Abstract
Background
N, N-Diethyl-meta-toluamide (DEET) is the predominant active ingredient found in insect repellents utilized by consumers. Exposure to DEET has been associated with notable risks to human health. Nonetheless, there is a scarcity of extensive cohort studies investigating the precise correlation between DEET exposure and mortality rates among cancer survivors. The objective of this study is to thoroughly evaluate the connection between DEET exposure and mortality rates in cancer survivors.
Methods
This study employed individual samples obtained from the National Health and Nutrition Examination Survey (NHANES). Utilizing data from NHANES spanning 2007 to 2016, this study incorporated a cohort of 5,859 cancer survivors for subsequent analysis, following the exclusion of incomplete datasets. Through subgroup analysis, the research examined the impact of quartile levels of 3-diethyl-carbamoyl benzoic acid (DCBA), the primary metabolite of DEET, on cancer survivors across various subgroups within the broader population. Furthermore, the research utilized a multivariable Cox proportional hazards regression model and Kaplan-Meier (KM) curves to investigate the relationship between 3-diethyl-carbamoyl benzoic acid (DCBA), a principal metabolite of DEET, and mortality rates in individuals who have survived cancer.
Results
The study identified an association between specific quartiles of DCBA concentration and a decreased risk of all-cause mortality among cancer survivors, specifically in the second (Q2: 0.665–1.95) and third quartiles (Q3: 1.95–6.845). Furthermore, a significant correlation was observed between the third quartile and cancer-specific mortality (Q3: 1.95–6.845), as well as between the second quartile and non-cancer mortality (Q2: 0.665–1.95). The quartiles of DCBA concentration exhibit a statistically significant correlation with total deaths (P < 0.001), cancer-specific deaths (P = 0.009), and non-cancer deaths (P < 0.001) among cancer survivors. The correlation between DCBA and reduced mortality risk in cancer survivors is particularly notable among females and individuals of non-Hispanic Black descent.
Conclusion
The detection of DCBA in the urine of adult cancer survivors is strongly associated with increased mortality risks, particularly among females and non-Hispanic Black individuals, warranting further investigation and targeted interventions.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12885-024-13178-6.
Keywords: DEET, NHANES, Cancer survivors, Mortality
Environmental implication
DEET, an active ingredient utilized for repelling biting insects, has raised concerns regarding potential health effects due to its widespread use. However, existing research on the environmental toxicity and human health risks associated with DEET remains limited. This study, based on a national cohort investigation, aims to examine the impact of DEET on mortality among cancer survivors and has revealed that DEET may offer a degree of risk reduction in this population.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12885-024-13178-6.
Introduction
The global population of cancer survivors is on the rise [1, 2]. In 2020, there were 19.3 million new cases, which is expected to grow to 28.4 million by 2040 [3]. While survival rates for various types of cancer have improved in recent decades, the life expectancy of cancer survivors is still relatively limited, leading to disparities compared to individuals without a cancer diagnosis [4–6]. Additionally, the physical and emotional well-being of cancer survivors is adversely affected, placing a substantial strain on public health systems [7].
Due to global warming, there is a heightened risk for humans to be exposed to diseases transmitted by mosquitoes and other insects [8]. This has led to a rise in public apprehension regarding mosquito-borne illnesses such as the Zika virus, prompting an increased reliance on insect repellents. Among these repellents, N, N-diethyl-m-toluamide (DEET) stands out as a commonly utilized option that has been historically utilized for the prevention of mosquito bites. DEET, known for its longevity and efficacy as an insecticide, is frequently found in human urine, plasma samples, and even drinking water [9–12]. It can permeate the human body through skin absorption or inhalation via aerosols. Due to the extensive utilization of DEET among the U.S. populace, its safety has become a subject of examination by both the public and scientific communities [13]. Although existing research does not definitively establish a causal relationship between DEET and cancer, ongoing scrutiny is being conducted by scientists to evaluate the potential health consequences of prolonged DEET exposure. Specifically, DEET has been shown to stimulate endothelial cells, thereby fostering angiogenesis and potentially facilitating tumor progression [14]. Consequently, additional research is necessary to elucidate the influence of DEET on the survival rates of individuals who have survived cancer.
Hence, our study seeks to explore the correlation between DEET exposure among adult cancer survivors and the ensuing risks of all-cause mortality, cancer-specific mortality, and non-cancer mortality. Drawing upon data from the National Health and Nutrition Examination Survey (NHANES), our study seeks to examine the association between the DEET urinary metabolite 3-diethyl-carbamoyl benzoic acid (DCBA) and mortality rates among adult cancer survivors. Furthermore, our objective is to explore the potential associations between varying concentrations of DCBA and the rates of all-cause mortality, cancer-specific mortality, and non-cancer mortality in this population.
