癌症病患體重愈重活得愈久？研究揭「肥胖悖論」新解釋！

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一般認為超重或肥胖會增加罹癌風險，但一項針對11萬名癌症病患的研究顯示，診斷後BMI較高的病患存活率反而較高，甚至可延長壽命3至6年。研究指出，在24種癌症中，BMI高於22.5的患者死亡風險較低，最佳存活區間為BMI 29.6至34.2。這一現象被稱為「肥胖悖論」，顯示癌症患者維持較高體重可能有助於提高存活率，顛覆了傳統減重建議。

Body mass index and survival after cancer diagnosis: A pan-cancer cohort study of 114 430 patients with cancer

體重指數與癌症診斷後的生存率：一項涵蓋 114,430 名癌症患者的全癌症隊列研究

Tu H, McQuade JL, Davies MA, et al. 體重指數與癌症診斷後的生存率：一項針對 114,430 名癌症患者的全癌症隊列研究。創新 (劍橋)。2022;3(6):100344。發表於 2022 年 10 月 18 日。doi:10.1016/j.xinn.2022.100344
Tu H, McQuade JL, Davies MA, et al. Body mass index and survival after cancer diagnosis: A pan-cancer cohort study of 114 430 patients with cancer. Innovation (Camb). 2022;3(6):100344. Published 2022 Oct 18. doi:10.1016/j.xinn.2022.100344

https://pmc.ncbi.nlm.nih.gov/articles/PMC9638833/

摘要 Abstract

鼓勵癌症患者保持正常體重指數（BMI）的建議主要是基於癌症發展風險的數據。我們測試了診斷周圍（診斷後 1 年內）BMI 與新發癌症患者全因死亡率之間的前瞻性關聯。在 7.2 年的隨訪中，114,430 名癌症患者中有 42%（48,340 人）去世。樣條分析顯示，與 BMI 為 22.5 相比，BMI 低於 22.5 與 24 種癌症類型的全因死亡風險增加相關。BMI 高於 22.5 則與降低的全因死亡率相關，並且觀察到非線性關聯；在 BMI 為 29.6–34.2 時風險最低，而在非常高的 BMI 值下風險開始回升至 1 或以上。高 BMI 的降低死亡風險在 24 種癌症類型中的 23 種中均有觀察到，並在試圖消除潛在的選擇偏差、吸煙和合併症的混淆以及保留因果關係後仍然保持。 與正常的體重指數（BMI）18.5–24.9 相比，超重 BMI（25–29.9）的危險比為 0.85（95%信心區間[CI]，0.83–0.87），而肥胖 BMI（≥30）的危險比為 0.82（0.80–0.85），這些關聯在不同癌症類型和各種子群中通常是一致的。肥胖 BMI 與男性可增加長達 6 年的壽命，女性則可增加 3 年。總之，雖然超重/肥胖的 BMI 會增加一般人群中發展癌症的風險，但在癌症患者中，超重/肥胖的診斷前 BMI 與更長的生存期相關。
The recommendation encouraging patients with cancer to keep a normal body mass index (BMI) is largely extrapolated from data on risk of developing cancer. We tested the prospective association between peri-diagnostic (within 1 year post-diagnosis) BMI and all-cause mortality in patients with incident cancers. During 7.2 years of follow-up, 42% (48,340) of the 114 430 patients with cancer died. Spline analysis revealed that compared with a BMI of 22.5, a BMI lower than 22.5 was associated with increased risk of all-cause mortality across 24 cancer types. A BMI higher than 22.5 was associated with reduced all-cause mortality, while a non-linear association was observed; the lowest risk was found at a BMI of 29.6–34.2, and the risk started to return to and above unity at very high BMI values. The reduced mortality risk of high BMI was observed in 23 of 24 cancer types and maintained after attempts to remove potential selection bias, confounding by smoking and comorbidities, and reserve causality. Compared with a normal BMI of 18.5–24.9, the hazard ratios were 0.85 (95% confidence interval [CI], 0.83–0.87) for an overweight BMI (25–29.9) and 0.82 (0.80–0.85) for an obese BMI (≥30), and the associations were generally consistent across cancer types and various subgroups. Obese BMI was associated with increased life expectancy, up to 6 years among men and 3 years among women. In conclusion, while overweight/obese BMI increases the risk of developing cancer in the general population, overweight/obese peri-diagnostic BMI was associated with longer survival in cancer patients.

引言 Introduction

已經確立，過多的體重會增加整體死亡率的風險以及在一般人群中發展許多不同惡性腫瘤的風險。 1 2 相反，在患有慢性疾病（包括心血管疾病、肺部疾病和末期腎病）的人群中發現了矛盾的關聯，超重或肥胖的身體質量指數（BMI）似乎與較低的死亡風險相關，這一現象被稱為“肥胖悖論”。 3
It is well established that excess body weight increases risk of overall mortality and risk of developing many different malignancies in the general population.1,2 In contrast, paradoxical associations were found in populations with a chronic disease (including cardiovascular disease, pulmonary disease, and end-stage renal disease) where an overweight or obese body mass index (BMI) appears associated with lower mortality risk, a phenomenon called the “obesity paradox.”3

癌症患者和癌症倖存者是一個龐大且不斷增長的人群，他們非常關心尋求有關改變生活方式因素以改善預後的信息。 4 當前的指導方針建議癌症患者和癌症倖存者達到或維持正常體重， 5 6 7 而這些建議主要是從預防數據中推斷出來的。然而，過多體重在癌症發展中的生物學可能與在癌症預後中的生物學不同。迄今為止，診斷後體重對癌症結果的影響尚未完全了解，且沒有隨機試驗的證據來檢查有意識的體重變化對癌症結果的影響。 8
Patients with cancer and cancer survivors, a large and growing population, are greatly interested in seeking information on modifying lifestyle factors to improve prognosis.4 Current guidelines recommend patients with cancer and cancer survivors to achieve or maintain a normal body weight,5,6,7 and those recommendations are largely extrapolated from prevention data. However, the biology of excess body weight in cancer development may differ from that in cancer prognosis. To date, the effects of body weight after diagnosis on cancer outcomes are not fully understood, and no evidence from a randomized trial to examine the impact of intentional weight change on cancer outcomes is available.8

