Studies implicate choline and betaine metabolite trimethylamine N-oxide (TMAO) in cardiovascular disease (CVD). We conducted a systematic review and random-effects meta-analysis to quantify a summary ...estimated effect of dietary choline and betaine on hard CVD outcomes (incidence and mortality). Eligible studies were prospective studies in adults with comprehensive diet assessment and follow-up for hard CVD endpoints. We identified six studies that met our criteria, comprising 18,076 incident CVD events, 5343 CVD deaths, and 184,010 total participants. In random effects meta-analysis, incident CVD was not associated with choline (relative risk (RR): 1.00; 95% CI: 0.98, 1.02) or betaine (RR: 0.99; 95% CI: 0.98, 1.01) intake. Results did not vary by study outcome (incident coronary heart disease, stroke, total CVD) and there was no evidence for heterogeneity among studies. Only two studies provided data on phosphatidylcholine and CVD mortality. Random effects meta-analysis did not support an association between choline and CVD mortality (RR: 1.09, 95% CI: 0.89, 1.35), but one study supported a positive association and there was significant heterogeneity (
² = 84%,
-value < 0.001). Our findings do not support an association between dietary choline/betaine with incident CVD, but call for further research into choline and CVD mortality.
Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Strategies designed for genomic data to mitigate batch ...effects usually fail to address the zero-inflated and over-dispersed microbiome data. Most strategies tailored for microbiome data are restricted to association testing or specialized study designs, failing to allow other analytic goals or general designs. Here, we develop the Conditional Quantile Regression (ConQuR) approach to remove microbiome batch effects using a two-part quantile regression model. ConQuR is a comprehensive method that accommodates the complex distributions of microbial read counts by non-parametric modeling, and it generates batch-removed zero-inflated read counts that can be used in and benefit usual subsequent analyses. We apply ConQuR to simulated and real microbiome datasets and demonstrate its advantages in removing batch effects while preserving the signals of interest.
The food supply and dietary preferences have changed in recent decades.
We studied time- and age-related individual and population-wide changes in a dietary quality score and food groups during ...1985-2006.
The Coronary Artery Risk Development in Young Adults (CARDIA) study of 5115 black and white men and women aged 18-30 y at year 0 (1985-1986) assessed diet at examinations at study years 0, 7 (1992-1993), and 20 (2005-2006). The dietary quality score, which was validated by its inverse association with cardiovascular disease risk, summed 46 food groups rated by investigators as positive or negative on the basis of hypothesized health effects. We used repeated-measures regression to estimate time-specific mean diet scores and servings per day of food groups.
In 2652 participants with all 3 diet assessments, the mean (±SD) dietary quality score increased from 64.1 ± 13.0 at year 0 to 71.1 ± 12.6 at year 20, which was mostly attributable to increased age. However, the secular trend, which was estimated from differences of dietary quality scores across time at a fixed age (age-matched time trend) decreased. The diet score was higher in whites than in blacks and in women than in men and increased with education, but demographic gaps in the score narrowed over 20 y. There tended to be increases in positively rated food groups and decreases in negatively rated food groups, which were generally similar in direction across demographic groups.
The CARDIA study showed many age-related, desirable changes in food intake over 20 y of observation, despite a secular trend toward a lower diet quality. Nevertheless, demographic disparities in diet persist.
Over the past decade, the gut microbiome has emerged as a novel and largely unexplored source of variability for metabolic and cardiovascular disease risk, including diabetes. Animal and human ...studies support several possible pathways through which the gut microbiome may impact health, including the production of health-related metabolites from dietary sources. Diet is considered important to shaping the gut microbiota; in addition, gut microbiota influence the metabolism of many dietary components. In the present paper, we address the distinction between compositional and functional analysis of the gut microbiota. We focus on literature that highlights the value of moving beyond surveys of microbial composition to measuring gut microbial functioning to delineate mechanisms related to the interplay between diet and gut microbiota in cardiometabolic health.
Background
Clinical studies implicate trimethylamine N‐oxide (TMAO; a gut microbiota‐dependent nutrient metabolite) in cardiovascular disease risk. There is a lack of population‐based data on the ...role of TMAO in advancing early atherosclerotic disease. We tested the prospective associations between TMAO and coronary artery calcium (CAC) and carotid intima‐media thickness (cIMT).
