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Systematic Review and Meta-Analysis of the Associations Between Body Mass Index, Prostate Cancer, Advanced Prostate Cancer and Prostate Specific Antigen

09 Sep 2019-medRxiv (Cold Spring Harbor Laboratory Press)-pp 19005421

TL;DR: There is little or no evidence of a association between BMI and risk of prostate cancer or advanced prostate cancer, and strong evidence of an inverse and non-linear association between body-mass index and PSA.
Abstract: Purpose The relationship between body-mass index (BMI) and prostate cancer remains unclear. However, there is an inverse association between BMI and prostate-specific antigen (PSA), used for prostate cancer screening. We conducted this review to estimate the associations between BMI and (1) prostate cancer, (2) advanced prostate cancer, and (3) PSA. Methods We searched PubMed and Embase for studies until 02 October 2017 and obtained individual participant data from four studies. In total, 78 studies were identified for the association between BMI and prostate cancer, 21 for BMI and advanced prostate cancer, and 35 for BMI and PSA. We performed random-effects meta-analysis of linear associations of log PSA and prostate cancer with BMI and, to examine potential non-linearity, of associations between categories of BMI and each outcome. Results In the meta-analyses with continuous BMI, a 5 kg/m2 increase in BMI was associated with a percentage change in PSA of −5.88% (95% CI −6.87% to −4.87%). Using BMI categories, compared to normal weight men the PSA levels of overweight men were 3.43% lower (95% CI −5.57% to −1.23%), and obese men were 12.9% lower (95% CI −15.2% to −10.7%). Prostate cancer and advanced prostate cancer analyses showed little or no evidence associations. Conclusion There is little or no evidence of an association between BMI and risk of prostate cancer or advanced prostate cancer, and strong evidence of an inverse and non-linear association between BMI and PSA. The association between BMI and prostate cancer is likely biased if missed diagnoses are not considered.
Topics: Prostate cancer screening (66%), Prostate cancer (60%), Prostate-specific antigen (58%), Body mass index (50%)

Summary (5 min read)

Introduction

  • Vol.:(0123456789) Keywords Prostate cancer · Prostate-specific antigen · Body mass index · Screening · Meta-analysis · Systematic review Electronic supplementary material.
  • The online version of this article (https ://doi.org/10.1007/s1055 2-020-01291 -3) contains supplementary material, which is available to authorized users.

Background

  • Prostate cancer is the second commonest male cancer worldwide, [1] and the most commonly diagnosed cancer in men in the UK, with an estimated 47,151 diagnoses in 2015 [2].
  • Generally, most prostate cancers are slow growing, but can metastasize to the bones, lungs, and brain.
  • The authors therefore sought to perform an updated review of the literature, including more studies, and additionally examining non-linear associations.
  • A negative association between BMI and PSA could also induce a spurious positive association between BMI and advanced prostate cancer, as obese men may be diagnosed later, due to their lower PSA levels.
  • The authors objectives were to i) precisely quantify the (assumed linear) associations between BMI and prostate cancer, advanced prostate cancer, and PSA; ii) update previous meta-analyses using all relevant evidence, including case–control studies; and iii) explore potential non-linearity in these associations.

Eligibility criteria

  • The authors performed a systematic review in which they included original articles published in peer reviewed journals that measured an association between BMI and total prostate cancer incidence and/or advanced prostate cancer; and studies that measured an association between BMI and PSA, including supplements and meeting abstracts; human randomized controlled trials (RCTs), case–control, cohort, crosssectional, and non-randomized experimental studies.
  • If the abstract did not specifically mention BMI but mentioned height or weight, the authors acquired the full text to determine if BMI was calculable from data included in the publication.
  • The authors excluded reviews, books, commentaries, letters, and animal and cell-line studies; studies examining pre-malignant disease if there was no mention of prostate cancer or PSA; studies where BMI was measured after diagnosis of prostate cancer, as this increases the likelihood of reverse causality; and studies that they considered to be at critical risk of bias (see ‘Risk of Bias Assessment’ below).
  • The authors determined the effect estimate to be for advanced prostate cancer if the individual studies labeled the effect as “advanced” or “aggressive,” or if the effect was for locally advanced, extra-prostatic, nodular or metastatic prostate cancer.
  • Advanced prostate cancer represents clinically meaningful cancer, with lower survival rates than nonadvanced cancers.

Data sources

  • The authors searched Medline and Embase databases up to 02 October 2017 for studies in humans associating BMI with either prostate cancer or PSA.
  • The search query was as follows (each term as a text word search): (BMI or body mass index or obese or obesity or body weight or body size or adiposity) AND (prostate cancer or prostate neoplasm or PSA or prostate-specific antigen) NOT psoriatic arthritis.
  • Psoriatic arthritis was excluded as its initialism is also PSA.
  • The authors also reviewed the reference lists of previous meta-analyses for further studies for inclusion [6, 8, 14].
  • Duplicate studies were removed prior to download using the Ovid deduplication tool.

