Association between Percent Body Fat and Low High-Density Lipoproteinemia in Middle-Aged Men in Korea
Article information
Abstract
Background
Obesity is a significant health risk factor for cardiovascular diseases. Dyslipidemia, defined as a low high-density lipoprotein cholesterol (HDL-C) level, is associated with these risks. Recent bioelectrical impedance analysis (BIA) devices offer precise measurements of the percent body fat (PBF). We aimed to determine the association between PBF and HDL-C levels in middle-aged men in Korea.
Methods
We conducted a cross-sectional sstudy of men aged 40-65 years who visited a health examination center. Body composition was analyzed using BIA. Health habits were assessed using a self-administered questionnaire. The participants were divided into four groups based on their PBF: group 1 (<21%), group 2 (21%–23.99%), group 3 (24%–28.99%), and group 4 (≥29%). Logistic regression was used to obtain the odds ratio (OR) between the PBF group and the low HDL-C level and adjusted for other variables.
Results
In this study, 2,685 men were analyzed. The number of individuals diagnosed with low HDL-C levels increased significantly as the group-specific PBF increased. Group 4 showed a 5.5-fold greater association with low HDL-C compared to group 1 (P<0.01), whereas group 3 and group 2 showed an OR of 4.38 and 2.95 (P<0.01 and P<0.01), respectively.
Conclusion
These results suggest that if middle-aged men are able to decrease their body fat by <5%, their HDL-C levels will increase. We suggest that 3%–5% PBF is a useful guideline for general body fat reduction in Korean middle- aged men in primary care.
INTRODUCTION
Obesity is a significant health burden on society [1]. Obesity is associated with various risk factors for atherosclerosis and carcinogenesis. This trend aggravates with age. The relationship between obesity and cardiovascular diseases is well established [2]. Abdominal obesity is associated with insulin resistance, which leads to dyslipidemia, hyperglycemia, and other metabolic abnormalities [3].
High-density lipoprotein cholesterol (HDL-C) is regarded as a protective factor against cardiovascular diseases. HDL-C acts as “good cholesterol” because it can reduce the accumulation of “bad cholesterol” in the coronary arteries [4]. Therefore, unsurprisingly, lower risk of heart disease is associated with higher HDL-C levels.
Obesity is characterized by the excessive accumulation of body fat. For Asian men, the World Health Organization defines obesity as having a body mass index (BMI) of ≥25.0 kg/m2 [5]. However, BMI is calculated based on height and weight; therefore, it has limitations in distinguishing body composition components such as percentage body fat (PBF) and skeletal muscle mass [6]. Recent bioelectrical impedance analysis (BIA) devices enable the accurate and convenient measurement of body composition. It provides a precise parameter, especially the PBF [7]. To date, whether PBF is a superior parameter compared to BMI in obesity remains debatable because preceding studies showed inconsistent results [8-10]. Furthermore, no guidelines regarding an established PBF-based obesity range. Specifically, establishing ideal cutoff values for PBF applicable to Asians, including Koreans, remains difficult. The manufacturer of the BIA suggested an ideal cutoff PBF of 20% in Korean men. However, no studies have proven the association between the value and cardiovascular risk factors in practice in Korea.
Health examinations were conducted in individuals aged ≥40 years in Korea [11]. These checkups were performed with an emphasis on the middle-aged population who are actively engaged in occupational and social activities. In terms of occupation, a higher proportion of men visited university hospitals.
Therefore, we aimed to identify the association between PBF and low HDL-C levels in apparently healthy and asymptomatic men aged 40–65 years. Participants included men who visited a health examination center at a university hospital in Korea. Additionally, this study aimed to demonstrate the degree to which PBF is associated with the risk of low HDL-C levels. Therefore, we aimed to identify an effective guide for PBF as basic information to prevent low HDL-C levels through body weight management.
