Causal factors underlying diabetes risk informed by Mendelian randomisation analysis: evidence, opportunities and challenges

Diabetes and its complications cause a heavy disease burden globally. Identifying exposures, risk factors and molecular processes causally associated with the development of diabetes can provide important evidence bases for disease prevention and spur novel therapeutic strategies. Mendelian randomisation (MR), an epidemiological approach that uses genetic instruments to infer causal associations between an exposure and an outcome, can be leveraged to complement evidence from observational and clinical studies. This narrative review aims to summarise the evidence on potential causal risk factors for diabetes by integrating published MR studies on type 1 and 2 diabetes, and to reflect on future perspectives of MR studies on diabetes. Despite the genetic influence on type 1 diabetes, few MR studies have been conducted to identify causal exposures or molecular processes leading to increased disease risk. In type 2 diabetes, MR analyses support causal associations of somatic, mental and lifestyle factors with development of the disease. These studies have also identified biomarkers, some of them derived from the gut microbiota, and molecular processes leading to increased disease risk. These studies provide valuable data to better understand disease pathophysiology and explore potential therapeutic targets. Because genetic association studies have mostly been restricted to participants of European descent, multi-ancestry cohorts are needed to examine the role of different types of physical activity, dietary components, metabolites, protein biomarkers and gut microbiome in diabetes development.

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.

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results.. [DOI: 10.1101/2023.03.22.533869] [Cited by in Crossref: 0] [Impact Index Per Article: 0] Studies combining metabolomics and genetics, known as metabolite genome-wide association studies (mGWAS), have provided valuable insights into our understanding of the genetic control of metabolite levels. However, the biological interpretation of these associations remains challenging due to a lack of existing tools to annotate mGWAS gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we computed the shortest reactional distance (SRD) based on the curated knowledge of the KEGG database to explore its utility in enhancing the biological interpretation of results from three independent mGWAS, including a case study on sickle cell disease patients. Results show that, in reported mGWAS pairs, there is an excess of small SRD values and that SRD values and p-values significantly correlate, even beyond the standard conservative thresholds. The added-value of SRD annotation is shown for identification of potential false negative hits, exemplified by the finding of gene-metabolite associations with SRD ≤1 that did not reach standard genome-wide significance cut-off. The wider use of this statistic as an mGWAS annotation would prevent the exclusion of biologically relevant associations and can also identify errors or gaps in current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs that can be used to integrate statistical evidence to biological networks

Studies combining metabolomics and genetics, known as metabolite genome-wide association studies (mGWAS), have provided valuable insights into our understanding of the genetic control of metabolite levels. However, the biological interpretation of these associations remains challenging due to a lack of existing tools to annotate mGWAS gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we computed the shortest reactional distance (SRD) based on the curated knowledge of the KEGG database to explore its utility in enhancing the biological interpretation of results from three independent mGWAS, including a case study on sickle cell disease patients. Results show that, in reported mGWAS pairs, there is an excess of small SRD values and that SRD values and p-values significantly correlate, even beyond the standard conservative thresholds. The added-value of SRD annotation is shown for identification of potential false negative hits, exemplified by the finding of gene-metabolite associations with SRD ≤1 that did not reach standard genome-wide significance cut-off. The wider use of this statistic as an mGWAS annotation would prevent the exclusion of biologically relevant associations and can also identify errors or gaps in current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs that can be used to integrate statistical evidence to biological networks.

Analysis of protein expression changes in patients with prediabetes using proteomics approaches

RATIONALE

Proteomics and metabolomics are widely used in the study of diabetes, but rarely in prediabetes research. This study aimed to explore the mechanisms of early-onset type 2 diabetes mellitus (T2DM) by analyzing proteomic changes at different stages of glucose metabolism.

METHODS

A total of 40 individuals undergoing routine physical health examinations between December 2016 and April 2017 were enrolled. Subjects were divided into four groups based on fasting blood glucose (FPG) levels: FPG < 5.6 mmol/L (group A); FPG ≥ 5.6 mmol/L and <6.1 mmol/L (group B); FPG ≥ 6.1 mmol/L and <7.0 mmol/L (group C); and FPG ≥ 7.0 mmol/L (group D). Each group had 10 cases. Sera from these 40 subjects were analyzed by label-free quantitative liquid chromatography coupled with tandem mass spectrometry (LC/MS/MS). LC/MS/MS with selected reaction monitoring mode was also performed for qualitative and quantitative metabolomics analysis. Differentially expressed proteins were identified. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to analyze the differentially expressed metabolites.

RESULTS

A total of 202 differentially expressed proteins were screened and were identified as mainly secreted proteins. Comparing group A with group B, 32 proteins were up-regulated and 18 proteins were down-regulated. Comparing group A with group C, 24 proteins were up-regulated and 24 proteins were down-regulated. Comparing group A with group D, 19 proteins were up-regulated and 17 proteins were down-regulated. The fold change for up-regulated proteins was >1.2, p < 0.05, while the fold change for down-regulated proteins was <-1.2, p < 0.05. PLS-DA and OPLS-DA revealed 113 differentially expressed metabolites. Correlation analysis of differentially expressed metabolites of group A versus group B revealed that among the down-regulated differential proteins, transforming growth factor β-induced protein ig-h3 correlated negatively with metabolite L-saccharin, while among the up-regulated differential proteins, apolipoprotein C-IV correlated negatively with metabolite 3-methyloxindole. Among all differentially expressed proteins, 19 proteins were associated with early initiation of chronic inflammation, including CD14 and CSF-1R, which were newly identified in the early onset of T2DM.

CONCLUSIONS

Many proteins are differentially expressed between prediabetes and after T2DM diagnosis, although the specific mechanism remains unclear. The expression level of CD14 was significantly up-regulated and that of CSF-1R was significantly down-regulated when FPG was ≥5.6 mmol/L, suggesting that CD14 and CSF-1R may be important markers for early-onset T2DM and may serve as new targets for T2DM treatment.

Exploiting the mediating role of the metabolome to unravel transcript-to-phenotype associations

Despite the success of genome-wide association studies (GWASs) in identifying genetic variants associated with complex traits, understanding the mechanisms behind these statistical associations remains challenging. Several methods that integrate methylation, gene expression, and protein quantitative trait loci (QTLs) with GWAS data to determine their causal role in the path from genotype to phenotype have been proposed. Here, we developed and applied a multi-omics Mendelian randomization (MR) framework to study how metabolites mediate the effect of gene expression on complex traits. We identified 216 transcript-metabolite-trait causal triplets involving 26 medically relevant phenotypes. Among these associations, 58% were missed by classical transcriptome-wide MR, which only uses gene expression and GWAS data. This allowed the identification of biologically relevant pathways, such as between ANKH and calcium levels mediated by citrate levels and SLC6A12 and serum creatinine through modulation of the levels of the renal osmolyte betaine. We show that the signals missed by transcriptome-wide MR are found, thanks to the increase in power conferred by integrating multiple omics layer. Simulation analyses show that with larger molecular QTL studies and in case of mediated effects, our multi-omics MR framework outperforms classical MR approaches designed to detect causal relationships between single molecular traits and complex phenotypes.

Ubiquitin-specific protease 28: the decipherment of its dual roles in cancer development

As significant posttranslational modifications, ubiquitination and deubiquitination, whose balance is modulated by ubiquitin-conjugating enzymes and deubiquitinating enzymes (DUBs), can regulate many biological processes, such as controlling cell cycle progression, signal transduction and transcriptional regulation. Belonging to DUBs, ubiquitin-specific protease 28 (USP28) plays an essential role in turning over ubiquitination and then contributing to the stabilization of quantities of substrates, including several cancer-related proteins. In previous studies, USP28 has been demonstrated to participate in the progression of various cancers. Nevertheless, several reports have recently shown that in addition to promoting cancers, USP28 can also play an oncostatic role in some cancers. In this review, we summarize the correlation between USP28 and tumor behaviors. We initially give a brief introduction of the structure and related biological functions of USP28, and we then introduce some concrete substrates of USP28 and the underlying molecular mechanisms. In addition, the regulation of the actions and expression of USP28 is also discussed. Moreover, we concentrate on the impacts of USP28 on diverse hallmarks of cancer and discuss whether USP28 can accelerate or inhibit tumor progression. Furthermore, clinical relevance, including impacting clinical prognosis, influencing therapy resistance and being the therapy target in some cancers, is depicted systematically. Thus, assistance may be given to future experimental designs by the information provided here, and the potential of targeting USP28 for cancer therapy is emphasized.

