Journal Logo

Clinical Methods and Pathophisiology

Waist circumference was associated with 2-year blood pressure change in community dwelling adults independently of BMI

Tebar, William R.a; Ritti-Dias, Raphael M.b; Silva, Kelly Samara dac; Mielke, Gregore Ivend; Canhin, Daniel S.a; Scarabottolo, Catarina C.a; Mota, Jorgee; Christofaro, Diego G.D.a

Author Information
doi: 10.1097/MBP.0000000000000558
  • Free

Abstract

Introduction

Adiposity is a main risk factor for increases in blood pressure (BP) in adult population [1–3]. Longitudinal studies analyzing the relationship between changes in adiposity and changes in BP over time have shown a positive association of BMI with SBP and DBP in both sexes, with higher association in adults over 50 years [4,5], as an increase in abdominal fat was associated to a rise in resting BP in male adults after 5 years [6].

Distribution of adiposity in the body has been considered an important factor related to cardiovascular risk. Interestingly, no consensual findings exist on whether BMI or waist circumference is more strongly associated with changes in BP, once studies did not adjusted for each other [4–6]. A previous longitudinal evidence in adult population which observed association of changes in waist circumference with BP values regardless BMI and age was reported three decades ago [7].

The controversy among studies was potentially influenced by the several confounders and moderators involved in these relationships (e.g. age, ethnicity, socioeconomic status, and physical activity levels). Thereby, this study aimed to analyze the association of changes in different adiposity indicators with changes in BP, adjusting for confounding factors and for each other. This study hypothesis is that BMI and waist circumference may be differently related to BP, but which one is more related is unclear.

Methods

This observational study presents data of adults with 18 years and more, collected at baseline and after 2 years of follow-up. The research was approved by the Ethics in Research Committee of Sao Paulo State University at protocol CAAE 45486415.4.0000.5402, from 19 June 2015. All the subjects signed the informed consent term agreeing to participate, being informed about research procedures, with confidentiality of personal information and guaranteed the possibility of giving up at any time with no cost. The patients and the public were not involved in the design or planning of the study.

Sample selection process

The sample was selected in the city of Presidente Prudente, which is located in the Southeast region of Brazil and has a population of 176 124 adults. The participants were selected in a stratified sampling process, where the city was stratified into five regions according to geographical location, postal codes, and urban mobility (north, south, east, west, and central region). After this division, all the streets of each region were registered and a random list was generated by a randomization on-line tool (www.random.org/lists). Following the randomized list order, all the households from selected streets were visited door to door by the researchers. On this visit, the researchers looked for residents who have 18 years and more, presenting the objective of the study and inviting them to participate. The assessments were performed at the household of participant and in a single day. After the assessment or refusal of the visited household, the next household was visited, and so on until the end of street, when another street in the same region was randomly selected and fully visited until reach the minimum sample for each region.

After 2 years, the researchers returned to each one of households to do the same assessments in the participants. A total of 449 participants were enrolled in the longitudinal study, where sample losses occurred for giving up during the assessment period (n = 105, 23.4%), unable to participate (n = 7, 1.5%), and for death (n = 6, 1.3%). The exclusion criteria were defined as: (1) not having performed anthropometric measurements and (2) not having answered all questions of the survey. At the end, a total of 331 participants were assessed (73.7%), since no participants were excluded. The data collection occurred between April 2016 and October 2019.

Blood pressure

The measurement of SBP and DBP was performed through a portable automatic oscillometric device (OMRON HEM-4200, Kyoto, Japan), previously validated to adult population [8]. The measurement was made twice, with the participant seated with a minimum rest of 10 min until the first measurement while the second measurement was collected 5 min later. The mean of the two measures was calculated for SBP and DBP. Participants with values of ≥140/90 mmHg for SBP and DBP were classified as high BP, according to the International Society of Hypertension [9]. The changes in BP were obtained by subtracting baseline values from follow-up.

Adiposity indicators

Anthropometric measurements were used as adiposity indicators in this study. BMI = body mass (kg)/height (m2) was calculated by objectively assessed body mass and height, collected through a digital scale and portable stadiometer, respectively. The waist circumference (in centimeters) was collected in the middle point between the iliac crest and last rib, with an inextensible tape, with the measurement performed in twice to avoid inconsistency, being confirmed with a third measurement when necessary. All the participants were assessed barefoot and wearing light clothes. Overweight and obesity was defined as BMI between 25.0–29.9 kg/m2 and 30 kg/m2 and more, respectively, according to WHO recommendations [10]. Abdominal obesity was defined as waist circumference ≥102 for men and ≥88 for women, according to cutoff points for metabolic risk [11]. The changes in BMI and waist circumference were obtained by subtracting baseline values from follow-up.

