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Leukocytosis, the expansion of white blood cells, is associated with increased cardiovascular risk. Studies in animal models have shown that high-density lipoprotein cholesterol (HDL-c) suppresses leukocytosis by mediating cholesterol efflux from hematopoietic stem and progenitor cells. HDL-c showed a moderate negative association with leukocyte numbers in the UK Biobank and Multi-Ethnic Study of Atherosclerosis. Cholesterol efflux capacity of HDL (HDL-CEC) or HDL particle (HDL-P) number has been proposed as improved inverse predictor of CVD compared to plasma HDL-c. In the LifeLines DEEP (LLD) cohort (n = 962), a sub-cohort representing the prospective population-based LL cohort from the North of The Netherlands, we tested the hypothesis that HDL-CEC and HDL-P were associated with lower leukocyte counts. We carried out multivariable regression and causal mediation analyses (CMA) to test associations between HDL-c, HDL-CEC, or HDL-P and leukocyte counts. We measured HDL-CEC in THP-1 macrophages and HDL-P and composition using nuclear magnetic resonance. HDL-c associated negatively with leukocyte counts, as did extra-large and large HDL-P, while HDL-CEC showed no association. Each one-standard deviation (SD) increase in extra-large HDL-P was associated with 3.0% and 4.8% lower leukocytes and neutrophils, respectively (q < 0.001). In contrast, plasma concentration of small HDL-P associated positively with leukocyte and neutrophil counts, as did small HDL-P triglycerides (TG) and total plasma TG. CMA showed that the association between S-HDL-P and leukocytes was mediated by S-HDL-TG. The association between HDL-P and leukocyte counts in the general population is dependent on HDL-P size and composition, but not HDL-CEC. [Display omitted] • High-density lipoprotein cholesterol (HDL-c) negatively associates with leukocyte counts independent of cholesterol efflux capacity of HDL (HDL-CEC). • Extra-large and large HDL particles (HDL-P) negatively associate with leukocyte counts. • The strong positive association between small HDL-P and leukocyte counts is mediated by HDL-triglycerides (TG). • S-HDL-TG showed a strong positive correlation with total plasma TG. [ABSTRACT FROM AUTHOR]

Host genetics are known to influence the gut microbiome, yet their role remains poorly understood. To robustly characterize these effects, we performed a genome-wide association study of 207 taxa and 205 pathways representing microbial composition and function in 7,738 participants of the Dutch Microbiome Project. Two robust, study-wide significant (P < 1.89 × 10 -10 ) signals near the LCT and ABO genes were found to be associated with multiple microbial taxa and pathways and were replicated in two independent cohorts. The LCT locus associations seemed modulated by lactose intake, whereas those at ABO could be explained by participant secretor status determined by their FUT2 genotype. Twenty-two other loci showed suggestive evidence (P < 5 × 10 -8 ) of association with microbial taxa and pathways. At a more lenient threshold, the number of loci we identified strongly correlated with trait heritability, suggesting that much larger sample sizes are needed to elucidate the remaining effects of host genetics on the gut microbiome., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Importance: Previous studies have indicated that gut microbiome may be associated with development of type 2 diabetes. However, these studies are limited by small sample size and insufficient for confounding. Furthermore, which specific taxa play a role in the development of type 2 diabetes remains unclear., Objective: To examine associations of gut microbiome composition with insulin resistance and type 2 diabetes in a large population-based setting controlling for various sociodemographic and lifestyle factors., Design, Setting, and Participants: This cross-sectional analysis included 2166 participants from 2 Dutch population-based prospective cohorts: the Rotterdam Study and the LifeLines-DEEP study., Exposures: The 16S ribosomal RNA method was used to measure microbiome composition in stool samples collected between January 1, 2012, and December 31, 2013. The α diversity (Shannon, richness, and Inverse Simpson indexes), β diversity (Bray-Curtis dissimilarity matrix), and taxa (from domain to genus level) were identified to reflect gut microbiome composition., Main Outcomes and Measures: Associations among α diversity, β diversity, and taxa with the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) and with type 2 diabetes