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. 2026 Jun 11;134(4):485–493. doi: 10.1021/EHP.6c00520

Household Fuel Use and Kidney Disease-Related Mortality: The Golestan Cohort Study

Michele Sassano †, Monireh Sadat Seyyedsalehi †,‡, Sudabeh Alatab §, Hossein Poustchi ∥, Mahdi Sheikh ⊥, Arash Etemadi #, Reza Malekzadeh §, Paolo Boffetta †,¶,■,*
PMCID: PMC13445265  PMID: 42564593

Abstract

BACKGROUND: A large proportion of the global population uses solid fuels for household purposes, and limited evidence from previous studies suggests that it might be associated with reduced renal function. OBJECTIVES: To investigate the association between household use of different types of fuels and kidney disease-related mortality. METHODS: We analyzed data from the Golestan Cohort Study, a population-based prospective cohort study conducted in northeastern Iran, with 50 045 individuals aged 40–75 years enrolled in the period 2004–2008 and followed through April 2023. Information about household fuel use was collected using validated questionnaires. We estimated adjusted hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) using Cox proportional hazards models. The outcome of interest was death due to any kidney disease, excluding kidney cancer (ICD-10 codes: N00–N19 and N25–N29). RESULTS: During 724,063.62 person-years of follow-up, 262 participants died due to kidney disease. The risk of kidney disease-related mortality was higher with increasing duration of biomass use for cooking or house heating (HR for every 10-year increase: 1.20; 95% CI: 1.04–1.37), while it was not associated with increased duration of using kerosene (10-y HR: 1.09; 95% CI: 0.95–1.24) or gas (10-y HR: 1.00; 95% CI: 0.86–1.16). Estimates for lifetime duration of fuel burning for both cooking and house heating (exclusive fuel use) did not differ according to whether used heating stoves were chimney-equipped or not for kerosene, while they differed for biomass (10-y HR, chimney-equipped: 1.06 [95% CI: 0.95–1.18]; 10-y HR, not chimney-equipped: 1.19 [95% CI: 1.06–1.34]; P difference = 0.025). DISCUSSION: The findings of our study suggest that burning biomass for household purposes under poor ventilating conditions is associated with kidney disease-related mortality.

Background

Kidney disease, and in particular chronic kidney disease (CKD), is a leading cause of disease burden and mortality worldwide. Indeed, it affects more than 850 million people worldwide, and it has been estimated that more than 10% of global population live with prevalent CKD. CKD also led to approximately 1.2 million deaths in 2017, further highlighting the public health relevance of these conditions. In addition, CKD has also been predicted to become the fifth leading cause of death by 2040 with more than 3 million expected deaths. Type 2 diabetes mellitus and hypertension are the leading causes of kidney disease, although it has also been linked to other risk factors, such as smoking, lack of physical activity, and exposure to environmental factors, including air pollution.

Household air pollution is a relevant issue in low- and middle-income countries, particularly in rural areas. It is primarily caused by use of inefficient and polluting fuels and technologies, such as open fires and inefficient and poorly ventilated stoves, which generate a range of health-damaging pollutants. − Approximately 2.3 billion people worldwide have been estimated to use solid fuels like wood, crop waste, charcoal, coal, dung, or kerosene/paraffin for cooking and heating. − Chronic exposure to particulate matter and other pollutants, such as polycyclic aromatic hydrocarbons (PAHs), which can be released from burning such types of fuel, can result in health complications, including cardiopulmonary disease and cancer. −

A recent cohort study showed an inverse association between use of solid fuels for cooking purposes, including coal and biomass, and renal function. This finding, however, was not fully confirmed by a cross-sectional study conducted later, suggesting the need for further investigation in this area. In addition, no previous study evaluated use of individual fuel types in detail, and evidence on whether the observed association between solid fuel use and kidney disease is modified by poor ventilating conditions, which might significantly influence individuals’ exposure to indoor pollutants, is also lacking. Also, evidence on the association between kerosene use, which is very common worldwide, − and kidney disease is lacking. Eventually, to the best of our knowledge, no previous reports investigating the potential association between household fuel use and kidney disease-related mortality have been published so far, except for a previous report focused on coal use only with a limited number of cases of deaths due to kidney disease.

The Golestan Cohort Study (GCS) enrolled approximately 50,000 adults from the mostly rural population of Golestan in Iran, and within this study, detailed data on the use of different fuels for household purposes, such as kerosene and biomass, were collected. Hence, we aimed to evaluate the association between household fuel use and kidney-disease-related mortality within this cohort.

