DNA methylation has been shown to be associated with kidney function and diabetic kidney disease (DKD), but prospective studies are scarce. Therefore, we conducted epigenome-wide association studies (EWASs) on early- and late-stage DKD progression using DNA methylation data obtained by analysing baseline blood samples from participants in the Finnish Diabetic Nephropathy Study type 1 diabetes cohort. We included 403 individuals with normal AER (early-stage progression group) and 372 individuals with severe albuminuria (late-stage progression group), and followed up DKD progression, defined as a decrease in eGFR to <60 ml/min per 1.73 m2 in the early-stage progression group, and end-stage kidney disease (ESKD) in the late-stage group. Replication was conducted in two type 1 diabetes cohorts in addition to publicly available EWAS summary statistics from diabetes and general population cohorts. Significant loci were further characterised by integration with genetic and proteomic data. We identified 11 methylation sites associated with DKD progression (p<9.4 × 10−8). Methylation at cg01730944 near the podocyte-specific gene CDKN1C and three other CpGs associated with early-stage DKD progression were independent of baseline eGFR, whereas late-stage progression CpGs were strongly associated with eGFR. The identified lead ESKD risk locus cg17944885 (chr19p13.2, p=2.6 × 10−17) and several novel methylation sites associated with late-stage DKD progression were supported by the results of previous studies. Proteomic analysis of cis proteins identified potential target genes for two CpGs: cg14999724 methylation was associated with PRG3 and PRG2, and cg12272104 was associated with BSG, FSTL3 and PALM. Furthermore, UK Biobank data show associations between these proteins and severe kidney endpoints. Finally, survival models that included methylation markers in addition to clinical risk factors significantly improved the identification of individuals at risk of early-stage DKD progression. The current study detected 11 loci associated with DKD progression, identifying methylation changes predictive of early-stage DKD progression in type 1 diabetes for the first time. Future research is needed to establish prognostic DNA methylation markers for DKD progression.

Although genome-wide association studies (GWAS) have identified loci associated with type 1 diabetes, the specific pathways and regulatory networks linking these loci to disease pathology remain largely unknown. We hypothesised that type 1 diabetes genetic risk factors disrupt tissue-specific biological pathways and gene networks that ultimately lead to beta cell loss. We conducted a multi-tissue multi-omics analysis that integrates human GWAS data for type 1 diabetes with tissue-specific regulatory data for gene expression and gene network models across relevant tissues to highlight key pathways and key driver (KD) genes contributing to type 1 diabetes pathogenesis. KD genes were validated using islet-specific gene expression and protein data from non-obese diabetic (NOD) mice compared with type 2 diabetic and non-diabetic mouse models. Drug repositioning predictions were generated using the INCS L1000 and PharmOmics platforms, and candidate drugs were tested using electronic medical records (EMRs) of individuals with type 1 diabetes from the OneFlorida+ Clinical Data Network. Our integrative genomics approach identified known immune pathways across multiple tissues, such as adaptive immune responses, cytokine-mediated inflammation, primary immunodeficiency, and interactions between lymphoid and non-lymphoid cells. Tissue-specific signals included genes related to type 2 diabetes in lymphocytes, viral response pathways in macrophages and monocytes, and Notch signalling in adipose tissue and immune cells. In pancreatic islet analysis, we observed significant enrichment for type 1 diabetes and type 2 diabetes gene sets alongside immune-related pathways, including antigen processing, systemic lupus erythematosus and IFN signalling. Removing HLA genes from the analysis revealed additional immune pathways, such as retinoic acid-inducible gene I (RIG-I)/melanoma differentiation-associated protein 5 (MDA5) induction of interferons, together with melanogenesis, steroid hormone synthesis and iron transport. Network modelling highlighted the autoimmune basis of disease, with KDs such as FYN, TAP1, WAS and HLA-B/C/G, as well as additional immunomodulatory proteins such as LCK, LCP2 and genes such as EMR1 and GC. These KDs were further supported by gene and protein expression data from NOD mice. We additionally highlight various drug classes that target the type 1 diabetes genetic networks and may be useful to delay type 1 diabetes development; some of these were supported by our EMR screen. Our multi-tissue multi-omics approach provides a detailed landscape of the tissue-specific genetic networks and regulators underlying type 1 diabetes. This analysis confirms the roles of known immune pathways while uncovering additional regulatory elements and disease-associated networks, thus expanding our understanding of type 1 diabetes pathogenesis. The identification of potential drug candidates through network analysis, with supporting evidence from EMRs, offers potential therapeutic strategies for targeting disease pathways and holds promise for delaying or preventing type 1 diabetes progression.