Data extraction

  • One author (SH) screened the titles and abstracts of all papers for inclusion and retrieved full texts for all studies that met the inclusion criteria.
  • If no full text could be found, and the abstract provided insufficient information for inclusion, the study was excluded.
  • One author (SH) screened all full texts for inclusion, and one of three independent reviewers (KT, ET, HJ) reviewed the first 60 full texts to check for consistency.
  • Specifically, the authors estimated linear associations between BMI and the log odds of prostate cancer or advanced prostate cancer, and between BMI and log transformed PSA.
  • If both adjusted (e.g., for potential confounders such as age, ethnicity, etc.) and unadjusted results were given in the same paper, the most-adjusted model was used in the meta-analysis.

Risk of bias assessment

  • SH and RL assessed the risk of bias in each study independently using an assessment tool created for a previous meta-analysis [18], with disagreements resolved by discussion.
  • The authors assessed risk of bias in six categories: confounding, selection of participants, missing data, outcome measurement, exposure measurement, and results’ reporting.
  • The authors included baseline age, BMI, log-PSA, family history of prostate cancer, and study as explanatory variables to predict prostate cancer status using multiple imputation.
  • Additionally, the authors visually inspected a plot of estimated prostate cancer risk against PSA for the imputed studies, to see whether the predicted risk of prostate cancer at low PSA levels for each study was plausible .
  • In each of the three included IPD studies, the authors estimated associations between BMI and (1) prostate cancer, (2) advanced prostate cancer, and (3) PSA.

Combining data

  • The authors combined estimates from studies identified through the systematic review and the IPD studies using random-effects and fixed-effect meta-analyses.
  • Studies presenting HRs and ORs were analyzed and presented separately.
  • In meta-analyses of categorical associations, studies from the systematic review were included if they presented ORs or HRs for overweight and/or obese men relative to normal weight men (for the outcomes of prostate cancer and advanced prostate cancer) or means and SDs of PSA or log-PSA for each of these BMI categories (for the outcome of PSA).
  • ORs and HRs that were presented for other categories of BMI were not used (such as morbidly obese, BMI ≥ 35 kg/m2), though the authors combined the mean and SD of PSA for different categories with neighboring categories when sufficient information was available.
  • Meta‑regression Meta-regression [33] was used to determine if the effect estimates from individual studies included in the metaanalyses varied by study-level factors.

Funnel plots

  • Funnel plots [34] were drawn to assess for small study effects in each analysis [35].

Albatross plots

  • As not all studies reported enough information to be included in the meta-analyses, the authors also present albatross plots containing results from studies with and without sufficient information to be included in the meta-analyses [17].
  • These are plots of the p value of an association against the number of participants and can be used to assess heterogeneity between studies and assess the rough magnitude of an association using limited information.
  • By indicating which studies had insufficient data to contribute to meta-analysis on the albatross plots, the authors determined whether inclusion of the remaining studies would have altered the overall interpretation of the evidence.

Results

  • In total, 9,127 papers were found that had keywords for BMI and prostate cancer or PSA.
  • After title and abstract screening, 725 papers remained (see Fig. 1, PRISMA flow diagram).
  • After full text screening, risk of bias assessment, and removal of papers reporting the same studies, 78 studies examined the association between BMI and prostate cancer [67 with data for meta-analysis], 21 studies examined the association between BMI and advanced prostate cancer [18 with data for meta-analysis], and 35 studies examined the association between BMI and PSA [20 with data for meta-analysis, one of which only had data for categorical associations].

Continuous BMI

  • Of the 34 studies providing information on the association between BMI (as a continuous variable) and PSA [25, 27, 28, 115–145], 15 studies (42%) could not be included in the meta-analysis due to insufficient data but were included in an albatross plot [131–145].
  • All studies in the meta-analysis adjusted for age in either the study design or analysis, while 9 studies (47%) adjusted for ethnicity.
  • No other variable (of 13 other variables) was adjusted for in more than four studies.
  • There was strong evidence for heterogeneity in effect estimates across studies (I2 = 60.0%, p < 0.001).
  • The funnel plot (Supplementary Fig. 11) showed little evidence of small study effects.

Categorical BMI

  • Sixteen of the studies included in the continuous meta-analysis presented PSA or log-PSA levels for overweight and/ or obese men and normal weight men [25, 27, 28, 115–122, 124, 125, 127, 129, 146], and one further study presented only categorical results [147].
  • Overall, there were 17 studies and 218,700 participants included in this analysis.
  • Forest plots are presented in Supplementary Figs.  13 and 14.
  • The pooled estimates from fixed-effect meta-analyses were slightly lower for the change in PSA between overweight and normal weight men (percentage change = -2.56%, 95% CI − 3.34 to − 1.78, p < 0.001), but similar for the change in PSA between obese and normal weight men (percentage change = -12.1%, 95% CI − 13.2 to − 11.1, p < 0.001).
  • The weighted mean BMI across all studies was 22.2 kg/m2 for the normal BMI category, 26.5 kg/m2 for the overweight category, and 31.3 kg/m2 for the obese category.

