Abstract
Background
Remnant cholesterol, the cholesterol content of triglyceride-rich lipoproteins, has emerged as a novel cardiovascular risk factor. However, its relationship with isolated diastolic hypertension, particularly among young adults, remains unclear. This study aimed to investigate the association between remnant cholesterol levels and isolated diastolic hypertension in a population aged under 45 years.
Methods
A total of 6,153 participants aged 18–44 years who underwent routine health check-ups at Chinese PLA General Hospital were included after exclusion of secondary or non-isolated hypertension. Participants were categorized into remnant cholesterol tertiles (T1 ≤ 0.6 mmol/L, T2 0.6–0.88 mmol/L, T3 > 0.88 mmol/L). Multiple linear and logistic regression models were used to assess associations between remnant cholesterol and blood pressure levels as well as isolated diastolic hypertension, with progressive adjustment for demographic, metabolic, inflammatory, and renal factors. Restricted cubic spline analysis further examined the dose–response relationship between remnant cholesterol and isolated diastolic hypertension.
Results
Higher remnant cholesterol levels were significantly associated with increased systolic and diastolic blood pressure (both P < 0.001). Compared with the lowest remnant cholesterol tertile, participants with remnant cholesterol > 0.88 mmol/L showed higher odds of isolated diastolic hypertension in both unadjusted and fully adjusted models (OR = 1.73, 95% CI: 1.12–2.68, P = 0.003). Restricted cubic spline analysis revealed a linear positive correlation. Subgroup analyses showed consistent associations across sex, age, body mass index, hyperuricemia, and diabetes status (all P for interaction > 0.05).
Conclusions
Elevated remnant cholesterol is independently associated with higher blood pressure and greater odds of isolated diastolic hypertension in young adults, suggesting remnant cholesterol may contribute to early diastolic blood pressure elevation.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12872-026-05545-x.
Keywords: Remnant cholesterol, Isolated diastolic hypertension, Young adults, Blood pressure, Logistic regression, Cardiovascular risk
Introduction
Hypertension represents a leading worldwide public health concern and a major contributor to cardiovascular disease (CVD) morbidity and mortality. It was reported that more than 1.2 billion adults worldwide live with hypertension, and its prevalence continues to rise, especially in lower-middle-income nations [1]. Although the burden posed by elevated blood pressure increases with age, a growing number of studies have highlighted its emergence among younger adults [2, 3]. In China, the proportion of adults aged 18–44 years with hypertension has risen markedly over the last thirty years [4, 5]. Among the various subtypes of hypertension, isolated diastolic hypertension (IDH) is defined by increased diastolic blood pressure (DBP) with preserved systolic blood pressure (SBP). This phenotype is more frequently observed in younger adults and has been consistently linked to subsequent cardiovascular events [6, 7]. However, the potential mechanisms driving IDH in young adults remain largely unclear.
Emerging evidence indicates that lipid metabolism abnormalities, particularly remnant cholesterol (RC), may contribute substantially to the emergence of hypertension [8, 9]. RC corresponds to the cholesterol component of triglyceride-rich lipoproteins (primarily VLDL and IDL) which has been identified as a potent atherogenic component distinct from LDL cholesterol [10, 11]. Elevated RC has been reported to contribute to endothelial dysfunction, arterial stiffness, and vascular inflammation. These pathological changes may lead to the occurrence of hypertension [12, 13]. Moreover, elevated RC in hypertensive individuals is associated with a heightened probability of subsequent composite cardiovascular events (such as stroke, acute coronary syndrome) and death [14]. RC levels are closely linked to adiposity, impaired insulin sensitivity and hyperuricemia (HUA), which are increasingly common metabolic disorders among young adults [15, 16].
Recent evidence from both cohort and cross-sectional investigations has consistently connected increased RC levels with incident hypertension [17, 18]. Cui and colleagues demonstrated that elevated RC was associated with a 28% greater risk of developing hypertension, independent of traditional lipid measures. Similarly, longitudinal analyses have shown that prolonged exposure and fluctuations in RC concentrations significantly increase hypertension risk [19, 20]. Despite accumulating evidence linking RC to overall hypertension risk, most prior studies have concentrated on adults of middle or older age, and the specific association between RC and IDH remains understudied, especially in younger populations. Thus, the present analysis was designed to assess the relationship between RC and IDH in individuals under 45 years of age. This approach may provide new insights into early lipid-related vascular dysregulation.
Methods
Data collection and participants
This cross-sectional investigation was carried out among young adults between 18 and 44 years of age who underwent comprehensive health examinations at Chinese PLA General Hospital between Jan. 2022 and Dec. 2024. The research focused on assessing the relationship between RC and IDH among relatively healthy individuals. A total of 13,415 individuals were initially screened. Participants with missing data on exposure or outcome variables, possible secondary hypertension, non-IDH, abnormal RC levels, age out of the target range were excluded from the analysis. After exclusions, 6,153 individuals were retained for the final evaluation (as shown in Fig. 1). The research protocol was reviewed and approved by the Ethics Committee of Chinese PLA General Hospital (Approval No.: 2025K717O-XS001).
