Association Between Platelet to High-Density Lipoprotein Cholesterol Ratio and Risk of Diabetes and Prediabetes: Recent Findings from NHANES 2005– 2018

Association Between Platelet to High-Density Lipoprotein Cholesterol Ratio and Risk of Diabetes and Prediabetes: Recent Findings from NHANES 2005– 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 Article Association Between Platelet to High-Density Lipoprotein Cholesterol Ratio and Risk of Diabetes and Prediabetes: Recent Findings from NHANES 2005– 2018 Pengfei Chen, Meilin Zhu, Dazhuo Shi, Jianpeng Du This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4956704/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Purpose: To explore the relationship between the platelet-to-high-density lipoprotein cholesterol ratio (PHR) and the risk of diabetes and prediabetes. Methods: This study analyzes data from the 2005-2018 National Health and Nutrition Examination Survey (NHANES). The prevalence of diabetes and prediabetes, as well as levels of HDL-C and platelet counts, were derived from cross-sectional surveys. The PHR was calculated by dividing platelet count by HDL-C concentration, and diabetes or prediabetes were classified according to established clinical criteria. We used multivariate logistic regression analyses to estimate odds ratios (ORs) and 95% CIs. The logistic regression models were classified into categorical and continuous models. The potential non-linear relationship was assessed using restricted cubic splines (RCSs) and two-piecewise linear regression to identify any inflection points. Additionally, subgroup and interaction analyses were conducted to determine variations across different population groups. Result: A total of 20,229 eligible participants were included in the study, with a mean age of 47.84 years, and 51.80% of them were female. Among these participants, 3,884 (14.29%) were diagnosed with diabetes, and 8,863 (44.36%) were prediabetes. The result showed a positive association between PHR and the risk of diabetes and prediabetes. After adjusting for model 3, the OR for diabetes and prediabetes was associated with a per unit increase in PHR of 1.14 (95% CI: 1.00–1.29, P<0.05). The OR for participants in the highest PHR quartile was 2.46 (95% CI: 1.34–4.51, P<0.01) compared to those in the lowest quartile. Two-piecewise regression analysis identified a breakpoint at PHR = 4.55, with a positive association observed when PHR was below this value (OR = 1.32, 95% CI: 1.01–1.73, P<0.05). Subgroup and interaction analyses demonstrated that the positive association remained consistent across various demographic groups. Conclusions: Our study indicates that a higher PHR may be associated with an increased risk of developing diabetes and prediabetes. Therefore, PHR could potentially be used as a marker for assessing the likelihood of these conditions. Health sciences/Endocrinology/Endocrine system and metabolic diseases Health sciences/Biomarkers/Predictive markers Diabetes Prediabetes Platelet High-density lipoprotein PHR NHANES Figures Figure 1 Figure 2 1. Introduction Diabetes is the most common chronic metabolic disorder, with global prevalence steadily increasing. In 2019, around 9.3% of the global population were affected by diabetes, and this figure is expected to rise to 10.9% by 2045 [ 1 ] . Prediabetes is a high-risk precursor to diabetes, characterized by elevated blood sugar levels that have not yet reached the threshold for diabetes [ 2 ] . Currently, 374 million adults worldwide have prediabetes, and this number is expected to grow to nearly 540 million by 2045 [ 1 ] . As the prevalence of prediabetes rises, the burden of diabetes is likely to escalate. Studies have shown that diabetes increases the risk of cardiovascular disease (CVD), chronic kidney disease (CKD), retinopathy, and neuropathy [ 3 – 5 ] . Additionally, diabetes imposes a substantial economic burden, with global costs estimated at $ 1.31 trillion [ 6 ] . Therefore, targeted prevention and treatment strategies of diabetes and prediabetes are essential to reduce clinical prevalence. PHR integrates platelet activity and HDL-C levels and is an emerging indicator for assessing inflammation and hypercoagulability [ 7 ] . Hyperglycemia, insulin resistance, and chronic inflammation are common pathological features of diabetes, which can lead to increased platelet activation, resulting in a procoagulant state and impaired fibrinolysis [ 8 , 9 ] . Activated platelets release pro-inflammatory and pro-coagulant factors, which aggravate inflammation and increase the risk of thrombosis, further contributing to the occurrence and progression of diabetes and its cardiovascular complications [ 10 – 12 ] . HDL-C is known for its antiplatelet, antithrombotic, and anti-inflammatory properties [ 13 , 14 ] . Diabetic patients often have lower HDL-C levels, leading to impaired cholesterol reverse transport, reduced anti-inflammatory and antioxidant capabilities, and endothelial dysfunction [ 15 – 17 ] . PHR was first introduced by Jialal et al. as an effective biomarker for predicting metabolic syndrome (MetS) [ 7 ] and hyperuricemia [ 18 ] . However, its association with diabetes or prediabetes has not yet been thoroughly explored. An ideal predictor should provide independent predictive parameters, be easily identifiable during diagnosis, and be cost-effective in clinical practice. This study uses NHANES data to investigate the association of PHR with diabetes and prediabetes. 2. Methods 2.1 Study population NHANES, conducted by the National Center for Health Statistics (NCHS), is collected biennially using a stratified sampling method. The survey was conducted with approval from the NCHS Institutional Review Board, and informed consent was obtained from all participants [ 19 , 20 ] . This study used NHANES data from 2005 to 2018, spanning seven biennial cycles, initially including 70,190 participants. We excluded those without diabetes or prediabetes data (n = 5,058) and those missing platelet or HDL-C information (n = 17,403). Additional exclusions included participants lacking essential covariates (n = 13,735), those under 20 years old (n = 12,781), and pregnant individuals (n = 984). Ultimately, 20,229 individuals were included, as illustrated in Fig. 1 . 2.2 Diagnosis of diabetes and prediabetes Diabetes was diagnosed in individuals who satisfied one or more of the following criteria: (1) fasting blood glucose (FSG) ≥ 7.0 mmol/L or random blood glucose ≥ 11.1 mmol/L; (2) 2-hour oral glucose tolerance test (OGTT) ≥ 11.1 mmol/L; (3) glycosylated hemoglobin (HbA1c) ≥ 6.5%; (4) use of diabetes medication or insulin; or (5) a self-reported diagnosis of diabetes by a doctor. Prediabetes was diagnosed in individuals who met one or more of the following criteria: (1) FSG between 5.6 and 7.0 mmol/L; (2) HbA1c between 5.7 and 6.5%; (3) 2-hour OGTT between 7.8 and 11.1 mmol/L; or (4) a self-reported diagnosis of prediabetes by a doctor. 2.3 Calculation of PHR The exposure variable, PHR, was calculated as the ratio of platelet count (1000 cells/µL) to HDL-C (mg/dL) [ 7 , 18 ] . Blood samples collected at the Mobile Examination Center were analyzed to measure biochemical parameters. 2.3 Covariates Demographic data were gathered through questionnaire interviews and included variables such as sex (male, female), age, race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Mexican American, other Hispanic, Non-Hispanic Asian, Other), education (below high school, high school, above high school), family poverty income ratio (PIR, 3.0), alcohol consumption (no drinks, 1–5 drinks/month, 5–10 drinks/month, > 10 drinks/month), BMI ( 29 kg/m²), and smoking status [never (< 100 cigarettes in lifetime), current (≥ 100 cigarettes and currently smoking), former (≥ 100 cigarettes but not currently smoking)]. Complications included hypertension (based on systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, a prior diagnosis, or a history of anti-hypertensive medication use), CVD (self-reported doctor-diagnosed conditions such as coronary heart disease, heart failure, heart attack, stroke, and angina pectoris), and CKD (self-reported doctor-diagnosed). Laboratory covariates encompassed serum insulin, FSG, HbA1c, total triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), serum uric acid (SUA), high-sensitivity C-reactive protein (hs-CRP), and creatinine (Cr). Detailed procedures for collecting blood biochemical measurements are provided on the NHANES website. 3. Statistical analysis For all statistical analyses, NHANES sampling weights were applied using R 4.3.3 to account for the survey's stratification and complexity. The participants in this study were weighted to represent a population of 879,614,278. Categorical variables were displayed as unweighted counts (percentages). Continuous variables were presented as weighted means (standard errors) or medians (interquartile range). Differences between the two groups were assessed using the weighted Student’s t-test, Mann-Whitney U test, and Chi-squared test. A two-sided P-value of less than 0.05 was considered statistically significant. We used multivariate logistic regression analyses to estimate odds ratios (ORs) and 95% CIs. The logistic regression models were classified into categorical and continuous models. For the categorical model, PHR was divided into quartiles, using the lowest quartile as the reference group. Trend tests (p-trend) were conducted using the median PHR in each quartile. Model 1 included only PHR as the independent variable. Model 2 adjusted for sex, age, race, PIR, smoking status, alcohol status, education, and BMI. Model 3 further adjusted for hypertension, CVD, CKD, TG, TC, LDL-C, hs-CRP, SUA, and Cr. Subgroup heterogeneity was assessed through interaction analyses. To investigate potential linear and non-linear associations, a restricted cubic spline (RCS) model featuring three knots was applied, with the third knot chosen according to the Akaike information criterion (AIC). The log-likelihood ratio test was employed to determine the presence of linear or non-linear relationships. 4. Results 4.1 Study characteristics Table 1 presents the weighted study characteristics of participants categorized by PHR quartiles. The study included 20,229 participants from the NHANES 2005–2018 survey, with 9,818 males (48.20%) and 10,481 females (51.80%), and an average age of 47.84 ± 17.07 years. Among them, 3,884 (14.29%) were diagnosed with diabetes, 8,863 (44.36%) had prediabetes, 7,493 (32.85%) had hypertension, and 2,207 (8.88%) had CVD. The majority of participants were Non-Hispanic white (65.18%), 14.75% lived in poverty, and 94.97% had a high school diploma or higher. Additionally, 56.58% had never smoked, and 22.31% had never consumed alcohol. The weighted mean (standard error) BMI was 29.30 ± 6.99 kg/m². The mean (standard error) platelet count was 238.69 ± 60.08 cells/µL, HDL-C was 53.85 ± 16.52 mg/dL, LDL-C was 2.93 ± 0.92 mmol/L, TG was 1.35 ± 1.07 mmol/L, TC was 4.97 ± 1.07 mmol/L, SUA was 320.42 ± 84.06 mmol/L, and Cr was 77.56 ± 30.68 mmol/L. Significant differences were observed across all PHR quartiles for the other variables, except for the number of CVD cases, which showed no significant difference. Table 1 Baseline characteristics of study participants stratified by PHR Characteristic Overall, N = 20,229 Q1, N = 5,075 ( 5.87) p-value Age (years) 47.84 ± 17.07 52.06 ± 17.61 48.32 ± 17.51 46.23 ± 16.52 44.56 ± 15.56 < 0.001 Sex, n (%) < 0.001 female 10,481(51.80%) 2,941 (61.92%) 2,680 (51.65%) 2,484 (47.95%) 2,376 (45.28%) male 9,818 (48.20%) 2,134 (38.08%) 2,405 (48.35%) 2,583 (52.05%) 2,696 (54.72%) Race, n (%) < 0.001 Non-Hispanic White 7,596 (65.18%) 2,018 (69.66%) 1,916 (66.85%) 1,870 (63.77%) 1,792 (60.16%) Non-Hispanic Black 4,454 (10.82%) 1,351 (12.32%) 1,120 (10.57%) 1,051 (10.50%) 932 (9.862%) Mexican American 2,806 (8.775%) 469 (5.311%) 687 (8.451%) 767 (9.644%) 883 (11.87%) Other Hispanic 2,126 (6.351%) 411 (4.378%) 508 (5.630%) 594 (7.468%) 613 (8.033%) Non-Hispanic Asian 2,583 (5.436%) 678 (5.595%) 686 (5.566%) 609 (5.100%) 610 (5.479%) Other Race 734 (3.429%) 148 (2.733%) 168 (2.934%) 176 (3.520%) 242 (4.592%) Alcohol status, n (%) < 0.001 Non-drinker 4,005 (22.31%) 996 (21.32%) 1,008 (21.93%) 996 (22.37%) 1,005 (23.73%) 1-5drinks/month 6,985(50.60%) 1,499(41.96%) 1,724(49.93%) 1,820(54.22%) 1,942(57.07%) 5-10drinks/month 1,089 (9.354%) 298 (9.698%) 286 (8.984%) 266 (10.19%) 239 (8.533%) > 10 drinks/month 1,890 (17.74%) 737 (27.02%) 522 (19.15%) 344 (13.22%) 287 (10.70%) PIR, n (%) < 0.001 3.0 6,604 (49.11%) 1,859 (56.02%) 1,768 (51.26%) 1,590 (46.81%) 1,387 (41.92%) Smoke, n (%) < 0.001 Current 3,103 (14.91%) 654 (11.71%) 665 (12.70%) 775 (15.38%) 1,009 (20.13%) Former 4,753 (24.65%) 1,263 (26.71%) 1,194 (24.25%) 1,194 (24.85%) 1,102 (22.68%) Never 11,633 (56.58%) 2,967 (58.15%) 3,031 (59.24%) 2,888 (55.51%) 2,747 (53.24%) NA 810 (3.862%) 191 (3.427%) 195 (3.815%) 210 (4.263%) 214 (3.956%) Education, n (%) < 0.001 Below high school 1,914 (4.975%) 423 (4.066%) 457 (4.714%) 518 (5.386%) 516 (5.782%) High school 7,046 (31.72%) 1,610 (27.10%) 1,736 (30.42%) 1,777 (33.11%) 1,923 (36.55%) Above high school 11,319 (63.25%) 3,036 (68.78%) 2,887 (64.80%) 2,767 (61.45%) 2,629 (57.65%) NA 20(< 0.1%) 6(< 0.1%) 5(< 0.1%) 5(< 0.1%) 4(< 0.1%) CVD, n (%) 2,207 (8.883%) 615 (9.555%) 558 (8.757%) 504 (8.178%) 530 (9.038%) 0.3 Hypertension, n (%) 7,493 (32.85%) 1,835 (30.64%) 1,841 (31.54%) 1,827 (33.04%) 1,990 (36.35%) < 0.001 Diabetes, n (%) 3,884 (14.29%) 747 (9.829%) 910 (13.12%) 1,020 (14.69%) 1,207 (19.82%) < 0.001 Prediabetes, n (%) 8,863 (44.36%) 2,090 (39.01%) 2,186 (42.54%) 2,236 (45.36%) 2,351 (51.36%) < 0.001 CKD, n (%) 576 (3.029%) 147 (3.549%) 148 (3.128%) 134 (2.593%) 147 (2.831%) < 0.001 BMI, kg/m2 29.30 ± 6.99 26.44 ± 5.78 28.37 ± 6.35 30.26 ± 6.97 32.31 ± 7.39 < 0.001 Platelet, 1000 cells/µL 238.69 ± 60.08 194.79 ± 43.31 223.65 ± 41.49 248.07 ± 45.45 291.15 ± 62.60 < 0.001 HDL-C, mg/dL 53.85 ± 16.52 71.05 ± 17.27 55.62 ± 10.60 48.14 ± 8.94 39.76 ± 8.29 < 0.001 LDL-C, mmol/L 2.93 ± 0.92 2.80 ± 0.88 2.94 ± 0.91 3.02 ± 0.92 2.97 ± 0.94 < 0.001 TG, mmol/L 1.35 ± 1.07 0.93 ± 0.51 1.22 ± 0.79 1.48 ± 0.97 1.91 ± 1.60 < 0.001 TC, mmol/L 4.97 ± 1.07 5.09 ± 1.03 4.93 ± 1.03 4.96 ± 1.07 4.91 ± 1.14 < 0.001 SUA, mmol/L 320.42 ± 84.06 302.15 ± 79.54 312.93 ± 80.56 328.06 ± 84.99 339.56 ± 86.32 < 0.001 Sr, umol/L 77.56 ± 30.68 77.30 ± 36.54 77.46 ± 30.05 77.79 ± 23.99 77.70 ± 30.76 < 0.001 hs-CRP, mg/L 3.89 ± 7.24 2.50 ± 4.80 3.15 ± 6.03 3.95 ± 5.90 6.05 ± 10.50 < 0.001 Normally distributed continuous variables are described as means ± SEs, and continuous variables without a normal distribution are described as medians (interquartile ranges). Categorical variables are presented as numbers (percentages). All estimates accounted for complex survey designs. 