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

The National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. An ongoing annual survey combines interviews and physical examinations. The NHANES interview includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as laboratory tests administered by highly trained medical personnel.

Ancillary studies include the NHANES National Youth Fitness Survey (NNYFS) and NHANES Epidemiologic Followup Study (NHEFS). NNYFS was conducted in 2012 to evaluate the physical activity and fitness of children aged 3 to 15 years old through interviews and fitness tests. NHEFS is a longitudinal survey of adults aged 25 to 74 years old in the NHANES I (1971-1975) cohort who completed a medical examination. Data was collected in follow-up rounds in 1982-1984, 1986, 1987, and 1992 through subject and proxy interviews and vital record search. Available data files include vital and tracing status, demographic information, interview data on health status, health care facility inpatient data, and mortality data.

The third National Health and Nutrition Examination Survey (NHANES III, ICPSR 2231), conducted in 1988-1994, was designed to obtain nationally representative information on the health and nutritional status of the population of the United States through interviews and direct physical examinations. This release, Series II, No. 3A, contains data obtained from a second exam of selected survey participants who had had a primary exam. This release does not replace any previous NHANES III data releases. The second exam sample consists of seven separate data files. The Combination Foods file contains information on food weight, nutrient data, and descriptions about combination foods. The Total Nutrient Intake file records respondent intake of foods and beverages in a 24-hour time period. The Examination file consists of a comprehensive physical/dental examination. The Individual Foods file lists the food records and component food records for single and multi-component combination foods. The Laboratory file contains data collected through whole blood, serum, plasma, and urine specimens collected from respondents. The Second Laboratory file contains blood and urine assessments by specimen type and age group. The Variable Ingredient file reports data pertaining to the variable ingredients for many recipe foods in the Individual Foods file.

Data from the National Health and Nutrition Examination Survey (NHANES) in 2005-2010, including 6,662 adults aged 20 or older, were utilized for this cross-sectional study. The baseline data was used to display the distribution of each characteristic visually. Multiple linear regression and smooth curve fitting were used to study the linear and non-linear correlations between PIR and nocturia. Subgroup analysis and interaction tests were conducted to examine the stability of intergroup relationships., We obtained data from the NHANES database website for the three cycles of 2005-2006, 2007-2008, and 2009-2010. Data analysis, including baseline characteristic distribution, logistic regression analysis, RCS curves, and subgroup analysis, was conducted using StataMP17.0 and R language 4.2.2., , # Association between family income to poverty ratio and nocturia

The “Data†originates from the NHANES database and represents the data obtained after our screening process. “RCS†includes the code for conducting restricted cubic spline regression on the data after applying weights. “subgroup†contains the code for performing subgroup analysis on the weighted dataset. “1.4svyscitb5†is utilized to weigh the dataset during subgroup analysis. Through “RCS,†“subgroup,†and “1.4svyscitb5,†we analyzed “Data†and identified a significant nonlinear relationship between PIR and nocturia. We also listed the correlations between various subgroups and their associations with PIR and nocturia.

Description of the data and file structure

Data

Seqn: The patient sequence number corresponding to the NHANES database.

Gender: The gender of the participants.

Age: The age range of the participants.

Race: The race of the participants.

Education: The education level of the participants.

Ma...

Non-genetic exposures—including nutrients, lifestyle factors, pollutants, and infections—substantially contribute to phenotypic variation. Most studies assess only a few exposures or phenotypes, yielding fragmented exposome-phenome relationships. Systematic approaches are needed to quantify how the exposome—the totality of environmental exposures—relates broadly to clinically relevant phenotypes. We developed a resource benchmarking the exposome’s role using data from the National Health and Nutrition Examination Survey (NHANES), cataloging 619 exposures and 278 phenotypes, and systematically testing associations (Phenotype-exposure-wide association study [P-ExWAS]). Among ~119k associations, 5% (n=5,661) were Bonferroni significant, and 40% replicated across independent population samples. Single exposures explained modest variance (median R²=0.5%; interquartile range [IQR]: 0.27–1.10%). Twenty simultaneous exposome factors increased median variance explained to 3.5% (IQR: 1.8–7.8%), comparable to 1M genetic variants. The exposome-phenome atlas is freely available at: http://apps.chiragjpgroup.org/pe_atlas/.