Confounders

Baecke’s questionnaire was used to assess habitual practice of physical activity in the sample [12], previously validated to Brazilian adults [13] and against gold standard methods as doubly-labeled water [14]. This instrument is composed by 16 questions about physical activities performed in three different domains: work (8 questions), sport (4 questions), and leisure time/commuting (4 questions), and provided a dimensionless score for each assessed domain, ranging from 1 to 5. The sum of three scores corresponds to total physical activity score, which ranges from 3 to 15. Participants who were located in the fourth quartile of Baecke score were classified as physically actives. The changes in physical activity were obtained by subtracting baseline values from follow-up.

Self-reported screen time hours were used to assess sedentary behavior of sample. This method has been widely used in epidemiological studies [15,16]. The mean of hours spent in TV viewing, computer and cell phone use were asked for a typical weekday and for a typical weekend day. The sum of time spent in these three screen devices was made through weekday and weekend day and divided by two. This mean of hours per day corresponded to total daily sedentary behavior. Participants with 8 and more hours were classified as high sedentary behavior, according to Ku et al. [17]. The changes in sedentary behavior were obtained by subtracting baseline values from follow-up.

Participants were asked if they had medical diagnosis of hypertension and used lowering pressure drugs. Those subjects who respond yes for these questions were classified with hypertension for the adjustment of analysis. The self-reported hypertension has been showed high sensitivity and good accuracy in adult population[18], including Brazilian sample [19]. It was also included information about self-reported medical diagnosis and use of medications for diabetes and high low-density lipoprotein (LDL)-cholesterol.

The economic classification of sample was made through Brazilian Criteria [20], which considered the presence and quantity of specific rooms and consumer goods, as well the educational level. According to its specific score, this instrument classifies into classes from highest to lowest: A, B1, B2, C1, C2, and D–E. The sample was stratified according to the power of consumption recommendation of this instrument, as high socioeconomic status (class A), middle socioeconomic status (classes B1, B2, and C1), and low socioeconomic status (classes C2 and D–E).

The ethnicity was assessed by a self-reported closed question: ‘What is your ethnicity?’. Responses were: ‘Black’, ‘Caucasian’, ‘Mixed black/brown’, ‘Asian’, and ‘Other’.

Statistical analysis

Characteristics of sample were presented as means and SD for continue variables and in frequencies for categorical information. Mean comparisons at baseline and follow-up were analyzed through one-sample t test, whereas comparison of proportions were analyzed by chi-square test. Relationship between BP values and independent variables were analyzed by linear regression models, with hierarchical insertion of covariates: baseline values (SBP/DBP and waist circumference/BMI, depending on the model); sociodemographic factors (sex, age, socioeconomic status, and ethnicity); chronic diseases and medications (hypertension, diabetes, and high LDL-cholesterol – medical diagnosis or use of medications); lifestyle habits (physical activity and sedentary behavior); and BMI (for waist circumference model) or waist circumference (for BMI model). Interaction was tested in the models statistically significant by a sex-stratified analysis. The level of significance was fixed at P < 0.05 and confidence interval in 95%, with analysis performed by IBM SPSS Statistical Package, version 24.0 (Armonk, New York, USA).

Results

A total sample of 331 participants were assessed, being 68.3% of females. A flowchart of sampling process is presented in Fig. 1. The mean age of sample was 59.6 (±17.3) years (minimum = 18 years, maximum = 97 years), with no difference according to sex. In regard socioeconomic status, most of the sample was from middle class (74.6%), followed by high (20.5%), and low (4.8%). About ethnicity, 62.5% of sample reported as Caucasian (n = 207), 26.6% as Mixed black/Brown (n = 88), 5.4% as Black (n = 18), 3.9% as Asian (n = 13), and 1.5% as other ethnicity (n = 5). Regarding chronic diseases, 17.2% of sample reported to have medical diagnosis or to use medications for Diabetes (n = 57) and 18.1% for high LDL-cholesterol (n = 60). Sample characteristics in regard BP, adiposity and lifestyle at baseline and follow-up are presented in Table 1.

