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Adiposity rather than BMI determines metabolic risk
International Journal of Cardiology 166 (2013) 111–117
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Adiposity rather than BMI determines metabolic risk☆ Antonino De Lorenzo a, b,⁎, Alessia Bianchi a, Pasquale Maroni c, Annarita Iannarelli c, Nicola Di Daniele d, Leonardo Iacopino a, Laura Di Renzo a, b a
Department of Neuroscience, Division of Human Nutrition, University of Tor Vergata, Via Montpellier 1, I-00133 Rome, Italy I.N.Di.M, National Institute for Mediterranean Diet and Nutrigenomic, Reggio Calabria, Italy Capgemini Italia S.p.a., Via di Torre Spaccata n. 140, Rome, Italy d From the Department of Internal Medicine, University of Tor Vergata, Italy b c
a r t i c l e
i n f o
Article history: Received 14 July 2011 Received in revised form 7 September 2011 Accepted 13 October 2011 Available online 15 November 2011 Keywords: Obesity Body composition Dual X-ray absorptiometry BMI Cardiometabolic risk
a b s t r a c t Background and aim: There is increasing evidence suggesting that WHO body mass index (BMI) cut-off values are outdated and should not be applied to different population. To overcome misclassifications, direct measurements of percentage body fat (PBF) would be a better tool for preobesity and obesity diagnosis. The aim of this study was to analyze the body composition in a adult population in Centre-South of Italy, by age and gender, and to verify the accordance between BMI and PBF cut-off points for health status classification. Methods: The total subject pool cover a total of 4408 participants adults. A completed screening of anthropometry and body composition by Dual X-ray Absorptiometry, (DXA) was assessed on 3.258 subjects. Results: Distributions and quantitative reliable estimates of PBF, total body fat and lean, according to gender and age are provided. The prevalence of “at risk” subjects (preobese and obese) was 69% and 85%, for men and women respectively, according to PBF cut-off points. The agreement of BMI and PBF categories resulted low for the total and male population, even scarce for female population (all P ≤ 0.001). The false negative classification of BMI was stronger for women than men and for younger than older subjects. Conclusions: Screening for adiposity in subjects with a normal BMI could better identify those at higher risk for cardiometabolic disturbances and cardiovascular mortality. The herein used cut-offs points of PBF, by age and gender, may provide a useful reference in clinical settings and public health services, in particular for the Italian Caucasian population. © 2011 Elsevier Ireland Ltd. All rights reserved.
1. Introduction The World Health Organization (WHO) defines obesity as a condition in which percentage body fat (PBF) is increased to an extent in which health and well-being are impaired [1]. The adipose tissue has traditionally been considered an energy storage organ, but over the last decade, its new role has emerged, as an endocrine organ [2]. Recently, the significant role of adipokines, peptides derived mainly from adipocytes, in the pathogenesis of obesity, insulin resistance and cardiovascular diseases (CVD) has been discussed. Additionally, obesity partially influences some cardiovascular risk factors such as: dyslipidaemia [hypercholesterolaemia, hypertriglyceridaemia and low high-density lipoprotein (HDL) cholesterol], hypertension and glucose intolerance [3,4]. ☆ Funding/support: this study was supported in part by grants from Ministero Politiche Agricole e Forestali and from I.N.Di.M., National Institute for Mediterranean Diet and Nutrigenomic, Reggio Calabria, Italy (MenSa, D.M. Dicembre 2010, n. 19663, cap 7743/3). ⁎ Corresponding author at: Department of Neuroscience, Division of Human Nutrition, University of Tor Vergata, Via Montpellier 1, 00133 Rome, Italy. Tel./fax: + 39 0672596415. E-mail address: [email protected] (A. De Lorenzo). 0167-5273/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.ijcard.2011.10.006
The currently used WHO body mass index (BMI) cut-off values for pre-obesity and obesity are based on morbidity and mortality studies in relation to Caucasian population BMI [1,5]. Because BMI does not measure PBF directly and poorly distinguishes between total body fat (TBFat) and total body lean (TBLean), or bone mass, the use of BMI as an index of PBF for a person may be inaccurate and not useful as a cardiovascular risk factor [6,7]. Moreover, PBF at a given BMI will tend to vary across gender, age, and race-ethnicity [8–10]. The clinical use of WHO BMI cut-off values when applied to the Italian population cause misclassifications, and a considerable number of subjects, both males and females, will not be classified as obese based on their BMI alone [11]. To overcome misclassifications, direct measurements of PBF would be a better tool for diagnosing obesity. According to a WHO expert committee, “there is no agreement about cut-off points for the PBF that constitutes obesity” [5]. Current research suggests that the obesity cut-off points of PBF are in the 23%–25% range in men and 30%–33%–35% range in women [12]. Moreover, TBFat distribution may be different in subjects with the same BMI, and lean and obese subjects share different metabolic characteristics [8,13–19]. In fact, a person with the same BMI, may have a large proportion of TBFat and be preobese or obese [11], or may have a considerable muscle mass and be a weight-lifter. It has
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been estimated that normal weight individuals could have abnormal metabolic profiles and be at increased risk of developing obesityassociated diseases [20–23]. Thus, body composition (BC) evaluation is indispensable to assess nutritional status and thus health and to assess the efficacy of primary and secondary preventive nutritional strategies. Various methods have been developed for measuring body composition accurately “in vivo”, such as isotopic dilution techniques, hydrodensitometry, bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA). Among these, DXA has proved the most reliable in clinical practice to directly assess total and regional body fat and fat-free mass (FFM), which includes lean soft issues and bone mineral, because it is easily applicable to persons with and without diseases, highly accurate and reproducible, involves a moderate cost and minimal irradiation, and is quick to perform [24]. Percentage body fat (PBF), which is calculated as total body fat (TBFat) divided by total mass multiplied by 100, is a direct measure of a person's relative body fat. Although, there exist significant differences in body fat across gender, age, and race-ethnicity [25]. On the basis of these criticisms and previous our results [11,26], this study has been carried out in a large cross sectional sample of healthy adult Italian Caucasians: 1) to analyze the distributions of PBF, TBFat and TBLean, measured by Dual X-ray Absorptiometry (DXA); 2) to assess differences in the means of PBF, TBFat, and TBLean by age and gender; 3) to verify the accordance between BMI and PBF cut-off points to classify adult Italian Caucasians. 1.1. Subjects and methods 1.1.1. Study design and subjects A cross-sectional study was conducted on a sample coming from Centre-South of Italy of apparently healthy Italian adults consecutively recruited at the University of Rome Tor Vergata, Human Nutrition Unit from 2002 to 2009. All subjects were volunteers, between 19 and 90 years old, who had already been recruited to create a reference normal sample among hospital staff members, university students, lay people contacted by word of mouth, subjects presenting spontaneously for DXA measurement. For what regards race ethnicity, the sample included only Caucasian participants. Participation in the study included a complete medical history to gather information about health status, current medications history, including supplements of vitamin and mineral, social habits, like alcohol drinking and smoking, eating habits, physical activity and family history for chronic diseases. Subjects with acute diseases, severe liver, heart or kidney dysfunctions, endocrine disorders (diabetes, hypo- or hyperthyroidism), cancer or other conditions capable of altering body composition (AIDS, Paget's, gastroenteropathies with malabsorption, neuromuscular diseases, rheumatic diseases, mild to severe cognitive impairment or disability) were excluded. The use of certain drugs (steroids, diuretics) was also a reason for exclusion. A completed screening of anthropometry and body composition was assessed. Participants were stratified into 5 macro-groups according to age: 19–34, 35–49, 50–64, 65–79 and ≥80 years. The subjects were also categorized in BMI subgroups according to World Health Organization (WHO) criteria [1,5]. Moreover, gender and age PBF cutoff points were also used to classify the total population [26]. Firstly, the prevalence of underweight/underfat, normal, preobese and obese subjects, according to BMI and gender and age PBF cutoff points, was examined. Secondly, the distributions of PBF, TBFat and TBLean and differences in the means of PBF, TBFat, and TBLean by age and gender were estimated. Finally, Kappa test was performed to estimate in which extent BMI and PBF cut-off agree with each other and move on the same direction. A statement of informed consent was signed by all participants in accordance with principles of the Declaration of Helsinki. The study