有關體重與癌症患者預後之間的關聯性存在矛盾的數據。雖然一些研究表明，超重/肥胖的 BMI 可能預測癌症患者的良好預後，支持癌症中的肥胖悖論，但其他研究則表明相反的結果。有證據表明，這些關聯可能因癌症部位、階段和治療而異。先前研究中的不一致結果也可能源於方法論問題，例如樣本量相對較小、BMI 的單次測量、BMI 測量的時間（即診斷前/早期生活、診斷期間或某些癌症治療之前）、自我報告的體重和身高、選擇的 BMI 臨界值的細緻程度、統計方法不佳（即將 BMI 視為分類或線性）、選擇偏差、未測量變量的混淆（特別是吸煙和合併症）以及反向因果關係。此外，到目前為止，大多數研究都是在常見癌症中進行的，而有關相對不常見癌症的數據則稀少。
There is conflicting data on the association of body weight with outcomes among patients with cancer. While some studies suggest that an overweight/obese BMI may predict favorable outcomes in patients with cancer,9,10,11,12,13,14,15,16,17,18 supporting the obesity paradox in cancer,15,19 other studies suggest the opposite.20,21,22,23 There is some evidence that associations may vary by cancer site, stage, and treatment.13,14,24 Inconsistent results in previous studies may also arise from methodological issues such as relatively small sample size, single measurement of BMI, timing of BMI measurement (ie, pre-diagnostic/early life, peri-diagnostic, or before certain cancer therapy), body weight and height that were self-reported, granularity of BMI cutoffs selected, poor statistical methodology (ie, treating BMI as categorized or linear), selection bias, confounding by unmeasured variables (especially smoking and comorbidities), and reverse causality.15,19,25,26,27,28 Moreover, most studies to date have been conducted in common cancers, and data on relatively uncommon cancers are sparse.

為了解決這些潛在的限制，我們系統性地研究了一個由 114,430 名成人癌症患者組成的大型前瞻性隊列，以調查診斷周圍（診斷後 1 年內 15 29 ）的 BMI 與 24 種癌症類型的全因死亡率之間的關聯。
To address these potential limitations, we systematically studied a large prospective cohort of 114 430 adult patients with cancer to investigate the association of peri-diagnostic (within 1 year after diagnosis15,29) BMI with all-cause mortality across 24 cancer types.

方法 Methods

研究參與者來自 MD 安德森癌症患者與生還者隊列，該隊列已詳細描述。 30 本次分析的納入標準為：1）新診斷（在診斷後 1 年內在 MD 安德森註冊）且組織學確認的癌症；2）診斷時年齡≥18 歲；3）在診斷後 1 年內至少有一次 BMI 測量；4）患者歷史數據庫（PHDB）中有可用的核心流行病學數據。最終研究隊列由 114,430 名在 2001 年至 2014 年間診斷的癌症患者組成。本研究已獲得德克薩斯大學 MD 安德森癌症中心機構審查委員會的批准，並通過協議 Lab03-0320 獲得知情同意，以授權數據處理和分析。
Study participants were accrued from the MD Anderson Cancer Patients and Survivors Cohort, which was previously described in detail.30 The inclusion criteria for the current analysis were 1) newly diagnosed (registered at MD Anderson within 1 year of diagnosis) and histologically confirmed cancer; 2) age ≥18 years at the time of diagnosis; 3) at least one BMI measurement obtained within 1 year after diagnosis; 4) core epidemiological data available from the patient history database (PHDB). The final study cohort consisted of 114 430 patients with cancer diagnosed between 2001 and 2014. This study was approved by The University of Texas MD Anderson Cancer Center institutional review board, and informed consent was obtained through protocol Lab03-0320 to authorize data processing and analysis.

醫療專業人員在醫療訪問中測量體重和身高，作為標準護理評估，這些數據用於在每次訪問時推導 BMI（體重[公斤]/身高[米] 2 ）。每位患者的體重和 BMI 測量的中位數為 12 次（範圍 1–1076）。標準化的 PHDB 問卷是每位患者主要醫療評估的強制組成部分，收集在 MD Anderson 首次訪問時的全面基線信息，如人口統計學、煙草和酒精使用歷史、病史、當前合併症和生活質量（基於短表-12 v.1 31 ）。患有更晚期/侵襲性癌症的患者通常會減輕體重，並可能轉向較低的 BMI；因此，關於先前體重減輕的信息對於評估由於反向因果關係引起的潛在偏見非常重要。關於先前體重減輕的信息在隨機子集的 24,962 名患者中可用，通過手動抽取 PHDB 問卷中有關先前體重減輕的問題（這些信息最初未輸入數據庫）。 為了評估這個子集之間的關聯是否能代表整體隊列之間的關聯，我們將這個子集中的患者特徵分佈與整體隊列中的分佈進行了比較。這個子集中的患者特徵分佈與整體隊列中的分佈相當。機構腫瘤登記處的臨床編碼專家抽取了有關腫瘤部位、分期、組織學、分級、先前治療和在 MD 安德森的治療的臨床數據。
Healthcare professionals measured weight and height at medical visits as standard of care assessments, which were used to derive BMI (weight [kg]/height [m]2) at each visit. The median number of weight and BMI measurements per patient was 12 (range 1–1076). The standardized PHDB questionnaire, a mandatory component of each patient’s primary medical evaluation, collects comprehensive baseline information at first visit to MD Anderson such as demographics, tobacco and alcohol use history, medical history, current comorbid conditions, and quality of life (based on the Short Form-12 v.131). Patients with more advanced/aggressive cancer often lose weight and may migrate to a lower BMI; therefore, information on prior weight loss is important for assessing potential bias due to reverse causality. Information regarding prior weight loss was available in a random subset of 24 962 patients through manual abstraction from the question on prior weight loss in the PHDB questionnaire (this information was not entered into the database initially). To assess whether the associations among the subset could represent those among the overall cohort, we compared the distribution of patient characteristics in this subset with that in the overall cohort. The distribution of patient characteristics in this subset was comparable to that in the overall cohort. The clinical coding specialists at the institutional tumor registry abstracted clinical data on tumor site, stage, histology, grade, prior treatment, and treatment at MD Anderson.

