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Article(id=1198558113776369761, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1198558106218230069, articleNumber=null, orderNo=null, doi=10.11855/j.issn.0577-7402.1574.2024.0327, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1701100800000, receivedDateStr=2023-11-28, revisedDate=null, revisedDateStr=null, acceptedDate=1703692800000, acceptedDateStr=2023-12-28, onlineDate=1763688122456, onlineDateStr=2025-11-21, pubDate=1727452800000, pubDateStr=2024-09-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1763688122456, onlineIssueDateStr=2025-11-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1763688122456, creator=13701087609, updateTime=1763688122456, updator=13701087609, issue=Issue{id=1198558106218230069, tenantId=1146029695717560320, journalId=1189873630562394117, year='2024', volume='49', issue='9', pageStart='977', pageEnd='1098', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1763688120655, creator=13701087609, updateTime=1763689155065, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1198562444915339352, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1198558106218230069, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1198562444915339353, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1198558106218230069, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1004, endPage=1010, ext={EN=ArticleExt(id=1198558114082553982, articleId=1198558113776369761, tenantId=1146029695717560320, journalId=1189873630562394117, language=EN, title=Clinical characterization and prediction modeling of lung cancer patients with high energy metabolism, columnId=1190310109000602400, journalTitle=Medical Journal of Chinese People’s Liberation Army, columnName=Clinical Research, runingTitle=null, highlight=null, articleAbstract=

Objective To analyze the clinical characteristics of high energy metabolism in lung cancer patients and its correlation with body composition, nutritional status, and quality of life, and to develop a corresponding risk prediction model. Methods Retrospectively analyzed 132 primary lung cancer patients admitted to the First Hospital of Shanxi Medical University from January 2022 to May 2023, and categorized into high (n=94) and low energy metabolism group (n=38) based on their metabolic status. Differences in clinical data, body composition, Patient Generated Subjective Global Assessment (PG-SGA) scores, and European Organization for Research and treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 (QLQ-C30) scores were compared between the two groups. Logistic regression was used to identify the risk factors for high energy metabolism in lung cancer patients, and a risk prediction model was established accordingly; the Hosmer-Lemeshow test was used to assess the model fit, and the ROC curve was used to test the predictive efficacy of the model. Results Of the 132 patients with primary lung cancer, 94 (71.2%) exhibited high energy metabolism. Compared with low energy metabolism group, patients in high-energy metabolism group had a smoking index of 400 or higher, advanced disease staging of stage Ⅲ or Ⅳ, and higher levels of IL-6 level, low adiposity index, low skeletal muscle index, and malnutrition (P<0.05), and lower levels of total protein, albumin, hemoglobin level, and prognostic nutritional index (PNI) (P<0.05). There was no significant difference in age, gender, height, weight, BMI and disease type between the two groups (P>0.05). Logistic regression analysis showed that smoking index ≥400, advanced disease stage, IL-6 ≥3.775 ng/L, and PNI <46.43 were independent risk factors for high energy metabolism in lung cancer patients. The AUC of the ROC curve for the established prediction model of high energy metabolism in lung cancer patients was 0.834(95%CI 0.763-0.904). Conclusion The high energy metabolic risk prediction model of lung cancer patients established in this study has good fit and prediction efficiency.

, correspAuthors=Jun-Mei Jia, authorNote=null, correspAuthorsNote=
E-mail:
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目的 分析肺癌患者高能量代谢的临床特征及其与人体成分、营养状况、生活质量的相关性，并建立高能量代谢风险预测模型。方法 回顾性选取2022年1月-2023年5月山西医科大学第一医院收治的132例原发性肺癌患者，根据是否出现高能量代谢分为高能量代谢组(n=94)和非高能量代谢组(n=38)。比较两组临床资料、人体成分、患者主观整体营养状况评估量表(PG-SGA)评分、EORTC生命质量测定量表(QLQ-C30)评分的差异。采用logistic回归分析肺癌患者高能量代谢的危险因素，并据此建立风险预测模型；采用Hosmer-Lemeshow 检验评估模型拟合度，采用受试者工作特征(ROC)曲线下面积(AUC)检测其预测效能。结果 132例原发性肺癌中，94例(71.2%)出现高能量代谢。与低能量代谢组比较，高能量代谢组患者吸烟指数≥400、疾病分期为Ⅲ或Ⅳ期、IL-6水平、低脂肪指数、低骨骼肌指数、营养不良风险均增高(P<0.05)，总蛋白、白蛋白、血红蛋白水平及预后营养指数(PNI)降低(P<0.05)；两组患者年龄、性别、身高、体重、BMI及疾病分型差异无统计学意义(P>0.05)。Logistic回归分析结果显示，吸烟指数≥400、疾病分期为晚期、IL-6≥3.775 ng/L、PNI<46.43为肺癌患者高能量代谢的独立危险因素；据此建立的预测模型预测肺癌患者高能量代谢风险ROC曲线的AUC为0.834(95%CI 0.763~0.904)。结论 本研究建立的肺癌患者高能量代谢风险预测模型具有较好的拟合度和预测效能。

, correspAuthors=贾军梅, authorNote=null, correspAuthorsNote=
贾军梅，E-mail：
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任江珊，硕士研究生，主要从事肺恶性肿瘤诊治方面的研究

