Correlation of weight-adjusted waist index with diabetic retinopathy in US adults aged ≥ 40 years: the NHANES 2005-2008.

The effect of obesity on diabetic retinopathy (DR) has been under scrutiny in recent years. The weight-adjusted waist index (WWI) has been reported to better assess the degree of centripetal obesity in humans, with a higher WWI indicating a higher amount of body fat. The present study is the first to investigate the relationship between WWI and DR and to assess the difference in the predictive ability of WWI and other obesity indices for DR. This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2008. Researchers collected complete anthropometric data (weight and waist circumference), dilated fundus images, and adult baseline information. Independent interactions between WWI and DR were investigated using multivariate regression and subgroup analyses. In addition, nonlinear associations and threshold effects between WWI and DR were searched for by smoothed curve fitting and by two-stage linear regression modeling. Finally, the receiver operating characteristic curve (ROC) was plotted to compare the predictive power of WWI with other indices of obesity for DR. A total of 1228 eligible patients with diabetes were collected in this study. There were 631 (51.38%) males and 597 (48.62%) females. Among them, 545 (44.38%) were without diabetic retinopathy, 555 (45.20%) had mild diabetic retinopathy, 100 (8.14%) had moderate/severe diabetic retinopathy and 28 (2.28%) had proliferative diabetic retinopathy (PDR). In the fully adjusted model, each unit increase in WWI was associated with a corresponding 31% reduction in the prevalence of DR [OR (95% CI) = 0.69 (0.58, 0.83)]. Compared with subjects in the lowest quartile (quartile 1), subjects in the highest quartile of WWI levels (quartile 4) were 45% less likely to have DR [OR (95% CI) = 0.55 (0.38, 0.78)]. In the case of female participants, a U-shaped correlation was observed between WWI and DR with an inflection point of 11.49. WWI also possesses a better predictive ability for DR compared to obesity indicators such as weight, BMI, and ABSI. This study showed a negative association between WWI and DR in the U.S. population aged 40 years and older.

Polyunsaturated fatty acids and diabetic microvascular complications: a Mendelian randomization study.

Background: Observational studies and clinical trials have implicated polyunsaturated fatty acids (PUFAs) in potentially safeguarding against diabetic microvascular complication. Nonetheless, the causal nature of these relationships remains ambiguous due to conflicting findings across studies. This research employs Mendelian randomization (MR) to assess the causal impact of PUFAs on diabetic microvascular complications. Methods: We identified instrumental variables for PUFAs, specifically omega-3 and omega-6 fatty acids, using the UK Biobank data. Outcome data regarding diabetic microvascular complications were sourced from the FinnGen Study. Our analysis covered microvascular outcomes in both type 1 and type 2 diabetes, namely diabetic neuropathy (DN), diabetic retinopathy (DR), and diabetic kidney disease (DKD). An inverse MR analysis was conducted to examine the effect of diabetic microvascular complications on PUFAs. Sensitivity analyses were performed to validate the robustness of the results. Finally, a multivariable MR (MVMR) analysis was conducted to determine whether PUFAs have a direct influence on diabetic microvascular complications. Results: The study indicates that elevated levels of genetically predicted omega-6 fatty acids substantially reduce the risk of DN in type 2 diabetes (odds ratio (OR): 0.62, 95% confidence interval (CI): 0.47-0.82, p = 0.001). A protective effect against DR in type 2 diabetes is also suggested (OR: 0.75, 95% CI: 0.62-0.92, p = 0.005). MVMR analysis confirmed the stability of these results after adjusting for potential confounding factors. No significant effects of omega-6 fatty acids were observed on DKD in type 2 diabetes or on any complications in type 1 diabetes. By contrast, omega-3 fatty acids showed no significant causal links with any of the diabetic microvascular complications assessed. Conclusions: Our MR analysis reveals a causal link between omega-6 fatty acids and certain diabetic microvascular complications in type 2 diabetes, potentially providing novel insights for further mechanistic and clinical investigations into diabetic microvascular complications.

Differential Capillary and Large Vessel Analysis Improves OCTA Classification of Diabetic Retinopathy.

