Epigenetic markers associated with metformin response and intolerance in drug-naïve patients with type 2 diabetes
Science Translational Medicine • 16 Sep 2020 • Vol 12, Issue 561 • DOI: 10.1126/scitranslmed.aaz1803
How to mete out metformin
Metformin is the most commonly used drug to treat type 2 diabetes (T2D), though not all patients respond to it, and still, others do not tolerate it. García-Calzón et al. analyzed genome-wide DNA methylation in the blood of drug-naïve patients who were recently diagnosed with T2D. They found that DNA methylation at specific loci associated with future metformin response or tolerance, respectively, across multiple cohorts. These epigenetic markers may have theranostic potential regarding which patients should receive metformin.
Abstract
Metformin is the first-line pharmacotherapy for managing type 2 diabetes (T2D). However, many patients with T2D do not respond to or tolerate metformin well. Currently, there are no phenotypes that successfully predict glycemic response to, or tolerance of, metformin. We explored whether blood-based epigenetic markers could discriminate metformin response and tolerance by analyzing genome-wide DNA methylation in drug-naïve patients with T2D at the time of their diagnosis. DNA methylation of 11 and 4 sites differed between glycemic responders/nonresponders and metformin-tolerant/intolerant patients, respectively, in discovery and replication cohorts. Greater methylation at these sites associated with a higher risk of not responding to or not tolerating metformin with odds ratios between 1.43 and 3.09 per 1-SD methylation increase. Methylation risk scores (MRSs) of the 11 identified sites differed between glycemic responders and nonresponders with areas under the curve (AUCs) of 0.80 to 0.98. MRSs of the 4 sites associated with future metformin intolerance generated AUCs of 0.85 to 0.93. Some of these blood-based methylation markers mirrored the epigenetic pattern in adipose tissue, a key tissue in diabetes pathogenesis, and genes to which these markers were annotated to had biological functions in hepatocytes that altered metformin-related phenotypes. Overall, we could discriminate between glycemic responders/nonresponders and participants tolerant/intolerant to metformin at diagnosis by measuring blood-based epigenetic markers in drug-naïve patients with T2D. This epigenetics-based tool may be further developed to help patients with T2D receive optimal therapy.
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Supplementary Material
Summary
Materials and Methods
Fig. S1. Flowchart and participant selection criteria of the ANDIS discovery cohort for metformin response.
Fig. S2. Flowchart and participant selection criteria of the ANDIS replication cohort for metformin response.
Fig. S3. Flowchart and selection criteria of ANDiU and OPTIMED participants for investigation of glycemic response to metformin therapy: “The European replication cohort for metformin response.”
Fig. S4. Combined MRSs discriminate between glycemic responders and nonresponders to metformin in drug-naïve participants with T2D from the ANDIS discovery and the European replication cohorts.
Fig. S5. Combined MRSs discriminate between glycemic responders and nonresponders to metformin in drug-naïve participants with T2D from the ANDIS discovery and the ANDIS replication cohorts.
Fig. S6. ROC curves for response and intolerance to metformin incorporating different clinical baseline phenotypes in all subjects from discovery and replication cohorts for metformin response and intolerance.
Fig. S7. ROC curves for response to metformin incorporating additional clinical baseline phenotypes in ANDIS participants from the discovery and replication cohorts for metformin response combined.
Fig. S8. Combined MRSs discriminate between tolerant and intolerant participants to metformin in drug-naïve participants with T2D from the ANDIS discovery and the European replication cohorts.
Fig. S9. Combined MRSs discriminate between tolerant and intolerant participants to metformin in drug-naïve participants with T2D from the ANDIS discovery and the ANDIS replication cohorts.
Fig. S10. Correlations between DNA methylation in blood and DNA methylation in adipose tissue (n = 28) from the same participant (monozygotic twin cohort).
Fig. S11. In vitro methylation of the SAP130 promoter resulted in decreased transcriptional activity.
Table S1. Clinical characteristics of the full discovery and replication cohorts for metformin response including drug-naïve and newly diagnosed participants with T2D from the ANDIS cohort.
Table S2. Clinical characteristics of case-control discovery and replication cohorts including patients who fulfill the criteria of being glycemic responders and nonresponders to metformin therapy.
Table S3. CpG sites with a significant association (FDR < 5%) between DNA methylation in whole blood before taking metformin and the change in HbA1c after ~1.5 years on metformin in drug-naïve participants with T2D from the discovery cohort (n = 63) (Excel).
Table S4. Comparison of the 2577 significant CpG sites (FDR < 5%) with an association between DNA methylation and the ∆HbA1c after ~1.5 years in drug-naïve participants with T2D from the discovery cohort, with two other linear models (Excel).
Table S5. CpG sites with DNA methylation associated with the change in HbA1c (ΔHbA1c) in both the discovery cohort and in the ANDIS replication cohort for metformin response (Excel).
Table S6. CpG sites exhibiting differences in DNA methylation in whole blood between glycemic responders (n = 26) and nonresponders (n = 21) to metformin therapy in drug-naïve participants with T2D from the discovery cohort (Excel).
Table S7. Comparison of the 7916 significant CpG sites (FDR < 5%) between metformin responders and nonresponders in drug-naïve participants with T2D from the discovery cohort, with three other linear models (Excel).
Table S8. Methylated CpG sites associated with response to metformin in the discovery cohort and in the ANDIS replication cohort for metformin response (Excel).
Table S9. Methylated CpG sites associated with response to metformin in the discovery cohort and in the European replication cohort for metformin response (Excel).
