
==== Front
J Occup Health
J Occup Health
joh
Journal of Occupational Health
1341-9145
1348-9585
Oxford University Press

39012028
10.1093/joccuh/uiae037
uiae037
Original Article
AcademicSubjects/MED00010
AcademicSubjects/MED00640
Effects of cold and hot temperatures on the renal function of people with chronic disease
https://orcid.org/0000-0002-7812-7912
Park Min Young Department of Occupational and Environmental Medicine, Seoul St Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

Ahn Joonho Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Seoul, Republic of Korea

Bae S Department of Preventive Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

Chung B H Division of Nephrology, Department of Internal Medicine, Seoul St Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

Myong Jun-Pyo Department of Occupational and Environmental Medicine, Seoul St Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

Lee Jongin Department of Occupational and Environmental Medicine, Seoul St Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

https://orcid.org/0000-0002-1682-865X
Kang Mo-Yeol Department of Occupational and Environmental Medicine, Seoul St Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

Corresponding author: Mo-Yeol Kang, (snaptoon@naver.com).
Jan-Dec 2024
16 7 2024
16 7 2024
66 1 uiae03701 2 2024
30 5 2024
14 7 2024
05 9 2024
© The Author(s) [2024]. Published by Oxford University Press on behalf of Journal of Occupational Health.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

Objectives

This study investigated the effects of hot and cold temperature on the renal function of people with chronic diseases, such as diabetes, hypertension, and chronic kidney disease, using large-scale clinical data.

Methods

We used retrospective cohort data from the Clinical Data Warehouse of the Seoul St Mary’s Hospital, which contains clinical, diagnostic, laboratory, and other information about all patients who have visited the hospital since 1997. We obtained climate data from the Automated Synoptic Observing System of the Korea Meteorological Administration. The heat index was used as a measuring tool to evaluate heat exposure by indexing the actual heat that individuals feel according to temperature and humidity. The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation. To investigate changes in renal function trends with heat index, this study used generalized additive mixed models.

Results

Renal function decreased linearly with increasing heat index after approximately 25°C, which was considered the flexion point of temperature. A linear decrease in the eGFR was observed with the effects of 0 to 5 lag days. Although there was a correlation observed between the decrease in eGFR and temperatures below −10°C, the results did not indicate statistical significance.

Conclusions

The results of our study provide scientific evidence that high temperatures affect the renal function of people with chronic diseases. These results can help prevent heat-related morbidity by identifying those who are more likely to develop renal disease and experience worsening renal function.

climate change
temperature
renal insufficiency, chronic
glomerular filtration rate
chronic disease
Big Data Utilization Contest at Seoul St Mary’s Hospital (2020)
==== Body
pmcKey points

What is already known on this topic 

▪ Climate change has a profound impact on human health, but the evidence on the kidneys is still lacking.

What this study adds 

▪ This study investigated the effects of temperature on the renal function of people with chronic diseases, such as diabetes, hypertension, and chronic kidney disease, using large-scale clinical data.

▪ An increase in temperature to more than 25°C was significantly correlated with a decrease in the eGFR during the generalized mixed model analysis.

How this study might affect research, practice, or policy 

▪ These results will serve as a sound scientific foundation for policy applications for ongoing public health initiatives aimed at preventing heat-related morbidity by identifying those who are more likely to develop or deteriorate renal disease and raise public awareness of the risks associated with climate change.

1. Introduction

The integration of multidimensional evidence enhances the comprehensive understanding of past, present, and potential future impact of climate change as well as regional-scale knowledge distillation about climate change.1 The impacts of climate change are pervasive and affect everyone in important ways. Many epidemiological studies have shown that climate change has profound effects on human health and confirmed that exposure to high temperature impacts mortality, cardiovascular and respiratory diseases, birth outcomes, infectious diseases, and mental health.2 In particular, exposure to extreme heat exacerbates several diseases, thus creating a new era of climate health crises during which new diseases are emerging. To prevent and treat illnesses that are linked to climate change, it is essential to have an evidence-based understanding of the health risks associated with climate change and effectively communicate that knowledge to decision-makers and clinicians. Climate change is known to increase the frequency and intensity of extreme weather events, including prolonged periods of high temperatures. Several studies have documented the occurrence of cardiovascular and respiratory diseases attributable to such increased exposure to high temperatures3; however, knowledge of the effects of temperature on kidney disease is insufficient.