先前已報告了隨訪程序。 30 簡而言之，所有患者的生命狀態每年通過機構腫瘤登記處的主動和被動方法進行確認。與 MD 安德森的約診檔案匹配，將最近有醫療訪問的患者與在過去 12-15 個月內沒有醫療訪問的患者分開。對於後一組患者，則通過隨訪信件和對未回覆信件的患者進行電話聯繫來查詢其生命狀態。對於未通過這些主動方法聯繫到的患者（估計不到 5%），其生命狀態進一步通過與社會安全死亡指數和州生命統計局的被動匹配來確認。最後一次可用的隨訪日期為 2018 年 2 月 16 日。
Follow-up procedures were previously reported.30 Briefly, the vital status of all patients is ascertained annually via active and passive approaches by the institutional tumor registry. Matching with appointment files at MD Anderson separates patients with a recent medical visit from those without one in the previous 12–15 months. The vital status of the latter group is then inquired by follow-up letters and by telephone calls to patients who have not responded to letters. For patients who are not reached by these active approaches (estimated to be <5%), the vital status is further ascertained by passive matching to the Social Security Death Index and State Bureau of Vital Statistics. The last date of available follow up was February 16, 2018.

STATA 統計軟體和統計分析系統 (SAS) 被用來執行所有統計分析。事件的時間是從癌症診斷時開始計算到死亡或最後聯繫的時間，以先到者為準。我們根據這段時間內所有的診斷後 BMI 測量，計算了平均的診斷周圍 (診斷後 1 年內 15 29 ) BMI，並根據測量之間的時間加權。 32 在所有癌症類型及 24 種癌症類型中，我們使用限制性立方樣條分析來評估 BMI 作為連續變數與全因死亡率之間的多變量調整關聯。考慮的潛在混雜因素是根據先驗知識選擇的，最終模型包括與 BMI 和全因死亡率均有關聯且不在因果路徑中的潛在混雜因素。樣條分析中的結點數量選擇為三個。選擇 22.5 的 BMI 作為參考，因為它是 BMI 類別 20 到 25 的中點，並且在大型前瞻性研究中與一般人群中最低的全因死亡率相關。 此外，22.5 的 BMI 將提供比極端 BMI 值更穩定的參考。作為一項敏感性分析，以評估由於先前體重減輕而可能產生的偏差，我們在具有或不具有先前體重減輕的患者中進行了分層分析，該分析基於體重減輕信息的子集。然後，根據標準的 WHO 標準將 BMI 分組（體重過輕<18.5；正常 18.5–24.9；超重 25–29.9；肥胖≥30）。四個 BMI 組別的死亡風險被繪製並使用 Kaplan-Meier 函數進行估算。 我們使用 Cox 比例風險模型，以研究時間作為時間尺度，估計危險比（HRs）和 95%置信區間（CIs），並調整潛在的混雜因素，包括診斷年齡（連續）；性別（男性，女性）；種族/民族（白人，黑人，西班牙裔，亞裔/太平洋島民，其他）；婚姻狀況（已婚，單身，其他）；教育程度（高中或以下，一些大學/副學士學位，學士或更高）；吸煙狀況（目前，曾經，從不）；酒精消費（目前，曾經，從不）；自評整體健康狀況（優秀，非常好，好，公平，差）；合併症數（0，1，2，3，≥4）；腫瘤分期（原位癌，局部，區域，遠端，治療後無疾病證據）；先前治療（手術，放射治療，化學治療，內分泌治療，免疫治療）；在 MD 安德森的治療（手術，放射治療，化學治療，內分泌治療，免疫治療）；以及特定癌症類型。通過對對數-對數生存圖的視覺檢查來檢查 Cox 比例風險模型的比例性。 我們對缺失數據進行了 10 次迭代的多重插補，並使用 STATA 中的 mi estimate 命令對插補數據集進行了分析。然而，未插補的結果相似。預期壽命是通過江氏的簡化生命表方法估算的，該方法具有 85 歲以上的開放端和 5 年的年齡間隔。 33
STATA statistical software and Statistics Analysis System (SAS) were used to perform all statistical analyses. The time to event was accumulated from the time at cancer diagnosis to the time at death or last contact, whichever came first. We calculated mean peri-diagnostic (within 1 year after diagnosis15,29) BMI based on all the post-diagnosis BMI measurements in this time frame weighted proportionally to the time elapsed between measurements.32 Overall and within each of the 24 cancer types, we used restricted cubic spline analysis to assess the multivariable-adjusted association between BMI as a continuous variable and all-cause mortality. Potential confounders to consider were selected according to a priori knowledge, and the final model included potential confounders that were in association with both BMI and all-cause mortality and were not in the causal pathway. The number of knots in spline analysis was chosen to be three. A BMI of 22.5 was chosen as the reference as it is the mid-point of the BMI category 20 to 25, which was associated with the lowest all-cause mortality in large prospective studies in the general population. Also, a BMI of 22.5 would provide a more stable reference compared with extreme BMI values. As a sensitivity analysis to assess potential bias due to prior weight loss, we conducted a stratified analysis among patients with or without weight loss prior in the subset with weight-loss information. Then, BMI was grouped according to standard WHO criteria (underweight <18.5; normal 18.5–24.9; overweight 25–29.9; obese ≥30). The risk of death across four BMI groups was plotted and estimated using the Kaplan–Meier function. We used the Cox proportional hazards model with time on study as the timescale to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) after adjustment of potential confounders including age at diagnosis (continuous); sex (male, female); race/ethnicity (white, black, Hispanic, Asian/Pacific islander, other); marital status (married, single, other); education (high school or less, some college/associate degree, bachelor or higher); smoking status (current, former, never); alcohol consumption (current, former, never); self-rated overall health status (excellent, very good, good, fair, poor); number of comorbidities (0, 1, 2, 3, ≥4); tumor stage (carcinoma in situ, localized, regional, distant, post-treatment no evidence of disease); prior treatment (surgery, radiation therapy, chemotherapy, endocrine therapy, immunotherapy); treatment at MD Anderson (surgery, radiation therapy, chemotherapy, endocrine therapy, immunotherapy); and specific cancer types. The proportionality of the Cox proportional hazards model was examined by visual inspection of the log–log survival plots. We performed multiple imputation with 10 iterations for missing data, and analyses were performed on the dataset with imputed data using the mi estimate command in STATA. However, the results without imputation were similar. Life expectancy was estimated by the Chiang’s method of abridged life table having 85+ open ends with 5-year age interval.33

