- Email: [email protected]
Antibiotic body burden of elderly Chinese population and health risk assessment: A human biomonitoring-based study
Journal Pre-proof Antibiotic body burden of Chinese elderly population and health risk assessment: A biomonitoring-based study Yitian Zhu, Kaiyong Liu, Jingjing Zhang, Xinji Liu, Linsheng Yang, Rong Wei, Sufang Wang, Dongmei Zhang, Shaoyu Xie, Fangbiao Tao PII:
S0269-7491(19)33487-6
DOI:
https://doi.org/10.1016/j.envpol.2019.113311
Reference:
ENPO 113311
To appear in:
Environmental Pollution
Received Date: 30 June 2019 Revised Date:
24 September 2019
Accepted Date: 25 September 2019
Please cite this article as: Zhu, Y., Liu, K., Zhang, J., Liu, X., Yang, L., Wei, R., Wang, S., Zhang, D., Xie, S., Tao, F., Antibiotic body burden of Chinese elderly population and health risk assessment: A biomonitoring-based study, Environmental Pollution (2019), doi: https://doi.org/10.1016/ j.envpol.2019.113311. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
22 20
Clinical Use Self-Medication
19.0
18 16.5
Antibiotics
Elderly
Urine
Population proportion (%)
16 14 12.5 11.5
12 9.6
10 8
11.2
7.0
6.8 5.9
6 4
Animal-derived Food Drinking Water
2 0
Estimated daily intake (µg/kg/day)
1
1
Antibiotic body burden of Chinese elderly population and health
2
risk assessment: a biomonitoring-based study
3
Yitian Zhu1^, Kaiyong Liu1,5^, Jingjing Zhang1, Xinji Liu1, Linsheng Yang2, Rong Wei1, Sufang Wang1,
4
Dongmei Zhang3, Shaoyu Xie4, Fangbiao Tao6*
5 6
1. Department of Nutrition and Food Hygiene, School of Public Health, Anhui Medical University,
7
81 Meishan Road, Hefei, Anhui 230032, PR China.
8
2. Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical
9
University, 81 Meishan Road, Hefei, Anhui 230032, PR China.
10
3. School of Health Management, Anhui Medical University, Hefei, Anhui 230032, PR China.
11
4. Lu’an Center of Disease Control and Prevention, Lu’an, Anhui, 237000, PR China
12
5. Department of Microbiology and Immunology, University of Michigan Medical School, Ann
13
Arbor, MI 48109, USA.
14
6. Anhui Provincial Key Laboratory of Population Health and Aristogenics, 81 Meishan Road,
15
Hefei, Anhui 230032, PR China.
16 17
^
18
*Corresponding author: Email: [email protected]. Anhui Provincial Key Laboratory of
19
Population Health and Aristogenics, 81 Meishan Road, Hefei, Anhui 230032, PR China.
These authors contributed equally to this work.
20 21 22 1
23
Abstract
24
Limited biomonitoring studies have demonstrated an extensive exposure
25
of different populations to antibiotics, but antibiotic body burden and the
26
potential health risks in the Chinese elderly population remains unclear.
27
In the present study, we investigated 990 elderly (aged 60 years and over)
28
from the Cohort of Elderly Health and Environment Controllable Factors
29
in West Anhui of China. Then, 45 representative antibiotics and 2
30
metabolites were monitored in urines by liquid chromatography
31
electrospray tandem mass spectrometry. The results revealed that 34
32
antibiotics were overall detected in 93.0% of all urine samples and the
33
detection frequencies of each antibiotic varied between 0.2% and 35.5%.
34
The overall detection frequencies of 7 human antibiotics (HAs), 10
35
veterinary antibiotics (VAs), 3 antibiotics preferred as HA (PHAs) and 14
36
preferred as VA (PVAs) in urines were 27.4%, 62.9%, 30.9% and 72.7%,
37
respectively. Notably, the samples with the concentrations of 6 PVAs
38
(sulfamethoxazole,
39
norfloxacin and lincomycin) above 5.0 × 103 ng/mL account for 1.7% of
40
total urine samples. Besides, in the remaining 62.7% of urine samples, the
41
total antibiotic concentration was in the range of the limits of detection
42
(LODs) to 20.0 ng/mL. Furthermore, the elderly with the sum of
43
estimated daily intake (EDI) of VAs and PVAs more than 1 µg/kg/day
44
accounted for 15.2% of all participants, and 6.7% of the elderly might
trimethoprim,
2
oxytetracycline,
danofloxacin,
45
under a health risk related to the gut microbiota changing under
46
antibiotics stimulation. Especially, ciprofloxacin was the foremost
47
contributor to the health risk and its hazard quotient value more than 1
48
was seen in 3.5% of all subjects. Taken together, the Chinese elderly were
49
extensively exposed to veterinary antibiotics and some elderly may under
50
a health risk linked to dysbiosis of the gut microbiota.
51
Capsule: 34 antibiotics were detected in 93% urines of 990 elderly and
52
health risk related to gut microbiota was revealed in 6.7% of the elderly.
53
Keywords: Antibiotics; Elderly; Urine; Health risk assessment.
54 55 56 57 58 59 60 61 62 63 64 65 66 3
67
1. Introduction
68
Antibiotics are not only used in the prevention and treatment of bacterial
69
infection occurred in humans and animals (Nathan, 2014) but also
70
extensively added to serve as growth factors in aquaculture and animal
71
husbandry (Van Boeckel et al., 2017). Owing to the abuse and improper
72
usage, these antibiotics were often detected in surface water, soil and food
73
(Guo et al., 2019; Sun et al., 2017; Yamaguchi et al., 2015). To date,
74
numerous publications have demonstrated that approximately 70-80
75
antibiotics and their metabolites were measured in animal-derived food
76
and drinking water worldwide (Chen et al., 2015; Wang et al., 2017a;
77
Wang et al., 2016b; Yamaguchi et al., 2015). Thus, the antibiotics
78
residues in food and the environment can be transferred to humans
79
through the food chain, which may cause bioaccumulation of antibiotics
80
in human body. Consequently, the ingestion of low-dose antibiotics for
81
extended periods of time could aggravate the growing battle with
82
emerging antibiotic-resistant pathogenic strains. What's the worse, the
83
exposure to antibiotics could increase the pesticide bioavailability via
84
disturbing gut microbiota and further may increase the pesticide exposure
85
risk (Zhan J, 2018). Additionally, increasing evidence has indicated that
86
antibiotics exposure can affect the composition and functionality of the
87
host microbiota (Becattini et al., 2016; Leclercq et al., 2017), and is one
88
of the potential risk factor for obesity (Bailey et al., 2014), diabetes 4
89
(Pearson JA, 2019), and even some nervous system diseases (Lurie et al.,
90
2015).
91
It is well-known that human biomonitoring is a powerful tool for
92
simultaneously analyzing the actual internal levels of bodily chemicals
93
from all potential routes of exposure, which may make contribution to
94
improve risk assessments. In recent years, several population-based
95
studies used urine as a sample to bio-monitor antibiotics (Li et al., 2017;
96
Wang et al., 2015; Wang et al., 2016a). For instances, more than 20
97
antibiotics were detected in Shanghai children’ urine samples and the
98
overall detected frequencies reached 79.6% (Wang et al., 2016a). Another
99
study has shown that 9 veterinary antibiotics were measured in 77.4% of
100
pre-school children urine samples in Hong Kong (Li et al., 2017). These
101
studies demonstrated that urine samples were suitable or convenient for
102
monitoring the amount of total antibiotic exposures in human body from
103
contaminated food and drinking water. Based on the studies conducted in
104
eastern China, the results showed that 4.3% of pregnant women (Wang et
105
al., 2017b), 6.0% of children (Wang et al., 2018a) and also 7.2% of
106
general adults (Wang et al., 2018b) had a health risk relevant to gut
107
microbiota.
108
Interestingly, a previous study conducted in Dalian of China has revealed
109
that in the general population (n=107), the serum concentrations for
110
sulfonamides, macrolides and chloramphenicols in elderly were higher 5
111
than those in young adults (Liu et al., 2017a). This finding implied that
112
the age may link to the antibiotic accumulation in human. As is well
113
known, ageing is accompanied by some physiologic alterations, including
114
reduced species diversity and declined metabolic activities of gut
115
microbiota, which may negatively influence the excretion process of
116
antibiotics (An R, 2018; Woodmansey et al., 2004). Moreover, the elderly
117
are vulnerable to bacterial infection disease because of their compromised
118
immune function (An R, 2018) which is the reason why antibiotics are
119
prescribed more frequently to elderly than that to younger. The
120
abovementioned results indicated that the elderly have a higher risk of
121
antibiotics bioaccumulation compared with younger. However, until now,
122
the information for antibiotic body burden and potential health risk in the
123
elderly population is seldom being investigated.
124
To our best knowledge, this is the first study focusing on antibiotics
125
exposure examination using bio-monitoring data based on an elderly
126
cohort. Here, we monitored 45 antibiotics and 2 metabolites in urine
127
samples from approximately 1000 elderly in Lu’an city of west Anhui
128
province in China and assessed health risks of antibiotics for the elderly
129
based on microbiological or toxicological effects. Our study may provide
130
new insights into the effect of antibiotics exposure on healthy ageing.
131
2. Materials and methods
132
2.1. Study population and sample collection 6
133
The data reported in this study came from the baseline survey of the
134
Elderly Health and Environment Risk Factor (EHERF) cohort, which was
135
conducted in Lu'an Municipality, West Anhui, China, since June to
136
September in 2016. Detailed procedures of elderly person recruitment and
137
of questionnaire survey were described in our previous article (Li et al.,
138
2019). Briefly, all participants aged more than 60 years and live in two
139
communities including rural community in Ji’an (JA) and an urban
140
community in Yu’an (YA). For physical examination, each participant
141
was asked to collect a morning urine sample (at least 30 mL) before they
142
came to the local community hospitals. The collected urine samples were
143
stored at −80 °C prior to the analyses of the target antibiotics. Of 1080
144
participants, 59 participants did not provide urine samples, 31 participants’
145
urine sample analysis lack urine creatinine information; therefore, we
146
eventually included 990 subjects in our analysis. No significant
147
differences were found in age (mean age, 71.8 and 73.2 years) and gender
148
ratio (54.4% and 53.3% female) between the included and the excluded
149
(P > 0.05). In fact, we measured 1021 urine samples in this study, the
150
detected frequencies and volume-based concentrations were shown in
151
Table S1. The whole participants have provided informed consent form.
152
This study was approved by the ethical committee of Anhui Medical
153
University.
154
2.2. Selection of Antibiotics 7
155
Because of the abuse and unreasonable application in aquaculture and
156
animal husbandry, antibiotics were frequently detected in aquatic
157
products and animal-derived food (Chen et al., 2015; Wang et al., 2017a),
158
therefore, exposure to antibiotics via dietary intake for the elderly should
159
not be omitted. In this study, apart from human antibiotics (HAs) and
160
antibiotics preferred as HA (PHAs), we also screened the veterinary
161
antibiotics (VAs) and antibiotics preferred as VA (PVAs). Ultimately,
162
according to the detected antibiotics in foods of animal origin (Du et al.,
163
2019; Wang et al., 2017a; Zhao et al., 2018; Zhou et al., 2013) and human
164
urines (Wang et al., 2015; Wang et al., 2017b; Wang et al., 2016a; Wang
165
et al., 2018b), we selected 45 antibiotics and 2 metabolites (Table S2)
166
including 9 sulfonamides (SAs), 10 fluoroquinolones (FQs), 7 macrolides
167
(MAs), 8 β-lactams (LAs), 4 tetracyclines (TCs), 3 phenicols (PCs), 2
168
quinoxalines (QUs), lincomycin (LIN), spectinomycin (SPC), florfenicol
169
amine, N4-acetyl -sulfamonomethoxine. All standards were purchased
170
from Sigma-Aldrich (St Louis, MO, USA), Dr Ehrenstorfer (Augsburg,
171
Germany) and TRC (North York, Canada).
172
2.3. Urine sample preparation
173
The sample preparation was referred to the previous methodology with
174
minor modification (He-Xing et al., 2014). Briefly, urine samples were
175
thawed and centrifuged for 5 min at 2000 rpm at 4 °C, 1.0-mL urine
176
supernatant was buffered with 200 µL of Na2EDTA-McIlvaine buffer (pH 8
177
4.0). Then the mixture was spiked with 15 µL β-glucuronidase aqueous
178
solution (
10000 units/mL) from Helix pomatia (type H-2,
179
Sigma-Aldrich) and 20 µL of mixed internal standards (2 µg/mL of
180
sulfamethoxazole-D4, trimethoprim-D3, ofloxacin-D3, azithromycin-D3,
181
ceftiofur-D3,
182
lincomycin-D3, 4 µg/mL of amoxicillin-D4), vortexed thoroughly and kept
183
at 37 °C overnight to hydrolyze the analyte conjugates. After hydrolysis,
184
the mixture was transferred onto a reversed phase Waters® Oasis
185
hydrophilic–lipophilic–balanced (HLB) cartridge (60 mg, 3 mL; 30 µm),
186
which was preconditioned with 1.2 mL of pure methanol and equilibrated
187
with 1.2 mL of ultrapure water. Subsequently, the cartridge was washed
188
with 1.2 mL of ultrapure water and 1.2 mL of 30 % methanol-water
189
solution to remove matrix interferences. The antibiotics kept in cartridges
190
were eluted by 2 mL of pure methanol and concentrated to near dryness at
191
45 °C under a gentle nitrogen stream, then re-dissolved in 200 µL of 5 %
192
acetonitrile-water solution. Finally, the solution was filtered by 0.22 µm
193
hydrophilic membrane and 15 µL of the extracts were injected for
194
HPLC-MS/MS analysis.
195
2.4. Instrument analysis
196
In the Anhui Provincial Key Laboratory of Population Health &
197
Aristogenics (Anhui Medical University, Hefei City, China), urinary
198
antibiotics
doxycycline-D3,
were
measured
florfenicol-D3,
by 9
a
penicillinV-D5
high-performance
and
liquid
199
chromatograph-tandem mass spectrometry (HPLC, Agilent 1200; MS,
200
Agilent 6410B). Based on the previous method with a few minor
201
modifications (Liu et al., 2017a), the analytes were separated on the
202
ZORBAX SB-C18 column (2.10 mm ×150 mm 3.50 µm Agilent,
203
USA), at a column temperature 35 °C and a flow rate of 0.4 mL/min. The
204
gradient elution program was different between positive ion and negative
205
ion modes and shown in Table S3 and Table S4. All LC-MS grade
206
solvents (water, formic acid and acetonitrile) were from Honeywell
207
International Inc (Morristown, NJ, USA). The fragment voltages,
208
collision energies and retention times of individual antibiotic are shown
209
in Table S2. Other MS conditions are as follows: drying gas temperature,
210
350 °C; drying gas flow, 10 L/min; capillary voltage, 4000 V; nebulizer
211
pressure, 40 Psi. The dynamic multiple reaction monitoring (dMRM)
212
mode was used for all data acquisition.
213
2.5. Quantification and quality control
214
Urinary antibiotics were quantified using an isotope dilution method. A
215
nine-point calibration curve from 0.5 to 200 ng/mL was prepared for all
216
analyzed antibiotics. The regression coefficients (R2) ranged from 0.990
217
to 0.999. The limits of detection (LODs) and limits of quantitation (LOQs)
218
were defined as a signal-to-noise ratio of 3 and 10, respectively. The
219
LODs of the screened antibiotics ranged from 0.03 to 2.15 ng/mL, while
220
the LOQs between 0.11 and 6.02 ng/mL. Two spiked urine samples at 20 10
221
ng/mL were used to monitor the precision and trueness of analytical
222
procedure. Procedural and instrumental blanks were also prepared for
223
each batch to avoid laboratory contamination and analytical interferences.
224
The recoveries of analyzed antibiotics ranged from 73.5% to 112.2% with
225
the relative standard deviations varying between 8.5% and 14.6%, and the
226
matrix effects between 57.5% and 123.7% with the relative standard
227
deviations varying between 11.4% and 19.7%.
228
2.6. Daily exposure dose estimation
229
We calculated the estimated daily intake (EDI) of antibiotics using the
230
following equation (Katrine et al., 2014; Wang et al., 2017b):
231
EDI (µg/kg/day)
Caj (µg/g)×CE(g/day)
(1)
bw(kg)×P
232
Caj was the creatinine-adjusted concentration of antibiotics, bw was the
233
body weight, P represented antibiotic excretion rate in urine as
234
unchanged and glucuronide-conjugated forms, which were derived from
235
human pharmacokinetic studies (Table S5). As for sulfachinoxalin,
236
sulfaclozine, sulfachloropyridazine, sarafloxacin and cyadox, the urinary
237
excretion rates from animals were used due to lack of data in human. CE
238
was the daily output of urinary creatinine, which calculated using
239
equation 2 and 3 (Mage et al., 2008) (Ht: height, cm).
