Cardiometabolic risks profile of normal weight obese and multi-ethnic women in a developing country

Maturitas 81 (2015) 389–393

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Cardiometabolic risks profile of normal weight obese and multi-ethnic women in a developing country Foong Ming Moy ∗ , Debbie Ann Loh Julius Centre University of Malaya, Department of Social & Preventive Medicine, Faculty of Medicine, University of Malaya, Lembah Pantai, Kuala Lumpur 50603, Malaysia

a r t i c l e

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Article history: Received 4 February 2015 Received in revised form 17 April 2015 Accepted 20 April 2015 Keywords: Cardiometabolic risks Normal weight obesity Body fat Women

a b s t r a c t Objectives: To determine the prevalence of normal weight obesity among multi-ethnic women in Peninsular Malaysia and examine its associations with cardiometabolic risks and lifestyle behaviours. Methods: This was a cross-sectional study involving women recruited via multi-stage sampling from six states in Malaysia. Anthropometric and body composition analysis were performed. Normal weight obese (NWO) was defined as normal body mass index for Asians and the highest tertile of % body fat (BF). Biochemical measurements included fasting lipid and blood glucose levels. Metabolic syndrome was diagnosed based on the Harmonization criteria. Participants completed self-reported questionnaires that included physical activity, smoking, alcohol consumption, fruit and vegetable intake and sleep duration. Main outcome measure: Body mass index, %BF, cardiometabolic risk factors, lifestyle behaviours. Results: A total of 6854 women were recruited and the prevalence of NWO was 19.8% (95% CI: 17.3–22.5). NWO was more prevalent among the Indians and older women. NWO women had higher odds for abdominal obesity (OR: 2.64, 95% CI: 1.73–4.04), hypertriglyceridemia (2.51, 1.47–4.29) and hypertension (1.63, 1.15–2.31) compared to women with lower % body fat after adjusted for age and ethnicity. The prevalence of metabolic syndrome among NWO women was 5.4% (95% CI: 3.0–9.8). None of the lifestyle behaviours were significantly associated with NWO. Conclusions: Women with NWO had cardiometabolic abnormalities including abdominal obesity, dyslipidaemia and increased blood pressure. Health promotion efforts should include NWO women who may be oblivious of their deleterious health risks. © 2015 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Cardiovascular disease (CVD) remains the leading cause of death in women worldwide with one in three of all female deaths due to CVD or stroke [1]. Approximately 81% of all cardiovascular mortality in women occurs in low- and middle-income countries. The misconception that CVD is a ‘man’s disease has now been dispelled with women accounting for over half of CVD deaths, however, initial presentation of coronary artery diseases in women often surface 10 to 20 years later than men [2]. This under-recognition and differences in clinical presentation of CVD in women calls for the need to raise awareness and identify cardiometabolic risk factors in both premenopausal and postmenopausal women including the rising obesity trend towards the prevention of CVD.

∗ Corresponding author. Tel.: +60 3 7697 6657. E-mail addresses: [email protected], [email protected] (F.M. Moy). http://dx.doi.org/10.1016/j.maturitas.2015.04.011 0378-5122/© 2015 Elsevier Ireland Ltd. All rights reserved.

Globally, overweight and obesity is estimated to contribute to 44% of diabetes burden and 23% of ischaemic heart disease [3]. Obesity is a state of excessive adipose tissue, precariously linked to adverse health outcomes [4,5]. The widely-used body mass index (BMI) does not differentiate between fat and lean mass [4]. This may result in under-diagnosis of individuals at risk of obesity-related diseases more so, when the onset of co-morbidities such as type 2 diabetes in Asian populations present at lower BMI compared to Caucasians at a similar weight [6]. Normal weight obesity (NWO) defined as the combination of normal BMI with high body fat [7] is receiving increased attention. Individuals with normal BMI but high body fat may have increased risks for CVD [8]. A normal BMI therefore does not necessarily imply protection from consequences of increased body fat. NWO might be a key factor in the emerging worldwide epidemic of obesity, metabolic syndrome, diabetes, and coronary artery disease. Adipose tissue in abdominal obesity, visceral adiposity and ectopic fat are considered as an endocrinal organ orchestrating key pathophysiological pathways in inflammation and lipid

