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1. A reference map of potential determinants for the human serum metabolome

Author

Bar, N., Korem, T., Weissbrod, O., Zeevi, D., Rothschild, D., Leviatan, S., Kosower, N., Lotan-Pompan, M., Weinberger, A., Le Roy, C. I., Menni, C., Visconti, A., Falchi, M., Spector, T. D., Vestergaard, H., Arumugam, M., Hansen, T., Allin, K., Hong, Mun-Gwan, Schwenk, Jochen M., Häussler, Ragna S., Dale, Matilda, Giorgino, T., Rodriquez, M., Perry, M., Nice, R., McDonald, T., Hattersley, A., Jones, A., Graefe-Mody, U., Baum, P., Grempler, R., Thomas, C. E., Masi, F. D., Brorsson, C. A., Mazzoni, G., Allesøe, R., Rasmussen, S., Gudmundsdóttir, V., Nielsen, A. M., Banasik, K., Tsirigos, K., Nilsson, B., Pedersen, H., Brunak, S., Karaderi, T., Lundgaard, A. T., Johansen, J., Gupta, R., Sackett, P. W., Tillner, J., Lehr, T., Scherer, N., Dings, C., Sihinevich, I., Loftus, H., Cabrelli, L., McEvoy, D., Mari, A., Bizzotto, R., Tura, A., ’t Hart, L., Dekkers, K., Leeuwen, N., Slieker, R., Rutters, F., Beulens, J., Nijpels, G., Koopman, A., Oort, S., Groeneveld, L., Groop, L., Elders, P., Viñuela, A., Ramisch, A., Dermitzakis, E., Ehrhardt, B., Jennison, C., Froguel, P., Canouil, M., Boneford, A., McVittie, I., Wake, D., Frau, F., Staerfeldt, H. -H, Adragni, K., Thomas, M., Wu, H., Pavo, I., Steckel-Hamann, B., Thomsen, H., Giordano, G. N., Fitipaldi, H., Ridderstråle, M., Kurbasic, A., Pasdar, N. A., Pomares-Millan, H., Mutie, P., Koivula, R., McRobert, N., McCarthy, M., Wesolowska-Andersen, A., Mahajan, A., Abdalla, M., Fernandez, J., Holl, R., Heggie, A., Deshmukh, H., Hennige, A., Bianzano, S., Thorand, B., Sharma, S., Grallert, H., Adam, J., Troll, M., Fritsche, A., Hill, A., Thorne, C., Hudson, M., Kuulasmaa, T., Vangipurapu, J., Laakso, M., Cederberg, H., Kokkola, T., Jiao, Y., Gough, S., Robertson, N., Verkindt, H., Raverdi, V., Caiazzo, R., Pattou, F., White, M., Donnelly, L., Brown, A., Palmer, C., Davtian, D., Dawed, A., Forgie, I., Pearson, E., Ruetten, H., Musholt, P., Bell, J., Thomas, E. L., Whitcher, B., Haid, M., Nicolay, C., Mourby, M., Kaye, J., Shah, N., Teare, H., Frost, G., Jablonka, B., Uhlen, M., Eriksen, R., Vogt, J., Dutta, A., Jonsson, A., Engelbrechtsen, L., Forman, A., Sondertoft, N., de Preville, N., Baltauss, T., Walker, M., Gassenhuber, J., Klintenberg, M., Bergstrom, M., Ferrer, J., Adamski, J., Franks, P. W., Pedersen, O., Segal, E., consortium, The