Most studies of the occurrence and exacerbation of kidney disease attributable to exposure to high temperatures have been conducted in Central America. Since the late 1990s, numerous kidney disease cases have been reported in Central America and southern Mexico, described as “chronic renal failure of unknown etiology.” Subsequently, attempts have been made to determine the cause of the phenomenon termed “Mesoamerican nephropathy.”4 Studies that have focused on exposure to high temperatures have been conducted among those who work in sugarcane fields.5 More recently, similar disease patterns have been observed in North America, South America, the Middle East, Africa, and India; therefore, the occurrence and exacerbation of kidney disease caused by exposure to high temperatures have increased worldwide.4 In addition, studies targeting workers who are highly likely to be exposed to high temperatures during work, other than agricultural workers, and studies examining the relationship between the degree of exposure to high temperatures and the occurrence of kidney disease without specifying occupational groups have been reported.4–7

Most of the existing studies have focused on the risk of acute kidney injury (AKI) by exposure to high temperatures,8 but now attention should be expanded to renal dysfunction among patients with chronic disease in relation to extreme temperature exposure. Chronic kidney disease (CKD) has become a growing global public health concern,9 and its associated burden is rapidly increasing worldwide.10 It is predicted that CKD will be the fifth most prevalent cause of years lost globally by 2040. The recent increase in CKD in many countries is partly attributable to the aging of the population; however, the effect of global warming is obviously too large to be ignored.7 Moreover, if CKD remains uncontrolled, then end-stage renal disease (ESRD) will develop, resulting in the need for dialysis or kidney transplantation, which entail catastrophic health expenditures in high-income countries.11 Hence, it is essential to identify prognostic factors among high-risk groups to control disease progression.12

Given that there is increasing evidence that exposure to extreme temperatures can cause subclinical damage, which can result in the acute worsening of CKD, also referred to as acute exacerbation of CKD,13 it is necessary to study the effects of temperature on renal function, particularly among high-risk groups with chronic diseases. Therefore, we investigated the effects of temperature on the renal function of people with chronic diseases, such as diabetes, hypertension, and CKD, using large-scale clinical data.

2. Methods

2.1. Data sources

We used data from the Clinical Data Warehouse (CDW) of the Catholic University of Korea. Because the demand for research using data resources of clinical fields has increased, the Catholic Medical Center has established its own CDW and linked hospital data stored in electronic medical records to support research by implementing personal information anonymization and access security.14 The CDW of the Catholic Medical Center is a research data platform that integrates medical big data from 7 affiliated hospitals and includes information about approximately 14 million patients. The web platform allows researchers to define the desired cohort by retrieving optional clinical information.14 We established a study dataset that included demographic information, clinical diagnoses, and laboratory test data obtained from Seoul St Mary’s Hospital.

2.2. Study population extraction

The study participants were selected as patients who had visited the endocrinology, cardiology, or nephrology departments of Seoul St Mary's Hospital and had the following Korean Standard Classification of Diseases codes entered in their electronic medical records: diabetes (E10-E14), hypertension (I10-I15), or kidney disease [glomerular disease (N00-N08) or chronic kidney disease(N18)]. They were expected to have a predisposition to worsening kidney function and needed to visit the hospital at relatively short intervals to have their kidney function assessed. The total extracted was 70 023 patients who visited between January 1, 2010 and December 31, 2019.

Patients younger than 19 years, those whose residential address was not registered in the electronic medical records, and those who did not undergo serum creatinine testing were excluded from the study. In addition, serum creatinine levels below 0.6 mg/dL were excluded because the value may be unreliable due to prerenal or renal causes, muscle-related diseases, or liver disease.15 After further excluding ESRD patients already on dialysis, 55 350 participants were included in the study (Figure 1).

Figure 1 Schematic diagram of study participants (n = 55 350).

2.3. Outcome measurements and other variables from CDW

Baseline demographic information of study participants was obtained, including sex, age, and residential address. Blood chemistry data, including serum creatinine, and test dates were obtained from each blood test performed during outpatient visits or hospitalization between 2010 and 2019. The serum creatinine level was measured using the rate-blanked compensated kinetic Jaffe method. The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation: GFR (mL/min per 1.73 m2) = 141 × min(Scr/κ, 1)α × max(Scr/κ, 1) − 1.209 × 0.993Age × 1.018 [if female] _ 1.159 [if black], where Scr is serum creatinine, κ is 0.7 for females and 0.9 for males, α is −0.329 for females and −0.411 for males, min indicates the minimum of Scr/κ or 1, and max indicates the maximum of Scr/κ or 1.16 This equation categorizes the risks of mortality and ESRD more accurately than the Modification of Diet in Renal Disease study equation across a broad range of populations.17

