Tumor necrosis factor-alpha mediates the association between telomere length and kidney dysfunction in patients with hypertension

doi:10.21203/rs.3.rs-2815542/v1

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Tumor necrosis factor-alpha mediates the association between telomere length and kidney dysfunction in patients with hypertension

https://doi.org/10.21203/rs.3.rs-2815542/v1

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Aim/hypothesis: The relationship between peripheral blood leukocyte telomere length (LTL) and kidney dysfunction, especially in people with hypertension, remains unclear. No clinical study has explored the role of oxidative stress and inflammatory markers in the relationship between LTL and kidney dysfunction. Therefore, we examined the relationship between baseline LTL and albuminuria progression and/or rapid renal function decline in Chinese patients with or without hypertension, and investigated whether oxidative stress and inflammation play a mediating role in this relationship.

Methods: We conducted a prospective study including 262 patients in a 7-year follow-up period from 2014 to 2021. Data on LTL, inflammation, oxidative markers, renal function, and urine protein levels were assessed. Kidney dysfunction was defined as either albuminuria progression, rapid decline in renal function, or the composite endpoint (albuminuria progression and rapid decline in renal function). Logistic regression and simple mediation models were used for the analysis.

Results: In this cohort (mean age, 53.18±11.32 years; follow-up period, 5.97±1.16 years), 43, 22, and 59 patients developed albuminuria progression, rapid renal decline, and the composite endpoint of kidney dysfunction, respectively. Logistic regression analysis showed that each standard deviation decrease in the lower quartile (Q) of baseline LTL was correlated with an increased risk of albuminuria progression (odds ratio [OR]=1.493 [95% confidence interval (CI) 0.985, 2.263], P=0.059; OR=3.307 [95% CI 1.033, 10.586], P=0.044; Q2 vs. Q4); rapid renal function decline (OR=2.402 [95% CI 1.361, 4.239], P=0.002; OR=13.457 [95% CI 1.610, 112.472], P=0.016; Q1 vs. Q4); and the composite endpoint of kidney dysfunction (OR=1.797 [95% CI 1.244, 2.594], P=0.002; OR=4.062 [95% CI 1.426, 11.568], P=0.009; Q1 vs. Q4). Subgroup analyses showed that LTL was inversely associated with albuminuria progression and the composite endpoint of kidney dysfunction in patients with hypertension (OR=5.671 [95% CI 1.203, 26.726], P=0.028; OR=4.223 [95% CI 1.297, 13.753], P=0.017), but not in those without hypertension. The mediating analysis showed that tumor necrosis factor (TNF)-α partly mediated the relationship between LTL and rapid decline in renal function (direct effect: β= –0.6791 [–1.2145, –0.1438]; indirect effect: β= –0.2919 [–0.5190, –0.1177]).

Conclusion: Baseline LTL could independently predict kidney dysfunction at follow-up, especially in participants with hypertension. TNF-α partially mediated the negative association between LTL and kidney dysfunction.

Leukocyte Telomere Length

Renal Dysfunction

Albuminuria

Renal Function Decline

Hypertension

TNF-α

Telomeres are tandem repeats of the eukaryotic chromosomal terminal DNA sequence TTAGGG. Leukocyte telomere length (LTL) progressively shortens with each cell division, and telomeres attrition is a hallmark of cellular aging. Additionally, LTL has been associated with cardiovascular diseases, diabetes, cancer, and chronic kidney disease (CKD)[1].

LTL is associated with CKD and end-stage renal disease (ESKD) development, and albuminuria progression[2–6]. However, the association between LTL and the declining estimated glomerular filtration rate (eGFR) remains inconclusive. The rate of eGFR decline is a useful indicator of kidney dysfunction; and an annual eGFR decline of 3.3% or more, defined as ‘rapid decline,’ has been associated with a higher risk of developing ESKD[7]. Prospective studies have reported a high incidence of a rapid decline in eGFR in patients with diabetes with short telomeres[8]. However, in another prospective study during 8 years of follow-up, buccal telomere length was not associated with the changing rate of eGFR[9].

The most common causes of CKD are poorly controlled diabetes and hypertension[10]. However, the relationship between LTL and kidney dysfunction has mostly been studied in general populations and participants with either diabetes, heart failure, or cardiovascular risk[3, 11–13]. Hypertension causes small renal artery sclerosis, interstitial inflammatory fibrosis, and tubular atrophy or loss, thereby resulting in renal damage or decreased renal function[14]. A meta-analysis of 16 studies showed that patients with hypertension and prehypertension have an increased risk of decreased eGFR[15]. However, the relationship between LTL and kidney dysfunction, a leading cause of CKD, has not been studied in participants with hypertension.