240
CEmale (g/day)
×(bw1.5×Ht0.5)/106
241 242
0.926×1.93×(140–age)
CEfemale (g/day)
0.993×1.64×(140–age) 11
(2)
×(bw1.5×Ht0.5)/ 106
243
(3)
244 245
2.7. Health risk assessment
246
On the basis of EDI estimations, we further calculated HQs and HIs to
247
assess health risks from a single antibiotic exposure and combined
248
exposure, respectively. The formula was as follows (Gao et al., 2017;
249
Wang et al., 2017b): HQ
250
EDI(µg/kg bw/day) ADI(µg/kg bw/day)
;
HI=∑ HQ
251
Given that most of antibiotics belong to antibacterial agents, they mainly
252
pose impacts on gut microbiota (Becattini et al., 2016; Leclercq et al.,
253
2017). Thus, the microbiological effect-based acceptable daily intake
254
(ADI) of individual antibiotic was used to calculate HQ. The ADI used in
255
this study was derived from literature or established by authorities and
256
shown in Table S6. In the current study, HI was the sum of the HQ for
257
individual antibiotic, but except for HAs and PHAs. We assumed a
258
potential health risk would occur when the HI value for an elderly was >
259
1.
260
2.8. Statistical analysis
261
In the current study, based on the antibacterial mechanisms, we
262
categorized 34 antibiotics into eight categories (SAs, FQs, MAs, LAs,
263
TCs, PCs, QUs and LINs). Moreover, four new variables grouped by their
264
usages were HAs, VAs, PHAs and PVAs. Generally, in one urine sample, 12
265
a detection of one or more antibiotics in a category was regard as a
266
positive detection of the corresponding category. Creatinine corrected
267
concentrations of antibiotics were used to assess their exposure in urines.
268
Body mass index (BMI) was calculated as the ratio of body weight (kg)
269
to height squared (m2). All subjects were categorized into two groups of
270
normal weight (BMI < 24) and overweight (BMI
271
BMI-based cutoff values of Chinese adults proposed by the Working
272
Group on Obesity in China(Zhou, 2002). Pearson chi-square test or the
273
non-parameters test was used to examine the gender, age, regional and
274
BMI differences in the detection frequencies or concentrations of
275
individual antibiotics or categories. The binomial logistic regression
276
model was performed to estimate the relation and odds ratios for gender,
277
age, study site and BMI in association with antibiotic detection rates.
278
Values below the LODs were treated as zero for the calculation of total
279
concentrations, means, and medians. However, when performing
280
statistical analyses, those below the LODs were taken as 1/2 LODs.
281
Statistical analyses were performed using SPSS Version 23.0 software
282
(Chicago, IL, USA), statistical significance was set at P < 0.05 in the
283
current study.
284
3. Results
285
3.1. Detection concentrations and frequencies of antibiotics
286
In this study, Table 1 showed that 34 antibiotics were detected in 93.0% 13
24) according to the
287
of all urines samples, the detection frequencies of individual antibiotic
288
ranged from 0.2% to 35.5%. The predominantly categories in urines were
289
sulfonamides and fluoroquinolones, with the detection frequencies of
290
55.8% and 50.1%, respectively. In addition, two or more antibiotics were
291
simultaneously detected in above 70% samples. Moreover, 10 VAs or
292
PVAs, including sulfaclozine, ofloxacin, florfenicol, trimethoprim,
293
tetracycline, oxytetracycline, doxycycline, ciprofloxacin, norfloxacin and
294
enrofloxacin, were extensively detected in urines, which detection rates
295
were more than 10%. Sulfaclozine (35.5%) and ofloxacin (23.7%) were
296
the most frequently detected in VAs and PVAs, respectively. And the total
297
detection rates of both VAs and PVAs in our study were 62.9% and 72.7%
298
(Table S7).
299
Figure 1 showed that the majority of antibiotic concentration values in
300
the urine samples were in the range of the LODs to 20 ng/mL. However,
301
it is worth noting that 4.8% of urines were detected to contain antibiotics
302
more
303
sulfamethoxazole, trimethoprim, oxytetracycline, danofloxacin and
304
lincomycin) which content were even higher than 5000 ng/mL in urines
305
of very few elderly (Table 1). Perhaps, the extreme values of urinary
306
antibiotic concentrations were achieved by direct intakes of antibiotics,
307
such as clinical use, self-medication or an unknown cause. However, the
308
abovementioned antibiotics, biological half-lives were quite low usually
than
500
ng/mL,
especially
14
the
6
PVAs
(norfloxacin,
309
less than 20 hours (Liu et al., 2017a), could be quickly excreted from
310
human bodies. Therefore, we speculated that the higher concentrations
311
probably were obtained from a continuous intake of antibiotics seriously
312
contaminated foods or drinking water.
313
3.2. Relationship among detection frequencies with study site, age,
314
gender, and BMI
315
Table 2 presented the differences in detection frequencies of urinary
316
antibiotics among different study sites, age, gender and BMI. The
317
detection
318
sulfaclozine, oxytetracycline, ofloxacin and ciprofloxacin, were higher in
319
subjects from the rural area than those from the urban area (P < 0.05) and
320
a similar trend was observed for the category of VAs (P > 0.05). For
321
age-related
322
tetracycline and enrofloxacin) were more likely to be detected in older
323
subjects (aged > 70 years). The categories of SAs and PVAs were more
324
frequently found in men than women. Furthermore, BMI-related
325
differences were also observed in this study. The detection frequencies of
326
3 PVAs (chlortetracycline, norfloxacin and sulfachloropyridazine)
327
showed a significantly higher level in overweight group (BMI ≥ 24) than
328
normal weight group (BMI < 24), but sulfaclozine and ofloxacin were the
329
opposite (P < 0.05). Table S10 showed the adjusted associations between
330
study site, age, gender, and BMI and the detection frequencies of
frequencies
differences,
of
4
5
PVAs,
PVAs
15
including
sulfamethoxazole,
(sulfamethoxazole,
sulfadiazine,
331
antibiotics in urines, the above differences and trends remained the same
332
after adjustment for covariates. Table S11 presented the correlation
333
between study site, age, gender, and BMI and antibiotics concentration in
334
urines, the results revealed that the group with a higher detection
335
frequency often had a higher concentration of antibiotic in urine sample.
336
3.3. Estimated daily intake and health risk assessment
337
Figure 2 displayed the elderly with the total EDI greater than 1 µg/kg/day
338
accounted for 17.1% of all participants. Table 3 revealed that the extreme
339
values of 3 PVAs (sulfamethoxazole, norfloxacin and lincomycin) were
340
more than 500 µg/kg/day and the maximum value of 5450.45 µg/kg/day
341
was seen in a VA (danofloxacin). Moreover, as presented in Table S8, the
342
PVAs had the higher EDI than either HAs or PHAs. Table 4 showed that
343
12 antibiotics (2 VAs and 10 PVAs) were observed to pose a
344
microbiological effect-related health risk on 6.7% of the elderly in this
345
study. Specifically, ciprofloxacin (3.5%) was a most important
346
contributor to the potential health risk related to gut microbiota, followed
347
by
348
sulfamethoxazole were found to pose a toxicological effect-related health
349
risk in 0.3% of all subjects. Although the detection frequencies of
350
florfenicol and ofloxacin reached 23.0% and 23.7%, respectively, they
351
did not pose health risks on the elderly in this study. Even so, it did not
352
mean there were no negative effects on human health under a continuous
oxytetracycline
(1.1%)
and
16
lincomycin
(0.7%).
Besides,
353
exposure scenario.
354
4. Discussion
355
In the current study, 34 antibiotics were detected in the urine of the
356
elderly from West Anhui of China, with a total detection frequency
357
reaching 93%. The 10 veterinary antibiotics were found in 62.9% of urine
358
samples. Furthermore, the majority of the subjects had a total antibiotic
359
concentration ranging from the LODs to 20.0 ng/mL. And the HI value of
360
more than 1 was observed in 6.7% of the elderly. These results indicated
361
that the elderly were extensively exposed to antibiotics, especially VAs
362
and PVAs, and some elderly were under a health risk linked to dysbiosis
363
of the gut microbiota.
364
4.1. Exposure sources of antibiotics in the elderly
365
In general, antibiotics are the primary drugs for treat and prevent bacterial
366
infections in human and animal (Nathan, 2014). Also, antibiotics are
367
extensively added to serve as growth factors in aquaculture and animal
368
husbandry (Van Boeckel et al., 2017). Owing to the highest rates of
369
antibiotic use, first, clinical use is the main source for the elderly exposed
370
to HAs and PHAs in our study. Second, ingesting contaminated food
371
might be the most important source for the indirect exposure to
372
antibiotics (Wang et al., 2015; Wang et al., 2017b). For instance,
373
fluoroquinolones were the most frequently detected category in aquatic
374
products and had high residual levels (Liu et al., 2017b). In the current 17
375
study, 50.0% of the elderly were exposed to fluoroquinolones and the
376
detection frequencies of both ciprofloxacin and ofloxacin were more than
377
15.0%. Moreover, sulfonamides were detected in meat foods, the
378
maximum residue level in pork and chicken reached 3.6 × 103 µg/kg and
379
2.7 × 103 µg/kg (Yamaguchi et al., 2015), respectively. Also,
380
sulfonamides were frequently detected in the elderly of this study, with a
381
detection rate of 55.8%. A study conducted in Korea adults indicated that
382
after 5-day diet without animal-derived food, the urinary levels of 2 PVAs
383
(trimethoprim and ciprofloxacin) were significantly reduced (Ji et al.,
384
2010). Finally, drinking water was contaminated by some antibiotics with
385
lower concentrations, but played a limited role in antibiotic exposure of
386
elderly (Li et al., 2017; Wang et al., 2016b). Therefore, the VAs and PVAs
387
detected in urines of the elderly might primarily derive from long-term
388
ingesting contaminated food of animal origin, such as pork, fish and milk.
389
4.2. Differences of antibiotic across study sites, age, gender and BMI
390
In the current study, the detected frequencies and concentrations of some
391
antibiotics varied by age, gender, study site and BMI. For example,
392
sulfamethoxazole, tetracycline and enrofloxacin were more likely to be
393
found in the older age group which might be related to a declining
394
excretory capacity in the older. The detection frequency of PVAs was
395
higher in males than females, this could be partly explained as the sex
396
difference of dietary habits or lifestyles. Generally, the males or the 18
397
overweight adults may intake higher amount of animal food than females
398
or the normal weight ones (Fraser et al., 2000; Zou et al., 2017), which
399
may increase the risk of exposure to VAs or PVAs from food.
400
Consequently, both sulfachloropyridazine and chlortetracycline showed
401
significantly higher levels in overweight group than in the normal weight.
402
We also found some differences of urinary antibiotics across various
403
study sites. For example, the detected frequencies and concentrations of
404
VAs or PVAs, including lincomycin, ofloxacin and oxytetracycline, were
405
higher in subjects from rural area than those in urban area. In the majority
406
of rural areas of China, the treatment rate of sewage and livestock wastes
407
are quite low due to the limited infrastructure (Zhang et al., 2015). As a
408
result, antibiotics contained by animal wastes may be discharged directly
409
into the environment, which is the reason why the rural population has
410
higher opportunity to exposure the VAs and PVAs from environment.
411
Additionally, the elderly from urban area were more likely to
412
self-medication with antibiotics than those from rural area (Torres et al.,
413
2019), which is consistent with our study that the HAs (macrolides) have
414
a higher exposure level in subjects from the urban area.
415
4.3. Comparisons with other studies
416
Bio-monitoring data have indicated a widespread antibiotics exposure of
417
different populations, but most of subjects are children or younger adults
418
rather than the elderly (Wang et al., 2018a; Wang et al., 2015; Wang et al., 19
419
2017b; Wang et al., 2016a; Wang et al., 2018b). In our current study, the
420
overall detection frequency (93.0%) showed a higher level compared to
421
that school children in Shanghai (79.6%) (Wang et al., 2016a) or Hong
422
Kong (77.4%) (Li et al., 2017), pregnant women in Eastern China (41.6%)
423
(Wang et al., 2017b) and normal adults in Shanghai (45.9%) (Wang et al.,
424
2018b) or Dalian (41.1%) (Liu et al., 2017a). Probably, the elderly are
425
more susceptible to bacterial infection and some of them with chronic
426
disease (An R, 2018), which is the reason why antibiotics are prescribed
427
more frequently to elderly. If so, it is reasonable that the elderly have a
428
higher exposure level of antibiotics compared with adults in China. For
429
instance, azithromycin, the most commonly prescribed antibiotic in the
430
elderly (Kabbani et al., 2018), was detected in 18.2% of urine samples in
431
our study, much higher than 1.6% of adults (Wang et al., 2018b) and 1.3%
432
of pregnant women (Wang et al., 2017b). As for the VAs or PVAs, due to
433
they were mainly obtained from ingesting contaminated food of animal
434
origin, the exposure level of them in urines would influence by the
435
dietary habits. Compared with the youngers, the elderly ate red meats
436
more frequently (Fraser et al., 2000), such as pork and beef often
437
contaminated by tetracyclines, norofloxacin and florfenicol (Li et al.,
438
2017; Wang et al., 2017a). Consequently, as shown in Figure 3,
439
oxytetracycline (18.9%), chlorotetracycline (7.8%), tetracycline (19.5%),
440
doxycycline (18.4%), norofloxacin (11.8%) and florfenicol (23.0%) were 20
441
at a higher level in urines from the elderly than other populations (Wang
442
et al., 2018a; Wang et al., 2015; Wang et al., 2017b; Wang et al., 2016a;
443
Wang et al., 2018b).
444
Ciprofloxacin had a comparable detection frequency to the recent studies,
445
but its concentration in elderly of this study (99th percentile concentration:
446
33.6 ng/mL) was higher than other populations (99th percentile
447
concentration: 19.0 ng/mL in general adults (Wang et al., 2018b), 9.2
448
ng/mL in pregnant women (Wang et al., 2017b) and 5.5 ng/mL in children
449
(Wang et al., 2015)). Maybe, it is the reason that aging is accompanied by
450
the decline of nephron number and renal cortical volume, which induces a
451
decline in glomerular filtration rate and alterations in the endocrine
452
activity of the kidney (Rowland et al., 2018). As a result, there would be a
453
lower excretion rate of antibiotics in the elderly, which might explain the
454
higher concentrations in urines of the elderly in our study. Also, the 99th
455
percentile concentration (217 ng/mL) of norfloxacin in this study was far
456
higher than those in adults, pregnant women and children (Wang et al.,
457
2015; Wang et al., 2017b; Wang et al., 2018b). Taken together, there was
458
a heavy antibiotic body burden in the Chinese elderly and it is essential to
459
assess the potential health risks.
460
4.4. Health risk assessment
461
In the resent years, numerous studies confirmed that the composition and
462
function diversity of gut microbiota could be changed or even disrupted 21
463
in a low dose of antibiotic stimulation. For instance, oral vancomycin,
464
ciprofloxacin and metronidazole had a profound and long-lasting effect
465
on microbiota composition (Ferrer et al., 2017; Haak et al., 2018; Isaac et
466
al., 2017). This disruption of the gut microbiota is harmful to the human
467
host because the gut microbiota is important for human immune and
468
metabolic functions (Zmora et al., 2017). Growing evidence has
469
demonstrated that exposure to antibiotics may increase the risk of getting
470
obesity (Stark CM, 2019), inflammatory bowel diseases (Balram et al.,
471
2019) and colorectal adenoma (Cao et al., 2017). Most of the previous
472
studies mainly focused on the effects of clinical antibiotics, which were
473
commonly with a short-term and high-dose exposure mode, on human
474
health. Pitifully, there is still limited information regarding the effects on
475
human health of long-term exposure to low-dose antibiotics.
476
In the current study, we assessed the health risk of 20 VAs or PVAs were
477
assessed based on microbiological effect, that of three sulfonamides and
478
cyadox were based on toxicological effect. Of the detected antibiotics, the
479
ones including 3 tetracyclines, 6 fluoroquinolones, and 3 other antibiotics
480
(trimethoprim, thiamphenicol and lincomycin) were found to pose a
481
microbiological effect-related health risk in 6.7% of the elderly, which
482
was higher than that 4.3% of the pregnant women (Wang et al., 2017b)
483
and 6.0% of the children (Wang et al., 2018a), but lower than 7.2% of the
484
general adults (Wang et al., 2018b). Notably, ciprofloxacin was a most 22
485
important contributor to the potential health risk related to gut microbiota
486
in this study, consistent with the published articles (Wang et al., 2018a;
487
Wang et al., 2017b; Wang et al., 2018b). Therefore, the government
488
should formulate the usage guidelines of ciprofloxacin for human and
489
animals. Moreover, it is worthy of paying attention that 1.1% of the
490
elderly had HQ value of oxytetracycline more than 1 in our study. We
491
also found that sulfamethoxazole caused a toxicological effect-based
492
health risk in 0.3% of all subjects, which was not reported in previous
493
studies. These findings showed exposures of VAs or PVAs might pose a
494
higher health risk to the elderly, either related to microbiological or
495
toxicological effects. Further investigation should be required to elucidate
496
the underlying mechanisms by which low level antibiotic exposure
497
induces adverse health effects in elderly.
