Cardiometabolic risk factors and Framingham Risk Score in severely obese patients: Baseline data from DieTBra trial

Journal Pre-proof Cardiometabolic risk factors and Framingham Risk Score in severely obese patients: baseline data from DieTBra Trial Annelisa SAC. Santos, Ana Paula S. Rodrigues, Lorena PS. Rosa, Nizal Sarrafzadegan, Erika A. Silveira PII:

S0939-4753(19)30409-0

DOI:

https://doi.org/10.1016/j.numecd.2019.10.010

Reference:

NUMECD 2172

To appear in:

Nutrition, Metabolism and Cardiovascular Diseases

Received Date: 4 May 2019 Revised Date:

1 September 2019

Accepted Date: 29 October 2019

Please cite this article as: Santos AS, Rodrigues APS, Rosa LP, Sarrafzadegan N, Silveira EA, Cardiometabolic risk factors and Framingham Risk Score in severely obese patients: baseline data from DieTBra Trial, Nutrition, Metabolism and Cardiovascular Diseases, https://doi.org/10.1016/ j.numecd.2019.10.010. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V. on behalf of The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University.

Title Cardiometabolic risk factors and Framingham Risk Score in severely obese patients: baseline data from DieTBra Trial

Author names and affiliations Annelisa SAC Santosa Ana Paula S Rodriguesa Lorena PS Rosaa Nizal Sarrafzadeganb Erika A Silveiraa

a

Programa de Pós-Graduação em Ciências da Saúde, Faculdade de Medicina,

Universidade Federal de Goiás, Brazil. b

Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan

University of Medical Sciences, Isfahan,Iran & School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, Canada.

Corresponding author Name: Annelisa Silva e Alves de Carvalho Santos Mailing address: Programa de Pós-Graduação em Ciências da Saúde Universidade Federal de Goiás, Goiânia, Brazil. ZIP Code: 74.605-020, Rua 235 c/ 1ª Avenida, s/n, Setor Universitário, Goiania, Brazil. Phone/Fax: +55-62-3209-6151 E-mail: [email protected]

Word counts Abstract: 248 Text (including list of authors and their affiliations, corresponding author, acknowledgements, key words and figure legends): 3483 Number of references: 51 Figures: 4 Tables: 2

Keywords morbid obesity; risk factors; hypertension; c-reactive protein; body composition

Acronyms ABEP - Brazilian Association of Population Studies (acronym in Brazilian Portuguese) BF – body fat BMI – body mass index BP – blood pressure CAPES - Coordination for the Improvement of Higher Education Personnel (acronym in Brazilian Portuguese) CHD – coronary heart diseases CMRF – cardiometabolic risk factors CRP – C-reactive protein CVD – cardiovascular diseases FAPEG - Goiás State Research Support Foundation (acronym in Brazilian Portuguese) FRS – Framingham Risk Score GEOG – Severe Obesity Study Group (acronym in Brazilian Portuguese) HbA1c – hemoglobin A1C

HDL-c – high-density lipoprotein cholesterol HOMA-IR – homeostatic model assessment for insulin resistance LDL-c – low-density lipoprotein cholesterol RCT – randomized clinical trial SB – sedentary behavior TC – total cholesterol TG - triglycerides VFA – visceral fat area WC – waist circumference

Competing interests The authors have nothing to disclose.

ABSTRACT Background and aims: Little is known about differences of cardiometabolic risk factors (CMRF) and the function of Framingham Risk Score (FRS) within severe obesity, thus we aimed to study not only CMRF and FRS, but to determine significant differences between BMI ranges within severe obesity. Methods and Results: In this baseline analysis of the Traditional Brazilian Diet (DieTBra) Trial, several CMRF were assessed in 150 adult patients in two BMI ranges: 35.0-44.9 kg/m2 (n=76) and ≥ 45 kg/m2 (n=74). Body composition was evaluated by multifrequency bioelectrical impedance analysis to measure the percent of body fat, visceral fat area and waist circumference. Pearson’s Chi-squared, Fisher’s Exact, Student’s t-test, and MannWhitney’s test were used in the statistical analysis with a 5% significance level. Hypertension, C-reactive protein, systolic and diastolic blood pressure and positive family history for heart diseases were more prevalent in BMI ≥ 45.0 kg/m2 (p<0.05). Mean values of waist circumference, body fat %, visceral fat area, and systolic blood pressure were significantly higher in patients with BMI ≥ 45.0 kg/m2. Regarding the function of FRS, 40.0% of the patients were at high risk. No differences were found for diabetes, lifestyle, lipid parameters, and FRS within different BMI ranges, except for dyslipidemia, significantly higher among participants with BMI 35.0 – 44.9 kg/m2. Conclusion: BMI >45 kg/m2 was associated with higher prevalence of hypertension, systolic and diastolic blood pressure, C-reactive protein, waist circumference, body fat % and family history of heart diseases, enhancing the risk for the occurrence of cardiovascular diseases.

