Iodine intake from brown seaweed and the related nutritional risk assessment in Koreans.

BACKGROUND/OBJECTIVES: Although iodine is essential for thyroid hormone production and controls many metabolic processes, there are few reports on the iodine intake of the population because of the scarcity of information on the iodine content in food. This study estimated the iodine intake of Koreans from brown seaweed, the major source of iodine in nature. SUBJECTS/METHODS: The dietary intake data from the recent Korea National Health and Nutrition Examination Survey (2016-2021) and the iodine content in brown seaweed were used for the estimation. Nationwide brown seaweed samples were collected and prepared using the representative preparation/cooking methods in the Koreans' diet before iodine analysis by alkaline digestion followed by inductively coupled plasma mass spectrometry. RESULTS: The mean (± SE) iodine intake from sea mustard was 96.01 ± 2.36 µg/day in the Korean population. Although the iodine content in kelp was approximately seven times higher than that in sea mustard, the mean iodine intake from kelp (except broth) was similar to that of sea mustard, 115.58 ± 7.71 µg/day, whereas that from kelp broth was 347.57 ± 10.03 µg/day. The overall mean iodine intake from brown seaweed was 559.16 ± 13.15 µg/day, well over the Recommended Nutrient Intake of iodine for Koreans. Nevertheless, the median intake was zero because only 37.6% of the population consumed brown seaweed on the survey date, suggesting that Koreans do not consume brown seaweed daily. CONCLUSION: The distribution of the usual intake of iodine from brown seaweed in Koreans would be much tighter, resulting in a lower proportion of people exceeding the tolerable upper intake levels and possibly a lower mean intake than this study presented. Further study evaluating the iodine nutriture of Koreans based on the usual intake is warranted. Nevertheless, this study adds to the few reports on the iodine nutriture of Koreans.

Dementia Incidence Rate Before and After Implementing the National Responsibility Policy for Dementia Care in Patients With Vascular Risk Factors in Korea.

Background and Purpose: The National Responsibility Policy for Dementia Care was implemented in September 2017 in Korea. This study aimed to compare dementia incidence in Seoul and Gangwon-do before and after the implementation of this policy. Methods: We extracted insurance claim data from the Korean Health Insurance Review and Assessment Service for people diagnosed with diabetes, hypertension, or dyslipidemia for the first time in Seoul and Gangwon-do, Korea. We defined two enrollment groups based on the policy implementation date: 1) January 1, 2015 to December 31, 2016 (Index 1, pre-implementation), and 2) January 1, 2017 to December 31, 2018 (Index 2, post-implementation). Each group was followed up for 1 year from the time of enrollment. Then, we calculated hazard ratios to compare the incidence of dementia between the two groups, and between Seoul and Gangwon-do. Results: In Seoul, the incidence of dementia was significantly lower in Index 2 than in Index 1 (hazard ratio [HR], 0.926; 95% confidence interval [CI], 0.875-0.979). However, the incidence rate did not differ between the 2 groups (HR, 1.113; 95% CI, 0.966-1.281) in Gangwon-do. In Index 1, the incidence of dementia did not differ between Seoul and Gangwon-do (HR, 1.043; 95% CI, 0.941-1.156), but in Index 2, was significantly higher in Gangwon-do than in Seoul (HR, 1.240; 95% CI, 1.109-1.386). Conclusions: After implementing the National Responsibility Policy for Dementia Care, the dementia incidence rate decreased significantly in Seoul, consistent with other studies, but not in Gangwon-do.

Growth, Nutritional Status, and Dietary Intake Patterns Associated With Prolonged Breastfeeding in Young Korean Children: A Nationwide Cross-Sectional Study.

