Risk factors for diabetic retinopathy in northern Chinese patients with type 2 diabetes mellitus

Nomogram for Prediction of Diabetic Retinopathy Among Type 2 Diabetes Population in Xinjiang, China

PURPOSE

To establish an accurate risk prediction model of diabetic retinopathy (DR) using cost effective and easily available patients' characteristics and clinical biomarkers.

PATIENTS AND METHODS

Totally 18,904 cases diagnosed type 2 diabetes mellitus (T2DM) were collected, among which 13,980 cases were selected after quality screening. The least absolute shrinkage and selection operator (LASSO) regression models were used for univariate analysis and factors selection, and the multi-factor logistic regression analysis was used to establish the prediction model. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using AUC/ Harrell's C statistic, calibration plot, and decision curve analysis. Both the development group and validation group were assessed.

RESULTS

Candidate variables were selected by Lasso regression and multivariate logistic regression analysis. Finally, the candidate predictive variables were included diabetic peripheral neuropathy (DPN), age, neutrophilic granulocyte (NE), high-density lipoprotein (HDL), hemoglobin A1c (HbA1C), duration of T2DM, and glycosylated serum protein (GSP) were used to establish a nomogram model for predicting the risk of DR. In the development group, the area under the receiver operating characteristic curve (AUC) was 0.882 (95% CI, 0.875-0.888). In the validation group, the AUC was 0.870 (95% CI, 0.856-0.881). Meanwhile, the optimism-corrected Harrell's C statistic were 0.878 and 0.867 in the development group and the validation group, respectively. Decision curve analysis demonstrated that the nomogram was clinically useful.

CONCLUSION

We constructed and verified nomograms that could accurately predict the risk of DR in T2DM patients, which could be used to predict the personalized risk of DR patients in Xinjiang, China.

Global and Regional Prevalence of Diabetic Retinopathy; A Comprehensive Systematic Review and Meta-analysis

PURPOSE

We conducted a systematic search to estimate DR prevalence in different age and gender groups, and to evaluate the determinants of heterogeneity in its prevalence.

METHODS

A systematic and comprehensive search from inception to August 10, 2020, was done in international databases, including Scopus, PubMed, Web of Science, Embase, and other data sources without any restriction to find cross-sectional studies related to the prevalence of DR.

RESULTS

Of 6399 studies, 90 articles with a sample size of 563460 individuals and 204189 diabetic patients were analyzed. The estimated pooled prevalence of DR in the diabetic population in general; in female and in male was 28.41% (95% CI: 25.98 to 30.84); 25.93% (95% CI: 23.54 to 28.31) and 28.95% (95% CI: 26.57 to 31.32); respectively and the prevalence of DR showed no inter-gender difference. The heterogeneity of the pooled prevalence according to I² was 99% (p < .001). According to the meta-regression results, the variables of WHO region (Coefficient of AMRO vs SEARO: 15.56; p: 0.002), age (Coefficient of above 60 years vs below 40 year: 18.67; p: 0.001), type of DR (Coefficient: 19.01; p < .001), and publication year (Coefficient: -0.60; p: 0.001) had a significant correlation with heterogeneity.

CONCLUSION

One third of diabetic patients suffered from DR, mostly NPDR. DR increased markedly after the age of 60 years, which could be due to the longer duration of diabetes. Age, WHO region, type of DR, and publication year affected the heterogeneity in the prevalence of DR.

Association between smoking history and optical coherence tomography angiography findings in diabetic patients without diabetic retinopathy

Purpose

To investigate any associations between cigarette smoking and retinal microvascular changes in diabetic patients without visible retinopathy.

Design

Retrospective, cross-sectional study.

Participants

1099 eyes from 1099 diabetic patients with no clinical evidence of diabetic retinopathy (DR) were included in this study.

Methods

Diabetic patients underwent optical coherence tomography angiography (OCTA) scanning at Zuckerberg San Francisco General Hospital and Trauma Center between April 2018 and September 2019. Patient demographic and clinical information was collected. Standard bivariate statistics and multivariate linear regression were performed.