Table S10. Methylated CpG sites associated with response to metformin in the discovery cohort and in both the ANDIS and the European replication cohorts for metformin response (Excel).
Table S11. Clinical characteristics of drug-naïve and newly diagnosed patients with T2D included in the metformin intolerance discovery and replication cohorts.
Table S12. CpG sites exhibiting differences in DNA methylation in whole blood between metformin-intolerant (n = 17) and metformin-tolerant (n = 66) drug-naïve participants with T2D from the discovery cohort (Excel).
Table S13. Comparison of the 9676 significant CpG sites (FDR < 5%) between metformin-tolerant and metformin-intolerant drug-naïve participants with T2D from the discovery cohort, with two other linear models (Excel).
Table S14. CpG sites with DNA methylation associated with intolerance to metformin in the discovery cohort and in the ANDIS replication cohort for metformin intolerance (Excel).
Table S15. CpG sites with DNA methylation associated with intolerance to metformin in the discovery cohort and in the European replication cohort for metformin intolerance (Excel).
Table S16. CpG sites with DNA methylation associated with intolerance to metformin in the discovery cohort and in both the ANDIS and the European replication cohorts for metformin intolerance (Excel).
Table S17. Assessing discrimination between glycemic responders and nonresponders to metformin using SNPs and MRSs associated with metformin response.
Table S18. Assessing discrimination between tolerant and intolerant participants to metformin using SNPs and MRSs associated with metformin intolerance.
Table S19. Clinical characteristics of all study participants in the monozygotic twin cohort (MZ).
Table S20. Available data from the monozygotic twin cohort used for cross-tissue methylation analysis in human tissues in the present study.
Table S21. Promoter sequence upstream of SAP130 inserted into the CpG-free firefly luciferase reporter vector (pCpGL-basic) and used for luciferase experiments.
Data file S1. Tables S3 to S10 and S12 to S16 (Excel).
Data file S2. Raw data from figures (Excel).
Resources
File (aaz1803_data_file_s1.xlsx)
File (aaz1803_data_file_s2.xlsx)
File (aaz1803_sm.pdf)
REFERENCES AND NOTES
1
S. E. Inzucchi, R. M. Bergenstal, J. B. Buse, M. Diamant, E. Ferrannini, M. Nauck, A. L. Peters, A. Tsapas, R. Wender, D. R. Matthews, Management of hyperglycaemia in type 2 diabetes, 2015: A patient-centred approach. Update to a position statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetologia 58, 429–442 (2015).
2
M. N. Cook, C. J. Girman, P. P. Stein, C. M. Alexander, Initial monotherapy with either metformin or sulphonylureas often fails to achieve or maintain current glycaemic goals in patients with Type 2 diabetes in UK primary care. Diabet. Med. 24, 350–358 (2007).
3
S. E. Kahn, S. M. Haffner, M. A. Heise, W. H. Herman, R. R. Holman, N. P. Jones, B. G. Kravitz, J. M. Lachin, M. C. O’Neill, B. Zinman, G. Viberti; ADOPT Study Group, Glycemic durability of rosiglitazone, metformin, or glyburide monotherapy. N. Engl. J. Med. 355, 2427–2443 (2006).
4
L. A. Donnelly, A. S. F. Doney, A. T. Hattersley, A. D. Morris, E. R. Pearson, The effect of obesity on glycaemic response to metformin or sulphonylureas in Type 2 diabetes. Diabet. Med. 23, 128–133 (2006).
5
T. Dujic, A. Causevic, T. Bego, M. Malenica, Z. Velija-Asimi, E. R. Pearson, S. Semiz, Organic cation transporter 1 variants and gastrointestinal side effects of metformin in patients with Type 2 diabetes. Diabet. Med. 33, 511–514 (2016).
6
T. Dujic, K. Zhou, L. A. Donnelly, R. Tavendale, C. N. A. Palmer, E. R. Pearson, Association of organic cation transporter 1 with intolerance to metformin in Type 2 diabetes: A GoDARTS Study. Diabetes 64, 1786–1793 (2015).
7
T. Dujic, K. Zhou, R. Tavendale, C. N. A. Palmer, E. R. Pearson, Effect of serotonin transporter 5-HTTLPR polymorphism on gastrointestinal intolerance to metformin: A GoDARTS Study. Diabetes Care 39, 1896–1901 (2016).
8
GoDARTS and UKPDS Diabetes Pharmacogenetics Study Group; Wellcome Trust Case Control Consortium 2, Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes. Nat. Genet. 43, 117–120 (2011).
9
K. A. Jablonski, J. B. McAteer, P. I. W. de Bakker, P. W. Franks, T. I. Pollin, R. L. Hanson, R. Saxena, S. Fowler, A. R. Shuldiner, W. C. Knowler, D. Altshuler, J. C. Florez; Diabetes Prevention Program Research Group, Common variants in 40 genes assessed for diabetes incidence and response to metformin and lifestyle intervention in the diabetes prevention program. Diabetes 59, 2672–2681 (2010).
10
D. M. Rotroff, S. W. Yee, K. Zhou, S. W. Marvel, H. S. Shah, J. R. Jack, T. M. Havener, M. M. Hedderson, M. Kubo, M. A. Herman, H. Gao, J. C. Mychaleckyi, H. L. McLeod, A. Doria, K. M. Giacomini, E. R. Pearson, M. J. Wagner, J. B. Buse, A. A. Motsinger-Reif; MetGen Investigators; ACCORD/ACCORDion Investigators, Genetic variants in CPA6 and PRPF31 are associated with variation in response to metformin in individuals with Type 2 diabetes. Diabetes 67, 1428–1440 (2018).
