Active surveillance (AS) is recommended for low-risk papillary thyroid microcarcinoma (PTMC) in many guidelines. However, while its clinical application requires incorporation of patient values, implementing shared decision-making (SDM) in practice remains challenging. To generate reliable evidence, facilitate the integration of SDM into routine PTMC managements and improve patient satisfaction, this study developed a PTMC-specific SDM model (SM group) and aims to evaluate whether it improves patient-reported outcomes (PROs) compared to usual care (UC group) in patients with low-risk PTMC. This multicenter, parallel-group, cluster-randomized controlled trial will enroll 310 patients with low-risk PTMC across seven academic hospitals in Korea. Participants will be assigned to either the SM group (model) or the UC group (control) through cluster randomization of 26 clinicians, stratified by specialty and AS experience. The SM group will receive structured counseling using a newly developed PTMC-specific SDM model, supported by decision aids such as educational videos, web-based card news, and illustrated leaflets regarding disease-information and patients' values. The UC group will receive standard counseling. The primary outcome is the Decisional Conflict Scale score. Secondary outcomes include satisfaction with decision-making process, decision regret, anxiety, and thyroid-specific quality of life. Data will be collected via the iCReaT v2.0 electronic Case Report Form, supplemented by electronic and paper-based PRO surveys. Assessments will be conducted at baseline, 1-4 weeks, and 6 months after the treatment decision. Trial Registration: ClinicalTrials.gov Identifier: NCT06730893.

Endocrine glands rely on specialized vasculature to orchestrate hormone trafficking and tissue homeostasis; however, the transcriptomic landscape underlying endothelial cell heterogeneity across these organs remains poorly understood. Here, we constructed a comprehensive single-cell atlas of endothelial cells from four endocrine organs, utilizing liver and brain tissues as structural references. Our analysis not only distinguishes capillary endothelial cells by their fenestrated, non-fenestrated, and sinusoidal architectures but also reveals distinct organotypic programs: vascular remodeling in the adrenal gland and kidney versus stress responsiveness in the pituitary gland and thyroid. Strikingly, we identified that the thyroid displays a unique restriction in endothelial–immune communication, mirroring the immune-privileged phenotype of the brain. This protective vascular interface is compromised during aging and tumorigenesis. These findings define the molecular basis of endocrine vascular specialization and offer new perspectives on the role of endothelial plasticity in aging and cancer.

Progression from differentiated thyroid cancer to anaplastic thyroid cancer (ATC) involves profound epithelial plasticity and remodeling of the tumor microenvironment (TME), but how BRAFV600E and RAS driver mutations shape these processes remains unclear. Here, we integrated single-nucleus RNA-seq, spatial transcriptomics, and bulk RNA-seq across BRAFV600E- and RAS-driven thyroid tumors to delineate mutation-specific progression trajectories. BRAFV600E-driven tumors exhibited a gradual dedifferentiation trajectory with immune pathway activation, whereas RAS-driven tumors displayed abrupt transitions characterized by aneuploidy, epithelial–mesenchymal transition, hypoxia, and extracellular matrix remodeling. Cancer-associated fibroblasts (CAFs) emerged as key regulators, with mutation-specific ligand–receptor interactions: integrin-based signaling predominated in BRAFV600E-mutant ATCs, while PLAU–PLAUR, TNFSF10–TNFRSF10B, and AREG–EGFR were additionally enriched in RAS-driven ATCs. These CAF–epithelial circuits were spatially validated and associated with poor prognosis. Together, our findings reveal mutation-dependent epithelial and TME dynamics associated with thyroid cancer dedifferentiation and highlight the potential importance of molecular-tailored approaches in the management of advanced thyroid cancer.

