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1. Role of human plasma metabolites in prediabetes and type 2 diabetes from the IMI-DIRECT study

Author

Sharma, Sapna, Dong, Qiuling, Haid, Mark, Adam, Jonathan, Bizzotto, Roberto, Fernandez-Tajes, Juan J., Jones, Angus G., Tura, Andrea, Artati, Anna, Prehn, Cornelia, Kastenmüller, Gabi, Koivula, Robert W., Franks, Paul W., Walker, Mark, Forgie, Ian M., Giordano, Giuseppe, Pavo, Imre, Ruetten, Hartmut, Dermitzakis, Manolis, McCarthy, Mark I., Pedersen, Oluf, Schwenk, Jochen M., Tsirigos, Konstantinos D., De Masi, Federico, Brunak, Soren, Viñuela, Ana, Mari, Andrea, McDonald, Timothy J., Kokkola, Tarja, Adamski, Jerzy, Pearson, Ewan R., and Grallert, Harald

Published
2024
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3. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Author

Tobias, Deirdre K, Merino, Jordi, Ahmad, Abrar, Aiken, Catherine, Benham, Jamie L, Bodhini, Dhanasekaran, Clark, Amy L, Colclough, Kevin, Corcoy, Rosa, Cromer, Sara J, Duan, Daisy, Felton, Jamie L, Francis, Ellen C, Gillard, Pieter, Gingras, Véronique, Gaillard, Romy, Haider, Eram, Hughes, Alice, Ikle, Jennifer M, Jacobsen, Laura M, Kahkoska, Anna R, Kettunen, Jarno LT, Kreienkamp, Raymond J, Lim, Lee-Ling, Männistö, Jonna ME, Massey, Robert, Mclennan, Niamh-Maire, Miller, Rachel G, Morieri, Mario Luca, Most, Jasper, Naylor, Rochelle N, Ozkan, Bige, Patel, Kashyap Amratlal, Pilla, Scott J, Prystupa, Katsiaryna, Raghavan, Sridharan, Rooney, Mary R, Schön, Martin, Semnani-Azad, Zhila, Sevilla-Gonzalez, Magdalena, Svalastoga, Pernille, Takele, Wubet Worku, Tam, Claudia Ha-ting, Thuesen, Anne Cathrine B, Tosur, Mustafa, Wallace, Amelia S, Wang, Caroline C, Wong, Jessie J, Yamamoto, Jennifer M, Young, Katherine, Amouyal, Chloé, Andersen, Mette K, Bonham, Maxine P, Chen, Mingling, Cheng, Feifei, Chikowore, Tinashe, Chivers, Sian C, Clemmensen, Christoffer, Dabelea, Dana, Dawed, Adem Y, Deutsch, Aaron J, Dickens, Laura T, DiMeglio, Linda A, Dudenhöffer-Pfeifer, Monika, Evans-Molina, Carmella, Fernández-Balsells, María Mercè, Fitipaldi, Hugo, Fitzpatrick, Stephanie L, Gitelman, Stephen E, Goodarzi, Mark O, Grieger, Jessica A, Guasch-Ferré, Marta, Habibi, Nahal, Hansen, Torben, Huang, Chuiguo, Harris-Kawano, Arianna, Ismail, Heba M, Hoag, Benjamin, Johnson, Randi K, Jones, Angus G, Koivula, Robert W, Leong, Aaron, Leung, Gloria KW, Libman, Ingrid M, Liu, Kai, Long, S Alice, Lowe, William L, Morton, Robert W, Motala, Ayesha A, Onengut-Gumuscu, Suna, Pankow, James S, Pathirana, Maleesa, Pazmino, Sofia, Perez, Dianna, Petrie, John R, Powe, Camille E, Quinteros, Alejandra, Jain, Rashmi, Ray, Debashree, and Ried-Larsen, Mathias

Subjects
Health Services and Systems, Biomedical and Clinical Sciences, Health Sciences, Precision Medicine, Diabetes, Minority Health, Good Health and Well Being, Humans, Consensus, Diabetes Mellitus, Evidence-Based Medicine, Medical and Health Sciences, Immunology, Biomedical and clinical sciences, Health sciences
Abstract

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.

Published
2023

7. Long-term outcomes after severe childhood malnutrition in adolescents in Malawi (LOSCM): a prospective observational cohort study

Author

Abera, Mubarek, Ameya, Gemechu, Anujo, Kenneth, Arefayine, Melkamu, Girma, Tsinuel, Gonzales, Gerard Bryan, Olga, Laurentya, McKenzie, Kimberley, Thompson, Debbie, Wells, Jonathan C, Kirolos, Amir, Harawa, Philliness P, Chimowa, Takondwa, Divala, Oscar, Freyne, Bridget, Jones, Angus G, Lelijveld, Natasha, Lissauer, Samantha, Maleta, Kenneth, Gladstone, Melissa J, and Kerac, Marko

Published
2024
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11. Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

Author

Brown, Andrew A., Fernandez-Tajes, Juan J., Hong, Mun-gwan, Brorsson, Caroline A., Koivula, Robert W., Davtian, David, Dupuis, Théo, Sartori, Ambra, Michalettou, Theodora-Dafni, Forgie, Ian M., Adam, Jonathan, Allin, Kristine H., Caiazzo, Robert, Cederberg, Henna, De Masi, Federico, Elders, Petra J. M., Giordano, Giuseppe N., Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison J., Howald, Cédric, Jones, Angus G., Kokkola, Tarja, Laakso, Markku, Mahajan, Anubha, Mari, Andrea, McDonald, Timothy J., McEvoy, Donna, Mourby, Miranda, Musholt, Petra B., Nilsson, Birgitte, Pattou, Francois, Penet, Deborah, Raverdy, Violeta, Ridderstråle, Martin, Romano, Luciana, Rutters, Femke, Sharma, Sapna, Teare, Harriet, ‘t Hart, Leen, Tsirigos, Konstantinos D., Vangipurapu, Jagadish, Vestergaard, Henrik, Brunak, Søren, Franks, Paul W., Frost, Gary, Grallert, Harald, Jablonka, Bernd, McCarthy, Mark I., Pavo, Imre, Pedersen, Oluf, Ruetten, Hartmut, Walker, Mark, Adamski, Jerzy, Schwenk, Jochen M., Pearson, Ewan R., Dermitzakis, Emmanouil T., and Viñuela, Ana

Published
2023
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15. Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials

Author

't Hart, L.M., Abdalla, M., Adam, J., Adamski, J., Adragni, K., Allin, K.H., Arumugam, M., Atabaki Pasdar, N., Baltauss, T., Banasik, K.B., Baum, P., Bell, J.D., Bergstrom, M., Beulens, J.W., Bianzano, S., Bizzotto, R., Bonneford, A., Brorsson, C.A.B., Brown, A.A., Brunak, S.B., Cabrelli, L., Caiazzo, R., Canouil, M., Dale, M., Davtian, D., Dawed, A.Y., De Masi, F.M., de Preville, N., Dekkers, K.F., Dermitzakis, E.T., Deshmukh, H.A., Dings, C., Donnelly, L., Dutta, A., Ehrhardt, B., Elders, P.J.M., Engel Thomas, C.E.T., Engelbrechtsen, L., Eriksen, R.G., Eriksen, R.E., Fan, Y., Fernandez, J., Ferrer, J., Fitipaldi, H., Forgie, I.M., Forman, A., Franks, P.W., Frau, F., Fritsche, A., Froguel, P., Frost, G., Gassenhuber, J., Giordano, G.N., Giorgino, T., Gough, S., Graefe-Mody, U., Grallert, H., Grempler, R., Groeneveld, L., Groop, L., Gudmundsdóttir, V.G., Gupta, R.G., Haid, M., Hansen, T., Hansen, T.H., Hattersley, A.T., Haussler, R.S., Heggie, A.J., Hennige, A.M., Hill, A.V., Holl, R.W., Hong, M.-G., Hudson, M., Jablonka, B., Jennison, C., Jiao, J., Johansen, J.J., Jones, A.G., Jonsson, A., Karaderi, T.K., Kaye, J., Klintenberg, M., Koivula, R.W., Kokkola, T., Koopman, A.D.M., Kurbasic, A, Kuulasmaa, T., Laakso, M., Lehr, T., Loftus, H., Lundbye Allesøe, R.L.A, Mahajan, A., Mari, A., Mazzoni, G.M., McCarthy, M.I., McDonald, T.J., McEvoy, D., McRobert, N., McVittie, I., Mourby, M., Musholt, P., Mutie, P, Nice, R., Nicolay, C., Nielsen, A.M.N., Nilsson, B.N., Palmer, C.N., Pattou, F., Pavo, I., Pearson, E.R., Pedersen, O., Pedersen, H.K.P., Perry, M.H., Pomares-Millan, H., Ramisch, A., Rasmussen, S.R., Raverdi, V., Ridderstrale, M., Robertson, N., Roderick, R.C., Rodriquez, M., Ruetten, H., Rutters, F., Sackett, W., Scherer, N., Schwenk, J.M., Shah, N., Sharma, S., Sihinevich, I., Sondertoft, N.B., Staerfeldt, H., Steckel-Hamann, B., Teare, H., Thomas, M.K., Thomas, E.L., Thomsen, H.S., Thorand, B., Thorne, C.E., Tillner, J., Troen Lundgaard, A.T.L., Troll, M., Tsirigos, K.D.T., Tura, A., Uhlen, M., van Leeuwen, N., van Oort, S., Verkindt, H., Vestergaard, H., Viñuela, A., Vogt, J.K, Wad Sackett, P.W.S, Wake, D., Walker, M., Wesolowska-Andersen, A., Whitcher, B., White, M.W., Wu, H., Dawed, Adem Y, Mari, Andrea, Brown, Andrew, McDonald, Timothy J, Li, Lin, Wang, Shuaicheng, Hong, Mun-Gwan, Sharma, Sapna, Robertson, Neil R, Mahajan, Anubha, Wang, Xuan, Walker, Mark, Gough, Stephen, Hart, Leen M ‘t, Zhou, Kaixin, Forgie, Ian, Ruetten, Hartmut, Pavo, Imre, Bhatnagar, Pallav, Jones, Angus G, and Pearson, Ewan R

Published
2023
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21. Clinical Prediction Models Combining Routine Clinical Measures Have High Accuracy in Identifying Youth-Onset Type 2 Diabetes Defined by Maintained Endogenous Insulin Secretion: The SEARCH for Diabetes in Youth Study.

Author

Jones, Angus G., Shields, Beverley M., Oram, Richard A., Dabelea, Dana M., Hagopian, William A., Sharp, Seth A., Lustigova, Eva, Shah, Amy S., Knupp, Julieanne, Mottl, Amy K., D'Agostino Jr., Ralph B., Williams, Adrienne, Marcovina, Santica M., Pihoker, Catherine, Divers, Jasmin, and Redondo, Maria J.

Subjects
*GENETIC risk score, *TYPE 2 diabetes, *TYPE 1 diabetes, *RECEIVER operating characteristic curves, *HDL cholesterol
Abstract

OBJECTIVE: With high prevalence of obesity and overlapping features between diabetes subtypes, accurately classifying youth-onset diabetes can be challenging. We aimed to develop prediction models that, using characteristics available at diabetes diagnosis, can identify youth who will retain endogenous insulin secretion at levels consistent with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We studied 2,966 youth with diabetes in the prospective SEARCH for Diabetes in Youth study (diagnosis age ≤19 years) to develop prediction models to identify participants with fasting C-peptide ≥250 pmol/L (≥0.75 ng/mL) after >3 years' (median 74 months) diabetes duration. Models included clinical measures at the baseline visit, at a mean diabetes duration of 11 months (age, BMI, sex, waist circumference, HDL cholesterol), with and without islet autoantibodies (GADA, IA-2A) and a type 1 diabetes genetic risk score (T1DGRS). RESULTS: Models using routine clinical measures with or without autoantibodies and T1DGRS were highly accurate in identifying participants with C-peptide ≥0.75 ng/mL (17% of participants; 2.3% and 53% of those with and without positive autoantibodies) (area under the receiver operating characteristic curve [AUCROC] 0.95–0.98). In internal validation, optimism was very low, with excellent calibration (slope 0.995–0.999). Models retained high performance for predicting retained C-peptide in older youth with obesity (AUCROC 0.88–0.96) and in subgroups defined by self-reported race and ethnicity (AUCROC 0.88–0.97), autoantibody status (AUCROC 0.87–0.96), and clinically diagnosed diabetes types (AUCROC 0.81–0.92). CONCLUSIONS: Prediction models combining routine clinical measures at diabetes diagnosis, with or without islet autoantibodies or T1DGRS, can accurately identify youth with diabetes who maintain endogenous insulin secretion in the range associated with T2D. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Autoantibodies to Truncated GAD(96-585) Antigen Stratify Risk of Early Insulin Requirement in Adult-Onset Diabetes.

Author

Grace, Sian L., Gillespie, Kathleen M., Williams, Claire L., Lampasona, Vito, Achenbach, Peter, Pearson, Ewan R., Williams, Alistair J.K., Long, Anna E., McDonald, Timothy J., and Jones, Angus G.

