An Inflammation Score Is Better Associated with Basal than Stimulated Surrogate Indexes of Insulin Resistance
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《临床内分泌与代谢杂志》
Abstract
Most studies describe the association between one particular inflammatory marker and insulin resistance (IR), features of the metabolic syndrome, or progression to type 2 diabetes. We aimed to build an Inflammation Score as a tool to measure IR-associated inflammatory activity and to evaluate the ability of different surrogate indexes of IR to reflect the inflammatory state.
We studied 81 subjects, aged 47.7 ± 12 yr with a body mass index of 28.3 ± 4 kg/m2. The Inflammation Score was composed of: white blood cell count, erythrocyte sedimentation rate, C-reactive protein, and soluble fraction of TNF- receptors 1 and 2. All the subjects underwent a frequently sampled iv glucose tolerance test, an oral glucose tolerance test, and surrogate indexes of IR were calculated.
Each increase in the Inflammation Score was associated with a progressive increase in IR. We found significant differences across categories (0–1, 2, 3, and 4–5 points in the score) in age (P = 0.048), waist circumference (P = 0.015), body mass index (P = 0.013), blood pressure (P = 0.005), and uric acid (P = 0.031). The Inflammation Score was significantly associated with all but three of the surrogate IR indexes [2-h insulin glucose ratio, Gutt’s insulin sensitivity (SI) index, and Avignon’s 2-h SI index]. Surrogate indexes obtained from basal values showed a similar correlation with the Inflammation Score than the SI from frequently sampled iv glucose tolerance test.
In summary, the Inflammation Score is a useful tool in the evaluation of IR-associated inflammatory activity. The surrogate indexes obtained using fasting glucose and insulin appear to better reflect this inflammatory state. Basal rather than stimulated indexes should be used in the evaluation of therapeutic measures aimed at modifying IR-associated inflammatory activity.
Introduction
IMPAIRED GLUCOSE TOLERANCE (IGT) and type 2 diabetes mellitus (DM2) commonly occur with a cluster of clinical and biochemical features that have been named metabolic syndrome (MS), which is associated with an increased risk for cardiovascular disease and mortality (1, 2, 3). Many researches believe that insulin resistance (IR) is the pathophysiological process underlying the clustering of cardiovascular risk factors in the MS.
One of the main alterations that leads to impaired insulin action seems to be related to a low-grade systemic inflammation (4). Positive correlations between C-reactive protein (CRP) and features of the MS (5, 6, 7) as well as with the progression to DM2 (8, 9, 10) have been described. High white blood cell count (WBC) (11, 12), elevated erythrocyte sedimentation rate (ESR) (13), and increased circulating concentration of soluble fraction of TNF- receptor (sTNFR) (14, 15) have been related to a worsening of insulin sensitivity (SI) or increased DM2 incidence. Among the tools to define IR and measure whole-body insulin action, the euglycemic hyperinsulinemic clamp and the frequently sampled iv glucose tolerance test (FSIVGT) with minimal model analysis are the most reliable indexes, but they require insulin infusion and repeated blood sampling. Surrogate measures of SI based on single measurements of fasting insulin and glucose [homeostasis model assessment (HOMA) and quantitative SI check index (QUICKI)] have been demonstrated to correlate with gold standard procedures (16). These indexes are useful in defining MS and in predicting the development of cardiovascular disease and DM2 (17, 18). Other indexes, based on stimulated insulin and glucose concentrations after an oral glucose tolerance test (OGTT) [Gutt (19), Composite (20), and Avignon (21)] have also demonstrated an acceptable correlation with the gold standard measurements.
Most studies describe the association of one particular inflammatory marker with IR, features of the MS, or progression to DM2. In this study, we aimed to: measure subclinical inflammation with an Inflammation Score composed of WBC, ESR, CRP, and sTNFR; investigate the association between the Inflammation Score and IR; and evaluate the suitability/ability of the surrogate IR indexes to reflect the inflammatory state.
Subjects and Methods
We included 81 consecutive subjects from an ongoing longitudinal study dealing with SI. Inclusion criteria for that study were: body mass index (BMI) less than 40 kg/m2, absence of any systemic disease, and absence of any infection in the previous month.