Table 1 - Sample characteristics at baseline and after 2 years of follow-up (n = 331)
Baseline Follow-up
Variable Mean (SD) Min.–Max. Mean (SD) Min.–Max. P value*
BMI (kg/m2) 27.7 (5.5) 15.8–49.4 28.1 (5.1) 16.8–47.3 0.001
Waist circumference (cm) 94.5 (14.9) 55.0–152.0 96.6 (13.7) 57.5–137.5 0.001
SBP (mmHg) 134.3 (20.8) 89.0–229.5 132.1 (17.5) 94.5–199.0 0.001
DBP (mmHg) 80.1 (11.5) 54.5–124.0 80.8 (9.9) 50.5–110.5 0.001
Sedentary behavior (mean hours/day) 7.2 (3.6) 3.0–18.0 9.0 (4.6) 3.0–18.0 0.001
Physical activity (Baecke’ score) 6.9 (1.4) 3.5–11.3 6.8 (1.2) 3.5–10.1 0.001
*P value for difference between means by one sample t test.

Fig. 1
Fig. 1:
Flowchart of sampling process.

It was observed a significant increase in the prevalence of abdominal obesity (55.3% vs. 64.4%, P < 0.001) and high sedentary behavior (15.9% vs. 29.6%, P < 0.001) at follow-up, when compared to baseline. Although there were no significant changes from baseline to follow-up, the diagnosed hypertension was reported by almost half of sample (46.8% at baseline and 48.6% at follow-up), whereas more than one third presented elevated BP at data collection (38.4% at baseline and 37.2% at follow-up). These findings are presented in Fig. 2.

Fig. 2
Fig. 2:
Prevalence of health conditions at baseline and after 2-years of follow-up among middle-aged and older adults (n = 331). *Statistical significance for chi-square test at P < 0.05 level. Overweight = BMI between 25.0 and 29.9 kg/m2; Obesity = BMI ≥30.0 kg/m2; Abdominal obesity = waist circumference ≥102 cm for men and ≥88 cm for women; Physically active= fourth quartile of Baecke score; Elevated blood pressure = ≥140 mmHg for SBP or ≥90 mmHg for DBP; Diagnosed hypertension = self-reported medical diagnosis of hypertension.

The cross-sectional relationship of BP with independent variables at baseline and at follow-up is presented in Table 2. BMI and waist circumference were positively related with SBP and DBP at baseline and remained significantly related at 2-years follow-up. No relationship was observed in regard physical activity and sedentary behavior with BP in the sample in both moments.

Table 2 - Cross-sectional relationship at baseline and at follow-up of BMI, waist circumference, physical activity, and sedentary behavior with blood pressure in middle-aged and older adults (n = 331)
SBP DBP
Independent variables β 95% CI P value Β 95% CI P value
Baseline
 BMI (kg/m2) 0.48 0.10 to 0.86 0.013 0.31 0.08 to 0.54 0.009
 Waist circumference (cm) 0.21 0.06 to 0.35 0.005 0.12 0.04 to 0.21 0.006
 Sedentary behavior (mean hours/day) 0.02 −0.65 to 0.69 0.953 0.12 −0.29 to 0.52 0.565
 Physical activity (Baecke’ score) 1.25 −0.23 to 2.72 0.096 0.46 −0.43 to 1.35 0.306
Follow-up
 BMI (kg/m2) 0.34 −0.18 to 0.70 0.063 0.42 0.21 to 0.63 0.001
 Waist circumference (cm) 0.20 0.06 to 0.37 0.007 0.18 0.09 to 0.26 0.001
 Sedentary behavior (mean hours/day) −0.24 −0.69 to 0.21 0.299 −0.13 −0.40 to 0.14 0.356
 Physical activity (Baecke’ score) 0.37 −1.26 to 2.00 0.657 0.59 −0.38 to 1.57 0.232
CI, confidence interval. Adjusted by sex, age, ethnicity, socioeconomic status, and hypertension (medical diagnosis or use of lowering pressure drugs).

Table 3 presents the relationship of 2-year changes in BP and waist circumference. It was observed that each increase of 1 cm in waist circumference was associated with an increase of 0.30 mmHg in SBP (P = 0.019). This association remained significant even after simultaneous adjustment for confounding factors in multiple linear regression models (β = 0.33, P = 0.013). However, when interaction was tested in a sex-stratified analysis, the relationship remained significant for men in the simple model and in the model adjusted by baseline values, while lost significance in all the other models for both men and women. No relationship was observed between changes in DBP and waist circumference.

