【作者】 张丽丽；

【导师】 崔久嵬；

【作者基本信息】 吉林大学 ， 临床医学硕士（专业学位）， 2025， 硕士

【摘要】 研究背景及目的:近年来的研究表明,体型表型与乳腺癌的预后密切相关。然而,传统的体型特征指标,如体质指数(Body mass index,BMI)尚无法全面反映体型的复杂变化。主成分分析法(Principal component analysis,PCA)是一种利用降维的思想,把多个相关变量转化为少数几个综合变量(即主成分,PC)的统计学方法。本研究旨在利用PCA从多个体型特征中提取出主要的体型表型,并探讨体型表型对乳腺癌患者的预后影响,为患者个性化营养和运动指导提供一定的依据。研究方法:本研究回顾性收集了2014年1月至2023年12月首次就诊于吉林大学第一医院、符合纳入排除标准乳腺癌患者的资料。采用Cox回归分析单一体型特征(身高、体重、BMI、腰围、内脏脂肪面积、骨骼肌含量及握力)与肿瘤预后的关系。基于上述指标利用PCA提取3个PC。采用Cox回归分析PC与乳腺癌预后之间的关系。研究结果:1.研究总共纳入了347名经病理诊断为乳腺癌的患者。研究参与者的确诊中位年龄为53岁(范围:44-61岁)。I期、II期、III期和Ⅳ期的例数分别为75(21.61%)、118(34.00%)、63(18.16%)和91(26.22%)。病理分子分型为Luminal A型、Luminal B型、HER-2阳性型和三阴型的例数分别为65(18.73%)、148(42.65%)、78(22.48%)和56(16.14%)。2.单因素和多因素Cox回归分析探讨单一体型特征与总体乳腺癌预后的关系,结果显示,高体重(HR=1.80,95%CI:1.44-2.26)、BMI(HR=2.13,95%CI:1.67-2.72)、腰围(HR=2.48,95%CI:1.95-3.16)和内脏脂肪面积(HR=2.30,95%CI:1.81-2.91)与死亡风险增加相关,高握力(HR=0.96,95%CI:0.92-0.99)与死亡风险下降相关。3.基于身高、体重、BMI、腰围、内脏脂肪面积、骨骼肌含量及握力进行PCA,提取3个PC。PC1(贡献率为53.95%)代表以腹型肥胖为特征的体型;PC2(贡献率为23.92%)代表以身材高大伴有高骨骼肌含量为特征的体型;PC3(贡献率为12.42%)代表以身材矮小并伴有高握力为特征的体型。4.单因素和多因素Cox回归分析探讨PC与总体乳腺癌预后的关系,结果显示,PC1增加与死亡风险增加相关(HR=1.45,95%CI:1.28-1.64);PC2增加与死亡风险下降相关(HR=0.70,95%CI:0.57-0.85)。5.单因素和多因素Cox回归分析探讨PC与不同分期乳腺癌预后的关系,结果显示,Ⅰ期中,PC1增加与死亡风险增加相关(HR=1.75,95%CI:1.10-2.76);PC2增加与死亡风险下降相关(HR=0.42,95%CI:0.21-0.85)。Ⅱ期中,PC1增加与死亡风险增加相关(HR=2.79,95%CI:1.90-4.10);PC2增加与死亡风险下降相关(HR=0.58,95%CI:0.37-0.92)。Ⅲ期中,PC1增加与死亡风险增加相关(HR=1.29,95%CI:1.02-1.62);PC2增加与死亡风险下降相关(HR=0.56,95%CI:0.38-0.84)。Ⅳ期中,PC1增加与死亡风险增加相关(HR=1.33,95%CI:1.06-1.67)。6.单因素和多因素Cox回归分析及探讨PC与不同分子分型乳腺癌预后的关系,结果显示,Luminal A型中,PC1增加与死亡风险增加相关(HR=1.44,95%CI:1.11-1.88);PC2增加与死亡风险下降相关(HR=0.43,95%CI:0.25-0.71)。Luminal B型中,PC1增加与死亡风险增加相关(HR=1.49,95%CI:1.20-1.85)。HER-2阳性型中,PC1增加与死亡风险增加相关(HR=1.62,95%CI:1.19-2.21)。三阴型中,PC1增加与死亡风险增加相关(HR=1.42,95%CI:1.04-1.94);PC2增加与死亡风险下降显著相关(HR=0.54,95%CI:0.31-0.92)。结论:1.当仅考虑单一体型特征时,高体重、BMI、腰围、内脏脂肪面积与乳腺癌死亡风险增加相关,高握力与乳腺癌死亡风险下降相关。2.以腹型肥胖为特征的体型表型与乳腺癌死亡风险增加相关,无论是何种分期或何种分子分型乳腺癌患者。3.以身材高大伴有高骨骼肌含量为特征的体型表型与乳腺癌死亡风险下降相关,尤其是早期及Luminal A型、三阴型乳腺癌患者。更多还原

