Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

Body mass index and cancer risk among adults with and without cardiometabolic diseases: evidence from the EPIC and UK Biobank prospective cohort studies

BMC Medicine volume 21, Article number: 418 (2023) Cite this article

Abstract

Background

Whether cancer risk associated with a higher body mass index (BMI), a surrogate measure of adiposity, differs among adults with and without cardiovascular diseases (CVD) and/or type 2 diabetes (T2D) is unclear. The primary aim of this study was to evaluate separate and joint associations of BMI and CVD/T2D with the risk of cancer.

Methods

This is an individual participant data meta-analysis of two prospective cohort studies, the UK Biobank (UKB) and the European Prospective Investigation into Cancer and nutrition (EPIC), with a total of 577,343 adults, free of cancer, T2D, and CVD at recruitment. We used Cox proportional hazard regressions to estimate multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between BMI and incidence of obesity-related cancer and in turn overall cancer with a multiplicative interaction between BMI and the two cardiometabolic diseases (CMD). HRs and 95% CIs for separate and joint associations for categories of overweight/obesity and CMD status were estimated, and additive interaction was quantified through relative excess risk due to interaction (RERI).

Results

In the meta-analysis of both cohorts, BMI (per ~ 5 kg/m2) was positively associated with the risk of obesity-related cancer among participants without a CMD (HR: 1.11, 95%CI: 1.07,1.16), among participants with T2D (HR: 1.11, 95% CI: 1.05,1.18), among participants with CVD (HR: 1.17, 95% CI: 1.11,1.24), and suggestively positive among those with both T2D and CVD (HR: 1.09, 95% CI: 0.94,1.25). An additive interaction between obesity (BMI ≥ 30 kg/m2) and CVD with the risk of overall cancer translated into a meta-analytical RERI of 0.28 (95% CI: 0.09–0.47).

Conclusions

Irrespective of CMD status, higher BMI increased the risk of obesity-related cancer among European adults. The additive interaction between obesity and CVD suggests that obesity prevention would translate into a greater cancer risk reduction among population groups with CVD than among the general population.

Peer Review reports

Background

The prevalence of overweight and obesity (body mass index, BMI ≥ 25 kg/m2) has increased globally in recent decades, from 20% in 1975 to over 39% in 2016 [1]. Overweight and obesity increase the risk of non-communicable diseases (NCDs) including cardiometabolic diseases (CMD) [2]—such as type 2 diabetes (T2D) and cardiovascular diseases (CVD)—and cancer [3]. A high BMI, a surrogate measure of body fatness, is an established risk factor for at least 13 types of cancer, including adenocarcinoma of the breast (postmenopausal women), colorectal, gallbladder, kidney, liver, meningioma, multiple myeloma, oesophageal, ovarian, pancreatic, thyroid, gastric cardia (upper stomach), and corpus uteri [1, 3]. These cancers alone (described hereafter as obesity-related cancers) comprise approximately 37% of the total global burden of cancer (based on GLOBOCAN 2020 data [4]) and at present impact more on populations in high-income countries, where 67% of all obesity-related cancers are diagnosed [5]. The co-occurrence of CMD and cancer in an individual has become common, resulting in multimorbidity [6]. In turn, T2D has also been associated with increased risk of cancers of the colorectum, pancreas, liver, gallbladder, breast, and corpus uteri [7,8,9,10,11,12], all of which are also obesity-related cancers. Emerging evidence suggests that CVD might be an independent risk factor for certain cancers even after accounting for shared risk factors including BMI [13,14,15]. T2D and CVD also share common biological pathways with cancer, such as inflammation, oxidative stress, or hormonal processes [6]. Taken together, the presence of one or more of these CMDs may exacerbate the negative health effects of overweight and obesity in the development of cancer, though this is not well understood.

Prior studies focused on the BMI–cancer association in the general population, but little attention has been given to the co-occurrence of CMD [16,17,18]. In a 2021 meta-analysis of six cohort studies in patients with T2D, a higher BMI was associated with an increased risk of total cancer and breast cancer, a suggestive increased risk of colorectal cancer, but not with the risk of cancers of the prostate and pancreas [19]. Similar studies investigating BMI–cancer associations among population groups with CVD are largely lacking. Furthermore, joint associations of overweight and/or obesity and CMD with cancer risk have not been studied with the exception of our companion study by Recalde et al. [16]. Considering that prevalent cases of both CVD and T2D are likely to increase substantially as a result of population growth and aging [20, 21], it is important to investigate potential differences in the BMI–cancer relationship among population groups affected by CMD [22]. Such knowledge would enable evaluating the burden of cancer in populations with different prevalence of overweight and obesity, T2D, and CVD and inform health recommendations such as cancer screening programs and lifestyle interventions to prevent and control the co-occurrence of multiple NCDs in a targeted population.

We evaluated whether the association between BMI and obesity-related cancer risk differs among adults with and without CMD in the European Prospective Investigation into Cancer and Nutrition (EPIC) and the UK Biobank (UKB) prospective cohort studies. We investigated potential interaction between BMI and CMD and risk of obesity-related cancer on both the multiplicative and additive scale. In this context, we also investigated total cancer (all cancers combined) as a secondary outcome.

Methods

Study population

The UKB is a prospective cohort study of around 500,000 individuals aged 40–69 years enrolled between 2006 and 2010 across 22 centers located in England, Scotland, and Wales. At recruitment, information on socio-demographic characteristics, lifestyle factors, diet, anthropometry, and biological samples were collected [23]. Participants were followed from recruitment until the earliest of cancer, death, loss to follow-up, or end of the study period (between February 2020 and March 2021 depending on center).

EPIC is a prospective cohort with approximately 521,000 adults mostly aged 35–69 years at enrolment (between 1992 and 2000) from 23 research centers across 10 European countries (Denmark, France, Germany, Greece, Italy, Norway, Spain, Sweden, the Netherlands, and the UK) [24]. At recruitment, participants completed questionnaires covering socio-demographic, lifestyle, diet, and reproductive factors and anthropometric measurements and blood samples were also collected [24]. Participants were followed from recruitment until end of follow-up (i.e., last date of center- and event-specific ascertainment of CVD, T2D, or cancer, whichever came first), death, loss to follow-up, or end of the study [25].