METHODS
1. Study Participants
The participants were asymptomatic, apparently healthy men aged 40–65 years who underwent a health examination at Dongguk University Ilsan Hospital, located in a metropolitan area in Korea. A total of 2,685 individuals who visited the center between March 2021 and February 2022 were included in this study. The study protocol adhered to the ethical principles of the Declaration of Helsinki, 1975. The Institutional Review Board (IRB) of Dongguk University Ilsan Hospital approved the study protocol (DUIH IRB no., 2022-10-032-001). Owing to the retrospective nature of this study, the need for informed consent was waived.
2. Data Collection
The participants were asked to complete a standard questionnaire regarding their sociodemographic characteristics, past medical conditions, medications including lipid-lowering agents, and lifestyle factors (e.g., cigarette smoking [12], alcohol use [13], and physical activity [14]). Serum levels of total cholesterol, triglycerides, HDL-C, and low-density lipoprotein cholesterol (LDL-C) were measured after 12 hours of fasting. Body composition, including height, weight, BMI, PBF, and skeletal muscle mass, was analyzed using the InBody 970 model (2019; InBody Co. Ltd., Seoul, Korea), which uses BIA. It performs a precise body composition analysis by measuring the impedance in 40 different segments divided into eight frequency ranges (1 kHz, 5 kHz, 50 kHz, 250 kHz, 500 kHz, 1 MHz, 2 MHz, and 3 MHz) for each of the five body parts (right arm, left arm, trunk, right leg, and left leg). The complete questionnaire, blood tests, and body composition analysis were conducted at the health examination center of Dongguk University Ilsan Hospital.
3. Definitions
The dyslipidemia diagnostic criterion for HDL-C presented by the Committee of Clinical Practice Guidelines of the Korean Society of Lipid and Atherosclerosis is <40 mg/dL [15]. To divide the participants as evenly as possible, we classified the PBF into four categories based on the reference for Asian men: group 1 (<21%), group 2 (21%–23.99%), group 3 (24%–28.99%) and group 4 (≥29%) [16]. According to the equation in reference, it yielded PBF 13 for BMI 18.5 kg/m2, PBF 21 for BMI 23.0 kg/m2, PBF 24 for BMI 25.0 kg/m2, and PBF 29 for BMI 30.0 kg/m2. Because a very low proportion (11/2,685 [0.4%]) of our study participants were <PBF 13, we combined the <PBF 13 and <PBF 21 groups. We created an additional classification for PBF 21 based on BMI 23.0 kg/m2. This study aimed to suggest a practically achievable range for body fat reduction in primary care. We also defined physical activity in terms of its frequency and quantity. The frequency of physical activity groups was expressed as follows: zero times per week categorized as inactive, 1–2 times as moderate, and ≥3 as active. The amount of physical activity was calculated by converting weekly moderate-intensity, high-intensity, and strength exercises into kilocalories (kcal). Notably, <1,000 kcal per week was classified as the lower group, 1,000–1,500 kcal as the middle group, and ≥1,500 kcal as the upper group.
4. Statistical Analysis
A one-way analysis of variance was used to compare the baseline characteristics of the participants based on their PBF status. Normally distributed continuous variables are reported as mean and standard deviation. All categorical variables are represented numerically and proportionally. Using cross-tabulation analysis, we examined the number and proportion of participants diagnosed with low HDL-C levels in each PBF group. Logistic regression analysis was used to compare the risk of being diagnosed with low HDL-C levels across the PBF groups. Additionally, the risk was compared based on weekly physical activity levels, smoking status, alcohol consumption, and the use of dyslipidemia medication. The IBM SPSS for Windows ver. 24.0 (IBM Corp., Armonk, NY, USA) was used for performing all statistical analyses, and a P-value <0.05 was considered statistically significant.
