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美國 NHANES 大數據分析揭示：當以身高標準化肌肉量（FFMi）衡量時，肌肉量高者罹患代謝症候群風險反而升高，年輕男女高 FFMi 者 MetS 發生機率最高達 5 倍，顛覆「越多肌肉越健康」的傳統觀念。
增加無脂肪質量與代謝症候群的機率：來自國家健康與營養檢查調查數據庫的反直覺結果
Increased odds of having the metabolic syndrome with greater fat-free mass: counterintuitive results from the National Health and Nutrition Examination Survey database
Lagacé JC, Marcotte-Chenard A, Paquin J, Tremblay D, Brochu M, Dionne IJ. Increased odds of having the metabolic syndrome with greater fat-free mass: counterintuitive results from the National Health and Nutrition Examination Survey database. J Cachexia Sarcopenia Muscle. 2022;13(1):377-385. doi:10.1002/jcsm.12856
https://pubmed.ncbi.nlm.nih.gov/34825787/
摘要 Abstract
背景 Background
已知身體組成會影響代謝健康，但新興數據與普遍認為的大量無脂肪質量（FFM）對健康有保護作用的觀點相矛盾。這些差異的潛在解釋可能在於 FFM 的表現方式。第一個目標是確定代謝症候群（MetS）與 FFM 之間的關聯，當 FFM 以三種不同方式表示時：1—絕對 FFM；2—相對於身高的平方（FFMi）；3—相對於體重（FFM%）。第二個目標是在考慮脂肪質量、身體活動和社會人口變數後，評估 FFM 對於患有 MetS 的相對風險的影響。
It is well established that body composition influences metabolic health, but emerging data are conflicting with the largely purported idea that a large fat-free mass (FFM) has a protective effect on health. A potential explanation for these discrepancies is the way FFM is represented. The first objective is to determine the association between the metabolic syndrome (MetS) and FFM when the latter was represented in three different ways: 1—absolute FFM; 2—relative to squared height (FFMi); and 3—relative to body weight (FFM%). The second objective is to assess the impact of FFM on the relative risk of having the MetS after taking fat mass, physical activity, and sociodemographic variables into account.
方法 Methods
本研究分析了來自國家健康與營養檢查調查數據庫的 5274 名個體。定義了三種 FFM 表現方式的年齡特定和性別特定四分位數，並確定了每個四分位數中的 MetS 患病率。使用 FFMi（kg/m 2 ）的四分位數來計算在不考慮脂肪質量、身體活動水平和社會人口變數的情況下，患有 MetS 的比值比。
A total of 5274 individuals from the National Health and Nutrition Examination Survey database were studied. Age-specific and sex-specific quartiles of the three representations of FFM were defined, and the prevalence of MetS was determined in each of them. Quartiles of FFMi (kg/m2) were used to calculate the odds ratios of having the MetS independently of FM, physical activity levels, and sociodemographic variables.
結果 Results
MetS 的盛行率隨著全身 FFM%四分位數的增加而下降（Q1: 40%；Q4: 10%），但隨著絕對 FFM（Q1: 13%；Q4: 40%）和 FFMi（Q1: 10%；Q4: 44%）的四分位數增加而上升。對於附肢和軀幹 FFM 也觀察到了類似的結果。與每個特定子組的第一四分位數相比，獨立於脂肪量、身體活動和社會人口變數，FFMi 的第四四分位數的 MetS 發生比值比顯著更高 [Q4 vs. Q1: 年輕男性: 4.16 (1.99–8.68); 年輕女性: 5.74 (2.46–13.39); 年長男性: 1.98 (1.22–3.22); 年長女性: 2.88 (1.69–4.90); 所有 P ≤ 0.01]。
The prevalence of MetS decreased with increasing quartiles of whole-body FFM% (Q1: 40%; Q4: 10%) but grew with increasing quartiles of absolute FFM (Q1: 13%; Q4: 40%) and FFMi (Q1: 10%; Q4: 44%). Similar results were observed for appendicular and truncal FFM. The odds ratios of having the MetS, independently of fat mass, physical activity, and sociodemographic variables, were significantly greater in the fourth quartile of FFMi when compared with the first quartiles of each specific subgroup [Q4 vs. Q1: younger men: 4.16 (1.99–8.68); younger women: 5.74 (2.46–13.39); older men: 1.98 (1.22–3.22); older women: 2.88 (1.69–4.90); all P ≤ 0.01].
結論 Conclusions
這些結果支持了 FFM 的表現顯著影響其與 MetS 的關聯，並且無論是絕對 FFM 還是相對於身高的 FFM，較大的 FFM 與心臟代謝健康的變化相關聯。
These results support the notion that the representation of FFM significantly influences its association with MetS and that a larger FFM, whether absolute or relative to height, is associated with alterations in cardiometabolic health.
引言 Introduction
眾所周知，身體組成會影響代謝健康。例如，較高的脂肪量（FM）和內臟脂肪的積累顯著增加罹患代謝症候群（MetS）、 1 第二型糖尿病（T2D）、 2 和心血管疾病（CVD）的風險。 3 此外，許多研究表明，肌少症，即與年齡相關的無脂肪量（FFM）減少的狀態，與不健康的代謝健康、 4–6 胰島素抵抗（IR）、T2D、 7, 8 和 CVD 有關。 9 在這方面，Lee 等人報告指出，相對於總體重，較高的基線 FFM 與 4 年隨訪後更有利的代謝健康相關。 6 同樣，Atlantis 等人得出結論，較高的 FFM 百分比（FFM∕總體重 × 100）是被分類為 MetS 的強保護因素。 5
It is well known that body composition influences metabolic health. For instance, greater fat mass (FM) and visceral fat accumulations significantly increase the risk of having the metabolic syndrome (MetS),1 type 2 diabetes (T2D),2 and cardiovascular disease (CVD).3 Furthermore, many studies have suggested that sarcopenia, a state of age-related reduced fat-free mass (FFM), is associated with an unhealthy metabolic health,4–6 insulin resistance (IR), T2D,7, 8 and CVD.9 In this regard, Lee et al. reported that a greater baseline FFM, relative to total body weight, was associated with a more favourable metabolic health after a 4 year follow-up.6 Similarly, Atlantis et al. concluded that a greater FFM percentage (FFM∕total body weight × 100) was a strong protective factor against being classified with the MetS.5
擁有較大無脂肪量（FFM）被視為代謝健康的保護因素，這一觀點可以通過兩個主要且廣為接受的機制來解釋。首先，早已確立無脂肪量在胰島素刺激條件下佔據了葡萄糖攝取的很大比例，這導致了人們假設較大的無脂肪量能更好地調節葡萄糖穩態。其次，考慮到無脂肪量的生物活性特性以及無脂肪量與靜息能量消耗之間的關聯，通常會聲稱較大的無脂肪量可能通過提高靜息能量消耗來保護個體免受脂肪積累的影響。
The standpoint that having a large FFM is a protective factor for metabolic health is rationalized by two main and well-accepted mechanisms. First, it has long been established that FFM accounts for a large proportion of glucose uptake under insulin-stimulated conditions,10 which led to the purported assumption that a larger FFM better regulates glucose homeostasis.11 Second, considering the bioactive nature of FFM and the association between FFM and resting energy expenditure,12 it is often claimed that a greater FFM may protect individuals from fat accumulations through greater resting energy expenditure.13
然而，根據我們研究小組 14–19 和其他研究者 20–23 獲得的多項結果，我們最近報告指出，與一般認為的觀點相反，較高的無脂肪質量（FFM）可能與各種族群中的胰島素敏感性和代謝健康呈負相關。這些矛盾結論之間的差異可能源於在眾多相關研究中，FFM 的表現方式不同。先前的研究顯示，以不同方式表現 FFM（相對於體重或相對於身高）會導致關於其與胰島素敏感性 8 或代謝症候群（MetS） 4, 24 之間關聯的不同結論。例如，Park 和 Yoon 觀察到，在韓國族群中，當 FFM 相對於體重呈現時，較大的腰圍（WC）、血壓（BP）和三酸甘油脂（TG）水平，以及 MetS 的機率比顯著降低。 4 與他們在以體重為基準表現 FFM 時所獲得的結果相反，當 FFM 相對於身高的平方（kg/m 2 ）呈現時，較高的 FFM 與呈現改變的代謝特徵或擁有 MetS 的機率比相關聯。 4 雖然無脂肪質量百分比（FFM%）是一個有用的指標來識別代謝症候群（MetS）的風險，但它實際上只是身體組成的表現，因此無法用來評估 FFM 的數量對代謝健康的影響。相反，應該使用無脂肪質量指數（FFMi），這是一個針對身高修正的全身 FFM 指標。重要的是要注意，在使用 FFMi 時，Park 和 Yoon 以及 Scott 等人都沒有調整主要的混雜因素，如脂肪質量（FM）， 4, 24 這可能使他們的結果產生偏差。Bijlsma 等人的數據支持這些調整的必要性，他們報告了較高的肢體 FFMi 對胰島素抵抗（IR）的有害影響，該影響是通過胰島素抵抗的穩態模型評估（HOMA-IR）來評估的。然而，當調整 FM 時，這種關聯在女性中消失，而在男性中仍然顯著，儘管程度較低。 8
