生物電阻抗分析 vs. BMI：德黑蘭研究顯示體脂百分比無明顯預測優勢

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一項來自德黑蘭脂質與葡萄糖研究（TLGS）的橫斷面分析納入1271名成人，探討生物電阻抗分析（BIA）測得的體脂百分比（PBF）與傳統體重指數（BMI）在預測心血管代謝風險因素的效能。結果顯示，無論男性（PBF臨界值25.6%、BMI 27.2 kg/m²）或女性（PBF 36.2%、BMI 27.5 kg/m²），PBF 在預測代謝症候群（MetS）及其組成因子上未優於 BMI。研究指出，BMI 簡便、便宜、易獲取，仍是臨床評估風險的實用工具，而 PBF 在實務中的應用價值有限。未來建議進行前瞻性研究，探討這些指標對心血管疾病和全因死亡率的預測力。

通過生物電阻抗分析進行的身體組成評估，以預測心血管代謝風險因素：德黑蘭脂質和葡萄糖研究（TLGS）

Body Composition Assessment by Bioelectrical Impedance Analysis in Prediction of Cardio-Metabolic Risk Factors: Tehran Lipid and Glucose Study (TLGS)

Heidari Almasi M, Barzin M, Serahati S, et al. Body Composition Assessment by Bioelectrical Impedance Analysis in Prediction of Cardio-Metabolic Risk Factors: Tehran Lipid and Glucose Study (TLGS). Iran J Public Health. 2022;51(4):851-859. doi:10.18502/ijph.v51i4.9246

https://pmc.ncbi.nlm.nih.gov/articles/PMC9288404/

摘要 Abstract

我們旨在評估與心血管代謝風險因素相關的最佳體重指數（BMI）和體脂百分比（PBF）臨界值，並比較 PBF 和 BMI 在預測這些風險因素方面的區分能力。
We aimed at evaluating the best body mass index (BMI) and percent body fat (PBF) cutoffs related to cardio-metabolic risk factors and comparing the discriminative power of PBF and BMI for predicting these risk factors.

在這項橫斷面研究的第五階段（2012–2015 年），共招募了 1271 名參與者（年齡≥20 歲；54.3%為女性）。使用生物電阻抗分析（BIA）來估算體脂百分比（PBF）。使用聯合臨時聲明標準來定義代謝綜合症（MetS）。我們通過邏輯回歸和接收者操作特徵（ROC）曲線下的面積比較了 PBF 和 BMI。男性的體脂百分比臨界值為> 25，女性為> 35。
In this cross-sectional study in phase V (2012–2015), 1271 participants (age ≥ 20 yr; 54.3% women) were enrolled. Bioelectrical impedance analysis (BIA) was used to estimate PBF. Joint Interim Statement criteria were used for defining metabolic syndrome (MetS). We compared PBF with BMI through logistic regression and area under the curve of the receiver operating characteristic (ROC) curve. Percent body fat cutoff points were > 25 in men and >35 in women.

預測 MetS 的體脂百分比和 BMI 臨界值分別為男性的 25.6%和 27.2 kg/m ² ，女性的 36.2%和 27.5 kg/m ² 。除了男性的腹部肥胖和女性的低高密度脂蛋白（HDL）外，BMI 和 PBF 在預測 MetS 及其組成部分的 ROC 曲線下的面積之間沒有顯著差異。邏輯回歸分析顯示，女性的 BMI 在預測 MetS 及其組成部分方面表現更佳，除了腹部肥胖。此外，除了低 HDL 和高三酸甘油脂水平外，男性的 BMI 與 PBF 相等或更優。
Percent body fat and BMI cutoff points for predicting MetS were 25.6% and 27.2 kg/m² in men and 36.2% and 27.5 kg/m² in women, respectively. There were no significant differences between BMI and PBF area under the ROC curves for predicting MetS and its components, except for abdominal obesity in men and low high-density lipoprotein (HDL) in women in favor of BMI. Logistic regression analysis indicated that BMI in women was better for predicting MetS and its components, except for abdominal obesity. Moreover, BMI was equal or superior to PBF in men, except for low HDL and high triglyceride levels.

與 BMI 的比較顯示，PBF 的使用在預測一般人群的心血管代謝風險方面並不顯著優於 BMI。
Comparison of PBF with BMI showed that the use of PBF is not significantly better than BMI in predicting cardio-metabolic risks in the general population.