was conducted according to the guidelines of the “Tor Vergata” University Medical Ethical Committee, Rome, Italy. 1.1.2. Anthropometric measurements After a 12-hour overnight fast, all subjects underwent anthropometric evaluation. Anthropometric measurements for all participants according to standard methods, were carried out [27]. All the individuals were instructed to take off their clothes and shoes before undergoing the measurements. Body weight (kg) was measured to the nearest 0.1 kg, using a balance scale (Invernizzi, Rome, Italy). Height (m) was measured using a stadiometer to the nearest 0.1 cm (Invernizzi, Rome, Italy). Body mass index (BMI) was calculated using the formula: BMI = body weight(kg)/height(m) 2 (kg/m 2). 1.1.3. Dual X-ray absorptiometry (DXA) The total body composition was assessed by DXA (Lunar DPX and iDXA, G.E. Medical Systems, WI, USA), according to the previously described procedure [28,29]. The technique combined a total body scanner, an X-ray source, an internal wheel to calibrate the bone mineral compartment, and an external lucite/aluminium phantom to calibrate the fat compartment. Standard DXA quality control and calibration measures were performed prior to each testing session. Individuals were asked to remove all clothing except for undergarments including shoes, socks and metal items prior to being positioned on the DXA table. Scans were performed with individuals in a supine position. The entire body was scanned beginning from the top of the head and moving in a rectilinear pattern down the body to the feet. Mean measurement time was 15 min. The average measurement time was 20 min. The effective radiation dose from this procedure is about 0.01 mSv. The coefficient of variation (CV% = 100× SD/mean) intra and inter subjects ranged from 1% to 5%. The coefficient of variation for bone measurements is less than 1%; CVs on this instrument for five subjects scanned six times over a nine month period were 2.2% for TBFat, and 1.1% for TBLean. 1.1.4. Statistical analysis Data are presented as group means ± SD or number (percentage), and were analyzed to check assumptions about the distribution of the measured variables. We stratified all our analyses by gender and age. Age was grouped for analysis as 19–34, 35–49, 50–64, 65–79 and ≥80 y. The overall classification by PBF and BMI categories was evaluated by calculating the percentage of the population that fell into the corresponding category by 2 measures (exact agreement) and using separate logistic regression to model the dependence of exact agreement between each pair of measures on gender and age group. We calculated the percentage that agreed to within one category. The measure used to evaluate Cohen's kappa (CK) coefficient is a statistical measure of inter-rater agreement for qualitative items, it ranges from 0 to 1, where a coefficient equal to 0 means that there is no concordance between the indices whilst 1 represents perfect agreement [30]. The results of logistic regression were expressed as odds-ratio (OR) and [95% confidence interval] using male and younger age as the reference. All tests were considered significant at P ≤ 0.05. Statistical analysis was performed by Capgemini Italia S.p.a., through a computer software package (SPSS for Windows, version 17.0; SPSS, Chicago, IL). 2. Results The total subject pool cover a total of 4408 participants adults (≥19 y of age). The analytic sample consisted of 3.258 Italian Caucasian adults (1.250 men and 2.008 women), range 19–88 years, who had complete data on anthropometric and body composition analysis. The distributions of TBFat, PBF, TBLean according to gender are shown in Fig. 1. The means of TBFat, PBF, and TBLean by age and gender groups are shown in Fig. 2. The width of the confidence intervals
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Fig. 1. Population distributions of TBFat, PBF, and TBLean, by gender. Data are expressed as percentage (%) of subjects according to TBFat (Kg), PBF (%), and TBLean (Kg). TBFat, Total Body Fat; PBF, Percentage Body Fat; TBLean, Total Body Lean.
(CI), with level of 95%, is directly related to the number of participants in a group. In the group with age > = 80 y, the small sample size leads to more variability in the data and a wider CI. PBF increases with age before 70 y and decreases afterwards in women, whilst in men the turning point occurs at 60 y. TBFat shows a non-linear trend: it increases up to 35 y both in men and women, it remains constant between 35 y and 60 y in men, and between 35 y and 65 y in women, and then decreases. TBLean trend moves downwards with age in both men and women; it decreases faster in men than in women. The main characteristics of the subjects are summarized in Table 1. Differences in mean weight, height, BMI, PBF, TBFat and TBLean were observed between men and women (for all, P ≤ 0.001). A significant less content of TBLean, reflecting in an higher PBF but not in an higher BMI, in women respect to men was observed. As defined above, the study population was stratified in 5 set of age groups. According to gender, an equal distribution in age set groups was obtained. The prevalence of underweight/underfat, normal, preobese and obese subjects, according to BMI and PBF cut-off points, respectively, is also shown. Table 2 shows the degree of agreement between BMI and PBF classification of obesity, in total, male and female population. BMI and PBF agreement was measured by CK coefficient. These data are useful to understand to what extent the two indices agree with each other to estimate obesity. The results herein shown represent how people in the same class of BMI are categorized according to PBF. We applied age- and gender-specific cut-offs to create PBF and BMI categories and calculate the corresponding prevalence of underweight/underfat, normal, pre-obese and obese subjects. For example, according to PBF, 64% of the total population fell into the obese category. Of this percentage, less than 50% (≈31%) fell into the corresponding obese BMI
category, ≈22% fell into the preobese BMI category, ≈11% fell into the normal BMI category. The agreement measure, expressed by CK values, resulted low for the total and male population, even scarce for female population (all P ≤ 0.001). Among men, the agreement measure resulted low for all 5 sets of age (CK = 0.298 in 19–34 y group, P ≤ 0.001; CK = 0.403 in 35–49 y group, P ≤ 0.001; CK = 0.381 in 50–64 y group, P ≤ 0.001; CK = 0.417 in 65–79 y group, P ≤ 0.001; CK = 0.130 in > =80y; ). Among women, the agreement measure resulted scarce for all 5 sets of age (CK = 0.139 in 19–34 y group, P ≤ 0.001; CK = 0.132 in 35–49 y group, P ≤ 0.001; CK = 0.154 in 50–64 y group, P ≤ 0.001; CK = 0.228 in 65–79 y group, P ≤ 0.001; CK not determined in > =80y). The agreement of BMI categories with PBF categories was significantly lower for women than men (OR M vs. F: 0.58; 95% CI: 0.50, 0.67, P ≤ 0.05) and for younger than older subjects (OR 19–34y vs. 65–79y: 0.50; 95% CI: 0.39, 0.58; OR 35–49y vs. 65–79y: 0.74; 95% CI: 0.56, 0.98; OR 50–64y vs. 65–79y: 0.90; 95% CI: 0.66, 1.19; all P ≤ 0.05). Moreover, ≈17% and≈ 35% of PBF defined obese were normal and preobese according to BMI, respectively; ≈59% of preobese according to PBF categories were defined normal by BMI; ≈7% and≈ 21% of PBF defined normal subjects were considered underweight and preobese, respectively, by considering BMI cut-off points; while ≈ 70% of underfat subjects were classified as normal according to BMI (data not shown). BMI differences according to age and gender and PBF categories are shown in Table 3. These data show how mean BMI of our sample depends on gender and age across underfat, normal, preobese and obese categories, evaluated by age and gender specific PBF cut-off points. On average, men had higher BMI compared to women, in all PBF categories. Such difference persists across all age set groups. Moreover, differences in BMI among all classes of age are also
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Fig. 2. Mean (and 95% CI) of TBFat, PBF, and TBLean, by age and gender. Data are expressed as as arithmetic mean and 95% CI according to age and gender. CI, Confidence Interval; TBFat, Total Body Fat; Percentage Body Fat, PBF; TBLean, Total Body Lean.
