scholarly journals Predictive ability of current machine learning algorithms for type 2 diabetes mellitus ‐ A meta‐analysis

2021 ◽  
Author(s):  
Satoru Kodama ◽  
Kazuya Fujihara ◽  
Chika Horikawa ◽  
Masaru Kitazawa ◽  
Midori Iwanaga ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Predictive Ability ◽  
Machine Learning Algorithms

841-P: Predictive Ability of Incident Type 2 Diabetes Mellitus (T2DM) Using Machine Learning Algorithms: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 841-P
Author(s):  
SATORU KODAMA ◽  
TAKAAKI SATO ◽  
MASAHIKO YAMAMOTO ◽  
HAJIME ISHIGURO ◽  
MIDORI IWANAGA ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Meta Analysis ◽  
Predictive Ability ◽  
Machine Learning Algorithms ◽  
Incident Type ◽  
Incident Type 2 Diabetes

Mitochondrial Import of Malat1 Regulates Cardiac Mitochondrial Function in Type 2 Diabetes Mellitus through Interaction with MicroRNA-23b

2020 ◽  
Author(s):  
Quincy A. Hathaway ◽  
Andrew D. Taylor ◽  
Amina Kunovac ◽  
Mark V. Pinti ◽  
Mackenzie S. Newman ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Mitochondrial Function ◽  
Secondary Structures ◽  
Machine Learning Algorithms ◽  
12S Rrna ◽  
Human Patient

Abstract Background: The pathogenesis of type 2 diabetes mellitus is known to alter cardiac mitochondrial bioenergetics, but the role of non-coding RNAs (ncRNAs) in regulating this process remains poorly understood, specifically within the mitochondrion. Here we characterized the mitochondrial localization of nuclear-encoded lncRNAs in the heart in an effort to understand how import of lncRNAs can modify mitochondrial function during diabetes mellitus.Methods: Human patient and FVB/NJ mouse cardiac tissues were collected from type 2 diabetics and non-diabetics. Immunoblotting was used to validate purity of mitochondrial fractionation. NcRNA from subcellular compartments (cytoplasmic and mitochondrial) were sequenced through the Illumina 2500 HiSeq. LncRNA mitochondrial targeting sequences were acquired through crosslinking immunoprecipitation (CLIP) with Polynucleotide Phosphorylase (PNPase) and secondary structures were analyzed using supervised and unsupervised machine learning algorithms. Fluorescence in situ hybridization (FISH) allowed for visualization of Malat1 localization. Mitochondrial function was assessed through oxygen consumption rate.Results: 2448 LncRNAs were discovered in human cardiac mitochondria. Malat1 and microRNA-23b abundance were significantly reduced in human and mouse diabetic mitochondria. Modification of the import protein PNPase in HL-1 cells decreased binding affinity (~80%) of transcript variant 4 of Malat1 (Malat1-204), with machine learning (AUC 0.75) identifying stem-loops as shared secondary structures of lncRNAs bound to PNPase. Knockdown of Malat1-204 and microRNA-23b in mitochondria decreased protein expression of mt-Nd4 (~55%), though showed little/no binding homology to the mt-Nd4 mRNA transcript. Malat1-204 and microRNA-23b were identified bound to mt-Rnr1 rRNA, which could influence translation of mt-Nd4 through the mitochondrial 12S rRNA pathway.Conclusions: Malat1-204 and microRNA-23b, decreased in cardiac diabetic mitochondria, likely stabilize the mitochondrial ribosomal complexes through binding of mt-Rnr1. The significant decrease in total abundance of lncRNAs in diabetic mitochondria could suggest maladaptation for regulating bioenergetics and managing future insults.

