Abstract
Background
The relationship between leukocyte telomere length (LTL) and mortality risk in individuals with metabolic syndrome (MetS) remains poorly understood. This study aimed to investigate the association between telomere length and long-term all-cause mortality, and cardiovascular disease (CVD) mortality, in individuals with MetS in the United States.
Methods
A total of 1980 participants with MetS aged 18 years or older from the National Health and Nutrition Examination Survey (NHANES) prospective cohort study (1999–2002) were included in this cohort study. Medical records review was used to identify the cause of deaths as of December 2018. We employed Kaplan-Meier curves, fitted curves, and Cox proportional hazards regression models to estimate hazard ratios (HRs) for all-cause and CVD mortality, stratified by tertiles of LTL.
Results
Over a median follow-up of 17.75 years of participants with metabolic syndrome, 819 deaths occurred, including 231 cardiovascular deaths. After adjusting for multiple covariates, participants with shorter telomere length had a significantly higher risk of all-cause mortality (HR, 1.33; 95% CI, 1.11–1.6) and CVD mortality (HR, 1.36; 95% CI, 0.96–1.93) compared with those in the highest tertile of telomere length. All-cause mortality (P < 0.001) and cardiovascular disease mortality (P = 0.028) followed a similar pattern across tertiles of telomere length.
Conclusion
In individuals with MetS, shorter telomere length is associated with increased risks of death from cardiovascular disease and all causes. The underlying mechanisms and clinical implications of these findings require additional investigation.
Keywords: Leucocyte telomere length, Metabolic syndrome, All-cause mortality, Cardiovascular Disease mortality, NHANES
Introduction
Metabolic syndrome (MetS) is a highly prevalent constellation of metabolic abnormalities including central obesity, hyperglycemia, hypertension, and dyslipidemia [1, 2]. The prevalence of MetS has increased significantly in recent years, and it is estimated that 34.7% of adults in the United States have MetS [3]. It is a significant global health challenge due to its association with increased cardiovascular disease (CVD) and mortality risk [4]. The development of metabolic syndrome is also linked to a higher lifetime risk of CVD and a shorter life expectancy without CVD [5].
Telomere length (TL) has gained attention as a possible biomarker of age and age-related illnesses in recent years. Telomeres are DNA-protein complexes that prevent damage to and malfunction at the ends of chromosomes. Cellular aging is associated with a reduction in leukocyte telomere length (LTL), which can trigger genomic instability, DNA damage, cellular senescence, or apoptosis. Several age-related disorders, such as cardiovascular disease and cancer, have this as a proposed mechanism of onset [6]. Increasing evidence from recent studies suggests that telomere length may contribute to metabolic health [4, 7, 8]. Reduced LTT has been connected to cellular senescence, inflammation, and an increased risk of age-related illnesses like CVD, diabetes, and mortality [6, 9].
Shorter LTL is associated with an increased risk of mortality in contemporary humans, particularly for non-cancer causes of death such as cardiovascular disease, and LTL may serve as a marker for the natural lifespan limit in humans [10]. However, studies have found contradictory results, indicating that longer leukocyte telomere length increases the risk of cardiovascular mortality in patients with type 2 diabetes [11]. Shorter TL is strongly correlated with multiple components of metabolic syndrome, such as abdominal obesity, dyslipidemia, hyperglycemia, and overall metabolic health, and it also serves as a predictor for an unfavorable metabolic profile [12, 13]. Each standard deviation decrease in TL was associated with a 1.19-fold higher odds of having MetS [12]. These associations persist even after a six-year follow-up [12]. Mendelian randomization study revealed a paradoxical association between LTL (leukocyte telomere length) and the risk of metabolic syndrome [14]. It was observed that higher BMI (body mass index) was linked to shorter LTL, while higher levels of low-density lipoprotein cholesterol were associated with longer LTL [14]. Obesity may be linked to shorter LTL through the mechanisms of increased subclinical inflammation and lower circulating levels of linoleic acid [14]. In another Mendelian randomization study, it was found that longer LTL was associated with a higher waist-to-hip ratio adjusted for body mass index, elevated blood pressure, and increased risk of metabolic syndrome [15]. However, there is limited data regarding how LTL relates to mortality risk in those who have MetS. Consequently, the purpose of this investigation was to examine the potential association between LTL and long-term all-cause and CVD mortality risk in MetS patients in the United States.
Materials and methods
Data sources and preparation
Information from the prospective cohort research known as the National Health and Nutrition Examination Survey (NHANES) was analyzed for this paper. NHANES is a large-scale, nationally representative survey of citizens residing in the United States who are not in institutional settings. Each participant gave their written informed permission after the study was authorized by the NCHS institutional review board. Using a multilevel, stratified probability design, the survey sampled 5,000 participants every year, who were subjected to standardized questionnaires and physical examination procedures. Since 1999, the survey has been carried out, with updated data sets made accessible every two years at https://www.cdc.gov/nchs/nhanes/index.htm. The institutional review board at Shenzhen People’s Hospital determined that the study did not require informed permission because it used publically available, de-identified data.
The NHANES data utilized in this study were collected between 1999 and 2002 and were made available to the public. Our study was limited by the following criteria for elimination: Missing data on telomere length (n = 13,296), height (n = 161), weight (n = 80); marital status (n = 364), education (n = 8), alcohol consumption (n = 353), tobacco use (n = 8), annual family income (n = 223), chronic kidney disease (n = 32), heart attack (n = 7), congestive heart failure (n = 15), coronary heart disease (n = 22), hypertension (n = 1), angina (n = 1), stroke (n = 4), anemia (n = 2), mortality (n = 3), and Non-Mets (n = 4427). Overall, 1980 individuals with MetS were enrolled in this study.
