ABSTRACT
Background and hypothesis
Evidence on the role of smoking in the development of chronic kidney disease (CKD) has mostly relied on self-reported smoking status. We aimed to compare the associations of smoking status as assessed by self-reports and urine cotinine with CKD risk.
Methods
Using the PREVEND prospective study, smoking status was assessed at baseline using self-reports and urine cotinine in 4333 participants (mean age, 52 years) without a history of CKD at baseline. Participants were classified as never, former, light current, and heavy current smokers according to self-reports and comparable cutoffs for urine cotinine. Hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated for CKD.
Results
The percentages of self-reported and cotinine-assessed current smokers were 27.5% and 24.0%, respectively. During a median follow-up of 7.0 years, 593 cases of CKD were recorded. In analyses adjusted for established risk factors, the HRs (95% CI) of CKD for self-reported former, light current, and heavy current smokers compared with never smokers were 1.17 (0.95–1.44), 1.48 (1.10–2.00), and 1.48 (1.14–1.93), respectively. On further adjustment for urinary albumin excretion (UAE), the HRs (95% CI) were 1.07 (0.87–1.32), 1.26 (0.93–1.70), and 1.20 (0.93–1.57), respectively. For urine cotinine-assessed smoking status, the corresponding HRs (95% CI) were 0.81 (0.52–1.25), 1.17 (0.92–1.49), and 1.32 (1.02–1.71), respectively, in analyses adjusted for established risk factors plus UAE.
Conclusion
Self-reported current smoking is associated with increased CKD risk, but dependent on UAE. The association between urine cotinine-assessed current smoking and increased CKD risk is independent of UAE. Urine cotinine-assessed smoking status may be a more reliable risk indicator for CKD incidence than self-reported smoking status.
Keywords: chronic kidney disease, cohort study, cotinine, risk factor, smoking
Graphical Abstract
Graphical Abstract.
KEY LEARNING POINTS.
What was known:
The evidence linking smoking to chronic kidney disease (CKD) has largely depended on self-reported smoking status.
Urine cotinine is a more objective measure of smoking status, but no studies have compared the associations of smoking exposure as assessed by self-reports and urine cotinine with CKD risk in the same participants.
This study adds:
Assessment of smoking status based on self-reports appears to underestimate the risk between smoking status and CKD as a result of misclassification.
Urine cotinine-assessed smoking status may be a more reliable risk indicator for CKD incidence than self-reported smoking status.
Potential impact:
The study highlights the importance of using objective measures such as urine cotinine in conjunction with self-reports for a more accurate assessment of smoking status in clinical settings.
INTRODUCTION
Chronic kidney disease (CKD) represents a significant public health challenge, characterized by its high morbidity, mortality, and the substantial economic burden associated with its treatment [1–4]. The epidemiology of CKD reflects a complex interplay of various established risk factors, including hypertension, diabetes, and several lifestyle factors [1, 5]. Among lifestyle factors, smoking has been increasingly recognized as a major contributor to the development and progression of cardiometabolic conditions [6, 7], including CKD [8]. Historically, the evidence linking smoking to these cardiometabolic conditions has largely depended on self-reported smoking status. However, this method of assessment comes with limitations. Inaccuracies in self-reporting due to denial of smoking habits or difficulty in recalling the quantity and duration of smoking can lead to a significant underestimation of the true impact of smoking on health [9, 10]. This is where cotinine, the primary metabolite of nicotine and a constituent of tobacco, becomes relevant. With a biological half-life of 19–40 hr, as opposed to nicotine's 30 min to 2 hr [11], cotinine serves as a highly sensitive and specific biomarker for cigarette smoking exposure [11, 12]. Its levels can be accurately determined in serum or urine, offering a more reliable measure than self-reports [13].
The rationale for our study stems from the need to further elucidate the relationship between smoking and CKD using an objective measure of smoking status. Although several studies have explored cotinine-assessed smoke exposure's association with adverse cardiovascular outcomes [14–16], data on its link with CKD remains non-existent, particularly in comparison to self-reported smoking status. Previous research indicates that reliance on self-reports might underestimate the risk of smoking in relation to cardiovascular disease (CVD) due to misclassification [16]. However, it remains unclear whether this also applies to CKD. Our prospective cohort study aimed to compare the associations of smoking exposure as assessed by self-reports and urine cotinine with CKD risk in the same sample of participants. Furthermore, we investigated whether self-reported smoking assessments might underestimate the association between smoking status and CKD risk due to potential misclassification.
