Mammographic breast features and risk of cardiovascular diseases in korean women.

BACKGROUND: There is a growing amount of evidence on the association between cardiovascular diseases (CVDs) and breast calcification. Thus, mammographic breast features have recently gained attention as CVD predictors. OBJECTIVE: This study assessed the association of mammographic features, including benign calcification, microcalcification, and breast density, with cardiovascular diseases. METHODS: This study comprised 6,878,686 women aged ≥40 who underwent mammographic screening between 2009 and 2012 with follow-up until 2020. The mammographic features included benign calcification, microcalcification, and breast density. The cardiovascular diseases associated with the mammographic features were assessed using logistic regression. RESULTS: The prevalence of benign calcification, microcalcification, and dense breasts were 9.6 %, 0.9 % and 47.3 % at baseline, respectively. Over a median follow-up of 10 years, benign calcification and microcalcification were positively associated with an increased risk of chronic ischaemic heart disease whereas breast density was inversely associated with it; the corresponding aOR (95 % CI) was 1.14 (1.10-1.17), 1.19 (1.03-1.15), and 0.88 (0.85-0.90), respectively. A significantly increased risk of chronic ischaemic heart disease (IHD) was observed among women with benign calcifications (aHR, 1.14; 95 % CI 1.10-1.17) and microcalcifications (aOR, 1.19; 95 % CI 1.06-1.33). Women with microcalcifications had a 1.16-fold (95 % CI 1.03-1.30) increased risk of heart failure. CONCLUSIONS: Mammographic calcifications were associated with an increased risk of chronic ischaemic heart diseases, whereas dense breast was associated with a decreased risk of cardiovascular disease. Thus, the mammographic features identified on breast cancer screening may provide an opportunity for cardiovascular disease risk identification and prevention.

Glycemic status, insulin resistance, and mortality from lung cancer among individuals with and without diabetes.

BACKGROUND: The effects of glycemic status and insulin resistance on lung cancer remain unclear. We investigated the associations between both glycemic status and insulin resistance, and lung cancer mortality, in a young and middle-aged population with and without diabetes. METHODS: This cohort study involved individuals who participated in routine health examinations. Lung cancer mortality was identified using national death records. Cox proportional hazards models were used to calculate hazard ratios (HRs) with 95% CIs for lung cancer mortality risk. RESULTS: Among 666,888 individuals (mean age 39.9 ± 10.9 years) followed for 8.3 years (interquartile range, 4.6-12.7), 602 lung cancer deaths occurred. Among individuals without diabetes, the multivariable-adjusted HRs (95% CI) for lung cancer mortality comparing hemoglobin A1c categories (5.7-5.9, 6.0-6.4, and ≥ 6.5% or 39-41, 42-46, and ≥ 48 mmol/mol, respectively) with the reference (< 5.7% or < 39 mmol/mol) were 1.39 (1.13-1.71), 1.72 (1.33-2.20), and 2.22 (1.56-3.17), respectively. Lung cancer mortality was associated with fasting blood glucose categories in a dose-response manner (P for trend = 0.001) and with previously diagnosed diabetes. Insulin resistance (HOMA-IR ≥ 2.5) in individuals without diabetes was also associated with lung cancer mortality (multivariable-adjusted HR, 1.41; 95% CI, 1.13-1.75). These associations remained after adjusting for changing status in glucose, hemoglobin A1c, insulin resistance, smoking status, and other confounders during follow-up as time-varying covariates. CONCLUSIONS: Glycemic status within both diabetes and prediabetes ranges and insulin resistance were independently associated with an increased risk of lung cancer mortality.

Overactive bladder and cognitive impairment in middle-aged women: A cross-sectional study.

