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Baskal S, Posma RA, Bollenbach A, Dieperink W, Bakker SJL, Nijsten MW, Touw DJ, Tsikas D. GC-MS analysis of 4-hydroxyproline: elevated proline hydroxylation in metformin-associated lactic acidosis and metformin-treated Becker muscular dystrophy patients. Amino Acids 2024; 56:21. [PMID: 38461423 PMCID: PMC10925573 DOI: 10.1007/s00726-024-03383-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 01/31/2024] [Indexed: 03/12/2024]
Abstract
Metformin (N,N-dimethylbiguanide), an inhibitor of gluconeogenesis and insulin sensitizer, is widely used for the treatment of type 2 diabetes. In some patients with renal insufficiency, metformin can accumulate and cause lactic acidosis, known as metformin-associated lactic acidosis (MALA, defined as lactate ≥ 5 mM, pH < 7.35, and metformin concentration > 38.7 µM). Here, we report on the post-translational modification (PTM) of proline (Pro) to 4-hydroxyproline (OH-Pro) in metformin-associated lactic acidosis and in metformin-treated patients with Becker muscular dystrophy (BMD). Pro and OH-Pro were measured simultaneously by gas chromatography-mass spectrometry before, during, and after renal replacement therapy in a patient admitted to the intensive care unit (ICU) because of MALA. At admission to the ICU, plasma metformin concentration was 175 µM, with a corresponding lactate concentration of 20 mM and a blood pH of 7.1. Throughout ICU admission, the Pro concentration was lower compared to healthy controls. Renal excretion of OH-Pro was initially high and decreased over time. Moreover, during the first 12 h of ICU admission, OH-Pro seems to be renally secreted while thereafter, it was reabsorbed. Our results suggest that MALA is associated with hyper-hydroxyprolinuria due to elevated PTM of Pro to OH-Pro by prolyl-hydroxylase and/or inhibition of OH-Pro metabolism in the kidneys. In BMD patients, metformin, at the therapeutic dose of 3 × 500 mg per day for 6 weeks, increased the urinary excretion of OH-Pro suggesting elevation of Pro hydroxylation to OH-Pro. Our study suggests that metformin induces specifically the expression/activity of prolyl-hydroxylase in metformin intoxication and BMD.
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Affiliation(s)
- Svetlana Baskal
- Institute of Toxicology, Core Unit Proteomics, Hannover Medical School, Carl-Neuberg-Strasse 1, 30625, Hannover, Germany
| | - Rene A Posma
- Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Alexander Bollenbach
- Institute of Toxicology, Core Unit Proteomics, Hannover Medical School, Carl-Neuberg-Strasse 1, 30625, Hannover, Germany
| | - Willem Dieperink
- Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Stephan J L Bakker
- Division of Nephrology, Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Maarten W Nijsten
- Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Daan J Touw
- Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Dimitrios Tsikas
- Institute of Toxicology, Core Unit Proteomics, Hannover Medical School, Carl-Neuberg-Strasse 1, 30625, Hannover, Germany.
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Pearson ER. New Insights Into the Genetics of Glycemic Response to Metformin. Diabetes Care 2024; 47:193-195. [PMID: 38241501 DOI: 10.2337/dci23-0060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2024]
Affiliation(s)
- Ewan R Pearson
- Division of Population Health & Genomics, University of Dundee, Dundee, U.K
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Wu B, Yee SW, Xiao S, Xu F, Sridhar SB, Yang M, Hochstadt S, Cabral W, Lanfear DE, Hedderson MM, Giacomini KM, Williams LK. Genome-Wide Association Study Identifies Pharmacogenomic Variants Associated With Metformin Glycemic Response in African American Patients With Type 2 Diabetes. Diabetes Care 2024; 47:208-215. [PMID: 37639712 PMCID: PMC10834390 DOI: 10.2337/dc22-2494] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 08/03/2023] [Indexed: 08/31/2023]
Abstract
OBJECTIVE Metformin is the most common treatment for type 2 diabetes (T2D). However, there have been no pharmacogenomic studies for T2D in which a population of color was used in the discovery analysis. This study sought to identify genomic variants associated with metformin response in African American patients with diabetes. RESEARCH DESIGN AND METHODS Patients in the discovery set were adult, African American participants from the Diabetes Multi-omic Investigation of Drug Response (DIAMOND), a cohort study of patients with T2D from a health system serving southeast Michigan. DIAMOND participants had genome-wide genotype data and longitudinal electronic records of laboratory results and medication fills. The genome-wide discovery analysis identified polymorphisms correlated to changes in glycated hemoglobin (HbA1c) levels among individuals on metformin monotherapy. Lead associations were assessed for replication in an independent cohort of African American participants from Kaiser Permanente Northern California (KPNC) and in European American participants from DIAMOND. RESULTS The discovery set consisted of 447 African American participants, whereas the replication sets included 353 African American KPNC participants and 466 European American DIAMOND participants. The primary analysis identified a variant, rs143276236, in the gene ARFGEF3, which met the threshold for genome-wide significance, replicated in KPNC African Americans, and was still significant in the meta-analysis (P = 1.17 × 10-9). None of the significant discovery variants replicated in European Americans DIAMOND participants. CONCLUSIONS We identified a novel and biologically plausible genetic variant associated with a change in HbA1c levels among African American patients on metformin monotherapy. These results highlight the importance of diversity in pharmacogenomic studies.
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Affiliation(s)
- Baojun Wu
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences and Institute for Human Genetics, School of Pharmacy, University of California San Francisco, San Francisco, CA
| | - Shujie Xiao
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - Fei Xu
- Division of Research, Kaiser Permanente Northern California, Oakland, CA
| | - Sneha B. Sridhar
- Division of Research, Kaiser Permanente Northern California, Oakland, CA
| | - Mao Yang
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - Samantha Hochstadt
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - Whitney Cabral
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - David E. Lanfear
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | | | - Kathleen M. Giacomini
- Department of Bioengineering and Therapeutic Sciences and Institute for Human Genetics, School of Pharmacy, University of California San Francisco, San Francisco, CA
| | - L. Keoki Williams
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI
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Kelesoglu N, Kori M, Yilmaz BK, Duru OA, Arga KY. Differential co-expression network analysis elucidated genes associated with sensitivity to farnesyltransferase inhibitor and prognosis of acute myeloid leukemia. Cancer Med 2023; 12:22420-22436. [PMID: 38069522 PMCID: PMC10757125 DOI: 10.1002/cam4.6804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 11/13/2023] [Accepted: 11/27/2023] [Indexed: 12/31/2023] Open
Abstract
Acute myeloid leukemia (AML) is a heterogeneous disease and the most common form of acute leukemia with a poor prognosis. Due to its complexity, the disease requires the identification of biomarkers for reliable prognosis. To identify potential disease genes that regulate patient prognosis, we used differential co-expression network analysis and transcriptomics data from relapsed, refractory, and previously untreated AML patients based on their response to treatment in the present study. In addition, we combined functional genomics and transcriptomics data to identify novel and therapeutically potential systems biomarkers for patients who do or do not respond to treatment. As a result, we constructed co-expression networks for response and non-response cases and identified a highly interconnected group of genes consisting of SECISBP2L, MAN1A2, PRPF31, VASP, and SNAPC1 in the response network and a group consisting of PHTF2, SLC11A2, PDLIM5, OTUB1, and KLRD1 in the non-response network, both of which showed high prognostic performance with hazard ratios of 4.12 and 3.66, respectively. Remarkably, ETS1, GATA2, AR, YBX1, and FOXP3 were found to be important transcription factors in both networks. The prognostic indicators reported here could be considered as a resource for identifying tumorigenesis and chemoresistance to farnesyltransferase inhibitor. They could help identify important research directions for the development of new prognostic and therapeutic techniques for AML.
