Accelerated biological aging, genetic susceptibility, and incident severe MASLD, liver cirrhosis and cancer

Accelerated biological aging, genetic susceptibility, and incident severe MASLD, liver cirrhosis and cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Accelerated biological aging, genetic susceptibility, and incident severe MASLD, liver cirrhosis and cancer Tian Tian, Jing Zeng, Shi-Yin Meng, Xiang Wang, Shang-Xin Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4170717/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background There is an explicit link between biological age (BA) and chronic liver disease (CLD). This study aimed to explore the association between clinical biomarker-based BA and potential interaction with genetic risk on incident CLD. Methods This prospectively cohort study was conducted in UK Biobank included 347,917 participants. We quantified clinical biomarker-based BAs using the KDM-BA and PhenoAge algorithms and constructed the polygenic risk score (PRS) to examine its interactions with BAs on CLD risk. Results We first identified acceleration for KDM-BA (KDM-BAaccel) and PhenoAge (PhenoAgeAccel) were significantly associated with prevalent severe metabolic dysfunction-associated steatotic liver disease (MASLD), as well as liver cirrhosis and cancer. Each SD increase in KDM-BAaccel and PhenoAgeAccel was correlated with an 10% elevated risk of MASLD. Particularly, we observed the deleterious effects of advanced biological aging on three CLDs in males were mostly stronger than in females. In predicting MASLD, the two BA indicators showed better performance than chronological age, with AUC values of 0.526, 0.571 and 0.595 for chronological age, KDM-BAaccel and PhenoAgeAccel, respectively. Moreover, individuals with the highest BA acceleration and PRS had the highest risk of developing severe MASLD, although no significant additive and multiplicative interactions were found. Additionally, participants who at a high genetic risk level had the greatest 10-year absolute risk reduction of severe MASLD (6.74 per 1000 person-years) if their PhenoAgeAccel decreased. Conclusion Our findings elucidate that relieving biological aging is important for preventing serious fatty liver-related diseases and could offset the adverse effects of inherent genetic risk. Biological aging Genetic risk score Chronic liver diseases Cohort study Figures Figure 1 Figure 2 Figure 3 Introduction It is well known aging is a complex biological process that can induce pathological changes in liver organ and potentiate the progression of age-related metabolic chronic liver disease (CLD) [ 1 ]. Metabolic dysfunction-associated steatotic liver disease (MASLD), as the most prevalent CLD, affects more than 30% of the worldwide general population [ 2 – 3 ]. In recent decades, with the progressively aging of global, the incidence of MASLD and its serious form including cirrhosis and hepatocellular carcinoma (HCC) has been elevating. Although chronological age (CA) is a major risk factor for most CLDs, extensive heterogeneity is also existed among elderly individuals. Accumulating experimental evidence has shown that aging effects the liver volume, blood flow and liver regeneration [ 4 – 5 ]. Nie et al estimated biological age (BA) of several organs by utilizing multi-omics features, and they found liver ages has the most variance from CA [ 6 ]. Actually, BA reflects a decline in body function and considers as an ideal age predicted indicator than CA [ 7 – 8 ]. Multiple measurements have been proposed to estimate the BA, including telomere length, clinical biomarkers as well as epigenetic clocks [ 8 – 9 ]. For example, a longitudinal follow-up study has reported that short telomere length was significantly associated with a 1.39-fold increased risk of developing liver fibrosis and cirrhosis [ 10 ]. Based on DNA methylation signatures, epigenetic age acceleration was observed among metabolic dysfunction associated steatohepatitis (MASH) patients compared with healthy controls [ 11 ]. Among them, clinical biomarkers are likely to be routinely applied and monitor liver health status in large population. However, there is limited evidence on the association of clinical biomarker-based age acceleration with the risk of incident CLDs. In a large-scale cohort, we quantified clinical biomarker-based BA using the Klemera-Doubal method biological age (KDM-BA) and PhenoAge algorithms and explored whether accelerated BA pose risks to severe MASLD and its progress form (liver cirrhosis and cancer). Additionally, we evaluated the potential interaction and joint effect of BA acceleration and genetic susceptibility on the incidence of severe MASLD. Methods Study design and population The UK Biobank is an ongoing large-scale prospective cohort study that enrolled more than 500,000 participants aged 37–73 years between 2006 and 2010, with multiple follow-ups. Participants’ lifestyle, health information, and biological samples were collected at baseline. The UK Biobank research had approval from the North West Multicenter Research Ethical Committee. And all participants provided Written informed consent. As shown in Fig. S1 , a total of 502,411 participants were initially included in the study. After the initial exclusion