最大攝氧量揭示心臟健康關鍵：腰圍比體重更具預測力！

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研究發現，最大攝氧量（VO2max）與心血管疾病及全因死亡風險呈顯著反比關係。雖然以體重調整的傳統指標已具參考價值，但加入腰圍的模型可提升預測力，特別是在女性與50歲以下族群中效果更強，為臨床評估提供新方向。

將 VO 2 max 按身體大小差異進行標準化，以評估與心血管疾病發生率和全因死亡風險的關聯

Scaling VO2max to body size differences to evaluate associations to CVD incidence and all-cause mortality risk

Salier Eriksson J, Ekblom B, Andersson G, Wallin P, Ekblom-Bak E. Scaling VO2max to body size differences to evaluate associations to CVD incidence and all-cause mortality risk. BMJ Open Sport Exerc Med. 2021;7(1):e000854. Published 2021 Jan 29. doi:10.1136/bmjsem-2020-000854

https://pubmed.ncbi.nlm.nih.gov/33537151/

摘要 Abstract

引言 Introduction

心肺適能以最大氧氣消耗量（VO 2 max）評估，是心血管疾病（CVD）的強獨立預測因子。絕對 VO 2 max 水平（L·min −1 ）主要依賴於遺傳因素、中等至高強度的身體活動水平和體型。為了在表現相關和健康相關的方面進行個體內比較，VO 2 max 傳統上使用比率縮放（Y=bX）來調整體型差異。最常用的體重單位是公斤（以 mL·min −1 ·kg −1 表示）。然而，越來越多的證據表明，線性、每比率標準的 VO 2 max 表達方式可能導致幾種類型的錯誤和誤解，包括較大體型的受試者受到懲罰，而較輕體型的受試者受到偏愛。

Cardiorespiratory fitness assessed as maximal oxygen consumption (VO2max) is a strong independent predictor for cardiovascular disease (CVD). Absolute VO2max level (L·min−1) is mainly dependent on genetic contribution, moderate-to-vigorous intensity levels of physical activity and body size. To enable intraindividual comparisons in terms of both performance-related and health-related aspects, VO2max is traditionally scaled for body size differences using ratio scaling (Y=bX). Most commonly, body mass in kg is used (expressed as mL·min−1·kg−1). However, a growing body of evidence indicates that the linear, per-ratio standard way of expressing VO2max can lead to several types of errors and misinterpretations, including larger subjects being penalised and lighter subjects favouritised.

幾何相似性的理論指出，當比較不同大小的人類之間的生物功能時，測量應該在維度上是同質的。靜態和動態功能以線性尺寸（L）的倍數表示。使用傳統比例縮放的身體大小標準化的 VO 2 max 不符合幾何相似性的理論，因為在線性尺寸中，絕對 VO 2 max 以 L·分鐘 −1 ·公斤 −1 表示，這是以 L 3 除以分鐘（L）和體重（L 3 ）的形式表達的，這並不導致維度同質性（≠ 1）。另一方面，異速縮放是一種遵循幾何相似性理論的模型，與比例縮放相比，已被提議在個體內部的大小無關比較中更為準確。異速縮放模型方程為 Y=aX b 。在這個上下文中，Y 是 VO 2 （升 = L 3 ），a 是常數，X 是身體大小變量，b 是指數參數。身高和體重是兩個易於獲取的身體大小測量，可以用於 VO 2 的異速縮放。 在異質樣本中，理論上建議的 VO 2 max 的縮放指數可以是身高 2 或體重 2/3 （兩者均等於 L 2 ）。此外，身體脂肪分佈，特別是以腰圍（WC）測量的腹部過多脂肪，與心血管疾病（CVD）風險有著強烈的關聯。因此，納入一個易於獲得的腹部脂肪測量（例如，WC）將具有臨床相關性。

The theory of geometric similarity states that when comparing biological functions between humans of different sizes, the measures should be dimensionally homogenous. Static and dynamic functions are expressed as multiples of the linear dimension (L). VO2max scaled for body size using traditional ratio scaling does not comply to the theory of geometric similarity, as absolute VO2max in L·min−1·kg−1 in linear dimensions is expressed as L3 divided by minutes (L) and body mass (L3), which does not result in dimensional homogeneity (≠ 1). Allometric scaling, on the other hand, is a model that follows the theory of geometric similarity and has been proposed to be more accurate, compared with ratio scaling, for intraindividual size-independent comparisons. The allometric scaling model equation reads Y=aXb. In this context, Y is VO2 (litre = L3), a is the constant, X is the body size variable and b is the exponent parameter. Height and body mass are two easy accessible measures of body size that can be used for allometric scaling of VO2. In heterogenic samples, theoretical suggested exponents for scaling for VO2max can be either height2 or body mass2/3 (both equal to L2). Furthermore, body fat distribution, in particular excess fat in the abdominal region measured as waist circumference (WC), has a strong association with CVD risk. Thus, also including an easy accessible measure of abdominal fat (eg, WC) would be clinically relevant.

