ABSTRACT
Background
A higher body fat percentage is associated with hypertension, even in non-obese individuals. The difference in body composition may be related to hypertension. The fat mass index (FMI) and fat-free mass index (FFMI) are proposed indicators of body composition. This study aimed to examine the relationship of a combination of FMI and FFMI with hypertension.
Methods
We conducted a cross-sectional study of 5,058 men and 11,842 women aged ≥ 20 years in the Miyagi Prefecture, northeastern Japan. The FMI and FFMI were calculated as the fat mass and fat-free mass divided by the height squared, respectively. The indices were classified into quartiles and combined into 16 groups. Hypertension was defined as casual blood pressure ≥ 140/90 mmHg and/or self-reported treatment for hypertension. Multivariable logistic regression models, adjusted for potential confounders, were used to assess the relationship of a combination of FMI and FFMI with hypertension.
Results
Higher FMI was associated with hypertension in most of the FFMI subgroups. Similarly, a higher FFMI was associated with hypertension in most of FMI subgroups. For men, the association between FFMI and hypertension in the lowest FMI group was not significant.
Conclusions
Reducing the FMI and FFMI may be important in preventing hypertension. For men, the relationship between the FFMI and hypertension in the lowest FMI group might be weak.
Introduction
Body mass index (BMI) is a widely used index for assessing obesity. Many epidemiological studies have shown that a higher BMI is associated with incident hypertension (Citation1–7). However, a recent study has shown that a higher body fat percentage (BF%) is associated with an increased risk of hypertension, even in non-obese individuals (Citation8). Furthermore, an increase in fat mass (FM) and fat-free mass (FFM) is associated with incident hypertension (Citation9). These findings suggest that not only BMI but also differences in body composition are associated with hypertension.
VanItallie et al. (Citation10) proposed the fat mass index (FMI) and fat-free mass index (FFMI) as indices of body composition. The FMI and FFMI were calculated as the FM and FFM divided by the height squared, respectively (Citation11). Thus, the FMI and FFMI are useful for comparing individuals with different height measurements (Citation10–13).
Previous studies have shown that the FMI and FFMI are associated with blood pressure (BP) and the prevalence of hypertension, respectively (Citation14,Citation15). However, the FMI and FFMI are not independent of each other because height is used to compute both indices. When the FMI and FFMI are simultaneously adopted in the same statistical model, the estimation results may become unstable. Thus, to avoid this instability, we hypothesized that a combination of the FMI and FFMI may be more effective. However, no studies have investigated the association between the combined FMI and FFMI and hypertension.
Therefore, this study aimed to examine the association between the combined FMI and FFMI and the prevalence of hypertension. We also examined whether the combined FMI and FFMI model is superior to the BMI model for assessing the prevalence of hypertension.
Methods
Participants
We conducted a cross-sectional study using data from the Tohoku Medical Megabank Community-Based Cohort Study, a population-based prospective cohort study of individuals aged ≥ 20 years in the Miyagi or Iwate Prefecture, northeastern Japan (Citation16). In this study, only participants recruited in the Miyagi Prefecture were included. Participants were recruited between May 2013 and March 2016 using three approaches. The type 1 survey (40,433 participants) was conducted at a municipal-specific health checkup site. This approach collected basic information from blood and urine, a questionnaire, and municipal health checkups. The type 1 additional survey (664 participants) was conducted on different dates from those of the municipal health checkups. This approach collected the same information as the type 1 survey. The type 2 survey (13,855 participants) was conducted at the Community Support Center. This approach collected the same information as the type 1 survey, in addition to several physiological measurements (carotid echography, calcaneal ultrasound bone mineral density, body composition, etc.). Informed consent was obtained from all participants. The study was approved by the Institutional Review Board of the Tohoku Medical Megabank Organization (approval number: 2019–4–065).
To be included in the analysis, participants were required to undergo several physiological measurements. In addition to the participants of the type 2 survey (13,855 participants), those who underwent several physiological measurements at the Community Support Center after the type 1, type 1 additional surveys were also included (type 1 survey, 3,833; type 1 additional survey, 134). In total, 17,822 participants were eligible (). Those who withdrew from the study by June 10, 2019, failed to return the self-reported questionnaire, did not undergo physiological measurements, or had missing height, weight, BF%, and BP data were excluded (922 participants). Thus, the data of 16,900 participants were analyzed.
