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Leukocyte telomere shortening in metabolic dysfunction-associated steatotic liver disease and all-cause/cause-specific mortality

Donghee Kim1orcid, Pojsakorn Danpanichkul2, Karn Wijarnpreecha3,4, George Cholankeril5,6, Aijaz Ahmed1
Clinical and Molecular Hepatology 2024;30(4):982-986.
Published online: August 27, 2024

1Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, CA, USA

2Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA

3Department of Medicine, Division of Gastroenterology and Hepatology, University of Arizona College of Medicine, Phoenix, AZ, USA

4Department of Internal Medicine, Division of Gastroenterology and Hepatology, Banner University Medical Center, Phoenix, AZ, USA

5Liver Center, Division of Abdominal Transplantation, Michael E De-Bakey Department of General Surgery, Baylor College of Medicine, Houston, TX, USA

6Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA

Corresponding author : Donghee Kim Division of Gastroenterology and Hepatology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94304, USA Tel: +1-650-497-9261, Fax: +1-650-723-5488, E-mail: dhkimmd@stanford.edu

Editor: Gi-Ae Kim, Kyung Hee University, Korea

• Received: August 21, 2024   • Revised: August 23, 2024   • Accepted: August 26, 2024

Copyright © 2024 by The Korean Association for the Study of the Liver

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Dear Editor,
Metabolic dysfunction-associated steatotic liver disease (MASLD) has increasingly been recognized as the most prevalent liver disease globally [1-3]. Efforts to investigate predictors of mortality in MASLD are needed to slow the growing disease burden [4]. Several studies suggest that telomere shortening may play a role in developing cirrhosis and advanced fibrosis in MASLD and liver-related outcomes [5,6]. Leukocyte telomere length (LTL) may be explored as a possible predictor for early detection of all-cause/causespecific mortality in MASLD. We analyzed using the US National Health and Nutrition Examination Survey 1999– 2002 dataset, which evaluated LTL using DNA samples collected (≥20 years). Out of 7,827 participants with available LTL data, we included 3,618 individuals with MASLD after excluding individuals with viral hepatitis by serology, significant alcohol consumption, those who have taken steatogenic medications, and those in whom data on serum aminotransferase, body mass index (BMI), and mortality data were not available. The methods employed for LTL have been described in detail elsewhere [7]. Steatotic liver disease (SLD) was defined using the hepatic steatosis index (HSI=8×[alanine aminotransferase/aspartate aminotransferase ratio]+BMI [+2, if diabetes; +2, if female]) [8]. We used the published cutoff of 36 to define the presence of SLD [8]. MASLD was defined as the presence of SLD with one or more of the cardiometabolic criteria [9]. Because of the strong effect of age on LTL, LTL was categorized into quartiles based on the weighted sample distribution of LTL by the age group (ages 20–39, 40–59, and ≥60 years). The weighted multivariable Cox proportional model investigated the independent association of the weighted quartile of LTL with all-cause mortality and cause-specific mortalities using the longest quartile of LTL as a reference group.
Among the 3,618 individuals with MASLD (mean ages, 46.8 years and men: 48.0%), there were no differences in age, sex, race/ethnicity, BMI, waist circumference, smoking status, liver enzymes, or metabolic profiles according to the LTL quartile. The median follow-up in the 3,618 individuals with MASLD was 18.4 years (interquartile range 17.3–19.4). There were a total of 990 deaths during the follow-up. The two leading causes of death were cardiovascular (n=325), followed by cancer (n=211). In the age-, sex- and race/ethnicity-adjusted Cox-regression model (Table 1), shorter LTL were associated with progressively higher hazards of allcause mortality (P for trend=0.020). An additional multivariable model taking into account variables such as marital status, education status, smoking status, diabetes, hypertension, and total cholesterol showed essentially identical results (hazards ratio [HR] 1.29; 95% confidence interval [CI] 0.99–1.68 for 3rd quartile; and HR 1.42; 95% CI 1.02– 1.99 for 4th quartile comparing with 1st quartile [longest LTL]; P for trend=0.018). This association was attenuated but was still statistically significant after further adjusting waist circumference and advanced fibrosis (P for trend=0.028). When we broke into cause-specific mortality, decreasing LTL showed increasing cardiovascular mortality hazards but were statistically insignificant. Regarding cancer-related mortality, decreasing LTL showed more dosedependently increasing cancer-related mortality with a marginal significance (P for trend=0.070) than cardiovascular mortality. When stratified by age, sex, and race/ethnicity (Table 1), the association between shorter LTL and allcause mortality remained more pronounced and significant in the older population, men, and non-Hispanic white than the younger population, women, and other race/ethnicity.
Despite the independent association between short LTL and MASLD and fibrosis [7], we found that shorter LTL in MASLD was associated with a higher risk of all-cause mortality independent of known demographic, clinical, metabolic risk factors, and advanced fibrosis in this US population-based study. This is expected as hypertension and insulin resistance, prevalent in MASLD, contribute to telomere attrition [10]. Maintaining a healthy lifestyle, including regular physical activity, a balanced diet rich in antioxidants, sufficient sleep, and avoiding harmful habits like smoking, may potentially support an increase in LTL [10-13]. Among elderly participants, men, or non-Hispanic white, we found that LTL was inversely associated with all-cause mortality, which suggests that shortening of telomere length may be modulated and affected by high-risk populations. Although some reports suggested that longer LTL may be linked to higher cancer risk [14,15], it may also correlate with a reduced risk of other age-related diseases and enhanced overall health. Our study is not without limitations. First, we used noninvasive markers to define MASLD, which may misclassify the true prevalence of MASLD. Second, we were unable to obtain liver-related mortality, which NHANES restricts due to small outcomes. A recent study showed a clear association between shorter LTL and excess mortality due to digestive disorders, including liver-related mortality [10]. However, these flaws may be steadied by the advantage of a national, population-based study.
Although US FDA recently approved resmetirom for treating noncirrhotic nonalcoholic steatohepatitis, identifying a high-risk population for mortality in conjunction with focusing on lifestyle modifications is still critical. Hence, investigating LTL as a potential early predictor for identifying the high-risk population in MASLD could be essential. Subsequently, individual counseling may be required to enable timely intervention.

