Association between Body Mass Index and Gastric Cancer Risk According to Effect Modification by Helicobacter pylori Infection

Purpose Few studies investigated roles of body mass index (BMI) on gastric cancer (GC) risk according to Helicobacter pylori infection status. This study was conducted to evaluate associations between BMI and GC risk with consideration of H. pylori infection information. Materials and Methods We performed a case-cohort study (n=2,458) that consists of a subcohort (n=2,193 including 67 GC incident cases) randomly selected from the Korean Multicenter Cancer Cohort (KMCC) and 265 incident GC cases outside of the subcohort. H. pylori infection was assessed using an immunoblot assay. GC risk according to BMI was evaluated by calculating hazard ratios (HRs) and their 95% confidence intervals (95% CIs) using weighted Cox hazard regression model. Results Increased GC risk in lower BMI group (= 25 kg/m(2)) showed non-significantly increased GC risk (HR, 10.82; 95% CI, 1.25 to 93.60 and HR, 11.33; 95% CI, 1.13 to 113.66, respectively). However, these U-shaped associations between BMI and GC risk were not observed in the group who had ever been infected by H. pylori. Conclusion This study suggests the U-shaped associations between BMI and GC risk, especially in subjects who had never been infected by H. pylori.Peer reviewe

The Effect of Breastfeeding Duration and Parity on the Risk of Epithelial Ovarian Cancer: A Systematic Review and Meta-analysis

