Body mass index, abdominal fatness, weight gain and the risk

of urinary incontinence: A systematic review and dose-

response meta-analysis of prospective studies

Dagfinn Aune, PhD1,2,3, Mahamat-Saleh Yahya, MS4,5, Teresa Norat, PhD1, Elio Riboli, MD1

Affiliations

1 Department of Epidemiology and Biostatistics, School of Public Health, Imperial College

London, London, United Kingdom

2 Department of Nutrition, Bjørknes University College, Oslo, Norway

3 Department of Endocrinology, Morbid Obesity and Preventive Medicine,

Oslo University Hospital, Oslo, Norway4CESP, Fac. de médecine - Univ. Paris-Sud, Fac. demédecine - UVSQ, INSERM, Université

Paris-Saclay, 94805, Villejuif, France

5 Gustave Roussy, F-94805, Villejuif, France

Correspondence to: Dr. Dagfinn Aune, Department of Epidemiology and Biostatistics,

School of Public Health, Imperial College London, St. Mary's Campus, Norfolk Place,

Paddington, London W2 1PG, UK.

Telephone: +44 (0) 20 7594 8478

E-mail: d.aune@imperial.ac.uk

Word count (main text, introduction through conclusion): 3395

Word count abstract: 247

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Abstract

Background: Adiposity has been associated with elevated risk of urinary incontinence in

epidemiological studies, however, the strength of the association has differed between studies.

Objectives: To conduct a systematic literature review and dose-response meta-analysis of

prospective studies on adiposity and risk of urinary incontinence.

Search strategy: We searched PubMed and Embase databases up to July 19th 2017.

Selection criteria: Prospective cohort studies were included.

Data collection and analysis: Data were extracted by one reviewer and checked for accuracy

by a second reviewer. Summary relative risks (RRs) and 95% confidence intervals (CIs) were

calculated using random effects models.

Main results: Twenty four prospective studies were included. The summary RR per 5 kg/m2

increment in BMI was 1.20 (95% confidence interval: 1.16-1.25, I2=58%, n=13) for

population-based studies and 1.19 (95% CI: 1.08-1.30, I2=87.1%, n=8) for pregnancy-based

studies, 1.18 (95% CI: 1.14-1.22, I2=0%, n=2) per 10 cm increase in waist circumference and

1.34 (95% CI: 1.11-1.62, I2=90%, n=2) per 10 kg of weight gain. Although the test for

nonlinearity was significant for BMI, p=0.04, the association was approximately linear. For

subtypes of urinary incontinence the summary RR per 5 BMI units was 1.45 (95% CI: 1.25-

1.68, I2=85%, n=3) for frequent incontinence, 1.52 (95% CI: 1.37-1.68, I2=34%, n=4) for

severe incontinence, 1.33 (95% CI: 1.26-1.41, I2=0%, n=8) for stress incontinence, 1.26 (95%

CI: 1.14-1.40, I2=70%, n=7) for urge incontinence, and 1.52 (95% CI: 1.36-1.69, I2=0%, n=3)

for mixed incontinence.

Conclusion: These results suggest excess weight may increase risk of urinary incontinence.

Key words: Overweight; obesity; BMI; waist circumference; urinary incontinence;

prospective studies; meta-analysis.

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Tweetable abstract: Overweight and obesity increases the risk of urinary incontinence.

Funding statement: This project was funded by the South-East Regional Health Authorities

of Norway and the School of Public Health, Imperial College London.

