Search Results (6,072)

Mastectomy with immediate breast reconstruction (IBR) has become increasingly common, driven by advances in mastectomy techniques and the growing number of autologous, implant-based, and hybrid reconstructive options. The expansion of reconstructive strategies has introduced greater complexity in surgical planning and outcomes. Postoperative outcomes are influenced not only by mastectomy-related complications but also by complications specific to each reconstructive technique. Consequently, each combination of mastectomy and reconstructive modality carries unique benefits, limitations, and complication profiles that must be evaluated in the context of patient-specific factors, such as comorbidities, oncologic treatment, and reconstructive goals. This narrative review aims to characterize complication profiles associated with mastectomy and IBR to support improved perioperative management and individualized surgical care. To enhance clinical utility, this review includes representative clinical images that demonstrate variability in complication presentation across surgical approaches and patient contexts. This review presents a framework for understanding the factors that influence patient candidacy and outcomes in the setting of mastectomy with IBR, thereby supporting improved risk stratification and tailored treatment algorithms. Full article

Background: Radiation maculopathy (RM) is a common complication after radiotherapy for intraocular tumors, causing permanent visual loss. Intravitreal dexamethasone (IV DEX) is effective for macular edema (ME) resolution, but functional outcomes remain highly heterogeneous. This study aimed to assess the prognostic utility of CEA classification on visual outcomes following IV DEX based on three parameters: largest cyst diameter (C), ellipsoid zone (EZ) disruption (E) and retinal pigment epithelium (RPE) atrophy (A). Methods: A retrospective analysis of 50 patients with RM secondary to Iodine-125 brachytherapy treated with IV DEX was performed. Best-corrected visual acuity (BCVA) and CEA parameters were analyzed with OCT before and after treatment. Results: IV DEX induced significant anatomical improvement (mean Δcyst: −203.3 μm, p < 0.001), regardless of functional response. Visual outcomes were related to baseline CEA stratification: eyes with intact outer retina gained +6.4 ETDRS letters (p = 0.002); eyes with EZ disruption demonstrated stability (−2.8 letters; p = 0.183); eyes with RPE atrophy significantly worsened (−11.7 letters; p = 0.014). Baseline RPE atrophy was the strongest negative predictor (p = 0.007). Subgroup analysis of patients with intact outer retina identified a baseline BCVA cutpoint of ≤70 letters to predict clinically significant visual improvement (≥5 letters), demonstrating a functional ceiling effect. Conclusions: The CEA classification effectively stratifies functional response in RM. EZ disruption and RPE atrophy identify eyes unlikely to achieve visual improvement despite a reduction in ME, aiding clinical decision-making and establishing functional expectations while promoting a fundamental shift from an edema-driven management to a biomarker-centered approach. Full article

We consider sequential monitoring in early-phase clinical trial designs with patient noncompliance. Unlike most existing methods that focus on modeling a single endpoint, we consider the entire outcome distribution and propose a Bayesian causal effect estimation method based on the Wasserstein distance. Using a principal stratification framework and random forest to classify patient compliance statuses, we quantify a distributional causal-effect magnitude among Compliers by calculating the Wasserstein distance between the barycenters of their potential outcome distributions. The proposed method also adaptively determines early trial termination based on accumulated outcomes. Simulation results show favorable operating characteristics under the scenarios investigated, with the proposed analysis remaining close to the oracle benchmark as noncompliance increases. Full article

