Inflammation and Triglycerides Partially Mediate the Effect of Prepregnancy Body Mass Index on the Risk of Preeclampsia

Lisa M. Bodnar1,2, Roberta B. Ness1,2,3,4, Gail F. Harger1,2 and James M. Roberts1,2,3

1 Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
2 Magee-Women's Research Institute, Pittsburgh, PA
3 Department of Obstetrics, Gynecology, and Reproductive Sciences, School of Medicine, University of Pittsburgh, Pittsburgh, PA
4 Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA

Correspondence to Dr. Lisa M. Bodnar, A742 Crabtree Hall, Graduate School of Public Health, University of Pittsburgh, 130 DeSoto Street, Pittsburgh, PA 15261 (e-mail: Bodnar{at}edc.pitt.edu).

Received for publication March 22, 2005. Accepted for publication June 28, 2005.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 References
 
The objective of this study was to quantify the mediating role of inflammation and triglycerides in the association between prepregnancy body mass index (weight (kg)/height (m)2) and preeclampsia. The authors conducted a nested case-control study of 55 preeclamptic women and 165 pregnant controls from the Pregnancy Exposures and Preeclampsia Prevention Study (Pittsburgh, Pennsylvania, 1997–2001). Serum samples collected at ≤20 weeks' gestation were analyzed for levels of C-reactive protein and triglycerides. The adjusted odds ratio (AOR) from a multivariable conditional logistic regression model assessing the total effect of body mass index on preeclampsia risk was compared with the AOR from the same model after results were controlled for C-reactive protein, triglycerides, and confounding factors (direct-effects model). The percentage of the total effect that was mediated through inflammation and triglycerides was calculated as 100 – [ln(direct-effects AOR)/ln(total-effects AOR)]. In the total-effects model, 4- and 8-unit increases in body mass index were associated with 1.7-fold (95% confidence interval (CI): 1.3, 2.3) and 2.9-fold (95% CI: 1.6, 5.2) increases in preeclampsia risk, whereas in the direct-effects model, these AORs were 1.4 (95% CI: 1.0, 1.9) and 2.0 (95% CI: 1.0, 3.8), respectively. Inflammation was a more important mediator than triglycerides. These findings suggest that approximately one third of the total effect of body mass index on preeclampsia risk is mediated through inflammation and triglyceride levels.

body mass index; C-reactive protein; inflammation; obesity; pre-eclampsia; pregnancy; triglycerides


Abbreviations: AOR, adjusted odds ratio; BMI, body mass index; CRP, C-reactive protein


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 References
 
Preeclampsia is a multisystemic, pregnancy-specific disorder that is diagnosed by new-onset hypertension and proteinuria after 20 weeks' gestation. It is a leading cause of maternal and infant morbidity and mortality worldwide (1Go), yet its etiology remains unclear. Numerous studies with varying definitions of preeclampsia have shown that high maternal prepregnancy body mass index (BMI; weight (kg)/height (m)2) is a strong, modifiable risk factor for preeclampsia (2Go–7Go). Using the same definition of preeclampsia as in the current study, we previously reported that the risk of preeclampsia rose strikingly from a prepregnancy BMI of 15 to a BMI of 35, such that, compared with a BMI of 21, the risk of preeclampsia was approximately doubled at a BMI of 26, tripled at a BMI of 30, and halved at a BMI of 18 (2Go).

Two commonly hypothesized mechanisms underlying the BMI-preeclampsia relation are hyperlipidemia and inflammation, but judging from published reports, neither these factors nor others have been formally tested as mediators. Overweight is associated with alterations in lipid concentrations and an activation of inflammatory markers (8Go, 9Go), and both of these metabolic abnormalities are characteristic of preeclamptic pregnancies before the onset of clinically evident disease (10Go, 11Go). Dyslipidemia and an exaggerated inflammatory response in preeclampsia are thought to contribute to widespread endothelial dysfunction and the subsequent maternal syndrome (12Go). If adiposity is causally important in preeclampsia, it may contribute to the pathophysiology in one of five ways: 1) solely through an exaggerated inflammatory response; 2) solely through lipid abnormalities; 3) through both of these pathways only; 4) through one or both of these pathways while also having a direct, residual effect on one or more alternative pathways not mediated by either inflammation or hyperlipidemia; or 5) solely through its residual direct effect on alternative pathways. Our objective was to estimate the proportion of the effect of BMI on preeclampsia risk that is mediated by triglyceride concentrations and inflammation, as well as the direct effect on pathways not involving triglycerides and inflammation.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 References
 
