Metabolic syndrome (MS) is frequently associated with an increased body mass index (BMI), and related to an adverse cardiovascular prognosis. The purpose of this study is to evaluate the prevalence and association between MS, obesity and subclinical atherosclerosis (SA).
This cross-sectional study included healthy adults, allocated to normal weight (NW) when BMI <25 kg/m2, overweight (OW) BMI ≥25 and <30 kg/m2, or obese (OB) BMI ≥30 kg/m2 groups. Presence of MS was defined according to National Cholesterol Education Program (NCEP) criteria. SA was evidenced with vascular ultrasound. Association between SA, obesity and MS, was evaluated by logistic regression models.
There were 3,716 patients studied (female 66.7%, mean age 47 ± 17.5 years). According to BMI, NW represented 28.2%, OW 39.4% and OB 32.4%. MS showed a strong correspondence with BMI (NW 4.9%, OW 21.4%, OB 49.7%; p<0.001). SA was more prevalent in each group when MS was present: NW (25.4% vs. 45.1%, p<0.005), OW (43.2% vs. 58.9%, p<0.0001) and OB (44.2% vs. 57.8%, p<0.0001). Logistic-regression models showed an independent association of SA with MS criteria (arterial hypertension p<0.001; high-density lipoprotein [HDL] p<0.05; and triglycerides p<0.005) adjusted by gender, age and BMI.
In conclusion, overweight and obesity are frequent and strongly linked with MS and SA. Prevalence of SA is high, and is independently associated with MS components. However, BMI could not retain statistical significance in the multi-variate analyses.
Introduction
Overweight and obesity are a global pandemic.1 These evermore frequent conditions are associated with serious chronic diseases (e.g. diabetes mellitus, hyperlipidaemia, cancer), and incremented risk for cardiovascular events.2,3 Metabolic syndrome (MS), a constellation of anthropometric and metabolic anomalies, is frequently associated with an increased body mass index (BMI) and related to an adverse cardiovascular prognosis.4 Despite the well-established association between BMI and cardiovascular prognosis, the concept of healthy obesity has emerged in more recent years, resembling a phenotype of obesity without metabolic disturbances.5 Whether this condition is associated with better cardiovascular prognosis is under debate. To date, few studies have addressed the association of MS, body weight and subclinical atherosclerosis (SA). This relationship holds major implications in cardiovascular risk prediction, since SA is associated with incident cardiovascular events.6 Hence, the aim of the present study is to evaluate the importance of MS components across BMI categories, and on the prevalence of SA as a manifestation of arterial vascular disease.
Materials and method
This study was of cross-sectional design and conducted at the Hospital Universitario René G Favaloro in Buenos Aires, Argentina. Participants were recruited from a cardiovascular health prevention programme if they were above 18 years, with no history of cardiovascular disease (acute coronary syndrome, transient ischaemic attack [TIA] or stroke, any arterial revascularisation). Those with untreated hypothyroidism, history of acute or chronic hepatic or kidney disease, were excluded. Medical records were used as the source for general data collection. This study was approved by the institutional ethics committee (Comité de Bioética del Hospital Universitario Fundación Favaloro).
Subjects were allocated according to BMI to normal weight (NW) when BMI <25 kg/m2, overweight (OW) BMI ≥25 and <30 kg/m2, or obese (OB) BMI ≥30 kg/m2 groups.
MS was diagnosed according to the NCEP ATP III (National Cholesterol Education Program Adult Treatment Panel III) criteria.7 SA was diagnosed with vascular ultrasound using an Affinity 50 ultrasound system (Philips HealthCare, USA) with a 9–12 MHz vascular probe. Plaques were defined as a focal protrusion into the arterial lumen of >50% of the surrounding intima-media thickness or a thickness >1.5 mm measured between the media-adventitia and intima-lumen interfaces.8
Quantitative data are presented as mean ± standard deviation [SD] and categorical variables with number and percentage. For comparison between groups of quantitative variables, one-way ANOVA, with post-hoc Tukey test was performed. Categorical variables were analysed with Chi-square and exact Fisher test. The association between SA, BMI and MS components, was evaluated by multi-nomial logistic-regression models.