4.2 Association of PHR with the diabetes and prediabetes Table 2 presents the associations of PHR with diabetes and prediabetes. In all three models, the highest PHR quartiles were significantly associated with an increased risk of diabetes and prediabetes compared to the lowest quartiles. Even after adjusting for all covariates, these positive associations remained significant for Q2 (1.85 [1.18, 2.91], P < 0.05), Q3 (2.12 [1.20, 3.74], P < 0.05), and Q4 (2.46 [1.34, 4.51], P < 0.05). When PHR was analyzed as a continuous variable in the linear regression model, similar findings emerged, showing a positive association between PHR and diabetes and prediabetes in Model 1 (1.13 [1.11, 1.16], P < 0.05), Model 2 (1.13 [1.09, 1.16], P < 0.05), and Model 3 (1.14 [1.00, 1.29], P < 0.05). Table 2 Associations between PHR and the risk of diabetes and prediabetes PHR Range Model 1 Model 2 Model 3 Continuous 1.13 (1.11, 1.16) 1.13 (1.09, 1.16) 1.14 (1.00, 1.29) Categories Q1 5.87 1.93 (1.73, 2.16) 1.90 (1.61, 2.24) 2.46 (1.34, 4.51) p-trend < 0.001 < 0.001 < 0.001 The PHR was categorized into four quartiles and tests for trend (p–trend) based on variable containing the median value for each quartiles. PHR also was utilized as continuous variables and p-value was used to test significance. Model 1, each serum carotenoid was the sole independent variable. Model 2 included adjustments for sex, race, age, poverty status, smoking status, alcohol status, education, and BMI. Model 3 built on Model 2 by further adjusting for hypertension, CVD, CKD, TG, TC, LDL-C, hs-CRP, SUA and Cr. After full adjustments, Fig. 2 illustrates the nonlinear relationship of PHR with diabetes and prediabetes, as modeled using a smoothed curve from a generalized additive model. The two-stage linear regression analysis pinpointed an inflection point at 4.55 (see Table 3 ). The results indicated that the OR for PHR < 4.55 was 1.32 (95% CI: 1.01–1.73, P 0.05). This suggests that below the inflection point, a lower PHR is associated with a decreased risk of diabetes and prediabetes. Table 3 Threshold analysis of the effect of PHR on diabetes and prediabetes using two-piece linear regression models. Diabetes and prediabetes Adjusted OR (95% CI) P–value Fitting by binary logistic regression model 1.14 (1.00, 1.29) 0.04 Fitting by the two–piecewise linear model Inflection point 4.55 PHR < 4.55 1.32 (1.01, 1.73) 0.03 PHR ≥ 4.55 1.07 (0.92, 1.24) 0.12 Log–likelihood ratio < 0.001 4.3 Stratified assessment Stratified analysis was conducted to evaluate the impact of confounding factors and specific populations on the results. Table 4 reveals that, with the exceptions of Mexican Americans, Other Hispanics, and individuals with a history of CVD (P > 0.05), most demographic groups show a significant association with PHR. This association suggests that these groups may be particularly vulnerable to increased risks of diabetes and prediabetes (P < 0.05). Additionally, we examined how various patient characteristics—such as age, sex, race, BMI, education, smoking status, alcohol status, PIR, hypertension, and CVD—might influence the observed associations. The analysis found significant interactions with sex, alcohol status, and BMI (P for interaction < 0.05), suggesting that these factors may modify the relationship between PHR and the risk of diabetes and prediabetes. Table 4 Subgroup analyses of the relationship between PHR and the risk of diabetes and prediabetes PHR Continuous Q1 Q2 Q3 Q4 P for trend P for interaction Age 0.96 < 60 1.09(1.02,1.16) 1 1.18(0.91,1.54) 1.12(0.84,1.51) 1.93(1.36,2.74) < 0.001 ≥ 60 1.09(1.05,1.13) 1 1.19(0.98,1.44) 1.36(1.12,1.65) 1.55(1.28,1.89) < 0.001 Sex < 0.01 male 1.07(1.03,1.12) 1 1.02(0.86,1.21) 1.23(1.04,1.45) 1.45(1.23,1.72) < 0.001 female 1.19(1.14,1.24) 1 1.32(1.06,1.64) 1.62(1.29,2.04) 2.33(1.84,2.95) < 0.001 Race 0.37 Non-Hispanic White 1.12(1.07,1.17) 1 1.32(1.07,1.63) 1.46(1.17,1.83) 1.97(1.56,2.48) < 0.001 Non-Hispanic Black 1.12(1.06,1.18) 1 1.02(0.79,1.32) 1.42(1.09,1.84) 1.92(1.43,2.56) < 0.001 Mexican American 1.13(1.05,1.22) 1 0.67(0.45,1.01) 0.95(0.64,1.42) 1.32(0.88,1.96) < 0.001 Other Hispanic 1.12(1.03,1.21) 1 1.00(0.65,1.56) 1.20(0.77,1.88) 1.49(0.96,2.32) < 0.001 Non-Hispanic Asian 1.13(1.05,1.22) 1 1.85(1.28,2.67) 1.32(0.89,1.96) 1.74(1.15,2.64) < 0.001 Education 0.06 Below high school 1.13(1.02,1.26) 1 1.03(0.60,1.77) 1.04(0.61,1.78) 1.97(1.15,3.36) < 0.001 High school 1.14(1.08,1.20) 1 1.22(0.92,1.61) 1.43(1.08,1.89) 1.98(1.49,2.63) < 0.001 Above high school 1.11(1.07,1.16) 1 1.29(1.05,1.58) 1.44(1.17,1.78) 1.78(1.43,2.21) < 0.001 Smoke 0.06 Current 1.15(1.08,1.24) 1 1.90(1.24,2.90) 1.98(1.28,3.05) 2.71(1.77,4.13) < 0.001 Former 1.11(1.03,1.20) 1 1.07(0.79,1.47) 1.62(1.15,2.26) 2.08(1.45,2.98) < 0.001 Never 1.10(1.06,1.15) 1 1.15(0.94,1.41) 1.15(0.92,1.42) 1.55(1.24,1.93) < 0.001 Alcohol status,n (%) 0.02 Non-drinker 1.10(1.04,1.16) 1 1.28(0.94,1.75) 1.44(1.05,1.96) 1.62(1.19,2.20) < 0.001 1-5drinks/month 1.12(1.07,1.17) 1 1.03(0.82,1.30) 1.16(0.91,1.47) 1.70(1.34,2.17) < 0.001 5-10drinks/month 1.19(1.06,1.32) 1 1.44(0.84,2.48) 2.43(1.37,4.32) 2.51(1.39,4.53) 10 drinks/month 1.13(1.03,1.25) 1 1.73(1.21,2.46) 1.65(1.11,2.45) 2.18(1.38,3.45) < 0.001 PIR, n (%) 0.70 < 1.0 1.15(1.09,1.21) 1 1.46(1.05,2.03) 1.47(1.07,2.02) 2.06(1.47,2.88) < 0.001 1.0–3.0 1.13(1.08,1.18) 1 1.20(0.93,1.54) 1.48(1.15,1.91) 1.87(1.45,2.42) 3.0 1.14(1.09,1.19) 1 1.26(0.99,1.59) 1.37(1.06,1.77) 1.80(1.38,2.36) < 0.001 BMI < 0.01 < 25 1.09(1.04,1.14) 1 1.36(1.05,1.77) 1.64(1.21,2.23) 2.12(1.52,2.97) < 0.001 ≥ 25 1.07(1.04,1.10) 1 1.29(1.06,1.56) 1.53(1.26,1.85) 2.18(1.80,2.64) < 0.001 Hypertension, n (%) 0.06 1 1.12(1.06,1.18) 1 1.55(1.19,2.03) 1.55(1.18,2.04) 2.00(1.51,2.65) < 0.001 0 1.12(1.08,1.17) 1 1.14(0.94,1.39) 1.37(1.12,1.67) 1.79(1.45,2.20) < 0.001 CVD, n (%) 0.20 1 1.03(0.92,1.17) 1 2.12(1.27,3.53) 1.18(0.73,1.94) 1.85(1.05,3.26) < 0.001 0 1.13(1.09,1.17) 1 1.22(1.03,1.44) 1.44(1.21,1.71) 1.86(1.56,2.22) < 0.001 5. Discussion This study reveals a positive correlation between PHR and the risk of diabetes and prediabetes, which remains significant after accounting for various confounding factors. After full adjustment, the OR for diabetes and prediabetes per unit increase in PHR was 1.14 (95% CI: 1.00–1.29, P < 0.05). Participants in the highest PHR quartile had an OR of 2.46 (95% CI: 1.34–4.51, P < 0.05) compared to those in the lowest quartile. Subgroup analyses and interaction tests suggest that this positive correlation is consistent across different demographic groups. Additionally, the RCS analysis revealed a non-linear relationship. The two-piecewise regression identified an inflection point at PHR = 4.55, and showing a stronger positive association when PHR is below this threshold. In recent years, PHR has attracted extensive attention as a new biomarker in the study of metabolic diseases [ 21 – 23 ] . PHR not only reflects platelet activity but also integrates an individual's lipid metabolic status. One study has found that PHR is significantly elevated in patients with metabolic syndrome (MetS) and increases with MetS severity [ 7 ] . Another study has reported a positive correlation between PHR and the risk of hyperuricemia [ 18 ] . MetS is characterized by a constellation of metabolic disorders centered on insulin resistance, including abdominal obesity, dyslipidemia, and hypertension, which are closely associated with the development of diabetes [ 24 , 25 ] . In addition to being a component of MetS, hyperuricemia is also an independent risk factor for the development of insulin resistance and diabetes [ 26 – 28 ] . Large-scale prospective cohort studies have confirmed that MetS and hyperuricemia significantly increase the risk of developing diabetes [ 29 , 30 ] . Therefore, it is logical to suggest that PHR is closely related to diabetes or prediabetes. The relationship between PHR and diabetes involves complex pathophysiological mechanisms that are not yet fully understood. In this paper, we attempt to explore the pathophysiological links by focusing on three aspects: chronic inflammation, oxidative stress, and lipid metabolism disorders. Chronic low-grade inflammation is a key characteristic in the development of diabetes and prediabetes. Studies have shown that the pro-inflammatory cytokines tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) can inhibit the expression and translocation of glucose transporter 4 (GLUT4) through the nuclear factor-kappa B (NF-κB) and c-Jun N-terminal kinase (JNK) pathways, leading to a decrease in insulin sensitivity [ 31 , 32 ] . Platelets promote the spread of systemic inflammation and contribute to chronic inflammatory responses by releasing various pro-inflammatory mediators, such as platelet factor 4 (PF4), transforming growth factor-β (TGF-β), and platelet-activating factor (PAF) [ 33 – 35 ] . Study has shown that elevated levels of PF4 are closely associated with insulin resistance. PF4 binds to chemokine receptors, activating monocytes and macrophages, which then promote the release of additional pro-inflammatory cytokines [ 36 ] . Platelets also interact directly with neutrophils and monocytes, forming platelet-leukocyte aggregates that activate leukocytes and enhance their pro-inflammatory activity [ 37 ] . HDL-C is considered to have anti-inflammatory properties, and can reduce inflammatory responses by promoting reverse cholesterol transport, inhibiting the oxidation of LDL-C, and clearing circulating pro-inflammatory factors [ 38 , 39 ] . When PHR is elevated, it may indicate an increase in the pro-inflammatory effects of platelets and a reduction in the anti-inflammatory effects of HDL-C. This imbalance exacerbates chronic inflammation, may lead to increased insulin resistance and the development of diabetes. Pancreatic β cells are more susceptible to damage by reactive oxygen species (ROS) due to their relatively weak antioxidant capacity. Excessive platelet activation can increase oxidative stress by producing ROS, which can damage not only vascular endothelial cells but also pancreatic β cells and weaken the ability to secrete insulin [ 40 – 42 ] . Platelets in diabetic patients are often in a highly activated state, which is closely linked to heightened oxidative stress [ 43 ] . HDL-C has antioxidant properties and can protect pancreatic β-cells by scavenging excess ROS [ 44 ] . Therefore, this imbalance between oxidative stress and the antioxidant system could be one of the key mechanisms underlying the association between PHR and diabetes. Lipid metabolism disorders affect the secretion and function of insulin, and have a significant impact on platelet activity. HDL-C plays a crucial role in maintaining lipid metabolic balance, and low levels of HDL-C are often associated with high triglycerides and elevated LDL-C levels [ 45 ] . These lipid abnormalities can exacerbate insulin resistance [ 46 , 47 ] . Hyperlipidemia is a major feature of diabetes and prediabetes, usually manifested as an abnormal combination of high TG, low HDL-C levels, and elevated LDL-C levels [ 48 , 49 ] . Research indicates that HDL-C facilitates the reverse transport of cholesterol, transporting excess cholesterol from peripheral tissues to the liver for metabolism, thereby reducing the formation of atherosclerotic plaques. HDL-C inhibits the progression of atherosclerosis by reducing the generation of lipid peroxidation products. In addition, platelet function is affected by lipid metabolism, and hyperlipidemia is often accompanied by increased platelet reactivity and thrombotic tendency [ 50 ] . PHR reflects the lipid metabolism status of an individual, and its increase usually indicates abnormal lipid metabolism, especially low HDL-C levels. These lipid metabolism disorders will further promote the development of insulin resistance, leading to the progression of diabetes. The major strengths of this study are as follows. First, it is the first study to use a nationally representative sample to examine the associations of the PHR with diabetes and prediabetes. Second, a wide array of potential confounding variables was accounted for in the analysis. Third, the accuracy and reliability of the data were bolstered by employing trained staff who adhered to standardized protocols for collecting key information and conducting participant interviews. Our study also has some limitations. First, as a cross-sectional study, this study cannot establish causal relationships. Additionally, the study's capacity to explore and test etiological hypotheses is limited, and its findings may not be fully generalizable. Therefore, further prospective longitudinal studies are needed to confirm these findings. Second, potential confounding from unknown or unmeasurable factors cannot be entirely excluded. Third, due to the presence of randomly missing data and the large sample size, we did not use multiple imputation methods to address the missing data, which may impact the precision of the results. 