88 datasets found
  1. i

    Chronotype in Carbohydrates intake and cardiovascular risk

    • ieee-dataport.org
    Updated Jan 30, 2020
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    Victorine Raissa Nkondjock (2020). Chronotype in Carbohydrates intake and cardiovascular risk [Dataset]. https://ieee-dataport.org/documents/chronotype-carbohydrates-intake-and-cardiovascular-risk-nhanes-2015
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    Dataset updated
    Jan 30, 2020
    Authors
    Victorine Raissa Nkondjock
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    HSCRP records from 5

  2. A

    ‘nhanes_2015_2016’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Feb 14, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘nhanes_2015_2016’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-nhanes-2015-2016-4888/ffed9194/?iid=024-118&v=presentation
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    Dataset updated
    Feb 14, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘nhanes_2015_2016’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ramendrapandey/nhanes-2015-2016 on 14 February 2022.

    --- No further description of dataset provided by original source ---

    --- Original source retains full ownership of the source dataset ---

  3. Z

    Inter-Chemical Correlation results for the study: NHANES20152016 (NHANES...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Oct 17, 2024
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    Sili Fan (2024). Inter-Chemical Correlation results for the study: NHANES20152016 (NHANES Survey 2015-2016) [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_11319110
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    Dataset updated
    Oct 17, 2024
    Dataset provided by
    Sili Fan
    Dinesh Barupal
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Title: NHANES Survey 2015-2016 Species: Homo sapiens Number of samples: 9170 Number of named analytes: 341 Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory

  4. d

    Data from: USDA National Nutrient Database for Standard Reference Dataset...

    • catalog.data.gov
    • agdatacommons.nal.usda.gov
    • +3more
    Updated Apr 21, 2025
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    Agricultural Research Service (2025). USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES (Survey-SR) [Dataset]. https://catalog.data.gov/dataset/usda-national-nutrient-database-for-standard-reference-dataset-for-what-we-eat-in-america--37895
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    Dataset updated
    Apr 21, 2025
    Dataset provided by
    Agricultural Research Service
    Area covered
    United States
    Description

    The dataset, Survey-SR, provides the nutrient data for assessing dietary intakes from the national survey What We Eat In America, National Health and Nutrition Examination Survey (WWEIA, NHANES). Historically, USDA databases have been used for national nutrition monitoring (1). Currently, the Food and Nutrient Database for Dietary Studies (FNDDS) (2), is used by Food Surveys Research Group, ARS, to process dietary intake data from WWEIA, NHANES. Nutrient values for FNDDS are based on Survey-SR. Survey-SR was referred to as the "Primary Data Set" in older publications. Early versions of the dataset were composed mainly of commodity-type items such as wheat flour, sugar, milk, etc. However, with increased consumption of commercial processed and restaurant foods and changes in how national nutrition monitoring data are used (1), many commercial processed and restaurant items have been added to Survey-SR. The current version, Survey-SR 2013-2014, is mainly based on the USDA National Nutrient Database for Standard Reference (SR) 28 (2) and contains sixty-six nutrientseach for 3,404 foods. These nutrient data will be used for assessing intake data from WWEIA, NHANES 2013-2014. Nutrient profiles were added for 265 new foods and updated for about 500 foods from the version used for the previous survey (WWEIA, NHANES 2011-12). New foods added include mainly commercially processed foods such as several gluten-free products, milk substitutes, sauces and condiments such as sriracha, pesto and wasabi, Greek yogurt, breakfast cereals, low-sodium meat products, whole grain pastas and baked products, and several beverages including bottled tea and coffee, coconut water, malt beverages, hard cider, fruit-flavored drinks, fortified fruit juices and fruit and/or vegetable smoothies. Several school lunch pizzas and chicken products, fast-food sandwiches, and new beef cuts were also added, as they are now reported more frequently by survey respondents. Nutrient profiles were updated for several commonly consumed foods such as cheddar, mozzarella and American cheese, ground beef, butter, and catsup. The changes in nutrient values may be due to reformulations in products, changes in the market shares of brands, or more accurate data. Examples of more accurate data include analytical data, market share data, and data from a nationally representative sample. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES 2013-14 (Survey SR 2013-14). File Name: SurveySR_2013_14 (1).zipResource Description: Access database downloaded on November 16, 2017. US Department of Agriculture, Agricultural Research Service, Nutrient Data Laboratory. USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES (Survey-SR), October 2015. Resource Title: Data Dictionary. File Name: SurveySR_DD.pdf

