Association between weight-adjusted waist index and the risk of cardiovascular and cerebrovascular diseases in prediabetic and diabetic patients in the United States: A Cross-Sectional Study

Association between weight-adjusted waist index and the risk of cardiovascular and cerebrovascular diseases in prediabetic and diabetic patients in the United States: A Cross-Sectional Study | 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 Association between weight-adjusted waist index and the risk of cardiovascular and cerebrovascular diseases in prediabetic and diabetic patients in the United States: A Cross-Sectional Study Xiangkun Wang, Liang Zheng, Feng Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7281291/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 Objective: To examine the association between weight-adjusted waist index (WWI) and the risk of cardiovascular and cerebrovascular diseases (CCDs) in prediabetic and diabetic patients in the United States. Methods: This study included 18,007 participants from the National Health and Nutrition Examination Survey (NHANES) 2003-2018. Weighted multivariate logistic regression models and smoothed fit curves were employed to assess the association between WWI and CCDs among participants diagnosed with prediabetes and diabetes, and subgroup analysis was used to reflect the interaction of different covariates. Results: In the fully adjusted categorical model, the highest tertiary subgroup of WWI (T3) exhibited a significantly elevated risk of CCDs in comparison to the lowest tertiary subgroup of WWI (T1): hypertension，1.17-fold increase (2.17 [1.91,2.45], P < 0.0001); coronary heart disease, 0.64-fold increase (1.64 [1.22,2.21], P < 0.002); stroke, 0.9-fold increase (1.90 [1.39,2.59], P < 0.0001); heart failure, 0.97-fold increase (1.97 [1.47,2.64], P < 0.0001); angina, 0.72-fold increase (1.72 [1.27,2.31], P < 0.001); and heart attack, 0.95-fold increase (1.95 [ 1.46,2.59], P < 0.0001). The results indicate that there is a positive association between WWI and the risk of CCDs in prediabetic and diabetic patients. This finding is consistent with the results of the smoothed fit curves. In the subgroup analyses, in addition to consistency with the overall population results, we identified some interactions in the age subgroups. Conclusion: WWI was found to be positively associated with the risk of developing CCDs in prediabetic and diabetic patients in the United States. weight-adjusted waist index cardiovascular and cerebrovascular diseases prediabetes diabetes NHANES cross-sectional study Figures Figure 1 Figure 2 Figure 3 1 Introduction Diabetes is a leading cause of premature morbidity and mortality worldwide and one of the 21st century's greatest global health emergencies. Prediabetes, on the other hand, is a precursor to diabetes and can be described as a continuum from normal blood glucose to abnormally elevated blood glucose. The number of people with prediabetes and diabetes is expected to increase to 1.635 billion by 2030, according to the International Diabetes Federation (IDF)( 1 ).In addition, prediabetes and diabetes are associated with an increased risk of cardiovascular disease. Cardiovascular and cerebrovascular diseases (CCDs) are the leading cause of death and disability among pre-diabetic and diabetic patients( 2 ). Patients with prediabetes and diabetes have at least a 2-fold increased risk of CCDs compared to non-diabetics and a 2-4-fold increased risk of cardiovascular disease mortality, which accounts for about half of all deaths from diabetes ( 3 , 4 ). Consequently, it is of paramount importance to identify patients at high risk for CCDs in prediabetes and diabetes, which is essential to prevent cardiovascular complications and premature death, improve quality of life, and reduce healthcare costs( 5 – 7 ). Obesity has reached alarming proportions in the United States, where 27% of adults are obese, and another 34% are overweight( 8 ). The American Heart Association (AHA) and the American Diabetes Association (ADA) have concluded that diabetes and prediabetes are associated with obesity, particularly abdominal or visceral obesity, and hyperlipidemia( 9 , 10 ). Several studies have confirmed the strong association between obesity, especially central obesity, and the development of cardiovascular and cerebrovascular diseases( 11 – 13 ). Body mass index (BMI) and waist circumference (WC) are frequently utilized in clinical settings to assess obesity. However, these indices do not accurately reflect central obesity( 14 , 15 ). To address this limitation, the study employed the weight-adjusted waist index (WWI), which combines the advantages of WC and BMI to provide a good reflection of body fat distribution, muscle mass, and central obesity ( 16 , 17 ). Previous studies have illustrated the association of visceral adiposity index (VAI) and WWI with cardiovascular disease( 18 , 19 ), respectively, as well as the association of WWI with the incidence and poor prognosis of diabetes( 20 , 21 ).To the best of our knowledge, high-quality evidence focused on the relationships between WWI and the risk of CCDs in prediabetic and diabetic patients is scarce. Consequently, we employed a substantial sample of data obtained from the NHANES database spanning 2003 to 2018 to further substantiate the correlation between WWI and the risk of CCDs in prediabetic and diabetic patients. 2 Methods 2.1 Study population National Health and Nutrition Examination Survey (NHANES) is an ongoing, nationally representative survey of the health and nutritional status of the U.S. population that has been approved by the National Center for Health Statistics Institutional Ethics Review Board and all subjects, and the full data are publicly available. The study included 80,312 participants from eight survey cycles (2003-2018) in the NHANES database. To ensure the authenticity of the data, 57,132 non-diabetics and non-prediabetics were successively excluded, 3,307 with missing waist circumference data, 37 with missing weight data, and 1,829 participants with missing CCDs data. As shown in Figure 1, a total of 18,007 eligible participants were finally included in the study. 2.2 Exposure Variable and Covariates As mentioned above, WWI was used as the exposure variable in this study, and WWI (cm/kg 1/2 ) was calculated as WC (cm) divided by the square root of weight (kg). All WC data and weight data were measured and recorded by trained professional technicians using specialized instruments. And age, sex, race/ethnicity, educational level, marital status, poverty-to-income ratio (PIR), smoking status, alcohol user, waist circumference (WC), weight, total cholesterol (TC, mmol/L), triglycerides (TG, mmol/L), high-density lipoprotein cholesterol (HDL-c, mmol/L), low-density lipoprotein cholesterol (LDL-c, mmol/L), BMI, and hyperlipidemia were used as covariates in the study. 2.3 Diagnosis of CCDs, Diabetes and Prediabetes The risk of CCDs, including hypertension, coronary heart disease (CHD), stroke, heart failure, angina, and heart attack, were used as outcome variables in this study. All CCD diagnosis data were obtained by a physician or health professional by asking all participants the following questions: “Has a doctor or other healthcare professional ever told you that you have hypertension/coronary heart disease /stroke/ heart failure/angina/heart attack?” Hypertension was defined as any of the following: 1) self-reported physician diagnosis of hypertension; 2) current use of antihypertensive medication; 3) an average of three measurements of systolic blood pressure (SBP) greater than 140 mmHg and/or diastolic blood pressure (DBP) greater than 90 mmHg. Participants who answered "yes" to any of the above questions were considered to have CCD(s). According to the American Diabetes Association (ADA) 2021 diagnostic criteria, diabetes was identified as self-reported diabetes previously diagnosis of diabetes by a physician, current use of anti-diabetic agents or FPG ≥7.0 mmol/L or 2-h post-load glucose ≥11.1 mmol/L or HbA1c ≥6.5% (48 mmol/mol). Prediabetes was defined as FPG ≥5.6-6.9mmol/L or 2-h post-load glucose ≥7.8-11.0mmol/L or HbA1c：5.7–6.4% (39−47 mmol/mol)(22). 2.4 Statistical analysis All statistical analyses were conducted using the R4.3.2 software for data processing, and appropriate weights were employed (23). Continuous variables in the sample characteristics are expressed as mean ± standard deviation (SD), and categorical variables are expressed as numbers and percentages, with P<0.05 being the level at which the difference is statistically significant. Three multivariate logistic regression models were constructed: Model 1 did not adjust for covariates; Model 2 adjusted only for age, sex, and race/ethnicity; and Model 3 further adjusted for educational level, marital status, PIR, smoking status, alcohol user, and hyperlipidemia, and trisected the WWI into three intervals (T1, T2, and T3). The nonlinear association of WWI with the risk of hypertension, CHD, stroke, heart failure, angina, and heart attack was then assessed separately using smoothed fit curves, and subgroup analyses were performed. 