of 5,224 participants with basic liver diseases at baseline (Table S1 ), 497,187 participants remained. Next, participants who without trait data for BA algorithms (n = 91,463) or without data for leukocyte telomere length (LTL) measurements (n = 15,642) were removed. Finally, we excluded participants who without genetic data for polygenic risk score (PRS) algorithms (n = 4,751) or who had other covariates missing (n = 37,414). Ultimately, 347,917 participants were included in the preliminary analysis. Calculation of PRS The detail of genotyping process, arrays and quality control used in the UK Biobank has been discussed elsewhere [ 12 ]. Brifely, the genotyping of participants was obtained using the Affymetrix UK BiLEVE Axiom or UK Biobank Axiom array. PRS was calculated used four single nucleotide polymorphisms (SNPs): PNPLA3 -rs738409, TM6SF2 -rs58542926, MBOAT7 -rs641738 and GCKR -rs1260326, which have been shown to be closely associated with liver damage and the occurrence of severe liver disease (Table S2) [ 13 ]. The calculation formula of PRS is shown in Supplementary Method section 1 . Exposure assessment In order to better assess individuals’ degree of aging, three approaches (KDM-BA, PhenoAge acceleration and LTL) were adopted to calculate the BAs which are able to completely depict the whole landscape of the aging process of individuals as far as possible. According to the method originally described, both KDM-BA and PhenoAge were trained using data from the National Health and Nutrition Examination Survey (NHANES) with two sets of nine clinical traits [ 14 – 16 ]. KDM-BA and PhenoAge were obtained based on different algorithms. The detailed calculation methods of KDM-BA, PhenoAge and LTL are shown in Supplementary Method section 2 . To quantify the difference between participants’ CA and BA, we regress the computed BA values on their CA and calculate the residual values. These residuals are referred to as "BA acceleration" [ 17 ]. Meanwhile, age acceleration indicators and log-transformed LTL measurements (T/S ratio) were normalized by Z-scores to ensure comparability between different BA indicators in this analysis. Assessment of outcomes Outcome events were defined according to the 10th edition of the International Classification of Diseases (ICD) and obtained through electronic links to inpatient admission registries in England, Wales and Scotland. Severe MASLD was defined as MASLD or MASH, with codes K76.0 and K75.8, respectively. Definitions of liver cirrhosis and cancer included codes [ 18 ]: K70.2 (alcoholic fibrosis and cirrhosis), K70.3 (alcoholic cirrhosis), K70.4 (alcoholic liver failure), K74.0 (hepatic fibrosis), K74.1 (cirrhosis), K74.2 (hepatic fibrosis with cirrhosis), K74.6 (other nonspecific cirrhosis), K76.6 (portal hypertension), or I85.0 (esophageal varices, bleeding), I85.9 (esophageal varices, no bleeding) and C22 (liver cancer). The follow-up time was from recruitment until the data of first diagnosis, loss to follow-up, death or censoring data, whichever occurred first. Covariates We adjusted age, sex, ethnicity, Townsend deprivation index, body mass index (BMI), alcohol status, smoking status, physical activity, history of hypertension, history of diabetes and history of heart disease as potential covariates. In model 1, we only adjusted for sex and age. In model 2, all potential covariates included in this study were adjusted. The definition of covariates see in Supplementary Method section 3 . Statistical analysis We used multivariable Cox proportional hazards model to estimate the hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). To investigate the dose-response associations between BA and risk of severe MASLD, liver cirrhosis and cancer, we performed restricted cubic spline regressions (RCS) fitted by Cox hazard regression with three knots (5th, 50th, and 95th). We assessed the P values for trends by fitting categories as continuous variables in models and used Schoenfeld residuals to test the proportional hazards assumption. The joint analysis was used to investigate the combined effects of BAs and PRS on the risk of severe MASLD, liver cirrhosis and cancer, using individuals with youngest BA and the lowest PRS as a reference. The area under the receiver operating characteristic (ROC) curve (AUC) was used to test the predictive ability of different models for the risk of outcomes. To investigate the potential effect of the relationship between BA indicators and PRS on MASLD and its adverse results, we used additive and multiplicative interaction to evaluate the interaction between them. The computing method of interaction see in Supplementary Method section 4 . The following sensitivity analyses were performed to better assess the robustness of this study. 