Shifts in Metabolic Biomarkers Related to Cardiovascular Disease and Diabetes from 2013 to 2023: A Decade of Change, Including the COVID-19 Era

Shifts in Metabolic Biomarkers Related to Cardiovascular Disease and Diabetes from 2013 to 2023: A Decade of Change, Including the COVID-19 Era | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Shifts in Metabolic Biomarkers Related to Cardiovascular Disease and Diabetes from 2013 to 2023: A Decade of Change, Including the COVID-19 Era Caishan Fang, Xiangjun Qi, Tianhui Yuan, Zhaohua Zhu, Jiaojiao Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5704576/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract IMPORTANCE Understanding trends in cardiovascular and diabetes-related metabolic biomarkers across populations, especially during the COVID-19 pandemic, is essential for informing public health strategies targeting the prevention and management of cardiovascular diseases (CVD) and diabetes. This study aimed to assess trends in cardiovascular and diabetes-related metabolic biomarkers among U.S. adults from 2013-2014 to 2021-2023. DESIGN, SETTING, AND PARTICIPANTS This study analyzed five cycles of cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) spanning 2013-2014 to 2021-2023. The sample was weighted to reflect the noninstitutionalized civilian U.S. population aged 18 and older. Data analysis was conducted from August to October 2024. EXPOSURES Calendar year and sociodemographic subgroups, including age, gender, race, educational level, and family poverty-to-income ratio. MAIN OUTCOMES AND MEASURES Primary outcomes included body mass index (BMI), waist circumference, body fat percentage, systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse rate, estimated pulse wave velocity (ePWV), fasting glucose, glycohemoglobin, total fasting cholesterol, high-density lipoprotein cholesterol (HDL-C), fasting insulin, and insulin resistance index. Trends were estimated using survey-weighted linear regression models. RESULTS A total of 10,337 participants were included. BMI, waist circumference, and body fat percentage showed significant increases (all P for trend < 0.05). Specifically, BMI increased from 28.54 kg/m² (95% CI: 28.18-28.91) to 29.43 kg/m² (95% CI: 28.85-30.01); waist circumference rose from 97.63 cm (95% CI: 96.86-98.40) to 100.11 cm (95% CI: 98.77-101.44); and body fat percentage increased from 33.59% (95% CI: 31.11-34.07%) to 35.68% (95% CI: 34.90-36.46%). Significant interactions for these biomarkers were observed among various education and income subgroups. DBP ( P < 0.0001) and ePWV ( P < 0.0001) also increased, with DBP rising from 68.01 mmHg (95% CI: 67.42-68.60) to 74.17 mmHg (95% CI: 73.29-76.06) and ePWV from 7.89 m/s (95% CI: 7.75-8.02) to 8.41 m/s (95% CI: 8.27-8.55), while pulse rate declined from 72.27 bpm (95% CI: 71.17-73.37) to 70.59 bpm (95% CI: 69.96-71.23) ( P < 0.0001). Although SBP did not show an overall significant trend, increases were observed among men (from 121.06 mmHg [95% CI: 119.87-122.25] to 123.27 mmHg [95% CI: 122.41-124.12], P for trend = 0.005) and individuals with less than a high school education (from 117.56 mmHg [95% CI: 115.77-119.34] to 124.55 mmHg [95% CI: 121.81-127.30], P for trend < 0.0001). No significant trends were found for total cholesterol and HDL-C. Fasting glucose and glycohemoglobin showed significant upward trends (P for trend = 0.001 and 0.027, respectively), with notable increases in Mexican Americans (fasting glucose: 5.90 mmol/L [95% CI: 5.81-6.00] to 6.64 mmol/L [95% CI: 6.26-7.01], P for trend < 0.0001; glycohemoglobin: 5.59% [95% CI: 5.51-5.68] to 6.06% [95% CI: 5.84-6.28], P for trend < 0.001). CONCLUSIONS AND RELEVANCE Analysis of NHANES data indicates that most cardiovascular and diabetes-related metabolic biomarkers significantly increased from 2013-2014 to 2021-2023, with notable differences across demographic groups. These findings can help shape targeted prevention strategies, especially for addressing the needs of diverse populations. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cardiovascular disease (CVD) and diabetes are leading causes of disability and mortality globally 1 , 2 . From 1990 to 2019, total CVD cases nearly doubled, rising from 271 million to 523 million, while CVD-related deaths grew from 12.1 million to 18.6 million 3 . Managing CVD imposes a significant economic burden; in 2020, one in three adults in the U.S. required care for a cardiovascular risk factor or condition. By 2035, the annual healthcare costs for CVD are projected to increase nearly fourfold from $ 393 billion to $ 1.49 trillion, with productivity losses expected to grow by 54%, from $ 234 billion to $ 361 billion 4 . Between 1990 and 2021, the global age-standardized prevalence of diabetes rose by 90.5%, from 3.2–6.1%, affecting 529 million individuals by 2021. Forecasts indicate a further 59.7% increase in prevalence by 2050, reaching 9.8%, with 1.31 billion people expected to have diabetes 5 . In the U.S., diabetes-related costs were estimated at $ 327 billion in 2017, accounting for 24% of healthcare expenditures, rising to $ 412.9 billion by 2022, including $ 306.6 billion in direct medical costs and $ 106.3 billion in indirect costs 6 , 7 . CVD and diabetes are closely interconnected, sharing several risk factors, such as obesity, smoking, and physical inactivity, with diabetes itself serving as a CVD risk factor 8 . Adults with diabetes face a 2–4 times higher risk of cardiovascular events compared to those without diabetes, and this risk escalates with poor glycemic control 9 . Modifiable risk factors, including body mass index (BMI), blood pressure, cholesterol levels, smoking, and diabetes, contribute significantly to the incidence and prevalence of CVD 10 – 12 . Preventing CVD in diabetic patients requires managing risk factors, such as glycohemoglobin, blood pressure, and cholesterol levels 13 , 14 . Although control over these factors (e.g., glycohemoglobin < 7.0%, blood pressure < 130/80 mmHg, low-density lipoprotein cholesterol < 100 mg/dL) improved from 1988 to 2010, only about 18.8% of U.S. adults met all three targets by 2007-2010 15,16 . Between 2020 and 2023, the Coronavirus disease 2019 (COVID-19) pandemic added additional complexities, with CVD and diabetes being potential long-term outcomes of infection. COVID-19 patients exhibited a significantly higher risk of all cardiovascular outcomes than non-infected individuals (HR 1.66 [1.62–1.71] for those with diabetes; HR 1.75 [1.73–1.78]) 17 . Additionally, "Long COVID" may continue affecting cardiovascular health with fluctuating symptoms over time 18 . The trends in CVD and diabetic metabolic biomarkers remain unclear, especially following the pandemic, the understanding of these trends is paramount for population-level health complications and prevention efforts. Using recent National Health and Nutrition Examination Survey (NHANES) data, this study primarily aimed to provide national estimates to assess trends in CVD and diabetes-related metabolic biomarkers among adults in U.S from 2013 to 2023. Methods The First Affiliated Hospital of Guangzhou University of Chinese Medicine Ethics Committee deemed this cross-sectional study exempt from ethical review and informed consent because publicly available and deidentified NHANES data were used. For NHANES cycles, researchers recruit participants using established consent protocols approved by the National Center for Health Statistics Institutional Review Board 19 . Details of the data collection procedures, study design, and protocol were published previously 20 . This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Data source and study population We used data from the NHANES, which is conducted biennially by the US Centers for Disease Control and Prevention (CDC) for public health surveillance 21 . The CDC has disclosed NHANES data during the COVID-19 pandemic. Therefore, we used data from NHANES cycles from 2013 to August 2023. Because data collection for the 2019-2020 NHANES cycle was not completed and the collected data were not nationally representative, we used 2017-2020 prepandemic NHANES data to ensure nationally representative estimates 22 . NHANES cycle August 2021- August 2023 was used to represent data on the pandemic. We excluded participants with missing data on age, gender, race, FPIR, educational attainment, and cardiovascular disease or diabetes-related metabolic biomarkers. The patient selection flowchart is shown in Supplementary Figure 1. Measurement of cardiovascular disease and diabetes-related metabolic biomarkers We included a total of 15 cardiovascular and diabetes-related metabolic biomarkers, including body mass index (BMI), waist circumference, body fat percentage, systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse rate, estimated pulse wave velocity (ePWV), fasting glucose, glycohemoglobin, fasting total cholesterol, high-density lipoprotein-cholesterol(HDL-C), fasting insulin, and insulin resistance index. BMI and waist circumference were derived from body measurements collected by trained health technicians at the Mobile Examination Center (MEC). The body fat percentage (BF%) was calculated using the following formula: BF% = -44.988 + (0.503 × age) + (10.689 × sex) + (3.172 × BMI) - (0.026 × BMI 2 ) + (0.181 × BMI × sex) - (0.02 × BMI × age) - (0.005 × BMI 2 × sex) + (0.00021 × BMI 2 × age). Where sex is coded as 0 for male and 1 for female, and age is in years 23 . The SBP and DBP measurements were taken following these guidelines: after a 5-minute rest in a seated position, three consecutive BP readings were obtained once the maximum inflation level was established. If a measurement was interrupted or incomplete, a fourth attempt was made. The pulse rate was measured by locating the radial pulse with the participant's elbow and forearm resting comfortably on a table, palm facing up, and counting for 30 seconds. The ePWV was calculated with the following formula 24 : No cardiovascular disease risk factor: 4.62-0.13 × age + 0.0018 × age 2 + 0.0006 × age × mean arterial pressure (MAP) + 0.0284 × MAP ≥1 cardiovascular disease risk factor: 9.587-0.402 × age + 4.560 × 10 -3 × age 2 -2.621 × 10 -5 × age 2 × MAP + 3.176 × 10 -3 × age × MAP - 1.832 × 10 -2 × MAP The MAP was calculated as: (DBP) + 0.4 × ([SBP]-DBP) Fasting glucose levels were determined using an enzymatic method in which glucose is converted to glucose-6-phosphate by hexokinase in the presence of ATP. The glucose-6-phosphate dehydrogenase then converts G-6-P to gluconate-6-phosphate in the presence of NADP+. The reduction of NADP+ to NADPH was measured by the increase in absorbance at 340 nm (secondary wavelength = 700 nm). Glycohemoglobin was measured by diluting the whole blood sample with a hemolysis solution, followed by injection into an HPLC analytical column. The separated hemoglobin components passed through a photometric flow cell, with absorbance changes measured at 415 nm. Total cholesterol was measured using an enzymatic assay in which esterified cholesterol was converted to free cholesterol by cholesterol esterase. Cholesterol oxidase then converted the cholesterol to cholest-4-en-3-one and hydrogen peroxide, which reacted with 4-aminophenazone in the presence of peroxidase, forming a colored product measured at 505 nm (secondary wavelength = 700 nm). HDL cholesterol was measured using a method involving magnesium/dextran sulfate to form water-soluble complexes with non-HDL fractions, followed by conversion of HDL cholesterol esters to HDL cholesterol. Subsequent oxidation produced hydrogen peroxide, which combined with 4-amino-antipyrine and HSDA under the action of peroxidase to form a pigment measured at 600 nm (secondary wavelength = 700 nm). Fasting insulin was quantified using the AIA-PACK IRI, a two-site immunoenzymometric assay performed on a Tosoh AIA System analyzer. Insulin in the sample was bound to a monoclonal antibody on a magnetic solid phase and an enzyme-labeled monoclonal antibody. The amount of enzyme-labeled antibody bound was proportional to the insulin concentration and was quantified using a standard curve. The Homeostasis Model Assessment (HOMA) included calculations for HOMA-IR (insulin resistance), HOMA-IS (insulin sensitivity), and HOMA-β% (β-cell function), with the formulas as follows 25 : HOMA-IR: (fasting glucose [mmol/L)] × fasting insulin [mIU/L]/22.5) HOMA-IS：1/HOMA-IR HOMA-β%: 20 × fasting insulin (uU/mL) / (fasting glucos(mmol/L) - 3.5) Statistical analysis We adjusted the weights for the prepandemic period following official guidelines to reduce potential bias. The analysis utilized NHANES Mobile Examination Center (MEC) exam weights, incorporating sampling, stratification, and clustering to produce nationally representative estimates 26 . While the 2017-2020 prepandemic NHANES data file spans 3.2 years, the other NHANES cycles cover 2 years each. To account for this discrepancy, we adjusted the survey weights when combining the 2017-2020 data with other 2-year cycles, ensuring they reflected the extended time frame and larger population. New multicycle sample weights were derived based on the combined survey periods (2013-2014, 2015-2016, 2017-2020, and 2021-2023, totaling 9.2 years) using the formulas: (1) Weight = Weight × (2/9.2) for the 2013-2014, 2015-2016, or 2021-2023 cycles, and (2) Weight = Weight × (3.2/9.2) for the 2017-2020 cycle. In this study, baseline characteristics of participants were described by survey cycle. Means (with standard errors) were reported for continuous variables, while percentages were presented for categorical variables. Given the skewed distribution ofcardiovascular disease and diabetes-related metabolic biomarkers , we calculated geometric means (referred to