Direct Measure of Insulin Sensitivity with the Hyperinsulinemic-Euglycemic Clamp and Surrogate Measures of Insulin Sensitivity with the Oral Glucose Tolerance Test: Correlations with Aberrant Crypt Foci Promotion in Rats¹

CRC³ is one of the leading causes of cancer mortality in industrialized countries (1). However, it appears to be a modifiable disease because lifestyle factors may account for as much as 80% of the cases (1). McKeown-Eyssen (2) and Giovannucci (3) noted that the lifestyle risk factors for CRC such as diets high in calories, saturated fat, and carbohydrates with a high glycemic index, as well as low levels of physical activity, are remarkably similar to those for the development of insulin resistance and type 2 diabetes. Insulin resistance is the reciprocal of insulin sensitivity and can be defined as a reduced ability of insulin to exert its expected metabolic actions. Insulin resistance can lead to elevated circulating levels of NEFA and triglycerides and to compensatory hyperinsulinemia (4). Taken together, these elevated levels comprise part of the insulin resistance or metabolic syndrome. McKeown-Eyssen (2) thus hypothesized that the high-risk lifestyle factors listed above lead, possibly through insulin resistance, to elevated levels of insulin, glucose, NEFA, and triglycerides, which act either individually or in concert as growth factors and energy sources to promote CRC. Giovannucci (3) proposed that these high-risk lifestyles lead, in part, to high insulin levels that promote CRC.

Evidence for the insulin resistance syndrome-CRC hypothesis has been reported by epidemiological and animal studies. Several prospective studies have examined the relationship between risk of CRC and insulin resistance-related biomarkers such as nonfasted blood glucose level, nonfasted C-peptide level (a measure of insulin secretion), measures from OGTT, and clusters of risk factors related to the insulin resistance syndrome (5, 6, 7, 8, 9, 10). These studies show a wide range of relative risk of CRC from 0.90 to 3.96, presumably because of the different risk factors examined. Animal studies also support the insulin resistance syndrome-CRC hypothesis. First, hyperinsulinemia from exogenous insulin may act as a growth factor for colorectal tumors (11) and for putative preneoplastic lesions termed ACF (12), lesions that have been widely used to examine agents that promote or inhibit early stages of CRC development (13). Second, insulin resistance-related parameters may be involved in CRC development. In initiated rats fed a wide range of diets (differing in caloric intake, type and level of fat and carbohydrate, and availability of food choices), glucose intolerance, and basal insulin levels showed a strong correlation with multiplicity of ACF (number of aberrant crypts/ACF; Ref. 14). However, no study has measured insulin resistance directly with a hyperinsulinemic-euglycemic clamp during the development of CRC, and no study has simultaneously compared the relative importance of insulin resistance and its related metabolic parameters on the development of CRC.

Direct determination of insulin sensitivity with the hyperinsulinemic-euglycemic clamp technique is laborious and impractical in large human prospective studies. Indirect but more feasible methods of determining insulin sensitivity often use glucose and insulin data obtained at fasting and/or after a glucose load. Indices of insulin sensitivity or insulin resistance include simple ratios and products of insulin and glucose levels at individual time points or integrated over time during an OGTT as proposed by Perley et al. (15) and Yalow et al. (16) and further investigated by Yeni-Komshian et al. (17) and others. More complex formulas for indices of insulin sensitivity or insulin resistance have been proposed by several investigators such as Duncan et al. (FIRI), Katz et al. (QUICKI), Matthews et al. (HOMA), Avignon et al. (Si), Belfiore et al. (ISI(gly)), Cederholm et al. (SI), Gutt et al. (ISI0,120), Mari et al. (OGIS), Matsuda et al. (ISI(composite)), and Stumvoll et al. (ISIest(OGTT); Refs. 18, 19, 20, 21, 22, 23, 24, 25, 26, 27). In humans, the above methods have correlated well with more rigorous but laborious measurements of insulin sensitivity as assessed by either the steady-state plasma glucose method (17), the frequently sampled i.v. glucose tolerance test and minimal model method (18, 19, 21), or the gold-standard hyperinsulinemic-euglycemic clamp method (19, 20, 22, 23, 24, 25, 26, 27). To our knowledge, there have been no previous reports that have compared the association between colon carcinogenesis and direct measures of insulin sensitivity with the association between colon carcinogenesis and simultaneous measurements of the above surrogates of insulin sensitivity in human studies or animal models.

