The first four PCs together explained 99.8% of the total variation of the six anthropometric variables. Thus, all analyses were restricted to these PCs (Table 1). Each PC described a distinct body morphology (Fig. 1). The loadings for each anthropometric trait are presented in Table 1 (men and women combined), which were very similar among men and women (Supplementary Tables 2 and 3). For better comparability with Ried et al. [16], the directionality of PC4 was reversed.

Table 1 Loadings and explained variance of the principal components (PCs) for the analytic study population in EPIC (n = 340,152) and average loadings and average explained variance derived by Ried et al. [16] labelled “avPC”.Fig. 1: Loadings for the four different body shape phenotypes.

PC1: blue; PC2: magenta; PC3: green; PC4: orange.

PC1 explained 63.0% of the total variation, with high loadings for all anthropometric measures except height, describing individuals characterised by general obesity (Supplementary Fig. 3). PC2 (19.6% of total variation) was characterised by opposite loadings for height and WHR (Supplementary Fig. 4), mainly discriminating between tall individuals with low WHR and short individuals with high WHR. PC3 (14.4% of the total variation) was characterised by loadings for height and WHR in the same direction, HC loadings in the opposite direction, and low loadings for BMI (Supplementary Fig. 5), distinguishing between tall individuals with high WHR but low HC and short individuals with low WHR and high HC. PC4 represents a rare phenotype explaining only 2.8% of the total variation and was characterised by high loadings for body weight and BMI and low loadings for WC and HC (Supplementary Fig. 6). Pearson’s correlation coefficients for the six anthropometric variables were consistent with the loadings for the individual PCs (Supplementary Fig. 7).

Baseline characteristics

Baseline characteristics are presented for sex-specific quintiles of loadings for PC1 (Table 2). Anthropometric measures were notably larger in quintile 5 than in quintile 1, except for WHR and height. Participants in the lowest two PC1 quintiles had a healthier diet, a higher educational attainment, were more physically active, and smoked more frequently compared with the top 20% of the study population. Additional baseline characteristics are provided in Supplementary Table 4.

Table 2 Characteristics of participants according to sex-specific quintiles of loadings of principal component 1 (overall adiposity) in EPICa.Body shapes and cancer risk

After a median follow-up of 15.3 years (interquartile range = 12.8–16.8 years) and 4,841,860 person-years, 47,110 incident cancer cases were diagnosed. Among participants, 65% were women; the mean age at recruitment was 50.9 years (SD = ±10.5 years) for women and 52.7 years (SD = ±9.6 years) for men.

Results for PC1 (overall adiposity)

The HR for overall cancer risk per 1 SD increment in PC1 was 1.07 (95% CI = 1.05–1.08) (Fig. 2). In cancer type-specific analyses, a 1 SD increment in PC1 was associated with increased risks for malignant tumours of the corpus uteri, oesophagus (adeno), liver, kidney, gallbladder, colon, pancreas, myeloma, breast (postmenopausal), and rectum. An inverse relationship was observed between PC1 and cancers of the prostate and oesophagus (SCC). All these associations passed the Bonferroni-corrected P ≤ 0.001. Among never smokers, these estimates remained largely unchanged, except for smoking-related cancers, where the point estimate increased (lips, oral cavity, pharynx: 1.13; 0.98–1.30) or showed a tendency towards the null (lung, larynx, oesophagus (SCC)).

Fig. 2: Hazard ratios (HRs) for total cancer and 24 cancer subtypes per 1 SD increment in the first principal component (PC1; overall adiposity).

HRs with corresponding 95% confidence intervals (95% CIs) from Cox proportional hazards regressions in the total population (n = 340,152) and in never smokers (n = 160,111); n number of cancer incidence cases, CNS central nervous system, SCC squamous cell carcinomas.

