Self-perceived bodyweight status among adults who are overweight or have obesity, with and without high cardiovascular risk.

Background: Though low-density lipoprotein cholesterol (LDL) is a proven cardiovascular risk factor, substantive data on LDL levels in victims of sudden cardiac or sudden unexpected death is lacking. Additionally, post-mortem studies have shown higher concentrations of remnant-like lipoprotein particles (RLP) in sudden cardiac death victims. Triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL) is associated with RLP concentration, but has not been reported for victims prior to sudden death. Hypothesis: We assessed the hypothesis that out-of-hospital sudden unexpected death (OHSUD) victims would have similar or higher calculated LDL levels and higher TG/HDL ratios when compared with National Health and Nutrition Examination Survey (NHANES) participants. Methods: From 2013-15, all free living adults aged 18-64 who died out-of-hospital as reported by emergency medical services in Wake County, North Carolina (population 974,289) were adjudicated to identify OHSUD victims (n=408). Medical records were requested from area healthcare providers; 138 victims had a lipid panel available at an average of 1.2 years prior to death. To emulate a similar follow-up period, 18-64 year old NHANES (2009-2010) participants with a lipid panel who were alive at the end of 2011 served as a comparison group (n=1316). Covariates were abstracted from medical records for OHSUD victims and self-reported in NHANES participants. Subjects with triglycerides>400 mg/dL were excluded for analysis pertaining to LDL. We used multiple linear regression to assess the difference in lipid measures between OHSUD victims and NHANES participants, adjusting for demographics, prevalent dyslipidemia, diabetes, hypertension, body mass index, and coronary artery disease, use of lipid-lowering medication and clinic visits per year. Results: OHSUD victims had a lower mean LDL than NHANES participants (91.6 mg/dL; 95% CI 84.7, 98.5 vs. 115.8 mg/dL; 95%CI 113.8, 117.7 respectively). After multivariate adjustment, mean LDL of victims was still 22.3 mg/dL lower than NHANES participants (p<0.001). OHSUD victims had a higher unadjusted mean TG/HDL ratio than NHANES participants (4.2; 95% CI 3.2, 5.2 vs. 2.9; 95% CI 2.7, 3.2 respectively); this difference was mildly attenuated on adjustment for age, gender and race and insignificant upon additional adjustment for dyslipidemia and diabetes status. Conclusion: Contrary to expectations, out-of-hospital sudden unexpected death victims had a more favorable LDL cholesterol profile unexplained by differences in demographics, comorbid conditions or use of lipid lowering medication. The elevated TG/HDL ratio in victims, though explained by a higher prevalence of comorbidities, corroborates an evolving hypothesis on the contributory nature of vasoactive, prothrombotic remnant-like lipoprotein particles to sudden unexpected death.

Health hazards of obesity have been recognized for centuries, appearing, for example, in writings attributed to Hippocrates. From the later decades of the 20th century through the present, there have been numerous epidemiological studies of the relationship between excess weight and the total, or all-cause, mortality rate,1 a critical cumulative measure of the public health impact of any health condition. Using body mass index (BMI), an indicator of relative weight for height (weight [kg]/height [m]2) and a frequently used surrogate for assessment of excess body fat, these studies have found linear, U-shaped, or J-shaped relationships between total mortality and BMI. That is, in some studies, both the thin and the obese were more likely to die than those in between. There is, however, always a point at which increasing BMI is associated with increasing mortality risk, but the BMI at which this occurs varies across studies and populations.2 Currently,3 overweight in adults is defined as a BMI of 25.0 to <30.0 kg/m2 and obesity as a BMI of ≥30.0 kg/m2 (Table 1). A number of studies have found no significant relationship between BMI in the overweight range and mortality rate4 and have shown the nadir of mortality risk to be in the overweight range. In particular, commentaries in both the lay press5–7 and scientific literature2,8,9 subsequent to recent reports from National Health and Nutrition Examination Surveys (NHANES)10,11 have highlighted the confusion and controversy regarding this issue. Some have interpreted the recent data to mean that overweight is not detrimental to health and is not in itself a public health concern and that drawing attention to the need for weight loss in this range will have negative effects on the health and well-being of the general population.8 Others have argued …

