Original Article Basal Metabolic Rate Is Correlated with Blood Pressure Among Young Population

Int J Clin Exp Med 2016;9(6):12375-12383 www.ijcem.com /ISSN:1940-5901/IJCEM0021503

Original Article Basal metabolic rate is correlated with blood pressure among young population

Tao Chen1,2*, Xiaofang Chu4*, Yongkai Zhu1*, Yiting Lan1, Hailong Zhao1, Huifeng Zhu3, Chong Shen1

1Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China; 2Division of Health and Social Care Research, King’s College London, London SE1 1UL, UK; 3Pa- thology Pharmacology Teaching and Research Group, Nanjing Health School of Jiangsu Union Technical Institute, Nanjing 210038, China; 4Department of Health Inspection and Quarantine, School of Public Health, Nanjing Medical University, Nanjing 211166, China. *Equal contributors. Received December 9, 2015; Accepted March 19, 2016; Epub June 15, 2016; Published June 30, 2016

Abstract: Backgrounds: Our study was to confirm the positive relationship of predicted basal metabolic rate (BMR) and blood pressure (BP) and to determine if BMR was still predictive, irrespective of demographic and anthropometric variables in young Chinese population. Methods: BP measurements were performed by trained workers through a standardized method using Omron HE: 7071 electronic sphygmomanometer. BMR values were calculated by a given formula of Omron HBF-306 Body Fat Monitor. Results: The mean systolic blood pressures (SBP) and diastolic blood pressures (DBP) as well as pulse pressure (PP) increased with increasing BMR deciles (SBP by 20 mmHg,

DBP by 4 mmHg and PP by 15 mmHg from the 1st decile to the 5th decile, Ptrends<0.001 for all). Pearson correlation analyses indicated that BMR was positively correlated with SBP (r=0.601, P<0.001), DBP (r=0.243, P<0.001) and PP (r=0.588, P<0.001). After adjusting with potential confounding factors, the regression coefficients indicated that every 1 Kcal/day higher BMR level was associated with an increase of 0.021 mmHg SBP (P<0.001), 0.008 mmHg DBP (P<0.001) and 0.013 mmHg PP (P<0.001). This did not alter substantially after further adjustment for waist circumference. In addition, we also found that the relationship between BMR and BP did not materially change, particularly for SBP and PP, after using a series of equations for BMR calculation. Conclusions: The study showed that the predicted BMR might be a good metabolic surrogate to predict the development of high blood pressure considering its simplicity and feasibility in clinical practices.

Keywords: Blood pressure, basal metabolic rate, hypertension

Introduction found that body mass index (BMI) in adoles- cents was significantly associated with systolic High blood pressure (BP) is considered as an blood pressure (SBP) and DBP in both genders independent risk factor of cardiovascular dis- and in both the normal weight and overweight eases [1]. It was suggested that in East Asian groups [6]. populations, diastolic blood pressure (DBP) values of 80 mmHg or more could explain more The exact mechanisms that contribute to ele- than 50% of deaths due to strokes and almost vated BP in people with obesity are not clear. 25% of deaths associated with coronary heart Some studies reported that obesity-related disease [2]. In China, hypertension was respon- hypertension were associated with abnormali- sible for 24.6% of all-cause mortality and ties of metabolism, including abnormal levels 64.0% of the deaths related to cardiovascular of insulin, leptin and cytokines, renin-angioten- diseases in 2010 [3]. sin-aldosterone system activation, and stimu- lation of sympathetic nervous system [9, 10]. Many factors contribute to the variations of Furthermore, studies showed that people with BP, including age, gender, smoking, drinking, a high sensitivity of energy imbalance were less metabolism and heredity [4, 5]. Previous stud- prone to gain weight [11] and another study ies have proved that obesity is strongly corre- revealed that obesity could be prevented by lated with blood pressure [6-8]. A study in Israel increasing energy expenditure [12]. Therefore, Basal metabolic rate and blood pressure several studies have investigated the interrela- standard posture and taken measurements. All tionship to discover whether basal metabolic participants were needed to measure BP for rate (BMR), measured by open/close-circuit three times. SBP, DBP and heart rate were metabolic analyzer, may potentially mediate recorded. The mean values of three measure- resting BP and results revealed that BMR was a ments were used in the statistical analyses. significant determinant of SBP and DBP even after adjustment for many well-cited factors Measurement of risk factors such as body size, fat mass and physical activity [13, 14]. Age, gender, waist circumference (WC), BMI and family history of hypertension (FHH) were Apart from the direct measured BMR, seve- considered as risk factors of hypertension. ral predictive equations were widely used be- Relevant data were recorded on their individual cause of its simplicity and feasibility in most questionnaires. Height and weight were mea- time [15]. However, few studies were perform- sured when participants took off heavy clothes ed to testify whether the positive relationship and shoes. Besides, subjects were asked to between predictive BMR and SBP, DBP and keep an erect posture with their eyes looking at pulse pressure (PP) still exist among young the front horizontally. Electronic weight meter population. Therefore the purpose of the pre- and meter ruler should been calibrated before sent study was two-fold. The first was to con- every measurement. BMI was calculated as the firm the previous findings in young Chinese weight in kilograms divided by the square of the population even using various predictive for- height in meters. WC was measured on bare mulations. The second was to determine if skin as the narrowest circumference between BMR was predictive of BP, irrespective of de- the lower costal margin and the iliac crest in mographic and anthropometric variables. centimeters. FHH was recorded on the condi- tion that at least one immediate family relative Methods was recalled as hypertension.