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metabolism [9–11]. Adipose tissue synthesises and secretes various adipocytokines that create a pro-inflammatory milieu [12], important antecedents of cardiometabolic risks reflected in atherogenic lipid profiles and insulin resistance leading to heightened CVD risks [9]. This phenomenon is clearly evidenced in large cohorts [13] with a growing prevalence of CVD mortality observed among NWO women [2,14,15]. Lifestyle factors associated with NWO include physical inactivity, unhealthy diet, smoking, alcohol consumption and short sleep duration [16]. Literature is replete with evidence that regular physical activity is related to lower odds of metabolic syndrome. There is convincing evidence that a diet rich in fruit and vegetables lowers the risk for CVD and cancer attributed to the benefits of antioxidants, fibre, vitamins and minerals [17]. Avoiding smoking and limiting alcohol intake have also been recommended as guidelines for CVD prevention [2]. The impact of sleep deprivation and disorders on CVD health is increasingly acknowledged [18]. To the best of our knowledge, a large epidemiological study on the prevalence and association of cardiometabolic risks and NWO among multi-ethnic Asian adults has yet to be conducted. Therefore, this study aims to determine the prevalence of NWO among women in Peninsular Malaysia and to identify its associations with cardiometabolic risks and lifestyle behaviours. 2. Methods 2.1. Study design and sampling method This cross-sectional study was conducted using multi-stage sampling involving six randomly selected states in Peninsular Malaysia (Penang, Kuala Lumpur, Selangor, Melaka, Terengganu and Johor). From each selected state, 70% of all public secondary schools in each district were invited to participate in the study. All permanently employed female teachers from the selected schools were eligible to participate voluntarily. Data collection was carried out from March 2013 to March 2014. This paper will report findings from the states of Penang, Kuala Lumpur, Selangor and Melaka.

was used to define NWO as there was no established cut off values for %BF among Asians. The use of tertiles to classify individuals as high %BF is more valid than using an arbitrary cut-off not previously validated [8]. 2.4. Metabolic risks assessment Fasting venous blood samples were drawn after an overnight fast of a minimum 8 h to determine serum total cholesterol, triglycerides (TG), high-density lipoprotein (HDL) cholesterol and fasting blood glucose (FBG) levels analysed with Dimension® clinical chemistry system which is an in-vitro diagnostic test. Low-density lipoprotein (LDL) cholesterol was calculated with the Friedewald equation [20]. Biochemical analyses for blood samples collected from all states were conducted at the Clinical Diagnostic Laboratory of UMMC. Samples from Melaka and Penang were frozen at −20 C and transported back to the Clinical Diagnostic Laboratory of UMMC in Kuala Lumpur following standard procedures. Resting systolic and diastolic blood pressure (BP) was measured once on the left arm with participants seated in an upright position using a validated oscillometric blood pressure monitor (OMRON HEM-907, Japan). Metabolic syndrome was defined using the Harmonization criteria [21] with three or more of the following risk factors present: (1) abdominal obesity: WC ≥80 cm in women; (2) hypertriglyceridemia: TG ≥ 1.7 mmol/L; (3) reduced HDL cholesterol: HDL-C ≤ 1.3 mmol/L in women; (4) elevated blood pressure (systolic ≥130 mmHg and/or diastolic ≥85 mmHg) or on antihypertensive treatment; and (5) hyperglycaemia: FBG ≥ 5.6 mmol/L. The questionnaire comprised socio-demographic characteristics, medical history, family history of chronic diseases, occupation-related items, lifestyle behaviours encompassing physical activity measured using the International Physical Activity Questionnaire (IPAQ)-short form in Malay language, smoking, alcohol consumption, fruit and vegetable consumption (servings/day) and sleep duration (in hours) on weekdays and weekends. Further details can be found in the published protocol [22]. 2.5. Statistical analyses