Review Objectives: We conducted a systematic review and meta-analysis to summarize current evidence regarding the association of parity and duration of breastfeeding with the risk of epithelial ovarian cancer (EOC). Methods: A systematic search of relevant studies published by December 31, 2015 was performed in PubMed and EMBASE. A random-effect model was used to obtain the summary relative risks (RRs) and 95% confidence intervals (CIs). Results: Thirty-two studies had parity categories of 1, 2, and ≥3. The summary RRs for EOC were 0.72 (95% CI, 0.65 to 0.79), 0.57 (95% CI, 0.49 to 0.65), and 0.46 (95% CI, 0.41 to 0.52), respectively. Small to moderate heterogeneity was observed for one birth (p<0.01; Q=59.46; I 2 =47.9%). Fifteen studies had breastfeeding categories of <6 months, 6-12 months, and >13 months. The summary RRs were 0.79 (95% CI, 0.72 to 0.87), 0.72 (95% CI, 0.64 to 0.81), and 0.67 (95% CI, 0.56 to 0.79), respectively. Only small heterogeneity was observed for <6 months of breastfeeding (p=0.17; Q=18.79, I 2 =25.5%). Compared to nulliparous women with no history of breastfeeding, the joint effects of two births and <6 months of breastfeeding resulted in a 0.5-fold reduced risk for EOC. Conclusions: The first birth and breastfeeding for <6 months were associated with significant reductions in EOC risk. Key words: Ovarian neoplasms, Parity, Breast feeding, Reproduction, Risk factors, Meta-analysis Received: June 29, 2016 Accepted: September 8, 2016 Corresponding author: Suekyung Park, MD, PhD 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea Tel: +82-2-740-8338, Fax: +82-2-747-4830 E-mail: [email protected] This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. INTRODUCTION Worldwide, ovarian cancer is the seventh most common cancer in women. Furthermore, it is the sixth leading cause of cancer deaths in women and the second most common cause of death among those with gynecologic cancers 350 to 8%), germ cell tumors (3% to 5%), and other rare types of ovarian cancer Most ovarian cancers are life-threatening and are notorious for having a poor prognosis, as they are usually diagnosed at an advanced stage. Moreover, screening results based on pelvic imaging or tumor markers for early detection remain unsatisfactory Reproductive risk factors for epithelial ovarian cancer (EOC) have been extensively explored in epidemiologic studies. For instance, a pooled analysis of 12 US case-control studies in 1992 showed that parous women and those who had breastfed had a lower risk of EOC Since 1992, many studies from around the world have reported associations of parity and breastfeeding with ovarian cancer. However, findings concerning the protective role of increasing parity and duration of breastfeeding remain inconsistent. For parity, some studies have indicated that the first birth reduces ovarian cancer risk more than subsequent births Therefore, we conducted a systematic review and metaanalysis to summarize the current evidence regarding the association of parity and duration of breastfeeding with EOC risk. The aim of this study was to clarify the threshold for risk reduction among the studies without heterogeneity across the results. An additional aim was to perform a meta-analysis to estimate the joint risk reductions associated with parity and breastfeeding. METHODS Search Strategy We performed a literature search including studies published through December 2015 using the following search terms in the PubMed and EMBASE databases (1) (parity or "number of live births") and (ovary or ovarian) and (cancer or tumor or neoplasm or malignancy) or (2) (breastfeeding or lactation) and (ovary or ovarian) and (cancer or tumor or neoplasm or malignancy). Furthermore, to find any additional published studies, a manual search was performed by checking all references of prior meta-analyses [5,6.8,20-23] and of all the original studies. This systematic review was planned, conducted, and reported in adherence to the standards of quality for reporting meta-analyses Study Selection To be included, studies had to meet the following criteria: (1) the studies were observational (case-control or cohort studies), (2) the exposures of interest were the number of live births and the total duration of breastfeeding, (3) the outcome of interest was EOC, (4) odds ratios (ORs) or relative risk (RR) estimates with 95% confidence intervals (CIs) were reported or sufficient data were present to allow the calculation of these effect measures, and (5) articles were published in the English language. In the case of overlapping data, the study with the largest number of cases was included. As fertility treatments and BRCA mutation effects on EOC may alter the association between parity/breastfeeding and EOC [26], we excluded studies conducted on specific populations, such as BRCA-1 or BRCA-2 mutation carriers or infertile women treated with fertility drugs. The detailed steps of our literature search are shown in Data Extraction Data extraction was conducted independently by two authors. Disagreements were discussed and resolved by consensus. The following data were collected from each study: the first author's last name, publication year, study region and design, study period, participant age, sample size (cases and 351 Parity and Breastfeeding Effects on Ovarian Cancer Risk controls or cohort size), exposure variables (parity or total breastfeeding duration), study-specific adjusted RR or OR with 95% CIs for each exposure category, and factors matched or adjusted for in the design or data analysis. If no adjusted RR or OR was presented, we included crude estimates. If no RRs or ORs were presented in a given study, we calculated them and the 95% CIs according to the raw frequencies presented in the article. The quality of the study was assessed independently by two authors using the 9-star Newcastle-Ottawa Scale (range, 0 to 9 stars) Statistical Analysis The study-specific RRs or ORs with 95% CIs were used to determine the principal outcome. Because the OR closely approximates the RR for rare diseases, the RR can be estimated from a case-control study using the OR as an approximation One study did not provide the required risk estimates for analysis or separate the risk estimates for different categories of parity or breastfeeding duration. For this study, we used the method proposed by Fleiss and Gross [30]. This method allows adjusted effect estimates and CIs to be calculated for any alternative comparison of levels and can help in a dose-response meta-analysis. Briefly, we combined risk estimates obtained through a simple fixed-effects meta-analysis wherein the subjects were divided into unexposed groups (i=0) and exposed groups (i=1, …, n), and estimates (Ri) with lower and upper 95% CIs were available. To obtain the R1+, we meta-analyzed R1, R2, R3, …, Rn using a fixed-effect model. The categories of parity or breastfeeding duration varied across studies; accordingly, the