Overall prostate cancer

  • There was no compelling evidence to suggest there is a linear association between BMI and prostate cancer risk as the effect estimate was null with a very tight confidence interval, nor an association between being overweight and prostate cancer risk, and only weak evidence for a small reduction in prostate cancer risk in obesity.
  • There is likely a reduced risk of being diagnosed with prostate cancer in overweight/obese men due to the role of PSA screening or testing in many prostate cancer diagnoses.
  • This finding is consistent with their hypothesis regarding the expected direction of bias due to the negative association of BMI with PSA.
  • Overall, their results are consistent with previous metaanalyses.
  • In addition, an umbrella review of systematic reviews and meta-analysis by Kyrgiou et al. [3] concluded that there was no strong evidence for an association between BMI and prostate cancer risk, with a summary OR for prostate cancer for a 5 kg/m2 increase in BMI of 1.03 (95% CI 0.99–1.06).

Advanced prostate cancer

  • There was some evidence to suggest a positive linear association between BMI and the risk of advanced prostate cancer, but only among studies reporting an HR (HR = 1.06, 95% CI 1.01–1.12, p = 0.013).
  • This association was null in studies reporting an OR (OR = 1.00, 95% CI 0.94–1.06), but still consistent with a small positive association in studies, such that the difference between the two groups of studies may be due to chance or differences in study design or population.
  • Additionally, there may be collider bias [24] in both estimates from conditioning on prostate cancer, since any unmeasured confounders associated with both prostate cancer and advanced prostate cancer could induce an association between BMI and advanced prostate cancer.
  • The effect estimate may be increased in the WCRF analysis by the inclusion of high-grade and/or fatal prostate cancers or exclusion of case–control studies.
  • Kyrgiou et al. [3] concluded that there was weak evidence for a positive association between increasing BMI and advanced prostate cancer risk, with a RR for advanced prostate cancer for a 5 kg/ m2 increase in BMI of 1.09 (95% CI 1.02–1.16), although their meta-analysis included more up-to-date studies with a stricter inclusion criteria.

PSA

  • There was strong evidence of an inverse association between BMI and PSA, which the authors found to be likely non-linear, decreasing more quickly between overweight and obese than normal weight and overweight.
  • On average, obese men have an estimated 12.9% lower PSA than a normal weight man, and overweight men 3.4% lower PSA.
  • The authors could only find one previous review of the association between BMI and PSA, which did not include a meta-analysis or estimate effect size [152].
  • Their conclusion was that many studies reported an inverse association between BMI and PSA, in agreement with their findings.
  • It could thus be potentially beneficial to account for BMI when interpreting the results of a PSA test, however, prospective research would be necessary to confirm whether this would have a beneficial effect on prostate cancerrelated outcomes.

Strengths and Limitations

  • The authors synthesized data from many studies, including participants from many different populations at different time points, improving generalizability.
  • By including IPD studies and imputing prostate cancer status in men who were not biopsied, the authors were able to show and account for bias in the association between BMI and prostate cancer from PSA testing.
  • It is also possible the association between BMI and PSA varies by population, though their meta-regressions did not find any explanatory factors.
  • There was at least a moderate risk of bias for all studies, as all studies were observational and therefore could have been biased by unobserved confounding.
  • As such, relatively few studies were included; a superior approach would be to gather IPD from all eligible studies and to determine the precise form of any non-linear associations, which would also allow more accurate corrections to men’s PSA levels.

Conclusion

  • There was little evidence of any association between BMI and prostate cancer risk, and some evidence for a small positive association with advanced prostate cancer risk.
  • There was evidence from IPD studies to suggest this could bias the association between BMI and prostate cancer in screening studies.
  • The authors would like to acknowledge the support of the National Cancer Research Institute (NCRI) formed by the Department of Health, the Medical Research Council (MRC), and Cancer Research UK.
  • The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
  • This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