Results
A total of 3,716 patients were included (table 1). According to BMI, NW represented 28.2%, OW 39.4% and OB 32.4%. MS was present in 25.9% (n=964), with a strong correspondence with BMI (NW 4.9%, OW 21.4%, OB 49.7%; p<0.001). The OW and OB groups showed higher prevalence of men and older age. The proportion of patients with dyslipidaemia, arterial hypertension (HT) and diabetes was higher in OW and OB groups. Triglycerides (TG) were increased in OW and OB patients with a lower high-density lipoprotein (HDL)-cholesterol in these two groups compared with NW. In addition, a higher use of statins was observed in OW and OB groups.
Table 1. Clinical features and complications
| NW group (n=1,047) |
OW group (n=1,464) |
OB group (n=1,205) |
p value for trend | |
|---|---|---|---|---|
| Female gender, n (%) | 698 (66.7) | 548 (37.4)a | 429 (35.6)a | <0.0001 |
| Mean age ± SD, years | 47 ± 17.5 | 55.6 ± 14.6a | 57.2 ± 13.5a,b | <0.001 |
| Mean weight ± SD, kg | 61.4 ± 8.9 | 77.4 ± 9.8a | 96.1 ± 15.1a,b | <0.001 |
| Mean BMI ± SD, kg/m2 | 22.5 ± 2.0 | 27.4 ± 1.4a | 34.3 ± 4.1a,b | <0.001 |
| Mean waist circumference ± SD, cm | 79.8 ± 8.6 | 93.3 ± 8.5a | 108.7 ± 11.9a,b | <0.001 |
| Mean systolic BP ± SD, mmHg | 113.3 ± 13.5 | 120.9 ± 14.0a | 126.7 ± 14.8a,b | <0.001 |
| Mean diastolic BP ± SD, mmHg | 72.1 ± 10.0 | 76.6 ± 9.0 | 80.1 ± 8.8 | <0.001 |
| Hypertension, n (%) | 156 (14.9) | 430 (29.4)a | 591 (49)a,b | <0.0001 |
| Type 1 diabetes, n (%) | 2 (0.2) | 6 (0.4) | 3 (0.2) | ns |
| Type 2 diabetes, n (%) | 21 (2) | 109 (7.4)a | 174 (14.4)a,b | <0.0001 |
| Current smoking, n (%) | 200 (19.1) | 258 (17.6) | 198 (16.4) | ns |
| Former smoking, n (%) | 162 (15.5) | 317 (21.6)a | 315 (26.1)a,b | <0.001 |
| Family history of CVD, n (%) | 90 (8.6) | 118 (8.1) | 80 (6.6) | ns |
| Dyslipidaemia, n (%) | 310 (29.6) | 665 (45.4)a | 621 (52.6)a,b | <0.0001 |
| Mean glycaemia ± SD, mg/dL | 93.7 ± 17.0 | 99.0 ± 22.1a | 105.6 ± 33.7a,b | <0.001 |
| Mean HbA1c ± SD, mg/dL | 4.3 ± 2.2 | 5.0 ± 1.8a | 5.2 ± 2.0a,b | <0.001 |
| Mean HDL-cholesterol ± SD, mg/dL | 61.5 ± 15.4 | 53.7 ± 13.6a | 51.3 ± 13.1a,b | <0.001 |
| Mean total cholesterol ± SD, mg/dL | 197.8 ± 39.9 | 199.4 ± 39.6a | 200.1 ± 41.2a,b | ns |
| Mean triglycerides ± SD, mg/dL | 99.0 ± 54.5 | 129.5 ± 82.4a | 148.2 ± 98.0a,b | <0.001 |
| Mean LDL-cholesterol ± SD, mg/dL | 116.5 ± 34.5 | 119.7 ± 35.2a | 119.2 ± 39.9a | ns |
| Treatment with statins, n (%) | 106 (10.1) | 308 (21) | 263 (21.8) | <0.001 |
| BMI categories are divided into NW (normal weight group) when <25 kg/m2, OW (overweight group) with BMI ≥25 and <30 kg/m2, and OB (obese group) for BMI >30 kg/m2. a p<0.001 vs. NW; b p<0.05 vs. OW. Key: BMI = body mass index; BP = blood pressure; CVD = cardiovascular disease; HbA1c = glycated haemoglobin; HDL = high-density lipoprotein; LDL = low-density lipoprotein; ns = not significant; SD = standard deviation |
||||
Prevalence of SA was also associated with BMI (OB 50.9%, OW 46.6%, NW 26.4%; p<0.001). MS was associated with a higher SA prevalence in the NW group (without MS 25.4% vs. 45.1% with MS; p<0.005), and was also increased in OW (without MS 43.2% vs. 58.9% with MS; p<0.0001) and OB (without MS 44.2% vs. 57.8% with MS; p<0.0001) groups.
