(2024) 22:355
Gong et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03592-9

BMC Medicine

RESEARCH ARTICLE

Open Access

Sex disparity in the association
between metabolic‑anthropometricphenotypes and risk of obesity‑related cancer:
a prospective cohort study
Jianxiao Gong1†, Fubin Liu1†, Yu Peng1, Peng Wang1, Changyu Si1, Xixuan Wang1, Huijun Zhou1, Jiale Gu1,
Ailing Qin1 and Fangfang Song1*   

Abstract
Background Sex disparity between metabolic-obesity (defined by body mass index, BMI) phenotypes and obesityrelated cancer (ORC) remains unknown. Considering BMI reflecting overall obesity but not fat distribution, we aimed
to systematically assess the association of our newly proposed metabolic-anthropometric phenotypes with riskof overall and site-specific ORC by sex.
Methods A total of 141,579 men (mean age: 56.37 years, mean follow-up time: 12.04 years) and 131,047 women(mean age: 56.22 years, mean follow up time: 11.82 years) from the UK Biobank was included, and designated as metabolic-anthropometric phenotypes based on metabolic status (metabolically healthy/unhealthy), BMI (non-obesity/
obesity) and body shape (pear/slim/apple/wide). The sex-specific association of different phenotypes with overalland site-specific ORC was assessed by hazard ratios (HRs) and 95% confidence intervals (CIs) using Cox proportional
hazards regression models.
Results We found metabolically unhealthy and/or obesity phenotypes conveyed a higher risk in men than in womenfor overall ORC and colorectal cancer compared with metabolically healthy non-obesity phenotype (Pinteraction < 0.05).
Of note, metabolically healthy obesity phenotype contributed to increased risks of most ORC in men (HRs: 1.58 ~ 2.91),
but only correlated with higher risks of endometrial (HR = 1.89, 95% CI: 1.54–2.32) and postmenopausal breast cancers(HR = 1.17, 95% CI: 1.05–1.31) in women. Similarly, even under metabolically healthy, men carrying apple and wide
shapes phenotypes (metabolically healthy apple/wide and metabolically healthy non-obesity apple/wide) suffered
an increased risk of ORC (mainly colorectal, liver, gastric cardia, and renal cancers, HRs: 1.20 ~ 3.81) in comparison
with pear shape or non-obesity pear shape.
Conclusions There was a significant sex disparity between metabolic-anthropometric phenotypes and ORC risk. We
advised future ORC prevention and control worth taking body shape and sex disparity into account.
Keywords Metabolic status, Obesity, Body shape, Phenotype, Obesity-related cancer, UK Biobank†

Jianxiao Gong and Fubin Liu contributed equally to this work.

*Correspondence:
Fangfang Song
songfangfang@tmu.edu.cn
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article orparts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise 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
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Gong et al. BMC Medicine(2024) 22:355

BackgroundObesity has become a serious public health threat worldwide. More than 600 million are obese and over 4 million
people dying from overweight or obese in 2015 according
to the global burden of disease [1]. Elevated body massindex (BMI) increases the risk of non-communicable diseases, such as cancer [2]. According to the International
Agency for Research on Cancer, the following 13 cancers
are categorized as causally related to obesity: colorectal, liver, pancreatic, gastric cardia, renal, thyroid, ovarian, endometrial, postmenopausal breast and gallbladdercancers, esophageal adenocarcinoma, multiple myeloma,
and meningioma [3–5]. The development of these obesity-related cancers (ORC) may be related to metabolic
dysfunction caused by obesity, but the etiology remains
unclear and further prevention strategies are urgently
required.
Metabolic abnormalities vary among obese individuals.
Specifically, some obese individuals have been observed
not to exhibit metabolic abnormalities. This subtype isreferred to as metabolically healthy obesity (MHO) compared to metabolically unhealthy obesity (MUO), which
has recently been a concerned topic defined by BMI andmetabolic status [6, 7]. Over the years, this metabolicobesity phenotypes have been extensively explored not
only in the cardiovascular field [8–11], but also in thearea of cancer [6, 12–17]. Nevertheless, findings of studies examining the association between metabolic-obesity phenotypes and overall or site-specific ORC remaininconclusive [6, 15]. Additionally, BMI reflects overallobesity but cannot indicate fat distribution [18–21]. The
traditional anthropometric indices used to represent
body shape, i.e., waist and hip circumferences, may produce biased risk estimates when combined with BMI
due to the influence by the strong association between
BMI and cancer [22]. Previous studies have proposed toapply the allometric anthropometric body shape indices independent of BMI, i.e., a body shape index (ABSI)
and hip index (HI), to measure body shape. We hereinconstructed "metabolic-body shape phenotypes" using
metabolic status and body shape defined by ABSI and
HI. Notably, the deposition of adipose tissue varies by sex[23–26]. There are also sex differences in the associationof BMI and ABSI-HI with the risk of colorectal cancer [4,27–29]. However, only one study examined the relationship between metabolic-obesity phenotypes and ORC
by sex, and found MUO phenotype had a higher risk ofcolon cancer in men than in women [15], let alone thesex disparity in the association between metabolic-bodyshape phenotypes and ORC.
Therefore, the primary aim of this study was to prospectively assess the sex-specific associations of the metabolic-anthropometric phenotypes based on metabolic

Page 2 of 10

status, BMI and ABSI-HI categories with risks of overall and site-specific ORC in the UK Biobank. The results
would help to identify individuals that should pay more
attention to obesity management, metabolic abnormalities surveillance and body shape control to reduce theburden of future ORC.