Methods

Study Population

The GCS, described in detail elsewhere, is a population-based prospective cohort study conducted in the region of Golestan in northeastern Iran. In particular, a total of 50,045 individuals aged 40–75 years were enrolled between January 2004 and June 2008 from urban and rural areas, represented by Gonbad City for the former and by villages in Gonbad, Kalaleh, and Aq-Qala counties for the latter. From an initial number of 16,599 potential urban participants randomly selected using systematic clustering based on household number, 10,032 agreed to participate, with participation rates of ∼70% among women and 50% among men. As for rural areas, all residents from the aforementioned villages who were eligible based on their age (n = 53,121) were invited to participate by contacting them at their home with support from staff of primary health care centers present in each group of villages, and 40,013 of them agreed, with final participation rates of 84% among women and 70% among men.

Exclusion criteria of the GCS were a history of an upper gastrointestinal cancer, temporary resident status, and lack of will or ability to participate for any reason. All enrolled participants provided written informed consent to participate in this study. The GCS was approved by the institutional review boards of the Digestive Disease Research Institute of Tehran University of Medical Sciences, the International Agency for Research on Cancer, and the U.S. National Cancer Institute.

Data Collection

At enrollment, trained professionals interviewed participants using two validated structured questionnaires, which included a general questionnaire , and a food-frequency questionnaire. The former was focused on sociodemographic characteristics and lifestyle and contained questions about demographics, education, history of diseases, cigarette smoking, opium consumption, alcohol use, and socioeconomic status. Education was categorized based on the years of formal education (none, up to 8 years, which is equivalent to junior high school, high school, and university level). In addition, we computed cumulative cigarette smoking in pack-years (with a pack corresponding to 20 cigarettes) by multiplying the calculated number of packs smoked per day by the duration of smoking in years. As an indicator of socioeconomic status, we used quartiles of a wealth score that was computed using multiple correspondence analysis according to the following items: property ownership; structure and size of the house; vehicle ownership; and having a television, refrigerator, freezer, vacuum, or washing machine at home. Further details on methods used for estimation of the wealth score can be found elsewhere.

At enrollment, participants also underwent anthropometric measurements. They were weighed by trained professionals while wearing light clothes and using a standard scale. Participants also had their height measured in the full upright position, without wearing shoes, by the same professionals. Hence, we computed body mass index by dividing participants’ weight in kilograms by their squared height in meters.

Fuel Use Information

The general questionnaire included questions regarding fuel use for both cooking and heating purposes. Participants were asked about the type of fuel used in their household during their entire life through separate items for cooking and house heating in the questionnaire. The fuels that the questionnaire inquired about included natural gas (liquefied petroleum gas), kerosene, and biomass (wood and animal dung), which represented those available in the study area. For all of these types of fuel, participants were asked to report their age when the use of the fuel was started and stopped (if applicable) in their household. In addition, participants were also asked whether heating stoves used in their household had chimneys or not, while this was not asked for stoves used for cooking, since chimney-equipped cooking stoves were not available in the study area.

Follow-Up and Outcome Ascertainment

Detailed follow-up methods of the GCS were reported elsewhere. , Briefly, all study participants have been followed since enrollment through direct telephone contact every 12 months to ascertain their vital and health status, or by contacting family members, friends, or local health workers whenever study participants themselves could not be reached. The proportion of participants lost to follow-up has been 1.2% so far. If a case of death is reported, a trained general practitioner visits the deceased study participant’s family members or primary care givers to complete a verbal autopsy interview using a previously validated questionnaire. Death certificates and all available medical documents are also collected. Hence, all available documentation, including death certificates, medical records, and the verbal autopsy information, is independently reviewed by two medical internists to determine the cause of death, which is coded according to the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10). Whenever disagreement occurs among the two internists, a third expert and more experienced internist reviews all available documents, as well as the initial diagnosis proposed by the two internists, and determines the cause of death. The cause of death is considered “unknown” whenever a diagnosis cannot be made based on available information. The outcome of interest of the current analysis was represented by death due to kidney disease, excluding kidney cancer (ICD-10 codes: N00–N19, N25–N29).

Statistical Analysis

We used Cox proportional hazards regression models to estimate hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) for the association between household fuel use and kidney disease-related mortality. Age was used as the time scale, with the entry time being represented by the age at enrollment. The exit time was represented by the age at death (due to kidney disease or to any other causes) or the age at the last follow-up, whichever occurred first, through April 2023. Analyses were adjusted for the following potential confounders, which were chosen according to previous clinical knowledge and based on modification of the estimated HR by at least 10% when including the covariate under consideration in the model: sex (male, female), age (<45, 45–54, 55–64, ≥65 years), self-reported ethnicity (Turkman, Non-Turkman), residence (urban, rural), education (no education, up to 8 years, high school, university), wealth score (quartiles), regular alcohol drinking (never, ever), body mass index (according to WHO classification: underweight [<18.5 kg/m2], normal weight [18.5–24.9 kg/m2], overweight [25–29.9 kg/m2], obese [(≥30 kg/m2]), cigarette smoking (never smoker, quartiles of pack years), opium consumption (never, ever), self-reported diagnosis of diabetes (yes, no), self-reported diagnosis of hypertension (yes, no). − As for ethnicity, in particular, it was included in the regression model to take into account for potential culture-related differences in fuel use, as well as potential disparities in access to care. We also considered additional adjustment for self-reported aspirin use at baseline, but this variable was not included in the final regression model since results did not change.

