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The Journal of Clinical Endocrinology & Metabolism Vol. 85, No. 8 2758-2766
Copyright © 2000 by The Endocrine Society

Original Studies

Impairment and Recovery of Elementary Cognitive Function Induced by Hypoglycemia in Type-1 Diabetic Patients and Healthy Controls

Ralf Lobmann, Henderikus G. O. M. Smid, Gesine Pottag, Katrin Wagner, Hans-Joachim Heinze and Hendrik Lehnert

Departments of Endocrinology and Metabolism (R.L., G.P., K.W., H.L.) and Neurology (H.G.O.M.S., H.-J.H.), Magdeburg University Medical School, 39120 Magdeburg, Germany

Address correspondence and requests for reprints to: Prof. Dr. med. Hendrik Lehnert, Dr. R. Lobmann, Otto-von-Guericke-Universität Magdeburg, Medizinische Fakultät, Zentrum für Innere Medizin, Klinik für Endokrinologie und Stoffwechselkrankheiten, Leipziger Str. 44, 39120 Magdeburg, Germany. E-mail: hendrik.lehnert{at}medizin.uni-magdeburg.de


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Although neuroendocrine changes after induction of hypoglycemia, in patients with diabetes and healthy persons, are thoroughly investigated, cognitive adaptation processes are still insufficiently understood. Changes in cognitive functions are mainly investigated by psychometric tests, which represent a summation of different cognitive processes. We aimed at dissecting cognitive adaptation into single components, i.e. stimulus selection, response choice, and reaction speed during a hyperinsulinemic hypoglycemic clamp in patients with type-1 diabetes and matched healthy controls. Using novel neurophysiological analyses, the event-related potentials of early stimulus selection (selection negativity) and response selection (lateralized readiness potential) were studied, in addition to reaction time (RT). A total of 12 diabetic patients and 12 normal volunteers were studied while receiving a hyperinsulinemic hypoglycemic clamp. RTs and the event-related potentials related to stimulus selection and response selection were significantly delayed during hypoglycemia in both groups, whereas early evoked potentials (P100) were unaltered. This suggests that hypoglycemia delays stimulus selection, with the consequence that also central and motor processing are delayed. In addition, patients with diabetes showed an earlier negative shift over the frontal cortex, which, when compared with the controls, reveals better adaptation to hypoglycemia in frontal cortical brain regions. After restoration of euglycemia stimulus selection, response selection and RT returned to baseline level in the type-1 group. In the control group, however, response selection and RTs were still delayed. This suggests that type-1 patients, possibly because of the past occurrence of hypoglycemic events, might be able to better cope with the hypoglycemic state than healthy volunteers who lack such a history. In summary, our data demonstrate, for the first time, that cognitive adaptation processes to an experimental hypoglycemic episode can clearly be dissected into their single components.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
WITH AN increased acceptance and performance of intensive insulin therapy (ICT), as supported by the diabetes control and complications trial data, recurrent hypoglycemia in insulin-dependent diabetes mellitus has become even more a major focus of research and clinical interest (1, 2, 3). The incidence of severe hypoglycemia in patients with diabetes treated by ICT is two to six times higher as in conventionally treated patients with diabetes. In particular, recurrent hypoglycemic episodes during the night represent a relevant risk for the patient, because they are often not realized and lead to a deterioration in the awareness for subsequent hypoglycemic episodes (4). Recent data show that recurrent hypoglycemia not only affects neuroendocrine counterregulation but also autonomic and neuroglucopenic symptoms (5, 6, 7).

Although knowledge about neuroendocrine processes in normal persons and in patients with insulin-dependent diabetes mellitus (IDDM) is rather conclusive, very little is known about the precise alterations in cognitive function and neurophysiological changes in target brain cortical areas, the hippocampus, and the basal ganglia (8, 9, 10). Various data, based on psychometric tests (11) and neurophysiological techniques using N2 and P300 waves or event-related potentials (ERPs), point to compromised selective attention and delayed reaction time (RT) (12, 13, 14, 15, 16). A major drawback of both these methods is the analysis of dependent measures (RT, averaged ERP) that represent a summation of different, partly unknown, cognitive processes (17). Also, the modification of an ERP’s amplitude may simply represent a gross effect of hypoglycemia on overall electrophysiological brain activity, rather than on specific cognitive changes.

In contrast, the ERP subtraction technique makes it possible to isolate distinct cognitive processes in the brain before they result in motor action (18, 19, 20, 21, 22, 23, 24, 25). Stimulus selection and response selection are two elementary cognitive processes engaged in almost every cognitive task (26, 27). They can be analyzed with this technique when subjects perform a task in which they have to select objects on the basis of one visual feature (e.g. color) for a subsequent response choice on the basis of another feature (e.g. letter shape). The ERPs to selected objects and those to not-selected objects start to differ shortly (~120 msec) after object presentation. This difference is largest over temporooccipital scalp sites (i.e. over brain areas involved in object perception) and is called selection negativity (SN). Because the SN is related to the differential processing of selected and ignored objects, occurs with high reliability, and is sensitive to stimulus (but not response) parameters, it can be used to study the time course of selective stimulus processing (19, 20, 21, 24, 28).

Response selection can be analyzed when subjects must select between their right and left hand for making a subsequent keypress (29). Preceding the unilateral hand movement, the ERP over the motor cortex contralateral to the responding hand contains more negativity than the ERP over the ipsilateral motor cortex. The motor-related component of this negativity is made available by a double-subtraction technique (see Subjects and Methods) that cancels other nonmotor lateralized components and results in the lateralized readiness potential (LRP). Because the LRP is related to the differential performance of right and left motor responses, is largest over precentral motor cortex, and also occurs with high reliability, it can be used to study the time course of selective response activation (29). The study of SN and LRP allows, for the first time, separate analysis of stimulus selection and response selection during euglycemic and hypoglycemic states in diabetic patients and healthy controls.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Twelve healthy nonobese adult volunteers and twelve patients with type-1 diabetes, whose clinical characteristics are given in Table 1Go, were investigated. The methodological evaluation of the novel neurophysiological technique employed has been performed in the control group and been communicated in a recent report of our group (17). Each gave written informed consent to participate in the study protocol, which was approved by the ethical committee of the University of Magdeburg.