對於身體大小差異的 VO 2 max 的比率和全尺度縮放，主要是從與心肺適能相關的表現方面進行評估，通常使用小樣本來進行個體內比較。據我們所知，只有兩項研究評估了 VO 2 max 在與健康相關方面（心血管疾病風險因素、全因死亡率）相關的不同身體測量的縮放。之前的研究沒有比較不同的 VO 2 max 縮放方式，包括不同的身體大小變數，並將這些應用於健康視角的維度理論。因此，本研究的目的是評估和比較不同模型對絕對 VO 2 max 進行身體大小縮放，與心血管疾病風險和全因死亡率的關係，針對不同年齡的男性和女性進行大樣本研究。

Both ratio and allometric scaling of VO2max for body size differences have mainly been evaluated in terms of the performance-related aspect of cardiorespiratory fitness, often using small sample sizes to enable intraindividual comparisons. To our knowledge, only two studies have evaluated scaling of VO2max for different body measurements in association with health-related aspects (CVD risk factors, all-cause mortality). No previous study has compared different ways of scaling VO2max including different variables of body size, applying these, with the dimensional theory, to a health perspective. Thus, the aim of this study was to evaluate and compare different models scaling absolute VO2max for body size, in relation to CVD risk and all-cause mortality in a large sample of men and women of different ages.

The theory of geometric similarity states that when comparing biological functions between humans of different sizes, the measures should be dimensionally homogenous. Static and dynamic functions are expressed as multiples of the linear dimension (L).6 7 VO₂max scaled for body size using traditional ratio scaling does not comply to the theory of geometric similarity, as absolute VO₂max in L·min⁻¹·kg⁻¹ in linear dimensions is expressed as L³ divided by minutes (L) and body mass (L³), which does not result in dimensional homogeneity (≠ 1). Allometric scaling, on the other hand, is a model that follows the theory of geometric similarity and has been proposed to be more accurate, compared with ratio scaling, for intraindividual size-independent comparisons. The allometric scaling model equation reads Y=aX^b. In this context, Y is VO₂ (litre = L³), a is the constant, X is the body size variable and b is the exponent parameter.7 8 Height and body mass are two easy accessible measures of body size that can be used for allometric scaling of VO₂. In heterogenic samples, theoretical suggested exponents for scaling for VO₂max can be either height² or body mass^2/3 (both equal to L²).9 Furthermore, body fat distribution, in particular excess fat in the abdominal region measured as waist circumference (WC), has a strong association with CVD risk.10 11 Thus, also including an easy accessible measure of abdominal fat (eg, WC) would be clinically relevant.

對於身體大小差異的 VO ₂ max 的比率和全尺度縮放，主要是從與心肺適能相關的表現方面進行評估，通常使用小樣本來進行個體內比較。據我們所知，只有兩項研究評估了 VO ₂ max 在與健康相關方面（心血管疾病風險因素、全因死亡率）相關的不同身體測量的縮放。12 13 之前的研究沒有比較不同的 VO ₂ max 縮放方式，包括不同的身體大小變數，並將這些應用於健康視角的維度理論。因此，本研究的目的是評估和比較不同模型對絕對 VO ₂ max 進行身體大小縮放，與心血管疾病風險和全因死亡率的關係，針對不同年齡的男性和女性進行大樣本研究。

Both ratio and allometric scaling of VO₂max for body size differences have mainly been evaluated in terms of the performance-related aspect of cardiorespiratory fitness, often using small sample sizes to enable intraindividual comparisons. To our knowledge, only two studies have evaluated scaling of VO₂max for different body measurements in association with health-related aspects (CVD risk factors, all-cause mortality).12 13 No previous study has compared different ways of scaling VO₂max including different variables of body size, applying these, with the dimensional theory, to a health perspective. Thus, the aim of this study was to evaluate and compare different models scaling absolute VO₂max for body size, in relation to CVD risk and all-cause mortality in a large sample of men and women of different ages.

