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

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.

Blood pressure and risk of cancer in the European Prospective Investigation into Cancer and Nutrition.

Several studies have reported associations of hypertension with cancer, but not all results were conclusive. We examined the association of systolic (SBP) and diastolic (DBP) blood pressure with the development of incident cancer at all anatomical sites in the European Prospective Investigation into Cancer and Nutrition (EPIC). Hazard ratios (HRs) (95% confidence intervals) were estimated using multivariable Cox proportional hazards models, stratified by EPIC-participating center and age at recruitment, and adjusted for sex, education, smoking, body mass index, physical activity, diabetes and dietary (in women also reproductive) factors. The study included 307,318 men and women, with an average follow-up of 13.7 (standard deviation 4.4) years and 39,298 incident cancers. We confirmed the expected positive association with renal cell carcinoma: HR = 1.12 (1.08-1.17) per 10 mm Hg higher SBP and HR = 1.23 (1.14-1.32) for DBP. We additionally found positive associations for esophageal squamous cell carcinoma (SCC): HR = 1.16 (1.07-1.26) (SBP), HR = 1.31 (1.13-1.51) (DBP), weaker for head and neck cancers: HR = 1.08 (1.04-1.12) (SBP), HR = 1.09 (1.01-1.17) (DBP) and, similarly, for skin SCC, colon cancer, postmenopausal breast cancer and uterine adenocarcinoma (AC), but not for esophageal AC, lung SCC, lung AC or uterine endometroid cancer. We observed weak inverse associations of SBP with cervical SCC: HR = 0.91 (0.82-1.00) and lymphomas: HR = 0.97 (0.93-1.00). There were no consistent associations with cancers in other locations. Our results are largely compatible with published studies and support weak associations of blood pressure with cancers in specific locations and morphologies.

Lifestyle factors and risk of multimorbidity of cancer and cardiometabolic diseases: a multinational cohort study.

BACKGROUND: Although lifestyle factors have been studied in relation to individual non-communicable diseases (NCDs), their association with development of a subsequent NCD, defined as multimorbidity, has been scarcely investigated. The aim of this study was to investigate associations between five lifestyle factors and incident multimorbidity of cancer and cardiometabolic diseases. METHODS: In this prospective cohort study, 291,778 participants (64% women) from seven European countries, mostly aged 43 to 58 years and free of cancer, cardiovascular disease (CVD), and type 2 diabetes (T2D) at recruitment, were included. Incident multimorbidity of cancer and cardiometabolic diseases was defined as developing subsequently two diseases including first cancer at any site, CVD, and T2D in an individual. Multi-state modelling based on Cox regression was used to compute hazard ratios (HR) and 95% confidence intervals (95% CI) of developing cancer, CVD, or T2D, and subsequent transitions to multimorbidity, in relation to body mass index (BMI), smoking status, alcohol intake, physical activity, adherence to the Mediterranean diet, and their combination as a healthy lifestyle index (HLI) score. Cumulative incidence functions (CIFs) were estimated to compute 10-year absolute risks for transitions from healthy to cancer at any site, CVD (both fatal and non-fatal), or T2D, and to subsequent multimorbidity after each of the three NCDs. RESULTS: During a median follow-up of 11 years, 1910 men and 1334 women developed multimorbidity of cancer and cardiometabolic diseases. A higher HLI, reflecting healthy lifestyles, was strongly inversely associated with multimorbidity, with hazard ratios per 3-unit increment of 0.75 (95% CI, 0.71 to 0.81), 0.84 (0.79 to 0.90), and 0.82 (0.77 to 0.88) after cancer, CVD, and T2D, respectively. After T2D, the 10-year absolute risks of multimorbidity were 40% and 25% for men and women, respectively, with unhealthy lifestyle, and 30% and 18% for men and women with healthy lifestyles. CONCLUSION: Pre-diagnostic healthy lifestyle behaviours were strongly inversely associated with the risk of cancer and cardiometabolic diseases, and with the prognosis of these diseases by reducing risk of multimorbidity.

The Obesity Paradox in Cancer: a Review.

There is a common perception that excess adiposity, commonly approximated by body mass index (BMI), is associated with reduced cancer survival. A number of studies have emerged challenging this by demonstrating that overweight and early obese states are associated with improved survival. This finding is termed the "obesity paradox" and is well recognized in the cardio-metabolic literature but less so in oncology. Here, we summarize the epidemiological findings related to the obesity paradox in cancer. Our review highlights that many observations of the obesity paradox in cancer reflect methodological mechanisms including the crudeness of BMI as an obesity measure, confounding, detection bias, reverse causality, and a specific form of the selection bias, known as collider bias. It is imperative for the oncologist to interpret the observation of the obesity paradox against the above methodological framework and avoid the misinterpretation that being obese might be "good" or "protective" for cancer patients.

Glycemic index, glycemic load, and risk of coronary heart disease: a pan-European cohort study.