MATERIALS AND METHODS
Study design and population
This research was carried out adhering to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines, which are standards for reporting epidemiological observational studies (see Supplementary Material 1) [17]. The subjects of this research were participants of the Prevention of Renal and Vascular End-stage Disease (PREVEND) study. This prospective cohort study, based on a general population, aimed to explore the progression of urinary albumin excretion (UAE) and its association with renal diseases and cardiometabolic conditions. The methodology, recruitment strategy, and measurement techniques of the study have been documented in earlier publications [18–21]. In summary, the PREVEND study incorporated a representative group of individuals from Groningen, Netherlands. Our research focused on the second screening phase of the PREVEND study, which initially included 6894 participants aged between 32 and 80 years at the time of entry into the study. These examinations and evaluations were conducted from 2001 to 2003. For our analysis, we excluded participants with a known history of CKD, resulting in a final sample of 4333 individuals. This group had complete data on urine cotinine levels, self-reported smoking exposure, other relevant factors, and CKD outcomes. The study received approval from the Institutional Review Board of the University Medical Center Groningen (Medisch Ethische Toetsingscommissie, METc, IRB no. 01/139) and was conducted in alignment with the principles of the Declaration of Helsinki. All participants provided their written informed consent.
Assessment of exposures and covariates
In this study, baseline data including sociodemographic information, anthropometric measurements, medical histories, and medication usage were gathered during two outpatient visits. To complement this, we collected medication usage data from all community pharmacies in Groningen, providing comprehensive drug use information for 95% of the PREVEND study participants [22]. After an overnight fast and a 15-min rest period, venous samples of plasma and serum were collected from the participants for blood biomarker analysis, conducted between 8 and 10 a.m. The plasma samples underwent centrifugation at 4°C and were preserved at −80°C for later analysis. Similarly, 24-hr urine samples were obtained and stored at −80°C for cotinine level assessment. The Immulite 2500 assay, courtesy of Siemens in Los Angeles, CA, USA, was used to measure urinary cotinine concentrations, with the intra- and inter-assay coefficient of variation ranging between 2.2% and 5.7%. UAE was determined as the average of two 24-hr urine collections. Blood biomarkers such as total cholesterol, high-density lipoprotein cholesterol (HDL-C), serum creatinine, and serum cystatin C were measured following standard laboratory procedures [23–28]. Serum creatinine levels were determined using an enzymatic method on a Roche Modular analyzer with reagents and calibrators from Roche (Roche Diagnostics, Mannheim, Germany) maintaining intra- and inter-assay variability at 0.9% and 2.9%, respectively. Additionally, the serum cystatin C concentration was assessed using a Gentian cystatin C Immunoassay (Gentian AS, Moss, Norway) on a Modular analyzer (Roche Diagnostics, Risch-Rotkreuz, Switzerland). The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) combined creatinine-cystatin C equation [28, 29]. Body mass index (BMI) was calculated as weight divided by height squared. Alcohol consumption was obtained by self-reports [21]. To collect data on smoking habits, self-reported questionnaires were utilized. Participants' self-reported smoking status was categorized into four groups: never smokers, former smokers, light current smokers (smoking ≤10 cigarettes per day), and heavy current smokers (smoking >10 cigarettes per day). Former smokers were identified as those who had smoked previously but were non-smokers at the time of the study, while current smokers were those actively smoking during the study period.
Ascertainment of CKD
In our study, the identification of new cases of CKD was based on the criteria set by the Kidney Disease: Improving Global Outcomes guidelines [28]. These guidelines define CKD as either having an eGFR <60 mL/min/1.73 m2 or UAE ≥30 mg/24 h. Incident new CKD cases were recognized among participants who were initially free of CKD at the start of the study and subsequently developed the condition during the follow-up period.