Association between SA, obesity and MS, was evaluated by three logistic-regression models with SA as an independent variable (table 2). In model 1, adjusted by gender and age, BMI (p<0.05), as a continuous variable, was independently associated with SA. Addition of MS as a dichotomic variable in model 2, reveals MS is also associated with SA (p<0.001), when adjusted by age, gender and BMI. In this second model, BMI loses statistical significance. Model 3 was further adjusted for age, gender and BMI (kg/m2), triglycerides (mg/dL), HDL (mg/dL), HT (yes or no), waist circumference (cm), and glycaemia (mg/dL). An independent association of SA with MS criteria was found: HT (p<0.001), HDL (p<0.05) and TG (p<0.005).
Table 2. Multi-variate analysis of association between BMI, metabolic syndrome (MS) and subclinical atherosclerosis
| Variables in equation | B | Standard error | Significance | Exp(B) | 95%CI for Exp(B) |
|---|---|---|---|---|---|
| Model 1 | |||||
| Age (years) | 0.117 | 0.004 | <0.001 | 1.124 | 1.115 to 1.134 |
| Gender (m or f) | 0.730 | 0.087 | <0.001 | 2.075 | 1.751 to 2.459 |
| BMI (kg/m2) | 0.16 | 0.008 | 0.044 | 1.017 | 1 to 1.033 |
| Model 2 | |||||
| Age (years) | 0.117 | 0.004 | <0.001 | 1.124 | 1.114 to 1.133 |
| Gender (m or f) | 0.735 | 0.087 | <0.001 | 2.086 | 1.759 to 2.473 |
| BMI (kg/m2) | 0.003 | 0.009 | ns | 1.003 | 0.986 to 1.021 |
| MS (yes or no) | 0.36 | 0.1 | <0.001 | 1.433 | 1.178 to 1.744 |
| Model 3 | |||||
| Age (years) | 0.113 | 0.004 | <0.001 | 1.12 | 1.11 to 1.13 |
| Gender (m or f) | 0.558 | 0.1 | <0.001 | 1.747 | 1.436 to 2.126 |
| BMI (kg/m2) | –0.014 | 0.015 | ns | 0.986 | 0.958 to 1.015 |
| HT (yes or no) | 0.494 | 0.091 | <0.001 | 1.638 | 1.371 to 1.957 |
| Glycaemia (mg/dL) | –0.002 | 0.002 | ns | 0.998 | 0.995 to 1.001 |
| HDL (mg/dL) | –0.007 | 0.003 | 0.049 | 0.993 | 0.987 to 1 |
| Triglycerides (mg/dL) | 0.002 | 0.001 | 0.004 | 1.002 | 1 to 1.003 |
| WC (cm) | 0.006 | 0.006 | ns | 1.006 | 0.995 to 1.017 |
| Key: BMI = body mass index; CI = confidence interval; Exp(B) = odds ratio; f = female; HDL = high-density lipoprotein; HT = hypertension; m = male; MS = metabolic syndrome; ns = not significant; WC = waist circumference | |||||
Discussion
Overweight and obesity were frequent in our study, reaching almost 72% of otherwise healthy primary prevention individuals. As expected, the increase in BMI was accompanied by a higher prevalence of cardiovascular risk factors, as well as MS diagnosis, which is concordant with other studies.9 Overall, there was a 40% prevalence of SA in our population, which reproduces the results of other cohorts.10 In our study, OB and OW individuals were more prone than their NW counterparts to present SA. However, including MS in the second logistic regression model caused BMI to lose its statistical significance. In addition, in model 3, HT, HDL, and TG were found to have an independent association to SA. The loss of independent prediction of BMI over SA is not new in literature. Prediction models of cardiovascular risk often attenuate significant associations between BMI and incidental events when adjusted by other risk factors as covariates.11 This interaction between BMI, SA and MS, could partly be attributed to ‘metabolically healthy obesity’. Without a homogeneous definition, this concept developed from the observation that some individuals with obesity do not exhibit metabolic disorders and have a lower incidence of cardiovascular events, reflecting that obesity itself would show less influence on vascular damage in the absence of metabolic factors.12,13
To conclude, we propose that the association of weight disorders and SA could mainly be attributed to the effect of MS components. Future research, focused on freedom from clinical events, will elucidate the influence of these findings on prognosis.