However, based on multiple results obtained from our research group14–19 and others,20–23 we recently reported, in contrast with the generally purported idea, that a greater FFM could be negatively associated with insulin sensitivity and metabolic health in various populations. The dissonance between these contradictory conclusions could stem from the different ways FFM is represented in the numerous pertinent studies. It was previously shown that representing FFM in different ways (relative to weight or relative to height) leads to different conclusions regarding the association with insulin sensitivity8 or MetS.4, 24 For instance, Park and Yoon observed that the odds ratios of having greater waist circumference (WC), blood pressure (BP), and triglyceride (TG) level, as well as the MetS, were significantly reduced when FFM was presented relative to body weight in a Korean population.4 In contrast with the results they obtained when representing FFM relative to body weight, reporting FFM relative to squared height (kg/m2) led to greater FFM being associated with higher odds ratios of presenting altered metabolic characteristics or having the MetS.4 While the percentage of FFM (FFM%) is a useful measure to identify the risk of having the MetS, it is effectively a representation of body composition and thus cannot be used to assess the impact of the quantity of FFM per se on metabolic health. Instead, FFM index (FFMi), a measure of whole-body FFM corrected for height, should be used. It is important to note that when using FFMi, neither Park and Yoon nor Scott et al. adjusted for major confounding factors such as FM,4, 24 which may have biased their results. Supporting the need for these adjustments are the data of Bijlsma et al., who reported a deleterious impact of greater appendicular FFMi on IR assessed with homeostatic model assessment of insulin resistance (HOMA-IR). However, this association was nulled in women when adjusting for FM and remained significant, although to a lesser extent, in men.8
本研究的目標因此有兩個。首先是確認代謝症候群（MetS）與無脂肪量（FFM）之間的關聯，當 FFM 以三種不同方式表示時：1—絕對 FFM（公斤）；2—相對於身高平方（FFMi）；3—相對於體重（FFM%）。第二是評估在排除混雜因素後，FFM 對於特定年齡子群中男性和女性罹患 MetS 或特定 MetS 組件的相對風險的影響。
The objectives of this study were thus two-fold. First is to confirm the association between MetS and FFM when the latter was represented in three different ways: 1—absolute FFM (kg); 2—relative to squared height (FFMi); and 3—relative to body weight (FFM%). Second is to assess the impact of FFM, once isolated from confounding factors, on the relative risk of having the MetS, or specific MetS components, in age-specific subgroups of men and women.
方法 Methods
研究人群 Study population
本研究使用了 1999 年至 2006 年美國國家健康與營養檢查調查（NHANES）的數據。NHANES 採用了多階段、分層和加權的抽樣設計，以招募能代表美國人口的個體。 25 年齡在 20 至 79 歲的成年人，如果有可用的身體測量、身體組成和代謝症候群（MetS）組件[腰圍（WC）、空腹血糖、高密度脂蛋白膽固醇（HDL-C）、三酸甘油脂（TG）和血壓（BP）]數據，則納入分析。考慮到年齡和性別對身體組成的影響， 26 分析分別在年輕（20-49 歲）和年長（50-79 歲）男性和女性中進行（年輕男性：n = 1662，年輕女性：n = 1379；年長男性：n = 1128，年長女性：n = 1105）。所有參與者均提供了書面知情同意，並且該計劃已獲得國家健康統計中心的批准。
This study used data from the 1999–2006 cohorts of the US National Health and Nutrition Examination Survey (NHANES). NHANES used a multistage, stratified, and weighted sampling design to recruit individuals who were representative of the US population.25 Adults aged 20 to 79 were included in the analyses if they had available data for anthropometrics, body composition, and MetS components [WC, fasting glucose, high-density lipoprotein cholesterol (HDL-C), TG, and BP]. Considering the impact of age and sex on body composition,26 analyses were performed separately in younger (20–49 years old) and older (50–79 years old) men and women (younger men: n = 1662 and younger women: n = 1379; older men: n = 1128 and older women: n = 1105). All participants provided written and informed consent, and the protocol was approved by the National Center for Health Statistics.
人體測量 Anthropometric
參與者僅穿著輕便衣物進行身高和體重的測量。結果用於計算身體質量指數（BMI：體重（kg）/身高（m） 2 ）。腰圍在髂骨上方最近的 0.1 厘米處進行測量。
Height and body weight were measured with participants wearing only light clothing. Results were used to calculate body mass index [BMI: body weight (kg)/height (m)2]. Waist circumference was measured at the nearest 0.1 cm just above the ilium.
身體組成、去脂體重和脂肪量的表示及四分位數
Body composition, fat-free mass, and fat mass representations and quartiles
身體組成是使用雙能量 X 光吸收法（Hologic 密度計 QDR4500A，Hologic Inc.，美國麻薩諸塞州貝德福德）來測量，以獲得脂肪量（FM）、去脂體重（FFM）和骨礦物質含量（BMC）。絕對去脂體重（kg）是計算為絕對去脂體重，排除骨礦物質含量。肢體去脂體重是計算雙臂和雙腿的去脂體重總和，排除骨礦物質含量。然後，通過將絕對去脂體重除以身高的平方來計算 FFMi [FFM（kg）/身高（m 2 ）]。FFM%是使用以下公式計算的：（絕對去脂體重∕體重）× 100。相同的公式用於確定絕對 FMi。將去脂體重和脂肪量相對於身高的平方進行標準化的好處在於，它更好地考慮了身材對這兩個變數的影響，從而使不同體型的個體之間的比較更加準確。 14 此外，確定肢體去脂體重可以通過排除其他瘦體組織（例如器官）的影響來評估骨骼肌本身的影響。 14
Body composition was measured using dual-energy X-ray absorptiometry (Hologic densitometer QDR4500A, Hologic Inc., Bedford, MA, USA) to obtain FM, FFM, and bone mineral content (BMC). Absolute FFM (kg) was calculated as absolute FFM, excluding BMC. Appendicular FFM was calculated as the sum of FFM from both arms and legs, excluding BMC. Then, FFMi was calculated by dividing absolute FFM by squared height [FFM (kg)/height (m2)]. FFM% was calculated using the following equation: (absolute FFM∕body weight) × 100. The same equations were used to determine absolute FMi. The benefit of normalizing FFM and FM relative to squared height is that it better considers the impact of stature on both variables, which then allows to better compare individuals of different sizes.14 Furthermore, the determination of appendicular FFM allows the assessment of the impact of skeletal muscle per se by excluding the impact of other lean tissues (e.g. organs).14
最後，考慮到先前報告的年齡和性別對身體組成的影響， 26 在年輕和年長的男性及女性中分別計算了 FFM 和 FM 的四分位數。使用 25th、50th 和 75th 百分位數的切割點來建立年輕和年長男性及女性的 FFM 和 FM 四分位數。
Lastly, considering the previously reported impact of age and sex on body composition,26 the quartiles of FFM and FM were calculated separately in younger and older men and women. The cut-offs for 25th, 50th, and 75th percentiles were used to establish quartiles of FFM and FM in younger and older men and women.