INTRODUCTION In the past few years, the epidemiological scenario of obesity has experienced ascending prevalence rates with an essential increase in severe obesity [1]. In a recent study on global adult body mass index (BMI) trends in 200 countries, the prevalence of severe obesity (BMI ≥ 35 kg/m2) was 2.3% for men and 5.0% for women in 2014, and Brazil is one of the top five countries in the ranking of severe obesity worldwide [1]. In this context, the development of studies with this specific BMI ranges is pertinent. The presence of cardiometabolic risk factors (CMRF) contributes to higher mortality rates for all causes and cardiovascular events in adults with obesity [2-4]. CMRF could be notably more significant as the BMI increases [5-7], and so the cardiometabolic complications [8]. However, little is known about possible differences in the occurrence of CMRF among different BMI ranges within severe obesity, including body composition. Studies assessing CMRF in severely obese patients are mainly focused on the effectiveness of surgical approaches on their reduction [9-11]. A broad characterization of the association of CMRF within higher obesity grades is relevant to advance the awareness of the matter that modern societies are going to confront as a result of the increasing prevalence of severe obesity [12]. Hence, this study aimed to describe the frequency of several CMRF and to assess potential BMI-range differences in CMRF and differences in Framingham Risk Score (FRS) according to different BMI categories in a population with severe obesity.

METHODS Study design This study analyzes baseline data from a randomized clinical trial (RCT) with severely obese patients, entitled “Effect of Nutritional Intervention and Olive Oil in Severe Obesity – DieTBra Trial” (NCT02463435). The principal study was conducted in

Midwestern Brazil between June 2015 and February 2016 in partnership with the Nutrition Outpatient Clinic in Severe Obesity from the Clinics Hospital from the Federal University of Goiás and the Severe Obesity Study Group (GEOG – Grupo de Estudos em Obesidade Grave) [13, 14].

Study population Two hundred and twenty-nine obese patients referred from the Goiania’s Municipal Health Department were screened for eligibility, and 150 patients met inclusion criteria. Inclusion criteria were: severe obesity (BMI ≥ 35 kgm2); age between 18 and 65 years; and residence in Goiânia or metropolitan area. Exclusion criteria were: nutritional treatment in the past two years; weight loss >8% in the past three months; pregnancy and lactation; illiteracy or less than three years of study; people with special needs – who couldn’t walk, hear or speak; use of medication to lose weight; daily use of anti-inflammatory drugs and corticosteroids; patients who underwent bariatric surgery. Those presenting the following diseases were also excluded: HIV/AIDS, cardiac insufficiency, liver or kidney failure, chronic obstructive pulmonary disease, and cancer.

Study variables A trained interviewer collected all variables through a pre-tested and structured questionnaire. Sociodemographic data were used only for participants’ characterization, except for age. Lifestyle, clinical history, body composition, and biochemical variables were evaluated according to two predefined BMI ranges, using as cutoff point the 50th percentile, i.e., dividing the sample in half: 35.0 – 44.9 kg/m2 and ≥ 45.0 kg/m2. Sociodemographic variables were sex, age, marital status, educational level, and socioeconomic class. Age was categorized as >45 years for men and >55 years for women (postmenopausal), as these age categories are independent risk factors for