Moreover, the mechanisms underlying the association between LTL and kidney dysfunction remain unclear. Some animal studies have shown that chronic oxidative stress and increased tumor necrosis factor (TNF)-α levels play important roles in LTL shortening as well as kidney dysfunction[16–18]; however, previous clinical studies that reported the relationship between LTL and kidney dysfunction failed to explore the role of inflammation and oxidative stress because of the lack of data on these indices[5, 13]. Therefore, it is necessary to examine these markers to study the relationship between LTL and kidney dysfunction.

The purpose of our study was to examine the relationship between baseline LTL and albuminuria progression and/or rapid renal function decline during follow-up in patients with or without hypertension in rural Chinese communities and to investigate whether oxidative stress plays a mediating role in this relationship.

2.1. Study population

The current prospective study, which included a Chinese cohort from the suburb of Changping, Beijing, was conducted between March 2014 and July 2021, during a mean follow-up of 5.97±1.16 years. The research protocol and consent procedures were approved by the Ethics Committee of the Peking Union Medical College Hospital. All the participants provided written informed consent. Of a total of 599 participants aged between 18 and 81 years recruited into the study in 2014, 198 (148, 25, 1, and 24 lacked baseline LTL results, albuminuria results, serum creatinine results, and had a baseline urine albumin-creatinine ratio [ACR] ≥300 mg/g Cr or eGFR <60 mL/min/1.73 m², respectively) were excluded. Finally, 401 participants were included in this study.

Demographic data collected at baseline included age; sex; history of use of anti-glycemic, antihypertensive, and anti-lipid drugs; and history of diabetes, hypertension, and hyperlipidemia. Anthropometric data, including height, weight, waist circumference (WC), hip circumference, systolic blood pressure (SBP), and diastolic blood pressure (DBP), were collected. The formula for calculating the body mass index (BMI) was height (in meter)/weight (in kilogram) squared. Overweight and obesity were defined as 24 kg/m² ≤BMI <28 kg/m² and BMI ≥ 28 kg/m², respectively.

Urine and blood samples were collected from participants during follow-up to assess albuminuria status and renal function progression. According to the World Health Organization criteria, normal glucose tolerance (NGT) was identified using the 75-g oral glucose tolerance test: fasting plasma glucose (FPG) <6.1 mmol/L and 2-hour post-load plasma glucose (2h-PG) <7.8 mmol/L. Pre-diabetes was indicated by impaired fasting glucose measured as 6.1 mmol/L ≤ FPG <7.0 mmol/L and 2h-PG <7.8 mmol/L, impaired glucose tolerance (IGT) measured as 7.8 ≤2h-PG <11.1 mmol/L and FPG <6.1 mmol/L, or both impaired fasting glucose and IGT. Diabetes was indicated by FPG ≥7.0 mmol/L or 2h-PG ≥11.1 mmol/L.

2.2. Calculations of eGFR and ACR

eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation, based on the measurement of serum creatinine and anthropometric data, such as age and sex. ACR was calculated based on urine microalbumin and urine creatinine levels. The equations are as follows:

eGFR=142 × min (standardized S_cr/K,1)^α× max (standardized S_cr/K,1)^-1.200× 0.9938^Age× 1.012 [if female] mL/min/1.73 m²,

where K=0.7 (female) or 0.9 (male), α= –0.241 (female) or –0.302 (male), and ACR=urinary albumin/urinary creatinine (mg/g Cr).

2.3. Measurement of LTL

LTL assays were performed using the blood samples collected at baseline. The details of the LTL measurements are described in our previous publication[19]. LTL was determined, as the ratio of the telomere repeat copy number to the single copy number (T/S ratio), using a monochrome multiplex quantitative polymerase chain reaction protocol.

2.4. Assessment of oxidative stress and inflammatory markers

Oxidative stress and inflammatory marker levels were measured using the blood samples collected at baseline. TNF-α concentrations was determined using an ELISA kit (Cloud-Clone Corp, Houston, USA) by the Beijing Institute of Biotechnology.

2.5. Definition of hypertension

Hypertension was defined either as having been diagnosed with hypertension, taking antihypertensive drugs, or SBP ≥140 mmHg and DBP ≥90 mmHg at rest.

