Effects of general and central adiposity on circulating lipoprotein, lipid, and metabolite levels in UK Biobank: A multivariable Mendelian randomization study

Analyses of potential causal contributors to increased waist/hip ratio-associated cardiometabolic disease: A combined and sex-stratified Mendelian randomization study

BACKGROUND

Increased waist/hip ratio (WHR) contributes to type 2 diabetes, fatty liver, dyslipidaemia, hypertension and coronary artery disease, with potential sex-differential effects. Postulated mediators include increased lipid flux, branched-chain amino acids, glycine and glycoprotein acetyl, but their relative contributions and sex-specific impact on WHR-associated cardiometabolic disease (CMD) are not established.

METHODS

We therefore undertook combined and sex-stratified Mendelian randomization (MR) to assess the relative causal contributions of these mediators to WHR-associated CMD using summary statistics from the largest genome-wide association studies in European ancestries.

RESULTS

In sex-combined MR analyses, increased WHR significantly reduces high-density lipoprotein (beta = -0.416, SE = 0.029, p = 2.87E-47), increases triglyceride (beta = 0.431, SE = 0.029, p = 1.87E-50), type 2 diabetes (odds ratio = 2.747, SE = 0.09, p = 26E-23), coronary artery disease (odds ratio = 1.478, SE = 0.045, p = 6.96E-18), alanine transaminase (beta = 0.062, SE = 0.004, p = 6.88E-67), and systolic (beta = 0.134, SE = 0.022, p = 7.81E-10) and diastolic blood pressure (beta = 0.162, SE = 0.026, p = 5.38E-10). Adjustment for the mediators attenuated WHR's effects, but the associations remained significant with concordant results in females. In males, a similar pattern was seen, except after adjusting for the effect of the ratio of monounsaturated fatty acid to total free fatty acid, the potential causal effect of WHR was no longer significant: high-density lipoprotein (beta = -0.117, SE = 0.069, p = .09) and triglyceride (beta = 0.051, SE = 0.068, p = .459).

CONCLUSIONS

MR suggests WHR increases the risk of CMD independent of these mediators, with the exception of dyslipidaemia in males, which is largely driven by the monounsaturated fatty acid to total free fatty acid ratio.

Development and validation of a cognitive dysfunction risk prediction model for the abdominal obesity population

Objectives

This study was aimed to develop a nomogram that can accurately predict the likelihood of cognitive dysfunction in individuals with abdominal obesity by utilizing various predictor factors.

Methods

A total of 1490 cases of abdominal obesity were randomly selected from the National Health and Nutrition Examination Survey (NHANES) database for the years 2011-2014. The diagnostic criteria for abdominal obesity were as follows: waist size ≥ 102 cm for men and waist size ≥ 88 cm for women, and cognitive function was assessed by Consortium to Establish a Registry for Alzheimer's Disease (CERAD), Word Learning subtest, Delayed Word Recall Test, Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). The cases were divided into two sets: a training set consisting of 1043 cases (70%) and a validation set consisting of 447 cases (30%). To create the model nomogram, multifactor logistic regression models were constructed based on the selected predictors identified through LASSO regression analysis. The model's performance was assessed using several metrics, including the consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA) to assess the clinical benefit of the model.

Results

The multivariate logistic regression analysis revealed that age, sex, education level, 24-hour total fat intake, red blood cell folate concentration, depression, and moderate work activity were significant predictors of cognitive dysfunction in individuals with abdominal obesity (p < 0.05). These predictors were incorporated into the nomogram. The C-indices for the training and validation sets were 0.814 (95% CI: 0.875-0.842) and 0.805 (95% CI: 0.758-0.851), respectively. The corresponding AUC values were 0.814 (95% CI: 0.875-0.842) and 0.795 (95% CI: 0.753-0.847). The calibration curves demonstrated a satisfactory level of agreement between the nomogram model and the observed data. The DCA indicated that early intervention for at-risk populations would provide a net benefit, as indicated by the line graph.

Conclusion

Age, sex, education level, 24-hour total fat intake, red blood cell folate concentration, depression, and moderate work activity were identified as predictive factors for cognitive dysfunction in individuals with abdominal obesity. In conclusion, the nomogram model developed in this study can effectively predict the clinical risk of cognitive dysfunction in individuals with abdominal obesity.