Metabolic syndrome
The criteria for diagnosing MetS include the presence of three or more specific abnormalities out of a total of five, as defined by the American Endocrine Association and the American Society of Clinical Endocrinology. These criteria are elevated waist circumference, elevated triglyceride levels, low levels of high-density lipoprotein cholesterol, elevated blood pressure, and elevated fasting plasma glucose levels. Abdominal obesity, measured by an increased waist circumference, is a significant risk factor for MetS. This criterion is gender-specific, with recommended cutoff values of ≥ 88 cm for females and ≥ 102 cm for males. Elevated triglyceride levels, or treatment for hypertriglyceridemia, is another diagnostic criterion for MetS, with a fasting triglyceride level of ≥ 150 mg/dL considered elevated. Low levels of high-density lipoprotein cholesterol (HDL-C), often referred to as “good” cholesterol, is also a MetS diagnostic criterion. The cutoff values differ between genders, with values < 40 mg/dL for males and < 50 mg/dL for females. Elevated blood pressure, particularly systolic blood pressure, is a major risk factor for heart disease, stroke, and kidney disease. A systolic blood pressure of ≥ 130 mmHg or diastolic blood pressure of ≥ 85 mmHg, or both, are considered indicative of MetS. Individuals who take antihypertensive medication are also considered to meet this criterion. Finally, elevated fasting plasma glucose levels or drug-treated hyperglycemia is also a diagnostic criterion for MetS. Fasting plasma glucose levels ≥ 100 mg/dL is considered a sign of impaired glucose tolerance, which can lead to type 2 diabetes [16].
Telomere length
Blood samples were obtained from the study participants using standardized protocols. The telomere length assay was performed using a polymerase chain reaction. Telomere length was measured as a relative ratio against standard reference DNA (T/S), with each sample tested three times on three different days, in duplicate wells (yielding six data points in total). Detailed information regarding the laboratory methodology, quality control procedures, and data analysis can be found on the NHANES website, under the laboratory section (http://cdc.gov/nchs/nhanes). The interassay coefficient of variation was determined to be 6.5%. Results are presented as the mean T/S ratio along with its corresponding standard deviation. We transformed this to base pairs for analysis based on previous studies [17, 18].
All-cause and CVD mortality
All-cause mortality and CVD mortality were the primary outcomes of interest in this study. All-cause mortality was defined as the number of participants who died due to any cause after completing their baseline survey but before December 31, 2018. The International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify causes of death to obtain data on mortality follow-up from NHANES Public-use Linked Mortality Files (https://www.cdc.gov/nchs/data-linkage/mortality.htm). ICD-10 codes are alphanumeric codes that are used to track diseases and other health-related problems [19]. By using these codes, researchers can track and categorize deaths based on the leading causes of death CVD deaths were categorized using certain ICD codes (054–068). These include coronary artery disease, heart failure,ischemic heart diseases, pericardium disease, acute myocarditis, other heart disease and peripheral artery disease. By categorizing deaths due to CVD using specific ICD codes, researchers can accurately track the number of deaths related to cardiovascular disease.
Covariates
Our study included various clinical and demographic factors as covariates to account for potential sources of confounding. These covariate variables comprised age, sex, body mass index (BMI), race and ethnicity, educational level, marital status, smoking status, alcohol drinking status, annual family income, and chronic diseases. The relevant information on these covariates was obtained from survey responses in NHANES. Participants were categorized according to five racial/ethnic groups: Mexican American, other Hispanic, non-Hispanic White, Black, or Other (including multiracial). Less than high school, high school graduate or equivalent, Some College or AA degree, and college graduate or above were the educational classifications [20]. Marital status was described using seven categories as follows: Never married, Married, Widowed, or Divorced or Separated. Medical conditions such as anemia, angina, heart attack, congestive heart failure, coronary heart disease, chronic kidney disease, asthma, chronic obstructive pulmonary disease (COPD), diabetes mellitus, hypertension, hyperlipidemia, and stroke were diagnosed by a physician or other qualified healthcare professional. The smoking and drinking behaviors were grouped into three categories: never, past, and current use. Using the standard formula, BMI was calculated as weight (kg)/[height (m2) × height (m2)].
Statistical analysis
For continuous variables, 95% confidence intervals (CIs) were provided, while for categorical variables, percentage frequencies were provided. T-tests and χ2 tests were used to compare continuous and categorical data. No imputation approach was applied because all variables had low missing data rates. The mortality risk is calculated using Cox proportional hazards regression models. Curve fitting and Kaplan-Meier curves are visually illustrated. Statistical analyses were carried out using the R software package (http://www.R-project.org, The R Foundation), the nhanesR package, and the Free Statistics software version 1.8. Statistical significance was determined by a two-sided P value < 0.05.
Results
Demographics
The study analyzed a cohort of 1980 individuals with MetS, with 971 (49%) men and 1009 (51%) women with a mean age of 58 ± 16.5 years (Table 1). Participants were divided into three groups based on LTL, with Tertile 1 having the shortest LTL and Tertile 3 having the longest LTL. Significant differences were found between groups for age, Gender, weight, BMI, race, education, income, alcohol use, and smoking history. The shorter telomere group had older patients, more male participants, lower weight, a higher proportion of non-Hispanic whites, former smokers, and a higher percentage of heavy alcohol users. The longer telomere group was younger, had a higher proportion of females, higher BMI, a higher proportion of other Hispanics, never alcohol users, and current smokers.
Table 1.