Subjects
GENETIC risk score, TYPE 2 diabetes, TYPE 1 diabetes, INSULIN therapy, INSULIN
Abstract

We investigated whether characterization of full-length GAD (f-GADA) antibody (GADA) responses could identify early insulin requirement in adult-onset diabetes. In 179 f-GADA–positive participants diagnosed with type 2 diabetes, we assessed associations of truncated GADA (t-GADA) positivity, f-GADA IgG subclasses, and f-GADA affinity with early insulin requirement (<5 years), type 1 diabetes genetic risk score (T1D GRS), and C-peptide. t-GADA positivity was lower in f-GADA–positive without early insulin in comparison with f-GADA–positive type 2 diabetes requiring insulin within 5 years, and T1D (75% vs. 91% and 95% respectively, P < 0.0001). t-GADA positivity (in those f-GADA positive) identified a group with a higher T1D genetic susceptibility (mean T1D GRS 0.248 vs. 0.225, P = 0.003), lower C-peptide (1,156 pmol/L vs. 4,289 pmol/L, P = 1 × 10−7), and increased IA-2 antigen positivity (23% vs. 6%, P = 0.03). In survival analysis, t-GADA positivity was associated with early insulin requirement compared with those only positive for f-GADA, independently from age of diagnosis, f-GADA titer, and duration of diabetes (adjusted hazard ratio 5.7 [95% CI 1.4, 23.5], P = 0.017). The testing of t-GADA in f-GADA–positive individuals with type 2 diabetes identifies those who have genetic and clinical characteristics comparable to T1D and stratifies those at higher risk of early insulin requirement. Article Highlights: Progression to insulin therapy is highly variable in adult-onset GAD antibody (GADA)–positive diabetes. We further characterized GADA in adult-onset diabetes and assessed whether these are associated with early insulin requirement. Truncated GADA positivity was associated with a type 1 diabetes–like phenotype and stratified risk of early insulin requirement. Those GADA positive who were negative for truncated GADA had the characteristics and progression of classic type 2 diabetes. Assessing full-length GADA IgG subclass and affinity did not further stratify risk of progression. Truncated GADA assessment remains underused in clinical practice but could assist correct therapy allocation in adult-onset diabetes. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Ethnic differences in the manifestation of early-onset type 2 diabetes

Author

Kibirige, Davis, primary, Hill, Anita V, additional, Katte, Jean Claude, additional, Sekitoleko, Isaac, additional, Lumu, William, additional, Knupp, Julieanne, additional, Squires, Steven, additional, Hattersley, Andrew, additional, Smeeth, Liam, additional, Jones, Angus G, additional, and Nyirenda, Moffat J, additional

Published
2024
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26. Long-term outcomes after severe childhood malnutrition in adolescents in Malawi (LOSCM): a prospective observational cohort study

Author

Kirolos, Amir, primary, Harawa, Philliness P, additional, Chimowa, Takondwa, additional, Divala, Oscar, additional, Freyne, Bridget, additional, Jones, Angus G, additional, Lelijveld, Natasha, additional, Lissauer, Samantha, additional, Maleta, Kenneth, additional, Gladstone, Melissa J, additional, Kerac, Marko, additional, Abera, Mubarek, additional, Ameya, Gemechu, additional, Anujo, Kenneth, additional, Arefayine, Melkamu, additional, Girma, Tsinuel, additional, Gonzales, Gerard Bryan, additional, Olga, Laurentya, additional, McKenzie, Kimberley, additional, Thompson, Debbie, additional, and Wells, Jonathan C, additional

Published
2024
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27. Clinical prediction models combining routine clinical measures have high accuracy in identifying youth-onset type 2 diabetes defined by maintained endogenous insulin secretion: The SEARCH for Diabetes in Youth Study

Author

Jones, Angus G., primary, Shields, Beverley M, primary, A Oram, Richard, primary, M Dabelea, Dana, primary, A Hagopian, William, primary, Lustigova, Eva, primary, S Shah, Amy, primary, Knupp, Julieanne, primary, K Mottl, Amy, primary, B. D`Agostino Jr, Ralph, primary, Williams, Adrienne, primary, Pihoker, Catherine, primary, Divers, Jasmin, primary, and Redondo, Maria J, primary

Published
2024
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29. Whole blood co-expression modules associate with metabolic traits and type 2 diabetes: an IMI-DIRECT study

Author

Gudmundsdottir, Valborg, Pedersen, Helle Krogh, Mazzoni, Gianluca, Allin, Kristine H., Artati, Anna, Beulens, Joline W., Banasik, Karina, Brorsson, Caroline, Cederberg, Henna, Chabanova, Elizaveta, De Masi, Federico, Elders, Petra J., Forgie, Ian, Giordano, Giuseppe N., Grallert, Harald, Gupta, Ramneek, Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison, Hong, Mun-Gwan, Jones, Angus G., Koivula, Robert, Kokkola, Tarja, Laakso, Markku, Løngreen, Peter, Mahajan, Anubha, Mari, Andrea, McDonald, Timothy J., McEvoy, Donna, Musholt, Petra B., Pavo, Imre, Prehn, Cornelia, Ruetten, Hartmut, Ridderstråle, Martin, Rutters, Femke, Sharma, Sapna, Slieker, Roderick C., Syed, Ali, Tajes, Juan Fernandez, Thomas, Cecilia Engel, Thomsen, Henrik S., Vangipurapu, Jagadish, Vestergaard, Henrik, Viñuela, Ana, Wesolowska-Andersen, Agata, Walker, Mark, Adamski, Jerzy, Schwenk, Jochen M., McCarthy, Mark I., Pearson, Ewan, Dermitzakis, Emmanouil, Franks, Paul W., Pedersen, Oluf, and Brunak, Søren

Published
2020
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31. Clinical prediction models combining routine clinical measures identify participants with youth-onset diabetes who maintain insulin secretion in the range associated with type 2 diabetes: The SEARCH for Diabetes in Youth Study

Author

Jones, Angus G, primary, Shields, Beverley, additional, Oram, Richard A, additional, Dabelea, Dana M, additional, Hagopian, William A, additional, Lustigova, Eva, additional, Shah, Amy S, additional, Knupp, Julieanne, additional, Mottl, Amy K, additional, D`Agostino, Ralph B, additional, Williams, Adrienne, additional, Marcovina, Santica M, additional, Pihoker, Catherine, additional, Divers, Jasmin, additional, and Redondo, Maria J, additional

Published
2023
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32. Phenotype-based targeted treatment of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes

Author

Cardoso, Pedro, primary, Young, Katie G., additional, Nair, Anand T.N., additional, Hopkins, Rhian, additional, McGovern, Andrew P, additional, Haider, Eram, additional, Karunaratne, Piyumanga, additional, Donnelly, Louise, additional, Mateen, Bilal A., additional, Sattar, Naveed, additional, Holman, Rury R., additional, Bowden, Jack, additional, Hattersley, Andrew T., additional, Pearson, Ewan R., additional, Jones, Angus G., additional, Shields, Beverley M., additional, McKinley, Trevelyan J., additional, and Dennis, John M., additional