Table 3 - Association of 2-years change in waist circumference with changes in blood pressure among middle-aged and older adults (n = 331)
Δ Waist circumference
β 95% Confidence interval P value
Simple linear regression*
 Δ SBP 0.30 0.05 to 0.54 0.019
 Δ DBP 0.07 −0.08 to 0.23 0.356
Model 1 – inclusion of baseline values*
 Δ SBP 0.34 0.08 to 0.59 0.009
 Δ DBP 0.13 0.04 to 0.17 0.104
Model 2 – inclusion of sociodemographic factors**
 Δ SBP 0.33 0.07 to 0.58 0.011
 Δ DBP 0.12 −0.03 to 0.27 0.127
Model 3 – inclusion chronic diseases and medications**
 Δ SBP 0.32 0.08 to 0.59 0.011
 Δ DBP 0.12 −0.03 to 0.28 0.114
Model 4 – inclusion of lifestyle habits**
 Δ SBP 0.32 0.07 to 0.57 0.013
 Δ DBP 0.12 −0.03 to 0.27 0.126
Model 5 – inclusion of BMI**
 Δ SBP 0.33 0.07 to 0.59 0.013
 Δ DBP 0.10 −0.04 to 0.22 0.180
Δ = Difference between follow-up and baseline values, expressed as mean. Baseline values = SBP/DBP and waist circumference; sociodemographic factors = age, sex, socioeconomic status, and ethnicity; Chronic diseases and medications = hypertension, diabetes, and high LDL-cholesterol; lifestyle habits = current physical activity and sedentary behavior.
*Interaction between sex: significance remained for men.
**Interaction between sex: significance lost for both men and women.

The relationship between 2-year changes in BP and BMI is presented in Table 4. No associations were observed between changes in BP and BMI.

Table 4 - Association of 2-years change in BMI with changes in blood pressure among middle-aged and older adults (n = 331)
Δ BMI
β 95% Confidence interval P value
Simple linear regression
 Δ SBP 0.64 −0.32 to 1.59 0.190
 Δ DBP −0.11 −0.71 to 0.49 0.719
Model 1 – inclusion of baseline values
 Δ SBP 0.07 −0.77 to 0.91 0.877
 Δ DBP 0.25 −0.26 to 0.77 0.332
Model 2 – inclusion of sociodemographic factors
 Δ SBP 0.10 −0.74 to 0.94 0.812
 Δ DBP 0.23 −0.28 to 0.75 0.377
Model 3 – inclusion chronic diseases and medications
 Δ SBP 0.08 −0.77 to 0.93 0.848
 Δ DBP 0.26 −0.25 to 0.78 0.322
Model 4 – inclusion of lifestyle habits
 Δ SBP 0.14 −0.71 to 0.99 0.753
 Δ DBP 0.29 −0.22 to 0.79 0.265
Model 5 – inclusion of waist circumference
 Δ SBP 0.13 −0.72 to 0.98 0.761
 Δ DBP 0.30 −0.20 to 0.81 0.245
Δ = Difference between follow-up and baseline values, expressed as mean. Baseline values = SBP/DBP and waist circumference; sociodemographic factors = age, sex, socioeconomic status, and ethnicity; Chronic diseases and medications = hypertension, diabetes, and high LDL-cholesterol; lifestyle habits = current physical activity and sedentary behavior.

Discussion

On the basis of this study findings, BMI and waist circumference remained positively related to BP both at baseline and follow-up. However, the relationship of changes in adiposity across the time with BP remained significant only for waist circumference in middle-aged and older adults, regardless of several confounding factors.

After 2 years of follow-up, the sample presented an increase in abdominal obesity and high sedentary behavior. These results could be related to a reduction in energy expenditure through changes in lifestyle habits, once sedentary activities showed to have low energy expenditure [21] and its high amount takes place from daily physical activities, even of light intensity, which may lead to positive caloric imbalance and, consequently, adiposity gain [22,23]. However, both sedentary behavior and physical activity levels were not associated with BP in the present study. It is possible that these variables are mediating factors of caloric imbalance in the relationship of body fat accumulation and BP, not being significantly related to BP values regardless of body fat.

