Joint Effects Model Research Articles

232-OR: The Joint Contribution of Social Determinants Factors to Mortality Risk in Adults with Diabetic Kidney Disease

Introduction: No studies have examined whether social determinants of health (SDOH) have a synergistic effect on health outcomes. The study aim is to examine the synergistic impact of socioeconomic status (SES), psychosocial and neighborhood factors on mortality in U.S. adults with diabetes and CKD. Methods: We analyzed data from NHANES (2005-2010) for adults linked to mortality follow-up. Primary outcome was all-cause mortality. CKD stage was based on eGFR and albumin-to-creatinine ratio. Diabetes was based on self-report or HbA1c ≥6.5%. Cox proportional hazards models was used to estimate the association between SES (poverty income ratio(PIR)), psychosocial (depression) and neighborhood factors (food insecurity-FI) on all-cause mortality as independent predictors and in a joint-effect model while controlling for covariates. Results: This study included 994 adults (representing 8,188,150 U.S. adults) with diabetes. The sample mean age was 66 years, 40% Non-Hispanic white, 27% Non-Hispanic black, 68% had a ≤high school degree, 39% had PIR of ≤130%, 49% with CKD stage 1-2 and 51% with CKD stage 3-5. In individuals with diabetes and CKD, an increase in PIR was associated with 19% lower mortality (unadjusted HR=0.81, 95% CI=0.68-0.96) while food insecurity (unadjusted HR=0.92, 95% CI=0.72-1.18) and depression (unadjusted HR=1.03, 95% CI=0.98-1.08) did not have a significant effect. After adjusting for demographics, lifestyle, access to care, risk factor control and comorbidities, FI was associated with a 29% higher mortality (adjusted HR=1.29, 95% CI=1.04-1.60); PIR was associated with 22% lower mortality (adjusted HR=0.78, 95% CI=0.61-1.01), marginally significant and depression remained non-significant (adjusted HR=1.02, 95% CI=0.98-1.07). SDOH factors such as FI are an important determinant of mortality risk in this population independent of other SDOH such as SES, psychosocial factor etc. and non-SDOH factors such as lifestyle, access to care, diabetes control etc. Disclosure M.N. Ozieh: None. E. Garacci: None. L.E. Egede: Consultant; Self; Novo Nordisk Inc. Research Support; Self; National Institute of Diabetes and Digestive and Kidney Diseases.

Assessment of Molecular Subtypes in Thyrotoxic Periodic Paralysis and Graves Disease Among Chinese Han Adults

Thyrotoxic periodic paralysis (TPP) is a potentially lethal complication of hyperthyroidism. However, only 1 specific susceptibility locus for TPP has been identified. Additional genetic determinants should be detected so that a prediction model can be constructed. To investigate the genetic architecture of TPP and distinguish TPP from Graves disease cohorts. This population-based case-control study used a 2-stage genome-wide association study to investigate the risk loci of TPP and weighted genetic risk score to construct a TPP prediction model with data from a Chinese Han population recruited in hospitals in China from March 2003 to December 2015. The analysis was conducted from November 2014 to August 2016. Loci specifically associated with TPP risk and those shared with Graves disease and prediction model of joint effects of TPP-specific loci. A total of 537 patients with TPP (mean [SD] age, 35 [11] years; 458 male) 1519 patients with Graves disease and no history of TPP (mean [SD] age, 38 [13] years; 366 male), and 3249 healthy participants (mean [SD] age, 46 [10] years; 1648 male) were recruited from the Han population by hospitals throughout China. Two new TPP-specific susceptibility loci were identified: DCHS2 on 4q31.3 (rs1352714: odds ratio [OR], 1.58; 95% CI, 1.35-1.85; P = 1.24 × 10-8) and C11orf67 on 11q14.1 (rs2186564: OR, 1.50; 95% CI, 1.29-1.74; P = 2.80 × 10-7). One previously reported specific locus was confirmed on 17q24.3 near KCNJ2 (rs312729: OR, 2.08; 95% CI, 1.83-2.38; P = 8.02 × 10-29). Meanwhile, 2 risk loci (MHC and Xq21.1) were shared by Graves disease and TPP. After 2 years of treatment, the ratio of persistent thyrotropin receptor antibody positivity was higher in patients with TPP than in patients with Graves disease and no history of TPP (OR, 3.82; 95% CI, 2.04-7.16; P = 7.05 × 10-6). The prediction model using a weighted genetic risk score and 11 candidate TPP-specific single-nucleotide polymorphisms had an area under the curve of 0.80. These findings provide evidence that TPP is a novel molecular subtype of Graves disease. The newly identified loci, along with other previously reported loci, demonstrate the growing complexity of the heritable contribution to TPP pathogenesis. A complete genetic architecture will be helpful to understand the pathophysiology of TPP, and a useful prediction model could prevent the onset of TPP.