Identification of a weight loss-associated causal eQTL in MTIF3 and the effects of MTIF3 deficiency on human adipocyte function

Genetic variation at the MTIF3 (Mitochondrial Translational Initiation Factor 3) locus has been robustly associated with obesity in humans, but the functional basis behind this association is not known. Here, we applied luciferase reporter assay to map potential functional variants in the haplotype block tagged by rs1885988 and used CRISPR-Cas9 to edit the potential functional variants to confirm the regulatory effects on MTIF3 expression. We further conducted functional studies on MTIF3-deficient differentiated human white adipocyte cell line (hWAs-iCas9), generated through inducible expression of CRISPR-Cas9 combined with delivery of synthetic MTIF3-targeting guide RNA. We demonstrate that rs67785913-centered DNA fragment (in LD with rs1885988, r² > 0.8) enhances transcription in a luciferase reporter assay, and CRISPR-Cas9-edited rs67785913 CTCT cells show significantly higher MTIF3 expression than rs67785913 CT cells. Perturbed MTIF3 expression led to reduced mitochondrial respiration and endogenous fatty acid oxidation, as well as altered expression of mitochondrial DNA-encoded genes and proteins, and disturbed mitochondrial OXPHOS complex assembly. Furthermore, after glucose restriction, the MTIF3 knockout cells retained more triglycerides than control cells. This study demonstrates an adipocyte function-specific role of MTIF3, which originates in the maintenance of mitochondrial function, providing potential explanations for why MTIF3 genetic variation at rs67785913 is associated with body corpulence and response to weight loss interventions.

Causal evidence for an ApoB-independent metabolomic risk profile associated with coronary artery disease.. [DOI: 10.1101/2023.03.01.23286619] [Cited by in Crossref: 0] [Impact Index Per Article: 0] Background and aims1-H nuclear magnetic resonance (1H-NMR) metabolomic measures in plasma have yielded significant insight into the pathophysiology of cardiometabolic disease, but their interrelated nature complicates causal inference and clinical interpretation. This study aimed to investigate the associations of unrelated1H-NMR metabolomic profiles with coronary artery disease (CAD), type 2 diabetes (T2D) and ischemic stroke (ISTR).MethodsPrincipal component (PC) analysis was performed on 1681H-NMR metabolomic measures in 56,712 unrelated European participants from UK Biobank to retrieve unrelated PCs, which were used in multivariable-adjusted cox-proportional hazard models and genome-wide association analyses for Mendelian Randomization (MR). Two-sample MR analyses were conducted in three non-overlapping databases which were subsequently meta-analysed, resulting in combined sample sizes of 755,481 (128,728 cases), 1,017,097 (121,977 cases), and 1,002,264 (56,067 cases) for CAD, T2D, and ISTR, respectively.ResultsWe identified six PCs which collectively explained 88% of the total variance. For CAD in particular, results from both multivariable-adjusted and MR analyses were generally directionally consistent. The pooled odds ratios (ORs) [95% CI] of per one-SD increase in genetically-influenced PC1 and PC3 (both characterized by distinct ApoB-associated lipoprotein profiles) were 1.04 [1.03, 1.05] and 0.94 [0.93, 0.96], respectively. In addition, the pooled OR for CAD of PC4, characterized by simultaneously decreased small HDL and increased large HDL, and independent of ApoB, was 1.05 [1.03, 1.07]. For the other outcomes, PC5 (characterized by increased amino acids) was associated with a higher risk of T2D and ISTR.ConclusionsThis study highlights the existence of an ApoB-independent lipoprotein profile driving CAD. Interestingly, this profile is characterized by a distinctive HDL sub-particle distribution, providing evidence for a role of HDL in the development of CAD

Background and aims

1-H nuclear magnetic resonance (1H-NMR) metabolomic measures in plasma have yielded significant insight into the pathophysiology of cardiometabolic disease, but their interrelated nature complicates causal inference and clinical interpretation. This study aimed to investigate the associations of unrelated1H-NMR metabolomic profiles with coronary artery disease (CAD), type 2 diabetes (T2D) and ischemic stroke (ISTR).

Methods

Principal component (PC) analysis was performed on 1681H-NMR metabolomic measures in 56,712 unrelated European participants from UK Biobank to retrieve unrelated PCs, which were used in multivariable-adjusted cox-proportional hazard models and genome-wide association analyses for Mendelian Randomization (MR). Two-sample MR analyses were conducted in three non-overlapping databases which were subsequently meta-analysed, resulting in combined sample sizes of 755,481 (128,728 cases), 1,017,097 (121,977 cases), and 1,002,264 (56,067 cases) for CAD, T2D, and ISTR, respectively.

Results

We identified six PCs which collectively explained 88% of the total variance. For CAD in particular, results from both multivariable-adjusted and MR analyses were generally directionally consistent. The pooled odds ratios (ORs) [95% CI] of per one-SD increase in genetically-influenced PC1 and PC3 (both characterized by distinct ApoB-associated lipoprotein profiles) were 1.04 [1.03, 1.05] and 0.94 [0.93, 0.96], respectively. In addition, the pooled OR for CAD of PC4, characterized by simultaneously decreased small HDL and increased large HDL, and independent of ApoB, was 1.05 [1.03, 1.07]. For the other outcomes, PC5 (characterized by increased amino acids) was associated with a higher risk of T2D and ISTR.

Conclusions

This study highlights the existence of an ApoB-independent lipoprotein profile driving CAD. Interestingly, this profile is characterized by a distinctive HDL sub-particle distribution, providing evidence for a role of HDL in the development of CAD.

Relationship Between Regional Adiposity Distribution and Incident Heart Failure in General Populations without Cardiovascular Disease

BACKGROUND

Obesity is associated with a high risk of heart failure. However, the contribution of regional fat distribution evaluated using bioimpedance analysis toward heart failure risk in the general population without cardiovascular disease has rarely been studied.

METHODS

This study included 483,316 participants without heart failure and cardiovascular disease from the UK Biobank study. The regional fat mass was determined by bioimpedance analysis and calculated by dividing the square of height in meters (kg/m²). This study evaluated the association of regional fat mass (arm fat index [AFI], trunk fat index [TFI], and leg fat index [LFI]) with the risk of incident heart failure and whether regional fat mass adds a further prognostic value for heart failure besides body mass index (BMI) in a large prospective cohort study.

RESULTS

During the median 12.1 years, 3134 incident heart failure cases occurred. After adjustment for BMI and other confounding factors, each 1-standard deviation increase in LFI was associated with a 21% lower heart failure risk even after adjusting for BMI and other confounding factors (hazard ratio [HR] 0.79; 95% confidence interval [CI], 0.73-0.85). However, we did not observe heart failure-associated risks with AFI and TFI (HR 1.04; 95% CI, 0.99-1.09; HR 0.97, 95% CI, 0.91-1.04, respectively). Subgroup analysis demonstrated that the protective role of LFI was more prominent in the elderly and female participants (P < .01).

CONCLUSION

Regional fat measurement other than BMI can improve heart failure risk stratification; leg fat plays a protective role, yet arm and trunk fat do not, in the general population without cardiovascular disease.

PPM1K-regulated impaired catabolism of branched-chain amino acids orchestrates polycystic ovary syndrome

BACKGROUND

Polycystic ovary syndrome (PCOS) is one of the most common diseases with the coexistence of reproductive malfunction and metabolic disorders. Previous studies have found increased branched chain amino acid (BCAA) levels in women with PCOS. However, it remains unclear whether BCAA metabolism is causally associated with the risk of PCOS.

METHODS

The changes of BCAA levels in the plasma and follicular fluids of PCOS women were detected. Mendelian randomization (MR) approaches were used to explore the potential causal association between BCAA levels and the risk of PCOS. The function of the gene coding the protein phosphatase Mg²⁺/Mn²⁺-dependent 1K (PPM1K) was further explored by using Ppm1k-deficient mouse model and PPM1K down-regulated human ovarian granulosa cells.

FINDINGS

BCAA levels were significantly elevated in both plasma and follicular fluids of PCOS women. Based on MR, a potential direct, causal role for BCAA metabolism was revealed in the pathogenesis of PCOS, and PPM1K was detected as a vital driver. Ppm1k-deficient female mice had increased BCAA levels and exhibited PCOS-like traits, including hyperandrogenemia and abnormal follicle development. A reduction in dietary BCAA intake significantly improved the endocrine and ovarian dysfunction of Ppm1k^-/- female mice. Knockdown of PPM1K promoted the conversion of glycolysis to pentose phosphate pathway and inhibited mitochondrial oxidative phosphorylation in human granulosa cells.

INTERPRETATION

Ppm1k deficiency-impaired BCAA catabolism causes the occurrence and development of PCOS. PPM1K suppression disturbed energy metabolism homeostasis in the follicular microenvironment, which provided an underlying mechanism of abnormal follicle development.

FUNDING

This study was supported by the National Key Research and Development Program of China (2021YFC2700402, 2019YFA0802503), the National Natural Science Foundation of China (81871139, 82001503, 92057107), the CAMS Innovation Fund for Medical Sciences (2019-I2M-5-001), Key Clinical Projects of Peking University Third Hospital (BYSY2022043), the China Postdoctoral Science Foundation (2021T140600), and the Collaborative Innovation Program of Shanghai Municipal Health Commission (2020CXJQ01).