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  		Table 1. Clinical characteristics of subjects studied

 
Volunteers, healthy and with diabetes, were excluded from the study if they had signs or symptoms of autonomic or peripheral neuropathy by diabetic or other causes. In addition, retinopathy, peripheral vascular disease, hypertension, chronic heart failure, and renal or hepatic diseases lead to exclusion. Cardial autonomic neuropathy was tested by function tests that have previously been described by Ziegler at al. (30). Peripheral neuropathy was measured by testing thermal and sensory discrimination (Medoc Systems, Ramat Yishai, Israel). No patient had any severe hypoglycemic episode (requiring external assistance) in the past 3 months. Furthermore, the glycemic control between the different subjects did not strongly deviate (HbA1c 7.38 ± 1.8). All subjects had normal or corrected vision and were right-handed.

Hyperinsulinemic hypoglycemic clamp (for protocol see below and Fig. 1Go)

Each subject was studied in the morning (starting at 1000 h), after a 12-h overnight fast, on a single day. Coffee and nicotine intake were not allowed. The diabetic patients received their long-acting insulin at nighttime and in the morning before the clamp. No regular insulin was given before the clamp procedure. All subjects received an euglycemic clamp using the artificial pancreas (Biostator, Life Science Instruments, Miles Laboratories, Elkhart, IN). This instrument consists of the following modules: an analyzer pump to control the continuous withdrawal and mixing of the blood; a glucose analyzer for the continuous on-line analysis of blood-glucose; a computer programmed with a set of algorithms, which (depending on the dynamic and/or static blood glucose concentrations) calculates the amount of insulin and/or dextrose to be infused; and a computer-controlled infusion pump to deliver the insulin and/or dextrose to the patient.



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  		Figure 1. Study protocol. For further description, see Subjects and Methods.

 
Blood glucose was monitored by continuous sampling through a cannula inserted into a forearm vein. A second cannula was inserted into an ipsilateral antecubital vein for intermittent sampling, whereas a third catheter was inserted into a contralateral antecubital vein for dextrose (20%), insulin (regular insulin), and saline infusions.

The diabetic subjects had higher blood glucose levels in the morning (6.9 ± 0.9 mmol/L); it took 1 h to stabilize glucose levels before studying. After 60 min, to reach the steady state of 5.5–6.2 mmol/L, we used a three-phase model for clamping. First, a hyperinsulinemic euglycemic phase, with a mean blood-glucose baseline level of 6.1 mmol/L, was clamped. The infusion of insulin was set to 1 mU x kg-1 x min-1 and variable glucose infusion, to maintain the steady state for the following 30 min. Thereafter, plasma glucose was reduced, in a stepped clamp, in 0.8-mmol/L steps, every 20 min, over 1.5 h, to a final plateau of 2.6 mmol/L, by increasing the insulin infusion rate to 2 mU x kg-1 x min-1. The hypoglycemic plateau phase lasted for 30 min, after which glucose infusion was increased and insulin infusion rate decreased to 1 mU x kg-1 x min-1 to restore an euglycemic level (phase 3). Each plateau phase was clamped for 30 min to study the electrophysiological parameters.

At fixed time points of blood glucose levels (6 mmol/L before the first euglycemic phase, 6 mmol/L after the first euglycemic phase, 4.5 mmol/L, 3.8 mmol/L, 3.3 mmol/L, 2.8 mmol/L), blood samples were taken for measurements of counterregulatory hormones (epinephrine, norepinephrine, cortisol, glucagon, and ACTH) and blood glucose levels. Also, blood samples after the hypoglycemic clamp phase and after reaching the second euglycemic level were taken.

Simultaneously with blood sampling, we administered a semiquantitative symptom score questionnaire. Subjects scored from 0 (none) to 3 (severe) symptoms. The sum of the 15 individual rating scores from each questionnaire provided a total symptom score for each observation time. The classification of symptoms comprised primarily autonomic (sweating, trembling, warmness, palpitations), neuroglucopenic (tiredness, dizziness, confusion, lack of concentration, light-headedness), and not clearly attributable [weakness, hunger, speech disorder, double images, nausea, paresthesia (especially perioral)] items. Effects, over time, on symptom awareness were assessed by a general linear model with repeated measures (4, 31, 32).

Cognitive task

At each of the three plateaus, the same selective attention task was administered to each subject. In this task, a sequence of colored letters was presented, and the letters in one color (e.g. red) had to be selected to decide whether they required a right-hand movement (e.g. D), a left-hand movement (e.g. H), or no movement (all other letters). A single letter (from the set C, D, F, G, H, J, L, M, N, Q, T, W; 2 x 2 degrees of visual angle) was presented on each trial, in the center of a PC-controlled video monitor. The letter appeared either in green or in red on a black background. The subjects responded by making a keypress with either their left or right index finger, if one of the two letters requiring a response (i.e. the targets) was presented.

In each of the 3 test phases, 4 blocks of this task were administered. Each block consisted of a randomized sequence of 200 letter presentations. In the first 2 blocks, 2 letters in one color were targets; and in the second 2 blocks, 2 other letters in the other color were targets. Each new assignment of stimuli to responses was preceded by short training blocks (40 trials). The order of color assignment (red relevant first or green relevant first) and target letter assignment were balanced within and between subjects. All 12 letters equally often served as a target.

The subjects were instructed to respond to the targets in the correct color, as fast and accurately as possible, to fixate a fixation aid that replaced the letter after it disappeared from the screen and to minimize eye movements and blinking. The stimulus duration was 150 msec. The interstimulus interval, in which the fixation aid remained on the screen, varied between 650 and 950 msec, to minimize time-based preparation effects.

