Abstract
Rationale & Objective
Dietary factors may impact inflammation and interferon production, which could influence phenotypic expression of Apolipoprotein1 (APOL1) genotypes. We investigated whether associations of dietary patterns with kidney outcomes differed by APOL1 genotypes.
Study Design
Prospective cohort.
Settings & Participants
5,640 Black participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS).
Exposures
Five dietary patterns derived from food frequency questionnaires: Convenience foods, Southern, Sweets and Fats, Plant-based, and Alcohol/Salads.
Outcomes
Incident chronic kidney disease (CKD), CKD progression, and kidney failure. Incident CKD was defined as a change in estimated glomerular filtration rate (eGFR) to <60 mL/min/1.73 m2 accompanied by a ≥25% decline from baseline eGFR or development of kidney failure among those with baseline eGFR ≥60 mL/1.73 m2 body surface area. CKD progression was defined as a composite of 40% reduction in eGFR from baseline or development of kidney failure in the subset of participants who had serum creatinine levels at baseline and completed a second in-home visit/follow-up visit.
Analytical Approach
We examined associations of dietary pattern quartiles with incident CKD (n=4,188), CKD progression (n=5,640), and kidney failure (n=5,640). We tested for statistical interaction between dietary patterns and APOL1 genotypes for CKD outcomes and explored stratified analyses by APOL1 genotypes.
Results
Among 5,640 Black REGARDS participants, mean age was 64 years (standard deviation = 9), 35% were male, and 682 (12.1%) had high-risk APOL1 genotypes. Highest versus lowest quartiles (Q4 vs Q1) of Southern dietary pattern were associated with higher adjusted odds of CKD progression (OR, 1.28; 95% CI, 1.01-1.63) but not incident CKD (OR, 0.92; 95% CI, 0.74-1.14) or kidney failure (HR, 1.48; 95% CI, 0.90-2.44). No other dietary patterns showed significant associations with CKD. There were no statistically significant interactions between APOL1 genotypes and dietary patterns. Stratified analysis showed no consistent associations across genotypes, although Q3 and Q4 versus Q1 of Plant-based and Southern patterns were associated with lower odds of CKD progression among APOL1 high- but not low-risk genotypes.
Limitations
Included overlapping dietary patterns based on a single time point and multiple testing.
Conclusions
In Black REGARDS participants, Southern dietary pattern was associated with increased risk of CKD progression. Analyses stratified by APOL1 genotypes suggest associations may differ by genetic background, but these findings require confirmation in other cohorts.
Index Words: APOL1 genotypes, Apolipoprotein L1, chronic kidney disease, dietary patterns, ESKD, inflammation, kidney failure, Plant pattern, Southern pattern
Graphical abstract
Plain-Language Summary.
Dietary patterns may influence phenotypic expression of Apolipoprotein1 (APOL1) high-risk genotypes and chronic kidney disease (CKD). Using data from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort, we examined whether associations of 5 dietary patterns with CKD differed by APOL1 genotypes. Mean age of Black REGARDS participants (n=5,640) was 64 years and 35% were male; 12.1% had high-risk APOL1 genotypes. Highest versus lowest Southern dietary pattern quartile was significantly associated with CKD progression (odds ratio, 1.28; 95% confidence interval 1.01-1.63) but not incident CKD or kidney failure. In exploratory stratified analyses, highest versus lowest quartiles of Plant-based and Southern patterns were associated with lower odds of CKD progression in APOL1 high-risk genotypes. Dietary patterns are associated with CKD progression, but more studies are needed.
An individual’s preferences and access to healthy and unhealthy foods translates to decades of dietary behaviors that can influence the development of chronic diseases. Examining dietary patterns as an exposure for disease risk such as chronic kidney disease (CKD) may be more informative than focusing solely on 1 particular nutrient or food group.1 Previous studies that examined associations of dietary patterns with CKD outcomes did not show consistent associations of dietary patterns with CKD risk, but such differences could in part be due to heterogeneity in genetic risk for CKD. Approximately 13% of African Americans carry ApolipoproteinL1 (APOL1) high-risk genotypes, a 2-allele haplotype consisting of 2 nonsynonymous coding variants rs73885319 (S342G) or rs60910145 (I384M) (G1) and rs71785313—a 6 base pair deletion (G2).2 Only a minority of adults with the APOL1 high-risk genotypes will develop CKD, which indicates that a second factor is needed for phenotypic expression of APOL1 high-risk variants.3 To date, factors that increase interferon levels such as viral infections strongly influence the phenotypic CKD risk in individuals with APOL1 high-risk genotypes,4, 5, 6, 7 but few studies have examined the interaction of diet and APOL1 high-risk genotypes on CKD outcomes. Diet influences innate immunity and promotes pro- or anti-inflammatory effects by immune cells and adipocytes, and this metabolic inflammation/meta-inflammation may impact development and progression of CKD.8,9 Diet also shapes the gut microbiome, which may modulate the production of interferon in response to viral infection.10,11 To our knowledge, no previous study has examined whether the association of dietary patterns with CKD outcomes differs by the presence of APOL1 high-risk genotypes.
The aim of this study was to investigate whether the association of dietary patterns with CKD outcomes differs by the presence of APOL1 genotypes. We used data from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort to examine associations of 5 empirically derived dietary patterns with CKD outcomes.12 We tested for statistical interactions between dietary patterns and presence of APOL1 high-risk genotypes. Subsequently, as an exploratory analysis, we examined associations of dietary patterns and CKD outcomes according to presence of APOL1 high-risk genotypes. We hypothesized that association of dietary patterns with kidney outcomes differ by the presence of APOL1 genotypes.
Methods
Setting and Participants
The REGARDS cohort is a population-based cohort designed to examine geographic differences in stroke incidence in Black and White adults in the United States aged 45 years and older. Approximately half of the participants (55.5%) were recruited from 8 Southeastern US states with disproportionately higher stroke mortality rates (Georgia, North Carolina, South Carolina, Tennessee, Mississippi, Alabama, Louisiana, and Arkansas) compared with other states, and 44.5% were recruited from all other US states except for Hawaii and Alaska.13 Inclusion and exclusion criteria for the REGARDS study have been described, and race and ethnicity were self-reported.13 The study was designed to recruit an equal number of men and women and to oversample Black adults. Potential participants were identified from commercially available lists; residents were recruited through an initial mailing followed by telephone contact. Overall, 30,239 individuals were enrolled in REGARDS between January 2003 and October 2007. Participants completed computer-assisted telephone interviews followed by in-home study visits conducted by trained health professionals that included collection of fasting blood and urine samples. Research personnel provided a 1998 Block Food Frequency Questionnaire (Block 98 FFQ, Nutrition Quest) for participants to complete and mail back to the study center.14 Starting in April 2013, participants were invited to undergo a second in-person visit during which the baseline procedures were repeated. Among the 16,150 participants who participated in the second assessment, 15,938 completed the second telephone-administered assessment and 14,449 had in-person assessments, which were completed in December 2016. The REGARDS study protocol was approved by the Institutional Review Boards at the participating centers, and all participants provided informed consent.
Figure 1 shows the selection of the 5,640 Black REGARDS participants included in the analyses of CKD progression and incident kidney failure and the 4,188 Black participants included in the incident CKD analyses (Fig 1).
Figure 1.
Final Sample for (A) Exposures and incident ESKD/CKD Progression (B) Dietary Exposures and incident CKD (Bottom) in the REGARDS study.
Exposures
The primary exposures were the 5 dietary patterns previously derived from the REGARDS cohort: Convenience foods, Southern, Sweets and Fats, Plant-based, and Alcohol/Salads.12
Empirically Derived Dietary Patterns
The food frequency questionnaire includes frequency of consumption and portion sizes of specific fruits and beverages.15 Food frequency questionnaires were completed by participants at home and mailed to the study center, where they were checked for completeness. The scanned files were sent to Nutrition Quest for analysis of nutrient contents using algorithms.
Dietary patterns and factor loading values for 56 food groups have previously been derived in the REGARDS cohort using principal component analysis.12 Using the scree plot and eigenvalues ≥1.5, a 5-factor solution was retained based on loadings that contributed the most to each pattern. In total, the 5 retained dietary patterns explained ∼24% of the total variance in the study population. All other factors explained less than 3% of the variance. Congruence by ethnicity, region, and sex was confirmed across the 5 patterns by deriving patterns separately in each subpopulation.12
Five retained dietary patterns were defined according to food group loadings;12 (1) The Convenience dietary pattern was characterized by high factor loadings for Chinese and Mexican food, pasta dishes, pizza, soup, and other mixed dishes including frozen or take-out meals; (2) the Plant-based pattern, by fruits, vegetables, and fish; (3) the Sweets/Fats pattern, by desserts and carbohydrate-heavy items; (4) the Southern dietary pattern, by organ meats, fried foods, sugar-sweetened beverages, and greens; and (5) the Alcohol/Salads pattern, by alcohol, green-leafy vegetables, and salad dressing.
A dietary pattern score was calculated for each participant by adding observed intakes of component food groups, weighted by their respective factor loadings. The scores were later divided into quartiles for the statistical analyses, and dietary patterns were considered as continuous and categorical variables, with quartile 1 (Q1) having the lowest accordance to the dietary pattern and quartile 4 (Q4) having the highest accordance to each dietary pattern. Unlike cluster analysis, individuals may be in accordance with more than 1 dietary pattern in factor analysis (Table S1).16
APOL1 Risk Genotypes
APOL1 risk variants G1 and G2 were genotyped in Black participants using TaqMan SNP Genotyping Assays (Applied Biosystems/Thermofisher Scientific). We defined APOL1 risk genotype for each individual using a recessive model, with the high-risk genotype individuals carrying 2 risk alleles (G1/G1, G2/G2 or G1/G2) and low-risk genotype individuals carrying 1 or 0 risk allele (G1/G0, G2/G0 or G0/G0).17
Outcomes
Serum creatinine was measured using isotope dilution mass spectrometry-traceable methods. The 2021 race-free CKD Epidemiology Collaboration creatinine-cystatin equation was used to calculate estimated glomerular filtration rate (eGFR).18
Among those who had a baseline eGFR ≥60 mL/min/1.73 m2 at the first visit and completed the second in-home visit (n=4,188), incident CKD was defined as a change in eGFR to <60 mL/min/1.73 m2 accompanied by a ≥25% decline from baseline eGFR or development of kidney failure among those with baseline eGFR ≥60 mL/1.73 m2 body surface area. CKD progression was defined as a composite of 40% reduction in eGFR from baseline or development of kidney failure in the subset of participants who had serum creatinine level measured at baseline and completed a second in-home visit/follow-up visit. Time to kidney failure was assessed via linkage with the US Renal Data System through June 30, 2018. Participants were followed from the first visit to kidney failure, death, or June 30, 2018, whichever came first.
