Changes in Diet Quality, Risk of CKD Progression, and All-Cause Mortality in the CRIC Study.

Adherence to healthy dietary patterns is associated with reduced risk of chronic kidney disease (CKD) progression and mortality in adults with CKD.1Gutiérrez O.M. Muntner P. Rizk D.V. et al.Dietary patterns and risk of death and progression to ESRD in individuals with CKD: a cohort study.Am J Kidney Dis. 2014; 64: 204-213https://doi.org/10.1053/J.AJKD.2014.02.013Abstract Full Text Full Text PDF PubMed Google Scholar, 2Kelly J.T. Palmer S.C. Wai S.N. et al.Healthy dietary patterns and risk of mortality and ESRD in CKD: a meta-analysis of cohort studies.Clin J Am Soc Nephrol. 2017; 12: 272-279https://doi.org/10.2215/CJN.06190616Crossref PubMed Scopus (173) Google Scholar, 3Hu E.A. Coresh J. Anderson C.A.M. et al.Adherence to healthy dietary patterns and risk of CKD progression and all-cause mortality: findings from the CRIC (Chronic Renal Insufficiency Cohort) Study.Am J Kidney Dis. 2021; 77: 235-244https://doi.org/10.1053/J.AJKD.2020.04.019Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar However, diet is modifiable, and changes in diet quality may predict disease course and survival.4Sotos-Prieto M. Bhupathiraju S.N. Mattei J. et al.Changes in diet quality scores and risk of cardiovascular disease among US men and women.Circulation. 2015; 132: 2212-2219https://doi.org/10.1161/CIRCULATIONAHA.115.017158Crossref PubMed Scopus (148) Google Scholar,5Sotos-Prieto M. Bhupathiraju S.N. Mattei J. et al.Association of changes in diet quality with total and cause-specific mortality.N Engl J Med. 2017; 377: 143-153https://doi.org/10.1056/NEJMoa1613502Crossref PubMed Scopus (297) Google Scholar Using data from the Chronic Renal Insufficiency Cohort (CRIC) Study, we assessed the associations of 4-year changes in diet quality with the subsequent risk of CKD progression and all-cause mortality in adults with CKD. The CRIC Study enrolled adults with reduced estimated glomerular filtration rate (eGFR; 20-70 mL/min/1.73 m2) (Item S1).6Feldman H.I. Appel L.J. Chertow G.M. et al.The Chronic Renal Insufficiency Cohort (CRIC) Study: design and methods.J Am Soc Nephrol. 2003; 14: S148-S153https://doi.org/10.1097/01.asn.0000070149.78399.ceCrossref PubMed Google Scholar Diet was assessed at baseline and year 4. We included participants with nonmissing dietary assessments and covariates (Fig S1). We calculated diet quality using 4 index scores: Healthy Eating Index (HEI) 2015, Alternative Healthy Eating Index (AHEI) 2010, Dietary Approaches to Stop Hypertension (DASH) diet score, and alternate Mediterranean diet score (aMed) (Table S1). We classified scores as low (less than or equal to the baseline median) or high (greater than the median) and categorized changes as sustained low (low on both assessments), sustained high (high on both assessments), worsened (high at baseline, low at follow-up), or improved (low at baseline, high at follow-up). We also examined absolute diet score changes (year 4 less baseline), categorized as increased, stable, or decreased. We calculated covariate-adjusted hazard ratios for associations between changes in diet quality and time to CKD progression (defined as 50% reduction in eGFR from year 4, or initiation of kidney replacement therapy) and all-cause mortality (Item S1). Person-years were calculated from year 4 until the event, study withdrawal, loss to follow-up, or administrative censoring (January 2019). We assessed robustness of findings across subgroups by sex, race, diabetes, year 4 eGFR, and year 4 proteinuria. Mean diet scores did not change substantially over 4 years (Fig S2), but there was considerable variation in observed changes (Fig S3). Participants with sustained high diet scores were more educated and reported higher incomes than those with sustained low scores or scores that changed (Table 1; Table S2). Food and nutrient changes associated with categorized score changes are in Table S3.Table 1Participant Characteristics by 4-Year AHEI-2010 Score ChangeSustained Low (Low-Low)Improved (Low-High)Worsened (High-Low)Sustained High (High-High)PaPearson χ2 test (categorical variables) or analysis of variance (continuous variables) comparing categories of diet score change.No. of participants510197162527Age, y61.1 ± 10.961.9 ± 10.964.8 ± 9.263.2 ± 9.5<0.001Female sex225 (44%)99 (50%)83 (51%)309 (59%)<0.001Race and ethnicity<0.001 Non-Hispanic White273 (54%)116 (59%)82 (51%)363 (69%) Non-Hispanic Black211 (41%)71 (36%)64 (40%)119 (23%) Hispanic15 (3%)6 (3%)7 (4%)14 (3%) Other11 (2%)4 (2%)9 (6%)31 (6%)College graduate155 (30%)87 (44%)59 (36%)312 (59%)<0.001Income<0.001 >$50,000173 (34%)75 (38%)62 (38%)268 (51%) Do not wish to answer78 (15%)30 (15%)25 (15%)68 (14%)eGFR, mL/min/1.73 m243 ± 1947 ± 2042 ± 1750 ± 20<0.001Physical activity, MET h/wk188 ± 117210 ± 123187 ± 126197 ± 1090.1Change in physical activity, MET h/wk−21 ± 14110 ± 115−10 ± 128−12 ± 1110.03BMI, kg/m231.8 ± 7.333.7 ± 9.632.1 ± 7.530.4 ± 6.8<0.001Change in BMI, kg/m20 ± 2.90.2 ± 3.80.4 ± 3.00.2 ± 2.50.4Energy intake, kcal/d1,669 ± 7491,777 ± 7791,618 ± 6181,637 ± 6190.08Change in energy intake, kcal/d−214 ± 7733 ± 629−258 ± 708−141 ± 607<0.001Alcohol intake status<0.001 Consistent nondrinker396 (78%)142 (72%)117 (72%)314 (60%) Consistent drinker63 (12%)35 (18%)28 (17%)146 (28%) Changed from drinker to nondrinker27 (5%)10 (5%)14 (9%)26 (5%) Changed from nondrinker to drinker24 (5%)10 (5%)3 (2%)41 (8%)Smoking status<0.001 Consistent never smoker235 (46%)100 (51%)66 (41%)278 (53%) Changed from current to former smoker or remained former smoker210 (41%)82 (42%)79 (49%)226 (43%) Changed from never or former smoker to current smoker11 (2%)4 (2%)0 (0%)3 (1%) Consistent smoker54 (11%)11 (6%)17 (10%)20 (4%)ProteinuriabDefined as 24-hour urinary protein>1.5g/d; n = 810.46 (15%)9 (8%)21 (23%)26 (9%)0.009Hypertension480 (94%)178 (90%)148 (91%)439 (83%)<0.001Diabetes218 (43%)94 (48%)70 (43%)224 (43%)0.6Cardiovascular disease203 (40%)65 (33%)79 (49%)164 (31%)<0.001Lipid-lowering medication use345 (68%)135 (69%)111 (69%)367 (70%)0.9ACEI/ARB use361 (71%)142 (72%)113 (70%)348 (66%)0.3Antiplatelet medication use273 (54%)106 (54%)91 (56%)292 (55%)0.9Cohort for analysis of time to CKD progression (n = 1,396). Variables are at year 4 time point unless otherwise specified; change is from initial visit to year 4. Continuous variables given as mean ± SD; categorical as count (%). Abbreviations: ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; BMI, body mass index; MET, metabolic equivalent of task.a Pearson χ2 test (categorical variables) or analysis of variance (continuous variables) comparing categories of diet score change.b Defined as 24-hour urinary protein >1.5 g/d; n = 810. Open table in a new tab Cohort for analysis of time to CKD progression (n = 1,396). Variables are at year 4 time point unless otherwise specified; change is from initial visit to year 4. Continuous variables given as mean ± SD; categorical as count (%). Abbreviations: ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; BMI, body mass index; MET, metabolic equivalent of task. There were 412 CKD progression events (308 were kidney replacement therapy initiation) observed over a median of 7.0 (IQR, 3.3-9.5) years. The association between categorized diet change and CKD progression differed by diabetes status. Among adults without diabetes, those with sustained low AHEI-2010 and aMed scores had 41% and 39% lower risk of CKD progression, respectively, while those with improved aMed scores had 58% lower risk relative to those with sustained high scores (Fig 1A; Table S4). Among adults with diabetes, those with AHEI-2010 scores that worsened had 93% higher risk of CKD progression, while both improvements and declines in DASH scores were associated with higher risk relative to those with sustained high scores (Fig 1B). Changes in HEI-2015 scores were not associated with CKD progression. Results were similar across sex, race, eGFR, and proteinuria subgroups. When analyzed as categorized 4-year absolute score changes, among adults without diabetes, increased DASH scores were associated with 39% lower risk of CKD progression compared to stable scores (Table S5). Among adults with diabetes, those with decreased AHEI-2010 scores had 63% higher risk of CKD progression compared to those with stable scores. There were 393 deaths over a median of 9.4 (IQR, 7.9-10.4) years. Associations between diet change and mortality were consistent across all subgroups. Compared to those with sustained high scores, those with worsened DASH scores had 41% higher risk of death, and those with sustained low aMed scores had 41% higher risk of death (Fig 1C; Table S6). Analyzed as categorized 4-year absolute score changes, adults with decreased AHEI-2010 scores had 34% higher risk of death compared to those with stable scores (Table S7). As diet was not assessed after year 4, we could not measure subsequent changes that may have influenced