Abstract
Background and objectives
In the United States population, high dietary fiber intake has been associated with a lower risk of inflammation and mortality in individuals with kidney dysfunction. This study aimed to expand such findings to a Northern European population.
Design, setting, participants, & measurements
Dietary fiber intake was calculated from 7-day dietary records in 1110 participants aged 70–71 years from the Uppsala Longitudinal Study of Adult Men (examinations performed during 1991–1995). Dietary fiber was adjusted for total energy intake by the residual method. Renal function was estimated from the concentration of serum cystatin C, and deaths were registered prospectively during a median follow-up of 10.0 years.
Results
Dietary fiber independently and directly associated with eGFR (adjusted difference, 2.6 ml/min per 1.73 m2 per 10 g/d higher; 95% confidence interval [95% CI], 0.3 to 4.9). The odds of C-reactive protein >3 mg/L were lower (linear trend, P=0.002) with higher fiber quartiles. During follow-up, 300 participants died (incidence rate of 2.87 per 100 person-years at risk). Multiplicative interactions were observed between dietary fiber intake and kidney dysfunction in the prediction of mortality. Higher dietary fiber was associated with lower mortality in unadjusted analysis. These associations were stronger in participants with kidney dysfunction (eGFR<60 ml/min per 1.73 m2) (hazard ratio [HR], 0.58; 95% CI, 0.35 to 0.98) than in those without (HR, 1.30; 95% CI, 0.76 to 2.22; P value for interaction, P=0.04), and were mainly explained by a lower incidence of cancer-related deaths (0.25; 95% CI, 0.10 to 0.65) in individuals with kidney dysfunction versus individuals with an eGFR≥60 ml/min per 1.73 m2 (1.61; 95% CI, 0.69 to 3.74; P value for interaction, P=0.01).
Conclusions
High dietary fiber was associated with better kidney function and lower inflammation in community-dwelling elderly men from Sweden. High dietary fiber was also associated with lower (cancer) mortality risk, especially in individuals with kidney dysfunction.
Keywords: CKD, chronic inflammation, GFR, mortality risk, nutrition
Introduction
CKD is increasingly recognized as a public health burden, affecting >10% of the general population and with a much larger prevalence among elderly persons (1,2). Patients with CKD have a substantially increased risk of malnutrition, persistent inflammation, and cardiovascular disease (CVD), which collectively leads to high mortality risk (3,4). Identification of modifiable risk factors that could reduce the risk of complications in this vulnerable population is of great importance.
The role of dietary fiber in the prevention of CVD and cancer has received increasing attention in the community, prompting health care organizations to recommend increased dietary fiber intake (5,6). Although the biologic mechanisms explaining how fiber may protect against these outcomes have yet to be fully elucidated, epidemiologic evidence suggests that the beneficial effect of high-fiber diets is coupled with their effect on inflammation (7). A recent study from the Third National Health and Nutrition Examination Survey (NHANES III) showed that the inverse association of dietary fiber intake with inflammation and mortality risk was stronger in individuals with kidney dysfunction than in those without (8). The authors suggest that both inflammation and kidney function may be important mediators in the association between dietary fiber and mortality outcomes. Validation of this finding in other societies with different dietary habits is needed in order to substantiate and motivate preventive dietary recommendations. Therefore, this study aimed to evaluate the association between fiber intake, kidney function, inflammation, and death in Swedish community-dwelling elderly men of similar age. Within this aim, we additionally assessed possible effect modification by underlying kidney function.
Materials and Methods
Study Population
This study was performed in the Uppsala Longitudinal Study of Adult Men (ULSAM) (http://www2.pubcare.uu.se/ULSAM/). The present analyses are based on the third examination cycle of the ULSAM cohort (examinations performed during 1991–1995; n=1221), when dietary records were collected for the first time and participants were aged approximately 70–71 years (mean 70.9 ±0.5 years; range, 69.5–71.9). Exclusion criteria were unavailable data on 7-day dietary records and/or serum cystatin C (n=116), and extreme values of reported energy intake (<765 or >4300 kcal/d; n=5). This study therefore comprises 1110 participants. All participants gave written consent, and the Ethics Committee of Uppsala University approved the study.