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Affiliation(s)
| | - Medi Kori
- Department of BioengineeringMarmara UniversityIstanbulTürkiye
| | - Betul Karademir Yilmaz
- Genetic and Metabolic Diseases Research and Investigation CenterMarmara UniversityIstanbulTürkiye
- Department of Biochemistry, Faculty of MedicineMarmara UniversityIstanbulTürkiye
| | - Ozlem Ates Duru
- Department of Nutrition and Dietetics, School of Health SciencesNişantaşı UniversityIstanbulTürkiye
- Department of Chemical Engineering, Faculty of EngineeringBolu Abant İzzet Baysal UniversityBoluTürkiye
| | - Kazim Yalcin Arga
- Department of BioengineeringMarmara UniversityIstanbulTürkiye
- Genetic and Metabolic Diseases Research and Investigation CenterMarmara UniversityIstanbulTürkiye
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5
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Li JH, Perry JA, Jablonski KA, Srinivasan S, Chen L, Todd JN, Harden M, Mercader JM, Pan Q, Dawed AY, Yee SW, Pearson ER, Giacomini KM, Giri A, Hung AM, Xiao S, Williams LK, Franks PW, Hanson RL, Kahn SE, Knowler WC, Pollin TI, Florez JC. Identification of Genetic Variation Influencing Metformin Response in a Multiancestry Genome-Wide Association Study in the Diabetes Prevention Program (DPP). Diabetes 2023; 72:1161-1172. [PMID: 36525397 PMCID: PMC10382652 DOI: 10.2337/db22-0702] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022]
Abstract
Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not been replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in prediabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in the metformin (MET; n = 876) and placebo (PBO; n = 887) arms. Multiple linear regression assessed association with 1-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (P < 9 × 10-9). In the MET arm, rs144322333 near ENOSF1 (minor allele frequency [MAF]AFR = 0.07; MAFEUR = 0.002) was associated with an increase in percentage of glycated hemoglobin (per minor allele, β = 0.39 [95% CI 0.28, 0.50]; P = 2.8 × 10-12). rs145591055 near OMSR (MAF = 0.10 in American Indians) was associated with weight loss (kilograms) (per G allele, β = -7.55 [95% CI -9.88, -5.22]; P = 3.2 × 10-10) in the MET arm. Neither variant was significant in PBO; gene-by-treatment interaction was significant for both variants [P(G×T) < 1.0 × 10-4]. Replication in individuals with diabetes did not yield significant findings. A GWAS for metformin response in prediabetes revealed novel ethnic-specific associations that require further investigation but may have implications for tailored therapy.
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Affiliation(s)
- Josephine H. Li
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - James A. Perry
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD
| | - Kathleen A. Jablonski
- Department of Epidemiology and Biostatistics, George Washington University Biostatistics Center, Washington, DC
| | - Shylaja Srinivasan
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, University of California, San Francisco, San Francisco, CA
| | - Ling Chen
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Jennifer N. Todd
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
- Division of Endocrinology, Department of Pediatrics, Boston Children’s Hospital, Boston, MA
| | - Maegan Harden
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Josep M. Mercader
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Qing Pan
- Department of Epidemiology and Biostatistics, George Washington University Biostatistics Center, Washington, DC
| | - Adem Y. Dawed
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee, U.K
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA
| | - Ewan R. Pearson
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee, U.K
| | - Kathleen M. Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA
| | - Ayush Giri
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN
| | - Adriana M. Hung
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Shujie Xiao
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - L. Keoki Williams
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Robert L. Hanson
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Steven E. Kahn
- Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle
| | - William C. Knowler
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Toni I. Pollin
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD
| | - Jose C. Florez
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
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6
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Srinivasan S, Chen L, Udler M, Todd J, Kelsey MM, Haymond MW, Arslanian S, Zeitler P, Gubitosi-Klug R, Nadeau KJ, Kutney K, White NH, Li JH, Perry JA, Kaur V, Brenner L, Mercader JM, Dawed A, Pearson ER, Yee SW, Giacomini KM, Pollin T, Florez JC. Initial Insights into the Genetic Variation Associated with Metformin Treatment Failure in Youth with Type 2 Diabetes. Pediatr Diabetes 2023; 2023:8883199. [PMID: 38590442 PMCID: PMC11000826 DOI: 10.1155/2023/8883199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/10/2024] Open
Abstract
Metformin is the first-line treatment for type 2 diabetes (T2D) in youth but with limited sustained glycemic response. To identify common variants associated with metformin response, we used a genome-wide approach in 506 youth from the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study and examined the relationship between T2D partitioned polygenic scores (pPS), glycemic traits, and metformin response in these youth. Several variants met a suggestive threshold (P < 1 × 10-6), though none including published adult variants reached genome-wide significance. We pursued replication of top nine variants in three cohorts, and rs76195229 in ATRNL1 was associated with worse metformin response in the Metformin Genetics Consortium (n = 7,812), though statistically not being significant after Bonferroni correction (P = 0.06). A higher β-cell pPS was associated with a lower insulinogenic index (P = 0.02) and C-peptide (P = 0.047) at baseline and higher pPS related to two insulin resistance processes were associated with increased C-peptide at baseline (P = 0.04,0.02). Although pPS were not associated with changes in glycemic traits or metformin response, our results indicate a trend in the association of the β-cell pPS with reduced β-cell function over time. Our data show initial evidence for genetic variation associated with metformin response in youth with T2D.
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Affiliation(s)
- Shylaja Srinivasan
- Division of Pediatric Endocrinology, University of California at San Francisco, San Francisco, CA, USA
| | - Ling Chen
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Miriam Udler
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jennifer Todd
- Division of Pediatric Endocrinology, University of Vermont, Burlington, VA, USA
| | - Megan M. Kelsey
- Division of Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Morey W. Haymond
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Silva Arslanian
- UPMC Children’s Hospital of Pittsburgh, Departments of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Philip Zeitler
- Division of Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Rose Gubitosi-Klug
- Division of Pediatric Endocrinology and Metabolism, Case Western Reserve University and Rainbow Babies and Children’s Hospital, Cleveland, OH, USA
| | - Kristen J. Nadeau
- Division of Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Katherine Kutney
- Division of Pediatric Endocrinology and Metabolism, Case Western Reserve University and Rainbow Babies and Children’s Hospital, Cleveland, OH, USA
| | - Neil H. White
- Division of Endocrinology, Metabolism & Lipid Research, Washington University School of Medicine, St Louise, MO, USA
| | - Josephine H. Li
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA, USA
| | - James A. Perry
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Varinderpal Kaur
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Laura Brenner
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Josep M. Mercader
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Adem Dawed
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Ewan R. Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Sook-Wah Yee
- Department of Bioengineering and Therapeutics, University of California, San Francisco, CA, USA
| | - Kathleen M. Giacomini
- Department of Bioengineering and Therapeutics, University of California, San Francisco, CA, USA
| | - Toni Pollin
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jose C. Florez
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA, USA
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7
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Vohra M, Sharma AR, Mallya S, Prabhu NB, Jayaram P, Nagri SK, Umakanth S, Rai PS. Implications of genetic variations, differential gene expression, and allele-specific expression on metformin response in drug-naïve type 2 diabetes. J Endocrinol Invest 2022; 46:1205-1218. [PMID: 36528847 DOI: 10.1007/s40618-022-01989-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022]
Abstract
PURPOSE Metformin is widely used to treat type 2 diabetes mellitus (T2DM) individuals. Clinically, inter-individual variability of metformin response is of significant concern and is under interrogation. In this study, a targeted exome and whole transcriptome analysis were performed to identify predictive biomarkers of metformin response in drug-naïve T2DM individuals. METHODS The study followed a prospective study design. Drug-naïve T2DM individuals (n = 192) and controls (n = 223) were enrolled. T2DM individuals were administered with metformin monotherapy and defined as responders and non-responders based on their glycated haemoglobin change over three months. 146 T2DM individuals were used for the final analysis and remaining samples were lost during the follow-up. Target exome sequencing and RNA-seq was performed to analyze genetic and transcriptome profile. The selected SNPs were validated by genotyping and allele specific gene expression using the TaqMan assay. The gene prioritization, enrichment analysis, drug-gene interactions, disease-gene association, and correlation analysis were performed using various tools and databases. RESULTS rs1050152 and rs272893 in SLC22A4 were associated with improved response to metformin. The copy number loss was observed in PPARGC1A in the non-responders. The expression analysis highlighted potential differentially expressed targets for predicting metformin response (n = 35) and T2DM (n = 14). The expression of GDF15, TWISTNB, and RPL36A genes showed a maximum correlation with the change in HbA1c levels. The disease-gene association analysis highlighted MAGI2 rs113805659 to be linked with T2DM. CONCLUSION The results provide evidence for the genetic variations, perturbed transcriptome, allele-specific gene expression, and pathways associated with metformin drug response in T2DM.
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Affiliation(s)
- M Vohra
- Department of Biotechnology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - A R Sharma
- Department of Biotechnology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - S Mallya
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - N B Prabhu
- Department of Biotechnology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - P Jayaram
- Department of Cell and Molecular Biology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - S K Nagri
- Department of Medicine, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India
| | - S Umakanth
- Department of Medicine, Dr. T.M.A. Pai Hospital, Manipal Academy of Higher Education, Manipal, India
| | - P S Rai
- Department of Biotechnology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India.
