The Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio (NHHR) as a Predictor of All-Cause and Cardiovascular Mortality in US Adults with Diabetes or Prediabetes: NHANES 1998-2018

The Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio (NHHR) as a Predictor of All-Cause and Cardiovascular Mortality in US Adults with Diabetes or Prediabetes: NHANES 1998-2018 | 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 The Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio (NHHR) as a Predictor of All-Cause and Cardiovascular Mortality in US Adults with Diabetes or Prediabetes: NHANES 1998-2018 Binyang Yu, Min Li, Zongliang Yu, Tao Zheng, Xue Feng, Anran Gao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4207993/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Aug, 2024 Read the published version in BMC Medicine → Version 1 posted 9 You are reading this latest preprint version Abstract Background The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) serves as a novel composite lipid indicator for atherosclerosis. However, the association between the NHHR and mortality in patients with diabetes or prediabetes remains unclear. Therefore, the present study aimed to examine the correlation between the NHHR and both all-cause and cardiovascular mortality in U.S. adults with diabetes or prediabetes. Methods This study enrolled a total of 12,578 adult participants with diabetes or prediabetes from the National Health and Nutrition Examination Survey in the US (1998–2018). The mortality outcomes were ascertained through linkage with the National Death Index (NDI) records available until December 31, 2019. We employed weighted multivariate Cox proportional hazards models to estimate the associations between the NHHR and both all-cause and cardiovascular mortality. Restricted cubic splines (RCS) were employed to evaluate nonlinear correlations. Moreover, a segmented Cox proportional hazards model was utilized to assess the associations between the NHHR and mortality on both sides of the inflection point. Results During a median follow-up period of 8.08 years, 2403 participants experienced all-cause mortality, with 662 of them specifically succumbing to cardiovascular mortality. The RCS revealed a U-shaped association between the NHHR and all-cause mortality in participants with diabetes or prediabetes, while an L-shaped association was observed for cardiovascular mortality. The analysis of threshold effects revealed that the inflection points for the NHHR and all-cause and cardiovascular mortality were 2.71 and 2.85, respectively. Specifically, when the baseline NHHR was below the inflection points, a negative correlation was observed between the NHHR and both all-cause mortality (HR: 0.80, 95% CI: 0.73–0.88) and cardiovascular mortality (HR: 0.78, 95% CI: 0.67–0.92). Conversely, when the baseline NHHR exceeded the inflection points, a positive correlation was observed between the NHHR and all-cause mortality (HR: 1.07, 95% CI: 1.03–1.11). Conclusions In U.S. adults with diabetes or prediabetes, a U-shaped correlation was observed between the NHHR and all-cause mortality, whereas an L-shaped correlation was identified with cardiovascular mortality. The inflection points for all-cause and cardiovascular mortality were 2.71 and 2.85, respectively. NHHR Diabetes Prediabetes Mortality Cardiovascular Disease NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Diabetes and its complications are among the leading causes of death and disability worldwide, posing a significant challenge to global health. The global incidence of diabetes is increasing annually due to factors such as population aging and unhealthy dietary habits[ 1 , 2 ]. Epidemiological surveys indicated that in 2021, there were approximately 529 million participants worldwide with diabetes, and by 2050, the number of affected participants is projected to reach 1.31 billion[ 3 ]. Moreover, diabetes ranks as the eighth leading cause of death globally, with associated healthcare expenditures reaching a staggering $ 966 billion, imposing a significant burden on healthcare systems[ 3 – 5 ]. Notably, diabetes is closely associated with several major causes of death worldwide and is a primary risk factor for cardiovascular disease (CVD) and stroke[ 3 ]. Although cancer is also emerging as a primary cause of death in participants with diabetes in certain regions, CVD remains the leading cause of mortality among diabetes patients[ 6 , 7 ]. CVD among participants with diabetes is multifactorial, and controlling cardiovascular risk factors can significantly reduce cardiovascular events. Research has demonstrated that atherosclerotic cardiovascular diseases (ASCVDs) caused by factors such as hypertension, high blood glucose, dyslipidemia, obesity, and insulin resistance are the primary causes of mortality in diabetes patients[ 7 – 9 ]. Notably, dyslipidemia is a crucial factor contributing to ASCVD in participants with diabetes. Dyslipidemia in diabetes patients is characterized by increased levels of non-high-density lipoprotein cholesterol (non-HDL-C), including low-density lipoprotein cholesterol (LDL-C), intermediate-density lipoprotein, and very low-density lipoprotein remnants, all of which contribute to the development of atherosclerosis[ 10 , 11 ]. In recent years, the clinical value of non-HDL-C has gained widespread attention and recognition. In 2021, the National Institute for Health and Care Excellence (NICE) in the US recommended that non-HDL-C be used as the primary target for reducing CVD risk in patients with diabetes, replacing LDL-C. This recommendation highlights the significance of non-HDL-C in assessing CVD risk and treatment efficacy [ 12 , 13 ]. Furthermore, a novel composite lipid indicator, the non-HDL-C to high-density lipoprotein cholesterol (HDL-C) ratio (NHHR), has shown promising predictive value in assessing the risk of various diseases, including coronary artery disease, diabetes, abdominal aortic aneurysm, and carotid atherosclerosis[ 14 – 17 ]. Compared to non-HDL-C, the NHHR is a superior and comprehensive indicator that takes into account both the risk factor (non-HDL-C) and the protective factor (HDL-C) in atherosclerosis[ 18 , 19 ]. However, the prognostic value of the NHHR in patients with diabetes remains unclear. In order to address this gap, we conducted a study utilizing the National Health and Nutrition Examination Survey (NHANES) database. This research sought to explore the correlation between the NHHR and all-cause and cardiovascular mortality in participants with diabetes or prediabetes, as well as its association with various population characteristics, aiming to provide valuable insights into disease management and prevention strategies. Methods Study population and design The NHANES is a nationwide survey study conducted to assess the nutritional and health status of adults and children in the US. The survey employs a stratified, complex multistage sampling methodology and includes interviews, physical examinations, and laboratory tests[ 20 ]. The study protocol received formal approval from the National Center for Health Statistics (NCHS) Research Ethics Review Board, and all participants involved in the study provided informed consent by signing consent forms[ 21 ]. This study utilized data from the NHANES database spanning from 1998 to 2018, with a total of 101,316 participants. Initially, participants under the age of 20 were excluded (n = 46,235). Subsequently, participants who did not meet the diagnostic criteria for diabetes or prediabetes according to the 2021 American Diabetes Association guidelines[ 22 ] were excluded (n = 31,214). Diabetes was defined as: (1) self-reported diabetes; (2) insulin injection or hypoglycemic medication usage; (3) hemoglobin A1c (HbA1c) ≥ 6.5%; (4) fasting blood glucose (FBG) ≥ 7.0 mmol/L; (5) 2-hour postprandial glucose (2hPG) ≥ 11.1 mmol/L. Prediabetes was defined as: (1) HbA1c 5.7–6.4%; (2) FBG 5.6–6.9 mmol/L; (3) 2hPG 7.8–11.0 mmol/L. Subsequently, participants lacking the NHHR data were excluded (n = 11,250). Finally, after excluding participants with missing mortality data (n = 39), a total of 12,578 participants were included in this study (Fig. 1 ). Assessment of the NHHR Blood lipid parameters are fundamental indicators included in the NHANES database for assessing CVD risk. In our study, we used the non-HDL-C to HDL-C ratio to evaluate participants’ lipid levels and CVD risk. The NHHR was calculated based on serum total cholesterol (TC) and HDL-C levels from the NHANES database for the years 1999–2018. Non-HDL-C is defined as TC minus HDL-C, and the NHHR is calculated as the ratio of non-HDL-C to HDL-C[ 23 ]. Participants were categorized into four groups (Q1, Q2, Q3, and Q4) based on the NHHR quartiles, with the Q1 group serving as the reference group. Ascertainment of mortality To ascertain the all-cause and cardiovascular mortality status of the participants in this study, we utilized the Public-Use Linked Mortality Files provided by the NCHS. These files link survey participants’ data with death certificate records from the National Death Index (NDI) using a probabilistic matching algorithm. The follow-up mortality data were updated until December 31, 2019. The specific cause of death was determined based on the International Classification of Diseases, Tenth Revision (ICD-10), with cardiovascular deaths including codes I00-I09, I11, I13, and I20-I51[ 24 ]. All-cause mortality encompassed the sum of all specific cause deaths. Assessment of covariates The selection of covariates in this study considered a range of demographic characteristics and health-related information, including age, sex (male/female), race (Mexican American, Non-Hispanic White, Non-Hispanic Black, Other), education (less than high school, high school or equivalent, some college or above), and marital status (married/living with partner, widowed/divorced/separated, never married). The poverty income ratio (PIR) was divided into the following categories: low income ( 3.5). Body mass index (BMI) was classified as normal/underweight ( 30). Smoking status was categorized as never smoking (smoking fewer than 100 cigarettes in a lifetime), former smoking (smoking more than 100 cigarettes in a lifetime but currently not smoking), or current smoking (smoking more than 100 cigarettes in a lifetime and currently smoking every day). Drinking status was dichotomized as yes/no (defined as whether the individual consumed more than 12 drinks per year). Physical activity was classified as vigorous, moderate, or other. Hypertension was defined as self-reported hypertension, the use of antihypertensive medication, or an average blood pressure > 130 mmHg or a diastolic pressure > 80 mmHg. Hyperlipidemia was defined as self-reported high cholesterol level, use of cholesterol-lowering medications, or laboratory examination indicating TC ≥ 200 mg/dl, triglycerides ≥ 150 mg/dl, LDL-C ≥ 130 mg/dl, HDL < 40 mg/dl (male) or < 50 mg/dl (female). Statistical analyses The NHANES survey was conducted using a complex, multistage, probability sampling design to ensure the representativeness of the research results for the U.S. civilian non-institutionalized population. In our study, we incorporated sample weights, stratification, and clustering for analysis purposes. The participants were divided into four groups based on the quartiles (Q1-Q4) of the NHHR. Continuous variables were reported as the means and standard deviations (SDs), while categorical variables were reported as frequencies and percentages. The baseline characteristics of the four participant groups were compared using analysis of the variance (ANOVA) for continuous variables and the chi-square (χ2) test for categorical variables. We established three models to control for confounding factors and used weighted multivariate Cox proportional hazards models to estimate the associations between the NHHRs and all-cause and cardiovascular mortality. Model 1 did not include any covariate adjustments. Model 2 was adjusted for age, sex, race, education, marital status, and PIR. Model 3 was further adjusted for BMI, smoking status, drinking status, physical activity, hypertension, and hyperlipidemia. Moreover, the linear trend test was performed to assess the trend by designating the median NHHR for each quartile as a continuous variable in the regression model. Multiple imputation was employed as a method to reduce the decrease in sample size caused by missing covariate data. To investigate the dose-response relationship between the NHHR and mortality, we employed Cox proportional hazards regression models incorporating restricted cubic splines (RCSs) and smooth curve fitting. If the RCS indicated a nonlinear association, we determined the inflection point using the “segmented” package based on the likelihood-ratio test and bootstrap resampling method. Subsequently, we assessed the correlation between the NHHR and mortality using a segmented Cox proportional hazards model on both sides of the inflection point. We performed subgroup analyses based on age (< 60 or ≥ 60 years), sex (male or female), race (Mexican American, non-Hispanic White, non-Hispanic Black, or Other), smoking status (former, current, or never), drinking status (yes or no), physical activity (vigorous, moderate, or other), BMI (normal/underweight, overweight, or obese), and classification of diabetes (diabetes or prediabetes). Likelihood ratio tests were employed