Abstract
Background
High glycemic index (GI) and glycemic load (GL) are associated with increased levels of oxidative stress and systemic inflammation in the general population. Maintenance hemodialysis (MHD) patients are known to have excessive oxidative stress burden and inflammation. In this study, we examined the relationship between dietary GI or GL and markers of oxidative stress or inflammation among prevalent MHD patients.
Methods
A registered dietitian obtained GI, GL and other dietary data from 58 MHD patients. Two separate 24-h diet recalls (a hemodialysis day and a non-hemodialysis day) were analyzed using the Nutrition Data System for Research (NDS-R) software. Plasma or serum concentrations of F2-isoprostanes, high sensitivity C-reactive protein (hsCRP), leptin and adiponectin (ADPN) were measured in fasting state. Fat mass was measured by dual-energy X-ray absorptiometry (DEXA). Cross-sectional associations between GI, GL and markers of interest were examined by multiple regression analysis with adjustment for potential covariates.
Results
Mean (±SD) age, body mass index (BMI) and total trunk fat were 47 ± 12 years, 29.5 ± 6.8 kg/m2 and 16.4 ± 8.8 kg, respectively. Dietary GI was associated with trunk fat (r = −0.182, P = 0.05) but not with F2-isoprostanes and hsCRP. In contrast, GL was significantly associated with F2-isoprostanes (P = 0.002), in unadjusted analysis, which remained in adjusted analyses, adjusting for age and sex (P = 0.005), and after adjusting for BMI, trunk fat and waist/hip ratio (P = 0.004). Addition of leptin or ADPN did not alter the significance of the association. GL also correlated with hsCRP (P = 0.03), but this association was modified by BMI and trunk fat.
Conclusions
Dietary GL is significantly associated with markers of oxidative stress and inflammation among prevalent MHD patients, independent of the body composition and adipocytokines. These data indicate the importance of the contents of dietary nutrient intake composition and its potential role in determining the metabolic disturbances in MHD patients.
Keywords: carbohydrate, diet, nutrition, oxidative stress
INTRODUCTION
The risk of cardiovascular disease (CVD) and its associated mortality in end-stage renal disease (ESRD) patients on maintenance hemodialysis (MHD) is exponentially higher than the general population [1]. These adverse outcomes may plausibly be related to or precipitated by metabolic derangements that accompany advanced kidney disease including oxidative stress, systemic inflammation and insulin resistance [2].
Glycemic index (GI) ranks different carbohydrate foods according to their effect on blood sugar response compared with a reference food, usually glucose or white bread. Glycemic load (GL) quantifies glycemic effect of a food serving. The Western-style diet is high in sugar and refined grains and almost 50% of its calories derive from carbohydrates. However, the same amount of carbohydrates may produce a different blood glucose response, depending on their GI. Reduction of dietary glycemic response has been proposed as methods of reducing the risk of diabetes and coronary heart disease [3]. The underlying rationale is the observation that chronic consumption of diets with a high GI may lead to high levels of oxidative stress markers and may adversely modulate plasma leptin and adiponectin (ADPN) levels [4, 5]. Additional data indicate that lower GI and GL are associated with decreased markers of inflammation [6–8].
While the high prevalence of exaggerated systemic inflammation and oxidative stress burden is well established among MHD patients, the role of dietary nutrient intake, especially in terms of GI and GL in this process, has not been studied in detail. Given the increasing prevalence of obesity and diabetes in this population, these patients represent a unique cohort to study the relevance of GI and GL in relation to dietary nutrient intake and co-existing metabolic abnormalities.
This study aimed to evaluate the dietary and disease-specific determinants of dietary GI and GL and their relationship to metabolic markers of oxidative stress and systemic inflammation in prevalent MHD patients. We hypothesized that dietary patterns associated with higher GI and GL would be associated with higher levels of markers of oxidative stress and systemic inflammation. We further hypothesized that the degree of adiposity and associated adipokine concentrations will influence this association.
