Perez‑Cornago et al. BMC Medicine 2022, 20(1):143
https://doi.org/10.1186/s12916-022-02336-x

RESEARCH ARTICLE

Open Access

Adiposity and risk of prostate cancer
death: a prospective analysis in UK Biobank
and meta‑analysis of published studies
Aurora Perez‑Cornago*, Yashvee Dunneram, Eleanor L. Watts, Timothy J. Key and Ruth C. Travis

Abstract
Background: The association of adiposity with prostate cancer specific mortality remains unclear. We examined how
adiposity relates to fatal prostate cancer and described the cross-sectional associations of commonly used adiposity
measurements with adiposity estimated by imaging in UK Biobank. We also conducted a dose-response meta-analy‑
sis to integrate the new data with existing prospective evidence.
Methods: 218,237 men from UK Biobank who were free from cancer at baseline were included. Body mass index(BMI), total body fat percentage (using bioimpedance), waist circumference (WC) and waist-to-hip ratio (WHR) werecollected at recruitment. Risk of dying from prostate cancer (primary cause) by the different adiposity measurements
was estimated using multivariable-adjusted Cox proportional hazards models. Results from this and other prospective
cohort studies were combined in a dose-response meta-analysis.
Results: In UK Biobank, 661 men died from prostate cancer over a mean follow-up of 11.6 years. In the subsampleof participants with magnetic resonance imaging and dual-energy X-ray absorptiometry, BMI, body fat percentage
and WC were strongly associated with imaging estimates of total and central adiposity (e.g. visceral fat, trunk fat).
The hazard ratios (HR) for prostate cancer death were 1.07 (95% confidence interval = 0.97–1.17) per 5 kg/m2 higher
BMI, 1.00 (0.94–1.08) per 5% increase in total body fat percentage, 1.06 (0.99–1.14) per 10 cm increase in WC and1.07 (1.01–1.14) per 0.05 increase in WHR. Our meta-analyses of prospective studies included 19,633 prostate cancer
deaths for BMI, 670 for body fat percentage, 3181 for WC and 1639 for WHR, and the combined HRs for dying fromprostate cancer for the increments above were 1.10 (1.07–1.12), 1.03 (0.96–1.11), 1.07 (1.03–1.11), and 1.06 (1.01–1.10),
respectively.
Conclusion: Overall, we found that men with higher total and central adiposity had similarly higher risks of prostatecancer death, which may be biologically driven and/or due to differences in detection. In either case, these findings
support the benefit for men of maintaining a healthy body weight.
Keywords: Adiposity, Imaging, Prostate cancer, Mortality, Population-attributable risk

*Correspondence: aurora.perez-cornago@ndph.ox.ac.uk
Nuffield Department of Population Health, Cancer Epidemiology Unit,
University of Oxford, OX3 7LF Oxford, UK

Background
Prostate cancer is the second most common cause ofcancer death in men in the UK [1]. Age, black ethnicity, family history of prostate cancer, genetic factors andendogenous hormones are known risk factors for prostate cancer, but apart from hormones none of them ismodifiable [2–5]. While many prostate cancer tumours© The Author(s) 2022. Open Access 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 theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Perez‑Cornago et al. BMC Medicine 2022, 20(1):143

are indolent (slow-growing), others are lethal, and thesedifferent tumours may have different risk factors [3].
However, the aetiology of lethal prostate cancer is notwell understood, and there is a need to identify risk factors for this most clinically relevant form of the disease.
There is substantial evidence that relates adiposity toprostate cancer risk, but the association appears to varyby tumour behaviour. Previous studies have found an
inverse association of obesity with risk of overall prostatecancer and non-aggressive forms of the disease, possibly due to later diagnosis in men with obesity. However,a positive association of greater adiposity with risk for
aggressive prostate cancer, including risk of dying fromprostate cancer, has been reported [2, 6], although it isunclear whether this positive association may be due tolate detection (and thus more advanced tumours withpoorer prognosis), a role of excessive adiposity in promoting metabolic and hormonal dysfunction that inturn may stimulate the growth and progression of prostate cancer cells [7], or a combination of both. Moreover, some evidence suggests that fat located within theabdominal cavity may be more aetiologically important
for aggressive prostate cancer than total adiposity [6],and the use of “gold standard” methods to determine
body fat distribution (e.g. magnetic resonance imaging(MRI)) [8, 9] may help to better understand these associations. However, due to the limited number of prostatecancer deaths in most prospective studies, relatively few
studies have investigated whether adiposity or its distribution is related to prostate cancer mortality [2], andmore research is needed.
In this report, we describe the results from a prospective analysis using UK Biobank data, and then from adose-response meta-analysis of findings from all published prospective studies. To inform the interpretationof our findings, we also first describe the cross-sectional
associations in UK Biobank of commonly used indices ofadiposity with MRI- and dual-energy X-ray absorptiometry (DXA)-derived estimates of adiposity.