11
K. Zhou, L. Donnelly, J. Yang, M. Li, H. Deshmukh, N. Van Zuydam, E. Ahlqvist; Wellcome Trust Case Control Consortium 2, C. C. Spencer, L. Groop, A. D. Morris, H. M. Colhoun, P. C. Sham, M. I. McCarthy, C. N. A. Palmer, E. R. Pearson, Heritability of variation in glycaemic response to metformin: A genome-wide complex trait analysis. Lancet Diabetes Endocrinol. 2, 481–487 (2014).
12
K. Zhou, S. W. Yee, E. L. Seiser, N. van Leeuwen, R. Tavendale, A. J. Bennett, C. J. Groves, R. L. Coleman, A. A. van der Heijden, J. W. Beulens, C. E. de Keyser, L. Zaharenko, D. M. Rotroff, M. Out, K. A. Jablonski, L. Chen, M. Javorský, J. Židzik, A. M. Levin, L. K. Williams, T. Dujic, S. Semiz, M. Kubo, H.-C. Chien, S. Maeda, J. S. Witte, L. Wu, I. Tkáč, A. Kooy, R. H. N. van Schaik, C. D. A. Stehouwer, L. Logie; MetGen Investigators; DPP Investigators; ACCORD Investigators, C. Sutherland, J. Klovins, V. Pirags, A. Hofman, B. H. Stricker, A. A. Motsinger-Reif, M. J. Wagner, F. Innocenti, L. M. ‘t Hart, R. R. Holman, M. I. McCarthy, M. M. Hedderson, C. N. A. Palmer, J. C. Florez, K. M. Giacomini, E. R. Pearson, Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin. Nat. Genet. 48, 1055–1059 (2016).
13
T. Dayeh, P. Volkov, S. Salo, E. Hall, E. Nilsson, A. H. Olsson, C. L. Kirkpatrick, C. B. Wollheim, L. Eliasson, T. Rönn, K. Bacos, C. Ling, Genome-wide DNA methylation analysis of human pancreatic islets from type 2 diabetic and non-diabetic donors identifies candidate genes that influence insulin secretion. PLOS Genet. 10, e1004160 (2014).
14
E. Nilsson, P. A. Jansson, A. Perfilyev, P. Volkov, M. Pedersen, M. K. Svensson, P. Poulsen, R. Ribel-Madsen, N. L. Pedersen, P. Almgren, J. Fadista, T. Rönn, B. Klarlund Pedersen, C. Scheele, A. Vaag, C. Ling, Altered DNA methylation and differential expression of genes influencing metabolism and inflammation in adipose tissue from subjects with type 2 diabetes. Diabetes 63, 2962–2976 (2014).
15
E. Nilsson, A. Matte, A. Perfilyev, V. D. de Mello, P. Käkelä, J. Pihlajamäki, C. Ling, Epigenetic alterations in human liver from subjects with type 2 diabetes in parallel with reduced folate levels. J. Clin. Endocrinol. Metab. 100, E1491–E1501 (2015).
16
K. Bacos, L. Gillberg, P. Volkov, A. H. Olsson, T. Hansen, O. Pedersen, A. P. Gjesing, H. Eiberg, T. Tuomi, P. Almgren, L. Groop, L. Eliasson, A. Vaag, T. Dayeh, C. Ling, Blood-based biomarkers of age-associated epigenetic changes in human islets associate with insulin secretion and diabetes. Nat. Commun. 7, 11089 (2016).
17
P. Volkov, K. Bacos, J. K. Ofori, J. L. S. Esguerra, L. Eliasson, T. Rönn, C. Ling, Whole-genome bisulfite sequencing of human pancreatic islets reveals novel differentially methylated regions in type 2 diabetes pathogenesis. Diabetes 66, 1074–1085 (2017).
18
J. C. Chambers, M. Loh, B. Lehne, A. Drong, J. Kriebel, V. Motta, S. Wahl, H. R. Elliott, F. Rota, W. R. Scott, W. Zhang, S.-T. Tan, G. Campanella, M. Chadeau-Hyam, L. Yengo, R. C. Richmond, M. Adamowicz-Brice, U. Afzal, K. Bozaoglu, Z. Y. Mok, H. K. Ng, F. Pattou, H. Prokisch, M. A. Rozario, L. Tarantini, J. Abbott, M. Ala-Korpela, B. Albetti, O. Ammerpohl, P. A. Bertazzi, C. Blancher, R. Caiazzo, J. Danesh, T. R. Gaunt, S. de Lusignan, C. Gieger, T. Illig, S. Jha, S. Jones, J. Jowett, A. J. Kangas, A. Kasturiratne, N. Kato, N. Kotea, S. Kowlessur, J. Pitkäniemi, P. Punjabi, D. Saleheen, C. Schafmayer, P. Soininen, E.-S. Tai, B. Thorand, J. Tuomilehto, A. R. Wickremasinghe, S. A. Kyrtopoulos, T. J. Aitman, C. Herder, J. Hampe, S. Cauchi, C. L. Relton, P. Froguel, R. Soong, P. Vineis, M.-R. Jarvelin, J. Scott, H. Grallert, V. Bollati, P. Elliott, M. I. McCarthy, J. S. Kooner, Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: A nested case-control study. Lancet Diabetes Endocrinol. 3, 526–534 (2015).