Methods
Study design and participants

The UK Biobank is a large prospective study database of
500,000 participants aged 37–73 years recruited between
2006 and 2010 by 22 assessment centers in the United
Kingdom (detailed information: https://​www.​ukbio​bank.​
ac.​uk). Information on sociodemographic characteristics,
lifestyles, diseases and drug use of study participants was
obtained through touch-screen questionnaires and brieforal interviews. Physical measurements and blood assays
were also performed to collect physical indices and bloodindicators. The UK Biobank study was approved by theNorth West Multi-Centre Research Ethics Committee(REC reference for the UK Biobank 11/NW/0382). All
participants provided written informed consent and theiridentity information was kept confidential in the datasets.
In the primary analysis of this study, after excluding
participants with cancer diagnosis except for non-melanoma skin cancer at baseline (15,514 men and 28,480
women), BMI ≤ 18.5 kg/m2, and missing phenotype information and covariates, 141,579 men and 131,047 women
were included. The detailed study design and analysis
process is shown in Additional file 1: Fig S1.
Definitions of metabolic‑anthropometric phenotypes

As shown in Table 1, we classified BMI into obesity(≥ 30 kg/m2) and non-obesity (< 30 kg/m2) according to
the World Health Organization criteria [30]. Based on
National Cholesterol Education Program Adult Treatment Panel-III (NCEP ATP-III) [31] criteria and previous
research [32, 33], we used six markers including bloodpressure, c-reactive protein, triacylglycerol, low-density
lipoprotein cholesterol, high-density lipoprotein cholesterol and glycated hemoglobin to assess metabolic healthstatus. For the body shape, sex-specific medians of ABSI
and HI in the complete dataset of the present study were
employed [34, 35]. Subsequently, participants were categorized into four metabolic-obesity phenotypes basedon BMI and metabolic status: metabolically healthy
non-obesity (MHN), metabolically healthy obesity(MHO), metabolically unhealthy non-obesity (MUN),
and metabolically unhealthy obesity (MUO). Similarly,
eight groups of metabolic-body shape phenotypes were
designated based on the combination of body shape andmetabolic health status: metabolically healthy/unhealthy
pear (MHP/MUP), metabolically healthy/unhealthy slim

Gong et al. BMC Medicine(2024) 22:355

Page 3 of 10

Table 1 Definition of metabolic status, degree of obesity and body shape
Criteria

Method of calculation

Healthy

Score ≥ 4

Unhealthy

0 ≤ Score < 4(1) SBP < 130 mmHg and DBP < 85 mmHg
and no antihypertensive medications, score1 = 1;
(2) CRP < 3 mg/L, score2 = 1;
(3) Triacylglycerols < 2.3 mmol/L, score3 = 1;
(4) LDL-C < 3 mmol/L and no cholesterol lowering
medication, score4 = 1;
(5) HDL-C > 1 mmol/L, score5 = 1;
(6) HbA1c < 42 mmol/mol and no diabetes medications, score6 = 1;
Score = sum (score1-score6)

MetabolicObesityNon-obesity

18.5 ≤ BMI < 30

Obesity

BMI ≥ 30

BMI = Weight (kg) * Height (m)−2Body shape
Pear

ABSI ≤ Median (ABSI) & HI > Median (HI)Slim

ABSI ≤ Median (ABSI) & HI ≤ Median (HI)Apple

ABSI > Median (ABSI) & HI ≤ Median (HI)

Wide

ABSI > Median (ABSI) & HI > Median (HI)ABSI = WC (mm)*Weight (kg)−2/3*Height (m)5/6
HIwomen = HC (cm)*Weight (kg)−0.482*Height (cm)0.310
HImen = HC (cm)*Weight (kg)−2/5*Height (cm)1/5
Median (ABSI): 74 for women and 80 for men
Median (HI): 64 for women and 49 for men

ABSI A body shape index, CRP C-reactive protein, DBP Diastolic blood pressure, HbA1c Glycated hemoglobin, HDL-C High-density lipoprotein cholesterol, HI Hip index,
LDL-C Low-density lipoprotein cholesterol, SBP Systolic blood pressure(MHS/MUS), metabolically healthy/unhealthy apple(MHA/MUA), and metabolically healthy/unhealthy wide
(MHW/MUW).
Outcomes

The outcomes were incident overall and site-specific
ORC, which was determined utilizing the first cancer
record from hospitalization, cancer registries, death registries, and self-reported diseases. We defined ORC using
the International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) codes, as well
as self-reported information verified by interviewing witha nurse (Additional file 1: Table S1). The follow-up time
was calculated from the period of baseline enrollment to
the first ORC diagnosis, loss to follow-up, death or end offollow-up (30 September 2021 for England, 31 July 2021
for Scotland, and 28 February 2018 for Wales), whicheveroccurred first.
Covariates

Sociodemographic
characteristics
includedage,
Townsend deprivation index, and ethnicity. Lifestyle factors comprised frequency of alcohol consumption (formdaily or almost daily to never), smoking status (never,previous, current), and physical activity level (low, moderate, high). Physical activity levels were evaluated using
modified questions from the validated short International
Physical Activity Questionnaire (IPAQ), encompassing
inquiries about the frequency, intensity, and duration ofwalking, as well as moderate and vigorous activities [36].

Information on family history of cancer (no, yes) was collected from the touchscreen questionnaire. Women wereadditionally asked for women reproductive factors (ageat menarche, menopausal status, number of live births,
number of stillbirths, use of hormone replacement therapy [HRT], and oral contraceptives).
Statistical analysis

The Analysis of Variance (ANOVA) was used to compare continuous variables, which were reported as means(standard deviations). The Chi-square tests were used tocompare categorical variables, which were expressed as
the quantity (percentage).
The association of metabolic-obesity phenotypes andmetabolic-body shape phenotypes with overall andsite-specific ORC was assessed using Cox proportional
hazards regression models in men and women, respectively. The results were expressed as hazard ratios (HRs)and 95% confidence intervals (CIs). We tested the proportional hazards assumption based on Schoenfeld
residuals and no statistically significant deviations wereobserved. The multivariate models were adjusted forage, Townsend deprivation index, ethnicity, smoking
status, alcohol consumption frequency, physical activity
level, and family history of cancer. Additional adjustments were made for age at menarche, menopausal
status, number of live births, number of stillbirths, useof HRT, oral contraceptive use in analyses restricted to
women. We also performed subgroup analyses to assess
the associations of these two phenotypes with overall

