Association of Body Mass Index and Waist Circumference with Hypertension in Older Adults

DOI: https://doi.org/10.21203/rs.3.rs-65456/v1

Abstract

Background: The relationship between obesity and prevalent hypertension in older adults has predominantly been estimated using categorical measures of body mass index (BMI) and waist circumference (WC), masking the shape of the dose-response relationship. We aimed to examine the precise relationship of BMI, WC with hypertension, and to assess the appropriate level of BMI and WC for hypertension.

Methods: We examined data for 126,123 individuals in Xinzheng city aged ≥60 years from a population based study from January to December 2019. Logistic regression and restricted cubic spline models were applied to assess the relationship and the appropriate level of BMI and WC for hypertension. An additive interaction analysis was used to test synergistic effects between a higher BMI and WC for hypertension.

Results: The full-adjusted odds ratios (ORs) with 95% confidence intervals (CIs) of an increase of 1 kg/m2 in BMI and 1 cm in WC for hypertension were 1.084 (1.080-1.087) and 1.025 (1.024-1.027), respectively. Multivariable adjusted restricted cubic spline analyses showed the nonlinear relationships of BMI and WC with hypertension in both men and women (all P<0.001). The risk of hypertension increased steeply with increasing BMI from ≥25 kg/m2 and WC ≥ 88 cm or 86 cm for males and females, respectively. And we observed a significant additive interaction between a higher BMI and WC such that the prevalence of hypertension was significantly enhanced.

Conclusion: These findings suggest increased hypertension prevalence in the elderly population with increased BMI and WC. BMI ≤ 25 kg/m2 and WC ≤ 88 cm or 86 cm for males and females may be the best suggestion with regard to primary prevention of hypertension in older adults.  

1. Introduction

Hypertension is a substantial public health burden[1] and a strong modifiable risk factor for cardiovascular disease[2], which has become one of the leading causes of global mortality, accounting for 9.4 million deaths each year[3, 4]. From 2010 to 2017, the weighted prevalence of hypertension increased by 23.4% and is increasing with aging in China [4]; therefore, the identification of modifiable risk factors for hypertension onset has become a public health priority. Obesity is one of the modifiable risk factors associated with cardiovascular disease [57], which is most generally assessed by body mass index (BMI) and waist circumference (WC). Commonly, BMI is used as a substitute for general obesity because of its strong correlation with gold standard body fat[810], and WC is regarded as a better index for evaluating abdominal obesity [11].

Accumulating evidence suggests a potential link between obesity-related hypertension [1214], but controversy exists about the degree of the associations between two anthropometric indicators and the risk of hypertension. Nonetheless, information about the relationship between BMI, WC and hypertension is limited in older adults. In particular, few studies have explored the dose–response relationship and investigated the interaction between BMI and WC. Furthermore, the definition of general overweight and obesity usually ignore ethnicity and age; and are based on their associations with chronic non-communicable diseases. Their utility in predicting hypertension remains uncertain. In addition, higher BMI and WC increase the risk of hypertension alone, but their interrelations with an increased risk of hypertension remain uncertain.

Thus, in this study, we aimed to assess the independent association of BMI and WC with hypertension as well as their possible additive interactions on the risk of hypertension using a large and contemporary population in central China and to explore the appropriate level of BMI and WC for the elderly group.

2. Methods

2.1| Study Population

The study population consisted of participants of a comprehensive health check-up program conducted at fourteen medical examination centers (Supplementary 1). Generally, the Central People’s Government of China demands that residents aged ≥ 60 years participate in health examinations annually to promote good health by enabling early detection of chronic diseases and associated risk factors. The study collected clinical, demographic, and lifestyle information from all participants by face-to-face interviews, physical examinations and blood biochemical examinations. Cross-sectional study data from Xinzheng from January to December 2019 were combined for analyses. For this study, 1969 people were excluded from the current study due to missing physical examination data (n = 117) or biochemical test data (n = 1852). Finally, we had data for 58,115 men and 68,008 women resident ≥ 60 years of age, who were enrolled to assess the association between BMI, WC and hypertension (Fig. 1). Written informed consent was obtained from each participant before data collection. The research ethics committee of Zheng Zhou University approved the current study methodology, protocol, and procedures. (Reference Number: ZZUIRB2019-019)

2.2| Exposure

Height, weight, and WC were measured twice by trained nurses following rigorous protocols. Body height was measured without shoes with a stadiometer, and body weight was measured with participants in light clothing and without shoes by electronic scales. WC was measured with gentle breathing at the midpoint between the lowest rib and the iliac crest to the nearest 0.1 cm[15]. BMI was calculated as weight (kg) divided by the square of height (m). BMI and WC were divided into quintiles.