Study population BMR determination

A cross-sectional study was conducted in BMR was obtained from a hand-to-hand bio- a medical university and data of question- electric impedance analysis instrument (Omron naire were collected by uniformly trained sur- HBF-306, OMRON (Dalian) CO., LTD. China). All veyors. In total, 802 students in grade one were procedures were performed according to the chosen and recruited. The participants came manufacturer’s instructions. The subjects did from different majors, including Clinical Me- not exercise or consume caffeine or alcohol dicine, Medicine, Health Law, Optometry and prior to the measurement. Having rested quiet- Ophthalmology, Oral Medicine, English, Clini- ly for at least two hours after dinner, partici- cal Medicine, Rehabilitation Medicine, Labo- pants took measurements of BMR in a stan- ratory Medical Science, Nursing, Preventive dardized position under a relaxed state. Medicine, Hygienic Detection and Analysis, and Anthropometric data like height and weight as Biostatistics. well as gender and age information were filled into the instrument and the device was held Measurement and definition of BP while both arms were stretched horizontally in front of the body. The equation used by this Three separate times of BP measurements instrument to calculate BMR is based on were performed by uniformly trained survey Dietary Reference intakes for Japanese and or through a standardized procedure using indicated as follows: Male: 18.6*weight+347; Omron HE: 7071 electronic sphygmomanome- Female: 18.3*weight+272 (age range: 18-29 ter (OMRON (Dalian) CO., LTD. China). Partici- years old). pants were required to avoid to do intense sports and fast in one hour before measure- Statistical methods ments. Besides, they were asked whether they had taken any medicines which could affect Data was input with Epi Data 3.1 software blood pressure. After a 5 minute resting, par- package. Continuous data with normal distribu- ticipants were asked to sit in a relaxed and tion were reported as mean (± standard devia-

12376 Int J Clin Exp Med 2016;9(6):12375-12383 Basal metabolic rate and blood pressure

Table 1. Characteristics of enrolled participants ple and different genders were Measure Female (n=548) Male (n=226) Total (n=774) presented in Table 1. Generally, Males had significantly higher he- Age (years) 19.9±0.7 20.1±0.9 19.9±0.8 ight, weight, BMI, WC, SBP, DBP, Height (cm) 161.2±5.6 173.8±7.2 164.9±8.4* PP and BMR than females. The Weight (kg) 54.9±7.6 66.7±10.2 58.3±10.0* percent of participants with family BMI (kg/m2) 21.0±2.6 22.0±3.2 21.3±2.8* history of hypertension in females WC (cm) 73.5±6.7 79.2±8.1 75.2±7.6* was 71.53%, which was higher SBP (mmHg) 110.3±9.0 126.1±12.1 114.9±12.3* than that in males (59.29%). All dif- DBP (mmHg) 67.8±6.8 70.1±7.7 68.5±7.1* ferences of parameters except age PP (mmHg) 42.5±6.4 56.0±9.6 46.4±9.7* between female and male were BMR (Kcal/day) 1277.0±143.4 1590.7±214.7 1368.6±219.9* statistically significant at P<0.05. FHH (n, %) 392 (71.53%) 134 (59.29%) 526 (67.96%)* Mean SBP has increased along Differences between female and male were statistically significant at with the increasing BMR deciles *P<0.001. Abbreviations: BMI, Body mass index; WC, Waist circumference; (from 106.3±7.5 mmHg in the 1st SBP, Systolic blood pressure; DBP, Diastolic blood pressure; PP, Pulse pressure; BMR, Basal metabolic rate; FHH, Family history of hypertension. decile to 126.6±12.0 mmHg in the 5th decile, P for trend <0.001) and mean DBP has increased along tion). Categorical data were shown as number with increasing BMR deciles (from 66.2±6.3 (percentage). Categorical variables were com- mmHg in the 1st decile to 70.7±7.9 mmHg in pared using chi-square test and continuous the 5th decile, P for trend <0.001). The same variables were compared using the indepen- pattern persisted in mean PP (from 40.1±5.7 dent sample t test. mmHg in the 1st decile to 55.9±10.2 mmHg in the 5th decile, P for trend <0.001). Data was Pearson correlation analysis was used to test shown in Table 2. Supplementary analysis by the correlation of age, height, weight, BMI, WC, the gender did not find materially change as BMR, SBP, DBP and PP. Multiple linear regres- seen from total population (Tables S1 and S2). sions were performed to evaluate the effect of From the above results in tables, we could not BMR on BP by adjusting for age, gender, and find a threshold where the association became FHH and then additionally adjusting for WC. significant.