Baseline Characteristics of Participants with metabolic syndrome in NHANES 1999–2002
| Characteristics | Total (n = 1980) | Tertile 1 (n = 660) | Tertile 2 (n = 660) | Tertile3 (n = 660) | P value |
|---|---|---|---|---|---|
| Age (years), Mean ± SD | 58.0 ± 16.5 | 65.2 ± 14.0 | 57.6 ± 15.6 | 51.1 ± 16.6 | < 0.001 |
| Age group, n (%) | < 0.001 | ||||
| <45 years | 434 (21.9) | 60 (9.1) | 142 (21.5) | 232 (35.2) | |
| 45–64 years | 775 (39.1) | 232 (35.2) | 272 (41.2) | 271 (41.1) | |
| ≥ 65 years | 771 (38.9) | 368 (55.8) | 246 (37.3) | 157 (23.8) | |
| Gender, n (%) | 0.001 | ||||
| Female | 1009 (51.0) | 307 (46.5) | 329 (49.8) | 373 (56.5) | |
| Male | 971 (49.0) | 353 (53.5) | 331 (50.2) | 287 (43.5) | |
| Height (cm), Mean ± SD | 167.2 ± 10.3 | 166.8 ± 10.3 | 167.2 ± 10.5 | 167.5 ± 10.1 | 0.476 |
| Weight(kg), Mean ± SD | 86.8 ± 19.8 | 84.5 ± 19.0 | 88.0 ± 20.9 | 87.9 ± 19.2 | 0.001 |
| BMI(kg/m2), Mean ± SD | 31.0 ± 5.9 | 30.2 ± 5.5 | 31.4 ± 6.3 | 31.3 ± 5.9 | < 0.001 |
| Race, n (%) | 0.002 | ||||
| Mexican American | 438 (22.1) | 151 (22.9) | 162 (24.5) | 125 (18.9) | |
| Non-Hispanic Black | 335 (16.9) | 90 (13.6) | 110 (16.7) | 135 (20.5) | |
| Non-Hispanic White | 1075 (54.3) | 384 (58.2) | 350 (53) | 341 (51.7) | |
| Other Hispanic | 87 ( 4.4) | 24 (3.6) | 24 (3.6) | 39 (5.9) | |
| Other Race - Including Multi-Racial | 45 ( 2.3) | 11 (1.7) | 14 (2.1) | 20 (3) | |
| Marital status, n (%) | < 0.001 | ||||
| Single | 147 ( 7.4) | 22 (3.3) | 44 (6.7) | 81 (12.3) | |
| Married | 1249 (63.1) | 421 (63.8) | 427 (64.7) | 401 (60.8) | |
| Divorced or separated or widowed | 584 (29.5) | 217 (32.9) | 189 (28.6) | 178 (27) | |
| Education, n (%) | < 0.001 | ||||
| College Graduate or above | 351 (17.7) | 103 (15.6) | 124 (18.8) | 124 (18.8) | |
| High School Grad/GED or Equivalent | 475 (24.0) | 149 (22.6) | 152 (23) | 174 (26.4) | |
| less than high school | 691 (34.9) | 277 (42) | 216 (32.7) | 198 (30) | |
| Some College or AA degree | 463 (23.4) | 131 (19.8) | 168 (25.5) | 164 (24.8) | |
| Alcohol user, n (%) | < 0.001 | ||||
| former | 483 (24.4) | 184 (27.9) | 162 (24.5) | 137 (20.8) | |
| heavy | 324 (16.4) | 74 (11.2) | 103 (15.6) | 147 (22.3) | |
| mild | 646 (32.6) | 222 (33.6) | 228 (34.5) | 196 (29.7) | |
| moderate | 209 (10.6) | 65 (9.8) | 73 (11.1) | 71 (10.8) | |
| never | 318 (16.1) | 115 (17.4) | 94 (14.2) | 109 (16.5) | |
| smoking, n (%) | < 0.001 | ||||
| former | 704 (35.6) | 266 (40.3) | 249 (37.7) | 189 (28.6) | |
| never | 958 (48.4) | 305 (46.2) | 307 (46.5) | 346 (52.4) | |
| now | 318 (16.1) | 89 (13.5) | 104 (15.8) | 125 (18.9) | |
| Annual family income, n (%) | < 0.001 | ||||
| $0 to $19,999 | 644 (32.5) | 264 (40) | 182 (27.6) | 198 (30) | |
| $20,000 to $34,999 | 488 (24.6) | 156 (23.6) | 184 (27.9) | 148 (22.4) | |
| $35,000 to $54,999 | 327 (16.5) | 95 (14.4) | 111 (16.8) | 121 (18.3) | |
| more than $55,000 | 521 (26.3) | 145 (22) | 183 (27.7) | 193 (29.2) | |
| Telomere Length (BP), Mean ± SD | 5593.1 ± 591.4 | 5018.5 ± 202.4 | 5515.8 ± 131.2 | 6245.0 ± 479.6 | < 0.001 |
| Telomere T/S, Mean ± SD | 1.0 ± 0.2 | 0.7 ± 0.1 | 0.9 ± 0.1 | 1.2 ± 0.2 | < 0.001 |
| Anemia, n (%) | 0.956 | ||||
| Mild | 90 ( 4.5) | 31 (4.7) | 30 (4.5) | 29 (4.4) | |
| Moderate | 30 ( 1.5) | 11 (1.7) | 11 (1.7) | 8 (1.2) | |
| Non-Aemia | 1859 (93.9) | 618 (93.6) | 619 (93.8) | 622 (94.2) | |
| Severe | 1 ( 0.1) | 0 (0) | 0 (0) | 1 (0.2) | |
| Asthma, n (%) | 0.579 | ||||
| no | 1696 (85.7) | 571 (86.5) | 558 (84.5) | 567 (85.9) | |
| yes | 284 (14.3) | 89 (13.5) | 102 (15.5) | 93 (14.1) | |
| Chronic kidney disease, n (%) | < 0.001 | ||||
| no | 1463 (73.9) | 435 (65.9) | 491 (74.4) | 537 (81.4) | |
| yes | 517 (26.1) | 225 (34.1) | 169 (25.6) | 123 (18.6) | |
| Angina, n (%) | 0.291 | ||||
| no | 1871 (94.5) | 617 (93.5) | 624 (94.5) | 630 (95.5) | |
| yes | 109 ( 5.5) | 43 (6.5) | 36 (5.5) | 30 (4.5) | |
| Heart attack, n (%) | < 0.001 | ||||
| no | 1868 (94.3) | 603 (91.4) | 631 (95.6) | 634 (96.1) | |
| yes | 112 ( 5.7) | 57 (8.6) | 29 (4.4) | 26 (3.9) | |
| Congestive heart failure, n (%) | 0.069 | ||||
| no | 1895 (95.7) | 623 (94.4) | 632 (95.8) | 640 (97) | |
| yes | 85 ( 4.3) | 37 (5.6) | 28 (4.2) | 20 (3) | |
| COPD, n (%) | 0.008 | ||||
| no | 1918 (96.9) | 628 (95.2) | 645 (97.7) | 645 (97.7) | |
