by Health Data Science

A) The cumulative incidence of acute kidney disorder in exposed (black) and unexposed (gray) participants during 28-day follow-up. B) Hazard function smoothed with B spline depicting the instantaneous risk of COVID-19 on acute kidney disorder in exposed (blue) and unexposed (red) participants during follow-up. The dark blue/red lines represent the estimated hazard functions with 95% CIs in the light blue/red regions. C) Time-varying effects of acute kidney disorder and subgroup analysis. The results were presented as HR with 95% CI. HR, hazard ratio. CI, confidence interval. Credit: Li Chunyang and Zeng Xiaoxi, West China Biomedical Big Data Center, West China Hospital, Sichuan University

Researchers from West China Hospital, Sichuan University, have conducted a study revealing a significant association between COVID-19 and acute kidney disorders (AKD), including acute kidney injury (AKI), that varies over time. The study, led by Dr. Li Chunyang and Dr. Zeng Xiaoxi from the West China Biomedical Big Data Center, was published in the journal Health Data Science.

COVID-19, known for its impact on the respiratory system, also affects other organs, including the kidneys. The study aimed to investigate the time-dependent effects of COVID-19 on acute kidney disorders. Using data from the UK Biobank, the researchers conducted a matched cohort study and a Mendelian randomization analysis to explore both the association and potential causality between COVID-19 and AKD.

"Our research highlights the time-varying risk of acute kidney disorders in COVID-19 patients, particularly in the first three weeks following infection," said Dr. Li Chunyang, a research associate at the West China Biomedical Big Data Center, West China Hospital, Sichuan University. "We observed that the hazard effects peak in the second week post-infection and decline by the fourth week."

The study involved 10,121 COVID-19 patients matched with 29,004 unexposed historical controls based on age, sex, deprivation index, and hospitalization status. A conditional and time-varying Cox proportional hazard regression model was used to assess the association between COVID-19 and AKD within four weeks of infection.

The results indicated that the risk of AKD peaked during the second week after infection (hazard ratio, 12.77; 95% confidence interval, 5.93–27.70) and decreased by the fourth week (hazard ratio, 2.28; 95% confidence interval, 0.75–6.93).

The study also found that only patients with moderate to severe COVID-19 showed a significant risk of acute worsening of renal function. This risk was not observed in patients with mild COVID-19. A one-sample Mendelian randomization analysis further demonstrated a potential "short-term" causal effect of COVID-19 on AKD risk, primarily confined to the first week after infection.

The findings suggest that health care providers should closely monitor kidney function in COVID-19 patients, particularly those with moderate to severe cases, during the critical first few weeks after infection. The study provides important insights into the temporal nature of COVID-19's impact on kidney health, which may guide clinical management and follow-up strategies.

Looking ahead, the research team plans to further explore the time-varying impact of COVID-19 on the risk of incident acute kidney disorders in East Asian populations. Additionally, they aim to investigate the underlying molecular mechanisms that may link COVID-19 to subsequent acute kidney disorders to establish more definitive causal pathways.

"The molecular mechanisms behind the association between COVID-19 and kidney damage remain unclear," added Dr. Zeng Xiaoxi, an associate professor in the Nephrology Department at West China Hospital. "Our future research will focus on elucidating these mechanisms and verifying causality, which could pave the way for targeted interventions."

More information: Chunyang Li et al, The Time-Varying Impact of COVID-19 on the Acute Kidney Disorders: A Historical Matched Cohort Study and Mendelian Randomization Analysis, Health Data Science (2024). DOI: 10.34133/hds.0159

Provided by Health Data Science