Body size and composition and risk of site-specific cancers in the UK Biobank and large international consortia: A mendelian randomisation study

Mathew Vithayathil; Paul Carter; Siddhartha Kar; Amy M Mason; Stephen Burgess; Susanna C Larsson

doi:10.1371/journal.pmed.1003706

. 2021 Jul 29;18(7):e1003706. doi: 10.1371/journal.pmed.1003706

Body size and composition and risk of site-specific cancers in the UK Biobank and large international consortia: A mendelian randomisation study

Mathew Vithayathil ¹, Paul Carter ², Siddhartha Kar ^2,³, Amy M Mason ², Stephen Burgess ^2,^4,^‡,^*, Susanna C Larsson ^5,^6,^‡

Editor: Matthew Haggett Law⁷

PMCID: PMC8320991 PMID: 34324486

Abstract

Background

Evidence for the impact of body size and composition on cancer risk is limited. This mendelian randomisation (MR) study investigates evidence supporting causal relationships of body mass index (BMI), fat mass index (FMI), fat-free mass index (FFMI), and height with cancer risk.

Methods and findings

Single nucleotide polymorphisms (SNPs) were used as instrumental variables for BMI (312 SNPs), FMI (577 SNPs), FFMI (577 SNPs), and height (293 SNPs). Associations of the genetic variants with 22 site-specific cancers and overall cancer were estimated in 367,561 individuals from the UK Biobank (UKBB) and with lung, breast, ovarian, uterine, and prostate cancer in large international consortia. In the UKBB, genetically predicted BMI was positively associated with overall cancer (odds ratio [OR] per 1 kg/m² increase 1.01, 95% confidence interval [CI] 1.00–1.02; p = 0.043); several digestive system cancers: stomach (OR 1.13, 95% CI 1.06–1.21; p < 0.001), esophagus (OR 1.10, 95% CI 1.03, 1.17; p = 0.003), liver (OR 1.13, 95% CI 1.03–1.25; p = 0.012), and pancreas (OR 1.06, 95% CI 1.01–1.12; p = 0.016); and lung cancer (OR 1.08, 95% CI 1.04–1.12; p < 0.001). For sex-specific cancers, genetically predicted elevated BMI was associated with an increased risk of uterine cancer (OR 1.10, 95% CI 1.05–1.15; p < 0.001) and with a lower risk of prostate cancer (OR 0.97, 95% CI 0.94–0.99; p = 0.009). When dividing cancers into digestive system versus non-digestive system, genetically predicted BMI was positively associated with digestive system cancers (OR 1.04, 95% CI 1.02–1.06; p < 0.001) but not with non-digestive system cancers (OR 1.01, 95% CI 0.99–1.02; p = 0.369). Genetically predicted FMI was positively associated with liver, pancreatic, and lung cancer and inversely associated with melanoma and prostate cancer. Genetically predicted FFMI was positively associated with non-Hodgkin lymphoma and melanoma. Genetically predicted height was associated with increased risk of overall cancer (OR per 1 standard deviation increase 1.09; 95% CI 1.05–1.12; p < 0.001) and multiple site-specific cancers. Similar results were observed in analyses using the weighted median and MR–Egger methods. Results based on consortium data confirmed the positive associations between BMI and lung and uterine cancer risk as well as the inverse association between BMI and prostate cancer, and, additionally, showed an inverse association between genetically predicted BMI and breast cancer. The main limitations are the assumption that genetic associations with cancer outcomes are mediated via the proposed risk factors and that estimates for some lower frequency cancer types are subject to low precision.

Conclusions

Our results show that the evidence for BMI as a causal risk factor for cancer is mixed. We find that BMI has a consistent causal role in increasing risk of digestive system cancers and a role for sex-specific cancers with inconsistent directions of effect. In contrast, increased height appears to have a consistent risk-increasing effect on overall and site-specific cancers.

Mathew Vithayathil and colleagues study associations of body mass index and other measures with incidence of specific cancers.

Author summary

Why was this study done?

The causal relevance of body size and composition as risk factors for specific cancers is unclear based on traditional observational studies.
By considering the relationships between genetically predicted values of body size and composition with cancer risk, our estimates are less influenced by confounding variables, and, hence, more reliably reflect the underlying causal relationships between these measures and cancer risk.

What did the researchers do and find?

We assessed the associations between genetically predicted body mass index (BMI), fat mass index (FMI), fat-free mass index (FFMI), and height with 22 specific cancers in the UK Biobank (UKBB), a population-based sample of the United Kingdom residents.
Although genetically predicted height was consistently associated with increased risk of site-specific cancers, genetically predicted BMI was associated with an increased risk of certain digestive system cancers (esophageal, stomach, liver, and pancreas), plus lung and uterine cancer, but a decreased risk of breast and prostate cancer.
When dividing cancers into digestive system cancers versus non-digestive system cancers, genetically predicted BMI was associated with increased risk of digestive system cancers, but not associated with non-digestive system cancers.

What do these findings mean?

Our findings suggest that BMI is a causal risk factor for some cancers, but is not a generic risk factor for all cancers.
Body fat may play a role in development of specific cancers and should be studied further to identify future targets to prevent cancer.
Public health strategies should focus on reducing obesity as a risk factor for cancer, but should be clear that benefit may be limited to certain cancers.

Background

Obesity is a global epidemic [1] that is predicted to affect 20% of the world’s population by 2025 [2]. The relationship between obesity and cancer risk has been subject to extensive investigation. Body mass index (BMI) is the most commonly measured marker for obesity and correlates most strongly with fat mass [3]. Observational studies have shown that raised BMI is associated with increased risk [4–7], no risk [4,7], and even reduced risk [4,8] of cancers. In particular, consistent positive associations have been observed between BMI and risk of colorectal, stomach, esophagus, liver, gallbladder, breast, endometrial, ovarian, kidney, and pancreatic cancers [9–11]. However, traditional epidemiological studies are influenced by confounding factors, such as smoking [12,13], and reverse causation due to subclinical disease [14,15]. Thus, the true relationship between obesity and cancer remains unclear.

Mendelian randomisation (MR) uses genetic variants as instrumental variables for an exposure to investigate evidence for a causal effect of the exposure on a disease outcome [16]. MR estimates represent associations of genetically predicted levels of risk factors with outcomes, as opposed to standard epidemiological estimates, which represent associations of the risk factor levels with outcomes. As a result of Mendel’s laws of segregation and independent assortment, estimates from MR are less susceptible to bias due to confounding factors than those from conventional observational epidemiological studies [17]. As the genetic code cannot be influenced by environmental factors or preclinical disease, MR estimates are also less susceptible to bias due to reverse causation.

MR investigations have previously been performed to investigate the effect of obesity on various cancer types. Studies have suggested risk-increasing effects of BMI for cancers of the colorectum, pancreas, kidney, lung, uterus, and ovary, for adenocarcinoma in the esophagus, and for overall cancer [18–20]. A separate study evidenced a risk-increasing effect of BMI on renal cell carcinoma [21]. Risk-decreasing effects of BMI have been evidenced for both pre- and postmenopausal breast cancer [22] and for prostate cancer in one study [23], but not others [19,24]. However, a comprehensive MR investigation into the effect of BMI on a wide range of cancer types has not been performed. Additionally, previous investigations have not considered the specific contribution of fat mass and fat-free mass to cancer risk.

Our aim in this paper is to perform a wide-angled MR investigation to provide independent evidence that replicates and extends analyses for the impact of obesity on cancer outcomes where MR investigations have already been performed and provides new evidence for cancer outcomes that have not previously been investigated using this approach. In particular, we want to assess the consistency of findings across different site-specific cancers. We define fat mass index (FMI) and fat-free mass index (FFMI) analogously to BMI, as fat mass or fat-free mass divided by height squared. As a comparative analysis, we consider height as a risk factor, as this has also been suggested to have a causal effect on multiple cancer types [25–27]. We used MR to investigate the causal roles of genetically predicted BMI, FMI, FFMI, and height on the risk of developing 22 cancers in 367,561 individuals from the UK Biobank (UKBB) study. We supplement our investigation with publicly available genetic association data from large international consortia for certain site-specific cancers. We aimed to elucidate the causal role of body composition for site-specific cancers and so extend and focus the evidence base for targeted public health strategies.

