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High body mass index is a risk factor for transition to hemodialysis or hybrid therapy and peritoneal dialysis-related infection in Japanese patients undergoing peritoneal dialysis

  • Nephrology - Original Paper
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Abstract

Purpose

Obesity may negatively impact the clinical outcomes of patients undergoing peritoneal dialysis (PD). However, the impact of obesity on PD-related outcomes remains unclear. We herein examined the association of high body mass index (BMI) with complete hemodialysis (HD) transfer, transition to HD and PD/HD hybrid therapy, peritonitis, catheter exit-site and tunnel infection (ESI/TI), and heart failure-related hospitalization.

Methods

This retrospective cohort study included 120 patients who underwent PD-catheter insertion between January 2008 and June 2018. BMI ≥ 25 kg/m2 at the time of PD-catheter insertion was defined as high BMI, and its association with outcomes was analyzed using the log-rank test and Cox proportional hazards models.

Results

The follow-up duration was 46.2 (23.3–75.3) months. The time until transfer to HD and hybrid therapy was significantly shorter in the high BMI group than that in the low BMI group, whereas the time until HD transfer was not significantly different between the two groups (P < 0.001 and 0.18, respectively). Peritonitis-free and ESI/TI-free survivals were significantly shorter in the high BMI group than those in the low BMI group (P = 0.006 and 0.03, respectively). After adjusting for age, sex, diabetes mellitus, and estimated glomerular filtration rate, high BMI remained a significant risk factor for transferring to HD and hybrid therapy, peritonitis, and ESI/TI (hazard ratio [HR] 2.60, P < 0.001; HR 2.08, P = 0.01; HR 2.64, P = 0.02, respectively).

Conclusion

BMI ≥ 25 kg/m2 is a risk factor for transition to HD and hybrid therapy, peritonitis, and ESI/TI, but not for complete HD transfer in Japanese patients with PD.

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References

  1. Nitta K, Goto S, Masakane I et al (2020) Annual dialysis data report for 2018, JSDT renal data registry: survey methods, facility data, incidence, prevalence, and mortality. Ren Replace Ther. https://doi.org/10.1186/s41100-020-00286-9

    Article  Google Scholar 

  2. Jaar BG, Plantinga LC, Crews DC et al (2009) Timing, causes, predictors and prognosis of switching from peritoneal dialysis to hemodialysis: a prospective study. BMC Nephrol 10:3. https://doi.org/10.1186/1471-2369-10-3

    Article  PubMed  PubMed Central  Google Scholar 

  3. Guo A, Mujais S (2003) Patient and technique survival on peritoneal dialysis in the United States: evaluation in large incident cohorts. Kidney Int. https://doi.org/10.1046/j.1523-1755.2003.08801.x

    Article  PubMed  Google Scholar 

  4. Huisman RM, Nieuwenhuizen MG, de Charro Th, F, (2002) Patient-related and centre-related factors influencing technique survival of peritoneal dialysis in the Netherlands. Nephrol Dial Transplant 17:1655–1660. https://doi.org/10.1093/ndt/17.9.1655

    Article  PubMed  Google Scholar 

  5. Johnson DW, Herzig KA, Purdie DM et al (2000) Is obesity a favorable prognostic factor in peritoneal dialysis patients? Perit Dial Int 20:715–721. https://doi.org/10.1177/089686080002000623

    Article  CAS  PubMed  Google Scholar 

  6. Aslam N, Bernardini J, Fried L, Piraino B (2002) Large body mass index does not predict short-term survival in peritoneal dialysis patients. Perit Dial Int 22:191–196. https://doi.org/10.1177/089686080202200205

    Article  PubMed  Google Scholar 

  7. McDonald SP, Collins JF, Johnson DW (2003) Obesity is associated with worse peritoneal dialysis outcomes in the Australia and New Zealand patient populations. J Am Soc Nephrol 14:2894–2901. https://doi.org/10.1097/01.asn.0000091587.55159.5f