Background: Nocturia, a prevalent chronic condition, impacts individuals' quality of life but remains underexplored. This study aimed to assess the association between serum albumin levels and nocturia.Methods: Based on the analysis of the National Health and Nutrition Examination Survey (NHANES) database (2005-2012), our study included a total of 6345 adults (≥20 years old). Nocturia was defined as ≥2 nocturnal voiding episodes. Logistic regression and smooth curve fitting analyzed the linear and nonlinear correlations between serum albumin and nocturia, with subgroup analysis.Results: Among 6345 participants, 1821 (28.7%) experienced nocturia. Logistic regression analysis revealed a linear negative correlation between serum albumin and nocturia risk (OR = 0.9549, 95% CI = 0.9280 ~ 0.9827, P = 0.002). Even after quartile division of serum albumin concentration, this correlation persisted within each group, and a smooth curve fitting validated the nonlinear negative correlation between the two. Subgroup analysis further demonstrated significant impacts of body mass index (BMI), alcohol consumption, and age on this association.Conclusion: This cross-sectional study indicated that higher serum albumin levels were associated with a reduced risk of nocturia in U.S. adults aged 20 and older, highlighting the importance of serum albumin in the prevention and treatment of nocturia and providing clinical guidance.

*Population Weight in Millions ± SE.

[Note: Integrated as part of FoodData Central, April 2019.] USDA's Food and Nutrient Database for Dietary Studies (FNDDS) is a database that is used to convert food and beverages consumed in What We Eat In America (WWEIA), National Health and Nutrition Examination Survey (NHANES) into gram amounts and to determine their nutrient values. Because FNDDS is used to generate the nutrient intake data files for WWEIA, NHANES, it is not required to estimate nutrient intakes from the survey. FNDDS is made available for researchers using WWEIA, NHANES to review the nutrient profiles for specific foods and beverages as well as their associated portions and recipes. Such detailed information makes it possible for researchers to conduct enhanced analysis of dietary intakes. FNDDS can also be used in other dietary studies to code foods/beverages and amounts eaten and to calculate the amounts of nutrients/food components in those items. FNDDS is released every two-years in conjunction with the WWEIA, NHANES dietary data release. The FNDDS is available for free download from the FSRG website. Resources in this dataset:Resource Title: Website Pointer to Food and Nutrient Database for Dietary Studies. 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/fndds/ USDA's Food and Nutrient Database for Dietary Studies (FNDDS) is a database that is used to convert food and beverages consumed in What We Eat In America (WWEIA), National Health and Nutrition Examination Survey (NHANES) into gram amounts and to determine their nutrient values.

See https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2021/DataFiles/DEMO_L.htm#DMDHHSIZ for a complete description.

Data and code for ' Food insecurity and patterns of dietary intake in a sample of UK adults' by Shinwell et al.

For the UK data, the script 'analysis UK dataset.r' is required along with the csv data file.

For the NHANES data analyses, the user needs to:

a) Download the required 2013-4 NHANES data files as described at https://zenodo.org/record/3361283

b) Run the script 'merging.script.r' from https://zenodo.org/record/3361283

c) Using the resulting .csv file in conjunction with the script 'analysis NHANES dataset.r' to reproduce the analyses in the paper.

The reason for doing it this indirect way is that the raw NHANES data are not ours to share.

AimTo investigate the associations of the estimated glucose disposal rate (eGDR) and the Dietary Antioxidant Quality Score (DAQS) with periodontitis, and assessed the potential regulating effect of DAQS on relationship between eGDR and periodontitis, to provide some references for management and control of periodontitis.MethodsData of 9,588 individuals were extracted from the National Health and Nutrition Examination Survey (NHANES) database in 2009–2014 in this cross-sectional study. Associations of eGDR and DAQS with periodontitis were evaluated by multivariate logistic regression analysis, with odds ratio (OR) and 95% confidence interval (CI). The potential regulating effect of DAQS on association between eGDR and periodontitis was investigated using multiplicative interaction term, and analyzed in subgroups of age, gender, overweight and diabetes mellitus (DM). Additionally, the value of eGDR on periodontitis identification was compared with common insulin resistance (IR)-related indexes via the receiver operating characteristic (ROC) curve with area under the curve (AUC) and Delong test.ResultsAmong eligible participants, 3,111 had periodontitis stage I/II, and 6,477 had periodontitis stage III/IV. After adjusting for the selected covariates, an eGDR of

