Publications by authors named "Ewan R Pearson"

129 Publications

PRESCRIBING PATTERNS AND RESPONSE TO ANTIHYPERGLYCEMIC AGENTS AMONG NOVEL CLUSTERS OF TYPE 2 DIABETES IN ASIAN INDIANS.

Diabetes Technol Ther 2021 Oct 5. Epub 2021 Oct 5.

Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialities Centre, Diabetology, No. 4, Conran Smith Road, Gopalapuram, Chennai, Tamilnadu, India, 600 086;

Aim: To assess the prescribing patterns and response to different classes of antihyperglycemic agents in novel clusters of type 2 diabetes (T2D) described in India.

Materials And Methods: We attempted to replicate the earlier described clusters of T2D In 32,867 individuals with new-onset T2D (within 2 years of diagnosis) registered between October 2013 and December 2020 at 15 diabetes clinics located across India, by means of k-means clustering utilising six clinically relevant variables. Individuals who had followup HbA1c upto 2 years were included for the drug response analysis (n=13,247).

Results: Among the 32,867 participants included in the study, 20779 (63.2%) were males. The average age at diagnosis was 45 years and mean HbA1c at baseline was 8.9 %. The same four clusters described in India earlier were replicated. Forty percent of the study participants belonged to the Mild Age-Related Diabetes [MARD] cluster, followed by Insulin Resistant Obese Diabetes [IROD] (27%), Severe Insulin Deficient Diabetes [SIDD] (21%) and Combined Insulin Resistant and Deficient Diabetes [CIRDD] (12%) clusters. The most frequently used antihyperglycemic agents were sulphonylureas, metformin and dipeptidyl peptidase-4 inhibitors apart from insulin. While there were significant differences in HbA1c reduction between drugs across clusters, these were largely driven by differences in the baseline (pre-treatment) HbA1c.

Conclusions: In this new cohort we were able to reliably replicate the four subtypes of T2D earlier described in Asian Indians. Prescribing patterns show limited usage of newer antihyperglycemic agents across all clusters. Randomized clinical trials are required to establish differential drug responses between clusters.
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http://dx.doi.org/10.1089/dia.2021.0277DOI Listing
October 2021

Genome-Wide Meta-analysis Identifies Genetic Variants Associated With Glycemic Response to Sulfonylureas.

Diabetes Care 2021 Oct 4. Epub 2021 Oct 4.

Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA.

Objective: Sulfonylureas, the first available drugs for the management of type 2 diabetes, remain widely prescribed today. However, there exists significant variability in glycemic response to treatment. We aimed to establish heritability of sulfonylurea response and identify genetic variants and interacting treatments associated with HbA reduction.

Research Design And Methods: As an initiative of the Metformin Genetics Plus Consortium (MetGen Plus) and the DIabetes REsearCh on patient straTification (DIRECT) consortium, 5,485 White Europeans with type 2 diabetes treated with sulfonylureas were recruited from six referral centers in Europe and North America. We first estimated heritability using the generalized restricted maximum likelihood approach and then undertook genome-wide association studies of glycemic response to sulfonylureas measured as HbA reduction after 12 months of therapy followed by meta-analysis. These results were supported by acute glipizide challenge in humans who were naïve to type 2 diabetes medications, expression quantitative trait loci (eQTL), and functional validation in cellular models. Finally, we examined for possible drug-drug-gene interactions.

Results: After establishing that sulfonylurea response is heritable (mean ± SEM 37 ± 11%), we identified two independent loci near the and genes associated with HbA reduction at a genome-wide scale ( < 5 × 10). The C allele at rs1234032, near , was associated with 0.14% (1.5 mmol/mol), = 2.39 × 10), lower reduction in HbA. Similarly, the C allele was associated with higher glucose trough levels (β = 1.61, = 0.005) in healthy volunteers in the SUGAR-MGH given glipizide ( = 857). In 3,029 human whole blood samples, the C allele is a eQTL for increased expression of (β = 0.21, = 2.04 × 10). The C allele of rs10770791, in an intronic region of , was associated with 0.11% (1.2 mmol/mol) greater reduction in HbA ( = 4.80 × 10). In 1,183 human liver samples, the C allele at rs10770791 is a eQTL for reduced expression ( = 1.61 × 10), which, together with functional studies in cells expressing , supports a key role for hepatic (encoding OATP1B1) in regulation of sulfonylurea transport. Further, a significant interaction between statin use and SLCO1B1 genotype was observed (p = 0.001). In statin nonusers, C allele homozygotes at rs10770791 had a large absolute reduction in HbA (0.48 ± 0.12% [5.2 ± 1.26 mmol/mol]), equivalent to that associated with initiation of a dipeptidyl peptidase 4 inhibitor.

Conclusions: We have identified clinically important genetic effects at genome-wide levels of significance, and important drug-drug-gene interactions, which include commonly prescribed statins. With increasing availability of genetic data embedded in clinical records these findings will be important in prescribing glucose-lowering drugs.
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http://dx.doi.org/10.2337/dc21-1152DOI Listing
October 2021

Genomic editing of metformin efficacy-associated genetic variants in SLC47A1 does not alter SLC47A1 expression.

Hum Mol Genet 2021 Sep 9. Epub 2021 Sep 9.

Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, 20502, Sweden.