BACKGROUND:Current molecular classification model for thyroid cancer (TC), which relies on BRAF-RAS score genes has limited efficacy in differentiating follicular-patterned tumors from normal thyroid (NT) tissues and lack predictive power for disease progression. This study aimed to refine TC classification and develop predictive systems for tumor progression. METHODS:A multi-omics analysis integrating transcriptomics (SNUH-mRNASeq) and proteomics (SNUH-TMT-Pro) from retrospectively collected human tissues representing NT and tumors with mutation profiles was conducted in a single center. A novel gene set, termed NPF genes, was identified and used to construct a decision-tree based classification system and a risk stratification model for tumor progression. The discriminatory potential of protein markers was validated through immunohistochemistry (SNUH-IHC) using tissue microarrays. External datasets (TCGA-THCA, SNUH-DIA-Pro, and CellDis-Pro) were employed to validate both the classification and risk stratification systems. RESULTS:Clustering based on NPF genes separated papillary thyroid cancer (PTC) with BRAFV600E mutation (PTC-B), follicular thyroid cancer (FTC) regardless of mutation status and NT, classifying them into BRAFV600E-like, RAS-like, and NT-like subtypes. The decision-tree based classification model with NPF genes demonstrated high accuracy (0.92, 95% CI 0.88-0.95) and Kappa statistics (0.88). IHC of three protein biomarkers (MATN2, FN1, and PLSCR4) confirmed the molecular subtypes in SNUH-IHC, with findings consistent across external datasets. Additionally, higher NP-score and NF-score predicted poorer prognosis in BRAFV600E-like and RAS-like TCs. CONCLUSION:The NPF gene set and classification model refine TC classification, improve diagnostic accuracy, and enable better risk stratification. These advancements offer a foundation for personalized therapeutic strategies in TC management.

Abstract Background: DICER1 is an essential RNase III enzyme for microRNA (miRNA) processing. Germline loss-of-function (LoF) variants cause DICER1 syndrome and predispose younger individuals to tumors including follicular thyroid carcinoma (FTC). Somatic RNase IIIb hotspot mutations are characteristic, but how germline LoF, somatic hits, and tissue-specific regulatory networks drive FTC is not fully understood. Methods: We analyzed DICER1-mutant FTC (n=20) using whole-exome sequencing, miRNA-seq, RNA-seq, and proteomics, and compared them with wild-type tumors (wt-T, n=61). Tumors were classified as syndrome-associated (syn-T, n=7) or sporadic (spo-T, n=13). Integrative analyses included allelic imbalance, pathway enrichment, and network modeling (WGCNA). Results: Clinically, spo-T patients were significantly younger than wt-T, indicating an age-specific window for DICER1-driven FTC. Most tumors showed biallelic disruption through RNase IIIb hotspot mutations with secondary LoF events, and allelic imbalance confirmed two-hit inactivation even when only one mutation was detected.In syndrome-normal tissues, substantial mRNA dysregulation occurred without miRNA changes, suggesting DICER1 haploinsufficiency acts independently of global miRNA loss.In DICER1-mutant tumors, cell-cycle, mTOR, and Wnt pathways were strongly upregulated, whereas immune programs were broadly suppressed. The thyroid stem-cell marker REXO1 was specifically elevated, indicating a stem-like phenotype. Network analysis highlighted CTNNB1 and let-7i as key regulators of the DICER1 transcriptional program. Although DICER1- and RAS-mutant FTCs shared some downstream signaling modules, DICER1-mutant tumors retained a distinct expression identity. WGCNA identified a DICER1-specific cell-cycle module and a partially shared DICER1-RAS Wnt/MAPK module. Conclusion: DICER1 functions as a distinct oncogenic driver in FTC, following a biallelic inactivation model and producing unique proliferative, immune-suppressed, and stem-like transcriptional states. These findings refine the mechanism of DICER1-associated thyroid tumorigenesis and suggest potential lineage-specific therapeutic targets. Citation Format: Dakyung Lee, Young Ah Lee, Yeonju Kyoung, Seong-Keun Yoo, Sun-Wha Im, Jaeyong Choi, Yoo Hyung Kim, Dohyun Han, Young Joo Park, Jong-Il Kim. Comprehensive characterization of DICER1 mutations and two hit tumorigenesis mechanisms in follicular thyroid carcinoma using multi-omics analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5013.

Abstract Introduction Endocrine glands rely on specialized vasculature to orchestrate hormone trafficking and tissue homeostasis. However, the transcriptomic landscape of endothelial cells (ECs) as gatekeepers of the immune microenvironment remains poorly defined. We aimed to map the molecular programs governing endocrine EC specialization and their role in regulating organ-specific immune communication. Methods We constructed a single-cell transcriptomic atlas of ECs from four endocrine organs (adrenal gland, pituitary, thyroid, kidney), using liver and brain as structural and immunological references. We categorized ECs by architectural features and quantified endothelial-immune crosstalk via ligand-receptor interaction analysis across these distinct vascular beds. Results Our analysis revealed distinct organotypic programs: while adrenal and kidney ECs showed vascular remodeling signatures, pituitary and thyroid ECs exhibited high stress-responsiveness. Crucially, we identified a stark divergence in immunological signatures. While the adrenal, pituitary, kidney, and liver were enriched in pro-inflammatory gene sets, the thyroid and brain were uniquely prominent in anti-inflammatory programs. This reflects a specialized “immune-shielding” interface in the thyroid, characterized by significant downregulation of leukocyte adhesion molecules and pro-inflammatory signaling, mirroring the immune-privileged phenotype of the blood-brain barrier. Strikingly, this protective endothelial interface is significantly compromised during aging and tumorigenesis, leading to a breakdown of the immune-restricted vascular state. Conclusion These findings define the molecular basis of endocrine vascular specialization and identify a unique endothelial-immune communication restriction in the thyroid. The loss of this immune-privileged phenotype is a hallmark of thyroid aging and cancer, suggesting that restoring the endothelial “immune-shielding” capacity could be a novel therapeutic strategy for endocrine pathologies. Funding Source RS-2023-00214581 Topic Categories Immune Response Regulation: Cellular Mechanisms (IRC)