Figure 1. Flowchart of study participants
Anthropometry
Height was measured to the nearest 0.1 cm using a stadiometer (AD-6400; A&D Co., Ltd.). Weight was measured in increments of 0.1 kg using a body composition analyzer (InBody720; Biospace Co., Ltd., Seoul, Korea) and subtracted by 1.0 kg to account for the weight of the participants’ clothing. BMI was calculated as the weight (kg) divided by the height (m2). BF% was measured using the same body composition analyzer. A tetra-polar eight-point tactile electrode system was used. The system separately measured the impedance of the participants’ right arm, left arm, trunk, right leg, and left leg at six different frequencies (1, 5, 50, 250, 500, and 1,000 kHz) for each body segment. The participants were instructed to stand upright and to grasp the handles of the analyzer, thereby providing contact with a total of eight electrodes (two for each foot and hand). The participants were not instructed to refrain from exercise and eating or drinking before the measurements were taken. FM was calculated by multiplying the weight (kg) by the BF%. The FMI was calculated as the FM divided by the height squared. To calculate the FFMI (Citation1): FFM% was calculated by subtracting the BF% from 100 (Citation2), FFM was calculated by multiplying the weight by the FFM%, and (Citation3) FFMI was calculated as the FFM divided by the height squared.
Hypertension
BP was measured at the Community Support Center. After resting in a sitting position for ≥ 2 min, BP was measured twice in the upper right arm using a digital automatic BP monitor (HEM-9000AI; Omron Healthcare Co., Ltd., Kyoto, Japan). The mean value of the two recorded measurements was used in the analysis. Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg, diastolic blood pressure (DBP) ≥ 90 mmHg, and/or self-reported treatment for hypertension.
Potential confounders
A self-reported questionnaire was used to assess the participants’ demographic characteristics, smoking and drinking status, and diabetes, and dyslipidemia treatment. Age was determined at the time of visiting the Community Support Center. Smoking status was classified as “never-smoker” (smoked < 100 cigarettes in their lifetime), “ex-smoker” (smoked ≥ 100 cigarettes in their lifetime, but answered “current non smoker” in the questionnaire), or current smoker (smoked ≥ 100 cigarettes in their lifetime and answered “current smoker” in the questionnaire) (Citation17). Drinking status was classified as “never-drinker” (participants who answered “little or no drinking” or could not drink constitutionally), “ex-drinker” (participants who answered “stopped drinking”), “< 23 g/day,” or “≥ 23 g/day.” To determine whether participants drank < 23 or ≥ 23 g/day, the type of alcohol was classified as follows: sake, distilled spirits, shochu-based beverages, beer, whiskey, or wine. The frequency of alcohol consumption was classified as “almost never,” “1–3 days/month,” “1–2 days/week,” “3–4 days/week,” “5–6 days/week,” or “daily.” Participants answered how much of each type of alcohol they drank. The type of alcohol was multiplied by the frequency and amount, and converted to the amount of ethanol. We determined the cutoff value to be 23 g as it is the traditional Japanese unit of sake. Participants who answered that undergoing treatment in self-reported questionnaire were defined as treatment for diabetes, and dyslipidemia, respectively.
Data of participants recruited using the type 2 survey were obtained using a self-reported questionnaire administered upon enrollment in the study. Participants recruited using the type 1 and type 1 additional surveys were asked to respond to the self-administered questionnaire sent by mail. Afterward, body composition and BP were measured when participants visited the Community Support Center. Results showed that the lifestyle at the time of body composition and BP measurements may be different from that at the time of answering the self-administered questionnaire. Thus, participants’ lifestyle data were collected at different times in the type 1 and type 1 additional surveys.
Statistical analysis
Data were presented as mean (standard deviation) or median (interquartile range) for continuous variables and number (percentage) for categorical variables. Analyses were performed separately for men and women, because the distribution of the FMI and FFMI differ between men and women. The FMI was categorized into the following quartile groups using whole population: Q1 (lowest group), Q2, Q3, and Q4 (highest group). The FFMI was also categorized into the following quartile groups using whole population: Q1 (lowest group), Q2, Q3, and Q4 (highest group). The FMI and FFMI were also combined and categorized into 16 groups.