Published
2023
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33. Corrigendum to ‘Identifying type 1 and 2 diabetes in research datasets where classification biomarkers are unavailable: assessing the accuracy of published approaches’ [Journal of Clinical Epidemiology (2023) 34-44]

Author

Thomas, Nicholas J., primary, McGovern, Andrew, additional, Young, Katherine G., additional, Sharp, Seth A., additional, Weedon, Michael N., additional, Hattersley, Andrew T., additional, Dennis, John, additional, and Jones, Angus G., additional

Published
2023
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35. Treatment effect heterogeneity following type 2 diabetes treatment with GLP1-receptor agonists and SGLT2-inhibitors:a systematic review

Author

Young, Katherine G., McInnes, Eram Haider, Massey, Robert J., Kahkoska, Anna R., Pilla, Scott J., Raghavan, Sridharan, Stanislawski, Maggie A., Tobias, Deirdre K., McGovern, Andrew P., Dawed, Adem Y., Jones, Angus G., Pearson, Ewan R., Dennis, John M., Vilsbøll, Tina, Loos, Ruth J.F., Njølstad, Pål Rasmus, Nakabuye, Mariam, Hansen, Torben, Guasch-Ferré, Marta, Clemmensen, Christoffer, Andersen, Mette K., Thuesen, Anne Cathrine B., Merino, Jordi, Young, Katherine G., McInnes, Eram Haider, Massey, Robert J., Kahkoska, Anna R., Pilla, Scott J., Raghavan, Sridharan, Stanislawski, Maggie A., Tobias, Deirdre K., McGovern, Andrew P., Dawed, Adem Y., Jones, Angus G., Pearson, Ewan R., Dennis, John M., Vilsbøll, Tina, Loos, Ruth J.F., Njølstad, Pål Rasmus, Nakabuye, Mariam, Hansen, Torben, Guasch-Ferré, Marta, Clemmensen, Christoffer, Andersen, Mette K., Thuesen, Anne Cathrine B., and Merino, Jordi

Abstract

Background: A precision medicine approach in type 2 diabetes requires the identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy. Methods: We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. Results: Here we show that the majority of included papers have methodological limitations precluding robust assessment of treatment effect heterogeneity. For SGLT2-inhibitors, multiple observational studies suggest lower renal function as a predictor of lesser glycaemic response, while markers of reduced insulin secretion predict lesser glycaemic response with GLP1-receptor agonists. For both therapies, multiple post-hoc analyses of randomized control trials (including trial meta-analysis) identify minimal clinically relevant treatment effect heterogeneity for cardiovascular and renal outcomes. Conclusions: Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care.

Published
2023

36. Pharmacogenomics of GLP-1 receptor agonists : a genome-wide analysis of observational data and large randomised controlled trials

Author

Dawed, Adem Y., Mari, Andrea, Brown, Andrew, McDonald, Timothy J., Li, Lin, Wang, Shuaicheng, Hong, Mun-Gwan, Sharma, Sapna, Robertson, Neil R., Mahajan, Anubha, Wang, Xuan, Walker, Mark, Gough, Stephen, Hart, Leen M. 't, Zhou, Kaixin, Forgie, Ian, Ruetten, Hartmut, Pavo, Imre, Bhatnagar, Pallav, Jones, Angus G., Pearson, Ewan R., DIRECT consortium, for the D. I. R. E. C. T. consortium, Dawed, Adem Y., Mari, Andrea, Brown, Andrew, McDonald, Timothy J., Li, Lin, Wang, Shuaicheng, Hong, Mun-Gwan, Sharma, Sapna, Robertson, Neil R., Mahajan, Anubha, Wang, Xuan, Walker, Mark, Gough, Stephen, Hart, Leen M. 't, Zhou, Kaixin, Forgie, Ian, Ruetten, Hartmut, Pavo, Imre, Bhatnagar, Pallav, Jones, Angus G., Pearson, Ewan R., and DIRECT consortium, for the D. I. R. E. C. T. consortium

Abstract

Background: In the treatment of type 2 diabetes, GLP-1 receptor agonists lower blood glucose concentrations, body weight, and have cardiovascular benefits. The efficacy and side effects of GLP-1 receptor agonists vary between people. Human pharmacogenomic studies of this inter-individual variation can provide both biological insight into drug action and provide biomarkers to inform clinical decision making. We therefore aimed to identify genetic variants associated with glycaemic response to GLP-1 receptor agonist treatment. Methods: In this genome-wide analysis we included adults (aged >= 18 years) with type 2 diabetes treated with GLP-1 receptor agonists with baseline HbA1c of 7% or more (53 mmol/mol) from four prospective observational cohorts (DIRECT, PRIBA, PROMASTER, and GoDARTS) and two randomised clinical trials (HARMONY phase 3 and AWARD). The primary endpoint was HbA1c reduction at 6 months after starting GLP-1 receptor agonists. We evaluated variants in GLP1R, then did a genome-wide association study and gene-based burden tests. Findings: 4571 adults were included in our analysis, of these, 3339 (73%) were White European, 449 (10%) Hispanic, 312 (7%) American Indian or Alaskan Native, and 471 (10%) were other, and around 2140 (47%) of the participants were women. Variation in HbA1c reduction with GLP-1 receptor agonists treatment was associated with rs6923761G -> A (Gly168Ser) in the GLP1R (0.08% [95% CI 0.04-0.12] or 0.9 mmol/mol lower reduction in HbA1c per serine, p=6.0 x 10(-5)) and low frequency variants in ARRB1 (optimal sequence kernel association test p=6.7 x 10(-8)), largely driven by rs140226575G -> A (Thr370Met; 0.25% [SE 0.06] or 2.7 mmol/mol [SE 0.7] greater HbA1c reduction per methionine, p=5.2 x 10(-6)). A similar effect size for the ARRB1 Thr370Met was seen in Hispanic and American Indian or Alaska Native populations who have a higher frequency of this variant (6-11%) than in White European populations. Combining these two genes i

Published
2023
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37. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Author