Circulating Metabolites and Dental Traits: A Mendelian Randomization Study

It is of great importance to uncover causal biomarkers to gain insight into the pathogenesis of oral diseases and identify novel treatment targets for prevention and treatment thereof. This study aimed to systematically evaluate the causal effects of hundreds of metabolites on 10 dental traits using a 2-sample Mendelian randomization (MR) approach. Genetic variants from genome-wide association studies of 309 known metabolites were used as instrumental variables. We selected 10 dental traits, including clinical measures of dental diseases, from the Gene-Lifestyle Interactions in Dental Endpoints Consortium and self-reported oral health data from the UK Biobank. The causal relationships between metabolites and dental traits were inferred using the inverse variance-weighted approach and further controlled for horizontal pleiotropy using 5 additional MR methods. After correcting for multiple tests, 5 metabolites were identified as causal biomarkers. Genetically predicted increased levels of mannose were associated with lower risk of bleeding gums (odds ratio [OR] = 0.72; 95% confidence interval [CI], 0.61-0.85; P = 9.9 × 10-5). MR also indicated 4 metabolites on the causal pathway to dentures, with fructose (OR = 0.50; 95% CI, 0.36-0.70; P = 5.2 × 10-5) and 1-palmitoleoyl-glycerophosphocholine (OR = 0.67; 95% CI, 0.56-0.81; P = 4.8 × 10-5) as potential protective factors and glycine (OR = 1.22; 95% CI, 1.11-1.35; P = 5.6×10-5) and 1,5-anhydroglucitol (OR = 1.32; 95% CI, 1.14-1.52; P = 1.5 × 10-4) as risk factors. The causal associations were robust in various sensitivity analyses. We further observed some shared metabolites among different dental traits, implying similar biological mechanisms underlying the pathogenic processes. Finally, the pathway analysis revealed several significant metabolic pathways that may be involved in the development of dental disorders. Our study provides novel insights into the combination of metabolomics and genomics to reveal the pathogenesis of and therapeutic strategies for dental disorders. It highlighted 5 metabolites and several pathways as causal candidates, warranting further investigation.

Low levels of small HDL particles predict but do not influence risk of sepsis

BACKGROUND

Low levels of high-density lipoprotein (HDL) cholesterol have been associated with higher rates and severity of infection. Alterations in inflammatory mediators and infection are associated with alterations in HDL cholesterol. It is unknown whether the association between HDL and infection is present for all particle sizes, and whether the observed associations are confounded by IL-6 signalling.

METHODS

In the UK Biobank, ~ 270,000 individuals have data on HDL subclasses derived from nuclear magnetic resonance analysis. We estimated the association of particle count of total HDL and HDL subclasses (small, medium, large, and extra-large HDL) with sepsis, sepsis-related death, and critical care admission in a Cox regression model. We subsequently utilised genetic data from UK Biobank and FinnGen to perform Mendelian randomisation (MR) of each HDL subclass and sepsis to test for a causal relationship. Finally, we explored the role of IL-6 signalling as a potential causal driver of changes in HDL subclasses.

RESULTS

In observational analyses, higher particle count of small HDL was associated with protection from sepsis (Hazard ratio, HR 0.80; 95% CI 0.74-0.86, p = 4 × 10-9 comparing Quartile 4, highest quartile of HDL to Quartile 1, lowest quartile of HDL), sepsis-related death (HR 0.80; 95% CI 0.74-0.86, p = 2 × 10-4), and critical care admission with sepsis (HR 0.72 95% CI 0.60-0.85, p = 2 × 10-4). Parallel associations with other HDL subclasses were likely driven by changes in the small HDL compartment. MR analyses did not strongly support causality of small HDL particle count on sepsis incidence (Odds ratio, OR 0.98; 95% CI 0.89-1.07, p = 0.6) or death (OR 0.94, 95% CI 0.75-1.17, p = 0.56), although the estimate on critical care admission with sepsis supported protection (OR 0.73, 95% CI 0.57-0.95, p = 0.02). Bidirectional MR analyses suggested that increased IL-6 signalling was associated with reductions in both small (beta on small HDL particle count - 0.16, 95% CI - 0.10 to - 0.21 per natural log change in SD-scaled CRP, p = 9 × 10-8).and total HDL particle count (beta - 0.13, 95% CI - 0.09 to - 0.17, p = 7 × 10-10), but that the reverse effect of HDL on IL-6 signalling was largely null.

