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J Appl Physiol 103: 340-346, 2007. First published April 5, 2007; doi:10.1152/japplphysiol.01321.2006
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Utility of circulating IGF-I as a biomarker for assessing body composition changes in men during periods of high physical activity superimposed upon energy and sleep restriction

Bradley C. Nindl,1 Joseph A. Alemany,1 Mark D. Kellogg,2 Jennifer Rood,3 Steven A. Allison,1 Andrew J. Young,2 and Scott J. Montain2

1Military Performance Division and 2Military Nutrition Division, US Army Research Institute of Environmental Medicine, Natick, Massachusetts; and 3Clinical Research Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana

Submitted 21 November 2006 ; accepted in final form 4 April 2007


   ABSTRACT
TOP
 ABSTRACT
METHODS
 RESULTS
 DISCUSSION
 GRANTS
 DISCLAIMER
 ACKNOWLEDGMENTS
 REFERENCES
 
Insulin-like growth factor (IGF)-I is a biomarker that may have greater utility than other conventional nutritional biomarkers in assessing nutritional, health, and fitness status. We hypothesized that the IGF-I system would directionally track a short-term energy deficit and would be more related to changes in body composition than other nutritional biomarkers. Thirty-five healthy men (24 ± 0.3 yr) underwent 8 days of exercise and energy imbalance. Total and free IGF-I, IGF binding proteins-1, -2, and -3, the acid labile subunit, transferrin, ferritin, retinol binding protein, prealbumin, testosterone, triiodothyronine, thyroxine, and leptin responses were measured. Dual-energy X-ray absorptiometry assessed changes in body mass and composition. Repeated-measures ANOVA, correlation analysis, and receiver operator characteristic curves were used for statistical analyses (P ≤ 0.05). Body mass (–3.8%), fat-free mass (–2.2%), and fat mass (–12.9%) all decreased. Total and free IGF-I, IGF binding protein-3, and the acid labile subunit and prealbumin, but not transferrin, retinol-binding protein, and ferritin, directionally tracked the energy deficit and losses in body composition. The correlation (r = 0.43) between changes in free IGF-I and body and fat-free mass was the only significant association observed. Receiver operator characteristic curve analysis revealed that a baseline value < 1.67 for the molar volume ratio of IGF-I to acid labile subunit had an area under the curve of 0.745 and was a significant discriminator for those subjects losing >5% body mass. The IGF-I system is an important adjunct in the overall assessment of adaptation to stress imposed by high levels of physical activity superimposed on energy and sleep restriction and is more closely associated with losses in body mass and fat-free mass than other conventional nutritional biomarkers.

somatotrophic hormones; military operation stress; nutritional biomarkers; soldier

CIRCULATING INSULIN-LIKE GROWTH factor (IGF)-I is a liver-derived 7.6-kDa polypeptide that plays a pivotal role in mediating metabolic, mitogenic, and anabolic cellular responses during altered energy states (1–3, 28). Due to its short half-life (2–4 h), specific and sensitive decline during negative energy balance (12, 15, 24, 30) and protein-energy malnutrition (6, 11, 13, 15, 27), somatotrophic influences on body composition, and role in modulating the growth-promoting effect of physical activity, circulating IGF-I is receiving increasing attention as a biomarker reflecting metabolic status (6, 15, 18, 19, 28).

Traditional evaluation of nutritional/metabolic status utilizes a global assessment of parameters that include crude anthropometric indexes (i.e., circumferences) and the assay of serum proteins [i.e., albumin, transferrin, ferritin, retinal binding protein (BP)]. While these conventional nutritional biomarkers have their utility, they are limited by their relatively long half-lives (2–20 days) and can be affected by nonnutritional factors (18, 25). Unlike many hormones used as biomarkers of nutritional/metabolic status, such as growth hormone (GH), IGF-I concentrations exhibit minimal circadian variability through the day (26); therefore, single time samples are valid indicators of IGF-I status.

