Endocrinology and Metabolism

Original Articles

Comprehensive Proteomics and Machine Learning Analysis to Distinguish Follicular Adenoma and Follicular Thyroid Carcinoma from Indeterminate Thyroid Nodules: Hee-Sung Ahn, Eyun Song, Chae A Kim, Min Ji Jeon, Yu-Mi Lee, Tea-Yon Sung, Dong Eun Song, Jiyoung Yu, Ji Min Shin, Yeon-Sook Choi, Kyunggon Kim, Won Gu Kim; Received October 16, 2024 Accepted February 24, 2025 Published online April 10, 2025; DOI: https://doi.org/10.3803/EnM.2024.2208 [Epub ahead of print]

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Abstract PDF Supplementary Material PubReader ePub: Background
The preoperative diagnosis of follicular thyroid carcinoma (FTC) is challenging because it cannot be readily distinguished from follicular adenoma (FA) or benign follicular nodular disease (FND) using the sonographic and cytological features typically employed in clinical practice.
Methods
We employed comprehensive proteomics and machine learning (ML) models to identify novel diagnostic biomarkers capable of classifying three subtypes: FTC, FA, and FND. Bottom-up proteomics techniques were applied to quantify proteins in formalin-fixed, paraffin-embedded (FFPE) thyroid tissues. In total, 202 FFPE tissue samples, comprising 62 FNDs, 72 FAs, and 68 FTCs, were analyzed.
Results
Close spectrum-spectrum matching quantified 6,332 proteins, with approximately 9% (780 proteins) differentially expressed among the groups. When applying an ML model to the proteomics data from samples with preoperative indeterminate cytopathology (n=183), we identified distinct protein panels: five proteins (CNDP2, DNAAF5, DYNC1H1, FARSB, and PDCD4) for the FND prediction model, six proteins (DNAAF5, FAM149B1, RPS9, TAGLN2, UPF1, and UQCRC1) for the FA model, and seven proteins (ACTN4, DSTN, MACROH2A1, NUCB1, SPTAN1, TAGLN, and XRCC5) for the FTC model. The classifiers’ performance, evaluated by the median area under the curve values of the random forest models, was 0.832 (95% confidence interval [CI], 0.824 to 0.839) for FND, 0.826 (95% CI, 0.817 to 0.835) for FA, and 0.870 (95% CI, 0.863 to 0.877) for FTC.
Conclusion
Quantitative proteome analysis combined with an ML model yielded an optimized multi‐protein panel that can distinguish FTC from benign subtypes. Our findings indicate that a proteomic approach holds promise for the differential diagnosis of FTC.

Mineral, Bone & Muscle
Higher Plasma Stromal Cell-Derived Factor 1 Is Associated with Lower Risk for Sarcopenia in Older Asian Adults: Sunghwan Ji, Kyunggon Kim, So Jeong Park, Jin Young Lee, Hee-Won Jung, Hyun Ju Yoo, Il-Young Jang, Eunju Lee, Ji Yeon Baek, Beom-Jun Kim; Endocrinol Metab. 2023;38(6):701-708. Published online October 18, 2023; DOI: https://doi.org/10.3803/EnM.2023.1783

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Abstract PDF Supplementary Material PubReader ePub

Background
Despite the protective effects of stromal cell-derived factor 1 (SDF-1) in stimulating muscle regeneration shown in experimental research, there is a lack of clinical studies linking circulating SDF-1 concentrations with muscle phenotypes. In order to elucidate the role of SDF-1 as a potential biomarker reflecting human muscle health, we investigated the association of plasma SDF-1 levels with sarcopenia in older adults.
Methods
This cross-sectional study included 97 community-dwelling participants who underwent a comprehensive geriatric assessment at a tertiary hospital in South Korea. Sarcopenia was defined by specific cutoff values applicable to the Asian population, whereas plasma SDF-1 levels were determined using an enzyme immunoassay.
Results
After accounting for sex, age, and body mass index, participants with sarcopenia and low muscle mass exhibited plasma SDF-1 levels that were 21.8% and 18.3% lower than those without these conditions, respectively (P=0.008 and P=0.009, respectively). Consistently, higher plasma SDF-1 levels exhibited a significant correlation with higher skeletal muscle mass index (SMI) and gait speed (both P=0.043), and the risk of sarcopenia and low muscle mass decreased by 58% and 55% per standard deviation increase in plasma SDF-1 levels, respectively (P=0.045 and P=0.030, respectively). Furthermore, participants in the highest SDF-1 tertile exhibited significantly higher SMI compared to those in the lowest tertile (P=0.012).
Conclusion
These findings clinically corroborate earlier experimental discoveries highlighting the muscle anabolic effects of SDF- 1 and support the potential role of circulating SDF-1 as a biomarker reflecting human muscle health in older adults.

Citations

Citations to this article as recorded by

Circulating BMP-7 Level is Independent of Sarcopenia in Older Asian Adults
Ahin Choi, Ji Yeon Baek, Eunhye Ji, Il-Young Jang, Hee-Won Jung, So Jeong Park, Yunju Jo, Eunju Lee, Dongryeol Ryu, Beom-Jun Kim
Annals of Geriatric Medicine and Research.2025; 29(1): 75. CrossRef
From a Solitary Blood-Derived Biomarker to Combined Biomarkers of Sarcopenia: Experiences From the Korean Frailty and Aging Cohort Study
Chang Won Won, Miji Kim, Hyung Eun Shin, Gustavo Duque
The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences.2025;[Epub] CrossRef
Unlocking diagnosis of sarcopenia: The role of circulating biomarkers – A clinical systematic review
F. Veronesi, F. Salamanna, V. Borsari, A. Ruffilli, C. Faldini, G. Giavaresi
Mechanisms of Ageing and Development.2024; 222: 112005. CrossRef

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