Model to Calculate Harms and Benefits of Early vs Delayed Liver Transplantation for Patients With Alcohol-Associated Hepatitis.

Early liver transplantation (without requiring a minimum period of sobriety) for severe alcohol-associated hepatitis (AH) is controversial: many centers delay eligibility until a specific period of sobriety (such as 6 months) has been achieved. To inform ongoing debate and policy, we modeled long-term outcomes of early vs delayed liver transplantation for patients with AH. We developed a mathematical model to simulate early vs delayed liver transplantation for patients with severe AH and different amounts of alcohol use after transplantation: abstinence, slip (alcohol use followed by sobriety), or sustained use. Mortality of patients before transplantation was determined by joint-effect model (based on Model for End-Stage Liver Disease [MELD] and Lille scores). We estimated life expectancies of patients receiving early vs delayed transplantation (6-month wait before placement on the waitlist) and life years lost attributable to alcohol use after receiving the liver transplant. Patients offered early liver transplantation were estimated to have an average life expectancy of 6.55 life years, compared with an average life expectancy of 1.46 life years for patients offered delayed liver transplantation (4.49-fold increase). The net increase in life expectancy from offering early transplantation was highest for patients with Lille scores of 0.50-0.82 and MELD scores of 32 or more. Patients who were offered early transplantation and had no alcohol use afterward were predicted to survive 10.85 years compared with 3.62 years for patients with sustained alcohol use after transplantation (7.23life years lost). Compared with delayed transplantation, early liver transplantation increased survival times in all simulated scenarios and combinations of Lille and MELD scores. In a modeling study of assumed carefullyselected patients with AH, early vs delayed liver transplantation (6months of abstinence from alcohol before transplantation) increased survival times of patients, regardless of estimated risk of sustained alcohol use after transplantation. These findings support early liver transplantation for patients with severe AH. The net increasein life expectancy was maintained in all simulated extreme scenarios but should be confirmed in prospective studies. Sustained alcohol use after transplantation significantly reduced but did not eliminate thebenefits of early transplantation. Strategies are needed toprevent and treat posttransplantation use of alcohol.

A Proof-Of-Concept Approach for Quantifying Multipollutant Health Impacts Using Joint Effects Models within the Open-Source BenMAP-CE Software Program

Air pollution risk assessments often use results from epidemiologic studies to quantify health impacts of air quality changes to individual pollutants, but tend not to account for exposure to complex mixtures. Multipollutant statistical models consider collinearity among pollutants by identifying mixtures commonly emitted from specific sources. Using a proof-of-concept version of the environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE) we aim to answer: (1) can the results from multipollutant statistical models (joint effect models) be used in air pollution risk assessments?; (2) how does the procedure for quantifying population health impacts differ between a single and multipollutant context? We identified studies using joint effect models with/without interactions to estimate the risk of air pollutant-attributable asthma emergency department (ED) visits. These studies examine associations between short-term criteria pollutant exposures (O3, PM2.5, CO, NO2, SO2) and PM components, which represent predefined source groupings (oxidant gases, secondary pollutants, traffic, power plant, criteria pollutants). We use results from these studies and daily air quality data in BenMAP-CE for a case study in the city of Atlanta and the state of Georgia. Preliminary results indicate: (1) interaction models yield larger estimates of pollutant-attributable asthma ED visits; (2) warm season impacts are greater than cold season; and (3) certain pollutant groups yield a negative number of cases. BenMAP-CE runtime for multipollutant models was commensurate with runtime for single pollutant models. This study suggests that risk assessments for multipollutant exposures are feasible, but data-intensive. Future risk assessments using single and multipollutant approaches can potentially provide a more comprehensive evaluation to inform air quality management strategies. Disclaimer: The views expressed do not necessarily reflect the views/policies of U.S. EPA.