Statement of ethics

The National Center for Health Statistics review board approved the National Health and Nutrition Examination Survey. The National Center for Health Statistics obtained informed consent from all participants.

Data availability statement

The National Health and Nutrition Examination Survey 1998–2002 dataset are publicly available at the National Center for Health Statistics of the Center for Disease Control and Prevention (https://www.cdc.gov/nchs/nhanes/).

Authors’ contribution

Donghee Kim was involved in the study concept and design, acquisition of data, analysis and interpretation of data, drafting of the manuscript, and study supervision. Pojsakorn Danpanichkul, Karn Wijarnpreecha, and George Cholankeril were involved in the interpretation of data and critical revision of the manuscript. Aijaz Ahmed was involved in the study concept and design, interpretation of data, critical revision of the manuscript, and study supervision. All authors were involved in the final approval of the submitted manuscript and agreed to be accountable for all aspects of the work.

Conflicts of Interest

The authors have no conflicts to disclose.

Table 1.
Association between leukocyte telomere length and all-cause and cause-specific mortality among individuals with MASLD (n=3,618)
Table 1.
Age, sex, race/ethnicity-adjusted model
 Multivariable model 1
 Multivariable model 2
HR (95% CI) P-value HR (95% CI) P-value HR (95% CI) P-value
All-cause mortality
 Quartile 1 1 0.020* 1 0.018* 1 0.028*
 Quartile 2 1.03 (0.79–1.33) 0.838 1.11 (0.81–1.52) 0.508 1.16 (0.88–1.53) 0.292
 Quartile 3 1.22 (0.96–1.54) 0.103 1.29 (0.99–1.68) 0.060 1.22 (0.95–1.56) 0.109
 Quartile 4 1.33 (1.01–1.76) 0.044 1.42 (1.02–1.99) 0.039 1.41 (1.03–1.94) 0.035
Cardiovascular mortality
 Quartile 1 1 0.137* 1 0.181* 1 0.257*
 Quartile 2 1.06 (0.64–1.76) 0.815 1.17 (0.67–2.05) 0.568 1.19 (0.70–2.04) 0.506
 Quartile 3 1.48 (0.95–2.30) 0.078 1.58 (0.94–2.65) 0.080 1.44 (0.90–2.32) 0.126
 Quartile 4 1.30 (0.78–2.18) 0.304 1.33 (0.74–2.39) 0.333 1.29 (0.72–2.30) 0.378
Cancer-related mortality
 Quartile 1 1 0.076* 1 0.070* 1 0.091*
 Quartile 2 1.36 (0.77–2.37) 0.276 1.37 (0.74–2.55) 0.306 1.40 (0.75–2.60) 0.282
 Quartile 3 1.42 (0.89–2.26) 0.134 1.53 (0.87–2.66) 0.131 1.50 (0.85–2.64) 0.152
 Quartile 4 1.63 (0.98–2.74) 0.061 1.69 (0.95–3.01) 0.073 1.68 (0.93–3.02) 0.081
Subgroup analysis for all-cause mortality
 Age <50 (n=1,792)
  Quartile 1 1 0.858* 1 0.762* 1 0.753*
  Quartile 2 0.66 (0.32–1.39) 0.259 0.68 (0.32–1.43) 0.298 0.68 (0.33–1.41) 0.283
  Quartile 3 0.85 (0.50–1.44) 0.534 0.82 (0.45–1.47) 0.472 0.72 (0.38–1.38) 0.306