材料與方法 Materials and methods

本研究的數據來自由 HPI 健康檔案研究所（瑞典斯德哥爾摩）管理的健康檔案評估（HPA）數據庫。該研究所自 1970 年代末以來負責標準化方法並教育數據收集人員。參與是自願的，對個人免費，並提供給所有為與職業或其他健康服務相關的公司或組織工作的員工。HPA 包括一份廣泛的問卷、人體測量、用於估算 VO 2 max 的亞最大自行車測試，以及以人為中心的對話。所有數據隨後都記錄在數據庫中。從 1982 年 1 月到 2015 年 12 月，共有 316,116 名參與者的數據被納入分析，這些參與者具有有效的估算 VO 2 max 測試且沒有先前的心血管疾病事件。WC 於 2001 年作為一項測量加入，因此所有包括 WC 的分析均自此日期開始。該子組由 63,380 名參與者組成，參與者的特徵顯示在表 1A、B 中，並按性別以及 50 歲以下和 50 歲以上進行劃分。這一年齡的切點是隨意決定的。

Data were obtained for this study from the Health Profile Assessment (HPA) database managed by the HPI Health Profile Institute (Stockholm, Sweden). The institute has been responsible for standardising methods and educating the data collection staff since the late 1970s.14 Participation is optional and cost-free for the individual and is offered to all employees working for a company or organisation connected to occupational or other health services. The HPA comprises an extensive questionnaire, anthropometric measurements, a submaximal cycle test for estimation of VO2max, and a person-centred dialogue. All data are subsequently recorded in the database. From January 1982 to December 2015, data from a total of 316 116 participants with a valid estimated VO2max test and no previous CVD event were included in the analyses. WC was added as a measurement in 2001 so all analyses including WC are from this date. This subgroup consisted of 63 380 participants Characteristics of the participants are shown in table 1A,B where they have been divided by sex as well as under and over 50 years of age. This cut-point of ages was an arbitrary decision.

表 1 參與者的特徵

VO 2 max 是使用標準化的亞最大 Åstrand 自行車測試估算的。為了最小化亞最大測試中已知的誤差，參與者被要求在測試前 24 小時內避免劇烈活動，在測試前 3 小時和 1 小時內分別避免吃重餐或吸煙/使用鼻煙，並避免壓力。經過標準效度測試，Åstrand 測試顯示在估算和直接測量的 VO 2 max 之間的平均差異沒有系統性偏差且變異有限，跑步機測試的平均差異為 0.01 L∙min −1 ，95% CI −0.10 至 0.11，絕對誤差和變異係數與其他亞最大測試相似（SEE=0.48 L∙min −1 ，CV=18.1%）。因此，亞最大測試適合用於大型未選擇的隊列。

VO2max was estimated using the standardised submaximal Åstrand cycle ergometer test. In order to minimise well-known errors with submaximal testing, participants were asked to abstain from vigorous activity 24 hours before the test, eating a heavy meal or smoking/using snuff 3 hours and 1 hour before the test, respectively, as well as avoiding stress. Tested for criterion validity, the Åstrand test shows no systematic bias and limited variation in mean differences between estimated and directly measured VO2max while treadmill running (mean difference 0.01 L∙min−1, 95% CI −0.10 to 0.11), with an absolute error and coefficient of variance similar to other submaximal tests (SEE=0.48 L∙min−1, CV=18.1%). The submaximal test is thus suitable for use in large unselected cohorts.

身高和體重分別以 0.5 厘米和 0.5 公斤的精度進行評估，使用經校準的秤和壁掛式身高計。腰圍在正常呼氣後，使用卷尺在髂嵴頂部和最後一根可觸及肋骨下緣之間的中點測量，精度為 0.5 厘米。

Body height and weight were assessed to the nearest 0.5 cm and 0.5 kg, respectively, by a calibrated scale and wall-mounted stadiometer. WC was measured with a tape measure to the nearest 0.5 cm at the midpoint between the top of the iliac crest and the lower margin of the last palpable rib in the mid-axillary line after normal exhalation.

有關首次心血管疾病事件或全因死亡的數據來自瑞典國家登記，並使用獨特的瑞典個人身份號碼納入個別分析。所有參與者從其健康檢查開始，跟蹤至首次心血管疾病事件、死亡或截至 2015 年 12 月 31 日。首次心血管疾病事件的發生案例（致命或非致命心肌梗死、心絞痛或缺血性中風；ICD8，410–414 和 430–438；ICD9，410–414、427、429 和 430–437；ICD10，I20-I25、I46 和 I60-I66）以及任何原因的死亡通過瑞典國家死亡原因登記和國家住院登記進行確認。

Data on first-time CVD event or all-cause mortality were derived from Swedish national registers and included in the analyses on an individual level using the unique Swedish personal identity number. All participants were followed from their HPA to the first CVD event, death or until 31 December 2015. Incident cases of first-time CVD event after the HPA (fatal or non-fatal myocardial infarction, angina pectoris or ischaemic stroke; ICD8, 410–414 and 430–438; ICD9, 410–414, 427, 429 and 430–437; ICD10, I20-I25, I46 and I60-I66) and death from any cause were ascertained through the Swedish national cause of death registry and the national in-hospital registry.