hereafter as means) for these measures. Confidence intervals were determined using Taylor series linearization. To assess trends over time, we applied a survey-weighted linear regression model, treating the survey cycle as a continuous variable 26 . A total of three statistical models were constructed in each analysis. The crude model was the non-adjusted model with no covariates adjusted. Model I adjusted for age and gender. Model II adjusted for age, gender, race, education, and FPIR. Potential variations in trends across subgroups were evaluated using survey-weighted Wald F statistics 27 , assessing interactions between survey cycles and subgroup indicators such as age, gender, race, educational level, and family poverty-to-income ratio (FPIR). Statistical analysis was conducted using R software (version 4.3.1), with the NHANES survey design accounted for via the survey package. Statistical significance was set at p < 0.05 (two-tailed). Data analysis was performed between August and October 2024. Results Baseline characteristics of the participants A total of 10,337 participants were included in the study, distributed across four NHANES cycles: 2,650 from the 2013-2014 cycle, 2,446 from the 2015-2016 cycle, 2,755 from the 2017-2020 cycle, and 2,486 from the 2021-2023 cycle ( Table 1 ). There were no significant differences in age distribution, gender, or race across the four cycles. However, significant variations were observed in educational attainment and FPIR, with a higher proportion of participants having higher education and income levels in the 2017-2020 and 2021-2023 cycles. Additionally, BMI, waist circumference, systolic blood pressure, diastolic blood pressure, body fat percentage, fasting blood glucose, total cholesterol, glycohemoglobin, HOMA-IS, ePWV, and pulse rate showed significant differences across the cycles. Trends in BMI, Waist Circumference, and BF% From 2013-2014 to 2021-2023, the mean BMI rose from 28.54 kg/m² (95% CI: 28.18-28.91) to 29.43 kg/m² (95% CI: 28.85-30.01), with a significant trend ( P = 0.003; Figure 1A and Table 2 ). Similarly, the mean waist circumference increased from 97.63 cm (95% CI: 96.86-98.40) to 100.11 cm (95% CI: 98.77-101.44), with a trend P -value < 0.001 ( Figure 1B and Table 2 ). BF% also showed an upward trend, increasing from 33.59% (95% CI: 31.11-34.07%) to 35.68% (95% CI: 34.90-36.46%), with a trend P -value < 0.0001 ( Figure 1C and Table 2 ). While BMI and waist circumference saw a decline in 2021-2023 compared to the pre-pandemic period (2017-2020), body fat percentage continued to rise. Notably, changes in BMI, waist circumference, and BF% varied significantly based on educational level, with interaction P -values of 0.026, <0.001, and <0.001, respectively ( Supplementary Table 1 ). Among participants with less than high school education during the same period, BMI increased from 26.92 kg/m² (95% CI: 26.33-27.51) to 29.71 kg/m² (95% CI: 28.74-30.67), with a trend P -value < 0.0001 ( Supplementary Table 1 and Supplementary Figure 2A ). Waist circumference also rose from 92.06 cm (95% CI: 90.32-93.80) to 101.43 cm (95% CI: 99.23-103.62), with a trend P-value < 0.0001 ( Supplementary Table 1 and Supplementary Figure 2B ). BF% increased from 30.13% (95% CI: 28.97-31.29%) to 35.49% (95% CI: 33.48-37.51%), with a trend P -value < 0.0001 ( Supplementary Table 1 and Supplementary Figure 2C ). BMI, waist circumference, and BF% trends also varied significantly by FPIR level, with interaction P -values of 0.003, <0.001, and 0.002, respectively ( Supplementary Table 1 ). A marked increase was observed among low-income groups (0 < FPIR < 1). From 2013-2014 to 2021-2023, BMI rose from 28.75 kg/m² (95% CI: 28.28-29.23) to 29.82 kg/m² (95% CI: 28.79-30.86), with a trend P -value of 0.002 ( Supplementary Table 1 and Supplementary Figure 2D ). Waist circumference increased from 97.08 cm (95% CI: 95.41-98.74) to 100.39 cm (95% CI: 98.04-102.74), with a trend P -value < 0.001 ( Supplementary Table 1 and Supplementary Figure 2E ). BF% grew from 33.39% (95% CI: 32.80-33.98%) to 36.66% (95% CI: 34.98-38.54%), with a trend P -value < 0.0001 ( Supplementary Table 1 and Supplementary Figure 2F ). Trends in SBP, DBP, Pulse Rate, and ePWV The rise in DBP was more prominent than that in SBP. The mean SBP increased from 119.63 mmHg (95% CI: 118.61-120.65) in 2013-2014 to 120.25 mmHg (95% CI: 119.47-121.03) in 2021-2023 ( P for trend = 0.0689) ( Figure 2A and Table 2 ). Meanwhile, the mean DBP significantly rose from 68.01 mmHg (95% CI: 67.42-68.60) to 74.17 mmHg (95% CI: 73.29-76.06), with a trend P -value < 0.0001 ( Figure 2B and Table 2 ). Conversely, the pulse rate decreased from 72.27 bpm (95% CI: 71.17-73.37) to 70.59 bpm (95% CI: 69.96-71.23), with a trend P -value < 0.0001 ( Figure 2C and Table 2 ), though there was a subsequent rise in 2021-2023 compared to 2017-2020. The ePWV increased significantly from 7.89 m/s (95% CI: 7.75-8.02) to 8.41 m/s (95% CI: 8.27-8.55), with a trend P -value < 0.0001 ( Figure 2D and Table 2 ). SBP trends differed significantly by gender and educational level ( P for interaction = 0.006 and < 0.0001, respectively) ( Supplementary Table 2 and Supplementary Figure 3A-B ). In males, SBP rose from 121.06 mmHg (95% CI: 119.87-122.25) to 123.27 mmHg (95% CI: 122.41-124.12) ( P for trend = 0.005). For females, a slight decrease was observed from 118.24 mmHg (95% CI: 116.97-119.51) to 117.41 mmHg (95% CI: 116.17-118.66), though the trend was not statistically significant ( P for trend = 0.101). Participants with less than a high school education experienced a significant increase in SBP from 117.56 mmHg (95% CI: 115.77-119.34) to 124.55 mmHg (95% CI: 121.81-127.30) ( P for trend < 0.0001). DBP showed significant variations across race, education, and FPIR ( P for interaction = 0.004, <0.0001, and 0.004, respectively) ( Supplementary Table 2 and Supplementary Figure 3C-E ). The largest increases in DBP occurred among non-Hispanic Black individuals (68.16 mmHg [95% CI: 66.74-69.58] in 2013-2014 to 78.71 mmHg [95% CI: 76.29-81.14] in 2021-2023, P for trend < 0.0001), participants with less than a high school education (64.15 mmHg [95% CI: 63.14-65.17] to 75.17 mmHg [95% CI: 73.03-77.30], P for trend < 0.0001), and low-income individuals (67.65 mmHg [95% CI: 66.25-69.05] to 74.87 mmHg [95% CI: 72.96-76.79], P for trend < 0.0001). Differences in pulse rate were observed between elderly and non-elderly individuals ( P for interaction = 0.027) ( Supplementary Table 2 and Supplementary Figure 3F ). The mean pulse rate (95% CI) in non-elderly individuals showed a significant decrease, from 73.16 bpm (72.02, 74.3) in 2013-2014 to 71.42 bpm (70.84, 72) in 2021-2023 ( P for trend < 0.0001). Differences in ePWV were noted among populations with varying educational attainment and different FPIR levels ( P for interaction < 0.0001 and 0.005, respectively), with the most pronounced differences observed in individuals with less than a high school education and in low-income groups ( Supplementary Table 2 and Supplementary Figure 3G-H ). Among individuals with less than a high school education, the mean ePWV (95% CI) increased from 7.55 m/s (7.34, 7.76) in 2013-2014 to 8.79 m/s (8.38, 9.2) in 2021-2023 ( P for trend < 0.0001). In the low-income group (0 < FPIR < 1), the mean ePWV (95% CI) rose from 7.41 m/s (7.26, 7.56) in 2013-2014 to 8.16 m/s (7.9, 8.41) in 2021-2023 ( P for trend < 0.0001). Trends in Total Cholesterol and HDL-C Overall, no significant changes were observed in total cholesterol or HDL-C levels from 2013 to 2023 ( P for trend = 0.083 and 0.657, respectively) ( Figure 3 and Table 2 ). However, significant interactions with age and educational level were found for total cholesterol ( P for interaction < 0.001 and 0.006). Among non-elderly individuals, total cholesterol rose from 4.78 mmol/L (95% CI: 4.72-4.85) to 4.96 mmol/L (95% CI: 4.89-5.03) ( P for trend = 0.005), while in the elderly, it decreased from 4.81 mmol/L (95% CI: 4.72-4.90) to 4.66 mmol/L (95% CI: 4.58-4.74) ( P for trend = 0.002) ( Supplementary Table 3 and Supplementary Figure 3A ). Participants with less than a high school education saw a rise in total cholesterol from 4.48 mmol/L (95% CI: 4.42-4.54) to 4.76 mmol/L (95% CI: 4.60-4.92) ( P for trend < 0.0001) ( Supplementary Table 3 and Supplementary Figure 3B ). Trends in Fasting Glucose, Glycohemoglobin, Fasting Insulin, and HOMA From 2013 to 2023, fasting glucose and glycohemoglobin levels showed upward trends ( P for trend = 0.001 and 0.027, respectively) ( Figure 4A-B and Table 2 ), while no significant changes were noted in fasting insulin or HOMA scores ( Figure 4C-F and Table 2 ). Fasting glucose increased from 5.77 mmol/L (95% CI: 5.69-5.85) to 5.99 mmol/L (95% CI: 5.88-6.09), and glycohemoglobin rose from 5.58% (95% CI: 5.53-5.62) to 5.67% (95% CI: 5.60-5.74). Among participants who graduated high school, fasting glucose levels increased from 5.82 mmol/L (95% CI: 5.64-6.01) to 6.16 mmol/L (95% CI: 5.93-6.39) ( P for trend = 0.021). For those with less than a high school education, levels rose from 5.75 mmol/L (95% CI: 5.60-5.89) to 6.54 mmol/L (95% CI: 6.24-6.85) ( P for trend < 0.0001) ( Supplementary Table 4 and Supplementary Figure 5A ). Although there was no significant interaction for race ( P = 0.179), a marked increase was seen in Mexican Americans (5.90 mmol/L [95% CI: 5.81-6.00] to 6.64 mmol/L [95% CI: 6.26-7.01], P for trend < 0.0001) ( Supplementary Table 4 and Supplementary Figure 5B ). Similar trends were observed for glycohemoglobin, with significant variations based on educational level and FRIR level ( P for interaction < 0.0001 and 0.022) ( Supplementary Table 4 and Supplementary Figure 5C-D ). Among those with lower education levels, glycohemoglobin increased from 5.58% (95% CI: 5.51-5.66) to 6.14% (95% CI: 5.96-6.32) ( P for trend < 0.0001). There were also differences across FRIR levels, particularly in low-income and middle-income groups, where glycohemoglobin rose from 5.58% (95% CI: 5.48-5.67) to 5.76% (95% CI: 5.63-5.89) ( P for trend = 0.002) and from 5.64% (95% CI: 5.57-5.71) to 5.76% (95% CI: 5.68-5.84) ( P for trend = 0.004). Despite a non-significant interaction P -value for race (P = 0.118), Mexican Americans exhibited a significant increase (5.59% [95% CI: 5.51-5.68] to 6.06% [95% CI: 5.84-6.28], P for trend < 0.001) ( Supplementary Table 4 and Supplementary Figure 5E ). No significant subgroup differences were found for HOMA-IR and HOMA-β. However, HOMA-IS varied with age, declining in the elderly from 0.60 (95% CI: 0.54-0.65) to 0.51 (95% CI: 0.48-0.55) ( Supplementary Table 4 and Supplementary Figure 5F ). Although the interaction P -value for race was not statistically significant ( P = 0.072), a substantial decline was noted among non-Hispanic Black participants (0.75 [95% CI: 0.65-0.85] to 0.51 [95% CI: 0.45-0.57], P for trend < 0.001) ( Supplementary Table 4 and Supplementary Figure 5G ). Discussion To our knowledge, this study represents the first in-depth analysis of trends in cardiovascular and diabetic metabolic biomarkers over time in the US population, encompassing the period during the COVID-19 pandemic. We observed an upward trend in most biomarkers, including BMI, waist circumference, BF%, DBP, ePWV, fasting glucose, and glycohemoglobin, while pulse rate showed a declining trend. Peak values for BMI, waist circumference, and fasting glucose were reached in the 2017-2020 cycle, while body fat, DBP, and ePWV continued to rise in the 2021-2023 cycle. Trends varied across different population subgroups, with education and income identified as significant subgroup characteristics. BMI, waist circumference, BF%, SBP, ePWV, and glycated hemoglobin showed differences between these subgroups. Differences in pulse rate, total cholesterol, and HOMA-IS were noted between older and younger groups. Variations in SBP were also observed across racial groups; although these differences were not mirrored in fasting glucose and glycohemoglobin, both biomarkers significantly increased in the Mexican American subgroup. We first assessed trends in BMI, waist circumference, and BF%, as these three indicators are commonly used to evaluate an individual's cardiometabolic risk. BMI is strongly associated with an increased risk of CVD and diabetes. Compared with individuals of normal weight, competing hazard ratios for incident CVD were 1.21 (95% CI: 1.14-1.28) and 1.32 (95% CI: 1.24-1.40) among overweight men and women (BMI, 25.0-29.9 kg/m 2 ), respectively 28 . Lifetime diabetes risk at age 18 rose from 7.6% to 70.3% among men and from 12.2% to 74.4% among women between underweight and severely obese (BMI >35 kg/m 2 ) categories 29 . However, the limitations of BMI in fully capturing cardiometabolic risk are partly due to its inadequacy as a standalone biomarker for abdominal adiposity 30 . Waist circumference, which is simple to standardize clinically, provides a straightforward measure of abdominal adiposity and is strongly associated with all-cause mortality 31 . Additionally, we assessed BF%, which reflects body composition more accurately than BMI and demonstrates stronger associations with cardiometabolic conditions 32 . Notably, a longitudinal study reported a statistically significant change in total fat percentage before and after the COVID-19 pandemic 33 . The observed subgroup differences in these three biomarkers align with previous studies. For example, a multi-round survey of California adults from 2011 to 2014 indicated an inverse relationship between obesity and household income 34 . Furthermore, BMI and waist circumference were significantly lower across all three higher education categories (vocational secondary, other secondary, and university degree) compared to the