The National Health and Nutrition Examination Surveys (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The NHANES combines personal interviews and physical examinations, which focus on different population groups or health topics. These surveys have been conducted by the National Center for Health Statistics (NCHS) on a periodic basis from 1971 to 1994. In 1999, the NHANES became a continuous program with a changing focus on a variety of health and nutrition measurements designed to meet current and emerging concerns. The sample for the survey is selected to represent the U.S. population of all ages. Many of the NHANES 2001-2002 questions also were asked in NHANES II 1976-1980, Hispanic HANES 1982-1984, NHANES III 1988-1994. New questions were added to the survey based on recommendations from survey collaborators, NCHS staff, and other interagency work groups.

In the 2001-2002 wave, the NHANES includes more than 100 datasets. Most have been combined into three datasets for convenience. Each starts with the demographic dataset and includes datasets of a specific type.

1. National Health and Nutrition Examination Survey (NHANES), Demographic & Examination Data, 2001-2002 (The base of the Demographic dataset + all data from medical examinations).

2. National Health and Nutrition Examination Survey (NHANES), Demographic & Laboratory Data, 2001-2002 (The base of the Demographic dataset + all data from medical laboratories).

3. National Health and Nutrition Examination Survey (NHANES), Demographic & Questionnaire Data, 2001-2002 (The base of the Demographic dataset + all data from questionnaires)

Not all files from the 2001-2002 wave are included. This is for two reasons, both of which related to the merging variable (SEQN). For a subset of the files, SEQN is not a unique identifier for cases (i.e. some respondents have multiple cases) or SEQN is not in the file at all. The following datasets from this wave of the NHANES are not included in these three files and can be found individually from the "https://www.cdc.gov/nchs/nhanes/index.htm" Target="_blank">NHANES website at the CDC:

Examination: Dietary Interview (Individual Foods File)
Examination: Dual Energy X-ray Absorptiometry (DXX)
Examination: Dual Energy X-ray Absorptiometry (DXX)
Questionnaire: Analgesics Pain Relievers
Questionnaire: Dietary Supplement Use -- Ingredient Information
Questionnaire: Dietary Supplement Use -- Supplement Blend
Questionnaire: Dietary Supplement Use -- Supplement Information
Questionnaire: Drug Information
Questionnaire: Dietary Supplement Use -- Participants Use of Supplement
Questionnaire: Physical Activity Individual Activity File
Questionnaire: Prescription Medications

Variable SEQN is included for merging files within the waves. All data files should be sorted by SEQN.

Additional details of the design and content of each survey are available at the "https://www.cdc.gov/nchs/nhanes/index.htm" Target="_blank">NHANES website.

The National Health and Nutrition Examination Surveys (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The NHANES combines personal interviews and physical examinations, which focus on different population groups or health topics. These surveys have been conducted by the National Center for Health Statistics (NCHS) on a periodic basis from 1971 to 1994. In 1999 the NHANES became a continuous program with a changing focus on a variety of health and nutrition measurements which were designed to meet current and emerging concerns. The surveys examine a nationally representative sample of approximately 5,000 persons each year. These persons are located in counties across the United States, 15 of which are visited each year. The 1999-2000 NHANES contains data for 9,965 individuals (and MEC examined sample size of 9,282) of all ages. Many questions that were asked in NHANES II, 1976-1980, Hispanic HANES 1982-1984, and NHANES III, 1988-1994, were combined with new questions in the NHANES 1999-2000. The 1999-2000 NHANES collected data on the prevalence of selected chronic conditions and diseases in the population and estimates for previously undiagnosed conditions, as well as those known to and reported by respondents. Risk factors, those aspects of a person's lifestyle, constitution, heredity, or environment that may increase the chances of developing a certain disease or condition, were examined. Data on smoking, alcohol consumption, sexual practices, drug use, physical fitness and activity, weight, and dietary intake were collected. Information on certain aspects of reproductive health, such as use of oral contraceptives and breastfeeding practices, were also collected. The interview includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as laboratory tests. Demographic data file variables are grouped into three broad categories: (1) Status Variables: Provide core information on the survey participant. Examples of the core variables include interview status, examination status, and sequence number. (Sequence number is a unique ID assigned to each sample person and is required to match the information on this demographic file to the rest of the NHANES 1999-2000 data). (2) Recoded Demographic Variables: The variables include age (age in months for persons through age 19 years, 11 months; age in years for 1-84 year olds, and a top-coded age group of 85+ years), gender, a race/ethnicity variable, an education variable (high school, and more than high school education), country of birth (United States, Mexico, or other foreign born), and pregnancy status variable. Some of the groupings were made due to limited sample sizes for the two-year dataset. (3) Interview and Examination Sample Weight Variables: Sample weights are available for analyzing NHANES 1999-2000 data. For a complete listing of survey contents for all years of the NHANES see the document -- Survey Content -- NHANES 1999-2010.

