Abstract
Background
Telomere length is closely associated with the occurrence and development of cardiovascular and other diseases. Monocyte to high-density lipoprotein cholesterol ratio (MHR) is a novel indicator of inflammation, oxidative stress, and metabolic syndrome, with some predictive ability for related disease risks in clinical practice. However, there is no research on the correlation between these two factors.
Methods
Using data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2002, we conducted analysis and research on the correlation between MHR and telomere length using the Kruskal-Wallis H test, Spearman rank correlation analysis, and partial correlation analysis. Weighted linear regression analysis assessed the strength of the association between the two variables, while restricted cubic spline regression (RCS) explored potential nonlinear relationships between them.
Results
The results of correlation analysis showed that MHR levels were negatively correlated with telomere length (ρ=-0.083, P < 0.001), and this relationship remained statistically significant after controlling for other covariates (P all < 0.001). Weighted linear regression analysis showed that after adjusting for all covariates, MHR remained negatively associated with telomere length (β = -0.020; 95% CI: -0.039 to -0.002; P = 0.037). Subgroup analysis shows that the negative association between MHR and telomere length appeared more striking among females (𝛽 = -0.024; 95%CI: -0.050 to 0.001; P = 0.058), the Non-Hispanic White (𝛽 = -0.022; 95%CI: -0.045 to 0.002; P = 0.066), and other race (𝛽 = -0.067; 95%CI: -0.134 to -0.000; P = 0.049). Using RCS explored potential nonlinear relationships between MHR and telomere length, revealing no nonlinear relationship between the two (P = 0.102).
Conclusions
This study suggests a negative correlation between MHR levels and telomere length in American adults. More comprehensive research is needed to confirm these findings in the future.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12872-024-04301-3.
Keywords: Telomere length, Monocyte to high-density lipoprotein cholesterol ratio, Cardiovascular disease, NHANES
Introduction
Telomeres are highly repetitive sequences of TTAGGG nucleotides located at the ends of DNA double helices, playing a crucial role in protecting the ends of chromosomes during cell division [1]. Due to the inability of DNA polymerase to fully replicate the 3’ end of linear DNA, telomere repeat sequences are gradually lost during somatic cell replication, a process ultimately leading to cell senescence or apoptosis. Therefore, telomere length naturally shortens with increasing age, serving as a hallmark of cellular aging [2, 3]. Research indicates that chronic inflammatory states, oxidative stress, unhealthy lifestyles, and nutritional status contribute to telomere attrition through oxidative DNA damage in the telomeric region and dysfunction of telomerase [4]. Telomere length is closely associated with the development of cardiovascular diseases, Alzheimer’s disease, metabolic disorders, infections, tumors, and other conditions, holding significant implications for human health [2, 5–8].
Monocytes are a major component of the body’s immune system and can promote the production of inflammatory cytokines and pro-oxidants, leading to the formation of atherosclerosis [9]. High-density lipoprotein cholesterol (HDL-C), on the other hand, possesses antioxidant and anti-inflammatory properties by reducing the release of pro-inflammatory cytokines from monocytes and macrophages [10]. HDL-C also inhibits the expression of adhesion molecules activated by cytokines and mediates the efflux of cholesterol from peripheral tissues, thereby slowing the progression of inflammation [11]. The monocyte to high-density lipoprotein cholesterol ratio (MHR) is a novel indicator of inflammation, oxidative stress, and metabolic syndrome, reflecting the balance between monocyte inflammation and oxidative stress and HDL-C. MHR’s predictive ability for clinical outcomes may even surpass that of independent monocyte counts and HDL-C concentrations, serving as a prognostic marker for cardiovascular diseases and diabetic nephropathy [12–14].
Previous studies on the value of MHR have primarily focused on cardiovascular diseases, with limited research on its correlation with cellular aging. Therefore, this study will utilize data from the National Health and Nutrition Examination Survey (NHANES) to conduct statistical research, supplementing current knowledge on the relationship between MHR levels and telomere length to enhance our understanding of the interplay between cardiovascular health and cellular aging.
Methods
Study Population
This cross-sectional study utilized data from the NHANES database (https://www.cdc.gov/nchs/nhanes). NHANES is a nationwide survey conducted by the National Center for Health Statistics (NCHS) in the United States, employing a complex, multi-stage sampling design to collect and reflect data from the non-institutionalized civilian population. Our analysis utilized data from two cycles of NHANES conducted between 1999 and 2002, as only these cycles contained information on telomere length. A total of 21,004 individuals participated in the survey during 1999–2002. We excluded individuals without telomere length data (n = 13,179) and those with missing monocytes, or high-density lipoprotein (n = 53), resulting in a final sample of 7,772 participants included in the analysis, as illustrated in Fig. 1.