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任江珊，硕士研究生，主要从事肺恶性肿瘤诊治方面的研究

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任江珊，硕士研究生，主要从事肺恶性肿瘤诊治方面的研究

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figureFileSmall=adM0VDT/WQyNw4yTs7/0sA==, figureFileBig=ld7NMFOZqQJH+qrD09PAcQ==, tableContent=null), ArticleFig(id=1198558124455068211, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558113776369761, language=CN, label=图1, caption=CRP、IL-6预测肺癌患者高能量代谢的ROC曲线, figureFileSmall=adM0VDT/WQyNw4yTs7/0sA==, figureFileBig=ld7NMFOZqQJH+qrD09PAcQ==, tableContent=null), ArticleFig(id=1198558124627034683, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558113776369761, language=EN, label=Fig.2, caption=ROC curves of indicators as NLR, SII and PNI for prediction of lung cancer patients with high energy metabolism, figureFileSmall=DI3XyPq6nniRSYiCJYjSzg==, figureFileBig=mV74oKhbIWklizuaWHLwFQ==, tableContent=null), ArticleFig(id=1198558124723503680, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558113776369761, language=CN, label=图2, caption=NLR、SII、PNI等指标预测肺癌患者高能量代谢的ROC曲线

NLR. 中性粒细胞与淋巴细胞比值；SII. 系统免疫炎症指数；PNI. 预后营养指数；A. NLR、SII、PNI；B. NLR+SII、NLR+PNI、SII+PNI；C. NLR+SII+PNI

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Comparison of clinical data between two groups of patients with lung cancer

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指标

高能量代
谢组(n=94)

非高能量代
谢组(n=38)

χ2/tP
年龄[例(%)]0.4450.505
<60岁53(56.4)19(50.0)
≥60岁41(43.6)19(50.0)
性别[例(%)]0.3330.564
男69(73.4)26(68.4)
女25(26.6)12(31.6)
身高(m, $\bar{x}±s$)1.67±0.061.69±0.071.7260.087
体重(kg, $\bar{x}±s$)62.71±7.6660.69±7.201.3970.165
BMI(kg/m2, $\bar{x}±s$)21.87±2.7121.71±2.570.3000.764
疾病分期[例(%)]12.6540.005
Ⅰ3(3.2)5(13.2)
Ⅱ16(17.0)14(36.8)
Ⅲ35(37.2)10(26.3)
Ⅳ40(42.6)9(23.7)
疾病分型[例(%)]0.4760.788
腺癌48(51.6)22(57.9)
鳞状细胞癌10(10.8)4(10.5)
小细胞肺癌35(37.6)12(31.6)
吸烟指数[例(%)]6.1030.013
<40028(29.8)20(52.6)
≥40066(70.2)18(47.4)
总蛋白[例(%)]4.4300.035
<60 g/L29(30.90)5(13.20)
≥60 g/L65(69.10)33(86.80)
白蛋白[例(%)]7.5450.006
<35 g/L32(34.00)4(10.50)
≥35 g/L62(66.00)34(89.50)
血红蛋白[例(%)]4.6990.030
<11 g/L(女)/12 g/L(男)23(24.50)3(7.90)
≥11 g/L(女)/12 g/L(男)71(75.50)35(92.10)
IL-6 [例(%)]14.746<0.001
<3.775 ng/L30(31.9)26(68.4)
≥3.775 ng/L64(68.1)12(31.6)
PNI[例(%)]18.839<0.001
<46.4359(62.8)8(21.1)
≥46.4335(37.2)30(78.9)
), ArticleFig(id=1198558125197460046, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558113776369761, language=CN, label=表1, caption=

两组肺癌患者临床资料比较

, figureFileSmall=null, figureFileBig=null, tableContent=
指标

高能量代
谢组(n=94)

非高能量代
谢组(n=38)

χ2/tP
年龄[例(%)]0.4450.505
<60岁53(56.4)19(50.0)
≥60岁41(43.6)19(50.0)
性别[例(%)]0.3330.564
男69(73.4)26(68.4)
女25(26.6)12(31.6)
身高(m, $\bar{x}±s$)1.67±0.061.69±0.071.7260.087
体重(kg, $\bar{x}±s$)62.71±7.6660.69±7.201.3970.165
BMI(kg/m2, $\bar{x}±s$)21.87±2.7121.71±2.570.3000.764
疾病分期[例(%)]12.6540.005
Ⅰ3(3.2)5(13.2)
Ⅱ16(17.0)14(36.8)
Ⅲ35(37.2)10(26.3)
Ⅳ40(42.6)9(23.7)
疾病分型[例(%)]0.4760.788
腺癌48(51.6)22(57.9)
鳞状细胞癌10(10.8)4(10.5)
小细胞肺癌35(37.6)12(31.6)
吸烟指数[例(%)]6.1030.013
<40028(29.8)20(52.6)
≥40066(70.2)18(47.4)
总蛋白[例(%)]4.4300.035
<60 g/L29(30.90)5(13.20)
≥60 g/L65(69.10)33(86.80)
白蛋白[例(%)]7.5450.006
<35 g/L32(34.00)4(10.50)
≥35 g/L62(66.00)34(89.50)
血红蛋白[例(%)]4.6990.030
<11 g/L(女)/12 g/L(男)23(24.50)3(7.90)
≥11 g/L(女)/12 g/L(男)71(75.50)35(92.10)
IL-6 [例(%)]14.746<0.001
<3.775 ng/L30(31.9)26(68.4)
≥3.775 ng/L64(68.1)12(31.6)
PNI[例(%)]18.839<0.001
<46.4359(62.8)8(21.1)
≥46.4335(37.2)30(78.9)
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Comparison of body composition and PG-SGA between high-energy metabolism and non-high-energy metabolism groups of patients with lung cancer [n(%)]

