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血浆蛋白质组学与 2 型糖尿病患者和非糖尿病患者死亡率的相关性:来自两项基于人群的 KORA 队列研究的结果。

Association of plasma proteomics with mortality in individuals with and without type 2 diabetes: Results from two population-based KORA cohort studies.

机构信息

Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.

Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, Pettenkofer School of Public Health, LMU Munich, Munich, Germany.

出版信息

BMC Med. 2024 Sep 27;22(1):420. doi: 10.1186/s12916-024-03636-0.

Abstract

BACKGROUND

Protein biomarkers may contribute to the identification of vulnerable subgroups for premature mortality. This study aimed to investigate the association of plasma proteins with all-cause and cause-specific mortality among individuals with and without baseline type 2 diabetes (T2D) and evaluate their impact on the prediction of all-cause mortality in two prospective Cooperative Health Research in the Region of Augsburg (KORA) studies.

METHODS

The discovery cohort comprised 1545 participants (median follow-up 15.6 years; 244 with T2D: 116 total, 62 cardiovascular, 31 cancer-related and 23 other-cause deaths; 1301 without T2D: 321 total, 114 cardiovascular, 120 cancer-related and 87 other-cause deaths). The validation cohort comprised 1031 participants (median follow-up 6.9 years; 203 with T2D: 76 total, 45 cardiovascular, 19 cancer-related and 12 other-cause deaths; 828 without T2D: 169 total, 74 cardiovascular, 39 cancer-related and 56 other-cause deaths). We used Cox regression to examine associations of 233 plasma proteins with all-cause and cause-specific mortality and Lasso regression to construct prediction models for all-cause mortality stratifying by baseline T2D. C-index, category-free net reclassification index (cfNRI), and integrated discrimination improvement (IDI) were conducted to evaluate the predictive performance of built prediction models.

RESULTS

Thirty-five and 62 proteins, with 29 overlapping, were positively associated with all-cause mortality in the group with and without T2D, respectively. Out of these, in the group with T2D, 35, eight, and 26 were positively associated with cardiovascular, cancer-related, and other-cause mortality, while in the group without T2D, 55, 41, and 47 were positively associated with respective cause-specific outcomes in the pooled analysis of both cohorts. Regulation of insulin-like growth factor (IGF) transport and uptake by IGF-binding proteins emerged as a unique pathway enriched for all-cause and cardiovascular mortality in individuals with T2D. The combined model containing the selected proteins (five and 12 proteins, with four overlapping, in the group with and without T2D, respectively) and clinical risk factors improved the prediction of all-cause mortality by C-index, cfNRI, and IDI.

CONCLUSIONS

This study uncovered shared and unique mortality-related proteins in persons with and without T2D and emphasized the role of proteins in improving the prediction of mortality in different T2D subgroups.

摘要

背景

蛋白质生物标志物可能有助于确定过早死亡的脆弱亚组。本研究旨在探讨血浆蛋白与有和没有基线 2 型糖尿病(T2D)个体全因和特定原因死亡率之间的关系,并评估它们在两个前瞻性奥格斯堡合作健康研究(KORA)研究中对全因死亡率预测的影响。

方法

发现队列包括 1545 名参与者(中位随访 15.6 年;244 名患有 T2D:116 名总死亡,62 名心血管死亡,31 名癌症相关死亡和 23 名其他原因死亡;1301 名无 T2D:321 名总死亡,114 名心血管死亡,120 名癌症相关死亡和 87 名其他原因死亡)。验证队列包括 1031 名参与者(中位随访 6.9 年;203 名患有 T2D:76 名总死亡,45 名心血管死亡,19 名癌症相关死亡和 12 名其他原因死亡;828 名无 T2D:169 名总死亡,74 名心血管死亡,39 名癌症相关死亡和 56 名其他原因死亡)。我们使用 Cox 回归来研究 233 种血浆蛋白与全因和特定原因死亡率的关系,并使用 Lasso 回归构建基于基线 T2D 的全因死亡率分层预测模型。C 指数、无分类净重新分类指数(cfNRI)和综合判别改善(IDI)用于评估所构建预测模型的预测性能。

结果

在有和没有 T2D 的组中,分别有 35 种和 62 种蛋白与全因死亡率呈正相关,其中 29 种蛋白重叠。在有 T2D 的组中,35 种、8 种和 26 种蛋白分别与心血管、癌症相关和其他原因的死亡率呈正相关,而在没有 T2D 的组中,在两个队列的合并分析中,55 种、41 种和 47 种蛋白分别与各自的特定原因结局呈正相关。胰岛素样生长因子(IGF)结合蛋白对 IGF 转运和摄取的调节作为一个独特的途径,在有 T2D 的个体中与全因和心血管死亡率相关。包含选定蛋白(有和没有 T2D 的组分别有 5 种和 12 种蛋白,其中有 4 种重叠)和临床危险因素的联合模型提高了全因死亡率的 C 指数、cfNRI 和 IDI。

结论

本研究揭示了有和没有 T2D 的个体中存在共同和独特的与死亡率相关的蛋白,并强调了蛋白在改善不同 T2D 亚组死亡率预测中的作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b38a/11438072/147148e2013a/12916_2024_3636_Fig1_HTML.jpg

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