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Diabetes Care 26:168-171, 2003
© 2003 by the American Diabetes Association, Inc.

Pathophysiology/Complications
Original Article

L-Arginine: An Ultradian-Regulated Substrate Coupled With Insulin Oscillations in Healthy Volunteers

Adrien Schaefer, PHD, Chantal Simon, PHD, Antoine Viola, François Piquard, PHD, Bernard Geny, PHD and Gabrielle Brandenberger, PHD

From Laboratoire des Régulations Physiologiques et des Rythmes Biologiques chez l’Homme, Strasbourg Cedex, France


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESEARCH DESIGN AND METHODS
 RESULTS
 CONCLUSIONS
 References
 
OBJECTIVE—Coupled oscillations of 50–110 min in insulin and glucose have been found previously in healthy men under continuous enteral nutrition. Because L-arginine induces insulin release as glucose does, we tested the hypothesis that L-arginine can also display such an ultradian rhythm.

RESEARCH DESIGN AND METHODS—Seven healthy male subjects participated in one experimental night during which blood was sampled every 10 min from 2300 to 0700. Plasma glucose, C-peptide, and L-arginine levels were measured simultaneously. The insulin secretion rate (ISR) was calculated from plasma C-peptide levels by a deconvolution procedure.

RESULTS—Plasma glucose followed the recognizable profiles, with oscillations closely linked to similar changes in the ISR. Pulse analysis of L-arginine profiles revealed significant oscillations linked to glucose and ISR oscillations, with the highest cross-correlation coefficients at time lag 0 ranging from 0.380 to 0.680 for glucose and L-arginine and from 0.444 to 0.726 for ISR and L-arginine (P < 0.01). The mean period of L-arginine oscillations was 77.2 ± 6.2 min, and their mean amplitude was 19.9 ± 1.7%, similar to that of glucose (17.0 ± 1.9%), when expressed as the percentage of mean overnight levels.

CONCLUSIONS—This newly discovered ultradian rhythm of L-arginine and its coupling with glucose and ISR oscillations sheds new light on the regulation of L-arginine, the substrate of numerous metabolic pathways, including nitric oxide synthesis. These oscillations may be of significance in conditions of hyperinsulinemia or abnormal glucose tolerance.

Abbreviations: CV, coefficient of variation • ISR, insulin secretion rate • NO, nitric oxide


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESEARCH DESIGN AND METHODS
 RESULTS
 CONCLUSIONS
 References
 
A number of studies have demonstrated that L-arginine triggers insulin release in the presence of D-glucose (1–4). The underlying mechanisms are not fully elucidated. The insulinotropic action of L-arginine has been ascribed to transporter-mediated accumulation of this cationic amino acid inside the ß-cells, with resulting depolarization of the plasma membrane (2,3). One other putative mechanism is the formation of nitric oxide (NO) from L-arginine by the action of NO synthase in pancreatic ß-cells (4–7). It has been reported that systemic infusion of L-arginine induces vasodilation, inhibits platelet aggregation, and reduces blood viscosity and that these effects are mediated by NO release (8,9). Although there might be a dietary need for L-arginine, it is produced endogenously from the turnover of the urea cycle within the liver and via conversion of citrulline to arginine in the renal proximal tubule (10,11).

In normal humans, the insulin secretion rate (ISR) presents an oscillatory pattern characterized by slow ultradian oscillations with a period of 50–110 min coexisting with rapid small fluctuations of 8–15 min. These ISR oscillations are closely associated with similar oscillations in plasma glucose and are best seen in situations of insulin stimulation, such as during continuous enteral nutrition (12), after meal ingestion (13), and during intravenous glucose infusion (14). Abnormalities in their pattern have been observed in type 2 diabetes (15,16) and in cases of impaired glucose tolerance (17,18). In these situations, the ISR oscillations are less regular. They then have a reduced amplitude, and the tight coupling with glucose oscillations is altered. Because both arginine and glucose stimulate insulin secretion from the pancreatic ß-cells, we have tested in healthy volunteers the hypothesis that these substrates can display a common ultradian rhythm.