Primary thyroid hemangioma is a rare entity whose radiologic and intraoperative features may resemble those of a malignant disease, increasing the risk of misdiagnosis. Herein, we report a case of papillary thyroid carcinoma coexisting with a venous hemangioma that mimicked locally invasive cancer, which complicated surgical planning. A 48-year-old man presented with a thyroid nodule confirmed as papillary thyroid carcinoma. Preoperative imaging suggested extrathyroidal extension toward the esophagus and trachea, raising concern for locally invasive disease. Because vascular features were also suspected, total thyroidectomy was planned with an intraoperative reassessment of the surgical extent. Intraoperatively, the lesion was identified as a dilated vascular structure rather than an infiltrative tumor, allowing the procedure to be limited to thyroidectomy. Final histopathology confirmed papillary thyroid carcinoma with a coexisting venous hemangioma without true capsular invasion. This case highlights an important diagnostic pitfall: vascular lesions may mimic aggressive thyroid carcinoma and lead to overtreatment. A careful integration of imaging, intraoperative assessment, and pathology is essential for the appropriate management of primary thyroid hemangioma.

Background:Biological age (BA) is increasingly recognized as a valuable alternative to chronological age (CA) for assessing an individual's health and aging status. However, existing models are based on limited clinical parameters and have not thoroughly integrated morbidity and mortality data. Objective:This study aimed to develop and validate a novel transformer-based model, referred to as the BA - CA gap model, for BA estimation that incorporates morbidity and mortality information to improve predictive accuracy and enhance clinical use in the early identification of the risk of age-related diseases. Methods:We retrospectively analyzed data from 151,281 adults aged 18 years or older who underwent routine health checkups between 2003 and 2020. Participants were classified into normal, predisease, and disease groups based on comorbidities (diabetes mellitus, hypertension, and dyslipidemia) to evaluate the model's ability to discriminate health status along a clinically relevant spectrum. Variables with less than 50% missingness had missing values imputed using the mean, while features with 50% or more missingness were excluded. We develop a custom transformer architecture that learns multiple objectives simultaneously, including input feature reconstruction, BA and CA alignment, health status discrimination, and mortality prediction. Model training used unsupervised and self-supervised strategies. We compared our model's performance with conventional BA estimation approaches, including Klemera and Doubal's method, a CA cluster-based model, and a deep neural network, by examining BA gap distributions, health status stratification, and mortality prediction. Results:The proposed BA - CA gap model provided a more accurate reflection of health status and superior stratification of mortality risk than existing methods. The model effectively distinguished among normal, predisease, and disease groups, with a clear gradient of BA gap values. Kaplan-Meier analyses demonstrated stronger discrimination of future mortality in men, while a similar but not statistically significant trend was observed in women. Sensitivity analyses across multiple random splits and training subsets confirmed the robustness of the model's performance. Conclusions:By integrating morbidity and mortality information within a transformer-based framework, the BA - CA gap model offers a more granular and clinically meaningful assessment of aging and health status than CA alone. This approach supports the potential for personalized health management and risk stratification, although external validation in diverse populations is warranted to further confirm its generalizability.