Fig. 1.
Screening conditions and process for the study population
Measurement of telomere length
Detailed information on telomere length measurement has been reported on the official NHANES website [15]. Peripheral blood samples are collected from participants in the NHANES survey. The measurement process starts with the extraction and purification of deoxyribonucleic acid (DNA). Dr. Elizabeth Blackburn’s laboratory at the University of California, San Francisco, uses quantitative polymerase chain reaction (qPCR) to measure telomere length. Telomere length is measured relative to a standard reference DNA (the T/S ratio) derived from the human diploid fibroblast cell line IMR90. Each sample is tested in duplicate wells kept confidential from the researchers. Each assay plate includes 8 control DNA samples for normalization of inter-run variability. If four or more control DNA values differ from the mean of all assay runs by 2.5 standard deviations, they are flagged as potential outliers and excluded from further analysis (< 6% of runs). Telomere length is ultimately expressed as the T/S ratio.
Measurement of blood lipids and cell count
Professionals in the MEC collected blood specimens from study participants, which were then processed, stored, and shipped to the University of Minnesota in Minneapolis, MN for analysis. The HDL-C concentration was determined using either direct precipitation or immunoassay methods [16]. The Beckman Coulter DXH 800 instrument was utilized to measure blood cell counts, including monocyte number, neutrophils number, lymphocyte number, and Platelet count. MHR was calculated based on the monocyte count to HDL-C ratio, as suggested by previous studies [17].
Demographic, medical information, and other covariant
Demographic information included age, gender (male and female), and race/ethnicity (Non-Hispanic Black, Non-Hispanic White, Mexican American, and Other). Household poverty was calculated as the ratio of monthly family income to poverty levels defined by Department of Health and Human Services guidelines and categorized as low income (≤ 1.30), middle income (1.31–3.50), and high income (> 3.50) [18]. Smoking history was defined as having smoked 100 or more cigarettes in a lifetime. Alcohol use was assessed by asking about consuming 12 or more alcoholic drinks in a year. Histories of medical conditions were verified by asking participants if they had been diagnosed with diabetes, hypertension, coronary heart disease, or stroke by a doctor. The body mass index (BMI) was calculated as weight (kg) divided by height (m) squared (kg/m2). The DxC800 chemistry module uses the Jaffe rate method to measure creatinine concentration in serum. The systemic immune inflammation index (SII) serves as an indicator of the equilibrium between systemic inflammation and immune status, calculated as platelet count multiplied by neutrophil count divided by lymphocyte count, and was also included as a covariate in this study [19].
Statistical analyses
According to the NHANES database analysis guidelines and recommended weighting, statistical analysis was conducted. The Kolmogorov-Smirnov test was used to assess the normality of the data, revealing non-normal distributions. For non-normally distributed continuous data, median and interquartile ranges were reported, and group comparisons were performed using the Kruskal-Wallis H test. Categorical data were presented as proportions, and group comparisons were conducted using the Kruskal-Wallis H test for ordinal data and the chi-square test for count data.
In the analysis, continuous variable telomere length was categorized based on quartiles and subjected to statistical description. Spearman rank correlation analysis and partial correlation analysis were employed to assess the correlation between MHR and telomere length. As the distribution of telomere length was mainly below 1 (with percentiles 5–95% ranging from 0.6618 to 1.4943), telomere length was multiplied by 10 for transformation. Subsequently, both original MHR data and quartile-grouped data were used as independent variables, with telomere length as the dependent variable, for weighted linear regression analysis. β coefficients were calculated to estimate the strength of the association between the two variables and presented in a forest plot. Subgroup analysis was conducted by stratifying gender, age, race/ethnicity, and household poverty ratio, followed by weighted linear regression and interaction analysis. Finally, restricted cubic spline regression (with 3 knots) was employed to explore potential nonlinear relationships between MHR and telomere length, with a likelihood ratio test used for nonlinear assessment.
All statistical analyses were performed using SPSS 21.0 and R 4.0.0 software. Two-tailed statistical tests were conducted, and statistical significance was set at P < 0.05.