The objectives of this study were to determine: (a) whether direct assessment of insulin sensitivity with a hyperinsulinemic-euglycemic clamp correlates with ACF promotion in rats fed various levels of saturated fat, a well known inducer of hyperinsulinemia and insulin resistance, as well as a promoter of ACF and CRC (14, 28, 29, 30, 31); and (b) the extent to which direct and surrogate measures of insulin resistance and its related metabolic parameters correlate with promotion of ACF in the same rat model.

Male Fischer 344 rats (Harlan Sprague Dawley, Inc., Indianapolis, IN) weighing ∼180 g were housed individually in wire-bottom cages. The room was maintained on a 12-h light/dark cycle (dark cycle extended from 7 p.m. to 7 a.m.) at ∼22°C and 50% humidity. Care and treatment of the rats were in compliance with the guidelines of the Canadian Council on Animal Care, and the protocol was approved by the University of Toronto Animal Care Committee.

Rats were acclimatized for 7 days on rodent chow (Purina 5001; Ralston Purina International, Strathroy, Ontario, Canada), initiated i.p. with 20 mg/kg of the carcinogen azoxymethane (Sigma Chemicals, St. Louis, MO), fed rodent chow for another 7 days, and then randomized to one of three dietary groups with various levels of saturated fat. Thirty rats were in the LF, 15 in the IF, and 30 in the HF diet group (Dyets, Inc., Bethlehem, PA). The randomization produced three groups of animals with initially similar body weights (mean ± SE were 209 ± 2, 209 ± 4, and 207 ± 2 g for the LF, IF, and HF groups, respectively). The diet composition is outlined in Table 1. All groups were fed ad libitum. At ∼40 days after initiation, rats underwent surgery to have the left carotid artery and right jugular vein cannulated. This was followed by at least a 5-day recovery period. Insulin sensitivity in the rats was assessed directly with a hyperinsulinemic-euglycemic clamp and indirectly with an OGTT and fasting measurements between days 45 and 55. Body weight was measured at the time of randomization to dietary treatment and when fasting measurements, OGTT, and hyperinsulinemic-euglycemic clamps were performed. At day 100, rats were euthanized by halothane overdose and cervical dislocation, and their colons were removed for the ACF assay. All blood samplings, assays, and scoring of ACF were coded to ensure that dietary treatment was not known during these measurements.

After rats were anesthetized with ketamine:xylazine:acepromazine (100:0.1:0.5 mg/ml, 1 μl/g body weight), two polyethylene catheters (PE-50; Cay Adams, Boston, MA), with a 2.5-cm silastic tubing (Dow Corning Corp., Midland, MI) that overlapped each catheter by 0.5 cm, were inserted into the right jugular vein and left carotid artery so that one catheter reached the right atrium and the other reached the aortic arch. Catheters were exteriorized at the back of the neck and cut to leave 4 cm of the catheter outside. Patency was maintained by filling the catheters with a polyvinyl-pyrrolidone mixture [60% w/v polyvinyl pyrrolidone, Sigma Chemicals) and 60% v/v heparin (1000 USP/ml)], closing the catheter with a pin, and replacing the polyvinyl pyrrolidone/heparin mixture every third day.

At ∼50 days after initiation, rats were fasted overnight from 5 p.m. Fasting samples were taken at time 0 from the clamps and OGTT via lines attached to the catheters. Fasting glucose, insulin, NEFA, triglycerides, and total IGF-1 levels were measured. In rats that had both OGTT and clamps performed, the mean fasting levels were calculated.

At ∼50 days after initiation, rats were fasted overnight, had a line connected to the carotid catheter to facilitate repeated blood samples, and were then given a gavage of 4 mg anhydrous glucose/g body weight with a solution of 625 mg of glucose/ml (14). 200 μl blood samples were taken before the glucose load at time 0, and at 30, 60, 90, 120, and 180 min thereafter for plasma glucose and insulin measurements.

