Project description:BackgroundHigh diet quality is associated with a lower risk of chronic diseases. Metabolomics can be used to identify objective biomarkers of diet quality.ObjectivesWe used metabolomics to identify serum metabolites associated with 4 diet indices and the components within these indices in 2 samples from African Americans and European Americans.MethodsWe studied cross-sectional associations between known metabolites and Healthy Eating Index (HEI)-2015, Alternative Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension Trial (DASH) diet, alternate Mediterranean diet (aMED), and their components using untargeted metabolomics in 2 samples (n1 = 1,806, n2 = 2,056) of the Atherosclerosis Risk in Communities study (aged 45-64 y at baseline). Dietary intakes were assessed using an FFQ. We used multivariable linear regression models to examine associations between diet indices and serum metabolites in each sample, adjusting for participant characteristics. Metabolites significantly associated with diet indices were meta-analyzed across 2 samples. C-statistics were calculated to examine if these candidate biomarkers improved prediction of individuals in the highest compared with lowest quintile of diet scores beyond participant characteristics.ResultsSeventeen unique metabolites (HEI: n = 6; AHEI: n = 5; DASH: n = 14; aMED: n = 2) were significantly associated with higher diet scores after Bonferroni correction in sample 1 and sample 2. Six of 17 significant metabolites [glycerate, N-methylproline, stachydrine, threonate, pyridoxate, 3-(4-hydroxyphenyl)lactate)] were associated with ≥1 dietary pattern. Candidate biomarkers of HEI, AHEI, and DASH distinguished individuals with highest compared with lowest quintile of diet scores beyond participant characteristics in samples 1 and 2 (P value for difference in C-statistics <0.02 for all 3 diet indices). Candidate biomarkers of aMED did not improve C-statistics beyond participant characteristics (P value = 0.930).ConclusionsA considerable overlap of metabolites associated with HEI, AHEI, DASH, and aMED reflects the similar food components and similar metabolic pathways involved in the metabolism of healthy diets in African Americans and European Americans.

Project description:The prevalence of type 2 diabetes (T2D) is greater in populations of African descent compared to European-descent populations. Genetic risk factors may underlie the disparity in disease prevalence. Genome-wide association studies (GWAS) have identified >60 common genetic variants that contribute to T2D risk in populations of European, Asian, African and Hispanic descent. These studies have not comprehensively examined population differences in cumulative risk allele load. To investigate the relationship between risk allele load and T2D risk, 46 T2D single nucleotide polymorphisms (SNPs) in 43 loci from GWAS in European, Asian, and African-derived populations were genotyped in 1,990 African Americans (n = 963 T2D cases, n = 1,027 controls) and 1,644 European Americans (n = 719 T2D cases, n = 925 controls) ascertained and recruited using a common protocol in the southeast United States. A genetic risk score (GRS) was constructed from the cumulative risk alleles for each individual. In African American subjects, risk allele frequencies ranged from 0.024 to 0.964. Risk alleles from 26 SNPs demonstrated directional consistency with previous studies, and 3 SNPs from ADAMTS9, TCF7L2, and ZFAND6 showed nominal evidence of association (p < 0.05). African American individuals carried 38-67 (53.7 ± 4.0, mean ± SD) risk alleles. In European American subjects, risk allele frequencies ranged from 0.084 to 0.996. Risk alleles from 36 SNPs demonstrated directional consistency, and 10 SNPs from BCL11A, PSMD6, ADAMTS9, ZFAND3, ANK1, CDKN2A/B, TCF7L2, PRC1, FTO, and BCAR1 showed evidence of association (p < 0.05). European American individuals carried 38-65 (50.9 ± 4.4) risk alleles. African Americans have a significantly greater burden of 2.8 risk alleles (p = 3.97 × 10(-89)) compared to European Americans. However, GRS modeling showed that cumulative risk allele load was associated with risk of T2D in European Americans, but only marginally in African Americans. This result suggests that there are ethnic-specific differences in genetic architecture underlying T2D, and that these differences complicate our understanding of how risk allele load impacts disease susceptibility.

