Relation between cutaneous and extracutaneous complications in pediatric patients with type 1 diabetes

Type 2 Diabetes Mellitus with Early Dry Skin Disorder: A Comparison Study Between Primary and Tertiary Care in Indonesia

BACKGROUND

The prevalence of Type 2 Diabetes Mellitus (T2DM) in Indonesia has continued to increase over the years. Management of T2DM is challenging across clinical settings, including primary and tertiary care. Uncontrolled T2DM puts patients at risk of the development of T2DM complications, especially early-stage dry skin that is neglected by most of the patients. This study aimed to investigate the comparison between the T2DM management profile and dry skin clinical profile of T2DM patients in primary care and tertiary care settings.

METHODS

The study was conducted as a cross-sectional epidemiological study by comparing T2DM patient profiles in primary and tertiary care. The data collected included sociodemographic, clinical, and laboratory data that were correlated with T2DM and early dry skin related-T2DM. This study included early dry skin within the SRRC score of 3-11 and excluded infection, ulcer, and severe erythema.

RESULTS

The patients in primary and tertiary care presented poorly controlled T2DM with median HbA1c levels of 7.8% and 7.6%. The patients in primary care also presented with high triglyceride, 179 mg/dl. Furthermore, several significant differences were found in the duration of T2DM, duration of dry skin, and DM treatment (OAD and insulin).

CONCLUSION

Significant differences in the duration of T2DM, duration of dry skin, and DM treatment (OAD and insulin) might be affected by the parameter of T2DM glycemic control (Blood pressure (BP), body mass index (BMI), HbA1c, random blood glucose (RBG), and triglyceride).

Non-invasive detection of diabetic complications via pattern analysis of temporal facial colour variations

BACKGROUND AND OBJECTIVE

Diabetes mellitus is a common disorder amounting to 400 million patients worldwide. It is often accompanied by a number of complications, including neuropathy, nephropathy, and cardiovascular diseases. For example, peripheral neuropathy is present among 20-30% of diabetics before the diagnosis is substantiated. For this reason, a reliable detection method for diabetic complications is crucial and attracts a lot of research attention.

METHODS

In this paper, we introduce a non-invasive detection framework for patients with diabetic complications that only requires short video recordings of faces from a standard commercial camera. We employed multiple image processing and pattern recognition techniques to process video frames, extract relevant information, and predict the health status. To evaluate our framework, we collected a dataset of 114 video files from diabetic patients, who were diagnosed with diabetes for years and 60 video files from the control group. Extracted features from videos were tested using two conceptually different classifiers.

RESULTS

We found that our proposed framework correctly identifies patients with diabetic complications with 92.86% accuracy, 100% sensitivity, and 80% specificity.

CONCLUSIONS

Our study brings a novel perspective on diagnosis procedures in this field. We used multiple techniques from image processing, pattern recognition, and machine learning to robustly process video frames and predict the health status of our subjects with high efficiency.

Invisible Color Variations of Facial Erythema: A Novel Early Marker for Diabetic Complications?

AIM

(1) To quantify the invisible variations of facial erythema that occur as the blood flows in and out of the face of diabetic patients, during the blood pulse wave using an innovative image processing method, on videos recorded with a conventional digital camera and (2) to determine whether this "unveiled" facial red coloration and its periodic variations present specific characteristics in diabetic patients different from those in control subjects.

METHODS

We video recorded the faces of 20 diabetic patients with peripheral neuropathy, retinopathy, and/or nephropathy and 10 nondiabetic control subjects, using a Canon EOS camera, for 240 s. Only one participant presented visible facial erythema. We applied novel image processing methods to make the facial redness and its variations visible and automatically detected and extracted the redness intensity of eight facial patches, from each frame. We compared average and standard deviations of redness in the two groups using t-tests.

RESULTS

Facial redness varies, imperceptibly and periodically, between redder and paler, following the heart pulsation. This variation is consistently and significantly larger in diabetic patients compared to controls (p value < 0.001).

CONCLUSIONS

Our study and its results (i.e., larger variations of facial redness with the heartbeats in diabetic patients) are unprecedented. One limitation is the sample size. Confirmation in a larger study would ground the development of a noninvasive cost-effective automatic tool for early detection of diabetic complications, based on measuring invisible redness variations, by image processing of facial videos captured at home with the patient's smartphone.