It has been proposed that inflammation or infection may contribute to the development of type 2 diabetes. We examined whether serum gamma globulin, a nonspecific measure of the humoral immune system, predicted changes in glucose tolerance in 2,530 members of the Pima Indian population, a group with a marked predisposition to type 2 diabetes. Cross-sectionally, gamma globulin was positively related to age (r = 0.08, P < 0.0005), BMI (r = 0.09; P < 0.0001), and female sex (P < 0.0001). Gamma globulin concentrations were familial, being positively correlated among siblings (r = 0.23; P < 0.0001) and between parents and their children (mother/child: r = 0.17, P < 0.0001; father/child: r = 0.25, P < 0.0001). Gamma globulin concentrations were higher with greater degrees of American Indian heritage (P < 0.004, with adjustment for age, sex, and BMI) and in the presence of a family history of type 2 diabetes (P < 0.04). Higher gamma globulin levels predicted risk of diabetes. In univariate analysis, a 1 SD difference in gamma globulin was associated with a 20% higher incidence of diabetes in those who were normal glucose tolerant at baseline (hazard rate ratio 1.20 [CI 1.11–1.30]; P < 0.0001) and remained as a significant predictor of diabetes, even when controlled for effects of sex, BMI, and 2-h glucose as additional predictors (hazard rate ratio for 1 SD difference in gamma globulin, 1.14 [1.05–1.24]; P = 0.002). Gamma globulin was also associated in univariate analysis with later development of impaired glucose tolerance (IGT) (hazard rate ratio 1.15 [1.07–1.23]; P < 0.0001), but not with the transition from IGT to diabetes (hazard rate ratio 1.04 [0.90–1.20]; P = 0.6). Thus, gamma globulin levels predict increased risk of diabetes in the Pima population. We suggest that immune function or activation may play a role in the development of type 2 diabetes.
Type 2 diabetes and obesity are associated with a cytokine-associated acute phase reaction (1). This has led to a recently proposed hypothesis that elements of the innate immune system, such as cytokines or the acute phase reactants that they stimulate, contribute to the development of type 2 diabetes and obesity (1). In this model, the innate immune system mediates the effects of factors such as nutrition, age, genes, fetal programming, and ethnicity on the later development of metabolic disease (1). Inflammatory mediators have been extensively studied in coronary heart disease, with convincing evidence that increased concentrations of fibrinogen, C-reactive protein, albumin, and leukocyte count are associated with higher rates of subsequent coronary events (2). Similar evidence for prediction of type 2 diabetes is less extensive. Higher fibrinogen and white cell count and lower serum albumin were all found to predict later type 2 diabetes in a single study (3). More recently, a preliminary report has suggested that plasminogen activator inhibitor-1 (PAI-1) predicts the development of diabetes (4).
The Pima Indian community of Arizona has a very high prevalence of type 2 diabetes and obesity (5). As part of the long-standing epidemiological study in this population, community members have been invited to attend examinations for the presence of diabetes since 1965. In the early years of the study (1966–1973), serum concentrations of gamma globulin were also measured. Because polyclonal increases in gamma globulins are known to accompany infection and inflammatory disease (6,7), we used gamma globulin levels as a marker of activation of the adaptive immune system, examining cross-sectional and prospective relationships of gamma globulin levels to obesity and type 2 diabetes in the Pima population. Prospective relationships of type 2 diabetes to rheumatoid factor, another immune marker measured routinely, were examined for comparison.
RESEARCH DESIGN AND METHODS
The study subjects are participants in the National Institutes of Health survey of diabetes in the Gila River Indian community. Informed consent was obtained from all participants, and ethical approval was received from both the National Institutes of Health and the Gila River Indian community. All members of the community older than age 5 years are invited to a biennial examination, with measurement of a 75-g oral glucose tolerance test (OGTT). Diabetes was diagnosed at these examinations by World Health Organization (WHO) 1985 criteria (8) or, if made between research examinations, the clinical diagnosis was reviewed using these criteria. Height and weight were measured, with subjects wearing light clothing and no shoes, for calculation of BMI (kg/m2).