  Quartile 4 0.98 (0.51–1.86) 0.947 0.85 (0.41–1.76) 0.644 0.86 (0.42–1.79) 0.684
 Age ≥50 (n=1,826)
  Quartile 1 1 0.005* 1 0.003* 1 0.004*
  Quartile 2 1.12 (0.88–1.43) 0.328 1.22 (0.91–1.64) 0.168 1.30 (0.99–1.70) 0.055
  Quartile 3 1.30 (1.04–1.64) 0.023 1.41 (1.10–1.82) 0.009 1.37 (1.09–1.73) 0.009
  Quartile 4 1.42 (1.10–1.84) 0.009 1.55 (1.14–2.12) 0.007 1.55 (1.16–2.09) 0.005
 Men (n=1,713)
  Quartile 1 1 0.015* 1 0.005* 1 0.007*
  Quartile 2 0.91 (0.60–1.38) 0.637 0.96 (0.60–1.55) 0.871 0.96 (0.60–1.56) 0.872
  Quartile 3 1.50 (1.04–2.16) 0.030 1.65 (1.09–2.51) 0.021 1.60 (1.06–2.42) 0.027
  Quartile 4 1.33 (0.91–1.93) 0.135 1.51 (1.00–2.28) 0.052 1.47 (0.98–2.20) 0.061
 Women (n=1,905)
  Quartile 1 1 0.120* 1 0.232* 1 0.265*
  Quartile 2 1.14 (0.87–1.50) 0.342 1.21 (0.85–1.72) 0.283 1.31 (0.94–1.82) 0.110
  Quartile 3 1.00 (0.76–1.30) 0.982 1.01 (0.74–1.39) 0.936 0.96 (0.70–1.32) 0.805
  Quartile 4 1.36 (1.00–1.84) 0.048 1.35 (0.93–1.98) 0.111 1.39 (0.96–2.01) 0.079
 Non-Hispanic white (n=1,713)
  Quartile 1 1 0.026* 1 0.022* 1 0.034*
  Quartile 2 1.08 (0.80–1.45) 0.618 1.22 (0.84–1.77) 0.283 1.28 (0.92–1.78) 0.135
  Quartile 3 1.35 (1.05–1.75) 0.023 1.47 (1.09–1.98) 0.014 1.38 (1.04–1.83) 0.026
  Quartile 4 1.40 (0.98–1.99) 0.061 1.54 (1.00–2.36) 0.048 1.52 (1.01–2.28) 0.044
 Non-Hispanic black (n=646)
  Quartile 1 1 0.709* 1 0.832* 1 0.988*
  Quartile 2 0.90 (0.58–1.38) 0.604 0.77 (0.48–1.24) 0.274 0.76 (0.49–1.18) 0.208
  Quartile 3 0.95 (0.58–1.56) 0.832 0.87 (0.48–1.60) 0.649 0.82 (0.46–1.49) 0.488
  Quartile 4 1.11 (0.68–1.80) 0.657 1.09 (0.60–1.98) 0.767 1.03 (0.59–1.81) 0.919
 Hispanic (n=1,190)
  Quartile 1 1 0.885* 1 0.830* 1 0.793*
  Quartile 2 0.86 (0.59–1.27) 0.448 0.82 (0.55–1.24) 0.337 0.81 (0.53–1.23) 0.303
  Quartile 3 0.75 (0.53–1.07) 0.112 0.65 (0.41–1.03) 0.064 0.66 (0.43–1.03) 0.064
  Quartile 4 1.04 (0.64–1.69) 0.872 0.96 (0.55–1.68) 0.888 0.93 (0.51–1.69) 0.794

The present analyses used leukocyte telomere length as the exposure variable. All individuals over 20 years in the NHNAES 1999–2002 had passive mortality follow-up until December 31st, 2019. The leading cause of death was recorded as the Underlying Cause of Death 113 (UCOD_113) code. All-cause and two leading cause-specific mortalities were defined as cardiovascular disease (UCOD_113: 55–68, 70) and cancer (UCOD_113: 19–43). Liver-related mortality was publicly restricted due to the small number. Given the complex sample design employed by NHANES 1999-2002, appropriate sample weights were applied to restructure population-level data for the US.