AimThe aim of this study was to assess the relationship of circadian syndrome and stroke.MethodsWe performed a cross-sectional analysis of 11,855 participants from the National Health and Nutrition Examination Survey (NHANES) database between 2005 and 2018, and collected the baseline characteristics. Multivariate logistic regression models were developed to explore the association between circadian syndrome and stroke. Simultaneously, subgroup analyses based on the difference of gender, race, and components associated with circadian syndrome also were performed. The odds ratio (OR) and 95% CI were calculated in this study.ResultsAll the participants were divided into the non-stroke group and the stroke group. There were approximately 3.48% patients exclusively with stroke and 19.03% patients exclusively with circadian syndrome in our study. The results suggested that the risk of stroke in patients with circadian syndrome was higher than that in patients without circadian syndrome (OR = 1.322, 95 CI%: 1.020–1.713). Similar associations were found in women with circadian syndrome (OR = 1.515, 95 CI%: 1.086–2.114), non-Hispanic whites with circadian syndrome (OR = 1.544, 95 CI%: 1.124–2.122), participants with circadian syndrome who had elevated waist circumference (OR = 1.395, 95 CI%: 1.070–1.819) or short sleep (OR = 1.763, 95 CI%: 1.033–3.009).ConclusionCircadian syndrome was associated with the risk of stroke. Particularly, we should pay more close attention to the risk of stroke in those populations who were female, non-Hispanic whites, had the symptoms of elevated waist circumference or short sleep.

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)

The National Health and Nutrition Examination Survey (NHANES) is a population survey implemented by the Centers for Disease Control and Prevention (CDC) to monitor the health of the United States whose data is publicly available in hundreds of files. This Data Descriptor describes a single unified and universally accessible data file, merging across 255 separate files and stitching data across 4 surveys, encompassing 41,474 individuals and 1,191 variables. The variables consist of phenotype and environmental exposure information on each individual, specifically (1) demographic information, physical exam results (e.g., height, body mass index), laboratory results (e.g., cholesterol, glucose, and environmental exposures), and (4) questionnaire items. Second, the data descriptor describes a dictionary to enable analysts find variables by category and human-readable description. The datasets are available on DataDryad and a hands-on analytics tutorial is available on GitHub. Through a new b...

The Nutrient Data Laboratory is responsible for developing authoritative nutrient databases that contain a wide range of food composition values of the nation's food supply. This requires updating and revising the USDA Nutrient Database for Standard Reference (SR) and developing various special interest databases. However, with over 7,000 food items in SR and a complete nutrient profile costing approximately $2,000 for one sample, analyzing every food item for every nutrient and meeting all user requirements is impossible. Consequently, priorities must be determined. Procedures using food consumption data and nutrient values for developing the Key Foods list are explained. Key Foods have been identified as those food items that contribute up to 75% of any one nutrient to the dietary intake of the US population. These Key Foods will be used to set priorities for nutrient analyses under the National Food and Nutrient Analysis Program. The tables describe key foods based on Continuing Survey Of Food Intakes By Individuals (CSFII, 1989-) and WWEIA-NHANES (What We Eat In America - National Health and Nutrition Examination Survey 2001-) survey data. Resources in this dataset:Resource Title: List of Key Foods based on CSFII 1989-91. File Name: KeyFoods_key_ls91.txtResource Description: Key Foods based on CSFII 1989-91 https://www.ars.usda.gov/ARSUserFiles/80400525/Data/KeyFoods/key_ls91.txtResource Title: List of Key Foods based on CSFII 1994-96 . File Name: KeyFoods_key_ls9496.txtResource Description: List of Key Foods based on CSFII 1994-96 https://www.ars.usda.gov/ARSUserFiles/80400525/Data/KeyFoods/key_ls9496.txtResource Title: List of Key Foods based on WWEIA-NHANES 2001-02. File Name: KeyFoods_key_ls0102.txtResource Description: List of Key Foods based on WWEIA-NHANES 2001-02 https://www.ars.usda.gov/ARSUserFiles/80400525/Data/KeyFoods/key_ls0102.txtResource Title: List of Key Foods based on WWEIA-NHANES 2003-04 . File Name: KeyFoods_key_ls0304.txtResource Description: https://www.ars.usda.gov/ARSUserFiles/80400525/Data/KeyFoods/key_ls0304.txtResource Title: List of Key Foods based on WWEIA-NHANES 2007-08. File Name: Keyfoods_0708.xlsxResource Description: List of Key Foods based on WWEIA-NHANES 2007-08 https://www.ars.usda.gov/ARSUserFiles/80400525/Data/KeyFoods/Keyfoods_0708.xlsxResource Title: List of Key Foods based on WWEIA-NHANES 2009-10. File Name: Keyfoods_0910.xlsxResource Description: List of Key Foods based on WWEIA-NHANES 2009-10 https://www.ars.usda.gov/ARSUserFiles/80400525/Data/KeyFoods/Keyfoods_0910.xlsxResource Title: List of Key Foodsbased on WWEIA-NHANES 2011-12. File Name: Keyfoods_1112.xlsxResource Description: List of Key Foodsbased on WWEIA-NHANES 2011-12 https://www.ars.usda.gov/ARSUserFiles/80400525/Data/KeyFoods/Keyfoods_1112.xlsx