Supplemental Material for A Test of the Main-Effects, Stress-Buffering, Stress-Exacerbation, and Joint-Effects Models Among Mexican-Origin Adults

Supplemental Material for A Test of the Main-Effects, Stress-Buffering, Stress-Exacerbation, and Joint-Effects Models Among Mexican-Origin Adults

Performance of Incubator Centres in Kenya

Globally, countries are strategically positioning themselves for market leadership due to dynamic business environment. Entrepreneurial spirit is seen as the strategy that will deliver this agenda. This spirit, is believed to be behind the innovative business that revolutionizes the business world. In a dynamic and complex environment, the success of any business is pegged on the entrepreneurial operations of a firm. Entrepreneurial-oriented firms have been proven to be ahead of competition because they are always introduced new products and services and in turn improve their financial results. Scholars believe that learning and development can occur amongst people who actively engage in a common enterprise. This makes learning empowering and productive and thus sustains entrepreneurial orientation. This in turn produces communities of entrepreneurial practice. The role of the entrepreneurial manager is to nurture communities of growth-oriented firms where entrepreneurial learning takes place. The purpose of this study is to assess the relationship between entrepreneurial management and performance of incubator centres in Kenya. The study used a correlation design because it focused on a causal-effect relationship. The study population was 41 incubator managers in Kenya. After missing data analysis two respondents were expunged leaving 39 respondents. Secondary data was obtained from published sources such as company reports, manuals and research done by other scholars. Structural Equation Modelling (SEM) approach was used to analyze the measurement model and test the hypothesized relationship in this study. Simple linear regression model was used to measure the strength of the relationship between entrepreneurial management and performance incubator centre in Kenya. The joint effect model results indicated that entrepreneurial management had a significant relationship with performance of incubator centre.

Transforming Summary Statistics from Logistic Regression to the Liability Scale: Application to Genetic and Environmental Risk Scores

Objective: Stratified medicine requires models of disease risk incorporating genetic and environmental factors. These may combine estimates from different studies, and the models must be easily updatable when new estimates become available. The logit scale is often used in genetic and environmental association studies; however, the liability scale is used for polygenic risk scores and measures of heritability, but combining parameters across studies requires a common scale for the estimates. Methods: We present equations to approximate the relationship between univariate effect size estimates on the logit scale and the liability scale, allowing model parameters to be translated between scales. Results: These equations are used to build a risk score on the liability scale, using effect size estimates originally estimated on the logit scale. Such a score can then be used in a joint effects model to estimate the risk of disease, and this is demonstrated for schizophrenia using a polygenic risk score and environmental risk factors. Conclusion: This straightforward method allows the conversion of model parameters between the logit and liability scales and may be a key tool to integrate risk estimates into a comprehensive risk model, particularly for joint models with environmental and genetic risk factors.

Analysis of the joint effect of SNPs to identify independent loci and allelic heterogeneity in schizophrenia GWAS data

We have tested published methods for capturing allelic heterogeneity and identifying loci of joint effects to uncover more of the “hidden heritability” of schizophrenia (SCZ). We used two tools, cojo-GCTA and multi-SNP, to analyze meta-statistics from the latest genome-wide association study (GWAS) on SCZ by the Psychiatric Genomics Consortium (PGC). Stepwise regression on markers with p values <10−7 in cojo-GCTA identified 96 independent signals. Eighty-five passed the genome-wide significance threshold. Cross-validation of cojo-GCTA by CLUMP was 76%, i.e., 26 of the loci identified by the PGC using CLUMP were found to be dependent on another locus by cojo-GCTA. The overlap between cojo-GCTA and multi-SNP was better (up to 92%). Three markers reached genome-wide significance (5 × 10−8) in a joint effect model. In addition, two loci showed possible allelic heterogeneity within 1-Mb genomic regions, while CLUMP analysis had identified 16 such regions. Cojo-GCTA identified fewer independent loci than CLUMP and seems to be more conservative, probably because it accounts for long-range LD and interaction effects between markers. These findings also explain why fewer loci with possible allelic heterogeneity remained significant after cojo-GCTA analysis. With multi-SNP, 86 markers were selected at the threshold 10−7. Multi-SNP identifies fewer independent signals, due to splitting of the data and use of smaller samples. We recommend that cojo-GCTA and multi-SNP are used for post-GWAS analysis of all traits to call independent loci. We conclude that only a few loci in SCZ show joint effects or allelic heterogeneity, but this could be due to lack of power for that data set.

