Agreement between laboratory methods and the 4-compartment model in assessing fat mass in obese older Hispanic-American adults

Adult obesity diagnostic tool: A narrative review

Obesity is a complex chronic metabolic disorder characterized by abnormalities in lipid metabolism. Obesity is not only associated with various chronic diseases but also has negative effects on physiological functions such as the cardiovascular, endocrine and immune systems. As a global health problem, the incidence and prevalence of obesity have increased significantly in recent years. Therefore, understanding assessment methods and measurement indicators for obesity is critical for early screening and effective disease control. Current methods for measuring obesity in adult include density calculation, anthropometric measurements, bioelectrical impedance analysis, dual-energy X-ray absorptiometry, computerized imaging, etc. Measurement indicators mainly include weight, hip circumference, waist circumference, neck circumference, skinfold thickness, etc. This paper provides a comprehensive review of the literature to date, summarizes and analyzes various assessment methods and measurement indicators for adult obesity, and provides insights and guidance for the innovation of obesity assessment indicators.

Assessment of the performance of the body mass index in diagnosing obesity in community-dwelling older adults in Latin American and Caribbean countries

BACKGROUND

The body mass index (BMI) ≥30 kg/m2 is the universally accepted cut-off point for defining obesity; however, its accuracy in classifying obesity in older adults is poorly understood.

OBJECTIVES

To assess the performance of the BMI cut-off point ≥30 kg/m2 in classifying obesity in older adults, using the fat mass index (FMI) and fat mass percentage (FM%) as reference criteria; and to establish region- and sex-specific BMI-based cut-off points to classify obesity in older adults.

METHODS

The present study is a secondary analysis derived from a cross-sectional project that included a sample of 1463 older adults from ten Latin American and Caribbean countries. Volunteers underwent total body water measurements using the deuterium dilution technique to determine FMI and FM%. Accuracy of the BMI and derived cutoff points was assessed by the area under the receiver operating characteristic curve (AUC).

RESULTS

The BMI cut-off point ≥30 kg/m2 had low sensitivity for classifying obesity in these older adults compared to the FMI and FM%. The AUC values for the optimal BMI-derived cut-off points showed an acceptable-to-outstanding discriminatory capacity in diagnosing obesity defined by the FMI. There was also a better balance between sensitivity and specificity than with the values obtained by a BMI ≥30 kg/m2 in older subjects in both regions.

CONCLUSION

The BMI cut-off point ≥30 kg/m2 had poor sensitivity for accurately diagnosing obesity in older adults from two regions. The region- and sex-specific BMI-derived cut-off points for defining obesity using the FMI are more accurate in classifying obesity in older men and women subjects from both regions.

Predictive equations for fat mass in older Hispanic adults with excess adiposity using the 4-compartment model as a reference method

BACKGROUND

Predictive equations are the best option for assessing fat mass in clinical practice due to their low cost and practicality. However, several factors, such as age, excess adiposity, and ethnicity can compromise the accuracy of the equations reported to date in the literature.

OBJECTIVE

To develop and validate two predictive equations for estimating fat mass: one based exclusively on anthropometric variables, the other combining anthropometric and bioelectrical impedance variables using the 4C model as the reference method.

SUBJECTS/METHODS

This is a cross-sectional study that included 386 Hispanic subjects aged ≥60 with excess adiposity. Fat mass and fat-free mass were measured by the 4C model as predictive variables. Age, sex, and certain anthropometric and bioelectrical impedance data were considered as potential predictor variables. To develop and to validate the equations, the multiple linear regression analysis, and cross-validation protocol were applied.

RESULTS

Equation 1 included weight, sex, and BMI as predictor variables, while equation 2 considered sex, weight, height squared/resistance, and resistance as predictor variables. R2 and RMSE values were ≥0.79 and ≤3.45, respectively, in both equations. The differences in estimates of fat mass by equations 1 and 2 were 0.34 kg and -0.25 kg, respectively, compared to the 4C model. This bias was not significant (p < 0.05).

CONCLUSIONS

The new predictive equations are reliable for estimating body composition and are interchangeable with the 4C model. Thus, they can be used in epidemiological and clinical studies, as well as in clinical practice, to estimate body composition in older Hispanic adults with excess adiposity.

Body composition assessment in adult females with anorexia nervosa and bulimia nervosa - a cross-sectional study comparing dual-energy X-ray absorptiometry scan and isotopic dilution of deuterium

BACKGROUND

Eating disorders (EDs) compromise individuals' nutritional status, affecting among other organs and systems, bone health.

OBJECTIVE

This study aimed to assess and compare bone mineral density (BMD) from dual-energy x-ray absorptiometry (DXA) scan and deuterium (D₂O) dilution of adult females with anorexia nervosa (AN) and bulimia nervosa (BN).

