The effect of hyperlipidemia on overall survival in patients with cancer was differentiated by BMI and hyperlipidemia type.

IF 3.9 2区医学 Q2 NUTRITION & DIETETICS

Nutrition & Metabolism Pub Date : 2024-06-24 DOI:10.1186/s12986-024-00811-1

Hai-Ying Tian, Ming Yang, Hai-Lun Xie, Guo-Tian Ruan, Yi-Zhong Ge, Xiao-Wei Zhang, He-Yang Zhang, Chen-An Liu, Tong Liu, Han-Ping Shi

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引用次数: 0

Abstract

Background and aims: The impact of lipids on the overall survival (OS) of patients with malignancy has not yet been clarified. This study aimed to evaluate the effect of hyperlipidemia on the OS among Chinese patients based on Body Mass Index (BMI) stratifications and hyperlipidemia types.

Method: The patients in this study were derived from the Investigation of the Nutrition Status and Clinical Outcome of Common Cancers (INSCOC) trial. Kaplan-Meier was used to draw the survival curve, and the log-rank test was used to estimate the survival rates between each group. Cox proportional hazards regression models were used to estimate the hazard ratios (HR) and 95% confidence intervals (CI).

Results: A total of 9054 patients were included in the final study, with a median age of 59 years, and 55.3% (5004) of them were males. Regarding types of hyperlipidemia, only low high-density lipoprotein was an independent risk factor for the prognosis of all patients (HR = 1.35, 95% CI: 1.25-1.45, P < 0.001), while high total cholesterol (HR = 1.01, 95% CI: 0.90-1.15, P = 0.839) and high low-density lipoprotein (HR = 1.03, 95%CI: 0.91-1.16, P = 0.680) were not. In terms of BMI stratification, the effect of triglycerides on prognosis varied; high triglycerides were an independent risk factor for the prognosis of underweight patients (HR = 1.56, 95% CI:1.05-2.32, P = 0.027) and a protective factor for overweight patients (HR = 0.75, 95% CI: 0.63-0.89, P = 0.001). However, for normal-weight patients, there was no significant statistical difference (HR = 0.88, 95%CI: 0.75-1.03, P = 0.108).

Conclusions: The impact of hyperlipidemia on the OS among patients with cancer varied by different BMI and hyperlipidemia types. BMI and hyperlipidemia type ought to be considered in combination to estimate the prognosis of patients with malignancy.

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根据体重指数和高脂血症类型来区分高脂血症对癌症患者总生存期的影响。

背景和目的：血脂对恶性肿瘤患者总生存期（OS）的影响尚未明确。本研究旨在根据体质指数（BMI）分层和高脂血症类型，评估高脂血症对中国患者OS的影响：方法：本研究的患者来自常见癌症营养状况和临床结果调查（INSCOC）试验。采用 Kaplan-Meier 法绘制生存曲线，用 log-rank 检验法估算各组间的生存率。采用 Cox 比例危险回归模型估算危险比（HR）和 95% 置信区间（CI）：最终研究共纳入 9054 名患者，中位年龄为 59 岁，其中 55.3% （5004 名）为男性。关于高脂血症的类型，只有低高密度脂蛋白是所有患者预后的独立危险因素（HR = 1.35，95% CI：1.25-1.45，P 结论：高脂血症对所有患者预后的影响是不确定的：高脂血症对癌症患者OS的影响因BMI和高脂血症类型的不同而异。在估计恶性肿瘤患者的预后时，应综合考虑体重指数和高脂血症类型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nutrition & Metabolism 医学-营养学

CiteScore

8.40

自引率

0.00%

发文量

审稿时长

4-8 weeks

期刊介绍： Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects. The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases. Key areas we wish to encourage submissions from include: -how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes; -the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components; -how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved; -how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.