498
4.5. Strengths and limitations
499
To our best knowledge, this is the first study dedicated to investigate the
500
antibiotic body burden of the elderly in larger sample sizes using a
501
biomonitoring approach. We measured 45 common used antibiotics and 2
502
metabolites in urines of the elderly, which may better reflect the
503
cumulative antibiotic exposure from environment, food or drinking water.
504
Moreover, we also assessed the potential health risk relevant to the gut
505
microbiota for the elderly. This study may provide new insights into the
506
effect of antibiotics exposure on healthy ageing. 23
507
However, there are some uncertainties and limitations in this study. First,
508
antibiotics residues and utilization vary widely by geographical area
509
(Zhang et al., 2015), but the participants only come from the Lu’an city,
510
West Anhui. Therefore, the subjects might not entirely represent the
511
elderly population in China. Second, the two metabolites, N4-acetyl
512
-sulfamonomethoxine and florfenicol amine, were used to estimate the
513
total exposure of sulfamonomethoxine and florfenicol, but the other
514
antibiotics were only measured unchanged species, which might
515
underestimate true antibiotic exposure in human. In the future study, more
516
metabolites of targeted antibiotics should be bio-monitored in human
517
urines. Third, most detected antibiotics have a relatively low biological
518
half-live (< 20 h) (Liu et al., 2017a), thus, the first morning urine is
519
merely able to reflect the antibiotics exposure condition in a short period
520
of
521
sulfachloropyridazine, sarafloxacin and cyadox), due to lacking of human
522
pharmacokinetic data, the reference data of proportion of antibiotic
523
excretion in urine are consulted from animals’ experiments which might
524
have an impact on the EDI of antibiotics.
525
Declaration of conflicting interests
526
The authors declare no conflicts of interest.
527
Acknowledgements
528
This work was supported by the Key Projects of Natural Science
time.
Finally,
for
several
24
antibiotics
(sulfaclozine,
529
Research in Colleges and Universities of Anhui province (KJ2018A0164,
530
KJ2017A189) National Natural Science Foundation of China (81202209)
531
and the Key Projects introduced and funded by leading talent teams of
532
colleges and universities of Anhui province (0303011224). We are deeply
533
grateful for the help provided by all the members in the experimental
534
center platform for physical and chemical of Anhui Medical University.
535
Supporting information
536
Additional text, tables, and figures providing details of analytical method
537
of urinary antibiotics, detection frequencies, concentration distribution,
538
estimated daily intakes and health risk assessment of HAs, VAs, PHAs
539
and PVAs; differences of antibiotic concentrations and detection
540
frequencies by age, gender and study site.
541 542
References:
543
An R., Wilms E., Masclee AAM., et al., 2018. Age-dependent changes in GI physiology and
544 545 546
microbiota: time to reconsider? Gut. 67, 2213-2222. Bailey, L.C., Forrest, C.B., Zhang, P., et al., 2014. Association of antibiotics in infancy with early childhood obesity. JAMA Pediatr. 168 (11), 1063-1069.
547
Balram, B., Battat, R., Al-Khoury, A., et al., 2019. Risk Factors Associated with Clostridium
548
difficile Infection in Inflammatory Bowel Disease: A Systematic Review and
549
Meta-Analysis. J Crohns Colitis. 13 (1), 27-38.
550
Becattini, S., Taur, Y., Pamer, E.G., 2016. Antibiotic-Induced Changes in the Intestinal 25
551 552 553
Microbiota and Disease. Trends Mol Med. 22 (6), 458-478. Cao, Y., Wu, K., Mehta, R., et al., 2017. Long-term use of antibiotics and risk of colorectal adenoma. Gut. 67 (4), 672-678.
554
Chen, H., Liu, S., Xu, X.R., et al., 2015. Antibiotics in typical marine aquaculture farms
555
surrounding Hailing Island, South China: occurrence, bioaccumulation and human
556
dietary exposure. Mar Pollut Bull. 90 (1-2), 181-187.
557 558 559 560
Du, B., Wen, F., Zhang, Y., et al., 2019. Presence of tetracyclines, quinolones, lincomycin and streptomycin in milk. Food Control. 100, 171-175. Ferrer, M., Mendez-Garcia, C., Rojo, D., et al., 2017. Antibiotic use and microbiome function. Biochem Pharmacol. 134, 114-126.
561
Fraser, G.E., Welch, A., Luben, R., et al., 2000. The effect of age, sex, and education on food
562
consumption of a middle-aged English cohort-EPIC in East Anglia. Prev Med. 30 (1),
563
26-34.
564
Gao, H., Xu, Y.Y., Huang, K., et al., 2017. Cumulative risk assessment of phthalates
565
associated with birth outcomes in pregnant Chinese women: A prospective cohort study.
566
Environ Pollut. 222, 549-556.
567
Guo, X., Feng, C., Gu, E., et al., 2019. Spatial distribution, source apportionment and risk
568
assessment of antibiotics in the surface water and sediments of the Yangtze Estuary. Sci
569
Total Environ. 671, 548-557.
570
Haak, B.W., Lankelma, J.M., Hugenholtz, F., et al., 2019. Long-term impact of oral
571
vancomycin, ciprofloxacin and metronidazole on the gut microbiota in healthy humans. J
572
Antimicrob Chemother. 74 (3), 782-786. 26
573
HeXing, W., Bin, W., Ying, Z., et al., 2014. Rapid and sensitive screening and selective
574
quantification of antibiotics in human urine by two-dimensional ultraperformance liquid
575
chromatography coupled with quadrupole time-of-flight mass spectrometry. Ana Bioanal
576
Chem. 406 (30), 8049-8058.
577
Isaac, S., Scher, J.U., Djukovic, A., et al., 2017. Short- and long-term effects of oral
578
vancomycin on the human intestinal microbiota. J Antimicrob Chemother. 72 (1),
579
128-136.
580
Ji, K., Lim, K.Y., Park, Y., et al., 2010. Influence of a five-day vegetarian diet on urinary
581
levels of antibiotics and phthalate metabolites: a pilot study with "Temple Stay"
582
participants. Environ Res. 110 (4), 375-382.
583 584 585 586
Kabbani, S., Palms, D., Bartoces, M., et al., 2018. Outpatient Antibiotic Prescribing for Older Adults in the United States: 2011 to 2014. J Am Geriatr Soc. 66 (10), 1998-2002. Katrine, R.M., Hanne, F., Jeppe, S.C., et al., 2014. Current exposure of 200 pregnant Danish women to phthalates, parabens and phenols. Reproduction. 147 (4), 443-453.
587
Leclercq, S., Mian, F.M Stanisz, A.M., et al., 2017. Low-dose penicillin in early life induces
588
long-term changes in murine gut microbiota, brain cytokines and behavior. Nat Commun.
589
8, 15062.
590 591
Li, N., Ho, K.W.K., Ying, G.G., et al., 2017. Veterinary antibiotics in food, drinking water, and the urine of preschool children in Hong Kong. Environ Int, 108, 246-252.
592
Li, X.D., Cao, H.J., Xie, S.Y., et al., 2019. Adhering to a vegetarian diet may create a greater
593
risk of depressive symptoms in the elderly male Chinese population. J Affect Disord.
594
243, 182-187. 27
595
Liu, S., Zhao, G., Zhao, H., et al., 2017. Antibiotics in a general population: Relations with
596
gender, body mass index (BMI) and age and their human health risks. Sci Total Environ.
597
599-600, 298-304.
598 599 600
Liu, X., Steele, J.C., Meng, X.Z., 2017. Usage, residue, and human health risk of antibiotics in Chinese aquaculture: A review. Environ Pollut. 223, 161-169. Lurie, I., Yang, Y.X., Haynes, K., et al., 2015. Antibiotic exposure and the risk for depression,
601
anxiety, or psychosis: a nested case-control study. J Clin Psychiatry. 76 (11), 1522-1528.
602
Mage, D.T., Allen, R.H., Kodali, A., 2008. Creatinine corrections for estimating children's
603
and adult's pesticide intake doses in equilibrium with urinary pesticide and creatinine
604
concentrations. J Expo Sci Environ Epidemiol. 18 (4), 360-368.
605 606 607 608
Nathan, C., 2014. Antibiotic Resistance — Problems, Progress, and Prospects. N Engl J Med. 371 (19), 1761-1763. James A P., Dimitri K., Joanne D., et al., 2019. Altered gut microbiota activate and expand Insulin B15-23-reactive CD8+ T-cells. Diabetes. 68 (5), 1002-1013.
609
Rowland, J., Akbarov, A., Maan, A., et al., 2018. Tick-Tock Chimes the Kidney Clock - from
610
Biology of Renal Ageing to Clinical Applications. Kidney Blood Press Res. 43 (1),
611
55-67.
612 613 614 615 616
Stark, CM.S.A., Emerick, J., Nylund, CM., 2019. Antibiotic and acid-suppression medications during early childhood are associated with obesity. Gut. 68 (1), 62-69. Sun, J., Zeng, Q., Tsang, D.C.W., et al., 2017. Antibiotics in the agricultural soils from the Yangtze River Delta, China. Chemosphere. 189, 301-308. Torres, N.F., Chibi, B., Middleton, L.E., et al., 2019. Evidence of factors influencing 28
617
self-medication with antibiotics in low and middle-income countries: a systematic
618
scoping review. Public Health. 168, 92-101.
619 620 621 622 623 624
Van Boeckel, T.P., Glennon, E.E., Chen, D., et al., 2017. Reducing antimicrobial use in food animals. Science. 357 (6358), 1350-1352. Wang, H., Ren, L., Yu, X., et al., 2017a. Antibiotic residues in meat, milk and aquatic products in Shanghai and human exposure assessment. Food Control. 80, 217-225. Wang, H., Tang, C., Yang, J., et al., 2018a. Predictors of urinary antibiotics in children of Shanghai and health risk assessment. Environ Int. 121 (Pt 1), 507-514.
625
Wang, H., Wang, B., Zhao, Q., et al., 2015. Antibiotic body burden of Chinese school
626
children: a multisite biomonitoring-based study. Environ Sci Technol. 49 (8),
627
5070-5079.
628
Wang, H., Wang, N., Qian, J., et al., 2017b. Urinary Antibiotics of Pregnant Women in
629
Eastern China and Cumulative Health Risk Assessment. Environ Sci Technol. 51 (6),
630
3518-3525.
631 632
Wang, H., Wang, N., Wang, B., et al., 2016a. Antibiotics detected in urines and adipogenesis in school children. Environ Int. 89-90, 204-211.
633
Wang, H., Wang, N., Wang, B., et al., 2016. Antibiotics in Drinking Water in Shanghai and
634
Their Contribution to Antibiotic Exposure of School Children. Environ Sci Technol. 50
635
(5), 2692-2699.
636
Wang, H., Yang, J., Yu, X., et al., 2018b. Exposure of Adults to Antibiotics in a Shanghai
637
Suburban Area and Health Risk Assessment: A Biomonitoring-Based Study. Environ Sci
638
Technol. 52 (23), 13942-13950. 29
639
Woodmansey, E.J, Mcmurdo, M.E.T., Macfarlane, G.T., et al., 2004. Comparison of
640
compositions and metabolic activities of fecal microbiotas in young adults and in
641
antibiotic-treated and non-antibiotic-treated elderly subjects. Appl Environ Microbiol. 70
642
(10), 6113-6122.
643
Yamaguchi, T., Okihashi, M., Harada, K., et al., 2015. Antibiotic residue monitoring results
644
for pork, chicken, and beef samples in Vietnam in 2012-2013. J Agric Food Chem. 63
645
(21), 5141-5145.
646 647
Zhan, J.L.Y, Liu, D., Ma, X., 2018. Antibiotics may increase triazine herbicide exposure risk via disturbing gut microbiota. Microbiome. 6 (1), 224.
648
Zhang, Q.Q., Ying, G.G., Pan, C.G., et al., 2015. Comprehensive evaluation of antibiotics
649
emission and fate in the river basins of China: source analysis, multimedia modeling, and
650
linkage to bacterial resistance. Environ Sci Technol. 49 (11), 6772-6782.
651
Zhao, F., Yang, L., Chen, L., et al., 2018. Bioaccumulation of antibiotics in crops under
652
long-term manure application: Occurrence, biomass response and human exposure.
653
Chemosphere. 219, 882-895.
654
Zhou, B.F., 2002. Predictive values of body mass index and waist circumference for risk
655
factors of certain related diseases in Chinese adults--study on optimal cut-off points of
656
body mass index and waist circumference in Chinese adults. Biomed Environ Sci. 15 (1),
657
83-96.
658
Zhou, L.J., Ying, G.G., Liu, S., et al., 2013. Excretion masses and environmental occurrence
659
of antibiotics in typical swine and dairy cattle farms in China. Sci Total Environ. 444(2),
660
183-195. 30
661 662
Zmora, N., Bashiardes, S., Levy, M., et al., 2017. The Role of the Immune System in Metabolic Health and Disease. Cell Metab. 25 (3), 506-521.
663
Zou, Y., Zhang, R., Xia, S., et al., 2017. Dietary Patterns and Obesity among Chinese Adults:
664
Results from a Household-Based Cross-Sectional Study. Int J of Environ Res Public
665
Health. 14 (5), 487.