Fig. 1.
Flowchart of participant selection in the study. A total of 13,415 participants who underwent comprehensive health check-ups at Chinese PLA General Hospital were initially enrolled. After data cleaning, 3,770 participants were excluded due to missing data on HDL-C or LDL-C (n = 336), missing SBP/DBP (n = 240), possible secondary hypertension (n = 124; including 13 with a history of hyperthyroidism and 111 with abnormal FT3, FT4, or TSH), and 3,070 with non-IDH. Among the remaining individuals, 3,492 were excluded because of abnormal RC levels (n = 158) or age outside the 18–44 years range (n = 3,334). Finally, 6,153 participants were included in the final analysis, comprising 147 with IDH and 6,006 without IDH
Definition of IDH
Blood pressure was assessed with an electronic monitor while participants were seated and had rested for at least 5 min. SBP and DBP for all participants were registered as the mean of two consecutive measurements at 5-minute intervals. Since this study was conducted based on the Chinese population, we followed the Chinese hypertension management guidelines, in which IDH is characterized by SBP < 140 mmHg and DBP ≥ 90 mmHg [21]. Subjects with elevated SBP or secondary hypertension were excluded.
Data collection
The analysis for lipid profiles (including TC, TG, LDL-C and HDL-C) were quantified from fasting blood samples. RC was derived by the formula: RC = TC − LDL-C − HDL-C [11]. Individuals were grouped according to RC tertiles: T1 (≤ 0.60 mmol/L), T2 (0.60–0.88 mmol/L), and T3 (> 0.88 mmol/L), with the lowest third group (T1) serving as the reference group. Other laboratory parameters included white blood cell (WBC) and neutrophil (NEU) counts, serum creatinine (SCr), estimated glomerular filtration rate (eGFR), serum uric acid (SUA) and blood urea nitrogen (BUN). Type 2 diabetes mellitus (T2DM) was diagnosed based on fasting plasma glucose levels of at least 126 mg/dL, 2-hour glucose values ≥ 200 mg/dL on OGTT, or current use of antidiabetic medications. Prediabetes was identified by a fasting plasma glucose of 100–125 mg/dL or a 2-hour OGTT glucose level of 140–199 mg/dL [22]. The diagnosis of Chronic kidney disease (CKD) was based on an eGFR ≤ 90 mL/min/1.73 m² [23]. HUA was identified when SUA concentration ≥ 7.0 mg/dL in males and ≥ 5.7 mg/dL in females [24]. Anthropometric indices (such as BMI) were collected by trained staff using standardized methods.
Statistical analysis
Continuous variables were expressed as mean ± standard deviation (SD) and compared using ANOVA or Kruskal-Wallis tests, while categorical variables were analyzed using the chi-square test. We respectively applied linear regression to examine how RC related to blood pressure measurements, and multivariable logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the relationship between RC and IDH. We developed four models: (1) non-adjusted, (2) adjusted for sex, age, BMI, (3) additionally adjusted for metabolic comorbidities (T2DM, CKD, HUA), and (4) additionally adjusted for inflammatory and renal indices (WBC, NEU, SCr, eGFR, SUA, and BUN). Restricted cubic spline (RCS) regression was applied to explore potential nonlinear relationships, with four knots placed at the 5th, 35th, 65th, and 95th percentiles of RC, using the median value as the reference point [25]. We performed subgroup analyses to assess potential effect modification by sex, age, BMI, and additional relevant factors. In supplementary analyses, receiver operating characteristic (ROC) curve analyses were performed to evaluate the discriminative performance of RC across the four adjustment models, and to compare RC with traditional lipid parameters using ROC curves as well as net reclassification improvement (NRI) and integrated discrimination improvement (IDI) metrics. To assess the robustness of the findings, a sensitivity analysis was conducted using a simplified multivariable logistic regression model with a reduced set of clinically representative covariates. We considered P < 0.05 to indicate statistical significance. Data analyses were carried out in SPSS (V26.0) and Free Statistics software version 1.9, which is built upon R software (V4.3.2).
Results
Selection of participants and baseline characteristics
According to the selection of participants process shown in Figs. 1, 6 and 153 participants aged 18–44 were finally included for subsequent analysis. Among them, 147 (2.4%) had IDH. Participants were classified into three tertiles according to RC concentrations: ≤0.60 mmol/L (normal), 0.60–0.88 mmol/L (mild increase), and > 0.88 mmol/L (significant increase) (Table 1).
Table 1.