Statistical analyses
To normalize distributions, skewed variables underwent logarithmic transformation. Baseline characteristics were presented as either means (with standard deviation, SD) or medians (with interquartile range, IQR) for continuous variables, and as numbers (with percentages) for categorical variables. Group comparisons of continuous and categorical variables were performed using ANOVA and chi-square tests, respectively. We estimated Spearman's rank correlation coefficient to assess the association between self-reported smoking status and urine cotinine-based measurements. The agreement in smoking exposure classification via self-report and urine cotinine was evaluated using Cohen's kappa (κ). A κ value <0.21 indicates poor agreement, 0.21 to 0.40 signals weak agreement, 0.41 to 0.60 is moderate, 0.61 to 0.80 is strong, and >0.80 is considered very strong [30]. Smoking exposure, as determined by cotinine levels, was categorized into non-smokers (never and former), light current smokers, and heavy current smokers, using predefined urine cotinine thresholds: <100 ng/mL for non-smokers, 100–500 ng/mL for former smokers, and >500 ng/mL for current smokers [31–34]. Furthermore, current smokers were divided into light and heavy categories based on median cotinine levels [16, 32]. We calculated the misclassification rate of active smokers by self-reports (i.e. the number of misclassified active smokers divided by the total number of self-reported active smokers [35]). Time-to-event Cox proportional hazards models were used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) for the association of smoking exposure (self-reported and cotinine-based) with CKD risk, following confirmation of no major deviation from the proportionality of hazards assumption [36]. Confounder adjustments were made using four progressive models: (Model 1) age and sex; (Model 2) Model 1 plus systolic blood pressure (SBP), total cholesterol, history of T2D, use of antihypertensives, and baseline eGFR; (Model 3) Model 2 plus BMI, 24 hr urinary sodium (Na+) excretion, 24 hr urinary potassium (K+) excretion, and alcohol consumption; and (Model 4) Model 3 plus UAE. These confounders were selected based on the following criteria: (i) their roles as risk factors for CKD; (ii) published associations with CKD in the PREVEND study [28]; and (iii) their potential as confounders based on known associations with CKD and observed associations with the exposure using the available data [37]. Statistical analyses were conducted using Stata v.17 (Stata Corp, College Station, TX, USA).
RESULTS
Baseline characteristics
Table 1 presents the baseline characteristics of the study's participants, categorized by their self-reported smoking habits. The mean (SD) age of the participants at the start of the study was 52 (11) years, and males constituted ∼48% of the cohort. Notably, former smokers tended to be older, had higher BMI and blood pressure, and were more frequently using antihypertensive medications compared to participants in other smoking categories. Participants identified as heavy current smokers exhibited higher levels of total cholesterol, triglycerides, and UAE, along with reduced HDL-C levels, relative to other groups. The study found that 27.5% of participants were self-reported current smokers, while cotinine assessments identified 24.0% as current smokers. A strong positive correlation was observed between self-reported smoking status and smoking status verified through urine cotinine levels (Spearman's rho = 0.76, P < 0.001). However, smoking status classification based on self-report corresponded weakly with that based on urine cotinine, as indicated by Cohen's κ of 0.25 and an interrater agreement of 45.7% (P < 0.001).