  5. Prevalence of Selected Measures Among Adults Aged 20 and Over: United...

    • catalog.data.gov
    • healthdata.gov
    • +2more
    Updated Apr 23, 2025
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    Centers for Disease Control and Prevention (2025). Prevalence of Selected Measures Among Adults Aged 20 and Over: United States, 1999-2000 through 2017-2018 [Dataset]. https://catalog.data.gov/dataset/prevalence-of-selected-measures-among-adults-aged-20-and-over-united-states-1999-2000-2017-42e36
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    Dataset updated
    Apr 23, 2025
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Area covered
    United States
    Description

    This data represents the age-adjusted prevalence of high total cholesterol, hypertension, and obesity among US adults aged 20 and over between 1999-2000 to 2017-2018. Notes: All estimates are age adjusted by the direct method to the U.S. Census 2000 population using age groups 20–39, 40–59, and 60 and over. Definitions Hypertension: Systolic blood pressure greater than or equal to 130 mmHg or diastolic blood pressure greater than or equal to 80 mmHg, or currently taking medication to lower high blood pressure High total cholesterol: Serum total cholesterol greater than or equal to 240 mg/dL. Obesity: Body mass index (BMI, weight in kilograms divided by height in meters squared) greater than or equal to 30. Data Source and Methods Data from the National Health and Nutrition Examination Surveys (NHANES) for the years 1999–2000, 2001–2002, 2003–2004, 2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018 were used for these analyses. NHANES is a cross-sectional survey designed to monitor the health and nutritional status of the civilian noninstitutionalized U.S. population. The survey consists of interviews conducted in participants’ homes and standardized physical examinations, including a blood draw, conducted in mobile examination centers.

  6. f

    Additional file 2 of Perfluoroalkyl and polyfluoroalkyl substance exposure...

    • springernature.figshare.com
    xls
    Updated Feb 7, 2024
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    Xin Xie; Xueqiong Weng; Shan Liu; Jingmin Chen; Xinrong Guo; Xinyu Gao; Qiaoyuan Fei; Guang Hao; Chunxia Jing; Liping Feng (2024). Additional file 2 of Perfluoroalkyl and polyfluoroalkyl substance exposure and association with sex hormone concentrations: results from the NHANES 2015–2016 [Dataset]. http://doi.org/10.6084/m9.figshare.14741200.v1
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    xlsAvailable download formats
    Dataset updated
    Feb 7, 2024
    Dataset provided by
    figshare
    Authors
    Xin Xie; Xueqiong Weng; Shan Liu; Jingmin Chen; Xinrong Guo; Xinyu Gao; Qiaoyuan Fei; Guang Hao; Chunxia Jing; Liping Feng
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Additional file 2. Additional tables.

  7. f

    Data_Sheet_1_Association Between the Children's Dietary Inflammatory Index...

    • frontiersin.figshare.com
    docx
    Updated Jun 6, 2023
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    Chuang Zhang; Weirui Ren; Meng Li; Wenbo Wang; Chi Sun; Lin Liu; Yanbin Fang; Xiaofeng Yang; Xiangjian Zhang; Suolin Li (2023). Data_Sheet_1_Association Between the Children's Dietary Inflammatory Index (C-DII) and Markers of Inflammation and Oxidative Stress Among Children and Adolescents: NHANES 2015-2018.DOCX [Dataset]. http://doi.org/10.3389/fnut.2022.894966.s001
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    docxAvailable download formats
    Dataset updated
    Jun 6, 2023
    Dataset provided by
    Frontiers
    Authors
    Chuang Zhang; Weirui Ren; Meng Li; Wenbo Wang; Chi Sun; Lin Liu; Yanbin Fang; Xiaofeng Yang; Xiangjian Zhang; Suolin Li
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    ObjectivesTo explore the association of Children's Dietary Inflammatory Index (C-DII) scores with inflammation and markers of inflammatory factors in children and adolescents.MethodsData on dietary nutrient intake, markers of inflammation (ferritin, alkaline phosphatase, C-reactive protein (CRP), absolute neutrophil cell count and lymphocyte count) and oxidative stress (serum bilirubin, albumin, and iron) were available for participants aged 6–19 years (n = 1281). Each participant's C-DII score was calculated based on a 24-h diet and recall. Generalized linear models were applied to examine associations between C-DII and markers of inflammation and oxidative stress, while adjusting for covariates. Restricted cubic splines were used to explore the dose-response association of C-DII scores with indicators of inflammatory oxidative stress. Akaike's Information Criterionwas applied to compare the performance of linear and non-linear models.ResultsAfter adjusting for potential confounders, quantile regression results showed that when comparing C-DII quartile 4 (most pro-inflammatory) and quartile 1 (most anti-inflammatory), lymphocytes, ferritin, CRP were statistically significant differences in serum bilirubin, albumin and serum iron (P < 0.05). The C-DII score showed a non-linear relationship with inflammatory oxidative stress indicators. Overweight/obese children and adolescents who ate a high pro-inflammatory diet were more likely to have higher levels of inflammatory cytokines (P = 0.002).ConclusionsThe dietary inflammatory index in children is associated with markers of chronic inflammation and oxidative stress. A pro-inflammatory diet resulted in increased serum concentrations of these markers, implying that early dietary interventions have implications for reducing chronic inflammation and oxidative stress in children and adolescents.