Methods
UK Biobank
Study design and population

UK Biobank is a prospective study of ~ 500,000 UK adults
aged 40–69 years at recruitment (including 229,000 men)established between 2006 and 2010 to study risk factors
for disease. Details of the study protocol and information about data access are available online (https://​www.​ukbio​bank.​ac.​uk/​media/​gnkey​h2q/​study-​ratio​nale.​pdf )and elsewhere [10]. All individuals provided informed
consent to participate and the study was approved by
the National Information Governance Board for Healthand Social Care and the National Health Service North

Page 2 of 13

West Multicentre Research Ethics Committee (reference
number 06/MRE08/65). In brief, approximately 9.2 million people living within reasonable travelling distance(∼25 km) of 1 of the 22 assessment centres across England, Wales, and Scotland were identified from National
Health Service (NHS) registers and invited to participate
in the study, with a participation rate of 5.5% [11].
After excluding 9869 men with prevalent cancer(except C44: non-melanoma skin cancer), 1 man censored on entry day and 999 men with no adiposity measurements, the analyses included a total of 218,237 men(Additional File 1: Figure S1).
Assessment of adiposity and other predictor variables

At recruitment, participants provided detailed information on a range of sociodemographic, physical, lifestyleand health-related factors via self-completed touchscreen questionnaires and a computer assisted personal
interview [11]. Anthropometric measurements (standingheight, weight, waist and hip circumferences) were taken
by trained research clinic staff at the assessment centre,
while body mass index (BMI) was calculated and percentage body fat was estimated through bioimpedance
measures [12].
UK Biobank imaging sub‑cohort

In 2014, the UK Biobank imaging study re-invited a
subsample of participants to undergo abdominal MRI
and DXA, which has been detailed elsewhere [13, 14].
In brief, participants were scanned in a Siemens MAGNETOM Aera 1.5 T MRI scanner (Siemens Healthineers,
Germany) using a 6-min dual-echo Dixon Vibe protocol, providing water-and-fat-separated volumetric information for fat and muscle. Body composition analyses
for MRI images were performed using AMRA Profiler
Research (AMRA Medical AB, Linköping, Sweden). DXA
captures whole-body composition (e.g. bone, fat and leanmass) with no extensive additional processing and analysis. However, it is not possible to obtain direct compartmental volumetric measurements using this method, andtherefore, regional volume estimates are obtained indirectly using anatomical models [13, 14]. By December
2021, imaging data on ~ 18,800 men were available. BMI,
WC, hip circumference and body fat percentage werealso re-assessed at the imaging visit.
Ascertainment of prostate cancer mortality

Our endpoint was prostate cancer as the underlying
cause of death recorded on the death certificate (International Classification of Diseases Tenth revision codes:
C61 [15]). Men were followed-up until 31 December
2020 for England and Scotland and 19 July 2020 for
Wales. Mortality data were provided by NHS Digital forPerez‑Cornago et al. BMC Medicine 2022, 20(1):143

England and Wales and by the NHS Central Register andInformation and Statistics Division for Scotland.
Statistical analysis in UK Biobank

Cross‑sectional analyses of adiposity measure‑ments Pearson correlations between different anthropometric measurements were calculated. A subsample
of men had both commonly used anthropometric measures of adiposity (i.e. BMI, body fat percentage, WC andWHR) and adiposity information (MRI and DXA) fromthe imaging visit. Multivariable linear regression adjusted
for categories of age and height was used to estimate themean differences in each MRI- and DXA-derived measure of body composition per 1-SD difference in the levels of each commonly used anthropometric measure of
adiposity. We also used Pearson correlations to assess theassociations between adiposity measures. Moreover, menwere categorised into tenths of BMI, body fat percentage, WC and WHR, and multivariable linear regressions(adjusted for age and height) were conducted to calculatemean values for MRI and DXA. Moreover,
Prospective analyses Cox proportional hazards models
were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for prostate cancer death, using
age as the underlying time variable. Person-years were
calculated from the date of recruitment to the date ofdeath, loss to follow-up, or the censoring date, whicheveroccurred first. The proportional hazards assumption was
examined using time-varying covariates and Schoenfeld
residuals, and this revealed no evidence of deviation.
Men were categorised into fourths of adiposity measurements based on the distribution in the cohort. We alsomodelled HRs per predefined increments and categories of the adiposity measurements: (i) BMI [per 5 kg/m2
increase, and as predefined World Health Organization(WHO) categories [16] (< 25, 25–29.9, and ≥ 30 kg/m2)];(ii) body fat percentage (per 5% increase); (iii) WC [per10 cm increase, and as predefined WHO categories [17](< 94, 94–101.9, ≥ 102 cm)]; and (iv) WHR [per 0.05 unit
increase, and as predefined WHO categories [17] (< 0.90,≥ 0.90)]. Potential nonlinear associations between theanthropometric variables and prostate cancer mortality were evaluated using likelihood ratio tests comparing
the model with the anthropometric variable entered as anordered categorical (ordinal) variable to a model with thecategorical variable treated as continuous, and no evidence of non-linearity was observed.
Adjustment covariates were defined a priori based on
previous analyses by our group using UK Biobank data[18]. The minimally-adjusted models were stratified byPage 3 of 13

geographical region of recruitment (ten UK regions)and age (< 45, 45–49, 50–54, 55–59, 60–64, ≥ 65 years)
at recruitment. The fully adjusted model was further adjusted for Townsend deprivation score (fifths,
unknown [0.1%]), ethnic group (white, mixed background, Asian, black, other, and unknown [0.6%]), height(< 170, 170–174.9, 175–179.9, ≥ 180 cm, and unknown[0.2%]), lives with a wife or partner (no, yes), cigarettesmoking (never, former, current 1– < 15 cigarettes perday, current ≥ 15 cigarettes per day, current but number of cigarettes per day unknown, and smoking status
unknown [0.6%]), physical activity (low [0–9.9 METs/
week], moderate [10–49.9 METs/week], and high [≥50
METs/week], unknown [3.6%]), alcohol consumption (non-drinkers, < 1–9.9, 10–19.9, ≥20 g ethanol/
day, unknown [0.5%]), diabetes (no, yes, and unknown[0.5%]) and history of PSA testing at recruitment (no, yes,
unknown [5%]).
Sensitivity analyses We also performed the following
sensitivity analyses: excluding the first 5 years of followup. excluding men with BMI ≥ 27.5 kg/m2, excluding men
with BMI ≥ 25 kg/m2, excluding extreme values of exposure variables (percentiles outside 1–99), excluding men< 50 years of age at recruitment, running the statisticalanalyses per 1 standard deviation (SD) increment, using
the BMI-adjusted residuals of WC (or WHR, dependingon which one is the exposure of interest) by regressingthese variables in a linear model and using the residuals(that are statistically independent of BMI) as the exposures of interest.
Dose‑response meta‑analysis