Several pharmacogenetics studies have identified an association between a greater metformin-dependent reduction in HbA1c levels and the minor A allele at rs2289669 in intron 10 of SLC47A1, encoding multidrug and toxin extrusion 1 (MATE1), a presumed metformin transporter. It is currently unknown if the rs2289669 locus is a cis-eQTL, which would validate its role as predictor of metformin efficacy. We looked at association between common genetic variants in the SLC47A1 gene region and HbA1c reduction after metformin treatment using locus-wise meta-analysis from the MetGen consortium. CRISPR-Cas9 was applied to perform allele editing of, or genomic deletion around, rs2289669 and of the closely linked rs8065082 in HepG2 cells. The genome-edited cells were evaluated for SLC47A1 expression and splicing. None of the common variants including rs2289669 showed significant association with metformin response. Genomic editing of either rs2289669 or rs8065082 did not alter SLC47A1 expression or splicing. Experimental and in silico analyses show that the rs2289669-containing haploblock does not appear to carry genetic variants that could explain its previously reported association with metformin efficacy.
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http://dx.doi.org/10.1093/hmg/ddab266DOI Listing
September 2021

Distinct Molecular Signatures of Clinical Clusters in People with Type 2 Diabetes: an IMIRHAPSODY Study.

Diabetes 2021 Aug 10. Epub 2021 Aug 10.

Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, CRC, Lund University, SUS, Malmö, Sweden.

Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity a previous study clustered people with diabetes into five diabetes subtypes. The aim of the current study is to investigate the aetiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic- (N=12828), metabolomic- (N=2945), lipidomic- (N=2593) and proteomic (N=1170) data were obtained in plasma. In each datatype each cluster was compared with the other four clusters as the reference. The insulin resistant cluster showed the most distinct molecular signature, with higher BCAAs, DAG and TAG levels and aberrant protein levels in plasma enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher cytokines. A subset of the mild diabetes cluster with high HDL showed the most beneficial molecular profile with opposite effects to those seen in the insulin resistant cluster. This study showed that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous disease.
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http://dx.doi.org/10.2337/db20-1281DOI Listing
August 2021

Polymorphism in Locus Modifies Risk of Atrial Fibrillation in Patients on Thyroid Hormone Replacement Therapy.

Front Genet 2021 23;12:652878. Epub 2021 Jun 23.

Division of Population Health and Genomics, School of Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, United Kingdom.

Aims: Atrial fibrillation (AF) is a risk for patients receiving thyroid hormone replacement therapy. No published work has focused on pharmacogenetics relevant to thyroid dysfunction and AF risk. We aimed to assess the effect of L-thyroxine on AF risk stratified by a variation in a candidate gene.

Methods And Results: A retrospective follow-up study was done among European Caucasian patients from the Genetics of Diabetes Audit and Research in Tayside Scotland cohort (Scotland, United Kingdom). Linked data on biochemistry, prescribing, hospital admissions, demographics, and genetic biobank were used to ascertain patients on L-thyroxine and diagnosis of AF. A GWAS-identified insulin receptor- locus (rs4804416) was the candidate gene. Cox survival models and sensitivity analyses by taking competing risk of death into account were used. Replication was performed in additional sample (The Genetics of Scottish Health Research register, GoSHARE), and meta-analyses across the results of the study and replication cohorts were done. We analyzed 962 exposed to L-thyroxine and 5,840 unexposed patients who were rs4804416 genotyped. The rarer G/G genotype was present in 18% of the study population. The total follow-up was up to 20 years, and there was a significant increased AF risk for patients homozygous carriers of the G allele exposed to L-thyroxine (RHR = 2.35, = 1.6e-02). The adjusted increased risk was highest within the first 3 years of exposure (RHR = 9.10, = 8.5e-04). Sensitivity analysis yielded similar results. Effects were replicated in GoSHARE ( = 3,190).

Conclusion: Homozygous G/G genotype at the locus (rs4804416) is associated with an increased risk of AF in patients on L-thyroxine, independent of serum of free thyroxine and thyroid-stimulating hormone serum concentrations.
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http://dx.doi.org/10.3389/fgene.2021.652878DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8260687PMC
June 2021

Profiles of Glucose Metabolism in Different Prediabetes Phenotypes, Classified by Fasting Glycemia, 2-Hour OGTT, Glycated Hemoglobin, and 1-Hour OGTT: An IMI DIRECT Study.

Diabetes 2021 Sep 7;70(9):2092-2106. Epub 2021 Jul 7.

Department of Epidemiology and Data Science, Amsterdam Medical Centre, location VUMC, Amsterdam, the Netherlands.

Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants ( = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or HbA indicative of prediabetes [IA1c]), two defects (IFG+IGT, IFG+IA1c, or IGT+IA1c), or all defects (IFG+IGT+IA1c). β-Cell function (BCF) and insulin sensitivity were assessed from OGTT. At baseline, in pooling of participants with isolated defects, they showed impairment in both BCF and insulin sensitivity compared with healthy control subjects. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, those with IGT showed lower insulin sensitivity, insulin secretion at reference glucose (ISR), and insulin secretion potentiation ( < 0.002). Conversely, those with IA1c showed higher insulin sensitivity and ISR ( < 0.0001). Among groups with two defects, we similarly found differences in both BCF and insulin sensitivity. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio >2, < 0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared with the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence.
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http://dx.doi.org/10.2337/db21-0227DOI Listing
September 2021

Utilizing Large Electronic Medical Record Data Sets to Identify Novel Drug-Gene Interactions for Commonly Used Drugs.

Clin Pharmacol Ther 2021 09 23;110(3):816-825. Epub 2021 Jul 23.

Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK.