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Vol.:(0123456789)
1 3
Cancer Causes & Control (2020) 31:431–449
https://doi.org/10.1007/s10552-020-01291-3
REVIEW ARTICLE
Systematic review andmeta‑analysis oftheassociations
betweenbody mass index, prostate cancer, advanced prostate cancer,
andprostate‑specic antigen
SeanHarrison
1,2
· KateTilling
1,2
· EmmaL.Turner
1
· RichardM.Martin
1,3
· RosieLennon
4
· J.AtheneLane
1,3
·
JennyL.Donovan
1,5
· FreddieC.Hamdy
6
· DavidE.Neal
6,7
· J.L.H.RuudBosch
8
· HayleyE.Jones
1
Received: 23 August 2019 / Accepted: 27 February 2020 / Published online: 11 March 2020
© The Author(s) 2020
Abstract
Purpose The relationship between body mass index (BMI) and prostate cancer remains unclear. However, there is an inverse
association between BMI and prostate-specific antigen (PSA), used for prostate cancer screening. We conducted this review
to estimate the associations between BMI and (1) prostate cancer, (2) advanced prostate cancer, and (3) PSA.
Methods We searched PubMed and Embase for studies until 02 October 2017 and obtained individual participant data
from four studies. In total, 78 studies were identified for the association between BMI and prostate cancer, 21 for BMI and
advanced prostate cancer, and 35 for BMI and PSA. We performed random-effects meta-analysis of linear associations of
log-PSA and prostate cancer with BMI and, to examine potential non-linearity, of associations between categories of BMI
and each outcome.
Results In the meta-analyses with continuous BMI, a 5kg/m
2
increase in BMI was associated with a percentage change
in PSA of −5.88% (95% CI −6.87 to −4.87). Using BMI categories, compared to normal weight men the PSA levels of
overweight men were 3.43% lower (95% CI −5.57 to −1.23), and obese men were 12.9% lower (95% CI −15.2 to −10.7).
Prostate cancer and advanced prostate cancer analyses showed little or no evidence associations.
Conclusion There is little or no evidence of an association between BMI and risk of prostate cancer or advanced prostate
cancer, and strong evidence of an inverse and non-linear association between BMI and PSA. The association between BMI
and prostate cancer is likely biased if missed diagnoses are not considered.
Keywords Prostate cancer· Prostate-specific antigen· Body mass index· Screening· Meta-analysis· Systematic review
Electronic supplementary material The online version of this
article (https ://doi.org/10.1007/s1055 2-020-01291 -3) contains
supplementary material, which is available to authorized users.
* Sean Harrison
sean.harrison@bristol.ac.uk
1
Department ofPopulation Health Sciences, Bristol Medical
School, University ofBristol, Bristol, England
2
Medical Research Council Integrative Epidemiology Unit,
University ofBristol, Bristol, England
3
National Institute forHealth Research Bristol Biomedical
Research Centre, University Hospitals Bristol NHS
Foundation Trust andUniversity ofBristol, Bristol, England
4
Department ofEnvironment andGeography, University
ofYork, York, England
5
National Institute forHealth Research Collaboration
forLeadership inApplied Health Research andCare West,
University Hospitals Bristol NHS Trust, Bristol, England
6
Nuffield Department ofSurgical Sciences, University
ofOxford, Oxford, England
7
Department ofOncology, Addenbrooke’s Hospital,
University ofCambridge, Cambridge, England
8
Department ofUrology, University Medical Centre Utrecht,
Utrecht, TheNetherlands

432 Cancer Causes & Control (2020) 31:431–449
1 3
Background
Prostate cancer is the second commonest male cancer
worldwide, [1] and the most commonly diagnosed cancer
in men in the UK, with an estimated 47,151 diagnoses in
2015 [2]. Generally, most prostate cancers are slow grow-
ing, but can metastasize to the bones, lungs, and brain.
Worldwide, there were an estimated 307,000 deaths from
prostate cancer in 2012 [1], and in the UK, around 11,600
men died from prostate cancer in 2016 [2].
Body mass index (BMI) has been associated with
many cancers [3], but its association with prostate can-
cer is unclear. Previous meta-analyses and reviews have
suggested that BMI is not associated with prostate cancer
[4, 5], positively associated with prostate cancer [6, 7],
inversely associated with localized prostate cancer [8], and
positively associated with advanced [8], aggressive [9],
high-grade, and fatal prostate cancers [4]. These meta-
analyses were either limited to cohort studies [4, 5, 7, 8] or
in need of updating [6, 7]. Additionally, no meta-analysis
assessed potential non-linear associations between BMI
and risk of prostate cancer or advanced prostate cancer.
We therefore sought to perform an updated review of the
literature, including more studies, and additionally exam-
ining non-linear associations.
BMI has also been inversely associated with prostate-
specific antigen (PSA) [10], although no previous meta-
analysis of this relationship exists. The presence of such an
association could bias observed relationships between BMI
and prostate cancer as PSA testing often plays a key role in
diagnosis. More specifically, a negative association between
BMI and PSA could lead to a spurious negative association
or mask a positive association between BMI and localized
prostate cancer, as obese men, with lower PSA values, would
be less likely to be offered a biopsy as the result of a PSA
test. A negative association between BMI and PSA could
also induce a spurious positive association between BMI
and advanced prostate cancer, as obese men may be diag-
nosed later, due to their lower PSA levels. In addition, if the
association between BMI and prostate cancer (or advanced
prostate cancer) is non-linear, then studies with different
distributions of BMI will give rise to different estimates
of the BMI-prostate cancer association. There may also be
an association between BMI and prostate cancer screening
behavior (including uptake of PSA testing), though stud-
ies have shown conflicting results. In the USA, men with
high BMI values were more likely to receive PSA tests [11],
whereas in the UK men with both very low and high BMI
values were less likely to receive a PSA test [12]. This fur-
ther complicates the relationship between BMI and prostate
cancer diagnosis (though not BMI and PSA values), and this
review does not aim to assess this association.
We systematically reviewed the literature for all relevant
studies and performed meta-analyses. We also examined
these relationships using individual participant data (IPD)
from prostate cancer studies. In analyzing the IPD studies,
we aimed to account for incomplete and PSA-dependent
diagnosis by imputing prostate cancer status for all men who
did not receive a biopsy, and in doing, avoid potential bias
resulting from an association between BMI and PSA.
Our objectives were to i) precisely quantify the (assumed
linear) associations between BMI and prostate cancer,
advanced prostate cancer, and PSA; ii) update previ-
ous meta-analyses using all relevant evidence, including
case–control studies; and iii) explore potential non-linear-
ity in these associations. Our overall aim was to understand
whether BMI is a risk factor for prostate cancer, and to iden-
tify whether failure to account for the role of PSA in many
prostate cancer diagnoses is likely to lead to biased estimates
of the association between BMI and prostate cancer.
Methods
Eligibility criteria
We performed a systematic review in which we included
original articles published in peer reviewed journals that
measured an association between BMI and total prostate
cancer incidence and/or advanced prostate cancer; and stud-
ies that measured an association between BMI and PSA,
including supplements and meeting abstracts; human rand-
omized controlled trials (RCTs), case–control, cohort, cross-
sectional, and non-randomized experimental studies. If the
abstract did not specifically mention BMI but mentioned
height or weight, we acquired the full text to determine if
BMI was calculable from data included in the publication.
We excluded reviews, books, commentaries, letters, and
animal and cell-line studies; studies examining pre-malig-
nant disease if there was no mention of prostate cancer or
PSA; studies where BMI was measured after diagnosis of
prostate cancer, as this increases the likelihood of reverse
causality; and studies that we considered to be at critical risk
of bias (see ‘Risk of Bias Assessment’ below).
We determined the effect estimate to be for advanced
prostate cancer if the individual studies labeled the effect as
“advanced” or “aggressive,” or if the effect was for locally
advanced, extra-prostatic, nodular or metastatic prostate
cancer. Advanced prostate cancer represents clinically
meaningful cancer, with lower survival rates than non-
advanced cancers. High-grade prostate cancer on its own
was not considered equivalent to advanced prostate cancer
and was not extracted, as the definition of “high-grade” has
been inconsistent over time, incorporating Gleason scores

