Telmisartan use and risk of dementia in type 2 diabetes patients with hypertension: A population-based cohort study.

BACKGROUND: Angiotensin receptor blockers (ARBs) may have protective effects against dementia occurrence in patients with hypertension (HTN). However, whether telmisartan, an ARB with peroxisome proliferator-activated receptor γ (PPAR-γ)-modulating effects, has additional benefits compared to other ARBs remains unclear. METHODS AND FINDINGS: Between 1997 and 2013, 2,166,944 type 2 diabetes mellitus (T2DM) patients were identified from the National Health Insurance Research Database of Taiwan. Patients with HTN using ARBs were included in the study. Patients with a history of stroke, traumatic brain injury, or dementia were excluded. Finally, 65,511 eligible patients were divided into 2 groups: the telmisartan group and the non-telmisartan ARB group. Propensity score matching (1:4) was used to balance the distribution of baseline characteristics and medications. The primary outcome was the diagnosis of dementia. The secondary outcomes included the diagnosis of Alzheimer disease and occurrence of symptomatic ischemic stroke (IS), any IS, and all-cause mortality. The risks between groups were compared using a Cox proportional hazard model. Statistical significance was set at p < 0.05. There were 2,280 and 9,120 patients in the telmisartan and non-telmisartan ARB groups, respectively. Patients in the telmisartan group had a lower risk of dementia diagnosis (telmisartan versus non-telmisartan ARBs: 2.19% versus 3.20%; HR, 0.72; 95% CI, 0.53 to 0.97; p = 0.030). They also had lower risk of dementia diagnosis with IS as a competing risk (subdistribution HR, 0.70; 95% CI, 0.51 to 0.95; p = 0.022) and with all-cause mortality as a competing risk (subdistribution HR, 0.71; 95% CI, 0.53 to 0.97; p = 0.029). In addition, the telmisartan users had a lower risk of any IS (6.84% versus 8.57%; HR, 0.79; 95% CI, 0.67 to 0.94; p = 0.008) during long-term follow-up. Study limitations included potential residual confounding by indication, interpretation of causal effects in an observational study, and bias caused by using diagnostic and medication codes to represent real clinical data. CONCLUSIONS: The current study suggests that telmisartan use in hypertensive T2DM patients may be associated with a lower risk of dementia and any IS events in an East-Asian population.

Macular Ischemia Quantification Using Deep-Learning Denoised Optical Coherence Tomography Angiography in Branch Retinal Vein Occlusion.

Purpose: To examine whether deep-learning denoised optical coherence tomography angiography (OCTA) images could enhance automated macular ischemia quantification in branch retinal vein occlusion (BRVO). Methods: This retrospective, single-center, cross-sectional study enrolled 74 patients with BRVO and 46 age-matched healthy subjects. The severity of macular ischemia was graded as mild, moderate, or severe. Denoised OCTA images were produced using a neural network model. Quantitative parameters derived from denoised images, including vessel density and nonperfusion area, were compared with those derived from the OCTA machine. The main outcome measures were correlations between quantitative parameters, and areas under receiver operating characteristic curves (AUCs) in classifying the severity of the macular ischemia. Results: The vessel density and nonperfusion area from denoised images were correlated strongly with the corresponding parameters from machine-derived images in control eyes and BRVO eyes with mild or moderate macular ischemia (all P < 0.001). However, no such correlation was found in eyes with severe macular ischemia. The vessel density and nonperfusion area from denoised images had significantly larger area under receiver operating characteristic curve than those derived from the original images in classifying moderate versus severe macular ischemia (0.927 vs 0.802 [P = 0.042] and 0.946 vs 0.797, [P = 0.022], respectively). There were no significant differences in the areas under receiver operating characteristic curve between the denoised images and the machine-derived parameters in classifying control versus BRVO, and mild versus moderate macular ischemia. Conclusions: A neural network model is useful for removing speckle noise on OCTA images and facilitating the automated grading of macular ischemia in eyes with BRVO. Translational Relevance: Deep-learning denoised optical coherence tomography angiography images could enhance automated macular ischemia quantification.

The Risks of Corneal Surface Damage in Aqueous-Deficient Dry Eye Disease: A 17-Year Population-Based Study in Taiwan.

PURPOSE: To investigate the epidemiologic characteristics and risk of corneal surface damage in patients with aqueous-deficient dry eye disease (DED) in Taiwan. DESIGN: Retrospective, population-based cohort study. METHODS: We used claims data in the Taiwan National Health Insurance Research Database from 1997 to 2013 of patients with DED, defined according to diagnoses, drug codes, and clinical follow-up. A comparison cohort without DED was selected through propensity score matching. The main outcome measures were corneal surface damage, including corneal erosion, corneal ulcers, or corneal scars. RESULTS: Patients with DED had a significantly higher rate of corneal surface damage (hazard ratio [HR]: 2.70; 95% confidence interval [CI] 2.38-3.06, P < .001), especially higher in patients aged <18 years (HR 6.66; 95% CI 3.58-12.41) than in older patients and in women (HR 2.98; 95% CI 2.57-3.46) than in men (HR 2.22; 95% CI 1.78-2.77), compared to those in the non-DED cohort. DED with diabetes mellitus (P = .002), rheumatoid arthritis (P = .029), or systemic lupus erythematosus (P = .005) was positively associated with corneal surface damage. The overall prevalence of DED was 7.85%, higher among women (10.49%) than men (4.92%), and increased with age (0.53%, 3.94%, 10.08%, and 20.72% for ages <18, 18-39, 40-64, and >65 years, respectively). The prevalence increased gradually during the study period. CONCLUSIONS: The younger age group (<18 years) had the highest risk of corneal surface damage in aqueous-deficient DED. Other predisposing factors included female sex, diabetes, and autoimmune diseases. To improve clinical care, special attention is required for patients with DED with these risk factors.

Stroke Rate Increases Around the Time of Cancer Diagnosis.

Objective: To test whether strokes increase around the time of cancer diagnosis, we comprehensively examined the correlations of cancer and stroke by employing a population-based cohort study design. Methods: One million people insured under the Taiwan's National Health Insurance program in 2005 were randomly sampled to create the study's dataset. According to the presence of cancer and/or stroke, patients were separated into cancer and stroke, cancer-only, and stroke-only groups. Diagnoses of cancer, stroke, and comorbidities were defined according to ICD9-CM codes. Cancer and non-cancer populations were matched by age at cancer diagnosis, gender, and stroke risk factors, and each patient with cancer was matched with two non-cancer controls nested in the same year of cancer diagnosis. The hazards of stroke and cumulative incidences within a year after cancer diagnosis were evaluated using Fine and Gray's subdistributional hazard model. Results: The temporal distribution of first-ever stroke in patients with both cancer and stroke was a sharpened bell shape that peaked between 0.5 years before and after cancer diagnosis. Frequencies of stroke were further adjusted by number of cancer survivors. The monthly event rate of stroke remained nested around the time of cancer diagnosis in all strokes. Brain malignancies, lung cancer, gastric cancer, prostate cancer, and leukemia patients obtained higher ratio of stroke, while breast cancer and thyroid cancer patients had low percentage of combining stroke. When compared to non-cancer matched control, the hazard of stroke within one year after cancer diagnosis was increased by cancer at a subdistributional hazard ratio of 1.72 (95% confident interval 1.48 to 2.01; p < 0.0001). Conclusions: Cancer increased the risk of stroke and stroke events were nested around the time of cancer diagnosis, occurring 0.5 years prior to cancer on average regardless of stroke type.