關鍵詞：身體組成，生物電阻抗分析（BIA），心血管代謝風險因素
Keywords: Body composition, Bioelectrical impedance analysis (BIA), Cardio-metabolic risk factor

引言 Introduction

肥胖的普遍流行已成為公共健康的嚴重威脅，因為它與多種併發症有關，如 2 型糖尿病、代謝綜合症（MetS）、心血管疾病以及幾種癌症（ 1 ）。
The universal prevalence of obesity has become a serious threat to public health as it is related to different complications, such as type 2 diabetes mellitus, metabolic syndrome (MetS), cardiovascular diseases, and several types of cancer (1).

肥胖通常通過身體質量指數（BMI）來衡量，但 BMI 無法區分瘦體重和體脂肪含量。為了克服由 BMI 臨界值引起的錯誤分類，直接測量體脂百分比（PBF）將更適合於檢測肥胖（ 2 ）。
Obesity is usually measured via body mass index (BMI), which is not able to discriminate between lean mass and body fat content. To overcome the misclassifications caused by BMI cutoff values, direct measurement of percent body fat (PBF) would be preferable for detecting obesity (2).

雖然間接方法如雙能 X 射線吸收法（DEXA）提供準確的身體組成數據，但它們對於重複測量來說是無法接觸且不安全的，並且需要技術專業知識。直接方法如 MRI 和 CT 掃描則昂貴且不適合流行病學和常規用途。相比之下，生物電阻抗分析（BIA）是一種相對簡單、非侵入性且快速的方法，能夠準確評估身體組成（ 3 ）。
Although indirect methods such as dual-energy X-ray absorptiometry (DEXA) provide exact body composition data, they are inaccessible and unsafe for repeated measurements and need technical expertise. Direct methods such as MRI and CT scans are expensive and not suitable for epidemiologic and routine purposes. In contrast, bioelectrical impedance analysis (BIA) is a comparatively easy, non-invasive, and rapid method that provides accurate evaluation of body composition (3).

儘管在這個領域進行了大量研究，但反映心血管代謝疾病的最適當的體脂百分比（PBF）臨界值尚不明確（ 4 ， 5 ）。本研究的第一個目的是確定與心血管代謝異常相關的最佳體重指數（BMI）和 PBF 臨界值，並調查 PBF 在預測德黑蘭成人群體中的心血管代謝風險因素方面與 BMI 的區分能力。
Despite the large number of studies performed in this area, the most appropriate PBF cutoffs reflecting cardio-metabolic disorders are not clear yet (4, 5). The first objective of this research was to determine the optimal BMI and PBF cutoffs related to cardio-metabolic abnormalities and investigate the discriminative power of PBF in comparison with BMI for predicting cardio-metabolic risk factors among the adult population in Tehran.

材料與方法 Materials and Methods

德黑蘭脂質與葡萄糖研究（TLGS）是一項針對非傳染性疾病及相關風險因素的發病率調查，並進行了近三年的前瞻性隨訪評估。關於 TLGS 的理由、數據收集和抽樣的額外數據已經發表（ 6 ）。在這項 2012 年至 2015 年進行的第五階段橫斷面研究中，我們從 10733 名年齡≥20 歲的參與者中，通過簡單隨機抽樣選擇了 1271 名 BMI≥18.5 的個體，並排除了懷孕者、患有心力衰竭、肝硬化或慢性腎病等嚴重慢性疾病者、使用利尿劑或皮質類固醇的藥物史者，以及有肢體截肢、脊柱側彎和植入心臟去顫器或使用心臟起搏器的病史者。
The Tehran Lipid and Glucose Study (TLGS) is an incidence survey of non-communicable diseases and the related risk factors with prospective follow-up evaluation intervals of nearly three years. Additional data regarding the rationale, data collection, and sampling of the TLGS has been already published (6). In this cross-sectional study in phase V (2012–2015), we selected 1271 individuals with BMI ≥ 18.5 via simple random sampling from a total 10733 participants aged ≥ 20, and excluded those who were pregnant, had a severe chronic disease such as heart failure, cirrhosis or chronic kidney disease, drug history of using diuretics or corticosteroids, and history of limb amputation, kyphoscoliosis, and intracardiac defibrillator or using pacemakers.

