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

doi:10.21203/rs.3.rs-4956704/v1

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

https://doi.org/10.21203/rs.3.rs-4956704/v1

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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

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.

2.1 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, < 1.0, 1.0–3.0, > 3.0), alcohol consumption (no drinks, 1–5 drinks/month, 5–10 drinks/month, > 10 drinks/month), BMI (< 25, 25–29, > 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.

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.

4.1 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 (< 3.51)	Q2, N = 5,085 (3.51–4.56)	Q3, N = 5,067 (4.56–5.87)	Q4, N = 5,072 (> 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
< 1.0	3,994 (14.75%)	859 (11.59%)	943 (13.87%)	1,034 (15.52%)	1,158 (18.22%)
1.0–3.0	7,751 (36.14%)	1,864 (32.40%)	1,899 (34.87%)	1,958 (37.67%)	2,030 (39.87%)
> 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	< 3.51	1	1	1
Q2	3.51–4.56	1.21 (1.08, 1.35)	1.28 (1.09, 1.49)	1.85 (1.18, 2.91)
Q3	4.56–5.87	1.39 (1.25, 1.56)	1.45 (1.23, 1.70)	2.12 (1.20, 3.74)
Q4	> 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), while for PHR ≥ 4.55, the OR was 1.07 (95% CI: 0.92–1.24, 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)	< 0.001
> 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)	< 0.001
> 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

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.

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.

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.

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

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Abstract

Figures

1. Introduction

2. Methods

2.1 Study population

2.2 Diagnosis of diabetes and prediabetes

2.3 Calculation of PHR

2.3 Covariates

3. Statistical analysis

4. Results

4.1 Study characteristics

4.2 Association of PHR with the diabetes and prediabetes

4.3 Stratified assessment

5. Discussion

6. Conclusions

Declarations

References

Additional Declarations

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