To enhance readability, we use the following abbreviations. The target-letters in the to-be-attended color (e.g. red G and N) are called relevant targets (RTG). The letters in the to-be-attended color not requiring a motor response (nontargets) are called relevant nontargets (RNT, e.g. red C, D, F, H, J, L, M, Q, T, and W). Target letters in the to-be-ignored color (e.g. green G and N) are called irrelevant targets (ITG), and nontarget letters in the to-be-ignored color are called irrelevant nontargets (e.g. green C, D, F, H, J, L, M, Q, T, and W). The RT was measured to the RTG letters. The LRP was derived by subtracting the averaged C3'-C4' ERP response obtained to left-hand RTG from that obtained to right-hand RTG (33). The SN was derived by subtracting the averaged occipital ERP response to INT from that to RNT. The SN and LRP concern difference potentials that are directly associated with distinct aspects of selective cognitive processing [discrimination of task-relevant vs. task-irrelevant information (SN); activation of a unilateral hand movement (LRP)]. By observing the SN and LRP, we do not need to rely on the end result of information processing, such as a button press, but instead have observations of the intervening cognitive processes (like a so-called window on the mind) that eventually lead to that end-result. By repeating the task in different glucose level states and observing the SN and LRP, we can learn about different cognitive functions uncontaminated by more peripheral effects. Note that SN and LRP have been used to this aim in a myriad of studies and that we have combined them in a single protocol.

ERP analyses

The ERPs were recorded synchronously to each presentation of a letter and the eventual motor processing it required. This was done using standard procedures (see Ref. 17 for details) from electrode positions F7, F8, F3, F4, Fz, FC1, FC2, C3', C4', Cz, CP1, CP2, P3, P4, Pz, T3, T4, T5, T6, PO1, PO2, TO1, TO2, O1, O2, Oz, IN1, IN2, and INz, all referenced to an electrode on the left ear lobe. Blinks and eye movements were monitored with electrodes at the outer canthi [horizontal electrooculogram (EOG)] and below the right eye (vertical EOG), also referenced to the left earlobe. The ERPs were filtered with a bandpass of 0.01–70 Hz (half-amplitude cutoffs) and digitized at a rate of 250 HZ. Off-line automated artefact rejection eliminated data epochs contaminated by blinks, saccades, excessive muscle activity, and amplifier saturation (criterion = 50 µV).

The ERP was averaged separately for each stimulus type (RTG, RNT, ITG, and INT), clamp condition, subject, and response side, over epochs of 1080 msec, starting 100 msec before onset of the stimulus and ending 980 msec post stimulus. These averages were next used for statistical analyses [multivariate ANOVA (MANOVA), SPSSPC+ V5.02]1, forming the basis for inferences about the time-course of the SN and the LRP, and topographical ERP amplitude measures. We first tested the mean ERP amplitudes in each clamp condition in five latency windows: 0–120 msec, 124–200 msec, 204–300 msec, 304–400 msec, and 404–500 msec for effects of the factors group (healthy controls, type-1 diabetics), test phase [hypoglycemia effect (euglycemia phase 1 vs. hypoglycemia] and euglycemia restoration effect (euglycemia phase 2 vs. hypoglycemia phase 2), and scalp distribution (ERP amplitude at Fz, Cz, Pz and Oz). Note that the number of subjects (24) was 3-fold the number of factor levels in the designs [eight in each (glucose level, 2; electrode position, 4; group, 2], so that the use of MANOVA was justified (34).

A second set of analyses was carried out to find the onset latencies of the SN and LRP in each clamp condition and group. First, the 0–480-msec poststimulus interval of each averaged ERP was divided into 60 epochs of 8 msec, averaging the amplitudes of each sequential pair of 4-msec samples in the interval, correcting for differences in the 100-msec prestimulus baseline. The amplitude of the SN was maximal at the IN1 electrode site; therefore, the ERPs at this electrode were used to analyze the SN. Next, the RNT and INT ERPs were statistically analyzed to find the first epoch in which they significantly differed, representing the onset latency of the SN. In a similar manner, the right-hand C3'-C4' difference ERPs and left-hand C3'-C4' difference ERPs were analyzed to find the onset latency of the LRP. Mathematically, the LRP is derived according to the formula: LRP = right-hand (C3'-C4') - left-hand (C3'-C4').

These analyses were performed by MANOVAs, repeated on 25 of the 8-msec epochs, starting at 120 msec post stimulus, by means of planned comparisons according to a repeated-measures, within-subjects design. For determining the onset latency of the SN, these comparisons concerned the difference between the averaged ERPs to RNT and INT (color-related SN). To determine the onset latency of the LRP, the comparisons concerned the difference between the averaged C3'-C4' ERPs to right-hand RTG and those to left-hand RTG. The first epoch of a consecutive series of at least 5 epochs (representing a 40-msec interval) with P-values below 0.01 was taken as the onset latency of a significant SN or LRP. The criterion of finding at least 5 consecutive epochs with P < 0.01 is a correction for performing many pairwise comparisons (18). The experimental factor in the SN tests concerned color-relevance (relevant, irrelevant) and, in the LRP tests, the factor response hand (left, right). These test procedures closely follow those previously used (17, 35, 36, 37, 38, 39). The RTs to RTGs were tested in a MANOVA with clamp- condition (euglycemia phase 1, hypoglycemia, euglycemia phase 2) and group (healthy controls, type-1 diabetics) as main factors.

Hormone analysis

Blood samples were immediately processed (centrifugation at 4 C for 10 min with 2500 x g) and stored deep-frozen at -70 C until assay.

Catecholamines were measured in EDTA-plasma by high-performance liquid chromatography with electrochemical detection (Chrom-Systems, Alltec Associates, Deerfield).

The between-series coefficient of variation (CV) for adrenaline was 5.6%, and that for noradrenaline was 6.1%; the within-series CV for adrenaline was 5.4%, and that for noradrenaline was 5.8%.