Project description:Translational Relevance Historically, African Americans have been underrepresented in clinical cancer research. Diversity helps to ensure equal access to new cancer therapies and better treatment for everyone. Cancer research is increasingly focused on classifying patients according to molecular profiles for particular groups. We provide a detailed molecular analysis from paired NSCLC tissues that identified differential coding and noncoding RNA expression in NSCLC from African Americans (AA) and European Americans (EA). Similar to other tumor types, we determined that race-enriched gene and microRNA expression signatures suggest a more aggressive disease in African Americans. Based on predicted drug resistance to adjuvant chemotherapies, AA may not equally benefit from the same range of clinical drugs as EA. Our findings provide a rationale for integrating coding and noncoding transcriptome profiles, along with clinical, demographic, and genomic data, when determining treatment options. Abstract Purpose: To determine if racial differences in gene and microRNA expression translates to differences in lung tumor biology with clinical relevance in African Americans (AA) and European Americans (EA). Experimental Design: The NCI-Maryland Case Control Study includes seven Baltimore City hospitals and is overrepresented with AA patients (~40%). Patients that underwent curative NSCLC surgery between 1998 and 2014 were enrolled. Comparative molecular profiling used mRNA (n = 22 AAs and n = 19 EAs) and microRNA (n = 42 AAs and n = 55 EAs) expression arrays to track differences in paired fresh frozen normal tissues and lung tumor specimens from AA and EA. Pathway enrichment, predicted drug response, tumor microenvironment infiltration, cancer immunotherapy antigen profiling, and microRNA target enrichment were assessed. Results: AA-enriched differential gene expression was characterized by stem-cell and invasion pathways. Differential gene expression in lung tumors from EA were primarily characterized by cell proliferation pathways. Population-specific gene expression was partly driven by population-specific miRNA expression profiles. Drug susceptibility predictions revealed a strong inverse correlation between AA resistance and EA sensitivity to the same panel of drugs. Statistically significant differences in M1 and M2 macrophage infiltration was observed in AA (P <0.05), however, PD-L1, PD-L2 expression was similar between both. Conclusions: Comparative transcriptomic profiling revealed clear differences in lung tumor biology between AA and EA. Increased participation by AA in lung cancer clinical trials are needed to integrate, and leverage, transcriptomic differences with other clinical information to maximize therapeutic benefit in both AA and EA.

Project description:Translational Relevance Historically, African Americans have been underrepresented in clinical cancer research. Diversity helps to ensure equal access to new cancer therapies and better treatment for everyone. Cancer research is increasingly focused on classifying patients according to molecular profiles for particular groups. We provide a detailed molecular analysis from paired NSCLC tissues that identified differential coding and noncoding RNA expression in NSCLC from African Americans (AA) and European Americans (EA). Similar to other tumor types, we determined that race-enriched gene and microRNA expression signatures suggest a more aggressive disease in African Americans. Based on predicted drug resistance to adjuvant chemotherapies, AA may not equally benefit from the same range of clinical drugs as EA. Our findings provide a rationale for integrating coding and noncoding transcriptome profiles, along with clinical, demographic, and genomic data, when determining treatment options. Abstract Purpose: To determine if racial differences in gene and microRNA expression translates to differences in lung tumor biology with clinical relevance in African Americans (AA) and European Americans (EA). Experimental Design: The NCI-Maryland Case Control Study includes seven Baltimore City hospitals and is overrepresented with AA patients (~40%). Patients that underwent curative NSCLC surgery between 1998 and 2014 were enrolled. Comparative molecular profiling used mRNA (n = 22 AAs and n = 19 EAs) and microRNA (n = 42 AAs and n = 55 EAs) expression arrays to track differences in paired fresh frozen normal tissues and lung tumor specimens from AA and EA. Pathway enrichment, predicted drug response, tumor microenvironment infiltration, cancer immunotherapy antigen profiling, and microRNA target enrichment were assessed. Results: AA-enriched differential gene expression was characterized by stem-cell and invasion pathways. Differential gene expression in lung tumors from EA were primarily characterized by cell proliferation pathways. Population-specific gene expression was partly driven by population-specific miRNA expression profiles. Drug susceptibility predictions revealed a strong inverse correlation between AA resistance and EA sensitivity to the same panel of drugs. Statistically significant differences in M1 and M2 macrophage infiltration was observed in AA (P <0.05), however, PD-L1, PD-L2 expression was similar between both. Conclusions: Comparative transcriptomic profiling revealed clear differences in lung tumor biology between AA and EA. Increased participation by AA in lung cancer clinical trials are needed to integrate, and leverage, transcriptomic differences with other clinical information to maximize therapeutic benefit in both AA and EA. GSEA, Connectivity Map, CIBERSORT, cancer immunotherapy antigen profiling, and hypergeometric testing for overlapping miRNA targets were performed.

Dataset Information

Adiposity is inversely associated with hippocampal volume in African Americans and European Americans with diabetes.

Publications

Adiposity is inversely associated with hippocampal volume in African Americans and European Americans with diabetes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets