METHODS: Single nucleotide polymorphisms (SNP) in the fatty acid desaturase 1 (FADS1) gene is suggested as risk factor of metabolic diseases in genome-wide association studies (GWAS). This study hypothesized that FADS1_rs174546T associates with serum triglycerides (TG) in Korean Genome and Epidemiology Study (KoGES). In addition, functional study of SNP genotypes in cultured cells is performed.
RESULTS: FADS1_rs174546T is associated with high level of serum TG (effect size of variant: 6.48 ± 1.84 mg dL-1) in Korean individuals (normotriglyceridemia, n = 5128; hypertriglyceridemia, n = 3714). Functional study in cells with FADS1_rs174546T, shows reduced transcriptional activity, when compared with rs174546C. MiR-6728-3p, which is predicted to bind with rs174546T, decreases transcriptional activity of rs174546T but not in rs174546C, and it is reversed by miR-6728-3p inhibitor. Formononetin is selected as binding molecule to 3\'-UTR of FADS1 and increases luciferase activity in both rs174546 (C/T). Moreover, formononetin compensates for the reduced luciferase activity by rs174546T and miR-6728-3p. Formononetin also increases endogenous FADS1 expression and long-chain polyunsaturated fatty acid (LC-PUFA) ratio.
CONCLUSIONS: FADS1_rs174546T is a crucial risk factor for hypertriglyceridemia in the Koreans potentially through the interaction with miR-6728-3p. Formononetin can be a potent dietary intervention to prevent and improve hypertriglyceridemia in both rs174546 (C/T) populations.

Insights from public DNA methylation data derived from cancer or normal tissues from cancer patients or healthy people can be obtained by machine learning. The goal is to determine methylation patterns that could be useful for predicting the prognosis for cancer patients and correcting lifestyles for healthy people. DNA methylation data were obtained from the DNA of 446 healthy participants from the Korean Genome Epidemiology Study (KoGES) and from the DNA of normal tissues or from cancer tissues of 11 types of carcinomas from The Cancer Genome Atlas (TCGA) database. To correct for the batch effect, R\'s ComBat function was used. Using the K-mean clustering (k = 3), the survival rates of the cancer patients and the incidence of chronic diseases were compared between the three clusters for TCGA and KoGES, respectively. Based on the public DNA methylation and clinical data of healthy participants and cancer patients, I present an analysis pipeline that integrates and clusters the methylation data from the two groups. As a result of clustering, CpG sites from gene or genomic regions, such as AFAP1, NINJ2, and HOOK2 genes, that correlated with survival rate and chronic disease are presented.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a dynamic disease with a high socioeconomic burden. Using data collected prospectively from the general population, we examined factors related to the transition of at-risk individuals to COPD.
METHODS: We used the Korean Genome Epidemiology Study (KoGES) database, defining pre-COPD based on respiratory symptoms and radiological abnormalities suggestive of COPD; the preserved ratio impaired spirometry (PRISm) was defined as a forced expiratory volume in 1s (FEV1)/forced vital capacity ratio≥70% and FEV1<80%, as predicted by spirometry. We determined group differences in the rate of lung function decline, risk of future airflow obstruction (AFO).
RESULTS: The study included 4762 individuals, and longitudinal analysis revealed distinct trends in pulmonary function indicators. Compared to the normal group, the pre-COPD group showed a more rapid decline in lung function, while the PRISm group showed a slower decline. In the pre-COPD and PRISm groups, 4.4% and 3.5%, and 13.6% and 10.8%, respectively, of patients had progressed to COPD at the first and second visits. Pre-COPD and PRISm contributed to an earlier time to first AFO, but consideration of comorbid cardiovascular disease weakened this relationship in the PRISm group. Multivariate logistic regression showed that pre-COPD and PRISm are significant risk factors for future development of COPD (OR 1.80, p<0.001; OR 4.26, p<0.001, respectively).
CONCLUSIONS: Pre-COPD and PRISm patients showed different trends in lung function changes over time and both were significant risk factors for future development of COPD.

UNASSIGNED: The epidemiological evidence regarding nut consumption and metabolic diseases focuses on Western populations. Nut consumption among Koreans is relatively low, and the prevalence of metabolic syndrome is rapidly increasing, highlighting the need for more focused studies in this population. This cross-sectional study aimed to investigate the relationship between nut consumption and metabolic syndrome in Korean adults.
UNASSIGNED: A total of 112,501 adults (39,481 men and 73,020 women) aged 40-79 years were selected from baseline data of the Korean Genome and Epidemiology Study-Health Examinees. Nut consumption was assessed using a validated semi-quantitative food frequency questionnaire and categorized as non-consumers, <1 serving/week, ≥1 to <2 servings/week, or ≥2 servings/week (15 g per serving). Metabolic syndrome and its components were defined according to the Korean Society of CardioMetabolic Syndrome criteria. Associations between nut consumption and metabolic syndrome and its components were examined using multiple logistic regression with adjustments for potential confounders.
UNASSIGNED: In the study population, the prevalence of metabolic syndrome was 21.4% (26.9% in men and 18.4% in women), and the mean nut intake was 0.8 serving/week (0.7 serving/week in men and 0.8 serving/week in women). After adjusting for confounders, higher nut consumption was associated with a lower odds ratio (OR) of metabolic syndrome in individuals consuming ≥2 servings/week compared with non-consumers [OR = 0.85, 95% confidence interval (CI) = 0.80-0.91, p for trend <0.001]. Specifically, in men, this level of consumption was associated with a 14% reduction in the OR of metabolic syndrome (OR = 0.86, 95% CI = 0.77-0.95, p for trend = 0.028). In women, a similar reduction of 14% was observed (OR = 0.86, 95% CI = 0.80-0.93, p for trend <0.001). Among the metabolic syndrome components, nut consumption was inversely associated with abdominal obesity, low high-density lipoprotein-cholesterol, and elevated triglycerides in men and women, whereas no associations were observed for elevated blood pressure or elevated fasting blood glucose.
UNASSIGNED: Our findings suggest that higher nut consumption is inversely associated with metabolic syndrome and its components in Korean adults. Further studies are needed to examine the longitudinal association between nut consumption and metabolic diseases in this population.