As shown in Additional File 1: Figures S1 and S2, we excluded participants who had cancer, CVD, or T2D prior to enrolment in both UKB and EPIC. The rationale to exclude participants with a history of CVD or T2D was to avoid potential reverse causation (i.e., CVD or T2D affecting BMI at recruitment). We further excluded participants with missing values in any covariate (~ 21% and 3% in UKB and EPIC, respectively). All analyses were performed in a sample restricted to participants with no missing data (complete-case analysis). In EPIC, participants from France, Greece, and Norway were excluded due to the lack of data on incident events of CVD or T2D. Last, we excluded participants from Sweden due to uncertain dates for a majority of T2D diagnoses.

Anthropometry

Weight and height were measured by trained staff in both cohorts using comparable procedures [24]. In UKB, height was measured to the nearest centimeter using a Seca 202 stadiometer, and body weight to the nearest 0.1kg using a Tanita BC-418 body composition analyser [23]. In EPIC, body measurements were obtained using a standard protocol in all centres, except in Oxford (UK) where measurements were self-reported [26]. Depending on study center, height was measured to the nearest 0.1, 0.5, or 1.0 cm and weight to the nearest 0.1 kg [27]. BMI was calculated as weight/height2 (kg/m2) and categorized according to WHO definitions [1] into overweight/obesity (BMI ≥ 25 kg/m2) and obesity (BMI ≥ 30 kg/m2).

Ascertainment of cardiometabolic diseases

Incident cases of both CVD and T2D were coded using the 10th Edition of the International Classification of Diseases (ICD-10). In both cohorts, CVD was defined as a composite of ischemic heart diseases (I20-I25), atrial fibrillation (I48), and cerebrovascular disease (I60-I69), and T2D was defined as E11 (Additional File 1: Table S1) [28].

In the UKB, cases for both CVD and T2D were identified via linked hospital admissions records (primary diagnosis). The inpatient hospital data were obtained through linked medical records, mapped across England, Scotland, and Wales using the Hospital Episode Statistics in England, Scottish Morbidity Record, and Patient Episode Database for Wales.

In EPIC, the diagnoses of CVD were ascertained within the framework of the EPIC-Heart study using active follow-up through questionnaires, medical records, hospital morbidity registers, contact with medical professionals, retrieving and assessing death certificates, or verbal autopsy [29]. T2D cases were identified within the framework of the EPIC-Interact through a review of several sources including self-report, linkage to primary care registers, secondary care registers, medication use, hospital admissions, and mortality data, depending on the center [28]. Both EPIC-Heart and EPIC-InterAct were designed as case-cohort studies nested in the full EPIC cohort; however, the nested case-cohort design of these studies was not used in the current analysis.

Ascertainment of cancer

The outcomes of interest were the occurrence of any first primary malignant cancer (excluding non-melanoma skin cancer and in situ cancers), combined as overall cancer, and obesity-related cancers. Obesity-related cancers were defined as meningioma, multiple myeloma, adenocarcinoma of the esophagus, and cancers of the thyroid, postmenopausal breast, gallbladder, stomach, liver, pancreas, kidney, ovary, uterus, colon, and rectum (colorectal) [3]. Type of incident cancer cases were coded according to ICD-10 and information on tumor morphology and histology was ascertained using the 3rd Edition of the International Classification of Diseases for Oncology (ICD-O-3), detailed in Additional File 1: Table S1. In UKB, data on cancer diagnoses were provided by NHS Digital and Public Health England for participants from England and Wales and by NHS Central Register (NHSCR) for participants residing in Scotland and were ascertained through cancer registries. In EPIC, cases were identified by linkage to cancer registries for Denmark, Italy, the Netherlands, Spain, and the UK, and through a combination of health insurance records, cancer pathology registries, and active follow-up in Germany.

Statistical analysis

We used Cox proportional hazards regression to estimate cause-specific hazard ratios (HRs) and 95% confidence intervals (CIs) for associations with obesity-related cancers and all-cancers per 1 standard deviation (SD) increment in BMI (~ 5 kg/m2). The entry time was age at recruitment and the exit time was age at first primary cancer diagnosis, end of follow-up, loss to follow-up, or death, whichever occurred first. Deaths from any cause were modelled as a censored observation. Follow-up for CVD and T2D, i.e., the CMD, also started at age at recruitment and the exit time was the same as described for cancer.

Our base model was stratified by center of recruitment, age (5-year categories), and sex and adjusted for educational level, alcohol consumption, smoking status, height, physical activity, diet score, and in women additionally for use of hormone therapy and menopausal status (Additional File 1: Table S2). The menopausal status variable was allowed to change from pre- to post-menopausal among women, who turned 55 years during follow-up [30].We used a directed acyclic graph (DAG) [31] to identify confounding variables (Additional File 1: Figure S3). Our “overall adjusted model” further included CVD and T2D and their duration (time-varying accounting for non-linearity through natural splines) (Additional File 1: Figure S4). CVD and T2D were modelled as a time-varying variable with four categories (1: neither CVD nor T2D; 2: T2D; 3: CVD; and 4: T2D and CVD). Our main model further included a multiplicative interaction term between BMI (continuous) and the CMD variable (time-varying categorical) and estimates for the four categories were reported. We performed a likelihood ratio test to evaluate the multiplicative interaction between BMI and the CMD variable by comparing a model with the interaction term to a nested model without the interaction term.

We next evaluated the separate and joint associations of overweight and/or obesity (BMI ≥ 25 kg/m2 or BMI ≥ 30 kg/m2) and CMD (T2D, CVD, or both) with the risk of obesity-related cancer and total cancer. We created three variables each with four exclusive categories of combinations of overweight (and alternatively obesity) and in turn T2D, CVD, and both CMDs: (1) BMI < 25 kg/m2, without CMD of interest (reference); (2) BMI < 25 kg/m2, with CMD of interest; (3) BMI ≥ 25 kg/m2, without CMD of interest; and (4) BMI ≥ 25 kg/m2, with CMD of interest (joint effect). Based on this categorization, we quantified additive interaction for each of the three variables with the relative excess risk due to interaction (RERI) (as recommended in the STROBE statement [32, 33]) of the joint effect. The RERI was estimated as RERIRR = RR11 – RR10 – RR01 + 1, with RR11 the relative risk of being exposed to both factors (e.g., overweight/obesity and T2D), RR10 exposed to one of the factors (overweight/obesity) and RR01 to the other one (e.g., T2D). Estimations of 95% CI were based on the delta method described by Hosmer and Lemeshow [34]. A RERI of 0 means a lack of additive interaction. The model was adjusted for the same variables as our base model and further adjusted for the two binary variables of the other two CMD statuses not studied [35].

