The impact of phenotype, ethnicity and genotype on progression of type 2 diabetes mellitus

Type 2 diabetes progression in an adult Ugandan population with new-onset diabetes: an observational prospective study

BACKGROUND

The rate of progression of type 2 diabetes following diagnosis varies across individuals and populations. Studies investigating the progression of type 2 diabetes in adult African populations with newly diagnosed diabetes are limited. We aimed to investigate the prevalence and predictors of short-term (one year) diabetes progression in an adult Ugandan population with new-onset type 2 diabetes (type 2 diabetes diagnosed in < 3 months) initiated on oral hypoglycaemic agents (OHA).

METHODS

Two hundred and seven adult participants with type 2 diabetes diagnosed within the previous three months were followed up for 12 months. We investigated the association of specific demographic, clinical, and metabolic characteristics, and short-term diabetes progression (defined as glycated haemoglobin or HbA1c ≥ 8% on ≥ 2 OHA and/or treatment intensification).

RESULTS

One hundred sixteen participants (56%) completed the follow-up period. Sixty-four participants (55.2%, 95% CI 45.7-64.4) showed evidence of diabetes progression during the 12-month period of follow-up. An HbA1c ≥ 8% on ≥ 2 OHA and treatment intensification were noted in 44.8% and 29.3% of the participants, respectively. On multivariate analysis, only the female gender (AOR 3.2, 95% CI 1.1-9.2, p = 0.03) was noted to be independently associated with short-term diabetes progression.

CONCLUSION

Short-term diabetes progression was relatively common in this study population and was independently associated with the female gender. Early intensified diabetes therapy in adult Ugandan female patients with new-onset type 2 diabetes should be emphasised to avert rapid short-term diabetes progression.

Identification of biomarkers for glycaemic deterioration in type 2 diabetes

We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

Ethnic Variability in Glucose and Insulin Response to Rice Among Healthy Overweight Adults: A Randomized Cross-Over Study

BACKGROUND

The influence of ethnicity on postprandial glucose and insulin responses has been reported earlier and rice is a major contributor to the overall glycaemic load of Asian and Arab diets. This study aims to compare postprandial glycaemic and insulinaemic responses to rice among healthy overweight Asian, Arab and European participants.

METHODS

In a randomized crossover design, 47 healthy overweight participants (23 Asian, 16 Arab, and 8 European) consumed 75 grams of glucose beverage or ate 270 grams of cooked basmati rice (75 g of available carbohydrate) on two separate occasions, separated by a one 1-week washout period. Blood glucose and insulin levels were determined at fasting 0 (fasting), 30, 60, and 120 minutes and used to determine the incremental area under the curve (iAUC).

RESULTS

The three groups were matched on body mass index and gender. Although no differences were noted statistically in most clinical features, a wide range of variation was noted in age, systolic, diastolic blood pressure. The fasting blood glucose and insulin levels were highest among Asians, followed by Arabs and Europeans (p < 0.01). According to the HOMA-IR test and the Matsuda index, Asians have a higher insulin resistance than Arabs or Europeans when consuming a glucose beverage (p < 0.001) and rice (p < 0.01). Postprandial glucose and insulin responses to glucose beverage did not differ between ethnic groups (p = 0.28; p = 0.10). Based on an unadjusted regression model, European participants had significantly lower iAUC-glucose (p = 0.02) and iAUC-insulin (p = 0.01) after rice consumption than Asian participants. In the adjusted model, the difference between the two groups remained for iAUC-insulin (p = 0.04) but not for iAUC-glucose (p = 0.07).

CONCLUSION

Our study found that ethnic differences exist among healthy overweight adults in terms of insulin resistance, glycaemic response and insulinaemic response to rice. As a result of their high insulin resistance, Asian participants had a higher postprandial insulin spike than Europeans after eating rice. These findings could have substantial implications for nutrition recommendations based on ethnicity, particularly for Asians.

Precision Medicine in Diabetes

Tailoring treatment or management to groups of individuals based on specific clinical, molecular, and genomic features is the concept of precision medicine. Diabetes is highly heterogenous with respect to clinical manifestations, disease progression, development of complications, and drug response. The current practice for drug treatment is largely based on evidence from clinical trials that report average effects. However, around half of patients with type 2 diabetes do not achieve glycaemic targets despite having a high level of adherence and there are substantial differences in the incidence of adverse outcomes. Therefore, there is a need to identify predictive markers that can inform differential drug responses at the point of prescribing. Recent advances in molecular genetics and increased availability of real-world and randomised trial data have started to increase our understanding of disease heterogeneity and its impact on potential treatments for specific groups. Leveraging information from simple clinical features (age, sex, BMI, ethnicity, and co-prescribed medications) and genomic markers has a potential to identify sub-groups who are likely to benefit from a given drug with minimal adverse effects. In this chapter, we will discuss the state of current evidence in the discovery of clinical and genetic markers that have the potential to optimise drug treatment in type 2 diabetes.