HR, hazard ratio; CI, confidence; MASLD, metabolic dysfunction-associated steatotic liver disease.

The multivariable model 1 was adjusted for age, sex, race/ethnicity, marital status, education status, smoking status, diabetes, hypertension, and total cholesterol.

The multivariable model 2 was adjusted for waist circumference and advanced fibrosis in addition to multivariable model 1.

Quartile 1, ≥6.380; quartile 2, 5.985–6.380; quartile 3, 5.616–5.985; quartile 4, <5.616 in participants aged 20–39 years.

Quartile 1, ≥6.090; quartile 2, 5.696–6.090; quartile 3, 5.377–5.696; quartile 4, <5.377 in participants aged 40–59 years.

Quartile 1, ≥5.763; quartile 2, 5.401–5.763; quartile 3, 5.122–5.401; quartile 4, <5.122 in participants aged over 60 years.

*P-value for the test of trend of hazards.

BMI

body mass index

CI

confidence interval

HSI

hepatic steatosis index

HR

hazard ratio

LTL

leukocyte telomere length

MASLD

metabolic dysfunction-associated steatotic liver disease

SLD

steatotic liver disease
  • 1. Murag S, Ahmed A, Kim D. Recent epidemiology of nonalcoholic fatty liver disease. Gut Liver 2021;15:206-216.
  • 2. Kim GA, Moon JH, Kim W. Critical appraisal of metabolic dysfunction-associated steatotic liver disease: Implication of Janus-faced modernity. Clin Mol Hepatol 2023;29:831-843.
  • 3. Younossi ZM, Kalligeros M, Henry L. Epidemiology of metabolic dysfunction-associated steatotic liver disease. Clin Mol Hepatol 2024 Aug 19. doi: 10.3350/cmh.2024.0431.
  • 4. Konyn P, Ahmed A, Kim D. Causes and risk profiles of mortality among individuals with nonalcoholic fatty liver disease. Clin Mol Hepatol 2023;29(Suppl):S43-S57.
  • 5. Calado RT, Brudno J, Mehta P, Kovacs JJ, Wu C, Zago MA, et al. Constitutional telomerase mutations are genetic risk factors for cirrhosis. Hepatology 2011;53:1600-1607.
  • 6. Aravinthan A, Scarpini C, Tachtatzis P, Verma S, Penrhyn-Lowe S, Harvey R, et al. Hepatocyte senescence predicts progression in non-alcohol-related fatty liver disease. J Hepatol 2013;58:549-556.
  • 7. Kim D, Li AA, Ahmed A. Leucocyte telomere shortening is associated with nonalcoholic fatty liver disease-related advanced fibrosis. Liver Int 2018;38:1839-1848.
  • 8. Lee JH, Kim D, Kim HJ, Lee CH, Yang JI, Kim W, et al. Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease. Dig Liver Dis 2010;42:503-508.
  • 9. Rinella ME, Lazarus JV, Ratziu V, Francque SM, Sanyal AJ, Kanwal F, et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. J Hepatol 2023;79:1542-1556.
  • 10. Schneider CV, Schneider KM, Teumer A, Rudolph KL, Hartmann D, Rader DJ, et al. Association of telomere length with risk of disease and mortality. JAMA Intern Med 2022;182:291-300.
  • 11. Balan E, Decottignies A, Deldicque L. Physical activity and nutrition: Two promising strategies for telomere maintenance? Nutrients 2018;10:1942.
  • 12. Astuti Y, Wardhana A, Watkins J, Wulaningsih W; PILAR Research Network. Cigarette smoking and telomere length: A systematic review of 84 studies and meta-analysis. Environ Res 2017;158:480-489.
  • 13. Sabot D, Lovegrove R, Stapleton P. The association between sleep quality and telomere length: A systematic literature review. Brain Behav Immun Health 2023;28:100577.
  • 14. McNally EJ, Luncsford PJ, Armanios M. Long telomeres and cancer risk: the price of cellular immortality. J Clin Invest 2019;129:3474-3481.
  • 15. DeBoy EA, Tassia MG, Schratz KE, Yan SM, Cosner ZL, McNally EJ, et al. Familial clonal hematopoiesis in a long telomere syndrome. N Engl J Med 2023;388:2422-2433.