CONCLUSIONS

Low number of small HDL particles are associated with increased hazard of sepsis, sepsis-related death, and sepsis-related critical care admission. However, genetic analyses did not strongly support this as causal. Instead, we demonstrate that increased IL-6 signalling, which is known to alter infection risk, could confound associations with reduced HDL particle count, and suggest this may explain part of the observed association between (small) HDL particle count and sepsis.

Prediagnostic Glycoprotein Acetyl Levels and Incident and Recurrent Flare Risk Accounting for Serum Urate Levels: A Population-Based, Prospective Study and Mendelian Randomization Analysis

OBJECTIVE

To prospectively investigate population-based metabolomics for incident gout and reproduce the findings for recurrent flares, accounting for serum urate.

METHODS

We conducted a prediagnostic metabolome-wide analysis among 105,615 UK Biobank participants with nuclear magnetic resonance metabolomic profiling data (168 total metabolites) from baseline blood samples collected 2006-2010 in those without history of gout. We calculated hazard ratios (HRs) for incident gout, adjusted for gout risk factors, excluding and including serum urate levels, overall and according to fasting duration before sample collection. Potential causal effects were tested with 2-sample Mendelian randomization. Poisson regression was used to calculate rate ratios (RRs) for the association with recurrent flares among incident gout cases.

RESULTS

Correcting for multiple testing, 88 metabolites were associated with risk of incident gout (N = 1,303 cases) before serum urate adjustment, including glutamine and glycine (inversely), and lipids, branched-chain amino acids, and most prominently, glycoprotein acetyls (GlycA; P = 9.17 × 10-32 ). Only GlycA remained associated with incident gout following urate adjustment (HR 1.52 [95% confidence interval (95% CI) 1.22-1.88] between extreme quintiles); the HR increased progressively with fasting duration before sample collection, reaching 4.01 (95% CI 1.36-11.82) for ≥8 hours of fasting. Corresponding HRs per SD change in GlycA levels were 1.10 (95% CI 1.04-1.17) overall and 1.54 (95% CI 1.21-1.96) for ≥8 hours of fasting. GlycA levels were also associated with recurrent gout flares among incident gout cases (RR 1.90 [95% CI 1.27-2.85] between extreme quintiles) with larger associations with fasting. Mendelian randomization corroborated a potential causal role for GlycA on gout risk.

CONCLUSION

This prospective, population-based study implicates GlycA, a stable long-term biomarker reflecting neutrophil overactivity, in incident and recurrent gout flares (central manifestation from neutrophilic synovitis) beyond serum urate.

Large-scale metabolomic profiling and incident non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent disease with no specific drug therapy. High-throughput metabolomics present an unprecedented opportunity to identify biomarkers and potentially causal risk factors for NAFLD. Here, we determined the impact of 21 circulating metabolites, 17 lipids, and 132 lipoprotein particle characteristics on NAFLD combining prospective observational and two-sample Mendelian randomization (MR) analyses in 121,032 UK Biobank participants. We identified several metabolic factors associated with NAFLD risk in observational and MR analyses including triglyceride-rich and high-density lipoprotein particles composition, as well as the ratio of polyunsaturated fatty acids to total fatty acids. This study, is one of the largest to investigate incident NAFLD, provides concordant observational and genetic evidence that therapies aimed at reducing circulating triglycerides and increasing large HDL particles, as well as interventions aimed at increasing polyunsaturated fatty acid content may warrant further investigation into NAFLD prevention and treatment.

Metabolic profiling of MRI-measured liver fat in the UK Biobank

OBJECTIVE

Liver fat associates with obesity-related metabolic disturbances and may precede incident diseases. Metabolomic profiles of liver fat in the UK Biobank were investigated.

METHODS

Regression models assessed the associations between 180 metabolites and proton density liver fat fraction (PDFF) measured 5 years later through magnetic resonance imaging, as the difference (in SD units) of each log metabolite measure with 1-SD higher PDFF among those without chronic disease and not taking statins, and by diabetes and cardiovascular diseases.

RESULTS

After accounting for confounders, multiple metabolites were associated positively with liver fat (p < 0.0001 for 152 traits), particularly extremely large and very large lipoprotein particle concentrations, very low-density lipoprotein triglycerides, small high-density lipoprotein particles, glycoprotein acetyls, monounsaturated and saturated fatty acids, and amino acids. Extremely large and large high-density lipoprotein concentrations had strong inverse associations with liver fat. Associations were broadly comparable among those with versus without vascular metabolic conditions, although negative, rather than positive, associations were observed between intermediate-density and large low-density lipoprotein particles among those with BMI ≥25 kg/m² , diabetes, or cardiovascular diseases. Metabolite principal components showed a 15% significant improvement in risk prediction for PDFF relative to BMI, which was twice as great (but nonsignificant) compared with conventional high-density lipoprotein cholesterol and triglycerides.