Methods

Study population

Data for genetic associations with site-specific cancer risk were obtained from the UKBB. The UKBB comprises demographic, clinical, biochemical, and genetic data from around 500,000 adults (aged 37 to 73 years old at baseline) recruited between 2006 and 2010 and followed up until June 30, 2020 [28]. Only unrelated individuals of European ancestries (defined by self-report and genetics) were included in our analysis in order to reduce population stratification bias. For each group of related individuals (third-degree relatives or closer), only 1 individual was included in analyses. After performing quality control filters as described previously [29], 367,561 individuals were included in analyses (S1 Fig). We defined cancer outcomes in the UKBB for the 22 most common site-specific cancers in the UK using the International Classification of Diseases (ICD)-9 and ICD-10 coding (S1 Table). Overall cancer analyses included individuals with any of the 22 site-specific cancers. Cancer outcomes were obtained from electronic health records, hospital episodes statistics (HES) data, the National Cancer Registry, death certification data, and self-reporting validated by nurse interview. Both prevalent and incident events were included in analyses. Genetic association estimates were obtained for each cancer outcome by logistic regression adjusting for age, sex, and 10 genetic principal components. Associations for sex-specific cancers (breast, ovarian, cervical, and uterine for women and testicular and prostate for men) were estimated in participants of the relevant sex only (198,825 women and 168,736 men).

In addition, publicly available data were extracted from the MR-Base platform [30] for lung, breast, ovarian, uterine, and prostate cancer from the International Lung Cancer Consortium (11,348 cases and 15,861 controls) [31], Breast Cancer Association Consortium (BCAC) (122,977 cases and 105,974 controls) [32], the Ovarian Cancer Association Consortium (25,509 cases and 40,941 controls) [33], a meta-analysis of genome-wide association studies (GWASs) for endometrial cancer (12,906 cases and 108,979 controls from 17 studies identified from the Endometrial Cancer Association Consortium, the Epidemiology of Endometrial Cancer Consortium, and the UKBB) [34], and the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome consortium (79,148 cases and 61,106 controls) [35].

Genetic instruments

The genetic instrument for BMI compromised 312 uncorrelated single nucleotide polymorphisms (SNPs) (linkage disequilibrium R² < 0.001) associated with BMI at genome-wide significance (p < 5 × 10⁻⁸) in a GWAS of up to 806,834 individuals of European ancestries [36] (S2 Table). For fat mass and fat-free mass (measured using bioelectrical impedance), we used 577 uncorrelated SNPs associated with body composition among 331,291 UKBB participants [37] (S3 Table). FMI and FFMI were computed by dividing fat mass or fat-free mass by the square of height. A GWAS of 253,288 European ancestry individuals identified 697 genome-wide significant SNPs (p < 5 × 10⁻⁸) for adult height [38], of which 293 were uncorrelated (linkage disequilibrium R² < 0.001) and were used as instrumental variables (S4 Table). Genetic associations with these body composition measures were obtained from the relevant discovery GWAS and were adjusted for age, sex, and genetic principal components.

Statistical analysis

Associations of genetically predicted BMI and height with the 22 site-specific cancers and overall cancer for the UKBB cohort were obtained using the random-effects inverse-variance weighted method [39]. We performed additional analyses using the weighted median [39] and MR–Egger [40] methods. The analyses of FMI and FFMI were based on the multivariable random-effects inverse-variance weighted method with both exposures included in the same model. Although overall cancer is a composite outcome comprising malignancies with different etiologies, analyses for overall cancer are important from a public health perspective to estimate the overall impact of the risk factors on cancer risk.

Our analysis did not have an explicit prespecified analysis plan. The analysis was conducted similarly to previous published efforts for investigating the causal relationships of BMI, FMI, and FFMI with cardiovascular diseases [37,41]. In response to comments from peer reviewers, we made a number of changes to the analysis: We changed the overall cancer outcome from including all cancers to including any of the 22 named site-specific cancers, we updated the genetic variants used as instrumental variables to those from the latest GWAS for the relevant risk factor, and we added analyses based on large-scale consortia for lung, breast, ovarian, uterine, and prostate cancer.

The odds ratios (ORs) are expressed per 1 kg/m² increase in genetically predicted BMI, FMI, and FFMI and per 1 standard deviation (approximately 6.5 cm) increase in height. As the number of cases and thus statistical power differed between analyses, we did not set a fixed threshold for statistical significance. Statistical analyses were performed in Stata/SE 14.2 using the mrrobust package [42] and R 3.6.0 software using the MendelianRandomization package [39]. Power calculations were performed using the web-based tool at https://sb452.shinyapps.io/power/ [43].

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist).

Results

Baseline characteristics of the 367,561 participants in the UKBB are provided in Table 1. The mean age was 57.2 years, with 45.9% males. Moreover, 10.3% of participants were current smokers. In the UKBB, the 312 SNPs explained 4.1% of the variance in BMI, whereas the 577 SNPs for body composition explained 3.1% of the variance in FMI and 2.3% of the variance in FFMI. The 293 SNPs for height explained around 5.5% of the variance in height. The phenotypic correlation of BMI with FMI was 0.84 and with FFMI was 0.66. The correlation between FMI and FFMI was 0.14. The relatively low correlation between FMI and FFMI means multivariable analyses can likely differentiate between these 2 risk factors. A total of 59,647 participants had one of the 22 defined site-specific cancers. When excluding outcomes for which only self-reported data were available, 55 674 events remained.

Table 1. Baseline characteristics of the UKBB participants included in this study.

	Mean (SD) or n (%)^†
Sample size	367,561 (100)
Male	168,736 (45.9)
Age at baseline (years)	57.2 (8.1)
Body composition
BMI (kg/m²)	27.4 (4.8)
FMI (kg/m²)	8.8 (3.6)
FFMI (kg/m²)	18.6 (2.6)
Height (m)	1.69 (0.07)
Blood pressure (mm Hg)
Systolic blood pressure	137.6 (18.6)
Diastolic blood pressure	82.0 (10.1)
Smoking status
Current	37,866 (10.3)
Ex	185,704 (50.5)
Never	143,777 (39.1)
Alcohol status
Current	342,797 (93.2)
Ex	12,732 (3.5)
Never	11,646 (3.2)
Type 2 diabetes	15,834 (4.3)

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^† Mean (standard deviation) for continuous variables; n (%) for categorical variables.

A total of 214 participants had missing data on smoking status, and 386 participants had missing data on alcohol status.

BMI, body mass index; FFMI, fat-free mass index; FMI, fat mass index; SD, standard deviation; UKBB, UK Biobank.

Power calculations are provided in S2 Fig. For BMI, there was 80% power to detect an OR of 1.2 per 1 kg/m² increase in genetically predicted BMI even for outcomes with only 300 events and an OR of 1.05 for outcomes with 3,800 events. Power was lower for FMI and FFMI, although still adequate to detect a moderate effect size for more common cancers.

