Scott, Christopher

BACKGROUND: Although diabetes is a known driver of heart failure with preserved ejection fraction, whether antecedent hyperinsulinemia drives preclinical diastolic dysfunction (DD) progression before overt hyperglycemia remains unknown. Therefore, we investigated whether elevated endogenous insulin and insulin resistance independently predict longitudinal DD progression and adverse cardiovascular outcomes. METHODS: In a community-based cohort study, we evaluated 1191 participants from the Olmsted County Heart Function Study (median age, 58.7 [interquartile range, 52.4–66.6 years]) who underwent echocardiography at baseline and after 4 years. Fasting insulin and homeostatic model assessment of insulin resistance were assessed. The primary end point was DD progression. Secondary outcomes (mortality, heart failure) were assessed in 2030 participants followed for a median of 10.97 (95% CI, 10.90–11.05) years for heart failure and a median of 18.1 (95% CI, 18.0–18.2) years for mortality. RESULTS: DD progression occurred in 7.5% of participants. Baseline fasting insulin and homeostatic model assessment of insulin resistance strongly predicted progression ( P <0.001). In multivariable analysis, every 1-SD increase in log-transformed insulin was associated with 65% increased odds of progression (odds ratio, 1.65 [95% CI, 1.33–2.07]; P <0.001), adjusting for age, creatinine, and NT-proBNP (N-terminal pro-B-type natriuretic peptide). Similar results were observed for homeostatic model assessment of insulin resistance (odds ratio, 1.66 [95% CI, 1.34–2.08]; P <0.001). In secondary analyses, elevated insulin independently predicted all-cause (hazard ratio, 1.11 per 1-SD [95% CI, 1.02–1.21]) and cardiovascular mortality (hazard ratio, 1.16 [95% CI, 1.02–1.33]). Homeostatic model assessment of insulin resistance was similarly predictive of all-cause (hazard ratio, 1.11 [95% CI, 1.03–1.20]) and cardiovascular mortality (hazard ratio, 1.16 [95% CI, 1.02–1.32]). CONCLUSIONS: Elevated endogenous insulin and insulin resistance are predictors of progressive DD and long-term mortality, even after accounting for body mass index. Metabolic dysregulation may drive myocardial stiffening before diabetes onset, identifying a potential early therapeutic window for heart failure with preserved ejection fraction prevention.

Background Prognostic staging systems in transthyretin amyloid (ATTR) cardiomyopathy were developed before the introduction of disease‐modifying treatment. We therefore evaluated contemporary risk stratification according to different staging systems in patients with ATTR cardiomyopathy. Methods All patients with newly diagnosed ATTR cardiomyopathy and baseline biomarker measurements from 2019 to 2023 were included from Mayo Clinic, Rochester, MN. Prognostic outcomes were calculated with Kaplan–Meier analysis for 3e staging systems in the main analysis: the National Amyloidosis Centre (NAC) staging system; a modified Mayo staging system using hsTNT (high‐sensitivity troponin T) cutoff >65 ng/L; the extended NAC model with a fourth stage (NT‐proBNP [N‐terminal pro‐B‐type natriuretic peptide] >10 000 ng/L). Results The study population consisted of 441 patients with a median age of 76 years, 93% were men, 85% were initiated on tafamidis, and 94% had wild‐type ATTR. Median follow‐up was 3.4 years, and 109 deaths occurred during follow‐up. The 3‐year survival for the Mayo stages were 92.3% (88.7–95.9) for stage I, 71.6% (63.0–81.3) for stage II, and 43.5% (32.4–58.5) for stage III. For the NAC system, 3‐year survival were 92.0% (89.0–95.7), 67.5% (59.0–77.3), and 47.0% (34.7–63.5) for stages I, II, and III, respectively. The 3‐year survival by the extended NAC system was 92.0 (69.0–79.2), 69.0% (60.1–79.2), 53.2% (39.9–70.8) and 34.4% (17.4–68.0) for stages I, II, III, and IV, respectively. Conclusions In a contemporary cohort of patients with ATTR cardiomyopathy, the NAC and modified Mayo staging systems remained equally effective in delineating clinically meaningful prognostic groups, whereas the extended NAC system identified a stage IV subgroup with high mortality.

