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European Heart Journal Advance Access originally published online on September 1, 2007
European Heart Journal 2007 28(21):2637-2643; doi:10.1093/eurheartj/ehm360
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Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2007. For permissions please email: journals.permissions@oxfordjournals.org

ApoB/apoA-I ratio: an independent predictor of insulin resistance in US non-diabetic subjects

Justo Sierra-Johnson1,2, Abel Romero-Corral1, Virend K. Somers1, Francisco Lopez-Jimenez1, Göran Walldius2,3, Anders Hamsten2, Mai-Lis Hellénius2,4 and Rachel M. Fisher2,*

1 Department of Internal Medicine, Division of Cardiovascular Diseases, Mayo Clinic and Foundation, Rochester MN, USA
2 Department of Medicine, Atherosclerosis Research Unit, King Gustaf V Research Institute, Karolinska Insititutet, Karolinska University Hospital, S-171 76, Stockholm, Sweden
3 AstraZeneca, Södertälje, Sweden
4 (MLH), Center for Family and Community Medicine, Karolinska Intstitutet Huddinge, Sweden

Received 18 February 2007; revised 19 July 2007; accepted 26 July 2007; online publish-ahead-of-print 1 September 2007.

* Corresponding author. Tel: +46 8 517 732 45; fax: +46 8 31 12 98. E-mail address: rachel.fisher{at}ki.se

See page 2563 for the editorial comment on this article (doi:10.1093/eurheartj/ehm434)


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 Conclusion
 Acknowledgements
 References
 
Background: Recently, the apoB/apoAI ratio has been associated with the metabolic syndrome; however, is unclear if its association with insulin resistance is mediated through traditional risk factors or if it adds an independent risk by itself. The aim of this study was to assess the independent association between apoB/apoAI ratio and insulin resistance in the US non-diabetic population.

Methods: We examined the association between high apoB/apoAI ratio and insulin resistance among 2955 adults (mean age 47 years; 1457 women) without diabetes (fasting glucose ≤7 mmol/L and not taking diabetes medication), who participated in the Third National Health and Nutrition Examination Survey. Insulin resistance was estimated using the computer homeostatic model assessment (HOMA2) and defined as the upper quartile. The updated ATP-III definition of the metabolic syndrome was used. First, logistic regression was applied to estimate the cross-sectional association between apoB/apoAI (highest quartile vs. lowest quartile) and insulin resistance adjusting for metabolic syndrome components excluding glucose. Finally, multiple linear regression was used to assess the relationships between apoB/apoAI and insulin sensitivity.

Results: Overall, median of apoB/apoAI ratio was significantly higher in subject with insulin resistance than without (0.85, IQR 0.69–0.99 vs. 0.69, IQR 0.56–0.85; P < 0.0001). High apoB/apoAI ratio was independently associated with insulin resistance after adjustment for age and race, and remained significant after further adjustment for metabolic syndrome components, traditional and inflammatory risk factors (in men: OR, 4.12–95% CI, 1.97–8.81; in women: OR, 3.69–95% CI, 1.94–7.27). When apoB/apoAI was considered as a quantitative trait rather than dichotomized, use of the ratio improved the prediction of HOMA2 independently of metabolic syndrome components, traditional and inflammatory risk factors (in men: additional R2 = 0.09, P < 0.001; in women: additional R2 = 0.05, P < 0.001).

Conclusion: In the US population, apoB/apoAI ratio is significantly associated with insulin resistance in non-diabetic subjects, independently of the traditional risk factors, metabolic syndrome components, and inflammatory risk factors. Important clinical risk information provided by apoB/apoAI ratio should be recognized and implemented in future clinical guidelines.