to assess the interactions among the subgroups. To evaluate the stability of the models, several sensitivity analyses were conducted. First, we excluded participants who self-reported having a cancer at baseline. Second, participants who died within 2 years of follow-up were excluded. Finally, participants with extreme NHHR values beyond the mean ± 3SD were excluded. All analyses were performed using R software, version 4.2.1, and a P-value < 0.05 was considered to indicate statistical significance. Results Characteristics of the study participants A total of 12,578 participants were included in this study, with an average age of 53.06 years and a male proportion of 53.9%. The mean NHHR of the participants was calculated to be 3.14 ± 1.47. Table 1 presents the baseline characteristics of the participants according to NHHR quartiles. Compared to participants in the lowest quartile, participants with higher NHHRs were often younger, male, and of Mexican American ethnicity. Additionally, these participants tend to exhibit characteristics such as obesity, current smoking, alcohol consumption, and physical inactivity, indicating a potential association between unhealthy dietary habits and an elevated NHHR. Table 1 Baseline characteristics according to the NHHR quartiles Characteristics Quartiles of NHHR Overall Q1( 3.88) P value N (%) 12578 3267 3186 3068 3057 Age, years, mean (SD) 53.05(16.10) 55.93(17.52) 54.51(16.17) 51.97(15.55) 49.80(14.33) < 0.001 Sex, n (%) Male 6732(53.9) 1406(42.3) 1532(48.1) 1766(57.9) 2028(67.3) < 0.001 Female 5846(46.1) 1861(57.7) 1654(51.9) 1302(42.1) 1029(32.7) Race, n (%) Mexican American 2337(8.8) 428(6.7) 556(8.7) 646(9.5) 707(10.3) < 0.001 non-Hispanic White 5355(66.7) 1336(65.2) 1363(66.3) 1281(67.1) 1375(68.4) non-Hispanic Black 2523(11.5) 905(16.1) 669(12.1) 552(10.3) 397(7.6) Other 2363(12.9) 598(12.1) 598(12.8) 589(13.1) 578(13.6) Education, n (%) Less than high school 3828(20.4) 902(18.0) 932(19.2) 941(20.7) 1053(23.7) < 0.001 High school grad or equivalent 2976(25.7) 722(23.4) 763(25.6) 755(26.7) 736(27.0) Some college or above 5774(53.9) 1643(58.6) 1491(55.2) 1372(52.6) 1268(49.3) Marital, n (%) Married/Living with partner 7875(66.2) 1845(60.9) 2002(67.6) 2000(67.1) 2028(69.3) < 0.001 Widowed/Divorced/Separated 3214(21.5) 968(25.1) 842(22.4) 721(19.9) 683(18.7) Never married 1489(12.2) 454(14.0) 342(9.9) 347(13.0) 346(12.0) PIR, n (%) Low 4009(22.6) 886(19.1) 1011(22.3) 991(22.8) 1121(26.2) < 0.001 Moderate 4994(38.3) 1373(39.6) 1254(38.0) 1215(37.8) 1152(37.9) High 3575(39.1) 1008(41.3) 921(39.7) 862(39.4) 784(35.9) BMI (kg/m 2 ), n (%) Normal/Underweight 2720(20.9) 1125(35.2) 706(22.1) 513(15.2) 376(10.9) < 0.001 Overweight 4314(33.9) 1018(32.2) 1087(33.4) 1077(35.1) 1132(35.1) Obese 5544(45.2) 1124(32.6) 1393(44.5) 1478(49.7) 1549(54.0) Smoking status, n (%) Never 6463(50.4) 1794(54.1) 1719(53.1) 1566(50.2) 1384(44.2) < 0.001 Former 3695(29.7) 971(31.6) 918(28.8) 917(29.4) 889(29.0) Current 2420(19.9) 502(14.3) 549(18.2) 585(20.4) 784(26.7) Drinking status, n (%) Yes 8231(69.9) 2084(69.5) 2005(67.4) 1998(69.5) 2144(73.2) 0.002 No 4347(30.1) 1183(30.5) 1181(32.6) 1070(30.5) 913(26.8) physical activity, n (%) Vigorous 2205(20.7) 602(23.1) 538(19.7) 524(19.3) 541(20.6) 0.034 Moderate 3455(29.9) 942(30.5) 875(29.7) 843(31.0) 795(28.4) Other 6918(49.4) 1723(46.4) 1773(50.5) 1701(49.7) 1721(51.0) Hypertension, n (%) Yes 8185(61.1) 2149(59.3) 2090(62.3) 1967(61.2) 1979(61.7) 0.323 No 4393(38.9) 1118(40.7) 1096(37.7) 1101(38.8) 1078(38.3) Hyperlipidemia, n (%) Yes 9813(77.9) 1631(48.2) 2310(71.6) 2816(91.6) 3056(100.0) < 0.001 No 2765(22.1) 1636(51.8) 876(28.4) 252(8.4) 1(0.0) Diabetes, n (%) 4089(27.1) 1095(27.1) 1024(27.8) 965(26.3) 1005(27.3) 0.748 Prediabetes, n (%) 8489(72.9) 2172(72.9) 2162(72.2) 2103(73.7) 2052(72.7) The data are presented as the mean (SD) or n (%). All estimates were obtained from complex survey designs, analysis of variance or χ 2 tests where appropriate. BMI body mass index, PIR poverty income ratio, SD standard deviation Associations between the NHHR and mortality During a mean follow-up period of 8.08 years, a total of 2,403 participant deaths were recorded, of which 662 were attributed to cardiovascular causes. The correlation between the NHHR and mortality is presented in Table 2 . We employed the Cox proportional hazards regression model to investigate the independent association between the NHHR and the risk of mortality. After adjusting for multiple variables (Model 3), the hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality across the lowest to highest quartiles of the NHHR (Q1-Q4) were 1.00 (reference), 0.87 (0.75, 1.00), 0.79 (0.68, 0.92), and 1.00 (0.86, 1.18), respectively. For cardiovascular mortality, the HRs and 95% CIs were 1.00 (reference), 0.75 (0.57, 0.99), 0.76 (0.58, 1.00), and 0.80 (0.60, 1.07), respectively. According to the trend analysis, no significant linear relationships were detected between the NHHR and all-cause mortality (P for trend = 0.859) or cardiovascular mortality (P for trend = 0.169). Table 2 HRs (95% CIs) for mortality according to the NHHR quartiles Quartiles of NHHR P for trend Q1( 3.88) All-cause mortality Number of deaths 658 613 534 598 Model 1 HR (95%CI) P value 1 0.83(0.72,0.95)0.007 0.66(0.57,0.75)0.002 0.70(0.62,0.79)0.001 0.001 Model 2 HR (95%CI) P value 1 0.88(0.77,1.01)0.074 0.79(0.69,0.89)0.001 1.00(0.87,1.15)0.992 0.579 Model 3 HR (95%CI) P value 1 0.87(0.75,1.00)0.053 0.79(0.68,0.92)0.002 1.00(0.86,1.18)0.954 0.859 CVD mortality Number of deaths 157 181 171 153 Model 1 HR (95%CI) P value 1 0.76(0.59,0.98)0.038 0.70(0.54,0.91)0.006 0.63(0.48,0.82)0.001 0.001 Model 2 HR (95%CI) P value 1 0.82(0.63,1.07)0.153 0.86(0.68,1.10)0.236 0.93(0.71,1.23)0.619 0.671 Model 3 HR (95%CI) P value 1 0.75(0.57,0.99)0.040 0.76(0.58,1.00)0.051 0.80(0.60,1.07)0.135 0.169 Model 1 was unadjusted Model 2 was adjusted for sex, age, race, education, marital status, and poverty income ratio. Model 3 was further adjusted for BMI, smoking status, drinking status, physical activity, hypertension, and hyperlipidemia, based on Model 2 The results are presented as HRs and 95% CIs. BMI, body mass index; CI, confidence interval; CVD, cardiovascular disease; HR, hazard ratio Detection of nonlinear relationships To examine potential nonlinear trends, we employed RCS fitted for Cox proportional hazards models to further investigate the correlation between the NHHR and mortality. Notably, we discovered a significant U-shaped association between the NHHR and all-cause mortality (Fig. 2 A), as well as a significant L-shaped relationship between the NHHR and cardiovascular mortality (Fig. 2 B). By utilizing the “segmented” package, we determined that the inflection points for the NHHR in relation to the risks of all-cause and cardiovascular mortality were 2.71 and 2.85, respectively. To further investigate these relationships, we employed a segmented Cox proportional hazards model and found that the results of the likelihood ratio tests were statistically significant (Table 3 ). In particular, the NHHR exhibited a negative correlation with all-cause and cardiovascular mortality below the inflection points. For each unit increase in the NHHR, the risk of all-cause mortality decreased by 20% (HR: 0.80, 95% CI: 0.73–0.88, p = 0.001), and the risk of cardiovascular mortality decreased by 22% (HR: 0.78, 95% CI: 0.67–0.92, p = 0.003). Above the inflection points, the NHHR demonstrated a positive correlation with all-cause mortality, with a 7% increase in the risk for each unit increase in the NHHR (HR: 1.07, 95% CI: 1.03–1.11, p = 0.001). However, the risk of cardiovascular mortality increased by 5% (HR: 1.05, 95% CI: 0.98–1.13, p = 0.189), although this increase did not reach statistical significance. We also examined the associations between the NHHR and mortality in diabetic and prediabetic populations, respectively (Fig. 3 ). The results revealed that in participants with diabetes, the NHHR exhibited a U-shaped correlation with all-cause mortality (P for nonlinear < 0.001), while it still presented an approximately L-shaped correlation with cardiovascular mortality (P for nonlinear = 0.008). For participants with prediabetes, the NHHR also demonstrated a U-shaped correlation with all-cause mortality (P for nonlinear = 0.007), but showed a linear correlation with cardiovascular mortality (P for nonlinear = 0.445). Table 3 Threshold effect analysis of the NHHR on all-cause and CVD mortality in participants with diabetes or prediabetes Adjusted HR (95%CI), P value All-cause mortality Total 1.01(0.98,1.04)0.57 Segmented cox proportional hazards model Inflection point 2.71 NHHR<2.71 0.80(0.73,0.88)0.001 NHHR ≥ 2.71 1.07(1.03,1.11)0.001 P for Log-likelihood ratio < 0.001 CVD mortality Total 0.98(0.92,1.04)0.50 Segmented cox proportional hazards model Inflection point 2.85 NHHR<2.85 0.78(0.67,0.92)0.003 NHHR ≥ 2.85 1.05(0.98,1.13)0.189 P for Log-likelihood ratio 0.005 The model was adjusted for sex, age, race, education, marital status, poverty income ratio, BMI, smoking status, drinking status, physical activity, hypertension, and hyperlipidemia. BMI, body mass index; CI, confidence interval; CVD, cardiovascular disease; HR, hazard ratio Figure 3 Restricted cubic splines were used to test the hypothesis of nonlinear associations between the NHHR and all-cause (A) and cardiovascular (B) mortality in participants with diabetes. The NHHRs of 2.52 for A and 2.53 for B were chosen as reference estimates for each hazard ratio (HR). Similarly, restricted cubic splines were used to examine the nonlinear relationships between the NHHR and all-cause (C) and cardiovascular (D) mortality in participants with prediabetes. Using NHHR of 3.29 for A and 2.93 for B as reference estimates for each HR, the analysis was adjusted for sex, age, race, education, marital status, poverty income ratio, BMI, smoking status, drinking status, physical activity, hypertension, and hyperlipidemia. The Cox proportional hazards model was used to fit the data, with solid lines representing the estimated values and shaded areas indicating the corresponding 95% CIs. The black dashed line denotes the location of the inflection point in the curve. BMI, body mass index; CVD, cardiovascular disease Subgroup analyses According to the subgroup analysis (Fig. 4 ) stratified by sex, age, race, smoking status, drinking status, physical activity, BMI, and classification of diabetes, higher NHHRs were consistently less strongly correlated with the risk of all-cause and cardiovascular mortality than lower NHHRs were (all-cause mortality < 2.71, cardiovascular mortality < 2.85). No significant interaction was observed across the subgroups for all-cause mortality. However, for cardiovascular mortality, a significant interaction was found when stratified by race (P for interaction = 0.021). Therefore, further investigation is warranted to explore the differences in the relationship between the NHHR and cardiovascular mortality among different racial populations. Sensitivity analysis In the sensitivity analysis, a total of 11,109 participants were included in the study after excluding participants who self-reported cancer at baseline (Additional file 1: Table S1 ). The results demonstrated that the associations between the NHHR and all-cause and cardiovascular mortality remained relatively stable. After adjusting for all confounding factors, participants in the second quartile (Q2) and third quartile (Q3) of the NHHR experienced 17% and 22% decreases in all-cause mortality, respectively, compared to participants in the lowest quartile (Q1). Additionally, participants in the Q2, Q3, and fourth quartile (Q4) showed reductions in cardiovascular mortality of 36%, 29%, and 26%, respectively. After excluding participants who died within the first two years of follow-up, a total of 11,576 participants were included in the study (Additional file 1: Table S2). The results demonstrated a similar relationship between the NHHR and both all-cause and cardiovascular mortality. Compared to participants in Q1 of the NHHR, those in Q2 and Q3 experienced a 15% and 27% decrease in all-cause mortality, respectively. Furthermore, participants in Q3 exhibited a 34% decrease in cardiovascular mortality. After excluding extreme values (mean ± 3 SD) of the NHHR, a total of 12,447 participants were included in the study (Additional file 1: Table S3). Compared to participants in Q1 of the NHHR, those in Q3 experienced a 21% decrease in all-cause mortality. Additionally, participants in both Q2 and Q3 exhibited a 25% decrease in cardiovascular mortality. Trend line analysis did not reveal any statistically significant findings. Discussion To the best of our knowledge, our study is the first prospective cohort study to reveal the association between the NHHR and the risk of all-cause and cardiovascular mortality in participants with diabetes or prediabetes. Our findings indicate a U-shaped correlation between the NHHR and all-cause mortality, as well as an L-shaped correlation with cardiovascular mortality among participants with diabetes or prediabetes. The results of the threshold effect analysis reveal that the NHHR exhibits inflection points at 2.71 and 2.85 for the risks of all-cause and cardiovascular mortality, respectively. Our study underscores the significance of the NHHR as a valuable clinical prognostic indicator for both all-cause and cardiovascular mortality in patients