隸屬於沙希德·貝赫什提醫科大學的內分泌科學研究所的機構倫理委員會（IR.SBMU.MSP.REC.1397.628）批准了這項研究。在進行這項研究時遵循了赫爾辛基宣言的所有原則。參與者簽署了書面知情同意書。
The institutional ethics committee of the Research Institute for Endocrine Sciences affiliated to Shahid Beheshti University of Medical Sciences (IR.SBMU.MSP.REC.1397.628) approved the research. All the tenets of Declaration of Helsinki were followed while performing this study. The participants signed a written informed consent.

面試官收集了數據，包括人口統計信息和藥物及醫療歷史。所有的人體測量均根據標準協議進行。參與者的體重在穿著最少衣物且不穿鞋的情況下使用數字秤進行評估（精確到 100 克）。參與者的身高在不穿鞋的情況下站立時使用卷尺進行評估，當肩膀處於正常位置時。身體質量指數（BMI）計算為體重除以身高的平方（kg/m ² ）。根據國際 BMI 的臨界值，患者被分類為正常（BMI < 25）、超重（25 ≤ BMI < 30）和肥胖（BMI ≥ 30）。腰圍（WC）在肚臍水平使用卷尺進行評估，對身體表面不施加壓力（精確到 0.1 厘米）。身體成分使用可轉換的生物電阻抗分析儀（型號：GAIA 359 Plus，Cosmed 公司，意大利）進行評估。過多的 PBF 在男性中表現為 PBF >25，在女性中表現為 PBF >35（ 7 ）。
An interviewer collected the data, including demographic information and drug and medical history. All the anthropometric measurements were performed based on the standard protocol. The participant’s weight was evaluated with minimal clothing and no shoes using digital scales (to the nearest 100 g). The participants’ height was assessed while standing with no shoes using a tape measure when the shoulders were in a normal position. Body mass index was measured as weight divided by the square of height (kg/m²). The patients were categorized with respect to the international cutoff points for BMI as normal (BMI < 25), overweight (25 ≤ BMI < 30), and obese (BMI ≥ 30). Waist circumference (WC) was evaluated at the umbilicus level using a tape meter with no pressure toward the body surface (to the nearest 0.1 cm). Body composition was evaluated using a transferable bioelectrical impedance analyzer (Model: GAIA 359 Plus, Co. Cosmed, Italy). Excess PBF was expressed as PBF >25 in men and PBF >35 in women (7).

在用電解質紙巾擦拭手掌和腳底後，參與者站立時腳底接觸腳電極，雙手握住手持電極。其他信息如性別、身高、體重和年齡也被記錄下來。使用八個電極的生物電阻抗分析在 5、50 和 250 kHz 的頻率下評估不同的區段阻抗（即軀幹、右臂和左臂、右腿和左腿）。參與者脫掉鞋子和襪子，換上輕便的衣物。在分析儀的平台上站立時，評估對交流電（500-μA，50/60 kHz）的阻抗。結果數據在無運動且雙臂放在身側的情況下通過“標準”選項進行解釋。瘦體重（LBM，公斤）和體脂百分比（PBF）通過生物電阻抗分析（BIA）根據標準方程計算（ 8 ）。評估由一位研究人員進行。根據標準協議測量收縮壓和舒張壓（SBP 和 DBP）（ 9 ）。
Following wiping the case’s palm and sole using an electrolyte tissue, the participants stood with their soles touching the foot electrodes and their hands grabbing the hand-held paddles electrodes. Other information such as sex, height, weight, and age were also recorded. The bioelectrical impedance analysis with eight electrodes assessed different segmental impedances (i.e., trunk, right and left arms, and right and left legs) at the frequencies of 5, 50, and 250 kHz from tetra-polar electrodes. Participants removed their shoes and socks changing to light clothing. The resistance against the alternating current (500- μA, 50/60 kHz) was assessed while standing on the analyzer’s platform. The resulted data were interpreted by the “standard” option while standing with no motion and arms at the sides. Lean body mass (LBM, kg) and PBF were calculated by BIA via standard equations (8). The evaluations were done by one of the researchers. Systolic and diastolic blood pressure (SBP and DBP) were measured based on the standard protocol (9).