The direct method for assessing insulin sensitivity, the reciprocal of insulin resistance, is the hyperinsulinemic-euglycemic clamp (32, 33). With this technique, a constant rate of insulin is infused i.v. to increase the uptake of circulating glucose into insulin-sensitive tissues and to inhibit endogenous glucose production by the liver. The decline in plasma glucose is prevented by a concomitant variable rate of glucose infusion. The amount of exogenous glucose required to maintain plasma glucose at its initial level is quantified by the GIR. Thus, GIR is a measure of the ability of insulin to increase glucose uptake and to suppress glucose production in a given subject, i.e., a measure of the insulin sensitivity of that subject.

At ∼50 days after initiation, the glucose solution was prepared with 5 ml of saline and 15 ml of 50% dextrose (Abbott Laboratories Ltd., Montreal, Quebec, Canada). Five units of insulin (Iletin II regular pork insulin; Eli Lilly, Indianapolis, IN) were mixed with a variable volume of 0.1% BSA (Sigma Chemicals), which would later produce an insulin infusion rate of 18 mU insulin/kg body weight × min. This rate maximally stimulates glucose uptake and completely suppresses hepatic glucose production (34). The glucose and insulin solutions were placed in digital syringe pumps (Harvard Apparatus Pump 22; Ealing Scientific Ltd., Saint Laurent, Quebec, Canada) that were connected to two PE-50 lines and joined by a Y connector (Bifurcation cannula 21/22Y; Plastics One Inc., Roanoke, VA) to the jugular catheter of the rat. Another line was connected to the rat’s carotid catheter to allow blood sampling. Two baseline fasting blood samples were obtained: 600 μl of blood for the measurement of hematocrit, glucose, insulin, NEFA, and triglyceride levels, and after a 20-min adaptation period, 200 μl of blood for glucose and insulin levels. The average of these glucose values was the target fasting glucose level for the clamp. At the start of the clamp, the insulin pump was set at a constant rate of 10 μl/min. Euglycemia was maintained by a variable rate of glucose infusion that was adjusted according to the determination of plasma glucose at 5-min intervals throughout the 2-h clamp. During the last 20 min of the clamp when GIR reached steady state, 200 μl of blood aliquots were taken at 100, 110, and 120 min, and plasma was collected and stored at −20°C. A total of ∼2.5 ml of blood was withdrawn during the clamp, however, the RBCs were reinfused after each sample in a 1:1 dilution with saline.

All plasma glucose samples were measured by the glucose oxidase method with a Beckman Glucose Analyzer II (Beckman Instruments Co., Fullerton, CA). Plasma insulin was determined by a radioimmunoassay kit specific for rat insulin with 100% cross-reactivity for porcine insulin (used during infusions; Linco Research, Inc., St. Charles, MO). Plasma NEFA were assessed by a colorimetric kit (Wako Chemicals USA, Inc., Richmond, VA). Plasma triglycerides were quantified with a colorimetric assay (Boehringer Mannheim Co., Indianapolis, IN). Plasma total IGF-1 was determined by a radioimmunoassay kit specific for rat IGF-1 (Diagnostic Systems Laboratories, Inc., Webster, TX). The CV for the duplicate measurements of glucose were <5%. The intra-assay CV for insulin, NEFA, triglycerides, and total IGF-1 were not calculated because there were limited blood volumes, and duplicate measurements were not performed. The interassay CV for the quality controls for the insulin, NEFA, and triglyceride assays were <13.8, 14.2, and 11.9%, respectively, and our values were always within the companies’ reference range. The assay for total IGF-1 was performed once, and the measures for the quality controls were within the reference range.

Colons were removed from the rat, flushed with PBS, cut longitudinally, laid flat between filter papers, and kept in 10% phosphate-buffered formalin until analysis of ACF. Colons were subsequently stained with 0.2% methylene blue and examined under a light microscope at ×40 magnification (35). ACF were identified by their enlarged crypt openings, their thicker cell walls, and the clustering of the aberrant crypts into foci.