Real-world prescribing of drugs differs from the experimental systems, physiological-pharmacokinetic models, and clinical trials used in drug development and licensing, with drugs often used in patients with multiple comorbidities with resultant polypharmacy. The increasing availability of large biobanks linked to electronic healthcare records enables the potential to identify novel drug-gene interactions in large populations of patients. In this study we used three Scottish cohorts and UK Biobank to identify drug-gene interactions for the 50 most commonly used drugs and 162 variants in genes involved in drug pharmacokinetics. We defined two phenotypes based upon prescribing behavior-drug-stop or dose-decrease. Using this approach, we replicate 11 known drug-gene interactions including, for example, CYP2C9/CYP2C8 variants and sulfonylurea/thiazolidinedione prescribing and ABCB1/ABCG2 variants and statin prescribing. We identify eight novel associations after Bonferroni correction, three of which are replicated or validated in the UK Biobank or have other supporting results: The C-allele at rs4918758 in CYP2C9 was associated with a 25% (15-44%) lower odds of dose reduction of quinine, P = 1.6 × 10 ; the A-allele at rs9895420 in ABCC3 was associated with a 46% (24-62%) reduction in odds of dose reduction with doxazosin, P = 1.2 × 10 , and altered blood pressure response in the UK Biobank; the CYP2D6*2 variant was associated with a 30% (18-40%) reduction in odds of stopping ramipril treatment, P = 1.01 × 10 , with similar results seen for enalapril and lisinopril and with other CYP2D6 variants. This study highlights the scope of using large population bioresources linked to medical record data to explore drug-gene interactions at scale.
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http://dx.doi.org/10.1002/cpt.2352DOI Listing
September 2021

The genetic association of the transcription factor NPAT with glycemic response to metformin involves regulation of fuel selection.

PLoS One 2021 1;16(7):e0253533. Epub 2021 Jul 1.

Division of Cellular Medicine, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, James Arnott Drive, Dundee, United Kingdom.

The biguanide, metformin, is the first-choice therapeutic agent for type-2 diabetes, although the mechanisms that underpin metformin clinical efficacy remain the subject of much debate, partly due to the considerable variation in patient response to metformin. Identification of poor responders by genotype could avoid unnecessary treatment and provide clues to the underlying mechanism of action. GWAS identified SNPs associated with metformin treatment success at a locus containing the NPAT (nuclear protein, ataxia-telangiectasia locus) and ATM (ataxia-telangiectasia mutated) genes. This implies that gene sequence dictates a subsequent biological function to influence metformin action. Hence, we modified expression of NPAT in immortalized cell lines, primary mouse hepatocytes and mouse tissues, and analysed the outcomes on metformin action using confocal microscopy, immunoblotting and immunocytochemistry. In addition, we characterised the metabolic phenotype of npat heterozygous knockout mice and established the metformin response following development of insulin resistance. NPAT protein was localised in the nucleus at discrete loci in several cell types, but over-expression or depletion of NPAT in immortalised cell models did not change cellular responses to biguanides. In contrast, metformin regulation of respiratory exchange ratio (RER) was completely lost in animals lacking one allele of npat. There was also a reduction in metformin correction of impaired glucose tolerance, however no other metabolic abnormalities, or response to metformin, were found in the npat heterozygous mice. In summary, we provide methodological advancements for the detection of NPAT, demonstrate that minor reductions in NPAT mRNA levels (20-40%) influence metformin regulation of RER, and propose that the association between NPAT SNPs and metformin response observed in GWAS, could be due to loss of metformin modification of cellular fuel usage.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0253533PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248654PMC
July 2021

The role of genetics in fetal programming of adult cardiometabolic disease.

J Dev Orig Health Dis 2021 Jun 28:1-8. Epub 2021 Jun 28.

The University of Edinburgh, The Queen's Medical Research Institute, Deanery of Molecular, Genetic and Population Health Sciences, Edinburgh, Midlothian, EH16 4TJ, UK.

Disturbances affecting early development have broad repercussions on the individual's health during infancy and adulthood. Multiple observational studies throughout the years have shown that alterations of fetal growth are associated with increased cardiometabolic disease risks. However, the genetic component of this association only started to be investigated in the last 40 years, when single genes with distinct effects were investigated. Birth weight (BW), commonly reported as the outcome of developmental growth, has been estimated to be 20% to 60% heritable. Through Genome-Wide Association (GWA) meta-analyses, 190 different loci have been identified being associated with BW, and while many of these loci designate genes involved in glucose and lipid metabolism, with clear ties to fetal development, the role of others is not yet understood. In addition, due to its influence over the intrauterine environment, the maternal genotype also plays an important part in the determination of offspring BW, with the same loci having independent effects of different magnitude or even direction. There is still much to uncover regarding the genetic determinants of BW and the interactions between maternal, offspring, and even paternal genotype. To fully understand these, diverse and novel cohorts from multiple ancestries collecting extensive neonatal phenotype will be needed. This review compiles, chronologically, the main findings in the investigation of the genetics of BW.
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http://dx.doi.org/10.1017/S2040174421000350DOI Listing
June 2021

Replication and cross-validation of type 2 diabetes subtypes based on clinical variables: an IMI-RHAPSODY study.

Diabetologia 2021 Sep 10;64(9):1982-1989. Epub 2021 Jun 10.

Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.

Aims/hypothesis: Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic.

Methods: In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA, random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort's cluster centres. Finally, we compared the time to insulin requirement for each cluster.

Results: Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6-90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requirement, while the MDH cluster showed the slowest progression.

Conclusions/interpretation: Clusters based on C-peptide instead of HOMA2 measures resemble those based on HOMA2 measures, especially for SIDD, SIRD and MOD. By adding HDL-cholesterol, the MARD cluster based upon HOMA2 measures resulted in the current clustering into two clusters, with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA, HDL-cholesterol, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts. Adding HDL-cholesterol to the clustering resulted in the identification of a cluster with very slow glycaemic deterioration.
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http://dx.doi.org/10.1007/s00125-021-05490-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8382625PMC
September 2021

Type 2 Diabetes, Metabolic Traits, and Risk of Heart Failure: A Mendelian Randomization Study.