BACKGROUND: Technetium-based cardiac amyloid radionuclide imaging enables noninvasive diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM), but conventional planar and visual semiquantitative approaches have important limitations. We developed the quantitative 3-dimensional Mayo 3D Score, defined as the ratio of mean left ventricular myocardial radiotracer uptake to right atrial blood-pool uptake using computed tomography (CT)-fused single-photon emission CT (SPECT) volumes of interest, and sought to validate its diagnostic performance against myocardial pathology. METHODS: Consecutive patients who underwent 99m Tc-pyrophosphate SPECT/CT at Mayo Clinic for suspected ATTR-CM between June 2017 and January 2025 with archived myocardial tissue were included. Baseline characteristics, including demographics, echocardiogram, biomarkers, visual semiquantitative, and Mayo 3D Score metrics, were collected. Myocardial specimens underwent histopathologic confirmation of amyloid, with amyloid subtype determination and further semiquantitative grading of amyloid burden and deposition patterns. RESULTS: Among 230 patients (median age, 72 years; 78% male), 107 (47%) had ATTR-CM, of whom 9 had hereditary ATTR-CM. The Mayo 3D Score showed excellent discrimination against pathology (area under the receiver operating characteristic curve, 0.92 [0.88–0.96]), exceeding planar Perugini (0.87 [0.83–0.92]; P =0.006), and heart-to-contralateral lung ratio (0.86 [0.81–0.91]; P =0.004) and comparable to SPECT Perugini (0.90 [0.86–0.94]; P =0.12). A cutoff ≥1.0 yielded 86.0% sensitivity, 92.7% specificity, 91.1% positive predictive value, and 88.4% negative predictive value. Among ATTR-positive cases, the Mayo 3D Score correlated strongly with semiquantitative amyloid burden (Spearman ρ=0.69), was higher in pericellular than nodular deposition patterns ( P <0.001), and was not associated with vascular involvement ( P =0.42). CONCLUSIONS: In patients with suspected ATTR-CM undergoing 99m Tc-pyrophosphate SPECT/CT, the Mayo 3D Score demonstrated superior diagnostic performance compared with planar metrics and comparable to SPECT Perugini, while correlating with myocardial ATTR burden and deposition patterns on pathology. These findings support its integration as a quantitative adjunct within clinical diagnostic ATTR-CM workflows.

Image-derived artificial intelligence (AI) risk models have shown promise in short-term risk assessment for improving breast cancer (BC) screening. No image-derived long-term AI risk model for primary prevention has been developed and externally validated. Individuals aged 31 to 94 years were recruited between 2010 and 2020 in two population-based case cohorts (Olmsted County, Minnesota, US, and KARMA, Sweden) and one hospital-based case-control study (EMBED, Atlanta, US). Median follow-up in the case cohorts was 10 years, with BCs diagnosed through June 2022. Additional validation was performed in EMBED with 3-year follow-up. An AI risk model was developed in Sweden, and we report independent validation in Olmsted/KARMA/EMBED. Absolute 10-year risks were estimated at study entry. Time-dependent discriminatory performance [AUC( t )] and expected-to-observed events (E/O) were estimated. Comparisons were performed with clinical risk tools and the image-based Mirai tool. Across Olmsted/KARMA, 8696 individuals (mean age, 54.4±10.6) and 1633 individuals with incident BC (mean age, 57.0±10.6) were included. Average 10-year risks were 3.83 and 3.14%, and E/O ratios were 0.99 and 0.99. Ten-year AUC( t ) for invasive BCs were 0.72 in Olmsted and 0.72 in KARMA. Similar discriminatory results were observed in EMBED. Our AI risk tool performed significantly better than Mirai in Olmsted/KARMA/EMBED. In KARMA, in the top 10% of high-risk individuals, the AI risk tool predicted 33% of BCs compared with 23%/20%/24% predicted by Tyrer-Cuzick-v8/BCSC-v3/Mirai (all P < 0.01). The 10-year image-derived AI risk model showed significantly higher performance than clinical risk models and Mirai in diverse populations, supporting its clinical potential for primary prevention.