   RESEARCH DESIGN AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS
RESULTS
 CONCLUSIONS
 References
 
Subjects
Seven healthy male volunteers aged 20–26 years participated in the experiment after giving their written informed consent. The study was approved by the local ethics committee. All subjects had normal weight, with an average BMI of 22.4 ± 0.7 kg/m2 and normal routines of work, meals, and sleep. They were selected after medical examination and completing questionnaires on their usual sleep-wake cycle, and work and eating habits. Subjects with a personal history of obesity or sleep disorders, smokers, subjects with underlying signs of disease, and subjects taking medication were excluded from the study.

Procedure
The experiments were performed in soundproof air-conditioned sleep chambers, which communicated with an adjoining room where blood samples were collected via a catheter inserted in an antecubital vein and kept patent with heparinized solution. Because plasma glucose and insulin oscillations are amplified during sleep (19), blood was sampled during one experimental night, which was preceded by a habituation night. Subjects were assessed under constant conditions: bed rest and continuous enteral nutrition 4 h before blood sampling (nutrition: Sondalis Iso [Nestlé Clinical Nutrition, Marne-la-Vallée, France]; 50% carbohydrates, 35% fat, and 15% protein; 378 kJ/h).

Blood sampling and assays
Blood was sampled every 10 min from 2300 to 0700 and placed in ethylenediamine tetraacetate-K2-treated tubes (1 mg/ml) using a peristaltic pump. Samples were immediately centrifuged at 4°C, and plasma was stored at -25°C until assay. Plasma glucose levels were measured using a glucose oxidase method (BioMérieux, Marcy-l’Etoile, France), with an intra-assay coefficient of variation (CV) of <1.3%. Plasma C-peptide was measured by radioimmunoassay (Diagnostic Systems Laboratories, Webster, TX) with a detection limit of 0.01 ng/ml. The mean intra-assay CV was 4.3% for concentrations between 0.01 and 4.4 ng/ml and 7.9% for concentrations >4.4 ng/ml. L-Arginine was measured by high-performance liquid chromatography. The intra-assay CV was 5.0% for values between 90 and 140 µmol/l. All samples from one individual were analyzed in a single series.

Data analysis
Determination of the ISR.
For each subject, the ISR during each 10-min interval was mathematically derived from plasma C-peptide levels using a two-compartment model. This deconvolution method is based on the fact that insulin and C-peptide are cosecreted in equimolar amounts and that C-peptide, unlike insulin, is not significantly extracted by the liver and has a constant metabolism throughout the day. The kinetic parameters for C-peptide distribution and metabolism were obtained from published data adjusted for sex, age, and body surface area (20). No assumption was made for the shape of the secretory pulses. Statistical error propagation of the uncertainty in the C-peptide measurements was taken into account in the determination of the secretory profiles, and the SD associated with each estimated secretory rate was calculated.

Ultradian rhythm analysis.
The individual nocturnal plasma glucose, ISR, and L-arginine profiles were analyzed for pulse identification using the computer program ULTRA (21). The threshold for pulse detection was set at twice the intra-assay CV in the relevant range of concentration for glucose and L-arginine and at thrice the SD associated with the estimated ISR. For each significant pulse, the time of occurrence, the increment and decrement, and the total duration were determined.

The temporal link between the overnight profiles of L-arginine, glucose, and ISR was quantified using cross-correlation analysis. The association between individual oscillations of L-arginine and glucose and ISR oscillations was tested by lagged coincidence analysis, based on a model of conditional probability derived from two binomial distributions leading to a hypergeometric probability density function, as proposed by Veldhuis et al. (22). Two pulses were considered to be concomitant if their peaks occurred within ±10 min of each other.

Mean ISR oscillations were calculated by aligning significant individual pulses by their maximum and averaging them for each subject. A mean pulse was then obtained for the group of seven subjects.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS
 RESULTS
CONCLUSIONS
 References
 
Figure 1 illustrates the overnight profiles of ISR, glucose, and L-arginine in one representative subject. Figure 2 gives the mean oscillations aligned by the maximum of ISR. ISR and glucose followed the recognizable profiles (19), with oscillations of ISR having a period of 75.2 ± 6.7 min. Their mean number was 5.6 ± 0.4 and their mean amplitude was 123.9 ± 12.4%, expressed as a percentage of the mean overnight levels. Plasma glucose showed similar oscillations with a mean period of 75.0 ± 6.6 min. An average of 5.7 ± 0.5 glucose oscillations was detected throughout the night, with a mean amplitude of 17.0 ± 1.9%. ISR and glucose oscillations were closely linked, with the highest cross-correlation coefficients at time lag 0 ranging from 0.767 to 0.918 (P < 0.001).