Cross-sectionally, BMI and waist circumference were positively related to SBP and DBP in both baseline and follow-up moments, regardless of age, sex, ethnicity, socioeconomic status, and diagnosed hypertension. This finding has been supported by previous studies [1–3]. When considered the change in adiposity across the time, only the 2-years change in waist circumference remained significantly related to changes in SBP, independently of sociodemographic factors, lifestyle habits, diagnosed hypertension, and current adiposity values. Similar association has been previously reported in a small sample of young adults (n = 17) after 5 years of follow-up [6]. Otherwise, the change in BMI was not associated to changes in BP in this study.

The present study observed that 1-cm increase in waist circumference was associated with 0.30 mmHg increase in SBP after 2-year follow-up. If considered a proportional adjustment by the years and scale, these values were 50% higher than what was observed in a previous study by Wang et al. [24], which reported that an annual increase of 10-cm in waist circumference was associated with 0.98-mmHg increase in SBP among Chinese adults. However, Wang et al. [24] considered a wide range of follow-up periods from 2 to 22 years, which did not differ for short-term and long-term changes. Our findings were observed even after adjustment for sociodemographic factors, lifestyle habits, and multiple chronic conditions as hypertension, diabetes, and high LDL-cholesterol. No epidemiological studies which reported the association between changes in waist circumference and BP across the years in low-to-middle income countries were found in literature for comparison with this study finding.

It is important to highlight that adipose tissue is currently considered as a highly active metabolic tissue and a dynamic endocrine organ, with an important role in release of cytokines that contributes to inflammatory condition [25]. The cytokines secreted by adipose tissue (adipocytokines) are responsible to transmit information to other cells, triggering an inflammatory response [26]. Each adipose cell (adipocyte) release a small quantity of substances; however, as adiposity tissue can be considered as the largest human organ, this tissue may promote great impact in body functions [27]. In this sense, the higher the adiposity, the higher will be inflammation, which affect other physiological manifestations, such as increased activity of the sympathetic autonomic nervous system, which contributes to the increase of BP levels [28].

Moreover, changes in BMI were not associated with changes in BP in the present study. Conversely, some longitudinal studies reported that changes in BMI were associated with BP [4,5]. However, the analysis in those cited studies was categorical (normal weight vs. overweight/obese), not considering the anthropometric changes as continue variables. The linear analysis of the present study may have been more sensible for changes within groups and could justify these different findings. Another possible reason is that BMI has a limitation to estimate total body fat in overall population, once it only considers the body density through absolute body mass instead of body composition [29]. In this sense, the changes in BMI may also has been related to fat-free mass changes, which may result in different adaptations of cardiovascular condition [30]. Another aspect is that the sample of this study was composed in majority by women (68.3%) and adiposity tend to be more prevalent in hips and thighs, being less harmful for cardiometabolic health than adiposity in abdominal region, which is more prevalent in men [31].

Moreover, the fat distribution has been associated to BP, mainly in regard adiposity in abdominal region [32]. Higher fat in abdominal region was associated with higher values of SBP in short-term and in long-term, and it is suggested that may be a determinant of BP values regardless of BMI [33]. Convergent of the findings of this study, fat accumulation in abdominal region was associated with cardiometabolic risk in adults after 6-years of follow-up [34]. The amount of abdominal fat is associated with renal and neurohumoral mechanisms, which can raise BP levels, as compression of kidneys through fat around and inside of them, as well the higher activation of renin-angiotensin-aldosterone and sympathetic nervous system [35]. In the present study, the relationship between changes in waist circumference and SBP remained significant even after adjustment for current adiposity levels, and it is suggested that these impairment in mechanisms may occur even in those people with normal adiposity values, not only in those with abdominal obesity.

As limitation of the study, is important to highlight that BP measurements were performed in the household, which is susceptible of environmental conditions as temperature, humidity, noise, and people circulation. The class of hypertensive medication was not assessed and may influence the association between adiposity and BP, mainly in regard renin–angiotensin system inhibitors. Besides that, the BMI is not an accurate measure of body fat and may be susceptible to body composition changes across the time. In addition, physical activity was assessed by questionnaire, being susceptible to biases of memory and classification of intensity, as well sedentary behavior was assessed through self-reported screen time, which not considered other sitting time behaviors and simultaneous screen time use. The interaction of relationships according to sex limits the extrapolation of the findings, so that further studies need to consider stratified analysis for more robust evidence. Otherwise, the 2 years of follow-up, the randomization of sample, and adjustment of analysis by several confounding factors are considered as strengths of this study. In addition, it is emphasized the application of the questionnaire face to face with the objective of reducing bias and doubts of the participant in the responses made.