Diabetes Care 2021 Jun 4. Epub 2021 Jun 4.

Medical Research Council Population Health Research Unit, University of Oxford, Oxford, U.K.

Objective: The aim of this study was to use Mendelian randomization (MR) techniques to estimate the causal relationships between genetic liability to type 2 diabetes (T2D), glycemic traits, and risk of heart failure (HF).

Research Design And Methods: Summary-level data were obtained from genome-wide association studies of T2D, insulin resistance (IR), glycated hemoglobin, fasting insulin and glucose, and HF. MR was conducted using the inverse-variance weighted method. Sensitivity analyses included the MR-Egger method, weighted median and mode methods, and multivariable MR conditioning on potential mediators.

Results: Genetic liability to T2D was causally related to higher risk of HF (odds ratio [OR] 1.13 per 1-log unit higher risk of T2D; 95% CI 1.11-1.14; < 0.001); however, sensitivity analysis revealed evidence of directional pleiotropy. The relationship between T2D and HF was attenuated when adjusted for coronary disease, BMI, LDL cholesterol, and blood pressure in multivariable MR. Genetically instrumented higher IR was associated with higher risk of HF (OR 1.19 per 1-log unit higher risk of IR; 95% CI 1.00-1.41; = 0.041). There were no notable associations identified between fasting insulin, glucose, or glycated hemoglobin and risk of HF. Genetic liability to HF was causally linked to higher risk of T2D (OR 1.49; 95% CI 1.01-2.19; = 0.042), although again with evidence of pleiotropy.

Conclusions: These findings suggest a possible causal role of T2D and IR in HF etiology, although the presence of both bidirectional effects and directional pleiotropy highlights potential sources of bias that must be considered.
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http://dx.doi.org/10.2337/dc20-2518DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323186PMC
June 2021

Interaction between Omeprazole and Gliclazide in Relation to CYP2C19 Phenotype.

J Pers Med 2021 May 3;11(5). Epub 2021 May 3.

Department of Pharmacology, Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina.

The antidiabetic drug gliclazide is partly metabolized by CYP2C19, the main enzyme involved in omeprazole metabolism. The aim of the study was to explore the interaction between omeprazole and gliclazide in relation to CYP2C19 phenotype using physiologically based pharmacokinetic (PBPK) modeling approach. Developed PBPK models were verified using in vivo pharmacokinetic profiles obtained from a clinical trial on omeprazole-gliclazide interaction in healthy volunteers, CYP2C19 normal/rapid/ultrarapid metabolizers (NM/RM/UM). In addition, the association of omeprazole cotreatment with gliclazide-induced hypoglycemia was explored in 267 patients with type 2 diabetes (T2D) from the GoDARTS cohort, Scotland. The PBPK simulations predicted 1.4-1.6-fold higher gliclazide area under the curve (AUC) after 5-day treatment with 20 mg omeprazole in all CYP2C19 phenotype groups except in poor metabolizers. The predicted gliclazide AUC increased 2.1 and 2.5-fold in intermediate metabolizers, and 2.6- and 3.8-fold in NM/RM/UM group, after simulated 20-day dosing with 40 mg omeprazole once and twice daily, respectively. The predicted results were corroborated by findings in patients with T2D which demonstrated 3.3-fold higher odds of severe gliclazide-induced hypoglycemia in NM/RM/UM patients concomitantly treated with omeprazole. Our results indicate that omeprazole may increase exposure to gliclazide and thus increase the risk of gliclazide-associated hypoglycemia in the majority of patients.
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http://dx.doi.org/10.3390/jpm11050367DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8147656PMC
May 2021

Cohort profile: DOLORisk Dundee: a longitudinal study of chronic neuropathic pain.

BMJ Open 2021 05 5;11(5):e042887. Epub 2021 May 5.

Chronic Pain Research Group, Division of Population Health and Genomics, Mackenzie Building, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK

Purpose: Neuropathic pain is a common disorder of the somatosensory system that affects 7%-10% of the general population. The disorder places a large social and economic burden on patients as well as healthcare services. However, not everyone with a relevant underlying aetiology develops corresponding pain. DOLORisk Dundee, a European Union-funded cohort, part of the multicentre DOLORisk consortium, was set up to increase current understanding of this variation in onset. In particular, the cohort will allow exploration of psychosocial, clinical and genetic predictors of neuropathic pain onset.

Participants: DOLORisk Dundee has been constructed by rephenotyping two pre-existing Scottish population cohorts for neuropathic pain using a standardised 'core' study protocol: Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) (n=5236) consisting of predominantly type 2 diabetics from the Tayside region, and Generation Scotland: Scottish Family Health Study (GS:SFHS; n=20 221). Rephenotyping was conducted in two phases: a baseline postal survey and a combined postal and online follow-up survey. DOLORisk Dundee consists of 9155 participants (GoDARTS=1915; GS:SFHS=7240) who responded to the baseline survey, of which 6338 (69.2%; GoDARTS=1046; GS:SFHS=5292) also responded to the follow-up survey (18 months later).

Findings To Date: At baseline, the proportion of those with chronic neuropathic pain (Douleur Neuropathique en 4 Questions questionnaire score ≥3, duration ≥3 months) was 30.5% in GoDARTS and 14.2% in Generation Scotland. Electronic record linkage enables large scale genetic association studies to be conducted and risk models have been constructed for neuropathic pain.

Future Plans: The cohort is being maintained by an access committee, through which collaborations are encouraged. Details of how to do this will be available on the study website (http://dolorisk.eu/). Further follow-up surveys of the cohort are planned and funding applications are being prepared to this effect. This will be conducted in harmony with similar pain rephenotyping of UK Biobank.
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http://dx.doi.org/10.1136/bmjopen-2020-042887DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103377PMC
May 2021

The Impact of Low-dose Gliclazide on the Incretin Effect and Indices of Beta-cell Function.