2.2. Ethics approval and study procedures Ethical approval was obtained from the Medical Ethics Committee of the University Malaya Medical Committee (UMMC) (Ref No. 950.1). Written permission was granted from the Ministry of Education, Malaysia and the Education Department in the respective states and the school principals. Informed consent from participants was obtained prior to data collection. 2.3. Anthropometric measurements and body composition Trained research assistants performed the anthropometric measurements following standardised protocol. Body weight and height was measured with participants in light clothing, with shoes removed, to the nearest 0.1 kg with a digital floor scale (SECA 813, Hamburg, Germany) and the nearest 0.1 cm with a stadiometer (SECA 217, Hamburg, Germany), respectively. Body mass index (BMI) was calculated in kg/m2 and categorised according to the Asian WHO BMI cut-offs of ≥23 kg/m2 to define overweight and ≥27.5 kg/m2 for obesity [19]. Waist circumference (WC) was measured to the nearest 0.1 cm at the umbilicus, between the tenth rib and the iliac crest using a flexible measuring tape (SECA 203, Hamburg, Germany). Body composition was assessed using a 4-point foot-to-foot bioelectrical impedance analysis (BIA) equipment (TANITA TBF-300A, Tanita, Japan). Normal weight obesity was defined as individuals with a normal BMI for Asians (18.5–22.9 kg/m2 ) and excess %BF with the highest tertile of %BF (>28.52%). An arbitrary cut-off for %BF based on tertiles

Samples were weighted to account for unequal probabilities of selection and non-response as multi-stage sampling was used. Complex sample analyses were performed to determine the associations between lifestyle behaviours and cardiometabolic risks according to %BF tertiles of the normal weight participants. Multivariate logistic regression was conducted to estimate the odds ratio (OR) with 95% confidence intervals (95% CI) of cardiometabolic risks across %BF tertiles, adjusted for age and ethnicity. Significance level was pre-set at 0.05 and data analyses were performed with SPSS 21.0. 3. Results A total of 6854 women were recruited, of which 1095 women with normal BMI. From these normal weight women, 19.8% (95% CI: 17.3–22.5) were normal weight obese (normal BMI with %BF in the third tertile). The anthropometric measurements, cardiometabolic risk factors and lifestyle behaviours of the women are presented in Table 1. Percent BF increased with age and Indian women had the highest %BF followed by Malays and Chinese (p < 0.001). All anthropometric measurements in the second and third tertiles were significantly higher compared to the lowest %BF tertile (p < 0.001). NWO women had significantly higher levels of blood pressure, total cholesterol, low-density lipoprotein (LDL), triglycerides and decreased high-density lipoprotein (HDL) (p < 0.05) when compared to the lowest %BF tertile. There was no significant difference in levels of physical activity, fruit and vegetable intake and sleep

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Table 1 Anthropometric, cardiometabolic parameters and lifestyle habits in normal weight women according to body fat tertiles (n = 1095).

Age, years (mean ± SE) Age groups (years) 20–29 years 30–39 years 40–49 years 50–59 years Ethnicity Malay Chinese Indians Anthropometric parameters Weight (kg) Height (cm) Body mass index (kg/m2 ) Waist circumference (cm) Hip circumference (cm) Waist-to-hip ratio Waist to height ratio Body fat (%) Fat mass (kg) Cardiometabolic risk factors Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Total cholesterol (mmol/L) Low-density lipoprotein (mmol/L) High-density lipoprotein (mmol/L) Triglycerides (mmol/L) Fasting blood glucose (mmol/L) Lifestyle behaviours Physical activity, MET-mins/week Servings of fruits & vegetables/day Sleep, hours per day a,b,c

Tertile 1 BF <22.95% (n = 511)

Tertile 2 BF: 22.95–28.52% (n = 347)

Tertile 3 BF >28.52% (n = 237)

p

Unweighted count (%) or Mean + S.E.