Table 1:
Baseline: participant characteristics overall and according to self-reported smoking status.
| Self-reported smoking status | ||||||
|---|---|---|---|---|---|---|
| Variable | Overall (N = 4333) mean (SD) or median (IQR) | Never (N = 1341) mean (SD) or median (IQR) | Former (N = 1799) mean (SD) or median (IQR) | Light current (N = 454) mean (SD) or median (IQR) | Heavy current (N = 739) mean (SD) or median (IQR) | P-value |
| Urine cotinine, ng/ml | 0 (0, 429) | 0 (0, 0) | 0 (0, 0) | 654 (317, 1217) | 1539 (1102, 1950) | <0.001 |
| Questionnaire | ||||||
| Males (%) | 2074 (47.9%) | 571 (42.6%) | 926 (51.5%) | 213 (46.9%) | 364 (49.3%) | <0.001 |
| Age, years | 52 (11) | 51 (12) | 54 (11) | 50 (11) | 50 (9) | <0.001 |
| Alcohol consumers (%) | 3283 (75.8%) | 942 (70.2%) | 1423 (79.1%) | 364 (80.2%) | 554 (75.0%) | <0.001 |
| History of T2D (%) | 184 (4.2%) | 60 (4.5%) | 77 (4.3%) | 20 (4.4%) | 27 (3.7%) | 0.84 |
| Use of antihypertensives (%) | 644 (14.9%) | 176 (13.1%) | 335 (18.6%) | 58 (12.8%) | 75 (10.1%) | <0.001 |
| Physical measurements | ||||||
| BMI, kg/m2 | 26.4 (4.2) | 26.4 (4.2) | 27.1 (4.2) | 25.5 (4.0) | 25.6 (3.8) | <0.001 |
| Waist circumference, cm | 91.0 (12.4) | 89.7 (12.0) | 93.2 (12.5) | 88.4 (12.0) | 89.8 (12.0) | <0.001 |
| SBP, mmHg | 124 (17) | 123 (17) | 126 (17) | 120 (16) | 122 (16) | <0.001 |
| DBP, mmHg | 73 (9) | 72 (9) | 74 (9) | 71 (9) | 73 (8) | <0.001 |
| Lipid markers | ||||||
| Total cholesterol, mmol/l | 5.44 (1.03) | 5.32 (1.04) | 5.48 (1.03) | 5.35 (1.02) | 5.59 (1.03) | <0.001 |
| HDL-C, mmol/l | 1.27 (0.31) | 1.29 (0.29) | 1.29 (0.32) | 1.27 (0.33) | 1.20 (0.30) | <0.001 |
| Triglycerides, mmol/l | 1.09 (0.79, 1.56) | 1.01 (0.74, 1.41) | 1.09 (0.78, 1.57) | 1.09 (0.79, 1.56) | 1.27 (0.92, 1.77) | <0.001 |
| Metabolic and renal markers | ||||||
| Fasting plasma glucose, mmol/l | 4.95 (1.03) | 4.93 (1.08) | 5.01 (0.96) | 4.88 (0.94) | 4.89 (1.13) | 0.019 |
| Urinary Na+ excretion, mmol/24 hr | 143.9 (54.3) | 141.8 (51.0) | 148.9 (55.3) | 139.8 (54.7) | 138.1 (56.3) | <0.001 |
| Urinary K+ excretion, mmol/24 hr | 68.8 (21.5) | 69.7 (21.2) | 70.7 (21.8) | 64.8 (20.4) | 65.1 (21.1) | <0.001 |
| Creatinine, mg/dl | 0.80 (0.14) | 0.80 (0.14) | 0.82 (0.14) | 0.78 (0.13) | 0.77 (0.13) | <0.001 |
| Cystatin C, mg/l | 0.87 (0.14) | 0.85 (0.13) | 0.88 (0.14) | 0.88 (0.14) | 0.91 (0.14) | <0.001 |
| Estimated GFR, ml/min/1.73m2 | 86.0 (6.9) | 86.5 (6.5) | 85.4 (7.4) | 86.3 (6.6) | 86.7 (6.3) | <0.001 |
| UAE, mg/24 hr | 7.85 (5.88, 11.52) | 7.52 (5.74, 10.74) | 8.01 (5.93, 12.34) | 8.04 (5.84, 11.44) | 8.10 (5.96, 11.21) | 0.002 |
Continuous variables are reported as mean (SD) or median (interquartile range) and categorical variables are reported as n (%).
Abbreviations: DBP, diastolic blood pressure; IQR, interquartile range
Former smokers were those who were non-smokers at the time of study inclusion but had ever smoked in their life; current smokers were those who reported smoking at the time of inclusion; light current smokers were current smokers who reported smoking 10 cigarettes or less per day; and heavy current smokers were current smokers who reported smoking more than 10 cigarettes per day.
Cross-tabulation presented in Table 2 compares self-reported smoking status with that measured by urine cotinine. Among 1341 individuals who reported never smoking, 10 (0.7%) had cotinine levels indicative of active smoking. Similarly, out of 1799 self-declared former smokers, 45 (2.5%) showed cotinine levels aligning with active smoking, leading to a 4.6% misclassification rate of active smokers.
Table 2:
Cross-tabulation of participants by self-reported smoking status and urine cotinine measured smoking status.