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指标高能量代谢组(n=94)非高能量代谢组(n=38)χ2/tP
脂肪指数4.2540.039
<3.9 kg/m2(女)/1.8 kg/m2(男)17(18.1)(1)1(2.6)
≥3.9 kg/m2(女)/1.8 kg/m2(男)77(81.9)37(97.4)
骨骼肌指数3.9100.048
<5.7 kg/m2(女)/7.0 kg/m2(男)20(21.3)(1)2(5.3)
≥5.7 kg/m2(女)/7.0 kg/m2(男)74(78.7)36(94.7)
PG-SGA评分7.1640.028
0~1分(营养良好)7(7.4)8(21.1)
2~8分(可疑或中度营养不良)71(75.5)28(73.7)
≥9分(重度营养不良)16(17.0)(1)2(5.3)
), ArticleFig(id=1198558125436535383, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558113776369761, language=CN, label=表2, caption=

两组肺癌患者人体成分及PG-SGA评分的比较[例(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
指标高能量代谢组(n=94)非高能量代谢组(n=38)χ2/tP
脂肪指数4.2540.039
<3.9 kg/m2(女)/1.8 kg/m2(男)17(18.1)(1)1(2.6)
≥3.9 kg/m2(女)/1.8 kg/m2(男)77(81.9)37(97.4)
骨骼肌指数3.9100.048
<5.7 kg/m2(女)/7.0 kg/m2(男)20(21.3)(1)2(5.3)
≥5.7 kg/m2(女)/7.0 kg/m2(男)74(78.7)36(94.7)
PG-SGA评分7.1640.028
0~1分(营养良好)7(7.4)8(21.1)
2~8分(可疑或中度营养不良)71(75.5)28(73.7)
≥9分(重度营养不良)16(17.0)(1)2(5.3)
), ArticleFig(id=1198558125516227162, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558113776369761, language=EN, label=Tab.3, caption=

Comparison of EORTC QLQ-C30 scores between two groups of patients with lung cancer [M(Q1, Q3)]

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指标高能量代谢组(n=94)非高能量代谢组(n=38)χ2/tP
身体功能86.67(80.00, 93.33)93.33(86.67, 100.00)3.1250.002
角色功能100.00(83.33, 100.00)1003.707<0.001
情绪功能91.67(83.33, 100.00)100.00(91.67, 100.00)1.9700.049
认知功能100.00(83.33, 100.00)100.00(83.33, 100.00)0.8060.420
社会功能83.33(66.67, 100.00)83.33(83.33, 100.00)1.4920.136
总健康状况91.67(83.33, 100.00)91.67(91.67, 100.00)2.4780.013
疲劳11.11(0.00, 22.22)0.00(0.00, 11.11)2.6220.009
恶心与呕吐0.00(0.00, 16.67)0.00(0.00, 16.67)0.2170.828
疼痛0.00(0.00, 16.67)03.0690.002
气促0.00(0.00, 33.33)0.00(0.00, 33.33)0.7550.450
失眠0.00(0.00, 33.33)0.00(0.00, 33.33)0.3900.697
食欲丧失0.00(0.00, 16.67)02.0170.044
便秘0.00(0.00, 33.33)0.00(0.00, 33.33)0.6990.485
腹泻001.6350.102
经济困难0.00(0.00, 33.33)0.00(0.00, 33.33)0.2970.766
), ArticleFig(id=1198558125591724637, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558113776369761, language=CN, label=表3, caption=

两组肺癌患者EORTC QLQ-C30评分比较 [分，M(Q1，Q3)]

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指标高能量代谢组(n=94)非高能量代谢组(n=38)χ2/tP
身体功能86.67(80.00, 93.33)93.33(86.67, 100.00)3.1250.002
角色功能100.00(83.33, 100.00)1003.707<0.001
情绪功能91.67(83.33, 100.00)100.00(91.67, 100.00)1.9700.049
认知功能100.00(83.33, 100.00)100.00(83.33, 100.00)0.8060.420
社会功能83.33(66.67, 100.00)83.33(83.33, 100.00)1.4920.136
总健康状况91.67(83.33, 100.00)91.67(91.67, 100.00)2.4780.013
疲劳11.11(0.00, 22.22)0.00(0.00, 11.11)2.6220.009
恶心与呕吐0.00(0.00, 16.67)0.00(0.00, 16.67)0.2170.828
疼痛0.00(0.00, 16.67)03.0690.002
气促0.00(0.00, 33.33)0.00(0.00, 33.33)0.7550.450
失眠0.00(0.00, 33.33)0.00(0.00, 33.33)0.3900.697
食欲丧失0.00(0.00, 16.67)02.0170.044
便秘0.00(0.00, 33.33)0.00(0.00, 33.33)0.6990.485
腹泻001.6350.102
经济困难0.00(0.00, 33.33)0.00(0.00, 33.33)0.2970.766
), ArticleFig(id=1198558125667222113, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558113776369761, language=EN, label=Tab.4, caption=

Multifactorial logistic regression analysis of high energy metabolism in lung cancer patients