Haplotype analysis of XRCC2 gene polymorphisms and association with increased risk of head and neck cancer

We aimed to investigate the effect of hotspot variations of XRCC2 gene on the risk of head and neck cancer (HNC) in 400 patients and 400 controls. Five polymorphisms of XRCC2 gene G4234C (rs3218384), G4088T (rs3218373), G3063A (rs2040639), R188H (rs3218536) and rs7802034 were analyzed using Allele- specific polymerase chain reaction (ARMS-PCR) followed by sequence analysis. For rs3218373, the GG genotype indicated a statistically significant 3-fold increased risk of HNC (P < 0.001) after multivariate adjustment. For rs7802034, the GG genotype suggested statistically significant 2-fold increased risk of HNC (P < 0.001). For SNP of rs3218536, the AA genotype indicated a significant 3-fold increased risk of HNC (P < 0.001). Additionally, haplotype analysis revealed that TACAG, TGGAG, TACGG and TAGGA haplotypes of XRCC2 polymorphisms are associated with HNC risk. Two SNPs in XRCC2 (rs2040639 and rs3218384) were found increased in strong linkage disequilibrium. Furthermore, joint effect model showed 20 fold (OR = 19.89; 95% CI = 2.65–149.36, P = 0.003) increased HNC risk in patients carrying four homozygous risk alleles of selected polymorphisms. These results show that allele distributions and genotypes of XRCC2 SNPs are significantly associated with increased HNC risk and could be a genetic adjuster for the said disease.

Abstract 1295: Genomewide gene-diabetes and gene-obesity interaction scan in 7095 cases and 10710 controls from Pancreatic Cancer Cohort Consortium and Pancreatic Cancer Case Control Consortium

