Abstract
Background
The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) represents a novel composite lipid marker for atherosclerosis and cardiovascular disease (CVD). Nevertheless, the correlation between NHHR and mortality in the non-diabetic population remains indistinct.
Methods
This study included 20,774 non-diabetic individuals from the 1999–2018 National Health and Nutrition Examination Survey (NHANES). We employed a weighted multivariate Cox proportional hazards model and restricted cubic splines to assess the associations between NHHR, its combination with obesity indicators, and all-cause and CVD mortality.
Results
During a mean follow-up period of 62 months, a total of 897 participant deaths were recorded, of which 155 were attributed to cardiovascular causes. The restricted cubic splines revealed a U-shaped association between NHHR and all-cause mortality, while an L-shaped association was observed for CVD mortality. The analysis of threshold efects revealed that the infection points for NHHR and all-cause and CVD mortality were 2.65 and 2.07, respectively. The cubic spline revealed a nonlinear correlation was observed between NHHR-BMI, NHHR-WC and NHHR-WHtR and all-cause and CVD mortality.
Conclusion
NHHR and its combination with obesity indicators can be a meaningful predictor of all-cause mortality and CVD mortality in non-diabetic individuals.
Keywords: NHHR, Non-diabetic, Mortality, Cardiovascular disease, NHANES
Introduction
Diabetes mellitus, a chronic noncommunicable ailment, exhibits multifaceted metabolic irregularities [1]. Among these, lipid metabolism dysfunction constitutes one of the significant elements. Recent investigations reveal that in diabetic patients, dyslipidemia is marked by elevated levels of non-high-density lipoprotein cholesterol (non-HDL-C), such as low density lipoprotein cholesterol (LDL-C), intermediate density lipoprotein, along with very low-density lipoprotein remnants [2]. As prior studies have pointed out, these augmented levels play a crucial role in hastening the progression of atherosclerosis [3, 4]. The emergence of atherosclerotic plaques gives rise to luminal constriction, affecting the cardiac blood supply. When the coronary artery stenosis attains a certain extent, myocardial ischemia may ensue, possibly provoking angina pectoris. Should the plaque rupture and trigger thrombus formation, completely blocking the coronary artery, it could precipitate cardiovascular incidents like myocardial infarction [5]. Consequently, handling these intricate lipid metabolic disorders and obesity is a key facet of comprehensive diabetes prevention, substantially diminishing the risks of cardiovascular morbidity and mortality [6, 7].
Substantial research efforts have been directed towards probing the connection between traditional lipid parameters – encompassing high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), LDL-C, and triglycerides (TG) – and diabetes [8, 9]. A prevalent agreement among metabolic specialists underlines that heightened TG levels and diminished HDL-C concentrations can markedly augment the diabetes risk. Nevertheless, the direct association between LDL-C, TC, and diabetes remains a topic under continuous deliberation [10, 11, 12]. Recently, non-conventional lipid parameters, like lipid ratios and non-HDL-C, have attracted significant notice and acclaim [13, 14, 15]. Across diverse systems, namely the endocrine, cardiovascular, digestive, respiratory, and urinary ones, these non-conventional lipid parameters have served as novel biomarkers in numerous epidemiological studies [16, 17, 18]. In 2021, the National Institute for Health and Care Excellence put forward a suggestion advocating the employment of non-HDL-C as the principal biomarker for curtailing the cardiovascular disease (CVD) risk among diabetic patients. This indicates that non-HDL-C occupies a critical position in evaluating CVD risk and treatment effectiveness [19].
Remarkably, a newly emerged composite lipid indicator, the ratio of non-HDL-C to HDL-C (NHHR), has demonstrated promising predictive potential in gauging the risks of assorted diseases, including coronary artery disease, diabetes, and carotid atherosclerosis [20, 21]. In contrast to non-HDL-C by itself, NHHR proves to be a more excellent and comprehensive metric since it combines both the risk factor (non-HDL-C) and the protective factor (HDL-C) within the framework of atherosclerosis [22, 23]. However, the prognostic implications of NHHR in the non-diabetic populace remain indistinct. While previous studies have explored NHHR in diabetic cohorts [20, 21], limited evidence exists for its prognostic value in non-diabetic individuals. For instance, Sheng et al. [20] demonstrated NHHR’s association with diabetes risk but did not address mortality outcomes in non-diabetic groups. Investigating NHHR in this population is critical, as dyslipidemia remains a modifiable risk factor for atherosclerosis and CVD even in the absence of diabetes [24, 25]. Obesity is prevalent worldwide and is closely associated with various health risks [26, 27], which can lead to the onset progression and prognosis of cardiovascular disease [28, 29, 30]. Combining NHHR with anthropometric indices (e.g., BMI, waist circumference) may better reflect the interplay between lipid metabolism and adiposity. To bridge this knowledge gap, we initiated a study leveraging the National Health and Nutrition Examination Survey (NHANES) database. The objective of this research is to investigate the correlation between NHHR and all-cause as well as CVD mortality in non-diabetic individuals, in addition to its relationship with diverse demographic traits. Our outcomes are poised to offer valuable perspectives for disease management and prevention blueprints.