The assumption of proportionality of hazards was evaluated based on Schoenfeld residuals, showing no violation of the assumption for the main exposures of interest. Occasional evidence of nonproportionality of hazards for other covariates included in the models was dealt with by treating their coefficients as time varying, hence allowing the interaction between the covariate itself and time.

Study participants with missing values for the main exposures of interest or for any of the covariates included in the regression models were excluded from the analyses.

We considered both any-purpose use (i.e., for cooking or heating, even if with combined use of another fuel at the same time) and exclusive use (i.e., use of a specific type of fuel only, which was used both for cooking and heating purposes, over a period of time) of fuels in the analysis. First, we computed the lifelong duration of use for each fuel type in years for cooking or for house heating separately according to the starting and stopping ages reported by participants. Hence, we calculated the duration of use of the different fuels for any purposes, regardless of whether they were used for cooking or for house heating. Instead, we calculated the duration of exclusive use as the period during which the fuel was used for both cooking and house heating. Hence, we computed tertiles of durations among ever users for both types of variables (i.e., use for any purpose and exclusive use) to evaluate their associations with kidney-disease-related mortality compared with never use of the corresponding fuel, and we assessed linear trends by including these categorical variables as continuous in the models. Additionally, for both the duration of fuel use for any purpose and the duration of exclusive fuel use, we evaluated the risk of death due to kidney disease associated with a 10-year increase in duration. For analyses on the duration of exclusive fuel use, we also considered in the analysis the type of stoves used for heating purposes (with chimney/without chimney), and we used Wald tests to assess whether risk estimates for the duration of exclusive use of the different types of fuels differed according to the type of heating stove. In the analysis on the duration of use of the different fuels for any purpose, we first carried out the analysis by considering one type of fuel at a time, and then we repeated it by including all types of fuel in the same model. The latter option was used for the analysis of the duration of exclusive use of the different fuels considered.

We carried out stratified analyses according to participants’ sex (male, female), age at enrollment (<50 years, ≥50 years), residence (urban, rural), education (no education, any formal education), wealth score (<median, ≥median), cigarette smoking status (never, ever), self-reported diagnosis of hypertension (negative, positive), and self-reported diagnosis of diabetes (negative, positive) for the association between a 10-year increase in duration of use of the different fuels and kidney disease-related mortality. The choice of variables to stratify for was based on their potential influence on the outcome, the exposure, or both. − ,,− Statistical heterogeneity between strata of considered participants’ characteristics was assessed using likelihood ratio tests comparing the models with inclusion or exclusion of the interaction term between the exposure of interest and the covariate under consideration.

Eventually, we repeated the analysis by dropping the first year of follow-up. This approach is based on the inclusion of participants with a follow-up duration longer than one year only, hence allowing an appropriate time interval between the exposure and the outcome for the latter to occur.

All analyses were carried out using Stata software version 18 (StataCorp LLC, College Station, TX)

Results

Among 50,045 individuals enrolled in the GCS, 63 (0.1%) had no follow-up data (i.e., lost immediately after enrollment), leaving a total of 49,982 participants, 49,972 of whom reported information on household fuel use. Among them, 49,792 individuals also had available data on covariates included in the regression models and were thus included in the analysis. Their main characteristics according to lifetime duration of use of biomass fuel for any purpose are reported in Table , and further details can be found in Tables S1–S7. Participants in the highest tertile of duration of biomass use were more commonly male, older, and Turkmen compared with never users or those with lower durations of use, and higher proportions of them also lived in rural areas and had lower levels of education and wealth score. They also reported higher levels of cigarette smoking and were more frequently opium users, too. During a follow-up period with a median duration of 15.9 (interquartile range: 1.5) years and a total of 724,063.62 person-years, 226 cases of death due to kidney disease occurred, and 166 (73.5%) of them were due to chronic kidney disease (Table S8).

1. Main Study Participants’ Characteristics According to Lifetime Duration of Biomass Fuel Use for Any Purpose (i.e., Cooking or House Heating), Golestan Cohort Study.