What clinical metabolomics will bring to the medicine of tomorrow

The purpose of this review is to explore how clinical metabolomics could help physicians in the future. The recent advent of medical genomics brings new and interesting technological tools to measure genetic predispositions to a disease. But metabolomics will allow us to go even further by linking the patient’s pathological phenotype with gene expression defects and metabolic disorders. It is in this context that the clinical chemist must adapt and be a force of proposal to meet these health challenges. He must help the clinician by mastering these new innovative tools, in order to participate in the implementation of clinical studies for the discovery of biomarkers, but also to propose the assays of biomarkers called “signatures,” which can be composite biomarkers or fingerprints, which will ultimately guide the clinician. He will have to propose them as clinical chemistry tests. In the first part, we will look at some concrete examples of the use of clinical metabolomics in clinical research projects that have led to the identification of a new biomarker. We will use the example of trimethylamine N-oxide (or TMAO) and review the clinical studies that have proposed TMAO as a biomarker for cardiovascular diseases. In a second part, we will see through bibliographic studies, how the metabolomic fingerprint can be useful to build a supervised model for patient stratification. In conclusion, we will discuss the limitations currently under debate.

Proteogenomic links to human metabolic diseases

Studying the plasma proteome as the intermediate layer between the genome and the phenome has the potential to identify new disease processes. Here, we conducted a cis-focused proteogenomic analysis of 2,923 plasma proteins measured in 1,180 individuals using antibody-based assays. We (1) identify 256 unreported protein quantitative trait loci (pQTL); (2) demonstrate shared genetic regulation of 224 cis-pQTLs with 575 specific health outcomes, revealing examples for notable metabolic diseases (such as gastrin-releasing peptide as a potential therapeutic target for type 2 diabetes); (3) improve causal gene assignment at 40% (n = 192) of overlapping risk loci; and (4) observe convergence of phenotypic consequences of cis-pQTLs and rare loss-of-function gene burden for 12 proteins, such as TIMD4 for lipoprotein metabolism. Our findings demonstrate the value of integrating complementary proteomic technologies with genomics even at moderate scale to identify new mediators of metabolic diseases with the potential for therapeutic interventions.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

A New In Vivo Zebrafish Bioassay Evaluating Liver Steatosis Identifies DDE as a Steatogenic Endocrine Disruptor, Partly through SCD1 Regulation

Non-alcoholic fatty liver disease (NAFLD), which starts with liver steatosis, is a growing worldwide epidemic responsible for chronic liver diseases. Among its risk factors, exposure to environmental contaminants, such as endocrine disrupting compounds (EDC), has been recently emphasized. Given this important public health concern, regulation agencies need novel simple and fast biological tests to evaluate chemical risks. In this context, we developed a new in vivo bioassay called StAZ (Steatogenic Assay on Zebrafish) using an alternative model to animal experimentation, the zebrafish larva, to screen EDCs for their steatogenic properties. Taking advantage of the transparency of zebrafish larvae, we established a method based on fluorescent staining with Nile red to estimate liver lipid content. Following testing of known steatogenic molecules, 10 EDCs suspected to induce metabolic disorders were screened and DDE, the main metabolite of the insecticide DDT, was identified as a potent inducer of steatosis. To confirm this and optimize the assay, we used it in a transgenic zebrafish line expressing a blue fluorescent liver protein reporter. To obtain insight into DDE's effect, the expression of several genes related to steatosis was analyzed; an up-regulation of scd1 expression, probably relying on PXR activation, was found, partly responsible for both membrane remodeling and steatosis.

Widespread natural selection on metabolite levels in humans.. [DOI: 10.1101/2023.02.07.527420] [Cited by in Crossref: 0] [Impact Index Per Article: 0] Natural selection acts ubiquitously on complex human traits, predominantly constraining the occurrence of extreme phenotypes (stabilizing selection). These constrains propagate to DNA sequence variants associated with traits under selection. The genetic imprints of such evolutionary events can thus be detected via combining effect size estimates from genetic association studies and the corresponding allele frequencies. While this approach has been successfully applied to high-level traits, the prevalence and mode of selection acting on molecular traits remains poorly understood. Here, we estimate the action of natural selection on genetic variants associated with metabolite levels, an important layer of molecular traits. By leveraging summary statistics of published genome-wide association studies with large sample sizes, we find strong evidence of stabilizing selection for 15 out of 97 plasma metabolites, with an overrepresentation of amino acids among such cases. Mendelian randomization analysis revealed that metabolites under stronger stabilizing selection display larger effects on key cardiometabolic traits, suggesting that maintaining a healthy cardiometabolic profile may be an important source of selective constraints on the metabolome. Metabolites under strong stabilizing selection in humans are also more conserved in their concentrations among diverse mammalian species, suggesting shared selective forces across micro and macroevolutionary time scales. Finally, we also found evidence for both disruptive and directional selection on specific lipid metabolites, potentially indicating ongoing evolutionary adaptation in humans. Overall, this study demonstrates that variation in metabolite levels among humans is frequently shaped by natural selection and this may be acting indirectly through maintaining cardiometabolic fitness

Natural selection acts ubiquitously on complex human traits, predominantly constraining the occurrence of extreme phenotypes (stabilizing selection). These constrains propagate to DNA sequence variants associated with traits under selection. The genetic imprints of such evolutionary events can thus be detected via combining effect size estimates from genetic association studies and the corresponding allele frequencies. While this approach has been successfully applied to high-level traits, the prevalence and mode of selection acting on molecular traits remains poorly understood. Here, we estimate the action of natural selection on genetic variants associated with metabolite levels, an important layer of molecular traits. By leveraging summary statistics of published genome-wide association studies with large sample sizes, we find strong evidence of stabilizing selection for 15 out of 97 plasma metabolites, with an overrepresentation of amino acids among such cases. Mendelian randomization analysis revealed that metabolites under stronger stabilizing selection display larger effects on key cardiometabolic traits, suggesting that maintaining a healthy cardiometabolic profile may be an important source of selective constraints on the metabolome. Metabolites under strong stabilizing selection in humans are also more conserved in their concentrations among diverse mammalian species, suggesting shared selective forces across micro and macroevolutionary time scales. Finally, we also found evidence for both disruptive and directional selection on specific lipid metabolites, potentially indicating ongoing evolutionary adaptation in humans. Overall, this study demonstrates that variation in metabolite levels among humans is frequently shaped by natural selection and this may be acting indirectly through maintaining cardiometabolic fitness.

Causal relationships between genetically predicted circulating levels of amino acids and NAFLD risk: a Mendelian randomisation study.. [DOI: 10.1101/2023.02.03.23285451] [Cited by in Crossref: 0] [Impact Index Per Article: 0] Background and AimsEmerging metabolomics-based studies suggested links between amino acids metabolism and NAFLD risk, however, whether there exists an aetiological role of amino acid metabolism in NAFLD development remains unknown. The aim of the present study was to assess the causal relationship between circulating levels of amino acids and NAFLD risk.Approach and ResultsWe performed two-sample Mendelian randomisation (MR) analyses using summary level data from genome-wide association studies (GWAS) to assess causal relationships between genetically predicted circulating levels of amino acids and NAFLD risk. Data from the largest GWAS on NAFLD (8,434 cases and 770,180 controls) were used in discovery MR analysis, and from a GWAS on NAFLD (1,483 cases and 17,781 controls) where NAFLD cases were diagnosed using liver biopsy, were used in replication MR analysis. Wald ratios or multiplicative random-effect inverse variance weighted (IVW) methods were used in the main MR analysis, and weighted median and MR-Egger regression analysis were used in sensitivity analyses. We additionally performed an MR conservative analysis by restricting genetic instruments to those directly involved in amino acid metabolism pathways.We found that genetically predicted higher alanine (OR=1.45, 95% CI 1.15-1.83) and lower glutamine (OR = 0.81, 95% CI 0.66-1.00) levels were associated with a higher risk of developing NAFLD. Results from MR sensitivity analyses and conservative analysis supported the main findings.ConclusionGenetically predicted higher circulating levels of alanine was associated with an increased risk of NAFLD, whereas higher glutamine was associated with a decreased risk of NAFLD

Background and Aims

Emerging metabolomics-based studies suggested links between amino acids metabolism and NAFLD risk, however, whether there exists an aetiological role of amino acid metabolism in NAFLD development remains unknown. The aim of the present study was to assess the causal relationship between circulating levels of amino acids and NAFLD risk.

Approach and Results

We performed two-sample Mendelian randomisation (MR) analyses using summary level data from genome-wide association studies (GWAS) to assess causal relationships between genetically predicted circulating levels of amino acids and NAFLD risk. Data from the largest GWAS on NAFLD (8,434 cases and 770,180 controls) were used in discovery MR analysis, and from a GWAS on NAFLD (1,483 cases and 17,781 controls) where NAFLD cases were diagnosed using liver biopsy, were used in replication MR analysis. Wald ratios or multiplicative random-effect inverse variance weighted (IVW) methods were used in the main MR analysis, and weighted median and MR-Egger regression analysis were used in sensitivity analyses. We additionally performed an MR conservative analysis by restricting genetic instruments to those directly involved in amino acid metabolism pathways.