結果 Results

研究人群中 BMI 的分佈如 Table S1 所示。根據診斷前的體重狀態，MD 安德森首次就診的選定患者特徵如 Table 1 所示。在 114,430 名參與的癌症患者中，30.7%為肥胖，35.8%為超重，31.5%為正常體重，2%為體重不足。癌症患者中遠端疾病的百分比為 28.7%（區域性疾病：20.4%；局部疾病：21.7%；原位癌：2.2%；治療後無疾病證據：11.5%；未分期：15.4%）。肥胖或超重的患者更可能年齡較大，男性，西班牙裔/黑人種族和民族，已婚，教育程度較低，合併症以及早期腫瘤分期。
The distribution of BMI in the study population is presented in Table S1. The selected patient characteristics at first visit to MD Anderson by peri-diagnostic weight status are presented in Table 1. Among the 114 430 patients with cancer enrolled, 30.7% were obese, 35.8% were overweight, 31.5% were normal weight, and 2% were underweight. The percentage of patients with cancer with distant disease was 28.7% (regional disease: 20.4%; localized disease: 21.7%; carcinoma in situ: 2.2%; post-treatment with no evidence of disease: 11.5%; unstaged: 15.4%). Patients who were obese or overweight were more likely to be of increased age, male sex, Hispanic/black race and ethnicity, married marital status, lower education, comorbid conditions, and earlier tumor stages.

表 1 根據 BMI（公斤/米 2 ）的體重狀態患者特徵

在隨訪期間（中位數為 7.2 年），累積了 529,712 人年，記錄了 48,340 例死亡。樣條分析顯示，診斷期間的 BMI 與死亡風險之間存在 J 型關聯（ Figure 1 ），在不同癌症類型的合併人群中尤為明顯。具體而言，與 BMI 為 22.5 相比，BMI 低於 22.5 與全因死亡風險增加相關。BMI 高於 22.5 則與全因死亡風險降低相關，但觀察到非線性關聯；在 BMI 為 29.6–34.2 時風險最低，而在非常高的 BMI 值下風險開始回升至 1 或以上。當使用診斷後 1 年內或 90 天內的首個可用 BMI 時，這些關聯也相似。
During follow up (median of 7.2 years), 529 712 person years were accumulated, and 48 340 deaths were recorded. Spline analysis revealed a J-shaped association of peri-diagnostic BMI with risk of death (Figure 1) in the pooled population across cancer types. Specifically, compared with a BMI of 22.5, a BMI lower than 22.5 was associated with increased risk of all-cause mortality. A BMI higher than 22.5 was associated with reduced all-cause mortality, while a non-linear association was observed; the lowest risk was found at a BMI of 29.6–34.2, and the risk started to return to and above unity at very high BMI values. The associations were similar when using the first available BMI within 1 year or 90 days after diagnosis.

圖 1 診斷期間的身體質量指數（BMI）與全因死亡率的關聯性 spline 分析

注意：22.5 kg/m 2 的 BMI 為參考值。實線代表危險比（HR），虛線為在限制立方樣條 Cox 比例風險回歸中計算的 95% 置信區間（95% CIs），已調整年齡、性別、種族/族裔、婚姻狀況、教育程度、吸煙狀況、酒精消費、自評整體健康狀況、合併症數、腫瘤分期、先前治療、在 MD 安德森的治療以及特定癌症類型。
Note: BMI of 22.5 kg/m2 was the reference value. Solid lines represent hazard ratios (HRs), and dashed lines are 95% confidence intervals (95% CIs) calculated in restricted cubic spline Cox proportional hazards regression adjusted for age, sex, race/ethnicity, marital status, education, smoking status, alcohol consumption, self-rated overall health status, number of comorbidities, tumor stage, prior treatment, treatment at MD Anderson, and specific cancer types