Methods
Study population and design
NHANES is a research program designed to assess the nutrition and health status of United States adults and children. The program began in the early 60s of the 20th century as a survey of different populations or health topics. The survey used a stratified sampling approach that combined interviews, physical examinations, and laboratory tests [31]. Details about the surveys and the corresponding death index are available at www.cdc.gov/nchs/nhanes and www.cdc.gov/nchs/ndi/, respectively. The study drew on data from the NHANES database from 1999 to 2018.
In our analysis, we included people aged ≥ 20 years old. The exclusion criteria for this study are as follows: (1) Diabetic population: glycohemoglobin ≥ 6.5% or fasting blood glucose ≥ 7.0 mmol/L [32]. At the same time, people who use insulin or hypoglycemic drugs are also considered diabetic. (2) Participants with missing data. The study included a total of 20,774 participants. The survival status of participants was followed up to December 31, 2019. The studies involving human participants were reviewed and approved by the National Center for Health Statistics Research Ethics Review Board (protocol number: #2021-05). The participants provided written informed consent to participate in this study. The study protocol complied with the Declaration of Helsinki.
Defnitions of NHHR, NHHR-BMI, NHHR-WC, and NHHR-WHtR
Lipid parameters are a key component of CVD assessment in the NHANES database and are essential for assessing CVD risk. These parameters include TC, HDL-C, LDL-C, and TG. The relevant tests were carried out at Mobile Inspection Centres (MECs). After collection, serum samples were processed and stored. The samples were then sent to the University of Minnesota for comprehensive analysis. In our study, we used NHHR to assess participants lipid levels and CVD risk. NHHR was calculated based on serum TC and HDL-C levels in the NHANES database from 1999 to 2018. Non-HDL-C was converted to TC minus HDL-C, and NHHR was calculated as the ratio of non-HDL-C to HDL-C [33]. Body weight, height, and waist were obtained when people participated in the physical examinations at MECs. Furthermore, the body mass index along with the waist-to-height ratio were calculated. The participants were classified into four groups (Q1, Q2, Q3, Q4) by the quartiles of the NHHR index, NHHR-WC, NHHR-WHtR, and NHHR-BMI, respectively, and the Q1 group was used as the reference group.
NHHR-WC, NHHR-WHtR, and NHHR-BMI were calculated according to the following formulas: (1) WHtR = waist circumference/height; (2) NHHR-WC = NHHR×waist circumference; NHHR-WHtR = NHHR×WHtR; NHHR-BMI = NHHR×BMI.
Ascertainment of mortality
As of December 31, 2019, the mortality rate data came from the NHANES public related mortality documents. The documents used a probabilistic matching algorithm to integrate data from survey participants with death certificate records from the National Death Index. The specific cause of death is determined according to the International Classification of Diseases, 10th Edition (ICD-10), and CVD mortality refers to death due to heart disease and cerebrovascular disease. All-cause mortality refers to the total number of deaths from all specific causes.
Data collection
The selection of covariates in this study considered a range of demographic characteristics and health-related information, including age, gender, race, education, marriage, poverty-to-income ratio (PIR), BMI, waist circumference, drinking, smoking, hypertension, congestive heart failure, atherosclerotic cardiovascular diseases (ASCVD), dietary supplements, energy, caffeine, glycohemoglobin, uric acid, creatinine, and sleep time. History of ASCVD, hypertension or congestive heart failure was defined as self-reported physician diagnosis. “Has a doctor or other health professional ever told {you/SP} that {you/s/he} had a coronary heart disease/angina, also called angina pectoris/heart attack (also called myocardial infarction)/stroke?” was a question on the medical conditions section of the household questionnaires via home interview, and those who answered “yes” were deemed to have a history of ASCVD. The detailed acquisition process and measuring method of each variable are available at www.cdc.gov/nchs/nhanes.
Statistical analyses
All analyses were performed using R statistical software (Project R, version 4.4.1). Participants’ baseline characteristics were described according to the quartiles of NHHR (Q1-Q4). Continuous variables were reported as mean ± standard deviation (SD) or median (interquartile range). Categorical variables were expressed as percentages. Differences in baseline characteristics were compared using analysis of the variance ANOVA for continuous variables and a weighted chi-square test for categorical variables.
Firstly, we established three models to control for confounders and used a weighted multivariate Cox proportional hazards model to estimate the relationship between NHHR and all-cause mortality and CVD mortality. Model 1 did not include any covariate adjustments. Model 2 was adjusted for age, gender, race, education, marriage and PIR. Model 3 further adjusted BMI, waist circumference, drinking, smoking, hypertension, congestive heart failure, ASCVD, dietary supplements, energy, caffeine, glycohemoglobin, uric acid, creatinine, and sleep time.