BACKGROUND: Overactive bladder (OAB) is a common condition in middle-aged and older women. It has been reported to be potentially linked to cognitive decline, particularly in older adults. This study investigated the association between OAB symptoms and cognitive impairment in middle-aged women. MATERIALS AND METHODS: This cross-sectional study had a sample of 1652 women (mean age 49.3 ± 2.8 years) who were not taking medication for either urinary tract infection or OAB. OAB symptoms and cognitive function were evaluated by self-administered questionnaires: the Overactive Bladder Symptom Score and the Alzheimer's disease 8. Logistic regression models estimated prevalence ratios (PRs) with 95 % confidence intervals (CI) for cognitive impairment according to the presence/absence of OAB. Mediation analyses assessed the impact of poor sleep quality on this association. RESULTS: Cognitive impairment was more prevalent in women with OAB than in those without OAB (multivariable-adjusted PR: 1.88 [95 % CI: 1.52-2.24]). Women experiencing nocturia (≥twice a night), urinary urgency at least once a week, and urgency urinary incontinence at least once a week had multivariable-adjusted PRs (95 % CI) for cognitive impairment of 2.08 (1.50-2.65), 2.12 (1.66-2.58), and 1.75 (1.17-2.34), respectively. Poor sleep quality mediated 10.81 % [95 % CI: 4.55-19.44 %] of the relationship between OAB and cognitive impairment. CONCLUSIONS: Among middle-aged women not taking OAB medications, OAB symptoms were associated with cognitive impairment, partly because of poor sleep quality. Further research is needed to determine whether early screening of patients with OAB can help identify those susceptible to cognitive impairment associated with OAB medication and if preventive measures should be targeted at this group.

Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction.

The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1-5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%). To determine the feasibility of the AI prediction model, mammograms and detailed clinical information were collected. C-indices and area under the receiver operating characteristic curves (AUCs) for 1-5-year outcomes were obtained. We compared the AUCs of our AI prediction model, Mirai, and clinical statistical risk models, including the Tyrer-Cuzick (TC) model and Gail model, using DeLong's test. A total of 16,894 mammograms were independently collected for external validation, of which 4002 were followed by a cancer diagnosis within 5 years. Our AI prediction model obtained a C-index of 0.76, with AUCs of 0.90, 0.84, 0.81, 0.78, and 0.81, to predict the 1-5-year risks. Our AI prediction model showed significantly higher AUCs than those of the TC model (AUC: 0.57; p < 0.001) and Gail model (AUC: 0.52; p < 0.001), and achieved similar performance to Mirai. The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction.

Network Analysis Revealed the Role of Helplessness as a Central Feature Among Late-Life Depressive Symptoms in Patients With Mild Cognitive Impairment and Early Stage Dementia.

OBJECTIVE: It has been reported that depressive symptoms in older adults are different from those in younger adults, especially when accompanied by cognitive decline. However, few studies have investigated the network structure of depressive symptoms in this population. METHODS: The participants consisted of 627 older adults (>60 yr) who were diagnosed with mild cognitive impairment (MCI) or early stage dementia. Among them, 36.7% were male and the mean age was 76.20±7.71 years. The Korean form of Geriatric Depression Scale (KGDS) was used to evaluate their depressive symptoms and network analyses were performed using bootnet R-package to identify the central features among depressive symptoms. RESULTS: Of all the KGDS items, we found that KGDS 2 (often feel helpless) had the highest node strength followed by KGDS 21 (in good spirits), KGDS 14 (not confident at all), and KGDS 15 (cheerful and happy). In terms of node betweenness, KGDS 2 also showed the highest value. The edge weights of edges connected to node KGDS 2 were strongest in KGDS 3 (restless and fidgety) and KGDS 28 (easily get tired). CONCLUSION: In this study, we presented which symptoms are central among the elderly with MCI and early stage dementia. This result not only increases the understanding of depressive symptoms in this group but would also help determine target symptoms in the treatment program.

Adiposity, Body Composition Measures, and Breast Cancer Risk in Korean Premenopausal Women.