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变量βSEWald χ2OR95%CIP
吸烟指数1.0760.4715.2302.9341.166~7.3790.022
疾病分期0.5780.2614.9121.7831.069~2.9740.027
血红蛋白-0.4070.8470.2310.6650.126~3.5020.631
总蛋白0.6630.8830.5631.9410.344~10.9620.453
白蛋白-0.0840.9640.0080.9190.139~6.0820.930
IL-61.2790.4807.0843.5921.401~9.2120.008
PNI-1.5920.6955.2410.2040.052~0.7950.022
常量-0.6371.0030.4030.529-0.525
), ArticleFig(id=1198558125763691106, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558113776369761, language=CN, label=表4, caption=

肺癌患者高能量代谢的多因素logistic回归分析结果

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变量βSEWald χ2OR95%CIP
吸烟指数1.0760.4715.2302.9341.166~7.3790.022
疾病分期0.5780.2614.9121.7831.069~2.9740.027
血红蛋白-0.4070.8470.2310.6650.126~3.5020.631
总蛋白0.6630.8830.5631.9410.344~10.9620.453
白蛋白-0.0840.9640.0080.9190.139~6.0820.930
IL-61.2790.4807.0843.5921.401~9.2120.008
PNI-1.5920.6955.2410.2040.052~0.7950.022
常量-0.6371.0030.4030.529-0.525
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高能量代谢肺癌患者临床特征分析及其预测模型建立
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任江珊 1 , 贾军梅 2, * , 孙萍 3 , 平梅 2 , 张琼琼 2 , 刘燕燕 2 , 赵和平 2 , 陈妍 2 , 戎冬文 2 , 王康 2 , 仇海乐 2 , 刘宸安 4 , 樊羽羽 1 , 于德刚 1
解放军医学杂志 | 临床研究 2024,49(9): 1004-1010
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解放军医学杂志 | 临床研究 2024, 49(9): 1004-1010
高能量代谢肺癌患者临床特征分析及其预测模型建立
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任江珊1, 贾军梅2, * , 孙萍3, 平梅2, 张琼琼2, 刘燕燕2, 赵和平2, 陈妍2, 戎冬文2, 王康2, 仇海乐2, 刘宸安4, 樊羽羽1, 于德刚1
作者信息
  • 1山西医科大学第一临床医学院，山西太原 030001
  • 2山西医科大学第一医院肿瘤科，山西太原 030001
  • 3山西医科大学第一医院营养科，山西太原 030001
  • 4首都医科大学附属北京世纪坛医院胃肠外科与临床营养科，北京 100038

    • 任江珊，硕士研究生，主要从事肺恶性肿瘤诊治方面的研究

通讯作者:

贾军梅，E-mail：
Clinical characterization and prediction modeling of lung cancer patients with high energy metabolism
Jiang-Shan Ren1, Jun-Mei Jia2, * , Ping Sun3, Mei Ping2, Qiong-Qiong Zhang2, Yan-Yan Liu2, He-Ping Zhao2, Yan Chen2, Dong-Wen Rong2, Kang Wang2, Hai-Le Qiu2, Chen-An Liu4, Yu-Yu Fan1, De-Gang Yu1
Affiliations
  • 1The First Medical College of Shanxi Medical University, Taiyuan, Shanxi 030001, China
  • 2Department of Oncology,The First Medical College of Shanxi Medical University, Taiyuan, Shanxi 030001, China
  • 3Department of Nutrition, the First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China
  • 4Department of Gastrointestinal Surgery and Clinical Nutrition, Beijing Shijitan Hospital Affiliated to Capital Medical University, Beijing 100038, China
出版时间: 2024-09-28 doi: 10.11855/j.issn.0577-7402.1574.2024.0327
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摘要
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目的 分析肺癌患者高能量代谢的临床特征及其与人体成分、营养状况、生活质量的相关性，并建立高能量代谢风险预测模型。方法 回顾性选取2022年1月-2023年5月山西医科大学第一医院收治的132例原发性肺癌患者，根据是否出现高能量代谢分为高能量代谢组(n=94)和非高能量代谢组(n=38)。比较两组临床资料、人体成分、患者主观整体营养状况评估量表(PG-SGA)评分、EORTC生命质量测定量表(QLQ-C30)评分的差异。采用logistic回归分析肺癌患者高能量代谢的危险因素，并据此建立风险预测模型；采用Hosmer-Lemeshow 检验评估模型拟合度，采用受试者工作特征(ROC)曲线下面积(AUC)检测其预测效能。结果 132例原发性肺癌中，94例(71.2%)出现高能量代谢。与低能量代谢组比较，高能量代谢组患者吸烟指数≥400、疾病分期为Ⅲ或Ⅳ期、IL-6水平、低脂肪指数、低骨骼肌指数、营养不良风险均增高(P<0.05)，总蛋白、白蛋白、血红蛋白水平及预后营养指数(PNI)降低(P<0.05)；两组患者年龄、性别、身高、体重、BMI及疾病分型差异无统计学意义(P>0.05)。Logistic回归分析结果显示，吸烟指数≥400、疾病分期为晚期、IL-6≥3.775 ng/L、PNI<46.43为肺癌患者高能量代谢的独立危险因素；据此建立的预测模型预测肺癌患者高能量代谢风险ROC曲线的AUC为0.834(95%CI 0.763~0.904)。结论 本研究建立的肺癌患者高能量代谢风险预测模型具有较好的拟合度和预测效能。

