The influence of body composition and fat distribution on circadian blood pressure rhythm and nocturnal mean arterial pressure dipping in patients with obesity

Marek Tałałaj; Agata Bogołowska-Stieblich; Michał Wąsowski; Ada Sawicka; Piotr Jankowski

doi:10.1371/journal.pone.0281151

. 2023 Jan 31;18(1):e0281151. doi: 10.1371/journal.pone.0281151

The influence of body composition and fat distribution on circadian blood pressure rhythm and nocturnal mean arterial pressure dipping in patients with obesity

Marek Tałałaj ^1,^*, Agata Bogołowska-Stieblich ², Michał Wąsowski ², Ada Sawicka ², Piotr Jankowski ²

Editor: Jeremy P Loenneke³

PMCID: PMC9888712 PMID: 36719897

Abstract

Loss of physiological nocturnal blood pressure (BP) decline is an independent predictor of cardiovascular risk and mortality. The aim of the study was to investigate the influence of body composition and fat distribution on 24-hour BP pattern and nocturnal dipping of mean arterial pressure (MAP) in patients with obesity. The study comprised 436 patients, 18 to 65 years old (306 women), with BMI ≥30 kg/m². Body composition was assessed with dual-energy X-ray absorptiometry (DXA) and blood pressure was assessed by 24-hour BP monitoring. The prevalence of hypertension was 64.5% in patients with BMI <40 kg/m² and increased to 78.7% in individuals with BMI ≥50 kg/m² (p = 0.034). The whole-body DXA scans showed that the hypertensive patients were characterized by a greater lean body mass (LBM) and a higher abdominal-fat-to-total-fat-mass ratio (AbdF/FM), while the normotensive participants had greater fat mass, higher body fat percentage and more peripheral fat. Loss of physiological nocturnal MAP decline was diagnosed in 50.2% of the patients. The percentage of non-dippers increased significantly: from 38.2% in patients with BMI <40 kg/m² to 50.3% in those with BMI 40.0–44.9 kg/m², 59.0% in patients with BMI 45.0–49.9 kg/m², 71.4% in those with BMI 50.0–54.9 kg/m² and 83.3% in patients with BMI ≥55 kg/m² (p = 0.032, p = 0.003, p<0.001, and p = 0.002 vs. BMI <40 kg/m², respectively). The multivariable regression analysis showed that patients at the highest quartiles of body weight, BMI, LBM and AbdF/FM had significantly reduced nocturnal MAP dipping compared with patients at the lowest quartiles, respectively.

Introduction

The prevalence of obesity is increasing dramatically worldwide. If the current trend continues, by the year 2025, 18% of men and 21% of women will be obese [1]. The simplest and most common method for diagnosing obesity is to calculate the body mass index (BMI), but this measure cannot differentiate between individuals with excess fat and those with high muscle mass [2]. Body composition and fat distribution can be assessed precisely by dual-energy X-ray absorptiometry (DXA), which is an established technique in the non-invasive assessment of body composition. Whole-body DXA scans offer accurate measures of three main body components: bone mineral content, fat mass and lean body mass. Moreover, they allow to determine the amount of centrally located (abdominal) fat, which is more significant than peripheral, subcutaneous adipose tissue in predicting the risk of cardiometabolic sequelae associated with obesity [3]. Visceral obesity appears to be especially important in activation of the sympathetic nervous system and the renin-angiotensin-aldosterone system, thereby increasing the risk of developing hypertension [4,5].

Twenty-four-hour ambulatory blood pressure monitoring (ABPM) is regarded as the best currently available method of measuring blood pressure (BP) and its circadian profile [6]. According to 2018 European Society of Cardiology guidelines, the ABPM threshold for a diagnosis of hypertension is ≥130/80 mm Hg, while a BP below those values with a nocturnal BP decline of 10% to 20% are considered normal values for ABPM [7]. The maintenance of a physiological circadian rhythm in blood pressure with a nocturnal “dip” is important for cardiovascular health. A loss of nocturnal BP decline (non-dipping) is an independent predictor of cardiovascular risk and mortality [8,9]. Moreover, it is associated with a higher risk of microalbuminuria, hypertensive retinopathy [10] and asymptomatic lacunar infarction of the brain [11].

Both, office BP measurements and ABPM, measure the peripheral blood pressure at the brachial artery. However, vital organs such as the brain, the heart and the kidneys are exposed to central (aortic) pressure; therefore, central systolic BP (SBP) is a better predictor of cardiovascular events than SBP measured at the upper arm [12,13]. Systolic BP amplifies from the aorta toward the peripheral arteries due to the arterial vessels’ progressive reduction in diameter and increase in stiffness [14]. In young and middle-aged people the difference between central and peripheral SBP is approximately 10 mmHg [15,16], but this significantly increases with ageing [17]. Contrary to systolic BP, mean arterial pressure (MAP) remains relatively constant along the arterial tree, and it therefore better reflects central BP and shows a closer relationship with hypertension-associated organ damage [12,18].

This study aimed to investigate (1) the influence of body composition and fat distribution–as determined with DXA–on the 24-hour systolic and diastolic BP profiles in patients with obesity as well as (2) the associations of obesity and central obesity with a non-dipping pattern of MAP, as an indirect indicator of central SBP.

Material and methods

A group of 436 Caucasian patients with obesity (306 women and 130 men) were enrolled in the study. The exclusion criteria comprised pregnancy, secondary or drug-induced obesity (e.g. due to endocrine disorders) or any physical disability that might interfere with the measurement of body composition (amputation or orthopedic prosthesis). Participants with a body weight >190 kg and/or a height >190 cm were also excluded due to the limitations of the densitometry device.

The patients were between the ages of 18 and 65 years and had BMI ≥30 kg/m². In all patients main anthropometric parameters were determined: body weight (BW) was measured to the nearest 0.1 kg with a digital scale, and standing height was measured to the nearest 0.1 cm with a fixed wall stadiometer. The weight and height were measured with the participants wearing light indoor clothing and no shoes. Waist and hip circumferences (WC, HC) were measured to the nearest 1 cm, and the waist-to-hip ratio (WHR) was calculated. WC was determined in a horizontal plane, midway between the iliac crest and the costal margin, at the end of a normal expiration; HC was measured at the level of the greater trochanter, using a non-stretch tape measure [19].