Direct assessment of insulin sensitivity was performed by calculating GIR during the last 20 min of the hyperinsulinemic-euglycemic clamp (when steady state was reached) from the concentration of the glucose solution that was infused, the flow rate of the glucose pump, the corresponding times between changes in these flow rates, and the weight of the rat (32). Next, indices of insulin resistance or insulin sensitivity were determined with 11 methods that use glucose and insulin measurements at times (t) 0, 30, 60, 90, 120, and/or 180 min after a glucose load as follows. (a) Glucose levels, insulin levels, glucose-to-insulin ratios (an index of insulin sensitivity), and insulin-and-glucose products (an index of insulin resistance) were determined for all times (t) during the OGTT (Gt, It, G/It, and IxGt), and auc was determined for the 120 and 180 min of the OGTT (G auct, I auct, G/I auct, and IxG auct) with the trapezoid rule (14). (b) FIRI (18) was calculated with the formula: [fasting insulin (μU/ml) × fasting glucose (mm)]/25. (c) QUICKI (19) was calculated with the formula: 1/[log(fasting insulin (μU/ml)) × log(fasting glucose (mg/dl)]. (d) The HOMA method (20) is a computer-solved model used to assess insulin resistance. A simpler although less accurate method is the HOMA-formula: [fasting insulin (μU/ml) × fasting glucose (mm)]/22.5 (18, 20). (This equation is rather similar to the simplified FIRI.) (e) Si (21) was determined at times (t) 0 and 120 min after a glucose load with the formulas: Si t = 10⁸/[insulin t (μU/ml) × glucose t (mg/dl) × VD], where VD is the apparent glucose distribution volume and is 150 ml/kg of body weight; and the weighted mean of Si is SiMean0,t = [(0.137 × Si0) + Sit]/2. (f) ISI(gly) (22) was calculated with the formula: 2/[(INSp × GLYp) +1], where INSp and GLYp are insulin and glucose auc of the OGTT and calculated from data at 0, 30, 60, 90, 120, and 180 min. INSp and GLYp can also be simplified by considering values only at 0, 60, and 120 min or only at 0 and 120 min (by calculating the sum of half the value at 0 min + the value at 60 min + half the value at 120 min, or the sum of values at 0 and 120 min). The INSp and GLYp values for each animal are expressed as the observed value divided by the mean value of the normal reference group (which is the LF group in this study), and thus the values are expressed as a ratio with the mean normal value as one (22). (g) SI (23) was calculated with the formula: glucose uptake rate/[(mean glucose level during the OGTT (mm)) × log (mean insulin level during the OGTT (mU/liter)], where the glucose uptake rate = [(glucose load (mg) + (glucose 0 min (mm) − glucose 120 min (mm))) × 1.15 × 180 × 0.19 × body weight (kg)]/120 min, and the mean glucose and insulin values are calculated from values at 0, 30, 60, and 120 min. (h) ISI0,120 (24) was simplified from the SI proposed by Cederholm et al. (23). The formula is: glucose uptake rate/[(mean glucose level during the OGTT (mm)) × log (mean insulin level during the OGTT (mU/liter))], where the glucose uptake rate = [(glucose load (mg) + (glucose 0 min (mg/liter) − glucose 120 min (mg/liter))) × 0.19 liter/kg × body weight (kg)]/120 min, and the mean glucose and insulin values are calculated from values at 0 and 120 min only. (i) OGIS (25) was determined from a spreadsheet that can be downloaded from the world wide web.⁴ One modification was made in this study in which the oral glucose dose was changed from g/m² to g/kg body weight of the rats. (j) ISI(composite) (26) was calculated with the formula: 10,000/square root of [fasting glucose × fasting insulin × mean glucose during the OGTT × mean insulin during the OGTT]. (k) ISIest(OGTT) (27) was calculated from two multivariate regression equations, with or without body mass index. For the rats in this study, the following equation without body mass index was used: ISI = 0.157 − (4.576 × 10⁻⁵ × insulin 120 min) − (0.00519 × glucose 90 min) − (0.000299 × insulin 0 min). All of the above indices were developed for 75 g-OGTT in humans. Because rats were used in this study, their body weight and oral glucose load of 4 mg/g body weight were entered into the formulas for methods (e), (g), (h), and (i). Also, the 180-min data from the OGTT was not used in most of the original publications for the above indices but was used in this study [methods (a) and (e) through (j)] mainly because of the prolonged elevation in plasma glucose observed with the glucose load used in these rats. Next, the number (occurrence) of ACF and multiplicity of ACF (number of aberrant crypts/ACF) were calculated.