Key Words: apoB • apoAI • apoB/apoAI • Insulin resistance • Metabolic syndrome • Risk factors • NHANES • Centres for disease control and prevention • HOMA2

Insulin resistance is a fundamental metabolic disorder that independently increases risk for coronary heart disease (CHD).1–4 Insulin resistance is associated with aging and obesity and can lead to the clustering of important risk factors, such as high blood pressure, dyslipidaemia, and hyperglycaemia; thus, the importance of detecting insulin resistance in early stages is of relevance.5 The metabolic syndrome is a clinical concept created to identify patients at high cardiovascular risk who have a particular clustering of risk factors (high blood pressure, dyslipidaemia, hyperglycaemia, and central obesity); all of which are associated with varying degree to insulin resistance, which is believed to be the underlying shared pathophysiological disturbance.6,7 However, meeting the clinical criteria for the metabolic syndrome using widely used definitions that do not include a measure of insulin resistance—such as the Adult Treatment Panel III (ATP-III)8,9 and the International Diabetes Federation (IDF)10— does not necessarily equal a very high absolute risk of cardiovascular disease;11 therefore, it is important to identify potential additional metabolic markers that can help predict cardiovascular risk better than the ones already available.

Apolipoproteins are important structural and functional proteins in lipoprotein particles, which transport lipids. Recent reports from prospective risk studies, such as AMORIS,12 INTERHEART,13 EPIC-Norfolk study,14 ULSAM,15 and the MONICA/KORA16 studies indicate that the apoB/apoAI ratio is a useful predictor of risk of both non-fatal and fatal myocardial infarction. A recent meta-analysis by Thompson and Danesh17 on the apoB/apoAI ratio supports the use of apoB/apoAI ratio as a future risk marker of cardiovascular disease.

Recently, the apoB/apoAI ratio was associated with the presence of individual metabolic syndrome components in a representative sample of the US population;18 however, it is unclear whether its association with insulin resistance is independent of traditional cardiovascular risk factors, and whether the ratio adds to the prediction of cardiovascular disease by itself. The aim of this study was to assess the independent association between the apoB/apoAI ratio and insulin resistance in the US non-diabetic population. In particular, we posed the question whether apoB/apoAI can improve the ability to predict HOMA2 beyond what is possible with traditional risk factors, metabolic syndrome components, and inflammatory risk factors.


   Methods
Top
 Abstract
 Methods
Results
 Discussion
 Conclusion
 Acknowledgements
 References
 
Subjects
The National Health and Examination Nutrition Survey (NHANES) is a representative sample of the US non-institutionalized civilian population from 1988 to 1994. It consists of a periodic survey conducted by the United States National Center for Health Statistics designed to provide an estimate of the health of the nation. NHANES III covers the years 1988–94. Detailed methods used in NHANES III are published elsewhere18 and are available for public access on the Internet.19

We limited the present analysis of both surveys to men and non-pregnant women aged ≥20 years, <90 years (excluded n = 602), who attended the morning medical examination (excluded n = 3979) and who had fasted ≥8 h (excluded n = 5874). We excluded data for those judged by their interviewer to have provided unreliable data, and those missing data for apoB, apoA-I, or the metabolic syndrome components (n = 10,409). We also excluded all self-reported diabetics and/or subjects on medication for this and/or those with fasting glucose >7 mmol/L. This resulted in a final analytic sample of 2955 subjects (1498 men; 1457 women). Height, weight, and waist circumference were obtained using standardized techniques and equipment. The lipids were measured enzymatically with the use of commercially available reagents (Cholesterol/HP, cat. no. 816302, and Triglycerides/GPO, cat. no. 816370, both from Boehringer Mannheim). HDL cholesterol was measured in the clear supernatant after precipitating the other lipoproteins with heparin and MnCl2 (1.3 g/L and 0.046 mol/L, respectively) and removing excess Mn2+ by precipitation with NaHCO3. The biases (CVs) averaged –0.3% (1.7%), –2.1% (3.9%), and 0.3% (3.4%) for cholesterol, triglycerides, and HDL-C, respectively. Fasting glucose was measured using the Glucose standard assay (Sigma chemical, St Louis), and plasma insulin was measured with the Pharmacia insulin RIA kit (Pharmacia diagnostics, Sweden). The C-reactive protein concentrations were measured by latex-enhanced nephelometry on a Behring Nephelometer (Dade Behring Diagnostics Inc., Somerville, NJ, USA). The Clauss clotting method, on a STA-Compact (Diagnostica Stago, Parsippany, NJ, USA), was used to quantitatively determine the fibrinogen concentration in plasma. We determined the insulin sensitivity index using the updated computer model homeostatic model assessment (HOMA2) index.20

