Prevalence and Risk Factors for Self-Report Diabetes Mellitus: A Population-Based Study

Self-reported prevalence and risk factors associated with keratoconus among the adult population of Trinidad and Tobago: a cross-sectional study

AIM

To determine the prevalence of keratoconus and its associated risk factors within the adult population of Trinidad and Tobago.

METHOD

A population-based cross-sectional study was conducted among adults in Trinidad and Tobago, utilizing the Keratoconus Risk Assessment Questionnaire (KRIS) for data collection. Data were exported to the Statistical Package for Social Sciences (SPSS) version 27 for analysis. Descriptive statistics were employed to summarize the variables, while logistic regression was used to identify associated risk factors, with a significance level set at p < 0.05.

RESULTS

A total of 413 participants were included in the study, yielding a response rate of 97.4% (413/424). The prevalence of keratoconus (KC) was 1.5% (95% CI: 0.33 - 2.67%). The condition was more prevalent among females (98.5%), individuals aged 19-30 years (66.7%), and participants of mixed race (50%). Allergic or atopic diseases were identified as the most common established risk factor for KC, affecting 50.1% of all participants. Among those diagnosed with KC, eye rubbing emerged as the most common established risk factor. Family history of KC (95% CI: 1.841-48.352, p < 0.007) and the use of rigid contact lenses (95% CI: 8.696-286.051, p < 0.001) were statistically significant predictors of KC. Specifically, participants with a family history of KC were 49.8 times more likely to develop the condition (OR = 49.875), while those using rigid contact lenses were 9.4 times more likely (OR = 9.436).

CONCLUSION

The prevalence of KC in Trinidad and Tobago was found to be significant. Among the identified risk factors, atopy was the most common. Additionally, a positive family history and the use of rigid contact lenses were significant predictors of KC. Early screening for keratoconus in this country is strongly recommended to facilitate prompt detection and appropriate management of the condition.

Integrating Laser-Induced Breakdown Spectroscopy and Ensemble Learning as Minimally Invasive Optical Screening for Diabetes

Diabetes mellitus is a prevalent chronic disease necessitating timely identification for effective management. This paper introduces a reliable, straightforward, and efficient method for the minimally invasive identification of diabetes mellitus through nanosecond pulsed laser-induced breakdown spectroscopy (LIBS) by integrating a state-of-the-art machine learning approach. LIBS spectra were collected from urine samples of diabetic and healthy individuals. Principal component analysis and an ensemble learning classification model were used to identify significant changes in LIBS peak intensity between the diseased and normal urine samples. The model, integrating six distinct classifiers and cross-validation techniques, exhibited high accuracy (96.5%) in predicting diabetes mellitus. Our findings emphasize the potential of LIBS for diabetes mellitus identification in urine samples. This technique may hold potential for future applications in diagnosing other health conditions.

Advances and challenges of the cell-based therapies among diabetic patients

Diabetes mellitus is a significant global public health challenge, with a rising prevalence and associated morbidity and mortality. Cell therapy has evolved over time and holds great potential in diabetes treatment. In the present review, we discussed the recent progresses in cell-based therapies for diabetes that provides an overview of islet and stem cell transplantation technologies used in clinical settings, highlighting their strengths and limitations. We also discussed immunomodulatory strategies employed in cell therapies. Therefore, this review highlights key progresses that pave the way to design transformative treatments to improve the life quality among diabetic patients.

Blood lipid profile, HbA1c, fasting glucose, and diabetes: a cohort study and a two-sample Mendelian randomization analysis

PURPOSE

The prevalence of diabetes is increasing worldwide. The associations between the lipid profile and glycated hemoglobin (HbA1c), fasting glucose, and diabetes remain unclear, so we aimed to perform a cohort study and a two-sample Mendelian randomization (MR) study to investigate the causality between blood lipid profile and HbA1c, fasting glucose, and diabetes.

METHODS

A total of 25,171 participants from the Taiwan Biobank were enrolled. We applied a cohort study and an MR study to assess the association between blood lipid profile and HbA1c, fasting glucose, and diabetes. The summary statistics were obtained from the Asian Genetic Epidemiology Network (AGEN), and the estimates between the instrumental variables (IVs) and outcomes were calculated using the inverse-variance weighted (IVW) method. A series of sensitivity analyses were performed.

RESULTS

In the cohort study, high-density lipoprotein cholesterol (HDL-C) was negatively associated with HbA1c, fasting glucose, and diabetes, while the causal associations between HDL-C and HbA1c (βIVW = - 0.098, p = 0.003) and diabetes (βIVW = - 0.594, p < 0.001) were also observed. Furthermore, there was no pleiotropy effect in this study using the MR-Egger intercept test and MR-PRESSO global test.

CONCLUSIONS

Our results support the hypothesis that a genetically determined increase in HDL-C is causally related to a reduction in HbA1c and a lower risk of diabetes.

Non-coding RNAs in diabetes mellitus and diabetic cardiovascular disease

More than 10% of the world's population already suffers from varying degrees of diabetes mellitus (DM), but there is still no cure for the disease. Cardiovascular disease (CVD) is one of the most common and dangerous of the many health complications that can be brought on by DM, and has become the leading cause of death in people with diabetes. While research on DM and associated CVD is advancing, the specific mechanisms of their development are still unclear. Given the threat of DM and CVD to humans, the search for new predictive markers and therapeutic ideas is imminent. Non-coding RNAs (ncRNAs) have been a popular subject of research in recent years. Although they do not encode proteins, they play an important role in living organisms, and they can cause disease when their expression is abnormal. Numerous studies have observed aberrant ncRNAs in patients with DM complications, suggesting that they may play an important role in the development of DM and CVD and could potentially act as biomarkers for diagnosis. There is additional evidence that treatment with existing drugs for DM, such as metformin, alters ncRNA expression levels, suggesting that regulation of ncRNA expression may be a key mechanism in future DM treatment. In this review, we assess the role of ncRNAs in the development of DM and CVD, as well as the evidence for ncRNAs as potential therapeutic targets, and make use of bioinformatics to analyze differential ncRNAs with potential functions in DM.