Methods
Research population
American households are surveyed as part of NHANES, a cross-sectional study designed to measure health, nutrition, and sociodemographic characteristics. Approximately 10,000 participants from 30 selected counties out of the about 3,000 counties in the U.S. take part in household interviews, physical examinations, and laboratory tests at a Mobile Examination Center (MEC) every survey cycle. The interview portion of NHANES encompasses inquiries pertaining to demographic, socioeconomic, dietary, and health-related topics.
The examination component of this study encompasses a range of basic medical assessments, including blood pressure, hearing tests, oral health, grip strength, and various laboratory tests administered by qualified medical professionals, including certain radiological data. Data for this analysis was obtained from adult participants aged 20 and older in the NHANES spanning from 2007 to 2016. To be eligible for analysis, participants had to meet the following inclusion criteria: (1) completion of the Medical Conditions questionnaire, (2) availability of data on mosquito repellent use, and (3) assessment of baseline covariates. An analysis of 5,859 eligible patients was performed after excluding those with missing data (Fig. 1).
Fig. 1.
Flowchart depicting the cohort included in this study
DEET assessment
DEET is commonly utilized as a highly efficient mosquito and tick repellent, predominantly through dermal contact. Upon application to the skin, DEET is absorbed into the systemic circulation. Subsequently, DEET undergoes rapid metabolism and is predominantly excreted via urine [15]. The major metabolite of DEET, DCBA, accounts for 83% of urinary excretion in the United States and is frequently employed as a specific biomarker for DEET exposure [16, 17]. This method uses online solid phase extraction coupled with high performance liquid chromatography-tandem mass spectrometry (SPE-HPLC-MS/MS) for quantifying DCBA, in 100 µL of human urine. Sample preparation begins with an over-night enzymatic deconjugation of the glucuronide-bound metabolites. On the second day, the four compounds being measured are concentrated via online SPE and then chromatographically separated from each other and from other urine biomolecules using reversed phase HPLC. The eluting molecular ions are converted to gas phase ions using Atmospheric Pressure Chemical Ionization (APCI) and then selectively filtered by mass-to-charge ratios at unit resolution. Select molecular ions are then fragmented with chemical induced dissociation (CID) and the resulting product ions are filtered at unit resolution before detection via an electron multiplier [18]. The aim of this study is to investigate whether DEET may have an impact on cancer survivors’ mortality, DCBA(µg/L) was stratified into four quartiles: Q1 [0.3352, 0.665], Q2 (0.665, 1.95], Q3 (1.95, 6.845], and Q4 (6.845, 382000].
Diagnosis of cancer
A prior study evaluating the precision and reliability of NHANES self-reported cancer diagnoses revealed that medical documentation aligns well with self-reported information for the most prevalent cancer types [19, 20]. Participants’ cancer history is obtained from the “Medical Conditions” section of the NHANES database. Cancer survivors are distinguished by their response to the inquiry: “Have you received a diagnosis of cancer or malignancy from a medical professional?” Upon affirming this question, participants are classified as individuals who have experienced cancer.
Mortality ascertainment
NHANES public-use mortality files were linked to the National Death Index (NDI) using a probabilistic matching algorithm to collect mortality information for the follow-up group. Furthermore, the International Classification of Diseases, 10th Revision (ICD-10) was employed to ascertain the primary cause of death. All-cause mortality was defined as death resulting from any cause, encompassing cancer (C00-C97), cardiovascular diseases (I00-I09, I11, I13, I20-I51), cerebrovascular diseases (I60-I69), respiratory diseases (J40-J47), and other etiologies (residual). Cancer mortality is defined as death resulting from any malignant tumor during the follow-up period, which is measured from the baseline interview until the date of death or December 31, 2019 [21].
Covariates
Based on prior research on cancer survivor mortality rates, our study considers several potential factors that may influence the relationship between cancer survivor mortality rates and DEET. We collected and statistically analyzed various covariates, including age, gender (female, or male), race/ethnicity (Hispanic, Non-Hispanic Black, Non-Hispanic White, or Others), marital status (Married/Living with partner, Not married), Educational level (College or above, High school or GED, or Less than high school), poverty income ratio (PIR, < 1.3, 1.3–3.49, > 3.49), body mass index (BMI, < 25, 25–30, or > 30), smoking status (Former, Never, or Now), alcohol consumption (Former, Mild, Moderate, Heavy, Never), and health-related factors. The health-related variables considered in this study encompass hypertension, diabetes mellitus (DM including: DM, IFG, IGT, or No), cardiovascular disease (CVD including: No, or Yes), and chronic obstructive pulmonary disease (COPD including: No, or Yes), ascertained through physician self-reported diagnoses collected via standardized medical condition questionnaires during individual interviews. These variables were incorporated into our multivariable-adjusted model for analysis. Through statistical examination of these variables, our objective was to enhance comprehension of their influence on the association between cancer survivor mortality rates and DEET. By adjusting for these potential confounding variables, we aimed to improve the reliability and credibility of our research results.