3 Results 3.1 Characteristics of the study population Table 1 presents the demographic and clinical characteristics of the 18,007 participants with prediabetes and diabetes, stratified by tertiles of baseline WWI values. The unweighted mean values for the first, second, and third tertiles were T1 (≤10.992), T2 (10.992–11.669), and T3 (>11.669). Notably, the odds of risk of the six types of a CCD increased with increasing WWI values. From group T1 to T3, age, WC, weight, BMI, TG, and LDL-c increased while HDL-c decreased, and compared to the T1 group, most of the participants in the T3 group were female. Table 1. Essential characteristics of the study population by WWI. V ariable T otal WWI (cm/kg 1/2 ) p- value T1(=11.669) Age (years old) 53.95±0.22 47.47±0.30 55.27±0.28 60.38±0.29 < 0.0001 Sex, (%) < 0.0001 Female 8727(48.65) 2111(36.85) 2792(47.78) 3824(63.93) Male 9280(51.35) 3890(63.15) 3206(52.22) 2184(36.07) Race/ethnicity, (%) < 0.0001 Mexican American 2972(9.00) 711(7.33) 1096(10.32) 1165(9.59) Non-Hispanic Black 4259(12.68) 1879(16.20) 1341(11.41) 1039(9.77) Non-Hispanic White 6891(64.30) 2057(61.52) 2237(63.88) 2597(68.14) Others 3885(14.02) 1354(14.95) 1324(14.39) 1207(12.49) Educational level, (%) < 0.0001 College or above 3082(19.17) 1273(23.41) 1009(18.71) 800(14.54) High school or equivalent 9748(61.80) 3444(61.84) 3261(61.81) 3043(61.75) Less than high school 5177(19.02) 1284(14.75) 1728(19.48) 2165(23.72) Marital status, (%) < 0.0001 Divorced/widowed/separated 4762(22.52) 1210(16.47) 1498(21.33) 2054(31.17) Married/living with partner 11045(65.74) 3815(68.64) 3876(68.90) 3354(58.77) Never married 2200(11.74) 976(14.90) 624(9.77) 600(10.06) PIR < 0.0001 =3.50 5137(40.61) 2101(47.33) 1763(40.93) 1273(32.10) Smoking status, (%) < 0.0001 Former 5091(29.16) 1379(23.48) 1829(32.16) 1883(32.79) Never 9423(51.51) 3213(54.72) 3065(49.28) 3145(50.07) Now 3493(19.32) 1409(21.80) 1104(18.56) 980(17.14) Alcohol user, (%) < 0.0001 Former 3236(15.27) 794(10.62) 1097(15.97) 1345(20.15) Heavy 2672(15.62) 1074(18.29) 916(15.87) 682(12.09) Mild 5639(35.58) 2115(38.41) 1893(35.11) 1631(32.66) Moderate 2101(13.41) 809(14.98) 717(13.95) 575(10.93) Never 4359(20.11) 1209(17.69) 1375(19.10) 1775(24.17) Waist circumference (cm) 104.78±0.23 94.35±0.20 105.84±0.23 116.31±0.31 < 0.0001 Weight (kg) 87.58±0.28 82.25±0.32 88.57±0.38 92.97±0.50 < 0.0001 Total cholesterol (mmol/L) 5.08±0.02 5.10±0.02 5.13±0.02 5.01±0.02 < 0.001 Triglyceride (mmol/L) 1.91±0.02 1.69±0.03 2.00±0.03 2.09±0.03 < 0.0001 HDL-c (mmol/L) 1.32±0.01 1.38±0.01 1.29±0.01 1.28±0.01 < 0.0001 LDL-c (mmol/L) 2.91±0.01 2.95±0.02 2.95±0.02 2.81±0.02 < 0.0001 BMI (kg/m2) 29.04±0.12 25.56±0.11 29.53±0.10 33.35±0.17 < 0.0001 Hypertension, (%) < 0.0001 No 7964(47.96) 3527(62.41) 2556(45.50) 1881(33.08) Yes 10043(52.04) 2474(37.59) 3442(54.50) 4127(66.92) Coronary heart disease, (%) < 0.0001 No 16937(94.27) 5821(97.14) 5642(94.04) 5474(91.02) Yes 1070(5.73) 180(2.86) 356(5.96) 534(8.98) Stroke, (%) < 0.0001 No 17061(95.71) 5848(98.16) 5678(95.51) 5535(92.95) Yes 946(4.29) 153(1.84) 320(4.49) 473(7.05) Heart failure, (%) < 0.0001 No 17194(96.30) 5867(98.39) 5747(96.55) 5580(93.48) Yes 813(3.70) 134(1.61) 251(3.45) 428(6.52) Angina, (%) < 0.0001 No 17325(96.40) 5890(98.07) 5790(96.82) 5645(93.90) Yes 682(3.60) 111(1.93) 208(3.18) 363(6.10) Heart attack, (%) < 0.0001 No 16866(94.46) 5808(97.30) 5621(94.35) 5437(91.12) Yes 1141(5.54) 193(2.70) 377(5.65) 571(8.88) Hyperlipidemia, (%) < 0.0001 No 3574(19.09) 1776(27.69) 998(14.88) 800(13.24) Yes 14433(80.91) 4225(72.31) 5000(85.12) 5208(86.76) PIR, poverty-to-income ratio; HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol; BMI, body mass index. 3.2 Association between the WWI and the risk of CCDs As shown in Table 2, in the fully adjusted model, compared with T1 as the reference group, T3 was associated with a 1.17-fold increase in the risk of hypertension (2.17, 95% CI: 1.91-2.45, P<0.0001); a 0.64-fold increase in the risk of CHD (1.64, 95% CI: 1.22-2.21, P<0.002); a 0.9-fold increase in the risk of stroke (1.90, 95% CI: 1.39-2.59, P<0.0001); a 0.97-fold increase in the risk of heart failure (1.97, 95% CI: 1.47-2.64, P<0.0001); a 0.72-fold increase in the risk of angina (1.72, 95% CI: 1.27-2.31, P<0.001); and a 0.95-fold increase in the risk of heart attack (1.95, 95% CI:1.46-2.59, P<0.0001). The implication is that higher WWI values are associated with an increased risk of CCDs. Smoothed fit curves were used to examine the nonlinear relationship between WWI and the risk of CCDs in prediabetic and diabetic patients in the United States. As shown in Figure 2, a nonlinear relationship was observed between WWI and hypertension (P value for nonlinearity: 0.0073), angina (P value for nonlinearity: 0.0013), and stroke (P value for nonlinearity: 0.0168). However, no nonlinear association was observed between WWI and CHD (P value for nonlinearity: 0.6185), heart failure (P value for nonlinearity: 0.9793), and heart attack (P value for nonlinearity: 0.2333). Table 2. The association between WWI and the risk of CCDs was demonstrated by different models. variable Model 1 P Model 2 P Model 3 P Hypertension T1 1.00(reference) 1.00(reference) 1.00(reference) T2 1.99(1.80,2.20) <0.0001 1.59(1.42,1.78) <0.0001 1.51(1.35,1.69) <0.0001 T3 3.36(3.01,3.75) <0.0001 2.32(2.05,2.62) <0.0001 2.17(1.91,2.45) <0.0001 Coronary heart disease T1 1.00(reference) 1.00(reference) 1.00(reference) T2 2.16(1.66,2.81) <0.0001 1.41(1.07,1.85) 0.01 1.29(0.98,1.70) 0.07 T3 3.35(2.54,4.43) <0.0001 1.85(1.38,2.47) <0.0001 1.64(1.22,2.21) <0.002 Stroke T1 1.00(reference) 1.00(reference) 1.00(reference) T2 2.51(1.87,3.35) <0.0001 1.79(1.32,2.43) <0.001 1.64(1.21,2.24) 0.002 T3 4.05(3.09,5.30) <0.0001 2.30(1.69,3.13) <0.0001 1.90(1.39,2.59) <0.0001 Heart failure T1 1.00(reference) 1.00(reference) 1.00(reference) T2 2.19(1.67,2.86) <0.0001 1.57(1.17,2.12) 0.003 1.38(1.02,1.87) 0.04 T3 4.26(3.31,5.49) <0.0001 2.54(1.93,3.36) <0.0001 1.97(1.47,2.64) <0.0001 Angina T1 1.00(reference) 1.00(reference) 1.00(reference) T2 1.67(1.22,2.28) 0.002 1.21(0.86,1.68) 0.27 1.08(0.77,1.52) 0.63 T3 3.30(2.52,4.33) <0.0001 2.01(1.49,2.70) <0.0001 1.72(1.27,2.31) <0.001 Heart attack T1 1.00(reference) 1.00(reference) 1.00(reference) T2 2.16(1.67,2.78) <0.0001 1.61(1.23,2.11) <0.001 1.43(1.09,1.89) 0.01 T3 3.51(2.72,4.53) <0.0001 2.35(1.79,3.08) <0.0001 1.95(1.46,2.59) <0.0001 Model 1 did not adjust for covariates; Model 2 adjusted for age, sex, and race/ethnicity; Model 3 adjusted for age, sex, race/ethnicity, educational level, marital status, PIR, smoking status, alcohol user, and hyperlipidemia. 3.3 Subgroup analysis As shown in Figure 3, subgroup analyses were performed to assess the association between WWI and the risk of CCDs in different subgroups of prediabetes and diabetes. These results found significant associations between age and the odds of CHD (P for interaction = 0.01, Figure 3B), heart failure (P for interaction = 0.03, Figure 3D), and heart attack (P for interaction < 0.001, Figure 3F) in subgroups based on age (=60). For participants under the age of 60, the risk of CHD, heart failure, and heart attack increased by 1.14-fold, 0.97-fold, and 1.04-fold, respectively, for each 1-unit increase in WWI. In contrast, for participants aged 60 years or older, the risk of CHD, heart failure, and heart attack increased 0.41-fold, 0.54-fold, and 0.4-fold for each 1-unit increase in WWI. This suggests that participants younger than 60 years of age are at higher risk for cardiovascular and cerebrovascular diseases. In addition, we found an association between educational level and heart attack (P for interaction = 0.01, Figure 3F). For each 1-unit increase in WWI, the risk of heart attack increased 1.41-fold in participants with college or higher educational level, 0.51-fold in participants with high school or equivalent, and 0.15-fold in participants with less than a high school educational level. There was an interaction of smoking status on the risk of hypertension (P for interaction = 0.02, Figure 3A), with a 0.96-fold increase in the risk of hypertension in the Former group, a 0.58-fold increase in the Now group, and a 0.61-fold increase in the Never group. Race/ethnicity appeared to have an interaction effect on the risk of heart failure (P for interaction =0.04, Figure 3D), with a 1.31-fold increase in the risk of heart failure in the Others group, compared to a 0.25-fold increase in the Non-Hispanic Black group, a 0.62-fold increase in the Non-Hispanic White group, and a 0.62-fold increase in the Mexico American group. and 0.48-fold, respectively. 