The clinical relevance and functional significance of monitoring IGF-I resides in its essential role in stimulating protein synthesis and maintaining muscle mass (14). IGF-I is temporally correlated with entry into positive nitrogen balance during nutritional repletion in malnourished patients and is more strongly correlated with nitrogen balance in critically ill patients than either albumin or transferrin (11). While IGF-I appears to be a promising addition in the assessment of metabolic status, more information is required linking the predictive ability to changes in the circulating IGF-I system to measurable biological outcomes (i.e., changes in body composition) before definitive statements can be made regarding its utility as a biomarker.

The bioavailability of IGF-I is governed by a great deal of regulatory complexity, involving at least six different BPs (IGFBPs) that differently modulate (i.e., either stimulate or inhibit) IGF-I bioavailability and the proportion of IGF-I that is unbound (i.e., free IGF-I) (3, 14, 20, 21, 23, 25–29). The efficacy of monitoring free IGF-I, IGFBP, or the molar volume ratios of IGF-I to IGFBPs in assessing metabolic status has not been examined. Equally important, studies have not explored the efficacy of IGF-I and its associated BPs as predictors of subjects who may be most susceptible to losses in body mass or fat-free mass during short-term energy deficits. Such information would be useful in terms of identifying individuals in need of recovery and intervention strategies.

The present study sought to broaden our understanding of IGF-I physiology within the context of short-term metabolic stress (i.e., an 8-day energy deficit characterized by high levels of physical activity and restricted caloric intake) in young, healthy men. We examined the hypotheses that the IGF-I system (IGF-I and its associated IGFBPs) would be more reflective of the metabolic stress imposed by an 8-day energy deficit than other anabolic hormones (i.e., testosterone), adipokines (i.e., leptin), and conventional nutritional status biomarkers, and that a relationship would exist between changes in the IGF-I system and changes in body composition. Additionally, receiver operator characteristic (ROC) curves were employed with all baseline hormonal metabolic and body composition variables in an attempt to identify a priori any initial baseline characteristics that were predictive of individuals most at risk for losses in body mass and fat-free mass.


   METHODS
TOP
 ABSTRACT
 METHODS
RESULTS
 DISCUSSION
 GRANTS
 DISCLAIMER
 ACKNOWLEDGMENTS
 REFERENCES
 
Subjects.   Thirty-four male Marine infantry officer candidates (mean ± SD: 82.3 ± 7.8 kg, 181.2 ± 6.3 cm, 15.9 ± 3.4% body fat), who participated in an Infantry Officer Course 8-day field exercise characterized by near-continuous physical activity, restricted caloric intake, and disrupted sleep, volunteered to participate in this study. The Marines were briefed on the study purpose, methods, and procedures, both orally and in writing, and voluntary written, informed consent was obtained before participation. The study had received scientific and human use approval by institutional review boards before the volunteers were briefed. The research was conducted in adherence with the provisions of 45 CFR Part 46 and Army Regulation 70–25.

Experimental paradigm.   A repeated-measures (3 time points: Pre, Mid, and Post) research design was employed to monitor responses in outcome variables (IGF-I system proteins, nutritional and metabolic biomarkers, and body composition). Subjects were exposed to 8 days of military operational stress. During this period, continuous physical activity, negative energy balance [energy intake: 1,079 ± 543 kcal/day from meals ready to eat (MRE) plus 460 kcal from a supplemental sports bar and drink], and energy expenditure (3,862 ± 200 kcal/day), limited periods of sleep/disrupted sleep (~4 h/day), and cognitive/mental stress were all deliberate stressors. These stressors were intended to test and develop the leadership skills of Marine Corps Infantry officers. High-energy expenditure during these 8 days of military operational stress was measured by doubly labeled water methods described previously (7) and achieved by having the subjects involved with nearly continuous military operations and maneuvers. The 8 days were partitioned into three phases: dismounted infantry training (4 days), mechanized infantry training (3 days), and amphibious assault training (1 day) activities. Subjects consumed daily one MRE each. Along with MREs, subjects were supplied with a chocolate beverage (180 kcal/drink) and a chocolate sports bar (280 kcal) each day. All food was supplied from the Combat Feeding Program (Soldier Biological and Chemical Command, Natick, MA). The data presented herein is from a larger study that evaluated the effects of 1) protein content on hormonal, body composition, and physical performance measures; and 2) efficacy of using a filter paper blood spot assay to measure IGF-I (24). For the purposes of evaluating the utility of the IGF-I system as a biomarker, data from all subjects were pooled for analysis in this report.

