Altered cerebral regulation in type 2 diabetic patients with cardiac autonomic neuropathy

Aims/hypothesis Assessment of cerebral regulation in diabetic patients is often problematic because of the presence of cardiac autonomic neuropathy. We evaluated the technique of oscillatory neck suction at 0.1 Hz to quantify cerebral regulation in diabetic patients and healthy control subjects.Subjects and methods In nine type 2 diabetic patients with cardiac autonomic neuropathy and 11 age-matched controls, we measured blood pressure and cerebral blood flow velocity responses to application of 0.1 Hz neck suction. We determined spectral powers and calculated the transfer function gain and phase shift between 0.1 Hz blood pressure and cerebral blood flow velocity oscillations as parameters of cerebral regulation.Results In the patients and control subjects, neck suction did not significantly influence mean values of the RR interval, blood pressure and cerebral blood flow velocity. The powers of 0.1 Hz blood pressure and cerebral blood flow velocity oscillations increased in the control subjects, but remained stable in the patients. Transfer function gain remained stable in both groups. Phase shift decreased in the patients, but remained stable in control subjects.Conclusions/interpretation The absence of an increase in the power of 0.1 Hz blood pressure and cerebral blood flow velocity oscillations confirmed autonomic neuropathy in the diabetic patients. Gain analysis did not show altered cerebral regulation. The decrease in phase shift in the patients indicates a more passive transmission of neck suction-induced blood pressure fluctuations onto the cerebrovascular circulation, i.e. altered cerebral regulation, in the patients, and is therefore suited to identifying subtle impairment of cerebral regulation in these patients

Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in its clinical presentation. This makes diagnosis and effective treatment difficult, so better tools for estimating disease progression are needed. Here, we report results from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. In this crowdsourcing competition, competitors developed algorithms for the prediction of disease progression of 1,822 ALS patients from standardized, anonymized phase 2/3 clinical trials. The two best algorithms outperformed a method designed by the challenge organizers as well as predictions by ALS clinicians. We estimate that using both winning algorithms in future trial designs could reduce the required number of patients by at least 20%. The DREAM-Phil Bowen ALS Prediction Prize4Life challenge also identified several potential nonstandard predictors of disease progression including uric acid, creatinine and surprisingly, blood pressure, shedding light on ALS pathobiology. This analysis reveals the potential of a crowdsourcing competition that uses clinical trial data for accelerating ALS research and development