ABSTRACT Background Multimorbidity in type 1 diabetes has previously not been studied in detail. Therefore, we aimed to assess the prevalence of multimorbidity and its association with mortality in type 1 diabetes. Methods This observational follow‐up study includes 4069 individuals with type 1 diabetes from the Finnish Diabetic Nephropathy study. The prevalence of multimorbidity (coexistence of two or more chronic conditions) was based on 32 conditions at baseline. Conditions were grouped into three subcategories: vascular comorbidities, autoimmune disorders, and other conditions. Hazard ratios (HR) for all‐cause mortality were calculated. Results The prevalence of multimorbidity was 60.4% and increased with age and especially diabetes duration. Multimorbidity was associated with increased risk of mortality, HR 6.0 (95% CI 4.6–7.8), p < 0.001. The HR for mortality increased by each additional condition and was 37.9 (95% CI 25.7–56.0) in those with ≥eight conditions. Vascular comorbidities and other conditions were associated with increased mortality, HRs 5.9 (4.4–7.9) and 3.8 (2.4–5.9), p < 0.001, separately, and in combination, HR 11.2 (8.3–15.2), p < 0.001. Autoimmune disorders did not influence mortality. Conclusions Multimorbidity in type 1 diabetes is common and is associated with increased mortality. Comprehensive evaluation of all additional conditions is needed to tailor treatment individually.

Abstract Hyperglycaemia is a hallmark of diabetes and a major risk factor for diabetic kidney disease (DKD). However, the molecular consequences of long-term cumulative hyperglycaemia (CH) remain unclear. As a stable epigenetic modification, DNA methylation may capture past glycaemic exposure. Here, we assessed CH-associated DNA methylation in 1,245 participants with type 1 diabetes (T1D) from Finland and the United Kingdom-Republic of Ireland cohorts. We identified 17 CH-associated CpGs, with the strongest association at cg19693031 ( TXNIP ). Longitudinal analyses demonstrate that these CH-associated DNA methylation levels remain stable despite short-term glycaemic fluctuations, suggesting lasting epigenetic imprints of earlier metabolic control. Integrative analyses combining genomic, epigenetic, and proteomic data characterized these CpGs and potential target proteins. Mendelian randomization suggested a causal association between cg20853880 ( KLF11 ) and DKD, supported by chromatin accessibility and kidney KLF11 expression. Our findings suggest that epigenetic changes contribute to metabolic memory and may mediate the effects of hyperglycaemia on DKD.

Hyperglycaemia and dyslipidaemia are well-known risk factors for coronary artery disease (CAD) in type 1 diabetes. The impact of long-term cumulative exposure to these risk factors is less explored. We investigated the relationship between cumulative glycaemic and lipid exposure and CAD in individuals with type 1 diabetes. This longitudinal study included 3495 adults with type 1 diabetes from the FinnDiane cohort, without end-stage kidney disease and no history of CAD or stroke at the study baseline. Total cumulative glycaemic exposure (CGEtot) and cumulative hyperglycaemic exposure (CGEhg), accounting only for time spent above an HbA1c of 53 mmol/mol (7