Purpose: This study aimed to investigate the impact of distinctive capillary-large vessel (CLV) analysis in optical coherence tomography angiography (OCTA) on the classification performance of diabetic retinopathy (DR). Methods: This multicenter study analyzed 212 OCTA images from 146 patients, including 28 controls, 36 diabetic patients without DR (NoDR), 31 with mild non-proliferative DR (NPDR), 28 with moderate NPDR, and 23 with severe NPDR. Quantitative features were derived from the whole image as well as the parafovea and perifovea regions. A support vector machine classifier was employed for DR classification. The accuracy and area under the receiver operating characteristic curve were used to evaluate the classification performance, utilizing features derived from the whole image and specific regions, both before and after CLV analysis. Results: Differential CLV analysis significantly improved OCTA classification of DR. In binary classifications, accuracy improved by 11.81%, rising from 77.45% to 89.26%, when utilizing whole image features. For multiclass classifications, accuracy increased by 7.55%, from 78.68% to 86.23%. Incorporating features from the whole image, parafovea, and perifovea further improved binary classification accuracy from 83.07% to 93.80%, and multiclass accuracy from 82.64% to 87.92%. Conclusions: This study demonstrated that feature changes in capillaries are more sensitive during DR progression, and CLV analysis can significantly improve DR classification performance by extracting features that are specific to large vessels and capillaries in OCTA. Incorporating regional features further improves DR classification accuracy. Differential CLV analysis promises better disease screening, diagnosis, and treatment outcome assessment.

Association between gut microbiota and diabetic microvascular complications: a two-sample Mendelian randomization study.

Background: Gut microbiota (GM) homeostasis in the human body is closely associated with health, which can be used as a regulator for preventing the onset and progression of disease. Diabetic microvascular complications bring about not only a huge economic burden to society, but also miserable mental and physical pain. Thus, alteration of the GM may be a method to delay diabetic microvascular complications. Objective: A two-sample Mendelian randomization (MR) analysis was conducted to reveal the causal inference between GM and three core diabetic microvascular complications, namely, diabetic kidney disease (DKD), diabetic retinopathy (DR), and diabetic neuropathy (DNP). Methods: First, genome-wide association study (GWAS) summary statistics for GM from the MiBioGen consortium and three main diabetic microvascular complications acquired from the FinnGen research project were assessed. Second, a forward MR analysis was conducted to assess the causality of GM on the risk of DKD, DR, and DNP. Third, a series of sensitivity studies, such as heterogeneity tests, pleiotropy evaluations, and leave-one-out analyses, were further conducted to assess the accuracy of MR analysis. Finally, Steiger tests and reverse MR analyses were performed to appraise the possibility of reverse causation. Results: A total of 2,092 single-nucleotide polymorphisms related to 196 bacterial traits were selected as instrumental variables. This two-sample MR analysis provided strongly reasonable evidence that 28 genetically predicted abundance of specific GM that played non-negligible roles in the occurrence of DKD, DR, and DNP complications were causally associated with 23 GM, the odds ratio of which generally ranged from 0.9 to 1.1. Further sensitivity analysis indicated low heterogeneity, low pleiotropy, and high reliability of the causal estimates. Conclusion: The study raised the possibility that GM may be a potential target to prevent and delay the progression of diabetic microvascular complications. Further experiments of GM therapy on diabetic microvascular complications are warranted to clarify their effects and specific mechanisms.

Application of smartphone-based infrared thermography devices for ocular surface thermal imaging.