  8. f

    Trends in food insecurity for adults with cardiometabolic disease in the...

    • plos.figshare.com
    • figshare.com
    docx
    Updated May 30, 2023
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    Seth A. Berkowitz; Theodore S. Z. Berkowitz; James B. Meigs; Deborah J. Wexler (2023). Trends in food insecurity for adults with cardiometabolic disease in the United States: 2005-2012 [Dataset]. http://doi.org/10.1371/journal.pone.0179172
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    docxAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Seth A. Berkowitz; Theodore S. Z. Berkowitz; James B. Meigs; Deborah J. Wexler
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundFood insecurity, the uncertain ability to access adequate food, can limit adherence to dietary measures needed to prevent and manage cardiometabolic conditions. However, little is known about temporal trends in food insecurity among those with diet-sensitive cardiometabolic conditions.MethodsWe used data from the Continuous National Health and Nutrition Examination Survey (NHANES) 2005–2012, analyzed in 2015–2016, to calculate trends in age-standardized rates of food insecurity for those with and without the following diet-sensitive cardiometabolic conditions: diabetes mellitus, hypertension, coronary heart disease, congestive heart failure, and obesity.Results21,196 NHANES participants were included from 4 waves (4,408 in 2005–2006, 5,607 in 2007–2008, 5,934 in 2009–2010, and 5,247 in 2011–2012). 56.2% had at least one cardiometabolic condition, 24.4% had 2 or more, and 8.5% had 3 or more. The overall age-standardized rate of food insecurity doubled during the study period, from 9.06% in 2005–2006 to 10.82% in 2007–2008 to 15.22% in 2009–2010 to 18.33% in 2011–2012 (p for trend < .001). The average annual percentage change in food insecurity for those with a cardiometabolic condition during the study period was 13.0% (95% CI 7.5% to 18.6%), compared with 5.8% (95% CI 1.8% to 10.0%) for those without a cardiometabolic condition, (parallelism test p = .13). Comparing those with and without the condition, age-standardized rates of food insecurity were greater in participants with diabetes (19.5% vs. 11.5%, p < .0001), hypertension (14.1% vs. 11.1%, p = .0003), coronary heart disease (20.5% vs. 11.9%, p < .001), congestive heart failure (18.4% vs. 12.1%, p = .004), and obesity (14.3% vs. 11.1%, p < .001).ConclusionsFood insecurity doubled to historic highs from 2005–2012, particularly affecting those with diet-sensitive cardiometabolic conditions. Since adherence to specific dietary recommendations is a foundation of the prevention and treatment of cardiometabolic disease, these results have important implications for clinical management and public health.

  9. f

    Table 4_Positive association between serum lactate dehydrogenase levels and...