Demographics and Comorbidities
Body mass index (BMI) was calculated as body weight in kilograms divided by the square of height in meters. Smoking was defined as current smoking versus nonsmoking. Exercise habits were self-reported according to four categories (sedentary, moderate, regular, and athletic) (9). Education level was recorded as low (elementary school), medium (secondary school), and high (university studies). Previous CVD was established from the Swedish Hospital Discharge Registry (International Classification of Diseases, Ninth Revision [ICD-9] codes 390–459 or International Classification of Diseases, Tenth Revision [ICD-10] codes I00–I99). Previous cancer diagnosis was defined as ICD-9 codes 150–250 or ICD-10 codes C00–D48. BP was measured by the Accutracker II ambulatory BP monitoring device (Suntech Medical Instruments, Raleigh, NC). Hypertension was defined as either average daytime BP from ambulatory BP monitoring ≥135/85 mmHg (10,11) or intake of antihypertensive drugs. Hyperlipidemia was defined as serum cholesterol >250 mg/dl (6.5 mmol/L), serum triglycerides >200 mg/dl (2.3 mmol/L), or treatment with lipid-lowering medications. Diabetes was defined as fasting plasma glucose ≥126 mg/dl (7.0 mmol/L), a 2-hour postload glucose level ≥200 mg/dl (11.1 mmol/L), or the use of oral hypoglycemic agents or insulin (12).
Laboratory Measurements
Serum cystatin C was measured by a latex-enhanced reagent (N Latex Cystatin C; Dade Behring, Deerfield, IL) with a Behring BN ProSpec analyzer (Dade Behring). The total analytical imprecision of the method was 4.8% at 0.56 mg/L and 3.7% at 2.85 mg/L. The eGFR was calculated from serum cystatin C concentrations (in milligrams per liter) by the following formula: eGFR = 77.24 × cystatin C−1.2623, which has been shown to be closely correlated with iohexol clearance (13). This equation was used in our primary analysis. We also used the CKD Epidemiology Collaboration (CKD-EPI) cystatin C equation to calculate the eGFR (14,15) and aimed to confirm our findings. Kidney dysfunction was defined as an eGFR<60 ml/min per 1.73 m2 according to the current Kidney Disease Outcomes Quality Initiative definition (1). C-reactive protein (CRP) measurements were performed by a latex-enhanced reagent (Dade Behring) using a Behring BN ProSpec analyzer (Dade Behring). The intra-assay coefficient of variation of the CRP method was 1.4% at both 1.23 mg/L and 5.49 mg/L. Serum CRP >3 mg/L was defined as elevated CRP according to a consensus statement of the US Centers for Disease Control and Prevention and the American Heart Association (16). IL-6 was analyzed by an ELISA kit (IL-6HS; R&D Systems, Minneapolis, MN). The interassay coefficient of variation was 5%. The urinary albumin excretion rate (UAER) was measured in one overnight urine collection (expressed in micrograms per minute). The assay utilized a commercially available RIA kit (Albumin RIA 100; Pharmacia, Uppsala, Sweden).
Dietary Assessment
Dietary habits were evaluated by a 7-day dietary record based on a validated precoded menu book, which was prepared and previously used by the Swedish National Food Administration (NFA) (17). The participants were given oral instructions by a dietitian on how to perform the dietary registration, and the amounts consumed were reported in household measurements or specified as portion sizes. The daily intakes of energy as well as macronutrients and micronutrients were calculated by using a database from the NFA. To reduce extraneous variation and predict the effect of dietary interventions, the daily intakes of macronutrients and micronutrients in this study were corrected for total energy intake by regression analysis of the residual method (18).
Follow-Up and Mortality
Follow-up for mortality was conducted, with no loss to follow-up, from the examination date until death or December 31, 2003. The Swedish National Registry recording for date and cause of death was used to define end points. During a median of 10 years (range, 0.1–12.4 years), 300 participants died. There were 138 deaths from CVD (ICD-9 codes 390–459 or ICD-10 codes I00–I99), 111 from cancer (ICD-9 codes 150–250 or ICD-10 codes C00–D48), 19 from infections (ICD-10 codes J180–K578), and 33 from other causes.