cardiovascular diseases [15]. Socioeconomic class was classified according to the ABEP (Associação Brasileira de Estudos Populacionais - Brazilian Association of Population Studies) parameters [16]: A, B, C, D and E. This classification is based on a scoring system assigned to the amount of some household items owned by the participants, such as color television, radio, bathroom, car, monthly maid, washing machine, VCR / DVD, refrigerator, freezer, and the degree of education of the head of the family. The higher the score, the higher the socioeconomic class. Lifestyle variables were smoking status, alcohol consumption, and sedentary behavior. Although it is an important lifestyle component, dietary habits were not in the scope of this paper, once it will be explored in another study from our study group. Smoking status was defined as nonsmoker, former smoker, and current smoker. Regarding alcohol consumption, the questions were adapted from the GENACIS - Gender, Alcohol, and Culture: An International Study [17]. The subjects were then classified as lifetime abstainers (never have drunk alcoholic beverages), current drinkers, and former drinkers (those who have not drunk alcoholic beverages for the past year). Binge drinking was also investigated, classified as the consumption of more than five drinks in a single occasion for men and more than four for women in the past 12 months. Sedentary behavior (SB) was defined as the participation in activities that do not significantly increase energy expenditure, such as sitting and reclining posture during the non-sleep time [18]. SB was evaluated by accelerometry using the triaxial accelerometer ActiGraph wGT3X (ActiGraph, Pensacola, FL, USA) in the patients’ non-dominant wrist for 24 hours a day, during six consecutive days, including the weekend. The accelerometer’s raw data was evaluated in gravitational equivalent units called milligravities (mg), being 1000 mg = 1 g = 9.81 m/s2. Activities with mean acceleration <50 mg were classified as SB. Accelerometry data were collected with 30 Hz frequency and were downloaded to the Actilife v. 6 software. Mean minutes per day spent in SB were used in statistical analysis.

Clinical history included investigation on information about blood sugar lowering drugs, blood pressure (BP) lowering drugs, and BP measurement. We defined positive family history if our participants reported first degree relatives to have obesity, high blood pressure, CVD, diabetes or dyslipidemia. BP was measured while the patient was seated and at rest for at least 30 minutes. Two BP measurements were taken within 2-3 minutes apart using the automatic sphygmomanometer Omron HEM-742INT (Omron Healthcare, São Paulo, Brazil) in the brachial artery, with an appropriate cuff size according to the arm diameter for severely obese patients. The mean of the two measurements was used in the statistical analysis [19]. Body composition was examined through a multifrequency bioelectrical impedance analysis using the Inbody S10 equipment according to international standard procedures [20]. Body composition variables were the mean values of waist circumference (WC, cm), body fat percentage (BF%), and visceral fat area (VFA, cm3). The following biochemical parameters were evaluated: fasting blood glucose, hemoglobin A1C (HbA1c), fasting insulinemia, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), non-HDL cholesterol, homocysteine, semi-quantitative C-reactive protein (CRP), and homeostatic model assessment for insulin resistance (HOMA-IR). The patients were advised to remain in a 12-hour fasting period for blood collection. Fasting blood glucose and lipid parameters were determined through the enzymatic colorimetric method. Fasting insulinemia and HOMA-IR were determined through electrochemiluminescence. HbA1c was assessed through immunoturbidimetry and semi-quantitative CRP through immunochemical agglutination reaction. All biochemical assessments were performed according to standardized procedures in a reference clinical laboratory, rated as excellent in quality by the National Quality Control Program of the Brazilian Society of Clinical Analyzes.

Diabetes was defined as a fasting blood glucose ≥ 126 mg/dl and/or HbA1c ≥ 6.5% [21] and/or use of oral blood sugar lowering drugs. Hypertension was defined as systolic BP ≥ 140 mmHg and/or diastolic BP ≥ 90 mmHg and/or use of BP lowering drugs [22]. Dyslipidemia was defined as the presence of at least one of the following: LDL ≥ 160 mg/dl, hypertriglyceridemia, low-HDL and/or use of lipid-lowering agents [23]. Hypercholesterolemia was set at a total cholesterol ≥ 200 mg/dl; elevated LDL-c at ≥ 100 mg/dl; hypertriglyceridemia when triglycerides ≥ 150 mg/dl; low HDL-c at <40 for men and < 50 for women [23]; and elevated non-HDL-c at ≥ 130 mg/dl [24]. Castelli I (TC/HDL-c) and Castelli II (LDL-c/HDL-c) atherogenic indexes were also calculated, with the following cutoff points: Castelli I > 4.4 for females and 5.1 for males; Castelli II > 2.9 for females and 3.3 for males, considering the absence of coronary heart disease [25]. In the lack of a specific cutoff point for hyperhomocysteinemia in this population, the 75th percentile of the study sample was used for classification (10.19 mmol/l). CRP was classified as reagent (> 6) or non-reagent. The cutoff point for high HOMA IR was > 2.72 [26]. Traditional Framingham Risk Score (FRS) was used to estimate the risk of fatal or nonfatal coronary events in 10-years for men and women, considering the following variables: sex, -age, total cholesterol, HDL-c, BP, diabetes, and smoking status. Diabetes was considered as CVD equivalent, i. e., diabetic patients were automatically classified as high 10-year coronary heart disease (CHD) risk. The individuals were classified as low risk when the score was < 10%, intermediate risk 10-20% and high risk > 20% [27-29]. The FRS was also calculated for all participants and analyzed by different BMI ranges: 35.0 – 39.9 kg/m2; 40.0 – 44.9 kg/m2; 45.0 – 49.9 kg/m2; and ≥ 50.0 kg/m2.