433Cancer Causes & Control (2020) 31:431–449
1 3
(the definition of which has changed over time [13]), tumor,
node, metastases [TNM] scores, and PSA levels.
Data sources
We searched Medline and Embase databases up to 02 Octo-
ber 2017 for studies in humans associating BMI with either
prostate cancer or PSA. The search query was as follows
(each term as a text word search): (BMI or body mass index
or obese or obesity or body weight or body size or adipos-
ity) AND (prostate cancer or prostate neoplasm or PSA or
prostate-specific antigen) NOT psoriatic arthritis. Psoriatic
arthritis was excluded as its initialism is also PSA. We also
reviewed the reference lists of previous meta-analyses for
further studies for inclusion [6, 8, 14]. Duplicate studies
were removed prior to download using the Ovid deduplica-
tion tool.
Data extraction
One author (SH) screened the titles and abstracts of all
papers for inclusion and retrieved full texts for all studies
that met the inclusion criteria. Full texts were also sought
if no abstract was available or if the abstract did not include
sufficient information to decide on inclusion. We also sought
full texts for conference abstracts, if a corresponding full text
was not found in the original search. If no full text could be
found, and the abstract provided insufficient information for
inclusion, the study was excluded. We excluded one pub-
lished paper where we could not locate a full text [15].
One author (SH) screened all full texts for inclusion, and
one of three independent reviewers (KT, ET, HJ) reviewed
the first 60 full texts to check for consistency. We resolved
any inconsistency with discussion to clarify screening crite-
ria. A random subset of the remaining studies [30 full texts]
was also reviewed by the independent reviewers to check for
drift from inclusion/exclusion criteria.
Both SH and RL independently extracted all relevant data
from included studies, with disagreements resolved by dis-
cussion. The first ten extractions were also performed by
HEJ, KT, and ELT to check for consistency.
We categorized prostate cancer studies as “before” if BMI
was measured on average at least two years before diagnosis
(prospective studies), and “same time” if BMI was measured
on average less than two years before diagnosis. In general,
“before” studies were cohort studies and “same time” stud-
ies were case–control studies. We considered the “before”
studies to be at lower risk of reverse causation.
We extracted data that were (or could be transformed to)
an odds ratio (OR) or hazard ratio (HR) quantifying the con-
tinuous association between BMI and total and advanced
prostate cancer risk, and a regression coefficient for the
association between BMI and log-PSA. Log-PSA was used
as an outcome rather than PSA as we assumed a multiplica-
tive association between BMI and PSA, which fits with the
theory that haemodilution is responsible for any observed
association [16]. Studies reported associations in a variety
of ways; a detailed list of the statistical conversions used to
estimate the ORs, HRs, and regression coefficients and their
standard errors (SEs) is in Supplementary appendix 1.
We estimated linear associations, taking BMI as a con-
tinuous exposure variable, and assessing the possibility
of non-linear associations by coding BMI as a categori-
cal exposure. Specifically, we estimated linear associa-
tions between BMI and the log odds of prostate cancer or
advanced prostate cancer, and between BMI and log trans-
formed PSA. For simplicity, we refer to linear associations
as “continuous” throughout. The following BMI categories
were used: normal weight (BMI < 25kg/m
2
), overweight
(25kg/m
2
≤ BMI < 30kg/m
2
), and obese (BMI 30kg/m
2
).
We refer to these as “categorical” associations throughout.
When several papers reported on the same study, for con-
tinuous associations we prioritized papers that presented
continuous effect estimates (e.g., HR or OR per 1kg/m
2
increase in BMI) over papers presenting categorical effect
estimates (e.g., HR or OR for overweight and obese groups
versus normal weight), and these were prioritized over mean
differences. For categorical associations, we extracted esti-
mates from papers presenting categorical associations only.
If duplicate studies presented the same effect estimate types
in multiple papers, the paper with the largest number of
participants was used in the meta-analysis. If both adjusted
(e.g., for potential confounders such as age, ethnicity, etc.)
and unadjusted results were given in the same paper, the
most-adjusted model was used in the meta-analysis.
If the data were insufficient to estimate a regression coef-
ficient, OR or HR and SE, we extracted a p value, the num-
ber of participants and direction of association from the most
relevant analysis for use in an albatross plot [17].
Risk ofbias assessment
SH and RL assessed the risk of bias in each study inde-
pendently using an assessment tool created for a previous
meta-analysis [18], with disagreements resolved by discus-
sion. This tool uses the categories of assessment from a draft
of the ROBINS-I tool [19], and questions from the CASP
case–control and cohort questionnaires [20, 21], see Sup-
plementary appendix 2.
We assessed risk of bias in six categories: confounding,
selection of participants, missing data, outcome measure-
ment, exposure measurement, and results’ reporting. We
assigned overall and category-specific risks of bias: either
low, moderate, high, critical, or unclear (if there was insuffi-
cient information to assign a risk). We based the overall risk
of bias on a subjective combination of the category-specific

