scholarly journals Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1383
Author(s):  
Sakifa Aktar ◽  
Ashis Talukder ◽  
Md. Martuza Ahamad ◽  
A. H. M. Kamal ◽  
Jahidur Rahman Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Meta Analysis ◽  
Individual Risk ◽  
Severe Illness ◽  
Chronic Obstructive ◽  
Learning Approaches ◽  
Machine Learning Classification ◽  
Increased Risk

Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom–comorbidity combinations, it was observed that Pneumonia–Hypertension, Pneumonia–Diabetes, and Acute Respiratory Distress Syndrome (ARDS)–Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resources.

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.

scholarly journals Breakfast skipping and the risk of type 2 diabetes: a meta-analysis of observational studies

2015 ◽  
Vol 18 (16) ◽  
pp. 3013-3019 ◽  
Author(s):  
Huashan Bi ◽  
Yong Gan ◽  
Chen Yang ◽  
Yawen Chen ◽  
Xinyue Tong ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Observational Studies ◽  
Meta Analysis ◽  
Adjusted Relative Risk ◽  
China National Knowledge Infrastructure ◽  
Cross Sectional ◽  
Breakfast Skipping ◽  
Risk Estimates ◽  
Increased Risk

AbstractObjectiveBreakfast skipping has been reported to be associated with type 2 diabetes (T2D), but the results are inconsistent. No meta-analyses have applied quantitative techniques to compute summary risk estimates. The present study aimed to conduct a meta-analysis of observational studies summarizing the evidence on the association between breakfast skipping and the risk of T2D.DesignSystematic review and meta-analysis.SettingRelevant studies were identified by a search of PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI) and SINOMED up to 9 August 2014. We also reviewed reference lists from retrieved articles. We included studies that reported risk estimates (including relative risks, odds ratios and hazard ratios) with 95 % confidence intervals for the association between breakfast skipping and the risk of T2D.SubjectsEight studies involving 106 935 participants and 7419 patients with T2D were included in the meta-analysis.ResultsA pooled adjusted relative risk for the association between exposure to breakfast skipping and T2D risk was 1·21 (95 % CI 1·12, 1·31; P=0·984; I2=0·0 %) in cohort studies and the pooled OR was 1·15 (95 % CI, 1·05, 1·24; P=0·770; I2=0·0 %) in cross-sectional studies. Visual inspection of a funnel plot and Begg’s test indicated no evidence of publication bias.ConclusionsBreakfast skipping is associated with a significantly increased risk of T2D. Regular breakfast consumption is potentially important for the prevention of T2D.

scholarly journals TheType 2 Deiodinase Thr92Ala PolymorphismIs Associated with Worse Glycemic Control in Patients with Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Xiaowen Zhang ◽  
Jie Sun ◽  
Wenqing Han ◽  
Yaqiu Jiang ◽  
Shiqiao Peng ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Systematic Review ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Glycemic Control ◽  
Meta Analysis ◽  
Increased Risk ◽  
Design And Methods ◽  
Hba1c Levels

Objective. Type 2 deiodinase (Dio2) is an enzyme responsible for the conversion of T4 to T3. The Thr92Ala polymorphism has been shown related to an increased risk for developing type 2 diabetes mellitus (T2DM). The aim of this study is to assess the association between this polymorphism and glycemic control in T2DM patients as marked by the HbA1C levels.Design and Methods.The terms “rs225014,” “thr92ala,” “T92A,” or “dio2 a/g” were used to search for eligible studies in the PubMed, Embase, and Cochrane databases and Google Scholar. A systematic review and meta-analysis of studies including both polymorphism testing and glycated hemoglobin (HbA1C) assays were performed.Results. Four studies were selected, totaling 2190 subjects. The pooled mean difference of the studies was 0.48% (95% CI, 0.18–0.77%), indicating that type 2 diabetics homozygous for the Dio2 Thr92Ala polymorphism had higher HbA1C levels.Conclusions. Homozygosity for the Dio2 Thr92Ala polymorphism is associated with higher HbA1C levels in T2DM patients. To confirm this conclusion, more studies of larger populations are needed.

scholarly journals The association between insulin therapy and depression in patients with type 2 diabetes mellitus: a meta-analysis

BMJ Open ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. e020062 ◽  
Author(s):  
Xiaosu Bai ◽  
Zhiming Liu ◽  
Zhisen Li ◽  
Dewen Yan
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Insulin Therapy ◽  
Depressive Disorders ◽  
Meta Analysis ◽  
Epidemiological Studies ◽  
Cochrane Library ◽  
Increased Risk