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8
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Lee HA, Park H, Hong YS. Sex Differences in the Effects of CDKAL1 Variants on Glycemic Control in Diabetic Patients: Findings from the Korean Genome and Epidemiology Study. Diabetes Metab J 2022; 46:879-889. [PMID: 35130687 PMCID: PMC9723206 DOI: 10.4093/dmj.2021.0265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 11/26/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Using long-term data from the Korean Genome and Epidemiology Study, we defined poor glycemic control and investigated possible risk factors, including variants related to type 2 diabetes mellitus (T2DM). In addition, we evaluated interaction effects among risk factors for poor glycemic control. METHODS Among 436 subjects with newly diagnosed diabetes, poor glycemic control was defined based on glycosylated hemoglobin trajectory patterns by group-based trajectory modeling. For the variants related to T2DM, genetic risk scores (GRSs) were calculated and divided into quartiles. Risk factors for poor glycemic control were assessed using a logistic regression model. RESULTS Of the subjects, 43% were in the poor-glycemic-control group. Body mass index (BMI) and triglyceride (TG) were associated with poor glycemic control. The risk for poor glycemic control increased by 11.0% per 1 kg/m2 increase in BMI and by 3.0% per 10 mg/dL increase in TG. The risk for GRS with poor glycemic control was sex-dependent (Pinteraction=0.07), and a relationship by GRS quartiles was found in females but not in males. Moreover, the interaction effect was found to be significant on both additive and multiplicative scales. The interaction effect was evident in the variants of cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like (CDKAL1). CONCLUSION Females with risk alleles of variants in CDKAL1 associated with T2DM had a higher risk for poor glycemic control than males.
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Affiliation(s)
- Hye Ah Lee
- Clinical Trial Center, Ewha Womans University Mokdong Hospital, Seoul, Korea
- Corresponding author: Hye Ah Lee https://orcid.org/0000-0002-4051-0350 Clinical Trial Center, Ewha Womans University Mokdong Hospital, 1071 Anyangcheon-ro, Yangcheon-gu, Seoul 07985, Korea E-mail:
| | - Hyesook Park
- Department of Preventive Medicine, Ewha Womans University College of Medicine, Seoul, Korea
- Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Korea
| | - Young Sun Hong
- Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul, Korea
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Abstract
Data generated over nearly two decades clearly demonstrate the importance of epigenetic modifications and mechanisms in the pathogenesis of type 2 diabetes. However, the role of pharmacoepigenetics in type 2 diabetes is less well established. The field of pharmacoepigenetics covers epigenetic biomarkers that predict response to therapy, therapy-induced epigenetic alterations as well as epigenetic therapies including inhibitors of epigenetic enzymes. Not all individuals with type 2 diabetes respond to glucose-lowering therapies in the same way, and there is therefore a need for clinically useful biomarkers that discriminate responders from non-responders. Blood-based epigenetic biomarkers may be useful for this purpose. There is also a need for a better understanding of whether existing glucose-lowering therapies exert their function partly through therapy-induced epigenetic alterations. Finally, epigenetic enzymes may be drug targets for type 2 diabetes. Here, I discuss whether pharmacoepigenetics is clinically relevant for type 2 diabetes based on studies addressing this topic.
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Affiliation(s)
- Charlotte Ling
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Scania University Hospital, Malmö, Sweden.
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10
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Li S, Xu B, Fan S, Kang B, Deng L, Chen D, Yang B, Tang F, He Z, Xue Y, Zhou JC. Effects of single-nucleotide polymorphism on the pharmacokinetics and pharmacodynamics of metformin. Expert Rev Clin Pharmacol 2022; 15:1107-1117. [PMID: 36065506 DOI: 10.1080/17512433.2022.2118714] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Metformin has been recognized as the first-choice drug for type 2 diabetes mellitus (T2DM). The potency of metformin in the treatment of type 2 diabetes has always been in the spotlight and shown significant individual differences. Based on previous studies, the efficacy of metformin is related to the single-nucleotide polymorphisms of transporter genes carried by patients, amongst which a variety of gene polymorphisms of transporter and target protein genes affect the effectiveness and adverse repercussion of metformin. AREAS COVERED Here, we reviewed the current knowledge about gene polymorphisms impacting metformin efficacy based on transporter and drug target proteins. EXPERT OPINION The reason for the difference in clinical drug potency of metformin can be attributed to the gene polymorphism of drug transporters and drug target proteins in the human body. Substantial evidence shows that genetic polymorphisms in transporters such as organic cation transporter 1 (OCT1) and organic cation transporter 2 (OCT2) affect the glucose-lowering effectiveness of metformin. However, optimization of individualized dosing regimens of metformin is necessary to clarify the role of several polymorphisms.
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Affiliation(s)
- Shaoqian Li
- The First Affiliated Hospital, Clinical Pharmacology Research Center, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Hengyang Key Laboratory of Clinical Pharmacology, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Department of Clinical Laboratory Medicine, Institution of Microbiology and Infectious Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Bo Xu
- The First Affiliated Hospital, Clinical Pharmacology Research Center, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Hengyang Key Laboratory of Clinical Pharmacology, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Shangzhi Fan
- The First Affiliated Hospital, Clinical Pharmacology Research Center, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Hengyang Key Laboratory of Clinical Pharmacology, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Department of Clinical Laboratory Medicine, Institution of Microbiology and Infectious Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Bo Kang
- The First Affiliated Hospital, Clinical Pharmacology Research Center, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Hengyang Key Laboratory of Clinical Pharmacology, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Department of Clinical Laboratory Medicine, Institution of Microbiology and Infectious Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Lijing Deng
- The First Affiliated Hospital, Clinical Pharmacology Research Center, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Hengyang Key Laboratory of Clinical Pharmacology, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Pharmacy Department, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Danjun Chen
- The First Affiliated Hospital, Clinical Pharmacology Research Center, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Hengyang Key Laboratory of Clinical Pharmacology, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Pharmacy Department, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Bo Yang
- The First Affiliated Hospital, Clinical Pharmacology Research Center, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Hengyang Key Laboratory of Clinical Pharmacology, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Pharmacy Department, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Fan Tang
- The First Affiliated Hospital, Clinical Pharmacology Research Center, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Hengyang Key Laboratory of Clinical Pharmacology, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Pharmacy Department, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Zunbo He
- The First Affiliated Hospital, Clinical Pharmacology Research Center, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Hengyang Key Laboratory of Clinical Pharmacology, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Department of Anesthesiology, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Yong Xue
- The Second Affiliated Hospital, Pharmacy Department, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Jie-Can Zhou
- The First Affiliated Hospital, Clinical Pharmacology Research Center, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Hengyang Key Laboratory of Clinical Pharmacology, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Department of Clinical Laboratory Medicine, Institution of Microbiology and Infectious Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,The First Affiliated Hospital, Pharmacy Department, Hengyang Medical School, University of South China, Hengyang, Hunan, China
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11
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Li C, Dou P, Lu X, Guan P, Lin Z, Zhou Y, Lu X, Lin X, Xu G. Identification and Validation of TRIM25 as a Glucose Metabolism Regulator in Prostate Cancer. Int J Mol Sci 2022; 23:ijms23169325. [PMID: 36012594 PMCID: PMC9408812 DOI: 10.3390/ijms23169325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/10/2022] [Accepted: 08/16/2022] [Indexed: 11/16/2022] Open
Abstract
Prostate cancer (PCa) malignant progression is accompanied with the reprogramming of glucose metabolism. However, the genes involved in the regulation of glucose metabolism in PCa are not fully understood. Here, we propose a new method, DMRG, which constructs a weighted differential network (W-K-DN) to define the important metabolism-related genes. Based on biological knowledge and prostate cancer transcriptome data, a tripartite motif-containing 25 (TRIM25) was defined using DMRG; TRIM25 was involved in the regulation of glucose metabolism, which was verified by overexpressing or knocking down TRIM25 in PCa cell lines. Differential expression analysis of TCA cycle enzymes revealed that TRIM25 regulated isocitrate dehydrogenase 1 (IDH1) and fumarate hydratase (FH) expression. Moreover, a protein–RNA interaction network of TRIM25 revealed that TRIM25 interacted with RNA-binding proteins, including DExH-box helicase 9 and DEAD-box helicase 5, to play a role in regulating the RNA processing of metabolic enzymes, including IDH1 and FH. Furthermore, TRIM25 expression level was found to be positively correlated with Gleason scores in PCa patient tissues. In conclusion, this study provides a new method to define genes influencing tumor progression, and sheds light on the role of the defined TRIM25 in regulating glucose metabolism and promoting PCa malignancy.
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Affiliation(s)
- Chao Li
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Peng Dou
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Xin Lu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Pengwei Guan
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Zhikun Lin
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Yanyan Zhou
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Xin Lu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
- Correspondence: (X.L.); (G.X.)
| | - Xiaohui Lin
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
- Correspondence: (X.L.); (G.X.)