2.4. Environmental variables

We obtained climate data from the Automated Synoptic Observing System of the Korea Meteorological Administration, which has 102 weather-monitoring stations and regularly accumulates data. Using ambient temperature and humidity, we calculated the 24-hour average heat index, which is also called the apparent temperature in hot weather, using the following equation of multiple regression analysis reported by Rothfusz18: HI = −42.379 + 2.04901523 T + 10.14333127R − 0.22475541TR − 6.83783 × 10−3 T2 − 5.481717 × 10−2R2 + 1.22874 × 10−3T2R +  8.5282 × 10−4TR2 − 1.99 × 10−6T2R2 where T is ambient temperature (°F) and R is relative humidity(percentage). The calculated heat index was used in the analysis by converting the units to Celsius. The heat index is used as a measuring tool to evaluate heat exposure during various environmental health studies by indexing the actual heat that individuals feel according to temperature and humidity.19 During this study, we defined each participant’s exposed heat index based on the data of the meteorological monitoring station closest to the residential area of that participant.

2.5. Statistical analysis

We defined each participant’s first visit with a serum creatinine test as baseline, and identified basic information such as sex, age, and diagnoses of hypertension, diabetes, and CKD. To investigate changes in renal function trends with heat index, this study used generalized additive mixed models (GAMMs). GAMMs are an effective modeling strategy because they can separate out the interdependence of observations and account for nonlinear patterns.20 The dependent variable of GAMM is the heat index, and the assumption follows the Gaussian distribution. The general equation for GAMM is as follows:

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $$ \mathrm{g}\left(\mathrm{E}\left(\mathrm{Y}|{X}_1,{X}_2,\cdots, {X}_p\right)\right)={s}_0+\sum_{j=1}^p{s}_j\left({X}_j\right)+\sum_{j=m+1}^p{\beta}_j{X}_j+\overset{\sim }{R_j}+\theta $$\end{document}

where Y is the dependent variable; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${X}_p$\end{document} is the explanatory variable; p is the number of explanatory variables; E(Y) is the average of Y; g() is the connection function; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${s}_0$\end{document} is the intercept term; s() is a smooth function to specify the nonlinear dependence of the dependent variable on the explanatory variable (ie, cubic regression splines in this study); m is the number of smoothing items, namely the number of explanatory variables in the model that have nonlinear effects on dependent variables; βj is the linear regression coefficient for the explanatory variables; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\overset{\sim}{R_j}$\end{document} is the random effect at the individual level; and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\theta$\end{document} is the error term.21 The model established in this study included the registration number randomly assigned to each individual as a random term \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\overset{\sim}{R_j}$\end{document}. Models were established with either a linear or spline term of the heat index, and the Akaike information criteria were compared to confirm the goodness of fit of the nonlinear model with smaller values. The estimated degrees of freedom (EDF) are reported for the estimation of nonlinear trends using the smooth term, where EDF = 1 corresponds to a linear relationship and EDF >2 indicates a strong nonlinear relationship. The significance test of the smooth term was performed by reporting the P value for the EDF. The shape of the graph of the spline model was also visually analyzed to determine the presence of a threshold. The estimation of GAMM was implemented using the R mgcv and gamm4 packages.

Using a generalized linear mixed model, we investigated the effects of high and low temperatures referencing with 25°C and −10°C, respectively, which are the points estimated to be the flexion points in the schematic figure from GAMMs, on renal function. To estimate the lag effects of the heat index, single lag models were used. Because many previous studies have shown that most health effects of ambient temperature have a short lag period,22,23 we analyzed the lag effect of heat index up to 5 days before the date of the blood test and arranged the values in sequential order, such as from the same day of exposure (Lag0) to 5 days prior (Lag5). We performed all statistical analyses using R version 4.0.3 (The Comprehensive R Archive Network; http://cran.r-project.org). P < .05 was considered significant.

3. Results

The mean baseline eGFR values of the 55 350 participants at the time of the first hospital visit, grouped by sex, age, and disease status, are shown in Table 1. Overall, there were more males (58.02%) and more participants older than 60 years (55.00%). Renal function was significantly different among all subgroups; it was especially different among the CKD (difference, −28.85), age (difference, 18.55), and hypertension (difference, −6.04) subgroups. Participants with CKD had a lower mean eGFR than those without (54.14 vs 83.00), and participants younger than 60 years had a higher eGFR than those 60 years or older (90.02 vs 71.47). Participants with hypertension had a lower eGFR than those without hypertension (78.04 vs 84.08).

Table 1 Estimated glomerular filtration rate (eGFR) of participants at first visit by baseline characteristics (mean, SD, and P value for subgroup difference).