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.

What We Eat in America (WWEIA) is the dietary intake interview component of the National Health and Nutrition Examination Survey (NHANES). WWEIA is conducted as a partnership between the U.S. Department of Agriculture (USDA) and the U.S. Department of Health and Human Services (DHHS). Two days of 24-hour dietary recall data are collected through an initial in-person interview, and a second interview conducted over the telephone within three to 10 days. Participants are given three-dimensional models (measuring cups and spoons, a ruler, and two household spoons) and/or USDA's Food Model Booklet (containing drawings of various sizes of glasses, mugs, bowls, mounds, circles, and other measures) to estimate food amounts. WWEIA data are collected using USDA's dietary data collection instrument, the Automated Multiple-Pass Method (AMPM). The AMPM is a fully computerized method for collecting 24-hour dietary recalls either in-person or by telephone. For each 2-year data release cycle, the following dietary intake data files are available: Individual Foods File - Contains one record per food for each survey participant. Foods are identified by USDA food codes. Each record contains information about when and where the food was consumed, whether the food was eaten in combination with other foods, amount eaten, and amounts of nutrients provided by the food. Total Nutrient Intakes File - Contains one record per day for each survey participant. Each record contains daily totals of food energy and nutrient intakes, daily intake of water, intake day of week, total number foods reported, and whether intake was usual, much more than usual or much less than usual. The Day 1 file also includes salt use in cooking and at the table; whether on a diet to lose weight or for other health-related reason and type of diet; and frequency of fish and shellfish consumption (examinees one year or older, Day 1 file only). DHHS is responsible for the sample design and data collection, and USDA is responsible for the survey’s dietary data collection methodology, maintenance of the databases used to code and process the data, and data review and processing. USDA also funds the collection and processing of Day 2 dietary intake data, which are used to develop variance estimates and calculate usual nutrient intakes. Resources in this dataset:Resource Title: What We Eat In America (WWEIA) main web page. File Name: Web Page, url: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/wweianhanes-overview/ Contains data tables, research articles, documentation data sets and more information about the WWEIA program. (Link updated 05/13/2020)

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).

ObjectiveWe aimed to assess the association between weight-adjusted waist circumference index (WWI) and gallstone prevalence in US adults.MethodsWe selected individuals from the National Health and Nutrition Examination Survey (NHANES) database from 2017 to 2020 and used logistic regression analyses, subgroup analyses, and dose-response curves to assess the association between WWI and gallbladder stone prevalence and age, sex, and ethnicity.ResultsA total of 7971 participants aged ≥20 years were enrolled in our study; 828 patients had a self-reported history of gallstones. After correcting for confounders, for each unit of WWI after Ln conversion, the prevalence of gallbladder stones increased by 34% (OR=1.34, 95% CI:1.20, 1.50). Dose-response curves showed a positive correlation between WWI and gallbladder stone prevalence.According to the subgroup analysis, the positive association between TyG index and high-frequency HI was more significant in males(OR=1.34, 95% CI:1.07, 1.69),