Ethics The study was conducted in accordance with the Declaration of Helsinki and was approved by the Research Ethics Committee of the Clinical Hospital from the Universidade

Federal de Goiás (reference number 747 792). All study participants provided informed consent.

Statistical analysis All statistical analysis was performed in Stata 12.0 (StataCorp, College Station, TX, USA). Descriptive analysis of the total sample and according to two BMI ranges (35.0-44.9 kg/m2 and ≥ 45.0 kg/m2) were conducted. Kolmogorov-Smirnov's test tested normal distribution of continuous variables test. Differences between proportions were tested by Pearson’s Chi-squared test or Fisher’s Exact test and between two means by Student’s ttest or Mann-Whitney’s test. Statistical significance was set at p<0.05.

RESULTS Out of 150 patients, 85.3% were females, with a mean age of 39.6 ± 8.8 years, 63.3% were married, 66.3% with > 8 years of education and 16.0% were classified in the lowest economic classes. Age varied between 18 and 62 years, being 90% of the study subjects aged lower than 52.5 years old (data not shown). No statistical differences were found between the two BMI ranges for sociodemographic data. Additionally, 5.33% (BMI 35-44.9 kg/m2: 5.26%; BMI ≥ 45 kg/m2: 5.41%; p= 1.000) and 18.0% (BMI 35-44.9 kg/m2: 15.72%; BMI ≥ 45 kg/m2: 20.27%; p= 0.475) of study participant were in hypolipidemic and oral antidiabetic agents use, respectively. There were no statistical differences for the use of both medications in the two BMI ranges. Dyslipidemia, high HOMA-IR, and positive family history were found in over 70% of the study sample, while CRP >6, high LDL-c, low HDL-c, hypertension, and current drinking had a general prevalence around 50-60% (Figure 1). The following variables were significantly more prevalent in the patients with BMI ≥ 45.0 kg/m2: hypertension, systolic and diastolic BP, CRP >6, WC, BF%, VFA, and family

history of heart diseases. Dyslipidemia was significantly higher among participants with BMI 35.0 – 44.9 kg/m2 (Table 1 and 2; Figure 2). When comparing by sex, dyslipidemia was significantly higher in patients with BMI 32.0 – 44.9 kg/m2 only in men (Supplementary Table 4). Regarding the FRS, the mean absolute risk was 7.39 ± 6.78 in the total sample, 6.87 ± 6.44 for BMI range 35.0 – 44,9 kg/m2, 7.92 ± 7.11 for BMI range ≥ 45.0 kg/m2 (Table 2), 7.58 ± 6.93 in women, and 6.22 ± 5.80 in men (Supplementary Table 5) without statistically significant difference between sex and BMI category. As for the risk stratification, 55.3% of the study participants were classified as low risk (50.0% of men and 56.2% of women), 4.7% as intermediate risk (9.1% of men and 3.9% of women), and 40.0% as high 10-year CHD risk (40.9% of men and 39.8% of women), with no statistical differences between sex, either when comparing men and women alone in different BMI ranges (Supplementary Tables 3 and 4). Across different BMI categories, the prevalence of high 10-year CHD risk varied between 35.3% to 48.0%, but no statistical differences were found (Figures 3 and 4).