2.6. Definition of the outcomes

Kidney dysfunction was defined according to the following: (1) Decline in eGFR ≤60 mL/min/1.73 m²or a rapid decline in eGFR. A total of 261 participants with at least two eGFR measurements during follow-up were included in the calculation of the eGFR slope. Linear mixed-effects regression was used to calculate the eGFR slope for each individual, which was then re-expressed as the percentage change per year of eGFR. A rapid decline in eGFR was defined as a decline of ≥3.3% in eGFR per year[20, 21]. (2) Albuminuria progression was defined as the development of either microalbuminuria or macroalbuminuria from normal albuminuria at baseline, or macroalbuminuria from microalbuminuria at baseline. (3) Decline in eGFR and albuminuria progression (the composite endpoint, defined as the presence of albuminuria progression or a rapid decline in eGFR of ≥3.3% per year).

2.7. Statistical analysis

Participants were divided into quartiles (Q) based on their LTL levels. Continuous variables with normal distribution are expressed as mean ± standard deviations (SDs), whereas non-normally distributed variables are presented as median and interquartile range. Categorical variables are expressed as numbers with corresponding percentages. Comparisons between groups were performed using a one-way analysis of variance for continuous variables and the c² test for categorical variables. Bonferroni correction was used for post-hoc comparisons.

The associations between LTL and the outcomes were analyzed using logistic regression analysis, with odds ratios (OR) and associated 95% confidence intervals (CIs) computed for quartiles of baseline LTL and one SD change in baseline LTL. For the computation of ORs for one SD decrease in baseline LTL, a minus z-score for LTL was calculated for each participant. Four models were generated: Model 1 was adjusted for age and sex; Model 2 for Model 1 plus BMI and WC; Model 3 for Model 2 plus diabetes status, history of hypertension, and hyperlipidemia; and Model 4 for Model 3 plus SBP, DBP, and biochemical indicators (total cholesterol [TC], triglycerides [TG], high-density lipoprotein cholesterol [HDL-C], and low-density lipoprotein cholesterol [LDL-C]).

To explore whether oxidative stress and inflammatory markers mediated the effect of LTL on kidney dysfunction, PROCESS macro version 3.4.1 (www.afhayes.com)was used to generate simple mediation models with ordinary least squares.

A two-sided P<0.05 was considered statistically significant. Statistical analysis was performed using IBM SPSS Statistics for Windows, version 26.0 (IBM Corporation, Armonk, NY, USA).

3.1. Baseline characteristics of the study populations

The baseline characteristics of the study populations in different LTL quartiles are shown in Table 1. Participants in the first LTL quartile had a lower eGFR (P < 0.05) than those in the other quartiles. TNF-α decreased with increasing LTL, with the lowest concentration of 17.61 ± 7.94 pmol/L in the Q4 group (P = 0.000). The groups did not differ in terms of age, sex, BMI, waist-to-hip ratio, SBP, DBP, FPG, HbA1_C TC, TG, HDL-C and LDL-C (all P > 0.05). Additionally, groups did not differ in terms of the percentages of participants with microalbuminuria, diabetes or pre-diabetes, hypertension, hyperlipidemia, hyperuricemia, and obesity (all P > 0.05).