4 Discussion The findings of this cross-sectional study, which included a total of 18,007 participants, indicated that prediabetic and diabetic patients who exhibited higher WWI were at a higher risk of cardiovascular and cerebrovascular diseases (CCDs). This suggests that WWI may be a useful predictor of cardiovascular disease in prediabetic and diabetic patients. Furthermore, subgroup analyses revealed interactions in the age subgroups of coronary heart disease, heart failure, and heart attack. A prospective cohort study with a mean follow-up of 15 years (n = 26822) observed that WWI values were positively associated with the risk of developing cardiovascular disease in U.S. adults( 24 ), suggesting that WWI may be an independent predictor of cardiovascular disease occurrence in U.S. adults( 19 ). A rural Chinese cohort study (n = 10,338) with a mean follow-up of 6 years found that WWI was significantly and positively associated with the risk of hypertension ( 25 ). Other studies have also illustrated that WWI can be used to identify and recognize the risk of hypertension( 26 , 27 ). WWI has also been identified as an independent predictor of heart failure and stroke( 28 , 29 ). Furthermore, several studies have demonstrated that WWI is positively associated with the risk of cardiovascular disease (CVD) in East Asian populations( 16 , 30 , 31 ). It follows that WWI appears to be a reliable predictor of CDDs in East Asian populations. In this study, a nationally representative sample of prediabetes and diabetes from the NHANES database was utilized to investigate the association between WWI levels and the risk of CCDs in prediabetic and diabetic populations. The results indicated a significant and positive association between WWI levels and the risk of CCDs in prediabetic and diabetic populations. The analysis of the dose-response relationship demonstrated that the risk of CCDs was particularly significant at a WWI level of T3 (≥ 11.669). These findings suggest that WWI may be a useful predictor of CCDs in prediabetic and diabetic populations. Further randomized clinical trials are required to substantiate these findings in prediabetic and diabetic populations, particularly those with elevated levels of WWI. In addition, subgroup analyses showed that for every 1-unit increase in WWI in the group aged < 60 years, the incidence of CHD, heart failure, angina, and heart attack increased by 1.14-fold, 0.97-fold, and 1.04-fold, respectively, all of which were higher than the incidence in the group aged 60 years and older. This is due to the malnutrition that occurs with age, decreased physical activity, and decreased muscle strength due to prolonged sedentary or bedridden behavior( 32 ), and changes in both fat distribution and composition, with not only an increase in fat mass and a decrease in muscle mass ( 33 , 34 ) but also a concomitant decrease in white fat and an increase in brown fat( 35 ). Some studies have shown that WWI is positively correlated with fat mass and negatively correlated with muscle mass in older adults( 17 ). With regard to the increased risk of hypertension due to smoking, several studies indicate that smoking enhances inflammatory markers in the peripheral blood and is accompanied by alterations in the composition of the intestinal microbiota and even transcriptional gene dysfunction, which ultimately leads to the development of hypertension( 36 , 37 ). The interaction between education level and heart attack risk may be related to the fact that participants with higher levels of education had a more accurate level of knowledge about the disease. Additionally, the study identified an interaction between different racial/ethnic pairs on heart failure. The evidence indicates that WWI is significantly correlated with three definitions of sarcopenic obesity( 38 ). Moreover, it represents the most efficacious screening tool for obesity when compared to waist-height ratio (WHtR), WC, and BMI( 19 , 38 ), and it has been proposed as an indicator that can reflect both high-fat mass and low muscle mass( 17 ). A multiracial study of 1,946 participants showed no statistically significant differences in WWI in identifying obesity based on race( 39 ). These findings suggest that WWI may be a superior indicator of obesity and is generally applicable to diverse populations( 19 ). Several potential mechanisms could explain the relationship between WWI and the incidence of CCDs in prediabetic and diabetic patients. Obesity, a known risk factor for prediabetes and diabetes mellitus, is frequently associated with low-grade, chronic, systemic inflammation and also thickening of epicardial adipose tissue (EAT)( 40 ), which is characterized by elevated levels of pro-inflammatory markers, including tumor necrosis factor (TNF-α) and interleukin-6 (IL-6)( 41 – 43 ). Aberrant expression of pro-inflammatory cytokines leads to oxidative stress as well as increased inflammation. In turn, increased oxidative stress and inflammation lead to cellular damage, which may ultimately increase arterial tone and stiffness, thereby increasing the risk of cardiovascular diseases such as hypertension, coronary heart disease, stroke, and heart failure( 41 , 44 ). In addition, abnormal blood glucose is a strong risk factor, as high blood glucose causes an inflammatory response similar to that of adipose tissue, on the one hand, and decreases glucose metabolism in the heart, on the other hand( 45 , 46 ). When blood glucose is controlled within the normal range, obesity becomes a critical factor, and the use of WWI at this point is able to predict the risk of developing CCDs more accurately. The strengths of this study are based on the use of the National Health and Nutrition Examination Survey (NHANES) data, a graded multistage probability sampling strategy for data collection, a large and representative sample size of the study population, and a long follow-up period. These factors collectively enhance the credibility and representativeness of the study. Secondly, the validity of the findings was enhanced by the adjustment for a multitude of potential confounding variables in this study. Nevertheless, it is important to acknowledge the limitations of this study. Firstly, as the current study was a cross-sectional study, causal conclusions cannot be drawn from the results. Second, although we adjusted for a number of potential confounding variables in our model, we cannot rule out the possibility that other unmeasured factors, such as dietary intake and environmental exposures, may have influenced the results. Finally, due to the differences between countries, the current findings cannot be extrapolated to the broader population or to other ethnic groups, thus limiting the generalizability of the findings. In conclusion, our results suggest that higher levels of WWI are significantly associated with prediabetes and the risk of CCDs in diabetic patients. Furthermore, these associations were partially modified by age. These findings highlight the importance of WWI as a simple and effective anthropometric measure in the prevention of CCDs in prediabetic and diabetic populations. Declarations Ethics approval and consent to participate The NHANES procedure was in accordance with the ethical guidelines established by the Declaration of Helsinki and received approval from the Institutional Review Board of the National Center for Health Statistics. Informed consent was obtained from all participants in written form. Consent for publication Not Applicable Availability of data and materials All data are from the NHANES survey, sponsored by the National Center for Health Statistics. These publicly available datasets can be accessed at the official website: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx. Competing interests The authors declare that this study was conducted from beginning to end without any commercial or economic conflicts. Funding The authors affirm that no financial support was received for the research, writing, and publication of this article. Authors' contributions Wang completed the first draft of the manuscript and used software to organize the raw data. Zheng and Lu reviewed and supervised the article and provided the methodology. References International Diabetes Federation.IDF Diabetes Atlas, 10th ed. Brussels,Belgium: 2021. [Internet]. International Diabetes Federation; Available from: https://www.diabetesatlas.org Low Wang CC, Hess CN, Hiatt WR, Goldfine AB. 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American Diabetes Association. Standards of Medical Care in Diabetes—2021 Abridged for Primary Care Providers. Clinical Diabetes. 2021 Jan 1;39(1):14–43. Johnson CL, Paulose-Ram R, Ogden CL, Carroll MD, Kruszon-Moran D, Dohrmann SM, et al. National health and nutrition examination survey: analytic guidelines, 1999-2010. Vital Health Stat 2. 2013 Sep;(161):1–24. Han Y, Shi J, Gao P, Zhang L, Niu X, Fu N. The weight-adjusted-waist index predicts all-cause and cardiovascular mortality in general US adults. Clinics (Sao Paulo). 2023;78:100248. Li Q, Qie R, Qin P, Zhang D, Guo C, Zhou Q, et al. Association of weight-adjusted-waist index with incident hypertension: The Rural Chinese Cohort Study. Nutrition, Metabolism and Cardiovascular Diseases. 2020 Sep;30(10):1732–41. Wang J, Yang QY, Chai DJ, Su Y, Jin QZ, Wang JH. The relationship between obesity associated weight-adjusted waist index and the prevalence of hypertension in US adults aged ≥60 years: a brief report. Front Public Health. 2023;11:1210669. Xiong Y, Shi W, Huang X, Yu C, Zhou W, Bao H, et al. Association between weight-adjusted waist index and arterial stiffness in hypertensive patients: The China H-type hypertension registry study. Front Endocrinol. 2023 Mar 17;14:1134065. Zhang D, Shi W, Ding Z, Park J, Wu S, Zhang J. Association between weight-adjusted-waist index and heart failure: Results from National Health and Nutrition Examination Survey 1999–2018. Front Cardiovasc Med. 2022 Dec 14;9:1069146. Ye J, Hu Y, Chen X, Yin Z, Yuan X, Huang L, et al. Association between the weight-adjusted waist index and stroke: a cross-sectional study. BMC Public Health. 2023 Sep 1;23(1):1689. Ding C, Shi Y, Li J, Li M, Hu L, Rao J, et al. Association of weight-adjusted-waist index with all-cause and cardiovascular mortality in China: A prospective cohort study. Nutrition, Metabolism and Cardiovascular Diseases. 2022 May;32(5):1210–7. Cai S, Zhou L, Zhang Y, Cheng B, Zhang A, Sun J, et al. Association of the Weight-Adjusted-Waist Index With Risk of All-Cause Mortality: A 10-Year Follow-Up Study. Front Nutr. 2022 May 25;9:894686. Perna S, Francis MD, Bologna C, Moncaglieri F, Riva A, Morazzoni P, et al. Performance of Edmonton Frail Scale on frailty assessment: its association with multi-dimensional geriatric conditions assessed with specific screening tools. BMC Geriatr. 