Tobias, Deirdre K., Merino, Jordi, Ahmad, Abrar, Aiken, Catherine, Benham, Jamie L., Bodhini, Dhanasekaran, Clark, Amy L., Colclough, Kevin, Corcoy, Rosa, Cromer, Sara J., Duan, Daisy, Felton, Jamie L., Francis, Ellen C., Gillard, Pieter, Gingras, Véronique, Gaillard, Romy, Haider, Eram, Hughes, Alice, Ikle, Jennifer M., Jacobsen, Laura M., Kahkoska, Anna R., Kettunen, Jarno L.T., Kreienkamp, Raymond J., Lim, Lee Ling, Männistö, Jonna M.E., Massey, Robert, Mclennan, Niamh Maire, Miller, Rachel G., Morieri, Mario Luca, Most, Jasper, Naylor, Rochelle N., Ozkan, Bige, Patel, Kashyap Amratlal, Pilla, Scott J., Prystupa, Katsiaryna, Raghavan, Sridharan, Rooney, Mary R., Schön, Martin, Semnani-Azad, Zhila, Sevilla-Gonzalez, Magdalena, Svalastoga, Pernille, Takele, Wubet Worku, Tam, Claudia Ha ting, Thuesen, Anne Cathrine B., Tosur, Mustafa, Wallace, Amelia S., Wang, Caroline C., Wong, Jessie J., Yamamoto, Jennifer M., Young, Katherine, Amouyal, Chloé, Andersen, Mette K., Bonham, Maxine P., Chen, Mingling, Cheng, Feifei, Chikowore, Tinashe, Chivers, Sian C., Clemmensen, Christoffer, Dabelea, Dana, Dawed, Adem Y., Deutsch, Aaron J., Dickens, Laura T., DiMeglio, Linda A., Dudenhöffer-Pfeifer, Monika, Evans-Molina, Carmella, Fernández-Balsells, María Mercè, Fitipaldi, Hugo, Fitzpatrick, Stephanie L., Gitelman, Stephen E., Goodarzi, Mark O., Grieger, Jessica A., Guasch-Ferré, Marta, Habibi, Nahal, Hansen, Torben, Huang, Chuiguo, Harris-Kawano, Arianna, Ismail, Heba M., Hoag, Benjamin, Johnson, Randi K., Jones, Angus G., Koivula, Robert W., Leong, Aaron, Leung, Gloria K.W., Libman, Ingrid M., Liu, Kai, Long, S. Alice, Lowe, William L., Morton, Robert W., Motala, Ayesha A., Onengut-Gumuscu, Suna, Pankow, James S., Pathirana, Maleesa, Pazmino, Sofia, Perez, Dianna, Petrie, John R., Powe, Camille E., Quinteros, Alejandra, Jain, Rashmi, Ray, Debashree, Ried-Larsen, Mathias, Saeed, Zeb, Santhakumar, Vanessa, Kanbour, Sarah, Sarkar, Sudipa, Monaco, Gabriela S.F., Scholtens, Denise M., Selvin, Elizabeth, Sheu, Wayne Huey Herng, Speake, Cate, Stanislawski, Maggie A., Steenackers, Nele, Steck, Andrea K., Stefan, Norbert, Støy, Julie, Taylor, Rachael, Tye, Sok Cin, Ukke, Gebresilasea Gendisha, Urazbayeva, Marzhan, Van der Schueren, Bart, Vatier, Camille, Wentworth, John M., Hannah, Wesley, White, Sara L., Yu, Gechang, Zhang, Yingchai, Zhou, Shao J., Beltrand, Jacques, Polak, Michel, Aukrust, Ingvild, de Franco, Elisa, Flanagan, Sarah E., Maloney, Kristin A., McGovern, Andrew, Molnes, Janne, Nakabuye, Mariam, Njølstad, Pål Rasmus, Pomares-Millan, Hugo, Provenzano, Michele, Saint-Martin, Cécile, Zhang, Cuilin, Zhu, Yeyi, Auh, Sungyoung, de Souza, Russell, Fawcett, Andrea J., Gruber, Chandra, Mekonnen, Eskedar Getie, Mixter, Emily, Sherifali, Diana, Eckel, Robert H., Nolan, John J., Philipson, Louis H., Brown, Rebecca J., Billings, Liana K., Boyle, Kristen, Costacou, Tina, Dennis, John M., Florez, Jose C., Gloyn, Anna L., Gomez, Maria F., Gottlieb, Peter A., Greeley, Siri Atma W., Griffin, Kurt, Hattersley, Andrew T., Hirsch, Irl B., Hivert, Marie France, Hood, Korey K., Josefson, Jami L., Kwak, Soo Heon, Laffel, Lori M., Lim, Siew S., Loos, Ruth J.F., Ma, Ronald C.W., Mathieu, Chantal, Mathioudakis, Nestoras, Meigs, James B., Misra, Shivani, Mohan, Viswanathan, Murphy, Rinki, Oram, Richard, Owen, Katharine R., Ozanne, Susan E., Pearson, Ewan R., Perng, Wei, Pollin, Toni I., Pop-Busui, Rodica, Pratley, Richard E., Redman, Leanne M., Redondo, Maria J., Reynolds, Rebecca M., Semple, Robert K., Sherr, Jennifer L., Sims, Emily K., Sweeting, Arianne, Tuomi, Tiinamaija, Udler, Miriam S., Vesco, Kimberly K., Vilsbøll, Tina, Wagner, Robert, Rich, Stephen S., Franks, Paul W., Tobias, Deirdre K., Merino, Jordi, Ahmad, Abrar, Aiken, Catherine, Benham, Jamie L., Bodhini, Dhanasekaran, Clark, Amy L., Colclough, Kevin, Corcoy, Rosa, Cromer, Sara J., Duan, Daisy, Felton, Jamie L., Francis, Ellen C., Gillard, Pieter, Gingras, Véronique, Gaillard, Romy, Haider, Eram, Hughes, Alice, Ikle, Jennifer M., Jacobsen, Laura M., Kahkoska, Anna R., Kettunen, Jarno L.T., Kreienkamp, Raymond J., Lim, Lee Ling, Männistö, Jonna M.E., Massey, Robert, Mclennan, Niamh Maire, Miller, Rachel G., Morieri, Mario Luca, Most, Jasper, Naylor, Rochelle N., Ozkan, Bige, Patel, Kashyap Amratlal, Pilla, Scott J., Prystupa, Katsiaryna, Raghavan, Sridharan, Rooney, Mary R., Schön, Martin, Semnani-Azad, Zhila, Sevilla-Gonzalez, Magdalena, Svalastoga, Pernille, Takele, Wubet Worku, Tam, Claudia Ha ting, Thuesen, Anne Cathrine B., Tosur, Mustafa, Wallace, Amelia S., Wang, Caroline C., Wong, Jessie J., Yamamoto, Jennifer M., Young, Katherine, Amouyal, Chloé, Andersen, Mette K., Bonham, Maxine P., Chen, Mingling, Cheng, Feifei, Chikowore, Tinashe, Chivers, Sian C., Clemmensen, Christoffer, Dabelea, Dana, Dawed, Adem Y., Deutsch, Aaron J., Dickens, Laura T., DiMeglio, Linda A., Dudenhöffer-Pfeifer, Monika, Evans-Molina, Carmella, Fernández-Balsells, María Mercè, Fitipaldi, Hugo, Fitzpatrick, Stephanie L., Gitelman, Stephen E., Goodarzi, Mark O., Grieger, Jessica A., Guasch-Ferré, Marta, Habibi, Nahal, Hansen, Torben, Huang, Chuiguo, Harris-Kawano, Arianna, Ismail, Heba M., Hoag, Benjamin, Johnson, Randi K., Jones, Angus G., Koivula, Robert W., Leong, Aaron, Leung, Gloria K.W., Libman, Ingrid M., Liu, Kai, Long, S. Alice, Lowe, William L., Morton, Robert W., Motala, Ayesha A., Onengut-Gumuscu, Suna, Pankow, James S., Pathirana, Maleesa, Pazmino, Sofia, Perez, Dianna, Petrie, John R., Powe, Camille E., Quinteros, Alejandra, Jain, Rashmi, Ray, Debashree, Ried-Larsen, Mathias, Saeed, Zeb, Santhakumar, Vanessa, Kanbour, Sarah, Sarkar, Sudipa, Monaco, Gabriela S.F., Scholtens, Denise M., Selvin, Elizabeth, Sheu, Wayne Huey Herng, Speake, Cate, Stanislawski, Maggie A., Steenackers, Nele, Steck, Andrea K., Stefan, Norbert, Støy, Julie, Taylor, Rachael, Tye, Sok Cin, Ukke, Gebresilasea Gendisha, Urazbayeva, Marzhan, Van der Schueren, Bart, Vatier, Camille, Wentworth, John M., Hannah, Wesley, White, Sara L., Yu, Gechang, Zhang, Yingchai, Zhou, Shao J., Beltrand, Jacques, Polak, Michel, Aukrust, Ingvild, de Franco, Elisa, Flanagan, Sarah E., Maloney, Kristin A., McGovern, Andrew, Molnes, Janne, Nakabuye, Mariam, Njølstad, Pål Rasmus, Pomares-Millan, Hugo, Provenzano, Michele, Saint-Martin, Cécile, Zhang, Cuilin, Zhu, Yeyi, Auh, Sungyoung, de Souza, Russell, Fawcett, Andrea J., Gruber, Chandra, Mekonnen, Eskedar Getie, Mixter, Emily, Sherifali, Diana, Eckel, Robert H., Nolan, John J., Philipson, Louis H., Brown, Rebecca J., Billings, Liana K., Boyle, Kristen, Costacou, Tina, Dennis, John M., Florez, Jose C., Gloyn, Anna L., Gomez, Maria F., Gottlieb, Peter A., Greeley, Siri Atma W., Griffin, Kurt, Hattersley, Andrew T., Hirsch, Irl B., Hivert, Marie France, Hood, Korey K., Josefson, Jami L., Kwak, Soo Heon, Laffel, Lori M., Lim, Siew S., Loos, Ruth J.F., Ma, Ronald C.W., Mathieu, Chantal, Mathioudakis, Nestoras, Meigs, James B., Misra, Shivani, Mohan, Viswanathan, Murphy, Rinki, Oram, Richard, Owen, Katharine R., Ozanne, Susan E., Pearson, Ewan R., Perng, Wei, Pollin, Toni I., Pop-Busui, Rodica, Pratley, Richard E., Redman, Leanne M., Redondo, Maria J., Reynolds, Rebecca M., Semple, Robert K., Sherr, Jennifer L., Sims, Emily K., Sweeting, Arianne, Tuomi, Tiinamaija, Udler, Miriam S., Vesco, Kimberly K., Vilsbøll, Tina, Wagner, Robert, Rich, Stephen S., and Franks, Paul W.