2.3| Outcome

Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were collected according to the World Health Organization definition [16]. After sitting quietly for five minutes, certified nurses measured each participant’s seated blood pressure three times using a mercury sphygmomanometer (Omron HEM-7125, Kyoto, Japan). We calculated the means of SBP and DBP. Diagnosis of hypertension was defined as SBP ≥ 140 mm Hg/or DBP ≤ 90 mm Hg or use of antihypertensive medication within 2 weeks [16].

2.4| Covariates

Information on demographic characteristics (age, sex, marital status, and place of residence) and behavioral measures (smoking, physical exercise and alcohol consumption) were obtained by a standardized questionnaire, which was in strict accordance with the National Standards for Basic Public Health Services (2011). Marital status was categorized as unmarried, married, divorced and death of a spouse. Smoking status was defined as current smoker, ex-smoker, or never smoker[17]; Alcohol consumption and physical exercise status were categorized as never, once in a while, more than once a week and every day. Place of residence (rural and urban) was defined according to country-specific definitions. Overnight fasting blood samples were collected into vacuum tubes for assessing serum levels of blood glucose using standard methods.

2.5| Statistical Analysis

Continuous data were expressed as the mean ± standard deviation (SD) and categorical data as the number (percentage) for quintiles of BMI or WC, using different values for men and women. Comparisons of the basic characteristics of the quintiles were performed with the χ2test, ANOVA, and Kruskal-Wallis test.

Logistic regression models were used to estimate the adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for BMI, WC and hypertension, taking the quintile with the lowest baseline BMI or WC values as the reference. ORs and 95% CIs for hypertension were estimated for each group with adjustment of multiple confounders. One obesity parameter was introduced at a time in each model to avoid the collinear effect. Model 1 was unadjusted. Model 2 was adjusted for sex and age. Model 3 was adjusted for Model 2 and marital status, alcohol consumption, smoking, physical activity, place of residence and blood glucose. A linear trend test was performed by modeling the median value of each exposure category as a continuous variable in the models. Fully adjusted restricted cubic spline analyses were used to characterize the dose-response association and explore the potential linear or nolinear relationship of BMI, and WC with hypertension. The knots were placed at the 5th, 25th, 50th, 75th and 95th percentiles. The test result for overall association was checked first. If the test for overall association was significant, the test result for nonlinearity and linearity were checked, and the P-value for non-linear association < 0.05 indicated a significant result indicating the linear association. We also evaluated the additive interaction between BMI and WC for hypertension with BMI and WC analyzed as continuous variable in two categories [(BMI: BMI < 25 and BMI ≥ 25. WC: WC < 102 for males, WC < 88 for females and WC ≥ 102 for males, WC ≥ 88 for females) or (WHO proposed cut-off points[18])]. We applied three indicators to evaluate the additive interaction: relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP) or synergy index (S). RERI > 0, AP > 0, or S > 1 was regarded as a significant additive interaction.

Statistical analyses involved the use of SAS V .9.1 (SAS Institute) and R x64 4.0.0.All reported P values were two-sided, with P < 0.05 considered statistically significant.

3. Results

3.1 | Basic demographic characteristics

A total of 126,123 participants were eligible for inclusion in this study. The mean (SD) age was 70.29 (6.94) years. The basic demographic characteristics of the study population according to BMI and WC quintiles are shown in Table 1 and Table 2. The prevalence of hypertension, levels of alcohol consumption, physical exercise and blood glucose showed a significant progressive increase from the quintile with the lowest BMI to the quintile with the highest BMI. This was also the case for the WC quintiles.