Unweighted count (%) or Mean + S.E.

Unweighted count (%) or Mean + S.E.

81 (54.7) 210 (55.3) 148 (40.3) 72 (36.0)

44 (29.7) 97 (25.5) 131 (35.7) 75 (37.5)

23 (15.5) 73 (19.2) 88 (24.0) 53 (26.5)

<0 .001

369 (49.0) 136 (48.6) 6 (9.7)

220 (29.2) 96 (34.3) 31 (50.0)

164 (21.8) 48 (17.1) 25 (40.3)

<0 .001

49.54 ± 0.27a 154.81 ± 0.31a 20.65 ± 0.07a 69.56 ± 0.38a 89.86 ± 0.27a 0.78 ± 0.00a 0.45 ± 0.00a 17.95 ± 0.24a 8.95 ± 0.14a

53.75 ± 0.22b 157.56 ± 0.37b 21.66 ± 0.07b 72.66 ± 0.43b 92.61 ± 0.38b 0.79 ± 0.00b 0.46 ± 0.00b 25.44 ± 0.11b 13.70 ± 0.09b

52.63 ± 0.35b 157.10 ± 0.46b 21.30 ± 0.08c 71.99 ± 0.46b 91.90 ± 0.34b 0.78 ± 0.00a 0.46 ± 0.00b 41.39 ± 0.75c 21.63 ± 0.37c

<0.001 <0.001 <0.001 <0.001 <0.001 0.03 <0.001 <0.001 <0.001

114.31 ± 0.82a 70.51 ± 0.55a 5.11 ± 0.05a 3.06 ± 0.05a 1.69 ± 0.02a 0.91 ± 0.03a 4.67 ± 0.03a

116.95 ± 0.97b 72.28 ± 0.69b 5.29 ± 0.06b 3.18 ± 0.05b 1.61 ± 0.02b 1.09 ± 0.05b 4.83 ± 0.10b

117.59 ± 0.98b 70.56 ± 0.69a 5.29 ± 0.06a 3.18 ± 0.05b 1.61 ± 0.02b 1.09 ± 0.05b 4.78 ± 0.07c

<0.001 0.02 0.002 0.002 0.01 <0.001 0.04

3337.95 ± 327.27 3.19 ± 0.13 6.37 ± 0.08

3090.10 ± 362.62 3.28 ± 0.19 6.33 ± 0.12

3015.57 ± 765.40 3.46 ± 0.23 6.33 ± 0.07

40.47 ± 8.97 years

0.49 0.47 0.38

denotes significance differences at p < 0.05.

duration across the %BF tertiles. However, physical activity seemed to decline as body fat increased, however this was not statistically significant (Table 1). The prevalence of metabolic syndrome among the normal weight women was 5.4% (95% CI: 3.0–9.8). The highest proportion of women with no metabolic risk factor was in Tertile 1 while NWO women (in Tertile 3) had the highest proportion with four metabolic risk factors (Fig. 1). The crude odds of cardiometabolic abnormalities and metabolic syndrome among those in higher %BF tertiles were significantly increased compared to those in Tertile 1 (Table 2). After adjustment for age and ethnicity, higher odds for abdominal obesity and hypertriglyceridemia were found in %BF Tertiles 2 and 3, while the odds of hypertension was found to be significantly higher in Tertile 3 (NWO). NWO women had significantly higher odds of abdominal obesity (OR:2.64, 95% CI:1.73–4.04), hypertriglyceridemia (2.51, 1.47–4.29) and hypertension (1.63, 1.15–2.31) compared to those in the lowest %BF tertile.