| yes | 62 ( 3.1) | 32 (4.8) | 15 (2.3) | 15 (2.3) | |
| Coronary heart disease, n (%) | 0.331 | ||||
| no | 1873 (94.6) | 619 (93.8) | 623 (94.4) | 631 (95.6) | |
| yes | 107 ( 5.4) | 41 (6.2) | 37 (5.6) | 29 (4.4) | |
| DM, n (%) | 0.515 | ||||
| DM | 436 (23.1) | 160 (24.7) | 150 (23.7) | 126 (20.9) | |
| IFG | 205 (10.9) | 73 (11.2) | 69 (10.9) | 63 (10.4) | |
| no | 1244 (66.0) | 416 (64.1) | 414 (65.4) | 414 (68.7) | |
| Hypertension, n (%) | < 0.001 | ||||
| no | 601 (30.4) | 165 (25) | 201 (30.5) | 235 (35.6) | |
| yes | 1379 (69.6) | 495 (75) | 459 (69.5) | 425 (64.4) | |
| Hyperlipidemia, n (%) | 0.31 | ||||
| no | 291 (14.7) | 100 (15.2) | 86 (13) | 105 (15.9) | |
| yes | 1689 (85.3) | 560 (84.8) | 574 (87) | 555 (84.1) | |
| stroke, n (%) | 0.073 | ||||
| no | 1905 (96.2) | 629 (95.3) | 632 (95.8) | 644 (97.6) | |
| yes | 75 ( 3.8) | 31 (4.7) | 28 (4.2) | 16 (2.4) | |
| Cancer, n (%) | 0.007 | ||||
| no | 1741 (87.9) | 561 (85) | 582 (88.2) | 598 (90.6) | |
| yes | 239 (12.1) | 99 (15) | 78 (11.8) | 62 (9.4) | |
| All-cause mortality, n (%) | < 0.001 | ||||
| Alive | 1161 (58.6) | 281 (42.6) | 415 (62.9) | 465 (70.5) | |
| Dead | 819 (41.4) | 379 (57.4) | 245 (37.1) | 195 (29.5) | |
| Death cause, n (%) | 0.105 | ||||
| Accidents | 25 ( 3.1) | 7 (1.8) | 5 (2) | 13 (6.7) | |
| Alzheimer’s disease | 34 ( 4.2) | 18 (4.7) | 6 (2.4) | 10 (5.1) | |
| Cerebrovascular diseases | 44 ( 5.4) | 24 (6.3) | 13 (5.3) | 7 (3.6) | |
| Chronic lower respiratory diseases | 33 ( 4.0) | 15 (4) | 10 (4.1) | 8 (4.1) | |
| Diabetes mellitus | 33 ( 4.0) | 11 (2.9) | 10 (4.1) | 12 (6.2) | |
| Cardiovascular disease | 231 (28.2) | 108 (28.5) | 71 (29) | 52 (26.7) | |
| Influenza and pneumonia | 11 ( 1.3) | 8 (2.1) | 3 (1.2) | 0 (0) | |
| Cancer | 183 (22.3) | 81 (21.4) | 55 (22.4) | 47 (24.1) | |
| Nephritis, nephrotic syndrome and nephrosis | 12 ( 1.5) | 7 (1.8) | 2 (0.8) | 3 (1.5) | |
| All other causes (residual) | 213 (26.0) | 100 (26.4) | 70 (28.6) | 43 (22.1) | |
| Follow time, Mean ± SD | 15.3 ± 5.4 | 13.8 ± 6.1 | 15.6 ± 5.2 | 16.6 ± 4.3 | < 0.001 |
Mortality distribution
The study reported a total of 819 deaths including 231 cardiovascular deaths during the follow-up period. The distribution of deaths was not uniform among the tertiles, with the highest percentage of deaths occurring in the Tertile1 (57.4%), followed by the Tertile2 (37.1%), and the Tertile3 (29.5%). The causes of death were classified into different disease groups, with diseases of the heart being the leading cause of death, accounting for 28.2% of all deaths. Other causes of death included cancer (22.3%), cerebrovascular diseases (5.4%), chronic lower respiratory diseases (4.0%), diabetes mellitus (4.0%), Alzheimer’s disease (4.2%), nephritis/nephrotic syndrome/nephrosis (1.5%), influenza and pneumonia (1.3%), and accidental injuries (3.1%). While there was no statistically significant difference in the cause of death between the LTL tertiles, there was a lower percentage of deaths and the longest mean follow-up time in the Tertile1, suggesting a link between shorter telomere length and increased mortality risk in MetS patients.
The relationship between LTL and all-cause mortality or CVD mortality
According to the fitted curves, shorter LTL in MetS increases all-cause mortality (Fig. 2) and CVD mortality (Fig. 3). Conversely, as telomere length increases, the risk of both all-cause and CVD mortality decreases significantly (Figs. 2 and 3). Kaplan-Meier survival curves indicated that low LTL was associated with an increased all-cause mortality and CVD mortality risk (P < 0.05) (Figs. 4 and 5).
Fig. 2.
The relationship between Telomere Length with all-cause mortality by curve fitting in participants with metabolic syndrome
Fig. 3.
The relationship between Telomere Length with CVD mortality by curve fitting in participants with metabolic syndrome
Fig. 4.
Kaplan-Meier survival curves for LTL associated with all-cause mortality risk
Fig. 5.
Kaplan-Meier survival curves for LTL associated with CVD mortality risk
Fig. 1.
The flow chart of the participants with metabolic syndrome in NHANES of this study
Four models were used to stratify hazard ratios (HRs) for all-cause mortality and cardiovascular mortality by tertiles of telomere length (Table 2).
Table 2.