    Article  PubMed  Google Scholar 

  8. Snyder JJ, Foley RN, Gilbertson DT, Vonesh EF, Collins AJ (2003) Body size and outcomes on peritoneal dialysis in the United States. Kidney Int 64:1838–1844. https://doi.org/10.1046/j.1523-1755.2003.00287.x

    Article  PubMed  Google Scholar 

  9. Abbott KC, Glanton CW, Trespalacios FC et al (2004) Body mass index, dialysis modality, and survival: analysis of the United States renal data system dialysis morbidity and mortality wave II study. Kidney Int 65:597–605. https://doi.org/10.1111/j.1523-1755.2004.00385.x

    Article  PubMed  Google Scholar 

  10. Johansen KL, Young B, Kaysen GA, Chertow GM (2004) Association of body size with outcomes among patients beginning dialysis. Am J Clin Nutr 80:324–332. https://doi.org/10.1093/ajcn/80.2.324

    Article  CAS  PubMed  Google Scholar 

  11. de Mutsert R, Grootendorst DC, Boeschoten EW, Dekker FW, Krediet RT (2009) Is obesity associated with a survival advantage in patients starting peritoneal dialysis? Contrib Nephrol 163:124–131. https://doi.org/10.1159/000223790

    Article  PubMed  Google Scholar 

  12. Obi Y, Streja E, Mehrotra R, Rivara MB, Rhee CM, Soohoo M et al (2018) Impact of obesity on modality longevity, residual kidney function, peritonitis, and survival among incident peritoneal dialysis patients. Am J Kidney Dis 71:802–813. https://doi.org/10.1053/j.ajkd.2017.09.010

    Article  PubMed  Google Scholar 

  13. Shibagaki Y, Faber MD, Divine G, Shetty A (2002) Feasibility of adequate solute clearance in obese patients on peritoneal dialysis: a cross-sectional study. Am J Kidney Dis 40:1295–1300. https://doi.org/10.1053/ajkd.2002.36904

    Article  CAS  PubMed  Google Scholar 

  14. Yoshiike NWN, Matsushima S, Ito C et al (2000) Obese degree and diabetes, hypertension based on body mass index, epidemiological examination - by associated - multicenter study with the risk factor of hyperlipidemia. J Japan Soc Study Obes 6:4–17

    Google Scholar 

  15. Kawanishi H, Marshall MR, Zhao J et al (2021) Mortality, hospitalization and transfer to haemodialysis and hybrid therapy, in Japanese peritoneal dialysis patients. Perit Dial Int. https://doi.org/10.1177/08968608211016127

    Article  PubMed  Google Scholar 

  16. Kang SH, Cho KH, Park JW, Yoon KW, Do JY (2013) Geriatric nutritional risk index as a prognostic factor in peritoneal dialysis patients. Perit Dial Int 33:405–410. https://doi.org/10.3747/pdi.2012.00018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Figueiredo A, Goh BL, Jenkins S et al (2010) Clinical practice guidelines for peritoneal access. Perit Dial Int 30:424–429. https://doi.org/10.3747/pdi.2010.00087

    Article  PubMed  Google Scholar 

  18. Nakayama M, Miyazaki M, Honda K et al (2014) Encapsulating peritoneal sclerosis in the era of a multi-disciplinary approach based on biocompatible solutions: the NEXT-PD study. Perit Dial Int 34:766–774. https://doi.org/10.3747/pdi.2013.00074

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kolesnyk I, Dekker FW, Boeschoten EW, Krediet RT (2010) Time-dependent reasons for peritoneal dialysis technique failure and mortality. Perit Dial Int 30:170–177. https://doi.org/10.3747/pdi.2008.00277

    Article  PubMed  Google Scholar 

  20. Chidambaram M, Bargman JM, Quinn RR, Austin PC, Hux JE, Laupacis A (2011) Patient and physician predictors of peritoneal dialysis technique failure: a population based, retrospective cohort study. Perit Dial Int 31:565–573. https://doi.org/10.3747/pdi.2010.00096