Whole-body DXA scans were performed using HOLOGIC DELPHI system with QDR Software (v.11.1, Hologic, Bedford, MA). Automatic daily quality control of the system was performed with a spine phantom provided by the manufacturer. Using the DXA technique, lean body mass (LBM), whole body fat mass (FM), body fat percentage (BF%) and peripheral fat (PerF) were determined. Abdominal fat (AbdF) was measured within the region from the top of the pubic bone, extending cranially up to the line between twelfth thoracic and first lumbar vertebrae, as previously described [20]. After the scan was completed, the abdominal-fat-to-total-fat-mass ratio (AbdF/FM) was calculated.

Each patient underwent 24-hour BP monitoring with a BTL 08 CardioPoint ABP monitor (BTL Industries Ltd., Stevenage, UK). The measurements were performed according to the American Heart Association guidelines [6]. Systolic and diastolic BP (DBP) were measured every 30 minutes during the day (between 6.00 and 22.00) and every 60 minutes during the night (from 22.00 to 6.00). The average daytime and nighttime BP were computed as the means of all readings during each period. The average circadian SBP and DBP were calculated according to the formula (2 x mean daytime BP + mean nightime BP) / 3, that reflected the times of diurnal and nocturnal BP measurements, 16 hours and 8 hours, respectively, and eliminated the influence of different measurement frequencies on average 24-hour BP values. Hypertension was diagnosed according to the 2018 European Society of Cardiology guidelines [7]. Non-dipping of BP was defined as a decline in MAP values <10% from the average daytime to the average nighttime values.

The study was performed in accordance with the principles of the Declaration of Helsinki, and it was approved by the Ethics Committee of the Centre of Postgraduate Medical Education, Warsaw, Poland. Written informed consent was obtained from all participants.

Statistical analysis

The results are presented as mean ± standard deviation (±SD). The normality of the continuous variables’ distribution was verified with the Shapiro-Wilk test, and the homogeneity of the variance was determined with Levene’s test. Paired or nonpaired Student’s t-tests were performed to compare parametric data, while the Mann-Whitney U test was used to compare nonparametric variables. For the correlation assessment, the values of Spearman’s coefficient (r) were computed. Categorical variables are expressed as numbers and percentages. Univariate comparisons were made between groups using the χ2 test for categorical variables. Multivariable linear regression models were used to investigate associations between anthropometric parameters/body composition and MAP dipping. Independent variables were categorized according to the quartiles. Stepwise selection with Akaike’s information criterion was performed to select variables for multivariable models. The calculations were performed using Statistica software, version 13.1. A p-value < 0.05 was considered statistically significant.

Results

The mean age of the patients was 43 ± 11 years. Their basic anthropometric data, body composition and daytime, nighttime and average BP are presented in Table 1.

Table 1. Anthropometric parameters, body composition and blood pressure in patients with obesity.

	Mean ± SD (range)
BW (kg)	120.4 ± 20.2	(73.3–189.0)
BMI (kg/m²)	42.4 ± 6.0	(30.0–64.9)
WC (cm)	123 ± 14	(88–175)
HC (cm)	128 ± 15	(95–175)
WHR (cm/cm)	0.96 ± 0.10	(0.72–1.25)
LBM (kg)	62.9 ± 11.5	(38.0–100.9)
FM (kg)	49.1 ± 11.0	(22.3–83.1)
BF%	42.7 ± 6.3	(21.7–59.5)
PerF (kg)	35.9 ± 9.8	(11.6–64.9)
AbdF (kg)	13.2 ± 2.9	(5.6–24.2)
AbdF/FM (%)	27.8 ± 6.4	(9.7–52.1)
SBP—daytime (mmHg)	131 ± 15	(91–195)
DBP—daytime (mmHg)	78 ± 8	(58–106)
SBP—nighttime (mmHg)	121 ± 17	(87–198)
DBP—nighttime (mmHg)	69 ± 9	(47–109)
SBP—mean (mmHg)	127 ± 15	(90–194)
DBP—mean (mmHg)	75 ± 8	(58–102)

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BW, body weight; BMI, body mass index; WC, waist circumference; HC, hip circumference; WHR, waist-to-hip ratio; LBM, lean body mass; FM, fat mass; BF%, body fat percentage; PerF, peripheral fat; AbdF, abdominal fat; AbdF/FM, abdominal-fat-to-total-fat-mass ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure.

Out of 436 obese patients, 395 (90.6%) had BMI ≥35 kg/m², 284 (65.1%) suffered from morbid obesity with BMI ≥40 kg/m² and 47 (10.8%) had BMI ≥50 kg/m². Total of 291 patients (66.7%) were considered hypertensive. Up to 6 blood pressure-lowering drugs were used by 289 participants; the mean number of medications was 2.54 ±1.11. The prevalence of hypertension increased together with BMI values, from 64.5% (BMI <40 kg/m²) to 78.7% (BMI ≥50 kg/m²) (Table 2).

Table 2. The number and percentage of hypertensive patients depending on BMI values.

BMI (kg/m²)	All patients (n)	Hypertensive patients (n)	Hypertensive patients (%)	p-value
<40	152	98	64.5
40.0–44.9	159	102	64.2	0.478
45.0–49.9	78	54	69.2	0.224
≥50.0	47	37	78.7	0.034

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BMI, body mass index; p-value vs. BMI <40 kg/m².

Hypertension was found in 179 women (58.5%) and 112 men (86.2%) (p<0.001), despite the participants of both sexes being of similar age and having similar BMI values. The men were characterized by higher SBP and DBP than the women. Daytime SBP and DBP were 4.9% and 6.7% higher, respectively, while the nighttime values were 6.3% and 10.4% higher, respectively. Pulse pressure (PP) was similar in both sexes (Table 3).

Table 3. Age, BMI and blood pressure in female and male patients with obesity.

	Women (n = 306)	Men (n = 130)	p-value
Age (years)	42 ± 11	45 ± 11	0.057
BMI (kg/m2)	42.5 ± 5.8	42.1 ± 6.4	0.529
SBP—daytime (mmHg)	129 ± 15	135 ± 14	< 0.001
DBP—daytime (mmHg)	76 ± 8	81 ± 8	< 0.001
PP—daytime (mmHg)	53 ± 10	54 ± 9	0.247
SBP—nighttime (mmHg)	118 ± 17	126 ± 15	0.001
DBP—nighttime (mmHg)	67 ± 9	74 ± 10	< 0.001
PP—nighttime (mmHg)	51 ± 11	52 ± 9	0.628
SBP—mean (mmHg)	125 ± 15	132 ± 14	< 0.001
DBP—mean (mmHg)	73 ± 8	79 ± 8	< 0.001
PP—mean (mmHg)	52 ± 10	53 ± 9	0.345

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SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, pulse pressure.

