An Apple a Day Keeps the Doctor Away – Inter-Relationship Between Apple Consumption, the Gut Microbiota and Cardiometabolic Disease Risk Reduction

AbstractBackgroundCardiometabolic affections greatly contribute to the global burden of disease. The susceptibility to these conditions associates with the ancestral genetic composition and gut microbiota. However, studies explicitly testing associations between genetic ancestry and gut microbes are rare. We examined whether the ancestral genetic composition was associated with gut microbiota, and split apart the effects of genetic and non-genetic factors on host health.ResultsWe performed a cross-sectional study of 441 community-dwelling Colombian mestizos from five cities. We characterized the host genetic ancestry using 40 ancestry informative markers and gut microbiota through 16S rRNA gene sequencing. We measured variables related to cardiometabolic health (adiposity, blood chemistry and blood pressure), diet (calories, macronutrients and fiber) and lifestyle (physical activity, smoking and medicament consumption). The ancestral genetic composition of the studied population was 67±6% European, 21±5% Native American and 12±5% African. While we found limited evidence of associations between genetic ancestry and gut microbiota or disease risk, we observed a strong link between gut microbes and cardiometabolic health. Multivariable-adjusted linear models indicated that gut microbiota was more likely to explain variance in host health than genetic ancestry. Further, we identified 9 OTUs associated with increased disease risk and 11 with decreased risk.ConclusionsGut microbiota seems to be more meaningful to explain cardiometabolic disease risk than genetic ancestry in this mestizo population. Our study suggests that novel ways to control cardiometabolic disease risk, through modulation of the gut microbial community, could be applied regardless of the genetic ancestry of the intervened population.

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Identification of Robust Metabotypes Associated With Increased Cardiometabolic Disease Risk- an Approach for Improved Prevention Through Precision Nutrition

Current Developments in Nutrition ◽

10.1093/cdn/nzab044_035 ◽

2021 ◽

Vol 5 (Supplement_2) ◽

pp. 604-604

Author(s):

Marie Palmnäs-Bédard ◽

Viktor Skantze ◽

Agnetha Rastgaard-Hansen ◽

Johan Dicksved ◽

Jytte Halkjaer ◽

...

Keyword(s):

Physical Activity ◽

Gut Microbiota ◽

Disease Risk ◽

Risk Group ◽

Clustering Algorithms ◽

Cardiometabolic Disease ◽

Future Research ◽

Metabolomics Data ◽

Mean Values ◽

Cardiometabolic Disease Risk

Abstract Objectives We hypothesize that individuals can be grouped into robust metabolic phenotypes (metabotypes) based on biochemical, anthropometric, gut microbial and metabolomics data and that such metabotypes will reflect differences in cardiometabolic disease risk and can act as targets for tailored nutritional prevention. We furthermore hypothesize that diet-gut microbiota interactions will be major determinants of the metabotypes. Methods Metabotyping is currently performed based on baseline data from 628 Danish adults from a validation sub-study of the Danish Diet Cancer and Health-Next Generation cohort. Participants were followed for one year, also providing data at 6 and 12 months. Dietary data was obtained by food frequency questionnaire and repeated 24h recalls and data on physical activity, smoking, sociodemographic factors, disease prevalence and use of medication was collected by questionnaires. Untargeted metabolomics and gut microbiota are currently determined. Metabotypes will be identified using clustering algorithms, variable optimization and data integration of the plasma metabolome, the 16S rRNA microbiota and 14 biochemical and anthropometric variables. Differences in habitual diet and physical activity across metabotypes will be determined as well as main metabotype determinants and potential plasma metabolite biomarkers. We will also assess the reproducibility of the metabotypes and biomarkers over time. Results Two clusters of individuals were identified by using the currently available clinical and anthropometric data. One of the clusters presented with mean values consistent with overweight, hypertension and dyslipidemia. Next, we will integrate the plasma metabolomics and gut microbial data into the analysis to determine the metabotypes. Conclusions We have identified one higher risk group and one lower risk group for cardiometabolic disease and will identify and characterize metabotypes based on more extensive data. Future research will assess whether individuals belonging to different metabotypes respond differently to dietary interventions. Funding Sources Formas, Sweden.