All subjects underwent a full medical study and were studied in the research laboratory in the postabsortive state. BMI was calculated as weight in kilograms divided by squared height in meters. The subjects’ waists were measured with a soft tape midway between the lowest rib and the iliac crest. Blood pressure was measured in the supine position on the right arm after 10-min rest; a standard sphygmomanometer of appropriate cuff size was used. Values used in the analysis are the average of three readings taken at 5-min intervals. Alcohol and caffeine were withheld within 12 h of the different tests.
Subjects were aged 47.7 ± 12 yr and were classified as having normal glucose tolerance (NGT), IGT, or DM-2 (previously undiagnosed) after an OGTT, according to WHO criteria (22). Clinical and metabolic characteristics of the study subjects are shown in Table 1.
Informed written consent was obtained after the purpose, nature, and potential risks of the study were explained to the subjects. The experimental protocol was approved by the Ethics Committee of our Hospital.
Analytical methods
Serum glucose concentrations were measured in duplicate by the glucose oxidase method using a Beckman Glucose Analyzer II (Beckman Instruments, Brea, CA). The coefficient of variation was 1.9%. Total serum cholesterol was measured through the reaction of cholesterol esterase/cholesterol oxidase/peroxidase. High-density lipoprotein-cholesterol was quantified after precipitation with polyethylene glycol at room temperature. Total serum triglycerides were measured through the reaction of glycerol-phosphate-oxidase and peroxidase. Serum insulin levels during the FSIVGT were measured in duplicate by monoclonal immunoradiometric assay (Medgenix Diagnostics, Fleunes, Belgium). Intra- and interassay coefficients of variation were similar to those previously reported (23).
sTNFR1 and sTNFR2 levels were analyzed as surrogate markers of TNF- actions using a commercially available solid-phase enzyme- amplified sensitivity immunoassay, sTNFR1 (Medgenix Diagnostics), and sTNFR2 enzyme-amplified sensitivity immunoassay (Biosource Technologies, Inc. Europe S.A., Fleunes, Belgium). The intra- and interassay coefficients of variation were less than 9%
ESR was measured by a modified Westergren method at 1 h and CRP analyzed by immunoturbidimetry (Beckman, Fullerton, CA).
WBC was determined by routine laboratory tests (Coulter Electronics, Hialeah, FL).
Inflammation Score
We aimed to quantify the inflammatory state using an Inflammatory Score inspired by the results of two recently published studies (24, 25) that show an additive effect of inflammatory markers on diabetes incidence. This score was composed of five inflammatory markers: WBC, CRP, ESR, sTNFR1, and sTNFR2. This inflammation score ranged from 0–5, increasing by one unit for each value greater than the median of the study sample for each inflammatory marker. Data for these markers are shown in Table 1.
SI
We used the SI obtained from the FSIVGT (SI FSIVGT) with minimal model analysis as a reference test for SI. An iv catheter was placed in an antecubital vein of each arm. Baseline samples for glucose and insulin determinations were drawn at 5, 10, and 20 min after iv placement; 0.3 g/kg of glucose as 50% dextrose solution was administered over 1 min. At 20 min after the completion of the glucose bolus, 0.03 U/kg insulin (Actrapid, Novo-Nordisk A/S, Bagsvaerd, Denmark) was administered iv. This increase of insulin facilitates measurement of SI using the minimal model technique. Blood samples for glucose and insulin determinations were obtained from a contralateral antecubital vein up to 180 min, as previously described (14, 15, 23, 26).
The surrogate indexes of IR (Table 2) were calculated as previously described (17, 20, 21, 27, 28, 29, 30, 31, 32, 33).
Statistical analysis
Data were analyzed with the version 9 statistical software package (SPSS Inc., Chicago, IL). Descriptive results of continuous variables are expressed as mean ± SD, median, range, or interquartile range as appropriate. Before statistical analysis, normal distribution was evaluated using Kolmogorov test, and variables were log-transformed if necessary. We used the variable (SI measured with each formula + 1) to avoid values of SI = 0 and allow for logarithmic transformation. Comparison among groups was performed by ANOVA, followed by Bonferroni’s multiple t test. Spearman rank correlation coefficients were used to quantify the relation between IR indexes and the Inflammation Score. Multiple linear regression analysis in an enter mode was also used to correct for confounding variables. Results were considered statistical significant at P < 0.05.