ACTH and glucagon were determined in EDTA-plasma by RIA (IBL) (interassay CV, 3.34%; intraassay CV, 12.9%).

Cortisol was determinated in EDTA-plasma by immunofluorescent essay (Immulite/Biermann, Bad Nauheim, Germany) (interassay CV, 10.3%; intraassay CV, 7.0%).

Effects, over time, on the neuroendocrine response were assessed by a general linear model with repeated measures.


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Neuroendocrine response basal levels of counterregulatory hormones were similar between the two groups (see Table 2Go). When blood glucose was lowered to 2.8 mmol/L, adrenaline (P < 0.001), glucagon (P = 0.007), ACTH (P = 0.017), and cortisol (P = 0.001) increased significantly in the group of normal participants. Only noradrenaline (P = 0.067) responses just failed to reach statistical significance.


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  		Table 2. Data of hormone analysis (mean concentration of adrenaline, noradrenaline, cortisol, ACTH) at the different time points for both investigated groups

 
Also, in the group of subjects with diabetes, adrenaline (P = 0.002), noradrenaline (P = 0.007), and cortisol (P = 0.001) increased. The augmentation of glucagon and ACTH secretion did not reach statistical significance (P = 0.102 and 0.26, respectively).

After restoration of euglycemia, the hormone concentrations also did not differ between groups.

Symptom awareness

The autonomic and neuroglycopenic symptom scores of 15 hypoglycemic symptoms increased significantly during stepped hypoglycemia (healthy group, P < 0.001; group with diabetes, P < 0.001). We found no statistical significant differences for the autonomic and neuroglycopenic symptom scores between both groups at the different time points (euglycemia phase 1, hypoglycemia, euglycemia phase 2).

Neurophysiological data

Behavior. Overall, the RTs increased as a result of the hypoglycemic clamp [F(1, 22) = 15.67, P < 0.001]. The RTs increased by 27 msec in the healthy group, during hypoglycemia, when compared with the initial euglycemia baseline [F(1, 11) = 5.53, P < 0.038]. In the type-1 group, the RTs also increased during hypoglycemia [30 msec: F(1, 11) = 11.84, P < 0.006] but no more than in the healthy controls [group by test-phase interaction: F(1, 22) < 1)]. The overall difference in RT between the groups was not significant. Across groups, restoring euglycemia resulted in significantly shorter RTs [F(1, 22) = 15.48, P < 0.001]. Restoration of euglycemia did not significantly decrease RTs in the healthy group [-18 msec: F(1, 11) = 2.71, P < 0.128]. In the type-1 group, the RTs significantly decreased with restoration of euglycemia [-37 msec: F (1, 11) = 18.12, P < 0.001]. The group by test-phase interaction, however, did not reach significance [F(1, 22) = 1.81, P < 0.19]. None of the baseline euglycemia vs. posttreatment euglycemia comparisons reached significance [all F(1, 22) < 2.68, P > 0.115]; that is, RTs in euglycemia phase 1 and euglycemia phase 2 were not significantly different for both groups. There were no significant effects on error frequencies of hypoglycemic treatment [largest F(1, 22) = 3.08, P < 0.093; largest F(1, 11) = 4.37, P < 0.061] nor of the restoration of euglycemia [all F(1, 22) < l; F(1, 11) < 1] (Table 3Go).


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  		Table 3. Averaged mean RT, total error frequencies (Terr), false alarms (FA), onset latencies of the SN, and LRP

 
These results indicate that induction of hypoglycemia produced comparable effects on task performance in the healthy subjects and in the type-1 patients. The RTs returned to baseline level in the type-1 patients; but in the healthy subjects, the statistical analysis did not show clear effects of restoration, suggesting no restoration of RT.

ERPs: morphology. Figure 2Go shows the ERPs separately for each group and clamp condition, averaged across stimulus types, at the midline electrodes (Fz, Cz, Pz, and Oz). These ERPs show the typical sequences of positive and negative deflections usually seen in studies applying visual stimuli. Thus, in each clamp condition and group, there was an initial positivity, peaking at about 160 msec (P160, Fz, Cz, and Pz); an occipital negativity, peaking at about 180 msec (N180, Oz); and a large positive deflection, peaking at about 400 msec at all electrodes (this concerns the P3 or P300 component, as defined by its centroparietal maximum). Of importance for the present purposes is how these ERP deflections were modulated by the hypoglycemia treatment and the subsequent restoration of euglycemia.



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  		Figure 2. Averaged ERP for healthy young probands and the group with diabetes type-1.

 
The effects of hypoglycemia Hypoglycemia treatment produced a strong effect on the amplitude of the averaged ERP, consisting of a shift in amplitude with negative polarity. This negative shift is visible in Fig. 2Go at the Fz and Cz electrodes, as the upward deflection of the dashed waveform, relative to the other two waveforms, and occurring in the 200–500 msec interval after stimulus presentation. Note that, at the posterior Oz electrode, hypoglycemia (dashed waveform) produced a shift in amplitude, with positive polarity (downward deflection). This anterior-negative/posterior-positive shift was present in both type-1 subjects and healthy controls (significant test phase by scalp distribution interactions between 204 and 500 msec, P < 0.011 to P < 0.002). The scalp distribution of the ERPs, as a function of hypoglycemic treatment, did not differ between the healthy and the type-1 group (no significant group by test phase by scalp distribution interactions). Overall, the averaged ERP did not differ between the two groups in any interval [largest F(1, 22) < 3.6, P > 0.07], and the main group by test-phase interactions were also not significant [largest F(1, 22) < 2.5, P > 0.13]. The negative shift was highly significant at the Fz and Cz electrodes between 124 and 500 msec (P < 0.041 to P < 0.0005) and not significant at the Pz electrode. At the Oz electrode, the positive shift was significant between 124 and 300 msec (P < 0.036 to P < 0.004). In the healthy volunteers, the negative shift lasted from 204–500 msec at Fz and from 304–500 msec at Cz (P < 0.036 to P < 0.0005). In the type-1 group, the negative shift started and ended earlier, both at Fz and at Cz (124–400 msec, P < 0.042 to P < 0.013). The hypoglycemia-induced positive shift at Oz was significant in the healthy volunteers (204–300 msec; P < 0.014) but not in the type-1 group (P > 0.12). As Fig. 2Go also shows, the ERPs up to 160 msec were not different in the euglycemic and hypoglycemic states (P = 0.20). In summary, hypoglycemic treatment produced a large frontally maximal negative shift in the ERPs that started and ended later in the healthy volunteers than in the type-1 patients.