Main outcome measures

OCTA parameters included metrics related to the foveal avascular zone (FAZ; area, perimeter, circularity), perfusion density (PD; full, center, inner), and vessel length density (VLD; full, center, inner).

Results

The study population included 750 non-smokers and 349 smokers. FAZ perimeter was the only OCTA parameter that was significantly different between the two groups on uncontrolled analysis (P = 0.033). Multivariate regression analyses revealed significant associations between lower VLD full (β = -0.31, P = 0.048), lower VLD inner (β = -0.35, P = 0.046) and a history of smoking. No significant associations between cigarette smoking and either FAZ or PD were detected.

Conclusions

Our results suggest that smoking is likely associated with deleterious changes in the retinal microvasculature of patients with a history of diabetes and no visible DR. Based on these findings, diabetic patients with a history of smoking may benefit from higher prioritization in terms of ophthalmic screening.

Study on the Risk Factors for Hyperuricaemia and Related Vascular Complications in Patients with Type 2 Diabetes Mellitus

Purpose

The study aimed to identify diseases that exhibit significant differences between hyperuricaemia (HUA) and non-hyperuricaemia (NHUA) groups and analyse the risk factors for HUA based on the related diseases in type 2 diabetes mellitus (T2DM).

Methods

A total of 3264 T2DM patients were investigated from 2013 to 2017 in the Jinyang and Sanlin communities by obtaining basic data from the electronic medical record system (EMRS). From September 2018 to July 2019, 3000 patients (264 patients were missing during follow-up) were investigated with questionnaires, physical examinations and biochemical index tests. After removing missing values, 2899 patients were divided into HUA and NHUA groups. The chi-square test was used to identify diseases with differences. Using Lasso analysis and logistic regression analysis, risk factors for HUA based on the related diseases were obtained. The C-index, receiver operating characteristic (ROC) curve and calibration plot were used to validate the discrimination and accuracy of the factors.

Results

The chi-square test showed that there were significant differences in coronary heart disease (CHD) and diabetic nephropathy (DN) between the HUA group and the NHUA group. Through Lasso regression, glycosylated haemoglobin A1c (HbA1c), triglyceride (TG), blood urea nitrogen (BUN) and serum creatinine (SCR) were screened in the CHD group. Body mass index (BMI), HbA1c, total cholesterol (TC), TG, BUN, SCR and urine microalbumin (UMA) were screened in the DN group. The P-value of all the variables was less than 0.05. Through the C-index, calibration, and ROC curve analyses, these risk factors had medium accuracy.

Conclusion

HUA was significantly related to CHD and DN. The level of UA was correlated with HbA1c, TG, BUN, and SCR based on CHD. The level of UA was associated with BMI, HbA1c, TC, TG, BUN, SCR, and UMA based on DN.

Nomogram for the Risk of Diabetic Nephropathy or Diabetic Retinopathy Among Patients with Type 2 Diabetes Mellitus Based on Questionnaire and Biochemical Indicators: A Cross-Sectional Study

PURPOSE

This study aimed to develop a diabetic nephropathy (DN) or diabetic retinopathy (DR) incidence risk nomogram in China's population with type 2 diabetes mellitus (T2DM) based on a community-based sample.

METHODS

We carried out questionnaire evaluations, physical examinations and biochemical tests among 4219 T2DM patients in Shanghai. According to the incidence of DN and DR, 4219 patients in our study were divided into groups of T2DM patients with DN or DR, patients with both, and patients without any complications. We successively used least absolute shrinkage and selection operator regression analysis and logistic regression analysis to optimize the feature selection for DN and DR. To ensure the accuracy of the results, we carried out multivariable logistic regression analysis of the above significant risk factors on the sample data for both DN and DR. The selected features were included to establish a prediction model. The C-index, calibration plot, curve analysis and internal validation were used to validate the distinction, calibration, and clinical practicality of the model.

