Angelo Pietrobelli

Body mass index as a measure of adiposity among children and adolescents: A validation study☆☆☆★★★

OBJECTIVES To test the hypothesis that in a healthy pediatric population body mass index (BMI) (kilograms per meter square) is a valid measure of fatness that is independent of age for both sexes. METHODS Total body fat (TBF) (in kilograms) and percent of body weight as fat (PBF) were estimated by dual energy x-ray absorptiometry (DXA) in 198 healthy Italian children and adolescents between 5 and 19 years of age. We developed multiple regression analysis models with TBF and percent body fat as dependent variables and BMI, age, and interaction terms as independent variables. Separate analyses were conducted for boys and girls. RESULTS BMI was strongly associated with TBF (R2 = 0.85 and 0.89 for boys and girls, respectively) and PBF (R2 =0.63 and 0.69 for boys and girls, respectively). Confidence limits on BMI-fatness association were wide, with individuals of similar BMI showing large differences in TBF and in PBF. Age was a significant covariate in all regression models. Addition of nonlinear terms for BMI did not substantially increase the R2 for TBF and PBF models in boys and girls. CONCLUSION Our results support the use of BMI as a fatness measure in groups of children and adolescents, although interpretation should be cautious when comparing BMI across groups that differ in age or when predicting a specific individuals TBF or PBF.

Putative contributors to the secular increase in obesity: exploring the roads less traveled

Objective:To investigate plausible contributors to the obesity epidemic beyond the two most commonly suggested factors, reduced physical activity and food marketing practices.Design:A narrative review of data and published materials that provide evidence of the role of additional putative factors in contributing to the increasing prevalence of obesity.Data:Information was drawn from ecological and epidemiological studies of humans, animal studies and studies addressing physiological mechanisms, when available.Results:For at least 10 putative additional explanations for the increased prevalence of obesity over the recent decades, we found supportive (although not conclusive) evidence that in many cases is as compelling as the evidence for more commonly discussed putative explanations.Conclusion:Undue attention has been devoted to reduced physical activity and food marketing practices as postulated causes for increases in the prevalence of obesity, leading to neglect of other plausible mechanisms and well-intentioned, but potentially ill-founded proposals for reducing obesity rates.

What is the best measure of adiposity change in growing children: BMI, BMI %, BMI z-score or BMI centile?

Background:Weight control programs for obese children monitor change in body mass index (BMI) adjusted for age. However, change can be measured in several ways: raw (kg/m2) units, percentage, z-scores or centiles. The suitability of the different measures is not known.Aim:To identify the optimal BMI measure for change, whose short-term variability is most consistent for children across the spectrum of adiposity.Setting:An Italian kindergarten.Subjects:A total of 135 (66 female) children aged 29–68 months at baseline, with BMI measured three times over a 9-month period.Methods:Each childs short-term variability in adiposity was summarized by the standard deviation (s.d.) of BMI and BMI % adjusted for age, and BMI z-score and BMI centile. The s.d.s were then compared in obese and nonobese children, and also correlated with each childs baseline BMI z-score.Results:The within-child s.d.s of BMI z-score and BMI centile were significantly smaller in obese than nonobese children, while the s.d.s of BMI and BMI % were similar in the two groups. Also, the within-child s.d.s of z-score and centile, and to a lesser extent BMI %, were significantly inversely correlated with baseline z-score, whereas the s.d. of BMI was not. The changes in adiposity over time, as assessed by the four measures, were very highly correlated with each other, particularly for BMI with BMI %.Discussion:Even though BMI z-score is optimal for assessing adiposity on a single occasion, it is not necessarily the best scale for measuring change in adiposity, as the within-child variability over time depends on the childs level of adiposity. Better alternatives are BMI itself or BMI %. Our results underscore the importance of using a relatively stable method to assess adiposity change when following children at risk of obesity.

