The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Milk and dairy consumption and risk of cardiovascular diseases and all-cause mortality: dose–response meta-analysis of prospective cohort studies
Open accessAbstract
With a growing number of prospective cohort studies, an updated dose–response meta-analysis of milk and dairy products with all-cause mortality, coronary heart disease (CHD) or cardiovascular disease (CVD) have been conducted. PubMed, Embase and Scopus were searched for articles published up to September 2016. Random-effect meta-analyses with summarised dose–response data were performed for total (high-fat/low-fat) dairy, milk, fermented dairy, cheese and yogurt. Non-linear associations were investigated using the spine models and heterogeneity by subgroup analyses. A total of 29 cohort studies were available for meta-analysis, with 938,465 participants and 93,158 mortality, 28,419 CHD and 25,416 CVD cases. No associations were found for total (high-fat/low-fat) dairy, and milk with the health outcomes of mortality, CHD or CVD. Inverse associations were found between total fermented dairy (included sour milk products, cheese or yogurt; per 20 g/day) with mortality (RR 0.98, 95% CI 0.97–0.99; I 2 = 94.4%) and CVD risk (RR 0.98, 95% CI 0.97–0.99; I 2 = 87.5%). Further analyses of individual fermented dairy of cheese and yogurt showed cheese to have a 2% lower risk of CVD (RR 0.98, 95% CI 0.95–1.00; I 2 = 82.6%) per 10 g/day, but not yogurt. All of these marginally inverse associations of totally fermented dairy and cheese were attenuated in sensitivity analyses by removing one large Swedish study. This meta-analysis combining data from 29 prospective cohort studies demonstrated neutral associations between dairy products and cardiovascular and all-cause mortality. For future studies it is important to investigate in more detail how dairy products can be replaced by other foods.
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Cites
Quantifying heterogeneity in a meta‐analysis
Higgins, J. P. T., & Thompson, S. G. (2002) · Statistics in Medicine · doi:10.1002/sim.1186 · Cited by 36,763
Meta-analysis of Observational Studies in Epidemiology<SUBTITLE>A Proposal for Reporting</SUBTITLE>
Stroup, D. F. (2000) · JAMA · doi:10.1001/jama.283.15.2008 · Cited by 20,623
Mozaffarian, D. (2016) · Circulation · doi:10.1161/circulationaha.115.018585 · Cited by 2,216 · cited 1×
“Together with smoking, obesity and inactivity, diet is considered to be one of the most important prevention strategies for CVD [2].”
Micha, R., Wallace, S. K., & Mozaffarian, D. (2010) · Circulation · doi:10.1161/circulationaha.109.924977 · Cited by 1,244
Added sugar intake and cardiovascular diseases mortality among US adults
Yang, Q., Zhang, Z., Gregg, E. W., Flanders, W. D., Merritt, R., & Hu, F. B. (2014) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2013.13563 · Cited by 947 · cited 1×
“…with mortality and CVD risk could be because of replacement by other foods, for example, those with high intake of dairy products may consume less sugar sweetened beverages which could lead to lower CVD mortality [66] or consume more processed meat which could lead to higher CVD risks [67, 68].”
Hu, F. B., Stampfer, M. J., Manson, J. E., Ascherio, A., Colditz, G. A., Speizer, F. E., Hennekens, C. H., & Willett, W. C. (1999) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/70.6.1001 · Cited by 661 · cited 2×
“A total of 6 studies presented sex-specific results, 3 studies were in men [18–20] and 3 in women [15, 16, 30].”
Red and processed meat consumption and risk of stroke: a meta-analysis of prospective cohort studies
Chen, G.-C., Lv, D.-B., Pang, Z., & Liu, Q.-F. (2013) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2012.180 · Cited by 184
Sonestedt, E., Wirfält, E., Wallström, P., Gullberg, B., Orho-Melander, M., & Hedblad, B. (2011) · European Journal of Epidemiology · doi:10.1007/s10654-011-9589-y · Cited by 171
Haring, B., Gronroos, N., Nettleton, J. A., Wyler von Ballmoos, M. C., Selvin, E., & Alonso, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0109552 · Cited by 82
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Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems
Willett, W., Rockström, J., Loken, B., Springmann, M., Lang, T., Vermeulen, S., Garnett, T., Tilman, D., DeClerck, F., Wood, A., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(18)31788-4 · Cited by 10,880
Zhang, X., Chen, X., Xu, Y., Yang, J., Du, L., Li, K., & Zhou, Y. (2021) · Nutrition & Metabolism · doi:10.1186/s12986-020-00527-y · Cited by 195
Eating Fermented: Health Benefits of LAB-Fermented Foods
Castellone, V., Bancalari, E., Rubert, J., Gatti, M., Neviani, E., & Bottari, B. (2021) · Foods · doi:10.3390/foods10112639 · Cited by 178
Soedamah-Muthu, S. S., & de Goede, J. (2018) · Current Nutrition Reports · doi:10.1007/s13668-018-0253-y · Cited by 162
Self-citationFermented food and non-communicable chronic diseases: a review
Gille, D., Schmid, A., Walther, B., & Vergères, G. (2018) · Nutrients · doi:10.3390/nu10040448 · Cited by 142
Metadata
- Authors
- Guo, J., Astrup, A., Lovegrove, J. A., Gijsbers, L., Givens, D. I., & Soedamah-Muthu, S. S.
- Venue
- European Journal of Epidemiology
- Published
- 2017-04
- Volume
- 32
- Issue
- 4
- Pages
- 269-287
- ISSN
- 0393-2990 (ISSN-L)
- 1573-7284
- OpenAlex
- W2604851700
- Cited by
- 387
- References
- 9
- FWCI
- 8.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
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