We searched on PubMed, Embase, and Web of Sciencefor prospective studies examining the associations of
BMI, body fat percentage, WC and WHR with prostatecancer as the underlying cause of death, independently by
two researchers up to 15 March 2021; please see details in
the Additional File 1: Supplementary Methods [2, 6, 19–38], Tables S1-S3 and Figure S2) [6, 22–38]. We excluded
reviews, abstract-only publications or editorials. Whenthe same cohort study published more than one originalarticle looking at these associations, the paper reporting
the longest follow-up time was retained.
In the dose-response meta-analysis, we calculated the
HR estimates in the studies that reported results for a different increment (e.g. per 1 SD increase) or from the categorical data using generalised least-squares [39] for theincrements mentioned above (details in Additional File 1:
Supplementary Methods). We then pooled study-specific log HRs to obtain a summarised effect size using
a fixed effects model. The I2 statistic was used to assessPerez‑Cornago et al. BMC Medicine 2022, 20(1):143

heterogeneity across studies, and we assessed publication
bias with funnel plots and Egger’s test.

Page 4 of 13

Table 1 Baseline characteristics in all men and in men who diedfrom prostate cancer in men from UK Biobank
Characteristics at baseline

All menMen who diedfrom prostatecancerNo. of men

218237661

56.5 (8.2)

63.1 (4.9)

Most deprived quintile, n (%)

44,804 (20.5)

109 (16.5)

No qualifications, n (%)

29,465 (13.5)

83 (12.6)

Black ethnicity, n (%)

3,225 (1.5)

4 (0.6)

Population‑attributable risk

The number of prostate cancer deaths attributable toobesity as measured by BMI (population-attributablerisk (PAR)) in the UK was calculated using the number of deaths in the UK in 2019, the estimate of relativerisk from our dose response meta-analysis and information on the prevalence of obesity in English men aged
55–64 years in 2019 (as a surrogate for the UK; mean
BMI 28.9 kg/m2) [40].
All analyses were performed using Stata version 14.1
(Stata Corporation, College Station, TX, USA), and figures were plotted in R version 3.2.3. All tests of significance were two-sided, and P-values < 0.05 wereconsidered statistically significant.

Results
UK Biobank participants’ characteristics

After an average of 11.6 years of follow-up, a total of 661
men died from prostate cancer among the 218,237 men
included in the UK Biobank study. The main baselinecharacteristics of the participants are shown in Table 1,
while baseline characteristics of participants according to categories of BMI and WC are reported in Additional File 1: Tables S4 and S5. 12.4% of men reported
that they were current cigarette smokers, 43.3% reported
drinking ≥ 20 g of alcohol per day and 27.6% of men
reported being physically inactive. Men who subsequently died from prostate cancer had higher values of
all adiposity measurements at recruitment (Table 1).
Moreover, men with higher adiposity at baseline weremore likely to be older, drink ≥ 20 g of alcohol per day, bephysically inactive and to have hypertension and diabetes
than men in the lowest quartiles of BMI and WC (Additional File 1: Tables S4-S6).
Cross‑sectional associations in UK Biobank

BMI, body fat percentage and WC were strongly correlated (correlation coefficients (r) = 0.79–0.88), althoughthese measures were less strongly correlated with WHR(r = 0.59–0.79, Additional File 1: Table S7). BMI, body fat
percentage and WC were strongly associated with total
and central adiposity (e.g. visceral fat, trunk fat) obtained
from MRI- and DXA-derived measures of body composition, while the associations for WHR were somewhat smaller (Additional File 1: Tables S7-S12). Muscle
fat mass infiltration and liver proton density fat fraction
were moderately correlated with the commonly usedanthropometric measurements (r = 0.36–0.54) (Additional File 1: Tables S8 & S9).

Sociodemographic
Age at recruitment (years), mean(SD)

Not in paid/self-employment, % (n) 84,578 (38.8)

410 (62.2)

Living with partner, n (%)

493 (74.8)

166,378 (76.2)

Anthropometric
Height (cm), mean (SD)

175.6 (6.8)

175.4 (7.0)

BMI (kg/m2), mean (SD)27.8 (4.3)

28.1 (4.3)

Body fat (%), mean (SD)25.3 (5.8)

26.1 (5.8)

Waist circumference (cm), mean(SD)

96.9 (11.4)

98.9 (11.2)

Waist to hip ratio, mean (SD)

0.936 (0.065)

0.950 (0.064)

Current cigarette smokers, n (%)

27,247 (12.4)

68 (9.8)

Drinking alcohol ≥ 20 g/day, n (%)

94,407 (43.3)

299 (45.4)

60,228 (27.6)

178 (27.0)

Vasectomy, n (%)

11,343 (5.2)

25 (3.8)

Hypertension, n (%)

113,874 (52.2)

416 (62.0)

Diabetes, n (%)

15,088 (6.9)

72 (10.9)

Lifestyle

Physically inactive, n (%)

Health status

Prostate specific factors prior recruitment
PSA test, n (%)

60,441 (27.7)

206 (31.3)

Enlarged prostate, n (%)

7,074 (3.2)

35 (5.3)