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12
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Srinivasan S, Todd J. The Genetics of Type 2 Diabetes in Youth: Where We Are and the Road Ahead. J Pediatr 2022; 247:17-21. [PMID: 35660490 PMCID: PMC9833991 DOI: 10.1016/j.jpeds.2022.05.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 05/24/2022] [Accepted: 05/27/2022] [Indexed: 01/13/2023]
Affiliation(s)
- Shylaja Srinivasan
- Department of Pediatrics, University of California San Francisco, San Francisco, CA.
| | - Jennifer Todd
- Department of Pediatrics, University of Vermont, Burlington, VT
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13
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Nasykhova YA, Barbitoff YA, Tonyan ZN, Danilova MM, Nevzorov IA, Komandresova TM, Mikhailova AA, Vasilieva TV, Glavnova OB, Yarmolinskaya MI, Sluchanko EI, Glotov AS. Genetic and Phenotypic Factors Affecting Glycemic Response to Metformin Therapy in Patients with Type 2 Diabetes Mellitus. Genes (Basel) 2022; 13:genes13081310. [PMID: 35893047 PMCID: PMC9330240 DOI: 10.3390/genes13081310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/19/2022] [Accepted: 07/21/2022] [Indexed: 12/10/2022] Open
Abstract
Metformin is an oral hypoglycemic agent widely used in clinical practice for treatment of patients with type 2 diabetes mellitus (T2DM). The wide interindividual variability of response to metformin therapy was shown, and recently the impact of several genetic variants was reported. To assess the independent and combined effect of the genetic polymorphism on glycemic response to metformin, we performed an association analysis of the variants in ATM, SLC22A1, SLC47A1, and SLC2A2 genes with metformin response in 299 patients with T2DM. Likewise, the distribution of allele and genotype frequencies of the studied gene variants was analyzed in an extended group of patients with T2DM (n = 464) and a population group (n = 129). According to our results, one variant, rs12208357 in the SLC22A1 gene, had a significant impact on response to metformin in T2DM patients. Carriers of TT genotype and T allele had a lower response to metformin compared to carriers of CC/CT genotypes and C allele (p-value = 0.0246, p-value = 0.0059, respectively). To identify the parameters that had the greatest importance for the prediction of the therapy response to metformin, we next built a set of machine learning models, based on the various combinations of genetic and phenotypic characteristics. The model based on a set of four parameters, including gender, rs12208357 genotype, familial T2DM background, and waist–hip ratio (WHR) showed the highest prediction accuracy for the response to metformin therapy in patients with T2DM (AUC = 0.62 in cross-validation). Further pharmacogenetic studies may aid in the discovery of the fundamental mechanisms of type 2 diabetes, the identification of new drug targets, and finally, it could advance the development of personalized treatment.
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Affiliation(s)
- Yulia A. Nasykhova
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
| | - Yury A. Barbitoff
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
- St. Petersburg State University, 199034 Saint-Petersburg, Russia
| | - Ziravard N. Tonyan
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
| | - Maria M. Danilova
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
| | - Ivan A. Nevzorov
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
| | | | - Anastasiia A. Mikhailova
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
| | | | - Olga B. Glavnova
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
| | - Maria I. Yarmolinskaya
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
| | | | - Andrey S. Glotov
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
- Correspondence: ; Tel.: +7-9117832003
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14
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Auwerx C, Sadler MC, Reymond A, Kutalik Z. From Pharmacogenetics to Pharmaco-Omics:Milestones and Future Directions. HGG ADVANCES 2022; 3:100100. [PMID: 35373152 PMCID: PMC8971318 DOI: 10.1016/j.xhgg.2022.100100] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.
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15
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Zepeda-Carrillo EA, Ramos-Lopez O, Martínez-López E, Barrón-Cabrera E, Bernal-Pérez JA, Velasco-González LE, Rangel-Rios E, Bustamante Martínez JF, Torres-Valadez R. Effect of Metformin on Glycemic Control Regarding Carriers of the SLC22A1/OCT1 (rs628031) Polymorphism and Its Interactions with Dietary Micronutrients in Type 2 Diabetes. Diabetes Metab Syndr Obes 2022; 15:1771-1784. [PMID: 35711690 PMCID: PMC9196279 DOI: 10.2147/dmso.s354579] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 04/08/2022] [Indexed: 12/19/2022] Open
Abstract
PURPOSE Differences in metformin effect on glycemic control in type 2 Diabetes (T2D) have been associated with diet, obesity, years since T2D diagnosis and genetic factors, such as the Met408Val (rs628031) SLC22A1/OCT1 gene polymorphism. This study aimed to analyze the effect of metformin and diet on glycemic control and its association with the Met408Val polymorphism in patients with T2D from western Mexico. PATIENTS AND METHODS A total of 240 T2D adult patients were enrolled in this cross-sectional study. Anti-hyperglycemic therapy, dietary intake, body composition and glycemic profile were recorded and the determination of genotypes of SLC22A1/OCT1 gene (rs628031) was performed using an allelic discrimination assay. RESULTS The type of metformin therapy was 47% monotherapy, 45% dual therapy (metformin+glibenclamide or metformin+insulin) and 8% triple therapy (metformin+glibenclamide+insulin). Individuals with metformin monotherapy had a higher glycemic control frequency (%HbA1c <7.0) compared with the dual and triple treatment schemes (77% vs 35% and 15%, respectively; p<0.001). Interestingly, a high potassium intake was documented in the three anti-hyperglycemic therapies and a lower intake of micronutrients, including calcium, magnesium, and zinc. An interaction was found between calcium intake and carriers of the risk allele A (408Val) with %HbA1c (P interaction=0.028), and potassium intake with the TyG index (P interaction=0.027). In addition, there was a positive correlation between calcium intake and %HbA1c (r=0.682; p=0.010), and potassium intake vs TyG index (r=0.593; p=0.033) in risk allele A (408Val) carriers with metformin monotherapy. Genotype frequencies were GG homozygotes (76.6%), GA heterozygotes (21.5%) and AA homozygotes (1.9%). The allele frequency was 87.4% for the ancestral allele G and 12.6% for the risk allele A. CONCLUSION These findings suggest a differing effect of metformin on glycemic control regarding calcium and potassium intake and the Met408Val SLC22A1/OCT1 gene polymorphism in T2D patients.
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Affiliation(s)
- Eloy A Zepeda-Carrillo
- Specialized Unit in Research, Development and Innovation in Genomic Medicine, Nayarit Center for Innovation and Technology Transfer, Autonomous University of Nayarit, Tepic, Nayarit, Mexico
- Civil Hospital “Dr. Antonio González Guevara”, Health Services in Nayarit, Tepic, Nayarit, Mexico
| | - Omar Ramos-Lopez
- Medicine and Psychology School, Autonomous University of Baja California, Tijuana, B.C, Mexico
| | - Erika Martínez-López
- Institute of Translational Nutrigenetics and Nutrigenomics, Department of Molecular and Genomic Biology, University Center of Health Sciences, University of Guadalajara, Guadalajara, Jalisco, Mexico
| | - Elisa Barrón-Cabrera
- Faculty of Nutrition and Gastronomy Sciences, Autonomous University of Sinaloa, Culiacan, Sinaloa, Mexico
| | - J Antonio Bernal-Pérez
- Family Medicine Unit No. 24 “Ignacio García Téllez”, Mexican Social Security Institute, Tepic, Nayarit, Mexico
| | - Luisa E Velasco-González
- Family Medicine Unit No. 24 “Ignacio García Téllez”, Mexican Social Security Institute, Tepic, Nayarit, Mexico
| | - Ernesto Rangel-Rios
- Family Medicine Unit No. 24 “Ignacio García Téllez”, Mexican Social Security Institute, Tepic, Nayarit, Mexico
| | | | - Rafael Torres-Valadez
- Specialized Unit in Research, Development and Innovation in Genomic Medicine, Nayarit Center for Innovation and Technology Transfer, Autonomous University of Nayarit, Tepic, Nayarit, Mexico
- Integral Health Academic Unit, Autonomous University of Nayarit, Tepic, Nayarit, Mexico
- Correspondence: Rafael Torres-Valadez, Nayarit Center for Innovation and Technology Transfer, Autonomous University of Nayarit, Tepic, Nayarit, Mexico, Tel +52-3312523644, Email
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16
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Venkatachalapathy P, Padhilahouse S, Sellappan M, Subramanian T, Kurian SJ, Miraj SS, Rao M, Raut AA, Kanwar RK, Singh J, Khadanga S, Mondithoka S, Munisamy M. Pharmacogenomics and Personalized Medicine in Type 2 Diabetes Mellitus: Potential Implications for Clinical Practice. Pharmgenomics Pers Med 2021; 14:1441-1455. [PMID: 34803393 PMCID: PMC8598203 DOI: 10.2147/pgpm.s329787] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 10/04/2021] [Indexed: 12/20/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) is the most common form of diabetes, and is rising in incidence with widespread prevalence. Multiple gene variants are associated with glucose homeostasis, complex T2DM pathogenesis, and its complications. Exploring more effective therapeutic strategies for patients with diabetes is crucial. Pharmacogenomics has made precision medicine possible by allowing for individualized drug therapy based on a patient's genetic and genomic information. T2DM is treated with various classes of oral hypoglycemic agents, such as biguanides, sulfonylureas, thiazolidinediones, meglitinides, DPP4 inhibitors, SGLT2 inhibitors, α-glucosidase inhibitors, and GLP1 analogues, which exhibit various pharmacogenetic variants. Although genomic interventions in monogenic diabetes have been implemented in clinical practice, they are still in the early stages for complex polygenic disorders, such as T2DM. Precision DM medicine has the potential to be effective in personalized therapy for those suffering from various forms of DM, such as T2DM. With recent developments in genetic techniques, the application of candidate-gene studies, large-scale genotyping investigations, genome-wide association studies, and "multiomics" studies has begun to produce results that may lead to changes in clinical practice. Enhanced knowledge of the genetic architecture of T2DM presents a bigger translational potential. This review summarizes the genetics and pathophysiology of T2DM, candidate-gene approaches, genome-wide association studies, personalized medicine, clinical relevance of pharmacogenetic variants associated with oral hypoglycemic agents, and paths toward personalized diabetology.