Gong et al. BMC Medicine(2024) 22:355

ORC risk stratified by age (< 60 years, ≥ 60 years), alcohol consumption frequency, smoking status, and physical activity. Furthermore, we examined the sex-specificassociation between metabolic-obesity phenotypes
and ORC risk across the strata of body shape, as well
as between metabolic-body shape phenotypes and ORC
risk stratified by obese state. We then established metabolic-obesity-body shape phenotypes with the combination of metabolic status, obese state and body shape
to examine its relationship with ORC risk in both sexes.
To evaluate the robustness of results, we performed
sensitivity analyses. First, we excluded participants whodeveloped ORC or died within 2 years of follow-up andrepeated the main analyses. Second, Fine and Gray subdistribution hazards model was used to examine sexspecific associations of the metabolic-anthropometricphenotypes with risks of overall and site-specific ORC.
All analyses were performed using SAS version 9.4(SAS Institute, USA) and R software (The R Foundation, http://​w ww.r-​proje​ct.​org, version 4.0.2). A levelof < 0.05 for bilateral P values was considered statistically significant.

Page 4 of 10

Results
Characteristics of the study participants

Among 141,579 men and 131,047 women included in thepresent study, MHN phenotype accounted for the highest proportion while MHO phenotype for the lowest
proportion (Fig. 1A), and the proportion of metabolicallyunhealthy phenotypes (especially MUN phenotype) was
higher in men than in women. On the other hand, theproportion of MHS and MHP phenotypes respectively
ranked the largest in men and women, while MUP phenotype constituted the smallest proportion in both sexes(Fig. 1B). All the metabolically healthy-body shape phenotypes took a proportion of more than 75% in womenwhereas a higher proportion of 41% for the metabolically unhealthy-body shape phenotypes was observed inmen. Among both sexes, subjects with MUO and MUWphenotypes were more likely to be older, more deprived,
physically inactive, current smokers, non-drinkers, havea family history of cancer, and were more likely to be
wide-type when compared to those with MHN and MHP
phenotypes, respectively (Additional file 1: Table S2 andS3). During a median follow-up of 12.5 years (interquartile range: 11.8–13.2 years), 5,258 and 8,333 overall ORC
cases occurred in men and women, respectively, with theFig. 1 Proportion of metabolic-obesity/body shape phenotypes in men and women. A Proportion of metabolic-obesity phenotypes in men
and women. B Proportion of metabolic-body shape phenotypes in men and women. Abbreviations: A, apple shape; MH, metabolically healthy; MU,
metabolically unhealthy; N, non-obesity; O, obesity; P, pear shape; S, slim shape; W, wide shapeGong et al. BMC Medicine(2024) 22:355

Page 5 of 10

number of site-specific ORC cases shown in Additional
file 1: Table S4.
Association between metabolic‑obesity phenotypes
and ORCAs shown in Table 2, MHO phenotype contributed to anaugmented risk of overall ORC in men (HR = 1.36, 95%

CI: 1.23–1.50) and women (HR = 1.17, 95% CI: 1.09–1.26)
when compared with MHN phenotype. Specifically,
increased risks of all ORC types in men (with HRs from1.58 to 2.91), except colorectal, thyroid cancers and multiple myeloma, were ascribed to MHO phenotype, which,however, only correlated with higher risks of endometrial (HR = 1.89, 95% CI: 1.54–2.32) and postmenopausal

Table 2 Association of metabolic-obesity phenotypes with risk of ORC incidence
Men
MHN
ORC

Case/total

Women
MHO

2117/70843 508/12330

MUN

MUO

MHN

MHO

MUN

MUO

1486/35651

1147/22755

4655/84919 870/12889

1417/17840

1391/15399

1.36(1.23–1.50)

1.22(1.15–1.31)

1.56(1.45–1.68)

ref

1.17
(1.09–1.26)

1.09(1.03–1.16)

1.30(1.22–1.38)

1090/70843 216/12330

HR (95%CI) refColorectal
cancerCase/total

702/35651

520/22755

966/84919

148/12889

263/17840

243/15399

HR (95%CI) ref

1.13(0.98–1.31)

1.16(1.05–1.27)

1.42(1.28–1.59)

ref

0.99
(0.83–1.18)

1.01(0.88–1.16)

1.18(1.02–1.37)

Liver cancer

Case/total

36/12330

125/35651

122/22755

102/84919

18/12889

38/17840

43/15399

HR (95%CI) ref

121/70843

1.64(1.13–2.39)

1.64(1.27–2.12)

2.66(2.05–3.45)

ref

1.07
(0.65–1.78)

1.24(0.85–1.81)

1.73(1.19–2.51)

PancreaticcancerCase/total

68/12330

175/35651

124/22755

195/84919

41/12889

83/17840

73/15399

HR (95%CI) ref

1.66(1.27–2.18)

1.24(1.02–1.51)

1.53(1.23–1.91)

ref

1.37
(0.97–1.92)

1.42(1.09–1.84)

1.67(1.26–2.20)

Esophageal
adenocarci‑
noma

Case/total

60/12330

107/35651

95/22755

31/84919

7/12889

10/17840

17/15399

HR (95%CI) ref

2.91(2.12–3.99)

1.55(1.18–2.03)

2.18(1.64–2.89)

ref

1.45
(0.63–3.32)

1.08(0.52–2.22)

2.33(1.25–4.35)

Gastric car‑dia cancer

Case/total

41/12330

101/35651

80/22755

33/84919

7/12889

13/17840

26/15399

2.38(1.64–3.44)

1.77(1.33–2.35)

2.24(1.65–3.04)

ref

1.20
(0.52–2.73)

1.32(0.69–2.53)

2.93(1.71–5.07)

239/70843

110/70843

93/70843

HR (95%CI) ref

Renal cancer Case/total

83/12330

215/35651

188/22755

148/84919

32/12889

62/17840

80/15399

HR (95%CI) ref295/70843

1.58(1.24–2.02)

1.26(1.05–1.50)

1.79(1.48–2.16)

ref

1.24
(0.84–1.83)

1.42(1.05–1.92)

2.20(1.65–2.92)