666 667
31
Captions Table 1 Detection frequencies and concentrations of antibiotic in urines of Chinese Elderly (n = 990) Table 2 Differences of urinary antibiotic detection frequencies among different study site, age, sex and BMI (n = 990) Table 3 Estimated daily intake dose (EDI) of 34 antibiotics (n = 990) Table 4 Hazard quotient (HQ) based on acceptable daily intakes (ADIs) (n = 990)
Table 1 Detection frequencies and concentrations of antibiotic in urines of Chinese Elderly (n = 990)
Antibiotic
LOD
Sulfonamidesa
N (%)
Unadjusted (Creatinine adjusted)e P50
552 (55.8) 0.40(0.36)
-
Maximum
P75
P90
P95
P99
6.12 (5.15)
16.3 (14.6)
31.5 (27.8)
108 (130)
5.29 × 104 (5.18 × 104)
-
-
-
5.37 (6.45)
2.25 × 104 (2.20 × 104)
Sulfamethoxazole
0-0.45
26 (2.6)
Sulfaclozine
0-0.17
351 (35.5) -
4.30 (4.00)
13.9 (11.8)
23.7 (22.0)
76.3 (54.3)
1.52 × 103 (681)
Trimethoprimc
0-0.05
201 (20.3) -
-
0.53 (0.44)
1.38 (1.64)
11.3 (13.1)
3.05 × 104 (2.98 × 104)
Sulfamethazine
0-0.07
6 (0.6)
-
-
-
-
-
4.32 (5.90)
Sulfadiazine
0-0.16
28 (2.8)
-
-
-
-
1.85 (1.68)
118 (120)
Sulfachloropyridazine
0-0.09
45 (4.5)
-
-
-
-
3.19 (2.02)
13.6 (28.7)
d
0-0.10
65 (6.6)
-
-
-
0.78 (0.71)
5.06 (4.67)
75.2 (68.6)
0.07 (0.05)
0.55 (0.58)
2.91 (2.72)
30.8 (25.8)
7.75 × 103 (4.97 × 103)
Sulfamonomethoxine Macrolidesa
282 (28.5)
Erythromycin
0-0.12
85 (8.6)
-
-
-
1.15 (1.40)
13.5 (16.1)
7.75 × 103 (4.97 × 103)
Clarithromycin
0-0.05
27 (2.7)
-
-
-
-
0.22 (0.24)
4.23 (4.26)
Azithromycin
0-0.04
180 (18.2) -
-
0.09 (0.09)
0.20 (0.25)
5.93 (7.02)
1.35 × 103 (981)
Roxithromycin
0-0.04
27 (2.7)
-
-
-
-
0.39 (0.29)
1.28 × 103 (1.66 × 103)
0.25 (0.19)
2.54 (2.27)
7.07 (5.70)
136 (216)
8.77 × 103 (1.35 × 104)
β-lactamsa
253 (25.6)
Cefaclor
0-0.22
3 (0.3)
-
-
-
-
-
3.71 (9.60)
Cefotaxime
0-0.51
8 (0.8)
-
-
-
-
-
46.2 (24.0)
Penicillin V
0-0.20
176 (17.8) -
-
1.18 (1.02)
2.82 (2.35)
13.4 (9.72)
97.0 (164)
Amoxicillin
0-0.10
77 (7.8)
-
-
-
0.98 (1.03)
135 (215)
8.77 × 103 (1.35 × 104)
440 (44.4)
1.21 (1.03)
3.54 (3.23)
6.85 (7.41)
568 (608)
4.33 × 104 (3.06 × 104)
Tetracyclinesa Oxytetracycline
0-0.15
187 (18.9) -
-
0.70 (0.64)
1.87 (1.79)
349 (569)
4.30 × 104 (3.02 × 104)
Chlorotetracycline
0-0.11
77 (7.8)
-
-
-
1.26 (0.95)
7.79 (8.84)
531 (906)
Tetracycline
0-0.20
193 (19.5) -
-
1.01 (0.88)
2.18 (2.01)
14.2 (17.1)
706 (1.04 × 103)
0-0.19
182 (18.4) -
-
0.85 (0.73)
1.85 (1.58)
11.6 (10.4)
Doxycycline Fluoroquinolones
a
495 (50.0) 0.03(0.02)
3
54.6 (80.3) 3
2.66 (3.02)
23.6 (20.7)
62.3 (65.2)
1.51 × 10 (1.06 × 10 )
3.03 × 105 (1.80 × 105)
Pefloxacin
0-0.05
40 (4.0)
-
-
-
-
7.89 (5.11)
264 (343)
Lomefloxacin
0-0.08
14 (1.4)
-
-
-
-
0.38 (0.54)
3.68 × 103 (3.66 × 103)
Danofloxacin
0-0.12
40(4.0)
-
-
-
-
24.0 (19.5)
3.03 × 105 (1.58 × 105)
Sarafloxacin
0-0.05
16 (1.6)
-
-
-
-
0.38 (0.29)
196 (215)
Ofloxacin
0-0.03
235 (23.7) -
-
0.94 (0.92)
3.74 (3.92)
38.5 (42.7)
99.6 (114)
Levofloxacin
0-0.04
33 (3.3)
-
-
-
-
141 (157)
2.16 × 105 (1.80 × 105)
Difloxacin
0-0.05
7 (0.7)
-
-
-
-
-
16.0 (13.0)
Enrofloxacin
0-0.04
103 (10.4) -
-
0.08 (0.07)
0.74 (0.66)
6.71 (7.47)
48.1 (22.4)
Ciprofloxacin
0-0.06
163 (16.5) -
-
1.67 (1.51)
5.06 (4.41)
33.6 (34.9)
101 (123)
Norfloxacin
0-0.04
117 (11.8) -
-
1.00 (0.68)
5.36 (4.50)
217 (252)
4.83 × 104 (6.27 × 104)
247 (24.9)
0.07 (0.08)
3.36 (3.35)
6.06 (6.83)
79.2 (90.0)
1.90 × 103 (1.81 × 103)
Phenicolsa Chloramphenicol
0-0.51
27 (2.7)
-
-
-
-
54.6 (78.8)
1.90 × 103 (1.81 × 103)
Thiamphenicol
0-2.15
2 (0.2)
-
-
-
-
-
179 (204)
Florfenicold
0-0.10
228 (23.0) -
-
2.74 (2.60)
4.36 (4.64)
12.6 (11.7)
79.1 (112)
165 (162)
2.44 × 105 (1.95 × 105)
165 (162)
2.44 × 105 (1.95 × 105)
15.6 (12.0)
113 (79.3)
Lincosamides
a
Lincomycin
36 (3.6) 0-0.05
Quinoxalinesa Cyadox All antibioticb
36 (3.6)
-
-
-
-
29 (2.9) 0-1.45
29 (2.9)
-
921 (93.0) 8.82(7.94)
-
-
-
15.6 (12.0)
113 (79.3)
26.5(23.9)
100 (91.0)
413(437)
1.83 × 104 (2.28 × 104)
3.03 × 105 (1.95 × 105)
Notes: a, sum of concentrations of antibiotics in corresponding category for individual; b, sum of concentrations of all antibiotics; c, due to the similar antibacterial mechanisms, trimethoprim was included in the sulfonamides; d, the urinary levels of sulfamonomethoxine and florfenicol were separately considered to be the sum of their prototypes and metabolites (sulfamonomethoxine-N4-acetyl and florfenicol amine); e, volume-based urinary antibiotic concentration, ng/mL (creatinine adjusted urinary antibiotics concentration, µg/g); LODs, limits of detection ng/mL; N (%), the number of positive detection (detection frequency, %); P, percentile; -, < limits of detection (LODs).
Roxithromycin
3.7
1.6
0.042
3.0
2.5
0.614
3.8
1.9
0.0
Cefaclor
0.4
0.2
0.680
0.0
0.6
0.102
0.2
0.4
0.6
Cefotaxime
0.6
1.1
0.322
0.4
1.1
0.209
0.2
1.3
0.0
Penicillin V
16.6
19.2
0.275
17.0
18.5
0.522
16.6
18.7
0.3
Amoxicillin
10.5
4.5
<0.001
8.2
7.4
0.676
7.1
8.3
0.4
Oxytetracycline
23.6
13.2
<0.001
18.5
19.3
0.742
19.7
18.2
0.5
Chlorotetracycline
5.3
10.7
0.002
9.7
6.1
0.037
7.8
7.8
0.9
Tetracycline
21.0
17.7
0.189
14.8
23.7
<0.001
22.8
16.7
0.0
Doxycycline
14.0
23.7
<0.001
17.4
19.3
0.443
18.2
18.6
0.8
Pefloxacin
3.9
4.3
0.761
4.1
4.0
0.956
5.1
3.2
0.1
Lomefloxacin
1.8
0.9
0.209
1.5
1.3
0.825
1.1
1.7
0.4
Danofloxacin
5.0
2.9
0.101
3.2
4.8
0.216
3.5
4.5
0.4
Sarafloxacin
1.3
2.0
0.368
1.5
1.7
0.780
2.2
1.1
0.1
Ofloxacin
28.5
17.9
<0.001
23.4
24.0
0.809
22.8
24.5
0.5
Levofloxacin
3.7
2.9
0.499
2.6
4.0
0.210
2.4
4.1
0.1
Difloxacin
0.2
1.3
0.030
0.4
1.0
0.325
0.4
0.9
0.3
Enrofloxacin
10.5
10.3
0.916
8.2
12.4
0.029
11.1
9.8
0.5
Ciprofloxacin
18.8
13.6
0.030
17.0
16.0
0.696
17.1
16.0
0.6
Norfloxacin
12.9
10.7
0.298
9.9
13.7
0.061
13.3
10.8
0.2
Chloramphenicol
2.8
2.7
0.940
2.6
2.9
0.782
2.7
2.8
0.9
Thiamphenicol
0.4
0.0
0.199
0.0
0.4
0.182
0.0
0.4
0.1
Florfenicol
23.9
21.3
0.548
21.0
24.2
0.394
22.2
23.2
0.8
Lincomycin
5.3
1.6
0.002
2.8
4.4
0.180
4.4
3.0
0.2
Cyadox
4.4
1.3
0.005
2.6
3.4
0.431
3.3
2.8
0.6
Sulfonamides
56.7
54.6
0.501
56.4
55.2
0.684
60.8
51.6
0.0
Macrolides
19.7
39.1
<0.001
27.3
29.6
0.418
26.8
29.9
0.2
β-lactams
26.7
24.2
0.361
24.9
26.1
0.652
23.5
27.3
0.1
Tetracyclines
44.9
43.8
0.732
42.9
45.8
0.362
45.5
43.6
0.5
Fluoroquinolones
54.9
44.3
<0.001
47.6
52.3
0.144
49.7
50.5
0.8
Chloramphenicols
26.7
22.8
0.160
23.4
26.3
0.285
23.9
25.8
0.5
Veterinary antibiotic
65.4
60.0
0.079
63.1
62.8
0.921
64.5
61.6
0.3
Human antibiotic
19.3
37.1
<0.001
25.3
29.2
0.172
26.2
28.4
0.4
Preferred as HA
31.3
30.4
0.765
31.1
30.7
0.894
28.4
33.0
0.1
Preferred as VA
74.0
71.1
0.309
73.4
72.1
0.659
75.8
70.1
0.0
All antibiotic
93.7
92.2
0.335
93.1
92.9
0.905
94.7
91.7
0.0
Categories
a
b
Notes: , detection rate corresponding to specific characteristics, %; , p-values were calculated by Chi-squ VA, antibiotic; BMI, body mass index; two study sites: the rural area (n = 543) and the urban area (n = 44 (n = 466) and > 70 years (n = 524); two sex categorizes: males (n = 451) and females (n = 539); two BMI 24, n = 488) and overweight (BMI ≥ 24, n = 502).
Azithromycin
-
-
-
0.02
0.05
1.30
255
Roxithromycin
-
-
-
-
-
0.01
30.9
0.05
0.12
2.81
234
β-lactamsa Cefaclor
-
-
-
-
-
-
0.13
Cefotaxime
-
-
-
-
-
-
0.50
Penicillin V
-
-
-
0.02
0.05
0.18
2.74
Amoxicillin
-
-
-
-
0.01
2.34
234
0.01
0.02
0.06
0.14
10.9
430
Tetracyclinesa Oxytetracycline
-
-
-
0.01
0.03
9.30
424
Chlorotetracycline
-
-
-
-
0.02
0.21
19.0
Tetracycline
-
-
-
0.02
0.04
0.29
18.6
Doxycycline
-
-
-
0.01
0.03
0.18
1.27
0.02
0.06
0.52
1.30
20.4
5.45
Fluoroquinolonesa Pefloxacin
-
-
-
-
-
0.63
36.1
Lomefloxacin
-
-
-
-
-
0.01
53.9
Danofloxacin
-
-
-
-
-
0.89
5.45
Sarafloxacin
-
-
-
-
-
0.01
5.30
Ofloxacin
-
-
-
0.01
0.05
0.45
1.69
Levofloxacin
-
-
-
-
-
1.73
1.82
Difloxacin
-
-
-
-
-
-
0.20
Enrofloxacin
-
-
-
-
0.04
0.34
1.19
Ciprofloxacin
-
-
-
0.03
0.10
0.74
2.32
Norfloxacin
-
-
-
0.01
0.09
3.83
1.14
0.08
0.14
1.12
22.9
Phenicolsa Chloramphenicol
-
-
-
-
-
0.78
22.9
Thiamphenicol
-
-
-
-
-
-
3.24
Florfenicol
-
-
-
0.06
0.11
0.26
2.10
3.33
3.38
3.33
3.38
0.89
4.91
Lincosamides
a
Lincomycin Quinoxalines
-
Cyadox
-
All antibioticb a
-
-
-
-
a
0.18
0.36
0.60
2.09
9.04
0.89
4.91 3
3.78 × 10
5.45
Notes: , sum of estimated daily intakes of antibiotics in corresponding catego individual; b, sum of estimated daily intakes of all antibiotics; c, due to the si antibacterial mechanisms, trimethoprim was included in the sulfonamide percentile; -, < limits of detection (LODs).
Azithromycin
HA
-
-
-
0.01
0.03
0.77
150
Roxithromycin
HA
-
-
-
-
-
0.01
77.3
0.01
0.04
3.34
334
β-lactamsa Cefaclor
HA
-
-
-
-
-
-
-
Cefotaxime
HA
-
-
-
-
-
-
0.01
Penicillin V
PHA
-
-
-
-
0.01
0.03
0.46
Amoxicillin
PHA
-
-
-
-
0.02
3.34
334
0.01
0.02
0.05
3.62
143
Tetracyclinesa Oxytetracycline
PVA
-
-
-
-
0.01
3.10
141
Chlorotetracycline
PVA
-
-
-
-
0.01
0.07
6.34
Tetracycline
PVA
-
-
-
0.01
0.01
0.10
6.20
PVA
-
-
-
-
0.01
0.06
0.42
0.02
0.46
1.10
9.66
910
Doxycycline Fluoroquinolones
a
Pefloxacin
PVA
-
-
-
-
-
0.05
2.58
Lomefloxacin
PVA
-
-
-
-
-
-
26.9
Danofloxacin
VA
-
-
-
-
-
-
9.08
Sarafloxacin
VA
-
-
-
-
-
0.02
13.3
Ofloxacin
PVA
-
-
-
-
0.02
0.14
0.53
Levofloxacin
HA
-
-
-
-
-
0.87
910
Difloxacin
VA
-
-
-
-
-
-
0.11
Enrofloxacin
VA
-
-
-
-
0.01
0.06
0.19
Ciprofloxacin
PVA
-
-
-
0.21
0.65
4.96
15.5
Norfloxacin
PVA
-
-
-
-
0.01
0.27
81.3
0.03
0.05
0.45
9.16
Phenicolsa Chloramphenicol
HA
-
-
-
-
-
0.31
9.16
Thiamphenicol
PVA
-
-
-
-
-
-
1.30
Florfenicol
VA
-
-
-
0.02
0.04
0.09
0.70
0.33
338
0.33
338
0.01
0.03
Lincosamides
a
PVA
Lincomycin Quinoxalines Cyadox All antibioticb a
-
-
-
-
-
a
VA
-
-
0.02 0.04
-
-
-
0.01
0.03
0.08
0.58
2.76
73.5
2.92 × 10
Notes: , sum of hazard quotient of antibiotics in corresponding category for individu the sum of the HQ for individual antibiotic based on microbiological effects, but exce HAs and PHAs; c, due to the similar antibacterial mechanisms, trimethoprim was inc in the sulfonamides; HAs, human antibiotics; VAs, veterinary antibiotics; PVAs, antib preferred as VA; PHAs, antibiotics preferred as HA; P, percentile; N (%), number o or HI > 1 (percent in all, %); -, < limits of detection (LODs).
Figure captions: Figure 1 Frequency distribution of sum of concentrations of 34 antibiotics (n = 990) Figure 2 Frequency distribution of sum of estimated daily intake of 34 antibiotics (n = 990) Figure 3 Comparison of detection frequencies of 14 veterinary antibiotics or preferred as VA in urines between our study and four previous studies (SMX: Sulfamethoxazole; SMZ: Sulfamethazine; SDZ: Sulfadiazine; TMP: Trimethoprim; OTC: Oxytetracycline; CTC: Chlortetracycline; TC: Tetracycline; DC: Doxycycline; OFX: Ofloxacin; EFX: Enrofloxacin; CFX: Ciprofloxacin; NFX: Norfloxacin; TAP: Thiamphenicol; FF: Florfenico; #, corresponding antibiotic is not measured in urines)
22 20 17.0
Population proportion (%)
18 15.3
16
14.8
14.7
14 12
10.8
10.7
10 8
7.0
6
4.9
4.8
4 2 0
LOD-0.5
0.5-2.0
2.0-5.0
5.0-10.0 10.0-20.0 20.0-50.0 50.0-500.0500.0-Max
Antibiotics concentration (ng/mL)
Figure 1 Frequency distribution of sum of concentrations of 34 antibiotics (n = 990)
22 19.0
20
Population proportion (%)
18
16.5
16 14
12.5 11.5
12 9.6
10 8
11.2
7.0
6.8
5.9
6 4 2 0
LOD-0.02 0.02-0.05 0.05-0.10 0.10-0.20 0.20-0.50 0.50-1.00 1.00-5.00 5.00-Max
Estimated daily intake (µg/kg/day)
Figure 2 Frequency distribution of sum of estimated daily intake of 34 antibiotics (n = 990)
70
Wang et al., 2016 (586 school children in Shanghai) Ji et al., 2010 (541 general adults in Korea)
60
Wang et al., 2017 (536 pregnant women in East China)
Detection frequency (%)
Wang et al., 2018 (822 general adults in Shanghai) 50
Our research, 2019 (990 elderly in West Anhui)
40
30
20
10 #
0 TMP SMX SMZ
SDZ
#
OTC
# #
CTC
#
TC
##
DC
#
NFX
EFX
CFX
OFX
#
#
FF
TAP
Figure 3 Comparison of detection frequencies of 14 veterinary antibiotics or preferred as VA in urines between this study and four previous studies (SMX: Sulfamethoxazole; SMZ: Sulfamethazine; SDZ: Sulfadiazine; TMP: Trimethoprim; OTC: Oxytetracycline; CTC: Chlortetracycline; TC: Tetracycline; DC: Doxycycline; OFX: Ofloxacin; EFX: Enrofloxacin; CFX: Ciprofloxacin; NFX: Norfloxacin; TAP: Thiamphenicol; FF: Florfenico; #, corresponding antibiotic is not measured in urines)
Highlights 1. Thirty-four antibiotics were detected in urines of 990 elderly from the Elderly Health and Environment Risk Factor cohort. 2. Fourteen antibiotics preferred for veterinary use were found in 72.7% of the elderly urines. 3. A higher detection frequency of the antibiotics preferred for veterinary use in males than in females. 4. Ciprofloxacin was the foremost contributor to the health risk in 3.5% of all subjects. 5. A health risk related to gut microbiota was revealed in 6.7% of the elderly.