MATERIALS AND METHODS
Study design and patient population
This was a retrospective cross-sectional study. We gathered demographic, clinical and nutritional data from 67 MHD patients who were recruited for multiple clinical studies at Vanderbilt University Medical Center (VUMC) between 2003 and 2012. All studies were approved by the Institutional Review Board of VUMC. For this analysis, subjects who were 18 years of age or older, had been on MHD for >3 months and were adequately dialyzed with biocompatible membranes and had detailed nutritional and body composition data available were included. Patients with autoimmune disease, active inflammatory or infectious disease, pregnancy, hospitalization within 1 month before the study, documented malignancy within the last 12 months, on unusual dietary restrictions or with a life-expectancy of <6 months were excluded.
The dietary intake information was collected by a trained registered dietitian for at least two 24-h diet recalls including a hemodialysis day and a non-hemodialysis day. Key dietary variables collected included total energy intake, carbohydrate, fat, protein, total sugar, total fibers, cholesterol, GI and GL (using glucose as the reference) data. Glycemic index is a measure that compares how carbohydrate content of a food affects blood glucose levels. GI is determined by measuring the area under the postprandial glucose curve of 50 g of available carbohydrate compared with 50 g of a standard food, usually glucose or white bread. Low GI foods have a score of <55, moderate GI foods 55–70 and high GI foods >70. Glycemic load is a GI-weighted measure of carbohydrate content of a food or meal and is calculated by multiplying the available grams of carbohydrates by the GI divided by 100. The results are based on the averaged GI of all foods consumed in a 24-h period and the GL is a sum of all the foods consumed in one day.
Diet recall analysis was completed using the Nutritional Data System for Research (NDS-R) software version 2011 (University of Minnesota, Minneapolis, MN, USA). The primary data source for GI/GL was from the 2008 International Tables of GI and GL Values [9]. Dietary data were excluded for under- or over-reporting of energy intake considered as intake of ≤800 or ≥4000 kcal/day in men and ≤500 or ≥3500 kcal/day in women. Nine participants were excluded, eight due to incomplete dietary data records and one for under reporting calorie intake. Information was obtained through clinic interviews, physical examination, laboratory tests and review of medical records. Blood was collected after an overnight fast for the measurement of all study variables at the visit closest to the dietary recalls.
Blood sampling
Venous blood was drawn into Vacutainer (Becton-Dickinson, Franklin Lakes, NJ, USA) tubes containing ethyldiaminetetraacetic acid supplemented with 1000 U/mL catalase and serum separator tubes containing clot activator for plasma and serum separation, respectively. Samples for plasma collection were transported on ice and immediately centrifuged at 4°C at 1700 g for 15 min, whereas the samples for serum collection were allowed to clot at room temperature before centrifugation. Plasma and serum samples were thereafter stored at −70°C until analysis.
All blood samples were collected fasted at the time of study visit, performed at the Vanderbilt General Clinical Research Center. Biochemical indices specific to the study included inflammatory markers: high sensitivity C-reactive protein (hsCRP) analyzed at a specialized chemistry laboratory (RenaLab, Richland, MS, USA), oxidative stress marker F2-isoprostanes (F2-IsoPs) measured by gas chromatography/negative-ion chemical ionization-mass spectrometry [10] with data expressed in nanograms per milliliter, adipokines: total ADPN measured using the MILLIPLEX™ MAP, Human Serum Adipokine Panel A kit (Millipore, Billerica, MA, USA) and leptin measured by human Leptin RIA kit. The intra-assay variability for ADPN and leptin was 5.6 and 6.0%, respectively.
Body composition
Dual-energy X-ray absorptiometry (DEXA) was performed to assess body composition. During the scan, participants were asked to remain motionless on the scanning bed until the whole body scan was completed. Body Mass Index (BMI) was defined as the individual's body weight in kilograms divided by the square of height in meters. Waist/hip ratio was calculated for each patient.