2017 Dec;17(1):2. Szulc P, Duboeuf F, Chapurlat R. Age-Related Changes in Fat Mass and Distribution in Men—the Cross-Sectional STRAMBO Study. Journal of Clinical Densitometry. 2017 Oct;20(4):472–9. Larsson L, Degens H, Li M, Salviati L, Lee YI, Thompson W, et al. Sarcopenia: Aging-Related Loss of Muscle Mass and Function. Physiol Rev. 2019 Jan 1;99(1):427–511. Conte M, Martucci M, Sandri M, Franceschi C, Salvioli S. The Dual Role of the Pervasive “Fattish” Tissue Remodeling With Age. Front Endocrinol (Lausanne). 2019;10:114. Shahida B, Planck T, Singh T, Åsman P, Lantz M. Smoking enhances proliferation, inflammatory markers, and immunoglobulins in peripheral blood mononuclear cells from Graves’ patients. Endocr Connect. 2024 Jun 1;13(6):e230374. Wang P, Dong Y, Jiao J, Zuo K, Han C, Zhao L, et al. Cigarette smoking status alters dysbiotic gut microbes in hypertensive patients. J Clin Hypertens (Greenwich). 2021 Jul;23(7):1431–46. Kim JE, Choi J, Kim M, Won CW. Assessment of existing anthropometric indices for screening sarcopenic obesity in older adults. Br J Nutr. 2023 Mar 14;129(5):875–87. Kim JY, Choi J, Vella CA, Criqui MH, Allison MA, Kim NH. Associations between Weight-Adjusted Waist Index and Abdominal Fat and Muscle Mass: Multi-Ethnic Study of Atherosclerosis. Diabetes Metab J. 2022 Sep 30;46(5):747–55. Malavazos AE, Di Leo G, Secchi F, Lupo EN, Dogliotti G, Coman C, et al. Relation of Echocardiographic Epicardial Fat Thickness and Myocardial Fat. The American Journal of Cardiology. 2010 Jun;105(12):1831–5. Huang D, Refaat M, Mohammedi K, Jayyousi A, Al Suwaidi J, Abi Khalil C. Macrovascular Complications in Patients with Diabetes and Prediabetes. BioMed Research International. 2017;2017:1–9. Liang Y, Wang M, Wang C, Liu Y, Naruse K, Takahashi K. The Mechanisms of the Development of Atherosclerosis in Prediabetes. IJMS. 2021 Apr 15;22(8):4108. Shulman GI. Cellular mechanisms of insulin resistance. J Clin Invest. 2000 Jul 15;106(2):171–6. Ryan S, Taylor CT, McNicholas WT. Selective Activation of Inflammatory Pathways by Intermittent Hypoxia in Obstructive Sleep Apnea Syndrome. Circulation. 2005 Oct 25;112(17):2660–7. Nielsen R, Jorsal A, Iversen P, Tolbod L, Bouchelouche K, Sørensen J, et al. Heart failure patients with prediabetes and newly diagnosed diabetes display abnormalities in myocardial metabolism. Journal of Nuclear Cardiology. 2018 Feb;25(1):169–76. De Wit-Verheggen VHW, Van De Weijer T. Changes in Cardiac Metabolism in Prediabetes. Biomolecules. 2021 Nov 12;11(11):1680. 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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-7281291","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500174228,"identity":"5b47781e-dc71-4a16-a0f1-8da3a8623f43","order_by":0,"name":"Xiangkun Wang","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiangkun","middleName":"","lastName":"Wang","suffix":""},{"id":500174229,"identity":"111a81c0-e126-4510-9279-efda9b004e32","order_by":1,"name":"Liang Zheng","email":"","orcid":"","institution":"Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Zheng","suffix":""},{"id":500174230,"identity":"0df86c71-126e-4729-8a2b-860afe33345b","order_by":2,"name":"Feng Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIie3RrwvCQBTA8YPBpafWG5PbXyA8HAhD0X/lDmHJYDROBJfshv0XwvINg2V/wMCiCKYF7YIuatqzCd63f+D9YMxm+8G4f8hPCkfQ8S+GRtoCpnibR9KNI0UjUrCBu73tAzQzJA7mxZEH6OjYFPeyYmPZi5tIN98PAbleLje7MGXTYGCaCFP6CAh65bQyD5jRGYFgPZjQaw5XIhGq724RAwDgRAJFfWRUUggehCkSdvGTpH7l4wmT0jmX1WIsG8lHAoiveSffCpvNZvuLXuZjPpAL5aYnAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Feng","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2025-08-03 04:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7281291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7281291/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89230478,"identity":"416c9044-6054-4759-a5d3-a4d90c803e35","added_by":"auto","created_at":"2025-08-17 14:14:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":95296,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of processing the participants' data.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7281291/v1/6c34f50f7a4ba8b186911389.png"},{"id":89230477,"identity":"cbe76401-bfe0-4067-a3d1-a803975890fe","added_by":"auto","created_at":"2025-08-17 14:14:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":377840,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe association between WWI and the risk of CCDs was demonstrated by smoothed-fit curves.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdjusted for age, sex, race/ethnicity, educational level, marital status, PIR, smoking status, alcohol user, and hyperlipidemia.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7281291/v1/67573b8b4e5305e8c4952d72.png"},{"id":89230480,"identity":"e524db99-9248-41a3-a8f5-fc1d46dd0ce7","added_by":"auto","created_at":"2025-08-17 14:14:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":611984,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analysis of the association between WWI and the risk of CCDs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7281291/v1/4f0680bd332ca420746f9d3c.png"},{"id":91417638,"identity":"40be8ea1-7400-4c73-a8c7-bcbc1a4f3e22","added_by":"auto","created_at":"2025-09-16 09:39:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2189074,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7281291/v1/1246958e-9fc3-4aed-95c4-ae46ef0ac0a5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between weight-adjusted waist index and the risk of cardiovascular and cerebrovascular diseases in prediabetic and diabetic patients in the United States: A Cross-Sectional Study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eDiabetes is a leading cause of premature morbidity and mortality worldwide and one of the 21st century's greatest global health emergencies. Prediabetes, on the other hand, is a precursor to diabetes and can be described as a continuum from normal blood glucose to abnormally elevated blood glucose. The number of people with prediabetes and diabetes is expected to increase to 1.635\u0026nbsp;billion by 2030, according to the International Diabetes Federation (IDF)(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).In addition, prediabetes and diabetes are associated with an increased risk of cardiovascular disease. Cardiovascular and cerebrovascular diseases (CCDs) are the leading cause of death and disability among pre-diabetic and diabetic patients(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Patients with prediabetes and diabetes have at least a 2-fold increased risk of CCDs compared to non-diabetics and a 2-4-fold increased risk of cardiovascular disease mortality, which accounts for about half of all deaths from diabetes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Consequently, it is of paramount importance to identify patients at high risk for CCDs in prediabetes and diabetes, which is essential to prevent cardiovascular complications and premature death, improve quality of life, and reduce healthcare costs(\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eObesity has reached alarming proportions in the United States, where 27% of adults are obese, and another 34% are overweight(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The American Heart Association (AHA) and the American Diabetes Association (ADA) have concluded that diabetes and prediabetes are associated with obesity, particularly abdominal or visceral obesity, and hyperlipidemia(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Several studies have confirmed the strong association between obesity, especially central obesity, and the development of cardiovascular and cerebrovascular diseases(\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Body mass index (BMI) and waist circumference (WC) are frequently utilized in clinical settings to assess obesity. However, these indices do not accurately reflect central obesity(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). To address this limitation, the study employed the weight-adjusted waist index (WWI), which combines the advantages of WC and BMI to provide a good reflection of body fat distribution, muscle mass, and central obesity (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrevious studies have illustrated the association of visceral adiposity index (VAI) and WWI with cardiovascular disease(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), respectively, as well as the association of WWI with the incidence and poor prognosis of diabetes(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).To the best of our knowledge, high-quality evidence focused on the relationships between WWI and the risk of CCDs in prediabetic and diabetic patients is scarce. Consequently, we employed a substantial sample of data obtained from the NHANES database spanning 2003 to 2018 to further substantiate the correlation between WWI and the risk of CCDs in prediabetic and diabetic patients.