6. Conclusions Our study suggests that an increase in PHR may be correlated with a heightened likelihood of developing diabetes and prediabetes. Consequently, PHR could potentially serve as a marker for estimating the probability of diabetes and prediabetes development. Declarations Data availability statement The original contributions presented in this study are included in the article. For further inquiries, please contact the corresponding author, Jianpeng Du, at [email protected] . Acknowledgements We would like to appreciate the support by participants involved in the NHANES study. Authors' contributions Dazhuo Shi: conceptualization and supervision. Pengfei Chen and Meilin Zhu: data curation and writing the original draft. Jianpeng Du: methodology. All authors contributed to and approved the submitted version of the article. Funding This work was supported by the project of Hospital capability enhancement project of Xiyuan Hospital, CACMS. (NO. XYZX0204-02, NO. XYZX0201-03). Conflicts of interest The authors declare that they have no conflicts of interest. Ethics approval and consent to participate The study protocol was approved by the NHANES Institutional Review Board, and was performed in accordance with the Declaration of Helsinki, with all NHANES participants providing signed informed consent. Details are available at https://www.cdc.gov/nchs/nhanes/irba98.htm . Consent for publication Not applicable. Clinical trial number Not applicable. References Saeedi, P. et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9 edition. Diabetes Res. Clin. Pract. 157 , 107843 (2019). Sánchez, E. et al. Characteristics of atheromatosis in the prediabetes stage: a cross-sectional investigation of the ILERVAS project. Cardiovasc. Diabetol. 18 (1), 154 (2019). Lin, X. et al. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci. Rep. 10 (1), 14790 (2020). GBD 2021 Fertility and Forecasting Collaborators. Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. Lancet . 403 (10440), 2057–2099 (2024). Bragg, F. et al. Association Between Diabetes and Cause-Specific Mortality in Rural and Urban Areas of China. JAMA . 317 (3), 280–289 (2017). Bommer, C. et al. The global economic burden of diabetes in adults aged 20–79 years: a cost-of-illness study. Lancet Diabetes Endocrinol. 5 (6), 423–430 (2017). Jialal, I., Jialal, G. & Adams-Huet, B. The platelet to high density lipoprotein -cholesterol ratio is a valid biomarker of nascent metabolic syndrome. Diabetes Metab. Res. Rev. 37 (6), e3403 (2021). Davì, G. et al. In vivo formation of 8-iso-prostaglandin f2alpha and platelet activation in diabetes mellitus: effects of improved metabolic control and vitamin E supplementation. Circulation . 99 (2), 224–229 (1999). Al-Sofiani, M. E. et al. Diabetes and Platelet Response to Low-Dose Aspirin. J. Clin. Endocrinol. Metab. 103 (12), 4599–4608 (2018). Xin, G. et al. Metformin Uniquely Prevents Thrombosis by Inhibiting Platelet Activation and mtDNA Release. Sci. Rep. 6 , 36222 (2016). Gajos, G. et al. Polyhedrocytes in blood clots of type 2 diabetic patients with high cardiovascular risk: association with glycemia, oxidative stress and platelet activation. Cardiovasc. Diabetol. 17 (1), 146 (2018). Zhou, A. M. et al. Salvianolic acid a inhibits platelet activation and aggregation in patients with type 2 diabetes mellitus. BMC Cardiovasc. Disord . 20 (1), 15 (2020). Chan, L. W. et al. High levels of LDL-C combined with low levels of HDL-C further increase platelet activation in hypercholesterolemic patients. Braz J. Med. Biol. Res. 48 (2), 167–173 (2015). Flierl, U. & Schäfer, A. Fractalkine–a local inflammatory marker aggravating platelet activation at the vulnerable plaque. Thromb. Haemost . 108 (3), 457–463 (2012). Zheng, Y. Y. et al. Low HDL Cholesterol Is Associated with Reduced Bleeding Risk in Patients Who Underwent PCI: Findings from the PRACTICE Study. Thromb. Haemost Jul 8. (2023). Gao, Y. et al. The predictive value of the hs-CRP/HDL-C ratio, an inflammation-lipid composite marker, for cardiovascular disease in middle-aged and elderly people: evidence from a large national cohort study. Lipids Health Dis. 23 (1), 66 (2024). Sohrabi, Y., Schwarz, D. & Reinecke, H. LDL-C augments whereas HDL-C prevents inflammatory innate immune memory. Trends Mol. Med. 28 (1), 1–4 (2022). Yan, L. et al. Association of platelet to high-density lipoprotein cholesterol ratio with hyperuricemia. Sci. Rep. 14 (1), 15641 (2024). Ahluwalia, N. et al. Update on NHANES Dietary Data: Focus on Collection, Release, Analytical Considerations, and Uses to Inform Public Policy. Advances in nutrition. (Bethesda Md) . 7 (1), 121–134 (2016). Zou, Q. et al. Longitudinal association between physical activity and blood pressure, risk of hypertension among Chinese adults: China Health and Nutrition Survey 1991–2015. Eur. J. Clin. Nutr. 75 (2), 274–282 (2021). Zhang, H., Xu, Y. & Xu, Y. The association of the platelet/high-density lipoprotein cholesterol ratio with self-reported stroke and cardiovascular mortality: a population-based observational study. Lipids Health Dis. 23 (1), 121 (2024). Ni, J. et al. Associations between the platelet/high-density lipoprotein cholesterol ratio and likelihood of nephrolithiasis: a cross-sectional analysis in United States adults. Front. Endocrinol. (Lausanne) . 15 , 1289553 (2024). Ni, J. et al. Examining the cross-sectional relationship of platelet/high-density lipoprotein cholesterol ratio with depressive symptoms in adults in the United States. BMC Psychiatry . 24 (1), 427 (2024). Hudish, L. I., Reusch, J. E. & Sussel, L. β Cell dysfunction during progression of metabolic syndrome to type 2 diabetes. J. Clin. Invest. 129 (10), 4001–4008 (2019). Thaisetthawatkul, P. et al. Prediabetes, diabetes, metabolic syndrome, and small fiber neuropathy. Muscle Nerve . 61 (4), 475–479 (2020). Jiang, J. et al. Prevalence of Diabetes in Patients with Hyperuricemia and Gout: A Systematic Review and Meta-analysis. Curr. Diab Rep. 23 (6), 103–117 (2023). Mortada, I. & Hyperuricemia Type 2 Diabetes Mellitus, and Hypertension: an Emerging Association. Curr. Hypertens. Rep. 19 (9), 69 (2017). Vareldzis, R., Perez, A. & Reisin, E. Hyperuricemia: An Intriguing Connection to Metabolic Syndrome, Diabetes, Kidney Disease, and Hypertension. Curr. Hypertens. Rep. 26 (6), 237–245 (2024). Woyesa, S. B., Hirigo, A. T. & Wube, T. B. Hyperuricemia and metabolic syndrome in type 2 diabetes mellitus patients at Hawassa university comprehensive specialized hospital, South West Ethiopia. BMC Endocr. Disord . 17 (1), 76 (2017). Weinstock, R. S. et al. Metabolic syndrome is common and persistent in youth-onset type 2 diabetes: Results from the TODAY clinical trial. Obes. (Silver Spring) . 23 (7), 1357–1361 (2015). Esser, N. et al. Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes. Diabetes Res. Clin. Pract. 105 (2), 141–150 (2014). Tong, H. V. et al. Adiponectin and pro-inflammatory cytokines are modulated in Vietnamese patients with type 2 diabetes mellitus. J. Diabetes Investig . 8 (3), 295–305 (2017). Ludwig, N., Hilger, A., Zarbock, A. & Rossaint, J. Platelets at the Crossroads of Pro-Inflammatory and Resolution Pathways during Inflammation. Cells . 11 (12), 1957 (2022). Morrell, C. N. et al. Emerging roles for platelets as immune and inflammatory cells. Blood . 123 (18), 2759–2767 (2014). Tunjungputri, R. N. et al. Differential effects of platelets and platelet inhibition by ticagrelor on TLR2- and TLR4-mediated inflammatory responses. Thromb. Haemost . 113 (5), 1035–1045 (2015). Gruden, G. et al. Plasma beta-thromboglobulin and platelet factor 4 are not increased in insulin-dependent diabetic patients with microalbuminuria. Acta Diabetol. 31 (3), 130–132 (1994). Hafner, C. et al. Brief High Oxygen Concentration Induces Oxidative Stress in Leukocytes and Platelets: A Randomized Cross-over Pilot Study in Healthy Male Volunteers. Shock . 56 (3), 384–395 (2021). Zhang, J. et al. The mediation effect of HDL-C: Non-HDL-C on the association between inflammatory score and recurrent coronary events. Heliyon . 10 (1), e23731 (2023). Kolahi Ahari, R. et al. Association of Three Novel Inflammatory Markers: Lymphocyte to HDL-C Ratio, High-Sensitivity C-Reactive Protein to HDL-C Ratio and High-Sensitivity C-Reactive Protein to Lymphocyte Ratio With Metabolic Syndrome. Endocrinol. Diabetes Metab. 7 (3), e00479 (2024). Zhang, S. et al. Matrine Impairs Platelet Function and Thrombosis and Inhibits ROS Production. Front. Pharmacol. 12 , 717725 (2021). Han, M. et al. Luteolin Protects Pancreatic β Cells against Apoptosis through Regulation of Autophagy and ROS Clearance. Pharmaceuticals (Basel) . 16 (7), 975 (2023). Huang, Q. et al. Selenium Nanodots (SENDs) as Antioxidants and Antioxidant-Prodrugs to Rescue Islet β Cells in Type 2 Diabetes Mellitus by Restoring Mitophagy and Alleviating Endoplasmic Reticulum Stress. Adv. Sci. (Weinh) . 10 (19), e2300880 (2023). Leoncini, S. et al. Oxidative stress, erythrocyte ageing and plasma non-protein-bound iron in diabetic patients. Free Radic Res. 42 (8), 716–724 (2008). León-Reyes, G. et al. Oxidative modifications of foetal LDL-c and HDL-c lipoproteins in preeclampsia. Lipids Health Dis. 17 (1), 110 (2018). Ascaso, J. F. et al. Lipoprotein phenotype and insulin resistance in familial combined hyperlipidemia. Metabolism . 49 (12), 1627–1631 (2000). Cicero, A. F. et al. Short-term effects of a combined nutraceutical of insulin-sensitivity, lipid level and indexes of liver steatosis: a double-blind, randomized, cross-over clinical trial. Nutr. J. 14 , 30 (2015). Liu, W. et al. Major lipids and lipoprotein levels and risk of blood pressure elevation: a Mendelian Randomisation study. EBioMedicine . 100 , 104964 (2024). Oliveri, A. et al. Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank. Nat. Genet. 56 (2), 212–221 (2024). Moran, C. et al. Dyslipidemia, Insulin Resistance, Ectopic Lipid Accumulation, and Vascular Function in Resistance to Thyroid Hormone β. J. Clin. Endocrinol. Metab. 106 (5), e2005–e2014 (2021). Pedreño, J. et al. Platelet function in patients with familial hypertriglyceridemia: evidence that platelet reactivity is modulated by apolipoprotein E content of very-low-density lipoprotein particles. Metabolism . 49 (7), 942–949 (2000). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Oct, 2024 Reviews received at journal 10 Oct, 2024 Reviewers agreed at journal 07 Oct, 2024 Reviewers agreed at journal 07 Oct, 2024 Reviewers agreed at journal 07 Oct, 2024 Reviews received at journal 06 Oct, 2024 Reviewers agreed at journal 06 Oct, 2024 Reviewers invited by journal 04 Oct, 2024 Editor assigned by journal 04 Oct, 2024 Editor invited by journal 03 Sep, 2024 Submission checks completed at journal 02 Sep, 2024 First submitted to journal 22 Aug, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4956704","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":358635033,"identity":"5c4aad0d-8037-47bb-9c41-2d6495ed5c3c","order_by":0,"name":"Pengfei Chen","email":"","orcid":"","institution":"Xiyuan Hospital, China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Chen","suffix":""},{"id":358635035,"identity":"bb896fac-6267-4739-b174-8fab0f3f6663","order_by":1,"name":"Meilin Zhu","email":"","orcid":"","institution":"Xiyuan Hospital, China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Meilin","middleName":"","lastName":"Zhu","suffix":""},{"id":358635039,"identity":"f931385e-a03a-4d7c-b919-5e22ce84349e","order_by":2,"name":"Dazhuo Shi","email":"","orcid":"","institution":"Xiyuan Hospital, China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Dazhuo","middleName":"","lastName":"Shi","suffix":""},{"id":358635041,"identity":"ab3d9ca2-c20a-4832-bfdc-5136cf6c0747","order_by":3,"name":"Jianpeng Du","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACNv7mAwcS/0jIsbE3H3yQUFFDWAufxLHEAw8bLIz5eY4lGzw4c4ywFjmGHOODDxsqEmfO8DGTfNjCTITDGI4lHEjcIWFscIPBrCKxgY2Bv707Ab8WZpBfzkjIGdxuSLuRuEOGQeLM2Q2EbUlgA9py58CxG4ln2BgMJHIJackxAGlJ3HAjsa0gsY2ZSC2JbRJA7yezMRCnRQLksDMSoEBmlkg4c4yHoF/k+5sPf/xRUQeMyv6PQEaNHH97L34tGICHNOWjYBSMglEwCrACANafUf9C5wgJAAAAAElFTkSuQmCC","orcid":"","institution":"Xiyuan Hospital, China Academy of Chinese Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Jianpeng","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2024-08-22 09:11:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4956704/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4956704/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-81637-y","type":"published","date":"2024-12-03T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66944652,"identity":"b85fd7c4-a13e-4f11-bd52-2f12b926be99","added_by":"auto","created_at":"2024-10-18 09:37:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148711,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of study participants\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4956704/v1/9e3760cc9d7e90c0d8d79d67.jpg"},{"id":66944653,"identity":"e364e3ee-d5fd-4eda-a91a-0e1122221971","added_by":"auto","created_at":"2024-10-18 09:37:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":55510,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRCS analysis of PHR with diabetes and prediabetes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4956704/v1/b4b9061a4f2ac6ffb96cb779.jpg"},{"id":70965299,"identity":"11e8cf2c-6789-4831-b919-7ce15557ec95","added_by":"auto","created_at":"2024-12-09 16:18:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1289692,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4956704/v1/8260dc7f-c822-494e-850c-e2bfe2f8fcbd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Platelet to High-Density Lipoprotein Cholesterol Ratio and Risk of Diabetes and Prediabetes: Recent Findings from NHANES 2005– 2018","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDiabetes is the most common chronic metabolic disorder, with global prevalence steadily increasing. In 2019, around 9.3% of the global population were affected by diabetes, and this figure is expected to rise to 10.9% by 2045\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Prediabetes is a high-risk precursor to diabetes, characterized by elevated blood sugar levels that have not yet reached the threshold for diabetes\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Currently, 374\u0026nbsp;million adults worldwide have prediabetes, and this number is expected to grow to nearly 540\u0026nbsp;million by 2045\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. As the prevalence of prediabetes rises, the burden of diabetes is likely to escalate. Studies have shown that diabetes increases the risk of cardiovascular disease (CVD), chronic kidney disease (CKD), retinopathy, and neuropathy\u003csup\u003e[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Additionally, diabetes imposes a substantial economic burden, with global costs estimated at \u003cspan\u003e$\u003c/span\u003e1.31 trillion\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Therefore, targeted prevention and treatment strategies of diabetes and prediabetes are essential to reduce clinical prevalence.