AimHeart failure (HF) is a severe manifestation or late stage of various heart diseases. As an anti-inflammatory nutrient, dietary fiber has been shown to be associated with the progression and prognosis of cardiovascular diseases (CVDs). However, little is known about the relationship between dietary fiber intake and mortality in HF survivors. This study evaluated the association between dietary fiber intake and all-cause and CVD-caused mortality among HF survivors.MethodsData for the study were extracted from the National Health and Nutrition Examination Survey 1999–2018. Dietary fiber intake information was obtained by a 24-h dietary recall interview. Death outcomes were ascertained by linkage to National Death Index records through 31 December 2019. Covariates, including sociodemographic, lifestyle, disease history, and laboratory data, were extracted from the database. The weighted univariate and multivariate Cox proportional hazard models were utilized to explore the association between dietary fiber intake and mortality among HF survivors, with hazard ratios and 95% confidence intervals. Further stratified analyses were performed to explore this association based on age, gender, a history of diabetes and dyslipidemia, and duration of HF.ResultsA total of 1,510 patients were included. Up to 31 December 2019, 859 deaths had occurred over a mean follow-up of 70.00 months. After multivariable adjustment, a higher dietary fiber intake was associated with a lower risk of all-cause and CVD-caused mortality in HF survivors, especially in male patients, those aged

Mean nutritional intakes in NHANES 2011–12 children and adolescents aged 4 to 19 years (Day 1), at baseline and in the reformulation and substitution scenarios.

The National Health and Nutrition Examination Survey I Epidemiologic Follow-Up Study (NHEFS) is a longitudinal study which uses as its baseline those adult persons aged 25 to 74 years who were examined in the first National Health and Nutrition Examination Survey (NHANES I). The NHEFS surveys were designed to investigate the association between factors measured at the baseline and the development of specific health conditions. The NHEFS is comprised of a series of follow-up surveys, three of which have been completed. The first wave of data collection, the 1982-1984 NHEFS (ICPSR 8900), included all persons who were between 25 and 74 years of age at their NHANES I examination. The second wave of data collection, the 1986 NHEFS (ICPSR 9466), included the NHEFS cohort who were 55-74 years at their baseline examination and not known to be deceased at the time of the 1982-1984 NHEFS. The third wave, the 1987 NHEFS, was conducted for the entire nondeceased NHEFS cohort. The 1982-1984 NHEFS consisted of five steps. The first step focused on tracing and locating all subjects in the cohort or their proxies and determining their vital status. The second step involved obtaining death certificates for subjects who were deceased. Interviews with the participants or their proxies constituted the third phase of the follow-up. The fourth phase of the follow-up included measurements of pulse, blood pressure, and weight for interviewed respondents, and the fifth step was the acquisition of relevant hospital and nursing home records, including pathology reports and electrocardiograms. The 1986 NHEFS assessed changes to the health and functional status of the oldest members of the NHEFS cohort since the last contact period. The 1987 NHEFS also collected information on changes in the health and functional status of the NHEFS cohort since the last contact period. The Vital and Tracing Status file contains summary information about the status of the entire NHEFS cohort. The Health Care Facility Record file contains information on reports of stays in hospitals and nursing homes as well as information abstracted from facility medical records. The Mortality Data file contains data abstracted from the death certificates from all three NHEFS surveys. The Interview Data file contains information on selected aspects of the subject's health history since the time of the NHANES I exam.

