Kokkorakis Michail, Folkertsma Pytrik, Forte José Castela, Wolffenbuttel Bruce H R, van Dam Sipko, Mantzoros Christos S
Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Metabolism. 2025 Feb;163:156047. doi: 10.1016/j.metabol.2024.156047. Epub 2024 Oct 12.
BACKGROUND & AIMS: Noninvasive tools (NITs) are currently used to stratify the risk of having or developing hepatic steatosis or fibrosis. Their performance and a proteomic-enabled improvement in forecasting long-term cardio-renal-metabolic morbidity, malignancies, as well as cause-specific and all-cause mortality, are lacking. Therefore, the performance of established NITs needs to be investigated in identifying cardio-renal-metabolic morbidity, malignancies, cause-specific and overall mortality and improve their performance with novel, proteomic-enabled NITs, including growth differentiation factor 15 (GDF-15), allowing multipurpose utilization.
502,359 UK Biobank participants free of the study outcomes at baseline with a 14-year median follow-up were grouped into three categories: a) general population, b) potentially metabolic dysfunction-associated steatotic liver disease (MASLD) population, c) individuals with type 2 diabetes mellitus. The investigated NITs include Aspartate aminotransferase to Platelet Ratio Index (APRI), Fibrosis 4 Index (FIB-4), Fatty Liver Index (FLI), Hepatic Steatosis Index (HSI), Lipid Accumulation Product (LAP), and metabolic dysfunction-associated fibrosis (MAF-5) score.
Adding GDF-15 to the existing NITs led to significantly increased prognostic performance compared to the traditional NITs in almost all instances, reaching substantially high C-indices, ranging between 0.601 and 0.808, with an overall >0.2 improvement in C-index. Overall, with the GDF-15 enhanced NITs, up to more than seven times fewer individuals need to be screened to identify more incident cases of adverse outcomes compared to the traditional NITs. The cumulative incidence of all outcomes, based on the continuous value percentiles of NITs, is increasing exponentially in the upper quintile of the GDF-15 enhanced NITs.
The herein-developed GDF-15 enhanced indices demonstrate higher screening effectiveness and significantly improved prognostic abilities, which are reduced to practice through an easy-to-use web-based calculator tool (https://clinicalpredictor.shinyapps.io/multimorbidity-mortality-risk/).
目前，非侵入性工具（NITs）用于对发生或发展为肝脂肪变性或肝纤维化的风险进行分层。然而，它们在预测长期心肾代谢疾病、恶性肿瘤以及特定病因和全因死亡率方面的表现，以及蛋白质组学助力下的改进尚不清楚。因此，需要研究现有NITs在识别心肾代谢疾病、恶性肿瘤、特定病因和总体死亡率方面的表现，并通过新型的、基于蛋白质组学的NITs（包括生长分化因子15（GDF - 15））来提高其性能，以实现多用途应用。
502359名英国生物银行参与者在基线时无研究结局，中位随访14年，分为三类：a）普通人群，b）潜在的代谢功能障碍相关脂肪性肝病（MASLD）人群，c）2型糖尿病患者。所研究的NITs包括天冬氨酸转氨酶与血小板比值指数（APRI）、纤维化4指数（FIB - 4）、脂肪肝指数（FLI）、肝脂肪变性指数（HSI）、脂质蓄积产物（LAP）和代谢功能障碍相关纤维化（MAF - 5）评分。
与传统NITs相比，在现有NITs中加入GDF - 15几乎在所有情况下都显著提高了预后性能，C指数大幅提高，范围在0.601至0.808之间，C指数总体提高>0.2。总体而言，与传统NITs相比，使用GDF - 15增强的NITs时，识别更多不良结局的新发病例所需筛查的个体数量减少了多达七倍以上。基于NITs连续值百分位数的所有结局的累积发生率，在GDF - 15增强的NITs的上五分位数中呈指数增长。
本文开发的GDF - 15增强指数显示出更高的筛查有效性和显著改善的预后能力，可通过一个易于使用的基于网络的计算器工具（https://clinicalpredictor.shinyapps.io/multimorbidity-mortality-risk/）应用于实际。