Blood collection.   Morning (between 0600 and 0700) venous blood samples were collected from the antecubital vein after an overnight fast on days 0 (Pre), 4 (Mid), and 8 (Post). Blood samples were taken at approximately the same time on all days for all subjects to eliminate fluctuations in circulating analyte concentration due to circadian rhythm. Participants were instructed to be seated for ~10 min before phlebotomy was performed. Blood was allowed to clot on ice and was centrifuged for 30 min at 3,000 g at 4°C. After centrifugation, serum was aliquoted into appropriate storage vials, flash frozen in liquid nitrogen, and stored at –80°C for later analysis. To eliminate interassay variances, all samples were assayed within the same batch.

IGF-I system analytes.   IGF-I system analytes were measured using commercially available kits without modifications (Diagnostic Systems Laboratories, Webster, TX). Circulating concentrations of total IGF-I, free IGF-I, IGFBP-1, and IGFBP-3 were quantified using a two-site immunoradiometric assay. Total IGF-I required an acid-ethanol extraction procedure to extract IGFBPs. The sensitivity for total IGF-I, free IGF-I, IGFBP-1, and IGFBP-3 was 0.3, 0.004, 0.1, and 0.02 nmol/l, respectively. Intra-assay variances for total IGF-I, free IGF-I, IGFBP-1, and IGFBP-3 were all less than ~5%. IGFBP-2 was measured using a competitive RIA. Sensitivity and intra-assay variance for IGFBP-2 was 0.02 nmol/l and <12%, respectively. Acid labile subunit (ALS) was quantified using a two-site ELISA. Sensitivity was 0.1 nmol/l. Intra- and interassay variances were 5.1 and 10.6%, respectively. All analytes quantified by immunoradiometric assay and RIA were counted on a gamma counter using 1-min counts (Cobra gamma counter, Packard Instruments, Downers Groove, IL).

IGF-I system molar volume ratios.   Molar volume ratios were calculated using the formulas described elsewhere (21). Briefly, the following formula was used: protein 1 (M), [(sample volume; µl)/analyte concentration] x (1 x 10–12)/(mass of hormone; kDa); protein 2 (M), [(sample volume; µl)/analyte concentration] x (1 x 10–12)/(mass of hormone; kDa). Molar ratio was calculated as protein 1 (M)/protein 2 (M). The sample volume was total IGF-I, 20 µl; ALS, 20 µl; and 10 µl for IGFBP-1, -2, and -3. Molecular masses that were used in the formula were IGF-I, 7.5 kDa; ALS, 80 kDa; IGFBP-1, 25.3 kDa; IGFBP-2, 31.4 kDa; and 28.7 kDa for IGFBP-3. Concentrations of analytes are expressed in nanograms per milliliter.

Nutritional and metabolic biomarker analytes.   Serum glucose concentrations (Bio-Chem Laboratory Systems, Lakewood, NJ), nonesterified fatty acids (Wako Chemicals, Richmond, VA), glycerol (Sigma Diagnostics, St. Louis, MO), and beta-hydroxybutyrate (Sigma Diagnostics) were measured according to manufacturer's instructions on an ATAC 8000 (Elan Diagnostics, Smithfield, RI). Ferritin and transferrin were determined with solid-phase ELISAs (Bethyl Laboratories, Montgomery, TX; American Laboratory Products, Windham, NH). Prealbumin concentrations were determined using a nephelometric method (Minineph, The Binding Site, San Diego, CA). The intra-assay variances were all <7%.