BackgroundCardiovascular health (CVH) has been associated with various systemic diseases. However, the relationship between CVH, as measured by Life’s Essential 8 (LE8), and liver function markers in the general population remains poorly understood.MethodsThis study analyzed data from 21,156 participants (aged ≥ 20) from the NHANES 2005–2018 to investigate the associations between CVH and liver function markers [alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), albumin and AST/ALT ratio]. Linear regression models were used, along with a restricted cubic spline (RCS) to assess dose-response. Weighted quantile sum (WQS) regression and quantile g-computation (QGC) analyses were employed to evaluate the association between CVH and liver function markers.ResultsLinear regression analysis showed that each 1-point increase in CVH score was significantly associated with decreased levels of liver enzymes [ALT: −0.200 U/L (95% CI: −0.223, −0.176), AST: −0.043 U/L (−0.062, −0.024), GGT: −0.453 U/L (−0.509, −0.397), ALP: −0.310 U/L (−0.340, −0.281)] and increased levels of albumin [0.040 g/dL (0.036, 0.045)] and AST/ALT ratio [0.0056 (0.0051, 0.0061)]. Notably, CVH score demonstrated non-linear dose-response relationships with ALT, ALP, and AST/ALT ratio. Age significantly modified these associations, while nicotine exposure, BMI, and blood lipids were identified as primary contributors through WQS and QGC analyses. E-value analysis suggested robustness to unmeasured confounding.ConclusionThis study demonstrates robust associations between CVH and liver function markers in United States adults, with nicotine exposure, BMI, and blood lipids identified as significant contributors. These findings suggest that maintaining optimal cardiovascular health may have beneficial effects on liver function, highlighting potential targets for integrated prevention strategies.

Basic characteristics of participants by WWI quartile.

BackgroundExcessive oxidative stress is one of the key pathophysiological mechanisms underlying migraine, and increasing antioxidant intake has proven to be an effective strategy for the prevention and improvement of migraine symptoms. To explore the relationship between the composite dietary antioxidant index (CDAI) and the occurrence of migraine attacks.MethodsCross-sectional data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999–2004 were utilized. Logistic regression, stratified analysis, and restricted cubic spline (RCS) models were employed to investigate the association between CDAI and migraine attacks.ResultsA total of 8,137 adults aged ≥20 were enrolled, comprising 1,610 patients with migraine and 6,527 non-migraine individuals. After adjusting for all covariates, CDAI was negatively correlated with migraine. In the overall participants, compared with the CDAI Q1 (−5.83 to −2.14) group, the adjusted odds ratio (OR) for migraine in Q3 (−0.59 to 1.53) and Q4 (1.53–44.63) groups were 0.71 [95% confidence interval (95% CI): 0.54–0.92, p = 0.011] and 0.64 (95% CI: 0.47–0.87, p = 0.005), respectively. After stratifying by age and gender, the protective effect was more pronounced in females aged 20–50, with adjusted OR for Q3 (−0.59 to 1.53) and Q4 (1.53–44.63) groups of 0.60 (95% CI: 0.40–0.90, p = 0.013) and 0.48 (95% CI: 0.30–0.78, p = 0.003), respectively. The RCS curve indicated a nonlinear relationship between CDAI and migraine in females aged 20–50, with a threshold of 0.006.ConclusionCDAI is negatively correlated with migraine attacks, and a higher CDAI may be an effective protective factor in preventing migraine attacks, especially in women aged 20–50.