    • frontiersin.figshare.com
    docx
    Updated Feb 28, 2025
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    Tao Hu; Linfeng Li; Qiqiang Cao; Weiling Tu; XianTao Huang; Tan Yuan (2025). Table 4_Positive association between serum lactate dehydrogenase levels and blood pressure: evidence from NHANES 2015–2016.docx [Dataset]. http://doi.org/10.3389/fcvm.2025.1554702.s006
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    docxAvailable download formats
    Dataset updated
    Feb 28, 2025
    Dataset provided by
    Frontiers
    Authors
    Tao Hu; Linfeng Li; Qiqiang Cao; Weiling Tu; XianTao Huang; Tan Yuan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundSerum lactate dehydrogenase (sLDH) is an enzyme implicated in tissue injury and inflammatory responses. Despite its established role in these pathophysiological processes, the association between sLDH and blood pressure remains underexplored. The present findings suggest that sLDH could emerge as a valuable biomarker for blood pressure regulation and may hold significant promise in the management of hypertension.MethodsOur investigation utilized data from the National Health and Nutrition Examination Survey (NHANES) 2015–2016, comprising 3,469 participants after excluding those under the age of 20, individuals on antihypertensive therapies, and cases with incomplete data. sLDH levels were categorized into tertiles, while blood pressure measurements were conducted under standardized protocols. To elucidate the relationship between sLDH levels and blood pressure, multivariate regression analyses and smooth curve fitting techniques were employed, adjusting for 17 covariates, including age, sex, and body mass index.ResultssLDH corresponds with both systolic blood pressure (SBP) and diastolic blood pressure (DBP). The adjusted smooth curve fitting diagram demonstrates a linear positive connection between sLDH and SBP, with an increment of 0.053 mmHg (95% CI: 0.032, 0.074; p 

  10. D

    Linkage file: LAFA, NHANES, and FCID

    • lifesciences.datastations.nl
    pdf, tsv, zip
    Updated Mar 19, 2018
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    ZC Conrad; ZC Conrad (2018). Linkage file: LAFA, NHANES, and FCID [Dataset]. http://doi.org/10.17026/DANS-X9Y-879T
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    pdf(409621), pdf(54600), pdf(37671), zip(15560), pdf(57883), tsv(295), tsv(371990)Available download formats
    Dataset updated
    Mar 19, 2018
    Dataset provided by
    DANS Data Station Life Sciences
    Authors
    ZC Conrad; ZC Conrad
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Improving diet quality while simultaneously reducing environmental impact is a critical focus globally. Metrics linking diet quality and sustainability have typically focused on a limited suite of indicators, and have not included food waste. To address this important research gap, we examine the relationship between food waste, diet quality, nutrient waste, and multiple measures of sustainability: use of cropland, irrigation water, pesticides, and fertilizers. Data on food intake, food waste, and application rates of agricultural amendments were collected from diverse US government sources. Diet quality was assessed using the Healthy Eating Index-2015. A biophysical simulation model was used to estimate the amount of cropland associated with wasted food. This analysis finds that US consumers wasted 422g of food per person daily, with 30 million acres of cropland used to produce this food every year. This accounts for 30% of daily calories available for consumption, one-quarter of daily food (by weight) available for consumption, and 7% of annual cropland acreage. Higher quality diets were associated with greater amounts of food waste and greater amounts of wasted irrigation water and pesticides, but less cropland waste. This is largely due to fruits and vegetables, which are health-promoting and require small amounts of cropland, but require substantial amounts of agricultural inputs. These results suggest that simultaneous efforts to improve diet quality and reduce food waste are necessary.. Increasing consumers’ knowledge about how to prepare and store fruits and vegetables will be one of the practical solutions to reducing food waste. Relationship between food waste, diet quality, and environmental sustainability

  11. f

    Basic characteristics of participants aged ≥20 y in the Japanese National...

    • plos.figshare.com
    xls
    Updated Jun 4, 2023
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    Kentaro Murakami; M. Barbara E. Livingstone; Aya Fujiwara; Satoshi Sasaki (2023). Basic characteristics of participants aged ≥20 y in the Japanese National Health and Nutrition Survey 2012 (n = 19717) and US NHANES 2011–2012 (n = 4614)1. [Dataset]. http://doi.org/10.1371/journal.pone.0228318.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Kentaro Murakami; M. Barbara E. Livingstone; Aya Fujiwara; Satoshi Sasaki
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Basic characteristics of participants aged ≥20 y in the Japanese National Health and Nutrition Survey 2012 (n = 19717) and US NHANES 2011–2012 (n = 4614)1.

  12. f

    Table_1_Association between triglyceride glucose-body mass index and...