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003ch2\u003e2.1 Study population\u003c/h2\u003e\n\u003cp\u003eNational Health and Nutrition Examination Survey (NHANES) is an ongoing, nationally representative survey of the health and nutritional status of the U.S. population that has been approved by the National Center for Health Statistics Institutional Ethics Review Board and all subjects, and the full data are publicly available. The study included 80,312 participants from eight survey cycles (2003-2018) in the NHANES database. To ensure the authenticity of the data, 57,132 non-diabetics and non-prediabetics were successively excluded, 3,307 with missing waist circumference data, 37 with missing weight data, and 1,829 participants with missing CCDs data. As shown in Figure 1, a total of 18,007 eligible participants were finally included in the study.\u003c/p\u003e\n\u003ch2\u003e2.2 Exposure Variable and Covariates\u003c/h2\u003e\n\u003cp\u003eAs mentioned above, WWI was used as the exposure variable in this study, and WWI (cm/kg\u003csup\u003e1/2\u003c/sup\u003e) was calculated as WC (cm) divided by the square root of weight (kg). All WC data and weight data were measured and recorded by trained professional technicians using specialized instruments. And age, sex, race/ethnicity, educational level, marital status, poverty-to-income ratio (PIR), smoking status, alcohol user, waist circumference (WC), weight, total cholesterol (TC, mmol/L), triglycerides (TG, mmol/L), high-density lipoprotein cholesterol (HDL-c, mmol/L), low-density lipoprotein cholesterol (LDL-c, mmol/L), BMI, and hyperlipidemia were used as covariates in the study.\u003c/p\u003e\n\u003ch2\u003e2.3 Diagnosis of CCDs, Diabetes and Prediabetes\u003c/h2\u003e\n\u003cp\u003eThe risk of CCDs, including hypertension, coronary heart disease (CHD), stroke, heart failure, angina, and heart attack, were used as outcome variables in this study. All CCD diagnosis data were obtained by a physician or health professional by asking all participants the following questions: \u0026ldquo;Has a doctor or other healthcare professional ever told you that you have hypertension/coronary heart disease /stroke/ heart failure/angina/heart attack?\u0026rdquo; Hypertension was defined as any of the following: 1) self-reported physician diagnosis of hypertension; 2) current use of antihypertensive medication; 3) an average of three measurements of systolic blood pressure (SBP) greater than 140 mmHg and/or diastolic blood pressure (DBP) greater than 90 mmHg. Participants who answered \u0026quot;yes\u0026quot; to any of the above questions were considered to have CCD(s).\u003c/p\u003e\n\u003cp\u003eAccording to the American Diabetes Association (ADA) 2021 diagnostic criteria, diabetes was identified as self-reported diabetes previously diagnosis of diabetes by a physician, current use of anti-diabetic agents or FPG\u0026nbsp;\u0026ge;7.0 mmol/L or 2-h post-load glucose\u0026nbsp;\u0026ge;11.1 mmol/L or HbA1c\u0026nbsp;\u0026ge;6.5% (48 mmol/mol). Prediabetes was defined as FPG\u0026nbsp;\u0026ge;5.6-6.9mmol/L or 2-h post-load glucose\u0026nbsp;\u0026ge;7.8-11.0mmol/L or HbA1c：5.7\u0026ndash;6.4% (39\u0026minus;47 mmol/mol)(22).\u003c/p\u003e\n\u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\n\u003cp\u003eAll statistical analyses were conducted using the R4.3.2 software for data processing, and appropriate weights were employed (23). Continuous variables in the sample characteristics are expressed as mean \u0026plusmn; standard deviation (SD), and categorical variables are expressed as numbers and percentages, with P\u0026lt;0.05 being the level at which the difference is statistically significant. Three multivariate logistic regression models were constructed: Model 1 did not adjust for covariates; Model 2 adjusted only for age, sex, and race/ethnicity; and Model 3 further adjusted for educational level, marital status, PIR, smoking status, alcohol user, and hyperlipidemia, and trisected the WWI into three intervals (T1, T2, and T3). The nonlinear association of WWI with the risk of hypertension, CHD, stroke, heart failure, angina, and heart attack was then assessed separately using smoothed fit curves, and subgroup analyses were performed.\u003c/p\u003e"},{"header":"3 Results","content":"\u003ch2\u003e3.1 Characteristics of the study population\u003c/h2\u003e\n\u003cp\u003eTable 1\u0026nbsp;presents the demographic and clinical characteristics of the 18,007 participants with prediabetes and diabetes, stratified by tertiles of baseline WWI values. The unweighted mean values for the first, second, and third tertiles were T1 (\u0026le;10.992), T2\u0026nbsp;(10.992\u0026ndash;11.669), and\u0026nbsp;T3 (\u0026gt;11.669).\u0026nbsp;Notably, the odds of risk of the six types of a CCD increased with increasing WWI values.\u0026nbsp;From group T1 to T3, age, WC, weight, BMI, TG, and LDL-c increased while HDL-c decreased,\u0026nbsp;and compared to the T1 group, most of the participants in the T3 group were female.\u003c/p\u003e\n\u003cp\u003eTable 1.\u0026nbsp;Essential characteristics of the study population by WWI.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eV\u003c/strong\u003e\u003cstrong\u003eariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003cstrong\u003eotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eWWI\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(cm/kg\u003csup\u003e1/2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eT1(=\u0026lt;10.992)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eT2(10.992-11.669)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eT3(=\u0026gt;11.669)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years old)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.95\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.47\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55.27\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.38\u0026plusmn;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSex, (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8727(48.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2111(36.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2792(47.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3824(63.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9280(51.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3890(63.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3206(52.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2184(36.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRace/ethnicity, (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2972(9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e711(7.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1096(10.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1165(9.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4259(12.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1879(16.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1341(11.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1039(9.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6891(64.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2057(61.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2237(63.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2597(68.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3885(14.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1354(14.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1324(14.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1207(12.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEducational level, (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3082(19.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1273(23.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1009(18.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e800(14.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh school or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9748(61.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3444(61.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3261(61.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3043(61.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5177(19.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1284(14.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1728(19.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2165(23.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status, (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDivorced/widowed/separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4762(22.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1210(16.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1498(21.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2054(31.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarried/living with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11045(65.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3815(68.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3876(68.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3354(58.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2200(11.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e976(14.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e624(9.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e600(10.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5853(22.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1667(18.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1868(21.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2318(27.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1.30-3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7017(37.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2233(34.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2367(37.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2417(40.