Table 1

Differences in participant baseline characteristics between LTL quartiles
	Q1 (n = 101)	Q2 (n = 100)	Q3 (n = 100)	Q4 (n = 100)	P
Age, years	54.00 ± 10.73	52.39 ± 11.64	52.89 ± 12.48	53.45 ± 10.43	0.769
Male sex, n, (%)	35(34.65)	27 (27.00)	38 (38.00)	36 (36.00)	0.377
BMI, kg/m²	25.92 ± 3.55	25.93 ± 4.30	27.00 ± 6.45	27.00 ± 6.49	0.269
WHR	0.95 ± 0.01	0.94 ± 0.01	0.97 ± 0.09	0.96 ± 0.07	0.743
SBP, mmHg	129.21 ± 19.10	125.00 ± 14.57	128.33 ± 17.21	129.34 ± 23.81	0.339
DBP, mmHg	77.38 ± 9.24	75.85 ± 9.38	75.69 ± 9.68	75.59 ± 10.66	0.526
FPG, mmol/L	6.52 ± 2.09	6.07 ± 1.07	6.60 ± 1.82	6.65 ± 2.16	0.109
HbA1c, %	6.03 ± 1.19	5.77 ± 0.79	5.91 ± 1.12	5.69 ± 0.95	0.101
TC, mmol/L	5.50 ± 1.16	5.46 ± 1.08	5.57 ± 1.08	5.53 ± 1.06	0.909
TG, mmol/L	1.47 (1.08, 2.08)	1.39 (0.98, 2.09)	1.50 (1.09, 2.12)	1.23 (0.87, 1.95)	0.267
HDL-C, mmol/L	1.34 ± 0.61	1.31 ± 0.25	1.33 ± 0.47	1.27 ± 0.29	0.657
LDL-C, mmol/L	2.87 ± 0.79	2.85 ± 0.70	2.86 ± 0.79	2.86 ± 0.71	0.995
eGFR, mL/min/1.73 m²	94.14 ± 16.20	100.09 ± 14.12	97.93 ± 16.08	96.88 ± 14.06	0.047*
ACR, mg/g Cr	8.82(5.27,15.84)	12.84 (6.72, 16.62)	11.12 (5.67, 21.34)	11.18 (6.66, 19.95)	0.754
Microalbuminuria, n (%)	19 (18.81)	18 (18.00)	16 (16.00)	17 (17.00)	0.958
Diabetes or pre-diabetes (%)	54 (53.5%)	57 (57%)	59 (59%)	60 (60%)	0.910
Hypertension (%)	33 (32.7%)	26 (26.0%)	26 (26.0%)	35 (35.0%)	0.382
Hyperlipidemia (%)	63 (62.4%)	56 (56.0%)	61 (61.0%)	66 (66.0%)	0.527
Hyperuricemia (%)	12 (11.9%)	14 (14.0%)	7 (7.0%)	6 (6.0%)	0.173
Obesity (%)	29 (28.7%)	25 (25.0%)	33 (33.0%)	27 (27.0%)	0.624
TNF-α(pmol/L)	28.54 ± 9.33	26.66 ± 11.01	21.97 ± 9.38	17.61 ± 7.94	0.000

Data are presented as mean ± standard deviation or median (Q1, Q3) for skewed variables or proportion of participants (%), as appropriate.

*Statistically significant

LTL, leukocyte telomere length; Q, quartile; BMI, body mass index; WHR, waist-to-hip ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; ACR, urine albumin-creatinine ratio; TNF-α, tumor necrosis factor-α; IL-6, interleukine-6; SOD, superoxide dismutase; GR, glutathione reductase

3.2. Associations of baseline LTL with the risk of albuminuria progression and/or rapid decline in renal function

Associations of baseline LTL with the risk of albuminuria progression

Baseline LTL was negatively correlated with albuminuria progression, but after adjusting for all confounding factors, the association became borderline significant (OR = 1.493 [95% CI 0.985, 2.263]; P = 0.059) (Table 2). When LTL was modeled as quartiles, the negative association reached a significant level when comparing Q2 with Q4 (OR = 3.307 [95% CI 1.033, 10.586]; P = 0.044; P for trend = 0.164) (Table 3).

Table 2

Results of the logistic regression models of the effect of covariates on the association between baseline LTL and the risk of albuminuria progression and a rapid decline in renal function
	Albuminuria progression		Rapid decline in renal function		Composite endpoint
	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P
Model 1	1.528 (1.072, 2.177) *	0.019	2.321 (1.430, 3.766) *	0.001	1.811 (1.317, 2.491) *	0.000
Model 2	1.530 (1.059, 2.211) *	0.024	2.368 (1.432, 3.915) *	0.001	1.846 (1.327, 2.568) *	0.000
Model 3	1.478 (0.997, 2.191)	0.052	2.400 (1.414, 4.073) *	0.001	1.822 (1.283, 2.587) *	0.001
Model 4	1.493 (0.985, 2.263)	0.059	2.402 (1.361, 4.239) *	0.002	1.797 (1.244, 2.594) *	0.002

Model 1: adjusted for sex and age. Model 2: adjusted for Model 1 + body mass index and waist circumference. Model 3: adjusted for Model 2 + history of hypertension, hyperlipidemia, albuminuria, and diabetes status. Model 4: adjusted for Model 3 + systolic blood pressure, diastolic blood pressure, and blood biochemistry (total cholesterol, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol).