  Lifetime duration of biomass fuel use
Baseline characteristics Never use Tertile 1 (≤16.00 years) Tertile 2 (16.01–27.00 years) Tertile 3 (>27.00 years)
  (n = 5,053) (n = 16,043) (n = 14,030) (n = 14,666)
  n (%) n (%) n (%) n (%)
Sex        
Male 1,425 (28.2) 6,640 (41.4) 5,950 (42.4) 7,089 (48.3)
Female 3,628 (71.8) 9,403 (58.6) 8,080 (57.6) 7,577 (51.7)
Age, years        
<45 2,592 (51.3) 6,119 (38.1) 2,712 (19.3) 1,517 (10.3)
45–54 2,035 (40.3) 7,104 (44.3) 6,972 (49.7) 4,208 (28.7)
55–64 343 (6.8) 2,250 (14.0) 3,247 (23.1) 5,174 (35.3)
≥65 83 (1.6) 570 (3.6) 1,099 (7.8) 3,767 (25.7)
Ethnicity        
Turkman 3,121 (61.8) 11,784 (73.5) 10,406 (74.2) 11,848 (80.8)
Non-Turkman 1,932 (38.2) 4,259 (26.5) 3,624 (25.8) 2,818 (19.2)
Residence        
Urban 2,288 (45.3) 4,313 (26.9) 2,364 (16.8) 924 (6.3)
Rural 2,765 (54.7) 11,730 (73.1) 11,666 (83.2) 13,742 (93.7)
Education        
No education 2,356 (46.6) 9,222 (57.5) 10,503 (74.9) 12,882 (87.8)
Up to 8 years 1,631 (32.3) 4,695 (29.3) 2,787 (19.9) 1,532 (10.4)
High school 824 (16.3) 1,535 (9.6) 553 (3.9) 213 (1.5)
University 242 (4.8) 591 (3.7) 187 (1.3) 39 (0.3)
Wealth score        
Quartile 1 (lowest) 680 (13.5) 2979 (18.6) 3913 (27.9) 6300 (43.0)
Quartile 2 759 (15.0) 3031 (18.9) 3161 (22.5) 3500 (23.9)
Quartile 3 1352 (26.8) 4692 (29.2) 3926 (28.0) 3180 (21.7)
Quartile 4 (highest) 2262 (44.8) 5341 (33.3) 3030 (21.6) 1686 (11.5)
Regular alcohol drinking        
Never 4,829 (95.6) 15,283 (95.3) 13,581 (96.8) 14,388 (98.1)
Ever 224 (4.4) 760 (4.7) 449 (3.2) 278 (1.9)
Body mass index        
Underweight (<18.5 kg/m2) 145 (2.9) 638 (4.0) 671 (4.8) 941 (6.4)
Normal weight (18.5–24.9 kg/m2) 1,369 (27.1) 5,013 (31.2) 5,036 (35.9) 6,417 (43.8)
Overweight (25–29.9 kg/m2) 1,817 (36.0) 5,751 (35.9) 4,725 (33.7) 4,594 (31.3)
Obese (≥30 kg/m2) 1,722 (34.1) 4,641 (28.9) 3,598 (25.6) 2,714 (18.5)
Cigarette smoking, pack-years        
Never smoker 4,393 (86.9) 13,221 (82.4) 11,597 (82.7) 12,025 (82.0)
Quartile 1 (<3.5) 177 (3.5) 720 (4.5) 596 (4.2) 650 (4.4)
Quartile 2 (3.5–11.6) 175 (3.5) 776 (4.8) 582 (4.2) 606 (4.1)
Quartile 3 (11.7–25.0) 211 (4.2) 753 (4.7) 663 (4.7) 587 (4.0)
Quartile 4 (>25.0) 97 (1.9) 573 (3.6) 592 (4.2) 798 (5.4)
Opium consumption        
Never 4,567 (90.4) 13,696 (85.4) 11,524 (82.1) 11,568 (78.9)
Ever 486 (9.6) 2,347 (14.6) 2,506 (17.9) 3,098 (21.1)
Self-reported history of diabetes        
Yes 320 (6.3) 1,091 (6.8) 1,058 (7.5) 948 (6.5)
No 4,733 (93.7) 14,952 (93.2) 12,972 (92.5) 13,718 (93.5)
Self-reported history of hypertension        
Yes 713 (14.1) 2,650 (16.5) 2,875 (20.5) 3,596 (24.5)
No 4,340 (85.9) 13,393 (83.5) 11,155 (79.5) 11,070 (75.5)
Self-reported history of heart disease        
Yes 186 (3.7) 834 (5.2) 944 (6.7) 1,065 (7.3)
No 4,867 (96.3) 15,208 (94.8) 13,085 (93.3) 13,601 (92.7)
Self-reported history of stroke        
Yes 19 (0.4) 125 (0.8) 119 (0.8) 154 (1.1)
No 5,034 (99.6) 15,918 (99.2) 13,911 (99.2) 14,512 (98.9)
Self-reported history of cancer        
Yes 17 (0.3) 44 (0.3) 53 (0.4) 39 (0.3)
No 5,036 (99.7) 15,999 (99.7) 13,977 (99.6) 14,627 (99.7)
Lifetime duration of gas fuel use for any purpose        
Never use 26 (0.5) 40 (0.3) 51 (0.4) 320 (2.2)
Tertile 1 (≤25.63 years) 490 (9.7) 2,293 (14.3) 4,738 (33.8) 8,941 (61.0)
Tertile 2 (25.64–32.00 years) 1,042 (20.6) 6,247 (38.9) 5,813 (41.4) 3,842 (26.2)
Tertile 3 (>32.00 years) 3,495 (69.2) 7,463 (46.5) 3,428 (24.4) 1,563 (10.7)
Lifetime duration of kerosene fuel use for any purpose        
Never use 13 (0.3) 5 (0.0) 24 (0.2) 371 (2.5)
Tertile 1 (≤26.00 years) 260 (5.1) 2,970 (18.5) 5,309 (37.8) 8,551 (58.3)
Tertile 2 (26.01–34.00 years) 1,106 (21.9) 6,156 (38.4) 5,285 (37.7) 3,440 (23.5)
Tertile 3 (>34.00 years) 3,674 (72.7) 6,912 (43.1) 3,412 (24.3) 2,304 (15.7)
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The HRs for the highest tertile of lifetime duration of fuel use for any purpose compared with never use were 0.48 (95% CI: 0.19, 1.23), 0.68 (95% CI: 0.21, 2.17), and 2.18 (95% CI: 1.00, 4.75) for natural gas, kerosene, and biomass, respectively. Those associated with a 10-year increase in the duration of use for any purpose were 0.94 (95% CI: 0.83, 1.06) for gas, 0.97 (95% CI: 0.87, 1.08) for kerosene, and 1.13 (95% CI: 1.02, 1.26) for biomass. The inclusion of all considered types of fuels in the model at the same time did not substantially modify the results (Table ). Similarly, estimates did not change after dropping the first year of follow-up (Table S9).