衍生了八種不同的 VO 2 max 標準化模型，其中一種不使用任何身體測量，六種使用體重和/或身高作為身體大小的測量，兩種使用腰圍。除了模型 1 和 2，分別使用每分鐘升和傳統的以公斤體重標準化的 VO 2 max 進行比較外，所有模型均根據幾何相似性的理論衍生出來，具有維度正確性。模型 4 使用來自大型人群樣本的性別特定體重指數。六個模型包含了研究人群中所有參與者的數據（n=316 116 參與者），而只有 63 380 個案例提供了腰圍數據並被納入模型 7 和 8。不同的模型在表 2 中描述。

Eight different models for scaling of VO2max were derived, one not using any body measurements, six using body mass and/or height as measures of body size, and two using WC. Apart from models 1 and 2, which used litres per minute and the traditional ratio scaling of VO2max by body mass in kg, respectively, for comparative purposes, all models were derived to be dimensionally correct according to the theory of geometric similarity. Model 4 uses sex-specific exponents for body mass derived from large population samples. Six models included data for all participants in the study population (n=316 116 participants), while only 63 380 cases provided data for WC and were included in models 7 and 8. The different models are described in table 2.

表 2 分析中包含的八種不同模型的描述

統計分析 Statistical analyses

每個連續模型的值範圍不同，因此每個模型進一步分為性別特定和年齡特定（18–50 歲；>50 歲）特定的十分位數（全樣本比較模型 1–6，n=316 116），或五分位數（在提供 WC 數據的參與者的限制樣本中比較所有模型，n=63 380）。使用 Cox 比例風險回歸模型來評估 HR 及 95% CI，以預測首次 CVD 發生率和全因死亡風險，與不同模型以及相應的十分位數和五分位數相關。為了比較不同模型中隨著縮放 VO 2 max 的增加而變化的風險關聯（HR）與最常用的縮放方法（模型 2；mL·min −1 ·kg −1 ），使用 R 核心團隊所描述的程序，對於依賴樣本進行 Bonferroni 調整以進行多重比較。全樣本中模型 1–6 之間的比較使用 P<0.01 作為顯著性水平，而在限制樣本中模型 1–8 之間的比較使用 p<0.007。 顯著性趨勢定義為 p<0.05。協調統計作為 Cox 回歸模型的擬合優度測量，包括模型的連續變數。使用縮放的 Schönfelts 殘差檢查 Cox 回歸的比例假設，我們發現沒有違反比例假設。數據使用 IBM SPSS 進行分析，版本 24.0.0，2016 年，SPSS。

The range of values for each continuous model varied, hence, each model was further divided into sex-specific and age-specific (18–50 years; >50 years) specific deciles (comparison of models 1–6 in full sample, n=316 116), or quintiles (comparison of all models in a restricted sample of participants that provided WC data, n=63 380). Cox proportional hazard regression modelling was used to assess HR with 95% CI to predict first time CVD incidence and all-cause mortality in relation to the different models and in relation to the deciles and quintiles, respectively. To compare risk associations (HR) with increasing deciles or quintiles of scaled VO2max between the different models in comparison to the method most commonly used for scaling (model 2; mL·min−1·kg−1), the procedure described by R Core Team was used for dependent samples with Bonferroni adjustment for multiple comparison. P<0.01 was used as level of significance for comparisons between models 1–6 in the full sample, and p<0.007 for comparisons between models 1–8 in the restricted sample. Trends of significance were defined as p<0.05. Concordance statistics were calculated as a measure of goodness-of-fit for Cox regression models including continuous variables for the models. The proportionality assumption for Cox regression was examined using scaled Schönfelts residuals, and we found no violation of the proportionality assumption. Data were analysed using IBM SPSS, V.24.0.0, 2016, SPSS.

結果 Results

總共有 316,116 名參與者（45%為女性）被納入比較模型 1-6，其中有 4,760 例心血管疾病（28%為女性，平均隨訪時間為 6.8±4.7 年），以及 2,936 例因各種原因死亡（43%為女性，平均隨訪時間為 6.8±4.7 年）。在比較所有模型（1-8）的限制樣本分析中，共納入 63,380 名參與者（39%為女性），有 391 例心血管疾病（24%為女性，平均隨訪時間為 3.5±2.5 年）和 185 例因各種原因死亡（30%為女性，平均隨訪時間為 3.5±2.5 年）。

A total of 316 116 participants (45% women) were included to compare models 1–6, where there were 4760 cases of CVD (28% women, mean follow-up time of 6.8±4.7 years), and 2936 deaths due to all causes (43% women a mean follow-up time of 6.8±4.7 years). In the restricted sample analyses comparing all models (1–8), a total of 63 380 participants (39% women) were included, with 391 cases of CVD (24% women, mean follow-up time of 3.5±2.5 years) and 185 deaths due to all causes (30% women, mean follow-up time 3.5±2.5 years.

