Impact of Heat Stress on Renal Function: A Systematic Literature Review Focusing on Workplace Heat
Vida Rezaei-Hachesu1, Shadi Naderyan Fe’li2, Rajabali Hokmabadi1,3, Meghdad Kazemi4, Farideh Golbabaei5*
1. Ph.D. Student in Occupational Health Engineering, Dept, of Occupational Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
2. Ph.D. Student in Epidemiology, Dept. of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
3. Instructor of Occupational Health Engineering, Dept. of Occupational Health Engineering, School of Public Health, North Khorasan University of Medical Sciences, Bojnurd, Iran.
4. Ph.D. Student in Occupational Health Engineering, Dept, of Occupational Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
5. Professor of Occupational Health Engineering, Dept, of Occupational Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
* Corresponding author: Farideh Golbabaei; E-mail: fgolbabaei@tums.ac.ir
Abstract
Background: There is evidence that exposure to heat stress over time may lead to chronic kidney disease. This study aimed to summarize the evidence on the effects of heat stress on renal function among individuals exposed to occupational heat stress.
Materials and Methods: In this systematic review, all papers on the effect of heat stress on kidney function among workers at any workplace and heat level were included. Reviews, case reports, conference proceedings, letters, editorials, abstracts without full text, in-vitro, and animal studies were excluded. Furthermore, studies conducted on children, general populations, and hospitalized patients, as well as those not measuring heat stress, were also excluded. Medline, Scopus, ISI, and Embase databases were searched from 1st January 1991 to 19th October 2021. Search criteria were prepared by combining an 'exposed population' AND 'exposure' AND 'outcome' keywords. Quality assessment was done using the National Institutes of Health Quality Assessment tool.
Results: A total of 24 articles with 14,282 participants were considered for qualitative synthesis. Although most papers indicated a positive association between heat stress and kidney dysfunction, especially regarding dehydration, the present study found heterogeneous evidence. Glomerular filtration rate, serum creatinine level, and albumin-to-creatinine ratio, due to occupational heat stress, were other markers mentioned in primary studies.
Conclusions: This review highlighted the impact of occupational heat stress on renal function. Among the markers investigated in this review, most studies reported a positive association between occupational heat stress and dehydration.
Keywords: COVID-19, Anxiety, Depression, Insomnia, Health Personnel
Introduction
Heat stress (HS) refers to an aggregation of both the heat produced by the body (metabolic heat) and the heat received from the environment (environmental heat) minus the heat transferred from the body to the environment [1, 2]. Heat exposure can cause a wide range of adverse effects on human health. Heat cramps, heat collapse, heat rashes, heat fatigue, heat exhaustion, heat stroke [3, 4], adverse effects on productivity [5, 6], performance impairment [7, 8], and kidney diseases [9] are among the reported consequences of HS.
Chronic Kidney Disease of non-traditional origin (CKDnt) is a condition not related to traditional risk factors such as diabetes or hypertension [10]. CKDnt is initially asymptomatic but later can lead to end-stage renal disease [11]. There is increasing evidence that HS is associated with a risk for kidney damage in populations at risk of CKDnt [10, 12]. HS can lead to high body temperature and dehydration with subsequent volume depletion, which may cause kidney damage [12, 13]. Heat-exposed workers such as miners and industrial and agricultural workers are prone to HS and subsequently develop CKDnt, because of manual work in hot environments, short breaks, and inadequate fluid intake [10, 14].
Timely and appropriate screening can be used to prevent kidney damage in high-risk populations such as workers exposed to occupational heat. The standard screening tests include the assessment of renal biomarkers such as serum creatinine and albuminuria. Further, urine-specific gravity is an inexpensive test that can accurately detect early kidney injury [13].
It is stated that CKDnt is prevalent mainly in young, male agricultural laborers [15]. Occupational HS has been associated with kidney disease in a longitudinal study that revealed a significant decrease in glomerular filtration rate from pre-harvest to late harvest among migrant and seasonal farmworkers [16]. Sugarcane cutters are also a particular group among other agricultural workers who are more susceptible to developing kidney dysfunction because of working in hot and humid conditions and inadequate fluid intake [5, 15, 17, 18]. Moreover, some non-agricultural occupations carry increased risks [15]. For example, miners have been considered a high-risk population for developing CKDnt since ambient temperatures in the mines can be in excess of more than 30°C wet bulb globe temperature [19]. Construction workers also risk developing kidney damage, as indicated in previous studies [20-22].
On the contrary, in some studies, no statistically significant difference has been observed between workers with and without HS in kidney function biomarkers [23-25].
Controversy can be observed between studies investigating the effect of HS on kidney function. The present review aimed to improve understanding of the renal disorders related to occupational heat exposure. . We attempted to describe and summarize any recently reported evidence of the association between workplace heat and kidney damage. A better understanding of early biomarkers of kidney damage serves to perform preventive and therapeutic interventions and can finally lead to promotion in workers' health.