Covariates
Covariates for adjustment in our analyses were a priori determined based on known association with both the exposure and the outcome via previous studies.19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 Self-reported highest levels of education achieved, family income, age, and smoking status were obtained during the baseline telephone interview. Systolic and diastolic blood pressures were defined as an average of 2 seated blood pressures taken after a 5-minute rest during the baseline in-home visit. Hypertension was defined as blood pressure ≥140/90 mm Hg or use of antihypertensive medication. Physical activity was assessed through a single question “How many times per week do you engage in intense physical activity, enough to work up sweat?” Diabetes mellitus was defined by a fasting serum glucose ≥126 mg/dL, nonfasting serum glucose ≥200 mg/dL, or use of anti-diabetes medications. Fasting blood samples were collected during baseline and the follow-up in-home visits. Body mass index urine albumin was measured by nephelometry using the BNII ProcSpec nephelometer (now Siemens, Munich). We did not include baseline eGFR and albuminuria in the main analyses because we felt that this was part of the causal pathway for kidney disease outcomes. Sensitivity analyses were conducted that included adjusting for baseline eGFR and albuminuria.
Analytical Approach
We examined baseline characteristics of participants included in the kidney failure analyses (n=5,640) by quartiles of dietary pattern scores for the 5 patterns derived by factor analysis. Results are reported as mean and standard deviation, median and interquartile range, or counts and proportions.
Multivariable logistic regression was used to determine associations of dietary patterns with incident CKD and CKD progression. We modeled the dietary patterns as quartiles. Models were repeated using Poisson regression. We estimated the associations of the same dietary patterns with kidney failure using Cox proportional hazard regression. We confirmed the assumptions of proportional hazards using Schoenfield residuals and log-log plots. Models adjusted for age, sex, region (random effect), caloric intake, exercise, smoking status, education level, income, hypertension, body mass index, history of diabetes, and history of cardiovascular disease. Models were repeated with death as a competing risk for kidney failure using Fine and Gray model.30
We tested for interactions between dietary patterns and APOL1 high-risk genotype status by introducing multiplicative interaction terms and adjusting for APOL1 high-risk genotype status as a covariate. Subsequently, we stratified the association of dietary patterns with kidney outcomes by the presence of APOL1 high-risk genotype status. We also tested for dietary patterns as a mediator of the association of APOL1 genotypes with CKD outcomes. A 2-tailed P value < 0.05 was considered statistically significant for all analyses. To account for multiple testing, we considered the P for interaction significant when P < 0.01. Analyses were conducted using SAS software version 9.4 (SAS Institute Inc).
Results
Descriptive Characteristics
Baseline characteristics by quartiles of dietary patterns for these 5,640 participants are shown in Table 1. Among the 5,640 Black REGARDS participants included in the analyses of CKD progression and kidney failure, mean age was 64 years (standard deviation = 9), 35% were male, and 682 (12.1%) had high-risk APOL1 genotypes (Table S2). Percentage of participants with high-risk APOL1 genotypes did not differ significantly by quartiles of any dietary pattern (Table 1). Prevalence of diabetes increased across Convenience and Southern dietary patterns. Table S2 shows differences in characteristics by presence of high-risk APOL1 genotypes. During a median follow-up of 11.6 years (interquartile range, 6.3-13.6 years), 3.2% (n=180) developed kidney failure (37 with high-risk and 143 with low-risk APOL1 genotypes), and 7.5% (n=440) showed CKD progression (67 with high-risk and 363 with low-risk APOL1 genotypes). High-risk APOL1 variants were present in 11.3% (n=474) of the 4,188 Black REGARDS participants with baseline eGFR ≥60 mL/min/1.73 m2 and who participated in a follow-up home visit. Over a median follow-up of 9.5 years (interquartile range, 8.8-9.9 years), incident CKD occurred in 7.4% (n=313) of this group (n=32 with high-risk and 281 with low-risk APOL1 genotypes).
Table 1.