Abstract Introduction: Obesity and diabetes are two major modifiable risk factors for pancreatic cancer. Genetic variation affecting the risk of obesity- and diabetes-related pancreatic cancer have yet to be comprehensively investigated at the genome-wide level. Aims: To identify novel gene-environment interactions (GxE) of obesity/diabetes on pancreatic cancer risk. Methods: We conducted a multi-stage genome-wide gene-diabetes and gene-obesity interaction study using GWAS data of 7095 cases and 10710 controls from Pancreatic Cancer Consortium (PanScan I, PanScan II and PanScan III) and Pancreatic Cancer Case Control Consortium (PanC4). About 870,000 SNPs, having MAF ≥ 0.005 and having been genotyped in at least one study, were analyzed. Missing genotypes were imputed using IMPUTE2 and the Haplotype Reference Consortium (HRC) reference panel. Case-control (CC) and case-only (CO) methods were used for SNP-level GxE analysis. Fixed-, random- and joint-effect models built in “rareGE” R package were used for gene-based GxE analysis. About 20,000 genes that have at least one SNP genotyped were included in the analysis. Age, gender and principal components accounting for subpopulation structure were adjusted in both SNP-level and gene-level analyses. Obesity was defined as body mass index ≥30 mg/m2. Diabetics with &amp;lt;3-year duration were excluded for gene-diabetes interaction analysis. The PanScan I, II, III and PanC4 datasets were analyzed separately followed by meta-analysis of the summary statistics. Fixed-effect and Fisher’s p-value based meta-analysis was applied for SNP-level and gene-level GxE analysis, respectively. Results: No genome-wide significant (P&amp;lt;5 x 10-8 ) interaction at the SNP level was detected in any of the PanScan or PanC4 datasets or in the meta-analysis. A significant interaction of diabetes and FAM63A (family with sequence similarity 63 member A) gene (P&amp;lt;2.5 x 10-6) was observed in the meta-analysis (PFixed= 3.4 x 10-5, PRandom= 1.9 x 10-6, PJoint= 6.8 x 10-7) . This interaction showed nominal significance in all individual dataset analyses. The corresponding P values for this interaction were 4.2 x 10-2, 3.8 x 10-2 and 6.8 x 10-2 in PanScan I, 2.8 x 10-2, 3.9 x 10-2 and 6.5 x 10-2 9.1 x 10-3, 2.2 x 10-4 and 8.8 x 10-6 in PanScan III, and 3.1 x 10-3, 3.4 x 10-3 and 8.8 x 10-3 in PanC4, respectively. Conclusions: In the largest genome-wide GxE analysis for pancreatic cancer to date, we identified a significant diabetes interacting gene in pancreatic cancer, which was consistently replicated in 4 individual GWAS studies. This observation identifies FAM63A as a candidate for future functional studies to understand its role in pancreatic cancer etiology. Further increasing the sample size may unveil additional genetic loci that contribute to the susceptibility to pancreatic cancer via G x E. Citation Format: Hongwei Tang, Lai Jiang, Laufey T. Amundadottir, Harvey A. Risch, Rachael S. Stolzenberg-Solomon, Alison P. Klein, Brian M. Wolpin, Gloria Petersen, Nilanjan Chatterjee, Donghui Li, Peter Kraft, Peng Wei. Genomewide gene-diabetes and gene-obesity interaction scan in 7095 cases and 10710 controls from Pancreatic Cancer Cohort Consortium and Pancreatic Cancer Case Control Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1295. doi:10.1158/1538-7445.AM2017-1295

Social and clinically-relevant cardiovascular risk factors in Asian Americans adults: NHANES 2011-2014.

Little evidence exists examining cardiovascular risk factors among Asian Americans and how social determinants such as nativity status and education pattern risk in the United States (U.S.) context. We used the National Health and Nutrition Examination Survey, which purposely oversampled Asian Americans from 2011 to 2014, and examined prevalence of Type II diabetes, smoking and obesity for Asian Americans (n=1363) and non-Latino Whites (n=4121). We classified Asian Americans as U.S. or foreign-born and by years in the U.S. Obesity status was based on standard body mass index (BMI) cut points of ≥30kg/m2 and Asian-specific cut points (BMI≥25kg/m2) that may be more clinically relevant for this population. We fit separate logistic regression models for each outcome using complex survey design methods and tested for the joint effect of race, nativity and education on each outcome. Diabetes and obesity prevalence (applying Asian-specific BMI cut points) were higher among Asian Americans when compared to non-Latino Whites but smoking prevalence was lower. These patterns remained in fully adjusted models and showed small increases with longer duration in the U.S. Joint effects models showed higher odds of prevalent Type II diabetes and obesity (Asian-specific) for foreign-born Asians, regardless of years in the U.S. and slightly higher risk for low education, when compared to non-Latino Whites with high education. Smoking models showed significant interaction effects between race and education for non-Latino Whites only. Our study supports the premise that social as well as clinical factors should be considered when developing health initiatives for Asian Americans.

The joint contribution of neighborhood poverty and social integration to mortality risk in the United States

PurposeA well-established literature has shown that social integration strongly patterns health, including mortality risk. However, the extent to which living in high-poverty neighborhoods and having few social ties jointly pattern survival in the United States has not been examined. MethodsWe analyzed data from the Third National Health and Nutrition Examination Survey (1988–1994) linked to mortality follow-up through 2006 and census-based neighborhood poverty. We fit Cox proportional hazards models to estimate associations between social integration and neighborhood poverty on all-cause mortality as independent predictors and in joint-effects models using the relative excess risk due to interaction to test for interaction on an additive scale. ResultsIn the joint-effects model adjusting for age, gender, race/ ethnicity, and individual-level socioeconomic status, exposure to low social integration alone was associated with increased mortality risk (hazard ratio [HR]: 1.42, 95% confidence interval [CI]: 1.28–1.59) while living in an area of high poverty alone did not have a significant effect (HR: 1.10; 95% CI: 0.95–1.28) when compared with being jointly unexposed. Individuals simultaneously living in neighborhoods characterized by high poverty and having low levels of social integration had an increased risk of mortality (HR: 1.63; 95% CI: 1.35–1.96). However, relative excess risk due to interaction results were not statistically significant. ConclusionsSocial integration remains an important determinant of mortality risk in the United States independent of neighborhood poverty.