肺癌  /  高能量代谢  /  预测模型  /  人体成分  /  生活质量

Objective To analyze the clinical characteristics of high energy metabolism in lung cancer patients and its correlation with body composition, nutritional status, and quality of life, and to develop a corresponding risk prediction model. Methods Retrospectively analyzed 132 primary lung cancer patients admitted to the First Hospital of Shanxi Medical University from January 2022 to May 2023, and categorized into high (n=94) and low energy metabolism group (n=38) based on their metabolic status. Differences in clinical data, body composition, Patient Generated Subjective Global Assessment (PG-SGA) scores, and European Organization for Research and treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 (QLQ-C30) scores were compared between the two groups. Logistic regression was used to identify the risk factors for high energy metabolism in lung cancer patients, and a risk prediction model was established accordingly; the Hosmer-Lemeshow test was used to assess the model fit, and the ROC curve was used to test the predictive efficacy of the model. Results Of the 132 patients with primary lung cancer, 94 (71.2%) exhibited high energy metabolism. Compared with low energy metabolism group, patients in high-energy metabolism group had a smoking index of 400 or higher, advanced disease staging of stage Ⅲ or Ⅳ, and higher levels of IL-6 level, low adiposity index, low skeletal muscle index, and malnutrition (P<0.05), and lower levels of total protein, albumin, hemoglobin level, and prognostic nutritional index (PNI) (P<0.05). There was no significant difference in age, gender, height, weight, BMI and disease type between the two groups (P>0.05). Logistic regression analysis showed that smoking index ≥400, advanced disease stage, IL-6 ≥3.775 ng/L, and PNI <46.43 were independent risk factors for high energy metabolism in lung cancer patients. The AUC of the ROC curve for the established prediction model of high energy metabolism in lung cancer patients was 0.834(95%CI 0.763-0.904). Conclusion The high energy metabolic risk prediction model of lung cancer patients established in this study has good fit and prediction efficiency.