The oral microbiome likely plays key roles in human health. Yet, population-representative characterizations are lacking. To characterize the composition, diversity, and correlates of the oral microbiome in US adults. This cross-sectional study analyzed data from the population-representative National Health and Nutrition Examination Survey (NHANES) from 2009 to 2012. Microbiome data were made publicly available in 2024. NHANES participants were aged 18 to 69 years and provided oral rinse samples in 1 of 2 consecutive NHANES cycles (2009-2010 and 2011-2012). Demographic, socioeconomic, behavioral, anthropometric, metabolic, and clinical characteristics. Oral microbiome measures, characterized through 16S ribosomal RNA gene sequencing, included α diversity (observed amplicon sequence variants [ASVs], Faith phylogenetic diversity, Shannon-Weiner Index, and Simpson Index); β diversity (unweighted UniFrac, weighted UniFrac, and Bray-Curtis dissimilarity); and prevalence and relative abundance at phylum level through genus level. Analyses accounted for the NHANES complex sample design. This study included 8237 US adults aged 18 to 69 years, representing 202 314 000 individuals (102 813 000 men [50.8%]; mean [SD] age, 42.3 [14.4] years; 9.3% self-reported as Mexican American, 12.1% as non-Hispanic Black, 64.7% as non-Hispanic White, 5.9% as other Hispanic, and 8.1% as other non-Hispanic individuals). The oral microbiome encompassed 37 bacterial phyla, 99 classes, 212 orders, 446 families, and 1219 genera. Five phyla (Firmicutes, Actinobacteria, Bacteroidetes, Proteobacteria, and Fusobacteria) and 6 genera (Veillonella, Streptococcus, Prevotella 7, Rothia, Actinomyces, and Gemella) were present in nearly all US adults (weighted prevalence, >99%). These genera were the most abundant, accounting for 65.7% of total abundance. Observed ASVs showed a quadratic pattern with age (peak at 30 years), were similar by sex, significantly lower among non-Hispanic White individuals, and increased with greater body mass index (BMI), alcohol use, and periodontal disease severity. All covariates together accounted for a modest proportion of oral microbiome variability as measured by β diversity: R2 = 8.7% (95% CI, 8.4%-9.1%) for unweighted UniFrac, R2 = 7.2% (95% CI, 6.6%-7.7%) for weighted UniFrac, and R2 = 6.3% (95% CI, 3.1%-6.7%) for Bray-Curtis matrices. By contrast, relative abundance of a few genera explained a high percentage of variability in β diversity for weighted UniFrac: Aggregatibacter (R2 = 22.4%; 95% CI, 22.1%-22.8%), Lactococcus (R2 = 21.6%; 95% CI, 20.9%-22.3%), and Haemophilus (R2 = 18.4%; 95% CI, 18.1%-18.8%). Prevalence and relative abundance of numerous genera were associated with age, race and ethnicity, smoking, BMI categories, alcohol use, and periodontal disease severity. This cross-sectional study of the oral microbiome in US adults showed that a few genera were universally present and a different set of genera explained a high percentage of oral microbiome diversity across the population. This comprehensive characterization provides a contemporary reference standard for future studies.

O verweight and obesity have become increasingly com- mon; worldwide, at least 1.1 billion adults are overweight and 312 million are obese, when overweight and obesity are defined conventionally as having a body mass index (BMI) of Ͼ25 kg/m 2 and Ͼ30 kg/m 2 , respectively. 1,2In the general population, overweight and obesity are associated with increased risk of developing cardiovascular disease, 3,4 and thus it is not surprising that in cohorts of patients with prevalent ischemic heart disease or acute coronary events, well over 50% are overweight or obese. 5,6Despite the association between obesity and cardiovascular risk in the general population, a multitude of studies have described an inverse correlation between BMI and mortality in patients with coronary artery disease (CAD), including post-coronary revascularization patients and those with acute myocardial infarction (MI); the association between elevated BMI and improved survival has been termed the obesity paradox. 7,8 Article p 482In this issue of Circulation, Zeller et al 9 further investigate the obesity paradox in a cohort of 2229 consecutive patients presenting with acute MI in the Côte d'Or region of France.In assessing the impact of obesity on mortality after MI, the group uses both BMI, a traditional index of obesity, as well as waist circumference, an alternate anthropometric index that is more specific for abdominal obesity.Approximately one-half of the subjects in the study were overweight (BMI 25 to 29.9 kg/m 2 ), one-quarter were obese (BMI Ͼ30 kg/m 2 ) and onehalf had increased waist circumference, which was defined as Ͼ102 cm in men and Ͼ88 cm in women.Left ventricular ejection fraction, type of MI, and acute treatment strategy did not generally differ by BMI or waist circumference values.Of note, BMI was inversely correlated with age and plasma N-terminal pro B-type natriuretic peptide, whereas waist circumference was positively correlated with age and did not correlate with N-terminal pro B-type natriuretic peptide.Consistent with prior studies, survival analysis showed that the risk of death decreased with increasing BMI tertile.In a waist-matched analysis of 832 subjects, BMI was a signifi-