Statistical Analyses
Values are expressed as the mean±SD for normally distributed continuous variables, the median (interquartile range [IQR]) for skewed variables, or the percentage of the total for categorical variables. Study participants were divided into four groups according to quartiles of energy-adjusted dietary fiber. The Jonckheere–Terpstra test was used to assess linear trends across these groups, and the P value for trend was reported.
For comparison purposes with the NHANES III study (8), we use the same cut point of per 10 g/d higher in fiber intake. Selection of covariates was done on the basis of consideration as confounders in the association of interest. In a final step, we considered CRP adjustment as a plausible mediator. In cross-sectional analyses, multivariable linear regressions were calculated to evaluate the association of dietary fiber and kidney function (eGFR). We also performed prespecified multicategory (fiber intake quartiles) models. Three hierarchical models were investigated (an unadjusted model and two adjusted models). Model 1 considered adjustment for lifestyle factors (protein intake, age, BMI, smoking status, physical activity, and education). Model 2 further adjusted for the presence of comorbidities (CVD, hypertension, hyperlipidemia, and diabetes) and UAER. Data are expressed as regression coefficients (difference) and 95% confidence interval (95% CIs). Unadjusted and multiple adjusted logistic regressions were fitted to evaluate the association of dietary fiber with the presence of elevated serum CRP. Covariates in the adjusted models included protein intake, age, BMI, physical activity, smoking status, education, comorbidities, UAER, and eGFR. Data are presented as odds ratios and 95% CIs.
In longitudinal analysis, the association of dietary fiber with mortality was investigated with Cox proportional hazards analyses. Proportional hazard assumptions were confirmed by the Schoenfeld test. The relations between dietary fiber and mortality were investigated in unadjusted analyses and in analyses adjusted for protein intake, age, BMI, smoking status, physical activity, education, comorbidities, eGFR, and UAER (all considered confounders) as well as CRP (possible mediator). Adding CRP as a final separate step was also tested, but the intermediate model is not presented herein because the results were similar. Data are presented as hazard ratios and 95% CIs. Due to the cause of death distribution, we addressed the association between dietary fiber and both CVD- and cancer-specific deaths. Given the potential for changes in dietary habits soon after a diagnosis, we performed a sensitivity analysis that excluded deaths that occurred within 2 years of baseline (n=20).
We examined multivariable adjusted models that included interaction terms for dietary fiber (as a continuous variable) and kidney dysfunction (as a binominal variable: eGFR≥60 or <60 ml/min per 1.73 m2), and performed the analyses after stratification of individuals according to the presence/absence of kidney dysfunction. P values for interaction were reported. A P value <0.05 was regarded as significant. All statistical analyses were performed using STATA software (version 12.0; StataCorp, College Station, TX).
Results
General Characteristics
The median absolute intake of fiber was 16.3 g/d (IQR, 13.4–20.2; range, 4.9–40.8) and the energy-adjusted fiber intake was 16.8 g/d (IQR, 14.5–19.3; range, 4.8–34.7). We used energy-adjusted values as the exposure for the analyses. Clinical and biochemical characteristics are shown in Table 1 as stratified by quartiles of dietary fiber. Across increasing quartiles, participants had higher physical activity and education levels, but there was a lower proportion of smokers. eGFR was higher across increasing dietary fiber quartiles, whereas CRP and IL-6 were lower. Dietary protein, carbohydrate, sodium, and potassium intake were higher, whereas dietary fat was lower. Sodium intake was correlated with dietary fiber intake (Spearman’s ρ=0.16; P=0.001).
Table 1.
