Scott, Christopher G.

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 Artificial intelligence (AI)-scores estimated from digital mammograms predict future breast cancer (BC) risk. MIRAI is a deep learning BC risk model that provides a continuous 5-year risk of overall BC from four screening full field digital mammograms. Common germline genetic variation in the form of a polygenic risk score (BC-PRS) is associated with increased BC risk and may improve MIRAI’s 5-year risk prediction. Our goal was to develop an updated MIRAI 5-year risk model that incorporates the BC-PRS (MIRAI+PRS) and to evaluate its discriminatory accuracy and calibration for both overall and invasive BC compared to MIRAI alone. We developed the MIRAI+PRS model by multiplying each woman’s 5-year MIRAI risk estimate by their relative risk based on their BC-PRS relative to the population mean. We evaluated the models within the Mayo Clinic Biobank mammography cohort, comprised of 12,307 women without a prior history of BC; 176 invasive and 250 overall BC were diagnosed within 5 years. MIRAI was estimated on screening mammograms closest to enrollment but at least 6 months prior to BC. Discriminatory accuracy, assessed by C-statistic, was high and similar for MIRAI+PRS vs. MIRAI models, for overall BC and invasive BC (Table). Calibration assessed by observed to expected (O/E) ratios was also similar for MIRAI+PRS compared to MIRAI predictions for BC outcomes (Table), although there was improvement in decile-specific O/E ratios across the lowest risk deciles (&lt;1.67%) for MIRAI+PRS. Calibration for invasive cancer was poor for MIRAI with or without PRS. In summary, the MIRAI+PRS risk model did not result in significant difference of discriminatory accuracy or overall calibration compared to the MIRAI model, but there was evidence for improved calibration for women with 5-year risk below 1.67%. For invasive BC, the model had poor calibration regardless of whether PRS was included, underscoring the importance of training AI models for BC outcomes that are associated with a clinical intervention. Citation Format: Christopher G. Scott, Peter Kraft, Imon Banerjee, Ramon Correa Medero, Aaron D. Norman, Fergus J. Couch, Karla Kerlikowske, Stacey J. Winham, Celine M. Vachon. Development and evaluation of a MIRAI 5-year risk model with a breast cancer polygenic risk score [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 2779.

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.

Abstract Background 99mTc-labelled cardiac scintigraphy enables noninvasive diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM). Guidelines recommend using planar semiquantitative (heart to contralateral ratio, H/CL) and single photon emission computed tomography (SPECT) qualitative visual grading for diagnostic interpretation. However, planar imaging has inherent limitations, and visual grading is prone to interobserver variability, underscoring the need for standardized quantitative assessment. We developed a 3D volumetric quantitative score (3D Score) and assessed its diagnostic performance compared to myocardial tissue, the gold standard for ATTR-CM diagnosis. Methods At a single, tertiary referral center, we retrospectively identified consecutive patients from 06/2017 to 06/2024 who underwent 3-hour 99mTc-pyrophosphate or 99mTc-hydroxymethylene diphosphonate planar and SPECT/CT for suspected ATTR-CM and had archived myocardial tissue (biopsy or myectomy). On planar imaging, H/CL was calculated. On SPECT/CT, volumes of interest were drawn around the entire left ventricle (LV), avoiding calcifications, and within the right atrium (RA) blood pool. Mean uptake values were used to calculate the 3D Score (LVmean:RAmean), Fig 1A. Myocardial tissue was stained via sulfated Alcian blue and/or Congo red histochemistry. Once histologically confirmed, amyloid deposits were subtyped by liquid chromatography tandem mass spectrometry. The diagnostic performance of H/CL and 3D Score was assessed using receiver operating characteristic (ROC) curve analysis and predictive characteristics were defined. ROC areas under the curve (AUC) were compared using Delong test. Optimal cutoffs were determined using Youden’s J statistic. Baseline characteristics (demographics, echocardiogram, biomarkers) and H/CL were compared between patients above and below 3D Score cutoff. Results 239 patients were identified (median age 72 [65-78], 78% male, 46% ATTR positive). Median time between cardiac scintigraphy and myocardial tissue collection was 22 (6-65) days. Distribution of the 3D Score stratified by ATTR-CM status is illustrated in Fig 1B-C. The discriminative ability of the 3D Score was excellent (AUC 0.92) and superior to H/CL (0.87, p =0.006, Fig 1D-E). The optimal 3D Score cutoff for identifying ATTR-CM was ≥1.0 with sensitivity of 86%, specificity of 92%, positive predictive value (PPV) 90%, and negative predictive value (NPV) 89%. The optimal H/CL ratio cutoff for identifying ATTR-CM was ≥1.4 with sensitivity of 72%, specificity of 86%, PPV 81%, NPV 78%. Baseline characteristics of the overall cohort stratified by 3D Score are displayed in Table 1. Conclusions The 3D Score demonstrated excellent diagnostic performance for detecting ATTR-CM and was superior to current quantitative metrics. Future studies are needed to externally validate its performance across diverse patient cohorts and imaging centers and to investigate correlation with myocardial amyloid burden. Figure 1