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Interethnic Differences in Serum Lipids and Implications for Cardiometabolic Disease Risk in African Ancestry Populations

Global Heart ◽

10.1016/j.gheart.2017.01.011 ◽

2017 ◽

Vol 12 (2) ◽

pp. 141 ◽

Cited By ~ 20

Author(s):

Amy R. Bentley ◽

Charles N. Rotimi

Keyword(s):

Serum Lipids ◽

Disease Risk ◽

African Ancestry ◽

Cardiometabolic Disease ◽

Interethnic Differences ◽

Cardiometabolic Disease Risk

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Inflammatory markers are altered in severe mental disorders independent of comorbid cardiometabolic disease risk factors – ERRATUM

Psychological Medicine ◽

10.1017/s0033291719000291 ◽

2019 ◽

Vol 49 (10) ◽

pp. 1758-1758

Author(s):

Ragni H. Mørch ◽

Ingrid Dieset ◽

Ann Færden ◽

Elina J. Reponen ◽

Sigrun Hope ◽

...

Keyword(s):

Risk Factors ◽

Mental Disorders ◽

Inflammatory Markers ◽

Disease Risk ◽

Cardiometabolic Disease ◽

Severe Mental Disorders ◽

Cardiometabolic Disease Risk

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Distinct phenotypic characteristics of normal-weight adults at risk of developing cardiovascular and metabolic diseases

American Journal of Clinical Nutrition ◽

10.1093/ajcn/nqaa194 ◽

2020 ◽

Vol 112 (4) ◽

pp. 967-978

Author(s):

Abishek Stanley ◽

John Schuna ◽

Shengping Yang ◽

Samantha Kennedy ◽

Moonseong Heo ◽

...

Keyword(s):

Health Risk ◽

Body Shape ◽

Metabolic Diseases ◽

Disease Risk ◽

White Men ◽

Normal Weight ◽

Cardiometabolic Disease ◽

Men And Women ◽

Distinct Phenotype ◽

Cardiometabolic Disease Risk

ABSTRACT Background The normal-weight BMI range (18.5–24.9 kg/m2) includes adults with body shape and cardiometabolic disease risk features of excess adiposity, although a distinct phenotype developed on a large and diverse sample is lacking. Objective To identify demographic, behavioral, body composition, and health-risk biomarker characteristics of people in the normal-weight BMI range who are at increased risk of developing cardiovascular and metabolic diseases based on body shape. Methods Six nationally representative waist circumference index (WCI, weight/height0.5) prediction formulas, with BMI and age as covariates, were developed using data from 17,359 non-Hispanic (NH) white, NH black, and Mexican-American NHANES 1999–2006 participants. These equations were then used to predict WCI in 5594 NHANES participants whose BMI was within the normal weight range. Men and women in each race/Hispanic-origin group were then separated into high, medium, and low tertiles based on the difference (residual) between measured and predicted WCI. Characteristics were compared across tertiles; P values for significance were adjusted for multiple comparisons. Results Men and women in the high WCI residual tertile, relative to their BMI and age-equivalent counterparts in the low tertile, had significantly lower activity levels; higher percent trunk and total body fat (e.g. NH white men, X ± SE, 25.3 ± 0.2% compared with 20.4 ± 0.2%); lower percent appendicular lean mass (skeletal muscle) and bone mineral content; and higher plasma insulin and triglycerides, higher homeostatic model assessment of insulin resistance (e.g. NH white men, 1.45 ± 0.07 compared with 1.08 ± 0.06), and lower plasma HDL cholesterol. Percent leg fat was also significantly higher in men but lower in women. Similar patterns of variable statistical significance were present within sex and race/ethnic groups. Conclusions Cardiometabolic disease risk related to body shape in people who are normal weight according to BMI is characterized by a distinct phenotype that includes potentially modifiable behavioral health risk factors.

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