with diabetes or prediabetes. Furthermore, it holds immense importance for disease risk stratification and prognosis. The subgroup analysis demonstrated ethnic-specific differences in the relationship between the NHHR and cardiovascular mortality. The NHHR is a novel lipid ratio indicator that is cost-effective and easily obtainable, and previous studies have revealed its clinical predictive value in various diseases. A longitudinal cohort study conducted by Sheng et al. [ 14 ] involving 15,464 participants demonstrated that the NHHR is a superior indicator for predicting the risk of diabetes compared to other conventional lipid markers, with an inflection point at approximately 2.74. This finding essentially aligns with the results of our study. You et al. [ 25 ] reported a correlation between the NHHR and coronary artery disease, with an elevated NHHR being associated with an increased risk of acute coronary syndrome. Similarly, Mao et al. [ 17 ] demonstrated that the NHHR serves as an independent predictor for severe coronary artery disease and major adverse cardiovascular events and is associated with the prognosis of patients with non-ST-segment elevation myocardial infarction. These studies indirectly support the conclusions of our research. Additionally, other studies have revealed a relationship between the NHHR and depression as well as suicidal ideation in adults, shedding light on the connection between lipid metabolism and mental health [ 23 , 26 ]. Lipid markers provide crucial information about lipid metabolism and an individual’s health status and can be used to predict the risk of certain diseases [ 27 ]. Although the biological mechanisms linking the NHHR to mortality remain unclear, several studies have suggested a potential association between the NHHR and atherosclerosis [ 14 , 28 ]. Arteriosclerosis in patients with diabetes is associated with several pathological mechanisms, including diabetes-related dyslipidemia, hyperglycemia, and insulin resistance, all of which play important roles in the development of arteriosclerosis and are associated with adverse CVD outcomes [ 29 , 30 ]. In 2013, the American Heart Association/American College of Cardiology reported that elevated non-HDL-C levels were the underlying cause of arteriosclerosis and the core driver of ASCVD [ 31 ]. Non-HDL-C comprises LDL-C and all other components contributing to the development of arteriosclerosis, making it a superior predictor for ASCVD [ 32 – 34 ]. Conversely, HDL-C may exert anti-inflammatory, antioxidant, and anti-atherogenic effects, displaying a negative correlation with the incidence of ASCVD[ 35 , 36 ]. The NHHR incorporates all lipid-related information relevant to both atherogenic and anti-atherogenic processes, offering a more comprehensive depiction of their balance. An increased NHHR may contribute to vascular endothelial layer impairment and the accumulation of atherosclerotic plaques. The ultimate outcome of this process can be thrombotic events triggered by plaque erosion or rupture, ultimately leading to acute cardiovascular events and, consequently, patient mortality. Our study revealed a negative correlation between the NHHR and age at baseline, while a positive correlation was observed between the NHHR and BMI. This finding is partially supported by Zhang et al.[ 37 ], who found a negative correlation between non-HDL-C levels and age in participants aged over 57 years. Our study also revealed a U-shaped correlation between the NHHR and all-cause mortality in participants with diabetes or prediabetes. Additionally, the NHHR exhibited an L-shaped association with cardiovascular mortality. We further developed a segmented Cox proportional hazards model, and the results revealed that when the baseline NHHR was below the inflection point, the NHHR was negatively associated with both all-cause and cardiovascular mortality. For every unit increase in the NHHR, the all-cause and cardiovascular mortality decreased by 22% and 20%, respectively. Conversely, when the NHHR exceeded the inflection point, there was a positive correlation between the NHHR and all-cause mortality. For every unit increase in the NHHR, the all-cause mortality increased by 7%. Our results are partially supported by multiple studies. A meta-analysis[ 38 ] showed that compared to coronary heart disease patients with low baseline levels of non-HDL-C, those with high levels of non-HDL-C experienced a 24% increase in mortality. This could be attributed to the accelerated development of atherosclerosis associated with extremely high non-HDL-C levels, leading to an elevated risk of mortality. However, extremely low levels of non-HDL-C can also lead to an increased risk of mortality. Several studies[ 39 – 41 ] have shown a U-shaped relationship between non-HDL-C and all-cause and cardiovascular mortality in populations of hypertensive participants, those with chronic kidney disease, and males not receiving statin therapy. These findings indicate the necessity of maintaining non-HDL-C levels within a reasonable range. Furthermore, two prospective cohort studies have shown a U-shaped relationship between HDL-C levels and all-cause mortality, indicating that both excessively high and excessively low HDL-C levels can increase the risk of death[ 42 ]. Another study from the CANHEART cohort supported this notion, demonstrating that participants with very low HDL-C levels (≤ 30 mg/dl) have higher rates of cardiovascular and non-cardiovascular mortality compared to those with intermediate levels of HDL-C[ 43 ]. Notably, our study demonstrates that both excessively high and excessively low NHHRs are associated with an increased risk of mortality. We also found that excessively low NHHRs can lead to a significant increase in the risk of mortality. Possible mechanisms for this observation are as follows: (1) patients with lower levels of TC may experience poorer health conditions[ 44 ] or a decrease in cholesterol levels due to weakness and illness[ 45 , 46 ]; (2) according to lipid calculation formulas, there is typically an inverse relationship between HDL-C and non-HDL-C, with low non-HDL-C levels being equivalent to high HDL-C levels. Furthermore, studies have shown that high HDL-C levels can increase the risk of mortality in patients[ 39 , 42 ]. Therefore, the specific mechanisms underlying the association between the NHHR and the risk of diabetes-related mortality remain to be further investigated. We further investigated the correlation between the NHHR and mortality in participants with diabetes and those with prediabetes, respectively. The results revealed a U-shaped relationship between the NHHR and all-cause mortality in participants with diabetes, while a similar L-shaped correlation was observed with cardiovascular mortality. In participants with prediabetes, the NHHR was still associated with all-cause mortality in a U-shaped manner but showed a linear correlation with cardiovascular mortality. The aforementioned discrepancies may be attributed to the relatively small number of participants experiencing cardiovascular mortality in the study. In the subgroup analysis, we found an interaction effect between the NHHR and race in terms of cardiovascular mortality, indicating that racial factors could potentially influence the association between NHHR and cardiovascular mortality. The sensitivity analysis results indicated that the relationships between the NHHR and both all-cause and cardiovascular mortality remained relatively stable when participants with self-reported cancer at baseline, those who died within the first 2 years of follow-up, and those with extreme NHHRs were excluded. There are several limitations in this study. First, as an observational study, we cannot establish a causal relationship between the NHHR and mortality. Second, this study only assessed the prognostic value of the baseline NHHR and did not investigate the association between changes in the NHHR over time and mortality. Third, we did not adjust for diabetes classification, disease duration, or medication use, which could lead to potential biases. Fourth, we did not control for the influences of factors such as diet, season, or the use of lipid-lowering medications on the NHHR or lipid levels. Fifth, the study population consisted primarily of the general population in the US, so caution should be exercised when extrapolating the findings to other ethnicities. Finally, the proportion of patients with CVD outcomes in the study population was relatively small, which may have limited the statistical power to detect differences between groups. Conclusions The findings of this study indicate that the NHHR serves as a valuable predictive indicator for all-cause and cardiovascular mortality in participants with diabetes or prediabetes. In our nationally representative sample of adults with diabetes or prediabetes in the US, we observed a U-shaped correlation between the NHHR and all-cause mortality, as well as an L-shaped correlation with cardiovascular mortality. Monitoring the NHHR may contribute to evaluating the mortality risk and prognosis of participants with diabetes or prediabetes. Additionally, our analysis revealed significant interactions between the NHHR and different racial groups. This finding suggests that race may potentially influence the relationship between the NHHR and cardiovascular mortality. Abbreviations ASCVD Atherosclerotic cardiovascular disease BMI Body mass index CI Confidence intervals CVD Cardiovascular disease DM Diabetes mellitus FBG Fasting blood glucose HbA1c Hemoglobin A1c HDL-C High-density lipoprotein-cholesterol HR Hazard ratios LDL-C Low-density lipoprotein cholesterol NCHS National Center for Health Statistics NDI National death index NHHR Non-high density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio Non-HDL-C Non-high density lipoprotein cholesterol PG Postprandial glucose RCS Restricted cubic splines TC Total cholesterol Declarations Acknowledgment We sincerely express our gratitude to the participants and investigators of the NHANES study for their invaluable contributions, which have provided significant support and assistance to our research endeavors. Supplementary Information Additional file1: Table S1. HRs (95% CIs) for mortality according to the NHHR quartiles after excluding participants with self-reported cancer at baseline. Additional file1: Table S2. HRs (95% CIs) for mortality according to the NHHR quartiles after excluding participants who died within the first two years of follow-up. Additional file1: Table S3. HRs (95% CIs) for mortality according to the NHHR quartiles after excluding extreme values (mean ± 3 standard deviations) of the NHHR. Author contributions The study was conceived by BY, ML, and RG, who were responsible for performing the data analysis and manuscript writing. TZ and ZY extracted the data from the official NHANES website. HZ contributed to the revision and review of the manuscript. AG and XF conducted a repeat analysis of the data and verified the results. All authors have reviewed and approved the final version of the manuscript. Funding This study was supported by the Science and Technology Innovation Project of the China Academy of Chinese Medical Sciences (CI2021A04701) and the National Key Research and Development Program of China (2021YFF 0901404). Availability of data and materials The dataset used for this study analysis can be found on the official website of the National Health and Nutrition Examination Survey (https://www.cdc.gov/nchs/nhanes/index.htm). Ethics approval and consent to participate The NHANES protocol was approved by the National Center for Health Statistics and the Institutional Review Board. All participants provided written informed consent. Consent for publication The authors declare no competing interests. Author details 1 Graduate School, Beijing University of Chinese Medicine, Beijing 100029, China 2 School of Nursing, Xi 'an Jiaotong University Health Science Center, Xi 'an 710061, China 3 Graduate School, China Academy of Chinese Medical Sciences, Beijing 100700, China 4 Xiyuan Hospital, Chinese Academy of Chinese Medical Sciences, Beijing 100091, China References Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14:88–98. Gregg E, Buckley J, Ali MK, Davies J, Flood D, Mehta R et al. Improving Health Outcomes of People with Diabetes Mellitus: Global Target Setting to Reduce the Burden of Diabetes Mellitus by 2030. Lancet (London, England). 2023;401:1302. GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402:203–34. 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The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and suicidal ideation in adults: a population-based study in the United States. Lipids Health Dis. 2024;23:17. International statistical classification of diseases. and related health problems [Internet]. [cited 2024 Feb 3]. https://iris.who.int/handle/10665/246208 . You J, Wang Z, Lu G, Chen Z. Association between the Non-high-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio and the Risk of Coronary Artery Disease. Biomed Res Int. 2020;2020:7146028. Qi X, Wang S, Huang Q, Chen X, Qiu L, Ouyang K, et al. The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and risk of depression among US adults: A cross-sectional NHANES study. J Affect Disord. 2024;344:451–7. Emerging Risk Factors Collaboration, Di Angelantonio E, Gao P, Pennells L, Kaptoge S, Caslake M, et al. 