代謝症候群 Metabolic syndrome
根據國家膽固醇教育計畫的成人治療小組第三版定義，擁有以下文本中列出的三個或更多組成部分的個體被視為患有代謝症候群。 27 成人治療小組第三版定義認可以下界限來定義代謝症候群：(i) 男性腰圍 > 102 公分或女性 > 88 公分；(ii) 收縮壓 ≥ 130 mmHg 或舒張壓 ≥ 85 mmHg；(iii) 三酸甘油脂 ≥ 1.7 mmol/L；(iv) 男性高密度脂蛋白膽固醇 < 1.04 mmol/L 或女性 < 1.30 mmol/L；(v) 空腹血糖 ≥ 6.1 mmol/L。
Individuals with three or more of the components listed in the succeeding text were considered as having the MetS, as defined by the Adult Treatment Panel III definition from the National Cholesterol Education Program.27 The Adult Treatment Panel III definition recognizes the following cut-offs for defining MetS: (i) WC > 102 cm for men or >88 cm for women; (ii) systolic BP ≥ 130 mmHg or diastolic BP ≥ 85 mmHg; (iii) TG ≥ 1.7 mmol/L; (iv) HDL-C < 1.04 mmol/L for men or <1.30 mmol/L for women; and (v) fasting glucose ≥6.1 mmol/L.
血壓在右臂連續測量三次，並在休息 5 分鐘後計算兩次或三次可用測量的平均值。如果參與者僅有一個可用的血壓讀數，則將其排除在外。血清樣本在禁食 9 小時後收集。葡萄糖和三酸甘油脂的測量首先在日立 917 型分析儀（羅氏診斷，印第安納波利斯，印第安納州，美國）上進行，分別使用己糖激酶法和顏色反應 Trinder 測定法。高密度脂蛋白膽固醇（HDL-C）則使用肝素-錳沉澱法或在日立 704、日立 717 和日立 912 分析儀（羅氏診斷）上進行的直接免疫測定技術進行定量。
Blood pressure was measured three consecutive times on the right arm after a 5 min rest, and the average of the two or three available measures was calculated. Participants were excluded if they had only one available BP reading. Serum samples were collected following a 9 h overnight fast. Glucose and TG measurements were performed at first on Hitachi Model 917 Analyzer (Roche Diagnostics, Indianapolis, IN, USA) using the hexokinase method and the colorimetric Trinder assay, respectively. HDL-C was quantified using a heparin–manganese precipitation method or a direct immunoassay technique on Hitachi 704, Hitachi 717, and Hitachi 912 analysers (Roche Diagnostics).
混淆因素 Confounding factors
資訊是針對社會人口學協變數獲得的，例如族裔（墨西哥裔美國人、其他西班牙裔族群、非西班牙裔白人、非西班牙裔黑人及其他族群）、教育程度（未滿 9 年級、9 至 11 年級、高中或同等學歷、部分大學或副學士學位，以及大學畢業或以上）和收入，通過貧困收入比進行評估。貧困收入比是家庭收入與由衛生與公共服務部確定的貧困門檻的比率。其值範圍從 0（即無收入）到 5，代表收入是貧困門檻的五倍。任何超過 5 的值在數據庫中都被編碼為 5，以便於披露考量。
Information was obtained for sociodemographic covariates such as ethnicity (Mexican American, other Hispanic ethnicities, non-Hispanic White, Non-Hispanic Black, and other ethnicities), education (<9th grade, 9th to 11th grade, high school grade or equivalent, some college or Associate of Arts degree, and college graduate or above), and income, assessed with the poverty income ratio. The poverty income ratio is the ratio of family income to poverty threshold as determined by the Department of Health and Human Services. The values range from 0 (i.e. no income) to 5, representing a revenue five-fold over the poverty threshold. Any values over 5 are coded as 5 in the database for disclosure concerns.
過去 30 天的身體活動透過身體活動與身體健康問卷進行評估。根據個體的身體活動水平，依據加拿大身體活動標準進行分類：(i) 身體活躍 = 在過去 30 天內進行了阻力訓練和中等或高強度的有氧活動；(ii) 中度活躍 = 至少進行了一項中等、高強度或阻力訓練；(iii) 不活躍 = 既沒有進行有氧運動也沒有進行阻力訓練。
Physical activities over the last 30 days were assessed using the Physical Activity and Physical Fitness Questionnaire. Individuals were then characterized according to their level of physical activity following the Canadian Physical Activity: (i) physically active = both resistance exercises and either moderate or vigorous aerobic activities over the past 30 days; (ii) moderately active = at least one of moderate, vigorous, or resistance exercise; and (iii) inactive = neither aerobic nor resistance exercise.
統計分析 Statistical analyses
連續數據以平均值 ± 標準差呈現，除非另有說明。考慮到本研究的目標並非對美國人口本身進行特徵描述，因此使用 1999–2006 年 NHANES 數據集進行了非加權分析。在統計分析之前，從數據集中移除了一些異常數據（例如，舒張壓值為 0）。組間差異使用單因子變異數分析進行評估，並使用 Bonferroni 事後檢定來識別具體差異。獨立性卡方檢定（χ 2 ）用於評估族裔分佈的差異。FFM、FFMi 和 FFM%四分位數之間的 MetS 流行率差異也使用χ 2 進行評估。
Continuous data are presented as mean ± standard deviation, unless otherwise specified. Given that the objective of this study is not to characterize the American population per se, non-weighted analyses were conducted with the 1999–2006 NHANES dataset. A few abnormal data were removed from the dataset prior to statistical analyses (e.g. value of diastolic BP of 0). Between-group differences were assessed using one-way analyses of variance, and Bonferroni post hoc tests were used to identify specific differences. χ2 tests of independence (χ2) were used for differences in the distribution of ethnicities. Differences in MetS prevalence between quartiles of FFM, FFMi, and FFM% were also assessed with χ2.