To investigate the dose-response relationship between NHHR and its associated indices and all-cause mortality and CVD mortality, restricted triple spline analyses were performed at the 5, 50, and 95th percentiles of the distributions of NHHR, NHHR-WC, NHHR-WHtR, and NHHR-BMI. If the association was nonlinear, the possible threshold inflection points were estimated, and a two-segmented Cox proportional hazards model on both sides of the inflection points was constructed. The solid line represented the risk ratio, and the shaded area represented the 95% confidence interval. Subsequently, we performed subgroup analyses by age (< 65 years or ≥ 65 years), gender (male or female), drinking (yes or never), smoking (non-smoker, current smoker, ex-smoker), ASCVD (yes or no), hypertension (yes or no), dietary supplements use (yes or no), and sleep time (less than 6 h, 6–8 h, more than 8 h).
The Cox proportional hazards regression model was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) between categorical NHHR-BMI, NHHR-WC, and NHHR-WHtR and all-cause mortality and cardiovascular mortality. P-values for trends were tested by treating the quartile level as an ordinal variable. Statistical significance was defined as a two-tailed P-value < 0.05. All analyses were conducted using R statistical software (version 4.4.1).
Results
Characteristics of the study participants
In this study, 20,774 eligible subjects, with a mean age of 46.77 years and a male proportion of 51.8%, were included and stratified by the level of NHHR. The mean NHHR of the participants was calculated to be 2.94 ± 1.45. The general characteristics were detailed in Table 1 according to NHHR quartiles. From Quartile 1 to Quartile 4, participants’ male proportion, smoker proportion, levels of waist circumference, BMI, and caffeine are on the rise, while PIR and non-married proportion gradually decrease. Decreases in NHHR is linked to those with high school or higher education, which indicates NHHR may be subject to education. Besides, in the higher NHHR quartiles, there is an increase in the number of participants with higher level of uric acid, creatinine and glycohemoglobin, which suggests potential correlation between metabolic index and an elevated NHHR. An elevated NHHR is associated with hypertension and ASCVD, but not congestive heart failure.
Table 1.
Baseline characteristics according to NHHR quartiles
| NHHR index | All participants | Quartile 1 (< 1.92) |
Quartile 2 (1.92–2.66) | Quartile 3 (2.66–3.65) | Quartile 4 (> 3.65) | P |
|---|---|---|---|---|---|---|
| N | 20,774 | 5165 | 5201 | 5194 | 5214 | |
| Age (years) | 46.77 ± 17.25 | 45.72 ± 18.93 | 47.10 ± 17.99 | 47.54 ± 16.66 | 46.73 ± 15.18 | < 0.001 |
| Gender (n, %) | < 0.001 | |||||
| Male | 10,759 (51.8) | 1854 (35.9) | 2360 (45.4) | 2946 (56.7) | 3599 (69.0) | |
| Female | 10,015 (48.2) | 3311 (64.1) | 2841 (54.6) | 2248 (43.3) | 1615 (31.0) | |
| Race (n, %) | < 0.001 | |||||
| Mexican American | 3034 (14.6) | 519 (10.0) | 679 (13.1) | 841 (16.2) | 995 (19.1) | |
| Other Hispanic | 1804 (8.7) | 334 (6.5) | 446 (8.6) | 491 (9.5) | 531 (10.2) | |
| Non-Hispanic White | 10,069 (48.5) | 2514 (48.7) | 2500 (48.1) | 2509 (48.3) | 2546 (48.8) | |
| Non-Hispanic Black | 3967 (19.1) | 1305 (25.3) | 1078 (20.7) | 912 (17.6) | 672 (12.9) | |
| Other Race - Including Multi-Racial | 1902 (9.2) | 493 (9.5) | 498 (9.6) | 441 (8.5) | 470 (9.0) | |
| Education (n, %) | < 0.001 | |||||
| Less than high school | 4189 (20.2) | 810 (15.7) | 942 (18.1) | 1111 (21.4) | 1326 (25.4) | |
| High school | 4837 (23.3) | 1076 (20.8) | 1168 (22.5) | 1269 (24.4) | 1324 (25.4) | |
| More than high school | 11,748 (56.6) | 3279 (63.5) | 3091 (59.4) | 2814 (54.2) | 2564 (49.2) | |
| Marriage (n, %) | < 0.001 | |||||
| Married/living with partner | 12,650 (60.9) | 2817 (54.5) | 3060 (58.8) | 3292 (63.4) | 3481 (66.8) | |
| Widowed/divorced/separated | 4139 (19.9) | 1051 (20.3) | 1053 (20.2) | 1032 (19.9) | 1003 (19.2) | |
| Never married | 3985 (19.2) | 1297 (25.1) | 1088 (20.9) | 870 (16.8) | 730 (14.0) | |
| PIR | 2.67 ± 1.65 | 2.79 ± 1.67 | 2.72 ± 1.65 | 2.67 ± 1.64 | 2.48 ± 1.61 | < 0.001 |
| BMI (kg/m²) | 28.68 ± 6.56 | 25.77 ± 5.86 | 28.30 ± 6.66 | 29.87 ± 6.65 | 30.78 ± 5.92 | < 0.001 |
| Waist circumference (cm) | 98.17 ± 15.85 | 89.74 ± 14.41 | 96.71 ± 15.59 | 101.45 ± 15.22 | 104.73 ± 13.98 | < 0.001 |
| Drinking (n, %) | < 0.001 | |||||
| Never | 3879 (18.7) | 737 (14.3) | 962 (18.5) | 1019 (19.6) | 1161 (22.3) | |
| 1 to 4 times a month | 11,184 (53.8) | 2585 (50.0) | 2830 (54.4) | 2865 (55.2) | 2904 (55.7) | |
| 5 to 8 times a month | 2069 (10.0) | 606 (11.7) | 485 (9.3) | 503 (9.7) | 475 (9.1) | |
| 9 to 16 times a month | 1792 (8.6) | 596 (11.5) | 456 (8.8) | 391 (7.5) | 349 (6.7) | |
| More than 16 times a month | 1850 (8.9) | 641 (12.4) | 468 (9.0) | 416 (8.0) | 325 (6.2) | |
| Smoking (n, %) | < 0.001 | |||||
| Non-smoker | 10,601 (51.0) | 2853 (55.2) | 2757 (53.0) | 2638 (50.8) | 2353 (45.1) | |
| Current smoker | 4905 (23.6) | 1090 (21.1) | 1118 (21.5) | 1150 (22.1) | 1547 (29.7) | |
| Ex-smoker | 5268 (25.4) | 1222 (23.7) | 1326 (25.5) | 1406 (27.1) | 1314 (25.2) | |
| Hypertension (n, %) | 6133 (29.5) | 1319 (25.5) | 1538 (29.6) | 1660 (32.0) | 1616 (31.0) | < 0.001 |
| Congestive heart failure (n, %) | 405 (1.9) | 115 (2.2) | 106 (2.0) | 85 (1.6) | 99 (1.9) | 0.170 |
| ASCVD (n, %) | 1475 (7.1) | 420 (8.1) | 377 (7.2) | 347 (6.7) | 331 (6.3) | 0.002 |
| Dietary supplements use (n, %) | 10,413 (50.1) | 2889(55.9) | 2758 (53.0) | 2533 (48.8) | 2233 (42.8) | < 0.001 |
| Energy (kcal) | 2213.56 ± 1030.18 | 2159.44 ± 1014.71 | 2152.89 ± 1008.32 | 2222.25 ± 1019.08 | 2319.03 ± 1068.91 | < 0.001 |
| Caffeine (mg) | 166.52 ± 220.95 | 148.02 ± 195.60 | 157.79 ± 207.33 | 170.09 ± 220.70 | 190.00 ± 253.48 | < 0.001 |
| Glycohemoglobin (%) | 5.47 ± 0.59 | 5.33 ± 0.43 | 5.42 ± 0.48 | 5.49 ± 0.51 | 5.63 ± 0.81 | < 0.001 |
| Uric acid (mg/dL) | 5.44 ± 1.41 | 4.91 ± 1.32 | 5.23 ± 1.33 | 5.61 ± 1.37 | 6.02 ± 1.39 | < 0.001 |
| Creatinine (mg/dL) | 0.89 ± 0.32 | 0.86 ± 0.35 | 0.87 ± 0.24 | 0.90 ± 0.31 | 0.93 ± 0.37 | < 0.001 |
| Sleep time (n, %) | < 0.001 | |||||
| Less than 6 h | 2754 (13.3) | 627 (12.1) | 660 (12.7) | 713 (13.7) | 754 (14.5) | |
| 6–8 h | 15,386 (74.1) | 3791 (73.4) | 3870 (74.4) | 3851 (74.1) | 3874 (74.3) | |
| More than 8 h | 2634 (12.7) | 747 (14.5) | 671 (12.9) | 630 (12.1) | 586 (11.2) |
PIR, Poverty income ratio; BMI, Body mass index; ASCVD, Atherosclerotic cardiovascular diseases
Associations between NHHR, NHHR-BMI, NHHR-WC and NHHR-WHtR and mortality
During a mean follow-up period of 62 months, a total of 897 participant deaths were recorded, of which 155 were attributed to cardiovascular causes. The relative risk of all-cause and CVD mortality in relation to NHHRs is shown in Table 2, using the weighted multivariate Cox proportional hazards regression model. Using the NHHR (Q1) as a reference, the HRs and 95% CIs for all-cause mortality of the remaining three groups, namely NHHR (Q2-Q4), were 0.81 (0.67, 0.97), 0.79 (0.65, 0.96) and 0.90 (0.74, 1.11), respectively, after adjustment for potential confounders (model 3). For CVD mortality, the HRs and 95% CIs were 0.56 (0.34, 0.93), 0.90 (0.56, 1.43) and 1.04 (0.65, 1.67), respectively. According to the trend analysis, no significant linear relationships were observed between NHHR and all-cause mortality (P for trend = 0.367) or CVD mortality (P for trend = 0.411).