In terms of the baseline characteristics of the FMI, trend tests were performed to evaluate the linear relationships between the FMI and the baseline characteristics. Similarly, we also performed the liner relationship of FFMI with baseline variable. For the trend tests, a simple linear model was used to analyze age, height, BMI, BF%, SBP, DBP, FM, and FFM (continuous variables), while a simple logistic regression model was used to analyze the prevalence and treatment of hypertension (categorical variables). A trend test was not performed to evaluate the linear relationship between the FMI and FFMI because of the instability of the estimation results due to multicollinearity. A chi-square test was used to compare smoking and drinking status between the FMI quartiles. Similarly, a chi-square test was used to compare smoking and drinking status between the FFMI quartiles. We examined the association between the BMI, FMI and FFMI by Pearson correlation coefficients.
Multivariable logistic regression analysis was used to examine the association between the FMI and the prevalence of hypertension. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were estimated. Two multivariable-adjusted models were applied. In model 1, covariates included age (years). In model 2, additional covariates included smoking (never-smoker, ex-smoker, or current smoker) and drinking (never-drinker, ex-drinker, or current drinker [< 23 or ≥ 23 g/day]) status. The FFMI was not adjusted to avoid multicollinearity. We also examined the association between the FFMI and the prevalence of hypertension. The FMI was not adjusted to avoid multicollinearity.
To avoid multicollinearity, the FMI and FFMI were combined, and the association between the combined FMI and FFMI and the prevalence of hypertension was evaluated using multivariable logistic regression analysis. To determine whether the combined FMI and FFMI model is superior to the BMI model for assessing the prevalence of hypertension, we calculated the area under the receiver operating characteristic curve (AUROC) and the 95% CIs. We performed multiple logistic regression analysis and calculated the AUROC for BMI and the combined FMI and FFMI. The same model was used as above. AUROCs were compared using the DeLong test.
We also conducted the analyses that selected only individuals who had no treatment for diabetes and/or dyslipidemia to eliminate the influence of treatment for diabetes or dyslipidemia. In this analysis, participants who answered that undergoing treatment in self-reported questionnaire were defined as treatment for diabetes and treatment for dyslipidemia, respectively.
All statistical analyses were conducted using SAS for Windows (version 9.4; SAS Inc., Cary, NC, USA) and JMP Pro 15. Two-tailed P-values of < 0.05 were considered significant.
Results
The characteristics of the study participants are shown in and . In total, 5,058 men and 11,842 women fulfilled the inclusion criteria and were included in the analyses. The mean (standard deviation) age was 61.3 (13.1) years for men and 57.3 (12.9) years for women. The prevalence rate of hypertension was 55.3% (n = 2,795) in men and 35.0% (n = 4,145) in women; the prevalence of hypertension was higher in men than in women. The median (interquartile range) FMI was higher in women (6.8 [5.2–8.7] kg/m2) than in men (5.6 [4.3–7.1] kg/m2), while the median (interquartile range) FFMI was higher in men (18.0 [17.0–18.9] kg/m2) than in women (15.2 [14.5–16.0] kg/m2). The proportions of current smokers and current drinkers were higher in men than in women. The correlations among BMI, FMI and FFMI are shown in Supplementary Table S1. The correlations between FMI and FFMI were r = 0.39 for men, r = 0.51 for women.
Table 1. Characteristics of the combined fat mass and fat-free mass index in mena.
Table 2. Characteristics of the combined fat mass and fat-free mass index in womena.
The FMI quartiles for men were Q1 (< 4.3 kg/m2), Q2 (4.3–5.5 kg/m2), Q3 (5.6–7.0 kg/m2), and Q4 (≥ 7.1 kg/m2), while those for women were Q1 (< 5.2 kg/m2), Q2 (5.2–6.7 kg/m2), Q3 (6.8–8.6 kg/m2), and Q4 (≥ 8.7 kg/m2). The characteristics of the participants according to the FMI are shown in Supplementary Table S2. The FMI was positively associated with age, BMI, and hypertension in both men and women (P for linear trend < 0.001). Smoking and drinking status also differed significantly between the quartiles in both men and women (P for difference < 0.01).