隨著 VO 2 max 的十等分增加，模型 1–6 在完整樣本分析中與較低的心血管疾病風險和全因死亡率相關（p<0.001）（圖 1A,B）。在完整樣本分析中，與模型 2 相比，每個更高十等分的心血管疾病風險關聯，模型 1 (L·min −1 ) 和模型 5 (mL·min·height −2 ) 與模型 2 (mL·min −1 ·kg −1 ) 的關聯顯著較弱（p<0.00001; p<0.00001）。模型 1 和 5 在所有子群中與模型 2 的關聯也顯著較弱（所有子群 p<0.00001）。模型 3 (mL·min·kg −0.67 ) 和 4 (mL·min·kg −0.76 and −0.52 ) 在整個樣本中與模型 2 的關聯顯著較弱（p=0.0004; p<0.00006），女性（p=0.0003; p=0.00009），以及 50 歲以下的人（p<0.00001; p=0.00001），男性則顯示出較弱關聯的趨勢（p=0.038; p=0.015）。模型 6 (mL·min −1 ·(kg 1 ·height −1 ) −1 ) 與模型 2 在整個樣本中有更強的關聯（p<0.00001），男性（p<0.00001）以及兩個年齡子群（p=0.0001; p<0.00002），（表 3A 和圖 1）。

Increasing deciles of VO2max were associated with lower CVD risk and all-cause mortality for models 1–6 in the full sample analyses (p<0.001) (figure 1A,B). For risk associations per each higher decile for each model compared with model 2 for CVD risk in the full sample analyses, model 1 (L·min−1) and model 5 (mL·min·height−2) had significantly weaker associations compared with model 2 (mL·min−1·kg−1) (p<0.00001; p<0.00001). Models 1 and 5 also had a significantly weaker association compared with model two in all subgroups (p<0.00001 for all subgroups). Models 3 (mL·min·kg−0.67) and 4 (mL·min·kg−0.76 and −0.52) had a significantly weaker association compared with model 2 in the whole sample (p=0.0004; p<0.00006), women (p=0.0003; p=0.00009), and those under 50 years (p<0.00001; p=0.00001), as well as a trend for a weaker association for men (p=0.038; p=0.015). Model 6 (mL·min−1·(kg1·height−1)−1) had a stronger association compared with model 2 for the whole sample (p<0.00001), men (p<0.00001) and both age subgroups (p=0.0001; p<0.00002), (table 3A and figure 1).

圖 1 每十個分位數的心血管疾病風險（左）和全因死亡率（右）的風險比（HRs），模型 1-6 的總樣本（n=316 116）。心血管疾病，CVD；最大氧氣消耗量，VO 2 max。 圖 2 每五個分位數的心血管疾病風險（左）和全因死亡率（右）的風險比（HRs），模型 1-8 的限制樣本（n=63 380）。心血管疾病，CVD；最大氧氣消耗量，VO 2 max。 表 3 不同模型的心血管疾病風險和全因死亡率的風險比（HR）及 95%置信區間（CI）和一致性。

對於全因死亡率，模型 1 (L·min ⁻¹ ) 和模型 5 (mL·min ⁻¹ ·height ⁻² ) 與增加的標準化 VO ₂ max 十分位數之間的關聯顯著較弱，與模型 2 相比，對於全樣本 (p=0.0008; p=0.001)、男性 (p=0.003; 0.0005) 和 50 歲以下者 (p<0.0001; p<0.00001) 均如此 (表 3A 和圖 1)。模型 4 對於 50 歲以上者與模型 2 之間顯示出顯著較強的關聯 (p=0.009)。

For all-cause mortality, significantly weaker associations with increasing deciles of scaled VO₂max were seen for model 1 (L·min⁻¹) and model 5 (mL·min⁻¹·height⁻²) in comparison to model 2 for the full sample (p=0.0008; p=0.001), men (p=0.003; 0.0005) and those under 50 (p<0.0001; p<0.00001), (table 3A and figure 1). Model 4 showed a significantly stronger association to model 2 for those over 50 (p=0.009).

在受限樣本中，所有模型與隨著 VO ₂ max 的五分位數增加而降低的心血管疾病風險和全因死亡率相關（p<0.001）（圖 2A,B）。對於心血管疾病風險，模型 5 (mL.min.height ⁻² ) 與整個樣本的五分位數增加相比，與模型 2 (mL.min ⁻¹ kg ⁻¹ ) 的關聯顯著較弱（p=0.0009），女性（p=0.001）。模型 1 (L·min ⁻¹ ) 與女性（p=0.003）和 50 歲以下的人（p=0.002）相比，與模型 2 (mL.min ⁻¹ kg ⁻¹ ) 的關聯顯著較弱，並且在整個樣本中有趨勢顯示關聯顯著較弱（p=0.025）（表 3B）。模型 5 對男性顯示出顯著較弱的關聯趨勢（p=0.031）。對於 50 歲以下的人，模型 7 (mL·min ⁻¹ ·WC ⁻² ) 和模型 8 (mL·min ⁻¹ ·(WC ³ ·height ⁻¹ ) ⁻¹ ) 與模型 2 的心血管疾病風險有顯著較強的關聯（兩個模型均為 p=0.002），並且模型 8 對女性有趨勢（p=0.033）。

