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Background: Observational studies have suggested an association between gut microbiota and Alzheimer's disease (AD); however, the causal relationship remains unclear, and the role of blood metabolites in this association remains elusive. Purpose: To elucidate the causal relationship between gut microbiota and AD and to investigate whether blood metabolites serve as potential mediators. Materials and methods: Univariable Mendelian randomization (UVMR) analysis was employed to assess the causal relationship between gut microbiota and AD, while multivariable MR (MVMR) was utilized to mitigate confounding factors. Subsequently, a two-step mediation MR approach was employed to explore the role of blood metabolites as potential mediators. We primarily utilized the inverse variance-weighted method to evaluate the causal relationship between exposure and outcome, and sensitivity analyses including Contamination mixture, Maximum-likelihood, Debiased inverse-variance weighted, MR-Egger, Bayesian Weighted Mendelian randomization, and MR pleiotropy residual sum and outlier were conducted to address pleiotropy. Results: After adjustment for reverse causality and MVMR correction, class Actinobacteria (OR: 1.03, 95% CI: 1.01-1.06, p = 0.006), family Lactobacillaceae (OR: 1.03, 95% CI: 1.00-1.05, p = 0.017), genus Lachnoclostridium (OR: 1.03, 95% CI: 1.00-1.06, p = 0.019), genus Ruminiclostridium9 (OR: 0.97, 95% CI: 0.94-1.00, p = 0.027) and genus Ruminiclostridium6 (OR: 1.03, 95% CI: 1.01-1.05, p = 0.009) exhibited causal effects on AD. Moreover, 1-ribosyl-imidazoleacetate levels (-6.62%), Metabolonic lactone sulfate levels (2.90%), and Nonadecanoate (19:0) levels (-12.17%) mediated the total genetic predictive effects of class Actinobacteria on AD risk. Similarly, 2-stearoyl-GPE (18:0) levels (-9.87%), Octadecanedioylcarnitine (C18-DC) levels (4.44%), 1-(1-enyl-stearoyl)-2-oleoyl-GPE (p-18:0/18:1) levels (38.66%), and X-23639 levels (13.28%) respectively mediated the total genetic predictive effects of family Lactobacillaceae on AD risk. Furthermore, Hexadecanedioate (C16-DC) levels (5.45%) mediated the total genetic predictive effects of genus Ruminiclostridium 6 on AD risk; Indole-3-carboxylate levels (13.91%), X-13431 levels (7.08%), Alpha-ketoglutarate to succinate ratio (-13.91%), 3-phosphoglycerate to glycerate ratio (15.27%), and Succinate to proline ratio (-14.64%) respectively mediated the total genetic predictive effects of genus Ruminiclostridium 9 on AD risk. Conclusion: Our mediation MR analysis provides genetic evidence suggesting the potential mediating role of blood metabolites in the causal relationship between gut microbiota and AD. Further large-scale randomized controlled trials are warranted to validate the role of blood metabolites in the specific mechanisms by which gut microbiota influence AD.
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Gliomas are highly complex and metabolically active brain tumors associated with poor prognosis. Recent reports have found altered levels of blood metabolites during early tumor development, suggesting that tumor development could be detected several years before clinical manifestation. In this study, we performed metabolite analyses of blood samples collected from healthy controls and future glioma patients, up to eight years before glioma diagnosis, and on the day of glioma surgery. We discovered that metabolites related to early glioma development were associated with an increased energy turnover, as highlighted by elevated levels of TCA-related metabolites such as fumarate, malate, lactate and pyruvate in pre-diagnostic cases. We also found that metabolites related to glioma progression at surgery were primarily high levels of amino acids and metabolites of amino acid catabolism, with elevated levels of 11 amino acids and two branched-chain alpha-ketoacids, ketoleucine and ketoisoleucine. High amino acid turnover in glioma tumor tissue is currently utilized for PET imaging, diagnosis and delineation of tumor margins. By examining blood-based metabolic progression patterns towards disease onset, we demonstrate that this high amino acid turnover is also detectable in a simple blood sample. These findings provide additional insight of metabolic alterations during glioma development and progression.