The Pregnancy Exposures and Preeclampsia Prevention Study, a prospective cohort study of the pathogenesis of preeclampsia, was conducted at Magee-Womens Hospital in Pittsburgh, Pennsylvania (1997–2001) (13Go). Eligible women were those aged 14–44 years with a singleton pregnancy who were planning to deliver at Magee-Womens Hospital. Women were recruited at ≤16 weeks of pregnancy after providing informed, written consent. A total of 2,981 women enrolled in the study (72 percent response rate). An interview-administered questionnaire at enrollment collected data on maternal sociodemographic characteristics, medical history, and prepregnancy behaviors. Nonfasting blood samples were collected at times of usual blood draws for clinical indications (initial visit, 16–18 weeks, 26–29 weeks, and predelivery). Medical records were abstracted to obtain data on prepregnancy weight and height, blood pressures and urinary protein measurements throughout gestation, use of hypertensive medicines, antepartum and delivery events, and neonatal outcomes. The study was approved by the institutional review board of Magee-Womens Hospital.

We conducted a nested case-control study among the 1,198 women in the Pregnancy Exposures and Preeclampsia Prevention Study who had had no previous deliveries at more than 20 weeks, no terminations (spontaneous or elective) in the index pregnancy, and no preexisting medical conditions (e.g., pregestational diabetes mellitus, chronic hypertension). We selected women whose index pregnancy was their first pregnancy, because the etiology of preeclampsia may differ by parity (14Go). Of the 59 preeclamptic cases in this cohort, 55 had an available banked blood sample taken at ≤20 weeks' gestation and were therefore eligible for this analysis. We chose 20 weeks as the cutpoint because it precedes the clinical onset of disease. We individually matched the 55 cases (3:1) to 165 nonpreeclamptic controls on the basis of maternal race/ethnicity (non-Hispanic White, non-Hispanic Black, other), age (within 3 years of the case's age), gestational age at blood sampling (within 3 weeks of the case's gestational age), and smoking status at enrollment (yes, no). Women who quit smoking before enrollment were considered nonsmokers.

Definition of study variables
Primary exposure variable: prepregnancy BMI.
Prepregnancy BMI was based on measured height and maternal self-report of prepregnancy weight at the initial visit. Prepregnancy BMI was categorized as underweight (BMI <18.5), normal weight (BMI 18.5–24.9), overweight (BMI 25.0–29.9), or obese (BMI ≥30.0) (15Go).

Primary outcome variable: preeclampsia.
Preeclampsia was defined as gestational hypertension and proteinuria and return of all abnormalities to normal levels by 12 weeks postpartum (16Go). Our research definition emphasized specificity in the diagnosis of preeclampsia. Gestational hypertension was defined as systolic blood pressure persistently at or above 140 mmHg and/or diastolic blood pressure persistently at or above 90 mmHg for the first time after 20 weeks' gestation. We determined blood pressure as the average of the last five pressures obtained after hospital admission for delivery, before administration of medications or clinical perturbations that would alter blood pressure. Proteinuria was defined as the excretion of more than 300 mg of protein in 24 hours, a random urine sample of 2+ protein, a catheterized urine sample of 1+ protein, or a protein:creatinine ratio greater 0.3. While the gold standard for the quantification of urinary protein is 24-hour urine collection, 24-hour urine samples are infrequently collected in clinical practice. Rather, dipsticks and catheterized samples are commonly used. We based proteinuria on a level of 2+ rather than 1+ for a random urine sample so as to minimize misclassification caused by subjective visual determination of color on the dipstick. When available, the protein:creatinine ratio, which is independent of urine concentration, was used. Protein:creatinine ratio correlates well with 24-hour urinary protein in most studies (17Go). This definition of proteinuria has been used in other large-scale epidemiologic studies of preeclampsia (18Go, 19Go). Blood pressure and protein measurements at 6 weeks postpartum were used to determine whether abnormalities had been resolved. If abnormalities persisted, women returned for reevaluation at 12 weeks postpartum.