Baseline characteristics of the study population according to RC
| Variables | Total (n = 6153) | RC | |||
|---|---|---|---|---|---|
| T1: RC ≤ 0.6 (n = 2060) |
T2: 0.6< RC ≤ 0.88 (n = 2044) |
T3: RC>0.88 (n = 2049) |
p value | ||
| Sex, n (%) | < 0.001 | ||||
| Male | 3637 (59.1) | 1162 (56.4) | 1139 (55.7) | 1336 (65.2) | |
| Female | 2516 (40.9) | 898 (43.6) | 905 (44.3) | 713 (34.8) | |
| Age(years), Mean ± SD | 32.8 ± 6.5 | 31.9 ± 6.6 | 33.0 ± 6.4 | 33.6 ± 6.2 | < 0.001 |
| BMI(kg/m²), Mean ± SD | 23.1 ± 3.5 | 22.5 ± 3.4 | 22.9 ± 3.5 | 23.8 ± 3.6 | < 0.001 |
| RC(mmol/L), Mean ± SD | 0.8 ± 0.4 | 0.4 ± 0.1 | 0.7 ± 0.1 | 1.2 ± 0.2 | < 0.001 |
| SBP(mmHg), Mean ± SD | 115.8 ± 11.9 | 114.7 ± 11.8 | 115.3 ± 11.8 | 117.4 ± 12.0 | < 0.001 |
| DBP(mmHg), Mean ± SD | 70.2 ± 9.2 | 69.0 ± 9.0 | 69.8 ± 9.1 | 71.8 ± 9.4 | < 0.001 |
| IDH, n (%) | < 0.001 | ||||
| No | 6006 (97.6) | 2029 (98.5) | 2004 (98) | 1973 (96.3) | |
| Yes | 147 (2.4) | 31 (1.5) | 40 (2) | 76 (3.7) | |
| T2DM, n (%) | < 0.001 | ||||
| No | 6005 (97.6) | 2025 (98.3) | 2007 (98.2) | 1973 (96.3) | |
| Yes | 19 ( 0.3) | 5 (0.2) | 4 (0.2) | 10 (0.5) | |
| prediabetes | 129 ( 2.1) | 30 (1.5) | 33 (1.6) | 66 (3.2) | |
| CKD, n (%) | < 0.001 | ||||
| No | 5979 (97.2) | 2019 (98.1) | 1990 (97.4) | 1970 (96.1) | |
| Yes | 172 ( 2.8) | 40 (1.9) | 53 (2.6) | 79 (3.9) | |
| HUA, n (%) | < 0.001 | ||||
| No | 4509 (73.3) | 1559 (75.7) | 1548 (75.7) | 1402 (68.4) | |
| Yes | 1644 (26.7) | 501 (24.3) | 496 (24.3) | 647 (31.6) | |
| WBC(*10^9/L), Mean ± SD | 5.9 ± 1.5 | 5.7 ± 1.5 | 5.9 ± 1.5 | 6.1 ± 1.6 | < 0.001 |
| NEU(*10^9/L), Mean ± SD | 3.4 ± 1.2 | 3.2 ± 1.1 | 3.4 ± 1.1 | 3.5 ± 1.2 | < 0.001 |
| SCr(µmol/L), Mean ± SD | 66.5 ± 14.8 | 65.0 ± 14.7 | 65.9 ± 14.8 | 68.6 ± 14.6 | < 0.001 |
| eGFR(mL/min/1.73 m²), Mean ± SD | 115.4 ± 11.1 | 117.2 ± 10.8 | 115.4 ± 10.9 | 113.7 ± 11.4 | < 0.001 |
| SUA(µmol/L), Mean ± SD | 357.4 ± 93.8 | 354.0 ± 90.1 | 348.7 ± 93.2 | 369.6 ± 96.9 | < 0.001 |
| BUN(mmol/L), Mean ± SD | 4.8 ± 1.1 | 4.7 ± 1.1 | 4.8 ± 1.1 | 4.9 ± 1.1 | < 0.001 |
Data presented are mean ± SD or n (%); T1, T2, T3 are tertile of RC. AbbreviationsBMI body mass index, RC remnant cholesterol, SBP systolic blood pressure, DBP diastolic blood pressure, IDH isolated diastolic hypertension, T2DM type 2 diabetes mellitus, CKD chronic kidney disease, HUA hyperuricemia, WBC white blood cell, NEU neutrophils, eGFR estimated glomerular filtration rate, SUA serum uric acid, BUN blood urea nitrogen
Notable variations were observed across RC tertiles. The proportion of males increased with RC (56.4% in T1 vs. 65.2% in T3, P < 0.001), as did age and BMI. Both SBP and DBP rose progressively with RC (P < 0.001). IDH prevalence increased notably from 1.5% (T1) to 3.7% (T3) (P < 0.001). Elevated RC concentrations were also related to elevated WBC, NEU, SUA, SCr, and BUN, alongside reduced eGFR (all P < 0.001). Participants with higher RC more frequently had HUA, hyperglycemia, CKD, and T2DM (P < 0.001), indicating pronounced metabolic and inflammatory disturbances.