Statistical analysis
Baseline characteristics were described by using medians and interquartile ranges. hsCRP was log-transformed because residuals indicated an influential point. We assessed five potential mediators: leptin, ADPN, BMI, trunk fat and waist/hip ratio. To assess whether mediators affect the association between inflammation markers and dietary characteristics, we used the following three regression-model approaches. First, the associations between potential mediators (leptin, ADPN, BMI, trunk fat and waist/hip ratio) and outcomes (F2-Iso and log CRP) were assessed. Second, the association between exposures (GI and GL) and potential mediators (leptin, ADPN, BMI, trunk fat, HOMA-IR and waist/hip ratio) were examined. Third, the associations between outcomes (F2-Iso and log CRP) and exposures (GI and GL), without and with adjustment for potential mediators (leptin, ADPN, BMI, trunk fat and waist/hip ratio) were evaluated. Mediation was evident when the effect of exposures on outcomes was attenuated after adjustment for potential mediators. All regression analyses were performed using ordinary least squares regression. Due to small sample size, we adjusted for three covariates: age, gender and race (African American versus Other). Analyses were performed using R version 2.15.2 (www.r-project.org).
RESULTS
The major demographic, clinical and nutritional characteristics among 58 MHD subjects in whom complete data were available is shown in Table 1. The median age was 48.0 (38.0, 55.8) years, 37 (63.8%) were male, 43 (74.1%) were African American and 21 (36.2%) had diagnosis of diabetes mellitus; 27.7% of participants are current smokers. Median BMI of the cohort was 27.9 (24.7, 34.2) kg/m2 with a body fat of 34.5 (29.7, 41.3).
Table 1.
Patient characteristics
| N | Value | |
|---|---|---|
| Age (year)a | 58 | 48.0 (38.0, 55.8) |
| Gender | 58 | |
| Male | 63.8% (37) | |
| Female | 36.2% (21) | |
| Race | 58 | |
| African American | 74.1% (43) | |
| Middle Eastern | 1.8% (1) | |
| White | 20.7% (12) | |
| Hispanic | 3.4% (2) | |
| Diabetes | 58 | |
| No | 63.8% (37) | |
| Yes | 36.2% (21) | |
| Tobacco use | 47 | |
| Never | 65.9% (31) | |
| Current | 27.7% (13) | |
| Former | 6.4% (3) | |
| Cardiovascular history | 58 | |
| No | 63.8% (37) | |
| Yes | 36.2% (21) | |
| BMIa (kg/m2) | 57 | 27.9 (24.7, 34.2) |
| DEXA percent body fata | 58 | 34.5 (29.7, 41.3) |
| Total fat massa (kg) | 58 | 26.7 (19.0, 35.6) |
| Trunk fata (kg) | 58 | 15.5 (10.2, 19.6) |
| Waist/hip ratioa | 51 | 0.9 (0.9, 1.0) |
aMedian (interquartile range).
Numbers after % are frequencies.
Dietary nutrient intake and related information is shown in Table 2. The median total calorie intake was 1495.4 (1162.0, 1971.5) kcal/kg/day. Total carbohydrate, fat and protein intakes were 174.6 (131.4, 232.4), 60.9 (47.7, 83.7) and 59.5 (48.0, 69.5) g/day, respectively. Total sugar and dietary fiber intakes were 74.3 (50.3, 114.8) and 9.1 (6.9, 11.4) g/day, respectively. The median GI was 66.5 (61.3, 68.5) and the median dietary GL was 109.7 (82.7, 146.5). Table 3 shows the mean concentrations of biomarkers of oxidative stress (F2-IsoPs), systemic inflammation (hsCRP) and adipokines (leptin and ADPN). As expected, all of the biomarkers were elevated similar to levels previously reported in MHD patients.
Table 2.
Dietary characteristics
| N | Valuea | |
|---|---|---|
| Total energy intake (kcal/day) | 58 | 1495.4 (1162.0, 1971.5) |
| Carbohydrates (g/day) | 58 | 174.6 (131.4, 232.4) |
| Fat intake (g/day) | 58 | 60.9 (47.7, 83.7) |
| Protein intake (g/day) | 58 | 59.5 (48.0, 69.5) |
| Cholesterol intake (mg/day) | 58 | 215.3 (129.2, 353.7) |
| Total sugar (g/day) | 58 | 74.3 (50.3, 114.8) |
| Dietary fiber intake (g/day) | 58 | 9.1 (6.9, 11.4) |
| GIb | 58 | 66.5 (61.3, 68.5) |
| GLb | 58 | 109.7 (82.7, 146.5) |
aAll values are median (interquartile range).
bCalculated by using glucose as the reference.
