scholarly journals Type 2 Diabetes Mellitus (T2DM) may have Four Subtypes Beneficial for Adequate Treatment

2021 ◽  
Vol 4 (1) ◽  
pp. 38-41
Author(s):  
Bando H
Keyword(s):  
Type 2 Diabetes ◽  
Diabetes Type 2 ◽  
Adequate Treatment ◽  
Insulin Resistant ◽  
Age Related ◽  
Cluster A ◽  
Diabetic Treatment ◽  
Mild Obesity

Diabetes includes various heterogeneous factors. Similar to subtypes of type 1 diabetes, type 2 diabetes may show four subtype clusters. They are cluster A: severe insulin-deficient diabetes, B: severe insulin-resistant diabetes, C: mild obesity-related diabetes, and D: mild age-related diabetes. Comparing them, the prevalence of nephropathy and cardiovascular events was highest in the cluster A. Reference data are i) the ratio of cluster A-D is 18.7%, 23.7%, 21.1%, 36.4%, ii) HbA1c for A-D is 11.05%, 8.17%, 8.49%, 7.95%, iii) event ratio of MACE is 14.4%, 10.6%, 11.4%, 9.1%. Future diabetic treatment is hopefully provided suitable for each subtype.

Prevalence and incidence of diabetes in Norway

1983 ◽  
Vol 104 (4_Suppl) ◽  
pp. S19-S20
Author(s):  
Kristian Midthjell
Keyword(s):  
Type 2 Diabetes ◽  
Diabetes Type 2 ◽  
Limited Data ◽  
Prevalence Data ◽  
Age Related ◽  
Type 2 Diabetes Treatment ◽  
Definition Of ◽  
Hospital Outpatients

ABSTRACT. There are limited data on the prevalence of Type 2 diabetes in Norway, since most studies conducted have not tried to separate between Type 1 and Type 2 diabetes. The prevalence of known diabetes in patients over 14 years was reported as 0.7 % in 1956. The present prevalence of known diabetes probably is about twice this percentage. This will be estimated in a planned study. The treatment behaviour of Norwegian physicians has been analysed in a preliminary study. Based on this, 90 % of the patients at hospital outpatients clinics used insulin compared with only 16 % in general practice. The criteria applied for definition of diabetes varied considerably among the physicians, only 50 % of them using the WHO criteria. Age related prevalence data will be determined in a future screening project. Key words: prevalence, incidence, Type 1 diabetes, Type 2 diabetes, treatment, Norway.

The use of apple cider vinegar as a secondary treatment line with conventional diabetic treatment as a new medical hypothesis linking type 1 diabetes and type 2 diabetes

2021 ◽  
Vol 11 (2) ◽  
pp. 45-46
Author(s):  
Ahed J Alkhatib
Keyword(s):  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
The Body ◽  
Secondary Treatment ◽  
Therapeutic Approaches ◽  
Apple Cider ◽  
Treatment Line ◽  
Diabetic Treatment

The diabetes as a disease has been reported for 3500 years. Although diagnostic and therapeutic approaches have continuously developed, no definitive therapeutic approaches have so far been reached. Diabetes is not a single disease; it interferes with various systems in the body including nervous system and cardiovascular system. The therapeutic lines for type 1 diabetes start with insulin and will need another treatment such as metformin. On the other hand, type 2 diabetes treatment strategies start with metformin and there will be a need for another treatment, insulin according to the disease progression. At certain point, both types of diabetes are treated applying the same strategies. In this study, we followed another strategy by applying the use of apple cider vinegar in patient with type 1 diabetes, and patient with type 2 diabetes following getting each meal. The results showed that glucose levels were within reference range after five days. Taken together, the use of apple cider vinegar as a secondary treatment line with conventional diabetic treatment is promising and needs to be further investigated

scholarly journals Increased Tau Phosphorylation and Cleavage in Mouse Models of Type 1 and Type 2 Diabetes

