Diabetes is the leading cause of severe health complications and one of the top 10 causes of death worldwide. To date, diabetes has no cure, and therefore, it is necessary to take precautionary measures to avoid its occurrence. The main aim of this systematic review is to identify the majority of the risk factors for the incidence/prevalence of type 2 diabetes mellitus on one hand, and to give a critical analysis of the cohort/cross-sectional studies which examine the impact of the association of risk factors on diabetes. Consequently, we provide insights on risk factors whose interactions are major players in developing diabetes. We conclude with recommendations to allied health professionals, individuals and government institutions to support better diagnosis and prognosis of the disease.

Association of risk factors with type 2 diabetes: A systematic review

Previous studies have revealed that triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) is one of major risk factors of insulin resistance and diabetes. However, study on the association between TG/HDL-C and diabetes mellitus (DM) risk is limited, especially in Chinese people. This study was undertaken to investigate the relationship between TG/HDL-C and incident of diabetes in a large cohort in Chinese population. The present study was a retrospective cohort study. A total of 114,787 adults from Rich Healthcare Group in China, which includes all medical records for participants who received a health check from 2010 to 2016. The target independent variable and the dependent variable were triglyceride to high-density lipoprotein cholesterol ratio measured at baseline and incident of diabetes mellitus appeared during follow-up respectively. Covariates involved in this study included age, gender, body mass index, diastolic blood pressure, systolic blood pressure, fasting plasma glucose, total cholesterol, low density lipoprotein cholesterol, serum creatinine, smoking and drinking status and family history of diabetes. Cox proportional-hazards regression was used to investigate the association of TG/HDL-C and diabetes. Generalized additive models was used to identify non-linear relationships. Additionally, we also performed a subgroup analysis. It was stated that the data had been uploaded to the DATADRYAD website. After adjusting age, gender, body mass index, systolic blood pressure, diastolic blood pressure, fasting blood glucose, total cholesterol, low density lipoprotein cholesterol, serum creatinine, smoking and drinking status and family history of diabetes, result showed TG/HDL-C was positively associated with incident of diabetes mellitus (HR = 1.159, 95%CI (1.104, 1.215)). A non-linear relationship was detected between TG/HDL-C and incident of diabetes, which had an inflection point of TG/HDL-C was 1.186. The effect sizes and the confidence intervals on the left and right sides of the inflection point were 1.718(1.433,2.060) and 1.049(0.981,1.120), respectively. Subgroup analysis showed, the stronger association can be found in the population with fasting plasma glucose (FPG) < 6.1 mmol/L (P for interaction< 0.0001; HR = 1.296 with FPG < 6.1 mmol/L vs HR = 1.051 with FPG ≥ 6.1 mmol/L).The same trend was also seen in the population with body mass index (BMI)(≥18.5, < 24 kg/m2) (P for interaction = 0.010,HR = 1.324) and family history without diabetes(P for interaction = 0.025, HR = 1.170). TG/HDL-C is positively associated with diabetes risk. The relationship between TG/HDL-C and incident of diabetes is also non-linear. TG/HDL-C was strong positively related to incident of diabetes when TG/HDL-C is less than 1.186.

Association of Triglyceride to high-density lipoprotein cholesterol ratio and incident of diabetes mellitus: a secondary retrospective analysis based on a Chinese cohort study.

Diabetes is one of the most prevalent chronic diseases in the world, and its prevalence is expected to rise further. To help understand the utility of the ratio of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol (NHHR) in diabetes prevention, this large-scale longitudinal cohort study aims to explore the association of NHHR with diabetes risk and compare it as a risk predictor with conventional lipid parameters.This observational study extracted data from the NAGALA longitudinal cohort study conducted in Japan between 2004 and 2015. Multivariate Cox regression analysis was used to evaluate the association between NHHR and the risk of diabetes. The dose-response relationship was analyzed by restricted cubic spline (RCS) regression and the potential risk threshold was estimated. The receiver operator characteristic curve (ROC) was used to analyze and calculate the predictive value and optimal threshold of NHHR and other conventional lipids for new-onset diabetes.Of the 15,464 people aged 18-79, 373 (2.41%) were diagnosed with new-onset diabetes during the study period, with a median age of 46 years. The sensitivity analysis based on multivariate adjustment showed that the independent positive correlation between diabetes and NHHR was stable in different populations. RCS and ROC analysis indicated that the association between NHHR and diabetes was non-linear, and the NHHR was a better marker for predicting diabetes risk than other conventional lipid parameters; Additionally, it is worth noting that an NHHR of approximately 2.74 may be the optimal threshold for intervention in diabetes risk.In the general population, NHHR is a better marker for predicting diabetes risk than conventional lipid parameters, and an NHHR of about 2.74 may be the optimal threshold for assessing diabetes risk. 