【Abstract】 Research background and purpose:Recent studies have demonstrated that body shape phenotypes are closely related to the prognosis of breast cancer.However,traditional body size characteristics,such as body mass index(BMI),fail to fully reflect the complex changes in body shape phenotypes.Principal component analysis(PCA)is a statistical method that uses the idea of dimensionality reduction to transform multiple variables into a few comprehensive variables(principal components,PCs).The purpose of this study is to apply PCA to extract the main body shape phenotypes from multiple body size characteristics and investigate the prognostic impact of body shape phenotypes on breast cancer patients in China.The findings may offer a basis for personalized nutrition and exercise guidance for patients.Research methods:This study retrospectively analyzed data from breast cancer patients who were first visited the First Hospital of Jilin University from January 2014 to December 2023 and met the inclusion and exclusion criteria.Cox regression analysis was employed to assess the relationship between individual body type characteristics(height,weight,BMI,waist circumference,visceral fat area,skeletal muscle mass,and grip strength)and tumor prognosis.Based on the above indicators,three PCs were extracted using PCA.Cox regression analysis was also used to analyze the relationship between PCs and the prognosis of breast cancer.The research results:1.A total of 347 patients with pathologically diagnosed breast cancer were included in the study.The median age at diagnosis of the study participants was 53years(range:44-61 years).The distribution of cases across stages was as follows:I(75,21.61%),II(118,34.00%),III(63,18.16%),and IV(91,26.22%).The distribution of molecular subtypes based on postoperative pathology was as follows:Luminal A(65,18.73%),Luminal B(148,42.65%),HER-2 positive(78,22.48%),and triple-negative(56,16.14%).2.Univariate and multivariate Cox regression analysis were used to explore the relationship between single body shape characteristics and the overall prognosis of breast cancer.The results indicated that elevated weight(HR=1.80,95%CI:1.44–2.26),BMI(HR=2.13,95%CI:1.67–2.72),waist circumference(HR=2.48,95%CI:1.95–3.16),and visceral fat area(HR=2.30,95%CI:1.81–2.91)were associated with an increased risk of mortality,whereas higher grip strength(HR=0.96,95%CI:0.92–0.99)was associated with a reduced risk of mortality.3.PCA was performed based on height,weight,BMI,waist circumference,visceral fat area,skeletal muscle mass,and grip strength.The first three PCs explained90.29%of all variables.PC1(accounting for 53.95%)represents a body shape mainly characterized by abdominal obesity;PC2(accounting for 23.92%)represents a body shape mainly characterized by tall stature with high skeletal muscle content;PC3(accounting for 12.42%)represents a body shape mainly characterized by being short with high grip strength.4.Univariate and multivariate Cox regression analyses were used to explore the relationship between PCs and the overall prognosis of breast cancer.The results showed that an increase in PC1 was associated with an increased risk of death(HR=1.45,95%CI:1.28-1.64);an increase in PC2 was associated with a decreased risk of death(HR=0.70,95%CI:0.57-0.85).5.Univariate and multivariate Cox regression analyses were used to explore the relationship between PCs and the prognosis of breast cancer in different stages.In stage I,an increase in PC1 was associated with an increased risk of death(HR=1.75,95%CI:1.10-2.76);an increase in PC2 was associated with a decreased risk of death(HR=0.42,95%CI:0.21-0.85).In stage II,an increase in PC1 was associated with an increased risk of death(HR=2.79,95%CI:1.90-4.10);an increase in PC2 was associated with a decreased risk of death(HR=0.58,95%CI:0.37-0.92).In stage III,an increase in PC1 was associated with an increased risk of death(HR=1.29,95%CI:1.02-1.62);an increase in PC2 was associated with a decreased risk of death(HR=0.56,95%CI:0.38-0.84).In stage IV,an increase in PC1 was associated with an increased risk of death(HR=1.33,95%CI:1.06-1.67).6.Univariate and multivariate Cox regression analyses were used to explore the relationship between PCs and the prognosis of breast cancer with different molecular subtypes.In Luminal A,an increase in PC1 was associated with an increased risk of death(HR=1.44,95%CI:1.11-1.88);an increase in PC2 was associated with a decreased risk of death(HR=0.43,95%CI:0.25-0.71).In Luminal B,an increase in PC1 was associated with an increased risk of death(HR=1.49,95%CI:1.20-1.85).In HER-2positive,an increase in PC1 was associated with an increased risk of death(HR=1.62,95%CI:1.19-2.21).In triple-negative,an increase in PC1 was associated with an increased risk of death(HR=1.42,95%CI:1.04-1.94);an increase in PC2 was associated with a decreased risk of death(HR=0.54,95%CI:0.31-0.92).Conclusions:1.When considering individual body type characteristic,elevated weight,BMI,waist circumference,and visceral fat area were associated with an increased risk of breast cancer mortality,whereas higher grip strength was associated with a reduced risk.2.The body shape phenotype characterized by abdominal obesity was associated with an increased risk of breast cancer mortality,irrespective of the disease stage or molecular subtype.3.The body shape phenotype characterized by tall stature with high skeletal muscle content was associated with a decreased risk of breast cancer death,especially in early-stage and Luminal A and triple-negative breast cancer patients.更多还原