BACKGROUND:Active surveillance (AS) has emerged as a viable management strategy for low-risk papillary thyroid microcarcinoma (PTMC), following pioneering trials at Kuma Hospital and the Cancer Institute Hospital in Japan. Numerous prospective cohort studies have since validated AS as a management option for low-risk PTMC, leading to its inclusion in thyroid cancer guidelines across various countries. From 2016 to 2020, the Multicenter Prospective Cohort Study of Active Surveillance on Papillary Thyroid Microcarcinoma (MAeSTro) enrolled 1,177 patients, providing comprehensive data on PTMC progression, sonographic predictors of progression, quality of life, surgical outcomes, and cost-effectiveness when comparing AS to immediate surgery. The second phase of MAeSTro (MAeSTro-EXP) expands AS to low-risk papillary thyroid carcinoma (PTC) tumors larger than 1 cm, driven by the hypothesis that overall risk assessment outweighs absolute tumor size in surgical decision-making. METHODS:This protocol aims to address whether limiting AS to tumors smaller than 1 cm may result in unnecessary surgeries for low-risk PTCs detected during their rapid initial growth phase. By expanding the AS criteria to include tumors up to 1.5 cm, while simultaneously refining and standardizing the criteria for risk assessment and disease progression, we aim to minimize overtreatment and maintain rigorous monitoring to improve patient outcomes. CONCLUSION:This study will contribute to optimizing AS guidelines and enhance our understanding of the natural course and appropriate management of low-risk PTCs. Additionally, MAeSTro-EXP involves a multinational collaboration between South Korea and Australia. This cross-country study aims to identify cultural and racial differences in the management of low-risk PTC, thereby enriching the global understanding of AS practices and their applicability across diverse populations.

Purpose: Shared decision-making (SDM) is a collaborative process in which patients and healthcare providers exchange medical information regarding treatment options and reach an optimal decision that reflects the patient’s values and preferences. In managing low-risk thyroid cancer, both surgery and active surveillance are valid treatment options, making SDM essential.Current Concepts: Although both surgery and active surveillance for low-risk thyroid cancer yield excellent long-term outcomes, each option has distinct advantages and disadvantages. The optimal treatment choice depends on the patient’s unique values, preferences, and clinical circumstances. Effective SDM in this setting requires active engagement from both patients and healthcare providers, interactive information exchange regarding treatment options and patient preferences, integration of patient values into the decision-making process, and, ultimately, agreement on a mutually acceptable treatment plan. To facilitate this process in practice, we applied a six-step SDM model and developed decision aids specifically tailored for patients with low-risk thyroid cancer.Discussion and Conclusion: SDM is expected to improve patient satisfaction with both the decision-making process and treatment outcomes, while reducing unnecessary interventions and decisional regret, thereby advancing truly patient-centered care.

INTRODUCTION:Radiofrequency ablation (RFA) is widely used to treat benign thyroid nodules. While pre-ablation cancer screenings can produce false-negatives, tumor seeding is rare, and aggressive thyroid cancers following RFA have not been reported. We present two cases of potential track seeding after RFA. CASE REPORT:The first patient is a 56-year-old female who developed anaplastic thyroid cancer, while the second is a 61-year-old female diagnosed with poorly differentiated thyroid cancer. Both had a history of RFA for presumed benign nodules, with cancer identified at the intervention sites. CONCLUSION:These cases highlight the need for thorough pre-treatment biopsy and careful needle handling during RFA. To date, no cases of cancer directly induced by RFA have been reported; therefore, it is likely that thyroid carcinoma coexisted at the time of the procedure or developed afterward. Early detection through regular follow-up could have prevented progression to aggressive disease. This emphasizes the importance of diligent post-procedural monitoring.

Figure S1-S10, Table S1-S10, Supplementary Methods

Background: Delayed postoperative hyponatremia (DPH) is the most common cause of readmission after pituitary surgery. In this study, we aimed to evaluate the cutoff values of serum copeptin and determine the optimal timing for copeptin measurement for the prediction of the occurrence of DPH in patients who undergo endoscopic transsphenoidal approach (eTSA) surgery and tumor resection.Methods: This was a prospective observational study of 73 patients who underwent eTSA surgery for pituitary or stalk lesions. Copeptin levels were measured before surgery, 1 hour after extubation, and on postoperative days 1, 2, 7, and 90.Results: Among 73 patients, 23 patients (31.5%) developed DPH. The baseline ratio of copeptin to serum sodium level showed the highest predictive performance (area under the curve [AUROC], 0.699), and its optimal cutoff to maximize Youden’s index was 2.5×10–11, with a sensitivity of 91.3% and negative predictive value of 92.0%. No significant predictors were identified for patients with transient arginine vasopressin (AVP) deficiency. However, for patients without transient AVP deficiency, the copeptin-to-urine osmolarity ratio at baseline demonstrated the highest predictive performance (AUROC, 0.725). An optimal cutoff of 6.5×10–12 maximized Youden’s index, with a sensitivity of 92.9% and a negative predictive value of 94.1%.Conclusion: The occurrence of DPH can be predicted using baseline copeptin and its ratio with serum sodium or urine osmolarity only in patients without transient AVP deficiency after pituitary surgery.