Ancillary biomarkers.   Serum total testosterone (TT) was measured using a competitive RIA (Diagnostic Systems Laboratories, Webster, TX). The sensitivity and intra-assay variance for TT was 0.3 nmol/l and 2%, respectively. Total triiodothyronine (T3) and total thyroxine (T4) was quantified using immunoassay with chemiluminescent detection. Leptin was quantified using an ELISA (Diagnostic Systems Laboratories, Webster, TX). The sensitivity for the leptin assay was 0.05 ng/ml with intra- and interassay variances of 7.9 and 8.8%, respectively. Serum glucose was assessed using the glucose oxidase method on the Beckman Synchron CX7 analyzer (Beckman, Brea, CA).

Dual-energy X-ray absorptiometry.   Body composition was assessed by whole body dual-energy X-ray absorptiometry. Total body estimates of body fat (%), bone mineral density, and bodily content of bone, percent body fat, and nonbone lean tissue were determined using manufacturer-described procedures and supplied algorithms (Total Body Analysis, version 3.6, Lunar, Madison, WI). Precision of this measurement is better than 0.5% body fat. Scanning was in 1-cm slices from head-to-toe using the 20-min scanning speed. The subjects had dual-energy X-ray absorptiometry scans performed before and the morning after the study.

Statistical analyses.   Within-group repeated-measures ANOVA was used to detect significant mean differences for the IGF-I system, nutritional biomarkers, ancillary biomarkers, and dietary intake values with time (either days 0, 4, and 8 for the biochemical measures or days 1–8 for the dietary intake values) as the repeated measure. When appropriate, Tukey's post hoc was utilized to determine where significant differences occurred from ANOVA. Changes in body composition variables (day 0 vs. day 8) were analyzed using a paired two-sided Student's t-test (dependent samples). Pearson product-moment correlations (r) were utilized to observe associations between serum analytes and body composition.

An additional objective of the study was to evaluate whether any baseline characteristics could be used for prognostic purposes in predicting those subjects most susceptible to losses in body mass, fat-free mass, and fat mass during an energy deficit. This was accomplished by evaluation of the sensitivity and specificity of selected baseline characteristics (all hormonal and metabolic and body composition variables) via ROCs (9, 13). To determine the predictive ability of the baseline hormonal and metabolic body composition variables in identifying subjects experiencing substantial losses in body mass, fat-free mass, and fat mass, an independent sample t-test was tabulated for those variables based on a criterion of >5% loss (n = 8) for body mass, >4% loss for fat-free mass (n = 73), and >15% loss for fat mass (n = 10). These cut scores were selected as natural cut points after visual inspection of the frequency distributions and supported by a review article, suggesting that military physical performance is compromised when weight loss exceeds 5% (9). Based on the t-test results, those variables with a significance of P < 0.05 were then retained as potential prediction variables. For these variables that were significantly different, sensitivity and (1 – specificity) values were calculated for possible cutoff points and then plotted as ROC curves. From each ROC curve, the point approximating the upper left-hand corner was selected as corresponding with the best possible threshold value for a positive test. Sensitivity, specificity, and likelihood ratios were also calculated. The positive likelihood was calculated as sensitivity (1 – specificity) and indicates the degree to which the posttest probability of a negative outcome will be increased, given a positive result on the baseline test. Similarly, the negative likelihood ratio was calculated as (1 – sensitivity)/specificity, which indicates the degree to which the posttest probability of a negative outcome will be reduced, given a negative result on the baseline test (9, 13).

Analyses were performed on Statistica for Windows (Statsoft, Tulsa, OK; version 6.1) and SPSS (version 13.0, Chicago, IL) statistical software packages for ROC analysis. A significance level was set at P ≤ 0.05. Unless otherwise noted, all data are presented as means ± SD.