Statistical analysis
NHANES utilizes a sophisticated, multi-stage probability sampling design. Our study performed weighted analyses to correct for potential over- or under-representation within the sample, thus enhancing the generalizability of the findings to the U.S. population. We utilized R software, version 4.3.2 (version 4.3.2, https://www.r-project.org/), and the “survey” package to conduct our statistical analyses. We compared weighted baseline characteristics across groups to detect statistical differences among the DCBA quartiles. In this study, for the data of normal distribution, the continuous variable is expressed as the average value with standard error, and for the data of non-normal distribution, the continuous variable is expressed as the median with quartile range. Categorical variables were presented as counts and percentages. Group differences were evaluated using the weighted Student’s t-test, Mann-Whitney U test, one-way ANOVA, Kruskal-Wallis test and chi-square test, with statistical significance set at a two-tailed P-value < 0.05.
A multivariable Cox regression model employed to investigate the association between DCBA levels and overall mortality, cancer-specific mortality, and non-cancer mortality, adjusting for gender, age, marital status, ethnicity, education, PIR, BMI, hypertension, DM, CVD, and COPD. To investigate the potential non-linear association between the DEET and both all-cause mortality, cancer mortality and non-cancer mortality in cancer survivors, a restricted cubic spline (RCS) methodology was utilized. It was constructed using the “RMS” package in R. Knot positions were varied from 3 to 7, with the model exhibiting the lowest RCS information criterion value ultimately chosen, employing 3 knots. Inflection points were identified based on the RCS curve. Additionally, to gain further insights into the varying survival probabilities of cancer survivors based on urinary DCBA concentrations, a Kaplan-Meier (KM) curves was constructed using the " survey " package in R.
Subgroup analyses were conducted to assess the influence of confounding factors on the prevalence of cancer survivors associated with DCBA. We evaluated interaction effects by incorporating multiplicative interaction terms between DCBA and the respective subgroup variables.
Results
Baseline characteristics of participants
This research involved a sample of 5,859 participants who were stratified into four groups according to the quartiles of DCBA (Q1-Q4). Participants ages ranged from 47.37 to 50.86, with 50% female. Table 1 presents demographic data, socioeconomic data, and behavioral data. A majority of the participants (62.91%) had attained a college education or higher, and the majority (78.43%) reported not having diabetes. The results presented in the table indicate statistically significant variations in DEET exposure across various demographic and health-related factors, such as age, gender, race, education level, marital status, BMI, PIR, smoking, alcohol consumption, and diabetes (P < 0.05). Conversely, no statistically significant differences were observed in DEET exposure levels among individuals with hypertension, cardiovascular disease, and chronic obstructive pulmonary disease. Specifically, a notable proportion of male, non-Hispanic white participants who were smokers exhibited elevated concentrations of DEET in their urine samples (Q4).
Table 1.
Characteristics of cancer survivors under, NHANES 2007–2016
| Variables | Total | Q1 | Q2 | Q3 | Q4 | P-value |
|---|---|---|---|---|---|---|
| Age, mean(SE) | 47.37(0.36) | 50.36(0.63) | 47.46(0.60) | 45.50(0.59) | 46.15(0.47) | < 0.0001 |
| Sex, n(%) | < 0.0001 | |||||
| Female | 50.86(0.02) | 56.98(1.57) | 54.57(1.87) | 48.56(1.75) | 43.23(1.71) | |
| Male | 49.14(0.02) | 43.02(1.57) | 45.43(1.87) | 51.44(1.75) | 56.77(1.71) | |
| Ethnicity, n(%) | < 0.001 | |||||
| Hispanic | 12.78(0.01) | 12.79(1.40) | 12.80(1.32) | 13.05(1.59) | 12.48(1.75) | |
| Non-Hispanic Black | 10.24(0.01) | 6.14(0.72) | 10.34(1.05) | 13.76(1.48) | 10.76(1.22) | |
