Objective assessment of the association between telomere length, a biomarker of aging, and health screening indicators: A cross-sectional study

Abstract

Physical examination data are used to indicate individual health status and organ health, and understanding which physical examination data are indicative of physiological aging is critical for health management and early intervention. There is a lack of research on physical examination data and telomere length. Therefore, the present study analyzed the association between blood telomere length and physical examination indices in healthy people of different ages to investigate the role and association of various organs/systems with physiological aging in the human body. The present study was a cross-sectional study. Sixteen physical examination indicators of different tissue and organ health status were selected and analyzed for trends in relation to actual age and telomere length (TL). The study included 632 individuals with a total of 11,766 data for 16 physical examination indicators. Age was linearly correlated with 11 indicators. Interestingly, telomere length was strongly correlated only with the renal indicators eGFR (P < .001), CYS-C (P < .001), and SCR (P < .001). The study established that renal aging or injury is a risk factor for Physical aging of the human body. Early identification and management are essential to healthcare.

1. Introduction

Aging is an inevitable process that unfolds continuously throughout an individual’s life, marked by the gradual degeneration of physiological functions within tissues and organs. This intricate phenomenon encompasses a reduction in the body’s metabolic energy, a decline in organ function, the progressive loss of cellular structures and components, and ultimately, the irreversible cessation of cell replication.^[1] Aging serves as a prominent risk factor for a spectrum of ailments, including cardiovascular diseases, neurodegenerative disorders, respiratory conditions, and various forms of cancer.^[2] According to the Global Burden of Disease 2019 findings, aging populations in industrialized countries are living longer but in poorer health, and healthcare systems are heavily burdened.^[3] Presently, the global population is witnessing a shift toward an unprecedented era of population aging. Recent data reveals that approximately 9% of the world’s populace has surpassed the age of 65, and this proportion is projected to escalate to 16% by 2050. Remarkably, China is experiencing an even more rapid advancement in population aging. Predictions indicate that by 2035, China’s elderly population (65 and above) will surpass 327 million, rising to a staggering 393 million by 2050. The aging of such a substantial portion of the population portends a substantial healthcare burden. China Research Center on Ageing, health care for elderly people is under pressure and this pressure has generated widespread social concern.^[4] Consequently, the scientific evaluation of organ aging emerges as an urgent priority, offering valuable insights into the promotion of healthy aging.

Traditionally, aging has been synonymous with chronological aging (CA), tethered solely to the passage of time. Nonetheless, it is imperative to acknowledge that aging encompasses dimensions extending beyond the temporal. Variation in physical resilience, individual physiological attributes, and diverse lifestyles result in disparate rates of physiological aging among individuals of the same chronological age. This insight led to the recognition, post-1970s, of biological aging as a more crucial consideration than CA.^[5] Biomarkers like telomere length, DNA methylation, and protein glycosylation have assumed central roles in appraising physiological aging. Among these, telomere length has emerged as a potent marker, demonstrating a linear correlation with age.^[6–8] Telomeres, which are situated at the termini of eukaryotic cell chromosomes, consist of highly repetitive sequences (5′-TTAGGG-3′) and typically span between 10 to 15 kilobases.^[6] Their primary function revolves around safeguarding chromosome ends, ensuring chromosomal integrity, and preventing end deterioration or fusion.^[9] Extensive research has revealed that chronic stress and diseases are associated with shortened telomeres.^[10–12] Furthermore, age-related telomere attrition can induce abnormal gene expression.^[13] Despite these insights, it remains a important question which specific physical examination indices correlate with telomere length. There is a great lack of research on the correlation of physical examination markers on telomere length, and most telomere studies are based on physical disease rather than physical aging. Additionally, understanding how the various tissues, organs, or systems represented by these examination parameters are intertwined with the physiological aging process requires further exploration.