在過夜禁食後，進行了血液採樣，並在血液採樣當天於 TLGS 實驗室進行測試。我們測量了空腹血漿葡萄糖（FPG）、總膽固醇（TC）、三酸甘油脂（TG）、高密度脂蛋白膽固醇（HDLC）和低密度脂蛋白膽固醇（LDL-C）（ 10 ）。所有測量方法的詳細信息可在其他地方找到（ 9 ）。
Following overnight fasting, blood sampling was performed, and the tests were conducted at the TLGS laboratory on the day on which blood sampling was done. We measured fasting plasma glucose (FPG), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDLC), and low-density lipoprotein cholesterol (LDL-C) (10). Details of all the measurement methods are available elsewhere (9).

通過聯合臨時聲明（JIS）標準，我們將代謝健康組件定義如下：1）FPG ≥100 mg/dl 或藥物治療，2）TG ≥ 150 mg/dl/藥物治療，3）女性 HDL<50 mg/dl 和男性<40 mg/dl/藥物治療，4）SBP ≥130 mmHg，DBP ≥85 mmHg/使用抗高血壓藥物，以及 5）腹部肥胖（WC 截止點男性≥89 cm 和女性≥91 cm）（ 6 ）。那些擁有三個或更多 JIS 組件的人被歸類為代謝不健康（存在代謝綜合症）（ 10 ）。
Through the Joint Interim Statement (JIS) criteria, we defined the metabolic health components as follows: 1) FPG ≥100 mg/dl or drug therapy, 2) TG ≥ 150 mg/dl/drug therapy, 3) HDL<50 mg/dl in females and <40 mg/dl in males/drug therapy, 4) SBP ≥130 mmHg, DBP ≥85 mmHg/using antihypertensives, and 5) abdominal obesity (WC cutoff points ≥89 cm in males and ≥91 cm in females) (6). Those with three or more JIS components were categorized as metabolically unhealthy (presence of MetS) (10).

數值以平均值 ± 標準差呈現，類別變數以數量 (%) 報告。連續變數的平均值及其相應的標準差是基於正態分佈考量的。由於三酸甘油脂的分佈高度正偏，因此報告了中位數的四分位數範圍。對於作為非正態變數的三酸甘油脂，進行了曼-惠特尼檢驗和克魯斯卡爾-瓦利斯檢驗。對於正態分佈的協變量，使用 t 檢驗和方差分析進行平均值比較。對於類別變數的單變量基線統計關聯性進行了卡方檢驗。我們對連續變數進行了方差分析的流行率趨勢檢驗，對類別變數進行了科克倫-阿米塔奇檢驗。使用皮爾森相關檢驗分析了身體脂肪百分比與體重指數之間的相關性；回歸分析用於年齡調整的相關性。使用卡帕檢驗檢測體重指數與身體脂肪百分比之間的協議。我們使用曲線下面積來比較肥胖測量對代謝風險因素的預測能力。應用 DeLong 技術檢驗 ROC 曲線下面積之間差異的顯著性。 我們使用了一個向後逐步的二元邏輯回歸模型，調整了吸煙狀態、身高和年齡，以檢測每個心血管代謝風險（設為因變量）與身體脂肪百分比（PBF）和身體質量指數（BMI）（設為自變量）之間的關係。P 值小於 0.05 被視為具有統計學意義。數據分析是通過 SPSS 版本 26（IBM 公司，阿蒙克，紐約，美國）和 R-3.6.3 統計軟件程序進行的。
Values are presented as mean ±SD, and categorical variables are reported as number (%). Mean values and the corresponding standard deviations of the continuous variables were considered based on normal distribution. The inter-quintile for median was reported due to the highly positive skewed distribution of TG. Mann-Whitney and Kruskal-Wallis tests were run for TG as a non-normal variable. Mean comparisons of the normally distributed covariates were conducted using t-test and ANOVA. Chi-square test was performed for testing the univariate baseline statistical association of the categorical variables. We performed the trend test for prevalence with ANOVA for continuous and Cochran-Armitage test for categorical variables. The correlations between PBF and BMI were analyzed using Pearson’s correlation test; regression analysis was used for age-adjusted correlations. Kappa was used to detect the agreements between BMI and PBF. We used AUC for comparing the predictive capacity of adiposity measurements for metabolic risk factors. The DeLong technique was applied for testing the significance of the difference between the areas under the ROC curves. We used a backward, stepwise binary logistic regression model adjusted for smoking status, height, and age for detecting the relationship between each cardio-metabolic risk (set as a dependent variable) and PBF and BMI (set as the independent variables). A P-value of less than 0.05 was regarded as statistically significant. Data analysis was performed by SPSS version 26 (IBM Corp., Armonk, NY, USA) and R-3.6.3 statistical software program.