Abstract

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.

Published
2023

38. Type 2 Diabetes risk alleles in Peptidyl-glycine Alpha-amidating Monooxygenase influence GLP-1 levels and response to GLP-1 Receptor Agonists

Author

Umapathysivam, Mahesh M, Araldi, Elisa, Hastoy, Benoit, Dawed, Adem Y, Vatandaslar, Hasan, Sengupta, Shahana, Kaufmann, Adrian, Thomsen, Søren, Hartmann, Bolette, Jonsson, Anna E, Kabakci, Hasan, Thaman, Swaraj, Grarup, Niels, Have, Christian T, Færch, Kristine, Gjesing, Anette P, Nawaz, Sameena, Cheeseman, Jane, Neville, Matthew J, Pedersen, Oluf, Walker, Mark, Jennison, Christopher, Hattersley, Andrew T, Hansen, Torben, Karpe, Fredrik, Holst, Jens J, Jones, Angus G, Ristow, Michael, McCarthy, Mark I, Pearson, Ewan R, Stoffel, Markus, Gloyn, Anna L, Umapathysivam, Mahesh M, Araldi, Elisa, Hastoy, Benoit, Dawed, Adem Y, Vatandaslar, Hasan, Sengupta, Shahana, Kaufmann, Adrian, Thomsen, Søren, Hartmann, Bolette, Jonsson, Anna E, Kabakci, Hasan, Thaman, Swaraj, Grarup, Niels, Have, Christian T, Færch, Kristine, Gjesing, Anette P, Nawaz, Sameena, Cheeseman, Jane, Neville, Matthew J, Pedersen, Oluf, Walker, Mark, Jennison, Christopher, Hattersley, Andrew T, Hansen, Torben, Karpe, Fredrik, Holst, Jens J, Jones, Angus G, Ristow, Michael, McCarthy, Mark I, Pearson, Ewan R, Stoffel, Markus, and Gloyn, Anna L

Abstract

UNLABELLED: Patients with type 2 diabetes vary in their response to currently available therapeutic agents (including GLP-1 receptor agonists) leading to suboptimal glycemic control and increased risk of complications. We show that human carriers of hypomorphic T2D-risk alleles in the gene encoding peptidyl-glycine alpha-amidating monooxygenase (PAM), as well as Pam- knockout mice, display increased resistance to GLP-1 in vivo . Pam inactivation in mice leads to reduced gastric GLP-1R expression and faster gastric emptying: this persists during GLP-1R agonist treatment and is rescued when GLP-1R activity is antagonized, indicating resistance to GLP-1's gastric slowing properties. Meta-analysis of human data from studies examining GLP-1R agonist response (including RCTs) reveals a relative loss of 44% and 20% of glucose lowering (measured by glycated hemoglobin) in individuals with hypomorphic PAM alleles p.S539W and p.D536G treated with GLP-1R agonist. Genetic variation in PAM has effects on incretin signaling that alters response to medication used commonly for treatment of T2D. (Funded by the Wellcome, Medical Research Council, European Union, NIHR Oxford Biomedical Research Centre, United Kingdom, Registered on ClinicalTrials.gov, NCT02723110 .). SUMMARY PARAGRAPH: Type 2 diabetes (T2D) is a leading cause of morbidity and mortality globally 1 . Current management of T2D patients focuses on lowering glycemic exposure and reducing complications with lifestyle and pharmacological interventions 2 . Despite the availability of multiple medications to lower glycated hemoglobin (HbA1c), only 53% of individuals with T2D reach the glycemic target (HbA1c <7%) 3, 4 . There is potential to improve medication selection through "precision medicine" where patient specific factors (e.g. genetic markers) are used to indicate whether a patient is more or less likely to respond to a medication. Here we show that human carriers of hypomorphic T2D-risk alleles in the gen