ContextIt is still unknown whether the dietary inflammatory index (DII) is associated with sex hormones and sex hormone binding globulin (SHBG) in adult women.ObjectiveThis study examined the association between DII and sex hormones and SHBG in U.S. adult women.Design and ParticipantsThis was a cross-sectional study. A total of 2,092 female participants (age ≥ 20) from the 2013–2016 National Health and Nutrition Examination Survey were enrolled. Dietary inflammatory potential was assessed by DII based on 24-h dietary recall. SHBG was assessed using immuno-antibodies and chemo-luminescence, whereas sex hormones were measured by ID-LC–MS/MS.ResultsThe average DII was 0.21 ± 1.68, ranging from −4.54 (most anti-inflammatory) to 4.28 (most pro-inflammatory). After adjusting all covariates, a per-unit DII increase in DII tertile 3 was related to an 8.05 nmol/L SHBG decrease compared to DII tertile 1 (P = 0.0366). Subgroup analysis stratified by perimenopausal period found that this negative association remained strong but only existed in women before (β = −3.71, 95% CI: −7.43, −0.12, P = 0.0423) the perimenopausal period. Interaction terms were added to both subgroup analyses and found no significant heterogeneity among different body mass index (BMI) or perimenopausal groups (P > 0.05). Treshold analyses showed that the association of age with SHBG was an inverted U-shaped curve (inflection point: age = 50 yrs).ConclusionA proinflammatory diet caused decreased SHBG. However, more well-designed studies are still needed to validate and verify the causal relationship between DII and sex hormones and SHBG.

BackgroundHypertension is a major public health problem, and its resulting other cardiovascular diseases are the leading cause of death worldwide. In this study, we constructed a convenient and high-performance hypertension risk prediction model to assist in clinical diagnosis and explore other important influencing factors.MethodsWe included 8,073 people from NHANES (2017—March 2020), using their 120 features to form the original dataset. After data pre-processing, we removed several redundant features through LASSO regression and correlation analysis. Thirteen commonly used machine learning methods were used to construct prediction models, and then, the methods with better performance were coupled with recursive feature elimination to determine the optimal feature subset. After data balancing through SMOTE, we integrated these better-performing learners to construct a fusion model based for predicting hypertension risk on stacking strategy. In addition, to explore the relationship between serum ferritin and the risk of hypertension, we performed a univariate analysis and divided it into four level groups (Q1 to Q4) by quartiles, with the lowest level group (Q1) as the reference, and performed multiple logistic regression analysis and trend analysis.ResultsThe optimal feature subsets were: age, BMI, waist, SBP, DBP, Cre, UACR, serum ferritin, HbA1C, and doctors recommend reducing salt intake. Compared to other machine learning models, the constructed fusion model showed better predictive performance with precision, accuracy, recall, F1 value and AUC of 0.871, 0.873, 0.871, 0.869 and 0.966, respectively. For the analysis of the relationship between serum ferritin and hypertension, after controlling for all co-variates, OR and 95% CI from Q2 to Q4, compared to Q1, were 1.396 (1.176–1.658), 1.499 (1.254–1.791), and 1.645 (1.360–1.989), respectively, with P 

BackgroundArterial stiffness is a significant determinant and evaluation of cardio-cerebrovascular disease and all-cause mortality risk in the stroke population. Estimated pulse wave velocity (ePWV) is a well-established indirect measure of arterial stiffness. We examined the association of ePWV with all-cause and cardio-cerebrovascular disease (CCD) mortality in the stroke population in a large sample of US adults.MethodsThe study design was a prospective cohort study with data from the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2014, between the ages of 18–85 years, with follow-up through December 31, 2019. 1,316 individuals with stroke among 58,759 participants were identified and ultimately, 879 stroke patients were included in the analysis. ePWV was calculated from a regression equation using age and mean blood pressure according to the following formula: ePWV = 9.587 − (0.402 × age) + [4.560 × 0.001 × (age2)] − [2.621 × 0.00001 × (age2) × MBP] + (3.176 × 0.001 × age × MBP) − (1.832 × 0.01 × MBP). Survey-weighted Cox regression models were used to assess the association between ePWV and all-cause and CCD mortality risk.ResultsThe high ePWV level group had a higher increased risk of all-cause mortality and CCD mortality compared to the low ePWV level group after fully adjusting for covariates. With an increase in ePWV of 1 m/s, the risk of all-cause and CCD mortality increased by 44%–57% and 47%–72% respectively. ePWV levels were linearly correlated with the risk of all-cause mortality (P for nonlinear = 0.187). With each 1 m/s increase in ePWV, the risk of all-cause mortality increased by 44% (HR 1.44, 95% CI: 1.22–1.69; P