4. Discussion This study investigated the prevalence of normal weight obese (NWO) among Malaysian women and determined its associations with cardiometabolic risks and lifestyle behaviours. About 20% of the women were identified as NWO, slightly lower than the Koreans [14]. The highest proportion of NWO women were found among Indian women, followed by Malays and Chinese. Deurenberg-Yap et al. reported similar findings in their study conducted in Singapore [23].

Women with normal BMI and lowest % body fat had the highest proportion with none or one risk factor for metabolic syndrome while NWO women had the highest proportion of four metabolic risk factors. Women with the lowest %BF also had smaller waist circumference than their counterparts. This further demonstrates that high %BF and body fat distribution contributes to the difference in cardiovascular risk. Ito et al. [24] reported that excessive body fat distributed in the upper body increased the risk of hyperlipidaemia in both men and women with a normal body weight. Coronary artery disease patients with normal BMI and central obesity are also linked to the highest mortality risk as compared to other adiposity patterns [25]. Our findings show that NWO women had significantly lower HDL-cholesterol, higher blood pressure, total cholesterol, LDLcholesterol and triglycerides in the univariate analysis. After adjustment for age and ethnicity, higher odds for abdominal obesity were observed in %BF tertiles 2 and 3. Although women are more likely to deposit fat on their lower extremities [26], they are at risk of abdominal obesity if their %BF was higher than normal. We also observed the odds of hypertriglyceridemia were higher among women in %BF tertile 2 and 3. This indicates that a normal BMI with excess accumulation of BF is not protective of metabolic risks. Women should try to maintain their body fat in the normal range as cardiometabolic risks start prevailing as soon as their body fat increases. However, this may require additional effort as women tend to put on weight/fat easily after childbirth. Our findings concur with other studies reporting that NWO adults had significantly higher risk for cardiometabolic risks or dyslipidemia [6,24]. NWO individuals are at increased risk of

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Fig. 1. Association of metabolic risk factors according to %BF tertile among women.

CVD mortality through the intermediary effect of cardiometabolic dysregulation [8]. Cut-offs for excess %BF described in literature varies according to ethnicity, limiting comparisons on prevalence of NWO between studies although %BF cut-offs among Asians were considerably lower than Caucasians [6]. Smoking, physical inactivity and alcohol intake were reported to contribute to cardiometabolic abnormalities [16,27]. Smoking and alcohol were not included in our analyses as less than 0.1% of the respondents reported these behaviours. There seemed to be a declining trend in physical activity as body fat increased, however this did not reach statistical significance. Dietary patterns characterised by low dietary fibre intake, high intake of meat, soft drinks and confectionery have been related to NWO [16]. However, our results showed no significant associations between fruit and vegetable consumption with cardiometabolic outcomes. This could be due to social desirability bias as our participants were secondary school teachers with tertiary education. Our results also

contradicted with a recent meta-analysis reported that short sleep duration was associated with obesity [28]. Possible reason could be due to them having similar sleep pattern or duration as they have similar occupation. There are several limitations in our study that need to be addressed. BIA was used to measure %BF although dual energy Xray absorptiometry (DEXA) is the gold standard. DEXA is not used as it is expensive and inconvenient to be used in the field. However, a good correlation between the %BF obtained from the two methods had been reported [29]. Furthermore, bioelectrical impedance’s acceptable accuracy, simplicity, lack of radiation and relatively low cost make it a practical and feasible alternative for measuring body fatness, especially in large population [30]. Recall bias cannot be ruled out as lifestyle behaviours were self-reported. Causality cannot be established due to the cross-sectional design. On the other hand, this study is a step forward as there was a dearth of studies investigating the prevalence of NWO and its association with

Table 2 Crude and adjusted odds ratio (OR) and 95% confidence intervals (95% CI) for metabolic risk factors in normal weight women across body fat tertiles (n = 1095).