In order to examine the consistency of the rela- Correlation analysis tionship between BMR and BP (SBP, DBP and PP), we further calculated the value of BMR and BMR was positively correlated with height (r= built multiple linear regression models by the 0.646, P<0.001), weight (r=0.963, P<0.001), following algorithms: Harris and Benedict BMI (r=0.741, P<0.001), WC (r=0.771, P< Equation (HBE) [16], Mifflin-St Jeor Equation 0.001), SBP (r=0.601, P<0.001), DBP (r=0.243, (MSJE) [17], LIU equation (LIUE) [18], Kleiber’s P<0.001) and PP (r=0.588, P<0.001) in who- Law (KL) [19] and Owen Equations (OE) [20, le study population (Table 3). Further partial 21]. Statistical analyses were conducted by correlation analysis by adjustment for age Stata 12.0 software package. Differences were showed the consistent results. BMR still had regarded statistically significant at P<0.05. a strong association with height (r=0.645, P<0.001), weight (r=0.963, P<0.001), BMI (r= Results 0.752, P<0.001), WC (r=0.771, P<0.001), SBP Characteristics of participants (r=0.601, P<0.001), DBP (r=0.244, P<0.001) and PP (r=0.588, P<0.001). Overall, 744 (548 females, 226 males) students finished the survey questionnaire and In males, BMR increased with the augmenta- accepted physical examination with a response tion of height (r=0.246, P<0.001), weight (r= rate of 92.8%. The average age of participants 0.932, P<0.001), BMI (r=0.844, P<0.001), WC was 19.9±0.8 years old. (r=0.788, P<0.001), SBP (r=0.369, P<0.001), DBP (r=0.139, P=0.037) and PP (r=0.353, P< Descriptive statistics for anthropometric, blood 0.01). In females, positive correlations were pressure, and metabolic data for the total sam- also observed between BMR and height (r=

12377 Int J Clin Exp Med 2016;9(6):12375-12383 Basal metabolic rate and blood pressure

Table 2. Distribution of BP and BMR data Deciles Mean BMR Number of SBP DBP PP of BMR (Range) (Kcal/day) valid subjects (mmHg) (mmHg) (mmHg) 1 1124.6±46.5 (953-1187) 155 106.3±7.5 66.2±6.3 40.1±5.7 2 1233.4±26.9 (1191-1279) 155 109.7±8.3 67.0±6.8 42.7±6.1 3 1326.2±29.7 (1279-1385) 154 112.0±9.9 68.3±6.7 43.8±6.7 4 1451.4±43.6 (1385-1530) 155 119.8±11.2 70.2±6.8 49.6±9.5 5 1706.9±186.5 (1531-2988) 155 126.6±12.0 70.7±7.9 55.9±10.2 P for trend <0.001 <0.001 <0.001 Abbreviations: BP, Blood pressure; BMR, Basal metabolic rate; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; PP, Pulse pressure.

Table 3. Correlation matrix for age, Anthropometric, metabolic and BP data Age Height Weight BMI WC SBP DBP PP BMR (years) (cm) (kg) (kg/m2) (cm) (mmHg) (mmHg) (mmHg) (Kcal/day) Age (years) 1.000 0.053 0.022 -0.022 0.026 0.016 -0.046 0.055 0.031 Height (cm) 1.000 0.610* 0.090** 0.360* 0.475* 0.124* 0.515* 0.646* Weight (kg) 1.000 0.814* 0.816* 0.572* 0.247* 0.548* 0.963* BMI (kg/m2) 1.000 0.780* 0.382* 0.246* 0.306* 0.741* WC (cm) 1.000 0.413* 0.222* 0.363* 0.771* SBP (mmHg) 1.000 0.623* 0.817* 0.601* DBP (mmHg) 1.000 0.058 0.243* PP (mmHg) 1.000 0.588* BMR (Kcal/day) 1.000 Significant difference at p* <0.001, **p<0.05. Abbreviations: BMI, Body mass index; WC, Waist circumference; BP, Blood pressure; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; PP, Pulse pressure; BMR, Basal metabolic rate.