Group	n	eGFR (mL/min/1.73 m 2 )	
Mean	SD	Difference	P value a	
Total	55 350	79.82	21.36			
Sex				3.01	<.0001	
 Male	32 116	81.08	21.73			
 Female	23 234	78.07	20.70			
Age				18.55	<.0001	
 <60 years	24 906	90.02	19.47			
 ≥60 years	30 444	71.47	19.08			
Hypertension				−6.04	<.0001	
 Yes	39 074	78.04	20.86			
 No	16 276	84.08	21.93			
Diabetes				0.79	<.0001	
 Yes	34 987	80.11	21.42			
 No	20 363	79.32	21.24			
Chronic kidney disease				−28.85	<.0001	
 Yes	6210	54.14	23.66			
 No	49 140	83.00	18.71			
a P value for Student t test for subgroup (with 2 levels) difference.

Figure 2 shows the nonlinear relationship between the daily mean heat index and eGFR. The significance test of the smoothing term showed an EDF of 8.77 and a P value of <.001, confirming the nonlinearity of the model. Renal function decreased linearly with increasing heat index at approximately 25°C, which was considered the flexion point of temperature. These patterns of decreasing eGFR with increasing heat index were similar after stratification by sex (Figure S1) and by CKD, with a greater tendency for eGFR to decrease with high heat index in those with CKD than in those without (Figure S2). Additionally, a linear decrease in the eGFR was observed with increases in the daily mean temperature, and the approximate flexion point was approximately 2°C higher than the heat index (Figure S3). On the other hand, the decrease in temperature was also correlated with the decrease in the eGFR at temperatures lower than −10°C.

Figure 2 Nonlinear relationship between daily mean heat index (°C) and estimated glomerular filtration rate (eGFR).

The effects of hot and cold temperatures, higher than 25°C and lower than −10°C, on renal function according to the lag days with the assumption of linearity are shown in Table 2. The estimated change in the eGFR with each increase of 1°C above the flexion point with 0 lag days was 0.214, with a greater change in females (0.232) and those aged ≥60 years (0.226) than in males (0.203) and those aged <60 years (0.139), respectively. Participants diagnosed with hypertension (0.257), diabetes (0.231), and chronic kidney disease (0.262) had greater eGFR declines than those who had never been diagnosed with each disease. Similar eGFR changes of 0.206, 0.210, and 0.214 were observed with 1, 2, and 3 lag days, respectively. Although the beta value decreased from lag day 4, the decrease in the eGFR attributable to high temperatures was still statistically significant. No statistically significant ambient temperature effects were seen at low temperatures.

Table 2 Effects of daily mean heat index on eGFR per 1°C increase/decrease at above/below the flexion temperature.