2. Association between Lifetime Duration of Fuel Use for Any Purpose (i.e., Cooking or House Heating) and Kidney Disease-Related Mortality, Golestan Cohort Study .

Fuel type n cases/n total (226/49,792) HR (95% CI) HR (95% CI)
Gas      
Never use 5/437 1.00 1.00
Tertile 1 (≤25.63 years) 65/16,462 0.49 (0.19, 1.22) 0.43 (0.16, 1.16)
Tertile 2 (25.64–32.00 years) 80/16,944 0.55 (0.22, 1.39) 0.55 (0.21, 1.50)
Tertile 3 (>32.00 years) 76/15,949 0.48 (0.19, 1.23) 0.53 (0.19, 1.46)
p for trend   0.675 0.677
Per 10-year increase   0.94 (0.83, 1.06) 1.00 (0.86, 1.16)
Kerosene      
Never use 3/413 1.00 1.00
Tertile 1 (≤26.00 years) 76/17,090 0.79 (0.25, 2.53) 1.08 (0.31, 3.78)
Tertile 2 (26.01–34.00 years) 69/15,987 0.74 (0.23, 2.36) 0.98 (0.28, 3.47)
Tertile 3 (>34.00 years) 78/16,302 0.68 (0.21, 2.17) 1.05 (0.30, 3.75)
p for trend   0.306 0.984
Per 10-year increase   0.97 (0.87, 1.08) 1.09 (0.95, 1.24)
Biomass      
Never use 8/5,053 1.00 1.00
Tertile 1 (≤16.00 years) 51/16,043 1.64 (0.77, 3.48) 1.66 (0.77, 3.56)
Tertile 2 (16.01–27.00 years) 62/14,030 1.81 (0.85, 3.88) 1.90 (0.86, 4.19)
Tertile 3 (>27.00 years) 105/14,666 2.18 (1.00, 4.75) 2.39 (1.01, 5.66)
p for trend   0.042 0.064
Per 10-year increase   1.13 (1.02, 1.26) 1.20 (1.04, 1.37)
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a

HR: hazard ratio, CI: confidence interval.

b

Estimated using Cox proportional hazards regression model adjusted for sex (male, female), age (<45, 45–54, 55–64, ≥65 years), ethnicity (Turkman, Non-Turkman), residence (urban, rural), education (no education, up to 8 years, high school, university), wealth score (quartiles), regular alcohol drinking (never, ever), body mass index (underweight, normal weight, overweight, obese), cigarette smoking (never smoker, quartiles of pack years), opium consumption (never, ever), self-reported history of diabetes (yes, no), self-reported history of hypertension (yes, no).

c

Estimated using Cox proportional hazards regression model. All types of fuels included in the model at the same time. Estimates are further adjusted for sex (male, female), age (<45, 45–54, 55–64, ≥65 years), ethnicity (Turkman, Non-Turkman), residence (urban, rural), education (no education, up to 8 years, high school, university), wealth score (quartiles), regular alcohol drinking (never, ever), body mass index (underweight, normal weight, overweight, obese), cigarette smoking (never smoker, quartiles of pack years), opium consumption (never, ever), self-reported history of diabetes (yes, no), self-reported history of hypertension (yes, no).

d

Wald test for the coefficient for the variable representing categories of duration of fuel use was included as a continuous variable in the Cox regression model.