Linear regression analysis of RC in relation to blood pressure level at baseline
According to Table 2, higher RC levels were positively related to both SBP and DBP at baseline. In the unadjusted model, subjects classified in the highest RC group (> 0.88 mmol/L) had significantly higher SBP (β = 2.67, 95% CI: 1.95–3.40, P < 0.001) and DBP (β = 2.76, 95% CI: 2.20–3.32, P < 0.001) compared with those in the lowest group (≤ 0.6 mmol/L). After progressive adjustments for demographic characteristics, metabolic indicators, and inflammatory markers, these associations remained statistically significant, though attenuated. After adjusting for all covariates (Model 3), elevated RC (> 0.88 mmol/L) was still significantly associated with higher SBP (β = 1.15, 95% CI: 0.48–1.82, P = 0.001) as well as DBP (β = 1.41, 95% CI: 0.88–1.94, P < 0.001). By contrast, moderate RC levels (0.6–0.88 mmol/L) did not show significant associations with either SBP or DBP after full adjustment (P > 0.1). (Table 2).
Table 2.
Association of RC and blood pressure level at baseline
| Outcomes | Model 1 (Basic-adjusted) | Model 2 (Metabolic-adjusted) | Model 3 (Fully adjusted) | |||
|---|---|---|---|---|---|---|
| β (95% CI) | P-value | β (95% CI) | P-value | β (95% CI) | P-value | |
| SBP | ||||||
| Remnant Cholesterol | ||||||
| ≤0.6, mmol/L | 0(Ref) | 0(Ref) | 0(Ref) | |||
| 0.6–0.88, mmol/L | 0.32 (-0.34 ~ 0.98) | 0.347 | 0.35 (-0.31 ~ 1.01) | 0.294 | 0.47 (-0.2 ~ 1.13) | 0.167 |
| >0.88, mmol/L | 1 (0.33 ~ 1.67) | 0.004 | 1 (0.33 ~ 1.67) | 0.003 | 1.15 (0.48 ~ 1.82) | 0.001 |
| DBP | ||||||
| Remnant Cholesterol | ||||||
| ≤0.6, mmol/L | 0(Ref) | 0(Ref) | 0(Ref) | |||
| 0.6–0.88, mmol/L | 0.32 (-0.21 ~ 0.84) | 0.236 | 0.34 (-0.18 ~ 0.86) | 0.202 | 0.4 (-0.12 ~ 0.92) | 0.134 |
| >0.88, mmol/L | 1.38 (0.85 ~ 1.91) | < 0.001 | 1.34 (0.81 ~ 1.87) | < 0.001 | 1.41 (0.88 ~ 1.94) | < 0.001 |
Data presented are β and 95% CIs. Model 1: adjusted for age, sex, BMI. Model 2: adjusted for Model 1 + T2DM, CKD, HUA. Model 3: adjusted for Model 2 + WBC, NEU, SCr, eGFR, SUA, BUN. AbbreviationsBMI body mass index, T2DM type 2 diabetes mellitus, CKD chronic kidney disease, HUA hyperuricemia, WBC white blood cell, NEU neutrophils, eGFR estimated glomerular filtration rate, SUA serum uric acid, BUN blood urea nitrogen
Logistic regression analysis of RC in relation to IDH
According to Table 3, elevated RC concentrations were significantly linked to an increased risk of IDH in the logistic regression analysis. In the unadjusted model, subjects classified in the highest RC group (> 0.88 mmol/L) had a markedly higher risk of IDH than individuals in the lowest tertile (≤ 0.6 mmol/L) (OR = 2.52, 95% CI: 1.65–3.85, P < 0.001). After adjusting for age, sex, and BMI (Model 1), this association remained statistically significant, with an OR of 1.79 (95% CI: 1.17–2.76, P = 0.002).
Table 3.
Associations between RC and IDH in the multiple regression model
| Variable | RC (n = 6153) | RC | |||
|---|---|---|---|---|---|
| RC ≤ 0.6 (n = 2060) |
0.6 < RC ≤ 0.88 (n = 2044) |
RC > 0.88 (n = 2049) |
|||
| OR (95% CI) | P-value | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
| Unadjusted | 3.24 (2.13 ~ 4.94) | < 0.001 | 1(Ref) | 1.31 (0.81 ~ 2.1) | 2.52 (1.65 ~ 3.85) |
| Model 1 (Basic-adjusted) | 1.98 (1.3 ~ 3.03) | 0.002 | 1(Ref) | 1.18 (0.73 ~ 1.91) | 1.79 (1.17 ~ 2.76) |
| Model 2 (Metabolic-adjusted) | 1.93 (1.26 ~ 2.95) | 0.003 | 1(Ref) | 1.18 (0.73 ~ 1.9) | 1.74 (1.13 ~ 2.69) |
| Model 3 (Fully adjusted) | 1.94 (1.26 ~ 2.99) | 0.003 | 1(Ref) | 1.17 (0.72 ~ 1.89) | 1.73 (1.12 ~ 2.68) |
Data presented are ORs and 95% CIs. Model 1: adjusted for age, sex, BMI. Model 2: adjusted for Model 1 + T2DM, CKD, HUA. Model 3: adjusted for Model 2 + WBC, NEU, SCr, eGFR, SUA, BUN. AbbreviationsRC remnant cholesterol, IDH isolated diastolic hypertension, BMI body mass index, T2DM type 2 diabetes mellitus, CKD chronic kidney disease, HUA hyperuricemia, WBC white blood cell, NEU neutrophils, eGFR estimated glomerular filtration rate, SUA serum uric acid, BUN blood urea nitrogen
Further adjustment for metabolic comorbidities including T2DM, CKD, and HUA (Model 2), as well as inflammatory and renal biomarkers such as WBC, NEU, SCr, eGFR, SUA, and BUN (Model 3), only slightly attenuated the association. In the fully adjusted model, individuals with RC > 0.88 mmol/L continued to have a substantially elevated odds of IDH (OR = 1.73, 95% CI: 1.12–2.68, P = 0.003). In contrast, moderate RC levels (0.6–0.88 mmol/L) showed no significant relationship with IDH across all models (P > 0.05).