Results
Spearman correlation coefficients between SI FSIVGT and each of the components of the Inflammation Score were: WBC (r = –0.345, P = 0.002), CRP (r = –0.247, P = 0.026), sTNFR1 (r = –0.198, P = 0.080), sTNFR2 (r = –0.297, P = 0.007), and ESR (r = –0.139, P = 0.215). All of the inflammatory markers correlated with at least one of the IR indexes. Subjects were classified in categories according to their Inflammation Score. For reasons of homogeneity, subjects with an Inflammation Scores of 0 and 1 and 4 and 5 were grouped (Table 3). Each increase in Inflammation Score was associated with a progressive increase in IR (P = 0.027) (Fig. 1). In a multiple linear regression analysis to predict SI FSIVGT, BMI, sex, and the Inflammation Score contributed to 40% of the SI FSIVGT variance (R square=0.4). A general lineal model was constructed to predict the decrease in SI FSIVGT according to the Inflammation Score. For each increase in the Inflammation Score, SI FSIVGT was decreased by 0.34 x 10–4 min/mU·liter (95% confidence interval, – 0.57 to –0.1; r2 = 0.1; P = 0.007). Thus healthy subjects with all five inflammatory markers present had an average SI FSIVGT that was 20.7% lower than those subjects with none or one single marker present.
We found significant differences across categories in age (P = 0.048), waist circumference (P = 0.015), BMI (P = 0.013), systolic blood pressure (SBP) (P = 0.005), diastolic blood pressure (P = 0.002), and uric acid (P = 0.031) (Table 3). Results were similar when men and women were analyzed separately. Although we did not find differences in fasting glycemia, the Inflammation Score was greater in DM-2 and IGT patients (2.63 ± 1.49) than NGT subjects (1.87 ± 1.51; P = 0.036; Fig. 2).
Statistically significant associations between the Inflammation Score and all but three of the simple IR indexes were found. The only three indexes without significant correlation with the Inflammation Score were those calculated using the 2-h values of the OGTT (Table 2). IR indexes obtained from basal values showed a similar correlation with Inflammation Score than the SI FSIVGT. After controlling for age, gender, smoking, SBP, and BMI, the indexes that maintained a statistically significant association with the Inflammation Score were: SI FSIVGT (P = 0.015), HOMA and fasting IR index (FIRI) (P = 0.017), QUICKI (P = 0.018), Bennett’s SI index (P = 0.045), Avignon’s SI index (P = 0.018), and Composite (P = 0.038).
SI FSIVGT was independently predicted by BMI (P < 0.001) and the Inflammation Score (P = 0.015) after adjusting for age, gender, smoking, and SBP.
Discussion
Inflammatory pathways have been increasingly recognized as having an important role in IR (34, 35, 36). In this report, we classified healthy subjects according to an Inflammation Score. This score was designed to measure subclinical inflammation. Each increase in the Inflammation Score was associated with an increase in IR. As expected, a greater score of inflammation was associated with increased BMI, waist circumference, and higher blood pressure. Aging was also associated with an increase in inflammatory parameters, as reported by others (37). In multiple regression analysis, we show that BMI and the Inflammation Score were independent predictors of IR.
Simple IR indexes have been developed as tools used to infer SI in large epidemiological studies. They are the result of combinations of insulin or insulin and glucose values in the fasting state or at different times during the OGTT. Different reports have shown fairly good correlations of HOMA, FIRI, QUICKI, Gutt, Avignon, or Composite either with measurements obtained from the euglycemic hyperinsulinemic clamp or with the FSIVGT. They have also been used to evaluate changes in SI after therapy with insulin sensitizers or antiinflammatory agents (38, 39), but they may be also suitable to reflect the inflammatory state underlying IR. Remarkably, we found that indexes obtained at least from basal glucose and insulin concentrations had a similar correlation with the inflammation score than the SI FSIVGT. The index with the strongest association with the Inflammation Score was the Composite from Matzuda and De Fronzo (20). Composite and Avignon’s SI index were the only two indexes obtained from OGTT that showed significant correlation with the Inflammation Score. An important difference with Gutt and Avignon’s 2-h SI index is that the Composite formula gives special importance to basal values. Considering that fasting glucose and insulin values are mainly determined by hepatic IR (40, 41), these results hint at the important role of the liver in the pathogenesis of MS and associated inflammatory activity. In fact, recent findings in dogs with central adiposity support the hypothesis of the primacy of hepatic IR in the development of the MS (42).