   RESULTS
TOP
 ABSTRACT
 METHODS
 RESULTS
DISCUSSION
 GRANTS
 DISCLAIMER
 ACKNOWLEDGMENTS
 REFERENCES
 
The mean daily caloric and macronutrient intake values from the MREs over the course of the study are presented in Table 1 (daily mean values for the macronutrient consumption). The energy deficit due to the low caloric intake and high levels of physical activity caused significant losses in body mass (Pre: 82.3 ± 7.8 kg; Post: 79.1 ± 7.3 kg; 3.8% decrease; see Table 2), fat-free mass (69.l ± 6.6 kg; Post: 67.6 ± 6.4 kg; 2.2% decrease), and fat mass (Pre: 13.2 ± 3.4 kg; Post: 11.5 ± 3.2 kg; 22.9% decrease). The total body and regional body composition results are all presented in Table 2. The average daily energy expenditure was 3,862 ± 200 kcal/day.


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  		Table 1. Mean daily caloric and macronutrient values from the meals ready to eat over the 8 days of military operational stress

 

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  		Table 2. DEXA-assessed changes in body composition

 
Responses for the IGF-I system components, nutritional biomarkers, and other ancillary metabolic variables are depicted in Table 3. Total IGF-I, free IGF-I, ALS, and IGFBP-3 all showed a progressive decline over the course of the study (Pre < Mid < Post). IGFBP-I and IGFBP-2 significantly increased from Pre to Mid, showing no further change at the Post time point (Pre < Mid = Post).


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  		Table 3. IGF-I components, nutritional biomarkers, and ancillary biomarkers during 8 days of military operational stress

 
For the nutritional biomarkers, ferritin (Pre < Post < Mid), transferrin (Pre = Post < Mid), prealbumin (Pre > Mid > Post), and retinol BP (Pre = Mid > Post) all showed changes over the course of the study. For the ancillary hormonal and metabolic biomarkers, TT (Pre > Mid = Post), total T3 (Pre = Mid > Post), total T4 (Pre < Mid < Post), leptin (Pre > Mid = Post), glucose (Pre > Mid = Post), nonesterified fatty acids (Pre < Post < Mid), and beta-hydroxybutyrate (Pre < Post < Mid) also showed significant time effects.

Similarly, changes in the IGF-I systems molar volume ratios are displayed in Table 4. Total IGF-I/IGFBP-2 (Pre > Mid = Post), total IGF-I/IGFBP-3 (Pre > Mid > Post), total IGF-I/ALS (Pre > Mid = Post), IGFBP-3/IGFBP-2 (Pre > Mid = Post), ALS/IGFBP-2 (Pre > Mid = Post), and ALS/IGFBP-3 (Pre = Mid > Post) all showed significant time effects.


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  		Table 4. Molar volume ratio comparison between IGF-I and selected IGF-I components during 8 days of military operational stress

 
Figure 1 illustrates the relative change from baseline for the ternary components of the IGF-I system (Fig. 1A) and for the conventional nutritional biomarkers (Fig. 1B) at the Mid and Post time points. It is evident that the ternary components of the IGF-I responded directionally with the progression of the energy deficit and resultant loss in body mass, while, in comparison, the conventional nutritional biomarkers display an inconsistent directional response.


Figure 1
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  		Fig. 1. A: responses of the ternary components of the insulin-like growth factor (IGF)-I system as a percentage of baseline [i.e., total IGF-I, free IGF-I, IGF binding protein (BP)-3, and the acid labile subunit (ALS)]. B: responses of the conventional nutritional biomarkers as a percentage of baseline. RBP, retinol binding protein; Pre, day 0 time period; Mid, day 4 time period; Post, day 8 time period.

 
Correlation analyses of the percent change between IGF-I components and body composition (Fig. 2) revealed that the only significant relationships among all of the blood measures monitored in the study were between the percent change in free IGF-I and the percent change in both body mass (r = 0.43; see Fig. 2A) and fat-free mass (r = 0.43; see Fig. 2B). The percent change in free IGF-I explained 20% of the variance of the percent change in both body mass and fat-free mass.