Mediating variables: C-reactive protein and triglycerides.
Nonfasting serum samples obtained at ≤20 weeks' gestation were stored in aliquots at –80°C until they were assayed for C-reactive protein (CRP), a sensitive index of systemic inflammation (20Go), and triglycerides, an indicator of lipid status which, if elevated, is a marker of hyperlipidemia (21Go). Serum CRP was measured by Quest Diagnostics, Inc. (Pittsburgh, Pennsylvania), using a high-sensitivity immunoturbidimetric assay. The detection limit of the assay was 0.1 mg/dl. For the 3.6 percent of the sample (n = 8) with CRP concentrations below the detectable limit, we imputed 0.1 mg/dl. The inter- and intraassay variabilities at Quest's laboratories were 2.9 percent and 1.8 percent, respectively. Serum triglyceride level was analyzed enzymatically using a triglyceride-GPO (glycerophosphate oxidase) reagent set from Pointe Scientific, Inc. (Canton, Michigan). The inter- and intraassay variabilities in our laboratory were 4.7 percent and 5.6 percent, respectively.

Covariates.
Maternal race/ethnicity was self-reported. Data on maternal education (<12, 12, or >12 years), marital status (married, unmarried), and income in the year before the index pregnancy were also available. Poverty index ratio (0–130 percent, 131–299 percent, or ≥300 percent) was defined as total household income divided by the year-specific poverty threshold (22Go). Women were classified as current smokers or nonsmokers at enrollment on the basis of self-report. Women who had quit smoking were included as nonsmokers. Women were classified as either users or nonusers of multivitamin/mineral supplements in the 6 months before conception. For assessment of physical activity, women were asked whether they had usually engaged in leisure-time physical activity during the year before the index pregnancy and, if so, to categorize the usual intensity of this activity as low, medium, or high.

Statistical analysis
To estimate the proportion of the BMI effect that was mediated by inflammation and triglycerides, we took several analytical steps. First, we estimated the total effect of BMI on the risk of preeclampsia. We defined the total effect as the effect of all potential pathways by which BMI could affect preeclampsia risk (e.g., inflammation, triglycerides, other lipids, oxidative stress, insulin resistance, etc.). To estimate the total effect, we fitted a conditional, multivariable logistic regression model that adjusted for confounders of the BMI-preeclampsia relation ("total-effects model"; see Appendix). Second, we estimated the effect of BMI that was directly mediated through pathways not involving inflammation. To do this, we included CRP and confounders of the CRP-preeclampsia relation in the conditional logistic total-effects model (23Go) to fit "direct-effects model 1" (Appendix). Because hyperlipidemia also causes inflammation, this BMI effect also represents the direct effect of BMI that is not mediated by the effect of elevated triglyceride concentrations on inflammation. Longitudinal triglycerides and CRP measurements, which we lacked, were needed to isolate the effect of inflammation that was independent of hypertriglyceridemia. Third, we estimated the direct effect of BMI that was exerted through pathways not mediated by either inflammation or triglycerides. We included CRP, triglycerides, and confounders of the CRP-preeclampsia and triglyceride-preeclampsia relations in the conditional logistic total-effects model to fit "direct-effects model 2" (Appendix). Unlike direct-effects model 1, this model attempts to eliminate all pathways by which triglycerides may mediate the BMI-preeclampsia relation (through inflammation or otherwise). Finally, after fitting each model and obtaining adjusted odds ratios (AORs), we calculated the percentage of the BMI effect that was direct (i.e., not mediated through pathways involving inflammation or triglycerides) as [ln(direct-effects AOR)/ln(total-effects AOR)] x 100. Because BMI was a continuous variable in our analysis, the percentage of the effect that was direct was calculated more simply as (direct-effects coefficient/total-effects coefficient) x 100. We may infer that the remaining proportion (100 – percentage direct effect) is the percentage of the BMI effect that is mediated through triglycerides and inflammation.