All models were fitted in each of the two cohorts separately and results then combined using random-effects meta-analysis [36]. The proportion of total variability due to between-study heterogeneity was assessed by I2.

All analyses were also performed stratified by sex. To investigate potential biases, the following sensitivity analyses were carried out. First, to investigate uncontrolled confounding and/or collider bias due to conditioning on CMD, we considered non-obesity-related cancers as a negative-control outcome, for which a null association of obesity with non-obesity-related cancer was expected. Second, we additionally adjusted our main models for CMD treatment (use of metformin and/or statins) to assess its impact on risk estimates. The data on CMD treatment were available only in the UKB. Third, to assess residual confounding by smoking, we ran the main models among never smokers only. Lastly, we also evaluated potential collinearity between BMI and height by replacing height with residuals of height, which we computed by regressing BMI on height. Comparing our main models to a model with adjustment for residuals of height instead of height by likelihood ratio test indicated that both models were equivalent.

Statistical tests were two-sided, and p-values < 0.05 were considered statistically significant. All analyses were performed using R version 4.1.2. Epi and InteractionR packages were used for the main analyses.

Results

Of a total of 577,343 study participants (UKB n = 344,094; EPIC n = 233,249), 53% and 62% were women in the UKB and EPIC cohorts, respectively. Baseline characteristics of the two study populations are reported, respectively, in Tables 1 and 2. In both cohorts, study participants with obesity (BMI ≥ 30 kg/m2) tended to have a lower educational level and were less physically active as compared to the other study participants. In UKB, during a median follow-up of 10.9 years (interquartile range, IQR: 10.1 – 11.7), a total of 32,549 first primary cancers (9.5% of participants) occurred, including 12,526 obesity-related cancers (of which 66% were in women). In EPIC, during a median follow-up of 10.9 years (IQR: 9.4 – 12.2), 19,833 first primary cancers (8.3% of participants) occurred, including 7892 obesity-related cancers (of which 76% were in women).

Table 1 Baseline characteristics of the study population in UK Biobank, by body mass index categories
Full size table
Table 2 Baseline characteristics of the study population in EPIC, by body mass index categories
Full size table

BMI and risk of obesity-related cancer by cardiometabolic disease status

In our multivariable-adjusted base model, where we ignored CMD status, an expected positive association was observed between BMI and obesity-related cancer risk. Per 1 SD increment in BMI (~ 5 kg/m2), HRs were 1.10 (95% CI 1.08–1.12) and 1.16 (95% CI: 1.14–1.18) in EPIC and UKB, respectively (Table 3). Adjustment for CMD and its duration slightly attenuated these associations (Table 3). In our main model, we estimated associations by CMD status. In the meta-analysis of both cohorts, BMI, per 1 SD increment, was positively associated with the risk of obesity-related cancers among participants without CMD (summary HR: 1.11, 95% CI: 1.07–1.16) and, similarly, among participants with T2D or CVD (Fig. 1) with little evidence for multiplicative interaction (all P-interaction ≥ 0.17) (Table 3). The strongest positive association was observed among participants with CVD in the UKB with a HR equal to 1.19 (95% CI: 1.12–1.25) (Fig. 1 and Table 3). The high proportion of total variability attributed to between-study heterogeneity (I2 = 92%) for models without CMD (Fig. 1) is likely due to a combination of the precise risk estimates and the different prevalence of obesity among participants in the UK Biobank (22%) as compared to EPIC (15%). Results did not materially differ between men and women (Additional File 1: Figures S5 and S6, Tables S3 and S4) except those for CVD, where we observed a stronger association between BMI- and obesity-related cancer risk among women than men (summary HR women: 1.22, 95% CI: 1.13–1.31; summary HR men: 1.07, 95% CI: 0.96–1.19).

Table 3 Association between BMI (per 1 standard deviation increment1) and the risk of obesity-related cancer by ascertainment of cardiometabolic diseases, in EPIC and UKB cohorts
Full size table
Fig. 1
figure 1

BMI and obesity-related cancer risk by cardiometabolic disease status in EPIC and UKB cohorts. I2, the proportion of total variability due to between-study heterogeneity from random effects meta-analysis. BMI body mass index, EPIC European Prospective Investigation into Cancer and nutrition, UKB UK Biobank, HR hazard ratio, CI confidence interval, T2D type 2 diabetes, CVD cardiovascular diseases, CMD cardiometabolic diseases

Full size image

In sensitivity analyses, further adjustments for the use of metformin or statins in the UKB or analyses among never smokers resulted in risk estimates similar to our main models (Additional File 1: Tables S5, S6, and S7). We also assessed associations between BMI and the risk of non-obesity related cancers as a negative control outcome and risk estimates were largely null (Additional File 1: Figures S5, S6 and S7, Tables S5, S7, and S8).

Joint association of overweight and/or obesity and cardiometabolic diseases with obesity-related cancer risk

The separate and joint associations of overweight/obesity and CMD with obesity-related cancer risk are shown in Table 4. In the UKB, there was evidence that the joint association of overweight and CVD was higher than the sum of the separate associations. Compared to participants with normal weight (BMI < 25 kg/m2) and without a CVD (reference group), participants with both exposures (BMI ≥ 25 kg/m2 and a CVD) had a 3.4 times higher risk of obesity-related cancer (95% CI: 2.95–3.95). Respective HR for participants with normal weight, but with a CVD, and for participants with overweight/obesity without CVD were 2.68 (95% CI 2.23–3.23) and 1.23 (95% CI 1.18–1.28) (Table 4). This additive interaction corresponded to a RERI of 0.50 (95% CI: 0.02–0.98), meaning that the joint association was 0.5 times higher than the sum of the separate associations. The joint association of obesity (BMI ≥ 30 kg/m2) and CVD resulted in a slightly stronger RERI (0.66, 95% CI: 0.16–1.17). However, these interactions were not confirmed in EPIC (Table 4) and the CI of the RERI in the meta-analysis of both cohorts included the null; for example, the meta-analysed RERI of obesity (BMI ≥ 30 kg/m2) and CVD was 0.39 (95% CI − 0.17–0.96) (Fig. 2). In sex-stratified analysis, the RERI included the null among men (Additional File 1: Figure S8 and Table S9) while in women, the joint association of obesity and CVD was more than additive (summary RERI: 0.90 95% CI: 0.34–1.45) (Additional File 1: Figure S9 and Table S10).