ObjectivesSeveral patients with type 2 diabetes mellitus (T2DM) have depressive disorders. Whether insulin treatment was associated with increased risk of depression remains controversial. We performed a meta-analysis to evaluate the association of insulin therapy and depression.DesignA meta-analysis.MethodsWe conducted a systematic search of PubMed, PsycINFO, Embase and the Cochrane Library from their inception to April 2016. Epidemiological studies comparing the prevalence of depression between insulin users and non-insulin users were included. A random-effects model was used for meta-analysis. The adjusted and crude data were analysed.ResultsTwenty-eight studies were included. Of these, 12 studies presented with adjusted ORs. Insulin therapy was significantly associated with increased risk of depression (OR=1.41, 95% CI 1.13 to 1.76, p=0.003). Twenty-four studies provided crude data. Insulin therapy was also associated with an odds for developing depression (OR=1.59, 95% CI 1.41 to 1.80, p<0.001). When comparing insulin therapy with oral antidiabetic drugs, significant association was observed for adjusted (OR=1.42, 95% CI 1.08 to 1.86, p=0.008) and crude (OR=1.61, 95% CI 1.35 to 1.93, p<0.001) data.ConclusionsOur meta-analysis confirmed that patients on insulin therapy were significantly associated with the risk of depressive symptoms.

scholarly journals Machine learning approaches revealed metabolic signatures of incident chronic kidney disease in persons with pre-and type 2 diabetes

2020 ◽  
Author(s):  
Ada Admin ◽  
Jialing Huang ◽  
Cornelia Huth ◽  
Marcela Covic ◽  
Martina Troll ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Population Based ◽  
Learning Approaches ◽  
Clinical Variables

Early and precise identification of individuals with pre-diabetes and type 2 diabetes (T2D) at risk of progressing to chronic kidney disease (CKD) is essential to prevent complications of diabetes. Here, we identify and evaluate prospective metabolite biomarkers and the best set of predictors of CKD in the longitudinal, population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort by targeted metabolomics and machine learning approaches. Out of 125 targeted metabolites, sphingomyelin (SM) C18:1 and phosphatidylcholine diacyl (PC aa) C38:0 were identified as candidate metabolite biomarkers of incident CKD specifically in hyperglycemic individuals followed during 6.5 years. Sets of predictors for incident CKD developed from 125 metabolites and 14 clinical variables showed highly stable performances in all three machine learning approaches and outperformed the currently established clinical algorithm for CKD. The two metabolites in combination with five clinical variables were identified as the best set of predictors and their predictive performance yielded a mean area value under the receiver operating characteristic curve of 0.857. The inclusion of metabolite variables in the clinical prediction of future CKD may thus improve the risk prediction in persons with pre- and T2D. The metabolite link with hyperglycemia-related early kidney dysfunction warrants further investigation.

scholarly journals Cadmium Is Associated with Type 2 Diabetes in a Superfund Site Lead Smelter Community in Dallas, Texas

2020 ◽  
Vol 17 (12) ◽  
pp. 4558
Author(s):  
Bert B. Little ◽  
Robert Reilly ◽  
Brad Walsh ◽  
Giang T. Vu
Keyword(s):  
Type 2 Diabetes ◽  
Logistic Regression ◽  
Fixed Effects ◽  
Smoking Status ◽  
Meta Analysis ◽  
Two Phase ◽  
Lead Smelter ◽  
Increased Risk ◽  
Cd Exposure

Objective: To test the hypothesis that cadmium (Cd) exposure is associated with type 2 diabetes mellitus (T2DM). Materials and Methods: A two-phase health screening (physical examination and laboratory tests) was conducted in a lead smelter community following a Superfund Cleanup. Participants were African Americans aged >19 years to <89 years. Multiple logistic regression was used to analyze T2DM regressed on blood Cd level and covariates: body mass index (BMI), heavy metals (Ar, Cd, Hg, Pb), duration of residence, age, smoking status, and sex. Results: Of 875 subjects environmentally exposed to Cd, 55 were occupationally exposed to by-products of lead smelting and 820 were community residents. In addition, 109 T2DM individuals lived in the community for an average of 21.0 years, and 766 non-T2DM individuals for 19.0 years. T2DM individuals (70.3%) were >50 years old. Blood Cd levels were higher among T2DM subjects (p < 0.006) compared to non-T2DM individuals. Logistic regression of T2DM status identified significant predictors: Cd level (OR = 1.85; 95% CI: 1.14–2.99, p < 0.01), age >50 years (OR = 3.10; 95% CI: 1.91–5.02, p < 0.0001), and BMI (OR = 1.07; CI: 1.04–1.09, 0.0001). In meta-analysis of 12 prior studies and this one, T2DM risk was OR = 1.09 (95% CI: 1.03–1.15, p < 0.004) fixed effects and 1.22 (95% CI: 1.04–1.44, p < 0.02) random effects. Discussion: Chronic environmental Cd exposure was associated with T2DM in a smelter community, controlling for covariates. T2DM onset <50 years was significantly associated with Cd exposure, but >50 years was not. Meta-analysis suggests that Cd exposure is associated with a small, but significant increased risk for T2DM. Available data suggest Cd exposure is associated with an increased propensity to increased insulin resistance.