The above data were reported as mean ± SE. One-way ANOVA was performed to compare the three dietary groups in terms of: (a) body weight; (b) fasting levels of glucose, insulin, NEFA, triglycerides, and total IGF-1; (c) GIR during the clamp; (d) the indices of insulin resistance or insulin sensitivity as described above, all at 50 days; and (e) number and multiplicity of ACF at 100 days. When a significant difference (P < 0.05) was detected, Tukey’s studentized range test was used to evaluate significance between each possible pair of groups.

Pearson’s correlation was performed to assess the association between GIR during the clamp (direct measure of insulin sensitivity) and metabolic parameters (surrogate measures of insulin resistance) as well as between multiplicity of ACF and metabolic measures. Stepwise multivariate regression analysis was performed, in which the independent variables were significant metabolic parameters (r² ≥ 0.13, n = 30, P < 0.05 in the univariate analyses) and the dependent variable was GIR during the clamp (direct assessment of insulin sensitivity). Stepwise multivariate analysis was also performed with metabolic parameters (r² ≥ 0.12, n = 24, P < 0.10 in the univariate analyses) as the independent variables and multiplicity of ACF as the dependent variable. Furthermore, to identify practical surrogate measures of insulin sensitivity that were associated with CRC promotion, multivariate analyses were performed using metabolic parameters that required blood samples at only one time point (i.e., excluded from the set of independent variables were GIR during the clamp, parameters involving auc, and complex indices of insulin resistance or insulin sensitivity). r, r², and Ps were reported for the appropriate analyses. All statistical analyses were performed with the SAS software program, version 8.00 (SAS Institute, Cary, NC).

The mean values for the metabolic parameters measured in the three dietary groups at ∼50 days after carcinogen initiation are shown in Figs. 1 and 2 as well as Tables 2 through 4. Sample sizes reflect animals that survived surgical cannulations, had patent cannulas, and completed OGTT and hyperinsulinemic-euglycemic clamps. There did not appear to be a consistent relationship between patency of cannulas or rats that survived to ACF end point with respect to type of diet.

The direct assessment of insulin sensitivity involved the determination of GIR during the last 20 min of the hyperinsulinemic-euglycemic clamp when GIR reached steady state. The insulin levels during the last 20 min of the clamp at 100, 110, and 120 min were not significantly different between the three dietary groups (4080 ± 294, 3578 ± 318, 3930 ± 354 pm for the LF group; 3864 ± 390, 3666 ± 510, 3852 ± 504 pm for the IF group; and 3750 ± 174, 3768 ± 300, and 4002 ± 348 pm for the HF group for the respective times) and thus indicated that the hyperinsulinemic clamp had reached a steady state to examine GIR. The quantity of glucose required to establish euglycemia during this period was affected by diet. The GIR required to maintain euglycemia was significantly lower in the HF than in the LF and IF groups and was similar between the LF and IF groups (Fig. 1). Thus, the HF group was less insulin sensitive, i.e., more insulin resistant than the LF and IF groups.