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Introduction

Urinary incontinence is a common disorder which affects between 12-42% of women

below age 60 years and 17-55% of older women,1;2 and between 1-16% of men.2;3 Cross-

sectional studies have reported increased prevalence of urinary incontinence among obese

compared to lean women.4-8 However, women might also reduce their activity levels because

of urinary incontinence and thereby increase their weight. Prospective studies avoids such a

temporal bias because the exposure is measured before the disease development. Several

prospective studies have also suggested positive associations between greater body mass

index (BMI, kg/m2) and risk of urinary incontinence,9-15 however, a few studies reported no

clear association or associations only for subtypes of urinary incontinence.16-19 Studies on BMI

in pregnancy and risk of urinary incontinence have also shown somewhat mixed results,20-27

with some studies reporting a positive association 20-23;26 and others showing no clear

association.24;25;27 In addition, a few studies found positive associations between waist

circumference 11;28 and weight gain 10;15 and the risk of urinary incontinence. The objective of

the current analysis was to clarify the association between adiposity and risk of urinary

incontinence and for this reason we conducted a systematic literature review and meta-

analysis of cohort studies on BMI, waist measures and weight gain and the risk of urinary

incontinence. We aimed to clarify the strength and shape of the dose-response relationship

between different adiposity measures and urinary incontinence overall as well as with

subtypes of urinary incontinence including frequent incontinence, severe incontinence, stress

incontinence, urge incontinence and mixed incontinence, and lastly to investigate potential

sources of heterogeneity between studies with subgroup and meta-regression analyses.

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Methods

Search strategy

We searched PubMed and Embase databases up to July 19th 2017 for eligible studies. The

search terms used are provided in the Appendix S1 and included terms for other exposures as

part of a larger project. MOOSE criteria for reporting meta-analyses was followed.29 We also

hand-searched the reference lists of the included publications for additional studies.

Study selection

Prospective and retrospective cohort studies and nested case-control studies (within

cohort studies) on different adiposity measures (BMI, waist circumference, weight gain) and

risk of urinary incontinence published in English were included. Other adiposity variables

(waist-to-hip ratio, hip circumference) were not investigated in a sufficient number of studies

to be analyzed. We excluded studies in high-risk populations (populations with patients with

type 2 diabetes or type 1 diabetes for example), grey literature and abstract only publications.

Relative risk (RR) such as hazard ratios, risk ratios, or odds ratios and 95% confidence

intervals (CIs) adjusted for at least one confounding factor had to be available in the

publication. A quantitative measure of adiposity and the total number of cases and person-

years or non-cases had to be available in the publication for studies to be included in the dose-

response meta-analysis. When several articles were published from the same study we used

the article with the largest number of cases or the article which provided most detail in the

reporting of the results for inclusion in dose-response analyses. A list of the studies that were

excluded and reasons for exclusion are found in the Table S1. DA and MSY did the literature

screening.

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Data extraction

We extracted from each study: The first author’s last name, publication year, country where

the study was conducted, study period, number of participants, number of cases, measure of

adiposity, level of adiposity, RR estimates and 95% CIs for each level of the adiposity

variables and confounding factors that were adjusted for in the analyses. Data were extracted

by DA and checked for accuracy by MSY.

Study quality

The Newcastle-Ottawa scale 30 was used to assess the quality of the cohort studies. The scale

ranks the studies based on the selection, comparability, and outcome assessment used in the

studies and gives a score of 0-9 stars. Studies with a score of 0-3, 4-6 and 7-9 stars were

considered to indicate low, moderate and high quality, respectively.

Statistical analysis

Summary RRs and 95% CIs were calculated for a 5 kg/m2 increment in BMI, 10 cm increase

in waist circumference and 10 kg increase in weight gain using random effects models, which

take into account heterogeneity between studies.31 The natural logarithm of the RR from each

study was calculated and a weighted average of these RRs was estimated with weights

according to the method of DerSimonian and Laird31, and then back-transformed to non-

logarithmic scale. Separate analyses were conducted for studies in the general population and

pregnancy-based studies because of the influence of pregnancy/childbirth on both weight and

risk of urinary incontinence. Associations which showed a two-tailed p<0.05 were considered

statistically significant. If studies reported results separately for subtypes of urinary

incontinence or other subgroups, but not overall, we combined the subgroup-specific

estimates using a fixed-effects model to generate an overall estimate which was included in

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the main analysis.