Conflict of Interest There is no conflict of interest.
S0269-7491(19)33487-6
DOI:
https://doi.org/10.1016/j.envpol.2019.113311
Reference:
ENPO 113311
To appear in:
Environmental Pollution
Received Date: 30 June 2019 Revised Date:
24 September 2019
Accepted Date: 25 September 2019
Please cite this article as: Zhu, Y., Liu, K., Zhang, J., Liu, X., Yang, L., Wei, R., Wang, S., Zhang, D., Xie, S., Tao, F., Antibiotic body burden of Chinese elderly population and health risk assessment: A biomonitoring-based study, Environmental Pollution (2019), doi: https://doi.org/10.1016/ j.envpol.2019.113311. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
22 20
Clinical Use Self-Medication
19.0
18 16.5
Antibiotics
Elderly
Urine
Population proportion (%)
16 14 12.5 11.5
12 9.6
10 8
11.2
7.0
6.8 5.9
6 4
Animal-derived Food Drinking Water
2 0
Estimated daily intake (µg/kg/day)
1
1
Antibiotic body burden of Chinese elderly population and health
2
risk assessment: a biomonitoring-based study
3
Yitian Zhu1^, Kaiyong Liu1,5^, Jingjing Zhang1, Xinji Liu1, Linsheng Yang2, Rong Wei1, Sufang Wang1,
4
Dongmei Zhang3, Shaoyu Xie4, Fangbiao Tao6*
5 6
1. Department of Nutrition and Food Hygiene, School of Public Health, Anhui Medical University,
7
81 Meishan Road, Hefei, Anhui 230032, PR China.
8
2. Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical
9
University, 81 Meishan Road, Hefei, Anhui 230032, PR China.
10
3. School of Health Management, Anhui Medical University, Hefei, Anhui 230032, PR China.
11
4. Lu’an Center of Disease Control and Prevention, Lu’an, Anhui, 237000, PR China
12
5. Department of Microbiology and Immunology, University of Michigan Medical School, Ann
13
Arbor, MI 48109, USA.
14
6. Anhui Provincial Key Laboratory of Population Health and Aristogenics, 81 Meishan Road,
15
Hefei, Anhui 230032, PR China.
16 17
^
18
*Corresponding author: Email: [email protected]. Anhui Provincial Key Laboratory of
19
Population Health and Aristogenics, 81 Meishan Road, Hefei, Anhui 230032, PR China.
These authors contributed equally to this work.
20 21 22 1
23
Abstract
24
Limited biomonitoring studies have demonstrated an extensive exposure
25
of different populations to antibiotics, but antibiotic body burden and the
26
potential health risks in the Chinese elderly population remains unclear.
27
In the present study, we investigated 990 elderly (aged 60 years and over)
28
from the Cohort of Elderly Health and Environment Controllable Factors
29
in West Anhui of China. Then, 45 representative antibiotics and 2
30
metabolites were monitored in urines by liquid chromatography
31
electrospray tandem mass spectrometry. The results revealed that 34
32
antibiotics were overall detected in 93.0% of all urine samples and the
33
detection frequencies of each antibiotic varied between 0.2% and 35.5%.
34
The overall detection frequencies of 7 human antibiotics (HAs), 10
35
veterinary antibiotics (VAs), 3 antibiotics preferred as HA (PHAs) and 14
36
preferred as VA (PVAs) in urines were 27.4%, 62.9%, 30.9% and 72.7%,
37
respectively. Notably, the samples with the concentrations of 6 PVAs
38
(sulfamethoxazole,
39
norfloxacin and lincomycin) above 5.0 × 103 ng/mL account for 1.7% of
40
total urine samples. Besides, in the remaining 62.7% of urine samples, the
41
total antibiotic concentration was in the range of the limits of detection
42
(LODs) to 20.0 ng/mL. Furthermore, the elderly with the sum of
43
estimated daily intake (EDI) of VAs and PVAs more than 1 µg/kg/day
44
accounted for 15.2% of all participants, and 6.7% of the elderly might
trimethoprim,
2
oxytetracycline,
danofloxacin,
45
under a health risk related to the gut microbiota changing under
46
antibiotics stimulation. Especially, ciprofloxacin was the foremost
47
contributor to the health risk and its hazard quotient value more than 1
48
was seen in 3.5% of all subjects. Taken together, the Chinese elderly were
49
extensively exposed to veterinary antibiotics and some elderly may under
50
a health risk linked to dysbiosis of the gut microbiota.
51
Capsule: 34 antibiotics were detected in 93% urines of 990 elderly and
52
health risk related to gut microbiota was revealed in 6.7% of the elderly.
53
Keywords: Antibiotics; Elderly; Urine; Health risk assessment.
54 55 56 57 58 59 60 61 62 63 64 65 66 3
67
1. Introduction
68
Antibiotics are not only used in the prevention and treatment of bacterial
69
infection occurred in humans and animals (Nathan, 2014) but also
70
extensively added to serve as growth factors in aquaculture and animal
71
husbandry (Van Boeckel et al., 2017). Owing to the abuse and improper
72
usage, these antibiotics were often detected in surface water, soil and food
73
(Guo et al., 2019; Sun et al., 2017; Yamaguchi et al., 2015). To date,
74
numerous publications have demonstrated that approximately 70-80
75
antibiotics and their metabolites were measured in animal-derived food
76
and drinking water worldwide (Chen et al., 2015; Wang et al., 2017a;
77
Wang et al., 2016b; Yamaguchi et al., 2015). Thus, the antibiotics
78
residues in food and the environment can be transferred to humans
79
through the food chain, which may cause bioaccumulation of antibiotics
80
in human body. Consequently, the ingestion of low-dose antibiotics for
81
extended periods of time could aggravate the growing battle with
82
emerging antibiotic-resistant pathogenic strains. What's the worse, the
83
exposure to antibiotics could increase the pesticide bioavailability via
84
disturbing gut microbiota and further may increase the pesticide exposure
85
risk (Zhan J, 2018). Additionally, increasing evidence has indicated that
86
antibiotics exposure can affect the composition and functionality of the
87
host microbiota (Becattini et al., 2016; Leclercq et al., 2017), and is one
88
of the potential risk factor for obesity (Bailey et al., 2014), diabetes 4
89
(Pearson JA, 2019), and even some nervous system diseases (Lurie et al.,
90
2015).
91
It is well-known that human biomonitoring is a powerful tool for
92
simultaneously analyzing the actual internal levels of bodily chemicals
93
from all potential routes of exposure, which may make contribution to
94
improve risk assessments. In recent years, several population-based
95
studies used urine as a sample to bio-monitor antibiotics (Li et al., 2017;
96
Wang et al., 2015; Wang et al., 2016a). For instances, more than 20
97
antibiotics were detected in Shanghai children’ urine samples and the
98
overall detected frequencies reached 79.6% (Wang et al., 2016a). Another
99
study has shown that 9 veterinary antibiotics were measured in 77.4% of
100
pre-school children urine samples in Hong Kong (Li et al., 2017). These
101
studies demonstrated that urine samples were suitable or convenient for
102
monitoring the amount of total antibiotic exposures in human body from
103
contaminated food and drinking water. Based on the studies conducted in
104
eastern China, the results showed that 4.3% of pregnant women (Wang et
105
al., 2017b), 6.0% of children (Wang et al., 2018a) and also 7.2% of
106
general adults (Wang et al., 2018b) had a health risk relevant to gut
107
microbiota.
108
Interestingly, a previous study conducted in Dalian of China has revealed
109
that in the general population (n=107), the serum concentrations for
110
sulfonamides, macrolides and chloramphenicols in elderly were higher 5
111
than those in young adults (Liu et al., 2017a). This finding implied that
112
the age may link to the antibiotic accumulation in human. As is well
113
known, ageing is accompanied by some physiologic alterations, including
114
reduced species diversity and declined metabolic activities of gut
115
microbiota, which may negatively influence the excretion process of
116
antibiotics (An R, 2018; Woodmansey et al., 2004). Moreover, the elderly
117
are vulnerable to bacterial infection disease because of their compromised
118
immune function (An R, 2018) which is the reason why antibiotics are
119
prescribed more frequently to elderly than that to younger. The
120
abovementioned results indicated that the elderly have a higher risk of
121
antibiotics bioaccumulation compared with younger. However, until now,
122
the information for antibiotic body burden and potential health risk in the
123
elderly population is seldom being investigated.
124
To our best knowledge, this is the first study focusing on antibiotics
125
exposure examination using bio-monitoring data based on an elderly
126
cohort. Here, we monitored 45 antibiotics and 2 metabolites in urine
127
samples from approximately 1000 elderly in Lu’an city of west Anhui
128
province in China and assessed health risks of antibiotics for the elderly
129
based on microbiological or toxicological effects. Our study may provide
130
new insights into the effect of antibiotics exposure on healthy ageing.
131
2. Materials and methods
132
2.1. Study population and sample collection 6
133
The data reported in this study came from the baseline survey of the
134
Elderly Health and Environment Risk Factor (EHERF) cohort, which was
135
conducted in Lu'an Municipality, West Anhui, China, since June to
136
September in 2016. Detailed procedures of elderly person recruitment and
137
of questionnaire survey were described in our previous article (Li et al.,
138
2019). Briefly, all participants aged more than 60 years and live in two
139
communities including rural community in Ji’an (JA) and an urban
140
community in Yu’an (YA). For physical examination, each participant
141
was asked to collect a morning urine sample (at least 30 mL) before they
142
came to the local community hospitals. The collected urine samples were
143
stored at −80 °C prior to the analyses of the target antibiotics. Of 1080
144
participants, 59 participants did not provide urine samples, 31 participants’
145
urine sample analysis lack urine creatinine information; therefore, we
146
eventually included 990 subjects in our analysis. No significant
147
differences were found in age (mean age, 71.8 and 73.2 years) and gender
148
ratio (54.4% and 53.3% female) between the included and the excluded
149
(P > 0.05). In fact, we measured 1021 urine samples in this study, the
150
detected frequencies and volume-based concentrations were shown in
151
Table S1. The whole participants have provided informed consent form.
152
This study was approved by the ethical committee of Anhui Medical
153
University.
154
2.2. Selection of Antibiotics 7
155
Because of the abuse and unreasonable application in aquaculture and
156
animal husbandry, antibiotics were frequently detected in aquatic
157
products and animal-derived food (Chen et al., 2015; Wang et al., 2017a),
158
therefore, exposure to antibiotics via dietary intake for the elderly should
159
not be omitted. In this study, apart from human antibiotics (HAs) and
160
antibiotics preferred as HA (PHAs), we also screened the veterinary
161
antibiotics (VAs) and antibiotics preferred as VA (PVAs). Ultimately,
162
according to the detected antibiotics in foods of animal origin (Du et al.,
163
2019; Wang et al., 2017a; Zhao et al., 2018; Zhou et al., 2013) and human
164
urines (Wang et al., 2015; Wang et al., 2017b; Wang et al., 2016a; Wang
165
et al., 2018b), we selected 45 antibiotics and 2 metabolites (Table S2)
166
including 9 sulfonamides (SAs), 10 fluoroquinolones (FQs), 7 macrolides
167
(MAs), 8 β-lactams (LAs), 4 tetracyclines (TCs), 3 phenicols (PCs), 2
168
quinoxalines (QUs), lincomycin (LIN), spectinomycin (SPC), florfenicol
169
amine, N4-acetyl -sulfamonomethoxine. All standards were purchased
170
from Sigma-Aldrich (St Louis, MO, USA), Dr Ehrenstorfer (Augsburg,
171
Germany) and TRC (North York, Canada).
172
2.3. Urine sample preparation
173
The sample preparation was referred to the previous methodology with
174
minor modification (He-Xing et al., 2014). Briefly, urine samples were
175
thawed and centrifuged for 5 min at 2000 rpm at 4 °C, 1.0-mL urine
176
supernatant was buffered with 200 µL of Na2EDTA-McIlvaine buffer (pH 8
177
4.0). Then the mixture was spiked with 15 µL β-glucuronidase aqueous
178
solution (
10000 units/mL) from Helix pomatia (type H-2,
179
Sigma-Aldrich) and 20 µL of mixed internal standards (2 µg/mL of
180
sulfamethoxazole-D4, trimethoprim-D3, ofloxacin-D3, azithromycin-D3,
181
ceftiofur-D3,
182
lincomycin-D3, 4 µg/mL of amoxicillin-D4), vortexed thoroughly and kept
183
at 37 °C overnight to hydrolyze the analyte conjugates. After hydrolysis,
184
the mixture was transferred onto a reversed phase Waters® Oasis
185
hydrophilic–lipophilic–balanced (HLB) cartridge (60 mg, 3 mL; 30 µm),
186
which was preconditioned with 1.2 mL of pure methanol and equilibrated
187
with 1.2 mL of ultrapure water. Subsequently, the cartridge was washed
188
with 1.2 mL of ultrapure water and 1.2 mL of 30 % methanol-water
189
solution to remove matrix interferences. The antibiotics kept in cartridges
190
were eluted by 2 mL of pure methanol and concentrated to near dryness at
191
45 °C under a gentle nitrogen stream, then re-dissolved in 200 µL of 5 %
192
acetonitrile-water solution. Finally, the solution was filtered by 0.22 µm
193
hydrophilic membrane and 15 µL of the extracts were injected for
194
HPLC-MS/MS analysis.
195
2.4. Instrument analysis
196
In the Anhui Provincial Key Laboratory of Population Health &
197
Aristogenics (Anhui Medical University, Hefei City, China), urinary
198
antibiotics
doxycycline-D3,
were
measured
florfenicol-D3,
by 9
a
penicillinV-D5
high-performance
and
liquid
199
chromatograph-tandem mass spectrometry (HPLC, Agilent 1200; MS,
200
Agilent 6410B). Based on the previous method with a few minor
201
modifications (Liu et al., 2017a), the analytes were separated on the
202
ZORBAX SB-C18 column (2.10 mm ×150 mm 3.50 µm Agilent,
203
USA), at a column temperature 35 °C and a flow rate of 0.4 mL/min. The
204
gradient elution program was different between positive ion and negative
205
ion modes and shown in Table S3 and Table S4. All LC-MS grade
206
solvents (water, formic acid and acetonitrile) were from Honeywell
207
International Inc (Morristown, NJ, USA). The fragment voltages,
208
collision energies and retention times of individual antibiotic are shown
209
in Table S2. Other MS conditions are as follows: drying gas temperature,
210
350 °C; drying gas flow, 10 L/min; capillary voltage, 4000 V; nebulizer
211
pressure, 40 Psi. The dynamic multiple reaction monitoring (dMRM)
212
mode was used for all data acquisition.
213
2.5. Quantification and quality control
214
Urinary antibiotics were quantified using an isotope dilution method. A
215
nine-point calibration curve from 0.5 to 200 ng/mL was prepared for all
216
analyzed antibiotics. The regression coefficients (R2) ranged from 0.990
217
to 0.999. The limits of detection (LODs) and limits of quantitation (LOQs)
218
were defined as a signal-to-noise ratio of 3 and 10, respectively. The
219
LODs of the screened antibiotics ranged from 0.03 to 2.15 ng/mL, while
220
the LOQs between 0.11 and 6.02 ng/mL. Two spiked urine samples at 20 10
221
ng/mL were used to monitor the precision and trueness of analytical
222
procedure. Procedural and instrumental blanks were also prepared for
223
each batch to avoid laboratory contamination and analytical interferences.
224
The recoveries of analyzed antibiotics ranged from 73.5% to 112.2% with
225
the relative standard deviations varying between 8.5% and 14.6%, and the
226
matrix effects between 57.5% and 123.7% with the relative standard
227
deviations varying between 11.4% and 19.7%.
228
2.6. Daily exposure dose estimation
229
We calculated the estimated daily intake (EDI) of antibiotics using the
230
following equation (Katrine et al., 2014; Wang et al., 2017b):
231
EDI (µg/kg/day)
Caj (µg/g)×CE(g/day)
(1)
bw(kg)×P
232
Caj was the creatinine-adjusted concentration of antibiotics, bw was the
233
body weight, P represented antibiotic excretion rate in urine as
234
unchanged and glucuronide-conjugated forms, which were derived from
235
human pharmacokinetic studies (Table S5). As for sulfachinoxalin,
236
sulfaclozine, sulfachloropyridazine, sarafloxacin and cyadox, the urinary
237
excretion rates from animals were used due to lack of data in human. CE
238
was the daily output of urinary creatinine, which calculated using
239
equation 2 and 3 (Mage et al., 2008) (Ht: height, cm).