Data are expressed as mean ± SD.

Significant differences were found in body size and composition between the hypertensive and normotensive patients (Table 4). The former had 4.1% higher BW, 4.4% larger WC and 4.3% higher WHR, while the BMI and HC values were similar between the groups. The DXA scans showed that the hypertensive participants had 6.3% higher LBM and 13.3% higher AbdF/FM, while the normotensive individuals had 7.5% higher FM, 7.5% higher BF%, and 12.2% higher PerF. The extended analysis examining variables by sex are shown in S1–S3 Tables.

Table 4. Anthropometric parameters and body composition in hypertensive and normotensive patients with obesity.

	Hypertensive patients (n = 291)	Normotensive patients (n = 145)	p-value
BW (kg)	122.0 ± 21.9	117.2 ± 15.8	0.019
BMI (kg/m²)	42.7 ± 6.4	41.7 ± 5.0	0.103
WC (cm)	125 ± 15	120 ± 13	0.005
HC (cm)	128 ± 16	128 ± 13	0.979
WHR (cm/cm)	0.98 ± 0.10	0.94 ± 0.09	0.001
LBM (kg)	64.3 ± 12.7	60.5 ± 8.7	0.004
FM (kg)	47.8 ± 11.3	51.4 ± 10.2	0.004
BF%	41.6 ± 6.6	44.7 ± 5.0	0.001
PerF (kg)	34.3 ± 9.9	38.5 ± 9.1	0.001
AbdF (kg)	13.4 ± 3.1	12.9 ± 2.4	0.093
AbdF/FM (%)	29.0 ± 6.8	25.6 ± 4.8	< 0.001

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Correlations between the anthropometric measures of obesity, body composition and blood pressure are presented in Table 5. It was found that BMI positively correlated with SBP and PP, while the other parameters related to body size—BW and LBM—positively correlated with both SBP and DBP, as well as with PP. The measures of central adiposity—WC, AbdF and AbdF/FM—positively correlated with SBP and DBP, but not with PP, whereas WHR positively correlated with DBP alone. Hip circumference, FM and PerF did not influence blood pressure, while BF% negatively correlated with both SBP and DBP values.

Table 5. Correlations between anthropometric characteristics/body composition and blood pressure in patients with obesity.

	Daytime			Nighttime			Average
	SBP	DBP	PP	SBP	DBP	PP	SBP	DBP	PP
BW	0.21^***	0.18^***	0.18^***	0.28^***	0.23^***	0.21^***	0.24^***	0.21^***	0.19^***
BMI	0.15^**	0.06	0.16^***	0.22^***	0.10	0.22^***	0.18^***	0.08	0.18^***
WC	0.12^*	0.17^**	0.06	0.22^***	0.25^***	0.10	0.16^**	0.21^***	0.07
HC	0.07	0.02	0.09	0.10	0.05	0.10	0.09	0.02	0.10
WHR	0.07	0.19^**	-0.03	0.10	0.22^***	-0.05	0.09	0.22^***	-0.04
LBM	0.20^***	0.20^***	0.14^**	0.25^***	0.26^***	0.13^*	0.22^***	0.24^***	0.13^*
FM	0.02	0.01	0.03	0.04	-0.01	0.09	0.03	0.00	0.06
BF%	-0.12^*	-0.13^*	-0.05	-0.13^*	-0.18^**	0.01	-0.12^*	-0.16^**	-0.03
PerF	-0.00	-0.02	0.02	0.00	-0.06	0.08	-0.01	-0.04	0.04
AbdF	0.15^**	0.16^**	0.10	0.23^***	0.22^***	0.11	0.18^***	0.18^***	0.10
AbdF/FM	0.13^*	0.15^**	0.07	0.18^**	0.20^***	0.06	0.15^**	0.18^***	0.06

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*p < 0.05

**p < 0.01

***p < 0.001.

The assessment of nocturnal BP decline revealed that the magnitude of SBP dipping (9.9 ± 8.2 mmHg) was greater than that of the DBP dipping (8.5 ± 7.0 mmHg) by 1.4 ± 5.2 mmHg, p<0.001, while the percentage of the DBP decline (10.8 ± 8.7%) was greater than that of SBP decline (7.6 ± 6.8%) by 3.2 ± 4.9%, p<0.001. The average MAP dipping was 9.4 ± 7.5%. Reduced nocturnal MAP decline was diagnosed in 219 patients (50.2%). The percentage of non-dippers increased significantly along with higher BMI from 38.2% of participants with a BMI <40 kg/m² up to 83.3% of patients with a BMI ≥55 kg/m² (Table 6). The extended analysis of how hypertension status within BMI group relates to the nocturnal MAP dipping status is shown in S1 and S2 Figs.

Table 6. The number and percentage of non-dippers by BMI values.

BMI (kg/m²)	All patients (n)	Non- dippers (n)	Non- dippers (%)	p-value
<40	152	58	38.2
40.0–44.9	159	80	50.3	0.032
45.0–49.9	78	46	59.0	0.003
50.0–54.9	35	25	71.4	< 0.001
≥55.0	12	10	83.3	0.002

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BMI, body mass index; p-value vs. BMI <40 kg/m².

The stepwise linear regression analysis (with BW, BMI, WC, WHR, LBM, FM, BF%, PerF, AbdF and AbdF/FM tested for inclusion in the multivariable models) revealed significant negative associations between BW, BMI, LBM, AbdF/F and nocturnal dipping of MAP (Table 7).

Table 7. Multiple stepwise regression analysis showing associations between anthropometric parameters/body composition and nocturnal mean arterial pressure decline.

	MAP dipping [%]
	β	95% CI
BW (kg) Ref.: 73.3–102.9
103.0–116.6	-0.33	-3.08 ÷ 2.42
116.7–129.9	0.65	-2.08 ÷ 3.39
130.0–180.0	-3.09^*	-5.84 ÷ -0.34
BMI (kg/m²) Ref.: 35.5–37.5
37.6–41.2	-0.97	-3.72 ÷ 1.78
41.3–45.4	-1.70	-4.44 ÷ 1.04
45.5–62.8	-3.51^*	-6.31 ÷ -0.72
LBM (kg) Ref.: 38.0–54.4
54.5–61.1	-1.54	-4.26 ÷ 1.18
61.2–69.0	-0.07	-2.79 ÷ 2.65
69.1–100.9	-4.01^**	-6.73 ÷ -1.29
AbdF/FM (%) Ref.: 9.7–23.5
23.6–26.6	0.88	-1.82 ÷ 3.59
26.7–30.8	0.71	-2.00 ÷ 3.43
30.9–50.3	-3.81^**	-6.59 ÷ -1.04

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MAP, mean arterial pressure; BW, body weight; BMI, body mass index; LBM, lean body mass; AbdF/FM, abdominal-fat-to-total-fat-mass ratio.