Group	The flexion temperature	Effects above/below flexion	Lag (days) a	
0	1	2	3	4	5	
Total	25°C	Beta (SE)	−.214** (0.014)	−.206** (0.014)	−.210** (0.014)	−.214** (0.014)	−.163** (0.014)	−.145** (0.013)	
−10°C	Beta (SE)	.063 (0.226)	−.019 (0.200)	−.031 (0.229)	.014 (0.227)	.054 (0.229)	.005 (0.199)	
Sex									
 Male	25°C	Beta (SE)	−.203** (0.017)	−.210** (0.017)	−.209** (0.018)	−.223** (0.017)	−.171** (0.017)	−.161** (0.017)	
−10°C	Beta (SE)	−.198 (0.285)	−.264 (0.254)	−.176 (0.292)	.108 (0.286)	.482 (0.295)	.187 (0.255)	
 Female	25°C	Beta (SE)	−.232** (0.023)	−.202** (0.023)	−.211** (0.023)	−.197** (0.023)	−.151** (0.022)	−.112** (0.022)	
−10°C	Beta (SE)	.386 (0.368)	.467 (0.324)	.473 (0.362)	−.278 (0.367)	−.634 (0.359)	−.346 (0.316)	
Age									
 <60 years	25°C	Beta (SE)	−.139** (0.025)	−.135** (0.026)	−.119** (0.026)	−.123** (0.025)	−.100** (0.025)	−.091** (0.025)	
−10°C	Beta (SE)	−.306 (0.385)	−.321 (0.350)	−.597 (0.407)	.306 (0.417)	−.375 (0.446)	.251 (0.364)	
 ≥60 years	25°C	Beta (SE)	−.226** (0.016)	−.222** (0.016)	−.224** (0.017)	−.232** (0.016)	−.169** (0.016)	−.141** (0.016)	
−10°C	Beta (SE)	.258 (0.262)	.115 (0.233)	.349 (0.244)	−.099 (0.226)	.119 (0.236)	−.106 (0.219)	
Hypertension									
 Yes	25°C	Beta (SE)	−.257** (0.016)	−.233** (0.016)	−.251** (0.016)	−.254** (0.016)	−.200** (0.016)	−.184** (0.016)	
−10°C	Beta (SE)	.073 (0.265)	.200 (0.235)	.155 (0.260)	−.133 (0.260)	.297 (0.264)	.041 (0.233)	
 No	25°C	Beta (SE)	−.109** (0.026)	−.139** (0.026)	−.108** (0.027)	−.110** (0.027)	−.064* (0.026)	−.043 (0.026)	
−10°C	Beta (SE)	.147 (0.417)	−.390 (0.373)	−.561 (0.463)	.270 (0.438)	−.743 (0.444)	−.129 (0.368)	
Diabetes								
 Yes	25°C	Beta (SE)	−.231** (0.015)	−.226** (0.015)	−.229** (0.016)	−.228** (0.015)	−.167** (0.015)	−.151** (0.015)	
−10°C	Beta (SE)	.039 (0.253)	−.168 (0.228)	−.354 (0.258)	.012 (0.251)	.018 (0.266)	.149 (0.232)	
 No	25°C	Beta (SE)	−.154** (0.030)	−.137** (0.032)	−.149** (0.032)	−.168** (0.031)	−.153** (0.030)	−.126** (0.030)	
−10°C	Beta (SE)	.181 (0.482)	.711 (0.400)	.849 (0.481)	.037 (0.499)	−.004 (0.431)	−.439 (0.377)	
Chronic kidney disease								
 Yes	25°C	Beta (SE)	−.262** (.033)	−.266** (.033)	−.267** (.033)	−.285** (.032)	−.207** (.032)	−.191** (.032)	
−10°C	Beta (SE)	.533 (0.477)	.407 (0.420)	.166 (0.450)	−.141 (0.437)	.201 (0.495)	−.228 (0.422)	
 No	25°C	Beta (SE)	−.184* (0.014)	−.172** (0.015)	−.172** (0.015)	−.173** (0.015)	−.133** (0.014)	−.114** (0.014)	
−10°C	Beta (SE)	−.030 (0.226)	−.117 (0.205)	−.205 (0.242)	.002 (0.238)	−.093 (0.231)	.043 (0.201)	
Abbreviation: eGFR, estimated glomerular filtration rate.

a* P value <.05

* * P value <.001; otherwise, not statistically significant.

4. Discussion

To the best of our knowledge, this is the first study to investigate the relationship between temperature and renal function among a large population of people with chronic diseases, including diabetes, hypertension, and CKD. The results of our analysis showed a nonlinear relationship between the daily mean heat index and eGFR with the effects of 0 to 5 lag days. An increase in temperature to higher than 25°C was significantly correlated with a decrease in the eGFR during the generalized mixed model analysis. On the other hand, the decrease in temperature was also correlated with the decrease in the eGFR at temperatures lower than −10°C, but the results do not indicate statistical significance in the linear regression model.

Previous studies revealed associations between high temperatures, heatwaves, and AKI. For instance, Gronlund et al24 analyzed data regarding the effects of high temperatures and heatwaves (temperatures ≥95th percentile for at least 2 consecutive days) among those older than 65 years in 114 cities in the United States from 1992 to 2006; they observed increases of 4.3% and 14.2% in admissions for kidney problems related to high temperatures and heatwaves, respectively. Liu et al8 reviewed 82 of 91 studies that satisfied the requirements for inclusion in their meta-analysis and reported that a 1°C increase in temperature increased the risk of AKI by 1.2%. A recent case-crossover study conducted in South Korea also showed that the odds ratios of AKI with each 1°C increase in temperature during the summer season were 1.051 with 0 lag days and 1.076 with 2 lag days.25

The proposed mechanism for this association is that heat exposure can induce sweating, thus leading to decreased extracellular fluid, subsequent dehydration, and changes in blood pressure, which can lead to acute or chronic renal disease, particularly when accompanied by overexertion.5 However, evidence of the intermediate mechanism is still theoretical; therefore, more empirical evidence is necessary. Masugata et al26 compared seasonal differences in the eGFR among 102 hypertensive patients with and without CKD and showed that the eGFR was lower during summer, regardless of the CKD status. Ranucci et al27 also calculated the monthly eGFRs of 16 023 patients who underwent heart surgery in Italy and showed that they were low in July and August. Similarly, Barski et al28 conducted a retrospective observational cohort study of all patients older than 65 years with a creatinine level ≤2.0 mg/dL who were hospitalized twice (during summer and winter) between 2010 and 2011; they noted that biochemical indicators representing compromised renal function were more pronounced during summer than during winter among the entire cohort of patients, especially those with hypertension, diabetes, and heart failure. However, these studies did not directly analyze the relationship with temperature; they only showed the distribution according to the season or month. Another study of patients who visited the emergency department showed a significant negative correlation between the daily temperature and eGFR (r = −0.033; P < .001), indicating the possibility of adverse effects with temperature rises.29 These results are consistent with our finding that renal function may deteriorate as the temperature increases to higher than 25°C.