0.458, P<0.001), weight (r=0.982, P<0.001), BMR was the most powerful predictor of SBP BMI (r=0.859, P<0.001), WC (r=0.763, P< (standardized β=0.371), DBP (standardized β= 0.001), SBP (r=0.353, P<0.001), DBP (r=0.235, 0.157) and PP (standardized β=0.358). P<0.001) and PP (r=0.250, P<0.001). Data of males and females were listed in Tables S3 Analysis of the association between BMR and and S4), respectively. BP indicated that BMR accounted for 36.1%, 5.9% and 34.6% of the variance in SBP, DBP Linear regression analysis and PP, respectively, while combining with other factors (age, gender, WC and FHH) explained Multiple linear regression analysis was used 42.7% for SBP, 6.6% for DBP and 45.8% for PP. to reveal the potential effects of BMR on BP (SBP, DBP and PP). The regression coefficients Additionally, we testified the positive finding of indicated that every 1 Kcal/day higher BMR BMR on BP with five equations. The predicted level was associated with an increase of values of BMR with these equations (HBE, 0.021 mmHg SBP (P<0.001), 0.008 mmHg MSJE, LIUE, KL and OE) were 1558.5±141.8, DBP (P<0.001) and 0.013 mmHg PP (P< 1401.8±196.0, 1402.1±198.1, 1473.7±187.9 0.001). Further adjustment for WC, the effect and 1297.2±183.7, respectively. Compared still presented statistical significance (0.021 with the measured BMR value 1368.6±219.9, mmHg SBP, P<0.001; 0.005 mmHg DBP, P= most of the calculated values were larger than 0.030 and 0.016 mmHg PP, P<0.001). Data measured values except that of OE. Multiple lin- were listed in Table 4. ear models were also built as before (Table 5). The regression coefficients for the relation Compared with other factors, the standardized between BMR and SBP were 0.032, 0.028, regression coefficient of BMR indicated that 0.028, 0.026 and 0.055, respectively. All of the

12378 Int J Clin Exp Med 2016;9(6):12375-12383 Basal metabolic rate and blood pressure

Table 4. Multiple linear regression models using SBP, DBP and PP as dependent variables, respectively SBP DBP PP Variables Coeff (SE) P Standardized β Coeff (SE) P Standardized β Coeff (SE) P Standardized β Model 1 Age -0.561 (0.446) 0.209 -0.035 -0.482 (0.330) 0.145 -0.051 -0.079 (0.341) 0.816 -0.006 BMR 0.021 (0.002) <0.001 0.38 0.008 (0.001) <0.001 0.245 0.013 (0.002) <0.001 0.304 Gender (female vs. male) -9.286 (0.989) <0.001 -0.343 0.009 (0.731) 0.99 0.001 -9.295 (0.755) <0.001 -0.438 FHH (yes vs. no) -0.305 (0.729) 0.675 -0.012 -0.415 (0.539) 0.442 -0.027 0.109 (0.556) 0.845 0.005 Constant 113.158 (10.147) <0.001 67.730 (7.501) <0.001 45.427 (7.747) <0.001 Model R2 0.427 0.062 0.457 Model 2 Age -0.566 (0.447) 0.206 -0.035 -0.508 (0.330) 0.124 -0.054 -0.057 (0.341) 0.867 -0.005 BMR 0.021 (0.003) <0.001 0.371 0.005 (0.002) 0.03 0.157 0.016 (0.002) <0.001 0.358 Gender (female vs. male) -9.357 (1.046) <0.001 -0.345 -0.389 (0.772) 0.614 -0.025 -8.967 (0.798) <0.001 -0.422 WC 0.015 (0.074) 0.835 0.009 0.087 (0.055) 0.111 0.093 -0.072 (0.056) 0.203 -0.057 FHH (yes vs. no) -0.295 (0.731) 0.687 -0.011 -0.354 (0.539) 0.512 -0.023 0.059 (0.557) 0.915 0.003 Constant 112.894 (10.233) <0.001 66.235 (7.552) <0.001 46.659 (7.804) <0.001 Model R2 0.427 0.066 0.458 Abbreviations: BMR, Body metabolic rate; WC, Waist circumference; FHH, Family history of hypertension; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; PP, Pulse pressure. Model 1: Adjusting age, gender, and FHH. Model 2: Additionally adjusting WC.