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Leukocyte telomere shortening in metabolic dysfunction-associated steatotic liver disease and all-cause/cause-specific mortality
Clin Mol Hepatol. 2024;30(4):982-986.   Published online August 27, 2024
DOI: https://doi.org/10.3350/cmh.2024.0691
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Leukocyte telomere shortening in metabolic dysfunction-associated steatotic liver disease and all-cause/cause-specific mortality
Clin Mol Hepatol. 2024;30(4):982-986.   Published online August 27, 2024
DOI: https://doi.org/10.3350/cmh.2024.0691
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Leukocyte telomere shortening in metabolic dysfunction-associated steatotic liver disease and all-cause/cause-specific mortality
Leukocyte telomere shortening in metabolic dysfunction-associated steatotic liver disease and all-cause/cause-specific mortality
Age, sex, race/ethnicity-adjusted model
 Multivariable model 1
 Multivariable model 2
HR (95% CI) P-value HR (95% CI) P-value HR (95% CI) P-value
All-cause mortality
 Quartile 1 1 0.020* 1 0.018* 1 0.028*
 Quartile 2 1.03 (0.79–1.33) 0.838 1.11 (0.81–1.52) 0.508 1.16 (0.88–1.53) 0.292
 Quartile 3 1.22 (0.96–1.54) 0.103 1.29 (0.99–1.68) 0.060 1.22 (0.95–1.56) 0.109
 Quartile 4 1.33 (1.01–1.76) 0.044 1.42 (1.02–1.99) 0.039 1.41 (1.03–1.94) 0.035
Cardiovascular mortality
 Quartile 1 1 0.137* 1 0.181* 1 0.257*
 Quartile 2 1.06 (0.64–1.76) 0.815 1.17 (0.67–2.05) 0.568 1.19 (0.70–2.04) 0.506
 Quartile 3 1.48 (0.95–2.30) 0.078 1.58 (0.94–2.65) 0.080 1.44 (0.90–2.32) 0.126
 Quartile 4 1.30 (0.78–2.18) 0.304 1.33 (0.74–2.39) 0.333 1.29 (0.72–2.30) 0.378
Cancer-related mortality
 Quartile 1 1 0.076* 1 0.070* 1 0.091*
 Quartile 2 1.36 (0.77–2.37) 0.276 1.37 (0.74–2.55) 0.306 1.40 (0.75–2.60) 0.282
 Quartile 3 1.42 (0.89–2.26) 0.134 1.53 (0.87–2.66) 0.131 1.50 (0.85–2.64) 0.152
 Quartile 4 1.63 (0.98–2.74) 0.061 1.69 (0.95–3.01) 0.073 1.68 (0.93–3.02) 0.081
Subgroup analysis for all-cause mortality
 Age <50 (n=1,792)
  Quartile 1 1 0.858* 1 0.762* 1 0.753*
  Quartile 2 0.66 (0.32–1.39) 0.259 0.68 (0.32–1.43) 0.298 0.68 (0.33–1.41) 0.283
  Quartile 3 0.85 (0.50–1.44) 0.534 0.82 (0.45–1.47) 0.472 0.72 (0.38–1.38) 0.306
  Quartile 4 0.98 (0.51–1.86) 0.947 0.85 (0.41–1.76) 0.644 0.86 (0.42–1.79) 0.684
 Age ≥50 (n=1,826)
  Quartile 1 1 0.005* 1 0.003* 1 0.004*
  Quartile 2 1.12 (0.88–1.43) 0.328 1.22 (0.91–1.64) 0.168 1.30 (0.99–1.70) 0.055
  Quartile 3 1.30 (1.04–1.64) 0.023 1.41 (1.10–1.82) 0.009 1.37 (1.09–1.73) 0.009
  Quartile 4 1.42 (1.10–1.84) 0.009 1.55 (1.14–2.12) 0.007 1.55 (1.16–2.09) 0.005
 Men (n=1,713)
  Quartile 1 1 0.015* 1 0.005* 1 0.007*
  Quartile 2 0.91 (0.60–1.38) 0.637 0.96 (0.60–1.55) 0.871 0.96 (0.60–1.56) 0.872
  Quartile 3 1.50 (1.04–2.16) 0.030 1.65 (1.09–2.51) 0.021 1.60 (1.06–2.42) 0.027
  Quartile 4 1.33 (0.91–1.93) 0.135 1.51 (1.00–2.28) 0.052 1.47 (0.98–2.20) 0.061
 Women (n=1,905)
  Quartile 1 1 0.120* 1 0.232* 1 0.265*
  Quartile 2 1.14 (0.87–1.50) 0.342 1.21 (0.85–1.72) 0.283 1.31 (0.94–1.82) 0.110
  Quartile 3 1.00 (0.76–1.30) 0.982 1.01 (0.74–1.39) 0.936 0.96 (0.70–1.32) 0.805
  Quartile 4 1.36 (1.00–1.84) 0.048 1.35 (0.93–1.98) 0.111 1.39 (0.96–2.01) 0.079
 Non-Hispanic white (n=1,713)
  Quartile 1 1 0.026* 1 0.022* 1 0.034*
  Quartile 2 1.08 (0.80–1.45) 0.618 1.22 (0.84–1.77) 0.283 1.28 (0.92–1.78) 0.135
  Quartile 3 1.35 (1.05–1.75) 0.023 1.47 (1.09–1.98) 0.014 1.38 (1.04–1.83) 0.026
  Quartile 4 1.40 (0.98–1.99) 0.061 1.54 (1.00–2.36) 0.048 1.52 (1.01–2.28) 0.044
 Non-Hispanic black (n=646)
  Quartile 1 1 0.709* 1 0.832* 1 0.988*
  Quartile 2 0.90 (0.58–1.38) 0.604 0.77 (0.48–1.24) 0.274 0.76 (0.49–1.18) 0.208
  Quartile 3 0.95 (0.58–1.56) 0.832 0.87 (0.48–1.60) 0.649 0.82 (0.46–1.49) 0.488
  Quartile 4 1.11 (0.68–1.80) 0.657 1.09 (0.60–1.98) 0.767 1.03 (0.59–1.81) 0.919
 Hispanic (n=1,190)
  Quartile 1 1 0.885* 1 0.830* 1 0.793*
  Quartile 2 0.86 (0.59–1.27) 0.448 0.82 (0.55–1.24) 0.337 0.81 (0.53–1.23) 0.303
  Quartile 3 0.75 (0.53–1.07) 0.112 0.65 (0.41–1.03) 0.064 0.66 (0.43–1.03) 0.064
  Quartile 4 1.04 (0.64–1.69) 0.872 0.96 (0.55–1.68) 0.888 0.93 (0.51–1.69) 0.794
Table 1. Association between leukocyte telomere length and all-cause and cause-specific mortality among individuals with MASLD (n=3,618)