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Stone NJ, Robinson JG, Lichtenstein AH, Bairey Merz CN, Blum CB, Eckel RH, et al. 2013 ACC/AHA Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63:2889–934. Visseren FLJ, Mach F, Smulders YM, Carballo D, Koskinas KC, Bäck M, et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice: Developed by the Task Force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies With the special contribution of the European Association of Preventive Cardiology (EAPC). Rev Esp Cardiol. 2021;42:3227–337. Pencina KM, Thanassoulis G, Wilkins JT, Vasan RS, Navar AM, Peterson ED, et al. Trajectories of Non-HDL Cholesterol Across Midlife: Implications for Cardiovascular Prevention. J Am Coll Cardiol. 2019;74:70–9. Raja V, Aguiar C, Alsayed N, Chibber YS, ElBadawi H, Ezhov M, et al. Non-HDL-cholesterol in dyslipidemia: Review of the state-of-the-art literature and outlook. Atherosclerosis. 2023;383:117312. Endo Y, Fujita M, Ikewaki K. HDL Functions—Current Status and Future Perspectives. Biomolecules. 2023;13:105. Xepapadaki E, Nikdima I, Sagiadinou EC, Zvintzou E, Kypreos KE. HDL and type 2 diabetes: the chicken or the egg? Diabetologia. 2021;64:1917–26. Zhang P, Su Q, Ye X, Guan P, Chen C, Hang Y, et al. Trends in LDL-C and Non-HDL-C Levels with Age. Aging Dis. 2020;11:1046–57. Liao P, Zeng R, Zhao X, Guo L, Zhang M. Prognostic value of non-high-density lipoprotein cholesterol for mortality in patients with coronary heart disease: A systematic review and meta-analysis. Int J Cardiol. 2017;227:950–5. Zeng R-X, Xu J-P, Kong Y-J, Tan J-W, Guo L-H, Zhang M-Z. U-Shaped Relationship of Non-HDL Cholesterol With All-Cause and Cardiovascular Mortality in Men Without Statin Therapy. Front Cardiovasc Med. 2022;9:903481. Cheng Q, Liu X-C, Chen C-L, Huang Y-Q, Feng Y-Q, Chen J-Y. The U-Shaped Association of Non-High-Density Lipoprotein Cholesterol Levels With All-Cause and Cardiovascular Mortality Among Patients With Hypertension. Front Cardiovasc Med. 2021;8:707701. Chiu H, Wu P-Y, Huang J-C, Tu H-P, Lin M-Y, Chen S-C, et al. There is a U shaped association between non high density lipoprotein cholesterol with overall and cardiovascular mortality in chronic kidney disease stage 3–5. Sci Rep. 2020;10:12749. Madsen CM, Varbo A, Nordestgaard BG. Extreme high high-density lipoprotein cholesterol is paradoxically associated with high mortality in men and women: two prospective cohort studies. Eur Heart J. 2017;38:2478–86. Ummarino D. HDL-C levels not specific to cardiovascular mortality. Nat Rev Cardiol. 2017;14:2–2. Tuikkala P, Hartikainen S, Korhonen MJ, Lavikainen P, Kettunen R, Sulkava R, et al. Serum total cholesterol levels and all-cause mortality in a home-dwelling elderly population: a six-year follow-up. Scand J Prim Health Care. 2010;28:121–7. Jacobs D, Blackburn H, Higgins M, Reed D, Iso H, McMillan G et al. Report of the Conference on Low Blood Cholesterol: Mortality Associations. Circulation. 1992;86:1046–60. Johannesen CDL, Langsted A, Mortensen MB, Nordestgaard BG. Association between low density lipoprotein and all cause and cause specific mortality in Denmark: prospective cohort study. BMJ. 2020;371:m4266. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additional file1: Table S1. HRs (95% CIs) for mortality according to the NHHR quartiles after excluding participants with self-reported cancer at baseline. Additional file1: Table S2. HRs (95% CIs) for mortality according to the NHHR quartiles after excluding participants who died within the first two years of follow-up. Additional file1: Table S3. HRs (95% CIs) for mortality according to the NHHR quartiles after excluding extreme values (mean ± 3 standard deviations) of the NHHR. Cite Share Download PDF Status: Published Journal Publication published 07 Aug, 2024 Read the published version in BMC Medicine → Version 1 posted Editorial decision: Revision requested 30 Apr, 2024 Reviews received at journal 24 Apr, 2024 Reviews received at journal 23 Apr, 2024 Reviewers agreed at journal 10 Apr, 2024 Reviewers agreed at journal 10 Apr, 2024 Reviewers invited by journal 09 Apr, 2024 Editor assigned by journal 03 Apr, 2024 Submission checks completed at journal 03 Apr, 2024 First submitted to journal 02 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4207993","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287061369,"identity":"0e826afc-6860-4767-a99d-c8ae04832325","order_by":0,"name":"Binyang Yu","email":"","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Binyang","middleName":"","lastName":"Yu","suffix":""},{"id":287061370,"identity":"a569a1fb-f963-43fb-8ea6-7196b28fe175","order_by":1,"name":"Min Li","email":"","orcid":"","institution":"School of Nursing, Xi 'an Jiaotong University Health Science Center","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Li","suffix":""},{"id":287061371,"identity":"14a5f30c-ccdb-4dc0-961f-c1d13d755f40","order_by":2,"name":"Zongliang Yu","email":"","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zongliang","middleName":"","lastName":"Yu","suffix":""},{"id":287061372,"identity":"164955df-4b00-4a0c-951d-7c96b62889f0","order_by":3,"name":"Tao Zheng","email":"","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Zheng","suffix":""},{"id":287061373,"identity":"44d210e8-6914-425e-b342-c108379ab6c8","order_by":4,"name":"Xue Feng","email":"","orcid":"","institution":"Chinese Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Feng","suffix":""},{"id":287061374,"identity":"1610ed97-6a97-4133-a5bb-75415aa72fc9","order_by":5,"name":"Anran Gao","email":"","orcid":"","institution":"Chinese Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Anran","middleName":"","lastName":"Gao","suffix":""},{"id":287061375,"identity":"ca2bae0d-2421-4776-a378-05378f97c098","order_by":6,"name":"Haoling Zhang","email":"","orcid":"","institution":"Xiyuan Hospital, Chinese Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Haoling","middleName":"","lastName":"Zhang","suffix":""},{"id":287061376,"identity":"4276487e-4d87-4596-bd99-7f27d249eba3","order_by":7,"name":"Rui Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYFACxgYgYcPDz95AmpY0GcmeA6RZddjG4IYDkWrNZyQ3v/hRcZ6H4QYD44ePOURokbmR2GbZc+Y2D+PsBmbJmduI0CIhkdhmwNt2m4dZ5gAbMy+xWgz/tp3jYZNIIF5L82PetgM8PMRr4XnYxixzJplHgudgM5F+YU9//PFNhZ29/fHmgx8+EqMFCNgkIDQ4TokDzB+IVjoKRsEoGAUjEwAAe4gzWxl9cRwAAAAASUVORK5CYII=","orcid":"","institution":"Xiyuan Hospital, Chinese Academy of Chinese Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Rui","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2024-04-02 16:22:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4207993/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4207993/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12916-024-03536-3","type":"published","date":"2024-08-07T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54321790,"identity":"b236233b-ea60-4c19-8236-f23ca7392b54","added_by":"auto","created_at":"2024-04-08 19:39:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":784398,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study participants\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4207993/v1/ab8db0ed4678ef036abd8491.png"},{"id":54322246,"identity":"5411520a-3faa-4cc2-abd4-a568e3e461f0","added_by":"auto","created_at":"2024-04-08 19:47:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89942,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic splines were used to test the hypothesis of nonlinear associations between the NHHR and all-cause (A) and cardiovascular (B) mortality in participants with diabetes or prediabetes. The NHHRs of 2.71 for A and 2.85 for B were chosen as reference estimates for each hazard ratio (HR). The analysis was adjusted for sex, age, race, education, marital status, poverty income ratio, BMI, smoking status, drinking status, physical activity, hypertension, and hyperlipidemia. The Cox proportional hazards models were fitted, with solid lines representing estimated values and shaded areas indicating the corresponding 95% confidence intervals (CIs). The black dashed line indicates the location of the inflection point in the curve. BMI, body mass index; CVD, cardiovascular disease\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4207993/v1/9f8a30c4b1e5183ea896380f.png"},{"id":54321794,"identity":"0b6fedc7-8d7c-4f61-86ca-c0c3a4e258da","added_by":"auto","created_at":"2024-04-08 19:39:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":340952,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic splines were used to test the hypothesis of nonlinear associations between the NHHR and all-cause (A) and cardiovascular (B) mortality in participants with diabetes. The NHHRs of 2.52 for A and 2.53 for B were chosen as reference estimates for each hazard ratio (HR). Similarly, restricted cubic splines were used to examine the nonlinear relationships between the NHHR and all-cause (C) and cardiovascular (D) mortality in participants with prediabetes. Using NHHR of 3.29 for A and 2.93 for B as reference estimates for each HR, the analysis was adjusted for sex, age, race, education, marital status, poverty income ratio, BMI, smoking status, drinking status, physical activity, hypertension, and hyperlipidemia. The Cox proportional hazards model was used to fit the data, with solid lines representing the estimated values and shaded areas indicating the corresponding 95% CIs. The black dashed line denotes the location of the inflection point in the curve. BMI, body mass index; CVD, cardiovascular disease\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4207993/v1/c84eb70a882ba9328efd6712.png"},{"id":54321793,"identity":"0437fc9d-9b63-4161-afee-aaecbb1dfd9c","added_by":"auto","created_at":"2024-04-08 19:39:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1425519,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the associations between the NHHR and all-cause and cardiovascular mortality. The reference NHHRs for all-cause mortality were NHHRs\u0026lt;2.71, while for cardiovascular mortality, the reference was NHHRs\u0026lt;2.85. The model was adjusted for sex, age, race, education, marital status, poverty income ratio, BMI, smoking status, drinking status, physical activity, hypertension, and hyperlipidemia. BMI, body mass index; CI, confidence interval; CVD, cardiovascular disease; HR, hazard ratio\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4207993/v1/25badcde24e3369db8ac2c41.png"},{"id":62298321,"identity":"42ffbc18-af0c-4271-a4f2-b3cd95d339be","added_by":"auto","created_at":"2024-08-12 16:12:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1700032,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4207993/v1/e7f252a0-eb6e-42fc-bd51-2af30476564e.pdf"},{"id":54321791,"identity":"c0077bd3-508f-4af0-aa96-00b6eb0a0ec2","added_by":"auto","created_at":"2024-04-08 19:39:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28578,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file1: Table S1. HRs (95% CIs) for mortality according to the NHHR quartiles after excluding participants with self-reported cancer at baseline. Additional file1: Table S2. HRs (95% CIs) for mortality according to the NHHR quartiles after excluding participants who died within the first two years of follow-up. Additional file1: Table S3. HRs (95% CIs) for mortality according to the NHHR quartiles after excluding extreme values (mean ± 3 standard deviations) of the NHHR.\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4207993/v1/757a9d06801f8093312b8720.