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Biomarcadores Tumorais , Neoplasias Encefálicas , Glioma , Humanos , Glioma/sangue , Glioma/cirurgia , Glioma/diagnóstico , Glioma/patologia , Neoplasias Encefálicas/sangue , Neoplasias Encefálicas/diagnóstico , Neoplasias Encefálicas/cirurgia , Neoplasias Encefálicas/metabolismo , Masculino , Feminino , Biomarcadores Tumorais/sangue , Pessoa de Meia-Idade , Adulto , Progressão da Doença , Aminoácidos/sangue , Aminoácidos/metabolismoRESUMO
OBJECTIVE: The observational association between blood metabolites and asthma has been extensively studied. However, it is still unclear whether this association is causal. In this study, we aimed to investigate the causal relationship between blood metabolites and asthma using a bidirectional Mendelian randomization (MR) analysis. Additionally, we aimed to explore the potential mechanisms underlying this relationship. METHODS: The study design involved the use of genetic instruments as instrumental variables (IVs) to fulfill the assumptions of MR analysis. The data on 1,091 metabolites and 309 metabolite ratios were obtained from the Canadian Longitudinal Study on Aging (CLSA), while the data on asthma were obtained from the Integrative Epidemiology Unit (IEU) Open GWAS Project. Utilizing the inverse variance-weighted (IVW) method as the primary MR analysis approach, sensitivity tests were conducted to assess the reliability of the findings, which involved employing Cochran's Q and the MR-Egger intercept. Furthermore, Bayesian weighted MR was used to further test the robustness of the results. Additionally, pathway analysis was conducted to explore the metabolic explanations underlying asthma. RESULT: In our study, a comprehensive MR Analysis identified 10 metabolites and 6 metabolite ratios significantly associated with the development of asthma (FDR < 0.05). The metabolites included glycerophosphocholines(GPCs), glycerophosphoethanolamines(GPEs), and an unknown metabolite. Of these, 1-arachidonoyl-GPC, 1-myristoyl-2-arachidonoyl-GPC, 1-palmitoyl-2-arachidonoyl-GPC, and 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC were associated with an increased risk of asthma, whereas 1,2-dilinoleoyl-GPC, 1-palmitoyl-2-linoleoyl-GPC, 1,2-dilinoleoyl-GPE, 1 - oleoyl - 2 - linoleoyl - GPE, 1-palmitoyl-2-linoleoyl-GPE, and X-21470 were found to have a protective effect. No heterogeneity and pleiotropy were observed in the significant metabolites (p > 0.05), and each metabolite exhibited a consistent effect direction across all five methods. BWMR analysis results confirmed the significance and direction of effects across exposures, except for Cholesterol to linoleoyl-arachidonoyl-glycerol ratio(p = 0.673). Pathway analysis suggests that glycerophospholipid metabolism may potentially be a mechanism underlying the development of asthma. CONCLUSION: Our MR findings suggest that the identified metabolites and pathways can serve as biomarkers for clinical asthma screening and prevention, while also providing new insights for future mechanistic exploration and drug target selection.
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BACKGROUND: Facial aging (FA) is a complex process influenced by both genetic and environmental factors. Gut microbiota (GM), gut microbiota metabolic pathways (GMMPs), and blood metabolites (BMs) have been implicated in the regulation of FA, but the causal and mediating effects of these factors remain unclear. METHODS: We used summary-level data from genome-wide association studies (GWAS) of 16S rRNA gene sequencing data for GM (n = 18 340), GWAS of GMMPs (n = 7738), BMs (n = 24 925), and GWAS of FA (n = 423 999). We applied Mendelian randomization (MR) methods to estimate the causal effects of GM, GMMPs, and BMs on FA. We performed mediation analysis to quantify the proportion of the effects mediated by blood metabolites. RESULTS: We identified nine genus, two phylum, two families of GM, nine GM metabolic pathways, and 73 BMs that showed potential causal effects on FA. After Bonferroni correction, three BMs remained causally associated with FA, including average number of methylene groups per double bond (ß, -0.023; 95% CI, -0.032â¼-0.014; p = 3.120×10-7) and average number of methylene groups in a fatty acid chain (ß, -0.031; 95% CI, -0.045â¼-0.016; p = 2.062×10-5), which had strong negative causal effects on FA, and ratio of bisallylic groups to total fatty acids (ß, 0.023; 95% CI, 0.017â¼-0.029; p = 8.441×10-15), which had a strong positive causal effect on FA. Mediation analysis revealed that histidine, average number of methylene groups in a fatty acid chain, and triglycerides in chylomicrons and largest VLDL particles mediated the effects of anaerofilum and/ or superpathway of Laspartate and Lasparagine biosynthesis on FA. CONCLUSION: Our study provides novel insights into the causal and mediating effects of GM, GMMPs, and BMs on FA. These findings may have implications for the development of new strategies for preventing or delaying FA.
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Microbioma Gastrointestinal , Estudo de Associação Genômica Ampla , Análise da Randomização Mendeliana , Redes e Vias Metabólicas , Humanos , Microbioma Gastrointestinal/fisiologia , Envelhecimento da Pele/fisiologia , Análise de Mediação , Face , RNA Ribossômico 16S/genética , Envelhecimento/sangue , Envelhecimento/fisiologiaRESUMO
Objective: To investigate the impact of gut microbiota on osteoporosis and identify the mediating role of blood metabolites in this process. Methods: This two-sample Mendelian randomization (MR) study utilized summary level data from genome-wide association studies (GWAS). Gut microbiota GWAS data were obtained from the MiBio-Gen consortium meta-analysis (n=13,266), while osteoporosis summary statistics were sourced from the FinnGen consortium R9 release data (7300 cases and 358,014 controls). Metabolite data, including 1400 metabolites or metabolite ratios, were derived from a study involving 8,299 unrelated individuals. The primary MR method employed was the inverse variance weighted (IVW) method. Reverse MR analysis was conducted on bacteria causally associated with osteoporosis in forward MR. The gut microbiota with the smallest p-value was selected as the top influencing factor for subsequent mediation analysis. A two-step MR approach quantified the proportion of the blood metabolite effect on gut microbiota influencing osteoporosis. IVW and Egger methods were used to assess heterogeneity and horizontal pleiotropy. Results: IVW estimates indicated a suggestive effect of family Christensenellaceae on osteoporosis (odds ratio(OR) = 1.292, 95% confidence interval(CI): 1.110-1.503, P =9.198 × 10-4). Reverse MR analysis revealed no significant causal effect of osteoporosis on family Christensenellaceae (OR = 0.947, 95% CI: 0.836-1.072, P =0.386). The proportion of the effect of family Christensenellaceae on osteoporosis mediated by circulating levels of 3,4-dihydroxybutyrate was 9.727%. No significant heterogeneity or horizontal pleiotropy was detected in the instrumental variables used for MR analysis. Conclusion: This study establishes a causal link between family Christensenellaceae and osteoporosis, with a minor proportion of the effect mediated by elevated circulating levels of 3,4-dihydroxybutyrate. Further randomized controlled trials (RCTs) are warranted to validate this conclusion.
