Type 2 diabetes and hypertension are common health conditions that often occur together, suggesting shared biological mechanisms. To explore this relationship, we analyse large-scale multiomic data to uncover genetic factors underlying type 2 diabetes and blood pressure comorbidity. We curate 1304 independent single-nucleotide variants associated with type 2 diabetes and blood pressure, grouping them into five clusters related to metabolic syndrome, inverse type 2 diabetes/blood pressure risk, impaired pancreatic beta-cell function, higher adiposity, and vascular dysfunction. Colocalization with tissue-specific gene expression highlights significant enrichment in pathways related to thyroid function and fetal development. Partitioned polygenic scores derived from these clusters improve risk prediction for type 2 diabetes/hypertension comorbidity, identifying individuals with more than twice the usual susceptibility. These results reveal a mechanistically heterogeneous genetic architecture shared between type 2 diabetes and blood pressure, enhancing comorbidity risk prediction. Partitioned polygenic risk scores offer a promising approach for early risk stratification, personalised prevention, and improved management of these interconnected conditions.

Age is the strongest risk factor for type 2 diabetes, yet their independent contribution to pancreatic islet dysfunction remains unclear. We integrate DNA methylation, transcriptomic, and genotyping data from 144 islet donors. We identify 996 age- and 902 T2D-associated CpGs with minimal overlap, and 251 age- and 310 diabetes CpG target genes, usually distant from the CpG. Age-linked CpGs are enriched in promoters, form co-regulated gene modules, link to beta-cell function, including insulin secretion. Diabetes-associated CpGs are enriched in enhancer/non-regulatory regions, and modules suggest stress-induced epigenetic drift. CpG-gene associations are independent of genetic variation. Mendelian randomisation supports a causal role for age-associated CpGs regulating KLHL42, a T2D GWAS locus. A blood-based methylation risk score based on age-linked CpGs correlates with insulin secretion and improves diabetes classification when combined with genetic risk (AUC = 0.91). Altogether, age is associated with a coordinated epigenetic programme, whereas diabetes links to a heterogeneous, stress-related epigenetic signature.

Background & Aims Metabolic dysfunction-associated steatotic liver disease (MASLD, previously NAFLD) is a frequent co-morbidity of obesity and diabetes, with prevalence increasing worldwide in all age groups and both sexes. Only early stages of the disease are fully reversible. Recognising liver disease stages and elucidating the molecular underpinning of their progression are thus medically important. We developed a deep learning model to recognise simple steatosis from steatohepatitis combining liver transcriptomics, epigenetics, and clinical data. Methods We used clinical data, liver gene expression and liver DNA methylation gathered from 300 patients with obesity of the ABOS cohort (80 without NAFLD, 137 with simple steatosis, 83 with steatohepatitis). We selected non-redundant clinical variables, gene expressions and CpGs methylation levels most associated with severity using unsupervised approaches. We designed a multi-module, multi-layer perceptron to predict patients’ liver status. We trained five model instances on independent training/test sets and combined the predictions. Results We used a score based on gene expression/DNA methylation and relevant principal component analysis (PCA) loadings to select 200 genes and 260 CpG methylations. Models trained on the three modalities reached an AUC of 0.945 overall on a validation set with accuracies above 81% for simple steatosis and 88% for NASH, outperforming any other machine learning model so far. We retrieved patient clusters previously found using clinical variables in the latent space of our clinical data module, but not in the gene expression and DNA methylation modules. While all three modules are needed to reach the best prediction accuracy in all classes, the gene expression module had the most impact on the decision. Independent models weighted gene expression inputs similarly, shining light on their importance. The most impactful genes were linked to immune responses and extracellular matrix. However, many of those genes were previously unassociated with steatotic liver disease onset or progression. Conclusions A multi-omics deep-learning model can recognise steatohepatitis from simple liver steatosis with an AUC of 0.945 and identify new genes potentially involved in NAFLD progression. Gene expressions profiles predicting disease severity are largely different from those specific of clinical variable clusters. Impact and implications This study suggests that clinical variables are not sufficient to recognise the severity of steatotic liver disease with high accuracy, but model efficiency increases when used together with liver epigenetics and transcriptomics. ### Competing Interest Statement The authors have declared no competing interest. * ABOS : Atlas Biologique de l’Obésité Sévère AUC : area under the (ROC) curve CLR : context likelihood of relatedness TPM : transcript per million readss CRP : c-reactive protein GENIE3 : gene network inference with ensemble of trees HDL : high-density lipoproteins kNN : k nearest neighbours LDL : low-density lipoproteins MAFL : metabolic dysfunction–associated steatotic liver MASH : metabolic dysfunction associated steatohepatitis MASLD : metabolic dysfunction associated steatotic liver disease MLP : multi-layer perceptron NAS : NAFLD activity score NASH : non-alcoholic steatohepatitis NAFL : non-alcoholic fatty liver NAFLD : non-alcoholic fatty liver disease OGTT : Oral Glucose Tolerance Test PCA : principal component analysis ROC : receiver operating characteristic Agence Nationale de la Recherche, 18-IBHU-0001, 16-RHUS-0006, I-SITE ULNE / ANR-16-IDEX-0004 ULNE, 10-LABX-0046 Region Hauts-de-France, 2020-R3-CTRL\_IPL\_Phase4, STaRS Gambardella European regional development fund, 20001891/NP0025517 European Commission, 101080465 European Research Council, 101043671 Metropole Europeenne de Lille, 2019\_ESR\_11

