Research progress of clinical nutrition in inflammatory bowel disease
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摘要:
炎症性肠病是一种病因尚不明确的慢性非特异性肠道炎性疾病, 包括溃疡性结肠炎及克罗恩病。炎症性肠病的发病机制复杂,包括遗传因素和环境因素,其中饮食是重要的环境因素之一。炎症性肠病患者通常会伴有营养不良及微量元素缺乏的表现。本文综述了近年来临床营养在炎症性肠病中的研究成果,分析了炎症性肠病的不同饮食治疗策略,为进一步研究炎症性肠病的预防与治疗策略提供参考。
Abstract:Inflammatory bowel disease is a chronic non-specific inflammatory disease of the gastrointestinal tract with unclear etiology, including ulcerative colitis and the Crohn's disease. The pathogenesis of inflammatory bowel disease is complex, involving genetic and environmental factors, and diet is one of the important environmental factors. Patients with inflammatory bowel disease often present with malnutrition and micronutrient deficiencies. This paper reviewed the research achievements of clinical nutrition in inflammatory bowel disease in recent years, analyzed the different dietary therapeutic strategies for inflammatory bowel disease, and provided reference for further research on the prevention and treatment of inflammatory bowel disease.
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肾细胞癌(RCC)是一种常见肿瘤,占所有癌症的3%。近20年来,世界范围内的肾癌发病率每年大致递增2%[1]。肾透明细胞癌(ccRCC)是一种常见的RCC,较乳头状细胞癌和嫌色细胞癌有更高的复发率、转移率及死亡率[2]。既往研究[3]显示肾透明细胞癌TNM分期1~4期的5年癌症特异性生存率分别为91%、74%、67%和32%。尽管肾脏相关手术可在一定程度上延长RCC患者的生存时间,但术后长期治疗效果并不理想,死亡率和复发率相对较高[4]。欧洲泌尿外科协会(EAU)指南[5-7]指出,术后预测系统、诺模图(Nomogram)可能较常规的TNM分期、Fuhrman分级对预后的预测更加准确。既往有一些RCC预后相关的Nomogram研究,但多数只纳入传统的临床信息,如肿瘤分期、分级等变量,并且仅关注模型的准确性,较少关注临床获益,从而无法全面评估模型的临床应用价值。本研究通过全面分析大样本ccRCC患者术后相关的临床资料,构建临床用于预测患者预后的预测模型,并使用决策曲线分析(DCA)等方法评估模型的预测价值,现将结果报告如下。
1. 材料与方法
1.1 诊断标准
收集2013年1月—2017年12月本院行肾根治或肾部分切手术患者,术后病理为ccRCC。选取病理分期为T1N0M0、T2N0M0的患者。排除标准: 非ccRCC患者; 既往临床资料不全者; 合并其他类型肿瘤者; 多发RCC者; 死于其他原因、术后接受过其他治疗者。
1.2 资料收集