RCS analysis: associations between RC and IDH
We also applied RCS analysis to illustrate the shape of the relationship between RC and IDH (Fig. 2). The spline curves showed a gradually increasing trend, with mild curvature and a visually apparent upward bend around RC ≈ 0.70 mmol/L. However, the formal test for nonlinearity did not reach statistical significance (p > 0.05), indicating that the association between RC and IDH should be interpreted as overall linear. Consistent patterns were observed across all adjustment models (A–D).
Fig. 2.
RCS analysis between RC levels and ORs for IDH. This figure depicts the dose–response relationship between RC levels and the OR for IDH using logistic regression combined with RCS regression analysis. The red line represents the fitted OR curve, and the shaded region indicates the 95% CI. The histogram below each plot shows the distribution of RC in the study population. Panels represent different adjustment models: A unadjusted, B Model 1, C Model 2, and (D) Model 3
Subgroup analysis
As revealed by subgroup analysis (Fig. 3), The association of increased RC levels with IDH was robust in every subgroup analyzed, and interaction tests were not statistically significant (P > 0.05). After full adjustment for potential confounders, the overall OR for IDH associated with high RC was 1.94 (95% CI: 1.26–2.99), confirming the robustness of this relationship.
Fig. 3.
Subgroup analysis of the association between RC and IDH. This forest plot displays the ORs and 95% CIs for the relationship between RC and IDH across different subgroups. Both crude and adjusted models are shown. The adjusted model accounts for age, sex, BMI, T2DM, CKD, HUA, WBC, NEU, SCr, eGFR, SUA, and BUN. No significant interactions were observed across subgroups of sex, age, BMI, hyperuricemia (HUA), or diabetes status (P for interaction > 0.05), indicating the association was consistent across these populations
When stratified by sex, the association appeared slightly stronger in women (OR = 4.77, 95% CI: 1.62–14.0) than in men (OR = 2.55, 95% CI: 1.63–4.01), though this difference did not reach statistical significance (P = 0.172). Similarly, no significant interactions were detected across age, BMI, HUA, or diabetes subgroups. Participants with BMI < 24 kg/m² or without HUA exhibited comparable risk elevations to those with higher BMI or HUA. Collectively, these results indicate that the relationship between RC and IDH was stable and independent of sex, age, metabolic status, and body composition (Fig. 3).
The ROC analyses of RC and the comparative discriminative performance of traditional lipid parameters are presented in the Supplementary Materials.
Discussion
Major findings
This cross-sectional analysis investigated the relationship between RC and IDH among young adults aged under 45 years. We observed a significant positive relationship between rising RC levels and both SBP and DBP, even after adjusting for sex, age, BMI, and metabolic confounders. Participants in the highest RC tertile (> 0.88 mmol/L) had approximately 1.7 times the odds of IDH than participants in the lowest tertile (≤ 0.6 mmol/L), suggesting an independent association between RC and diastolic pressure elevation. RCS analysis findings further supported a linear association (p for nonlinearity > 0.05), with mild curvature around RC ≈ 0.7 mmol/L. Subgroup analyses confirmed the consistency of this association across sex, BMI, and metabolic profiles. Supplementary analyses support a complementary, rather than standalone, clinical role of RC in the context of IDH. Collectively, these results demonstrate a robust observational association between RC levels and the presence of IDH in young adults, supporting the potential utility of RC as a correlated metabolic indicator in this population.
Although the present study was designed as an association analysis rather than a prediction study, the observed effect sizes suggest that RC is meaningfully associated with IDH in young adults. From a clinical perspective, IDH is increasingly recognized as a non-benign phenotype in younger populations. Identification of elevated RC may therefore help highlight individuals with early metabolic disturbances who warrant closer cardiovascular risk monitoring [26]. Importantly, these findings should not be interpreted as supporting RC as a standalone screening or diagnostic marker for IDH, but rather as a complementary lipid-related marker that may inform early preventive strategies for real-world health screening settings, particularly in lifestyle modification.