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Affiliation(s)
| | - Sruthi Padhilahouse
- Department of Pharmacy Practice, Karpagam College of Pharmacy, Coimbatore, Tamilnadu, India
| | - Mohan Sellappan
- Department of Pharmacy Practice, Karpagam College of Pharmacy, Coimbatore, Tamilnadu, India
| | | | - Shilia Jacob Kurian
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Sonal Sekhar Miraj
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Mahadev Rao
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Ashwin Ashok Raut
- Translational Medicine Centre, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
| | - Rupinder Kaur Kanwar
- Translational Medicine Centre, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
| | - Jitendra Singh
- Translational Medicine Centre, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
| | - Sagar Khadanga
- Department of General Medicine, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
| | - Sukumar Mondithoka
- Department of General Medicine, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
| | - Murali Munisamy
- Translational Medicine Centre, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
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17
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Gonzalez-Covarrubias V, Sánchez-Ibarra H, Lozano-Gonzalez K, Villicaña S, Texis T, Rodríguez-Dorantes M, Cortés-Ramírez S, Lavalle-Gonzalez F, Soberón X, Barrera-Saldaña H. Transporters, TBC1D4, and ARID5B Variants to Explain Glycated Hemoglobin Variability in Patients with Type 2 Diabetes. Pharmacology 2021; 106:588-596. [PMID: 34265779 DOI: 10.1159/000517462] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 05/15/2021] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Genetic variants could aid in predicting antidiabetic drug response by associating them with markers of glucose control, such as glycated hemoglobin (HbA1c). However, pharmacogenetic implementation for antidiabetics is still under development, as the list of actionable markers is being populated and validated. This study explores potential associations between genetic variants and plasma levels of HbA1c in 100 patients under treatment with metformin. METHODS HbA1c was measured in a clinical chemistry analyzer (Roche), genotyping was performed in an Illumina-GSA array and data were analyzed using PLINK. Association and prediction models were developed using R and a 10-fold cross-validation approach. RESULTS We identified genetic variants on SLC47A1, SLC28A1, ABCG2, TBC1D4, and ARID5B that can explain up to 55% of the interindividual variability of HbA1c plasma levels in diabetic patients under treatment. Variants on SLC47A1, SLC28A1, and ABCG2 likely impact the pharmacokinetics (PK) of metformin, while the role of the two latter can be related to insulin resistance and regulation of adipogenesis. CONCLUSIONS Our results confirm previous genetic associations and point to previously unassociated gene variants for metformin PK and glucose control.
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Affiliation(s)
| | | | | | - Sergio Villicaña
- Pharmacogenomics Laboratory, Instituto Nacional de Medicina Genómica, CDMX, Mexico
| | - Tomas Texis
- Pharmacogenomics Laboratory, Instituto Nacional de Medicina Genómica, CDMX, Mexico
| | | | | | - Fernando Lavalle-Gonzalez
- University Hospital Dr. José E. González, Endocrinology, Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Mexico
| | - Xavier Soberón
- Instituto de Biotecnología, Universidad Autónoma de México, UNAM, Cuernavaca, Mexico
| | - Hugo Barrera-Saldaña
- Genetics Laboratory, Vitagénesis, Monterrey, Mexico.,Medicine and Health Sciences Department, Tecnológico de Monterrey, Monterrey, Mexico
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18
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Xiao D, Liu JY, Zhang SM, Liu RR, Yin JY, Han XY, Li X, Zhang W, Chen XP, Zhou HH, Ji LN, Liu ZQ. A Two-Stage Study Identifies Two Novel Polymorphisms in PRKAG2 Affecting Metformin Response in Chinese Type 2 Diabetes Patients. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2021; 14:745-755. [PMID: 34188521 PMCID: PMC8236263 DOI: 10.2147/pgpm.s305020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/25/2021] [Indexed: 12/29/2022]
Abstract
Objective Individual differences in glycemic response to metformin in antidiabetic treatment exist widely. Although some associated genetic variations have been discovered, they still cannot accurately predict metformin response. In the current study, we set out to investigate novel genetic variants affecting metformin response in Chinese type 2 diabetes (T2D) patients. Methods A two-stage study enrolled 500 T2D patients who received metformin, glibenclamide or a combination of both were recruited from 2009 to 2012 in China. Change of HbA1c, adjusted by clinical covariates, was used to evaluate glycemic response to metformin. Selected single nucleotide polymorphisms (SNPs) were genotyped using the Infinium iSelect and/or Illumina GoldenGate genotyping platform. A linear regression model was used to evaluate the association between SNPs and response. Results A total of 3739 SNPs were screened in Stage 1, of which 50 were associated with drug response. Except for one genetic variant preferred to affect glibenclamide, the remaining SNPs were subsequently verified in Stage 2, and two SNPs were successfully validated. These were PRKAG2 rs2727528 (discovery group: β=−0.212, P=0.046; validation group: β=−0.269, P=0.028) and PRKAG2 rs1105842 (discovery group: β=0.205, P=0.048; validation group: β=0.273, P=0.025). C allele carriers of rs2727528 and C allele carriers of rs1105842 would have a larger difference of HbA1c level when using metformin. Conclusion Two variants rs2727528 and rs1105842 in PRKAG2, encoding γ2 subunit of AMP-activated protein kinase (AMPK), were found to be associated with metformin response in Chinese T2D patients. These findings may provide some novel information for personalized pharmacotherapy of metformin in China.
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Affiliation(s)
- Di Xiao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Department of pharmacy, Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Jun-Yan Liu
- Department of orthopaedics, Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Si-Min Zhang
- Department of Endocrinology and Metabolism, The People's Hospital of Peking University, Beijing, People's Republic of China
| | - Rang-Ru Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Key Laboratory of Tropical Diseases and Translational Medicine of the Ministry of Education & Hainan Provincial Key Laboratory of Tropical Medicine, Hainan Medical College, Haikou, People's Republic of China
| | - Ji-Ye Yin
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, People's Republic of China
| | - Xue-Yao Han
- Department of Endocrinology and Metabolism, The People's Hospital of Peking University, Beijing, People's Republic of China
| | - Xi Li
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, People's Republic of China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, People's Republic of China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Xiao-Ping Chen
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, People's Republic of China
| | - Hong-Hao Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, People's Republic of China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Li-Nong Ji
- Department of Endocrinology and Metabolism, The People's Hospital of Peking University, Beijing, People's Republic of China
| | - Zhao-Qian Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, People's Republic of China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, People's Republic of China
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19
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Mariam A, Miller-Atkins G, Pantalone KM, Zimmerman RS, Barnard J, Kattan MW, Shah H, McLeod HL, Doria A, Wagner MJ, Buse JB, Motsinger-Reif AA, Rotroff DM. A Type 2 Diabetes Subtype Responsive to ACCORD Intensive Glycemia Treatment. Diabetes Care 2021; 44:1410-1418. [PMID: 33863751 PMCID: PMC8247498 DOI: 10.2337/dc20-2700] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 02/23/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVE Current type 2 diabetes (T2D) management contraindicates intensive glycemia treatment in patients with high cardiovascular disease (CVD) risk and is partially motivated by evidence of harms in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Heterogeneity in response to intensive glycemia treatment has been observed, suggesting potential benefit for some individuals. RESEARCH DESIGN AND METHODS ACCORD was a randomized controlled trial that investigated whether intensively treating glycemia in individuals with T2D would reduce CVD outcomes. Using a novel approach to cluster HbA1c trajectories, we identified groups in the intensive glycemia arm with modified CVD risk. Genome-wide analysis and polygenic score (PS) were developed to predict group membership. Mendelian randomization was performed to infer causality. RESULTS We identified four clinical groupings in the intensive glycemia arm, and clinical group 4 (C4) displayed fewer CVD (hazard ratio [HR] 0.34; P = 2.01 × 10-3) and microvascular outcomes (HR 0.86; P = 0.015) than those receiving standard treatment. A single-nucleotide polymorphism, rs220721, in MAS1 reached suggestive significance in C4 (P = 4.34 × 10-7). PS predicted C4 with high accuracy (area under the receiver operating characteristic curve 0.98), and this predicted C4 displayed reduced CVD risk with intensive versus standard glycemia treatment (HR 0.53; P = 4.02 × 10-6), but not reduced risk of microvascular outcomes (P < 0.05). Mendelian randomization indicated causality between PS, on-trial HbA1c, and reduction in CVD outcomes (P < 0.05). CONCLUSIONS We found evidence of a T2D clinical group in ACCORD that benefited from intensive glycemia treatment, and membership in this group could be predicted using genetic variants. This study generates new hypotheses with implications for precision medicine in T2D and represents an important development in this landmark clinical trial warranting further investigation.