Objective: To investigate the association between body composition parameters and breast cancer (BC) risk in premenopausal women. Design, Setting, and Participants: Prospective cohort study using data from the Kangbuk Samsung Cohort Study. Participants were women aged 20 to 54 years who were enrolled from 2011 to 2019 and followed up for BC development until December 31, 2020. Data were analyzed from June to August 2023. Exposures: Trained nurses conducted anthropometric measurements and assessed body composition using segmental bioelectric impedance analysis. The analysis encompassed adiposity measures such as body mass index (BMI), waist circumference, and body composition parameters, including muscle mass, fat mass, ratio of muscle mass to weight, ratio of fat mass to weight, and fat mass index. Main outcomes and measures: Adjusted hazard ratios (aHR) for BC during the follow-up period. Results: Among 125 188 premenopausal women, the mean (SD) age was 34.9 (6.3) years. During a mean (range) follow-up of 6.7 (0.5-9.9) years, 1110 incident BC cases were identified. The mean (SD) BMI and waist circumference were 21.6 (3.1) and 75.3 (8.2) cm, respectively. Higher BMI and waist circumference were associated with decreased risk, with an aHR of 0.89 (95% CI, 0.84-0.95) per SD increase in BMI and 0.92 (95% CI, 0.86-0.98) per SD increase in waist circumference. A higher ratio of fat mass to weight was associated with decreased BC risk (aHR, 0.92; 95% CI, 0.86-0.99 per SD increase), whereas the opposite trend was observed for the ratio of muscle mass to weight, with an aHR of 1.08 (95% CI, 1.02-1.15) per SD increase. The results remained consistent even after additional adjustments for height in the model. The fat mass index was also inversely associated with BC risk, with an HR of 0.90 (95% CI, 0.85-0.97) per SD increase. Conclusions and Relevance: In this cohort study of premenopausal women, a higher level of adiposity, represented by increased BMI, waist circumference, and fat mass, was consistently associated with decreased breast cancer risk. Conversely, muscle mass and its ratio to weight displayed opposite or inconsistent patterns. These findings suggest an inverse association between excess adiposity and the risk of BC in premenopausal women, confirming earlier findings that BMI is an indirect measure of adiposity.

Risk prediction model for gastric cancer within 5 years in healthy Korean adults.

BACKGROUND: Although endoscopy is commonly used for gastric cancer screening in South Korea, predictive models that integrate endoscopy results are scarce. We aimed to develop a 5-year gastric cancer risk prediction model using endoscopy results as a predictor. METHODS: We developed a predictive model using the cohort data of the Kangbuk Samsung Health Study from 2011 to 2019. Among the 260,407 participants aged ≥20 years who did not have any previous history of cancer, 435 cases of gastric cancer were observed. A Cox proportional hazard regression model was used to evaluate the predictors and calculate the 5-year risk of gastric cancer. Harrell's C-statistics and Nam-D'Agostino χ2 test were used to measure the quality of discrimination and calibration ability, respectively. RESULTS: We included age, sex, smoking status, alcohol consumption, family history of cancer, and previous results for endoscopy in the risk prediction model. This model showed sufficient discrimination ability [development cohort: C-Statistics: 0.800, 95% confidence interval (CI) 0.770-0.829; validation cohort: C-Statistics: 0.799, 95% CI 0.743-0.856]. It also performed well with effective calibration (development cohort: χ2 = 13.65, P = 0.135; validation cohort: χ2 = 15.57, P = 0.056). CONCLUSION: Our prediction model, including young adults, showed good discrimination and calibration. Furthermore, this model considered a fixed time interval of 5 years to predict the risk of developing gastric cancer, considering endoscopic results. Thus, it could be clinically useful, especially for adults with endoscopic results.

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.

The association of blood eosinophil counts and FEV1 decline: a cohort study.

BACKGROUND: Accelerated lung function decline is characteristic of COPD. However, the association between blood eosinophil counts and lung function decline, accounting for current smoking status, in young individuals without prevalent lung disease is not fully understood. METHODS: This is a cohort study of 629 784 Korean adults without COPD or a history of asthma at baseline who participated in health screening examinations including spirometry and differential white blood cell counts. We used a linear mixed-effects model to estimate the annual change in forced expiratory volume in 1 s (FEV1) (mL) by baseline blood eosinophil count, adjusting for covariates including smoking status. In addition, we performed a stratified analysis by baseline and time-varying smoking status. RESULTS: During a mean follow-up of 6.5 years (maximum 17.8 years), the annual change in FEV1 (95% CI) in participants with eosinophil counts <100, 100-199, 200-299, 300-499 and ≥500 cells·µL-1 in the fully adjusted model were -23.3 (-23.9--22.7) mL, -24.3 (-24.9--23.7) mL, -24.8 (-25.5--24.2) mL, -25.5 (-26.2--24.8) mL and -26.8 (-27.7--25.9) mL, respectively. When stratified by smoking status, participants with higher eosinophil count had a faster decline in FEV1 than those with lower eosinophil count in both never- and ever-smokers, which persisted when time-varying smoking status was used. CONCLUSIONS: Higher blood eosinophil counts were associated with a faster lung function decline among healthy individuals without lung disease, independent of smoking status. The findings suggest that higher blood eosinophil counts contribute to the risk of faster lung function decline, particularly among younger adults without a history of lung disease.