Endocrinology ◽  
2009 ◽  
Vol 150 (12) ◽  
pp. 5294-5301 ◽  
Author(s):  
Bhumsoo Kim ◽  
Carey Backus ◽  
SangSu Oh ◽  
John M. Hayes ◽  
Eva L. Feldman
Keyword(s):  
Type 2 Diabetes ◽  
Mouse Models ◽  
The United States ◽  
Tau Phosphorylation ◽  
Age Related ◽  
Increased Risk ◽  
And Gender ◽  
Tau Cleavage

Abstract As the population of the United States ages, the incidence of age-related neurodegenerative and systemic diseases including Alzheimer’s disease (AD) and diabetes is increasing rapidly. Multiple studies report that patients with diabetes have a 50–75% increased risk of developing AD compared with age- and gender-matched patients without diabetes. Abnormally phosphorylated tau is a major building block of neurofibrillary tangles, a classic neuropathological characteristic of AD. In addition, proteolytic tau cleavage promotes AD progression due to cleaved tau serving as a nucleation center for the pathological assembly of tau filaments. The current study examines tau modification in type 1 (streptozotocin-injected) and type 2 (db/db) mouse models of diabetes. Tau phosphorylation is increased in the cortex and hippocampus of db/db mice compared with db+ control mouse brain. Interestingly, there is an age-dependent increase in tau cleavage that is not observed in age-matched control db+ animals. Streptozotocin injection also increased tau phosphorylation; however, the increase was less significant compared with the type 2 mouse model, and more importantly, no tau cleavage was detected. Our results suggest tau modification caused by insulin dysfunction and hyperglycemia may contribute to the increased incidence of AD in diabetes. We hypothesize that type 1 and type 2 diabetes may contribute to AD through different mechanisms; in type 2 diabetes, hyperglycemia-mediated tau cleavage may be the key feature, whereas insulin deficiency may be the major contributing factor in type 1 diabetes.

scholarly journals Assessing reproducibility and utility of clustering of patients with type 2 diabetes and established CV disease (SAVOR -TIMI 53 trial)

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259372
Author(s):  
Yasunori Aoki ◽  
Bengt Hamrén ◽  
Lindsay E. Clegg ◽  
Christina Stahre ◽  
Deepak L. Bhatt ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Renal Function ◽  
Disease Progression ◽  
Risk Scores ◽  
Predictive Utility ◽  
Insulin Resistant ◽  
Age Related ◽  
Cardiovascular Outcome Trial ◽  
Cv Disease

Objective To assess the reproducibility and clinical utility of clustering-based subtyping of patients with type 2 diabetes (T2D) and established cardiovascular (CV) disease. Methods The cardiovascular outcome trial SAVOR-TIMI 53 (n = 16,492) was used. Analyses focused on T2D patients with established CV disease. Unsupervised machine learning technique called “k-means clustering” was used to divide patients into subtypes. K-means clustering including HbA1c, age of diagnosis, BMI, HOMA2-IR and HOMA2-B was used to assign clusters to the following diabetes subtypes: severe insulin deficient diabetes (SIDD); severe insulin-resistant diabetes (SIRD); mild obesity-related diabetes (MOD); mild age-related diabetes (MARD). We refer these subtypes as “clustering-based diabetes subtypes”. A simulation study using randomly generated data was conducted to understand how correlations between the above variables influence the formation of the cluster-based diabetes subtypes. The predictive utility of clustering-based diabetes subtypes for CV events (3-point MACE), renal function reduction (eGFR decrease >30%) and diabetic disease progression (introduction of additional anti-diabetic medication) were compared with conventional risk scores. Hazard ratios (HR) were estimated by Cox-proportional hazard models. Results In the SAVOR-TIMI 53 trial based dataset, the percentage of the clustering-based T2D subtypes were; SIDD (18%), SIRD (17%), MOD (29%), MARD (37%). Using the simulated dataset, the diabetes subtypes could be largely reproduced from a log-normal distribution when including known correlations between variables. The predictive utility of clustering-based diabetic subtypes on CV events, renal function reduction, and diabetic disease progression did not show an advantage compared to conventional risk scores. Conclusions The consistent reproduction of four clustering-based T2D subtypes can be explained by the correlations between the variables used for clustering. Subtypes of T2D based on clustering had limited advantage compared to conventional risk scores to predict clinical outcome in patients with T2D and established CV disease.

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