    Article  PubMed  Google Scholar 

  21. See EJ, Johnson DW, Hawley CM et al (2018) Risk predictors and causes of technique failure within the first year of peritoneal dialysis: an Australia and New Zealand dialysis and transplant registry (ANZDATA) study. Am J Kidney Dis 72:188–197. https://doi.org/10.1053/j.ajkd.2017.10.019

    Article  PubMed  Google Scholar 

  22. Boundville N, Ullah S, Clayton P et al (2019) Differences in peritoneal dialysis technique survival between patients treated with peritoneal dialysis systems from different companies. Nephrol Dial Transplant 34:1035–1044. https://doi.org/10.1093/ndt/gfy340

    Article  Google Scholar 

  23. Cho Y, Johnson DW (2014) Peritoneal dialysis-related peritonitis: towards improving evidence, practices, and outcomes. Am J Kidney Dis 64:278–289. https://doi.org/10.1053/j.ajkd.2014.02.025

    Article  PubMed  Google Scholar 

  24. Nakayama T, Uchiyama K, Washida N et al (2021) Exercise parameters predict technique survival in patients on peritoneal dialysis. Blood Purif 50:380–389. https://doi.org/10.1159/000511293

    Article  CAS  PubMed  Google Scholar 

  25. Kanda Y (2013) Investigation of the freely available easy-to-use software “EZR” for medical statistics. Bone Marrow Transplant 48:452–458. https://doi.org/10.1038/bmt.2012.244

    Article  CAS  PubMed  Google Scholar 

  26. Isla RA, Mapiye D, Swanepoel CR, Rozumyk N, Hubahib JE, Okpechi IG (2014) Continuous ambulatory peritoneal dialysis in Limpopo province, South Africa: Predictors of patient and technique survival. Perit Dial Int 34:518–525. https://doi.org/10.3747/pdi.2013.00334

    Article  PubMed  PubMed Central  Google Scholar 

  27. Descoeudres B, Koller MT, Garzoni D et al (2008) Contribution of early failure to outcome on peritoneal dialysis. Perit Dial Int 28:259–267. https://doi.org/10.1177/089686080802800312

    Article  PubMed  Google Scholar 

  28. Cueto-Manzano AM, Quintana-Piña E, Correa-Rotter R (2001) Long-term CAPD survival and analysis of mortality risk factors: 12-year experience of a single Mexican center. Perit Dial Int 21:148–153. https://doi.org/10.1177/089686080102100207

    Article  CAS  PubMed  Google Scholar 

  29. Chen HL, Tarng DC, Huang LH (2019) Risk factors associated with outcomes of peritoneal dialysis in Taiwan: an analysis using a competing risk model. Med (Baltim) 98:e14385. https://doi.org/10.1097/MD.0000000000014385

    Article  Google Scholar 

  30. Shi Y, Zheng D, Zhang L et al (2017) Six-minute walk test predicts all-cause mortality and technique failure in ambulatory peritoneal dialysis patients. Nephrology (Carlton) 22:118–124. https://doi.org/10.1111/nep.12726

    Article  Google Scholar 

  31. Matsui M, Akai Y, Samejima KI et al (2017) Prognostic value of predialysis indices for technique failure and mortality in peritoneal dialysis patients. Ther Apher Dial 21:493–499. https://doi.org/10.1111/1744-9987.12546

    Article  CAS  PubMed  Google Scholar 

  32. Murashima M, Hamano T, Abe M, Masakane I (2021) Combination of once-weekly haemodialysis with peritoneal dialysis is associated with lower mortality compared with peritoneal dialysis alone: a longitudinal study. Clin Kidney J 14:1610–1617. https://doi.org/10.1093/ckj/sfaa173

    Article  PubMed  Google Scholar 

  33. Tanaka M, Ishibashi Y, Hamasaki Y et al (2020) Bioimpedance spectroscopy-based fluid status in combined dialysis compared with hemodialysis and peritoneal dialysis: a cross-sectional study. Ther Apher Dial 24:373–379. https://doi.org/10.1111/1744-9987.13444

