Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome

Cai, Xue; Xue, Zhangzhi; Zeng, Fang-Fang; Tang, Jun; Yue, Liang; Wang, Bo; Ge, Weigang; Xie, Yuting; Miao, Zelei; Gou, Wanglong; Fu, Yuanqing; Li, Sainan; Gao, Jinlong; Shuai, Menglei; Zhang, Ke; Xu, Fengzhe; Tian, Yunyi; Xiang, Nan; Zhou, Yan; Shan, Peng-Fei; Zhu, Yi; Chen, Yu-ming; Zheng, Ju-Sheng; Guo, Tiannan

doi:10.1016/j.xcrm.2023.101172

Cited by 8 publications

(5 citation statements)

References 82 publications

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“…Remaining molecular signatures were domain specific showing no connections among each other, such as the proteins VTN, APOD and a set of other amino acids (valine, leucine, alanine) being only related to the endocrine-metabolic domain. Such associations have also been reported previously in the literature [39][40][41] . Untargeted metabolomics analysis in the prospective, population-based EPIC-Norfolk study identified 420 metabolites that were shared among two or more chronic diseases 12 , further observing high connectivity among cardiometabolic and respiratory diseases across different biochemical classes of metabolites.…”

Section: Discussionsupporting

confidence: 89%

Identifying Metabolomic and Proteomic Biomarkers for Age-Related Morbidity in a Population-Based Cohort - the Cooperative Health Research in South Tyrol (CHRIS) study

Hantikainen,

Weichenberger,

Dordevic

et al. 2024

Preprint

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Identifying biomarkers able to discriminate individuals on different health trajectories is crucial to understand the molecular basis of age-related morbidity. We investigated multi-omics signatures of general health and organ-specific morbidity, as well as their interconnectivity. We examined cross-sectional metabolome and proteome data from 3,142 adults of the Cooperative Health Research in South Tyrol (CHRIS) study, an Alpine population study designed to investigate how human biology, environment, and lifestyle factors contribute to people’s health over time. We had 174 metabolites and 148 proteins quantified from fasting serum and plasma samples. We used the Cumulative Illness Rating Scale (CIRS) Comorbidity Index (CMI), which considers morbidity in 14 organ systems, to assess health status (any morbidity vs. healthy). Omics-signatures for health status were identified using random forest (RF) classifiers. Linear regression models were fitted to assess directionality of omics markers and health status associations, as well as to identify omics markers related to organ-specific morbidity.Next to age, we identified 21 metabolites and 10 proteins as relevant predictors of health status and results confirmed associations for serotonin and glutamate to be age-independent. Considering organ-specific morbidity, several metabolites and proteins were jointly related to endocrine, cardiovascular, and renal morbidity. To conclude, circulating serotonin was identified as a potential novel predictor for overall morbidity.

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Section: Discussionsupporting

confidence: 89%

Identifying Metabolomic and Proteomic Biomarkers for Age-Related Morbidity in a Population-Based Cohort - the Cooperative Health Research in South Tyrol (CHRIS) study

Hantikainen,

Weichenberger,

Dordevic

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…The criteria for significantly differential abundant proteins (DCP) were an adjusted p < 0.01 and a fold change >1.2 or <0.83. − The adjusted p value was calculated using the Benjamini and Hochberg method. Kyoto Encyclopedia of Genes and Genomes (KEGG) function analysis was performed using the “Metascape” tools (version 3.5) available at .…”

Section: Materials and Methodsmentioning

confidence: 99%

Proteomic Analysis Identifies GSN as a Noninvasive Circulating Serum Biomarker for Predicting Early Recurrence of Hepatocellular Carcinoma