Apolipoprotein analysis
Samples were thawed at room temperature and mixed thoroughly for 30 min on a blood- rotating device before analysis. The procedures used for apoB and apoAI analyses have been described in detail elsewhere.18 Briefly, apoB and apoAI were measured by radial immunodiffusion (RID) in the first 8.2% (1055 specimens) of the specimens during the first 5 months of the study and by rate immunonephelometry (INA) for the remaining specimens during the last 31 months. At the beginning of the survey, there were no standardized reference materials on which to base the measurements. Over the past few years, the WHO-IFCC First International Reference Materials for apoB and apoAI became available. The Northwest Lipid Research Laboratories, Seattle, WA, served as the coordinating laboratory for the development of these materials. The results were used to transform the INA values to equivalent WHO-IFCC International Reference Materials-based values, which are presented here.

Traditional risk factors definition
Subjects were considered to have dyslipidaemia if they reported current usage of medications to lower blood cholesterol, self reported diagnosis of hypercholesterolaemia, and/or HDL-cholesterol <1.03 mmol/L (40 mg/dL) in men and <1.30 mmol/L (50 mg/dL) in women, and/or triglycerides ≥1.7 mmol/L (150 mg/dL), and/or LDL-cholesterol ≥4.10 mmol/L (160 mg/dL). Subjects were considered to be hypertensive if they were taking antihypertensive medications, self reported diagnosis of hypertension and/or if their systolic pressure was ≥140 mmHg or diastolic pressure was ≥90 mmHg, as defined by the recent Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure guidelines and stratified in the Framingham cardiovascular risk score. Subjects were considered to be in the smoking group if they were current, former, or ever smokers (more than 100 cigarettes in their life).

Metabolic syndrome definition
The updated ATP-III definition of metabolic syndrome9 was met when three or more of the following criteria were present: waist circumference ≥102 cm (40 in) in men and ≥88 cm (35 in) in women; HDL <1.03 mmol/L (40 mg/dL) in men and <1.30 mmol/L (50 mg/dL) in women or specific treatment for this lipid abnormality; triglycerides ≥1.7 mmol/L (150 mg/dL) in men and women or specific treatment for this lipid abnormality; systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥85 mmHg in men and women or treatment of previously diagnosed hypertension; and fasting glucose ≥5.6 mmol/L (100 mg/dL) in men and women.

Statistical methods
The analysis of the NHANES III data was conducted following the guidelines in ‘Analytic and Reporting Guidelines: The Third National Health and Nutrition Examination Survey, NHANES III (1988 to 1994)’. To reduce the positive skewness of HOMA2, a log transformation was applied. Insulin resistance was defined as the upper quartile of HOMA2. We defined ‘high apoB/apoAI ratio’ as the highest sex-specific quartile of the apoB/apoAI ratio ( ≥0.97 in men; ≥0.86 in women) and we then compared it to the lowest quartile. Data were summarized by calculating sex-specific means and standard deviation for quantitative variables and percentages for categorical variables by high apoB/apoAI ratio and low apoB/apoAI ratio. We compared the median and the interquartile ranges of the apoB/apoAI ratio for individual components of the metabolic syndrome, ATP-III definition of the metabolic syndrome, and insulin resistance, all considered as qualitative variables using the t test for unequal variances. We applied logistic regression models adjusted for age and sex to determine the association between apoB/apoAI ratio and insulin resistance adjusting for metabolic syndrome components, traditional and inflammatory risk factors. Because of the high correlation between glucose and HOMA2, we did not include glucose in the model.