收集的资料包括患者的年龄、性别、体质量指数(BMI)、吸烟史、高血压史、糖尿病(DM)史、临床症状、全身症状、高脂血症、家族肿瘤史、D-二聚体、血清白蛋白、血清钙离子、白细胞、中性粒细胞、淋巴细胞、贫血、术前肌酐、肿瘤大小、肿瘤分期和肿瘤病理分级。吸烟史定义为患者连续或累积的吸烟时间超过6个月[8]; 临床症状指肉眼血尿、腰痛和腹部肿块; 全身症状指体质量减轻和发热; 肿瘤分期参考国际抗癌联盟TNM分期标准; 肿瘤分级根据1982年Fuhrman分类标准; 肿瘤直径通过CT测量,分为≤4 cm、>4~ < 7 cm、≥7 cm; 无病生存期(DFS)定义为术后至首次肿瘤复发转移、肿瘤原因导致死亡或随访结束的时间。所有患者术后2年内每3个月进行1次血常规、血生化检查及胸腹部CT检查,第3年开始每6个月复查1次,第5年后每年复查1次。
1.3 统计学方法
应用SPSS 25.0(IBM, Armonk, NY, USA)、STATA 15.0(Stata Corp., College Station, TX, USA)和R语言(version 3.0.1; http://www.Rproject.org)软件进行数据的统计分析处理,连续性变量的正态性由Kolmogorov-Smirnov检验确定。正态分布的连续变量采用(x±)表示,非正态连续变量采用中位数(四分位数间距)表示。通过单因素及多因素Cox比例风险回归模型分析预后的相关危险因素,并用于构建Nomogram。P < 0.05为差异有统计学意义。一致性指数(CI)用于评价模型的预测能力, 0.5为没有预测能力, 1.0为完美预测。吻合线(calibration plots)用于校准模型。完美标准为倾斜角为45 °的直线。DCA计算此模型在不同风险阈值下的临床净获益[9]。
2. 结果
本研究共纳入526例患者,平均随访时间44个月; 年龄 < 50岁151例(28.7%), 50~ < 60岁179例(34.0%), 60~ < 70岁177例(33.7%), ≥70岁19例(3.6%); 男335例(63.7%), 女191例(36.3%), 体质量指数为(22.2±1.4) kg/m2; 有吸烟史53例(10.1%), 有临床症状129例(24.5%), 有副瘤综合征57例(10.8%), 有高血压109例(20.7%), 有糖尿病47例(8.9%), 有高脂血症22例(4.2%), 有家族肿瘤史11例(2.1%), 有贫血140例(26.6%); D-二聚体正常477例(90.7%), 异常49例(9.3%); 白蛋白 < 40 g/L者119例(22.6%), ≥40 g/L者407例(77.4%); 血清钙离子为2.3 mmol/L(2.2, 2.4), 白细胞为7.8×109/L(6.2, 9.4), 中性粒细胞为4.1×109/L(3.1, 5.3), 淋巴细胞为1.8×109/L(1.5, 2.2), 血小板为228.0×109/L(190.5, 272.5), 术前肌酐为66.3 μmol/L(55.2, 76.8); 肿瘤病理分级Ⅰ~Ⅱ级460例(87.5%), Ⅲ~Ⅳ级66例(12.5%); T分期为T1期393例(74.7%), T2期133例(25.3%); 肿瘤大小 < 4 cm者281例(53.4%), 4~ < 7 cm者91例(17.3%), ≥7 cm者154例(29.3%)。
526例患者中, 43例出现终点事件(7.6%)。单因素分析显示年龄、临床症状、高血压病史、高脂血症、D-二聚体、白蛋白、贫血、术前肌酐、病理分级、肿瘤大小与预后相关(P < 0.05)。多因素Cox比例风险回归用于构建预测模型,最终模型包括年龄、临床症状、贫血、D-二聚体、肿瘤大小。见表 1、图 1。
表 1 预后相关危险因素的多因素分析变量 HR 95% CI P 年龄 1.428 1.059~1.925 0.020 贫血 1.782 0.949~3.346 0.072 D-二聚体 3.650 1.877~7.099 < 0.001 肿瘤大小 1.613 1.107~2.350 0.013 临床症状 1.909 0.999~3.648 0.005 HR: 风险比; CI: 置信区间。 CI用于评价模型预测能力。本模型的CI为0.78, 有较好的预测能力。本预测模型的校准曲线在理想模型下表现良好(图 2A、B)。术后36、60个月的DCA见图 2C、D。术后36个月时,此模型风险阈值为0.01~0.50时,净获益率大于0; 术后60个月时,风险阈值为0.01~0.58时,净获益率大于0,有临床意义。