240
CEmale (g/day)
×(bw1.5×Ht0.5)/106
241 242
0.926×1.93×(140–age)
CEfemale (g/day)
0.993×1.64×(140–age) 11
(2)
×(bw1.5×Ht0.5)/ 106
243
(3)
244 245
2.7. Health risk assessment
246
On the basis of EDI estimations, we further calculated HQs and HIs to
247
assess health risks from a single antibiotic exposure and combined
248
exposure, respectively. The formula was as follows (Gao et al., 2017;
249
Wang et al., 2017b): HQ
250
EDI(µg/kg bw/day) ADI(µg/kg bw/day)
;
HI=∑ HQ
251
Given that most of antibiotics belong to antibacterial agents, they mainly
252
pose impacts on gut microbiota (Becattini et al., 2016; Leclercq et al.,
253
2017). Thus, the microbiological effect-based acceptable daily intake
254
(ADI) of individual antibiotic was used to calculate HQ. The ADI used in
255
this study was derived from literature or established by authorities and
256
shown in Table S6. In the current study, HI was the sum of the HQ for
257
individual antibiotic, but except for HAs and PHAs. We assumed a
258
potential health risk would occur when the HI value for an elderly was >
259
1.
260
2.8. Statistical analysis
261
In the current study, based on the antibacterial mechanisms, we
262
categorized 34 antibiotics into eight categories (SAs, FQs, MAs, LAs,
263
TCs, PCs, QUs and LINs). Moreover, four new variables grouped by their
264
usages were HAs, VAs, PHAs and PVAs. Generally, in one urine sample, 12
265
a detection of one or more antibiotics in a category was regard as a
266
positive detection of the corresponding category. Creatinine corrected
267
concentrations of antibiotics were used to assess their exposure in urines.
268
Body mass index (BMI) was calculated as the ratio of body weight (kg)
269
to height squared (m2). All subjects were categorized into two groups of
270
normal weight (BMI < 24) and overweight (BMI
271
BMI-based cutoff values of Chinese adults proposed by the Working
272
Group on Obesity in China(Zhou, 2002). Pearson chi-square test or the
273
non-parameters test was used to examine the gender, age, regional and
274
BMI differences in the detection frequencies or concentrations of
275
individual antibiotics or categories. The binomial logistic regression
276
model was performed to estimate the relation and odds ratios for gender,
277
age, study site and BMI in association with antibiotic detection rates.
278
Values below the LODs were treated as zero for the calculation of total
279
concentrations, means, and medians. However, when performing
280
statistical analyses, those below the LODs were taken as 1/2 LODs.
281
Statistical analyses were performed using SPSS Version 23.0 software
282
(Chicago, IL, USA), statistical significance was set at P < 0.05 in the
283
current study.
284
3. Results
285
3.1. Detection concentrations and frequencies of antibiotics
286
In this study, Table 1 showed that 34 antibiotics were detected in 93.0% 13
24) according to the
287
of all urines samples, the detection frequencies of individual antibiotic
288
ranged from 0.2% to 35.5%. The predominantly categories in urines were
289
sulfonamides and fluoroquinolones, with the detection frequencies of
290
55.8% and 50.1%, respectively. In addition, two or more antibiotics were
291
simultaneously detected in above 70% samples. Moreover, 10 VAs or
292
PVAs, including sulfaclozine, ofloxacin, florfenicol, trimethoprim,
293
tetracycline, oxytetracycline, doxycycline, ciprofloxacin, norfloxacin and
294
enrofloxacin, were extensively detected in urines, which detection rates
295
were more than 10%. Sulfaclozine (35.5%) and ofloxacin (23.7%) were
296
the most frequently detected in VAs and PVAs, respectively. And the total
297
detection rates of both VAs and PVAs in our study were 62.9% and 72.7%
298
(Table S7).
299
Figure 1 showed that the majority of antibiotic concentration values in
300
the urine samples were in the range of the LODs to 20 ng/mL. However,
301
it is worth noting that 4.8% of urines were detected to contain antibiotics
302
more
303
sulfamethoxazole, trimethoprim, oxytetracycline, danofloxacin and
304
lincomycin) which content were even higher than 5000 ng/mL in urines
305
of very few elderly (Table 1). Perhaps, the extreme values of urinary
306
antibiotic concentrations were achieved by direct intakes of antibiotics,
307
such as clinical use, self-medication or an unknown cause. However, the
308
abovementioned antibiotics, biological half-lives were quite low usually
than
500
ng/mL,
especially
14
the
6
PVAs
(norfloxacin,
309
less than 20 hours (Liu et al., 2017a), could be quickly excreted from
310
human bodies. Therefore, we speculated that the higher concentrations
311
probably were obtained from a continuous intake of antibiotics seriously
312
contaminated foods or drinking water.
313
3.2. Relationship among detection frequencies with study site, age,
314
gender, and BMI
315
Table 2 presented the differences in detection frequencies of urinary
316
antibiotics among different study sites, age, gender and BMI. The
317
detection
318
sulfaclozine, oxytetracycline, ofloxacin and ciprofloxacin, were higher in
319
subjects from the rural area than those from the urban area (P < 0.05) and
320
a similar trend was observed for the category of VAs (P > 0.05). For
321
age-related
322
tetracycline and enrofloxacin) were more likely to be detected in older
323
subjects (aged > 70 years). The categories of SAs and PVAs were more
324
frequently found in men than women. Furthermore, BMI-related
325
differences were also observed in this study. The detection frequencies of
326
3 PVAs (chlortetracycline, norfloxacin and sulfachloropyridazine)
327
showed a significantly higher level in overweight group (BMI ≥ 24) than
328
normal weight group (BMI < 24), but sulfaclozine and ofloxacin were the
329
opposite (P < 0.05). Table S10 showed the adjusted associations between
330
study site, age, gender, and BMI and the detection frequencies of
frequencies
differences,
of
4
5
PVAs,
PVAs
15
including
sulfamethoxazole,
(sulfamethoxazole,
sulfadiazine,
331
antibiotics in urines, the above differences and trends remained the same
332
after adjustment for covariates. Table S11 presented the correlation
333
between study site, age, gender, and BMI and antibiotics concentration in
334
urines, the results revealed that the group with a higher detection
335
frequency often had a higher concentration of antibiotic in urine sample.
336
3.3. Estimated daily intake and health risk assessment
337
Figure 2 displayed the elderly with the total EDI greater than 1 µg/kg/day
338
accounted for 17.1% of all participants. Table 3 revealed that the extreme
339
values of 3 PVAs (sulfamethoxazole, norfloxacin and lincomycin) were
340
more than 500 µg/kg/day and the maximum value of 5450.45 µg/kg/day
341
was seen in a VA (danofloxacin). Moreover, as presented in Table S8, the
342
PVAs had the higher EDI than either HAs or PHAs. Table 4 showed that
343
12 antibiotics (2 VAs and 10 PVAs) were observed to pose a
344
microbiological effect-related health risk on 6.7% of the elderly in this
345
study. Specifically, ciprofloxacin (3.5%) was a most important
346
contributor to the potential health risk related to gut microbiota, followed
347
by
348
sulfamethoxazole were found to pose a toxicological effect-related health
349
risk in 0.3% of all subjects. Although the detection frequencies of
350
florfenicol and ofloxacin reached 23.0% and 23.7%, respectively, they
351
did not pose health risks on the elderly in this study. Even so, it did not
352
mean there were no negative effects on human health under a continuous
oxytetracycline
(1.1%)
and
16
lincomycin
(0.7%).
Besides,
353
exposure scenario.
354
4. Discussion
355
In the current study, 34 antibiotics were detected in the urine of the
356
elderly from West Anhui of China, with a total detection frequency
357
reaching 93%. The 10 veterinary antibiotics were found in 62.9% of urine
358
samples. Furthermore, the majority of the subjects had a total antibiotic
359
concentration ranging from the LODs to 20.0 ng/mL. And the HI value of
360
more than 1 was observed in 6.7% of the elderly. These results indicated
361
that the elderly were extensively exposed to antibiotics, especially VAs
362
and PVAs, and some elderly were under a health risk linked to dysbiosis
363
of the gut microbiota.
364
4.1. Exposure sources of antibiotics in the elderly
365
In general, antibiotics are the primary drugs for treat and prevent bacterial
366
infections in human and animal (Nathan, 2014). Also, antibiotics are
367
extensively added to serve as growth factors in aquaculture and animal
368
husbandry (Van Boeckel et al., 2017). Owing to the highest rates of
369
antibiotic use, first, clinical use is the main source for the elderly exposed
370
to HAs and PHAs in our study. Second, ingesting contaminated food
371
might be the most important source for the indirect exposure to
372
antibiotics (Wang et al., 2015; Wang et al., 2017b). For instance,
373
fluoroquinolones were the most frequently detected category in aquatic
374
products and had high residual levels (Liu et al., 2017b). In the current 17
375
study, 50.0% of the elderly were exposed to fluoroquinolones and the
376
detection frequencies of both ciprofloxacin and ofloxacin were more than
377
15.0%. Moreover, sulfonamides were detected in meat foods, the
378
maximum residue level in pork and chicken reached 3.6 × 103 µg/kg and
379
2.7 × 103 µg/kg (Yamaguchi et al., 2015), respectively. Also,
380
sulfonamides were frequently detected in the elderly of this study, with a
381
detection rate of 55.8%. A study conducted in Korea adults indicated that
382
after 5-day diet without animal-derived food, the urinary levels of 2 PVAs
383
(trimethoprim and ciprofloxacin) were significantly reduced (Ji et al.,
384
2010). Finally, drinking water was contaminated by some antibiotics with
385
lower concentrations, but played a limited role in antibiotic exposure of
386
elderly (Li et al., 2017; Wang et al., 2016b). Therefore, the VAs and PVAs
387
detected in urines of the elderly might primarily derive from long-term
388
ingesting contaminated food of animal origin, such as pork, fish and milk.
389
4.2. Differences of antibiotic across study sites, age, gender and BMI
390
In the current study, the detected frequencies and concentrations of some
391
antibiotics varied by age, gender, study site and BMI. For example,
392
sulfamethoxazole, tetracycline and enrofloxacin were more likely to be
393
found in the older age group which might be related to a declining
394
excretory capacity in the older. The detection frequency of PVAs was
395
higher in males than females, this could be partly explained as the sex
396
difference of dietary habits or lifestyles. Generally, the males or the 18
397
overweight adults may intake higher amount of animal food than females
398
or the normal weight ones (Fraser et al., 2000; Zou et al., 2017), which
399
may increase the risk of exposure to VAs or PVAs from food.
400
Consequently, both sulfachloropyridazine and chlortetracycline showed
401
significantly higher levels in overweight group than in the normal weight.
402
We also found some differences of urinary antibiotics across various
403
study sites. For example, the detected frequencies and concentrations of
404
VAs or PVAs, including lincomycin, ofloxacin and oxytetracycline, were
405
higher in subjects from rural area than those in urban area. In the majority
406
of rural areas of China, the treatment rate of sewage and livestock wastes
407
are quite low due to the limited infrastructure (Zhang et al., 2015). As a
408
result, antibiotics contained by animal wastes may be discharged directly
409
into the environment, which is the reason why the rural population has
410
higher opportunity to exposure the VAs and PVAs from environment.
411
Additionally, the elderly from urban area were more likely to
412
self-medication with antibiotics than those from rural area (Torres et al.,
413
2019), which is consistent with our study that the HAs (macrolides) have
414
a higher exposure level in subjects from the urban area.
415
4.3. Comparisons with other studies
416
Bio-monitoring data have indicated a widespread antibiotics exposure of
417
different populations, but most of subjects are children or younger adults
418
rather than the elderly (Wang et al., 2018a; Wang et al., 2015; Wang et al., 19
419
2017b; Wang et al., 2016a; Wang et al., 2018b). In our current study, the
420
overall detection frequency (93.0%) showed a higher level compared to
421
that school children in Shanghai (79.6%) (Wang et al., 2016a) or Hong
422
Kong (77.4%) (Li et al., 2017), pregnant women in Eastern China (41.6%)
423
(Wang et al., 2017b) and normal adults in Shanghai (45.9%) (Wang et al.,
424
2018b) or Dalian (41.1%) (Liu et al., 2017a). Probably, the elderly are
425
more susceptible to bacterial infection and some of them with chronic
426
disease (An R, 2018), which is the reason why antibiotics are prescribed
427
more frequently to elderly. If so, it is reasonable that the elderly have a
428
higher exposure level of antibiotics compared with adults in China. For
429
instance, azithromycin, the most commonly prescribed antibiotic in the
430
elderly (Kabbani et al., 2018), was detected in 18.2% of urine samples in
431
our study, much higher than 1.6% of adults (Wang et al., 2018b) and 1.3%
432
of pregnant women (Wang et al., 2017b). As for the VAs or PVAs, due to
433
they were mainly obtained from ingesting contaminated food of animal
434
origin, the exposure level of them in urines would influence by the
435
dietary habits. Compared with the youngers, the elderly ate red meats
436
more frequently (Fraser et al., 2000), such as pork and beef often
437
contaminated by tetracyclines, norofloxacin and florfenicol (Li et al.,
438
2017; Wang et al., 2017a). Consequently, as shown in Figure 3,
439
oxytetracycline (18.9%), chlorotetracycline (7.8%), tetracycline (19.5%),
440
doxycycline (18.4%), norofloxacin (11.8%) and florfenicol (23.0%) were 20
441
at a higher level in urines from the elderly than other populations (Wang
442
et al., 2018a; Wang et al., 2015; Wang et al., 2017b; Wang et al., 2016a;
443
Wang et al., 2018b).
444
Ciprofloxacin had a comparable detection frequency to the recent studies,
445
but its concentration in elderly of this study (99th percentile concentration:
446
33.6 ng/mL) was higher than other populations (99th percentile
447
concentration: 19.0 ng/mL in general adults (Wang et al., 2018b), 9.2
448
ng/mL in pregnant women (Wang et al., 2017b) and 5.5 ng/mL in children
449
(Wang et al., 2015)). Maybe, it is the reason that aging is accompanied by
450
the decline of nephron number and renal cortical volume, which induces a
451
decline in glomerular filtration rate and alterations in the endocrine
452
activity of the kidney (Rowland et al., 2018). As a result, there would be a
453
lower excretion rate of antibiotics in the elderly, which might explain the
454
higher concentrations in urines of the elderly in our study. Also, the 99th
455
percentile concentration (217 ng/mL) of norfloxacin in this study was far
456
higher than those in adults, pregnant women and children (Wang et al.,
457
2015; Wang et al., 2017b; Wang et al., 2018b). Taken together, there was
458
a heavy antibiotic body burden in the Chinese elderly and it is essential to
459
assess the potential health risks.
460
4.4. Health risk assessment
461
In the resent years, numerous studies confirmed that the composition and
462
function diversity of gut microbiota could be changed or even disrupted 21
463
in a low dose of antibiotic stimulation. For instance, oral vancomycin,
464
ciprofloxacin and metronidazole had a profound and long-lasting effect
465
on microbiota composition (Ferrer et al., 2017; Haak et al., 2018; Isaac et
466
al., 2017). This disruption of the gut microbiota is harmful to the human
467
host because the gut microbiota is important for human immune and
468
metabolic functions (Zmora et al., 2017). Growing evidence has
469
demonstrated that exposure to antibiotics may increase the risk of getting
470
obesity (Stark CM, 2019), inflammatory bowel diseases (Balram et al.,
471
2019) and colorectal adenoma (Cao et al., 2017). Most of the previous
472
studies mainly focused on the effects of clinical antibiotics, which were
473
commonly with a short-term and high-dose exposure mode, on human
474
health. Pitifully, there is still limited information regarding the effects on
475
human health of long-term exposure to low-dose antibiotics.
476
In the current study, we assessed the health risk of 20 VAs or PVAs were
477
assessed based on microbiological effect, that of three sulfonamides and
478
cyadox were based on toxicological effect. Of the detected antibiotics, the
479
ones including 3 tetracyclines, 6 fluoroquinolones, and 3 other antibiotics
480
(trimethoprim, thiamphenicol and lincomycin) were found to pose a
481
microbiological effect-related health risk in 6.7% of the elderly, which
482
was higher than that 4.3% of the pregnant women (Wang et al., 2017b)
483
and 6.0% of the children (Wang et al., 2018a), but lower than 7.2% of the
484
general adults (Wang et al., 2018b). Notably, ciprofloxacin was a most 22
485
important contributor to the potential health risk related to gut microbiota
486
in this study, consistent with the published articles (Wang et al., 2018a;
487
Wang et al., 2017b; Wang et al., 2018b). Therefore, the government
488
should formulate the usage guidelines of ciprofloxacin for human and
489
animals. Moreover, it is worthy of paying attention that 1.1% of the
490
elderly had HQ value of oxytetracycline more than 1 in our study. We
491
also found that sulfamethoxazole caused a toxicological effect-based
492
health risk in 0.3% of all subjects, which was not reported in previous
493
studies. These findings showed exposures of VAs or PVAs might pose a
494
higher health risk to the elderly, either related to microbiological or
495
toxicological effects. Further investigation should be required to elucidate
496
the underlying mechanisms by which low level antibiotic exposure
497
induces adverse health effects in elderly.