In conclusion, the change in waist circumference was positively related with SBP change, independently of current adiposity status, lifestyle factors, and hypertension condition. As practical application, strategies to face fat accumulation in abdominal region could be effective to preclude raising of BP levels among middle-aged and older adults, regardless of age, sex, ethnicity, socioeconomic status, and BMI.

Acknowledgements

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES, Brazil (Finance Code 001).

Conflicts of interest

There are no conflicts of interest.

References

1. Dua S, Bhuker M, Sharma P, Dhall M, Kapoor S. Body mass index relates to blood pressure among adults. N Am J Med Sci 2014; 6:89–95.
2. Lara M, Bustos P, Amigo H, Silva C, Rona RJ. Is waist circumference a better predictor of blood pressure, insulin resistance and blood lipids than body mass index in young Chilean adults? BMC Public Health 2012; 12:638.
3. Gnatiuc L, Alegre-Díaz J, Halsey J, Herrington WG, López-Cervantes M, Lewington S, et al. Adiposity and blood pressure in 110 000 Mexican adults. Hypertension 2017; 69:608–614.
4. Drøyvold WB, Midthjell K, Nilsen TI, Holmen J. Change in body mass index and its impact on blood pressure: a prospective population study. Int J Obes (Lond) 2005; 29:650–655.
5. Itoh H, Kaneko H, Kiriyama H, Nakanishi K, Mizuno Y, Daimon M, et al. Effect of body weight change on blood pressure in a Japanese general population with a body mass index ≥ 22 kg/m2. Int Heart J 2019; 60:1381–1386.
6. Allemann Y, Hutter D, Aeschbacher BC, Fuhrer J, Delacrétaz E, Weidmann P. Increased central body fat deposition precedes a significant rise in resting blood pressure in male offspring of essential hypertensive parents: a 5 year follow-up study. J Hypertens 2001; 19:2143–2148.
7. Cassano PA, Segal MR, Vokonas PS, Weiss ST. Body fat distribution, blood pressure, and hypertension. A prospective cohort study of men in the normative aging study. Ann Epidemiol 1990; 1:33–48.
8. Topouchian J, Agnoletti D, Blacher J, Youssef A, Ibanez I, Khabouth J, et al. Validation of four automatic devices for self-measurement of blood pressure according to the international protocol of the European Society of Hypertension. Vasc Health Risk Manag 2011; 7:709–717.
9. Weber MA, Schiffrin EL, White WB, Mann S, Lindholm LH, Kenerson JG, et al. Clinical practice guidelines for the management of hypertension in the community: a statement by the American Society of Hypertension and the International Society of Hypertension. J Clin Hypertens (Greenwich) 2014; 16:14–26.
10. Expert Panel on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults. Executive Summary of the Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults. Arch Intern Med 1998; 158:1855–1867.
11. Klein S, Allison DB, Heymsfield SB, Kelley DE, Leibel RL, Nonas C, et al. Waist circumference and cardiometabolic risk: a consensus statement from shaping America’s health: Association for Weight Management and Obesity Prevention; NAASO, the Obesity Society; the American Society for Nutrition; and the American Diabetes Association. Am J Clin Nutr 2007; 15:1061–1067.
12. Baecke JA, Burema J, Frijters JE. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr 1982; 36:936–942.
13. Florindo AA, Latorre MRDO. Validation and reliability of the Baecke questionnaire for the evaluation of habitual physical activity in adult men. Rev Bras Med Esporte 2003; 9:129–135.
14. Philippaerts RM, Westerterp KR, Lefevre J. Doubly labelled water validation of three physical activity questionnaires. Int J Sports Med 1999; 20:284–289.
15. Biddle SJH, García Bengoechea E, Pedisic Z, Bennie J, Vergeer I, Wiesner G. Screen time, other sedentary behaviours, and obesity risk in adults: a review of reviews. Curr Obes Rep 2017; 6:134–147.