Background: Estimated pulse wave velocity (ePWV) is a non-invasive marker of arterial stiffness with potential relevance for chronic kidney disease (CKD) risk assessment. This study aimed to investigate the association between ePWV and CKD risk in the US and Chinese populations and to evaluate its discriminative performance using machine learning approaches. Methods: Data were obtained from the National Health and Nutrition Examination Survey (NHANES, 2005–2018, weighted n ≈ 100.9 million) and the China Health and Retirement Longitudinal Study (CHARLS, 2011–2015, n = 21,853). In NHANES, CKD was defined according to the 2021 KDIGO criteria as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m² or a urinary albumin-to-creatinine ratio (ACR) ≥ 30 mg/g; in CHARLS, where urinary albumin was not measured, CKD was defined by the eGFR criterion alone, and this difference in case definition was taken into account when interpreting the results. Logistic regression and restricted cubic spline models were used to examine the association between ePWV and CKD, with subgroup analyses stratified by demographic and clinical characteristics. Multiple machine learning models were developed in NHANES and externally validated in CHARLS; model discrimination was assessed using the area under the receiver operating characteristic curve (AUROC), and feature importance was interpreted using SHapley Additive exPlanations (SHAP) values. Results: Higher ePWV was consistently associated with higher odds of CKD in both cohorts (NHANES: odds ratio [OR] = 1.505 per 1 m/s, 95% confidence interval [CI] = 1.459–1.552; CHARLS: OR = 1.434 per 1 m/s, 95% CI = 1.368–1.503; both p < 0.0001), with dose–response relationships observed. Formal interaction tests confirmed significant effect modification by sex (both cohorts) and by BMI and diabetes (NHANES only). Point estimates were higher in men and in NHANES obese and diabetic subgroups, but interactions across smoking and alcohol strata were not statistically significant. Among the machine learning models evaluated, discrimination was moderate and comparable across algorithms (LightGBM: AUROC = 0.804 in internal validation and 0.793 in external validation), and DeLong tests showed no significant difference between LightGBM and XGBoost (p = 0.0655 and 0.0684, respectively). SHAP analysis identified ePWV as the highest-ranking feature, surpassing uric acid, lipid levels, and diabetes history. Using the Youden index, the optimal ePWV cutoff for identifying CKD was 10.15 m/s in NHANES (sensitivity 0.658, specificity 0.695) and 10.568 m/s in CHARLS (sensitivity 0.658, specificity 0.718). Conclusions: Elevated ePWV is significantly associated with eGFR-defined CKD across the US and Chinese populations studied. These findings support ePWV as a potentially useful marker for CKD risk stratification; however, given the cross-sectional design of both cohorts, its predictive value requires confirmation in prospective studies. It is important to emphasize that no non-invasive calculated metric can replace direct measurement of serum creatinine and urinalysis for identifying individuals at risk of CKD in routine clinical practice. Full article

Although gut microbiota dysbiosis is associated with coronary artery disease (CAD), its dynamic alterations across distinct clinical stages remain largely unexplored. This study aimed to identify stage-specific microbial biomarkers across the CAD spectrum. We analyzed 16S rRNA sequencing data from 306 subjects, including non-CAD controls and patients with mild coronary stenosis (MCS), stable angina (SA), unstable angina (UA), and acute myocardial infarction (AMI). Diversity analyses, machine learning, and SPIEC-EASI network modeling were applied to evaluate microbial community shifts and identify core diagnostic markers. Significant structural remodeling of the gut microbiota was observed, particularly during the AMI stage. Veillonella, Intestinimonas, and Streptococcus were identified as the core biomarkers for the MCS, SA, and UA/AMI stages, respectively. From the MCS to the UA stage, ecological network connectivity continuously declined, highlighting critical barrier vulnerability, before transitioning into an antagonism-dominated network in the AMI stage. Machine learning models effectively discriminated among the various disease stages, and key microbial markers remained statistically robust after adjusting for major clinical confounders. The gut microbiota exhibits dynamic, stage-specific dysbiosis throughout CAD progression, providing promising non-invasive biomarkers for precise disease staging and diagnosis. Full article

Currently, no validated population-based screening strategy uses persistent oral high-risk human papillomavirus (HPV) detection to identify individuals at clinically meaningful risk of HPV-associated oropharyngeal squamous cell carcinoma (OPSCC). This narrative review evaluates sampling strategies, virologic assays, liquid-biopsy biomarkers, adjunct biomarkers, and integrative approaches across three clinical contexts: longitudinal oral HPV assessment; diagnostic evaluation and HPV attribution in suspected or newly diagnosed OPSCC; and treatment monitoring and post-treatment surveillance in established HPV-associated OPSCC. The literature published from January 2020 through June 2026 and identified in PubMed was synthesized by clinical context, specimen source, biological target, study population, and intended use. Repeated detection of the same high-risk HPV genotype in serial oral specimens provides a pragmatic measure of persistent oral HPV detection but does not establish uninterrupted infection, sustained viral oncogene transcription, malignant transformation, or future OPSCC risk. Oral HPV DNA, oral E6/E7 mRNA, tumor-tissue HPV testing, and plasma circulating tumor HPV DNA represent distinct biological and clinical signals. Plasma circulating tumor HPV DNA has the most mature evidence for treatment monitoring and post-treatment surveillance in established HPV-associated OPSCC, not population screening. Clinical translation requires standardized longitudinal protocols, validation in intended-use populations, actionable management pathways, and incremental predictive value for proposed risk-stratification models. Full article