To analyse the additional contribution of apoB/apoAI to insulin resistance, multiple linear regression modelling was used to assess the simple and joint associations of apoB/apoAI, metabolic syndrome components, traditional and inflammatory risk factors with HOMA2. Age and race were always included as covariates, and all analyses were stratified by sex. Initially traditional risk factors, metabolic syndrome components, inflammatory risk factors, and apoB/apoAI were considered one at a time. Selection of predictor variables was done using a forward stepwise fashion with strict variable entry and elimination criteria in each predictors group (traditional risk factors, metabolic syndrome components, and inflammatory risk factors group). Consequently, the final parsimonious models for each sex only included those measures that made independent contributions to the prediction of HOMA2. The predictive value of each predictor group was assessed by comparing R2 values of the models obtained from each group. Incremental additive value was judged by the increase in R2 obtained when apoB/apoAI was added to the most predictive cardiovascular risk factors. All analyses were performed using the SAS windows version and SUDAAN.


   Results
Top
 Abstract
 Methods
 Results
Discussion
 Conclusion
 Acknowledgements
 References
 
Descriptive characteristics
Insulin resistant subjects were older and had on average greater mean values for BMI, waist circumference, triglycerides, fasting glucose, blood pressure levels, LDL-cholesterol, C-reactive protein, fibrinogen, HOMA2 index, and apoB/apoAI ratio than insulin sensitive subjects. In contrast, insulin resistant subjects had significantly lower HDL-cholesterol than insulin sensitive subjects (Table 1). There were significant differences between sexes; therefore we stratified the analysis by sex. After adjusting for age and race, high apoB/apoAI ratio was significantly associated with insulin resistance in both sexes (in men: OR, 5.15; 95% CI, 3.51–7.72; in women: OR, 4.44; 95% CI, 3.04–6.57). Figure 1 shows the sex-specific association of apoB/apoAI ratio quartiles with insulin resistance after further adjustment for smoking, dyslipidaemia, hypertension, waist circumference, and C-reactive protein.


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  		Table 1 Descriptive characteristics

 

Figure 1
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  		Figure 1 Plot of the sex-specific odds ratio of apoB/apoAI quartiles by insulin resistance, after adjustment for age, race, dyslipidaemia, smoking, hypertension, and waist circumference and C-reactive protein. (A) Men and (B) Women.

 
Predictors of homeostatic model assessment index
To further evaluate the predictive effects of the apoB/apoAI ratio, quantitative traits rather than dichotomized were considered. Age and race accounted for 1% of the observed interindividual variation in HOMA2 in men (P < 0.001) and 3% of the variation in women (P < 0.001). In both sexes after controlling for age and race, each risk factor considered one-at-a-time made a significant additional contribution to the prediction of HOMA2 (P < 0.05) (Table 2).


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  		Table 2 Univariate age and race-adjusted correlation of cardiovascular risk factors and homeostatic model assessment

 
Overall, for the traditional risk factors (dyslipidaemia, hypertension, and smoking), the additional percentage of variation in HOMA2 explained by each measure ranged from 1 to 6%, with dyslipidaemia being the strongest predictor. For metabolic syndrome components (waist circumference, triglycerides, HDL-C and blood pressure), the additional percentage of variation in HOMA2 explained by each measure ranged from 1 to 25%, with waist circumference being the strongest predictor (Table 2). For the inflammatory risk factors (C-reactive protein and fibrinogen), the additional percentage of variation in HOMA2 explained by each measure ranged from less than 1 to 10%, with C-reactive protein being the strongest predictor. The additional percentage of variation in HOMA2 explained by apoB/apoAI ratio was 11% for men and 9% for women (P < 0.001). In a final parsimonious model adjusting for age, race, and the best predictors of HOMA2 from the metabolic syndrome components, traditional and inflammatory risk factors, apoB/apoAI ratio still made an additional independent contribution to the prediction of HOMA2 (in men: additional R2 = 0.09, P < 0.001; in women: additional R2 = 0.05, P < 0.001) (Table 3).