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Microbioma Gastrointestinal , Estudo de Associação Genômica Ampla , Análise da Randomização Mendeliana , Osteoporose , Humanos , Osteoporose/genética , Osteoporose/sangue , Polimorfismo de Nucleotídeo Único , Feminino , Fatores de RiscoRESUMO
BACKGROUND: Previous studies provide evidence that in vivo metabolites are associated with breast cancer (BC). However, the causal relationship between blood metabolites and BC remains unclear. METHOD: Comprehensive two-sample Mendelian randomization analysis was conducted to determine the causal association between 1400 publicly available genetic data on metabolic factors and human epidermal growth factor receptor positive (HER+) BC or HER- BC in this study. RESULT: Epiandrosterone sulfate levels (OR = 1.07, 95% CI = 1.02 ~ 1.10, p = 0.0013), 5alpha-androstan-3beta,17beta-diol monosulfate (2) levels (OR = 1.07, 95% CI = 1.03 ~ 1.12, p = 0.0012), glycohyocholate levels (OR = 0.85, 95% CI = 0.77 ~ 0.93, p = 0.0007) and etiocholanolone glucuronide levels (OR = 1.12, 95% CI = 1.05 ~ 1.20, p = 0.0013) were causally correlated with HER+ BC. 5 metabolites were causally correlated with HER- BC: Vanillic acid glycine levels (OR = 1.14, 95% CI = 1.06 ~ 1.22, p = 0.0003), Thyroxine levels (OR = 1.26, 95% CI = 1.11 ~ 1.44, p = 0.0004), 1-palmitoyl-2-linoleoyl-GPI (16:0/18:2) levels (OR = 0.86, 95% CI = 0.79 ~ 0.94, p = 0.0010), N-acetylphenylalanine levels (OR = 1.12, 95% CI = 1.05 ~ 1.19, p = 0.0007) and Glucose-to-mannose ratio (OR = 1.15, 95% CI = 1.06 ~ 1.24, p = 0.0008). Two common causally related metabolites were identified: Gamma-glutamyl glutamate and X-12849 levels. CONCLUSIONS: Our study has respectively demonstrated the connection between blood metabolites and HER+ or HER- BC by genetic means, thereby offering opportunities for therapeutic targets.
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Neoplasias da Mama , Análise da Randomização Mendeliana , Humanos , Feminino , Neoplasias da Mama/sangue , Neoplasias da Mama/genética , Receptor ErbB-2/genética , Receptor ErbB-2/metabolismo , Polimorfismo de Nucleotídeo ÚnicoRESUMO
The utilization of agro-industrial by-products, such as fruit residues, presents a promising strategy for providing alternative feed to ruminants amidst rising prices and limited availability of traditional roughage. In this study, we investigated the effects of Rosa roxburghii tratt residue, a local fruit residue in Guizhou province of China, on the growth, blood metabolites, rumen fermentation, and slaughter performance of Hu sheep. Ninety-six sheep were randomly divided into four groups, namely control, treatment 1, treatment 2, and treatment 3, and fed diets containing 0, 10, 20, and 30% Rosa roxburghii Tratt residue, respectively. Feeding varying levels of Rosa roxburghii Tratt residue showed no significant differences in dry matter intake, average daily gain, or the ratio of dry matter intake to average daily gain. However, sheep in the group fed with 30% Rosa roxburghii Tratt residue showed the highest gross profit. Plasma albumin content was lower in groups fed with Rosa roxburghii Tratt residue-containing diets compared to the control group (p < 0.05). Additionally, diet treatment 3 decreased plasma creatinine levels compared to control and treatment 1 (p < 0.05). Sheep in treatment 2 and treatment 3 exhibited higher plasma high-density lipoprotein level than control and treatment 1 (p < 0.05), as well as increased total cholesterol levels compared to control (p < 0.05). There were no significant differences in other plasma metabolites. Rumen pH, N-NH3, volatile fatty acids, and methane levels did not differ significantly among the four groups. However, feeding diets treatment 2 and treatment 3 resulted in decreased water holding capacity and increased shear force compared to control and treatment 1 (p < 0.05). Furthermore, pH, red chromaticity (a*), yellowness index (b*), and luminance (L*) were unaffected among the four groups of sheep. In conclusion, the inclusion of up to 30% Rosa roxburghii Tratt residue had no adverse effects on growth performance, allowing for feed cost savings without impacting rumen fermentation parameters. Rosa roxburghii tratt residue also showed benefits in improving plasma protein efficiency and enhancing lipid metabolism, albeit with limited effects on meat quality. Considering its affordability, Rosa roxburghii Tratt residue presents a practical choice for low-cost diets, ensuring economic returns.