relevant, particularly in normal and preobese categories. It should be noted that, in some cases, the comparisons made in this analysis do not take into account the age group > = 80y because the low sample size makes it impossible to provide reliable results. 3. Discussion The assessment of the physical state of pre-obesity and obesity represents a very important information both for the state of health of an individual, and for his quality of life. Many studies have shown an association between obesity, mortality and morbidity [31,32]. The increased prevalence of pre-obesity and obesity has become an epidemiological emergency, not only in USA but also in Mediterranean countries [33]. Recent analytical studies on CVD risk indicate that preobesity and obesity play a greater than-expected role in determining CVD (especially coronary), the leading cause of mortality, morbidity and hospitalisation in both genders in all countries of Europe [34]. A recent systematic analysis of population-based data sources revealed that between 1980 and 2008, age-standardized mean global BMI increased by 0.4–0.5 kg/m 2 per decade in men and women. Worldwide, age-standardized prevalence of obesity by BMI was 9.8% (9.2–10.4) in men and 13.8% (13.1–14.7) in women in 2008, which
was nearly twice the 1980 prevalences of 4.8% [35]. In Western Europe, an increased trend of obesity ranged from about 10% in 1980 to about 20% in 2008, both in men and women. Based on data collected in 2008 by the National Institute of Statistics (Istat) and published in 2010, in Italy 35.5% of the adult population were preobese (27.1% males, 44.6% females), and 9.9% obese (9.1 males, 10.8% females), according to BMI [36]. BMI is normally used in population studies in which overweight and obesity are related to morbidity and mortality. However, predictive methods have a relatively large error at an individual level, and thus, if subjects have to be classified into categories, misclassification can occur [10]. In fact, there is increasing criticism on the use of WHO BMI cut-off values to define pre-obesity and obesity [37]. Nevertheless it is possible that the surprisingly favourable prognostic implications associated with a mildly elevated BMI might actually reflect intrinsic limitations of BMI to differentiate TBFat from TBLean [38]. For example, some BMI defined preobese are not overfat [11]. Therefore, using gross weight and BMI may not accurately reflect the risk of complications in all obese individuals and may explain some of the paradoxical results seen, known as “obesity paradox” [39]. A recent meta-analysis, regarding the influence of body weight or obesity measures in patients with coronary artery disease (CAD) reported a better outcome for cardiovascular and total mortality
A. De Lorenzo et al. / International Journal of Cardiology 166 (2013) 111–117 Table 1 Descriptive characteristics of participantsa. Parameters
Men
Women
Sample size (n, %) Age (n, %)
1250 (38.4)
2008 (61.6)
19–34 y 35–49 y 50–64 y 65–79 y > = 80 y
506 (40.5) 345 (27.6) 285 (22.8) 106 (8.5) 8 (0.6)
768 (38.2) 569 (28.3) 500 (24.9) 155 (7.7) 16 (0.8)
Under-weight Normal Preobese Obese Under-fat Normal Preobese Obese
11 (0.9) 355 (28.4) 465 (37.2) 419 (33.5) 108 (8.6) 280 (22.4) 233 (18.6) 629 (50.3) 28.5 ± 9.5 26.0 ± 12.7 61.0 ± 8.9 87.2 ± 18.2 174.8 ± 7.2 28.5 ± 5.7
63 (3.1) 761 (37.9) 549 (27.3) 635 (31.6) 44 (2.1) 246 (12.3) 262 (13.1) 1456 (72.5) 39.5 ± 9.4d 29.5 ± 13.1d 41.6 ± 6.7d 71.8 ± 17.8d 160.9 ± 6.9d 27.8 ± 6.9d
BMI cut-off (n, %)b
PBF cut-off (n, %)c
PBF (%) TBFat (kg) TBLean (kg) Weight (kg) Height (cm) BMI (kg/m2)
a Sample size, age groups, BMI and PBF categories are expressed as number (percentage). PBF, TBFat, TBLean, Weight, Height, BMI are indicated as arithmetic mean± SD. Age groups are expressed as number (percentage). b WHO BMI categories were used: under-weight: b 18.5 kg/m2; Normal: 18.5–24.9 kg/ m2; preobese: 25.0–29.9 kg/m2; Obese: ≥30 kg/m2. c Gender and age specific cut-off PBF cut-off point were used. d Significantly different from men (independent t test; P≤ 0.001). Y, Year; BMI, Body Mass Index; PBF, Percentage Body Fat; TBFat, Total Body Fat; TBLean, Total Body Lean; SD, Standard Deviation.
seen in overweight and mildly obese groups. The authors suggested that this could be due to the lack of discriminatory power of BMI to reflect adiposity adequately [40]. Many other studies in the past suggested a need to further explore the appropriateness of gender-specific BMI cut-points for clinical risk assessment due the marked difference in the BMI-per cent for relation observed in men and women across the entire range of BMI [41]. In recent years, there is a growing debate on whether there are possible needs for developing different BMI cut-off points for different ethnic groups due to the increasing evidence that the associations between BMI, PBF, and TBFat distribution differ across populations [42]. Moreover, it's known that the relationship between BMI and PBF is age and gender dependent [8,9]. General adiposity and abdominal adiposity are associated with the risk of death and support the use of waist circumference or waist-to hip ratio in addition to BMI. Interesting, central obesity, gain in abdominal fat correlates closely with both hyperinsulinemia and insulin
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resistance and with the possibility to develop type 2 diabetes and coronary heart disease, both in preobese–obese and non preobese–obese individual [2,4,43]. Obese subjects are characterized by an unfavourable cardiovascular profile, increased diabetes and impaired liver function. The paracrine and endocrine adipose inflammatory events induce a systemic inflammatory, insulin-resistant, atherogenic state and metabolic dyslipidemia, resulting in type 2 diabetes and CVD [3]. Application of BC evaluation that target both adipose and lean tissue may be required to prevent, time in advance, type 2 diabetes and atherosclerotic CVD in the emerging epidemic of obesity. Different subtypes of obesity were known: the “at risk” obese with metabolic syndrome, the metabolically healthy but obese individuals (MHO), and the metabolically-obese normal weight subjects (MONW) [13,44]. The “metabolically- obese” normal weight (MONW), represents a subset of individuals who have normal weight and BMI, but display a cluster of metabolic characteristics that may increase the possibility to develop the metabolic syndrome, in the same manner of so called “at risk” obese individuals. MONW individuals are not obese but characterized by an excess visceral fat, predisposing to insulin resistance, hypertension and CVD, as referred for the metabolic syndrome. Moreover, De Lorenzo et al. 2006, have identified the Normal Weight Obese (NWO) syndrome, characterized by normal body weight and BMI, but high PBF [14]. Since, NWO subjects do not present metabolic syndrome, they are distinguished from MOWN individuals. NWO subjects were similar to preobese–obese women not only for TBLean distribution, but also for atherogenic indices (i.e. lipoprotein ratios) and oxidative stress [15,16]. An early inflammation and genetic predisposition characterized the syndrome [17–19]. A cross-sectional study realized to assess the prevalence of NWO in Switzerland, showed a low prevalence in the general population and higher in women than in men [21,22]. Romero Corral et al. observed that NWO had a higher prevalence of metabolic syndrome, dyslipidemia and of hypertension (men), CVD (women), and a 2.2-fold increased risk of CV mortality (women) compared with those with low TBFat [23]. The main focus of this study has been to assess whether the WHO guidelines for preobese and obese using the current BMI definitions, in a large cross sectional sample of Italy, are useful, or need to be adapted to age, gender and population for a better prediction of CVD risk and setting priorities on prevention and intervention policies. In this large study population, the prevalence of “at risk” subjects (preobese and obese) was 69% and 85%, for men and women respectively, according to PBF cut-off points, respect to 70% and 58%, according to BMI classification. The results obtained from our study suggest there is a low agreement between BMI and PBF classifications. In fact, among obese people according to PBF just 48% resulted obese also for BMI. The differences between BMI and PBF categories persisted within gender and age groups. More specifically, 50% and
Table 2 Agreement between BMI and PBF classification of obesity, in total, male and female populationa. Subjects (%)
BMI (Kg/m2) Underweight Normal Preobese Obese Total CK2
Total population
Male population
Female population
PBF (%)
PBF (%)
PBF (%)
Underfat Normal Preobese Obese Total
Underfat Normal Preobese Obese Total
Underfat Normal Preobese Obese Total
0.83 3.25 0.52 0.06 4.67 0.245b
0.95 1.25 0.00 0.00 2.19 0.369b
0.64 6.48 1.36 0.16 8.64 0.170b
1.20 11.26 3.35 0.34 16.14
0.18 8.96 4.97 1.07 15.19
0.06 10.77 22.28 30.88 64.00
2.27 34.25 31.12 32.35 100.00
1.79 9.51 0.80 0.15 12.25
0.30 11.25 1.25 0.25 13.05
0.10 15.89 25.30 31.23 72.51
3.14 37.90 27.34 31.62 100.00
0.24 14.08 7.44 0.64 22.40
0.00 5.28 10.96 2.40 18.64
0.00 2.56 17.44 30.32 50.32
0.88 28.40 37.20 33.52 100.00
a Data are expressed as percentage of subjects (%) categorized according to PBF cut-off points, and along BMI categories. We applied age- and gender-specific cut-offs to create PBF and BMI categories and calculate the corresponding prevalence of underweight/underfat, normal, pre-obese and obese subjects. WHO BMI categories were used: under-weight: b 18.5 kg/m2; normal: 18.5–24.9 kg/m2; preobese: 25.0–29.9 kg/m2; obese: ≥30 kg/m2. Gender and age specific cut-off PBF cut-off point were used. PBF, Percentage Body Fat; BMI, Body Mass Index; CK, Cohen's kappa coefficient. b Degree of agreement: b 0.01 none; 0.01–0.20 scarce; 0.21–0.40 low; 0.41–0.60 moderate; 0.61–0.80 good; 0.81–1.00 almost perfect/perfect. P ≤ 0.001.
