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Population Health Metrics
Published in: Population Health Metrics 1/2015

Open Access 01-12-2015 | Research

Abdominal vs. overall obesity among women in a nutrition transition context: geographic and socio-economic patterns of abdominal-only obesity in Tunisia

Authors: Pierre Traissac, Rebecca Pradeilles, Jalila El Ati, Hajer Aounallah-Skhiri, Sabrina Eymard-Duvernay, Agnès Gartner, Chiraz Béji, Souha Bougatef, Yves Martin-Prével, Patrick Kolsteren, Francis Delpeuch, Habiba Ben Romdhane, Bernard Maire

Published in: Population Health Metrics | Issue 1/2015

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Abstract

Background

Most assessments of the burden of obesity in nutrition transition contexts rely on body mass index (BMI) only, even though abdominal adiposity might be specifically predictive of adverse health outcomes. In Tunisia, a typical country of the Middle East and North Africa (MENA) region, where the burden of obesity is especially high among women, we compared female abdominal vs. overall obesity and its geographic and socio-economic cofactors, both at population and within-subject levels.

Methods

The cross-sectional study used a stratified, three-level, clustered sample of 35- to 70-year-old women (n = 2,964). Overall obesity was BMI = weight/height2 ≥ 30 kg/m2 and abdominal obesity waist circumference ≥ 88 cm. We quantified the burden of obesity for overall and abdominal obesity separately and their association with place of residence (urban/rural, the seven regions that compose Tunisia), plus physiological and socio-economic cofactors by logistic regression. We studied the within-subject concordance of the two obesities and estimated the prevalence of subject-level “abdominal-only” obesity (AO) and “overall-only” obesity (OO) and assessed relationships with the cofactors by multinomial logistic regression.

Results

Abdominal obesity was much more prevalent (60.4% [57.7-63.0]) than overall obesity (37.0% [34.5-39.6]), due to a high proportion of AO status (25.0% [22.8-27.1]), while the proportion of OO was small (1.6% [1.1-2.2]). We found mostly similar associations between abdominal and overall obesity and all the cofactors except that the regional variability of abdominal obesity was much larger than that of overall obesity. There were no adjusted associations of AO status with urban/rural area of residence (P = 0.21), education (P = 0.97) or household welfare level (P = 0.94) and only non-menopausal women (P = 0.093), lower parity women (P = 0.061) or worker/employees (P = 0.038) were somewhat less likely to be AO. However, there was a large residual adjusted regional variability of AO status (from 16.6% to 34.1%, adjusted P < 0.0001), possibly of genetic, epigenetic, or developmental origins.