| Non-Hispanic White | 70.22(0.04) | 71.95(2.10) | 70.62(2.08) | 67.25(2.41) | 71.02(2.44) | |
| Others | 6.76(0.00) | 9.13(1.02) | 6.24(0.81) | 5.94(0.79) | 5.74(0.81) | |
| Marital, n(%) | < 0.001 | |||||
| Married/Living with partner | 62.63(0.03) | 63.79(1.67) | 59.96(1.56) | 59.02(1.82) | 67.74(1.40) | |
| Not married | 37.37(0.01) | 36.21(1.67) | 40.04(1.56) | 40.98(1.82) | 32.26(1.40) | |
| Educational level, n(%) | 0.03 | |||||
| College or above | 62.91(0.03) | 66.50(1.82) | 63.12(1.97) | 62.76(1.92) | 59.29(2.46) | |
| High school or GED | 32.47(0.02) | 28.28(1.72) | 32.51(2.03) | 32.48(1.84) | 36.58(2.34) | |
| Less than high school | 4.62(0.00) | 5.22(0.52) | 4.38(0.57) | 4.76(0.54) | 4.12(0.49) | |
| BMI, mean(SE) | 0.003 | |||||
| < 25 | 29.70(0.01) | 33.56(1.68) | 32.15(1.73) | 27.21(1.57) | 25.82(1.74) | |
| 25–30 | 34.06(0.01) | 34.85(1.63) | 33.96(1.99) | 34.24(1.81) | 33.20(1.54) | |
| > 30 | 36.24(0.01) | 31.60(1.67) | 33.88(1.76) | 38.55(1.85) | 40.98(1.73) | |
| PIR, mean(SE) | 0.004 | |||||
| < 1.3 | 21.97(0.01) | 16.94(1.32) | 22.11(1.73) | 26.05(1.94) | 22.83(1.59) | |
| 1.3–3.49 | 34.58(0.01) | 35.01(1.41) | 35.74(1.61) | 31.71(1.84) | 35.77(1.70) | |
| > 3.49 | 43.45(0.02) | 48.05(1.95) | 42.16(2.22) | 42.24(1.97) | 41.39(2.45) | |
| Smoke, n(%) | < 0.0001 | |||||
| Former | 24.08(0.01) | 24.61(1.41) | 22.24(1.19) | 22.97(1.44) | 26.52(1.77) | |
| Never | 55.43(0.02) | 58.74(1.75) | 59.16(1.34) | 54.09(1.88) | 49.62(1.85) | |
| Now | 20.50(0.01) | 16.65(1.44) | 18.60(1.19) | 22.95(1.41) | 23.85(1.47) | |
| Drinking status, n(%) | < 0.0001 | |||||
| Former | 15.70(0.01) | 18.06(1.14) | 16.24(1.13) | 14.38(1.14) | 14.08(1.15) | |
| Mild | 35.68(0.02) | 39.01(1.68) | 37.41(1.87) | 31.92(1.64) | 34.27(1.80) | |
| Moderate | 16.51(0.01) | 14.14(1.23) | 16.19(1.31) | 18.72(1.01) | 17.01(1.29) | |
| Heavy | 20.78(0.01) | 16.11(1.22) | 18.35(1.46) | 23.48(1.60) | 25.26(1.48) | |
| Never | 11.34(0.01) | 12.68(1.22) | 11.80(1.24) | 11.51(1.55) | 9.37(1.05) | |
| Hypertension, n(%) | 0.51 | |||||
| No | 62.76(0.02) | 60.92(1.61) | 64.27(1.80) | 63.32(1.54) | 62.48(1.76) | |
| Yes | 37.24(0.01) | 39.08(1.61) | 35.73(1.80) | 36.68(1.54) | 37.52(1.76) | |
| DM, n(%) | 0.02 | |||||
| DM | 13.42(0.01) | 16.48(1.25) | 12.54(1.24) | 10.81(0.83) | 13.83(0.86) | |
| IFG | 3.72(0.00) | 3.01(0.65) | 4.11(0.68) | 4.08(0.55) | 3.67(0.63) | |
| IGT | 4.43(0.00) | 3.42(0.54) | 4.14(0.64) | 4.68(0.79) | 5.50(0.62) | |
| No | 78.43(0.03) | 77.10(1.43) | 79.21(1.59) | 80.43(1.25) | 77.00(1.23) | |
| CVD, n(%) | 0.8 | |||||
| No | 90.98(0.03) | 90.45(0.82) | 91.01(0.80) | 91.53(0.77) | 90.94(0.78) | |
| Yes | 9.02(0.00) | 9.55(0.82) | 8.99(0.80) | 8.47(0.77) | 9.06(0.78) | |
| COPD, n(%) | 0.6 | |||||
| No | 94.65(0.03) | 94.11(0.92) | 94.42(0.90) | 95.51(0.65) | 94.57(0.81) | |
| Yes | 5.35(0.00) | 5.89(0.92) | 5.58(0.90) | 4.49(0.65) | 5.43(0.81) |
Cox multivariate analysis
A multivariable Cox proportional hazards model was employed to evaluate the independent relationships between urinary DEET levels and overall mortality, cancer-specific mortality, and non-cancer mortality in a cohort of cancer survivors (Table 2). Model 1 was unadjusted for confounding variables, while Model 2 included adjustments for demographic factors such as age, sex, and ethnicity. Model 3 expanded upon Model 2 by additionally controlling for marital status, education level, smoking and drinking status, PIR, hypertension, and diabetes mellitus. Significant associations with overall mortality among cancer survivors were observed when urinary DCBA concentrations fell within the Q2 and Q3 ranges, as indicated by all three models (P < 0.05). However, these associations were not statistically significant in the Q4 concentration range. For cancer-specific and non-cancer mortality, patients experienced decreased death rates within the Q3 and Q2 concentration ranges, respectively, with statistically significant values. Notably, mortality rates among cancer survivors decreased with DEET exposure in the aforementioned scenarios (HR < 1).
