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Department of Pediatrics (L.J.M., J.G.W., S.R.D., L.M.D.), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229; University of Cincinnati School of Medicine (L.J.M., J.G.W., S.R.D., L.M.D.), Cincinnati, Ohio 45267; and The Heller School for Social Policy and Management (E.G.), Brandeis University, Waltham, Massachusetts 02454
Address all correspondence and requests for reprints to: Dr. Lisa J. Martin, Center for Epidemiology and Biostatistics, MLC 5041, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, Ohio 45229. E-mail: Lisa.Martin{at}cchmc.org.
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Abstract |
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 Top Abstract IntroductionSubjects and MethodsResultsDiscussionReferences |
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Objective: The aim of this study was to determine whether associations between adiponectin and insulin sensitivity and lipids are stronger with increasing adiposity.
Design: This cross-sectional study involved participants in The Princeton School District Study.
Setting: The study was conducted in the Princeton City schools (Cincinnati, OH) during the 2001–2002 school year.
Participants: A total of 1196 non-Hispanic White and Black students in grades 5–12 participated.
Main Outcome Measure: The relationships between adiponectin and high-density lipoprotein, triglycerides, and insulin were measured. To test our hypothesis, we: 1) compared correlation and regression coefficients of lean and nonlean individuals, and 2) incorporated an adiponectin by adiposity interaction in regression models.
Results: For high-density lipoprotein and triglycerides, the relationship with adiponectin, although present among lean adolescents, strengthened with increasing adiposity. However, with insulin, a relationship with adiponectin was only present among nonlean adolescents.
Conclusions: These analyses suggest that adiponectin’s relationship with insulin and lipids strengthens with increasing adiposity, such that heavier adolescents have a greater benefit from high levels of adiponectin than their lean counterparts.
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Introduction |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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The proinflammatory environment of obesity has been implicated in the pathogenesis of obesity-related comorbidities. With increasing adiposity, adipose tissue undergoes marked morphologic and physiologic changes, including the infiltration of macrophages and the release of proinflammatory cytokines (2). In physiologic studies, these proinflammatory cytokines, such as TNF-
, decrease insulin sensitivity and increase lipolysis (3, 4). Thus, adipose tissue changes may contribute to insulin resistance and dyslipidemia.
In such an environment, antiinflammatory factors such as adiponectin may play a central role in modulating obesity-related comorbidities. Although produced exclusively in adipose tissue, plasma adiponectin levels are paradoxically lower in obesity (5), for reasons that have not been fully elucidated. Adiponectin is a major circulating protein in blood and has important endocrine functions, including improving hepatic insulin sensitivity and lipid levels (6, 7, 8). Adiponectin also has strong antiinflammatory properties, because it inhibits macrophage activation and TNF- action (9, 10, 11). Because adiponectin is secreted by adipocytes, it may have the opportunity to act within adipose tissue to counteract the proinflammatory cytokines associated with insulin resistance and dyslipidemia.
We hypothesized that adiponectin may have a more important role in improving insulin sensitivity and lipid profiles in obese individuals, despite their lower plasma adiponectin levels. Our objective was to explore these relationships in a large epidemiologic cohort of adolescents and test whether associations between adiponectin and both insulin sensitivity and blood lipids are stronger with increased adiposity.
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Subjects and Methods |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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Sample. We randomly selected 1236 non-Hispanic White and Black students from 2501 students participating in a prospective, urban-suburban school-based study of carbohydrate metabolism (Princeton School District Study) (12). In this larger study, students in grades 5–12, in 2001–2002, in the Princeton City School District (Cincinnati, OH) were invited to participate. Exclusion criteria included chronic disease, medication use known to affect carbohydrate metabolism, and pregnancy. Our cohort consisted of 1196 students with complete data. This cohort did not differ from the overall Princeton School District Study population with regard to age, sex, race, adiposity, or family history of diabetes (data not shown). The Institutional Review Boards of Cincinnati Children’s Hospital Medical Center and the University of Cincinnati approved this study. Written informed consent was obtained from all participants 18 yr of age and older or from the parents/guardian, with written assent obtained from participants less than 18 yr of age.