Background/Objectives: Cerebral small vessel disease (CSVD) is an established determinant of functional outcome after stroke; however, its influence on the longitudinal course of neuropsychiatric recovery remains incompletely understood. We aimed to investigate the association between MRI-defined CSVD burden and longitudinal depressive symptom trajectories, as well as cognitive, anxiety, and functional outcomes after stroke. Methods: This prospective longitudinal cohort study included consecutive patients with acute ischemic stroke or intracerebral hemorrhage treated at a tertiary comprehensive stroke center between January 2022 and November 2025. CSVD burden was quantified using a validated MRI-based Global CSVD Score and categorized as none-to-mild (0–1) or moderate-to-severe (2–4). Depressive symptoms were assessed using the 17-item Hamilton Depression Rating Scale (HDRS-17), anxiety using the Generalized Anxiety Disorder-7 (GAD-7), cognitive function using the Montreal Cognitive Assessment (MoCA), and functional outcome using the modified Rankin Scale (mRS) at baseline and 3, 6, and 9–12 months after stroke. Longitudinal depressive symptom trajectories were evaluated using linear mixed-effects models, and multivariable logistic regression was used to assess associations with incident and persistent post-stroke depression, cognitive impairment, and functional outcome. Results: Of 550 participants enrolled, 480 (87.3%) underwent brain MRI and were included in the MRI-defined CSVD analyses. Moderate-to-severe CSVD burden was associated with a less favorable longitudinal depressive symptom trajectory, reflected by a significant CSVD burden × time interaction (β = 0.68, 95% CI 0.25–1.11; p = 0.002). Moderate-to-severe CSVD burden was also independently associated with poor functional outcome (adjusted odds ratio [aOR] 1.56, 95% confidence interval [CI] 1.04–2.32; p = 0.030). In contrast, baseline CSVD burden was not independently associated with incident post-stroke depression, persistent post-stroke depression, or global cognitive impairment after multivariable adjustment. Conclusions: Greater MRI-defined CSVD burden was associated with a less favorable longitudinal depressive symptom trajectory and poorer functional recovery after stroke, whereas it was not independently associated with incident or persistent post-stroke depression or global cognitive impairment. These findings suggest that cumulative MRI-defined CSVD burden may provide complementary information regarding longitudinal emotional and functional recovery, although its incremental prognostic value and clinical utility require further validation. Multicenter studies with longer follow-up and quantitative neuroimaging are needed to confirm these associations and determine whether CSVD assessment improves risk stratification and individualized post-stroke monitoring and rehabilitation. Full article

Immune checkpoint inhibitors (ICIs) have transformed the treatment of several malignancies, but their efficacy and gastrointestinal (GI) immune-related adverse events (irAEs) vary considerably among patients. Growing evidence suggests that the gut microbiota and intestinal barrier integrity are associated with both therapeutic response and toxicity, although their causal contribution and clinical utility remain incompletely defined. Dysbiosis may disrupt epithelial homeostasis, increase intestinal permeability, and promote mucosal immune activation, potentially influencing antitumor immunity and susceptibility to ICI-induced colitis. Current biomarkers of intestinal permeability are limited by their indirect nature, lack of standardization, or poor spatial resolution. Probe-based confocal laser endomicroscopy (pCLE) enables real-time, in vivo visualization of epithelial abnormalities associated with barrier dysfunction, including fluorescein leakage, epithelial gaps, and cell shedding. In parallel, dietary and microbiota-directed interventions are being investigated as potential strategies to modulate intestinal homeostasis during ICI therapy, although evidence for their clinical benefit and effects on intestinal barrier integrity remains limited. This review summarizes current evidence linking the gut microbiota, intestinal barrier dysfunction, and cancer immunotherapy and discusses the potential role of pCLE as a candidate functional imaging tool in ICI-treated patients. We highlight current knowledge gaps and propose a translational framework integrating pCLE with microbiome profiling, circulating biomarkers, histopathology, and clinical outcomes to determine whether this approach can improve patient stratification and the early identification and monitoring of GI toxicity. Full article

