Association between urinary antibiotic exposure and blood pressure parameters in reproductive-age men: a biomonitoring-based cross-sectional study in China

Introduction

Hypertension is a major preventable factor contributing to cardiovascular disease (CVD) and premature death worldwide. Currently, more than one billion people around the world are suffering from hypertension (1). Findings from the Global Burden of Disease (GBD) Study 2021 reveal that in 2021, roughly 10.9 million individuals across the globe lost their lives due to high systolic blood pressure (SBP) (2). In China, the standardized prevalence of hypertension among adults aged 18–69 years first rose from 20.8% in 2004 to 29.6% in 2010, and then declined to 24.7% in 2018 (3). Although the prevalence of hypertension in China has declined slightly, treatment and control remain low. A recent study based on the GBD 2021 shows that although the global burden of CVD caused by high SBP has an obvious decreasing trend among people aged 50 years and above, the control effect of such diseases is the poorest in the 15–49-year age group (4). A study conducted in Shenzhen from 1997 to 2018 indicated that the average SBP and diastolic blood pressure (DBP) of adults in Shenzhen had both increased, and there was no improvement in the awareness, treatment, and control rates of hypertension (5). Therefore, accurately identifying and thoroughly exploring various pathogenic factors that lead to elevated blood pressure (BP), particularly before it progresses to hypertension, is of great significance for the effective prevention and control of hypertension. Beyond the traditional risk factors for CVD, such as diabetes, drinking, smoking, hypercholesterolemia, and genetic susceptibility, a growing body of evidence suggests that environmental pollutants have emerged as a new and significant cause of elevated BP (6).

Antibiotics are widely used in medicine, animal husbandry, aquaculture, and agriculture, and are considered a new class of emerging contaminants (7). Humans are exposed to antibiotics through a variety of routes, including the use of medications, dietary intake, inhalation of air, ingestion of dust, and skin contact (8). A global study showed that global antibiotic usage increased by 65% and the usage rate increased by 39% between 2000 and 2015 (9). Studies indicate that the detection rates of antibiotics in Chinese soil, surface water, and coastal waters are at extremely high levels, at 100%, 98.0%, and 96.4%, respectively (10). A systematic review identified 15 antibiotics, including erythromycin, doxycycline, ciprofloxacin, and florfenicol, as the primary antibiotics with relatively high detection frequencies and concentrations (11).

Currently, research on the biological detection of antibiotics in the human body and their health effects mainly focuses on relatively vulnerable groups, including the elderly, children, and pregnant women. Another study from Anhui, China, found that the total detection rates of all antibiotics in reproductive-age couples reached 98.9% and 99.3%, respectively, with those of veterinary antibiotics (VA) and preferred as veterinary antibiotics (PVA) both exceeding 90% (12). A study from Jiangsu Province, China, detected 35 antibiotics in children’s urine, with a total detection rate of 98.31% and the detection rates of both sulfonamides and fluoroquinolones exceeding 60% (13). A multi-site biomonitoring study in China revealed that the overall exposure to antibiotics poses a health risk to 13.9% of the individuals. Among them, amoxicillin, florfenicol, and ciprofloxacin are the main factors contributing to the potential health risks caused by the disruption of the gut microbiota (14). A study on children’s antibiotic exposure shows that children’s contact with certain VA and PVA through food or water is associated with an increased risk of overweight/obesity (13). A cohort study on the elderly found that ciprofloxacin was positively correlated with triglyceride levels, florfenicol exposure was associated with an increased risk of hyper-β-lipoproteinemia and hypo-α-lipoproteinemia, and doxycycline levels were negatively correlated with total cholesterol levels (15). In addition, exposure to specific types of antibiotics, including doxycycline, ciprofloxacin, and florfenicol, was associated with an increased risk of obesity in older adults (16). In the elderly population in China, the levels of fluoroquinolones in urine are associated with elevated SBP in both arms, elevated DBP in the left arm, and an increased risk of hypertension (17).

Currently, existing research on the relationship between antibiotic exposure and cardiovascular health mainly focuses on blood lipid indicators and body mass index (BMI), with the majority of studies conducted among the elderly. There is a notable lack of research on the association between internal antibiotic exposure and BP parameters, especially in the reproductive-age population. Therefore, in this cross-sectional study based on biological monitoring, we aim to explore the associations between the single and combined exposure levels of multiple antibiotics in the urine samples of reproductive-age men and BP parameters. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-aw-817/rc).

Methods

Study population

This cross-sectional study was performed in Hefei, China. In brief, this study recruited healthy adult men who were free from reproductive-related diseases and viral infections (September 2022 to December 2023). Urine samples were collected from these participants, and their BP was measured. The detailed inclusion and exclusion criteria can be found in our previous study (18). Briefly, men meeting the following conditions were included: (I) no diseases; (II) no viruses or sexually transmitted infections; (III) self-reported no antibiotic use in the 3 months preceding sample collection; (IV) eligible participants were required to express willingness to provide both semen and urine samples. Based on these criteria, a consecutive sample of 986 healthy Chinese men aged 20–40 years was recruited. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University (No. P2020-12-36), and written informed consent was obtained from all participants.