16,383 (7.5)

65 (9.9)

Family history of prostate cancer,
n (%)

Abbreviations: BMI body mass index, PSA prostate specific antigen

Prospective analysis in UK Biobank

The multivariable-adjusted associations of BMI, body fat
percentage, WC and WHR with prostate cancer mortality are shown in Fig. 1 (minimally adjusted associations
are shown in Additional File 1: Table S13). Except for the
associations of WC and WHR with prostate cancer death,
there were no large changes between the minimally- andthe multivariable-adjusted models. BMI (HR per 5 kg/
m2 = 1.07 (95% CI 0.97–1.17), total body fat percentage(HR per 5% increase = 1.00, 0.94–1.08) and WC (HR per
10 cm increase = 1.06, 0.99–1.14) were not significantly
associated with prostate cancer death, whereas WHR(HR per 0.05 increase = 1.07, 1.01–1.14) was significantly
associated with risk of dying from prostate cancer; when
the highest quartiles were compared to the lowest the
HRs were 1.06 (0.84–1.34) for BMI, 0.95 (0.76–1.20) for
body fat percentage, 1.24 (0.98–1.57) for WC, and 1.25

Perez‑Cornago et al. BMC Medicine 2022, 20(1):143

Page 5 of 13

N pca deaths Total n

Hazard ratios (95% CI)

Q1, <=25.0

148

55669

1 ref

Q2, 25.1 - 27.2

160

54197

1.04 (0.83 - 1.31)

Q3, 27.3-30.0

191

54695

1.21 (0.98 - 1.51)

Q4, >=30.1

160

53286

1.06 (0.84 - 1.34)

Per 5 kg/m^2 increase659

217832

1.07 (0.97 - 1.17)

Q1, <=21.5

139

56238

1 ref

Q2, 21.6 - 25.4

137

57568

0.82 (0.65 - 1.04)

Q3, 25.5-29.1

178

50128

0.98 (0.78 - 1.23)

Q4, >=29.2

191

54291

0.95 (0.76 - 1.20)

Per 5 % increase

645

2142111.00 (0.94 - 1.08)

Q1, <=89

124

56238

1 ref

Q2, 89.1 - 96.0

165

57568

1.10 (0.87 - 1.40)

Q3, 96.1-103.0

165

50128

1.15 (0.91 - 1.46)

Q4, >=103.1

205

54291

1.24 (0.98 - 1.57)

Per 10 cm increase659

2182091.06 (0.99 - 1.14)

Q1, <=0.892

116

54,6361 ref

Q2, 0.893 - 0.934

139

54,4861.00 (0.78 - 1.28)

Q3, 0.935-0.978

190

54,5681.22 (0.96 - 1.54)

Q4, >=0.979

214

54,483

1.25 (0.99 - 1.58)

Per 0.05 increase659

2181571.07 (1.01 - 1.14)

P-trend

BMI, kg/m^2

0.19

Body fat, %0.892

Waist circumference, cm

0.116

Waist to hip ratio

0.5

0.75

1

1.5

0.028

2.5

Hazard ratios and 95% CI

Fig. 1 Multivariable-adjusted hazard ratios (95% CI) for prostate cancer death in relation to adiposity measurements at baseline in men from UK
Biobank. Abbreviations: BMI, body mass index. Cox regression analyses. All models are stratified by region and age at recruitment and adjusted forage (underlying time variable), Townsend deprivation score, ethnicity, lives with a wife or partner, smoking, physical activity, alcohol consumption,height, diabetes, and history of PSA test. Full details for each covariate are provided in the statistical section(0.99–1.58) for WHR. When BMI, WC and WHR werecategorised according to the WHO cutoff points, nosignificant associations were found (Additional File 1:
Table S14).

In sensitivity analyses, we found that the associations
remained largely unchanged after excluding the first
5 years of follow-up, men with BMI ≥ 25 kg/m2, extreme
values and men < 50 years of age (Table 2). When the0.93 (0.79–1.10)

1.07 (0.97–1.18)

144
Excluding men withBMI ≥ 25 kg/m2, per 1
SD increment

659

650

659

Excluding extreme
values: percentiles
1–99

Excluding men < 50years of age

Per 1 SD increment

1.06 (0.98–1.15)

0.170

0.223

0.170

0.404

0.265

0.190

p-trend

1

645

636

645

140

559

645

1.01 (0.93–1.10)

1.00 (0.93–1.08)

1.01 (0.94–1.08)

0.87 (0.74–1.03)

1.01 (0.93–1.09)

1.00 (0.94–1.08)

0.826

0.950

0.826

0.118

0.802

0.892

n cases Per 5 % increase p-trend

Body fat percentage
1

659

659

650

659

144

568

659

1.08 (0.92–1.27)

1.07 (0.99–1.16)

1.06 (0.98–1.14)

1.06 (0.99–1.14)

1.04 (0.87–1.24)

1.05 (0.98–1.14)

1.06 (0.99–1.14)

0.325

0.101

0.137

0.101

0.667

0.180

0.116

n cases Per 10 cm increase p-trend

Waist circumference
1

659

659

650

659

144

568

659

1.07 (0.99–1.15)

1.10 (1.01–1.19)

1.07 (1.01–1.14)

1.07 (1.01–1.14)

1.13 (0.96–1.33)

1.07 (1.00–1.14)

1.07 (1.01–1.14)

n cases Per 0.05 unit
increase

Waist to hip ratio

0.072

0.023

0.034

0.023

0.144

0.060

0.028

p-trend1

P-values for trend are obtained by entering the anthropometric variable per increment in the Cox regression model

