الفهرس 
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Abstract
T2DM is the most common type of diabetes present worldwide(128). It is a condition characterized by heterogeneity in its etiopathogenesis and clinical presentation(1). Hence, the classification of individuals with T2DM into distinct novel subgroups can help in our approach to precision diabetes, plan suitable therapies based on their pathophysiology, and predict the prognosis of complications with ways of their prevention.
A novel classification for adult-onset DM was published in 2018 by Ahlqvist. et al(5). They reported five unique “clusters” of individuals with newly diagnosed diabetes using a data-driven approach including six clinical variables.
In this study, our diabetic patients aged more than 18 years old were classified into subgroups (clusters) and showed the relation of these clusters with incidence of common microvascular complications. For this purpose, A total number of 800 patients were divided into 2 groups:
First group (300 patients): Which represented the prospective group. All patients in this group underwent detailed full examinations, history taking and investigations, during their clinic visit at the time of registration for this study. We used most of variables used in Ahlqvist. et al study(5) except for (GAD) antibodies in clustering of this group.
Second group (500 patients): Which represented the retrospective group. Data for those patients were collected from electronic records and/or paper records in collaboration with different diabetes clinics scattered in Upper Egypt governorates. All available data were used in the clustering of this group. We did not use HOMA2-B or HOMA2-IR in this group as fasting C-peptide is not done routinely in our population and it is costly. Therefore, we used another variable called Metabolic Score for Insulin Resistance (METS-IR) used before by Bello-Chavolla. et al in 2020(6) in clustering Mexican population as a parameter for insulin resistance state. Before using it, we confirmed the positive correlation between HOMA2-IR and METS-IR.
For both groups, We studied the relation between those new resulted clusters and presence of microvascular complications.
Results:
In the first group, three resulting clusters were obtained:
1. Severe Insulin Deficiency Diabetes (SIDD) cluster: consisted of 228 patients (76%). Those patients are characterized by lower HOMA2-B%, HOMA2-IR and BMI in comparison to other clusters with highest HbA1c%.
2. Severe Insulin Resistant Diabetes (SIRD) cluster: consisted of 59 patients (19.7%). Those patients are characterized by very high HOMA2-IR and very high HOMA2-B and intermediate BMI, also intermediate HbA1c% in comparison to other clusters.
3. Obesity-Related Diabetes (ORD) clusters: consisted of 13 patients (4.3%). Those patients had the highest BMI, highest WC with lowest HbA1c and intermediate HOMA2-IR and HOMA2-B in comparison with other clusters.
We confirmed the positive correlation between HOMA-IR and METS-IR. Accuracy of METS-IR in prediction of insulin resistance is good according to our study. It was found that patients Insulin Resistance state (IR) measured by HOMA-IR in this first group, had significantly higher METS-IR in comparison to those without IR regardless of their cluster.
In the second group, three resulting clusters were obtained which shared same criteria with those of the first group:
1- Cluster (A) mostly refers to Severe Insulin Deficiency Diabetes (SIDD) consisted of 245 patients (49%). Those patients were characterized by lower BMI, higher HbA1c%, lower METS-IR.
2- Cluster (B) mostly refers to Severe Insulin Resistant Diabetes (SIRD) consisting of 190 patients (38%). Those patients had higher METS-IR in comparison to other clusters with BMI in between cluster C and cluster A. HbA1c% is also intermediate between the other two clusters.
3- Cluster (C) mostly refers to Obesity-Related Diabetes (ORD) consisted of 65 patients (13%). Those patients were characterized by higher BMI and higher waist circumference in comparison to other clusters. METS-IR is intermediate between the other two clusters, While HbA1c% is the lowest between all groups.
For both groups, It was found that SIDD cluster ((cluster A in second group)) was a predictor for retinopathy, while ORD cluster ((cluster C in second group)) was predictor for albuminuria. Meanwhile, SIRD cluster (cluster B in second group) was predictor for Diabetic Kidney Disease (DKD), CKD and Albuminuria.