IMI DIRECT, Bar, N., Korem, T., Weissbrod, O., Zeevi, D., Rothschild, D., Leviatan, S., Kosower, N., Lotan-Pompan, M., Weinberger, A., Le Roy, C. I., Menni, C., Visconti, A., Falchi, M., Spector, T. D., Vestergaard, H., Arumugam, M., Hansen, T., Allin, K., Hong, Mun-Gwan, Schwenk, Jochen M., Häussler, Ragna S., Dale, Matilda, Giorgino, T., Rodriquez, M., Perry, M., Nice, R., McDonald, T., Hattersley, A., Jones, A., Graefe-Mody, U., Baum, P., Grempler, R., Thomas, C. E., Masi, F. D., Brorsson, C. A., Mazzoni, G., Allesøe, R., Rasmussen, S., Gudmundsdóttir, V., Nielsen, A. M., Banasik, K., Tsirigos, K., Nilsson, B., Pedersen, H., Brunak, S., Karaderi, T., Lundgaard, A. T., Johansen, J., Gupta, R., Sackett, P. W., Tillner, J., Lehr, T., Scherer, N., Dings, C., Sihinevich, I., Loftus, H., Cabrelli, L., McEvoy, D., Mari, A., Bizzotto, R., Tura, A., ’t Hart, L., Dekkers, K., Leeuwen, N., Slieker, R., Rutters, F., Beulens, J., Nijpels, G., Koopman, A., Oort, S., Groeneveld, L., Groop, L., Elders, P., Viñuela, A., Ramisch, A., Dermitzakis, E., Ehrhardt, B., Jennison, C., Froguel, P., Canouil, M., Boneford, A., McVittie, I., Wake, D., Frau, F., Staerfeldt, H. -H, Adragni, K., Thomas, M., Wu, H., Pavo, I., Steckel-Hamann, B., Thomsen, H., Giordano, G. N., Fitipaldi, H., Ridderstråle, M., Kurbasic, A., Pasdar, N. A., Pomares-Millan, H., Mutie, P., Koivula, R., McRobert, N., McCarthy, M., Wesolowska-Andersen, A., Mahajan, A., Abdalla, M., Fernandez, J., Holl, R., Heggie, A., Deshmukh, H., Hennige, A., Bianzano, S., Thorand, B., Sharma, S., Grallert, H., Adam, J., Troll, M., Fritsche, A., Hill, A., Thorne, C., Hudson, M., Kuulasmaa, T., Vangipurapu, J., Laakso, M., Cederberg, H., Kokkola, T., Jiao, Y., Gough, S., Robertson, N., Verkindt, H., Raverdi, V., Caiazzo, R., Pattou, F., White, M., Donnelly, L., Brown, A., Palmer, C., Davtian, D., Dawed, A., Forgie, I., Pearson, E., Ruetten, H., Musholt, P., Bell, J., Thomas, E. L., Whitcher, B., Haid, M., Nicolay, C., Mourby, M., Kaye, J., Shah, N., Teare, H., Frost, G., Jablonka, B., Uhlen, M., Eriksen, R., Vogt, J., Dutta, A., Jonsson, A., Engelbrechtsen, L., Forman, A., Sondertoft, N., de Preville, N., Baltauss, T., Walker, M., Gassenhuber, J., Klintenberg, M., Bergstrom, M., Ferrer, J., Adamski, J., Franks, P. W., Pedersen, O., Segal, E., and consortium, The IMI DIRECT