BACKGROUND: Although the optimal duration of breastfeeding remains unclear, breastfeeding is generally recommended exclusively for the first 6 months of life, which continues into late infancy. However, the awareness regarding the effects of long-term breastfeeding is relatively low compared with that of breastfeeding in early infancy. We aimed to investigate the growth and nutritional characteristics of the children with prolonged breastfeeding (PBF) over 1 year. METHODS: This cross-sectional study was based on the data of children aged 12 to 23 months from the National Health and Nutrition Examination Survey (2010-2020) conducted by the Korean Center for Disease Control and Prevention. Data on anthropometric measurements, dietary behavior, and food and nutrient intake were extracted, and the association between PBF and growth, nutritional status, and dietary patterns were analyzed. RESULTS: Of the 872 children with a birth weight of ≥ 2.5 kg in the final analysis, 34.2% continued breastfeeding over 12 months of age, and their median breastfeeding duration was 14.2 months. Children with PBF were more likely to have lower current body weight (P < 0.001) and weight gain (P < 0.001), lower daily protein (P = 0.012), calcium (P < 0.001), and iron (P < 0.001) intake per calorie compared with children weaned by 12 months of age or those who were never breastfed. Furthermore, they were started on complementary food at 6 months or later rather than 4-5 months (P < 0.001), consumed cow's milk earlier (P = 0.012), and consumed probiotics as dietary supplements (P < 0.001) significantly less commonly. When comparing the intake of food groups, children with PBF had a significantly higher intake of cereals and grains (P = 0.023) and fruits (P = 0.020) and a significantly lower intake of bean products (P = 0.020) and milk and dairy products (P = 0.003). CONCLUSION: Korean children who continued breastfeeding over 12 months of age showed distinct characteristics in terms of growth, nutritional status, and dietary patterns in the second year of life compared to children who did not. Long-term additional research on their growth and nutritional status may be needed; however, these findings are significant as important fundamental data for nutritional counseling to establish healthy PBF.

The association between MTHFR polymorphism, dietary methyl donors, and childhood asthma and atopy.

BACKGROUND: Studies investigating the genetic association of the C677T methylenetetrahydrofolate reductase (MTHFR) genotype and dietary methyl donors with asthma and atopy are limited, and have variable results. OBJECTIVE: To investigate the effect of dietary methyl donor intake on the risk of childhood asthma and atopy, based on the C677T polymorphism in the MTHFR gene. METHODS: This cross-sectional study included 2,333 elementary school children aged 6-8 years across Korea during 2005 and 2006, as part of the first Children's Health and Environmental Research survey. Genotyping for the MTHFR (rs1801133) polymorphism was performed using the TaqMan assay. Multivariable-adjusted logistic regression analysis was performed to determine a descriptive association between the dietary methyl donor intake, MTHFR polymorphism, and childhood asthma and atopy. RESULTS: Intake of dietary methyl donors like folates was significantly associated with a decreased risk of the wheezing symptom, in the past 12 months, and "ever asthma" diagnosis, respectively. Vitamin B6 intake was also associated with a decreased atopy risk. The T allele of the MTHFR (rs1801133) gene was significantly associated with a decreased risk of atopy. Increased intakes of folate, vitamin B2, and vitamin B6 were protective factors against atopy, especially in children with the T allele on the MTHFR gene, compared to those with lower intakes and the CC genotype. CONCLUSIONS: High intakes of dietary methyl donors were associated with reduced risk of atopy and asthma symptoms. These may have additive effects related to the susceptibility alleles of the MTHFR gene. The clinical implications require evaluation.

Evidence of risky driving in Korean older adults: A longitudinal cohort.

OBJECTIVES: The aim of this study was to determine the differences in the risk factors for dangerous driving between older adults with normal cognition and those with cognitive impairment. DESIGN: The driving risk questionnaire (DRQ) that was applied to a community-dwelling older adult cohort and 2 years of accident/violation records from the National Police Agency were analyzed. We conducted regression analyses with the presence or absence of risky driving based on records (accidents + violations) 2 years before and after evaluation as a dependent variable and dichotomized scores of each risky driving factor as independent variables. RESULTS: According to four identified factors-crash history, safety concern, reduced mileage, and aggressive driving-significant associations were found between risky driving over the past 2 years and crash history and for aggressive driving in the normal cognition group. In the cognitive impairment group, only crash history was significantly associated, although safety concerns showed a trend toward significance. CONCLUSIONS: In this study, it was suggested that the factors of DRQ have a significant association with actual risky driving. Our results are expected to contribute to establishing the evidence for evaluating and predicting risky driving and advising whether to continue driving in clinics.

The Association of Serum High-Sensitivity C-Reactive Protein Level With the Risk of Site-Specific Cancer Mortality: The Health Examinees (HEXA) Study Cohort.