Table 1

Basic demographic characteristics of Subjects According to BMI Quintiles

Characteristics

First Quintile

Second Quintile

Third Quintile

Fourth Quintile

Fifth Quintile

P for trend

Male

BMI < 22.20

22.20 ≤ BMI < 24.02

24.02 ≤ BMI < 25.73

25.73 ≤ BMI < 27.78

BMI ≥ 27.78

 

Female

BMI < 22.43

22.43 ≤ BMI < 24.42

24.42 ≤ BMI < 26.27

26.27 ≤ BMI < 28.58

BMI ≥ 28.58

 

N0.

25164

25236

25166

25268

25289

 

Hypertension

         

< 0.001

no

13812(54.89)

12206(48.37)

11346(45.08)

10508(41.59)

9205(36.4)

 

yes

11352(45.11)

13030(51.63)

13820(54.92)

14760(58.41)

16084(63.6)

 

Age, years

72.08 ± 7.75

70.54 ± 7.17

69.96 ± 6.66

69.59 ± 6.49

69.29 ± 6.17

< 0.001

sex,%

         

0.981

men

11619(46.17)

11584(45.9)

11612(46.14)

11642(46.07)

11658(46.1)

 

women

13545(53.83)

13652(54.1)

13554(53.86)

13626(53.93)

13631(53.9)

 

Marital status,%

         

< 0.001

unmarried

558(2.22)

432(1.71)

341(1.36)

285(1.13)

246(0.97)

 

married

19847(78.87)

20968(83.09)

21315(84.7)

21686(85.82)

21916(86.66)

 

divorced

135(0.54)

91(0.36)

124(0.49)

92(0.36)

83(0.33)

 

death of a spouse

4624(18.38)

3745(14.84)

3386(13.45)

3205(12.68)

3044(12.04)

 

Smoking,%

         

< 0.001

Never smokers

20950(83.25)

21506(85.22)

21557(85.66)

21879(86.59)

22079(87.31)

 

Former smokers

537(2.13)

591(2.34)

611(2.43)

623(2.47)

678(2.68)

 

Current smokers

3677(14.61)

3139(12.44)

2998(11.91)

2766(10.95)

2532(10.01)

 

Alcohol consumption,%

         

< 0.001

Never

23681(94.11)

23651(93.72)

23364(92.84)

23379(92.52)

23296(92.12)

 

Once in a while

906(3.6)

998(3.95)

1113(4.42)

1129(4.47)

1215(4.8)

 

More than once a week

243(0.97)

277(1.1)

305(1.21)

350(1.39)

352(1.39)

 

Every day

334(1.33)

310(1.23)

384(1.53)

410(1.62)

426(1.68)

 

Physical exercise,%

         

< 0.001

Never

16555(65.79)

15881(62.93)

15014(59.66)

14897(58.96)

15290(60.46)

 

Once in a while

863(3.43)

912(3.61)

943(3.75)

998(3.95)

966(3.82)

 

More than once a week

1576(6.26)

1809(7.17)

1937(7.7)

1914(7.57)

1830(7.24)

 

Every day

6170(24.52)

6634(26.29)

7272(28.9)

7459(29.52)

7203(28.48)

 

Residence

         

< 0.001

Rural areas

21838(86.78)

20703(82.04)

20322(80.75)

20314(80.39)

20365(80.53)

 

Urban areas

3326(13.22)

4533(17.96)

4844(19.25)

4954(19.61)

4924(19.47)

 

Blood glucose, mmol/L

5.44 ± 1.8

5.68 ± 1.86

5.82 ± 1.91

5.91 ± 1.91

6.04 ± 1.95

< 0.001

Data are shown as mean ± SD or n (%).BMI, body mass index; Residence, area of residence; Blood glucose, Fasting plasma glucose.

 

Table 2

Basic demographic characteristics of Subjects According to WC Quintiles

Characteristics

First Quintile

Second Quintile

Third Quintile

Fourth Quintile

Fifth Quintile

P for trend

Male

WC < 81

81 ≤ WC < 86

86 ≤ WC < 90

90 ≤ WC < 96

WC ≥ 96

 

Female

WC < 79

79 ≤ WC < 84

84 ≤ WC < 89

89 ≤ WC < 95

WC ≥ 95

 

N0.