Gate-bias stability of triple-gated feedback field-effect transistors with silicon nanosheet channels.

In this study, we investigate the gate-bias stability of triple-gated feedback field-effect transistors (FBFETs) with Si nanosheet channels. The subthreshold swing (SS) of FBFETs increases from 0.3 mV dec-1to 60 and 80 mV dec-1inp- andn-channel modes, respectively, when a positive bias stress (PBS) is applied for 1000 s. In contrast, the SS value does not change even after a negative bias stress (NBS) is applied for 1000 s. The difference in the switching characteristics under PBS and NBS is attributed to the ability of the interface traps to readily gain electrons from the inversion layer. The switching characteristics deteriorated by PBS are completely recovered after annealing at 300 °C for 10 min, and the characteristics remain stable even after PBS is applied again for 1000 s.

Association of Depression With the Progression of Multimorbidity in Older Adults: A Population-Based Cohort Study.

BACKGROUND: The relationship between depression and the risk of multimorbidity progression has rarely been studied in older adults. This study was aimed to determine whether depression is associated with progression in the severity and complexity of multimorbidity, considering the influence of depression's severity and subtype. METHODS: As a part of the Korean Longitudinal Study on Cognitive Aging and Dementia, this population-based cohort study followed a random sample of community-dwelling Koreans aged 60 and older for 8 years at 2-year intervals starting in 2010. Participants included those who completed mood and multimorbidity assessments and did not exhibit complex multimorbidity at the study's outset. Depression was assessed using the Geriatric Depression Scale, while multimorbidity was evaluated using the Cumulative Illness Rating Scale. The study quantified multimorbidity complexity by counting affected body systems and measured multimorbidity severity by averaging scores across 14 body systems. FINDINGS: The 2,486 participants (age = 69.1 ± 6.5 years, 57.6% women) were followed for 5.9 ± 2.4 years. Linear mixed models revealed that participants with depression had a faster increase in multimorbidity complexity score (ß = .065, SE = 0.019, p = 0.001) than those without depression, but a comparable increase in multimorbidity severity score (ß = .001, SE = .009, p = 0.870) to those without depression. Cox proportional hazard models revealed that depression was associated with the risk of developing highly complex multimorbidity affecting five or more body systems, particularly in severe or anhedonic depression. INTERPRETATION: Depression was associated with the worsening of multimorbidity in Korean older adults, particularly when severe or anhedonic. Early screening and management of depression may help to reduce the burden of multimorbidity in older adults.

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.

Binary and ternary logic-in-memory using nanosheet feedback field-effect transistors with triple-gated structure.

In this study, we demonstrate binary and ternary logic-in-memory (LIM) operations of inverters and NAND and NOR gates comprising nanosheet (NS) feedback field-effect transistors (FBFETs) with a triple-gated structure. The NS FBFETs are reconfigured in p- or n-channel modes depending on the polarity of the gate bias voltage and exhibit steep switching characteristics with an extremely low subthreshold swing of 1.08 mV dec-1 and a high ON/OFF current ratio of approximately 107. Logic circuits consisting of NS FBFETs perform binary and ternary logic operations of the inverters and NAND and NOR gates in each circuit and store their outputs under zero-bias conditions. Therefore, NS FBFETs are promising components for next-generation LIM.

Temperament Clusters in Patients With Panic Disorder in Relation to Character Maturity.