lowest education level 35 . Our findings suggest an upward trend in these three biomarkers over the past decade, with low-income and low-education populations being particularly vulnerable. We subsequently conducted a trend analysis of cardiovascular physical examination indicators, including SBP, DBP, pulse rate, and ePWV. Our findings indicate that SBP levels remained well-controlled with no significant upward trend; however, DBP levels showed a marked increase. A comparative study on the impact of systolic and diastolic blood pressures on cardiovascular outcomes found that both SBP and DBP independently contribute to adverse cardiovascular events. In survival models, continuous systolic hypertension (≥140 mmHg) was associated with a hazard ratio per unit increase in z-score of 1.18 (95% CI: 1.17-1.18), while diastolic hypertension (≥90 mmHg) had a hazard ratio of 1.06 (95% CI: 1.06-1.07) 36 . Additionally, the study demonstrated a "J"-shaped relationship between DBP and cardiovascular outcomes, suggesting that both excessively high and low DBP levels may increase the risk of adverse cardiovascular events 36 . Consequently, while elevated DBP in our study may not directly raise cardiovascular event risk, suggesting more robust SBP-lowering strategies that can avoid significant DBP reduction. ePWV, a marker of arterial stiffness reflecting arterial health and aging, was calculated from age and mean BP using an equation from the Reference Values for Arterial Stiffness Collaboration 37 . ePWV is predictive of mortality and cardiovascular risk, correlating with all-cause mortality (HR per 1 m/s increase, 1.13; 95% CI: 1.05-1.21) and myocardial infarction (HR per 1 m/s increase, 1.23; 95% CI: 1.09-1.39) 38 . Our study observed a significant rise in ePWV during the COVID-19 pandemic, consistent with prior research showing progressive ePWV increases across control groups, COVID-19 survivors, and deceased patients (mean adjusted increase per group 1.89 m/s, P < 0.001), suggesting its potential role as a marker related to COVID-19 severity and possibly indicating long-term impacts in cardiovascular diseases outcome 39 . Total cholesterol and HDL levels are indicators of lipid metabolism. Elevated total cholesterol has been associated with increased CVD risk 40 and is also commonly linked to metabolic disturbances in diabetes 41 . Although we did not observe a significant trend in cholesterol levels overall, subgroup analysis revealed a notable decline in TC among the elderly, suggesting improved cholesterol control in this group. Conversely, Total cholesterol showed a significant increase among participants with lower education levels, underscoring a need for targeted attention in these populations. To assess diabetes-related metabolic biomarkers, we included fasting blood glucose, fasting insulin, glycohemoglobin, and HOMA-IR. fasting blood glucose and glycohemoglobin showed significant upward trends, particularly among Mexican Americans. Another study based on NHANES data indicated that the estimated prevalence of diabetes has continued to rise significantly among Mexican American adults 42 , suggesting that this disparity may partly be attributed to higher insurance coverage and greater access to preventive services compared to other minority groups 43,44 . While some cohort studies have reported elevated blood glucose, reduced insulin levels, and increased insulin resistance during and post-COVID-19 infection compared to healthy controls, a broader, long-term national sampling study found that the COVID-19 pandemic did not significantly affect diabetes-related biomarkers 45,46 . Limitations This study has several limitations. First, our analysis included data encompassing the COVID-19 pandemic period (2021-2023 cycle). However, COVID-19 infection status was not reported for participants during this cycle, limiting our ability to evaluate the pandemic's impact on observed trends. Second, due to the partial release of data for 2021-2023, we could not analyze the following metabolic biomarkers, such as triglycerides, low-density lipoprotein cholesterol, glomerular filtration rate, and C-reactive protein. Finally, NHANES response rates have declined over time. To mitigate nonresponse bias, we incorporated survey weights provided by the National Center for Health Statistics 26 . Conclusion In this cross-sectional study, trends in cardiovascular and diabetes-related metabolic biomarkers were analyzed from 2013 to 2023. We identified variations in these biomarkers across subgroups defined by age, gender, race, income, and education level. Our findings offer insights for shaping prevention policies, particularly by addressing the needs of diverse populations. Such policies could contribute to a more equitable society, where everyone has the opportunity to reduce their risk of cardiovascular disease and diabetes. Declarations Ethics statement The current study was supported by the Ethics Review Board of U.S. National Center for Health Statistics, and written informed consents were obtained from all participants of the NAHNES survey. The datasets used and/or analyzed in the current study are available in the article or supplementary material. Disclosure The authors report no conflicts of interest in this work. Acknowledgements None. Author contributions CF designed the study. CF, XQ, TY and ZZ collected the data. XQ prepared figures 1-4. CF, XQ, TY, JLand JS analyzed the data and drafted the manuscript. QZ and JJ revised the final version of the manuscript. All the authors have read and approved the final version of the manuscript. Availability of data and materials The datasets used and/or analyzed in the current study are available in the article or supplementary material. Consent for publication Not Applicable. Funding sources This work was supported by National Traditional Chinese Medicine Inheritance and Innovation Center Special Research Project in 2023(2023QN16) and Guangzhou University of Traditional Chinese Medicine Provincial Student Innovation and Entrepreneurship Training Program Project in 2024 (202410572044). 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COVID-19 induces new-onset insulin resistance and lipid metabolic dysregulation via regulation of secreted metabolic factors. Signal Transduct Target therapy. 2021;6(1):427. Kartika R, Subekti I, Kurniawan F et al. Altered Body Composition and Cytokine Production in Patients with Elevated HOMA-IR after SARS-CoV-2 Infection: A 12-Month Longitudinal Study. Biomedicines 2024;12(7). Tables Table 1. Baseline Characteristics of Participants in NHANES Cycles (2013-2014 to 2021-2023). 2013-2014 2015-2016 2017-2020 2021-2023 P -value Age 44.009(0.619) 45.297(0.692) 47.521(0.842) 49.251(0.637) < 0.0001 Gender 0.72 Female 1358(50.677) 1238(51.149) 1404(50.021) 1371(51.477) Male 1292(49.323) 1208(48.851) 1351(49.979) 1115(48.523) Race 0.704 Mexican American 399(9.430) 408(8.066) 353(8.826) 171(7.127) Non-Hispanic Black 534(10.677) 494(10.110) 652( 9.821) 245( 8.985) Non-Hispanic White 1116(66.914) 851(66.617) 988(64.870) 1545(63.609) Other Hispanic 224(5.292) 327(6.363) 272(6.684) 249(9.164) Other Race - Including Multi-Racial 377( 7.687) 366( 8.844) 490( 9.799) 276(11.114) Education < 0.0001 College or above 578(28.236) 530(28.879) 707(33.684) 984(37.233) High school graduate 502(18.586) 500(20.659) 647(25.237) 489(24.543) Less than high school 869(23.571) 798(21.303) 478(10.205) 282( 9.091) Some college or associates degree 701(29.607) 618(29.158) 923(30.874) 731(29.134) Family poverty-to-income ratio 0.028 >=0,1,3 959(47.918) 781(47.815) 1100(55.249) 1274(54.609) Body mass index 28.542(0.187) 28.969(0.271) 29.635(0.216) 29.427(0.296) 0.002 Waist circumference 97.628(0.393) 99.205(0.666) 100.396(0.586) 100.108(0.681) < 0.001 Body fat percentage 33.590(0.245) 34.470(0.318) 35.484(0.355) 35.681(0.398) < 0.0001 Systolic blood pressure 119.630(0.519) 121.743(0.448) 120.710(0.464) 120.252(0.399) 0.018 Diastolic blood pressure 68.010(0.302) 68.750(0.543) 73.837(0.333) 74.172(0.452) < 0.0001 Pulse rate 72.271(0.563) 72.358(0.285) 68.406(0.285) 70.594(0.324) < 0.0001 Estimated pulse wave velocity 7.888(0.069) 8.054(0.070) 8.219(0.092) 8.410(0.071) < 0.0001 Fasting total cholesterol 4.786(0.025) 4.890(0.032) 4.815(0.046) 4.894(0.030) 0.021 High-density lipoprotein-cholesterol 1.392(0.013) 1.448(0.020) 1.388(0.013) 1.406(0.011) 0.069 Fasting insulin 12.842(0.610) 13.451(0.432) 13.930(0.620) 13.989(0.658) 0.534 Glycohemoglobin 5.576(0.021) 5.643(0.023) 5.670(0.031) 5.666(0.036) 0.034 Fasting glucose 5.768(0.041) 6.004(0.037) 6.087(0.059) 5.987(0.055) < 0.0001 HOMA-IR 3.618(0.212) 3.938(0.125) 4.116(0.228) 4.129(0.256) 0.338 HOMA-IS 0.608(0.021) 0.529(0.020) 0.542(0.017) 0.556(0.025) 0.045 HOMA-beta 108.955(12.134) 115.682( 3.687) 114.135( 4.420) 120.760( 3.532) 0.551 Table 2. Geometric Mean (95% CI) and P-values for Trends in Cardiovascular and Diabetic Metabolic Biomarkers. 2013-2014 2015-2016 2017-2020 2021-2023 P for trend Body mass index 28.54 (28.18, 28.91) 28.97 (28.44, 29.5) 29.63 (29.21, 30.06) 29.43 (28.85, 30.01) 0.003 Waist circumference 97.63 (96.86, 98.4) 99.2 (97.9, 100.51) 100.4 (99.25, 101.54) 100.11 (98.77, 101.44) <0.001 Body fat percentage 33.59 (33.11, 34.07) 34.47 (33.85, 35.09) 35.48 (34.79, 36.18) 35.68 (34.9, 36.46) <0.0001 Diastolic blood pressure 68.01 (67.42, 68.6) 68.75 (67.69, 69.81) 73.84 (73.19, 74.49) 74.17 (73.29, 75.06) <0.0001 Systolic blood pressure 119.63 (118.61, 120.65) 121.74 (120.86, 122.62) 120.71 (119.8, 121.62) 120.25 (119.47, 121.03) 0.689 Pulse rate 72.27 (71.17, 73.37) 72.36 (71.8, 72.92) 68.41 (67.85, 68.96) 70.59 (69.96, 71.23) <0.0001 Estimated pulse wave velocity 7.89 (7.75, 8.02) 8.05 (7.92, 8.19) 8.22 (8.04, 8.4) 8.41 (8.27, 8.55) <0.0001 Fasting total cholesterol 4.79 (4.74, 4.84) 4.89 (4.83, 4.95) 4.81 (4.72, 4.91) 4.89 (4.83, 4.95) 0.083 High density lipoprotein cholesterol 1.39 (1.37, 1.42) 1.45 (1.41, 1.49) 1.39 (1.36, 1.41) 1.41 (1.38, 1.43) 0.657 Fasting glucose 5.77 (5.69, 5.85) 6 (5.93, 6.08) 6.09 (5.97, 6.2) 5.99 (5.88, 6.09) 0.001 Fasting insulin 12.84 (11.65, 14.04) 13.45 (12.6, 14.3) 13.93 (12.71, 15.15) 13.99 (12.7, 15.28) 0.159 Glycohemoglobin 5.58 (5.53, 5.62) 5.64 (5.6, 5.69) 5.67 (5.61, 5.73) 5.67 (5.6, 5.74) 0.027 HOMA_β 108.95 (85.17, 132.74) 115.68 (108.45, 122.91) 114.14 (105.47, 122.8) 120.76 (113.84, 127.68) 0.396 HOMA_IR 3.62 (3.2, 4.03) 3.94 (3.69, 4.18) 4.12 (3.67, 4.56) 4.13 (3.63, 4.63) 0.096 HOMA_IS 0.61 (0.57, 0.65) 0.53 (0.49, 0.57) 0.54 (0.51, 0.57) 0.56 (0.51, 0.6) 0.154 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Supplementary Table 1. Geometric Mean (95% CI) and P -values for Trends in Body Mass Index, Waist Circumference, and Body Fat Percentage by Subgroups. Supplementary Table 2. Geometric Mean (95% CI) and P -values for Trends in Systolic Blood Pressure, Diastolic Blood Pressure, Pulse Rate, and Estimated Pulse Wave Velocity by Subgroups. Supplementary Table 3. Geometric Mean (95% CI) and P -values for Trends in Fasting Total Cholesterol and High-Density Lipoprotein Cholesterol by Subgroups. Supplementary Table 4. Geometric Mean (95% CI) and P -values for Trends in Fasting Glucose, Glycohemoglobin, Fasting Insulin, and HOMA Score by Subgroups. SupplementaryFigure1.pdf Supplementary Figure 1. Study Participant Flow Chart. SupplementaryFigure2.pdf Supplementary Figure 2. Trends in Body Mass Index, Waist Circumference, and Body Fat Percentage by Subgroups. SupplementaryFigure3.pdf Supplementary Figure 3. Trends in Systolic Blood Pressure, Diastolic Blood Pressure, Pulse Rate, and Estimated Pulse Wave Velocity by Subgroups. SupplementaryFigure4.pdf Supplementary Figure 4. Trends in Fasting Total Cholesterol and High-Density Lipoprotein Cholesterol by Subgroups. SupplementaryFigure5.pdf Supplementary Figure 5. Trends in Fasting Glucose, Glycohemoglobin, Fasting Insulin, and HOMA Score by Subgroups. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5704576","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":396170612,"identity":"b96e6748-1f97-41f5-8010-bce8fa9fc3ae","order_by":0,"name":"Caishan Fang","email":"","orcid":"","institution":"Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Caishan","middleName":"","lastName":"Fang","suffix":""},{"id":396170613,"identity":"d6e9719e-0eae-4c13-88f7-e3852a4cb6f9","order_by":1,"name":"Xiangjun 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Cholesterol.\u003c/p\u003e","description":"","filename":"Figure300.png","url":"https://assets-eu.researchsquare.com/files/rs-5704576/v1/b480d65b44250710d0b912f2.png"},{"id":72805389,"identity":"70c6ac8b-ad6a-4f7d-9f53-a4c138bdf104","added_by":"auto","created_at":"2025-01-02 10:16:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2126746,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in Fasting Glucose, Glycohemoglobin, Fasting Insulin, and HOMA Score.\u003c/p\u003e","description":"","filename":"Figure400.png","url":"https://assets-eu.researchsquare.com/files/rs-5704576/v1/be51b0a31bac8b677b46c5e1.png"},{"id":72833338,"identity":"b1374e8e-be18-4a2e-88aa-36a22be54d9a","added_by":"auto","created_at":"2025-01-02 16:16:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5782049,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5704576/v1/0efb9da0-19d8-4ab6-a8e8-6bbece43b7ef.pdf"},{"id":72805373,"identity":"8e910adc-509b-489e-bd72-2bb8582603ae","added_by":"auto","created_at":"2025-01-02 10:16:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":83861,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 1. Geometric Mean (95% CI) and \u003cem\u003eP\u003c/em\u003e-values for Trends in Body Mass Index, Waist Circumference, and Body Fat Percentage by Subgroups.