Utility of Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio in Evaluating Incident Diabetes Risk

BACKGROUND Recent studies have suggested the triglyceride-glucose index (TyG index) may serve as a suitable substitute for insulin resistance. However, evidence for the relationship between TyG index and risk of diabetes remains limited. This study sought to explore the association of baseline TyG index with risk of developing diabetes in Chinese adults. METHODS This retrospective cohort study was conducted using data from the health screening program in China. A total of 201,298 non-diabetic individuals were included. TyG index was calculated as Ln [fasting plasma glucose (mg/dL) × fasting triglyceride level (mg/dL) / 2]. Diabetes was defined as fasting plasma glucose ≥126 mg/dL and/or self-reported diabetes. Cox proportion-hazard model was employed to evaluate the independent impact of baseline TyG index on future diabetes risk. Sensitivity and subgroup analyses were implemented to verify the reliability of results. Notably, data were downloaded from the DATADRYAD website, and used only for secondary analyses. RESULTS During an average follow-up of 3.12 years, among 201,298 individuals aged ≥20 years, 3389 subjects developed diabetes. After adjusting for potential confounders, elevated TyG index were independently correlated with greater risk of incident diabetes (hazard ratio (HR), 3.34; 95% confidence interval (CI), 3.11-3.60). Compared with the lowest quartile (Q1), increasing TyG index (Q2, Q3, and Q4) was related to increased HR estimates of incident diabetes [HR (95% CI), 1.83 (1.49-2.26); 3.29 (2.70-4.01), and 6.26 (5.15-7.60), respectively]. Moreover, a nonlinear relationship was observed between TyG index and risk of diabetes and the slope of the curve increased accompanying the rise of TyG index. Subgroup analysis revealed the positive association was stronger among subjects with age < 40 years, body mass index ≥18.5 kg/m2 and < 24 kg/m2, or systolic blood pressure < 140 mmHg, or in females. CONCLUSIONS Elevated TyG index is independently correlated with increased risk of incident diabetes in Chinese adults, indicating it may represent a reliable predictor of diabetes in high-risk populations.

/pdf/association-between-triglyceride-glucose-index-and-risk-of-jtxmlfabj5.pdf

Association between triglyceride-glucose index and risk of incident diabetes: a secondary analysis based on a Chinese cohort study : TyG index and incident diabetes.

Importance/Background: The coronavirus disease (COVID-19) pandemic is a critical public health issue. Evidence has shown that metformin favorably influences COVID-19 outcomes. This study aimed to assess the benefits and risks of metformin in COVID-19 patients. Methods: We searched the PubMed, Embase, Cochrane Library, and Chinese Biomedical Literature Database from inception to February 18, 2021. Observational studies assessing the association between metformin use and the outcomes of COVID-19 patients were included. The primary outcome was mortality, and the secondary outcomes included intubation, deterioration, and hospitalization. Random-effects weighted models were used to pool the specific effect sizes. Subgroup analyses were conducted by stratifying the meta-analysis by region, diabetic status, the adoption of multivariate model, age, risk of bias, and timing for adding metformin. Results: We identified 28 studies with 2,910,462 participants. Meta-analysis of 19 studies showed that metformin is associated with 34% lower COVID-19 mortality [odds ratio (OR), 0.66; 95% confidence interval (CI), 0.56-0.78; I 2 = 67.9%] and 27% lower hospitalization rate (pooled OR, 0.73; 95% CI, 0.53-1.00; I 2 = 16.8%). However, we did not identify any subgroup effects. The meta-analysis did not identify statistically significant association between metformin and intubation and deterioration of COVID-19 (OR, 0.94; 95% CI, 0.77-1.16; I 2 = 0.0% for intubation and OR, 2.04; 95% CI, 0.65-6.34; I 2 = 79.4% for deterioration of COVID-19), respectively. Conclusions: Metformin use among COVID-19 patients was associated with a reduced risk of mortality and hospitalization. Our findings suggest a relative benefit for metformin use in nursing home and hospitalized COVID-19 patients. However, randomized controlled trials are warranted to confirm the association between metformin use and COVID-19 outcomes. Study Registration: The study was registered on the PROSPERO on Feb 23, 2021 (CRD42021238722).

/pdf/metformin-in-patients-with-covid-19-a-systematic-review-and-6afkt849o4.pdf

Metformin in Patients With COVID-19: A Systematic Review and Meta-Analysis.