Improving hydration is a strategy commonly used by clinicians to prevent overeating with the goal of promoting a healthy weight among patients. The relationship between weight status and hydration, however, is unclear. Our objective was to assess the relationship between inadequate hydration and BMI and inadequate hydration and obesity among adults in the United States. Our study used a nationally representative sample from the National Health and Nutrition Examination Survey (NHANES) 2009 to 2012, and included adults aged 18 to 64 years. The primary outcome of interest was body mass index (BMI), measured in continuous values and also categorized as obese (BMI ≥30) or not (BMI <30). Individuals with urine osmolality values of 800 mOsm/kg or greater were considered to be inadequately hydrated. Linear and logistic regressions were performed with continuous BMI and obesity status as the outcomes, respectively. Models were adjusted for known confounders including age, race/ethnicity, sex, and income-to-poverty ratio. In this nationally representative sample (n = 9,528; weighted n = 193.7 million), 50.8% were women, 64.5% were non-Hispanic white, and the mean age was 41 years. Mean urine osmolality was 631.4 mOsm/kg (SD = 236.2 mOsm/kg); 32.6% of the sample was inadequately hydrated. In adjusted models, adults who were inadequately hydrated had higher BMIs (1.32 kg/m(2); 95% CI, 0.85-1.79; P <.001) and higher odds of being obese (OR = 1.59; 95% CI, 1.35-1.88; P <.001) compared with hydrated adults. We found a significant association between inadequate hydration and elevated BMI and inadequate hydration and obesity, even after controlling for confounders. This relationship has not previously been shown on a population level and suggests that water, an essential nutrient, may deserve greater focus in weight management research and clinical strategies.

The present study assessed the relationship between night-time eating and body weight status among US adults, using in-person 24-h dietary recall data from a nationally representative survey. Individual-level data (n=23003) came from the 2007-2016 National Health and Nutrition Examination Survey (NHANES) (five waves). Multivariate linear and logistic regressions were performed to estimate the effect of energy, sugar, fat and saturated fat intake during night-time on body mass index (BMI), obesity (BMI≥30kgm-2 ), waist circumference (WC) and abdominal obesity (WC≥88cm in women; WC≥102cm in men), adjusting for daily total energy intake, physical activity, sleeping and other individual characteristics. Approximately 36.5% and 56.7% of the NHANES adult participants had obesity and abdominal obesity, respectively. The proportion of energy, total fat, saturated fat and total sugar intake within the time window of 00.00h to 05.59h and 22.00h to 23.59h averaged 5.7%, 5.3%, 5.7% and 6.8%, respectively. Energy intake within the time window was not found to be associated with BMI, WC, obesity or abdominal obesity in the regression analyses. Sensitivity analyses applying alternative time windows to capture night-time eating found night-time intakes of energy, total fat, saturated fat and total sugar not to be associated BMI, WC, obesity or abdominal obesity. Night-time eating was not found to be associated with body weight status in a US nationally representative adult sample. Restricting night-time food consumption alone without an overall reduction in daily caloric intake may not prevent obesity. The findings of the present study warrant replication in a future experimental study with habitual dietary behaviour measures.

Background: Use of single pill combination (SPC) antihypertensive medications is associated with improved medication adherence and blood pressure (BP) outcomes compared with combinations of the same medications and doses taken as separate pills. We analyzed time trends of SPC use and BP control among US adults taking multiple antihypertensive medications and identified factors associated with SPC use. Methods: Combining National Health and Nutrition Examination Survey (NHANES) data from 2009-2018, we included participants aged &gt;=20 years with hypertension and taking &gt;=2 antihypertensive medications. Number of antihypertensive medications and SPC use were identified from self-reported prescription medications. Controlled BP was defined as systolic BP &lt;140 mm Hg and diastolic BP &lt;90 mm Hg. The age-adjusted proportion of individuals using SPC medications and BP control were estimated across each NHANES cycle. Multivariable logistic regression was used to assess trends in SPC use, BP control, and factors associated with SPC use. All estimates accounted for the complex survey design and represented the US population. Findings: Overall, 5,365 NHANES participants were included; mean (SD) age was 63.9 (12.0) years and 53.0% were female. SPC antihypertensive medication use decreased from 38.2% in 2009-2010 to 28.8% in 2017-2018 ( Figure , p=0.01). Similarly, BP control decreased from 72.1% in 2009-2010 to 64.4% in 2017-2018 (p=0.01). Age, male sex, body mass index, concurrent use of lipid-lowering therapy, and history of cardiovascular disease were significantly associated with decreased odds of SPC use. Conclusions: In the last decade, use of SPC antihypertensive medications and BP control each have decreased significantly among US adults taking &gt;=2 antihypertensive medications. Randomized trial evidence supports SPCs over separate pill combinations to improve BP outcomes. Efforts should be made to increase SPC use in targeted populations (e.g., those with comorbidities or polypharmacy).