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 2. Geometric Mean (95% CI) and \u003cem\u003eP\u003c/em\u003e-values for Trends in Systolic Blood Pressure, Diastolic Blood Pressure, Pulse Rate, and Estimated Pulse Wave Velocity by Subgroups.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 3. Geometric Mean (95% CI) and \u003cem\u003eP\u003c/em\u003e-values for Trends in Fasting Total Cholesterol and High-Density Lipoprotein Cholesterol by Subgroups.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 4. Geometric Mean (95% CI) and \u003cem\u003eP\u003c/em\u003e-values for Trends in Fasting Glucose, Glycohemoglobin, Fasting Insulin, and HOMA Score by Subgroups.\u003c/p\u003e","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5704576/v1/faecf2b22b872ecd6f638893.docx"},{"id":72805372,"identity":"4897ede1-b6d4-403a-8dc6-dc6235dcb00a","added_by":"auto","created_at":"2025-01-02 10:16:52","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":38628,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure 1. Study Participant Flow Chart.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5704576/v1/001045bd3e59b2d1e98095ba.pdf"},{"id":72805376,"identity":"5ce4b207-bd4e-4aef-9109-3d0ff68c903c","added_by":"auto","created_at":"2025-01-02 10:16:53","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":538669,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure 2. Trends in Body Mass Index, Waist Circumference, and Body Fat Percentage by Subgroups.\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5704576/v1/32dbdd927cd122d6d925ae30.pdf"},{"id":72805378,"identity":"f2aa88b4-40b2-409b-8fbe-1ca0b2858a49","added_by":"auto","created_at":"2025-01-02 10:16:53","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":582941,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure 3. Trends in Systolic Blood Pressure, Diastolic Blood Pressure, Pulse Rate, and Estimated Pulse Wave Velocity by Subgroups.\u003c/p\u003e","description":"","filename":"SupplementaryFigure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5704576/v1/4a14335bb6aa2acf377e2fbe.pdf"},{"id":72806640,"identity":"2f9190cd-ab7d-415d-8553-e1f94c90ffa4","added_by":"auto","created_at":"2025-01-02 10:24:53","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":480168,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure 4. Trends in Fasting Total Cholesterol and High-Density Lipoprotein Cholesterol by Subgroups.\u003c/p\u003e","description":"","filename":"SupplementaryFigure4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5704576/v1/cab5e7ee63ec3d8c4552309e.pdf"},{"id":72805384,"identity":"950f5f39-831a-4cd4-bf2e-c27b57394e7c","added_by":"auto","created_at":"2025-01-02 10:16:53","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":576372,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure 5. Trends in Fasting Glucose, Glycohemoglobin, Fasting Insulin, and HOMA Score by Subgroups.\u003c/p\u003e","description":"","filename":"SupplementaryFigure5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5704576/v1/220e1baf5037e21a13854400.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Shifts in Metabolic Biomarkers Related to Cardiovascular Disease and Diabetes from 2013 to 2023: A Decade of Change, Including the COVID-19 Era","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) and diabetes are leading causes of disability and mortality globally \u003csup\u003e \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e \u003c/sup\u003e. From 1990 to 2019, total CVD cases nearly doubled, rising from 271\u0026nbsp;million to 523\u0026nbsp;million, while CVD-related deaths grew from 12.1\u0026nbsp;million to 18.6 million\u003csup\u003e \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e \u003c/sup\u003e. Managing CVD imposes a significant economic burden; in 2020, one in three adults in the U.S. required care for a cardiovascular risk factor or condition. By 2035, the annual healthcare costs for CVD are projected to increase nearly fourfold from \u003cspan\u003e$\u003c/span\u003e393\u0026nbsp;billion to \u003cspan\u003e$\u003c/span\u003e1.49 trillion, with productivity losses expected to grow by 54%, from \u003cspan\u003e$\u003c/span\u003e234\u0026nbsp;billion to \u003cspan\u003e$\u003c/span\u003e361 billion\u003csup\u003e \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e \u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBetween 1990 and 2021, the global age-standardized prevalence of diabetes rose by 90.5%, from 3.2\u0026ndash;6.1%, affecting 529\u0026nbsp;million individuals by 2021. Forecasts indicate a further 59.7% increase in prevalence by 2050, reaching 9.8%, with 1.31\u0026nbsp;billion people expected to have diabetes\u003csup\u003e \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e \u003c/sup\u003e. In the U.S., diabetes-related costs were estimated at \u003cspan\u003e$\u003c/span\u003e327\u0026nbsp;billion in 2017, accounting for 24% of healthcare expenditures, rising to \u003cspan\u003e$\u003c/span\u003e412.9\u0026nbsp;billion by 2022, including \u003cspan\u003e$\u003c/span\u003e306.6\u0026nbsp;billion in direct medical costs and \u003cspan\u003e$\u003c/span\u003e106.3\u0026nbsp;billion in indirect costs\u003csup\u003e \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e \u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCVD and diabetes are closely interconnected, sharing several risk factors, such as obesity, smoking, and physical inactivity, with diabetes itself serving as a CVD risk factor\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Adults with diabetes face a 2\u0026ndash;4 times higher risk of cardiovascular events compared to those without diabetes, and this risk escalates with poor glycemic control\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Modifiable risk factors, including body mass index (BMI), blood pressure, cholesterol levels, smoking, and diabetes, contribute significantly to the incidence and prevalence of CVD\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Preventing CVD in diabetic patients requires managing risk factors, such as glycohemoglobin, blood pressure, and cholesterol levels \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Although control over these factors (e.g., glycohemoglobin\u0026thinsp;\u0026lt;\u0026thinsp;7.0%, blood pressure\u0026thinsp;\u0026lt;\u0026thinsp;130/80 mmHg, low-density lipoprotein cholesterol\u0026thinsp;\u0026lt;\u0026thinsp;100 mg/dL) improved from 1988 to 2010, only about 18.8% of U.S. adults met all three targets by 2007-2010\u003csup\u003e15,16\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBetween 2020 and 2023, the Coronavirus disease 2019 (COVID-19) pandemic added additional complexities, with CVD and diabetes being potential long-term outcomes of infection. COVID-19 patients exhibited a significantly higher risk of all cardiovascular outcomes than non-infected individuals (HR 1.66 [1.62\u0026ndash;1.71] for those with diabetes; HR 1.75 [1.73\u0026ndash;1.78])\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Additionally, \"Long COVID\" may continue affecting cardiovascular health with fluctuating symptoms over time\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The trends in CVD and diabetic metabolic biomarkers remain unclear, especially following the pandemic, the understanding of these trends is paramount for population-level health complications and prevention efforts.\u003c/p\u003e \u003cp\u003eUsing recent National Health and Nutrition Examination Survey (NHANES) data, this study primarily aimed to provide national estimates to assess trends in CVD and diabetes-related metabolic biomarkers among adults in U.S from 2013 to 2023.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe First Affiliated Hospital of Guangzhou University of Chinese Medicine Ethics Committee deemed this cross-sectional study exempt from ethical review and informed consent because publicly available and deidentified NHANES data were used. For NHANES cycles, researchers recruit participants using established consent protocols approved by the National Center for Health Statistics Institutional Review Board\u003csup\u003e19\u003c/sup\u003e.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDetails of the data collection procedures, study design, and protocol were published previously\u003csup\u003e20\u003c/sup\u003e. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData source and study population\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used data from the NHANES, which is conducted biennially by the US Centers for Disease Control and Prevention (CDC) for public health surveillance\u003csup\u003e21\u003c/sup\u003e. The CDC has disclosed NHANES data during the COVID-19 pandemic. Therefore, we used data from NHANES cycles from 2013 to August 2023. Because data collection for the 2019-2020 NHANES cycle was not completed and the collected data were not nationally representative, we used 2017-2020 prepandemic NHANES data to ensure nationally representative estimates\u003csup\u003e22\u003c/sup\u003e. NHANES cycle August 2021- August 2023 was used to represent data on the pandemic. We excluded participants with missing data on age, gender, race, FPIR, educational attainment, and cardiovascular disease or diabetes-related metabolic biomarkers. The patient selection flowchart is shown in \u003cstrong\u003eSupplementary Figure 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of cardiovascular disease and diabetes-related metabolic biomarkers\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe included a total of 15 cardiovascular and diabetes-related metabolic biomarkers, including body mass index (BMI), waist circumference, body fat percentage, systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse rate, estimated pulse wave velocity (ePWV), fasting glucose, glycohemoglobin, fasting total cholesterol, high-density lipoprotein-cholesterol(HDL-C), fasting insulin, and insulin resistance index.\u003c/p\u003e\n\u003cp\u003eBMI and waist circumference were derived from body measurements collected by trained health technicians at the Mobile Examination Center (MEC). The body fat percentage (BF%) was calculated using the following formula:\u003c/p\u003e\n\u003cp\u003eBF% = -44.988 + (0.503 \u0026times; age) + (10.689 \u0026times; sex) + (3.172 \u0026times; BMI) - (0.026 \u0026times; BMI\u003csup\u003e2\u003c/sup\u003e) + (0.181 \u0026times; BMI \u0026times; sex) - (0.02 \u0026times; BMI \u0026times; age) - (0.005 \u0026times; BMI\u003csup\u003e2\u003c/sup\u003e \u0026times; sex) + (0.00021 \u0026times; BMI\u003csup\u003e2\u003c/sup\u003e \u0026times; age). Where sex is coded as 0 for male and 1 for female, and age is in years\u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe SBP and DBP measurements were taken following these guidelines: after a 5-minute rest in a seated position, three consecutive BP readings were obtained once the maximum inflation level was established. If a measurement was interrupted or incomplete, a fourth attempt was made. The pulse rate was measured by locating the radial pulse with the participant\u0026apos;s elbow and forearm resting comfortably on a table, palm facing up, and counting for 30 seconds.\u003c/p\u003e\n\u003cp\u003eThe ePWV was calculated with the following formula\u003csup\u003e24\u003c/sup\u003e:\u003c/p\u003e\n\u003col class=\"decimal_type\"\u003e\n \u003cli\u003eNo cardiovascular disease risk factor: 4.62-0.13 \u0026times; age + 0.0018 \u0026times; age\u003csup\u003e2\u003c/sup\u003e + 0.0006 \u0026times; age \u0026times; mean arterial pressure (MAP) + 0.0284 \u0026times; MAP\u003c/li\u003e\n \u003cli\u003e\u0026ge;1 cardiovascular disease risk factor: 9.587-0.402 \u0026times; age + 4.560 \u0026times; 10\u003csup\u003e-3\u003c/sup\u003e \u0026times; age\u003csup\u003e2\u003c/sup\u003e-2.621 \u0026times; 10\u003csup\u003e-5\u003c/sup\u003e \u0026times;\u0026nbsp;age\u003csup\u003e2\u003c/sup\u003e \u0026times; MAP + 3.176 \u0026times; 10\u003csup\u003e-3\u003c/sup\u003e \u0026times; age \u0026times; MAP - 1.832 \u0026times; 10\u003csup\u003e-2\u003c/sup\u003e \u0026times; MAP\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe MAP was calculated as: (DBP) + 0.4 \u0026times;\u0026nbsp;([SBP]-DBP)\u003c/p\u003e\n\u003cp\u003eFasting glucose levels were determined using an enzymatic method in which glucose is converted to glucose-6-phosphate by hexokinase in the presence of ATP. The glucose-6-phosphate dehydrogenase then converts G-6-P to gluconate-6-phosphate in the presence of NADP+. The reduction of NADP+ to NADPH was measured by the increase in absorbance at 340 nm (secondary wavelength = 700 nm).\u003c/p\u003e\n\u003cp\u003eGlycohemoglobin was measured by diluting the whole blood sample with a hemolysis solution, followed by injection into an HPLC analytical column. The separated hemoglobin components passed through a photometric flow cell, with absorbance changes measured at 415 nm.\u003c/p\u003e\n\u003cp\u003eTotal cholesterol was measured using an enzymatic assay in which esterified cholesterol was converted to free cholesterol by cholesterol esterase. Cholesterol oxidase then converted the cholesterol to cholest-4-en-3-one and hydrogen peroxide, which reacted with 4-aminophenazone in the presence of peroxidase, forming a colored product measured at 505 nm (secondary wavelength = 700 nm).\u003c/p\u003e\n\u003cp\u003eHDL cholesterol was measured using a method involving magnesium/dextran sulfate to form water-soluble complexes with non-HDL fractions, followed by conversion of HDL cholesterol esters to HDL cholesterol. Subsequent oxidation produced hydrogen peroxide, which combined with 4-amino-antipyrine and HSDA under the action of peroxidase to form a pigment measured at 600 nm (secondary wavelength = 700 nm).