Table 2.
HRs (95% CIs) for mortality according to NHHR quartiles
| NHHR | P for trend | ||||
|---|---|---|---|---|---|
| Quartile 1 (< 1.92) | Quartile 2 (1.92–2.66) |
Quartile 3 (2.66–3.65) | Quartile 4 (> 3.65) |
||
| All-cause mortality | |||||
| Number | 234 | 222 | 204 | 237 | |
|
Model 1 HR (95% CI) |
1 | 0.90 (0.75,1.08) | 0.81 (0.67,0.98) * | 0.91 (0.76,1.09) | 0.210 |
|
Model 2 HR (95% CI) |
1 | 0.79 (0.66,0.96) * | 0.75 (0.62,0.91) * | 0.89 (0.74,1.08) | 0.221 |
|
Model 3 HR (95% CI) |
1 | 0.81 (0.67,0.97) * | 0.79 (0.65,0.96) * | 0.90 (0.74,1.11) | 0.367 |
| CVD mortality | |||||
| Number | 40 | 27 | 39 | 49 | |
|
Model 1 HR (95% CI) |
1 | 0.64 (0.39, 1.04) | 0.91 (0.59,1.41) | 1.11 (0.73,1.68) | 0.340 |
|
Model 2 HR (95% CI) |
1 | 0.56 (0.34,0.91) * | 0.86 (0.55,1.34) | 1.17 (0.76,1.80) | 0.188 |
|
Model 3 HR (95% CI) |
1 | 0.56 (0.34,0.93) * | 0.90 (0.56,1.43) | 1.04 (0.65,1.67) | 0.411 |
Model 1 was unadjusted
Model 2 was adjusted for age, gender, race, education, marriage, and PIR
Model 3 was further adjusted for BMI, waist circumference, drinking, smoking, hypertension, congestive heart failure, ASCVD, dietary supplements, energy, caffeine, glycohemoglobin, uric acid, creatinine, and sleep time, based on model 2
HR, Hazard ratio; CI, Confdence interval; BMI, Body mass index; PIR, Poverty income ratio; ASCVD, Atherosclerotic cardiovascular diseases
* Compared with Quartile 1, P < 0.05
After adjustment for potential confounders, a stepwise incremental association was observed between increasing NHHR-BMI, NHHR-WC and NHHR-WHtR and risk of all-cause mortality (P for trend < 0.05). Specifically, the adjusted hazard ratios for the NHHR-BMI index quartile 4 vs. quartile 1 were 0.76 (95% CI 0.62–0.94) for all-cause mortality. For the NHHR-WC index, the corresponding hazard ratios were 0.75 (0.62–0.93). For the NHHR-WHtR index, the corresponding hazard ratios were 0.82 (0.67-1.00). (Fig. 1).
Fig. 1.
Cox proportional hazards regression analyses for the association of NHHR-related indices with all-cause and cause-specific mortality. All-cause and CVD mortality for quartiles of A NHHR-BMI index, B NHHR-WC index, and C NHHR-WHtR index. Adjusted for age, gender, race, education, marriage, PIR, drinking, smoking, hypertension, congestive heart failure, ASCVD, dietary supplements, energy, caffeine, glycohemoglobin, uric acid, creatinine, and sleep time
Detection of nonlinear relationships
In Fig. 2, we used restricted cubic spline to flexibly Cox proportional hazards models and visualize the relationship between NHHR and mortality in non-diabetic adults for examining potential nonlinear trends further. Notably, we discovered a significant U-shaped association between NHHR and all-cause mortality (Fig. 2A) as well as a significant L-shaped association between NHHR and CVD mortality (Fig. 2B), with P-nonlinear = 0.009 and 0.026, respectively. Utilizing the “segmented” package, we identified that the infection points for NHHR in relation risks of all-cause and CVD mortality were 2.65 and 2.07, respectively. Furthermore, we employed a segmented Cox proportional hazards model and Table 3 depicts the cumulative hazard of all-cause mortality and CVD mortality in the groups with different NHHR index. In particular, below the infection points, NHHR showed an inverse association with all-cause mortality. For each unit increase in NHHR, the risk of all-cause mortality decreased by 21% (HR: 0.79, 95% CI: 0.65–0.97, P = 0.021). Above the infection points, NHHR showed a positive association with CVD mortality. For each unit increase in the NHHR, the risk of CVD mortality increased by 16% (HR: 1.16, 95% CI: 1.02–1.31, P = 0.023).