number of studies included in each metaanalysis and the summary RRs in each meta-analysis were different depending upon the number of categories. Statistical heterogeneity among studies was evaluated with the Cochran Q and I-squared statistics 352 with ≤7 stars considered low-quality as per the 9-star Newcastle-Ottawa Scale; and (3) year of publication (<2000, ≥ 2000), respectively. Publication bias was evaluated using the Begg rank correlation and the Egger linear regression test, in which p-vlaue <0.05 were considered representative of statistically significant publication bias From the meta-analyzed result, to calculate the RR for the joint effect of parity and breastfeeding, we applied the log-linear dose-response model proposed by Berlin et al. We configured the following formula for the multivariate linear logit regression of two factors: Logit P=α + β1χ1 + β2χ2; where P is the probability of a particular outcome (EOC risk), α is the intercept from the linear regression equation, β is the regression coefficient multiplied by some value of the predictor, and χ is the risk factor (parity and breastfeeding). Using this equation yields the value of the RR for the joint effects of parity and breastfeeding duration. For example, in the case of a subject who has no risk factors, logit(P) is α. In this case, the probability of EOC is exp(α)=1.0. In the case of a subject with only χ1, logit(P) is α+β1. In the case of a subject with both χ1 and χ2, logit(P) is α+β1+β2. Accordingly, the probability of EOC is exp(β1+β2)=OR1×OR2. Since the category of parity and breastfeeding duration varied across studies, to calculate the RR for the joint effect of parity and breastfeeding, we used the summary RR for parity and breastfeeding duration that contained the largest number of studies. All statistical analyses were performed with Stata version 12.0 (StataCorp., College Station, TX, USA). RESULTS Study Characteristics The characteristics of the 32 studies included with data regarding parity and the 15 studies included with data regarding breastfeeding are shown in Supplemental 353 Parity and Breastfeeding Effects on Ovarian Cancer Risk Africa. For breastfeeding, two cohort studies and 13 case-control studies were included. The included studies were conducted between 1978 and 2008. Of the 15 studies, seven were performed in North America, six in Europe, one in Asia, and one in Australia. Parity and Epithelial Ovarian Cancer Risk Thirty-two studies had parity categories of 1, 2, and ≥3. The summary RRs for the first, second, and third births were 0.72 (95% CI, 0.65 to 0.79), 0.57 (95% CI, 0.49 to 0.65), and 0.46 (95% CI, 0.41 to 0.52), respectively Duration of Breastfeeding and Epithelial Ovarian Cancer Risk Fifteen studies had breastfeeding categories of <6 months, 6-12 months, and ≥13 months. The summary RRs for these categories were 0.79 (95% CI, 0.72 to 0.87), 0.72 (95% CI, 0.64 to 0.81) and 0.67 (95% CI, 0.56 to 0.79), respectively Subgroup Analysis According to Study Design, Study Quality, and Publication Year The results from the subgroup analysis according to study design, study quality, and publication year are shown in Relative Risk for the Joint Effect of Parity and Breastfeeding The RR for the joint effect of parity and breastfeeding, obtained using the summary RR from the analysis of 32 studies with parity categories of 1, 2, and ≥3 and 15 studies with breastfeeding categories of <6 months, 6-12 months, and ≥ 13 months, is shown in DISCUSSION The findings of this meta-analysis indicate that parity and breastfeeding experiences in women can help prevent EOC, which is typically life-threatening and has a poor prognosis. In particular, the first birth and the first six months of breastfeeding had a greater protective effect than did subsequent births and/or additional breastfeeding, although multiparity and additional breastfeeding did provide some additional protection. The risk reduction effect of the first birth on EOC risk was almost 30%, and the combined effect of the first birth and <6 months of breastfeeding was 40%; thus, breastfeeding provided a nearly 10% greater risk reduction. In regards to parity, the EOC risk reduction was highest for the first birth, with some additional protection from the second birth. However, slightly less risk reduction was observed for the third birth Pregnancy and breastfeeding are thought to reduce EOC risk Ho Kyung Sung, et al. 354 by decreasing pituitary gonadotropin levels and inducing anovulation [7,35]. Pregnancy and breastfeeding are expected to decrease the likelihood of spontaneous genetic mutation under the incessant ovulation hypothesis and of the hyperproliferation of inclusion cysts under the gonadotropin hypothesis. However, the observation that multiparity and additional breastfeeding did not provide an equal amount of protection does not provide evidence for either of these hypotheses. Nev- The summary RRs (95% CIs) in each meta-analysis were estimated using a random effect model. 3 Studies with ≥8 stars were considered high-quality as per the 9-star Newcastle-Ottawa Scale. 4 Studies with ≤7 stars were considered low-quality as per the 9-star Newcastle-Ottawa Scale. 355 Parity and Breastfeeding Effects on Ovarian Cancer Risk ertheless, the results of two experimental studies provide biological evidence for the relatively weaker protective effect of additional parity and breastfeeding [36,37]. For instance, high progesterone levels during pregnancy can increase apoptosis, which may clear transformed cells from the ovarian epithelium, meaning that all the accumulated transformed cells are washed fully out by the first pregnancy. Therefore, the first pregnancy provides a stronger protective effect than subsequent pregnancies [36]. In regards to breastfeeding, breastfeeding in the first few months completely inhibits the pulsatile secretion of gonadotropin-releasing hormone and luteinizing hormone, leading to suppression of ovulation [37]. After a couple of months, ovulatory activity may return, even though breastfeeding continues [37]; thus, a longer duration of breastfeeding does not provide an additional protective effect. Our finding of decreased EOC risk with longer breastfeeding is similar to that reported by prior meta-analyses in 2013 and 2014 [22,23], but differs from that of a meta-analysis of nine case-control studies conducted in developed countries in 2001, in which breastfeeding for ≥12 months was associated with a significant 0.72-fold reduced risk of EOC compared to never having breastfed, while breastfeeding <12 months did not show such an association (OR, 0.95; 95% CI, 0.80 to 1.12) The strength of this meta-analysis is that it included all available studies, and the large number of EOC cases allowed for the investigation of the risk associated with different categories of parity and breastfeeding duration. However, the current study also has several limitations. First, our meta-analysis wa