498
4.5. Strengths and limitations
499
To our best knowledge, this is the first study dedicated to investigate the
500
antibiotic body burden of the elderly in larger sample sizes using a
501
biomonitoring approach. We measured 45 common used antibiotics and 2
502
metabolites in urines of the elderly, which may better reflect the
503
cumulative antibiotic exposure from environment, food or drinking water.
504
Moreover, we also assessed the potential health risk relevant to the gut
505
microbiota for the elderly. This study may provide new insights into the
506
effect of antibiotics exposure on healthy ageing. 23
507
However, there are some uncertainties and limitations in this study. First,
508
antibiotics residues and utilization vary widely by geographical area
509
(Zhang et al., 2015), but the participants only come from the Lu’an city,
510
West Anhui. Therefore, the subjects might not entirely represent the
511
elderly population in China. Second, the two metabolites, N4-acetyl
512
-sulfamonomethoxine and florfenicol amine, were used to estimate the
513
total exposure of sulfamonomethoxine and florfenicol, but the other
514
antibiotics were only measured unchanged species, which might
515
underestimate true antibiotic exposure in human. In the future study, more
516
metabolites of targeted antibiotics should be bio-monitored in human
517
urines. Third, most detected antibiotics have a relatively low biological
518
half-live (< 20 h) (Liu et al., 2017a), thus, the first morning urine is
519
merely able to reflect the antibiotics exposure condition in a short period
520
of
521
sulfachloropyridazine, sarafloxacin and cyadox), due to lacking of human
522
pharmacokinetic data, the reference data of proportion of antibiotic
523
excretion in urine are consulted from animals’ experiments which might
524
have an impact on the EDI of antibiotics.
525
Declaration of conflicting interests
526
The authors declare no conflicts of interest.
527
Acknowledgements
528
This work was supported by the Key Projects of Natural Science
time.
Finally,
for
several
24
antibiotics
(sulfaclozine,
529
Research in Colleges and Universities of Anhui province (KJ2018A0164,
530
KJ2017A189) National Natural Science Foundation of China (81202209)
531
and the Key Projects introduced and funded by leading talent teams of
532
colleges and universities of Anhui province (0303011224). We are deeply
533
grateful for the help provided by all the members in the experimental
534
center platform for physical and chemical of Anhui Medical University.
535
Supporting information
536
Additional text, tables, and figures providing details of analytical method
537
of urinary antibiotics, detection frequencies, concentration distribution,
538
estimated daily intakes and health risk assessment of HAs, VAs, PHAs
539
and PVAs; differences of antibiotic concentrations and detection
540
frequencies by age, gender and study site.
541 542
References:
543
An R., Wilms E., Masclee AAM., et al., 2018. Age-dependent changes in GI physiology and
544 545 546
microbiota: time to reconsider? Gut. 67, 2213-2222. Bailey, L.C., Forrest, C.B., Zhang, P., et al., 2014. Association of antibiotics in infancy with early childhood obesity. JAMA Pediatr. 168 (11), 1063-1069.
547
Balram, B., Battat, R., Al-Khoury, A., et al., 2019. Risk Factors Associated with Clostridium
548
difficile Infection in Inflammatory Bowel Disease: A Systematic Review and
549
Meta-Analysis. J Crohns Colitis. 13 (1), 27-38.
550
Becattini, S., Taur, Y., Pamer, E.G., 2016. Antibiotic-Induced Changes in the Intestinal 25
551 552 553
Microbiota and Disease. Trends Mol Med. 22 (6), 458-478. Cao, Y., Wu, K., Mehta, R., et al., 2017. Long-term use of antibiotics and risk of colorectal adenoma. Gut. 67 (4), 672-678.
554
Chen, H., Liu, S., Xu, X.R., et al., 2015. Antibiotics in typical marine aquaculture farms
555
surrounding Hailing Island, South China: occurrence, bioaccumulation and human
556
dietary exposure. Mar Pollut Bull. 90 (1-2), 181-187.
557 558 559 560
Du, B., Wen, F., Zhang, Y., et al., 2019. Presence of tetracyclines, quinolones, lincomycin and streptomycin in milk. Food Control. 100, 171-175. Ferrer, M., Mendez-Garcia, C., Rojo, D., et al., 2017. Antibiotic use and microbiome function. Biochem Pharmacol. 134, 114-126.
561
Fraser, G.E., Welch, A., Luben, R., et al., 2000. The effect of age, sex, and education on food
562
consumption of a middle-aged English cohort-EPIC in East Anglia. Prev Med. 30 (1),
563
26-34.
564
Gao, H., Xu, Y.Y., Huang, K., et al., 2017. Cumulative risk assessment of phthalates
565
associated with birth outcomes in pregnant Chinese women: A prospective cohort study.
566
Environ Pollut. 222, 549-556.
567
Guo, X., Feng, C., Gu, E., et al., 2019. Spatial distribution, source apportionment and risk
568
assessment of antibiotics in the surface water and sediments of the Yangtze Estuary. Sci
569
Total Environ. 671, 548-557.
570
Haak, B.W., Lankelma, J.M., Hugenholtz, F., et al., 2019. Long-term impact of oral
571
vancomycin, ciprofloxacin and metronidazole on the gut microbiota in healthy humans. J
572
Antimicrob Chemother. 74 (3), 782-786. 26
573
HeXing, W., Bin, W., Ying, Z., et al., 2014. Rapid and sensitive screening and selective
574
quantification of antibiotics in human urine by two-dimensional ultraperformance liquid
575
chromatography coupled with quadrupole time-of-flight mass spectrometry. Ana Bioanal
576
Chem. 406 (30), 8049-8058.
577
Isaac, S., Scher, J.U., Djukovic, A., et al., 2017. Short- and long-term effects of oral
578
vancomycin on the human intestinal microbiota. J Antimicrob Chemother. 72 (1),
579
128-136.
580
Ji, K., Lim, K.Y., Park, Y., et al., 2010. Influence of a five-day vegetarian diet on urinary
581
levels of antibiotics and phthalate metabolites: a pilot study with "Temple Stay"
582
participants. Environ Res. 110 (4), 375-382.
583 584 585 586
Kabbani, S., Palms, D., Bartoces, M., et al., 2018. Outpatient Antibiotic Prescribing for Older Adults in the United States: 2011 to 2014. J Am Geriatr Soc. 66 (10), 1998-2002. Katrine, R.M., Hanne, F., Jeppe, S.C., et al., 2014. Current exposure of 200 pregnant Danish women to phthalates, parabens and phenols. Reproduction. 147 (4), 443-453.
587
Leclercq, S., Mian, F.M Stanisz, A.M., et al., 2017. Low-dose penicillin in early life induces
588
long-term changes in murine gut microbiota, brain cytokines and behavior. Nat Commun.
589
8, 15062.
590 591
Li, N., Ho, K.W.K., Ying, G.G., et al., 2017. Veterinary antibiotics in food, drinking water, and the urine of preschool children in Hong Kong. Environ Int, 108, 246-252.
592
Li, X.D., Cao, H.J., Xie, S.Y., et al., 2019. Adhering to a vegetarian diet may create a greater
593
risk of depressive symptoms in the elderly male Chinese population. J Affect Disord.
594
243, 182-187. 27
595
Liu, S., Zhao, G., Zhao, H., et al., 2017. Antibiotics in a general population: Relations with
596
gender, body mass index (BMI) and age and their human health risks. Sci Total Environ.
597
599-600, 298-304.
598 599 600
Liu, X., Steele, J.C., Meng, X.Z., 2017. Usage, residue, and human health risk of antibiotics in Chinese aquaculture: A review. Environ Pollut. 223, 161-169. Lurie, I., Yang, Y.X., Haynes, K., et al., 2015. Antibiotic exposure and the risk for depression,
601
anxiety, or psychosis: a nested case-control study. J Clin Psychiatry. 76 (11), 1522-1528.
602
Mage, D.T., Allen, R.H., Kodali, A., 2008. Creatinine corrections for estimating children's
603
and adult's pesticide intake doses in equilibrium with urinary pesticide and creatinine
604
concentrations. J Expo Sci Environ Epidemiol. 18 (4), 360-368.
605 606 607 608
Nathan, C., 2014. Antibiotic Resistance — Problems, Progress, and Prospects. N Engl J Med. 371 (19), 1761-1763. James A P., Dimitri K., Joanne D., et al., 2019. Altered gut microbiota activate and expand Insulin B15-23-reactive CD8+ T-cells. Diabetes. 68 (5), 1002-1013.
609
Rowland, J., Akbarov, A., Maan, A., et al., 2018. Tick-Tock Chimes the Kidney Clock - from
610
Biology of Renal Ageing to Clinical Applications. Kidney Blood Press Res. 43 (1),
611
55-67.
612 613 614 615 616
Stark, CM.S.A., Emerick, J., Nylund, CM., 2019. Antibiotic and acid-suppression medications during early childhood are associated with obesity. Gut. 68 (1), 62-69. Sun, J., Zeng, Q., Tsang, D.C.W., et al., 2017. Antibiotics in the agricultural soils from the Yangtze River Delta, China. Chemosphere. 189, 301-308. Torres, N.F., Chibi, B., Middleton, L.E., et al., 2019. Evidence of factors influencing 28
617
self-medication with antibiotics in low and middle-income countries: a systematic
618
scoping review. Public Health. 168, 92-101.
619 620 621 622 623 624
Van Boeckel, T.P., Glennon, E.E., Chen, D., et al., 2017. Reducing antimicrobial use in food animals. Science. 357 (6358), 1350-1352. Wang, H., Ren, L., Yu, X., et al., 2017a. Antibiotic residues in meat, milk and aquatic products in Shanghai and human exposure assessment. Food Control. 80, 217-225. Wang, H., Tang, C., Yang, J., et al., 2018a. Predictors of urinary antibiotics in children of Shanghai and health risk assessment. Environ Int. 121 (Pt 1), 507-514.
625
Wang, H., Wang, B., Zhao, Q., et al., 2015. Antibiotic body burden of Chinese school
626
children: a multisite biomonitoring-based study. Environ Sci Technol. 49 (8),
627
5070-5079.
628
Wang, H., Wang, N., Qian, J., et al., 2017b. Urinary Antibiotics of Pregnant Women in
629
Eastern China and Cumulative Health Risk Assessment. Environ Sci Technol. 51 (6),
630
3518-3525.
631 632
Wang, H., Wang, N., Wang, B., et al., 2016a. Antibiotics detected in urines and adipogenesis in school children. Environ Int. 89-90, 204-211.
633
Wang, H., Wang, N., Wang, B., et al., 2016. Antibiotics in Drinking Water in Shanghai and
634
Their Contribution to Antibiotic Exposure of School Children. Environ Sci Technol. 50
635
(5), 2692-2699.
636
Wang, H., Yang, J., Yu, X., et al., 2018b. Exposure of Adults to Antibiotics in a Shanghai
637
Suburban Area and Health Risk Assessment: A Biomonitoring-Based Study. Environ Sci
638
Technol. 52 (23), 13942-13950. 29
639
Woodmansey, E.J, Mcmurdo, M.E.T., Macfarlane, G.T., et al., 2004. Comparison of
640
compositions and metabolic activities of fecal microbiotas in young adults and in
641
antibiotic-treated and non-antibiotic-treated elderly subjects. Appl Environ Microbiol. 70
642
(10), 6113-6122.
643
Yamaguchi, T., Okihashi, M., Harada, K., et al., 2015. Antibiotic residue monitoring results
644
for pork, chicken, and beef samples in Vietnam in 2012-2013. J Agric Food Chem. 63
645
(21), 5141-5145.
646 647
Zhan, J.L.Y, Liu, D., Ma, X., 2018. Antibiotics may increase triazine herbicide exposure risk via disturbing gut microbiota. Microbiome. 6 (1), 224.
648
Zhang, Q.Q., Ying, G.G., Pan, C.G., et al., 2015. Comprehensive evaluation of antibiotics
649
emission and fate in the river basins of China: source analysis, multimedia modeling, and
650
linkage to bacterial resistance. Environ Sci Technol. 49 (11), 6772-6782.
651
Zhao, F., Yang, L., Chen, L., et al., 2018. Bioaccumulation of antibiotics in crops under
652
long-term manure application: Occurrence, biomass response and human exposure.
653
Chemosphere. 219, 882-895.
654
Zhou, B.F., 2002. Predictive values of body mass index and waist circumference for risk
655
factors of certain related diseases in Chinese adults--study on optimal cut-off points of
656
body mass index and waist circumference in Chinese adults. Biomed Environ Sci. 15 (1),
657
83-96.
658
Zhou, L.J., Ying, G.G., Liu, S., et al., 2013. Excretion masses and environmental occurrence
659
of antibiotics in typical swine and dairy cattle farms in China. Sci Total Environ. 444(2),
660
183-195. 30
661 662
Zmora, N., Bashiardes, S., Levy, M., et al., 2017. The Role of the Immune System in Metabolic Health and Disease. Cell Metab. 25 (3), 506-521.
663
Zou, Y., Zhang, R., Xia, S., et al., 2017. Dietary Patterns and Obesity among Chinese Adults:
664
Results from a Household-Based Cross-Sectional Study. Int J of Environ Res Public
665
Health. 14 (5), 487.