2. Methods

2.1. Study population and data

A cross-sectional study was conducted, enlisting 651 participants spanning the period from October 2021 to March 2022. Recruitment occurred via three distinct centers: the Department of Physical Examination, the Department of Geriatrics, and the Guangta Community of Guangzhou First People’s Hospital. Inclusion Criteria for Study Participants are as follows: Individuals aged 18 years or older, irrespective of gender, and residing within the geographic boundaries of Guangzhou were eligible for inclusion. Participants were expected to possess a certain level of cognitive and social competence. This included the ability to fully comprehend study-related instructions, make independent judgments, engage in effective communication, and actively participate in completing questionnaires. Participants concerned about the aging condition of the body have a willingness to assess aging and voluntarily undergo a physical examination and provide data from their own physical examination report. In order to ensure that the focus of the study was on those who did not have significant health problems that could affect the analysis. Therefore, only those who showed no signs of serious systemic diseases, tumors, or cancer based on their medical reports were included. These inclusion criteria ensured that the overall goal of the study was to create a cohort of genuinely motivated, physically healthy individuals who could provide accurate and reliable data to facilitate the study of the relationship between telomere length, aging, and body metrics. Nineteen of the 651 participants did not meet health inclusion criteria. Ultimately, a total of 632 participants were enrolled in this study (Fig. 1).

Figure 1.

Flow chart of the enrolled participants.

The study encompassed an array of physical examination indicators, encompassing parameters such as Age, body mass index (BMI), blood pressure (PP), heart rate (P), P-R interval,^[14] QRS waves,^[15] serum albumin (ALB), serum globulin (GLB), alanine aminotransferase (ALT),^[16] aspartate aminotransferase (AST), creatinine, estimated glomerular filtration rate (eGFR),^[17] cystatin C (CYS-C), random blood glucose (GLU), glycosylated hemoglobin (HbA1c), total cholesterol (TCHO), triglycerides (TG) and telomere length (TL). Data sources of questionnaire, electronic balance, sphygmometer, electrocardiography, blood test, and qPCR. Medical histories were taken, anthropometric measurements and physical tests were performed with the assistance of medical students, trained general practitioners and nurses. All study investigators and staff members completed a training programme that taught the methods and process of the study. All blood samples used for physical examination were analyzed in the central laboratory of Guangzhou First People’s Hospital. All the study laboratories successfully completed a standardization and certification program. Whole blood telomere length is measured by extracting blood genomic DNA and then using the Real-time fluorescence quantitative PCR (Q-PCR) method.^[18] All telomere length assays were analyzed in the laboratory of the Institute of Aging and Regenerative Medicine, Jinan University, Guangzhou, China.

2.2. Real-time fluorescence quantitative PCR assay (Q-PCR)

Fresh whole blood samples were collected by a specialized clinical nurse from a vein in the forearm of the participant’s elbow. Each blood sample was extracted using blood DNA extraction kits from Magen Biotechnology, and the procedure was performed strictly according to the instructions. The quality and quantity were determined at an absorbance of 260 and 280 nm using a nanodrop microvolume spectrophotometers (Thermo Scientific: Waltham). Pure DNA samples with an A260/A280 ratio of 1.8 to 2.0 were processed for the study. Relative telomere length was determined by the real-time quantitative PCR method using specific PCR primers. Telomere-specific primers are: Telg (forward) 5′- ACACTAAGGTTTGGGTTTGGGTTTGGGTTTGGGTTAGTGT-3′ and Telc (reverse) 5′- TGTTAGGTATCCCTATCCCTATCCCTATCCCTATCCCTAACA-3′. Human single copy genes (SCG) control primers are: Albu (forward) 5′- GCTGGGCGGAAATGCTGCACAGAATCCTTG-3′, and Albd (reverse) 5′- TCCCGCCGGAAAAGCATGGTCGCCTGTT-3′. PCR reactions were set up by aliquoting 20 µL of master mix into each reaction well of a 96-well plate compatible with the QuantStudio™ 6 Flex (Thermo Scientific). For QPCR, the reaction master mixture was prepared using 2× SYBR qPCR mix (GenStar: Taicang, Jiangsu, China) in a 20 µL reaction. The final concentration of primers is 900 nM each, and the final content of DNA is 50 ng. The reaction conditions were as follows: Stage 1: 15 minutes at 95°C; Stage 2: 2 cycles of 15 seconds at 94°C, 15 seconds at 49°C; and Stage 3: 32 cycles of 15 seconds at 94°C, 10 seconds at 62°C, 15 seconds at 74°C with signal acquisition.^[18] The relative telomere length was measured as the T/S ratio, the T/S ratio is approximately 2^{–[Ct(telo)- Ct(Alb)]}.