OBJECTIVE: This cross-sectional study investigated the relationship of nightmares with cardio-cerebrovascular disease (CVD), hypertension and hyperlipidemia which are major preceding diseases of CVD in older adults.
METHODS: Participants (n = 2824; mean age 63.6 ± 6.6 years, females 49.3%) completed the Disturbing Dream and Nightmare Severity Index (DDNSI), which was used to divide the sample into either the Nightmare or Non-Nightmare group (cut-off score ≥ 10). Demographic information, history of CVD (cerebrovascular disease, myocardial infarction, congestive heart failure, coronary artery disease, and arrhythmia), hypertension, hyperlipidemia, and self-report questionnaires about stress (Perceived Stress Scale), depression (Beck Depression Inventory), sleep quality (Pittsburgh Sleep Quality Index), and insomnia symptoms were also collected.
RESULTS: Among the sample, 379 participants (13.4%) reported experiencing nightmares more than once a year, and 73 participants (2.6%) were classified as having nightmare disorder based on DDNSI scores (≥10). 11.3% of participants (n = 319) reported having more than one CVD. Approximately half of the participants reported a history of hypertension (52.1%, n = 1471) and hyperlipidemia (47.7%, n = 1346). Logistic regression analysis indicated the Nightmare group was 2.04 times at higher risk for hyperlipidemia (OR = 2.04, 95% CI 1.22-3.40, p = .006) after controlling for covariates compared to the Non-Nightmare group. Although non-significant, there was a trend toward a higher risk of hypertension in the Nightmare group (OR = 1.67, 95% CI 0.99-2.84, p = .056).
CONCLUSIONS: Results of this study indicate frequent nightmares in older adults may be associated with hyperlipidemia, which are risk factors for CVD. Further studies are needed to explore nightmares\' directionality and health consequences in an aging population.

UNASSIGNED: This study constructed a comprehensive database of dietary antioxidants and total antioxidant capacity (TAC) to facilitate the estimation of daily antioxidant intake using a food frequency questionnaire (FFQ). This database was applied to 3 general population-based cohorts (n=195,961) within the Korean Genome and Epidemiology Study (KoGES).
UNASSIGNED: To establish a database of 412 foods derived from recipes of a 106-item FFQ, we followed a pre-established standardized protocol. This included the selection of source databases, matching of foods, substitution of unmatched items with identical foods and input of values, and assessment of coverage. For each food, the TAC was estimated by summing the individual antioxidant capacities, calculated by multiplying the amount of each antioxidant by its vitamin C equivalent antioxidant capacity.
UNASSIGNED: We identified 48 antioxidants across 5 classes: retinol, carotenoids, vitamins C and E, and flavonoids, with flavonoids divided into 7 subclasses. TAC values were then established. Coverage exceeded 90.0% for retinol, carotenoids, vitamin C, and vitamin E, while coverage for flavonoids was 60.9%. The daily intakes of 4 antioxidant classes-all but vitamin E-were higher in women than in men. The Ansan-Ansung cohort exhibited the highest levels of dietary TAC, vitamin E, and flavonoids, while the Health Examinees Study cohort displayed the highest values for retinol, carotenoids, and vitamin C.
UNASSIGNED: We customized a comprehensive antioxidant database for the KoGES FFQ, achieving relatively high coverage. This expansion could support research investigating the impact of dietary antioxidants on the development of chronic diseases targeted by the KoGES.