RESULTS

The predictors in the prediction model included disease course, body mass index (BMI), total triglycerides (TGs), systolic blood pressure (SBP), postprandial blood glucose (PBG), haemoglobin A1C (HbA1c) and blood urea nitrogen (BUN). The model displayed moderate predictive power with a C-index of 0.807 and an area under the receiver operating characteristic curve of 0.807. In internal verification, the C-index reached 0.804. The risk threshold was 16-75% according to the analysis of the decision curve, and the nomogram could be applied in clinical practice.

CONCLUSION

This DN or DR incidence risk nomogram incorporating disease course, BMI, TGs, SBP, PBG, HbA1c and BUN can be used to predict DN or DR incidence risk in T2DM patients. The research team has developed an online app based on a clinical prediction model incorporating risk factors for rapid and simple prediction.

Indicating and predicting role of the horizontal C/D ratio in preclinical diabetic retinopathy associated with chronic angle-closure glaucoma

AIM

To observe morphological optic disc characteristics in patients with preclinical diabetic retinopathy (DR) associated with chronic angle-closure glaucoma (CACG).

METHODS

Twenty-two cases (43 eyes) of preclinical DR associated with CACG were enrolled in group A; 24 preclinical DR cases (46 eyes) were enrolled in group B; 26 CACG cases (51 eyes) were enrolled in group C; and 49 normal controls (49 eyes) were enrolled in group D. All underwent optical coherence tomography to measure the horizontal C/D ratio (HCDR), C/D area ratio (CDaR), vertical C/D ratio (VCDR), rim area (RA), cup volume (CV), disc area (DA) and average retinal nerve fiber layer (RNFL) thickness.

RESULTS

The ages of groups A, B, C, and D were 67.60±3.36, 66.78±3.33, 65.98±3.83, and 67.54±3.17y, respectively. The HCDR values in groups A, B, and C were distinct relative to those in group D (P<0.0001, P<0.01, and P<0.05, respectively). The HCDR values in group A were higher compared with those in groups B (P<0.0001) and D (P<0.0001); while these values were virtually identical statistically between groups A and C (P>0.05). The CDaR values in group A were higher in comparison to those in groups B and D (P<0.0001 in both groups); while these values were virtually identical statistically between groups A and C (P>0.05). The RA values in group A were smaller relative to those in groups B and D (P<0.0001 in both groups); while groups A and C were not distinct statistically (P>0.05). The CV values in group A were greater in comparison to those in groups B and D (P<0.0001 in both groups); while groups A and C were not distinct statistically (P>0.05). DA was not distinct for comparisons of two groups among the four groups (P>0.05). HCDR value correlated with mean nasal RNFL thickness (r=-0.909, P<0.0001), mean superior RNFL thickness (r=-0.866, P<0.0001), mean inferior RNFL thickness (r=-0.650, P<0.001) and mean temporal RNFL thickness (r=-0.562, P<0.01) in group A.

CONCLUSION

The HCDR was a sensitive morphological parameter in detecting structural visual disc changes in preclinical DR associated with CACG, which can reflect optic nerve damage caused jointly by CACG and diabetes. A higher HCDR may predict optic nerve atrophy.

Smoking and diabetes: dangerous liaisons and confusing relationships

The combined harmful effects of cigarette smoking and hyperglycemia can accelerate vascular damage in patients with diabetes who smoke, as is well known. Can smoking cause diabetes? What are the effects of smoking on macro and microvascular complications? Now growing evidence indicates that regular smokers are at risk of developing incident diabetes. Since the prevalence rates of smoking in patients with diabetes are relatively similar to those of the general population, it is essential to address the main modifiable risk factor of smoking to prevent the onset of diabetes and delay the development of its complications. Quitting smoking shows clear benefits in terms of reducing or slowing the risk of cardiovascular morbidity and mortality in people with diabetes. Does quitting smoking decrease the incidence of diabetes and its progression? What are the effects of quitting smoking on complications? The current evidence does not seem to unequivocally suggest a positive role for quitting in patients with diabetes. Quitting smoking has also been shown to have a negative impact on body weight, glycemic control and subsequent increased risk of new-onset diabetes. Moreover, its role on microvascular complications of the disease is unclear. What are the current smoking cessation treatments, and which ones are better for patients with diabetes? Stopping smoking may be of value for diabetes prevention and management of the disease and its macrovascular and microvascular complications. Unfortunately, achieving long-lasting abstinence is not easy and novel approaches for managing these patients are needed. This narrative review examines the evidence on the impact of smoking and smoking cessation in patients with diabetes and particularly in type 2 diabetes mellitus and its complications. In addition, management options and potential future directions will be discussed.