Baseline Sociodemographic and Clinical Characteristics Across Quartiles of Dietary Patterns Among Black Individuals in the REGARDS Cohort
| Q1 | Q2 | Q3 | Q4 | P for trend | |
|---|---|---|---|---|---|
| Convenience Dietary Pattern – Q1 desirable | |||||
| Total N | 1,410 | 1,410 | 1,410 | 1,410 | |
| Age (y) | 66 (9) | 64 (9) | 62 (9) | 62 (9) | <0.001 |
| Male | 438 (31.1%) | 455 (32.3%) | 508 (36.0%) | 566 (40.1%) | <0.001 |
| Income <$20,000 | 419 (29.7%) | 354 (25.11%) | 313 (22.2%) | 354 (25.1%) | <0.001 |
| Smoking | 0.005 | ||||
| Current | 221 (15.7%) | 246 (17.5%) | 235 (16.7%) | 299 (21.2%) | |
| Past | 542 (38.4%) | 533 (37.8%) | 552 (39.2%) | 490 (34.8%) | |
| Never | 647 (45.9%) | 631 (44.8%) | 623 (44.2%) | 621 (44.0%) | |
| BMI (kg/m2) | 30.4 (6.6) | 30.9 (6.7) | 30.6 (6.5) | 31.5 (7.1) | <0.001 |
| Physical activity | 0.37 | ||||
| None | 512 (36.3%) | 512 (36.3%) | 500 (35.5%) | 522 (37.0%) | |
| 1 to 3 times per wk | 508 (36.0%) | 551 (39.1%) | 552 (39.2%) | 514 (36.5%) | |
| 4 or more times per wk | 390 (27.7%) | 347 (24.6%) | 358 (25.4%) | 374 (26.5%) | |
| Diabetes | 392 (27.8%) | 428 (30.4%) | 356 (25.3%) | 368 (26.1%) | 0.01 |
| eGFR (mL/min/1.73 m2) | 80.5 (21.7) | 83.0 (22.2) | 85.9 (21.0) | 87.2 (22.1) | <0.001 |
| ACR (mg/g) | 8.0 [4.6-17.8] | 7.4 [4.5-16.2] | 7.1 [4.3-17.2] | 7.3 [4.4-18.1] | 0.08 |
| APOL1 status | 0.37 | ||||
| High risk | 163 (11.6%) | 159 (11.3%) | 187 (13.3%) | 173 (12.3%) | |
| Low risk | 1,247 (88.4%) | 1,251 (88.7%) | 1,223 (86.7%) | 1,237 (87.7%) | |
| Plant-Based Dietary Pattern – Q4 desirable | |||||
| Total N | 1,410 | 1,410 | 1,410 | 1,410 | |
| Age (y) | 61 (9) | 64 (9) | 65 (9) | 64 (9) | <0.001 |
| Male | 588 (41.7%) | 516 (36.6%) | 449 (31.8%) | 414 (29.4%) | <0.001 |
| Income <$20,000 | 378 (26.8%) | 364 (25.8%) | 358 (25.4%) | 340 (24.1%) | 0.43 |
| Smoking | <0.001 | ||||
| Current | 403 (28.6%) | 249 (17.7%) | 196 (13.9%) | 153 (10.9%) | |
| Past | 493 (35.0%) | 552 (39.2%) | 553 (39.22%) | 519 (36.8%) | |
| Never | 514 (36.5%) | 609 (43.2%) | 661 (46.9%) | 738 (52.3%) | |
| BMI (kg/m2) | 30.7 (6.9) | 30.8 (7.0) | 30.9 (6.6%) | 31.0 (6.5) | 0.25 |
| Physical activity | <0.001 | ||||
| None | 612 (43.4%) | 540 (38.3%) | 492 (34.9%) | 402 (28.5%) | |
| 1 to 3 times per wk | 475 (33.7%) | 524 (37.2%) | 538 (38.2%) | 588 (41.7%) | |
| 4 or more times per wk | 323 (22.9%) | 346 (24.5%) | 380 (27.0%) | 420 (29.8%) | |
| Diabetes | 330 (23.4%) | 412 (29.2%) | 393 (27.9%) | 409 (29.0%) | 0.001 |
| eGFR (mL/min/1.73 m2) | 85.6 (23.0) | 82.6 (22.3) | 83.2 (21.7) | 85.1 (20.4%) | 0.72 |
| ACR (mg/g) | 7.2 [4.4-17.6] | 7.5 [4.4-18.5] | 7.4 [4.5-17.1] | 7.6 [4.6-16.7] | 0.40 |
| APOL1 status | 0.54 | ||||
| High risk | 165 (11.7%) | 182 (12.9%) | 159 (11.3%) | 176 (12.5%) | |
| Low risk | 1,245 (88.3%) | 1,228 (87.1%) | 1,251 (88.7%) | 1,234 (87.5%) | |
| Sweets and Fats Dietary Pattern – Q1 desirable | |||||
| Total N | 1410 | 1,410 | 1,410 | 1,410 | |
| Age (y) | 63 (8) | 64 (9) | 64 (9) | 63 (9) | 0.89 |
| Male | 450 (31.9%) | 460 (32.6%) | 509 (36.1%) | 548 (38.9%) | <0.001 |
| Income <$20,000 | 329 (23.3%) | 358 (25.4%) | 359 (25.5%) | 394 (27.9%) | 0.05 |
| Smoking | <0.001 | ||||
| Current | 220 (15.6%) | 224 (15.9%) | 263 (18.7%) | 294 (20.9%) | |
| Past | 501 (35.5%) | 525 (37.2%) | 538 (38.2%) | 553 (39.2%) | |
| Never | 689 (48.9%) | 661 (46.9%) | 609 (43.2%) | 563 (39.9%) | |
| BMI (kg/m2) | 30.9 (6.7) | 31.0 (6.6) | 30.9 (6.9) | 30.7 (6.8) | 0.38 |
| Physical activity | <0.001 | ||||
| None | 462 (32.8%) | 501 (35.5%) | 498 (35.3%) | 585 (41.5%) | |