Few studies have investigated the association between high-sensitivity C-reactive protein (hsCRP) level and site-specific cancer mortality. In this study, we aimed to examine the associations of hsCRP with overall and site-specific cancer mortality among South Koreans using data on the Health Examinees (HEXA) Study cohort (41,070 men and 81,011 women aged ≥40 years). We obtained mortality information from the National Statistical Office of Korea, which provided the dates and causes of all deaths occurring through December 31, 2015, by linking mortality data with each participant's unique national identifier. Cox proportional hazards and restricted cubic spline models were used to assess the association between hsCRP and cancer mortality with adjustment for covariates. An analysis of site-specific cancer mortality was focused on 5 major cancers (lung, liver, gastric, colorectal, and breast/prostate). Median hsCRP levels were 0.77 mg/L and 0.59 mg/L for men and women, respectively. A dose-response association between hsCRP and overall cancer mortality was observed in men but disappeared in women after exclusion of deaths occurring in the first 1 or 2 years of follow-up. Elevated hsCRP levels increased the risks of lung, liver, and gastric cancer mortality in men, but the risks of colorectal and breast cancer mortality were not increased. The dose-response association between hsCRP and cancer mortality was observed differently depending on site-specific cancer mortality by sex.

Association of serum high-sensitivity C reactive protein with risk of mortality in an Asian population: the Health Examinees cohort.

OBJECTIVES: This study aimed to examine the association of high-sensitivity C reactive protein (hsCRP) with mortality risk and the attenuated effect of non-communicable disease history (NCD history ) on the association. DESIGN: Prospective cohort study. SETTING: Health Examinees cohort. PARTICIPANTS: A total of 41 070 men and 81 011 women aged ≥40 years were involved (follow-up: 6.8 years). OUTCOME MEASURES: Data and cause of death occurring until 31 December 2015 were confirmed by death statistics from the National Statistical Office. We conducted advanced analysis after stratification by NCD history and sensitivity analysis after excluding death before 1 or 2 years from recruitment. Cox proportional hazard and restricted cubic spline models were used to assess the association. RESULTS: The association between serum hsCRP and risk of all-cause mortality was observed with strong linearity in both genders and was not influenced by NCD history . The association of serum hsCRP with risk of cancer mortality was not observed in women with NCD history , but the association with risk of cardiovascular disease (CVD) mortality was predominantly observed in men with NCD history . CONCLUSIONS: This study suggests a dose-response association of hsCRP with mortality risk, including cancer and CVD mortality, in Koreans with low serum hsCRP, although the association with cancer and CVD mortality risk could be influenced by gender and NCD history .

Association of body mass index and COPD exacerbation among patients with chronic bronchitis.

BACKGROUND AND OBJECTIVE: Chronic obstructive pulmonary disease (COPD) patients with a body mass index (BMI) < 25 kg/m2 are prone to develop adverse event of pharmacological treatment for frequent exacerbation. As chronic bronchitis (CB) is one of the strong risk factors of exacerbation, we investigated the associations between BMI and COPD exacerbations in patients with CB. METHODS: Patients with COPD were included from the Korean COPD Subgroup Study (KOCOSS), a multicenter observational cohort study. CB was defined using the St. George's Respiratory Questionnaire and the participants were categorized according to BMI cut-off of 25 kg/m2. Exacerbations during a 1-year follow-up were compared among four groups: non-CB with BMI ≥ 25 kg/m2, non-CB with BMI < 25 kg/m2, CB with BMI ≥ 25 kg/m2, and CB with BMI < 25 kg/m2. RESULTS: Among the 1264 patients with COPD, 451 (35.7%) had CB and 353 (27.9%) had both CB and BMI < 25 kg/m2. The COPD exacerbation risk increased across the non-CB with BMI < 25 kg/m2, CB with BMI ≥ 25 kg/m2, and CB with BMI < 25 kg/m2 groups (adjusted incidence rate ratio [95% confidence interval] 1.21 [0.89-1.62], 1.20 [0.77-1.88], and 1.41 [1.02-1.91], respectively, compared to the non-CB with BMI ≥ 25 kg/m2 group). CONCLUSIONS: COPD patients having both CB and a BMI < 25 kg/m2 are at higher risk of exacerbations. Considering that a BMI < 25 kg/m2 often limits treatment options preventing exacerbations, modified guidelines might be needed for non-obese CB patients in Asia.

Coffee consumption and diabetic retinopathy in adults with diabetes mellitus.