scholarly journals Association of the type 2 deiodinase Thr92Ala polymorphism with type 2 diabetes: case–control study and meta-analysis

2010 ◽  
Vol 163 (3) ◽  
pp. 427-434 ◽  
Author(s):  
José Miguel Dora ◽  
Walter Escouto Machado ◽  
Jakeline Rheinheimer ◽  
Daisy Crispim ◽  
Ana Luiza Maia
Keyword(s):  
Type 2 Diabetes ◽  
Case Control Study ◽  
Association Studies ◽  
Meta Analysis ◽  
Genetic Association Studies ◽  
Case Control ◽  
Increased Risk ◽  
Key Enzyme ◽  
Control Study

ObjectiveThe type 2 deiodinase (D2) is a key enzyme for intracellular triiodothyronine (T3) generation. A single-nucleotide polymorphism in D2 (Thr92Ala) has been associated with increased insulin resistance in nondiabetic and type 2 diabetes (DM2) subjects. Our aim was to evaluate whether the D2 Thr92Ala polymorphism is associated with increased risk for DM2.Design and methodsA case–control study with 1057 DM2 and 516 nondiabetic subjects was performed. All participants underwent genotyping of the D2 Thr92Ala polymorphism. Additionally, systematic review and meta-analysis of the literature for genetic association studies of D2 Thr92Ala polymorphism and DM2 were performed in Medline, Embase, LiLacs, and SciELO, and major meeting databases using the terms ‘rs225014’ odds ratio (OR) ‘thr92ala’ OR ‘T92A’ OR ‘dio2 a/g’.ResultsIn the case–control study, the frequencies of D2 Ala92Ala homozygous were 16.4% (n=173) versus 12.0% (n=62) in DM2 versus controls respectively resulting in an adjusted OR of 1.41 (95% confidence intervals (CI) 1.03–1.94, P=0.03). The literature search identified three studies that analyzed the association of the D2 Thr92Ala polymorphism with DM2, with the following effect estimates: Mentuccia (OR 1.40 (95% CI 0.78–2.51)), Grarup (OR 1.09 (95% CI 0.92–1.29)), and Maia (OR 1.22 (95% CI 0.78–1.92)). The pooled effect of the four studies resulted in an OR 1.18 (95% CI 1.03–1.36, P=0.02).ConclusionsOur results indicate that in a case–control study, the homozygosity for D2 Thr92Ala polymorphism is associated with increased risk for DM2. These results were confirmed by a meta-analysis including 11 033 individuals, and support a role for intracellular T3 concentration in skeletal muscle on DM2 pathogenesis.

scholarly journals Association between sugar-sweetened and artificially sweetened soft drinks and type 2 diabetes: systematic review and dose–response meta-analysis of prospective studies

2014 ◽  
Vol 112 (5) ◽  
pp. 725-734 ◽  
Author(s):  
D. C. Greenwood ◽  
D. E. Threapleton ◽  
C. E. L. Evans ◽  
C. L. Cleghorn ◽  
C. Nykjaer ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Dose Response ◽  
Prospective Studies ◽  
Meta Analysis ◽  
Lifestyle Factors ◽  
Soft Drinks ◽  
Reverse Causality ◽  
Increased Risk ◽  
Causal Pathway

The intake of sugar-sweetened soft drinks has been reported to be associated with an increased risk of type 2 diabetes, but it is unclear whether this is because of the sugar content or related lifestyle factors, whether similar associations hold for artificially sweetened soft drinks, and how these associations are related to BMI. We aimed to conduct a systematic literature review and dose–response meta-analysis of evidence from prospective cohorts to explore these issues. We searched multiple sources for prospective studies on sugar-sweetened and artificially sweetened soft drinks in relation to the risk of type 2 diabetes. Data were extracted from eleven publications on nine cohorts. Consumption values were converted to ml/d, permitting the exploration of linear and non-linear dose–response trends. Summary relative risks (RR) were estimated using a random-effects meta-analysis. The summary RR for sugar-sweetened and artificially sweetened soft drinks were 1·20/330 ml per d (95 % CI 1·12, 1·29,P< 0·001) and 1·13/330 ml per d (95 % CI 1·02, 1·25,P= 0·02), respectively. The association with sugar-sweetened soft drinks was slightly lower in studies adjusting for BMI, consistent with BMI being involved in the causal pathway. There was no evidence of effect modification, though both these comparisons lacked power. Overall between-study heterogeneity was high. The included studies were observational, so their results should be interpreted cautiously, but findings indicate a positive association between sugar-sweetened soft drink intake and type 2 diabetes risk, attenuated by adjustment for BMI. The trend was less consistent for artificially sweetened soft drinks. This may indicate an alternative explanation, such as lifestyle factors or reverse causality. Future research should focus on the temporal nature of the association and whether BMI modifies or mediates the association.

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