A directed acyclic graph (24Go, 25Go) (figure 1) was used to determine potential confounders entered into each model. Potential confounders included maternal race/ethnicity, age, smoking status, gestational age at blood sampling, education, marital status, income, prepregnancy multivitamin/mineral use, and prepregnancy physical activity. Effect modification by race and gestational age of blood sampling were tested separately in each of the three models using likelihood ratio tests (p < 0.15). Covariates that did not satisfy our a priori change-in-estimate criterion (a change in the AOR of more than 8 percent) were considered not to be influential and were removed from the models. Confounders were tested separately in each of the three models. BMI was linear in the logit of preeclampsia and thus was specified as a linear term in the models. CRP, triglycerides, and gestational age at blood sampling were curvilinear in the logit of preeclampsia and were specified as spline terms.



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FIGURE 1. Directed acyclic graph of the mediating role of inflammation and triglycerides in the association between prepregnancy body mass index (BMI; weight (kg)/height (m)2) and preeclampsia, Pregnancy Evaluation and Preeclampsia Prevention Study, 1997–2001. The graph corresponds to the assumption of no unmeasured confounders given control for measured factors. x, y, and z represent vectors of measured confounders that may be associated with BMI, lipids, and inflammation, respectively. u denotes a vector of unmeasured covariates. Arrows (directed edges) represent causal effects. Lack of directed edges from u to BMI, lipids, or inflammation represents the unverifiable assumption of no unmeasured confounders associated with BMI, lipids, or inflammation, conditional on the x, y, and z. If there were directed edges from u to BMI, lipids, or inflammation, u would be considered a vector of unmeasured confounders.

 

    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 References
 
Preeclamptic women were not significantly different from controls in terms of age, race/ethnicity, smoking status, gestational age at blood sampling, marital status, education, income, multivitamin use, or physical activity (table 1). However, the distribution of prepregnancy BMIs varied by preeclampsia status. Approximately 32 percent of controls were overweight or obese, whereas 56 percent of preeclamptic women were defined as such. Mean CRP concentration and the proportion of women with markedly elevated CRP concentrations (26Go) were significantly higher in preeclamptic women than in controls; triglyceride concentrations and the percentages of women with high triglyceride levels (27Go) were not significantly different.0


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TABLE 1. Characteristics of pregnant women by case/control status, Pregnancy Exposures and Preeclampsia Prevention Study, Pittsburgh, Pennsylvania, 1997–2001

 
Overweight and obese women tended to have higher CRP concentrations at ≤20 weeks' gestation than their leaner counterparts (table 2). In fact, BMI had a positive, linear relation with log CRP concentration. After adjustment for race/ethnicity, marital status, education, smoking status, and age in a multiple linear regression model, a 5-unit increase in prepregnancy BMI was associated with a 46 percent rise in CRP (95 percent confidence interval: 33, 61). In contrast, there was no significant association between BMI and triglycerides after adjustment for the covariates mentioned above.


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TABLE 2. Geometric mean* C-reactive protein and triglyceride concentrations by prepregnancy body mass index{dagger} category, Pregnancy Exposures and Preeclampsia Prevention Study, Pittsburgh, Pennsylvania, 1997–2001{ddagger}