OBJECTIVE: This study explored whether temperament profiles are associated with psychological functioning and whether character maturity affects this association in patients with panic disorders (PD). METHODS: A total of 270 patients with PD were enrolled in this study. Measurements included the Temperament and Character Inventory-revised-short (TCI-RS), a self-report version of the Panic Disorder Severity Scale (PDSS-SR), Beck Depression Inventory-II (BDI-II), and Spielberger State-Trait Anxiety Inventory (STAI). Cluster analysis was used to define the patients' temperament profiles, and the differences in discrete variables among temperament clusters were calculated using a one-way analysis of variance. An analysis of covariance was conducted to control for the impact of character maturity on psychological functioning among clusters. RESULTS: We identified four temperament clusters of patients with PD. Significant differences in the PDSS-SR, BDI-II, STAI-state, and STAI-trait scores among the four clusters were detected [F(3, 262)=9.16, p<0.001; F(3, 266)=33.78, p<0.001; F(3, 266)=19.12, p<0.001; F(3, 266)=39.46, p<0.001]. However, after controlling for the effect of character maturity, the effect of cluster type was either eliminated or reduced ([STAI-state] cluster type: F(3, 262)=0.94, p>0.05; SD+CO: F(1, 262)=65.95, p<0.001, ηp2 =0.20). CONCLUSION: This study enabled a more comprehensive and integrated understanding of patients by exploring the configuration of all temperament dimensions together rather than each temperament separately. Furthermore, we revealed that depending on the degree of character maturity, the psychological functioning might differ even within the same temperament cluster. These results imply that character maturity can complement inherently vulnerable temperament expression.

The future of International Classification of Diseases coding in steatotic liver disease: An expert panel Delphi consensus statement.

BACKGROUND: Following the adoption of new nomenclature for steatotic liver disease, we aimed to build consensus on the use of International Classification of Diseases codes and recommendations for future research and advocacy. METHODS: Through a two-stage Delphi process, a core group (n = 20) reviewed draft statements and recommendations (n = 6), indicating levels of agreement. Following revisions, this process was repeated with a large expert panel (n = 243) from 73 countries. RESULTS: Consensus ranged from 88.8% to 96.9% (mean = 92.3%). CONCLUSIONS: This global consensus statement provides guidance on harmonizing the International Classification of Diseases coding for steatotic liver disease and future directions to advance the field.

Baseline and change in serum uric acid level over time and resolution of nonalcoholic fatty liver disease in young adults: The Kangbuk Samsung Health Study.

AIMS: To determine the association between: (i) baseline serum uric acid (SUA) level and (ii) SUA changes over time, and nonalcoholic fatty liver disease (NAFLD) resolution. MATERIALS AND METHODS: A retrospective cohort study, comprising 38 483 subjects aged <40 years with pre-existing NAFLD, was undertaken. The effects of SUA changes over time were studied in 25 266 subjects. Participants underwent a health examination between 2011 and 2019, and at least one follow-up liver ultrasonography scan up to December 2020. Exposures included baseline SUA level and SUA changes between baseline and subsequent visits, categorized into quintiles. The reference group was the third quintile (Q3) containing zero change. The primary endpoint was resolution of NAFLD. RESULTS: During a median follow-up of 4 years, low baseline SUA level and decreases in SUA levels over time were independently associated with NAFLD resolution (p for trend <0.001). Using SUA as a continuous variable, the likelihood of NAFLD resolution was increased by 10% and 13% in men and women, respectively, per 1-mg/dL decrease in SUA. In a time-dependent model with changes in SUA treated as a time-varying covariate, adjusted hazard ratios (95% confidence intervals) for NAFLD resolution comparing Q1 (highest decrease) and Q2 (slight decrease) to Q3 (reference) were 1.63 (1.49-1.78) and 1.23 (1.11-1.35) in men and 1.78 (1.49-2.12) and 1.18 (0.95-1.46) in women, respectively. CONCLUSIONS: Low baseline SUA levels and a decrease in SUA levels over time were both associated with NAFLD resolution in young adults.

Impact of metabolic unhealthiness and its changes on the risk of erosive esophagitis: a cohort study.