Antibiotics measurement

Morning urine specimens were obtained using sterile polyethylene sample cups. Immediately after collection, the urine was aliquoted into multiple polyethylene specimen cups and promptly stored in a −20 ℃ freezer until the analysis phase. The target antibiotic was accomplished via liquid chromatography-triple quadrupole tandem mass spectrometry. This analytical approach was based on a method previously established by our collaborators (18,19). Following thawing and centrifugation of the spot urine samples, 1 mL of the supernatant was collected. This aliquot was then spiked with 15 µL of β-glucosidase, 200 µL of McIlvaine-Na₂EDTA buffer, and 20 µL of a mixture containing 36 isotopically labeled internal standards, prior to solid-phase extraction and subsequent quantitative detection. The concentration range of the calibration curve was set between 0.05 and 100 ng/mL. Both the inter and intraday precisions were less than 13%. In each batch of analysis, two spiked urine samples were included. These samples were used to evaluate the accuracy and precision of the entire monitoring process. Additionally, two blank urine samples were added to remove any background interference. The limit of detection (LOD) for the selected antibiotics spanned from 0.016 to 1.481 ng/mL (Table S1). In this study, antibiotics with a detection rate exceeding 60% were chosen for further in-depth analysis (Table S2). To reduce data loss, a common practice in environmental epidemiology, values below the LOD were replaced with half of the LOD value (20,21). Urinary antibiotic levels greater than or equal to the LOD were used to calculate creatinine-adjusted concentrations. A portable specific gravity (SG) refractometer (Atago PAL-10S, Tokyo, Japan) was employed to measure the urine SG. The urinary antibiotic concentrations adjusted by SG were considered as internal exposure levels. These concentrations were computed with the formula: P-_SG = P[(SG_m − 1)/(SG − 1)] (P-_SG: urinary antibiotic concentrations adjusted by SG; P: original concentrations of urinary antibiotic; SG_m: the median value (1.016) of SG for the whole population; SG: SG values of urine samples for each individual).

Measurement and analysis of BP parameters

BP was measured using an Omron HBP-1300 upper-arm electronic BP monitor (Omron Corporation, Kyoto, Japan). An accuracy verification test of this BP monitor model was conducted in accordance with the standards of the American Association for the Advancement of Medical Instrumentation. The results indicated that the BP monitor met the test requirements (22). After participants had rested quietly for at least 5 minutes, their SBP and DBP were measured. During the measurement, a warm and comfortable environment was ensured. The cuff of the BP monitor was placed on the right upper arm at the same level as the heart. The width of the cuff was set to two-thirds of the length of the right upper arm. During the measurement, participants are required to sit upright with their upper arms hanging naturally, and it is essential to keep the BP monitor at the same level as the heart. Additionally, the elbows and back were supported to maintain a proper posture. Participants were instructed not to speak or move during the measurement to minimize BP fluctuations. BP was measured three consecutive times, with an interval of approximately 1–2 minutes between each measurement. If the difference between two consecutive BP measurements exceeded 10 mmHg, the measurement was repeated. After the measurement was completed, the physical examination doctor recorded the BP data in detail. To calculate an individual’s pulse pressure (PP), their average DBP was subtracted from their average SBP. Mean arterial pressure (MAP) was calculated using the formula: DBP + 1/3(SBP − DBP) (23).

Covariates

Potential confounders were first identified from prior literature and our study outcomes; directed acyclic graph analysis (Figure S1) was then used to determine the final set for adjustment, which included staying up late, age, education, occupation, smoking status, alcohol use, monthly income, BMI and physical activity. The characteristics of the participants were gathered via a questionnaire. These characteristics included age (years), educational attainment, occupation, monthly income (¥), smoking status (smoking or exposure to secondhand smoke; non-smoking), alcohol consumption status (alcohol consumption; no alcohol consumption), abstinence duration (days), frequency of physical activity (times per week), and daily sleep duration (hours). Regarding the participants’ BMI, it was computed based on the height and weight data. These measurements were taken by professional medical staff.

Statistical analysis

Participant characteristics were summarized as mean ± standard deviation (SD) or number (percent), while the distribution of antibiotic concentrations was characterized using selected percentiles. Antibiotic concentrations displayed right skewness, necessitating a natural log transformation to mitigate the influence of outliers and satisfy the assumptions of normality and homogeneity of variance. The normal distribution was confirmed by using Q-Q plots. Prior to regression modeling, we assessed multicollinearity among independent variables in the generalized linear model using variance inflation factor (VIF) analysis. All VIF values were below 2, indicating no significant multicollinearity issues in the model.

Associations between urinary antibiotic levels and BP were analyzed using generalized linear models, specifying a Gaussian distribution and an identity link function. Additionally, to investigate potential exposure-response associations, we categorized urinary antibiotic concentrations into quartiles and designated the first quartile as the reference group. Given that age and BMI influence BP parameters (24,25), we conducted stratified analyses based on age and BMI. The P value for multiplicative interaction was calculated. To evaluate the combined impact, we employed a quantile-based g-computation (QGComp) method (26). The QGComp method integrates weighted quantile sum regression and g-computation techniques. It can assess how a simultaneous one-quantile increase in all antibiotics within the mixture jointly affects BP parameters. The QGComp model relaxes key requirements of traditional methods by forgoing the need for homogeneity, linearity, and additivity assumptions regarding exposures. It also quantifies contributions’ direction with positive or negative weights for antibiotics. Similar to the generalized linear models, the QGComp analysis incorporated the same confounding variables (R package, “qgcomp”).