結果 Results

Table 1 顯示了參與者的基線數據。他們的平均年齡為 43.2 ± 13.7 歲，54.3% 的參與者為女性。男性和女性之間的平均 PBF 存在顯著差異 (P<0.001)。與女性相比，男性的代謝症候群及其大多數組成部分（如高三酸甘油脂、低高密度脂蛋白、空腹血糖受損 (IFG) 和腹部肥胖）的患病率較高 (P<0.05)。
Table 1 presents the participant’s baseline data. Their mean age was 43.2 ± 13.7 yr, and 54.3% of the participants were women. A significant difference was found in the mean PBF between men and women (P<0.001). Men were found with a higher prevalence of MetS and most of its components such as high TG, low HDL, impaired fasting glucose (IFG), and abdominal obesity compared with women (P<0.05).

表 1：按性別組別劃分的參與者基線特徵

年齡調整的相關性分析結果顯示，BMI 與女性的體脂百分比 (PBF) 之間的相關性 (r=0.892, P<0.001) 高於男性 (r=0.736, P<0.001)。根據 BMI 的 ROC 曲線，檢測過多的 PBF (>25 在男性和 >35 在女性)，AUC 在男性為 0.87，在女性為 0.96。識別 PBF 的最佳 BMI 界限為男性 26.4 (83% 敏感性和 77% 特異性) 和女性 27.3 (90% 敏感性和特異性) ( Fig. 1 )。
The results of the age-adjusted correlation analysis indicated that BMI had a higher correlation with PBF in women (r=0.892, P<0.001) than in men (r =0.736, P<0.001). According to the ROC curves for BMI, to detect excess PBF (>25 in males and >35 in females), AUC was 0.87 in men and 0.96 in women. The best BMI cutoff points to identify PBF was 26.4 in men (83% sensitivity and 77% specificity) and 27.3 in women (90% sensitivity and specificity) (Fig. 1).

圖 1: 用於檢測男性（A）和女性（B）過多體脂肪百分比的身體質量指數 ROC 曲線

身體質量指數和體脂肪百分比的協議在兩性中分別計算；我們應用了性別特定的臨界值來創建體脂肪百分比和身體質量指數類別（男性體脂肪百分比臨界值 25%，女性 35%，身體質量指數臨界值 30）。男性的 Kappa 值為 0.288，女性為 0.571。
Body mass index and PBF agreements were calculated separately in both sexes; we applied sex-specific cutoffs to create PBF and BMI categories (PBF cutoffs 25% in men and 35% in women, BMI cutoff 30). Kappa values were 0.288 in men and 0.571 in women.

參與者的代謝特徵按性別在體脂肪百分比三分位數中進行分層（ Fig. 2 ）。在兩性中，第三體脂肪百分比三分位數的受試者中，代謝綜合症及其所有組成部分顯著較高（所有比較的趨勢 P 值<0.05）。
Metabolic characteristics of the participants are stratified by sex across PBF tertiles (Fig. 2). MetS and all of its components were significantly higher in subjects in the third PBF tertile in both sexes (P-value for trend for all comparisons <0.05).

圖 2

Tables 2 和 3 顯示了 PBF、LBM 和 BMI 在按性別檢測 MetS 及其組件的診斷性能的詳細信息。為了預測 MetS，男性的 PBF 切點為 25.6（68% 敏感性和 66% 特異性），女性的 PBF 切點為 36.2（80% 敏感性和 65% 特異性）。預測 MetS 的 BMI 切點分別為 27.2（83% 敏感性和 77% 特異性）和 27.5（90% 敏感性和特異性）。除了男性的腹部肥胖和女性的低 HDL 之外，BMI 和 PBF 在預測 MetS 及其組件的 ROC 曲線下沒有顯著差異（P<0.001）。
Tables 2 and 3 display the details of the diagnostic performance of PBF, LBM, and BMI for detecting MetS and its components by sex. To predict MetS, the PBF cut points were 25.6 in men (68% sensitivity and 66% specificity) and 36.2 in women (80% sensitivity and 65% specificity). The BMI cutoff points for predicting MetS were 27.2 (83% sensitivity and 77% specificity) and 27.5 (90% sensitivity and specificity), respectively. There were no significant differences between BMI and PBF under the ROC curves for predicting MetS and its components except for abdominal obesity in men and low HDL in women in favor of BMI (P<0.001).