To compare the association of obesity and abdominal obesity with cardiometabolic risk factor burden and global estimated coronary heart disease (CHD) risk among multiethnic US adults. Cross-sectional, survey study. A total of 4456 participants (representing 194.9 million adults) aged 20-79 years in the 2003-2004 National Health and Nutrition Examination Survey (NHANES). Body mass index (BMI) and waist circumference (WC) measures, CHD risk factors and a 10-year estimated CHD risk based on Framingham algorithms. Obesity was defined as a BMI >or=30 kg/m(2) and abdominal obesity as a WC >88 cm in women and >102 cm in men. High CHD risk status included diabetes, cardiovascular disease (CVD) or a 10-year Framingham risk score of >20%. Overall, abdominal obesity was present in 42.3% of men and 62.5% of women and in 53.6% of whites, 56.9% of blacks and 50.5% of Hispanics (P<0.001 between gender and ethnicity). However, using International Diabetes Federation (IDF)-recommended WC cut points for Hispanics, the prevalence of abdominal obesity was 78.3%. Mean levels of low-density lipoprotein cholesterol (LDL-C), systolic and diastolic blood pressure, fasting glucose and C-reactive protein increased, and high-density lipoprotein cholesterol (HDL-C) decreased (P<0.001) according to BMI and WC categories, although these associations were attenuated in blacks for blood pressure, LDL-C, HDL-C and triglycerides. Of those with high WC, 25-35% had >or=3 cardiometabolic risk factors. High CHD risk among those with high WC was most common in men (27.9%) and non-Hispanic whites (23.9%). Persons with a high vs normal WC, adjusted for age, gender, ethnicity and BMI were more likely to have >or=3 cardiometabolic risk factors (odds ratio (OR)=5.1, 95% confidence interval (CI)=3.9-6.6) and were classified as high CHD risk (OR=1.5, 95% CI=1.1-2.0). The association of abdominal obesity with risk factors varies by ethnicity and is independently associated with high CHD risk status, further validating its clinical significance.

Background This study aimed to investigate the association of the difference of dietary consumption (energy, macronutrients, and foods) at dinner versus breakfast with obesity among U.S adults. Subjects/Methods: This study adopted the data from the National Health and Nutrition Examination Survey (2003–2016), involving a total of 27911 participants. The differences in the ratio of total energy and three macronutrients with six subgroups at dinner versus breakfast (ΔRatio) were categorized into quartiles. The differences in the consumption of 17 types of food at dinner versus breakfast (ΔFoods) were considered as continuous variables. Body Mass Index (BMI) and Waist circumference (WC) were used to define general obesity (30.0 ≤ BMI &lt; 40.0), morbid obesity (BMI ≥ 40.0), and abdominal obesity (WC &gt; 102 cm for men or WC &gt; 88 cm for women). Multiple logistic and linear regression models were developed. Results: After a variety of covariates were adjusted, participants in the highest quartile (higher energy/macronutrient intake at dinner than breakfast) of ΔRatio in terms of energy, fat, saturated fatty acids (SFA), and unsaturated fatty acids (USFA) was positively associated with morbid obesity compared with those in the lowest quartile (ORΔRatio of energy 1.27, 95%CI 1.01;1.61; ORΔRatio of fat 1.27, 95%CI 1.01;1.60; ORΔRatio of SFA 1.27, 95%CI 1.01;1.59; ORΔRatio of USFA 1.28, 95%CI 1.02;1.59). The highest quartile of ΔRatio of low-quality carbohydrate led to higher odds of abdominal obesity (ORΔRatio of low−quality carbohydrate 1.16; 95%CI 1.03–1.31). Meanwhile, ΔRatio of low-quality carbohydrates was significantly positively associated with BMI (coefficient: 0.562, 95%CI: 0.217–0.907). ΔFoods including whole fruits, other starchy vegetables, added sugars, poultry, dairy, and nuts were positively associated with obesity. Conclusions: This study indicated that among US adults, higher intake of energy, macronutrients (low-quality carbohydrate, fat, SFA, and USFA), and foods (whole fruits, other starchy vegetables, added sugars, poultry, dairy, and nuts) at dinner than breakfast was associated with higher odds of having obesity. In conclusion, this study emphasized the importance of diet quality and meal-timing in the prevention of obesity.