Body weights were not significantly different between the three dietary groups at ∼50 days after initiation when fasting, OGTT, and clamp measurements were performed (Table 2). Fasting levels for glucose, insulin, NEFA, triglycerides and total IGF-1 were similar between the three dietary groups. During the OGTT, glucose levels were significantly higher in the HF group than in the LF group only at 30 min after the glucose load; glucose levels in the IF group at 30 min were between and not significantly different from those of the LF and HF groups (Fig. 2). Insulin levels at 30, 120, and 180 min of the OGTT were significantly higher in the HF group than in the LF group, and levels in the IF group were between those of the LF and HF groups. G/I is an index of insulin sensitivity, and values at 30, 120, and 180 min and for 2- and 3-h auc were generally higher in the LF group than in the HF group (Table 3). IxG is an index of insulin resistance (the reciprocal of insulin sensitivity), and values at 30, 120, and 180 min and for 2- and 3-h auc were significantly higher in the HF than in the LF group. Levels in the IF group tended to be between those of the LF and HF groups.

Indices of insulin resistance or insulin sensitivity that use only fasting values, specifically FIRI, QUICKI, and HOMA-formula, were not significantly different between the three dietary groups (Table 4). Indices of insulin sensitivity that use 120- and/or 180-min data, specifically Si, ISI(gly), SI, ISI, OGIS, ISI(composite), and ISIest(OGTT), were generally lower in the HF group than in the LF group, with values for the IF group between those of the HF and LF groups, with varying levels of significance.

ACF, a marker of the effect of the diets on colon carcinogenesis, was assessed in colons removed at 100 days after initiation. The mean number of ACF/colon, a measure of CRC initiation, was not significantly different between the three dietary groups (Table 5). The mean multiplicity of ACF, a measure of CRC promotion, was significantly higher in the HF group than in the LF group. The IF group was between, and not significantly different from, the LF and HF groups.

Table 6 presents Pearson’s correlation between direct measures of insulin sensitivity (as measured by GIR during the hyperinsulinemic-euglycemic clamp) and metabolic parameters. The four strongest correlates of GIR were insulin levels and G/I each integrated over 120 and 180 min (auc) after the glucose load. Each of these four surrogate measures explained ∼50% of the variation in GIR (P < 0.001 each). There was an inverse association between insulin levels (surrogate measure of insulin resistance) and GIR (direct measure of insulin sensitivity), and a positive association between glucose-to-insulin ratio (surrogate measure of insulin sensitivity) and GIR. Our result for insulin auc integrated over 120 min in this animal model confirms the results by Yeni-Komshian et al. (17), who reported that insulin auc integrated over 120 min, also accounted for ∼50% of the variation in the direct measure of insulin sensitivity in 490 nondiabetic volunteers. Other surrogate measures of insulin sensitivity, which explained a significant 14–46% of the variation in GIR, are also shown in Table 6. In contrast to the above significant correlates of GIR, fasting levels of glucose, insulin, total IGF-1, NEFA, triglycerides, glucose, as well as FIRI, QUICKI, and HOMA-formula each accounted for <12% of the variation in GIR (P for each of these parameters >0.05; data not shown).

Stepwise multivariate regression analysis of metabolic parameters (with r² ≥ 0.13, n = 30, P < 0.05 in the univariate analysis) indicated that insulin levels integrated over 180 min (auc) were most strongly correlated with GIR during the clamp (direct measure of insulin sensitivity) and accounted for ∼52% (P < 0.001) of the variation in GIR. No other metabolic parameter provided a significant additional contribution to the model. Because the clamp is labor intensive and impractical for large studies and determination of the integrated insulin level requires sampling at multiple time points, practical surrogate measures of insulin sensitivity were entered into a second multivariate analysis to identify combinations of other metabolic parameters that best correlate with GIR. The practical surrogate measures of insulin sensitivity entered into the regression analysis were body weight and measurements taken at one time point, i.e., no complex indices of insulin sensitivity were included. Results show that insulin levels at 180 min, insulin levels at 120 min, and body weight (partial r² = 0.38, 0.12, and 0.08, respectively) were significantly and independently associated with GIR, and together they accounted for ∼58% (P = 0.04) of the variation in GIR.

