It doesn’t matter if you’re a computer geek or complete technophobe: If you’ve ever made the effort to download your blood glucose meter, you probably don’t have a clue about what to do with the data once you’ve gotten it.  That needs to change. Those of us who live with diabetes need to become more adept at analyzing our own data, to see what’s working and what isn’t both for our own sake and that of our time-strapped healthcare providers. .

“One reason why people with diabetes can suffer more damage during strokes has been discovered,” reported BBC News.

Drugmaker Bristol-Myers Squibb Co., which reports its fourth-quarter results before the stock market opens Thursday, likely will focus on how recent and pending approvals of new drugs could help offset a looming revenue plunge as patents on key drugs expire.

Q: I have diabetes and high blood pressure . Is the combination of lisinopril and amlodipine a good choice? A: Your combination of drugs to treat high blood pressure is one of the best.

OBJECTIVE

Variation in the fat mass and obesity-associated (FTO) gene is associated with obesity. The extent to which separate and combined effects of physical activity and caloric intake modify this association remains unclear.

RESEARCH DESIGN AND METHODS

FTO polymorphism rs8050136 was measured, and physical activity, caloric intake, and anthropometrics were self-reported in 21,675 apparently healthy Caucasian women.

RESULTS

The effect of the risk allele (A) on BMI was larger among inactive or higher intake women, with additive effects of inactivity and high intake on the associated genetic risk. Specifically, each A allele was associated with mean BMI difference of +0.73 (SE 0.08) kg/m² among inactive women (≤median, 8.8 MET-hours/week), compared with +0.31 (0.06) kg/m², P < 0.0001, among active women (>8.8 MET-hours/week). Similarly, each A allele was associated with mean BMI difference of +0.65 (0.07) among high intake women (>median, 1,679 kcals/day), compared with +0.38 (0.07) kg/m², P = 0.005, among low intake women (≤1,679 kcals/day). Among inactive/high intake women, each A allele was associated with mean BMI difference of +0.97 (0.11) kg/m² vs. +0.22 (0.08) kg/m² among inactive/low intake women, P < 0.0001. Among inactive/high intake women, each A allele carried increased risk of obesity (odds ratio 1.39, 95% CI 1.27–1.52) and diabetes (odds ratio 1.36, 95% CI 1.07–1.73).

CONCLUSIONS

In this study, lifestyle factors modified the genetic risk of FTO on obesity phenotypes, particularly among women who were both inactive and had high intake. Healthier lifestyle patterns blunted but did not completely eliminate the associated genetic risk.

OBJECTIVE

To determine the relationship between mean sensor glucose concentrations and hemoglobin A_1c (HbA_1c) values measured in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications laboratory at the University of Minnesota in a cohort of subjects with type 1 diabetes from the Juvenile Diabetes Research Foundation continuous glucose monitoring randomized trial.

RESEARCH DESIGN AND METHODS

Near-continuous glucose sensor data (≥4 days/week) were collected for 3 months before a central laboratory–measured HbA_1c was performed for 252 subjects ages 8–74 years, the majority of whom had stable HbA_1c values (77% within ±0.4% of the patient mean).

RESULTS

The slope (95% CI) for mean sensor glucose concentration (area under the curve) versus a centrally measured HbA_1c was 24.4 mg/dL (22.0–26.7) for each 1% change in HbA_1c, with an intercept of –16.2 mg/dL (–32.9 to 0.6). Although the slope did not vary with age or sex, there was substantial individual variability, with mean sensor glucose concentrations ranging from 128 to 187 mg/dL for an HbA_1c of 6.9–7.1%. The root mean square of the errors between the actual mean sensor glucose concentration versus the value calculated using the regression equation was 14.3 mg/dL, whereas the median absolute difference was 10.1 mg/dL.

CONCLUSIONS

There is substantial individual variability between the measured versus calculated mean glucose concentrations. Consequently, estimated average glucose concentrations calculated from measured HbA_1c values should be used with caution.

OBJECTIVE

To demonstrate that Diabeo software enabling individualized insulin dose adjustments combined with telemedicine support significantly improves HbA_1c in poorly controlled type 1 diabetic patients.

RESEARCH DESIGN AND METHODS

In a six-month open-label parallel-group, multicenter study, adult patients (n = 180) with type 1 diabetes (>1 year), on a basal-bolus insulin regimen (>6 months), with HbA_1c ≥8%, were randomized to usual quarterly follow-up (G1), home use of a smartphone recommending insulin doses with quarterly visits (G2), or use of the smartphone with short teleconsultations every two weeks but no visit until point end (G3).

RESULTS

Six-month mean HbA_1c in G3 (8.41 ± 1.04%) was lower than in G1 (9.10 ± 1.16%; P = 0.0019). G2 displayed intermediate results (8.63 ± 1.07%). The Diabeo system gave a 0.91% (0.60; 1.21) improvement in HbA_1c over controls and a 0.67% (0.35; 0.99) reduction when used without teleconsultation. There was no difference in the frequency of hypoglycemic episodes or in medical time spent for hospital or telephone consultations. However, patients in G1 and G2 spent nearly 5 h more than G3 patients attending hospital visits.

CONCLUSIONS

The Diabeo system gives a substantial improvement to metabolic control in chronic, poorly controlled type 1 diabetic patients without requiring more medical time and at a lower overall cost for the patient than usual care.