07. Diabetes

Carrillo-Larco RM, Miranda JJ, Gilman RH, Checkley W, Smeeth L, Bernabe-Ortiz A, Cronicas Cohort Study Group.

J Diabetes Res. 2018 Dec 16.

Prognostic thresholds to identify new type 2 diabetes mellitus (T2DM) cases using the HOMA-IR have not been defined. We studied the HOMA-IR performance to identify incident T2DM cases and to assess if the thresholds varied according to urbanization and altitude in Peru.

Using the HOMA-IR to identify incident T2DM cases seems to yield moderate accuracy. The HOMA-IR could help improve identifying people at high risk of T2DM.

Lazo-Porras, M., Bernabe-Ortiz, A., Sacksteder, K., Gilman, R., Malaga, G., Armstrong, D., and Miranda, J.

Trials. 2016; 17: 206. Published online 2016 Apr 19.

Abstract

Background

Diabetic foot neuropathy (DFN) is one of the most important complications of diabetes mellitus; its early diagnosis and intervention can prevent foot ulcers and the need for amputation. Thermometry, measuring the temperature of the feet, is a promising emerging modality for diabetic foot ulcer prevention. However, patient compliance with at-home monitoring is concerning. Delivering messages to remind patients to perform thermometry and foot care might be helpful to guarantee regular foot monitoring. This trial was designed to compare the incidence of diabetic foot ulcers (DFUs) between participants who receive thermometry alone and those who receive thermometry as well as mHealth (SMS and voice messaging) over a year-long study period.

Methods/design

This is an evaluator-blinded, randomized, 12-month trial. Individuals with a diagnosis of type 2 diabetes mellitus, aged between 18–80 years, having a present dorsalis pedis pulse in both feet, are in risk group 2 or 3 using the diabetic foot risk classification system (as specified by the International Working Group on the Diabetic Foot), have an operating cell phone or a caregiver with an operating cell phone, and have the ability to provide informed consent will be eligible to participate in the study. Recruitment will be performed in diabetes outpatient clinics at two Ministry of Health tertiary hospitals in Lima, Peru.

Interventions: participants in both groups will receive education about foot care at the beginning of the study and they will be provided with a thermometry device (TempStat™). TempStat™ is a tool that captures a thermal image of the feet, which, depending on the temperature of the feet, shows different colors. In this study, if a participant notes a single yellow image or variance between one foot and the contralateral foot, they will be prompted to notify a nurse to evaluate their activity within the previous 2 weeks and make appropriate recommendations. In addition to thermometry, participants in the intervention arm will receive an mHealth component in the form of SMS and voice messages as reminders to use the thermometry device, and instructions to promote foot care.

Outcomes: the primary outcome is foot ulceration, evaluated by a trained nurse, occurring at any point during the study.

Discussion

This study has two principal contributions towards the prevention of DFU. First, the introduction of messages to promote self-management of diabetes foot care as well as using reminders as a strategy to improve adherence to daily home-based measurements. Secondly, the implementation of a thermometry-based strategy complemented by SMS and voice messages in an LMIC setting, with wider implications for scalability.

Trial registration

This study is registered in ClinicalTrials.gov: Identifier NCT02373592.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-016-1333-1) contains supplementary material, which is available to authorized users.

Keywords: Diabetic neuropathies, Thermometry, Diabetes mellitus, Type 2 ulcer, mHealth

Bernabe-Ortiz, A., Smeeth, L., Gilman, R., Sanchez-Abanto, J., Checkley, W., Miranda, J., and CRONICAS Cohort Study Group

Received 16 June 2016; Accepted 27 July 2016

Abstract

Objective. To develop and validate a risk score for detecting cases of undiagnosed diabetes in a resource-constrained country. Methods. Two population-based studies in Peruvian population aged ≥35 years were used in the analysis: the ENINBSC survey () and the CRONICAS Cohort Study (). Fasting plasma glucose ≥7.0 mmol/L was used to diagnose diabetes in both studies. Coefficients for risk score were derived from the ENINBSC data and then the performance was validated using both baseline and follow-up data of the CRONICAS Cohort Study. Results. The prevalence of undiagnosed diabetes was 2.0% in the ENINBSC survey and 2.9% in the CRONICAS Cohort Study. Predictors of undiagnosed diabetes were age, diabetes in first-degree relatives, and waist circumference. Score values ranged from 0 to 4, with an optimal cutoff ≥2 and had a moderate performance when applied in the CRONICAS baseline data (AUC = 0.68; 95% CI: 0.62–0.73; sensitivity 70%; specificity 59%). When predicting incident cases, the AUC was 0.66 (95% CI: 0.61–0.71), with a sensitivity of 69% and specificity of 59%. Conclusions. A simple nonblood based risk score based on age, diabetes in first-degree relatives, and waist circumference can be used as a simple screening tool for undiagnosed and incident cases of diabetes in Peru.

Miranda JJ, Bernabe-Ortiz A, Stanojevic S, Malaga G, Gilman RH, Smeeth L.

PLoS One. 2011 Mar 25;6(3):e18069.

This study shows that the use of A1C as diagnostic criteria for type 2 diabetes mellitus identifies people of different characteristics than fasting glucose. In the PERU MIGRANT population using A1C to define diabetes tripled the prevalence; the increase was more marked among poorer and rural populations. More than half the newly diagnosed people with diabetes using A1C had normal fasting glucose.