進行了二元邏輯回歸分析，以計算年輕（20–49 歲）和年長（50–79 歲）男性和女性在 FFMi 四分位數增加下，患有代謝症候群（MetS）或特定 MetS 組件的機率比（ORs）及 95%信賴區間（CIs）。模型中納入了 FMi、身體活動水平、種族、教育程度和貧困收入比作為協變數。回歸分析中的分類臨界值是根據整體樣本中 MetS 和/或特定組件的流行率來確定的。統計顯著性設定為 P ≤ 0.05。所有分析均使用
SPSS 25 for Windows（IBM Corp., Armonk, NY, USA）進行。
Binary logistic regressions were performed to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) of having the MetS or specific MetS components in younger (20–49) and older (50–79) men and women with increasing quartiles of FFMi. FMi, physical activity level, ethnicity, education, and poverty income ratio were included in the model as covariates. The classification cut-offs in the regressions were determined on whole-sample prevalence of the MetS and/or specific components. Statistical significance was set at P ≤ 0.05. All analyses were performed using SPSS 25 for Windows (IBM Corp., Armonk, NY, USA).
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結果 Results
總體而言，來自 NHANES 1999–2006 數據集的 5274 名個體（46.4 ± 16.1 歲），平均 BMI 為 27.5 ± 5.0 kg/m 2 ，被納入分析。年齡特定和性別特定子群的描述性特徵如表 1 所示。男性的身高和體重均高於女性（P < 0.05）。年輕個體的 FFM 較高，FM、靜息血壓、三酸甘油脂、空腹血糖和 HOMA-IR 均較低（均 P < 0.05），與年長者相比。用於確定 FFMi、附肢 FFMi、軀幹 FFMi 和 FMi 的年齡特定和性別特定四分位數的 25th、50th 和 75th 百分位數的切點如表 2 所示。
Overall, 5274 individuals (46.4 ± 16.1 years) with an average BMI of 27.5 ± 5.0 kg/m2 from the NHANES 1999–2006 dataset were included in the analyses. Descriptive characteristics for age-specific and sex-specific subgroups are presented in Table 1. Men displayed greater height and body weight than women (P < 0.05). Younger individuals had greater FFM and lower FM, resting BP, TGs, fasting glucose, and HOMA-IR (all P < 0.05) than older counterparts. The cut-offs for the 25th, 50th, and 75th percentiles used to determine age-specific and sex-specific quartiles of FFMi, appendicular FFMi, trunk FFMi, and FMi are displayed in Table 2.
Table 1. Descriptive characteristics| 年輕 20–49 歲 Younger 20–49 years old | 年長 50–79 歲 Older 50–79 years old | |||
|---|---|---|---|---|
| 男性 (n = 1662) Men (n = 1662) | 女性 (n = 1379) Women (n = 1379) | 男性 (n = 1128) Men (n = 1128) | 女性 (n = 1105) Women (n = 1105) | |
| 年齡 (歲) Age (years) | 34.3 ± 8.8 34.3 ± 8.8 | 34.9 ± 8.8 34.9 ± 8.8 | 62.5 ± 7.8a,b | 62.4 ± 7.8a,b |
| 族裔 Ethnicity | ||||
| 墨西哥裔美國人 (%) Mexican American (%) | 26.7 | 23.2 | 23.2 | 24.7 |
| 其他西班牙裔 (%) Other Hispanic (%) | 4.9 | 5.1 | 3.8 | 4.3 |
| 非西班牙裔白人 (%) Non-Hispanic White (%) | 43.6 | 46.5 | 54.8a,b | 51.7a |
| 非西班牙裔黑人 (%) Non-Hispanic Black (%) | 20.8 | 21.7 | 15.1a,b | 16.2a,b |
| 其他 (%) Other (%) | 4.0 | 3.6 | 3.1 | 3.1 |
| 人體測量變數 Anthropometric variables |
||||
| 身高 (公尺) Height (m) | 1.75 ± 0.07 1.75 ± 0.07 | 1.62 ± 0.07a | 1.74 ± 0.08a,b | 1.60 ± 0.07a,b,c |
| 體重 (公斤) Weight (kg) | 82.5 ± 15.2 82.5 ± 15.2 | 72.5 ± 16.2a | 84.6 ± 14.4a,b | 72.4 ± 14.3a,c |
| BMI （公斤/公尺 2 ） BMI (kg/m2) | 26.9 ± 4.4 26.9 ± 4.4 | 27.4 ± 5.9a | 27.8 ± 4.2a | 28.2 ± 5.2a,b |
| 絕對無脂肪體重 (公斤) Absolute FFM (kg) | 58.4 ± 8.5 58.4 ± 8.5 | 42.2 ± 6.8a | 57.1 ± 7.9a,b | 40.2 ± 6.2a,b,c |
| 絕對無脂肪質量 (公斤) Absolute FM (kg) | 22.2 ± 8.2 22.2 ± 8.2 | 28.8 ± 10.3a | 25.4 ± 7.8a,b | 30.8 ± 9.1a,b,c |
| 腰圍 (公分) Waist circumference (cm) |
94.2 ± 12.4 94.2 ± 12.4 | 90.2 ± 13.5a | 101.9 ± 11.3a,b | 95.3 ± 12.6b,c |
| 代謝變數 Metabolic variables | ||||
| 收縮壓 (mmHg) Systolic BP (mmHg) | 119.6 ± 12.2 119.6 ± 12.2 | 113.3 ± 13.7a | 132.3 ± 19.5a,b | 135.1 ± 21.7a,b,c |
| 舒張壓 (mmHg) Diastolic BP (mmHg) | 72.4 ± 11.6 72.4 ± 11.6 | 70.2 ± 9.4a | 73.6 ± 11.6b | 72.0 ± 11.8b,c |
| 三酸甘油脂 （mmol/L） Triglycerides (mmol/L) | 1.66 ± 1.48 1.66 ± 1.48 | 1.32 ± 1.12a | 1.93 ± 2.26a,b | 1.79 ± 1.29b |
| HDL-C (mmol/L) | 1.23 ± 0.34 1.23 ± 0.34 | 1.44 ± 0.40a | 1.24 ± 0.35b | 1.53 ± 0.43a,b,c |
| 葡萄糖 （mmol/L） Glucose (mmol/L) | 5.26 ± 1.43 5.26 ± 1.43 | 5.02 ± 1.25a | 6.23 ± 2.45a,b | 5.99 ± 2.41a,b,c |
| MetS，n (%) MetS, n (%) | 241 (14.5) | 235 (17.0) | 416 (36.9)a,b | 474 (42.9)a,b,c |
| 百分位數 Percentiles | 年輕者 20–49 歲 Younger 20–49 years old |
年長者 50–79 歲 Older 50–79 years old | |||
|---|---|---|---|---|---|
| 男性 (n = 1662) Men (n = 1662) | 女性 (n = 1379) Women (n = 1379) | 男性 (n = 1128) Men (n = 1128) | 女性 (n = 1105) Women (n = 1105) | ||
| 全身無脂肪質量指數 (kg/m 2 ) Whole-body FFMi (kg/m2) |
25 | 17.42 | 14.24 | 17.43 | 14.08 |
| 50 | 18.98 | 15.62 | 18.74 | 15.45 | |
| 75 | 20.49 | 17.38 | 20.10 | 17.01 | |
| 肢體無脂肪質量指數 (kg/m 2 ) Appendicular FFMi (kg/m2) |
25 | 7.79 | 5.90 | 7.50 | 5.67 |
| 50 | 8.54 | 6.63 | 8.13 | 6.29 | |
| 75 | 9.33 | 7.54 | 8.81 | 7.01 | |
| 軀幹無脂肪質量指數 (kg/m 2 ) Trunk FFMi (kg/m2) |
25 | 8.50 | 7.19 | 8.75 | 7.29 |
| 50 | 9.25 | 7.89 | 9.43 | 8.00 | |
| 75 | 10.02 | 8.74 | 10.21 | 8.78 | |
| FMi (公斤/平方米 2 ) FMi (kg/m2) | 25 | 5.32 | 8.03 | 6.66 | 9.63 |
| 50 | 7.04 | 10.38 | 8.16 | 11.72 | |
| 75 | 8.84 | 13.35 | 9.95 | 14.13 | |
無脂肪質量的代謝症候群盛行率
Prevalence of metabolic syndrome by representations of fat-free mass
總體而言，樣本中有 25.9%（n = 1366）呈現代謝症候群。最常見的成分是高腰圍（47.3%），其次是高靜息血壓（34.9%）、低高密度脂蛋白膽固醇（34.4%）、高三酸甘油脂（32.5%）和高血糖（13.8%）。
Altogether, 25.9% of the sample (n = 1366) presented a MetS. The most prevalent component was a high WC (47.3%), followed by high resting BP (34.9%), low HDL-C (34.4%), high TG (32.5%), and high blood glucose (13.8%).