HR (95% CI) is from the multiple adjusted model (above) after accounting for the residuals of waist circumference and waist to hip ratio regressed on BMI for analyses of waist circumference and waist to hip ratio as
exposuresa1

Cox regression analyses. All models are stratified by region and age at recruitment and adjusted for age (underlying time variable), Townsend deprivation score, ethnicity, lives with a wife or partner, smoking, physicalactivity, alcohol consumption, height, diabetes, and history of PSA test. Full details for each covariate are provided in the statistical section

Abbreviations: BMI body mass index

Residualsa

1.06 (0.96–1.18)

Excluding first 5 years 568
of follow-up

1.06 (0.96–1.17)

1.07 (0.97–1.17)

Per 5 kg/m
increase

2659

Overalln prostatecancerdeathsBMI

Table 2 Sensitivity analyses. Multivariable-adjusted hazard ratios (95 % CI) for prostate cancer death in relation to adiposity measurements at recruitment in 218,237 men fromUK Biobank

Perez‑Cornago et al. BMC Medicine 2022, 20(1):143
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Page 7 of 13

BMI, per 5 kg/m^2 increase% Weight

Study

Year

Prostate cancer deaths

CHAC

2001

176

0.97 (0.77, 1.25)

1.14

CPS I

2001

1590

1.05 (0.93, 1.19)

3.88

CPS II

2003

4004

1.08 (1.02, 1.13)

21.69

Basel Prospective Study

2005

30

0.77 (0.43, 1.40)

0.1

JACC

2007

156

1.40 (1.00, 1.95)

0.29

HUNT 22009

1101.01 (0.77, 1.36)

0.75

Glasgow Alumni Cohort

2010

102

1.10 (0.70, 1.69)

0.27

WS

2011

578

1.07 (0.90, 1.25)

2.14

NHEFS

2011

44

1.12 (0.75, 1.68)

0.3

Swedish men cohort

2011

225

0.73 (0.53, 1.02)

1.09

Harvard Alumni Health Study

2012

417

1.08 (0.83, 1.40)

0.8

Metabolic Syndrome and Cancer Project

2012

961

1.16 (1.03, 1.29)

3.88

Vlagtwedde-Vlaardingen cohort study

2015

61

1.80 (1.21, 2.59)

0.14

Korean Veterans Health Study

2016

46

0.98 (0.56, 1.72)

0.2

EPIC (not included in DCPP, 2004-2010)

2017

668

1.16 (1.02, 1.33)

2.73

AGES-Reykjavik

2019

31

1.56 (0.97, 2.53)

0.11

DCPP

2020

3027

1.10 (1.05, 1.16)

21.69

Five Swedish cohorts

2020

6748

1.12 (1.08, 1.17)

32.41

UK Biobank [this report]

2021

659

1.07 (0.97, 1.17)

6.37

19,633

1.10 (1.07, 1.12)

100

Overall (I-Squared = 16.8%, Het=21.64, P=0.248)

0.5

HR (95% CI)

1.0

1.5

2.0

Hazard ratio (95% CI)

Fig. 2 Meta-analysis of prospective studies on the risk of prostate cancer death in relation to BMI. Study-specific hazard ratios (HR) are represented
by squares (with their 95% confidence intervals [CIs] as lines). HRs were combined using inverse-variance-weighted averages of the log HRs in
the separate studies, yielding a result and its 95% CI, which is plotted as a diamond. Please see Supplementary Table 1 for further details about
each study. Abbreviations: AGES-Reykjavik, Age, Gene/Environment Susceptibility-Reykjavik; CHAC, The Chicago Heart Association; CPS I, Cancer
Prevention Study I Nutrition Cohort Study; CPS II, Cancer Prevention Study II Nutrition Cohort Study; DCPP, Diet and Cancer Pooling Project; EPIC,
European Prospective Investigation into Cancer and Nutrition; JACC, Japan Collaborative Cohort Study; HUNT 2, Nord-Trøndelag Health Study;
NHEFS, Nutrition Examination Survey Epidemiology Follow-Up Study; WS, Whitehall study

associations between commonly used anthropometricmeasurements and prostate cancer death were assessedusing a 1 SD increment of each exposure of interest(4.3 kg/m2 for BMI, 5.8% for body fat percentage, 11.4 cm
for WC, and 0.065 for WHR), the HRs were 1.06 (0.98–
1.15), 1.01 (0.93–1.10), 1.07 (0.99–1.16), and 1.10 (1.01–
1.19), respectively for BMI, body fat percentage, WC andWHR (Table 2).
Dose‑response meta‑analyses

A total of 19, 2, 6, and 3 prospective studies (including the current report on UK Biobank) were identified
that had reported on BMI, body fat percentage, WC and/or WHR, respectively, in relation to prostate cancer-specific mortality (Additional File 1: Tables S1-S3). Whenthese results were combined with UK Biobank, data froma total of 19,633 (for BMI), 670 (for body fat percentage),
3181 (for WC) and 1639 (for WHR) men who died fromprostate cancer were available.

In the dose-response meta-analyses, the weighted
average HRs were 1.10 (1.07–1.12) for every 5 kg/m2
increase in BMI, 1.03 (0.96–1.11) for every 5% increasein body fat percentage, 1.07 (1.03–1.11) for every 10 cmincrease in WC and 1.06 (1.01–1.10) for every 0.05
increase in WHR. There was no statistically significant
heterogeneity between studies for any of these associations (Figs. 2 and 3). When we assessed the associations
by region, we observed similar associations in European(1.10 (1.07–1.14)) and USA studies (1.08 (1.04–1.13)) for
BMI. The Egger test showed no evidence of publication bias for associations of BMI or waist circumferencewith prostate cancer death (p value = 0.54 for BMI andp value = 0.71 for WC). On examination of funnel plots(Additional File 1: Figure S3), we observed no evidenceof publication bias for the associations of BMI or waist
circumference with prostate cancer death. Assessing
evidence of possible publication bias via funnel plots forbody fat percentage and WHR was not appropriate dueto the small number of studies.