Abstract Background Common genetic variation has been linked to an increased risk of developing dilated cardiomyopathy (DCM) in case-control studies. However, the phenotypic profile and biological mechanisms by which genetic background may predispose individuals to DCM and outcomes in the general population remain unclear. Aim The primary objectives of this study were twofold: (1) to validate a DCM polygenic score (PRS-1) based on a prior meta-analysis of case–control genetic association studies [Tadros R et al. Nat Genet (2021)]; and (2) To apply this PRS, along with two recently developed PRSs for DCM [PRS-2 from Zheng SL et al. Nat Genet (2024) and PRS-3 from Jurgens SJ et al. Nat Genet (2024)] to a general population that underwent comprehensive longitudinal phenotypic assessments and had long-term outcomes available. Methods While the PRS-1 was derived from only 13 SNPs, PRS-2 and PRS-3 were generated from 709,534 and 1,072,247 SNPs, respectively with significant overlap between them. These DCM-PRSs were applied to an unselected community-based cohort (n=1897) aged &gt;45 years, who underwent a physical examination, echocardiogram, and blood sample at baseline. Subsequent clinical outcomes were collected over a median 16 year follow-up (IQR: 15-17). Results No significant differences between the quartiles were present in terms of age, sex (48% male), and cardiovascular risk factors (CV-RF) (such as hypertension, diabetes, and coronary artery disease). However, linear regression models demonstrated a significant correlation between all DCM-PRSs and left ventricular (LV) parameters, including LV ejection fraction, LV end-systolic diameter, and LV end-diastolic diameter (Table). Multivariable regression analysis confirmed that these correlations were independent of age, sex, CV-RF, and population stratification (adjusted for the first 10 principal components [PCs]). During follow-up, 47 individuals developed DCM. A logistic regression analysis, using a 3:1 matched cohort of non-DCM vs. DCM individuals based on age and sex and adjusted for the same 10 PCs, found a significant correlation between DCM development and both PRS-1 [OR=2.27 (1.06–4.85), p=0.03] and PRS-3 [OR=2.63 (1.20–5.75), p=0.02], while PRS-2 did not show a significant association [OR=1.85 (0.84–4.06), p=0.12]. No association was found between the DCM-PRSs and heart failure (HF) biomarkers (aldosterone, natriuretic peptides, ST2, Galectin-3) or HF outcomes (all-cause mortality/HF hospitalization). Conclusion PRS-1 for DCM despite being derived from only 13 risk alleles, performs at least as well as the newer and more extensive PRSs, in its correlation with LV function, dimensions, and DCM development (except for PRS-2) in a general population. The absence of correlation between these DCM-PRSs and the renin-angiotensin-aldosterone system or natriuretic peptides suggests that genetic background influences LV traits through mechanisms independent of these biological pathways.