\u003c/p\u003e\n\u003cp\u003eFasting insulin was quantified using the AIA-PACK IRI, a two-site immunoenzymometric assay performed on a Tosoh AIA System analyzer. Insulin in the sample was bound to a monoclonal antibody on a magnetic solid phase and an enzyme-labeled monoclonal antibody. The amount of enzyme-labeled antibody bound was proportional to the insulin concentration and was quantified using a standard curve.\u003c/p\u003e\n\u003cp\u003eThe Homeostasis Model Assessment (HOMA) included calculations for HOMA-IR (insulin resistance), HOMA-IS (insulin sensitivity), and HOMA-\u0026beta;% (\u0026beta;-cell function), with the formulas as follows\u003csup\u003e25\u003c/sup\u003e:\u003c/p\u003e\n\u003cp\u003eHOMA-IR: (fasting glucose [mmol/L)] \u0026times; fasting insulin [mIU/L]/22.5)\u003c/p\u003e\n\u003cp\u003eHOMA-IS：1/HOMA-IR\u003c/p\u003e\n\u003cp\u003eHOMA-\u0026beta;%: 20 \u0026times; fasting insulin (uU/mL) / (fasting glucos(mmol/L) - 3.5)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe adjusted the weights for the prepandemic period following official guidelines to reduce potential bias. The analysis utilized NHANES Mobile Examination Center (MEC) exam weights, incorporating sampling, stratification, and clustering to produce nationally representative estimates\u003csup\u003e26\u003c/sup\u003e. While the 2017-2020 prepandemic NHANES data file spans 3.2 years, the other NHANES cycles cover 2 years each. To account for this discrepancy, we adjusted the survey weights when combining the 2017-2020 data with other 2-year cycles, ensuring they reflected the extended time frame and larger population. New multicycle sample weights were derived based on the combined survey periods (2013-2014, 2015-2016, 2017-2020, and 2021-2023, totaling 9.2 years) using the formulas: (1) Weight = Weight \u0026times; (2/9.2) for the 2013-2014, 2015-2016, or 2021-2023 cycles, and (2) Weight = Weight \u0026times; (3.2/9.2) for the 2017-2020 cycle.\u003c/p\u003e\n\u003cp\u003eIn this study, baseline characteristics of participants were described by survey cycle. Means (with standard errors) were reported for continuous variables, while percentages were presented for categorical variables. Given the skewed distribution ofcardiovascular disease and diabetes-related metabolic biomarkers , we calculated geometric means (referred to hereafter as means) for these measures. Confidence intervals were determined using Taylor series linearization. To assess trends over time, we applied a survey-weighted linear regression model, treating the survey cycle as a continuous variable\u003csup\u003e26\u003c/sup\u003e. A total of three statistical models were constructed in each analysis. The crude model was the non-adjusted model with no covariates adjusted. Model I adjusted for age and gender. Model II adjusted for age, gender, race, education, and FPIR. Potential variations in trends across subgroups were evaluated using survey-weighted Wald F statistics\u003csup\u003e27\u003c/sup\u003e, assessing interactions between survey cycles and subgroup indicators such as age, gender, race, educational level, and family poverty-to-income ratio (FPIR).\u003c/p\u003e\n\u003cp\u003eStatistical analysis was conducted using R software (version 4.3.1), with the NHANES survey design accounted for via the \u003cem\u003esurvey\u003c/em\u003e package. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (two-tailed). Data analysis was performed between August and October 2024.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of the participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 10,337 participants were included in the study, distributed across four NHANES cycles: 2,650 from the 2013-2014 cycle, 2,446 from the 2015-2016 cycle, 2,755 from the 2017-2020 cycle, and 2,486 from the 2021-2023 cycle (\u003cstrong\u003eTable 1\u003c/strong\u003e). There were no significant differences in age distribution, gender, or race across the four cycles. However, significant variations were observed in educational attainment and FPIR, with a higher proportion of participants having higher education and income levels in the 2017-2020 and 2021-2023 cycles. Additionally, BMI, waist circumference, systolic blood pressure, diastolic blood pressure, body fat percentage, fasting blood glucose, total cholesterol, glycohemoglobin, HOMA-IS, ePWV, and pulse rate showed significant differences across the cycles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrends in BMI, Waist Circumference, and BF%\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 2013-2014 to 2021-2023, the mean BMI rose from 28.54 kg/m\u0026sup2; (95% CI: 28.18-28.91) to 29.43 kg/m\u0026sup2; (95% CI: 28.85-30.01), with a significant trend (\u003cem\u003eP\u003c/em\u003e = 0.003; \u0026nbsp;\u003cstrong\u003eFigure 1A and Table 2\u003c/strong\u003e). Similarly, the mean waist circumference increased from 97.63 cm (95% CI: 96.86-98.40) to 100.11 cm (95% CI: 98.77-101.44), with a trend \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.001 (\u003cstrong\u003eFigure 1B and Table 2\u003c/strong\u003e). BF% also showed an upward trend, increasing from 33.59% (95% CI: 31.11-34.07%) to 35.68% (95% CI: 34.90-36.46%), with a trend \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.0001 (\u003cstrong\u003eFigure 1C and Table 2\u003c/strong\u003e). While BMI and waist circumference saw a decline in 2021-2023 compared to the pre-pandemic period (2017-2020), body fat percentage continued to rise.\u003c/p\u003e\n\u003cp\u003eNotably, changes in BMI, waist circumference, and BF% varied significantly based on educational level, with interaction \u003cem\u003eP\u003c/em\u003e-values of 0.026, \u0026lt;0.001, and \u0026lt;0.001, respectively (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e). Among participants with less than high school education during the same period, BMI increased from 26.92 kg/m\u0026sup2; (95% CI: 26.33-27.51) to 29.71 kg/m\u0026sup2; (95% CI: 28.74-30.67), with a trend \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.0001 (\u003cstrong\u003eSupplementary Table 1 and Supplementary Figure 2A\u003c/strong\u003e). Waist circumference also rose from 92.06 cm (95% CI: 90.32-93.80) to 101.43 cm (95% CI: 99.23-103.62), with a trend P-value \u0026lt; 0.0001 (\u003cstrong\u003eSupplementary Table 1 and Supplementary Figure 2B\u003c/strong\u003e). BF% increased from 30.13% (95% CI: 28.97-31.29%) to 35.49% (95% CI: 33.48-37.51%), with a trend \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.0001 (\u003cstrong\u003eSupplementary Table 1 and Supplementary Figure 2C\u003c/strong\u003e). BMI, waist circumference, and BF% trends also varied significantly by FPIR level, with interaction \u003cem\u003eP\u003c/em\u003e-values of 0.003, \u0026lt;0.001, and 0.002, respectively \u0026nbsp;(\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e). A marked increase was observed among low-income groups (0 \u0026lt; FPIR \u0026lt; 1). From 2013-2014 to 2021-2023, BMI rose from 28.75 kg/m\u0026sup2; (95% CI: 28.28-29.23) to 29.82 kg/m\u0026sup2; (95% CI: 28.79-30.86), with a trend \u003cem\u003eP\u003c/em\u003e-value of 0.002 (\u003cstrong\u003eSupplementary Table 1 and Supplementary Figure 2D\u003c/strong\u003e). Waist circumference increased from 97.08 cm (95% CI: 95.41-98.74) to 100.39 cm (95% CI: 98.04-102.74), with a trend \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.001 (\u003cstrong\u003eSupplementary Table 1 and Supplementary Figure 2E\u003c/strong\u003e). BF% grew from 33.39% (95% CI: 32.80-33.98%) to 36.66% (95% CI: 34.98-38.54%), with a trend \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.0001 (\u003cstrong\u003eSupplementary Table 1 and Supplementary Figure 2F\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrends in SBP, DBP, Pulse Rate, and ePWV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rise in DBP was more prominent than that in SBP. The mean SBP increased from 119.63 mmHg (95% CI: 118.61-120.65) in 2013-2014 to 120.25 mmHg (95% CI: 119.47-121.03) in 2021-2023 (\u003cem\u003eP\u003c/em\u003e for trend = 0.0689) (\u003cstrong\u003eFigure 2A and Table 2\u003c/strong\u003e). Meanwhile, the mean DBP significantly rose from 68.01 mmHg (95% CI: 67.42-68.60) to 74.17 mmHg (95% CI: 73.29-76.06), with a trend \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.0001 (\u003cstrong\u003eFigure 2B and Table 2\u003c/strong\u003e). Conversely, the pulse rate decreased from 72.27 bpm (95% CI: 71.17-73.37) to 70.59 bpm (95% CI: 69.96-71.23), with a trend \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.0001 (\u003cstrong\u003eFigure 2C and Table 2\u003c/strong\u003e), though there was a subsequent rise in 2021-2023 compared to 2017-2020. The ePWV increased significantly from 7.89 m/s (95% CI: 7.75-8.02) to 8.41 m/s (95% CI: 8.27-8.55), with a trend \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.0001 (\u003cstrong\u003eFigure 2D and Table 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eSBP trends differed significantly by gender and educational level (\u003cem\u003eP\u003c/em\u003e for interaction = 0.006 and \u0026lt; 0.0001, respectively) (\u003cstrong\u003eSupplementary Table 2 and Supplementary Figure 3A-B\u003c/strong\u003e). In males, SBP rose from 121.06 mmHg (95% CI: 119.87-122.25) to 123.27 mmHg (95% CI: 122.41-124.12) (\u003cem\u003eP\u003c/em\u003e for trend = 0.005). For females, a slight decrease was observed from 118.24 mmHg (95% CI: 116.97-119.51) to 117.41 mmHg (95% CI: 116.17-118.66), though the trend was not statistically significant (\u003cem\u003eP\u003c/em\u003e for trend = 0.101). Participants with less than a high school education experienced a significant increase in SBP from 117.56 mmHg (95% CI: 115.77-119.34) to 124.55 mmHg (95% CI: 121.81-127.30) (\u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.0001).\u003c/p\u003e\n\u003cp\u003eDBP showed significant variations across race, education, and FPIR (\u003cem\u003eP\u003c/em\u003e for interaction = 0.004, \u0026lt;0.0001, and 0.004, respectively) (\u003cstrong\u003eSupplementary Table 2 and Supplementary Figure 3C-E\u003c/strong\u003e). The largest increases in DBP occurred among non-Hispanic Black individuals (68.16 mmHg [95% CI: 66.74-69.58] in 2013-2014 to 78.71 mmHg [95% CI: 76.29-81.14] in 2021-2023, \u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.0001), participants with less than a high school education (64.15 mmHg [95% CI: 63.14-65.17] to 75.17 mmHg [95% CI: 73.03-77.30], \u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.0001), and low-income individuals (67.65 mmHg [95% CI: 66.25-69.05] to 74.87 mmHg [95% CI: 72.96-76.79], \u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.0001).\u003c/p\u003e\n\u003cp\u003eDifferences in pulse rate were observed between elderly and non-elderly individuals (\u003cem\u003eP\u003c/em\u003e for interaction = 0.027) (\u003cstrong\u003eSupplementary Table 2 and Supplementary Figure 3F\u003c/strong\u003e). The mean pulse rate (95% CI) in non-elderly individuals showed a significant decrease, from 73.16 bpm (72.02, 74.3) in 2013-2014 to 71.42 bpm (70.84, 72) in 2021-2023 (\u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.0001).\u003c/p\u003e\n\u003cp\u003eDifferences in ePWV were noted among populations with varying educational attainment and different FPIR levels (\u003cem\u003eP\u003c/em\u003e for interaction \u0026lt; 0.0001 and 0.005, respectively), with the most pronounced differences observed in individuals with less than a high school education and in low-income groups (\u003cstrong\u003eSupplementary Table 2 and Supplementary Figure 3G-H\u003c/strong\u003e). Among individuals with less than a high school education, the mean ePWV (95% CI) increased from 7.55 m/s (7.34, 7.76) in 2013-2014 to 8.79 m/s (8.38, 9.2) in 2021-2023 (\u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.0001). In the low-income group (0 \u0026lt; FPIR \u0026lt; 1), the mean ePWV (95% CI) rose from 7.41 m/s (7.26, 7.56) in 2013-2014 to 8.16 m/s (7.9, 8.41) in 2021-2023 (\u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.0001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrends in Total Cholesterol and HDL-C\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, no significant changes were observed in total cholesterol or HDL-C levels from 2013 to 2023 (\u003cem\u003eP\u003c/em\u003e for trend = 0.083 and 0.657, respectively) (\u003cstrong\u003eFigure 3 and Table 2\u003c/strong\u003e). However, significant interactions with age and educational level were found for total cholesterol (\u003cem\u003eP\u003c/em\u003e for interaction \u0026lt; 0.001 and 0.006). Among non-elderly individuals, total cholesterol rose from 4.78 mmol/L (95% CI: 4.72-4.85) to 4.96 mmol/L (95% CI: 4.89-5.03) (\u003cem\u003eP\u003c/em\u003e for trend = 0.005), while in the elderly, it decreased from 4.81 mmol/L (95% CI: 4.72-4.90) to 4.66 mmol/L (95% CI: 4.58-4.74) (\u003cem\u003eP\u003c/em\u003e for trend = 0.002) (\u003cstrong\u003eSupplementary Table 3 and Supplementary Figure 3A\u003c/strong\u003e). Participants with less than a high school education saw a rise in total cholesterol from 4.48 mmol/L (95% CI: 4.42-4.54) to 4.76 mmol/L (95% CI: 4.60-4.92) (\u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.0001) (\u003cstrong\u003eSupplementary Table 3 and Supplementary Figure 3B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrends in Fasting Glucose, Glycohemoglobin, Fasting Insulin, and HOMA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 2013 to 2023, fasting glucose and glycohemoglobin levels showed upward trends (\u003cem\u003eP\u003c/em\u003e for trend = 0.001 and 0.027, respectively) (\u003cstrong\u003eFigure 4A-B and Table 2\u003c/strong\u003e), while no significant changes were noted in fasting insulin or HOMA scores (\u003cstrong\u003eFigure 4C-F and Table 2\u003c/strong\u003e). Fasting glucose increased from 5.77 mmol/L (95% CI: 5.69-5.85) to 5.99 mmol/L (95% CI: 5.88-6.09), and glycohemoglobin rose from 5.58% (95% CI: 5.53-5.62) to 5.67% (95% CI: 5.60-5.74).