Background: Periodontal charting remains the gold standard for periodontal diagnosis but is time-consuming, operator-dependent, and not always feasible in routine clinical practice. In contrast, panoramic radiographs are widely available and increasingly amenable to automated analysis through artificial intelligence (AI). Recent AI systems have demonstrated high accuracy in the detection and quantification of periodontal bone loss and in radiographic disease staging. Objective: To review current evidence on AI applications in periodontal radiology and explore whether routinely acquired panoramic radiographs may support the development of AI-assisted radiographic periodontal charting. Methods: This narrative review summarizes recent evidence regarding AI-based periodontal and peri-implant radiographic assessment. The literature was identified through searches of PubMed, Scopus, and Google Scholar using combinations of terms related to artificial intelligence, periodontal disease, radiographic diagnosis, panoramic radiography, and peri-implantitis, with emphasis on studies relevant to AI-assisted periodontal assessment. Results: Recent deep learning architectures have demonstrated high diagnostic performance for periodontal bone loss detection, quantification, and disease staging. Current AI systems are capable of identifying radiographic manifestations of periodontal destruction and localising key anatomical landmarks, including the cemento-enamel junction and the alveolar bone crest. However, they remain unable to assess clinical parameters such as probing depth, bleeding on probing, suppuration, and tooth mobility. The review highlights the distinction between Clinical Attachment Level (CAL), Clinical Attachment Loss (CALoss), and radiographic measures of periodontal destruction. In this context, the concept of Radiographic Attachment Loss (RAL) is proposed as the radiographically measurable distance between the cemento-enamel junction and the alveolar bone crest, providing a conceptual estimate of periodontal support loss. Discussion: The principal novelty of this review is the proposal of AI-assisted radiographic periodontal charting as a clinically oriented framework for translating AI-derived radiographic information into meaningful periodontal assessment. Rather than replacing conventional periodontal charting, this approach aims to transform routinely acquired panoramic radiographs into a source of structured periodontal information that may support screening, risk stratification, longitudinal monitoring, and clinical decision-making. Conclusions: Artificial intelligence is unlikely to replace conventional periodontal examination. Nevertheless, AI-assisted radiographic periodontal charting may represent a realistic intermediate step between routine radiographic interpretation and comprehensive digital periodontal assessment. Future research should focus on validating AI-derived radiographic biomarkers, particularly Radiographic Attachment Loss (RAL), determining their clinical utility, and evaluating their potential contribution to periodontal screening, longitudinal monitoring and population-based oral health assessment. Full article

Acute coronary syndromes (ACS) remain a major cause of cardiovascular mortality and long-term morbidity worldwide despite substantial advances in revascularization techniques, antithrombotic therapies, and lipid-lowering strategies. Although contemporary management has significantly improved survival, a considerable residual risk of recurrent ischemic events, adverse ventricular remodelling, heart failure, and cardiovascular death persists following ACS. Consequently, accurate risk stratification has become increasingly important to guide individualized secondary prevention and optimize long-term outcomes. Traditional risk assessment after ACS relies largely on clinical characteristics, conventional cardiovascular risk factors, and established risk scores such as GRACE and TIMI. However, growing evidence indicates that several emerging biomarkers and pathophysiological pathways provide additional prognostic information beyond conventional models. These markers reflect distinct biological processes including residual inflammatory activity, myocardial injury and remodelling, renal dysfunction, metabolic impairment, and visceral adiposity. This review summarizes current evidence regarding both established and emerging prognostic markers after ACS across six major pathophysiological domains: lipid abnormalities and traditional cardiovascular risk factors; systemic inflammation and residual inflammatory risk; myocardial injury and adverse ventricular remodelling; cardiorenal dysfunction; visceral adiposity; and anti-inflammatory therapeutic strategies. For each domain, we discuss underlying biological mechanisms, key findings from major clinical trials and registries, and the potential incremental value of biomarkers in contemporary risk stratification. Collectively, these markers support a multidimensional approach to post-ACS risk assessment and may contribute to more personalized therapeutic strategies aimed at reducing residual cardiovascular risk. Full article