圖 1 顯示了整體、肢體和軀幹無脂肪量（FFM）三種表現形式的每個四分位數的代謝症候群（MetS）盛行率。隨著整體 FFM 和 FFMi 的四分位數增加，MetS 的盛行率也隨之上升（即 FFM 和 FFMi 增加；P < 0.001）。相反地，隨著整體 FFM%（即相對於體重的 FFM 百分比）的四分位數增加，盛行率則下降（P < 0.001）。對於肢體和軀幹 FFM 也觀察到了類似的結果（圖 1），即隨著 FFM 和 FFMi 的增加，MetS 的盛行率較高，而隨著 FFM%的增加，MetS 的盛行率較低。
Figure 1 shows the prevalence of MetS for each quartile of the three representations of whole-body, appendicular, and truncal FFM. A greater prevalence of MetS was observed with increasing quartiles of whole-body FFM and FFMi (i.e. greater FFM and FFMi; P < 0.001). In contrast, prevalence decreased with greater quartiles of whole-body FFM% (i.e. greater percentage of FFM relative to body weight; P < 0.001). Similar results were observed for appendicular and trunk FFM (Figure 1), that is, a higher prevalence of MetS with increased FFM and FFMi and lower prevalence of MetS with greater FFM%.
圖 1 Figure 1
代謝症候群（MetS）在絕對、肢體和軀幹無脂肪量百分比（FFM∕體重 × 100）（FFM%）、絕對無脂肪量[FFM（kg）]和無脂肪量指數[FFMi（kg/m 2 ）]的每個四分位數（Q）中的盛行率。匹配的四分位數被合併以測量 MetS 的盛行率（即所有 Q1 合併：25%的年輕和年長男性及 25%的年輕和年長女性）。WB，整體。
Prevalence of metabolic syndrome (MetS) per quartile (Q) of absolute, appendicular, and trunk fat-free mass percentage (FFM∕body weight × 100) (FFM%), absolute fat-free mass [FFM (kg)], and fat-free mass index [FFMi (kg/m2)]. Matching quartiles were pooled to measure MetS prevalence (i.e. all Q1 pooled: 25% of young and older men and 25% of young and older women). WB, whole-body.
罹患代謝症候群或代謝症候群組件的機率比
Odds ratios of having the metabolic syndrome or metabolic syndrome components
代謝症候群的比值比
Odds ratios of having the metabolic syndrome
我們接著尋求驗證在前一部分觀察到的隨著 FFMi 四分位數增加而增加的 MetS 盛行率是否是協變量的結果。因此，模型中納入了脂肪質量指數（kg/m 2 ）、身體活動、種族、教育程度和貧困收入比。進一步的分析使用全身 FFMi（kg/m 2 ）的表示，因為它考慮了身高並且能夠真實地描述 FFM 對 MetS 的影響，而非身體組成，如 FFM%。此外，由於肢體和軀幹部分的結果與全身相似，因此在其他分析中僅使用後者。
We then sought to verify if the increased prevalence of MetS with increasing quartiles of FFMi observed in the previous section was the consequence of covariates. Hence, FM index (kg/m2), physical activity, ethnicity, education, and the poverty income ratio were included in the models. Further analyses were performed with whole-body FFMi (kg/m2) representation because it accounts for height and characterizes the actual role of FFM on the MetS and not body composition, such as FFM%. Additionally, because appendicular and truncal compartments showed results similar to whole-body, only the latter was used in other analyses.
每個年齡和性別子群的 FFMi 四分位數的 MetS 比值比（ORs）如表 3 所示。與其參考值相比，所有子群中 FFMi 的 Q4 的 MetS ORs 顯著增高（1.98 ≤ β ≥ 5.74；所有 P < 0.01），且不受協變量的影響。除了 Q4，年輕和年長女性的 Q2 和 Q3 的 MetS ORs 也顯著高於 FFMi 的 Q1（1.93 ≤ β ≥ 3.41；0.001 ≤ P ≥ 0.04）。
The ORs of having the MetS per quartiles of FFMi are presented in Table 3 for each age and sex subgroup. The ORs of having the MetS were significantly greater in Q4 of FFMi in every subgroup compared with its reference (1.98 ≤ β ≥ 5.74; all P < 0.01), independently of covariates. In addition to Q4, the ORs of having the MetS were also significantly greater in Q2 and Q3 compared with Q1 of FFMi in younger and older women (1.93 ≤ β ≥ 3.41; 0.001 ≤ P ≥ 0.04).