We used the method by Greenland and Longnecker 32 for the dose–response analysis

to calculate study-specific linear trends and 95% CIs from the natural logs of the reported

RRs and CIs across categories of each adiposity measure. For studies that reported BMI, waist

circumference or weight gain categories in ranges we calculated the average of the upper and

the lower cut-off point for each category which then was used as a midpoint which was

assigned to the corresponding RR for each study. For open-ended categories we used the

width of the adjacent interval to calculate an upper or lower cut-off point. Fractional

polynomial models (combined with random effects models) were used to examine whether

there was a nonlinear dose-response relationship between BMI and risk of urinary

incontinence. 33 The best fitting second order fractional polynomial regression model, which

was defined as the one with the lowest deviance, was determined and a likelihood ratio test

was used to test for nonlinearity.33

To investigate potential sources of heterogeneity subgroup analyses were conducted

stratified by sex, measurement vs. self-report of adiposity measures, duration of follow-up,

geographic location, number of cases, study quality scores, and adjustment for confounders

(age, race/ethnicity, education, socio-economic status, smoking, diabetes mellitus,

hypertension, physical activity, parity, oral contraceptive use, hysterectomy, hormone

replacement therapy, and enuresis. The Q test and I2 was used to quantitatively assess

heterogeneity between studies.34 Between subgroup differences in summary estimates were

examined using meta-regression analyses. Publication bias was assessed with Egger’s test 35

and Begg’s test 36 (p<0.10) and by inspecting the funnel plots for asymmetry. Sensitivity

analyses were conducted excluding one study at a time from the analysis to clarify whether

the results were simply driven by a large study or a study with an extreme result.

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Patient involvement

Because the meta-analysis uses already published data there was no requirement for

patient involvement.

Funding

This work has been supported by funding from the School of Public Health Imperial

College London and the South-East Regional Health Authorities of Norway. The

study sponsor had no role in the study design, collection of data, analysis, and interpretation

of data.

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Results

We included 14 population-based cohort studies 9-19;28;37;38 and 10 pregnancy-based

cohort studies 20-27;39;40 in the systematic review and meta-analysis of BMI, waist

circumference, weight gain and risk of urinary incontinence (Table S2, S3, Figure 1). Five

population-based studies were from Europe, eight were from the USA, and one was from

Australia (Table S2). Five pregnancy-based studies were from Europe, one from North

America, two from Asia and two from Australia (Table S3). Study characteristics of the

included studies, including publication year, location, study name, recruitment and follow-up

period, number of participants, number of cases, age of participants, assessment method of

height and weight, adiposity variables, levels of adiposity, RR estimates (CIs) and

confounders adjusted for, are provided in Table S2 and Table S3).

Body mass index and urinary incontinence

Eleven prospective studies (18,164 incident cases, 106,346 participants) 9-19 were

included in the dose-response meta-analysis of BMI and urinary incontinence. The summary

RR for a 5 kg/m2 increment in BMI was 1.20 (95% confidence interval: 1.16-1.25, I2=61.7%,

pheterogeneity=0.004) (Figure 2a). When studies were excluded one by one in sensitivity analyses,

the summary RR ranged from 1.19 (95% CI: 1.15-1.24) when excluding the Nurses' Health

Study 2 10 to 1.21 (95% CI: 1.17-1.26) when excluding the Cardiovascular Health Study 17

(Figure S1). There was no evidence of publication bias with Egger’s test, p=0.48, or with

Begg’s test, p=0.16, and there was no indication of asymmetry by inspection of the funnel

plot (Figure S2). Although the test for nonlinearity was significant, pnonlinearity=0.04, the

association appeared to be nearly linear (Figure 2b, Table S4).