Whereas our study focuses on the short-term effects of high temperatures on estimated eGFR, it is important to consider how these acute impacts can contribute to long-term disease progression. Short-term exposure to high temperatures can lead to immediate physiological stress, such as dehydration and heat stress, which temporarily reduce renal function as evidenced by decreased eGFR levels. These acute episodes, if recurrent, may cause cumulative damage to the kidneys.30 For instance, AKI episodes, which are often precipitated by high temperatures, have been shown to accelerate the progression of CKD into ESRD.31 This is particularly problematic in high-risk populations such as those with pre-existing hypertension or diabetes, who are more vulnerable to temperature-related renal stress.

On the other hand, there have been only few epidemiological studies of the occurrence and exacerbation of kidney disease with exposure to low temperatures, which can cause a delayed normal hemodynamic response, affect the cardiovascular system, and lead to renal hypoperfusion. One study investigated the association between the temperature and dehydration index (blood urea nitrogen-to-creatinine ratio, urine specific gravity, plasma osmotic pressure, and hematocrit) and suggested that the risk of dehydration increases at low and high temperatures; furthermore, dehydration was suggested as a mechanism of the relationship between temperature change and cardiovascular burden.32 During the current study, we observed that renal function may deteriorate as the temperature decreases in a cryogenic environment, which has a temperature lower than −10°C. However, to reflect the low-temperature environment sensed by the human body, additional analyses are needed by applying an apparent temperature that also considers wind speed.

Exposure to extreme temperatures has significant health effects that disproportionately impact socioeconomically vulnerable groups, leading to a vicious cycle where poverty deepens and health conditions worsen. This is consistent with our stratification analysis results: females, those ≥60 years old, and participants with diseases (hypertension, diabetes, or CKD) are more affected by exposure to extreme temperatures. Deteriorating health caused by climate change also diminishes labor productivity, further aggravating the financial hardships of low-income populations.33 Notably, however, many of these negative health impacts are preventable34 through proactive measures such as the assessment and monitoring of vulnerable groups.35 It is particularly important to monitor kidney function in individuals with chronic diseases who work in extreme weather conditions, such as outdoor laborers including agricultural workers or construction workers. This monitoring would be crucial to prevent the progression of kidney disease and the associated loss of productivity. Additionally, further research is necessary to gain a deeper understanding of the clinical, social, and behavioral risk factors that affect individuals vulnerable to occupational exposure to extreme temperatures.

Several limitations in the current study should be considered. First, it was possible to misclassify exposure by using the heat index adjacent to the participants’ residential addresses instead of individual measurements, and by omission of air pressure, cloudiness, and wind speed among weather factors. Additionally, the relationship between temperature and renal function may have been affected by the differences between the indoor and outdoor temperatures. However, individual measurements are challenging to use in large-scale studies such as this one; therefore, the ambient air temperatures at the participants’ residential addresses were used for practicality. Second, bias caused by interindividual variations could be minimized by analyzing repeated measurement data that consider intraindividual correlations; however, unknown factors other than interindividual variations could not be modified due to a lack of information. Third, we could not investigate the seasonal change and the long-term trend of GFR decline to identify the development of clinically significant ESRD among the participants included in the study. Moreover, the single lag effect of renal function by heat index confirmed the intuitive lag pattern but did not explore the cumulative lag effect. It is necessary to explore the cumulative effect of temperature using a distributed lag model in future studies. Fourth, although Seoul St Mary’s Hospital is one of the major hospitals in the capital of South Korea and treats patients from across the country, this was a single-center study; therefore, the generalizability of our findings might be limited, especially in countries with different climates and population characteristics like diet, overall health, and genetics. Fifth, we did not identify all the detailed mechanisms that are important in the effect of temperature on renal function. Further studies should be conducted using blood tests, such as blood urea nitrogen or hematocrit, as an indicator of dehydration or measured blood pressure data.

In conclusion, the results of our study suggest that high and low temperatures affect the renal function of people with chronic diseases. We hope these results will serve as a sound scientific foundation for policy applications for ongoing public health initiatives aimed at preventing heat-related morbidity by identifying those who are more likely to develop or deteriorate renal disease and raise public awareness of the risks associated with climate change.