Ten Putative Contributors to the Obesity Epidemic

The obesity epidemic is a global issue and shows no signs of abating, while the cause of this epidemic remains unclear. Marketing practices of energy-dense foods and institutionally-driven declines in physical activity are the alleged perpetrators for the epidemic, despite a lack of solid evidence to demonstrate their causal role. While both may contribute to obesity, we call attention to their unquestioned dominance in program funding and public efforts to reduce obesity, and propose several alternative putative contributors that would benefit from equal consideration and attention. Evidence for microorganisms, epigenetics, increasing maternal age, greater fecundity among people with higher adiposity, assortative mating, sleep debt, endocrine disruptors, pharmaceutical iatrogenesis, reduction in variability of ambient temperatures, and intrauterine and intergenerational effects as contributing factors to the obesity epidemic are reviewed herein. While the evidence is strong for some contributors such as pharmaceutical-induced weight gain, it is still emerging for other reviewed factors. Considering the role of such putative etiological factors of obesity may lead to comprehensive, cause specific, and effective strategies for prevention and treatment of this global epidemic.

Crossvalidation of Anthropometry Against Magnetic Resonance Imaging for the Assessment of Visceral and Subcutaneous Adipose Tissue in Children

Background:The study of the relationship between anthropometry and visceral adipose tissue (VAT) is of great interest because VAT is associated with many risk factors for noncommunicable diseases and anthropometry is easy to perform in clinical practice. The studies hitherto available for children have, however, been performed on small sample sizes.Design:Pooling of the data of studies published from 1992 to 2004 as indexed on Medline.Aims:To assess the relationship between anthropometry and VAT and subcutaneous adipose tissue (SAT) as measured by magnetic resonance imaging (MRI) in children and to analyze the effect of age, gender, pubertal status and ethnicity.Subjects and methods:Eligible subjects were 7–16 year-old, with availability of VAT and SAT, gender, ethnicity, body mass index (BMI) and waist circumference (WC). A total of 497 subjects were collected from seven different investigators and 407 of them (178 Caucasians and 229 Hispanics) were analyzed.Results:Despite ethnic differences in MRI data, BMI, WC and age, no difference in VAT was found between Caucasians and Hispanics after correction for SAT and BMI. Univariate regression analysis identified WC as the best single predictor of VAT (64.8% of variance) and BMI of SAT (88.9% of variance). The contribution of ethnicity and gender to the unexplained variance of the VAT–WC relationship was low (⩽3%) but significant (P⩽0.002). The different laboratories explained a low (⩽4.8%) but significant (P<0.0001) portion of the unexplained variance of the VAT–WC and SAT–BMI relationships. Prediction equations for VAT (VAT (cm2)=1.1 × WC (cm)−52.9) and SAT (SAT (cm2)=23.2 × BMI (kg/m2)−329) were developed on a randomly chosen half of the population and crossvalidated in the remaining half. The pure error of the estimate was 13 cm2 for VAT and 57 cm2 for SAT.Conclusions:WC can be considered a good predictor of VAT as well as BMI of SAT. The importance of ethnicity and gender on VAT estimation is not negligible.

Depressive mood and obesity in US adults: comparison and moderation by sex, age, and race.

Objective:Sustained depressive mood is a gateway symptom for a major depressive disorder. This paper investigated whether the association between depressive mood and obesity differs as function of sex, age, and race in US adults after controlling for socio-economic variables of martial status, employment status, income level and education level.Methods:A total of 44 800 nationally representative respondents from the 2001 Behavioral Risk Factor Surveillance Survey were studied. Respondents were classified as having experienced a depressive mood if they felt sad, blue, or depressed at least for 1 week in the previous month. The depressive mood was operationalized in terms of duration and sustenance, both defined based on number of days with depressive mood: 7+ and 14+ days. Age groups were classified as young (18–64 years) and old (65+ years). Obesity status was classified as: not overweight/obese (BMI<25); overweight (25⩽BMI<30); obese (BMI⩾30).Results:Prevalence of prior-month depressive mood was 14.3 and 7.8% for 7+ and 14+ days, respectively. Controlling for race and socio-economic variables, both young overweight and obese women were significantly more likely to have experienced depressive mood than nonoverweight/nonobese women. Young overweight, but not obese, men were significantly more likely to have experienced depressive mood than nonoverweight/nonobese men. Young obese women were also significantly more likely to have a sustained depressive mood than nonoverweight/nonobese women. For old respondents, depressive mood and its sustenance were not associated with obesity in either sex.Discussion:The relationship between the depressive mood and obesity is dependent upon gender, age, and race. Young obese women, Hispanics in particular, are much more prone to depressive mood than nonobese women. Future studies testing associations between depression and obesity should be sensitive to the influence of these demographic and socio-economic variables.