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The current study evaluated the effects of parsley essential oil on broiler growth performance, carcass features, liver and kidney functions, immunity and antioxidant activity, and lipid profile. A total of 160 unsexed 7-day broiler chicks (Cobb500) were distributed into five groups; each group contained five replicates with eight birds each. The treatments were (1) basal diet (no additive, T1), (2) basal diet + 0.5 mL parsley essential oil/kg (T2), (3) basal diet + 1 mL parsley essential oil/kg (T3), (4) basal diet + 1.5 mL parsley essential oil/kg (T4), and (5) basal diet + 2 mL parsley essential oil/kg (T5). According to GC-MS analysis, parsley oil contains D-limonene, hexadecanoic acid, α-cyclocitral, globulol, α-pinene, myristicin, cryophyllene, bergapten, α-chamigrene, etc. The current results indicated that the most abundant molecules in parsley oil were D-limonene (18.82%), oleic acid (14.52%), α-cyclocitral (11.75%), globulol (11.24%), α-guaiene (7.34%), apiol (5.45%), and hexadecanoic acid (4.69%). Adding parsley essential oil to the broiler diet quadratically increased body weight (BW) during 1-3 weeks of age. The T5 group recorded the highest value (869.37 g) of BW in comparison to other treatments and the control group. The cholesterol, triglyceride, low-density lipoprotein (LDL) cholesterol, and total immunoglobulin, including immunoglobulin G (IgG) and immunoglobulin M (IgM) levels in the birds fed parsley essential oil were not affected. The T3 group recorded the highest value (159 ng/mL) of superoxide dismutase (SOD) and the lowest value (2.01 ng/mL) of malondialdehyde (MDA) when compared to the control and other treatment. In conclusion, we recommend using parsley oil at levels of 1 mL/kg diet of broiler chicks.
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Ração Animal , Antioxidantes , Galinhas , Dieta , Rim , Fígado , Óleos Voláteis , Petroselinum , Animais , Galinhas/crescimento & desenvolvimento , Galinhas/metabolismo , Galinhas/imunologia , Galinhas/fisiologia , Antioxidantes/metabolismo , Ração Animal/análise , Óleos Voláteis/administração & dosagem , Óleos Voláteis/farmacologia , Fígado/metabolismo , Dieta/veterinária , Rim/metabolismo , Petroselinum/química , Óleos de Plantas/farmacologia , Óleos de Plantas/administração & dosagem , Lipídeos/sangue , Lipídeos/análise , Fenômenos Fisiológicos da Nutrição Animal , Aditivos Alimentares , Suplementos Nutricionais , MasculinoRESUMO
Background: Metabolic dysregulation represents a defining characteristic of Type 2 diabetes (T2DM). Nevertheless, there remains an absence of substantial evidence establishing a direct causal link between circulating blood metabolites and the promotion or prevention of T2DM. In addressing this gap, we employed Mendelian randomization (MR) analysis to investigate the potential causal association between 1,091 blood metabolites, 309 metabolite ratios, and the occurrence of T2DM. Methods: Data encompassing single-nucleotide polymorphisms (SNPs) for 1,091 blood metabolites and 309 metabolite ratios were extracted from a Canadian Genome-wide association study (GWAS) involving 8,299 participants. To evaluate the causal link between these metabolites and Type 2 diabetes (T2DM), multiple methods including Inverse Variance Weighted (IVW), Weighted Median, MR Egger, Weighted Mode, and Simple Mode were employed. p-values underwent correction utilizing False Discovery Rates (FDR). Sensitivity analyses incorporated Cochran's Q test, MR-Egger intercept test, MR-PRESSO, Steiger test, leave-one-out analysis, and single SNP analysis. The causal effects were visualized via Circos plot, forest plot, and scatter plot. Furthermore, for noteworthy, an independent T2DM GWAS dataset (GCST006867) was utilized for replication analysis. Metabolic pathway analysis of closely correlated metabolites was conducted using MetaboAnalyst 5.0. Results: The IVW analysis method utilized in this study revealed 88 blood metabolites and 37 metabolite ratios demonstrating a significant causal relationship with T2DM (p < 0.05). Notably, strong causal associations with T2DM were observed for specific metabolites: 1-linoleoyl-GPE (18:2) (IVW: OR:0.930, 95% CI: 0.899-0.962, p = 2.16 × 10-5), 1,2-dilinoleoyl-GPE (18:2/18:2) (IVW: OR:0.942, 95% CI: 0.917-0.968, p = 1.64 × 10-5), Mannose (IVW: OR:1.133, 95% CI: 1.072-1.197, p = 1.02 × 10-5), X-21829 (IVW: OR:1.036, 95% CI: 1.036-1.122, p = 9.44 × 10-5), and Phosphate to mannose ratio (IVW: OR:0.870, 95% CI: 0.818-0.926, p = 1.29 × 10-5, FDR = 0.008). Additionally, metabolic pathway analysis highlighted six significant pathways associated with T2DM development: Valine, leucine and isoleucine biosynthesis, Phenylalanine metabolism, Glycerophospholipid metabolism, Alpha-Linolenic acid metabolism, Sphingolipid metabolism, and Alanine, aspartate, and glutamate metabolism. Conclusion: This study identifies both protective and risk-associated metabolites that play a causal role in the development of T2DM. By integrating genomics and metabolomics, it presents novel insights into the pathogenesis of T2DM. These findings hold potential implications for early screening, preventive measures, and treatment strategies for T2DM.
