Aim/hypothesis
Five subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this ...phenotyping in Indian individuals with young-onset type 2 diabetes.
Methods
We applied the European-derived centroids to Indian individuals with type 2 diabetes diagnosed before 45 years of age from the WellGen cohort (
n
= 1612). We also applied de novo
k
-means clustering to the WellGen cohort to validate the subgroups. We then compared clinical and metabolic-endocrine characteristics and the complication rates between the subgroups. We also compared characteristics of the WellGen subgroups with those of two young European cohorts, ANDIS (
n
= 962) and DIREVA (
n
= 420). Subgroups were also assessed in two other Indian cohorts, Ahmedabad (
n
= 187) and PHENOEINDY-2 (
n
= 205).
Results
Both Indian and European young-onset type 2 diabetes patients were predominantly classified into severe insulin-deficient (SIDD) and mild obesity-related (MOD) subgroups, while the severe insulin-resistant (SIRD) and mild age-related (MARD) subgroups were rare. In WellGen, SIDD (53%) was more common than MOD (38%), contrary to findings in Europeans (Swedish 26% vs 68%, Finnish 24% vs 71%, respectively). A higher proportion of SIDD compared with MOD was also seen in Ahmedabad (57% vs 33%) and in PHENOEINDY-2 (67% vs 23%). Both in Indians and Europeans, the SIDD subgroup was characterised by insulin deficiency and hyperglycaemia, MOD by obesity, SIRD by severe insulin resistance and MARD by mild metabolic-endocrine disturbances. In WellGen, nephropathy and retinopathy were more prevalent in SIDD compared with MOD while the latter had higher prevalence of neuropathy.
Conclusions /interpretation
Our data identified insulin deficiency as the major driver of type 2 diabetes in young Indians, unlike in young European individuals in whom obesity and insulin resistance predominate. Our results provide useful clues to pathophysiological mechanisms and susceptibility to complications in type 2 diabetes in the young Indian population and suggest a need to review management strategies.
Graphical abstract
Diabetes increases the risk of bacterial infections. We investigated whether common genetic variants associate with infection susceptibility in Finnish diabetic individuals. We performed genome-wide ...association studies and pathway analysis for bacterial infection frequency in Finnish adult diabetic individuals (FinnDiane Study; N = 5092, Diabetes Registry Vaasa; N = 4247) using national register data on antibiotic prescription purchases. Replication analyses were performed in a Swedish diabetic population (ANDIS; N = 9602) and in a Finnish non-diabetic population (FinnGen; N = 159,166). Genome-wide data indicated moderate but significant narrow-sense heritability for infection susceptibility (h
= 16%, P = 0.02). Variants on chromosome 2 were associated with reduced infection susceptibility (rs62192851, P = 2.23 × 10
). Homozygotic carriers of the rs62192851 effect allele (N = 44) had a 37% lower median annual antibiotic purchase rate, compared to homozygotic carriers of the reference allele (N = 4231): 0.38 IQR 0.22-0.90 and 0.60 0.30-1.20 respectively, P = 0.01). Variants rs6727834 and rs10188087, in linkage disequilibrium with rs62192851, replicated in the FinnGen-cohort (P < 0.05), but no variants replicated in the ANDIS-cohort. Pathway analysis suggested the IRAK1 mediated NF-κB activation through IKK complex recruitment-pathway to be a mediator of the phenotype. Common genetic variants on chromosome 2 may associate with reduced risk of bacterial infections in Finnish individuals with diabetes.
Urinary extracellular vesicles (uEV) are a largely unexplored source of kidney-derived mRNAs with potential to serve as a liquid kidney biopsy. We assessed ∼200 uEV mRNA samples from clinical studies ...by genome-wide sequencing to discover mechanisms and candidate biomarkers of diabetic kidney disease (DKD) in Type 1 diabetes (T1D) with replication in Type 1 and 2 diabetes. Sequencing reproducibly showed >10,000 mRNAs with similarity to kidney transcriptome. T1D DKD groups showed 13 upregulated genes prevalently expressed in proximal tubules, correlated with hyperglycemia and involved in cellular/oxidative stress homeostasis. We used six of them (GPX3, NOX4, MSRB, MSRA, HRSP12, and CRYAB) to construct a transcriptional “stress score” that reflected long-term decline of kidney function and could even identify normoalbuminuric individuals showing early decline. We thus provide workflow and web resource for studying uEV transcriptomes in clinical urine samples and stress-linked DKD markers as potential early non-invasive biomarkers or drug targets.