12379 Int J Clin Exp Med 2016;9(6):12375-12383 Basal metabolic rate and blood pressure

Table 5. Regression coefficients (standardized regres- years old [24], and the relation persist- sion coefficients) of BMR for SBP, DBP and PPǂ ed across 4.5 years [25]. Different- Equation SBP DBP PP ly, unless patients were regarded as HBE 0.032 (0.365)* 0.005 (0.096) 0.027 (0.396)* obesity with a high BMI value (>30 kg/ m2), normotensive and hypertensive MSJE 0.028 (0.447)* 0.005 (0.142) 0.023 (0.466)* individuals presented no obvious differ- LIUE 0.028 (0.448)* 0.006 (0.161) 0.022 (0.453)* ence in resting metabolic rate [26]. In KL 0.026 (0.401)* 0.006 (0.163)** 0.020 (0.392)* this study, the results indicated that OE 0.055 (0.823)* 0.010 (0.261) 0.045 (0.857)* BMR was an independent risk factor Regression coefficient for the relationship between BMR and SBP, of high BP, after adjustment for age, DBP and PP was statistical significant at p* <0.001, **p<0.05. Ab- breviations: HBE, Harris and Benedict Equation; MSJE, Mifflin-St Jeor gender, WC, FHH and it was the most Equation; LIUE, LIU equation; KL, Kleiber’s Law; OE, Owen Equations; powerful predictor to BP elevation, SBP, Systolic blood pressure; DBP, Diastolic blood pressure; PP, Pulse especially for SBP and PP. Therefore, ǂ pressure; BMR, Basal metabolic rate. Adjusting for age, gender, WC the remaining variation in blood pres- and FHH. sure which cannot be explained by WC may have something to do with meta- regression coefficients were larger than that in bolic processes. Furthermore, by comparing original model (0.021). The comparable results predicted BMR values, which were reported to existed in other two models using DBP and PP be suitable for different people [27-29] with as dependent factors. Moreover, BMR was still those determined by indirect calorimetry, our the most powerful predictor by the standard- result revealed that predicted BMR might be a ized regression coefficient in comparison with good metabolic surrogate to monitor BP varia- other factors and the magnitude did not mate- tions due to its simplicity and feasibility in clini- rially vary. cal practices.

Discussion Nowadays, the mechanism contributed to the relation between BMR and BP is nebulous. As a measurement of least energy used by Several possible mechanisms may be respon- bodies of warm-blooded animals when they are sible. First, the blood pressure-raising systems under a relaxed, awake and fasting state [22], played an important role in hypertension and BMR can mirror the interaction between extrin- over 50% of cases of high blood pressure could sic and intrinsic environment [23]. Generally, be classified as neurogenic essential hyperten- there are two main methods to determine the sion [30]. Stimulated renal sympathetic ner- value of calories that we need in a given day: vous system (RSNS) links to upregulating BMR, one is called indirect calorimetry (relatively which resulted in increased arterial pressure in accurate, but more difficult), which uses ex- essential hypertensive patients, particularly in pired gases to calculate the amounts and ty- young hypertensive persons [31]. Second, thy- pes of fuel being utilized; the other one is to roid hormones were reported to mediate the use several different formulas (less accurate, association between BMR and blood pressure. but much easier). In this study, we evaluated Studies have found thyroid hormone levels (T3 the association between predicted BMR and [triiodothyronine] and T4 [thyroxine]) are closely BP in a young population (mean age, 19.9 related to BMR through their direct effects on years). We had shown that predicted BMR was rates of oxidative metabolism in most tissues significantly associated with SBP and DBP as [32], which in turn precipitates changes in car- well as PP. This relationship persisted after diovascular function and blood pressure regu- adjusting WC, an important factor with BP. lation [33]. Third, the relation between BMR and BP may be partly explained by heritable The main findings of this study are in con- regulation. A loss-of-function mutation of the gruence with the findings of previous research- gene encoding the melanocortin4 receptor es. Previous study found that resting energy (MC4R) was reported to be associated with a expenditure was positively connected with SBP low risk of high blood pressure [34], while independent of height, resting heart rate and genetic variation in Neuropeptide Y interacting body composition among overweight African- with Y1 receptor (NPY1R) could lead to dramatic and European-American women with 34.8±6.3 increase of DBP [35]. Finally, developmental