\u003c/p\u003e \u003cp\u003ePHR integrates platelet activity and HDL-C levels and is an emerging indicator for assessing inflammation and hypercoagulability\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Hyperglycemia, insulin resistance, and chronic inflammation are common pathological features of diabetes, which can lead to increased platelet activation, resulting in a procoagulant state and impaired fibrinolysis\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Activated platelets release pro-inflammatory and pro-coagulant factors, which aggravate inflammation and increase the risk of thrombosis, further contributing to the occurrence and progression of diabetes and its cardiovascular complications\u003csup\u003e[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. HDL-C is known for its antiplatelet, antithrombotic, and anti-inflammatory properties\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Diabetic patients often have lower HDL-C levels, leading to impaired cholesterol reverse transport, reduced anti-inflammatory and antioxidant capabilities, and endothelial dysfunction\u003csup\u003e[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. PHR was first introduced by Jialal et al. as an effective biomarker for predicting metabolic syndrome (MetS)\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e and hyperuricemia\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. However, its association with diabetes or prediabetes has not yet been thoroughly explored.\u003c/p\u003e \u003cp\u003eAn ideal predictor should provide independent predictive parameters, be easily identifiable during diagnosis, and be cost-effective in clinical practice. This study uses NHANES data to investigate the association of PHR with diabetes and prediabetes.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eNHANES, conducted by the National Center for Health Statistics (NCHS), is collected biennially using a stratified sampling method. The survey was conducted with approval from the NCHS Institutional Review Board, and informed consent was obtained from all participants\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. This study used NHANES data from 2005 to 2018, spanning seven biennial cycles, initially including 70,190 participants. We excluded those without diabetes or prediabetes data (n\u0026thinsp;=\u0026thinsp;5,058) and those missing platelet or HDL-C information (n\u0026thinsp;=\u0026thinsp;17,403). Additional exclusions included participants lacking essential covariates (n\u0026thinsp;=\u0026thinsp;13,735), those under 20 years old (n\u0026thinsp;=\u0026thinsp;12,781), and pregnant individuals (n\u0026thinsp;=\u0026thinsp;984). Ultimately, 20,229 individuals were included, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Diagnosis of diabetes and prediabetes\u003c/h2\u003e \u003cp\u003eDiabetes was diagnosed in individuals who satisfied one or more of the following criteria: (1) fasting blood glucose (FSG)\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L or random blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L; (2) 2-hour oral glucose tolerance test (OGTT)\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L; (3) glycosylated hemoglobin (HbA1c)\u0026thinsp;\u0026ge;\u0026thinsp;6.5%; (4) use of diabetes medication or insulin; or (5) a self-reported diagnosis of diabetes by a doctor.\u003c/p\u003e \u003cp\u003ePrediabetes was diagnosed in individuals who met one or more of the following criteria: (1) FSG between 5.6 and 7.0 mmol/L; (2) HbA1c between 5.7 and 6.5%; (3) 2-hour OGTT between 7.8 and 11.1 mmol/L; or (4) a self-reported diagnosis of prediabetes by a doctor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Calculation of PHR\u003c/h2\u003e \u003cp\u003eThe exposure variable, PHR, was calculated as the ratio of platelet count (1000 cells/\u0026micro;L) to HDL-C (mg/dL)\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Blood samples collected at the Mobile Examination Center were analyzed to measure biochemical parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Covariates\u003c/h2\u003e \u003cp\u003eDemographic data were gathered through questionnaire interviews and included variables such as sex (male, female), age, race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Mexican American, other Hispanic, Non-Hispanic Asian, Other), education (below high school, high school, above high school), family poverty income ratio (PIR, \u0026lt;\u0026thinsp;1.0, 1.0\u0026ndash;3.0, \u0026gt;\u0026thinsp;3.0), alcohol consumption (no drinks, 1\u0026ndash;5 drinks/month, 5\u0026ndash;10 drinks/month, \u0026gt;\u0026thinsp;10 drinks/month), BMI (\u0026lt;\u0026thinsp;25, 25\u0026ndash;29, \u0026gt;\u0026thinsp;29 kg/m\u0026sup2;), and smoking status [never (\u0026lt;\u0026thinsp;100 cigarettes in lifetime), current (\u0026ge;\u0026thinsp;100 cigarettes and currently smoking), former (\u0026ge;\u0026thinsp;100 cigarettes but not currently smoking)]. Complications included hypertension (based on systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, a prior diagnosis, or a history of anti-hypertensive medication use), CVD (self-reported doctor-diagnosed conditions such as coronary heart disease, heart failure, heart attack, stroke, and angina pectoris), and CKD (self-reported doctor-diagnosed). Laboratory covariates encompassed serum insulin, FSG, HbA1c, total triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), serum uric acid (SUA), high-sensitivity C-reactive protein (hs-CRP), and creatinine (Cr). Detailed procedures for collecting blood biochemical measurements are provided on the NHANES website.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Statistical analysis","content":" \u003cp\u003eFor all statistical analyses, NHANES sampling weights were applied using R 4.3.3 to account for the survey's stratification and complexity. The participants in this study were weighted to represent a population of 879,614,278. Categorical variables were displayed as unweighted counts (percentages). Continuous variables were presented as weighted means (standard errors) or medians (interquartile range). Differences between the two groups were assessed using the weighted Student\u0026rsquo;s t-test, Mann-Whitney U test, and Chi-squared test. A two-sided P-value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eWe used multivariate logistic regression analyses to estimate odds ratios (ORs) and 95% CIs. The logistic regression models were classified into categorical and continuous models. For the categorical model, PHR was divided into quartiles, using the lowest quartile as the reference group. Trend tests (p-trend) were conducted using the median PHR in each quartile. Model 1 included only PHR as the independent variable. Model 2 adjusted for sex, age, race, PIR, smoking status, alcohol status, education, and BMI. Model 3 further adjusted for hypertension, CVD, CKD, TG, TC, LDL-C, hs-CRP, SUA, and Cr. Subgroup heterogeneity was assessed through interaction analyses. To investigate potential linear and non-linear associations, a restricted cubic spline (RCS) model featuring three knots was applied, with the third knot chosen according to the Akaike information criterion (AIC). The log-likelihood ratio test was employed to determine the presence of linear or non-linear relationships.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Study characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the weighted study characteristics of participants categorized by PHR quartiles. The study included 20,229 participants from the NHANES 2005\u0026ndash;2018 survey, with 9,818 males (48.20%) and 10,481 females (51.80%), and an average age of 47.84\u0026thinsp;\u0026plusmn;\u0026thinsp;17.07 years. Among them, 3,884 (14.29%) were diagnosed with diabetes, 8,863 (44.36%) had prediabetes, 7,493 (32.85%) had hypertension, and 2,207 (8.88%) had CVD. The majority of participants were Non-Hispanic white (65.18%), 14.75% lived in poverty, and 94.97% had a high school diploma or higher. Additionally, 56.58% had never smoked, and 22.31% had never consumed alcohol. The weighted mean (standard error) BMI was 29.30\u0026thinsp;\u0026plusmn;\u0026thinsp;6.99 kg/m\u0026sup2;. The mean (standard error) platelet count was 238.69\u0026thinsp;\u0026plusmn;\u0026thinsp;60.08 cells/\u0026micro;L, HDL-C was 53.85\u0026thinsp;\u0026plusmn;\u0026thinsp;16.52 mg/dL, LDL-C was 2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92 mmol/L, TG was 1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07 mmol/L, TC was 4.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07 mmol/L, SUA was 320.42\u0026thinsp;\u0026plusmn;\u0026thinsp;84.06 mmol/L, and Cr was 77.56\u0026thinsp;\u0026plusmn;\u0026thinsp;30.68 mmol/L. Significant differences were observed across all PHR quartiles for the other variables, except for the number of CVD cases, which showed no significant difference.\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 of study participants stratified by PHR\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\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall, N\u0026thinsp;=\u0026thinsp;20,229\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1, N\u0026thinsp;=\u0026thinsp;5,075 (\u0026lt;\u0026thinsp;3.51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2, N\u0026thinsp;=\u0026thinsp;5,085 (3.51\u0026ndash;4.56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3, N\u0026thinsp;=\u0026thinsp;5,067 (4.56\u0026ndash;5.87)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4, N\u0026thinsp;=\u0026thinsp;5,072 (\u0026gt;\u0026thinsp;5.87)\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\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.84\u0026thinsp;\u0026plusmn;\u0026thinsp;17.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.06\u0026thinsp;\u0026plusmn;\u0026thinsp;17.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.32\u0026thinsp;\u0026plusmn;\u0026thinsp;17.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.23\u0026thinsp;\u0026plusmn;\u0026thinsp;16.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.56\u0026thinsp;\u0026plusmn;\u0026thinsp;15.56\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=\"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\u003e10,481(51.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,941 (61.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,680 (51.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,484 (47.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,376 (45.28%)\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\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,818 (48.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,134 (38.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,405 (48.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,583 (52.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,696 (54.72%)\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=\"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\u003e7,596 (65.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,018 (69.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,916 (66.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,870 (63.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,792 (60.16%)\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\u003e4,454 (10.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,351 (12.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,120 (10.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,051 (10.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e932 (9.862%)\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\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,806 (8.775%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e469 (5.311%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e687 (8.451%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e767 (9.644%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e883 (11.87%)\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 Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,126 (6.351%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e411 (4.378%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e508 (5.630%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e594 (7.468%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e613 (8.033%)\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 Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,583 (5.436%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e678 (5.595%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e686 (5.566%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e609 (5.100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e610 (5.479%)\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 Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e734 (3.429%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148 (2.733%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (2.934%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e176 (3.520%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e242 (4.592%)\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\u003eAlcohol\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003estatus, 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=\"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-drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,005 (22.