Several sensitivity analyses were conducted. First, BMI was represented as a continuous variable within the generalized linear models. Second, given its potential association with BP, sleep duration (27) was included in the models for adjustment. Third, human antibiotics (HA) and preferred as human antibiotics (PHA) (except erythromycin) were merged into HA/PHA (28) and further adjusted as a confounding factor. Finally, for urinary antibiotic concentrations below the LOD, the values were handled using multiple imputation.

We did not perform multiple testing correction because the corrected P values would vary according to the number of tests conducted for different BP parameters and antibiotic types, which might artificially increase the probability of Type II errors. R software (4.4.2) and SPSS (23.0) were used for statistical analysis. Two-sided P values <0.05 and P values for interaction <0.10 (29) were indicated as statistical significance.

Results

The average BMI of participants was 22.96±2.93 kg/m², and the majority of them were between 20 and 30 years old (85.7%) with a bachelor’s degree or higher (71.7%). In addition, the proportions of smokers and alcohol drinkers were 17.2% and 23.6%, respectively. Among these men, 51.3% are students, 59.3% have a monthly income of less than ¥5,000, 68.5% have the habit of staying up late, 87.4% engage in physical exercise at least twice a week, and 69.0% sleep more than 7 hours per day. The mean values of SBP, DBP, PP, and MAP among participants were 120.13±10.31, 73.77±8.70, 46.37±2.13, and 89.22±9.22 mmHg, respectively (Table 1).

Table 2 and Table S2 present the positive detection rates of antibiotics and the selected levels of urinary antibiotic exposure. A proportion of 98.9% of the participants’ urine samples tested positive for antibiotics. The positive rates for erythromycin, doxycycline, ciprofloxacin, and florfenicol were 74.9%, 81.7%, 66.8%, and 65.1%, respectively. The concentration ranges corresponding to the 50th, 75th, 90th, and 95th percentiles of these four antibiotics are 0.06–0.66, 0.31–1.98, 0.96–13.92, and 2.19–26.76 ng/mL, respectively.

The relationship between urinary antibiotic exposure and BP parameters is shown in Figure 1 and Table S3. After adjusting for confounders, doxycycline [β=0.63, 95% confidence interval (CI): 0.18–1.09], ciprofloxacin (β=6.45, 95% CI: 6.30–6.61), and florfenicol (β=1.10, 95% CI: 0.60–1.61) exposure were significantly positively associated with SBP. Doxycycline (β=0.51, 95% CI: 0.12–0.89), ciprofloxacin (β=5.55, 95% CI: 5.44–5.66), and florfenicol (β=0.92, 95% CI: 0.50–1.35) exposure were significantly positively associated with DBP. Doxycycline (β=0.13, 95% CI: 0.03–0.22), ciprofloxacin (β=0.91, 95% CI: 0.84–0.97), and florfenicol (β=0.18, 95% CI: 0.08–0.29) exposure were significantly positively associated with PP. Doxycycline (β=0.55, 95% CI: 0.14–0.96), ciprofloxacin (β=5.85, 95% CI: 5.73–5.97), and florfenicol (β=0.98, 95% CI: 0.53–1.44) exposure were significantly positively associated with MAP (Table S3). Furthermore, positive exposure-response relationships between ciprofloxacin and florfenicol quartile concentration and SBP, DBP, PP, and MAP were observed (all P for trends <0.001) (Table S4). Based on the stratified analyses, we found that in the BMI <24 kg/m² group (Figure 2 and Table S5), doxycycline has stronger positive associations with SBP (β=1.06, 95% CI: 0.49–1.63), DBP (β=0.89, 95% CI: 0.41–1.37) and MAP (β=0.95, 95% CI: 0.44–1.45), while ciprofloxacin has stronger positive associations with SBP (β=6.77, 95% CI: 6.59–6.95), DBP (β=5.77, 95% CI: 5.64–5.89), PP (β=1.00, 95% CI: 0.91–1.09) and MAP (β=6.10, 95% CI: 5.96–6.24) (all P for interaction <0.10). Moreover, no differences within age groups for the associations between antibiotic exposure and BP parameters were observed (all P for interaction >0.10) (Table S6).

Figure 1 Associations of urinary antibiotics exposure with blood pressure parameters. DBP, diastolic blood pressure; MAP, mean arterial pressure; PP, pulse pressure; SBP, systolic blood pressure.

Figure 2 Association of urinary antibiotics exposure with blood pressure parameters stratified by BMI. BMI, body mass index; DBP, diastolic blood pressure; MAP, mean arterial pressure; PP, pulse pressure; SBP, systolic blood pressure.

Based on the QGComp model, we discovered that a one-quartile increase in the concentration of antibiotic mixtures was related to rises in SBP (β=8.96; 95% CI: 8.61–9.31), DBP (β=7.59; 95% CI: 7.34–7.83), PP (β=1.37; 95% CI: 1.21–1.53) and MAP (β=8.04; 95% CI: 7.77–8.32), respectively (Figure 3A; Table S7). Ciprofloxacin was the primary contributor to the positive weight of the QGComp index in SBP (positive weight =0.953) (Figure 3B), DBP (positive weight =0.966) (Figure 3C), PP (positive weight =0.848) (Figure 3D), and MAP (positive weight =0.961) (Figure 3E), respectively (Table S8 for numeric data). In sensitivity analyses, exposure-outcome associations were similar to the main findings (Tables S9-S12).