MultiplemyelomaCase/total

38/12330

119/35651

67/22755

156/84919

33/12889

39/17840

33/15399

HR (95%CI) ref1.07(0.75–1.51)

1.04(0.83–1.31)

0.98(0.74–1.30)

ref

1.34
(0.92–1.97)

0.88(0.62–1.27)

0.96(0.65–1.42)

ThyroidcancerCase/total

6/12330

31/35651

14/22755

105/84919

25/12889

26/17840

17/15399

HR (95%CI) ref

0.85(0.36–2.02)

1.45(0.90–2.36)

1.03(0.55–1.91)

ref

1.41
(0.90–2.20)

1.13(0.72–1.76)

0.79(0.46–1.34)

OvariancancerCase/total

/

/

/

/

439/84919

84/12889

92/17840

109/15399

HR (95%CI) /

/

/

/

ref

1.26
(0.99–1.60)

0.86(0.68–1.08)

1.25(1.01–1.56)

Endometrial
cancerCase/total

/

/

/

/

406/84919

125/12889

123/17840

251/15399

HR (95%CI) /

/

/

/

ref

1.89
(1.54–2.32)

1.22(0.99–1.50)

2.88(2.44–3.40)

Postmeno‑
pausalbreastcancerCase/total

/

/

/

/

2252/55495 393/8406

726/15558

580/12231

HR (95%CI) /

/

/

/

ref

1.13(1.04–
1.23)

1.17(1.07–
1.29)

205/70843

39/70843

1.17(1.05–
1.31)

Adjusted for age (continuous), Townsend deprivation index (in quintiles), ethnicity (White, Mixed, Asian, Black, Chinese, other), smoking status (never, previous,
current, prefer not to answer), alcohol frequency (daily or almost daily, 3 or 4 times a week, 1 or 2 times a week, 1 to 3 times a month, special occasions only, never),physical activity level (low, moderate, high), family history of cancer (no, yes). Additional adjustments were made for menarche (continuous) menopausal status (no,yes), number of live births (= 0, > 0), number of stillbirths (no, yes), use of HRT (no, yes), oral contraceptive use (no, yes) in women
CI confidence interval, HR hazard ration, MH metabolically healthy, MU metabolically unhealthy, N non-obesity, O obesity, ORC obesity-related cancer

Gong et al. BMC Medicine(2024) 22:355

breast cancers (HR = 1.17, 95% CI: 1.05–1.31) in women.
Increased risks of most cancers except multiple myeloma
and thyroid cancer were observed in men with MUN
phenotype and in both sexes with MUO phenotype,respectively. In addition, for overall ORC and colorectal
cancer, MHO, MUN and MUO phenotypes conveyed ahigher risk in men than in women (Pinteraction < 0.05).
In subgroup analysis (Additional file 1: Table S5), we
observed an interaction between age and metabolicobesity phenotypes in men, which exhibited a greater
effect by MHO, MUN and MUO phenotypes on overall
ORC risk in < 60 years than that aged 60 years and older(Pinteraction < 0.0001). Besides, among men, there was aninteraction between metabolic-obesity phenotypes andbody shape in gastric cardia cancer, with the risk effectsof MHO/MUO phenotypes more pronounced in pear
shape (Pinteraction = 0.0102, Additional file 1: Table S6).
In women, we found a more pronounced positive association between MUO phenotype and pancreatic cancer
in wide shape (Pinteraction = 0.0482) and between MHO/MUO phenotypes and endometrial cancer in pear andslim shapes (Pinteraction = 0.0055).
Association between metabolic‑body shape phenotypes
and ORCIn men, for overall ORC, especially colorectal, liver,gastric cardia and renal cancers, apple and wide shapes
remained a risk factor even at the healthy metabolic

Page 6 of 10

status (MHA and MHW phenotypes, Fig. 2 and Additional file 1: Table S7). This situation was devastatedamong men with unhealthy metabolic status, where
MUA phenotype conveyed the highest risk of esophageal adenocarcinoma and the aforementioned ORC
except liver cancer (HR from 1.55 to 3.33), and thehighest risk of about threefold for liver cancer was
found in MUW phenotype (HR = 2.95, 95%CI: 1.90–
4.57) as compared with MHP phenotype. However,in women, metabolically healthy phenotypes almost
had little effect on ORC risk, except increased risks ofoverall ORC and colorectal cancer by MHA phenotype
compared with MHP phenotype. Correspondingly, all
the metabolically unhealthy phenotypes (MUP, MUS,MUA and MUW phenotypes) were associated withincreased risks of overall ORC, especially endometrial
cancer.
In subgroup analysis, we observed metabolic-bodyshape phenotypes except MHS phenotype exerted amore pronounced positive effect on the risk of overall
ORC in those aged < 60 years than ≥ 60 years in men(Pinteraction < 0.0001, Additional file 1: Table S8). When
stratified by obesity, only non-obese men with MUS
and MUA phenotypes had a higher susceptibility todevelop esophageal adenocarcinoma and gastric cardia
cancer (Pinteraction = 0.0041 and 0.008, Additional file 1:
Table S9).

Fig. 2 Association of metabolic-body shape phenotypes with risk of ORC incidence in men (A) and women (B). The models adjusted for age(continuous), Townsend deprivation index (in quintiles), ethnicity (White, Mixed, Asian, Black, Chinese, other), smoking status (never, previous,
current, prefer not to answer), alcohol frequency (daily or almost daily, 3 or 4 times a week, 1 or 2 times a week, 1 to 3 times a month, special
occasions only, never), physical activity level (low, moderate, high), family history of cancer (no, yes). Additional adjustments were madefor menarche (continuous), menopausal status (no, yes), number of live births (= 0, > 0), number of stillbirths (no, yes), use of HRT (no, yes), oral
contraceptive use (no, yes) in women. The color of the bubbles represents the negative logarithm of the P-value, while the size of the bubbles
indicates the magnitude of the hazard ratio, with larger bubbles representing larger hazard ratios. ****: P < 0.0001; ***: P < 0.001; **: P < 0.01; *:P < 0.05. Abbreviations: A, apple shape; MH, metabolically healthy; MU, metabolically unhealthy; ORC, obesity-related cancer; P, pear shape; S, slim
shape; W, wide shapeGong et al. BMC Medicine(2024) 22:355