BACKGROUND: High carbohydrate intake raises blood triglycerides, glucose, and insulin; reduces HDLs; and may increase risk of coronary heart disease (CHD). Epidemiological studies indicate that high dietary glycemic index (GI) and glycemic load (GL) are associated with increased CHD risk. OBJECTIVES: The aim of this study was to determine whether dietary GI, GL, and available carbohydrates are associated with CHD risk in both sexes. METHODS: This large prospective study-the European Prospective Investigation into Cancer and Nutrition-consisted of 338,325 participants who completed a dietary questionnaire. HRs with 95% CIs for a CHD event, in relation to intake of GI, GL, and carbohydrates, were estimated using covariate-adjusted Cox proportional hazard models. RESULTS: After 12.8 y (median), 6378 participants had experienced a CHD event. High GL was associated with greater CHD risk [HR 1.16 (95% CI: 1.02, 1.31) highest vs. lowest quintile, p-trend 0.035; HR 1.18 (95% CI: 1.07, 1.29) per 50 g/day of GL intake]. The association between GL and CHD risk was evident in subjects with BMI (in kg/m2) ≥25 [HR: 1.22 (95% CI: 1.11, 1.35) per 50 g/d] but not in those with BMI <25 [HR: 1.09 (95% CI: 0.98, 1.22) per 50 g/d) (P-interaction = 0.022). The GL-CHD association did not differ between men [HR: 1.19 (95% CI: 1.08, 1.30) per 50 g/d] and women [HR: 1.22 (95% CI: 1.07, 1.40) per 50 g/d] (test for interaction not significant). GI was associated with CHD risk only in the continuous model [HR: 1.04 (95% CI: 1.00, 1.08) per 5 units/d]. High available carbohydrate was associated with greater CHD risk [HR: 1.11 (95% CI: 1.03, 1.18) per 50 g/d]. High sugar intake was associated with greater CHD risk [HR: 1.09 (95% CI: 1.02, 1.17) per 50 g/d]. CONCLUSIONS: This large pan-European study provides robust additional support for the hypothesis that a diet that induces a high glucose response is associated with greater CHD risk.

Body mass index trajectories across adulthood and smoking in relation to prostate cancer risks: the NIH-AARP Diet and Health Study.

BACKGROUND: Previously we showed that adulthood body mass index (BMI) trajectories that result in obesity were associated with elevated risks of fatal prostate cancer (PCA). To further explore this relationship, we conducted a study within the NIH-AARP Diet and Health Study. METHODS: Among 153 730 eligible men enrolled in the NIH-AARP cohort from 1995 to 1996 (median follow-up = 15.1 years), we identified 630 fatal PCA cases and 16 896 incident cases. BMI was assessed for ages 18, 35 and 50 and at study entry, enabling examination of latent class-identified BMI trajectories. Hazard ratios (HRs) and 95% confidence intervals (CI) were estimated using Cox proportional hazards regression. RESULTS: BMI at study entry (mean age = 63, HR = 1.12; 95% CI = 1.01, 1.24, per 5-unit increase) and maximum BMI during adulthood (HR = 1.12; 95% CI = 1.02, 1.24, per 5-unit increase) shared modest associations with increased risk of fatal PCA. Smoking status likely modified the relationship between BMI trajectories and fatal PCA (Pinteraction = 0.035 via change-in-estimate variable section, P = 0.065 via full a priori model). Among never-smokers, BMI trajectory of normal weight to obesity was associated with increased risk of fatal disease (HR = 2.37; 95% CI = 1.38, 4.09), compared with the maintained normal weight trajectory, whereas there was no association among former or current-smokers. Total and non-aggressive PCA exhibited modest inverse associations with BMI at all ages, whereas no association was observed for aggressive PCA. CONCLUSIONS: Increased BMI was positively associated with fatal PCA, especially among never-smokers. Future studies that examine PCA survival will provide additional insight as to whether these associations are the result of biology or confounding.

Patient Stratification Using Longitudinal Data - Application of Latent Class Mixed Models.

Analysis of longitudinal data in medical research is becoming increasingly important, in particular for the identification of patient subgroups, as the focus of medical research is shifting toward personalised medicine. Here we present the use of a statistical learning approach for the identification of subgroups of hypertension patients demonstrating different patterns of response to treatment. This method, applied to large-scale patient-level data, has identified three such groups found to be associated with different clinical characteristics. We further consider the utility of this method in medical research by comparison to the application in two additional studies.

Framework to construct and interpret latent class trajectory modelling.

OBJECTIVES: Latent class trajectory modelling (LCTM) is a relatively new methodology in epidemiology to describe life-course exposures, which simplifies heterogeneous populations into homogeneous patterns or classes. However, for a given dataset, it is possible to derive scores of different models based on number of classes, model structure and trajectory property. Here, we rationalise a systematic framework to derive a 'core' favoured model. METHODS: We developed an eight-step framework: step 1: a scoping model; step 2: refining the number of classes; step 3: refining model structure (from fixed-effects through to a flexible random-effect specification); step 4: model adequacy assessment; step 5: graphical presentations; step 6: use of additional discrimination tools ('degree of separation'; Elsensohn's envelope of residual plots); step 7: clinical characterisation and plausibility; and step 8: sensitivity analysis. We illustrated these steps using data from the NIH-AARP cohort of repeated determinations of body mass index (BMI) at baseline (mean age: 62.5 years), and BMI derived by weight recall at ages 18, 35 and 50 years. RESULTS: From 288 993 participants, we derived a five-class model for each gender (men: 177 455; women: 111 538). From seven model structures, the favoured model was a proportional random quadratic structure (model F). Favourable properties were also noted for the unrestricted random quadratic structure (model G). However, class proportions varied considerably by model structure-concordance between models F and G were moderate (Cohen κ: men, 0.57; women, 0.65) but poor with other models. Model adequacy assessments, evaluations using discrimination tools, clinical plausibility and sensitivity analyses supported our model selection. CONCLUSION: We propose a framework to construct and select a 'core' LCTM, which will facilitate generalisability of results in future studies.