Cycles, Arrows and Turbulence: Time Patterns in Renal Disease, a Path from Epidemiology to Personalized Medicine?

Jeroen P. Kooman*, Len A. Usvyat, Marijke J. E. Dekker, Dugan W. Maddux, Jochen G. Raimann, Frank M. van der Sande, Xiaoling Ye, Yuedong Wang, Peter Kotanko

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

8 Citations (Web of Science)

Abstract

Patients with end-stage renal disease (ESRD) experience unique patterns in their lifetime, such as the start of dialysis and renal transplantation. In addition, there is also an intricate link between ESRD and biological time patterns. In terms of cyclic patterns, the circadian blood pressure (BP) rhythm can be flattened, contributing to allostatic load, whereas the circadian temperature rhythm is related to the decline in BP during hemodialysis (HD). Seasonal variations in BP and interdialytic-weight gain have been observed in ESRD patients in addition to a profound relative increase in mortality during the winter period. Moreover, nonphysiological treatment patters are imposed in HD patients, leading to an excess mortality at the end of the long interdialytic interval. Recently, new evidence has emerged on the prognostic impact of trajectories of common clinical and laboratory parameters such as BP, body temperature, and serum albumin, in addition to single point in time measurements. Backward analysis of changes in cardiovascular, nutritional, and inflammatory parameters before the occurrence as hospitalization or death has shown that changes may already occur within months to even 1-2 years before the event, possibly providing a window of opportunity for earlier interventions. Disturbances in physiological variability, such as in heart rate, characterized by a loss of fractal patterns, are associated with increased mortality. In addition, an increase in random variability in different parameters such as BP and sodium is also associated with adverse outcomes. Novel techniques, based on time-dependent analysis of variability and trends and interactions of multiple physiological and laboratory parameters, for which machine-learning -approaches may be necessary, are likely of help to the clinician in the future. However, upcoming research should also evaluate whether dynamic patterns observed in large epidemiological studies have relevance for the individual risk profile of the patient. (c) 2018 S. Karger AG, Basel

Original languageEnglish
Pages (from-to)171-184
Number of pages14
JournalBlood Purification
Volume47
Issue number1-3
DOIs
Publication statusPublished - 2019

Keywords

  • End-stage renal disease
  • Pathophysiology
  • Circadian
  • Seasonal
  • Heart rate variability
  • Epidemiology
  • Interdialytic period
  • BLOOD-PRESSURE VARIABILITY
  • CHRONIC KIDNEY-DISEASE
  • CHRONIC DIALYSIS PATIENTS
  • HEART-RATE-VARIABILITY
  • INCIDENT HEMODIALYSIS-PATIENTS
  • SEASONAL-VARIATIONS
  • CARDIOVASCULAR MORTALITY
  • NUTRITIONAL COMPETENCE
  • PROGNOSTIC VALUE
  • FLUID OVERLOAD

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