Gene-diet interactions and cardiovascular diseases: a systematic review of observational and clinical trials.

BACKGROUND: Both genetic background and diet are important determinants of cardiovascular diseases (CVD). Understanding gene-diet interactions could help improve CVD prevention and prognosis. We aimed to summarise the evidence on gene-diet interactions and CVD outcomes systematically. METHODS: We searched MEDLINE® via Ovid, Embase, PubMed®, and The Cochrane Library for relevant studies published until June 6th 2022. We considered for inclusion cross-sectional, case-control, prospective cohort, nested case-control, and case-cohort studies as well as randomised controlled trials that evaluated the interaction between genetic variants and/or genetic risk scores and food or diet intake on the risk of related outcomes, including myocardial infarction, coronary heart disease (CHD), stroke and CVD as a composite outcome. The PROSPERO protocol registration code is CRD42019147031. RESULTS AND DISCUSSION: We included 59 articles based on data from 29 studies; six articles involved multiple studies, and seven did not report details of their source population. The median sample size of the articles was 2562 participants. Of the 59 articles, 21 (35.6%) were qualified as high quality, while the rest were intermediate or poor. Eleven (18.6%) articles adjusted for multiple comparisons, four (7.0%) attempted to replicate the findings, 18 (30.5%) were based on Han-Chinese ethnicity, and 29 (49.2%) did not present Minor Allele Frequency. Fifty different dietary exposures and 52 different genetic factors were investigated, with alcohol intake and ADH1C variants being the most examined. Of 266 investigated diet-gene interaction tests, 50 (18.8%) were statistically significant, including CETP-TaqIB and ADH1C variants, which interacted with alcohol intake on CHD risk. However, interactions effects were significant only in some articles and did not agree on the direction of effects. Moreover, most of the studies that reported significant interactions lacked replication. Overall, the evidence on gene-diet interactions on CVD is limited, and lack correction for multiple testing, replication and sample size consideration.

A 24-step guide on how to design, conduct, and successfully publish a systematic review and meta-analysis in medical research.

To inform evidence-based practice in health care, guidelines and policies require accurate identification, collation, and integration of all available evidence in a comprehensive, meaningful, and time-efficient manner. Approaches to evidence synthesis such as carefully conducted systematic reviews and meta-analyses are essential tools to summarize specific topics. Unfortunately, not all systematic reviews are truly systematic, and their quality can vary substantially. Since well-conducted evidence synthesis typically involves a complex set of steps, we believe formulating a cohesive, step-by-step guide on how to conduct a systemic review and meta-analysis is essential. While most of the guidelines on systematic reviews focus on how to report or appraise systematic reviews, they lack guidance on how to synthesize evidence efficiently. To facilitate the design and development of evidence syntheses, we provide a clear and concise, 24-step guide on how to perform a systematic review and meta-analysis of observational studies and clinical trials. We describe each step, illustrate it with concrete examples, and provide relevant references for further guidance. The 24-step guide (1) simplifies the methodology of conducting a systematic review, (2) provides healthcare professionals and researchers with methodologically sound tools for conducting systematic reviews and meta-analyses, and (3) it can enhance the quality of existing evidence synthesis efforts. This guide will help its readers to better understand the complexity of the process, appraise the quality of published systematic reviews, and better comprehend (and use) evidence from medical literature.

The Use of the Alcohol Use Disorders Identification Test - Consumption as an Indicator of Hazardous Alcohol Use among University Students.