Baseline characteristics according to quartile of dietary fiber intake (N=1110)
| Parameter | Quartile of Energy-Adjusted Dietary Fiber (g/d) | P Trend | |||
|---|---|---|---|---|---|
| Q1 (4.8, 14.5) | Q2 (>14.5, 16.8) | Q3 (>16.8, 19.2) | Q4 (>19.2, 34.7) | ||
| Participants, n | 277 | 278 | 277 | 278 | |
| Age, yr | 71±0.6 | 71±0.5 | 71±0.5 | 71±0.5 | 0.76 |
| BMI, kg/m2 | 25.9±3.3 | 26.6±3.5 | 26.3±3.4 | 26.0±3.4 | 0.58 |
| Smokers | 78 (28) | 59 (21) | 45 (16) | 34 (12) | <0.001 |
| Physical activity | 0.02 | ||||
| Sedentary | 13 (5) | 8 (3) | 14 (5) | 7 (3) | |
| Moderate | 103 (39) | 88 (33) | 89 (33) | 88 (32) | |
| Regular | 140 (53) | 152 (57) | 144 (54) | 158 (58) | |
| Athletic | 7 (3) | 17 (7) | 19 (8) | 20 (7) | |
| Education | 0.01 | ||||
| Elementary school | 170 (64) | 160 (60) | 171 (64) | 140 (51) | |
| Secondary school | 62 (23) | 66 (25) | 53 (20) | 74 (27) | |
| University or equivalent | 36 (13) | 42 (15) | 45 (16) | 58 (22) | |
| CVD | 86 (31) | 83 (30) | 85 (31) | 85 (31) | 0.96 |
| Hypertension | 194 (70) | 199 (72) | 177 (64) | 191 (69) | 0.15 |
| Hyperlipidemia | 98 (35) | 102 (37) | 102 (37) | 91 (33) | 0.54 |
| Diabetes | 37 (13) | 32 (12) | 41 (15) | 45 (16) | 0.21 |
| Cancer history | 10 (4) | 20 (7) | 16 (6) | 23 (8) | 0.06 |
| Serum CRP>3 mg/L | 104 (38) | 94 (34) | 78 (28) | 68 (24) | <0.001 |
| eGFR<60 ml/min per 1.73 m2 | 136 (49) | 134 (48) | 125 (45) | 111 (40) | 0.02 |
| eGFR, ml/min per 1.73 m2 | 60.1 (51.4–68.5) | 60.1 (51.9–69.3) | 61.4 (53.4–70.9) | 63.4 (54.4–73.5) | 0.001 |
| UAER, μg/min | 5.5 (3.4–10.3) | 5.0 (3.2–10.6) | 5.2 (3.5–13.0) | 5.1 (3.4–11.5) | 0.99 |
| CRP, mg/L | 2.2 (1.1–4.8) | 1.9 (1.0–3.9) | 1.8 (0.8–3.6) | 1.6 (0.8–3.0) | <0.001 |
| IL-6, ng/L | 4.0 (2.5–6.7) | 3.6 (2.3–6.3) | 3.2 (2.1–5.5) | 3.1 (2.0–5.2) | <0.001 |
| Energy intake, kcal/d | 1846±498 | 1706±414 | 1688±400 | 1830±460 | 0.75 |
| Carbohydrate intake, g/d | 191.7±23.4 | 201.8±20.7 | 209.6±19.7 | 224.6±21.8 | <0.001 |
| Protein intake, g/d | 65.0±8.9 | 66.2±8.1 | 67.3±8.4 | 68.3±8.4 | <0.001 |
| Fat intake, g/d | 75.3±11.0 | 71.0±9.0 | 68.5±8.1 | 61.6±9.2 | <0.001 |
| Potassium intake, g/d | 2.5±0.4 | 2.7±0.4 | 2.8±0.4 | 3.1±0.4 | <0.001 |
| Sodium intake, g/d | 2.4±0.3 | 2.5±0.3 | 2.6±0.4 | 2.6±0.5 | <0.001 |
Data are expressed as the mean±SD, median (25th percentile–75th percentile), or n (%), as appropriate. BMI, body mass index; CVD, cardiovascular disease; CRP, C-reactive protein; UAER, urinary albumin excretion rate.
Dietary Fiber, eGFR, and Elevated Serum CRP
In unadjusted and adjusted linear regression models, dietary fiber positively associated with eGFR regardless of confounders (Table 2). Similar associations were observed when applying the CKD-EPI equation (Supplemental Table 1).
Table 2.