19
E. Ahlqvist, P. Storm, A. Käräjämäki, M. Martinell, M. Dorkhan, A. Carlsson, P. Vikman, R. B. Prasad, D. M. Aly, P. Almgren, Y. Wessman, N. Shaat, P. Spégel, H. Mulder, E. Lindholm, O. Melander, O. Hansson, U. Malmqvist, Å. Lernmark, K. Lahti, T. Forsén, T. Tuomi, A. H. Rosengren, L. Groop, Novel subgroups of adult-onset diabetes and their association with outcomes: A data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 6, 361–369 (2018).
20
American Diabetes Association, 6. Glycemic targets: Standards of medical care in diabetes—2018. Diabetes Care 41, S55–S64 (2018).
21
E. A. Houseman, J. Molitor, C. J. Marsit, Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics 30, 1431–1439 (2014).
22
S. Wahl, A. Drong, B. Lehne, M. Loh, W. R. Scott, S. Kunze, P.-C. Tsai, J. S. Ried, W. Zhang, Y. Yang, S. Tan, G. Fiorito, L. Franke, S. Guarrera, S. Kasela, J. Kriebel, R. C. Richmond, M. Adamo, U. Afzal, M. Ala-Korpela, B. Albetti, O. Ammerpohl, J. F. Apperley, M. Beekman, P. A. Bertazzi, S. L. Black, C. Blancher, M.-J. Bonder, M. Brosch, M. Carstensen-Kirberg, A. J. M. de Craen, S. de Lusignan, A. Dehghan, M. Elkalaawy, K. Fischer, O. H. Franco, T. R. Gaunt, J. Hampe, M. Hashemi, A. Isaacs, A. Jenkinson, S. Jha, N. Kato, V. Krogh, M. Laffan, C. Meisinger, T. Meitinger, Z. Y. Mok, V. Motta, H. K. Ng, Z. Nikolakopoulou, G. Nteliopoulos, S. Panico, N. Pervjakova, H. Prokisch, W. Rathmann, M. Roden, F. Rota, M. A. Rozario, J. K. Sandling, C. Schafmayer, K. Schramm, R. Siebert, P. E. Slagboom, P. Soininen, L. Stolk, K. Strauch, E.-S. Tai, L. Tarantini, B. Thorand, E. F. Tigchelaar, R. Tumino, A. G. Uitterlinden, C. van Duijn, J. B. J. van Meurs, P. Vineis, A. R. Wickremasinghe, C. Wijmenga, T.-P. Yang, W. Yuan, A. Zhernakova, R. L. Batterham, G. D. Smith, P. Deloukas, B. T. Heijmans, C. Herder, A. Hofman, C. M. Lindgren, L. Milani, P. van der Harst, A. Peters, T. Illig, C. L. Relton, M. Waldenberger, M.-R. Järvelin, V. Bollati, R. Soong, T. D. Spector, J. Scott, M. I. McCarthy, P. Elliott, J. T. Bell, G. Matullo, C. Gieger, J. S. Kooner, H. Grallert, J. C. Chambers, Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 541, 81–86 (2017).
23
M. L. Becker, L. E. Visser, R. H. N. van Schaik, A. Hofman, A. G. Uitterlinden, B. H. C. Stricker, Genetic variation in the organic cation transporter 1 is associated with metformin response in patients with diabetes mellitus. Pharmacogenomics J. 9, 242–247 (2009).
24
M. M. H. Christensen, C. Brasch-Andersen, H. Green, F. Nielsen, P. Damkier, H. Beck-Nielsen, K. Brosen, The pharmacogenetics of metformin and its impact on plasma metformin steady-state levels and glycosylated hemoglobin A1c. Pharmacogenet. Genomics 21, 837–850 (2011).
25
T. Dujic, T. Bego, M. Malenica, Z. Velija-Asimi, E. Ahlqvist, L. Groop, E. R. Pearson, A. Causevic, S. Semiz, Effects of TCF7L2 rs7903146 variant on metformin response in patients with type 2 diabetes. Bosn. J. Basic Med. Sci. 19, 368–374 (2019).
26
T. Dujic, K. Zhou, S. W. Yee, N. van Leeuwen, C. E. de Keyser, M. Javorský, S. Goswami, L. Zaharenko, M. M. Hougaard Christensen, M. Out, R. Tavendale, M. Kubo, M. M. Hedderson, A. A. van der Heijden, L. Klimčáková, V. Pirags, A. Kooy, K. Brøsen, J. Klovins, S. Semiz, I. Tkáč, B. H. Stricker, C. N. A. Palmer, L. M. ‘t Hart, K. M. Giacomini, E. R. Pearson, Variants in pharmacokinetic transporters and glycemic response to metformin: A Metgen meta-analysis. Clin. Pharmacol. Ther. 101, 763–772 (2017).
27
E. Shikata, R. Yamamoto, H. Takane, C. Shigemasa, T. Ikeda, K. Otsubo, I. Ieiri, Human organic cation transporter (OCT1 and OCT2) gene polymorphisms and therapeutic effects of metformin. J. Hum. Genet. 52, 117–122 (2007).
28
Y. Shu, S. A. Sheardown, C. Brown, R. P. Owen, S. Zhang, R. A. Castro, A. G. Ianculescu, L. Yue, J. C. Lo, E. G. Burchard, C. M. Brett, K. M. Giacomini, Effect of genetic variation in the organic cation transporter 1 (OCT1) on metformin action. J. Clin. Invest. 117, 1422–1431 (2007).
29
J. N. Todd, J. C. Florez, An update on the pharmacogenomics of metformin: Progress, problems and potential. Pharmacogenomics 15, 529–539 (2014).