Novel approach for predicting the joint effects based on the enzyme-catalyzed kinetics

Organisms are exposed to mixtures of multiple contaminants and it is necessary to build prediction models for the joint effects, considering the high expense and the complexity of the traditional toxicity testing and the flood occurrence of environmental chemical pollutants. In this study, a new method for predicting the joint effects was developed and corresponding prediction models were constructed based on the kinetic models of enzyme-catalyzed reactions. While, we utilized Vibrio fischeri, Escherichia coli and Bacillus subtilis as model organisms and determined the chronic toxicity of the binary mixtures of sulfonamides (SAs) and sulfonamide potentiators (SAPs) (SA+SAP), the mixtures of two kinds of sulfonamides (SA+SA) and the binary mixtures of sulfonamide potentiators (SAPs) and tetracyclines (TCs) (SAP+TC) respectively. Finally, corresponding mixture toxicity data was utilized to fit and verify the prediction models for different joint effects.

Covering the Cover

Covering the Cover

Combining Data From Liver Disease Scoring Systems Better Predicts Outcomes of Patients With Alcoholic Hepatitis

Several models have been used to determine prognoses of patients with alcoholic hepatitis. These include static systems (the Maddrey discriminant function; age, bilirubin, international normalized ratio, creatinine [ABIC] score; and model for end-stage liver disease [MELD] score) and dynamic models (the Lille model). We aimed to combine features of all of these models to develop a better method to predict outcomes of patients with alcoholic hepatitis. We collected data from several databases of patients with severe alcoholic hepatitis treated with corticosteroids in France and the United Kingdom to create a model to predict patient survival (derivation cohort, n = 538 patients). We compared the performances of 3 joint-effect models (Maddrey+Lille, MELD+Lille, and ABIC+Lille) to determine which combination had the best prognostic value, based on known patient outcomes. The model was validated using data from trials of the effects of corticosteroids in patients in the United States, France, Korea, and Belgium (n = 604 patients). We created a joint-effect model to predict patient survival after 2 and 6 months; in the derivation and validation cohorts it predicted outcome significantly better than either static or dynamic models alone (P < .01 for all comparisons). The joint model accurately predicted patient survival regardless of patient risk level. The MELD+Lille combination was better than the Maddrey+Lille or ABIC+Lille combination in predicting patient survival, with Akaike information criterion values of 1305, 1313, and 1312, respectively. For example, based on the MELD+Lille combination model, the predicted 6-month mortality of complete responders with MELD scores of 15-45 (Lille score, 0.16) was 8.5% to 49.7%, compared with 16.4%-75.2% for nonresponders (Lille score, 0.45). According to the joint-effect model, for 2 patients with the same baseline MELD score of 21, the patient with a Lille score of 0.45 had a 1.9-fold higher risk of death than the patient with a Lille score of 0.16 (23.7% vs 12.5%). By combining results from static and dynamic scoring systems for liver disease, we can better predict outcomes of patients with alcoholic hepatitis, compared with either model alone. This may help patient management and design of clinical trials.

Gender differences in the association between tooth loss and obesity among older adults in Brazil.