* p < 0.05

** p < 0.01.

It was found that patients with BW ≥130 kg and those with BMI ≥45.5 kg/m² demonstrated over 3% lower nocturnal MAP dipping compared with patients in the lowest quartiles of BW and BMI (β -3.09, CI -5.84 ÷ -0.34 and β -3.51, CI -6.31 ÷ -0.72, respectively). It was also shown that participants in the highest quartiles of LBM (≥69.1 kg) and AbdF/FM (≥23,6%) showed a lower nocturnal MAP decrease, by 4.01% and by 3.81%, respectively (β -4.01, CI -6.73 ÷ -1.29 and β 3.81, CI -6.59 ÷ -1.04, respectively) compared with patients in the lowest quartiles of LBM and AbdF/FM.

Discussion

Our study examined the complex associations between body composition and fat distribution, and circadian SBP, DBP and MAP profile in obese patients. We found that the prevalence of hypertension in participants with obesity was 66.7%, which significantly increased to 78.7% in patients with BMI ≥50 kg/m². In a German study that included over 45,000 primary care patients, the prevalence of hypertension was found to be 72.9% in grade 1 obesity, 77.1% in grade 2 and 74.1% in grade 3 obesity [21]. These values were more than twice as high as the global age-standardized prevalence of hypertension: 32% in women and 34% in men aged 30–79 years [22]. The authors of the Korea National Health and Nutrition Examination Survey showed that obesity was associated with even higher odds of hypertension (OR, 7.54; 95% CI, 5.89–9.65) as compared to BMI 18.5–23 kg/m² [23]. Among the participants of the second Nurses’ Health Study, the incidence of hypertension in obese women was almost five times higher than in those with BMI <23.0 kg/m² [24]. In contrast, a cross-sectional study that comprised young participants aged 5–21 years showed that the prevalence of hypertension was not significantly different between lean individuals (40.3%) and obese participants (43.7%) [25].

Previous studies had documented the relationships between BP and anthropometric measures of central obesity, such as WC and WHR. It had been suggested that these parameters, especially when used in combination, might be a superior predictor of obesity-related cardiometabolic risk [26,27]. A study by Momin et al. found that, when compared with participants with normal WC, abdominal obesity was positively associated with the incidence of hypertension in both men (OR = 1.79, 95% CI: 1.10–2.91) and women (OR = 1.61, 95% CI: 1.09–2.40) [28]. A Chinese study on over 4,000 adult participants showed that hypertension was associated with central obesity (OR = 2.21, 95% CI: 1.90–2.57) [29].

Few studies have evaluated the relationship between BP and body composition. Lee et al. revealed that the percentage of whole body fat, measured with DXA, was positively associated with higher odds of hypertension, determined with a standard mercury sphygmomanometer (OR, 3.56 for highest vs. lowest quartile) [23]. A study carried out among South African children found significant positive correlations between systolic BP and body fat mass, fat percentage and fat-free mass. However, body composition was estimated based on triceps, gluteal and subscapular skinfold measurements [30]. Goto et al. showed that the prevalence of hypertension was positively associated with both visceral and subcutaneous fat area, measured with computed tomography at the level of the umbilicus [31]. The opposite results were shown in the Dallas Heart Study, which included 2595 adult participants with a mean BMI 29 kg/m². In multivariable-adjusted models, more lower body subcutaneous fat—as measured with DXA—delineated by oblique lines crossing the femoral neck and including gluteal-femoral fat—was associated with lower BP values. In contrast, more visceral fat, as determined by magnetic resonance imaging, was associated with higher SBP [32].

A study on 8802 US residents who participated in the 1999–2004 US National Health and Nutrition Examination Survey (NHANES) investigated the association between regional fat distribution, measured with DXA, and cardiometabolic risk factors. In multivariate-adjusted models, leg fat accumulation was inversely associated with systolic and diastolic BP [33]. The results of our study resemble those reported in the NHANES and the Dallas Heart Study. We found that a higher amount of centrally located, abdominal fat was associated with increased BP, while peripheral adipose tissue seemed to play a protective role against hypertension. It should be noted that the measures of central adiposity correlated positively with SBP and DBP, but not with PP; BW, BMI and LBM, on the other hand, were associated with higher SBP and PP reflecting the stiffness of large arteries [34].

The circadian BP rhythm is essential for maintaining the body’s normal physiological functions [11,35,36]. It was estimated that 25%-30% of the normal population exhibited insufficient nocturnal SBP decline, and approximately 10%-20% of the population had less than normal DBP dipping [37]. In overweight individuals with hypertension, the prevalence of physiological SBP decline was found to be 15% lower than in their lean counterparts [38]. The authors of a European study on 3216 adults referred to a hypertension clinic, found a non-dipping pattern of BP in 45% of normal-weight patients and 65% of obese participants. However, the association between BMI and nighttime BP was not statistically significant after multivariable adjustment [39]. Similar results were obtained in studies involving young populations. A study by Macumber et al. reported that 34.4% of obese children (≥95th BMI percentile) and 13.6% of children in the lean group (15th–85th BMI percentiles) were non-dippers. Compared to the lean subjects, obese children had an adjusted prevalence ratio for SBP non-dipping of 2.15 (95% CI: 1.25–3.42), but increasing severity of obesity was not further associated with nocturnal non-dipping [25]. A retrospective chart review conducted among 263 young study participants revealed that the prevalence of SBP non-dipping among the obese patients was 45%, which increased as the severity of obesity increased [40].

