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.
Table 1.
Association between leukocyte telomere length and all-cause and cause-specific mortality among individuals with MASLD (n=3,618)
| 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.
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.
Abbreviations
- 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
Footnotes
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.
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