DISCUSSION Few studies have extensively evaluated CMRF in severely obese individuals as here presented, including body composition, which is a crucial contribution of this study. Several CMRF were present in more than half of the study participants. In general, higher prevalence rates were found for inadequate levels of LDL-c, CRP, HOMA-IR, HDL-c, hypertension, and positive family history. We highlight the significantly higher prevalence of some important CMRF in the patients with BMI ≥ 45 kg/m2, such as hypertension and other obesity indices like waist circumference, visceral fat area, and %BF. In our study, over 50% of the patients presented high LDL-c and low-HDL, while hypertriglyceridemia was found in 46.0% of the study participants. Also, dyslipidemia was

prevalent in over 75% of the patients. Compared to our study, another investigation with severely obese patients to be submitted to bariatric surgery found a lower prevalence of high LDL-c, higher prevalence of low HDL-c, and a similar prevalence of hypertriglyceridemia [11]. This dyslipidemic profile is common among obese subjects, related to excessive visceral adipose tissue, and has been documented before in other studies [30,31]. Dyslipidemia was higher in the patients with BMI between 35.0 – 44.9 kg/m2, mainly in male subjects. The relationship between lipid disorders and obesity is probably due to the insulin resistance secondary to adipose tissue augmentation, contributing to elevated fasting and postprandial triglycerides [32]. In obesity, hypertriglyceridemia may be the primary cause of other lipid abnormalities [33] and accumulation of small-dense LDL particles [34]. Several factors are associated with hypertriglyceridemia other than the adiposity status itself, including other medical conditions sigh as diabetes, renal disease, hypothyroidism, and lifestyle factors as alcohol consumption, insufficient physical activity, and dietary habits [35], which could explain this difference among the two BMI ranges in our study. CRP – an inflammatory marker, and HOMA-IR – an indicator of insulin resistance, were expressively frequent, reaching 58.0% and 74.0% of our study participants, respectively. However, only CRP was significantly higher among participants with BMI > 45.0 kg/m2, while no difference was observed for HOMA-IR levels between the two BMI categories. Mean value for HOMA-IR in the total sample found in our study was slightly higher than the mean found in a study that evaluated subclinical atherosclerosis determinants in individuals with BMI ≥ 40.0 kg/m2 in Spain [36]. A pro-inflammatory environment, as encountered in obese patients, may favor the occurrence of lipid abnormalities, directly affecting the endothelium and contributing to higher cardiovascular risk [32].

Prevalence of higher mean systolic and diastolic BP values, as well as hypertension, were significantly increased in patients with BMI ≥ 45.0 kg/m2. The prevalence of hypertension in this BMI range was 72.9% in our study, while in other studies was 60.9% in morbidly obese patients (BMI ≥ 40.0 kg/m2), similar to our study [33]. However, another study with severely obese patients (BMI ≥ 35.0 kg/m2), hypertension prevalence (53.0%) was lower than our study with BMI ≥ 45 kg/m2 individuals [11]. Increased adiposity is directly related to the occurrence of hypertension, being responsible for 65-75% of hypertension in adults [37]. Indeed, there are many complex mechanisms linking hypertension to obesity, particularly related to visceral adipose tissue augmentation [38,39]. Obesity associated hypertension is characterized by several hemodynamic and metabolic mechanisms such as activation of the sympathetic nervous system, activation of the renin-angiotensin axis and sodium retention which ultimately may increase blood volume and systemic vascular resistance [8, 37, 40]. Additionally, a deficiency in cardiac natriuretic peptides, especially the natriuretic peptide, appear to have a critical role in the link between heart and adipose tissue in obesity-associated hypertension [38, 39]. Considering that severely obese individuals have a higher risk of cardiovascular events primarily due to hypertension [41], hypertension management and BP control are crucial to prevent cardiovascular events in this population. BMI > 45 kg/m2 was associated with increased measures of adiposity (WC, BF%, and VFA). Consistent evidence has shown that the adipose tissue distribution is critical to determine the cardiometabolic risk associated with body fatness [42,43]. Study with severely obese observed that, although the subcutaneous adipose tissue has a role on cardiometabolic risk, visceral adipose tissue has a stronger association with an atherogenic and dysmetabolic phenotype (markers of dyslipidemia, insulin resistance, hepatic steatosis, and subclinical atherosclerosis) [43]. Visceral adipose tissue is also involved in ectopic fat deposition in organs, such as heart, blood vessels, kidneys, and

