Daily Endocrinology Research Analysis

BACKGROUND: Lipoprotein(a) [Lp(a)] is a risk factor for coronary heart disease. Whether baseline Lp(a) identifies higher risk patients who derive more benefit from evolocumab is not established in a population without prior myocardial infarction (MI) or stroke. METHODS: From June 2019 to November 2021, the VESALIUS-CV trial enrolled patients with qualifying atherosclerosis or high-risk diabetes, without prior MI or stroke and randomized them to evolocumab or placebo (median follow-up 4.6 years). In a prespecified analysis, Lp(a) was assessed at baseline in 7557 patients. Cox models were used to assess the adjusted risk of cardiovascular events by baseline Lp(a) in the placebo arm, and the efficacy of evolocumab by baseline Lp(a). The primary outcome of interest was the composite of major coronary events (coronary heart disease death, MI or urgent coronary revascularization). RESULTS: Median age was 66 [interquartile range 60-71] years and 42.8% were women; median Lp(a) was 28 (interquartile range 9-132) nmol/L. Higher baseline Lp(a) was associated with an increased risk of major coronary events (HR CONCLUSIONS: In patients with atherosclerosis or high-risk diabetes but without prior MI or stroke, Lp(a) was independently associated with an increased risk of major coronary events, but not ischemic stroke. Evolocumab reduced the relative risk of major coronary events to a similar degree irrespective of baseline Lp(a), with a numerically greater absolute risk reduction in patients with elevated Lp(a).

BACKGROUND: Dynamic risk stratification (DRS) enables response-adapted follow-up in differentiated thyroid carcinoma (DTC). However, prospective data supporting surveillance de-escalation and discharge to primary care (PC) after an excellent response (ER) is lacking. METHODS: We conducted a longitudinal cohort study with a prospectively conducted postdischarge reassessment, including 154 patients with DTC treated with total thyroidectomy, with or without radioiodine ablation, who completed ≥5 years of specialist follow-up and were discharged to PC after achieving ER. A small subset ( RESULTS: Baseline American Thyroid Association-2025 recurrence risk was low 78/154 (50.6%), intermediate-low 44/154 (28.6%), intermediate-high 23/154 (14.9%), and high 9/154 (5.8%). ER increased from 118/154 (76.6%) at 6 months to 145/154 (94.2%) at predischarge and to 148/154 (96.1%) at postdischarge reassessment ( CONCLUSIONS: Our findings suggest that patients with DTC who complete ≥5 years of specialist follow-up and achieve ER may be safely transitioned to PC. ER represents a stable clinical state, with minimal risk of clinically meaningful recurrence even with markedly reduced biochemical and imaging surveillance. These findings support a response-adapted long-term care model centered in PC, focused on thyroid hormone replacement and TSH monitoring. Further studies are warranted to confirm these findings.

BACKGROUND: Artificial Intelligence (AI)-based opportunistic risk stratification solutions can help to counter rising fragility fracture rates. Existing tools estimate bone mineral density (BMD) alone, while the present study incorporates trabecular bone score (TBS), a surrogate of bone microarchitecture, more closely mirroring fracture pathophysiology. We aimed to evaluate the performance of an AI tool that estimates bone fragility directly from standard radiographs to identify individuals at highest risk of fracture. METHODS: This retrospective, multinational cohort study included 18,858 paired radiographs and lumbar spine dual-energy X-ray absorptiometry (DXA) scans from adult patients (aged at least 20 years) from three clinical sites in Europe and two sites in the United States. Routine clinical radiographs of the spine, abdomen, chest, or pelvis acquired in the anteroposterior or posteroanterior view and including visualisation of the lumbar spine were included. Eligible radiographs had an in-plane spatial resolution of ≤0.2 mm per pixel, independent of vendor, and had a corresponding DXA examination within 6 months, and included at least two lumbar vertebrae (L1-L4). The AI model training used a composite Bone Fragility Index, combining TBS and BMD. Training, internal validation and testing was performed on two European sites (n = 10,692; Italy and Austria); and external validation involved three sites with ethnically diverse populations (n = 7079): Slovakia, US site 1 (Wisconsin) and US site 2 (New York). Model performance for identifying very high bone fragility (characterised by degraded TBS and osteoporosis) prioritised specificity (as per the intended clinical use to prioritise low false-positive rates) and was evaluated with accuracy, sensitivity, specificity, AUC and precision. FINDINGS: Between Jan 1, 2010 and Dec 31, 2023, 18,858 paired radiographs and lumbar spine dual-energy X-ray absorptiometry (DXA) scans from 11,138 participants across five international sites were retrospectively aggregated. Internal testing on two European sites demonstrated an accuracy of 0.86 (95% CI: 0.78, 0.92), specificity of 0.93 (0.85, 0.99), and sensitivity of 0.53 (0.41, 0.67). External testing on three sites demonstrated consistently high specificity of 0.88 (0.77, 0.99) in the European cohort, 0.94 (0.91, 0.97) in the American White dataset, and 0.96 (0.81, 0.99) in the American Non-White. External sensitivity ranged from 0.53 (0.41, 0.67) to 0.64 (0.50, 0.88). INTERPRETATION: The proposed approach provides rapid identification of individuals with very high bone fragility from routine radiographs. Its specificity across diverse populations supports clinical use for opportunistic osteoporosis screening in real-world settings. Future work should assess the model's performance in more sex-balanced cohorts without prior DXA assessment, and evaluate its ability to predict incident fractures. FUNDING: The Swiss National Science Foundation, the Foundation of the Orthopaedic Hospital of the Vaudois University Hospital (Lausanne, Switzerland), and Medimaps Group SA, Switzerland.