Published
2023

40. Type 2 Diabetes risk alleles in Peptidyl-glycine Alpha-amidating Monooxygenase influence GLP-1 levels and response to GLP-1 Receptor Agonists

Author

Umapathysivam, Mahesh M, primary, Araldi, Elisa, additional, Hastoy, Benoit, additional, Dawed, Adem Y, additional, Vatandaslar, Hasan, additional, Sengupta, Shahana, additional, Kaufmann, Adrian, additional, Thomsen, Søren, additional, Hartmann, Bolette, additional, Jonsson, Anna E, additional, Kabakci, Hasan, additional, Thaman, Swaraj, additional, Grarup, Niels, additional, Have, Christian T, additional, Færch, Kristine, additional, Gjesing, Anette P, additional, Nawaz, Sameena, additional, Cheeseman, Jane, additional, Neville, Matthew J, additional, Pedersen, Oluf, additional, Walker, Mark, additional, Jennison, Christopher, additional, Hattersley, Andrew T, additional, Hansen, Torben, additional, Karpe, Fredrik, additional, Holst, Jens J, additional, Jones, Angus G, additional, Ristow, Michael, additional, McCarthy, Mark I, additional, Pearson, Ewan R, additional, Stoffel, Markus, additional, and Gloyn, Anna L, additional

Published
2023
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41. Precision medicine in type 2 diabetes: A systematic review of treatment effect heterogeneity for GLP1-receptor agonists and SGLT2-inhibitors

Author

Young, Katherine G, McInnes, Eram Haider, Massey, Robert J, Kahkohska, Anna R, Pilla, Scott J, Raghaven, Sridharan, Stanislawski, Maggie A, Tobias, Deirdre K, McGovern, Andrew P, Dawed, Adem Y, Jones, Angus G, Pearson, Ewan R, and Dennis, John M

Subjects
Article
Abstract

BACKGROUND: A precision medicine approach in type 2 diabetes requires identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy. METHODS: We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. RESULTS: After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. The majority of papers had methodological limitations precluding robust assessment of treatment effect heterogeneity. For glycaemic outcomes, most cohorts were observational, with multiple analyses identifying lower renal function as a predictor of lesser glycaemic response with SGLT2-inhibitors and markers of reduced insulin secretion as predictors of lesser response with GLP1-receptor agonists. For cardiovascular and renal outcomes, the majority of included studies were post-hoc analyses of randomized control trials (including meta-analysis studies) which identified limited clinically relevant treatment effect heterogeneity. CONCLUSIONS: Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care. PLAIN LANGUAGE SUMMARY: This review identifies research that helps understand which clinical and biological factors that are associated with different outcomes for specific type 2 diabetes treatments. This information could help clinical providers and patients make better informed personalized decisions about type 2 diabetes treatments. We focused on two common type 2 diabetes treatments: SGLT2-inhibitors and GLP1-receptor agonists, and three outcomes: blood glucose control, heart disease, and kidney disease. We identified some potential factors that are likely to lessen blood glucose control including lower kidney function for SGLT2-inhibitors and lower insulin secretion for GLP1-receptor agonists. We did not identify clear factors that alter heart and renal disease outcomes for either treatment. Most of the studies had limitations, meaning more research is needed to fully understand the factors that influence treatment outcomes in type 2 diabetes.

Published
2023

44. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

Author

Atabaki-Pasdar, Naeimeh, Ohlsson, Mattias, Viñuela, Ana, Frau, Francesca, Pomares-Millan, Hugo, Haid, Mark, Jones, Angus G., Thomas, E. Louise, Koivula, Robert W., Kurbasic, Azra, Mutie, Pascal M., Fitipaldi, Hugo, Fernandez, Juan, Dawed, Adem Y., Giordano, Giuseppe N., Forgie, Ian M., McDonald, Timothy J., Rutters, Femke, Cederberg, Henna, Chabanova, Elizaveta, Dale, Matilda, Masi, Federico De, Thomas, Cecilia Engel, Allin, Kristine H., Hansen, Tue H., Heggie, Alison, Hong, Mun-Gwan, Elders, Petra J. M., Kennedy, Gwen, Kokkola, Tarja, Pedersen, Helle Krogh, Mahajan, Anubha, McEvoy, Donna, Pattou, Francois, Raverdy, Violeta, Häussler, Ragna S., Sharma, Sapna, Thomsen, Henrik S., Vangipurapu, Jagadish, Vestergaard, Henrik, 't Hart, Leen M., Adamski, Jerzy, Musholt, Petra B., Brage, Soren, Brunak, Søren, Dermitzakis, Emmanouil, Frost, Gary, Hansen, Torben, Laakso, Markku, Pedersen, Oluf, Ridderstråle, Martin, Ruetten, Hartmut, Hattersley, Andrew T., Walker, Mark, Beulens, Joline W. J., Mari, Andrea, Schwenk, Jochen M., Gupta, Ramneek, McCarthy, Mark I., Pearson, Ewan R., Bell, Jimmy D., Pavo, Imre, Franks, Paul W., Howard, Helen, and Moyer, Caitlin

Subjects
Risk factors, Health aspects, Diagnostic imaging -- Health aspects, Machine learning -- Health aspects, Type 2 diabetes -- Risk factors, Enzymes -- Health aspects, Fatty liver -- Risk factors
Abstract

Author(s): Naeimeh Atabaki-Pasdar 1, Mattias Ohlsson 2,3, Ana Viñuela 4,5,6, Francesca Frau 7, Hugo Pomares-Millan 1, Mark Haid 8, Angus G. Jones 9, E. Louise Thomas 10, Robert W. Koivula [...], Background Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and findings We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content ( Conclusions In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community. Trial registration ClinicalTrials.gov NCT03814915.

Published
2020
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45. Additional file 1 of Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine

Author

Venkatasubramaniam, Ashwini, Mateen, Bilal A., Shields, Beverley M., Hattersley, Andrew T., Jones, Angus G., Vollmer, Sebastian J., and Dennis, John M.