BMI and cancer risk

Associations between genetically predicted BMI and site-specific and overall cancer in the UKBB are shown in Fig 1. Genetically predicted BMI was associated with an increased risk of overall cancer (OR 1.01, 95% confidence interval [CI] 1.00 to 1.02; p = 0.043); certain digestive system cancers, including stomach (OR 1.13, 95% CI 1.06 to 1.21; p < 0.001), esophagus (OR 1.10, 95% CI 1.10 (1.03, 1.17; p = 0.003), liver (OR 1.13, 95% CI 1.03 to 1.25; p = 0.012), and pancreas (OR 1.06, 95% CI 1.01 to 1.12; p = 0.016); and lung cancer (OR 1.08, 95% CI 1.04 to 1.12; p < 0.001). For sex-specific cancers, elevated BMI was associated with an increased risk of uterine cancer (OR 1.10, 95% CI 1.05 to 1.15; p < 0.001) and with a lower risk of prostate cancer (OR 0.97, 95% CI 0.94 to 0.99; p = 0.009). After omission of self-reported cancer events, the strength of association with risk of overall cancer was diminished (OR 1.01, 95% CI 1.00 to 1.02; p = 0.055). Results for site-specific cancers excluding self-reported outcomes were generally similar but marginally less precise (S3 Fig). Complementary analyses based on the weighted median and MR–Egger methods revealed similar but less precise estimates (S5 Table).

In analyses based on consortium data, genetically predicted BMI was positively associated with increased risk of overall lung cancer (OR 1.04, 95% CI 1.01 to 1.07; p = 0.006) and the squamous cell cancer subtype (OR 1.08, 95% CI 1.03 to 1.12; p < 0.001) (Fig 2). For sex-specific cancers, elevated BMI was associated with increased risk of uterine cancer (OR 1.13, 95% CI 1.10 to 1.16; p < 0.001), but with lower risk of breast (OR 0.96, 95% CI 0.95 to 0.98; p < 0.001) and prostate cancer (OR 0.98, 95% CI 0.96 to 1.00; p = 0.020). Inverse associations with similar magnitude were observed for both estrogen receptor positive (ER+) and estrogen receptor negative (ER−) breast cancer. Positive associations were observed for mucinous and endometrioid ovarian cancer, but not overall ovarian cancer.

Fig 2 — ORs are expressed per 1 kg/m² increase in BMI. Results are obtained from the random-effects inverse-variance weighted method. BMI, body mass index; CI, confidence interval; ER−, estrogen receptor negative; ER+, estrogen receptor positive; OR, odds ratio; SNP, single nucleotide polymorphism.

FMI and FFMI and cancer risk

Similar to BMI, genetically predicted FMI was associated with an increased risk of liver (OR 2.40, 95% CI 1.02 to 5.65; p = 0.045), pancreatic (OR 1.68, 95% CI 1.06 to 2.66; p = 0.028), and lung cancer (OR 1.57, 95% CI 1.16 to 2.13; p = 0.004) in the UKBB (Fig 3). Elevated FMI was associated with lower risk of prostate cancer (OR 0.77, 95% CI 0.61 to 0.97; p = 0.027) and melanoma (OR 0.68, 95% CI 0.51 to 0.91; p = 0.008). From the consortia, elevated FMI was associated with increased uterine cancer risk (OR 1.69, 95% CI 1.33 to 2.15; p < 0.001) (Fig 4).

Fig 3 — ORs are expressed per one 1 kg/m² increase in FMI. Results are obtained from the multivariable random-effects inverse-variance weighted method. CI, confidence interval; FFMI, fat-free mass index; FMI, fat mass index; OR, odds ratio; UKBB, UK Biobank.

Fig 4 — ORs are expressed per one 1 kg/m² increase in FMI. Results are obtained from the multivariable random-effects inverse-variance weighted method. CI, confidence interval; ER−, estrogen receptor negative; ER+, estrogen receptor positive; FFMI, fat-free mass index; FMI, fat mass index; OR, odds ratio; SNP, single nucleotide polymorphism.

Genetically predicted FFMI was associated with increased risk of non-Hodgkin lymphoma (OR 1.89, 95% CI 1.21 to 2.96; p = 0.005) and melanoma (OR 1.44, 95% CI 1.02 to 2.05; p = 0.039) in the UKBB (Fig 3). From the consortia, elevated FFMI was associated with a decreased risk of breast cancer (0.77, 95% CI 0.64 to 0.93; p = 0.007) (Fig 4).

Height and cancer risk

In the UKBB, genetically predicted height was positively associated with overall cancer (OR 1.09; 95% 1.05 to 1.12; p < 0.001) and multiple site-specific cancers, including kidney (OR 1.19, 95% CI 1.02 to 1.38; p = 0.027), colorectal (OR 1.09, 95% CI 1.01 to 1.19; p = 0.034), biliary tract (OR 1.45, 95% CI 1.14 to 1.84; p = 0.003), breast (OR 1.12, 95% CI 1.06 to 1.19; p < 0.001), and ovarian cancer (OR 1.21, 95% CI 1.05 to 1.40; p = 0.008) (Fig 5). Estimates were generally similar in additional analyses using the MR–Egger and weighted median methods (S6 Table). From the consortia, genetically predicted height was positively associated with ovarian (OR 1.37, 95% CI 1.14 to 1.64; p = 0.001) and breast cancer (OR 1.10, 95% CI 1.05 to 1.15; p < 0.001), similar to the UKBB (Fig 6).

Fig 5 — ORs are expressed per 1 standard deviation (6.5 cm) increase in height. Results are obtained from the random-effects inverse-variance weighted method. CI, confidence interval; OR, odds ratio; UKBB, UK Biobank.

Fig 6 — ORs are expressed per 1 standard deviation (6.5 cm) increase in height. Results are obtained from the random-effects inverse-variance weighted method. CI, confidence interval; ER−, estrogen receptor negative; ER+, estrogen receptor positive; OR, odds ratio; SNP, single nucleotide polymorphism.

Digestive system versus non-digestive system cancer risk

When dividing cancers into digestive system (esophagus, stomach, colorectum, liver, biliary tract, and pancreas; 11,061 cases) versus non-digestive system (48,586 cases), estimates for BMI were OR 1.04 (95% CI 1.02 to 1.06; p < 0.001) for digestive system and OR 1.01 (95% CI 0.99 to 1.02; p = 0.37) for non-digestive system cancers. For FMI, estimates were OR 1.17 (95% CI 0.95 to 1.44; p = 0.13) for digestive system and OR 0.99 (95% CI 0.88 to 1.11; p = 0.86) for non-digestive system cancers. For FFMI, estimates were OR 1.11 (95% CI 0.86 to 1.43; p = 0.42) for digestive system and OR 1.12 (95% CI 0.96 to 1.29; p = 0.15) for non-digestive system cancers. Genetically predicted height was positively associated with both digestive system and non-digestive system cancers (Fig 5).

Discussion

This MR study investigated the causal role of clinically relevant measures of body composition for a wide range of site-specific cancers. Genetically predicted BMI was associated with risk of overall cancer. Elevated BMI was positively associated with several digestive system cancers, including at the esophagus, stomach, liver, and pancreas. Additionally, BMI was positively associated with lung and uterine cancer, but inversely associated with breast cancer (only in the BCAC) and prostate cancer. Genetically predicted FMI was positively associated with lung, liver, and pancreatic cancer, with inverse associations seen for melanoma and prostate cancer. FFMI was positively associated with non-Hodgkin lymphoma, melanoma, and uterine cancer, with inverse associations seen for breast cancer. Genetically predicted height was positively associated with overall and multiple site-specific cancers.

The relationship between adiposity and cancer has been assessed in many traditional observational studies. An umbrella review of 204 meta-analyses found adiposity to be consistently associated with 10 site-specific cancers: esophageal, gastric, colorectal, biliary tract, pancreas, breast, uterine, ovarian, and kidney cancer as well as multiple myeloma [11]. Current MR studies have replicated the associations with esophageal, colorectal, pancreas, pancreas, uterine, ovarian, and kidney cancer [18–21,44,45]. However, further positive associations were observed in MR analyses for lung cancer [19,20], and inverse associations were seen with breast and prostate cancer [22,23]. These discrepancies between observational and MR studies may be due to the effect of environmental confounders (such as smoking) and reverse causation bias in traditional observational studies.