Abdominal obesity Crude OR Adjusted OR# Hypertriglyceridemia Crude OR Adjusted OR# Low HDL Crude OR Adjusted OR# Hypertension Crude OR Adjusted OR# Hyperglycaemia Crude OR Adjusted OR# Metabolic syndrome Crude OR Adjusted OR# Hypercholesterolemia Crude OR Adjusted OR# Diabetes Crude OR Adjusted OR# #

Tertile 1 BF <22.95% (n = 511)

Tertile 2 BF: 22.95–28.52% (n = 347)

Tertile 3 BF >28.52% (n = 237)

1.00 1.00

2.12 (1.36–3.29) 1.93 (1.23–3.03)

3.04 (2.00–4.61) 2.64 (1.73–4.04)

1.00 1.00

2.85 (1.68–4.86) 2.62 (1.53–4.50)

2.89 (1.69–4.92) 2.51 (1.47–4.29)

1.00 1.00

1.58 (1.11–2.25) 1.41 (0.98–2.02)

1.36 (0.96–1.93) 1.09 (0.75–1.58)

1.00 1.00

1.48 (1.04–2.12) 1.35 (0.94–1.95)

1.85 (1.31–2.61) 1.63 (1.15–2.31)

1.00 1.00

2.07 (1.10–3.90) 1.76 (0.94–3.30)

2.22 (1.24–3.97) 1.67 (0.90–3.08)

1.00 1.00

1.42 (0.69–2.91) 1.18 (0.58–2.43)

2.28 (1.18–4.41) 1.70 (0.87–3.32)

1.00 1.00

1.76 (0.18–17.59) 2.02 (0.19–21.04)

1.84 (0.23–14.71) 2.22 (0.21–23.20)

1.00 1.00

2.79 (0.63–12.33) 2.23 (0.50–9.91)

1.90 (0.46–7.85) 1.28 (0.34–4.92)

Adjusted OR for age and ethnicity; significant associations are in bold.

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cardiometabolic risk profile among women in a multi-ethnic Asian population. In conclusion, older women and those of Indian ethnicity were predisposed to NWO. The clustering of cardiometabolic abnormalities was present among NWO women significantly. Our findings provide important directions for future planning of health promotion programs for women. An estimated 31.4% normal weight Malaysian women [31] may be unaware of their increased risks of cardiometabolic perturbations. Therefore, screening for adiposity particularly body fat in normal weight women should be incorporated in both epidemiological and clinical settings to identify NWO and their cardiometabolic risks. Behavioural modification begins with awareness and motivation to change, thus, this will spur women to adopt healthy lifestyle changes in their dietary habits and physical activity as precautionary measures in preventing future cardiovascular events. Conflict of interest statement The authors declare no conflict of interest. Contributors Foong Ming Moy designed and carried out the study. Data was analysed and manuscript written by Foong Ming Moy and Debbie Ann Loh. Both authors went through and approved the final version of the manuscript. Competing interests None declared. Funding This project is funded by the Ministry of Education High Impact Research Grant, Malaysia (H-20001-00-E2000069). Acknowledgements The approval from the Ministry of Education, Malaysia and the Department of Education in the respective states for this study is acknowledged. We would like to thank all the schools and teachers who participated in this study. This project is funded by the Ministry of Education High Impact Research Grant, Malaysia (H-20001-00-E2000069). References [1] Caboral MF. Update on cardiovascular disease prevention in women. Am J Nurs 2013;113:26–33. [2] Mosca L, Benjamin EJ, Berra K, et al. Effectiveness-based guidelines for the prevention of cardiovascular disease in women—2011 update: a guideline from the American Heart Association. Circulation 2011;123:1243–62. [3] World Health Organization. Obesity and overweight. World Health Organization; 2014. [4] Gomez-Ambrosi J, Silva C, Galofre JC, et al. Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity. Int J Obes (2005) 2012;36:286–94. [5] Guh D, Zhang W, Bansback N, Amarsi Z, Birmingham CL, Anis A. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Publ Health 2009;9:88.

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