Table 4 Relative excess risk of obesity-related cancers due to interaction between overweight and/or obesity (BMI ≥ 25 kg/m2 or BMI ≥ 30 kg/m2) and cardiometabolic diseases, in EPIC and UKB cohorts
Full size table
Fig. 2
figure 2

Relative excess risk of obesity-related cancers due to interaction between overweight/obesity and cardiometabolic diseases in EPIC and UKB cohorts. I2, the proportion of total variability due to between-study heterogeneity from random effects meta-analysis. CI confidence interval, EPIC European Prospective Investigation into Cancer and nutrition, UKB UK Biobank, RERI relative excess risk due to interaction, BMI body mass index, T2D type 2 diabetes, CVD cardiovascular diseases

Full size image

In sensitivity analyses of the joint effect, further adjustments for the use of metformin or statins did not alter the results while analyses among never smokers in UKB showed a higher positive additive interaction for the joint association of obesity and CVD (RERI: 0.83, 95% CI: 0.03–1.64) (Additional File 1: Table S11).

BMI and risk of total cancer by cardiometabolic disease status

In our multivariable-adjusted base model, ignoring CMD status, BMI (per 1 SD) was positively associated with overall cancer risk with HRs of 1.04 (95% CI 1.02–1.05) and 1.05 (95% CI: 1.04–1.06) in EPIC and UKB, respectively (Additional File 1: Table S3). Adjustment for CMD and its duration did not change these associations (Additional File 1: Table S3). After stratification by CMD, in the meta-analysis of both cohorts, BMI (per 1 SD increment) was positively associated with the risk of overall cancer among participants without CMD (summary HR: 1.04, 95% CI: 1.03–1.05) and among those with CVD (the summary HR: 1.05, 95% CI: 1.01–1.08). Among individuals with T2D, or with both T2D and CVD, associations were largely null (Additional File 1: Figure S7). Associations across categories of CMD were stronger among women as compared to men. A positive association among women without CMD (summary HR: 1.06, 95% CI: 1.02–1.10), with T2D (summary HR: 1.09, 95% CI: 1.00–1.19) and with CVD (summary HR: 1.13, 95% CI: 1.07–1.19) was found whereas associations were null among men (Additional File 1: Figures S5 and S6).

In sensitivity analyses, we observed that the associations were similar in UKB when further adjusted for metformin and statins use (Additional File 1: Tables S5 and S6). The associations among never-smokers tended to be slightly stronger than those among the total population (Additional File 1: Table S7).

Joint association of overweight and/or obesity and cardiometabolic diseases with total cancer risk

The separate and joint associations of overweight/obesity and CMD with overall cancer risk are shown in Additional File 1: Table S12. Compared to participants with normal weight (BMI < 25 kg/m2) and without a CVD (reference group), participants with obesity (BMI ≥ 30 kg/m2) and a CVD had a 2 times higher risk of overall cancer (95% CI: 1.62–2.51) in the EPIC study. Similar estimates were obtained in the UKB (HR 2.59, 95% CI: 2.32–2.88). In the meta-analysis of both cohorts, these joint associations of obesity and CVD translated into a summary RERI of 0.28 (95% CI: 0.09–0.47) (Additional File 1: Figure S10). Among participants with overweight/obesity and CVD, the joint association was attenuated (summary RERI: 0.11, 95% CI: − 0.07–0.29) (Additional File 1: Figure S10). In sex-stratified analysis, the RERI included the null among men (Additional File 1: Figure S8) while in women, the joint association of obesity and CVD was more than additive (summary RERI: 0.75, 95% CI: 0.35–1.16) as well as the joint association of overweight/obesity and T2D (summary RERI: 0.41, 95% CI: 0.02–0.81) (Additional File 1: Figure S9).

In sensitivity analyses of the joint effect, further adjustments for the use of metformin or statins did not alter the results (Additional File 1: Tables S13 and S14), while analyses among never smokers in UKB showed a positive additive interaction for the joint association of overweight/obesity and CVD and for obesity and CVD (respectively, RERI: 0.35, 95% CI: 0.01–0.70 and RERI: 0.45, 95% CI: 0.07–0.83) (Additional File 1: Table S11).

Discussion

In this meta-analysis of two of the largest European prospective cohorts with together almost 600,000 adult participants, we found that the joint association of obesity and CVD with cancer risk was greater than the sum of the separate associations of the two exposures with cancer risk. This finding was consistent across the two cohorts for overall cancer risk but was only observed in the UKB for obesity-related cancer risk. Provided that the findings of an additive interaction are confirmed in subsequent studies, this implies that obesity prevention would translate into a greater cancer risk reduction among population groups with CVD than among the general population.

While a high BMI, which is indicative of overweight or obesity, is a recognised risk factor for certain cancers in the general population [3, 37,38,39], the differences in cancer risk among population groups affected by CMD remained poorly understood [18]. We addressed this knowledge gap and showed that a high BMI was equally associated with an increased risk of obesity-related cancer in population groups with and without a CMD. Our findings were similar for the risk of total cancer, except that in the population group with T2D only, BMI was not associated with the risk of total cancer. This contrasts with a meta-analysis of six cohort studies, where BMI was positively associated with the risk of total cancer among T2D patients [19]. A possible explanation for this discrepancy is that the meta-analysis included cohorts from North America and Asia with likely differences in the proportion of cancer types included in the composite outcome of total cancer, and that individuals with diabetes at baseline were included, thus BMI was assessed after diabetes diagnosis.

We provide novel evidence for an additive interaction between overweight/obesity and a concurrent CVD in relation to overall and obesity-related cancer risk. This is supported by the findings of our companion study using real-world data, where a similar additive interaction between overweight/obesity and a CVD was observed in relation to obesity-related cancer risk (overall cancer was not investigated) [16]. In the context of a rising prevalence of multimorbidity [22], i.e., multiple diseases affecting individuals and groups, it is important to understand why certain diseases cluster and whether or to what degree their clustering impacts the disease burden of affected populations. This emergent approach to health and clinical practice has been described as the syndemics model of health [40]. Syndemics provides a framework for empirically evaluating how disease clustering arises in a population and what health interventions might be most effective for mitigating them [40].