Infrared thermography (IRT) is a well-known imaging technique that provides a non-invasive displaying of the ocular surface temperature distribution. Currently, compact smartphone-based IRT devices, as well as special software for processing thermal images, have become available. The study aimed to determine the possible use of smartphone-based IRT devices for real-time ocular surface thermal imaging. This study involved 32 healthy individuals (64 eyes); 10 patients (10 eyes) with proliferative diabetic retinopathy (PDR) and absolute glaucoma; 10 patients (10 eyes) with PDR, who underwent vitreoretinal surgery. In all cases, simultaneous ocular surface IRT of both eyes was performed. In healthy individuals, the ocular surface temperature (OST) averaged 34.6 ± 0.8 °C and did not differ substantially between the paired eyes, in different age groups, and after pupil dilation. In our study, high intraocular pressure was accompanied by a decrease in OST in all cases. After vitreoretinal surgery in cases with confirmed subclinical inflammation, the OST was higher than the baseline and differed from that of the paired eye by more than 1.0 °C. These results highlight that smartphone-based IRT imaging could be useful for the non-invasive detection of OST asymmetry between paired eyes due to increased intraocular pressure or subclinical inflammation.

A novel approach for automatic classification of macular degeneration OCT images.

Age-related macular degeneration (AMD) and diabetic macular edema (DME) are significant causes of blindness worldwide. The prevalence of these diseases is steadily increasing due to population aging. Therefore, early diagnosis and prevention are crucial for effective treatment. Classification of Macular Degeneration OCT Images is a widely used method for assessing retinal lesions. However, there are two main challenges in OCT image classification: incomplete image feature extraction and lack of prominence in important positional features. To address these challenges, we proposed a deep learning neural network model called MSA-Net, which incorporates our proposed multi-scale architecture and spatial attention mechanism. Our multi-scale architecture is based on depthwise separable convolution, which ensures comprehensive feature extraction from multiple scales while minimizing the growth of model parameters. The spatial attention mechanism is aim to highlight the important positional features in the images, which emphasizes the representation of macular region features in OCT images. We test MSA-NET on the NEH dataset and the UCSD dataset, performing three-class (CNV, DURSEN, and NORMAL) and four-class (CNV, DURSEN, DME, and NORMAL) classification tasks. On the NEH dataset, the accuracy, sensitivity, and specificity are 98.1%, 97.9%, and 98.0%, respectively. After fine-tuning on the UCSD dataset, the accuracy, sensitivity, and specificity are 96.7%, 96.7%, and 98.9%, respectively. Experimental results demonstrate the excellent classification performance and generalization ability of our model compared to previous models and recent well-known OCT classification models, establishing it as a highly competitive intelligence classification approach in the field of macular degeneration.

Choroidal and retinal exudative changes following extensive endolaser pan retinal photocoagulation.

BACKGROUND: In this report, we describe a case of proliferative diabetic retinopathy that developed into exudative changes confusing with central serous chorioretinopathy (CSCR) following extensive endolaser pan retinal photocoagulation. CASE DESCRIPTION: A 49-year-old male patient with diabetic retinopathy in both eyes presented with vitreous hemorrhage and 6/60 visual acuity in his left eye. Optical coherence tomography (OCT) scans at presentation revealed serous PEDs in both eyes. On day 10 after vitreoretinal surgery and complete peripheral endolaser PRP for the left eye, there was serous retinal detachment (SRD) and an increase in PED heights, mimicking CSCR. No additional treatment was considered. At the three-week post-operative visit, OCT scans revealed that the SRD had resolved and the PED heights had decreased without rupture. At the final follow-up visit, 12 weeks after surgery, the SRD had not recurred, and the PEDs had stabilized. Despite no additional ocular therapy for the right eye, the serous PED height had decreased. The choroidal thickness (CT) at the fovea at various points during the follow-up visits revealed a reduction in both eyes. CONCLUSION: This case demonstrated the course of SRD, PED, and CT following extensive PRP. These changes may be associated with intraocular VEGF changes. In the presence of SRD and serous PED, the PED morphology may help differentiate the condition from CSCR. Although caution should be exercised when performing PRP during surgery or as an outpatient procedure, the SRD usually resolves without problem.

Follow-up in a point-of-care diabetic retinopathy program in Pittsburgh: a non-concurrent retrospective cohort study.