12380 Int J Clin Exp Med 2016;9(6):12375-12383 Basal metabolic rate and blood pressure factors could relate BMR to BP. Congenital Suhong Chen, Xiaomin Lu, Wen Zhang, Xin- undernutrition might lead to elevated food ming Chu, Wenwen Yuan, Wencheng Zhou intake at early postnatal age [36] and a high-fat for their assistance with field survey. Finally, we diet may increase renal sympathetic activity, as want to express our gratitude to all subjects well as blood pressure [37]. Combined with the in this study. This work was supported by correlation results of BMR and BP, these find- the key project of College Students Practice ings supported that the elevated BMR was Innovation Training of Jiangsu Province (2013- strongly relevant to the augmentation of BP, 10312006Z), the Research Project of Health which may provide valuable orientation for Vocational and Technical Education of Jiang- future research. su Province (JZ201312), the National Natural Science Foundation of China (No. 81273165) Generally, this study has the following poten- and the Priority Academic Program for the tial limitations. First, as a cross-sectional Development of Jiangsu Higher Education In- survey, the subjects were the young medical stitutions (Public Health and Preventive Medi- college students with a similar age, low obe- cine). The funders had no role in study design, sity (1.4%) and unhealthy lifestyle rate (smok- data collection and analysis, decision to pub- ing: 0.6%; drinking: 0.8%), in a specific region. lish, or preparation of the manuscript. These results may be different from older population as well as the population in different Disclosure of conflict of interest countries, although a positive relationship between resting metabolic rate and SBP was None. reported to be independent of age and race Address correspondence to: Dr. Chong Shen, De- [24]. Second, gender difference may cause potential stratum effect nonetheless previous partment of Epidemiology and Biostatistics, Sch- study among Indigenous Siberians indicated ool of Public Health, Nanjing Medical University, that gender had no obvious influence on the 101 Longmian Avenue, Jiangning, Nanjing 211166, association between BMR and BP [14]. In this China. Tel: +86 25 86868443; Fax: +86 25 8652- study, we adjusted for age, gender, WC and FHH 7613; E-mail: [email protected] by multiple regression models and that would References help to control potential bias in the effect of BMR on BP for whole population. Third, there [1] Bloomfield GS, Wang TY, Boulware LE, Califf might be residual confounding factors which RM, Hernandez AF, Velazquez EJ, Peterson can affect BP, such as mental stress [38], phys- ED, Li JS. Implementation of Management ical exercise, lifestyles [39] and so on. Strategies for Diabetes and Hypertension: From Local to Global Health in Cardiovascular In conclusion, the study showed that predicted Diseases. Glob Heart 2015; 10: 31-38. BMR had a strong relationship with BP (SBP, [2] Rodgers A, Lawes C, Macmahon S. Reducing DBP and PP), especially SBP and PP. The pre- the global burden of blood pressure-related dicted BMR might be a good metabolic surro- cardiovascular disease. J Hypertens Suppl gate to predict the development of high blood 2000; 18: S3-S6. pressure considering its simplicity and feasibil- [3] Liu M, Li Y, Liu S, Wang W, Zhou M. [Burden on ity in clinical practices. Further replication of blood-pressure-related diseases among the predicted BMR and BP would be warranted in a Chinese population, in 2010]. Zhonghua Liu large sample and prospective study to testify Xing Bing Xue Za Zhi 2014; 35: 680-683. [4] Hypertension editors’ picks: obesity-associat- which equations will be more suitable to cardio- ed hypertension. Hypertension 2015; 65: e10- vascular risk stratification. e16. Acknowledgements [5] Kunes J, Kadlecova M, Vaneckova I, Zicha J. Critical developmental periods in the patho- genesis of hypertension. Physiol Res 2012; 61 We thank Lulu Ren, Zhaoqi Zhang, Jiaqian Feng, Suppl 1: S9-S17. Lei Zhang for preparation of the project. We [6] Chorin E, Hassidim A, Hartal M, Havakuk O, also appreciate Yuchao Li, Yinjia Deng, Huiwen Flint N, Ziv-Baran T, Arbel Y. Trends in Ado- Qian, Xinxin Xu, Zhuoyi Shen, Hui Huang, Xue lescents Obesity and the Association between Wang, Fan Zhao, Congcong Zhang, Dongxin BMI and Blood Pressure: A Cross-Sectional Jiang, Yixuan Li, Yingru Lin, Lujue Liu, Rui Dai, Study in 714,922 Healthy Teenagers. Am J Xiaoqiang Sun, Shuying Guo, Jiali Jin, Rong Wu, Hypertens 2015; 28:1157-63.