Perez‑Cornago et al. BMC Medicine 2022, 20(1):143

Page 8 of 13

A

Body fat percentage, per 5% increase

Study

Year

Prostate cancer deaths

AGES-Reykjavik

2019

25

1.19 (0.81, 1.75)

2.3

UK Biobank [this report]

2021

645

1.01 (0.94, 1.08)

97.7

670

1.03 (0.96, 1.11)

100

Overall (I-Squared = 0.0%, Het=0.57, P=0.451)

HR (95% CI)% Weight

670
0.8

0.9

1.0

1.1

1.2

1.3

Hazard ratio (95% CI)

B

Waist circumference, per 10 cm increase
Year

Prostate cancer deaths

HUNT 22009

107

0.90 (0.74, 1.12)

4.48

EPIC (not included in DCPP, 2004-2010) 2017

632

1.17 (1.06, 1.30)

11.28

AGES-Reykjavik

2019

31

1.43 (1.01, 2.03)

0.63

DCPP

2020

1365

1.07 (1.01, 1.13)

45.13

Five Swedish cohorts

2021

387

1.07 (0.95, 1.20)

10.4

UK Biobank [this report]

2021

659

1.06 (0.99, 1.14)

28.09

3,181

1.07 (1.03, 1.11)

100

Overall (I-Squared = 34.2%, Het=7.59, P=0.180)

0.8

1.0

HR (95% CI)% Weight

Study

1.2

1.4

1.6

Hazard ratio (95% CI)

C

Waist to hip ratio, per 0.05 increase

Study

Year

Prostate cancer deaths

HUNT 22009

110

0.92 (0.79, 1.09)

9.09

EPIC

2017

870

1.07 (1.00, 1.14)

42.86

UK Biobank

2021

659

1.07 (1.01, 1.14)

48.05

1639

1.06 (1.01, 1.10)

100Overall (I-Squared =38.4%, Het= 3.25, P=0.197)

0.8

HR (95% CI)

0.9

1.0

1.1

1.2

% Weight

1.3

Hazard ratio (95% CI)

Fig. 3 Meta-analysis of prospective studies on the risk of prostate cancer death in relation to body fat percentage (A), waist circumference (B),and waist to hip ratio (C). Study-specific hazard ratios (HR) are represented by squares (with their 95% confidence intervals [CIs] as lines). HRs werecombined using inverse-variance-weighted averages of the log HRs in the separate studies, yielding a result and its 95% CI, which is plotted as adiamond. Please see Supplementary Tables 2 and 3 for further details about each study. Abbreviations: AGES-Reykjavik, Age, Gene/Environment
Susceptibility-Reykjavik; DCPP, Diet and Cancer Pooling Project; EPIC, European Prospective Investigation into Cancer and Nutrition; HUNT 2,
NordTrøndelag Health Study

Perez‑Cornago et al. BMC Medicine 2022, 20(1):143

A total of 11,900 men die from prostate cancer eachyear (2016-2018 average) in the UK [41]. If it is assumed
that this number of annual deaths would otherwise
remain stable, that the HR from our meta-analysis isaccurate and unbiased, and that the mean BMI in men
aged 55–64 years is 28.9 kg/m2 (UK data from 2019), a
reduction in the mean BMI of 5 kg/m2 would decrease
the mean BMI to within the ideal range and would lead toan estimated 1309 fewer prostate cancer deaths annuallyin the UK.

Discussion
The dose-response meta-analysis integrating the UK
Biobank study and other prospective studies showed
positive and similar associations for both total and central adiposity in relation to risk for prostate cancer death.
We also report here separately the new analyses from UK
Biobank, and they are in line with the results from ourmeta-analysis.
Our dose-response meta-analysis, which included
more than double the number of prostate cancer deaths
than previous meta-analyses, suggested that the associations with prostate cancer mortality were similar for
total and central adiposity. The totality of the prospective
data on central adiposity as assessed by WC and WHR
in relation to subsequent prostate cancer death, however, is still relatively limited (e.g. 6 studies with a total of
3181 prostate cancer deaths for WC) [6, 19, 27, 36, 38],
and more studies are needed to confirm the magnitudeof the association with central adiposity. The latest World
Cancer Research Fund meta-analysis on prostate cancer
published in 2014 also reported a positive association
between overall adiposity (assessed using BMI) and prostate cancer death based on 10,100 prostate cancer deaths
in a total of 12 studies; however, it did not have enoughdata from previous prospective studies to look at theassociation with central adiposity measurements (i.e. WC
and WHR) [2]. A more recent pooled analysis of individual participant data from up to 15 prospective studies that included 3000 prostate cancer deaths for total
adiposity and 1300 for central adiposity found a positive
association of both total and central adiposity with prostate cancer mortality [19].
Obesity is defined as excessive fat accumulation, butsome commonly used measures of adiposity such as
BMI do not differentiate reliably between fat and fat-freemass. WC has been proposed as a better marker than
BMI of adiposity in middle-aged men [42]; however, in
men in UK Biobank, WC and BMI are highly correlated,
and they showed similar associations with “gold standard” measurements of adiposity (MRI and DXA) in ourcross-sectional analyses. We found that BMI, body fat
percentage and WC were strongly positively associatedPage 9 of 13