The HRs of all-cause mortality or CVD mortality in the participants with metabolic syndrome
| Variable | Total | Event(%) | Model 1 | P value | Model 2 | P value | Model 3 | P value | Model 4 | P value |
|---|---|---|---|---|---|---|---|---|---|---|
| All-cause mortality | ||||||||||
| LTL Tertile3 | 660 | 195 (29.5) | 1(Ref) | 1(Ref) | 1(Ref) | 1(Ref) | ||||
| LTL Tertile2 | 660 | 245 (37.1) | 1.35 (1.12 ~ 1.63) | 0.002 | 1.03 (0.85 ~ 1.24) | 0.788 | 1.03 (0.85 ~ 1.25) | 0.769 | 1 (0.83 ~ 1.21) | 0.999 |
| LTL Tertile1 | 660 | 379 (57.4) | 2.43 (2.05 ~ 2.89) | < 0.001 | 1.43 (1.19 ~ 1.7) | < 0.001 | 1.4 (1.17 ~ 1.68) | < 0.001 | 1.35 (1.12 ~ 1.61) | 0.001 |
| Trend test | 1980 | 819 (41.4) | 1.58 (1.45 ~ 1.73) | < 0.001 | 1.21 (1.11 ~ 1.33) | < 0.001 | 1.2 (1.1 ~ 1.32) | < 0.001 | 1.18 (1.08 ~ 1.29) | < 0.001 |
| Cardiovascular mortality | ||||||||||
| LTL Tertile3 | 660 | 52 (7.9) | 1(Ref) | 1(Ref) | 1(Ref) | 1(Ref) | ||||
| LTL Tertile2 | 660 | 71 (10.8) | 1.47 (1.03 ~ 2.1) | 0.036 | 1.09 (0.76 ~ 1.57) | 0.626 | 1.11 (0.77 ~ 1.59) | 0.589 | 1.1 (0.77 ~ 1.59) | 0.594 |
| LTL Tertile1 | 660 | 108 (16.4) | 2.58 (1.85 ~ 3.59) | < 0.001 | 1.46 (1.04 ~ 2.04) | 0.03 | 1.44 (1.02 ~ 2.03) | 0.038 | 1.41 (1 ~ 2) | 0.049 |
| Trend test | 1980 | 231 (11.7) | 1.62 (1.38 ~ 1.92) | < 0.001 | 1.22 (1.03 ~ 1.45) | 0.02 | 1.21 (1.02 ~ 1.44) | 0.028 | 1.2 (1.01 ~ 1.42) | 0.037 |
Model 1: Crude Model
Model 2: Adjusted for age and gender
Model 3: Adjusted for age, gender, race, marital status, body mass index, alcohol user, smoking status, and annual family income
Model 4: Adjusted for age, gender, race, marital status, body mass index, alcohol user, smoking status, annual family income, congestive heart failure, coronary heart disease, chronic kidney disease, chronic obstructive pulmonary disease (COPD), diabetes mellitus, hypertension, hyperlipidemia, and stroke
The crude model, which did not include any covariates, found that compared to individuals in Tertile 3, those in Tertile 2 and Tertile 1 had higher HRs for all-cause mortality: 1.35 (95% CI 1.12–1.63, P = 0.002) and 2.43 (95% CI 2.05–2.89, P < 0.001), respectively. A similar pattern was observed for cardiovascular mortality, with corresponding HRs of 1.47 (95% CI 1.03–2.10, P = 0.036) and 2.58 (95% CI 1.85–3.59, P < 0.001) for Tertile 2 and Tertile 1, respectively (Table 2).
The second model adjusted for age and gender as covariates. This analysis found that Tertile 1 had a significantly higher HR for all-cause mortality than Tertile 3 (1.43, 95% CI 1.19–1.70, P < 0.001), but no significant difference was observed between Tertile 2 and Tertile 3 (1.03, 95% CI 0.85–1.24, P = 0.788). For cardiovascular mortality, Tertile 1 had a higher HR than Tertile 3 (1.46, 95% CI 1.04–2.04, P = 0.030), whereas Tertile 2 did not (1.09, 95% CI 0.76–1.57, P = 0.626) (Table 2).
The third model was further adjusted for a variety of covariates, such as race, marital status, body mass index, alcohol use, smoking status, and annual family income. Tertile 1 remained significantly associated with a higher HR for all-cause mortality than Tertile 3 in this analysis (1.40, 95% CI 1.17–1.68, P0.001), whereas Tertile 2 did not (1.03, 95% CI 0.85–1.20, P = 0.769). For cardiovascular mortality, Tertile 1 had a higher HR than Tertile 3 (1.44, 95% CI 1.02–2.03, P = 0.038), while Tertile 2 did not (1.11, 95% CI 0.77–1.59, P = 0.589) (Table 2).
Finally, the fourth model included adjustment for a wide range of comorbidities in addition to the covariates included in Model 3, such as congestive heart failure, coronary heart disease, chronic kidney disease, COPD, diabetes mellitus, hypertension, hyperlipidemia, and stroke. This analysis found that Tertile 1 remained significantly associated with a higher HR for all-cause mortality (1.35, 95% CI 1.12–1.61, P = 0.001) compared to Tertile 3, while Tertile 2 did not (1.00, 95% CI 0.83–1.21, P = 0.999). Similarly, Tertile 1 had a higher HR than Tertile 3 for cardiovascular mortality (1.41, 95% CI 1.00–2.00, P = 0.049), while Tertile 2 did not (1.10, 95% CI 0.77–1.59, P = 0.594) (Table 2).
Subgroup analyses
To investigate the robustness of the association between telomere length and all-cause mortality or cardiovascular (CVD) mortality in individuals with metabolic syndrome, stratified analyses were performed across various subgroups. None of the examined variables, including gender, chronic kidney disease, coronary heart disease, diabetes mellitus, hypertension, hyperlipidemia, stroke, and cancer, significantly modified the relationship between telomere length and the risk of all-cause mortality or CVD mortality in individuals with metabolic syndrome (all p-values for interaction > 0.05) (Table 3).
Table 3.