| Urine cotinine-assessed smoking status | |||||
|---|---|---|---|---|---|
| Self-reported smoking status | Never smokers | Former smokers | Light current smokers | Heavy current smokers | Total |
| Never smokers | 1319 (98.4) | 12 (0.9) | 6 (0.5) | 4 (0.3) | 1341 |
| Former smokers | 1706 (94.8) | 48 (2.7) | 31 (1.7) | 14 (0.8) | 1799 |
| Light current smokers | 58 (12.8) | 115 (25.3) | 200 (44.1) | 81 (17.8) | 454 |
| Heavy current smokers | 15 (2.0) | 20 (2.7) | 291 (39.4) | 413 (55.9) | 739 |
| Total | 3098 (71.5) | 195 (4.5) | 528 (12.2) | 512 (11.8) | 4333 |
Smoking exposures and CKD risk
During a median follow-up of 7.0 (IQR, 4.8–7.4) years, 593 cases of CKD were recorded (annual rate 23.5/1000 person-years at risk; 95% CI: 21.7 to 25.5). Table 3 shows the associations of smoking status assessed by self-reports and urine cotinine with the risk of CKD. Compared with self-reported never smokers, the HRs (95% CIs) of CKD for self-reported former, light current, and heavy current smokers were 1.18 (0.96–1.45), 1.46 (1.09–1.97), and 1.47 (1.13–1.90), respectively, in analysis adjusted for age, sex, SBP, total cholesterol, history of T2D, use of antihypertensives, and baseline eGFR. These remained consistent on further adjustment for BMI, 24 hr urinary Na+ excretion, 24 hr urinary K+ excretion, and alcohol consumption: 1.17 (0.95–1.44), 1.48 (1.10–2.00), and 1.48 (1.14–1.93), respectively. The associations were attenuated to non-significance on further adjustment for UAE.
Table 3:
Associations of smoking exposures with CKD.
| Model 1 | Model 2 | Model 3 | Model 4 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Smoking exposure | Events/Total | HR (95% CI) | P-value | HR (95% CI) | P-value | HR (95% CI) | P-value | HR (95% CI) | P-value |
| Self-reported smoking | |||||||||
| Never smokers | 142/1341 | ref | ref | ref | ref | ||||
| Former smokers | 285/1799 | 1.19 (0.97–1.47) | .089 | 1.18 (0.96–1.45) | .12 | 1.17 (0.95–1.44) | .14 | 1.07 (0.87–1.32) | .51 |
| Light current smokers | 65/454 | 1.45 (1.08–1.95) | .013 | 1.46 (1.09–1.97) | .012 | 1.48 (1.10–2.00) | .009 | 1.26 (0.93–1.70) | .13 |
| Heavy current smokers | 101/739 | 1.51 (1.17–1.95) | .002 | 1.47 (1.13–1.90) | .004 | 1.48 (1.14–1.93) | .003 | 1.20 (0.93–1.57) | .16 |
| Urine cotinine | |||||||||
| Per 1000 ng/mL increase | 593/4333 | 1.22 (1.10–1.36) | <0.001 | 1.22 (1.09–1.36) | <0.001 | 1.23 (1.10–1.37) | <0.001 | 1.14 (1.02–1.28) | .019 |
| Never smokers | 417/3098 | ref | ref | ref | ref | ||||
| Former smokers | 21/195 | 0.95 (0.61–1.47) | .82 | 0.97 (0.62–1.50) | .88 | 0.98 (0.63–1.52) | .93 | 0.81 (0.52–1.25) | .34 |
| Light current smokers | 79/528 | 1.37 (1.08–1.75) | .011 | 1.34 (1.05–1.71) | .018 | 1.36 (1.07–1.73) | .014 | 1.17 (0.92–1.49) | .21 |
| Heavy current smokers | 76/512 | 1.44 (1.12–1.84) | .004 | 1.49 (1.16–1.91) | .002 | 1.52 (1.18–1.96) | .001 | 1.32 (1.02–1.71) | .034 |
ref, denotes the reference category used for comparison
Model 1: Age and sex.