LTL, leukocyte telomere length; OR, odds ratio; CI, confidence interval

*Statistically significant

Table 3

Results of logistic regression models of the effect of covariates on the association between baseline LTL and the risk of albuminuria progression and/or rapid decline in renal function
	Albuminuria progression				Rapid decline in renal function				Composite endpoint
	Q1 vs. Q4	Q2 vs. Q4	Q3 vs. Q4	P for trend	Q1 vs. Q4	Q2 vs. Q4	Q3 vs. Q4	P for trend	Q1 vs. Q4	Q2 vs. Q4	Q3 vs. Q4	P for trend
	OR (95% CI)	OR (95% CI)	OR (95% CI)		OR (95% CI)	OR (95% CI)	OR (95% CI)		OR (95% CI)	OR (95% CI)	OR (95% CI)
Model 1	2.473 (0.882, 6.937)	2.482 (0.875, 7.043)	1.179 (0.369, 3.773)	0.165	13.943* (1.768, 109.984)	5.142 (0.579, 45.655)	2.012 (0.177, 22.855)	0.007	4.159* (1.635, 10.579)	3.224* (1.233, 8.430)	1.321 (0.456, 3.827)	0.004
Model 2	2.870 (0.954, 8.637)	2.991 (0.990, 9.037)	1.512 (0.447, 5.111)	0.147	13.669* (1.720, 108.628)	5.494 (0.617, 48.940)	1.990 (0.174, 22.718)	0.009	4.855* (1.809, 13.035)	3.841* (1.398, 10.551)	1.650 (0.545, 4.996)	0.004
Model 3	2.356 (0.749, 7.411)	3.004 (0.996, 9.155)	1.296 (0.370, 4.547)	0.177	14.215* (1.741, 116.071)	5.449 (0.606, 48.968)	1.800 (0.155, 20.917)	0.010	4.255* (1.539, 11.762)	3.882* (1.403, 10.740)	1.437 (0.462, 4.467)	0.008
Model 4	2.434 (0.732, 8.090)	3.307* (1.033, 10.586)	1.363 (0.379, 4.902)	0.164	13.457* (1.610, 112.472)	4.706 (0.505, 43.901)	1.796 (0.153, 21.113)	0.015	4.062* (1.426, 11.568)	3.742* (1.325, 10.572)	1.442 (0.459, 4.532)	0.016
Model 1: adjusted for sex and age. Model 2: adjusted for Model 1 + body mass index and waist circumference. Model 3: adjusted for Model 2 + history of hypertension, hyperlipidemia, and diabetes status. Model 4: adjusted for Model 3 + systolic blood pressure, diastolic blood pressure, and blood biochemistry (total cholesterol, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol).
LTL, leukocyte telomere length; Q, quartile; OR, odds ratio; CI confidence interval
* Statistically significant

Associations of baseline LTL with the risk of rapid decline in renal function

After adjusting for traditional risk factors, LTL was inversely correlated with a rapid decline in renal function; and with each SD reduction in LTL, the risk of a rapid decline in renal function increased by 140.2% (OR = 2.402 [95% CI 1.362, 4.239]; P = 0.002) (Table 2). In addition, the ORs of rapid decline in renal function increased with decreasing LTL quartiles; and the OR of the highest quartile was 13.457 in the fully adjusted Model 4 (95% CI 1.610, 112.472]; P = 0.016; P for trend = 0.015) (Table 3).

Associations of baseline LTL with the risk of kidney dysfunction

After adjusting for traditional risk factors, LTL was inversely associated with the composite endpoint of kidney dysfunction (albuminuria progression and rapid decline in renal function). With each SD decline in LTL, the risk of composite endpoint increased by 79.7% (OR = 1.797 [95% CI 1.244, 2.594]; P = 0.002), and the OR was 4.062 for Q1 versus Q4 of LTL (95% CI 1.426, 11.568]; P = 0.009; P for trend = 0.016) (Tables 2 and 3).

3.3. Mediation model of the association among TNF-α, LTL, and a rapid decline in renal function

In the mediation analysis, LTL was found to have both significant direct and indirect effects on rapid decline in renal function (direct effect: β=–0.7909 [–1.4571, – 0.1248)]; indirect effect: β=–0.3445 [–0.6411, − 0.1310]), whereas TNF-α partly mediated this negative effect of LTL on rapid decline in renal function (Fig. 1). Mediation analysis between LTL and albuminuria progression or the composite endpoint of kidney dysfunction did not reach a significant level.

