The association between C-reactive protein-to-lymphocyte ratio and telomere length: A cross-sectional population-based study

Abstract

The association of inflammation and telomere length (TL) have drawn much attention. However, the association between the C-reactive protein-to-lymphocyte ratio (CLR) and TL has never been reported before. The authors aimed to investigate the association between CLR and TL in US population. Data for this cross-sectional analysis were extracted from the 1999 to 2001 National Health and Nutrition Examination Survey, including only participants with complete data on CLR and TL. We employed multivariable regression and logistic regression analyses to investigate the independent association between CLR and TL. Additionally, subgroup analyses and interaction tests were conducted to further explore these relationships. A total of 5517 participants were enrolled in the study, with those in the highest quartile of CLR exhibiting a tendency toward shorter TL. The mean TL was 5747.491 ± 677.02 base pairs. Individuals in quartiles with shorter TL showed a trend toward higher CLR values (quartile 1: 5866.17 ± 831.57; quartile 2: 5756.49 ± 632.42; quartile 3: 5704.13 ± 585.09; quartile 4: 5663.31 ± 614.61; P < .001). Those in the highest CLR quartile displayed a mean TL that was 202.86 base pairs shorter (β = −202.86, 95% CI: −253.09 to −152.63). Subgroup analyses and interaction tests revealed that the negative association between CLR and TL was consistent across populations with varying demographics, including gender, race, smoking, and alcohol consumption, suggesting its applicability to diverse population settings. Higher CLR was significantly associated with shorter TL, suggesting a link between systemic inflammation and telomere attrition.

1. Introduction

Telomeres are repetitive DNA sequences – specifically the TTAGGG motif – that act to safeguard chromosome ends.^[1] Clinical research has solidly confirmed their utility as biomarkers of aging.^[2,3] A striking observation is the considerable interindividual variability in telomere shortening rates, with mounting evidence implicating chronic inflammation as a significant driver.^[4]

In clinical practice, 2 key inflammatory markers are regularly assessed: C-reactive protein (CRP), a thoroughly validated gauge of systemic inflammation^[5]; Lymphocytes, which serve as essential cellular effectors of immune function.^[6] While each marker yields meaningful information, neither alone can fully encapsulate the intricate biological mechanisms underpinning inflammation-associated aging. To overcome this limitation, the C-reactive protein-to-lymphocyte ratio (CLR) was devised as a composite metric that combines both parameters. Prior clinical studies have shown that CLR outperforms its individual components in prognostic value across contexts like coronavirus disease 2019 and various malignancies, with improved capacity to predict patient outcomes.^[7–13] Despite multiple studies linking inflammatory markers to telomere shortening, the specific interplay between CLR and telomere length (TL) has received insufficient attention.^[7–12] The current study investigates this relationship using data from the National Health and Nutrition Examination Survey (NHANES), which contains standardized measurements of both CLR components and TL in a representative sample. Importantly, given that both CRP and lymphocyte counts are routinely obtainable in clinical laboratories, CLR could emerge as a practical biomarker for evaluating inflammation-related aging processes, pending confirmation of its relevance.

2. Methods

2.1. Study population

Data from the NHANES, a cross-sectional study conducted by the National Center for Health Statistics (NCHS), were utilized. NHANES employs a complex, stratified, multistage probability cluster sampling method to obtain a representative sample of the US civilian non-institutionalized population.^[14] The study protocols were approved by the NCHS Research Ethics Review Board. The NHANES dataset is accessible to the public at the Centers for Disease Control and Prevention website: https://www.cdc.gov/nchs/nhanes/.