Results
Baseline characteristics
Table 1 presents the baseline characteristics of the study population. A total of 7,772 adults aged ≥ 20 years were included, with 3,758 males, accounting for 48.35% of the population. Telomere length was categorized into four groups based on quartiles, and differences between each group were compared. The results showed that the median age of the study population was 47 years, with telomere length declining with increasing age (P < 0.001). There was a negative correlation between MHR and telomere length, with MHR levels decreasing as telomere length increased (P < 0.001). In the telomere length quartile groups, the MHR level was highest in the Q1 group and lowest in the Q4 group. Although both the Q2 and Q3 groups had the same median MHR level of 11.11, the MHR range in Q3 (7.69–14.29) was narrower than that in Q2 (8.11–15.15), indicating that the overall MHR level in Q3 was lower than in Q2. We also grouped MHR levels by quartile. The results showed that MHR was predominantly in the Q4 group for telomere length Q1 group (28.46%), while it was predominantly in the Q1 group for telomere length Q4 group (27.84%), indicating a negative correlation between MHR and telomere length. Additionally, telomere length was significantly associated with several covariates, including gender, and race, and these associations were statistically significant (all P < 0.05).
Table 1.
Baseline characteristics of the study population
| Variables | Total (n = 7772) | Q1 (n = 1943) | Q2 (n = 1943) | Q3 (n = 1943) | Q4 (n = 1943) | H/χ2 | P |
|---|---|---|---|---|---|---|---|
| MHR, mean(Q₁, Q₃) | 11.11 (8.11, 14.89) | 11.76 (8.70,15.69) | 11.11 (8.11,15.15) | 11.11 (8.00,14.78) | 10.71 (7.69,14.29) | 50.920 | < 0.001 |
| MHRquartiles, n(%) | 40.629 | < 0.001 | |||||
| Q1 | 1947 (25.05) | 413 (21.26) | 492 (25.32) | 501 (25.78) | 541 (27.84) | ||
| Q2 | 1975 (25.41) | 475 (24.45) | 487 (25.06) | 499 (25.68) | 514 (26.45) | ||
| Q3 | 1920 (24.70) | 502 (25.84) | 464 (23.88) | 478 (24.60) | 476 (24.50) | ||
| Q4 | 1930 (24.83) | 553 (28.46) | 500 (25.73) | 465 (23.93) | 412 (21.20) | ||
| Age, years, mean (Q₁, Q₃) | 47.00 (33.00, 65.00) | 64.00 (49.00,75.00) | 51.00 (37.00,66.00) | 43.00 (31.00,58.00) | 37.00 (26.00,49.00) | 1405.649 | < 0.001 |
| Gender, n (%) | 47.156 | < 0.001 | |||||
| Male | 3758 (48.35) | 1050 (54.04) | 969 (49.87) | 870 (44.78) | 869 (44.72) | ||
| Female | 4014 (51.65) | 893 (45.96) | 974 (50.13) | 1073 (55.22) | 1074 (55.28) | ||
| Race/Ethnicity, n (%) | 108.402 | < 0.001 | |||||
| Mexican American | 1862 (23.96) | 482 (24.81) | 487 (25.06) | 507 (26.09) | 386 (19.87) | ||
| Other Hispanic | 412 (5.30) | 85 (4.37) | 93 (4.79) | 103 (5.30) | 131 (6.74) | ||
| Non-Hispanic White | 3942 (50.72) | 1075 (55.33) | 997 (51.31) | 954 (49.10) | 916 (47.14) | ||
| Non-Hispanic Black | 1322 (17.01) | 257 (13.23) | 305 (15.70) | 315 (16.21) | 445 (22.90) | ||
| Other race/multiracial | 234 (3.01) | 44 (2.26) | 61 (3.14) | 64 (3.29) | 65 (3.35) | ||
| Poverty ratio, n (%) | 13.151 | 0.004 | |||||
| Low income ≤ 1.3 | 1980 (27.97) | 530 (29.98) | 458 (25.88) | 479 (27.00) | 513 (29.05) | ||
| Middle income (1.31–3.5) | 2727 (38.53) | 705 (39.88) | 708 (40.00) | 665 (37.49) | 649 (36.75) | ||
| High income (>3.5) | 2371 (33.50) | 533 (30.15) | 604 (34.12) | 630 (35.51) | 604 (34.20) | ||
| Education, n (%) | 99.302 | < 0.001 | |||||
| Less Than 9th Grade | 1237 (15.94) | 437 (22.54) | 325 (16.76) | 270 (13.91) | 205 (10.56) | ||
| 9-11th Grade | 1383 (17.82) | 369 (19.03) | 333 (17.17) | 352 (18.13) | 329 (16.95) | ||
| High School Grad/GED or Equivalent | 1805 (23.26) | 437 (22.54) | 436 (22.49) | 435 (22.41) | 497 (25.61) | ||
| Some College or AA degree | 1906 (24.56) | 411 (21.20) | 468 (24.14) | 485 (24.99) | 542 (27.92) | ||
| College Graduate or above | 1429 (18.41) | 285 (14.70) | 377 (19.44) | 399 (20.56) | 368 (18.96) | ||
| BMI, kg/m2, mean (Q₁, Q₃) | 27.40 (24.12, 31.40) | 27.73 (24.60,31.39) | 27.51 (24.24,31.74) | 27.40 (24.18,31.17) | 26.76 (23.51,31.10) | 22.026 | < 0.001 |
| Total cholesterol, mg/dl, mean (Q₁, Q₃) | 201.00 (176.00, 229.00) | 205.00 (181.00,233.00) | 203.00 (178.00,231.00) | 200.00 (175.00,229.00) | 195.00 (169.00,223.50) | 64.572 | < 0.001 |
| SII, mean (Q₁, Q₃) | 527.65 (376.51, 741.03) | 515.76 (373.79,722.37) | 530.78 (380.00,742.48) | 535.27 (380.37,746.23) | 528.57 (370.27,747.12) | 2.344 | 0.504 |
| Creatinine, umol/L, mean (Q₁, Q₃) | 70.70 (53.04, 79.60) | 70.72 (61.88,88.40) | 70.72 (53.04,88.40) | 70.70 (53.04,79.60) | 70.70 (53.04,79.56) | 82.665 | < 0.001 |
| Hypertension, n (%) | 161.463 | < 0.001 | |||||
| Yes | 2312 (30.04) | 768 (39.83) | 604 (31.39) | 525 (27.29) | 415 (21.61) | ||
| No | 5384 (69.96) | 1160 (60.17) | 1320 (68.61) | 1399 (72.71) | 1505 (78.39) | ||
| Diabetes, n (%) | 93.984 | < 0.001 | |||||
| Yes | 722 (9.29) | 263 (13.54) | 196 (10.09) | 146 (7.52) | 117 (6.03) | ||
| No | 6936 (89.29) | 1637 (84.29) | 1715 (88.27) | 1774 (91.35) | 1810 (93.25) | ||
| Borderline | 110 (1.42) | 42 (2.16) | 32 (1.65) | 22 (1.13) | 14 (0.72) | ||
| Coronary heart disease, n (%) | 53.755 | < 0.001 | |||||
| Yes | 327 (4.23) | 129 (6.72) | 92 (4.76) | 61 (3.15) | 45 (2.32) | ||
| No | 7399 (95.77) | 1790 (93.28) | 1840 (95.24) | 1875 (96.85) | 1894 (97.68) | ||
| Stroke, n (%) | 50.223 | < 0.001 | |||||
| Yes | 235 (3.03) | 100 (5.15) | 63 (3.25) | 43 (2.21) | 29 (1.49) | ||
| No | 7529 (96.97) | 1840 (94.85) | 1875 (96.75) | 1900 (97.79) | 1914 (98.51) | ||
| Smoke, n (%) | 29.138 | < 0.001 | |||||
| Yes | 3774 (48.65) | 1023 (52.68) | 971 (50.08) | 919 (47.37) | 861 (44.45) | ||
| No | 3984 (51.35) | 919 (47.32) | 968 (49.92) | 1021 (52.63) | 1076 (55.55) | ||
| Alcohol, n (%) | 6.638 | 0.084 | |||||
| Yes | 4980 (67.97) | 1213 (65.71) | 1265 (67.94) | 1260 (68.97) | 1242 (69.31) | ||
| No | 2347 (32.03) | 633 (34.29) | 597 (32.06) | 567 (31.03) | 550 (30.69) |
Notes non-normally distributed continuous variables: median (interquartile range); categorical variables: percentages
Abbreviations: MHR: monocyte to high-density lipoprotein cholesterol ratio; SII: systemic immune inflammation index; BMI: body max index
Correlation analysis between MHR and telomere length
Spearman rank correlation analysis and partial correlation analysis were performed to assess the correlation between MHR and telomere length. As shown in Table 2, the Spearman rank correlation coefficient for the entire population was − 0.083 (P < 0.001), indicating a significant negative correlation. To ensure that the observed negative correlation between MHR and telomere length was not influenced by other covariates, partial correlation analysis was conducted while controlling for the remaining covariates. In all cases, the obtained P values were less than 0.001.