A study that examined the association between measures of central obesity and circadian BP profile in 104 adult patients, found that WC and waist-to-height ratio were significantly higher in non-dippers. It also found that the percentage of systolic and diastolic nocturnal drop was significantly correlated with waist-to-height ratio [41]. In contrast, the authors of a Korean study involving 1290 subjects found no association between central obesity (defined as having a waist circumference ≥90 cm in males and ≥ 85 cm in females) and nocturnal dipping patterns. The percentage of non-dippers was 46% in the non-obese group and 47% in the central obesity group. Nocturnal SBP decline was also similar among the non-obese (7.32 ± 8.64%) and obese (7.17 ± 8.62%) participants [42]. A Polish study on 206 young adults, aged 18‐35 years with BMI 18.5‐35.0 kg/m², assessed the association between visceral adiposity and SBP non-dipping pattern. Visceral adipose tissue, measured with DXA at the upper part of the abdomen, was a relatively small fat deposit, constituting only 1.5% of patient weight and 3.1% of total body fat. In multiple regression analysis, significant correlations were found between visceral fat-to-BW ratio and the percentage of SBP nocturnal dipping (β = −0.375; 95% CI: −0.7167 to −0.042; p = 0.015). In the young men (but not the women), excess visceral fat (≥0.993 kg) was found to increase the odds by 2.3 times for non‐dipping SBP [43].

It has been documented that nighttime BP was a stronger predictor of total mortality and major cardiovascular events than daytime BP [44]. Boggia et al. pooled data from 6 population-based cohorts and reported that a higher SBP at night and a higher night-to-day SBP ratio were associated with a higher risk of all-cause and cardiovascular mortality, independent of mean awake and 24-hour SBP [45]. In children with pediatric-onset systemic lupus erythematosus, isolated nocturnal BP non-dipping was associated with endothelial dysfunction and atherosclerotic changes [46]. The MAPEC study found that subjects taking at least 1 antihypertensive medication at bedtime exhibited a lower cardiovascular risk than those who ingested all drugs in the morning [47].

To the best of our knowledge, the present study is the first to show the association between total body weight/composition and nocturnal decline in MAP. Previous studies have documented that peripheral SBP and DBP followed a similar nocturnal dipping profile to that of central BP, but MAP better reflected central, aortic SBP and better predicted cardiovascular risk than brachial SBP [15,48,49].

The studies that determined central BP through the acquisition of brachial waveform, with an automated oscillometric device, showed that waveform calibration with cuff-based MAP/DBP provided better estimation of invasive central SBP than brachial SBP/DBP calibration [50,51]. It was found that nocturnal SBP decline at the aorta and central arteries was significantly less pronounced than brachial SBP. Moreover, it was not related to a dipping of the heart rate and was virtually absent in the young individuals [52,53]. In our study, a nocturnal MAP decline <10% was diagnosed in 50.2% of the obese participants. The percentage of non-dippers was found to increase significantly, up to 83.3%, in patients with BMI ≥55 kg/m². The multivariable regression analysis revealed 3 groups of independent factors that reduced nocturnal MAP dipping: BW and BMI, reflecting body size; lean body mass, consisting mainly of muscle tissue; and AbdF/FM—the measure of central adiposity. It should be noted that abdominal fat in our study comprised almost the whole abdominal cavity and constituted 27.8% of total body fat—much more than the visceral adipose tissue measured in the aforementioned study [43]. Furthermore, it is important to note that a significant reduction of nocturnal MAP decline was observed only in participants in the highest quartiles of BW, BMI, LBM, and AbdF/F.

Conclusion

The study found that the prevalence of hypertension in patients with obesity was as high as 66.7%, which value increased to 78.7% of the participants with BMI ≥50 kg/m². It was also found that hypertensive participants were characterized by larger measures of abdominal obesity, whereas normotensive individuals had higher FM, BF% and PerF. Furthermore, BW, BMI and LBM positively correlated with systolic BP and PP, while the measures of central adiposity positively correlated with SBP and DBP, but not with PP. A reduced nocturnal MAP decline was found in 50.2% of patients with obesity (BMI ≥30 kg/m²) and in 83.3% of the participants with morbid obesity (BMI ≥55 kg/m²). The multivariable analysis revealed that BW, BMI, LBM and AbdF/FM were independent factors that influenced MAP decline, and showed that the patients in the highest quartiles of BW, BMI, LBM and AbdF/FM had reduced nocturnal MAP dipping compared with the individuals in the lowest respective quartiles.

Supporting information

S1 Fig. The number and percentage of non-dippers among hypertensive patients by BMI values.

(TIF)

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S2 Fig. The number and percentage of non-dippers among normotensive patients by BMI values.

(TIF)

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S1 Table. The number and percentage (in brackets) of hypertensive and normotensive women and men.

(DOCX)

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S2 Table. Anthropometric parameters and body composition in hypertensive and normotensive women with obesity.

(DOCX)

Click here for additional data file.^{(37.7KB, docx)}

S3 Table. Anthropometric parameters and body composition in hypertensive and normotensive men with obesity.

(DOCX)

Click here for additional data file.^{(38.4KB, docx)}

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

The study was supported by the Grant Number 501-3-40-10-15, Centre of Postgraduate Medical Education, Warsaw, Poland. The funders had no role in the study design, data collection and analysis, the decision to publish or the preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0281151.r001

Decision Letter 0

Jeremy P Loenneke

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

8 Aug 2022

PONE-D-22-12990The influence of body composition and fat distribution on circadian blood pressure rhythm and nocturnal mean arterial pressure dipping in patients with obesityPLOS ONE

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: General Comments:

This paper explores an interesting question, however, some significant rewriting of the article for clarity and correction of grammatical error is required. Also, there are some issues with the paper described below.

It would’ve been interesting to explore further how hypertension status within BMI group relates to the nocturnal blood pressure dipping status (even though non-dipping is an independent predictor of CV risk and mortality, as stated by the authors.)

Introduction

The rationale built here is difficult to follow. Particularly, the section on central blood pressure does not fit in. This paper does not measure central blood pressure.

Methods

generally sound

Results

-only report results to the decimal to which they were measured (rules of significant figures)

-line 136 and 137 – “blood pressure lowering drugs, 1-6” To what is this referring?

-Tables 2 and 6 – the “(No)” is confusing; please find a way to present this differently

-Table 3 - as men and women should statistically significant differences in systolic and diastolic pressure that day, night, and in the mean value, it would strengthen the paper to provide further analysis examining variables with men alone and with women alone. It is interesting to note that the females alone would not even reach the hypertension classification while the males would. If this kind of analysis were added, in my opinion, some of the other analyses performed could be eliminated so as not to bloat the paper. Keep the analyses simple and related to the main purpose of the paper.

Discussion

-Line 311 – This is interesting: “whereas normotensive individuals had larger FM, BF%, and PerF.” the authors mention this finding in passing (line 250, “a possible protective role for peripheral adipose tissue” but it is not really discussed.