Fig. 2.
Restricted cubic spline were utilized to evaluate the hypothesis of potential nonlinear relationships between NHHR and all-cause (A) and CVD (B) mortality in non-diabetic population. Solid lines represent hazard ratios, and shaded areas represent 95% confidence intervals. Adjusted for age, gender, race, education, marriage, PIR, BMI, waist circumference, drinking, smoking, hypertension, congestive heart failure, ASCVD, dietary supplements, energy, caffeine, glycohemoglobin, uric acid, creatinine, and sleep time
Table 3.
Threshold efect analysis of NHHR on all-cause and CVD mortality in non-diabetic population
| Adjusted HR (95%CI), P value | |
|---|---|
| All-cause mortality | |
| Total | 0.99 (0.94–1.04) 0.676 |
| Segmented cox proportional hazards mode | |
| Infection point | 2.65 |
| NHHR < 2.65 | 0.79 (0.65–0.97) 0.021 |
| NHHR ≥ 2.65 | 1.04 (0.96–1.11) 0.366 |
| CVD mortality | |
| Total | 1.07 (0.95–1.21) 0.282 |
| Segmented cox proportional hazards mode | |
| Infection point | 2.07 |
| NHHR < 2.07 | 0.62 (0.26–1.44) 0.265 |
| NHHR ≥ 2.07 | 1.16 (1.02–1.31) 0.023 |
The model was adjusted for age, gender, race, education, marriage, PIR, BMI, waist circumference, drinking, smoking, hypertension, congestive heart failure, ASCVD, dietary supplements, energy, caffeine, glycohemoglobin, uric acid, creatinine, and sleep time
CVD, Cardiovascular disease; HR, Hazard ratio; CI, Confdence interval; PIR, Poverty income ratio; BMI, Body mass index; ASCVD, Atherosclerotic cardiovascular diseases
What’s more, We employed restricted cubic spline to visualize the associations between NHHR-BMI, NHHR-WC and NHHR-WHtR, and mortality (Fig. 3). after adjusting for all covariates in the master analytical model 3 above, a nonlinear correlation was observed between NHHR-BMI, NHHR-WC and NHHR-WHtR and all-cause and CVD mortality (P-overall < 0.0001 and P-nonlinear < 0.05).
Fig. 3.
Restricted cubic spline curve for the association of NHHR-related indices with all-cause mortality and CVD mortality. (A) All-cause mortality, (B) CVD mortality. Solid lines represent hazard ratios, and shaded areas represent 95% confidence intervals. Adjusted for age, gender, race, education, marriage, PIR, BMI, waist circumference, drinking, smoking, hypertension, congestive heart failure, ASCVD, dietary supplements, energy, caffeine, glycohemoglobin, uric acid, creatinine, and sleep time
Subgroup analyses
To further elucidate the relationship between NHHR and the risks of all-cause and CVD mortality, we conducted a series of subgroup analyses. In the subgroup analysis (Fig. 4), stratified by age, gender, drinking, smoking, hypertension, ASCVD, dietary supplements, and sleep time. Across all subgroups, the relationship between NHHR and all cause and CVD mortality coincided. Clearly, there was no significant interaction observed between the baseline NHHR and the stratifed variables.
Fig. 4.
Subgroup analysis of the associations between NHHR and all-cause and CVD mortality. The reference NHHR for all-cause mortality were NHHR < 2.65, while for cardiovascular mortality, the reference was NHHR ≥ 2.07. Adjusted for age, gender, race, education, marriage, PIR, BMI, waist circumference, drinking, smoking, hypertension, congestive heart failure, ASCVD, dietary supplements, energy, caffeine, glycohemoglobin, uric acid, creatinine, and sleep time
Discussion
Our research disclosed the associations among NHHR, NHHR-WHtR, NHHR-WC, and NHHR-BMI with all-cause mortality and CVD mortality in non-diabetic individuals. All of NHHR, NHHR-WHtR, NHHR-WC, and NHHR-BMI manifested independent dose-response correlations regarding all-cause mortality and CVD mortality. This investigation spotlighted NHHR as one of the clinically significant markers for forecasting all-cause mortality and CVD mortality in those without diabetes.