表 2：敏感性、特異性、PPV、NPV、PBF、LBM 和 BMI 在預測男性心代謝異常中的 AUC 表 3：敏感性、特異性、PPV、NPV、PBF、LBM 和 BMI 在預測女性心代謝異常中的 AUC

AUC，曲線下面積；PPV，正確預測值；NPV，負確定預測值；PBF，體脂百分比；LBM，瘦體重；BMI，身體質量指數；MetS，代謝綜合症；HTN，高血壓；IFG，空腹血糖受損；HDL，高密度脂蛋白；TG，三酸甘油脂。
AUC, area under curve; PPV, positive predictive value; NPV, negative predictive value; PBF, percent body fat; LBM, lean body mass; BMI, body mass index; MetS, metabolic syndrome; HTN, hypertension; IFG, impaired fasting glucose; HDL, high density lipoprotein; TG, triglyceride.

調整年齡、身高和吸煙狀況的邏輯回歸模型被用來評估每個心血管代謝風險因素與 BMI 和 PBF（ Table 4 ）的關係。在女性中，PBF 僅與腹部肥胖相關。然而，BMI 與所有變數都有獨立的關聯。在男性中，BMI 與低 HDL 和高 TG 無關。關於所有其他變數，BMI 和 PBF 均保留在模型中。
The logistic regression model adjusted for age, height, and smoking status was used to evaluate the relation of each cardio-metabolic risk factor with BMI and PBF (Table 4). In women, PBF was only associated with abdominal obesity. However, BMI had an independent association with all the variables. In men, BMI was not associated with low HDL and high TG. Regarding all the other variables, both BMI and PBF remained in the model.

表 4：與心血管代謝風險因素相關的肥胖指標的邏輯回歸

BMI，身體質量指數；PBF，體脂百分比；CI，置信區間；HTN，高血壓；HDL，高密度脂蛋白；TG，三酸甘油脂；IFG，受損空腹血糖；MetS，代謝綜合症；數據來自調整年齡、身高和吸煙的邏輯回歸分析；破折號表示變量通過向後逐步選擇從方程中移除。代謝綜合症定義為滿足五個標準中的三個或更多（ 1 ）FPG ≥100 mg/dl（IFG）或藥物治療；（ 2 ）TG ≥150mg/dl 或藥物治療；（ 3 ）女性 HDL< 50 mg/dl 和男性< 40 mg/dl 或藥物治療；（ 4 ）高血壓定義為 SBP ≥ 130 mmHg，DBP ≥ 85 mmHg 或抗高血壓藥物治療；（ 5 ）腹部肥胖，WC 的臨界值為男性≥ 89 cm 和女性≥ 91cm。
BMI, body mass index; PBF, percent body fat; CI, confidence interval; HTN, hypertension; HDL, high density lipoprotein; TG, triglyceride; IFG, impaired fasting glucose MetS, metabolic syndrome; Data from logistic regression analysis adjusted for age, height, and smoking; dashes mean variable removed from the equation by backward stepwise selection. Metabolic syndrome defined as three or more of five criteria (1) FPG ≥100 mg/dl (IFG) or drug treatment; (2) TG ≥150mg/dl or drug treatment; (3) HDL< 50 mg/dl in women and < 40 mg/dl in men or drug treatment; (4) hypertension defined as SBP ≥ 130 mmHg, DBP ≥ 85 mmHg or antihypertensive drug treatment (5) Abdominal obesity, WC cut off points as ≥ 89 cm for men and ≥ 91cm for women