Supplementary Material

Web_Material_uiae037

Acknowledgments

This research was supported by the research support project of the Big Data Utilization Contest at Seoul St Mary’s Hospital (2020).

Ethics approval

This study was approved by the Institutional Review Board of Seoul St Mary’s Hospital, Catholic University of Korea (approval number: KC20WISI0304).

Author contributions

M.-Y.K. conceptualized and designed the study. M.Y.P. conducted the data analysis. M.-Y.K., J.A., and S.B. contributed to the statistical analyses and validated the results of the data analysis. M.-Y.K. and M.Y.P. wrote initial draft. M.-Y.K., J.A., S.B., B.H.C, J.-P.M., and J.L. participated in the data interpretation, reviewed the initial draft of the manuscript with important intellectual content, and approved the final version of the manuscript.

Funding

None.

Conflicts of interest

None declared.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy or ethical restrictions.
==== Refs
References

1. IPCC . Climate change 2021: The physical science basis. In: Masson-DelmotteV, ZhaiP, PiraniAet al. eds. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press; 2021.
2. Haines A , EbiK. The imperative for climate action to protect health. N Engl J Med. 2019;380 (3 ):263–273. 10.1056/NEJMra1807873 30650330
3. Rocque RJ , BeaudoinC, NdjaboueR, et al. Health effects of climate change: an overview of systematic reviews. BMJ Open. 2021;11 (6 ):e046333. 10.1136/bmjopen-2020-046333
4. Johnson RJ , WesselingC, NewmanLS. Chronic kidney disease of unknown cause in agricultural communities. N Engl J Med. 2019;380 (19 ):1843–1852. 10.1056/NEJMra1813869 31067373
5. Glaser J , LemeryJ, RajagopalanB, et al. Climate change and the emergent epidemic of CKD from heat stress in rural communities: the case for heat stress nephropathy. Clin J Am Soc Nephrol. 2016;11 (8 ):1472–1483. 10.2215/CJN.13841215 27151892
6. de Lorenzo A , LiañoF. High temperatures and nephrology: the climate change problem. Nefrologia. 2017;37 (5 ):492–500. 10.1016/j.nefro.2016.12.008 28946962
7. Sorensen C , Garcia-TrabaninoR. A new era of climate medicine—addressing heat-triggered renal disease. N Engl J Med. 2019;381 (8 ):693–696. 10.1056/NEJMp1907859 31433914
8. Liu J , VargheseBM, HansenA, et al. Hot weather as a risk factor for kidney disease outcomes: a systematic review and meta-analysis of epidemiological evidence. Sci Total Environ. 2021;801 :149806. 10.1016/j.scitotenv.2021.149806 34467930
9. Hill NR , FatobaST, OkeJL, et al. Global prevalence of chronic kidney disease–a systematic review and meta-analysis. PLoS One. 2016;11 (7 ):e0158765. 10.1371/journal.pone.0158765 27383068
10. Foreman KJ , MarquezN, DolgertA, et al. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016–40 for 195 countries and territories. Lancet. 2018;392 (10159 ):2052–2090. 10.1016/S0140-6736(18)31694-5 30340847
11. Essue BM , LabaT-L, KnaulF. Economic burden of chronic ill-health and injuries for households in low-and middle-income countries. In: JamisonDT, GelbandH, HortonSet al. eds. Disease Control Priorities: Improving Health and Reducing Poverty. The World Bank; 2018.
12. Li PK-T , Garcia-GarciaG, LuiS-F, et al. Kidney health for everyone everywhere–from prevention to detection and equitable access to care. Braz J Med Biol Res. 2020;53 (3 ):e9614. 10.1590/1414-431X20209614 32159613
13. Venkatachalam MA , GriffinKA, LanR, GengH, SaikumarP, BidaniAK. Acute kidney injury: a springboard for progression in chronic kidney disease. Am J Physiol Renal Physiol. 2010;298 (5 ):F1078–F1094. 10.1152/ajprenal.00017.2010 20200097
14. Ko SJ , ParkSJ, ChangD-J. Experience of converting clinical data warehouse to common data model and additional data loading. Health Insurance Review & Assessment Service Research. 2021;1 (2 ):179–195. 10.52937/hira.21.1.2.179
15. Diago CAA , SeñarisJAA. Should we pay more attention to low creatinine levels? Endocrinol Diabetes y Nutr (Eng Ed). 2020;67 (7 ):486–492. 10.1016/j.endinu.2019.12.008