Model 2: Model 1 plus SBP, total cholesterol, history of type 2 diabetes, use of antihypertensives, and eGFR (as calculated using the Chronic Kidney Disease Epidemiology Collaboration combined creatinine– cystatin C equation)
Model 3: Model 2 plus BMI, 24 hr urinary sodium excretion, 24 hr urinary potassium excretion, and alcohol consumption
Model 4: Model 3 plus UAE
Compared with urine cotinine-assessed never smokers, the HRs (95% CIs) of CKD for former, light current, and heavy current smokers were 0.97 (0.62–1.50), 1.34 (1.05–1.71), and 1.49 (1.16–1.91), respectively, in analyses adjusted for age, sex, SBP, total cholesterol, history of T2D, use of antihypertensives, and eGFR. These were minimally attenuated on further adjustment for BMI, 24 hr urinary Na+ excretion, 24 hr urinary K+ excretion, and alcohol consumption: 0.98 (0.63–1.52), 1.36 (1.07–1.73), and 1.52 (1.18–1.96), respectively. The associations were attenuated but remained statistically significant on further adjustment for UAE. When urine cotinine was modeled as a continuous variable, a per 1000 ng/mL increase in urine cotinine was associated with an increased risk of CKD, independent of adjusted covariates (Table 3). In subsidiary analyses, we assessed the associations of urinary cotinine × urinary volume (24-hr urinary cotinine excretion) and urinary cotinine to urine creatinine ratio (CCR) with the risk of CKD. The subsidiary analyses sought to dissect the cumulative effects and transient spikes in smoking exposure, thereby offering a comprehensive picture of its impact on kidney function. The 24-hr urinary cotinine excretion value offers a stable representation of the exposure [38] and the CCR was chosen to adjust for potential variations in urine concentration; this ratio helps to mitigate the influence of hydration status and other factors affecting urine concentration on cotinine levels [39], thus providing a more consistent marker of exposure. Both measures are widely recognized in epidemiological studies as proxies of tobacco smoke exposure [38, 40], making them appropriate choices for our subsidiary analyses. In analyses adjusted for established risk factors and other potential confounders, the top compared to the bottom tertiles of 24-hr urinary cotinine excretion and CCR were each associated with an increased risk of CKD, but the associations were attenuated on further adjustment for UAE (Table 4).
Table 4:
Associations of other smoking exposures (urine cotinine x urinary volume and urine CCR) with CKD.
| Model 1 | Model 2 | Model 3 | Model 4 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Smoking exposure | Events/total | HR (95% CI) | P-value | HR (95% CI) | P-value | HR (95% CI) | P-value | HR (95% CI) | P-value |
| Urine cotinine × urinary volume, ng | |||||||||
| Tertile 1 | 351/2537 | ref | ref | ref | ref | ||||
| Tertile 2 | 40/298 | 1.02 (0.74–1.42) | .89 | 1.00 (0.72–1.38) | .98 | 1.03 (0.74–1.43) | .86 | 0.98 (0.71–1.37) | .92 |
| Tertile 3 | 195/1418 | 1.21 (1.01–1.44) | .038 | 1.21 (1.01–1.45) | .035 | 1.23 (1.03–1.48) | .022 | 1.07 (0.89–1.28) | .49 |
| Urine CCR, ngl/mmolml | |||||||||
| Tertile 1 | 351/2537 | ref | ref | ref | ref | ||||
| Tertile 2 | 38/299 | 0.96 (0.69–1.34) | .82 | 0.92 (0.66–1.29) | .64 | 0.95 (0.68–1.34) | .78 | 0.90 (0.65–1.27) | .56 |
| Tertile 3 | 197/1417 | 1.22 (1.02–1.46) | .026 | 1.23 (1.03–1.47) | .021 | 1.26 (1.05–1.50) | .013 | 1.09 (0.91–1.30) | .37 |
ref, denotes the reference category used for comparison
Model 1: Age and sex.
Model 2: Model 1 plus SBP, total cholesterol, history of type 2 diabetes, use of antihypertensives, and eGFR (as calculated using the Chronic Kidney Disease Epidemiology Collaboration combined creatinine– cystatin C equation)
Model 3: Model 2 plus BMI, 24 hr urinary sodium excretion, 24 hr urinary potassium excretion, and alcohol consumption
Model 4: Model 3 plus UAE
DISCUSSION
In this cohort study, primarily involving White men and women without previous CKD, we evaluated the relationship between smoking status, determined by both self-reports and urine cotinine, and the risk of developing CKD. Although a strong positive correlation was noted between self-reported smoking status and that assessed by urine cotinine, the kappa statistic indicated only a weak agreement in the classification of smoking status by these two methods. Utilizing cotinine-verified data, 55 individuals (1.8%) out of 3140 who had self-reported as never or former smokers were identified as active smokers, indicating a misclassification rate of 4.6%. Compared with self-reported never smokers, self-reported light and heavy current smoking were each associated with an increased risk of CKD in analysis adjusted for several established and emerging risk factors; however, these associations were abrogated on adjusting for UAE. For cotinine-assessed smoking status in the same set of participants, light and heavy current smoking were each associated with an increased risk of CKD in analysis adjusted for several established and emerging risk factors including levels of UAE. The associations were slightly stronger for cotinine-assessed smoking status.