In the restricted sample, all models were associated with lower CVD risk and all-cause mortality with increasing quintiles of VO₂max (p<0.001) (figure 2A,B). For CVD risk, model 5 (mL.min.height⁻²) had a significantly weaker association with increasing quintiles compared with model 2 (mL.min⁻¹ kg⁻¹) for the whole sample (p=0.0009), and women (p=0.001). Model 1 (L·min⁻¹) had a significantly weaker association compared with model 2 (mL.min⁻¹ kg⁻¹) for women (p=0.003) and those under 50 (p=0.002), and a trend towards a significantly weaker association in the whole sample (p=0.025) (table 3B). Model 5 showed a trend of a significantly weaker association (p=0.031) for men. There was a significantly stronger association to CVD risk for model 7 (mL·min⁻¹·WC⁻²) and model 8 (mL·min⁻¹·(WC³·height⁻¹)⁻¹) compared with model 2 for those under 50 (p=0.002 for both models) and a trend for women (p=0.033) for model 8.

對於全因死亡率，模型 2 與其他任何模型之間沒有顯著差異（表 3B 和圖 2）。與模型 2 相比，模型 7（mL·min ⁻¹ ·WC ⁻² ）和模型 8（mL·min ⁻¹ ·(WC ³ ·height ⁻¹ ) ⁻¹ ）在女性的全因死亡率方面顯示出顯著更強的關聯趨勢（p=0.012；p=0.033）。
For all-cause mortality, model 2 did not differ significantly from any of the other models (table 3B and figure 2). There was a trend towards a significantly stronger association for model 7 (mL·min⁻¹·WC⁻²,) and model 8 (mL·min⁻¹·(WC³·height⁻¹)⁻¹) compared with model 2 for all-cause mortality in women (p=0.012; p=0.033).

討論 Discussion

本研究的主要發現是，所有根據不同身體測量標準調整的 VO₂max 模型，在全樣本和限制樣本中均與較低的心血管疾病 (CVD) 風險和全因死亡率相關。在全樣本分析中，模型 1 (L·min⁻¹) 和模型 5 (mL·min⁻¹·height⁻²) 相較於參考模型 2 (mL·min⁻¹·kg⁻¹)，在每增加十分位數的風險關聯上較為平緩，無論是對於 CVD 風險還是全因死亡率。這一現象在所有 CVD 風險的子群中均有觀察到，並且在男性及年齡低於 50 歲的群體中對全因死亡率亦是如此。在限制樣本中，包括使用腰圍 (WC) 的調整模型，模型 7 (mL·min⁻¹·WC⁻²) 和模型 8 (mL·min⁻¹·(WC³·height⁻¹)⁻¹) 在某些子群中對 CVD 風險和全因死亡率的關聯顯示出顯著增強的趨勢。

The main findings in this study are that all models of VO₂max scaled to different body measurements, both in the full sample and in the restricted sample, are associated with lower CVD risk and all-cause mortality. In the full sample analyses, model 1 (L·min⁻¹) and model 5 (mL·min⁻¹·height⁻²) had less steep risk associations per increased deciles compared with reference model 2 (mL·min⁻¹·kg⁻¹) for both CVD risk and all-cause mortality. This was seen in all subgroups for CVD risk, and in men and in those younger than 50 years for all-cause mortality. In the restricted sample, including scaling models with WC, there was an additional trend towards a significantly stronger association for model 7 (mL·min⁻¹·WC⁻²) and model 8 (mL·min⁻¹·(WC³·height⁻¹)⁻¹) in some subgroups for CVD risk and all-cause mortality.