Results for PC2 (tall stature; low WHR)

The association between PC2 and overall cancer showed a slightly increased risk per 1 SD increment (HR = 1.03; 95% CI = 1.02–1.04) (Fig. 3). Positive associations were observed for cancers of the thyroid, breast (post- and premenopausal) and malignant melanoma. All these associations passed the Bonferroni-corrected P ≤ 0.001, except thyroid cancer (P = 0.003). An inverse relationship was observed for tumours of the rectum and lips, oral cavity, pharynx. Inverse associations were also observed for cancers of the stomach (non-cardia), liver, and oesophagus (adeno), but these associations did not pass the Bonferroni-corrected P ≤ 0.001. When these analyses were repeated among never smokers, the point estimates remained largely unchanged, except for a stronger positive association for cancers of the brain and CNS, no association for cancers of the lips, oral cavity, pharynx, and a stronger inverse association for non-cardia stomach cancer.

Fig. 3: Hazard ratios (HRs) for total cancer and 24 cancer subtypes per 1 SD increment in the second principal component (PC2; tall stature, low waist-to-hip ratio).

HRs with corresponding 95% confidence intervals (95% CIs) from Cox proportional hazards regressions in the total population (n = 340,152) and in never smokers (n = 160,111); n number of cancer incidence cases, CNS central nervous system, SCC squamous cell carcinomas.

Results for PC3 (tall stature; high WHR)

PC3 was positively associated with overall cancer risk, with an HR of 1.04 (95% CI = 1.03–1.05) per 1 SD increment (Fig. 4). Positive associations were observed for 12 of 24 different cancers, of which 8 also passed the Bonferroni-corrected P ≤ 0.001 (Supplementary Table 7). However, among never smokers, associations with five of these cancer types were substantially attenuated (larynx, oesophageal SCC, stomach cardia, lips, oral cavity, pharynx, and lung). An inverse association was found for cancer of the corpus uteri (P < 0.001), with a more pronounced inverse association among never smokers.

Fig. 4: Hazard ratios (HRs) for total cancer and 24 cancer subtypes per 1 SD increment in the third principal component (PC3; tall stature, high waist-to-hip ratio).

HRs with corresponding 95% confidence intervals (95% CIs) from Cox proportional hazards regressions in the total population (n = 340,152) and in never smokers (n = 160,111); n number of cancer incidence cases, CNS central nervous system, SCC squamous cell carcinomas.

Results for PC4 (high BMI and weight; low WC and HC)

There was no association between PC4 and overall cancer risk (HR = 1.00; 95% CI = 0.99–1.01) (Supplementary Fig. 8). A relatively robust positive association was observed with thyroid cancer risk (HR = 1.10, 95% CI = 1.00–1.21), which however did not pass the Bonferroni-corrected P ≤ 0.001.

Sensitivity analyses

After excluding the first 2 years of follow-up, the point estimates for PC1 remained largely unchanged, except for cervical cancer, for which the HR decreased (Supplementary Table 5). There was also little change for PC2, except for an even lower risk for laryngeal cancer (Supplementary Table 6). For PC3 and PC4, no sizeable changes in the associations were observed (Supplementary Tables 7 and 8).

In analyses stratified by age, most HRs were largely consistent across the two age groups (<52.3 vs. ≥52.3 years). Exceptions were as follows. For PC1, HRs for cancers of the pancreas and thyroid were stronger in the younger as compared to the older age group (Supplementary Table 5). For PC2, the HR for gallbladder cancer was stronger in the younger as compared to older age group, whereas HRs were less strong for cancers of the brain and CNS, breast (premenopausal), and thyroid (Supplementary Table 6). For PC3, HRs for oesophageal (adeno and SCC), laryngeal, stomach (cardia), and thyroid cancers were stronger in the younger as compared to the older age group (Supplementary Table 7). For PC4, a positive association was observed with liver cancer in the younger age group, while this association was inverse in the older age group (Supplementary Table 8).

In a further analysis, BMI (per 5 kg/m2 increment = 1 SD) was positively associated with risk of cancers of the corpus uteri, oesophagus (adeno), kidney, thyroid, gallbladder, breast (postmenopausal), colon, pancreas, rectum, and multiple myeloma (Supplementary Fig. 9). Positive associations were also seen for tumours of the stomach (cardia) and ovary, but confidence intervals included the null. An inverse relationship was found for seven cancer types, including cancers of the oesophagus (SCC), cervix, lung, lips, oral cavity, pharynx, stomach (non-cardia), larynx, and malignant melanoma. After restricting the analyses to never smokers, BMI remained inversely associated only with malignant melanoma.