| 1 to 3 times per wk | 554 (39.3%) | 560 (39.7%) | 523 (37.1%) | 488 (34.6%) | |
| 4 or more times per wk | 394 (27.9%) | 349 (24.8%) | 389 (27.6%) | 337 (23.9%) | |
| Diabetes | 425 (30.1%) | 398 (28.2%) | 387 (27.5%) | 334 (26.7%) | 0.001 |
| eGFR (mL/min/1.73 m2) | 85.1 (22.3) | 85.3 (22.1) | 83.0 (21.8) | 85.2 (21.5) | 0.98 |
| ACR (mg/g) | 7.5 [4.5-17.4] | 7.6 [4.5-18.8] | 7.1 [4.4-16.5] | 7.5 [4.5-16.7] | 0.47 |
| APOL1 status | 0.68 | ||||
| High risk | 163 (11.6%) | 167 (11.8%) | 183 (13.0%) | 169 (12.0%) | |
| Low risk | 1,247 (88.4%) | 1,243 (88.2%) | 1,227 (87.0%) | 1,241 (88.0%) | |
| Southern Dietary Pattern – Q1 desirable | |||||
| Total N | 1,410 | 1,410 | 1,410 | 1,410 | |
| Age (y) | 64 (9) | 65 (9) | 64 (9) | 62 (9) | <0.001 |
| Male | 294 (20.9%) | 411 (29.2%) | 577 (40.9%) | 685 (48.6%) | <0.001 |
| Income <$20,000 | 256 (18.2%) | 358 (25.4%) | 365 (25.9%) | 461 (32.7%) | <0.001 |
| Smoking | <0.001 | ||||
| Current | 151 (10.7%) | 235 (16.7%) | 277 (19.7%) | 338 (24.0%) | |
| Past | 562 (39.9%) | 525 (37.2%) | 527 (37.4%) | 503 (35.7%) | |
| Never | 697 (49.4%) | 650 (46.1%) | 606 (43.0%) | 569 (40.4%) | |
| BMI (kg/m2) | 30.4 (6.4) | 30.5 (6.5) | 31.3 (6.9) | 31.2 (7.1) | <0.001 |
| Physical activity | 0.16 | ||||
| None | 501 (35.5%) | 480 (34.0%) | 531 (37.7%) | 534 (37.9%) | |
| 1 to 3 times per wk | 552 (39.2%) | 553 (39.2%) | 523 (37.1%) | 497 (35.3%) | |
| 4 or more times per wk | 357 (25.3%) | 377 (26.7%) | 356 (25.3%) | 379 (26.9%) | |
| Diabetes | 336 (23.8%) | 379 (26.9%) | 408 (28.9%) | 421 (29.9%) | 0.002 |
| eGFR (mL/min/1.73 m2) | 84.5 (21.2) | 83.4 (21.3) | 84.3 (22.2) | 84.4 (22.8) | 0.91 |
| ACR (mg/g) | 6.8 [4.4-13.7] | 7.1 [4.4-16.2] | 7.7 [4.5-19.1] | 8.5 [4.7-23.5] | <0.001 |
| APOL1 status | 0.25 | ||||
| High risk | 150 (10.6%) | 183 (13.0%) | 177 (12.5%) | 172 (12.2%) | |
| Low risk | 1,260 (89.4%) | 1,227 (87.0%) | 1,233 (87.5%) | 1,238 (87.8%) | |
| Alcohol and Salads Dietary Pattern | |||||
| Total N | 1,410 | 1,410 | 1,410 | 1,410 | |
| Age | 65 (9) | 64 (9) | 63 (9) | 62 (9) | <0.001 |
| Male | 460 (32.6%) | 417 (29.6%) | 525 (37.2%) | 565 (40.1%) | <0.001 |
| Income <$20,0000 | 483 (34.3%) | 372 (26.4%) | 326 (23.1%) | 259 (18.4%) | <0.001 |
| Smoking | <0.001 | ||||
| Current | 198 (14.0%) | 235 (16.7%) | 262 (18.6%) | 306 (21.7%) | |
| Past | 466 (33.1%) | 517 (36.7%) | 542 (38.4%) | 592 (42.0%) | |
| Never | 746 (52.9%) | 658 (46.7%) | 606 (43.0%) | 512 (36.3%) | |
| BMI (kg/m2) | 30.4 (6.6) | 31.0 (6.9) | 31.0 (6.7) | 31.1 (6.7) | 0.005 |
| Physical activity | 0.06 | ||||
| None | 507 (36.0%) | 537 (38.1%) | 501 (35.5%) | 501 (35.5%) | |
| 1 to 3 times per wk | 506 (35.9%) | 526 (37.3%) | 569 (40.4%) | 524 (37.2%) | |
| 4 or more times per wk | 397 (28.2%) | 347 (24.6%) | 340 (24.1%) | 385 (27.3%) | |
| Diabetes | 372 (26.4%) | 396 (28.1%) | 410 (29.1%) | 366 (26.0%) | 0.21 |
| eGFR (mL/min/1.73 m2) | 81.0 (22.3) | 83.0 (22.4) | 84.8 (22.1) | 87.8 (20.2) | <0.001 |
| ACR (mg/g) | 7.5 [4.6-18.0] | 7.6 [4.6-17.3] | 7.4 [4.5-18.5] | 7.1 [4.3-15.4] | 0.04 |
| APOL1 status | 0.06 | ||||
| High risk | 187 (13.3%) | 187 (13.3%) | 149 (10.6%) | 159 (11.3%) | |
| Low risk | 1223 (86.7%) | 1223 (86.7%) | 1261 (89.4%) | 1251 (88.7%) | |
Note: Categorical variables are expressed as n (%), and continuous variables are expressed as mean (standard deviation) or median [interquartile range]. N=5,640 sample size includes individuals in the incident end-stage renal disease and chronic kidney disease progression analyses.