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  		Table 3 Parsimonious multiple regression models predicting homeostatic model assessment after controlling for age, race, and smoking

 
Previous studies have shown that adverse effects of excess body fat on cardiovascular outcomes only become apparent in subjects with BMI ≥30 kg/m2 and paradoxically subjects with a BMI ranging 25–29.9 kg/m2 have better survival and fewer cardiovascular events than lean subjects (BMI≤25 kg/m2).21,22 Therefore, we investigated whether the relationship between HOMA2 and apoB/apoAI was found in both obese (BMI ≥30 kg/m2 and/or high waist circumference according to the metabolic syndrome definition) and non-obese subjects and in both of these groups the apoB/apoAI ratio remained a significant predictor of HOMA2 (P < 0.001), data not shown.


   Discussion
Top
 Abstract
 Methods
 Results
 Discussion
Conclusion
 Acknowledgements
 References
 
In this study of a representative sample of the US non-institutionalized civilian population, apoB/apoAI ratio is associated with insulin resistance in both men and women. Our findings indicate that the apoB/apoAI ratio predicts HOMA index independently of the traditional risk factors, metabolic syndrome components, and inflammatory risk factors; thus, adding independent information for the prediction of insulin resistance. Our results extend upon the findings in previous studies suggesting that apoB/apoAI is related to the metabolic syndrome, and go further by adding important information on its pathophysiologic link with cardiometabolic disorders. The fact that the association between apoB/apoAI ratio and insulin resistance is independent of traditional risk factors, metabolic syndrome components, and inflammatory risk factors suggests the importance of including apoB/apoAI ratio in future guidelines.23 Recently, we published an association between the apoB/apoAI ratio and the metabolic syndrome definition in a similar representative sample of the US population that included diabetics;18 moreover, none of the metabolic syndrome definitions used take into account insulin resistance as a cofactor and it was not clear if the association between the apoB/apoAI ratio and insulin resistance was in fact mediated by the other risk factors such as traditional risk factors, metabolic syndrome components, and/or inflammatory risk factors. It is this issue that we address in the present study of US non-diabetics subjects. Furthermore, these conclusions are strongly supported by the major findings of the INTERHEART study,13 a case–control study, which showed that in all 52 countries investigated, the apoB/apoA-I ratio was not only the strongest factor in explaining risk of acute MI, but that the ratio was also the most prevalent risk factor of all the nine conventional risk factors investigated irrespective of age, sex, race, and other lipids or lipid ratios.

Previous studies
Previous reports have described a relationship between the apoB/apoAI ratio and the metabolic syndrome. In a sample of 313 Caucasian men (mean age 58 years), those with increasing numbers of metabolic syndrome components had a linear increase in the apoB/apoAI ratio.24

In a sample of 1522 men and women aged 40 to 69 years from three ethnic groups in the Insulin Resistance and Atherosclerosis Study, Sattar et al.25 compared the associations of apoB and non-HDL cholesterol with other cardiovascular risk factors in subjects with the metabolic syndrome. They reported that subjects with higher levels of apoB had a significantly higher proportion of the metabolic syndrome components than patients with higher LDL-cholesterol and normal levels of apoB. Other studies have tested the association between different apolipoproteins and cardiovascular risk factors, however none have explored its relationship with insulin sensitivity and insulin resistance further to determine the independence of the association.

Mechanistic considerations
Recent studies have shown that measurement of different forms of apolipoproteins may improve cardiovascular risk prediction. Higher apoB/apoAI ratio has been associated with increased cardiac events.17 ApoB, apoAI, and the apoB/apoAI ratio have been reported to be better predictors of cardiovascular events than LDL-cholesterol, even in subjects taking lipid-modifying therapy.26,27 Apolipoproteins regulate the synthesis and metabolism of lipoprotein particles and in addition stabilize their structure. These lipoprotein particles are composed of phospholipids, free cholesterol, triglycerides, cholesterol esters, and apolipoprotein molecules.28 The total value of apoB indicates the number of potentially atherogenic lipoproteins. ApoAI is important in removing excess cholesterol from tissues and incorporating it into HDL for reverse transport to the liver.29 The ratio of apoB/apoAI hence reflects the balance of cholesterol transport, so the higher the value, the higher the propensity for cholesterol deposition, and consequently the higher the risk for atherogenesis.23 The evidence reported in this study provides strong support for the notion that the lipoprotein abnormalities that are part of the metabolic-risk-factor clustering are significantly associated with insulin resistance and that they may provide additional mechanistic information on the complex metabolic syndrome.