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Table 3 Mean BMI according to PBF categories1. PBF categories Underfat
BMI (kg/m2) Men Crude Age 19–34 35–49 50–64 65–79 > = 80 Women Crude Age 19–34 35–49 50–64 65–79 > = 80
Normal
Preobese
Obese
Mean
SE
Min
Max
SD
Mean
SE
Min
Max
SD
Mean
SE
Min
Max
SD
Mean
SE
Min
Max
SD
22,52a
0,3
16,10
33,83
3,1
24,37b
0,2
17,66
33,56
2,7
26,83c
0,2
19,57
40,20
3,1
32,05d
0,2
21,4
61,5
5,4
22,50a 24,10a 20,54 20,09a –
0,3 0,9 2,0 0,7 –
16,10 20,13 16,34 18,52 –
32,40 33,83 25,80 22,23 –
3,0 3,5 4,0 1,6 –
24,08b 24,85a 25,30a 23,64b 21,39
0,2 0,4 0,4 0,7 0,0
17,7 20,4 21,6 18,8 21,4
33,5 33,6 33,2 29,7 21,4
2,6 2,7 2,4 2,9 0,0
25,77c 26,85b 28,01b 27,79c 22,26
0,3 0,4 0,4 0,7 0,0
19,57 20,48 23,15 23,69 22,26
34,82 35,08 40,20 34,81 22,26
2,9 3,1 2,8 3,1 0,0
31,49d 32,53c 32,27c 31,74d 28,40
0,4 0,4 0,4 0,6 0,8
21,6 21,4 23,9 22,9 26,7
52,9 61,5 51,4 48,0 31,1
5,7 5,8 4,7 5,2 1,9
19,05a,2
0,3
15,53
23,88
2,2
21,01b,2
0,2
16,35
42,92
2,9
22,54c,2
0,2
17,14
36,93
2,7
30,15d,2
0,2
16,9
59,5
6,5
19,08a 19,06a 19,03a 16,94 22,15
0,4 0,5 1,1 0,7 0,0
15,53 17,86 16,20 16,23 22,15
23,88 21,48 22,67 17,64 22,15
2,4 1,3 2,4 1,0 0,0
20,52b 21,24a 22,61a,b 22,13a -
0,2 0,5 0,8 0,6 -
16,9 16,4 16,9 18,8 -
28,9 32,6 42,9 25,8 -
2,3 3,1 4,6 2,3 -
21,65c 22,82b 23,76b 24,77a 30,27
0,2 0,3 0,5 0,9 4,3
17,14 19,38 19,10 20,52 25,97
29,80 36,93 31,93 31,50 34,57
2,1 2,5 3,0 3,2 6,1
28,31d 30,73c 31,00c 31,66b 31,98
0,3 0,3 0,3 0,5 1,8
16,9 19,2 19,4 21,5 24,1
59,5 57,6 52,6 51,6 46,5
6,8 6,7 5,7 5,8 6,5
1 Data are expressed as mean, with SE, Min, Max and SD and represent BMI value along PBF categories, according to age and gender. PBF, Percentage Body Fat; BMI, Body Mass Index; SE, Standard Error; Min, Minimun; Max, Maximum; SD, Standard Deviation. a,b,c Values with different subscript letters are significantly different among age groups in the same BMI category (independent t sample P ≤ 0.05).2 Significantly different from men (P ≤ 0.05).
73% of screened men and women were obese according to PBF, while only 34% and 32% respectively were obese according to BMI. In addition, the false negative classification was stronger for women than men and for younger than older subjects. Overall, 56% of men and 43% of women were in the same category by PBF and BMI cut-off points. These data are likely similar to Flegal et al., that observed a correspondence of 46% and 49% between both measures, for men and women respectively [8]. Gallageher et al., for the first time, presented age, gender and population specific PBF ranges, from current BMI guidelines. There is no consensus on how BF is linked with morbidity and mortality because of the absence of appropriate prospective studies. Specifically, no universally accepted published BF ranges exist; those reported based on empirically set limits, population percentiles, and z scores have serious limitations [10]. In an obesogenic environment, the positive skewing of the distribution curve of BMIs increases over time as heavier individuals gain more weight than lighter individuals [45]. Although the comparison with the BMI distribution data [32] showed that the sample examined referring to our center was not representative of the general population, the main objective of the study, i.e. o verify the accordance between BMI and PBF cut-off points, was satisfied. The herein used, cutoffs points of PBF, by age and gender, may provide a useful reference in clinical settings and public health services, in particular for the Italian Caucasian population. In the present study, we also provided quantitative reliable estimates of PBF, TBFat, and TBLean according to gender and age. Our results highlight gender and age dependent differences in PBF, TBFat and TBLean, in the general adult population. Similar to previous findings in adults [9,46], women in this study generally had greater estimated mean PBF and TBFat, but less TBLean, than men. Our results are also consistent with the findings of previous studies [47,48] in which the researchers showed that mean values of PBF were higher in older than younger subjects, supporting the idea that, particularly at older age, TBFat increases at the expense of TBLean. According to Shaw et al., TBLean decreased with ageing, after 50 y, particularly in men, toward sarcopoenia [49]. Decrease in muscle density and increase in the accumulation of TBFat with age may partially explain the differences in PBF between the younger and older adults [50]. Loss of skeletal muscle
mass with ageing increases an individual's risk for sarcopoenia, which is characterized by low skeletal muscle mass, reduced muscle strength and increased risk for adverse health outcomes such as falls, fractures, impaired physical functioning, disability and frailty in older populations [51]. Age-related changes in BC, including the increase and redistribution of fat tissue and the decrease in skeletal muscle and bone mass, begin as early as the fourth decade of life. Preventive and therapeutic options for optmising BC in old age is central to the care of patients in midlife and beyond [52]. Consequently, we suggest that age and gender independent BC reference values derived from adult populations may not be applicable for clinical practice. The present study has its limitation and strengths. The study population was not randomly selected from the general population but consisted mainly of subjects, from Centre-South of Italy, referred for DXA measurement. The strength of the present study is that it was considered a large cohort of both men and women. Moreover, a statistical analysis was performed by high professional specialists. Finally, by using DXA data, we described the distributions and showed the differences in BC by gender and age. Moreover, our results highlighted the low-scarce agreement of BMI and PBF cut-off points. In conclusion, screening for adiposity in subjects with a normal BMI could better identify those at higher risk for cardiometabolic diseases and CV mortality. Therefore, the determination of adiposity by methods more accurate than BMI and the definition of universally recognized PBF cut-off by gender, age, race and ethnicity, could have public health implications. Conflict of interest The authors declared no conflict of interest. Acknowledgments Author contributions: Prof. De Lorenzo, Dr(s) Di Renzo, Bianchi had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Prof. De Lorenzo and Dr Di Renzo. Acquisition of data: Dr(s) Iacopino, Bianchi. Study supervision and Critical
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revision: Prof. De Lorenzo, Dr Di Renzo. Statistical analysis: Dr Iannarelli and Maroni. Analysis and interpretation of data: Dr(s) Di Renzo, Bianchi, De Lorenzo. Manuscript preparation and editing: Prof. De Lorenzo Dr(s) Di Renzo and Bianchi. The authors thank Emidio Domino, Mariagiovanna Rizzo and Alessandra Feraco for the contributions to the study. The authors of this manuscript have certified that they comply with the Principles of Ethical Publishing in the International Journal of Cardiology.
