Height and breast cancer risk in premenopausal Korean women aged under 40 years of age.

BACKGROUND: Taller women are at an increased risk of breast cancer; however, evidence regarding this in younger women is limited. This study investigated the association between body height and breast cancer risk in premenopausal Korean women aged <40 years. METHODS: Premenopausal women aged <40 years enroled in the Kangbuk Samsung Health Study (KSHS) and National Health Insurance Service-National Health Information Database (NHIS-NHID) were included in the analysis. Trained staff members performed anthropometry, including height measurements. Breast cancer incidence was followed up until December 2019. Cox regression model and restricted cubic- spline regression were applied. RESULTS: The mean (standard deviation [SD]) age was 33.3 (3.6) years and 32.9 (4.2) in KSHS and NHIS-NHID cohorts, respectively. After adjusting for age at baseline and other confounders, every 10 cm of height was associated with a 1.44-fold increased risk of developing breast cancer (adjusted hazard ratio [aHR], 1.44; 95% confidence interval [CI], 1.17-1.78) in the KSHS. The restricted cubic spline regression showed an almost linear association between height and breast cancer risk. Compared to women with height <155 cm, aHRs (95% CI) among those with height 160-165 cm, 165-170 cm, and ≥170 cm were 1.67 (1.07-2.60), 1.75 (1.09-2.81), and 2.31 (1.18-3.86), respectively (P = 0.009). Results were similar in the NHIS-NHID cohort (aHR, 1.20 [95% CI, 1.10-1.31] per 10-cm increase in height). CONCLUSION: In young Korean women, greater body height was associated with increased breast cancer risk.

Menopausal stage transitions and their associations with overall and individual sleep quality in middle-aged Korean women.

BACKGROUND: Understanding the association between the menopausal transition and declining sleep quality can guide optimal timing for preventive interventions in transitioning women. However, studies lack representation of Asian women and sufficient data on the progression of menopausal stages and sleep quality changes over time in this population. METHODS: This study included 3305 women in the pre-menopause stage at baseline. The sleep quality and its components were assessed using the Pittsburgh Sleep Quality Index (PSQI). Menopausal stages were classified as pre-menopause, early transition, late transition, and post-menopause according to the Stages of Reproductive Aging Workshop+10 (STRAW+10) criteria. We estimated the longitudinal association between menopausal stage changes over time and the PSQI score, and examined the effect of being overweight. RESULTS: The trends in the PSQI scores and its components according to the menopausal stage changes over time showed that with the exception of sleep duration and habitual sleep efficiency, an overall decline was noted in sleep health during late transition and post-menopause compared to pre-menopause. These associations were independent of time-variant annual chronological aging, which was not significantly associated with sleep deterioration. Additionally, although the associations between menopausal stages and sleep quality did not significantly differ by adiposity level, the overweight group exhibited worse PSQI scores and components than did the non-overweight group. LIMITATION: Sleep quality and menopausal stage were assessed using self-reported questionnaires without objective measures. CONCLUSION: Our study underscores the importance of screening for sleep quality deterioration and implementing appropriate measures for women experiencing menopausal transition.

Masticatory Function, Sex, and Risk of Dementia Among Older Adults: A Population-Based Cohort Study.

BACKGROUND: A decline in masticatory function may indicate brain dysfunction related to dementia, but the relationship between masticatory function and dementia risk remains unclear. This study aimed to investigate whether masticatory function is associated with the risk of cognitive decline and dementia. METHODS: Data were obtained from the nationwide prospective cohort study of randomly sampled community-dwelling Koreans aged ≥ 60 years. The 5,064 non-demented participants, whose number of chewing cycles per bite was assessed by clinical interview, were followed for 8 years with biennial assessments of cognitive performance and clinical diagnoses of all-cause dementia and Alzheimer's disease (AD). Structural brain magnetic resonance imaging was collected from a subset of cohort participants and their spouses for imaging analyses. RESULTS: Males who chewed ≥ 30 cycles/bite had faster decline in global cognition and memory function and were at higher risk for incident all-cause dementia (hazard ratio [HR], 2.91; 95% confidence interval [CI], 1.18-7.18) and AD (HR, 3.22; 95% CI, 1.14-9.11) compared to males with less than 10 cycles/bite. Additionally, increased chewing cycles in males were associated with reduced brain volume, particularly in regions involved in compensatory cognitive control of mastication. There was no significant association between chewing cycles and the risk of dementia or brain volume in females. CONCLUSION: Older men who frequently chew their meals could be considered a notable population at risk for dementia who should be carefully assessed for their cognitive trajectories.