Conclusion

Measures of abdominal adiposity need to be included in population-level appraisals of the burden of obesity, especially among women in the MENA region. The causes of the highly prevalent abdominal-only obesity status among women require further investigation.
Literature
1.
2.
Musaiger AO. Overweight and obesity in eastern mediterranean region: prevalence and possible causes. J Obes. 2011;2011:Article ID 407237.CrossRef
3.
4.
Jones-Smith JC, Gordon-Larsen P, Siddiqi A, Popkin BM. Is the burden of overweight shifting to the poor across the globe? Time trends among women in 39 low- and middle-income countries (1991-2008). Int J Obes. 2011;36(8):1114–20.CrossRef
5.
Subramanian SV, Perkins JM, Ozaltin E, Davey Smith G. Weight of nations: a socioeconomic analysis of women in low- to middle-income countries. Am J Clin Nutr. 2010;93(2):413–21.PubMedPubMedCentralCrossRef
6.
Ko GT, Tang JS, Chan JC. Worsening trend of central obesity despite stable or declining body mass index in Hong Kong Chinese between 1996 and 2005. Eur J Clin Nutr. 2010;64(5):549–52.PubMedCrossRef
7.
8.
Song X, Jousilahti P, Stehouwer CD, Soderberg S, Onat A, Laatikainen T, et al. Comparison of various surrogate obesity indicators as predictors of cardiovascular mortality in four European populations. Eur J Clin Nutr. 2013;67(12):1298–302.PubMedCrossRef
9.
Alberti KG, Zimmet P, Shaw J. The metabolic syndrome–a new worldwide definition. Lancet. 2005;366(9491):1059–62.PubMedCrossRef
10.
World Health Organisation. The WHO STEPwise approach to Surveillance of Noncommunicable Diseases (STEPS): a framework for surveillance. Geneva: World Health Organisation; 2003. p. 42.
11.
Belfki H, Ali SB, Aounallah-Skhiri H, Traissac P, Bougatef S, Maire B, et al. Prevalence and determinants of the metabolic syndrome among Tunisian adults: results of the Transition and Health Impact in North Africa (TAHINA) project. Public Health Nutr. 2012;16(4):582–90.PubMedCrossRef
12.
Ben Romdhane H, Ben Ali S, Aissi W, Traissac P, Aounallah-Skhiri H, Bougatef S, et al. Prevalence of diabetes in Northern African countries: the case of Tunisia. BMC Public Health. 2014;14(1):86.PubMedPubMedCentralCrossRef
13.
El Ati J, Traissac P, Delpeuch F, Aounallah-Skhiri H, Beji C, Eymard-Duvernay S, et al. Gender obesity inequities are huge but differ greatly according to environment and socio-economics in a North African setting: a national cross-sectional study in Tunisia. PLoS One. 2012;7(10):e48153.PubMedPubMedCentralCrossRef
14.
Howe LD, Galobardes B, Matijasevich A, Gordon D, Johnston D, Onwujekwe O, et al. Measuring socio-economic position for epidemiological studies in low- and middle-income countries: a methods of measurement in epidemiology paper. Int J Epidemiol. 2012;41(3):871–86.PubMedPubMedCentralCrossRef
15.
Traissac P, Martin-Prevel Y. Alternatives to principal components analysis to derive asset-based indices to measure socio-economic position in low- and middle-income countries: the case for multiple correspondence analysis. Int J Epidemiol. 2012;41(4):1207–8.PubMedCrossRef
16.
Lohman T, Roche A, Martorell R. Anthropometric Standardization Reference Manual. Champaign, IL: Human Kinetics; 1988.
17.
World Health Organisation. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894(i-xii):1–253.
18.
Korn EL, Graubard BI. Analysis of Health Surveys. New York: John Wiley & Sons; 1999.CrossRef
19.
Janghorbani M, Amini M, Willett WC, Mehdi Gouya M, Delavari A, Alikhani S, et al. First nationwide survey of prevalence of overweight, underweight, and abdominal obesity in Iranian adults. Obesity. 2007;15(11):2797–808.PubMedCrossRef
20.
Al-Lawati JA, Mohammed AJ, Al-Hinai HQ, Jousilahti P. Prevalence of the metabolic syndrome among Omani adults. Diabetes Care. 2003;26(6):1781–5.PubMedCrossRef
21.
Han TS, van Leer EM, Seidell JC, Lean ME. Waist circumference action levels in the identification of cardiovascular risk factors: prevalence study in a random sample. BMJ. 1995;311(7017):1401–5.PubMedPubMedCentralCrossRef
22.
Bouguerra R, Alberti H, Smida H, Salem LB, Rayana CB, El Atti J, et al. Waist circumference cut-off points for identification of abdominal obesity among the tunisian adult population. Diabetes Obes Metab. 2007;9(6):859–68.PubMedCrossRef
23.
Ashwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr. 2005;56(5):303–7.PubMedCrossRef
24.
Visscher TL, Seidell JC. Time trends (1993-1997) and seasonal variation in body mass index and waist circumference in the Netherlands. Int J Obes Relat Metab Disord. 2004;28(10):1309–16.PubMedCrossRef
25.
Monteiro CA, Moura EC, Conde WL, Popkin BM. Socioeconomic status and obesity in adult populations of developing countries: a review. Bull World Health Organ. 2004;82(12):940–6.PubMed
26.
Stevens J, Katz EG, Huxley RR. Associations between gender, age and waist circumference. Eur J Clin Nutr. 2009;64(1):6–15.PubMedCrossRef
27.
Tchernof A, Poehlman ET. Effects of the menopause transition on body fatness and body fat distribution. Obes Res. 1998;6(3):246–54.PubMedCrossRef
28.
Blaudeau TE, Hunter GR, Sirikul B. Intra-abdominal adipose tissue deposition and parity. Int J Obes (Lond). 2006;30(7):1119–24.CrossRef
29.