30
Y. Zhou, W. Ye, Y. Wang, Z. Jiang, X. Meng, Q. Xiao, Q. Zhao, J. Yan, Genetic variants of OCT1 influence glycemic response to metformin in Han Chinese patients with type-2 diabetes mellitus in Shanghai. Int. J. Clin. Exp. Pathol. 8, 9533–9542 (2015).
31
L. Tarasova, I. Kalnina, K. Geldnere, A. Bumbure, R. Ritenberga, L. Nikitina-Zake, D. Fridmanis, I. Vaivade, V. Pirags, J. Klovins, Association of genetic variation in the organic cation transporters OCT1, OCT2 and multidrug and toxin extrusion 1 transporter protein genes with the gastrointestinal side effects and lower BMI in metformin-treated type 2 diabetes patients. Pharmacogenet. Genomics 22, 659–666 (2012).
32
M. D. Nitert, T. Dayeh, P. Volkov, T. Elgzyri, E. Hall, E. Nilsson, B. T. Yang, S. Lang, H. Parikh, Y. Wessman, H. Weishaupt, J. Attema, M. Abels, N. Wierup, P. Almgren, P.-A. Jansson, T. Rönn, O. Hansson, K.-F. Eriksson, L. Groop, C. Ling, Impact of an exercise intervention on DNA methylation in skeletal muscle from first-degree relatives of patients with type 2 diabetes. Diabetes 61, 3322–3332 (2012).
33
M. R. Luizon, W. L. Eckalbar, Y. Wang, S. L. Jones, R. P. Smith, M. Laurance, L. Lin, P. J. Gallins, A. S. Etheridge, F. Wright, Y. Zhou, C. Molony, F. Innocenti, S. W. Yee, K. M. Giacomini, N. Ahituv, Genomic characterization of metformin hepatic response. PLOS Genet. 12, e1006449 (2016).
34
L. J. McCreight, C. J. Bailey, E. R. Pearson, Metformin and the gastrointestinal tract. Diabetologia 59, 426–435 (2016).
35
N. Moreno-Castellanos, A. Rodríguez, Y. Rabanal-Ruiz, A. Fernández-Vega, J. López-Miranda, R. Vázquez-Martínez, G. Frühbeck, M. M. Malagón, The cytoskeletal protein septin 11 is associated with human obesity and is involved in adipocyte lipid storage and metabolism. Diabetologia 60, 324–335 (2017).
36
C. Wolfrum, E. Asilmaz, E. Luca, J. M. Friedman, M. Stoffel, Foxa2 regulates lipid metabolism and ketogenesis in the liver during fasting and in diabetes. Nature 432, 1027–1032 (2004).
37
S. Y.-W. Wong, T. Gadomski, M. van Scherpenzeel, T. Honzik, H. Hansikova, K. S. B. Holmefjord, M. Mork, F. Bowling, J. Sykut-Cegielska, D. Koch, J. Hertecant, G. Preston, J. Jaeken, N. Peeters, S. Perez, D. D. Nguyen, K. Crivelly, T. Emmerzaal, K. M. Gibson, K. Raymond, N. Abu Bakar, F. Foulquier, G. Poschet, A. M. Ackermann, M. He, D. J. Lefeber, C. Thiel, T. Kozicz, E. Morava, Oral D-galactose supplementation in PGM1-CDG. Genet. Med. 19, 1226–1235 (2017).
38
J.-Y. Hwang, X. Sim, Y. Wu, J. Liang, Y. Tabara, C. Hu, K. Hara, C. H. T. Tam, Q. Cai, Q. Zhao, S. Jee, F. Takeuchi, M. J. Go, R. T. H. Ong, T. Ohkubo, Y. J. Kim, R. Zhang, T. Yamauchi, W. Y. So, J. Long, D. Gu, N. R. Lee, S. Kim, T. Katsuya, J. H. Oh, J. Liu, S. Umemura, Y.-J. Kim, F. Jiang, S. Maeda, J. C. N. Chan, W. Lu, J. E. Hixson, L. S. Adair, K. J. Jung, T. Nabika, J.-B. Bae, M. H. Lee, M. Seielstad, T. L. Young, Y. Y. Teo, Y. Kita, N. Takashima, H. Osawa, S.-H. Lee, M.-H. Shin, D. H. Shin, B. Y. Choi, J. Shi, Y.-T. Gao, Y.-B. Xiang, W. Zheng, N. Kato, M. Yoon, J. He, X. O. Shu, R. C. W. Ma, T. Kadowaki, W. Jia, T. Miki, L. Qi, E. S. Tai, K. L. Mohlke, B.-G. Han, Y. S. Cho, B.-J. Kim, Genome-wide association meta-analysis identifies novel variants associated with fasting plasma glucose in East Asians. Diabetes 64, 291–298 (2015).