with total and central adiposity (e.g. visceral fat, trunkfat) from the imaging data, with associations smaller for
WHR. Previous studies have suggested that visceral fat
is more strongly related than subcutaneous fat to metabolic and hormonal dysfunction (e.g. insulin resistance,
impaired glucose metabolism, low-grade inflammation)[43, 44] and hence might play a more important role inprostate cancer progression, although the roles of thesefactors in prostate cancer are not clear. To the best of ourknowledge, only one small prospective study (n < 2000
men, 31 prostate cancer deaths) has examined the associations between different fat depots (visceral and subcutaneous fat, and thigh intermuscular and subcutaneous fat)estimated using computed tomography scans and risk of
prostate cancer death, finding similar positive associations of these specific fat depots measured by CT, BMI
and WC with aggressive and fatal prostate cancer [36];due to the small sample size of this study and the lack ofother prospective studies looking at different fat depots
as exposures, more research looking at body fat distribution based on imaging data in relation to risk of prostatecancer mortality is needed before conclusions can be
drawn.
Commonly used measures of adiposity also do notassess ectopic fat (fat stored in tissues other than adipose
tissue, for example liver proton density fat fraction andmuscle mass infiltration) [42]. Correspondingly, in UK
Biobank, we found that while correlations of BMI andWC with visceral fat estimates from imaging data werelarge, the correlations of BMI and WC with liver protondensity fat fraction and muscle mass infiltration were
weaker. These weaker associations, and also the biological plausibility of associations of liver fat with prostatecancer risk or progression, suggest that there may be
additional utility in assessing the associations with risk of
prostate cancer mortality using these measures.
Obesity has been associated with a higher risk of being
diagnosed with high grade prostate tumours [19], whichhave poorer prognosis, and several biological mechanisms have been proposed for the association between
adiposity and prostate cancer development and progression [7]. However, it does not seem likely that any of the
known biological risk factors for prostate cancer may
mediate this association. Both IGF-I and free testosterone are positively associated with prostate cancer risk
in observational and Mendelian randomisation studies [4, 5]; however, men with obesity have moderately
lower concentrations of IGF-I and free testosterone thanmen with a healthy BMI [45, 46]. Higher BMI is associated with lower concentrations of IGFBP-1 and IGFBP-2[45], which might lead to higher bioavailability of IGF-I,
but evidence on the association of these binding proteins with prostate cancer mortality is very limited. Other

Perez‑Cornago et al. BMC Medicine 2022, 20(1):143

biomarkers that are altered in men with obesity include
pro-inflammatory cytokines [47], insulin [43], and oxidative stress biomarkers [48], which have been hypothesised to increase prostate cancer risk [49, 50]. Further,some evidence suggests that visceral, periprostatic andpelvic fat might promote proliferation and inhibit apoptosis of prostate cancer cells through paracrine mechanisms, such as the secretion of growth factors andpro-inflammatory cytokines [51–53]. However, althoughthese mechanisms are possible, the current evidence
is too limited to suggest that they mediate the positive
association between adiposity and prostate cancer mortality and more research is needed. Emerging tools suchas metabolomics, proteomics and epigenetics and theintegration of this information with the gold standard
measures of adiposity have the potential to reveal novel
mechanisms through which adiposity may increase prostate cancer development and progression [54].
Although the association between adiposity and prostatecancer mortality may be mediated by metabolic changes,
it is likely that differences in detection also play a role.
Men with obesity may have a delayed diagnosis of prostate cancer tumours due to their lower prostate-specificantigen (PSA) concentrations (owing to increased blood
volume with higher BMI) and to the greater difficulty of
performing a thorough digital rectal examination and thus
their lower likelihood of undergoing a biopsy [37, 55, 56].
For example, a previous meta-analysis showed that, compared to men with a normal weight, those with obesity
have on average 12.9% lower PSA concentrations [55]. Furthermore, men with obesity may have enlarged prostates,
which may make cancer detection by biopsy more difficult
due to the large size, also resulting in a higher likelihood of
the needles missing the cancer [37, 55, 56]. A later detection of a prostate tumour will lead to worse prognosis anda higher risk of dying from the disease. Therefore, further
research about how obesity impacts the pathway to prostate cancer diagnosis is needed.
Strengths of our analyses in UK Biobank include its
prospective design, detailed information on potential
confounders and the large cohort size (though with onlya moderate number of prostate cancer deaths). Analyses excluding the first 5 years of follow-up did not suggest that the observed associations were influenced byreverse causality, but substantially longer follow-up
time is needed to be more confident about this. Adiposity measurements were assessed by trained researchclinic staff instead of being self-reported, and we hadhigh-quality body composition data (i.e. DXA- andMRI-derived adiposity measurements) in a subsample,
which allowed us to assess the associations of commonly used adiposity measurements with “gold standard” measurements.

Page 10 of 13

Our analyses also have some limitations. UK Biobank
includes participants from multiple regions across theUK, including deprived areas; however, it may suffer fromselection bias as it is not representative of the whole UK
population [11, 57], although the directions of some majorrisk factor associations in the UK Biobank seem to be generalisable [58]. As in every observational study, residual
confounding is possible in both our prospective analysis
in UK Biobank and the meta-analysis. Moreover, there
may be some misclassification of the underlying cause ofdeath, which could be differential; obese men with prostate cancer are at increased risk of dying from several
conditions, and some may die for example from cardiovascular disease but have their cause of death recorded as
prostate cancer. Finally, due to the small number of prostate cancer deaths (probably due to the limited follow-up
time, as 78% survive prostate cancer after ≥ 10 years [1]),we may have had limited power to find associations withoverall adiposity (i.e. BMI and body fat percentage) in UK
Biobank; we also had limited data in our meta-analysis forcentral adiposity and body fat percentage.