Analysis of Changes in Retinal Thickness in Type 2 Diabetes without Diabetic Retinopathy

OBJECTIVE

To examine the changes in retinal thickness of patients with diabetes without DR.

DESIGNS

A randomization, crossover, retrospective practice.

PARTICIPANTS

43 diabetic patients and 43 ethnic-, age-, and sex-matched controls.

METHODS

Full retinal thicknesses of ten areas were assessed using spectral domain optical coherence tomography. Confounding variables, such as age, gender, and glycated haemoglobin (HbA1c) level, were assessed by regression analysis.

MAIN OUTCOME MEASURES

Mean retinal thickness of ten areas.

RESULTS

The mean thickness of the fovea was 215.8 ± 18.9 μm in the diabetes group and 222.0 ± 18.6 μm in the control group (p = 0.04). The mean thickness of the temporal parafovea was 319.9 ± 16.7 μm in the diabetes group and 326.0 ± 14.4 μm in the control group (p = 0.01). The mean thickness of the temporal perifovea was 276.4 ± 27.9 μm in the diabetes group and 284.8 ± 17.4 μm in the control group (p = 0.02). There were no significant differences in retinal thickness between groups in other areas (p > 0.05). Regression analysis revealed that decreased retinal thickness of the temporal perifovea was associated with a higher HbA1c level (>8.7%) (p = 0.04).

CONCLUSION AND RELEVANCE

Subtle structural changes in the retina may occur in diabetes without DR. Decreased retinal thickness appeared to begin in the fovea and temporal areas. A high HbA1c level was the main factor influencing retinal thickness.

Risk factors of diabetic retinopathy and sight-threatening diabetic retinopathy: a cross-sectional study of 13 473 patients with type 2 diabetes mellitus in mainland China

OBJECTIVE

To explore the risk factors of diabetic retinopathy (DR) and sight-threatening diabetic retinopathy (STDR) among Chinese patients with diabetes.

DESIGN, SETTING AND PARTICIPANTS

A cross-sectional investigation was performed in eight screening clinics in six provinces across mainland China. Information about the risk factors was recorded in screening clinics. Some risk factors (sex, age, diagnosis age, diabetes duration, systolic blood pressure (SBP), diastolic blood pressure, fasting blood glucose (FBG) and glycosylated haemoglobin (HbA1c)) were recorded in all eight clinics, while others were collected only in a subset of the clinics. The relationships between the risk factors and DR and between the risk factors and STDR were explored for the eight factors mentioned above and for all factors studied.

MAIN OUTCOMES AND MEASURES

Risk factors of DR and STDR were assessed, and a nomogram of the results was produced.

RESULTS

Younger age, longer diabetes duration, higher SBP, higher FBG and higher HbA1c were found to be independent risk factors for both DR and STDR in the eight-factor analyses. In the all-factor analysis, younger age, longer diabetes duration, higher SBP, oral medicine use and insulin use were independent risk factors for both DR and STDR; higher postprandial blood glucose (PBG), HbA1c, triglyceride andlow-density lipoprotein were independent risk factors for DR only, and higher FBG was a risk factor for STDR only.

CONCLUSIONS

In this cross-sectional investigation, several risk factors were found for DR and STDR. Notably, FBG, PBG and HbA1c were all risk factors for DR or STDR, suggesting that stricter blood glucose control in clinical practice is required.