METHODS

This was a cross-sectional study with 53 female participants (18-49 years) with a diagnosis of AN (N = 25) or BN (N = 28). DXA scan was performed to assess BMD, fat mass, and fat-free mass, and D₂O dilution was used to assess total body water (TBW), fat mass, and fat-free mass. Interviews/questionnaires were used to assess symptoms, illness trajectory, and physical activity. T-test, Chi-squared test, Pearson's linear correlation, linear regressions, and Bland-Altman analyses were performed, with a significance level of 5%.

RESULTS

TBW below the recommended level for adult females (≥ 45%) was more frequent in BN (60%) compared with AN (21%; P = 0.013). FMI (soft tissue only) (t-test P = 0.06), and FFMI (t-test P = 0.08) agreed between DXA scan and D₂O dilution. Only FFMI did not show systematic bias of proportion (β:-0.2, P = 0.177). The diagnosis of BN, binge-eating episodes, and physical activity in AN were associated with the differences in the methods' results. FMI was positively associated with BMD in AN, and both FMI and FFMI were positively associated with BMD in BN.

CONCLUSION

In adult females with EDs, DXA scan and D₂O dilution achieved agreement for FMI and FFMI. Changes in fat mass and fat-free mass are important in understanding the mechanisms behind bone loss in EDs. Protocols for body composition assessment in EDs can help to minimize the effect of the ED diagnosis, ED behaviors (i.e., excessive exercise and purging behaviors) and weight on the accuracy of measurements.

Determination of Fat-Free Mass Density and its Components in Older Hispanic Adults by In Vivo Methods

The densitometry method estimates body composition based on cadaver reference values, mainly the fat-free mass density value of 1.100 g/cm³. However, several changes in fat-free mass components by aging, ethnicity, and excess adiposity could influence their density and affect body composition estimations. The present study aimed to compare the mean fat-free mass component values in older Hispanic adults to cadaver reference values. This cross-sectional study included a sample of 420 subjects aged ≥60 yr from northern Mexico. Fat-free mass was determined by the four-compartment model using air displacement plethysmography, the deuterium dilution technique, and dual-energy X-ray absorptiometry for body density, aqueous and mineral fractions of body weight, respectively. A 1-sample t test was used to compare the fat-free mass density and aqueous, mineral, and residue fractions of fat-free mass from subjects in the study to the assumed cadaver reference values. The mean fat-free mass density value for the total sample of older Hispanic adults (1.096 ± 0.011 g/cm³) was significantly (p < 0.001) lower than the assumed value of 1.100 g/cm³, except in obese older men. The mean aqueous fraction of fat-free mass (74.8 ± 3.3%) was higher than the assumed value of 73.8%, and the mean residue fraction of fat-free mass value was lower (18.3 ± 3.4%) than the reference value of 19.4%. Indeed, only the mean mineral fraction of fat-free mass value (6.8 ± 0.8%) was similar to the reference value. In the total sample, all characteristic mean fat-free mass values in these older Hispanic adults differed from cadaver reference values, except the mineral fraction of fat-free mass value.

External validation of BIA equations to estimate appendicular skeletal muscle mass in older adults: Importance of the bias analysis and derivation of correction factors to achieve agreement

There are several equations based on bioelectrical impedance analysis (BIA) to estimate with high precision appendicular skeletal muscle mass (ASM). However, most of the external validation studies have reported that these equations are inaccurate or biased when applied to different populations. Furthermore, none of the published studies has derived correction factors (CFs) in samples of community-dwelling older adults, and none of the published studies have assessed the influence of the dual-energy X-ray absorptiometry (DXA) model on the validation process. This study assessed the agreement between six BIA equations and DXA to estimate ASM in non-Caucasian older adults considering the DXA model and proposed a CF for three of them. This analysis included 547 non-institutionalized subjects over 60 years old from the northwest of Mexico who were physically independent and without cognitive impairment: 192 subjects were measured using DXA Hologic, while 355 were measured by DXA Lunar. The agreement between each of the equations and DXA was tested considering the DXA model used as a reference method for the design of each equation, using the Bland and Altman procedure, a paired t test, and simple linear regression as objective tests. This process was supported by the differences reported in the literature and confirmed in a subsample of 70 subjects measured with both models. Only six published BIA equations were included. The results showed that four equations overestimated ASM_DXA, and two underestimated it (p < 0.001, 95% CI for Kim's equation:−5.86-−5.45, Toselli's:−0.51-−0.15, Kyle's: 1.43–1.84, Rangel-Peniche's: 0.32–0.74, Sergi's: 0.83–1.23, and Yoshida's: 4.16–4.63 kg). However, Toselli's, Kyle's and Rangel-Peniche's equations were the only ones that complied with having a homogeneous bias. This finding allowed the derivation of CFs, which consisted of subtracting or adding the mean of the differences from the original equation. After estimating ASM applying the respective CF, the new ASM estimations showed no significant bias and its distribution remained homogeneously distributed. Therefore, agreement with DXA in the sample of non-Caucasian was achieved. Adding valid CFs to some BIA equations allowed to reduce the bias of some equations, making them valid to estimate the mean values of ASM at group level.