23098

23498

24971

26736

27820

 

Hypertension

         

< 0.001

no

12346(53.45)

11421(48.6)

11219(44.93)

11539(43.16)

10552(37.93)

 

yes

10752(46.55)

12077(51.4)

13752(55.07)

15197(56.84)

17268(62.07)

 

Age, years

71.29 ± 7.65

70.48 ± 7.15

69.96 ± 6.79

69.86 ± 6.57

70.01 ± 6.51

< 0.001

sex,%

         

0.001

men

10850(46.97)

10887(46.33)

10190(40.81)

13076(48.91)

13112(47.13)

 

women

12248(53.03)

12611(53.67)

14781(59.19)

13660(51.09)

14708(52.87)

 

Marital status,%

         

< 0.001

unmarried

546(2.36)

427(1.82)

312(1.25)

289(1.08)

288(1.04)

 

married

18446(79.86)

19404(82.58)

21106(84.52)

22908(85.68)

23868(85.79)

 

divorced

120(0.52)

99(0.42)

106(0.42)

103(0.39)

97(0.35)

 

death of a spouse

3986(17.26)

3568(15.18)

3447(13.8)

3436(12.85)

3567(12.82)

 

Smoking,%

         

< 0.001

Never smokers

19441(84.17)

19920(84.77)

21755(87.12)

22749(85.09)

24106(86.65)

 

Former smokers

469(2.03)

534(2.27)

555(2.22)

748(2.8)

734(2.64)

 

Current smokers

3188(13.8)

3044(12.95)

2661(10.66)

3239(12.11)

2980(10.71)

 

Alcohol consumption,%

       

< 0.001

Never

21794(94.35)

22082(93.97)

23500(94.11)

24555(91.84)

25440(91.45)

 

Once in a while

799(3.46)

913(3.89)

927(3.71)

1322(4.94)

1400(5.03)

 

More than once a week

208(0.9)

217(0.92)

233(0.93)

406(1.52)

463(1.66)

 

Every day

297(1.29)

286(1.22)

311(1.25)

453(1.69)

517(1.86)

 

Physical exercise,%

         

< 0.001

Never

14960(64.77)

14768(62.85)

15405(61.69)

15830(59.21)

16674(59.94)

 

Once in a while

808(3.5)

835(3.55)

996(3.99)

998(3.73)

1045(3.76)

 

More than once a week

1507(6.52)

1790(7.62)

1871(7.49)

1962(7.34)

1936(6.96)

 

Every day

5823(25.21)

6105(25.98)

6699(26.83)

7946(29.72)

8165(29.35)

 

Residence

         

< 0.001

Rural areas

20201(87.46)

20096(85.52)

20773(83.19)

21110(78.96)

21362(76.79)

 

Urban areas

2897(12.54)

3402(14.48)

4198(16.81)

5626(21.04)

6458(23.21)

 

Blood glucose, mmol/L

5.44 ± 1.71

5.64 ± 1.81

5.76 ± 1.88

5.9 ± 1.97

6.08 ± 2.01

< 0.001

Data are shown as mean ± SD or n (%).WC, waist circumference; Residence, area of residence; Blood glucose, Fasting plasma glucose.

 

3.2 | OR and 95% confidence intervals (CIs) for HYPERTENSION according to BMI and WC

Table 3 presents the results from the logistic regression that estimated the association between the levels of BMI, WC and hypertension. The multivariable adjusted OR (95% CI) per 1 kg/m2 increase in BMI was 1.084 (1.08 to 1.087). In all three models, the ORs for hypertension increased significantly with increasing BMI quintiles (P for trend < 0.01). In Model 3, the multivariate-adjusted OR (95% CIs) for hypertension with the highest BMI quintile group compared with the lowest quintile group was 2.297 (2.214 to 2.383). The multivariable adjusted OR (95% CI) per 1 cm increase in WC was 1.025 (1.024 to 1.027).The highest quintile group had a greater hypertension prevalence than the other quintile group, and the crude ORs (95% CIs) for hypertension compared with the lowest quintile group was 1.879 (1.814 to 1.947). After adjustment for sex, age, marital status, alcohol consumption, smoking, physical activity, place of residence and blood glucose, the ORs were enhanced, and the multivariate-adjusted ORs (95% CIs) for hypertension with the highest WC quintile group compared with the lowest quintile group was 1.974 (1.904 to 2.046).