Figure 2 顯示了每種類型癌症的樣條分析結果。對於大多數癌症類型（24 種中的 15 種），其關聯的形狀與跨癌症類型的綜合分析的整體關聯相似，且通常在 BMI 介於 30 到 35 之間觀察到最低的死亡風險。對於肺癌、結直腸癌、非黑色素瘤皮膚癌和甲狀腺癌，當 BMI 超過 30 時，風險達到平穩。對於胰腺癌、內分泌相關癌症、卵巢癌和子宮癌，BMI 與死亡風險呈反比關聯。對於中樞神經系統癌症，BMI 與死亡風險呈正相關。如 Figure 3 所示，BMI 與死亡風險之間的關聯在由腫瘤分期（ Figure 3 A）和其他預後意義的變量（包括在 MD 安德森首次就診前的體重減輕（ Figure 3 B）、性別（ Figure 3 C）、診斷年齡（ Figure S2 ）、種族/族裔（ Figure S3 ）、吸煙狀態（ Figure S4 ）、合併症數量（ Figure S5 ）、腫瘤分化（ Figure S6 ）和治療方案（ Figure S7 ）定義的（疾病綜合）子組中通常是一致的。 此外，對於與肥胖相關的癌症（根據 IARC 工作組定義 2 ）和非肥胖相關的癌症（ Figure S8 ），在跨癌症類型的合併人群中，經過逐步刪除 1 至 4 年的個人年數後（ Figure S9 ），在進一步調整癌症家族史後（ Figure S10 A），隨訪從每位患者最後一次 BMI 測量的時間開始（ Figure S10 B），以及在非肥胖相關癌症中僅限於從不吸煙者，並在排除隨訪的前兩年後（ Figure S11 ）觀察到一般一致的趨勢。
Figure 2 shows the results from spline analysis for each type of cancer. For most cancer types (15 out of 24), the shape of the association was similar to the overall association from the pooled analysis across cancer types, and the lowest mortality risk was generally observed for a BMI between 30 and 35. For lung cancer, colorectal cancer, non-melanoma skin cancer, and thyroid cancer, the risk plateaued for a BMI over 30. For pancreatic cancer, endocrine related cancer, ovary cancer, and uterine cancer, BMI was inversely associated with the risk of death. For central nervous system cancer, BMI was positively associated with risk of death. As shown in Figure 3, the association between BMI and risk of death was generally consistent within (disease-combined) subgroups defined by tumor stage (Figure 3A) and other variables of prognostic significance including weight loss prior to the first visit at MD Anderson (Figure 3B), sex (Figure 3C), age at diagnosis (Figure S2), race/ethnicity (Figure S3), smoking status (Figure S4), number of comorbid conditions (Figure S5), tumor differentiation (Figure S6), and treatment regimen (Figure S7). Also, generally consistent trends were observed for obesity-related cancers (defined according to the IARC Working Group2) and non–obesity-related cancers (Figure S8), after sequentially deleting person years within 1 to 4 years in the pooled population across cancer types (Figure S9), after further adjustment of family history of cancers (Figure S10A), where the follow up started from the time of the last BMI measurement for each patient (Figure S10B), and among never smokers for non–obesity-related cancers and after excluding the first 2 years of follow up (Figure S11).

圖 2 針對特定癌症的周邊診斷 BMI 與死亡風險的關聯性進行樣條分析

注意：22.5 kg/m 2 的 BMI 為參考值。實線表示風險比（HR），虛線為在限制立方樣條 Cox 比例風險回歸中計算的 95%置信區間（CIs），該回歸已調整年齡、性別、種族/民族、婚姻狀況、教育程度、吸煙狀況、酒精消費、自評整體健康狀況、合併症數、腫瘤分期、先前治療和在 MD 安德森的治療。每種癌症類型下的數字表示該癌症類型的死亡人數/總患者數。
Note: BMI of 22.5 kg/m2 was the reference value. Solid lines represent HRs, and dashed lines are 95% CIs calculated in restricted cubic spline Cox proportional hazards regression adjusted for age, sex, race/ethnicity, marital status, education, smoking status, alcohol consumption, self-rated overall health status, number of comorbidities, tumor stage, prior treatment, and treatment at MD Anderson. Numbers under each cancer type represent the number of deaths/total number of patients for that cancer type

圖 3 诊断期间 BMI 与死亡风险的关联（样条分析）

診斷前後 BMI 與死亡風險的樣條分析關聯
Association of peri-diagnostic BMI with risk of death in spline analysis

;; 根據疾病分期（A）、先前體重減輕（B）和性別（C）的樣條分析中，診斷前後 BMI 與死亡風險的關聯。
Association of peri-diagnostic BMI with risk of death in spline analysis by disease stage (A), prior weight loss (B), and sex (C).

註：以 22.5 kg/m²的 BMI 作為參考值。實線代表經限制性立方樣條 Cox 比例風險回歸模型計算的風險比（HR），該模型已針對年齡、性別、種族/民族、婚姻狀況、教育程度、吸煙狀況、飲酒量、自評總體健康狀況、合併症數量、腫瘤分期、既往治療、在 MD 安德森癌症中心的治療以及特定癌症類型（如適用）進行調整。按疾病分期、既往體重減輕和性別進行樣條分析的交互作用 p 值分別為<0.001、0.54 和<0.001。關於既往體重減輕的信息是通過對 24,962 名患者（19,027 名無既往體重減輕和 5,935 名有既往體重減輕）的隨機子集進行人工提取獲得的。
Note: BMI of 22.5 kg/m2 was the reference value. Solid lines represent HRs calculated in restricted cubic spline Cox proportional hazards regression adjusted for age, sex, race/ethnicity, marital status, education, smoking status, alcohol consumption, self-rated overall health status, number of comorbidities, tumor stage, prior treatment, treatment at MD Anderson, and specific cancer types, wherever appropriate. p for interaction was <0.001, 0.54, and <0.001 for spline analysis by disease stage, prior weight loss, and sex, respectively. Information regarding prior weight loss was available through manual abstraction in a random subset of 24 962 patients (19 027 patients without prior weight loss and 5935 with prior weight loss)

跨癌症類型匯總人群的卡普蘭-梅爾生存曲線（ Figure S12 ）顯示，較高的 BMI 組別與較好的總體生存率相關。與體重正常的患者相比，超重和肥胖患者的死亡風險分別降低了 15%（HR = 0.85，95% CI = 0.83–0.87）和 18%（HR = 0.82，95% CI = 0.80–0.85），而體重不足患者的死亡風險則增加了 44%（HR = 1.44，95% CI = 1.36–1.53）。
Kaplan–Meier survival curves in the pooled population across cancer types (Figure S12) showed that higher BMI groups were associated with better overall survival. Compared with patients of normal weight, patients who were overweight and obese had a 15% (HR = 0.85, 95% CI = 0.83–0.87) and 18% (HR = 0.82, 95% CI = 0.80–0.85) reduced risk of death, respectively, and patients who were underweight had a 44% increased risk of death (HR = 1.44, 95% CI = 1.36–1.53).