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  		Figure 1— Ultradian oscillations in ISR, plasma glucose, and L-arginine in one representative subject studied overnight during continuous enteral nutrition with a 10-min blood sampling interval.

 


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  		Figure 2— Mean (±SE) oscillations in ISR, plasma glucose, and L-arginine in seven subjects. L-Arginine and glucose were aligned by the maximum of ISR oscillations.

 
Pulse analysis of L-arginine profiles revealed an average of 5.1 ± 0.4 significant oscillations during the night. Their mean period was 77.2 ± 6.2 min, and their mean amplitude was 19.9 ± 1.7%, similar to that of glucose. These oscillations were in phase with ISR and glucose. The highest cross-correlation coefficients were at time lag 0 and ranged from 0.380 to 0.680 for glucose and L-arginine and from 0.444 to 0.726 for ISR and L-arginine. Coincidence analysis revealed that, on average, 85.7% of ISR and 78.4% of the glucose oscillations were associated with L-arginine oscillations within a time lag of 0 ± 10 min (P < 0.001).


   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS
 RESULTS
 CONCLUSIONS
References
 
This newly discovered ultradian rhythm of L-arginine and its coupling with ISR and glucose oscillations lead to an unconventional mode of thinking on the regulation of L-arginine, a nonessential amino acid for healthy subjects. In our experimental conditions, confusing or masking effects of repeated food ingestion were avoided by enteral nutrition, which provides a constant L-arginine intake. Any influence of external factors was eliminated because the subjects were asleep, which amplified glucose and insulin oscillations (19). An habituation session minimized the stress effect due to laboratory procedures. Under these constant conditions, the ultradian rhythm of L-arginine was clearly apparent.

The concomitant rhythmic increases of L-arginine, glucose, and ISR may reflect the stimulatory action of both substrates on insulin secretion. Over the past years, there has been considerable interest in the involvement of L-arginine in the release of insulin through NO pathways (4–7,23), which may explain the hemodynamic and vascular effects of insulin (8,9). The coupling of L-arginine, insulin, and glucose in a rhythmic way sheds new light on this complex interplay that might be significant in pathological states. In type 2 diabetic patients, in whom an altered ultradian insulin rhythm has been reported (15,16) together with an impaired L-arginine uptake in platelets (24), a disordered ultradian organization of L-arginine might occur, and such an imbalance could be implicated in pancreatic ß-cell dysregulation. Also, one can inquire whether such coupled ultradian rhythms persist in heart failure patients in whom insulin resistance and impaired NO synthesis have been described (25,26).

The numerous therapeutic properties of L-arginine, namely its anti-asthenic and detoxifying activities, have led to the use of this amino acid as a dietary supplement consumed by millions of people. L-Arginine also exerts favorable effects in the prevention and treatment of endothelial damage and the restoration of endothelial function in patients with cardiovascular risk factors or severe chronic cardiovascular disorders. L-Arginine administration is likely to represent a potentially novel therapeutic strategy (27,28). The possibility that L-arginine acts synergistically in mediating insulin release has been poorly investigated.

In conclusion, the results presented here suggest that L-arginine and glucose share not only their insulinotropic action but also their ultradian regulatory mechanisms, which raises the question of whether there is common insulin control of both substrates. It may be conceivable that L-arginine, a substrate of numerous pathways, protein synthesis, ureagenesis, and NO release, is submitted to mechanisms controlling its cellular availability, which have already been described for glucose. Therefore, the present findings of coupled oscillations of arginine and glucose suggest that further research on common regulatory pathways for these substrates is warranted.


   Acknowledgments
 
We are indebted to M. Siméoni and F. Goupilleau for experimental assistance and radioimmunoassay analysis. We thank I. Georg-Bentz for high-performance liquid chromatography analysis and S. Zahn for data analysis.


   Footnotes
 
Address correspondence and reprint requests to Adrien Schaefer, Institut de Physiologie, Laboratoire des Régulations Physiologiques et des Rythmes Biologiques chez l’Homme, 4 rue Kirschleger, 67085 Strasbourg Cedex, France. E-mail: adrien.schaefer{at}physio-ulp.u-strasbg.fr.

Received for publication 27 June 2002 and accepted in revised form 2 October 2002.

A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.


   References
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS
 RESULTS
 CONCLUSIONS
 References
 

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