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 role of alcohol consumption in the risk of vasomotor symptoms (VMS), the most cardinal climacteric symptoms, is not well established. We examined their relationship with early-onset VMS among premenopausal women. Moderately-to-severely bothersome VMS, the primary outcome, was assessed using the Korean version of the Menopause-Specific Quality of Life questionnaire. The alcohol consumption categories included lifetime abstainer, former drinker, or current drinker, categorized as light, moderate, heavy, and very heavy. Compared with the lifetime-abstinence (reference), the multivariable-adjusted odds ratio (95% CIs) for prevalent VMS in alcohol consumption of <10, 10–19, 20–39, and ≥40 g/day were 1.42 (1.02–1.99), 1.99 (1.27–3.12), 2.06 (1.19–3.57), and 3.52 (1.72–7.20), respectively (p trend <0.01). Compared with the lifetime-abstinence, the multivariable-adjusted hazard ratios (95% CIs) for incident bothersome VMS among average alcohol consumption of <10, 10–19, 20–39, and ≥40 g/day were 1.10 (0.85–1.41), 1.03 (0.70–1.51), 1.72 (1.06–2.78), and 2.22 (1.16–4.23), respectively (p trend = 0.02). Increased alcohol consumption positively and consistently showed a relationship with increased risk of both prevalent and incident early-onset VMS. Refraining from alcohol consumption may help prevent bothersome VMS in premenopausal women.

Previous studies have shown relatively low correlations between self-reported and accelerometer-assessed physical activity (PA). However, this association differs by socio-demographic factors, and this relationship has not been fully investigated in the general population. Thus, we investigated the correlation between self-reported and accelerometer-assessed PA and whether it differed by demographic and socioeconomic factors among the Korean general population. This cross-sectional study included 623 participants (203 men and 420 women) aged 30 to 64 years, who completed a PA questionnaire and wore a wrist-worn accelerometer on the non-dominant wrist for 7 days. We examined the agreement for metabolic equivalent task minutes per week (MET-min/wk) between the 2 measures and calculated Spearman correlation coefficients according to demographic and socioeconomic factors. The kappa coefficient between tertiles of self-reported and accelerometer-assessed total MET-min/wk was 0.16 in the total population, suggesting overall poor agreement. The correlation coefficient between the 2 measurements was 0.26 (p<0.001) in the total population, and the correlation tended to decrease with increasing age (p for trend <0.001) and depression scores (p for trend <0.001). We found a low correlation between self-reported and accelerometer-assessed PA among healthy Korean adults, and the correlation decreased with age and depression score. When studying PA using accelerometers and/or questionnaires, age and depression need to be considered, as should differences between self-reported and accelerometer-assessed PA.

OBJECTIVES: We analyzed whether gender role stereotypes (GRS) and patriarchal attitudes are associated with cognitive function in an elderly community.METHODS: We analyzed data from 580 people enrolled in the Korean Social Life, Health, and Aging Project. The degrees to which respondents held stereotypical beliefs about gender roles and had patriarchal mindsets were measured using a questionnaire. Based on participants’ responses, we divided respondents into 2 groups—those with conservative mindsets and those with open mindsets—according to the median score for each of the 2 variables. Cognitive function was assessed using the Mini-Mental State Examination, Korean version (MMSE-K). Cognitive impairment was defined as an MMSE-K score ≤21 points. Multivariable logistic regression was performed, adjusting for gender, age, socio-demographic and lifestyle factors, and social network size. Age and lifestyle factors were stratified.RESULTS: Compared to those with open mindsets, those with conservative mindsets regarding gender roles and patriarchal norms had adjusted odds ratios of 1.88 (95% confidence interval [CI], 1.11 to 3.19) and 1.67 (95% CI, 1.00 to 2.79) for cognitive impairment, respectively. In the stratified analysis, subgroups with younger age and a good lifestyle maintained a protective association with cognitive impairment.CONCLUSIONS: GRS and a patriarchal mindset were marginally significantly associated with cognitive impairment among women later in life.

The role of nonalcoholic fatty liver disease (NAFLD) in vasomotor symptom (VMS) risk in premenopausal women is unknown. We examined the prevalence of early-onset VMSs according to NAFLD status in lean and overweight premenopausal women. This cross-sectional study included 4242 premenopausal Korean women (mean age 45.4 years). VMSs (hot flashes and night sweats) were assessed using the Korean version of the Menopause-Specific Quality of Life questionnaire. Hepatic steatosis was determined using liver ultrasound; lean was defined as a body mass index of <23 kg/m². Participants were categorized into four groups: NAFLD-free lean (reference), NAFLD-free overweight, lean NAFLD, and overweight NAFLD. Compared with the reference, the multivariable-adjusted prevalence ratios (PRs) (95% confidence intervals (CIs)) for VMSs in NAFLD-free overweight, lean NAFLD, and overweight NAFLD were 1.22 (1.06–1.41), 1.38 (1.06–1.79), and 1.49 (1.28–1.73), respectively. For moderate-to-severe VMSs, the multivariable-adjusted PRs (95% CIs) comparing NAFLD-free overweight, lean NAFLD, and overweight NAFLD to the reference were 1.38 (1.10–1.74), 1.73 (1.16–2.57), and 1.74 (1.37–2.21), respectively. NAFLD, even lean NAFLD, was significantly associated with an increased risk of prevalent early-onset VMSs and their severe forms among premenopausal women. Further studies are needed to determine the longitudinal association between NAFLD and VMS risk.

Objective: We examined the association between menopause symptoms and the prevalence of ideal cardiovascular health (CVH) metrics among premenopausal women. Methods: This cross-sectional study comprised 4,611 premenopausal women aged 42 to 52 years. Data for CVH metrics were collected during health screening examinations. Menopause symptoms were measured using the Korean version of the Menopause-Specific Quality of Life questionnaire. For vasomotor, psychosocial, physical, and sexual symptoms, participants were divided into absent or symptomatic groups, further divided into tertiles (range, 0-7; 7 being the most bothersome). Ideal CVH metrics were defined according to the American Heart Association Life Simple 7 metrics, except dietary component. Cardiovascular health metrics were scored from 0 (unhealthy) to 6 (healthy) and classified as poor (0-2), intermediate (3-4), and ideal (5-6). Multinomial logistic regression models were used to estimate the prevalence ratios for intermediate and poor CVH metrics using ideal CVH as the reference. Results: The overall and 4 menopause-specific quality of life domain scores were significantly associated with poorer CVH metrics scores in a dose-response manner (P < 0.05). After adjusting for age, parity, education level, anti-Mullerian hormone levels, and alcohol intake, women with the most bothersome degree for vasomotor, psychosocial, physical, and sexual symptoms had significantly higher prevalence of poor CVH metrics, with corresponding prevalence ratios (95% confidence interval) of 2.90 (1.95-4.31), 2.07 (1.36-3.15), 3.01 (1.19-7.65), and 1.66 (1.15-2.39), respectively, compared with those without each vasomotor, psychosocial, physical, and sexual symptom. Conclusions: Premenopausal stage women with either vasomotor or nonvasomotor menopausal symptoms have significantly higher prevalence of poor CVH metrics, compared with those without any menopausal symptoms.