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Background: The diagnostic accuracy of preoperative radiologic findings in predicting the tumor characteristics and clinical outcomes of papillary thyroid microcarcinoma (PTMC) was evaluated across all risk groups.Methods: In total, 939 PTMC patients, comprising both low-risk and non-low-risk groups, who underwent surgery were enrolled. The preoperative tumor size and lymph node metastasis (LNM) were evaluated by ultrasonography within 6 months before surgery and compared with the postoperative pathologic findings. Discrepancies between the preoperative and postoperative tumor sizes were analyzed, and clinical outcomes were assessed.Results: The agreement rate between radiological and pathological tumor size was approximately 60%. Significant discrepancies were noted, including an increase in tumor size in 24.3% of cases. Notably, in 10.8% of patients, the postoperative tumor size exceeded 1 cm, despite being initially classified as 0.5 to 1.0 cm based on preoperative imaging. A postoperative tumor size >1 cm was associated with aggressive pathologic factors such as multiplicity, microscopic extrathyroidal extension, and LNM, as well as a higher risk of distant metastasis. In 30.1% of patients, LNM was diagnosed after surgery despite not being suspected before the procedure. This group was characterized by smaller metastatic foci and lower risks of distant metastasis or recurrence than patients with LNM detected both before and after surgery.Conclusion: Among all risk groups of PTMCs, a subset showed an increase in tumor size, reaching 1 cm after surgery. These cases require special consideration due to their association with adverse clinical outcomes, including an elevated risk of distant metastasis.

AbstractPurpose: Thyroid cancer metabolic characteristics vary depending on the molecular subtype determined by mutational status. We aimed to investigate the molecular subtype-specific metabolic characteristics of thyroid cancers. Experimental Design: An integrative multi-omics analysis was conducted, incorporating transcriptomics, metabolomics, and proteomics data obtained from human tissues representing distinct molecular characteristics of thyroid cancers: BRAF-like (papillary thyroid cancer with BRAFV600E mutation; PTC-B), RAS-like (follicular thyroid cancer with RAS mutation; FTC-R), and ATC-like (anaplastic thyroid cancer with BRAFV600E or RAS mutation; ATC-B or ATC-R). To validate our findings, we employed tissue microarray of human thyroid cancer tissues and performed in vitro analyses of cancer cell phenotypes and metabolomic assays after inducing genetic knockdown. Results: Metabolic properties differed between differentiated thyroid cancers of PTC-B and FTC-R, but were similar in dedifferentiated thyroid cancers of ATC-B/R, regardless of their mutational status. Tricarboxylic acid (TCA) intermediates and branched-chain amino acids (BCAA) were enriched with the activation of TCA cycle only in FTC-R, whereas one-carbon metabolism and pyrimidine metabolism increased in both PTC-B and FTC-R and to a great extent in ATC-B/R. However, the protein expression levels of the BCAA transporter (SLC7A5) and a key enzyme in one-carbon metabolism (SHMT2) increased in all thyroid cancers and were particularly high in ATC-B/R. Knockdown of SLC7A5 or SHMT2 inhibited the migration and proliferation of thyroid cancer cell lines differently, depending on the mutational status. Conclusions: These findings define the metabolic properties of each molecular subtype of thyroid cancers and identify metabolic vulnerabilities, providing a rationale for therapies targeting its altered metabolic pathways in advanced thyroid cancer.