We aimed to evaluate the association between the prevalence of diabetic retinopathy (DR) and coffee consumption in a Korean population. This cross-sectional study was based on data from the 2008-2011 Korean National Health and Nutrition Survey. Among 37,753 survey participants, the data of 1350 subjects with type 2 diabetes who underwent DR examination were analyzed. DR was graded using the modified Airlie House classification system. Coffee consumption data were obtained through food frequency questionnaires and categorized into four groups: almost none, < 1 cup/day, 1 cup/day, and ≥ 2 cups/day. The relationship between DR and coffee consumption was evaluated using multivariable logistic regression models adjusted for age, sex, education, occupation, income, smoking, alcohol intake, body mass index, physical activity, hypertension, dyslipidemia, diabetes duration, and glycated hemoglobin. The prevalence of DR was 20.0%. Non-proliferative DR was observed in 87.8% of all DR patients, and proliferative DR in 12.2%. The prevalence of DR and vision-threatening DR showed a significantly decreasing tendency according to daily coffee consumption (P for trend 0.025 and 0.005, respectively) after adjustment for possible confounders. This tendency was more prominent in those aged < 65 years (P for trend 0.005 and 0.003, respectively). Our findings suggest coffee consumption might be associated with DR reduction especially in Koreans with diabetes mellitus aged < 65 years.

Analysis of treatment pattern of anti-dementia medications in newly diagnosed Alzheimer's dementia using OMOP CDM.

Anti-dementia medications are widely prescribed to patients with Alzheimer's dementia (AD) in South Korea. This study investigated the pattern of medical management in newly diagnosed patients with AD using a standardized data format-the Observational Medical Outcome Partnership Common Data Model from five hospitals. We examined the anti-dementia treatment patterns from datasets that comprise > 5 million patients during 2009-2019. The medication utility information was analyzed with respect to treatment trends and persistence across 11 years. Among the 8653 patients with newly diagnosed AD, donepezil was the most commonly prescribed anti-dementia medication (4218; 48.75%), followed by memantine (1565; 18.09%), rivastigmine (1777; 8.98%), and galantamine (494; 5.71%). The rising prescription trend during observation period was found only with donepezil. The treatment pathways for the three cholinesterase inhibitors combined with N-methyl-D-aspartate receptor antagonist were different according to the drugs (19.6%; donepezil; 28.1%; rivastigmine, and 17.2%; galantamine). A 12-month persistence analysis showed values of approximately 50% for donepezil and memantine and approximately 40% for rivastigmine and galantamine. There were differences in the prescribing pattern and persistence among anti-dementia medications from database using the Observational Medical Outcome Partnership Common Data Model on the Federated E-health Big Data for Evidence Renovation Network platform in Korea.

Quantitative computed tomography imaging-based classification of cement dust-exposed subjects with an artificial neural network technique.

BACKGROUND AND OBJECTIVE: Cement dust exposure is likely to affect the structural and functional alterations in segmental airways and parenchymal lungs. This study develops an artificial neural network (ANN) model for identifying cement dust-exposed (CDE) subjects using quantitative computed tomography-based airway structural and functional features. METHODS: We obtained the airway features in five central and five sub-grouped segmental regions and the lung features in five lobar regions and one total lung region from 311 CDE and 298 non-CDE (NCDE) subjects. The five-fold cross-validation method was adopted to train the following classification models:ANN, support vector machine (SVM), logistic regression (LR), and decision tree (DT). For all the classification models, linear discriminant analysis (LDA) and genetic algorithm (GA) were applied for dimensional reduction and hyperparameterization, respectively. The ANN model without LDA was also optimized by the GA method to observe the effect of the dimensional reduction. RESULTS: The genetically optimized ANN model without the LDA method was the best in terms of the classification accuracy. The accuracy, sensitivity, and specificity of the GA-ANN model with four layers were greater than those of the other classification models (i.e., ANN, SVM, LR, and DT using LDA and GA methods) in the five-fold cross-validation. The average values of accuracy, sensitivity, and specificity for the five-fold cross-validation were 97.0%, 98.7%, and 98.6%, respectively. CONCLUSIONS: We demonstrated herein that a quantitative computed tomography-based ANN model could more effectively detect CDE subjects when compared to their counterpart models. By employing the model, the CDE subjects may be identified early for therapeutic intervention.

Assessment of dietary exposure to heterocyclic amines based on the Korean total diet study.