We found that genetically predicted higher alanine (OR=1.45, 95% CI 1.15-1.83) and lower glutamine (OR = 0.81, 95% CI 0.66-1.00) levels were associated with a higher risk of developing NAFLD. Results from MR sensitivity analyses and conservative analysis supported the main findings.

Conclusion

Genetically predicted higher circulating levels of alanine was associated with an increased risk of NAFLD, whereas higher glutamine was associated with a decreased risk of NAFLD.

Genetically determined serum serine level has a novel causal effect on multiple sclerosis risk and predicts disability progression

BACKGROUND

There are currently no specific biomarkers for multiple sclerosis (MS). Identifying robust biomarkers for MS is crucial to improve disease diagnosis and management.

METHODS

This study first used six Mendelian randomisation methods to assess causal relationship of 174 metabolites with MS, incorporating data from European-ancestry metabolomics (n=8569-86 507) and MS (n=14 802 MS cases, 26 703 controls) genomewide association studies. Genetic scores for identified causal metabolite(s) were then computed to predict MS disability progression in an independent longitudinal cohort (AusLong study) of 203 MS cases with up to 15-year follow-up.

RESULTS

We found a novel genetic causal effect of serine on MS onset (OR=1.67, 95% CI 1.51 to 1.84, p=1.73×10^-20), such that individuals whose serine level is 1 SD above the population mean will have 1.67 times the risk of developing MS. This is robust across all sensitivity methods (OR ranges from 1.49 to 1.67). In an independent longitudinal MS cohort, we then constructed time-dynamic and time-fixed genetic scores based on serine genetic instrument single-nucleotide polymorphisms, where higher scores for raised serum serine level were associated with increased risk of disability worsening, especially in the time-dynamic model (RR=1.25, 95% CI 1.10 to 1.42, p=7.52×10^-4).

CONCLUSIONS

These findings support investigating serine as an important candidate biomarker for MS onset and disability progression.

Using genetic association data to guide drug discovery and development: Review of methods and applications

Evidence on the validity of drug targets from randomized trials is reliable but typically expensive and slow to obtain. In contrast, evidence from conventional observational epidemiological studies is less reliable because of the potential for bias from confounding and reverse causation. Mendelian randomization is a quasi-experimental approach analogous to a randomized trial that exploits naturally occurring randomization in the transmission of genetic variants. In Mendelian randomization, genetic variants that can be regarded as proxies for an intervention on the proposed drug target are leveraged as instrumental variables to investigate potential effects on biomarkers and disease outcomes in large-scale observational datasets. This approach can be implemented rapidly for a range of drug targets to provide evidence on their effects and thus inform on their priority for further investigation. In this review, we present statistical methods and their applications to showcase the diverse opportunities for applying Mendelian randomization in guiding clinical development efforts, thus enabling interventions to target the right mechanism in the right population group at the right time. These methods can inform investigators on the mechanisms underlying drug effects, their related biomarkers, implications for the timing of interventions, and the population subgroups that stand to gain the most benefit. Most methods can be implemented with publicly available data on summarized genetic associations with traits and diseases, meaning that the only major limitations to their usage are the availability of appropriately powered studies for the exposure and outcome and the existence of a suitable genetic proxy for the proposed intervention.

A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes

Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.

Causal effects of maternal circulating amino acids on offspring birthweight: a Mendelian randomisation study

BACKGROUND

Amino acids are key to protein synthesis, energy metabolism, cell signaling and gene expression; however, the contribution of specific maternal amino acids to fetal growth is unclear.

METHODS

We explored the effect of maternal circulating amino acids on fetal growth, proxied by birthweight, using two-sample Mendelian randomisation (MR) and summary data from a genome-wide association study (GWAS) of serum amino acids levels (sample 1, n = 86,507) and a maternal GWAS of offspring birthweight in UK Biobank and Early Growth Genetics Consortium, adjusting for fetal genotype effects (sample 2, n = 406,063 with maternal and/or fetal genotype effect estimates). A total of 106 independent single nucleotide polymorphisms robustly associated with 19 amino acids (p < 4.9 × 10^-10) were used as genetic instrumental variables (IV). Wald ratio and inverse variance weighted methods were used in MR main analysis. A series of sensitivity analyses were performed to explore IV assumption violations.

FINDINGS

Our results provide evidence that maternal circulating glutamine (59 g offspring birthweight increase per standard deviation increase in maternal amino acid level, 95% CI: 7, 110) and serine (27 g, 95% CI: 9, 46) raise, while leucine (-59 g, 95% CI: -106, -11) and phenylalanine (-25 g, 95% CI: -47, -4) lower offspring birthweight. These findings are supported by sensitivity analyses.

INTERPRETATION

Our findings strengthen evidence for key roles of maternal circulating amino acids during pregnancy in healthy fetal growth.

FUNDING

A full list of funding bodies that contributed to this study can be found under Acknowledgments.

Epigenome-wide association study of DNA methylation in maternal blood leukocytes with BMI in pregnancy and gestational weight gain.. [DOI: 10.21203/rs.3.rs-2517570/v1] [Cited by in Crossref: 0] [Impact Index Per Article: 0] Objectives We aimed to discover CpG sites with differential methylation in peripheral blood leukocytes associated with body mass index (BMI) in pregnancy and gestational weight gain (GWG) in women of European and South Asian ancestry. Furthermore, we aimed to investigate how the identified sites were associated with methylation quantitative trait loci, gene ontology, and cardiometabolic parameters. Methods In the Epigenetics in pregnancy (EPIPREG) sample we quantified maternal DNA methylation in peripheral blood leukocytes in gestational week 28 with Illumina’s MethylationEPIC BeadChip. In women with European (n = 303) and South Asian (n = 164) ethnic ancestry, we performed an epigenome-wide association study of BMI in gestational week 28 and GWG between gestational weeks 15 and 28 using a meta-analysis approach. Replication was performed in the Norwegian Mother, Father, and Child Cohort Study, the Study of Assisted Reproductive Technologies (MoBa-START) (n = 877, mainly European/Norwegian). Results We identified five CpG sites associated with BMI at gestational week 28 (p from 4.0 x 10− 8 to 2.1 x 10− 10). Of these, we were able to replicate three in MoBa-START; cg02786370, cg19758958 and cg10472537. Two sites are located in genes previously associated with blood pressure and BMI. Methylation at the three replicated CpG sites were associated with levels of blood pressure, lipids and glucose in EPIPREG (p from 1.2 x10− 8 to 0.04). Pathway analysis suggested involvation in inflammatory pathways (p from 1.9 x10− 8 to 4.7 x10− 5). No CpG sites were significantly associated with GWG. Conclusions We identified five CpG sites associated with BMI at gestational week 28, three of which were replicated in an independent cohort. Several gene variants were associated with methylation at cg02786379, suggesting a genetic component influencing differential methylation. The identified CpG sites were associated with cardiometabolic traits, as well as with inflammatory pathways

Objectives We aimed to discover CpG sites with differential methylation in peripheral blood leukocytes associated with body mass index (BMI) in pregnancy and gestational weight gain (GWG) in women of European and South Asian ancestry. Furthermore, we aimed to investigate how the identified sites were associated with methylation quantitative trait loci, gene ontology, and cardiometabolic parameters. Methods In the Epigenetics in pregnancy (EPIPREG) sample we quantified maternal DNA methylation in peripheral blood leukocytes in gestational week 28 with Illumina’s MethylationEPIC BeadChip. In women with European (n = 303) and South Asian (n = 164) ethnic ancestry, we performed an epigenome-wide association study of BMI in gestational week 28 and GWG between gestational weeks 15 and 28 using a meta-analysis approach. Replication was performed in the Norwegian Mother, Father, and Child Cohort Study, the Study of Assisted Reproductive Technologies (MoBa-START) (n = 877, mainly European/Norwegian). Results We identified five CpG sites associated with BMI at gestational week 28 (p from 4.0 x 10− 8 to 2.1 x 10− 10). Of these, we were able to replicate three in MoBa-START; cg02786370, cg19758958 and cg10472537. Two sites are located in genes previously associated with blood pressure and BMI. Methylation at the three replicated CpG sites were associated with levels of blood pressure, lipids and glucose in EPIPREG (p from 1.2 x10− 8 to 0.04). Pathway analysis suggested involvation in inflammatory pathways (p from 1.9 x10− 8 to 4.7 x10− 5). No CpG sites were significantly associated with GWG. Conclusions We identified five CpG sites associated with BMI at gestational week 28, three of which were replicated in an independent cohort. Several gene variants were associated with methylation at cg02786379, suggesting a genetic component influencing differential methylation. The identified CpG sites were associated with cardiometabolic traits, as well as with inflammatory pathways.