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;=3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5137(40.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2101(47.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1763(40.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1273(32.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status, (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5091(29.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1379(23.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1829(32.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1883(32.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9423(51.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3213(54.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3065(49.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3145(50.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3493(19.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1409(21.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1104(18.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e980(17.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol user, (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3236(15.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e794(10.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1097(15.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1345(20.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHeavy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2672(15.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1074(18.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e916(15.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e682(12.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5639(35.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2115(38.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1893(35.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1631(32.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2101(13.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e809(14.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e717(13.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e575(10.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4359(20.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1209(17.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1375(19.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1775(24.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWaist circumference (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e104.78\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94.35\u0026plusmn;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105.84\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116.31\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWeight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87.58\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.25\u0026plusmn;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88.57\u0026plusmn;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92.97\u0026plusmn;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cholesterol (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.08\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.10\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.13\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.01\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTriglyceride (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.91\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.69\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.09\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-c (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.32\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.38\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.29\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.28\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-c (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.91\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.95\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.95\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.81\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.04\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.56\u0026plusmn;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.53\u0026plusmn;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.35\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7964(47.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3527(62.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2556(45.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1881(33.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10043(52.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2474(37.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3442(54.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4127(66.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoronary heart disease, (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16937(94.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5821(97.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5642(94.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5474(91.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1070(5.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e180(2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e356(5.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e534(8.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStroke, (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17061(95.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5848(98.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5678(95.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5535(92.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e946(4.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e153(1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e320(4.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e473(7.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHeart failure, (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17194(96.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5867(98.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5747(96.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5580(93.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e813(3.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e134(1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e251(3.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e428(6.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAngina, (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17325(96.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5890(98.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5790(96.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5645(93.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e682(3.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e111(1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e208(3.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e363(6.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHeart attack, (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16866(94.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5808(97.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5621(94.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5437(91.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1141(5.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e193(2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e377(5.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e571(8.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHyperlipidemia, (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3574(19.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1776(27.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e998(14.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e800(13.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14433(80.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4225(72.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5000(85.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5208(86.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ePIR, poverty-to-income ratio; HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol; BMI, body mass index.\u003c/p\u003e\n\u003ch2\u003e3.2 Association between the WWI and the risk of CCDs\u003c/h2\u003e\n\u003cp\u003eAs shown in Table 2, in the fully adjusted model, compared with T1 as the reference group, T3 was associated with a 1.17-fold increase in the risk of hypertension (2.17, 95% CI: 1.91-2.45, P\u0026lt;0.0001); a 0.64-fold increase in the risk of CHD (1.64, 95% CI: 1.22-2.21, P\u0026lt;0.002); a 0.9-fold increase in the risk of stroke (1.90, 95% CI: 1.39-2.59, P\u0026lt;0.0001); a 0.97-fold increase in the risk of heart failure (1.97, 95% CI: 1.47-2.64, P\u0026lt;0.0001); a 0.72-fold increase in the risk of angina (1.72, 95% CI: 1.27-2.31, P\u0026lt;0.001); and a 0.95-fold increase in the risk of heart attack (1.95, 95% CI:1.46-2.59, P\u0026lt;0.0001). The implication is that higher WWI values are associated with an increased risk of CCDs. Smoothed fit curves were used to examine the nonlinear relationship between WWI and the risk of CCDs in prediabetic and diabetic patients in the United States. As shown in Figure 2, a nonlinear relationship was observed between WWI and hypertension (P value for nonlinearity: 0.0073), angina (P value for nonlinearity: 0.0013), and stroke (P value for nonlinearity: 0.0168). However, no nonlinear association was observed between WWI and CHD (P value for nonlinearity: 0.6185), heart failure (P value for nonlinearity: 0.9793), and heart attack (P value for nonlinearity: 0.2333).