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio (NHHR) as a Predictor of All-Cause and Cardiovascular Mortality in US Adults with Diabetes or Prediabetes: NHANES 1998-2018","fulltext":[{"header":"Background","content":"\u003cp\u003eDiabetes and its complications are among the leading causes of death and disability worldwide, posing a significant challenge to global health. The global incidence of diabetes is increasing annually due to factors such as population aging and unhealthy dietary habits[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Epidemiological surveys indicated that in 2021, there were approximately 529\u0026nbsp;million participants worldwide with diabetes, and by 2050, the number of affected participants is projected to reach 1.31 billion[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Moreover, diabetes ranks as the eighth leading cause of death globally, with associated healthcare expenditures reaching a staggering \u003cspan\u003e$\u003c/span\u003e966\u0026nbsp;billion, imposing a significant burden on healthcare systems[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Notably, diabetes is closely associated with several major causes of death worldwide and is a primary risk factor for cardiovascular disease (CVD) and stroke[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although cancer is also emerging as a primary cause of death in participants with diabetes in certain regions, CVD remains the leading cause of mortality among diabetes patients[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCVD among participants with diabetes is multifactorial, and controlling cardiovascular risk factors can significantly reduce cardiovascular events. Research has demonstrated that atherosclerotic cardiovascular diseases (ASCVDs) caused by factors such as hypertension, high blood glucose, dyslipidemia, obesity, and insulin resistance are the primary causes of mortality in diabetes patients[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Notably, dyslipidemia is a crucial factor contributing to ASCVD in participants with diabetes. Dyslipidemia in diabetes patients is characterized by increased levels of non-high-density lipoprotein cholesterol (non-HDL-C), including low-density lipoprotein cholesterol (LDL-C), intermediate-density lipoprotein, and very low-density lipoprotein remnants, all of which contribute to the development of atherosclerosis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In recent years, the clinical value of non-HDL-C has gained widespread attention and recognition. In 2021, the National Institute for Health and Care Excellence (NICE) in the US recommended that non-HDL-C be used as the primary target for reducing CVD risk in patients with diabetes, replacing LDL-C. This recommendation highlights the significance of non-HDL-C in assessing CVD risk and treatment efficacy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, a novel composite lipid indicator, the non-HDL-C to high-density lipoprotein cholesterol (HDL-C) ratio (NHHR), has shown promising predictive value in assessing the risk of various diseases, including coronary artery disease, diabetes, abdominal aortic aneurysm, and carotid atherosclerosis[\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Compared to non-HDL-C, the NHHR is a superior and comprehensive indicator that takes into account both the risk factor (non-HDL-C) and the protective factor (HDL-C) in atherosclerosis[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, the prognostic value of the NHHR in patients with diabetes remains unclear. In order to address this gap, we conducted a study utilizing the National Health and Nutrition Examination Survey (NHANES) database. This research sought to explore the correlation between the NHHR and all-cause and cardiovascular mortality in participants with diabetes or prediabetes, as well as its association with various population characteristics, aiming to provide valuable insights into disease management and prevention strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population and design\u003c/h2\u003e \u003cp\u003eThe NHANES is a nationwide survey study conducted to assess the nutritional and health status of adults and children in the US. The survey employs a stratified, complex multistage sampling methodology and includes interviews, physical examinations, and laboratory tests[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The study protocol received formal approval from the National Center for Health Statistics (NCHS) Research Ethics Review Board, and all participants involved in the study provided informed consent by signing consent forms[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This study utilized data from the NHANES database spanning from 1998 to 2018, with a total of 101,316 participants. Initially, participants under the age of 20 were excluded (n\u0026thinsp;=\u0026thinsp;46,235). Subsequently, participants who did not meet the diagnostic criteria for diabetes or prediabetes according to the 2021 American Diabetes Association guidelines[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] were excluded (n\u0026thinsp;=\u0026thinsp;31,214). Diabetes was defined as: (1) self-reported diabetes; (2) insulin injection or hypoglycemic medication usage; (3) hemoglobin A1c (HbA1c)\u0026thinsp;\u0026ge;\u0026thinsp;6.5%; (4) fasting blood glucose (FBG)\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L; (5) 2-hour postprandial glucose (2hPG)\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L. Prediabetes was defined as: (1) HbA1c 5.7\u0026ndash;6.4%; (2) FBG 5.6\u0026ndash;6.9 mmol/L; (3) 2hPG 7.8\u0026ndash;11.0 mmol/L. Subsequently, participants lacking the NHHR data were excluded (n\u0026thinsp;=\u0026thinsp;11,250). Finally, after excluding participants with missing mortality data (n\u0026thinsp;=\u0026thinsp;39), a total of 12,578 participants were included in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of the NHHR\u003c/h2\u003e \u003cp\u003eBlood lipid parameters are fundamental indicators included in the NHANES database for assessing CVD risk. In our study, we used the non-HDL-C to HDL-C ratio to evaluate participants\u0026rsquo; lipid levels and CVD risk. The NHHR was calculated based on serum total cholesterol (TC) and HDL-C levels from the NHANES database for the years 1999\u0026ndash;2018. Non-HDL-C is defined as TC minus HDL-C, and the NHHR is calculated as the ratio of non-HDL-C to HDL-C[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Participants were categorized into four groups (Q1, Q2, Q3, and Q4) based on the NHHR quartiles, with the Q1 group serving as the reference group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAscertainment of mortality\u003c/h2\u003e \u003cp\u003eTo ascertain the all-cause and cardiovascular mortality status of the participants in this study, we utilized the Public-Use Linked Mortality Files provided by the NCHS. These files link survey participants\u0026rsquo; data with death certificate records from the National Death Index (NDI) using a probabilistic matching algorithm. The follow-up mortality data were updated until December 31, 2019. The specific cause of death was determined based on the International Classification of Diseases, Tenth Revision (ICD-10), with cardiovascular deaths including codes I00-I09, I11, I13, and I20-I51[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. All-cause mortality encompassed the sum of all specific cause deaths.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of covariates\u003c/h2\u003e \u003cp\u003eThe selection of covariates in this study considered a range of demographic characteristics and health-related information, including age, sex (male/female), race (Mexican American, Non-Hispanic White, Non-Hispanic Black, Other), education (less than high school, high school or equivalent, some college or above), and marital status (married/living with partner, widowed/divorced/separated, never married). The poverty income ratio (PIR) was divided into the following categories: low income (\u0026lt;\u0026thinsp;1.3), moderate income (1.3\u0026ndash;3.5), and high income (\u0026gt;\u0026thinsp;3.5). Body mass index (BMI) was classified as normal/underweight (\u0026lt;\u0026thinsp;25), overweight (25\u0026ndash;30), or obese (\u0026gt;\u0026thinsp;30). Smoking status was categorized as never smoking (smoking fewer than 100 cigarettes in a lifetime), former smoking (smoking more than 100 cigarettes in a lifetime but currently not smoking), or current smoking (smoking more than 100 cigarettes in a lifetime and currently smoking every day). Drinking status was dichotomized as yes/no (defined as whether the individual consumed more than 12 drinks per year). Physical activity was classified as vigorous, moderate, or other. Hypertension was defined as self-reported hypertension, the use of antihypertensive medication, or an average blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;130 mmHg or a diastolic pressure\u0026thinsp;\u0026gt;\u0026thinsp;80 mmHg. Hyperlipidemia was defined as self-reported high cholesterol level, use of cholesterol-lowering medications, or laboratory examination indicating TC\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dl, triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dl, LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;130 mg/dl, HDL\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dl (male) or \u0026lt;\u0026thinsp;50 mg/dl (female).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eThe NHANES survey was conducted using a complex, multistage, probability sampling design to ensure the representativeness of the research results for the U.S. civilian non-institutionalized population. In our study, we incorporated sample weights, stratification, and clustering for analysis purposes. The participants were divided into four groups based on the quartiles (Q1-Q4) of the NHHR. Continuous variables were reported as the means and standard deviations (SDs), while categorical variables were reported as frequencies and percentages. The baseline characteristics of the four participant groups were compared using analysis of the variance (ANOVA) for continuous variables and the chi-square (χ2) test for categorical variables. We established three models to control for confounding factors and used weighted multivariate Cox proportional hazards models to estimate the associations between the NHHRs and all-cause and cardiovascular mortality. Model 1 did not include any covariate adjustments. Model 2 was adjusted for age, sex, race, education, marital status, and PIR. Model 3 was further adjusted for BMI, smoking status, drinking status, physical activity, hypertension, and hyperlipidemia. Moreover, the linear trend test was performed to assess the trend by designating the median NHHR for each quartile as a continuous variable in the regression model. Multiple imputation was employed as a method to reduce the decrease in sample size caused by missing covariate data.\u003c/p\u003e \u003cp\u003eTo investigate the dose-response relationship between the NHHR and mortality, we employed Cox proportional hazards regression models incorporating restricted cubic splines (RCSs) and smooth curve fitting. If the RCS indicated a nonlinear association, we determined the inflection point using the \u0026ldquo;segmented\u0026rdquo; package based on the likelihood-ratio test and bootstrap resampling method. Subsequently, we assessed the correlation between the NHHR and mortality using a segmented Cox proportional hazards model on both sides of the inflection point. We performed subgroup analyses based on age (\u0026lt;\u0026thinsp;60 or \u0026ge;\u0026thinsp;60 years), sex (male or female), race (Mexican American, non-Hispanic White, non-Hispanic Black, or Other), smoking status (former, current, or never), drinking status (yes or no), physical activity (vigorous, moderate, or other), BMI (normal/underweight, overweight, or obese), and classification of diabetes (diabetes or prediabetes). Likelihood ratio tests were employed to assess the interactions among the subgroups. To evaluate the stability of the models, several sensitivity analyses were conducted. First, we excluded participants who self-reported having a cancer at baseline. Second, participants who died within 2 years of follow-up were excluded. Finally, participants with extreme NHHR values beyond the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;3SD were excluded. All analyses were performed using R software, version 4.2.1, and a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study participants\u003c/h2\u003e \u003cp\u003eA total of 12,578 participants were included in this study, with an average age of 53.06 years and a male proportion of 53.9%. The mean NHHR of the participants was calculated to be 3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of the participants according to NHHR quartiles. Compared to participants in the lowest quartile, participants with higher NHHRs were often younger, male, and of Mexican American ethnicity. Additionally, these participants tend to exhibit characteristics such as obesity, current smoking, alcohol consumption, and physical inactivity, indicating a potential association between unhealthy dietary habits and an elevated NHHR.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics according to the NHHR quartiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eQuartiles of NHHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1(\u0026lt;\u0026thinsp;2.12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2(2.12\u0026ndash;2.91)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3(2.91\u0026ndash;3.88)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4(\u0026gt;\u0026thinsp;3.88)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, years, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.05(16.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.93(17.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.51(16.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.97(15.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.80(14.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6732(53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1406(42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1532(48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1766(57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2028(67.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5846(46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1861(57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1654(51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1302(42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1029(32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2337(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e428(6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e556(8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e646(9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e707(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5355(66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1336(65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1363(66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1281(67.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1375(68.