The FFMI was categorized into the following sex-specific quartiles: Q1 (< 17.0 kg/m2), Q2 (17.0–17.9 kg/m2), Q3 (18.0–18.8 kg/m2), and Q4 (≥ 18.9 kg/m2) for men and Q1 (< 14.5 kg/m2), Q2 (14.5–15.1 kg/m2), Q3 (15.2–15.9 kg/m2), and Q4 (≥ 16.0 kg/m2) for women. The characteristics of the participants according to the FFMI are shown in Supplementary Table S3. The FFMI was positively associated with BMI and hypertension in both men and women (P for linear trend < 0.01). In men, age was inversely related to the FFMI (P for linear trend <0 .001). Smoking and drinking status differed significantly between the quartiles in both men and women (P for difference < 0.05).
The participants’ characteristics according to the combined FMI and FFMI are shown in and . Participants with a higher FMI and FFMI were more likely to have a higher BMI in both men and women. Furthermore, the combined FMIQ4 and FFMIQ1 (highest FMI quartile and lowest FFMI quartile) was associated with a higher age and a higher prevalence of hypertension in both men and women.
The FMI was positively associated with the prevalence of hypertension in both men and women, even after adjusting for potential confounders. Compared to the Q1 group (lowest FMI quartile), the adjusted ORs (95% CIs) for men in the Q2–4 groups were 1.50 (1.27–1.77), 2.06 (1.73–2.44), and 3.66 (3.06–4.37), respectively. For women, the adjusted ORs (95% CIs) were 1.33 (1.17–1.51), 1.68 (1.48–1.90), and 3.45 (3.05–3.90), respectively (Supplementary Figure S1.).
In the multivariate analysis, an association between a higher FFMI and a higher prevalence of hypertension was observed in both men and women. Compared to the Q1 group, the multivariate ORs (95% CIs) for men in the Q2–4 groups were 1.13 (0.96–1.34), 1.28 (1.08–1.52), and 2.07 (1.73–2.47), respectively. For women, the multivariate ORs (95% CIs) were 1.25 (1.11–1.41), 1.65 (1.46–1.85), and 2.39 (2.12–2.70), respectively (Supplementary Figure S2.).
shows the association between the combined FMI and FFMI and the prevalence of hypertension. In the multivariate analysis, compared to the combined FMIQ1 and FFMIQ1 (lowest FMI quartile and lowest FFMI quartile), a higher FMI was associated with hypertension in most of the FFMI subgroups. Similarly, a higher FFMI was associated with hypertension in most of the FMI subgroups. In addition, the combined FMIQ4 and FFMIQ1 was significantly associated with the prevalence of hypertension in both men (OR: 2.86 [95% CI: 1.89–4.31]) and women (OR: 3.00 [95% CI: 2.12–4.25]). For men, the FFMI was positively associated with the prevalence of hypertension in the FMIQ2–4 groups. However, in the FMIQ1 group, FFMI was not associated with the prevalence of hypertension.
Figure 2. Association between the combined FMI and FFMI with the prevalence of hypertension in men (adjusted for age, smoking status, drinking status). P values for difference were derived from multiple logistic regression analysis. Bars represent 95% confidence intervals. Abbreviations: CI, confidence interval; FFMI, fat free mass index; FMI, fat mass index; OR, odds ratio; Q, quartile.
Figure 3. Association between the combined FMI and FFMI with the prevalence of hypertension in women (adjusted for age, smoking status, drinking status). P values for difference were derived from multiple logistic regression analysis. Bars represent 95% confidence intervals. Abbreviations: CI, confidence interval; FFMI, fat free mass index; FMI, fat mass index; OR, odds ratio; Q, quartile.
shows the AUROC values (95% CIs) for the prevalence of hypertension, BMI, and the combined FMI and FFMI. In the multivariate analysis, the AUROC values (95% CIs) for men were 0.727 (0.713–0.741) for BMI and 0.729 (0.715–0.743) for the combined FMI and FFMI, which were not significantly different. For women, the AUROC values (95% CIs) for BMI and the combined FMI and FFMI were 0.761 (0.753–0.770) and 0.759 (0.750–0.767), respectively, which were significantly different (P for difference = 0.018).