Hu,

Yang,

Cai

et al. 2024

J. Proteome Res.

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Hepatocellular carcinoma (HCC) is susceptible to early recurrence, but it lacks effective predictive biomarkers. In this study, we retrospectively selected 179 individuals as a discovery cohort (126 HCC patients and 53 liver cirrhosis (LC) patients) for screening candidate serum biomarkers of early recurrence based on data independent acquisition-mass spectrometry strategy. And then, the candidate biomarkers were validated in an additional independent cohort with 192 individuals (142 HCC patients and 50 LC patients) using parallel reaction monitoring targeted quantitative techniques (PXD047852). Eventually, we validated that gelsolin (GSN) concentrations were significantly lower in HCC than in LC (p < 0.0001), patients with low GSN concentrations had a poor prognosis (p < 0.0001), and GSN concentrations were significantly lower in early recurrence HCC than in late recurrence HCC (p < 0.0001). These trends were also observed in alpha-fetoprotein (AFP)-negative HCC patients. The area under the curve of machine-learning-based predictive model (GSN and microvascular invasion) for predicting early recurrence risk reached 0.803 (95% confidence interval (CI): 0.786−0.820) and maintained the same efficacy in AFP-negative patients. In conclusion, GSN is a novel serum biomarker for early recurrence of HCC. The model could provide timely warning to HCC patients at high risk of recurrence.

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“…Briefly, serum proteome analysis was performed as previously described (Cai et al., 2023 ). The peptides were extracted from the serum samples and were then digested with a two‐step overnight tryptic digestion (Hualishi Tech.…”

Section: Methodsmentioning

confidence: 99%

Proteome‐wide profiling reveals dysregulated molecular features and accelerated aging in osteoporosis: A 9.8‐year prospective study

Xu,

Cai,

Miao

et al. 2023

Aging Cell

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The role of circulatory proteomics in osteoporosis is unclear. Proteome‐wide profiling holds the potential to offer mechanistic insights into osteoporosis. Serum proteome with 413 proteins was profiled by liquid chromatography–tandem mass spectrometry (LC–MS/MS) at baseline, and the 2nd, and 3rd follow‐ups (7704 person‐tests) in the prospective Chinese cohorts with 9.8 follow‐up years: discovery cohort (n = 1785) and internal validation cohort (n = 1630). Bone mineral density (BMD) was measured using dual‐energy X‐ray absorptiometry (DXA) at follow‐ups 1 through 3 at lumbar spine (LS) and femoral neck (FN). We used the Light Gradient Boosting Machine (LightGBM) to identify the osteoporosis (OP)‐related proteomic features. The relationships between serum proteins and BMD in the two cohorts were estimated by linear mixed‐effects model (LMM). Meta‐analysis was then performed to explore the combined associations. We identified 53 proteins associated with osteoporosis using LightGBM, and a meta‐analysis showed that 22 of these proteins illuminated a significant correlation with BMD (p < 0.05). The most common proteins among them were PHLD, SAMP, PEDF, HPTR, APOA1, SHBG, CO6, A2MG, CBPN, RAIN APOD, and THBG. The identified proteins were used to generate the biological age (BA) of bone. Each 1 SD‐year increase in KDM‐Proage was associated with higher risk of LS‐OP (hazard ratio [HR], 1.25; 95% CI, 1.14–1.36, p = 4.96 × 10−06), and FN‐OP (HR, 1.13; 95% CI, 1.02–1.23, p = 9.71 × 10−03). The findings uncovered that the apolipoproteins, zymoproteins, complements, and binding proteins presented new mechanistic insights into osteoporosis. Serum proteomics could be a crucial indicator for evaluating bone aging.

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Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome

Cited by 8 publications

References 82 publications

Identifying Metabolomic and Proteomic Biomarkers for Age-Related Morbidity in a Population-Based Cohort - the Cooperative Health Research in South Tyrol (CHRIS) study

Identifying Metabolomic and Proteomic Biomarkers for Age-Related Morbidity in a Population-Based Cohort - the Cooperative Health Research in South Tyrol (CHRIS) study

Proteomic Analysis Identifies GSN as a Noninvasive Circulating Serum Biomarker for Predicting Early Recurrence of Hepatocellular Carcinoma

Proteome‐wide profiling reveals dysregulated molecular features and accelerated aging in osteoporosis: A 9.8‐year prospective study

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