434 Cancer Causes & Control (2020) 31:431–449
1 3
risk of biases, looking at the maximum risk of bias that
could have been introduced into the study by each category.
The overall risk of bias was not low in any study, as all
studies were observational and thus potentially subject to
unmeasured confounding.
We determined that a study had a critical risk of bias if
i) age was not accounted for in either the design or analy-
sis of the study and, for BMI-prostate cancer case–control
studies, if there was more than a 3-year difference in the
mean or median ages of cases and controls, because age is
strongly associated with BMI [22], prostate cancer risk [23],
and PSA [23]; or ii) if the design of the study was such that
participation was conditional upon PSA levels, both for the
association between BMI and PSA (as this would involve
conditioning on the outcome) and the association between
BMI and prostate cancer (as this would involve conditioning
on a collider) [24].
Studies with a critical risk of bias were excluded prior to
analysis and were not considered further.
In the studies found in the systematic review, it was gen-
erally unclear whether men considered as not having prostate
cancer had received biopsies. Usually, the controls were “not
known to have prostate cancer,” rather than “known not to
have prostate cancer.” Therefore, screening could have intro-
duced bias in the association between BMI and prostate can-
cer. Although we did not consider this a critical risk of bias,
we sought to investigate and quantify this bias using large
studies where biopsy status was known, and IPD available.
Individual participant data studies
Studies that offered prostate biopsies if the participants
PSA were above threshold values (screening studies) were
excluded from our systematic review for having a critical
risk of bias. However, we noted that some of the largest
potentially relevant studies for our research questions were
screening studies, and that bias due to screening could
potentially be accounted for using imputation of prostate
cancer status if IPD were available. This would then allow
these studies to be included in the meta-analyses.
We approached four prospective studies looking at pros-
tate cancer to obtain IPD: Krimpen [25], Prostate Cancer
Prevention Trial (PCPT) [26], Prostate, Lung, Colorectal,
and Ovarian cancer screening trial (PLCO) [27] and Pros-
tate Testing for cancer and treatment trial (ProtecT) [28].
These studies were chosen because they were large studies of
prostate cancer with known PSA screening protocols, or the
biopsy status of all participants was known. Key to inform-
ing the imputation model was PCPT, which offered biopsies
to all participants regardless of PSA level. This information
allowed us to predict prostate cancer status for men with a
PSA level below the threshold for biopsy in the other three
studies using multiple imputation. However, PCPT only
included men with a PSA less than 3.0ng/ml, biasing both
the BMI-PSA and BMI-prostate cancer analyses, and as such
was excluded from the meta-analyses due to the critical risk
of bias from conditioning on a collider or outcome. Imputa-
tion is valid if the missing data (prostate cancer status) is
missing at random given other variables in the imputation
model, so imputing prostate cancer is not biased even though
PCPT is restricted to men with a PSA less than 3.0ng/ml, as
PSA is in the imputation model [29].
For each IPD study, we requested data measured at
baseline on BMI and PSA, as well as age, family history
of prostate cancer and ethnicity. We also requested data on
prostate cancer status (including tumor, node, metastases
[TNM], and Gleason scores). For each man who was not
biopsied, we imputed prostate cancer status by the end of
the study in which he participated using multiple imputa-
tion. We included baseline age, BMI, log-PSA, family his-
tory of prostate cancer, and study as explanatory variables
to predict prostate cancer status using multiple imputation.
BMI, log-PSA, and family history of prostate cancer were
also imputed if missing.
We checked the validity of the imputation model by
checking whether the predicted incidence of prostate cancer
among men without prostate biopsies was credible, given
results from autopsy studies [30]. Additionally, we visually
inspected a plot of estimated prostate cancer risk against
PSA for the imputed studies, to see whether the predicted
risk of prostate cancer at low PSA levels for each study was
plausible (see Supplementary Appendix3.4).
In each of the three included IPD studies, we estimated
associations between BMI and (1) prostate cancer, (2)
advanced prostate cancer, and (3) PSA. We restricted the
analyses to men with white ethnicity (due to low numbers
of non-white men and therefore difficulties in imputation),
and adjusted the analyses for age, family history of prostate
cancer (for prostate cancer analyses), and prostate cancer
status (for the PSA analyses). Full details of the IPD studies,
the imputation method, and statistical analyses are available
in Supplementary Appendix3.
Combining data
Meta‑analysis
We combined estimates from studies identified through the
systematic review and the IPD studies using random-effects
and fixed-effect meta-analyses. We performed separate meta-
analyses of continuous and categorical associations for each
outcome (prostate cancer, advanced prostate cancer, and
PSA). All meta-analysis results are presented in forest plots.
Studies presenting HRs and ORs were analyzed and pre-
sented separately. For studies presenting ORs, “same time”
and “before” studies were meta-analyzed in subgroups,