skeletal muscle, contributing to an impaired metabolic profile [42,44]. Thus, body composition measurement in studies with severely obese patients is relevant to contribute to the scientific knowledge related to cardiometabolic risk in this population. The present study observed higher values of FRS in both sexes than other research with preoperative obese individuals which include both BMI <35 kg/m2 and BMI ≥ 35 kg/m2 [45]. In a Canadian study with 73 patients with an indication for bariatric surgery, 36% of women and 12% of men were classified as high 10-year CHD risk according to FRS, similar for women and lower for men when compared to our results [46]. Our study limitations include the proportion of women and men in our study sample, which was approximately 5:1. In a study with preoperative obese individuals, a higher proportion of women in relation to men was reported, similarly to our study [45]. The study investigators argued that this gender difference could be because women seek health care more than men [45]. However, our primary objective was to evaluate differences in the frequency of several CMRF within different BMI ranges and not gender differences. It is also important to highlight that no difference was found for sociodemographic variables between the two BMI ranges here analyzed, including sex. Another possible limitation is about FRS use. Although other methods are available for assessing global cardiovascular risk, the FRS is the most prediction equation used worldwide [47]. Some limitations are related to the FRS employment in other populations than that of the original study, as it may overestimate the risk in Asians [48] and underestimate in older people [49]. However, no validation studies were performed for its use in obese or severely obese individuals. Most of the studies evaluating cardiovascular risk through risk stratification methods in severely obese patients use FRS, primarily to investigate the effect of surgical procedures on cardiovascular risk reduction [45, 46, 50]. It would be interesting to compare different cardiovascular risk stratification models in severely obese subjects in future studies.

We conclude that our results add essential contributions to the field of severe obesity and cardiometabolic risk factors, as most studies on this population are focused on the effectiveness of bariatric surgery [45, 46, 50]. Our study shows that some but not all CMRF are significantly increased in patients with BMI ≥ 45 kg/m2, although the prevalence of other CMRF were higher in both BMI ranges. It is important to address CMRF in obese people due to the increased prevalence of severe obesity worldwide, aiming to contribute with useful information for the development of effective treatment approaches to reduce cardiovascular risk, primarily lifestyle interventions [51]. Particular attention should be given to patients with higher BMI ranges during clinical assessment and treatment, primarily concerning high BP in order to prevent cardiovascular events in this population.

ACKNOWLEDGEMENTS Santos ASAC received a PhD scholarship from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil (CAPES - Coordination for the Improvement of Higher Education Personnel).

FUNDING The major study was supported by the Fundação de Amparo à Pesquisa de Estado de Goiás, Brazil (FAPEG - Goiás State Research Support Foundation; grant number 201310267000003). FAPEG did not participated in any of these study stages: collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

COMPETING INTERESTS The authors have nothing to disclose.

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TABLES

Table 1. Cardiometabolic risk factors in severely obese patients according to BMI ranges. Total

BMI 35.0 – 44.9 kg/m2

BMI ≥ 45 kg/m2

(n=76)

(n=74)