Systematic Literature Review and Network Meta-analysis (NMA) of SGLT2i as Monotherapy for Type 2 Diabetes Mellitus (T2DM)

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1159-P
Author(s):  
GLENN M. DAVIES ◽  
ANN MARIE MCNEILL ◽  
ELIZA KRUGER ◽  
STACEY L. KOWAL ◽  
FLAVIA EJZYKOWICZ ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Meta Analysis

Plant-based diet and risk of type 2 diabetes mellitus: a systematic review and meta-analysis

2020 ◽  
pp. 1301-1306
Author(s):  
Arwa Aljabali ◽  
Roaa Maghrabi ◽  
Ahmad Shok ◽  
Ghufran Alshawmali ◽  
Abdullah Alqahtani ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Systematic Review ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Meta Analysis

Cinammon (Cinnamomum Spp.) and Type 2 Diabetes Mellitus

2019 ◽  
Vol 18 (3) ◽  
pp. 247-255
Author(s):  
Sierra-Puente D. ◽  
Abadi-Alfie S. ◽  
Arakanchi-Altaled K. ◽  
Bogard-Brondo M. ◽  
García-Lascurain M. ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Systematic Reviews ◽  
Meta Analysis ◽  
Fasting Blood Glucose ◽  
Tolerance Test ◽  
Oral Glucose ◽  
Key Aspects

Spices such as cinnamon (Cinnamomum Spp.) have been of interest due to their phytochemical composition that exert hypoglycemic effects with potential for management of type 2 diabetes mellitus (T2DM). We summarize data from 27 manuscripts that include, one book chapter, 3 review articles, 10 randomized controlled trials, 4 systematic reviews with meta-analysis, and 9 preclinical studies. The most frequently used cinnamon variety was Cinnamomum cassia rather than the Cinnamomum zeylanicum, whereas outcomes were defined as fasting blood glucose, glycated hemoglobin, and oral glucose tolerance test. A great variability in methodology such as different doses (from 120 mg to 6 g), duration of intervention, data retrieved and use of different concomitant medication, were found to be key aspects of most of trials and systematic reviews with meta-analysis available to date. Low quality studies have been made in most cases with a lot of heterogeneity clouding significance of results. More research needs to be done in order to yield accurate evidence for evidence-based recommendations. Its use is not currently a reliable nor advisable option for the treatment of T2DM.

The GSTM1 and GSTT1 Null Genotypes Increase the Risk for Type 2 Diabetes Mellitus and the Subsequent Development of Diabetic Complications: A Meta-analysis

2018 ◽  
Vol 15 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Sayantan Nath ◽  
Sambuddha Das ◽  
Aditi Bhowmik ◽  
Sankar Kumar Ghosh ◽  
Yashmin Choudhury
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Meta Analysis ◽  
Subsequent Development ◽  
Asian Population ◽  
Gstm1 Null Genotype ◽  
Increased Risk

Background:Studies pertaining to association of GSTM1 and GSTT1 null genotypes with risk of T2DM and its complications were often inconclusive, thus spurring the present study.Methods:Meta-analysis of 25 studies for evaluating the role of GSTM1/GSTT1 null polymorphisms in determining the risk for T2DM and 17 studies for evaluating the role of GSTM1/GSTT1 null polymorphisms in development of T2DM related complications were conducted.Results:Our study revealed an association between GSTM1 and GSTT1 null polymorphism with T2DM (GSTM1; OR=1.37;95% CI =1.10-1.70 and GSTT1; OR=1.29;95% CI =1.04-1.61) with an amplified risk of 2.02 fold for combined GSTM1-GSTT1 null genotypes. Furthermore, the GSTT1 null (OR=1.56;95%CI=1.38-1.77) and combined GSTM1-GSTT1 null genotypes (OR=1.91;95%CI=1.25- 2.94) increased the risk for development of T2DM related complications, but not the GSTM1 null genotype. Stratified analyses based on ethnicity revealed GSTM1 and GSTT1 null genotypes increase the risk for T2DM in both Caucasians and Asians, with Asians showing much higher risk of T2DM complications than Caucasians for the same. </P><P> Discussion: GSTM1, GSTT1 and combined GSTM1-GSTT1 null polymorphism may be associated with increased risk for T2DM; while GSTT1 and combined GSTM1-GSTT1 null polymorphism may increase the risk of subsequent development of T2DM complications with Asian population carrying an amplified risk for the polymorphism.Conclusion:Thus GSTM1 and GSTT1 null genotypes increases the risk for Type 2 diabetes mellitus alone, in combination or with regards to ethnicity.

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