Two NHANES cycles from 1999 to 2002 were selected to assess the association between CLR and the length of telomere, since only these survey cycles included complete data on CRP, lymphocyte and telomere. The exclusion criteria for participants in our analysis were missing complete data about education, poverty-to-income ratio (PIR), alcohol, body mass index (BMI), hypertension, total cholesterol, CLR and telomere. Our analysis began with 21,004 NHANES participants. The exclusion cascade shows: Primary exclusion: 13,177 (62.74%) missing TL data. Secondary exclusions: CLR (0.37%), Covariates (range: 0.01% for cholesterol to 6.64% for alcohol). Final analytic sample: 5517 (26.26%) (Fig. 1). Consistent with the NCHS subsampling protocols, TL measurements were intentionally obtained for a randomly selected subset of participants (37.26% in our study period). As documented in the NHANES survey design files, this design-based missingness is inherently random, with subsample selection independent of participant characteristics through equal-probability sampling across demographic strata.

Figure 1.

Flowchart of participants selection. BMI = body mass index, CLR = C-reactive protein-to-lymphocyte ratio, CRP = C-reactive protein, HDL = high-density lipoprotein, LDL = low-density lipoprotein, NHANES = National Health and Nutrition Examination Survey, PIR = poverty-to-income ratio.

2.2. Exposure and outcome definitions

Blood samples collected from participants in the NHANES were analyzed to determine TL. The assays were conducted in Dr Elizabeth Blackburn Laboratory at the University of California, San Francisco, utilizing polymerase chain reaction to measure the ratio of TL to that of a standard reference DNA sample (ratio of TL to that of a standard reference DNA sample). Each sample was subjected to triplicate assays performed on 3 separate occasions. The assay protocol included quality controls to identify and exclude outliers, which were defined as values lying beyond 2 standard deviations from the mean across all assays (<6% of runs). Samples with outliers (<2% of total samples) were similarly identified and excluded. Comprehensive details of the TL quantification methodology and analytical techniques are detailed on the NHANES official website. The TL was treated as an outcome variable in our analysis.

CLR was designed as an exposure variable. Blood specimens were measured at the NHANES Mobile Examination Centers. CRP levels were quantified using latex-enhanced nephelometry. For the quantitative analysis of CRP, latex particles with a polystyrene core and a hydrophilic shell were utilized to covalently bind anti-CRP antibodies. A complete blood count, a standard hematological assay, assesses an individual’s general health status and screens for various conditions. The complete blood count measurements are obtained utilizing the Beckman Coulter analytical technique. We calculated CLR (mg/dL per 1000 lymphocytes as CRP (mg/dL)/ lymphocyte (cells/µL).

2.3. Covariates

Covariates were selected a priori based on established biological relevance to TL and inflammation.^[4,15–18] Covariates were selected a priori based on established biological relevance to TL and inflammation.^[4,15–18] Potential covariates that might confound the association between CLR and TL were summarized in the multivariable-adjusted models. Covariates in our study included gender (male/female), age (years), race, education level, PIR, alcohol intake, total cholesterol level (mg/dL), BMI, smoking status (smoking or not), hypertension (yes or no), CRP (mg/dL), triglycerides (mg/dL), low-density lipoprotein (LDL) (mg/dL) and high-density lipoprotein (HDL) (mg/dL). All detailed measurement processes of study variables were publicly available at www.cdc.gov/nchs/nhanes/.

3. Statistical analyses

All statistical analyses were conducted according to Centers for Disease Control and Prevention guidelines. Continuous variables were presented as mean with standard deviation, and categorical variables were presented as a percentage. Either a weighted Student t test (for continuous variables) or weighted χ² test (for categorical variables) was used to evaluate the differences in groups divided by CLR (quartiles). CLR quartiles were used to align with clinical stratification in prior studies.^[19] Multivariate logistic regression models were employed to explore the independent relationship between CLR and TL in 3 different models. In model 1, no covariates were adjusted. Model 2 was adjusted for gender, age, and race. Model 3 was adjusted for gender, age, race, education level, PIR, alcohol intake, total cholesterol, BMI, smoking status, hypertension, triglycerides HDL and LDL. Smooth curve fitting (penalized spline method) and weighted generalized additive model regression were conducted to further assess the nonlinear relationship between CLR and TL. Subgroup analysis stratified by gender, age, hypertension, and education level was also performed by stratified multivariate regression analysis. In addition, an interaction term was added to test the heterogeneity of associations between the subgroups using log likelihood ratio test model. Forest plots illustrating the sensitivity analyses consistently demonstrated the robustness of the CLR-TL association across all stratified subgroups. P < .05 was considered statistically significant. All analyses were preformed using Empower software (https://www.empowerstats.net/cn/; X&Y solutions, Inc., Boston) and R version 3.4.3 (http://www.R-project.org, The R Foundation).