討論 Discussion

在這項基於人群的橫斷面研究中，我們確定了預測代謝綜合症（MetS）的體脂百分比（PBF）和身體質量指數（BMI）臨界值，男性分別為 25.6%和 27.2 kg/m ² ，女性則為 36.2%和 27.5 kg/m ² 。在預測 MetS 及其組成部分方面，AUC 顯示 BMI 和 PBF 的準確性相似，除了男性的腹部肥胖和女性的低高密度脂蛋白（HDL）方面，BMI 更具優勢。此外，根據邏輯回歸分析，女性的 BMI 對所有風險因素的預測能力更佳，除了腹部肥胖。在男性中，BMI 的預測能力等同或優於 PBF，除了低 HDL 和高三酸甘油脂（TG）。
In this population-based cross-sectional study, we identified that PBF and BMI cutoff points for predicting MetS were 25.6% and 27.2 kg/m² in men and 36.2% and 27.5 kg/m² in women, respectively. For the prediction of MetS and its components, AUC revealed a similar accuracy of BMI and PBF, except for abdominal obesity in men and low HDL in women in favor of BMI. Moreover, according to logistic regression analysis, BMI in women was a better predictor for all the risk factors, except for abdominal obesity. In addition, in men BMI was equal or superior compared to PBF, except for low HDL and high TG.

許多嘗試已經被進行，以確定用於檢測心血管代謝異常的 BMI 和 PBF 臨界值。我們關於 PBF 和 BMI 臨界值以預測代謝綜合症的數據（男性分別為 25.6%和 27.2 kg/m ² ，女性分別為 36.2%和 27.5 kg/m ² ）與之前的結果（ 4 ， 11 ）有所不同。使用足對足的生物電阻抗分析（BIA）和日本代謝綜合症標準（JIS）定義代謝綜合症的最佳 PBF 臨界值分別為中國男性的 24%和女性的 33%，這兩者均低於我們提出的臨界值。造成這一差異的原因可能是所採用的方法、種族和終點（ 4 ）。需要進一步的研究來估計不同人群中預測心血管代謝風險因素的 PBF 臨界值。
Many attempts have been made to identify the BMI and PBF cutoff points for detecting cardio-metabolic abnormalities. Our data about PBF and BMI cutoff points for predicting MetS (25.6% and 27.2 kg/m² in men and 36.2% and 27.5 kg/m² in women, respectively) differ from the previous results (4, 11). The optimal PBF cutoff values for predicting cardio-metabolic risk factors were 24% and 33% using a foot-to-foot BIA and JIS criteria for defining MetS in Chinese men and women, respectively, which were both lower than our proposed cutoffs. The reasons behind this difference may be the applied methodologies, race, and end points (4). Further studies are needed to estimate PBF cutoff points for predicting cardio-metabolic risk factors in different populations.

關於 BMI 和 PBF 預測心血管代謝風險因素的能力存在矛盾的結果。儘管 BMI 在評估脂肪量和瘦體重方面的效率有限（ 12 ），正如先前研究所報導的（ 13 ， 14 ），其對身體脂肪的測量在預測代謝異常方面並不優於 BMI。先前研究的結果在於使用脂肪估算設備是否可以通過 BMI 改善以識別有心血管代謝疾病風險的受試者（ 15 ）上存在不一致。這些爭議可能是由於脂肪測量工具的差異、對代謝綜合症及其各個元素的不同定義，而不是將代謝綜合症作為一個整體概念，甚至是樣本特徵的不同所解釋的。
There are conflicting results regarding the capability of BMI and PBF for predicting cardio-metabolic risk factors. Although BMI is potentially restricted by its inefficiency to assess fat mass and lean body mass (12) as reported in previous studies (13, 14), its measures of body fat had no superiority over BMI in predicting metabolic abnormalities. The results of the former studies are discrepant as to whether the use of adiposity estimation devices can be improved via BMI for identifying subjects at risk for cardio-metabolic disorders (15). These controversies may be explained by the differences in adiposity measurement tools, applying different definitions of MetS and its individual elements instead of MetS as a whole concept, or even divergent sample characteristics.

使用邏輯回歸分析，我們的結果顯示女性的 BMI 是代謝症候群及其大多數組成部分的更好預測指標，除了腹部肥胖；然而，男性的 BMI 和 PBF 都與心血管代謝風險因素相關，而 PBF 對低 HDL 和高 TG 的預測更佳。這支持了報告的結果，即 BMI 在泰國女性中對高血壓、胰島素抵抗、高三酸甘油脂血症、高瘦素血症和高血糖的預測優於 PBF，而在男性中則不是這樣（ 16 ）。這可能可以解釋為男性和女性在脂肪酸動員、儲存、氧化和脂肪分佈方面的差異。
Using logistic regression analysis, our results revealed that BMI in women was a better predictor of MetS and most of its components, except for abdominal obesity; however, both BMI and PBF were related to cardio-metabolic risk factors in men, while PBF was a better predictor for low HDL and high TG. This supports the results that reported BMI was a better predictor of HTN, insulin resistance, hypertriglyceridemia, hyperleptinemia, and hyperglycemia in Thai women than PBF, while it was not the case in men (16). This may be explained by the differences in the fatty acid mobilization, storage, oxidation, and distribution of fat in men and women.