Figure 2
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  		Fig. 2. A: scatterplot of the relationship between the percentage change in body mass vs. the percentage change in free IGF-I. B: scatterplot of the relationship between the percentage change in fat-free mass vs. the percentage change in free IGF-I.

 
The ROC analysis yielded the baseline value for the molar volume ratio of IGF-I/ALS and appears to be a useful discriminator between those individuals losing more or less than 5% of their body mass (n = 8). Figure 3A shows the ROC curve and the cut-point for IGF-I/ALS (a potential surrogate measure for free IGF-I). This analysis indicated that those subjects with a baseline value less than the ROC-derived cut score of 1.67 would have a posttest probability of 50% (positive likelihood = 3.25, 95% confidence interval: 1.45–7.29) for having a loss of at least 5% of their body mass. This is a large and meaningful shift in probability from the best guess one would be able to make at baseline (23.5%) based on prevalence only. Furthermore, those subjects with a baseline total IGF-I/ALS value of ≤1.67 would have a posttest probability of only 9.1% (negative likelihood ratio = 0–33, 95% confidence interval 0.10–1.01; see Table 3) for having a loss of at least 5% of their body mass. The area under the curve for the ROC curve was 0.745 (P = 0.038).


Figure 3
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  		Fig. 3. A: receiver operator characteristic (ROC) curve for the molar volume ratio of total IGF-I to ALS. B: pretest probability, likelihood ratio, and the posttest probability of the molar volume ratio of total IGF to ALS.

 

   DISCUSSION
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
GRANTS
 DISCLAIMER
 ACKNOWLEDGMENTS
 REFERENCES
 
This study monitored an array of hormonal and metabolic biomarkers with a particular emphasis on the IGF-I system during an 8-day energy deficit in young healthy men. The main findings from the study were 1) all components of the ternary complex (IGF-I, IGFBP-3, and the ALS) directionally tracked the progression of the energy deficit and resultant body mass loss more so than did the other conventional nutritional biomarkers (i.e., transferrin, ferritin, and retinol BP); 2) the most biologically meaningful component of the IGF-I system, free IGF-I, significantly correlated with the relative losses in body mass and fat-free mass, explaining nearly 20% of the variance; 3) another index of free IGF-I, the molar volume ratio of IGF-I/ALS at baseline, was able to serve as a reasonable predictor for those subjects who experienced greater than a 5% loss in body mass. We conclude from the collective findings of the present study that, compared with other traditional nutritional and metabolic biomarkers, several aspects of the IGF-I system have greater utility in terms of providing an assessment of the severity of an energy deficit and in the prognostic value (as determined by the ROC analysis) for alterations in body composition. Our data also support the use of the IGF-I system as an important adjunct in the overall assessment of metabolic strain attributed to high levels of physical activity superimposed on energy and sleep restriction (9, 25, 26).

It is clear from this study that the temporal responses of the IGF-I ternary components vs. traditional nutritional biomarkers were not in accordance from either a directional or a magnitude perspective (refer to Fig. 1, A and B). The subjects in this study were exposed to an energy deficit attributed to both restricted caloric intake and a high-energy expenditure that resulted in mean losses of 3.8% for body mass, 2.2% for fat-free mass, and 12.9% for fat mass (see Table 2). Since the seminal reports by Clemmons et al. (5, 6) and Isley et al. (12) reporting that IGF-I was a more sensitive index of acute directional changes in nutritional status than other plasma proteins and was temporally correlated with entry into nitrogen balance during energy repletion, these findings have been confirmed in a variety of clinical populations, such as malnourished children and hospital patients, experimental fasting, critical illness, chronic diseases, eating disorders, and premature infants (2, 5, 11, 18, 27, 30, 31). The present study extends on these prior observations in clinical populations by providing experimental data demonstrating the utility of IGF-I for monitoring metabolic stress attributed to high levels of physical activity superimposed on energy and sleep restriction in healthy men during energy restriction, resulting in subsequent weight loss. Due to graduation requirements, it was not possible to remove a group of subjects from this training to serve as a control group. However, based on previous published work (21, 23) from our laboratory, we know that IGF-I exhibits a great deal of day-to-day stability, and we believe the changes observed in the IGF-I system to be a result of our intervention and not due to day-to-day variability. Although it is difficult to discern the individual effects of physical exertion, sleep deprivation, and energy restriction on changes in the IGF-I, it is likely that the major influence was the energy deficit. Older et al. (26a) have reported that 3 consecutive days of delta-wave sleep interruption had no effect on IGF-I, and acute physical exercise has either shown no change or increases in circulating IGF-I (21). Furthermore, our laboratory has previously reported that the IGF-I declines observed during military stress cannot be attributed to a decline in GH secretion, as GH secretion is actually increased. The fact that IGF-I decreased even in the presence of amplified GH release perhaps suggests that the liver was "GH resistant" (26).