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International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Adiposity rather than BMI determines metabolic risk☆ Antonino De Lorenzo a, b,⁎, Alessia Bianchi a, Pasquale Maroni c, Annarita Iannarelli c, Nicola Di Daniele d, Leonardo Iacopino a, Laura Di Renzo a, b a
Department of Neuroscience, Division of Human Nutrition, University of Tor Vergata, Via Montpellier 1, I-00133 Rome, Italy I.N.Di.M, National Institute for Mediterranean Diet and Nutrigenomic, Reggio Calabria, Italy Capgemini Italia S.p.a., Via di Torre Spaccata n. 140, Rome, Italy d From the Department of Internal Medicine, University of Tor Vergata, Italy b c
a r t i c l e
i n f o
Article history: Received 14 July 2011 Received in revised form 7 September 2011 Accepted 13 October 2011 Available online 15 November 2011 Keywords: Obesity Body composition Dual X-ray absorptiometry BMI Cardiometabolic risk
a b s t r a c t Background and aim: There is increasing evidence suggesting that WHO body mass index (BMI) cut-off values are outdated and should not be applied to different population. To overcome misclassifications, direct measurements of percentage body fat (PBF) would be a better tool for preobesity and obesity diagnosis. The aim of this study was to analyze the body composition in a adult population in Centre-South of Italy, by age and gender, and to verify the accordance between BMI and PBF cut-off points for health status classification. Methods: The total subject pool cover a total of 4408 participants adults. A completed screening of anthropometry and body composition by Dual X-ray Absorptiometry, (DXA) was assessed on 3.258 subjects. Results: Distributions and quantitative reliable estimates of PBF, total body fat and lean, according to gender and age are provided. The prevalence of “at risk” subjects (preobese and obese) was 69% and 85%, for men and women respectively, according to PBF cut-off points. The agreement of BMI and PBF categories resulted low for the total and male population, even scarce for female population (all P ≤ 0.001). The false negative classification of BMI was stronger for women than men and for younger than older subjects. Conclusions: Screening for adiposity in subjects with a normal BMI could better identify those at higher risk for cardiometabolic disturbances and cardiovascular mortality. The herein used cut-offs points of PBF, by age and gender, may provide a useful reference in clinical settings and public health services, in particular for the Italian Caucasian population. © 2011 Elsevier Ireland Ltd. All rights reserved.
1. Introduction The World Health Organization (WHO) defines obesity as a condition in which percentage body fat (PBF) is increased to an extent in which health and well-being are impaired [1]. The adipose tissue has traditionally been considered an energy storage organ, but over the last decade, its new role has emerged, as an endocrine organ [2]. Recently, the significant role of adipokines, peptides derived mainly from adipocytes, in the pathogenesis of obesity, insulin resistance and cardiovascular diseases (CVD) has been discussed. Additionally, obesity partially influences some cardiovascular risk factors such as: dyslipidaemia [hypercholesterolaemia, hypertriglyceridaemia and low high-density lipoprotein (HDL) cholesterol], hypertension and glucose intolerance [3,4]. ☆ Funding/support: this study was supported in part by grants from Ministero Politiche Agricole e Forestali and from I.N.Di.M., National Institute for Mediterranean Diet and Nutrigenomic, Reggio Calabria, Italy (MenSa, D.M. Dicembre 2010, n. 19663, cap 7743/3). ⁎ Corresponding author at: Department of Neuroscience, Division of Human Nutrition, University of Tor Vergata, Via Montpellier 1, 00133 Rome, Italy. Tel./fax: + 39 0672596415. E-mail address: [email protected] (A. De Lorenzo). 0167-5273/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.ijcard.2011.10.006
The currently used WHO body mass index (BMI) cut-off values for pre-obesity and obesity are based on morbidity and mortality studies in relation to Caucasian population BMI [1,5]. Because BMI does not measure PBF directly and poorly distinguishes between total body fat (TBFat) and total body lean (TBLean), or bone mass, the use of BMI as an index of PBF for a person may be inaccurate and not useful as a cardiovascular risk factor [6,7]. Moreover, PBF at a given BMI will tend to vary across gender, age, and race-ethnicity [8–10]. The clinical use of WHO BMI cut-off values when applied to the Italian population cause misclassifications, and a considerable number of subjects, both males and females, will not be classified as obese based on their BMI alone [11]. To overcome misclassifications, direct measurements of PBF would be a better tool for diagnosing obesity. According to a WHO expert committee, “there is no agreement about cut-off points for the PBF that constitutes obesity” [5]. Current research suggests that the obesity cut-off points of PBF are in the 23%–25% range in men and 30%–33%–35% range in women [12]. Moreover, TBFat distribution may be different in subjects with the same BMI, and lean and obese subjects share different metabolic characteristics [8,13–19]. In fact, a person with the same BMI, may have a large proportion of TBFat and be preobese or obese [11], or may have a considerable muscle mass and be a weight-lifter. It has
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been estimated that normal weight individuals could have abnormal metabolic profiles and be at increased risk of developing obesityassociated diseases [20–23]. Thus, body composition (BC) evaluation is indispensable to assess nutritional status and thus health and to assess the efficacy of primary and secondary preventive nutritional strategies. Various methods have been developed for measuring body composition accurately “in vivo”, such as isotopic dilution techniques, hydrodensitometry, bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA). Among these, DXA has proved the most reliable in clinical practice to directly assess total and regional body fat and fat-free mass (FFM), which includes lean soft issues and bone mineral, because it is easily applicable to persons with and without diseases, highly accurate and reproducible, involves a moderate cost and minimal irradiation, and is quick to perform [24]. Percentage body fat (PBF), which is calculated as total body fat (TBFat) divided by total mass multiplied by 100, is a direct measure of a person's relative body fat. Although, there exist significant differences in body fat across gender, age, and race-ethnicity [25]. On the basis of these criticisms and previous our results [11,26], this study has been carried out in a large cross sectional sample of healthy adult Italian Caucasians: 1) to analyze the distributions of PBF, TBFat and TBLean, measured by Dual X-ray Absorptiometry (DXA); 2) to assess differences in the means of PBF, TBFat, and TBLean by age and gender; 3) to verify the accordance between BMI and PBF cut-off points to classify adult Italian Caucasians. 1.1. Subjects and methods 1.1.1. Study design and subjects A cross-sectional study was conducted on a sample coming from Centre-South of Italy of apparently healthy Italian adults consecutively recruited at the University of Rome Tor Vergata, Human Nutrition Unit from 2002 to 2009. All subjects were volunteers, between 19 and 90 years old, who had already been recruited to create a reference normal sample among hospital staff members, university students, lay people contacted by word of mouth, subjects presenting spontaneously for DXA measurement. For what regards race ethnicity, the sample included only Caucasian participants. Participation in the study included a complete medical history to gather information about health status, current medications history, including supplements of vitamin and mineral, social habits, like alcohol drinking and smoking, eating habits, physical activity and family history for chronic diseases. Subjects with acute diseases, severe liver, heart or kidney dysfunctions, endocrine disorders (diabetes, hypo- or hyperthyroidism), cancer or other conditions capable of altering body composition (AIDS, Paget's, gastroenteropathies with malabsorption, neuromuscular diseases, rheumatic diseases, mild to severe cognitive impairment or disability) were excluded. The use of certain drugs (steroids, diuretics) was also a reason for exclusion. A completed screening of anthropometry and body composition was assessed. Participants were stratified into 5 macro-groups according to age: 19–34, 35–49, 50–64, 65–79 and ≥80 years. The subjects were also categorized in BMI subgroups according to World Health Organization (WHO) criteria [1,5]. Moreover, gender and age PBF cutoff points were also used to classify the total population [26]. Firstly, the prevalence of underweight/underfat, normal, preobese and obese subjects, according to BMI and gender and age PBF cutoff points, was examined. Secondly, the distributions of PBF, TBFat and TBLean and differences in the means of PBF, TBFat, and TBLean by age and gender were estimated. Finally, Kappa test was performed to estimate in which extent BMI and PBF cut-off agree with each other and move on the same direction. A statement of informed consent was signed by all participants in accordance with principles of the Declaration of Helsinki. The study