Association of fiber intake with eGFR (N=1110)
| Linear Regression Model | Cut Point/Limits (g/d) | Unadjusted | Model 1 | Model 2 | |||
|---|---|---|---|---|---|---|---|
| Difference (95% CI) | P | Difference (95% CI) | P | Difference (95% CI) | P | ||
| Continuous | 10 higher | 3.7 (1.6 to 5.9) | 0.001 | 3.1 (0.8 to 5.3) | 0.01 | 2.6 (0.3 to 4.9) | 0.03 |
| Multicategory | |||||||
| Quartile 1 | ≤14.5 | Reference | Reference | Reference | |||
| Quartile 2 | >14.5–16.8 | 1.4 (−0.9 to 3.7) | 0.24 | 1.2 (−1.2 to 3.5) | 0.34 | 1.5 (−1.0 to 4.0) | 0.23 |
| Quartile 3 | >16.8–19.2 | 2.3 (0.1 to 4.6) | 0.05 | 1.7 (−0.7 to 4.1) | 0.17 | 1.4 (−1.1 to 3.9) | 0.27 |
| Quartile 4 | > 19.2 | 4.2 (1.9 to 6.5) | <0.001 | 3.3 (0.9 to 5.7) | 0.01 | 2.9 (0.5 to 5.4) | 0.02 |
| P for trend | 0.01 | 0.05 | 0.08 | ||||
eGFR values are expressed in milliliters per minute per 1.73 m2. Covariates in model 1 include protein intake (energy adjusted), age, BMI, smoking, physical activity, and education. Covariates in model 2 include protein intake (energy adjusted), age, BMI, smoking, physical activity, education, CVD, diabetes, hyperlipidemia, hypertension, and UAER. 95% CI, 95% confidence interval.
In unadjusted logistic models, dietary fiber significantly associated with lower odds of having a state of higher CRP, which was progressively reduced after adjustment for confounders and became nonsignificant in the final model (Table 3). We also observed linear trends associating fiber quartiles with lower CRP odds. Similar associations were observed in individuals with and without kidney dysfunction. Comparable results were observed when using IL-6 as the exposure (per Log2 higher) (Supplemental Table 2). Results were confirmed when applying the CKD-EPI equation (Supplemental Table 3).
Table 3.
Associations of fiber intake with elevated serum CRP (>3 mg/L) in the entire survey (N=1110) and after stratification for kidney dysfunction
| Linear Regression Model | Cut Point/Limits (g/d) | Unadjusted | Model 1 | Model 2 | |||
|---|---|---|---|---|---|---|---|
| OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P | ||
| Continuous | 10 higher | 0.57 (0.40 to 0.81) | 0.002 | 0.68 (0.46 to 0.99) | 0.04 | 0.73 (0.50 to 1.08) | 0.12 |
| Multicategory | |||||||
| Quartile 1 | ≤14.5 | Reference | Reference | Reference | |||
| Quartile 2 | >14.5–16.8 | 0.84 (0.59 to 1.19) | 0.32 | 0.86 (0.59 to 1.25) | 0.43 | 0.87 (0.59 to 1.30) | 0.51 |
| Quartile 3 | >16.8–19.2 | 0.63 (0.44 to 0.90) | 0.01 | 0.63 (0.43 to 0.93) | 0.02 | 0.68 (0.46 to 0.99) | 0.05 |
| Quartile 4 | > 19.2 | 0.52 (0.36 to 0.76) | 0.001 | 0.61 (0.41 to 0.90) | 0.01 | 0.65 (0.43 to 0.98) | 0.04 |
| P for trend | 0.002 | 0.03 | 0.10 | ||||
| Stratified by the presence of kidney dysfunction | |||||||
| eGFR≥60 (n=604) | 10 higher | 0.59 (0.36 to 0.98) | 0.04 | 0.67 (0.38 to 1.17) | 0.16 | 0.63 (0.35 to 1.12) | 0.12 |
| eGFR<60 (n=506) | 10 higher | 0.60 (0.37 to 0.99) | 0.05 | 0.74 (0.43 to 1.26) | 0.26 | 0.86 (0.49 to 1.50) | 0.58 |
| P for interaction | 0.97 | 0.98 | 0.65 | ||||
eGFR values are expressed in milliliters per minute per 1.73 m2. Covariates in model 1 include protein intake (energy adjusted), age, BMI, smoking, physical activity, and education. Covariates in model 2 include protein intake (energy adjusted), age, BMI, smoking, physical activity, education, CVD, diabetes, hyperlipidemia, hypertension, eGFR, and UAER. OR, odds ratio.
