Metabolic Signatures Elucidate the Effect of Body Mass Index on Type 2 Diabetes

Dong, Qiuling; Sidra, Sidra; Gieger, Christian; Wang-Sattler, Rui; Rathmann, Wolfgang; Prehn, Cornelia; Adamski, Jerzy; Köenig, Wolfgang; Peters, Annette; Grallert, Harald; Sharma, Sapna

doi:10.3390/metabo13020227

Cited by 16 publications

(8 citation statements)

References 84 publications

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“…Another related study also proved that BMI had a direct causal relationship with T2DM, and found that weight control and blood lipid control played an important role in preventing diabetes [29]. The research results of Qiuling Dong et al clari ed the causal relationship between BMI and T2DM from the perspective of metabolic characteristics, and provided new ideas and directions for treatment strategies related to diabetes [30].…”

Section: Discussionmentioning

confidence: 95%

BMI and DM: a two-sample mendelian randomization study

Yuan,

Jiang,

2023

Preprint

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Introduction: Diabetes mellitus (DM) patients are often accompanied by an increase in BMI (body mass index). Obesity in clinical work is also often considered to be a high risk factor for diabetes, but whether there is a causal relationship between BMI and DM has not been clearly confirmed. Therefore, we conducted a two sample Mendelian randomization (MR) study to investigate the potential relationship. Method: We downloaded relevant data of "body mass index", "diabetes" and "type 2 diabetes (T2DM)" from the IEU OpenGWAS project database, used four different methods to conduct two sample MR analysis, and performed sensitivity analysis for verification. Results: The results of four MR analysis methods indicated that high BMI was indeed an independent risk factor for DM. Since T2DM was a main subtype of diabetes, we further analyzed the impact of BMI on T2DM and got the same results. The two OR values obtained through the IVW method in this study were: OR = 1.032 (95% CI, 1.014 to 1.051), P < 0.001 and OR = 1.005 (95% CI, 1.001 to 1.008), P = 0.011. Conclusion: Through our research, it was confirmed that high BMI was indeed an independent risk factor affecting the incidence rate of diabetes.

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Section: Discussionmentioning

confidence: 95%

BMI and DM: a two-sample mendelian randomization study

Yuan,

Jiang,

2023

Preprint

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“…Other lipids, as sphingolipid C16:1 SM, were also included in the legume-related-metabolite signature and showed a decreased risk association for T2D. Biological pathway analysis in combination with genetics and mice experiments indicates that the downregulation or upregulation of sphingolipid is a causal factor in early-stage T2D pathophysiology and is associated with a decreased risk of T2D [ 55 , 56 ]. The positive association between C7 and C18:2 acylcarnitines and legume consumption could be an indirect reflection of high fish consumption by those individuals allocated in the highest tertile of legume consumption, as fish has been established as one of the main dietary sources of acylcarnitines, although no association between short chain acylcarnitines and diabetes risk has been previously reported [ 57 ].…”

Section: Discussionmentioning

confidence: 99%

Plasma metabolite profile of legume consumption and future risk of type 2 diabetes and cardiovascular disease

Margara-Escudero,

Paz-Graniel,

García-Gavilán

et al. 2024

Cardiovasc Diabetol

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Background Legume consumption has been linked to a reduced risk of type 2 diabetes (T2D) and cardiovascular disease (CVD), while the potential association between plasma metabolites associated with legume consumption and the risk of cardiometabolic diseases has never been explored. Therefore, we aimed to identify a metabolite signature of legume consumption, and subsequently investigate its potential association with the incidence of T2D and CVD. Methods The current cross-sectional and longitudinal analysis was conducted in 1833 PREDIMED study participants (mean age 67 years, 57.6% women) with available baseline metabolomic data. A subset of these participants with 1-year follow-up metabolomics data (n = 1522) was used for internal validation. Plasma metabolites were assessed through liquid chromatography-tandem mass spectrometry. Cross-sectional associations between 382 different known metabolites and legume consumption were performed using elastic net regression. Associations between the identified metabolite profile and incident T2D and CVD were estimated using multivariable Cox regression models. Results Specific metabolic signatures of legume consumption were identified, these included amino acids, cortisol, and various classes of lipid metabolites including diacylglycerols, triacylglycerols, plasmalogens, sphingomyelins and other metabolites. Among these identified metabolites, 22 were negatively and 18 were positively associated with legume consumption. After adjustment for recognized risk factors and legume consumption, the identified legume metabolite profile was inversely associated with T2D incidence (hazard ratio (HR) per 1 SD: 0.75, 95% CI 0.61–0.94; p = 0.017), but not with CVD incidence risk (1.01, 95% CI 0.86–1.19; p = 0.817) over the follow-up period. Conclusions This study identified a set of 40 metabolites associated with legume consumption and with a reduced risk of T2D development in a Mediterranean population at high risk of cardiovascular disease. Trial registration: ISRCTN35739639.

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“…during which analytical procedures have been upgraded several times 26 . The targeted metabolomics data of F4 were measured with the analytical kit AbsoluteIDQ ® p150 42,43 , while S4 and FF4 data were quantified using the kit AbsoluteIDQ ® p180 [44][45][46] (Biocrates Life Sciences AG, Innsbruck, Austria). To assess the technical variation introduced by different kits, samples from 288 individuals from the F4 were remeasured (September-October 2019) using the p180 kit.…”

Section: Mgh Covid Datasetmentioning

confidence: 99%

LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer

Han,

Yu,

Shi

et al. 2023

Preprint

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Longitudinal multi-view omics data offer unique insights into the temporal dynamics of individual-level physiology that is instrumental to advancements in personalized healthcare. However, the common occurrence of incomplete view makes inference tasks difficult, and there is a lack of tailored methods for solving this critical issue. Here, we introduce LEOPARD, an approach specifically designed to complete missing views for multi-timepoint omics data. By disentangling longitudinal omics data into content and temporal representations, LEOPARD transfers temporal knowledge to omics-specific content to complete missing views. Compared to generic imputation methods, LEOPARD gives the most robust and reliable results across three longitudinal proteomics and metabolomics datasets. LEOPARD-imputed data also achieve the closest performance to observed data in our regression and classification analyses. Our work takes the first step towards a principled and generalized treatment of missing views in longitudinal omics data, enabling comprehensive exploration of temporal changes within omics data.

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Metabolic Signatures Elucidate the Effect of Body Mass Index on Type 2 Diabetes

Cited by 16 publications

References 84 publications

BMI and DM: a two-sample mendelian randomization study

BMI and DM: a two-sample mendelian randomization study

Plasma metabolite profile of legume consumption and future risk of type 2 diabetes and cardiovascular disease

LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer

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