Figure 3 Associations between antibiotics mixture with blood pressure parameters. (A) Joint effects. Relative weight of each antibiotic: (B) SBP; (C) DBP; (D) PP; (E) MAP. DBP, diastolic blood pressure; MAP, mean arterial pressure; PP, pulse pressure; SBP, systolic blood pressure.

Discussion

In this study, we observed positive correlations of doxycycline, ciprofloxacin, and florfenicol exposure with SBP, DBP, PP, and MAP in reproductive-age men. The associations between urinary doxycycline and ciprofloxacin concentrations and elevated BP parameters were more pronounced in men with a BMI of less than 24 kg/m². Furthermore, there exists a positive tendency in the combined effects of the urinary antibiotic mixtures on SBP, DBP, PP, and MAP.

Research shows that the biological half-lives of the vast majority of antibiotics are relatively short (less than 20 hours), which indicates that they are metabolized rapidly in the human body (30). Given that the study participants had not used antibiotics in the past 3 months, the detection rates of erythromycin, doxycycline, ciprofloxacin, and florfenicol in urine samples were all above 65.0%, with doxycycline having the highest detection rate at 81.7%. This finding indicates that apart from medical use, healthy populations might ingest environmental antibiotic residues via dietary and other routes, and these environmental antibiotic residues, along with long-term low-dose exposure, are likely significant sources of human antibiotic exposure. Doxycycline boasts remarkable features such as a long elimination half-life, high bioavailability, wide distribution range, and strong tissue penetration (31). It is a commonly used feed additive in the livestock industry to promote animal growth (32). Ciprofloxacin is one of the four fluoroquinolone antibiotics with the highest detection frequency and concentration, and is widely used in human medicine, veterinary medicine, and as an additive in animal husbandry, agriculture, and aquaculture (33). Due to its excellent tissue permeability, rapid absorption, minimal side effects, low cost, and good antibacterial effects against both Gram-negative and Gram-positive bacteria, florfenicol has replaced chloramphenicol and has become one of the commonly used antibiotics in aquaculture and veterinary medicine (34). Antibiotic exposure levels vary across regions. A study conducted in Tianjin, China, reported that the overall detection rate of common antibiotics in adult urine exceeded 40%. Specifically, the detection rate of ciprofloxacin was 67%, whereas that of florfenicol was only 17% (35). A report from Lu’an City showed that the elderly’s urine samples had an antibiotic detection rate of 93%. Among them, the detection rate of doxycycline was 18.4%, ciprofloxacin was 16.5%, erythromycin was 8.6%, and florfenicol was 23.0% (17). Antibiotics are widely used in disease treatment and in safeguarding animal health and livestock farming. However, due to the poor absorption capacity of antibiotics in the animal gut, most antibiotics are excreted unchanged in feces and urine and then enter the natural environment (36). Previous studies have also indicated that certain types of food may be potential sources of antibiotic exposure, with fluoroquinolones and tetracyclines being the antibiotics with the highest exposure levels (37,38). In this study, among the four antibiotics with the highest detection rates, three belong to VA or PVA. The outcomes of this study indicate that contaminated food likely constitutes a primary source of antibiotic exposure for men of reproductive age, independent of medical sources.

This finding implies that contaminated food is probably one of the primary sources of antibiotic exposure for men of reproductive age beyond medical pathways.

The current study found that doxycycline, ciprofloxacin, and florfenicol showed significant positive correlations with SBP, DBP, PP, and MAP. To our knowledge, this is the first report of epidemiological evidence linking urinary antibiotic exposure with BP parameters in reproductive-age men. Currently, epidemiological studies on the health risks of antibiotic exposure have primarily focused on vulnerable populations such as the elderly (16), children (39), and pregnant women (19), with the health hazards for healthy adults often overlooked. Studies have found that antibiotic exposure, including multiple antibiotics such as tetracyclines, fluoroquinolones, and florfenicol, is associated with various health outcomes in the elderly, including dyslipidaemia, obesity, hypertension, decreased muscle strength, and depression (15-17,40,41). Similar to our results, Li et al. found that high levels of fluoroquinolones are associated with an increase in SBP and DBP, and higher concentrations of PHA and the combination of tetracyclines and fluoroquinolones is associated with an increase in SBP (17). A small sample case series study suggested that doxycycline is associated with intracranial hypertension, and discontinuation of doxycycline (regardless of whether it was used in combination with other intracranial pressure-lowering drugs) improved the condition (42). A recent meta-analysis shows that tetracycline-class and quinolone-class antibiotics are significantly associated with drug-induced intracranial hypertension (43). Even though most of the research about antibiotic biomarkers is carried out in China, large-scale epidemiological studies from Europe and the United States have indicated that taking antibiotics is associated with a higher risk of CVDs and death from cardiovascular problems (44,45).

Based on the stratified analysis, we found that two antibiotics (doxycycline and ciprofloxacin) had significant interactions with BMI on BP parameters (SBP, DBP, PP, and MAP). A study exploring the association between urinary antibiotic exposure and blood lipid levels in the elderly also suggests that there is an interaction between waist circumference and exposure to multiple antibiotics (15). Interestingly, after stratification by BMI, only in the normal BMI group (BMI <24 kg/m²), doxycycline was positively correlated with SBP, DBP, PP, and MAP. For ciprofloxacin, this positive association was stronger in the normal BMI group (BMI <24 kg/m²). It has been suggested that obesity can alter the metabolic and excretory functions of the liver and kidneys, accelerating the metabolism and excretion of antibiotics, shortening their duration of action, and weakening their impact on BP (46). A study comparing the pharmacokinetics of piperacillin in patients with severe obesity and non-obesity who suffer from severe sepsis or septic shock found that when using the conventional dosage of antibiotics, obese patients are more likely to experience piperacillin underdosing when facing high minimal inhibitory concentration pathogens (46). In contrast, the liver and kidneys of people with normal body weight are more stable, allowing the antibiotics to act for a longer time (47). Our findings indicate that non-overweight/obese individuals exhibit greater sensitivity to the effects of antibiotic exposure on BP parameters, suggesting that dietary antibiotic exposure may be a potential BP-influencing factor requiring particular attention in individuals of the normal BMI population.