3.4. Risk association of baseline LTL with the risk of albuminuria progression and a rapid decline in renal function in participants with or without hypertension

Among 262 participants that were followed-up, 81 had hypertension. Subgroup analysis showed that LTL was significantly negatively associated with albuminuria progression (OR = 5.671 [95% CI 1.203, 26.726], P = 0.028) and the composite endpoint of kidney dysfunction (OR = 4.223 [95% CI 1.297, 13.753], P = 0.017) in participants with hypertension, only after adjusting for confounding factors (Table 4). However, the negative relationship between LTL and rapid decline in renal function was not significant in participants with hypertension, even after adjusting for all confounding factors (OR = 4.308 [95% CI 0.458, 40.544], P = 0.202; OR = 8.761 [95% CI 1.820, 42.182], P = 0.007) (Table 4). These results can probably be attributed to the small number of participants. However, no mediating effects were found in the subgroup analysis of participants with and without hypertension.

Table 4

Results of the logistic regression models of the effect of covariates on the association between baseline LTL and the risk of albuminuria progression and rapid decline in renal function in patients with or without hypertension
	With hypertension			Without hypertension
	Albuminuria progression	Rapid decline in renal function	Composite endpoint	Albuminuria progression	Rapid decline in renal function	Composite endpoint
	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
Model 1	2.481* (1.250, 4.923)	4.251* (1.544, 11.706)	3.286* (1.675, 6.443)	1.211 (0.788, 1.859)	1.840* (1.015, 3.336)	1.404 (0.963, 2.046)
Model 2	2.298* (1.112, 4.748)	4.973* (1.556, 15.893)	3.255* (1.587, 6.675)	1.276 (0.824, 1.978)	1.832 (0.998, 3.363)	1.478* (1.004, 2.176)
Model 3	2.189 (0.974, 4.916)	8.761* (1.820, 42.182)	3.407* (1.549, 7.495)	1.317 (0.835, 2.078)	1.852 (0.999, 3.434)	1.503* (1.005, 2.245)
Model 4	5.671* (1.203, 26.726)	4.308 (0.458, 40.544)	4.223* (1.297, 13.753)	1.347 (0.829, 2.186)	1.764 (0.895, 3.477)	1.494 (0.975, 2.290)

Model 1: adjusted for sex and age. Model 2: adjusted for Model 1 + body mass index and waist circumference. Model 3: adjusted for Model 2 + history of hypertension, hyperlipidemia, and diabetes status. Model 4: adjusted for Model 3 + systolic blood pressure, diastolic blood pressure, and blood biochemistry (total cholesterol, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol).

* Statistically significant

In this prospective study of 401 adults in a rural Chinese community, we demonstrated an independent negative association between baseline LTL and kidney dysfunction (including rapid decline in renal function, albuminuria progression, and the composite endpoint of kidney dysfunction) during follow-up. Additionally, we showed that TNF-α partly mediated the association between LTL and rapid renal function decline. Lastly, we found a negative association between baseline LTL and kidney dysfunction (albuminuria progression and the composite endpoint of kidney dysfunction), which remained significant only in participants with hypertension in the subgroup analysis.

This study posed three central questions: 1) whether there was an association between LTL and kidney dysfunction; 2) whether oxidative stress and inflammation played a mediating role in the association between LTL and kidney dysfunction; and 3) what were the different associations between baseline LTL and kidney dysfunction with or without hypertension.

For the first question, we found a negative association between LTL and albuminuria progression and the composite endpoint of kidney dysfunction, which is consistent with previous studies. A prospective study including 132 individuals with type 1 diabetes and another study including 691 individuals with type 2 diabetes showed that telomere length is associated with proteinuria progression[6, 22]. In addition, an American study with 889 participants (a third of whom had diabetes) and another study with 4085 participants with type 2 diabetes showed that LTL was negatively associated with CKD progression (defined as advanced renal failure or requiring renal replacement therapy [dialysis treatment or kidney transplantation])[3, 8]. Furthermore, we also found a negative relationship between LTL and rapid decline in renal function, a finding that was not always consistent with that of previous studies. The Mild to Moderate Kidney Disease (MMKD) study including 166 patients with CKD at a median follow-up of 4.5 years found that LTL was shorter in patients who exhibited doubling of baseline serum creatinine levels[22]. Another study including 3964 participants at a follow-up of 8 years found that participants with shorter buccal telomere lengths were more likely to have a normal-to-impaired kidney function trajectory[9]. Recently, a large and long-term follow-up study of 4085 Chinese individuals with type 2 diabetes showed robust results, indicating that shorter LTL at baseline is associated with a rapid decline in eGFR (> 4% per year) during 14 years follow-up[3], which directly supports our results. However, a study including 151 patients with type 1 diabetes and a follow-up of 11.1 years did not find any association between telomere length and the rate of decline in eGFR[9]. Similarly, another study found that telomere length was not associated with the rate of change in eGFR over a follow-up of 8 years in healthy individuals[23].