2.3. Statistical analysis

In the application of aging evaluation in this experiment, simple linear regression analysis model was used to analyze the correlation between physical examination indicators and chronological age, and the correlation between physical examination indicators and telomere length T/S ratio of blood cells. Correlation analyses were performed using GraphPad prism 9 with 95% confidence intervals (CIs) of two-sided P < .05 considered statistically significant. Values of physical examination indicators for all participant characteristics are presented as numbers (%) or mean ± standard deviation. Multifactorial stepwise regression analysis was conducted using SPSS 26 software (IBM: Chicago), with an inclusion criterion of P < .05 to determine significant factors influencing telomere length.

3. Results

3.1. Participant characteristics

We investigated Baseline data of the total 632 healthy participants recruited, 351 males (56%) and 281 females (44%), The age of the participants in this study ranged from 18 to 99 years, with a mean age of 59 years. The participants were recruited with a balanced ratio of men to women and a balanced age distribution. Statistics are shown in Table 1. Minors under 18 years of age were not included because there was no aging assessment need for minors under 18 years of age. The average life expectancy in China is around 78 years, so the sample size of the data above 70 years old appears to be reduced. The distribution of each age group was balanced and consistent with the aging assessment needs model. The values for the main characteristics of the participants are shown in Table 1.

Table 1.

Participant characteristics.

Gender
Male (n [%])	351 (55)
Female (n [%])	281 (45)
Parameter
Age (mean ± SD)	59.19 ± 17.13
BMI (mean ± SD)	23.69 ± 3.38
PP (mean ± SD)	51.62 ± 12.47
Pulse (mean ± SD)	73.26 ± 11.68
PR interval (mean ± SD)	151.52 ± 23.27
QRS waves (mean ± SD)	98.84 ± 12.95
ALB (mean ± SD)	42.84 ± 2.99
GLB (mean ± SD)	30.48 ± 3.71
ALT (mean ± SD)	24.00 ± 14.49
AST (mean ± SD)	25.08 ± 8.28
Creatine (mean ± SD)	80.57 ± 21.98
eGFR (mean ± SD)	93.79 ± 23.52
CYS-C (mean ± SD)	0.87 ± 0.24
GLU (mean ± SD)	5.57 ± 1.16
HbA1c (mean ± SD)	5.80 ± 0.66
TOHO (mean ± SD)	5.02 ± 1.05
TG (mean ± SD)	1.46 ± 1.06
T/S ratio (mean ± SD)	0.18 ± 0.07

ALB = serum albumin, ALT = alanine aminotransferase, AST = aspartate aminotransferase, BMI = body mass index, CYS-C = cystatin C, eGFR = estimated glomerular filtration rate, GLB = serum globulin, GLU = random blood glucose, HbA1c = glycosylated hemoglobin, PP = blood pressure, PR = P-R interval, QRS = QRS waves, TG = triglycerides.

3.2. Telomere length analysis

Analyzed genomic DNA extracted from all study participants. Subsequently, their telomere length T/S ratio was detected by quantitative polymerase chain reaction (Q-PCR) method. a general linear regression model was applied to assess the data. In Figure 2, each data point signifies the distribution of a sample, while the accompanying lines denote linear regression relationships with 95% confidence intervals. The analysis revealed a compelling and statistically significant linear correlation between age and the telomere length T/S ratio (P < .001). This outcome firmly establishes the progressive shortening of telomeres with advancing age, reinforcing the notion that telomere length is intricately linked to the aging process.

Figure 2.

Trend of telomere length T/S ratio with age.