Table 3

Association between BMI, WC and hypertension

Characteristics

Model 1

Model 2

Model 3

OR(95%CI)

OR(95%CI)

OR(95%CI)

BMI,per1kg/m2 change

1.077(1.074,1.08)

1.085(1.081,1.088)

1.084(1.080,1.087)

BMI group

     

Q1

1.000(ref)

1.000(ref)

1.000(ref)

Q2

1.299(1.254,1.345)

1.361(1.313,1.41)

1.365(1.317,1.414)

Q3

1.482(1.431,1.535)

1.581(1.525,1.638)

1.579(1.524,1.637)

Q4

1.709(1.65,1.77)

1.844(1.779,1.911)

1.836(1.771,1.903)

Q5

2.126(2.051,2.203)

2.317(2.235,2.403)

2.297(2.214,2.383)

P for trend

< 0.001

< 0.001

< 0.001

WC,per1 cm change

1.022(1.021,1.024)

1.025(1.024,1.026)

1.025(1.024,1.027)

WC group

     

Q1

1.000(ref)

1.000(ref)

1.000(ref)

Q2

1.214(1.171,1.259)

1.239(1.195,1.286)

1.238(1.194,1.285)

Q3

1.408(1.358,1.459)

1.443(1.391,1.496)

1.445(1.393,1.499)

Q4

1.512(1.46,1.567)

1.577(1.522,1.635)

1.593(1.536,1.651)

Q4

1.879(1.814,1.947)

1.95(1.881,2.021)

1.974(1.904,2.046)

P for trend

< 0.001

< 0.001

< 0.001

BMI, body mass index; WC, waist circumference. Model1: unadjusted. Model2: adjusted for sex and age. Model3 adjusted for Model2 and marital status, alcohol consumption, smoking, physical activity, place of residence and blood glucose. Linear trend test was performed by modeling the median value of each exposure category as a continuous variable in the models.

 

3.3| Dose response analysis

Multivariable adjusted restricted cubic spline analyses showed the nonlinear relationships of BMI with hypertension (all P < 0.001; Figure.2.A). The risk of hypertension increased with increasing BMI. As BMI increased, the ORs increased from 0.30 (0.27 to 0.34) to 2.03 (1.77 to 2.33) in the 15–42 kg/m2 range. As a result, the ORs were inversely associated with hypertension when BMI was below 25 kg/m2, but presented a significant risk effect above this value. Subgroup analyses on men-women did not show significant differences (Figure.2.B; Figure.2.C).

The results of the dose-response relationship analysis between WC and hypertension are shown in Figure.3.A. A nonlinear association (all P < 0.001) between WC and hypertension was detected. As WC increased, the ORs increased from 0.26 (0.20 to 0.37) to 2.57 (2.23 to 2.95) in the 40–130 cm range. When stratified by sex, the ORs increased from 0.31 (0.25 to 0.38) to 3.23 (2.62 to 3.99) in the male population. With an increase in WC, when WC was over 88 cm and 86 cm for males and females, respectively, WC was more steeply positively associated with the risk of hypertension (Figure.3.B; Figure.3.C).

3.4| Additive interaction analysis

We observed a significant additive interaction between higher BMI and WC such that the prevalence of hypertension increased (RERI = 1.28, 95% CI: 1.13–1.43; AP = 0.43, 95% CI: 0.41–0.45; S = 2.88, 95% CI: 2.79–2.97). If BMI < 25.0 kg/m2 and WC < 88 cm for males and WC < 86 cm for females were used as the reference, BMI ≥ 25 alone and WC ≥ 88 for males or WC ≥ 86 for females alone were both associated with increased risks of hypertension. The copresence of both factors greatly enhanced the adjusted ORs of higher BMI alone 1.477 (1.42 to 1.538) and higher WC alone 1.228 (1.184 to 1.274)) to 1.728 (1.683 to 1.773) for hypertension, with significant additive interactions. When BMI and WC were classified by the proposed cut-off points[18] on the waist circumference continuum (BMI: 25 kg/m2, WC: 88 cm for females and 102 cm for males), there was still significant additive interaction (RERI = 1.68, 95% CI: 1.48–1.87; AP = 0.48, 95% CI: 0.46–0.50; S = 3.00, 95% CI: 2.91–3.10).