were examined. Glucose and insulin were measured to calculate the HOMA-IR. Type 2 diabetes cases were identified based on glucose levels and medical records from January 2012 to December 2013. Analyses were adjusted for technical covariates, lifestyle, sociodemographic, and medical factors. Data analysis was performed from January 1, 2018, to December 31, 2020., Results: There were 2166 participants in this study: 1418 from the Rotterdam Study (mean [SD] age, 62.4 [5.9] years; 815 [57.5%] male) and 748 from the LifeLines-DEEP study (mean [SD] age, 44.7 [13.4] years; 431 [57.6%] male); from this total, 193 type 2 diabetes cases were identified. Lower microbiome Shannon index and richness were associated with higher HOMA-IR (eg, Shannon index, -0.06; 95% CI, -0.10 to -0.02), and patients with type 2 diabetes had a lower richness than participants without diabetes (odds ratio [OR], 0.93; 95% CI, 0.88-0.99). The β diversity (Bray-Curtis dissimilarity matrix) was associated with insulin resistance (R2 = 0.004, P = .001 in the Rotterdam Study and R2 = 0.005, P = .002 in the LifeLines-DEEP study). A total of 12 groups of bacteria were associated with HOMA-IR or type 2 diabetes. Specifically, a higher abundance of Christensenellaceae (β = -0.08; 95% CI, -0.12 to -0.03: P < .001), Christensenellaceae R7 group (β = -0.07; 95% CI, -0.12 to -0.03; P < .001), Marvinbryantia (β = -0.07; 95% CI, -0.11 to -0.03; P < .001), Ruminococcaceae UCG005 (β = -0.09; 95% CI, -0.13 to -0.05; P < .001), Ruminococcaceae UCG008 (β = -0.07; 95% CI, -0.11 to -0.03; P < .001), Ruminococcaceae UCG010 (β = -0.08; 95% CI, -0.12 to -0.04; P < .001), or Ruminococcaceae NK4A214 group (β = -0.09; 95% CI, -0.13 to -0.05; P < .001) was associated with lower HOMA-IR. A higher abundance of Clostridiaceae 1 (OR, 0.51; 95% CI, 0.41-0.65; P < .001), Peptostreptococcaceae (OR, 0.56; 95% CI, 0.45-0.70; P < .001), C sensu stricto 1 (OR, 0.51; 95% CI, 0.40-0.65; P < .001), Intestinibacter (OR, 0.60; 95% CI, 0.48-0.76; P < .001), or Romboutsia (OR, 0.55; 95% CI, 0.44-0.70; P < .001) was associated with less type 2 diabetes. These bacteria are all known to produce butyrate., Conclusions and Relevance: In this cross-sectional study, higher microbiome α diversity, along with more butyrate-producing gut bacteria, was associated with less type 2 diabetes and with lower insulin resistance among individuals without diabetes. These findings could help provide insight into the etiology, pathogenesis, and treatment of type 2 diabetes.

Rationale: Altered gut microbial composition has been linked to cardiovascular diseases (CVDs), but its functional links to host metabolism and immunity in relation to CVD development remain unclear., Objectives: To systematically assess functional links between the microbiome and the plasma metabolome, cardiometabolic phenotypes, and CVD risk and to identify diet-microbe-metabolism-immune interactions in well-documented cohorts., Methods and Results: We assessed metagenomics-based microbial associations between 231 plasma metabolites and microbial species and pathways in the population-based LLD (Lifelines DEEP) cohort (n=978) and a clinical obesity cohort (n=297). After correcting for age, sex, and body mass index, the gut microbiome could explain ≤11.1% and 16.4% of the variation in plasma metabolites in the population-based and obesity cohorts, respectively. Obese-specific microbial associations were found for lipid compositions in the VLDL, IDL, and LDL lipoprotein subclasses. Bacterial L-methionine biosynthesis and a Ruminococcus species were associated to cardiovascular phenotypes in obese individuals, namely atherosclerosis and liver fat content, respectively. Integration of microbiome-diet-inflammation analysis in relation to metabolic risk score of CVD in the population cohort revealed 48 microbial pathways associated to CVD risk that were largely independent of diet and inflammation. Our data also showed that plasma levels rather than fecal levels of short-chain fatty acids were relevant to inflammation and CVD risk., Conclusions: This study presents the largest metagenome-based association study on plasma metabolism and microbiome relevance to diet, inflammation, CVD risk, and cardiometabolic phenotypes in both population-based and clinical obesity cohorts. Our findings identified novel bacterial species and pathways that associated to specific lipoprotein subclasses and revealed functional links between the gut microbiome and host health that provide a basis for developing microbiome-targeted therapy for disease prevention and treatment.