3.3. Analysis of physical examination indicators by organ/system

The physical examination program selected 16 indicators of health state in different tissues and organs. Indicators mainly for physical signs, heart, liver, kidney, vasculature, glucose metabolism, and lipid metabolism. The 16 selected physical examination indicators were analyzed linearly by actual sample size in order of age. The indicators that represent the vasculature are Pulse Pressure (PP = SBP − DBP), reference rangepulse is 30 to 40 mm Hg, data sources in sphygmometer. The indicators that represent the physical signs are BMI (BMI = Ht(kg)/Wt(m)²), reference rangepulse is 18.5 to 24.9, data sources in electronic balance. The indicators that represent the heart are Pulse, PR interval and QRS wave, data sources in electrocardiography. The indicators that represent the liver are ALB, GLB, ALT, and AST, data sources in Blood Test. The indicators that represent the kidney are Scr, eGFR, and CYS-C, data sources in Blood Test. The indicators that represent the glucose metabolism are GLU and HbA1c. The indicators that represent the lipid metabolism are TCHO and TG.

By general linear regression model analysis, the small boxes mark the analysis with linear correlation as shown in Figure 3, Pulse Pressure, pulse, PR interval, ALB, creatinine, eGFR, cystatin C, random blood glucose, glycosylated hemoglobin, (all with (P < .001), triglycerides (P ≥ .02), and total cholesterol (P < .01)). These findings held statistically significant differences (P < .001). specifically: the pulse pressure increases with age the pulse rate slowed with age; the P-R interval lengthened with age, indicating delayed atrioventricular conduction; the serum albumin level decreased with age, indicating decreased hepatic synthesis; the creatinine levels and cystatin C levels increase with age, the estimated glomerular filtration rate decreased with age, indicating decreased renal filtration; random blood glucose, glycated hemoglobin, total cholesterol, and triglycerides gradually increased with age, indicating a slowed efficiency of metabolism. This analysis underscores a notable decline in the functioning of the heart, liver, kidney, and essential organs/systems related to glucose and lipid metabolism with advancing age. Furthermore, the data reveals that older individuals tend to exhibit more pronounced signs of organ/system aging.

Figure 3.

Trend graph of each physical examination index with age. Note: (A) Distribution of Pulse Pressure with age. (B) Distribution of Pulse with age. (C) Distribution of PR interval with age. (D) Distribution of QRS complex with age. (E) Distribution of serum albumin with age. (F) Distribution of serum globulin with age. (G) Distribution of alanine aminotransferase with age. (H) Distribution of aspartate aminotransferase with age. (I) Distribution of body mass index with age. (J) Distribution of creatinine with age. (K) Distribution of estimated glomerular filtration rate with age. (L) Distribution of cystatin C with age. (M) Distribution of random blood glucose with age. (N) Distribution of glycated hemoglobin with age. (O) Distribution of total cholesterol with age. (P) Distribution of triglycerides with age. Each point represents the distribution of a sample, and the lines represent linear correlations within 95% confidence intervals.

Subsequently, we conducted an analysis of the T/S ratio of telomere length in relation to each physical examination index (Fig. 4). Notably, the T/S ratio exhibited a linear correlation with 3 kidney-related physical examination indexes: eGFR (P < .001), cystatin C (P < .001), and creatinine (P < .001), with statistically significant differences. In clinical medical diagnostics eGFR, cystatin C and creatinine are used for early detection of kidney health, especially useful for screening for chronic kidney disease and monitoring the progression of kidney disease. This suggests that when assessing the biological aging of the heart, liver, and kidneys, the telomere length assay predominantly reflects the biological aging characteristics of the kidneys.

Figure 4.

Trend graph of each physical examination index with T/S ratio. Note: (A) Distribution of Pulse Pressure with T/S ratio. (B) Distribution of Pulse with T/S ratio. (C) Distribution of PR interval with T/S ratio. (D) Distribution of QRS complex with T/S ratio. (E) Distribution of serum albumin with T/S ratio. (F) Distribution of serum globulin with T/S ratio. (G) Distribution of alanine aminotransferase with T/S ratio. (H) Distribution of aspartate aminotransferase with T/S ratio. (I) Distribution of body mass index with T/S ratio. (J) Distribution of creatinine with T/S ratio. (K) Distribution of estimated glomerular filtration rate with T/S ratio. (L) Distribution of cystatin C with T/S ratio. (M) Distribution of random blood glucose with T/S ratio. (N) Distribution of glycated hemoglobin with T/S ratio. (O) Distribution of total cholesterol with T/S ratio. (P) Distribution of triglycerides with T/S ratio. Each point represents the distribution of one sample, and the lines represent linear correlations within 95% confidence intervals.