Abbreviations: ACR, albumin creatinine ratio; APOL1, Apolipoprotein L1; BMI, body mass index, eGFR, estimated glomerular filtration rate.
Characteristics of individuals who were and were not included in the cohort examined for CKD progression and kidney failure outcomes are shown in Table S1. Participants included in the analyses were more likely to be female, have diabetes and a higher body mass index, and income <$20,000 (all P < 0.001) (Table S1).
Empirically Derived Dietary Pattern Scores and CKD outcomes
Table 2 shows the associations of dietary pattern quartiles with CKD outcomes. The highest quartile of a Southern dietary pattern versus the lowest quartile was significantly associated with CKD progression (odds ratio, 1.28; 95% confidence interval [CI], 1.01-1.63) but not with incident CKD (odds ratio, 0.92; 95% CI 0.74-1.14). The adjusted risk of incident kidney failure was increased in the highest versus lowest quartile of a Southern dietary pattern (hazard ratio, 1.48; 95% CI 0.90-2.44), but the confidence interval was wide and included 1.0 (Table 2). The upper quartiles of all other dietary patterns compared with lowest quartile were not significantly associated with any CKD outcome. In models that used Poisson regression and adjusted for baseline eGFR and albuminuria, the highest quartile of the Southern dietary pattern versus lowest quartile was significantly associated with increased rate of incident CKD (relative rate, 1.41; 95% CI, 1.12-1.79) but not CKD progression (relative rate, 1.11; 95% CI, 0.95-.29) (Table S4). No dietary pattern was significantly associated with kidney failure, and findings were similar when analyses accounted for death as a competing risk (Table 2).
Table 2.
Associations of Dietary Patterns With CKD Outcomes Among Black Individuals in the REGARDS Cohort (N=4,188 for Incident CKD Outcomes and 5,640 for CKD Progression and Incident CKD)
| Incident CKD aOR (95% CI)∗ | CKD Progression aOR (95% CI)∗ | Incident Kidney Failure aHR (95% CI)∗ | Incident Kidney Failure (With Death As a Competing Risk) | |
|---|---|---|---|---|
| Events | 313 | 440 | 180 | 180 |
| Convenience Dietary Pattern (Q1 desirable) | ||||
| Q1 | Ref | Ref | Ref | Ref |
| Q2 | 1.41 (1.06-1.88) | 1.08 (0.67-1.73) | 0.92 (0.61-1.38) | 0.96 (0.63-1.47) |
| Q3 | 1.63 (1.48-1.79) | 1.01 (0.80-1.26) | 0.79 (0.51-1.24) | 0.79.(0.49-1.27) |
| Q4 | 1.01 (0.84-1.22) | 0.92 (0.71-1.19) | 1.01 (0.63-1.61) | 1.03 (0.62-1.72) |
| Plant-Based Dietary Pattern (Q4 desirable) | ||||
| Q1 | Ref | Ref | Ref | Ref |
| Q2 | 0.89 (0.72-1.10) | 0.92 (0.86-0.97) | 1.17 (0.77-1.79) | 1.16 (0.75-1.79) |
| Q3 | 0.72 (0.52-0.99) | 0.98 (0.91-1.04) | 0.93 (0.59-1.47) | 0.94 (0.59-1.50) |
| Q4 | 0.95 (0.79-1.13) | 0.95 (0.81-1.11) | 1.24 (0.77-2.02) | 1.25 (0.76-2.06) |
| Sweets and Fat Dietary Pattern (Q1 desirable) | ||||
| Q1 | Ref | Ref | Ref | Ref |
| Q2 | 1.36 (1.00-1.83) | 1.12 (0.96-1.30) | 0.97 (0.63-1.48) | 0.87 (0.56-1.34) |
| Q3 | 1.15 (0.75-1.75) | 1.26 (1.02-1.57) | 1.14 (0.74-1.74) | 1.10 (0.71-1.71) |
| Q4 | 1.07 (0.71-1.60) | 1.19 (0.90-1.58) | 1.00 (0.59-1.69) | 1.03 (0.61-1.72) |
| Southern Dietary Pattern (Q1 desirable) | ||||
| Q1 | Ref | Ref | Ref | Ref |
| Q2 | 1.43 (1.15-1.78) | 1.16 (0.85-1.59) | 0.94 (0.60-1.48) | 0.89(0.56-1.41) |
| Q3 | 1.18 (1.12-1.24) | 1.07 (0.75-1.54) | 0.88 (0.55-1.40) | 0.86 (0.53-1.39) |
| Q4 | 0.92 (0.74-1.14) | 1.28 (1.01-1.63) | 1.48 (0.90-2.44) | 1.42 (0.84-2.41) |
| Alcohols and Salad Dietary Pattern | ||||
| Q1 | Ref | Ref | Ref | Ref |
| Q2 | 0.82 (0.66-1.03) | 0.91 (0.68-1.21) | 0.73 (0.48-1.11) | 0.77 (0.51-1.18) |
| Q3 | 0.78 (0.53-1.15) | 0.87 (0.77-0.98) | 0.72 (0.47-1.10) | 0.68 (0.43-1.05) |
| Q4 | 0.84 (0.48-1.45) | 0.88 (0.71-1.11) | 0.77 (0.50-1.18) | 0.72 (0.46-1.13) |
Note: ∗Models were adjusted for age, sex, region, caloric intake, exercise, smoking status, cardiovascular disease, hypertension, education level, income, body mass index, and diabetes.