The parameter that correlated most strongly with multiplicity of ACF was the direct measure of insulin sensitivity (GIR during the clamp; r = −0.52, P = 0.009), which explained ∼27% of the variation in multiplicity of ACF (Fig. 3). The negative correlation of GIR indicated an inverse association between insulin sensitivity and multiplicity of ACF or a direct association between insulin resistance and multiplicity of ACF. The second highest correlate of multiplicity of ACF was insulin levels at 180 min after the glucose load (r = 0.42, P = 0.044), which explained 18% of the variation in multiplicity of ACF. Insulin levels at 180 min are a practical surrogate measure of insulin resistance and involve only a single blood determination rather than the labor intensive procedure of the hyperinsulinemic-euglycemic clamp. Body weight, fasting levels of glucose, insulin, total IGF-1, NEFA, and triglycerides, glucose levels at individual time points and for auc during the OGTT, and indices of insulin sensitivity or insulin resistance [specifically FIRI, QUICKI, HOMA-formula, Si, ISI(gly), SI, ISI0,120, OGIS, ISI(composite), and ISIest(OGTT)] each accounted for ≤10% of the variation in multiplicity of ACF (P for each of these parameters > 0.05; data not shown).

In stepwise multivariate regression analysis, only GIR was significantly associated with ACF, and no other measure provided a significant additional contribution.

This report provides additional experimental evidence for an association between CRC risk and insulin resistance with its related metabolic parameters. We determined the extent to which direct and surrogate measures of insulin resistance at 50 days correlated with multiplicity of ACF at 100 days in initiated rats fed various levels of dietary saturated fat. Multiplicity of ACF was most strongly correlated with direct measures of insulin sensitivity, as assessed with the hyperinsulinemic-euglycemic clamp. It was next most strongly correlated with a practical surrogate measure of insulin resistance, insulin levels at 180 min after a glucose load. Multiplicity of ACF was not significantly correlated with the following surrogate measures of insulin resistance: glucose values during the OGTT; complex insulin sensitivity indices; fasting levels of insulin; glucose; NEFA; triglycerides or total IGF-1; or body weight in this animal model. The correlations that were observed do not prove causality, but they suggest factors that could affect these correlations, as well as add biological plausibility to mechanisms that could link diet, insulin resistance, and its related metabolic parameters with CRC development.

Several factors could affect the strength of correlations in this study. First, the chronological development of the insulin resistance syndrome appears to strongly affect the correlations. Typically, insulin resistance occurs first, followed by hyperinsulinemia, and finally changes in fasting levels (4, 36). It is thus not surprising that early stages of CRC are most strongly associated with insulin resistance and hyperinsulinemia but less with fasting levels in this study. The sequence is also consistent with the results from most epidemiological studies in which risk of CRC was strongly associated with nonfasted C-peptide levels and insulin levels after a glucose load (6, 7) but less strongly associated with fasting measures (7, 37, 38, 39). Second, the diets used to produce the range of insulin resistance could affect the strength of correlations. In contrast to this study, our previous investigation used a wider range of diets (including a caloric restricted diet and a combination of HF, high glycemic diet with multiple food choices) and observed correlations such as between glucose auc and multiplicity of ACF (14) that were not observed in this study. It is thus possible that stronger correlations between metabolic parameters and ACF would have been observed in this study if a wider range of diets had been tested. Indeed, the range of insulin resistance observed in humans is greater than that observed in this study (17). Third, precision of measurements can affect correlations. The precision obtained during the hyperinsulinemic-euglycemic clamp is greater than the precision obtained during the OGTT (25) or complex insulin sensitivity indices such as the HOMA or OGIS methods (20, 25), which could affect the strength of correlations observed. Fourth, although fasting total IGF-1 did not correlate with multiplicity of ACF, a role for IGF-1 in carcinogenesis cannot be excluded. This is because bioactive or local levels of IGF-1 around the colon were not assessed (because of limited volumes of blood at 50 days and to methodological limitations) and because an early stage of carcinogenesis was examined. Fifth, body weight at 50 days was not associated with multiplicity of ACF. Nonetheless, in the absence of body weight gain, a metabolically obese state may still occur with increased abdominal visceral fat stores, insulin resistance, and other components of the insulin resistance syndrome (40).