Table 2.
Multivariable Cox regression analysis results of urinary metabolite DCBA with specific mortality rate in cancer survivors from NHANES
| Mortality outcome | Model1 | Model2 | Model3 | |||
|---|---|---|---|---|---|---|
| HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | |
| All-cause mortality | ||||||
| DCBA | ||||||
| Q1 | Reference | Reference | Reference | Reference | Reference | Reference |
| Q2 | 0.66(0.53, 0.81) | < 0.0001 | 0.73(0.60, 0.90) | 0.003 | 0.72(0.57, 0.90) | 0.005 |
| Q3 | 0.06(0.48, 0.77) | < 0.0001 | 0.77(0.60, 0.98) | 0.04 | 0.78(0.61, 1.00) | 0.05 |
| Q4 | 0.83(0.65, 1.06) | 0.14 | 1.04(0.83, 1.30) | 0.73 | 1.01(0.80, 1.29) | 0.91 |
| Cancer mortality | ||||||
| DCBA | ||||||
| Q1 | Reference | Reference | Reference | Reference | Reference | Reference |
| Q2 | 0.81(0.54, 1.22) | 0.31 | 0.90(0.59, 1.38) | 0.62 | 0.89(0.57, 1.37) | 0.59 |
| Q3 | 0.45(0.28, 0.72) | < 0.001 | 0.57(0.36, 0.90) | 0.02 | 0.58(0.36, 0.93) | 0.02 |
| Q4 | 0.70(0.48, 1.01) | 0.06 | 0.88(0.59, 1.31) | 0.53 | 0.83(0.54, 1.30) | 0.42 |
| Non-cancer mortality | ||||||
| DCBA | ||||||
| Q1 | Reference | Reference | Reference | Reference | Reference | Reference |
| Q2 | 0.60(0.47, 0.78) | < 0.001 | 0.68(0.53, 0.86) | 0.001 | 0.66(0.51, 0.86) | 0.002 |
| Q3 | 0.65(0.51, 0.85) | 0.001 | 0.83(0.64, 1.09) | 0.19 | 0.85(0.64, 1.13) | 0.26 |
| Q4 | 0.87(0.65, 1.17) | 0.37 | 0.88(0.59, 1.31) | 0.51 | 1.07(0.81, 1.41) | 0.62 |
Association of DCBA with the mortality for cancer patients
Restricted cubic spline (RCS) analyses between urinary DEET and mortality rates among cancer survivors showed that before the inflection point, mortality rates decreased with increasing levels of DEET exposure (Fig. 2). This may suggest that a certain dose of DEET may have a protective effect on the human body. However, beyond the inflection point, as urinary DEET concentrations increased, mortality rates gradually decreased. Significantly, the rate of cancer-specific mortality increase among cancer survivors was notably slower in comparison to the other two groups, thus emphasizing the potential protective impact of DEET on individuals who have survived cancer.To mitigate potential bias in estimating the relationship attributable to differing follow-up durations, we restricted the restricted cubic spline (RCS) analysis to patients who completed their baseline interviews in 2016. This approach ensured consistent trend outcomes (Supplement Fig. 1).
Fig. 2.
The association between DCBA and all-cause mortality (A), cancer-specific mortality (B), and non-cancer mortality (C) in cancer survivors was examined using RCS in this study
Exploring associations between urinary DCBA concentrations and survival outcomes in cancer survivors: insights from Kaplan-Meier analysis
Kaplan-Meier (KM) curves were utilized to illustrate the varying survival probabilities of cancer survivors based on urinary DCBA concentrations (Fig. 3). The findings indicated that cancer survivors in the Q3 category (1.95,6.845] of DCBA concentration demonstrated notably elevated overall survival probability (P < 0.001). Furthermore, the Q3 group exhibited the highest cancer-specific survival probability (P = 0.009) and non-cancer-specific survival probability (P < 0.001) in comparison to the other groups. To enhance the understanding of the influence of DCBA concentration on cancer survival rates, the study cohort was stratified into 11 distinct cancer types according to the anatomical site of the malignancy (Supplement Fig. 2). The findings revealed a statistically significant variation in survival risk across DCBA concentration quartiles specifically for breast cancer (P = 0.028). However, for the remaining cancer types, the analysis did not yield valid results, likely due to inadequate sample sizes.
Fig. 3.
