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| 024 | 7 | _ | |a 10.1002/cam4.5896 |2 doi |
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| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Mahamat-Saleh, Y. |0 0000-0002-5892-8886 |b 0 |
| 245 | _ | _ | |a Metabolically defined body size and body shape phenotypes and risk of postmenopausal breast cancer in the European Prospective Investigation into Cancer and Nutrition. |
| 260 | _ | _ | |a Hoboken, NJ |c 2023 |b Wiley |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1687349954_6283 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a 2023 Jun;12(11):12668-12682 |
| 520 | _ | _ | |a Excess body fatness and hyperinsulinemia are both associated with an increased risk of postmenopausal breast cancer. However, whether women with high body fatness but normal insulin levels or those with normal body fatness and high levels of insulin are at elevated risk of breast cancer is not known. We investigated the associations of metabolically defined body size and shape phenotypes with the risk of postmenopausal breast cancer in a nested case-control study within the European Prospective Investigation into Cancer and Nutrition.Concentrations of C-peptide-a marker for insulin secretion-were measured at inclusion prior to cancer diagnosis in serum from 610 incident postmenopausal breast cancer cases and 1130 matched controls. C-peptide concentrations among the control participants were used to define metabolically healthy (MH; in first tertile) and metabolically unhealthy (MU; >1st tertile) status. We created four metabolic health/body size phenotype categories by combining the metabolic health definitions with normal weight (NW; BMI < 25 kg/m2 , or WC < 80 cm, or WHR < 0.8) and overweight or obese (OW/OB; BMI ≥ 25 kg/m2 , or WC ≥ 80 cm, or WHR ≥ 0.8) status for each of the three anthropometric measures separately: (1) MHNW, (2) MHOW/OB, (3) MUNW, and (4) MUOW/OB. Conditional logistic regression was used to compute odds ratios (ORs) and 95% confidence intervals (CIs).Women classified as MUOW/OB were at higher risk of postmenopausal breast cancer compared to MHNW women considering BMI (OR = 1.58, 95% CI = 1.14-2.19) and WC (OR = 1.51, 95% CI = 1.09-2.08) cut points and there was also a suggestive increased risk for the WHR (OR = 1.29, 95% CI = 0.94-1.77) definition. Conversely, women with the MHOW/OB and MUNW were not at statistically significant elevated risk of postmenopausal breast cancer risk compared to MHNW women.These findings suggest that being overweight or obese and metabolically unhealthy raises risk of postmenopausal breast cancer while overweight or obese women with normal insulin levels are not at higher risk. Additional research should consider the combined utility of anthropometric measures with metabolic parameters in predicting breast cancer risk. |
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| 650 | _ | 7 | |a body mass index |2 Other |
| 650 | _ | 7 | |a breast cancer |2 Other |
| 650 | _ | 7 | |a concentrations of C-peptide |2 Other |
| 650 | _ | 7 | |a metabolic health |2 Other |
| 650 | _ | 7 | |a waist circumference |2 Other |
| 650 | _ | 7 | |a waist-to-hip ratio |2 Other |
| 700 | 1 | _ | |a Rinaldi, S. |0 0000-0002-6846-1204 |b 1 |
| 700 | 1 | _ | |a Kaaks, R. |0 P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a |b 2 |u dkfz |
| 700 | 1 | _ | |a Biessy, C. |b 3 |
| 700 | 1 | _ | |a Gonzalez-Gil, E. M. |b 4 |
| 700 | 1 | _ | |a Murphy, N. |b 5 |
| 700 | 1 | _ | |a Le Cornet, Charlotte |0 P:(DE-He78)2d4cac0f4bf270325b98fe07036dc6c6 |b 6 |u dkfz |
| 700 | 1 | _ | |a Huerta, J. M. |0 0000-0002-9637-3869 |b 7 |
| 700 | 1 | _ | |a Sieri, S. |b 8 |
| 700 | 1 | _ | |a Tjønneland, A. |b 9 |
| 700 | 1 | _ | |a Mellemkjaer, L. |0 0000-0001-9222-6215 |b 10 |
| 700 | 1 | _ | |a Guevara, M. |b 11 |
| 700 | 1 | _ | |a Overvad, K. |b 12 |
| 700 | 1 | _ | |a Perez-Cornago, A. |0 0000-0002-5652-356X |b 13 |
| 700 | 1 | _ | |a Tin Tin, S. |0 0000-0002-7303-061X |b 14 |
| 700 | 1 | _ | |a Padroni, L. |b 15 |
| 700 | 1 | _ | |a Simeon, V. |b 16 |
| 700 | 1 | _ | |a Masala, G. |b 17 |
| 700 | 1 | _ | |a May, A. |b 18 |
| 700 | 1 | _ | |a Monninkhof, E. |b 19 |
| 700 | 1 | _ | |a Christakoudi, S. |0 0000-0001-9219-4436 |b 20 |
| 700 | 1 | _ | |a Heath, A. K. |0 0000-0001-6517-1300 |b 21 |
| 700 | 1 | _ | |a Tsilidis, K. |0 0000-0002-8452-8472 |b 22 |
| 700 | 1 | _ | |a Agudo, A. |b 23 |
| 700 | 1 | _ | |a Schulze, M. B. |b 24 |
| 700 | 1 | _ | |a Rothwell, J. |b 25 |
| 700 | 1 | _ | |a Cadeau, C. |b 26 |
| 700 | 1 | _ | |a Severi, S. |0 0000-0001-7157-419X |b 27 |
| 700 | 1 | _ | |a Weiderpass, E. |0 0000-0003-2237-0128 |b 28 |
| 700 | 1 | _ | |a Gunter, M. J. |b 29 |
| 700 | 1 | _ | |a Dossus, L. |b 30 |
| 773 | _ | _ | |a 10.1002/cam4.5896 |g p. cam4.5896 |0 PERI:(DE-600)2659751-2 |n 11 |p 12668-12682 |t Cancer medicine |v 12 |y 2023 |x 2045-7634 |
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