The levels of dietary saturated fat in this study were associated with multiplicity of ACF but not with number of ACF/colon. Results from our previous studies (14, 30) and those from other laboratories (41, 42) also showed that a high saturated fat diet increased multiplicity of ACF but not number of ACF. Only one study reported that a HF diet with mixed lipids increased number of ACF (43). The relative degree of CRC promotion in this study, however, was less than in previous studies (14, 30). A major difference in these studies is the likely increased level of stress such as from the metabolic caging, surgery, and long-term presence of cannulas in this study. It has previously been reported that direct administration of epinephrine (a hormone associated with stress) decreased colorectal epithelial proliferation (44), whereas effects of cortisol (another stress hormone) on colorectal proliferation and carcinogenesis are controversial (45, 46, 47). Despite the narrower range of CRC promotion in this study, a strong correlation between carcinogenesis and insulin resistance was observed.

Previous studies that have examined CRC in relation to insulin resistance are small case-control studies that used the hyperinsulinemic-euglycemic clamp technique and demonstrated that insulin resistance is positively associated with late-stage CRC (48, 49). However, in these studies, insulin resistance could have been secondary to the cancer state itself because removal of these cancers was associated with a reversal of insulin resistance (50). In contrast, in this study, insulin sensitivity was assessed during the promotion of ACF, i.e., before tumors developed. Our observations also suggest that a practical surrogate measure of insulin resistance could be provided by a single measurement of insulin level at 180 min after a glucose load. No epidemiological study has examined the relationship between 180-min insulin levels and CRC incidence. Schoen et al. (7) have reported that 120-min insulin levels are associated with a 2.0-fold increase in CRC incidence between the highest and lowest quartile. In this study, the 120-min insulin levels are less strongly correlated with multiplicity of ACF than are 180-min insulin levels. This result, however, may simply reflect the more prolonged peak in insulin levels with the oral glucose load of 4 mg/g body weight in rats than with a standard OGTT in humans.

Several possible mechanisms have been proposed to relate dietary saturated fat with colon carcinogenesis. We have suggested that a high saturated fat diet promotes CRC through insulin resistance, perhaps via compensatory hyperinsulinemia. In CRC cell cultures, insulin can increase DNA synthesis, stimulate cell division, and inhibit apoptosis (51, 52) in addition to promoting metabolic effects such as increased glucose consumption, glycolysis, and glycogen synthesis (53, 54). Insulin receptors in colonic tumors and cell cultures are more resistant to down-regulation than insulin receptors in normal colonic epithelial cells (55, 56). Hyperinsulinemia could thus act as a growth factor and contribute to a clonal expansion of transformed cells. Moreover, a HF diet could promote CRC through insulin’s possible effect on other tumor promoters such as IGF-1 and reactive oxygen species (6, 57, 58, 59, 60). It is also possible that a high saturated fat diet promotes CRC through a mechanism independent of insulin resistance but with similar intermediates such as reactive oxygen species and protein kinase C (60, 61, 62, 63). A HF diet could also promote CRC by mechanisms completely independent of insulin resistance such as through increased secondary bile acids or prostaglandins (64, 65). Additional studies are needed to investigate the possible causal pathways between insulin resistance with its related metabolic parameters and CRC, as well as the importance of other postulated mechanisms of CRC development relative to those involving the insulin resistance syndrome in human and animal models.

In summary, the results of this study are the first to demonstrate that direct measures of insulin resistance, as assessed by the hyperinsulinemic-euglycemic clamp, are strongly correlated with promotion of CRC, as assessed by multiplicity of ACF. Practical surrogate measures of insulin sensitivity that correlated with multiplicity of ACF were insulin levels at 180 min after a glucose load in this model. In contrast, complex indices of insulin sensitivity, fasting levels of glucose, insulin, total IGF-1, NEFA, and triglycerides and body weight were poorly correlated with multiplicity of ACF. These results add support to the insulin resistance-CRC hypothesis. Additional studies are needed to investigate the possible causal link between insulin resistance with its related metabolic parameters and the development of CRC and to find the optimal combination of biomarkers to assess CRC risk and development.

We thank Frank Giuliano, Diana Hiesl, Loretta Lam, Karen Parisien, and An Jing Wang for technical assistance and Dr. Alan Medline for helpful discussions.