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2523(11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e905(16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e669(12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e552(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e397(7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2363(12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e598(12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e598(12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e589(13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e578(13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3828(20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e902(18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e932(19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e941(20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1053(23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school grad or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2976(25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e722(23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e763(25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e755(26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e736(27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5774(53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1643(58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1491(55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1372(52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1268(49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7875(66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1845(60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2002(67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2000(67.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2028(69.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3214(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e968(25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e842(22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e721(19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e683(18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1489(12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e454(14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e342(9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e347(13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e346(12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIR, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4009(22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e886(19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1011(22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e991(22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1121(26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4994(38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1373(39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1254(38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1215(37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1152(37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3575(39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1008(41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e921(39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e862(39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e784(35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal/Underweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2720(20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1125(35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e706(22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e513(15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e376(10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4314(33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1018(32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1087(33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1077(35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1132(35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5544(45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1124(32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1393(44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1478(49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1549(54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6463(50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1794(54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1719(53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1566(50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1384(44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3695(29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e971(31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e918(28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e917(29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e889(29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2420(19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e502(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e549(18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e585(20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e784(26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8231(69.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2084(69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2005(67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1998(69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2144(73.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4347(30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1183(30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1181(32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1070(30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e913(26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ephysical activity, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2205(20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e602(23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e538(19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e524(19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e541(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3455(29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e942(30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e875(29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e843(31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e795(28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6918(49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1723(46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1773(50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1701(49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1721(51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8185(61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2149(59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2090(62.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1967(61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1979(61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4393(38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1118(40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1096(37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1101(38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1078(38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHyperlipidemia, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9813(77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1631(48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2310(71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2816(91.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3056(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2765(22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1636(51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e876(28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e252(8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4089(27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1095(27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1024(27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e965(26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1005(27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrediabetes, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8489(72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2172(72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2162(72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2103(73.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2052(72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data are presented as the mean (SD) or n (%). All estimates were obtained from complex survey designs, analysis of variance or χ\u003csup\u003e2\u003c/sup\u003e tests where appropriate.\u003c/p\u003e \u003cp\u003eBMI body mass index, PIR poverty income ratio, SD standard deviation\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between the NHHR and mortality\u003c/h2\u003e \u003cp\u003eDuring a mean follow-up period of 8.08 years, a total of 2,403 participant deaths were recorded, of which 662 were attributed to cardiovascular causes. The correlation between the NHHR and mortality is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. We employed the Cox proportional hazards regression model to investigate the independent association between the NHHR and the risk of mortality. After adjusting for multiple variables (Model 3), the hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality across the lowest to highest quartiles of the NHHR (Q1-Q4) were 1.00 (reference), 0.87 (0.75, 1.00), 0.79 (0.68, 0.92), and 1.00 (0.86, 1.18), respectively. For cardiovascular mortality, the HRs and 95% CIs were 1.00 (reference), 0.75 (0.57, 0.99), 0.76 (0.58, 1.00), and 0.80 (0.60, 1.07), respectively. According to the trend analysis, no significant linear relationships were detected between the NHHR and all-cause mortality (P for trend\u0026thinsp;=\u0026thinsp;0.859) or cardiovascular mortality (P for trend\u0026thinsp;=\u0026thinsp;0.169).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHRs (95% CIs) for mortality according to the NHHR quartiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eQuartiles of NHHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1(\u0026lt;\u0026thinsp;2.12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2(2.12\u0026ndash;2.91)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3(2.91\u0026ndash;3.88)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4(\u0026gt;\u0026thinsp;3.88)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of deaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003eHR (95%CI) P value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83(0.72,0.95)0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66(0.57,0.75)0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70(0.62,0.79)0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003cp\u003eHR (95%CI) P value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88(0.77,1.01)0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79(0.69,0.89)0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00(0.87,1.15)0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003cp\u003eHR (95%CI) P value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87(0.75,1.00)0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79(0.68,0.92)0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00(0.86,1.18)0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of deaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003eHR (95%CI) P value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76(0.59,0.98)0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70(0.54,0.91)0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63(0.48,0.82)0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003cp\u003eHR (95%CI) P value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82(0.63,1.07)0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86(0.68,1.10)0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93(0.71,1.23)0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003cp\u003eHR (95%CI) P value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75(0.57,0.99)0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76(0.58,1.00)0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80(0.60,1.07)0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 1 was unadjusted\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel 2 was adjusted for sex, age, race, education, marital status, and poverty income ratio.