16. Levey AS , StevensLA, SchmidCH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150 (9 ):604–612. 10.7326/0003-4819-150-9-200905050-00006 19414839
17. Matsushita K , MahmoodiBK, WoodwardM, et al. Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate. JAMA. 2012;307 (18 ):1941–1951. 10.1001/jama.2012.3954 22570462
18. Rothfusz LP . The Heat Index "Equation". National Weather Service Technical Attachment (SR 90–23). 1990.
19. Anderson GB , BellML, PengRD. Methods to calculate the heat index as an exposure metric in environmental health research. Environ Health Perspect. 2013;121 (10 ):1111–1119. 10.1289/ehp.1206273 23934704
20. Gyorda JA , LekkasD, PriceG, JacobsonNC. Evaluating the impact of mask mandates and political party affiliation on mental health internet search behavior in the United States during the COVID-19 pandemic: generalized additive mixed model framework. J Med Internet Res. 2023;25 :e40308. 10.2196/40308 36735836
21. Wood SN . Generalized Additive Models: An Introduction with R. CRC Press; 2017.
22. Chen T , SarnatSE, GrundsteinAJ, WinquistA, ChangHH. Time-series analysis of heat waves and emergency department visits in Atlanta, 1993 to 2012. Environ Health Perspect. 2017;125 (5 ):057009. 10.1289/EHP44 28599264
23. Basu R , PearsonD, MaligB, BroadwinR, GreenR. The effect of high ambient temperature on emergency room visits. Epidemiology. 2012;23 (6 ):813–820. 10.1097/EDE.0b013e31826b7f97 23007039
24. Gronlund CJ , ZanobettiA, SchwartzJD, WelleniusGA, O’NeillMS. Heat, heat waves, and hospital admissions among the elderly in the United States, 1992–2006. Environ Health Perspect. 2014;122 (11 ):1187–1192. 10.1289/ehp.1206132 24905551
25. Ahn J , BaeS, ChungBH, et al. Association of summer temperatures and acute kidney injury in South Korea: a case-crossover study. Int J Epidemiol. 2022;52 (3 ):774–782. 10.1093/ije/dyac163
26. Masugata H , SendaS, InukaiM, et al. Seasonal variation in estimated glomerular filtration rate based on serum creatinine levels in hypertensive patients. Tohoku J Exp Med. 2011;224 (2 ):137–142. 10.1620/tjem.224.137 21617334
27. Ranucci M , CastelvecchioS, La RovereMT, Surgical and Clinical Outcome Research (SCORE) Group. Renal function changes and seasonal temperature in patients undergoing cardiac surgery. Chronobiol Int. 2014;31 (2 ):175–181. 10.3109/07420528.2013.836533 24164101
28. Barski L , BartalC, SagyI, et al. Seasonal influence on the renal function in hospitalized elderly patients. Eur Geriatr Med. 2015;6 (3 ):232–236. 10.1016/j.eurger.2014.10.007
29. Mahmut T , AbdullahŞ, AvincaÖ, KarakoçY. Effects of average air temperatures on patients' urea, creatinine, eGFR, sodium and potassium levels. Anatolian J Emerg Med. 2020;3 (2 ):47–50
30. Hansson E , GlaserJ, JakobssonK, et al. Pathophysiological mechanisms by which heat stress potentially induces kidney inflammation and chronic kidney disease in sugarcane workers. Nutrients. 2020;12 (6 ):1639. 10.3390/nu12061639 32498242
31. Herath C , JayasumanaC, De SilvaPMC, et al. Kidney diseases in agricultural communities: a case against heat-stress nephropathy. Kidney Int Rep. 2018;3 (2 ):271–280. 10.1016/j.ekir.2017.10.006 29725631
32. Lim Y-H , ParkM-S, KimY, KimH, HongYC. Effects of cold and hot temperature on dehydration: a mechanism of cardiovascular burden. Int J Biometeorol. 2015;59 (8 ):1035–1043. 10.1007/s00484-014-0917-2 25344017
33. Nerbass FB , Pecoits-FilhoR, ClarkWF, SontropJM, McIntyreCW, MoistL. Occupational heat stress and kidney health: from farms to factories. Kidney Int Rep. 2017;2 (6 ):998–1008. 10.1016/j.ekir.2017.08.012 29270511
34. Williams S , NitschkeM, TuckerG, BiP. Extreme heat arrangements in South Australia: an assessment of trigger temperatures. Health Promot J Austr. 2011;22 (4 ):21–27. 10.1071/he11421
35. Borg M , BiP, NitschkeM, WilliamsS, McDonaldS. The impact of daily temperature on renal disease incidence: an ecological study. Environ Health. 2017;16 (1 ):114. 10.1186/s12940-017-0331-4 29078794