The effects of restoration of euglycemia Figure 2Go shows that, at the Fz and Cz electrodes, the amplitude of the ERPs in the restored euglycemia condition (dot-dash waveform) is more positive than in the other two conditions. This positive amplitude shift started at about 130 msec (124–400 msec, P < 0.043 to P < 0.0005). In the type-1 group, this shift occurred with a somewhat different scalp distribution than in the healthy group (304–400 msec, P < 0.025). In the healthy group, it was also present at the Pz electrode but not in the type-1 group (P < 0.029 and P < 0.014). In the healthy group, the positive shift was present between 124 and 300 msec at the frontal electrode (P < 0.017 and P < 0.05), and between 0 and 300 msec at the central electrode (P < 0.049 to P < 0.012). In the type-1 group, it reached significance in only two intervals (124–200 msec, P < 0.04 and P < 0.03; 304–400 msec, P < 0.015 and P < 0.015). These results indicate that the positivity visible in the restored euglycemia waveforms, compared with the baseline euglycemia waveforms, was most prominently present in the healthy group and of only minor significance in the type-1 group.

Difference potentials: SN and LRP Table 3Go shows the onset latencies of SN and LRP. These measures were determined by the first epoch of a series of at least five consecutive 8-msec epochs (40 msec), in which the ERP difference was significant, with P < 0.01. In both groups, hypoglycemic treatment delayed the SN and the LRP. After restoration of euglycemia, the onset latency of the SN returned to baseline level in both groups. The onset latency of the LRP in the type-1 group also returned to baseline level, but the onset latency of the LRP in the healthy group did not (Fig. 3Go). These results indicate that hypoglycemia delayed the selection of a stimulus on the basis of its color (SN) and also delayed selection of the motor response (LRP) on the basis of the letter shape in the healthy and type-1 groups to a comparable degree. After restoration of euglycemia, color selection occurred practically as early as in the initial baseline condition in both groups. Response selection, however, was still delayed in the healthy group but not in the type-1 group. This is in agreement with the behavioral results showing that the RTs of the type-1 group returned to baseline after restoration of euglycemia but not those of the control group.



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  		Figure 3. Grand average of SN and LRP for the control group and the group with type-1 diabetes. VEOG, Vertical electro-oculogram.

 

   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
The data presented show that the cognitive adaptation processes to hypoglycemia can be dissected into more elementary components, such as stimulus selection, response choice, and reaction speed both in patients with diabetes and healthy controls. In both groups, hypoglycemia did not influence precognitive processes (as reflected by the initial 160-msec ERPs), but it delayed stimulus selection (SN) and selective central motor activation (LRP). After restoration of euglycemia, RT and response selection returned to normal, although this was significant only in the patient group with diabetes and not significant in the controls. The counterregulatory hormones measured did not differ between the two groups, with respect to baseline values. The maximum increase was significantly lower for adrenaline and glucagon in the IDDM group. This finding is in accordance with numerous other studies reporting a typical time course of augmentation in the secretion of glucagon, catecholamines, ACTH, and cortisol (1). In addition, the semiquantitative symptoms score applied did not differ significantly between the healthy group and the group with diabetes. The typical autonomic and neuroglycopenic symptoms were noted in both groups at blood glucose levels of approximately 3 mmol/L. This is also in accordance with other studies demonstrating that cognitive disturbances arise at a threshold of 2.8 mmol/L (5, 40, 41, 42, 43). A direct effect of these cognitive impairments on hypoglycemia unawareness is still speculative but of great clinical relevance (44, 45).

In previous neurophysiological investigations, the elementary components of cognitive information processing could not be analyzed in great detail. By employing event-related brain potentials (ERPs), we could analyze stimulus selection and response selection as separate cognitive processes in the present study. Previous studies examined mainly averaged N2 and/or P300 waves (12, 46, 47, 48). The major problem of these techniques concerns the ambiguous assignment of averaged ERP waves to specific cognitive processes [e.g. Churchland (49)]. With the neurophysiological paradigm developed by our group and described in this paper, it seems possible to distinguish the effects on different cognitive processes, such as stimulus and response selection. This paradigm has also been proven to be useful in other areas of neurophysiological research (17, 50, 51).

