Trimethylamine N-oxide (TMAO) is a circulating metabolite that has been implicated in the development of atherosclerosis and cardiovascular disease (CVD). In this paper, we identify blood markers, ...metabolites, proteins, gut microbiota patterns, and diets that are significantly associated with levels of plasma TMAO. We find that kidney markers are strongly associated with TMAO and identify CVD-related proteins that are positively correlated with TMAO. We show that metabolites derived by the gut microbiota are strongly correlated with TMAO and that the magnitude of this correlation varies with kidney function. Moreover, we identify diet-associated patterns in the microbiome that are correlated with TMAO. These findings suggest that both the process of TMAO accumulation and the mechanism by which TMAO promotes atherosclerosis are a complex interplay between diet and the microbiome on one hand and other system-level factors such as circulating proteins, metabolites, and kidney function.
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•TMAO is independently associated with demographic factors and kidney function•Gut microbiota-derived metabolites are significantly associated with TMAO•Diet- and disease-associated metabolites are significantly associated with TMAO•Proteins linked to cardiovascular and kidney disease are correlated with TMAO
Trimethylamine N-oxide (TMAO) is a circulating metabolite that has been implicated in the development of atherosclerosis and cardiovascular disease. Manor et al. identify blood markers, metabolites, proteins, gut microbiota patterns, and diets that are significantly associated with levels of plasma TMAO.
Both genetic and lifestyle factors contribute to an individual's disease risk, suggesting a multi-omic approach is essential for personalized prevention. Studies have examined the effectiveness of ...lifestyle coaching on clinical outcomes, however, little is known about the impact of genetic predisposition on the response to lifestyle coaching. Here we report on the results of a real-world observational study in 2531 participants enrolled in a commercial "Scientific Wellness" program, which combines multi-omic data with personalized, telephonic lifestyle coaching. Specifically, we examined: 1) the impact of this program on 55 clinical markers and 2) the effect of genetic predisposition on these clinical changes. We identified sustained improvements in clinical markers related to cardiometabolic risk, inflammation, nutrition, and anthropometrics. Notably, improvements in HbA1c were akin to those observed in landmark trials. Furthermore, genetic markers were associated with longitudinal changes in clinical markers. For example, individuals with genetic predisposition for higher LDL-C had a lesser decrease in LDL-C on average than those with genetic predisposition for average LDL-C. Overall, these results suggest that a program combining multi-omic data with lifestyle coaching produces clinically meaningful improvements, and that genetic predisposition impacts clinical responses to lifestyle change.
Colorectal cancer (CRC) is a complex disease that develops as a consequence of both genetic and environmental risk factors. A small proportion (3-5%) of cases arise from hereditary syndromes ...predisposing to early onset CRC as a result of mutations in over a dozen well defined genes. In contrast, CRC is predominantly a late onset 'sporadic' disease, developing in individuals with no obvious hereditary syndrome. In recent years, genome wide association studies have discovered that over 40 genetic regions are associated with weak effects on sporadic CRC, and it has been estimated that increasingly large genome wide scans will identify many additional novel genetic regions. Subsequent experimental validations have identified the causally related variant(s) in a limited number of these genetic regions. Further biological insight could be obtained through ethnically diverse study populations, larger genetic sequencing studies and development of higher throughput functional experiments. Along with inherited variation, integration of the tumour genome may shed light on the carcinogenic processes in CRC. In addition to summarising the genetic architecture of CRC, this review discusses genetic factors that modify environmental predictors of CRC, as well as examples of how genetic insight has improved clinical surveillance, prevention and treatment strategies. In summary, substantial progress has been made in uncovering the genetic architecture of CRC, and continued research efforts are expected to identify additional genetic risk factors that further our biological understanding of this disease. Subsequently these new insights will lead to improved treatment and prevention of colorectal cancer.