Table 2.
Correlation analysis between MHR level and telomere length
| Group | ρ | P value | |
|---|---|---|---|
| Spearman rank correlation analysis | The entire population | -0.083 | < 0.001 |
| Partial correlation analysis | Control gender | -0.055 | < 0.001 |
| Control age | -0.066 | < 0.001 | |
| Control race/ethnicity | -0.070 | < 0.001 | |
| Control household poverty ratio | -0.068 | < 0.001 | |
| Control BMI | -0.062 | < 0.001 | |
| Control education | -0.064 | < 0.001 |
Linear regression analysis of MHR and telomere length
Weighted linear regression analysis was conducted to explore the strength of the association between MHR and telomere length. Three models were provided: Model 1 = unadjusted variables, Model 2 = adjusted for gender, age, race, family poverty ratio, and education level, and Model 3 = additional adjustments for BMI, total cholesterol, SII, creatinine, hypertension, diabetes, coronary heart disease, stroke, alcohol, and smoke. As shown in Table 3; Fig. 2, when MHR was treated as the original continuous variable, the beta coefficients of MHR in models 1 to 3 were − 0.028 (95% CI: -0.046 to -0.009; P = 0.005), -0.028 (95% CI: -0.043 to -0.012; P = 0.002), and − 0.020 (95% CI: -0.039 to -0.002; P = 0.037), respectively, indicating that the negative association between MHR and telomere length was not influenced by other covariates. When MHR was categorized into quartiles and included in linear regression, MHR was divided into 4 groups, with the Q1 group serving as the reference group. From model 1 to model 3, relative to the Q1 group, the β coefficients for the Q4 group were all negative − 0.433 (95% CI: -0.721 to -0.145), -0.478(95% CI: -0.742 to -0.214), -0.359 (95% CI: -0.690 to -0.027), respectively (P < 0.05 for all), indicating that other covariates did not influence the negative association between MHR and telomere length.
Table 3.
Linear regression analyses for the associations between MHR level and telomere length
| Model 1 | Model 2 | Model 3 | ||||||
|---|---|---|---|---|---|---|---|---|
| β (95% CI) | P | β (95% CI) | P | β (95% CI) | P | |||
| MHR | -0.028(-0.046, -0.009) | 0.005 | -0.028(-0.043, -0.012) | 0.002 | -0.020(-0.039, -0.002) | 0.037 | ||
| MHR(Q1) | Reference | / | Reference | / | Reference | / | ||
| MHR(Q2) | -0.146(-0.430,0.138) | 0.301 | -0.228(-0.485,0.029) | 0.079 | -0.178(-0.462,0.106) | 0.186 | ||
| MHR(Q3) | -0.163(-0.462,0.136) | 0.274 | -0.253(-0.494, -0.012) | 0.041 | -0.197(-0.450,0.055) | 0.109 | ||
| MHR(Q4) | -0.433(-0.721, -0.145) | 0.005 | -0.478(-0.742, -0.214) | 0.001 | -0.359(-0.690, -0.027) | 0.037 | ||
| P-trend | 0.006 | 0.001 | 0.028 | |||||
Model 1: unadjusted; Model 2: adjusted for gender, age, race/ethnicity, household poverty ratio, and education; Model 3: additional adjustments for BMI, total cholesterol, SII, creatinine, hypertension, diabetes, coronary heart disease, stroke, alcohol, and smoke. 95%CI, Confidence Interval
Fig. 2.