The HRs of all-cause mortality or CVD mortality in the participants with metabolic syndrome in subgroup analyses
| All-cause mortality | Cardiovascular mortality | ||||||
|---|---|---|---|---|---|---|---|
| Subgroup | Total | Event(%) | HR(95%CI) | P for interaction | Event(%) | HR(95%CI) | P for interaction |
| Gender = Female | 0.726 | 0.115 | |||||
| LTL Tertile3 | 373 | 101 (27.1) | 1(Ref) | 19 (5.1) | 1(Ref) | ||
| LTL Tertile2 | 329 | 120 (36.5) | 1.01 (0.77 ~ 1.33) | 32 (9.7) | 1.46 (0.81 ~ 2.62) | ||
| LTL Tertile1 | 307 | 162 (52.8) | 1.19 (0.92 ~ 1.55) | 47 (15.3) | 1.61 (0.92 ~ 2.81) | ||
| Trend test | 1009 | 383 (38) | 1.1 (0.96 ~ 1.25) | 98 (9.7) | 1.24 (0.95 ~ 1.62) | ||
| Gender = Male | |||||||
| LTL Tertile3 | 287 | 94 (32.8) | 1(Ref) | 33 (11.5) | 1(Ref) | ||
| LTL Tertile2 | 331 | 125 (37.8) | 0.91 (0.69 ~ 1.19) | 39 (11.8) | 0.82 (0.51 ~ 1.31) | ||
| LTL Tertile1 | 353 | 217 (61.5) | 1.16 (0.9 ~ 1.5) | 61 (17.3) | 0.91 (0.58 ~ 1.41) | ||
| Trend test | 971 | 436 (44.9) | 1.1 (0.97 ~ 1.25) | 133 (13.7) | 0.97 (0.77 ~ 1.21) | ||
| Chronic kidney disease = no | 0.659 | 0.403 | |||||
| LTL Tertile3 | 537 | 128 (23.8) | 1(Ref) | 33 (6.1) | 1(Ref) | ||
| LTL Tertile2 | 491 | 145 (29.5) | 0.92 (0.72 ~ 1.17) | 37 (7.5) | 0.86 (0.53 ~ 1.39) | ||
| LTL Tertile1 | 435 | 197 (45.3) | 1.15 (0.91 ~ 1.45) | 54 (12.4) | 1.09 (0.69 ~ 1.72) | ||
| Trend test | 1463 | 470 (32.1) | 1.08 (0.96 ~ 1.22) | 124 (8.5) | 1.06 (0.84 ~ 1.34) | ||
| Chronic kidney disease = yes | |||||||
| LTL Tertile3 | 123 | 67 (54.5) | 1(Ref) | 19 (15.4) | 1(Ref) | ||
| LTL Tertile2 | 169 | 100 (59.2) | 1.2 (0.87 ~ 1.65) | 34 (20.1) | 1.61 (0.89 ~ 2.91) | ||
| LTL Tertile1 | 225 | 182 (80.9) | 1.32 (0.98 ~ 1.79) | 54 (24) | 1.4 (0.8 ~ 2.44) | ||
| Trend test | 517 | 349 (67.5) | 1.14 (0.99 ~ 1.32) | 107 (20.7) | 1.12 (0.87 ~ 1.45) | ||
| Corory heart disease = no | 0.351 | 0.695 | |||||
| LTL Tertile3 | 631 | 174 (27.6) | 1(Ref) | 43 (6.8) | 1(Ref) | ||
| LTL Tertile2 | 623 | 222 (35.6) | 1 (0.82 ~ 1.22) | 59 (9.5) | 1.07 (0.71 ~ 1.59) | ||
| LTL Tertile1 | 619 | 346 (55.9) | 1.24 (1.02 ~ 1.49) | 91 (14.7) | 1.21 (0.83 ~ 1.77) | ||
| Trend test | 1873 | 742 (39.6) | 1.12 (1.02 ~ 1.24) | 193 (10.3) | 1.11 (0.92 ~ 1.33) | ||
| Corory heart disease = yes | |||||||
| LTL Tertile3 | 29 | 21 (72.4) | 1(Ref) | 9 (31) | 1(Ref) | ||
| LTL Tertile2 | 37 | 23 (62.2) | 0.69 (0.36 ~ 1.32) | 12 (32.4) | 0.85 (0.34 ~ 2.14) | ||
| LTL Tertile1 | 41 | 33 (80.5) | 1.12 (0.62 ~ 2.03) | 17 (41.5) | 1.39 (0.58 ~ 3.33) | ||
| Trend test | 107 | 77 (72) | 1.07 (0.79 ~ 1.47) | 38 (35.5) | 1.21 (0.77 ~ 1.91) | ||
| DM = yes | 0.207 | 0.241 | |||||
| LTL Tertile3 | 126 | 66 (52.4) | 1(Ref) | 24 (19) | 1(Ref) | ||
| LTL Tertile2 | 150 | 74 (49.3) | 0.87 (0.62 ~ 1.23) | 21 (14) | 0.71 (0.39 ~ 1.3) | ||
| LTL Tertile1 | 160 | 113 (70.6) | 1.02 (0.74 ~ 1.41) | 37 (23.1) | 0.98 (0.57 ~ 1.7) | ||
| Trend test | 436 | 253 (58) | 1.02 (0.87 ~ 1.2) | 82 (18.8) | 1.01 (0.76 ~ 1.34) | ||
| DM = no | |||||||
| LTL Tertile3 | 477 | 126 (26.4) | 1(Ref) | 28 (5.9) | 1(Ref) | ||
| LTL Tertile2 | 483 | 169 (35) | 0.98 (0.78 ~ 1.24) | 50 (10.4) | 1.23 (0.77 ~ 1.97) | ||
| LTL Tertile1 | 489 | 266 (54.4) | 1.25 (1 ~ 1.56) | 71 (14.5) | 1.33 (0.84 ~ 2.1) | ||
| Trend test | 1449 | 561 (38.7) | 1.13 (1.02 ~ 1.27) | 149 (10.3) | 1.14 (0.92 ~ 1.42) | ||
| Hypertension = no | 0.846 | 0.689 | |||||
| LTL Tertile3 | 235 | 43 (18.3) | 1(Ref) | 10 (4.3) | 1(Ref) | ||
| LTL Tertile2 | 201 | 42 (20.9) | 0.88 (0.57 ~ 1.37) | 10 (5) | 0.99 (0.4 ~ 2.46) | ||
| LTL Tertile1 | 165 | 63 (38.2) | 1.12 (0.75 ~ 1.7) | 12 (7.3) | 0.96 (0.39 ~ 2.37) | ||
| Trend test | 601 | 148 (24.6) | 1.07 (0.87 ~ 1.32) | 32 (5.3) | 0.98 (0.63 ~ 1.54) | ||
| Hypertension = yes | |||||||
| LTL Tertile3 | 425 | 152 (35.8) | 1(Ref) | 42 (9.9) | 1(Ref) | ||
| LTL Tertile2 | 459 | 203 (44.2) | 0.97 (0.78 ~ 1.2) | 61 (13.3) | 1.02 (0.68 ~ 1.52) | ||
| LTL Tertile1 | 495 | 316 (63.8) | 1.22 (1 ~ 1.5) | 96 (19.4) | 1.22 (0.84 ~ 1.78) | ||
| Trend test | 1379 | 671 (48.7) | 1.12 (1.02 ~ 1.24) | 199 (14.4) | 1.12 (0.93 ~ 1.35) | ||
| Hyperlipidemia = no | 0.857 | 0.987 | |||||
| LTL Tertile3 | 105 | 37 (35.2) | 1(Ref) | 10 (9.5) | 1(Ref) | ||
| LTL Tertile2 | 86 | 34 (39.5) | 0.83 (0.49 ~ 1.4) | 10 (11.6) | 1.06 (0.34 ~ 3.33) | ||
| LTL Tertile1 | 100 | 59 (59) | 1.24 (0.79 ~ 1.96) | 15 (15) | 1.18 (0.42 ~ 3.31) | ||
| Trend test | 291 | 130 (44.7) | 1.15 (0.91 ~ 1.45) | 35 (12) | 1.09 (0.66 ~ 1.8) | ||
| Hyperlipidemia = yes | |||||||