J Clin Endocrinol Metab 2021 06;106(7):2036-2046

Division of Population Health and Genomics, School of Medicine, University of Dundee, UK.

Aims/hypothesis: Studies in permanent neonatal diabetes suggest that sulphonylureas lower blood glucose without causing hypoglycemia, in part by augmenting the incretin effect. This mechanism has not previously been attributed to sulphonylureas in patients with type 2 diabetes (T2DM). We therefore aimed to evaluate the impact of low-dose gliclazide on beta-cell function and incretin action in patients with T2DM.

Methods: Paired oral glucose tolerance tests and isoglycemic infusions were performed to evaluate the difference in the classical incretin effect in the presence and absence of low-dose gliclazide in 16 subjects with T2DM (hemoglobin A1c < 64 mmol/mol, 8.0%) treated with diet or metformin monotherapy. Beta-cell function modeling was undertaken to describe the relationship between insulin secretion and glucose concentration.

Results: A single dose of 20 mg gliclazide reduced mean glucose during the oral glucose tolerance test from 12.01 ± 0.56 to 10.82 ± 0.5mmol/l [P = 0.0006; mean ± standard error of the mean (SEM)]. The classical incretin effect was augmented by 20 mg gliclazide, from 35.5% (lower quartile 27.3, upper quartile 61.2) to 54.99% (34.8, 72.8; P = 0.049). Gliclazide increased beta-cell glucose sensitivity by 46% [control 22.61 ± 3.94, gliclazide 33.11 ± 7.83 (P = 0.01)] as well as late-phase incretin potentiation [control 0.92 ± 0.05, gliclazide 1.285 ± 0.14 (P = 0.038)].

Conclusions/interpretation: Low-dose gliclazide reduces plasma glucose in response to oral glucose load, with concomitant augmentation of the classical incretin effect. Beta-cell modeling shows that low plasma concentrations of gliclazide potentiate late-phase insulin secretion and increase glucose sensitivity by 50%. Further studies are merited to explore whether low-dose gliclazide, by enhancing incretin action, could effectively lower blood glucose without risk of hypoglycemia.
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http://dx.doi.org/10.1210/clinem/dgab151DOI Listing
June 2021

Monogenic Diabetes: From Genetic Insights to Population-Based Precision in Care. Reflections From a Editors' Expert Forum.

Diabetes Care 2020 12;43(12):3117-3128

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K.

Individualization of therapy based on a person's specific type of diabetes is one key element of a "precision medicine" approach to diabetes care. However, applying such an approach remains difficult because of barriers such as disease heterogeneity, difficulties in accurately diagnosing different types of diabetes, multiple genetic influences, incomplete understanding of pathophysiology, limitations of current therapies, and environmental, social, and psychological factors. Monogenic diabetes, for which single gene mutations are causal, is the category most suited to a precision approach. The pathophysiological mechanisms of monogenic diabetes are understood better than those of any other form of diabetes. Thus, this category offers the advantage of accurate diagnosis of nonoverlapping etiological subgroups for which specific interventions can be applied. Although representing a small proportion of all diabetes cases, monogenic forms present an opportunity to demonstrate the feasibility of precision medicine strategies. In June 2019, the editors of convened a panel of experts to discuss this opportunity. This article summarizes the major themes that arose at that forum. It presents an overview of the common causes of monogenic diabetes, describes some challenges in identifying and treating these disorders, and reports experience with various approaches to screening, diagnosis, and management. This article complements a larger American Diabetes Association effort supporting implementation of precision medicine for monogenic diabetes, which could serve as a platform for a broader initiative to apply more precise tactics to treating the more common forms of diabetes.
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http://dx.doi.org/10.2337/dci20-0065DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8162450PMC
December 2020

Efficacy of Modern Diabetes Treatments DPP-4i, SGLT-2i, and GLP-1RA in White and Asian Patients With Diabetes: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

Diabetes Care 2020 08;43(8):1948-1957

University of Dundee, Dundee, U.K.

Background: The pathophysiology of type 2 diabetes differs markedly by ethnicity.

Purpose: A systematic review and meta-analysis was conducted to assess the impact of ethnicity on the glucose-lowering efficacy of the newer oral agents, sodium-glucose cotransporter 2 inhibitors (SGLT-2i), glucagon-like peptide 1 receptor agonists (GLP-1RA), and dipeptidyl peptidase 4 inhibitors (DPP-4i), using evidence from randomized clinical trials (RCTs).

Data Sources: A literature search was conducted in PubMed of all randomized, placebo-controlled trials of DPP-4i, SGLT-2i, and GLP-1RA. The search strategy was developed based on Medical Subject Headings (MeSH) terms and keywords.

Study Selection: A total of 64 studies that qualified for meta-analysis after full-text review based on predefined inclusion and exclusion criteria-RCTs with at least 50 patients in each arm, >70% of population from Asian or white group, duration ≥24 weeks, and publication up to March 2019-were selected for systematic review and meta-analysis.

Data Extraction: Data extraction was done for aggregated study-level data by two independent researchers. Absolute changes in HbA (%) from baseline to 24 weeks between the drug and placebo were considered as the primary end point of the study.

Data Synthesis: Change in HbA was evaluated by computing mean differences and 95% CIs between treatment and placebo arms.

Limitations: The study is based on summarized data and could not be separated based on East Asians and South Asians.