 
After adjustment for marital status and gestational age of blood sampling, prepregnancy BMI was strongly associated with preeclampsia risk in the total-effects model (table 3). A 4-unit rise in BMI was associated with a 70 percent increase in the risk of preeclampsia; an 8-unit increase in BMI nearly tripled the risk. Direct-effects model 1, which theoretically eliminated the inflammatory pathway as a mediator of the BMI-preeclampsia relation, showed attenuated AORs. In this model, after adjustment for marital status, gestational age of blood sampling, and prepregnancy multivitamin/mineral use (a confounder of the CRP-preeclampsia relation), a 4-unit rise in BMI was associated with a 40 percent increase in preeclampsia risk, and an 8-unit rise approximately doubled the risk. The reduction in the AORs in direct-effects model 1 indicated that approximately 69 percent of the BMI effect was directly mediated by pathways not involving inflammation (likelihood ratio test of direct-effects model 1 vs. total-effects model: p = 0.08). We can estimate that approximately 31 percent of the residual effect may be mediated by an exaggerated inflammatory response. Adjustment for triglycerides and the aforementioned confounders, and thus theoretical elimination of both the inflammatory pathway and the triglyceride pathway, further reduced the AORs only slightly. Approximately 64 percent of the BMI effect went directly through pathways not mediated by triglycerides or inflammation (likelihood ratio test: direct-effects model 2 vs. total-effects model, p = 0.03; direct-effects model 2 vs. direct-effects model 1, p < 0.08). We can therefore estimate that approximately 36 percent of the residual effect may be mediated through inflammation and triglyceride levels. We did not observe any effect modification on the multiplicative scale by race/ethnicity or sample gestational age.


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TABLE 3. Total and direct effects of prepregnancy body mass index* on the risk of preeclampsia (n = 220), Pregnancy Exposures and Preeclampsia Prevention Study, Pittsburgh, Pennsylvania, 1997–2001

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 References
 
Our results confirm previous findings that prepregnancy BMI is a strong independent risk factor for preeclampsia, and they extend previous findings by demonstrating that inflammation and triglyceride levels at ≤20 weeks may be important mediators of the BMI-preeclampsia association. Inflammation, however, was clearly the more influential mediator in our population. Our data suggested that after adjustment for measured confounders, 31 percent of the effect of prepregnancy BMI on the risk of preeclampsia was mediated by a heightened inflammatory response and that 36 percent of the effect was mediated by both a heightened inflammatory response and increased triglyceride levels. The importance of CRP as a mediator in our population was not unexpected given that CRP concentrations at ≤20 weeks were strongly associated with prepregnancy BMI in multivariable analysis and with preeclampsia in bivariable analysis, whereas triglyceride concentrations were not significantly related to the exposure or outcome. Because mean triglyceride concentrations were within the range of normal in the vast majority of cases and controls, it is unlikely that the effect of inflammation that we observed was attributable to altered triglyceride levels. However, we could not directly separate these effects because we lacked longitudinal CRP and triglyceride measurements.

Our results agree with those of studies that have shown a heightened inflammatory response as measured by serum CRP (28Go, 29Go) and proinflammatory cytokine (30Go, 31Go) levels before clinically evident preeclampsia, though not all investigators have reported positive associations between CRP and preeclampsia incidence (32Go, 33Go). Our findings are inconsistent with previous reports showing that women who subsequently develop preeclampsia evidence higher fasting triglyceride concentrations than controls in the first and second trimesters, long before clinical manifestations of the disorder (34Go–37Go). The discrepancies we noted in our triglyceride data may be partially explained by our use of nonfasting blood samples, our wide range of gestational ages for study samples (though this was controlled for in our BMI-preeclampsia multivariable models), and/or our predominance of mild, late-onset preeclampsia cases. A previous study found that hypertriglyceridemia before 20 weeks' gestation increased the risk of early-onset preeclampsia but had no effect on late-onset disease (34Go). Lipid level may be a more important mediator of the BMI-preeclampsia association in other populations in which triglyceride concentrations are significantly elevated before the onset of the disease among cases as compared with controls.

Few studies have examined the association between pregravid overweight and dysregulation of metabolic and inflammatory pathways during pregnancy. CRP concentrations in the third trimester among women who are obese at 10–12 weeks' gestation (BMI ≥27.7) were reported in one study to be double those of leaner women (BMI <27.7)—results consistent with our data (38Go). In contrast to our findings, these investigators reported significantly higher fasting triglyceride concentrations in obese women compared with lean women (38Go).