Abstract

The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites—in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites., QC 20211001

Published
2020
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2. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Author

Wilman, H. R., Parisinos, C. A., Atabaki-Pasdar, N., Kelly, M., Thomas, E. L., Neubauer, S., Jennison, C., Ehrhardt, B., Baum, P., Schoelsch, C., Freijer, J., Grempler, R., Graefe-Mody, U., Hennige, A., Dings, C., Lehr, T., Scherer, N., Sihinecich, I., Pattou, F., Raverdi, V., Caiazzo, R., Torres, F., Verkindt, H., Mari, A., Tura, A., Giorgino, T., Bizzotto, Froguel, P., Bonneford, A., Canouil, M., Dhennin, V., Brorsson, C., Brunak, S., De Masi, F., Gudmundsdóttir, V., Pedersen, H., Banasik, K., Thomas, C., Sackett, P., Staerfeldt, H. -H, Lundgaard, A., Nilsson, B., Nielsen, A., Mazzoni, G., Karaderi, T., Rasmussen, S., Johansen, J., Allesøe, R., Fritsche, A., Thorand, B., Adamski, J., Grallert, H., Haid, M., Sharma, S., Troll, M., Adam, J., Ferrer, J., Eriksen, H., Frost, G., Häussler, Ragna S., Hong, Mun-Gwan, Schwenk, Jochen M., Uhlén, Mathias, Nicolay, C., Pavo, I., Steckel-Hamann, B., Thomas, M., Adragni, K., Wu, H., Hart, L., Roderick, S., van Leeuwen, N., Dekkers, K., Frau, F., Gassenhuber, J., Jablonka, B., Musholt, P., Ruetten, H., Tillner, J., Baltauss, T., Bernard Poenaru, O., de Preville, N., Rodriquez, M., Arumugam, M., Allin, K., Engelbrechtsen, L., Hansen, T., Forman, A., Jonsson, A., Pedersen, O., Dutta, A., Vogt, J., Vestergaard, H., Laakso, M., Kokkola, T., Kuulasmaa, T., Franks, P., Giordano, N., Pomares-Millan, H., Fitipaldi, H., Mutie, P., Klintenberg, M., Bergstrom, M., Groop, L., Ridderstrale, M., Atabaki Pasdar, N., Deshmukh, H., Heggie, A., Wake, D., McEvoy, D., McVittie, I., Walker, M., Hattersley, A., Hill, A., Jones, A., McDonald, T., Perry, M., Nice, R., Hudson, M., Thorne, C., Dermitzakis, E., Viñuela, A., Cabrelli, L., Loftus, H., Dawed, A., Donnelly, L., Forgie, I., Pearson, E., Palmer, C., Brown, A., Koivula, R., Wesolowska-Andersen, A., Abdalla, M., McRobert, N., Fernandez, J., Jiao, Y., Robertson, N., Gough, S., Kaye, J., Mourby, M., Mahajan, A., McCarthy, M., Shah, N., Teare, H., Holl, R., Koopman, A., Rutters, F., Beulens, J., Groeneveld, L., Bell, J., Thomas, L., Whitcher, B., Hingorani, A. D., Patel, R. S., Hemingway, H., Franks, P. W., Bell, J. D., Banerjee, R., Yaghootkar, H., Wilman, H. R., Parisinos, C. A., Atabaki-Pasdar, N., Kelly, M., Thomas, E. L., Neubauer, S., Jennison, C., Ehrhardt, B., Baum, P., Schoelsch, C., Freijer, J., Grempler, R., Graefe-Mody, U., Hennige, A., Dings, C., Lehr, T., Scherer, N., Sihinecich, I., Pattou, F., Raverdi, V., Caiazzo, R., Torres, F., Verkindt, H., Mari, A., Tura, A., Giorgino, T., Bizzotto, Froguel, P., Bonneford, A., Canouil, M., Dhennin, V., Brorsson, C., Brunak, S., De Masi, F., Gudmundsdóttir, V., Pedersen, H., Banasik, K., Thomas, C., Sackett, P., Staerfeldt, H. -H, Lundgaard, A., Nilsson, B., Nielsen, A., Mazzoni, G., Karaderi, T., Rasmussen, S., Johansen, J., Allesøe, R., Fritsche, A., Thorand, B., Adamski, J., Grallert, H., Haid, M., Sharma, S., Troll, M., Adam, J., Ferrer, J., Eriksen, H., Frost, G., Häussler, Ragna S., Hong, Mun-Gwan, Schwenk, Jochen M., Uhlén, Mathias, Nicolay, C., Pavo, I., Steckel-Hamann, B., Thomas, M., Adragni, K., Wu, H., Hart, L., Roderick, S., van Leeuwen, N., Dekkers, K., Frau, F., Gassenhuber, J., Jablonka, B., Musholt, P., Ruetten, H., Tillner, J., Baltauss, T., Bernard Poenaru, O., de Preville, N., Rodriquez, M., Arumugam, M., Allin, K., Engelbrechtsen, L., Hansen, T., Forman, A., Jonsson, A., Pedersen, O., Dutta, A., Vogt, J., Vestergaard, H., Laakso, M., Kokkola, T., Kuulasmaa, T., Franks, P., Giordano, N., Pomares-Millan, H., Fitipaldi, H., Mutie, P., Klintenberg, M., Bergstrom, M., Groop, L., Ridderstrale, M., Atabaki Pasdar, N., Deshmukh, H., Heggie, A., Wake, D., McEvoy, D., McVittie, I., Walker, M., Hattersley, A., Hill, A., Jones, A., McDonald, T., Perry, M., Nice, R., Hudson, M., Thorne, C., Dermitzakis, E., Viñuela, A., Cabrelli, L., Loftus, H., Dawed, A., Donnelly, L., Forgie, I., Pearson, E., Palmer, C., Brown, A., Koivula, R., Wesolowska-Andersen, A., Abdalla, M., McRobert, N., Fernandez, J., Jiao, Y., Robertson, N., Gough, S., Kaye, J., Mourby, M., Mahajan, A., McCarthy, M., Shah, N., Teare, H., Holl, R., Koopman, A., Rutters, F., Beulens, J., Groeneveld, L., Bell, J., Thomas, L., Whitcher, B., Hingorani, A. D., Patel, R. S., Hemingway, H., Franks, P. W., Bell, J. D., Banerjee, R., and Yaghootkar, H.