    • frontiersin.figshare.com
    pdf
    Updated Aug 16, 2024
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    Xingru Meng; Haihua Wen; Leshen Lian (2024). Table_1_Association between triglyceride glucose-body mass index and obstructive sleep apnea: a study from NHANES 2015–2018.pdf [Dataset]. http://doi.org/10.3389/fnut.2024.1424881.s001
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    pdfAvailable download formats
    Dataset updated
    Aug 16, 2024
    Dataset provided by
    Frontiers
    Authors
    Xingru Meng; Haihua Wen; Leshen Lian
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundThe association between TyG-BMI index and the risk of obstructive sleep apnea (OSA), a recently identified biomarker indicating insulin resistance, has yet to be elucidated. Therefore, this study aimed to investigate the association between TyG-BMI index and the risk of OSA using the NHANES database.MethodsAnalyses were performed on NHANES data conducted between 2015 and 2018. Logistic regression, stratified analyses, curve-fitting analyses, and threshold effects analyses were utilized to assess the association between TyG-BMI index and the risk of OSA.ResultsThe study included 4,588 participants. Multifactorial logistic regression analyses found a significant association between TyG-BMI and increased risk of OSA [OR: 1.54 (CI:1.39–1.70)]. In stratified analyses, age interacted with the association, with TyG-BMI being associated with increased risk of OSA only in a subgroup of subjects younger than 60 years [1.31 (1.14–1.50)], but gender, smoking status, and alcohol use, did not influence the association. The presence of diabetes, hypertension, and cardiovascular diseases also modified the association, but the number of the included subjects with such conditions was significantly lower, therefore the significance of associations was not observed in those subgroups. Additionally, the risk was non-linearly associated, with the inflection point of TyG-BMI at 12.09, after which the lower slope in the risk was observed.ConclusionThis study demonstrates that elevated levels of the TyG-BMI index are correlated with risk for OSA, underscoring the significance of these findings in facilitating early prevention or timely intervention for OSA.

  13. f

    Application of the Healthy Eating Index-2015 and the Nutrient-Rich Food...

    • plos.figshare.com
    docx
    Updated Jun 2, 2023
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    Kentaro Murakami; M. Barbara E. Livingstone; Aya Fujiwara; Satoshi Sasaki (2023). Application of the Healthy Eating Index-2015 and the Nutrient-Rich Food Index 9.3 for assessing overall diet quality in the Japanese context: Different nutritional concerns from the US [Dataset]. http://doi.org/10.1371/journal.pone.0228318
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    docxAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Kentaro Murakami; M. Barbara E. Livingstone; Aya Fujiwara; Satoshi Sasaki
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    ObjectivesWhile it is widely perceived that the diet consumed by Japanese is healthy, empirical evidence supporting this notion is limited. In this cross-sectional study, we assessed the overall diet quality of Japanese using the Healthy Eating Index-2015 (HEI-2015) and Nutrient-Rich Food Index 9.3 (NRF9.3), and compared diet quality scores between Japanese and Americans.MethodsWe used 1-d dietary record data from 19,719 adults (aged ≥20 y) in the Japanese National Health and Nutrition Survey 2012 and the first 24-h dietary recall data from 4614 adults in the US NHANES 2011–2012.ResultsAs expected, a higher total score of the HEI-2015 and NRF9.3 was associated with favorable patterns of overall diet in the Japanese population. The range of total score was wide enough for both HEI-2015 (5th percentile 37.2; 95th percentile 67.2) and NRF9.3 (5th percentile 257; 95th percentile 645). Both HEI-2015 and NRF9.3 distinguished known differences in diet quality between sex, age, and smoking status. The mean total scores of HEI-2015 and NRF9.3 were similar between Japanese (51.9 and 448, respectively) and US adults (52.8 and 435, respectively). However, component scores between the 2 populations were considerably different. For HEI-2015, Japanese had higher scores for whole fruits, total vegetables, green and beans, total protein foods, seafood and plant proteins, fatty acids, added sugars, and saturated fats, but lower scores for total fruits, whole grains, dairy, refined grains, and sodium. For NRF9.3, the intakes of vitamin C, vitamin D, potassium, added sugars, and saturated fats were more favorable in Japanese, while those of dietary fiber, vitamin A, calcium, iron, magnesium, and sodium were less favorable.ConclusionsThis study suggests the usefulness of HEI-2015 and NRF9.3 for assessing the diet quality of Japanese, as well as for highlighting different nutritional concerns between Japan and the US.