Abstract Background Artificial intelligence (AI)–based, mammography breast cancer risk prediction models show improved discriminatory accuracy relative to clinical risk models. However, data on their calibration are limited. This study compared model performance of 3 clinical breast cancer risk models: Gail, Tyrer–Cuzick v8, and Breast Cancer Surveillance Consortium v3 with the MIRAI AI-risk model. Methods Digital mammograms were ascertained from a screening mammography cohort of 12 308 women within the Mayo Clinic Biobank with 250 incident breast cancers (176 invasive) within 5 years. We predicted 5-year breast cancer risk, estimated discriminatory accuracy (C-index), and calibration (observed-to-expected ratio) of both overall and invasive breast cancer and compared estimates using bootstrapping approaches. Results MIRAI demonstrated similar or improved discriminatory accuracy of overall breast cancer (C-index = 0.71, 95% confidence interval [CI] = 0.68 to 0.74) and invasive breast cancer (C-index = 0.71, 95% CI = 0.67 to 0.75) compared with clinical models (overall breast cancer: C-index = 0.59-0.68; invasive breast cancer: C-index = 0.60-0.68). MIRAI’s calibration for risk of overall breast cancer (observed-to-expected ratio = 0.96, 95% CI = 0.85 to 1.08) was improved compared with Gail (observed-to-expected ratio = 1.22, 95% CI = 1.07 to 1.38) and Breast Cancer Surveillance Consortium (observed-to-expected ratio = 1.38, 95% CI = 1.22 to 1.56) but similar to TC with volumetric percent density and polygenic risk score (observed-to-expected ratio = 0.99, 95% CI = 0.87 to 1.13). However, for low-risk women (approximately 50%), MIRAI overestimated risk of overall breast cancer. MIRAI also overestimated risk of invasive breast cancer across the risk spectrum (observed-to-expected ratio = 0.68, 95% CI = 0.58 to 0.78), whereas clinical models had good calibration (observed-to-expected ratio = 0.86–0.99). Conclusion MIRAI demonstrated stronger discriminatory accuracy than clinical models for 5-year overall and invasive breast cancer risk prediction but overestimated risk for both breast cancer endpoints. AI-based risk models should consider discriminatory accuracy and calibration for invasive cancer before implementation.

Abstract Aims We aimed to assess regression and development rates of ≥moderate secondary mitral regurgitation (MR) and tricuspid regurgitation (TR) in patients with heart failure and reduced ejection fraction (HFrEF) receiving contemporary guideline-directed medical therapy (GDMT). Methods Patients with a &lt;1 year diagnosis of HFrEF with mildly (40% ≤ EF &lt; 50%) or moderately (&lt;40%) reduced EF enrolled in GDMT titration programme 2019–2024 were identified. Regression rates of ≥moderate MR/TR to ≤mild and development rates of incident ≥moderate MR/TR were calculated. Results Among 844 patients [median age 64; 68% men; 26% mildly reduced EF; 24% New York Heart Association (NYHA) Class III/IV], 28% had ≥moderate MR and 14% had ≥moderate TR. Over median 7.1 months, 171 (75%) patients with ≥moderate MR and 87 (75%) of patients with ≥moderate TR experienced regression to no/mild MR and TR, respectively. Conversely, over median 13 months, incident ≥moderate MR and TR developed in 5% and 6%, respectively, of patients with no/mild baseline MR and TR. Number of GDMT medications at follow-up was less in patients who did not have MR/TR regression (vs who did) and in patients who developed MR/TR (vs who did not). Patients with NYHA Class I/II received a similar number of GDMT and had a similar rate of MR and TR regression and development vs patients with NYHA Class III/IV. However, patients with mildly reduced EF received less GDMT medications vs patients with moderately reduced EF and experienced less MR regression. Conclusion In patients with a recent diagnosis of HFrEF receiving contemporary GDMT, the majority with ≥moderate secondary MR and TR achieved regression of MR/TR, and the minority without significant baseline MR/TR developed ≥moderate regurgitation at follow-up.