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Pregnancy-induced hypertension (PIH), a prominent determinant of maternal mortality and morbidity worldwide, is hindered by the absence of efficacious biomarkers for early diagnosis, contributing to suboptimal outcomes. Here, we explored potential causal relationships between blood metabolites and the risk of PIH using Mendelian randomization (MR). We employed a two-sample univariable MR approach to empirically estimate the causal relationships between 249 circulating metabolites and PIH. Inverse variance weighted, MR-egger, weight median, simple mode, and weighted mode methods were used for causal estimates. The exposure-to-outcome directionality was confirmed with the MR Steiger test. The Bayesian model averaging MR (MR-BMA) method was applied to detect the predominant causal metabolic traits with alignment for pleiotropy effects. In the primary analysis, analyzing 249 metabolites, we identified 25 causally linked to PIH, including 11 lipid-related traits and 6 associated with fatty acid (un)saturation. Importantly, MR-BMA analyses corroborated the total concentration of branched-chain amino acids(total-BCAA) to be the highest rank causal metabolite, followed by leucine (Leu), phospholipids to total lipids ratio in medium LDL (M-LDL-PL-pct), and Val (all P < 0.05). The directionality of causality predicted by univariable MR and MR-BMA for these metabolites remained consistent. This study highlights the causal connection between metabolites and PIH risk. It highlighted BCAAs as the strongest causal candidates warranting further investigation. Since PIH typically occurs in the second and third trimesters, extending these findings could inform earlier strategies to reduce its risk. Directed acyclic graph of the MR framework investigating the causal relationship between metabolites and PIH. MR: Mendelian randomization; GIVs: genetic instrument variables; SNPs: single-nucleotide polymorphism; IVW: inverse variance weighted; WM: weighted median; PIH: pregnancy-induced hypertension; SM: significant metabolite; MR-BMA: Bayesian model averaging MR.
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Teorema de Bayes , Hipertensão Induzida pela Gravidez , Análise da Randomização Mendeliana , Humanos , Feminino , Gravidez , Hipertensão Induzida pela Gravidez/sangue , Hipertensão Induzida pela Gravidez/genética , Biomarcadores/sangueRESUMO
BACKGROUND: Psoriasis is a chronic inflammatory disease that causes significant disability. However, little is known about the underlying metabolic mechanisms of psoriasis. Our study aims to investigate the causality of 975 blood metabolites with the risk of psoriasis. MATERIALS AND METHODS: We mainly applied genetic analysis to explore the possible associations between 975 blood metabolites and psoriasis. The inverse variance weighted (IVW) method was used as the primary analysis to assess the possible association of blood metabolites with psoriasis. Moreover, generalized summary-data-based Mendelian randomization (GSMR) was used as a supplementary analysis. In addition, linkage disequilibrium score regression (LDSC) was used to investigate their genetic correction further. Metabolic pathway analysis of the most suggested metabolites was also performed using MetaboAnalyst 5.0. RESULTS: In our primary analysis, 17 metabolites, including unsaturated fatty acids, phospholipids, and triglycerides traits, were selected as potential factors in psoriasis, with odd ratios (OR) ranging from 0.986 to 1.01. The GSMR method confirmed the above results (ß = 0.001, p < 0.05). LDSC analysis mainly suggested the genetic correlation of psoriasis with genetic correlations (rg) from 0.088 to 0.155. Based on the selected metabolites, metabolic pathway analysis suggested seven metabolic pathways including ketone body that may be prominent pathways for metabolites in psoriasis. CONCLUSION: Our study supports the causal role of unsaturated fatty acid properties and lipid traits with psoriasis. These properties may be regulated by the ketone body metabolic pathway.
