Insulin use in chronic kidney disease and the risk of hypoglycemic events.

BACKGROUND: We examined in persons with type 2 diabetes (T2D) whether the use of insulin and the risk of serious hypoglycemic events with insulin is higher in persons with more advanced CKD. METHODS: In a national cohort of 855,133 veterans with T2D seen at Veteran Affairs clinics between Jan 1, 2008 and December 31, 2010 with at least two serum creatinine measurements, we defined insulin use from pharmacy records and serious hypoglycemic events by ICD-9/10 codes from emergency room visits or hospitalizations that occurred until December 31, 2016. RESULTS: Mean age was 66 ± 11 years and 97% were men. Mean baseline eGFR was 73 ± 22 ml/min/1.73 m2. In a multivariable Cox regression model of those without insulin use at baseline (N = 653,200), compared to eGFR ≥90 group, eGFR < 30 group had higher hazard (HR 1.80, 95% CI 1.74 to 1.88) of subsequent insulin use. In a multivariable Cox model with propensity score matching for baseline insulin use (N = 305,570), both insulin use (HR 2.34, 95% CI 2.24 to 2.44) and advanced CKD (HR 2.28, 95% CI 2.07 to 2.51 for comparison of eGFR < 30 to eGFR ≥90 ml/min/1.73 m2 groups) were associated with increased risk of subsequent serious hypoglycemic events. CONCLUSIONS AND RELEVANCE: In T2D, more advanced CKD was associated with greater insulin use. Both insulin use and advanced CKD were risk factors for serious hypoglycemic events. The safety of insulin compared to newer glycemic agents in more advanced CKD needs further study.

An intervention to decrease sedentary behavior in older adults: A secondary analysis of a randomized controlled trial.

Background: Sedentary behaviors are associated with adverse health outcomes in older adults. The feasibility of behavioral interventions in this population is unclear. Methods: In the Sit Less, Interact, Move More (SLIMM) trial of 106 participants who had obesity, those randomized to the SLIMM intervention (N = 54) were instructed to replace sedentary activities with stepping. An accelerometer was used to measure physical activity. In this secondary analysis, mixed effect models were used to examine the effects of the SLIMM intervention on sedentary and stepping durations and steps/day by age (<70 and ≥ 70 years). Results: Mean ages in the <70 years (N = 47) and ≥70 years (N = 59) groups were 58 ± 11 and 78 ± 5. In the older subgroup, compared to standard-of-care (N = 29), the SLIMM intervention (N = 30) significantly increased stepping duration (13, 95%CI 1-24 min/d, p = 0.038) and steps per day (1330, 95% CI 322-2338, p = 0.01) and non-significantly decreased sedentary duration by (28,95% CI -61-5 min/d, p = 0.09). In the age <70 subgroup, there was no separation between the standard of care (N = 23) and SLIMM (N = 24) groups. Discussion: In older adults who had obesity, SLIMM intervention significantly increased stepping duration and steps per day. Interventions targeting sedentary behaviors by promoting low intensity physical activity may be feasible in this population.

Predicting hospitalization from real-world measures in patients with chronic kidney disease: A proof-of-principle study.

Objective: To investigate if in-clinic measures of physical function and real-world measures of physical behavior and mobility effort are associated with one another and to determine if they predict future hospitalization in participants with chronic kidney disease (CKD). Methods: In this secondary analysis, novel real-world measures of physical behavior and mobility effort, including the best 6-minute step count (B6SC), were derived from passively collected data from a thigh worn actigraphy sensor and compared to traditional in-clinic measures of physical function (e.g. 6-minute walk test (6MWT). Hospitalization status during 2 years of follow-up was determined from electronic health records. Correlation analyses were used to compare measures and Cox Regression analysis was used to compare measures with hospitalization. Results: One hundred and six participants were studied (69 ± 13 years, 43% women). Mean ± SD baseline measures for 6MWT was 386 ± 66 m and B6SC was 524 ± 125 steps. Forty-four hospitalization events over 224 years of total follow-up occurred. Good separation was achieved for tertiles of 6MWT, B6SC and steps/day for hospitalization events. This pattern persisted in models adjusted for demographics (6MWT: HR = 0.63 95% CI 0.43-0.93, B6SC: HR = 0.75, 95% CI 0.56-1.02 and steps/day: HR = 0.75, 95% CI 0.50-1.13) and further adjusted for morbidities (6MWT: HR = 0.54, 95% CI 0.35-0.84, B6SC: HR = 0.70, 95% CI 0.49-1.00 and steps/day: HR = 0.69, 95% CI 0.43-1.09). Conclusion: Digital health technologies can be deployed remotely, passively, and continuously to collect real-world measures of physical behavior and mobility effort that differentiate risk of hospitalization in patients with CKD.