Table 3.
Inflammatory, oxidative stress and adipokine markers
| Variable | N | Valuea |
|---|---|---|
| C-reactive protein (mg/L) | 58 | 5.8 (2.7, 15.6) |
| log CRP | 58 | 1.7 (1.0, 2.7) |
| F2 isoprostane (ng/mL) | 49 | 0.10 (0.07, 0.13) |
| Insulin (μU/mL) | 58 | 14.0 (8.5, 22.9) |
| ADPN (μg/mL) | 58 | 28.5 (15.3, 42.8) |
| Leptin (ng/mL) | 58 | 14.8 (7.4, 64.6) |
| Resistin (ng/mL) | 58 | 51.8 (31.55, 86.4) |
| Leptin/ADPN ratio | 56 | 0.11 (0.02, 0.32) |
| HOMA-IR | 58 | 3.5 (2.2, 7.1) |
aAll values are median (interquartile ratio).
Associations between glycemic index, glycemic load and study variables
Tables 4 and 5 present the effect of GI and GL on F2-isop and hsCRP with and without adjustment for leptin, ADPN, BMI, truncal fat and waist/hip ratio (Tables 4 and 5). The significant associations between GL and F2-IsoPs and non-significant associations between GI and F2-IsoPs were unchanged by including the set of potential mediators. On the other hand, addition of BMI and truncal fat attenuated the significant association between log-transformed CRP and GL, indicating mediation, whereas leptin, ADPN and waist/hip ratio appeared not to mediate the effect of GL and GI. The non-significant associations between GI and log-transformed CRP were unchanged after adjusting for potential mediators, including comorbidities, HOMA-IR. ADPN, leptin, HOMA-IR or body composition markers were not associated with GI or GL (data not shown).
Table 4.
Association between GI, GL and F2-isoprostanes, log CRP, with and without adjustment for potential mediators leptin and ADPN (adjusted for age, gender and race)
| 25th and 75th percentiles of exposure | Difference | Without adjustment for potential mediators |
With adjustment for leptin |
With adjustment for ADPN |
||||
|---|---|---|---|---|---|---|---|---|
| Coefficient (95% CI)a | P-value | Coefficient (95% CI)a | P-value | Coefficient (95% CI)a | P-value | |||
| F2-isop-GI | 61.3, 68.5 | 7.2 | 0.007 (−0.008, 0.022) | 0.369 | 0.006 (−0.009, 0.022) | 0.438 | 0.008 (−0.008, 0.024) | 0.332 |
| F2-isop-GL | 82.7, 146.5 | 63.8 | 0.022 (0.009, 0.036) | 0.002 | 0.022 (0.008, 0.036) | 0.004 | 0.023 (0.009, 0.037) | 0.002 |
| log (hsCRP)-GI | 61.3, 68.5 | 7.2 | −0.333 (−0.761, 0.096) | 0.134 | −0.314 (−0.747, 0.120) | 0.162 | −0.388 (−0.814, 0.037) | 0.080 |
| log(hsCRP)-GL | 82.7, 146.5 | 63.8 | −0.490 (−0.933, −0.047) | 0.035 | −0.469 (−0.921, −0.016) | 0.047 | −0.504 (−0.941, −0.067) | 0.028 |
aCoefficients and 95% confidence intervals reflect a comparison between the interquartile range of the exposure variable.
Table 5.