In our study, we observed a positive association between genetically predicted BMI and digestive system cancers. This link between BMI and adiposity with risk of certain digestive system cancers replicates and extends previous findings. High BMI has been associated with esophageal and stomach cancers in a meta-analysis of observational studies [46] and MR studies [20]. Increased adipose tissue is associated with insulin resistance and hyperinsulinemia [47], with raised circulating insulin enhancing colorectal epithelial cell proliferation in rat models [48]. Additionally, ghrelin is a gut hormone produced in the stomach, with reduced levels seen in obesity [49]. Ghrelin reduces pro-inflammatory cytokines and inflammatory stress [50], and reduced levels are associated with increased risk of esophageal [51] and stomach [52] cancers. Adiposity is also well established in causing nonalcoholic fatty liver disease and has been implicated in its progression to hepatocellular carcinoma [53]. In line with this, we observed an increased risk of liver cancer with raised BMI and FMI. Similarly, we observed a positive association between elevated BMI and FMI and pancreas cancer, in line with adipose tissue driving low grade inflammation and carcinogenesis in pancreatic tissue through pro-inflammatory cytokines [54]. We also observe low-precision positive associations between elevated BMI and colorectal and biliary tract cancers, suggesting a causal role between adiposity and digestive system cancers. Further research should assess the impact of weight loss interventions and dietary interventions in reducing cancers of the digestive system.

In this MR study, elevated genetically predicted BMI was associated with sex-specific cancers: increased risk of uterine cancer and decreased risk of breast and prostate cancer. BMI and breast cancer has been extensively studied in previous MR studies [19,22,55,56]. A previous MR study based on data from the BCAC and DRIVE consortia of 46,325 cases of breast cancer found that genetically predicted BMI based on 84 SNPs was inversely associated with breast cancer risk in both pre- and postmenopausal women [22], consistent with the findings of our study based on data from the BCAC. A further MR study of 98,842 cases of breast cancer confirmed this finding [57]. These MR results contrast with observational study findings that have demonstrated adult obesity to be associated with increased risk of postmenopausal breast cancer [10,46]. Furthermore, our study corroborates the findings of a previous MR study of 6,609 cases of uterine cancer, showing genetically predicted BMI was positively associated with incidence [58]. In males, our findings are in line with a previous MR of 22 European cohorts, which showed weak evidence for an inverse association between genetically predicted BMI and prostate cancer [24], as well as a larger analysis showing stronger evidence for an association [23]. The association of BMI and these sex-specific cancers is likely to be at least in part hormonally mediated. In males, elevated BMI reduces serum testosterone [59], with androgens recognised as promoting prostate cancer. In premenopausal women, increased BMI is associated with anovulation, reducing lifetime exposure of circulating estrogen and progesterone [60], and thus a consequent reduction in breast cancer risk [61] and an increase in uterine cancer risk [62]. While observational associations of BMI with pre- and postmenopausal breast cancers are directionally discordant, MR estimates have consistent direction. This may be because the association of genetically predicted BMI is mediated via lifelong exposure to elevated estrogen levels, the majority of which is premenopausal. We report an inverse association between FFMI and breast cancer risk, suggesting that increased non-adipose tissue density may have a protective role against malignancy. Observational studies have demonstrated that sarcopenia is associated with increased breast cancer mortality [63,64], although the association may be subject to reverse causation. However, reduced muscle mass and atrophy is associated with systemic inflammation [65,66] and increased TNF-alpha levels [66], which may drive carcinogenesis, although the mechanistic links need to be assessed further.

We report a positive association between genetically predicted BMI and squamous cell lung cancer risk, consistent with previous MR findings [19]. Our findings of no association between BMI and melanoma risk are consistent with findings of a recent MR investigation [67]. However, we observed an inverse association between FMI and melanoma risk, but a positive association for FFMI. These findings suggest that body composition influences melanoma risk, with increased adiposity being protective against melanoma. This may be because those with higher FFMI and lower FMI spend more time outdoors and so have greater exposure to the sun. Previous observational studies have shown that obesity is associated with improved survival of melanoma patients [68], with a down-regulation of key lipid genes shown in melanoma cells [69], suggesting that lipids play a role in carcinogenesis.

We observed a positive association between genetically predicted height and overall cancer risk, which was consistent across a wide range of site-specific cancers, including kidney, colorectal, biliary tract, ovarian, and breast cancers. Our findings are consistent with previous MR studies showing a positive association of height with colorectal [70–72] and breast cancer [72–74]. Increased height is associated with elevated insulin-like growth factor 1 (IGF1) [75], which is a growth factor that drives cellular proliferation and survival and has thus been implicated in carcinogenesis of IGF-responsive tissues. Increased expression of IGF-1 and its cellular receptors are present in cancer tumours [76,77]. Our recent MR investigation demonstrated that genetically predicted IGF-1 was associated with increased risk of colorectal cancer, and, possibly, breast cancer, but not associated with overall or other site-specific cancers [78]. This suggests that the effect of height on cancer risk operates via pathways independent of IGF-1.

We observed a positive association between elevated genetically predicted BMI and overall cancer risk. This result replicates a recent MR investigation using 520 genetic variants for BMI in the UKBB that showed that overall cancer risk (excluding nonmelanoma skin cancer) and mortality was associated with elevated genetically predicted BMI. However, when dividing cancers into digestive system versus non-digestive system, genetically predicted BMI was only associated with digestive system cancers. This result has important clinical implications. Previous public health recommendations have advocated obesity as a generic risk factor for cancer prevention [79,80]. While our research supports a causal role of obesity in driving and protecting against certain cancers, it suggests differential effects of BMI and obesity in different malignancies, which should be explored further. A more nuanced message public health message with regard to obesity as a risk factor for digestive system cancers may be more appropriate.

Our study has several strengths. The MR design minimises the influence of environmental confounding factors and reverse causality, allowing for causal relationships to be better characterised. The UKBB is a large prospective cohort, allowing multiple cancer types to be studied in a single dataset and comparisons of estimates across cancers to be made. However, there are some limitations. The main limitation is the assumption that the genetic associations with cancer risk are mediated via the proposed risk factors. Additionally, estimates for some lower frequency cancer types are subject to low precision, and, therefore, results should be interpreted based on the magnitude of the associations rather than on p-values alone. Another shortcoming is that our findings may not be applicable to other ethnic groups as we confined the study population to individuals of European ancestries to minimise bias from population stratification. As we analysed middle- to early late-aged individuals, some cancers, particularly those with early onset and poor survival, may not be well captured in our analysis. Associations in the UKBB may be subject to selection bias [81], as participants in the UKBB are overall more healthy and better educated compared to the overall UK population [82]. Another potential source of bias is detection bias. The probability of diagnosis for less severe cancers (such as prostate cancer) may be more likely if the individual has comorbidities and so has more extensive contact with health services. While we wanted to perform analyses for specific cancer subtypes, we were unable to define these in a reliable way for the majority of site-specific cancers in the data available. Results for overall cancer are dependent on the characteristics of the analytic sample and the relative prevalence of different cancer types. In particular, cancers with greater survival chances will be overrepresented in the case sample. Selection of genetic variants was based on datasets that include the UKBB participants, and for FMI and FFMI, on a dataset comprised solely of the UKBB participants. This may lead to bias due to sample overlap and winner’s curse. However, results were similar when genetic associations with cancers were taken from independent consortia. Our analysis assumes a linear relationship between the risk factors and the outcome. Quantitative estimates may be misleading if the true relationship is nonlinear, although estimates are still reflective of the presence and direction of the population-averaged causal effect [83]. As with all methodologies that aim to assess causal relationships, MR makes untestable assumptions. The approach relies on the genetic associations with cancer risk being mediated via the body composition measures. While we were able to perform robust methods to assess sensitivity to this assumption, it remains a possibility that some genetic variants may influence cancer risk through other pathways than obesity. A further limitation is that the relationship between obesity and cancer risk may change over the life course. Typically, MR estimates reflect the impact of a lifelong difference in the trajectory of a risk factor, as they represent associations between genetically predicted levels of risk factors and outcomes. Finally, our study does not provide understanding of the physiological pathways by which obesity and height may affect carcinogenesis.