OBJECTIVE To analyze if differences according to gender exists in the association between tooth loss and obesity among older adults.METHODS We analyzed data on 1,704 older adults (60 years and over) from the baseline of a prospective cohort study conducted in Florianopolis, SC, Southern Brazil. Multivariable logistic regression models were used to assess the association between tooth loss and general and central obesity after adjustment for confounders (age, gender, skin color, educational attainment, income, smoking, physical activity, use of dentures, hypertension, and diabetes). Linear regressions were also assessed with body mass index and waist circumference as continuous outcomes. Interaction between gender and tooth loss was further assessed.RESULTS Overall mean body mass index was 28.0 kg/m2. Mean waist circumference was 96.8 cm for males and 92.6 cm for females. Increasing tooth loss was positively associated with increased body mass index and waist circumference after adjustment for confounders. Edentates had 1.4 (95%CI 1.1;1.9) times higher odds of being centrally obese than individuals with a higher number of teeth; however, the association lost significance after adjustment for confounders. In comparison with edentate males, edentate females presented a twofold higher adjusted prevalence of general and central obesity. In the joint effects model, edentate females had a 3.8 (95%CI 2.2;6.6) times higher odds to be centrally obese in comparison with males with more than 10 teeth present in both the arches. Similarly, females with less than 10 teeth in at least one arch had a 2.7 (95%CI 1.6;4.4) times higher odds ratio of having central obesity in comparison with males with more than 10 teeth present in both the arches.CONCLUSIONS Central obesity was more prevalent than general obesity among the older adults. We did not observe any association between general obesity and tooth loss. The association between central obesity and tooth loss depends on gender – females with tooth loss had greater probability of being obese.

Abstract 2209: Effects of IL10 haplotypes and atomic bomb radiation exposure on risks of gastric cancer subtypes

Abstract Background: Gastric cancer is one of the cancers whose risks of incidence and mortality clearly increased among Japanese atomic-bomb (A-bomb) survivors. Gastric cancer incidence in the Radiation Effects Research Foundation's (RERF) Life Span Study cohort population increased with radiation dose and remains high even today, more than 65 years since exposure. In this study, we specifically examined the association between risk of gastric cancer (intestinal and diffuse types) and radiation exposure by haplotypes of the immunosuppressive gene IL10 to investigate inter-individual differences in gastric cancer susceptibility associated with radiation exposure. Methods: We analyzed the follow-up data from the RERF Immunogenome Study Cohort of 4,690 A-bomb survivors at baseline covering 1981-2005: a total of 200 gastric cancer cases comprising 93 intestinal-type, 96 diffuse-type, and 11 other subtypes. Relative risk (RR) for IL10 and excess relative risk (ERR) for radiation of gastric cancer subtypes were estimated. Using a single haplotype block composed of four haplotype-tagging single nucleotide polymorphisms (comprising major haplotype allele IL10-ATTA and minor haplotype allele IL10-GGCG), multiplicative and additive models for joint effects of radiation and this IL10 haplotyping were examined. Results: The RR of intestinal-type gastric cancer among A-bomb survivors harboring the minor homozygote was significantly high, being 2.2 (95% confidence interval (CI): 1.1-4.3), compared to those harboring the major homozygote. In other words, the IL10 minor haplotype allele was considered to be a risk factor for intestinal-type gastric cancer. However, no significant association was observed between radiation exposure and intestinal-type gastric cancer, and no statistical interaction was observed between radiation exposure and IL10 haplotypes. The IL10 minor haplotype allele was not a risk factor for diffuse-type gastric cancer. The ERR per Gy of radiation exposure by haplotype showed statistical significance only for the major homozygote (0.46/Gy, 95% CI: 0.02-1.4). On the other hand, the ERR per Gy for the minor homozygote was close to 0, with no statistical significance. Therefore, the IL10 minor haplotype allele might act to suppress the radiation-associated risk elevation of diffuse-type gastric cancer. Conclusion: This study of A-bomb survivors suggests that IL10 haplotypes may be associated with the development of radiation-related diffuse-type gastric cancer. There is a possibility, therefore, that immune/inflammatory-related gene polymorphisms may be involved in inter-individual differences in radiation-related cancer risks. Further studies based on genome-wide association analysis are currently underway. Citation Format: Tomonori Hayashi, John B. Cologne, Yiqun Hu, Kengo Yoshida, Waka Ohishi, Ikue Hayashi, Junko Kajimura, Seishi Kyoizumi, Yoichiro Kusunoki, Kei Nakachi. Effects of IL10 haplotypes and atomic bomb radiation exposure on risks of gastric cancer subtypes. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2209. doi:10.1158/1538-7445.AM2014-2209

Size-class effect contributes to tree species assembly through influencing dispersal in tropical forests.