12381 Int J Clin Exp Med 2016;9(6):12375-12383 Basal metabolic rate and blood pressure

[7] Rodrigues Barbosa A, Balduino Munaretti D, of the caloric requirements of men. Am J Clin Da Silva Coqueiro R, Ferreti Borgatto A. Nutr 1987; 46: 875-885. Anthropometric indexes of obesity and hyper- [21] Owen OE, Kavle E, Owen RS, Polansky M, tension in elderly from Cuba and Barbados. J Caprio S, Mozzoli MA, Kendrick ZV, Bushman Nutr Health Aging 2011; 15: 17-21. MC, Boden G. A reappraisal of caloric require- [8] Chen T, Li W, Wang Y, Xu T, Hu B, Chen W, Zhu ments in healthy women. Am J Clin Nutr 1986; M. Body mass index and hypertension among 44: 1-19. Chinese governmental and institutional em- [22] Mcnab BK. On the utility of uniformity in the ployees in Beijing. Angiology 2012; 63: 337- definition of basal rate of metabolism. Physiol 342. Zool 1997; 70: 718-720. [9] Vaneckova I, Maletinska L, Behuliak M, [23] Parati G, Ochoa JE, Lombardi C, Bilo G. Nagelova V, Zicha J, Kunes J. Obesity-related Assessment and management of blood-pres- hypertension: possible pathophysiological me- sure variability. Nat Rev Cardiol 2013; 10: 143- chanisms. J Endocrinol 2014; 223: R63-R78. 155. [10] Grassi G. Sympathetic neural activity in hyper- [24] Brock DW, Tompkins CL, Fisher G, Hunter GR. tension and related diseases. Am J Hypertens Influence of resting energy expenditure on 2010; 23: 1052-1060. blood pressure is independent of body mass [11] Cornier MA, Mcfadden KL, Thomas EA, Bechtell and a marker of sympathetic tone. Metabolism JL, Bessesen DH, Tregellas JR. Propensity to 2012; 61: 237-241. Obesity Impacts the Neuronal Response to [25] Sriram N, Hunter GR, Fisher G, Brock DW. Energy Imbalance. Front Behav Neurosci Resting energy expenditure and systolic blood 2015; 9: 52. pressure relationships in women across 4.5 [12] Niwa M, Numaguchi Y, Ishii M, Kuwahata T, years. J Clin Hypertens (Greenwich) 2014; 16: Kondo M, Shibata R, Miyata K, Oike Y, 172-176. Murohara T. IRAP deficiency attenuates diet- [26] Kunz I, Schorr U, Klaus S, Sharma AM. Resting induced obesity in mice through increased en- metabolic rate and substrate use in obesity hy- ergy expenditure. Biochem Biophys Res pertension. Hypertension 2000; 36: 26-32. Commun 2015; 457: 12-18. [27] Frankenfield DC, Rowe WA, Smith JS, Cooney [13] Luke A, Adeyemo A, Kramer H, Forrester T, RN. Validation of several established equa- Cooper RS. Association between blood pres- tions for resting metabolic rate in obese and sure and resting energy expenditure indepen- nonobese people. J Am Diet Assoc 2003; 103: dent of body size. Hypertension 2004; 43: 1152-1159. 555-560. [28] Rao ZY, Wu XT, Liang BM, Wang MY, Hu W. [14] Snodgrass JJ, Leonard WR, Sorensen MV, Comparison of five equations for estimating Tarskaia LA, Mosher MJ. The influence of basal resting energy expenditure in Chinese young, metabolic rate on blood pressure among indig- normal weight healthy adults. Eur J Med Res enous Siberians. Am J Phys Anthropol 2008; 2012; 17: 26. 137: 145-155. [29] Reidlinger DP, Willis JM, Whelan K. Resting [15] Henry CJ. Basal metabolic rate studies in hu- metabolic rate and anthropometry in older mans: measurement and development of new people: a comparison of measured and calcu- equations. Public Health Nutr 2005; 8: 1133- lated values. J Hum Nutr Diet 2015; 28: 72-84. 1152. [30] Parati G, Esler M. The human sympathetic ner- [16] Roza AM, Shizgal HM. The Harris Benedict vous system: its relevance in hypertension and equation reevaluated: resting energy require- heart failure. Eur Heart J 2012; 33: 1058- ments and the body cell mass. Am J Clin Nutr 1066. 1984; 40: 168-182. [31] Johns EJ, Kopp UC, Dibona GF. Neural control [17] Mifflin MD, St Jeor ST, Hill LA, Scott BJ, of renal function. Compr Physiol 2011; 1: 731- Daugherty SA, Koh YO. A new predictive equa- 767. tion for resting energy expenditure in healthy [32] Fazio S, Palmieri EA, Lombardi G, Biondi B. individuals. Am J Clin Nutr 1990; 51: 241-247. Effects of thyroid hormone on the cardiovascu- [18] Liu HY, Lu YF, Chen WJ. Predictive equations lar system. Recent Prog Horm Res 2004; 59: for basal metabolic rate in Chinese adults: a 31-50. cross-validation study. J Am Diet Assoc 1995; [33] Danzi S, Klein I. Thyroid hormone and blood 95: 1403-1408. pressure regulation. Curr Hypertens Rep. [19] Smil V. Laying down the law. Nature 2000; 2003; 5: 513-520. 403: 597. [34] Greenfield JR, Miller JW, Keogh JM, Henning E, [20] Owen OE, Holup JL, D’Alessio DA, Craig ES, Satterwhite JH, Cameron GS, Astruc B, Mayer Polansky M, Smalley KJ, Kavle EC, Bushman JP, Brage S, See TC, Lomas DJ, O’Rahilly S, MC, Owen LR, Mozzoli MA, Et A. A reappraisal Farooqi IS. Modulation of blood pressure by

12382 Int J Clin Exp Med 2016;9(6):12375-12383 Basal metabolic rate and blood pressure

central melanocortinergic pathways. N Engl J [37] Lim K, Burke SL, Head GA. Obesity-related hy- Med 2009; 360: 44-52. pertension and the role of insulin and leptin in [35] Wang L, Rao F, Zhang K, Mahata M, Rodriguez- high-fat-fed rabbits. Hypertension 2013; 61: Flores JL, Fung MM, Waalen J, Cockburn MG, 628-634. Hamilton BA, Mahata SK, O’Connor DT. [38] Van Woudenberg M, Shin J, Bernard M, Syme Neuropeptide Y(1) Receptor NPY1R discovery C, Abrahamowicz M, Leonard G, Perron M, of naturally occurring human genetic variants Richer L, Veillette S, Gaudet D, Paus T, Pausova governing gene expression in cella as well as Z. CYP17A1 and Blood Pressure Reactivity to pleiotropic effects on autonomic activity and Stress in Adolescence. Int J Hypertens 2015; blood pressure in vivo. J Am Coll Cardiol 2009; 2015: 734586. 54: 944-954. [39] Sfrantzis KD, How JM, Sartor DM. Implications [36] Vickers MH, Breier BH, Cutfield WS, Hofman of diet modification on sympathoinhibitory PL, Gluckman PD. Fetal origins of hyperphagia, mechanisms and hypertension in obesity. obesity, and hypertension and postnatal ampli- Auton Neurosci 2015; 189:25-30. fication by hypercaloric nutrition. Am J Physiol Endocrinol Metab 2000; 279: E83-E87.