Impact of the gut microbiota and associated metabolites on cardiometabolic traits, chronic diseases and human longevity: a Mendelian randomization study

Features of the gut microbiota have been associated with several chronic diseases and longevity in preclinical models as well as in observational studies. Whether these relations underlie causal effects in humans remains to be established. We aimed to determine whether the gut microbiota influences cardiometabolic traits as well as the risk of chronic diseases and human longevity using a comprehensive 2-Sample Mendelian randomization approach. We included as exposures 10 gut-associated metabolites and pathways and 57 microbial taxa abundance. We included as outcomes nine cardiometabolic traits (fasting glucose, fasting insulin, systolic blood pressure, diastolic blood pressure, HDL cholesterol, LDL cholesterol, triglycerides, estimated glomerular filtration rate, body mass index [BMI]), eight chronic diseases previously linked with the gut microbiota in observational studies (Alzheimer's disease, depression, type 2 diabetes, non-alcoholic fatty liver disease, coronary artery disease (CAD), stroke, osteoporosis and chronic kidney disease), as well as parental lifespan and longevity. We found 7 associations with evidence of causality before and after sensitivity analyses, but not after multiple testing correction (1198 tests). Most effect sizes (4/7) were small. The two largest exposure-outcome effects were markedly attenuated towards the null upon inclusion of BMI or alcohol intake frequency in multivariable MR analyses. While finding robust genetic instruments for microbiota features is challenging hence potentially inflating type 2 errors, these results do not support a large causal impact of human gut microbita features on cardiometabolic traits, chronic diseases or longevity. These results also suggest that the previously documented associations between gut microbiota and human health outcomes may not always underly causal relations.

Hepatic Lipid Homeostasis in NAFLD

Non-alcoholic fatty liver disease (NAFLD) is currently the most common liver disease, affecting 25% of world population. Hepatic steatosis has 60–90% prevalence among obese patients. It is also associated with multitude of detrimental effects and increased mortality. This narrative chapter investigates hepatic lipid homeostasis in NAFLD, focusing on the four molecular pathways of hepatic steatosis to lipid homeostasis in the liver. Hepatic steatosis is a consequence of lipid acquisition pathways exceeding lipid disposal pathways. In NAFLD, hepatic uptake of fatty acids and de novo lipogenesis surpass fatty acid oxidation and lipid export. The imbalance of the hepatic lipid may promote cellular damage by inducing oxidative stress in peroxisomes and cytochromes, especially with compromised mitochondrial function. Lipid export may even decrease with disease progression, sustaining the accumulation of lipids. NAFLD has a complex molecular mechanism regulating hepatic lipid homeostasis. Thus, as well as inter-individual differences, any intervention targeting one or more pathway is likely to have consequences on multiple cellular signaling pathways. We should be taken into careful consideration when developing future treatment options for NAFLD.

Genetic influences on human blood metabolites in the Japanese population

An increase in ethnic diversity in genetic studies has the potential to provide unprecedented insights into how genetic variations influence human phenotypes. In this study, we conducted a quantitative trait locus (QTL) analysis of 121 metabolites measured using gas chromatography-mass spectrometry with plasma samples from 4,888 Japanese individuals. We found 60 metabolite-gene associations, of which 13 have not been previously reported. Meta-analyses with another Japanese and a European study identified six and two additional unreported loci, respectively. Genetic variants influencing metabolite levels were more enriched in protein-coding regions than in the regulatory regions while being associated with the risk of various diseases. Finally, we identified a signature of strong negative selection for uric acid ( S ˆ  = -1.53, p = 6.2 × 10^-18). Our study expanded the knowledge of genetic influences on human blood metabolites, providing valuable insights into their physiological, pathological, and selective properties.

Large scale phenotype imputation and in vivo functional validation implicate ADAMTS14 as an adiposity gene

Obesity remains an unmet global health burden. Detrimental anatomical distribution of body fat is a major driver of obesity-mediated mortality risk and is demonstrably heritable. However, our understanding of the full genetic contribution to human adiposity is incomplete, as few studies measure adiposity directly. To address this, we impute whole-body imaging adiposity phenotypes in UK Biobank from the 4,366 directly measured participants onto the rest of the cohort, greatly increasing our discovery power. Using these imputed phenotypes in 392,535 participants yielded hundreds of genome-wide significant associations, six of which replicate in independent cohorts. The leading causal gene candidate, ADAMTS14, is further investigated in a mouse knockout model. Concordant with the human association data, the Adamts14^-/- mice exhibit reduced adiposity and weight-gain under obesogenic conditions, alongside an improved metabolic rate and health. Thus, we show that phenotypic imputation at scale offers deeper biological insights into the genetics of human adiposity that could lead to therapeutic targets.

Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes

We tested associations between 13 established genetic variants and type 2 diabetes (T2D) in 1371 study participants from the Volga-Ural region of the Eurasian continent, and evaluated the predictive ability of the model containing polygenic scores for the variants associated with T2D in our dataset, alone and in combination with other risk factors such as age and sex. Using logistic regression analysis, we found associations with T2D for the CCL20 rs6749704 (OR = 1.68, P_FDR = 3.40 × 10^-5), CCR5 rs333 (OR = 1.99, P_FDR = 0.033), ADIPOQ rs17366743 (OR = 3.17, P_FDR = 2.64 × 10^-4), TCF7L2 rs114758349 (OR = 1.77, P_FDR = 9.37 × 10^-5), and CCL2 rs1024611 (OR = 1.38, P_FDR = 0.033) polymorphisms. We showed that the most informative prognostic model included weighted polygenic scores for these five loci, and non-genetic factors such as age and sex (AUC 85.8%, 95%CI 83.7-87.8%). Compared to the model containing only non-genetic parameters, adding the polygenic score for the five T2D-associated loci showed improved net reclassification (NRI = 37.62%, 1.39 × 10^-6). Inclusion of all 13 tested SNPs to the model with age and sex did not improve the predictive ability compared to the model containing five T2D-associated variants (NRI = -17.86, p = 0.093). The five variants associated with T2D in people from the Volga-Ural region are linked to inflammation (CCR5, CCL2, CCL20) and glucose metabolism regulation (TCF7L, ADIPOQ2). Further studies in independent groups of T2D patients should validate the prognostic value of the model and elucidate the molecular mechanisms of the disease development.

The I148M PNPLA3 variant mitigates niacin beneficial effects: How the genetic screening in non-alcoholic fatty liver disease patients gains value

Background

The PNPLA3 p.I148M impact on fat accumulation can be modulated by nutrients. Niacin (Vitamin B3) reduced triglycerides synthesis in in vitro and in vivo NAFLD models.

Objectives

In this study, we aimed to investigate the niacin-I148M polymorphism crosstalk in NAFLD patients and examine niacin's beneficial effect in reducing fat by exploiting hepatoma cells with different PNPLA3 genotype.

Design

We enrolled 172 (Discovery cohort) and 358 (Validation cohort) patients with non-invasive and histological diagnosis of NAFLD, respectively. Dietary niacin was collected from food diary, while its serum levels were quantified by ELISA. Hepatic expression of genes related to NAD metabolism was evaluated by RNAseq in bariatric NAFLD patients (n = 183; Transcriptomic cohort). Hep3B (148I/I) and HepG2 (148M/M) cells were silenced (siHep3B) or overexpressed (HepG2^I148+ ) for PNPLA3, respectively.

Results

In the Discovery cohort, dietary niacin was significantly reduced in patients with steatosis ≥ 2 and in I148M carriers. Serum niacin was lower in subjects carrying the G at risk allele and negatively correlated with obesity. The latter result was confirmed in the Validation cohort. At multivariate analysis, the I148M polymorphism was independently associated with serum niacin, supporting that it may be directly involved in the modulation of its availability. siHep3B cells showed an impaired NAD biosynthesis comparable to HepG2 cells which led to lower niacin efficacy in clearing fat, supporting a required functional protein to guarantee its effectiveness. Conversely, the restoration of PNPLA3 Wt protein in HepG2^I148+ cells recovered the NAD pathway and improved niacin efficacy. Finally, niacin inhibited de novo lipogenesis through the ERK1/2/AMPK/SIRT1 pathway, with the consequent SREBP1-driven PNPLA3 reduction only in Hep3B and HepG2^I148M+ cells.

Conclusions

We demonstrated a niacin-PNPLA3 I148M interaction in NAFLD patients which possibly pave the way to vitamin B3 supplementation in those with a predisposing genetic background.

From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators

PURPOSE OF REVIEW

'Omics studies provide a comprehensive characterisation of a biological entity, such as the genome, epigenome, transcriptome, proteome, metabolome, or microbiome. This review covers the unique properties of these types of 'omics and their roles as causal mediators in cardiovascular disease. Moreover, applications and challenges of integrating multiple types of 'omics data to increase predictive power, improve causal inference, and elucidate biological mechanisms are discussed.

RECENT FINDINGS

Multi-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of 'omics data. Studies with multiple types of 'omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of 'omics data. For instance, disease-associated loci in the genome can be supplemented with other 'omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. As technologies improve, costs for 'omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of 'omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of 'omics data to provide insights greater than the sum of its parts.