BackgroundChronic respiratory diseases (CRPD) are a global health threat characterized by oxidative stress, systemic inflammation, hypoxemia, and respiratory distress. Inflammatory indicators such as hemoglobin-to-red blood cell distribution width ratio (HRR) have been explored in relation to diseases of the respiratory system, but the correlation between HRR and pulmonary function has not been established. As part of this study, a representative sample of the National Health and Nutrition Examination Survey (NHANES) respondents aged 40 or over was used to examine the correlation between HRR and pulmonary function indices.MethodsData from the 2007–2012 NHANES were used for this study. HRR and four pulmonary function parameters were compared using regression and subgroup analyses. The Restricted Cubic Spline (RCS) model was employed to find out if there are any non-linear relationships between these associations. Multiple sensitivity analyses were used to verify the correlation between the two.ResultsAfter adjusting for confounding variables, the data showed that for each unit increase in HRR among the population as a whole, for each unit increase in HRR, FVC increased by 0.11, FEV1 increased by 0.22, peak expiratory flow (PEF) increased by 0.24 and forced expiratory flow at 25–75% (FEF25-75%) was elevated by 0.49. In addition, we determined linear and positive correlations between FVC, FEV1, PEF or PEF 25–75% and HRR by constructing the RCS model curves. The positive correlation between HRR and pulmonary function parameters was affirmed through sensitivity analysis. Furthermore, except for the PEF 25–75%, FVC, FEV1, PEF all showed a significant upward trend with the increase of HRR in non-Hispanic white female population.ConclusionAccording to our study, HRR was positively correlated with FVC, FEV1, PEF, and PEF25-75% in a middle-aged and older adult US population. It would be useful to study the specific impact of HRR on pulmonary function and to investigate the potential pathophysiological mechanisms that might link them.

IntroductionObesity, especially abdominal obesity, is more common in patients with heart failure (HF), but body mass index (BMI) cannot accurately describe fat distribution. Several surrogate adiposity markers are available to reflect fat distribution and quantity. The objective of this study was to explore which adiposity marker is most highly correlated with HF prevalence, all-cause mortality and patients’ long-term survival.MethodsThe National Health and Nutrition Examination Survey (NHANES) database provided all the data for this study. Logistic regression analyses were adopted to compare the association of each surrogate adiposity marker with the prevalence of HF. Cox proportional hazards models and restricted cubic spline (RCS) analysis were employed to assess the association between surrogate adiposity markers and all-cause mortality in HF patients. The ability of surrogate adiposity markers to predict long-term survival in HF patients was assessed using time-dependent receiver operating characteristic (ROC) curves.Results46,257 participants (1,366 HF patients) were encompassed in this retrospective study. An area under the receiver operating characteristic curve (AUC) for the prevalence of HF assessed by weight-adjusted-waist index (WWI) was 0.70 (95% CI: 0.69-0.72). During a median follow-up of 70 months, 700 of 1366 HF patients’ death were recorded. The hazard ratio (HR) for HF patients’ all-cause mortality was 1.33 (95% CI: 1.06-1.66) in the a body shape index (ABSI) quartile 4 group and 1.43 (95% CI: 1.13-1.82) in the WWI quartile 4 group, compared with the lowest quartile group. The AUC for predicting 5-year survival of HF patients using the ABSI was 0.647 (95% CI: 0.61-0.68).ConclusionsWWI is strongly correlated with the prevalence of HF. In HF patients, those with higher WWI and ABSI tend to higher all-cause mortality. ABSI can predict patients’ long-term survival. We recommend the use of WWI and ABSI for assessing obesity in HF patients.