Heterocyclic amines (HCAs) are contaminants in proteinaceous foods produced by cooking at high temperatures. This study was the first assessment of exposure to HCAs using the Korean total diet study. Twelve HCAs were analysed in 1,232 pooled samples using six isotope-labelled internal standards and HPLC-MS/MS. The daily intake of HCAs in the Korean population was estimated based on the concentration of HCAs in the total diet study samples and individual food consumption data from the Korean National Health and Nutrition Examination Survey. Among HCAs, the intake of ß-carbolines, such as harman and norharman, was the highest, followed by the intake of PhIP. The primary sources of HCA intake were meat, fish, shellfish, and beverages, including alcohol. The margin of exposure to PhIP was 2,349,000 at the average level and 373,000 at the 95th percentile in the Korean population. The estimated daily intake of all HCAs in the Korean population was considered safe.

Predictors of Newborn's Weight for Height: A Machine Learning Study Using Nationwide Multicenter Ultrasound Data.

There has been no machine learning study with a rich collection of clinical, sonographic markers to compare the performance measures for a variety of newborns' weight-for-height indicators. This study compared the performance measures for a variety of newborns' weight-for-height indicators based on machine learning, ultrasonographic data and maternal/delivery information. The source of data for this study was a multi-center retrospective study with 2949 mother-newborn pairs. The mean-squared-error-over-variance measures of five machine learning approaches were compared for newborn's weight, newborn's weight/height, newborn's weight/height2 and newborn's weight/hieght3. Random forest variable importance, the influence of a variable over average node impurity, was used to identify major predictors of these newborns' weight-for-height indicators among ultrasonographic data and maternal/delivery information. Regarding ultrasonographic fetal biometry, newborn's weight, newborn's weight/height and newborn's weight/height2 were better indicators with smaller mean-squared-error-over-variance measures than newborn's weight/height3. Based on random forest variable importance, the top six predictors of newborn's weight were the same as those of newborn's weight/height and those of newborn's weight/height2: gestational age at delivery time, the first estimated fetal weight and abdominal circumference in week 36 or later, maternal weight and body mass index at delivery time, and the first biparietal diameter in week 36 or later. These six predictors also ranked within the top seven for large-for-gestational-age and the top eight for small-for-gestational-age. In conclusion, newborn's weight, newborn's weight/height and newborn's weight/height2 are more suitable for ultrasonographic fetal biometry with smaller mean-squared-error-over-variance measures than newborn's weight/height3. Machine learning with ultrasonographic data would be an effective noninvasive approach for predicting newborn's weight, weight/height and weight/height2.

Prediction of newborn's body mass index using nationwide multicenter ultrasound data: a machine-learning study.

BACKGROUND: This study introduced machine learning approaches to predict newborn's body mass index (BMI) based on ultrasound measures and maternal/delivery information. METHODS: Data came from 3159 obstetric patients and their newborns enrolled in a multi-center retrospective study. Variable importance, the effect of a variable on model performance, was used for identifying major predictors of newborn's BMI among ultrasound measures and maternal/delivery information. The ultrasound measures included biparietal diameter (BPD), abdominal circumference (AC) and estimated fetal weight (EFW) taken three times during the week 21 - week 35 of gestational age and once in the week 36 or later. RESULTS: Based on variable importance from the random forest, major predictors of newborn's BMI were the first AC and EFW in the week 36 or later, gestational age at delivery, the first AC during the week 21 - the week 35, maternal BMI at delivery, maternal weight at delivery and the first BPD in the week 36 or later. For predicting newborn's BMI, linear regression (2.0744) and the random forest (2.1610) were better than artificial neural networks with one, two and three hidden layers (150.7100, 154.7198 and 152.5843, respectively) in the mean squared error. CONCLUSIONS: This is the first machine-learning study with 64 clinical and sonographic markers for the prediction of newborns' BMI. The week 36 or later is the most effective period for taking the ultrasound measures and AC and EFW are the best predictors of newborn's BMI alongside gestational age at delivery and maternal BMI at delivery.

