Automated Comprehensive CT Assessment of the Risk of Diabetes and Associated Cardiometabolic Conditions.

Background CT performed for various clinical indications has the potential to predict cardiometabolic diseases. However, the predictive ability of individual CT parameters remains underexplored. Purpose To evaluate the ability of automated CT-derived markers to predict diabetes and associated cardiometabolic comorbidities. Materials and Methods This retrospective study included Korean adults (age ≥ 25 years) who underwent health screening with fluorine 18 fluorodeoxyglucose PET/CT between January 2012 and December 2015. Fully automated CT markers included visceral and subcutaneous fat, muscle, bone density, liver fat, all normalized to height (in meters squared), and aortic calcification. Predictive performance was assessed with area under the receiver operating characteristic curve (AUC) and Harrell C-index in the cross-sectional and survival analyses, respectively. Results The cross-sectional and cohort analyses included 32166 (mean age, 45 years ± 6 [SD], 28833 men) and 27 298 adults (mean age, 44 years ± 5 [SD], 24 820 men), respectively. Diabetes prevalence and incidence was 6% at baseline and 9% during the 7.3-year median follow-up, respectively. Visceral fat index showed the highest predictive performance for prevalent and incident diabetes, yielding AUC of 0.70 (95% CI: 0.68, 0.71) for men and 0.82 (95% CI: 0.78, 0.85) for women and C-index of 0.68 (95% CI: 0.67, 0.69) for men and 0.82 (95% CI: 0.77, 0.86) for women, respectively. Combining visceral fat, muscle area, liver fat fraction, and aortic calcification improved predictive performance, yielding C-indexes of 0.69 (95% CI: 0.68, 0.71) for men and 0.83 (95% CI: 0.78, 0.87) for women. The AUC for visceral fat index in identifying metabolic syndrome was 0.81 (95% CI: 0.80, 0.81) for men and 0.90 (95% CI: 0.88, 0.91) for women. CT-derived markers also identified US-diagnosed fatty liver, coronary artery calcium scores greater than 100, sarcopenia, and osteoporosis, with AUCs ranging from 0.80 to 0.95. Conclusion Automated multiorgan CT analysis identified individuals at high risk of diabetes and other cardiometabolic comorbidities. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Pickhardt in this issue.

Lifelog-based daily step counts, walking speed, and metabolically healthy status.

Objective: Optimal metabolically healthy status is important to prevent various chronic diseases. This study investigated the association between lifelog-derived physical activity and metabolically healthy status. Methods: This cross-sectional study included 51 Korean adults aged 30-40 years with no history of chronic diseases. Physical activity data were obtained by the International Physical Activity Questionnaire-Short Form (IPAQ-SF). Lifelog-derived physical activity was defined by step counts and walking speed for 1 week, as recorded by the Samsung Health application on both the Samsung Galaxy Fit2 and mobile phones. Participants without metabolic syndrome components were categorized as the metabolically healthy group (n = 31) and the remaining participants as the metabolically unhealthy group (n = 20). Prevalence ratios and 95% confidence intervals were estimated using Poisson regression models. The predictive ability of each physical activity measure was evaluated according to the area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) values. Results: Among the physical activity measures, lifelog-derived walking speed was significantly inversely associated with prevalent metabolically unhealthy status. The lifelog component model including walking speed, age, and sex had the highest AUC value for metabolically unhealthy status. Adding lifelog-derived step counts to the IPAQ-SF-derived metabolic equivalent (MET) model (including age, sex, and IPAQ-SF-METs) yielded 37% and 13% increases in the NRI and IDI values, respectively. Incorporating walking speed into the IPAQ-SF-derived MET model improved metabolically unhealthy status prediction by 42% and 21% in the NRI and IDI analyses, respectively. Conclusions: Slow walking speed derived from the lifelog was associated with a higher prevalence of metabolically unhealthy status. Lifelog-derived physical activity information may aid in identifying individuals with metabolic abnormalities.

Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection.

