MELODI: Mining Enriched Literature Objects to Derive Intermediates

Elsworth, Benjamin; Dawe, Karen; Vincent, Emma E.; Langdon, Ryan; Lynch, Brigid M.; Martin, Richard M.; Relton, Caroline L; Higgins, Julian P T; Gaunt, Tom R.

doi:10.1093/ije/dyx251

Cited by 16 publications

(14 citation statements)

References 19 publications

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“…CKD risk factors were identified from a literature review using the MELODI-Presto (9)(10) to search the PubMed database (for more details on this method see Supplementary Note 1 ). We identified 49 risk factors/phenotypes for CKD, including blood pressure phenotypes, diabetic phenotypes (glucose- and insulin-related phenotypes), lipids phenotypes, obesity, smoking, alcohol intake, sleep disorders, nephrolithiasis, serum uric acid, coronary artery disease, bone mineral density, homocysteine, C-reactive protein, micro-nutrient phenotypes (serum metals and vitamins), dehydration and thyroid phenotypes, were selected.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we aimed to investigate the causal effects of 45 previously reported risk factors on CKD in general European and East Asian populations. To achieve this, a systematic search of risk factors for CKD was conducted in PubMed using the literature mining tool MELODI (9)(10). A set of two-sample linear MR analyses was performed using CKD and estimated glomerular filtration rate (eGFR) summary data from over 1 million participants from CKDGen consortium (11), UK Biobank (12), Nord-Trøndelag Health (HUNT) Study (13), Biobank Japan (14), China Kadoorie Biobank (15) and Japan-Kidney-Biobank/ToMMo consortium, in conjunction with the largest available genome-wide association study (GWAS) for risk factors in European and East Asian ancestries.…”

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confidence: 99%

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Trans-ethnic Mendelian randomization study reveals causal relationships between cardio-metabolic factors and chronic kidney disease

Zheng

Zhang

Rasheed

et al. 2020

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BACKGROUND The chronic kidney disease (CKD) public health burden is substantial and has not declined as expected with current interventions on disease treatments. A large number of clinical, biological, and behavioural risk factors have been associated with CKD. However, it is unclear which of them are causal. OBJECTIVE To systematically test whether previously reported risk factors for CKD are causally related to the disease in European and East Asian ancestries. DESIGN Two-sample Mendelian randomization (MR) and non-linear MR analyses. PARTICIPANTS 53,703 CKD cases and 960,624 controls of European ancestry from CKDGen, UK Biobank and HUNT, and 13,480 CKD cases and 238,118 controls of East Asian ancestry from Biobank Japan, China Kadoorie Biobank and Japan-Kidney-Biobank/ToMMo. MEASURES Systematic literature mining of PubMed studies identified 45 clinical risk factors and biomarkers with robustly associated genetic variants, including phenotypes related to blood pressure, diabetes, glucose, insulin, lipids, obesity, smoking, sleep disorders, nephrolithiasis, uric acid, coronary artery disease, bone mineral density, homocysteine, C-reactive protein, micro-nutrients and thyroid function, which were selected as exposures. The outcome was CKD (defined by clinical diagnosis or by estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73m2). RESULTS Eight risk factors showed evidence of causal effects on CKD in European ancestry, including body mass index (BMI), hypertension, systolic blood pressure, high density lipoprotein cholesterol, apolipoprotein A-I, lipoprotein A, type 2 diabetes (T2D) and nephrolithiasis. In East Asian ancestry, BMI, T2D and nephrolithiasis showed evidence of causal effects on CKD. Hypertension showed reliable evidence of a strong causal effect on CKD in Europeans but in contrast appeared to show a null effect in East Asians, suggesting the possibility of different causal risk factors in Europeans and East Asians. Although liability to T2D showed consistent effects on CKD, the effect of glycemic traits on CKD was weak, suggesting T2D may have glucose-independent mechanisms to influence CKD. Non-linear MR indicated a threshold relationship between genetically predicted BMI and CKD, with increased risk at BMI above 25 kg/m2. LIMITATION Due to the unbalanced distribution of data between ancestries, we could only test 17 of the 45 risk factors in East Asian participants. CONCLUSIONS Eight CKD-associated risk factors showed evidence of causal effects on the disease in over 1.2 million European and East Asian ancestries. These risk factors were predominantly related to cardio-metabolic health, which supports the shared causal link between cardio-metabolic health and kidney function. This study provides evidence of potential intervention targets for primary prevention of CKD, which could help reduce the global burden of CKD and its cardio-metabolic co-morbidities.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Trans-ethnic Mendelian randomization study reveals causal relationships between cardio-metabolic factors and chronic kidney disease

Zheng

Zhang

Rasheed

et al. 2020

Preprint

Self Cite

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“…Previously, we have demonstrated that existing literature can be used to derive relationships and mechanisms between defined biomedical traits (Elsworth et al, 2018). By integrating this knowledge with causal estimates in EpiGraphDB, we can triangulate evidence, identifying where these two sources of evidence are in agreement, and where they are not (Lawlor et al, 2017).…”

Section: Triangulating Causal Estimates With Literature Evidencementioning

confidence: 99%

“…SemMedDB (Kilicoglu et al, 2012), MELODI (Elsworth et al, 2018), Met-aMap (Demner-Fushman et al, 2017), Monarch (Mungall et al, 2017) Mapping of biomedical entities to literature terms.…”

Section: Gwas Top Hitsmentioning

confidence: 99%

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EpiGraphDB: A database and data mining platform for health data science

Liu

Elsworth

Erola

et al. 2020

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Motivation: The wealth of data resources on human phenotypes, risk factors, molecular traits and therapeutic interventions presents new opportunities for population health sciences. These opportunities are paralleled by a growing need for data integration, curation and mining to increase research efficiency, reduce mis-inference and ensure reproducible research. Results: We developed EpiGraphDB (https://epigraphdb.org/), a graph database containing an array of different biomedical and epidemiological relationships and an analytical platform to sup-port their use in human population health data science. In addition, we present three case studies that illustrate the value of this platform. The first uses EpiGraphDB to evaluate potential pleiotropic relationships, addressing mis-inference in systematic causal analysis. In the second case study we illustrate how protein-protein interaction data offer opportunities to identify new drug targets. The final case study integrates causal inference using Mendelian randomization with relationships mined from the biomedical literature to "triangulate" evidence from different sources. Availability: The EpiGraphDB platform is openly available at https://epigraphdb.org. Code for repli-cating case study results is available at https://github.com/MRCIEU/epigraphdb as Jupyter notebooks using the API, and https://mrcieu.github.io/epigraphdb-r using the R package.

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