Excess body weight is a risk factor common to CMD and cancer, which is one likely reason for the clustering of these diseases. Different biological mechanisms are proposed to explain the adiposity-cancer link including altered sex hormone metabolism, increased insulin levels and the bioavailability of insulin-like growth factor 1 [IGF1], adipokine pathophysiology, and systemic inflammation [37]. T2D, an established risk factor for many cancers that are obesity-related [7,8,9,10,11,12], likely influences cancer risk through mechanisms similar to obesity [6, 39]. In contrast, the CVD–cancer relationship is only emerging. A seminal study demonstrated in a mouse model that heart failure due to myocardial infarction resulted in increased intestinal tumor growth [13]. Secreted cardiac proteins into the bloodstream, in particular SerpinA3, after cardiac injury were identified as the cause of this phenomenon [13]. A subsequent in vivo study in mice supported cardiac remodeling as an acute pathologic stressor that induced breast and lung cancer growth by the secretion of protumorigenic factors [41]. Moreover, in a breast cancer model, myocardial infarction induced a systemic host response toward an immunosuppressive state that accelerated breast cancer growth [42]. Taken together, CVD can promote growth of certain cancers in the experimental setting and might also do so in humans.

Our study has several strengths. First, we meta-analysed individual-level participant data from two large prospective cohorts of adults across 6 European countries with validated assessments of cancer, CVD, and T2D. Second, to our knowledge, this is the largest study using multinational data to investigate the association between BMI and cancer incidence by ascertainment of CMD. Third, associations were modelled in a time-varying fashion accounting for the sequence of incident chronic conditions. Fourth, we performed sensitivity analyses to address potential biases.

Our findings need to be interpreted considering the following limitations. First, information on lifestyle habits and BMI assessed at recruitment for both cohorts were used while possible changes in modifiable habits during follow-up could not be considered. However, there is evidence that a large proportion of individuals diagnosed with a new onset chronic condition did not change their lifestyle habits [43]. Additionally reassuring are the results of our companion study, where the cancer risk estimates based on baseline BMI were very similar to those when updated BMI assessments during follow-up were used [16]. Second, metformin, a first line medication for T2D, may lead to weight loss and may also reduce the risk of certain cancers [44, 45]. While we could account for metformin use, and statin use, in the UKB, such data are not available in EPIC. However, in the UKB, adjustment for CMD treatment did not alter our findings. Third, selection bias, due to excluding participants with missing data and due to restricting analyses to adults without a CMD at recruitment, could have influenced our results. Moreover, a major limitation of the UK Biobank cohort is its low response rate to recruitment invitations (~ 5%), significantly lower compared to many cohorts [46, 47]. The participants have been observed to be less socioeconomically deprived, have fewer risk factors, and have a lower prevalence of long-term conditions compared to the general UK population [48]. We partially addressed this concern of selection bias by providing estimates for a negative control outcome (i.e., non-obesity related cancers). Respective associations were largely null, supporting our main findings. Fourth, BMI neither captures body fat distribution nor differentiates between lean mass and fat mass. Future studies using detailed body composition assessments (e.g., from magnetic resonance imaging) could provide insight into the role of different fat compartments in the cardiometabolic disease–cancer relationship. Last, recruitment into the two cohorts varied between 1992 and 2000 for EPIC and between 2006 and 2010 for UKB. This temporal discrepancy may explain some of the observed inconsistencies in the strength of the associations between the two cohorts, as changes in lifestyle or in treatment and management of CMD could have occurred during this period.

Conclusions

In these two large cohorts, higher BMI was associated with an increased risk of obesity-related cancer among European adults irrespective of their CMD status. However, the joint exposure to overweight/obesity and CVD may be associated with a higher risk of overall and obesity-related cancer than the sum of the separate associations of the two exposures. These findings suggest that obesity prevention in population groups with CVD would lead to a greater reduction in cancer incidence as compared to the general population. Further studies that investigate associations of adiposity with the incidence of specific cancer sites among population groups with T2D or CVD are warranted.

Availability of data and materials

The UK Biobank resource is available to bona fide researchers for health-related research in the public interest. All researchers who wish to access the research resource must register with UK Biobank by completing the registration form in the Access Management System (AMS- https://bbams.ndph.ox.ac.uk/ams/).

EPIC data are available for investigators who seek to answer important questions on health and disease in the context of research projects that are consistent with the legal and ethical standard practices of IARC/WHO and the EPIC Centres. For information on how to submit an application for gaining access to EPIC data and/or biospecimens, please follow the instructions http://epic.iarc.fr/access/index.php.

The datasets used and/or analyzed during the current study were accessed in 2021 and are available from the corresponding author on reasonable request.

Abbreviations

BMI:

Body mass index

CI:

Confidence interval

CMD:

Cardiometabolic diseases

CVD:

Cardiovascular disease

EPIC:

European Prospective Investigation into Cancer and nutrition

HR:

Hazard ratio

NCD:

Noncommunicable diseases

RERI:

Relative excess risk due to interaction

SD:

Standard deviation

T2D:

Type 2 diabetes

UKB:

UK Biobank

WHO:

World Health Organization

References

  1. Obesity and overweight. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. Accessed 21 Feb 2023.

  2. Singh GM, Danaei G, Farzadfar F, Stevens GA, Woodward M, Wormser D, et al. The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: a pooled analysis. PLoS One. 2013;8:e65174.

  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;375:794–8.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Arnold M, Pandeya N, Byrnes G, Renehan AG, Stevens GA, Ezzati M, et al. Global burden of cancer attributable to high body-mass index in 2012: a population-based study. Lancet Oncol. 2015;16:36–46.

    Article  PubMed  Google Scholar 

  5. Arnold M, Leitzmann M, Freisling H, Bray F, Romieu I, Renehan A, et al. Obesity and cancer: an update of the global impact. Cancer Epidemiol. 2016;41:8–15.

    Article  PubMed  Google Scholar 

  6. Koene RJ, Prizment AE, Blaes A, Konety SH. Shared risk factors in cardiovascular disease and cancer. Circulation. 2016;133:1104–14.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Starup-Linde J, Karlstad Ø, Eriksen SA, Vestergaard P, Bronsveld HK, de Vries F, et al. CARING (CAncer Risk and INsulin analoGues): the association of diabetes mellitus and cancer risk with focus on possible determinants - a systematic review and a meta-analysis. Curr Drug Saf. 2013;8:296.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ohkuma T, Peters SAE, Woodward M. Sex differences in the association between diabetes and cancer: a systematic review and meta-analysis of 121 cohorts including 20 million individuals and one million events. Diabetologia. 2018;61:2140.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Jiang Y, Ben Q, Shen H, Lu W, Zhang Y, Zhu J. Diabetes mellitus and incidence and mortality of colorectal cancer: a systematic review and meta-analysis of cohort studies. Eur J Epidemiol. 2011;26:863–76.