New bioimpedance analysis system: improved phenotyping with whole-body analysis.

Objective: Bioimpedance analysis (BIA) is a potential field and clinical method for evaluating skeletal muscle mass (SM) and %fat. A new BIA system has 8-(two on each hand and foot) rather than 4-contact electrodes allowing for rapid ‘whole-body’ and regional body composition evaluation.Design: This study evaluated the 50 kHz BC-418 8-contact electrode and TBF-310 4-contact electrode foot–foot BIA systems (Tanita Corp., Tokyo, Japan).Subjects: There were 40 subject evaluations in males (n=20) and females (n=20) ranging in age from 6 to 64 y. BIA was evaluated in each subject and compared to reference lean soft-tissue (LST) and %fat estimates in the appendages and remainder (trunk+head) provided by dual-energy X-ray absorptiometry (DXA). Appendicular LST (ALST) estimates from both BIA and DXA were used to derive total body SM mass.Results: The highest correlation between total body LST by DXA and impedance index (Ht2/Z) by BC-418 was for the foot–hand segments (r=0.986; left side only) compared to the arm (r=0.970–0.979) and leg segments (r=0.942–0.957)(all P<0.001). The within- and between-day coefficient of variation for %fat and ALST evaluated in five subjects was <1% and ∼1–3.7%, respectively. The correlations between 8-electrode predicted and DXA appendicular (arms, legs, total) and trunk+head LST were strong and highly significant (all r⩾0.95, P<0.001) and group means did not differ across methods. Skeletal muscle mass calculated (Kim equation) from total ALST by DXA (X±s.d.)(23.7±9.7 kg) was not significantly different and highly correlated with BC-418 estimates (25.2±9.6 kg; r=0.96, P<0.001). There was a good correlation between total body %fat by 8-electrode BIA vs DXA (r=0.87, P<0.001) that exceeded the corresponding association with 4-electrode BIA (r=0.82, P<0.001). Group mean segmental %fat estimates from BC-418 did not differ significantly from corresponding DXA estimates. No between-method bias was detected in the whole body, ALST, and skeletal muscle analyses.Conclusions: The new 8-electrode BIA system offers an important new opportunity of evaluating SM in research and clinical settings. The additional electrodes of the new BIA system also improve the association with DXA %fat estimates over those provided by the conventional foot–foot BIA.

Weight loss increases and fat loss decreases all-cause mortality rate: Results from two independent cohort studies

OBJECTIVE: In epidemiological studies, weight loss is usually associated with increased mortality rate. Contrarily, among obese people, weight loss reduces other risk factors for disease and death. We hypothesised that this paradox could exist because weight is used as an implicit adiposity index. No study has considered the independent effects of weight loss and fat loss on mortality rate. We studied mortality rate as a function of weight loss and fat loss.DESIGN: Analysis of ‘time to death’ in two prospective population-based cohort studies, the Tecumseh Community Health Study (1890 subjects; 321 deaths within 16 y of follow-up) and the Framingham Heart Study (2731 subjects; 507 deaths within 8 y of follow-up), in which weight and fat (via skinfolds) loss were assessable.RESULTS: In both studies, regardless of the statistical approach, weight loss was associated with an increased, and fat loss with a decreased, mortality rate (P<0.05). Each standard deviation (s.d.) of weight loss (4.6 kg in Tecumseh, 6.7 kg in Framingham) was estimated to increase the hazard rate by 29% (95% confidence interval CI), (14%, 47%, respectively) and 39% (95% CI, 25%, 54% respectively), in the two samples. Contrarily, each s.d. of fat loss (10.0 mm in Tecumseh, 4.8 mm in Framingham) was estimated to reduce the hazard rate 15% (95% CI, 4%, 25%) and 17% (95% CI, 8%, 25%) in Tecumseh and Framingham, respectively. Generalisability of these results to severely (that is, body mass index BMI) ≥34) obese individuals is unclear.CONCLUSIONS: Among individuals that are not severely obese, weight loss is associated with increased mortality rate and fat loss with decreased mortality rate.