Type 2 diabetes (T2D) and hypertension are common health conditions that often occur together, suggesting shared biological mechanisms. To explore this relationship, we analysed large-scale multiomic data to uncover genetic factors underlying T2D and blood pressure (BP) comorbidity. We curated 1,304 independent single-nucleotide variants (SNVs) associated with T2D/BP, grouping them into five clusters related to metabolic syndrome, inverse T2D-BP risk, impaired pancreatic beta-cell function, higher adiposity, and vascular dysfunction. Colocalisation with tissue-specific gene expression highlighted significant enrichment in pathways related to thyroid function and fetal development. Partitioned polygenic scores (PGS) derived from these clusters improved risk prediction for T2D-hypertension comorbidity, identifying individuals with more than twice usual susceptibility. These results reveal complex genetic basis of shared T2D and BP mechanistic heterogeneity, enhancing comorbidity risk prediction. Partitioned PGSs offer promising approach for early risk stratification, personalised prevention, and improved management of these interconnected conditions, supporting precision medicine and public health initiatives. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research has been conducted using the UK Biobank Resource under application number 236. This project was in part funded by the Agence Nationale de la Recherche under the Programme d'Investissement d'Avenir (PreciDIAB, ANR-18-IBHU-0001 and RHU PreciNASH ANR-16-RHUS-0006), by the European Union through the "Fonds Europeen de Developpement Regional" (FEDER), by the "Conseil Regional des Hauts-de-France" (Hauts-de-France Regional Council), by the "Metropole Europeenne de Lille" (MEL, European Metropolis of Lille), and by the European Research Council (ERC OpiO - 101043671, to AB) The authors would like to thank all the investigators from different consortia that built and shared the GWAS meta-analysis, eQTLs, and scATAC-seq atlases used in this study, as well as the UK Biobank participants and dedicated staff. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The GWAS used in this study are all publicly available and listed in Supplementary Table 4. The UK Biobank Resource (UKB, https://ukbiobank.ac.uk/) was accessed using the Application Number 236. GTEx (https://www.gtexportal.org/home/datasets/) and TIGER (http://tiger.bsc.es/) eQTLs are publicly available. The ATAC-seq data from CATLAS are publicly available http://catlas.org/humanenhancer/. Data from the ABOS cohort are not publicly available, as the study is ongoing. The Biological Atlas of Severe Obesity (Atlas Biologique de l'Obesité Sévère [ABOS]) cohort (ClinicalTrials.gov: [NCT01129297][1]) is an ongoing prospective study that aims to identify the determinants of bariatric surgery outcomes. Patients were recruited at the Centre Hospitalier Universitaire de Lille (France), as previously described in DOI: 10.1097/SLA.0000000000000945, DOI: 10.1016/S2213-8587(22)00005-5, and DOI: 10.1038/s41467-024-51078-2. All human procedures were ethically approved by the Comité de Protection des Personnes Nord Ouest IV or by the ethics committee of Liège University Hospital. The analysis performed in this study aligned with the original scopes and objectives of the ABOS and Liège cohort studies; therefore, no additional ethical approval was requested. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The GWAS used in this study are all publicly available and listed in Supplementary Table 4. The UK Biobank Resource (UKB, https://ukbiobank.ac.uk/) was accessed using the Application Number 236. GTEx (https://www.gtexportal.org/home/datasets/) and TIGER (http://tiger.bsc.es/) eQTLs are publicly available. Data from the ABOS cohort are not publicly available, as the study is ongoing. The ATAC-seq data from CATLAS are publicly available http://catlas.org/humanenhancer/. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT01129297&atom=%2Fmedrxiv%2Fearly%2F2025%2F03%2F06%2F2025.03.02.25323190.atom