    Article  PubMed  Google Scholar 

  10. Song S, Wang B, Zhang X, Hao L, Hu X, Li Z, et al. Long-term diabetes mellitus is associated with an increased risk of pancreatic cancer: a meta-analysis. PLoS ONE. 2015;10:e0134321.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Friberg E, Orsini N, Mantzoros CS, Wolk A. Diabetes mellitus and risk of endometrial cancer: a meta-analysis. Diabetologia. 2007;50:1365–74.

    Article  CAS  PubMed  Google Scholar 

  12. Tsilidis KK, Kasimis JC, Lopez DS, Ntzani EE, Ioannidis JPA. Type 2 diabetes and cancer: umbrella review of meta-analyses of observational studies. BMJ. 2015;350.

  13. Meijers WC, Maglione M, Bakker SJL, Oberhuber R, Kieneker LM, de Jong S, et al. Heart failure stimulates tumor growth by circulating factors. Circulation. 2018;138:678–91.

    Article  CAS  PubMed  Google Scholar 

  14. Lau ES, Paniagua SM, Liu E, Jovani M, Li SX, Takvorian K, et al. Cardiovascular risk factors are associated with future cancer. JACC CardioOncol. 2021;3:48–58.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wang C, Lu D, Cronin-Fenton D, Huang C, Liew Z, Wei D, et al. Cardiovascular disease and risk of lung cancer incidence and mortality: a nationwide matched cohort study. Front Oncol. 2022;12:4640.

    Google Scholar 

  16. Recalde M, Pistillo A, Viallon V, Fontvieille E, Duarte-Salles T, Freisling H. Body mass index and incident cardiometabolic conditions in relation to obesity-related cancer risk: a population-based cohort study in Catalonia, Spain. Cancer Med. 2023;12(19):20188–200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Reeves GK, Pirie K, Beral V, Green J, Spencer E, Bull D. Cancer incidence and mortality in relation to body mass index in the Million Women Study: cohort study. BMJ: Br Med J. 2007;335:1134.

    Article  Google Scholar 

  18. Recalde M, Davila-Batista V, Díaz Y, Leitzmann M, Romieu I, Freisling H, et al. Body mass index and waist circumference in relation to the risk of 26 types of cancer: a prospective cohort study of 3.5 million adults in Spain. BMC Med. 2021;19:1–14.

    Article  Google Scholar 

  19. Soltani S, Abdollahi S, Aune D, Jayedi A. Body mass index and cancer risk in patients with type 2 diabetes: a dose-response meta-analysis of cohort studies. Sci Rep. 2021:11–9.

  20. Khan MAB, Hashim MJ, King JK, Govender RD, Mustafa H, Kaabi J Al. Epidemiology of type 2 diabetes – global burden of disease and forecasted trends. J Epidemiol Glob Health. 2020;10:107.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. J Am Coll Cardiol. 2020;76:2982–3021.

    Article  PubMed  PubMed Central  Google Scholar 

  22. World Health Organization. Noncommunicable Diseases Country Profiles 2018. World Health Organization. 2018;223.

  23. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Riboli E, Hunt K, Slimani N, Ferrari P, Norat T, Fahey M, et al. European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr. 2002;5:1113–24.

    Article  CAS  PubMed  Google Scholar 

  25. Freisling H, Viallon V, Lennon H, Bagnardi V, Ricci C, Butterworth AS, et al. Lifestyle factors and risk of multimorbidity of cancer and cardiometabolic diseases: a multinational cohort study. BMC Med. 2020;18:1–11.

    Article  Google Scholar 

  26. Spencer EA, Appleby PN, Davey GK, Key TJ. Validity of self-reported height and weight in 4808 EPIC–Oxford participants. Public Health Nutr. 2002;5:561–5.

    Article  PubMed  Google Scholar 

  27. Haftenberger M, Lahmann P, Panico S, Gonzalez C, Seidell J, Boeing H, et al. Overweight, obesity and fat distribution in 50- to 64-year-old participants in the European Prospective Investigation into Cancer and Nutrition (EPIC). Public Health Nutr. 2002;5:1147–62.

    Article  CAS  PubMed  Google Scholar 

  28. Langenberg C, Sharp S, Forouhi NG, Franks PW, Schulze MB, Kerrison N, et al. Design and cohort description of the InterAct Project: an examination of the interaction of genetic and lifestyle factors on the incidence of type 2 diabetes in the EPIC Study. Diabetologia. 2011;54:2272–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Danesh J, Saracci R, Berglund G, Feskens E, Overvad K, Panico S, et al. EPIC-Heart: the cardiovascular component of a prospective study of nutritional, lifestyle and biological factors in 520,000 middle-aged participants from 10 European countries. Eur J Epidemiol. 2007;22:129–41.

    Article  PubMed  Google Scholar 

  30. Phipps AI, Ichikawa L, Bowles EJ, Carney PA, Kerlikowske K, Miglioretti DL, et al. Defining menopausal status in epidemiologic studies: a comparison of multiple approaches and their effects on breast cancer rates. 2010. https://doi.org/10.1016/j.maturitas.2010.04.015.

  31. Textor J, Hardt J. DAGitty: a graphical tool for analyzing causal diagrams. Epidemiology. 2011;22:745.

    Article  PubMed  Google Scholar 

  32. Vandenbroucke JP, von Elm E, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med. 2007;4:e297.

  33. Knol MJ, van der Tweel I, Grobbee DE, Numans ME, Geerlings MI. Estimating interaction on an additive scale between continuous determinants in a logistic regression model. Int J Epidemiol. 2007;36:1111–8.

    Article  PubMed  Google Scholar 

  34. Hosmer DW, Lemeshow S. Confidence interval estimation of interaction. Epidemiology. 1992;3:452–6.

    Article  CAS  PubMed  Google Scholar 

  35. Van Der Weele TJ, Knol MJ. A tutorial on interaction. Epidemiol. Methods. 2014;3:33–72.

    Google Scholar 

  36. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557–60.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Renehan AG, Zwahlen M, Egger M. Adiposity and cancer risk: new mechanistic insights from epidemiology. Nat Rev Cancer. 2015;15(8):484–98.

    Article  CAS  PubMed  Google Scholar 

  38. Bhaskaran K, Douglas I, Forbes H, dos Santos-Silva I, Leon DA, Smeeth L. Body-mass index and risk of 22 specific cancers: a population-based cohort study of 5·24 million UK adults. Lancet. 2014;384:755–65.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Scully T, Ettela A, LeRoith D, Gallagher EJ. Obesity, type 2 diabetes, and cancer risk. Front Oncol. 2021;10:615375.