435Cancer Causes & Control (2020) 31:431–449
1 3
and labeled as such in forest plots. Studies presenting HRs
were all classed as “before” studies, and labeled simply
“HR.” The results are presented as the HR or OR for pros-
tate cancer or advanced prostate cancer and percentage
change in PSA for a 5kg/m
2
increase in BMI. Heterogene-
ity was tested for and quantified using the Cochrans Q and
I
2
statistics [31, 32].
In meta-analyses of categorical associations, studies
from the systematic review were included if they presented
ORs or HRs for overweight and/or obese men relative to
normal weight men (for the outcomes of prostate cancer
and advanced prostate cancer) or means and SDs of PSA
or log-PSA for each of these BMI categories (for the out-
come of PSA). ORs and HRs that were presented for other
categories of BMI were not used (such as morbidly obese,
BMI ≥ 35kg/m
2
), though we combined the mean and SD
of PSA for different categories with neighboring catego-
ries when sufficient information was available.
Meta‑regression
Meta-regression [33] was used to determine if the effect
estimates from individual studies included in the meta-
analyses varied by study-level factors. For all meta-regres-
sions, we considered ethnicity (non-white versus white in
each study, defined as > 80% white participants or from a
country with a majority white population), mid-year of
recruitment, mean BMI in the study, and the overall risk of
bias (high versus medium). For the associations between
BMI and prostate cancer and advanced prostate cancer, we
also considered the mean age at diagnosis, and study mean
time between BMI measurement and diagnosis.
Funnel plots
Funnel plots [34] were drawn to assess for small study
effects in each analysis [35].
Albatross plots
As not all studies reported enough information to be
included in the meta-analyses, we also present albatross
plots containing results from studies with and without suf-
ficient information to be included in the meta-analyses [17].
These are plots of the p value of an association against the
number of participants and can be used to assess heteroge-
neity between studies and assess the rough magnitude of an
association using limited information. By indicating which
studies had insufficient data to contribute to meta-analysis on
the albatross plots, we determined whether inclusion of the
remaining studies would have altered the overall interpreta-
tion of the evidence.
Results
In total, 9,127 papers were found that had keywords for
BMI and prostate cancer or PSA. After title and abstract
screening, 725 papers remained (see Fig.1, PRISMA flow
diagram). After full text screening, risk of bias assessment,
and removal of papers reporting the same studies, 78 stud-
ies examined the association between BMI and prostate
cancer [67 with data for meta-analysis], 21 studies exam-
ined the association between BMI and advanced prostate
cancer [18 with data for meta-analysis], and 35 studies
examined the association between BMI and PSA [20 with
data for meta-analysis, one of which only had data for
categorical associations].
A summary of all results is given in Table1.
BMI andprostate cancer
Continuous BMI
Of the 78 studies examining the association between BMI
and prostate cancer [25, 27, 28, 36110], 11 (14%) could
not be included in the meta-analysis due to insufficient
data but were included in the albatross plot [100110].
All studies are detailed in Supplementary Table1, with
the results of the risk of bias assessment in Supplementary
Table2. All studies in the meta-analysis adjusted for age
in either the study design or analysis, while 23 studies
(34%) adjusted for smoking status, 22 (33%) for ethnicity,
20 (30%) for family history of prostate cancer, 13 (19%)
for education, 10 (15%) for area, 10 (15%) for diabetes,
10 (15%) for physical activity, 9 (13%) for alcohol, 6 (9%)
for diet, and 6 (9%) for income. No other variable (of 24
other variables) was adjusted for in more than four studies.
In total, 9,513,326 men from 67 studies were included
in the HR and OR meta-analyses, (9,351,795 in 30 HR
studies, 161,531,383 in 37 OR studies); of these, 201,311
(2.1%) men had prostate cancer (157,990 cases [1.7%] in
HR studies, 41,863 [25.9%] in OR studies). The random-
effects meta-analyses (Figs.2 and 3) estimated the average
HR and OR for prostate cancer for a 5kg/m
2
increase in
BMI to be 1.01 (95% CI 0.99–1.04, p = 0.29) and 0.99
(95% CI 0.96–1.02, p = 0.64), respectively. There was
strong evidence for heterogeneity in effect estimates
across studies for the studies reporting an HR (p < 0.001,
I
2
= 79.9%), and studies reporting an OR (p < 0.001,
I
2
= 65.8%). Pooled estimates from fixed-effect meta-
analyses were essentially the same.