The combined effect of metabolic‑obesity‑body shapephenotypes on ORC

The proportions of metabolic status, obesity and body
shape phenotypes in men and women are showed in
Additional file 1: Fig S2. The proportion of MHN-slim(MHNS) and MHN-pear (MHNP) phenotypes was thelargest in men and in women, respectively, while MHOapple (MHOA) phenotype accounted for the smallest share in both sexes. As expected, the proportion ofeach phenotype with metabolically healthy was higher
in women, by contrast to the higher proportion of eachphenotype with metabolically unhealthy in men. As
shown in Fig. 3, we observed that phenotypes carrying
metabolically unhealthy or obesity increased overall ORC
risk in both sexes. It was noteworthy that as comparedwith MHNP phenotype, apple and wide shapes (MHNapple [MHNA] and MHN-wide [MHNW] phenotypes)
increased the risk of ORC, particularly esophageal adenocarcinoma, liver, gastric cardia and renal cancers in men,even under metabolically healthy and non-obese (Fig. 3A
and Additional file 1: Table S10). By contrast in women,
this situation just occurred in MHNA phenotype, which
was positively associated with risk of colorectal cancer(HR = 1.26, 95% CI: 1.05–1.51). Although MUN phenotype in the metabolic-obesity phenotypes contributed to
an increased risk of overall ORC (Table 2), only the wide
shape (MUN-wide [MUNW] phenotype) increased therisk of ORC when combined with body shape among
women. We also observed all combinations of body

Page 7 of 10

shapes with MHO and MUO phenotypes were associatedwith higher risk of overall women ORC, especially endometrial cancer (Fig. 3B and Additional file 1: Table S11).
Sensitivity analysis

As shown in Additional file 1: Table S12, the main results
generally remained consistent after excluding participants who developed ORC or died within two years offollow-up. Similarly, most associations remained robustin Fine & Gray competing models (Additional file 1:
Tables S13-16).

Discussion
In this study, although a higher risk of overall ORC
was ascribed to MUO phenotype in both sexes, we yetobserved a conspicuous sex disparity. Specific, MHO
phenotype possessed an adverse effect on the majority of
ORC in men but only endometrial and postmenopausal
breast cancers in women. Additionally, the risk effects ofmetabolic-obesity phenotypes on ORC were not modified by body shape in men, but in women, MUN phenotype exhibited a dangerous effect only when combinedwith wide shape. More importantly, even when metabolically healthy, phenotypes carrying apple and wide shapes(MHA/MHW/MHNA/MHNW) predominantly contributed to an increased risk of ORC (mainly colorectal, liver,gastric cardia, and renal cancers) in men.
The association between metabolic-obesity phenotypes
and ORC risk has been reported, but evidence on sex

Fig. 3 Association of metabolic-obesity-body shape phenotypes with risk of ORC incidence in men (A) and women (B). The models adjusted
for age (continuous), Townsend deprivation index (in quintiles), ethnicity (White, Mixed, Asian, Black, Chinese, other), smoking status (never,previous, current, prefer not to answer), alcohol frequency (daily or almost daily, 3 or 4 times a week, 1 or 2 times a week, 1 to 3 times a month,
special occasions only, never), physical activity level (low, moderate, high), family history of cancer (no, yes). Additional adjustments were madefor menarche (continuous), menopausal status (no, yes), number of live births (= 0, > 0), number of stillbirths (no, yes), use of HRT (no, yes), oral
contraceptive use (no, yes) in women. The color of the bubbles represents the negative logarithm of the P-value, while the size of the bubbles
indicates the magnitude of the hazard ratio, with larger bubbles representing larger hazard ratios. ****: P < 0.0001; ***: P < 0.001; **: P < 0.01; *:P < 0.05. Abbreviations: A, apple shape; MH, metabolically healthy; MU, metabolically unhealthy; N, non-obesity; O, obesity; ORC, obesity-related
cancer; P, pear shape; S, slim shape; W, wide shapeGong et al. BMC Medicine(2024) 22:355

differences was scarce. A prospective study of postmenopausal women from Women’s Health Initiative foundno significant association between MUN and colorectal
cancer risk (HR = 1.65, 95% CI: 0.99–2.74) [37]. Anotherprospective study from the UK Biobank observed that
MHO phenotype was associated with increased risks ofendometrial, renal, pancreatic, esophageal, and postmenopausal breast cancers but MUN phenotype was notcorrelated with ORC [12], without considering sex differences. A recent pooled analysis of 797,193 participants in
the Metabolic Syndrome and Cancer (Me-Can) Project
2.0 found MUO was associated with higher relative risksof overall ORC in men and women, especially endometrial, liver and renal cell cancers, but was not associatedwith multiple myeloma risk in both sexes [15], which
was consistent with our study. This pooled study further
reported that MUO phenotype was associated with ahigher risk of colon cancer in men than in women, which
was similar with our results for MHO/MUN/MUO phenotypes with overall ORC and colorectal cancer. Ourin-depth study further elucidated MHO phenotype wasassociated with elevated risks of most ORC in men, but
only endometrial and postmenopausal breast cancers inwomen. Moreover, we observed MUN phenotype wasassociated with most ORC in men and pancreatic, renal
and postmenopausal breast cancers in women. Morestudies and subjects are needed in the future to confirm
these findings of gender differences in the association of
metabolic-obesity phenotypes with ORC.
As a measure of general obesity, BMI neither reflects
the distribution of fat nor the ratio of fat to lean mass
[20]. High BMI and a slim figure may occur simultaneously due to a higher proportion of lean mass. Waist/hip
circumferences are closely related to BMI and may leadto biased risk estimates when combining with BMI [19].
Besides, they cannot distinguish subcutaneous fat fromvisceral fat or lean from fat mass. ABSI and HI, based on
the allometric principle used to derive BMI, stand out
among the alternative methods for designing a waist/
hip index independent of BMI [22]. In analogy to BMI,
comparing body mass among individuals with the sameheight, ABSI and HI, which are related to body volume,
compare the transversal body dimensions (waist circumference and hip circumference, respectively) amongindividuals with the same weight and height. The previous studies found that of all body shapes, apple shapecontributed to the highest risk of colorectal cancer
in both sexes [35]. To explore the effect of fat distribution on ORC, we combined metabolic status with body
shape for the first time to examine its relationship with
ORC and to explore whether sex differences similar to
the metabolic-obesity phenotypes existed. We found thatmetabolically healthy phenotypes, especially MHA andPage 8 of 10