BACKGROUND: The Point-of-Care Diabetic Retinopathy Examination Program (POCDREP) was initiated in 2015 at the University of Pittsburgh/UPMC in response to low diabetic retinopathy (DR) examination rates, a condition affecting a quarter of people with diabetes mellitus (PwDM) and leading to blindness. Early detection and treatment are critical with DR prevalence projected to triple by 2050. Approximately, half of PwDM in the U.S. undergo yearly examinations, and there are reported varying follow-up rates with eye care professionals, with limited data on the factors influencing these trends. POCDREP aimed to address screening and follow-up gap, partnering with diverse healthcare entities, including primary care sites, free clinics, and federally qualified health centers. METHODS: A non-concurrent retrospective cohort study spanning 2015-2018 examined data using electronic health records of patients who underwent retinal imaging. Imaging was performed using 31 cameras across various settings, with results interpreted by ophthalmologists. Follow-up recommendations were made for cases with vision-threatening DR (VTDR), incidental findings, or indeterminate results. Factors influencing follow-up were analyzed, including demographic, clinical, and imaging-related variables. We assessed the findings at follow-up of patients with indeterminate results. RESULTS: Out of 7,733 examinations (6,242 patients), 32.25% were recommended for follow-up. Among these, 5.57% were classified as having VTDR, 14.34% had other ocular findings such as suspected glaucoma and age-related macular degeneration (AMD), and 12.13% were indeterminate. Of those recommended for follow-up, only 30.87% were assessed by eye care within six months. Older age, marriage, and severe DR were associated with higher odds of following up. Almost two thirds (64.35%) of the patients with indeterminate exams were found with a vision-threatening disease at follow-up. CONCLUSION: The six-month follow-up rate was found to be suboptimal. Influential factors for follow-up included age, marital status, and the severity of diabetic retinopathy (DR). While the program successfully identified a range of ocular conditions, screening initiatives must extend beyond mere disease detection. Ensuring patient follow-up is crucial to DR preventing programs mission. Recommended strategies to improve follow-up adherence include education, incentives, and personalized interventions. Additional research is necessary to pinpoint modifiable factors that impact adherence and to develop targeted interventions.

Evaluation of thickness of individual macular retinal layers in diabetic eyes from optical coherence tomography.

The effect of diabetes mellitus (DM) on individual retinal layers remains incompletely understood. We evaluated the intra-retinal layer thickness alterations in 71 DM eyes with no diabetic retinopathy (DR), 90 with mild DR, and 63 with moderate DR without macular edema, using spectral-domain optical coherence tomography (SD-OCT) and the Iowa Reference Algorithm for automated retinal layer segmentation. The average thickness of 10 intra-retinal layers was then corrected for ocular magnification using axial length measurements, and pairwise comparisons were made using multivariable linear regression models adjusted for gender and race. In DM no DR eyes, significant thinning was evident in the ganglion cell layer (GCL; p < 0.001), inner nuclear layer (INL; p = 0.001), and retinal pigment epithelium (RPE; p = 0.014) compared to normal eyes. Additionally, mild DR eyes exhibited a thinner inner plexiform layer (IPL; p = 0.008) than DM no DR eyes. Conversely, moderate DR eyes displayed thickening in the INL, outer nuclear layer, IPL, and retinal nerve fiber layer (all p ≤ 0.002), with notably worse vision. These findings highlight distinctive patterns: early diabetic eyes experience thinning in specific retinal layers, while moderate DR eyes exhibit thickening of certain layers and slightly compromised visual acuity, despite the absence of macular edema. Understanding these structural changes is crucial for comprehending diabetic eye complications.

Long-Term Impact of Diabetic Retinopathy on Response to Anti-VEGF Treatment in Neovascular AMD.