Figure 4. Area under the ROC curve (AUROC) for the prevalence of hypertension, BMI and the combined FMI and FFMI. We used BMI as continuous variable, combination of FMI and FFMI as categorical variable. The AUROC values (95% confidence intervals) for BMI were 0.727 (0.713–0.741) for men, 0.761 (0.753–0.770) for women. The AUROC values (95% confidence intervals) for the combined FMI and FFMI were 0.729 (0.715–0.743) for men, 0.759 (0.750–0.767) for women. All models were adjusted for age, smoking status, and drinking status. P for difference were derived from DeLong test. Abbreviations: BMI, body mass index; FFMI, fat free mass index; FMI, fat mass index.
When we excluded participants with treatment for diabetes and/or dyslipidemia, the results were substantially unchanged compared with those using all participants (Supplementary Figure S3 and S4).
Discussion
In this study, we showed that the FMI and FFMI were associated with an increased prevalence of hypertension in both men and women. For the combined FMI and FFMI, a higher FMI was associated with hypertension in most of the FFMI subgroups, while a higher FFMI was associated with hypertension in most of the FMI subgroups. For men, FFMI was positively associated with the prevalence of hypertension in the FMIQ2–4 groups; in the FMIQ1 group, the FFMI was not associated with the prevalence of hypertension. The suitability of the combined FMI and FFMI model for assessing the prevalence of hypertension was comparable to that of the BMI model.
Previous studies have shown that the BF% was associated with an increased prevalence of incident hypertension (Citation6,Citation18). However, the BF% is affected by the FFM since weight is equal to the sum of the FM and FFM (Citation13). Thus, in this study, we used the FMI, which was not affected by FFM. Consequently, the FMI was associated with the prevalence of hypertension, which is consistent with the findings of previous studies (Citation6,Citation15,Citation18). The relationship between body fat and hypertension could be due to increased insulin resistance, increased sympathetic tone, and abnormalities in the renin–angiotensin–aldosterone system (Citation19–21). Therefore, our findings suggest that a reduced FMI is associated with lower BP.
The FFMI was associated with the prevalence of hypertension as reported in a previous study of South Indians (Citation15). The FFMI differed between four ethnic groups: Caucasian, African American, Hispanic, and Asian. African Americans had the highest FFMI, while Asians had the lowest FFMI (Citation22). South Asians also had more body fat and less skeletal muscle mass than Caucasians (Citation23). In this study, we investigated the association between the FFMI and the prevalence of hypertension in Japan. Our results were consistent with those of a previous study (Citation15). The mechanism underlying the relationship between the FFMI and hypertension is unknown. However, it has been reported that FFM increment by exercise training was associated with increment of the left ventricle mass and the wall thickness (Citation24). Further study has also reported that the increase FFM associated with increased total left ventricular mass increase in obesity (Citation25). Therefore, the possible mechanisms underlying the relationship between FFMI and hypertension might be due to increased left ventricular mass and the wall thickness Further studies are warranted to clarify the mechanism underlying the association between the FFMI and BP.
To evaluate body composition, we investigated the association between the combined FMI and FFMI and the prevalence of hypertension. We observed that a higher FMI was associated with hypertension in most of the FFMI subgroups. Similarly, a higher FFMI was associated with hypertension in most of the FMI subgroups. Since BMI is equal to the sum of the FMI and FFMI, a higher FMI and/or FFMI indicates a higher BMI (Citation11–13). Previous studies showed that individuals with a higher BMI have a higher risk of incident hypertension (Citation1–7). Thus, our findings were consistent with those of previous studies. These results suggest that a reduction in the FMI and FFMI (i.e., a reduction in body weight) may be important for the prevention of hypertension.