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Body mass index and subtypes of urinary incontinence

With regard to subtypes of urinary incontinence three studies (3,610 cases, 68,175

participants) of BMI and frequent incontinence,9-11 four studies (1,463 cases, 69,270

participants) of severe incontinence,10;11;16;18 eight studies (>3,906 cases, 85,576 participants)

of stress incontinence,9-11;16;18;19;37;38 seven studies (>1,567 cases, 77,953 participants) of urge

incontinence,9-11;14;16;18;19 and three studies (809 cases, 68,175 participants) of mixed

incontinence 9-11 were included in the meta-analysis. The summary RR per 5 kg/m2 was 1.45

(95% CI: 1.25-1.68, I2=85%, n=3) for frequent incontinence (Figure 2c), 1.52 (95% CI: 1.37-

1.68, I2=34%, n=4) for severe incontinence (Figure 2e), 1.33 (95% CI: 1.26-1.41, I2=0%, n=8)

for stress incontinence (Figure 3a), 1.26 (95% CI: 1.14-1.40, I2=70%, n=7) for urge

incontinence (Figure 3c), and 1.52 (95% CI: 1.36-1.69, I2=0%, n=3) for mixed incontinence

(Figure 3e). These associations persisted in sensitivity analyses excluding one study at a time

(Figure S3-S8). There was no indication of publication bias for stress incontinence with

Egger's test, p=0.67, or Begg'st test, p=0.54 (Figure S9), however, there was some indication

of publication bias for urge incontinence with both Egger's test, p=0.03, and with Begg's test,

p=0.04 (Figure S10). When excluding two outlying studies 10;19 the tests were attenuated,

Egger's test, p=0.7, and Begg's test, p=0.46, and the summary estimate was attenuated and the

heterogeneity disappeared, but the association remained significant, summary RR=1.15 (95%

CI: 1.09-1.21, I2=0%).

There was no indication of nonlinearity for frequent, severe, stress and mixed

incontinence, but the test for nonlinearity was significant for urge incontinence, p=0.004

(Figure 2d, 2f, 3b, 3d, 3f, Table S4), with a flat dose-response curve at a BMI around 20-22,

but with increased risk above that BMI level (Figure S3d).

Maternal body mass index and urinary incontinence

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Eight studies 20-27 (>29,431 cases, 65,754 participants) were included in the meta-

analysis of BMI in prepregnancy and risk of urinary incontinence. The summary RR per 5

kg/m2 increase in BMI was 1.19 (95% CI: 1.08-1.30, I2=87.1%, pheterogeneity<0.0001) (Figure

4a). In sensitivity analyses excluding the most influential studies, the summary RR ranged

from 1.15 (95% CI: 1.05-1.26) when excluding a British study 23 to 1.23 (95% CI: 1.10-1.38)

when excluding a Chinese study 24 (Figure S11). No indication of publication bias was

apparent with Egger's test, p=0.31, or with Begg's test, p=0.54, but there was some indication

of asymmetry in the funnel plot with potentially negative studies missing (Figure S12). There

was no indication of nonlinearity, pnonlinearity=0.49 (Figure 4b).

Maternal body mass index and subtypes of urinary incontinence

With regard to subtypes of urinary incontinence three studies (>1,112 cases, 17,700

participants) of maternal BMI and stress urinary incontinence,24;25;39 two studies (>87 cases,

16,751 participants) of urge incontinence,24;25 and two studies (>35 cases, 16,751 participants)

of mixed incontinence 24;25 were included in the meta-analysis. The summary RR per 5 kg/m2

was 1.20 (95% CI: 1.08-1.33, I2=61.5%, pheterogeneity=0.08) for stress incontinence, 1.00 (95%

CI: 0.95-1.05, I2=0%, pheterogeneity=0.71) for urge incontinence, and 0.91 (95% CI: 0.67-1.23,

I2=92.0%, pheterogeneity<0.0001) for mixed incontinence (Figure 4c, 4d, Figure S13). The

summary RR for stress incontinence ranged from 1.16 (95% CI: 1.07-1.25) when excluding a

Canadian study 39 to 1.27 (1.09-1.47) when excluding a Taiwanese study 25 (Figure S14).

Waist circumference

Two prospective studies (4,400 cases and 22,866 participants) 11;28 were included in the

analysis of waist circumference and risk of urinary incontinence. The summary RR was 1.18

(95% CI: 1.14-1.22, I2=0%, n=2) per 10 cm increase in waist circumference (Figure S15a).