\u003c/p\u003e\n\u003cp\u003eAmong participants who graduated high school, fasting glucose levels increased from 5.82 mmol/L (95% CI: 5.64-6.01) to 6.16 mmol/L (95% CI: 5.93-6.39) (\u003cem\u003eP\u003c/em\u003e for trend = 0.021). For those with less than a high school education, levels rose from 5.75 mmol/L (95% CI: 5.60-5.89) to 6.54 mmol/L (95% CI: 6.24-6.85) (\u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.0001) (\u003cstrong\u003eSupplementary Table 4 and Supplementary Figure 5A\u003c/strong\u003e). Although there was no significant interaction for race (\u003cem\u003eP\u003c/em\u003e = 0.179), a marked increase was seen in Mexican Americans (5.90 mmol/L [95% CI: 5.81-6.00] to 6.64 mmol/L [95% CI: 6.26-7.01], \u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.0001) (\u003cstrong\u003eSupplementary Table 4 and Supplementary Figure 5B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eSimilar trends were observed for glycohemoglobin, with significant variations based on educational level and FRIR level (\u003cem\u003eP\u003c/em\u003e for interaction \u0026lt; 0.0001 and 0.022) (\u003cstrong\u003eSupplementary Table 4 and Supplementary Figure 5C-D\u003c/strong\u003e). Among those with lower education levels, glycohemoglobin increased from 5.58% (95% CI: 5.51-5.66) to 6.14% (95% CI: 5.96-6.32) (\u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.0001). There were also differences across FRIR levels, particularly in low-income and middle-income groups, where glycohemoglobin rose from 5.58% (95% CI: 5.48-5.67) to 5.76% (95% CI: 5.63-5.89) (\u003cem\u003eP\u003c/em\u003e for trend = 0.002) and from 5.64% (95% CI: 5.57-5.71) to 5.76% (95% CI: 5.68-5.84) (\u003cem\u003eP\u003c/em\u003e for trend = 0.004). Despite a non-significant interaction \u003cem\u003eP\u003c/em\u003e-value for race (P = 0.118), Mexican Americans exhibited a significant increase (5.59% [95% CI: 5.51-5.68] to 6.06% [95% CI: 5.84-6.28], \u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.001) (\u003cstrong\u003eSupplementary Table 4 and Supplementary Figure 5E\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eNo significant subgroup differences were found for HOMA-IR and HOMA-\u0026beta;. However, HOMA-IS varied with age, declining in the elderly from 0.60 (95% CI: 0.54-0.65) to 0.51 (95% CI: 0.48-0.55) (\u003cstrong\u003eSupplementary Table 4 and Supplementary Figure 5F\u003c/strong\u003e). Although the interaction \u003cem\u003eP\u003c/em\u003e-value for race was not statistically significant (\u003cem\u003eP\u003c/em\u003e = 0.072), a substantial decline was noted among non-Hispanic Black participants (0.75 [95% CI: 0.65-0.85] to 0.51 [95% CI: 0.45-0.57], \u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.001) (\u003cstrong\u003eSupplementary Table 4 and Supplementary Figure 5G\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this study represents the first in-depth analysis of trends in cardiovascular and diabetic metabolic biomarkers over time in the US population, encompassing the period during the COVID-19 pandemic. We observed an upward trend in most biomarkers, including BMI, waist circumference, BF%, DBP, ePWV, fasting glucose, and glycohemoglobin, while pulse rate showed a declining trend. Peak values for BMI, waist circumference, and fasting glucose were reached in the 2017-2020 cycle, while body fat, DBP, and ePWV continued to rise in the 2021-2023 cycle. Trends varied across different population subgroups, with education and income identified as significant subgroup characteristics. BMI, waist circumference, BF%, SBP, ePWV, and glycated hemoglobin showed differences between these subgroups. Differences in pulse rate, total cholesterol, and HOMA-IS were noted between older and younger groups. Variations in SBP were also observed across racial groups; although these differences were not mirrored in fasting glucose and glycohemoglobin, both biomarkers significantly increased in the Mexican American subgroup.\u003c/p\u003e\n\u003cp\u003eWe first assessed trends in BMI, waist circumference, and BF%, as these three indicators are commonly used to evaluate an individual\u0026apos;s cardiometabolic risk. BMI is strongly associated with an increased risk of CVD and diabetes. Compared with individuals of normal weight, competing hazard ratios for incident CVD were 1.21 (95% CI: 1.14-1.28) and 1.32 (95% CI: 1.24-1.40) among overweight men and women (BMI, 25.0-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e), respectively\u003csup\u003e28\u003c/sup\u003e. Lifetime diabetes risk at age 18 rose from 7.6% to 70.3% among men and from 12.2% to 74.4% among women between underweight and severely obese (BMI \u0026gt;35 kg/m\u003csup\u003e2\u003c/sup\u003e) categories\u003csup\u003e29\u003c/sup\u003e. However, the limitations of BMI in fully capturing cardiometabolic risk are partly due to its inadequacy as a standalone biomarker for abdominal adiposity\u003csup\u003e30\u003c/sup\u003e. Waist circumference, which is simple to standardize clinically, provides a straightforward measure of abdominal adiposity and is strongly associated with all-cause mortality\u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAdditionally, we assessed BF%, which reflects body composition more accurately than BMI and demonstrates stronger associations with cardiometabolic conditions\u003csup\u003e32\u003c/sup\u003e. Notably, a longitudinal study reported a statistically significant change in total fat percentage before and after the COVID-19 pandemic\u003csup\u003e33\u003c/sup\u003e. The observed subgroup differences in these three biomarkers align with previous studies. For example, a multi-round survey of California adults from 2011 to 2014 indicated an inverse relationship between obesity and household income\u003csup\u003e34\u003c/sup\u003e. Furthermore, BMI and waist circumference were significantly lower across all three higher education categories (vocational secondary, other secondary, and university degree) compared to the lowest education level\u003csup\u003e35\u003c/sup\u003e. Our findings suggest an upward trend in these three biomarkers over the past decade, with low-income and low-education populations being particularly vulnerable.\u003c/p\u003e\n\u003cp\u003eWe subsequently conducted a trend analysis of cardiovascular physical examination indicators, including SBP, DBP, pulse rate, and ePWV. Our findings indicate that SBP levels remained well-controlled with no significant upward trend; however, DBP levels showed a marked increase. A comparative study on the impact of systolic and diastolic blood pressures on cardiovascular outcomes found that both SBP and DBP independently contribute to adverse cardiovascular events. In survival models, continuous systolic hypertension (\u0026ge;140 mmHg) was associated with a hazard ratio per unit increase in z-score of 1.18 (95% CI: 1.17-1.18), while diastolic hypertension (\u0026ge;90 mmHg) had a hazard ratio of 1.06 (95% CI: 1.06-1.07)\u003csup\u003e36\u003c/sup\u003e. Additionally, the study demonstrated a \u0026quot;J\u0026quot;-shaped relationship between DBP and cardiovascular outcomes, suggesting that both excessively high and low DBP levels may increase the risk of adverse cardiovascular events\u003csup\u003e36\u003c/sup\u003e. Consequently, while elevated DBP in our study may not directly raise cardiovascular event risk, suggesting more robust SBP-lowering strategies that can avoid significant DBP reduction. ePWV, a marker of arterial stiffness reflecting arterial health and aging, was calculated from age and mean BP using an equation from the Reference Values for Arterial Stiffness Collaboration\u003csup\u003e37\u003c/sup\u003e. ePWV is predictive of mortality and cardiovascular risk, correlating with all-cause mortality (HR per 1 m/s increase, 1.13; 95% CI: 1.05-1.21) and myocardial infarction (HR per 1 m/s increase, 1.23; 95% CI: 1.09-1.39)\u003csup\u003e38\u003c/sup\u003e. Our study observed a significant rise in ePWV during the COVID-19 pandemic, consistent with prior research showing progressive ePWV increases across control groups, COVID-19 survivors, and deceased patients (mean adjusted increase per group 1.89 m/s, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), suggesting its potential role as a marker related to COVID-19 severity and possibly indicating long-term impacts in cardiovascular diseases outcome\u003csup\u003e39\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTotal cholesterol and HDL levels are indicators of lipid metabolism. Elevated total cholesterol has been associated with increased CVD risk\u003csup\u003e40\u003c/sup\u003e and is also commonly linked to metabolic disturbances in diabetes\u003csup\u003e41\u003c/sup\u003e. Although we did not observe a significant trend in cholesterol levels overall, subgroup analysis revealed a notable decline in TC among the elderly, suggesting improved cholesterol control in this group. Conversely, Total cholesterol showed a significant increase among participants with lower education levels, underscoring a need for targeted attention in these populations.\u003c/p\u003e\n\u003cp\u003eTo assess diabetes-related metabolic biomarkers, we included fasting blood glucose, fasting insulin, glycohemoglobin, and HOMA-IR. fasting blood glucose and glycohemoglobin showed significant upward trends, particularly among Mexican Americans. Another study based on NHANES data indicated that the estimated prevalence of diabetes has continued to rise significantly among Mexican American adults\u003csup\u003e42\u003c/sup\u003e, suggesting that this disparity may partly be attributed to higher insurance coverage and greater access to preventive services compared to other minority groups\u003csup\u003e43,44\u003c/sup\u003e. While some cohort studies have reported elevated blood glucose, reduced insulin levels, and increased insulin resistance during and post-COVID-19 infection compared to healthy controls, a broader, long-term national sampling study found that the COVID-19 pandemic did not significantly affect diabetes-related biomarkers\u003csup\u003e45,46\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, our analysis included data encompassing the COVID-19 pandemic period (2021-2023 cycle). However, COVID-19 infection status was not reported for participants during this cycle, limiting our ability to evaluate the pandemic\u0026apos;s impact on observed trends. Second, due to the partial release of data for 2021-2023, we could not analyze the following metabolic biomarkers, such as triglycerides, low-density lipoprotein cholesterol, glomerular filtration rate, and C-reactive protein. Finally, NHANES response rates have declined over time. To mitigate nonresponse bias, we incorporated survey weights provided by the National Center for Health Statistics\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this cross-sectional study, trends in cardiovascular and diabetes-related metabolic biomarkers were analyzed from 2013 to 2023. We identified variations in these biomarkers across subgroups defined by age, gender, race, income, and education level. Our findings offer insights for shaping prevention policies, particularly by addressing the needs of diverse populations. Such policies could contribute to a more equitable society, where everyone has the opportunity to reduce their risk of cardiovascular disease and diabetes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study was supported by the Ethics Review Board of U.S. National Center for Health Statistics, and written informed consents were obtained from all participants of the NAHNES survey. The datasets used and/or analyzed in the current study are available in the article or supplementary material.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no conflicts of interest in this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCF designed the study. CF, XQ, TY and ZZ collected the data. XQ prepared figures 1-4. CF, XQ, TY, JLand JS analyzed the data and drafted the manuscript. QZ and JJ revised the final version of the manuscript. All the authors have read and approved the final version of the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed in the current study are available in the article or supplementary material.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Traditional Chinese Medicine Inheritance and Innovation Center Special Research Project in 2023(2023QN16) and Guangzhou University of Traditional Chinese Medicine Provincial Student Innovation and Entrepreneurship Training Program Project in 2024 (202410572044). \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal regional et al. and national comparative risk assessment of 84 behaviournvironmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. \u003cem\u003eLancet (London, England).\u003c/em\u003e 2018;392(10159):1923\u0026ndash;1994.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsao CW, Aday AW, Almarzooq ZI, et al. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association. Circulation. 2022;145(8):e153\u0026ndash;639.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoth GA, Mensah GA, Johnson CO, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990\u0026ndash;2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982\u0026ndash;3021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKazi DS, Elkind MSV, Deutsch A, et al. Forecasting the Economic Burden of Cardiovascular Disease and Stroke in the United States Through 2050: A Presidential Advisory From the American Heart Association. Circulation. 2024;150(4):e89\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal regional, national burden of diabetes. from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. 