Previous studies have shown that high-fat and low-muscle mass are associated with the prevalence of hypertension (Citation26,Citation27). In this study, the FFMI was used, not muscle mass. Although the FFM measured by dual energy X-ray absorptiometry was highly correlated with the skeletal muscle mass measured by magnetic resonance imaging (r = 0.88; P < .001), the FFM includes not only muscles, but also organs such as the liver, heart, and bones, as well as total body water (Citation12,Citation14,Citation28,Citation29). Thus, this study cannot determine whether muscle mass itself is low even if the FFMI is low. However, even when the FFM was replaced by the FFMI, the body composition with the highest FM and lowest FFM (FMIQ4・FFMIQ1) was significantly associated with the prevalence of hypertension, consistent with previous studies.
For men, the FFMI was positively associated with the prevalence of hypertension in the FMIQ2–4 groups. However, in the FMIQ1 group, the FFMI increment was not associated with the prevalence of hypertension. To our knowledge, this is the first study to suggest that FFMI might not be associated with the prevalence of hypertension in the lowest FMI subgroup in men only. Therefore, the mechanism underlying the relationship between the FFMI and hypertension in the lowest FMI subgroup is unknown. Further examinations are necessary to confirm and underly mechanism this weaker association of FFMI and hypertension in the lowest FMI subgroup.
A previous study has shown that a higher BF% is significantly associated with the incidence of hypertension, even in individuals with a normal BMI (Citation8). This suggests that not only BMI but also differences in body composition are associated with hypertension. Thus, we hypothesized that body composition may be more suitable for assessing the prevalence of hypertension than BMI. However, in our study, although there was a statistically significant difference in women, the AUROC for the prevalence of hypertension using the combined FMI and FFMI was comparable to that using the BMI. Our findings suggest that the combined FMI and FFMI may have a somewhat different role than BMI. However, in clinical practice, BMI measurements alone are sufficient for predicting hypertension.
This study has several strengths. It is the first study to examine the association between the combined FMI and FFMI and the prevalence of hypertension. Because this study was conducted in a large population of 16,000 participants, we were able to identify 16 groups by combining the quartiles of the FMI and FFMI and investigating the association between the combination FMI and FFMI and the prevalence of hypertension.
Our study also has some limitations. First, bioelectrical impedance analysis was used to measure the BF%, which may have caused measurement errors. However, Kim et al. (Citation30) evaluated 551 community-dwelling elderly individuals in Japan and reported a high correlation between the whole body FM measured using bioelectrical impedance analysis and that measured using dual energy X-ray absorptiometry (men, r = 0.95; women, r = 0.92). Thus, we believed that these methods would be available for large epidemiological studies. Second, for some participants, the date of measurement of body composition and BP did not match the date of response to the questionnaire. However, the results did not change, even after excluding those participants whose date of measurement of body composition and BP did not match the date of response to the questionnaire. Third, residual or unmeasured confounding may exist. Although we used combined FMI and FFMI to avoid instability, even in the same FMI subgroup, higher FFMI tended to be higher FMI. Therefore, we might not have shown that FFMI was associated with hypertension completely independently of FMI. In this study, we did not include physical activity and diet as covariates because the results of physical activity and dietary habits may appear as differences in body composition. We considered adjustment for these determinants of body composition might underestimate the relationship between body composition and hypertension. Finally, this study had a cross-sectional design. Participants diagnosed with hypertension may reduce their body fat by following the advice of their physicians or nurses. Therefore, our results may overestimate the lower FMI subgroups. This may underestimate the relationship between the FMI and hypertension.
In conclusion, the FMI and FFMI were positively associated with the prevalence of hypertension. Additionally, when the FMI and FFMI were combined, a higher FMI was associated with hypertension in most of the FFMI subgroups. Similarly, a higher FFMI was associated with hypertension in most of the FMI subgroups. However, for men, the association between the FFMI and hypertension was not significant in the lowest FMI group. Thus, we suggest that a reduction in the FMI and FFMI (i.e., weight loss) may be important for preventing hypertension in both men and women. Additionally, for men, the association between the FFMI and hypertension may be weak in the lowest FMI group.
Conflicts of interest
The authors declare that they have no conflict of interest with respect to this research study.
Supplemental Material
Download Zip (619.6 KB)Acknowledgments
The authors would like to thank the members of the Tohoku Medical Megabank Organization, including the Genome Medical Research Coordinators and the office and administrative personnel for their assistance. The complete list of members is available at: https://www.megabank.tohoku.ac.jp/english/a200601
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