-line 293 - if central blood pressure measurement is arguably better, and “nocturnal SPB decline of the aorta and central arteries was significantly less pronounced compared with brachial SBP” (refs 42,43), how important are the peripheral nocturnal blood pressure measurements? What was the relationship between central dipping and peripheral dipping in other studies?

Reviewer #2: Thank you for the opportunity to review this manuscript. The authors sought to investigate the relationship between markers of body composition and the distribution of fat on nocturnal blood pressure dipping. The results suggest that those with the highest body weight and, BMI, and lean body mass, had reduced nocturnal blood pressure dipper. The manuscript is very interesting; however, improvement is required in some areas before it is ready for publication.

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2023 Jan 31;18(1):e0281151. doi: 10.1371/journal.pone.0281151.r002

Author response to Decision Letter 0

25 Sep 2022

Response to reviewers

Reviewer #1:

General Comments:

This paper explores an interesting question, however, some significant rewriting of the article for clarity and correction of grammatical error is required.

The whole article has been changed and corrected.

As the number of normotensive patients in the study is low, the additional analysis is shown as supporting data.

Introduction

The rationale built here is difficult to follow. Particularly, the section on central blood pressure does not fit in. This paper does not measure central blood pressure.

The introduction has been extended and corrected to make the rationale clearer and easy to follow.

Results

-only report results to the decimal to which they were measured (rules of significant figures)

The results have been corrected according to the suggestion.

-line 136 and 137 – “blood pressure lowering drugs, 1-6” To what is this referring?

The sentence has been changed to make it clearer.

-Tables 2 and 6 – the “(No)” is confusing; please find a way to present this differently

‘No’ was replaced by ‘n’.

Extended analysis has been included in the supporting information files.

Discussion

The discussion on the role of subcutaneous fat has been expanded significantly.

The role of peripheral nocturnal blood pressure measurements, and the relationship between central dipping and peripheral dipping, are additionally discussed.

Reviewer #2

Introduction

Line 54-55 “As the best currently available method of measuring blood pressure (BP), and its circadian profile, has been regarded 24-hour ambulatory blood pressure monitoring (ABPM).”

Please consider changing this line to read “24-hour ambulatory blood pressure monitoring (ABPM) has been regarded as the best currently available method of measuring blood pressure (BP), and its circadian profile.”

Additionally, who has made this claim please support this with a citation.

The sentence has been changed according to the suggestion. A citation has been added.

Methods

Lines 84-86 “Waist circumference was determined in a horizontal plane, midway between the iliac crest and the costal margin, at the end of a normal expiration, using a non stretch tape measure.”

Which organization recommends taking it this way? This is different than how the American College of Sports Medicine describes the waist measurement. Please clarify why it was measured this way. Additionally, how was the hip measurement determined.

There are different waist circumference measurement protocols, and there is no consensus.

The measurement ‘midway between the iliac crest and the costal margin’ is recommended by the WHO, ASTM International and ISO 7250-1:2017, among others. By taking the measurement ‘midway between the iliac crest and the costal margin’, we wanted to obtain better reliability due to stable landmarks and better correlation with visceral adipose tissue mass (the abdomen in people with obesity is more in the shape of a ball than an hour-glass).

Hip circumference was measured at the level of the femoral great trochanter.

Piqueras P et al. Anthropometric indicators as a tool for diagnosis of obesity and other health risk factors: a literature review. Front Psychol 2021;12:631179. doi: 10.3389/fpsyg.2021.631179

Serviente C, Sforzo GA. A simple yet complicated tool measuring waist circumference to determine cardiometabolic risk. ACSM’s Health & Fitness Journal 2013; 17: 29-34.

doi: 10.1249/FIT.0b013e3182a956f5

Lines 97-99 “Systolic and diastolic BP were measured every 30 minutes at daytime (between 6.00 and 22.00) and every 60 minutes at night (from 22.00 to 6.00). Average daytime and nighttime BP were computed as the means of all readings during each period.”

How was mean blood pressure determined? There are 32 daytime measurements and 8 nighttime measurements. This may be problematic as this unfairly weights the influence of day time blood pressure. Honestly, I am not sure the best way to do this. But the current method is problematic. Either way please clarify how you calculated mean blood pressure.

I have to agree that ‘this unfairly weights the influence of daytime blood pressure’. The mean 24-hour values of SBP and DBP presented in the paper were 127.8 and 75.4 mmHg, respectively. Assuming that all 40 measurements (32 + 8) are legible, mean SBP and DBP should be 128.5 and 75.9 mmHg, respectively (daytime/nighttime measurements in the proportion of 4 to 1). If we calculate mean 24-hour SBP and DBP values including diurnal and nocturnal measurements in a 2-to-1 ratio (16 + 8 hours), the average SBP and DBP should be 127.2 and 74.8 mmHg, respectively. This means that approximately 1/3 of the daytime readings were not legible, and that the real daytime-to-nighttime measurement ratio was approximately 3 to 1.

Unfortunately, the exact numbers of legible measurements were not collected in our database, and are contained in individual patients’ source data only.

In the paper we decided to write that ‘the average circadian systolic and diastolic BP were calculated as the means of all legible measurements within a 24-hour period.’

Results.

Lines 184-185 “The assessment of nocturnal BP decline revealed that the magnitude of DBP dipping (10.8 +/- 8.7%) was significantly greater than SBP decline (7.6 +/- 6.8%, p<0.001).”

How was this determined? It is also more informative to the reader if you put the difference

Between SBP and DBP. Please clarify the percentages listed. It was stated in the statistical analyses section that it is standard deviations. That does not seem to be what is presented here.

The assessment of nocturnal BP decline revealed that the magnitude of SBP dipping (9.9 ± 8.2 mmHg) was significantly greater than DBP decline (8.5 ± 7.0 mmHg, p<0.001), while the percentage decline of DBP (10.8 ± 8.7%) was greater than SBP decline (7.6 ± 6.8%, p<0.001).

Discussion

Lines 300-301 “It should be noted, that abdominal fat determined in our study comprised almost whole abdominal cavity and constituted 27,8% of”. That comma should be a period.

The proper correction was made

Attachment

Submitted filename: 5 Response to reviewers.docx

Click here for additional data file.^{(22.1KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0281151.r003

Decision Letter 1

Jeremy P Loenneke

19 Oct 2022

PONE-D-22-12990R1The influence of body composition and fat distribution on circadian blood pressure rhythm and nocturnal mean arterial pressure dipping in patients with obesityPLOS ONE

Dear Dr. Tałałaj,

Both reviewers found that the manuscript was improved, however, each had additional minor comments that need to be addressed.