Key PointsA custom-made Joslin OLINK proteomics platform was developed to quantify 21 Joslin kidney panel (JKP) proteins in circulation to predict ESKD risk.Each JKP protein discriminated moderately/well ESKD risk in diabetes; optimal predictive model included three clinical markers and eight JKP proteins.In the type 2 diabetes subgroup from the Action to Control Cardiovascular Risk in Diabetes trial, three JKP proteins identified subjects in whom fenofibrate dramatically reduced risk of fast kidney decline.BackgroundTo facilitate personalized treatment of diabetic kidney disease (DKD), we developed the Joslin kidney panel (JKP) of 21 circulating proteins associated with progression to ESKD. Prognostic models using baseline concentrations of JKP proteins in circulation and clinical markers were then developed to stratify individuals according to ESKD risk and according to response to fenofibrate, a potential renoprotective drug.MethodsThe custom-made Joslin OLINK multipurpose proteomics platform was used to quantify JKP proteins. Association between baseline serum/plasma concentrations of these proteins and kidney outcomes was examined in five independent study groups.ResultsIn type 1 diabetes individuals from Joslin (N=59), FinnDiane (N=389), and Steno (N=283), all JKP proteins were good discriminators of ESKD risk during a 10-year follow-up. Baseline concentrations of KIM-1 and WFDC2 performed the best, matching or outperforming the clinical markers. An optimal model to discriminate ESKD risk that included three clinical markers and eight JKP proteins (KIM1, TNF-R2, TNF-R3, TNF-R19L, PVRL4, WFDC2, DLL1, and SYND1) was developed using the FinnDiane cohort (C-index 0.868, SEM +/- 0.019) and validated in the Steno cohort (C-index 0.913, SEM +/- 0.104) and in type 2 diabetes Joslin study (C-index 0.807, SEM +/- 0.036). In each of these studies, the optimal model performed better than models based solely on three clinical markers. In a subgroup of 450 individuals with type 2 diabetes from the Action to Control Cardiovascular Risk in Diabetes-Lipid trial, high levels of three JKP proteins (EFNA4, DLL1, IL-1RT1) predicted amelioration of fast kidney function decline during 4 years of follow-up in those treated with fenofibrate compared with placebo.ConclusionsQuantification of circulating JKP proteins using the Joslin OLINK platform discriminates ESKD risk in individuals with diabetes and their response to renoprotective drugs. The use of this multipurpose precision medicine tool should facilitate studies on the etiology of DKD and enable the development of effective personalized treatment protocols for individuals with DKD.

To investigate the association between depression and refill adherence to cardioprotective medications in a representative cohort of type 1 diabetes adults. This Finnish Diabetic Nephropathy (FinnDiane) sub-study included 1,588 adults with type 1 diabetes who had purchased antihypertensive or lipid-lowering drugs within ± 0.5 years from study baseline. The proportion of days covered (PDC) method was used to calculate overall refill adherence over a 10-year follow-up. Adherence was classified into good (> 80

Background Soluble receptor for advanced glycation end-products (sRAGE) modulates RAGE-mediated inflammation and oxidative stress. We investigated if sRAGE stratifies cardiovascular and kidney disease risk in individuals with type 1 diabetes and baseline treatment-resistant hypertension (TRH). Methods This study included 1262 adults with type 1 diabetes from the FinnDiane study who were on antihypertensive therapy and whose sRAGE concentration was measured at baseline. Participants were divided into groups: controlled blood pressure (BP) (n = 295), uncontrolled BP (n = 730) or TRH (n = 237). Prospective analyses were performed in those with baseline TRH. Of them, 62 developed coronary artery disease (CAD) and 38 stroke (median follow-up 12 years), while 99 progressed to end-stage kidney disease (ESKD) (median follow-up 9.2 years). Results Every 100 units increase in baseline sRAGE was associated with 4% higher odds for TRH, compared to those with uncontrolled BP ( P = 0.003), and 6% higher odds than those with controlled BP ( P = 0.0006). Associations attenuated after adjusting for kidney markers. In the competing risk analysis, higher sRAGE was associated with greater risk of CAD (SHR 1.05, P = 0.01) in those with TRH. After adjusting for eGFR, the association attenuated (SHR 1.04, P = 0.052), but the same trend remained. sRAGE was not associated with stroke. Furthermore, sRAGE was associated with higher risk of ESKD (SHR 1.06, P < 0.0001), but no longer after adjusting for eGFR ( P = 0.4). Conclusions Elevated sRAGE is associated with increased odds of TRH in individuals with type 1 diabetes. sRAGE further stratifies high risk of incident CAD and ESKD, even after accounting for clinical variables. Along with eGFR, sRAGE may help to identify individuals at the highest risk of adverse cardiovascular and kidney outcomes.