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2.\u0026nbsp;The association between WWI and the risk of CCDs was demonstrated by different models.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003evariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.99(1.80,2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.59(1.42,1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.51(1.35,1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e3.36(3.01,3.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2.32(2.05,2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2.17(1.91,2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoronary heart disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2.16(1.66,2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.41(1.07,1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.29(0.98,1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e3.35(2.54,4.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.85(1.38,2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.64(1.22,2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2.51(1.87,3.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.79(1.32,2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.64(1.21,2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4.05(3.09,5.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2.30(1.69,3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.90(1.39,2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeart failure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2.19(1.67,2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.57(1.17,2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.38(1.02,1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4.26(3.31,5.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2.54(1.93,3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.97(1.47,2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAngina\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.67(1.22,2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.21(0.86,1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.08(0.77,1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e3.30(2.52,4.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2.01(1.49,2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.72(1.27,2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeart attack\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2.16(1.67,2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.61(1.23,2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.43(1.09,1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e3.51(2.72,4.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2.35(1.79,3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.95(1.46,2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1 did not adjust for covariates;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 2 adjusted for age, sex, and race/ethnicity;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 3 adjusted for age, sex, race/ethnicity, educational level, marital status, PIR, smoking status, alcohol user, and hyperlipidemia.\u003c/p\u003e\n\u003ch2\u003e3.3 Subgroup analysis\u003c/h2\u003e\n\u003cp\u003eAs shown in Figure 3, subgroup analyses were performed to assess the association between WWI and the risk of CCDs in different subgroups of prediabetes and diabetes. These results found significant associations between age and the odds of CHD (P for interaction = 0.01, Figure 3B), heart failure (P for interaction = 0.03, Figure 3D), and heart attack (P for interaction \u0026lt; 0.001, Figure 3F) in subgroups based on age (\u0026lt;60, \u0026gt;=60). For participants under the age of 60, the risk of CHD, heart failure, and heart attack increased by 1.14-fold, 0.97-fold, and 1.04-fold, respectively, for each 1-unit increase in WWI. In contrast, for participants aged 60 years or older, the risk of CHD, heart failure, and heart attack increased 0.41-fold, 0.54-fold, and 0.4-fold for each 1-unit increase in WWI. This suggests that participants younger than 60 years of age are at higher risk for cardiovascular and cerebrovascular diseases. In addition, we found an association between educational level and heart attack (P for interaction = 0.01, Figure 3F). For each 1-unit increase in WWI, the risk of heart attack increased 1.41-fold in participants with college or higher educational level, 0.51-fold in participants with high school or equivalent, and 0.15-fold in participants with less than a high school educational level. There was an interaction of smoking status on the risk of hypertension (P for interaction = 0.02, Figure 3A), with a 0.96-fold increase in the risk of hypertension in the Former group, a 0.58-fold increase in the Now group, and a 0.61-fold increase in the Never group. Race/ethnicity appeared to have an interaction effect on the risk of heart failure (P for interaction =0.04, Figure 3D), with a 1.31-fold increase in the risk of heart failure in the Others group, compared to a 0.25-fold increase in the Non-Hispanic Black group, a 0.62-fold increase in the Non-Hispanic White group, and a 0.62-fold increase in the Mexico American group. and 0.48-fold, respectively.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe findings of this cross-sectional study, which included a total of 18,007 participants, indicated that prediabetic and diabetic patients who exhibited higher WWI were at a higher risk of cardiovascular and cerebrovascular diseases (CCDs). This suggests that WWI may be a useful predictor of cardiovascular disease in prediabetic and diabetic patients. Furthermore, subgroup analyses revealed interactions in the age subgroups of coronary heart disease, heart failure, and heart attack.\u003c/p\u003e\u003cp\u003eA prospective cohort study with a mean follow-up of 15 years (n\u0026thinsp;=\u0026thinsp;26822) observed that WWI values were positively associated with the risk of developing cardiovascular disease in U.S. adults(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), suggesting that WWI may be an independent predictor of cardiovascular disease occurrence in U.S. adults(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). A rural Chinese cohort study (n\u0026thinsp;=\u0026thinsp;10,338) with a mean follow-up of 6 years found that WWI was significantly and positively associated with the risk of hypertension (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Other studies have also illustrated that WWI can be used to identify and recognize the risk of hypertension(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). WWI has also been identified as an independent predictor of heart failure and stroke(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Furthermore, several studies have demonstrated that WWI is positively associated with the risk of cardiovascular disease (CVD) in East Asian populations(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). It follows that WWI appears to be a reliable predictor of CDDs in East Asian populations. In this study, a nationally representative sample of prediabetes and diabetes from the NHANES database was utilized to investigate the association between WWI levels and the risk of CCDs in prediabetic and diabetic populations. The results indicated a significant and positive association between WWI levels and the risk of CCDs in prediabetic and diabetic populations. The analysis of the dose-response relationship demonstrated that the risk of CCDs was particularly significant at a WWI level of T3 (\u0026ge;\u0026thinsp;11.669). These findings suggest that WWI may be a useful predictor of CCDs in prediabetic and diabetic populations. Further randomized clinical trials are required to substantiate these findings in prediabetic and diabetic populations, particularly those with elevated levels of WWI.\u003c/p\u003e\u003cp\u003eIn addition, subgroup analyses showed that for every 1-unit increase in WWI in the group aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years, the incidence of CHD, heart failure, angina, and heart attack increased by 1.14-fold, 0.97-fold, and 1.04-fold, respectively, all of which were higher than the incidence in the group aged 60 years and older. This is due to the malnutrition that occurs with age, decreased physical activity, and decreased muscle strength due to prolonged sedentary or bedridden behavior(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), and changes in both fat distribution and composition, with not only an increase in fat mass and a decrease in muscle mass (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) but also a concomitant decrease in white fat and an increase in brown fat(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Some studies have shown that WWI is positively correlated with fat mass and negatively correlated with muscle mass in older adults(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). With regard to the increased risk of hypertension due to smoking, several studies indicate that smoking enhances inflammatory markers in the peripheral blood and is accompanied by alterations in the composition of the intestinal microbiota and even transcriptional gene dysfunction, which ultimately leads to the development of hypertension(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The interaction between education level and heart attack risk may be related to the fact that participants with higher levels of education had a more accurate level of knowledge about the disease. Additionally, the study identified an interaction between different racial/ethnic pairs on heart failure.