與我們的結果相反，在一項針對 12,287 名 30 至 69 歲的日本男性和 6657 名女性的研究中（ 17 ），體脂百分比（使用生物電阻抗分析）與血清脂質的關聯性顯著高於身體質量指數。我們的結果不支持之前對 2483 名日本人的研究結果，該研究表明除了高密度脂蛋白外，通過生物電阻抗分析測量體脂百分比可能比身體質量指數更能預測血清脂質（ 15 ），儘管他們並未提供腰圍和代謝綜合症作為整體概念的數據。因此，普通人群中身體組成測量與心血管代謝風險之間的關聯尚不明確，需要未來的考量以確定體脂百分比和身體質量指數的預測價值。
In contrast to our results, in a study of 12,287 Japanese men and 6657 women aged 30–69 y (17), PBF (using BIA) was more significantly associated with serum lipids as compared with BMI. Our results do not support the findings of the previous investigation among 2,483 Japanese suggesting that except for HDL, measuring PBF by BIA may be better than BMI for anticipating serum lipids (15), although they did not present the data on waist circumference and MetS as a whole concept. Therefore, the associations between measures of body composition and cardio-metabolic risk in the general population are not clear and require future consideration to determine the predictive values of PBF and BMI.

雖然通過生物電阻抗分析（BIA）測量體脂百分比（PBF）被認為是檢測肥胖的最可靠方法之一，並且一些研究顯示使用 BIA 與 DEXA 相比的體脂測量之間有良好的相關性（ 11 ），但這種方法有某些限制，包括無法檢測體脂分佈。此外，我們沒有使用影像學研究來檢測區域脂肪分佈，因為其費用較高。在本研究中，一些已知的風險因素，如身體活動和飲食攝入，也未被考慮。此外，我們沒有評估胰島素抵抗作為心血管代謝風險因素。此外，考慮到我們受試者在體成分方面的年齡特定變化，截止點可能在不同年齡組之間有所不同，這需要進一步的測試來進行。在我們研究的優勢中，包括其基於人群的設計和對男性和女性數據的分別分析。
Although PBF measurement via BIA is regarded as one of the most reliable methods for detecting adiposity and some studies have shown a good correlation between body fat measurements using BIA compared with DEXA (11), this method has certain limitations including the incapability to detect body fat distribution. Moreover, we did not use imaging studies to detect regional fat distribution because of their expenses. Some known risk factors such as physical activity and dietary intake were not taken into account in this study as well. In addition, we did not assess insulin resistance as a cardio-metabolic risk factor. What’s more, considering the age-specific changes in body composition in our subjects, the cutoff points may differ among various age groups which needs further tests to be performed. Among the strengths of our study were its population-based design and analysis of the data for men and women separately.

結論 Conclusion

雖然 BMI 有其局限性，但 PBF 與 BMI 的比較顯示，PBF 在確定心血管代謝風險方面並不顯著優於 BMI。體脂百分比在臨床實踐中似乎並不有用，而 BMI 仍然是一種簡單、相對便宜且易於獲得的方法，用於評估兩性心血管代謝風險因素。應進一步研究 PBF 與其他人體測量指標的診斷準確性。此外，未來應進行隊列研究，以探討 BMI 和 PBF 對心血管疾病及全因死亡率的預後價值。
Although BMI has its limitations, comparison of PBF with BMI showed that the use of PBF is not significantly superior to BMI in determining cardio-metabolic risks. Percent body fat does not seem to be useful in clinical practice, and BMI remains a simple, relatively inexpensive, and easily obtainable method to assess the cardio-metabolic risk factors in both sexes. Further research should be performed to compare the diagnostic accuracy of PBF with other anthropo-metric indices. Moreover, future cohort studies be conducted on the prognostic values of BMI and PBF for cardiovascular diseases and all-cause mortality.

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