Conclusions: The glucose-lowering efficacy of SGLT-2i, and to a lesser extent DPP-4i, was greater in studies of predominantly Asian ethnicity compared with studies of predominantly white ethnicity. There was no difference seen by ethnicity for GLP-1RA.
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http://dx.doi.org/10.2337/dc19-2419DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7372059PMC
August 2020

Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study.

Diabetes Care 2021 02 15;44(2):511-518. Epub 2020 Dec 15.

Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.

Objective: We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D).

Research Design And Methods: A total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression.

Results: Faster HbA progression was independently associated with faster deterioration of OGIS and GS and increasing CLIm; visceral or liver fat, HDL-cholesterol, and triglycerides had further independent, though weaker, roles ( = 0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from area under the receiver operating characteristic = 0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS, and CLIm was relatively stable (odds ratios 0.07-0.09). T2D polygenic risk score and baseline pancreatic fat, glucagon-like peptide 1, glucagon, diet, and physical activity did not show an independent role.

Conclusions: Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of patients with T2D in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression.
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http://dx.doi.org/10.2337/dc20-1567DOI Listing
February 2021

Post-load glucose subgroups and associated metabolic traits in individuals with type 2 diabetes: An IMI-DIRECT study.

PLoS One 2020 30;15(11):e0242360. Epub 2020 Nov 30.

Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.

Aim: Subclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D.

Methods: The study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders.

Results: At baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1-3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18-1.92) for subgroup 2 and 1.88 (-0.08-3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose.

Conclusions: Different glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242360PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703960PMC
January 2021

In a cohort of individuals with type 2 diabetes using the drug sulfasalazine, HbA lowering is associated with haematological changes.

Diabet Med 2021 Sep 8;38(9):e14463. Epub 2020 Dec 8.

Division of Cellular Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK.

Objectives: Several small studies indicate the sulphonamide component of the drug sulfasalazine lowers HbA We investigated reduction of HbA following incident prescription of sulfasalazine and related aminosalicylates, lacking the sulphonamide group, in an observational cohort.

Research Design And Methods: Individuals in the Scottish Care Information Diabetes Collaboration (SCI-Diabetes) with type 2 diabetes and incident prescription for an aminosalicylate drug (sulfasalazine, mesalazine, olsalazine or balsalazide) were identified. Baseline and 6-month HbA were required for eligibility, to calculate HbA response. To investigate association with haemolysis, change in components of full blood count was assessed. Paired t-tests compared difference in baseline and treatment HbA measures and other clinical variables.

Results: In all, 113 individuals treated with sulfasalazine and 103 with mesalazine (lacking the sulphonamide group) were eligible, with no eligible individuals treated with olsalazine or balsalazide. Baseline characteristics were similar. Mean (SD) HbA reduction at 6 months was -9 ± 16 mmol/mol (-0.9 ± 1.4%) (p < 0.0001) in those taking sulfasalazine with no reduction in those taking mesalazine (2 ± 16 mmol/mol (0.2 ± 1.4%). Sulfasalazine but not mesalazine was associated with a mean (SD) increase in mean cell volume of 3.7 ± 5.6 fl (p < 0.0001) and decrease in red cell count of -0.2 ± 0.4 × 10 /L (p < 0.0001).

Conclusions: In this observational, population-based study, sulfasalazine initiation was associated with a 6-month reduction in HbA . This correlated with haematological changes suggesting haemolytic effects of sulfasalazine. Haemolysis is proposed to contribute to HbA lowering through the sulphonamide pharmacophore. This suggests that HbA is not a reliable measure of glycaemia in individuals prescribed sulfasalazine.
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http://dx.doi.org/10.1111/dme.14463DOI Listing
September 2021

A Polygenic Score for Type 2 Diabetes Risk Is Associated With Both the Acute and Sustained Response to Sulfonylureas.

Diabetes 2021 01 26;70(1):293-300. Epub 2020 Oct 26.

Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA

There is a limited understanding of how genetic loci associated with glycemic traits and type 2 diabetes (T2D) influence the response to antidiabetic medications. Polygenic scores provide increasing power to detect patterns of disease predisposition that might benefit from a targeted pharmacologic intervention. In the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH), we constructed weighted polygenic scores using known genome-wide significant associations for T2D, fasting glucose, and fasting insulin, comprising 65, 43, and 13 single nucleotide polymorphisms, respectively. Multiple linear regression tested for associations between scores and glycemic traits as well as pharmacodynamic end points, adjusting for age, sex, race, and BMI. A higher T2D score was nominally associated with a shorter time to insulin peak, greater glucose area over the curve, shorter time to glucose trough, and steeper slope to glucose trough after glipizide. In replication, a higher T2D score was associated with a greater 1-year hemoglobin A reduction to sulfonylureas in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study ( = 0.02). Our findings suggest that individuals with a higher genetic burden for T2D experience a greater acute and sustained response to sulfonylureas.
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http://dx.doi.org/10.2337/db20-0530DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7881853PMC
January 2021

The Relationship between AKI and CKD in Patients with Type 2 Diabetes: An Observational Cohort Study.

J Am Soc Nephrol 2021 01 18;32(1):138-150. Epub 2020 Sep 18.

Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom

Background: There are few observational studies evaluating the risk of AKI in people with type 2 diabetes, and even fewer simultaneously investigating AKI and CKD in this population. This limits understanding of the interplay between AKI and CKD in people with type 2 diabetes compared with the nondiabetic population.

Methods: In this retrospective, cohort study of participants with or without type 2 diabetes, we used electronic healthcare records to evaluate rates of AKI and various statistical methods to determine their relationship to CKD status and further renal function decline.