The fraction of American Indian heritage was calculated from self-report of individuals at interview regarding the heritage of their parents, grandparents, and great-grandparents. In this sample, it overwhelmingly derives from either Pima or Tohono O’odham heritage, these being two closely related groups geographically, culturally, and genetically. Although self-reported, this measure is strongly related to estimation of admixture by testing of a range of genetic markers (9). In this population, non−American Indian heritage represents a variety of influences, mainly a mixture of the population with Mexican-American and European-American groups (9).
Total gamma globulin levels were measured in serum between 1966 and 1973 with the zinc sulfate turbidity method (10). Because concentrations of total gamma globulin were skewed, a log-transformed value was used in statistical analysis, as this approximates more closely a normal distribution. Results in the main part of this study are limited to samples measured between September 1966 and September 1969 because of a change in measurement in 1969. Across these 3 years, assay measurement appeared stable (date of examination accounting for <1% of the variance in the population mean across these years). Measurements taken from September 1969 to September 1973 were also stable, but systematically lower, than those taken in earlier years. Although unable to pool measurements of gamma globulin across all available years, we repeated the main analysis (modeling of the relationship of baseline gamma globulin to later type 2 diabetes) using these later measures (September 1969 to September 1973) to assess results for consistency. Quintiles and tertiles of gamma globulin were calculated within sex and age in 10-year groups from age 5 years on. Rheumatoid factor was measured by the bentonite flocculation test (11).
Relationships of gamma globulin to age and BMI were assessed by calculation of partial correlation coefficients. Relationships to sex, glucose tolerance, parental diabetes, and ethnicity were assessed with general linear models after adjustment for other covariates (BMI and age in assessment of sex; BMI, age, and sex for other models) in baseline examinations (the first examination for each subject). Parents were considered 1) nondiabetic if all recorded OGTTs were nondiabetic (by 1985 WHO criteria), and at least one test had been performed when they were over age 30 years, or 2) diabetic if a diagnosis of diabetes had been made at any time using WHO 1985 criteria.
Familial relationships of gamma globulin levels were assessed by calculation of the intraclass correlation coefficient conditional upon family membership (for siblings) or Pearson interclass correlation coefficients (between pairs of parents and the mean of their children or between mothers and fathers). The intraclass correlation coefficient was calculated by a maximum likelihood method (assessment of the influence of family membership on gamma globulin when entered as a random effect in a “mixed” model). Analysis was restricted to families with at least two siblings in the study (2,215 siblings in 636 nuclear families). The correlation of gamma globulin between parents and their children was examined by Pearson interclass correlation coefficients after pairing maternal or paternal values to the mean of their children in 690 families with measurement of gamma globulin in mother and child (1,814 children) and in 417 families with measurement of gamma globulin in father and child (1,260 children). Finally, the Pearson interclass correlation of gamma globulin between parents was assessed in 307 mother-father pairs when gamma globulin was available for both parents. In all cases, gamma globulin levels were adjusted for age, a quadratic term for age, sex, year of birth, and 2-h glucose during the OGTT before the correlation analysis.
Diabetes incidence during follow-up was computed for subjects from the first visit when gamma globulin had been measured, glucose tolerance was normal (2-h glucose <140 mg/dl), and a measure of BMI was available. Incidence rates (as the number of new cases divided by the person years at risk of diabetes) were calculated for ages 5−19, and in 10-year age groups for ages 20−59, after stratification for sex and tertile of gamma globulin at baseline. Because of the small numbers of person years above age 60 years, individuals were censored at age 60 years for this analysis. Incident cases were defined as individuals diagnosed as diabetic at ages within the range of the age group. Person years at risk were counted from baseline examination to either the last examination (for those who did not develop diabetes) or the date of diagnosis of diabetes, divided into age strata. Standard errors of age- and sex-adjusted incidence rates were calculated (12).