Our search of the literature did not identify any studies that have compared the associations of smoking exposure as assessed by self-reports and urine cotinine with the risk of CKD. Furthermore, whereas there is an abundance of studies on the prospective association between self-reported smoking status and the risk of CKD [8], relatively fewer studies exist on the association between cotinine-assessed smoking status and CKD risk. These studies were either based on cross-sectional designs, used serum cotinine, or did not specifically evaluate CKD outcomes, but rather other kidney measures such as albuminuria and eGFR [41–43]. In a cross-sectional study of 283 sugarcane workers at risk for CKD of unknown origin, Butler-Dawson and colleagues [44] examined the accuracy of self-reported smoking status compared to urinary cotinine levels. Their results showed the prevalence of self-reported and cotinine-assessed smoking to be 12% and 34%, respectively [44]. The authors concluded that smoking prevalence was underestimated in this worker population and that smoking status should be objectively measured with biomarkers rather than self-reported in epidemiological studies of CKD [44].
The weak agreement between self-reported and urine cotinine-assessed smoking status may be attributed to underreporting or misreporting of smoking habits using self-reports. Individuals might underreport smoking due to social desirability bias or not remember their smoking history. The finding that a significant proportion of self-reported never or former smokers were actually active smokers, as per cotinine levels, suggests a considerable rate of misclassification. This could be due to either intentional underreporting or a lack of awareness about the extent of exposure to tobacco smoke. The increased risk of CKD among light and heavy smokers, as per both self-report and cotinine levels, confirms the detrimental impact of smoking on kidney health. However, the relationship between self-reported smoking and CKD risk was dependent on levels of UAE. Smokers have significantly higher levels of urinary albumin [45], which is associated with the risk of CKD, substantiating the importance of UAE as a confounding factor. The association between urine cotinine-assessed smoking status and CKD risk was also attenuated on accounting for UAE, but this was minimal. Furthermore, the attenuation in the association between self-reported or urine cotinine-assessed smoking status and CKD on adjustment for UAE may align with the plausibility that UAE lies in the causal pathway between smoking and CKD risk. This suggests that smoking may contribute to kidney damage, partly mediated through its effects on or biological mechanisms related to increasing albumin excretion [45–47]. The minimal attenuation of the association between cotinine-assessed smoking status and CKD risk after UAE adjustment suggests that while UAE is a confounder, the relationship between actual smoking exposure (as objectively measured by cotinine levels) and CKD risk is robust and less influenced by UAE levels. This highlights the potential for misclassification in self-reported smoking data and supports the use of urine cotinine as a more reliable biomarker for smoking exposure. This is also reflected in the fact that stronger associations were observed for cotinine-assessed smoking status compared to self-reported smoking status.
These findings underscore the need for healthcare providers to routinely screen smokers for CKD, given the established association between smoking and increased CKD risk. The study highlights the importance of using objective measures like urine cotinine in conjunction with self-reports for a more accurate assessment of smoking status in clinical settings. Within the limits of its half-life, cotinine is considered a highly sensitive and specific biomarker of cigarette smoking and it is cheap, easy to measure and use [11, 12]. Whereas cotinine offers several benefits as a biomarker, its application comes with certain limitations. Primarily, cotinine may not effectively differentiate between groups such as secondhand smokers versus non-smokers [48], and between individuals who have never smoked and former smoker [32]. A strong positive correlation has been observed between the cotinine levels of smokers and those of the non-smokers living in the same family [49]. Cotinine, with a maximum half-life of around 40 hr [11], becomes undetectable ∼1 week after smoking cessation, leading to potential inaccuracies in measurements if there is an interruption in smoking. Furthermore, the timing of urine collection relative to the last smoking episode can influence the measured levels. Delayed sample collection might result in lower cotinine levels, not reflecting recent smoking behaviors. Additionally, passive smokers may show elevated urine cotinine levels, and nicotine intake from sources like gums or patches can also be metabolized into cotinine. Another limitation lies in the variability of nicotine conversion to cotinine, with rates ranging from 55% to 92% [50]. The individual differences in metabolism and excretion can affect the conversion of nicotine to cotinine and its subsequent excretion in urine. This variability could be influenced by genetic factors, age, kidney function, and overall health status [51].
