difference in predicting IR (95%CI: -0.18-0.41, p = 0.456).

CONCLUSION

These results indicated that FFMI and FMI could significantly increase the risk of IR, both of which could be feasible predictors of IR in PCOS women.

Associations between 47 anthropometric markers derived from a body scanner and relative fat-free mass in a population-based study

BACKGROUND

Low relative fat free mass (FFM) is associated with a greater risk of chronic diseases and mortality. Unfortunately, FFM is currently not being measured regularly to allow for individuals therapy.

OBJECTIVE

One reason why FFM is not being used may be related to additional equipment and resources, thus we aimed to identify easily accessible anthropometric markers related with FFM.

MATERIALS AND METHODS

We analyzed data of 1,593 individuals (784 women; 49.2%, age range 28-88 years) enrolled in the population-based Study of Health in Pomerania (SHIP-TREND 1). Forty-seven anthropometric markers were derived from a 3D optical body-scanner. FFM was assessed by bioelectrical impedance analysis (FFMBIA) or air displacement plethysmography (FFMADP). In sex-stratified linear regression models, FFM was regressed on anthropometric measurements adjusted for body height and age. Anthropometric markers were ranked according to the coefficient of determination (R2) derived from these regression models.

RESULTS

Circumferences of high hip, belly, middle hip, waist and high waist showed the strongest inverse associations with FFM. These relations were stronger in females than in males. Associations of anthropometric markers with FFMAPD were greater compared to FFMBIA.

CONCLUSION

Anthropometric measures were more strongly associated with FFMADP compared to FFMBIA. Anthropometric markers like circumferences of the high or middle hip, belly or waist may be appropriate surrogates for FFM to aid in individualized therapy. Given that the identified markers are representative of visceral adipose tissue, the connection between whole body strength as surrogate for FFM and fat mass should be explored in more detail.

Conflicting Associations among Bioelectrical Impedance and Cardiometabolic Health Parameters in Young White and Black Adults

PURPOSE

The purpose of this cross-sectional evaluation was to determine the associations between raw bioelectrical impedance and cardiometabolic health parameters in a sample of young non-Hispanic White and African American adults.

METHODS

A total of 96 (female: 52, male: 44) non-Hispanic White ( n = 45) and African American adults ( n = 51) between the ages of 19 and 37 yr (22.7 ± 3.83 yr) completed several fasted assessments including resting systolic blood pressure (rSBP), blood glucose (FBG), blood lipids, and bioelectrical impedance spectroscopy. Bioelectrical impedance spectroscopy-derived measurements included phase angle, bioimpedance index (BI), impedance ratio (IR), reactance index (XCi), fat-free mass (FFM), FFM index (FFMi), and absolute (a) and relative (%) total body water (TBW) and extracellular (ECF) and intracellular fluid (ICF). All bioelectric variables were collected at 50 kHz other than IR (250 kHz/5 kHz). Multiple regressions were conducted and adjusted for sex, age, and body mass index.

RESULTS

rSBP was positively, and HDL was inversely, associated with all bioelectrical impedance and absolute hydration variables (all P ≤ 0.050) other than XCi for rSBP and XCi and FFMi for HDL. rSBP ( P < 0.001) was inversely, and HDL ( P = 0.034) was positively, associated with IR. FBG was positively associated with BI, XCi, FFM, TBWa, and ECFa (all P < 0.050). Metabolic syndrome severity was positively associated with BI, FFM, TBWa, and ECFa for women (all P ≤ 0.050) and with ICFa for African American women ( P = 0.016).

CONCLUSIONS

Given the rapid increase in the prevalence of cardiometabolic health risks among young adults and the broad use of bioelectrical impedance in practice, the conflicting associations we observed in this age group suggest that bioelectrical impedance parameters should be used with caution in the context of cardiometabolic health risks and age.

Physical activity in metabolic syndrome

Obesity has become one of the global epidemics, contributing to the burden of disease in society, increasing the risk of diabetes, cardiovascular and liver diseases. Inadequate energy balance resulting from excessive energy intake and insufficient physical activity (PA) is one of the main factors contributing to the incidence of obesity and the development of metabolic syndrome (MetS). Treatment options for obesity include lifestyle modifications, pharmacotherapy and bariatric surgery, with the latter being the most effective treatment. Lifestyle interventions involving increased PA and reduced caloric intake improve metabolic outcomes. Early implementation of exercise leads to improved physical fitness, better glycemic control and lipid profile. Undertaking systematic PA is associated with better quality of life, improves insulin sensitivity, causes additional weight loss, reduces its adverse effects on bone mass and results in better body composition. In this narrative review we summarized the current state of knowledge on the impact of PA on the components of MetS and the latest recommendations for PA in patients with MetS.