Our data show that RT increases as a function of hypoglycemia, as described by several other groups (4, 11, 50). Blackman et al. (46) studied P300 latency and RT. Under hypoglycemic conditions, P300 latency and RT were found to be prolonged. Interestingly, this prolongation remained as plasma glucose returned to euglycemic levels. For example, an augmented RT after presentation of a visual stimulus was observed both in patients with diabetes and healthy controls during controlled hypoglycemia (52). In our study, the induction of hypoglycemia delayed all cognitive processes for which an electrophysiological measure was applied. With regard to perception, the selection of stimuli with the relevant color was delayed (SN); with regard to motor processing, the selection of the appropriate response hand was delayed, as was RT. This occurred both in the healthy and type-1 groups. This delay did not seem to be different between the two groups, and importantly, precognitive processes occurring before the SN were not affected by hypoglycemia. The induction of hypoglycemia further produced a notable and highly significant negative deflection in ERP amplitude with a frontal maximum. It can be argued that this deflection is related to a frontally located cognitive mechanism, which is more highly activated during hypoglycemia and responsible for executive control (coordination) over subordinate cognitive mechanisms involved in the modality-specific operations of color and letter shape selection and selective motor activation (17). Evidence based on psychometric methods (e.g. frontal function sensitive tests) that supports this argument was reported by Tamburrano et al. (53) and Jarjour et al. (54), in children with IDDM. Thus, the most important cognitive mechanism, that of executive control, seems to be affected by hypoglycemia. Interestingly, the negative amplitude shift started and ended earlier in the type-1 group (see Fig. 2Go, dashed waveform). This might represent a consequence of the occurrence of previous hypoglycemic episodes. MacLeod et al. (55) suggested that repetitive hypoglycemia may lead to regionally selective capillary recruitment as an adaptive response to maintain glucose supply during hypoglycemia in vulnerable areas of the brain cortex. This relationship between cerebral glucose supply and regional blood flow has been supported by other studies. Nevertheless, the findings are not unequivocal (56). Keymeulen et al. (57) described regional hypoperfusion in the frontotemporal cortex in patients with long-term diabetes. A different regional distribution pattern of cerebral blood flow during hypoglycemia was described in another study. Here, blood flow was increased in the superior frontal cortex and the right thalamus, and reduced blood flow was found in the right posterior cingulate cortex and the right putamen (58). A significantly higher cerebral blood flow was found in the right hemisphere, when compared with the left hemisphere, in some studies (54, 59). In addition to these data, one study has investigated cerebral glucose uptake. During hypoglycemia, patients with IDDM and low HbAlc had an increased glucose uptake in the brain (60). This suggested that a relative enhanced glucose uptake could preserve cerebral metabolism, possibly explaining the rapid restoration of cognitive function after a hypoglycemic event. On the other hand, this also represents a misbalance, because the enhanced cerebral glucose uptake impairs counterregulatory responses and hypoglycemia awareness. In summary, these and our previous findings (14) suggest that the frontal cortex is preferentially activated during acute hypoglycemia in normal men and well-controlled patients with type-1 diabetes. This seems to be particularly true for executive control functions, regulating perceptive and motor processes. Differences observed in the starting and ending times of the effects in the frontal cortex might represent the sequel of recurrent hypoglycemia in patients with diabetes. After restoration of euglycemia, color selection, response selection, and RT occurred as early as during the baseline performance in the type-1 group. In the healthy group, however, color selection, but not response and RT, returned to baseline level. This also suggests that type-1 patients, although not having suffered from severe hypoglycemic episodes in the previous three months, are able to cope better with the hypoglycemic state, possibly because they experienced less severe episodes more frequently. The generalization of these findings is probably high because we used a rather simple and elementary task that can be viewed as a basic task-structure on which many more complex tasks are built (e.g. Stroop color-word task, continuous performance task, trail-making task). However, because it is a simple task, it may mask stronger effects of hypoglycemia and stronger delay effects after restoration of euglycemia. More complex tasks demand more mental effort (higher glucose consumption), which might result in both a stimulus selection and a response selection delay after reinstalling euglycemia.

This analytical approach represents a novel paradigm for the investigation of distinct and early components of cognitive function during hypoglycemia. In contrast to existing methods, this approach seems to be a more sensitive means to detect alterations in cognitive processes. These data also suggest that this method can now be employed to study different groups of patients with diabetes (e.g. elderly type 2 patients) and hypoglycemia unawareness and may also be useful in studying the effects of insulin and its analogs on central nervous system functions.


   Footnotes
 
1 The MANOVA approach is to be preferred for psychophysiological measurements (34 61 ). The mixed-model univariate approach to repeated-measurement analysis (ANOVA) requires the measurement data to meet several assumptions, such as the sphericity assumption (62 ). In their prescriptive key-reviews, Vasey and Thayer (61 ) and O’Brien and Kaiser (34 ) concluded that: 1) psychophysiological data (e.g. ERP data) hardly ever meet these assumptions; 2) the MANOVA multivariate approach provides for analyses equivalent to ANOVA that do not require that these assumptions are met; 3) if the number of subjects exceeds the number of factor levels by more than a few, MANOVA yields more powerful tests than ANOVA; 4) if there are only two levels of a factor (as in our subtests), the sphericity assumptions always hold and the ANOVA and MANOVA approaches yield identical results (i.e. t-test equivalent results). Back

Received October 20, 1999.

Revised May 4, 2000.