  14. Data for the article 'Association between various physical activity domains...

    • figshare.com
    xlsx
    Updated Jul 9, 2024
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    Yanxue Lian (2024). Data for the article 'Association between various physical activity domains and overall cancer risk, National Health and Nutrition Examination Survey (NHANES) 2007-2018' submitted to PLOS one [Dataset]. http://doi.org/10.6084/m9.figshare.26226836.v1
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    xlsxAvailable download formats
    Dataset updated
    Jul 9, 2024
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Yanxue Lian
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Title: "Association between various physical activity domains and overall cancer risk, National Health and Nutrition Examination Survey (NHANES) 2007-2018" Purpose: There are very few studies concurrently evaluating the association between multiple physical activity (PA) domains and cancer prevalence. Therefore, this study aims to fill this gap by investigating the link between multiple PA subdomains [occupational PA (OPA), transportation-related PA (TPA), leisure-time PA (LTPA), and total PA] and the likelihood of cancer. Method: The data from National Health and Nutrition Examination Survey (NHANES) 2007-2008, 2009-2010, 2011-2012, 2013-2014, 2015-2016, and 2017-2018 were used in this study. Cancers are the primary outcome variable of interest in this study. PA was self- or proxy-reported using the Global Physical Activity Questionnaire (GPAQ). Multivariable logistic regression models were used, adjusted for covariates. Results: The trend analysis revealed that the prevalence of cancer statistically decreased with the increase in total PA amount. The participants achieving twice the minimum recommended PA guidelines (≥300 minutes) for total PA were 32% [0.68 (0.54, 0.86)] less likely to have cancer. However, significant associations between three PA subdomains (OPA, TPA, and LTPA) and cancers were not found in this study. Conclusion: There is no significant association between any of these three single PA subdomains and cancer prevalence other than total PA. Therefore, this study recommends clinical practice should prioritize promoting comprehensive PA that integrates OPA, TPA, and LTPA to achieve at least 150 minutes per week (i.e. per seven days) initially and progressing towards 300 minutes for optimal cancer prevention.

  15. A

    ‘Prevalence of Selected Measures Among Adults Aged 20 and Over: United...

    • analyst-2.ai
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com), ‘Prevalence of Selected Measures Among Adults Aged 20 and Over: United States, 1999-2000 through 2017-2018’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-prevalence-of-selected-measures-among-adults-aged-20-and-over-united-states-1999-2000-through-2017-2018-fbbc/c46ba0a0/?iid=003-786&v=presentation
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    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    Analysis of ‘Prevalence of Selected Measures Among Adults Aged 20 and Over: United States, 1999-2000 through 2017-2018’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/dede1456-2d8e-48b8-8ad6-f1133fbcf06a on 26 January 2022.

    --- Dataset description provided by original source is as follows ---

    This data represents the age-adjusted prevalence of high total cholesterol, hypertension, and obesity among US adults aged 20 and over between 1999-2000 to 2017-2018.

    Notes:

    • All estimates are age adjusted by the direct method to the U.S. Census 2000 population using age groups 20–39, 40–59, and 60 and over.

    Definitions

    Hypertension: Systolic blood pressure greater than or equal to 130 mmHg or diastolic blood pressure greater than or equal to 80 mmHg, or currently taking medication to lower high blood pressure

    High total cholesterol: Serum total cholesterol greater than or equal to 240 mg/dL.

    Obesity: Body mass index (BMI, weight in kilograms divided by height in meters squared) greater than or equal to 30.

    Data Source and Methods

    Data from the National Health and Nutrition Examination Surveys (NHANES) for the years 1999–2000, 2001–2002, 2003–2004, 2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018 were used for these analyses.

    NHANES is a cross-sectional survey designed to monitor the health and nutritional status of the civilian noninstitutionalized U.S. population. The survey consists of interviews conducted in participants’ homes and standardized physical examinations, including a blood draw, conducted in mobile examination centers.

    --- Original source retains full ownership of the source dataset ---

  16. f

    Table 3_Higher HEI-2015 score is associated with reduced risk of Parkinson’s...