Background: We explored the utility of a small multi-gene DNA panel for assessing molecular profiles of thyroid nodules and influencing clinical decisions by comparing outcomes between tested and untested nodules.Methods: Between April 2022 and May 2023, we prospectively performed fine-needle aspiration (FNA) with gene testing via DNA panel of 11 genes (BRAF, RAS [NRAS, HRAS, KRAS], EZH1, DICER1, EIF1AX, PTEN, TP53, PIK3CA, TERT promoter) in 278 consecutive nodules (panel group). Propensity score-matching (1:1) was performed with 475 nodules that consecutively underwent FNA without gene testing between January 2021 and December 2021 (control group).Results: In the panel group, positive call rate for mutations was 41.7% (BRAF 16.2%, RAS 12.6%, others 11.5%, double mutation 1.4%) for all nodules, and 40.0% (BRAF 4.3%, RAS 19.1%, others 15.7%, double mutation 0.9%) for indeterminate nodules. Benign call rate was 69.8% for all nodules, and 75.7% for indeterminate nodules. In four nodules, additional TP53 (in addition to BRAF or EZH1) or PIK3CA (in addition to BRAF or TERT) mutations were co-detected. Sensitivity, specificity, positive predictive value, and negative predictive value were 80.0%, 53.3%, 88.1%, 38.1% for all nodules, and 78.6%, 45.5%, 64.7%, 62.5% for indeterminate nodules, respectively. Panel group exhibited lower surgical resection rates than the control group for all nodules (27.0% vs. 52.5%, P<0.001), and indeterminate nodules (23.5% vs. 68.2%, P<0.001). Malignancy risk was significantly different between the panel and control groups (81.5% vs. 63.9%, P=0.008) for all nodules.Conclusion: Our panel aids in managing thyroid nodules by providing information on malignancy risk based on mutations, potentially reducing unnecessary surgery in benign nodules or patients with less aggressive malignancies.

Aim/IntroductionWe assess the efficacy of artificial intelligence (AI)-based, fully automated, volumetric body composition metrics in predicting the risk of diabetes.Materials and MethodsThis was a cross-sectional and 10-year retrospective longitudinal study. The cross-sectional analysis included health check-up data of 15,330 subjects with abdominal computed tomography (CT) images between January 1, 2011, and September 30, 2012. Of these, 10,570 subjects with available follow-up data were included in the longitudinal analyses. The volume of each body segment included in the abdominal CT images was measured using AI-based image analysis software.ResultsVisceral fat (VF) proportion and VF/subcutaneous fat (SF) ratio increased with age, and both strongly predicted the presence and risk of developing diabetes. Optimal cut-offs for VF proportion were 24% for men and 16% for women, while VF/SF ratio values were 1.2 for men and 0.5 for women. The subjects with higher VF/SF ratio and VF proportion were associated with a greater risk of having diabetes (adjusted OR 2.0 [95% CI 1.7-2.4] in men; 2.9 [2.2-3.9] in women). In subjects with normal glucose tolerance, higher VF proportion and VF/SF ratio were associated with higher risk of developing prediabetes or diabetes (adjusted HR 1.3 [95% CI 1.1-1.4] in men; 1.4 [1.2-1.7] in women). These trends were consistently observed across each specified cut-off value.ConclusionsAI-based volumetric analysis of abdominal CT images can be useful in obtaining body composition data and predicting the risk of diabetes.

Background: We aimed to assess how the ageing-related changes in body composition contribute to the prevalence and incidence of diabetes using artificial intelligence (AI)-based analysis of abdominal computed tomography (CT) images. Methods: In this retrospective cohort study, we identified 15330 subjects age ≥18 years with abdominal CT scans at baseline, who underwent medical checkup at Seoul National University Hospital Healthcare System Gangnam Center from January 1, 2011 to September 30, 2012. Of these, 11693 subjects with available follow-up data were included in the longitudinal analysis. The volume of each body segment involved in abdominal CT images was measured by using an AI-based image analysis software. Findings: The ratio of visceral fat to subcutaneous fat (VF/SF ratio) increased with ageing. The optimal cutoffs of VF/SF ratio to predict the prevalence of diabetes were 1.2 and 0.5 in men and women, respectively. A VF/SF ratio over the cutoff was associated with a higher prevalence of diabetes (age and BMI adjusted OR 2.1, [95% CI 1.8-2.4] in men; 3.1, [2.4-3.9] in women). The same cutoffs of VF/SF ratio were used to predict incident diabetes in ten years of follow-up. Subjects with normal glucose tolerance at baseline who had higher VF/SF ratio had increased risk of progression to prediabetes or diabetes (age and BMI adjusted HR 1.2, [95% CI 1.1-1.4] in men; 1.4, [1.2-1.6] in women). Subjects with prediabetes at baseline who had higher VF/SF ratio also more frequently progressed to diabetes (age and BMI adjusted HR 1.4, [95% CI 1.2-1.6] in men; 1.8, [1.5-2.3] in women). Interpretation: VF/SF ratio in the abdomen change with ageing and are associated with the prevalence and future incidence of diabetes. AI-based analysis of abdominal CT images may help easily obtain body composition data to clinically assess the risk of incident diabetes. Disclosure Y.Kim: None. H.Son: None. J.Yoon: None. H.Choe: None. T.Oh: None. Y.Cho: Consultant; LG Chem. Funding Ministry of Health & Welfare, Republic of Korea