Utilizing KM curves to examine the temporal trends in all-cause mortality (A), cancer mortality (B), and non-cancer mortality (C) among cancer survivors, stratified by quartiles of urinary DCBA levels
Investigating the impact of DCBA exposure on cancer survivor survival rates: subgroup analysis
This study employed subgroup analyses to explore potential variations in the relationship between DCBA exposure and mortality rates. While no statistically significant interactions were observed across all subgroups (all P > 0.05), notable associations were found in the female and non-Hispanic Black subgroups (Table 3). Specifically, DCBA concentrations were linked to increased overall survival probability among cancer survivors in both subgroups, with statistically significant results (P = 0.033, P = 0.028).
Table 3.
Subgroup analyses of the associations between quartiles of urinary DCBA levels and the mortality rate among cancer survivors
| Variables | N | All-cause mortality | ||
|---|---|---|---|---|
| HR(95%CI) | P-value | P-interaction | ||
| Age | 0.643 | |||
| <= 65 | 4556 | 1.12 (0.81 to 1.56) | 0.497 | |
| > 65 | 1303 | 1.00 (0.86 to 1.17) | 0.969 | |
| Sex | 0.052 | |||
| Male | 2908 | 1.03 (0.86 to 1.23) | 0.755 | |
| Female | 2951 | 0.77 (0.61 to 0.98) | 0.033 | |
| Ethnicity | 0.287 | |||
| Hispanic | 1393 | 0.84 (0.59 to 1.19) | 0.318 | |
| Non-Hispanic Black | 1162 | 0.74 (0.57 to 0.97) | 0.028 | |
| Non-Hispanic White | 2755 | 0.95 (0.79 to 1.15) | 0.606 | |
| Others | 549 | 1.18 (0.63 to 2.20) | 0.609 | |
| Marital | 0.454 | |||
| Married/Living with partner | 3451 | 1.01 (0.84 to 1.22) | 0.877 | |
| Not married | 2408 | 0.89 (0.65 to 1.21) | 0.445 | |
| Educational level | 0.92 | 0.893 | ||
| College or above | 3148 | 0.66 (0.41 to 1.08) | ||
| High school or GED | 2153 | 0.62 (0.39 to 1.00) | ||
| Less than high school | 558 | 0.70 (0.33 to 1.52) | ||
| BMI | 0.799 | |||
| < 25 | 1691 | 0.94 (0.71 to 1.25) | 0.68 | |
| 25–30 | 1957 | 0.99 (0.80 to 1.24) | 0.949 | |
| > 30 | 2211 | 0.89 (0.66 to 1.22) | 0.472 | |
| PIR | 0.812 | |||
| < 1.3 | 1905 | 0.87 (0.68 to 1.11) | 0.267 | |
| 1.3–3.49 | 2140 | 0.98 (0.77 to 1.26) | 0.882 | |
| > 3.49 | 1814 | 0.91 (0.68 to 1.22) | 0.524 | |
| Smoke | 0.253 | |||
| Former | 1422 | 0.86 (0.70 to 1.07) | 0.179 | |
| Never | 3176 | 0.86 (0.64 to 1.16) | 0.330 | |
| Now | 1261 | 1.11 (0.81 to 1.51) | 0.513 | |
| Drinking status | 0.108 | |||
| Former | 1108 | 0.95 (0.78 to 1.15) | 0.594 | |
| Mild | 1880 | 0.89 (0.66 to 1.20) | 0.437 | |
| Moderate | 889 | 1.03 (0.53 to 2.02) | 0.927 | |
| Heavy | 1165 | 1.43 (0.97 to 2.12) | 0.069 | |
| Never | 817 | 0.97 (0.66 to 1.42) | 0.863 | |
| Hypertension | 0.276 | |||
| No | 3361 | 1.08 (0.78 to 1.51) | 0.633 | |
| Yes | 2498 | 0.89 (0.75 to 1.05) | 0.164 | |
| DM | 0.212 | |||
| DM | 1040 | 0.84 (0.68 to 1.03) | 0.101 | |
| IFG | 231 | 1.03 (0.53 to 1.99) | 0.936 | |
| IGT | 277 | 0.72 (0.45 to 1.16) | 0.177 | |
| No | 4311 | 1.04 (0.83 to 1.30) | 0.758 | |
| CVD | 0.885 | |||
| No | 5211 | 0.95 (0.74 to 1.21) | 0.657 | |
| Yes | 648 | 0.97 (0.74 to 1.26) | 0.794 | |
| COPD | 0.787 | |||
| No | 5526 | 0.96 (0.81 to 1.15) | 0.655 | |
| Yes | 333 | 0.90 (0.67 to 1.22) | 0.505 | |
Discussion
Utilizing data from the NHANES, this study examined the relationship between DCBA, the primary metabolite of the insect-repellent DEET, and the likelihood of all-cause mortality, cancer-specific mortality, and non-cancer mortality in individuals with a history of cancer. The findings consistently indicated that certain levels of DCBA concentrations were correlated with reduced risks of all-cause mortality, cancer-specific mortality, and non-cancer mortality in cancer survivors. At the outset, a notable correlation was observed between quartiles of DCBA concentrations and the incidence of all-cause mortality, cancer-specific mortality, and non-cancer mortality among cancer survivors. Subsequently, employing multivariable Cox regression models and Kaplan-Meier curves, we established a connection between quartile levels of DCBA concentrations and the risks associated with all-cause mortality, cancer-specific mortality, and non-cancer mortality among cancer survivors, thereby reinforcing the association between DCBA and the three types of mortality risks. This study has demonstrated that among adults aged 20 and older in the NHANES population, concentrations of DCBA falling within the Q3 (1.95,6.845] range are linked to decreased risks of all-cause mortality, cancer-specific mortality, and non-cancer mortality in individuals with a history of cancer. Notably, this association was found to be particularly pronounced among females and individuals of non-Hispanic Black descent.