Dual-energy X-ray absorptiometry: fat estimation errors due to variation in soft tissue hydration

Dual-energy X-ray absorptiometry (DXA) is rapidly gaining acceptance as a reference method for analyzing body composition. An important and unresolved concern is whether and to what extent variation in soft tissue hydration causes errors in DXA fat estimates. The present study aim was to develop and validate a DXA physical hydration model and then to apply this model by simulating errors arising from hypothetical overhydration states. The DXA physical hydration model was developed by first linking biological substance elemental content with photon attenuation. The validated physical model was next extended to describe photon attenuation changes anticipated when predefined amounts of two known composition components are mixed, as would occur when overhydration develops. Two overhydration models were developed in the last phase of study, formulated on validated physical models, and error was simulated for fluid surfeit states. Results indicate that systematic errors in DXA percent fat arise with added fluids when fractional masses are varied as a percentage of combined fluid + soft tissue mass. Three independent determinants of error magnitude were established: elemental content of overhydration fluid, fraction of combined fluid + soft tissue as overhydration fluid, and initial soft tissue composition. Small but systematic and predictable errors in DXA soft tissue composition analysis thus can arise with fluid balance changes.Dual-energy X-ray absorptiometry (DXA) is rapidly gaining acceptance as a reference method for analyzing body composition. An important and unresolved concern is whether and to what extent variation in soft tissue hydration causes errors in DXA fat estimates. The present study aim was to develop and validate a DXA physical hydration model and then to apply this model by simulating errors arising from hypothetical overhydration states. The DXA physical hydration model was developed by first linking biological substance elemental content with photon attenuation. The validated physical model was next extended to describe photon attenuation changes anticipated when predefined amounts of two known composition components are mixed, as would occur when overhydration develops. Two overhydration models were developed in the last phase of study, formulated on validated physical models, and error was simulated for fluid surfeit states. Results indicate that systematic errors in DXA percent fat arise with added fluids when fractional masses are varied as a percentage of combined fluid + soft tissue mass. Three independent determinants of error magnitude were established: elemental content of overhydration fluid, fraction of combined fluid + soft tissue as overhydration fluid, and initial soft tissue composition. Small but systematic and predictable errors in DXA soft tissue composition analysis thus can arise with fluid balance changes.

Six-compartment body composition model: inter-method comparisons of total body fat measurement.

OBJECTIVE: To compare 16 currently used total body fat methods to a six-compartment criterion model based on in vivo neutron activation analysis.DESIGN: Observational, inter-method comparison study.SUBJECTS: Twenty-three healthy subjects (17 male and 6 female).MEASUREMENTS: Total body water (TBW) was measured by tritium dilution; body volume by underwater weighing (UWW); total body fat and bone mineral by dual-energy X-ray absorptiometry (DXA), total body potassium (TBK) by whole-body 40K counting; total body carbon, nitrogen, calcium, phosphorus, sodium and chlorine by in vivo neutron activation analysis; skinfolds/circumferences by anthropometry (Anth); and resistance by single-frequency bioimpedance analysis (BIA).RESULTS: The average of total body fat mass measurements by the six-compartment neutron activation model was 19.7±10.2 kg (mean±s.d.) and comparable estimates by other methods ranged from 17.4–24.3 kg. Although all 16 methods were highly correlated with the six-compartment criterion model, three groups emerged based on their comparative characteristics (technical error, coefficient of reliability, Bland-Altman analysis) relative to criterion fat estimates, in decreasing order of agreement: 1. multi-compartment model methods of Baumgartner (19.5±9.9 kg), Heymsfield (19.6±9.9 kg), Selinger (19.7±10.2 kg) and Siri-3C (19.6±9.9 kg); 2. DXA (20.0±10.8 kg), Pace-TBW (18.8±10.1 kg), Siri-2C (20.0±9.9 kg), and Brozek-UWW (19.4±9.2 kg) methods; and 3. Segal-BIA (17.4±7.2 kg), Forbes-TBN (21.8±10.5 kg), Durnin-Anth (22.1±9.5 kg), Forbes-TBK (22.9±11.9 kg), and Steinkamp-Anth (24.3±9.5 kg) methods.CONCLUSION: Relative to criterion fat estimates, body composition methods can be organized into three groups based on inter-method comparisons including technical error, coefficient of reliability and Bland-Altman analysis. These initial groupings may prove useful in establishing the clinical and research role of the many available fat estimation methods.