Given that humans are typically exposed to multiple antibiotics simultaneously, an increasing number of studies have begun to focus on the overall impact of mixed antibiotic exposure on health. Moreover, this research perspective also helps to identify pollutants that contribute more significantly to health effects. Using an advanced mixed exposure model, the QGComp approach, we found that the mixed exposure of the four antibiotics with the highest detection rates was significantly and positively correlated with four BP parameters. These findings complement the results of previous studies and enrich the scientific basis for the adverse health effects of co-exposure to multiple antibiotics in humans (18,48). In addition, we found that ciprofloxacin had the most significant positive contribution to all four BP parameters, with weights exceeding 0.8 for each parameter. pressure parameters, their combined contribution weight fell below 0.1. This finding indicates that antibiotics with elevated detection rates due to non-medical sources—particularly ciprofloxacin—should be prioritized for pollution monitoring and control. Currently, ciprofloxacin is one of the most widely used antibiotics globally, used to treat a variety of infections (49). Due to its relatively low mobility and high soil adsorption capacity, the presence of ciprofloxacin in agricultural environments has become an emerging issue (50). A biomonitoring study conducted in Anhui, China, shows that in the assessment of the hazard index (HI) for health risks, ciprofloxacin contributes the most to the HI among three generations of the population (51). For special populations such as pregnant women and children, the potential risk index for ciprofloxacin is also relatively high (39,52). Given its widespread global use, long-term environmental accumulation, and potential high risks for special populations, it is imperative to re-evaluate its environmental health hazards and management strategies. Animal studies suggest that the potential mechanisms by which antibiotic exposure leads to increased BP include gut microbiota dysbiosis, activation of the renin-angiotensin-aldosterone system (53-55), as well as induction of oxidative stress and inflammatory responses (56,57). These mechanisms may result in thickening of the vascular walls and increased vascular resistance, ultimately leading to elevated BP (58,59). However, other studies have indicated that the use of antibiotics such as doxycycline can inhibit the activity of matrix metalloproteinases, thereby alleviating the functional and structural abnormalities associated with hypertension, including the increase in arterial stiffness (60). Abovementioned mechanisms require validation from animal models or population cohort studies involving real-world antibiotic environmental exposures.

It must be acknowledged that while the study demonstrates a significant positive correlation between urinary antibiotic exposure (doxycycline, ciprofloxacin, and florfenicol) and BP parameters in men of reproductive age, this finding warrants careful consideration. First, regarding reverse causality, it cannot be ruled out that hypertension may alter renal excretory function, thereby increasing urinary antibiotic concentrations. Hypertension can disrupt normal renal physiology, altering glomerular filtration and tubular reabsorption processes. This may consequently change antibiotic metabolism and excretion within the body, ultimately manifesting as elevated urinary antibiotic concentrations (61,62). Secondly, regional dietary patterns represent a significant confounding factor in these findings. In certain areas, food preferences or unhealthy dietary pattern may lead to increased intake of foods containing antibiotic residues (38). Concurrently, other dietary components like salt and potassium influence BP (63). Additionally, individuals may face concurrent exposure to other pollutants such as heavy metals and pesticides. These pollutants may exhibit synergistic or antagonistic effects with antibiotics, collectively influencing BP (64). This study did not account for co-exposure to these pollutants, potentially compromising the accuracy of the findings.

Some limitations should be acknowledged. Firstly, as a cross-sectional study, it inherently cannot establish causal relationships. Specifically, we measured BP at a single point in time. Given the natural temporal and circadian fluctuations in BP, the cross-sectional design captures only the BP readings at the time of measurement. Therefore, prospective cohort studies are needed to validate and confirm the primary findings of this research. Secondly, the issue of residual confounding is a significant concern. Although we controlled for several important confounding variables that affect BP, including age, education level, occupation, monthly income, smoking, drinking, staying up late, and physical activity, there are still crucial unmeasured environmental and lifestyle factors. For instance, we lack information on individual diets, especially the frequency and timing of consumption. Moreover, we did not account for occupational antibiotic exposure (such as in farming or pharmaceutical industries) and local water quality, both of which are significant sources of environmental antibiotics. Additionally, recall bias is a problem as the exclusion of medical antibiotic use in the past 3 months relied entirely on participants’ self-reporting. Future studies should collect this critical information to prevent residual confounding. Thirdly, the measurement of antibiotics only offers a snapshot of the exposure level. Given that the antibiotic exposure situation changes dynamically over time, this one-time measurement may distort an individual’s overall exposure experience, even though some studies have shown that a single-time sample can be representative. Also, this study only focused on the exposure to antibiotic metabolites, and there may be interference from the synergistic or antagonistic effects resulting from co-exposure to other environmental pollutants. Finally, the participants in this study were solely young individuals from Anhui Province, China, who had not used antibiotics within 3 months. The results must be interpreted cautiously and may be affected by regional and demographic disparities. Consequently, future prospective cohort studies featuring multi-center designs and large sample sizes are required to validate and build upon our existing findings.