\u003c/p\u003e\u003cp\u003eThe evidence indicates that WWI is significantly correlated with three definitions of sarcopenic obesity(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Moreover, it represents the most efficacious screening tool for obesity when compared to waist-height ratio (WHtR), WC, and BMI(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), and it has been proposed as an indicator that can reflect both high-fat mass and low muscle mass(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). A multiracial study of 1,946 participants showed no statistically significant differences in WWI in identifying obesity based on race(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). These findings suggest that WWI may be a superior indicator of obesity and is generally applicable to diverse populations(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral potential mechanisms could explain the relationship between WWI and the incidence of CCDs in prediabetic and diabetic patients. Obesity, a known risk factor for prediabetes and diabetes mellitus, is frequently associated with low-grade, chronic, systemic inflammation and also thickening of epicardial adipose tissue (EAT)(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), which is characterized by elevated levels of pro-inflammatory markers, including tumor necrosis factor (TNF-α) and interleukin-6 (IL-6)(\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAberrant expression of pro-inflammatory cytokines leads to oxidative stress as well as increased inflammation. In turn, increased oxidative stress and inflammation lead to cellular damage, which may ultimately increase arterial tone and stiffness, thereby increasing the risk of cardiovascular diseases such as hypertension, coronary heart disease, stroke, and heart failure(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). In addition, abnormal blood glucose is a strong risk factor, as high blood glucose causes an inflammatory response similar to that of adipose tissue, on the one hand, and decreases glucose metabolism in the heart, on the other hand(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). When blood glucose is controlled within the normal range, obesity becomes a critical factor, and the use of WWI at this point is able to predict the risk of developing CCDs more accurately.\u003c/p\u003e\u003cp\u003eThe strengths of this study are based on the use of the National Health and Nutrition Examination Survey (NHANES) data, a graded multistage probability sampling strategy for data collection, a large and representative sample size of the study population, and a long follow-up period. These factors collectively enhance the credibility and representativeness of the study. Secondly, the validity of the findings was enhanced by the adjustment for a multitude of potential confounding variables in this study. Nevertheless, it is important to acknowledge the limitations of this study. Firstly, as the current study was a cross-sectional study, causal conclusions cannot be drawn from the results. Second, although we adjusted for a number of potential confounding variables in our model, we cannot rule out the possibility that other unmeasured factors, such as dietary intake and environmental exposures, may have influenced the results. Finally, due to the differences between countries, the current findings cannot be extrapolated to the broader population or to other ethnic groups, thus limiting the generalizability of the findings.\u003c/p\u003e\u003cp\u003eIn conclusion, our results suggest that higher levels of WWI are significantly associated with prediabetes and the risk of CCDs in diabetic patients. Furthermore, these associations were partially modified by age. These findings highlight the importance of WWI as a simple and effective anthropometric measure in the prevention of CCDs in prediabetic and diabetic populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe NHANES procedure was in accordance with the ethical guidelines established by the Declaration of Helsinki and received approval from the Institutional Review Board of the National Center for Health Statistics. Informed consent was obtained from all participants in written form.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eAll data are from the NHANES survey, sponsored by the National Center for Health Statistics. These publicly available datasets can be accessed at the official website: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that this study was conducted from beginning to end without any commercial or economic conflicts.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe authors affirm that no financial support was received for the research, writing, and publication of this article.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eWang completed the first draft of the manuscript and used software to organize the raw data. Zheng and Lu reviewed and supervised the article and provided the methodology.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eInternational Diabetes Federation.IDF Diabetes Atlas, 10th ed. Brussels,Belgium: 2021. [Internet]. 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Front Nutr. 2022 May 25;9:894686. \u003c/li\u003e\n\u003cli\u003ePerna S, Francis MD, Bologna C, Moncaglieri F, Riva A, Morazzoni P, et al. Performance of Edmonton Frail Scale on frailty assessment: its association with multi-dimensional geriatric conditions assessed with specific screening tools. BMC Geriatr. 2017 Dec;17(1):2. \u003c/li\u003e\n\u003cli\u003eSzulc P, Duboeuf F, Chapurlat R. Age-Related Changes in Fat Mass and Distribution in Men\u0026mdash;the Cross-Sectional STRAMBO Study. Journal of Clinical Densitometry. 2017 Oct;20(4):472\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eLarsson L, Degens H, Li M, Salviati L, Lee YI, Thompson W, et al. Sarcopenia: Aging-Related Loss of Muscle Mass and Function. Physiol Rev. 2019 Jan 1;99(1):427\u0026ndash;511. \u003c/li\u003e\n\u003cli\u003eConte M, Martucci M, Sandri M, Franceschi C, Salvioli S. The Dual Role of the Pervasive \u0026ldquo;Fattish\u0026rdquo; Tissue Remodeling With Age. 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Biomolecules. 2021 Nov 12;11(11):1680. \u003c/li\u003e\n\u003c/ol\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":"weight-adjusted waist index, cardiovascular and cerebrovascular diseases, prediabetes, diabetes, NHANES, cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-7281291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7281291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo examine the association between weight-adjusted waist index (WWI) and the risk of cardiovascular and cerebrovascular diseases (CCDs) in prediabetic and diabetic patients in the United States.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study included 18,007 participants from the National Health and Nutrition Examination Survey (NHANES) 2003-2018. Weighted multivariate logistic regression models and smoothed fit curves were employed to assess the association between WWI and CCDs among participants diagnosed with prediabetes and diabetes, and subgroup analysis was used to reflect the interaction of different covariates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn the fully adjusted categorical model, the highest tertiary subgroup of WWI (T3) exhibited a significantly elevated risk of CCDs in comparison to the lowest tertiary subgroup of WWI (T1): hypertension，1.17-fold increase (2.17 [1.91,2.45], P \u0026lt; 0.0001); coronary heart disease, 0.64-fold increase (1.64 [1.22,2.21], P \u0026lt; 0.002); stroke, 0.9-fold increase (1.90 [1.39,2.59], P \u0026lt; 0.0001); heart failure, 0.97-fold increase (1.97 [1.47,2.64], P \u0026lt; 0.0001); angina, 0.72-fold increase (1.72 [1.27,2.31], P \u0026lt; 0.001); and heart attack, 0.95-fold increase (1.95 [ 1.46,2.59], P \u0026lt; 0.0001). The results indicate that there is a positive association between WWI and the risk of CCDs in prediabetic and diabetic patients. This finding is consistent with the results of the smoothed fit curves. In the subgroup analyses, in addition to consistency with the overall population results, we identified some interactions in the age subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eWWI was found to be positively associated with the risk of developing CCDs in prediabetic and diabetic patients in the United States.\u003c/p\u003e","manuscriptTitle":"Association between weight-adjusted waist index and the risk of cardiovascular and cerebrovascular diseases in prediabetic and diabetic patients in the United States: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-17 14:14:35","doi":"10.21203/rs.3.rs-7281291/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":"aaf3488a-db19-48fa-82af-5ccca8c0baf2","owner":[],"postedDate":"August 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-16T09:38:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-17 14:14:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7281291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7281291","identity":"rs-7281291","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}