Comparison with prior research
Our findings extend prior research about the association between RC and hypertension. Although many studies have verified the link between RC and hypertension, evidence on the correlation between RC and abnormal blood pressure in young people as well as IDH is still limited. Zheng et al. (2022) reported that elevated RC levels were independently linked to a higher prevalence of hypertension and CVD in a large Chinese cohort [13]. Likewise, Cui et al. (2025) and Molavizadeh et al. (2025) found that prolonged exposure to increased RC was associated with a higher likelihood of new-onset hypertension, independent of LDL-C and triglycerides [18, 19]. However, most previous research has concentrated on adults of middle or older age, whereas our study extends these findings to young adults under 45 years, emphasizing the early association of RC with vascular dysfunction. Moreover, we specifically explored IDH which is more prevalent among young adults [27, 28], and we demonstrated that RC is independently linked to IDH after adjusting for metabolic and inflammatory factors. Interestingly, the magnitude of the association in our cohort was somewhat weaker than that reported for general hypertension in older populations [14, 29], possibly reflecting the lower overall vascular burden and greater arterial compliance among younger individuals. Nonetheless, the consistency across studies supports RC as an early metabolic indicator associated with elevated blood pressure and vascular dysfunction.
Potential mechanisms linking RC and IDH
Several interrelated biological pathways have been proposed to potentially explain the observed association between RC and IDH. RC has been reported to exhibit potent atherogenic, inflammatory, and endothelial-toxic properties that extend beyond the effect of LDL-C alone [11, 30]. RC-rich triglyceride remnants can cross the endothelium more readily than LDL, are trapped in the subendothelial space, and are taken up by macrophages, associated with local inflammation and oxidative stress [31, 32]. This inflammatory milieu has been associated with downregulation of endothelial nitric oxide synthase and reduced NO bioavailability. These changes have been linked to increased peripheral arteriolar tone while the large arteries remain compliant, and may contribute to a hemodynamic change that raises diastolic rather than systolic pressure [28, 33]. At the same time, RC-related low-grade inflammation has been associated with smooth-muscle hypertrophy and rarefaction of small vessels, potentially contributing to increased total peripheral resistance [33]. Recent longitudinal work also indicates that prolonged exposure and fluctuations in RC concentrations are associated with an increased risk of hypertension and other CVD, suggesting a possible link whereby chronic vascular exposure to remnant particles is related to structural arteriolar changes [19, 20]. Recent findings suggest that VLDL particles may exert direct endocrine effects on the adrenal cortex. VLDL has been shown to activate signaling pathways similar to angiotensin II thereby stimulating aldosterone production [34]. Additionally, RC is tightly linked to insulin resistance, a metabolic state that enhances VLDL production and remnant particle accumulation [35, 36]. Insulin resistance itself is associated with increased sympathetic activation, impaired microvascular dilation, and sodium retention which are preferentially linked to elevations in DBP.
Strengths and limitations
A notable strength of the present study lies in its focus on a young, health-screened population, which provides valuable insight into early metabolic disturbances associated with overt hypertension. By excluding individuals with secondary or non-IDH, we ensured that the observed associations reflect true IDH related changes rather than conventional hypertension. Furthermore, we adjusted multiple covariates for modeling, incorporated RCS modeling to explore the dose–response relationship, and revealed that other risk factors did not significantly modify the association between RC and IDH by subgroup analyses. These methodological strengths further enhance the reliability of our outcomes.
Despite these strengths, certain limitations must be recognized. First, this study’s cross-sectional framework prevents causal analysis between RC levels and IDH. Although our findings suggest a robust association, prospective cohort studies are required to clarify temporality and causality. Second, RC was estimated indirectly as TC minus LDL-C minus HDL-C, a method widely used in epidemiologic research but less precise than direct measurement by advanced lipid profiling [11]. Third, RC was assessed based on a single measurement, so we were unable to account for the duration of exposure to elevated RC or its intra-individual variability over time, which recent studies have shown to be important predictors of incident hypertension [19, 20]. Fourth, despite extensive adjustment for potential confounders, the possibility of residual confounding from unmeasured factors remains, including dietary patterns, physical activity, alcohol intake, and genetic determinants of lipid metabolism [37]. Fifth, medication use (particularly antihypertensive and lipid-lowering therapy) was not systematically recorded in the health check-up database, which is a known structural limitation of screening-oriented cohorts [38, 39]. Consequently, medication-related confounding could not be directly assessed, and residual confounding cannot be fully ruled out. Sixth, as this study was based on a non-recruitment, health examination center population, some degree of health-related selection bias is possible, which may limit generalizability to inpatient populations. Seventh, because exclusions were largely driven by predefined study design and eligibility criteria, and although participants excluded due to missing key data were further evaluated, potential selection bias cannot be completely excluded. Finally, despite the relatively large overall sample size, the limited number of IDH cases led to outcome imbalance that may reduce the precision of regression estimates and warrants confirmation in future studies.