3. 讨论
局限性ccRCC患者术后可能需要更长的随访时间,因此完善局限性ccRCC患者术后的预后评估,并针对性地建立完善的随访制度很有必要。目前ccRCC术后患者的预后判断仍主要基于TNM分期[10]。Nomogram作为一种可视化的、具有临床应用价值的医学预测模型,可以提供更加准确且个性化的预后评估。Nomogram可以客观评估新的预测变量。本研究选取的终点事件是DFS, 即首次出现ccRCC相关的预后问题,这比传统的总生存时间(OS)更能准确地反映患者术后的恢复情况。
本预测模型经过良好的校准和临床获益评估后,最终Nomogram纳入5项预测因子: 年龄、临床症状、贫血、D-二聚体和肿瘤大小。本研究认为临床症状与ccRCC患者预后有关,此结论在既往研究[6-7, 11]中存在争议,这可能是因为病情较轻的患者往往无明显的临床症状。既往研究[7, 11]表明贫血、肿瘤大小、年龄与ccRCC患者预后有关,与本研究结果一致。本研究中D-二聚体与ccRCC预后相关。既往研究[12]认为血液高凝状态与肾癌预后有关。D-二聚体作为常见的纤维蛋白原降解产物,可用于评估纤溶系统活性。ERDEM S等[13]研究表明,D-二聚体水平升高预示肾癌患者预后更差。
FRANK I等[7]认为钙离子与预后相关。钙离子作为胞内信号分子,参与调控细胞的增殖和凋亡,同时在肿瘤细胞中也有类似作用[14]。本研究中,并未发现钙离子与细胞增殖、凋亡的关联,这可能是2个研究的观察结局有差异所致。本研究的单因素分析中,白蛋白是预后相关危险因素。白蛋白可参与调节癌细胞系的增殖[15], 同时低蛋白血症与人体防御机制减弱相关[16]。既往研究[17-18]表明,血清白蛋白和RCC患者的预后有明显关联,且白蛋白水平越低,患者预后越差。全身炎症反应与肿瘤预后的关系已有相关探讨[19], 许多恶性肿瘤的发生、进展与感染、慢性炎症反应有关[20]。HU H等[21]研究发现,术前中性粒细胞和淋巴细胞比值可作为RCC术后患者预后的潜在预测因子。本研究中纳入的炎症相关细胞并未发现与预后相关,这可能与选取患者的纳入排除标准不同有关。本研究中发现贫血和白蛋白水平是与预后相关的危险因素,而这2项指标也被认为与慢性炎症相关[20]。
既往有研究建立Nomogram来评估RCC患者预后,其中KARAKIEWICZ P I等[11]的Nomogram较为经典且广为引用。KARAKIEWICZ P I等的研究用于评估RCC患者术后的肿瘤特异性生存,其研究纳入2 474例患者,经单因素和多因素Cox回归分析后,最终的Nomogram包括了肿瘤分期、Fuhrman分级、肿瘤大小和临床症状,预测准确性为89%。但该研究纳入变量较少,只分析传统的肿瘤学特征,并未纳入其他可能与预后有关的变量。YAYCIOGLU O等[22]研究非转移性RCC患者的无复发生存情况,发现年龄、性别、存在放射性淋巴结、临床症状、肿瘤大小和临床分期是预后相关预测因子,其最终构建的Nomogram的CI为0.747, 其模型的预测能力与本研究模型接近。
本研究全面分析大量ccRCC术后患者相关的临床资料,构建临床相关的用于预测患者预后的Nomogram, 并使用DCA等方法评估模型的预测和临床应用价值。本研究的局限性: ①本研究为单中心的回顾性研究; ②某些指标并无公认的分类截点,可能会产生误差。因此,后续仍需要采用具有统一标准的多中心、大样本的前瞻性队列研究来进一步验证和完善本模型。
综上所述,本研究通过分析患者人口学资料、实验室检查、肿瘤学资料,构建了准确度较高且有临床获益的Nomogram, 用于评估患者的无病生存情况,可为ccRCC患者术后随访及疾病干预提供参考。
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