Regarding the second study question, our result suggests that LTL shortening increases the risk of rapid decline in renal function, in which TNF-α plays a mediating role. The relationship between telomeres and inflammation is complex. Inflammation can lead to telomeres dysfunction and cause telomeres shortening[24, 25], whereas telomeres shortening can cause an inflammatory response[26]. In in vitro experiments, the cells with the shortest telomere exhibited the highest levels of TNF-α expression[27]. Animal studies have also shown that TNF-α levels are increased in animals with shorter telomeres[28]. An in vitro experiment revealed that short telomeres can mediate additional downstream activation of ZBP1(S), via TERRA, to form ZBP1 filaments on the mitochondria, thereby promoting the inflammatory cycle[29]. In contrast, other animal experiments have shown that inflammation and oxidative stress significantly contribute to renal damage associated with hypertension, and cytokines, such as interferon-γ, TNF-α, and IL-17, affect Na+/H + exchangers in the kidney[16]. In addition, blocking TNF-α using a murine anti-TNF-α antibody confers kidney protection compared to that in a vehicle-treated Ins2^Akita (with diabetes) mice[18]. Thus, we hypothesized that the adverse effect of telomeres shortening on mitochondrial function might worsen inflammation, increase TNF-α levels, and accelerate kidney dysfunction. Our study indicates that TNF-α might mediate the negative relationship between LTL and kidney dysfunction from a clinical perspective. Further studies are required to clarify the details of these mechanisms.

Regarding the last question, we found a more prominent association between shorter LTL and the risk of kidney dysfunction in participants with hypertension than that in participants without hypertension. Hypertension increases the risk of decreased eGFR, blood pressure is an important predictor of early renal function decline[30, 31], and oxidative stress and inflammation might play important roles in these outcomes[32]. Although no studies have reported such results, a previous study showed that the relationship between LTL and CKD progression is stronger in smokers and those with diabetes who are more likely to have higher inflammatory and oxidative stress statuses[22]. Thus, together with the previous study, our results suggest that renal function does not respond to LTL per se, but to the risk factors inflammation and oxidative stress, which are influenced by LTL shortening. LTL could be a useful predictor of renal dysfunction in patients with hypertension.

Our study has several strengths. First, our analysis focused on the relationship between LTL and kidney dysfunction in participants with hypertension, for which there are few related studies. Second, we analyzed the role of inflammatory stress in the LTL-CKD link and found that TNF-α mediates the effect between LTL and kidney dysfunction. However, our study has some drawbacks. This study included a small sample size and could not draw causal conclusions because of the study design. Future studies will expand the sample size and extend the follow-up period.

LTL was negatively associated with the development of albuminuria or rapid decline in renal function during follow-up. TNF-α partially mediates the association between LTL and rapid decline in renal function. The association between LTL and kidney dysfunction is more pronounced in patients with hypertension. Therefore, LTL may be an important clinical predictor of kidney dysfunction in patients with hypertension. Future studies should be conducted using a larger population to better understand the role of LTL in changes in kidney function.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Funding

This research was supported by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (CIFMS2021-I2M-1-002) and National Natural Science Foundation of China (Grant No. 82100947).

Acknowledgments

Not applicable.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Ethics Statement

The experiments complied with the current laws of China, including ethical approval for the clinical study. All phases of the study complied with the Ethical Principles for Medical Research Involving Human Subjects as expressed in the Declaration of Helsinki.

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Tumor necrosis factor-alpha mediates the association between telomere length and kidney dysfunction in patients with hypertension

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Abstract

Figures

1. Introduction

2. Methods

3. Results

3.1. Baseline characteristics of the study populations

Associations of baseline LTL with the risk of albuminuria progression

Associations of baseline LTL with the risk of rapid decline in renal function

Associations of baseline LTL with the risk of kidney dysfunction

4. Discussion

5. Conclusion

Declarations

References

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