Dietary Fiber and Mortality
Fiber intake was associated with lower all-cause and cancer-related mortality risk in unadjusted analyses, but full multivariate adjustment abrogated statistical significance (Table 4). A significant interaction product term in both all-cause and cancer mortality prediction was observed between dietary fiber and kidney dysfunction (Figure 1). After stratification, dietary fiber intake was an independent predictor of all-cause and cancer mortality in individuals with kidney dysfunction, but not in those without. Sensitivity analysis excluding deaths that occurred within 2 years from baseline (Supplemental Table 4), as well as analyses applying the CKD-EPI equation (Supplemental Table 5), showed similar results.
Table 4.
Associations of dietary fiber with the risk of mortality (N=1110)
| Outcome | Events/At Risk (n) | Unadjusted | Adjusted Model | ||
|---|---|---|---|---|---|
| HR (95% CI) | P | HR (95% CI) | P | ||
| All-cause mortality | 300/1110 | 0.66 (0.48 to 0.91) | 0.01 | 0.80 (0.55 to 1.15) | 0.23 |
| CVD mortality | 138/1110 | 0.87 (0.55 to 1.38) | 0.56 | 1.13 (0.68 to 1.54) | 0.62 |
| Cancer mortality | 111/1110 | 0.57 (0.33 to 0.97) | 0.04 | 0.60 (0.32 to 1.10) | 0.09 |
Dietary fiber is expressed per 10 g/d higher. Covariates in the adjusted model include protein intake (energy adjusted), age, BMI, smoking, physical activity, education, CVD, diabetes, hyperlipidemia, hypertension, eGFR, UAER, and CRP (cancer history was additionally included in the cancer-related mortality analysis). HR, hazard ratio.
Figure 1.
Associations of dietary fiber (per 10 g/d higher) with all-cause, CVD, and cancer-related mortality, stratified by the presence/absence of kidney dysfunction. Data are presented are HRs and 95% CIs (error bars). Units for eGFR are ml/min per 1.73 m2. Covariates in the adjusted model include protein intake (energy adjusted), age, body mass index, smoking, physical activity, education, CVD, diabetes, hyperlipidemia, hypertension, eGFR, urinary albumin excretion rate, and C-reactive protein (cancer history was additionally included in the cancer mortality analysis). 95% CI, 95% confidence interval; CVD, cardiovascular disease; HR, hazard ratio.
Discussion
This cross-sectional study with prospective mortality follow-up in Swedish community-dwelling elderly men has three main findings. First, higher dietary fiber was associated with better kidney function. Second, higher dietary fiber was associated with markers of inflammation in minimally adjusted models. Third, higher dietary fiber was more strongly associated with survival in individuals with kidney dysfunction than in those without. The association between dietary fiber and death risk was mainly attributed to cancer.
In our study, dietary fiber was positively associated with kidney function. Supporting this concept, several studies with small sample sizes have described an association between dietary fiber intervention and reductions of BUN and an increase in fecal nitrogen excretion in patients with CKD (19–23). Serum creatinine concentration decreased and eGFR increased after 4 weeks of 16.5 g/d added fiber in 13 patients with stages 3–5 CKD (23). This study therefore expands this knowledge into the general population including elderly individuals, of which a large proportion (nearly 50%) presented kidney dysfunction. However, it should be noted that intake of protein can increase serum creatinine directly as well as indirectly, thereby increasing creatinine-based measures of GFR. Therefore, a strength in our analysis is the use of cystatin C eGFR estimations, which are presumably less influenced by this bias. Unfortunately, our cohort does not have data on urinary nitrogen excretion and serum urea nitrogen. Several potential mechanisms have been proposed for linking dietary fiber and kidney function. Dietary fiber can increase fecal bacteria mass and nitrogen excretion (24). Consumption of fiber, which increases the energy substrate available to fecal bacteria and stimulates their proliferation, could reduce serum urea by providing a fecal route of excretion for accumulated nitrogenous wastes. Both animal (25,26) and human studies (19–23) have shown that fiber supplementation increases nitrogen excretion in feces and decreases serum nitrogen. Another hypothesis is that foods with fiber are also rich in antioxidants and vitamins (27), which could also relate to or influence the associations reported here. In fact, vegetarian diets associate with decreased production of uremic toxins such as p-cresyl sulfate and indoxyl sulfate, which have been implicated in CKD progression (28). Moreover, a diet high in vegetable sources of protein might lead to lower endogenous production of acid, and a higher intake of fruits and vegetables in patients with stages 1–4 CKD yielded similar acidosis control as oral bicarbonate (29). Our observational design also allows the possibility of reverse causality (e.g., that lower fiber intake is a consequence of dietary adaptations in the context of kidney dysfunction).