Obesity, type 2 diabetes (T2D), metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction-associated steatohepatitis (MASH), and dual-etiology metabolic dysfunction-associated alcohol-related liver disease (MetALD) form an overlapping metabolic dysfunction spectrum, rather than a single linear disease sequence. Proteomics offers a functional readout of this spectrum by measuring proteins, proteoforms, and protein species involved in tissue injury, inflammation, metabolic stress, and inter-organ communication. This review asks how proteomic data can support mechanism-based stratification, rather than simply generate disease-associated signatures. We summarize advances in circulating and tissue-based proteomics across obesity, T2D, MASLD/MASH, and MetALD, highlighting shared and disease-specific pathways such as mitochondrial dysfunction, extracellular matrix remodeling, immune activation, proteostasis stress, and endocrine crosstalk. We emphasize that proteomic clusters should be considered candidate endotypes only when they are reproducible, mechanistically coherent, linked to tissue or causal evidence, and clinically informative. We also evaluate bioinformatics and biostatistical strategies needed for reliable interpretation, including preprocessing, missing-data handling, normalization, longitudinal modeling, multi-omics integration, protein quantitative trait locus (pQTL) analysis, colocalization, and Mendelian randomization. Finally, we discuss how proteoforms, post-translational modifications (PTMs), and platform-dependent proteome complexity shape interpretation. Together, these concepts provide practical guidance for moving from proteomic signatures to candidate endotypes and for prioritizing clinically useful biomarkers and therapeutic targets. Full article

Background: Cardiovascular magnetic resonance (CMR) is the reference non-invasive imaging modality for evaluating cardiomyopathies, providing comprehensive assessment of cardiac morphology, function, and tissue characterization. Radiomics has recently emerged as an advanced image analysis technique that extracts quantitative imaging biomarkers from routine CMR images, potentially enhancing disease characterization beyond conventional visual assessment. This systematic review summarizes current evidence on the role of CMR-based radiomics in the diagnosis, phenotypic characterization and risk stratification of cardiomyopathies. Methods: A structured search identified English-language, peer-reviewed studies published up to August 2026 investigating CMR-based radiomics in hypertrophic, dilated, arrhythmogenic, and infiltrative cardiomyopathies, including cardiac amyloidosis, Fabry disease, and cardiac sarcoidosis. Study selection followed PRISMA 2020 guidelines. Methodological quality was assessed using the Methodological Index for Non-Randomized Studies (MINORS) and the Radiomics Quality Score 2.0 (RQS 2.0) The authors have reviewed and edited the output and take full responsibility for the content of this publication. Results: Current evidence indicates that CMR radiomics provides incremental diagnostic information beyond conventional CMR by quantifying myocardial tissue heterogeneity. Radiomic features derived from cine imaging, late gadolinium enhancement, native T1/T2 mapping, and extracellular volume maps showed promising performance in differentiating cardiomyopathy subtypes, distinguishing pathological from physiological remodeling, and identifying infiltrative and inflammatory myocardial diseases. Multiparametric radiomic models generally outperformed individual imaging biomarkers, with preliminary evidence supporting applications in risk stratification and outcome prediction. However, studies were predominantly retrospective, involved small cohorts, used heterogeneous imaging and radiomics workflows, and rarely included external validation. RQS 2.0 assessment demonstrated low-to-moderate methodological quality. Conclusions: CMR-based radiomics shows considerable potential for improving diagnosis and phenotypic characterization of cardiomyopathies. However, methodological standardization, multicenter prospective validation, and demonstration of incremental clinical value are required before routine clinical implementation. Full article

Inflammatory bowel disease (IBD), encompassing Crohn’s disease (CD) and ulcerative colitis (UC), is a chronic relapsing intestinal inflammatory disorder driven by complex interactions among host genetics, immune dysregulation, and gut microbiota dysbiosis. The functional contribution of microbial metabolites beyond short-chain fatty acids and bile acids remains incompletely understood. A critical and unresolved question is whether microbiota-associated amino acid metabolites are merely passive indicators of dysbiosis or active drivers of intestinal inflammation and tissue repair. In this review, we summarize recent advances in the roles of microbiota-associated amino acid metabolites and their derivatives in IBD, highlighting that amino acid metabolites constitute a functionally distinct class of bioactive signaling molecules that operate through four representative metabolic networks: tryptophan metabolism, aspartate-related metabolism, branched-chain amino acid (BCAA) metabolism, and arginine–polyamine metabolism. We synthesize recent clinical metabolomic data and identify reductions in tryptophan-derived indoles, glutamate, histidine, and selected BCAAs across IBD clinical cohorts and sample matrices, whereas metabolites such as serine, proline, and polyamine degradation products are frequently elevated or associated with disease activity or therapeutic response. Mechanistically, microbiota-associated amino acid metabolites are implicated in intestinal homeostasis and IBD pathogenesis through multifaceted pathways, including modulation of intestinal epithelial barrier integrity (e.g., cell–cell junction, mucus secretion, and stem cell-driven mucosal repair), innate and adaptive immune responses, host–pathogen interactions, and extraintestinal inflammatory signaling (e.g., systemic inflammation, and brain–gut axis communication). Finally, we highlight current challenges and limitations in achieving a causal understanding of microbiota-associated amino acid metabolic alterations in IBD, particularly regarding their origin, spatial distribution, and functional relevance, as well as their potential utility for disease stratification and treatment-response assessment, and discuss how these insights may inform the future development of next-generation biomarkers and precision therapeutic strategies tailored to individual metabolic phenotypes in IBD. Full article