Inhibition of Diacylglycerol Acyltransferase 2 Versus Diacylglycerol Acyltransferase 1: Potential Therapeutic Implications of Pharmacology

PURPOSE

Hepatic steatosis due to altered lipid metabolism and accumulation of hepatic triglycerides is a hallmark of nonalcoholic fatty liver disease (NAFLD). Diacylglycerol acyltransferase (DGAT) enzymes, DGAT1 and DGAT2, catalyze the terminal reaction in triglyceride synthesis, making them attractive targets for pharmacologic intervention. There is a common misconception that these enzymes are related; however, despite their similar names, DGAT1 and DGAT2 differ significantly on multiple levels. As we look ahead to future clinical studies of DGAT2 inhibitors in patients with NAFLD and nonalcoholic steatohepatitis (NASH), we review key differences and include evidence to highlight and support DGAT2 inhibitor (DGAT2i) pharmacology.

METHODS

Three Phase I, randomized, double-blind, placebo-controlled trials assessed the safety, tolerability, and pharmacokinetic properties of the DGAT2i ervogastat (PF-06865571) in healthy adult participants (Single Dose Study to Assess the Safety, Tolerability and Pharmacokinetics of PF-06865571 [study C2541001] and Study to Assess the Safety, Tolerability, and Pharmacokinetics of Multiple Doses of PF-06865571 in Healthy, Including Overweight and Obese, Adult Subjects [study C2541002]) or participants with NAFLD (2-Week Study in People With Nonalcoholic Fatty Liver Disease [study C2541005]). Data from 2 Phase I, randomized, double-blind, placebo-controlled trials of the DGAT1i PF-04620110 in healthy participants (A Single Dose Study of PF-04620110 in Overweight and Obese, Otherwise Healthy Volunteers [study B0961001] and A Multiple Dose Study of PF-04620110 in Overweight and Obese, Otherwise Healthy Volunteers [study B0961002]) were included for comparison. Safety outcomes were the primary end point in all studies, except in study C2541005, in which safety was the secondary end point, with relative change from baseline in whole liver fat at day 15 assessed as the primary end point. Safety data were analyzed across studies by total daily dose of ervogastat (5, 15, 50, 100, 150, 500, 600, 1000, and 1500 mg) or PF-04620110 (0.3, 1, 3, 5, 7, 10, 14, and 21 mg), with placebo data pooled separately across ervogastat and PF-04620110 studies.

FINDINGS

Published data indicate that DGAT1 and DGAT2 differ in multiple dimensions, including gene family, subcellular localization, substrate preference, and specificity, with unrelated pharmacologic inhibition properties and differing safety profiles. Although initial nonclinical studies suggested a potentially attractive therapeutic profile with DGAT1 inhibition, genetic and pharmacologic data suggest otherwise, with common gastrointestinal adverse events, including nausea, vomiting, and diarrhea, limiting further clinical development. Conversely, DGAT2 inhibition, although initially not pursued as aggressively as a potential target for pharmacologic intervention, has consistent efficacy in nonclinical studies, with reduced triglyceride synthesis accompanied by reduced expression of genes essential for de novo lipogenesis. In addition, early clinical data indicate antisteatotic effects with DGAT2i ervogastat, in participants with NAFLD, accompanied by a well-tolerated safety profile.

IMPLICATIONS

Although pharmacologic DGAT1is are limited by an adverse safety profile, data support use of DGAT2i as an effective and well-tolerated therapeutic strategy for patients with NAFLD, NASH, and NASH with liver fibrosis.

CLINICALTRIALS

gov identifiers: NCT03092232, NCT03230383, NCT03513588, NCT00799006, and NCT00959426.

Genetic Mimicry Analysis Reveals the Specific Lipases Targeted by the ANGPTL3-ANGPTL8 Complex and ANGPTL4

Angiopoietin-like proteins, ANGPTL3, ANGPTL4, and ANGPTL8, are involved in regulating plasma lipids. In vitro and animal-based studies point to LPL and endothelial lipase (EL, LIPG) as key targets of ANGPTLs. To examine the ANGPTL mechanisms for plasma lipid modulation in humans, we pursued a genetic mimicry analysis of enhancing or suppressing variants in the LPL, LIPG, lipase C hepatic type (LIPC), ANGPTL3, ANGPTL4, and ANGPTL8 genes using data on 248 metabolic parameters derived from over 110,000 nonfasted individuals in the UK Biobank and validated in over 13,000 overnight fasted individuals from 11 other European populations. ANGPTL4 suppression was highly concordant with LPL enhancement but not HL or EL, suggesting ANGPTL4 impacts plasma metabolic parameters exclusively via LPL. The LPL-independent effects of ANGPTL3 suppression on plasma metabolic parameters showed a striking inverse resemblance with EL suppression, suggesting ANGPTL3 not only targets LPL but also targets EL. Investigation of the impact of the ANGPTL3-ANGPTL8 complex on plasma metabolite traits via the ANGPTL8 R59W substitution as an instrumental variable showed a much higher concordance between R59W and EL activity than between R59W and LPL activity, suggesting the R59W substitution more strongly affects EL inhibition than LPL inhibition. Meanwhile, when using a rare and deleterious protein-truncating ANGPTL8 variant as an instrumental variable, the ANGPTL3-ANGPTL8 complex was very LPL specific. In conclusion, our analysis provides strong human genetic evidence that the ANGPTL3-ANGPTL8 complex regulates plasma metabolic parameters, which is achieved by impacting LPL and EL. By contrast, ANGPTL4 influences plasma metabolic parameters exclusively via LPL.

Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases

Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets.

External control arm analysis: an evaluation of propensity score approaches, G-computation, and doubly debiased machine learning

BACKGROUND

An external control arm is a cohort of control patients that are collected from data external to a single-arm trial. To provide an unbiased estimation of efficacy, the clinical profiles of patients from single and external arms should be aligned, typically using propensity score approaches. There are alternative approaches to infer efficacy based on comparisons between outcomes of single-arm patients and machine-learning predictions of control patient outcomes. These methods include G-computation and Doubly Debiased Machine Learning (DDML) and their evaluation for External Control Arms (ECA) analysis is insufficient.

METHODS

We consider both numerical simulations and a trial replication procedure to evaluate the different statistical approaches: propensity score matching, Inverse Probability of Treatment Weighting (IPTW), G-computation, and DDML. The replication study relies on five type 2 diabetes randomized clinical trials granted by the Yale University Open Data Access (YODA) project. From the pool of five trials, observational experiments are artificially built by replacing a control arm from one trial by an arm originating from another trial and containing similarly-treated patients.

RESULTS

Among the different statistical approaches, numerical simulations show that DDML has the smallest bias followed by G-computation. In terms of mean squared error, G-computation usually minimizes mean squared error. Compared to other methods, DDML has varying Mean Squared Error performances that improves with increasing sample sizes. For hypothesis testing, all methods control type I error and DDML is the most conservative. G-computation is the best method in terms of statistical power, and DDML has comparable power at [Formula: see text] but inferior ones for smaller sample sizes. The replication procedure also indicates that G-computation minimizes mean squared error whereas DDML has intermediate performances in between G-computation and propensity score approaches. The confidence intervals of G-computation are the narrowest whereas confidence intervals obtained with DDML are the widest for small sample sizes, which confirms its conservative nature.

CONCLUSIONS

For external control arm analyses, methods based on outcome prediction models can reduce estimation error and increase statistical power compared to propensity score approaches.

Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data

BACKGROUND

Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices.

RESULTS

We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

Genome-wide association study reveals loci with sex-specific effects on plasma bile acids.. [DOI: 10.1101/2022.12.16.22283452] [Cited by in Crossref: 0] [Impact Index Per Article: 0] Bile acids are essential for food digestion and nutrient absorption, but also act as signalling molecules involved in hepatobiliary diseases, gastrointestinal disorders and carcinogenesis. While many studies have focused on the genetic determinants of blood metabolites, research focusing specifically on genetic regulation of bile acids in the general population is currently lacking. Here we investigate the genetic architecture of primary and secondary bile acids in blood plasma, reporting associations with both common and rare variants. By performing genome-wide association analysis (GWAS) of plasma blood levels of 18 bile acids (N = 4923) we identify two significantly associated loci, a common variant mapping toSLCO1B1(encoding a liver bilirubin and drug transporter) and a rare variant inPRKG1(encoding soluble cyclic GMP-dependent protein kinase). For these loci, in the sex-stratified GWAS (N♂ = 820, N♀ = 1088), we observe sex-specific effects (SLCO1B1β ♂ = -0.51,P= 2.30×10−13, β♀ = -0.3,P= 9.90×10−07;PRKG1β ♂ = -0.18,P= 1.80×10−01, β ♀ = -0.79,P= 8.30×10−11), corroborating the contribution of sex to bile acid variability. Using gene-based aggregate tests and whole exome sequencing, we identify rare pLoF and missense variants potentially associated with bile acid levels in 3 genes (OR1G1, SART1andSORCS2), some of which have been linked with liver diseases

Bile acids are essential for food digestion and nutrient absorption, but also act as signalling molecules involved in hepatobiliary diseases, gastrointestinal disorders and carcinogenesis. While many studies have focused on the genetic determinants of blood metabolites, research focusing specifically on genetic regulation of bile acids in the general population is currently lacking. Here we investigate the genetic architecture of primary and secondary bile acids in blood plasma, reporting associations with both common and rare variants. By performing genome-wide association analysis (GWAS) of plasma blood levels of 18 bile acids (N = 4923) we identify two significantly associated loci, a common variant mapping toSLCO1B1(encoding a liver bilirubin and drug transporter) and a rare variant inPRKG1(encoding soluble cyclic GMP-dependent protein kinase). For these loci, in the sex-stratified GWAS (N♂ = 820, N♀ = 1088), we observe sex-specific effects (SLCO1B1β ♂ = -0.51,P= 2.30×10−13, β♀ = -0.3,P= 9.90×10−07;PRKG1β ♂ = -0.18,P= 1.80×10−01, β ♀ = -0.79,P= 8.30×10−11), corroborating the contribution of sex to bile acid variability. Using gene-based aggregate tests and whole exome sequencing, we identify rare pLoF and missense variants potentially associated with bile acid levels in 3 genes (OR1G1, SART1andSORCS2), some of which have been linked with liver diseases.