History and physical examination. Parents and/or students completed a medical history documenting chronic disease, medication use, and history of menarche for the girls. Trained study personnel conducted physical examinations in school facilities, behind portable screens. Height and weight were measured in street clothes, without shoes and with empty pockets. Participants were weighed to the nearest 0.01 kg (Seca 770 scale; Seca, Hamburg, Germany), and height was measured to the nearest 0.1 cm (Road Rod stadiometer; Quick Medical, North Bend, WA). Waist circumference was measured at the level of the umbilicus, to the nearest 0.1 cm, after an overnight fast. Two measurements were taken (by the same study personnel), and the average was used in the analysis. Axillary hair was documented in boys as none, minimal, or adult distribution (13, 14, 15).
Laboratory measurements. After a minimum 10-h fast, venipuncture was performed. Laboratory methods for assaying insulin, testosterone, estradiol, high-density lipoprotein (HDL), and triglycerides (TG) were previously described (12, 16). Plasma adiponectin levels were measured using a commercial RIA kit (Linco Research, Inc., St. Charles, MO) with a sensitivity of 0.5 µg/ml and intra- and interassay coefficients of variations of 5 and 15%, respectively.
Puberty. Pubertal status determination was described previously (12). Briefly, sex hormone cut points for testosterone and estradiol were established to distinguish prepuberty (Tanner I) from puberty (Tanner II-IV) using data from two large Cincinnati-based adolescent cohorts with full Tanner staging (17, 18). Post puberty was defined in girls with menarche duration of at least 2 yr and in boys with an adult distribution of axillary hair.
Calculated variables.
Body mass index (BMI) was calculated [weight (kilograms)/height (meters)2]. BMI percentile (BMI %) and Z-score (BMI-Z) were determined using Centers for Disease Control and Prevention growth charts, which take age and sex into consideration (www.cdc.gov/nccdphp/dnpa/growthcharts/sas.htm, accessed December 2002). Lean (<85th BMI %) and nonlean (
85th BMI %) categories of adolescents were defined, consistent with the clinical threshold of risk for overweight in children (19).
Because nationally representative age- and sex-specific waist Z-scores have not been established, we calculated waist Z-scores (waist-Z) based on our population distribution of waist circumference by age and sex (n = 1196).
Statistical analyses
Analyses were conducted using SAS, version 9.1 (SAS Institute, Inc., Cary, NC). As the outcomes are HDL, TG, and insulin, we used Bonferroni-adjusted P-values (0.05/3), resulting in a significance level of 0.017.
Data preparation. All continuous variables were examined for normality, and natural logarithm (ln) transformations of insulin and TG were used in the analysis.
Correlation analysis. Partial correlations were calculated using race, sex, and puberty as partial variables for the full sample and subsets of lean and nonlean adolescents. A partial correlation is the correlation of two variables while controlling for the covariate relationship between variables. For our sample, the ability to remove covariate effects on the relationship is crucial because sex, puberty, and race may all influence adiponectin, insulin, and lipids. Thus, these covariates may alter the level of correlation. Significant differences between Pearson correlation coefficients in various subgroups were ascertained using Fisher’s Z-transformation. The differences between Z-transformed scores for the subsets were compared with a standard normal distribution, and two-tailed probabilities were calculated.
Linear regression in lean and nonlean subsets. Linear regression models for each of the three outcome variables were evaluated in lean and nonlean subsets. Sex, puberty, age, race, and adiponectin were considered in the models, with male, prepuberty, and non-Hispanic White as reference categories. To identify the most parsimonious model, we first used best subsets selection with adjusted R2 as the criterion. Variables not reaching marginal significance (P > 0.10) were eliminated. Additionally, as many variables were correlated, variables exhibiting high collinearity (variance inflation factor > 10) were identified, and the one with the higher P-value was removed. Then, variables in the model were exchanged for correlated variables that had been eliminated from the model (e.g. age might be substituted for puberty). The Bayesian Information Criteria (BIC) from these models were compared and the model with the lowest BIC was selected. BIC differences greater than 6 were considered strong evidence for the model with the lower BIC (20). Differences in BIC values less than 2 were considered equally likely models, and all models within this window were combined to create the most parsimonious model. Regression coefficients are reported as ß ± SE.
To compare the regression coefficients for adiponectin in lean vs. nonlean groups, the following equation was used:
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Testing interaction terms in the whole cohort. To evaluate interactions, models including the whole cohort for the three outcome variables were developed allowing for interactions between adiponectin and adiposity (waist-Z, BMI-Z, and nonlean) to enter the model. Model selection followed the criteria described above, except that the interaction terms were evaluated separately due to issues with collinearity, and nonsignificant main effects were retained in the model if interactions were significant.