Purpose: To explore the long-term effect of diabetic retinopathy on response to anti-vascular endothelial growth factor (VEGF) treatment in age-related macular degeneration-associated type 1 macular neovascularization (MNV) using optical coherence tomography angiography (OCTA). Methods: A total of 45 eyes with exudative neovascular age-related macular degeneration (nAMD) with type 1 MNV were included in the analysis. Among them, 24 eyes of 24 patients had no history of diabetes mellitus (DM) in their anamnesis and were assigned to the Not Diabetic group; 21 eyes of 21 patients had mild diabetic retinopathy and were included in the Diabetic group. We considered the following outcome measures: (1) best-corrected visual acuity changes; (2) central macular thickness; (3) MNV lesion area; and (4) MNV flow area. The OCTA acquisitions were performed at the following time points: (1) baseline visit, which corresponded to the day before the first injection; (2) post-loading phase (LP), which was scheduled at 1 month after the last LP injection; and (3) 12-month follow-up visit. Results: All morphofunctional parameters showed a significant improvement after the LP and at the 12-month follow-up visit. Specifically, both the Diabetic group and the Not Diabetic group displayed a significant reduction of MNV lesion areas at both the post-LP assessment (P = 0.026 and P = 0.016, respectively) and the 12-month follow-up (P = 0.039 and P = 0.025, respectively). Similarly, the MNV flow area was significantly decreased in both the Diabetic group and the Not Diabetic group at the post-LP assessment (P < 0.001 and P = 0.012, respectively) and at the 12-month follow-up (P = 0.01 and P = 0.035, respectively) compared to baseline. A smaller reduction in the MNV lesion area was observed in the Diabetic group at both the post-LP evaluation (P = 0.015) and the 12-month follow-up (P = 0.032). No other significant differences were found between the groups for the other parameters (P > 0.05). Conclusions: Our results indicated that the Diabetic group exhibited a smaller reduction in MNV lesion area after 12 months of anti-VEGF treatment. This highlights the importance of considering diabetic retinopathy as a potential modifier of treatment outcomes in nAMD management, with DM serving as a crucial risk factor during anti-angiogenic treatment.

Provision of care for diabetic retinopathy in New Zealand: are there ethnic disparities?

AIMS: Ethnic disparities have been observed in treatment at first specialist appointments across various specialties within New Zealand. This study aimed to examine documentation and treatment decisions for diabetic retinopathy by ethnicity. METHODS: Retrospective audit of first specialist diabetic retinopathy clinic appointments for 388 patients at the Department of Ophthalmology, Te Whatu Ora Te Toka Tumai Auckland. Multiple domains of care were assessed, including comprehensiveness of history taking, examination, investigations and treatment decisions. RESULTS: Europeans comprised 42%, Maori only 9.5%, Pacific peoples 13.19%, Asian 32.7% and Middle Eastern/Latin American/African in 2%. Maori patients were eligible for a significantly greater number of treatments (p=0.001). The comprehensiveness of history taking (p=0.809), examination (p=0.513), investigations (p=0.623) and proportion of eligible treatments provided (p=0.788) was similar but did not reach the gold standard of care across all ethnicities. CONCLUSIONS: The standard of care provided in first specialist appointments for diabetic retinopathy appear to be similar across all ethnic groups, although Maori were underrepresented and had a higher disease burden at presentation. Our data highlights the need to reduce barriers faced by Maori in accessing GP, optometry and retinopathy screening referrals in Auckland, and improving local consultation and treatment guidelines.

Physics-informed deep generative learning for quantitative assessment of the retina.

Disruption of retinal vasculature is linked to various diseases, including diabetic retinopathy and macular degeneration, leading to vision loss. We present here a novel algorithmic approach that generates highly realistic digital models of human retinal blood vessels, based on established biophysical principles, including fully-connected arterial and venous trees with a single inlet and outlet. This approach, using physics-informed generative adversarial networks (PI-GAN), enables the segmentation and reconstruction of blood vessel networks with no human input and which out-performs human labelling. Segmentation of DRIVE and STARE retina photograph datasets provided near state-of-the-art vessel segmentation, with training on only a small (n = 100) simulated dataset. Our findings highlight the potential of PI-GAN for accurate retinal vasculature characterization, with implications for improving early disease detection, monitoring disease progression, and improving patient care.

Prevalence of diabetic retinopathy and its risk factors in rural patients with type 2 diabetes referring to Beijing Huairou Hospital, China.

BACKGROUND: China has the largest population of diabetic patients worldwide. A diverse population and regional discrepancy in access to health care and diabetes management may lead to unique risk factors for diabetic retinopathy (DR) in different regions of China. This study aimed to evaluate the prevalence and risk factors of DR in rural patients with type 2 diabetes. METHODS: This hospital-based cross-sectional study recruited a sample of 704 type 2 diabetic patients from rural areas referred to Beijing Huairou Hospital, China, from June 1, 2022, to June 1, 2023. The medical history, demographic information, and results of laboratory examinations of patients were collected and analyzed. The diagnosis of DR were performed by experienced ophthalmologists using mydriatic fundus photography. RESULTS: Out of all patients, 53.8% were male and 46.2% were female. The mean age of patients and duration of diabetes were 54.9 ± 13.0 and 6.2 ± 4.5 years, respectively. The DR prevalence was 16.8%. The independent risk factors for DR in multivariate analysis were diabetes duration > 10 years (OR = 9.16, 95%CI = 5.49-15.30), fasting plasma glucose ≥ 7.2 mmol/L (OR = 3.25, 95%CI = 1.42-7.42), glycosylated hemoglobin ≥ 7% (OR = 6.49, 95%CI = 2.59-16.23), hypertension (OR = 1.59, 95%CI = 1.05-2.40), hyperlipidemia (OR = 2.16, 95%CI = 1.30-3.59), diabetic nephropathy (OR = 1.95, 95%CI = 1.17-3.23), high uric acid level (OR = 3.57, 95%CI = 1.56-8.15), high albumin to creatinine ratio (OR = 2.48, 95%CI = 1.06-5.82), and insulin treatment (OR = 1.79, 95%CI = 1.12-2.88). CONCLUSIONS: This study evaluated the DR prevalence and its associated risk factors among type 2 diabetic patients from rural areas in Beijing's Huairou District, China. Paying attention to these risk factors may be useful in screening high-risk diabetic patients for DR and adopting early preventive and therapeutic interventions.

Association of continuous subcutaneous insulin therapy and diabetic retinopathy in type 1 diabetes: A national cohort study.

AIM: This study aimed to investigate the short-and long-term effect on diabetic retinopathy (DR) in individuals with type 1 diabetes treated with continuous subcutaneous insulin injections (CSII) compared to those using multiple daily injections (MDI). METHODS: We conducted a register-based matched cohort study utilizing data from the Danish Registry of Diabetic Retinopathy as well as several other national Danish health registers. Our cohort consisted of all individuals with type 1 diabetes who attended the Danish screening program for DR from 2013 to 2022. We included individuals registered with CSII treatment, and compared them to individuals using MDI, matched by age, sex, and DR level. Cox regression analysis was performed to evaluate the outcomes. RESULTS: The study included 674 individuals treated with CSII and 2006 matched MDI users. In our cohort 53.4 % were female and median age was 36 (IQR 27-47). Average follow-up risk-time was 4.8 years. There was no difference in the risk of DR worsening between the CSII group and MDI group (HR 1.05 [95%CI 0.91; 1.22], p = 0.49). However, an increased risk of focal photocoagulation was observed in the CSII group (HR 2.40 [95%CI 1.11; 5.19], p = 0.03). CONCLUSIONS: Our findings indicate that CSII treatment does not confer a significant difference in the overall short- and long-term risk of DR worsening or ocular intervention compared to MDI treatment. These results provide insights into the DR outcomes of CSII treatment in individuals with type 1 diabetes.

Associations of heavy metal exposure with diabetic retinopathy in the U.S. diabetic population: a cross-sectional study.

Background: Mounting evidence suggests a correlation between heavy metals exposure and diabetes. Diabetic retinopathy (DR) is a prevalent and irreversible complication of diabetes that can result in blindness. However, studies focusing on the effects of exposure to heavy metals on DR remain scarce. Thus, this study aimed to investigate the potential correlation between heavy metals exposure and DR. Methods: A total of 1,146 diabetics from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018 were included in this study. Heavy metal levels were measured via urine testing. Weighted logistic regression, Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS) regression, and restricted cubic spline (RCS) were utilized to investigate the potential relationships between exposure to 10 heavy metals and DR. Finally, subgroup analysis was conducted based on the glycemic control status. Results: Among the 1,146 participants, 239 (20.86%) were diagnosed with DR. Those with DR had worse glycemic control and a higher prevalence of chronic kidney disease compared to those without DR. Moreover, both the WQS regression and BKMR models demonstrated a positive relationship between exposure to mixed heavy metals and the risk of DR. The results of weighted logistic regression revealed a positive correlation between cobalt (Co) and antimony (Sb) exposure and the risk of DR (OR = 1.489, 95%CI: 1.064-2.082, p = 0.021; OR = 1.475, 95% CI: 1.084-2.008, p = 0.014), while mercury (Hg) exposure was found to promote DR exclusively in the group with good glycemic control (OR = 1.509, 95% CI: 1.157-1.967, p = 0.003). These findings were corroborated by the results of the RCS analysis. Conclusion: Heavy metal exposure is associated with an increased risk of DR, especially Sb, Co, and Hg exposure. Nevertheless, well-designed prospective studies are warranted to validate these findings.

Correlation between plasma fibrinogen levels and microvascular complications in type 2 diabetes.

Objectives: To determine how plasma fibrinogen levels impact the severity of microvascular complications in people with type 2 diabetes while focussing on the molecular mechanisms of fibrinogen's role in such complications. METHODS: The analytical, cross-sectional study was conducted from September 2022 to March 2023 at the Department of Medicine, Mardan Medical Complex and Teaching Hospital, Khyber Pakhtunkhwa, Pakistan, and comprised adult patients of either gender who had been diagnosed with type 2 diabetes and microvascular complications. Each patient was subjected to an evaluation of microvascular complications, including diabetic retinopathy, nephropathy and neuropathy, using validated diagnostic criteria and clinical examinations. Data was analysed using SPSS 26. RESULTS: Of the 174 patients 97(%) were males and 77(%) were females. Retinopathy was found in 57(32.7) patients with median age 53 years (interquartile range: 46-63 years). Nephropathy was found in 55(31.6%) subjects with median age 54 years (interquartile range: 50-61 years). Neuropathy was found in 62(35.6%) patients with median age 53 years (interquartile range: 48-58 years). Diabetic neuropathy was significantly associated with elevated plasma fibrinogen levels and various biomarkers, such as creatinine, urea, fasting blood glucose, glycated haemoglobin and estimated average glucose (p<0.05). Diabetic retinopathy was significantly linked with higher levels of fibrinogen, which manifested through symptoms, like floaters or dark spots, impaired colour vision, difficulty seeing at night, blurred or fluctuating vision and vision loss (p<0.05). Diabetic nephropathy and the progression of its severity was significantly associated with increased fibrinogen levels, as well as markers, like albuminuria, creatinine, urea, fasting blood glucose, glycated haemoglobin and estimated average glucose (p<0.05). CONCLUSIONS: Elevated plasma fibrinogen levels in patients with type 2 diabetes significantly correlated with increased microvascular complications, underscoring the importance of monitoring and managing fibrinogen levels to mitigate diabetes-associated vascular pathologies.

Diagnostic application in streptozotocin-induced diabetic retinopathy rats: A study based on Raman spectroscopy and machine learning.

Vision impairment caused by diabetic retinopathy (DR) is often irreversible, making early-stage diagnosis imperative. Raman spectroscopy emerges as a powerful tool, capable of providing molecular fingerprints of tissues. This study employs RS to detect ex vivo retinal tissue from diabetic rats at various stages of the disease. Transmission electron microscopy was utilized to reveal the ultrastructural changes in retinal tissue. Following spectral preprocessing of the acquired data, the random forest and orthogonal partial least squares-discriminant analysis algorithms were employed for spectral data analysis. The entirety of Raman spectra and all annotated bands accurately and distinctly differentiate all animal groups, and can identify significant molecules from the spectral data. Bands at 524, 1335, 543, and 435 cm-1 were found to be associated with the preproliferative phase of DR. Bands at 1045 and 1335 cm-1 were found to be associated with early stages of DR.

Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study.