Comprehensive characterization of putative genetic influences on plasma metabolome in a pediatric cohort

BACKGROUND

The human exposome is composed of diverse metabolites and small chemical compounds originated from endogenous and exogenous sources, respectively. Genetic and environmental factors influence metabolite levels, while the extent of genetic contributions across metabolic pathways is not yet known. Untargeted profiling of human metabolome using high-resolution mass spectrometry (HRMS) combined with genome-wide genotyping allows comprehensive identification of genetically influenced metabolites. As such previous studies of adults discovered and replicated genotype-metabotype associations. However, these associations have not been characterized in children.

RESULTS

We conducted the largest genome by metabolome-wide association study to date of children (N = 441) using 619,688 common genetic variants and 14,342 features measured by HRMS. Narrow-sense heritability (h²) estimates of plasma metabolite concentrations using genomic relatedness matrix restricted maximum likelihood (GREML) method showed a bimodal distribution with high h² (> 0.8) for 15.9% of features and low h² (< 0.2) for most of features (62.0%). The features with high h² were enriched for amino acid and nucleic acid metabolism, while carbohydrate and lipid concentrations showed low h². For each feature, a metabolite quantitative trait loci (mQTL) analysis was performed to identify genetic variants that were potentially associated with plasma levels. Fifty-four associations among 29 features and 43 genetic variants were identified at a genome-wide significance threshold p < 3.5 × 10^-12 (= 5 × 10^-8/14,342 features). Previously reported associations such as UGT1A1 and bilirubin; PYROXD2 and methyl lysine; and ACADS and butyrylcarnitine were successfully replicated in our pediatric cohort. We found potential candidates for novel associations including CSMD1 and a monostearyl alcohol triglyceride (m/z 781.7483, retention time (RT) 89.3 s); CALN1 and Tridecanol (m/z 283.2741, RT 27.6). A gene-level enrichment analysis using MAGMA revealed highly interconnected modules for dADP biosynthesis, sterol synthesis, and long-chain fatty acid transport in the gene-feature network.

CONCLUSION

Comprehensive profiling of plasma metabolome across age groups combined with genome-wide genotyping revealed a wide range of genetic influence on diverse chemical species and metabolic pathways. The developmental trajectory of a biological system is shaped by gene-environment interaction especially in early life. Therefore, continuous efforts on generating metabolomics data in diverse human tissue types across age groups are required to understand gene-environment interaction toward healthy aging trajectories.

Quantifying the role of transcript levels in mediating DNA methylation effects on complex traits and diseases

High-dimensional omics datasets provide valuable resources to determine the causal role of molecular traits in mediating the path from genotype to phenotype. Making use of molecular quantitative trait loci (QTL) and genome-wide association study (GWAS) summary statistics, we propose a multivariable Mendelian randomization (MVMR) framework to quantify the proportion of the impact of the DNA methylome (DNAm) on complex traits that is propagated through the assayed transcriptome. Evaluating 50 complex traits, we find that on average at least 28.3% (95% CI: [26.9%-29.8%]) of DNAm-to-trait effects are mediated through (typically multiple) transcripts in the cis-region. Several regulatory mechanisms are hypothesized, including methylation of the promoter probe cg10385390 (chr1:8'022'505) increasing the risk for inflammatory bowel disease by reducing PARK7 expression. The proposed integrative framework can be extended to other omics layers to identify causal molecular chains, providing a powerful tool to map and interpret GWAS signals.

The large GTPase Sey1/atlastin mediates lipid droplet- and FadL-dependent intracellular fatty acid metabolism ofLegionella pneumophila.. [DOI: 10.1101/2022.12.05.519141] [Cited by in Crossref: 0] [Impact Index Per Article: 0] The facultative intracellular bacteriumLegionella pneumophilaemploys the Icm/Dot type IV secretion system (T4SS) to replicate in a unique membrane-bound compartment, theLegionella-containing vacuole (LCV). The endoplasmic reticulum (ER)-resident large fusion GTPase Sey1/atlastin promotes remodeling and expansion of LCVs, and the GTPase is also implicated in the formation of ER-derived lipid droplets (LDs). Here we show that LCVs intimately interact with palmitate-induced LDs inDictyostelium discoideumamoeba. Comparative proteomics of LDs isolated from theD. discoideumparental strain Ax3 or ⊗sey1revealed 144 differentially produced proteins, of which 7 or 22 were exclusively detected in LDs isolated from strain Ax3 or ⊗sey1, respectively. Using dually fluorescence-labeled amoeba producing the LCV marker P4C-GFP or AmtA-GFP and the LD marker mCherry-perilipin, we discovered that Sey1 and theL. pneumophilaIcm/Dot T4SS as well as the effector LegG1 promote LCV-LD interactions.In vitroreconstitution of the LCV-LD interactions using purified LCVs and LDs fromD. discoideumAx3 or ⊗sey1revealed that Sey1 and GTP promote this process. The LCV-LD interactions were impaired for ⊗sey1-derived LDs, suggesting that Sey1 regulates LD composition. Palmitate promoted the growth of (i)L. pneumophilawild-type inD. discoideumAx3 but not in ⊗sey1mutant amoeba and (ii)L. pneumophilawild-type but not ⊗fadLmutant bacteria lacking a homologue of theE. colifatty acid transporter FadL. Finally, isotopologue profiling indicated that intracellularL. pneumophilametabolizes13C-palmitate, and its catabolism was reduced inD. discoideum⊗sey1andL. pneumophila⊗fadL. Taken together, our results reveal that Sey1 mediates LD- and FadL-dependent fatty acid metabolism of intracellularL. pneumophila

The facultative intracellular bacteriumLegionella pneumophilaemploys the Icm/Dot type IV secretion system (T4SS) to replicate in a unique membrane-bound compartment, theLegionella-containing vacuole (LCV). The endoplasmic reticulum (ER)-resident large fusion GTPase Sey1/atlastin promotes remodeling and expansion of LCVs, and the GTPase is also implicated in the formation of ER-derived lipid droplets (LDs). Here we show that LCVs intimately interact with palmitate-induced LDs inDictyostelium discoideumamoeba. Comparative proteomics of LDs isolated from theD. discoideumparental strain Ax3 or ⊗sey1revealed 144 differentially produced proteins, of which 7 or 22 were exclusively detected in LDs isolated from strain Ax3 or ⊗sey1, respectively. Using dually fluorescence-labeled amoeba producing the LCV marker P4C-GFP or AmtA-GFP and the LD marker mCherry-perilipin, we discovered that Sey1 and theL. pneumophilaIcm/Dot T4SS as well as the effector LegG1 promote LCV-LD interactions.In vitroreconstitution of the LCV-LD interactions using purified LCVs and LDs fromD. discoideumAx3 or ⊗sey1revealed that Sey1 and GTP promote this process. The LCV-LD interactions were impaired for ⊗sey1-derived LDs, suggesting that Sey1 regulates LD composition. Palmitate promoted the growth of (i)L. pneumophilawild-type inD. discoideumAx3 but not in ⊗sey1mutant amoeba and (ii)L. pneumophilawild-type but not ⊗fadLmutant bacteria lacking a homologue of theE. colifatty acid transporter FadL. Finally, isotopologue profiling indicated that intracellularL. pneumophilametabolizes13C-palmitate, and its catabolism was reduced inD. discoideum⊗sey1andL. pneumophila⊗fadL. Taken together, our results reveal that Sey1 mediates LD- and FadL-dependent fatty acid metabolism of intracellularL. pneumophila.