Conclusions

In conclusion, this comprehensive MR study provides evidence that elevated BMI increases the risk of digestive system cancers, and BMI increases the risk of uterine cancer but is protective for other sex-specific cancers, including breast and prostate. We showed that elevated genetically predicted FMI is associated with liver, lung, and pancreatic cancer, with FFMI inversely associated with breast cancer. In contrast, genetically predicted height was consistently positively associated with overall cancer and several site-specific cancers. These findings suggest that obesity and body composition have particular causal relevance to specific cancer types.

Supporting information

S1 Checklist. STROBE Statement.

STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

(DOCX)

Click here for additional data file.^{(34.2KB, docx)}

S1 Fig. Flowchart of participant exclusion criteria in the UKBB.

PCA, principal component analysis; SD, standard deviation; UKBB, UK Biobank.

(PDF)

Click here for additional data file.^{(216.6KB, pdf)}

S2 Fig. Effect sizes (OR per 1 kg/m² increase in risk factor) that can be detected for BMI, FMI, and FFMI with different number of cases in analyses based on 367,561 individuals, a significance level of 0.05, and a phenotypic variance of 4.05% for BMI, 3.15% for FMI, and 2.27% for FFMI.

As power calculators are not available for multivariable MR, all calculations are performed for univariable MR analyses based on each risk factor in turn. BMI, body mass index; FFMI, fat-free mass index; FMI, fat mass index; MR, mendelian randomisation; OR, odds ratio.

(PDF)

Click here for additional data file.^{(54.7KB, pdf)}

S3 Fig. Associations of genetically predicted BMI with overall and site-specific cancers in the UKBB excluding outcomes that were self-reported only.

ORs are expressed per 1 kg/m² increase in BMI. Results are obtained from the random-effects inverse-variance weighted method. BMI, body mass index; CI, confidence interval; OR, odds ratio; UKBB, UK Biobank.

(PDF)

Click here for additional data file.^{(48.8KB, pdf)}

S1 Table. Sources and definition of cancers in the UKBB. UKBB, UK Biobank.

(PDF)

Click here for additional data file.^{(413.2KB, pdf)}

S2 Table. SNPs used in the analyses of BMI. BMI, body mass index; SNP, single nucleotide polymorphism.

(PDF)

Click here for additional data file.^{(560.6KB, pdf)}

S3 Table. SNPs used as instrumental variables in the multivariable MR analyses of fat mass and fat-free mass indices.

MR, mendelian randomisation; SNP, single nucleotide polymorphism.

(PDF)

Click here for additional data file.^{(521.9KB, pdf)}

S4 Table. SNPs used in the analyses of height. SNP, single nucleotide polymorphism.

(PDF)

Click here for additional data file.^{(559.7KB, pdf)}

S5 Table. Supplementary analyses of the association between genetically predicted BMI (per 1 kg/m² increase) and cancer.

BMI, body mass index.

(PDF)

Click here for additional data file.^{(521.1KB, pdf)}

S6 Table. Supplementary analyses of the association between genetically predicted height (per 1 standard deviation increase) and cancer.

(PDF)

Click here for additional data file.^{(520.3KB, pdf)}

Acknowledgments

The authors thank all investigators from the UK Biobank (UKBB), where data were conducted under application 29202.

Disclaimers: The views expressed are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research, or the Department of Health and Social Care.

Abbreviations

BCAC: Breast Cancer Association Consortium
BMI: body mass index
CI: confidence interval
ER−: estrogen receptor negative
ER+: estrogen receptor positive
FFMI: fat-free mass index
FMI: fat mass index
GWAS: genome-wide association study
HES: hospital episodes statistics
ICD: International Classification of Diseases
IGF1: insulin-like growth factor 1
MR: mendelian randomisation
OR: odds ratio
SNP: single nucleotide polymorphism
STROBE: Strengthening the Reporting of Observational Studies in Epidemiology
UKBB: UK Biobank

Data Availability

All primary data are available from the UK Biobank on application to any bona fide researcher (url: https://www.ukbiobank.ac.uk/). Genetic associations with cancer outcomes have been deposited at https://figshare.com/articles/dataset/Genetic_associations_with_cancer_outcomes/14806638.

Funding Statement

Stephen Burgess is supported by Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 204623/Z/16/Z). This work was supported by the UK National Institute for Health Research Cambridge Biomedical Research Centre (BRC-1215-20014). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1003706.r001

Decision Letter 0

Adya Misra

18 Aug 2020

Dear Dr Burgess,

Thank you for submitting your manuscript entitled "Body size and composition and site-specific cancers in UK Biobank: a Mendelian randomisation study" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff [as well as by an academic editor with relevant expertise] and I am writing to let you know that we would like to send your submission out for external peer review.

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Kind regards,

Adya Misra, PhD,

Senior Editor

PLOS Medicine

PLoS Med. doi: 10.1371/journal.pmed.1003706.r002

Decision Letter 1

Adya Misra

24 Sep 2020

Dear Dr. Burgess,

Thank you very much for submitting your manuscript "Body size and composition and site-specific cancers in UK Biobank: a Mendelian randomisation study" (PMEDICINE-D-20-03916R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

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We look forward to receiving your revised manuscript.

Sincerely,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

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Requests from the editors:

Background- please provide brief context for this study, include 1-2 sentence of why this work may be important

Please structure the abstract to: background, methods and findings, conclusions

Please provide participant demographics

Please provide additional details about the analyses carried out in the methods and findings section.

In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

Please add p values along with 95% CI as needed

Please temper conclusions by adding “our results show” or similar

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

Please ensure that the study is reported according to the STROBE guideline, and include the completed STROBE checklist as Supporting Information. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)."

The STROBE guideline can be found here: http://www.equator-network.org/reporting-guidelines/strobe/

When completing the checklist, please use section and paragraph numbers, rather than page numbers.

Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

Please avoid using words such as “obese” and “overweight” throughout the text. Please amend to “with obesity” and “with overweight” throughout in line with people first principles.

Please add a space between text and reference brackets throughout

Methods- please provide further details on the data sources used, including names of registries or sources of electronic medical records

Table 1 please add units to the row “Age”

Comments from the reviewers:

Reviewer #1: In the present study, the authors analyzed the effect of body size and composition on cancer risk using UK Biobank data. Even though a wide-angled MR investigation was performed to evaluate the impact of obesity on cancer outcomes, there are several deficiencies that need to be clarified by the authors, amongst which some of these issues limited the interpretability of the findings.

1. In the methods section, it was unclear how the quality control was performed. A clearer flowchart was needed, at least, in the supplementary.

2. In the statistical analysis, it is not clear what kind of Mendelian randomized analysis was adopted in this study? And which R package is used for this analysis?

3. Because a recent Mendelian randomized study has shown a J shaped relation between BMI and all-cause mortality, is there a non-linear association between BMI and cancer risk? If so, will it affect the results of this study?

4. "The correlation of BMI with FMI was 0.84, and with FFMI was 0.66. The correlation between FMI and FFMI was 0.14." in this study, whether the instrumental variable of BMI is independent of FMI? Does the relation among those variables affect the findings of this study?

5. Could the authors explain why there was a so strong effect of fat mass index on the stomach risk (OR=4.23) and liver cancer (OR=4.28)?

6. The incident events for most cancers were too small to perform a genetic analysis, as a result, the associations was not robust as far as I can see.

7. How about the direct and indirect effects of genetic components, BMI/ FMI/ FFMI, and cancer risk?

8. The number of overall cancer seems inconsistent with cancer-specific events according to the figures.

Reviewer #2: ºWhat are the main claims of the paper and how significant are they for the discipline?

This study applied mendelian randomisation to explore the causal impact of BMI and height on all cancer, and site specific cancers. In addition to BMI the study also considers the impact of facets of BMI - fat and fat-free mass index.