Research suggests that DEET may influence gene expression in mammals [22] and impact human neurobehavior [23, 24]. Adverse reactions to DEET primarily involve the central nervous system (CNS) and the cardiovascular system, and may also manifest as allergic responses or other dermatological conditions. CNS involvement can present with symptoms including drowsiness, headache, confusion, disorientation, ataxia, tremors, seizures, and acute encephalopathy with psychosis. Incorrect usage may inadvertently intensify the adverse effects of DEET in susceptible individuals. Consequently, while nearly all DEET absorbed by the human body is eliminated via urine within 24 h, the swift metabolism of DEET raises concerns regarding its potential to induce other physiological changes or pose health risks, an issue that remains unresolved.The effects of DEET on human health have predominantly been examined in clinical research at present [25]. Recent studies indicate a potential association between DEET exposure and the incidence of kidney stones in adults, with higher concentrations of the metabolite DCBA showing a positive correlation with the risk of developing kidney stones. Specifically, elevated levels of DCBA are linked to an increased likelihood of kidney stone formation. These results underscore the potential connection between DEET and its metabolite DCBA in relation to the risk of kidney stone development, particularly at higher levels of exposure. Hence, it is imperative for individuals utilizing DEET to exercise caution in its application and limit prolonged exposure to mitigate the potential hazards of kidney stone formation. Additional research is warranted to enhance our knowledge of the impacts of DEET and its byproducts on human well-being [26]. Corresponding data suggests that increased levels of DCBA are linked to elevated rates of hyperuricemia, obesity (particularly central obesity), and cardiovascular conditions among adult populations [17, 27–29]. Therefore, individuals using DEET should be mindful of proper usage and avoid excessive exposure to reduce the potential risks of developing kidney stones. Further research may help to provide a more comprehensive understanding of the effects of DEET and its metabolites on human health [26]. However, while some studies suggest that exposure to DCBA is not significantly associated with heart attacks, congestive heart failure, angina, and stroke, there is a scarcity of research on the impact of DEET exposure on overall mortality in cancer survivors, cancer-specific mortality, and non-cancer mortality in the adult population [30]. Additional research is required to validate the potential relationship between these variables. As the body of evidence indicating the potential negative effects of DEET on human health grows, it is crucial to acknowledge that these studies have primarily examined the association between elevated levels of DCBA and the risk of disease in humans through correlational research. Previous research has found minimal toxicity in household insecticides, with the majority of individuals being exposed to low concentrations of DCBA. It is imperative to explore the potential impact of low concentrations of DCBA, falling within the typical usage range, on the risk of human disease, an area that has not received significant attention in research thus far. This study offers prospective evidence investigating the relationship between quartiles of DCBA, the primary urinary metabolite of DEET found in household insecticides, and the risks of overall mortality, cancer-specific mortality, and non-cancer mortality in adult cancer survivors in the United States. The findings indicate that certain intervals of dietary consumption of DCBA (Q2, Q3) are linked to decreased rates of overall mortality, cancer-specific mortality, and non-cancer mortality risk among adult cancer survivors included in the NHANES dataset.
This study suggests that among NHANES participants, adult cancer survivors with DCBA concentrations falling within the range of (0.665, 1.95] exhibit a reduced risk of both overall mortality and non-cancer mortality. Conversely, concentrations falling within the range of (1.95, 6.845] are linked to decreased risks of overall mortality, cancer-specific mortality, and non-cancer mortality. These findings are further supported by Kaplan-Meier analysis, which yields consistent results. These findings present a divergence from conventional research, as epidemiological studies have identified various potential adverse effects of DEET usage, such as neurological impacts, seizures, and carcinogenic properties [31–34]. Additionally, in vitro studies have shown that DEET exhibits a pro-angiogenic effect, facilitating the movement and attachment of endothelial cells, and consequently fostering tumor development in vivo [14]. Nevertheless, DEET does not demonstrate substantial proliferative effects on U87MG glioblastoma-astrocytoma cells. Various factors may account for these disparities, with the primary factor being the variability in research concentrations of DEET and its metabolite active ingredient DCBA. High concentrations have been linked to heightened cancer-specific mortality risk, which escalates as concentrations increase. Conversely, at lower concentrations, this study affirms that DCBA can diminish overall mortality, cancer-specific mortality, and non-cancer mortality risk in cancer survivors. Furthermore, a higher concentration of DCBA is found to have a more pronounced effect on reducing mortality risk among cancer survivors within a specific range. Early research on the adverse effects of DEET in cancer patients was hindered by small sample sizes, limiting the generalizability of findings [33, 34]. In contrast, this study involved a larger cohort of 5859 American adult cancer survivors, enhancing its representativeness and providing more comprehensive insights compared to earlier investigations.