Table 4

Additive interaction analysis of BMI and WC for hypertension

Characteristics

Model 1

Model 2

Model 3

OR(95%CI)

OR(95%CI)

OR(95%CI)

Additive interaction models1

     

BMI < 25 and WC < 88/102

1.000(ref)

1.000(ref)

1.000(ref)

BMI ≥ 25 and WC < 88/102

1.459(1.42,1.5)

1.546(1.503,1.589)

1.520(1.478,1.563)

BMI < 25 and WC ≥ 88/102

1.377(1.304,1.453)

1.286(1.216,1.36)

1.294(1.223,1.368)

BMI ≥ 25 and WC ≥ 88/102

1.749(1.699,1.799)

1.757(1.705,1.811)

1.763(1.71,1.817)

Additive interaction models2

     

BMI < 25 and WC < 86/88

1.000(ref)

1.000(ref)

1.000(ref)

BMI ≥ 25 and WC < 86/88

1.469(1.412,1.528)

1.512(1.453,1.574)

1.477(1.42,1.538)

BMI < 25 and WC ≥ 86/88

1.215(1.172,1.26)

1.214(1.171,1.259)

1.228(1.184,1.274)

BMI ≥ 25 and WC ≥ 86/88

1.662(1.62,1.705)

1.727(1.683,1.772)

1.728(1.683,1.773)

BMI, body mass index; WC, waist circumference. Model 1: unadjusted. Model 2: adjusted for sex and age. Model 3 adjusted for Model 2 and marital status, alcohol consumption, smoking, physical activity, place of residence and blood glucose.

4. Discussion

In this study, the prevalence of hypertension increased with rising BMI and WC in the older population of Xinzheng, China, in males and females and in the entire study population. These associations remained significant after adjustment for multiple factors and restricted cubic spline analysis showed clear dose-response relationships. To our knowledge, this is the first study to research the association between BMI, WC and hypertension risk in an older Chinese population. At the same time, this study conducted an additive interaction analysis, which concluded a significant additive interaction between BMI and WC such that the prevalence of hypertension increased. To some extent, this study provides a better understanding of the association of anthropometric indicators of obesity with hypertension rather than focusing on individual indices, which would be more enlightening for hypertension prevention.

Our results are consistent with most previous studies that showed that the risk of hypertension increases with rising BMI [14, 19]. The “Look up 7+” epidemiological study showed that blood pressure increased significantly across BMI levels[20], which indicated that there was not only a strong relationship between BMI and hypertension, but also an association between the continuous variables of BMI and blood pressure. Our study confirmed that there was a nonlinear dose-response relationship between BMI and the risk of hypertension, which was similar to previous studies [21, 22]. However, a prospective study including 1412 subjects provided evidence that an increase in BMI is associated with a linearly increased adjusted risk of developing conditions with high hypertension risk[23], possibly because of estimating the relationship using categorical measures of BMI, masking the shape of the dose-response relationship. Furthermore, in our large sample (n = 126123), BMI below 25 kg/m2 was regarded as a healthy weight for the elderly in terms of hypertension prevalence. However, our proposed appropriate level for hypertension were higher than those in some previous studies[24], which could be due to differences in the age range. The average age we sampled was much higher because BMI is greater in older populations, which might be a reason for the inconsistent findings, and when one of the studies stratified data by age (< 50 and ≥ 50 years), the appropriate level for the older group was ༜25 kg/m2 for men and women[25]. Contrary to the three studies above, the NHANES study suggested 27 kg/m2 as the value [26], but there may be innate or cultural differences between the U.S. and Asia, including dietary habits, macronutrient content, and physical activity habits. Given the marked variations in different world regions, countries and populations within countries, the use of unified range may underestimate or overestimate the health hazards[27]; thus, it is of great interest to determine the appropriate level for cardiovascular disease risks.