Reduced gluteofemoral (subcutaneous) fat mass in young Japanese women with family history of type 2 diabetes: an exploratory analysis

Limited expandability of subcutaneous adipose tissue may be characteristics of first-degree relatives of type 2 diabetes. We tested the hypothesis that family history of type 2 diabetes (FHD) may be associated with reduced peripheral fat mass. Body composition and metabolic variables were compared between 18 and 111 Japanese female collegiate athletes, and between 55 and 148 nonathletes with positive (FHD +) and negative FHD (FHD-), respectively. We had multivariate logistic regression analyses for FHD + as dependent variable in a total population.BMI averaged < 21 kg/m2 and did not differ between FHD + and FHD- nonathletes. Despite comparable BMI, body fat percentage and serum leptin were lower in FHD + nonathletes. This was due to lower arm and gluteofemoral fat percentage (both p = 0.02) whereas the difference in trunk fat percentage was not significant (p = 0.08). These differences were not found between two groups of athletes. FHD + women had lower HDL cholesterol despite lower BMI in a total population. Fasting insulin, serum adiponectin and high-sensitivity C-reactive protein did not differ between FHD + and FHD- athletes or nonathletes. Multivariate logistic regression analyses revealed independent associations of FHD + with BMI (odds ratio, 0.869; 95% confidential interval, 0.768–0.984; p = 0.02) and HDL cholesterol (odds ratio, 0.977; 95% confidential interval, 0.957–0.997, p = 0.02). In conclusion, FHD may be associated with reduced subcutaneous fat mass in young Japanese women, suggesting impaired adipose tissue expandability.

Hepatocyte- or macrophage-specific SREBP-1a deficiency in mice exacerbates methionine- and choline-deficient diet-induced nonalcoholic fatty liver disease

Sterol regulatory element-binding proteins (SREBPs) are master transcription factors for lipid synthesis, and SREBP-1 is important for fatty acid and triglyceride synthesis. SREBP-1 has two isoforms, SREBP-1a and SREBP-1c, which are splicing variants transcribed from the Srebf1 gene. Although SREBP-1a exhibits stronger transcriptional activity than SREBP-1c, hepatic SREBP-1c is considered more physiologically important. We generated SREBP-1a flox mice using the CRISPR/Cas9 system and hepatocyte- and macrophage-specific SREBP-1a knockout (KO) mice (LKO, liver-knockout; and mΦKO, macrophage-knockout). There were no significant differences among all the mouse genotypes upon feeding with a normal diet. However, feeding with a methionine- and choline-deficient (MCD) diet resulted in exacerbated liver injury in both KO mice. In LKO mice, fatty liver was unexpectedly exacerbated, leading to macrophage infiltration and inflammation. In contrast, in mΦKO mice, the fatty liver state was similar to that in flox mice, but the polarity of the macrophages in the liver was transformed into a proinflammatory M1 subtype, resulting in the exacerbation of inflammation. Taken together, we found that SREBP-1a does not contribute to hepatic lipogenesis, but in either hepatocytes or macrophages distinctly controls the onset of pathological conditions in MCD diet-induced hepatitis.NEW & NOTEWORTHY Hepatocyte- and macrophage-specific SREBP-1a knockout mice were generated for the first time. This study reveals that SREBP-1a does not contribute to hepatic lipogenesis, but in either hepatocytes or macrophages distinctly controls the onset of pathological conditions in methionine- and choline-deficient diet-induced hepatitis.

Exploring the Genetic Association between Obesity and Serum Lipid Levels Using Bivariate Methods

It is crucial to understand the genetic mechanisms and biological pathways underlying the relationship between obesity and serum lipid levels. Structural equation models (SEMs) were constructed to calculate heritability for body mass index (BMI), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and the genetic connections between BMI and the four classes of lipids using 1197 pairs of twins from the Chinese National Twin Registry (CNTR). Bivariate genomewide association studies (GWAS) were performed to identify genetic variants associated with BMI and lipids using the records of 457 individuals, and the results were further validated in 289 individuals. The genetic background affecting BMI may differ by gender, and the heritability of males and females was 71% (95% CI [.66, .75]) and 39% (95% CI [.15, .71]) respectively. BMI was positively correlated with TC, TG and LDL-C in phenotypic and genetic correlation, while negatively correlated with HDL-C. There were gender differences in the correlation between BMI and lipids. Bivariate GWAS analysis and validation stage found 7 genes (LOC105378740, LINC02506, CSMD1, MELK, FAM81A, ERAL1 and MIR144) that were possibly related to BMI and lipid levels. The significant biological pathways were the regulation of cholesterol reverse transport and the regulation of high-density lipoprotein particle clearance (p < .001). BMI and blood lipid levels were affected by genetic factors, and they were genetically correlated. There might be gender differences in their genetic correlation. Bivariate GWAS analysis found MIR144 gene and its related biological pathways may influence obesity and lipid levels.

Genetically personalised organ-specific metabolic models in health and disease

Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases.

Development and validation of a machine learning-augmented algorithm for diabetes screening in community and primary care settings: A population-based study

Background

Opportunely screening for diabetes is crucial to reduce its related morbidity, mortality, and socioeconomic burden. Machine learning (ML) has excellent capability to maximize predictive accuracy. We aim to develop ML-augmented models for diabetes screening in community and primary care settings.

Methods

8425 participants were involved from a population-based study in Hubei, China since 2011. The dataset was split into a development set and a testing set. Seven different ML algorithms were compared to generate predictive models. Non-laboratory features were employed in the ML model for community settings, and laboratory test features were further introduced in the ML+lab models for primary care. The area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (auPR), and the average detection costs per participant of these models were compared with their counterparts based on the New China Diabetes Risk Score (NCDRS) currently recommended for diabetes screening.

Results

The AUC and auPR of the ML model were 0·697and 0·303 in the testing set, seemingly outperforming those of NCDRS by 10·99% and 64·67%, respectively. The average detection cost of the ML model was 12·81% lower than that of NCDRS with the same sensitivity (0·72). Moreover, the average detection cost of the ML+FPG model is the lowest among the ML+lab models and less than that of the ML model and NCDRS+FPG model.

Conclusion

The ML model and the ML+FPG model achieved higher predictive accuracy and lower detection costs than their counterpart based on NCDRS. Thus, the ML-augmented algorithm is potential to be employed for diabetes screening in community and primary care settings.

Type 2 diabetes

Type 2 diabetes accounts for nearly 90% of the approximately 537 million cases of diabetes worldwide. The number affected is increasing rapidly with alarming trends in children and young adults (up to age 40 years). Early detection and proactive management are crucial for prevention and mitigation of microvascular and macrovascular complications and mortality burden. Access to novel therapies improves person-centred outcomes beyond glycaemic control. Precision medicine, including multiomics and pharmacogenomics, hold promise to enhance understanding of disease heterogeneity, leading to targeted therapies. Technology might improve outcomes, but its potential is yet to be realised. Despite advances, substantial barriers to changing the course of the epidemic remain. This Seminar offers a clinically focused review of the recent developments in type 2 diabetes care including controversies and future directions.

Linear and nonlinear analyses of the association between low-density lipoprotein cholesterol and diabetes: The spurious U-curve in observational study

Objective

Hyperlipidemia is traditionally considered a risk factor for diabetes. The effect of low-density lipoprotein cholesterol (LDL-C) is counterintuitive to diabetes. We sought to investigate the relationship between LDL-C and diabetes for better lipid management.

Methods

We tested the shape of association between LDL-C and diabetes and created polygenic risk scores of LDL-C and generated linear Mendelian randomization (MR) estimates for the effect of LDL-C and diabetes. We evaluated for nonlinearity in the observational and genetic relationship between LDL-C and diabetes.

Results

Traditional observational analysis suggested a complex non-linear association between LDL-C and diabetes while nonlinear MR analyses found no evidence for a non-linear association. Under the assumption of linear association, we found a consistently protective effect of LDL-C against diabetes among the females without lipid-lowering drugs use. The ORs were 0.84 (95% CI, 0.72-0.97, P=0.0168) in an observational analysis which was more prominent in MR analysis and suggested increasing the overall distribution of LDL-C in females led to an overall decrease in the risk of diabetes (P=0.0258).

Conclusions

We verified the liner protective effect of LDL-C against diabetes among the females without lipid-lowering drug use. Non-linear associations between LDL-C against diabetes in observational analysis are not causal.

Identification of 969 protein quantitative trait loci in an African American population with kidney disease attributed to hypertension

Investigations into the causal underpinnings of disease processes can be aided by the incorporation of genetic information. Genetic studies require populations varied in both ancestry and prevalent disease in order to optimize discovery and ensure generalizability of findings to the global population. Here, we report the genetic determinants of the serum proteome in 466 African Americans with chronic kidney disease attributed to hypertension from the richly phenotyped African American Study of Kidney Disease and Hypertension (AASK) study. Using the largest aptamer-based protein profiling platform to date (6,790 proteins or protein complexes), we identified 969 genetic associations with 900 unique proteins; including 52 novel cis (local) associations and 379 novel trans (distant) associations. The genetic effects of previously published cis-protein quantitative trait loci (pQTLs) were found to be highly reproducible, and we found evidence that our novel genetic signals colocalize with gene expression and disease processes. Many trans- pQTLs were found to reflect associations mediated by the circulating cis protein, and the common trans-pQTLs are enriched for processes involving extracellular vesicles, highlighting a plausible mechanism for distal regulation of the levels of secreted proteins. Thus, our study generates a valuable resource of genetic associations linking variants to protein levels and disease in an understudied patient population to inform future studies of drug targets and physiology.

Rare and common genetic determinants of metabolic individuality and their effects on human health

Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P < 1.25 × 10^-11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships.