Abnormal Lipid Profiles as Markers of Diabetic Macular Edema Among Patients with Type 2 Diabetes Mellitus Attending a Tertiary Hospital in Northern Tanzania: A One-Year Cross-Sectional Study
HUUD, M.; MAKUPA, W.; MAKUPA, A.; DEOCAR, R.; SANDI, F.
Show abstract
BackgroundDiabetes mellitus (DM) remains a major global health challenge and is associated with vision-threatening complications, including diabetic macular edema (DME), a leading cause of visual impairment. Dyslipidemia has been implicated in the development of macular edema through mechanisms involving vascular permeability, endothelial dysfunction, and chronic inflammation. However, evidence regarding the relationship between lipid abnormalities and macular edema remains inconsistent across studies. AimThis study aimed to evaluate the association between abnormal lipid profiles and diabetic macular edema among patients with type 2 diabetes mellitus attending Kilimanjaro Christian Medical Centre (KCMC). MethodsA hospital-based analytical cross-sectional study was conducted among 296 diabetic outpatients at KCMC. Participants underwent comprehensive ophthalmic evaluation including fundoscopy and imaging with optical coherence tomography (OCT) for assessment of macular edema. Blood samples were collected for biochemical lipid analysis. Data were cleaned and analyzed using STATA version 17. ResultsDiabetic macular edema was identified in 56.4% (167/296) of participants. Abnormal lipid parameters were common, with elevated total cholesterol observed in 48.6%, triglycerides in 43.6%, low-density lipoprotein (LDL) in 36.1%, and reduced high-density lipoprotein (HDL) in 38.9% of patients. Elevated total cholesterol, triglycerides, and LDL levels showed significant associations with macular edema (p < 0.05). After multivariable adjustment, serum triglycerides remained independently associated with macular edema (p = 0.002). ConclusionDyslipidemia demonstrated a significant association with diabetic macular edema, with serum triglycerides emerging as an independent predictor. These findings highlight the importance of lipid monitoring, lifestyle modification, and strengthened screening strategies in reducing the burden of vision-threatening diabetic complications.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A Normative Database of A-Scan Data Using the Heidelberg Spectralis Spectral Domain Optical Coherence Tomography Machine 96%
- Determinants of glycemic control among persons living with type 2 diabetes mellitus attending a district hospital in Ghana 95%
- Predictors of glycemic control, quality of life and diabetes self-management of patients with diabetes mellitus at a tertiary hospital in Ghana 95%
Similar papers in this journal
- Association of renal function with diabetic retinopathy and macular edema among patients with type 2 diabetes mellitus 96%
- Automated vision screening of children using a mobile graphic device 91%
- An Open-Source Dataset Of Anti-Vegf Therapy In Diabetic Macular Oedema Patients Over Four Years & Their Visual Outcomes 91%
Similar papers in this journal
- Clinicopathological Evaluation of Dry eyes and Ocular surface in Newly diagnosed patients of Hyperthyroidism and Hypothyroidism and its Comparison to Healthy Subjects 95%
- Validation of the patient reported outcome measures tool “Catquest” in Odia language 94%
- Safety and efficacy of long-acting insulins (degludec and glargine) among type 2 diabetic Asian Population: A Systematic Review and Meta-Analysis 93%
Similar papers in this journal
Similar papers in this journal
- Grip strength is associated with retinal and choroidal thickness in type 2 diabetes mellitus patients without retinopathy 94%
- Risk Factors for Non-Communicable Diseases among Bangladeshi Adults: An Application of Generalized Linear Mixed Model on Multilevel Demographic and Health Survey Data 94%
- Prevalence and causes of vision impairment in Norwest Portugal: a capture and recapture study 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.