A Body Mass Index-Based Cross-Classification Approach for the Assessment of Prognostic Factors in Chronic Kidney Disease Progression

Abstract

Background/Aims: Cross-classification analyses are rarely reported. We investigated the prognostic factors for chronic kidney disease (CKD) progression using a body mass index (BMI)-based cross-classification approach. Methods: Patients’ renal outcome (≥50% decline in the estimated glomerular filtration rate or end-stage renal disease) in each subcohort was examined. Results: The number of prognostic factors identified in the multivariate Cox analysis was smaller in the “BMI ≥25, female” and CKD stage 3 subcohorts than in other subcohorts. Prognostic factors identified in the “BMI ≥25, CKD stage 3” subcohort only comprised albuminuria and male sex, and those in the “BMI ≥25, female” subcohort only comprised albuminuria, hyperphosphatemia, and anemia. Albuminuria, kidney impairment, male sex, hyperphosphatemia, anemia, and increased pulse pressure × heart rate product (PP × HR; pulsatile stress) were stable renal prognostic factors in almost all subcohorts. On the other hand, the prognostic value of increased BMI, younger age, hypoalbuminemia, increased intact parathyroid hormone, and decreased estimated 24-h urinary potassium excretion (e24hUK) differed according to subcohort. BMI was positively associated with CKD progression in the “BMI ≥25, age ≥65 years” and “BMI ≥25, CKD stages 4–5” subcohorts, whereas it was negatively associated with CKD progression in the “BMI <25, diabetes mellitus” subcohort. PP × HR was independently associated with CKD progression in the “BMI <25, CKD stage 3” subcohort, which had relatively few identified renal prognostic factors. Decreased e24hUK was a renal prognostic factor for CKD progression in the “BMI <25, CKD stages 4–5” subcohort, while no significant factors were observed in the “BMI ≥25, CKD stages 4–5” subcohort. Conclusion: A BMI-based cross-classification approach, which provides more comprehensive findings than that in previous approaches, is expected to be an effective method for evaluating renal prognostic factors in patients with CKD who are affected by multiple risk factors.

Maximum Carotid Intima-Media Thickness in Association with Renal Outcomes

“…Interpreting the results of clinical studies can sometimes be challenging, as these factors complicatedly affect one another. Indeed, as we recently reported 27) , renal prognosis analyses are strongly influenced by the factors examined and the cohort analyzed. Therefore, the usefulness of IMT measurements for renal prognosis may change depending on the variable examined (ICA-risk factor over time.…”

“…The median follow-up period of 12.5 years in the present study was comparable to these previous studies. The independent association between Max IMT and CKD progression observed in long-term follow-up studies may be because of the stability of Max IMT as a renal development of patient-centered medicine requires disaggregation of data and analysis of differences in subgroups 26,27) .…”

“…However, until recently, whether carotid IMT is useful in predicting the progression of kidney disease was not clarified 14) . Generally, CKD is multifactorial 27) , and its progression is associated with various risk factors linked to atherosclerosis, such as Table 3). The P-value for the interaction is derived from the multivariate Cox analyses including interaction terms (all variables age ≥ 65 years) for the entire study population in Table 2.…”

Visceral to subcutaneous fat ratio as an indicator of a ≥30% eGFR decline in chronic kidney disease

“…of data and analyses of differences within sub-cohorts are required [37,38]. Furthermore, considering that multifaceted and comprehensive judgments based on abundant medical information are required in the treatment of patients in real clinical settings, there is merit in evaluating various cut-off values in various sub-cohorts, at various follow-up periods, for prognostic factors of importance.…”

“…Furthermore, considering that multifaceted and comprehensive judgments based on abundant medical information are required in the treatment of patients in real clinical settings, there is merit in evaluating various cut-off values in various sub-cohorts, at various follow-up periods, for prognostic factors of importance. In fact, the adequate cut-off value for a risk factor depends on the handling of the risk factor [38][39][40], the cohort evaluated [38,39,41,42], and the observation period [19,21]. Fat distribution generally differs between the sexes; men have relatively more visceral fat and women have relatively more subcutaneous fat.…”