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Affiliation(s)
- Arshiya Mariam
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
| | - Galen Miller-Atkins
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
| | - Kevin M Pantalone
- Endocrinology and Metabolism Institute, Cleveland Clinic, Cleveland, OH
| | | | - John Barnard
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
| | - Michael W Kattan
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
| | - Hetal Shah
- Joslin Diabetes Center and Harvard Medical School, Boston, MA
| | - Howard L McLeod
- Taneja College of Pharmacy, University of South Florida, Tampa, FL
| | | | - Michael J Wagner
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - John B Buse
- Division of Endocrinology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Alison A Motsinger-Reif
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC
| | - Daniel M Rotroff
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
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20
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Devarbhavi P, Telang L, Vastrad B, Tengli A, Vastrad C, Kotturshetti I. Identification of key pathways and genes in polycystic ovary syndrome via integrated bioinformatics analysis and prediction of small therapeutic molecules. Reprod Biol Endocrinol 2021; 19:31. [PMID: 33622336 PMCID: PMC7901211 DOI: 10.1186/s12958-021-00706-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 01/28/2021] [Indexed: 02/06/2023] Open
Abstract
To enhance understanding of polycystic ovary syndrome (PCOS) at the molecular level; this investigation intends to examine the genes and pathways associated with PCOS by using an integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing data GSE84958 derived from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) between PCOS samples and normal controls were identified. We performed a functional enrichment analysis. A protein-protein interaction (PPI) network, miRNA- target genes and TF - target gene networks, were constructed and visualized, with which the hub gene nodes were identified. Validation of hub genes was performed by using receiver operating characteristic (ROC) and RT-PCR. Small drug molecules were predicted by using molecular docking. A total of 739 DEGs were identified, of which 360 genes were up regulated and 379 genes were down regulated. GO enrichment analysis revealed that up regulated genes were mainly involved in peptide metabolic process, organelle envelope and RNA binding and the down regulated genes were significantly enriched in plasma membrane bounded cell projection organization, neuron projection and DNA-binding transcription factor activity, RNA polymerase II-specific. REACTOME pathway enrichment analysis revealed that the up regulated genes were mainly enriched in translation and respiratory electron transport and the down regulated genes were mainly enriched in generic transcription pathway and transmembrane transport of small molecules. The top 10 hub genes (SAA1, ADCY6, POLR2K, RPS15, RPS15A, CTNND1, ESR1, NEDD4L, KNTC1 and NGFR) were identified from PPI network, miRNA - target gene network and TF - target gene network. The modules analysis showed that genes in modules were mainly associated with the transport of respiratory electrons and signaling NGF, respectively. We find a series of crucial genes along with the pathways that were most closely related with PCOS initiation and advancement. Our investigations provide a more detailed molecular mechanism for the progression of PCOS, detail information on the potential biomarkers and therapeutic targets.
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Affiliation(s)
- Praveenkumar Devarbhavi
- Department of Endocrinology and Metabolism, Subbaiah Institute of Medical Sciences and Research Centre, Shimoga, Karnataka, 577201, India
| | - Lata Telang
- Department of Gynaecology and Obstetrics, Subbaiah Institute of Medical Sciences and Research Centre, Shimoga, Karnataka, 577201, India
| | - Basavaraj Vastrad
- Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka, 582103, India
| | - Anandkumar Tengli
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, Mysuru and JSS Academy of Higher Education & Research, Mysuru, Karnataka, 570015, India
| | - Chanabasayya Vastrad
- Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad, Karanataka, 580001, India.
| | - Iranna Kotturshetti
- Department of Ayurveda, Rajiv Gandhi Education Society's Ayurvedic Medical College, Ron, Karanataka, 562209, India
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21
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Masilela C, Pearce B, Ongole JJ, Adeniyi OV, Benjeddou M. Single Nucleotide Polymorphisms Associated with Metformin and Sulphonylureas' Glycaemic Response among South African Adults with Type 2 Diabetes Mellitus. J Pers Med 2021; 11:jpm11020104. [PMID: 33561991 PMCID: PMC7914534 DOI: 10.3390/jpm11020104] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 01/25/2021] [Accepted: 02/01/2021] [Indexed: 12/31/2022] Open
Abstract
Aims: To examine the association of polymorphisms belonging to SLC22A1, SP1, PRPF31, NBEA, SCNN1B, CPA6 and CAPN10 genes with glycaemic response to metformin and sulphonylureas (SU) combination therapy among South African adults with diabetes mellitus type 2 (T2DM). Methods: A total of 128 individuals of Swati (n = 22) and Zulu (n = 106) origin attending chronic care for T2DM were recruited. Nine SNPs previously associated with metformin and SUs were selected and genotyped using MassArray. Uncontrolled T2DM was defined as HbA1c > 7%. The association between genotypes, alleles and glycaemic response to treatment was determined using multivariate logistic regression model analysis. Results: About 85.93% (n = 110) of the study participants were female and 77.34% (n = 99) had uncontrolled T2DM (HbA1c > 7%). In the multivariate (adjusted) logistic regression model analysis, the CC genotype of rs2162145 (CPA6), GG and GA genotypes of rs889299 (SCNN1B) were significantly associated with uncontrolled T2DM. On the other hand, the C allele of rs254271 (PRPF31) and the GA genotype of rs3792269 (CAPN10) were associated with controlled T2DM. A significant interaction between rs2162145 and rs889299 in response to metformin and SU combination therapy was observed. Conclusions: In this study, we reported the association of rs2162145 (CC) and rs889299 (GG and GA) with uncontrolled T2DM. We also reported the association of rs254271 (C) and rs3792269 (GA) with controlled T2DM in response to metformin and SU combination therapy. Furthermore, an interaction between rs2162145 and rs889299 was established, where the genotype combination GA (rs889299) and TT (rs2162145) was associated with uncontrolled T2DM.
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Affiliation(s)
- Charity Masilela
- Department of Biotechnology, University of the Western Cape, Bellville 7535, South Africa; (B.P.); (M.B.)
- Correspondence:
| | - Brendon Pearce
- Department of Biotechnology, University of the Western Cape, Bellville 7535, South Africa; (B.P.); (M.B.)
| | - Joven Jebio Ongole
- Center for Teaching and Learning, Department of Family Medicine, Piet Retief Hospital, Mkhondo 2380, South Africa;
| | | | - Mongi Benjeddou
- Department of Biotechnology, University of the Western Cape, Bellville 7535, South Africa; (B.P.); (M.B.)
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22
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Nasykhova YA, Tonyan ZN, Mikhailova AA, Danilova MM, Glotov AS. Pharmacogenetics of Type 2 Diabetes-Progress and Prospects. Int J Mol Sci 2020; 21:ijms21186842. [PMID: 32961860 PMCID: PMC7555942 DOI: 10.3390/ijms21186842] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/11/2020] [Accepted: 09/16/2020] [Indexed: 12/11/2022] Open
Abstract
Type 2 diabetes mellitus (T2D) is a chronic metabolic disease resulting from insulin resistance and progressively reduced insulin secretion, which leads to impaired glucose utilization, dyslipidemia and hyperinsulinemia and progressive pancreatic beta cell dysfunction. The incidence of type 2 diabetes mellitus is increasing worldwide and nowadays T2D already became a global epidemic. The well-known interindividual variability of T2D drug actions such as biguanides, sulfonylureas/meglitinides, DPP-4 inhibitors/GLP1R agonists and SGLT-2 inhibitors may be caused, among other things, by genetic factors. Pharmacogenetic findings may aid in identifying new drug targets and obtaining in-depth knowledge of the causes of disease and its physiological processes, thereby, providing an opportunity to elaborate an algorithm for tailor or precision treatment. The aim of this article is to summarize recent progress and discoveries for T2D pharmacogenetics and to discuss the factors which limit the furthering accumulation of genetic variability knowledge in patient response to therapy that will allow improvement the personalized treatment of T2D.
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Affiliation(s)
- Yulia A. Nasykhova
- Department of Genomic Medicine, D.O. Ott’s Institute of Obstetrics, Gynecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Z.N.T.); (A.A.M.); (M.M.D.)