39
A. K. Manning, M.-F. Hivert, R. A. Scott, J. L. Grimsby, N. Bouatia-Naji, H. Chen, D. Rybin, C.-T. Liu, L. F. Bielak, I. Prokopenko, N. Amin, D. Barnes, G. Cadby, J.-J. Hottenga, E. Ingelsson, A. U. Jackson, T. Johnson, S. Kanoni, C. Ladenvall, V. Lagou, J. Lahti, C. Lecoeur, Y. Liu, M. T. Martinez-Larrad, M. E. Montasser, P. Navarro, J. R. B. Perry, L. J. Rasmussen-Torvik, P. Salo, N. Sattar, D. Shungin, R. J. Strawbridge, T. Tanaka, C. M. van Duijn, P. An, M. de Andrade, J. S. Andrews, T. Aspelund, M. Atalay, Y. Aulchenko, B. Balkau, S. Bandinelli, J. S. Beckmann, J. P. Beilby, C. Bellis, R. N. Bergman, J. Blangero, M. Boban, M. Boehnke, E. Boerwinkle, L. L. Bonnycastle, D. I. Boomsma, I. B. Borecki, Y. Böttcher, C. Bouchard, E. Brunner, D. Budimir, H. Campbell, O. Carlson, P. S. Chines, R. Clarke, F. S. Collins, A. Corbatón-Anchuelo, D. Couper, U. de Faire, G. V. Dedoussis, P. Deloukas, M. Dimitriou, J. M. Egan, G. Eiriksdottir, M. R. Erdos, J. G. Eriksson, E. Eury, L. Ferrucci, I. Ford, N. G. Forouhi, C. S. Fox, M. G. Franzosi, P. W. Franks, T. M. Frayling, P. Froguel, P. Galan, E. de Geus, B. Gigante, N. L. Glazer, A. Goel, L. Groop, V. Gudnason, G. Hallmans, A. Hamsten, O. Hansson, T. B. Harris, C. Hayward, S. Heath, S. Hercberg, A. A. Hicks, A. Hingorani, A. Hofman, J. Hui, J. Hung, M.-R. Jarvelin, M. A. Jhun, P. C. D. Johnson, J. W. Jukema, A. Jula, W. H. Kao, J. Kaprio, S. L. R. Kardia, S. Keinanen-Kiukaanniemi, M. Kivimaki, I. Kolcic, P. Kovacs, M. Kumari, J. Kuusisto, K. O. Kyvik, M. Laakso, T. Lakka, L. Lannfelt, G. M. Lathrop, L. J. Launer, K. Leander, G. Li, L. Lind, J. Lindstrom, S. Lobbens, R. J. F. Loos, J. Luan, V. Lyssenko, R. Mägi, P. K. E. Magnusson, M. Marmot, P. Meneton, K. L. Mohlke, V. Mooser, M. A. Morken, I. Miljkovic, N. Narisu, J. O’Connell, K. K. Ong, B. A. Oostra, L. J. Palmer, A. Palotie, J. S. Pankow, J. F. Peden, N. L. Pedersen, M. Pehlic, L. Peltonen, B. Penninx, M. Pericic, M. Perola, L. Perusse, P. A. Peyser, O. Polasek, P. P. Pramstaller, M. A. Province, K. Räikkönen, R. Rauramaa, E. Rehnberg, K. Rice, J. I. Rotter, I. Rudan, A. Ruokonen, T. Saaristo, M. Sabater-Lleal, V. Salomaa, D. B. Savage, R. Saxena, P. Schwarz, U. Seedorf, B. Sennblad, M. Serrano-Rios, A. R. Shuldiner, E. J. G. Sijbrands, D. S. Siscovick, J. H. Smit, K. S. Small, N. L. Smith, A. V. Smith, A. Stančáková, K. Stirrups, M. Stumvoll, Y. V. Sun, A. J. Swift, A. Tönjes, J. Tuomilehto, S. Trompet, A. G. Uitterlinden, M. Uusitupa, M. Vikström, V. Vitart, M.-C. Vohl, B. F. Voight, P. Vollenweider, G. Waeber, D. M. Waterworth, H. Watkins, E. Wheeler, E. Widen, S. H. Wild, S. M. Willems, G. Willemsen, J. F. Wilson, J. C. M. Witteman, A. F. Wright, H. Yaghootkar, D. Zelenika, T. Zemunik, L. Zgaga; DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium; The Multiple Tissue Human Expression Resource (MUTHER) Consortium, N. J. Wareham, M. I. McCarthy, I. Barroso, R. M. Watanabe, J. C. Florez, J. Dupuis, J. B. Meigs, C. Langenberg, A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669 (2012).
40
C. Xing, J. C. Cohen, E. Boerwinkle, A weighted false discovery rate control procedure reveals alleles at FOXA2 that influence fasting glucose levels. Am. J. Hum. Genet. 86, 440–446 (2010).
41
Z. Zhu, Y. Lin, X. Li, J. A. Driver, L. Liang, Shared genetic architecture between metabolic traits and Alzheimer’s disease: A large-scale genome-wide cross-trait analysis. Hum. Genet. 138, 271–285 (2019).
42
G. Rena, D. G. Hardie, E. R. Pearson, The mechanisms of action of metformin. Diabetologia 60, 1577–1585 (2017).
43
A. Y. Dawed, A. Ali, K. Zhou, E. R. Pearson, P. W. Franks, Evidence-based prioritisation and enrichment of genes interacting with metformin in type 2 diabetes. Diabetologia 60, 2231–2239 (2017).
44
S. L. Stocker, K. M. Morrissey, S. W. Yee, R. A. Castro, L. Xu, A. Dahlin, A. H. Ramirez, D. M. Roden, R. A. Wilke, C. A. McCarty, R. L. Davis, C. M. Brett, K. M. Giacomini, The effect of novel promoter variants in MATE1 and MATE2 on the pharmacokinetics and pharmacodynamics of metformin. Clin. Pharmacol. Ther. 93, 186–194 (2013).
45
K. Toyama, A. Yonezawa, S. Masuda, R. Osawa, M. Hosokawa, S. Fujimoto, N. Inagaki, K. Inui, T. Katsura, Loss of multidrug and toxin extrusion 1 (MATE1) is associated with metformin-induced lactic acidosis. Br. J. Pharmacol. 166, 1183–1191 (2012).