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e996 (21.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,008 (21.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e996 (22.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,005 (23.73%)\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\u003e1-5drinks/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,985(50.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,499(41.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,724(49.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,820(54.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,942(57.07%)\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\u003e5-10drinks/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,089 (9.354%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298 (9.698%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e286 (8.984%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e266 (10.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e239 (8.533%)\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\u0026gt;\u0026thinsp;10 drinks/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,890 (17.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e737 (27.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e522 (19.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e344 (13.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e287 (10.70%)\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=\"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\u0026lt;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,994 (14.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e859 (11.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e943 (13.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,034 (15.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,158 (18.22%)\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\u003e1.0\u0026ndash;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,751 (36.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,864 (32.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,899 (34.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,958 (37.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,030 (39.87%)\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\u0026gt;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,604 (49.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,859 (56.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,768 (51.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,590 (46.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,387 (41.92%)\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\u003eSmoke, 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=\"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\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,103 (14.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e654 (11.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e665 (12.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e775 (15.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,009 (20.13%)\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\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,753 (24.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,263 (26.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,194 (24.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,194 (24.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,102 (22.68%)\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,633 (56.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,967 (58.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,031 (59.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,888 (55.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,747 (53.24%)\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\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e810 (3.862%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191 (3.427%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195 (3.815%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e210 (4.263%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e214 (3.956%)\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=\"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\u003eBelow high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,914 (4.975%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e423 (4.066%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e457 (4.714%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e518 (5.386%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e516 (5.782%)\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 school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,046 (31.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,610 (27.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,736 (30.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,777 (33.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,923 (36.55%)\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\u003eAbove high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,319 (63.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,036 (68.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,887 (64.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,767 (61.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,629 (57.65%)\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\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(\u0026lt;\u0026thinsp;0.1%)\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\u003e\u003cb\u003eCVD, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,207 (8.883%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e615 (9.555%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e558 (8.757%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e504 (8.178%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e530 (9.038%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \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 \u003cp\u003e7,493 (32.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,835 (30.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,841 (31.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,827 (33.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,990 (36.35%)\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\u003eDiabetes, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,884 (14.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e747 (9.829%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e910 (13.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,020 (14.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,207 (19.82%)\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\u003ePrediabetes, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,863 (44.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,090 (39.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,186 (42.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,236 (45.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,351 (51.36%)\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\u003eCKD, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e576 (3.029%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147 (3.549%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148 (3.128%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134 (2.593%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e147 (2.831%)\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\u003eBMI, kg/m2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.30\u0026thinsp;\u0026plusmn;\u0026thinsp;6.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.44\u0026thinsp;\u0026plusmn;\u0026thinsp;5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.37\u0026thinsp;\u0026plusmn;\u0026thinsp;6.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.26\u0026thinsp;\u0026plusmn;\u0026thinsp;6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.31\u0026thinsp;\u0026plusmn;\u0026thinsp;7.39\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\u003ePlatelet, 1000 cells/\u0026micro;L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e238.69\u0026thinsp;\u0026plusmn;\u0026thinsp;60.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194.79\u0026thinsp;\u0026plusmn;\u0026thinsp;43.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e223.65\u0026thinsp;\u0026plusmn;\u0026thinsp;41.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e248.07\u0026thinsp;\u0026plusmn;\u0026thinsp;45.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e291.15\u0026thinsp;\u0026plusmn;\u0026thinsp;62.60\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\u003eHDL-C, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.85\u0026thinsp;\u0026plusmn;\u0026thinsp;16.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.05\u0026thinsp;\u0026plusmn;\u0026thinsp;17.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.14\u0026thinsp;\u0026plusmn;\u0026thinsp;8.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.76\u0026thinsp;\u0026plusmn;\u0026thinsp;8.29\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\u003eLDL-C, mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\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\u003eTG, mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\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\u003eTC, mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\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\u003eSUA, mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e320.42\u0026thinsp;\u0026plusmn;\u0026thinsp;84.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e302.15\u0026thinsp;\u0026plusmn;\u0026thinsp;79.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e312.93\u0026thinsp;\u0026plusmn;\u0026thinsp;80.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e328.06\u0026thinsp;\u0026plusmn;\u0026thinsp;84.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e339.56\u0026thinsp;\u0026plusmn;\u0026thinsp;86.32\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\u003eSr, umol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.56\u0026thinsp;\u0026plusmn;\u0026thinsp;30.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.30\u0026thinsp;\u0026plusmn;\u0026thinsp;36.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.46\u0026thinsp;\u0026plusmn;\u0026thinsp;30.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.79\u0026thinsp;\u0026plusmn;\u0026thinsp;23.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.70\u0026thinsp;\u0026plusmn;\u0026thinsp;30.76\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\u003ehs-CRP, mg/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.89\u0026thinsp;\u0026plusmn;\u0026thinsp;7.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.50\u0026thinsp;\u0026plusmn;\u0026thinsp;4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.15\u0026thinsp;\u0026plusmn;\u0026thinsp;6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.95\u0026thinsp;\u0026plusmn;\u0026thinsp;5.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.05\u0026thinsp;\u0026plusmn;\u0026thinsp;10.50\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNormally distributed continuous variables are described as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SEs, and continuous variables without a normal distribution are described as medians (interquartile ranges). Categorical variables are presented as numbers (percentages). All estimates accounted for complex survey designs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Association of PHR with the diabetes and prediabetes\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the associations of PHR with diabetes and prediabetes. In all three models, the highest PHR quartiles were significantly associated with an increased risk of diabetes and prediabetes compared to the lowest quartiles. Even after adjusting for all covariates, these positive associations remained significant for Q2 (1.85 [1.18, 2.91], P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Q3 (2.12 [1.20, 3.74], P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and Q4 (2.46 [1.34, 4.51], P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). When PHR was analyzed as a continuous variable in the linear regression model, similar findings emerged, showing a positive association between PHR and diabetes and prediabetes in Model 1 (1.13 [1.11, 1.16], P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Model 2 (1.13 [1.09, 1.16], P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and Model 3 (1.14 [1.00, 1.29], P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eAssociations between PHR and the risk of diabetes and prediabetes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13 (1.11, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13 (1.09, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14 (1.00, 1.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategories\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.51\u0026ndash;4.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21 (1.08, 1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28 (1.09, 1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85 (1.18, 2.