Please submit your revised manuscript by Dec 03 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Jeremy P Loenneke

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Overall, this paper is significantly improved. It addresses an important question is generally clearly presented. The grammar and punctuation issues have clearly been addressed although some minor grammar and punctuation issues remain.

Abstract

• Still some minor grammatical correct / punctuation issues

Intro

• Significantly improved! - although still some minor grammatical correct / punctuation issues

Results

• decimals are still shown for values for which it was unlikely the original measurement was to a decimal (age and BP, for example)

• “Blood pressure-lowering drugs, 1 137 through 6, have been taken by 289 participants” � where can the reader find the 1-6 info?

Discussion

• Still some minor grammatical correct / punctuation issues – review your comma usage particularly (while comma usage can be subjective, there are a few cases throughout the paper where the usage is clearly incorrect. For example, “Previous studies 288 have documented, that mean arterial pressure better reflected central, aortic SBP, and better 289 predicted cardiovascular risk, as compared with brachial SBP [38,39].” Between “documented” and “that” there clearly should not be a comma. This type of error occurs in other places.

Reviewer #2: Thank you for the opportunity to re-review this manuscript. The authors addressed most of my comments. I have still have three points that need to be addressed.

Page 5 lines 90-94

“Waist and hip circumferences (WC, HC) were measured to the nearest 1 cm, and the waist-to-hip ratio (WHR) was calculated. WC was determined in a horizontal plane, midway between the iliac crest and the costal margin, at the end of a normal expiration; HC was measured at the level of the greater trochanter, using a non-stretch tape measure.”

Sorry if my last comment on this was not clear. I agree that there is no consensus on how to measure waist circumference. I am just suggesting that the authors provide a reference for who recommends this method of measurement.

Page 6 lines 105-112

“Systolic and diastolic BP (DBP) were measured every 30 minutes during the day (between 6.00 and 22.00) and every 60 minutes during the night (from 22.00 to 6.00). The average daytime and nighttime BP were computed as the means of all readings during each period, and the average circadian SBP and DBP were calculated as the means of all legible measurements within a 24-hour period. Hypertension was diagnosed according to the 2018 European Society of Cardiology guidelines [7]. Non-dipping of BP was defined as a decline in MAP values <10% from the average daytime to the average nighttime values.

“I have to agree that ‘this unfairly weights the influence of daytime blood pressure’. The mean 24-hour values of SBP and DBP presented in the paper were 127.8 and 75.4 mmHg, respectively. Assuming that all 40 measurements (32 + 8) are legible, mean SBP and DBP should be 128.5 and 75.9 mmHg, respectively (daytime/nighttime measurements in the proportion of 4 to 1). If we calculate mean 24-hour SBP and DBP values including diurnal and nocturnal measurements in a 2-to-1 ratio (16 + 8 hours), the average SBP and DBP should be 127.2 and 74.8 mmHg, respectively. This means that approximately 1/3 of the daytime readings were not legible, and that the real daytime-to-nighttime measurement ratio was approximately 3 to 1.

Unfortunately, the exact numbers of legible measurements were not collected in our database, and are contained in individual patients’ source data only.

In the paper we decided to write that ‘the average circadian systolic and diastolic BP were calculated as the means of all legible measurements within a 24-hour period.”

I appreciate the authors honesty. But they need to explicitly state this in the manuscript. It is important the reader knows how the data was affected by the unequal number of measurements.

Page 12 lines 194-198

The assessment of nocturnal BP decline revealed that the magnitude of SBP dipping (9.9 ± 8.2

mmHg) was significantly greater than that of the DBP dipping (8.5 ± 7.0 mmHg, p<0.001), while the percentage of the DBP decline (10.8 ± 8.7%) was greater than that of SBP decline (7.6 ± 6.8%, p<0.001). The average MAP dipping was 9.4 ± 7.5%. Reduced nocturnal MAP decline was diagnosed in 219 patients (50.2%).

The authors should consider reported the difference and SD of that difference between the SBP and DBP dipping. That is the information we are interested in.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

PLoS One. 2023 Jan 31;18(1):e0281151. doi: 10.1371/journal.pone.0281151.r004

Author response to Decision Letter 1

20 Nov 2022

Response to reviewers

Reviewer #1:

Abstract

• Still some minor grammatical correct / punctuation issues

Intro

• Significantly improved! - although still some minor grammatical correct / punctuation issues

Discussion

The grammar and punctuation issues have clearly been addressed although some minor grammar and punctuation issues remain. Review your comma usage particularly (while comma usage can be subjective, there are a few cases throughout the paper where the usage is clearly incorrect. For example, “Previous studies 288 have documented, that mean arterial pressure better reflected central, aortic SBP, and better 289 predicted cardiovascular risk, as compared with brachial SBP [38,39].” Between “documented” and “that” there clearly should not be a comma. This type of error occurs in other places.

The text has been corrected. We believe no more grammar and punctuation issues remained.

Results

• decimals are still shown for values for which it was unlikely the original measurement was to a decimal (age and BP, for example)

Decimals were removed. The only exception can be found on page 12, lines 198-199, as the differences between mean BP values are very small.

• “Blood pressure-lowering drugs, 1 137 through 6, have been taken by 289 participants” where can the reader find the 1-6 info?

The sentence has been changed to make it more clear (lines 150-151).

Reviewer #2:

Thank you for the opportunity to re-review this manuscript. The authors addressed most of my comments. I have still have three points that need to be addressed.

Page 5 lines 90-94

A reference has been provided. Line 94 [19].

Page 6 lines 105-112

Unfortunately, the exact numbers of legible measurements were not collected in our database, and are contained in individual patients’ source data only.

In the paper we decided to write that ‘the average circadian systolic and diastolic BP were calculated as the means of all legible measurements within a 24-hour period.”

I appreciate the authors honesty. But they need to explicitly state this in the manuscript. It is important the reader knows how the data was affected by the unequal number of measurements.

The extended information on BP measurements was added: page 8, lines 144-147.

Page 12 lines 194-198

The assessment of nocturnal BP decline revealed that the magnitude of SBP dipping (9.9 ± 8.2 mmHg) was significantly greater than that of the DBP dipping (8.5 ± 7.0 mmHg, p<0.001), while the percentage of the DBP decline (10.8 ± 8.7%) was greater than that of SBP decline (7.6 ± 6.8%, p<0.001). The average MAP dipping was 9.4 ± 7.5%. Reduced nocturnal MAP decline was diagnosed in 219 patients (50.2%).