Polygenic risk scores of endo-phenotypes identify the effect of genetic background in congenital heart disease

Congenital heart disease (CHD) is a rare structural defect that occurs in ∼1% of live births. Studies on CHD genetic architecture have identified pathogenic single-gene mutations in less than 30% of cases. Single-gene mutations often show incomplete penetrance and variable expressivity. Therefore, we hypothesize that genetic background may play a role in modulating disease expression. Polygenic risk scores (PRSs) aggregate effects of common genetic variants to investigate whether, cumulatively, these variants are associated with disease penetrance or severity. However, the major limitations in this field have been in generating sufficient sample sizes for these studies. Here we used CHD-phenotype matched genome-wide association study (GWAS) summary statistics from the UK Biobank (UKBB) as our base study and whole-genome sequencing data from the CHD cohort (n₁ = 711 trios, n₂ = 362 European trios) of the Gabriella Miller Kids First dataset as our target study to develop PRSs for CHD. PRSs estimated using a GWAS for heart valve problems and heart murmur explain 2.5% of the variance in case-control status of CHD (all SNVs, p = 7.90 × 10^-3; fetal cardiac SNVs, p = 8.00 × 10^-3) and 1.8% of the variance in severity of CHD (fetal cardiac SNVs, p = 6.20 × 10^-3; all SNVs, p = 0.015). These results show that common variants captured in CHD phenotype-matched GWASs have a modest but significant contribution to phenotypic expression of CHD. Further exploration of the cumulative effect of common variants is necessary for understanding the complex genetic etiology of CHD and other rare diseases.

Characterisation of type 2 diabetes subgroups and their association with ethnicity and clinical outcomes: a UK real-world data study using the East London Database

BACKGROUND

Subgroups of type 2 diabetes (T2DM) have been well characterised in experimental studies. It is unclear, however, whether the same approaches can be used to characterise T2DM subgroups in UK primary care populations and their associations with clinical outcomes.

AIM

To derive T2DM subgroups using primary care data from a multi-ethnic population, evaluate associations with glycaemic control, treatment initiation, and vascular outcomes, and to understand how these vary by ethnicity.

DESIGN AND SETTING

An observational cohort study in the East London Primary Care Database from 2008 to 2018.

METHOD

Latent-class analysis using age, sex, glycated haemoglobin, and body mass index at diagnosis was used to derive T2DM subgroups in white, South Asian, and black groups. Time to treatment initiation and vascular outcomes were estimated using multivariable Cox-proportional hazards regression.

RESULTS

In total, 31 931 adults with T2DM were included: 47% South Asian (n = 14 884), 26% white (n = 8154), 20% black (n = 6423). Two previously described subgroups were replicated, 'mild age-related diabetes' (MARD) and 'mild obesity-related diabetes' (MOD), and a third was characterised 'severe hyperglycaemic diabetes' (SHD). Compared with MARD, SHD had the poorest long-term glycaemic control, fastest initiation of antidiabetic treatment (hazard ratio [HR] 2.02, 95% confidence interval [CI] = 1.76 to 2.32), and highest risk of microvascular complications (HR 1.38, 95% CI = 1.28 to 1.49). MOD had the highest risk of macrovascular complications (HR 1.50, 95% CI = 1.23 to 1.82). Subgroup differences in treatment initiation were most pronounced for the white group, and vascular complications for the black group.

CONCLUSION

Clinically useful T2DM subgroups, identified at diagnosis, can be generated in routine real-world multi-ethnic populations, and may offer a pragmatic means to develop stratified primary care pathways and improve healthcare resource allocation.

Agreement between Type 2 Diabetes Risk Scales in a Caucasian Population: A Systematic Review and Report

Early detection of people with undiagnosed type 2 diabetes (T2D) is an important public health concern. Several predictive equations for T2D have been proposed but most of them have not been externally validated and their performance could be compromised when clinical data is used. Clinical practice guidelines increasingly incorporate T2D risk prediction models as they support clinical decision making. The aims of this study were to systematically review prediction scores for T2D and to analyze the agreement between these risk scores in a large cross-sectional study of white western European workers. A systematic review of the PubMed, CINAHL, and EMBASE databases and a cross-sectional study in 59,042 Spanish workers was performed. Agreement between scores classifying participants as high risk was evaluated using the kappa statistic. The systematic review of 26 predictive models highlights a great heterogeneity in the risk predictors; there is a poor level of reporting, and most of them have not been externally validated. Regarding the agreement between risk scores, the DETECT-2 risk score scale classified 14.1% of subjects as high-risk, FINDRISC score 20.8%, Cambridge score 19.8%, the AUSDRISK score 26.4%, the EGAD study 30.3%, the Hisayama study 30.9%, the ARIC score 6.3%, and the ITD score 3.1%. The lowest agreement was observed between the ITD and the NUDS study derived score (κ = 0.067). Differences in diabetes incidence, prevalence, and weight of risk factors seem to account for the agreement differences between scores. A better agreement between the multi-ethnic derivate score (DETECT-2) and European derivate scores was observed. Risk models should be designed using more easily identifiable and reproducible health data in clinical practice.