lung cancer  /  high energy metabolism  /  risk prediction model  /  body composition  /  quality of life
任江珊, 贾军梅, 孙萍, 平梅, 张琼琼, 刘燕燕, 赵和平, 陈妍, 戎冬文, 王康, 仇海乐, 刘宸安, 樊羽羽, 于德刚. 高能量代谢肺癌患者临床特征分析及其预测模型建立. 解放军医学杂志, 2024 , 49 (9) : 1004 -1010 . DOI: 10.11855/j.issn.0577-7402.1574.2024.0327
Jiang-Shan Ren, Jun-Mei Jia, Ping Sun, Mei Ping, Qiong-Qiong Zhang, Yan-Yan Liu, He-Ping Zhao, Yan Chen, Dong-Wen Rong, Kang Wang, Hai-Le Qiu, Chen-An Liu, Yu-Yu Fan, De-Gang Yu. Clinical characterization and prediction modeling of lung cancer patients with high energy metabolism[J]. Medical Journal of Chinese People’s Liberation Army, 2024 , 49 (9) : 1004 -1010 . DOI: 10.11855/j.issn.0577-7402.1574.2024.0327
正文
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肺癌是全球发病率居第二、死亡原因居第一的恶性肿瘤[1]。世界卫生组织预测，2025年我国新增肺癌死亡将超过100万例，即平均每分钟2例死于肺癌[2]。营养不良和癌症恶病质是肺癌患者的常见临床表现，在接受化疗、免疫治疗和(或)放疗时发生率更高[3]；约20%的晚期肺癌患者死于营养问题[4]。因此，有必要将肺癌患者的营养状况纳入治疗管理。营养与患者的能量代谢密切相关，高能量代谢可能导致恶病质的发生，因此，了解临床指标与高能量代谢的关系至关重要。据报道，静息能量消耗(resting energy expenditure，REE)增高可导致患者食欲降低、体重减轻，并可能导致营养不良及恶病质；准确测定癌症患者的REE可更好地安排营养治疗，有利于提高生活质量，缩短住院时间和降低病死率[5-6]。目前预测肺癌患者能量代谢的预测公式未纳入人体成分及相关炎性指标，因而难以明确患者能量代谢占预计值的百分比，即是否存在高能量代谢。本研究分析肺癌患者高能量代谢与人体成分、营养状况、生活质量的相关性，了解高能量代谢对其生存状态的影响，并构建肺癌患者高能量代谢的预测模型，旨在尽早识别高能量代谢者，进而针对性增加患者的营养摄入，平衡能量摄入与代谢，减少营养不良，改善患者的预后。
1 资料与方法
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1.1 研究对象
回顾性选取2022年1月-2023年5月山西医科大学第一医院收治的原发性肺癌患者132例，男95例，女37例；年龄35~81(60.4±10.8)岁；病理类型为非小细胞肺癌(non-small cell lung cancer，NSCLC)84例(鳞状细胞癌14例，腺癌70例)，小细胞肺癌(small cell lung cancer，SCLC)48例。
纳入标准：(1)年龄≥18 岁；(2)经影像和病理检查明确诊断为肺癌；(3)愿意配合参与本研究；(4)具有正常的理解和沟通能力，能够单独完成或在其他人帮助下完成问卷调查。
排除标准：(1)拒绝参加本研究；(2)无法耐受停止吸氧的危重病；(3)测定身体成分时无法站立；(4)体内植入了金属医疗器具；(5)合并其他恶性肿瘤；(6)合并代谢性疾病、感染性疾病、慢性消耗性疾病、自身免疫性疾病、甲状腺疾病或血液系统疾病；(7)接受了粒细胞集落刺激因子或海曲泊帕乙醇胺片等影响血细胞分析的治疗药物；(8)有意识障碍或语言障碍。本研究已获山西医科大学第一医院伦理委员会审批(KYLL-2023-228)。
1.2 方法
1.2.1 资料收集
(1)一般资料。通过电子病历系统收集所有入组患者的年龄、性别、身高、体重、吸烟指数、病理类型、疾病分期、体重指数(body mass index，BMI)等一般资料。记录患者入院48 h内的血液学指标：总蛋白(total protein，TP)、白蛋白(albumin，ALB)、血红蛋白(hemoglobin，Hb)、中性粒细胞计数(neutrophil count，NEU)、血小板计数(platelet count，PLT)、淋巴细胞计数(lymphocyte count，Ly)、C-反应蛋白(C-reactive protein，CRP)、白细胞介素-6(interleukin-6，IL-6)；进一步得出中性粒细胞与淋巴细胞比值(neutrophil to lymphocyte ratio，NLR)、系统免疫炎症指数(systemic immune-inflammation index，SII)[SII=中性粒细胞计数(109/L)×血小板计数(109/L)/淋巴细胞计数(109/L)]、预后营养指数(prognostic nutrition index，PNI)[PNI=血清白蛋白值(g/L)+5×淋巴细胞计数(109/L)]。(2)人体成分、骨骼肌指数和脂肪指数。采用人体成分分析仪测量人体成分，记录骨骼肌指数及脂肪指数。(3)患者主观整体营养状况评估量表(the scored patient-generated subjective global assessment，PG-SGA)评分。该评估量表包括患者自评表(体重变化、进食、症状、活动与身体功能)和医务人员评估表(疾病、年龄和营养需求的关系、体格检查、代谢应激状态等)；自评表和医务人员评估表评分相加即为总评分，总评分越高，提示营养状况越差；其中0~1分为营养良好，2~8分为可疑或中度营养不良，≥9分为重度营养不良。(4)欧洲癌症研究与治疗组织生命质量测定量表(European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30，EORTC QLQ-C30)评分。该量表共有30个条目，包括 5个功能领域(躯体、角色、认知、情绪、社会功能，共15个条目)，3个症状领域(疲劳、疼痛、恶心呕吐，共7个条目)，1个总体健康状况领域(共2个条目)和6个单一条目。将各个领域所包括的条目得分相加并除以该领域的总条目数，即得到该领域的粗分；再将粗分经线性公式转化为在0~100取值的标准分。功能领域和总体健康状况领域得分越高，提示功能状况和生命质量越好；症状领域得分越高则提示生命质量越差。
1.2.2 能量代谢评估