Analyses on the overall duration of exclusive fuel use, regardless of the types of heating stoves used, showed no association between the different types of fuels and kidney disease-related mortality (Table ), with HRs associated with each 10-year increase in duration use of 0.89 (95% CI: 0.57, 1.38) for gas, 1.03 (95% CI: 0.92, 1.15) for kerosene, and 1.10 (95% CI: 0.98, 1.22) for biomass. When considering whether heating stoves were equipped with chimneys or not, results were not substantially different between the two for kerosene, which showed an HR of 1.02 (95% CI: 0.89, 1.16) associated with a 10-year increase in duration of use with heating stoves without chimneys and 1.06 (95% CI: 0.91, 1.23) with chimneys. Conversely, we found no association between exclusive duration of biomass use with chimney-equipped heating stoves and kidney disease-related mortality (Table ) and a positive association between the highest tertile of duration of exclusive biomass use with heating stoves not equipped with chimneys (HR: 1.81; 95% CI: 1.12, 2.91). Similarly, a 10-year increase in the duration of exclusive biomass use with heating stoves without chimneys was also positively associated with kidney disease-related mortality (HR: 1.19; 95% CI: 1.06, 1.34). Estimates differed according to whether used heating stoves were chimney-equipped or not for biomass but not for natural gas and kerosene (Table ). Furthermore, removal of the first year of follow-up did not substantially modify the results (Table S10).

3. Association between Lifetime Duration of Exclusive Fuel Use (i.e., for Both Cooking and House Heating) and Kidney Disease-Related Mortality, Golestan Cohort Study .

        Using chimney-equipped heating stoves
  Overall
 Yes
 No
  
Fuel type n cases/n total Duration, years HR (95% CI) n cases/n total Duration, years HR (95% CI) n cases/n total Duration, years HR (95% CI) P for the difference
Gas                    
Never use 150/32,544   1.00 150/32,544   1.00     -  
Tertile 1 22/6,403 ≤4.00 0.78 (0.49, 1.24) 22/6,403 ≤4.00 0.81 (0.51, 1.30)     -  
Tertile 2 30/5,864 4.01–12.00 0.77 (0.35, 1.69) 30/5,864 4.01–12.00 0.83 (0.38, 1.81)     -  
Tertile 3 24/4,981 >12.00 0.71 (0.31, 1.63) 24/4,981 >12.00 0.76 (0.33, 1.75)     -  
p for trend     0.235     0.293        
Per 10-year increase     0.89 (0.57, 1.38)     0.90 (0.58, 1.40)     -  
Kerosene                    
Never use 153/32,856   1.00 167/36,690   1.00 191/42,057   1.00 0.926
Tertile 1 18/6,558 ≤10.00 0.56 (0.34, 0.92) 15/4,625 ≤10.00 0.61 (0.36, 1.05) 6/2,789 ≤13.00 0.59 (0.26, 1.35)  
Tertile 2 20/5,266 10.01–20.00 0.78 (0.48, 1.26) 14/4,228 10.01–22.00 0.68 (0.39, 1.19) 9/2,441 13.01–25.00 0.83 (0.42, 1.64)  
Tertile 3 35/5,112 >20.00 1.16 (0.80, 1.70) 30/4,249 >22.00 1.23 (0.75, 2.02) 20/2,505 >25.00 1.17 (0.64, 2.16)  
p for trend     0.889     0.949     0.289  
Per 10-year increase     1.03 (0.92, 1.15)     1.02 (0.89, 1.16)     1.06 (0.91, 1.23) 0.764
Biomass                    
Never use 16/6,471   1.00 58/13,143   1.00 143/37,232   1.00 0.017
Tertile 1 50/14,869 ≤15.00 1.17 (0.66, 2.08) 62/16,050 ≤15.00 1.05 (0.71, 1.55) 23/5,338 ≤15.00 0.91 (0.57, 1.46)  
Tertile 2 65/14,306 15.01–26.00 1.20 (0.67, 2.15) 41/8,841 15.01–22.00 1.19 (0.75, 1.89) 18/3,136 15.01–24.00 1.10 (0.64, 1.89)  
Tertile 3 95/14,146 >26.00 1.19 (0.65, 2.21) 65/11,758 >22.00 1.07 (0.67, 1.72) 42/4,086 >24.00 1.81 (1.12, 2.91)  
p for trend     0.533     0.366     0.017  
Per 10-year increase     1.10 (0.98, 1.22)     1.06 (0.95, 1.18)     1.19 (1.06, 1.34) 0.025
Open in a new tab
a