Table 3. Odds ratios for metabolic syndrome by quartiles of FFMi| 代謝症候群 Metabolic syndrome | 年輕者 20–49 歲 Younger 20–49 years old | 年長者 50–79 歲 Older 50–79 years old | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 男性 (n = 1551) Men (n = 1551) | 女性 (n = 1265) Women (n = 1265) | 男性 (n = 1016) Men (n = 1016) | 女性 (n = 955) Women (n = 955) | |||||||||
| β | 95% 信賴區間 95% CI | P | β | 95% 信賴區間 95% CI | P | β | 95% CI | P | β | 95% CI | P | |
| FFMi Q1 (參考) FFMi Q1 (ref) | 0.000 | 0.000 | 0.053 | 0.000 | ||||||||
| FFMi Q2 | 1.27 | 0.58–2.77 | NS | 2.27 | 1.03–5.01 | 0.04 | 1.42 | 0.90–2.22 | NS | 1.93 | 1.25–2.97 | 0.003 |
| FFMi Q3 | 1.61 | 0.76–3.40 | NS | 3.41 | 1.55–7.52 | 0.002 | 1.49 | 0.94–2.37 | NS | 3.02 | 1.90–4.81 | 0.000 |
| FFMi Q4 | 4.16 | 1.99–8.68 | 0.000 | 5.74 | 2.46–13.39 | 0.000 | 1.98 | 1.22–3.22 | 0.006 | 2.88 | 1.69–4.90 | 0.000 |
| 全身脂肪無關指數 (FMi) Whole-body FMi | 3.03 | 2.41–3.82 | 0.000 | 1.78 | 1.40–2.25 | 0.000 | 2.20 | 1.87–2.58 | 0.000 | 1.39 | 1.17–1.64 | 0.000 |
| PA 水準 PA level | 1.40 | 1.08–1.81 | 0.01 | 1.31 | 1.01–1.71 | 0.04 | 1.15 | 0.89–1.49 | NS | 1.52 | 1.18–1.98 | 0.002 |
| 族裔 Ethnicity | 0.99 | 0.85–1.15 | NS | 0.85 | 0.74–0.99 | 0.03 | 0.86 | 0.74–0.99 | 0.04 | 0.79 | 0.69–0.91 | 0.001 |
| 教育 Education | 0.93 | 0.79–1.10 | NS | 0.91 | 0.78–1.07 | NS | 0.90 | 0.80–1.03 | NS | 0.87 | 0.76–1.00 | 0.05 |
| PIR | 1.06 | 0.94–1.19 | NS | 0.90 | 0.80–1.01 | 0.06 | 0.88 | 0.79–0.98 | 0.02 | 0.98 | 0.89–1.09 | NS |
| 常數 Constant | 0.001 | 0.000 | 0.017 | 0.000 | 0.107 | 0.000 | 0.174 | 0.000 | ||||
| Nagelkerke R2 | 0.343 | 0.250 | 0.273 | 0.209 | ||||||||
特定代謝症候群成分的機率比
Odds ratios of having specific metabolic syndrome components
考量到擁有較大無脂肪量指數（FFMi）與代謝症候群（MetS）之間的機率比（ORs）增加，我們想確認是否有特定的 MetS 成分在驅動這些結果。高密度脂蛋白膽固醇（HDL-C）和腰圍（WC）在各組之間顯示出一致的反應（圖 2）。與第一四分位數（Q1）相比，幾乎每個 FFMi 的四分位數中，擁有大腰圍的 ORs 顯著增加（1.75 ≤ β ≥ 18.46; 0.001 ≤ P ≥ 0.04）。至於 HDL-C，年輕和年長的女性以及年長男性在 FFMi 的四分位數增加時，擁有低 HDL-C 的 ORs 也隨之增加（1.50 ≤ β ≥ 3.27; 0.001 ≤ P ≥ 0.051）。
Given the increased ORs of having the MetS with a larger FFMi, we wanted to verify if a specific component of the MetS was driving these results. HDL-C and WC revealed a homogeneous response across groups (Figure 2). The ORs of having a large WC were significantly greater in almost every quartile of FFMi compared with Q1 (1.75 ≤ β ≥ 18.46; 0.001 ≤ P ≥ 0.04). As for HDL-C, younger and older women and older men had greater ORs of having low HDL-C with increasing quartiles of FFMi (1.50 ≤ β ≥ 3.27; 0.001 ≤ P ≥ 0.051).
圖 2 Figure 2
其他組件在年齡特定和性別特定的子群之間顯示出更異質的反應。例如，年長女性在 FFMi 的四分位數增加時，具有更高的空腹血糖勝算 (2.16 ≤ β ≥ 4.60; 0.001 ≤ P ≥ 0.004)，而其他組別在四分位數之間未顯示差異。此外，年輕個體在 FFMi 四分位數增加時，擁有高血漿 TG 的 OR 要麼顯著要麼趨向於增加 (1.44 ≤ β ≥ 2.46; 0.001 ≤ P ≥ 0.07)，但在年長個體中則沒有 (1.15 ≤ β ≥ 1.46; 0.09 ≤ P ≥ 0.5)。
Other components displayed a more heterogeneous response between age-specific and sex-specific subgroups. For instance, older women had greater odds of having high fasting glucose with increasing quartiles of FFMi (2.16 ≤ β ≥ 4.60; 0.001 ≤ P ≥ 0.004), whereas no other group showed differences between quartiles. Furthermore, the ORs of having high plasma TG were either significantly or tended to be greater with increasing FFMi quartiles in younger individuals (1.44 ≤ β ≥ 2.46; 0.001 ≤ P ≥ 0.07), but not in older individuals (1.15 ≤ β ≥ 1.46; 0.09 ≤ P ≥ 0.5).
最後，FFMi 對血壓的影響在年輕男性和年長女性之間顯示出對比的反應，年輕男性的高血壓風險比（ORs）較高（Q4 vs. Q1: β = 2.07; P = 0.001），而年長女性的高血壓風險比則較低（Q4 vs. Q1: β = 0.54; P = 0.02）。
Finally, the impact of FFMi on BP showed contrasting responses between groups with a higher ORs of having high BP in young men (Q4 vs. Q1: β = 2.07; P = 0.001) and lower ORs of high BP in older women (Q4 vs. Q1: β = 0.54; P = 0.02).
討論 Discussion
本研究的目標是 (i) 確定 MetS 與 FFM 之間的關聯，當 FFM 以三種不同方式表示（公斤、FFMi 或 FFM%）時，以及 (ii) 在排除混雜因素後，評估 FFM 對於特定年齡子群中男性和女性擁有 MetS 或特定 MetS 組件的相對風險的影響。
The objectives of this study were to (i) determine the association between MetS and FFM when represented in three different ways (kg, FFMi, or FFM%) and (ii) assess the impact of FFM, once isolated from confounding factors, on the relative risk of having the MetS, or specific MetS components, in age-specific subgroups of men and women.
本研究的一個主要發現是，FFM 的不同表示方式顯著且強烈地影響了其與 MetS 流行率之間關聯的方向。考慮到 MetS 與 T2D 和 CVD 的風險增加，以及普遍認為較高的 FFM 對代謝健康有益的觀點，這一結論至關重要。因此，這些結果為先前發表的報告 FFM 與 MetS 之間不一致關聯的數據提供了新的見解，並建議 FFM 的不同表示方式可能解釋了這些差異。
One main finding of this study is that the representations of FFM significantly and strongly influenced the direction of its association with MetS prevalence. This conclusion is of utmost importance considering the augmented risk for T2D and CVD with MetS and the largely purported idea that a greater FFM is beneficial for metabolic health. These results thus shed new light on previously published discrepant data reporting discordant associations between FFM and MetS and suggest that the different representations of FFM likely explain these discrepancies.
我們的觀察與其他人的研究結果一致。 4, 21, 24 例如，Park 和 Yoon 以及 Scott 等人報告了 FFM 與 MetS 之間的相反關聯，這取決於 FFM 的表現方式：相對於體重（負相關）或相對於身高（正相關）。 4, 24 然而，儘管存在這些相反的關聯，兩個研究團隊都得出結論，低 FFM 可能在 MetS 的發展中扮演角色，這僅基於 FFM% 與 MetS 之間的負相關結果。 4, 24
Our observations are in accordance with those of others.4, 21, 24 For instance, Park and Yoon and Scott et al. reported opposite associations between FFM and MetS depending on how FFM was represented: relative to weight (negative association) or relative to height (positive association).4, 24 However, despite these opposite associations, both research groups concluded that low FFM could play a role in the development of MetS based solely on the results from the negative association between FFM% and MetS.4, 24
不同的 FFM 表現與 MetS 之間的對比關聯可以用 FM 所扮演的中介角色來解釋。Scott 等人主張，較高的 FFMi 與 FM 呈正相關，而後者可能驅動了發展 MetS 的風險。然而，當將 FFM 表示為總體重的百分比時，也可能引發類似的擔憂。應強調的是，在三 compartment 的層面上，身體由兩種主要組織組成：FFM（包括肌肉質量、韌帶、肌腱和器官）和 FM。因此，將 FFM 表示為體重的百分比本質上代表了 FM，因而指示了組織分佈，而不是 FFM 本身。雖然 FFM%可以準確預測代謝疾病，但它不足以孤立 FFM 對於 MetS 風險的具體作用，將其用作這樣的指標將導致錯誤的推論。一個很好的例子來自 Lee 等人的文章，他們根據 FFM%數據得出結論：「骨骼肌質量可能對未來的代謝惡化起到保護作用」。 6 然而，在他們的研究中，肢體無脂肪質量（以公斤表示）在向不健康代謝表型發展的個體與保持健康的個體之間相似，兩組之間唯一的差異是絕對脂肪量較高。我們堅信，這些有缺陷的結論可以通過使用無脂肪質量指數（FFMi）並進一步調整脂肪質量指數（FMi）來抵消，從而允許對無脂肪質量本身在患有代謝症候群（MetS）風險上的評估。
The contrasting associations with MetS among the different representations of FFM could be explained by the mediating role played by FM. Scott et al. argued that greater FFMi was positively correlated with FM and that the latter could have driven the risk of developing the MetS.24 Yet similar concerns could be raised when representing FFM as a percentage of total body weight. It should be emphasized that at a three-compartment level, the body is made up of two main tissues: FFM (which includes muscle mass, ligaments, tendons, and organs) and FM. Hence, representing FFM as a percentage of body weight inherently represents FM and is thus indicative of tissue distribution, not FFM per se. While FFM% can accurately predict metabolic diseases,4, 24 it is inadequate to isolate the specific role of FFM on the risk of having the MetS and using it as such will lead to flawed inferences. A great example is from the article of Lee et al. who concluded that ‘skeletal muscle mass may play a protective role against future metabolic deterioration’ based on FFM% data.6 However, in their study, appendicular FFM (expressed in kg) was similar between individuals progressing towards an unhealthy metabolic phenotype and those remaining healthy, leaving the only difference between groups to be a higher absolute FM. We strongly believe that these flawed conclusions could be counteracted by using FFMi and further adjusting for FMi, thus allowing the assessment of FFM per se on the risk of having the MetS.