To control for additional predictors of diabetes, the relationship of gamma globulin at baseline to incidence of type 2 diabetes was assessed with proportional hazards regression in all subjects. In subjects with normal glucose tolerance (NGT) at baseline, gamma globulin was modeled as a continuous variable along with other baseline covariates (age, age2, BMI, sex, and 2-h glucose). The closing date for the proportional hazards model was either the date of the last examination (for those who had not developed diabetes) or the date of diabetes diagnosis. For comparison, a separate proportional hazards model was also applied to subjects with impaired glucose tolerance (IGT) at baseline (2-h glucose ≥140 mg/dl but <200 mg/dl) and a model including both groups (IGT and NGT at baseline) was also fitted. The assumption of proportionality inherent in these models was assessed by inclusion of an interaction term (gamma globulin* log of follow-up time), which was not significant in any model. To allow comparison, predictor variables (apart from age and sex) were standardized to a mean of 0 and SD of 1 using data from all subjects (IGT and NGT at baseline). To assess predictors of later IGT, subjects with NGT were assigned a closing date of either the first examination diagnostic for IGT, or the date of a last NGT examination in those remaining NGT. When subjects had progressed to diabetes without an examination showing IGT, an assumption was made that IGT had occurred at a point midway between their last NGT examination and the date of diagnosis with type 2 diabetes, as previously described (13).
Relationships of gamma globulin to weight gain were also examined. The presence of diabetes is associated with diminution of weight gain or weight loss in this population (5). Analysis of weight change over time was therefore restricted to subjects who had nondiabetic examinations at baseline and follow-up. Gamma globulin levels, along with other potentially confounding baseline covariates (age, BMI, and sex) and length of follow-up were assessed using a general linear model.
Baseline characteristics and cross-sectional relationships with diabetes and IGT.
Characteristics of the 2,530 subjects are shown in Table 1. Concentrations of gamma globulin were positively related with age (Pearson partial correlation coefficient: r = 0.08; P < 0.0005; adjusted for sex and BMI), BMI (r = 0.09; P < 0.0001; adjusted for sex and age), and female sex (geometric mean ± 1 SE; males, 1.159 [1.152–1.165] g/100 ml; females, 1.190 [1.187–1.199] g/100 ml; P < 0.0001; adjusted for age and BMI) across all subjects.
Both diabetes and IGT were associated at baseline with higher gamma globulin concentrations (P < 0.0001 adjusted for sex) (Table 1) as well as older age and higher BMI. Differences in gamma globulin dependent on glucose tolerance status appeared to be largely mediated by obesity (analysis of variance [ANOVA] for effect of glucose tolerance on gamma globulin, P = 0.13 with adjustment for age, sex, and BMI). In a similar fashion, although gamma globulin levels had a positive correlation to 2-h glucose (r = 0.06; P < 0.003; n = 2,530), this was not statistically significant after allowing for other covariates (r = 0.02; P = 0.4; adjusted for sex, BMI, and age).
Familial and ethnic relationships of total gamma globulin.
Gamma globulin levels (adjusted for age, sex, and BMI) were positively correlated among siblings (1,752 siblings in 528 nuclear families: intraclass coefficient of correlation r = 0.23; P < 0.0001), mothers and their children (1,391 children in 555 families: r = 0.18; P < 0.0001), and fathers and their children (926 children in 334 families: r = 0.25; P < 0.0001). Correlation between parents was not statistically significant (238 father-mother pairs: r = 0.08; P = 0.2).
Information on parental diabetes was available for 870 subjects who also had measurement of gamma globulin. Parental diabetes was associated with a significant increase in gamma globulin in their offspring (geometric mean [±1 SE]): neither parent diabetic, 1.126 [1.103–1.149] g/100 ml; one diabetic parent, 1.136 [1.125–1.148] g/100 ml; both parents diabetic, 1.172 [1.162–1.182] g/100 ml; P = 0.03 for between-groups effect) with adjustment for other predictors of gamma globulin (age, sex, and BMI).
An estimate of American Indian heritage was available in 2,524 subjects (99.8%, in 1,304 families) of those with baseline gamma globulin measurement. As a continuous variable, fraction of American Indian heritage was positively associated with gamma globulin (P < 0.004) after adjustment for age, year of birth, BMI, glucose tolerance, and sex. It should be noted that the great majority of subjects were of full American Indian heritage (2,303 [91%]) (Fig. 1).