Despite the substantial burden of hypertension in US minority populations, few genetic studies of blood pressure have been conducted in Hispanics and African Americans, and it is unclear whether many ...of the established loci identified in European-descent populations contribute to blood pressure variation in non-European descent populations. Using the Metabochip array, we sought to characterize the genetic architecture of previously identified blood pressure loci, and identify novel cardiometabolic variants related to systolic and diastolic blood pressure in a multi-ethnic US population including Hispanics (n = 19,706) and African Americans (n = 18,744). Several known blood pressure loci replicated in African Americans and Hispanics. Fourteen variants in three loci (KCNK3, FGF5, ATXN2-SH2B3) were significantly associated with blood pressure in Hispanics. The most significant diastolic blood pressure variant identified in our analysis, rs2586886/KCNK3 (P = 5.2 x 10-9), also replicated in independent Hispanic and European-descent samples. African American and trans-ethnic meta-analysis data identified novel variants in the FGF5, ULK4 and HOXA-EVX1 loci, which have not been previously associated with blood pressure traits. Our identification and independent replication of variants in KCNK3, a gene implicated in primary hyperaldosteronism, as well as a variant in HOTTIP (HOXA-EVX1) suggest that further work to clarify the roles of these genes may be warranted. Overall, our findings suggest that loci identified in European descent populations also contribute to blood pressure variation in diverse populations including Hispanics and African Americans-populations that are understudied for hypertension genetic risk factors.
With modernization, the Philippines has experienced increasing rates of obesity and related cardiometabolic diseases. Studying how risk factors cluster in individuals may offer insight into ...cardiometabolic disease etiology. We used cluster analysis to group women who share the following cardiometabolic biomarkers: fasting triglycerides, HDL-C and LDL-C, C-reactive protein, systolic and diastolic blood pressure, homeostasis model assessment of insulin resistance, and fasting glucose. Participants included 1,768 women (36-69 years) in the Cebu Longitudinal Health and Nutrition Survey. We identified five distinct clusters characterized by: 1) low levels of all risk factors (except HDL-C and LDL-C) or "healthy"; 2) low HDL-C in the absence of other risk factors; 3) elevated blood pressure; 4) insulin resistance; and 5) high C-reactive protein. We identified predictors of cluster membership using multinomial logistic regression. Clusters differed by age, menopausal status, socioeconomic status, saturated fat intake, and combinations of overweight (BMI >23) and high waist circumference (>80 cm). In comparison to the healthy cluster, overweight women without high waist circumference were more likely to be in the high CRP cluster (OR=2.26, 95% CI=1.24-4.11), while women with high waist circumference and not overweight were more likely to be in the elevated blood pressure (OR=2.56, 95% CI=1.20-5.46) or insulin resistant clusters (OR=4.05, 95% CI=1.39-11.8). In addition, a diet lower in saturated fat uniquely increased the likelihood of membership to the low HDL-C cluster. Cluster analysis identified biologically meaningful groups, predicted by modifiable risk factors; this may have implications for the prevention of cardiometabolic diseases.
Background: With modernization, cardiometabolic disease risk has increased in low and middle-income countries. To better understand cardiometabolic disease etiology, we evaluated the patterning risk ...factors in a susceptible young adult population.Methods and Results: Participants included 1,621 individuals from the 2005 Cebu Longitudinal Health and Nutrition Survey. Using cluster analysis, we grouped individuals by the following biomarkers: triglycerides, HDL and LDL cholesterol, C-reactive protein, blood pressure, homeostasis model assessment of insulin resistance, and fasting glucose. Using multinomial logistic regression models we assessed how diet, adiposity, and environment predicted cardiometabolic clusters. We identified 5 distinct sex-specific clusters: 1) Healthy/High HDL cholesterol (with the addition of high LDL cholesterol in women); 2) Healthy/Low blood pressure; 3) High blood pressure; 4) Insulin resistant/High triglycerides; and 5) High C-reactive protein. Low HDL cholesterol was the most prevalent risk factor (63%). In men and women, a higher intake of saturated fat increased the likelihood of being in the healthy clusters. In men, poorer environmental hygiene increased the likelihood of being in the High C-reactive protein cluster, compared to the healthy clusters (OR 0.74 95% CI 0.60-0.90 and 0.83 0.70-0.99). Adiposity most strongly associated with membership to the Insulin resistant/high triglyceride cluster.Conclusions: Despite the population's youth and leanness, cluster analysis found patterns of cardiometabolic risk. While adiposity measures predicted clustering, diet and environment also independently predicted clustering, emphasizing the importance of screening lean and overweight individuals for cardiometabolic risk. Finding predictors of risk in early adulthood could help inform prevention efforts for future disease.
Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery ...efforts are based on data from populations of European ancestry
. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific
. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations
. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions
-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.
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We aimed to identify how genetic risk scores predicted patterns of cardiometabolic (CM) risk factors in women from the 2005 Cebu Longitudinal Health and Nutrition Survey (
n
= 1,492, ...age =36–68 y).
Previous work identified clusters of women with similar CM features: healthy, high blood pressure (BP), low HDL‐c, insulin resistant (IR), and high inflammation. Genetic risk scores for BP, LDL‐c, HDL‐c, triglycerides, glucose, and C‐reactive protein (CRP) summed the number of effect alleles known to relate to each trait. Using multinomial logistic regression, we predicted CM clusters with genetic risk scores, adjusting for population substructure, age, menopause, parity, waist circumference, BMI, and energy intake.
Compared to the healthy cluster: a higher HDL‐c genetic risk score (OR 1.12, 95% CI= 1.03–1.02) increased odds of being in the elevated BP cluster; a lower LDL‐c score (0.90, 0.82–1.00) and higher HDL‐c score (1.15, 1.08–1.23) increased the odds of being in the low HDL‐c cluster; a higher CRP score (1.28, 1.05–1.55) and higher glucose score (1.23, 1.09–1.39) increased the odds of being in the IR cluster; a higher CRP score (1.32, 1.15–1.50) and higher BP score (1.11, 1.01–1.23) increased the odds of being in the high inflammation cluster.
For most CM clusters, genetic risk across multiple traits predicted cluster membership, emphasizing the notion that many synergistic genetic effects likely influence CM risk.
Grant Funding Source
: The University of North Carolina at Chapel Hill and National Institutes of Health:
DK078150
, HL0851445144
Guidelines for initiating colorectal cancer (CRC) screening are based on family history but do not consider lifestyle, environmental, or genetic risk factors. We developed models to determine risk of ...CRC, based on lifestyle and environmental factors and genetic variants, and to identify an optimal age to begin screening.
We collected data from 9748 CRC cases and 10,590 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colorectal Transdisciplinary study, from 1992 through 2005. Half of the participants were used to develop the risk determination model and the other half were used to evaluate the discriminatory accuracy (validation set). Models of CRC risk were created based on family history, 19 lifestyle and environmental factors (E-score), and 63 CRC-associated single-nucleotide polymorphisms identified in genome-wide association studies (G-score). We evaluated the discriminatory accuracy of the models by calculating area under the receiver operating characteristic curve values, adjusting for study, age, and endoscopy history for the validation set. We used the models to project the 10-year absolute risk of CRC for a given risk profile and recommend ages to begin screening in comparison to CRC risk for an average individual at 50 years of age, using external population incidence rates for non-Hispanic whites from the Surveillance, Epidemiology, and End Results program registry.
In our models, E-score and G-score each determined risk of CRC with greater accuracy than family history. A model that combined both scores and family history estimated CRC risk with an area under the receiver operating characteristic curve value of 0.63 (95% confidence interval, 0.62–0.64) for men and 0.62 (95% confidence interval, 0.61–0.63) for women; area under the receiver operating characteristic curve values based on only family history ranged from 0.53 to 0.54 and those based only E-score or G-score ranged from 0.59 to 0.60. Although screening is recommended to begin at age 50 years for individuals with no family history of CRC, starting ages calculated based on combined E-score and G-score differed by 12 years for men and 14 for women, for individuals with the highest vs the lowest 10% of risk.
We used data from 2 large international consortia to develop CRC risk calculation models that included genetic and environmental factors along with family history. These determine risk of CRC and starting ages for screening with greater accuracy than the family history only model, which is based on the current screening guideline. These scoring systems might serve as a first step toward developing individualized CRC prevention strategies.
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