HR: hazard ratio, CI: confidence interval.

b

Estimated using Cox proportional hazards regression model. The model included sex (male, female), age (<45, 45–54, 55–64, ≥65 years), ethnicity (Turkman, Non-Turkman), residence (urban, rural), education (no education, up to 8 years, high school, university), wealth score (quartiles), regular alcohol drinking (never, ever), body mass index (underweight, normal weight, overweight, obese), cigarette smoking (never smoker, quartiles of pack years), opium consumption (never, ever), self-reported history of diabetes (yes, no), self-reported history of hypertension (yes, no), duration of exclusive gas burning (continuous), duration of exclusive kerosene burning, and duration of exclusive biomass burning (continuous).

c

Estimated using Cox proportional hazards regression model. The model included sex (male, female), age (<45, 45–54, 55–64, ≥65 years), ethnicity (Turkman, Non-Turkman), residence (urban, rural), education (no education, up to 8 years, high school, university), wealth score (quartiles), regular alcohol drinking (never, ever), body mass index (underweight, normal weight, overweight, obese), cigarette smoking (never smoker, quartiles of pack years), opium consumption (never, ever), self-reported history of diabetes (yes, no), self-reported history of hypertension (yes, no), duration of exclusive gas burning using chimney-equipped heating stoves (continuous), duration of exclusive kerosene burning using chimney-equipped heating stoves (continuous), duration of exclusive kerosene burning using heating stoves without chimney (continuous), duration of exclusive biomass burning using chimney-equipped heating stoves (continuous), and duration of exclusive biomass burning using heating stoves without chimney (continuous).

d

Wald test for the coefficient for the variable representing categories of duration of fuel use was included as a continuous variable in the Cox regression model.

e

Computed using a Wald test comparing the two estimates of the duration of exclusive use of fuels according to the type of the heating stoves (with/without chimney).

f

Computed for the highest tertile of the duration of fuel use.

Results of the analyses stratified by subject characteristics are reported in Table S11. The association between a 10-year increase in duration of biomass use with heating stoves not equipped with chimneys and kidney disease-related mortality was stronger among male individuals than among female participants (HRs: 1.38 [95% CI: 1.17, 1.63]). vs 1.03 [95% CI: 0.86, 1.23]; P heterogeneity = 0.015). Associations for gas, kerosene, and biomass appeared to be slightly stronger among individuals with no history of hypertension, for heating stoves both equipped and not equipped with chimneys (P heterogeneity < 0.05). Instead, no substantial differences according to the strata of sex, age, residence, education, wealth score, cigarette smoking status, and history of diabetes were observed.

Discussion

The findings of our study suggest that solid fuel use for household purposes, especially burning biomass without proper ventilation, is associated with increased risk of death due to kidney disease. Our results also show that the association between biomass use and kidney disease-related mortality might be stronger among male individuals, although this finding was limited to the use of heating stoves without chimneys. This might be explained by a number of factors, including the higher proportion of high duration of exposure to biomass and the higher prevalence of cigarette smoking among male study participants. Cigarette smoking, indeed, has been shown to be a relevant risk factor for chronic kidney disease, and although we adjusted analyses for this confounder, it may still influence our estimates, also because residual confounding cannot be ruled out. In this regard, however, we did not observe substantially different results between ever and never cigarette smokers in stratified analyses, although the estimate of association was slightly weaker among the latter. Also, associations were stronger among those with no history of hypertension for all considered fuel types. This increase in kidney disease-related mortality associated with solid fuel use may be due to the detrimental effects of fine particulate matter and other pollutants, including polycyclic aromatic hydrocarbons (PAH), benzene, and formaldehyde, which are produced in large quantities when burning biomass. ,, As for particulate matter, the mechanisms underlying renal toxicity have been suggested to be similar to those involved in cardiovascular disease, , including systematic oxidative stress, inflammation, thrombosis, vascular dysfunction, and atherosclerosis, which eventually lead to both macro- and microvascular damage, which might thus occur also in the kidney, since it is a highly vascularized organ. In a rat model with type 1 diabetes, exposure to urban-air concentrations of particulate matter for 16 weeks has been shown to increase levels of glycated hemoglobin A1c, interleukin 6, and fibrinogen, and to cause glomerulosclerosis and renal tubular damage, hence suggesting that exposure to particulate matter may cause macro- and microvascular damage through systemic inflammation, as well as reduced glycemic control among individuals with diabetes. Air pollution has also been associated with hypertension, which might eventually lead to kidney damage too, although our results showed that the association between household fuel use and kidney disease was stronger among participants not reporting a history of hypertension at enrollment. In addition, PAHs are also released from the combustion of solid fuel, including biomass, hence they might also partially explain our results, since they have also been reported to be associated with kidney damage and disease. − Thus, kidney damage associated with air pollution and with solid fuel use for household purposes could be expected to be partially due to direct damage from toxicants released after incomplete combustion of these fuels, but also partially derive from their effect on blood pressure and glucose levels.