Prospective relationship of total gamma globulin with diabetes.
Of the total 2,088 subjects who were not diabetic at baseline, follow-up information on later glucose tolerance was available in 91% of those with NGT and 85% of those with IGT at baseline (Table 2). Progression to diabetes was higher in those with IGT at baseline (66% progressed to type 2 diabetes vs. 34% of those with NGT).
In those who had NGT at baseline, later development of IGT or diabetes was associated with higher initial age, BMI, and 2-h glucose (Table 2). There was a graded increase in gamma globulin levels, with the highest levels in those who later developed type 2 diabetes and intermediate levels in those who later developed IGT (Table 2). The sex-adjusted incidence of type 2 diabetes, stratified by age and tertile of gamma globulin, is shown in Fig. 2 for 1,694 subjects who were normal glucose tolerant at baseline. The association of diabetes incidence with gamma globulin was most marked in ages 40−49 and 50−59. Although stratified by age group and sex, the incidence rates do not take account of other potentially confounding variables, such as glucose concentration at baseline, age within age groups, and BMI. The prospective relationship of gamma globulin to later development of type 2 diabetes was therefore further analyzed by proportional hazards regression.
In univariate analysis, age, BMI, 2-h glucose, and gamma globulin were significant predictors of diabetes in those with NGT at baseline (Table 3). After adjustment for other covariates, gamma globulin remained a significant predictor of later type 2 diabetes, with a 1 SD difference in log gamma globulin being associated with a 14% difference in hazard rate of diabetes (Table 3).
Gamma globulin also predicted progression of NGT to IGT in univariate analysis (902 incident cases; hazard rate ratio 1.15; 95% CI 1.07–1.23; P < 0.0001), but in contrast to results for prediction of type 2 diabetes, this was not significant in the multivariate model (hazard rate ratio 1.05; 95% CI 0.98–1.13; P = 0.14).
In those who had IGT at baseline, although the highest gamma globulin concentrations were again found in those who progressed to diabetes, differences among groups dependent on progression were not statistically significant (Table 2). Gamma globulin did not appear to predict progression of IGT to diabetes. In the 193 subjects with IGT at baseline, gamma globulin was not a significant predictor in either univariate (hazard rate ratio 1.04; 95% CI 0.90–1.20; P = 0.6) or multivariate models (0.98; 0.85–1.14; P = 0.8; baseline age, sex, BMI, and 2-h glucose included as covariates).
Measurement of rheumatoid factor was available in 5,967 nondiabetic individuals along with follow-up examination for diabetes in the population study (1,325 incident cases). Rheumatoid factor did not predict development of type 2 diabetes (data not shown).
The consistency of gamma globulin in predicting later IGT or type 2 diabetes was also assessed by separate analysis of the relationship of measurements of gamma globulin made between 1969 and 1973 to later type 2 diabetes. In subjects who had NGT at baseline, gamma globulin again predicted type 2 diabetes in both univariate (475 incident cases; hazard rate ratio for 1 SD difference in gamma globulin = 1.19 [1.1–1.29]; P < 0.0001) and multivariate models (hazard rate ratio for 1 SD difference in gamma globulin = 1.09 [1.01–1.18]; P = 0.04; model including sex, age, BMI, and 2-h glucose). Gamma globulin was again associated with later development of IGT in univariate (768 incident cases, hazard rate ratio for 1 SD difference in gamma globulin = 1.15 [1.1–1.29]; P < 0.0001), but not multivariate analysis.
Prospective relationship of total gamma globulin with later weight gain.
A total of 1,616 subjects were nondiabetic at baseline and one follow-up examination (mean ± SD follow-up: 16.1 ± 9.6 years). Baseline gamma globulin was not significantly associated with BMI at later examination (P = 0.3) after adjustment for other predictors (age, a quadratic term for age, BMI at baseline, sex, length of follow-up), all of which were highly significant predictors (P < 0.0001). Similar analysis was also carried out in groups stratified by age (in 10-year age groups from age 5 years) at baseline, with no effect of gamma globulin being observed in any age group.