    • figshare.com
    doc
    Updated May 30, 2025
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    Wenting Hu; Hai Liu; Ying Zhang; Huanxian Liu (2025). Table 3_Higher HEI-2015 score is associated with reduced risk of Parkinson’s disease: a nationwide population-based study.doc [Dataset]. http://doi.org/10.3389/fnut.2025.1541271.s003
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    docAvailable download formats
    Dataset updated
    May 30, 2025
    Dataset provided by
    Frontiers
    Authors
    Wenting Hu; Hai Liu; Ying Zhang; Huanxian Liu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundRecent studies have highlighted the significant role of diet in the development of Parkinson’s disease (PD). However, research on the association between diet quality and PD in the general adult population of the United States remains limited. This study aims to assess the relationship between diet quality, measured by the Healthy Eating Index 2015 (HEI-2015) score, and the risk of PD.MethodsData for this cross-sectional analysis were obtained from the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2018, which includes a nationally representative sample of US adults. Diet quality was measured using the HEI-2015 score, and weighted multivariable logistic regressions and restricted cubic splines (RCS) were applied to examine the correlation between HEI-2015 and PD. Threshold effects were computed using a two-segment linear regression model. Subgroup and sensitivity analyses, including multiple imputations, unweighted logistic regression, and exclusion of participants with HEI-2015 scores beyond 3 standard deviations (mean ± 3SD), were performed to assess the robustness of the findings.ResultsA total of 29,581 US adults were included in the analysis, with 286 participants diagnosed with PD. In the fully adjusted multivariable model, each 10-point increase in the HEI-2015 score was associated with a 17% reduction in the likelihood of PD (odds ratio (OR):0.858,95% confidence interval (CI):0.742–0.992, p = 0.039). Additionally, individuals with higher HEI-2015 scores had a 62% lower probability of developing PD compared to those with lower scores (OR:0.518, 95%CI:0.297–0.906, p = 0.021). RCS analysis revealed a nonlinear relationship between HEI-2015 scores and PD (p = 0.022). In the two-segment regression models, participants with HEI-2015 scores ≥ 55.500 had an adjusted OR of 0.957 for developing PD (95% CI: 0.916–0.999, p = 0.045). In contrast, no association was observed between HEI-2015 scores and PD in participants with scores < 55.500. Subgroup analyses indicated the association was modified by race and hyperlipidemia (P for interaction = 0.039 and 0.024, respectively). Sensitivity analyses further confirmed the robustness of this association.ConclusionHEI-2015 is negatively associated with the prevalence of PD. This suggests that modifiable lifestyle factors, particularly diet quality, may play an important role in reducing the risk of PD.

  17. A

    USDA's Expanded Flavonoid Database for the Assessment of Dietary Intakes,...

    • data.amerigeoss.org
    • datadiscoverystudio.org
    • +2more
    pdf, zip
    Updated Aug 20, 2022
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    United States (2022). USDA's Expanded Flavonoid Database for the Assessment of Dietary Intakes, Release 1.1 - December 2015 [Dataset]. https://data.amerigeoss.org/ar/dataset/usdas-expanded-flavonoid-database-for-the-assessment-of-dietary-intakes-release-1-1-decemb
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    pdf, zipAvailable download formats
    Dataset updated
    Aug 20, 2022
    Dataset provided by
    United States
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This database was developed with support from the Office of Dietary Supplements, National Institutes of Health for flavonoid intake studies. The database is a useful tool for flavonoid intake and health outcome studies for any population globally. It contains data for 29 individual flavonoid compounds in six subclasses of flavonoids for every food in a subset of 2,926 food items which provide the basis for the Food and Nutrient Database for Dietary Studies (FNDDS 4.1). Proanthocyanidins data are not included at the present time. For flavonoid intake data for the U.S. population based on NHANES 2007-08, please refer to the Food Surveys Research Group website.

  18. Diabetes control is associated with environmental quality in the U.S.

    • catalog.data.gov
    • s.cnmilf.com
    Updated Jul 21, 2022
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    U.S. EPA Office of Research and Development (ORD) (2022). Diabetes control is associated with environmental quality in the U.S. [Dataset]. https://catalog.data.gov/dataset/diabetes-control-is-associated-with-environmental-quality-in-the-u-s
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    Dataset updated
    Jul 21, 2022
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Area covered
    United States
    Description