Abstract Introduction Type 1 diabetes is suspected to hamper brain growth, implying that people with earlier diabetes onset would, on average, achieve lower maximal brain volume. We set out to test this hypothesis. Methods Examining brain MRI scans of middle-aged people with type 1 diabetes, we related age at diabetes onset to intracranial volume in 180 participants, as well as to cerebral gray and white matter volumes in a subset of 113 (63%) participants, using fractional polynomial regression models. Of the participants, 118 (67%) had been diagnosed with diabetes before 18 years of age. Results Of our participants, 54% were women, the median age 40.0 (IQR 33.2–45.0) years and the range of age at diabetes onset was 1.2–39.0 years. We found no association between age at diabetes onset and intracranial volume (p = 0.85), cerebral white (p = 0.10), or gray matter volumes (p = 0.12). Further, correlations between age at diabetes onset and the measured brain volumes were poor in analyses stratified for sex (all correlation coefficients ρ ≤ 0.16). Conclusions We found no association between age at diabetes onset and attained intracranial volume or gray or white matter volumes, indicating that type 1 diabetes may not have a clinically significant influence on brain growth.

AbstractDissecting the genetic mechanisms underlying urinary metabolite concentrations can provide molecular insights into kidney function and open possibilities for causal assessment of urinary metabolites with risk factors and disease outcomes. Proton nuclear magnetic resonance metabolomics provides a high-throughput means for urinary metabolite profiling, as widely applied for blood biomarker studies. Here we report a genome-wide association study meta-analysed for 3 European cohorts comprising 8,011 individuals, covering both people with type 1 diabetes and general population settings. We identify 54 associations (p < 9.3 × 10−10) for 19 of 54 studied metabolite concentrations. Out of these, 33 were not reported previously for relevant urinary or blood metabolite traits. Subsequent two-sample Mendelian randomization analysis suggests that estimated glomerular filtration rate causally affects 13 urinary metabolite concentrations whereas urinary ethanolamine, an initial precursor for phosphatidylcholine and phosphatidylethanolamine, was associated with higher eGFR lending support for a potential protective role. Our study provides a catalogue of genetic associations for 53 metabolites, enabling further investigation on how urinary metabolites are linked to human health.

BACKGROUND:Insulin resistance and chronic kidney disease are both associated with increased coronary artery disease risk. Many formulae estimating glucose disposal rate in type 1 diabetes infer insulin sensitivity from clinical data. We compare associations and performance relative to traditional risk factors and kidney disease severity between three formulae estimating the glucose disposal rate and coronary artery disease in people with type 1 diabetes.METHODS:The baseline glucose disposal rate was estimated by three (Williams, Duca, and Januszewski) formulae in FinnDiane Study participants and related to subsequent incidence of coronary artery disease, by baseline kidney status.RESULTS:In 3517 adults with type 1 diabetes, during median (IQR) 19.3 (14.6, 21.4) years, 539 (15.3%) experienced a coronary artery disease event, with higher rates with worsening baseline kidney status. Correlations between the three formulae estimating the glucose disposal rate were weak, but the lowest quartile of each formula was associated with higher incidence of coronary artery disease. Importantly, only the glucose disposal rate estimation by Williams showed a linear association with coronary artery disease risk in all analyses. Of the three formulae, Williams was the strongest predictor of coronary artery disease. Only age and diabetes duration were stronger predictors. The strength of associations between estimated glucose disposal rate and CAD incidence varied by formula and kidney status.CONCLUSIONS:In type 1 diabetes, estimated glucose disposal rates are associated with subsequent coronary artery disease, modulated by kidney disease severity. Future research is merited regarding the clinical usefulness of estimating the glucose disposal rate as a coronary artery disease risk factor and potential therapeutic target.

Introduction: Type 1 diabetes has been linked to brain volume reductions as well as to cerebral small vessel disease (cSVD). This study concerns the relationship between normalized brain volumes (volume fractions) and cSVD, which has not been examined previously.Methods: We subjected brain magnetic resonance imaging studies of 187 adults of both sexes with Type 1 diabetes and 30 matched controls to volumetry and neuroradiological interpretation.Results: Participants with Type 1 diabetes had smaller thalami compared to controls without diabetes (p = 0.034). In subgroup analysis of the Type 1 diabetes group, having any sign of cSVD was associated with smaller cortical (p = 0.031) and deep gray matter volume fractions (p = 0.029), but a larger white matter volume fraction (p = 0.048). After correcting for age, the smaller putamen volume remained significant.Conclusions: We found smaller thalamus volume fractions in individuals with Type 1 diabetes as compared to those without diabetes, as well as reductions in brain volume fractions related to signs of cSVD in individuals with Type 1 diabetes.