BACKGROUND: Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women. METHODS: We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI's performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A-D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories. RESULTS: Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7-77.3] vs. 67.1% [95% CI, 58.8-74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9-93.2] vs. 77.6% [95% CI, 61.7-77.9]), PPV (1.5% [95% CI, 1.2-1.9] vs. 0.5% [95% CI, 0.4-0.6]), recall rate (7.1% [95% CI, 6.9-7.2] vs. 22.5% [95% CI, 22.2-22.7]), and AUC values (0.8 [95% CI, 0.76-0.84] vs. 0.74 [95% CI, 0.7-0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category. CONCLUSIONS: AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue.

Early-onset vasomotor symptoms and development of depressive symptoms among premenopausal women.

BACKGROUND: We investigated the association between vasomotor symptoms (VMSs) and the onset of depressive symptoms among premenopausal women. METHODS: This cross-sectional study included 4376 premenopausal women aged 42-52 years, and the cohort study included 2832 women without clinically relevant depressive symptoms at baseline. VMSs included the symptoms of hot flashes and night sweats. Depressive symptoms were evaluated using the Center for Epidemiological Studies Depression Scale; a score of ≥16 was considered to define clinically relevant depressive symptoms. RESULTS: Premenopausal Women with VMSs at baseline exhibited a higher prevalence of depressive symptoms compared with women without VMSs at baseline (multivariable-adjusted prevalence ratio 1.76, 95 % confidence interval [CI] 1.47-2.11). Among the 2832 women followed up (median, 6.1 years), 406 developed clinically relevant depressive symptoms. Women with versus without VMSs had a significantly higher risk of developing clinically relevant depressive symptoms (multivariable-adjusted hazard ratio, 1.72; 95 % CI 1.39-2.14). VMS severity exhibited a dose-response relationship with depressive symptoms (P for trend <0.05). LIMITATIONS: Self-reported questionnaires were only used to obtain VMSs and depressive symptoms, which could have led to misclassification. We also could not directly measure sex hormone levels. CONCLUSIONS: Even in the premenopausal stage, women who experience hot flashes or night sweats have an increased risk of present and developed clinically relevant depressive symptoms. It is important to conduct mental health screenings and provide appropriate support to middle-aged women who experience early-onset VMSs.

Association Between ECG Abnormalities and Mortality in a Low-Risk Population.

BACKGROUND: The ECG is a simple, noninvasive screening method for cardiovascular disease and arrhythmia. The impact of ECG abnormality on mortality is not certain in low-risk populations. To address this, we evaluated the association between ECG abnormality and mortality. METHODS AND RESULTS: We retrospectively assessed baseline ECG and all-cause mortality and cardiovascular mortality in 660 383 patients presenting for medical check-ups. Baseline ECG abnormalities were classified according to the Minnesota Code. Among the total 660 383 participants, 23 609 (3.6%) had major and 110 038 (16.7%) had minor ECG abnormalities. All-cause mortality occurred in 7751 patients (1.1%) and cardiovascular mortality in 1180 (0.18%) over a median follow-up period of 8.8 years. Major ECG abnormalities were associated with all-cause mortality (hazard ratio [HR], 1.11 [95%, 1.03-1.2]) and cardiovascular mortality (HR, 1.92 [95% CI, 1.63-2.27]) compared with no ECG abnormalities. All-cause mortality was associated with right atrial enlargement (HR, 2.11 [95% CI, 1.1-4.07]), left atrial enlargement (HR, 1.76 [95% CI, 1.1-2.84]), sinus tachycardia (HR, 1.52 [95% CI, 1.15-2.01]), complete atrioventricular block (HR, 2.1 [95% CI, 1.05-4.2]), atrial fibrillation (HR, 1.52 [95% CI, 1.26-1.84]), and left ventricular hypertrophy (HR, 1.15 [95% CI, 1.02-1.3]). Cardiovascular mortality was associated with left atrial enlargement (HR, 4.52 [95% CI, 2.15-9.5]), atrial fibrillation (HR, 3.22 [95% CI, 2.33-4.46]), left ventricular hypertrophy (HR, 1.72 [95% CI, 1.35-2.19]), major Q-wave abnormality (HR, 1.6 [95% CI, 1.08-2.39]), and major ST-T abnormality (HR, 1.76 [95% CI, 1.01-3.04]). CONCLUSIONS: ECG abnormalities, including left atrial enlargement, left ventricular hypertrophy, atrial fibrillation, and major Q-wave and ST-T abnormalities, were associated with cardiovascular mortality in a low-risk population.