Conclusions
This study suggests that elevated RC concentrations are linked to a higher prevalence of IDH among young adults aged 18–44 years receiving comprehensive health examinations at Chinese PLA General Hospital.
Supplementary Information
Acknowledgements
Not applicable.
Abbreviations
- RC
Remnant cholesterol
- IDH
Isolated diastolic hypertension
- CVD
Cardiovascular disease
- SBP
Systolic blood pressure
- DBP
Diastolic blood pressure
- BMI
Body mass index
- T2DM
Type 2 diabetes mellitus
- CKD
Chronic kidney disease
- HUA
Hyperuricemia
- WBC
White blood cell
- NEU
Neutrophils
- eGFR
Estimated glomerular filtration rate
- SUA
Serum uric acid
- BUN
Blood urea nitrogen
- SD
Standard deviation
- ORs
Odds ratios
- CIs
Confidence intervals
- RCS
Restricted cubic spline
- ROC
Receiver operating characteristic
- NRI
Net reclassification improvement
- IDI
Integrated discrimination improvement
Authors’ contributions
Ri Liu and Jianhao Su contributed equally to this work and share first authorship.Chen Qiu contributed to study supervision, statistical guidance, and critical manuscript revision.
Funding
This research received no external funding.
Data availability
The datasets supporting the findings of this study were provided by the Chinese PLA General Hospital. Due to institutional security regulations, access to these data is restricted. The data are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This research was approved by the Ethics Committee of Chinese PLA General Hospital (Approval No.: 2025K717O-XS001). The use of de-identified retrospective data exempted the study from the requirement for informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Ri Liu and Jianhao Su are co-first authors.
References
- 1.Milanović M et al. Whey proteins and metabolic Dysfunction-Associated steatotic liver disease features: evolving the current knowledge and future trends. Metabolites, 2025. 15(8). [DOI] [PMC free article] [PubMed]
- 2.Ma S, et al. Trends in hypertension prevalence, awareness, treatment and control rates among Chinese adults, 1991–2015. J Hypertens. 2021;39(4):740–8. [DOI] [PubMed] [Google Scholar]
- 3.He R, et al. Self-management in young and middle-aged patients with hypertension: a systematic review and meta-synthesis of qualitative studies. Syst Rev. 2024;13(1):254. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Gao Y, et al. Prevalence of hypertension in china: a cross-sectional study. PLoS ONE. 2013;8(6):e65938. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Lv X, et al. Awareness, treatment and control of hypertension among hypertensive patients aged 18 to 59 years old in the Northeast of China. Sci Rep. 2018;8(1):17019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Franklin SS, et al. Predictors of new-onset diastolic and systolic hypertension: the Framingham heart study. Circulation. 2005;111(9):1121–7. [DOI] [PubMed] [Google Scholar]
- 7.Hisamatsu T, et al. Isolated systolic hypertension and 29-year cardiovascular mortality risk in Japanese adults aged 30–49 years. J Hypertens. 2020;38(11):2230–6. [DOI] [PubMed] [Google Scholar]
- 8.Guo DC, et al. Remnant cholesterol and risk of incident hypertension: a population-based prospective cohort study. Hypertens Res. 2024;47(5):1157–66. [DOI] [PubMed] [Google Scholar]
- 9.Shi L, et al. Remnant cholesterol associates with hypertension beyond low-density lipoprotein cholesterol among the general US adult population. Front Endocrinol (Lausanne). 2023;14:1260764. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Stürzebecher PE, Katzmann JL, Laufs U. What is ‘remnant cholesterol’? Eur Heart J. 2023;44(16):1446–8. [DOI] [PubMed] [Google Scholar]
- 11.Castañer O, et al. Remnant Cholesterol, not LDL Cholesterol, is associated with incident cardiovascular disease. J Am Coll Cardiol. 2020;76(23):2712–24. [DOI] [PubMed] [Google Scholar]
- 12.Chen MM, et al. High remnant cholesterol level potentiates the development of hypertension. Front Endocrinol (Lausanne). 2022;13:830347. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Zheng X, Han L, Shen S. Hypertension, remnant cholesterol and cardiovascular disease: evidence from the China health and retirement longitudinal study. J Hypertens. 2022;40(11):2292–8. [DOI] [PubMed] [Google Scholar]
- 14.Yang R, et al. Remnant cholesterol and intensive blood pressure control in older patients with hypertension: a post hoc analysis of the STEP randomized trial†. Eur J Prev Cardiol. 2024;31(8):997–1004. [DOI] [PubMed] [Google Scholar]