was conducted according to the guidelines of the “Tor Vergata” University Medical Ethical Committee, Rome, Italy. 1.1.2. Anthropometric measurements After a 12-hour overnight fast, all subjects underwent anthropometric evaluation. Anthropometric measurements for all participants according to standard methods, were carried out [27]. All the individuals were instructed to take off their clothes and shoes before undergoing the measurements. Body weight (kg) was measured to the nearest 0.1 kg, using a balance scale (Invernizzi, Rome, Italy). Height (m) was measured using a stadiometer to the nearest 0.1 cm (Invernizzi, Rome, Italy). Body mass index (BMI) was calculated using the formula: BMI = body weight(kg)/height(m) 2 (kg/m 2). 1.1.3. Dual X-ray absorptiometry (DXA) The total body composition was assessed by DXA (Lunar DPX and iDXA, G.E. Medical Systems, WI, USA), according to the previously described procedure [28,29]. The technique combined a total body scanner, an X-ray source, an internal wheel to calibrate the bone mineral compartment, and an external lucite/aluminium phantom to calibrate the fat compartment. Standard DXA quality control and calibration measures were performed prior to each testing session. Individuals were asked to remove all clothing except for undergarments including shoes, socks and metal items prior to being positioned on the DXA table. Scans were performed with individuals in a supine position. The entire body was scanned beginning from the top of the head and moving in a rectilinear pattern down the body to the feet. Mean measurement time was 15 min. The average measurement time was 20 min. The effective radiation dose from this procedure is about 0.01 mSv. The coefficient of variation (CV% = 100× SD/mean) intra and inter subjects ranged from 1% to 5%. The coefficient of variation for bone measurements is less than 1%; CVs on this instrument for five subjects scanned six times over a nine month period were 2.2% for TBFat, and 1.1% for TBLean. 1.1.4. Statistical analysis Data are presented as group means ± SD or number (percentage), and were analyzed to check assumptions about the distribution of the measured variables. We stratified all our analyses by gender and age. Age was grouped for analysis as 19–34, 35–49, 50–64, 65–79 and ≥80 y. The overall classification by PBF and BMI categories was evaluated by calculating the percentage of the population that fell into the corresponding category by 2 measures (exact agreement) and using separate logistic regression to model the dependence of exact agreement between each pair of measures on gender and age group. We calculated the percentage that agreed to within one category. The measure used to evaluate Cohen's kappa (CK) coefficient is a statistical measure of inter-rater agreement for qualitative items, it ranges from 0 to 1, where a coefficient equal to 0 means that there is no concordance between the indices whilst 1 represents perfect agreement [30]. The results of logistic regression were expressed as odds-ratio (OR) and [95% confidence interval] using male and younger age as the reference. All tests were considered significant at P ≤ 0.05. Statistical analysis was performed by Capgemini Italia S.p.a., through a computer software package (SPSS for Windows, version 17.0; SPSS, Chicago, IL). 2. Results The total subject pool cover a total of 4408 participants adults (≥19 y of age). The analytic sample consisted of 3.258 Italian Caucasian adults (1.250 men and 2.008 women), range 19–88 years, who had complete data on anthropometric and body composition analysis. The distributions of TBFat, PBF, TBLean according to gender are shown in Fig. 1. The means of TBFat, PBF, and TBLean by age and gender groups are shown in Fig. 2. The width of the confidence intervals
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Fig. 1. Population distributions of TBFat, PBF, and TBLean, by gender. Data are expressed as percentage (%) of subjects according to TBFat (Kg), PBF (%), and TBLean (Kg). TBFat, Total Body Fat; PBF, Percentage Body Fat; TBLean, Total Body Lean.
(CI), with level of 95%, is directly related to the number of participants in a group. In the group with age > = 80 y, the small sample size leads to more variability in the data and a wider CI. PBF increases with age before 70 y and decreases afterwards in women, whilst in men the turning point occurs at 60 y. TBFat shows a non-linear trend: it increases up to 35 y both in men and women, it remains constant between 35 y and 60 y in men, and between 35 y and 65 y in women, and then decreases. TBLean trend moves downwards with age in both men and women; it decreases faster in men than in women. The main characteristics of the subjects are summarized in Table 1. Differences in mean weight, height, BMI, PBF, TBFat and TBLean were observed between men and women (for all, P ≤ 0.001). A significant less content of TBLean, reflecting in an higher PBF but not in an higher BMI, in women respect to men was observed. As defined above, the study population was stratified in 5 set of age groups. According to gender, an equal distribution in age set groups was obtained. The prevalence of underweight/underfat, normal, preobese and obese subjects, according to BMI and PBF cut-off points, respectively, is also shown. Table 2 shows the degree of agreement between BMI and PBF classification of obesity, in total, male and female population. BMI and PBF agreement was measured by CK coefficient. These data are useful to understand to what extent the two indices agree with each other to estimate obesity. The results herein shown represent how people in the same class of BMI are categorized according to PBF. We applied age- and gender-specific cut-offs to create PBF and BMI categories and calculate the corresponding prevalence of underweight/underfat, normal, pre-obese and obese subjects. For example, according to PBF, 64% of the total population fell into the obese category. Of this percentage, less than 50% (≈31%) fell into the corresponding obese BMI
category, ≈22% fell into the preobese BMI category, ≈11% fell into the normal BMI category. The agreement measure, expressed by CK values, resulted low for the total and male population, even scarce for female population (all P ≤ 0.001). Among men, the agreement measure resulted low for all 5 sets of age (CK = 0.298 in 19–34 y group, P ≤ 0.001; CK = 0.403 in 35–49 y group, P ≤ 0.001; CK = 0.381 in 50–64 y group, P ≤ 0.001; CK = 0.417 in 65–79 y group, P ≤ 0.001; CK = 0.130 in > =80y; ). Among women, the agreement measure resulted scarce for all 5 sets of age (CK = 0.139 in 19–34 y group, P ≤ 0.001; CK = 0.132 in 35–49 y group, P ≤ 0.001; CK = 0.154 in 50–64 y group, P ≤ 0.001; CK = 0.228 in 65–79 y group, P ≤ 0.001; CK not determined in > =80y). The agreement of BMI categories with PBF categories was significantly lower for women than men (OR M vs. F: 0.58; 95% CI: 0.50, 0.67, P ≤ 0.05) and for younger than older subjects (OR 19–34y vs. 65–79y: 0.50; 95% CI: 0.39, 0.58; OR 35–49y vs. 65–79y: 0.74; 95% CI: 0.56, 0.98; OR 50–64y vs. 65–79y: 0.90; 95% CI: 0.66, 1.19; all P ≤ 0.05). Moreover, ≈17% and≈ 35% of PBF defined obese were normal and preobese according to BMI, respectively; ≈59% of preobese according to PBF categories were defined normal by BMI; ≈7% and≈ 21% of PBF defined normal subjects were considered underweight and preobese, respectively, by considering BMI cut-off points; while ≈ 70% of underfat subjects were classified as normal according to BMI (data not shown). BMI differences according to age and gender and PBF categories are shown in Table 3. These data show how mean BMI of our sample depends on gender and age across underfat, normal, preobese and obese categories, evaluated by age and gender specific PBF cut-off points. On average, men had higher BMI compared to women, in all PBF categories. Such difference persists across all age set groups. Moreover, differences in BMI among all classes of age are also
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Fig. 2. Mean (and 95% CI) of TBFat, PBF, and TBLean, by age and gender. Data are expressed as as arithmetic mean and 95% CI according to age and gender. CI, Confidence Interval; TBFat, Total Body Fat; Percentage Body Fat, PBF; TBLean, Total Body Lean.
