BACKGROUND: Diet is a key modifiable risk for many chronic diseases, but it remains unclear whether dietary patterns from one study sample are generalizable to other independent populations. OBJECTIVE: The primary objective of this study was to assess whether data-driven dietary patterns from one study sample are applicable to other populations. The secondary objective was to assess the validity of two criteria of pattern similarity. METHODS: Six dietary patterns-Western (n=3), Mediterranean, Prudent, and Healthy- from three published studies on breast cancer were reconstructed in a case-control study of 973 breast cancer patients and 973 controls. Three more internal patterns (Western, Prudent, and Mediterranean) were derived from this case-control study's own data. STATISTICAL ANALYSIS: Applicability was assessed by comparing the six reconstructed patterns with the three internal dietary patterns, using the congruence coefficient (CC) between pattern loadings. In cases where any pair met either of two commonly used criteria for declaring patterns similar (CC ≥0.85 or a statistically significant [P<0.05] Pearson correlation), then the true similarity of those two dietary patterns was double-checked by comparing their associations to risk for breast cancer, to assess whether those two criteria of similarity are actually reliable. RESULTS: Five of the six reconstructed dietary patterns showed high congruence (CC >0.9) to their corresponding dietary pattern derived from the case-control study's data. Similar associations with risk for breast cancer were found in all pairs of dietary patterns that had high CC but not in all pairs of dietary patterns with statistically significant correlations. CONCLUSIONS: Similar dietary patterns can be found in independent samples. The P value of a correlation coefficient is less reliable than the CC as a criterion for declaring two dietary patterns similar. This study shows that diet scores based on a particular study are generalizable to other populations.
BACKGROUND: Diet is a key modifiable risk for many chronic diseases, but it remains unclear whether dietary patterns from one study sample are generalizable to other independent populations. OBJECTIVE: The primary objective of this study was to assess whether data-driven dietary patterns from one study sample are applicable to other populations. The secondary objective was to assess the validity of two criteria of pattern similarity. METHODS: Six dietary patterns-Western (n=3), Mediterranean, Prudent, and Healthy- from three published studies on breast cancer were reconstructed in a case-control study of 973 breast cancerpatients and 973 controls. Three more internal patterns (Western, Prudent, and Mediterranean) were derived from this case-control study's own data. STATISTICAL ANALYSIS: Applicability was assessed by comparing the six reconstructed patterns with the three internal dietary patterns, using the congruence coefficient (CC) between pattern loadings. In cases where any pair met either of two commonly used criteria for declaring patterns similar (CC ≥0.85 or a statistically significant [P<0.05] Pearson correlation), then the true similarity of those two dietary patterns was double-checked by comparing their associations to risk for breast cancer, to assess whether those two criteria of similarity are actually reliable. RESULTS: Five of the six reconstructed dietary patterns showed high congruence (CC >0.9) to their corresponding dietary pattern derived from the case-control study's data. Similar associations with risk for breast cancer were found in all pairs of dietary patterns that had high CC but not in all pairs of dietary patterns with statistically significant correlations. CONCLUSIONS: Similar dietary patterns can be found in independent samples. The P value of a correlation coefficient is less reliable than the CC as a criterion for declaring two dietary patterns similar. This study shows that diet scores based on a particular study are generalizable to other populations.
Authors: R De Vito; Yuan Chin Amy Lee; M Parpinel; D Serraino; Andrew Fergus Olshan; Jose Pedro Zevallos; F Levi; Zhuo Feng Zhang; H Morgenstern; W Garavello; K Kelsey; M McClean; S Schantz; Guo Pei Yu; P Boffetta; Shu Chun Chuang; M Hashibe; C La Vecchia; G Parmigiani; V Edefonti Journal: Epidemiology Date: 2019-01 Impact factor: 4.822