- Laboratory of Biobanking and Genomic Medicine, Saint-Petersburg State University, 199034 Saint-Petersburg, Russia
| | - Ziravard N. Tonyan
- Department of Genomic Medicine, D.O. Ott’s Institute of Obstetrics, Gynecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Z.N.T.); (A.A.M.); (M.M.D.)
| | - Anastasiia A. Mikhailova
- Department of Genomic Medicine, D.O. Ott’s Institute of Obstetrics, Gynecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Z.N.T.); (A.A.M.); (M.M.D.)
- Laboratory of Biobanking and Genomic Medicine, Saint-Petersburg State University, 199034 Saint-Petersburg, Russia
| | - Maria M. Danilova
- Department of Genomic Medicine, D.O. Ott’s Institute of Obstetrics, Gynecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Z.N.T.); (A.A.M.); (M.M.D.)
| | - Andrey S. Glotov
- Department of Genomic Medicine, D.O. Ott’s Institute of Obstetrics, Gynecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Z.N.T.); (A.A.M.); (M.M.D.)
- Laboratory of Biobanking and Genomic Medicine, Saint-Petersburg State University, 199034 Saint-Petersburg, Russia
- Correspondence: ; Tel.: +7-9117832003
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23
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García-Calzón S, Perfilyev A, Martinell M, Ustinova M, Kalamajski S, Franks PW, Bacos K, Elbere I, Pihlajamäki J, Volkov P, Vaag A, Groop L, Maziarz M, Klovins J, Ahlqvist E, Ling C. Epigenetic markers associated with metformin response and intolerance in drug-naïve patients with type 2 diabetes. Sci Transl Med 2020; 12:12/561/eaaz1803. [DOI: 10.1126/scitranslmed.aaz1803] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 01/27/2020] [Accepted: 08/24/2020] [Indexed: 12/14/2022]
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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Affiliation(s)
- Sonia García-Calzón
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 214 28 Malmö, Sweden
- Department of Nutrition, Food Science and Physiology, University of Navarra, 31008 Pamplona, Spain
| | - Alexander Perfilyev
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 214 28 Malmö, Sweden
| | - Mats Martinell
- Department of Public Health and Caring Sciences, Uppsala University, 751 22 Uppsala, Sweden
| | - Monta Ustinova
- Latvian Biomedical Research and Study Centre, Rātsupītes Street 1, k-1, Riga LV-1067, Latvia
| | - Sebastian Kalamajski
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University Diabetes Centre, 214 28 Malmö, Sweden
| | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University Diabetes Centre, 214 28 Malmö, Sweden
| | - Karl Bacos
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 214 28 Malmö, Sweden
| | - Ilze Elbere
- Latvian Biomedical Research and Study Centre, Rātsupītes Street 1, k-1, Riga LV-1067, Latvia
| | - Jussi Pihlajamäki
- Institute of Public Health and Clinical Nutrition, Internal Medicine, University of Eastern Finland, 70211 Kuopio, Finland
- Clinical Nutrition and Obesity Center, Kuopio University Hospital, 70210 Kuopio, Finland
| | - Petr Volkov
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 214 28 Malmö, Sweden
| | - Allan Vaag
- Type 2 Diabetes Biology Research, Steno Diabetes Center, 2820 Gentofte, Denmark
| | - Leif Groop
- Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 214 28 Malmö, Sweden
| | - Marlena Maziarz
- Bioinformatics Unit, Department of Clinical Sciences, Lund University Diabetes Centre, 214 28 Malmö, Sweden
| | - Janis Klovins
- Latvian Biomedical Research and Study Centre, Rātsupītes Street 1, k-1, Riga LV-1067, Latvia
- Faculty of Biology, University of Latvia, Riga LV-1004, Latvia
| | - Emma Ahlqvist
- Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 214 28 Malmö, Sweden
| | - Charlotte Ling
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 214 28 Malmö, Sweden
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24
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Zazuli Z, Duin NJCB, Jansen K, Vijverberg SJH, Maitland-van der Zee AH, Masereeuw R. The Impact of Genetic Polymorphisms in Organic Cation Transporters on Renal Drug Disposition. Int J Mol Sci 2020; 21:ijms21186627. [PMID: 32927790 PMCID: PMC7554776 DOI: 10.3390/ijms21186627] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 09/05/2020] [Accepted: 09/07/2020] [Indexed: 12/18/2022] Open
Abstract
A considerable number of drugs and/or their metabolites are excreted by the kidneys through glomerular filtration and active renal tubule secretion via transporter proteins. Uptake transporters in the proximal tubule are part of the solute carrier (SLC) superfamily, and include the organic cation transporters (OCTs). Several studies have shown that specific genetic polymorphisms in OCTs alter drug disposition and may lead to nephrotoxicity. Multiple single nucleotide polymorphisms (SNPs) have been reported for the OCT genes (SLC22A1, SLC22A2 and SLC22A3), which can influence the proteins’ structure and expression levels and affect their transport function. A gain-in-function mutation may lead to accumulation of drugs in renal proximal tubule cells, eventually leading to nephrotoxicity. This review illustrates the impact of genetic polymorphisms in OCTs on renal drug disposition and kidney injury, the clinical significances and how to personalize therapies to minimize the risk of drug toxicity.
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Affiliation(s)
- Zulfan Zazuli
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (S.J.H.V.); (A.H.M.-v.d.Z.)
- Department of Pharmacology-Clinical Pharmacy, School of Pharmacy, Bandung Institute of Technology, Jawa Barat 40132, Indonesia
- Correspondence: (Z.Z.); (R.M.)
| | - Naut J. C. B. Duin
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3584 CG Utrecht, The Netherlands; (N.J.C.B.D.); (K.J.)
| | - Katja Jansen
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3584 CG Utrecht, The Netherlands; (N.J.C.B.D.); (K.J.)
| | - Susanne J. H. Vijverberg
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (S.J.H.V.); (A.H.M.-v.d.Z.)
| | - Anke H. Maitland-van der Zee
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (S.J.H.V.); (A.H.M.-v.d.Z.)
| | - Rosalinde Masereeuw
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3584 CG Utrecht, The Netherlands; (N.J.C.B.D.); (K.J.)
- Correspondence: (Z.Z.); (R.M.)
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25
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Jiang G, Luk AO, Tam CHT, Lau ES, Ozaki R, Chow EYK, Kong APS, Lim CKP, Lee KF, Siu SC, Hui G, Tsang CC, Lau KP, Leung JYY, Tsang MW, Kam G, Lau IT, Li JK, Yeung VT, Lau E, Lo S, Fung SKS, Cheng YL, Chow CC, Pearson ER, So WY, Chan JCN, Ma RCW. Obesity, clinical, and genetic predictors for glycemic progression in Chinese patients with type 2 diabetes: A cohort study using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank. PLoS Med 2020; 17:e1003209. [PMID: 32722720 PMCID: PMC7386560 DOI: 10.1371/journal.pmed.1003209] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 06/22/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Type 2 diabetes (T2D) is a progressive disease whereby there is often deterioration in glucose control despite escalation in treatment. There is significant heterogeneity to this progression of glycemia after onset of diabetes, yet the factors that influence glycemic progression are not well understood. Given the tremendous burden of diabetes in the Chinese population, and limited knowledge on factors that influence glycemia, we aim to identify the clinical and genetic predictors for glycemic progression in Chinese patients with T2D. METHODS AND FINDINGS In 1995-2007, 7,091 insulin-naïve Chinese patients (mean age 56.8 ± 13.3 [SD] years; mean age of T2D onset 51.1 ± 12.7 years; 47% men; 28.4% current or ex-smokers; median duration of diabetes 4 [IQR: 1-9] years; mean HbA1c 7.4% ± 1.7%; mean body mass index [BMI] 25.3 ± 4.0 kg/m2) were followed prospectively in the Hong Kong Diabetes Register. We examined associations of BMI and other clinical and genetic factors with glycemic progression defined as requirement of continuous insulin treatment, or 2 consecutive HbA1c ≥8.5% while on ≥2 oral glucose-lowering drugs (OGLDs), with validation in another multicenter cohort of Hong Kong Diabetes Biobank. During a median follow-up period of 8.8 (IQR: 4.8-13.3) years, incidence of glycemic progression was 48.0 (95% confidence interval [CI] 46.3-49.8) per 1,000 person-years with 2,519 patients started on insulin. Among the latter, 33.2% had a lag period of 1.3 years before insulin was initiated. Risk of progression was associated with extremes of BMI and high HbA1c. On multivariate Cox analysis, early age at diagnosis, microvascular complications, high triglyceride levels, and tobacco use were additional independent predictors for glycemic progression. A polygenic risk score (PRS) including 123 known risk variants for T2D also predicted rapid progression to insulin therapy (hazard ratio [HR]: 1.07 [95% CI 1.03-1.12] per SD; P = 0.001), with validation in the replication cohort (HR: 1.24 [95% CI 1.06-1.46] per SD; P = 0.008). A PRS using 63 BMI-related variants predicted BMI (beta [SE] = 0.312 [0.057] per SD; P = 5.84 × 10-8) but not glycemic progression (HR: 1.01 [95% CI 0.96-1.05] per SD; P = 0.747). Limitations of this study include potential misdiagnosis of T2D and lack of detailed data of drug use during follow-up in the replication cohort. CONCLUSIONS Our results show that approximately 5% of patients with T2D failed OGLDs annually in this clinic-based cohort. The independent associations of modifiable and genetic risk factors allow more precise identification of high-risk patients for early intensive control of multiple risk factors to prevent glycemic progression.