relevant, particularly in normal and preobese categories. It should be noted that, in some cases, the comparisons made in this analysis do not take into account the age group > = 80y because the low sample size makes it impossible to provide reliable results. 3. Discussion The assessment of the physical state of pre-obesity and obesity represents a very important information both for the state of health of an individual, and for his quality of life. Many studies have shown an association between obesity, mortality and morbidity [31,32]. The increased prevalence of pre-obesity and obesity has become an epidemiological emergency, not only in USA but also in Mediterranean countries [33]. Recent analytical studies on CVD risk indicate that preobesity and obesity play a greater than-expected role in determining CVD (especially coronary), the leading cause of mortality, morbidity and hospitalisation in both genders in all countries of Europe [34]. A recent systematic analysis of population-based data sources revealed that between 1980 and 2008, age-standardized mean global BMI increased by 0.4–0.5 kg/m 2 per decade in men and women. Worldwide, age-standardized prevalence of obesity by BMI was 9.8% (9.2–10.4) in men and 13.8% (13.1–14.7) in women in 2008, which
was nearly twice the 1980 prevalences of 4.8% [35]. In Western Europe, an increased trend of obesity ranged from about 10% in 1980 to about 20% in 2008, both in men and women. Based on data collected in 2008 by the National Institute of Statistics (Istat) and published in 2010, in Italy 35.5% of the adult population were preobese (27.1% males, 44.6% females), and 9.9% obese (9.1 males, 10.8% females), according to BMI [36]. BMI is normally used in population studies in which overweight and obesity are related to morbidity and mortality. However, predictive methods have a relatively large error at an individual level, and thus, if subjects have to be classified into categories, misclassification can occur [10]. In fact, there is increasing criticism on the use of WHO BMI cut-off values to define pre-obesity and obesity [37]. Nevertheless it is possible that the surprisingly favourable prognostic implications associated with a mildly elevated BMI might actually reflect intrinsic limitations of BMI to differentiate TBFat from TBLean [38]. For example, some BMI defined preobese are not overfat [11]. Therefore, using gross weight and BMI may not accurately reflect the risk of complications in all obese individuals and may explain some of the paradoxical results seen, known as “obesity paradox” [39]. A recent meta-analysis, regarding the influence of body weight or obesity measures in patients with coronary artery disease (CAD) reported a better outcome for cardiovascular and total mortality
A. De Lorenzo et al. / International Journal of Cardiology 166 (2013) 111–117 Table 1 Descriptive characteristics of participantsa. Parameters
Men
Women
Sample size (n, %) Age (n, %)
1250 (38.4)
2008 (61.6)
19–34 y 35–49 y 50–64 y 65–79 y > = 80 y
506 (40.5) 345 (27.6) 285 (22.8) 106 (8.5) 8 (0.6)
768 (38.2) 569 (28.3) 500 (24.9) 155 (7.7) 16 (0.8)
Under-weight Normal Preobese Obese Under-fat Normal Preobese Obese
11 (0.9) 355 (28.4) 465 (37.2) 419 (33.5) 108 (8.6) 280 (22.4) 233 (18.6) 629 (50.3) 28.5 ± 9.5 26.0 ± 12.7 61.0 ± 8.9 87.2 ± 18.2 174.8 ± 7.2 28.5 ± 5.7
63 (3.1) 761 (37.9) 549 (27.3) 635 (31.6) 44 (2.1) 246 (12.3) 262 (13.1) 1456 (72.5) 39.5 ± 9.4d 29.5 ± 13.1d 41.6 ± 6.7d 71.8 ± 17.8d 160.9 ± 6.9d 27.8 ± 6.9d
BMI cut-off (n, %)b
PBF cut-off (n, %)c
PBF (%) TBFat (kg) TBLean (kg) Weight (kg) Height (cm) BMI (kg/m2)
a Sample size, age groups, BMI and PBF categories are expressed as number (percentage). PBF, TBFat, TBLean, Weight, Height, BMI are indicated as arithmetic mean± SD. Age groups are expressed as number (percentage). b WHO BMI categories were used: under-weight: b 18.5 kg/m2; Normal: 18.5–24.9 kg/ m2; preobese: 25.0–29.9 kg/m2; Obese: ≥30 kg/m2. c Gender and age specific cut-off PBF cut-off point were used. d Significantly different from men (independent t test; P≤ 0.001). Y, Year; BMI, Body Mass Index; PBF, Percentage Body Fat; TBFat, Total Body Fat; TBLean, Total Body Lean; SD, Standard Deviation.
seen in overweight and mildly obese groups. The authors suggested that this could be due to the lack of discriminatory power of BMI to reflect adiposity adequately [40]. Many other studies in the past suggested a need to further explore the appropriateness of gender-specific BMI cut-points for clinical risk assessment due the marked difference in the BMI-per cent for relation observed in men and women across the entire range of BMI [41]. In recent years, there is a growing debate on whether there are possible needs for developing different BMI cut-off points for different ethnic groups due to the increasing evidence that the associations between BMI, PBF, and TBFat distribution differ across populations [42]. Moreover, it's known that the relationship between BMI and PBF is age and gender dependent [8,9]. General adiposity and abdominal adiposity are associated with the risk of death and support the use of waist circumference or waist-to hip ratio in addition to BMI. Interesting, central obesity, gain in abdominal fat correlates closely with both hyperinsulinemia and insulin
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resistance and with the possibility to develop type 2 diabetes and coronary heart disease, both in preobese–obese and non preobese–obese individual [2,4,43]. Obese subjects are characterized by an unfavourable cardiovascular profile, increased diabetes and impaired liver function. The paracrine and endocrine adipose inflammatory events induce a systemic inflammatory, insulin-resistant, atherogenic state and metabolic dyslipidemia, resulting in type 2 diabetes and CVD [3]. Application of BC evaluation that target both adipose and lean tissue may be required to prevent, time in advance, type 2 diabetes and atherosclerotic CVD in the emerging epidemic of obesity. Different subtypes of obesity were known: the “at risk” obese with metabolic syndrome, the metabolically healthy but obese individuals (MHO), and the metabolically-obese normal weight subjects (MONW) [13,44]. The “metabolically- obese” normal weight (MONW), represents a subset of individuals who have normal weight and BMI, but display a cluster of metabolic characteristics that may increase the possibility to develop the metabolic syndrome, in the same manner of so called “at risk” obese individuals. MONW individuals are not obese but characterized by an excess visceral fat, predisposing to insulin resistance, hypertension and CVD, as referred for the metabolic syndrome. Moreover, De Lorenzo et al. 2006, have identified the Normal Weight Obese (NWO) syndrome, characterized by normal body weight and BMI, but high PBF [14]. Since, NWO subjects do not present metabolic syndrome, they are distinguished from MOWN individuals. NWO subjects were similar to preobese–obese women not only for TBLean distribution, but also for atherogenic indices (i.e. lipoprotein ratios) and oxidative stress [15,16]. An early inflammation and genetic predisposition characterized the syndrome [17–19]. A cross-sectional study realized to assess the prevalence of NWO in Switzerland, showed a low prevalence in the general population and higher in women than in men [21,22]. Romero Corral et al. observed that NWO had a higher prevalence of metabolic syndrome, dyslipidemia and of hypertension (men), CVD (women), and a 2.2-fold increased risk of CV mortality (women) compared with those with low TBFat [23]. The main focus of this study has been to assess whether the WHO guidelines for preobese and obese using the current BMI definitions, in a large cross sectional sample of Italy, are useful, or need to be adapted to age, gender and population for a better prediction of CVD risk and setting priorities on prevention and intervention policies. In this large study population, the prevalence of “at risk” subjects (preobese and obese) was 69% and 85%, for men and women respectively, according to PBF cut-off points, respect to 70% and 58%, according to BMI classification. The results obtained from our study suggest there is a low agreement between BMI and PBF classifications. In fact, among obese people according to PBF just 48% resulted obese also for BMI. The differences between BMI and PBF categories persisted within gender and age groups. More specifically, 50% and
Table 2 Agreement between BMI and PBF classification of obesity, in total, male and female populationa. Subjects (%)
BMI (Kg/m2) Underweight Normal Preobese Obese Total CK2
Total population
Male population
Female population
PBF (%)
PBF (%)
PBF (%)
Underfat Normal Preobese Obese Total
Underfat Normal Preobese Obese Total
Underfat Normal Preobese Obese Total
0.83 3.25 0.52 0.06 4.67 0.245b
0.95 1.25 0.00 0.00 2.19 0.369b
0.64 6.48 1.36 0.16 8.64 0.170b
1.20 11.26 3.35 0.34 16.14
0.18 8.96 4.97 1.07 15.19
0.06 10.77 22.28 30.88 64.00
2.27 34.25 31.12 32.35 100.00
1.79 9.51 0.80 0.15 12.25
0.30 11.25 1.25 0.25 13.05
0.10 15.89 25.30 31.23 72.51
3.14 37.90 27.34 31.62 100.00
0.24 14.08 7.44 0.64 22.40
0.00 5.28 10.96 2.40 18.64
0.00 2.56 17.44 30.32 50.32
0.88 28.40 37.20 33.52 100.00
a Data are expressed as percentage of subjects (%) categorized according to PBF cut-off points, and along BMI categories. We applied age- and gender-specific cut-offs to create PBF and BMI categories and calculate the corresponding prevalence of underweight/underfat, normal, pre-obese and obese subjects. WHO BMI categories were used: under-weight: b 18.5 kg/m2; normal: 18.5–24.9 kg/m2; preobese: 25.0–29.9 kg/m2; obese: ≥30 kg/m2. Gender and age specific cut-off PBF cut-off point were used. PBF, Percentage Body Fat; BMI, Body Mass Index; CK, Cohen's kappa coefficient. b Degree of agreement: b 0.01 none; 0.01–0.20 scarce; 0.21–0.40 low; 0.41–0.60 moderate; 0.61–0.80 good; 0.81–1.00 almost perfect/perfect. P ≤ 0.001.