  40. Singer M, Bulled N, Ostrach B, Mendenhall E. Syndemics and the biosocial conception of health. Lancet. 2017;389:941–50.

    Article  PubMed  Google Scholar 

  41. Avraham S, Abu-Sharki S, Shofti R, Haas T, Korin B, Kalfon R, et al. Early cardiac remodeling promotes tumor growth and metastasis. Circulation. 2020;142:670–83.

    Article  CAS  PubMed  Google Scholar 

  42. Koelwyn GJ, Newman AAC, Afonso MS, van Solingen C, Corr EM, Brown EJ, et al. Myocardial infarction accelerates breast cancer via innate immune reprogramming. Nat Med. 2020;26(9):1452–8.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Newsom JT, Huguet N, McCarthy MJ, Ramage-Morin P, Kaplan MS, Bernier J, et al. Health behavior change following chronic illness in middle and later life. J Gerontol Series B. 2012;67B:279–88.

    Article  Google Scholar 

  44. Han Y, Xie H, Liu Y, Gao P, Yang X, Shen Z. Effect of metformin on all-cause and cardiovascular mortality in patients with coronary artery diseases: a systematic review and an updated meta-analysis. Cardiovasc Diabetol. 2019;18:1–16.

    Article  Google Scholar 

  45. Gandini S, Puntoni M, Heckman-Stoddard BM, Dunn BK, Ford L, DeCensi A, et al. Metformin and cancer risk and mortality: a systematic review and meta-analysis taking into account biases and confounders. Cancer Prev Res. 2014;7:867–85.

    Article  CAS  Google Scholar 

  46. Allen N, Sudlow C, Downey P, Peakman T, Danesh J, Elliott P, et al. UK Biobank: current status and what it means for epidemiology. Health Policy Technol. 2012;1:123–6.

    Article  Google Scholar 

  47. Manolio TA, Collins R. Enhancing the feasibility of large cohort studies. JAMA. 2010;304:2290–1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Comparison of sociodemographic and health-related characteristics of UK biobank participants with those of the general population. Am J Epidemiol. 2017;186:1026–34.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We acknowledge the use of data from the EPIC-Bilthoven cohort, PI Roel Vermeulen, and thank the National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands, for their contribution and ongoing support to the EPIC Study. We further acknowledge the use of data from the EPIC-Asturias cohort, PI J. Ramón Quirós, the EPIC-Norfolk cohort, PI Nick Wareham, and of the EPIC-Ragusa cohort, PI Rosario Tumino.

UKB is an open access resource. Bona fide researchers can apply to use the UKB dataset by registering and applying at http://ukbiobank.ac.uk/register-apply/.

This research has been conducted using the UKB Resource under Application Number 55870 and we express our gratitude to the participants and those involved in building the resource.

Disclaimer

Where authors are identified as personnel of the International Agency for Research on Cancer/ World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/ World Health Organization.

Funding

Funding [grant number: IIG_2019_1978] was obtained from World Cancer Research Fund (UK), as part of the World Cancer Research Fund International grant program.

The coordination of EPIC is financially supported by the International Agency for Research on Cancer (IARC) and by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts are supported by: Danish Cancer Society (Denmark); German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS)—Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology—ICO (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk; MR/M012190/1 to EPIC-Oxford). (United Kingdom). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Nutrition and Metabolism Branch, International Agency for Research On Cancer (IARC-WHO), 25 Avenue Tony Garnier, CS 90627, 69366, Lyon, CEDEX 07, France

    Emma Fontvieille, Vivian Viallon, Martina Recalde, Reynalda Cordova, Anna Jansana, Laia Peruchet-Noray, Hannah Lennon, Elisabete Weiderpass, Marc J. Gunter, Pietro Ferrari & Heinz Freisling

  2. Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain

    Laia Peruchet-Noray

  3. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

    Alicia K. Heath, Dagfinn Aune, Sofia Christakoudi & Marc J. Gunter

  4. Department of Nutrition, Oslo New University College, Oslo, Norway

    Dagfinn Aune

  5. Department of Research, Cancer Registry of Norway, Oslo, Norway

    Dagfinn Aune

  6. Department of Inflammation Biology, School of Immunology and Microbial Sciences, King’s College London, London, UK

    Sofia Christakoudi

  7. Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Verena Katzke & Rudolf Kaaks

  8. Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany

    Elif Inan-Eroglu & Matthias B. Schulze

  9. Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany

    Matthias B. Schulze

  10. Danish Cancer Society Research Center, Copenhagen, Denmark

    Lene Mellemkjær & Anne Tjønneland

  11. Department of Public Health, University of Copenhagen, Copenhagen, Denmark

    Anne Tjønneland

  12. Department of Public Health, Aarhus University, Aarhus, Denmark

    Kim Overvad

  13. Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Institut Català d’Oncologia, Bellvitge Biomedical Research Institute (IDIBELL), 08908, L’Hospitalet de Llobregat, Spain

    Marta Farràs

  14. Escuela Andaluza de Salud Pública (EASP), 18011, Granada, Spain

    Dafina Petrova

  15. Instituto de Investigación Biosanitaria Ibs.GRANADA, 18012, Granada, Spain

    Dafina Petrova

  16. Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain

    Dafina Petrova, María-Dolores Chirlaque & Conchi Moreno-Iribas

  17. Ministry of Health of the Basque Government, Sub Directorate for Public Health and Addictions of Gipuzkoa, 2013, San Sebastian, Spain

    Pilar Amiano

  18. Biodonostia Health Research Institute, Epidemiology of Chronic and Communicable Diseases Group, 20014, San Sebastián, Spain

    Pilar Amiano

  19. Spanish Consortium for Research On Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain

    Pilar Amiano

  20. Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia University, Murcia, Spain

    María-Dolores Chirlaque

  21. Navarra Public Health Institute, Pamplona, Spain

    Conchi Moreno-Iribas

  22. Navarra Institute for Health Research (IdiSNA), Pamplona, Spain

    Conchi Moreno-Iribas

  23. Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK

    Sandar Tin Tin

  24. Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy

    Giovanna Masala

  25. Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori Di Milano, Milan, Italy

    Sabina Sieri

  26. Department of Clinical and Biological Sciences, Centre for Biostatistics, Epidemiology, and Public Health, University of Turin, Turin, Italy