\u003c/p\u003e \u003cp\u003eModel 3 was further adjusted for BMI, smoking status, drinking status, physical activity, hypertension, and hyperlipidemia, based on Model 2\u003c/p\u003e \u003cp\u003eThe results are presented as HRs and 95% CIs. BMI, body mass index; CI, confidence interval; CVD, cardiovascular disease; HR, hazard ratio\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDetection of nonlinear relationships\u003c/h2\u003e \u003cp\u003eTo examine potential nonlinear trends, we employed RCS fitted for Cox proportional hazards models to further investigate the correlation between the NHHR and mortality. Notably, we discovered a significant U-shaped association between the NHHR and all-cause mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), as well as a significant L-shaped relationship between the NHHR and cardiovascular mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). By utilizing the \u0026ldquo;segmented\u0026rdquo; package, we determined that the inflection points for the NHHR in relation to the risks of all-cause and cardiovascular mortality were 2.71 and 2.85, respectively. To further investigate these relationships, we employed a segmented Cox proportional hazards model and found that the results of the likelihood ratio tests were statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In particular, the NHHR exhibited a negative correlation with all-cause and cardiovascular mortality below the inflection points. For each unit increase in the NHHR, the risk of all-cause mortality decreased by 20% (HR: 0.80, 95% CI: 0.73\u0026ndash;0.88, p\u0026thinsp;=\u0026thinsp;0.001), and the risk of cardiovascular mortality decreased by 22% (HR: 0.78, 95% CI: 0.67\u0026ndash;0.92, p\u0026thinsp;=\u0026thinsp;0.003). Above the inflection points, the NHHR demonstrated a positive correlation with all-cause mortality, with a 7% increase in the risk for each unit increase in the NHHR (HR: 1.07, 95% CI: 1.03\u0026ndash;1.11, p\u0026thinsp;=\u0026thinsp;0.001). However, the risk of cardiovascular mortality increased by 5% (HR: 1.05, 95% CI: 0.98\u0026ndash;1.13, p\u0026thinsp;=\u0026thinsp;0.189), although this increase did not reach statistical significance. We also examined the associations between the NHHR and mortality in diabetic and prediabetic populations, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results revealed that in participants with diabetes, the NHHR exhibited a U-shaped correlation with all-cause mortality (P for nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while it still presented an approximately L-shaped correlation with cardiovascular mortality (P for nonlinear\u0026thinsp;=\u0026thinsp;0.008). For participants with prediabetes, the NHHR also demonstrated a U-shaped correlation with all-cause mortality (P for nonlinear\u0026thinsp;=\u0026thinsp;0.007), but showed a linear correlation with cardiovascular mortality (P for nonlinear\u0026thinsp;=\u0026thinsp;0.445).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold effect analysis of the NHHR on all-cause and CVD mortality in participants with diabetes or prediabetes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted HR (95%CI), P value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01(0.98,1.04)0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented cox proportional hazards model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHHR\u0026lt;2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.80(0.73,0.88)0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHHR\u0026thinsp;\u0026ge;\u0026thinsp;2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07(1.03,1.11)0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for Log-likelihood ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98(0.92,1.04)0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented cox proportional hazards model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHHR\u0026lt;2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78(0.67,0.92)0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHHR\u0026thinsp;\u0026ge;\u0026thinsp;2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05(0.98,1.13)0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for Log-likelihood ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e The model was adjusted for sex, age, race, education, marital status, poverty income ratio, BMI, smoking status, drinking status, physical activity, hypertension, and hyperlipidemia. BMI, body mass index; CI, confidence interval; CVD, cardiovascular disease; HR, hazard ratio\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e Restricted cubic splines were used to test the hypothesis of nonlinear associations between the NHHR and all-cause (A) and cardiovascular (B) mortality in participants with diabetes. The NHHRs of 2.52 for A and 2.53 for B were chosen as reference estimates for each hazard ratio (HR). Similarly, restricted cubic splines were used to examine the nonlinear relationships between the NHHR and all-cause (C) and cardiovascular (D) mortality in participants with prediabetes. Using NHHR of 3.29 for A and 2.93 for B as reference estimates for each HR, the analysis was adjusted for sex, age, race, education, marital status, poverty income ratio, BMI, smoking status, drinking status, physical activity, hypertension, and hyperlipidemia. The Cox proportional hazards model was used to fit the data, with solid lines representing the estimated values and shaded areas indicating the corresponding 95% CIs. The black dashed line denotes the location of the inflection point in the curve. BMI, body mass index; CVD, cardiovascular disease\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses\u003c/h2\u003e \u003cp\u003eAccording to the subgroup analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e) stratified by sex, age, race, smoking status, drinking status, physical activity, BMI, and classification of diabetes, higher NHHRs were consistently less strongly correlated with the risk of all-cause and cardiovascular mortality than lower NHHRs were (all-cause mortality\u0026thinsp;\u0026lt;\u0026thinsp;2.71, cardiovascular mortality\u0026thinsp;\u0026lt;\u0026thinsp;2.85). No significant interaction was observed across the subgroups for all-cause mortality. However, for cardiovascular mortality, a significant interaction was found when stratified by race (P for interaction\u0026thinsp;=\u0026thinsp;0.021). Therefore, further investigation is warranted to explore the differences in the relationship between the NHHR and cardiovascular mortality among different racial populations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eIn the sensitivity analysis, a total of 11,109 participants were included in the study after excluding participants who self-reported cancer at baseline (Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The results demonstrated that the associations between the NHHR and all-cause and cardiovascular mortality remained relatively stable. After adjusting for all confounding factors, participants in the second quartile (Q2) and third quartile (Q3) of the NHHR experienced 17% and 22% decreases in all-cause mortality, respectively, compared to participants in the lowest quartile (Q1). Additionally, participants in the Q2, Q3, and fourth quartile (Q4) showed reductions in cardiovascular mortality of 36%, 29%, and 26%, respectively. After excluding participants who died within the first two years of follow-up, a total of 11,576 participants were included in the study (Additional file 1: Table S2). The results demonstrated a similar relationship between the NHHR and both all-cause and cardiovascular mortality. Compared to participants in Q1 of the NHHR, those in Q2 and Q3 experienced a 15% and 27% decrease in all-cause mortality, respectively. Furthermore, participants in Q3 exhibited a 34% decrease in cardiovascular mortality. After excluding extreme values (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;3 SD) of the NHHR, a total of 12,447 participants were included in the study (Additional file 1: Table S3). Compared to participants in Q1 of the NHHR, those in Q3 experienced a 21% decrease in all-cause mortality. Additionally, participants in both Q2 and Q3 exhibited a 25% decrease in cardiovascular mortality. Trend line analysis did not reveal any statistically significant findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, our study is the first prospective cohort study to reveal the association between the NHHR and the risk of all-cause and cardiovascular mortality in participants with diabetes or prediabetes. Our findings indicate a U-shaped correlation between the NHHR and all-cause mortality, as well as an L-shaped correlation with cardiovascular mortality among participants with diabetes or prediabetes. The results of the threshold effect analysis reveal that the NHHR exhibits inflection points at 2.71 and 2.85 for the risks of all-cause and cardiovascular mortality, respectively. Our study underscores the significance of the NHHR as a valuable clinical prognostic indicator for both all-cause and cardiovascular mortality in patients with diabetes or prediabetes. Furthermore, it holds immense importance for disease risk stratification and prognosis. The subgroup analysis demonstrated ethnic-specific differences in the relationship between the NHHR and cardiovascular mortality.\u003c/p\u003e \u003cp\u003eThe NHHR is a novel lipid ratio indicator that is cost-effective and easily obtainable, and previous studies have revealed its clinical predictive value in various diseases. A longitudinal cohort study conducted by Sheng et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] involving 15,464 participants demonstrated that the NHHR is a superior indicator for predicting the risk of diabetes compared to other conventional lipid markers, with an inflection point at approximately 2.74. This finding essentially aligns with the results of our study. You et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] reported a correlation between the NHHR and coronary artery disease, with an elevated NHHR being associated with an increased risk of acute coronary syndrome. Similarly, Mao et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] demonstrated that the NHHR serves as an independent predictor for severe coronary artery disease and major adverse cardiovascular events and is associated with the prognosis of patients with non-ST-segment elevation myocardial infarction. These studies indirectly support the conclusions of our research. Additionally, other studies have revealed a relationship between the NHHR and depression as well as suicidal ideation in adults, shedding light on the connection between lipid metabolism and mental health [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLipid markers provide crucial information about lipid metabolism and an individual\u0026rsquo;s health status and can be used to predict the risk of certain diseases [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Although the biological mechanisms linking the NHHR to mortality remain unclear, several studies have suggested a potential association between the NHHR and atherosclerosis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Arteriosclerosis in patients with diabetes is associated with several pathological mechanisms, including diabetes-related dyslipidemia, hyperglycemia, and insulin resistance, all of which play important roles in the development of arteriosclerosis and are associated with adverse CVD outcomes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In 2013, the American Heart Association/American College of Cardiology reported that elevated non-HDL-C levels were the underlying cause of arteriosclerosis and the core driver of ASCVD [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Non-HDL-C comprises LDL-C and all other components contributing to the development of arteriosclerosis, making it a superior predictor for ASCVD [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Conversely, HDL-C may exert anti-inflammatory, antioxidant, and anti-atherogenic effects, displaying a negative correlation with the incidence of ASCVD[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The NHHR incorporates all lipid-related information relevant to both atherogenic and anti-atherogenic processes, offering a more comprehensive depiction of their balance. An increased NHHR may contribute to vascular endothelial layer impairment and the accumulation of atherosclerotic plaques. The ultimate outcome of this process can be thrombotic events triggered by plaque erosion or rupture, ultimately leading to acute cardiovascular events and, consequently, patient mortality.\u003c/p\u003e \u003cp\u003eOur study revealed a negative correlation between the NHHR and age at baseline, while a positive correlation was observed between the NHHR and BMI. This finding is partially supported by Zhang et al.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], who found a negative correlation between non-HDL-C levels and age in participants aged over 57 years. Our study also revealed a U-shaped correlation between the NHHR and all-cause mortality in participants with diabetes or prediabetes. Additionally, the NHHR exhibited an L-shaped association with cardiovascular mortality. We further developed a segmented Cox proportional hazards model, and the results revealed that when the baseline NHHR was below the inflection point, the NHHR was negatively associated with both all-cause and cardiovascular mortality. For every unit increase in the NHHR, the all-cause and cardiovascular mortality decreased by 22% and 20%, respectively. Conversely, when the NHHR exceeded the inflection point, there was a positive correlation between the NHHR and all-cause mortality. For every unit increase in the NHHR, the all-cause mortality increased by 7%.