Variants at the SLC30A8 locus are associated with type 2 diabetes (T2D) risk. The lead variant, rs13266634, encodes an amino acid change, Arg325Trp (R325W), at the C-terminus of the secretory granule-enriched zinc transporter, ZnT8. Although this protein-coding variant was previously thought to be the sole driver of T2D risk at this locus, recent studies have provided evidence for lowered expression of SLC30A8 mRNA in protective allele carriers. In the present study, combined allele-specific expression (cASE) analysis in human islets revealed multiple variants that influence SLC30A8 expression. Epigenomic mapping identified an islet-selective enhancer cluster at the SLC30A8 locus, hosting multiple T2D risk and cASE associations, which is spatially associated with the SLC30A8 promoter and additional neighbouring genes. Deletions of variant-bearing enhancer regions using CRISPR-Cas9 in human-derived EndoC-βH3 cells lowered the expression of SLC30A8 and several neighbouring genes, and improved insulin secretion. Whilst down-regulation of SLC30A8 had no effect on beta cell survival, loss of UTP23, RAD21 or MED30 markedly reduced cell viability. Although eQTL or cASE analyses in human islets did not support the association between these additional genes and diabetes risk, the transcriptional regulator JQ1 lowered the expression of multiple genes at the SLC30A8 locus and enhanced stimulated insulin secretion.

Abstract: We postulated that T2D predisposes to exocrine pancreatic diseases through (epi)genetic mechanisms. We explored the methylome (methylationEPIC arrays) of the exocrine pancreas of 141 donors, assessing the impact of T2D. Epigenome-wide association study (EWAS) for T2D identified a hypermethylation in an enhancer of the Pancreatic-Lipase-Related-Protein 1 (PNLIPRP1) gene, associated with decreased PNLIPRP1 expression. PNLIPRP1 null variants (in 191K participants of the UKbiobank) associated with elevated glycemia and LDL-cholesterol. Mendelian Randomisation using 2.5M SNP OmniArrays in 111 donors evidenced that T2D was causal of PNLIPRP1 hypermethylation, which was causal for LDL-cholesterol. Further AR42J rat exocrine cell studies demonstrated that Pnliprp1 knockdown induced acinar-to-ductal metaplasia, a known pre-pancreatic cancer state, and increased cholesterol levels, reversible with statin. This (epi)genetic study suggests a role for PNLIPRP1 in human metabolism and on exocrine pancreas function with potential implications for pancreatic diseases. Article Highlights: a. Why did we undertake this study? We performed this study to identify epigenetic changes with T2D in the pancreas. b. What is the specific question(s) we wanted to answer? This study addresses whether T2D induce epigenetic changes that could explain why T2D individuals are more prone to pancreas disease. c. What did we find? We found a hypermethylation at PNLIPRP1 associated with T2D and revealed a role of this gene in cholesterol metabolism. d. What are the implications of our findings? This study has important implications in the prevention of pancreatic diseases as their molecular mechanisms remain largely unknown.