MHW phenotypes, were still associated with risk of most
ORC among men, but only MHA phenotype contributed to a higher risk of colorectal cancer among women.
Notably, the Women’s Health Initiative study conductedamong postmenopausal women found, with or without
adjustment for metabolic status, BMI was not associatedwith colorectal cancer risk, whereas waist circumference
showed an increasing trend [37]. These imply abdominal adiposity may be a potent risk factor for ORC. Differently, a previous UK Biobank study found that central
obesity (defined by waist circumference) with metabolically healthy status increased risks of endometrial andpostmenopausal breast cancers [12]. It suggests thatbody shape also plays an important role in ORC risk.
While when BMI was used as a measure to define obesity,
apple or wide shape was not always included in "obesity".
To better investigate the role of metabolic status, obesity, and body shape in ORC, we combined them in our
analysis. Apple and wide shapes still increased ORC risk
in men even with the presence of metabolically healthy
status and non-obesity. Although all body shapes werelinked to a higher risk of ORC in men when combinedwith metabolically unhealthy or obese, MUN phenotype
was associated with a higher ORC risk in women only
accompanied with wide shape. This hints body shape may
provide more value for risk stratification of ORC basedon metabolic and obese state in a sex-specific pattern.
There are several strengths in this study. To our knowledge, this was the first study to explore the sex disparityof metabolic-obesity phenotypes with overall and sitespecific ORC using the UK Biobank database, of whichprospective design and relatively large sample size provided modest statistical power. Second, we combined
body shape, defined by ABSI-HI and better reflecting the
distribution of adiposity, with metabolic status to explore
its relationship with ORC risk for the first time. In addition, we combined metabolic status, obesity and body
shape for further risk stratification of high-risk population. A limitation of this study was that other definitionsof metabolically (un)healthy status were not considered.
For example, some studies defined metabolic status using
five criteria including abdominal obesity [6], while others
also included insulin sensitivity [38, 39]. In this study, six
biomarkers were used to define metabolic status, which
was consistent with a UK Biobank study [40]. Additionally, we classified metabolic states based on metabolic
indicators at baseline, and shifts across phenotypes mightoccur throughout follow-up. A recent cardiovascularstudy found a higher incidence of atherosclerotic cardiovascular disease (ASCVD) in participants who transitioned from MHO to MUO, however MHO-throughout
did not increase ASCVD risks [41]. Further considerationof metabolic status changes and ORC risk is needed.

Gong et al. BMC Medicine(2024) 22:355

Conclusions
Our study found sex disparity in the relationshipbetween metabolic-anthropometric phenotypes andORC. Generally, even if metabolically healthy and nonobese, apple- or wide-shape still increased the risk ofmost ORC in men, but only apple shape was positively
associated with colorectal cancer in women. Furthermore, body shape did not modify the risk effects of metabolic-obesity phenotype on ORC in men, but MUN
phenotype was a risk factor for ORC in women only
combined with wide shape. Therefore, more consideration of body shape and sex disparity was warranted in
the risk assessment of ORC, and more attention shouldbe paid to early body shape management to reduce theburden of ORC.
Abbreviations
A	Apple
ABSI	A body shape index
ASCVD	Atherosclerotic cardiovascular disease
BMI	Body mass index
CI	Confidence intervalHR	Hazard ratioHI	Hip index
ICD	International Classification of Diseases
MH	Metabolically healthy
MU	Metabolically unhealthyN	Non-obesity
O	Obesity
ORC	Obesity-related cancer
P	Pear shapeS	Slim shapeW	Wide shape

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03592-9.
Additional file: Fig S1. Flowchart of the study design. Fig S2. Proportion of
metabolic-obesity-body shape phenotypes in men and women. Table S1.
Cancer definitions in UK Biobank Study. Table S2. Baseline characteristics of participants according to metabolic-obesity phenotypes in men
and women. Table S3. Baseline characteristics of participants according
to metabolic-body shape phenotypes in men and women. Table S4.
Number of obesity-related cancer (ORC) cases. Table S5. Subgroup
analysis of metabolic-obesity phenotypes and ORC. Table S6. Association
between metabolic-obesity phenotypes with risk of ORC incidence after
stratification by body shape. Table S7. Association between metabolicbody shape phenotypes with risk of ORC incidence. Table S8. Subgroup
analysis of metabolic-body shape phenotypes and ORC. Table S9.
Association between metabolic-body shape phenotypes with risk of ORC
incidence after stratification by obesity. Table S10. Association between
metabolic-obesity-body shape phenotypes with risk of ORC incidence
in men. Table S11. Association between metabolic-obesity-body shapephenotypes with risk of ORC incidence in women. Table S12. Association between metabolic-anthropometric phenotypes with ORC after
excluding those developed ORC or died within 2 years of follow-up.
Table S13. Association between metabolic-obesity phenotypes with riskof ORC incidence using Fine & Gray models for competing risk. Table S14.
Association between metabolic-body shape phenotypes with risk of ORC
incidence using Fine & Gray models for competing risk. Table S15. Association between metabolic-obesity-body shape phenotypes with risk of ORC
incidence in men using Fine & Gray models for competing risk. Table S16.