The authors should consider reported the difference and SD of that difference between the SBP and DBP dipping. That is the information we are interested in.

The data on the difference and SD of that difference between the SBP and DBP dipping has been added: page 12, lines 198-201.

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(19KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0281151.r005

Decision Letter 2

Jeremy P Loenneke

14 Dec 2022

PONE-D-22-12990R2The influence of body composition and fat distribution on circadian blood pressure rhythm and nocturnal mean arterial pressure dipping in patients with obesityPLOS ONE

Dear Dr. Tałałaj,

A reviewer has requested additional minor revisions in order to add clarity related to the ratio of measurements used.

Please submit your revised manuscript by Jan 28 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Jeremy P Loenneke

Academic Editor

PLOS ONE

Journal Requirements:

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: No

**********

6. Review Comments to the Author

Reviewer #1: All comments have been addressed. I have no further comments. I appreciate the work the authors did on this manuscript.

Reviewer #2: Thank you again for the opportunity to review this manuscript. The authors have made substantial improvements to the paper. However, I still have one concern that should be addressed.

Page 8 lines 144-147.

“The 24-hour BP monitoring included 40 measurements: 32 daytime and 8 nighttime readings. The comparison of average daytime, average nighttime and mean diurnal SBP and DBP values shows that actual daytime-to-nighttime ratio of legible BP measurements was 3 to 1. That means that approximately 1/3 of the daytime readings were not legible.”

I appreciate the authors adding this section in. However, I believe more discussion is warranted about why this is a problem. Perhaps discussing this in the limitations could be useful. But ultimately, I think the authors needs to discuss why the ratio is important. Simply when looking at mean blood pressure the data is skewed to be more like the day time measurements compared to the night time measurements due to the increased number of measurements taken during the day.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

Attachment

Submitted filename: Obesity_nocturnal_r2.pdf

Click here for additional data file.^{(58.4KB, pdf)}

PLoS One. 2023 Jan 31;18(1):e0281151. doi: 10.1371/journal.pone.0281151.r006

Author response to Decision Letter 2

8 Jan 2023

Response to reviewers

Reviewer #1:

All comments have been addressed. I have no further comments. I appreciate the work the authors did on this manuscript.

Thank you for your opinion

Reviewer #2:

The authors have made substantial improvements to the paper. However, I still have one concern that should be addressed.

Page 8 lines 144-147.

To solve the problem the average circadian SBP and DBP were calculated according to the formula (2 x mean daytime BP + mean nighttime BP) / 3.

This formula takes into account the times of diurnal and nocturnal BP measurements in a ratio of 2-to-1 (16-to-8 hours) and eliminates the influence of different measurement frequencies on average 24-hour SBP and DBP values.

As a consequence, the text on page 8, lines 145-148, became redundant and could have been removed.

The new calculations minimally changed the figures in tables 1, 3 and 5 but did not affect the results.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0281151.r007

Decision Letter 3

Jeremy P Loenneke

16 Jan 2023

The influence of body composition and fat distribution on circadian blood pressure rhythm and nocturnal mean arterial pressure dipping in patients with obesity

PONE-D-22-12990R3

Dear Dr. Tałałaj,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Jeremy P Loenneke

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: The authors have made substantial improvements to the paper. I appreciate the authors hard work in preparing this manuscript

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

**********

PLoS One. doi: 10.1371/journal.pone.0281151.r008

Acceptance letter

Jeremy P Loenneke

23 Jan 2023

PONE-D-22-12990R3

The influence of body composition and fat distribution on circadian blood pressure rhythm and nocturnal mean arterial pressure dipping in patients with obesity

Dear Dr. Tałałaj:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Jeremy P Loenneke

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. The number and percentage of non-dippers among hypertensive patients by BMI values.

(TIF)

Click here for additional data file.^{(108KB, tif)}

S2 Fig. The number and percentage of non-dippers among normotensive patients by BMI values.

(TIF)

Click here for additional data file.^{(98.1KB, tif)}

S1 Table. The number and percentage (in brackets) of hypertensive and normotensive women and men.

(DOCX)

Click here for additional data file.^{(36.3KB, docx)}

S2 Table. Anthropometric parameters and body composition in hypertensive and normotensive women with obesity.

(DOCX)

Click here for additional data file.^{(37.7KB, docx)}

S3 Table. Anthropometric parameters and body composition in hypertensive and normotensive men with obesity.

(DOCX)

Click here for additional data file.^{(38.4KB, docx)}

Attachment

Submitted filename: BP_Bodycomp_Nocturnal.docx

Click here for additional data file.^{(19.3KB, docx)}

Attachment

Submitted filename: 5 Response to reviewers.docx

Click here for additional data file.^{(22.1KB, docx)}

Attachment

Submitted filename: Response to reviewers.docx

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Attachment

Submitted filename: Obesity_nocturnal_r2.pdf

Click here for additional data file.^{(58.4KB, pdf)}

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(17.6KB, docx)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

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PERMALINK

The influence of body composition and fat distribution on circadian blood pressure rhythm and nocturnal mean arterial pressure dipping in patients with obesity

Marek Tałałaj

Agata Bogołowska-Stieblich

Michał Wąsowski

Ada Sawicka

Piotr Jankowski

Roles

Abstract

Introduction

Material and methods

Statistical analysis

Results

Table 1. Anthropometric parameters, body composition and blood pressure in patients with obesity.

Table 2. The number and percentage of hypertensive patients depending on BMI values.

Table 3. Age, BMI and blood pressure in female and male patients with obesity.

Table 4. Anthropometric parameters and body composition in hypertensive and normotensive patients with obesity.

Table 5. Correlations between anthropometric characteristics/body composition and blood pressure in patients with obesity.

Table 6. The number and percentage of non-dippers by BMI values.

Table 7. Multiple stepwise regression analysis showing associations between anthropometric parameters/body composition and nocturnal mean arterial pressure decline.

Discussion

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Jeremy P Loenneke

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Transfer Alert

Author response to Decision Letter 0

Decision Letter 1

Jeremy P Loenneke

Roles

Author response to Decision Letter 1

Decision Letter 2

Jeremy P Loenneke

Roles

Author response to Decision Letter 2

Decision Letter 3

Jeremy P Loenneke

Roles

Acceptance letter

Jeremy P Loenneke

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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