Importantly, we observed that approximately two thirds of the effect of prepregnancy BMI on preeclampsia risk was mediated by pathways that are independent of inflammation and triglyceride levels. Future research will be needed to delineate the other mechanisms explaining this important association. Possible intermediates include insulin resistance, oxidative stress, other lipids, and endothelial dysfunction. Lifestyle factors such as inadequate dietary intake or physical inactivity during pregnancy may contribute to this association as well, either through these metabolic disturbances (i.e., by increasing oxidative stress) or through other means.

Our results are predicated on several important assumptions that, if not upheld, may have biased our results. We assumed that CRP is a valid measure of systemic inflammation and that triglyceride concentration is a valid marker of lipid abnormalities at ≤20 weeks' gestation. Both of these markers have been used in past studies of preeclampsia and cardiovascular health and should have been appropriate in our population. Although CRP was initially recognized as a nonspecific indicator of inflammation, recent data suggest that specific biologic functions of CRP may link it to the pathogenesis of atherosclerosis, including facilitating macrophage low density lipoprotein uptake, binding the phosphocholine of oxidized low density lipoprotein, up-regulating the expression of adhesion molecules in endothelial cells, and inhibiting endothelial nitric-oxide synthase expression in aortic endothelial cells (39Go). These mechanisms are probably relevant to preeclampsia, which has many of the same pathophysiologic features as atherosclerosis (1Go). We chose triglycerides over other lipid markers because past studies have reported that differences in triglyceride levels between preeclamptics and controls have tended to be larger than differences in other lipids (34Go–37Go). Nevertheless, we cannot rule out the possibility that inclusion of another biomarker of lipid status or inflammation in the direct-effects models would have yielded different results.

We also assumed that the direct effects are multiplicative and that we accurately measured all confounders of the BMI-preeclampsia, CRP-preeclampsia, and triglyceride-preeclampsia associations. However, we lacked data on dietary intake, access to health care, and genetic factors that may predispose people to obesity, inflammation, elevated triglyceride levels, and preeclampsia, and some of our covariates may have been measured with error. We encourage other investigators to test the robustness of our findings using covariate-rich data sets from populations with varying proportions of preeclampsia subtypes based on severity, gestational age of onset, and the presence of fetal growth restriction. The use of different biomarkers of inflammation and lipid status would be particularly illuminating.

Understanding mechanisms underlying the BMI-preeclampsia relation is of great public health importance. Although reducing body weight before conception may lower the risk of preeclampsia, weight loss during pregnancy is not recommended (40Go). Other interventions must be identified to reduce the risk of preeclampsia among women who enter pregnancy with a high BMI. If our aforementioned assumptions are correct and our results are confirmed by other investigators, these data suggest that interventions designed to reduce inflammation and triglyceride concentrations may be effective in lowering the risk of preeclampsia among overweight and obese women at the start of pregnancy.


    APPENDIX
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 References
 
We fitted the following logistic regression models by maximizing the conditional likelihood.

Total-effects model:

Direct-effects model 1:

Direct-effects model 2:

Above, i = 1, 2, ..., 55 denotes independent groups; t = 1, 2, 3, 4 denotes the observations for the ith group; A denotes body mass index; B denotes C-reactive protein; C denotes triglycerides; x denotes a vector of measured confounders for the body mass index-preeclampsia association; y denotes a vector of measured confounders for the C-reactive protein–preeclampsia association; and z denotes a vector of measured confounders for the triglyceride-preeclampsia association.


    ACKNOWLEDGMENTS
 
This work was partially supported by grants 2PO1 HD30367 and 5MO1 RR00056 from the National Institute of Child Health and Human Development. Dr. Lisa Bodnar was supported by grant K12 HD43441 from the National Institute of Child Health and Human Development.

The authors thank Drs. Stephen Cole, Miguel Hernàn, and Enrique Schisterman for guidance regarding their analytical approach. They also thank the staff of the Pregnancy Exposures and Preeclampsia Prevention Study for their dedication to this project.

Conflict of interest: none declared.


    References
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 References
 

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