Key messages
- This study was a cross-sectional design, which evaluated 3,716 healthy patients to evaluate the role of metabolic syndrome (MS), overweight (OW) and obesity (OB) in the presence of subclinical atherosclerosis (SA)
- MS showed a strong correspondence with body mass index (BMI) (normal weight 4.9%, OW 21.4%, OB 49.7%; p<0.001)
- Logistic-regression models showed an independent association of SA with MS criteria (arterial hypertension p<0.001, HDL p<0.05, and triglycerides p<0.005) adjusted by gender, age and BMI
- BMI could not retain statistical significance in the multi-variate analyses
Conflicts of interest
None declared.
Funding
None.
Study approval
This study was approved by the institutional ethics committee (Comité de Bioética del Hospital Universitario Fundación Favaloro).
Acknowledgements
For data collection: Miguel Cerda, Mónica Piraino, Juan Manuel Copello, Guillermo Ganum, Claudia Abraham, Carolina Lizzi, Raúl Héctor Bianco, Julieta Paolini, José Máximo Santos, Alfredo Rojo. For statistical support: Diego Giunta.
References
1. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet 2017;390:2627–42. https://doi.org/10.1016/S0140-6736(17)32129-3
2. Schelbert KB. Comorbidities of obesity. Prim Care 2009;36:271–85. https://doi.org/10.1016/j.pop.2009.01.009
3. Zhu J, Su X, Li G et al. The incidence of acute myocardial infarction in relation to overweight and obesity: a meta-analysis. Arch Med Sci 2014;10:855–62. https://doi.org/10.5114/aoms.2014.46206
4. McNeill AM, Rosamond WD, Girman CJ et al. The metabolic syndrome and 11-year risk of incident cardiovascular disease in the atherosclerosis risk in communities study. Diabetes Care 2005;28:385–9. https://doi.org/10.2337/diacare.28.2.385
5. Blüher M. Metabolically healthy obesity. Endocr Rev 2020;41:1–16. https://doi.org/10.1210/endrev/bnaa004
6. Baber U, Mehran R, Sartori S et al. Prevalence, impact, and predictive value of detecting subclinical coronary and carotid atherosclerosis in asymptomatic adults: the BioImage study. J Am Coll Cardiol 2015;65:1065–74. https://doi.org/10.1016/j.jacc.2015.01.017
7. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 2001;285:2486–97. https://doi.org/10.1001/jama.285.19.2486
8. Stein JH, Korcarz CE, Hurst RT et al. Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force. Endorsed by the Society for Vascular Medicine. J Am Soc Echocardiogr 2008;21:93–111. https://doi.org/10.1016/j.echo.2007.11.011
9. Ervin RB. Prevalence of metabolic syndrome among adults 20 years of age and over, by sex, age, race and ethnicity, and body mass index: United States, 2003–2006. Natl Health Stat Report 2009;(13):1–7. Available from: https://www.cdc.gov/nchs/data/nhsr/nhsr013.pdf
10. Fernández-Friera L, Peñalvo JL, Fernández-Ortiz A et al. Prevalence, vascular distribution, and multiterritorial extent of subclinical atherosclerosis in a middle-aged cohort: the PESA (Progression of Early Subclinical Atherosclerosis) study. Circulation 2015;131:2104–13. https://doi.org/10.1161/CIRCULATIONAHA.114.014310
11. Yusuf S, Hawken S, Ounpuu S et al. Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study. Lancet 2005;366:1640–9. https://doi.org/10.1016/S0140-6736(05)67663-5
12. Eckel N, Meidtner K, Kalle-Uhlmann T et al. Metabolically healthy obesity and cardiovascular events: a systematic review and meta-analysis. Eur J Prev Cardiol 2016;23:956–66. https://doi.org/10.1177/2047487315623884
13. Primeau V, Coderre L, Karelis AD et al. Characterizing the profile of obese patients who are metabolically healthy. Int J Obes (Lond) 2011;35:971–81. https://doi.org/10.1038/ijo.2010.216