    Population-based county-level estimates for prevalence of DC were obtained from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (16). DC prevalence rate was defined as the propor-tion of people within a county who had previously been diagnosed with diabetes (high fasting plasma glu-cose 126 mg/dL, hemoglobin A1c (HbA1c) of 6.5%, or diabetes diagnosis) but do not currently have high fasting plasma glucose or HbA1c for the period 2004-2012. DC prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or HbA1C levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (16). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or HbA1C status for each BRFSS respondent (16). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict county-level prevalence of diabetes-related outcomes, including DC (16). The EQI was constructed for 2006-2010 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). Results are reported as prevalence rate differences (PRD) with 95% confidence intervals (CIs) comparing the highest quintile/worst environmental quality to the lowest quintile/best environmental quality expo-sure metrics. PRDs are representative of the entire period of interest, 2004-2012. Due to availability of DC data and covariate data, not all counties were captured, however, the majority, 3134 of 3142 were utilized in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, K. Price, D. Lobdell, and R. Sargis. Diabetes control is associated with environmental quality in the USA. Endocrine Connections. BioScientifica Ltd., Bristol, UK, 10(9): 1018-1026, (2021).

  19. The association between environmental quality and diabetes in the U.S.

    • catalog.data.gov
    Updated Nov 12, 2020
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    U.S. EPA Office of Research and Development (ORD) (2020). The association between environmental quality and diabetes in the U.S. [Dataset]. https://catalog.data.gov/dataset/the-association-between-environmental-quality-and-diabetes-in-the-u-s
    Explore at:
    Dataset updated
    Nov 12, 2020
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Area covered
    United States
    Description

    Population-based county-level estimates for diagnosed (DDP), undiagnosed (UDP), and total diabetes prevalence (TDP) were acquired from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (Evaluation 2017). Prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or hemoglobin A1C (HbA1C) levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (Dwyer-Lindgren, Mackenbach et al. 2016). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or A1C status for each BRFSS respondent (Dwyer-Lindgren, Mackenbach et al. 2016). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict the county-level prevalence of each of the diabetes-related outcomes (Dwyer-Lindgren, Mackenbach et al. 2016). Diagnosed diabetes was defined as the proportion of adults (age 20+ years) who reported a previous diabetes diagnosis, represented as an age-standardized prevalence percentage. Undiagnosed diabetes was defined as proportion of adults (age 20+ years) who have a high FPG or HbA1C but did not report a previous diagnosis of diabetes. Total diabetes was defined as the proportion of adults (age 20+ years) who reported a previous diabetes diagnosis and/or had a high FPG/HbA1C. The age-standardized diabetes prevalence (%) was used as the outcome. The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, S. Shaikh, D. Lobdell, and R. Sargis. Association between environmental quality and diabetes in the U.S.A.. Journal of Diabetes Investigation. John Wiley & Sons, Inc., Hoboken, NJ, USA, 11(2): 315-324, (2020).

  20. f

    Table 1_Associations between blood ethylene oxide levels and bone mineral...

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    Updated May 22, 2025
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    Wenwen Chen; Sujuan Lu; Min Lin; Kun Chen; Feng Huang (2025). Table 1_Associations between blood ethylene oxide levels and bone mineral density.docx [Dataset]. http://doi.org/10.3389/fpubh.2025.1561920.s001
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    docxAvailable download formats
    Dataset updated
    May 22, 2025
    Dataset provided by
    Frontiers
    Authors
    Wenwen Chen; Sujuan Lu; Min Lin; Kun Chen; Feng Huang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundEthylene oxide (EO) is a toxic compound extensively used in industrial applications. This study quantified serum EO levels by measuring hemoglobin-bound ethylene oxide (HbEO). However, the link between bone mineral density (BMD) and HbEO levels remains unexplored.MethodsA total of 2,570 participants were evaluated using data from National Health and Nutrition Examination Survey (NHANES) (2015–2018). Generalized linear regression models (LRM) and restricted cubic spline (RCS) analyses were used to investigate the association between blood EO levels and BMD. Adjusted models were also applied for comprehensive analysis.ResultsBlood EO levels and BMD were inversely related (p = 0.007). This RCS analysis also showed an L-shaped dose–response correlation between EO levels and BMD (p for nonlinearity

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Victorine Raissa Nkondjock (2020). Chronotype in Carbohydrates intake and cardiovascular risk [Dataset]. https://ieee-dataport.org/documents/chronotype-carbohydrates-intake-and-cardiovascular-risk-nhanes-2015

Chronotype in Carbohydrates intake and cardiovascular risk

NHANES 2015

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Dataset updated
Jan 30, 2020
Authors
Victorine Raissa Nkondjock
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

HSCRP records from 5

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