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Risk of Cardiovascular Disease After COVID-19 Diagnosis Among Adults With and Without Diabetes. J Am Heart Association. 2023;12(13):e029696.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsampasian V, B\u0026auml;ck M, Bernardi M et al. Cardiovascular disease as part of Long COVID: A systematic review. Eur J Prev Cardiol 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Health and Nutrition Examination Survey. NCHS ethics review board (ERB) approval. US Centers for Disease Control and Prevention National Center for Health Statistics. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/irba98.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/irba98.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed February 15, 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNHANES survey methods and analytic guidelines. US Centers for Disease Control and Prevention National Center for Health Statistics. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/analyticguidelines.aspx#analytic-guidelines\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/analyticguidelines.aspx#analytic-guidelines\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed February 1, 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Health and Nutrition Examination Survey. US Centers for Disease Control and Prevention National Center for Health Statistics. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed February 26, 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkinbami LJ, Chen TC, Davy O et al. National Health and Nutrition Examination Survey, 2017-March 2020 Prepandemic File: Sample Design, Estimation, and Analytic Guidelines. Vital health Stat Ser 1 Programs Collect procedures 2022(190):1\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026oacute;mez-Ambrosi J, Silva C, Catal\u0026aacute;n V, et al. Clinical usefulness of a new equation for estimating body fat. Diabetes Care. 2012;35(2):383\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeffernan KS, Stoner L, London AS, Augustine JA, Lefferts WK. Estimated pulse wave velocity as a measure of vascular aging. PLoS ONE. 2023;18(1):e0280896.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang YS, Shi HZ, Huang X, et al. Urinary Concentrations of Organophosphate Flame-Retardant Metabolites in the US Population. JAMA Netw open. 2024;7(9):e2435484.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT L, P G, B S. Analysis of complex survey samples. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/survey/index.html\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/survey/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed April 1, 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan SS, Ning H, Wilkins JT, et al. Association of Body Mass Index With Lifetime Risk of Cardiovascular Disease and Compression of Morbidity. JAMA Cardiol. 2018;3(4):280\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNarayan KM, Boyle JP, Thompson TJ, Gregg EW, Williamson DF. Effect of BMI on lifetime risk for diabetes in the U.S. Diabetes Care. 2007;30(6):1562\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoss R, Neeland IJ, Yamashita S, et al. Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat reviews Endocrinol. 2020;16(3):177\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang C, Rexrode KM, van Dam RM, Li TY, Hu FB. Abdominal obesity and the risk of all-cause, cardiovascular, and cancer mortality: sixteen years of follow-up in US women. Circulation. 2008;117(13):1658\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMyint PK, Kwok CS, Luben RN, Wareham NJ, Khaw KT. Body fat percentage, body mass index and waist-to-hip ratio as predictors of mortality and cardiovascular disease. Heart. 2014;100(20):1613\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFukase Y, Kamide N, Sakamoto M, et al. An in-person survey of the influence of the COVID-19 pandemic on physical function, functional capacity, cognitive function, and mental health among community-dwelling older adults in Japan from 2016 to 2022. BMC Geriatr. 2024;24(1):457.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong S, Wang L, Zhou Z, Wang K, Alamian A. Income Disparities in Obesity Trends among U.S. Adults: An Analysis of the 2011\u0026ndash;2014 California Health Interview Survey. Int J Environ Res Public Health 2022;19(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHermann S, Rohrmann S, Linseisen J, et al. The association of education with body mass index and waist circumference in the EPIC-PANACEA study. BMC Public Health. 2011;11:169.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlint AC, Conell C, Ren X, et al. Effect of Systolic and Diastolic Blood Pressure on Cardiovascular Outcomes. N Engl J Med. 2019;381(3):243\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreve SV, Laurent S, Olsen MH. Estimated Pulse Wave Velocity Calculated from Age and Mean Arterial Blood Pressure. Pulse (Basel Switzerland). 2017;4(4):175\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaugesen E, Olesen KKW, Peters CD, et al. Estimated Pulse Wave Velocity Is Associated With All-Cause Mortality During 8.5 Years Follow-up in Patients Undergoing Elective Coronary Angiography. J Am Heart Association. 2022;11(10):e025173.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStamatelopoulos K, Georgiopoulos G, Baker KF, et al. Estimated pulse wave velocity improves risk stratification for all-cause mortality in patients with COVID-19. Sci Rep. 2021;11(1):20239.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeong SM, Choi S, Kim K et al. Effect of Change in Total Cholesterol Levels on Cardiovascular Disease Among Young Adults. J Am Heart Association 2018;7(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarraich HJ, Rana JS. Dyslipidemia in diabetes mellitus and cardiovascular disease. Cardiovasc Endocrinol. 2017;6(1):27\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Li X, Wang Z, et al. Trends in Prevalence of Diabetes and Control of Risk Factors in Diabetes Among US Adults, 1999\u0026ndash;2018. JAMA. 2021;326(8):1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFisher-Hoch SP, Vatcheva KP, Rahbar MH, McCormick JB. Undiagnosed Diabetes and Pre-Diabetes in Health Disparities. PLoS ONE. 2015;10(7):e0133135.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson KM, Chapko MK, Reiber G, Boyko EJ. The association between health insurance coverage and diabetes care; data from the 2000 Behavioral Risk Factor Surveillance System. Health Serv Res. 2005;40(2):361\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe X, Liu C, Peng J, et al. COVID-19 induces new-onset insulin resistance and lipid metabolic dysregulation via regulation of secreted metabolic factors. Signal Transduct Target therapy. 2021;6(1):427.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKartika R, Subekti I, Kurniawan F et al. Altered Body Composition and Cytokine Production in Patients with Elevated HOMA-IR after SARS-CoV-2 Infection: A 12-Month Longitudinal Study. Biomedicines 2024;12(7).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Baseline Characteristics of Participants in NHANES Cycles (2013-2014 to 2021-2023).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1030\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e2013-2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e2015-2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e2017-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e2021-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e44.009(0.619)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e45.297(0.692)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e47.521(0.842)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e49.251(0.637)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1358(50.677)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1238(51.149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1404(50.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1371(51.477)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1292(49.323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1208(48.851)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1351(49.979)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1115(48.523)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e399(9.430)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e408(8.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e353(8.826)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e171(7.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Non-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e534(10.677)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e494(10.110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e652( 9.821)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e245( 8.985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Non-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1116(66.914)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e851(66.617)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e988(64.870)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1545(63.609)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Other Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e224(5.292)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e327(6.363)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e272(6.684)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e249(9.164)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Other Race - Including Multi-Racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e377( 7.687)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e366( 8.844)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e490( 9.799)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e276(11.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; College or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e578(28.236)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e530(28.879)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e707(33.684)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e984(37.233)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; High school graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e502(18.586)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e500(20.659)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e647(25.237)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e489(24.543)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Less than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e869(23.571)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e798(21.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e478(10.205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e282( 9.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Some college or associates degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e701(29.607)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e618(29.158)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e923(30.874)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e731(29.134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eFamily poverty-to-income ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt;=0,\u0026lt;=1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e664(17.225)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e602(14.789)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e497(10.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e334(11.195)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt;1,\u0026lt;=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1027(34.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1063(37.396)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1158(33.759)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e878(34.195)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e959(47.918)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e781(47.815)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1100(55.249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1274(54.609)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e28.542(0.187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e28.969(0.271)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e29.635(0.216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e29.427(0.296)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eWaist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e\u0026nbsp;97.628(0.393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e\u0026nbsp;99.205(0.666)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e100.396(0.586)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e100.108(0.681)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eBody fat percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e33.590(0.245)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e34.470(0.318)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e35.484(0.355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e35.681(0.398)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eSystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e119.630(0.519)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e121.743(0.448)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e120.710(0.464)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e120.252(0.399)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eDiastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e68.010(0.302)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e68.750(0.543)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e73.837(0.333)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e74.172(0.452)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5292%;\"\u003e\n \u003cp\u003ePulse rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e72.271(0.563)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e72.358(0.285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e68.406(0.285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e70.594(0.324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eEstimated pulse wave velocity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e7.888(0.