Our subgroup analysis revealed that female and non-Hispanic black cancer survivors exhibit increased susceptibility to the effects of DCBA concentrations. Specifically, these populations demonstrate a decreased risk of all-cause mortality as DCBA concentrations rise, indicating a potential genetic predisposition towards heightened sensitivity to DCBA. Further investigation into the underlying genetic mechanisms is necessary to elucidate the precise pathways through which this heightened sensitivity influences mortality risk in cancer survivors.
Through a comprehensive examination of adult cancer survivors across the United States, our research has consistently demonstrated a correlation between the active ingredient DEET found in household insecticides and heightened risks of all-cause mortality, cancer-specific mortality, and non-cancer mortality. Based on a nationwide study of adult cancer survivors in the United States, we have provided consistent evidence linking the active ingredient DEET in household insecticides with the risk of all-cause mortality, cancer-specific mortality, and non-cancer mortality in adult cancer survivors. Through the correlation of the principal metabolite of DEET, DCBA, with reduced mortality risks in cancer survivors and the subsequent verification of these correlations, we have presented compelling observational data that establishes a prospective link between DCBA levels and future all-cause mortality, cancer-specific mortality, and non-cancer mortality among adult cancer survivors. Our study includes robust methodological considerations, including adjustments for potential confounders such as demographic variables, socioeconomic status, dietary habits, and comorbid non-cancerous conditions.
This study is subject to several limitations that warrant consideration in future research endeavors. Primarily, the investigation solely focused on the active ingredient DEET in household insecticides and its potential correlation with mortality risk among cancer survivors. Subsequent studies should contemplate conducting comprehensive analyses encompassing various active ingredients found in household insecticides. Additionally, the self-reporting of cancer diagnoses and types represents another limitation that should be acknowledged. While certain research has demonstrated a satisfactory level of agreement between self-reported cancer history and medical records, it is important to consider the potential impact of recall bias and inaccuracies in data recording on self-reported data. Thus, caution is warranted when interpreting the findings of our study. Additionally, the limitations of this study include constraints related to the availability of the NHANES database and the inability to fully account for residual confounding factors, such as genetic variations. Furthermore, the study solely assessed DCBA concentration at baseline and did not capture information on the dynamic fluctuations of these factors over the course of follow-up. Subsequent studies should prioritize the assessment of the effects of each active component present in household insecticides on the long-term outcomes of individuals who have survived cancer, by considering the evolving nature of active ingredient data and the unique characteristics of various types of cancer. This approach will enhance our comprehension of the potential association between household insecticides and the mortality risk among cancer survivors.
Conclusion
In this nationwide cohort analysis of adult cancer survivors, the primary metabolite of the active ingredient DEET in household insecticides, DCBA, was discovered to have a correlation with decreased risks of all-cause mortality, cancer-specific mortality, and non-cancer mortality. This correlation was particularly evident in the Q2 (0.665, 1.95] and Q3 (1.95, 6.845] categories of DCBA concentrations. Additionally, this correlation was more prominent among female and non-Hispanic Black individuals.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
Data from NHANES was employed in this research. The author thanks all the contributors and participants of NHANES.
Abbreviations
- DEET
N, N-diethyl-m-toluamide
- DCBA
3-diethyl-carbamoyl benzoic acid
- GED
General Educational Development
- BMI
Body mass index
- DM
Diabetes mellitus
- IFG
Impaired fasting glucose
- IGT
Impaired glucose tolerance
- CVD
Cardiovascular disease
- COPD
Chronic obstructive pulmonary disease
Author contributions
Lingjuan Liu: Conceptualization, Methodology, Writing – original draft, Data curation. Weicheng Qin: Conceptualization, Validation, Data curation, Software. Lixin Nie: Validation, Methodology. Ximing Wang: Validation, Methodology. Xiulan Dong: Project administration, Methodology, Supervision, Writing – review & editing.
Funding
There is no funding to declare.
Data availability
The datasets presented in this study can be found in online repositories (https://www.cdc.gov/nchs/nhanes/index.htm).
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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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 datasets presented in this study can be found in online repositories (https://www.cdc.gov/nchs/nhanes/index.htm).