4. Results

4.1. Baseline characteristics of participants

The average age of the participants was 48.78 ± 17.89 years; 53.24% of them were men. The average of TL was 5747.491 ± 677.02. The average CLR of the overall participants was 0.259 ± 0.653, and the averages of CLR for quartiles 1 to 4 were 0.03 ± 0.01, 0.08 ± 0.02, 0.17 ± 0.04, and 0.76 ± 1.17, respectively. Among the 4 CLR quartiles, differences with statistical significance were observed in age, PIR, BMI, triglycerides, LDL, CRP, total cholesterol, HDL, telomere, CLR, gender, education, smoking, hypertension (all P < .05). Compared with the lowest CLR group, participants in the increased CLR group were significantly more likely to have hypertension, higher education, smoking, alcohol, elevated age, BMI, triglycerides, LDL, CRP, total cholesterol and CLR, and decreased HDL, PIR and TL (all P < .05). Differences across quartiles in race (Table 1) and alcohol use (Table 2) were not statistically significant (both P > .05). Variables were categorized into 3 tables: Table 1(demographic characteristics), Table 2 (clinical characteristics), and Table 3 (metabolic characteristics).

Table 1.

Baseline demographic characteristics by CLR quartiles.

CLR quartile	Q1	Q2	Q3	Q4	P-value
N	1378	1380	1379	1380
Age (yr)	43.81 ± 16.64	48.73 ± 17.39	51.27 ± 18.14	51.31 ± 18.33	<.001
Gender (%)
Male	869 (63.06%)	829 (60.07%)	688 (49.89%)	551 (39.93%)	<.001
Female	509 (36.94%)	551 (39.93%)	691 (50.11%)	829 (60.07%)
Race (%)
1	321 (23.29%)	329 (23.84%)	288 (20.88%)	314 (22.75%)	.168
2	70 (5.08%)	70 (5.07%)	68 (4.93%)	73 (5.29%)
3	753 (54.64%)	754 (54.64%)	781 (56.64%)	712 (51.59%)
4	195 (14.15%)	189 (13.70%)	212 (15.37%)	244 (17.68%)
5	39 (2.83%)	38 (2.75%)	30 (2.18%)	37 (2.68%)
Education (%)
1	160 (11.61%)	186 (13.48%)	188 (13.63%)	190 (13.77%)	<.001
2	227 (16.47%)	212 (15.36%)	221 (16.03%)	274 (19.86%)
3	293 (21.26%)	337 (24.42%)	329 (23.86%)	323 (23.41%)
4	361 (26.20%)	349 (25.29%)	359 (26.03%)	367 (26.59%)
5	337 (24.46%)	296 (21.45%)	282 (20.45%)	226 (16.38%)
PIR	2.89 ± 1.63	2.85 ± 1.63	2.86 ± 1.60	2.63 ± 1.59	<.001

Continuous variables: mean ± standard deviation. Categorical variables: percentage (%). Bold values indicate statistically significant differences (P < .05).

CLR = C-reactive protein-to-lymphocyte ratio, PIR = poverty-to-income ratio.

Table 2.

Baseline clinical characteristics by CLR quartiles.