Strengths and limitations
This study's principal strength lies in its novelty—being the first comparative, prospective analysis of smoking exposure's associations with CKD risk, assessed via self-reports and urine cotinine, including the evaluation of misclassification rates. Additional strengths include a substantial sample size reflective of the general population, the exclusion of participants with pre-existing CKD, a sufficiently long follow-up period for accurate CKD detection, and the use of cutoffs for differentiating non-smoking and active smoking status based on cotinine verification [31–34, 48]. However, the study is not without limitations. Primarily, the analysis relied on a single cotinine measurement at baseline, without considering potential variations over time, raising concerns about regression dilution bias. Additionally, the categorization of light and heavy current smokers was based on median urine cotinine values from previous studies [16, 32], which might lead to misclassification due to the limited validation of this method. The study also did not consider the use of nicotine patches or gums, predominantly used by former smokers, which could affect cotinine levels. In addition to several factors mentioned previously, there are other factors that can interfere with the accuracy of urine cotinine measurements. These include issues related to assay type, sample storage, and handling. We used an immunoassay for urine cotinine measurement, which may be less accurate than mass spectrometry techniques (gas chromatography or liquid chromatography with mass spectrometry) [52]. However, disadvantages of these mass spectrometry techniques are higher cost and time consumption [53]. Furthermore, the degradation of cotinine can occur if urine samples are not stored properly or if they are exposed to extreme temperatures. In a study that assessed the stability of cotinine in human urine stored in a room at ambient temperature for 120 hr, no degradation was observed in the analytic sample until 120 hr [54]. Additionally, dilution or contamination of urine samples can also impact cotinine levels. As with observational study designs, the potential for residual confounding bias exists, stemming from unmeasured covariates and measurement errors in risk markers. Last, the study's findings, derived from a younger and middle-aged Caucasian cohort, may not extend to other demographic groups.
CONCLUSION
Self-reported light and heavy current smoking exposures were associated with the risk of CKD, but dependent on levels of UAE. The associations of cotinine-assessed light and heavy current smoking with CKD were stronger and independent of several established and potential confounders including levels of UAE. Assessment of smoking status based on self-reports appears to underestimate the risk between smoking status and CKD as a result of misclassification. Urine cotinine-assessed smoking status may be a more reliable risk indicator for CKD incidence than self-reported smoking status.
Supplementary Material
Contributor Information
Setor K Kunutsor, Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK.
Richard S Dey, Department of Medicine, University of Ghana Hospital, Legon, Ghana.
Daan J Touw, Department of Pharmacy and Clinical Pharmacology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.
Stephan J L Bakker, Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Robin P F Dullaart, Department of Internal Medicine, Division of Endocrinology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
FUNDING
The cotinine measurements were supported by the FoodBall project, which is funded by the BIO-NH call under the Joint Programming Initiative, ‘a Healthy Diet for a Healthy Life’ (grant number 529051002). The FoodBall project is funded nationally by the respective Research Councils. The Dutch Kidney Foundation supported the infrastructure of the PREVEND program from 1997 to 2003 (grant E.033). The University Medical Center Groningen supported the infrastructure from 2003 to 2006. Dade Behring, Ausam, Roche, and Abbott financed laboratory equipment and reagents by which various laboratory determinations could be performed. The Dutch Heart Foundation supported studies on lipid metabolism (grant 2001-005). S.K.K. is funded by the National Institute for Health and Care Research (NIHR) Leicester Biomedical Research Centre (BRC). The views expressed are those of the author and not necessarily those of the NIHR or the Department of Health and Social Care. The funders had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.
AUTHORS' CONTRIBUTIONS
S.K.K., R.S.D., D.J.T., S.J.L.B., and R.P.F.D. were responsible for the research concept and study design and protocol drafting. D.J.T., S.J.L.B., and R.P.F.D. were responsible for data acquisition. S.K.K. was responsible for data analysis. S.K.K., R.S.D., D.J.T., S.J.L.B., and R.P.F.D. were responsible for interpretation. S.K.K. wrote the first draft. S.J.L.B. and R.P.F.D. were responsible for supervision. Each author contributed important intellectual content in drafting the manuscript and approved the accuracy and integrity of the final version of the manuscript.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on reasonable request from the Principal Investigators.
CONFLICT OF INTEREST STATEMENT
None declared.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available on reasonable request from the Principal Investigators.