Abstract

Additional file 1: sFlowchart. A) CANTATA D and D2 trials. B) CPRD patient flow and inclusion criteria. sTable 1. Additional baseline clinical characteristics by initiated drug class in CPRD. sFigure 1. Distribution of the predicted individualized treatment effect of SGLT2-inhibitor treatment compared to DPP4-inhibitor treatment in the RCT derivation data. a) Penalized regression. sTable 2. Comparison of model performance for the penalised ridge regression model with lasso regression. sTable 3. Sensitivity analysis testing variation in predicted treatment effects varying the number of trees in the causal forest algorithm. sFigure 2. Treatment selection model performance for A) Penalized regression and B) Causal forest in CPRD validation data using double robust estimation incorporating inverse probability of treatment weighting and adjustment for all predictors and confounders in the outcome model. sFigure 3. Treatment selection model performance for A) Penalized regression and B) Causal forest in CPRD validation data using double robust estimation incorporating inverse probability of treatment weighting, inverse probability of censoring weighting, and adjustment for all predictors and confounders in the outcome model. sFigure 4. Treatment selection model performance for A) Penalized regression and B) Causal forest in CPRD validation data, in patients initiating second-line therapy only. TRIPOD Checklist.

Published
2023
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48. Long-Term Outcomes after Severe Childhood Malnutrition (LOSCM Study): Growth and Clinical Catch-Up in an Adolescent Cohort

Author

Kirolos, Amir, primary, Harawa, Philliness Prisca, additional, Chimowa, Takondwa, additional, Divala, Oscar, additional, Freyne, Bridget, additional, Jones, Angus G., additional, Lelijveld, Natasha, additional, Lissauer, Samantha, additional, Maleta, Kenneth, additional, Gladstone, Melissa J., additional, Kerac, Marko, additional, and Group, CHANGE Study Collaborators, additional

Published
2023
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49. Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials

Author

Dawed, Adem Y, primary, Mari, Andrea, additional, Brown, Andrew, additional, McDonald, Timothy J, additional, Li, Lin, additional, Wang, Shuaicheng, additional, Hong, Mun-Gwan, additional, Sharma, Sapna, additional, Robertson, Neil R, additional, Mahajan, Anubha, additional, Wang, Xuan, additional, Walker, Mark, additional, Gough, Stephen, additional, Hart, Leen M ‘t, additional, Zhou, Kaixin, additional, Forgie, Ian, additional, Ruetten, Hartmut, additional, Pavo, Imre, additional, Bhatnagar, Pallav, additional, Jones, Angus G, additional, Pearson, Ewan R, additional, 't Hart, L.M., additional, Abdalla, M., additional, Adam, J., additional, Adamski, J., additional, Adragni, K., additional, Allin, K.H., additional, Arumugam, M., additional, Atabaki Pasdar, N., additional, Baltauss, T., additional, Banasik, K.B., additional, Baum, P., additional, Bell, J.D., additional, Bergstrom, M., additional, Beulens, J.W., additional, Bianzano, S., additional, Bizzotto, R., additional, Bonneford, A., additional, Brorsson, C.A.B., additional, Brown, A.A., additional, Brunak, S.B., additional, Cabrelli, L., additional, Caiazzo, R., additional, Canouil, M., additional, Dale, M., additional, Davtian, D., additional, Dawed, A.Y., additional, De Masi, F.M., additional, de Preville, N., additional, Dekkers, K.F., additional, Dermitzakis, E.T., additional, Deshmukh, H.A., additional, Dings, C., additional, Donnelly, L., additional, Dutta, A., additional, Ehrhardt, B., additional, Elders, P.J.M., additional, Engel Thomas, C.E.T., additional, Engelbrechtsen, L., additional, Eriksen, R.G., additional, Eriksen, R.E., additional, Fan, Y., additional, Fernandez, J., additional, Ferrer, J., additional, Fitipaldi, H., additional, Forgie, I.M., additional, Forman, A., additional, Franks, P.W., additional, Frau, F., additional, Fritsche, A., additional, Froguel, P., additional, Frost, G., additional, Gassenhuber, J., additional, Giordano, G.N., additional, Giorgino, T., additional, Gough, S., additional, Graefe-Mody, U., additional, Grallert, H., additional, Grempler, R., additional, Groeneveld, L., additional, Groop, L., additional, Gudmundsdóttir, V.G., additional, Gupta, R.G., additional, Haid, M., additional, Hansen, T., additional, Hansen, T.H., additional, Hattersley, A.T., additional, Haussler, R.S., additional, Heggie, A.J., additional, Hennige, A.M., additional, Hill, A.V., additional, Holl, R.W., additional, Hong, M.-G., additional, Hudson, M., additional, Jablonka, B., additional, Jennison, C., additional, Jiao, J., additional, Johansen, J.J., additional, Jones, A.G., additional, Jonsson, A., additional, Karaderi, T.K., additional, Kaye, J., additional, Klintenberg, M., additional, Koivula, R.W., additional, Kokkola, T., additional, Koopman, A.D.M., additional, Kurbasic, A, additional, Kuulasmaa, T., additional, Laakso, M., additional, Lehr, T., additional, Loftus, H., additional, Lundbye Allesøe, R.L.A, additional, Mahajan, A., additional, Mari, A., additional, Mazzoni, G.M., additional, McCarthy, M.I., additional, McDonald, T.J., additional, McEvoy, D., additional, McRobert, N., additional, McVittie, I., additional, Mourby, M., additional, Musholt, P., additional, Mutie, P, additional, Nice, R., additional, Nicolay, C., additional, Nielsen, A.M.N., additional, Nilsson, B.N., additional, Palmer, C.N., additional, Pattou, F., additional, Pavo, I., additional, Pearson, E.R., additional, Pedersen, O., additional, Pedersen, H.K.P., additional, Perry, M.H., additional, Pomares-Millan, H., additional, Ramisch, A., additional, Rasmussen, S.R., additional, Raverdi, V., additional, Ridderstrale, M., additional, Robertson, N., additional, Roderick, R.C., additional, Rodriquez, M., additional, Ruetten, H., additional, Rutters, F., additional, Sackett, W., additional, Scherer, N., additional, Schwenk, J.M., additional, Shah, N., additional, Sharma, S., additional, Sihinevich, I., additional, Sondertoft, N.B., additional, Staerfeldt, H., additional, Steckel-Hamann, B., additional, Teare, H., additional, Thomas, M.K., additional, Thomas, E.L., additional, Thomsen, H.S., additional, Thorand, B., additional, Thorne, C.E., additional, Tillner, J., additional, Troen Lundgaard, A.T.L., additional, Troll, M., additional, Tsirigos, K.D.T., additional, Tura, A., additional, Uhlen, M., additional, van Leeuwen, N., additional, van Oort, S., additional, Verkindt, H., additional, Vestergaard, H., additional, Viñuela, A., additional, Vogt, J.K, additional, Wad Sackett, P.W.S, additional, Wake, D., additional, Walker, M., additional, Wesolowska-Andersen, A., additional, Whitcher, B., additional, White, M.W., additional, and Wu, H., additional

Published
2023
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