Sarcopenia and type 2 diabetes: Pathophysiology and potential therapeutic lifestyle interventions

AIMS

Sarcopenia generally refers to the age-related reduction in muscle strength, functional ability, and muscle mass. Sarcopenia is a multifactorial condition associated with poor glucose disposal, insulin resistance, and subsequently type 2 diabetes (T2D). The pathophysiological connection between sarcopenia and T2D is complex but likely involves glycemic control, inflammation, oxidative stress, and adiposity.

METHODS AND RESULTS

Resistance exercise and aerobic training are two lifestyle interventions that may improve glycemic control in older adults with T2D and counteract sarcopenia. Further, there is evidence that dietary protein, Omega-3 fatty acids, creatine monohydrate, and Vitamin D hold potential to augment some of these benefits from exercise.

CONCLUSIONS

The purpose of this narrative review is: (1) discuss the pathophysiological link between age-related sarcopenia and T2D, and (2) discuss lifestyle interventions involving physical activity and nutrition that may counteract sarcopenia and T2D.

Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning

Background

Metabolic syndrome (Mets) is considered a global epidemic of the 21st century, predisposing to cardiometabolic diseases. This study aims to describe and compare the body composition profiles between metabolic healthy (MH) and metabolic unhealthy (MU) phenotype in normal and obesity population in China, and to explore the predictive ability of body composition indices to distinguish MU by generating machine learning algorithms.

Methods

A cross-sectional study was conducted and the subjects who came to the hospital to receive a health examination were enrolled. Body composition was assessed using bioelectrical impedance analyser. A model generator with a gradient-boosting tree algorithm (LightGBM) combined with the SHapley Additive exPlanations method was adapted to train and interpret the model. Receiver-operating characteristic curves were used to analyze the predictive value.

Results

We found the significant difference in body composition parameters between the metabolic healthy normal weight (MHNW), metabolic healthy obesity (MHO), metabolic unhealthy normal weight (MUNW) and metabolic unhealthy obesity (MUO) individuals, especially among the MHNW, MUNW and MUO phenotype. MHNW phenotype had significantly lower whole fat mass (FM), trunk FM and trunk free fat mass (FFM), and had significantly lower visceral fat areas compared to MUNW and MUO phenotype, respectively. The bioimpedance phase angle, waist-hip ratio (WHR) and free fat mass index (FFMI) were found to be remarkably lower in MHNW than in MUNW and MUO groups, and lower in MHO than in MUO group. For predictive analysis, the LightGBM-based model identified 32 status-predicting features for MUNW with MHNW group as the reference, MUO with MHO as the reference and MUO with MHNW as the reference, achieved high discriminative power, with area under the curve (AUC) values of 0.842 [0.658, 1.000] for MUNW vs. MHNW, 0.746 [0.599, 0.893] for MUO vs. MHO and 0.968 [0.968, 1.000] for MUO and MHNW, respectively. A 2-variable model was developed for more practical clinical applications. WHR > 0.92 and FFMI > 18.5 kg/m2 predict the increased risk of MU.

Conclusion

Body composition measurement and validation of this model could be a valuable approach for the early management and prevention of MU, whether in obese or normal population.

Association between skeletal muscle mass or percent body fat and metabolic syndrome development in Japanese women: A 7-year prospective study

Previous cross-sectional studies have indicated that low relative appendicular lean mass (ALM) against body weight (divided by body weight, ALM/Wt, or divided by body mass index, ALM/BMI) was negatively associated with metabolic syndrome (MetS). Conversely, previous cross-sectional studies have indicated that the absolute ALM or ALM divided by squared height (ALM/Ht2) were positively associated with MetS. The aim of this longitudinal study was to investigate the association between low absolute or relative skeletal muscle mass, leg muscle power, or percent body fat and the development of MetS in Japanese women in a 7-y prospective study. The study participants included 346 Japanese women aged 26 to 85 years. The participants were divided into low and high groups based on the median values of ALM/Wt, ALM/BMI, ALM/Ht2, absolute ALM, or leg power. The longitudinal relationship between ALM indices or leg power and MetS development was examined using Kaplan-Meier curves and Cox regression models (average follow-up duration 7 years, range 1 to 10 years). During follow-up, 24 participants developed MetS. MetS incidence was higher in the low ALM/Wt group than the high ALM/Wt group even after controlling for age, obesity, waist circumference, family history of diabetes, smoking, and physical activity [adjusted hazard ratio = 5.60 (95% CI; 1.04-30.0)]. In contrast, MetS incidence was lower in the low ALM/Ht2 group than the high ALM/Ht2 group [adjusted hazard ratio = 10.6 (95%CI; 1.27-89.1)]. MetS incidence was not significantly different between the low and high ALM/BMI, absolute ALM, and leg power groups. Both ALM/Ht2 and ALM/Wt were not significant predictive variables for MetS development when fat mass or percent body fat was taken into account in the Cox model. At the very least, the results of this study underscore the importance of body composition measurements in that percent body fat, but not ALM, is associated with MetS development.