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© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/., In this study we examined the combined association of physical activity and sitting time with cardiometabolic risk factors in adults in Chile. This is a cross-sectional study based on 3201 adults aged from 18 to 98 years from the Chilean National Health Survey (2016-2017) who responded to the GPAQ questionnaire. Participants were considered inactive if spent < 600 METs-min/wk-1 in physical activity. High sitting time was defined as ≥ 8 h/day. We classified participants into the following 4 groups: active and low sitting time; active and high sitting time; inactive and low sitting time; inactive and high sitting time. The cardiometabolic risk factors considered were metabolic syndrome, body mass index, waist circumference, total cholesterol, and triglycerides. Multivariable logistic regression models were performed. Overall, 16.1% were classified as inactive and high sitting time. Compared to active participants with low sitting time, both inactive participants with low (OR: 1.51; 95% CI 1.10, 1.92) and high sitting time (1.66; 1.10, 2.22) had higher body mass index. Similar results were found for high waist circumference: inactive participants with low (1.57; 1.14, 2.00) and high sitting time (1.84; 1.25, 2.43). We found no combined association of physical activity and sitting time with metabolic syndrome, total cholesterol, and triglycerides. These findings may be useful to inform programs focused on obesity prevention in Chile.

Additional file 1: Supplementary Material A. World Cancer Research Fund/ American Institute for Cancer Research meta-analysis references. Supplementary Material B. Parameters considered in the macrosimulation model. Supplementary Material C. 10th revision of the International Statistical Classification of Diseases and Related Health Problems codes. Supplementary Material D. Meta-analysis of prostate (advanced) cancer by incidence as outcome. Supplementary Material E. Meta-analysis of ovary cancer by incidence as outcome. Supplementary Material F. Relative risk of excess body weight-associated cancers per exposition category and sex. Supplementary Material G. Hyperlinks to publicly archived datasets.

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Additional file 1: Figure S1. Formula for PAFs. Figure S2. Association of Adiposity markers with overall, liver, pancreas and colorectal cancer mortality. Figure S3. Association of Adiposity markers with colorectal cancer incidence. Figure S4. Association of Adiposity markers with colorectal cancer mortality. Figure S5. Association of Adiposity markers with gallbladder and stomach cancer mortality. Figure S6. Association of Adiposity markers oesophagus, oral and lung cancer mortality in no smokers. Figure S7. Association of Adiposity markers with lymphatic cancer mortality. Figure S8. Association of Adiposity markers with uterine, endometrial, ovary and cervical cancer incidence adjusted. Figure S9. Association of Adiposity markers with uterine, endometrial, ovary and cervical cancer mortality. Figure S10. Association of Adiposity markers with prostate, testicular cancer in men and breast cancer in postmenopausal women mortality. Figure S11. Association of adiposity markers with prostate, testicular, and breast cancer incidence additionally adjusted for sex-related covariates. Figure S12. Association of adiposity markers with brain, melanoma, thyroid, bladder and kidney cancer mortality. Figure S13. Association of Adiposity markers with overall, liver, pancreas, colorectal cancer and stomach cardia incidence with underweight people. Figure S14. Association of Adiposity markers with gallbladder, bladder, kidney, breast and endometrium cancer incidence with underweight people. Table S1. Association of adiposity markers with incidence from 24 cancer sites per 1 SD increase in adiposity markers. Table S2. Association of adiposity markers with mortality from 24 cancer sites per 1 SD increase in adiposity markers. Table S3. C-Index for the predictive ability of BMI versus other adiposity markers. Table S4. Association of adiposity markers with incidence from 24 cancer sites after accounting for competing risk. Table S5. Association of adiposity markers with incidence from 24 cancer sites per 1 SD increase in adiposity markers with height as covariate. Table S6. Association of adiposity markers with mortality from 24 cancer sites per 1 SD increase in adiposity markers with height as covariate.

Multiple studies have suggested that ω-3 fatty acid intake may have a protective effect on cancer risk; however, its true association with cancer risk remains controversial. We performed an umbrella review of meta-analyses to summarize and evaluate the evidence for the association between ω-3 fatty acid intake and cancer outcomes. We searched PubMed, Embase, and the Cochrane Database of Systematic Reviews from inception to December 1, 2018. We included meta-analyses of observational studies that examined associations between intake of fish or ω-3 fatty acid and cancer risk (gastrointestinal, liver, breast, gynecologic, prostate, brain, lung, and skin) and determined the level of evidence of associations. In addition, we appraised the quality of the evidence of significant meta-analyses by using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We initially screened 598 articles, and 15 articles, including 57 meta-analyses, were eligible. Among 57 meta-analyses, 15 reported statistically significant results. We found that 12 meta-analyses showed weak evidence of an association between ω-3 fatty acid intake and risk of the following types of cancer: liver cancer (n = 4 of 6), breast cancer (n = 3 of 14), prostate cancer (n = 3 of 11), and brain tumor (n = 2 of 2). In the other 3 meta-analyses, studies of endometrial cancer and skin cancer, there were no assessable data for determining the evidence levels. No meta-analysis showed convincing, highly suggestive, or suggestive evidence of an association. In the sensitivity analysis of meta-analyses by study design, we found weak associations between ω-3 fatty acid intake and breast cancer risk in cohort studies, but no statistically significant association in case-control studies. However, the opposite results were found in case of brain tumor risk. Although ω-3 fatty acids have been studied in several meta-analyses with regard to a wide range of cancer outcomes, only weak associations were identified in some cancer types, with several limitations. Considering the nonsignificant or weak evidence level, clinicians and researchers should cautiously interpret reported associations between ω-3 fatty acid consumption and cancer risks.