RESUMO
Objective: The objective of this study was to develop ANcam, a novel method for identifying acanthosis nigricans (AN) using a smartphone camera and computer-aided color analysis for noninvasive screening of people with impaired glucose tolerance (IGT). Research Design and Methods: Adult and juvenile participants with or without diagnosed type 2 diabetes were recruited in Trinidad and Tobago. After obtaining informed consent, participants' history, demographics, anthropometrics, and A1C were collected and recorded. Three subject matter experts independently graded pictures of the posterior neck and upper back using the ANcam smartphone application and Burke methods. A correlation matrix investigated 25 color channels for association with hyperpigmentation, and the diagnostic thresholds were determined with a receiver operating characteristic curve analysis. Results: For the 227 participants with captured images and A1C values, the cyan/magenta/yellow/black (CMYK) model color channel CMYK_K was best correlated with IGT at an A1C cutoff of 5.7% (39 mmol/mol) (R = 0.45, P <0.001). With high predictive accuracy (area under the curve = 0.854), the cutoff of 7.67 CMYK_K units was chosen, with a sensitivity of 81.1% and a specificity of 70.3%. ANcam had low interrater variance (F = 1.99, P = 0.137) compared with Burke grading (F = 105.71, P <0.001). ANcam detected hyperpigmentation on the neck at double the self-reported frequency. Elevated BMI was 2.9 (95% CI 1.9-4.3) times more likely, elevated blood pressure was 1.7 (95% CI 1.2-2.4) times more likely, and greater waist-to-hip ratio was 2.3 (95% CI 1.4-3.6) times more likely with AN present. Conclusion: ANcam offers a sensitive, reproducible, and user-friendly IGT screening tool to any smartphone user that performs well with most skin tones and lighting conditions.
RESUMO
The present study aims to determine the prevalence of self-reported sleep duration and sleep habits and their associated factors in patients with type 2 diabetes in Trinidad. This was a cross-sectional multicenter study. There were 291 patients with type 2 diabetes studied. Sleep habits were assessed using the Epworth Sleepiness Scale (ESS) and the National Health and Nutrition Examination Survey sleep disorder questionnaire. Demographic, anthropometric and biochemical data were also collected. The sample had a mean age of 58.8 years; 66.7% were female. The mean BMI was 28.9 kg/m(2). The prevalence of Excessive Daytime Sleepiness (EDS) was 11.3%. The prevalence of patients with short sleep (⩽6h) was 28.5%. The prevalence of patients with poor sleep was 63.9%. Poor sleep was associated with age, intensive anti-diabetic treatment and longer duration of diabetes. Short sleep was associated with intensive anti-diabetic treatment and BMI, while EDS was associated with increased BMI. In a sample of patients with type 2 diabetes, a high prevalence of self-reported sleep duration and unhealthy sleep habits was found. There needs to be an increased awareness of sleep conditions in adults with type 2 diabetes by doctors caring for these patients.