Conclusions

In summary, we found that exposure to doxycycline, ciprofloxacin, and florfenicol in urine was significantly positively correlated with four BP parameters (SBP, DBP, PP, MAP) in reproductive-age men. The associations between doxycycline, ciprofloxacin, and BP parameters were more pronounced in men with a normal BMI. Furthermore, urinary antibiotic mixtures were positively associated with SBP, DBP, PP, and MAP. However, the conclusions regarding causality drawn from this study still await further validation or refutation through larger-scale and more representative cohort studies. Furthermore, future research should prioritize investigating the impact of environmental antibiotic exposure on hypertension, thereby enhancing the clinical relevance of the findings.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-aw-817/rc

Data Sharing Statement: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-aw-817/dss

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-aw-817/prf

Funding: This work was supported by the Key Project of Natural Science Research of Anhui Educational Committee (No. 2023AH053287), Research Fund of Anhui Institute of translational medicine (No. 2025zh-04), Basic and Clinical Cooperative Research Enhancement Program Funding Project of Anhui Medical University (No. 2023xkjT036), Key Research Program of Anhui Science and Technology Innovation Platform (No. 202305a12020016), Anhui Provincial Sci-Tech Innovation Tackling Program (No. 202423k09020027), Project of MOE Key Laboratory of Population Health Across Life Cycle (No. JK20245), and Project of Engineering Research Center of Biopreservation and Artificial Organs, Ministry of Education (No. ERC-2025-04).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-aw-817/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University (No. P2020-12-36), and written informed consent was obtained from all participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Dzau VJ, Hodgkinson CP. Precision Hypertension. Hypertension 2024;81:702-8. [Crossref] [PubMed]
2. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024;403:2162-203. [Crossref] [PubMed]
3. Zhang M, Shi Y, Zhou B, et al. Prevalence, awareness, treatment, and control of hypertension in China, 2004-18: findings from six rounds of a national survey. BMJ 2023;380:e071952. [Crossref] [PubMed]
4. Li F, Li J, Tan S, et al. Cardiovascular Disease Attributable to High Systolic Blood Pressure in Younger Adults Requires Greater Attention: Insights from the Global Burden of Disease Study 2021. J Am Heart Assoc 2025;14:e041964. [Crossref] [PubMed]
5. Peng K, Cai W, Liu X, et al. Trends of mean systolic and diastolic blood pressure among adults in Shenzhen, China, 1997-2018: findings from three rounds of the population-based survey. BMJ Open 2024;14:e074575. [Crossref] [PubMed]
6. Münzel T, Hahad O, Sørensen M, et al. Environmental risk factors and cardiovascular diseases: a comprehensive expert review. Cardiovasc Res 2022;118:2880-902. [Crossref] [PubMed]
7. Yang W, Li J, Yao Z, et al. A review on the alternatives to antibiotics and the treatment of antibiotic pollution: Current development and future prospects. Sci Total Environ 2024;926:171757. [Crossref] [PubMed]
8. Khalifa HO, Shikoray L, Mohamed MI, et al. Veterinary Drug Residues in the Food Chain as an Emerging Public Health Threat: Sources, Analytical Methods, Health Impacts, and Preventive Measures. Foods 2024;13:1629. [Crossref] [PubMed]
9. Klein EY, Van Boeckel TP, Martinez EM, et al. Global increase and geographic convergence in antibiotic consumption between 2000 and 2015. Proc Natl Acad Sci U S A 2018;115:E3463-70. [Crossref] [PubMed]
10. Lyu J, Yang L, Zhang L, et al. Antibiotics in soil and water in China-a systematic review and source analysis. Environ Pollut 2020;266:115147. [Crossref] [PubMed]
11. Hu Y, Zhu Q, Wang Y, et al. A short review of human exposure to antibiotics based on urinary biomonitoring. Sci Total Environ 2022;830:154775. [Crossref] [PubMed]
12. Wang B, Geng M, Li M, et al. Preconception exposure to environmental antibiotics among childbearing couples in Anhui and health risk assessment: A multicenter population-based representative study. Ecotoxicol Environ Saf 2023;265:115514. [Crossref] [PubMed]
13. Sun H, Huang J, Zhou Y, et al. Antibiotic exposure and risk of overweight/obesity in children: a biomonitoring-based study from eastern Jiangsu, China. Front Public Health 2024;12:1494511. [Crossref] [PubMed]
14. Hu F, Liu Y, Liu X, et al. Body burden and health risk of pharmaceuticals in elderly population: A multi-site biomonitoring study in China. Ecotoxicol Environ Saf 2025;300:118468. [Crossref] [PubMed]
15. Kong L, Yu S, Gu L, et al. Associations of typical antibiotic residues with elderly blood lipids and dyslipidemia in West Anhui, China. Ecotoxicol Environ Saf 2022;242:113889. [Crossref] [PubMed]