Forest plot of the β coefficients for the associations between MHR level and telomere length. Model 1: unadjusted; Model 2: adjusted for gender, age, race/ethnicity, household poverty ratio, and education; Model 3: additional adjustments for BMI, total cholesterol, SII, creatinine, hypertension, diabetes, coronary heart disease, stroke, alcohol, and smoke. 95%CI, Confidence Interval
Subgroup analysis
To further explore the negative association between MHR and telomere length in different populations, interaction analysis was performed based on gender, age (grouped according to the median age of 47 years for the whole population), race/ethnicity, and household poverty ratio. The results showed no significant interaction across subgroups (P for interaction > 0.05). In the gender subgroup, the association was marginally significant in females (β = -0.024; 95% CI: -0.050 to 0.001; P = 0.058) compared to males (β = -0.016; 95% CI: -0.038 to 0.006; P = 0.134). In the race subgroup analysis, Non-Hispanic White (β = -0.022; 95% CI: -0.045 to 0.002; P = 0.066) and the other races (i.e., those outside the categories of “Mexican American,” “Other Hispanic,” “Non-Hispanic White,” and “Non-Hispanic Black”) (β = -0.067; 95% CI: -0.134 to -0.000; P = 0.049) showed a marginally significant negative association. In the age and poverty ratio subgroups, although there was a trend toward a negative association between MHR and telomere length, it did not reach statistical significance (Table S1 and Figure S1, supplementary data).
Exploration of the nonlinear relationship between MHR and telomere length using RCS
RCS was employed to explore the potential nonlinear relationship between MHR and telomere length. The results showed that there was no direct nonlinear relationship between the two (P = 0.102) (Fig. 3).
Fig. 3.
Restricted cubic spline regression explores the potential nonlinear relationship between MHR level and telomere length. The β coefficient (solid lines) and 95% confidence levels (shaded areas) were adjusted for gender, age, race/ethnicity, household poverty ratio, education, BMI, total cholesterol, SII, creatinine, hypertension, diabetes, coronary heart disease, stroke, alcohol, and smoke
Discussion
In this study, we investigated the relationship between MHR and telomere length in a large cohort of American adults. We found a significant negative association between these two variables, indicating that as MHR increases, telomere length decreases. Even after adjusting for various potential confounders, this association remained evident, suggesting that the association between MHR and telomere length was not influenced by these factors. This relationship appeared more striking among females, non-Hispanic whites, and other races. To our knowledge, this is the first study to explore the association between MHR and telomere length. Our findings provide important insights into the relationship between MHR and telomere length, deepening our understanding of the interplay between cardiovascular health and cellular aging. Future research should further investigate the mechanisms underlying this relationship to advance our understanding of the development of cardiovascular diseases and aging processes, thereby providing more effective strategies for the prevention and treatment of related diseases.
Telomeres are markers of cellular aging, naturally shortening with age [1–3]. This is consistent with the results of our study, where the oldest individuals in the Q1 group showed a decreasing trend in telomere length. Various factors such as genetics, gender, ethnicity, physical activity levels, smoking, and obesity can influence telomere length [20–24]. Consistent with prior research [20–24], our results also indicate that gender, ethnicity, BMI, and nicotine exposure were statistically significant factors associated with telomere length. Previous studies have shown that alcohol consumption also affects telomere length [25], although our study did not find a statistically significant association, possibly due to the assessment of alcohol consumption relying on self-reported questionnaires, which are susceptible to recall bias. Telomere length is closely related to cardiovascular diseases, including atherosclerosis, hypertension, vascular dementia, and coronary heart disease [2, 26]. Additionally, telomere length has been identified as an indicator of the severity of such diseases in many cases, correlating with stroke, heart attacks, and mortality rates [27], as well as chronic diseases like diabetes and metabolic syndrome [13]. The results of this study indicate that telomere length is also associated with hypertension, coronary heart disease, stroke, and diabetes; however, the underlying mechanisms require further investigation.
As a novel inflammatory marker, elevated MHR reflects increased inflammation levels in the body [12, 13], which is one of the key factors contributing to telomere shortening [4]. In inflammatory states, pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) rise, leading to increased intracellular oxidative stress and the production of more reactive oxygen species (ROS). ROS exacerbates DNA damage and causes oxidative modifications to DNA bases, such as the oxidation of guanine to 8-oxo guanine (8-oxoG), interfering with telomere replication and repair, thereby accelerating telomere shortening [28–30]. Moreover, inflammatory factors inhibit the expression or activity of telomerase reverse transcriptase (TERT), further diminishing the enzyme’s ability to repair telomeres [31]. In inflammatory conditions, cellular metabolism is reprogrammed to favor glycolytic pathways, while the activities of the tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS) decline, leading to insufficient energy supply and reduced antioxidant capacity, exacerbating telomere loss [32, 33]. The increase in MHR reflects either an increase in monocyte count and/or a decrease in HDL-C levels [17]. HDL-C has anti-inflammatory and antioxidant properties that reduce macrophage accumulation, inhibit monocyte migration, enhance endothelial nitric oxide synthase expression, and promote reverse cholesterol transport, thereby reducing oxidative stress damage to cells [10, 34–38]. However, low HDL-C levels weaken these protective mechanisms, making telomeres more susceptible to inflammation and oxidative stress, resulting in accelerated shortening [28, 30, 35]. Additionally, the increase in monocyte counts not only leads to greater ROS production but also drives their differentiation into foam macrophages, further releasing pro-inflammatory cytokines and causing excessive activation of the immune system [39]. Under chronic inflammatory conditions, the immune system becomes exhausted over time due to prolonged stress and repair demands, impairing its ability to effectively repair telomere damage and leading to continuous telomere shortening [40]. These mechanisms collectively contribute to the significant negative correlation between MHR and telomere length.