Abstract

Background & Aims: Excess liver iron content is common and is linked to the risk of hepatic and extrahepatic diseases. We aimed to identify genetic variants influencing liver iron content and use genetics to understand its link to other traits and diseases. Methods: First, we performed a genome-wide association study (GWAS) in 8,289 individuals from UK Biobank, whose liver iron level had been quantified by magnetic resonance imaging, before validating our findings in an independent cohort (n = 1,513 from IMI DIRECT). Second, we used Mendelian randomisation to test the causal effects of 25 predominantly metabolic traits on liver iron content. Third, we tested phenome-wide associations between liver iron variants and 770 traits and disease outcomes. Results: We identified 3 independent genetic variants (rs1800562 [C282Y] and rs1799945 [H63D] in HFE and rs855791 [V736A] in TMPRSS6) associated with liver iron content that reached the GWAS significance threshold (p <5 × 10−8). The 2 HFE variants account for ∼85% of all cases of hereditary haemochromatosis. Mendelian randomisation analysis provided evidence that higher central obesity plays a causal role in increased liver iron content. Phenome-wide association analysis demonstrated shared aetiopathogenic mechanisms for elevated liver iron, high blood pressure, cirrhosis, malignancies, neuropsychiatric and rheumatological conditions, while also highlighting inverse associations with anaemias, lipidaemias and ischaemic heart disease. Conclusion: Our study provides genetic evidence that mechanisms underlying higher liver iron content are likely systemic rather than organ specific, that higher central obesity is causally associated with higher liver iron, and that liver iron shares common aetiology with multiple metabolic and non-metabolic diseases. Lay summary: Excess liver iron content is common and is associated with liver diseases and metabolic diseases including diabetes, high blood pressure, and heart di, QC 20191128

Published
2019
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3. Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging.

Author

Leach, M.O., Morgan, B., Tofts, P.S., Buckley, D.L., Huang, W., Horsfield, M.A., Chenevert, T.L., Collins, D.J., Jackson, A., Lomas, D., Whitcher, B., Clarke, L., Plummer, R., Judson, I., Jones, R., Alonzi, R., Brunner, T., Koh, D.M., Murphy, P., Waterton, J.C., Parker, G., Graves, M.J., Scheenen, T.W.J., Redpath, T.W., Orton, M., Karczmar, G., Huisman, H.J., Barentsz, J.O., Padhani, A., Leach, M.O., Morgan, B., Tofts, P.S., Buckley, D.L., Huang, W., Horsfield, M.A., Chenevert, T.L., Collins, D.J., Jackson, A., Lomas, D., Whitcher, B., Clarke, L., Plummer, R., Judson, I., Jones, R., Alonzi, R., Brunner, T., Koh, D.M., Murphy, P., Waterton, J.C., Parker, G., Graves, M.J., Scheenen, T.W.J., Redpath, T.W., Orton, M., Karczmar, G., Huisman, H.J., Barentsz, J.O., and Padhani, A.