Predictors of all-cause mortality among 514,866 participants from the Korean National Health Screening Cohort

<div><p>Background</p><p>There is not enough evidence regarding how information obtained from general health check-ups can predict individual mortality based on long-term follow-ups and large sample sizes. This study evaluated the applicability of various health information and measurements, consisting of self-reported data, anthropometric measurements and laboratory test results, in predicting individual mortality.</p><p>Methods</p><p>The National Health Screening Cohort included 514,866 participants (aged 40–79 years) who were randomly selected from the overall database of the national health screening program in 2002–2003. Death was determined from causes of death statistics provided by Statistics Korea. We assessed variables that were collected at baseline and repeatedly measured for two consecutive years using traditional and time-variant Cox proportional hazards models in addition to random forest and boosting algorithms to identify predictors of 10-year all-cause mortality. Participants’ age at enrollment, lifestyle factors, anthropometric measurements and laboratory test results were included in the prediction models. We used c-statistics to assess the discriminatory ability of the models, their external validity and the ratio of expected to observed numbers to evaluate model calibration. Eligibility of Medicaid and household income levels were used as inequality indexes.</p><p>Results</p><p>After the follow-up by 2013, 38,031 deaths were identified. The risk score based on the selected health information and measurements achieved a higher discriminatory ability for mortality prediction (c-statistics = 0.832, 0.841, 0.893, and 0.712 for Cox model, time-variant Cox model, random forest and boosting, respectively) than that of the previous studies. The results were externally validated using the community-based cohort data (c-statistics = 0.814).</p><p>Conclusions</p><p>Individuals’ health information and measurements based on health screening can provide early indicators of their 10-year death risk, which can be useful for health monitoring and related policy decisions.</p></div

10-year mortality risk in the National Health Insurance Service—National Health Screening Cohort (NHIS-HEALS) from 2002 to 2013.

<p>10-year mortality risk in the National Health Insurance Service—National Health Screening Cohort (NHIS-HEALS) from 2002 to 2013.</p

Individualized Biological Age as a Predictor of Disease: Korean Genome and Epidemiology Study (KoGES) Cohort

Chronological age (CA) predicts health status but its impact on health varies with anthropometry, socioeconomic status (SES), and lifestyle behaviors. Biological age (BA) is, therefore, considered a more precise predictor of health status. We aimed to develop a BA prediction model from self-assessed risk factors and validate it as an indicator for predicting the risk of chronic disease. A total of 101,980 healthy participants from the Korean Genome and Epidemiology Study were included in this study. BA was computed based on body measurements, SES, lifestyle behaviors, and presence of comorbidities using elastic net regression analysis. The effects of BA on diabetes mellitus (DM), hypertension (HT), combination of DM and HT, and chronic kidney disease were analyzed using Cox proportional hazards regression. A younger BA was associated with a lower risk of DM (HR = 0.63, 95% CI: 0.55&ndash;0.72), hypertension (HR = 0.74, 95% CI: 0.68&ndash;0.81), and combination of DM and HT (HR = 0.65, 95% CI: 0.47&ndash;0.91). The largest risk of disease was seen in those with a BA higher than their CA. A consistent association was also observed within the 5-year follow-up. BA, therefore, is an effective tool for detecting high-risk groups and preventing further risk of chronic diseases through individual and population-level interventions

Contribution of major risk factors of all-cause of death risk score in the National Health Insurance Service—National Health Screening Cohort (NHIS-HEALS) from 2002 to 2013.

<p>Contribution of major risk factors of all-cause of death risk score in the National Health Insurance Service—National Health Screening Cohort (NHIS-HEALS) from 2002 to 2013.</p

Results from cross validation with bootstrapping and external validation of risk prediction models to estimate 10-year mortality risk by the combination of major risk factors of all-cause of death in the National Health Insurance Service—National Health Screening Cohort (NHIS-HEALS) from 2002 to 2013.