pVariables

value* n

%

n

%

95%CI

n

%

95%CI

Age1

12

8.00

7

9.21

3.78 – 18.06

5

6.76

2.23 – 15.07

0.580

Diabetes

60

40.00

30

39.47

28.44 – 51.35

30

40.54

29.27 – 52.59

0.894

Hypertension

85

56.67

32

42.11

30.86 – 53.98

53

71.62

59.95 – 81.49

<0.001

High Systolic Blood Pressure

38

25.33

12

15.79

8.43 – 25.96

26

35.14

24.39 – 47.11

<0.001

High Diastolic Blood Pressure

50

33.33

18

23.68

14.68 – 34.82

32

43.24

31.76 – 55.28

0.011

Hypertriglyceridemia

69

46.00

38

50.00

38.30 – 61.69

31

41.89

30.51 – 53.94

0.319

High LDL-c

85

57.82

43

58.11

46.06 – 69.48

42

57.53

45.40 – 69.03

0.944

Low HDL-c

82

54.67

44

57.89

46.02 – 69.14

38

51.35

39.44 – 63.15

0.421

High non-HDL-c

88

58.67

47

61.84

49.98 – 72.75

41

55.41

43.39 – 66.97

0.423

Dyslipidemia

115

76.67

64

84.21

74.04 – 91.56

51

68.92

57.09 – 79.17

0.027

Hyperhomocysteinemia

38

25.33

16

21.05

12.54 – 31.92

22

29.73

19.66 – 41.48

0.222

C-reactive protein >6

87

58.00

33

43.42

32.08 – 55.29

54

72.97

61.39 – 82.64

<0.001

High HOMA-IR

127

84.87

63

82.89

72.53 – 90.56

64

86.49

76.55 – 93.32

0.542

High Castelli I index

45

30.00

25

32.89

22.54 – 44.62

20

27.03

17.35 – 38.61

0.433

High Castelli II index

32

21.77

17

22.97

13.98 – 34.21

15

20.55

11.98 – 31.61

0.722

High TG/HDL ratio

47

31.33

24

31.58

21.39 – 43.25

23

31.08

20.83 – 72.90

0.948

Obesity

120

81.08

58

77.33

66.21 – 86.21

62

84.93

74.63 – 92.23

0.238

Dyslipidemia

112

78.87

56

78.87

67.56 – 87.67

56

78.87

67.56 – 87.67

1.000

High blood pressure

133

89.86

65

86.67

78.84 – 93.42

68

93.13

84.73 – 97.74

0.191

Heart diseases

105

70.47

46

61.33

49.38 – 72.36

59

79.73

68.78 – 88.19

0.014

Diabetes

116

77.33

59

77.63

66.62 – 86.39

57

77.03

65.79 – 86.01

0.930

Family history

0.125†

Smoking status Non-smoker

101

67.33

55

72.37

60.91 – 82.01

46

62.16

50.13 – 73.18

Former smoker

40

26.67

15

19.74

11.49 – 30.45

25

33.78

23.19 – 45.72

Current Smoker

9

6.00

6

7.89

2.95 – 16.39

3

4.05

0.84 – 11.39

Alcohol consumption

0.319

Lifetime abstainer

26

17.93

13

18.06

9.98 – 28.89

13

17.81

9.84 – 28.52

Current drinker

81

55.86

44

61.11

48.89 – 72.38

37

50.68

38.72 – 62.59

Former drinker

38

26.21

15

20.83

12.15 – 32.02

23

31.51

21.13 – 43.44

Binge drinking

43

28.67

23

30.26

20.24 – 41.87

20

27.03

17.35 – 38.61

0.661

Simultaneity of risk factors (≥4)2

47

31.33

21

27.63

17.98 – 39.08

26

35.14

24.39 – 47.11

0.322

BMI: body mass index. 1>45 years for men and >55 years for women. 2Age, diabetes, hypertension, dyslipidemia, current smoking, current alcohol consumption. *Pearson’s Chi-squared test. †Fisher’s Exact test. Bold p-values indicate statistical significance.

Table 2. Mean and standard deviation of cardiometabolic risk factors in severely obese patients according to BMI ranges. Variables

BMI 35.0 – 44.9 kg/m2

BMI ≥ 45 kg/m2

(n=76)

(n=74)