Page 9 of 10

Association between metabolic-obesity-body shape phenotypes with riskof ORC incidence in women using Fine & Gray models for competing risk.
Acknowledgements
We are grateful to all participants and project teams in the UK Biobank study.
This research was conducted using the UK Biobank resource under approved
project 76092.
Authors’ contributions
Conceptualization: FS; Data Curation: JXG; Formal Analysis: FL, YP and PW;
Funding Acquisition: MZ and FS; Project Administration: FS; Software: JXG, CS
and XW; Supervision: FS; Validation: HZ, CS and XW; Visualization: JXG, JLG and
AQ; Writing—Original Draft: JXG; Writing—Review and Editing: FL YP, PW, CS
XW, HZ, JG, AQ, FS. All authors read and approved the final manuscript.
Funding
This work was supported by the National Key R&D Program of China(2021YFC2500401), the National Natural Science Foundation of China(81974488), Tianjin Key Medical Discipline (Specialty) Construction Project(TJYXZDXK-009A). The funding agencies had no role in study design, data collection, analysis, decision to publish, or manuscript preparation.
Availability of data and materials
Data from the UK Biobank are available on application at www.​ukbio​bank.​ac.​uk/​regis​ter-​apply.

Declarations
Ethics approval and consent to participate
The study protocol was approved by the North West Multicenter Research
Ethics Committee (16/NW/0274) in the United Kingdom. All participants
provided written consent to their participation in the UK Biobank.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Epidemiology and Biostatistics, Key Laboratory of Molecular
Cancer Epidemiology, Key Laboratory of Prevention and Control of Major
Diseases in the Population, Ministry of Education, National Clinical ResearchCenter for Cancer, Tianjin Medical University Cancer Institute and Hospital,
Tianjin Medical University, Tianjin 300060, China.
Received: 2 January 2024 Accepted: 27 August 2024

References
1. Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, Lee A, Marczak
L, Mokdad AH, Moradi-Lakeh M, Naghavi M, et al. Health Effects ofOverweight and Obesity in 195 Countries over 25 Years. N Engl J Med.
2017;377(1):13–27.
2. Belladelli F, Montorsi F, Martini A. Metabolic syndrome, obesity and cancer
risk. Curr Opin Urol. 2022;32(6):594–7.
3. Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K.
Body Fatness and Cancer-Viewpoint of the IARC Working Group. N Engl J
Med. 2016;375(8):794–8.
4. Renehan AG, Zwahlen M, Egger M. Adiposity and cancer risk: new mechanistic insights from epidemiology. Nat Rev Cancer. 2015;15(8):484–98.
5. Chadid S, Kreger BE, Singer MR, Loring Bradlee M, Moore LL. Anthropometric measures of body fat and obesity-related cancer risk: sex-specificdifferences in Framingham Offspring Study adults. Int J Obes (Lond).
2020;44(3):601–8.

Gong et al. BMC Medicine

6.

7.
8.

9.
10.
11.
12.
13.

14.
15.

16.

17.
18.
19.

20.
21.

22.
23.
24.
25.

26.

(2024) 22:355

Winn M, Karra P, Freisling H, Gunter MJ, Haaland B, Litchman ML, Doherty
JA, Playdon MC, Hardikar S. Metabolic obesity phenotypes and obesityrelated cancer risk in the National Health and Nutrition Examination
Survey. Endocrinol Diabetes Metab. 2023;6(4):e433.
Tanriover C, Copur S, Gaipov A, Ozlusen B, Akcan RE, Kuwabara M, Hornum M, Van Raalte DH, Kanbay M. Metabolically healthy obesity: Misleading phrase or healthy phenotype? Eur J Intern Med. 2023;111:5–20.
Eckel N, Li Y, Kuxhaus O, Stefan N, Hu FB, Schulze MB. Transition frommetabolic healthy to unhealthy phenotypes and association with cardiovascular disease risk across BMI categories in 90 257 women (the Nurses’
Health Study): 30 year follow-up from a prospective cohort study. Lancet
Diabetes Endocrinol. 2018;6(9):714–24.
Adair KE, Padgett RN, von Waaden N, Wilson RL, Bowden RG. Metabolic
Health, Obesity, and Cardiovascular Disease: 2015–2016 National Healthand Nutrition Examination Survey. Am J Med Sci. 2021;361(2):244–52.
Liu Y, Douglas PS, Lip GYH, Thabane L, Li L, Ye Z, Li G. Relationshipbetween obesity severity, metabolic status and cardiovascular disease inobese adults. Eur J Clin Invest. 2023;53(3):e13912.
Eckel N, Meidtner K, Kalle-Uhlmann T, Stefan N, Schulze MB. Metabolically healthy obesity and cardiovascular events: A systematic review andmeta-analysis. Eur J Prev Cardiol. 2016;23(9):956–66.
Cao Z, Zheng X, Yang H, Li S, Xu F, Yang X, Wang Y. Association of obesity
status and metabolic syndrome with site-specific cancers: a populationbased cohort study. Br J Cancer. 2020;123(8):1336–44.
Kim JW, Ahn ST, Oh MM, Moon DG, Cheon J, Han K, Kim SM, Park HS.
Increased incidence of bladder cancer with metabolically unhealthy
status: analysis from the National Health Checkup database in Korea. Sci
Rep. 2020;10(1):6476.
Park YM, White AJ, Nichols HB, O’Brien KM, Weinberg CR, Sandler DP. The
association between metabolic health, obesity phenotype and the risk ofbreast cancer. Int J Cancer. 2017;140(12):2657–66.
Sun M, Fritz J, Häggström C, Bjørge T, Nagel G, Manjer J, Engeland A,
Zitt E, van Guelpen B, Stattin P, et al. Metabolically (un)healthy obesity
and risk of obesity-related cancers: a pooled study. J Natl Cancer Inst.
2023;115(4):456–67.
Kabat GC, Kim MY, Lee JS, Ho GY, Going SB, Beebe-Dimmer J, Manson
JE, Chlebowski RT, Rohan TE. Metabolic Obesity Phenotypes and Risk of
Breast Cancer in Postmenopausal Women. Cancer Epidemiol Biomarkers
Prev. 2017;26(12):1730–5.
Kwon H, Chang Y, Cho A, Ahn J, Park SE, Park CY, Lee WY, Oh KW, Park SW,
Shin H, et al. Metabolic Obesity Phenotypes and Thyroid Cancer Risk: A
Cohort Study. Thyroid. 2019;29(3):349–58.
Tsatsoulis A, Paschou SA. Metabolically Healthy Obesity: Criteria,
Epidemiology, Controversies, and Consequences. Curr Obes Rep.
2020;9(2):109–20.
Christakoudi S, Tsilidis KK, Muller DC, Freisling H, Weiderpass E, Overvad
K, Söderberg S, Häggström C, Pischon T, Dahm CC, et al. A Body Shape
Index (ABSI) achieves better mortality risk stratification than alternative
indices of abdominal obesity: results from a large European cohort. Sci
Rep. 2020;10(1):14541.
Müller MJ, Braun W, Enderle J, Bosy-Westphal A. Beyond BMI: Conceptual Issues Related to Overweight and Obese Patients. Obes Facts.
2016;9(3):193–205.
Moltrer M, Pala L, Cosentino C, Mannucci E, Rotella CM, Cresci B. Body
mass index (BMI), waist circumference (WC), waist-to-height ratio(WHtR) e waist body mass index (wBMI): Which is better? Endocrine.
2022;76(3):578–83.
Krakauer NY, Krakauer JC. A new body shape index predicts mortality
hazard independently of body mass index. PLoS ONE. 2012;7(7):e39504.
Palmer BF, Clegg DJ. The sexual dimorphism of obesity. Mol Cell Endocrinol. 2015;402:113–9.
Muscogiuri G, Verde L, Vetrani C, Barrea L, Savastano S, Colao A. Obesity: agender-view. J Endocrinol Invest. 2023;47(2):299–306.
Karastergiou K, Fried SK, Xie H, Lee MJ, Divoux A, Rosencrantz MA, Chang
RJ, Smith SR. Distinct developmental signatures of human abdominal andgluteal subcutaneous adipose tissue depots. J Clin Endocrinol Metab.
2013;98(1):362–71.
Tramunt B, Smati S, Grandgeorge N, Lenfant F, Arnal JF, Montagner A,
Gourdy P. Sex differences in metabolic regulation and diabetes susceptibility. Diabetologia. 2020;63(3):453–61.