Perhaps the most interesting finding of this study was the significant correlation between the percent change in free IGF-I and the percent change in body mass and fat-free mass. In fact, free IGF-I was the only analyte in the study to be associated with alterations in body composition. While it may seem intuitive that IGF-I is associated with changes in body composition, due to the somatotrophic influence that IGF-I exerts on body tissues (14, 22, 29), there are discordant data with regard to the relationship between IGF-I and body composition (10, 19, 27, 30, 31). While these discordant data could be attributable to a number of factors, it is notable that, to our knowledge, the prevailing measurement in previous studies has been total IGF-I, not free IGF-I. Free IGF-I is universally considered the most physiologically relevant component of the IGF-I system, as free IGF-I is not bound to any BPs and is therefore bioavailable to cellular receptors (3, 4, 17). By considering the regulatory complexity of IGF-I physiology and by measuring both free and total IGF-I, we have potentially revealed a significant association that other studies may have overlooked. The decline in free IGF-I undoubtedly reflects a diminished synthetic and mitotic capacity to maintain protein turnover and balance, as reflected by the loss of fat-free mass.

The variance for the loss of body mass and fat-free mass explained in this study by the change in free IGF-I is comparable to the variance explaining the change in nitrogen balance for 24-h (14%), 48-h (25%), 72-h (12%), and 120-h (21%) periods in critically ill patients reported by Hawker et al. (11). It is important to acknowledge that our study was limited to blood measures and that locally produced (paracrine and autocrine) IGF-I may be more important than circulating IGF-I. While the finding that 20% of the variance explaining the loss of body mass and fat-free mass was explained by the decrease in circulating free IGF-I should not be overstated, the fact that free IGF-I demonstrated a stronger correlation with weight loss than even leptin is a notable observation. Of further interest, free IGF-I also showed a stronger relationship with fat-free mass loss than did testosterone, a potent anabolic steroid hormone.

Employing ROC curves to assess if these putative biomarkers provided predictive value for changes in body mass, we found that the baseline value for the molar volume ratio of IGF-I/ALS was able to discriminate between those individuals more susceptible to body mass losses. Subjects with a baseline IGF-I/ALS molar volume ratio of <1.67 were 3.25 times more likely to lose 5% or more of their body mass than those with larger ratios, while those subjects possessing a value >1.67 were only about one-third as likely to lose >5% of their body mass. The ROC curves for IGF/ALS ratio were even more predictive than the ROC curve for initial levels of initial body mass (not significant). Using ROC curves to compare IGF-I as a discriminator for the changes in the body composition parameters of total body water, sodium, and potassium during refeeding, Minuto et al. (19) demonstrated that IGF-I possessed higher likelihood ratios and true positive ratios than transferrin and albumin. Based on baseline metabolic information, future efforts using ROC curve analyses might be fruitful in terms of identifying subject populations susceptible to extreme weight and in need of intervention strategies.