The main finding is that higher BMI has a causal but not consistent role in some site specific cancers e.g. increasing the risk of some, and protective for others. There was no evidence for a causal risk for overall cancer risk, contrary to previous reports (see following). In line with previous reports they found that genetically predicted increases in height is causally associated with increased rates of all cancers, and the majority of site specific cancers.

Significance for the discipline: This heterogeneity of direction with respects to BMI and site specific cancers is interesting but caution should be made in suggesting that as a result nuance should exist in regards to the obesity as a risk factor for specific cancers (discussion, third to last paragraph). Setting aside that a similar study using UK Biobank data (see following) found there was a consistent increase in risk for all cancer and all-cancer mortality for a genetically predicted increase in BMI (and thus reducing BMI would be expected to reduce the overall cancer burden in the population even if some subsets might rise), if the true causal estimate is that higher BMI does not increase overall risk for cancer as reported here, within the site specific cancers the protective effect from higher BMI would have to be balanced against both the increase in other cancers, and other BMI related morbidities. That is, the likely health advice would be to reduce BMI even in populations at risk of cancers where BMI may be protective. This is especially true given two of the cancers with reported reduced odds with higher BMI (melanoma and prostate) are either relatively less likely to kill compared to those that increase in risk with higher BMI, and/or have a range of interventions that could counter the potential increased risk (e.g. increased use of sunscreen for melanoma). That is, the advice would likely still be to reduce BMI in concert with appropriate cancer specific risk reduction strategies. Even for cancers with more severe prognosis, or fewer protective strategies (e.g. breast) the likely answer would be increased screening in those with genetically predicted higher BMI and still reduce BMI.

Action - The discussion should better explain what role nuance would play in BMI messaging in light of this paper

ºAre the claims properly placed in the context of the previous literature? Have the authors treated the literature fairly?

There are some important omissions in the reported literature that impact both the perceived novelty of this work, and its interpretation.

Gharahkani 2019 PMID: 30733581 performs a very similar analysis in the same UK Biobank dataset, using a larger BMI instrument to test both site specific and overall cancer risk. There are differences in the new analysis, but these should be interpreted with respect to that publication. This is especially relevant as this study found obesity is associated with all cancer, unlike this analysis, and this point has relevance with respects to the papers discussion.

While not significant at P< 0.05 Levy 2017 PMID: 28804972 reported an OR of 0.848 (0.658-1.093) for testicular cancer given a 1sd increase in BMI; while this is smaller than the causal estimate in this paper especially given the degree of change in BMI the Cis are wide for this estimate and likely overlap with the estimate and this should be discussed in the paper with respects to novelty

The discussion with respect to a positive association between height and overall cancer risk, and site specific cancer, while citing a similar paper (Ong et al 2018, ref 57) for specific cancers, does not note that ref 57 performed a very similar analysis in the same UK Biobank dataset and found consistent results; this should be addressed/contrasted in terms of novelty and what this study adds/improves.

The observation for BMI on melanoma is at odds with the uncited Dusingize 2020 PMID: 32068838 and this difference should be addressed; Dusingize 2020 also investigated height and found a consistent result for melanoma as this study.

Action: Cite these papers, and check for any additional relevant papers not reported here (I may have missed others) and then address them in their publication with respects to novelty, consistency and differences/advances.

ºDo the data and analyses fully support the claims? If not, what other evidence is required?

Partially:

More powerful BMI and height instruments are available using UK Biobank data or more recent GWAS meta-analyses e.g. Yengo 2018 PMID: 30124842 reports potential instruments explaining 6% of BMI and 24% of height variance. Gharahkani 2019 PMID: 30733581 constructed two instruments for BMI using cancer free UK Biobank participants that explained 4 or 7% of BMI variance. This would increase power for site specific estimates compared to those generated using their current instrument, which explains 1.6% of trait variance. While their power for height is greater (and arguably already sufficient), likewise Yengo 2018 or UK Biobank provides even more powerful instruments for height.

Action: The authors should take advantage of data published or in hand to construct more powerful BMI/height instruments to improve precision/power.

The overall cancer measure appears to include non-melanoma cancers (keratinocyte cancers, basal and squamous cell carcinoma) as the overall cancer N is ~20k more than individual site count, and there is ~ this many ICD C44 cancer cases in UK Biobank. However table S1 does not report if C44 is included in the overall cancer phenotype. Observational studies suggest an inverse relationship between BMI and non-melanoma skin cancer (e.g. Zhou 2016 PMID: 27898109); if this is a causal effect, given how common non-melanoma skin cancers are in UK Biobank, this may explain why in this study BMI was associated with no increase in all-cancer while Gharahkani 2019 PMID: 30733581, which excluded these cancers, did. That is, the overall cancer estimate here may (crudely) be the sum of a protective effect on non-melanoma skin cancer, and in aggregate an increase for all other cancers.

This is an important distinction as non-melanoma skin cancers are very rarely lethal, are in general easily treated, and adequately controlled by sun smart campaigns - that is, even if BMI is (causally) protective for non-melanoma skin cancer such that the total number of cases of cancer does not change as BMI increases, trading off higher rates of NMSC for all other cancers is likely to be an acceptable decision.

Action: the impact of including, or excluding, non-melanoma skin cancers in all cancer estimates should be reported.

Action/comment: For the discussion hypothesis that increased IGF1 expression may underlie the causal effect of height - is this amendable to analysis with summary statistic data-based Mendelian randomization (SMR)? GTex reports eQTLs for this gene

ºPLOS Medicine encourages authors to publish detailed methods as supporting information online. Do any particular methods used in the manuscript warrant such publication? If a protocol is already provided, for example for a randomized controlled trial, are there any important deviations from it? If so, have the authors explained adequately why the deviations occurred?

Methods are sufficient barring other comments.

°Is this paper outstanding in its discipline? If yes, what makes it outstanding? If not, why not?

The paper is interesting but given the overlap with previous work is not in its current form outstanding.

ºDoes the study conform to any relevant guidelines such as CONSORT, MIAME, QUORUM, STROBE, and the Fort Lauderdale agreement?

N/A

ºAre details of the methodology sufficient to allow the experiments to be reproduced?

Action: related to above the methods should clarify how the overall cancer phenotype was constructed (e.g. does it include additonal cancers/ICD codes to those reported in sup table 1

ºIs any software created by the authors freely available?

N/A

ºIs the manuscript well organized and written clearly enough to be accessible to non-specialists?

In general the paper is well written and clear; two minor points:

1) Results paragraph 1 final sentences - are the reported correlations phenotypic, genetic etc?

2) Results sub section BMI and cancer risk - the CIs for prostate cancer in line 4 don't match the figure/P-value.

Reviewer #3: The MR analyses run in the paper are pretty standard. I have a few comments on the Mendelian Randomization analyses that the authors have performed:

1. One concern I have is that GWAS data for all the traits involved (the three risk factors and various cancer traits) are using the same UK Biobank data. A consequence is that for the exposure and outcome, some individuals are shared thus the summary statistics are also correlated due to non-heritable factors. In other words, for any specific SNP, the estimation of its effect on the exposure and outcome are not independent, which may bring bias to the MR analysis.

2. Related the question 1, I think to make the authors' conclusion more convincing, they can check whether than see replicable effects of BMI/FMI/height on cancers using BMI/FMI and cancer GWAS data from other cohorts (either European ancestry or other ethics).

3. In the discussions, the authors explained why BMI/height can have a causal effect on certain cancers. For height, the authors explained that its effect on height can be mediated by IGF1. I'm wondering if there are any GWAS data available for IGF1 (or similar traits) so that MR analyses can be performed to support this explanation. In general, I think the authors may need to provide more evidence to support their conclusions instead of using only the UK Biobank data and MR.

4. Since the authors look across about 20 cancers, I think multiple testing adjustment are needed. Since the significant p-values are not that small, I'm wondering if there are still many interesting signals left after adjustment.

5. I recommend also performing MR reversing the role of cancer and BMI/height to see whether there is reserve causation (especially for BMI/FMI/FFMI).