Walls HL, Stevenson CE, Mannan HR, Abdullah A, Reid CM, McNeil JJ, et al. Comparing trends in BMI and waist circumference. Obesity (Silver Spring). 2011;19(1):216–9.CrossRef
30.
Ekelund U, Besson H, Luan J, May AM, Sharp SJ, Brage S, et al. Physical activity and gain in abdominal adiposity and body weight: prospective cohort study in 288,498 men and women. Am J Clin Nutr. 2011;93(4):826–35.PubMedCrossRef
31.
Wahlqvist ML, Hodgson JM, Ng FM, Hsu-Hage BH-H, Strauss BJ. The role of nutrition in abdominal obesity. Nutr Res. 1999;19(1):85–101.CrossRef
32.
Chaput JP, Després JP, Bouchard C, Tremblay A. Short sleep duration preferentially increases abdominal adiposity in adults: preliminary evidence. Clin Obes. 2011;1(4–6):141–6.PubMedCrossRef
33.
Aounallah-Skhiri H, Traissac P, El Ati J, Eymard-Duvernay S, Landais E, Achour N, et al. Nutrition transition among adolescents of a south-Mediterranean country: dietary patterns, association with socio-economic factors, overweight and blood pressure: a cross-sectional study in Tunisia. Nutr J. 2011;10:38.PubMedPubMedCentralCrossRef
34.
den Tonkelaar I, Seidell JC, van Noord PA, Baanders-van Halewijn EA, Ouwehand IJ. Fat distribution in relation to age, degree of obesity, smoking habits, parity and estrogen use: a cross-sectional study in 11,825 Dutch women participating in the DOM-project. Int J Obes. 1990;14(9):753–61.
35.
Akbartabartoori M, Lean ME, Hankey CR. Relationships between cigarette smoking, body size and body shape. Int J Obes. 2005;29(2):236–43.CrossRef
36.
Demerath EW, Reed D, Choh AC, Soloway L, Lee M, Czerwinski SA, et al. Rapid postnatal weight gain and visceral adiposity in adulthood: the Fels Longitudinal Study. Obesity. 2009;17(11):2060–6.PubMedPubMedCentralCrossRef
37.
Ravelli ACJ, van der Meulen JHP, Osmond C, Barker DJP, Bleker OP. Obesity at the age of 50 y in men and women exposed to famine prenatally. Am J Clin Nutr. 1999;70(5):811–6.PubMed
38.
39.
Wells JC. Ethnic variability in adiposity and cardiovascular risk: the variable disease selection hypothesis. Int J Epidemiol. 2009;38(1):63–71.PubMedCrossRef
40.
Batnitzky A. Obesity and household roles: gender and social class in Morocco. Sociol Health Illn. 2008;30(3):445–62.PubMedCrossRef
41.
Aounallah-Skhiri H, Ben Romdhane H, Traissac P, Eymard-Duvernay S, Delpeuch F, Achour N, et al. Nutritional status of Tunisian adolescents: associated gender, environmental and socio-economic factors. Public Health Nutr. 2008;11(12):1306–17.PubMedCrossRef
42.
Lahti-Koski M, Taskinen O, Simila M, Mannisto S, Laatikainen T, Knekt P, et al. Mapping geographical variation in obesity in Finland. Eur J Public Health. 2008;18(6):637–43.PubMedCrossRef
43.
Merlo J, Chaix B, Yang M, Lynch J, Rastam L. A brief conceptual tutorial of multilevel analysis in social epidemiology: linking the statistical concept of clustering to the idea of contextual phenomenon. J Epidemiol Community Health. 2005;59(6):443–9.PubMedPubMedCentralCrossRef
44.
Demerath EW. Causes and consequences of human variation in visceral adiposity. Am J Clin Nutr. 2010;91(1):1–2.PubMedCrossRef
45.
Fadhlaoui-Zid K, Martinez-Cruz B, Khodjet-el-khil H, Mendizabal I, Benammar-Elgaaied A, Comas D. Genetic structure of Tunisian ethnic groups revealed by paternal lineages. Am J Phys Anthropol. 2012;146(2):271–80.CrossRef
46.
Hajjej A, Hmida S, Kaabi H, Dridi A, Jridi A, El Gaaled A, et al. HLA genes in Southern Tunisians (Ghannouch area) and their relationship with other Mediterraneans. Eur J Med Genet. 2006;49(1):43–56.PubMedCrossRef
47.
Berhouma R, Kouidhi S, Ammar M, Abid H, Baroudi T, Ennafaa H, et al. Genetic susceptibility to type 2 diabetes: a global meta-analysis studying the genetic differences in tunisian populations. Hum Biol. 2012;84(4):423–35.PubMedCrossRef
48.
Newbold RR. Impact of environmental endocrine disrupting chemicals on the development of obesity. Hormones (Athens). 2010;9(3):206–17.CrossRef
49.
50.
Shimokata H, Tobin JD, Muller DC, Elahi D, Coon PJ, Andres R. Studies in the distribution of body fat: I. Effects of age, sex, and obesity. J Gerontol. 1989;44(2):M66–73.PubMedCrossRef
51.
Mason C, Katzmarzyk PT. Variability in waist circumference measurements according to anatomic measurement site. Obesity. 2009;17(9):1789–95.PubMedCrossRef
52.
Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, et al. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007;116(1):39–48.PubMedCrossRef
53.
Demerath EW, Reed D, Rogers N, Sun SS, Lee M, Choh AC, et al. Visceral adiposity and its anatomical distribution as predictors of the metabolic syndrome and cardiometabolic risk factor levels. Am J Clin Nutr. 2008;88(5):1263–71.PubMedPubMedCentral
54.
Metadata
Title
Abdominal vs. overall obesity among women in a nutrition transition context: geographic and socio-economic patterns of abdominal-only obesity in Tunisia
Authors
Pierre Traissac
Rebecca Pradeilles
Jalila El Ati
Hajer Aounallah-Skhiri
Sabrina Eymard-Duvernay
Agnès Gartner
Chiraz Béji
Souha Bougatef
Yves Martin-Prével
Patrick Kolsteren
Francis Delpeuch
Habiba Ben Romdhane
Bernard Maire
Publication date
01-12-2015
Publisher
BioMed Central
Published in
Population Health Metrics / Issue 1/2015
Electronic ISSN: 1478-7954
DOI
https://doi.org/10.1186/s12963-015-0035-3

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