Classification and Laboratory Diagnosis of Diabetes Mellitus

Soh, Shui Boon; Topliss, Duncan J.

doi:10.1007/978-1-4614-8684-8_27

Cited by 2 publications

(5 citation statements)

References 42 publications

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“…The aetiologic classification of diabetes recognizes several types besides Type 1, Type 2 and gestational diabetes 1 . The recognition that there is still a high degree of heterogeneity leads to an effort to adapt the numerous drugs with distinct mechanisms to the patients who benefit most from them 32 .…”

Section: Advancement Of Diabetes Management—travelling On the Road To...mentioning

confidence: 99%

“…The aetiologic classification of diabetes recognizes several types besides Type 1, Type 2 and gestational diabetes. 1 The recognition that there is still a high degree of heterogeneity leads to an effort to adapt the numerous drugs with distinct mechanisms to the patients who benefit most from them. 32 Weight control, hypertension and dyslipidemia, among others, have gained increasing relevance along with glycemic control.…”

Section: Advancement Of Diabetes Management—travelling On the Road To...mentioning

confidence: 99%

“…In diabetes, glucose metabolism is affected due to individual or simultaneous changes in insulin secretion, action or metabolism. Diabetes is diagnosed based on glycaemia and cut‐off values were defined based on the presence of microvascular complications, namely retinopathy 1 . However, dysglycaemia, or the glucose altered metabolism, is not an all‐or‐nothing phenomenon, and on the contrary, it occurs continuously.…”

Section: Introductionmentioning

confidence: 99%

“…Diabetes is diagnosed based on glycaemia and cut‐off values were defined based on the presence of microvascular complications, namely retinopathy. 1 However, dysglycaemia, or the glucose altered metabolism, is not an all‐or‐nothing phenomenon, and on the contrary, it occurs continuously. Prediabetes (PD) is a less severe hyperglycemic state that depicts a higher risk of progression to diabetes.…”

Section: Introductionmentioning

confidence: 99%

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Big data and machine learning to tackle diabetes management

Pina

Meneses

Sousa‐Lima

et al. 2022

Eur J Clin Investigation

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Background Type 2 Diabetes (T2D) diagnosis is based solely on glycaemia, even though it is an endpoint of numerous dysmetabolic pathways. Type 2 Diabetes complexity is challenging in a real‐world scenario; thus, dissecting T2D heterogeneity is a priority. Cluster analysis, which identifies natural clusters within multidimensional data based on similarity measures, poses a promising tool to unravel Diabetes complexity. Methods In this review, we scrutinize and integrate the results obtained in most of the works up to date on cluster analysis and T2D. Results To correctly stratify subjects and to differentiate and individualize a preventive or therapeutic approach to Diabetes management, cluster analysis should be informed with more parameters than the traditional ones, such as etiological factors, pathophysiological mechanisms, other dysmetabolic co‐morbidities, and biochemical factors, that is the millieu. Ultimately, the above‐mentioned factors may impact on Diabetes and its complications. Lastly, we propose another theoretical model, which we named the Integrative Model. We differentiate three types of components: etiological factors, mechanisms and millieu. Each component encompasses several factors to be projected in separate 2D planes allowing an holistic interpretation of the individual pathology. Conclusion Fully profiling the individuals, considering genomic and environmental factors, and exposure time, will allow the drive to precision medicine and prevention of complications.

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Section: Advancement Of Diabetes Management—travelling On the Road To...mentioning

confidence: 99%

Section: Advancement Of Diabetes Management—travelling On the Road To...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Big data and machine learning to tackle diabetes management

Pina

Meneses

Sousa‐Lima

et al. 2022

Eur J Clin Investigation

View full text Add to dashboard Cite

show abstract

“…D iabetes mellitus is a metabolic disease characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. 1,2 Exercise has been known to increase insulin sensitivity and glucose tolerance, making it a safe, effective, and noninvasive treatment modality for T2D. [3][4][5] Lifestyle modification interventions such as exercise have long been promoted as important in the management of T2D.…”

Section: Introductionmentioning

confidence: 99%

Video Call-based Fitness Assessment shows Poor Fitness in People with Type II Diabetes: Findings from Diabefly Digital Therapeutics Program

Bhagat

Mandlekar²,

Verma³

et al. 2022

JAPI

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Objective: Exercise and physical activity are integral aspects for the effective management of diabetes. Unsupervised home exercise although very accessible is limited by poor adherence, risk of injury, and a higher dropout rate of participants. A fitness assessment by a qualified physiotherapist can help in understanding the baseline fitness of individuals and thus generating appropriate exercise prescriptions. The current study assesses the feasibility of video call-based fitness assessment for people with diabetes. The study also assesses the effect of current physical activity status and pain on performance in physical fitness tests. Methods: One hundred participants with type II diabetes (T2D) underwent 6-minute walk test (6MWT), 1-minute push-up test, wall sit test, 1-minute sit-up test, and V-sit and reach test for measuring different components of physical fitness such as aerobic capacity, upper body strength, lower body strength, core strength, and flexibility, respectively. The performance in physical fitness of participants was analyzed after the video consult along with pain complaints and current exercise status. Results: All the participants underwent the physical fitness test safely based on video call. Out of all the participants, a good range score was achieved by 52% in 6MWT, 17% in push-up test, 1% in wall sit test, 6% in sit-up test, and 9% in V-sit and reach test. Current physical activity status (aerobic exercise for minimum 20 minutes) did not show any association with performance in fitness tests (p = 0.89 for push-up test, p = 0.50 for wall sit test, p = 0.23 for sit-up test, and p = 0.10 for V-sit and reach test). Presence of upper body and lower body pain affected the performance in push-up test and wall sit test with 71.4% and 95.6% of participants achieving scores in poor to below-average range (p-value < 0.001). Conclusion: The study showed the safety and feasibility of conducting video call-based assessment of physical fitness by physiotherapists. The study also highlighted the poor glycemic control, high cardiovascular risk, and poor level of physical fitness in people with diabetes in India. Insights based on physical fitness, current physical activity status, and pain can help in developing personalized exercise plans for people with diabetes.

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Classification and Laboratory Diagnosis of Diabetes Mellitus

Cited by 2 publications

References 42 publications

Big data and machine learning to tackle diabetes management

Big data and machine learning to tackle diabetes management

Video Call-based Fitness Assessment shows Poor Fitness in People with Type II Diabetes: Findings from Diabefly Digital Therapeutics Program

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