CONCLUSIONS

Hazardous metabolomic profiles are associated with ectopic hepatic fat and are relevant to risk of vascular-metabolic disease.

Association Between Obesity and Chronic Kidney Disease: Multivariable Mendelian Randomization Analysis and Observational Data From a Bariatric Surgery Cohort

Obesity is postulated to independently increase chronic kidney disease (CKD), even after adjusting for type 2 diabetes (T2D) and hypertension. Dysglycemia below T2D thresholds, frequently seen with obesity, also increases CKD risk. Whether obesity increases CKD independent of dysglycemia and hypertension is unknown and likely influences the optimal weight loss (WL) needed to reduce CKD. T2D remission rates plateau with 20-25% WL after bariatric surgery (BS), but further WL increases normoglycemia and normotension. We undertook bidirectional inverse variance weighted Mendelian randomization (IVWMR) to investigate potential independent causal associations between increased BMI and estimated glomerular filtration rate (eGFR) in CKD (CKDeGFR) (<60 mL/min/1.73 m2) and microalbuminuria (MA). In 5,337 BS patients, we assessed whether WL influences >50% decline in eGFR (primary outcome) or CKD hospitalization (secondary outcome), using <20% WL as a comparator. IVWMR results suggest that increased BMI increases CKDeGFR (b = 0.13, P = 1.64 × 10-4; odds ratio [OR] 1.14 [95% CI 1.07, 1.23]) and MA (b = 0.25; P = 2.14 × 10-4; OR 1.29 [1.13, 1.48]). After adjusting for hypertension and fasting glucose, increased BMI did not significantly increase CKDeGFR (b = -0.02; P = 0.72; OR 0.98 [0.87, 1.1]) or MA (b = 0.19; P = 0.08; OR 1.21 [0.98, 1.51]). Post-BS WL significantly reduced the primary outcome with 30 to <40% WL (hazard ratio [HR] 0.53 [95% CI 0.32, 0.87]) but not 20 to <30% WL (HR 0.72 [0.44, 1.2]) and ≥40% WL (HR 0.73 [0.41, 1.30]). For CKD hospitalization, progressive reduction was seen with increased WL, which was significant for 30 to <40% WL (HR 0.37 [0.17, 0.82]) and ≥40% WL (HR 0.24 [0.07, 0.89]) but not 20 to <30% WL (HR 0.60 [0.29, 1.23]). The data suggest that obesity is likely not an independent cause of CKD. WL thresholds previously associated with normotension and normoglycemia, likely causal mediators, may reduce CKD after BS.

Distinct metabolic features of genetic liability to type 2 diabetes and coronary artery disease: a reverse Mendelian randomization study

BACKGROUND

Type 2 diabetes (T2D) and coronary artery disease (CAD) both have known genetic determinants, but the mechanisms through which their associated genetic variants lead to disease onset remain poorly understood.

METHODS

We used large-scale metabolomics data in a two-sample reverse Mendelian randomization (MR) framework to estimate effects of genetic liability to T2D and CAD on 249 circulating metabolites in the UK Biobank (N = 118,466). We examined the potential for medication use to distort effect estimates by conducting age-stratified metabolite analyses.

FINDINGS

Using inverse variance weighted (IVW) models, higher genetic liability to T2D was estimated to decrease high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) (e.g.

, HDL-C

 -0.05 SD; 95% CI -0.07 to -0.03, per doubling of liability), whilst increasing all triglyceride groups and branched chain amino acids (BCAAs). IVW estimates for CAD liability suggested an effect on reducing HDL-C as well as raising very-low density lipoprotein cholesterol (VLDL-C) and LDL-C. In pleiotropy-robust models, T2D liability was still estimated to increase BCAAs, but several estimates for higher CAD liability reversed and supported decreased LDL-C and apolipoprotein-B. Estimated effects of CAD liability differed substantially by age for non-HDL-C traits, with higher CAD liability lowering LDL-C only at older ages when statin use was common.