CLR quartile	Q1	Q2	Q3	Q4	P-value
BMI (kg/m2)	25.23 ± 4.30	27.74 ± 5.03	29.18 ± 5.68	31.04 ± 7.41	<.001
Hypertension (%)
1	243 (17.63%)	346 (25.07%)	465 (33.72%)	533 (38.62%)	<.001
2	1135 (82.37%)	1034 (74.93%)	914 (66.28%)	847 (61.38%)
Smoking (%)
1	393 (28.52%)	382 (27.68%)	385 (27.92%)	380 (27.54%)	<.001
2	498 (36.14%)	420 (30.43%)	383 (27.77%)	409 (29.64%)
3	487 (35.34%)	578 (41.88%)	611 (44.31%)	591 (42.83%)
Alcohol (%)
1	223 (16.18%)	246 (17.83%)	241 (17.48%)	227 (16.45%)	0.607
2	1155 (83.82%)	1134 (82.17%)	1138 (82.52%)	1153 (83.55%)

Continuous variable: mean ± standard deviation. Categorical variables: percentage (%). Bold values indicate statistically significant differences (P < .05).

BMI = body mass index; CLR = C-reactive protein-to-lymphocyte ratio.

Table 3.

Baseline metabolic characteristics by CLR quartiles.

CLR quartile	Q1	Q2	Q3	Q4	P-value
CRP (mg/dL)	0.06 ± 0.03	0.17 ± 0.07	0.36 ± 0.14	1.31 ± 1.56	<.001
Triglycerides	143.25 ± 58.66	158.51 ± 77.27	159.38 ± 86.62	159.64 ± 72.30	<.001
LDL (mg/dL)	121.90 ± 22.09	125.01 ± 23.15	125.78 ± 22.76	124.13 ± 26.43	<.001
HDL (mg/dL)	53.55 ± 15.67	49.99 ± 15.25	50.84 ± 16.14	51.72 ± 16.01	<.001
Total cholesterol (mg/dL)	196.07 ± 37.79	205.09 ± 39.09	208.51 ± 40.81	206.52 ± 42.46	<.001
Telomere length (bp)	5866.17 ± 831.57	5756.49 ± 632.42	5704.13 ± 585.09	5663.31 ± 614.61	<.001
CLR	0.03 ± 0.01	0.08 ± 0.02	0.17 ± 0.04	0.76 ± 1.17	<.001

Continuous variables: mean ± standard deviation. Categorical variables: percentage (%). Bold values indicate statistically significant differences (P < .05).

CLR = C-reactive protein-to-lymphocyte ratio, CRP = C-reactive protein, HDL = high-density lipoprotein, LDL = low-density lipoprotein.

Table 4 shows the association between CLR and TL. Our results demonstrated that higher CLR was associated with the shorter TL. A negative association between CLR and TL was detected both in 3 models. In model 1, an unadjusted model, the lower CLR was correlated with the longer TL. In model 2, which was adjusted for age, race and gender, the positive association between them exhibited a weakening trend. However, in model 3, the model adjusted for all relative covariates, including age, gender, race, PIR, educational level, smoking, alcohol, CRP, HDL, LDL, triglycerides, total cholesterol, and hypertension, and the shorter of TL was positively correlated with elevated CLR. In models 3, the β (95% CIs) of the shorter TL for quartile 4 versus quartile 1 was −62.68 (−119.96, −5.40). In addition, the results of smooth curve fitting indicated that there was no non-linear relationship between CLR and the TL in total samples (Fig. 2). The Model 3’s R² value is 0.154 for model diagnostics. The decreasing magnitude of β coefficients across adjustment models (Q4: β = −202.86 in model 1 vs –62.68 in model 3) reflects the expected influence of confounding variables. Despite this reduction, the association remained statistically significant (P for trend = .0243), supporting CLR’s independent relationship with TL.

Table 4.

Association between CLR and TL.