OBJECTIVE. Depression is a common co-morbidity of type 2 diabetes. We assessed the causal relationships and shared genetics between them. RESEARCH DESIGN AND METHODS. We applied two-sample bi-directional Mendelian randomization (MR) to assess causality between type 2 diabetes and depression. We investigated potential mediation using two-step MR. To identify shared genetics, we performed 1) GWAS, separately, and 2) multi-phenotype GWAS (MP-GWAS) of type 2 diabetes (cases=19,344, controls=463,641) and depression, using major depressive disorder (MDD, cases=5,262, controls=86,275) and self-reported depressive symptoms (n=153,079) in UK biobank. We analyzed expression quantitative trait loci (eQTL) data from public databases to identify target genes in relevant tissues. RESULTS. MR demonstrated a significant causal effect of depression on type 2 diabetes (OR=1.26[1.11-1.44], p=5.46×10-4), but not in the reverse direction. Mediation analysis indicated that 36.5% [12.4-57.6%, p=0.0499] of the effect from depression to type 2 diabetes was mediated by BMI. GWAS of type 2 diabetes and depressive symptoms did not identify shared loci. MP-GWAS identified seven shared loci mapped to TCF7L2, CDKAL1, IGF2BP2, SPRY2, CCND2-AS1, IRS1, CDKN2B-AS1. MDD was insignificant in both GWAS and MP-GWAS. Most MP-GWAS loci had an eQTL, including SNPs implicating the cell cycle gene CCND2 in pancreatic islets and brain, and insulin signaling gene IRS1 in adipose tissue, suggesting a multi-tissue and pleiotropic underlying mechanism. CONCLUSION. Our results highlight the importance to prevent type 2 diabetes at the onset of depressive symptoms, and the need to maintain a healthy weight in the context of its effect on depression and type 2 diabetes co-morbidity.

OBJECTIVE:Human functional genomics has proven powerful in discovering drug targets for common metabolic disorders. Through this approach, we investigated the involvement of the purinergic receptor P2RY1 in type 2 diabetes (T2D). METHODS:P2RY1 was sequenced in 9,266 participants including 4,177 patients with T2D. In vitro analyses were then performed to assess the functional effect of each variant. Expression quantitative trait loci (eQTL) analysis was performed in pancreatic islets from 103 pancreatectomized individuals. The effect of P2RY1 on glucose-stimulated insulin secretion was finally assessed in human pancreatic beta cells (EndoCβH5), and RNA sequencing was performed on these cells. RESULTS:Sequencing P2YR1 in 9,266 participants revealed 22 rare variants, seven of which were loss-of-function according to our in vitro analyses. Carriers, except one, exhibited impaired glucose control. Our eQTL analysis of human islets identified P2RY1 variants, in a beta-cell enhancer, linked to increased P2RY1 expression and reduced T2D risk, contrasting with variants located in a silent region associated with decreased P2RY1 expression and increased T2D risk. Additionally, a P2RY1-specific agonist increased insulin secretion upon glucose stimulation, while the antagonist led to decreased insulin secretion. RNA-seq highlighted TXNIP as one of the main transcriptomic markers of insulin secretion triggered by P2RY1 agonist. CONCLUSION:Our findings suggest that P2RY1 inherited or acquired dysfunction increases T2D risk and that P2RY1 activation stimulates insulin secretion. Selective P2RY1 agonists, impermeable to the blood-brain barrier, could serve as potential insulin secretagogues.

OBJECTIVES:Focusing on policy discourse in the United Kingdom, we examine the chain of causation that is characteristic of the ways in which the concepts of avoidability and inappropriateness are defined and used in these contexts. With a particular focus on diabetes complications, we aim to elucidate the way in which avoidable admission to hospital is conceptualised, measured, and applied to policy development and implementation and build a more inclusive model of identification as a basis for further research in this area.STUDY DESIGN:Discourse analysis was used in combination with a scoping review.METHODS:We searched the online databases of the UK Houses of Parliament Hansard, Official reports of the Northern Ireland Assembly and transcripts of the Scottish Parliament in October 2021. We also conducted an electronic search in October 2021 on MEDLINE, PubMed, Google Scholar, EMBASE, CINAHL and The Cochrane Library to review the available literature. In addition, an analysis of policies in place in Scotland, England and Northern Ireland relating to urgent diabetes care was conducted.RESULTS:'Avoidable' and 'inappropriate' hospital admissions are categories used in health policy and practice internationally as ways of identifying targets for interventions intending to reduce the burden of care. Diabetes mellitus is a chronic condition that is often seen as a costly and avoidable use of health care services and so is a frequent target of such policies. Avoidable admission is interpreted as having a very long chain of causation. The assumption is that people requiring unscheduled hospital admission could have taken steps to prevent the onset of diabetes, or associated complications, arising in the first place. Definitions focus on primary and secondary prevention and largely place responsibility on the individual and their behaviour rather than on structural or social factors. Inadequate or inappropriate care prehospital or in the emergency department is seldom considered as a potential cause of avoidable admissions. Procedural definitions of avoidable admission are proposed whereby health care professionals and people living with diabetes collaborate to identify avoidable admissions in clinical audit rather than using statistical rates of avoidable admission within isolation in policy development and implementation.CONCLUSIONS:Avoidability and inappropriateness are characteristics of cases in which conduct of the individual or attendant health care professionals was a proximate cause of hospital admission, and but for such conduct, admission could have been avoided. This process of definition seeks to provide a basis for contextualised and considered evaluation of where there are problems in care and where there are reasonable opportunities for prevention.