Total p-value

Mean

± SD

Mean

± SD

Mean

± SD

39.57

8.82

39.41

8.92

39.73

8.78

0.824**

1 177.15

83.06

1 172.79

78.11

1 181.44

88.01

0.382‡

Waist circumference, cm

119.08

10.25

116.71

8.92

121.48

10.99

<0.01**

Body fat, %

51.93

5.07

49.33

4.63

54.57

4.05

<0.01‡

Visceral fat area, cm3

224.09

28.93

218.00

27.39

229.74

29.55

<0.01‡

Fasting blood glucose, mg/dL

109.8

45.13

106.80

38.97

112.88

50.77

0.967‡

Systolic blood pressure, mmHg

128.27

17.84

124.74

16.01

131.9

18.98

<0.01‡

Diastolic blood pressure, mmHg

85.68

13.61

84.47

14.65

86.93

12.42

0.082‡

Age, years Sedentary behavior, minutes

Total cholesterol, mg/dL

189.34

38.07

191.80

34.73

186.81

41.29

0.247‡

LDL-c, mg/dL

109.57

35.39

109.19

32.02

109.96

38.74

0.803‡

Triglycerides, mg/dL

160.38

78.15

168.45

83.81

152.09

71.48

0.279‡

HDL-c, mg/dL

47.71

11.36

48.28

11.59

47.12

11.15

0.521‡

Non-HDL-c, mg/dl

141.63

37.69

143.53

35.26

139.69

40.19

0.535**

Castelli I index

4.13

1.12

4.16

1.14

4.10

1.12

0.527‡

Castelli II index

2.4

0.96

2.38

0.91

2.45

1.01

0.932‡

TG/HDL ratio

3.58

2.11

3.75

2.39

3.39

1.77

0.718‡

Homocysteine, mmol/L

9.78

8.33

8.93

4.66

10.65

10.86

0.096‡

HOMA IR

6.39

4.88

6.28

5.56

6.51

4.09

0.169‡

FRS

7.39

6.78

6.87

6.44

7.92

7.11

0.344**

FRS: Framingham Risk Score. **Student’s t-test. ‡Mann-Whitney’s test. Bold p-values indicate statistical significance.

FIGURE LEGENDS

Figure 1. Prevalence and 95% confidence intervals for cardiometabolic risk factors in severely obese patients. Abbreviations: BP: blood pressure. FH: family history. HDL-c: high-density lipoprotein cholesterol. HOMA-IR: homeostatic model assessment for insulin resistance. LDL-c: low-density lipoprotein cholesterol.

Figure 2. Prevalence and 95% confidence intervals for cardiometabolic risk factors in severely obese patients according to BMI ranges. Abbreviations: BP: blood pressure. FH: family history. HDL-c: high-density lipoprotein cholesterol. HOMA-IR: homeostatic model assessment for insulin resistance. LDL-c: lowdensity lipoprotein cholesterol.

Figure 3. Cardiovascular risk assessment according to Framingham Risk Score by two BMI ranges in severely obese patients. BMI: body mass index.

Figure 4. Cardiovascular risk assessment according to Framingham Risk Score by four BMI ranges in severely obese patients. BMI: body mass index.

Prevalence (%) 100

90

80

70

60

50

40

30

20

10

00 High Castelli II index

Systolic BP High Castelli I index Tobacco exposure Diastolic BP Hypercholesterolemia Diabetes Hypertriglyceridemia Current drinking Low HDL-c Hypertension High LDL-c (LDL-c ≥ 100) C-reactive protein >6 FH of heart diseases Dyslipidemia FH of diabetes FH of dyslipidemia FH of obesity High HOMA-IR FH of high BP

95% CI

Hyperhomocysteinemia

Prevalence (%) 100

90

80

70

60

50

40

30

20

10

00 High Castelli II index

Systolic BP

*

High Castelli I index Tobacco exposure

BMI 35.0-44.9 kg/m2

*p<0.05 Pearson's Chi-squared

Hyperhomocysteinemia

BMI ≥ 45.0 kg/m2

Diastolic BP

* 95% CI

Hypercholesterolemia Diabetes Hypertriglyceridemia Current drinking Low HDL-c Hypertension

*

High LDL-c (LDL-c ≥ 100) C-reactive protein >6

*

FH of heart diseases

*

FH of diabetes FH of dyslipidemia FH of obesity High HOMA-IR FH of high BP

*

Dyslipidemia

60

57 54

50

39

Prevalence (%)

40

41

30

20

10

07 03

00 Low risk

Intermediate risk

BMI 35.0 - 44.9 kg/m2

High risk

BMI 40.0 - 44.9 kg/m2

70 61 60

57 52

Prevalence (%)

50

48

48 42 39

40

35

30

20

08

10 04

06 00

00 Low risk

Intermediate risk

High risk

BMI 35.0 - 39.9 kg/m2

BMI 40.0 - 44.9 kg/m2

BMI 45.0 - 49.9 kg/m2

BMI ≥ 50.0 kg/m2

HIGHLIGHTS

•

There are variances in CMRF frequency in different BMI ranges within severe obesity

•

In general, several CMRF were present in more than half of the study participants

•

40.0% were classified as high risk by FRS with no differences between sex and BMI

•

Hypertension, CRP and positive family history were higher in BMI ≥ 45 kg/m2

•

Only dyslipidemia was significantly higher in BMI 35.0 to 44.9 kg/m2

CONFLICT OF INTEREST STATEMENT

All authors have nothing to disclose.

Annelisa Silva e Alves de Carvalho Santos Ana Paula dos Santos Rodrigues Lorena Pereira de Souza Rosa Nizal Sarrafzadegan Erika Aparecida Silveira