\u003c/p\u003e \u003cp\u003eOur results are partially supported by multiple studies. A meta-analysis[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] showed that compared to coronary heart disease patients with low baseline levels of non-HDL-C, those with high levels of non-HDL-C experienced a 24% increase in mortality. This could be attributed to the accelerated development of atherosclerosis associated with extremely high non-HDL-C levels, leading to an elevated risk of mortality. However, extremely low levels of non-HDL-C can also lead to an increased risk of mortality. Several studies[\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] have shown a U-shaped relationship between non-HDL-C and all-cause and cardiovascular mortality in populations of hypertensive participants, those with chronic kidney disease, and males not receiving statin therapy. These findings indicate the necessity of maintaining non-HDL-C levels within a reasonable range. Furthermore, two prospective cohort studies have shown a U-shaped relationship between HDL-C levels and all-cause mortality, indicating that both excessively high and excessively low HDL-C levels can increase the risk of death[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Another study from the CANHEART cohort supported this notion, demonstrating that participants with very low HDL-C levels (\u0026le;\u0026thinsp;30 mg/dl) have higher rates of cardiovascular and non-cardiovascular mortality compared to those with intermediate levels of HDL-C[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, our study demonstrates that both excessively high and excessively low NHHRs are associated with an increased risk of mortality. We also found that excessively low NHHRs can lead to a significant increase in the risk of mortality. Possible mechanisms for this observation are as follows: (1) patients with lower levels of TC may experience poorer health conditions[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] or a decrease in cholesterol levels due to weakness and illness[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]; (2) according to lipid calculation formulas, there is typically an inverse relationship between HDL-C and non-HDL-C, with low non-HDL-C levels being equivalent to high HDL-C levels. Furthermore, studies have shown that high HDL-C levels can increase the risk of mortality in patients[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, the specific mechanisms underlying the association between the NHHR and the risk of diabetes-related mortality remain to be further investigated.\u003c/p\u003e \u003cp\u003eWe further investigated the correlation between the NHHR and mortality in participants with diabetes and those with prediabetes, respectively. The results revealed a U-shaped relationship between the NHHR and all-cause mortality in participants with diabetes, while a similar L-shaped correlation was observed with cardiovascular mortality. In participants with prediabetes, the NHHR was still associated with all-cause mortality in a U-shaped manner but showed a linear correlation with cardiovascular mortality. The aforementioned discrepancies may be attributed to the relatively small number of participants experiencing cardiovascular mortality in the study. In the subgroup analysis, we found an interaction effect between the NHHR and race in terms of cardiovascular mortality, indicating that racial factors could potentially influence the association between NHHR and cardiovascular mortality. The sensitivity analysis results indicated that the relationships between the NHHR and both all-cause and cardiovascular mortality remained relatively stable when participants with self-reported cancer at baseline, those who died within the first 2 years of follow-up, and those with extreme NHHRs were excluded.\u003c/p\u003e \u003cp\u003eThere are several limitations in this study. First, as an observational study, we cannot establish a causal relationship between the NHHR and mortality. Second, this study only assessed the prognostic value of the baseline NHHR and did not investigate the association between changes in the NHHR over time and mortality. Third, we did not adjust for diabetes classification, disease duration, or medication use, which could lead to potential biases. Fourth, we did not control for the influences of factors such as diet, season, or the use of lipid-lowering medications on the NHHR or lipid levels. Fifth, the study population consisted primarily of the general population in the US, so caution should be exercised when extrapolating the findings to other ethnicities. Finally, the proportion of patients with CVD outcomes in the study population was relatively small, which may have limited the statistical power to detect differences between groups.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe findings of this study indicate that the NHHR serves as a valuable predictive indicator for all-cause and cardiovascular mortality in participants with diabetes or prediabetes. In our nationally representative sample of adults with diabetes or prediabetes in the US, we observed a U-shaped correlation between the NHHR and all-cause mortality, as well as an L-shaped correlation with cardiovascular mortality. Monitoring the NHHR may contribute to evaluating the mortality risk and prognosis of participants with diabetes or prediabetes. Additionally, our analysis revealed significant interactions between the NHHR and different racial groups. This finding suggests that race may potentially influence the relationship between the NHHR and cardiovascular mortality.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003eASCVD Atherosclerotic cardiovascular disease\u003c/p\u003e \u003cp\u003eBMI Body mass index\u003c/p\u003e \u003cp\u003eCI Confidence intervals\u003c/p\u003e \u003cp\u003eCVD Cardiovascular disease\u003c/p\u003e \u003cp\u003eDM Diabetes mellitus\u003c/p\u003e \u003cp\u003eFBG Fasting blood glucose\u003c/p\u003e \u003cp\u003eHbA1c Hemoglobin A1c\u003c/p\u003e \u003cp\u003eHDL-C High-density lipoprotein-cholesterol\u003c/p\u003e \u003cp\u003eHR Hazard ratios\u003c/p\u003e \u003cp\u003eLDL-C Low-density lipoprotein cholesterol\u003c/p\u003e \u003cp\u003eNCHS National Center for Health Statistics\u003c/p\u003e \u003cp\u003eNDI National death index\u003c/p\u003e \u003cp\u003eNHHR Non-high density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio\u003c/p\u003e \u003cp\u003eNon-HDL-C Non-high density lipoprotein cholesterol\u003c/p\u003e \u003cp\u003ePG Postprandial glucose\u003c/p\u003e \u003cp\u003eRCS Restricted cubic splines\u003c/p\u003e \u003cp\u003eTC Total cholesterol\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely express our gratitude to the participants and investigators of the NHANES study for their invaluable contributions, which have provided significant support and assistance to our research endeavors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional file1: Table S1. HRs (95% CIs) for mortality according to the NHHR quartiles after excluding participants with self-reported cancer at baseline. Additional file1: Table S2. HRs (95% CIs) for mortality according to the NHHR quartiles after excluding participants who died within the first two years of follow-up. Additional file1: Table S3. HRs (95% CIs) for mortality according to the NHHR quartiles after excluding extreme values (mean \u0026plusmn; 3 standard deviations) of the NHHR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conceived by BY, ML, and RG, who were responsible for performing the data analysis and manuscript writing. TZ and ZY extracted the data from the official NHANES website. HZ contributed to the revision and review of the manuscript. AG and XF conducted a repeat analysis of the data and verified the results. All authors have reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Science and Technology Innovation Project of the China Academy of Chinese Medical Sciences (CI2021A04701) and the National Key Research and Development Program of China (2021YFF 0901404).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used for this study analysis can be found on the official website of the National Health and Nutrition Examination Survey (https://www.cdc.gov/nchs/nhanes/index.htm).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES protocol was approved by the National Center for Health Statistics and the Institutional Review Board. All participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eGraduate School, Beijing University of Chinese Medicine, Beijing 100029, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eSchool of Nursing, Xi \u0026apos;an Jiaotong University Health Science Center, Xi \u0026apos;an 710061, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eGraduate School, China Academy of Chinese Medical Sciences, Beijing 100700, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eXiyuan Hospital, Chinese Academy of Chinese Medical Sciences, Beijing 100091, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZheng Y, Ley SH, Hu FB. 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Circulation. 1992;86:1046\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohannesen CDL, Langsted A, Mortensen MB, Nordestgaard BG. Association between low density lipoprotein and all cause and cause specific mortality in Denmark: prospective cohort study. BMJ. 2020;371:m4266.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"NHHR, Diabetes, Prediabetes, Mortality, Cardiovascular Disease, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-4207993/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4207993/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) serves as a novel composite lipid indicator for atherosclerosis. However, the association between the NHHR and mortality in patients with diabetes or prediabetes remains unclear. Therefore, the present study aimed to examine the correlation between the NHHR and both all-cause and cardiovascular mortality in U.S. adults with diabetes or prediabetes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study enrolled a total of 12,578 adult participants with diabetes or prediabetes from the National Health and Nutrition Examination Survey in the US (1998–2018). The mortality outcomes were ascertained through linkage with the National Death Index (NDI) records available until December 31, 2019. We employed weighted multivariate Cox proportional hazards models to estimate the associations between the NHHR and both all-cause and cardiovascular mortality. Restricted cubic splines (RCS) were employed to evaluate nonlinear correlations. Moreover, a segmented Cox proportional hazards model was utilized to assess the associations between the NHHR and mortality on both sides of the inflection point.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring a median follow-up period of 8.08 years, 2403 participants experienced all-cause mortality, with 662 of them specifically succumbing to cardiovascular mortality. The RCS revealed a U-shaped association between the NHHR and all-cause mortality in participants with diabetes or prediabetes, while an L-shaped association was observed for cardiovascular mortality. The analysis of threshold effects revealed that the inflection points for the NHHR and all-cause and cardiovascular mortality were 2.71 and 2.85, respectively. Specifically, when the baseline NHHR was below the inflection points, a negative correlation was observed between the NHHR and both all-cause mortality (HR: 0.80, 95% CI: 0.73–0.88) and cardiovascular mortality (HR: 0.78, 95% CI: 0.67–0.92). Conversely, when the baseline NHHR exceeded the inflection points, a positive correlation was observed between the NHHR and all-cause mortality (HR: 1.07, 95% CI: 1.03–1.11).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Conclusions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn U.S. adults with diabetes or prediabetes, a U-shaped correlation was observed between the NHHR and all-cause mortality, whereas an L-shaped correlation was identified with cardiovascular mortality. The inflection points for all-cause and cardiovascular mortality were 2.71 and 2.85, respectively.\u003c/p\u003e","manuscriptTitle":"The Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio (NHHR) as a Predictor of All-Cause and Cardiovascular Mortality in US Adults with Diabetes or Prediabetes: NHANES 1998-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 19:39:46","doi":"10.21203/rs.3.rs-4207993/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-30T23:26:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-24T16:14:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-23T17:18:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"09251aef-da1f-44d9-a2f8-137addaee047","date":"2024-04-10T15:48:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"f13b1011-eb95-4bb1-8ca9-c82418454e63","date":"2024-04-10T10:46:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-10T01:36:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-03T10:51:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-03T09:16:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medicine","date":"2024-04-02T16:21:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7e3760a9-b59d-4d79-b419-80d52faedb5f","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-12T16:01:35+00:00","versionOfRecord":{"articleIdentity":"rs-4207993","link":"https://doi.org/10.1186/s12916-024-03536-3","journal":{"identity":"bmc-medicine","isVorOnly":false,"title":"BMC Medicine"},"publishedOn":"2024-08-07 15:57:19","publishedOnDateReadable":"August 7th, 2024"},"versionCreatedAt":"2024-04-08 19:39:46","video":"","vorDoi":"10.1186/s12916-024-03536-3","vorDoiUrl":"https://doi.org/10.1186/s12916-024-03536-3","workflowStages":[]},"version":"v1","identity":"rs-4207993","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4207993","identity":"rs-4207993","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}