There has been recent interest in interactions between factors in the innate immune system and metabolic and vascular disease (1). In this study, we showed that total gamma globulin concentration, a nonspecific measure of the adaptive immune system, is influenced in the Pima population by both familial and ethnic factors and is associated cross-sectionally with obesity. Furthermore, higher levels of gamma globulin are associated with higher incidence of type 2 diabetes prospectively.
Gamma globulin was measured by the zinc sulfate turbidometric technique as reported by Kunkel in 1947 (10) and acts as a nonspecific measure of activity of the humoral immune system. Concentrations of gamma globulin by this method agree with quantification by electrophoresis (14) but have been almost entirely replaced in clinical practice with newer methods allowing quantification of immunoglobulin subfractions. Kunkel’s method was developed mainly for use in the assessment of hepatocellular disease (10), but is also sensitive to nonspecific rises in gamma globulin, as observed in a variety of infectious diseases (6,7).
In our analysis, higher gamma globulin levels were related to higher BMI and were also higher in those who had IGT or diabetes, although this relationship was largely explained by effects of covariates such as age and BMI. Immunoglobulin concentrations (of IgA, IgG, and IgM classes) have previously been reported to be higher in those with diabetes (although these data were not adjusted for other covariates) (15). Interpretation of these cross-sectional observations is difficult, as they may be confounded by secondary effects; for example, diabetes may both increase the likelihood of infection and alter the immune response. There were too few subjects in the present study with repeated measures of gamma globulin before and after changes in glucose tolerance to explore this issue.
Gamma globulin concentrations were familial, being significantly correlated among siblings and between parents and their children but not between parents. Relatives share genetic determinants of the immune response but also environmental factors—importantly, exposure to infection. Clearly it is not possible to determine with this analysis whether the familial associations and associations with American Indian ethnicity we observed reflect the results of shared genes or shared environment. The lack of correlation between parents, however, suggests that the correlations among relatives are attributable to either genetic factors or environmental influences acting early in life. Evidence regarding differences in the immune system between American Indian populations and other ethnic groups are limited. One previous study has suggested ethnic differences in gamma globulin levels, with higher concentrations of IgG, IgA, and IgM in American Indian than Caucasian, U.S. military veterans (16). By contrast, levels of PAI-1 were not higher in Pimas than in either Caucasians or South Asians (17).
Our results add to the observations that factors in the immune system may be associated with later metabolic disease. A variety of measures of inflammation predicted later type 2 diabetes in the Atherosclerosis Risk in Communities (ARIC) Study (3), including raised fibrinogen, white cell count, sialic acid, and orosomucoid, as well as lower serum albumin. Such inflammatory markers are known to have positive cross-sectional associations with BMI (3), and recently have been shown also to predict weight gain in the ARIC Study (18). It is important, then, to recognize that the prospective effects observed may simply reflect associations with baseline adiposity. However, in the ARIC study, at least some of the inflammatory measures (white cell count, sialic acid, and orosomucoid) remained significant after adjustment for baseline BMI (3). We have shown that BMI is positively related to gamma globulin in our study. In previous studies in the Pima population, white cell count was also positively associated with obesity (19), with the relationship proposed, at least in part, to be mediated by leptin (20). Nevertheless, the prospective relationship of gamma globulin with later type 2 diabetes that we observed appears to be over and above effects of adiposity. In a similar fashion, insulin resistance might be a potential explanation and confounder of our findings. Insulin resistance is associated with inflammatory mediators independent of relationships with adiposity (21). Insulin resistance also predicts later type 2 diabetes in the Pima population (22). If gamma globulin concentrations were associated with insulin resistance, as appears to be the case for C-reactive protein (21), this might explain the prospective relationship of gamma globulin to type 2 diabetes that we observed. To our knowledge, such a relationship of gamma globulin to insulin resistance has not been assessed. Against this interpretation, the prospective relationship of some inflammatory mediators appears to remain significant after adjustment for baseline insulin (3), and white cell count shows only weak association with measures of insulin resistance in the Pima population (19).