患者禁食2 h以上，在停止吸氧和输液的状态下，于合适气温下平卧休息30 min后，连续测量 15~30 min。测定仪器采用美国MGC Diagnostics 公司生产的CCM Express 能量代谢车，由经过培训的 2 名代谢车操作员共同完成。患者能量代谢占预计值的百分比>110%时，纳入高能量代谢组，≤110%则为非高能量代谢组。
1.2.3 两组肺癌患者人体成分、营养状况和生活质量的差异分析
比较高能量代谢组与非高能量代谢组肺癌患者人体成分、PG-SGA评分和EORTC QLQ-C30评分的差异。
1.2.4 肺癌患者高能量代谢预测模型的建立
将筛选出的两组差异有统计学意义的因素行二项分类logistic回归模型分析，建立预测模型；并进一步评估预测模型的拟合度和预测效能。
1.3 统计学处理
采用SPSS 25.0软件进行统计分析。计量资料符合正态分布者以$\bar{x}±s$表示，两组间比较采用独立样本t检验；若不符合正态分布，以M(Q1，Q3)表示，两组间比较采用秩和检验。计数资料以例(%)表示，两组间比较采用χ2检验或Fisher确切概率法，进一步两两比较采用Scheffe法。Hosmer-Lemeshow 检验评估预测模型的拟合度。采用受试者工作特征(receiver operating characteristic，ROC)曲线检测预测模型的预测效能，确定炎性指标的临界值，得到模型的阳性预测值、敏感度及特异度。P<0.05为差异有统计学意义。
2 结果
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2.1 两组临床资料比较
132例原发性肺癌患者中，高能量代谢组94例(71.2%)，非高能量代谢组38例(28.8%)。两组患者的年龄、性别、身高、体重、BMI及疾病分型差异无统计学意义(P>0.05)；高能量代谢组吸烟指数≥400和疾病分期为Ⅲ或Ⅳ期增多，IL-6水平明显升高(P<0.05)，总蛋白、白蛋白、血红蛋白、PNI水平降低(P<0.05，表1)。
2.2 能量代谢水平与人体成分、营养状况的关系
不同能量代谢患者的脂肪指数、骨骼肌指数、PG-SGA评分结果显示，高能量代谢患者的低脂肪指数、低骨骼肌指数、重度营养不良风险更高(P<0.05，表2)。
2.3 两组生活质量评分比较
两组患者的EORTC QLQ-C30评分结果显示，高能量代谢组身体功能、角色功能、情绪功能、总健康状况评分低于非高能量代谢组，疲劳、疼痛、食欲丧失评分高于非高能量代谢组，差异均有统计学意义(P<0.05)；两组患者认知功能、社会功能、恶心与呕吐、气促、失眠、便秘、腹泻、经济困难评分差异无统计学意义(P>0.05)(表3)。
2.4 炎性指标预测
2.4.1 IL-6与CRP
以IL-6、CRP及CRP+IL-6为检验变量，是否高能量代谢为状态变量，绘制炎性指标预测肺癌患者高能量代谢的ROC曲线，IL-6、CRP、CRP+IL-6的曲线下面积(AUC)分别为0.673(95%CI 0.578~0.768)、0.649(95%CI 0.546~0.753)、0.668(95%CI 0.572~0.763)，差异有统计学意义(P<0.05)；根据最大约登指数，最佳临界值分别为IL-6 3.775 ng/L(敏感度68.1%，特异度68.4%)、CRP 6.650 mg/L(敏感度60.6%，特异度68.4%)、CRP+IL-6 0.666(敏感度60.6%，特异度71.1%，图1)。
2.4.2 NLR、SII与PNI
分别以NLR、SII、PNI、NLR+SII、NLR+PNI、SII+PNI、NLR+SII+PNI为检验变量，是否高能量代谢为状态变量，绘制复合炎性指标与能量代谢的ROC曲线，NLR、SII、PNI、NLR+SII、NLR+PNI、SII+PNI、NLR+SII+PNI的AUC分别为0.630(95%CI 0.528~0.731)、0.616(95%CI 0.511~0.721)、0.754(95%CI 0.661~0.848)、0.622(95%CI 0.519~0.725)、0.753(95%CI 0.660~0.847)、0.754(95%CI 0.660~0.847)、0.754(95%CI 0.660~0.847)，差异有统计学意义(P<0.05)。根据最大约登指数，最佳临界值分别为NLR 3.36(敏感度40.4%，特异度84.2%)、SII 603.60(敏感度58.5%，特异度65.8%)、PNI 46.43(敏感度62.8%，特异度78.9%)、NLR+SII 0.68(敏感度61.7%，特异度65.8%)、NLR+PNI 0.78(敏感度55.3%，特异度86.8%)、SII+PNI 0.78(敏感度55.3%，特异度86.8%)、NLR+SII+PNI 0.78(敏感度55.3%，特异度86.8%，图2)。
为避免本研究纳入的指标过多而造成模型的过度拟合，根据IL-6、CRP、CRP+IL-6指标ROC曲线下面积，将IL-6纳入本研究；根据NLR、SII、PNI、NLR+SII、NLR+PNI、SII+PNI、NLR+SII+PNI指标ROC曲线下面积，可知PNI、SII+PNI、NLR+SII+PNI的曲线下面积相同，为简化临床评估，将单独变量PNI纳入本研究。
2.5 高能量代谢危险因素的logistic回归分析
对影响肺癌患者能量代谢的差异变量进行赋值，检验水准α=0.05，非高能量代谢组为0，高能量代谢组为1；疾病分期Ⅰ期为0，Ⅱ期为1，Ⅲ期为2，Ⅳ期为3；吸烟指数<400为0，≥400为1；总蛋白<60 g/L为0，≥60 g/L为1；白蛋白<35 g/L为0，≥35 g/L为1；血红蛋白<110(女)/120(男)为0，≥110(女)/120(男)为1；IL-6<3.775 ng/L为0，≥3.775 ng/L为1；PNI<46.43为0，≥46.43为1；二分类logistic回归分析结果显示，吸烟指数≥400(OR=2.754，P=0.042)、疾病分期晚(OR=1.868，P=0.018)、IL-6 ≥3.775 ng/L(OR=2.995，P=0.046)、PNI<46.43(OR=0.239，P=0.049)均为肺癌患者高能量代谢的独立危险因素(表4)。
2.6 高能量代谢预测模型的建立
根据多因素logistic回归分析结果，得到肺癌患者高能量代谢的风险预测模型，模型预测概率P=1/[1+e-1.076×吸烟指数-0.578×疾病分期-1.279×IL-6+1.592×PNI]；模型的Omnibus检验示P<0.05。采用Hosmer-Lemeshow检验拟合模型校准度，Hosmer-Lemeshow χ2=11.442，P=0.178。对预测公式进行ROC曲线描绘，AUC为0.834，95%CI 0.763~0.904(P<0.05，图3)。
3 讨论