- 15.Zuo C, Shen ZD, Lu Y. Systemic immune-inflammation index and uric acid mediate the association between obesity and hypertension in young and middleaged adults. Sci Rep. 2025;15(1):35704. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Zhou W, et al. Associations of body mass index and remnant cholesterol with hyperuricemia in patients with hypertension. BMC Endocr Disord. 2025;25(1):73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Wang J, et al. The association of remnant cholesterol (RC) and interaction between RC and diabetes on the subsequent risk of hypertension. Front Endocrinol (Lausanne). 2022;13:951635. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Cui L, et al. Association of remnant cholesterol with new-onset hypertension among middle-aged and older adults in China. Sci Rep. 2025;15(1):25296. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Molavizadeh D et al. Long-term cumulative exposure and variability of remnant cholesterol and risk of incident hypertension: A prospective cohort study (2002–2018). J Clin Lipidol, 2025. [DOI] [PubMed]
- 20.Wu W, et al. Cumulative exposure to high remnant-cholesterol concentrations increases the risk of cardiovascular disease in patients with hypertension: a prospective cohort study. Cardiovasc Diabetol. 2023;22(1):258. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Liu LS. [2010 Chinese guidelines for the management of hypertension]. Zhonghua Xin Xue Guan Bing Za Zhi. 2011;39(7):579–615. [PubMed] [Google Scholar]
- 22.2. Classification and diagnosis of diabetes: standards of medical care in Diabetes-2018. Diabetes Care, 2018. 41(Suppl 1): pp. S13–27. [DOI] [PubMed]
- 23.Stevens PE, Levin A. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013;158(11):825–30. [DOI] [PubMed] [Google Scholar]
- 24.Zhu Y, Pandya BJ, Choi HK. Prevalence of gout and hyperuricemia in the US general population: the National health and nutrition examination survey 2007–2008. Arthritis Rheum. 2011;63(10):3136–41. [DOI] [PubMed] [Google Scholar]
- 25.Harrell FE. Multivariable Modeling Strategies, in Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis, J.F.E. Harrell, Editor. 2015, Springer International Publishing: Cham. pp. 63–102.
- 26.Lee H, et al. Cardiovascular risk of isolated systolic or diastolic hypertension in young adults. Circulation. 2020;141(22):1778–86. [DOI] [PubMed] [Google Scholar]
- 27.Zhang X, et al. Prevalence, awareness, and treatment of isolated diastolic hypertension in the united States. Eur J Intern Med. 2023;112:93–9. [DOI] [PubMed] [Google Scholar]
- 28.Yano Y, Lloyd-Jones DM. Isolated systolic hypertension in young and Middle-Aged adults. Curr Hypertens Rep. 2016;18(11):78. [DOI] [PubMed] [Google Scholar]
- 29.Wu Y, et al. Association of remnant cholesterol with hypertension, type 2 diabetes, and their coexistence: the mediating role of inflammation-related indicators. Lipids Health Dis. 2023;22(1):158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Pirillo A, Norata GD, Catapano AL. Beyond LDL-C levels, does remnant cholesterol Estimation matter? Eur J Prev Cardiol. 2020;27(10):1088–90. [DOI] [PubMed] [Google Scholar]
- 31.Nordestgaard BG. Triglyceride-Rich lipoproteins and atherosclerotic cardiovascular disease: new insights from Epidemiology, Genetics, and biology. Circ Res. 2016;118(4):547–63. [DOI] [PubMed] [Google Scholar]
- 32.Libby P. The changing landscape of atherosclerosis. Nature. 2021;592(7855):524–33. [DOI] [PubMed] [Google Scholar]
- 33.Dinh QN, et al. Roles of inflammation, oxidative stress, and vascular dysfunction in hypertension. Biomed Res Int. 2014;2014:406960. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Tsai YY, Rainey WE, Bollag WB. Very low-density lipoprotein (VLDL)-induced signals mediating aldosterone production. J Endocrinol. 2017;232(2):R115–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Inoue T, et al. Possible relationship between insulin resistance and remnant-like lipoprotein particles in coronary endothelial dysfunction. Clin Cardiol. 2002;25(11):532–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Ohnishi H, et al. Relationship between insulin-resistance and remnant-like particle cholesterol. Atherosclerosis. 2002;164(1):167–70. [DOI] [PubMed] [Google Scholar]
- 37.General and abdominal adiposity and hypertension in eight world regions: a pooled analysis of 837 population-based studies with 7·5 million participants. Lancet, 2024. 404(10455): pp. 851–863. [DOI] [PMC free article] [PubMed]
- 38.Li Y, et al. Association of Helicobacter pylori infection with lipid metabolism and 10-year cardiovascular risk in diabetes mellitus: A cross-sectional study. PLoS ONE. 2025;20(4):e0319688. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Yokoyama S, et al. Risk factors for decreased bone mineral density in patients with metabolic dysfunction-associated steatotic liver disease: A cross-sectional study at a health examination center. Clin Nutr. 2024;43(6):1425–32. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets supporting the findings of this study were provided by the Chinese PLA General Hospital. Due to institutional security regulations, access to these data is restricted. The data are available from the corresponding author upon reasonable request.