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•NGS reveals robust expression of >10,000 mRNAs in clinical uEV samples•uEV transcriptome enriches kidney mRNAs and shows global similarity with the kidney•In DKD, uEV express elevated levels of cellular/oxidative stress-linked genes•uEV stress score associates with long-term decline of eGFR in early and late DKD
Medicine; Clinical finding; Disease; Specimen; Biopsy sample
Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous. A refined classification could provide a powerful tool to ...individualise treatment regimens and identify individuals with increased risk of complications at diagnosis.
We did data-driven cluster analysis (k-means and hierarchical clustering) in patients with newly diagnosed diabetes (n=8980) from the Swedish All New Diabetics in Scania cohort. Clusters were based on six variables (glutamate decarboxylase antibodies, age at diagnosis, BMI, HbA
, and homoeostatic model assessment 2 estimates of β-cell function and insulin resistance), and were related to prospective data from patient records on development of complications and prescription of medication. Replication was done in three independent cohorts: the Scania Diabetes Registry (n=1466), All New Diabetics in Uppsala (n=844), and Diabetes Registry Vaasa (n=3485). Cox regression and logistic regression were used to compare time to medication, time to reaching the treatment goal, and risk of diabetic complications and genetic associations.
We identified five replicable clusters of patients with diabetes, which had significantly different patient characteristics and risk of diabetic complications. In particular, individuals in cluster 3 (most resistant to insulin) had significantly higher risk of diabetic kidney disease than individuals in clusters 4 and 5, but had been prescribed similar diabetes treatment. Cluster 2 (insulin deficient) had the highest risk of retinopathy. In support of the clustering, genetic associations in the clusters differed from those seen in traditional type 2 diabetes.
We stratified patients into five subgroups with differing disease progression and risk of diabetic complications. This new substratification might eventually help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes.
Swedish Research Council, European Research Council, Vinnova, Academy of Finland, Novo Nordisk Foundation, Scania University Hospital, Sigrid Juselius Foundation, Innovative Medicines Initiative 2 Joint Undertaking, Vasa Hospital district, Jakobstadsnejden Heart Foundation, Folkhälsan Research Foundation, Ollqvist Foundation, and Swedish Foundation for Strategic Research.
High blood pressure is a major risk factor for cardiovascular disease and premature death. However, there is limited knowledge on specific causal genes and pathways. To better understand the genetics ...of blood pressure, we genotyped 242,296 rare, low-frequency and common genetic variants in up to 192,763 individuals and used ∼155,063 samples for independent replication. We identified 30 new blood pressure- or hypertension-associated genetic regions in the general population, including 3 rare missense variants in RBM47, COL21A1 and RRAS with larger effects (>1.5 mm Hg/allele) than common variants. Multiple rare nonsense and missense variant associations were found in A2ML1, and a low-frequency nonsense variant in ENPEP was identified. Our data extend the spectrum of allelic variation underlying blood pressure traits and hypertension, provide new insights into the pathophysiology of hypertension and indicate new targets for clinical intervention.
Type 2 diabetes has been reproducibly clustered into five subtypes with different disease progression and risk of complications; however, etiological differences are unknown. We used genome-wide ...association and genetic risk score (GRS) analysis to compare the underlying genetic drivers. Individuals from the Swedish ANDIS (All New Diabetics In Scania) study were compared to individuals without diabetes; the Finnish DIREVA (Diabetes register in Vasa) and Botnia studies were used for replication. We show that subtypes differ with regard to family history of diabetes and association with GRS for diabetes-related traits. The severe insulin-resistant subtype was uniquely associated with GRS for fasting insulin but not with variants in the TCF7L2 locus or GRS reflecting insulin secretion. Further, an SNP (rs10824307) near LRMDA was uniquely associated with mild obesity-related diabetes. Therefore, we conclude that the subtypes have partially distinct genetic backgrounds indicating etiological differences.
Latent autoimmune diabetes in adults (LADA) shares clinical features with both type 1 and type 2 diabetes; however, there is ongoing debate regarding the precise definition of LADA. Understanding its ...genetic basis is one potential strategy to gain insight into appropriate classification of this diabetes subtype.
We performed the first genome-wide association study of LADA in case subjects of European ancestry versus population control subjects (
= 2,634 vs. 5,947) and compared against both case subjects with type 1 diabetes (
= 2,454 vs. 968) and type 2 diabetes (
= 2,779 vs. 10,396).