The present analyses used leukocyte telomere length as the exposure variable. All individuals over 20 years in the NHNAES 1999–2002 had passive mortality follow-up until December 31st, 2019. The leading cause of death was recorded as the Underlying Cause of Death 113 (UCOD_113) code. All-cause and two leading cause-specific mortalities were defined as cardiovascular disease (UCOD_113: 55–68, 70) and cancer (UCOD_113: 19–43). Liver-related mortality was publicly restricted due to the small number. Given the complex sample design employed by NHANES 1999-2002, appropriate sample weights were applied to restructure population-level data for the US.

HR, hazard ratio; CI, confidence; MASLD, metabolic dysfunction-associated steatotic liver disease.

The multivariable model 1 was adjusted for age, sex, race/ethnicity, marital status, education status, smoking status, diabetes, hypertension, and total cholesterol.

The multivariable model 2 was adjusted for waist circumference and advanced fibrosis in addition to multivariable model 1.

Quartile 1, ≥6.380; quartile 2, 5.985–6.380; quartile 3, 5.616–5.985; quartile 4, <5.616 in participants aged 20–39 years.

Quartile 1, ≥6.090; quartile 2, 5.696–6.090; quartile 3, 5.377–5.696; quartile 4, <5.377 in participants aged 40–59 years.

Quartile 1, ≥5.763; quartile 2, 5.401–5.763; quartile 3, 5.122–5.401; quartile 4, <5.122 in participants aged over 60 years.

P-value for the test of trend of hazards.

Table 1.

CMH : Clinical and Molecular Hepatology
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