應用這種方法導致了本研究的第二個主要發現：在年輕和年長的男性及女性中，擁有較大 FFMi 的代謝症候群（MetS）風險比值（ORs）增加，這與多個協變量無關。儘管這與普遍認為的無脂肪質量對代謝健康具有保護作用的觀點相悖，但這一結論與多項已發表研究中的先前反直覺觀察一致。然而，這些研究的作者並不總是提出或討論他們的結果。這一現象可能導致出版不平衡，並延續了更高無脂肪質量必然對代謝健康有益的觀念。
Applying this method led to the second main finding of this study: the ORs of having the MetS increased in younger and older men and women with larger FFMi, independently of multiple covariables. Although it opposes the widespread belief that FFM has a protective effect on metabolic health, this conclusion is in line with previous counterintuitive observations from multiple published studies.4, 15, 17, 18, 20, 23, 24, 28–31 The authors of these studies, however, have not always put forward, if discussed, their results. This phenomenon may contribute to a publication imbalance and perpetuate the notion that greater FFM is necessarily beneficial for metabolic health.
然而，儘管在擁有較高的無脂肪質量（FFMi）時，代謝症候群（MetS）的比值比（OR）反應相對穩定，但某些 MetS 組成部分卻顯示出年齡特異性和/或性別特異性的反應。例如，TG 升高的 OR 在男性中僅顯著，而在女性中則不顯著。相對而言，年長女性的高血漿葡萄糖 OR 僅在 FFMi 較高時顯著增加。這一結果與我們之前的一些觀察相矛盾，我們報告了 FFMi 與年輕和年長男性及女性的葡萄糖穩態之間的關係，即較大的 FFMi 與較高的胰島素抵抗（IR）相關。這些差異可能源於之前研究中使用的 HOMA-IR，該指標相比於本研究中僅使用的空腹血糖，提供了更廣泛的葡萄糖代謝視角。另一個可能解釋這些年齡特異性和性別特異性反應的原因可能來自於無脂肪質量（FFM）的分佈，如 Peppa 等人所示。 20 作者顯示，雖然全身的 FFMi 或軀幹 FFMi 與各種心臟代謝參數（收縮壓、空腹胰島素、HOMA-IR、QUICKI、HDL-C 和高敏感性 C 反應蛋白）有不利的關聯，但肢體 FFMi 僅與血壓相關。 20 此外，當上半身肢體 FFMi 和下半身肢體 FFMi 被分開考量時，下半身肢體 FFMi 與任何心臟代謝參數無關，但上半身肢體 FFMi 仍與幾乎所有估算的變數相關。 20 這些觀察結果表明，上半身和下半身的 FFM 可能與代謝健康的關係不同，這需要進一步研究。
However, despite a rather constant response in the ORs of having the MetS with greater FFMi, some MetS components showed age-specific and/or sex-specific responses. For instance, the ORs of having elevated TGs were only significant in men and not in women. In contrast, the ORs for high plasma glucose were only significantly greater with FFMi in older women. This latter result is contrasting with some of our previous observations, in which we reported a relation between FFMi and glucose homeostasis in younger and older men and women,14 that is, a greater IR with larger FFMi. These discrepancies could stem from the utilization of HOMA-IR in the previous study,14 which give a broader perspective of glucose metabolism compared with simply using fasting glucose in this study. Another potential explanation for these age-specific and sex-specific responses could stem from the distribution of FFM, as shown by Peppa et al.20 The authors showed that while whole-body FFMi or trunk FFMi was deleteriously associated with various cardiometabolic parameters (systolic BP, fasting insulin, HOMA-IR, QUICKI, HDL-C, and high sensitivity C-reactive protein), appendicular FFMi was only associated with BP.20 Furthermore, when upper-body appendicular FFMi and lower-body appendicular FFMi were considered distinctly, lower-body appendicular FFMi was not associated with any cardiometabolic parameters, but upper-body appendicular FFMi was still associated with practically all variables estimated.20 These observations suggest that upper-body and lower-body FFM may not have the same relation with metabolic health, which should further be investigated.