There are some evidences that show the importance of insulin in modulating acute phase response. Animal experimentation shows that the acute phase response is increased in insulin deficiency (43). Insulin inhibits acute-phase protein synthesis in animal and human hepatoma cell lines (44, 45). However, the lack of an increased acute-phase response in uncomplicated type 1 DM indicates that liver IR is much more important than insulin deficiency in the regulation of this response (34, 43, 44, 45). These findings suggest that IR can amplify the cytokine effects on the liver.
Recommendations for cardiovascular risk reduction through preventive and therapeutic strategies that target IR and associated inflammatory activity (46, 47) may reduce the vascular sequelae of diabetes and ameliorate the impact of other components of the MS. The findings of the present study may aid in the evaluation of changes in glucose metabolism and associated inflammatory activity. For instance, in a recent study, basal insulin, HOMA, the Composite index, and the concentration of CRP were favorably affected by antidiabetic treatment (48). The most significant and discriminant changes in SI after therapy were those obtained using Composite index (48).
In summary, the Inflammation Score appears to be a useful tool when evaluating IR and most of the features of the MS. The surrogate indexes obtained using fasting glucose and insulin appear to better reflect this inflammatory state. These findings hint at the liver as an important player in the regulation of both insulin action and inflammation. Basal rather than stimulated indexes of IR should be used in the evaluation of therapeutic measures aimed at modifying IR-associated inflammatory activity.
Footnotes
This work was supported by the Fondo de Investigaciones Sanitarias, Ministry of Health of Spain Grants G03/212 and G03/028.
First Published Online October 14, 2004
Abbreviations: BMI, Body mass index; CRP, C-reactive protein; DM2, type 2 diabetes mellitus; ESR, erythrocyte sedimentation rate; FIRI, fasting IR index; FSIVGT, frequently sampled iv glucose tolerance test; HOMA, homeostasis model assessment; IGT, impaired glucose tolerance; IR, insulin resistance; NGT, normal glucose tolerance; OGTT, oral glucose tolerance test; QUICKI, quantitative SI check index; SBP, systolic blood pressure; SI, insulin sensitivity; sTNFR, soluble fraction of TNF- receptor; WBC, white blood cell count.
Received April 26, 2004.
Accepted October 1, 2004.
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Most studies describe the association between one particular inflammatory marker and insulin resistance (IR), features of the metabolic syndrome, or progression to type 2 diabetes. We aimed to build an Inflammation Score as a tool to measure IR-associated inflammatory activity and to evaluate the ability of different surrogate indexes of IR to reflect the inflammatory state.
We studied 81 subjects, aged 47.7 ± 12 yr with a body mass index of 28.3 ± 4 kg/m2. The Inflammation Score was composed of: white blood cell count, erythrocyte sedimentation rate, C-reactive protein, and soluble fraction of TNF- receptors 1 and 2. All the subjects underwent a frequently sampled iv glucose tolerance test, an oral glucose tolerance test, and surrogate indexes of IR were calculated.
Each increase in the Inflammation Score was associated with a progressive increase in IR. We found significant differences across categories (0–1, 2, 3, and 4–5 points in the score) in age (P = 0.048), waist circumference (P = 0.015), body mass index (P = 0.013), blood pressure (P = 0.005), and uric acid (P = 0.031). The Inflammation Score was significantly associated with all but three of the surrogate IR indexes [2-h insulin glucose ratio, Gutt’s insulin sensitivity (SI) index, and Avignon’s 2-h SI index]. Surrogate indexes obtained from basal values showed a similar correlation with the Inflammation Score than the SI from frequently sampled iv glucose tolerance test.
In summary, the Inflammation Score is a useful tool in the evaluation of IR-associated inflammatory activity. The surrogate indexes obtained using fasting glucose and insulin appear to better reflect this inflammatory state. Basal rather than stimulated indexes should be used in the evaluation of therapeutic measures aimed at modifying IR-associated inflammatory activity.
Introduction
IMPAIRED GLUCOSE TOLERANCE (IGT) and type 2 diabetes mellitus (DM2) commonly occur with a cluster of clinical and biochemical features that have been named metabolic syndrome (MS), which is associated with an increased risk for cardiovascular disease and mortality (1, 2, 3). Many researches believe that insulin resistance (IR) is the pathophysiological process underlying the clustering of cardiovascular risk factors in the MS.