The recent NHANES III study (8) reported that lower fiber intake associated with higher serum CRP levels in individuals with CKD. In our study, although dietary fiber (per 10 g/d higher) was not strongly associated with CRP in the whole survey, both CRP and IL-6 levels decreased across higher dietary fiber quartiles, but associations were similar in magnitude in both kidney function strata. We report that lower fiber intake was more strongly associated with mortality in individuals with kidney dysfunction than in those without; this association was independent of lifestyle factors, comorbidities, as well as calorie and protein intake. These data confirm the original observations from the NHANES III in an independent Northern European population and in individuals with a different dietary pattern (8), and thus may incite considerations toward dietary recommendation strategies. Our study design also offers the opportunity to analyze cause-specific deaths. Against our initial hypothesis, and in agreement with recent community studies (30), we do not observe an association with CVD-related mortality. Instead, the association between fiber and survival was mainly attributed to a lower incidence of cancer-related deaths. Although this interaction with kidney dysfunction is certainly a novel finding, the “protective” association with cancer death is in line with previous large population-based prospective community analyses (31). Several potential mechanisms have been proposed for explaining these links. Dietary fiber may increase the bulk and shorten the bowel transit, diluting the effect of potential carcinogens (32). Previous studies seem to suggest that short-chain fatty acids converted from dietary fiber by bacterial fermentation may inhibit the growth of cancer cell lines (32). In addition, lower dietary fiber may also alter the gut flora, causing dysbiosis and a state of chronic low-grade inflammation (33). Both of these aspects may be relevant in a CKD population with low fiber intake, in which dysbiosis may lead to alteration of the intestinal mucosal barrier and low-grade endotoxemia (34). Gut-derived uremic toxins such as indoxyl sulfate and inflammation are associated with higher mortality and further progression of CKD (35). Upcoming research in the field of the gut-renal axis may help elucidate the above-proposed mechanisms.
Additional strengths of our study include the relatively large, community-based sample, the prospective follow-up collection, and the use of 7-day dietary records. However, we are not exempt from limitations. Although the homogeneity of participants in this survey (same age, sex, ethnicity, and geographical distribution) may be a strength toward the study of unbiased associations, it renders a selective population that is not necessarily representative of women or individuals in other age groups. Ours is a cohort of relatively healthy individuals, attributed in part to the lower prevalence of CVD risk factors in Nordic countries and to the nature of the recruiting screening program. We did not directly measure the creatinine clearance rate but instead based our eGFR on serum cystatin C concentrations. Finally, we do not have detailed data on smoking (e.g., cigarettes per day or previous smoking history) or physical activity (other than self-reports).
High fiber intake was associated with better kidney function in community-dwelling elderly men from Sweden. Moreover, high fiber intake was more strongly associated with survival in individuals with kidney dysfunction than in those without. Further interventional studies are warranted to evaluate the effects of increasing fiber intake on kidney function and its consequences.
Disclosures
B.L. is affiliated with Baxter Healthcare Corporation.
Supplementary Material
Acknowledgments
This work was supported by grants from the Swedish Research Council. The doctoral education of H.X. is partially supported by Karolinska Institute faculty for funding of postgraduates. Baxter Novum is the result of a grant from Baxter Healthcare Corporation to Karolinska Institute.
Footnotes
Published online ahead of print. Publication date available at www.cjasn.org.
This article contains supplemental material online at http://cjasn.asnjournals.org/lookup/suppl/doi:10.2215/CJN.02260314/-/DCSupplemental.
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