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Table 1 Human metagenomic studies of the gut microbiome and cardio-metabolic traits

From: The gut microbiome in cardio-metabolic health

Study Aim Number of participants Design Method Main findings
Ley et al. (2006) [41] To investigate the effect of either a carbohydrate- or fat-restricted diet on gut microbial ecology 14 adults; 12 obese, 2 lean Randomized intervention 16S rRNA • Increased abundance of Firmicutes and reduced abundance of Bacteroidetes in obese
• Increased abundance of Bacteroidetes and decreased abundance of Firmicutes following 1 year of either fat- or carbohydrate-restricted low-calorie diet
• Increase in Bacteroidetes correlated with body weight reduction regardless of diet
Zhang et al. (2009) [161] To investigate microbiota composition in morbid obesity and following RYGB 9 adults; 3 lean, 3 morbidly obese, 3 post RYGB Cross-sectional 16S rRNA, qPCR • Firmicutes dominant in normal weight and obese, decreased in post RYGB
• Gamma-Proteobacteria increased whereas Clostridia decreased post-RYGB
Prevotellaceae highly enriched in obese
• Methanobacteriales highly abundant in obese, while non-detectable in normal weight
Turnbaugh et al. (2009) [37] To investigate the influence of host genotype, environmental exposure and host adiposity 154 adults; 31 MZ twin pairs, 23 DZ twin pairs, (concordant for obesity or leanness), 46 mothers Cross-sectional 16S rRNA • Lower proportion of Bacteroidetes and a higher proportion of Actinobacteria in obese
• Obesity associated with reduced diversity
• Obese microbiome enriched for genes involved in macronutrient metabolism
Larsen et al. (2010) [12] To investigate differences in gut microbiota composition associated with T2D 20 adults; 10 T2D, 10 NGT Cross-sectional 16S rRNA, qPCR • Decreased diversity in T2D
• Firmicutes, including Clostridia, decreased in T2D
• The ratio of the phylogenetic groups Bacteroides-Prevotella to Clostridium coccoides-Eubacterium rectale and the Bacteroidetes to Firmicutes ratio correlated positively with 2 h p-glucose during an OGTT
• Beta-Proteobacteria highly enriched in T2D and correlated with 2 hour p-glucose during an OGTT
Jumpertz et al. (2011) [5] To investigate the effect of caloric intake on microbiota composition in lean and obese 21 adults; 12 lean, 9 obese Randomized cross-over intervention 16S rRNA • High-calorie diet changes the relative abundance of microbiota on the phylum (Bacteroidetes versus Firmicutes), class (Bacteroidetes versus Clostridia), and order level (Bacteroidales versus Clostridiales)
• Phylum-, class-, and order-level changes in microbiota composition during intervention associated with fecal caloric content in lean but not in obese
Koren et al. (2011) [59] To investigate the bacterial diversity of atherosclerotic plaque, oral cavity and gut in patients with CVD 30 adults; 15 CVD, 15 healthy Cross-sectional 16S rRNA, qPCR • No phylum- or genus level compositional difference between CVD patients and healthy controls
• Several shared OTUs between atherosclerotic plaque and fecal samples
Karlsson et al. (2012) [60] To investigate the microbiota composition in patients with CVD 25 adults; 13 CVD, 12 healthy controls Cross-sectional Quantitative metagenomics Colinsella enriched in patients, Eubacterium and Roseburia enriched in controls
• Genera of Clostridiales, Clostridium and Peptostreptococcus negatively correlated with hsCRP
• Atherosclerosis associated with the Ruminococcus enterotype
Qin et al. (2012) [13] To investigate differences in gut microbiota composition and function associated with T2D 368 adults; 183 T2D cases, 185 healthy controls Cross-sectional Quantitative metagenomics • T2D associated with moderate dysbiosis with a decline in butyrate-producing bacteria
• Gut-microbiome-based T2D index accurately classifies T2D individuals
Le Chatelier et al. (2013) [55] To investigate the bacterial abundance in lean and obese 292 adults; 123 lean, 169 obese Cross-sectional/retrospective Quantitative metagenomics and 16S rRNA • Low bacterial richness associates with increased overall adiposity, insulin resistance, dyslipidemia, and a more pronounced inflammatory phenotype
• Discrimination between high versus low gene count and obesity status possible from a combination of only four species with ROC analysis AUC of 0.97
• Increased weight gain in individuals with low microbial gene count
Karlsson et al. (2013) [16] To investigate differences in gut microbiota composition and function associated with T2D 145 adults; 53 T2D, 49 IGT, 43 NGT Cross-sectional Quantitative metagenomics • Increased abundance of Lactobacillus spp. and abundance of Clostridium spp. decreased
Clostridium spp. correlated with fasting glucose and HbA1c, whereas Lactobacillus spp. correlated negatively with fasting glucose, insulin, C-peptide and TAG, and positively with adiponectin and HDL
• Microbiota composition as determined by metagenomic clusters better correlated with T2D than known clinical risk factors (WC, WHR and BMI)
Zhang et al. (2013) [17] To investigate differences in gut microbiota composition associated with T2D 121 adults; 44 NGT, 64 IGT, 13 T2D Cross-sectional 16S rRNA • Higher abundance of Clostridia in T2D
• Negative trend of abundance of Streptococcus from NGT to IGT to T2D
• Enterotype classification not associated with glucose tolerance status
• 28 OTUs associated with glucose tolerance status
• Fasting glucose associated with microbiota composition
• Fasting insulin inversely associated with alpha (intraindividual) diversity
Kong et al. (2013) [160] To investigate the impact of RYGB on microbiota composition 30 adults; 7 T2D, 23 non-T2D obese Non-randomized intervention 16S rRNA • Increased bacterial richness following RYGB, mainly within the phylum Proteobacteria
• RYGB induced genus-level changes in microbiota composition correlated with changes in white adipose tissue gene expression
Graessler et al. (2013) [14] To investigate the impact of RYGB on microbiota composition and function 6 adults; 5 T2D, 1 non-T2D obese Non-randomized intervention Quantitative metagenomics • Relative abundance of 22 species an 11 genera affected 3 months after RYGB
• Overall, RYGB induced phylum-level changes characterized by reduction in Bacteroidetes and Firmicutes and an increase in Proteobacteria and Verrucomicrobia
• Species-level changes dominated by an increase in A. muciniphila, E. coli and K. pneumonia and a decrease in F. prausnitzii, E. rectale and D. invisus.
  1. Abbreviations: AUC area under the curve, BMI body mass index, CVD cardiovascular disease, DZ dizygotic, HDL high-density lipoprotein, hsCRP high-sensitivity C-reactive protein, IGT impaired glucose tolerance, MZ monozygotic, NGT normal glucose tolerance, OGTT oral glucose tolerance test, OTU operational taxonomic unit, qPCR quantitative PCR, ROC receiver operating characteristic, RYGB Roux-en-Y gastric bypass, TAG triacylglyceride, T2D type 2 diabetes, WC waist circumference, WHR waist-hip ratio.