T/S, an indicator of physiological aging in humans, exhibited a negative correlation with chronological age, as depicted in Figure 2. Furthermore, the T/S ratio showed negative correlations with renal indicators such as Scr and cystatin C, while displaying a positive correlation with glomerular filtration rate, as consistent with the findings illustrated in Figure 3. These trends suggest that a reduction in telomere length and T/S ratio may correspond to an increased risk of renal impairment, diminished filtration rate and metabolic function, and a compromised immune response to exogenous threats. This observation prompts speculation about a potential close link between renal function and telomere length. To explore this relationship further, we conducted a multifactorial stepwise regression analysis, utilizing physical examination indicators related to chronological age, including PP, Pulse, PR interval, ALB, creatinine, eGFR, CYS-C, GLU, HbA1c, TCHO, and TG, as independent variables. The T/S value of relative telomere length served as the dependent variable. Results presented in Table 2 revealed significant associations between TL and eGFR. Specifically, eGFR exhibited a significant positive association with TL (B = 0.045, SE = 0.013, β = 0.146, t = 3.367, P = .001**). The regression model accounted for 21% of the variance in TL, with an adjusted R-squared of 19%. The F test value (F = 11.339, P = .001) underscored the statistical significance of the regression model. Additionally, the Durbin–Watson statistic (D–W) of 1.565 indicated no significant autocorrelation in the residuals. The T/S ratio was considered as the dependent variable, with significance levels denoted as *P < .05 and **P < .01.

Table 2.

Multifactorial stepwise regression of TL and physical examination indicators.

	Non-standardized coefficients		Standardized coefficients	t	P
	B	SE	Beta (β)
Constant	0.138	0.013	–	10.333	.000**
eGFR	0.045	0.013	0.146	3.367	.001**
R-squared	0.21
Adjusted R-squared	0.19
F	F = 11.339, P = .001
D-W	1.565

Dependent variable: T/S ratio.

eGFR = estimated glomerular filtration rate, TL = telomere length.

*P < .05.

^**P < .01.

4. Discussion

In our current investigation, we embraced a diverse range of participants, spanning from 18 to 99 years old. Notably, we maintained an equitable distribution of both gender and age, a deliberate approach in alignment with the aging assessment needs model. On the downside, we did not specifically analyze the interference of factors such as the population’s sleep quality, dietary habits, whether or not they were physically active, and stress levels, and the analysis of the impact of such complex factors needs to be further explored. The outcomes of our study unequivocally demonstrated a noteworthy linear correlation between age and the telomere length T/S ratio of blood cells (r² = 0.067, P < .001). The observed linear correlation substantiates previous findings that telomere length shortens progressively with advancing age.^[6–8] It affirms that our used Q-PCR method is efficacy, convenience, and feasibility. Based on the statistical data of this study, it can be seen that there is a significant decline in heart, liver, kidney, and organ/system functions related to glucose and lipid metabolism with age, and the older the age, the more severe the organ/system function aging is exhibited. This chronological aging is reflected in various tissues and organs, suggesting that an increase in the duration and frequency of use is followed by a decrease in the function of organs and systems. However, it’s important to note that not all indicators exhibit a linear correlation with age. For instance, no significant correlation was observed between QRS complex in cardiac indicators,^[15] GLB, ALT, AST in liver indicators,^[16,17] and BMI in baseline measurements with actual age. This lack of correlation can likely be attributed to various factors. QRS complex are primarily regulated by ventricular depolarization, while GLB is influenced by immune disorders. Enzymes like ALT and AST are subject to regulation by stress within the body. These indicators typically remain relatively stable in normal conditions, and abnormal states are characterized by rapid and variable changes, often several times higher than normal levels,^[19] making it difficult to discern a clear correlation trend. Additionally, the correlation between BMI and age is not distinctly evident, as it is influenced by factors such as height, weight, lifestyle, and dietary habits.