超重和肥胖的 BMI 與較長的預期壽命相關（ Figure 4 ），而體重不足的 BMI 則與較短的預期壽命相關。在男性中，超重和肥胖患者（診斷年齡為 40 歲時）的預期壽命分別比體重正常患者長達 4.5 年和 5.9 年。同樣，女性超重和肥胖患者的預期壽命分別長達 2.4 年（診斷年齡為 45-55 歲）和 3 年（診斷年齡為 50 歲）。
Overweight and obese BMIs were associated with longer life expectancy (Figure 4), whereas an underweight BMI was associated with shorter life expectancy. Among men, patients who were overweight and obese had up to 4.5- and 5.9-year-longer life expectancies (at diagnosis age of 40), respectively, than patients of normal weight. Likewise, female patients who were overweight and obese had up to 2.4- (at diagnosis age of 45–55) and 3-year-longer (at diagnosis age of 50) life expectancies, respectively.

圖 4 根據診斷前後體重狀況的預期壽命

男女患者根據診斷前後體重狀況的預期壽命
Life expectancy by peri-diagnostic weight status among male and female patients.

註：體重過輕：BMI <18.5 kg/m²；正常：BMI 18.5–24.9 kg/m²；過重：BMI 25–29.9 kg/m²；肥胖：BMI ≥30 kg/m²。
Note: Underweight: BMI <18.5 kg/m2; normal: BMI 18.5–24.9 kg/m2; overweight: BMI 25–29.9 kg/m2; obese: BMI ≥30 kg/m2.

討論 Discussion

與當前臨床指南鼓勵癌症患者達到或維持 18.5–24.9 kg/m²的正常 BMI 相反，我們研究中的樣條分析顯示，BMI 在 29.6–34.2 之間的患者死亡風險最低。在 24 種特定癌症中的 23 種裡，發現超重或肥胖 BMI 的死亡風險降低，並且在嘗試消除潛在選擇偏差、吸煙和共病症的混淆以及反向因果關係後，這一結果仍然成立。超重或肥胖的癌症患者死亡風險分別降低了 15%和 18%。
In contrast to the current clinical guidelines encouraging patients with cancer to achieve or maintain a normal BMI of 18.5–24.9 kg/m2, spline analysis in our study showed that patients with a BMI of 29.6–34.2 had the lowest mortality risk. The reduced mortality risk of overweight or obese BMI was found in 23 of 24 specific cancers and was maintained after attempts to remove potential selection bias, confounding by smoking and comorbidities, and reserve causality. Patients with cancer who were overweight or obese had a 15% and 18% reduced risk of death, respectively.

有些人認為，觀察到的肥胖或超重癌症患者的生存悖論反映了真實的生物學效應，而其他人則懷疑這是由於方法學限制，如稱為碰撞偏差的選擇偏差、吸煙和共病症的混淆，或反向因果關係。在我們解讀這些發現對癌症患者的臨床意義之前，必須徹底排除這些潛在的方法學限制。
Some posit that the observed paradoxical survival benefit among patients with cancer who were obese or overweight reflects a true biologic effect,15,19,26,28,34,35 while others suspect that it is due to methodologic limitations such as a selection bias called collider bias, confounding by smoking and comorbidities, or reverse causality.15,19,25,26,27,28 Before we interpret the clinical implications of our findings for patients with cancer, we must exhaustively exclude these potential methodologic limitations.

在研究 BMI 與肥胖相關癌症死亡率之間的關聯時，可能會發生碰撞偏差（collider bias），因為參與者是根據肥胖相關癌症（一個碰撞變量）的發生情況被選入分析的，而該變量受 BMI 影響且與死亡率共享風險因素。 36 儘管這種偏差在肥胖相關癌症（如乳腺癌和結直腸癌）中是可能存在的，但已有研究表明，要逆轉因果效應，碰撞偏差必須非常強烈。 37 根據定義，碰撞偏差不應是非肥胖相關癌症的方法學問題。在本研究中，為了評估碰撞偏差的潛在影響，我們按肥胖相關癌症與非肥胖相關癌症進行了分層分析，結果發現無論是在肥胖相關還是非肥胖相關癌症中，額外體重均帶來生存益處。第二個關注點是吸煙和合併症造成的混淆，這些因素與較低的 BMI 和癌症患者較差的生存率相關。 在我們的研究中，無論吸菸狀態如何，包括從未吸菸者，以及在進一步調整吸菸包年數（數據未顯示）和無論有無合併症的情況下，都觀察到了額外體重帶來的生存益處。這些發現表明，碰撞偏差以及吸菸和合併症的混淆因素無法解釋超重和肥胖 BMI 在癌症中的生存益處。
Collider bias might occur when associations between BMI and mortality in obesity-related cancers are studied because participants were selected into the analysis based on occurrence of obesity-related cancer (a collider) that is affected by BMI and shares risk factors with mortality.36 Even though this bias is plausible for obesity-related cancers (eg, breast and colorectal), it has been shown that in order to reverse the causal effect, the collider bias has to be very strong.37 By definition, collider bias should not be a methodological concern for non–obesity-related cancers. In the present study, to assess the potential impact of collider bias, we conducted stratified analysis by obesity-related cancers versus non–obesity-related cancers, and we found survival benefits of extra weight in obesity-related cancers as well as in non–obesity-related cancers. The second concern is confounding by smoking and comorbidities, which are associated with a lower BMI and poor survival in patients with cancer. In our study, the survival benefits of extra weight were observed regardless of smoking status, including in never smokers, and with additional adjustment of pack year of smoking (data not shown) and regardless of comorbidities. These findings suggest that collider bias and confounding by smoking and comorbidities cannot explain the survival benefits of overweight and obese BMIs in cancer.

