Smartphone-based Identification of Critical Levels of Glycated Hemoglobin A1c using Transdermal Optical Imaging

Worldwide, the prevalence of diabetes has continued to increase rapidly. This gives rise to concerns regarding appropriate diabetes management to ensure optimal glycemic control. Untreated or uncontrolled diabetes can lead to a host of complications, such as cardiovascular diseases, an increased likelihood of morbidity and mortality (Deshpande, Harris-Hayes, & Schootman, 2008). A challenging problem which arises in diabetes management is the limitations of current blood glucose monitoring techniques. Electronic medical devices can potentially overcome the persistent problems in the healthcare industry. Thus, for this study, it was of interest to investigate whether advanced machine learning methods and Anura, a smartphone-based transdermal optical imaging technology (TOI) that assess health markers, can be a viable solution for diabetes management. Objectives: To examine the validity of TOI and a novel machine algorithm for diabetes prediction (i.e diabetes and non-diabetes). We compared the diabetes classification from TOI’s obtained glycated hemoglobin A1c (HbA1c) concentrations against data obtained from FDA approved blood immunoassay. Methods: In the present study, we used a kitchen sink random forest machine algorithm for diabetes prediction. The data set was obtained from 513 participants recruited during their annual physical examination at the Health Management Center of The Affiliated Hospital of Hangzhou Normal University, China. This included participant’s TOI and blood immunoassay determined HbA1c concentrations. To validate the model, pristine testing was done on 400 pristine participants pseudo randomly selected during 20 trials of training/testing. Results: The confusion matrix found TOI to have a classification accuracy of 66%, and the ROC curve of the RF classifier found TOI to have a ROC AUC of 0.69. Conclusions: The present study provides evidence for the potential use of the TOI technology, Anura, for contactless, non-invasive, and inexpensive assessments of diabetes.

Fruit intake, genetic risk and type 2 diabetes: a population-based gene–diet interaction analysis

Purpose Whether the association between fruit and type 2 diabetes (T2D) is modified by the genetic predisposition of T2D was yet elucidated. The current study is meant to examine the gene–dietary fruit intake interactions in the risk of T2D and related glycemic traits. Methods We performed a cross-sectional study in 11,657 participants aged ≥ 40 years from a community-based population in Shanghai, China. Fruit intake information was collected by a validated food frequency questionnaire by asking the frequency of consumption of typical food items over the previous 12 months. T2D-genetic risk score (GRS) was constructed by 34 well established T2D common variants in East Asians. The risk of T2D, fasting, 2 h-postprandial plasma glucose, and glycated hemoglobin A1c associated with T2D-GRS and each individual single nucleotide polymorphisms (SNPs) were tested. Results The risk of T2D associated with each 1-point of T2D-GRS was gradually decreased from the lower fruit intake level (< 1 times/week) [the odds ratio (OR) and 95% confidence interval (CI) was 1.10 (1.07–1.13)], to higher levels (1–3 and > 3 times/week) [the corresponding ORs and 95% CIs were 1.08 (1.05–1.10) and 1.07 (1.05–1.08); P for interaction = 0.04]. Analyses for associations with fasting, 2 h-postprandial plasma glucose and glycated hemoglobin A1c demonstrated consistent tendencies (all P for interaction ≤ 0.03). The inverse associations of fruit intake with risk of T2D and glucose traits were more prominent in the higher T2D-GRS tertile. Conclusions Fruit intakes interact with the genetic predisposition of T2D on the risk of diabetes and related glucose metabolic traits. Fruit intake alleviates the association between genetic predisposition of T2D and the risk of diabetes; the association of fruit intake with a lower risk of diabetes was more prominent in population with a stronger genetic predisposition of T2D.

Gingival Crevicular Blood Glucose Evaluation in Patients with Periodontitis: Evolution of A New Screening Technique

OBJECTIVE: The aim and objective of this study was to assess if gingival crevicular blood could be utilized for blood glucose evaluation in patients with periodontitis and to check the reliability of this screening method. METHODOLOGY: The study was conducted at the department of Oral Medicine, Ziauddin Dental Hospital, Karachi. The sample size involved 348 participants with chronic periodontitis. The gingival crevicular blood produced during periodontal probing was collected on a glucometer strip to assess random glucose levels. Glycemic levels were also checked by finger capillary blood via a glucometer. Intravenous blood glycated hemoglobin A1c test was performed in patients found with blood glucose levels in pre-diabetic or diabetic range. RESULTS: The results of this study demonstrate a strong correlation (0.987, p< 0.001) between gingival crevicular blood and finger capillary blood glucose values and good correlation (0.709, p<0.001) between gingival crevicular bloodglucose and glycated Hemoglobin A1c levels. Receiver operating characteristic curve showed 94.1% sensitivity and 100% specificity of GCB glucose screening at the cut-off value of 168mg/dl. Gingival crevicular blood glucose showed significant regression with Finger capillary bloodglucose (R=0.987, R2=0.974, p<0.001) andglycated hemoglobin A1clevels (R=0.709, R2=0.502, p<0.001). CONCLUSIONS: From this study we conclude that gingival crevicular blood can be relied upon for screening of blood glucose levels in periodontitis patients presenting with bleeding on probing. KEYWORDS: Blood glucose, Diabetes Mellitus, Gingival Crevicular blood,Inflammation, Periodontitis. HOW TO CITE: Saeed Q, Memon S, Hosein M. Gingival crevicular blood glucose evoluation in patients with periodontitis: Evolution of a new screening technique. J Pak Dent Assoc 2020;29(4):193-198.