Background:DNA methylation differences are associated with kidney function and diabetic kidney disease (DKD), but prospective studies are scarce. Therefore, we aimed to study DNA methylation in a prospective setting in the Finnish Diabetic Nephropathy Study type 1 diabetes (T1D) cohort. Methods:We analysed baseline blood sample-derived DNA methylation (Illumina's EPIC array) of 403 individuals with normal albumin excretion rate (early progression group) and 373 individuals with severe albuminuria (late progression group) and followed-up their DKD progression defined as decrease in eGFR to <60 mL/min/1.73m2 (early DKD progression group; median follow-up 13.1 years) or end-stage kidney disease (ESKD) (late DKD progression group; median follow-up 8.4 years). We conducted two epigenome-wide association studies (EWASs) on DKD progression and sought methylation quantitative trait loci (meQTLs) for the lead CpGs to estimate genetic contribution. Results:Altogether, 14 methylation sites were associated with DKD progression (P<9.4×10-8). Methylation at cg01730944 near CDKN1C and at other CpGs associated with early DKD progression were not correlated with baseline eGFR, whereas late progression CpGs were strongly associated. Importantly, 13 of 14 CpGs could be linked to a gene showing differential expression in DKD or chronic kidney disease. Higher methylation at the lead CpG cg17944885, a frequent finding in eGFR EWASs, was associated with ESKD risk (HR [95% CI] = 2.15 [1.79, 2.58]). Additionally, we replicated meQTLs for cg17944885 and identified ten novel meQTL variants for other CpGs. Furthermore, survival models including the significant CpG sites showed increased predictive performance on top of clinical risk factors. Conclusions:Our EWAS on early DKD progression identified a podocyte-specific CDKN1C locus. EWAS on late progression proposed novel CpGs for ESKD risk and confirmed previously known sites for kidney function. Since DNA methylation signals could improve disease course prediction, a combination of blood-derived methylation sites could serve as a potential prognostic biomarker.

BackgroundType 1 diabetes increases the risk of coronary artery disease (CAD). High-throughput metabolomics may be utilized to identify metabolites associated with disease, thus, providing insight into disease pathophysiology, and serving as predictive markers in clinical practice. Urine is less tightly regulated than blood, and therefore, may enable earlier discovery of disease-associated markers. We studied urine metabolomics in relation to incident CAD in individuals with type 1 diabetes.MethodsWe prospectively studied CAD in 2501 adults with type 1 diabetes from the Finnish Diabetic Nephropathy Study. 209 participants experienced incident CAD within the 10-year follow-up. We analyzed the baseline urine samples with a high-throughput targeted urine metabolomics platform, which yielded 54 metabolites. With the data, we performed metabolome-wide survival analyses, correlation network analyses, and metabolomic state profiling for prediction of incident CAD.ResultsUrinary 3-hydroxyisobutyrate was associated with decreased 10-year incident CAD, which according to the network analysis, likely reflects younger age and improved kidney function. Urinary xanthosine was associated with 10-year incident CAD. In the network analysis, xanthosine correlated with baseline urinary allantoin, which is a marker of oxidative stress. In addition, urinary trans-aconitate and 4-deoxythreonate were associated with decreased 5-year incident CAD. Metabolomic state profiling supported the usage of CAD-associated urinary metabolites to improve prediction accuracy, especially during shorter follow-up. Furthermore, urinary trans-aconitate and 4-deoxythreonate were associated with decreased 5-year incident CAD. The network analysis further suggested glomerular filtration rate to influence the urinary metabolome differently between individuals with and without future CAD.ConclusionsWe have performed the first high-throughput urinary metabolomics analysis on CAD in individuals with type 1 diabetes and found xanthosine, 3-hydroxyisobutyrate, trans-aconitate, and 4-deoxythreonate to be associated with incident CAD. In addition, metabolomic state profiling improved prediction of incident CAD.