我們的結果顯示，除了模型 1 和 5 的關聯性較弱，以及模型 7 和 8 部分顯示出較強的關聯性趨勢外，這裡檢查的所有模型在與心血管疾病風險和全因死亡率的關聯上，與模型 2 相比顯示出小的差異，即使某些 p 值是顯著的。模型 1（L·min⁻¹）通常顯示出與心血管疾病和全因死亡率的關聯性較弱是可以理解的，因為該模型中未包含任何身體測量數據。文獻中對於哪種體重指數最適合 VO₂max 的功率函數縮放的持續不一致，促使了 VO₂max（mL·min⁻¹·kg）簡單比率縮放的持續使用，這幾乎可以被視為一種標準方法。儘管存在這種不一致，我們仍然使用 mL·min⁻¹·kg 作為標準方法來比較其他模型。令人驚訝的是，我們的結果部分確認了簡單比率縮放可能是足夠的，儘管它不符合維度理論。這與 Heil 等人的觀點相反，他們建議應停止使用 VO₂ 峰值的簡單比率縮放，而應改用介於 0.65 和 0.75 之間的體重冪指數。然而，我們的研究關注的是不同縮放模型與心血管疾病（CVD）發生率和全因死亡風險的關聯，而大多數先前的研究則研究了心肺適能的表現相關方面。這可能解釋了本研究中模型之間的小但顯著的差異，因為已知 VO₂max 水平與 CVD 發生率和全因死亡率有關。因此，僅在所有模型中包含 VO₂max 可能足以抵消模型中不同身體測量的影響，包括傳統的比率縮放。然而，這並不能解釋為什麼模型 5（mL·min⁻¹·height⁻²）與模型 2 相比，通常顯示出較弱的 CVD 發生率和全因死亡率的關聯。文獻中報導的許多不同可行的縮放指數也可能是由於這些研究中使用的小樣本量所致。我們研究中的大樣本量因此可能是未能找到類似差異的另一個原因。

Our results show that all the models examined here, except for model 1 and 5 that had a weaker association, and models 7 and 8 that partly had a trend of a stronger association, showed small differences in associations to CVD risk and all-cause mortality as compared with model 2 even if some p values were significant. That model 1 (L·min⁻¹) generally showed a weaker association to CVD and all-cause mortality is understandable as no body measurements were included in the model. The continual lack of agreement in the literature as to which body mass exponent is best for power function scaling of VO₂max has fuelled the continued use of the simple ratio scaling of VO₂max (mL·min⁻¹·kg) which can almost be considered a criterion method. In spite of this lack of agreement, we used mL·min⁻¹·kg as the criterion method to compare the other models with. Surprisingly our results partly confirm that the simple ratio scaling may be adequate to use in spite of it not adhering to the dimensional theory. This is contrary to Heil and others who suggest that the use of the simple ratio scaling of VO₂peak values should be discontinued in favour of body mass power exponents to powers between 0.65 and 0.75. However, our study concerns the use of different models of scaling in association to incidence of CVD and all-cause mortality, whereas most previous studies have studied performance-related aspects of cardiorespiratory fitness. This could account for the small, if significant, differences between the models in this study as VO₂max levels are known to be associated to incidence of CVD and all-cause mortality. Thus just including VO₂max in all the models may be enough to counteract the effect of the different body measurements in the models, including the traditional ratio scaling. However, this does not explain why model 5 (mL·min⁻¹·height⁻²) generally showed a weaker association to CVD incidence and all-cause mortality rate compared with model 2. The many different viable scaling exponents that have been reported in the literature concerning allometric scaling could also be due to the small sample sizes used in these studies. The large sample size in our study could therefore be another reason for not finding similar differences.

兩項先前的研究評估了與健康相關的不同縮放模型。Imboden 等人顯示，VO₂ 峰值與心血管疾病風險以及全因死亡率之間存在類似的反向關係，並且在將全因死亡率標準化為無脂肪質量而非總體重時，關係更為強烈。不幸的是，我們無法將無脂肪質量作為模型納入，因為我們沒有相關數據。在大多數臨床環境中，無脂肪質量的測量也比例如腰圍更難獲得。雖然可以使用體重和身高測量來計算，但有效性較低。未來的研究應評估無脂肪質量作為 VO₂max 縮放的身體測量的可能附加解釋。

Two previous studies have evaluated different scaling models in association with health-related aspects. Imboden et al showed a similar inverse relationship between VO₂peak and CVD risk as well as all-cause mortality scaled to both total body mass and fat-free mass, but with a stronger relationship when normalising to fat-free mass rather than total body mass for all-cause mortality. Unfortunately, we were not able to include scaling to fat-free mass as a model as we had no data for it. Fat-free mass is also a more difficult measurement to obtain than, for example, WC in most clinical environments. It might be calculated using weight and height measurements, however, with low validity. The possible added explanation of fat-free mass as a body measure for scaling of VO₂max should be evaluated in future studies.

