Population-level detection of early loss of kidney function: 7-year follow-up of a young adult cohort at risk of Mesoamerican nephropathy

Abstract

Background: Mesoamerican nephropathy is a leading contributor to premature mortality in Central America. Efforts to identify the cause are hampered by difficulties in distinguishing associations with potential initiating factors from common exposures thought to exacerbate the progression of all forms of established chronic kidney disease (CKD). We explored evidence of disease onset or departure from the healthy estimated glomerular filtration rate distribution [departure from ∼eGFR(healthy)] in an at-risk population.

Methods: Two community-based cohorts (adults aged 18-30 years, n = 351 and 420) from 11 rural communities in Northwest Nicaragua were followed up over 7 and 3 years respectively. We examined associations with both (i) incident CKD and (ii) the time point of departure from ∼eGFR(healthy), using a hidden Markov model.

Results: CKD occurred in men only (male incidence rate: 0.7%/year). Fifty-three (out of 1878 visits, 2.7%) and 8 (out of 1067 visits, 0.8%) episodes of probable departure from ∼eGFR(healthy) occurred in men and women, respectively. Cumulative time in sugarcane work and symptoms of excess occupational sun exposure were associated with incident CKD. The same exposures were associated with probability of departure from ∼eGFR(healthy) in time-updated analyses along with measured and self-reported weight loss, nausea, vomiting and cramps, as well as non-steroidal anti-inflammatory drug use.

Conclusions: CKD burden in this population is high and risk factors for established disease are occupational. Additionally, a syndrome suggesting an alternative exposure is associated with evidence of disease onset supporting a possible separate unknown initiating factor for which further investigation is needed. Interventions to reduce the impact of occupational risks should be pursued meanwhile.

Keywords: Chronic kidney disease of undetermined cause; chronic kidney disease of non-traditional cause; hidden Markov modelling.

Figure 1.

Outline of study design, location and analysis. (A) Recruitment and study visits over the 7-year follow-up with associated analyses described in this report. Numbers show the number of participants with estimated glomerular filtration rate (eGFR) results at each visit. *24 participants from Cohort 1 were recruited at Visit 2. (B) Leon and Chinandega regions highlighted (dark grey) on a map of Nicaragua. (C) Illustration of eGFR distribution of assumed unhealthy (dark grey) and estimated healthy (light grey) kidney states used in the hidden Markov model. The healthy eGFR distribution was estimated empirically. (D) Illustrative eGFR trajectories superimposed on healthy (light grey) and unhealthy (dark grey) Hidden Markov model distributions. Individuals (labelled 1) might be classified as remaining in a healthy kidney state (solid line) throughout. Other individuals (labelled 2) might be classified as in an unhealthy state (dotted line) at all time points. Individuals (labelled 3) who transition between states might be estimated to have a high probability (i.e. >50%) of transitioning from a healthy to unhealthy state since the previous study visit [marked with the arrowhead and referred to as departure from ∼eGFR_(healthy) episode in this study]. It is the probability of this departure from ∼eGFR_(healthy) at each study visit that is the outcome used in the time-updated risk-factor analyses in this study. Alternatively, an individual may sustain more gradual decline in eGFR (labelled 4) where they are estimated to have lower probabilities of moving from healthy to unhealthy across several time points [i.e. probabilities of >0% and <50% of a departure from ∼eGFR_(healthy) event at multiple time points]. Although no single time point of departure from ∼eGFR_(healthy) can be identified, these lower probabilities nonetheless contribute to the time-updated risk-factor analyses in this study. Individuals (labelled 5) with an isolated eGFR measure in the unhealthy distribution may remain classified as healthy throughout or revert to a healthy classification after a number of follow-up visits, as the model makes use of all data points in the follow-up period (see text). Again, the departure from ∼eGFR_(healthy) probabilities for these individuals contribute to the risk-factor analyses. Individuals estimated to already be in an unhealthy state do not contribute to the risk-factor analyses (dashed lines), i.e. all observations where the posterior probability of an unhealthy state was >50% at the previous study visit were censored. eGFR measures beyond 60 months were not used for the hidden Markov model to provide insight into the consequences of the estimated Markov states on the medium-term eGFR of participants. eGFR, estimated glomerular filtration rate; departure from ∼eGFR_(healthy), departure from the healthy eGFR distribution

Figure 2.

Kaplan–Meier plot of incident chronic kidney disease in the two cohorts. Chronic kidney disease (CKD) is defined as the first of two consecutive estimated glomerular filtration rate (eGFR) measures of <60 mL/min/1.7 m². These measures were >6 months apart by virtue of the study design. No individual classified as having incident CKD reverted to an eGFR of ≥60 mL/min/1.7 m² during the follow-up period

Figure 3.

Estimated glomerular filtration rate distributions in men and women at different time points during follow-up stratified by estimated hidden Markov model state at baseline and last visit. Last visit was the latest attended visit with a hidden Markov model estimate (up to Visit 8 for Cohort 1 or Visit 4 for Cohort 2). Participants who reverted from unhealthy to healthy at any point are excluded from this figure (n = 9). The number of observations in each group is indicated across the top of the figure. Healthy to unhealthy, healthy at baseline and unhealthy at last visit; extended follow-up, latest of Visits 9 and 10 (Cohort 1 only); eGFR, estimated glomerular filtration rate

Figure 4.

Estimated glomerular filtration rate distributions in men and women at baseline, visit prior to and time of probable departure from the healthy estimated glomerular filtration rate distribution_, along with extended follow-up, stratified by those participants who experience departure from the healthy estimated glomerular filtration rate distribution during follow-up. Probable departure from the healthy estimated glomerular filtration rate distribution [departure from ∼eGFR_(healthy)] was defined as a joint probability of >50% of transitioning from a healthy to unhealthy state since the previous study visit. Visit of departure from ∼eGFR_(healthy) varies by participant. The group not experiencing departure from ∼eGFR_(healthy) includes only participants estimated to be in a healthy state throughout follow-up. Last visit was the latest attended visit with an hidden Markov model estimate (up to Visit 8 for Cohort 1 or Visit 4 for Cohort 2). Extended follow-up, latest of Visits 9 and 10 (Cohort 1 only). Where an individual’s departure from ∼eGFR_(healthy) or pre-departure from ∼eGFR_(healthy) visit coincides with the baseline or last visit, the data point is included in both categories for the purposes of this figure. Number of observations in each group is indicated across the top of the figure. eGFR, estimated glomerular filtration rate