Results: We followed the cohort of 16,700 participants (9417 with type 2 diabetes and 7283 controls without diabetes) for a median of 8.2 years. Those with diabetes were more likely than controls to develop AKI (48.6% versus 17.2%, respectively) and have preexisting CKD or CKD that developed during follow-up (46.3% versus 17.2%, respectively). In the absence of CKD, the AKI rate among people with diabetes was nearly five times that of controls (121.5 versus 24.6 per 1000 person-years). Among participants with CKD, AKI rate in people with diabetes was more than twice that of controls (384.8 versus 180.0 per 1000 person-years after CKD diagnostic date, and 109.3 versus 47.4 per 1000 person-years before CKD onset in those developing CKD after recruitment). Decline in eGFR slope before AKI episodes was steeper in people with diabetes versus controls. After AKI episodes, decline in eGFR slope became steeper in people without diabetes, but not among those with diabetes and preexisting CKD.

Conclusions: Patients with diabetes have significantly higher rates of AKI compared with patients without diabetes, and this remains true for individuals with preexisting CKD.
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http://dx.doi.org/10.1681/ASN.2020030323DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7894655PMC
January 2021

Genome-Wide Association Analysis of Pancreatic Beta-Cell Glucose Sensitivity.

J Clin Endocrinol Metab 2021 01;106(1):80-90

Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.

Context: Pancreatic beta-cell glucose sensitivity is the slope of the plasma glucose-insulin secretion relationship and is a key predictor of deteriorating glucose tolerance and development of type 2 diabetes. However, there are no large-scale studies looking at the genetic determinants of beta-cell glucose sensitivity.

Objective: To understand the genetic determinants of pancreatic beta-cell glucose sensitivity using genome-wide meta-analysis and candidate gene studies.

Design: We performed a genome-wide meta-analysis for beta-cell glucose sensitivity in subjects with type 2 diabetes and nondiabetic subjects from 6 independent cohorts (n = 5706). Beta-cell glucose sensitivity was calculated from mixed meal and oral glucose tolerance tests, and its associations between known glycemia-related single nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) SNPs were estimated using linear regression models.

Results: Beta-cell glucose sensitivity was moderately heritable (h2 ranged from 34% to 55%) using SNP and family-based analyses. GWAS meta-analysis identified multiple correlated SNPs in the CDKAL1 gene and GIPR-QPCTL gene loci that reached genome-wide significance, with SNP rs2238691 in GIPR-QPCTL (P value = 2.64 × 10-9) and rs9368219 in the CDKAL1 (P value = 3.15 × 10-9) showing the strongest association with beta-cell glucose sensitivity. These loci surpassed genome-wide significance when the GWAS meta-analysis was repeated after exclusion of the diabetic subjects. After correction for multiple testing, glycemia-associated SNPs in or near the HHEX and IGF2B2 loci were also associated with beta-cell glucose sensitivity.

Conclusion: We show that, variation at the GIPR-QPCTL and CDKAL1 loci are key determinants of pancreatic beta-cell glucose sensitivity.
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http://dx.doi.org/10.1210/clinem/dgaa653DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7765651PMC
January 2021

Reducing Glut2 throughout the body does not result in cognitive behaviour differences in aged male mice.

BMC Res Notes 2020 Sep 16;13(1):438. Epub 2020 Sep 16.

Division of Cellular Medicine, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, James Arnott Drive, Dundee, DD1 9SY, UK.

Objectives: GLUT2 is a major facilitative glucose transporter, expressed from the SLC2A2 gene, with essential roles in the liver. Recent work in mice has shown that preventing Glut2 production in specific neuronal populations increases sugar-seeking behaviour, highlighting the importance of Slc2a2 gene expression in the brain. It implies that reduced GLUT2 in the brain, due to genetic polymorphisms or disease, impacts health through behaviour change. Defects in glucose transport in the brain are observed in conditions including type-2 diabetes and dementia. Few studies have directly examined the effect of modulating neuronal glucose transporter expression on cognitive function. The aim of this study was to investigate whether inactivating one Slc2a2 allele throughout the body had major effects on cognition. Cognitive tests to assess recognition memory, spatial working memory and anxiety were performed in Slc2a2 whole-body heterozygous mice (i.e. reduced Glut2 mRNA and protein), alongside littermates expressing normal levels of the transporter.

Results: No significant effects on neurological functions and cognitive capabilities were observed in mice lacking one Slc2a2 allele when fed a chow diet. This suggests that the minor variations in GLUT2 levels that occur in the human population are unlikely to influence behaviour and basic cognition.
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http://dx.doi.org/10.1186/s13104-020-05276-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7493158PMC
September 2020

Risk of Anemia With Metformin Use in Type 2 Diabetes: A MASTERMIND Study.

Diabetes Care 2020 10 14;43(10):2493-2499. Epub 2020 Aug 14.

Population Health & Genomics, School of Medicine, University of Dundee, Dundee, U.K.

Objective: To evaluate the association between metformin use and anemia risk in type 2 diabetes, and the time-course for this, in a randomized controlled trial (RCT) and real-world population data.

Research Design And Methods: Anemia was defined as a hemoglobin measure of <11 g/dL. In the RCTs A Diabetes Outcome Progression Trial (ADOPT; = 3,967) and UK Prospective Diabetes Study (UKPDS; = 1,473), logistic regression was used to model anemia risk and nonlinear mixed models for change in hematological parameters. In the observational Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) population ( = 3,485), discrete-time failure analysis was used to model the effect of cumulative metformin exposure on anemia risk.