Association between GI, GL and F2-isoprostanes, log CRP, with and without adjustment for potential mediators BMI, waist/hip ratio and trunk fat (adjusted for age, gender and race)
| 25th and 75th percentiles of exposure | Difference | With adjustment for BMI |
With adjustment for waist/hip ratio |
With adjustment for trunk fat |
||||
|---|---|---|---|---|---|---|---|---|
| Coefficient (95% CI)a | P-value | Coefficient (95% CI)a | P-value | Coefficient (95% CI)a | P-value | |||
| F2-isop-GI | 61.3, 68.5 | 7.2 | 0.004 (−0.012, 0.020) | 0.608 | 0.008 (−0.010, 0.025) | 0.400 | 0.004 (−0.012, 0.020) | 0.660 |
| F2-isop-GL | 82.7, 146.5 | 63.8 | 0.021 (0.007, 0.035) | 0.006 | 0.022 (0.006, 0.037) | 0.010 | 0.021 (0.007, 0.035) | 0.005 |
| log(hsCRP)-GI | 61.3, 68.5 | 7.2 | −0.225 (−0.654, 0.204) | 0.308 | −0.304 (−0.767, 0.159) | 0.205 | −0.201 (−0.632, 0.230) | 0.365 |
| log(hsCRP)-GL | 82.7, 146.5 | 63.8 | −0.400 (−0.842, 0.041) | 0.081 | −0.636 (−1.108,−0.163) | 0.011 | −0.416 (−0.848, 0.015) | 0.064 |
aCoefficients and 95% confidence intervals reflect a comparison between the 25th and 75th percentiles of the exposure variable.
DISCUSSION
In this study, we examined the relationship between certain characteristics of nutrient intake (i.e. GI and GL) and metabolic disturbances in MHD patients. As expected, carbohydrates were the primary calorie source for our patient population, followed by notably high fat intake. Our data further indicate that the glycemic effect (i.e. GL of the carbohydrate diet) is associated with increased oxidative stress, as measured by plasma F2-isoprostane levels. This association was independent of fat mass or adipokines suggesting a direct effect of the nutrient content on the metabolic milieu.
Nutritional management of MHD patients is complex and requires special consideration, especially in terms of optimal protein and calorie intake. According to the Kidney Disease Outcomes Quality Initiative guidelines, the recommended dietary protein and calorie intakes for MHD patients are 1.2 g/kg/day and 35 kcal/kg/day, respectively [11]. These levels are both significantly higher than the general population, especially in terms of calories. Therefore, the primary source for the increased oral intake defaults to carbohydrates, as shown in our patient population. While the need for the increased calorie intake has been studied in detail, there are very limited studies examining its metabolic consequences. In fact, it has been well established that high carbohydrate intake, especially ones with high GI, raises blood glucose concentrations particularly in postprandial stage, which potentiates high insulin levels. High glucose and insulin levels are shown to induce exaggerated oxidative stress burden and inflammation, in both animal models and humans [4, 12–14]. Our results are in line with these data and suggest that the characteristics of the carbohydrate intake determine the metabolic disturbances in MHD patients.
An interesting finding in our study is that GL but not GI was significantly elevated in our study patients. In line with these findings, F2- isoPs and hsCRP were significantly associated with GL, but not GI. While this might seem somewhat unexpected, it can be explained by the specific dietary restrictions imposed on MHD patients. Most of the low GI foods such as legumes, fruit and vegetables that are recommended in general population are restricted in quantity in MHD patients due to their potassium and phosphorus content. The result is ingestion of low GI foods in smaller portions and compensating for the increased calorie needs with larger portions of higher GI foods, leading to a cumulatively high GL. Since GI is primarily determined by food choices, a low GI can be observed in the setting of high GL, purely based on portion size. The apparent association between GL and oxidative stress burden is a reflection of these dietary preferences and their adverse consequences.
A notable finding in our study was the dietary intake characteristics. As expected, carbohydrates were the primary calorie source. Interestingly, our subjects ingested a significant amount of fat, almost 38% of the calorie intake. Dietary fat intake influences the physiological processes that transport fat between tissues as well as influencing the substrates for metabolic processes [15]. The amount and the characteristics of the fat intake could adversely affect postprandial lipidemia, leading to an atherosclerotic milieu. Increased oxidative stress as a result of high fat intake and lipoprotein oxidation could be involved in the pathogenesis of atherosclerosis. However, there are very limited data regarding the consequences of altering lipoprotein oxidation by means of increasing/decreasing the proportion of dietary polyunsaturated fatty acids or anti-oxidant therapies.