Improving the global assessment of cardiovascular disease risk
There is an ongoing controversy on the definitions used for the metabolic syndrome and how much they actually add to cardiovascular risk prediction. The basis for the metabolic-risk-factor clustering concept is that insulin resistance is the primary underlying pathophysiological disturbance that clusters along with atherogenic dyslipidaemia, elevated blood pressure, elevated plasma glucose, a prothrombotic state, and a proinflammatory state that ultimately may lead to increased cardiovascular risk.

With the exception of the WHO metabolic syndrome definition, current definitions of the metabolic do not take into account insulin resistance, and to date, no definition takes into account possible inflammatory and metabolic markers that can be used in clinical practice to identify patients at high risk. Furthermore, this study highlights the importance of studying men and women separately—i.e. apoB/apoAI ratio of more than 0.95 detected high-risk patients in men compared to 0.85 in women (P < 0.0001). The ApoB/apoAI ratio should therefore be considered in the assessment of future cardiovascular risk guidelines as an important risk factor that may be linked to the metabolic-risk-factor clustering phenomenon.

Limitations
An important limitation of this study is that the HOMA2 index calculated in the present study is not perfectly correlated with estimates of insulin sensitivity from the euglycemic clamp, which is the gold standard method for measurement of insulin sensitivity. However, the HOMA2 index provides correlated estimates of insulin sensitivity and provides a valuable epidemiological tool because of the laboriousness of the euglycemic clamp. The use of the recently updated HOMA2 computer model index provides a clear advantage over previous assessments of HOMA.30 Moreover, relationships of insulin sensitivity to its predictors have been consistent between the euglycemic clamp and the HOMA index. Given the cross-sectional nature of NHANES III, the impact of the apoB/apoAI ratio, and its interaction with metabolic syndrome components on cardiovascular outcomes could not be ascertained, thus we cannot deduct causality from this study.

Practical implications
The presence of the metabolic syndrome alone cannot predict global cardiovascular disease risk, as stated recently by Despres and Lemieux,11 it is only when we start taking into consideration the potential additional contribution of related metabolic markers —such as the apoB/apoAI ratio—to global cardiovascular risk, that we will advance in elucidating the pathophysiology of complex cardiometabolic disorders.


   Conclusion
Top
 Abstract
 Methods
 Results
 Discussion
 Conclusion
Acknowledgements
 References
 
The apoB/apoAI ratio is strongly associated with insulin resistance beyond the association explained by traditional risk factors, metabolic syndrome components, and inflammatory risk factors. These data suggest an additional mechanism that may help to explain the increased cardiovascular disease risk associated with insulin resistance and underscore the need to implement apoB/apoAI in future clinical guidelines.


   Acknowledgements
Top
 Abstract
 Methods
 Results
 Discussion
 Conclusion
 Acknowledgements
References
 
The data reported here have been analysed using National Health and Nutrition Examination survey files available for public use. All the analysis, interpretation, and/or conclusion reached in this paper are work of the authors and not of the National Center for Health Statistics. J.S.-J. was partially supported by faculty funds from the Board of Post-Graduate Education of the Karolinska Insitutet (KID Award) and the European Foundation for the Study of Diabetes Lilly Research Fellowship. V.K.S. was supported in part by NIH R01 HL73211. A.H. was supported in part by Swedish Heart and Lung Foundation. M.-L.H. was supported in part by the Swedish Heart Lung Foundation and the Swedish Council for Working Life and Social Research. R.M.F. was supported in part by the Swedish Research Council (project 15352) and the Swedish Diabetes Association.

Conflict of interest: none declared.


   References
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 Abstract
 Methods
 Results
 Discussion
 Conclusion
 Acknowledgements
 References
 

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