雖然其他成分顯示出特定群體的輕微、微弱或沒有與 FFMi 的關聯，但 WC 在各群體中與較高的 FFMi 之間顯示出穩健且一致的正相關。考慮到 WC 與 BMI 之間的已知關係，這種穩健的關聯是可以預期的。Scott 等人在澳大利亞和韓國的老年人中也報告了類似的觀察結果。 24 實際上，低四肢 FFMi 與 WC 的關聯最為顯著[OR = 0.12 (0.08–0.19)]，與 TGs 的關聯為輕微到中等[OR = 0.52 (0.37–0.72)]，與 HDL-C 的關聯[OR = 0.55 (0.40–0.78)]，以及與空腹血漿葡萄糖的關聯[OR = 0.65 (0.45–0.95)]，而與 BP 則沒有顯著的關聯[OR = 0.79 (0.58–1.08)]。 24
While other components showed group-specific and mild, weak, or no associations with FFMi, WC showed a robust and consistent positive association with greater FFMi across groups. This robust association was to be expected given the known relation between WC and BMI. Similar observations were reported by Scott et al. in Australian and Korean older adults.24 Indeed, low appendicular FFMi had the greatest association with WC [OR = 0.12 (0.08–0.19)], mild to moderate relation with TGs [OR = 0.52 (0.37–0.72)], HDL-C [OR = 0.55 (0.40–0.78)], and fasting plasma glucose [OR = 0.65 (0.45–0.95)], and no significant association with BP [OR = 0.79 (0.58–1.08)].24
總的來說，我們的結果顯示，在年輕和年長的男性及女性中，最高的 FFMi 四分位數與 MetS 的發生有更高的比值比（OR）。這些觀察結果與我們之前的一些結果 15, 17, 18, 28 以及其他研究的結果 8, 20, 22, 29–31 一致，但與許多研究的觀察結果 4, 7, 32–34 相悖。再次強調，大多數得出較高 FFM 對代謝健康有保護作用的研究在分析中使用了 FFM% 4, 7, 32–34 ，根據我們的結果，這可能解釋了差異。值得一提的是，許多報告 FFMi 與 IR 或 MetS 組件之間正相關的先前研究主要針對年長人群，特別是絕經後女性 8, 15–18, 20, 29, 31 和年長男性 8, 29, 31 。除了確認之前的觀察結果外，我們的研究顯示這些關聯也擴展到年輕個體。更廣泛地說，我們的結果表明，低 FFMi 似乎不是導致年長個體代謝健康改變的因素，還有其他機制在起作用。這與 Goulet 等人的先前結果一致。 誰得出結論，脆弱的瘦老年人在胰島素敏感性方面與正常健康的對照組沒有差異，儘管其 FFM 較低。 35
Altogether, our results demonstrate a greater OR of having the MetS in the highest FFMi quartiles in younger and older men and women. These observations are in agreement with some of our previous results15, 17, 18, 28 and those of others,8, 20, 22, 29–31 but contrast with the observations of many.4, 7, 32–34 Again, most studies concluding to a protective effect of a greater FFM on metabolic health used FFM% in their analyses,4, 7, 32–34 which, based on our results, can likely explain discrepancies. It is worth mentioning that many of the previous studies reporting a positive association between FFMi and IR or MetS components were mainly led in older populations, specifically postmenopausal women8, 15–18, 20, 29, 31 and older men.8, 29, 31 In addition to confirming previous observations, our results showed that these associations extend to younger individuals. More broadly, our results suggest that low FFMi does not seem to be a contributing factor to the altered metabolic health observed in elder individuals and that other mechanisms are at play. This is in line with previous results from Goulet et al. who concluded that frail, lean older adults showed no differences in insulin sensitivity compared with normal healthy counterparts despite lower FFM.35
考慮到這些結果的反直覺性，似乎沒有機制研究調查這一現象。我們小組之前提出了一些機制假設，可能解釋 FFMi 與 MetS 之間的有害關聯。 17, 19 簡而言之，較大的 FFM 通常以較高比例的第二型纖維為特徵，這些纖維的氧化能力和葡萄糖處理能力均低於第一型纖維。 36 此外，肌肉中的脂質滲透被顯示會改變不活躍個體的胰島素級聯途徑 37 ，因此可能促進 MetS 的發展。最後，具有較高 FFM 的個體中，毛細血管與纖維的比例降低和毛細血管密度減少也可能影響 FFMi 與 MetS 之間的關聯，因為有限的交換面積和血流已與胰島素抵抗相關聯。 38 然而，這些都涉及肌肉質量，並且是需要進一步研究的理論假設，將在不久的將來進行探討。
Considering the counterintuitive aspect of these results, no mechanistic study seems to have investigated this phenomenon. Our group has previously offered mechanistic hypotheses that could potentially explain the deleterious association between FFMi and MetS.17, 19 Briefly, a greater FFM is usually characterized by higher proportions of type 2 fibres, which have a lower oxidative capacity and a lower glucose-handling capacity compared with type 1 fibres.36 Furthermore, muscle infiltration of lipids was shown to alter the insulin cascading pathways in inactive individuals37 and could thus contribute to the development of MetS. Finally, a reduced capillary-to-fibre ratio and decreased capillary density in individuals with a higher FFM could also influence the association between FFMi and MetS because a limited exchange area and blood flow have been linked with IR.38 Nevertheless, these all refer to muscle quality and are theoretical hypotheses that require further investigation and will be addressed in the near future.
應注意的是，本研究存在一些限制。我們的結果僅限於非西班牙裔白人、非西班牙裔黑人和墨西哥裔美國人，因此可能不適用於其他族裔，如亞洲人和印度人。然而，其他大型隊列研究在亞洲人群中報告了類似的結果。其次，考慮到 1999 年至 2006 年 NHANES 數據庫中有大量缺失的吸煙狀態和酒精消費數據，這些與代謝症候群及其組成部分相關的混雜因素無法納入我們的分析。儘管存在這些限制，本研究在幾個方面得到了加強。其中之一是使用雙能 X 射線吸收法，這是量化身體組成部分最準確的方法之一，並被視為肌肉質量和脂肪質量測量的參考標準。此外，NHANES 的大樣本量涵蓋了廣泛的人口特徵，使得按年齡和性別進行的子組分析具有足夠的統計效能。最後，分析已針對包括族裔、教育水平和收入在內的多個混雜因素進行了修正。 此外，儘管是自我報告，控制身體活動使我們能夠考慮到在 FFM 與代謝狀態之間關聯中最具影響力的混雜因素之一。
It should be noted that the present study has some limitations. Our results are limited to non-Hispanic White, non-Hispanic Black, and Mexican American ethnicities and, therefore, may not be applicable to other ethnicities such as Asians and Indians. However, other large cohort studies have reported similar results in an Asian population.24 Secondly, considering a substantial amount of missing data for smoking status and alcohol consumption in the NHANES database from 1999 to 2006, these confounding factors, previously associated with MetS and its components,39 could not be included in our analyses. Despite these limits, this study is strengthened by several aspects. One of them is the use of dual-energy X-ray absorptiometry, which is one of the most accurate methods to quantify body composition compartments and is considered the reference standard for muscle mass and FM measurements.40 Furthermore, the large sample size from NHANES covering a wide range of demographic characteristics allowed the subgroup analyses by age and sex with sufficient statistical power. Finally, the analyses were corrected for multiple confounders including ethnicity, education level, and income. Furthermore, albeit self-reported, controlling for physical activity allowed us to take into account one of the most potent confounding factors in the association between FFM and metabolic status.
結論 Conclusions
綜合來看，這些結果為代謝狀態與無脂肪量（FFM）之間的關聯提供了新的見解。首先，我們顯示了相對於 FFM 的代謝症候群（MetS）盛行率會根據用來表示 FFM 的方法而有所不同。我們的研究結果也提供了一些指示，顯示 FFM 在多重混雜因素獨立下，對於年齡特定和性別特定的 MetS 發生比值（ORs）的影響。具體而言，我們觀察到在年輕和老年男性及女性中，FFMi 的較高四分位數與 MetS 的 OR 較高。因此，研究人員和臨床醫生應該意識到，FFM 的表示方式會大大影響他們對代謝健康與 FFM 之間關聯的結果和結論。例如，對數據的誤解可能導致在減重或慢性疾病的預防和管理（例如增加或維持肌肉量）方面的運動建議出現缺陷或錯誤。在這一點上，未來對 FFM 進行表型研究是非常必要的，以增進我們對於支撐我們觀察的結構和代謝機制的理解。
Taken together, these results shed new light on the association between metabolic status and FFM. First, we showed that the prevalence of MetS relative to FFM varies depending on the method used to represent the latter. Our findings also provide some indications of the role of FFM, independently of multiple confounders, on the ORs of having the MetS in an age-specific and sex-specific fashion. Specifically, we observed a greater OR of having the MetS in higher quartiles of FFMi in young and ageing men and women. Hence, researchers and clinicians should be aware that the way FFM is represented can greatly affect their results and conclusions regarding associations between metabolic health and FFM. For instance, a misinterpretation of the data can lead to flawed or erroneous exercise recommendations in the context of weight loss or the prevention and management of chronic diseases (e.g. to increase or maintain muscle mass). At this point, future investigations phenotyping FFM are highly necessary to improve our understanding of the structural and metabolic mechanisms underlying our observations.
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