Abstract

01 juli 2012, Contains fulltext : 108222.pdf (Publisher’s version ) (Open Access), Many therapeutic approaches to cancer affect the tumour vasculature, either indirectly or as a direct target. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important means of investigating this action, both pre-clinically and in early stage clinical trials. For such trials, it is essential that the measurement process (i.e. image acquisition and analysis) can be performed effectively and with consistency among contributing centres. As the technique continues to develop in order to provide potential improvements in sensitivity and physiological relevance, there is considerable scope for between-centre variation in techniques. A workshop was convened by the Imaging Committee of the Experimental Cancer Medicine Centres (ECMC) to review the current status of DCE-MRI and to provide recommendations on how the technique can best be used for early stage trials. This review and the consequent recommendations are summarised here. Key Points * Tumour vascular function is key to tumour development and treatment * Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess tumour vascular function * Thus DCE-MRI with pharmacokinetic models can assess novel treatments * Many recent developments are advancing the accuracy of and information from DCE-MRI * Establishing common methodology across multiple centres is challenging and requires accepted guidelines.

Published
2012

4. Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging.

Author

Leach, M.O., Morgan, B., Tofts, P.S., Buckley, D.L., Huang, W., Horsfield, M.A., Chenevert, T.L., Collins, D.J., Jackson, A., Lomas, D., Whitcher, B., Clarke, L., Plummer, R., Judson, I., Jones, R., Alonzi, R., Brunner, T., Koh, D.M., Murphy, P., Waterton, J.C., Parker, G., Graves, M.J., Scheenen, T.W.J., Redpath, T.W., Orton, M., Karczmar, G., Huisman, H.J., Barentsz, J.O., Padhani, A., Leach, M.O., Morgan, B., Tofts, P.S., Buckley, D.L., Huang, W., Horsfield, M.A., Chenevert, T.L., Collins, D.J., Jackson, A., Lomas, D., Whitcher, B., Clarke, L., Plummer, R., Judson, I., Jones, R., Alonzi, R., Brunner, T., Koh, D.M., Murphy, P., Waterton, J.C., Parker, G., Graves, M.J., Scheenen, T.W.J., Redpath, T.W., Orton, M., Karczmar, G., Huisman, H.J., Barentsz, J.O., and Padhani, A.

Abstract

01 juli 2012, Contains fulltext : 108222.pdf (Publisher’s version ) (Open Access), Many therapeutic approaches to cancer affect the tumour vasculature, either indirectly or as a direct target. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important means of investigating this action, both pre-clinically and in early stage clinical trials. For such trials, it is essential that the measurement process (i.e. image acquisition and analysis) can be performed effectively and with consistency among contributing centres. As the technique continues to develop in order to provide potential improvements in sensitivity and physiological relevance, there is considerable scope for between-centre variation in techniques. A workshop was convened by the Imaging Committee of the Experimental Cancer Medicine Centres (ECMC) to review the current status of DCE-MRI and to provide recommendations on how the technique can best be used for early stage trials. This review and the consequent recommendations are summarised here. Key Points * Tumour vascular function is key to tumour development and treatment * Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess tumour vascular function * Thus DCE-MRI with pharmacokinetic models can assess novel treatments * Many recent developments are advancing the accuracy of and information from DCE-MRI * Establishing common methodology across multiple centres is challenging and requires accepted guidelines.

Published
2012

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