Page 10 of 10

27. Kyrgiou M, Kalliala I, Markozannes G, Gunter MJ, Paraskevaidis E, Gabra
H, Martin-Hirsch P, Tsilidis KK. Adiposity and cancer at major anatomicalsites: umbrella review of the literature. BMJ. 2017;356:j477.
28. Christakoudi S, Tsilidis KK, Evangelou E, Riboli E. A Body Shape Index(ABSI), hip index, and risk of cancer in the UK Biobank cohort. Cancer
Med. 2021;10(16):5614–28.
29. Krakauer NY, Krakauer JC. An Anthropometric Risk Index Based on
Combining Height, Weight, Waist, and Hip Measurements. J Obes.
2016;2016:8094275.
30. Obesity: preventing and managing the global epidemic. Report of a
WHO consultation. World Health Organ Tech Rep Ser 2000, 894:i-xii,
1–253.
31. Executive Summary of The Third Report of The National Cholesterol
Education Program (NCEP) Expert Panel on Detection, Evaluation, And
Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III).
Jama 2001, 285(19):2486–2497.
32. Shao F, Chen Y, Xu H, Chen X, Zhou J, Wu Y, Tang Y, Wang Z, Zhang R,
Lange T, et al. Metabolic obesity phenotypes and risk of lung cancer: aprospective cohort study of 450,482 UK biobank participants. Nutrients.
2022;14(16):3370.
33. Bhaskaran K, Dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L. Association of BMI with overall and cause-specific mortality: a population-based
cohort study of 3·6 million adults in the UK. Lancet Diabetes Endocrinol.
2018;6(12):944–53.
34. Christakoudi S, Riboli E, Evangelou E, Tsilidis KK. Associations of body
shape phenotypes with sex steroids and their binding proteins in the UK
Biobank cohort. Sci Rep. 2022;12(1):10774.
35. Christakoudi S, Tsilidis KK, Evangelou E, Riboli E. Association of bodyshape phenotypes with imaging measures of body composition in
the UK Biobank cohort: relevance to colon cancer risk. BMC Cancer.
2021;21(1):1106.
36. The UK Biobank. Guidelines for data processing and analysis of the international physical activity questionnaire (IPAQ) [https://​bioba​nk.​ndph.​ox.​
ac.​uk/​ukb/​ukb/​docs/​ipaq_​analy​sis.​pdf.]
37. Kabat GC, Kim MY, Stefanick M, Ho GYF, Lane DS, Odegaard AO, Simon
MS, Bea JW, Luo J, Wassertheil-Smoller S, et al. Metabolic obesity phenotypes and risk of colorectal cancer in postmenopausal women. Int J
Cancer. 2018;143(3):543–51.
38. Kuk JL, Ardern CI. Are metabolically normal but obese individuals at lower
risk for all-cause mortality? Diabetes Care. 2009;32(12):2297–9.
39. Calori G, Lattuada G, Piemonti L, Garancini MP, Ragogna F, Villa M, Mannino S, Crosignani P, Bosi E, Luzi L, et al. Prevalence, metabolic features,and prognosis of metabolically healthy obese Italian individuals: theCremona Study. Diabetes Care. 2011;34(1):210–5.
40. Yuan Z, Cheng Y, Han J, Wang D, Dong H, Shi Y, Poulsen KL, Fan X, Zhao
J. Association between metabolic overweight/obesity phenotypes andreadmission risk in patients with lung cancer: A retrospective cohort
study. EClinicalMedicine. 2022;51:101577.
41. Zhou Z, Macpherson J, Gray SR, Gill JMR, Welsh P, Celis-Morales C, Sattar
N, Pell JP, Ho FK. Are people with metabolically healthy obesity really
healthy? A prospective cohort study of 381,363 UK Biobank participants.
Diabetologia. 2021;64(9):1963–72.

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