Exposure	Non-adjusted	Adjust I	Adjust II
CLR quartile
Q1	0	0	0
Q2	−109.68 (−159.91, −59.45) < 0.0001	−42.73 (−89.58, 4.12) 0.0739	−26.77 (−74.43, 20.89) 0.2710
Q3	−162.04 (−212.28, −111.81) < 0.0001	−71.65 (−119.07, −24.23) 0.0031	−44.96 (−94.46, 4.54) 0.0751
Q4	−202.86 (−253.09, −152.63) < 0.0001	−117.47 (−165.44, −69.49) < 0.0001	−62.68 (−119.96, −5.40) 0.0320
P for trend	<.0001	<.0001	.0243

Model 1: No covariate adjustments. Model 2: Demographic factors, such as age, race and gender, were taken into account for adjustments. Model 3: Comprehensive adjustments included age, PIR, BMI, triglycerides, LDL, CRP, total cholesterol, HDL, telomere, CLR, gender, race, education, smoking, alcohol, and hypertension.

BMI = body mass index, CLR = C-reactive protein-to-lymphocyte ratio, CRP = C-reactive protein, HDL = high-density lipoprotein, LDL = low-density lipoprotein, PIR = poverty-to-income ratio, TL = telomere length.

Figure 2.

Linear relationship between CLR (C-reactive protein-to-lymphocyte ratio) and TL (telomere length): β = −61.06 bp per CLR unit; 95% CI: −88.37 to −33.74; P < .001. CLR as continuous variable. CLR = C-reactive protein-to-lymphocyte ratio, TL = telomere length.

Across all 3 models (unadjusted, demographic-adjusted, and fully adjusted), the negative association between CLR and TL remained statistically significant. The persistence of this association despite progressive adjustment suggests that our primary conclusion is not highly sensitive to variable selection or potential missing data patterns. The persistent CLR-TL association across model 1, model 2, and model 3, despite attenuation of effect sizes (Q4: β = −202.86 to −62.68), suggests the relationship is robust to covariate selection (Table 4).

Subgroup analysis and interaction tests was conducted to evaluate whether the relationship between CLR and TL was stable among different population settings. As shown in Table 5, strong interaction was only found among hypertension and other stratifications, which include gender, race, education, smoking, and alcohol, significantly affected the positive association between CLR and TL (all P for interaction < .05). Of note, the association between CLR and TL remained significant in the hypertension subgroup, with a notable interaction effect (P for interaction = .0086), indicating potential differences in the strength of this relationship between hypertensive and non-hypertensive individuals.

Table 5.

Subgroup analysis of association between CLR and TL.

Subgroup	95% Cl	p	P-for interaction
Gender
Male	−68.75 (−103.86, −33.63)	0.0001	.6871
Female	−57.29 (−100.61, −13.97)	0.0096
Race
Mexican American	−72.08 (−127.87, −16.29)	0.0114	.7909
Other Hispanic	−57.77 (−196.66, 81.12)	0.415
Non-Hispanic White	−79.26 (−122.76, −35.75)	0.0004
Non-Hispanic Black	−38.17 (−86.97, 10.62)	0.1253
Other Race	−89.05 (−291.10, 113.01)	0.3877
Education
<9th grade	−53.70 (−113.84, 6.44)	0.0801	.8921
9–11th grade	−90.99 (−162.02, −19.96)	0.0121
High school	−48.57 (−100.35, 3.22)	0.0661
Some College or AA degree	−49.70 (−105.22, 5.82)	0.0794
College graduate	−48.01 (−121.38, 25.35)	0.1996
Smoking
Every day	−111.17 (−177.89, −44.44)	0.0011	.2625
Some days	−51.06 (−91.19, −10.94)	0.0126
Not at all	−49.31 (−93.53, −5.09)	0.0289
Alcohol
Yes	−122.65 (−212.77, −32.52)	0.0077	.1622
No	−55.23 (−83.87, −26.59)	0.0002
Hypertension
Yes	−20.74 (−56.21, 14.72)	0.2517	.0086
No	−95.03 (−137.57, −52.50)	<0.0001

Subgroup analysis was conducted using weighted multivariable logistic regression.

CLR = C-reactive protein-to-lymphocyte ratio, TL = telomere length.

Sensitivity analyses were conducted through stratification by 5 key demographic and clinical covariates (gender, race, education, smoking, and alcohol). As demonstrated in Figure 3, the inverse association between elevated CLR levels and shorter TL remained consistent across all stratified subgroup. Formal interaction testing revealed no significant effect modification across all examined subgroups (all P for interaction > .05).