One of the main alterations that leads to impaired insulin action seems to be related to a low-grade systemic inflammation (4). Positive correlations between C-reactive protein (CRP) and features of the MS (5, 6, 7) as well as with the progression to DM2 (8, 9, 10) have been described. High white blood cell count (WBC) (11, 12), elevated erythrocyte sedimentation rate (ESR) (13), and increased circulating concentration of soluble fraction of TNF- receptor (sTNFR) (14, 15) have been related to a worsening of insulin sensitivity (SI) or increased DM2 incidence. Among the tools to define IR and measure whole-body insulin action, the euglycemic hyperinsulinemic clamp and the frequently sampled iv glucose tolerance test (FSIVGT) with minimal model analysis are the most reliable indexes, but they require insulin infusion and repeated blood sampling. Surrogate measures of SI based on single measurements of fasting insulin and glucose [homeostasis model assessment (HOMA) and quantitative SI check index (QUICKI)] have been demonstrated to correlate with gold standard procedures (16). These indexes are useful in defining MS and in predicting the development of cardiovascular disease and DM2 (17, 18). Other indexes, based on stimulated insulin and glucose concentrations after an oral glucose tolerance test (OGTT) [Gutt (19), Composite (20), and Avignon (21)] have also demonstrated an acceptable correlation with the gold standard measurements.
Most studies describe the association of one particular inflammatory marker with IR, features of the MS, or progression to DM2. In this study, we aimed to: measure subclinical inflammation with an Inflammation Score composed of WBC, ESR, CRP, and sTNFR; investigate the association between the Inflammation Score and IR; and evaluate the suitability/ability of the surrogate IR indexes to reflect the inflammatory state.
Subjects and Methods
We included 81 consecutive subjects from an ongoing longitudinal study dealing with SI. Inclusion criteria for that study were: body mass index (BMI) less than 40 kg/m2, absence of any systemic disease, and absence of any infection in the previous month.
All subjects underwent a full medical study and were studied in the research laboratory in the postabsortive state. BMI was calculated as weight in kilograms divided by squared height in meters. The subjects’ waists were measured with a soft tape midway between the lowest rib and the iliac crest. Blood pressure was measured in the supine position on the right arm after 10-min rest; a standard sphygmomanometer of appropriate cuff size was used. Values used in the analysis are the average of three readings taken at 5-min intervals. Alcohol and caffeine were withheld within 12 h of the different tests.
Subjects were aged 47.7 ± 12 yr and were classified as having normal glucose tolerance (NGT), IGT, or DM-2 (previously undiagnosed) after an OGTT, according to WHO criteria (22). Clinical and metabolic characteristics of the study subjects are shown in Table 1.
Informed written consent was obtained after the purpose, nature, and potential risks of the study were explained to the subjects. The experimental protocol was approved by the Ethics Committee of our Hospital.
Analytical methods
Serum glucose concentrations were measured in duplicate by the glucose oxidase method using a Beckman Glucose Analyzer II (Beckman Instruments, Brea, CA). The coefficient of variation was 1.9%. Total serum cholesterol was measured through the reaction of cholesterol esterase/cholesterol oxidase/peroxidase. High-density lipoprotein-cholesterol was quantified after precipitation with polyethylene glycol at room temperature. Total serum triglycerides were measured through the reaction of glycerol-phosphate-oxidase and peroxidase. Serum insulin levels during the FSIVGT were measured in duplicate by monoclonal immunoradiometric assay (Medgenix Diagnostics, Fleunes, Belgium). Intra- and interassay coefficients of variation were similar to those previously reported (23).
sTNFR1 and sTNFR2 levels were analyzed as surrogate markers of TNF- actions using a commercially available solid-phase enzyme- amplified sensitivity immunoassay, sTNFR1 (Medgenix Diagnostics), and sTNFR2 enzyme-amplified sensitivity immunoassay (Biosource Technologies, Inc. Europe S.A., Fleunes, Belgium). The intra- and interassay coefficients of variation were less than 9%
ESR was measured by a modified Westergren method at 1 h and CRP analyzed by immunoturbidimetry (Beckman, Fullerton, CA).
WBC was determined by routine laboratory tests (Coulter Electronics, Hialeah, FL).