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Weight gain

Two prospective studies (6,015 cases and 41,679 participants) 10;15 were included in the

analysis of weight gain and risk of urinary incontinence. The summary RR was 1.34 (95% CI:

1.11-1.62, I2=90%, n=2) per 10 kg of weight gain (Figure S15b).

Subgroup and sensitivity analyses and study quality

The increased risk of urinary incontinence with higher BMI was evident in almost all

subgroup analyses defined by assessment of weight and height, duration of follow-up,

geographic location, number of cases, study quality and adjustment for confounding factors

(age, race/ethnicity, education, socio-economic status, smoking, diabetes mellitus,

hypertension, diuretics, physical activity, parity, oral contraceptive use, hysterectomy,

hormone replacement therapy, and enuresis) and meta-regression analyses showed little

evidence of between subgroup heterogeneity (Table S5). There was no heterogeneity between

subgroups in the analysis of maternal BMI and urinary incontinence, with the exception of the

subgroup stratified by duration of follow-up, where there was a stronger association among

studies with a longer duration of follow-up (Table S6).

Study quality was moderate with a mean (median) score of 6.3 (6) out of 9 points in

the analysis of BMI and urinary incontinence and 6 (6) for the studies on maternal BMI and

urinary incontinence.

Discussion

Main findings

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This meta-analysis of 24 prospective studies on adiposity and the risk of urinary incontinence

found 20%, 18% and 34% increases in the relative risk of urinary incontinence per 5 units

increase in BMI, per 10 cm increase in waist circumference and per 10 kg increase in weight

gain over time. For specific subtypes of incontinence there were 45%, 52%, 33%, 26% and

52% increases in risk of frequent, severe, stress, urge and mixed incontinence per 5 units

increase in BMI, respectively. Although the test for nonlinearity was significant for BMI and

urinary incontinence, the association appeared to be approximately linear with risk increasing

with significantly with increasing BMI even within the "normal" BMI range. There was no

evidence of nonlinearity in the analysis of BMI and severe, stress, and mixed type

incontinence, but the associations for urge incontinence was nonlinear with a flattening of the

dose-response curve between 17.5 and 25. The association between BMI in pregnancy and

urinary incontinence was also nonlinear with a steeper increase in risk at higher BMI values.

Strengths

Strengths of our meta-analysis include the prospective design of the studies which

avoids recall bias and provides less possibility for selection bias, the large number of cases

(up to >19000 cases and >107000 participants) which provided sufficient statistical power to

detect moderate associations between BMI and urinary incontinence. In addition, the detailed

dose-response analyses clarified the strength and shape of the dose-response relationship

between adiposity and urinary incontinence and the findings were robust in several subgroup

analyses. Lastly, the moderately high study quality of the included studies is another strength

of the analysis.

Limitations

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Potential limitations of our meta-analysis need to be mentioned. Confounding by other

risk factors cannot be entirely excluded. Nevertheless, the positive association between BMI

and urinary incontinence was also observed across a number of subgroup analyses when

stratified by whether the studies adjusted for confounding factors such as age, race/ethnicity,

education, socio-economic status, smoking, diabetes mellitus, hypertension, use of diuretics,

physical activity, parity, oral contraceptive use, hysterectomy, hormone replacement therapy

and enuresis. Although it is difficult to entirely rule out the possibility that confounding could

have had some influence on the results, the strong dose-response relationship between

increasing adiposity and urinary incontinence with RRs between 2 and 5 for severe obesity

make it seem less likely that confounding could account for all of the increased risk observed

in the current meta-analysis.

Measurement errors could have affected the assessment of weight, height, waist

circumference and weight changes, however, we found that the association between BMI and

urinary incontinence was similar among studies that used measured weight and height

compared to those that used self-reported weight and height. In addition, self-reported

anthropometric measures have shown to have high correlations with measured anthropometric

measures in several validation studies.41-44 Body mass index is an imperfect measure of body

fatness as it does not distinguish between body fat and muscle mass. However, studies have

shown high correlations between BMI and waist measures and body fat as measured by dual-

energy x-ray absorptiometry (DXA).45;46

Meta-analyses based on published literature may be affected by publication bias or

small study bias, however, we found evidence of publication bias in only one of the analyses

conducted and this appeared to be explained by two outlying studies which when excluded

did not significantly alter the conclusion of that particular analysis. Another limitation of the

present analysis is that not all the available studies reported results for subtypes of urinary

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incontinence and therefore the number of studies for some subtypes was limited and need

further examination in additional studies. Few studies reported on other anthropometric

measures than BMI such as waist circumference and weight change and therefore additional

studies are needed on these and other measures in relation to urinary incontinence as well.