Methods and Results
Data were from the Coronary Artery Risk Development in Young Adults Study (CARDIA), a biracial cohort of US adults recruited in 1985–1986 (n=5115). We randomly sampled 817 participants (aged 33–55 years) who attended examinations in 2000–2001, 2005–2006, and 2010–2011, at which CAC was measured by computed tomography and cIMT (2005–2006) by ultrasound. TMAO was quantified using liquid chromotography mass spectrometry on plasma collected in 2000–2001. Outcomes were incident CAC, defined as Agatston units=0 in 2000–2001 and >0 over 10‐year follow‐up, CAC progression (any increase over 10‐year follow‐up), and continuous cIMT. Over the study period, 25% (n=184) of those free of CAC in 2000–2001 (n=746) developed detectable CAC. In 2000–2001, median (interquartile range) TMAO was 2.6 (1.8–4.2) μmol/L. In multivariable‐adjusted models, TMAO was not associated with 10‐year CAC incidence (rate ratio=1.03; 95% CI: 0.71–1.52) or CAC progression (0.97; 0.68–1.38) in Poisson regression, or cIMT (beta coefficient: −0.009; −0.03 to 0.01) in linear regression, comparing the fourth to the first quartiles of TMAO.
Conclusions
In this population‐based study, TMAO was not associated with measures of atherosclerosis: CAC incidence, CAC progression, or cIMT. These data indicate that TMAO may not contribute significantly to advancing early atherosclerotic disease risk among healthy early‐middle‐aged adults.
Background: A priori diet scores such as the Alternative Healthy Eating Index (AHEI) and the food-based a priori diet quality score predict chronic disease risk.Objective: We compared the AHEI and a ...priori diet quality score relative to mortality.Design: Postmenopausal women who were free of diabetes, cardiovascular disease (CVD), and cancer in the Iowa Women's Health Study (in 1986, n = 29,634 with a mean ± SD age of 61.4 ± 4.2 y; in 2004, n = 15,076 with a mean ± SD age of 79.7 ± 4.0 y). A food-frequency questionnaire was used. Through 31 December 2008, 10,343 total, 3646 CVD, 3207 cancer, and 2888 inflammatory-related deaths were identified through record linkage. HRs were computed for quartiles of each diet score at baseline and 2004. To compare scores, the residual of each score given the other score was computed by using linear regression.Results: At baseline, indexes had a correlation of 0.65. For the AHEI, the multivariable-adjusted HRs (95% CIs) for total, CVD, cancer, and inflammatory-related mortality were 0.82 (0.77, 0.87), 0.79 (0.72, 0.88), 0.88 (0.79, 0.98), and 0.76 (0.68, 0.84), respectively. The a priori score had corresponding HRs of 0.80 (0.76, 0.85), 0.79 (0.72, 0.88), 0.86 (0.77, 0.95), and 0.75 (0.67, 0.84), respectively. Each score added information to the other score for total, CVD mortality, and inflammatory-related mortality. In 2004, both scores predicted total, CVD, and inflammatory-related mortality, and the a priori score also predicted cancer mortality. The a priori score added independent information for all outcomes except cancer, whereas the AHEI added information only for total mortality.Conclusion: Two correlated diet quality scores predicted total and disease-specific mortality, but their residuals also predicted complementarily.
Summary
Background
Aspirin is associated with decreased risk of colorectal cancer (CRC), potentially by modulating the gut microbiome.
Aims
To evaluate the effect of aspirin on the gut microbiome in ...a double‐blinded, randomised placebo‐controlled pilot trial.
Methods
Healthy volunteers aged 50‐75 received a standard dose of aspirin (325 mg, N = 30) or placebo (N = 20) once daily for 6 weeks and provided stool samples every 3 weeks for 12 weeks. Serial measurements of gut microbial community composition and bacterial abundance were derived from 16S rRNA sequences. Linear discriminant analysis of effect size (LEfSe) was tested for between‐arm differences in bacterial abundance. Mixed‐effect regression with binomial distribution estimated the effect of aspirin use on changes in the relative abundance of individual bacterial taxa via an interaction term (treatment × time).
Results
Over the study period, there were differences in microbial composition in the aspirin vs placebo arm. After treatment, four taxa were differentially abundant across arms: Prevotella, Veillonella, Clostridium XlVa and Clostridium XVIII clusters. Of pre‐specified bacteria associated with CRC (n = 8) or aspirin intake (n = 4) in published studies, interactions were significant for four taxa, suggesting relative increases in Akkermansia, Prevotella and Ruminococcaceae and relative decreases in Parabacteroides, Bacteroides and Dorea in the aspirin vs placebo arm.
Conclusion
Compared to placebo, aspirin intake influenced several microbial taxa (Ruminococcaceae, Clostridium XlVa, Parabacteroides and Dorea) in a direction consistent with a priori hypothesis based on their association with CRC. This suggests that aspirin may influence CRC development through an effect on the gut microbiome. The findings need replication in a larger trial.
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