Results: In ADOPT, compared with sulfonylureas, the odds ratio (OR) (95% CI) for anemia was 1.93 (1.10, 3.38) for metformin and 4.18 (2.50, 7.00) for thiazolidinediones. In UKPDS, compared with diet, the OR (95% CI) was 3.40 (1.98, 5.83) for metformin, 0.96 (0.57, 1.62) for sulfonylureas, and 1.08 (0.62, 1.87) for insulin. In ADOPT, hemoglobin and hematocrit dropped after metformin initiation by 6 months, with no further decrease after 3 years. In UKPDS, hemoglobin fell by 3 years in the metformin group compared with other treatments. At years 6 and 9, hemoglobin was reduced in all treatment groups, with no greater difference seen in the metformin group. In GoDARTS, each 1 g/day of metformin use was associated with a 2% higher annual risk of anemia.

Conclusions: Metformin use is associated with early risk of anemia in individuals with type 2 diabetes, a finding consistent across two RCTs and replicated in one real-world study. The mechanism for this early fall in hemoglobin is uncertain, but given the time course, is unlikely to be due to vitamin B deficiency alone.
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http://dx.doi.org/10.2337/dc20-1104DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7510037PMC
October 2020

Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study.

EBioMedicine 2020 Aug 4;58:102932. Epub 2020 Aug 4.

The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark.

Background: Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D.

Methods: We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (T) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous T score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models.

Findings: A higher T score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=-0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher T score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the T score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glucose (β=-0.2 mmol/L, 95% CI -0.4, -0.05) and insulin (β=-11.0 pmol/mol, 95% CI -19.5, -2.6). Longitudinal analysis showed at 18-month follow up a higher T score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, 95% CI -0.3, -0.01) and insulin (β=-9.2 pmol/mol, 95% CI -17.9, -0.4) concentrations in cohort 2.

Interpretation: Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health.

Funding: This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115,317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies.
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http://dx.doi.org/10.1016/j.ebiom.2020.102932DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7406914PMC
August 2020

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 07 28;17(7):e1003209. Epub 2020 Jul 28.

Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China.

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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http://dx.doi.org/10.1371/journal.pmed.1003209DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386560PMC
July 2020

Predicting post one-year durability of glucose-lowering monotherapies in patients with newly-diagnosed type 2 diabetes mellitus - A MASTERMIND precision medicine approach (UKPDS 87).

Diabetes Res Clin Pract 2020 Aug 21;166:108333. Epub 2020 Jul 21.

Diabetes Trials Unit, University of Oxford, Oxford, UK.

Aims: Predicting likely durability of glucose-lowering therapies for people with type 2 diabetes (T2D) could help inform individualised therapeutic choices.

Methods: We used data from UKPDS patients with newly-diagnosed T2D randomised to first-line glucose-lowering monotherapy with chlorpropamide-glibenclamide-basal insulin or metformin. In 2339 participants who achieved one-year HbA values <7.5% (<59 mmol/mol)-we assessed relationships between one-year characteristics and time to monotherapy-failure (HbA ≥ 7.5% or requiring second-line therapy). Model validation was performed using bootstrap sampling.

Results: Follow-up was median (IQR) 11.0 (8.0-14.0) years. Monotherapy-failure occurred in 72%-82%-75% and 79% for those randomised to chlorpropamide-glibenclamide-basal insulin or metformin respectively-after median 4.5 (3.0-6.6)-3.7 (2.6-5.6)-4.2 (2.7-6.5) and 3.8 (2.6- 5.2) years. Time-to-monotherapy-failure was predicted primarily by HbA and BMI values-with other risk factors varying by type of monotherapy-with predictions to within ±2.5 years for 55%-60%-56% and 57% of the chlorpropamide-glibenclamide-basal insulin and metformin monotherapy cohorts respectively.

Conclusions: Post one-year glycaemic durability can be predicted robustly in individuals with newly-diagnosed T2D who achieve HbA < 7.5% one year after commencing traditional monotherapies. Such information could be used to help guide glycaemic management for individual patients.
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http://dx.doi.org/10.1016/j.diabres.2020.108333DOI Listing
August 2020

Precision Medicine in Diabetes: A Consensus Report From the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD).

Diabetes Care 2020 07;43(7):1617-1635

Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Lund University, Malmo, Sweden

The convergence of advances in medical science, human biology, data science, and technology has enabled the generation of new insights into the phenotype known as "diabetes." Increased knowledge of this condition has emerged from populations around the world, illuminating the differences in how diabetes presents, its variable prevalence, and how best practice in treatment varies between populations. In parallel, focus has been placed on the development of tools for the application of precision medicine to numerous conditions. This Consensus Report presents the American Diabetes Association (ADA) Precision Medicine in Diabetes Initiative in partnership with the European Association for the Study of Diabetes (EASD), including its mission, the current state of the field, and prospects for the future. Expert opinions are presented on areas of precision diagnostics and precision therapeutics (including prevention and treatment), and key barriers to and opportunities for implementation of precision diabetes medicine, with better care and outcomes around the globe, are highlighted. Cases where precision diagnosis is already feasible and effective (i.e., monogenic forms of diabetes) are presented, while the major hurdles to the global implementation of precision diagnosis of complex forms of diabetes are discussed. The situation is similar for precision therapeutics, in which the appropriate therapy will often change over time owing to the manner in which diabetes evolves within individual patients. This Consensus Report describes a foundation for precision diabetes medicine, while highlighting what remains to be done to realize its potential. This, combined with a subsequent, detailed evidence-based review (due 2022), will provide a roadmap for precision medicine in diabetes that helps improve the quality of life for all those with diabetes.
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http://dx.doi.org/10.2337/dci20-0022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305007PMC
July 2020

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

PLoS Med 2020 06 19;17(6):e1003149. Epub 2020 Jun 19.

Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.

Background: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning.

Methods And Findings: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one.

Conclusions: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.

Trial Registration: ClinicalTrials.gov NCT03814915.
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http://dx.doi.org/10.1371/journal.pmed.1003149DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304567PMC
June 2020
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