RESUMEN
BACKGROUND: Dietary intake has been suggested to be associated with the oral microbiome, but no study has examined the association between overall diet quality and the oral microbiome. OBJECTIVES: This study aimed to investigate the cross-sectional association between the Healthy Eating Index-2020 (HEI-2020) and the diversity and composition of the oral microbiome among participants in the Buffalo Osteoporosis and Periodontal Disease (OsteoPerio) Study. METHODS: In 1175 postmenopausal women (mean age: 67 ± 7.0 y), we estimated the HEI-2020 scores for each woman from a food frequency questionnaire administered from 1997 to 2000. Bacterial DNA was extracted from subgingival plaque samples and analyzed using 16S ribosomal RNA sequencing. The alpha-diversity (within-sample diversity) and ß-diversity (between-sample diversity) across HEI-2020 quartiles were examined using analysis of covariance and PERMANOVA, respectively. The associations between the HEI-2020 score and the relative abundance of microbial taxa were examined by linear regression models. The analyses were further conducted for individual components of the HEI-2020. RESULTS: No statistically significant associations were observed between the HEI-2020 scores and the alpha- or beta-diversity. However, greater consumption of seafood, plant proteins, and total protein and lower consumption of added sugars were positively associated with alpha-diversity. After we applied a false detection rate (FDR) correction, higher HEI-2020 scores were significantly associated with decreased abundance of Lautropia, Streptococcus gordonii, Cardiobacterium valvarum, and Cardiobacterium hominis, and increased abundance of Selenomonas sp. oral taxon 133 and Selenomonas dianae (FDR-adjusted P values < 0.10). Additionally, 28 other taxa were identified as being associated with HEI-2020 components. CONCLUSIONS: Although the HEI-2020 was associated with the composition, but not the diversity, of the oral microbiome, individual HEI-2020 components were associated with both its diversity and composition. Specific dietary components may have more impact on the diversity and composition of oral microbiome than overall diet quality assessed by the HEI-2020.
RESUMEN
BACKGROUND: Few standardized and open-source tools exist for calculating multiple dietary pattern indexes from dietary intake data in epidemiological and clinical studies. OBJECTIVES: The primary aim is to develop and validate dietaryindex, a user-friendly and versatile R package that standardizes the calculation of dietary indexes. METHODS: Dietaryindex can calculate multiple dietary indexes of high interest in research, including the Healthy Eating Index (HEI)-2020, the Alternative HEI 2010, the Dietary Approaches to Stop Hypertension Index, the Alternate Mediterranean Diet Score, the Dietary Inflammatory Index, the American Cancer Society 2020 dietary index, and the Planetary Health Diet Index from the EAT-Lancet Commission. The package includes generic dietary index calculation functions that accept any dietary assessment with preprocessed serving sizes of food groups and nutrients as defined by the research group that developed each index. For ease of use and to eliminate any need for data preprocessing, dietaryindex also offers 1-step functions that directly reference common datasets and tools, including the National Health and Nutrition Examination Survey (NHANES), the Automated Self-Administered 24-h Dietary Assessment Tool (ASA24), the Diet History Questionnaire III (DHQ3), and the Block Food Frequency Questionnaire. At least 2 independent researchers validated the serving size definitions and scoring algorithms of dietaryindex. RESULTS: In our validation process, dietaryindex demonstrated full accuracy (100%) in all generic functions with 2-decimal rounding precision when compared with hand-calculated results. Similarly, using NHANES 2017-2018 data and ASA24 and DHQ3 example data, the HEI-2015 outputs from dietaryindex aligned (99.95%-100%) with results using the Statistical Analysis System codes from the National Cancer Institute. CONCLUSIONS: Dietaryindex is a user-friendly, versatile, and validated informatics tool for standardized dietary index calculations. We have open-sourced all validation files and codes with detailed tutorials on GitHub (https://github.com/jamesjiadazhan/dietaryindex).
RESUMEN
Background: Few standardized and open-source tools exist for calculating dietary pattern indexes from dietary intake data in epidemiological and clinical studies. Miscalculations of dietary indexes, with suspected erroneous findings, are occasionally noted in the literature. Objective: The primary aim is to develop and validate dietaryindex, a user-friendly and versatile R package that standardizes the calculation of dietary indexes. Methods: Dietaryindex utilizes a two-step process: an initial calculation of serving size for each food and nutrient category, followed by the calculation of individual dietary indexes. It includes generic functions that accept any preprocessed serving sizes of food groups and nutrients, with the standard serving sizes defined according to the methodologies used in well-known prospective cohort studies. For ease of use, dietaryindex also offers one-step functions that directly reference common datasets and tools, including the National Health and Nutrition Examination Survey (NHANES) and Block Food Frequency Questionnaire, eliminating the need for data preprocessing. At least two independent researchers validated the serving size definitions and scoring algorithms of dietaryindex. Results: Dietaryindex can calculate multiple dietary indexes of high interest in research, including Healthy Eating Index (HEI) - 2020, Alternative Healthy Eating Index 2010, Dietary Approaches to Stop Hypertension Index, Alternate Mediterranean Diet Score, Dietary Inflammatory Index, American Cancer Society 2020 dietary index, and Planetary Health Diet Index from the EAT-Lancet Commission. In our validation process, dietaryindex demonstrated full accuracy (100%) in all generic functions with two-decimal rounding precision in comparison to hand-calculated results. Similarly, using NHANES 2017-2018 data and ASA24 and DHQ3 example data, the HEI2015 outputs from dietaryindex aligned (99.95%-100%) with results using the SAS codes from the National Cancer Institute. Conclusions: Dietaryindex is a user-friendly, versatile, and validated informatics tool for standardized dietary index calculations. We have open-sourced all the validation files and codes with detailed tutorials on GitHub (https://github.com/jamesjiadazhan/dietaryindex).