BACKGROUND: Hazardous drinking among students in higher education is a growing concern. The alcohol use disorders identification test (AUDIT) is the gold standard screening instrument for hazardous drinking in the adult population, for which an abbreviated version has been developed: the -AUDIT-Consumption (AUDIT-C). Currently, there's no gold standard for identifying hazardous drinking among students in higher education and little evidence regarding the concurrent validity of the AUDIT-C as a screening instrument for this group. This study investigated the concurrent validity of the AUDIT-C in a sample of university students and suggests the most appropriate cutoff points. METHODS: Cross-sectional data of health surveys from 5,401 university and university of applied sciences in the Netherlands were used. Receiver operating characteristic (ROC) curves, sensitivity, specificity, and positive and negative predictive values for different cutoff scores of AUDIT-C were calculated for the total sample and for subgroups stratified by age, gender, and educational level. AUDIT-score ≥11 was used as the criterion of hazardous and harmful drinking. RESULTS: Twenty percent of students were hazardous and harmful drinkers. The area under the ROC curve was 0.922 (95% CI 0.914-0.930). At an AUDIT-C cutoff score of ≥7, sensitivity and specificity were both >80%, while other cutoffs showed less balanced results. A cutoff of ≥8 performed better among males, but for other subgroups ≥7 was most suitable. CONCLUSION: AUDIT-C seems valid in identifying hazardous and harmful drinking students, with suggested optimal cutoffs 7 (females) or 8 (males). However, considerations regarding avoiding false-positives versus false-negatives, in relation to the type of intervention following screening, could lead to selecting different cutoffs.

Comparison of different software for processing physical activity measurements with accelerometry.

Several raw-data processing software for accelerometer-measured physical activity (PA) exist, but whether results agree has not been assessed. We examined the agreement between three different software for raw accelerometer data, and associated their results with cardiovascular risk. A cross-sectional analysis conducted between 2014 and 2017 in 2693 adults (53.4% female, 45-86 years) living in Lausanne, Switzerland was used. Participants wore the wrist-worn GENEActive accelerometer for 14 days. Data was processed with the GENEActiv manufacturer software, the Pampro package in Python and the GGIR package in R. For the latter, two sets of thresholds "White" and "MRC" defining levels of PA and two versions (1.5-9 and 1.11-1) for the "MRC" threshold were used. Cardiovascular risk was assessed using the SCORE risk score. Time spent (mins/day) in stationary, light, moderate and vigorous PA ranged from 633 (GGIR-MRC) to 1147 (Pampro); 93 (GGIR-White) to 196 (GGIR-MRC); 19 (GGIR-White) to 161 (GENEActiv) and 1 (GENEActiv) to 26 (Pampro), respectively. Spearman correlations between results ranged between 0.317 and 0.995, while concordance coefficients ranged between 0.035 and 0.968. With some exceptions, the line of perfect agreement was not in the 95% confidence interval of the Bland-Altman plots. Compliance to PA guidelines varied considerably: 99.8%, 98.7%, 76.3%, 72.6% and 50.2% for Pampro, GENEActiv, GGIR-MRC v.1.11-1, GGIR-MRC v.1.4-9 and GGIR-White, respectively. Cardiovascular risk decreased with increasing time spent in PA across most software packages. We found large differences in PA estimation between software and thresholds used, which makes comparability between studies challenging.

Associations of Activity and Sleep With Quality of Life: A Compositional Data Analysis.

INTRODUCTION: Associations between time spent on physical activity, sedentary behavior, and sleep and quality of life are usually studied without considering that their combined time is fixed. This study investigates the reallocation of time spent on physical activity, sedentary behavior, and sleep during the 24-hour day and their associations with quality of life. METHODS: Data from the 2011-2016 Rotterdam Study were used to perform this cross-sectional analysis among 1,934 participants aged 51-94 years. Time spent in activity levels (sedentary, light-intensity physical activity, moderate-to-vigorous physical activity, and sleep) were objectively measured with a wrist-worn accelerometer combined with a sleep diary. Quality of life was measured using the EuroQoL 5D-3L questionnaire. The compositional isotemporal substitution method was used in 2018 to examine the association between the distribution of time spent in different activity behaviors and quality of life. RESULTS: Reallocation of 30 minutes from sedentary behavior, light-intensity physical activity, or sleep to moderate-to-vigorous physical activity was associated with a higher quality of life, whereas reallocation from moderate-to-vigorous physical activity to sedentary behavior, light-intensity physical activity, or sleep was associated with lower quality of life. To illustrate this, a reallocation of 30 minutes from sedentary behavior to moderate-to-vigorous physical activity was associated with a 3% (95% CI=2, 4) higher quality of life score. By contrast, a reallocation of 30 minutes from moderate-to-vigorous physical activity to sedentary behavior was associated with a 4% (95% CI=2, 6) lower quality of life score. CONCLUSIONS: Moderate-to-vigorous physical activity is important with regard to the quality of life of middle-aged and elderly individuals. The benefits of preventing less time spent in moderate-to-vigorous physical activity were greater than the benefits of more time spent in moderate-to-vigorous physical activity. These results could shift the attention to interventions focused on preventing reductions in moderate-to-vigorous physical activity levels. Further longitudinal studies are needed to confirm these findings and explore causality.