第三個擔憂是反向因果關係。 15 19 25 26 27 28 癌症較為晚期或侵襲性較強的患者往往會體重減輕，可能導致 BMI 下降，因此預後不良的癌症反而可能導致較低的 BMI，而非 BMI 影響癌症預後。為解決此問題，我們進行了多項敏感性分析。雖然我們並非所有患者都有診斷前的 BMI 數據，但我們確實擁有約 25,000 名患者先前體重減輕的資料，並發現這些關聯在沒有先前體重減輕的患者中仍然存在。我們進一步發現，超重和肥胖患者的死亡率風險降低，在局部腫瘤或分化良好的腫瘤亞組中、在整體生活品質良好的患者中，以及在逐步排除癌症診斷後早期死亡的患者後，這種風險降低仍然持續。因此，反向因果關係無法完全解釋癌症患者中超重和肥胖 BMI 的生存益處。 然而，值得指出的是，在嘗試消除潛在的選擇偏差並保留因果關係後，這些關聯性雖然減弱，但並未消失，這表明方法學上的限制確實起到了一定作用，但並非解釋癌症患者中超重和肥胖 BMI 生存益處的唯一原因。
A third concern is reverse causality.15,19,25,26,27,28 Patients with more advanced/aggressive cancer often lose weight and may migrate to a lower BMI, so the cancers with poor prognosis might reversely cause lower BMI instead of the other way around. Several sensitivity analyses were conducted to address this. Though we did not have pre-diagnosis BMI on all patients, we did have data on prior weight loss for ∼25,000 patients and found that associations were sustained among patients without prior weight loss. We further found that the reduced mortality risks of the patients who were overweight and obese were sustained in the subgroups with localized tumors or well-differentiated tumors, in those with good overall quality of life, and after sequentially removing patients who died early after cancer diagnosis. Thus, reverse causality cannot fully explain the survival benefits of overweight and obese BMIs in patients with cancer. However, it is worthwhile to point out that after attempts to remove potential selection bias and reserve causality, the associations were attenuated, but they did not disappear, suggesting that methodologic limitations indeed played a role but were not the sole explanation for the survival benefits of overweight and obese BMI in patients with cancer.

從生物學角度來看，癌症患者體重超重可能帶來生存優勢是合理的，因為額外的體重可作為生理和營養儲備，以克服腫瘤生長本身及癌症治療所帶來的負面代謝影響。 15 19 26 28 34 35 高 BMI 的患者通常擁有足夠的瘦體重， 34 38 39 這與癌症患者較好的預後相關。 19 34 38 39 40 此外，在慢性疾病的情況下，脂肪組織（特別是皮下和臀股部脂肪）具有多種有益效應（例如分泌具有心臟保護作用的脂肪因子如脂聯素、預防骨折等），這些可能抵消整體肥胖所帶來的不利影響。 28 41 42 43 44 45
It is biologically plausible that extra weight in patients with cancer may confer a survival advantage because extra weight serves as a physiologic and nutritional reserve to overcome the negative metabolic impact from tumor growth itself as well as treatments of cancers.15,19,26,28,34,35 Patients with a high BMI generally have adequate lean body mass,34,38,39 which is associated with better outcomes among patients with cancer.19,34,38,39,40 Also, in the context of chronic illness, fat tissue (in particular, subcutaneous and gluteofemoral) has several beneficial effects (eg, secretion of cardioprotective adipokines such as adiponectin, protection against bone fracture) that may offset the adverse effects of overall adiposity.28,41,42,43,44,45

我們發現，癌症患者在診斷後若體重指數（BMI）處於過重或輕度肥胖範圍，其整體生存率較佳。值得注意的是，我們認為這些發現絕不意味著反對全球遏制肥胖流行的必要性，因為肥胖增加了普通人群罹患癌症及許多其他疾病的負擔。然而，從生物學角度來看，假設所有個體在所有情況下的理想體重都相同是具有挑戰性的。我們的研究結果僅適用於癌症患者，這一人群的理想體重可能會向上偏移。此外，由於最低死亡風險出現在 BMI 為 29.6 至 34.2 的範圍內，我們的數據並不支持「越重越好」的觀點。最後需強調的是，當前研究僅評估了診斷後 1 年內的 BMI，結果應在此診斷週期背景下解讀；然而，BMI 可能在癌症治療及生存期間隨疾病進程而變化，未來還需進一步研究來探討恢復/生存階段 BMI 的預後影響。
We found better overall survival in patients with cancer with an overweight or mildly obese BMI after diagnosis. Notably, we feel that our findings by no means stand against the need to curb the obesity epidemic worldwide, which increases the general population’s burden from cancer and many other diseases.2,46 However, it is biologically challenging to assume that the ideal body weight is the same for all individuals under all conditions.47 Our findings are relevant only to patients with cancer among whom the ideal body weight may shift upward.47 Furthermore, as the lowest mortality risk was found at a BMI of 29.6–34.2, our data do not support “the heavier the better.” Finally, it should be noted that the current study only assessed BMI within 1 year after diagnosis, and the results should be interpreted within this peri-diagnosis setting; however, BMI may change throughout the cancer treatment and survivorship period along the course of the disease, and further studies are needed to examine the prognostic effect BMI during the recovery/survivorship phase.

由於肥胖流行病以及過量體重帶來的癌症風險增加，越來越多癌症患者在確診時處於肥胖或超重狀態， 8 而指導這些患者體重管理的證據有限。目前的臨床指南建議超重或肥胖的癌症患者減輕體重。 5 6 7 支持這一建議的最大證據來自早期乳腺癌研究， 6 15 但這些證據在「持續更新項目」中最近被評為「有限—提示性」。 29 若缺乏隨機對照試驗的確認性證據表明有意減重能改善癌症預後，那麼在診斷後時期對 BMI 處於超重或輕度肥胖範圍的癌症患者推薦減重可能並不合理。一項針對前列腺癌患者的隨機臨床試驗顯示，有意減重可能對腫瘤產生不利影響。 因此，與其專注於減重（這可能不會改善癌症預後，甚至可能對癌症患者造成傷害），或許更為謹慎的做法是建議其他生活方式的調整，如增加體能活動、健康飲食和戒菸。本研究結果支持進一步探索肥胖和超重癌症患者觀察到改善結果的潛在機制。
Because of the obesity epidemic and the elevated cancer risk conferred by excess body weight, increasing numbers of patients with cancer are obese or overweight at diagnosis,8 and evidence is limited to guide weight management in these patients. Current clinical guidelines recommend patients with cancer who are overweight or obese to lose weight.5,6,7 The largest body of evidence supporting this recommendation is from early-stage breast cancer,6,15 but the evidence was recently rated as “limited–suggestive” in the Continuous Update Project.29 Without confirmatory evidence from randomized controlled trials showing that losing weight intentionally can improve cancer prognosis, it may not be warranted to recommend weight loss among patients with cancer with an overweight or mildly obese BMI in the post-diagnosis period. One randomized clinical trial in patients with prostate cancer showed that intentional weight loss may have adverse effects on the tumor.48 Therefore, instead of focusing on weight loss, which may not improve cancer prognosis and even may even cause harms in patients with cancer, it might be more prudent to recommend other lifestyle modifications such as physical activity, healthy diet, and smoking cession. Results from this study warrant the exploration of potential mechanisms underlying the improved outcomes observed in patients with cancer who are obese and overweight.