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Análise da Randomização Mendeliana , Psoríase , Psoríase/sangue , Psoríase/genética , Psoríase/metabolismo , Humanos , Predisposição Genética para Doença/genética , Polimorfismo de Nucleotídeo Único , Desequilíbrio de Ligação , Metaboloma/fisiologia , Metaboloma/genética , Redes e Vias Metabólicas/genéticaRESUMO
Background and Aims: Blood metabolite abnormalities have revealed an association with cholestatic liver diseases (CLDs), while the underlying metabolic mechanisms have remained sluggish yet. Accordingly, the present evaluation aims to investigate the causal relationship between blood metabolites and the risk of two major CLDs, including primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC). Methods: Univariable and multivariable Mendelian randomization (MR) approaches were employed to uncover potential causal associations between blood metabolites and 2 CLDs, including PBS and PSC, through extracting instrumental variables (IVs) for metabolites from genome-wide association studies (GWAS) conducted on European individuals. The GWAS summary data of PBC or PSC were sourced from two distinct datasets. The initial analysis employed inverse variance weighted (IVW) and an array of sensitivity analyses, followed by replication and meta-analysis utilizing FinnGen consortium data. Finally, a multivariable MR analysis was carried out to ascertain the independent effects of each metabolite. Furthermore, the web-based tool MetaboAnalyst 5.0 was used to perform metabolic pathway examination. Results: A genetic causality between 15 metabolites and CLDs was recognized after preliminary analysis and false discovery rate (FDR) correction. Subsequently, 9 metabolites consistently represented an association through replication and meta-analysis. Additionally, the independent causal effects of 7 metabolites were corroborated by multivariable MR analysis. Specifically, the metabolites isovalerylcarnitine (odds ratio [OR] = 3.146, 95% confidence intervals [CI]: 1.471-6.726, p = 0.003), valine (OR = 192.44, 95%CI: 4.949-7483.27, p = 0.005), and mannose (OR = 0.184, 95%CI: 0.068-0.499, p < 0.001) were found to have a causal relationship with the occurrence of PBC. Furthermore, erythrose (OR = 5.504, 95%CI: 1.801-16.821, p = 0.003), 1-stearoylglycerophosphocholine (OR = 6.753, 95%CI: 2.621-17.399, p = 7.64 × 10-5), X-11847 (OR = 0.478, 95%CI: 0.352-0.650, p = 2.28 × 10-6), and X-12405 (OR = 3.765, 95%CI: 1.771-8.005, p = 5.71 × 10-4) were independently associated with the occurrence of PSC. Furthermore, the analysis of metabolic pathways identified seven significant pathways in two CLDs. Conclusion: The findings of the present study have unveiled robust causal relationships between 7 metabolites and 2 CLDs, thereby providing novel insights into the metabolic mechanisms and therapeutic strategies for these disorders.
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BACKGROUND: Various blood metabolites are known to be useful indicators of health status in dairy cattle, but their routine assessment is time-consuming, expensive, and stressful for the cows at the herd level. Thus, we evaluated the effectiveness of combining in-line near infrared (NIR) milk spectra with on-farm (days in milk [DIM] and parity) and genetic markers for predicting blood metabolites in Holstein cattle. Data were obtained from 388 Holstein cows from a farm with an AfiLab system. NIR spectra, on-farm information, and single nucleotide polymorphisms (SNP) markers were blended to develop calibration equations for blood metabolites using the elastic net (ENet) approach, considering 3 models: (1) Model 1 (M1) including only NIR information, (2) Model 2 (M2) with both NIR and on-farm information, and (3) Model 3 (M3) combining NIR, on-farm and genomic information. Dimension reduction was considered for M3 by preselecting SNP markers from genome-wide association study (GWAS) results. RESULTS: Results indicate that M2 improved the predictive ability by an average of 19% for energy-related metabolites (glucose, cholesterol, NEFA, BHB, urea, and creatinine), 20% for liver function/hepatic damage, 7% for inflammation/innate immunity, 24% for oxidative stress metabolites, and 23% for minerals compared to M1. Meanwhile, M3 further enhanced the predictive ability by 34% for energy-related metabolites, 32% for liver function/hepatic damage, 22% for inflammation/innate immunity, 42.1% for oxidative stress metabolites, and 41% for minerals, compared to M1. We found improved predictive ability of M3 using selected SNP markers from GWAS results using a threshold of > 2.0 by 5% for energy-related metabolites, 9% for liver function/hepatic damage, 8% for inflammation/innate immunity, 22% for oxidative stress metabolites, and 9% for minerals. Slight reductions were observed for phosphorus (2%), ferric-reducing antioxidant power (1%), and glucose (3%). Furthermore, it was found that prediction accuracies are influenced by using more restrictive thresholds (-log10(P-value) > 2.5 and 3.0), with a lower increase in the predictive ability. CONCLUSION: Our results highlighted the potential of combining several sources of information, such as genetic markers, on-farm information, and in-line NIR infrared data improves the predictive ability of blood metabolites in dairy cattle, representing an effective strategy for large-scale in-line health monitoring in commercial herds.
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Background: Substantial research evidence supports the correlation between mental disorders and sepsis. Nevertheless, the causal connection between a particular psychological disorder and sepsis remains unclear. Methods: For investigating the causal relationships between mental disorders and sepsis, genetic variants correlated with mental disorders, including anorexia nervosa (AN), attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), panic disorder (PD), posttraumatic stress disorder (PTSD), schizophrenia (SCZ), and tourette syndrome (TS), were all extracted from the Psychiatric Genomics Consortium (PGC). The causal estimates and direction between these mental disorders and sepsis were evaluated employing a two-sample bidirectional MR strategy. The inverse variance weighted (IVW) method was the primary approach utilized. Various sensitivity analyses were performed to confirm the validity of the causal effect. Meta-analysis, multivariable MR, and mediation MR were conducted to ensure the credibility and depth of this research. Results: The presence of AN was in relation to a greater likelihood of sepsis (OR 1.08, 95% CI 1.02-1.14; p = 0.013). A meta-analysis including validation cohorts supported this observation (OR 1.06, 95% CI 1.02-1.09). None of the investigated mental disorders appeared to be impacted when sepsis was set as the exposure factor. Even after adjusting for confounding factors, AN remained statistically significant (OR 1.08, 95% CI 1.02-1.15; p = 0.013). Mediation analysis indicated N-formylmethionine levels (with a mediated proportion of 7.47%), cystatin D levels (2.97%), ketogluconate Metabolism (17.41%) and N10-formyl-tetrahydrofolate biosynthesis (20.06%) might serve as mediators in the pathogenesis of AN-sepsis. Conclusion: At the gene prediction level, two-sample bidirectional MR analysis revealed that mental disorder AN had a causal association with an increased likelihood of sepsis. In addition, N-formylmethionine levels, cystatin D levels, ketogluconate metabolism and N10-formyl-tetrahydrofolate biosynthesis may function as potential mediators in the pathophysiology of AN-sepsis. Our research may contribute to the investigation of novel therapeutic strategies for mental illness and sepsis.