16. Healy GN, Clark BK, Winkler EA, Gardiner PA, Brown WJ, Matthews CE. Measurement of adults’ sedentary time in population-based studies. Am J Prev Med 2011; 41:216–227.
17. Ku PW, Steptoe A, Liao Y, Hsueh MC, Chen LJ. A cut-off of daily sedentary time and all-cause mortality in adults: a meta-regression analysis involving more than 1 million participants. BMC Med 2018; 16:74.
18. Thawornchaisit P, De Looze F, Reid CM, Seubsman SA, Sleigh A; Thai Cohort Study Team. Validity of self-reported hypertension: findings from the Thai Cohort Study compared to physician telephone interview. Glob J Health Sci 2013; 6:1–11.
19. de Menezes TN, Oliveira EC, de Sousa Fischer MA. Validity and concordance between self-reported and clinical diagnosis of hypertension among elderly residents in northeastern Brazil. Am J Hypertens 2014; 27:215–221.
20. ABEP – Associação Brasileira de Empresas de Pesquisa – Critério Brasil de Classificação Econômica. [Brazilian Association of Research Companies – Brazilian Criteria for Economic Classification]. 2015. http://www.abep.org/criterio-brasil. [Accessed 13 October 2020]
21. Newton RL Jr, Han H, Zderic T, Hamilton MT, Hamilton M. The energy expenditure of sedentary behavior: a whole room calorimeter study. PLoS One 2013; 8:e63171.
22. Heinonen I, Helajärvi H, Pahkala K, Heinonen OJ, Hirvensalo M, Pälve K, et al. Sedentary behaviours and obesity in adults: the cardiovascular risk in young Finns study. BMJ Open 2013; 3:e002901.
23. Henson J, Edwardson CL, Morgan B, Horsfield MA, Bodicoat DH, Biddle SJ, et al. Associations of sedentary time with Fat distribution in a high-risk population. Med Sci Sports Exerc 2015; 47:1727–1734.
24. Wang Y, Howard AG, Adair LS, Wang H, Avery CL, Gordon-Larsen P. Waist circumference change is associated with blood pressure change independent of BMI change. Obesity (Silver Spring) 2020; 28:146–153.
25. Lyon CJ, Law RE, Hsueh WA. Minireview: adiposity, inflammation, and atherogenesis. Endocrinology 2003; 144:2195–2200.
26. Prado WL, Lofrano MC, Oyama LM, Dâmaso AR. Obesidade e adipocinas inflamatórias: implicações práticas para a prescrição de exercício. Rev Bras Med Esporte 2009; 15:378–383.
27. Ronti T, Lupattelli G, Mannarino E. The endocrine function of adipose tissue: an update. Clin Endocrinol (Oxf) 2006; 64:355–365.
28. Smith MM, Minson CT. Obesity and adipokines: effects on sympathetic overactivity. J Physiol 2012; 590:1787–1801.
29. Rothman KJ. BMI-related errors in the measurement of obesity. Int J Obes (Lond) 2008; 32 Suppl 3:S56–S59.
30. Carbone S, Canada JM, Billingsley HE, Siddiqui MS, Elagizi A, Lavie CJ. Obesity paradox in cardiovascular disease: where do we stand? Vasc Health Risk Manag 2019; 15:89–100.
31. Karastergiou K, Smith SR, Greenberg AS, Fried SK. Sex differences in human adipose tissues – the biology of pear shape. Biol Sex Differ 2012; 3:13.
32. Malden D, Lacey B, Emberson J, Karpe F, Allen N, Bennett D, Lewington S. Body fat distribution and systolic blood pressure in 10,000 adults with whole-body imaging: UK Biobank and Oxford BioBank. Obesity (Silver Spring) 2019; 27:1200–1206.
33. Yano Y, Vongpatanasin W, Ayers C, Turer A, Chandra A, Carnethon MR, et al. Regional Fat distribution and blood pressure level and variability: the Dallas heart study. Hypertension 2016; 68:576–583.
34. Saunders TJ, Tremblay MS, Després JP, Bouchard C, Tremblay A, Chaput JP. Sedentary behaviour, visceral fat accumulation and cardiometabolic risk in adults: a 6-year longitudinal study from the Quebec Family Study. PLoS One 2013; 8:e54225.
35. Hall JE, do Carmo JM, da Silva AA, Wang Z, Hall ME. Obesity-induced hypertension: interaction of neurohumoral and renal mechanisms. Circ Res 2015; 116:991–1006.
Keywords:

abdominal obesity; adiposity; adults; cardiovascular health; overweight

Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.