ABSTRACTGestational diabetes mellitus (GDM) is associated with increased risk of pregnancy complications and adverse perinatal outcomes. GDM often reoccurs and is associated with increased risk of subsequent diagnosis of type 2 diabetes (T2D). To improve our understanding of the aetiological factors and molecular processes driving the occurrence of GDM, including the extent to which these overlap with T2D pathophysiology, the GENetics of Diabetes In Pregnancy (GenDIP) Consortium assembled genome-wide association studies (GWAS) of diverse ancestry in a total of 5,485 women with GDM and 347,856 without GDM. Through trans-ancestry meta-analysis, we identified five loci with genome-wide significant association (p<5×10−8) with GDM, mapping to/nearMTNR1B(p=4.3×10−54),TCF7L2(p=4.0×10−16),CDKAL1(p=1.6×10−14),CDKN2A-CDKN2B(p=4.1×10−9) andHKDC1(p=2.9×10−8). Multiple lines of evidence pointed to genetic contributions to the shared pathophysiology of GDM and T2D: (i) four of the five GDM loci (notHKDC1) have been previously reported at genome-wide significance for T2D; (ii) significant enrichment for associations with GDM at previously reported T2D loci; (iii) strong genetic correlation between GDM and T2D; and (iv) enrichment of GDM associations mapping to genomic annotations in diabetes-relevant tissues and transcription factor binding sites. Mendelian randomisation analyses demonstrated significant causal association (5% false discovery rate) of higher body mass index on increased GDM risk. Our results provide support for the hypothesis that GDM and T2D are part of the same underlying pathology but that, as exemplified by theHKDC1locus, there are genetic determinants of GDM that are specific to glucose regulation in pregnancy.

Background Type 2 diabetes (T2D) increases the risk of pancreatic ductal adenocarcinoma (PDAC), which could be due to an epigenetic mechanism. Methods We explored the association between T2D and whole pancreas methylation in 141 individuals, of which 28 had T2D, using Illumina MethylationEPIC 850K BeadChip arrays. We performed downstream functional assessment in the rat acinar pancreas cell line AR42J. To further understand the role of our candidate gene in humans, we tested whether null variants were associated with T2D and related traits using the UK biobank. Results Methylation analysis identified one significant CpG associated with T2D: hypermethylation in an enhancer in PNLIPRP1 , an acinar-specific gene. PNLIPRP1 expression was decreased in T2D individuals. Using a rat acinar cell line, we 1/ confirmed decreased Pnliprp1 in response to a diabetogenic treatment, and 2/ in Pnliprp1 knockdown, an up-regulation of cholesterol biosynthesis, cell cycle down-regulation, decreased expression of acinar markers and increased expression of ductal markers pointing towards acinar-to-ductal metaplasia (ADM), a hallmark of PDAC initiation. Using exome data from UK Biobank, we show that rare PNLIPRP1 null variants associated with increased glucose, BMI and LDL-cholesterol. Conclusions/interpretation We present evidence that an epigenetically-regulated gene associates with T2D risk, and might promote ADM and PDAC progression, opening new insights into early prevention of PDAC.