Conclusions
Classification of adult diabetic patients into sub-groups seems to be effective in differentiation of those patients according to their main pathogenesis of their diabetes. This may be helpful in choosing the best drugs for each patient in these sub-groups. It seems also that this classification is important in prediction of the incidence of microvascular complications even on the short term, which can be very useful in early prevention of these complications by using suitable antidiabetic medications and strict screening and follow-up for those patients.
Searching for new variables for determination of insulin resistance state and function of pancreatic beta-cells less expensive than serum insulin and C-peptide estimation is important specifically in low-income populations. Using variables like METS-IR seems to be effective in determining insulin resistance state, so it can be used as a substitute for HOMA-IR as it is cheaper and easier to obtain in our low-income populations.
It seems that there is a relation between these new clusters and prediction of incidence of microvascular complications. It was found that SIDD cluster was a predictor for retinopathy with hazard ratio (HR) was 2.01 while ORD cluster was predictor for albuminuria with HR was 1.23. Meanwhile, SIRD cluster was predictor for Diabetic Kidney Disease (DKD) (HR= 3.11), CKD (HR= 2.22) and Albuminuria (HR= 2.01).
In this study, some important data were concluded in diabetic patients in Upper Egypt population such as:
1- Most of our diabetic patients in the first group and second group were clustered in cluster of SIDD (76% and 49% respectively) despite relatively short time from diagnosis (within 3 years of diagnosis). Maybe this is due to the delay in diagnosis of newly discovered diabetes and this point should let us to concentrate our efforts in screening population which may be at high risk for developing diabetes. Moreover, we should increase awareness of the general population about the importance of checking their blood glucose at least annually, especially for those with multiple risk factors. Another probable cause is the clinical inertia of some physician in dealing with diabetes. Recent study suggested that early and tight control may delay secondary beta-cell failure and so delay needing for insulin use(129).
2- Most of our diabetic patients in both groups had uncontrolled blood glucose level using HbA1c (48%, 54.6% respectively). Indeed, this is considered as a dangerous alarm and means we need more attention for organizing patients’ education programs and physicians’ education programs to help in more strict control for our patients’ blood glucose.
3- Incidence of DKD (albuminuria and/or CKD) was relatively high in cluster of SIRD in both groups (69.5%, 68.4% respectively) despite short time of diabetes duration in them (maximum duration was 3 years). The starting of pathogenesis process in kidney (which leads to incidence of DKD mainly in patients with high insulin resistance state) mostly occurs years before the diagnosis of DM itself as suggested by many studies(130, 131). This is the most probable cause for this important notice besides poor controlling of blood sugar level in cluster of SIRD in the first and second group.
Recommendations
Novel classification of diabetes of the adults is considered now a good approach for classifying patients according to their diabetes pathogenesis, replication of this classification is recommended in different populations and ethnics for determination of each specific population clusters and sub-groups.
Usage of available clinical parameters and variables in each population is recommended mainly in poorer societies trying to classify patients into sub-groups without needing expensive investigations.
Follow up of patients in each sub-group for following years is recommended for exact determination of incidence of microvascular or macrovascular complications in each sub-group and its hazards ratio, So we can predict their incidence in each patient and try to use specific medications related to sub-group main pathogenesis to delay or even prevent incidence of those complications.
Studying effectiveness of new clinical variables like METS-IR which can be calculated from easily collected clinical related data, can be helpful in applying novel clustering without needing for other investigations like C-peptide or HOMA-IR which usually missed or not done for majority of patients specifically in developing countries.
Performing of similar studies on larger scale of patients is recommended with longer periods of follow-up to study incidence of microvascular complications and confirm its relationship with the new sup-groups.
It seems that studying of insulin resistance- as one of the most important factors in T2DM- and its relation to clinical related variables like METS-IR is important to help physicians to determine those patients as early as possible and starting suitable medications.