Most doctors have recognised for some time that dividing Diabetes into Type 1 early onset typically requiring insulin therapy and Type 2 late onset doesn’t really define the patterns that we see in clinical practice. In March 2018 a study in the Lancet
identified five clusters of diabetes that could revolutionise the way we diagnose and manage the disease, and screen for complications.
Current Classification of Diabetes
Diabetes is a metabolic disease underpinned by a pathological failure of glucose uptake by cells, which if left untreated can affect almost every organ system causing significant morbidity and reducing life expectancy. The disease can be caused either by reduced insulin production or insulin resistance and widespread complications result from chronic hyperglycaemia. Acute emergencies can occur either from insufficient insulin with diabetic ketoacidosis, dehydration and acute hyperglycaemia in HONC, or hypoglycaemia from diabetes therapy.
In 2006 the World Health Organisation published criteria for the diagnosis of diabetes
: patients should have symptoms of diabetes (polydipsia, polyuria) coupled with a random venous plasma glucose concentration >11.1 mmol/L, fasting plasma glucose concentration >7.0mmol/L or a two hour plasma glucose concentration of 11.1mmol/L after a 75g oral anhydrous glucose load. In 2011 they included the use of HbA1c > 48mmol/L (6.5%) for a diagnosis of diabetes
. Whilst these criteria establish inadequate glycaemic control it does not identify aetiology of the disorder, which may require further antibody and genetic screening, resulting in the risk of inadequate management of the disease.
Traditionally this disorder has been divided into two types. Type 1 affects 10-15% of patients with diabetes. It is characterised by early onset and is caused by autoimmune destruction of insulin-producing cells with patients requiring exogenous insulin administration. Type 2 represents 85% of those with diabetes, however, unlike Type 1, this group is largely heterogenous with patients having varying degrees of insulin deficiency and insulin resistance. It usually occurs in later life with sufferers tending towards obesity. Given its heterogeneity, current treatment strategies have required a trial and error approach to achieve adequate glycaemic control. Strategies for type 2 diabetes management range from lifestyle management with diet control through to differing classes of oral hypoglycaemic agents and insulin. This approach has meant that there can be a substantial delay in achieving adequate glucose control whilst determining the correct regime for individual patients, which in turn can have significant impact on the development of complications. In addition to these two types, there are a number of other types that have been described.
Latent Autoimmune Diabetes of Adults (LADA) is characterised by antibodies against insulin producing cells of the pancreas, similar to Type 1, but is frequently misdiagnosed as Type 2 due to age of onset. These patients can therefore have a significant period of mismanagement with oral hypoglycaemics instead of insulin. Maturity Onset Diabetes of the Young (MODY) is caused by a single gene mutation and is hereditary. It is a heterogenous group and presentation and management vary according to the gene mutation. Secondary diabetes is impaired glycaemic control caused by another medical condition and its diagnosis and management depends on the manner in which it prevents glycaemic control.
The New Classification of Diabetes
Researchers in Scandinavia followed 5 cohorts across Sweden and Finland but only the All New Diabetics in Scania (ANDIS) cohort had blood sampling performed at the time of diagnosis
. Analysing data from 8980 patients in the ANDIS cohort, who were followed for an average of 3.9 years, the authors identified 6 key variables: age at diagnosis, body mass index, HbA1c, function of insulin-producing cells of the pancreas, level of insulin resistance, presence of glutamatic acid decarboxylase antibodies (GADA). Using these variables they identified 5 distinct clusters that separated the cohort out as shown in Table 1.
Table 1. Cluster types as identified in the ANDIS cohort according to pattern of variables and propensity to develop complications.
||Severe autoimmune diabetes (SAID)
||Early disease onset, essentially corresponds with type 1 diabetes and LADA, relatively low BMI, high HbA1c
Insulin deficiency, GADA +ve
||Severe insulin-deficient diabetes (SIDD)
||GADA –ve otherwise similar to cluster 1, persistently high HbA1c
Highest incidence of retinopathy
||Severe-insulin resistant diabetes (SIRD)
||Insulin resistance, high BMI,
Highest incidence of nephropathy and risk of ESRF
Highest risk of developing chronic disease
||Mild obesity-related diabetes (MOD)
||Obesity, younger age of onset, not insulin resistant
||Older age of onset, modest metabolic alterations
In addition to these findings, the study authors were able to replicate the findings from the ANDIS cohort and reproduce the cluster types in three of the other four cohorts as well as identifying that there was not one unifying genetic code for “Type 2 Diabetes” but that each cluster had its own collection of genetic codes that distinguished it from that of another cluster.
By 2030 diabetes and its complications are forecast to affect 629 million and cost between 2.1-2.5 trillion USD globally
. With this rising health and economic burden, interventions aimed at early diagnosis, rapid and sustained glycaemic control, and aggressive screening for complications may have a significant effect both in terms of health outcomes and health economies.
This study reproducibly categorises diabetes further and more accurately than its traditional classification. The implications of these findings are that future diabetic management strategies may be focused on customised targeted treatment and screening options that result in early glycaemic control and detection and management of complications. Whilst blanket screening diabetic populations for complications is costly and may not be economically viable in various health systems, delayed diagnosis can result in markedly reduced life expectancy and significant economic impact for management of late stage complications. The identification of these clusters paves the way for targeted screening of at-risk individuals. Given that only 15% of the cohort (cluster 3) had the highest risk of developing nephropathy and 17% (cluster 2) had the highest risk of developing retinopathy, aggressive screening of these groups would enable effective allocation of resources with consequent early intervention and reduction in severity of morbidity and a significant increase in economic viability of such programmes.
Whilst this study has a number of limitations, particularly that the study group was a relatively homogenous population, it provides an exciting platform for future research to guide the way we diagnose and manage diabetes and reduce the associated global health and economic burden.
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