46
M. Foretz, S. Hébrard, J. Leclerc, E. Zarrinpashneh, M. Soty, G. Mithieux, K. Sakamoto, F. Andreelli, B. Viollet, Metformin inhibits hepatic gluconeogenesis in mice independently of the LKB1/AMPK pathway via a decrease in hepatic energy state. J. Clin. Invest. 120, 2355–2369 (2010).
47
C. Ling, L. Groop, Epigenetics: A molecular link between environmental factors and type 2 diabetes. Diabetes 58, 2718–2725 (2009).
48
D. W. Hosmer Jr., S. Lemeshow, Applied Logistic Regression, Wiley Series in Probability and Statistics (Wiley, ed. 2, 2000), pp. xii.
49
J. N. Mandrekar, Receiver operating characteristic curve in diagnostic test assessment. J. Thorac. Oncol. 5, 1315–1316 (2010).
50
J. J. Caro, A. J. Ward, J. A. O’Brien, Lifetime costs of complications resulting from type 2 diabetes in the U.S. Diabetes Care 25, 476–481 (2002).
51
A. Oussalah, S. Rischer, M. Bensenane, G. Conroy, P. Filhine-Tresarrieu, R. Debard, D. Forest-Tramoy, T. Josse, D. Reinicke, M. Garcia, A. Luc, C. Baumann, A. Ayav, V. Laurent, M. Hollenbach, C. Ripoll, R.-M. Guéant-Rodriguez, F. Namour, A. Zipprich, M. Fleischhacker, J.-P. Bronowicki, J.-L. Guéant, Plasma mSEPT9: A novel circulating cell-free DNA-based epigenetic biomarker to diagnose hepatocellular carcinoma. EBioMedicine 30, 138–147 (2018).
52
M. C. Benton, A. Johnstone, D. Eccles, B. Harmon, M. T. Hayes, R. A. Lea, L. Griffiths, E. P. Hoffman, R. S. Stubbs, D. Macartney-Coxson, An analysis of DNA methylation in human adipose tissue reveals differential modification of obesity genes before and after gastric bypass and weight loss. Genome Biol. 16, 8 (2015).
53
L. Gillberg, A. Perfilyev, C. Brøns, M. Thomasen, L. G. Grunnet, P. Volkov, F. Rosqvist, D. Iggman, I. Dahlman, U. Risérus, T. Rönn, E. Nilsson, A. Vaag, C. Ling, Adipose tissue transcriptomics and epigenomics in low birthweight men and controls: Role of high-fat overfeeding. Diabetologia 59, 799–812 (2016).
54
T. Rönn, P. Volkov, C. Davegårdh, T. Dayeh, E. Hall, A. H. Olsson, E. Nilsson, Å. Tornberg, M. Dekker Nitert, K.-F. Eriksson, H. A. Jones, L. Groop, C. Ling, A six months exercise intervention influences the genome-wide DNA methylation pattern in human adipose tissue. PLOS Genet. 9, e1003572 (2013).
55
Q. Chen, X. Yang, H. Zhang, X. Kong, L. Yao, X. Cui, Y. Zou, F. Fang, J. Yang, Y. Chang, Metformin impairs systemic bile acid homeostasis through regulating SIRT1 protein levels. Biochim. Biophys. Acta Mol. Cell Res. 1864, 101–112 (2017).
56
J. Hur, K. A. Sullivan, M. Pande, Y. Hong, A. A. F. Sima, H. V. Jagadish, M. Kretzler, E. L. Feldman, The identification of gene expression profiles associated with progression of human diabetic neuropathy. Brain 134, 3222–3235 (2011).
57
D.-n. Dai, Y. Li, B. Chen, Y. Du, S.-b. Li, S.-x. Lu, Z.-p. Zhao, A.-j. Zhou, N. Xue, T.-l. Xia, M.-s. Zeng, Q. Zhong, W.-d. Wei, Elevated expression of CST1 promotes breast cancer progression and predicts a poor prognosis. J. Mol. Med. 95, 873–886 (2017).
58
L. Zaharenko, I. Kalnina, K. Geldnere, I. Konrade, S. Grinberga, J. Židzik, M. Javorský, A. Lejnieks, L. Nikitina-Zake, D. Fridmanis, R. Peculis, I. Radovica-Spalvina, D. Hartmane, O. Pugovics, I. Tkáč, L. Klimčáková, V. Pīrāgs, J. Klovins, Single nucleotide polymorphisms in the intergenic region between metformin transporter OCT2 and OCT3 coding genes are associated with short-term response to metformin monotherapy in type 2 diabetes mellitus patients. Eur. J. Endocrinol. 175, 531–540 (2016).
59
V. Rovite, Y. Wolff-Sagi, L. Zaharenko, L. Nikitina-Zake, E. Grens, J. Klovins, Genome Database of the Latvian Population (LGDB): Design, goals, and primary results. J. Epidemiol. 28, 353–360 (2018).
60
American Diabetes Association, 8. Pharmacologic approaches to glycemic treatment: Standards of medical care in diabetes—2018. Diabetes Care 41, S73–S85 (2018).
61
F. M. Turnbull, C. Abraira, R. J. Anderson, R. P. Byington, J. P. Chalmers, W. C. Duckworth, G. W. Evans, H. C. Gerstein, R. R. Holman, T. E. Moritz, B. C. Neal, T. Ninomiya, A. A. Patel, S. K. Paul, F. Travert, M. Woodward, Intensive glucose control and macrovascular outcomes in type 2 diabetes. Diabetologia 52, 2288–2298 (2009).