Background: Immunotherapy has transformed cancer treatment and is widely used in the Chinese mainland. Though advances have been made, immune checkpoint inhibitor-related liver injury (ICILI) remains a significant clinical challenge. Existing risk models commonly lack time-specific risk stratification for ICILI. The present study aimed to develop and validate interpretable machine learning models to predict grade 2 or higher ICILI at multiple time points in patients with gastrointestinal cancer (GC). Methods: This retrospective cohort study encompassed GC patients who commenced their initial ICI medication between January 2019 and June 2023 at Zhongshan Hospital, Fudan University. Five machine learning algorithms, including Logistic Regression, Random Forest, Extreme Gradient Boosting (XGBoost), Gradient Boosting (GradientBoost), and Adaptive Boosting (AdaBoost), were utilized to develop predictive models for grade ≥ 2 ICILI at specific intervals of 3 months, 6 months, and 12 months. The evaluation of model performance was conducted using the area under the curve (AUC), accuracy, precision, recall, and F1-score. The Shapley Additive exPlanations (SHAP) method was employed to assess feature importance and interpret the final model. Results: A total of 1337, 849, and 401 patients were enrolled in the follow-up groups at 3 months, 6 months, and 12 months. The final model for grade ≥ 2 ICILI was developed with GradientBoost and achieved an AUC of 0.769 (95% CI: 0.732–0.806), with a test set accuracy of 0.834. XGBoost yielded AUCs of 0.671 (95% CI: 0.636–0.706) at 3-month indication, 0.678 (95% CI: 0.638–0.718) at 6 months, and 0.644 (95% CI: 0.589–0.699) at 12-month follow-up in the 5-fold cross-validation. The DCA curve demonstrated solid clinical benefit, whereas the calibration curve indicated good predictive reliability. SHAP analysis identified several parameters as predictive features at different intervals, which suggested that the ICILI determinants varied from acute inflammatory to host-related characteristics. Conclusions: A temporal stratification prediction model for grade ≥ 2 ICILI in GC patients was developed and validated at various intervals using ML algorithms with SHAP interpretability. This methodology facilitated early recognition of varying parameters across different treatment phases, enhancing clinical management and elevating treatment outcomes. Full article

The 2026 ACC/AHA Multisociety Dyslipidemia Management Guideline emphasizes the clinical utility of coronary artery calcium (CAC) and recommends integrating incidental CAC from non-ECG-gated chest CT into cardiovascular risk-stratification workflows; nevertheless, such incidental findings remain substantially underutilized in routine clinical practice. Many young-to-middle-aged patients present with acute myocardial infarction as their index cardiovascular event, even though prior chest CT could identify CAC that would trigger statin-based primary prevention. This single-center retrospective observational study enrolled 976 patients undergoing invasive coronary angiography (CAG) who had non-ECG-gated chest CT within the preceding 1 year (2020–2025) to assess cross-sectional associations between incidental CAC burden and angiographic coronary stenosis. CAC burden was categorized as Grade 0–3 using the validated Shemesh ordinal scoring system, with 79.2% of participants being CAC-positive. CAC positivity and obstructive CAD prevalence increased progressively with age. CAC positivity was associated with higher conditional odds of obstructive CAD among younger individuals, while this conditional risk signal was attenuated in older adults. CAC grade showed a step-wise positive correlation with stenosis severity. Following adjustment for age, sex, hypertension, diabetes mellitus, dyslipidemia, and smoking status, CAC was identified by multivariate regression as the strongest independent predictor of obstructive CAD (OR = 11.732, 95% CI: 7.700–17.875, p < 0.001). Among CAD patients, CAC-negative subjects had higher diabetes prevalence, in keeping with the propensity for non-calcified vulnerable plaques in diabetic individuals. Incidental CAC on routine chest CT represents a robust age-modulated biomarker for obstructive CAD, and considerable primary-prevention opportunities are missed in Chinese patients referred for CAG. Opportunistic age-stratified CAC screening assisted by artificial intelligence may improve real-world cardiovascular primary prevention. Full article