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.56\u0026ndash;5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39 (1.25, 1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45 (1.23, 1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.12 (1.20, 3.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.93 (1.73, 2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.90 (1.61, 2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.46 (1.34, 4.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eThe PHR was categorized into four quartiles and tests for trend (p\u0026ndash;trend) based on variable containing the median value for each quartiles. PHR also was utilized as continuous variables and p-value was used to test significance.\u003c/p\u003e \u003cp\u003eModel 1, each serum carotenoid was the sole independent variable. Model 2 included adjustments for sex, race, age, poverty status, smoking status, alcohol status, education, and BMI. Model 3 built on Model 2 by further adjusting for hypertension, CVD, CKD, TG, TC, LDL-C, hs-CRP, SUA and Cr.\u003c/p\u003e \u003cp\u003eAfter full adjustments, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the nonlinear relationship of PHR with diabetes and prediabetes, as modeled using a smoothed curve from a generalized additive model. The two-stage linear regression analysis pinpointed an inflection point at 4.55 (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results indicated that the OR for PHR\u0026thinsp;\u0026lt;\u0026thinsp;4.55 was 1.32 (95% CI: 1.01\u0026ndash;1.73, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while for PHR\u0026thinsp;\u0026ge;\u0026thinsp;4.55, the OR was 1.07 (95% CI: 0.92\u0026ndash;1.24, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This suggests that below the inflection point, a lower PHR is associated with a decreased risk of diabetes and prediabetes.\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 analysis of the effect of PHR on diabetes and prediabetes using two-piece linear regression models.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes and prediabetes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026ndash;value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitting by binary logistic regression model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14 (1.00, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitting by the two\u0026ndash;piecewise linear model\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 \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\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHR\u0026thinsp;\u0026lt;\u0026thinsp;4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.32 (1.01, 1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHR\u0026thinsp;\u0026ge;\u0026thinsp;4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07 (0.92, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog\u0026ndash;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 \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Stratified assessment\u003c/h2\u003e \u003cp\u003eStratified analysis was conducted to evaluate the impact of confounding factors and specific populations on the results. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals that, with the exceptions of Mexican Americans, Other Hispanics, and individuals with a history of CVD (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), most demographic groups show a significant association with PHR. This association suggests that these groups may be particularly vulnerable to increased risks of diabetes and prediabetes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAdditionally, we examined how various patient characteristics\u0026mdash;such as age, sex, race, BMI, education, smoking status, alcohol status, PIR, hypertension, and CVD\u0026mdash;might influence the observed associations. The analysis found significant interactions with sex, alcohol status, and BMI (P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that these factors may modify the relationship between PHR and the risk of diabetes and prediabetes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup analyses of the relationship between PHR and the risk of diabetes and prediabetes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP for interaction\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\u003eAge\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 \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09(1.02,1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.18(0.91,1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.12(0.84,1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.93(1.36,2.74)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09(1.05,1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.19(0.98,1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.36(1.12,1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.55(1.28,1.89)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\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 \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07(1.03,1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02(0.86,1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.23(1.04,1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.45(1.23,1.72)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19(1.14,1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.32(1.06,1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.62(1.29,2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.33(1.84,2.95)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\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 \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.37\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12(1.07,1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.32(1.07,1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.46(1.17,1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.97(1.56,2.48)\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 \u003ctd align=\"left\" colname=\"c8\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12(1.06,1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02(0.79,1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.42(1.09,1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.92(1.43,2.56)\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 \u003ctd align=\"left\" colname=\"c8\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13(1.05,1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67(0.45,1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95(0.64,1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.32(0.88,1.96)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12(1.03,1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00(0.65,1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.20(0.77,1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.49(0.96,2.32)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13(1.05,1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.85(1.28,2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.32(0.89,1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.74(1.15,2.64)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\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 \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13(1.02,1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03(0.60,1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.04(0.61,1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.97(1.15,3.36)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14(1.08,1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.22(0.92,1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.43(1.08,1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.98(1.49,2.63)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11(1.07,1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.29(1.05,1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.44(1.17,1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.78(1.43,2.21)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoke\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 \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15(1.08,1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.90(1.24,2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.98(1.28,3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.71(1.77,4.13)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11(1.03,1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.07(0.79,1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.62(1.15,2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.08(1.45,2.98)\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 \u003ctd align=\"left\" colname=\"c8\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10(1.06,1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.15(0.94,1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.15(0.92,1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.55(1.24,1.93)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003estatus,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 \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10(1.04,1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.28(0.94,1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.44(1.05,1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.62(1.19,2.20)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-5drinks/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12(1.07,1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03(0.82,1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16(0.91,1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.70(1.34,2.17)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-10drinks/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19(1.06,1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.44(0.84,2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.43(1.37,4.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.51(1.39,4.53)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 drinks/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13(1.03,1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.73(1.21,2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.65(1.11,2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.18(1.38,3.45)\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 \u003ctd align=\"left\" colname=\"c8\"\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 \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15(1.09,1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.46(1.05,2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.47(1.07,2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.06(1.47,2.88)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.0\u0026ndash;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13(1.08,1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.20(0.93,1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.48(1.15,1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.87(1.45,2.42)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14(1.09,1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.26(0.99,1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.37(1.06,1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.80(1.38,2.36)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\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 \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09(1.04,1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.36(1.05,1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.64(1.21,2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.12(1.52,2.97)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07(1.04,1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.29(1.06,1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.53(1.26,1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.18(1.80,2.64)\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 \u003ctd align=\"left\" colname=\"c8\"\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 \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12(1.06,1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.55(1.19,2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.55(1.18,2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.00(1.51,2.65)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12(1.08,1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14(0.94,1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.37(1.12,1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.79(1.45,2.20)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD, 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 \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03(0.92,1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.12(1.27,3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.18(0.73,1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.85(1.05,3.26)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13(1.09,1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.22(1.03,1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.44(1.21,1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.86(1.56,2.22)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study reveals a positive correlation between PHR and the risk of diabetes and prediabetes, which remains significant after accounting for various confounding factors. After full adjustment, the OR for diabetes and prediabetes per unit increase in PHR was 1.14 (95% CI: 1.00\u0026ndash;1.29, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Participants in the highest PHR quartile had an OR of 2.46 (95% CI: 1.34\u0026ndash;4.51, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to those in the lowest quartile. Subgroup analyses and interaction tests suggest that this positive correlation is consistent across different demographic groups. Additionally, the RCS analysis revealed a non-linear relationship. The two-piecewise regression identified an inflection point at PHR\u0026thinsp;=\u0026thinsp;4.55, and showing a stronger positive association when PHR is below this threshold.