Accepted May 4, 2000.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

  1. Cryer PE, Fisher IN, Shamoon H. 1994 Hypoglycemia. Diabetes Care. 17:734–755.[Medline]
  2. DCCT Research Group. 1993 The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin dependent diabetes mellitus. N Engl J Med. 329:977–986.[Abstract/Free Full Text]
  3. Cryer P, Gerich JE. 1985 Glucose counterregulation, hypoglycaemia and intensive insulin therapy in diabetes mellitus. N Engl J Med. 313:32–241.[Medline]
  4. Veneman T, Mitrakou A, Mokan M, Cryer P, Gerich I. 1993 Induction of hypoglycemia unawareness by asymptomatic nocturnal hypoglycemia. Diabetes. 42:1233–1237.[Abstract]
  5. Amiel SA, Gale E. 1993 Physiological response to hypoglycemia. Diabetes Care. 16:48–55.
  6. Cryer PE. 1997 Hierarchy of physiological responses to hypoglycemia: relevance to clinical hypoglycemia in type I (insulin dependent) diabetes mellitus. Horm Metab Res. 29:92–96.[Medline]
  7. Lingenfelser T, Ocerkamp D, Renn W et al. 1996 Insulin-associated modulation of neuroendocrine counterregulation, hypoglycemia-perception and cerebral function in insulin dependent diabetes mellitus: evidence for an intrinsic effect of insulin on the central nervous system. J Clin Endocrinol Metab. 8:1197–1205.
  8. Biessels GJ, Kappelle AC, Bravenboer B, Erkelens DW, Gispen WH. 1994 Cerebral function in diabetes mellitus. Diabetologia. 37:643–650.[Medline]
  9. Kalimo H, Olsson Y. 1980 Effects of severe hypoglycemia on the human brain. Acta Neurol Scand. 62:345–356.[Medline]
  10. Jones TW, McCarthy G, Tamborlane WV, et al. 1990 Mild hypoglycemia and impairment of brainstem and cortical evoked potentials in healthy subjects. Diabetes. 39:1550–1555.[Abstract]
  11. Draelos MT, Jacobson AM, Weinger K, et al. 1995 Cognitive function in patients with insulin-dependent diabetes mellitus during hyperglycemia and hypoglycemia. Am J Med. 98:135–144.[CrossRef][Medline]
  12. Münte TF, Berger D, Terkamp C, Schöfl C, Johannes S, Brabant G. 1995 Cognitive functioning in experimental hypoglycemia assessed with event-related potentials. Neuroreport. 6:1509–1512.[Medline]
  13. Ziegler D, Hübinger A, Mühlen H, Gries FA. 1995 Effects of previous glycemic control on the onset and magnitude of cognitive dysfunction during hypoglycemia in type-1 (insulin-dependent) diabetic patients. Diabetologia. 35:828–834.
  14. Donchin E, Coles MGH. 1988 Is the P300 component a manifestation of context updating? Behav Brain Sci. 11:355–372.
  15. Hernández-Collados A. 1995 Analysis of event-related potentials: a physiological interpretation. Neuropsychobiology. 32:52–58.[Medline]
  16. Hillyard SA, Picton TW. 1987 Electrophysiology of cognition. In: Plum F, ed. Handbook of physiology: sec. 1. The nervous system. Vol 5. Higher function of the nervous system, part 2. Baltimore: American Physiological Society; 519–584.
  17. Smid HGOM, Trümper B, Pottag G, et al. 1997 Differentiation of hypoglycemia-induced cognitive impairments: an electrophysiological approach. Brain. 120:1041–1056.[Abstract/Free Full Text]
  18. Guthrie D, Buchwald JS. 1991 Significance testing of difference potentials. Psychophysiology. 28:240–244.[Medline]
  19. Harter MR, Aine CJ. 1984 Brain mechanisms of visual selective attention. In: Parasuraman R, Davies DR, eds. Varieties of attention. Orlando: Academic Press; 293–321.
  20. Hillyard SA, Picton TW. 1987 Electrophysiology of cognition. In: Mountcastle VB, Plum F, Geiger SR, eds. Handbook of physiology. Sect 1, Vol 5, Pt 2. Bethesda, MD: American Physiological Society; 519–584.
  21. Mangun GR, Hillyard SA. 1995 Mechanisms and models of selective attention. In: Rugg MD, Coles MGH, eds. Electrophysiology of mind: event-related potentials and cognition. New York: Oxford University Press; 40–85.
  22. Smid HGOM, Böcker KBE, Van Touw DA, Mulder G, Brunia CHM. 1996 A psychophysiological investigation of the selection and use of partial stimulus information in response choice. J Exp Psychol Hum Percept Perform. 22:3–24.[Medline]
  23. Smid HGOM, Heinze H-J. 1997 An electrophysiological study of the selection of the color and shape of alphanumeric characters in response choice. Biol Psychol. 244:186–193.
  24. Näätänen R. 1992 Attention and brain function. Hillsdale, NJ: Lawrence Erlbaum.
  25. Coles MGH. 1989 Modern mind-brain reading: psychophysiology, physiology and cognition. Psychophysiology. 26:251–269.[Medline]
  26. Allport A. 1993 Attention and control: have we been asking the wrong questions? In: Meyer D, Kornblum S, eds. A critical review, attention and performance XIV. Cambridge, MA: MIT Press; 183–218.
  27. Treisman A, Gelade G. 1980 A feature integration theory of attention. Cogn Psychol. 12:97–136.[CrossRef][Medline]
  28. Smid HGOM, Jakob A, Heinze HJ. 1999 An event-related brain potential study of visual selective attention to conjunctions of color and shape. Psychophysiology. 36:264–280.[CrossRef][Medline]
  29. Coles MGH, Smid HGOM, Scheffers MK, Otten LJ. 1995 Mental chronometry and the study of human information processing. In: Rugg MD, Coles MGH, eds. Electrophysiology of mind. Oxford psychology series: 25. Oxford: Oxford University Press; 86–132.
  30. Ziegler D, Laux G, Dannehl K, et al. 1992 Assessment of cardiovascular autonomic function: age-related normal ranges and reproducibility of spectral analysis, vector analysis and standard tests of heart rate variation and blood pressure responses. Diabet Med. 9:166–175.[Medline]
  31. Heller SR, Cryer PE. 1991 Reduced neuroendocrine and symptomatic responses to subsequent hypoglycemia after 1 episode of hypoglycemia in nondiabetic humans. Diabetes. 40:223–226.[Abstract]
  32. Rugg MD, Doyle MC. 1994 Event-related potentials and stimulus repetition in direct and indirect test of memory. In: Heinze H-J, Münte TF,. Mangun GR, eds. Cognitive electrophysiology. Boston: Birkhauser; 124–148.