| LTL Tertile3 | 555 | 158 (28.5) | 1(Ref) | 42 (7.6) | 1(Ref) | ||
| LTL Tertile2 | 574 | 211 (36.8) | 0.97 (0.78 ~ 1.19) | 61 (10.6) | 1 (0.67 ~ 1.49) | ||
| LTL Tertile1 | 560 | 320 (57.1) | 1.17 (0.96 ~ 1.43) | 93 (16.6) | 1.16 (0.79 ~ 1.7) | ||
| Trend test | 1689 | 689 (40.8) | 1.1 (0.99 ~ 1.21) | 196 (11.6) | 1.09 (0.9 ~ 1.31) | ||
| Stroke = no | 0.655 | 0.218 | |||||
| LTL Tertile3 | 644 | 183 (28.4) | 1(Ref) | 46 (7.1) | 1(Ref) | ||
| LTL Tertile2 | 632 | 226 (35.8) | 0.97 (0.8 ~ 1.18) | 67 (10.6) | 1.11 (0.76 ~ 1.62) | ||
| LTL Tertile1 | 629 | 351 (55.8) | 1.18 (0.98 ~ 1.42) | 100 (15.9) | 1.21 (0.84 ~ 1.73) | ||
| Trend test | 1905 | 760 (39.9) | 1.1 (1 ~ 1.21) | 213 (11.2) | 1.1 (0.92 ~ 1.31) | ||
| Stroke = yes | |||||||
| LTL Tertile3 | 16 | 12 (75) | 1(Ref) | 6 (37.5) | 1(Ref) | ||
| LTL Tertile2 | 28 | 19 (67.9) | 0.91 (0.32 ~ 2.52) | 4 (14.3) | 0.41 (0.04 ~ 4.26) | ||
| LTL Tertile1 | 31 | 28 (90.3) | 1.74 (0.66 ~ 4.58) | 8 (25.8) | 1.33 (0.22 ~ 8.09) | ||
| Trend test | 75 | 59 (78.7) | 1.46 (0.93 ~ 2.31) | 18 (24) | 1.32 (0.53 ~ 3.26) | ||
| Cancer = no | 0.425 | 0.138 | |||||
| LTL Tertile3 | 598 | 164 (27.4) | 1(Ref) | 44 (7.4) | 1(Ref) | ||
| LTL Tertile2 | 582 | 199 (34.2) | 0.91 (0.74 ~ 1.13) | 55 (9.5) | 0.86 (0.57 ~ 1.29) | ||
| LTL Tertile1 | 561 | 300 (53.5) | 1.14 (0.93 ~ 1.39) | 89 (15.9) | 1.12 (0.77 ~ 1.64) | ||
| Trend test | 1741 | 663 (38.1) | 1.08 (0.98 ~ 1.2) | 188 (10.8) | 1.09 (0.9 ~ 1.32) | ||
| Cancer = yes | |||||||
| LTL Tertile3 | 62 | 31 (50) | 1(Ref) | 8 (12.9) | 1(Ref) | ||
| LTL Tertile2 | 78 | 46 (59) | 1.21 (0.75 ~ 1.97) | 16 (20.5) | 1.75 (0.69 ~ 4.42) | ||
| LTL Tertile1 | 99 | 79 (79.8) | 1.49 (0.95 ~ 2.33) | 19 (19.2) | 1.53 (0.62 ~ 3.78) | ||
| Trend test | 239 | 156 (65.3) | 1.22 (0.98 ~ 1.52) | 43 (18) | 1.18 (0.77 ~ 1.81) | ||
Adjusted for age, Gender, race, marital status, body mass index, alcohol user, smoking status, and annual family income
Discussion
Telomeres are repetitive DNA sequences located at the end of chromosomes. Their primary role is to protect the genetic material from being damaged or lost during cell division. Each time a cell divides, the telomeres shorten, and after multiple rounds of cell division, they become critically short and fragile [8]. Cellular aging and oxidative stress have both been linked to telomere shortening and implicated in the pathogenesis of MetS [8].
MetS is a crucial risk factor for mortality [21].MetS pathophysiology includes cellular aging and oxidative stress, both of which are associated with decreased TL. [22]。Some studies reveal that MetS patients have shortened telomeres, suggesting that telomere shortening may contribute to MetS development and progression [7, 9, 15]. Nellie Y. Loh et al. concluded through Mendelian randomization that longer leukocyte LTL is associated with an increased risk of upper body fat distribution, hypertension, and MetS [15]. The precise mechanism by which MetS patients have shortened LTL remains unclear, but it may involve chronic inflammation, insulin resistance, and mitochondrial dysfunction [8, 9, 23].
Numerous studies have shown that shortened telomere length increased the risk of all-cause mortality [10, 24, 25]。In a study involving 472,432 participants from the UK Biobank, Schneider et al. found that shortened LTL was positively correlated with all-cause mortality as well as disease-specific mortality for respiratory, digestive, circulatory, musculoskeletal, and infectious diseases [6]. Konstantin G Arbeev analyzed three European cohorts including a total of 3259 individuals and revealed that the risk of death from any cause increased by 1.34 times for every 1 kb decrease in LTL [10]. The Wilheit et al. study tracked 787 cancer patients for 15 years and showed a robust correlation between shorter telomeres and cancer death [24]. Likewise, Ayodeji Adegunsoye et al. found that leukocyte telomere length was a biomarker for predicting mortality among 2046 patients with pulmonary fibrosis [25]。 Our study confirms this relationship and extends it to patients with metabolic syndrome. We discovered that in individuals with MetS, shorter LTL is associated with an increased risk of death, even after taking into account a variety of demographic and clinical factors. The median follow-up time in our study was 17.75 years, which is relatively long compared to other studies examining LTL and mortality rates, pointing to the importance of LTL as a biomarker for detecting high-risk populations in clinical practice.