本研究具有多項優勢。首先，我們利用了一個前瞻性的泛癌症隊列，包含 114,430 名癌症患者的全面流行病學和臨床數據以及長期追蹤資料。其次，同時研究了 24 種特定癌症的廣泛譜系，每種癌症至少包含 1,000 例病例。第三，我們從多次 BMI 測量中計算出加權平均 BMI，克服了僅使用單一時間點 BMI 的限制。第四，由受訓人員測量體重和身高，最小化了自我報告體重和身高可能造成的錯誤分類。第五，我們進行了多項敏感性分析，以排除先前研究中提出的潛在人為解釋。第六，該隊列中的患者均於過去十年間在一家三級轉診癌症醫院接受診斷和治療，因此治療策略現代化且標準化。最後，由於 MD Anderson 設有腫瘤登記部門，能全面長期監測癌症患者，因此極少個案失訪。
Several strengths are noted in this study. First, we utilized a prospective pan-cancer cohort of 114 430 patients with cancer with comprehensive epidemiological and clinical data and long-term follow-up data. Second, a broad spectrum of 24 specific cancers were simultaneously studied, with each cancer represented by at least 1000 cases. Third, we calculated the weighted mean BMI from multiple BMI measurements, which overcomes the limitation of using BMI at one time point. Fourth, trained staff measured weight and height, minimizing potential misclassification from self-reported weight and height.49 Fifth, we conducted multiple sensitivity analyses to exclude potential artifact explanations raised in previous studies. Sixth, the patients in the cohort were diagnosed and treated within the last decade at one tertiary referral cancer hospital, hence treatment strategies were modern and standardized. Finally, because MD Anderson has a tumor registry department that comprehensively monitors patients with cancer over time, few individuals were lost to follow up.

我們也承認我們的研究存在潛在的局限性。與任何觀察性研究一樣，我們無法確認因果關係。然而，前瞻性的設計、觀察到的強烈關聯、結果的一致性和生物學上的合理性，以及排除方法學解釋的全面努力，都為因果關係提供了支持。其次，即使對潛在的混雜因素進行了全面調整，我們仍不能排除殘餘混雜的可能性。例如，我們沒有收集關於被動吸煙、治療依從性和劑量的信息。此外，我們也沒有收集與肥胖和生活方式相關的詳細行為數據，包括飲食、體育活動和藥物使用情況。第三，我們使用 BMI 來定義體重狀態，而其他體測指標如腰圍、腰臀比和身體組成等數據則無法獲得。與其他指標和身體組成不同，BMI 在醫療訪問中常規且容易收集，並且是目前指南中用於指導癌症患者和腫瘤科提供者進行體重管理的指標。 第四，儘管在不同癌症部位觀察到一致的關聯性，我們不能排除這種關聯可能因階段、治療方式、分子亞型或其他因素而對每個特定癌症部位有所不同。最後，我們無法推斷超重或肥胖的 BMI 對癌症特異性死亡或本研究未涵蓋的後期臨床結果的影響。
We also acknowledge that our study has potential limitations. As in any observational study, we cannot confirm causality. However, the prospective design, strong association observed, results being consistent and biologically plausible, and comprehensive efforts to exclude methodological explanations lend support for causality. Second, even with comprehensive adjustment of potential confounding factors, we cannot exclude the possibility of residual confounding. For example, we did not collect information on passive smoking, treatment adherence, and dose. Also, we did not collect detailed behavioral data related to obesity and lifestyle, including diet, physical activity, and medication use. Third, we used BMI to define weight status, and data on other anthropometric indices such as waist circumference, waist–to–hip ratio, and body composition were not available. Unlike other indices and body composition, BMI is routinely and readily collected at medical visits, and it is the index currently used in guidelines to guide both patients with cancer and oncology providers on weight management.5,6,7 Fourth, though consistent associations were observed across cancer sites, we cannot rule out that the association may vary by stage, treatment, molecular subtypes, or other factors for each specific cancer site.14,23,24 Finally, we cannot infer the effects of an overweight or obese BMI on cancer-specific death or clinical outcomes at later time points not captured in this study.

總之，雖然過重的體重會增加罹癌風險，但我們的研究顯示，診斷時過重或輕度肥胖的體重指數（BMI）與癌症患者的存活率改善有關。即使在嘗試排除因方法學限制所導致的非因果解釋後，這些關聯性依然存在。這些發現為基於證據制定癌症照護中的體重管理策略提供了支持，並表明當前對於過重或肥胖癌症患者普遍建議減重的做法應重新審視。 5 6 7
In summary, though excessive body weight increases risk of developing cancer, our study shows that an overweight or mildly obese peri-diagnostic BMI is linked to improved survival among patients with cancer. These associations were maintained after attempts to remove non-causal explanations due to methodological limitations. These findings provide support for developing weight management strategy that is based on evidence in cancer care, and they suggest that the current universal recommendations for patients with cancer who are overweight or obese to lose weight should be revisited.5,6,7

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