Subgroup analysis can better utilize data to reveal potential truths [41]. Therefore, in this study, we conducted a subgroup interaction analysis of MHR and telomere length based on gender, age, ethnicity, and household poverty ratio. The negative association between MHR and telomere length appears to be marginally significant in the female population, Non-Hispanic White individuals, and other racial groups (including multi-racial). This may be attributed to the fact that telomeres are influenced by various factors, and females, due to higher estrogen levels, benefit from the anti-inflammatory and antioxidant properties of estrogen, which can promote the expression of telomerase [42]. However, women also face social stressors, lifestyle factors, and physiological changes (such as the sharp decline in estrogen during menopause) that can accelerate telomere shortening [42]. As a result, the negative association between MHR and telomere length is stronger in females, although it does not reach statistical significance. Regarding racial differences, these should not be solely attributed to ethnicity but must also consider other psychosocial factors, such as historical discrimination and poverty [22, 43]. This suggests that “race” may not be as significant a predictor of telomere length differences as previously anticipated.
Limitations
While this cross-sectional study provides valuable insights, its limitations should be considered. Due to the cross-sectional nature of the study design, causal relationships cannot be determined. Therefore, more longitudinal studies are needed to validate these associations. Additionally, there may be other unaccounted factors in the study, such as lifestyle and genetic factors, which could influence the results. Moreover, some influencing factors such as smoking and alcohol consumption status relied on self-reporting, which may introduce bias to the results. However, this highlights the limitations of using health behaviors in any epidemiological study. Future research should conduct more in-depth and meticulous longitudinal studies to explore the relationship between these factors and their specific mechanisms.
Conclusion
This study demonstrates a negative association between MHR levels and telomere length in American adults. More comprehensive research results are needed in the future to confirm these findings.
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Acknowledgements
Not applicable.
Abbreviations
- MHR
Monocyte to high-density lipoprotein cholesterol ratio
- NHANES
National Health and Nutrition Examination Survey
- RCS
Restricted cubic splines
- HDL-C
High-density lipoprotein cholesterol
- NCHS
Centers for Disease Control and Prevention
- DNA
Deoxyribonucleic acid
- qPCR
Quantitative polymerase chain reaction
- BMI
Body mass index
- SII
Systemic immune inflammation index
- TNF-α
Tumor necrosis factor-alpha
- IL-6
Interleukin-6
- ROS
Reactive oxygen species
- 8-oxoG
8-oxoguanine
- TERT
Telomerase reverse transcriptase
- TCA
Tricarboxylic acid
- OXPHOS
Oxidative phosphorylation
Author contributions
MHY: conceived the study, designed the study, wrote the manuscript, statistical calculation, and data interpretation. LT and HSS: statistical calculation, and data interpretation. XZY, CJL, SXK, DY, and XGZ: participated in the data interpretation and manuscript writing. CZK: review of revised manuscripts.
Funding
The present work was supported by the Medical and Health Science and Technology Project of Zhejiang Province (2023KY244).
Data availability
The raw data is provided within the supplementary information files.
Declarations
Ethical approval
This study was ethically approved by the NCHS Research Ethics Review Board (ERB) and followed ethical standards for human research. More information on the NCHS Research Ethics Review Board Approval can be found on the NHANES website (https://www.cdc.gov/nchs/nhanes/irba98.htm). Written informed consent was not required for this study, in compliance with national and institutional regulations.
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.
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Supplementary Materials
Data Availability Statement
The raw data is provided within the supplementary information files.