The ability to quantitatively assess telomere length contributes to our understanding of biological aging changes in organ and system function. The salient finding in this study is that telomere length measurements are particularly relevant to the kidney in assessing the aging process of vital organs and systems, as evidenced by strong correlations between telomere length and indices such as glomerular filtration rate, creatinine, and cystatin C. The results of this study are presented in the following table. Telomere length testing mainly reflects the biological aging characteristics of the kidneys. Furthermore, multifactorial stepwise regression results analyzed eGFR as an independent determinant of the effect of biological aging on telomere length wear. However, it is important to emphasize that these relationships do not necessarily directly explain the relationship between the kidney and telomere length injury processes. In this regard, the mechanisms explaining the relationship between telomere length and the kidney need to be further investigated.

There are some possible explanations and underlying mechanisms for this association. Firstly, kidney is an important organ that regulates both water and electrolytes in the body and maintains the osmotic pressure and acid-base balance in the body. The kidney helps the body filter harmful substances like a sieve in the blood and retains nutrients such as glucose sugar, proteins, and amino acids through its reabsorption function, thus maintaining the normal physiological activities of the body.^[20] The kidneys have about 2 million renal units which are almost nonrenewable and work all the time. The glomerular filtration rate of a healthy adult is about 120 ml/min. Healthy kidneys help the body filter 180 L of everyday. 99% is reabsorbed by the renal tubules and 1.8 L of urine is excreted.^[21] It can be seen that kidneys have a powerful regulatory role in the physiological health of the human body. It has been reported that renal unit obstruction or filtration dysfunction triggers inflammation, oxidative stress, and metabolic disorders, which in turn have an impact on telomere length.^[21–23] There is now growing evidence of profound crosstalk between the kidneys and other organs, with most organs of the body affected when there is a health problem in the kidneys, e.g., slowing of the fluid circulation, making it difficult to eliminate metabolites from the body; and increased cardiac preload, reducing myocardial contractility.^[24] The gut microbiota is disturbed and intestinal permeability is increased^[25]; this results in elevated levels of inflammatory molecules and further exacerbates systemic inflammation, oxidative stress and fibrosis, and the immune system is affected to some extent.^[26,27]

There are a number of limitations to our study. First, we used a cross-sectional design, which involves surveying and observing a group of people at a specific point in time or time period, collecting data and analyzing it. It has the inherent limitation of not being able to infer causality, only correlation can be observed, and therefore, we were unable to arrive at a causal judgment of kidney on telomere length. This would require a longitudinal design in future investigations to track changes in participants and validate our findings. Second, we did not consider certain potential confounders such as sleep quality, dietary habits, lifestyle and environmental factors. These factors may influence the relationship between physical examination markers and aging and need to be further explored in future studies. However, our study used a comprehensive range of physical examination indices and analyzed a large sample of people from different age groups to elucidate the relationship between organ/system function and age, and with telomere length. In particular, the discovery of a monetary reconnection between EGFR, a key physical examination indicator of renal function, and telomere length provides new insights into the relationship between biological age and health.

5. Conclusion

This study highlights the significant link between renal hypoplasia with shortened telomere length. Telomere length is more responsive to kidney risk than age and time. Notably, with changing demographics, these findings highlight the importance of renal maintenance as a potential contributor to accelerated physiologic aging, as renal hypoplasia is an important factor.

Acknowledgments

The authors thank all the survey teams of the study group for their contribution and the study participants who contributed their information.

Author contributions

Conceptualization: Yuanlong Ge, Huiling Lou.

Data curation: Yiting Fu, Yuanlong Ge.

Funding acquisition: Hansen Chen, Huiling Lou.

Investigation: Yiting Fu, Qiaocong Chen, Shu Wu, Yuanlong Ge.

Project administration: Hansen Chen, Chunzhen Zhao.

Supervision: Yuanlong Ge.

Writing – original draft: Yiting Fu, Kaixin Liang.

Writing – review & editing: Yuanlong Ge, Chunzhen Zhao.