666 667
31
Captions Table 1 Detection frequencies and concentrations of antibiotic in urines of Chinese Elderly (n = 990) Table 2 Differences of urinary antibiotic detection frequencies among different study site, age, sex and BMI (n = 990) Table 3 Estimated daily intake dose (EDI) of 34 antibiotics (n = 990) Table 4 Hazard quotient (HQ) based on acceptable daily intakes (ADIs) (n = 990)
Table 1 Detection frequencies and concentrations of antibiotic in urines of Chinese Elderly (n = 990)
Antibiotic
LOD
Sulfonamidesa
N (%)
Unadjusted (Creatinine adjusted)e P50
552 (55.8) 0.40(0.36)
-
Maximum
P75
P90
P95
P99
6.12 (5.15)
16.3 (14.6)
31.5 (27.8)
108 (130)
5.29 × 104 (5.18 × 104)
-
-
-
5.37 (6.45)
2.25 × 104 (2.20 × 104)
Sulfamethoxazole
0-0.45
26 (2.6)
Sulfaclozine
0-0.17
351 (35.5) -
4.30 (4.00)
13.9 (11.8)
23.7 (22.0)
76.3 (54.3)
1.52 × 103 (681)
Trimethoprimc
0-0.05
201 (20.3) -
-
0.53 (0.44)
1.38 (1.64)
11.3 (13.1)
3.05 × 104 (2.98 × 104)
Sulfamethazine
0-0.07
6 (0.6)
-
-
-
-
-
4.32 (5.90)
Sulfadiazine
0-0.16
28 (2.8)
-
-
-
-
1.85 (1.68)
118 (120)
Sulfachloropyridazine
0-0.09
45 (4.5)
-
-
-
-
3.19 (2.02)
13.6 (28.7)
d
0-0.10
65 (6.6)
-
-
-
0.78 (0.71)
5.06 (4.67)
75.2 (68.6)
0.07 (0.05)
0.55 (0.58)
2.91 (2.72)
30.8 (25.8)
7.75 × 103 (4.97 × 103)
Sulfamonomethoxine Macrolidesa
282 (28.5)
Erythromycin
0-0.12
85 (8.6)
-
-
-
1.15 (1.40)
13.5 (16.1)
7.75 × 103 (4.97 × 103)
Clarithromycin
0-0.05
27 (2.7)
-
-
-
-
0.22 (0.24)
4.23 (4.26)
Azithromycin
0-0.04
180 (18.2) -
-
0.09 (0.09)
0.20 (0.25)
5.93 (7.02)
1.35 × 103 (981)
Roxithromycin
0-0.04
27 (2.7)
-
-
-
-
0.39 (0.29)
1.28 × 103 (1.66 × 103)
0.25 (0.19)
2.54 (2.27)
7.07 (5.70)
136 (216)
8.77 × 103 (1.35 × 104)
β-lactamsa
253 (25.6)
Cefaclor
0-0.22
3 (0.3)
-
-
-
-
-
3.71 (9.60)
Cefotaxime
0-0.51
8 (0.8)
-
-
-
-
-
46.2 (24.0)
Penicillin V
0-0.20
176 (17.8) -
-
1.18 (1.02)
2.82 (2.35)
13.4 (9.72)
97.0 (164)
Amoxicillin
0-0.10
77 (7.8)
-
-
-
0.98 (1.03)
135 (215)
8.77 × 103 (1.35 × 104)
440 (44.4)
1.21 (1.03)
3.54 (3.23)
6.85 (7.41)
568 (608)
4.33 × 104 (3.06 × 104)
Tetracyclinesa Oxytetracycline
0-0.15
187 (18.9) -
-
0.70 (0.64)
1.87 (1.79)
349 (569)
4.30 × 104 (3.02 × 104)
Chlorotetracycline
0-0.11
77 (7.8)
-
-
-
1.26 (0.95)
7.79 (8.84)
531 (906)
Tetracycline
0-0.20
193 (19.5) -
-
1.01 (0.88)
2.18 (2.01)
14.2 (17.1)
706 (1.04 × 103)
0-0.19
182 (18.4) -
-
0.85 (0.73)
1.85 (1.58)
11.6 (10.4)
Doxycycline Fluoroquinolones
a
495 (50.0) 0.03(0.02)
3
54.6 (80.3) 3
2.66 (3.02)
23.6 (20.7)
62.3 (65.2)
1.51 × 10 (1.06 × 10 )
3.03 × 105 (1.80 × 105)
Pefloxacin
0-0.05
40 (4.0)
-
-
-
-
7.89 (5.11)
264 (343)
Lomefloxacin
0-0.08
14 (1.4)
-
-
-
-
0.38 (0.54)
3.68 × 103 (3.66 × 103)
Danofloxacin
0-0.12
40(4.0)
-
-
-
-
24.0 (19.5)
3.03 × 105 (1.58 × 105)
Sarafloxacin
0-0.05
16 (1.6)
-
-
-
-
0.38 (0.29)
196 (215)
Ofloxacin
0-0.03
235 (23.7) -
-
0.94 (0.92)
3.74 (3.92)
38.5 (42.7)
99.6 (114)
Levofloxacin
0-0.04
33 (3.3)
-
-
-
-
141 (157)
2.16 × 105 (1.80 × 105)
Difloxacin
0-0.05
7 (0.7)
-
-
-
-
-
16.0 (13.0)
Enrofloxacin
0-0.04
103 (10.4) -
-
0.08 (0.07)
0.74 (0.66)
6.71 (7.47)
48.1 (22.4)
Ciprofloxacin
0-0.06
163 (16.5) -
-
1.67 (1.51)
5.06 (4.41)
33.6 (34.9)
101 (123)
Norfloxacin
0-0.04
117 (11.8) -
-
1.00 (0.68)
5.36 (4.50)
217 (252)
4.83 × 104 (6.27 × 104)
247 (24.9)
0.07 (0.08)
3.36 (3.35)
6.06 (6.83)
79.2 (90.0)
1.90 × 103 (1.81 × 103)
Phenicolsa Chloramphenicol
0-0.51
27 (2.7)
-
-
-
-
54.6 (78.8)
1.90 × 103 (1.81 × 103)
Thiamphenicol
0-2.15
2 (0.2)
-
-
-
-
-
179 (204)
Florfenicold
0-0.10
228 (23.0) -
-
2.74 (2.60)
4.36 (4.64)
12.6 (11.7)
79.1 (112)
165 (162)
2.44 × 105 (1.95 × 105)
165 (162)
2.44 × 105 (1.95 × 105)
15.6 (12.0)
113 (79.3)
Lincosamides
a
Lincomycin
36 (3.6) 0-0.05
Quinoxalinesa Cyadox All antibioticb
36 (3.6)
-
-
-
-
29 (2.9) 0-1.45
29 (2.9)
-
921 (93.0) 8.82(7.94)
-
-
-
15.6 (12.0)
113 (79.3)
26.5(23.9)
100 (91.0)
413(437)
1.83 × 104 (2.28 × 104)
3.03 × 105 (1.95 × 105)
Notes: a, sum of concentrations of antibiotics in corresponding category for individual; b, sum of concentrations of all antibiotics; c, due to the similar antibacterial mechanisms, trimethoprim was included in the sulfonamides; d, the urinary levels of sulfamonomethoxine and florfenicol were separately considered to be the sum of their prototypes and metabolites (sulfamonomethoxine-N4-acetyl and florfenicol amine); e, volume-based urinary antibiotic concentration, ng/mL (creatinine adjusted urinary antibiotics concentration, µg/g); LODs, limits of detection ng/mL; N (%), the number of positive detection (detection frequency, %); P, percentile; -, < limits of detection (LODs).
Roxithromycin
3.7
1.6
0.042
3.0
2.5
0.614
3.8
1.9
0.0
Cefaclor
0.4
0.2
0.680
0.0
0.6
0.102
0.2
0.4
0.6
Cefotaxime
0.6
1.1
0.322
0.4
1.1
0.209
0.2
1.3
0.0
Penicillin V
16.6
19.2
0.275
17.0
18.5
0.522
16.6
18.7
0.3
Amoxicillin
10.5
4.5
<0.001
8.2
7.4
0.676
7.1
8.3
0.4
Oxytetracycline
23.6
13.2
<0.001
18.5
19.3
0.742
19.7
18.2
0.5
Chlorotetracycline
5.3
10.7
0.002
9.7
6.1
0.037
7.8
7.8
0.9
Tetracycline
21.0
17.7
0.189
14.8
23.7
<0.001
22.8
16.7
0.0
Doxycycline
14.0
23.7
<0.001
17.4
19.3
0.443
18.2
18.6
0.8
Pefloxacin
3.9
4.3
0.761
4.1
4.0
0.956
5.1
3.2
0.1
Lomefloxacin
1.8
0.9
0.209
1.5
1.3
0.825
1.1
1.7
0.4
Danofloxacin
5.0
2.9
0.101
3.2
4.8
0.216
3.5
4.5
0.4
Sarafloxacin
1.3
2.0
0.368
1.5
1.7
0.780
2.2
1.1
0.1
Ofloxacin
28.5
17.9
<0.001
23.4
24.0
0.809
22.8
24.5
0.5
Levofloxacin
3.7
2.9
0.499
2.6
4.0
0.210
2.4
4.1
0.1
Difloxacin
0.2
1.3
0.030
0.4
1.0
0.325
0.4
0.9
0.3
Enrofloxacin
10.5
10.3
0.916
8.2
12.4
0.029
11.1
9.8
0.5
Ciprofloxacin
18.8
13.6
0.030
17.0
16.0
0.696
17.1
16.0
0.6
Norfloxacin
12.9
10.7
0.298
9.9
13.7
0.061
13.3
10.8
0.2
Chloramphenicol
2.8
2.7
0.940
2.6
2.9
0.782
2.7
2.8
0.9
Thiamphenicol
0.4
0.0
0.199
0.0
0.4
0.182
0.0
0.4
0.1
Florfenicol
23.9
21.3
0.548
21.0
24.2
0.394
22.2
23.2
0.8
Lincomycin
5.3
1.6
0.002
2.8
4.4
0.180
4.4
3.0
0.2
Cyadox
4.4
1.3
0.005
2.6
3.4
0.431
3.3
2.8
0.6
Sulfonamides
56.7
54.6
0.501
56.4
55.2
0.684
60.8
51.6
0.0
Macrolides
19.7
39.1
<0.001
27.3
29.6
0.418
26.8
29.9
0.2
β-lactams
26.7
24.2
0.361
24.9
26.1
0.652
23.5
27.3
0.1
Tetracyclines
44.9
43.8
0.732
42.9
45.8
0.362
45.5
43.6
0.5
Fluoroquinolones
54.9
44.3
<0.001
47.6
52.3
0.144
49.7
50.5
0.8
Chloramphenicols
26.7
22.8
0.160
23.4
26.3
0.285
23.9
25.8
0.5
Veterinary antibiotic
65.4
60.0
0.079
63.1
62.8
0.921
64.5
61.6
0.3
Human antibiotic
19.3
37.1
<0.001
25.3
29.2
0.172
26.2
28.4
0.4
Preferred as HA
31.3
30.4
0.765
31.1
30.7
0.894
28.4
33.0
0.1
Preferred as VA
74.0
71.1
0.309
73.4
72.1
0.659
75.8
70.1
0.0
All antibiotic
93.7
92.2
0.335
93.1
92.9
0.905
94.7
91.7
0.0
Categories
a
b
Notes: , detection rate corresponding to specific characteristics, %; , p-values were calculated by Chi-squ VA, antibiotic; BMI, body mass index; two study sites: the rural area (n = 543) and the urban area (n = 44 (n = 466) and > 70 years (n = 524); two sex categorizes: males (n = 451) and females (n = 539); two BMI 24, n = 488) and overweight (BMI ≥ 24, n = 502).
Azithromycin
-
-
-
0.02
0.05
1.30
255
Roxithromycin
-
-
-
-
-
0.01
30.9
0.05
0.12
2.81
234
β-lactamsa Cefaclor
-
-
-
-
-
-
0.13
Cefotaxime
-
-
-
-
-
-
0.50
Penicillin V
-
-
-
0.02
0.05
0.18
2.74
Amoxicillin
-
-
-
-
0.01
2.34
234
0.01
0.02
0.06
0.14
10.9
430
Tetracyclinesa Oxytetracycline
-
-
-
0.01
0.03
9.30
424
Chlorotetracycline
-
-
-
-
0.02
0.21
19.0
Tetracycline
-
-
-
0.02
0.04
0.29
18.6
Doxycycline
-
-
-
0.01
0.03
0.18
1.27
0.02
0.06
0.52
1.30
20.4
5.45
Fluoroquinolonesa Pefloxacin
-
-
-
-
-
0.63
36.1
Lomefloxacin
-
-
-
-
-
0.01
53.9
Danofloxacin
-
-
-
-
-
0.89
5.45
Sarafloxacin
-
-
-
-
-
0.01
5.30
Ofloxacin
-
-
-
0.01
0.05
0.45
1.69
Levofloxacin
-
-
-
-
-
1.73
1.82
Difloxacin
-
-
-
-
-
-
0.20
Enrofloxacin
-
-
-
-
0.04
0.34
1.19
Ciprofloxacin
-
-
-
0.03
0.10
0.74
2.32
Norfloxacin
-
-
-
0.01
0.09
3.83
1.14
0.08
0.14
1.12
22.9
Phenicolsa Chloramphenicol
-
-
-
-
-
0.78
22.9
Thiamphenicol
-
-
-
-
-
-
3.24
Florfenicol
-
-
-
0.06
0.11
0.26
2.10
3.33
3.38
3.33
3.38
0.89
4.91
Lincosamides
a
Lincomycin Quinoxalines
-
Cyadox
-
All antibioticb a
-
-
-
-
a
0.18
0.36
0.60
2.09
9.04
0.89
4.91 3
3.78 × 10
5.45
Notes: , sum of estimated daily intakes of antibiotics in corresponding catego individual; b, sum of estimated daily intakes of all antibiotics; c, due to the si antibacterial mechanisms, trimethoprim was included in the sulfonamide percentile; -, < limits of detection (LODs).
Azithromycin
HA
-
-
-
0.01
0.03
0.77
150
Roxithromycin
HA
-
-
-
-
-
0.01
77.3
0.01
0.04
3.34
334
β-lactamsa Cefaclor
HA
-
-
-
-
-
-
-
Cefotaxime
HA
-
-
-
-
-
-
0.01
Penicillin V
PHA
-
-
-
-
0.01
0.03
0.46
Amoxicillin
PHA
-
-
-
-
0.02
3.34
334
0.01
0.02
0.05
3.62
143
Tetracyclinesa Oxytetracycline
PVA
-
-
-
-
0.01
3.10
141
Chlorotetracycline
PVA
-
-
-
-
0.01
0.07
6.34
Tetracycline
PVA
-
-
-
0.01
0.01
0.10
6.20
PVA
-
-
-
-
0.01
0.06
0.42
0.02
0.46
1.10
9.66
910
Doxycycline Fluoroquinolones
a
Pefloxacin
PVA
-
-
-
-
-
0.05
2.58
Lomefloxacin
PVA
-
-
-
-
-
-
26.9
Danofloxacin
VA
-
-
-
-
-
-
9.08
Sarafloxacin
VA
-
-
-
-
-
0.02
13.3
Ofloxacin
PVA
-
-
-
-
0.02
0.14
0.53
Levofloxacin
HA
-
-
-
-
-
0.87
910
Difloxacin
VA
-
-
-
-
-
-
0.11
Enrofloxacin
VA
-
-
-
-
0.01
0.06
0.19
Ciprofloxacin
PVA
-
-
-
0.21
0.65
4.96
15.5
Norfloxacin
PVA
-
-
-
-
0.01
0.27
81.3
0.03
0.05
0.45
9.16
Phenicolsa Chloramphenicol
HA
-
-
-
-
-
0.31
9.16
Thiamphenicol
PVA
-
-
-
-
-
-
1.30
Florfenicol
VA
-
-
-
0.02
0.04
0.09
0.70
0.33
338
0.33
338
0.01
0.03
Lincosamides
a
PVA
Lincomycin Quinoxalines Cyadox All antibioticb a
-
-
-
-
-
a
VA
-
-
0.02 0.04
-
-
-
0.01
0.03
0.08
0.58
2.76
73.5
2.92 × 10
Notes: , sum of hazard quotient of antibiotics in corresponding category for individu the sum of the HQ for individual antibiotic based on microbiological effects, but exce HAs and PHAs; c, due to the similar antibacterial mechanisms, trimethoprim was inc in the sulfonamides; HAs, human antibiotics; VAs, veterinary antibiotics; PVAs, antib preferred as VA; PHAs, antibiotics preferred as HA; P, percentile; N (%), number o or HI > 1 (percent in all, %); -, < limits of detection (LODs).
Figure captions: Figure 1 Frequency distribution of sum of concentrations of 34 antibiotics (n = 990) Figure 2 Frequency distribution of sum of estimated daily intake of 34 antibiotics (n = 990) Figure 3 Comparison of detection frequencies of 14 veterinary antibiotics or preferred as VA in urines between our study and four previous studies (SMX: Sulfamethoxazole; SMZ: Sulfamethazine; SDZ: Sulfadiazine; TMP: Trimethoprim; OTC: Oxytetracycline; CTC: Chlortetracycline; TC: Tetracycline; DC: Doxycycline; OFX: Ofloxacin; EFX: Enrofloxacin; CFX: Ciprofloxacin; NFX: Norfloxacin; TAP: Thiamphenicol; FF: Florfenico; #, corresponding antibiotic is not measured in urines)
22 20 17.0
Population proportion (%)
18 15.3
16
14.8
14.7
14 12
10.8
10.7
10 8
7.0
6
4.9
4.8
4 2 0
LOD-0.5
0.5-2.0
2.0-5.0
5.0-10.0 10.0-20.0 20.0-50.0 50.0-500.0500.0-Max
Antibiotics concentration (ng/mL)
Figure 1 Frequency distribution of sum of concentrations of 34 antibiotics (n = 990)
22 19.0
20
Population proportion (%)
18
16.5
16 14
12.5 11.5
12 9.6
10 8
11.2
7.0
6.8
5.9
6 4 2 0
LOD-0.02 0.02-0.05 0.05-0.10 0.10-0.20 0.20-0.50 0.50-1.00 1.00-5.00 5.00-Max
Estimated daily intake (µg/kg/day)
Figure 2 Frequency distribution of sum of estimated daily intake of 34 antibiotics (n = 990)
70
Wang et al., 2016 (586 school children in Shanghai) Ji et al., 2010 (541 general adults in Korea)
60
Wang et al., 2017 (536 pregnant women in East China)
Detection frequency (%)
Wang et al., 2018 (822 general adults in Shanghai) 50
Our research, 2019 (990 elderly in West Anhui)
40
30
20
10 #
0 TMP SMX SMZ
SDZ
#
OTC
# #
CTC
#
TC
##
DC
#
NFX
EFX
CFX
OFX
#
#
FF
TAP
Figure 3 Comparison of detection frequencies of 14 veterinary antibiotics or preferred as VA in urines between this study and four previous studies (SMX: Sulfamethoxazole; SMZ: Sulfamethazine; SDZ: Sulfadiazine; TMP: Trimethoprim; OTC: Oxytetracycline; CTC: Chlortetracycline; TC: Tetracycline; DC: Doxycycline; OFX: Ofloxacin; EFX: Enrofloxacin; CFX: Ciprofloxacin; NFX: Norfloxacin; TAP: Thiamphenicol; FF: Florfenico; #, corresponding antibiotic is not measured in urines)
Highlights 1. Thirty-four antibiotics were detected in urines of 990 elderly from the Elderly Health and Environment Risk Factor cohort. 2. Fourteen antibiotics preferred for veterinary use were found in 72.7% of the elderly urines. 3. A higher detection frequency of the antibiotics preferred for veterinary use in males than in females. 4. Ciprofloxacin was the foremost contributor to the health risk in 3.5% of all subjects. 5. A health risk related to gut microbiota was revealed in 6.7% of the elderly.
Conflict of Interest There is no conflict of interest.