Figure 3.

Sensitivity analysis between CLR and TL. CLR = C-reactive protein-to-lymphocyte ratio, TL = telomere length.

5. Discussion

In this cross-sectional analysis involving 5517 participants, the investigators identified a positive correlation between the CLR and TL, suggesting that elevated CLR values may be associated with shorter telomeres. This correlation remained significant across subgroups categorized by gender, race, smoking status, and hypertension, indicating that the relationship between CLR and TL may be consistent across diverse population demographics. Interestingly, there was no non-linear relationship between CLR and the TL.

As of our current understanding, no investigations have hitherto been conducted to examine the connection between CLR and TL. This research constitutes the initial inquiry into the association between CLR and TL within the US population. Previous studies have indicated that inflammatory activity may be related to telomere shortening.^[20–24] For example, Rode et al discovered an inverse relationship between leukocyte TL and C-reactive protein levels, suggesting that inflammation plays a role in telomere attrition.^[20] Pedroso et al also showed a significant correlation between inflammatory biomarkers and leukocyte TL in middle-aged and elderly Chinese individuals, suggesting that chronic inflammation could contribute to telomere erosion.^[21] Although previous investigations have delved into the role of inflammation in TL, the present study further explores the correlation between chronic low-grade inflammation (CLR) and TL. Additionally, this study provides a more meticulous analysis of subgroup variations within the association between CLR and TL. Hypertension, characterized by sustained vascular inflammation, oxidative stress, and immune dysfunction, is known to accelerate telomere shortening.^[25] Elevated CLR may reflect this pro-inflammatory state, while contributing to both hypertensive end-organ damage and telomere erosion via reactive oxygen species and DNA damage. This initial mechanistic study provides preliminary evidence for the above associations, though causality remains undetermined.

Furthermore, our study underscores the importance of considering CLR as a prognostic indicator beyond infectious diseases. CLR has been associated with adverse outcomes in specific clinical contexts. As a novel inflammatory biomarker, the CLR has shown its importance in the diagnosis and prognosis of coronavirus disease 2019 pneumonia.^[13,26] A recent analysis of NHANES III data revealed that a CRP level of 0.5 mg/dL or higher is an independent risk factor for all-cause mortality, cardiovascular mortality, and cancer-specific mortality in individuals with metabolic associated fatty liver disease, after adjusting for confounding risk factors.^[27] Beyond infectious diseases, the CLR is also a significant biomarker for evaluating cancer prognosis.^[28,29] These studies suggest that the combined measurement of CRP and lymphocyte counts in routine tests can reflect the systemic inflammatory and immune status, potentially serving as a convenient and accurate prognostic tool for a range of diseases.^[30] Previous research has firmly established CRP as a commonly recognized marker of inflammation. The concentration of C-reactive protein (CRP) shows an upward trend in response to cellular damage or tissue injury, as documented in references.^[31,32] Consequently, chronic low-grade inflammation (CLR) can act as a reflection of the balance between systemic inflammation and immune function. In our study, we elucidate the relationship between the CLR and TL. And, our study provides novel insights into the relationship between CLR and TL, suggesting CLR merits further investigation as a potential indicator of inflammation-associated aging.

TL shortage could be observed in cell cycles.^[33] Our study reveals a significant positive correlation between the CLR and TL shortening in a representative sample of the US adult population. This association remained even after adjusting for multiple confounding factors, indicating that the observed relationship is not simply caused by demographic or lifestyle elements. The findings are in line with the expanding body of evidence that links chronic low-grade inflammation to the biology of aging and age-related diseases.^[34,35] The CLR, as an indicator of systemic inflammation, reflects the balance between pro-inflammatory and anti-inflammatory processes within the body.^[36] Lymphocytes, a key component of the immune system, are sensitive to inflammatory signals and can modulate the production of CRP, which is a well-established marker of inflammation.^[37,38] Our results suggest that a higher CLR, indicative of a pro-inflammatory state, is associated with shorter telomeres, which are protective caps at the ends of chromosomes that shorten with each cell division. This oxidative stress can expedite telomere erosion, leading to an increased risk of cellular senescence and organ dysfunction, which are characteristic hallmarks of aging and age-related diseases.