INTERPRETATION

Overall, our results support largely distinct metabolic features of genetic liability to T2D and CAD, illustrating both challenges and opportunities for preventing these commonly co-occurring diseases.

FUNDING

Wellcome Trust [218495/Z/19/Z], UK MRC [MC_UU_00011/1; MC_UU_00011/4], the University of Bristol, Diabetes UK [17/0005587], World Cancer Research Fund [IIG_2019_2009].

Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank

Blood lipids and metabolites are markers of current health and future disease risk. Here, we describe plasma nuclear magnetic resonance (NMR) biomarker data for 118,461 participants in the UK Biobank. The biomarkers cover 249 measures of lipoprotein lipids, fatty acids, and small molecules such as amino acids, ketones, and glycolysis metabolites. We provide an atlas of associations of these biomarkers to prevalence, incidence, and mortality of over 700 common diseases ( nightingalehealth.com/atlas ). The results reveal a plethora of biomarker associations, including susceptibility to infectious diseases and risk of various cancers, joint disorders, and mental health outcomes, indicating that abundant circulating lipids and metabolites are risk markers beyond cardiometabolic diseases. Clustering analyses indicate similar biomarker association patterns across different disease types, suggesting latent systemic connectivity in the susceptibility to a diverse set of diseases. This work highlights the value of NMR based metabolic biomarker profiling in large biobanks for public health research and translation.

Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers

Background

Polygenic scores (PGS) are becoming an increasingly popular approach to predict complex disease risk, although they also hold the potential to develop insight into the molecular profiles of patients with an elevated genetic predisposition to disease.

Methods

We sought to construct an atlas of associations between 125 different PGS derived using results from genome-wide association studies and 249 circulating metabolites in up to 83,004 participants from the UK Biobank.

Results

As an exemplar to demonstrate the value of this atlas, we conducted a hypothesis-free evaluation of all associations with glycoprotein acetyls (GlycA), an inflammatory biomarker. Using bidirectional Mendelian randomization, we find that the associations highlighted likely reflect the effect of risk factors, such as adiposity or liability towards smoking, on systemic inflammation as opposed to the converse direction. Moreover, we repeated all analyses in our atlas within age strata to investigate potential sources of collider bias, such as medication usage. This was exemplified by comparing associations between lipoprotein lipid profiles and the coronary artery disease PGS in the youngest and oldest age strata, which had differing proportions of individuals undergoing statin therapy. Lastly, we generated all PGS-metabolite associations stratified by sex and separately after excluding 13 established lipid-associated loci to further evaluate the robustness of findings.

Conclusions

We envisage that the atlas of results constructed in our study will motivate future hypothesis generation and help prioritize and deprioritize circulating metabolic traits for in-depth investigations. All results can be visualized and downloaded at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas.

Funding

This work is supported by funding from the Wellcome Trust, the British Heart Foundation, and the Medical Research Council Integrative Epidemiology Unit.

Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers

BACKGROUND

Polygenic scores (PGS) are becoming an increasingly popular approach to predict complex disease risk, although they also hold the potential to develop insight into the molecular profiles of patients with an elevated genetic predisposition to disease.

METHODS

We sought to construct an atlas of associations between 125 different PGS derived using results from genome-wide association studies and 249 circulating metabolites in up to 83,004 participants from the UK Biobank.

RESULTS

As an exemplar to demonstrate the value of this atlas, we conducted a hypothesis-free evaluation of all associations with glycoprotein acetyls (GlycA), an inflammatory biomarker. Using bidirectional Mendelian randomization, we find that the associations highlighted likely reflect the effect of risk factors, such as adiposity or liability towards smoking, on systemic inflammation as opposed to the converse direction. Moreover, we repeated all analyses in our atlas within age strata to investigate potential sources of collider bias, such as medication usage. This was exemplified by comparing associations between lipoprotein lipid profiles and the coronary artery disease PGS in the youngest and oldest age strata, which had differing proportions of individuals undergoing statin therapy. Lastly, we generated all PGS-metabolite associations stratified by sex and separately after excluding 13 established lipid-associated loci to further evaluate the robustness of findings.

CONCLUSIONS

We envisage that the atlas of results constructed in our study will motivate future hypothesis generation and help prioritize and deprioritize circulating metabolic traits for in-depth investigations. All results can be visualized and downloaded at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas.

FUNDING

This work is supported by funding from the Wellcome Trust, the British Heart Foundation, and the Medical Research Council Integrative Epidemiology Unit.