OBJECTIVE -Measures of baroreflex sensitivity, heart rate variability (HRV), and the classical Ewing test parameters are currently used for the diagnosis of diabetic autonomic neuropathy and for mortality risk stratification after myocardial infarction. However, the strengths of the associations of these measures of autonomic function with risk of mortality have never been compared in one study population. Furthermore, no evidence is available on the possible effect of glucose tolerance on these associations. RESEARCH DESIGN AND METHODS-The study population (n ϭ 605) consisted of a glucose tolerance-stratified sample from a general population (50 -75 years of age). Cardiac cycle duration and continuous finger arterial pressure were measured under two conditions: at rest and on metronome breathing. From these readings, seven parameters of autonomic function were assessed (one Ewing, five HRV, and one baroreflex sensitivity).RESULTS -During 9 years of follow-up, 101 individuals died, 43 from cardiovascular causes. Subjects with diabetes and low levels of the autonomic function parameters, indicating impaired autonomic function, had an approximately doubled risk of mortality. This association was consistent, though not statistically significant, for all parameters. The elevated risk was not observed in subjects without diabetes, hypertension, or prevalent cardiovascular disease.CONCLUSIONS -Impaired autonomic function is associated with all-cause and cardiovascular mortality. Moreover, the results of the present study suggest that cardiac autonomic dysfunction in patients already at risk (diabetes, hypertension, or history of cardiovascular disease) may be especially hazardous.

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In vivo measurements of equivalent resistivities of skull (rho(skull)) and brain (rho(brain)) are performed for six subjects using an electric impedance tomography (EIT)-based method and realistic models for the head. The classical boundary element method (BEM) formulation for EIT is very time consuming. However, the application of the Sherman-Morrison formula reduces the computation time by a factor of 5. Using an optimal point distribution in the BEM model to optimize its accuracy, decreasing systematic errors of numerical origin, is important because cost functions are shallow. Results demonstrate that rho(skull)/rho(brain) is more likely to be within 20 and 50 rather than equal to the commonly accepted value of 80. The variation in rho(brain)(average = 301 omega x cm, SD = 13%) and rho(skull)(average = 12230 omega x cm, SD = 18%) is decreased by half, when compared with the results using the sphere model, showing that the correction for geometry errors is essential to obtain realistic estimations. However, a factor of 2.4 may still exist between values of rho(skull)/rho(brain) corresponding to different subjects. Earlier results show the necessity of calibrating rho(brain) and rho(skull) by measuring them in vivo for each subject, in order to decrease errors associated with the electroencephalogram inverse problem. We show that the proposed method is suited to this goal.

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