Background Dyslipidemia predicts the development and progression of diabetes. A higher non-high-density lipoprotein cholesterol (HDL-C): HDL-C ratio is reportedly associated with metabolic syndrome and insulin resistance, but its relationship with glycemic levels and diabetes remains unclear. Methods In all, 4882 subjects aged ≥40 years without diabetes and not using lipid-lowering drugs were enrolled in the study. The non-HDL-C: HDL-C ratio was log10 transformed to achieve normal distribution. Multivariate logistic regression was used to investigate the association between the log10 -transformed non-HDL-C: HDL-C ratio and diabetes. Stratified analyses of the association by age, gender, and body mass index (BMI) were also performed. Results After 3 years of follow-up, 704 participants developed diabetes. After adjustment for age, gender, current smoking, current drinking, physical activity, BMI, systolic blood pressure, and family history of diabetes, each 1-SD increase in the log(non-HDL-C: HDL-C ratio) was associated with higher fasting blood glucose (FPG) levels (β = 0.1; 95% confidence interval [CI] 0.1-0.1), 2-h postload plasma glucose levels (2-h glucose; β = 0.2; 95% CI 0.1-0.2), and risk of diabetes (odds ratio [OR] 1.1; 95% CI 1.0-1.2). In a multivariate model, subjects in the top quartile of non-HDL-C: HDL-C ratio had higher FPG (β = 0.2; 95% CI 0.2-0.3), 2-h glucose (β = 0.5; 95% CI 0.3-0.7) and HbA1c (β = 0.1; 95% CI 0.1-0.2) levels, and a 40% increased risk of diabetes (OR 1.4; 95% CI 1.1-1.8) than participants in the bottom quartile. Conclusions The non-HDL-C: HDL-C ratio was found to be an independent risk factor for diabetes.

Non-high-density lipoprotein cholesterol: High-density lipoprotein cholesterol ratio is an independent risk factor for diabetes mellitus: Results from a population-based cohort study.

Background: Diabetes has been found to increase severity and mortality under the current pandemic of coronavirus disease of 2019 (COVID-19). Up to date, the clinical characteristics of diabetes patients with COVID-19 and the risk factors for poor clinical outcomes are not clearly understood. Methods: The study was retrospectively carried out on enrolled diabetes patients with laboratory confirmed COVID-19 infection from a designated medical center for COVID-19 from January 25th, 2020 to February 14th, 2020 in Wuhan, China. The medical record was collected and reviewed. Univariate and multivariate analyses were performed to assess the risk factors associated with the severe events which were defined as a composite endpoint of admission to intensive care unit, the use of mechanical ventilation, or death. Results: A total of 52 diabetes patients with COVID-19 were finally included in the study. 21 (40.4%) patients had developed severe events in 27.50 (IQR 12.25-35.75) days follow-up, 15 (28.8%) patients experienced life-threatening complications and 8 patients died with a recorded mortality rate of 15.4%. Only 13 patients (41.9%) were in optimal glycemic control with HbA1c value of <7.0%. In addition to general clinical characteristics of COVID-19, the severe events diabetes patients showed higher counts of white blood cells and neutrophil, lower lymphocytes (40, 76.9%), high levels of hs-CRP, erythrocyte sedimentation rate (ESR) and procalcitonin (PCT) as compared to the non-severe diabetes patients. Mild higher level of cardiac troponin I (cTNI) (32.0 pg/ml; IQR 16.80-55.00) and D-dimer (1.70 μg/L, IQR 0.70-2.40) were found in diabetes patients with severe events as compared to the non-severe patients (cTNI:20.00 pg/ml, IQR5.38-30.00, p = 0.019; D-dimer: 0.70 μg/L, IQR 0.30-2.40, p = 0.037). After adjusting age and sex, increased level of cTNI was found to significantly associate with the incidence of severe events (HR: 1.007; 95% CI: 1.000-1.013; p = 0.048), Furthermore, using of α-glucosidase inhibitors was found to be the potential protectant for severe events (HR: 0.227; 95% CI: 0.057-0.904; p = 0.035). Conclusion: Diabetes patients with COVID-19 showed poor clinical outcomes. Vigorous monitoring of cTNI should be recommended for the diabetes patients with COVID-19. Usage of α-glucosidase inhibitors could be a potential protectant for the diabetes patients with COVID-19.

/pdf/risk-factors-for-poor-outcomes-of-diabetes-patients-with-5bliwzn0x0.pdf

Risk Factors for Poor Outcomes of Diabetes Patients With COVID-19: A Single-Center, Retrospective Study in Early Outbreak in China.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Peng Bai

Author Tools

Chat about Author

Papers

Non-high-density lipoprotein cholesterol: High-density lipoprotein cholesterol ratio is an independent risk factor for diabetes mellitus: Results from a population-based cohort study.

Risk Factors for Poor Outcomes of Diabetes Patients With COVID-19: A Single-Center, Retrospective Study in Early Outbreak in China.