6. In the section of "statistical analysis", the authors stated that "We performed sensitivity analyses using weighted median and MR-Egger", however I don't think weighted median and MR-Egger are sensitivity analyses, these are just two other MR methods.

Reviewer #4: It was a pleasure to comment on this well-conducted and concisely written study. My main concern is limited reference to the most recent literature on adiposity and cancer; in particular, meta-analyses/umbrella reviews of observational studies.

Background

Because the focus is on obesity-related traits and cancer, I don't see the necessity to state that obesity is a risk factor for cardiovascular, liver, and musculoskeletal diseases.

I suggest shortening the citation of existing MR studies on body fatness and cancer and instead highlight the available evidence from observational studies. Based on grading by the World Cancer Research Fund and American Cancer Institute, strong evidence from meta-analyses of observational studies exists for the BMI and waist circumference and increased risk of postmenopausal, colorectum, endometrium, ovary, kidney, liver, gall bladder, stomach, esophagus, and pancreas, and moderate evidence exists for an association with cancers of the mouth, pharynx, larynx, prostate (advanced), male breast, and diffuse large B-cell lymphoma. Consider citing the most current grading of observational evidence from WCRFI/AICR/IARC [1-3] and umbrella reviews (e.g., [4-6]).

Could you be more specific which biases plaque observational research on obesity and cancer? Key biases include confounding by smoking [7-9] and reverse causation by subclinical and prevalent disease (cancer, cvd)[7, 10-12].

Methods

Since adjustments (e.g. for waist in a GWAS of BMI) can bias the MR estimate, please report whether exposure GWAS adjusted for covariables [13-15].

There's a larger GWAS[16] on BMI than the GWAS by Locke - I am sure if summary data excluding UKBB is available. Reliance on BMI as a measure of general adiposity is limited by its inability to discriminate fat mass and lean body mass [12] and this can be more problematic in subclinical diseased individuals who often experience unintentional weight loss. I, therefore, welcome the use of fat mass and fat-free mass as additional exposures.

I doubt the validity of defining cancer cases based on 'self-report validated by a nurse', especially for less common cancers and histotypes. Can the authors provide data on validity? I suggest limiting outcome definition to cancer registry, outpatient and inpatient records, and death certificates.

I assume that using outcome data from the largest available GWAS meta-analyses would provide more cases and increase power. For example, a recent breast cancer GWAS[17] had 122,977 cases of European descent (another one[18] had 133,384 cases), compared to 13,666 cases included in the present MR study. Likewise, the largest available GWAS on lung carcinoma[19, 20] contains 10 times the number of cancer than the UK Biobank. I suggest adding replication analyses using the largest available cancer GWAS data. This would also address and circumvent possible biased introduced by weak instrumentation in 1-sample MR, winner's-curse (fmi,ffmi), and sample overlap. Although this could introduce new bias that should be discussed (e.g.,selection/survival bias). Can endometrial cancer be added as an outcome?

The 'overall cancer' outcome lumps together obesity-related cancers with cancers that are not affected by body fatness. I suggest omitting the total cancer analyses.

MR-Egger is sensitive to influential points in the regression[21] and point estimates are often imprecise[22, 23], as can be witnessed in Table S4. MR Egger is therefore not generally recommended as a preferred sensitivity analysis method [23-25]. I suggest to follow Slob and Burgess and report one of the three robust MR method classes discussed in [23] and omit MR Egger estimates. Consider adding E-values [26], variant-outcome associations (fewer assumptions [27, 28]), and negative controls [29].

Discussion

MR studies can, like observational studies, be subject to bias (pleiotropy - in particular when the biology of the variant-exposure association is ill-defined, population stratification, selection bias etc). Applying different study designs and analytical approaches may in the long run converge to provide triangulating evidence [30-32] and help strengthen answering causal relationships. I suggest separating the evidence from observational and MR studies; followed by a "grading" of the combined observational and MR evidence. It would be helpful to first summarize and grade the observational evidence based on the best available meta-analyses/umbrella reviews, possibly adapting the extensive work of WCRFI/AICR/IARC, then raise the limitations of observational studies (i..e., potential biases introduced by smoking and reverse causation by pre-existing disease). Then summarize the available MR-studies and illustrate where findings of observational and MR studies converge or diverge. Also, it would help to provide more details on the strengths and limitations of the available MR studies on obesity-traits and cancer. In particular, the number of cases or power achieved by the present MR study and previous MR studies should be discussed. Several recent large MR studies [33-37] that are not cited.

For breast cancer, according to observational research, being overweight or obese as an adult before menopause decreases the risk of premenopausal cancer of the female breast, but greater weight gain in adulthood increases the risk of postmenopausal breast cancer [1]. Observational research has provided the strongest evidence for obesity and postmenopausal [6]. The differential effects on pre- and postmenopausal breast cancer require further investigation A recent MR [36] study found that childhood adiposity might protect against adult breast cancer but did not consider breast cancer age of onset. Body composition is s time-varying exposure and the difficulties of MR to handle such exposures should be more clearly highlighted.

1. World Cancer Research Fund International and American Institute for Cancer Research, Diet, nutrition, physical activity and cancer: a global perspective. third expert report. 2018.

2. Wild, C.P., E. Weiderpass, and B.W. Stewart, World cancer report 2020. 2020, Lyon, France: International Agency for Research on Cancer.

3. Chan, D.S.M., et al., World Cancer Research Fund International: Continuous Update Project-systematic literature review and meta-analysis of observational cohort studies on physical activity, sedentary behavior, adiposity, and weight change and breast cancer risk. Cancer Causes Control, 2019. 30(11): p. 1183-1200.

4. Kyrgiou, M., et al., Adiposity and cancer at major anatomical sites: umbrella review of the literature. Bmj, 2017. 356: p. j477.

5. Raglan, O., et al., Risk factors for endometrial cancer: An umbrella review of the literature. Int J Cancer, 2019. 145(7): p. 1719-1730.

6. Kalliala, I., et al., Obesity and gynaecological and obstetric conditions: umbrella review of the literature. Bmj, 2017. 359: p. j4511.

7. Arnold, M., A.G. Renehan, and G.A. Colditz, Excess Weight as a Risk Factor Common to Many Cancer Sites: Words of Caution when Interpreting Meta-analytic Evidence. Cancer Epidemiol Biomarkers Prev, 2017. 26(5): p. 663-665.

8. Samet, J.M., Lung Cancer, Smoking, and Obesity: It's Complicated. J Natl Cancer Inst, 2018. 110(8): p. 795-796.

9. Song, M. and E. Giovannucci, Estimating the Influence of Obesity on Cancer Risk: Stratification by Smoking Is Critical. J Clin Oncol, 2016. 34(27): p. 3237-9.

10. Danaei, G., et al., Weight Loss and Coronary Heart Disease: Sensitivity Analysis for Unmeasured Confounding by Undiagnosed Disease. Epidemiology, 2016. 27(2): p. 302-10.

11. Flegal, K.M., B.I. Graubard, and S.W. Yi, Comparative effects of the restriction method in two large observational studies of body mass index and mortality among adults. European journal of clinical investigation, 2017. 47(6): p. 415-421.

12. Lee, D.H. and E.L. Giovannucci, The Obesity Paradox in Cancer: Epidemiologic Insights and Perspectives. Curr Nutr Rep, 2019. 8(3): p. 175-181.

13. Hartwig, F.P., et al., Bias in two-sample Mendelian randomization by using covariable-adjusted summary associations. BioRxiv, 2019: p. 816363.

14. Holmes, M.V. and G. Davey Smith, Problems in interpreting and using GWAS of conditional phenotypes illustrated by 'alcohol GWAS'. Mol Psychiatry, 2019. 24(2): p. 167-168.

15. Tan, V.Y., et al., Letter regarding article, "Associations of obesity and circulating insulin and glucose with breast cancer risk: a Mendelian randomization analysis". Int J Epidemiol, 2019. 48(3): p. 1014-1015.