Given the important role that IGFBPs play in modulating IGF-I bioactivity, both their independent concentrations and their molar volume ratio to IGF-I have been postulated to serve as reliable indicators of nutritional status. It was surprising that neither the molar volume ratio of IGF-I/IGFBP-3 or IGFBP-1 was also not discriminators for body composition changes. Most attention has been given to IGF-I/IGFBP-3 as a rough index of free IGF-I (23) and to IGFBP-1 as the most sensitive BP to energy balance. However, like IGFBP-3, the ALS is also a component of the ternary complex, which carries >75% of circulating IGF-I. The ternary complex can only be formed in the presence of ALS, and, when IGF-I is sequestered within the ternary complex, its half-life is extended from 10 min to >12 h (1, 4, 16, 31). Thus a high concentration of ALS would contribute to lower IGF/ALS ratio, which, in turn, might be reflective of an inhibition of IGF-I bioactivity. Additionally, within the IGF-I system, ALS is well suited to serve as a reliable biomarker, as it is largest in size (85 kDa), mainly restricting ALS to the circulation and synthesized exclusively by the liver (IGF-I and the IGFBPs are synthesized by a number of different tissues). In a study that involved a 30-day monitoring period after admission to an intensive care unit, Baxter et al. (2) reported that ALS correlated best with serum levels of nutritional status, particularly prealbumin. While the functional importance of ALS has largely been ignored (1, 4), our data suggest that the molar volume of IGF-I/ALS yields prognostic information for the metabolic response to metabolic stress, as it may be able to predict compromises in body composition. Further studies are required to determine why, via ROC analyses, the baseline molar volume ratio of IGF/ALS ratio was a better predictor of body mass loss than even baseline free IGF-I concentrations or other metrics of free IGF-I.

In summary, our results lend further support for the use of the IGF-I system as an adjunct in the monitoring metabolic strain attributed to high levels of physical activity superimposed on energy and sleep restriction in young, healthy, fit men. By including the regulatory components of the IGF-I system in our evaluation, specifically free IGF-I and the ALS, we have provided new findings regarding the utility of the IGF-I system for predicting responses to energy imbalance. Free IGF-I, rather than total IGF-I, appears to be more closely associated with changes in body mass and fat-free mass. Also, the baseline value for the molar volume ratio of IGF-I/ALS emerged as a discriminator for those individuals most susceptible to losing body mass during an energy deficit. Future applications of monitoring the IGF-I system during metabolic strain could reside in its ability to serve as an "early indicator" when additional recovery and/or energy repletion are required before further health maladaptations are imminent.


   GRANTS
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
DISCLAIMER
 ACKNOWLEDGMENTS
 REFERENCES
 
This study was funded, in part, by an Army Medical Research and Materiel Command Technologies in Metabolic Monitoring Grant to B. C. Nindl, and Scientific Technology Program III.B (Metabolic Regulators for the Warfighter and Science) and Technology Objective W (Optimization of Physical Performance) and S (Physical Training Interventions to Enhance Military Task Performance and Reduce Musculoskeletal Injuries).


   DISCLAIMER
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 DISCLAIMER
ACKNOWLEDGMENTS
 REFERENCES
 
The views, opinions, and/or findings contained in this publication are those of the authors and should not be construed as an official Department of the Amy position, policy, or decision unless so designated by other documentation. This paper is approved for public release; distribution is unlimited.


   ACKNOWLEDGMENTS
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 DISCLAIMER
 ACKNOWLEDGMENTS
REFERENCES
 
The authors thank the US Marine Corps Basic School in Quantico, VA for their participation and gratefully acknowledge the efforts of all of the Marine Officers who volunteered and participated in this research effort. Special mention is given to Capt. Keith Parry and Maj. Gabriel Patricio for tremendous support. We also acknowledge the technical assistance of Thad Ross, Louis Marchetelli, David Adams, and Anthony Rogers.


   FOOTNOTES
 

Address for reprint requests and other correspondence: B. C. Nindl, Military Performance Division, US Army Research Institute of Environmental Medicine, Natick, MA 01760-5007 (e-mail: Bradley.Nindl{at}na.amedd.army.mil)

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.


   REFERENCES
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 DISCLAIMER
 ACKNOWLEDGMENTS
 REFERENCES
 

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