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e8.054(0.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e8.219(0.092)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e8.410(0.071)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eFasting total cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e4.786(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e4.890(0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e4.815(0.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e4.894(0.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eHigh-density lipoprotein-cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1.392(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1.448(0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1.388(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e1.406(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eFasting insulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e12.842(0.610)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e13.451(0.432)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e13.930(0.620)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e13.989(0.658)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eGlycohemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e5.576(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e5.643(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e5.670(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e5.666(0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eFasting glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e5.768(0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e6.004(0.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e6.087(0.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e5.987(0.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e3.618(0.212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e3.938(0.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e4.116(0.228)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e4.129(0.256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eHOMA-IS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.608(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.529(0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.542(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.556(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5292%;\"\u003e\n \u003cp\u003eHOMA-beta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e108.955(12.134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e115.682( 3.687)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e114.135( 4.420)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e120.760( 3.532)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4942%;\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Geometric Mean (95% CI) and P-values for Trends in Cardiovascular and Diabetic Metabolic Biomarkers.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1021\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e2013-2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e2015-2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e2017-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e2021-2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e28.54 (28.18, 28.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e28.97 (28.44, 29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e29.63 (29.21, 30.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e29.43 (28.85, 30.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003eWaist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e97.63 (96.86, 98.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e99.2 (97.9, 100.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e100.4 (99.25, 101.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e100.11 (98.77, 101.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003eBody fat percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e33.59 (33.11, 34.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e34.47 (33.85, 35.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e35.48 (34.79, 36.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e35.68 (34.9, 36.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003eDiastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e68.01 (67.42, 68.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e68.75 (67.69, 69.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e73.84 (73.19, 74.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e74.17 (73.29, 75.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003eSystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e119.63 (118.61, 120.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e121.74 (120.86, 122.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e120.71 (119.8, 121.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e120.25 (119.47, 121.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003ePulse rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e72.27 (71.17, 73.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e72.36 (71.8, 72.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e68.41 (67.85, 68.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e70.59 (69.96, 71.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003eEstimated pulse wave velocity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e7.89 (7.75, 8.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e8.05 (7.92, 8.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e8.22 (8.04, 8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e8.41 (8.27, 8.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003eFasting total cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e4.79 (4.74, 4.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e4.89 (4.83, 4.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e4.81 (4.72, 4.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e4.89 (4.83, 4.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003eHigh density lipoprotein cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e1.39 (1.37, 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e1.45 (1.41, 1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e1.39 (1.36, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e1.41 (1.38, 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003eFasting glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e5.77 (5.69, 5.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e6 (5.93, 6.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e6.09 (5.97, 6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e5.99 (5.88, 6.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003eFasting insulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e12.84 (11.65, 14.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e13.45 (12.6, 14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e13.93 (12.71, 15.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e13.99 (12.7, 15.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003eGlycohemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e5.58 (5.53, 5.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e5.64 (5.6, 5.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e5.67 (5.61, 5.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e5.67 (5.6, 5.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003eHOMA_\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e108.95 (85.17, 132.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e115.68 (108.45, 122.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e114.14 (105.47, 122.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e120.76 (113.84, 127.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003eHOMA_IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e3.62 (3.2, 4.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e3.94 (3.69, 4.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e4.12 (3.67, 4.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e4.13 (3.63, 4.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.7023%;\"\u003e\n \u003cp\u003eHOMA_IS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.9236%;\"\u003e\n \u003cp\u003e0.61 (0.57, 0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5113%;\"\u003e\n \u003cp\u003e0.53 (0.49, 0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e0.54 (0.51, 0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7689%;\"\u003e\n \u003cp\u003e0.56 (0.51, 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.32517%;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5704576/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5704576/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIMPORTANCE\u003c/strong\u003e Understanding trends in cardiovascular and diabetes-related metabolic biomarkers across populations, especially during the COVID-19 pandemic, is essential for informing public health strategies targeting the prevention and management of cardiovascular diseases (CVD) and diabetes. This study aimed to assess trends in cardiovascular and diabetes-related metabolic biomarkers among U.S. adults from 2013-2014 to 2021-2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDESIGN, SETTING, AND PARTICIPANTS\u003c/strong\u003e This study analyzed five cycles of cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) spanning 2013-2014 to 2021-2023. The sample was weighted to reflect the noninstitutionalized civilian U.S. population aged 18 and older. Data analysis was conducted from August to October 2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEXPOSURES\u003c/strong\u003e Calendar year and sociodemographic subgroups, including age, gender, race, educational level, and family poverty-to-income ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMAIN OUTCOMES AND MEASURES\u003c/strong\u003e Primary outcomes included body mass index (BMI), waist circumference, body fat percentage, systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse rate, estimated pulse wave velocity (ePWV), fasting glucose, glycohemoglobin, total fasting cholesterol, high-density lipoprotein cholesterol (HDL-C), fasting insulin, and insulin resistance index. Trends were estimated using survey-weighted linear regression models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS\u003c/strong\u003e A total of 10,337 participants were included. BMI, waist circumference, and body fat percentage showed significant increases (all \u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.05). Specifically, BMI increased from 28.54 kg/m² (95% CI: 28.18-28.91) to 29.43 kg/m² (95% CI: 28.85-30.01); waist circumference rose from 97.63 cm (95% CI: 96.86-98.40) to 100.11 cm (95% CI: 98.77-101.44); and body fat percentage increased from 33.59% (95% CI: 31.11-34.07%) to 35.68% (95% CI: 34.90-36.46%). Significant interactions for these biomarkers were observed among various education and income subgroups. DBP (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001) and ePWV (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001) also increased, with DBP rising from 68.01 mmHg (95% CI: 67.42-68.60) to 74.17 mmHg (95% CI: 73.29-76.06) and ePWV from 7.89 m/s (95% CI: 7.75-8.02) to 8.41 m/s (95% CI: 8.27-8.55), while pulse rate declined from 72.27 bpm (95% CI: 71.17-73.37) to 70.59 bpm (95% CI: 69.96-71.23) (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001). Although SBP did not show an overall significant trend, increases were observed among men (from 121.06 mmHg [95% CI: 119.87-122.25] to 123.27 mmHg [95% CI: 122.41-124.12], \u003cem\u003eP\u003c/em\u003e for trend = 0.005) and individuals with less than a high school education (from 117.56 mmHg [95% CI: 115.77-119.34] to 124.55 mmHg [95% CI: 121.81-127.30], \u003cem\u003eP\u003c/em\u003efor trend \u0026lt; 0.0001). No significant trends were found for total cholesterol and HDL-C. Fasting glucose and glycohemoglobin showed significant upward trends (P for trend = 0.001 and 0.027, respectively), with notable increases in Mexican Americans (fasting glucose: 5.90 mmol/L [95% CI: 5.81-6.00] to 6.64 mmol/L [95% CI: 6.26-7.01], \u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.0001; glycohemoglobin: 5.59% [95% CI: 5.51-5.68] to 6.06% [95% CI: 5.84-6.28], \u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSIONS AND RELEVANCE\u003c/strong\u003e Analysis of NHANES data indicates that most cardiovascular and diabetes-related metabolic biomarkers significantly increased from 2013-2014 to 2021-2023, with notable differences across demographic groups. These findings can help shape targeted prevention strategies, especially for addressing the needs of diverse populations.\u003c/p\u003e","manuscriptTitle":"Shifts in Metabolic Biomarkers Related to Cardiovascular Disease and Diabetes from 2013 to 2023: A Decade of Change, Including the COVID-19 Era","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-02 10:16:47","doi":"10.21203/rs.3.rs-5704576/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"713ba7c3-2426-47dd-9357-5af6ab9f7e3c","owner":[],"postedDate":"January 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-02T16:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-02 10:16:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5704576","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5704576","identity":"rs-5704576","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}