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Abstract: LIFETIME BODY SIZE AND PROSTATE CANCER RISK IN A POPULATION-BASED CASE-CONTROL STUDY IN SWEDEN

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Abstract: 1. Department of Ergonomics, Occupational health and safety research center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran 2. Center of the excellence of occupational health, occupational health and safety research center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran 3. Modeling of noncommunicable diseases research center, Hamadan University of medical sciences, Hamadan, Iran4Department of occupational health engineering, Occupational health and safety research center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran

References
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Journal ArticleDOI
04 Sep 2003-BMJ
TL;DR: A new quantity is developed, I 2, which the authors believe gives a better measure of the consistency between trials in a meta-analysis, which is susceptible to the number of trials included in the meta- analysis.
Abstract: Cochrane Reviews have recently started including the quantity I 2 to help readers assess the consistency of the results of studies in meta-analyses. What does this new quantity mean, and why is assessment of heterogeneity so important to clinical practice? Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care.1 Their value is especially clear when the results of the studies they include show clinically important effects of similar magnitude. However, the conclusions are less clear when the included studies have differing results. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The test seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity). However, the test is susceptible to the number of trials included in the meta-analysis. We have developed a new quantity, I 2, which we believe gives a better measure of the consistency between trials in a meta-analysis. Assessment of the consistency of effects across studies is an essential part of meta-analysis. Unless we know how consistent the results of studies are, we cannot determine the generalisability of the findings of the meta-analysis. Indeed, several hierarchical systems for grading evidence state that the results of studies must be consistent or homogeneous to obtain the highest grading.2–4 Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.5 6 But what does the test achieve in practice, and how should the resulting P values be interpreted? A test for heterogeneity examines the null hypothesis that all studies are evaluating the same effect. The usual test statistic …

37,135 citations


"Systematic Review and Meta-Analysis..." refers methods in this paper

  • ...Heterogeneity was tested for and quantified using the Cochran’s Q and I 2 statistics (27,28)....

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Journal ArticleDOI
13 Sep 1997-BMJ
TL;DR: Funnel plots, plots of the trials' effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials.
Abstract: Objective: Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses. Design: Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews . Main outcome measure: Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision. Results: In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias. Conclusions: A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution. Key messages Systematic reviews of randomised trials are the best strategy for appraising evidence; however, the findings of some meta-analyses were later contradicted by large trials Funnel plots, plots of the trials9 effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials Funnel plot asymmetry was found in 38% of meta-analyses published in leading general medicine journals and in 13% of reviews from the Cochrane Database of Systematic Reviews Critical examination of systematic reviews for publication and related biases should be considered a routine procedure

31,295 citations


Book
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9,339 citations


Journal ArticleDOI
12 Oct 2016-BMJ
TL;DR: Risk of Bias In Non-randomised Studies - of Interventions is developed, a new tool for evaluating risk of bias in estimates of the comparative effectiveness of interventions from studies that did not use randomisation to allocate units or clusters of individuals to comparison groups.
Abstract: Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.

4,211 citations


"Systematic Review and Meta-Analysis..." refers methods in this paper

  • ...This tool uses the categories of assessment from a draft of the ROBINS-I tool (17), and questions from the CASP casecontrol and cohort questionnaires (18,19), see Supplementary appendix 2....

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Journal ArticleDOI
Andrew G Renehan1, Margaret Tyson1, Matthias Egger2, Matthias Egger3  +2 moreInstitutions (3)
16 Feb 2008-The Lancet
TL;DR: Assessment of the strength of associations between BMI and different sites of cancer and differences in these associations between sex and ethnic groups should inform the exploration of biological mechanisms that link obesity with cancer.
Abstract: Summary Background Excess bodyweight, expressed as increased body-mass index (BMI), is associated with the risk of some common adult cancers. We did a systematic review and meta-analysis to assess the strength of associations between BMI and different sites of cancer and to investigate differences in these associations between sex and ethnic groups. Methods We did electronic searches on Medline and Embase (1966 to November 2007), and searched reports to identify prospective studies of incident cases of 20 cancer types. We did random-effects meta-analyses and meta-regressions of study-specific incremental estimates to determine the risk of cancer associated with a 5 kg/m 2 increase in BMI. Findings We analysed 221 datasets (141 articles), including 282 137 incident cases. In men, a 5 kg/m 2 increase in BMI was strongly associated with oesophageal adenocarcinoma (RR 1·52, p 2 increase in BMI and endometrial (1·59, p Interpretation Increased BMI is associated with increased risk of common and less common malignancies. For some cancer types, associations differ between sexes and populations of different ethnic origins. These epidemiological observations should inform the exploration of biological mechanisms that link obesity with cancer.

4,045 citations


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