NHHR represents a novel composite lipid index, specifically the ratio of non-HDL-C to HDL-C cholesterol. A meta-analysis involving 90,056 participants from 14 randomized trials indicated that a reduction in LDL-C levels was directly correlated with a decline in cardiovascular incidents like coronary events and ischemic stroke [34]. Previous studies had illustrated a U-shaped connection between HDL-C and atherosclerosis. In males, HDL-C ≥ 80 mg/dL and in females, HDL-C ≥ 100 mg/dL were associated with elevated all-cause mortality and ASCVD mortality [35, 36, 37]. Hence, NHHR is a more advantageous composite indicator as it takes into account both the risk elements (non-HDL-C) and the protective ones (HDL-C) relevant to atherosclerosis. Jiuling Liu et al. [38] examined NHHR levels in 2251 patients with coronary artery disease (CAD) who underwent percutaneous coronary intervention (PCI). By employing multivariate logistic regression analysis and restricted cubic splines, they evaluated the relationship between NHHR and major adverse cardiovascular and cerebrovascular events (MACCEs), ultimately uncovering a U-shaped relationship between NHHR and MACCEs. Besides cardiovascular diseases, NHHR is also utilized to predict other ailments. For instance, diabetes mellitus ranks among the most prevalent chronic conditions, and patients with this disease frequently exhibit lipid abnormalities along with significant disruptions in blood glucose metabolism [39]. Guotai Sheng et al. [20] conducted a multivariate Cox regression analysis on the NAGALA longitudinal cohort dataset and demonstrated an independent positive correlation between NHHR and diabetes. They posited that NHHR could be a relatively reliable indicator for assessing diabetes risk. Zhimeng Jiang [40] adopted a multivariate logistic regression model, subgroup analysis, and smooth-fitting curves to explore the correlation between NHHR and hyperuricemia (HUA) in 7,876 adult participants from NHANES data. They verified a significant positive correlation between NHHR and HUA and proposed that NHHR might serve as a potential risk evaluation marker for HUA. These examples underscore the escalating value of NHHR in appraising cardiovascular and metabolic diseases, indirectly bolstering our conclusions.
NHHR furnishes crucial details about lipid metabolism and can be harnessed to predict the susceptibility to certain diseases [24, 41]. Although the biological mechanisms linking NHHR to mortality remain obscure, some studies hint at a potential connection between NHHR and diabetes mellitus as well as atherosclerosis [42, 43]. Research has demonstrated that lipid irregularities are closely intertwined with the development of diabetes mellitus [44]. Diabetic dyslipidemia is typified by elevated levels of non-HDL-C, encompassing LDL-C and other constituents, while HDL-C is low [45, 46]. Elevated non-HDL-C levels substantially contribute to the progression of atherosclerosis, whereas HDL-C possesses anti-inflammatory, antioxidant, and antiatherosclerotic properties [25, 47]. Abundant evidence-based studies have shown that dyslipidemia can augment the risk of myocardial infarction and ischemic stroke [48, 49, 50]. Excessive deposition of lipids and lipoproteins in the arterial intima leads to structural impairment and functional degradation of endothelial cells, triggering the recruitment and migration of monocytes across the endothelial barrier. The migrating monocytes differentiate into macrophages that scavenge accumulated lipids and lipoproteins but transform into foamy cells when overloaded. Subsequently, they exacerbate the formation and development of atherosclerotic plaques and instigate an inflammatory response via the release of pro-inflammatory factors [51, 52, 53]. Concurrently, some studies have indicated that dyslipidemia can induce endothelial cell senescence and damage the endothelial barrier, thereby promoting lipid deposition and culminating in endothelial cell dysfunction and atherosclerosis. The underlying mechanism involves not only oxidative stress but also inflammation and autophagy through classical signaling pathways [54, 55, 56]. When atherosclerotic plaques are eroded or ruptured, thrombotic episodes can occur, potentially culminating in acute cardiovascular events that may prove fatal [57, 58, 59].
Consequently, dyslipidemia constitutes a pivotal link in the development of atherosclerosis and the poor prognosis of non-diabetic patients [60]. NHHR integrates all lipid-related information pertinent to atherosclerotic and anti-atherosclerotic processes, proffering a more comprehensive portrayal of its balance. It is an indicator that more aptly reflects the health status of the body’s blood lipids. Our study ascertained that in the higher quartiles of NHHR, there was an increment in the number of participants with elevated levels of uric acid, creatinine, and glycohemoglobin. This suggested a probable correlation between metabolic indices and elevated NHHR values, a finding congruent with those of other researchers [43]. Simultaneously, an elevated NHHR was associated with hypertension and ASCVD, intimating that NHHR might be a risk factor for certain cardiovascular diseases and was correlated with unfavorable outcomes in individuals. Our study revealed that NHHR exhibited a U-shaped relationship with all-cause mortality and an L-shaped relationship with CVD mortality. The inflection points for these associations were NHHR values of 2.65 and 2.07, respectively. Beneath the inflection points, NHHR was inversely related to all-cause mortality. For each unit increase in NHHR, the risk of all-cause mortality diminished by 21%. Above the inflection points, NHHR was positively correlated with CVD mortality. For each unit increase in the NHHR, the risk of CVD mortality augmented by 16%. Therefore, maintaining NHHR within the approximate range of 2.07 to 2.65 for non-diabetic patients markedly curtails the risk of adverse outcomes. This conclusion was partly corroborated by several studies. Several investigations [61, 62, 63] have demonstrated a U-shaped relationship between non-HDL-C and all-cause mortality and CVD mortality in individuals not treated with statin medications, peritoneal dialysis patients, and the general adult population. A prospective cohort study illustrated a U-shaped relationship between HDL-C levels and all-cause mortality [64]. These findings advocate that HDL and non-HDL-C levels should be maintained within reasonable confines.