Why should gamma globulin concentration predict type 2 diabetes? Apart from the influences of ethnicity, familiality, age, sex, and BMI that we have described, we do not know what underpins variations in total gamma globulin in our population and, therefore, potentially the association with later type 2 diabetes. If this variation reflects the presence of infectious or inflammatory disease, or indeed activation of the innate immune system by these or other factors (23), then this would support a possible role for these agents in influencing the development of type 2 diabetes. The place of infectious agents in the etiology of type 2 diabetes is currently speculative, but evidence does exist that exposure to hepatitis C increases risk of type 2 diabetes (24). By contrast with our results for total gamma globulin, rheumatoid factor does not appear to predict later type 2 diabetes in this population, making it unlikely that the relationship we observed is explained by associations with diseases that increase rheumatoid factor, such as rheumatoid arthritis. It is also possible that gamma globulin concentration is a marker of underlying genetic differences in the reactivity of the immune system. Fernandez-Real and Ricart (25) suggested that genetic variation in cytokine responses may underpin the predictive relationship of inflammatory mediators and metabolic disease. The ability of gamma globulin to predict later diabetes in this report may therefore reflect underlying genetic differences in the population. Importantly, the observations that gamma globulin levels are familial and related to both American Indian heritage and parental diabetes is consistent with this interpretation, although the possibility that these relationships might be underpinned by environmental rather than genetic influences cannot be excluded.
One weakness of this report is that we cannot determine which of several potential underlying mechanisms might underpin the association of gamma globulin with type 2 diabetes. It is also important to note that the predictive value of gamma globulin was confined to those with NGT. In subjects with IGT at baseline, although gamma globulin levels were higher on average, and highest in the group who progressed to later type 2 diabetes, gamma globulin was not significantly predictive of progression from IGT to diabetes. It is not possible to discern whether this reflected a difference in underlying biology or a failure to detect an effect of gamma globulin because of the smaller number of subjects with IGT at baseline.
Our data add to the body of evidence of relationships among immune mediators, obesity, and type 2 diabetes. This is of particular interest in the context of the high incidence of type 2 diabetes in American Indian populations. Fernandez-Real and Ricart proposed that the relationship of inflammatory mediators and metabolic risk may be important in understanding the evolutionary context of why certain individuals are at risk for type 2 diabetes (25). In particular, they hypothesized that an insulin-resistant genotype associated with a high cytokine response might have been advantageous in historical conditions of short life span, injury, and infectious disease, but disadvantageous today (25). They did not directly address why ethnic groups such as American Indians are so prone to obesity and type 2 diabetes compared with European populations. It is notable that, as well as being highly susceptibility to type 2 diabetes, American Indians have a dramatic history of recent epidemics of infectious disease. After first contact with Europeans in the late 15th century, American Indians were exposed to a range of novel infectious diseases to which they had no prior immunity, leading to repeated epidemics (26,27) and marked declines in population (27,28). It is tempting to speculate that these repeated epidemics may have led to selection of individuals both resistant to infectious disease but also highly prone to diabetes.
In conclusion, total gamma globulin concentrations predict development of type 2 diabetes in the Pima population. These observations support a number of recent observations suggesting a role of inflammation or infection in the pathogenesis of type 2 diabetes.
We thank the members of the Gila River Indian Community for their continued support and participation and the staff of the Diabetes and Arthritis Epidemiology Section for conducting this study.
Address correspondence and reprint requests to Robert Lindsay, MB, PhD, Visiting Associate, National Institute of Diabetes and Digestive and Kidney Diseases, 1550 East Indian School Rd., Phoenix, AZ 85014. E-mail:.
Received for publication 6 September 2000 and accepted in revised form 29 March 2001.
ANOVA, analysis of variance; ARIC Study, Atherosclerosis Risk in Communities Study; IGT, impaired glucose tolerance; NGT, normal glucose tolerance; OGTT, oral glucose tolerance test; PAI-1, plasminogen activator inhibitor-1; WHO, World Health Organization.