模型 5（mL·min⁻¹·身高⁻²）的心血管疾病（CVD）風險關聯較不陡峭的發現，以及模型 7（mL·min⁻¹·腰圍⁻²）和模型 8（mL·min⁻¹·（腰圍³·身高⁻¹）⁻¹）的 CVD 風險關聯趨勢較陡峭，應進一步討論。模型 5 是唯一僅包含身高作為身體測量的模型。顯然，這並未像包含體重或腰圍的測量那樣有效地區分個體，這可能表明（腹部）超重或肥胖。先前的研究顯示，心肺適能和體脂肪與 CVD 風險以及全因死亡率有著強烈的關聯，而那些肥胖且不健康的人風險最高。這意味著納入這兩項測量可能對進一步區分 CVD 風險的個體至關重要。目前的分析僅在限制樣本分析中包含 391 例 CVD 病例（佔總數的 0.6%），因此在未來的分析中納入更多 CVD 病例可能會提供顯著的關聯。

The findings of less steep CVD risk association of model 5 (mL·min⁻¹·height⁻²) and a trend of more steep CVD risk association of model 7 (mL·min⁻¹·WC⁻²) and 8 (mL·min⁻¹·(WC³·height⁻¹)⁻¹) should be further discussed. Model 5 was the only model that included only height as a body measurement. Evidently, this did not discriminate individuals as powerfully as when including measurements of either body mass or WC for CVD risk assessment, which in turn might indicate (abdominal) overweight or obesity. Previous research has shown that both cardiorespiratory fitness and body fatness are strongly associated to CVD risk as well as all-cause mortality, where those being obese and unfit are most at risk. This implies that including both these measurements may be of importance to further discriminate individuals for CVD risk. The present analyses included only 391 CVD cases in the restricted sample analyses (0.6% of total n), and hence inclusion of more CVD cases in future analyses may provide significant associations.

這項研究的一個優勢是樣本大小。以往的研究可能因為使用的小樣本而顯示出更多的分歧結果。我們樣本的異質性也是一個優勢，因為它反映了正常人口。潛在的弱點是該隊列可能略具選擇性，因為參加 HPA 是自願的。然而，隊列的大小和多樣性會削弱任何選擇性，以及 VO₂max 水平與在瑞典進行的其他人口研究的相似性。另一個可能的弱點是 VO₂max 是使用標準化的亞最大 Åstrand 自行車測功機測試估算的。然而，在這個主要由非運動員組成的大型人群中，直接測量 VO₂max 並不可行。

A strength of this study is the sample size. Previous studies may have shown more diverging results due to the small samples they used. The heterogeneity of our sample is also a strength as it mirrors a normal population. A potential weakness is that the cohort may be slightly selective, as participation in the HPA was voluntary. However, the size and diversity of the cohort would weaken any selectivity, as well as the similarity of VO₂max levels to other population studies conducted in Sweden. Another possible weakness is that VO₂max was estimated using the standardised submaximal Åstrand cycle ergometer test. It would not, however, have been feasible to measure VO₂max directly in this large and mainly non-athletic population.

另一個限制是，VO₂max 與心血管疾病 (CVD) 發生率及全因死亡風險之間的關聯依賴於許多其他風險因素，如肥胖、血脂異常、高血壓。我們選擇僅包括年齡和性別，因為我們的數據中其他風險因素的數量有限。

A further limitation is that the association between VO₂max and incidence of CVD and all-cause mortality risk is dependent on many other risk factors such as obesity, dyslipidaemia, hypertension. We chose to only include age and sex as we had a limited amount of other risk factors in our data.

結論 Conclusions

儘管 VO₂max 與體重的簡單比率縮放並未遵循維度理論，我們的結果顯示它與心血管疾病（CVD）和全因死亡率的關聯性與其他模型相似，這些模型中加入了不同的身體尺寸以符合維度理論。然而，僅將身高作為縮放的身體測量顯示出與 CVD 風險的關聯性較弱，與標準模型 2（mL·min⁻¹·kg⁻¹）相比。將腰圍（WC）作為縮放的身體測量顯示出與 CVD 風險的關聯性有增強的趨勢，與模型 2 相比。在一般人群中，當身體活動和 VO₂max 處於低水平時，這可能會加速慢性疾病的脆弱性，因此評估活動水平和 VO₂max 在臨床上具有高度相關性。本研究為臨床實踐如何考慮 VO₂max 的個體內尺寸差異與 CVD 發病率和全因死亡率的關聯提供了新的重要知識。然而，未來需要進行不同結果的研究以進一步澄清這一點。

In spite of the simple ratio scaling of VO₂max to body mass not following the dimensional theory, our results showed that it was associated to CVD and all-cause mortality in a similar way to the other models where varying body dimensions were added to comply with the dimensional theory. However, including only height as a body measurement for scaling showed a weaker association to CVD risk compared with the criterion model 2 (mL·min⁻¹·kg⁻¹). Inclusion of WC as body measurement for scaling showed a tendency for a stronger association to CVD risk in comparison to model 2. In times of low physical activity and VO₂max in the general population, which may potentially accelerate vulnerability for chronic disease, it is highly clinically relevant to evaluate activity levels and VO₂max. The present study adds new important knowledge of how clinical practices may consider intraindividual size differences in VO₂max for association to CVD incidence and all-cause mortality. However, future studies with different outcomes are required to clarify this further.

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