Comparison of the Physical Activity Frequency Questionnaire (PAFQ) with accelerometry in a middle-aged and elderly population: The CoLaus study.

OBJECTIVE: The Physical Activity Frequency Questionnaire (PAFQ) has been used in several studies, but its validation dates from 1998. We compared the PAFQ with accelerometry data for measuring levels of physical activity (PA) in a middle-aged and elderly population. DESIGN: Cross-sectional analysis was conducted with a sample of 1752 adults from the general population (50.7% female, age range 45.2-87.1 years) living in Switzerland. Participants completed the PAFQ and wore a wrist-worn accelerometer for 14 consecutive days. Spearman correlation, Lin's concordance coefficient and Bland-Altman plots were performed to compare PAFQ and accelerometry data. RESULTS: Compared with the accelerometer, the PAFQ overestimated total, light, moderate and vigorous activity by a median [interquartile range] of 143 [34.5; 249], 72 [12; 141], 23 [-46; 100] and 13 [-1; 41] minutes/day, respectively, and underestimated sedentary behaviour by 123 [14; 238] minutes/day. Spearman's correlation coefficients ranged from 0.171 for vigorous PA and 0.387 for total PA and sedentary behaviour. Lin's concordance coefficients ranged from 0.044 for vigorous PA and 0.254 for moderate to vigorous PA. The difference between PAFQ and accelerometer results increased with increasing time spent at each activity level. CONCLUSION: There is limited agreement between estimates of activity obtained by PAFQ and those obtained from accelerometers, suggesting that these tools measure activity differently. Although there is some degree of comparability, they should be considered as complementary tools to obtain comprehensive information on both individual and population activity levels.

Dietary Factors and Modulation of Bacteria Strains of Akkermansia muciniphila and Faecalibacterium prausnitzii: A Systematic Review.

Akkermansia muciniphila and Faecalibacterium prausnitzii are highly abundant human gut microbes in healthy individuals, and reduced levels are associated with inflammation and alterations of metabolic processes involved in the development of type 2 diabetes. Dietary factors can influence the abundance of A. muciniphila and F. prausnitzii, but the evidence is not clear. We systematically searched PubMed and Embase to identify clinical trials investigating any dietary intervention in relation to A. muciniphila and F. prausnitzii. Overall, 29 unique trials were included, of which five examined A. muciniphila, 19 examined F. prausnitzii, and six examined both, in a total of 1444 participants. A caloric restriction diet and supplementation with pomegranate extract, resveratrol, polydextrose, yeast fermentate, sodium butyrate, and inulin increased the abundance of A. muciniphila, while a diet low in fermentable oligosaccharides, disaccharides, monosaccharides, and polyols decreased the abundance of A. muciniphila. For F. prausnitzii, the main studied intervention was prebiotics (e.g. fructo-oligosaccharides, inulin type fructans, raffinose); seven studies reported an increase after prebiotic intervention, while two studies reported a decrease, and four studies reported no difference. Current evidence suggests that some dietary factors may influence the abundance of A. muciniphila and F. prausnitzii. However, more research is needed to support these microflora strains as targets of microbiome shifts with dietary intervention and their use as medical nutrition therapy in prevention and management of chronic disease.