16. Pulit, S.L., et al., Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet, 2019. 28(1): p. 166-174.

17. Michailidou, K., et al., Association analysis identifies 65 new breast cancer risk loci. Nature, 2017. 551(7678): p. 92-94.

18. Zhang, H., et al., Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses. Nat Genet, 2020. 52(6): p. 572-581.

19. McKay, J.D., et al., Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat Genet, 2017. 49(7): p. 1126-1132.

20. Wang, Y., et al., Association Analysis of Driver Gene-Related Genetic Variants Identified Novel Lung Cancer Susceptibility Loci with 20,871 Lung Cancer Cases and 15,971 Controls. Cancer Epidemiol Biomarkers Prev, 2020. 29(7): p. 1423-1429.

21. Burgess, S. and S.G. Thompson, Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol, 2017. 32(5): p. 377-389.

22. Bowden, J., et al., Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol, 2016. 45(6): p. 1961-1974.

23. Slob, E.A. and S. Burgess, A comparison of robust Mendelian randomization methods using summary data. Genet Epidemiol, 2020. 20: p. 1-17.

24. Burgess, S., C.N. Foley, and V. Zuber, Inferring Causal Relationships between Risk Factors and Outcomes Using Genetic Variation. Handbook of Statistical Genomics: Two Volume Set, 2019: p. 651-20.

25. Burgess, S., et al., Guidelines for performing Mendelian randomization investigations. Wellcome Open Research, 2020. 4(186): p. 186.

26. Swanson, S.A. and T.J. VanderWeele, E-Values for Mendelian Randomization. Epidemiology, 2020. 31(3): p. e23-e24.

27. VanderWeele, T.J., et al., Methodological challenges in mendelian randomization. Epidemiology, 2014. 25(3): p. 427-35.

28. Didelez, V. and N. Sheehan, Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res, 2007. 16(4): p. 309-30.

29. Sanderson, E., et al., The use of negative control outcomes in Mendelian Randomisation to detect potential population stratification or selection bias. BioRxiv, 2020.

30. Mariosa, D., et al., Commentary: What can Mendelian randomization tell us about causes of cancer? Int J Epidemiol, 2019. 48(3): p. 816-821.

31. Mamluk, L., et al., Evidence of detrimental effects of prenatal alcohol exposure on offspring birthweight and neurodevelopment from a systematic review of quasi-experimental studies. Int J Epidemiol, 2020.

32. Lawlor, D.A., K. Tilling, and G. Davey Smith, Triangulation in aetiological epidemiology. Int J Epidemiol, 2016. 45(6): p. 1866-1886.

33. Shu, X., et al., Associations of obesity and circulating insulin and glucose with breast cancer risk: a Mendelian randomization analysis. Int J Epidemiol, 2019. 48(3): p. 795-806.

34. Langdon, R.J., et al., A Phenome-Wide Mendelian Randomization Study of Pancreatic Cancer Using Summary Genetic Data. Cancer Epidemiol Biomarkers Prev, 2019. 28(12): p. 2070-2078.

35. Cornish, A.J., et al., Modifiable pathways for colorectal cancer: a mendelian randomisation analysis. Lancet Gastroenterol Hepatol, 2020. 5(1): p. 55-62.

36. Richardson, T.G., et al., Use of genetic variation to separate the effects of early and later life adiposity on disease risk: mendelian randomisation study. Bmj, 2020. 369: p. m1203.

37. Yarmolinsky, J., et al., Appraising the role of previously reported risk factors in epithelial ovarian cancer risk: A Mendelian randomization analysis. PLoS Med, 2019. 16(8): p. e1002893.

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2021 Jul 29;18(7):e1003706. doi: 10.1371/journal.pmed.1003706.r003

Author response to Decision Letter 1

21 Nov 2020

Attachment

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PLoS Med. doi: 10.1371/journal.pmed.1003706.r004

Decision Letter 2

Richard Turner

16 Jun 2021

Dear Dr. Burgess,

Thank you very much for re-submitting your manuscript "Body size and composition and risk of site-specific cancers in UK Biobank and large international consortia: a Mendelian randomisation study" (PMEDICINE-D-20-03916R2) for review by PLOS Medicine. We do apologize for the long delay in sending you a decision.

I have discussed the paper with editorial colleagues and it was also seen again by three reviewers. I am pleased to tell you that, provided the remaining editorial and production issues are fully dealt with, we expect to be able to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

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PLoS Med. doi: 10.1371/journal.pmed.1003706.r005

Decision Letter 3

Richard Turner

21 Jun 2021

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Checklist. STROBE Statement.

STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

(DOCX)

Click here for additional data file.^{(34.2KB, docx)}

S1 Fig. Flowchart of participant exclusion criteria in the UKBB.

PCA, principal component analysis; SD, standard deviation; UKBB, UK Biobank.

(PDF)

Click here for additional data file.^{(216.6KB, pdf)}

(PDF)

Click here for additional data file.^{(54.7KB, pdf)}

S3 Fig. Associations of genetically predicted BMI with overall and site-specific cancers in the UKBB excluding outcomes that were self-reported only.

(PDF)

Click here for additional data file.^{(48.8KB, pdf)}

S1 Table. Sources and definition of cancers in the UKBB. UKBB, UK Biobank.

(PDF)

Click here for additional data file.^{(413.2KB, pdf)}

S2 Table. SNPs used in the analyses of BMI. BMI, body mass index; SNP, single nucleotide polymorphism.

(PDF)

Click here for additional data file.^{(560.6KB, pdf)}

S3 Table. SNPs used as instrumental variables in the multivariable MR analyses of fat mass and fat-free mass indices.

MR, mendelian randomisation; SNP, single nucleotide polymorphism.

(PDF)

Click here for additional data file.^{(521.9KB, pdf)}

S4 Table. SNPs used in the analyses of height. SNP, single nucleotide polymorphism.

(PDF)

Click here for additional data file.^{(559.7KB, pdf)}

S5 Table. Supplementary analyses of the association between genetically predicted BMI (per 1 kg/m² increase) and cancer.

BMI, body mass index.

(PDF)

Click here for additional data file.^{(521.1KB, pdf)}

S6 Table. Supplementary analyses of the association between genetically predicted height (per 1 standard deviation increase) and cancer.

(PDF)

Click here for additional data file.^{(520.3KB, pdf)}

Attachment

Submitted filename: 2020_Nov_10 BMI Cancer_PLoS_Response_SB.docx

Click here for additional data file.^{(41.7KB, docx)}

Data Availability Statement

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PERMALINK

Body size and composition and risk of site-specific cancers in the UK Biobank and large international consortia: A mendelian randomisation study

Mathew Vithayathil

Paul Carter

Siddhartha Kar

Amy M Mason

Stephen Burgess

Susanna C Larsson

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Background

Methods

Study population

Genetic instruments

Statistical analysis

Results

Table 1. Baseline characteristics of the UKBB participants included in this study.

BMI and cancer risk

Fig 1. Associations of genetically predicted BMI with overall and site-specific cancers in the UKBB.

Fig 2. Associations of genetically predicted BMI with site-specific cancers in large international consortia.

FMI and FFMI and cancer risk

Fig 3. Associations of genetically predicted FMI and FFMI with overall and site-specific cancers in the UKBB.

Fig 4. Associations of genetically predicted FMI and FFMI with site-specific cancers in large international consortia.

Height and cancer risk

Fig 5. Associations of genetically predicted height with overall and site-specific cancers in the UKBB.

Fig 6. Associations of genetically predicted height with site-specific cancers in large international consortia.

Digestive system versus non-digestive system cancer risk

Discussion

Conclusions

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Adya Misra

Roles

Decision Letter 1

Adya Misra

Roles

Author response to Decision Letter 1

Decision Letter 2

Richard Turner

Roles

Decision Letter 3

Richard Turner

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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