62
I. M. Stratton, A. I. Adler, H. A. W. Neil, D. R. Matthews, S. E. Manley, C. A. Cull, D. Hadden, R. C. Turner, R. R. Holman, Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): Prospective observational study. BMJ 321, 405–412 (2000).
63
S. Moran, C. Arribas, M. Esteller, Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics 8, 389–399 (2016).
64
R. C. Gentleman, V. J. Carey, D. M. Bates, B. Bolstad, M. Dettling, S. Dudoit, B. Ellis, L. Gautier, Y. Ge, J. Gentry, K. Hornik, T. Hothorn, W. Huber, S. Iacus, R. Irizarry, F. Leisch, C. Li, M. Maechler, A. J. Rossini, G. Sawitzki, C. Smith, G. Smyth, L. Tierney, J. Y. H. Yang, J. Zhang, Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).
65
D. L. McCartney, R. M. Walker, S. W. Morris, A. M. McIntosh, D. J. Porteous, K. L. Evans, Identification of polymorphic and off-target probe binding sites on the Illumina Infinium MethylationEPIC BeadChip. Genom. Data 9, 22–24 (2016).
66
P. Du, X. Zhang, C.-C. Huang, N. Jafari, W. A. Kibbe, L. Hou, S. M. Lin, Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11, 587 (2010).
67
P. Du, W. A. Kibbe, S. M. Lin, lumi: A pipeline for processing Illumina microarray. Bioinformatics 24, 1547–1548 (2008).
68
W. E. Johnson, C. Li, A. Rabinovic, Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
69
A. E. Teschendorff, F. Marabita, M. Lechner, T. Bartlett, J. Tegner, D. Gomez-Cabrero, S. Beck, A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 29, 189–196 (2013).
70
X. Stephenne, M. Foretz, N. Taleux, G. C. van der Zon, E. Sokal, L. Hue, B. Viollet, B. Guigas, Metformin activates AMP-activated protein kinase in primary human hepatocytes by decreasing cellular energy status. Diabetologia 54, 3101–3110 (2011).
Information & Authors
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Published In
Science Translational Medicine
Volume 12 | Issue 561
September 2020
September 2020
Copyright
Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
This is an article distributed under the terms of the Science Journals Default License.
Submission history
Received: 20 August 2019
Accepted: 24 August 2020
Acknowledgments
We thank Y. Wessman, M. Sterner, E. Nilsson, the SCIBLU genomics facility at Lund University and the Genome Database of the Latvian Population for providing biological material and data for the OPTIMED cohort. We thank E. Pearson for help in the design of statistical models. Funding: This work was supported by grants from the Novo Nordisk Foundation, Swedish Research Council, Region Skåne (ALF), ERC-Co grant (PAINTBOX, no. 725840), H2020-Marie Skłodowska-Curie grant agreement no. 706081 (EpiHope), The Swedish Heart Lung Foundation, EFSD, Exodiab, Swedish Foundation for Strategic Research for IRC15-0067, Swedish Diabetes Foundation, Albert Påhlsson Foundation, and ERC-CoG-2015_681742_NASCENT. The group of Swedish twins was recruited from the Swedish Twin Registry, which is supported by grants from the Swedish Research Council. The funders had no role in study design, data collection, analysis and interpretation, decision to publish, or preparation of the manuscript. Author contributions: S.G.-C., L.G., E.A., and C.L. contributed to the conception of the work. S.G.-C., S.K., P.W.F., M.M., M.U., I.E., J.P., A.V., L.G., J.K., E.A., and C.L. contributed to the data collection. S.G.-C., A.P., M.M., S.K., K.B., E.A., and P.V. contributed to the data analysis. S.G.-C., S.K., and K.B. performed experiments. S.G.-C. and C.L. drafted the article. All authors contributed to the interpretation of data and critical revision of the article. All authors gave final approval of the version to be published. S.G.-C. and C.L. had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. S.G.-C. and C.L. are guarantors. Competing interests: S.G.-C., A.P., and C.L. have a pending patent (“DNA methylation level of specific CpG sites for prediction of glycemic response and tolerance to metformin treatment in type 2 diabetic patients,” P5066SE00) on using the degree of DNA methylation of specific sites for prediction of glycemic response and tolerance to metformin treatment in patients with T2D. P.W.F. has received research funding from Boehringer Ingelheim, Eli Lilly, Janssen, Novo Nordisk A/S, Sanofi Aventis, and Servier; received consulting fees from Eli Lilly, Novo Nordisk, and Zoe Global Ltd.; and has stock options in Zoe Global Ltd. P.V. works now at the pharmaceutical company AstraZeneca. The other authors declare that they have no competing interests. Data and materials availability: All data used to generate figures 3, 4, 5, and 6 are available in data file S2. DNA methylation data are deposited at the LUDC repository (www.ludc.lu.se/resources/repository) under the following accession numbers and are available upon request: LUDC2020.08.1 (discovery cohort for metformin response), LUDC2020.08.2 (ANDIS replication cohort for metformin response), LUDC2020.08.3 (discovery cohort for metformin response case control), LUDC2020.08.4 (ANDIS replication cohort for metformin response case control), LUDC2020.08.5 (European replication cohort for metformin response case control), LUDC2020.08.6 (discovery cohort for metformin intolerance), LUDC2020.08.7 (ANDIS replication cohort for metformin intolerance), and LUDC2020.08.8 (European replication cohort for metformin intolerance).
Authors
Funding Information
H2020 European Research Council: 725840
European Research Council: CoG-2015_681742_NASCENT
Swedish Foundation for Strategic Research: IRC15-0067
Excellence of Diabetes Research in Sweden (Exodiab)
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