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A. De Lorenzo et al. / International Journal of Cardiology 166 (2013) 111–117
Table 3 Mean BMI according to PBF categories1. PBF categories Underfat
BMI (kg/m2) Men Crude Age 19–34 35–49 50–64 65–79 > = 80 Women Crude Age 19–34 35–49 50–64 65–79 > = 80
Normal
Preobese
Obese
Mean
SE
Min
Max
SD
Mean
SE
Min
Max
SD
Mean
SE
Min
Max
SD
Mean
SE
Min
Max
SD
22,52a
0,3
16,10
33,83
3,1
24,37b
0,2
17,66
33,56
2,7
26,83c
0,2
19,57
40,20
3,1
32,05d
0,2
21,4
61,5
5,4
22,50a 24,10a 20,54 20,09a –
0,3 0,9 2,0 0,7 –
16,10 20,13 16,34 18,52 –
32,40 33,83 25,80 22,23 –
3,0 3,5 4,0 1,6 –
24,08b 24,85a 25,30a 23,64b 21,39
0,2 0,4 0,4 0,7 0,0
17,7 20,4 21,6 18,8 21,4
33,5 33,6 33,2 29,7 21,4
2,6 2,7 2,4 2,9 0,0
25,77c 26,85b 28,01b 27,79c 22,26
0,3 0,4 0,4 0,7 0,0
19,57 20,48 23,15 23,69 22,26
34,82 35,08 40,20 34,81 22,26
2,9 3,1 2,8 3,1 0,0
31,49d 32,53c 32,27c 31,74d 28,40
0,4 0,4 0,4 0,6 0,8
21,6 21,4 23,9 22,9 26,7
52,9 61,5 51,4 48,0 31,1
5,7 5,8 4,7 5,2 1,9
19,05a,2
0,3
15,53
23,88
2,2
21,01b,2
0,2
16,35
42,92
2,9
22,54c,2
0,2
17,14
36,93
2,7
30,15d,2
0,2
16,9
59,5
6,5
19,08a 19,06a 19,03a 16,94 22,15
0,4 0,5 1,1 0,7 0,0
15,53 17,86 16,20 16,23 22,15
23,88 21,48 22,67 17,64 22,15
2,4 1,3 2,4 1,0 0,0
20,52b 21,24a 22,61a,b 22,13a -
0,2 0,5 0,8 0,6 -
16,9 16,4 16,9 18,8 -
28,9 32,6 42,9 25,8 -
2,3 3,1 4,6 2,3 -
21,65c 22,82b 23,76b 24,77a 30,27
0,2 0,3 0,5 0,9 4,3
17,14 19,38 19,10 20,52 25,97
29,80 36,93 31,93 31,50 34,57
2,1 2,5 3,0 3,2 6,1
28,31d 30,73c 31,00c 31,66b 31,98
0,3 0,3 0,3 0,5 1,8
16,9 19,2 19,4 21,5 24,1
59,5 57,6 52,6 51,6 46,5
6,8 6,7 5,7 5,8 6,5
1 Data are expressed as mean, with SE, Min, Max and SD and represent BMI value along PBF categories, according to age and gender. PBF, Percentage Body Fat; BMI, Body Mass Index; SE, Standard Error; Min, Minimun; Max, Maximum; SD, Standard Deviation. a,b,c Values with different subscript letters are significantly different among age groups in the same BMI category (independent t sample P ≤ 0.05).2 Significantly different from men (P ≤ 0.05).
73% of screened men and women were obese according to PBF, while only 34% and 32% respectively were obese according to BMI. In addition, the false negative classification was stronger for women than men and for younger than older subjects. Overall, 56% of men and 43% of women were in the same category by PBF and BMI cut-off points. These data are likely similar to Flegal et al., that observed a correspondence of 46% and 49% between both measures, for men and women respectively [8]. Gallageher et al., for the first time, presented age, gender and population specific PBF ranges, from current BMI guidelines. There is no consensus on how BF is linked with morbidity and mortality because of the absence of appropriate prospective studies. Specifically, no universally accepted published BF ranges exist; those reported based on empirically set limits, population percentiles, and z scores have serious limitations [10]. In an obesogenic environment, the positive skewing of the distribution curve of BMIs increases over time as heavier individuals gain more weight than lighter individuals [45]. Although the comparison with the BMI distribution data [32] showed that the sample examined referring to our center was not representative of the general population, the main objective of the study, i.e. o verify the accordance between BMI and PBF cut-off points, was satisfied. The herein used, cutoffs points of PBF, by age and gender, may provide a useful reference in clinical settings and public health services, in particular for the Italian Caucasian population. In the present study, we also provided quantitative reliable estimates of PBF, TBFat, and TBLean according to gender and age. Our results highlight gender and age dependent differences in PBF, TBFat and TBLean, in the general adult population. Similar to previous findings in adults [9,46], women in this study generally had greater estimated mean PBF and TBFat, but less TBLean, than men. Our results are also consistent with the findings of previous studies [47,48] in which the researchers showed that mean values of PBF were higher in older than younger subjects, supporting the idea that, particularly at older age, TBFat increases at the expense of TBLean. According to Shaw et al., TBLean decreased with ageing, after 50 y, particularly in men, toward sarcopoenia [49]. Decrease in muscle density and increase in the accumulation of TBFat with age may partially explain the differences in PBF between the younger and older adults [50]. Loss of skeletal muscle
mass with ageing increases an individual's risk for sarcopoenia, which is characterized by low skeletal muscle mass, reduced muscle strength and increased risk for adverse health outcomes such as falls, fractures, impaired physical functioning, disability and frailty in older populations [51]. Age-related changes in BC, including the increase and redistribution of fat tissue and the decrease in skeletal muscle and bone mass, begin as early as the fourth decade of life. Preventive and therapeutic options for optmising BC in old age is central to the care of patients in midlife and beyond [52]. Consequently, we suggest that age and gender independent BC reference values derived from adult populations may not be applicable for clinical practice. The present study has its limitation and strengths. The study population was not randomly selected from the general population but consisted mainly of subjects, from Centre-South of Italy, referred for DXA measurement. The strength of the present study is that it was considered a large cohort of both men and women. Moreover, a statistical analysis was performed by high professional specialists. Finally, by using DXA data, we described the distributions and showed the differences in BC by gender and age. Moreover, our results highlighted the low-scarce agreement of BMI and PBF cut-off points. In conclusion, screening for adiposity in subjects with a normal BMI could better identify those at higher risk for cardiometabolic diseases and CV mortality. Therefore, the determination of adiposity by methods more accurate than BMI and the definition of universally recognized PBF cut-off by gender, age, race and ethnicity, could have public health implications. Conflict of interest The authors declared no conflict of interest. Acknowledgments Author contributions: Prof. De Lorenzo, Dr(s) Di Renzo, Bianchi had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Prof. De Lorenzo and Dr Di Renzo. Acquisition of data: Dr(s) Iacopino, Bianchi. Study supervision and Critical
A. De Lorenzo et al. / International Journal of Cardiology 166 (2013) 111–117
revision: Prof. De Lorenzo, Dr Di Renzo. Statistical analysis: Dr Iannarelli and Maroni. Analysis and interpretation of data: Dr(s) Di Renzo, Bianchi, De Lorenzo. Manuscript preparation and editing: Prof. De Lorenzo Dr(s) Di Renzo and Bianchi. The authors thank Emidio Domino, Mariagiovanna Rizzo and Alessandra Feraco for the contributions to the study. The authors of this manuscript have certified that they comply with the Principles of Ethical Publishing in the International Journal of Cardiology.
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