  33. Gratton G, Coles MG, Sirevaag EJ, Eriksen CW, Donchin E. 1988 Pre- and poststimulus activation of response channels: a psychophysiological analysis. J Exp Psychol Hum Percept Perform. 14:331–344.[CrossRef][Medline]
  34. O’Brien RG, Kaiser MK. 1985 MANOVA method for analyzing repeated measures designs: an extensive primer. Psychol Bull. 97:316–333.[CrossRef][Medline]
  35. De Jong R, Coles MGH, Logan GG, Gratton G. 1990 In search of the point of no return: the control of response processes. J Exp Psychol Hum Percept Perform. 16:164–182.[CrossRef][Medline]
  36. McCarthy G, Wood CC. 1985 Scalp distributions of event-related potentials: an ambiguity associated with analysis of variance models. Electroencephalogr Clin Neurophysiol. 62:203–208.[CrossRef][Medline]
  37. Smid HGOM, Lamain W, Hogeboom MM, Mulder G, Mulder LJM. 1991 Psychophysiological evidence for continuous information transmission between visual search and response processes. J Exp Psychol Hum Percept Perform. 17:696–714.[Medline]
  38. Smid HGOM, Mulder G, Mulder LJM, Brands GJ. 1992 A Psychophysiological study of the use of partial information in stimulus response translation. J Exp Psychol Hum Percept Perform. 18:1101–1119.[Medline]
  39. Wijers AA, Mulder G, Okita T, Mulder LJM, Scheffers MK. 1989 Attention to color: an analysis of selection, control search and motor activation, using event-related potentials. Psychophysiology. 26:89–109.[Medline]
  40. Fanelli C, Pampanelli S, Epifano L, et al. 1994 Relative roles of insulin and hypoglycemia on induction of neuroendocrine responses to symptoms of, and deterioration of cognitive function in hypoglycemia in male and female humans. Diabetologia. 37:797–807.[Medline]
  41. Widom B, Simonson DC. 1992 Intermittent hypoglycemia impairs glucose counterregulation. Diabetes. 41:1597–1602.[Abstract]
  42. Snorgaard O, Lassen LH, Rosenfalck AM, Binder C. 1991 Glycemic thresholds for hypoglycemic symptoms. Impairment of cognitive function, and release of counterregulatory hormones in subjects with functional hypoglycemia. J Intern Med. 229:343–350.[Medline]
  43. Holmes CS, Hayford JT, Gonzalez JL, Weydert JA. 1983 A survey of cognitive function at different glucose levels in diabetic persons. Diabetes Care. 6:180–185.[Abstract]
  44. Hepburn DA, Deary IJ, Frier BM. 1992 Classification of symptoms of hypoglycaemia in insulin-treated diabetic patients using factor analysis. Relationship to hypoglycaemia unawareness. Diabet Med. 9:70–75.[Medline]
  45. Lingenfelser T, Renn W, Sommerwerck U, et al. 1993 Compromised hormonal counterregulation, symptom awareness and neurophysiological function after recurrent short-term episodes of insulin-induced hypoglycemia in IDDM patients. Diabetes. 39:610–618.
  46. Blackman JD, Towle VL, Sturis J, Lewis GF, Spire J-P, Polonsky KS. 1992 Hypoglycemic thresholds for cognitive dysfunction in IDDM. Diabetes. 41:392–399.[Abstract]
  47. Ziegler D, Hübinger A, Gries FA. 1991 Changes in brainstem auditory-evoked potentials during insulin-induced hypoglycemia in type-1 diabetic patients. Diabet Med. 8:805–811.[Medline]
  48. Pramming S, Thorsteinsson B, Stigsby B, Binder C. 1988 Glycaemic threshold for changes in electroencephalograms during hypoglycaemia in patients with insulin dependent diabetes. Br Med J (Clin Res Ed). 296:665–667.
  49. Churchland PS. 1986 Neurophilosophy. Cambridge, MA: MIT Press.
  50. Lobmann R, Smid HGOM, Trümper B, et al. 1996 Distinctive changes of single components in cognitive functions during hypoglycemia in normal man. Diabetologia. 39(Suppl 1):829.
  51. Sailer M, Heinze HJ, Schoenfeld MA, Hauser U, Smid HGOM. 2000 Amantadine influences cognitive processing in patients with multiple sclerosis. Pharmacopsychiatry. 33:28–37.[Medline]
  52. Herold KC, Polonsky KS, Cohen RM, Levy J, Douglas F. 1985 Variable deterioration in cortical function during insulin-induced hypoglycemia. Diabetes. 34:677–685.[Abstract]
  53. Tamburrano G, Lala A, Locuratolo N, et al. 1988 Electroencephalography and evoked potentials during moderate hypoglycemia. J Clin Endocrinol Metab. 66:1301–1306.[Abstract]
  54. Jarjour IT, Ryan CM, Becker DJ. 1995 Regional cerebral blood flow during hypoglycaemia in children with IDDM. Diabetologia. 38:1090–1095.[Medline]
  55. MacLeod KM, Hepburn DA, Deary IJ, et al. 1994 Regional cerebral blood flow in IDDM patients: effect of diabetes and recurrent severe hypoglycemia. Diabetologia. 37:257–263.[Medline]
  56. Powers WJ, Hirsch IB, Cryer PE. 1996 Effect of stepped hypoglycemia on regional cerebral blood flow response to physiological brain activation. Am J Physiol. 270:H554–H559.
  57. Keymeulen B, de Metz K, Cluydts R, Bossuyt A, Somers G. 1996 Technetium-99 m hexamethylpropylene amine oxine single-photon emission tomography of regional cerebral blood flow in insulin-dependent diabetes. Eur J Nucl Med. 23:163–168.[Medline]
  58. MacLeod KM, Gold AE, Ebmeier KP, et al. 1996 The effects of acute hypoglycemia on relative cerebral blood flow distribution in patients with type I (insulin-dependent) diabetes and impaired hypoglycemia awareness. Metabolism. 44:974–980.
  59. Tallroth G, Ryding E, Agardh CD. 1992 Regional cerebra1 blood flow in normal man during insulin-induced hypoglycemia and in the recovery period following insulin infusion. Metabolism. 41:717–721.[Medline]
  60. Boyle PJ, Kempers SF, O’Connor AM, Nagy RJ. 1995 Brain glucose uptake and unawareness of hypoglycemia in patients with insulin-dependent diabetes mellitus. N Engl J Med. 333:1726–1731.[Abstract/Free Full Text]
  61. Vasey MW, Thayer JF. 1987 The continuing problem of false positives in repeated-measures ANOVA in psychophysiology: a multivariate solution. Psychophysiology. 24:479–486.
  62. Winer BJ. 1971 Statistical principles in experimental design. London: McGraw-Hill.



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