Inflammation Score
We aimed to quantify the inflammatory state using an Inflammatory Score inspired by the results of two recently published studies (24, 25) that show an additive effect of inflammatory markers on diabetes incidence. This score was composed of five inflammatory markers: WBC, CRP, ESR, sTNFR1, and sTNFR2. This inflammation score ranged from 0–5, increasing by one unit for each value greater than the median of the study sample for each inflammatory marker. Data for these markers are shown in Table 1.
SI
We used the SI obtained from the FSIVGT (SI FSIVGT) with minimal model analysis as a reference test for SI. An iv catheter was placed in an antecubital vein of each arm. Baseline samples for glucose and insulin determinations were drawn at 5, 10, and 20 min after iv placement; 0.3 g/kg of glucose as 50% dextrose solution was administered over 1 min. At 20 min after the completion of the glucose bolus, 0.03 U/kg insulin (Actrapid, Novo-Nordisk A/S, Bagsvaerd, Denmark) was administered iv. This increase of insulin facilitates measurement of SI using the minimal model technique. Blood samples for glucose and insulin determinations were obtained from a contralateral antecubital vein up to 180 min, as previously described (14, 15, 23, 26).
The surrogate indexes of IR (Table 2) were calculated as previously described (17, 20, 21, 27, 28, 29, 30, 31, 32, 33).
Statistical analysis
Data were analyzed with the version 9 statistical software package (SPSS Inc., Chicago, IL). Descriptive results of continuous variables are expressed as mean ± SD, median, range, or interquartile range as appropriate. Before statistical analysis, normal distribution was evaluated using Kolmogorov test, and variables were log-transformed if necessary. We used the variable (SI measured with each formula + 1) to avoid values of SI = 0 and allow for logarithmic transformation. Comparison among groups was performed by ANOVA, followed by Bonferroni’s multiple t test. Spearman rank correlation coefficients were used to quantify the relation between IR indexes and the Inflammation Score. Multiple linear regression analysis in an enter mode was also used to correct for confounding variables. Results were considered statistical significant at P < 0.05.
Results
Spearman correlation coefficients between SI FSIVGT and each of the components of the Inflammation Score were: WBC (r = –0.345, P = 0.002), CRP (r = –0.247, P = 0.026), sTNFR1 (r = –0.198, P = 0.080), sTNFR2 (r = –0.297, P = 0.007), and ESR (r = –0.139, P = 0.215). All of the inflammatory markers correlated with at least one of the IR indexes. Subjects were classified in categories according to their Inflammation Score. For reasons of homogeneity, subjects with an Inflammation Scores of 0 and 1 and 4 and 5 were grouped (Table 3). Each increase in Inflammation Score was associated with a progressive increase in IR (P = 0.027) (Fig. 1). In a multiple linear regression analysis to predict SI FSIVGT, BMI, sex, and the Inflammation Score contributed to 40% of the SI FSIVGT variance (R square=0.4). A general lineal model was constructed to predict the decrease in SI FSIVGT according to the Inflammation Score. For each increase in the Inflammation Score, SI FSIVGT was decreased by 0.34 x 10–4 min/mU·liter (95% confidence interval, – 0.57 to –0.1; r2 = 0.1; P = 0.007). Thus healthy subjects with all five inflammatory markers present had an average SI FSIVGT that was 20.7% lower than those subjects with none or one single marker present.
We found significant differences across categories in age (P = 0.048), waist circumference (P = 0.015), BMI (P = 0.013), systolic blood pressure (SBP) (P = 0.005), diastolic blood pressure (P = 0.002), and uric acid (P = 0.031) (Table 3). Results were similar when men and women were analyzed separately. Although we did not find differences in fasting glycemia, the Inflammation Score was greater in DM-2 and IGT patients (2.63 ± 1.49) than NGT subjects (1.87 ± 1.51; P = 0.036; Fig. 2).
Statistically significant associations between the Inflammation Score and all but three of the simple IR indexes were found. The only three indexes without significant correlation with the Inflammation Score were those calculated using the 2-h values of the OGTT (Table 2). IR indexes obtained from basal values showed a similar correlation with Inflammation Score than the SI FSIVGT. After controlling for age, gender, smoking, SBP, and BMI, the indexes that maintained a statistically significant association with the Inflammation Score were: SI FSIVGT (P = 0.015), HOMA and fasting IR index (FIRI) (P = 0.017), QUICKI (P = 0.018), Bennett’s SI index (P = 0.045), Avignon’s SI index (P = 0.018), and Composite (P = 0.038).