    Article  CAS  PubMed  Google Scholar 

  34. Wang AY (2007) The john F. Maher award recipient Lecture 2006. The “heart” of peritoneal dialysis: residual renal function. Perit Dial Int 27:116–124. https://doi.org/10.1177/089686080702700203

    Article  PubMed  Google Scholar 

  35. Ozener C, Arikan H, Karayaylali I et al (2014) The impact of diabetes mellitus on peritoneal dialysis: the Turkey multicenter clinic study. Ren Fail 36:149–153. https://doi.org/10.3109/0886022X.2013.843275

    Article  PubMed  Google Scholar 

  36. Nessim SJ, Bargman JM, Austin PC, Nisenbaum R, Jassal SV (2009) Predictors of peritonitis in patients on peritoneal dialysis: results of a large, prospective Canadian database. Clin J Am Soc Nephrol 4:1195–1200. https://doi.org/10.2215/CJN.00910209

    Article  PubMed  PubMed Central  Google Scholar 

  37. Tsai CC, Lee JJ, Liu TP, Ko WC, Wu CJ, Pan CF et al (2013) Effects of age and diabetes mellitus on clinical outcomes in patients with peritoneal dialysis-related peritonitis. Surg Infect (Larchmt) 14:540–546. https://doi.org/10.1089/sur.2012.195

    Article  Google Scholar 

  38. Conway EL, Sellick JA, Kurtzhalts K, Mergenhagen KA (2017) Obesity and heart failure as predictors of failure in outpatient skin and soft tissue infections. Antimicrobial Agents Chemother. https://doi.org/10.1128/AAC.02389-16

    Article  Google Scholar 

  39. Falagas ME, Kompoti M (2006) Obesity and infection. Lancet Infect Dis 6:438–446. https://doi.org/10.1016/S1473-3099(06)70523-0

    Article  PubMed  Google Scholar 

  40. Long C, Bartlett G, Macgibbon B et al (2013) The effect of obesity on antibiotic treatment failure: a historical cohort study. Pharmacoepidemiol Drug Saf 22:970–976. https://doi.org/10.1002/pds.3461

    Article  CAS  Google Scholar 

  41. Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y (2000) Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 72:694–701. https://doi.org/10.1093/ajcn/72.3.694

    Article  CAS  PubMed  Google Scholar 

  42. Alves Junior CA, Mocellin MC, Gonçalves ECA, Silva DA, Trindade EB (2017) Anthropometric indicators as body fat discriminators in children and adolescents: a systematic review and meta-analysis. Adv Nutr 8:718–727. https://doi.org/10.3945/an.117.015446

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Endocrinology, Metabolism and Nephrology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan

    Eriko Yoshida Hama, Kiyotaka Uchiyama, Tomoki Nagasaka, Ei Kusahana, Takashin Nakayama, Itaru Yasuda, Naoki Washida & Hiroshi Itoh

  2. Apheresis and Dialysis Center, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan

    Kohkichi Morimoto

  3. Department of Nephrology, International University of Health and Welfare School of Medicine, 4-3, Kozunomori, Narita, Chiba, Japan

    Naoki Washida

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  1. Eriko Yoshida Hama
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Contributions

KU designed the study, and EY wrote the initial draft of the manuscript. EY, KU, TN, EK, TN, IY, KM, and NW contributed to data collection. EY and KU contributed to analysis and interpretation of data and assisted in the preparation of the manuscript. KM, NW, and HI supervised the manuscript. All authors have approved the final version of the manuscript and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Kiyotaka Uchiyama.

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This study and all its protocols were reviewed and approved by the ethics committee of our hospital, and informed consent was obtained from all patients prior to participation.

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Hama, E.Y., Uchiyama, K., Nagasaka, T. et al. High body mass index is a risk factor for transition to hemodialysis or hybrid therapy and peritoneal dialysis-related infection in Japanese patients undergoing peritoneal dialysis. Int Urol Nephrol 54, 3193–3202 (2022). https://doi.org/10.1007/s11255-022-03252-y

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