<p>Results from cross validation with bootstrapping and external validation of risk prediction models to estimate 10-year mortality risk by the combination of major risk factors of all-cause of death in the National Health Insurance Service—National Health Screening Cohort (NHIS-HEALS) from 2002 to 2013.</p

Effects of Boundary Condition Models on the Seismic Responses of a Container Crane

In recent years, several large earthquakes have caused the collapse of container cranes, which have resulted in halting of freighting, and significantly affected the economy. Some reports are concerned the uplift and derailment events of crane legs, and the collapse of the crane itself. In this study, the effects of different boundary conditions used in the numerical method are investigated for a container crane under seismic excitation. Three different boundary conditions are considered in terms of the connection of the crane&rsquo;s legs (wheels) and the ground (rails), namely pin support (PIN), gap element (GAP), and Friction contact (FC) elements, by using the SAP2000 program for a typical container crane. Then, time history dynamic analyses are conducted using nine recorded ground motions. Dynamic behaviors of the container crane are studied in terms of the total base shear, portal drift, and relative displacement of legs, by investigating the three types of base boundary conditions. The results of the study show that when the intensity of earthquakes is large enough to create uplift and derailment events, the selection of the boundary condition model considerably affects the dynamic responses of the container crane. In addition, when uplift and derailment of the crane occur, the FC support condition is the most compatible with the real behavior of the crane. On the other hand, under low seismic excitation, there is no significant difference of the crane behavior according to the choice of boundary condition model

Sensitivity Analysis for Ship-to-Shore Container Crane Design

Ship-to-shore (STS) container cranes are important pieces of equipment in container terminals for container handling, so they need to be properly designed to avoid damage or collapse due to natural hazards (i.e., storms or earthquakes). However, the dynamic analyses necessary for this can be cost- and time-consuming because of the need to consider the time history of ground motions and several sources of uncertainty. Thus, sensitivity analysis on the input parameters to the responses of the structures is needed to categorize which sources of uncertainty are significant enough to be considered as random variables. In this study, an investigation is carried out into the sensitivity of some sources of uncertainty to the seismic response of a Korean container crane structure. The input random variables studied include ground motion intensity, ground motion profiles, mass, damping, and elastic modulus of steel. Nonlinear dynamic analyses are conducted using a set of 20 natural ground motions scaled to three ground motion intensity levels, in compliance with the Korean Design Standard. The method of deterministic sensitivity analysis using the so-called tornado diagram is applied for the evaluation of structural systems. For the studied type of Korean container crane, it can be stated that the intensity of ground motions (i.e., spectral acceleration) is the most significant input parameter on the response of the structure, as measured in terms of portal drift, vertical reaction of the crane&rsquo;s legs, and total base shear. The next most significant influencing factors are the mass of the structure and the characteristics of every ground motion. Damping plays a relatively important role on the total base shear, while it shows almost no impact on the axial reaction of the crane&rsquo;s legs. Of the three engineering design parameters (portal drift, vertical reaction, and total base shear), the elastic modulus exhibits a low effect, but it should be considered a source of uncertainty in seismic analysis

Associations of urinary sodium levels with overweight and central obesity in a population with a sodium intake

Abstract Background Previous studies have reported an association between dietary sodium intake and overweight/central obesity. However, dietary survey methods were prone to underestimate sodium intake. Therefore, this study investigated the associations of calculated 24-h urinary sodium excretion, an index of dietary sodium intake, with various obesity parameters including body mass index (BMI) and waist circumference (WC) in a population with a relatively high sodium intake. Methods A total of 16,250 adults (aged ≥19 years) and 1476 adolescents (aged 10-18 years), with available information on spot urine sodium levels and anthropometric measurements from the Korea National Health and Nutrition Examination Survey (KNHANES) were included in this study. We calculated 24-h urine sodium excretion levels from spot urine sodium levels using the Tanaka formula. Results In adults, those with high sodium excretion levels (≥ 3200 mg) showed increased odds of overweight and central obesity compared to those with low urinary sodium excretion level (< 2200 mg) (odds ratio [OR] = 2.17, 95% confidence interval [CI] = 1.90-2.49 for overweight; OR = 2.50, 95% CI = 2.13-2.94 for central obesity). These associations were also observed in adolescents (OR = 5.80, 95% CI = 3.17-10.60 for overweight; OR = 4.19, 95% CI = 1.78-9.89 for central obesity). Conclusions The present study suggests that reducing salt intake might be important for preventing overweight and central obesity, especially in adolescents. However, because the present study was conducted with cross-sectional study design, further longitudinal studies are warranted to confirm the causal relationship between urinary sodium excretion and overweight/central obesity