A limited number of studies previously investigated the association between fuels used for household purposes and kidney disease-related mortality or kidney function. In a cohort study carried out in Shanghai, China, use of coal as a fuel was not associated with kidney disease-related mortality among 74,941 never-smoking women aged 40–70 years, after adjustment for a number of potential confounders including those related to socioeconomic position, although the number of cases of death due to kidney disease was limited. We would expect that the effect of exposure to coal on kidney disease-related mortality, if any, be similar to that of biomass. However, it should be noted that the study evaluated the use of coal as a fuel for cooking only, and not for heating purposes, which could be expected to lead to nondifferential misclassification of the actual exposure, hence eventually biasing estimates toward the null. Furthermore, a recent study (n = 4,959) conducted in rural China showed an association between use of solid fuels (coal and biomass) for cooking and lower estimated glomerular filtration rate compared with use of clean fuel (solar, gas, and electric) for both cooking and heating, with some evidence suggesting that switching from solid to clean cooking fuel could be associated with lower risk of renal function decline. However, this finding was not confirmed by a later cross-sectional study conducted in the same country, perhaps due to the limited sample size (n = 646).

To the best of our knowledge, this is the first cohort study evaluating the association between biomass and kerosene use for household purposes and kidney disease-related mortality. Among its strength there are the large sample size, the low proportion of individuals lost to follow-up, the long duration of the follow-up period, the availability of detailed information from validated questionnaires on the types of fuel used for household purposes during their whole life, as well as on the type of stoves used for heating. In addition, we were able to control for a number of potential confounders, such as those related to the socioeconomic position and cigarette smoking, although occurrence of residual confounding cannot be excluded. Also, lack of information on intake of medications (other than aspirin) that may affect the kidney and on family history of kidney disease did not allow us to evaluate whether additional adjustment for such factors could change estimates of association. Furthermore, the use of self-reported information to assess study participants’ exposure may lead to misclassification of the exposure itself, even though it could be expected to be nondifferential according to the outcome status and thus it may potentially bias estimates toward the null. This represents a major limitation of the study, which requires confirmation by further studies with exposure assessment supported by detailed measurements of indoor air pollution. Additionally, no detailed information on kidney function based on laboratory tests prospectively during the follow-up period was available, hence we could not take it into account in the statistical analysis. Also, no data on participants’ exposure to extreme temperatures, which have been shown to negatively affect kidney health and related mortality, − as well as on climate change-related events were available, not allowing us to take them into account in our analysis. Eventually, the limited number of deaths due to kidney disease in some strata may have led to some spurious results, especially for those observed from the analyses on fuels used for any purpose, since the number of deaths among never users was low for all considered fuels.

In summary, the results of our study suggest that the use of solid fuels for household purposes under poorly ventilated conditions may be associated with kidney disease-related mortality. However, given the lack of detailed longitudinal data on kidney function and the use of self-reported information for exposure assessment, a confirmation by future studies is warranted. If proven causal, findings of this study suggest the need for actions aimed at fostering the adoption of chimney-equipped stoves and the use of clean fuels in world regions where the use of solid fuels is still common among the population such as in low-resource countries.

Supplementary Material

hp6c00520_si_001.pdf (485.8KB, pdf)

Acknowledgments

Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization. The Golestan Cohort Study was supported by Tehran University of Medical Sciences (grant no: 81/15), Cancer Research UK (grant no: C20/A5860), the Intramural Research Program of the National Cancer Institute, National Institutes of Health, and various collaborative research agreements with the International Agency for Research on Cancer. Deidentified participant data used for this study are available upon reasonable request for research purposes from the corresponding author.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/ehp.6c00520.

  • Tables S1–S11 (PDF)

The authors’ contributions to this work were as follows: Conceptualization – M.Sa., P.B.; Investigation – M.Sa., M.S.S., S.A., H.P., M.Sh., A.E., R.M., P.B.; Data curation – M.Sa., M.S.S., S.A., H.P., M.Sh., A.E., R.M., P.B.; Methodology – M.S., P.B.; Formal analysis – M.S.; Writing - original draft – M.S.; Writing - review and editing – M.S.S., S.A., H.P., M.Sh., A.E., R.M., P.B.; Project administration – A.E., R.M.; Supervision – P.B.

The authors declare no competing financial interest.

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