Materials and Methods
To conduct this systematic review, Preferred Items for Reporting Systematic Reviews and Meta-Analyses (PRISMA) statements were followed [26]. Given the considerable heterogeneity in the included studies, a quantitative meta-analysis was not applicable; hence, a qualitative synthesis was carried out.
Study selection, quality assessment of the included studies, and data extraction were carried out by two independent experts. Disagreements between the two reviewers were resolved through discussion or by the decision of the third expert.
Eligibility Criteria: All papers on the effect of occupational HS on kidney function among workers at any workplace and heat level were included in the research. Study designs included cohort, case-control, cross-sectional, and randomized (trial).
Review articles, case reports, conference proceedings, letters, editorials, abstracts without full text, in-vitro, and animal studies were excluded. Furthermore, studies conducted on children, general populations, and hospitalized patients, as well as those not measuring heat stress, were excluded.
Search Strategy: The Cochrane Library was searched initially to know whether there is any systematic review on the HS effect on renal function. International databases, including Web of Science, Medline (PubMed), EMBASE, and Scopus, were searched by two independent researchers to check any systematic review existence and find the relevant English articles published from 1st January 1991 to the 19th October 2021 without any restriction on study design. The search strategy was based on a combination of free text words (i.e., non-Mesh words or phrases) and medical subject heading (Mesh) such as ("Hot Temperature" [Mesh] OR Heat[Mesh] OR "Extreme Heat" [Mesh] OR "heat stress") AND ("Kidney Diseases" [Mesh] OR hydration OR Dehydration[Mesh] OR "kidney function" OR "kidney function tests" [Mesh] OR "Acute Kidney Injuries" OR "Acute Renal Failure" OR "Chronic kidney disease" OR Urea[Mesh] OR Creatinine[Mesh] OR "Urine specific gravity" OR "Glomerular filtration rate" [Mesh] OR "Blood urea nitrogen" [Mesh]) AND ("occupational exposure" [Mesh] OR "Occupational group" [Mesh] OR "Occupational groups" [Mesh] OR Workplace [Mesh] OR "Work Site" [Mesh] OR "Job Site" [Mesh] OR "Work Locations" [Mesh] OR Worker OR Employee). To avoid missing any relevant papers, the reference lists of all included articles were also searched manually.
Study Selection: To screen the results of the initial systematic search, all articles were exported to the EndNote software (version X8, for Windows, Thomson Reuters, and Philadelphia, PA, USA). In selecting the relevant articles, the terms related to the exposed population, exposure, and outcome were considered. Initially, the duplicates were excluded, and then the remaining articles were screened by title, abstract, and full text based on the eligibility criteria. In the cases where two similar articles were from a single study, we included the article that best suits our review objectives and eligibility criteria.
Quality Assessment of the Evidence: Quality assessment of the included studies was done using the National Institutes of Health (NIH) Quality Assessment tool for observational and interventional studies [27]. This tool includes items for evaluating the potential sources of bias (e.g., patient selection, performance, attrition, and detection), confounding factors, study power, the strength of causality in the association between interventions and outcomes, and other factors.
The tool items were evaluated as "yes," "no," "cannot determine," "not applicable," or "not reported." Later, studies were categorized (good, fair, or poor) in terms of their quality. In this regard, a study with 'good' quality has the least risk of bias. A fair-quality study is susceptible to some bias. A study with poor quality indicates a significant risk of bias. Arbitrary cut-off points were used to determine 'good' and 'fair' quality studies; articles that gained 60% of items as "yes" were defined to have good quality, and the others reached less than 60% of "yes" were evaluated as acceptable quality.
Data Collection Process: In order to extract the relevant data from each included primary study, a data extraction sheet was prepared in Excel software containing the variables of the first author's name, year of publication, country of study, study design, outcome, and exposure, sample size, age, gender, and occupation.
In the cases where some of the required information was missing, the corresponding author of that article was contacted via e-mail.
Results
Study Selection: The initial search retrieved 5,105 articles. After removing the duplicates, 4,906 papers remained. After screening the titles and abstracts, 4,805 articles were excluded due to a lack of relevance. As a result, 101 full-text articles were assessed for eligibility criteria. Subsequently, 77 articles were excluded due to not meeting the inclusion criteria. The manual reference checking of the searched articles yielded 16 papers. Finally, 24 articles were retained in the systematic review. The study selection flow diagram is represented in Figure 1.
Study Characteristics: Overall, 24 studies with 14,282 participants were investigated in this review. The selected studies included one non-randomized intervention, one prospective cohort study, one longitudinal study, and the rest were cross-sectional. Table 1 represents the characteristics of the included studies. Regarding the participants' gender, eight studies were conducted on both male and female workers, one study was performed only on women, and the remaining studies were carried out only on men.
In the studied research, exposure to HS was evaluated based on the temperature measurement and calculating thermal or physiological parameters; for example, core and skin temperature and heart rate were used to calculate the PSI index. Also, atmospheric parameters were required for calculating the WBGT and TWL indicators (dry bulb temperature, wet bulb temperature, radiant temperature, air velocity or wind speed, and atmospheric pressure).