RESULTS
1. General Characteristics of the Study Participants
Baseline characteristics of the participants according to their PBF status are shown in Table 1. The participants were categorized into four groups based on PBF: group 1 (<21%) comprised 447 individuals (16.8% of the total participants), group 2 (21%–23.99%) comprised 548 individuals (20.4%), group 3 (24%–28.99%) comprised 1,073 individuals (40.0%), and group 4 (≥29%) comprised 617 individuals (23.0%). Furthermore, the BMI value of 25.0 kg/m2, which is the cutoff value for obesity diagnosis, was similar to the mean of group 3, demonstrating consistency in obesity diagnosis using PBF.
General characteristics of the study participants (n=2,685)
2. Percentage Body Fat and Tendency of Low High-Density Lipoprotein Cholesterol Levels
Table 2 shows that the proportion of participants diagnosed with low HDL-C levels in groups 1–4 were 3.8%, 10.6%, 14.9%, and 18.0%, respectively. This demonstrates that as PBF increases, possibility of being diagnosed with low HDL-C levels increases. Figure 1 graphically shows that the proportion of participants with low HDL-C levels (red) increased as the PBF increased in each group.
Numbers of participants diagnosed with low HDL-C levels within each percent body fat group (n=2,685)
Proportion of individuals diagnosed with low high-density lipoprotein cholesterol (HDL-C) (<40 mg/dL) within each percent body fat group.
3. Percentage Body Fat and Other Factors Affect Low High-Density Lipoprotein Cholesterol
We observed a significant association between PBF and low HDL-C levels in the multivariable analysis, as shown in Table 3. Compared to group 1, which had the lowest PBF, group 2 had an odds ratio (OR) of 2.95 times (95% confidence interval [CI], 1.69–5.15; P<0.01) higher risk of being diagnosed with low HDL-C levels and group 3 had an OR of 4.38 times (95% CI, 2.62–7.33; P<0.01) higher risk of being diagnosed with low HDL-C levels. Group 4 had the highest observed risk of being diagnosed with low HDL-C levels at an OR of 5.5 times (95% CI, 3.24– 9.33; P<0.01). In contrast, the upper exercise group (≥1,500 kcal spent per week) suggested the possibility of a protective effect against low HDL-C levels, with an OR of 0.78 (95% CI, 0.60–1.02; P=0.07) compared to the inactive exercise group (<1,000 kcal spent per week). However, this difference was not statistically significant. Whereas, drinking alcohol showed an OR of 0.71 (95% CI, 0.51–0.97; P=0.03) for low HDL-C levels when compared to the group that did not drink alcohol. Similarly, cigarette smoking exhibited an adverse effect on low HDL-C levels, presenting an OR of 1.1 (95% CI, 0.85–1.42; P=0.46) compared to the non-smokers. However, this difference was not statistically significant. The use of dyslipidemia medication demonstrated an OR of 0.59 (95% CI, 0.34–1.04; P=0.07) for low HDL-C levels compared to the non-medication users. However, these effects were not statistically significant.
Results of a logistic regression analysis of low high-density lipoprotein cholesterol and percent body fat group adjusted for other variables
DISCUSSION
We confirmed that the possibility of low HDL-C levels increases with an increase in PBF [17]. Particularly, we showed that an increment of 3%–5% in PBF was associated with a significant risk of 2.95–5.5-fold for low HDL-C levels. Our results are consistent with those of previous studies. Previous studies have also demonstrated that the risk of dyslipidemia increases as individuals become more obese [18-20]. Notably, in our study, the average BMI of PBF group 3 was almost identical to the conventional obesity threshold of 25.0 kg/m2. This suggests that the categories of PBF (<21%, 21%–23.99%, 24%–28.99%, and ≥29%) presented in this study can serve as a basis for managing obesity in middle-aged Korean men. Because the PBF values were derived from an equation using BMI for Asian men, PBF 21% matched BMI 23.0 kg/m2, PBF 24% matched BMI 25.0 kg/m2, and PBF 29% matched BMI 30.0 kg/m2 [16]. We believe that the previously suggested criterion of a normal PBF range (approximately 16.7%–22.6%) may be too strict for practical application [21]. There is a lack of comprehensive studies on the appropriate criteria for PBF [22]. This warrants further investigation.