Interpretation of interaction terms is an important consideration in this study. The ß-estimate of an interaction between a categorical and a continuous variable indicates the difference in slope of the continuous variable between the nonreference and reference categories. The ß-estimate of an interaction between two continuous variables indicates that as one continuous variable increases (e.g. BMI-Z), the relationship between the other variable and the outcome (e.g. adiponectin and HDL) changes.
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Results |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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. Adiponectin and HDL concentrations are higher in the lean group, whereas insulin and TG are higher in the nonlean group (all P < 0.0001). Lean subjects are more likely to be non-Hispanic White than nonlean subjects (P < 0.0001).
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In the full dataset after accounting for the effects of sex, race, and puberty, plasma adiponectin was significantly negatively correlated with ln(insulin) and ln(TG) and positively correlated with HDL cholesterol (Table 2).
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). In addition, partial correlations between adiponectin and ln(insulin), HDL, and ln(TG) were significantly higher among nonlean than lean adolescents (P < 0.002 for all), indicating a significantly stronger relationship between adiponectin and these outcomes in the nonlean subset. Regression analysis
To further explore this finding, linear regression models were created separately in lean and nonlean subsets.
After adjusting for puberty, age, sex, and race, adiponectin was significantly associated with HDL in both lean and nonlean subsets (ß± SE, 0.47 ± 0.01 and 0.94 ± 0.13, respectively; both P < 0.0001). In addition, the relationship between adiponectin and HDL was significantly stronger in nonlean vs. lean adolescents (P = 0.0013; Fig. 1A), confirming our partial correlation findings.
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).
By contrast, after adjusting for puberty, sex, and race, adiponectin was significantly associated with ln(insulin) in the nonlean subset (ß± SE, –0.050 ± 0.008; P < 0.0001) but not in the lean subset (ß± SE, –0.008 ± 0.005; P = 0.13). These regression coefficients were significantly different from each other (P < 0.0001; Fig. 1C), again supporting the partial correlation analyses.
Regression analysis with interaction
To test for interaction between adiponectin and adiposity in the whole cohort, linear regression models were developed for HDL, ln(TG), and ln(insulin), including adiponectin by adiposity interaction terms. The best models for HDL included either adiponectin*BMI-Z (BIC = 5505) or adiponectin*nonlean (BIC = 5506), which were better than the model without an interaction (BIC = 5510). The best models for ln(TG) included either adiponectin*nonlean (BIC = –2307) or adiponectin*waist-Z (BIC = –2305), which were better than the model without an interaction (BIC = –2300). The best models for ln(insulin) included adiponectin*nonlean (BIC = –1375) or adiponectin*BMI-Z (BIC = –1375), which were superior to the model without an interaction (BIC = –1368). Table 3 reports the model for each outcome with the lowest BIC value. For each outcome, the adiponectin by adiposity interaction acts to increase adiponectin’s effect with increasing adiposity. However, given the similarities in BIC values, we are not able to address whether this is a function of central (waist) or overall (BMI) adiposity.
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Discussion |
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However, our results go beyond the concept that the levels of adiponectin, insulin, and lipids are different in lean and nonlean adolescents. Rather, these data suggest that the relationship between adiponectin and insulin and lipids strengthens with increasing adiposity. This is the first report of the relationship of adiponectin with insulin and lipids being conditional on adiposity. Weiss et al. (27) found that the association of adiponectin with intramyocellular lipid content was present only in their nonlean group; however, they did not formally test for interactions.
The relationships between adiponectin and HDL and ln(TG) were present in both lean and nonlean adolescents but were strengthened with increasing adiposity. The existence of an effect in both groups was supported by the partial correlations and the regression coefficients for adiponectin, which were significant in both lean and nonlean groups. Strengthening of the relationships was supported by statistically higher correlations and regression coefficients for the nonlean adolescents as well as statistically significant interaction terms between adiponectin and adiposity measures, suggesting that as adiposity increases, the relationship between adiponectin and lipids strengthens.
Adiponectin’s relationship with insulin resistance is complex. We demonstrated a strengthening of the association with increasing adiposity, because the partial correlations and regression coefficients were significantly higher in nonlean than lean adolescents. Additionally, the adiponectin by adiposity interaction was highly significant. However, our results from the partial correlations and regression subsets suggest that adiponectin and insulin are significantly associated only in the nonlean group. Additionally, the main effect for adiponectin became insignificant after including the interaction term. Our nonsignificant results in the lean group are not due to low power, given that there were nearly twice as many lean individuals as nonlean individuals. The lack of an effect of adiponectin on insulin sensitivity in the lean state has been noted in adiponectin knockout mice. On the standard diet, these mice fail to exhibit insulin resistance, but insulin resistance can be induced on a high-fat diet (7).