Our study builds on previous research by demonstrating that CLR, a novel inflammatory biomarker, is not only connected to traditional markers of inflammation such as CRP but also to a biological marker of aging, TL. However, any suggestion of CLR’s clinical utility in assessing age-related disease risk or monitoring interventions remains speculative at this stage. Such utility would require longitudinal studies to establish whether CLR predicts telomere shortening over time and, further, whether changes in CLR correlate with clinical outcome. Without such validation, CLR’s role in clinical practice cannot be definitively proposed. We also found no non-linear relationship between CLR and TL.

However, our study is not without limitations. First, 13,428 participants were excluded due to missing data (primarily for TL measurements or CLR components). This exclusion may introduce bias: individuals with missing data might differ in unmeasured characteristics that correlate with both CLR and TL, potentially limiting generalizability to populations with complete data. Second, the cross-sectional design of NHANES precludes any inference of causality between CLR and TL. Critically, we cannot determine the directionality of the relationship – whether elevated CLR drives telomere shortening, shorter telomeres promote inflammation, or both are downstream of a common factor. This limitation underscores the necessity of longitudinal data to clarify temporal relationships. Third, some subgroup analyses showed wide confidence intervals, likely due to smaller sample sizes in these strata. While the overall trends remained consistent, these estimates should be considered exploratory and require validation in larger, more diverse cohort. Additionally, while we adjusted for several potential confounders, there may be other unmeasured or unknown variables that could influence the relationship between CLR and TL. For instance, factors such as genetic predisposition to inflammation or telomere biology, which were not accounted for in this study, could potentially confound the observed association. Furthermore, the CLR was derived from a single measurement of CRP and lymphocyte counts, which may not capture long-term inflammatory status or dietary patterns accurately. Despite these limitations, our findings provide valuable insights into the role of inflammation in telomere biology and highlight the need for further research to validate CLR’s potential as a marker of systemic inflammation and aging.

6. Conclusion

In this cross-sectional analysis of a nationally representative adult cohort, elevated CLR levels showed correlation with shorter TL. This correlation persisted after adjustment for demographic and clinical covariates and was generally consistent across examined subgroups.

The study has important limitations that must be noted. First, the observational design precludes causal interpretation. Second, potential residual confounding cannot be excluded. These findings highlight the need for longitudinal investigations to better characterize the relationship between CLR and telomere biology.

Acknowledgments

We would like to thank Yunxi Lu give us help to finish this article (Yunxi Lu gives our permission to be named). This work was supported by the Program for Science and Technology Project of Guizhou [Province Qiankehe Platform Talents (No. [2021] 5613)], the Key Program for Science and Technology Project of Guizhou Province (No. ZK [2021] 007) and the National Natural Science Foundation of China (82160577).

Author contributions

Conceptualization: Wanshun Liu.

Data curation: Wanshun Liu.

Formal analysis: Wanshun Liu.

Funding acquisition: Wanshun Liu, Qingde Wa.

Investigation: Wanshun Liu.

Methodology: Wanshun Liu.

Project administration: Wanshun Liu.

Resources: Wanshun Liu.

Software: Wanshun Liu.

Supervision: Wanshun Liu, Yuanhao Lv, Jun Chen, Xiang Li, Qiping Huang, Zhenyu Wen, Xitao Linghu.

Validation: Wanshun Liu, Yuanhao Lv, Jun Chen, Xiang Li, Qiping Huang, Zhenyu Wen, Xitao Linghu.

Visualization: Wanshun Liu.

Writing – original draft: Wanshun Liu.

Writing – review & editing: Wanshun Liu, Qingde Wa.