A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder involving abnormalities of lung parenchymal morphology with different severities. COPD is assessed by pulmonary-function tests and computed tomography-based approaches. We introduce a new classification method for COPD grouping based on deep learning and a parametric-response mapping (PRM) method. We extracted parenchymal functional variables of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%) with an image registration technique, being provided as input parameters of 3D convolutional neural network (CNN). The integrated 3D-CNN and PRM (3D-cPRM) achieved a classification accuracy of 89.3% and a sensitivity of 88.3% in five-fold cross-validation. The prediction accuracy of the proposed 3D-cPRM exceeded those of the 2D model and traditional 3D CNNs with the same neural network, and was comparable to that of 2D pretrained PRM models. We then applied a gradient-weighted class activation mapping (Grad-CAM) that highlights the key features in the CNN learning process. Most of the class-discriminative regions appeared in the upper and middle lobes of the lung, consistent with the regions of elevated fSAD% and Emph% in COPD subjects. The 3D-cPRM successfully represented the parenchymal abnormalities in COPD and matched the CT-based diagnosis of COPD.

The impact of the shift system on health and quality of life of sleep technicians.

BACKGROUND: Sleep technicians are at high risk of shift work sleep disorders. We therefore aimed to identify the optimal shift system for sleep technicians. METHODS: We performed a nationwide survey of the work schedules, health and quality of life of sleep technicians using e-mail questionnaires including the Insomnia Severity Index (ISI), Epworth Sleep Scale (ESS), Functional Outcomes of Sleep Questionnaire-10 (FOSQ-10), Short Form-12 Health Survey (SF-12), and Hospital Anxiety and Depression Scale (HADS) in Korea. A multivariate general linear model was used to assess the effect of shift schedules on health and quality of life. RESULTS: Fifty-four technicians from 30 sleep laboratories participated. Their work schedules were classified as fixed night (F) (n = 18), slow rotation alternating from a night-only to a day-only schedule with a 3-months to one-year interval (S) (n = 20), rapid rotation within a week (R) (n = 5), night once a week (D+) (n = 5) and day (D) (n = 6). The adjusted ISI and HADS-anxiety scores were higher in F, S, and R than D and D+. Among night shift-dominant schedules, a less favorable profile was observed for R followed by F, and S regarding the ISI, FOSQ-10, mental SF-12 and HADS-depression. The physical SF-12 was lower in the order of R, S and F. The HADS-anxiety score was higher in the order of F, R and S. CONCLUSIONS: The S system appears to have the least negative effect on health and quality of life among night shift-dominant systems. The development of consensus guidelines for scheduling shifts in sleep laboratories is urged.

Long-term exposure to PM10 and NO2 in relation to lung function and imaging phenotypes in a COPD cohort.

BACKGROUND: Ambient air pollution can contribute to the development and exacerbation of COPD. However, the influence of air pollution on objective COPD phenotypes, especially from imaging, is not well studied. We investigated the influence of long-term exposure to air pollution on lung function and quantitative imaging measurements in a Korean cohort of participants with and without COPD diagnosis. METHODS: Study participants (N = 457 including 296 COPD cases) were obtained from the COPD in Dusty Areas (CODA) cohort. Annual average concentrations of particulate matter less than or equal to 10 µm in diameter (PM10) and nitrogen dioxide (NO2) were estimated at the participants' residential addresses using a spatial air pollution prediction model. All the participants underwent volumetric computerized tomography (CT) and spirometry measurements and completed survey questionnaires. We examined the associations of PM10 and NO2 with FVC, FEV1, emphysema index, and wall area percent, using linear regression models adjusting for age, gender, education, smoking, height, weight, and COPD medication. RESULTS: The age of study participants averaged 71.7 years. An interquartile range difference in annual PM10 exposure of 4.4 µg/m3 was associated with 0.13 L lower FVC (95% confidence interval (CI), - 0.22- -0.05, p = 0.003). Emphysema index (mean = 6.36) was higher by 1.13 (95% CI, 0.25-2.02, p = 0.012) and wall area percent (mean = 68.8) was higher by 1.04 (95% CI, 0.27-1.80, p = 0.008). Associations with imaging phenotypes  were not observed with NO2. CONCLUSIONS: Long-term exposure to PM10 correlated with both lung function and COPD-relevant imaging phenotypes in a Korean cohort.

Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison.

