Person:

Hsu, William

The prevalence of diabetes is rising dramatically among Asians, with increased consumption of the typical Western diet as one possible cause. We explored the metabolic responses in East Asian Americans (AA) and Caucasian Americans (CA) when transitioning from a traditional Asian diet (TAD) to a typical Western diet (TWD), which has not been reported before. This 16-week randomized control pilot feasibility study, included 28AA and 22CA who were at risk of developing type 2 diabetes. Eight weeks of TAD were provided to all participants, followed by 8 weeks of isoenergy TWD (intervention) or TAD (control). Anthropometric measures, lipid profile, insulin resistance and inflammatory markers were assessed. While on TAD, both AA and CA improved in insulin AUC (−960.2 µU/mL×h, P = 0.001) and reduced in weight (−1.6 kg; P<0.001), body fat (−1.7%, P<0.001) and trunk fat (−2.2%, P<0.001). Comparing changes from TAD to TWD, AA had a smaller weight gain (−1.8 to 0.3 kg, P<0.001) than CA (−1.4 to 0.9 kg, P = 0.001), but a greater increase in insulin AUC (AA: −1402.4 to 606.2 µU/mL×h, P = 0.015 vs CA: −466.0 to 223.5 µU/mL×h, P = 0.034) and homeostatic static model assessment-insulin resistance (HOMA-IR) (AA: −0.3 to 0.2, P = 0.042 vs CA: −0.1 to 0.0, P = 0.221). Despite efforts to maintain isoenergy state and consumption of similar energy, TAD induced weight loss and improved insulin sensitivity in both groups, while TWD worsened the metabolic profile. Trial Registration: ClinicalTrials.gov NCT00379548

Abstract Background: Overseeing proper insulin initiation and titration remains a challenging task in diabetes care. Recent advances in mobile technology have enabled new models of collaborative care between patients and healthcare providers (HCPs). We hypothesized that the adoption of such technology could help individuals starting basal insulin achieve better glycemic control compared with standard clinical practice. Materials and Methods: This was a 12 ± 2-week randomized controlled study with 40 individuals with type 2 diabetes who were starting basal insulin due to poor glycemic control. The control group (n = 20) received standard face-to-face care and phone follow-up as needed in a tertiary center, whereas the intervention group (n = 20) received care through the cloud-based diabetes management program where regular communications about glycemic control and insulin doses were conducted via patient self-tracking tools, shared decision-making interfaces, secure text messages, and virtual visits (audio, video, and shared screen control) instead of office visits. Results: By intention-to-treat analysis, the intervention group achieved a greater hemoglobin A1c decline compared with the control group (3.2 ± 1.5% vs. 2.0% ± 2.0%; P = 0.048). The Diabetes Treatment Satisfaction Questionnaire showed a significant improvement in the intervention group compared with the control group (an increase of 10.1 ± 11.7 vs. 2.1 ± 6.5 points; P = 0.01). HCPs spent less time with patients in the intervention group compared with those in the control group (65.9 min per subject vs. 81.6 min per subject). However, the intervention group required additional training time to use the mobile device. Conclusions: Mobile health technology could be an effective tool in sharing data, enhancing communication, and improving glycemic control while enabling collaborative decision making in diabetes care.