For WC, the relationship with hypertension was reported by most studies [28, 29]. Most studies have found a positive association between WC and hypertension[30], whereas a null association was found in a prospective study from European populations[29]. The low participation rate and the relatively high study drop-out rate may bias the result. We highlighted the increased risk of hypertension when WC was over 88 cm and 86 cm for males and females, respectively. Our proposed WC appropriate level is higher than those reported by M Gus et al[31]. As early as 2009, they suggested that the best range to predict hypertension were ༜87 cm in men and ༜80 cm in women, and increasing WC over time could be the cause of the difference. The worldwide upward trend in obesity has been dramatic; from 2013 to 2018, the mean WC increased from 82 cm to 86.3 cm for men and from 79.1 cm to 83.4 cm for older women [32]. However,lower appropriate level were suggested by previous studies[33, 34], and ethnic and racial differences might explain the discrepancy between different studies.

In China, rapid economic development and associated dramatic lifestyle changes have led to a substantial increase in the prevalence of obesity and related noncommunicable diseases, including cardiovascular diseases, which is concerning for a developing country with a population of 1.37 billion. To our knowledge, the present study is the first to report a synergistic effect of higher BMI and WC on the risk of hypertension in the aged. In other words, the copresence of higher BMI and WC greatly increased the risk of hypertension, more than the summation of the risks due to exposure to either of them. In a cohort of 17,803 pregnant Chinese women, the copresence of a higher BMI and WC interacts to further increase the risk of gestational diabetes mellitus [35]. In addition, RISKESDAS research showed that only when general obesity or overweight coexisted with central obesity was the prevalence of hypertension significantly increased [13]. This finding supported the stable relation between excess body fat and blood pressure. It is generally believed that the increased body mass would raise blood volume and cardiac output and then lead to the inadequate vasodilatation while the increased activity of the sympathetic nervous system, abnormal rennin-angiotensin-aldosterone relation and insulin resistance would arouse defects in the control of vascular resistance. These adverse vascular responses may dominate the development of obesity-associated hypertension [36, 37].

Several additional points warrant discussion. First, the findings of this cross-sectional study are not conclusive evidence of a causal relation of WC and BMI with hypertension. Thus, we must be cautious in interpreting the present results, and further cohort studies are needed to clarify our findings. Second, as the study data come from the Chinese middle area among the older population, our proposed appropriate level for the indices are only valid for this population. Third, selection of the appropriate levels for BMI and WC for hypertension was based on visual checking of the shapes of the OR curves. The true appropriate level of BMI and WC for hypertension remained arbitrary and might deviate slightly from the selected values. However, to the best of our knowledge, our study is the first to explore the association between anthropometric Indicators of obesity and hypertension among older people with a large sample size in central China, and it is of practical significance to improve relevant research. Second, this study is based on a comprehensive health check-up program, which not only contains data related to physical measurements and disease, but also includes information on demographic characteristics, physical activity, daily living habits and some blood biochemical tests, therefore, we can make full use of this information for a more comprehensive and reliable analysis.

5. Conclusion

Understanding the association between BMI, WC and risks of hypertension is very important because various interpretations can lead to conflicting recommendations of ideal BMIs and WCs among the elderly. The results of this study revealed that being as lean as possible within the normal range may be a best suggestion in reducing the risks of hypertension. However, further cohort studies and replication studies in Chinese and other populations are needed before the results can be used in clinical practice to detect high-risk elderly individuals for early intervention.

Abbreviations

Declarations

Ethics approval and consent to participate

Written informed consent was obtained from each participant before data collection. The research ethics committee of Zheng Zhou University approved the current study methodology, protocol, and procedures. (Reference Number: ZZUIRB2019-019)

Consent for publication

Not applicable.

Competing interests

None declared.

Funding

This study was supported by National Key Research and Development Program “Research on prevention and control of major chronic non-communicable diseases” of China (Grant NO: 2017YFC1307705).

Authors' contributions

Wenli Zhang: Conceptualization, Methodology, Software, Formal analysis, Data Curation, Writing - Original Draft. Kun He: Validation, Data Curation, Investigation, Formal analysis. Hao Zhao & Xueqi Hu: Data Curation, Investigation. Chunyu Yin & Xiaoyan Zhao: Funding acquisition. Songhe Shi: Funding acquisition, Project administration, Supervision, Revising it critically for important intellectual content, final approval of the version to be submitted.

Acknowledgements

The investigators thank the dedicated participants and all research staff of the study.

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