A possible mechanism by which the glycemic effects of nutrients foods are mediated is through the actions of adipokines, i.e. leptin and ADPN. For example, ADPN is an insulin sensitizing hormone that is inversely correlated with GI [16] and its levels were increased after low glycemic diets [17]. In this study, we did not observe any association between ADPN and both GI and GL. Inclusion of ADPN in the models did not influence results either. Similar findings were also applicable for leptin, another important adipokine that is closely associated with systemic inflammation and oxidative stress. In addition, there are data showing that long-term high GI diet (other than energy intake) is inversely associated with leptin levels in non-CKD patients [16, 18]. Overall, the lack of any confounding effect of these adipokines suggests that the association between GL and inflammation and oxidative stress are primarily driven by the nutrient intake rather than the co-existing metabolic miliue. Nevertheless, there may be other factors that mediate the observed associations that are not examined in this study. For example, some of the unmeasured uremic toxins that are end-products of the nutrients might also be influencing these results [19].
Our results have important clinical implications. In ESRD patients, biomarkers of systemic inflammation and oxidative stress inflammatory state, such as hsCRP, interleukin-6 (IL-6) and F2-IsoPs, are elevated and are also robust predictors of CVD and mortality. The etiology of the increased inflammatory response and oxidative stress burden is multi-factorial, and anti-oxidants and anti-inflammatory strategies are proposed as means to improve CVD mortality on these patients [2, 20, 21]. Interestingly, there are very limited data regarding the efficacy of these strategies [22–25]. While the calorie restriction had no significant effect on oxidative stress in certain animal models [26], certain food style and/or caloric restriction had some effects on oxidative stress biomarkers in the general population [27, 28]. Accordingly, adjustment of the dietary nutrient intake might be a safe and effective strategy to lower the oxidative stress burden in MHD patients.
An unexpected finding in our study was the inverse relationship between GL and inflammation, which is confounded by body composition. It is well established that there is a close direct correlation between visceral fat and inflammation. On the other hand, it is not clear how changes in BMI, especially in relation to dietary nutrient intake, are associated with systemic inflammation in the setting of ESRD. Our study included mostly overweight/obese prevalent MHD patients, and these results are clearly influenced by these variables. Further studies examining the effects of dietary manipulations on systemic inflammation are needed in MHD patients in order to understand the mechanisms leading to these findings.
Our study has multiple strengths, including use of gold standard measures for inflammation, oxidative stress and body composition, detailed nutritional analysis and a comprehensive statistical approach [29, 30]. On the other hand, several limitations need to be taken into account. This is a cross-sectional study and has a relatively small sample size. Therefore, a cause and effect relationship between GL/GI and oxidative stress and inflammation cannot be readily made. The 24-h dietary recalls are routinely used but are also based on patients' report, which may lead to bias. For example, there is a consistent underreporting, which was suggested in our study as well. It is possible that the underestimation, especially fat intake, could have influenced our results. In order to overcome the limitations, we maximized the reliability of the data by using two day recalls including one hemodialysis day and one non-hemodialysis day, assessing the plausibility of calorie intake, using picture of food portion size, and a registered dietitian carried out the interview [31].
In summary, dietary GL, but not GI, is increased in MHD patients and is significantly associated with F2-IsoPs, a marker of oxidative stress burden. The weak indirect correlation between GL and inflammation is dependent on BMI and trunk fat, whereas the association between GL and oxidative stress is independent of fat mass and adipokines. These data suggest that the dietary glycemic content of the foods should be considered as one of potential factors that affect oxidative stress among MHD patients. Specific dietary recommendations taking into account the GL of the nutrients may have potential to prevent some of the metabolic disturbances in MHD patients.
FUNDING
This study was supported in part by Clinical Translational Science Award 1UL-1RR024975 from the National Center for Research Resources, Vanderbilt Diabetes Research and Training Center Grant DK20593, K24 DK62849 and R01 DK45604 from the National Institute of Diabetes and Digestive and Kidney Diseases, Veterans Administration Merit Award 1I01CX000414 and Satellite Health Norman Coplon Extramural Grant Program. C. Limkunakul was supported in part by International Society of Nephrology fellowship award.
CONFLICT OF INTEREST STATEMENT
None declared.
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