The leading genetic signals were principally shared with type 1 diabetes, although we observed positive genetic correlations genome-wide with both type 1 and type 2 diabetes. Additionally, we observed a novel independent signal at the known type 1 diabetes locus harboring
, encoding a regulator of glycolysis and insulin signaling in type 2 diabetes and inflammation and autophagy in autoimmune disease, as well as an attenuation of key type 1-associated HLA haplotype frequencies in LADA, suggesting that these are factors that distinguish childhood-onset type 1 diabetes from adult autoimmune diabetes.
Our results support the need for further investigations of the genetic factors that distinguish forms of autoimmune diabetes as well as more precise classification strategies.
•Distribution of newly-defined diabetes subgroups in randomised clinical trials (RCTs) is unknown.•Mild obesity-related diabetes (MOD) was the predominant subgroup in a set of both pooled and single ...T2DM RCTs.•Severe insulin-deficient diabetes (SIRD) subgroup was least prevalent in RCTs.•Diabetes duration is a strong modifying factor of subgroup distribution and characteristics.•Subgroup-based randomisation in future RCTs may better define the target T2DM population by avoiding clinical heterogeneity.
Newly-defined subgroups of type 2 diabetes mellitus (T2DM) have been reported from real-world cohorts but not in detail from randomised clinical trials (RCTs).
T2DM participants, uncontrolled on different pre-study therapies (n = 12.738; 82 % Caucasian; 44 % with diabetes duration > 10 years) from 14 RCTs, were assigned to new subgroups according to age at onset of diabetes, HbA1c, BMI, and fasting C-peptide using the nearest centroid approach. Subgroup distribution, characteristics and influencing factors were analysed.
In both, pooled and single RCTs, “mild-obesity related diabetes” predominated (45 %) with mean BMI of 35 kg/m2. “Severe insulin-resistant diabetes” was found least often (4.6 %) and prevalence of “mild age-related diabetes” (23.9 %) was mainly influenced by age at onset of diabetes and age cut-offs. Subgroup characteristics were widely comparable to those from real-world cohorts, but all subgroups showed higher frequencies of diabetes-related complications which were associated with longer diabetes duration. A high proportion of “severe insulin-deficient diabetes” (25.4 %) was identified with poor pre-study glycaemic control.
Classification of RCT participants into newly-defined diabetes subgroups revealed the existence of a heterogeneous population of T2DM. For future RCTs, subgroup-based randomisation of T2DM will better define the target population and relevance of the outcomes by avoiding clinical heterogeneity.
Aims/hypothesis
Latent autoimmune diabetes in adults (LADA) is phenotypically a hybrid of type 1 and type 2 diabetes. Genetically LADA is poorly characterised but does share genetic predisposition ...with type 1 diabetes. We aimed to improve the genetic characterisation of LADA and hypothesised that type 2 diabetes-associated gene variants also predispose to LADA, and that the associations would be strongest in LADA patients with low levels of GAD autoantibodies (GADA).
Methods
We assessed 41 type 2 diabetes-associated gene variants in Finnish (phase I) and Swedish (phase II) patients with LADA (
n
= 911) or type 1 diabetes (
n
= 406), all diagnosed after the age of 35 years, as well as in non-diabetic control individuals 40 years or older (
n
= 4,002).
Results
Variants in the
ZMIZ1
(rs12571751,
p
= 4.1 × 10
−5
) and
TCF7L2
(rs7903146,
p
= 5.8 × 10
−4
) loci were strongly associated with LADA. Variants in the
KCNQ1
(rs2237895,
p
= 0.0012),
HHEX
(rs1111875,
p
= 0.0024 in Finns) and
MTNR1B
(rs10830963,
p
= 0.0039) loci showed the strongest association in patients with low GADA, supporting the hypothesis that the disease in these patients is more like type 2 diabetes. In contrast, variants in the
KLHDC5
(rs10842994,
p
= 9.5 × 10
−4
in Finns),
TP53INP1
(rs896854,
p
= 0.005),
CDKAL1
(rs7756992,
p
= 7.0 × 10
−4
; rs7754840,
p
= 8.8 × 10
−4
) and
PROX1
(rs340874,
p
= 0.003) loci showed the strongest association in patients with high GADA. For type 1 diabetes, a strong association was seen for
MTNR1B
(rs10830963,
p
= 3.2 × 10
−6
) and
HNF1A
(rs2650000,
p
= 0.0012).
Conclusions/interpretation
LADA and adult-onset type 1 diabetes share genetic risk variants with type 2 diabetes, supporting the idea of a hybrid form of diabetes and distinguishing them from patients with classical young-onset type 1 diabetes.
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