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Análise da Randomização Mendeliana , Transtornos Mentais , Sepse , Humanos , Transtornos Mentais/genética , FemininoRESUMO
BACKGROUND: An increasing amount of research demonstrates that metabolic disorders are related to rosacea. However, the correlations and causal relationships among them remain unknown. METHODS: We conducted not only forward 2-sample MR (Mendelian randomization) analyses but also reverse MR analyses which showed positive results in the forward MR analysis. In the forward MR analyses, inverse-variance weighted (IVW) and MR-Egger were performed as MR analyses. Cochran's Q test and the MR-Egger Intercept were used for sensitivity analyses. Concerning reverse MR analyses, IVW, MR-Egger, weighted median, simple mode, and weighted mode were applied. Cochran's Q test, MR-Egger Intercept, and MR pleiotropy residual sum and outlier (MR-PRESSO) outlier test were applied as sensitivity analyses. RESULTS: A total of 24 metabolites and 1 metabolite ratio were shown to have a causal effect on rosacea. N-lactoyl phenylalanine (N-Lac-Phe) was estimated as statistically significant by Bonferroni correction. Interestingly, we found three metabolites that were negatively associated with rosacea, especially caffeine, which are in line with the results of a large cohort study of females. For reverse MR analysis, we revealed that rosacea could potentially decrease the generation of two metabolites: octadecenedioate (C18:1-DC) and methyl vanillate sulfate. CONCLUSION: This study identified blood metabolites that may be associated with the development of rosacea. However, the exact mechanism by which these positive metabolites influence rosacea remains uncertain due to the paucity of experimental investigations. The combination of genetics and metabolomics offers novel viewpoints on the research of underlying mechanisms of rosacea and has significant value in screening and prevention of rosacea.
Assuntos
Análise da Randomização Mendeliana , Rosácea , Rosácea/sangue , Rosácea/genética , Humanos , Feminino , CausalidadeRESUMO
BACKGROUND: Unbalances in the gut microbiota have been proposed as a possible cause of esophageal cancer (ESCA), yet the exact causal relationship remains unclear. PURPOSE: To investigate the potential causal relationship between the gut microbiota and ESCA with Mendelian randomization (MR) analysis. METHODS: Genome-wide association studies (GWASs) of 207 gut microbial taxa (5 phyla, 10 classes, 13 orders, 26 families, 48 genera, and 105 species) and 205 gut microbiota metabolic pathways conducted by the Dutch Microbiome Project (DMP) and a FinnGen cohort GWAS of esophageal cancer specified the summary statistics. To investigate the possibility of a mediation effect between the gut microbiota and ESCA, mediation MR analyses were performed for 1091 blood metabolites and 309 metabolite ratios. RESULTS: MR analysis indicated that the relative abundance of 10 gut microbial taxa was associated with ESCA but all the 12 gut microbiota metabolic pathways with ESCA indicated no statistically significant association existing. Two blood metabolites and a metabolite ratio were discovered to be mediating factors in the pathway from gut microbiota to ESCA. CONCLUSION: This research indicated the potential mediating effects of blood metabolites and offered genetic evidence in favor of a causal correlation between gut microbiota and ESCA.