BACKGROUND: There is a noticeable lack of information on the levels of both non-essential and essential trace elements in women aged over 50. The main objective of this study is to investigate trace element concentrations and explore the influence of sociodemographic factors and dietary sources of exposure in this demographic.
METHODS: We analyzed 19 trace elements, including manganese, cobalt, copper, zinc, molybdenum, chromium, nickel, arsenic, strontium, cadmium, tin, antimony, cesium, barium, tungsten, mercury, thallium, lead, and uranium, using ICP-MS and mercury analyzer. Urine samples were obtained from a cohort of 851 women aged over 50 who participated in the 8th KoGES-Ansung study (2017-2018). Multiple linear models were employed to explore associations between urinary trace element concentrations and sociodemographic factors and dietary sources of exposure. We used K-means clustering to discern patterns of exposure to trace elements and identify contributing factors and sources.
RESULTS: Our findings indicate higher concentrations of molybdenum (Mo), arsenic (As), cadmium (Cd), and lead (Pb) in our study population compared to women in previous studies. The study population were clustered into two distinct groups, characterized by lower or higher urinary concentrations. Significant correlations between age and urinary concentrations were observed in Ni. Smoking exhibited positive associations with urinary Cd and As. Associations with dietary sources of trace elements were more distinct in women in the high-exposure group. Urinary antimony (Sb) was positively linked to mushroom and egg intake, As to mushroom and fish, and Hg to egg, dairy products, fish, seaweed, and shellfish.
CONCLUSIONS: Our study underscores the significant gap in understanding urinary concentrations of trace elements in women aged over 50. With higher concentrations of certain elements compared to previous studies and significant correlations between age, smoking, and specific food sources, it is imperative to address this gap through targeted dietary source-specific risk management.

OBJECTIVE: Late mealtime and short sleep are known to be associated with obesity risk due to a misaligned circadian rhythm. This study aimed to investigate the relationship between obesity and mealtime and sleep duration using the Korean Genome and Epidemiology Study (KoGES) data.
METHODS: Longitudinally prospective cohort study.
METHODS: Population-based.
METHODS: KoGES analysed data from 9,474 Korean adults with an average age of 54- years old at baseline.
METHODS: Meal timing was defined as the eating occasions of the day reported by the participant eating a 24-h dietary recall method. Sleep duration was categorized as <6, 6-7, 7-8, and ≥8 h. The Cox proportional hazard model was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident obesity according to meal timing, sleep duration, and nightly fasting duration.
RESULTS: During a mean follow-up of 3.5 years, 826 participants developed obesity. In the multivariable-adjusted analysis, midnight snack eating (HR, 1.20; 95% CI, 1.02-1.41) and higher energy intake from midnight snacks (HR, 1.26; 95% CI, 1.06-1.49) were associated with a higher risk of obesity. Sleeping 8 h or more (HR, 0.67; 95% CI, 0.53-0.85) was associated with a lower risk of obesity.
CONCLUSIONS: Our findings highlight the importance of meal and sleep times and suggest that healthy eating habits related to the time of day.

In recent studies, non-alcoholic fatty liver disease (NAFLD) has been associated with a high risk of ischemic heart disease. This study aimed to investigate a genetic variant within a specific gene associated with myocardial infarction (MI) among patients with NAFLD. We included 57,205 participants from a Korean genome and epidemiology study. The baseline population consisted of 45,400 individuals, with 11,805 identified as patients with NAFLD. Genome-wide association studies were conducted for three groups: the entire sample, the healthy population, and patients with NAFLD. We defined the p-value < 1 × 10-5 as the nominal significance and the p-value < 5 × 10-2 as statistically significant for the gene-by-nutrient interaction. Among the significant single-nucleotide polymorphisms (SNPs), the lead SNP of each locus was further analyzed. In this cross-sectional study, a total of 1529 participants (2.8%) had experienced MI. Multivariable logistic regression was performed to evaluate the association of 102 SNPs across nine loci. Nine SNPs (rs11891202, rs2278549, rs13146480, rs17293047, rs184257317, rs183081683, rs1887427, rs146939423, and rs76662689) demonstrated an association with MI in the group with NAFLD Notably, the MI-associated SNP, rs134146480, located within the SORCS2 gene, known for its role in secreting insulin in islet cells, showed the most significant association with MI (p-value = 2.55 × 10-7). Our study identifies candidate genetic polymorphisms associated with NAFLD-related MI. These findings may serve as valuable indicators for estimating MI risk and for conducting future investigations into the underlying mechanisms of NAFLD-related MI.

BACKGROUND: Genome-wide association studies have successfully identified genetic variants associated with human disease. Various statistical approaches based on penalized and machine learning methods have recently been proposed for disease prediction. In this study, we evaluated the performance of several such methods for predicting asthma using the Korean Chip (KORV1.1) from the Korean Genome and Epidemiology Study (KoGES).
RESULTS: First, single-nucleotide polymorphisms were selected via single-variant tests using logistic regression with the adjustment of several epidemiological factors. Next, we evaluated the following methods for disease prediction: ridge, least absolute shrinkage and selection operator, elastic net, smoothly clipped absolute deviation, support vector machine, random forest, boosting, bagging, naïve Bayes, and k-nearest neighbor. Finally, we compared their predictive performance based on the area under the curve of the receiver operating characteristic curves, precision, recall, F1-score, Cohen\'s Kappa, balanced accuracy, error rate, Matthews correlation coefficient, and area under the precision-recall curve. Additionally, three oversampling algorithms are used to deal with imbalance problems.
CONCLUSIONS: Our results show that penalized methods exhibit better predictive performance for asthma than that achieved via machine learning methods. On the other hand, in the oversampling study, randomforest and boosting methods overall showed better prediction performance than penalized methods.