these associations. We cannot discern the extent to which changes in scores were influenced by random measurement error associated with self-reported diet. Inclusion in our study sample required survival to year 4 and complete diet and covariate assessments, which may have introduced selection bias (Table S8). The main analysis does not account for albuminuria, a key predictor of CKD progression. Reverse causality may explain findings, as participants might have changed their diets in response to their health condition. Additional research is warranted to understand motivators of diet change in this population. Four-year changes in diet quality were not consistently associated with CKD progression in adults with CKD. Associations varied by diet quality index and diabetes status. Different associations by diabetes status may be due to differing motivations for change, as people with diabetes may be followed more closely by physicians and receive instruction to change their diet. Declines in DASH scores were associated with higher mortality risk. Worsening diet quality may predict earlier death in adults with CKD. Debbie L. Cohen, MD; Harold I. Feldman, MD, MSCE; Alan S. Go, MD; James P. Lash, MD; Mahboob Rahman, MD; and Panduranga S. Rao, MD. Authors LJA, C-yH, and JChen are also CRIC Study Investigators. Research idea and study design: VKS, CMR; CRIC idea and design: LJA, C-yH, VOS, MU, RGN, JS, JChen; data analysis/interpretation: VKS, LJA, CAMA, TCT, JB, ACR, SJS, C-yH, VOS, MU, RGN, JS, JChen, JH, JCharleston, CMR; mentorship: CMR. Each author contributed important intellectual content during manuscript drafting or revision and agrees to be personally accountable for the individual’s own contributions and to ensure that questions pertaining to the accuracy or integrity of any portion of the work, even one in which the author was not directly involved, are appropriately investigated and resolved, including with documentation in the literature if appropriate. Funding for CRIC was obtained under a cooperative agreement from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK; U01DK060990, U01DK060984, U01DK061022, U01DK061021, U01DK061028, U01DK060980, U01DK060963, U01DK060902, and U24DK060990). In addition, this work was supported in part by the Perelman School of Medicine at the University of Pennsylvania Clinical and Translational Science Award NIHNCATS UL1TR000003, Johns Hopkins University UL1 TR-000424, University of Maryland GCRC M01 RR-16500, Clinical and Translational Science Collaborative of Cleveland, UL1TR000439 from the National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health (NIH) and NIH roadmap for Medical Research, Michigan Institute for Clinical and Health Research (MICHR) UL1TR000433, University of Illinois at Chicago CTSA UL1RR029879, Tulane COBRE for Clinical and Translational Research in Cardiometabolic Diseases P20 GM109036, Kaiser Permanente NIH/ NCRR UCSF-CTSI UL1 RR-024131, and Department of Internal Medicine, University of New Mexico School of Medicine Albuquerque, NM R01DK119199. VKS is supported by National Heart, Lung, and Blood Institute (NHBLI) grant T32HL007024. ACR, SJS, and C-yH are supported by NIDDK grants R01DK118736, K23DK118198, and K24DK092291, respectively. VOS is supported by NIH grant R01MD015003 and project number 2P20GM103451. CMR is supported by grants R03DK128386 (NIDDK) and R01HL153178 (NHLBI). This work is also supported by the NIDDK Intramural Research Program. The funders had no role in the study design; collection, analysis, and interpretation of these data; writing the report; and the decision to submit the report for publication. The authors declare that they have no relevant financial interests. The authors thank the CRIC Study staff and participants for their contributions to this study, as well as Xiaoming Zhang and Jesse Hsu for preparing the datasets for statistical analyses. Received March 31, 2022. Evaluated by 3 external peer reviewers and a statistician, with editorial input from an Acting Editor-in-Chief (Editorial Board Member Tazeen Jafar, MD, MPH). Accepted in revised form September 30, 2022. The involvement of an Acting Editor-in-Chief to handle the peer-review and decision-making processes was to comply with AJKD’s procedures for potential conflicts of interest for editors, described in the Information for Authors & Journal Policies. Download .pdf (.79 MB) Help with pdf files Supplementary File (PDF)Figures S1-S3; Item S1; Tables S1-S8.