    Fulvio Ricceri

  27. Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy

    Salvatore Panico

  28. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands

    Anne M. May & Evelyn M. Monninkhof

Authors
  1. Emma Fontvieille
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vivian Viallon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martina Recalde
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Reynalda Cordova
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anna Jansana
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Laia Peruchet-Noray
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hannah Lennon
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Alicia K. Heath
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Dagfinn Aune
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Sofia Christakoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Verena Katzke
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Rudolf Kaaks
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Elif Inan-Eroglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Matthias B. Schulze
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Lene Mellemkjær
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Anne Tjønneland
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Kim Overvad
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Marta Farràs
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Dafina Petrova
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Pilar Amiano
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. María-Dolores Chirlaque
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Conchi Moreno-Iribas
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Sandar Tin Tin
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Giovanna Masala
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Sabina Sieri
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Fulvio Ricceri
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Salvatore Panico
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Anne M. May
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Evelyn M. Monninkhof
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Elisabete Weiderpass
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Marc J. Gunter
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Pietro Ferrari
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Heinz Freisling
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

EF, VV, and HL analyzed the data. EF, VV, MR, RC, AJ, LP-N, AH, DA, SC, VK, RK, EI-E, MBS, LM, AT, KO, MF, DP, PA, M-DC CM-I, STT, GM, SS, FR, SP, AM, EMM, EW, MJG, PF, and HF have drafted the manuscript or substantively revised it. HF conceived and designed the work. RK, MBS, AT, KO, PA, M-DC, SS, FR, SP, and EW contributed to data acquisition. EF, VV, HL, PF, and HF contributed to the interpretation of the data. All authors have read and approved the submitted manuscript.

Authors’ Twitter handles

Twitter handles: @e_fontvieille (Emma Fontvieille), @HFreisling (Heinz Freisling).

Corresponding author

Correspondence to Heinz Freisling.

Ethics declarations

Ethics approval and consent to participate

All participants from the UK Biobank and the EPIC studies have provided written informed consent to participate in the respective cohorts. UK Biobank has ethical approval from the Northwest Multi-Centre Research Ethics Committee. The EPIC study was approved by the Cancer Ethical Review Committee of IARC and by local ethical committees at the participating centers.

The current study was approved by the IARC’s Ethics Committee (No. 23–40).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Figure S1:

Flowchart with the inclusion and exclusion criteria of the study population, in UK Biobank; Figure S2: Flowchart with the inclusion and exclusion criteria of the study population, in EPIC; Table S1: Diagnostic codes for the definition of cancer, type 2 diabetes and cardiovascular diseases; Table S2: Main characteristics of both cohorts; Figure S3: Directed Acyclic Graph (DAG) describing the potential causal and confounding effects of obesity on cancer risk; Figure S4: Framework to study the natural history of multimorbidity in UKB and EPIC with cancer as the index disease and T2D and CVD as comorbidities; Table S3: Association between BMI (per 1 standard deviation increment) and cancer risk by ascertainment of incident cardiometabolic conditions, in EPIC and UKB cohorts, in men; Figure S5: Forest plot of the associations (with 95% CIs) between BMI and cancer risk depending on cardiometabolic status in EPIC and UKB cohorts and the results of the meta-analysis (random-effects models), in men; Table S4: Association between BMI (per 1 standard deviation increment) and cancer risk by ascertainment of incident cardiometabolic conditions, in EPIC and UKB cohorts, in women; Figure S6: Forest plot of the associations (with 95% CIs) between BMI and cancer risk depending on cardiometabolic status in EPIC and UKB cohorts and the results of the meta-analysis (random-effects models), in women; Table S5: Association between BMI (per 1 standard deviation increment) and cancer risk by ascertainment of incident cardiometabolic conditions, with 95% CIs, in UKB. Models further adjusted for metformin use; Table S6: Association between body mass index (per 1 standard deviation increment) and cancer risk by ascertainment of incident cardiometabolic conditions, with 95% CIs, in UKB. Models further adjusted for statin use; Table S7: Association between BMI (per 1 standard deviation increment) and cancer risk by ascertainment of incident cardiometabolic conditions, in EPIC and UKB cohorts, among never smokers; Table S8: Association between BMI (per 1 standard deviation increment) and cancer risk (total cancers and non-obesity-related cancers) by ascertainment of incident cardiometabolic conditions, in EPIC and UKB cohorts; Figure S7: Forest plot of the associations (with 95% CIs) between BMI and cancer risk (total cancers and non-obesity-related cancers) by ascertainment of incident cardiometabolic conditions, in EPIC and UKB cohorts and the results of the meta-analysis (random-effects models); Table S9: Relative excess risk of all-cancers due to interaction between overweight and/or obesity and incident cardiometabolic diseases, in EPIC and UKB cohorts, in men; Figure S8: Forest plot of the relative excess risk of all-cancers due to interaction (with 95% CIs) between overweight and/or obesity and incident cardiometabolic diseases in EPIC and UKB cohorts and the results of the meta-analysis (random-effects models), in men; Table S10: Relative excess risk of all-cancers due to interaction between overweight and/or obesity and incident cardiometabolic diseases, in EPIC and UKB cohorts, in women; Figure S9: Forest plot of the relative excess risk of all-cancers due to interaction (with 95% CIs) between overweight and/or obesity and incident cardiometabolic diseases in EPIC and UKB cohorts and the results of the meta-analysis (random-effects models), in women; Table S11: Relative excess risk of all-cancers due to interaction between overweight and/or obesity and incident cardiometabolic diseases, in EPIC and UKB cohorts, among never smokers; Figure S10: Forest plot of the relative excess risk of all-cancers due to interaction (with 95% CIs) between overweight and/or obesity and incident cardiometabolic diseases, in EPIC and UKB cohorts and the results of the meta-analysis (random-effects models); Table S12: Relative excess risk of all-cancers due to interaction between overweight and/or obesity and incident cardiometabolic diseases, in EPIC and UKB cohorts; Table S13: Relative excess risk of all-cancers due to interaction between overweight and/or obesity and incident cardiometabolic diseases, in EPIC and UKB cohorts. Models further adjusted for metformin use; Table S14: Relative excess risk of all-cancers due to interaction between overweight and/or obesity and incident cardiometabolic diseases, in EPIC and UKB cohorts. Models further adjusted for statin use.

Rights and permissions

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 the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other 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 this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fontvieille, E., Viallon, V., Recalde, M. et al. Body mass index and cancer risk among adults with and without cardiometabolic diseases: evidence from the EPIC and UK Biobank prospective cohort studies. BMC Med 21, 418 (2023). https://doi.org/10.1186/s12916-023-03114-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12916-023-03114-z

Keywords

BMC Medicine

ISSN: 1741-7015

Contact us