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Affiliation(s)
- Guozhi Jiang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Andrea O. Luk
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Claudia H. T. Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | | | - Risa Ozaki
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Elaine Y. K. Chow
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Alice P. S. Kong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Cadmon K. P. Lim
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Ka Fai Lee
- Department of Medicine and Geriatrics, Kwong Wah Hospital, Hong Kong, China
| | - Shing Chung Siu
- Diabetes Centre, Tung Wah Eastern Hospital, Hong Kong, China
| | - Grace Hui
- Diabetes Centre, Tung Wah Eastern Hospital, Hong Kong, China
| | - Chiu Chi Tsang
- Diabetes and Education Centre, Alice Ho Miu Ling Nethersole Hospital, Hong Kong, China
| | | | - Jenny Y. Y. Leung
- Department of Medicine and Geriatrics, Ruttonjee Hospital, Hong Kong, China
| | - Man-wo Tsang
- Department of Medicine and Geriatrics, United Christian Hospital, Hong Kong, China
| | - Grace Kam
- Department of Medicine and Geriatrics, United Christian Hospital, Hong Kong, China
| | - Ip Tim Lau
- Tseung Kwan O Hospital, Hong Kong, China
| | - June K. Li
- Department of Medicine, Yan Chai Hospital, Hong Kong, China
| | - Vincent T. Yeung
- Centre for Diabetes Education and Management, Our Lady of Maryknoll Hospital, Hong Kong, China
| | - Emmy Lau
- Department of Medicine, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China
| | - Stanley Lo
- Department of Medicine, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China
| | - Samuel K. S. Fung
- Department of Medicine and Geriatrics, Princess Margaret Hospital, Hong Kong, China
| | - Yuk Lun Cheng
- Department of Medicine, Alice Ho Miu Ling Nethersole Hospital, Hong Kong, China
| | - Chun Chung Chow
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Ewan R. Pearson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, Scotland, United Kingdom
| | - Wing Yee So
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Juliana C. N. Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Ronald C. W. Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
- * E-mail:
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26
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Wang H, Zhang M, Zhang M, Wang F, Liu J, Zhao Q. Carboxypeptidase A6 was identified and validated as a novel potential biomarker for predicting the occurrence of active ulcerative colitis. J Cell Mol Med 2020; 24:8803-8813. [PMID: 32570281 PMCID: PMC7412415 DOI: 10.1111/jcmm.15517] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 05/24/2020] [Accepted: 05/28/2020] [Indexed: 12/18/2022] Open
Abstract
Ulcerative colitis (UC) is a chronic, highly heterogeneous intestinal inflammation with changes in epithelial function and tissue damage. However, the pathogenesis is still unclear between active UC and inactive UC. Herein, weighted gene co‐expression network analysis was applied to explore the gene modules related to active UC. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to further investigate the underlying mechanism of selected genes. We found that in the blue module (r = −.72), carboxypeptidase A6 (CPA6) was chosen to validate because of its high intra‐modular connectivity and module membership. In the test sets, the expression level of CPA6 was down‐regulated in active UC compared with inactive UC and normal colon. Furthermore, CPA6 expression was decreased primarily in the descending colon and only in mucosa affected by active UC. The receiver operating characteristic curve indicated that CPA6 expression had a performed well in diagnosing active UC from inactive UC (area under the curve = 0.99). Importantly, anti‐tumour necrosis factor (TNF) treatment (infliximab and golimumab) significantly increased the CPA6 expression. Finally, GSEA and GSVA found that extracellular matrix receptor, inflammatory response and epithelial‐mesenchymal transition were highly enriched in active UC with low CPA6 expression. In conclusion, CPA6 was identified and validated as a novel potential biomarker for predicting the occurrence of active UC, probably through regulating extracellular matrix or immune response.
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Affiliation(s)
- Haizhou Wang
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, China
| | - Meng Zhang
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, China
| | - Mengna Zhang
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, China
| | - Fan Wang
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, China
| | - Jing Liu
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, China
| | - Qiu Zhao
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, China
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Metformin strongly affects transcriptome of peripheral blood cells in healthy individuals. PLoS One 2019; 14:e0224835. [PMID: 31703101 PMCID: PMC6839856 DOI: 10.1371/journal.pone.0224835] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 10/22/2019] [Indexed: 01/22/2023] Open
Abstract
Metformin is a commonly used antihyperglycaemic agent for the treatment of type 2 diabetes mellitus. Nevertheless, the exact mechanisms of action, underlying the various therapeutic effects of metformin, remain elusive. The goal of this study was to evaluate the alterations in longitudinal whole-blood transcriptome profiles of healthy individuals after a one-week metformin intervention in order to identify the novel molecular targets and further prompt the discovery of predictive biomarkers of metformin response. Next generation sequencing-based transcriptome analysis revealed metformin-induced differential expression of genes involved in intestinal immune network for IgA production and cytokine-cytokine receptor interaction pathways. Significantly elevated faecal sIgA levels during administration of metformin, and its correlation with the expression of genes associated with immune response (CXCR4, HLA-DQA1, MAP3K14, TNFRSF21, CCL4, ACVR1B, PF4, EPOR, CXCL8) supports a novel hypothesis of strong association between metformin and intestinal immune system, and for the first time provide evidence for altered RNA expression as a contributing mechanism of metformin’s action. In addition to universal effects, 4 clusters of functionally related genes with a subject-specific differential expression were distinguished, including genes relevant to insulin production (HNF1B, HNF1A, HNF4A, GCK, INS, NEUROD1, PAX4, PDX1, ABCC8, KCNJ11) and cholesterol homeostasis (APOB, LDLR, PCSK9). This inter-individual variation of the metformin effect on the transcriptional regulation goes in line with well-known variability of the therapeutic response to the drug.
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28
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Schulten HJ. Pleiotropic Effects of Metformin on Cancer. Int J Mol Sci 2018; 19:E2850. [PMID: 30241339 PMCID: PMC6213406 DOI: 10.3390/ijms19102850] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 09/07/2018] [Accepted: 09/14/2018] [Indexed: 12/19/2022] Open
Abstract
Metformin (MTF) is a natural compound derived from the legume Galega officinalis. It is the first line antidiabetic drug for type 2 diabetes (T2D) treatment. One of its main antidiabetic effects results from the reduction of hepatic glucose release. First scientific evidence for the anticancer effects of MTF was found in animal research, published in 2001, and some years later a retrospective observational study provided evidence that linked MTF to reduced cancer risk in T2D patients. Its pleiotropic anticancer effects were studied in numerous in vitro and in vivo studies at the molecular and cellular level. Although the majority of these studies demonstrated that MTF is associated with certain anticancer properties, clinical studies and trials provided a mixed view on its beneficial anticancer effects. This review emphasizes the pleiotropic effects of MTF and recent progress made in MTF applications in basic, preclinical, and clinical cancer research.
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Affiliation(s)
- Hans-Juergen Schulten
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, P.O. Box 80216, Jeddah 21589, Saudi Arabia.
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29
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Langenberg C, Lotta LA. Genomic insights into the causes of type 2 diabetes. Lancet 2018; 391:2463-2474. [PMID: 29916387 DOI: 10.1016/s0140-6736(18)31132-2] [Citation(s) in RCA: 102] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 04/30/2018] [Accepted: 05/15/2018] [Indexed: 01/05/2023]
Abstract
Genome-wide association studies have implicated around 250 genomic regions in predisposition to type 2 diabetes, with evidence for causal variants and genes emerging for several of these regions. Understanding of the underlying mechanisms, including the interplay between β-cell failure, insulin sensitivity, appetite regulation, and adipose storage has been facilitated by the integration of multidimensional data for diabetes-related intermediate phenotypes, detailed genomic annotations, functional experiments, and now multiomic molecular features. Studies in diverse ethnic groups and examples from population isolates have shown the value and need for a broad genomic approach to this global disease. Transethnic discovery efforts and large-scale biobanks in diverse populations and ancestries could help to address some of the Eurocentric bias. Despite rapid progress in the discovery of the highly polygenic architecture of type 2 diabetes, dominated by common alleles with small, cumulative effects on disease risk, these insights have been of little clinical use in terms of disease prediction or prevention, and have made only small contributions to subtype classification or stratified approaches to treatment. Successful development of academia-industry partnerships for exome or genome sequencing in large biobanks could help to deliver economies of scale, with implications for the future of genomics-focused research.
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Affiliation(s)
| | - Luca A Lotta
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
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