Adipose deposition and Barrett’s esophagus: Is all fat created equal?

Meats are high in energy and fat content, and thus may be associated with higher risk of obesity. Many controversies remain regarding the association between meat consumption (MC) and obesity. The aim of this study was to analyze the associations between MC and obesity assessed using body mass index (BMI > or = 30) and waist circumference (> or = 102 cm in men and > or = 88 cm in women) among US adults. Nationally representative data collected in the 1999-2004 National Health and Nutrition Examination Survey (NHANES) were used. Linear and logistic regression analyses were conducted to test the associations between MC and adiposity measures controlling for potential confounders. Considerable differences existed in MC across sociodemographic groups among US adults. Those who consumed more meat had a much higher daily total energy intake, for example, those in the upper vs bottom quintiles consumed around 700 more kcal day(-1) (P<0.05). Regression models showed consistent positive associations between MC and BMI, waist circumference, obesity and central obesity, respectively. Using quintile 1 (low MC) as the reference, the association (odds ratio (OR) and 95% confidence interval (CI)) between total MC quintiles and obesity were 1.03 (0.88; 1.21; 2nd quintile), 1.17 (1.00; 1.38), 1.27 (1.08; 1.51) and 1.27 (1.08; 1.49; upper quintile), respectively; whereas that with central obesity was 1.13 (0.96-1.33), 1.31 (1.10; 1.54), 1.36 (1.17-1.60) and 1.33 (1.13; 1.55), respectively. These US national cross-sectional data show positive associations between MC and risk for obesity and central obesity.

Body mass index (BMI) and waist circumference (WC) are important contributors to major causes of health risk factors such as high blood cholesterol, high fasting blood sugar and high blood pressure. This analysis was designed to assess the associations between WC, BMI and each health risk factor in suitable cut-off points for WC and BMI among middle aged men and women of Thai population. Cross sectional data from the Second National Health Survey in Thailand was analysed. This set of data collected by the Ministry of Public Health from 1996 to 1997 was obtained from a total of 998 subjects (comprising 396 men and 602 women) aged 45-50. Results from the Receiver Operating Characteristic curve (ROC curve) identified those with health risk factors demonstrating cut-off points for WC between 81.5-84 centimeters for men and 76-80.5 centimeters for women and BMI between 23.3-23.9 kg/m2 for both genders, respectively. Results from the Multiple logistic regression analysis demonstrated lower risks of having high blood cholesterol, high fasting blood sugar and high blood pressure with cutoff points 84 cm for men, 80 cm for women and BMI 23 kg/m2 for both genders compared to cut-off points 102 cm for men, 88 cm for women and BMI 25 kg/m2 for both genders as specified for the population in Western countries. This study has proposed a waist circumference of 84 cm (33.6 inches) for middle aged Thai men and 80 cm (32 inches) for middle aged Thai women and BMI of 23 kg/m2 for both genders. Monitoring changes in the waist circumference over time along with BMI may be convenient and useful for middle aged Thais to prevent health risk conditions, even in the absence of a change in BMI.

NHANES: No significant link between cardiorespiratory fitness and tinnitus

Role of a plausible nuisance contributor in the declining obesity-mortality risks over time

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .e3 1. About These Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .e7 2. American Heart Association's 2020 Impact Goals. . . . . . . . . . . . . . . . .e10 3. Cardiovascular Diseases . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . .e21 4. Subclinical Atherosclerosis . . . . . . . . . . . . . . . . . . . . .e45 5. Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectoris . . . . . . . . .e54 6. Stroke (Cerebrovascular Disease) . . . . . . . . . . . . . . . . . . . . . . . . . . . .e68 7. High Blood Pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . .e88 8. Congenital Cardiovascular Defects . . . . . . . . . . . . . . . . . . . . . . . . . . . .e97 9. Cardiomyopathy and Heart Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . .e102 10. Disorders …