\u003c/p\u003e \u003cp\u003eIn recent years, PHR has attracted extensive attention as a new biomarker in the study of metabolic diseases\u003csup\u003e[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. PHR not only reflects platelet activity but also integrates an individual's lipid metabolic status. One study has found that PHR is significantly elevated in patients with metabolic syndrome (MetS) and increases with MetS severity\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Another study has reported a positive correlation between PHR and the risk of hyperuricemia\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. MetS is characterized by a constellation of metabolic disorders centered on insulin resistance, including abdominal obesity, dyslipidemia, and hypertension, which are closely associated with the development of diabetes\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. In addition to being a component of MetS, hyperuricemia is also an independent risk factor for the development of insulin resistance and diabetes\u003csup\u003e[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Large-scale prospective cohort studies have confirmed that MetS and hyperuricemia significantly increase the risk of developing diabetes\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is logical to suggest that PHR is closely related to diabetes or prediabetes.\u003c/p\u003e \u003cp\u003eThe relationship between PHR and diabetes involves complex pathophysiological mechanisms that are not yet fully understood. In this paper, we attempt to explore the pathophysiological links by focusing on three aspects: chronic inflammation, oxidative stress, and lipid metabolism disorders.\u003c/p\u003e \u003cp\u003eChronic low-grade inflammation is a key characteristic in the development of diabetes and prediabetes. Studies have shown that the pro-inflammatory cytokines tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) can inhibit the expression and translocation of glucose transporter 4 (GLUT4) through the nuclear factor-kappa B (NF-κB) and c-Jun N-terminal kinase (JNK) pathways, leading to a decrease in insulin sensitivity\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Platelets promote the spread of systemic inflammation and contribute to chronic inflammatory responses by releasing various pro-inflammatory mediators, such as platelet factor 4 (PF4), transforming growth factor-β (TGF-β), and platelet-activating factor (PAF)\u003csup\u003e[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Study has shown that elevated levels of PF4 are closely associated with insulin resistance. PF4 binds to chemokine receptors, activating monocytes and macrophages, which then promote the release of additional pro-inflammatory cytokines\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Platelets also interact directly with neutrophils and monocytes, forming platelet-leukocyte aggregates that activate leukocytes and enhance their pro-inflammatory activity\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. HDL-C is considered to have anti-inflammatory properties, and can reduce inflammatory responses by promoting reverse cholesterol transport, inhibiting the oxidation of LDL-C, and clearing circulating pro-inflammatory factors\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. When PHR is elevated, it may indicate an increase in the pro-inflammatory effects of platelets and a reduction in the anti-inflammatory effects of HDL-C. This imbalance exacerbates chronic inflammation, may lead to increased insulin resistance and the development of diabetes.\u003c/p\u003e \u003cp\u003ePancreatic β cells are more susceptible to damage by reactive oxygen species (ROS) due to their relatively weak antioxidant capacity. Excessive platelet activation can increase oxidative stress by producing ROS, which can damage not only vascular endothelial cells but also pancreatic β cells and weaken the ability to secrete insulin\u003csup\u003e[\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Platelets in diabetic patients are often in a highly activated state, which is closely linked to heightened oxidative stress\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. HDL-C has antioxidant properties and can protect pancreatic β-cells by scavenging excess ROS\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Therefore, this imbalance between oxidative stress and the antioxidant system could be one of the key mechanisms underlying the association between PHR and diabetes.\u003c/p\u003e \u003cp\u003eLipid metabolism disorders affect the secretion and function of insulin, and have a significant impact on platelet activity. HDL-C plays a crucial role in maintaining lipid metabolic balance, and low levels of HDL-C are often associated with high triglycerides and elevated LDL-C levels\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. These lipid abnormalities can exacerbate insulin resistance\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Hyperlipidemia is a major feature of diabetes and prediabetes, usually manifested as an abnormal combination of high TG, low HDL-C levels, and elevated LDL-C levels\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Research indicates that HDL-C facilitates the reverse transport of cholesterol, transporting excess cholesterol from peripheral tissues to the liver for metabolism, thereby reducing the formation of atherosclerotic plaques. HDL-C inhibits the progression of atherosclerosis by reducing the generation of lipid peroxidation products. In addition, platelet function is affected by lipid metabolism, and hyperlipidemia is often accompanied by increased platelet reactivity and thrombotic tendency\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. PHR reflects the lipid metabolism status of an individual, and its increase usually indicates abnormal lipid metabolism, especially low HDL-C levels. These lipid metabolism disorders will further promote the development of insulin resistance, leading to the progression of diabetes.\u003c/p\u003e \u003cp\u003eThe major strengths of this study are as follows. First, it is the first study to use a nationally representative sample to examine the associations of the PHR with diabetes and prediabetes. Second, a wide array of potential confounding variables was accounted for in the analysis. Third, the accuracy and reliability of the data were bolstered by employing trained staff who adhered to standardized protocols for collecting key information and conducting participant interviews.\u003c/p\u003e \u003cp\u003eOur study also has some limitations. First, as a cross-sectional study, this study cannot establish causal relationships. Additionally, the study's capacity to explore and test etiological hypotheses is limited, and its findings may not be fully generalizable. Therefore, further prospective longitudinal studies are needed to confirm these findings. Second, potential confounding from unknown or unmeasurable factors cannot be entirely excluded. Third, due to the presence of randomly missing data and the large sample size, we did not use multiple imputation methods to address the missing data, which may impact the precision of the results.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eOur study suggests that an increase in PHR may be correlated with a heightened likelihood of developing diabetes and prediabetes. Consequently, PHR could potentially serve as a marker for estimating the probability of diabetes and prediabetes development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in this study are included in the article. For further inquiries, please contact the corresponding author, Jianpeng Du, at [email protected].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe would like to appreciate the support by participants involved in the NHANES study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDazhuo Shi: conceptualization and supervision. Pengfei Chen and Meilin Zhu: data curation and writing the original draft. Jianpeng Du: methodology. All authors contributed to and approved the submitted version of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the project of Hospital capability enhancement project of Xiyuan Hospital, CACMS. (NO. XYZX0204-02, NO. XYZX0201-03).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the NHANES Institutional Review Board, and was performed in accordance with the Declaration of Helsinki, with all NHANES participants providing signed informed consent. Details are available at\u003cu\u003e\u0026nbsp;https://www.cdc.gov/nchs/nhanes/irba98.htm\u003c/u\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSaeedi, P. et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9 edition. \u003cem\u003eDiabetes Res. Clin. Pract.\u003c/em\u003e \u003cb\u003e157\u003c/b\u003e, 107843 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026aacute;nchez, E. et al. Characteristics of atheromatosis in the prediabetes stage: a cross-sectional investigation of the ILERVAS project. \u003cem\u003eCardiovasc. Diabetol.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (1), 154 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, X. et al. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (1), 14790 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2021 Fertility and Forecasting Collaborators. Global fertility in 204 countries and territories, 1950\u0026ndash;2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. \u003cem\u003eLancet\u003c/em\u003e. \u003cb\u003e403\u003c/b\u003e (10440), 2057\u0026ndash;2099 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBragg, F. et al. Association Between Diabetes and Cause-Specific Mortality in Rural and Urban Areas of China. \u003cem\u003eJAMA\u003c/em\u003e. \u003cb\u003e317\u003c/b\u003e (3), 280\u0026ndash;289 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBommer, C. et al. The global economic burden of diabetes in adults aged 20\u0026ndash;79 years: a cost-of-illness study. \u003cem\u003eLancet Diabetes Endocrinol.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (6), 423\u0026ndash;430 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJialal, I., Jialal, G. \u0026amp; Adams-Huet, B. The platelet to high density lipoprotein -cholesterol ratio is a valid biomarker of nascent metabolic syndrome. \u003cem\u003eDiabetes Metab. Res. Rev.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e (6), e3403 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDav\u0026igrave;, G. et al. In vivo formation of 8-iso-prostaglandin f2alpha and platelet activation in diabetes mellitus: effects of improved metabolic control and vitamin E supplementation. \u003cem\u003eCirculation\u003c/em\u003e. \u003cb\u003e99\u003c/b\u003e (2), 224\u0026ndash;229 (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Sofiani, M. E. et al. Diabetes and Platelet Response to Low-Dose Aspirin. \u003cem\u003eJ. Clin. Endocrinol. Metab.\u003c/em\u003e \u003cb\u003e103\u003c/b\u003e (12), 4599\u0026ndash;4608 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXin, G. et al. Metformin Uniquely Prevents Thrombosis by Inhibiting Platelet Activation and mtDNA Release. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 36222 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGajos, G. et al. Polyhedrocytes in blood clots of type 2 diabetic patients with high cardiovascular risk: association with glycemia, oxidative stress and platelet activation. \u003cem\u003eCardiovasc. Diabetol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (1), 146 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, A. M. et al. Salvianolic acid a inhibits platelet activation and aggregation in patients with type 2 diabetes mellitus. \u003cem\u003eBMC Cardiovasc. Disord\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e (1), 15 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan, L. W. et al. High levels of LDL-C combined with low levels of HDL-C further increase platelet activation in hypercholesterolemic patients. \u003cem\u003eBraz J. Med. Biol. Res.\u003c/em\u003e \u003cb\u003e48\u003c/b\u003e (2), 167\u0026ndash;173 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlierl, U. \u0026amp; Sch\u0026auml;fer, A. Fractalkine\u0026ndash;a local inflammatory marker aggravating platelet activation at the vulnerable plaque. \u003cem\u003eThromb. Haemost\u003c/em\u003e. \u003cb\u003e108\u003c/b\u003e (3), 457\u0026ndash;463 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, Y. Y. et al. Low HDL Cholesterol Is Associated with Reduced Bleeding Risk in Patients Who Underwent PCI: Findings from the PRACTICE Study. \u003cem\u003eThromb. Haemost\u003c/em\u003e Jul 8. (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao, Y. et al. The predictive value of the hs-CRP/HDL-C ratio, an inflammation-lipid composite marker, for cardiovascular disease in middle-aged and elderly people: evidence from a large national cohort study. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (1), 66 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSohrabi, Y., Schwarz, D. \u0026amp; Reinecke, H. LDL-C augments whereas HDL-C prevents inflammatory innate immune memory. \u003cem\u003eTrends Mol. Med.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (1), 1\u0026ndash;4 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan, L. et al. Association of platelet to high-density lipoprotein cholesterol ratio with hyperuricemia. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (1), 15641 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhluwalia, N. et al. Update on NHANES Dietary Data: Focus on Collection, Release, Analytical Considerations, and Uses to Inform Public Policy. Advances in nutrition. \u003cem\u003e(Bethesda Md)\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e (1), 121\u0026ndash;134 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou, Q. et al. Longitudinal association between physical activity and blood pressure, risk of hypertension among Chinese adults: China Health and Nutrition Survey 1991\u0026ndash;2015. \u003cem\u003eEur. J. Clin. Nutr.\u003c/em\u003e \u003cb\u003e75\u003c/b\u003e (2), 274\u0026ndash;282 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, H., Xu, Y. \u0026amp; Xu, Y. The association of the platelet/high-density lipoprotein cholesterol ratio with self-reported stroke and cardiovascular mortality: a population-based observational study. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (1), 121 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNi, J. et al. Associations between the platelet/high-density lipoprotein cholesterol ratio and likelihood of nephrolithiasis: a cross-sectional analysis in United States adults. \u003cem\u003eFront. Endocrinol. (Lausanne)\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e, 1289553 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNi, J. et al. Examining the cross-sectional relationship of platelet/high-density lipoprotein cholesterol ratio with depressive symptoms in adults in the United States. \u003cem\u003eBMC Psychiatry\u003c/em\u003e. \u003cb\u003e24\u003c/b\u003e (1), 427 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHudish, L. I., Reusch, J. E. \u0026amp; Sussel, L. β Cell dysfunction during progression of metabolic syndrome to type 2 diabetes. \u003cem\u003eJ. Clin. Invest.\u003c/em\u003e \u003cb\u003e129\u003c/b\u003e (10), 4001\u0026ndash;4008 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThaisetthawatkul, P. et al. Prediabetes, diabetes, metabolic syndrome, and small fiber neuropathy. \u003cem\u003eMuscle Nerve\u003c/em\u003e. \u003cb\u003e61\u003c/b\u003e (4), 475\u0026ndash;479 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, J. et al. Prevalence of Diabetes in Patients with Hyperuricemia and Gout: A Systematic Review and Meta-analysis. \u003cem\u003eCurr. Diab Rep.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (6), 103\u0026ndash;117 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMortada, I. \u0026amp; Hyperuricemia Type 2 Diabetes Mellitus, and Hypertension: an Emerging Association. \u003cem\u003eCurr. Hypertens. Rep.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (9), 69 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVareldzis, R., Perez, A. \u0026amp; Reisin, E. Hyperuricemia: An Intriguing Connection to Metabolic Syndrome, Diabetes, Kidney Disease, and Hypertension. \u003cem\u003eCurr. Hypertens. Rep.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (6), 237\u0026ndash;245 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoyesa, S. B., Hirigo, A. T. \u0026amp; Wube, T. B. Hyperuricemia and metabolic syndrome in type 2 diabetes mellitus patients at Hawassa university comprehensive specialized hospital, South West Ethiopia. \u003cem\u003eBMC Endocr. Disord\u003c/em\u003e. \u003cb\u003e17\u003c/b\u003e (1), 76 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeinstock, R. S. et al. Metabolic syndrome is common and persistent in youth-onset type 2 diabetes: Results from the TODAY clinical trial. \u003cem\u003eObes. (Silver Spring)\u003c/em\u003e. \u003cb\u003e23\u003c/b\u003e (7), 1357\u0026ndash;1361 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsser, N. et al. Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes. \u003cem\u003eDiabetes Res. Clin. Pract.\u003c/em\u003e \u003cb\u003e105\u003c/b\u003e (2), 141\u0026ndash;150 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTong, H. V. et al. Adiponectin and pro-inflammatory cytokines are modulated in Vietnamese patients with type 2 diabetes mellitus. \u003cem\u003eJ. Diabetes Investig\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e (3), 295\u0026ndash;305 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLudwig, N., Hilger, A., Zarbock, A. \u0026amp; Rossaint, J. Platelets at the Crossroads of Pro-Inflammatory and Resolution Pathways during Inflammation. \u003cem\u003eCells\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e (12), 1957 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorrell, C. N. et al. Emerging roles for platelets as immune and inflammatory cells. \u003cem\u003eBlood\u003c/em\u003e. \u003cb\u003e123\u003c/b\u003e (18), 2759\u0026ndash;2767 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTunjungputri, R. N. et al. Differential effects of platelets and platelet inhibition by ticagrelor on TLR2- and TLR4-mediated inflammatory responses. \u003cem\u003eThromb. Haemost\u003c/em\u003e. \u003cb\u003e113\u003c/b\u003e (5), 1035\u0026ndash;1045 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGruden, G. et al. Plasma beta-thromboglobulin and platelet factor 4 are not increased in insulin-dependent diabetic patients with microalbuminuria. \u003cem\u003eActa Diabetol.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e (3), 130\u0026ndash;132 (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHafner, C. et al. Brief High Oxygen Concentration Induces Oxidative Stress in Leukocytes and Platelets: A Randomized Cross-over Pilot Study in Healthy Male Volunteers. \u003cem\u003eShock\u003c/em\u003e. \u003cb\u003e56\u003c/b\u003e (3), 384\u0026ndash;395 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, J. et al. The mediation effect of HDL-C: Non-HDL-C on the association between inflammatory score and recurrent coronary events. \u003cem\u003eHeliyon\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e (1), e23731 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolahi Ahari, R. et al. Association of Three Novel Inflammatory Markers: Lymphocyte to HDL-C Ratio, High-Sensitivity C-Reactive Protein to HDL-C Ratio and High-Sensitivity C-Reactive Protein to Lymphocyte Ratio With Metabolic Syndrome. \u003cem\u003eEndocrinol. Diabetes Metab.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (3), e00479 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, S. et al. Matrine Impairs Platelet Function and Thrombosis and Inhibits ROS Production. \u003cem\u003eFront. Pharmacol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 717725 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan, M. et al. Luteolin Protects Pancreatic β Cells against Apoptosis through Regulation of Autophagy and ROS Clearance. \u003cem\u003ePharmaceuticals (Basel)\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e (7), 975 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, Q. et al. Selenium Nanodots (SENDs) as Antioxidants and Antioxidant-Prodrugs to Rescue Islet β Cells in Type 2 Diabetes Mellitus by Restoring Mitophagy and Alleviating Endoplasmic Reticulum Stress. \u003cem\u003eAdv. Sci. (Weinh)\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e (19), e2300880 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeoncini, S. et al. Oxidative stress, erythrocyte ageing and plasma non-protein-bound iron in diabetic patients. \u003cem\u003eFree Radic Res.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (8), 716\u0026ndash;724 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe\u0026oacute;n-Reyes, G. et al. Oxidative modifications of foetal LDL-c and HDL-c lipoproteins in preeclampsia. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (1), 110 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAscaso, J. F. et al. Lipoprotein phenotype and insulin resistance in familial combined hyperlipidemia. \u003cem\u003eMetabolism\u003c/em\u003e. \u003cb\u003e49\u003c/b\u003e (12), 1627\u0026ndash;1631 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCicero, A. F. et al. Short-term effects of a combined nutraceutical of insulin-sensitivity, lipid level and indexes of liver steatosis: a double-blind, randomized, cross-over clinical trial. \u003cem\u003eNutr. J.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 30 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, W. et al. Major lipids and lipoprotein levels and risk of blood pressure elevation: a Mendelian Randomisation study. \u003cem\u003eEBioMedicine\u003c/em\u003e. \u003cb\u003e100\u003c/b\u003e, 104964 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliveri, A. et al. Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e56\u003c/b\u003e (2), 212\u0026ndash;221 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoran, C. et al. Dyslipidemia, Insulin Resistance, Ectopic Lipid Accumulation, and Vascular Function in Resistance to Thyroid Hormone β. \u003cem\u003eJ. Clin. Endocrinol. Metab.\u003c/em\u003e \u003cb\u003e106\u003c/b\u003e (5), e2005\u0026ndash;e2014 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedre\u0026ntilde;o, J. et al. Platelet function in patients with familial hypertriglyceridemia: evidence that platelet reactivity is modulated by apolipoprotein E content of very-low-density lipoprotein particles. \u003cem\u003eMetabolism\u003c/em\u003e. \u003cb\u003e49\u003c/b\u003e (7), 942\u0026ndash;949 (2000).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetes, Prediabetes, Platelet, High-density lipoprotein, PHR, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-4956704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4956704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eTo explore the relationship between the platelet-to-high-density lipoprotein cholesterol ratio (PHR) and the risk of diabetes and prediabetes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eThis study analyzes data from the 2005-2018 National Health and Nutrition Examination Survey (NHANES). The prevalence of diabetes and prediabetes, as well as levels of HDL-C and platelet counts, were derived from cross-sectional surveys. The PHR was calculated by dividing platelet count by HDL-C concentration, and diabetes or prediabetes were classified according to established clinical criteria. We used multivariate logistic regression analyses to estimate odds ratios (ORs) and 95% CIs. The logistic regression models were classified into categorical and continuous models. The potential non-linear relationship was assessed using restricted cubic splines (RCSs) and two-piecewise linear regression to identify any inflection points. Additionally, subgroup and interaction analyses were conducted to determine variations across different population groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult:\u003c/strong\u003eA total of 20,229 eligible participants were included in the study, with a mean age of 47.84 years, and 51.80% of them were female. Among these participants, 3,884 (14.29%) were diagnosed with diabetes, and 8,863 (44.36%) were prediabetes. The result showed a positive association between PHR and the risk of diabetes and prediabetes. After adjusting for model 3, the OR for diabetes and prediabetes was associated with a per unit increase in PHR of 1.14 (95% CI: 1.00–1.29, P\u0026lt;0.05). The OR for participants in the highest PHR quartile was 2.46 (95% CI: 1.34–4.51, P\u0026lt;0.01) compared to those in the lowest quartile. Two-piecewise regression analysis identified a breakpoint at PHR = 4.55, with a positive association observed when PHR was below this value (OR = 1.32, 95% CI: 1.01–1.73, P\u0026lt;0.05). Subgroup and interaction analyses demonstrated that the positive association remained consistent across various demographic groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eOur study indicates that a higher PHR may be associated with an increased risk of developing diabetes and prediabetes. Therefore, PHR could potentially be used as a marker for assessing the likelihood of these conditions.\u003c/p\u003e","manuscriptTitle":"Association Between Platelet to High-Density Lipoprotein Cholesterol Ratio and Risk of Diabetes and Prediabetes: Recent Findings from NHANES 2005– 2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 09:37:45","doi":"10.21203/rs.3.rs-4956704/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-10T20:32:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-10T14:13:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178352993132984441380071344316894888671","date":"2024-10-08T00:05:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124818109661909628692193756319935444709","date":"2024-10-08T00:02:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180550596060475827377442206141437472906","date":"2024-10-07T07:13:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-07T03:28:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1225161154850263752206914199436314193","date":"2024-10-06T11:53:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-04T20:40:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-04T05:04:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-03T20:36:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-02T10:45:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-08-22T09:08:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ace76cc3-01c4-40f8-b281-90d9e9044920","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":38157963,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases"},{"id":38157964,"name":"Health sciences/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2024-12-09T16:10:33+00:00","versionOfRecord":{"articleIdentity":"rs-4956704","link":"https://doi.org/10.1038/s41598-024-81637-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-12-03 15:57:44","publishedOnDateReadable":"December 3rd, 2024"},"versionCreatedAt":"2024-10-18 09:37:45","video":"","vorDoi":"10.1038/s41598-024-81637-y","vorDoiUrl":"https://doi.org/10.1038/s41598-024-81637-y","workflowStages":[]},"version":"v1","identity":"rs-4956704","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4956704","identity":"rs-4956704","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}