BACKGROUND: Guidelines recommend 3-hour cardiac amyloid radionuclide imaging (CARI) for transthyretin amyloid cardiomyopathy. Citing rapid blood clearance of 99m Tc-hydroxymethylene-diphosphonate (HMDP) and efficient laboratory throughput, 1-hour imaging is increasingly practiced despite limited supporting evidence. We sought to compare diagnostic performance and interpreter experience of 1-hour versus 3-hour HMDP-CARI. METHODS: Consecutive patients with suspected transthyretin amyloid cardiomyopathy (n=114) underwent both 1-hour and 3-hour HMDP single photon emission computed tomography (CT)/CT. Two cardiologist-radiologist reader teams, blinded to imaging timepoint (1 versus 3 hours), assessed overall interpretation, single photon emission CT–based Perugini grade, interpretation difficulty, interpreter confidence, and need for CT-fused images for anatomic localization. Discordant, equivocal, and difficult cases were arbitrated by a third tie-breaking team. The myocardial-to-blood-pool radiotracer uptake ratio (3-dimensional Score) was measured as a surrogate of contrast resolution. RESULTS: Interinterpreter agreement was high at both time points (κ≥0.81), with more cases requiring arbitration at 3 hours versus 1 hour (22% versus 13%; P =0.049). Overall interpretation and Perugini grades were concordant between time points in 111/114 (97%) and 106/114 (93%) patients, respectively. Three patients (3%) were negative at 1 hour but equivocal at 3 hours, all of which were clinically ruled out for transthyretin amyloid cardiomyopathy. Interpreter confidence was comparable at both timepoints (97% versus 95%; P =0.317). Compared with 3-hour imaging, contrast resolution was inferior (lower 3-dimensional score, P <0.001) and CT fusion was more frequently needed (57% versus 31%, P <0.001) at 1-hour imaging. CONCLUSIONS: In a prospective, blinded comparison of 1-hour versus 3-hour HMDP-CARI, diagnostic performance and interpreter experience were similar, with readers requesting CT fusion more frequently at 1 hour to optimize myocardial-to-blood pool discrimination.

BACKGROUND: Chronic aortic regurgitation (AR) is associated with significant cardiac remodeling, but the prevalence and prognostic impact of extravalvular cardiac damage remain unexplored. METHODS: Adults with moderate or greater chronic AR identified on echocardiogram between January 2008 and July 2024 were included. Exclusion criteria were acute AR, hypertrophic and infiltrative cardiomyopathies, prior cardiac surgery, and valve stenosis. Cardiac damage was classified into hierarchical stages: no cardiac damage (stage 0), left ventricular damage (stage 1), moderate or greater mitral regurgitation or left atrial enlargement or atrial fibrillation (stage 2), pulmonary hypertension or moderate or greater tricuspid regurgitation (stage 3), and significant right ventricular dysfunction (stage 4). The primary outcome was the association between cardiac damage stages and all-cause mortality under medical surveillance. RESULTS: Of 4026 patients (median age, 72 [61–80] years), 78% had moderate AR, 11% had moderate-severe, and 11% had severe AR. Cardiac damage was present in 87% of patients: 14% in stage 1, 53% in stage 2, 18% in stage 3, and 2% in stage 4. In a multivariable model, including age, sex, AR severity, and Charlson Comorbidity Index, cardiac damage stages were associated with mortality. Adjusted hazard ratios were 1.42 (95% CI, 1.04–1.96) for stage 1, 1.58 (95% CI, 1.21–2.06) for stage 2, 2.78 (95% CI, 2.10–3.67) for stage 3, and 5.34 (95% CI, 3.67–7.76) for stage 4. Adding cardiac damage staging to multivariable models improved predictive accuracy for mortality, increasing the concordance statistics from 0.73 (95% CI, 0.71–0.75) to 0.76 (95% CI, 0.74–0.77). CONCLUSIONS: Cardiac damage is present in nearly 90% of patients with moderate or greater AR and is associated with increased mortality, highlighting the need for a more comprehensive evaluation of cardiac structure and function beyond the aortic valve and left ventricle.

Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (<50 years) women (OR = 2.51, AUC = 0.622) than in women ≥ 50 years (OR = 2.06, AUC = 0.653) of European ancestry. PRS performance in Asians was lower (OR range = 1.62–1.64, AUC = 0.551–0.600). Gail performance was modest across groups and poor in younger Asian women (OR = 0.94–0.99, AUC = 0.523–0.533). Age interactions were observed for both PRS (p < 0.001) and Gail (p < 0.001) in Europeans, whereas in Asians, age interaction was observed only for Gail (invasive: p < 0.001; DCIS: p = 0.002). PRS identified more high-risk individuals than Gail in Asian populations, especially ≥50 years, while Gail identified more in Europeans. Overlap between PRS, Gail, and family history was limited at higher thresholds. Calibration analysis, comparing empirical and model-based ROC curves, showed divergence for both PRS and Gail (p < 0.001), which indicates miscalibration. In Europeans, family history and prior biopsies drove Gail discrimination. In younger Asians, age at first live birth was influential. Conclusions: PRS adds value to risk stratification beyond traditional tools, especially in younger women and Asian ancestry populations.