Lifestyle habits that contribute to obesity [23] include diet, overeating, lack of physical activity, stress, and genetics [24]. Previous studies have focused on sedentary lifestyles involving little or no physical movement during working hours, which can contribute to obesity and HDL-C levels [25,26]. We also identified various factors that might affect HDL-C levels. Physical activity and exercise can affect HDL-C levels. Exercise-induced changes in HDL-C levels result from the interaction between exercise intensity, frequency, duration of each exercise session, and the length of the exercise training period. A minimum level of habitual exercise intensity with an energy cost of ≥5–6 metabolic equivalents (METs) (a ratio of working metabolic rate relative to resting metabolic rate) is suggested as the threshold for favorable changes in HDL-C levels [27]. Physical activity can also be encouraged as long as they meet or exceed the caloric expenditure of 1,200–1,600 kcal per week, such as running, swimming, and weight training.
Alcohol intake increases HDL-C concentration through hepatic microsomal enzyme induction [13]. In our study, individuals who did not consume alcohol had lower HDL-C levels. Moderate alcohol consumption has a positive effect on HDL-C levels, which is beneficial for cardiovascular health. However, excessive alcohol consumption can have harmful health consequences.
Cigarette smoking is also associated with reduced HDL-C levels [12]. Cigarette smoking reduces HDL-C levels due to altered lipid transport enzymes, which negatively affect HDL-C metabolism and subfraction distribution. Therefore, smoking has a negative effect on both HDL-C levels and function, contributing to an increased risk of cardiovascular diseases in smokers.
The clinical implications of our study are as follows: we believe that reducing PBF by <5% will lead to a significantly lower risk and can be used as an effective motivation for weight management of patients in primary care. Owing to the widespread availability of BIA, patients can easily check their PBF in fitness clubs, obesity clinics, and health examination centers. These noninvasive measurements can provide accurate information for people to stay engaged in their proper body weight management.
Considering our grouping of exercises, it should be emphasized that engaging in exercise using >1,500 kcal per week can prevent dyslipidemia. For example, if a person weighing 80 kg engages in a 4 MET activity, such as walking at a speed of 6 km per hour, for approximately 5 days a week, they could burn a total of 1,600 kcal. This can also serve as an ideal guide for improving lifestyle and health outcomes. Although the influence of exercise was not significant (P=0.07) in our study, exercise is undoubtedly beneficial to health in various ways. We believe that this insignificance is due to the larger influence of PBF grouping in the multivariate model.
This study has some limitations. The target population comprised middle-aged men. Future studies involving different sexes and diverse age groups are warranted [27]. There is no relevant reference value for the PBF in Korean men. Therefore, approaching PBF in both sexes more comprehensively in the future is necessary. Due to its retrospective and self-reported nature, there might be over- or under-representation of health habits such as exercise types and alcohol amounts in the questionnaire by the participants. Our questionnaire did not provide quantitative data on smoking or alcohol consumption. In addition, we did not have sufficient information on the types and dosages of dyslipidemia medications or supplements that may affect blood lipid levels. Therefore, future prospective studies are required to adjust for possible recall bias. Another limitation is that our study only examined dyslipidemia in terms of HDL-C levels. Considering the complex nature of dietary habits and LDL-C, we excluded non-HDL cholesterol from the analysis. However, the non-HDL-C level is another important parameter of cardiovascular health. Further studies are needed to investigate the role of non-HDL-C levels in body fat.
In conclusion, our study proved that a 5% increment in PBF increased risk of low HDL-C levels by 2–3 folds in Korean middle-aged men. We suggest that a reduction of approximately 3%–5% PBF is a useful guideline for body fat reduction in Korean middle-aged men in primary care.
Notes
CONFLICT OF INTEREST
No potential conflict of interest relevant to this article was reported.