Conclusions
In summary, the totality of prospective evidence indicatesthat men with higher adiposity (both total and central
adiposity) have a higher risk of dying from prostate cancer than men with a healthy weight. Prospective studies
with more data on stage, grade and clinical information
on disease progression, and with high quality measurements of adiposity distribution (e.g. MRI measurements
which would allow the study of other characteristics suchas ectopic fat), together with better understanding of thebiological pathways, are needed to disentangle whether
the association is biologically driven or due to differencesin detection, but in either case, these findings provide further reason for men to maintain a healthy body weight.
Abbreviations
BMI: Body mass index; CIs: Confidence intervals; DXA: Dual-energy X-ray
absorptiometry; EPIC: European Prospective Investigation into Cancer andNutrition; IARC​: International Agency for Research on Cancer; ICD: Interna‑
tional Statistical Classification of Diseases; IGF-I: Insulin-like growth factor-I;
HRs: Hazard ratios; MRI: Magnetic resonance imaging; NHS: National HealthService; PCa: Prostate cancer; PSA: Prostate-specific antigen; UK: United
Kingdom; WC: Waist circumference; WCRF: World Cancer Research Fund; WHR:
Waist-to-hip ratio.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​022-​02336-x.
Additional file 1: Supplementary Methods. Meta-analyses fromprospective studies, literature search, study selection, data extraction,
displaying of findings. Figure S1. Flow chart of the study participants in
UK Biobank. Figure S2. Flow diagram of literature search and study selec‑tion for the meta-analysis. Table S1. Characteristics of prospective studies
and previous individual participant data meta-analysis of body massPerez‑Cornago et al. BMC Medicine 2022, 20(1):143

index and prostate cancer death. Table S2. Characteristics of prospective
studies and previous individual participant data meta-analysis of body fat
percentage, waist circumference and prostate cancer death. Table S3.
Characteristics of prospective studies and previous individual partici‑
pant data meta-analysis of waist to hip ratio and prostate cancer death.
Table S4. Baseline characteristics of participants according to fourths of
BMI at recruitment in men from UK Biobank. Table S5. Baseline character‑istics of participants according to fourths of waist at recruitment in men
from UK Biobank. Table S6. Mean and SD in men from UK Biobank withavailable imaging data (up to 4800 men). Table S7. Pearson correlation
coefficients between main adiposity measurements at baseline in 218,237
men from UK Biobank. Table S8. Pearson correlation coefficients between
adiposity measurements (imaging visit) with MRI adiposity measurementsfrom the imagining in up to 11,501 men from UK Biobank. Table S9.
Pearson correlation coefficients between adiposity measurements (imag‑
ing visit) with DXA adiposity measurements from the imagining in up to
18,827 men from UK Biobank. Table S10. Mean difference in MRI- andDXA-derived body fat compartments per 1 SD higher levels of BMI, bodyfat percentage, waist circumference, and waist to hip ratio in men fromUK Biobank. Table S11. Geometric means of selected MRI measurements
by tenths of anthropometric measurements at the imaging visit in up to
11,501 men from UK Biobank. Table S12. Geometric means of selected
DXA measurements by tenths of anthropometric measurements at theimaging visit in up to 18,827 men from UK Biobank. Table S13. Minimallyand multivariable-adjusted hazard ratios (95% CI) for prostate cancer
death in relation to adiposity measurements at baseline in men from UK
Biobank. Table S14. Multivariable-adjusted hazard ratios (95 % CI) for
prostate cancer in relation to BMI, waist circumference and WHR using the
WHO cut-off points at recruitment in men from UK Biobank.
Acknowledgements
This research has been conducted using the UK Biobank Resource underapplication numbers 24494. We would like to thank Georgina K. Fensom forher contributions to the preparation and standardisation. We also thank allparticipants, researchers and support staff who made the study possible.
Authors’ contributions
AP-C, TJK and RT conceived and designed the research question; AP-C man‑aged the project, was responsible for acquiring the data, prepared the data for
analysis, analysed the data, prepared the figures and tables and wrote the firstdraft of the manuscript; APC and YD independently screened papers for themeta-analysis. YD, EW, TJK and RT provided input on data analysis and inter‑
pretation of results. All authors revised the manuscript critically for important
intellectual content and read and approved the final manuscript.
Funding
This research was funded by Cancer Research UK Population Research Fellowship
number C60192/A28516 and Cancer Research UK programme grant number
C8221/A29017. APC is also supported by the World Cancer Research Fund (WCRF
UK), as part of the Word Cancer Research Fund International grant programme(2019/1953). ELW is supported by the NDPH Early Career Research Fellowship.
Availability of data and materials
The datasets generated/and or analysed in the current study will be madeavailable for bona fide researchers who apply to use the UK Biobank data set
by registering and applying at http://​www.​ukbio​bank.​ac.​uk/​regis​ter-​apply.

Declarations
Ethics approval and consent to participate
The study was approved by The National Information Governance Board for
Health and Social Care and the National Health Service North West Multi‑
centre Research Ethics Committee (reference number 06/MRE08/65), andparticipants provided informed consent at baseline and to be followed up
using data-linkage. The UK Biobank protocol is available online (http://​www.​ukbio​bank.​ac.​uk/​wp-​conte​nt/​uploa​ds/​2011/​11/​UK-​Bioba​nk-​Proto​col.​pdf ).

Page 11 of 13

Consent for publication
Not applicableCompeting interests
The authors declare that they have no competing interests.
Received: 21 October 2021 Accepted: 14 March 2022
Published: 5 May 2022

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