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肺癌按临床病理可分为非小细胞肺癌和小细胞肺癌两大类，其中非小细胞肺癌占80%~85%，包括腺癌、鳞状细胞癌等组织学亚型。肺癌患者发病早期可无任何症状，大多在行胸部X线片或CT检查时发现；晚期可出现声嘶、吞咽困难、恶性胸腔积液等症状。34.5%~69.0%的肺癌患者会出现营养不良或癌症恶病质。营养不良可降低患者免疫功能和对治疗的反应与耐受性，增加感染风险，降低生活质量，增加医疗保健成本及缩短生存时间，且患者死于营养不良的相对风险可增加1.8倍[7-8]。恶病质的影响包括身体活动和日常生活活动能力下降及心理健康问题。这些因素会导致生存状态较差，甚至造成恶性循环。2021年欧洲临床营养与代谢学会(European Society for Clinical Nutrition and Metabolism，ESPEN)的癌症患者临床营养指南[9]推荐，为了保持稳定的营养状态，营养摄入须满足癌症患者的能量需求，即REE、体力活动消耗及一小部分饮食诱导产热的总和。在总能量消耗中，REE 的贡献为 55%~80%[10-11]，占比较大且临床测量较多，因此，常通过测量REE来评估患者的能量代谢水平[12]。REE的多个预测方程(如HarriseBenedict、Schofield、IretoneJones、Mifflin-St.Jeor等)显示，身高、体重、性别、年龄等均可影响REE，但准确测量REE需要的远不止这些因素[13]。研究显示，癌症患者REE的主要决定因素是身体成分、肿瘤负荷、炎症变化[12,14]。一项研究显示，结直肠癌Ⅲ期或Ⅳ期患者REE水平与年龄、去脂体重、炎症及疾病分期有关[13]。另一项纳入了144例转移性非小细胞肺癌患者的研究显示，能量代谢占预计值的百分比升高(即高能量代谢)为非小细胞肺癌患者预后的独立危险因素[15]。
本研究纳入的132例肺癌患者中高能量代谢率为71.2%。曹冬兴等[16]报道，70.6%的恶性肿瘤患者处于高代谢状态，23.7%处于正常代谢状态，而5.7%处于低代谢状态；与本研究结果接近。
本研究的logistic 回归分析结果显示，吸烟指数≥400、疾病分期晚、IL-6 ≥3.775 ng/L、PNI <46.43均为肺癌患者高能量代谢的独立危险因素。有研究显示，吸烟指数越高，肺功能损害越严重[17]。因为香烟中的有害成分进入人体会激活核因子κB(NF-κB)转录因子，促进hs-CRP表达增加，加重炎症反应，并产生大量的TNF-α、IL-6、IL-8及其他炎性因子，最终损害肺组织。IL-6是引发高能量消耗和肌肉损失的主要炎性因子[18]，循环中的IL-6可与神经元细胞膜表面gp130受体结合，参与患者厌食症的发生，还可与肝细胞膜表面gp130受体结合而激活下游JAK/STAT通路，进而诱导急性期蛋白的转录和合成；IL-6是影响大多数组织器官能量消耗进而导致恶病质发生和发展的主要因素之一[19]，这与本研究结果一致。PNI是根据血清白蛋白和Ly计算的。淋巴细胞在各种癌症的细胞免疫中起重要作用，可反映癌症患者的全身炎症状况。既往研究显示，炎症通过促进肿瘤细胞的增殖、迁移、免疫逃逸和化疗耐药，与肿瘤的发生和进展密切相关。Xu等[20]的一项对照研究显示，在泌尿系统恶性肿瘤中，REE变化与肿瘤类型和分期有关；肾癌和肾上腺癌患者较膀胱癌患者的REE高，Ⅳ期肿瘤患者较早期(Ⅰ、Ⅱ期)患者的REE高。在101例接受新辅助放疗的结直肠癌患者中，治疗前后REE的影响因素包括肿瘤晚期、侵袭性组织学和较高水平的促炎细胞因子[21-22]。一项研究采用间接量热法，测定了714例癌症患者和642例对照的REE，结果显示，癌症患者的REE明显升高；癌症病理分期可影响REE[23]，与本研究结果一致。
通过对不同能量代谢肺癌患者的身体成分、营养状况、生活质量的分析，本研究的结论为，高能量代谢的肺癌患者更容易有低脂肪指数、低骨骼肌指数、高营养风险；高能量代谢的肺癌患者身体功能、角色功能、情绪功能、总健康状况评分较非高能量代谢者低，疲劳、疼痛、食欲丧失评分较非高能量代谢者高。能量消耗是机体组织和器官代谢活动的结果。肿瘤分泌的蛋白水解诱导因子(PIF)首先激活依赖三磷酸腺苷(ATP)的蛋白水解系统，从而增加蛋白质分解代谢并严重降低无脂肪质量(fat free mass，FFM)[24]。一项针对非小细胞肺癌患者的横断面研究显示，能量代谢升高可能导致营养不良甚至恶病质的发展；恶病质的特征是肌肉蛋白质降解增加，并导致身体成分的改变，包括瘦肌肉质量及脂肪组织的损失[25]。能量代谢与患者营养状况密切相关，相关研究显示，营养状况与生活质量呈正相关，肿瘤患者营养状况越好，其生命质量越高[26]。而本研究也显示，高能量代谢的肺癌患者营养状况差，生命质量也低。
综上所述，肺癌患者高能量代谢的独立危险因素为吸烟指数≥400、疾病分期为晚期、IL-6≥3.775 ng/L、PNI <46.43；而高能量代谢与较差的人体成分、营养状况和生活质量密切相关。本研究建立的肺癌患者高能量代谢logistic回归风险预测模型具有较好的拟合度和预测效能，有望用于高能量代谢肺癌患者的评估、识别。本研究的不足之处在于：为单中心回顾性研究，且样本量有限，可能存在多种偏倚。另外，仅基于单中心样本建立风险评估模型，其外推价值尚待进一步的大样本、多中心临床试验进行验证。
基金
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  • 吴阶平医学基金会临床科研专项资助基金(320.6750.2022-17-27)
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2024年第49卷第9期
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doi: 10.11855/j.issn.0577-7402.1574.2024.0327
  • 接收时间：2023-11-28
  • 首发时间：2025-11-21
  • 出版时间：2024-09-28
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  • 收稿日期：2023-11-28
  • 录用日期：2023-12-28
基金
Clinical Research Special Program of Wu Jieping Medical Foundation(320.6750.2022-17-27)
吴阶平医学基金会临床科研专项资助基金(320.6750.2022-17-27)
作者信息
    1山西医科大学第一临床医学院，山西太原 030001
    2山西医科大学第一医院肿瘤科，山西太原 030001
    3山西医科大学第一医院营养科，山西太原 030001
    4首都医科大学附属北京世纪坛医院胃肠外科与临床营养科，北京 100038

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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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