BACKGROUND: Cardiac autonomic neuropathy (CAN) in diabetes mellitus (DM) is independently associated with cardiovascular (CV) events and CV death. Diagnosis of this complication of DM is time-consuming and not routinely performed in the clinical practice, in contrast to fundus retinal imaging which is accessible and routinely performed. Whether artificial intelligence (AI) utilizing retinal images collected through diabetic eye screening can provide an efficient diagnostic method for CAN is unknown. METHODS: This was a single center, observational study in a cohort of patients with DM as a part of the Cardiovascular Disease in Patients with Diabetes: The Silesia Diabetes-Heart Project (NCT05626413). To diagnose CAN, we used standard CV autonomic reflex tests. In this analysis we implemented AI-based deep learning techniques with non-mydriatic 5-field color fundus imaging to identify patients with CAN. Two experiments have been developed utilizing Multiple Instance Learning and primarily ResNet 18 as the backbone network. Models underwent training and validation prior to testing on an unseen image set. RESULTS: In an analysis of 2275 retinal images from 229 patients, the ResNet 18 backbone model demonstrated robust diagnostic capabilities in the binary classification of CAN, correctly identifying 93% of CAN cases and 89% of non-CAN cases within the test set. The model achieved an area under the receiver operating characteristic curve (AUCROC) of 0.87 (95% CI 0.74-0.97). For distinguishing between definite or severe stages of CAN (dsCAN), the ResNet 18 model accurately classified 78% of dsCAN cases and 93% of cases without dsCAN, with an AUCROC of 0.94 (95% CI 0.86-1.00). An alternate backbone model, ResWide 50, showed enhanced sensitivity at 89% for dsCAN, but with a marginally lower AUCROC of 0.91 (95% CI 0.73-1.00). CONCLUSIONS: AI-based algorithms utilising retinal images can differentiate with high accuracy patients with CAN. AI analysis of fundus images to detect CAN may be implemented in routine clinical practice to identify patients at the highest CV risk. TRIAL REGISTRATION: This is a part of the Silesia Diabetes-Heart Project (Clinical-Trials.gov Identifier: NCT05626413).

Evaluating the potential of retinal photography in chronic kidney disease detection: a review.

Background: Chronic kidney disease (CKD) is a significant global health concern, emphasizing the necessity of early detection to facilitate prompt clinical intervention. Leveraging the unique ability of the retina to offer insights into systemic vascular health, it emerges as an interesting, non-invasive option for early CKD detection. Integrating this approach with existing invasive methods could provide a comprehensive understanding of patient health, enhancing diagnostic accuracy and treatment effectiveness. Objectives: The purpose of this review is to critically assess the potential of retinal imaging to serve as a diagnostic tool for CKD detection based on retinal vascular changes. The review tracks the evolution from conventional manual evaluations to the latest state-of-the-art in deep learning. Survey Methodology: A comprehensive examination of the literature was carried out, using targeted database searches and a three-step methodology for article evaluation: identification, screening, and inclusion based on Prisma guidelines. Priority was given to unique and new research concerning the detection of CKD with retinal imaging. A total of 70 publications from 457 that were initially discovered satisfied our inclusion criteria and were thus subjected to analysis. Out of the 70 studies included, 35 investigated the correlation between diabetic retinopathy and CKD, 23 centered on the detection of CKD via retinal imaging, and four attempted to automate the detection through the combination of artificial intelligence and retinal imaging. Results: Significant retinal features such as arteriolar narrowing, venular widening, specific retinopathy markers (like microaneurysms, hemorrhages, and exudates), and changes in arteriovenous ratio (AVR) have shown strong correlations with CKD progression. We also found that the combination of deep learning with retinal imaging for CKD detection could provide a very promising pathway. Accordingly, leveraging retinal imaging through this technique is expected to enhance the precision and prognostic capacity of the CKD detection system, offering a non-invasive diagnostic alternative that could transform patient care practices. Conclusion: In summary, retinal imaging holds high potential as a diagnostic tool for CKD because it is non-invasive, facilitates early detection through observable microvascular changes, offers predictive insights into renal health, and, when paired with deep learning algorithms, enhances the accuracy and effectiveness of CKD screening.