16. Sang Y, Zhang J, Liu K, et al. Antibiotics biomonitored in urine and obesogenic risk in a community-dwelling elderly population. Ecotoxicol Environ Saf 2021;210:111863. [Crossref] [PubMed]
17. Li Z, Liu K, Zhao J, et al. Antibiotics in elderly Chinese population and their relations with hypertension and pulse pressure. Environ Sci Pollut Res Int 2022;29:67026-45. [Crossref] [PubMed]
18. Shen Q, Ge L, Zhou Y, et al. Associations between urinary antibiotics exposure and semen parameters among adult men: A biomonitoring-based cross-sectional study. Environ Pollut 2025;375:126335. [Crossref] [PubMed]
19. Geng M, Yu Z, Wang B, et al. Associating prenatal antibiotics exposure with attention deficit hyperactivity disorder symptoms in preschool children: The role of maternal vitamin D. Ecotoxicol Environ Saf 2024;285:117037. [Crossref] [PubMed]
20. Chang C, Dai Y, Zhang J, et al. Associations between exposure to pesticides mixture and semen quality among the non-occupationally exposed males: Four statistical models. Environ Res 2024;257:119400. [Crossref] [PubMed]
21. Geng M, Gao H, Wang B, et al. Urinary tetracycline antibiotics exposure during pregnancy and maternal thyroid hormone parameters: A repeated measures study. Sci Total Environ 2022;838:156146. [Crossref] [PubMed]
22. Meng L, Zhao D, Pan Y, et al. Validation of Omron HBP-1300 professional blood pressure monitor based on auscultation in children and adults. BMC Cardiovasc Disord 2016;16:9. [Crossref] [PubMed]
23. Hannan J, Newman-Norlund S, Busby N, et al. Pulse Pressure, White Matter Hyperintensities, and Cognition: Mediating Effects Across the Adult Lifespan. Ann Clin Transl Neurol 2025;12:1520-7. [Crossref] [PubMed]
24. Thompson P, Logan I, Tomson C, et al. Obesity, Sex, Race, and Early Onset Hypertension: Implications for a Refined Investigation Strategy. Hypertension 2020;76:859-65. [Crossref] [PubMed]
25. Lu J, Lu Y, Wang X, et al. Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project). Lancet 2017;390:2549-58. [Crossref] [PubMed]
26. Keil AP, Buckley JP, O'Brien KM, et al. A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures. Environ Health Perspect 2020;128:47004. [Crossref] [PubMed]
27. Guo X, Zheng L, Wang J, et al. Epidemiological evidence for the link between sleep duration and high blood pressure: a systematic review and meta-analysis. Sleep Med 2013;14:324-32. [Crossref] [PubMed]
28. Geng M, Liu K, Huang K, et al. Urinary antibiotic exposure across pregnancy from Chinese pregnant women and health risk assessment: Repeated measures analysis. Environ Int 2020;145:106164. [Crossref] [PubMed]
29. Greenland S, Senn SJ, Rothman KJ, et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol 2016;31:337-50. [Crossref] [PubMed]
30. Liu S, Zhao G, Zhao H, et al. Antibiotics in a general population: Relations with gender, body mass index (BMI) and age and their human health risks. Sci Total Environ 2017;599-600:298-304. [Crossref] [PubMed]
31. Riviere JE, Papich MG. editors. Veterinary pharmacology and therapeutics. 10th ed. Hoboken, NJ: John Wiley & Sons; 2018.
32. Gutiérrez L, Zermeño J, Alcalá Y, et al. Higher bioavailability of doxycycline in broiler chickens with a novel in-feed pharmaceutical formulation. Poult Sci 2017;96:2662-9. [Crossref] [PubMed]
33. Oberoi AS, Jia Y, Zhang H, et al. Insights into the Fate and Removal of Antibiotics in Engineered Biological Treatment Systems A Critical Review. Environ Sci Technol 2019;53:7234-64. [Crossref] [PubMed]
34. Guo X, Chen H, Tong Y, et al. A review on the antibiotic florfenicol: Occurrence, environmental fate, effects, and health risks. Environ Res 2024;244:117934. [Crossref] [PubMed]
35. Zhang J, Liu Z, Song S, et al. The exposure levels and health risk assessment of antibiotics in urine and its association with platelet mitochondrial DNA methylation in adults from Tianjin, China: A preliminary study. Ecotoxicol Environ Saf 2022;231:113204. [Crossref] [PubMed]
36. Sarmah AK, Meyer MT, Boxall AB. A global perspective on the use, sales, exposure pathways, occurrence, fate and effects of veterinary antibiotics (VAs) in the environment. Chemosphere 2006;65:725-59. [Crossref] [PubMed]
37. Ben Y, Hu M, Zhong F, et al. Human daily dietary intakes of antibiotic residues: Dominant sources and health risks. Environ Res 2022;212:113387. [Crossref] [PubMed]
38. Chu L, Wang H, Su D, et al. Urinary Antibiotics and Dietary Determinants in Adults in Xinjiang, West China. Nutrients 2022;14:4748. [Crossref] [PubMed]
39. Wang H, Tang C, Yang J, et al. Predictors of urinary antibiotics in children of Shanghai and health risk assessment. Environ Int 2018;121:507-14. [Crossref] [PubMed]