Abbreviations: aHR, adjusted hazard ratio; aOR, adjusted odds ratio; CI, confidence interval; CKD, chronic kidney disease.
Interaction Analyses
After adjustment for all covariates, no significant statistical interaction was noted between the dietary patterns (examined as quartiles or when modeled continuously), APOL1 risk genotypes, and kidney outcomes (Fig 2A-C). As an exploratory analysis, we stratified the selected cohorts by presence of APOL1 risk genotypes and repeated analyses. Figure 2A-C shows the associations of dietary patterns with CKD outcomes in participants with high- and low-risk APOL1 genotypes. The associations of the Plant-based and Southern dietary pattern quartiles with CKD progression appeared to differ by the presence of APOL1 genotypes. For both dietary patterns, higher quartiles (Q3, Q4) versus lowest (Q1) were associated with significantly lower odds of CKD progression among participants with high-risk APOL1 genotypes. In contrast, with low-risk APOL1 genotypes, the highest quartile (Q4) versus lowest (Q1) for Plant-based dietary pattern showed no association with CKD progression whereas the highest quartile (Q4) versus lowest (Q1) showed significantly higher odds of CKD progression (odds ratio, 1.53; 95% CI, 1.18-1.98) (Fig 2B). All other associations of dietary pattern quartiles with CKD outcomes appeared similar by the presence of APOL1 high-risk genotypes. Additionally, in the mediation analyses, dietary patterns showed no evidence of statistical significance as a mediator of the association of APOL1 genotypes with CKD outcomes (Table S5).
Figure 2.
(A) Associations of Dietary Patterns across APOL1 Genotypes in REGARDS for Incident CKD. (B) Associations of Dietary Patterns across APOL1 Genotypes in REGARDS for CKD Progression. (C) Associations of Dietary Patterns across APOL1 Genotypes in REGARDS for Incident Kidney Failure
Discussion
This study examined the association of 5 dietary patterns with incident CKD, CKD progression, and kidney failure in a large cohort of Black individuals. Our analyses also explored potential differences in the associations of dietary patterns with CKD outcomes by the presence of APOL1 high-risk genotypes. Overall, no dietary pattern was significantly associated with kidney failure, but higher Southern dietary pattern accordance was significantly associated with higher odds of CKD progression. In the exploratory analyses stratified by APOL1 genotypes, the association of Southern dietary pattern accordance with CKD outcomes appeared to differ by presence of APOL1 high-risk genotypes, but the statistical interaction term did not meet statistical significance. Exploratory analyses also showed differences in the association of Plant-based dietary pattern with CKD progression, but interaction terms did not meet statistical significance.
The findings of different associations of dietary patterns with CKD progression in this study should be interpreted with caution given the lack of statistically significant interactions, but the findings deserve further investigation. Dietary factors may influence inflammation and interferon production in setting of a viral illness, which could potentially influence the phenotypic expression of APOL1 variants. For example, many fruits and vegetables have a high content of polyphenol compounds that downregulate the gene expression of pro-inflammatory factors via inactivation of nuclear factor kappa light chain enhancer of activated B cells.31 Polyphenols also impede eicosanoid production and block enzymes that lead to production of reactive oxygen species while upregulating production of antioxidant enzymes.31 The gut microbiota, shaped by decades of habitual dietary patterns, produces small chain fatty acids via fermentation of nondigestible dietary fiber. These small chain fatty acids have been shown to increase interferon production in response to a viral infection.31
Other mechanisms that may link dietary patterns with CKD also include higher blood pressure, inflammation, endothelial dysfunction, and insulin resistance.9,32,33 The Southern dietary pattern is characterized by consumption of fried foods, processed meats, and sugar-sweetened beverages and the intake of red meat and has been associated with heightened CKD risk and progression.34, 35, 36 The Southern dietary pattern often includes collard greens, which are rich in vitamins K and A and antioxidants.
We did not find consistent associations with Plant-based dietary patterns and CKD outcomes. Reasons for the inconsistent findings with the Plant-based pattern are unclear and may be because of the misclassification of dietary patterns. The finding of lower odds of CKD progression with high accordance to Plant-based dietary patterns in participants with high-risk APOL1 variants deserves further study given the recent report that higher potassium intake may reduce CKD risk in individuals with high-risk APOL1 variants.37 Plant-based diet could also decrease CKD risk via reduction of dietary acid load, inflammation, oxidative stress, and reductions in intraglomerular pressure.38, 39, 40, 41 In a study among Black participants in the African American Study of Kidney Disease and Hypertension (AASK), the high-risk APOL1 genotype was associated strongly with CKD progression among Blacks with low net endogenous acid production.42 However, several previous older studies have shown no modification of association of APOL1 genotypes with CKD progression by baseline urinary excretion of potassium or estimated net endogenous acid production.38,42, 43, 44
To our knowledge, this is the first report that explores the potential effect modification of APOL1 high-risk genotypes on the association of dietary patterns with CKD outcomes. In exploratory analyses, we found inconsistent differences in associations of dietary patterns with high- and low-risk APOL1 genotypes, which may warrant further investigations with larger sample sizes. To date, factors that increase interferon levels, such as infection with HIV (human immunodeficiency virus) and SARS-CoV2 (severe acute respiratory syndrome coronavirus 2), modify the phenotypic expression of the APOL1 high-risk genotypes because interferon upregulates APOL1 expression in podocytes.5,7,45 Although diet can influence inflammatory markers, a previous study by Chen et al46 found that inflammatory biomarkers did not modify the association of APOL1 genotypes and CKD outcomes.