OBJECTIVE: This study determined the effect of dose reduction and kernel selection on quantifying emphysema using low-dose computed tomography (LDCT) and evaluated the efficiency of a deep learning-based kernel conversion technique in normalizing kernels for emphysema quantification. METHODS: A sample of 131 participants underwent LDCT and standard-dose computed tomography (SDCT) at 1- to 2-year intervals. LDCT images were reconstructed with B31f and B50f kernels, and SDCT images were reconstructed with B30f kernels. A deep learning model was used to convert the LDCT image from a B50f kernel to a B31f kernel. Emphysema indices (EIs), lung attenuation at 15th percentile (perc15), and mean lung density (MLD) were calculated. Comparisons among the different kernel types for both LDCT and SDCT were performed using Friedman's test and Bland-Altman plots. RESULTS: All values of LDCT B50f were significantly different compared with the values of LDCT B31f and SDCT B30f (p < 0.05). Although there was a statistical difference, the variation of the values of LDCT B50f significantly decreased after kernel normalization. The 95% limits of agreement between the SDCT and LDCT kernels (B31f and converted B50f) ranged from - 2.9 to 4.3% and from - 3.2 to 4.4%, respectively. However, there were no significant differences in EIs and perc15 between SDCT and LDCT converted B50f in the non-chronic obstructive pulmonary disease (COPD) participants (p > 0.05). CONCLUSION: The deep learning-based CT kernel conversion of sharp kernel in LDCT significantly reduced variation in emphysema quantification, and could be used for emphysema quantification. KEY POINTS: • Low-dose computed tomography with smooth kernel showed adequate performance in quantifying emphysema compared with standard-dose CT. • Emphysema quantification is affected by kernel selection and the application of a sharp kernel resulted in a significant overestimation of emphysema. • Deep learning-based kernel normalization of sharp kernel significantly reduced variation in emphysema quantification.

Vegetable dietary pattern may protect mild and persistent allergic rhinitis phenotype depending on genetic risk in school children.

BACKGROUND: The effect of diet on allergic rhinitis (AR), its severity in children, and whether it modifies AR depending on genetic susceptibility are unknown. We investigated the association between dietary patterns and AR in school children and the influence of diet on AR according to a genetic risk score (GRS). METHODS: Totally, 435 7-year-old school children were recruited from the Panel Study on Korean Children. We used dietary patterns (vegetable, sugar, and meat) and dietary inflammatory index (DII) as dietary parameters. AR and its severity were defined by questionnaires about treatment in the previous 12 months and the Allergic Rhinitis and its Impact on Asthma (ARIA) guideline, respectively. A GRS was calculated using 6 single nucleotide polymorphisms for allergic diseases. RESULTS: A vegetable diet containing a lot of anti-inflammatory nutrients and higher vitamin D level in blood were negatively correlated, while DII was positively correlated with triglyceride level and triglyceride/HDL cholesterol. Vegetable diet (aOR, 95% CI = 0.73, 0.58-0.94) and DII (1.13, 1.01-1.28) were associated with AR risk. In particular, a high-vegetable diet resulted in a lower risk of mild and persistent AR (aOR, 95% CI = 0.24, 0.10-0.56) while a high DII represented a higher risk (2.33, 1.06-5.10). The protective effect of vegetable diet on AR appeared only among children with a lower GRS (adjusted P = .018). CONCLUSIONS: A vegetable dietary pattern characterized by high intake of anti-inflammatory nutrients and higher vitamin D level in blood might be associated with a lower risk of mild and persistent AR. This beneficial effect is modified by a genetic factor.

Dynamic association between AT(N) profile and cognition mediated by cortical thickness in Alzheimer's continuum.

BACKGROUND: The recently-proposed National Institute on Aging and Alzheimer's Association research framework organizes Alzheimer's disease (AD) biomarkers based on amyloid/tau/neurodegeneration (AT(N)). This study investigated the mediating effect of structural change in brain MRI on changes in cognitive function according to initial AT(N) profiles. METHODS: We included 576 subjects (cognitively unimpaired (N = 136), mild cognitive impairment (N = 294), dementia (N = 146)) from the Alzheimer's disease Neuroimaging Initiative study. The parallel-process latent growth curve model was applied to test the mediational effect of cortical thickness growth trajectory between the initial AT(N) profiles and cognitive growth trajectory. RESULTS: In Alzheimer's continuum, only the A + T + (N)+ profile showed a mediational effect of the cortical thickness growth trajectory. A + T - (N)- was not sufficient to induce direct or indirect effects on cognitive dysfunction, and A + T + (N)- showed a significant direct path from an altered cortical thickness to cognitive decline. CONCLUSION: The sequential effect between changes in brain MRI and cognition varied by baseline AT(N) profile, suggesting the dynamic changes in the relationships among biomarkers in the current cascade model.