Aim Understanding DNA methylation dynamics associated with progressive hyperglycaemia exposure could provide early diagnostic biomarkers and an avenue for delaying type 2 diabetes (T2D) disease. We aimed to identify DNA methylation changes during a 6-year period associated with early hyperglycaemia exposure using the longitudinal D.E.S.I.R. cohort. Methods We selected individuals with progressive hyperglycaemia exposure based on T2D diagnostic criteria: 27 with long-term exposure, 34 with short-term exposure and 34 normoglycaemic controls. DNA from blood at inclusion and at the 6-years visit was subjected to methylation analysis using 850K methylation-EPIC arrays. A linear mixed model was used to perform an epigenome-wide association study (EWAS) and identify methylated changes associated with hyperglycaemia exposure during 6-year time-period. Results We did not identify differentially methylated sites that reached FDR-significance in our cohort. Based on EWAS, we focused our analysis on methylation sites that had a constant effect during the 6-years across the hyperglycaemia groups compared to controls and found the most statistically significant site was the reported cg19693031 probe (TXNIP). We also performed an EWAS with HbA1c, using the inclusion and the 6-years methylation data and did not identify any FDR-significant CpGs. Conclusions Our study reveals that DNA methylation changes are not robustly associated with hyperglycaemia exposure or HbA1c during a short-term period, however, our top loci indicate potential interest and should be replicated in larger cohorts.

OBJECTIVEMaternal glycemic dysregulation during pregnancy increases the risk of adverse health outcomes in her offspring; a risk thought to be linearly related to maternal hyperglycemia. It is hypothesized that changes in offspring DNA methylation (DNAm) underline these associations.RESEARCH DESIGN AND METHODSTo address this hypothesis, we conducted fixed-effect meta-analyses of epigenome-wide association study (EWAS) results from eight birth cohorts investigating relationships between cord blood DNAm and fetal exposure to maternal glucose (Nmax= 3,503), insulin (Nmax= 2,062), and the area under the curve of glucose (AUCgluc) following oral glucose tolerance tests (OGTT, Nmax= 1,505). We performed look-up analyses for identified CpG dinucleotides (CpGs) in independent observational cohorts to examine associations between DNAm and cardiometabolic traits as well as tissue-specific gene expression.RESULTSGreater maternal AUCgluc was associated with lower cord blood DNAm at neighboring CpGs cg26974062 (β= -0.013 [SE=2.1x10-3], PFDR= 5.1x10-3) and cg02988288 (β= -0.013 [SE=2.3x10-3], PFDR =0.031) in TXNIP. These associations were attenuated in women with GDM. Lower blood DNAm at these two CpGs near TXNIP was associated with multiple metabolic traits later in life, including type 2 diabetes. TXNIP DNAm in liver biopsies was associated with hepatic expression of TXNIP. We observed little evidence of associations between either maternal glucose or insulin and cord blood DNAm.CONCLUSIONMaternal hyperglycemia, as reflected by AUCgluc, was associated with lower cord blood DNAm at TXNIP. Associations between DNAm at these CpGs and metabolic traits in subsequent look-up analyses suggest that these may be candidate loci to investigate in future causal and mediation analyses.

Aims: Pre-gestational diabetes mellitus (PGDM) is associated with adverse outcomes. We aimed to examine pregnancies affected by PGDM; report on these pregnancy outcomes and compare outcomes for patients with type 1 versus type 2 diabetes mellitus; compare our findings to published Irish and United Kingdom (UK) data and identify potential areas for improvement. Methods: Between 2016 and 2018 information on 679 pregnancies from 415 women with type 1 Diabetes Mellitus and 244 women with type 2 diabetes was analysed. Data was collected on maternal characteristics; pregnancy preparation; glycaemic control; pregnancy related complications; foetal and maternal outcomes; unscheduled hospitalisations; congenital anomalies and perinatal deaths. Results: Only 15.9% of women were adequately prepared for pregnancy. Significant deficits were identified in availability and attendance at pre-pregnancy clinic, use of folic acid, attaining appropriate glycaemic targets and appropriate retinal screening. The majority of pregnancies (n = 567, 83.5%) resulted in a live birth but the large number of infants born large for gestational age (LGA) (n = 280, 49.4%), born prematurely <37 weeks and requiring neonatal intensive care unit (NICU) admission continue to be significant issues. Conclusions: This retrospective cohort study identifies multiple targets for improvements in the provision of care to women with pre-gestational DM which are likely to translate into better pregnancy outcomes. (C) 2021 Elsevier B.V. All rights reserved.