Different studies evaluated various biomarkers as indicators of renal dysfunction. The most-reported biomarkers were urinary specific gravity (USG), estimated glomerular filtration rate (eGFR), serum creatinine (SCr), serum uric acid (SUA), blood urea nitrogen (BUN), and albumin-to-creatinine ratio (ACR), respectively.
Risk of Bias within Studies: According to the NIH Quality Assessment tool, 19 studies had good quality, three were fair in quality, and two had poor quality.
Table 1. Characteristics of the included studies
First author
(publication date)[reference] |
Country |
Sample size |
Occupation |
Gender |
Age
(mean±sd) |
Definition of
exposure |
Exposure (heat) |
Outcome |
Quality rating |
Cross-sectional studies |
|
|
|
|
|
|
|
|
Atan
(2005)[23] |
Brazil |
10,326 |
Steel industry workers |
M |
35.02±6.43 |
Air temperature |
Indoor |
SUA, SCr |
Good |
Nerbass
(2019)[24] |
Brazil |
31 |
Metallurgical workers |
M |
31 (26–39)† |
WBGT |
Indoor |
eGFR, SUA, SCr, ACR |
Good |
Moyce
(2017)[29] |
USA |
283 |
Agricultural workers |
Both |
38.6±12.4 |
PSI |
Outdoor |
eGFR, SCr |
Good |
Mix
(2017)[28] |
USA |
192 |
Agricultural workers |
Both |
38.0±8.2 |
HI |
Outdoor |
eGFR, SCr, USG, BUN |
Good |
Brake
(2003)[30] |
Australia |
39 |
Mine workers |
M |
35.0±8.0 |
WBGT and TWL |
Indoor |
USG |
Good |
Garcia-Trabanino
(2015)[17] |
El Salvador |
189 |
Sugarcane cutters |
Both |
30±7.33 |
WBGT and HI |
Outdoor |
eGFR, SUA, SCr, USG, BUN |
Good |
Meade
(2015)[40] |
USA |
32 |
Electrical utility workers |
M |
36.0±10.0 |
PSI |
Outdoor and
indoor |
USG |
Fair |
Spector
(2018)[36] |
USA |
46 |
Agricultural workers |
Both |
39.1±14.1 |
WBGT |
Outdoor |
USG |
Good |
Peiffer
(2012)[19] |
Australia |
77 |
Mine workers |
M |
34.88±10.97 |
TWL |
Outdoor |
USG |
Good |
Piil
(2018)[35] |
Denmark, Cyprus, Greece and Spain |
139 |
Manufacturing, agricultural, tourism, and construction workers; police officers |
M |
30.0±2.0 |
WBGT |
Outdoor and
indoor |
USG |
Good |
Paula Santos
(2014)[39] |
Brazil |
28 |
Sugarcane workers |
M |
24.9±5.6 |
Air temperature |
Outdoor |
eGFR, SCr |
Fair |
Brearley
(2016)[25] |
Australia |
20 |
Electric utility workers |
M |
31.5±10.0 |
WBGT and PSI |
Outdoor |
USG |
Good |
Farshad
(2014)[22] |
Iran |
60 |
Construction workers |
M |
30.8±6.59 |
WBGT and TWL |
Outdoor |
USG |
Good |
Ueno
(2018)[21] |
Japan |
23 |
Construction workers |
M |
41.0±15.0 |
WBGT |
Outdoor and indoor |
USG |
Fair |
Hunt
(2014)[34] |
Australia |
15 |
Mine workers |
Both |
36.7±9.7 |
WBGT |
Outdoor |
USG |
Poor |
Dang
(2014)[33] |
USA |
60 |
Aluminum
potroom workers |
Both |
32* |
WBGT |
Indoor |
USG, BUN, SCr |
Good |
Wagoner
(2020)[37] |
Mexico |
28 |
Agricultural workers |
M |
NR |
WBGT |
Outdoor |
USG |
Good |
Al-Bouwarthan
(2020)[31] |
Saudi Arabia |
23 |
Construction workers |
M |
42.7±8.8 |
WBGT |
Outdoor and indoor |
USG |
Good |
Venugopal
(2020)[12] |
India |
1842 |
8 work sectors |
Both |
36.8±12.6 |
WBGT |
Outdoor and indoor |
USG, eGFR |
Good |
Nainggolan
(2021)[13] |
Indonesia |
119 |
Shoe factory workers |
F |
38 (31–50)† |
WBGT |
Indoor |
eGFR, USG |
Good |
Butler-Dawson
(2021)[14] |
Guatemala |
107 |
Sugarcane cutters |
M |
28 (9)† |
WBGT |
Outdoor |
USG, SCr |
Good |
Longitudinal studies |
|
|
|
|
|
|
|
|
Sorensen
(2019)[18] |
Guatemala |
105 |
Sugarcane workers |
M |
30.1![](file:///C:UsersMISALI~1AppDataLocalTempmsohtmlclip1) |