40. Gu L, Yu S, Kong L, et al. Urinary antibiotic exposure and low grip strength risk in community-dwelling elderly Chinese by gender and age. Environ Geochem Health 2023;45:3865-89. [Crossref] [PubMed]
41. Zheng H, Ni Y, Wang S, et al. Associations between antibiotic exposure and abnormal cardiac enzyme profiles in older Chinese adults. Environ Sci Pollut Res Int 2023;30:123679-93. [Crossref] [PubMed]
42. Friedman DI, Gordon LK, Egan RA, et al. Doxycycline and intracranial hypertension. Neurology 2004;62:2297-9. [Crossref] [PubMed]
43. Tao BK, Lim K, Hwang J, et al. Antibiotic exposure and drug-induced intracranial hypertension: a systematic review and meta-analysis. Eye (Lond) 2025;39:2808-14. [Crossref] [PubMed]
44. Heianza Y, Ma W, Li X, et al. Duration and Life-Stage of Antibiotic Use and Risks of All-Cause and Cause-Specific Mortality: Prospective Cohort Study. Circ Res 2020;126:364-73. [Crossref] [PubMed]
45. Heianza Y, Zheng Y, Ma W, et al. Duration and life-stage of antibiotic use and risk of cardiovascular events in women. Eur Heart J 2019;40:3838-45. [Crossref] [PubMed]
46. Jung B, Mahul M, Breilh D, et al. Repeated Piperacillin-Tazobactam Plasma Concentration Measurements in Severely Obese Versus Nonobese Critically Ill Septic Patients and the Risk of Under- and Overdosing. Crit Care Med 2017;45:e470-8. [Crossref] [PubMed]
47. Hall RG 2nd, Swancutt MA, Meek C, et al. Ethambutol pharmacokinetic variability is linked to body mass in overweight, obese, and extremely obese people. Antimicrob Agents Chemother 2012;56:1502-7. [Crossref] [PubMed]
48. Xiong W, Wang B, Han F, et al. Association between maternal antibiotic exposure and emotional and behavioural problems in children at four years of age: A biomonitoring-based prospective study. Ecotoxicol Environ Saf 2024;284:116949. [Crossref] [PubMed]
49. Kanth S, Malgar Puttaiahgowda Y, Gupta S, et al. Recent advancements and perspective of ciprofloxacin-based antimicrobial polymers. J Biomater Sci Polym Ed 2023;34:918-49. [Crossref] [PubMed]
50. Martins MR, Pires MSG. Exposure of Enchytraeus crypticus to ciprofloxacin - A multi- and transgenerational study. Environ Pollut 2024;363:125270. [Crossref] [PubMed]
51. Zhang J, Liu X, Zhu Y, et al. Antibiotic exposure across three generations from Chinese families and cumulative health risk. Ecotoxicol Environ Saf 2020;191:110237. [Crossref] [PubMed]
52. Wang H, Wang N, Qian J, et al. Urinary Antibiotics of Pregnant Women in Eastern China and Cumulative Health Risk Assessment. Environ Sci Technol 2017;51:3518-25. [Crossref] [PubMed]
53. Yang Y, Li J, Zhou Z, et al. Gut Microbiota Perturbation in Early Life Could Influence Pediatric Blood Pressure Regulation in a Sex-Dependent Manner in Juvenile Rats. Nutrients 2023;15:2661. [Crossref] [PubMed]
54. Hsu CN, Chan JYH, Wu KLH, et al. Altered Gut Microbiota and Its Metabolites in Hypertension of Developmental Origins: Exploring Differences between Fructose and Antibiotics Exposure. Int J Mol Sci 2021;22:2674. [Crossref] [PubMed]
55. Robles-Vera I, de la Visitación N, Toral M, et al. Changes in Gut Microbiota Induced by Doxycycline Influence in Vascular Function and Development of Hypertension in DOCA-Salt Rats. Nutrients 2021;13:2971. [Crossref] [PubMed]
56. Badawy S, Yang Y, Liu Y, et al. Toxicity induced by ciprofloxacin and enrofloxacin: oxidative stress and metabolism. Crit Rev Toxicol 2021;51:754-87. [Crossref] [PubMed]
57. Liao Y, Chen Z, Yang Y, et al. Antibiotic intervention exacerbated oxidative stress and inflammatory responses in SD rats under hypobaric hypoxia exposure. Free Radic Biol Med 2023;209:70-83. [Crossref] [PubMed]
58. Kaur N, Kumar P, Dhami M, et al. Antibiotic-induced gut dysbiosis: unraveling the gut-heart axis and its impact on cardiovascular health. Mol Biol Rep 2025;52:319. [Crossref] [PubMed]
59. Tanaka M, Itoh H. Hypertension as a Metabolic Disorder and the Novel Role of the Gut. Curr Hypertens Rep 2019;21:63. [Crossref] [PubMed]
60. Castro MM, Tanus-Santos JE, Gerlach RF. Matrix metalloproteinases: targets for doxycycline to prevent the vascular alterations of hypertension. Pharmacol Res 2011;64:567-72. [Crossref] [PubMed]
61. Fanos V, Cataldi L. Renal transport of antibiotics and nephrotoxicity: a review. J Chemother 2001;13:461-72. [Crossref] [PubMed]
62. Hall JE, Granger JP, do Carmo JM, et al. Hypertension: physiology and pathophysiology. Compr Physiol 2012;2:2393-442. [Crossref] [PubMed]
63. Yin L, Deng G, Mente A, et al. Association patterns of urinary sodium, potassium, and their ratio with blood pressure across various levels of salt-diet regions in China. Sci Rep 2018;8:6727. [Crossref] [PubMed]
64. Habeeb E, Aldosari S, Saghir SA, et al. Role of environmental toxicants in the development of hypertensive and cardiovascular diseases. Toxicol Rep 2022;9:521-33. [Crossref] [PubMed]