美国人口中老年营养风险指数与全因死亡率、癌症特异性死亡率和心血管死亡率之间的关系:大规模汇总调查。

IF 3.9 2区 医学 Q2 NUTRITION & DIETETICS Nutrition & Metabolism Pub Date : 2024-07-12 DOI:10.1186/s12986-024-00827-7
Kun Han, Tianhong Wang, Congcong Zou, Tao Li, Leng Zhou
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引用次数: 0

摘要

背景:以往的研究报告显示,老年营养风险指数(GNRI)与各种疾病之间存在密切联系。然而,GNRI 与死亡率之间的关系仍不清楚。为了研究 GNRI 与全因死亡率、癌症特异性死亡率和心血管死亡率之间的相关性,我们进行了这项研究:我们分析了 2005 年至 2016 年国家健康与营养调查中的老年参与者。使用体重指数和血清白蛋白计算 GNRI。绘制 Kaplan-Meier 生存曲线,比较 GNRI 正常组和 GNRI 降低组的生存概率。采用加权多变量 Cox 回归和限制性立方样条(RCS)模型来确定 GNRI 与全因死亡率、癌症特异性死亡率和心血管死亡率的线性和非线性关系:共有 3276 名参与者参与了分析。Kaplan-Meier 生存曲线显示,GNRI 下降组的全因死亡率和癌症特异性死亡率的生存概率较低(P 0.05)。在完全回归模型中,GNRI下降组的全因死亡风险(HR = 1.67,95% CI = 1.21-2.30,P = 0.002)和癌症特异性死亡风险(HR = 2.20,95% CI = 1.32-3.67,P = 0.003)高于正常组。在心血管死亡率方面,未发现与 GNRI 有显著关联(HR = 1.39,95% CI = 0.60-3.22,P = 0.436)。值得注意的是,RCS 分析发现 GNRI 与全因死亡率和癌症特异性死亡率之间呈线性下降趋势(总体结论均为 P):GNRI 下降的个体全因死亡率和癌症特异性死亡率风险增加。GNRI与全因死亡率和癌症特异性死亡率呈线性相关。应仔细监测营养状况,这可能会改善普通人群的整体预后。
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The associations between the Geriatric Nutritional Risk Index and all-cause, cancer-specific, and cardiovascular mortality in the U.S. population: a large-scale pooled survey.

Background: Previous studies have reported a close association between the Geriatric Nutritional Risk Index (GNRI) and various conditions. However, the association between the GNRI and mortality remains unclear. To examine the correlation between the GNRI and all-cause, cancer-specific, and cardiovascular mortality, this study was performed.

Methods: We analyzed elderly participants in the National Health and Nutrition Examination Survey from 2005 to 2016. The GNRI was calculated using body mass index and serum albumin. Kaplan-Meier survival curves were drawn to compare the survival probability between the normal and decreased GNRI groups. Weighted multivariate Cox regression and restricted cubic spline (RCS) models were employed to determine the linear and non-linear associations of the GNRI with all-cause, cancer-specific, and cardiovascular mortality.

Results: A total of 3,276 participants were included in the analysis. The Kaplan-Meier survival curve showed that the decreased GNRI group had a lower survival probability for all-cause mortality and cancer-specific mortality (P < 0.001) but not for cardiovascular mortality (P > 0.05). In the full regression models, the decreased group had a higher risk of all-cause mortality (HR = 1.67, 95% CI = 1.21-2.30, P = 0.002), and cancer-specific mortality (HR = 2.20, 95% CI = 1.32-3.67, P = 0.003) than the normal group. For cardiovascular mortality, no significant association with GNRI (HR = 1.39, 95% CI = 0.60-3.22, P = 0.436) was detected. Notably, the RCS analysis identified a linear downward trend between the GNRI and all-cause, alongside cancer-specific mortalities (all P for overall < 0.05). The time-dependent Receiver Operating Characteristic (ROC) analysis unveiled the predictive power of the GNRI for 5-year all-cause mortality, cancer mortality, and cardiovascular mortality was 0.754, 0.757, and 0.836, respectively, after adjusting for covariates.

Conclusions: Individuals with a decreased GNRI had increased risks of all-cause, and cancer-specific mortality. There were linear associations of the GNRI with all-cause, and cancer-specific mortality. Nutritional status should be carefully monitored, which may improve the overall prognosis for the general population.

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来源期刊
Nutrition & Metabolism
Nutrition & Metabolism 医学-营养学
CiteScore
8.40
自引率
0.00%
发文量
78
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.
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