Furthermore, a stepwise incremental association was detected between escalating NHHR-BMI, NHHR-WC, and NHHR-WHtR and the risk of all-cause death following the adjustment for potential confounding factors. Subsequently, we utilized restricted cubic splines to visualize the relationships between NHHR-BMI, NHHR-WC, and NHHR-WHtR and mortality. After adjusting for covariates, there was a nonlinear correlation between NHHR-BMI, NHHR-WC, and NHHR-WHtR and all-cance mortality and CVD mortality. This affirmed the predictive capacity of NHHR-derived indicators for all-cause mortality and CVD mortality in non-diabetic populations. Hence, these NHHR-based indicators merit incorporation into routine health screening regimens and extensive application in clinical practice. Finally, we further executed subgroup analyses, stratified by age, sex, alcohol consumption, smoking, hypertension, ASCVD, dietary supplements, and sleep duration. The results of the subgroup analysis indicated that the relationship between NHHR and all-cause mortality and CVD mortality was consistent across all subgroups, thus the relationship between NHHR and all-cause mortality and CVD mortality remained relatively stable.
Nevertheless, this study has certain limitations. Firstly, as an observational study, we were incapable of establishing a causal connection between NHHR and mortality. Secondly, the study’s reliance on complex calculations raises concerns about its practical application in clinical settings, where simplicity is often prioritized. Automated electronic health record systems could facilitate real-time computation, minimizing clinician burden. Future studies should validate user-friendly tools for integrating these indices into clinical workflows. Thirdly, lipoprotein particle measurements offer a more direct assessment of atherogenic risk but were not available in NHANES. Their integration with NHHR could refine risk prediction and future work could explore synergies between NHHR and advanced lipid profiling [65, 66, 67]. Forth, the study population was predominantly composed of the general population in the United States, so the results of the study might not be applicable to other ethnic groups. Finally, the proportion of patients with cardiovascular outcomes in the study population was small, which might limit the statistical power to detect differences between groups.
Conclusion
The findings of this study suggest that NHHR and its combination with obesity indicators can be a meaningful predictor of all-cause mortality and CVD mortality in non-diabetic individuals. Routine monitoring of NHHR-related indices may be helpful in assessing the risk and prognosis of death in non-diabetic populations.
Acknowledgements
Not applicable.
Abbreviations
- NHHR
Non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio
- CVD
Cardiovascular disease
- NHANES
National Health and Nutrition Examination Survey
- non-HDL-C
Non-high-density lipoprotein cholesterol
- LDL-C
Low density lipoprotein cholesterol
- HDL-C
High-density lipoprotein cholesterol
- TC
Total cholesterol
- TG
Triglycerides
- MECs
Mobile Inspection Centres
- PIR
Poverty-to-income ratio
- ASCVD
Atherosclerotic cardiovascular diseases, SD: Standard deviation
- HRs
Hazard ratios
- CIs
Confidence intervals
- CAD
Coronary artery disease
- PCI
Percutaneous coronary intervention
- MACCEs
Major adverse cardiovascular and cerebrovascular events
- HUA
Hyperuricemia
Author contributions
Conceptualization: J.L., J.W., and P.L. Methodology: H.L., P.L., and Z.H. Data curation and validation: G.L. and Y.Z. Writing: B.L. and J.Z. All authors reviewed the manuscript.
Funding
This work was supported by the State Key Laboratory of Traditional Chinese Medicine Syndrome Open Project (SKLKY2024B0002), Chinese Medicine Guangdong Laboratory (HQL2024PZ028), Development Center for Medical Science & Technology National Health Commission of the People’s Republic of China (WKZX2022JG0118), National Key Laboratory of Traditional Chinese Medicine Syndrome Research Project (090056b51007) and the Project of Administration of Traditional Chinese Medicine of Guangdong Province of China (20241321).
Data availability
Details about the surveys and the corresponding death index are available at www.cdc.gov/nchs/nhanes and www.cdc.gov/nchs/ndi/, respectively.
Declarations
Ethics approval and consent to participate
The studies involving human participants were reviewed and approved by the National Center for Health Statistics Research Ethics Review Board (protocol number: #2021-05). The participants provided written informed consent to participate in this study. The study protocol complied with the Declaration of Helsinki.
Consent for publication
Not applicable.
Conflict of interest
The authors declare no conflict of interest in the preparation of this manuscript.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Details about the surveys and the corresponding death index are available at www.cdc.gov/nchs/nhanes and www.cdc.gov/nchs/ndi/, respectively.