This study has several limitations. First, the REGARDS cohort was not specifically designed to examine the associations of diet with CKD outcomes, and baseline urine albumin-to-creatinine ratios were low, reflecting a low risk for CKD outcomes. Secondly, dietary patterns were estimated from a single time point in this older cohort, which limits interpretability and generalizability when using dietary patterns to determine gene-diet interactions.47 Existing methodologies to model gene-diet interactions using dietary patterns may not be optimal as the effects of dietary patterns may be incremental and time-varying.47 Although largely employed in epidemiological studies, food frequency questionnaires have some limitations with nutrient accuracy and content, and this may pose a challenge in more complex analyses like diet by gene interactions.48, 49 Because the included cohort differs from the participants who were not included, results may not be representative of the entire REGARDS cohort.
In summary, we found that higher accordance with Southern dietary pattern was associated with increased risk for CKD progression, but this association may differ by APOL1 genotypes. More studies are needed to determine if the associations of dietary patterns with CKD outcomes differ by presence of APOL1 high-risk genotypes. Such information could help identify dietary patterns that can modulate CKD risk, especially in adults with high-risk APOL1 genotypes. Future studies of dietary patterns for CKD risk should also consider use of 24-hour recalls, duplicate dietary approach, food consumption records, and dietary history records or other informative assessments of dietary intake.
Article Information
Authors’ Full Names and Academic Degrees
Titilayo O. Ilori, MD, MS, Marquita S. Brooks, MSPH, Parin N. Desai, MD, Katharine L. Cheung, MD, PhD, Suzanne E. Judd, PhD, Deidra C. Crews, MD, ScM, Mary Cushman, MD, Cheryl A. Winkler, PhD, Michael G. Shlipak, MD, MPH, Jeffrey B. Kopp, MD, Rakhi P. Naik, MD, Michelle M. Estrella, MD, Orlando M. Gutiérrez, MD, and Holly Kramer, MD, MPH.
Authors’ Contributions
Contributions: research idea and study design: TOI, SEJ, OMG, HK; data acquisition: MSB, SEJ; data analysis/interpretation: MSB, DCC, MME, MGS, PND, RPN, SEJ, KLC, MC, JBK, CAW, TOI, OMG, HK; statistical analysis: MSB, SEJ; supervision or mentorship: SEJ, OMG, HK. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.
Support
This research project is supported by cooperative agreement U01 NS041588 co-funded by the National Institute of Neurological Disorders and Stroke (NINDS) and the National Institute on Aging (NIA), National Institutes of Health, Department of Health and Human Services. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NINDS or the NIA. Dr Ilori is funded by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) K23DK119542 and the Department of Medicine, Boston Medical Center. Dr Kopp is supported by the Intramural Research Program, NIDDK, NIH, Bethesda. Dr Crews was supported, in part, by grant 1 K24 HL148181 from the National Heart, Lung, and Blood Institute, National Institutes of Health.
Financial Disclosure
Dr Kramer is a consultant for Bayer Pharmaceuticals and AstraZeneca. The remaining authors declare that they have no relevant financial interests.
Acknowledgements
The authors thank the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions can be found at: https://www.uab.edu/soph/regardsstudy/
We would like to thank Sushrut Waikar MD, MPH and Josee Josée Dupuis, PhD for their contributions and insight toward the manuscript and reading early versions of the manuscript.
Peer Review
Received June 22, 2022 as a submission to the expedited consideration track with 4 external peer reviews. Direct editorial input from the Statistical Editor and the Editor-in-Chief. Accepted in revised form January 16, 2023.
Footnotes
Complete author and article information provided before references.
Figure S1: Kaplan-Meier Curve for the association of APOL1 risk genotypes with incident kidney failure.
Figure S2: Associations of dietary patterns across APOL1 genotypes in REGARDS for CKD outcomes.
Table S1: Food Group Loadings of the Dietary Patterns Derived From the REGARDS Cohort.
Table S2: Baseline Characteristics of REGARDS Participants Included in the Kidney Failure Outcomes Analyses Stratified by ApolipoproteinL1 (APOL1) Risk Genotypes.
Table S3: Baseline Characteristics of REGARDS Participants Versus Excluded Participants.
Table S4: Association of Dietary Patterns with Incident Chronic Kidney Disease and CKD Progression, Using Poisson Regression.
Table S5: Mediation Analysis Results for APOL1 High-Risk Variants and CKD Outcomes (CKD Progression, Kidney Failure, Incident CKD).
Supplementary Material
Fig S1-S2, Table S1-S5.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Fig S1-S2, Table S1-S5.