El análisis espectral de la variabilidad de la frecuencia cardiaca (VFC) permite estudiar la interacción entre los mecanismos psicológicos y neurofisiológicos del estrés. Con este análisis, se obtienen las bandas de frecuencia baja (LF) y alta (HF), cuyos valores se representan con diferentes unidades. No obstante, hay inconsistencias en algunas de estas unidades en la evaluación del estrés. El objetivo de este estudio fue analizar la similitud de cinco unidades de la VFC en una evaluación psicofisiológica de dicho fenómeno. Participaron dos grupos de estudiantes universitarios de primer ingreso. La evaluación psicofisiológica se hizo bajo tres condiciones: línea base, estrés y recuperación. Se compararon las condiciones de línea base y estrés. Además, se realizaron correlaciones de Spearman y un análisis de componentes principales entre todas las unidades de la VFC. En la condición de estrés se observaron reducciones significativas en todas las unidades de HF en ambos grupos. Solo un grupo mostró diferencias significativas en todas las unidades de LF. Con el análisis de componentes principales y de correlación se corroboró la dependencia entre LF y HF en unidades normalizadas y relativas. El uso de estas dos unidades debería de considerarse con precaución en la evaluación del estrés.
In Older Adults (OAs), Electroencephalogram (EEG) slowing in frontal lobes and a diminished muscle atonia during Rapid Eye Movement sleep (REM) have each been effective tracers of Mild Cognitive Impairment (MCI), but this relationship remains to be explored by non-linear analysis. Likewise, data provided by EEG, EMG (Electromyogram) and EOG (Electrooculogram)—the three required sleep indicators—during the transition from REM to Non-REM (NREM) sleep have not been related jointly to MCI. Therefore, the main aim of the study was to explore, with results for Detrended Fluctuation Analysis (DFA) and multichannel DFA (mDFA), the Color of Noise (CN) at the NREM to REM transition in OAs with MCI vs. subjects with good performances. The comparisons for the transition from NREM to REM were made for each group at each cerebral area, taking bilateral derivations to evaluate interhemispheric coupling and anteroposterior and posterior networks. In addition, stationarity analysis was carried out to explore if the three markers distinguished between the groups. Neuropsi and the Mini-Mental State Examination (MMSE) were administered, as well as other geriatric tests. One night polysomnography was applied to 6 OAs with MCI (68.1 ± 3) and to 7 subjects without it (CTRL) (64.5 ± 9), and pre-REM and REM epochs were analyzed for each subject. Lower scores for attention, memory and executive funcions and a greater index of arousals during sleep were found for the MCI group. Results confirmed that EOGs constituted significant markers of MCI, increasing the CN for the MCI group in REM sleep. The CN of the EEG from the pre-REM to REM was higher for the MCI group vs. the opposite for the CTRL group at frontotemporal areas. Frontopolar interhemispheric scaling values also followed this trend as well as right anteroposterior networks. EMG Hurst values for both groups were lower than those for EEG and EOG. Stationarity analyses showed differences between stages in frontal areas and right and left EOGs for both groups. These results may demonstrate the breakdown of fractality of areas especially involved in executive functioning and the way weak stationarity analyses may help to distinguish between sleep stages in OAs.
The human body secretes diabetogenic hormones such as cortisol in times of stress, a situation that is aggravated when there is already an undergoing pathology such as diabetes. Both stress and diabetes are important issues for public health, since each one puts the population’s quality of life at risk. This threatening scenario increases when both of them collude, becoming a dangerous combination that causes disease, a lack of metabolic control and early complications. This paper reviews the relationship between cortisol, stress, and diabetes, as well as how cortisol may function as a biological marker to reflect the activity of the corticotropic axis.
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