BACKGROUND AND AIM: We aimed to compare the risk of erosive esophagitis (EE) among individuals with different phenotypes based on metabolic health status and obesity and investigate the role of changes in metabolic health in EE risk. METHODS: A cohort of 258 892 asymptomatic adults without EE at baseline who underwent ollow-up esophagogastroduodenoscopy (EGD) were categorized into the following four groups according to metabolic health and obesity status: (i) metabolically healthy (MH) non-obese; (ii) metabolically unhealthy (MU) non-obese; (iii) MH obese; and (iv) MU obese. EE was defined as the presence of grade A or higher mucosal breaks on EGD. RESULTS: During a median follow-up of 4.5 years, the incidence rates of EE were 0.6/103 person-years (PY), 1.7/103 PY, 1.7/103 PY, and 3.1/103 PY in the MH non-obese, MU non-obese, MH obese, and MU obese groups, respectively. The multivariable-adjusted hazard ratio (HR) (95% confidence intervals [CI]) for developing EE comparing the MH obese, MU non-obese, and MU obese groups with the MH non-obese group were 1.49 (1.29-1.71), 1.56 (1.25-1.94), and 2.18 (1.90-2.49), respectively. The multivariable-adjusted HR (95% CI) comparing the progression of MH to MU, regression of MU to MH, and persistent MU with the persistent MH group were 1.39 (1.10-1.76), 1.39 (1.09-1.77), and 1.86 (1.56-2.21), respectively. The increased risk of EE among the persistent MU group was consistently observed in individuals without obesity or abdominal obesity. CONCLUSION: Metabolic unhealthiness and obesity were independent risk factors for the development of EE, suggesting that maintaining both normal weight and metabolic health may help reduce the risk of EE.

Human papillomavirus infection and cardiovascular mortality: a cohort study.

BACKGROUND AND AIMS: High-risk human papillomavirus (HR-HPV) infection-a well-established risk factor for cervical cancer-has associations with cardiovascular disease (CVD). However, its relationship with CVD mortality remains uncertain. This study examined the associations between HR-HPV infection and CVD mortality. METHODS: As part of a health examination, 163 250 CVD-free Korean women (mean age: 40.2 years) underwent HR-HPV screening and were tracked for up to 17 years (median: 8.6 years). National death records identified the CVD mortality cases. Hazard ratios (HRs) and 95% confidence intervals (CIs) for CVD mortality were estimated using Cox proportional hazard regression analyses. RESULTS: During 1 380 953 person-years of follow-up, 134 CVD deaths occurred, with a mortality rate of 9.1 per 105 person-years for HR-HPV(-) women and 14.9 per 105 person-years for HR-HPV(+) women. After adjustment for traditional CVD risk factors and confounders, the HRs (95% CI) for atherosclerotic CVD (ASCVD), ischaemic heart disease (IHD), and stroke mortality in women with HR-HPV infection compared with those without infection were 3.91 (1.85-8.26), 3.74 (1.53-9.14), and 5.86 (0.86-40.11), respectively. The association between HR-HPV infection and ASCVD mortality was stronger in women with obesity than in those without (P for interaction = .006), with corresponding HRs (95% CI) of 4.81 (1.55-14.93) for obese women and 2.86 (1.04-7.88) for non-obese women. CONCLUSIONS: In this cohort study of young and middle-aged Korean women, at low risks for CVD mortality, those with HR-HPV infection had higher death rates from CVD, specifically ASCVD and IHD, with a more pronounced trend in obese individuals.

Mammography-based deep learning model for coronary artery calcification.

AIMS: Mammography, commonly used for breast cancer screening in women, can also predict cardiovascular disease. We developed mammography-based deep learning models for predicting coronary artery calcium (CAC) scores, an established predictor of coronary events. METHODS AND RESULTS: We evaluated a subset of Korean adults who underwent image mammography and CAC computed tomography and randomly selected approximately 80% of the participants as the training dataset, used to develop a convolutional neural network (CNN) to predict detectable CAC. The sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), and overall accuracy of the model's performance were evaluated. The training and validation datasets included 5235 and 1208 women, respectively [mean age, 52.6 (±10.2) years], including non-zero cases (46.8%). The CNN-based deep learning prediction model based on the Resnet18 model showed the best performance. The model was further improved using contrastive learning strategies based on positive and negative samples: sensitivity, 0.764 (95% CI, 0.667-0.830); specificity, 0.652 (95% CI, 0.614-0.710); AUROC, 0.761 (95% CI, 0.742-0.780); and accuracy, 70.8% (95% CI, 68.8-72.4). Moreover, including age and menopausal status in the model further improved its performance (AUROC, 0.776; 95% CI, 0.762-0.790). The Framingham risk score yielded an AUROC of 0.736 (95% CI, 0.712-0.761). CONCLUSION: Mammography-based deep learning models showed promising results for predicting CAC, performing comparably to conventional risk models. This indicates mammography's potential for dual-risk assessment in breast cancer and cardiovascular disease. Further research is necessary to validate these findings in diverse populations, with a particular focus on representation from national breast screening programmes.