SI FSIVGT was independently predicted by BMI (P < 0.001) and the Inflammation Score (P = 0.015) after adjusting for age, gender, smoking, and SBP.
Discussion
Inflammatory pathways have been increasingly recognized as having an important role in IR (34, 35, 36). In this report, we classified healthy subjects according to an Inflammation Score. This score was designed to measure subclinical inflammation. Each increase in the Inflammation Score was associated with an increase in IR. As expected, a greater score of inflammation was associated with increased BMI, waist circumference, and higher blood pressure. Aging was also associated with an increase in inflammatory parameters, as reported by others (37). In multiple regression analysis, we show that BMI and the Inflammation Score were independent predictors of IR.
Simple IR indexes have been developed as tools used to infer SI in large epidemiological studies. They are the result of combinations of insulin or insulin and glucose values in the fasting state or at different times during the OGTT. Different reports have shown fairly good correlations of HOMA, FIRI, QUICKI, Gutt, Avignon, or Composite either with measurements obtained from the euglycemic hyperinsulinemic clamp or with the FSIVGT. They have also been used to evaluate changes in SI after therapy with insulin sensitizers or antiinflammatory agents (38, 39), but they may be also suitable to reflect the inflammatory state underlying IR. Remarkably, we found that indexes obtained at least from basal glucose and insulin concentrations had a similar correlation with the inflammation score than the SI FSIVGT. The index with the strongest association with the Inflammation Score was the Composite from Matzuda and De Fronzo (20). Composite and Avignon’s SI index were the only two indexes obtained from OGTT that showed significant correlation with the Inflammation Score. An important difference with Gutt and Avignon’s 2-h SI index is that the Composite formula gives special importance to basal values. Considering that fasting glucose and insulin values are mainly determined by hepatic IR (40, 41), these results hint at the important role of the liver in the pathogenesis of MS and associated inflammatory activity. In fact, recent findings in dogs with central adiposity support the hypothesis of the primacy of hepatic IR in the development of the MS (42).
There are some evidences that show the importance of insulin in modulating acute phase response. Animal experimentation shows that the acute phase response is increased in insulin deficiency (43). Insulin inhibits acute-phase protein synthesis in animal and human hepatoma cell lines (44, 45). However, the lack of an increased acute-phase response in uncomplicated type 1 DM indicates that liver IR is much more important than insulin deficiency in the regulation of this response (34, 43, 44, 45). These findings suggest that IR can amplify the cytokine effects on the liver.
Recommendations for cardiovascular risk reduction through preventive and therapeutic strategies that target IR and associated inflammatory activity (46, 47) may reduce the vascular sequelae of diabetes and ameliorate the impact of other components of the MS. The findings of the present study may aid in the evaluation of changes in glucose metabolism and associated inflammatory activity. For instance, in a recent study, basal insulin, HOMA, the Composite index, and the concentration of CRP were favorably affected by antidiabetic treatment (48). The most significant and discriminant changes in SI after therapy were those obtained using Composite index (48).
In summary, the Inflammation Score appears to be a useful tool when evaluating IR and most of the features of the MS. The surrogate indexes obtained using fasting glucose and insulin appear to better reflect this inflammatory state. These findings hint at the liver as an important player in the regulation of both insulin action and inflammation. Basal rather than stimulated indexes of IR should be used in the evaluation of therapeutic measures aimed at modifying IR-associated inflammatory activity.
Footnotes
This work was supported by the Fondo de Investigaciones Sanitarias, Ministry of Health of Spain Grants G03/212 and G03/028.
First Published Online October 14, 2004
Abbreviations: BMI, Body mass index; CRP, C-reactive protein; DM2, type 2 diabetes mellitus; ESR, erythrocyte sedimentation rate; FIRI, fasting IR index; FSIVGT, frequently sampled iv glucose tolerance test; HOMA, homeostasis model assessment; IGT, impaired glucose tolerance; IR, insulin resistance; NGT, normal glucose tolerance; OGTT, oral glucose tolerance test; QUICKI, quantitative SI check index; SBP, systolic blood pressure; SI, insulin sensitivity; sTNFR, soluble fraction of TNF- receptor; WBC, white blood cell count.
Received April 26, 2004.
Accepted October 1, 2004.
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