We have investigated the processes of community assembly using size classes of trees. Specifically our work examined (1) whether point process models incorporating an effect of size-class produce more realistic summary outcomes than do models without this effect; (2) which of three selected models incorporating, respectively environmental effects, dispersal and the joint-effect of both of these, is most useful in explaining species-area relationships (SARs) and point dispersion patterns. For this evaluation we used tree species data from the 50-ha forest dynamics plot in Barro Colorado Island, Panama and the comparable 20 ha plot at Bubeng, Southwest China. Our results demonstrated that incorporating an size-class effect dramatically improved the SAR estimation at both the plots when the dispersal only model was used. The joint effect model produced similar improvement but only for the 50-ha plot in Panama. The point patterns results were not improved by incorporation of size-class effects using any of the three models. Our results indicate that dispersal is likely to be a key process determining both SARs and point patterns. The environment-only model and joint-effects model were effective at the species level and the community level, respectively. We conclude that it is critical to use multiple summary characteristics when modelling spatial patterns at the species and community levels if a comprehensive understanding of the ecological processes that shape species’ distributions is sought; without this results may have inherent biases. By influencing dispersal, the effect of size-class contributes to species assembly and enhances our understanding of species coexistence.

The Role of Poverty Status and Obesity on School Attendance in the United States

PurposeSeveral studies have shown that obesity influences school performance. Little is known about the joint effect of poverty and obesity associated with school attendance. MethodsData are from the National Survey of Children's Health (N = 93,151), a nationally representative sample of U.S. youth aged 10–17 years. Our dependent variable was ≥11 days of school days missed per year. Body mass index was classified as normal, overweight, and obese using age- and sex-specific criteria. Federal poverty level (FPL) was classified as <200%, 200%–399%, and ≥400% (high income). Covariates included gender, age, child's race or ethnicity, maternal physical and mental health, child's health, family composition, and household tobacco use. Logistic regression models and prevalence ratios were estimated, accounting for the complex survey design. ResultsThe odds of missing ≥11 days of school among overweight youth was 1.5 times that of normal-weight youth (95% confidence interval (CI) = 1.22–1.85) and 1.7 (95% CI = 1.35–2.13) times among obese youth in fully adjusted models. In joint effects models, the probability of missing school was significantly greater for obese youth in both the <200% FPL group (prevalence ratio = 1.78, CI = 1.36–2.34) and the ≥400% FPL group (prevalence ratio = 2.88, CI = 1.91–4.35), when compared with their normal-weight, higher income peers. Predicted probabilities revealed sharper gradients for higher income youth. ConclusionsObesity influences school absenteeism across all income categories. Nonetheless, there may be distinct reasons for missing school for lower and higher income youth, and the long-term consequences of school absences may also differ for these populations.

Clashing paradigms: an empirical examination of cultural proxies and socioeconomic condition shaping Latino health

ObjectiveMuch debate exists regarding the role of culture versus socioeconomic position in shaping the health of Latino populations. We propose that both may matter for health and explicitly test their independent and joint effects on smoking and physical activity. MethodsWe used the 2010 National Health Interview Survey, a population-based survey of the U.S. population, to estimate the prevalence of smoking and physical activity by language use (cultural proxy) and education among Latino adults (n = 4929). We fit log binomial regression models to estimate prevalence ratios and test for interaction. ResultsEnglish-language use and educational attainment were each independently associated with smoking and physical activity. Joint effect models showed that individuals with both greater use of the English language and low levels of education were nearly three times more likely to smoke (prevalence ratio, 2.59; 95% confidence interval, 1.83-3.65) than those with low English language use and high education (referent group); high acculturation and high education were jointly associated with increased activity (prevalence ratio 2.24, 95% confidence interval, 1.79-2.81). ConclusionsCultural proxies such as language use and educational attainment are both important determinants of health among Latinos. Their joint effect suggests the need to simultaneously consider Latinos' socioeconomic position and their increased risk of adopting health-damaging behaviors while addressing culturally-specific factors that may mitigate risk.