Interpretation

Several mechanisms could contribute to the positive association observed between

body fatness and the risk of urinary incontinence. Studies of patients with urinary

incontinence have found high correlations between BMI or abdominal adiposity and intra-

abdominal pressure and intravesical pressure.47;48 Studies in rats found increased insulin

resistance, increased voiding frequency, and decreased leak point pressure among obese

compared to lean rats.49 The obese rats were shown to have more intramyocellular lipid

deposition in urethral striated muscle fibers which impaired urethral sphincter function, and

led to atrophy and distortion of the urethral striated muscle layer,49 and impaired contractility

and early fatiguing of muscle contractile activity of the urethra 50 compared to the lean rats.

Adiposity is associated with higher risk of diabetes 51 which is a risk factor for urinary

incontinence 52 perhaps through 1) microvascular damage of the pelvic floor leading to

dysfunction of the bladder or sphincter muscles, 2) diuresis due to hyperglycemia leading to

frequent urination and urge incontinence, and/or 3) structural changes including diabetic

neuropathy.52-54 Additional support for a causal interpretation of the evidence comes from a

randomized trial which showed a reduction in weekly urinary incontinence episodes by 60%

in a weight loss intervention group compared to 15% in the control group,55 while the Look

AHEAD Trial found a 42% reduction in odds of any incontinence among women with type 2

diabetes which had a weight loss of 5-10% compared to those with <5% weight loss.56 In

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addition, bariatric surgery trials have shown that weight loss improves urinary incontinence

and reduces the risk of urinary incontinence.57-61

Conclusion

These findings have important clinical and public health implications because of the

growing obesity epidemic worldwide 62 and add to the wide range of adverse health effects

that already have been established to be related to excess weight.63-75 The current study

suggests excess weight is a risk factor for urinary incontinence and underscore the importance

of population-wide policies and initiatives for prevention of overweight and obesity. Together

with other evidence from randomized trials which shows weight loss may reduce the

incidence of urinary incontinence the current findings provide further evidence excess weight

may be an important target for clinical intervention in patients with urinary incontinence.

In conclusion, increasing adiposity as measured by BMI, abdominal fatness and

weight gain was associated with an increased risk of urinary incontinence overall as well as

with different subtypes. Although further studies are needed on abdominal adiposity and

weight change and among men, these findings underscore the importance of weight control in

the prevention of urinary incontinence.

Acknowledgements: D. Aune takes primary responsibility for the integrity of the data and

the accuracy of the data analysis. We thank Darren C. Greenwood (Biostatistics Unit, Centre

for Epidemiology and Biostatistics, University of Leeds, Leeds, United Kingdom) for

providing the Stata code for the nonlinear dose-response analysis.

Disclosure of Interests: None of the authors have any conflict of interest.

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Contribution to Authorship:

Conceived and designed the research: DA.

Acquired the data: DA, MSY.

Analyzed and interpreted the data: DA, MSY, TN, ER.

Performed statistical analysis: DA.

Handled funding and supervision: ER, TN.

Drafted the manuscript: DA.

Made critical revision of the manuscript for intellectual content: DA, MSY, ER, TN.

Reference screening: DA, MSY.

Details of ethics approval: Since this is a meta-analysis of published studies ethical approval

was not needed.

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(2) Markland AD, Richter HE, Fwu CW, Eggers P, Kusek JW. Prevalence and trends of urinary incontinence in adults in the United States, 2001 to 2008. J Urol 2011; 186(2):589-593.

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