Telomere shortening is significantly associated with cardiovascular disease (CVD), heart failure, and disease-specific mortality rates [23, 26–29]. Metabolic syndrome is also a high-risk group for CVD. Age, inflammation, obesity, sedentary lifestyle, smoking, psychological stress, and oxidative stress are all risk factors for CVD that are also linked to telomere shortening [30]. Data from 2,151,597 middle-aged people showed that metabolic syndrome is a risk factor for heart failure in middle-aged men and women, with a higher impact on women [2]. In a cohort study of 290 patients with acute myocardial infarction, LTL was identified as a significant predictor of all-cause mortality and CVD mortality one year after myocardial infarction [31]. Genetic evidence has also identified several variants of telomere maintenance genes that may increase the risk of heart disease [30]. Telomere shortening can accelerate cellular aging and impair function, fueling atherosclerosis and other vascular diseases [30]. Studies indicate that weight loss surgery and calorie restriction can decrease BMI and increase telomere length in MetS patients, providing insights for managing their health [32]. However, a few studies indicate that short telomere burden and average LTL are not robust independent predictors of atherosclerosis at the preclinical stage [33]. Our findings emphasize the link between shortened telomere length and an elevated risk of CVD mortality in people with MetS. Shortened telomeres may serve as a potential biomarker and therapeutic target for CVD [30].
A possible explanation for the observed association is that shorter LTL is a marker of unhealthy aging. Our study found that individuals with shorter LTL were older, more likely to be male, have lower weight, and have a higher percentage of heavy alcohol use, suggesting that LTL could serve as a predictor of unhealthy aging in MetS patients. The process of cellular senescence is another hypothesized mechanism linking shorter LTL with higher mortality risk. When telomeres get too short, cells go through a process called senescence, which involves the release of pro-inflammatory chemicals and the permanent halting of cell division [34, 35]. Cellular senescence is thought to contribute to various age-related pathologies, including osteoarthritis, atherosclerosis, and frailty [22, 36, 37]. Thus, the link between shorter LTL and mortality in MetS patients could, in part, be mediated by the induction of cellular senescence.
The mechanism by which telomere shortening affects mortality risk is complex and likely involves multiple pathways and processes [6]. One potential mechanism is cellular senescence, a process that leads to the permanent cessation of cell division in response to telomere attrition [34]. Senescent cells can contribute to tissue dysfunction and inflammation, which have been associated with increased mortality risk in MetS patients. Additionally, telomere shortening can lead to oxidative stress, which can cause DNA damage, inflammation, and cellular dysfunction, which have all been linked to increased mortality risk in MetS patients [35]. Telomere shortening can also promote chronic inflammation and activation of the immune system, which has been implicated in a variety of age-related diseases, including MetS and its associated complications [34].
Our study possesses several strengths, including a large sample size and comprehensive adjustment for multiple confounding factors. Furthermore, our study had a long follow-up period, with a median follow-up time of 17.75 years, enabling us to evaluate the enduring mortality risk associated with LTL and MetS. However, it is important to acknowledge that our study was observational in design, thus precluding the establishment of causality. An important limitation of our study is the assessment of telomere length at a single time point, which may not capture potential fluctuations in telomere length over time. Additionally, the study population was drawn from the United States, and the results may not be generalizable to other populations with different demographics. Future research is warranted to investigate how changes in telomere length over time can impact the mortality risk in individuals with MetS.
Our analysis confirms that MetS patients with shorter LTL have higher all-cause and CVD mortality. LTL may be a biomarker for high-risk people who could benefit from focused interventions to reduce the risk of death. To create effective treatments for preventing age-related diseases, further research is needed to better understand the mechanisms behind the association between telomere length, MetS, and mortality risk.
Acknowledgements
We express our gratitude to the NHANSE research team and all of the respondents who participated in this study. Additionally, we thank the Free Statistics team for providing technical support, as well as valuable data analysis and visualization tools. Thanks to Zhang Jing (Shanghai Tongren Hospital) for nhanesR package on the NHANES database.
Authors’ contributions
Lijiao Xiong: conceiving the protocol, data analysis and interpretation, acquisition of data, statistical analysis and interpretation of data; manuscript preparation.Guangyan Yang: manuscript preparation.Tianting Guo, Zhaohao Zeng: Study concept and design, interpretation of data, Revision of the manuscript. Tingfeng Liao, Yanchun Li, Ying Li, Fujuan Chen: Revision of the manuscript. Yang Shu, Kang Lin, Liang Zhen: concept and design; final drafting of the manuscript; study supervision.All authors have agreed to assume full responsibility for the integrity and accuracy of the research presented in this paper. Furthermore, they have read and approved the final version of the manuscript.
Funding
This research was supported by grants from the Science and Technology Planning Project of Shenzhen City, Guangdong Province, China (No. KCXFZ20201221173600001 and JCYJ20220818102605013) and administration of Traditional Chinese Medicine of Jiangxi Province, China (grant No. 2021A374).
Data Availability
Publicly available datasets were analyzed in this study. This data can be found at: https://www.cdc.gov/nchs/nhanes/index.htm.
Declarations
Ethics approval and consent to participate
The National Center for Health Statistics Institutional Review Board authorized all study protocols, and subjects gave written informed permission. Since 1999, this representative survey has been published online every two years. https://www.cdc.gov/nchs/nhanes/index.htm offers public data sets. The study used publicly available deidentified data and waived informed consent. This study used a public database without ethical approval or informed permission.
Consent for publication
All participants agreed to publish.
Conflict of interest
The authors declare no conflicts of interest regarding the publication of this article.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Xionglijiao and Guangyan Yang have contributed equally to this work.
Contributor Information
Shu Yang, Email: yang.shu@szhosptial.com.
Lin Kang, Email: kang.lin@szhospital.com.
Zhen Liang, Email: liang.zhen@szhospital.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Publicly available datasets were analyzed in this study. This data can be found at: https://www.cdc.gov/nchs/nhanes/index.htm.