We speculate that adiponectin’s associations with lipids and insulin are strengthened in nonlean adolescents because of the proinflammatory, macrophage-rich environment associated with obesity (28). This proinflammatory state has been implicated in the pathogenesis of type 2 diabetes and dyslipidemia (29, 30, 31). Adiponectin, by contrast, has strong antiinflammatory properties, including suppression of proinflammatory cytokines and their actions in adipose tissue and the inhibition of macrophage accumulation and activity (9, 10, 11, 28). Thus, in overweight individuals, the antiinflammatory, macrophage-inhibiting actions of adiponectin are likely very important in influencing insulin sensitivity and lipid metabolism, given the proinflammatory adipose tissue milieu. However, in lean individuals, the absence of adipose tissue inflammation attenuates the impact of adiponectin on insulin and lipids.
The current study has several limitations. First, the proportion of the variability accounted for in our models is modest, indicating that other unmeasured factors also impact insulin and lipid profiles. In particular, genetic and environmental influences are likely, some of which we will be considering in future analyses, and some of which (e.g. diet, physical activity) were not measured in the current study. Although adiponectin may not account for the majority of variability in HDL, TG, or insulin in our population, it remains a strong independent factor and one that improves the model R2 and BIC. Second, the use of epidemiologic and anthropometric measures may limit our ability to precisely characterize the physiologic mechanism involved. However, large epidemiologic studies, such as this, provide important clues to interactions between factors that are not discernable in smaller cohorts or in vitro studies. Alternate study designs and populations should thus validate the proposed mechanisms suggested by our data.
In conclusion, we have provided novel evidence to suggest that the relationships between adiponectin and insulin and blood lipids (e.g. HDL and TG) are strengthened with increasing adiposity, representing a paradigm shift. The current philosophy with adiponectin is that higher levels are associated with better lipid and insulin profiles regardless of adiposity. Our data suggest that heavier individuals have a greater benefit from high levels of adiponectin than their lean counterparts. This could have important diagnostic and therapeutic implications, which should be explored in future research.
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Acknowledgments |
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Footnotes |
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First Published Online May 3, 2005
Abbreviations: BIC, Bayesian Information Criteria; BMI, body mass index; BMI %, BMI percentile; BMI-Z, BMI Z-score; HDL, high-density lipoprotein; ln, natural logarithm; TG, triglyceride(s); waist-Z, waist Z-scores.
Received January 5, 2005.
Accepted April 22, 2005.
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References |
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stimulates lipolysis in differentiated human adipocytes through activation of extracellular signal-related kinase and elevation of intracellular cAMP. Diabetes 51:2929–2935 inhibits signaling from the insulin receptor. Proc Natl Acad Sci USA 91:4854–4858, overexpressed in human fat cells from insulin-resistant subjects. J Biol Chem 278:45777–45784This article has been cited by other articles:
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  E. Wardaningsih, T. Miida, U. Seino, Y. Fueki, M. Ito, K. Nagasaki, T. Kikuchi, M. Uchiyama, S. Hirayama, O. Hanyu, et al. Low adiponectin state is associated with metabolic abnormalities in obese children, particularly depending on apolipoprotein E phenotype Ann Clin Biochem, September 1, 2008; 45(5): 496 - 503. [Abstract] [Full Text] [PDF]
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 J. G. Woo, L. M. Dolan, R. Deka, R. D. Kaushal, Y. Shen, P. Pal, S. R. Daniels, and L. J. Martin Interactions Between Noncontiguous Haplotypes in the Adiponectin Gene ACDC Are Associated With Plasma Adiponectin Diabetes, February 1, 2006; 55(2): 523 - 529. [Abstract] [Full Text] [PDF]
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H. Knobler, M. Benderly, V. Boyko, S. Behar, Z. Matas, A. Rubinstein, I. Raz, and J. Wainstein Adiponectin and the development of diabetes in patients with coronary artery disease and impaired fasting glucose Eur. J. Endocrinol., January 1, 2006; 154(1): 87 - 92. [Abstract] [Full Text] [PDF]
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