12383 Int J Clin Exp Med 2016;9(6):12375-12383 Basal metabolic rate and blood pressure

Table S1. Distribution of anthropometric, BP and BMR data for 226 males Deciles Mean BMR Number of SBP DBP PP of BMR (Range) (Kcal/day) valid subjects (mmHg) (mmHg) (mmHg) 1 1359.6±55.5 (1221-1432) 45 119.5±10.6 69.9±6.6 49.6±7.1 2 1469.4±24.4 (1433-1508) 45 127.3±11.5 70.8±7.1 56.5±9.7 3 1550.4±24.2 (1511-1601) 46 122.8±12.1 68.6±8.1 54.2±7.8 4 1665.2±41.5 (1601-1727) 45 128.5±9.3 69.6±7.2 58.9±8.2 5 1909.6±220.7 (1727-2988) 45 132.5±12.6 71.8±8.9 60.7±11.0 Abbreviations: BP, Blood pressure; BMR, Basal metabolic rate; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; PP, Pulse pressure.

Table S2. Distribution of anthropometric, BP and BMR data for 548 females Deciles Mean BMR Number of SBP DBP PP of BMR (Range) (Kcal/day) valid subjects (mmHg) (mmHg) (mmHg) 1 1104.8±40.7 (953-1158) 110 106.2±7.6 66.2±6.3 40.0±5.7 2 1193.0±19.3 (1158-1224) 109 108.4±8.1 66.6±6.5 41.8±6.2 3 1259.4±20.7 (1224-1293) 110 110.1±8.1 67.6±7.0 42.5±5.7 4 1332.3±25.6 (1293-1385) 109 110.8±9.9 67.7±6.7 43.1±6.6 5 1495.2±108.2 (1385-1865) 110 115.8±8.7 70.9±6.5 45.0±6.8 Abbreviations: BP, Blood pressure; BMR, Basal metabolic rate; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; PP, Pulse pressure.

1 Basal metabolic rate and blood pressure

Table S3. Correlation matrix for age, Anthropometric, metabolic and BP data for 226 males Age Height Weight BMI WC SBP DBP PP BMR (years) (cm) (kg) (kg/m2) (cm) (mmHg) (mmHg) (mmHg) (Kcal/day) Age (years) 1.000 -0.026 -0.011 -0.014 0.071 -0.065 -0.032 -0.056 -0.011 Height (cm) 1.000 0.269* -0.086 0.089 0.148*** 0.003 0.183** 0.246* Weight (kg) 1.000 0.900* 0.842* 0.400* 0.143*** 0.388* 0.932* BMI (kg/m2) 1.000 0.849* 0.365* 0.180** 0.315* 0.844* WC (cm) 1.000 0.264* 0.149*** 0.213** 0.788* SBP (mmHg) 1.000 0.605* 0.773* 0.369* DBP (mmHg) 1.000 -0.037 0.139*** PP (mmHg) 1.000 0.353* BMR (Kcal/day) 1.000 Significant difference at P* <0.001, **P<0.01 and ***P<0.05. Abbreviations: BMI, Body mass index; WC, Waist circumference; BP, Blood pressure; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; PP, Pulse pressure; BMR, Basal metabolic rate.

Table S4. Correlation matrix for age, Anthropometric, metabolic and BP data for 548 females Age Height Weight BMI WC SBP DBP PP BMR (years) (cm) (kg) (kg/m2) (cm) (mmHg) (mmHg) (mmHg) (Kcal/day) Age (years) 1.000 -0.045 -0.075 -0.060 -0.069 -0.063 -0.083 -0.002 -0.096*** Height (cm) 1.000 0.481* 0.015 0.245* 0.115** 0.047 0.112** 0.458* Weight (kg) 1.000 0.858* 0.770* 0.361* 0.236* 0.261* 0.982* BMI (kg/m2) 1.000 0.741* 0.358* 0.257* 0.234* 0.859* WC (cm) 1.000 0.292* 0.205* 0.196* 0.763* SBP (mmHg) 1.000 0.709* 0.663* 0.353* DBP (mmHg) 1.000 -0.058 0.235* PP (mmHg) 1.000 0.250* BMR (Kcal/day) 1.000 Significant difference at P* <0.001, **P<0.01 and ***P<0.05. Abbreviations: BMI, Body mass index; WC, Waist circumference; BP, Blood pressure; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; PP, Pulse pressure; BMR, Basal metabolic rate.