Assuntos
Neoplasias Esofágicas , Microbioma Gastrointestinal , Estudo de Associação Genômica Ampla , Análise da Randomização Mendeliana , Humanos , Neoplasias Esofágicas/genética , Neoplasias Esofágicas/microbiologia , Neoplasias Esofágicas/sangue , Microbioma Gastrointestinal/genética , MetabolomaRESUMO
Background: Numerous studies have established a link between coronary heart disease and metabolic disorders. Yet, causal evidence connecting metabolites and Coronary Heart Disease (CHD) remains scarce. To address this, we performed a bidirectional Mendelian Randomization (MR) analysis investigating the causal relationship between blood metabolites and CHD. Methods: Data were extracted from published genome-wide association studies (GWASs) on metabolite levels, focusing on 1,400 metabolite summary data as exposure measures. Primary analyses utilized the GWAS catalog database GCST90199698 (60,801 cases and 123,504 controls) and the FinnGen cohort (43,518 cases and 333,759 controls). The primary method used for causality analysis was random inverse variance weighting (IVW). Supplementary analyses included MR-Egger, weighted mode, and weighted median methods. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy. Reverse MR analysis was employed to evaluate the direct impact of metabolites on coronary heart disease. Additionally, replication and meta-analysis were performed. We further conducted the Steiger test and colocalization analysis to reflect the causality deeply. Results: This study identified eight metabolites associated with lipids, amino acids and metabolite ratios that may influence CHD risk. Findings include: 1-oleoyl-2-arachidonoyl-GPE (18:1/20:4) levels: OR = 1.08; 95% CI 1.04-1.12; P = 8.21E-06; 1-palmitoyl-2-arachidonoyl-GPE (16:0/20:4) levels: OR = 1.07; 95% CI 1.04-1.11; P = 9.01E-05; Linoleoyl-arachidonoyl-glycerol (18:2/20:4): OR = 1.08; 95% CI 1.04-1.22; P = 0.0001; Glycocholenate sulfate: OR = 0.93; 95% CI 0.90-0.97; P = 0.0002; 1-stearoyl-2-arachidonoyl-GPE (OR = 1.07; 95% CI 1.03-1.11; P = 0.0002); N-acetylasparagine (OR = 1.04; 95% CI 1.02-1.07; P = 0.0030); Octadecenedioate (C18:1-DC) (OR = 0.93; 95% CI 0.90-0.97; P = 0.0004); Phosphate to linoleoyl-arachidonoyl-glycerol (18:2-20:4) (1) ratio (OR = 0.92; 95% CI 0.88-0.97; P = 0.0005). Conclusion: The integration of genomics and metabolomics offers novel insights into the pathogenesis of CHD and holds significant importance for the screening and prevention of CHD.
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Background: Diabetic retinopathy (DR) is a microvascular complication of diabetes, severely affecting patients' vision and even leading to blindness. The development of DR is influenced by metabolic disturbance and genetic factors, including gene polymorphisms. The research aimed to uncover the causal relationships between blood metabolites and DR. Methods: The two-sample mendelian randomization (MR) analysis was employed to estimate the causality of blood metabolites on DR. The genetic variables for exposure were obtained from the genome-wide association study (GWAS) dataset of 486 blood metabolites, while the genetic predictors for outcomes including all-stage DR (All DR), non-proliferative DR (NPDR) and proliferative DR (PDR) were derived from the FinnGen database. The primary analysis employed inverse variance weighted (IVW) method, and supplementary analyses were performed using MR-Egger, weighted median (WM), simple mode and weighted mode methods. Additionally, MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis were also conducted to guarantee the accuracy and robustness of the results. Subsequently, we replicated the MR analysis using three additional datasets from the FinnGen database and conducted a meta-analysis to determine blood metabolites associated with DR. Finally, reverse MR analysis and metabolic pathway analysis were performed. Results: The study identified 13 blood metabolites associated with All DR, 9 blood metabolites associated with NPDR and 12 blood metabolites associated with PDR. In summary, a total of 21 blood metabolites were identified as having potential causal relationships with DR. Additionally, we identified 4 metabolic pathways that are related to DR. Conclusion: The research revealed a number of blood metabolites and metabolic pathways that are causally associated with DR, which holds significant importance for screening and prevention of DR. However, it is noteworthy that these causal relationships should be validated in larger cohorts and experiments.
Assuntos
Retinopatia Diabética , Estudo de Associação Genômica Ampla , Análise da Randomização Mendeliana , Humanos , Retinopatia Diabética/sangue , Retinopatia Diabética/genética , Polimorfismo de Nucleotídeo ÚnicoRESUMO
BACKGROUND: Pancreatic cancer is characterized by metabolic dysregulation and unique immunological profiles. Nevertheless, the comprehensive understanding of immune and metabolic dysregulation of pancreatic cancer remains unclear. In the present study, we aimed to investigate the causal relationship of circulating immune cells and pancreatic cancer and identify the blood metabolites as potential mediators. METHODS: The exposure and outcome genome-wide association studies (GWAS) data used in the present study were obtained from the GWAS open-access database (https://gwas.mrcieu.ac.uk). The study used 731 circulating immune cell features, 1400 types of blood metabolites and pancreatic cancer from GWAS. We then performed bidirectional Mendelian randomization (MR) analyses to explore the causal relationships between the circulating immune cells and pancreatic cancer, and two-step MR to discover potential mediating blood metabolites in this process. All statistical analyses were performed in R software. The STROBE-MR (i.e. Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization) checklist for the reporting of MR studies was also used. RESULTS: MR analysis identified seven types of circulating immune cells causally associated with pancreatic cancer. Furthermore, there was no strong evidence that genetically predicted pancreatic cancer had an effect on these seven types of circulating immune cells. Further two-step MR analysis found 10 types of blood metabolites were causally associated with pancreatic cancer and the associations between circulating CD39+CD8+ T cells and pancreatic cancer were mediated by blood orotates with proportions of 5.18% (p = 0.016). CONCLUSIONS: The present study provides evidence supporting the causal relationships between various circulating immune cells, especially CD39+CD8+ T cells, and pancreatic cancer, with a potential effect mediated by blood orotates. Further research is needed on additional risk factors as potential mediators and establish a comprehensive immunity-metabolism network in pancreatic cancer.