Risk of hepatic events associated with use of sodium-glucose cotransporter-2 inhibitors versus glucagon-like peptide-1 receptor agonists, and thiazolidinediones among patients with metabolic dysfunction-associated steatotic liver disease.

OBJECTIVE: To examine the hepatic effectiveness of sodium-glucose cotransporter-2 inhibitors (SGLT-2i) through a head-to-head comparison with glucagon-like peptide-1 receptor agonists (GLP-1RA) or thiazolidinediones (TZD) in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). DESIGN: This population-based cohort study was conducted using a nationwide healthcare claims database (2014-2022) of Korea. We included individuals with MASLD (aged ≥40 years) who initiated SGLT-2i or comparator drugs (GLP-1RA or TZD). Primary outcome was a composite of hepatic decompensation events, including ascites, oesophageal varices with bleeding, hepatic failure or liver transplant. Liver-cause death and all-cause death were also assessed as secondary outcomes. Cox proportional hazards models were used to estimated HRs with 95% CIs. RESULTS: After 1:1 propensity score matching, we included 22 550 patients who initiated SGLT-2i and GLP-1RA (median age=57 years, 60% male), and 191 628 patients who initiated SGLT-2i and TZD (median age=57 years, 72% male). Compared with GLP-1RA, SGLT-2i showed a similar risk of hepatic decompensation events (HR 0.93, 95% CI 0.76 to 1.14). Compared with TZD, SGLT-2i demonstrated a reduced risk of hepatic decompensation events (HR 0.77, 95% CI 0.72 to 0.82). As compared with TZD, the results of secondary analyses showed significantly lower hepatic decompensation event risks with SGLT-2i when stratified by sex (male: HR 0.87 (95% CI 0.80-0.94); female: HR 0.62 (95% CI 0.55-0.69)). CONCLUSIONS: In this nationwide cohort study, SGLT-2i was associated with a lower risk of hepatic decompensation events in patients with MASLD compared with TZD, while demonstrating similar effectiveness to GLP-1RA.

Sex-specific association between carbohydrate antigen 19-9 and incident type 2 diabetes.

Carbohydrate antigen 19-9 (CA19-9) levels are associated with glycemic control, insulin resistance, and chronic complications in patients with type 2 diabetes (T2D). Women generally show higher CA19-9 levels despite a greater T2D prevalence in men. We evaluated the sex-specific longitudinal associations between CA19-9 levels and T2D incidence. Korean adults (n = 329,380) without previous cancer or T2D were categorized into four groups based on their CA19-9 levels. The study end point was the development of incident T2D during follow-up. Cox proportional hazards models were used to estimate hazard ratios (HR) according to CA19-9 levels. During a median follow-up of 6.1 years (3.3-9.3 years), the incidence rates of T2D were 9.9 per 1,000 person-years in men and 3.6 per 1,000 person-years in women. In the time-dependent analysis, adjusted HRs (95% confidence intervals) for incident T2D comparing CA19-9 levels of 10.0-19.9, 20.0-29.9, and ≥ 30 U/mL to the reference (< 10 U/mL) were 1.08 (1.04-1.13), 1.18 (1.07-1.30), and 1.64 (1.35-1.99), respectively, among men. However, this association was not observed in women. The association between CA19-9 category and incident T2D significantly differed by sex (Pinteraction = 0.006). Among young and middle-aged Korean adults, elevated CA19-9 levels were significantly associated with increased risk of type 2 diabetes in men but not in women. Elevated CA19-9 levels in men could be a useful marker for identifying individuals at high risk of developing T2D. Evaluation approaches for individuals with elevated CA19-9 levels should be sex-specific.

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.

Different associations between body mass index and Alzheimer's markers depending on metabolic health.

BACKGROUND: Increasing evidence supports the association between body mass index (BMI), Alzheimer's disease, and vascular markers. Recently, metabolically unhealthy conditions have been reported to affect the expression of these markers. We aimed to investigate the effects of BMI status on Alzheimer's and vascular markers in relation to metabolic health status. METHODS: We recruited 1,736 Asians without dementia (71.6 ± 8.0 years). Participants were categorized into underweight, normal weight, or obese groups based on their BMI. Each group was further divided into metabolically healthy (MH) and unhealthy (MU) groups based on the International Diabetes Foundation definition of metabolic syndrome. The main outcome was Aß positivity, defined as a Centiloid value of 20.0 or above and the presence of vascular markers, defined as severe white matter hyperintensities (WMH). Logistic regression analyses were performed for Aß positivity and severe WMH with BMI status or interaction terms between BMI and metabolic health status as predictors. Mediation analyses were performed with hippocampal volume (HV) and baseline Mini-Mental State Examination (MMSE) scores as the outcomes, and linear mixed models were performed for longitudinal change in MMSE scores. RESULTS: Being underweight increased the risk of Aß positivity (odds ratio [OR] = 2.37, 95% confidence interval [CI] 1.13-4.98), whereas obesity decreased Aß positivity risk (OR = 0.63, 95% CI 0.50-0.80). Especially, obesity decreased the risk of Aß positivity (OR = 0.38, 95% CI 0.26-0.56) in the MH group, but not in the MU group. Obesity increased the risk of severe WMH (OR = 1.69, 1.16-2.47). Decreased Aß positivity mediate the relationship between obesity and higher HV and MMSE scores, particularly in the MH group. Obesity demonstrated a slower decline in MMSE (ß = 1.423, p = 0.037) compared to being normal weight, especially in the MH group. CONCLUSIONS: Our findings provide new evidence that metabolic health has a significant effect on the relationship between obesity and Alzheimer's markers, which, in turn, lead to better clinical outcomes.

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.

Menopausal stages and overactive bladder symptoms in middle-aged women: A cross-sectional study.

OBJECTIVE: To examine the prevalence of overactive bladder (OAB) according to menopausal stages in middle-aged women. DESIGN: Cross-sectional study. SETTING: Total Healthcare Center in South Korea. POPULATION: Middle-aged Korean women (n=3469, mean age, 49.5 ± 2.9 years). METHODS: Menopausal stages were defined according to the Stages of Reproductive Aging Workshop +10 criteria, and menopausal symptoms were assessed using the Korean version of Menopause-Specific Quality of Life (MENQOL). Logistic regression models were used to estimate prevalence ratios with 95% confidence intervals for OAB according to menopausal stage and to assess the associations with menopausal symptoms. MAIN OUTCOME MEASURES: OAB symptoms were evaluated using the Overactive Bladder Symptom Score (OABSS). RESULTS: The prevalence of OAB increased with menopausal stage; however, the multivariable-adjusted prevalence ratios for women in menopausal transition and postmenopausal stage were insignificant (ptrend = 0.160) compared to those for premenopausal women. Among individual OAB symptoms, the multivariable-adjusted prevalence ratios for nocturia increased with menopausal stage in a dose-response manner (ptrend = 0.005 for 1 time/day; ptrend < 0.001 for ≥2 times/day). The association between menopausal stages and nocturia occurring ≥2 times/day was evident in women without OAB and with relatively high MENQOL scores, vasomotor symptoms and difficulty sleeping. CONCLUSIONS: The prevalence of OAB, particularly nocturia, increased with menopausal stage, and the association was obvious in women with other menopausal symptoms. This finding underscores the importance of addressing nocturia as a potential menopausal symptom in middle-aged women. Further studies are required to understand the mechanisms linking OAB with menopausal symptoms in middle-aged women.

Exploring transfer effects on memory and its neural mechanisms through a computerized cognitive training in mild cognitive impairment: randomized controlled trial.

BACKGROUND: Computerized cognitive training (CCT) has been proposed as a potential therapy for cognitive decline. One of the benefits of CCT is a transfer effect, but its mechanism on the memory domain is unclear. This study aimed to investigate the transfer effect of non-memory multidomain CCT on the memory domain and its neural basis in patients with mild cognitive impairment (MCI) through a randomized controlled trial. METHODS: Patients with MCI recruited from memory clinics were randomly assigned to either the CCT or the control group. The CCT group received multidomain CCT training excluding memory training, while the control group read educational books with learning-based quizzes twice a week for 8 weeks. Participants underwent memory tests yielding a composite score, other cognitive domain tests, non-cognitive scales, and resting-state functional magnetic resonance imaging (rsfMRI), at baseline and after intervention. Within- and between-group comparisons, group × time interactions, and seed-to-voxel analyses in memory-involving brain networks were performed. RESULTS: The CCT group showed improvement over the control group in memory domain (Group × time, F = 5.87, P = 0.03, η2 = 0.31), which was related with the increased connectivity in the hippocampal-frontal and fusiform-occipital network. No other cognitive and non-cognitive symptoms differed between groups after adjusting for covariates. CONCLUSION: Eight weeks of multidomain CCT without memory training improved memory function and restored functional network in the hippocampal and medial temporal region in MCI patients. These results can provide evidence for the transferring ability of CCT on memory functioning with its neural basis.

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.

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.

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.

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.

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.

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.

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.

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.