1) Removing the individuals who with less than 2 years of follow-up, 2) filling in the miss covariates information by the chain inference (“mice” packet, all covariates < 1% missing), 3) Excluding individuals with absent or abnormal aspartate transaminase (AST) or alanine aminotransferase (ALT) values of liver function at baseline. All analyses were performed using R software (version 4.3.0). A two-sided P -value < 0.05 was considered statistically significant. Results Participants’ characteristics The baseline characteristics of 347,917 participants in the UK Biobank cohort were manifested in Table 1 . During 12.2 years of follow-up, there were a total of 3222 cases of MASLD and 1769 cases of liver cirrhosis and cancer, respectively. In general, participants were mostly aged 56 ± 8-year-old, 95.18% were White, 53.14% were female. The distributions of BA and CA for all included participants were shown in Fig. S2. We observed that the individuals’ BA was consistently younger than CA. Both KDM-BAaccel and PhenoAgeAccel were highly correlated with CA, with Pearson coefficients of 0.56 and 0.88, respectively (Fig. S3). Compared to participants without severe MASLD, those with severe MASLD had lower TDI, higher BMI, lower physical activity levels, higher genetic risk, were more likely to smoke and drink, and had a higher prevalence of hypertension, heart disease and diabetes. Participants who developed liver cirrhosis and cancer also had similar characteristics. Biological age and severe MASLD, liver cirrhosis and cancer incidence As shown in Table 2 , after adjusting for CA and other potential confounders, higher values of BAs were observed to be associated with a higher risk of MASLD, liver cirrhosis and cancer incidence. Each 1 SD increment in KDM-BAaccel increment yielded fully adjusted HRs of 1.10 (95%CI, 1.07–1.14) and 1.14 (95%CI, 1.07–1.19) for MASLD, liver cirrhosis and cancer, respectively. For PhenoAgeAccel, per SD increase was related to a 12%, 39% increase in the risk of MASLD (HR, 1.12; 95%CI, 1.09–1.16), liver cirrhosis and cancer (HR, 1.39; 95%CI, 1.35–1.42), respectively. Compared to individuals in the lowest quartiles of KDM-BA, those in the highest quartiles had a 1.28-fold increased risk of MASLD (95%CI, 1.15–1.41) and a 1.31-fold increased risk of liver cirrhosis and cancer (95%CI, 1.15–1.50). The HRs for the highest quartiles of PhenoAgeAccel, compared with the lowest quartiles, were 1.36 for MASLD (95%CI, 1.22–1.51) and 2.81 for liver cirrhosis and cancer (95%CI, 2.40–3.29). Similarly, when BA was divided into two groups, the biologically older group was associated with a greater risk of MASLD (Table S3). As expected, LTL was closely correlated with the risk of MASLD, and the longer the LTL, the lower the risk of MASLD. Interestingly, the deleterious effects of advanced biological aging on three CLDs in males were mostly larger than in females (Fig. 1 ; Table 4). Through the changing trend of RCS curve, we observed that the incidence risk of severe MASLD increased monotonically with the increase of BA measures. However, the incidence risk of cirrhosis and liver cancer and the pattern of BA measures were relatively complex (Fig. S4). PRS and severe MASLD, liver cirrhosis and cancer incidence After adjusting for potential covariates, we observed that participants who develop severe MASLD, liver cirrhosis and cancer tend to have a higher PRS than those without these diseases. When the PRS score was divided into five groups by quintile, the risk of three CLDs occurrence among PRS groups showed a significantly gradient-increasing trend (all P trend <0.001) (Table S5; Fig. 2 A, 2 C). Participants in the highest genetic risk category had a 1.85- and 1.76-fold increased risk of incident severe MASLD (95%CI, 1.67–2.06), liver cirrhosis and cancer (95%CI, 1.53–2.03) compared to those in the lowest category (Fig. 2 B, D ) . The consistent dose-risk relationship was also observed in liver cancer, with a HR of 1.13 (95%CI, 1.07–1.20) (Table S6; Fig. S5). Then, we assessed the predictive performance of different age indicators in predicting severe MASLD. As depicted in Table S7, the AUC values were 0.526, 0.571 and 0.595 for CA, KDM-BAaccel and PhenoAgeAccel, respectively. However, in predicting liver cirrhosis and cancer, PhenoAgeAccel had the largest AUC among the three age indicators. When combining PRS with different age indicators, the AUCs ranged from 0.532 to 0.620 for MASLD, 0.566 to 0.684 for liver cirrhosis and cancer, respectively. These observations were similar in subgroup analysis based on gender. Joint impact and interaction of biological age and genetic susceptibility on incident severe MASLD, liver cirrhosis and cancer risk When combining BA measurements and genetic risk, significant joint effects were observed on the risk of severe MASLD, liver cirrhosis and cancer. Compared to participants with the lowest quintile PhenoAgeAccel and lowest genetic risk, those with simultaneously highest PhenoAgeAccel and genetic risk had a 2.50-fold and 4.58-fold risk of severe MASLD and liver cirrhosis and cancer, respectively (Table S8). Similar joint impact were also observed for KDM-BAaccel and PRS on the development of severe MASLD, liver cirrhosis and cancer (Table S9). While there was no additive and multiplicative interaction between BA measurements and PRS on the risk of severe MASLD, as well as liver cirrhosis and cancer (Table S10). Benefits of adherence to a younger biological age with severe MASLD, liver cirrhosis and cancer prevention Individuals with the highest genetic risk and BA had the highest standardized 10-year absolute risk of severe MASLD, liver cirrhosis and cancer. However, with the reduction of BAaccel, participants in each genetic risk category showed a decreased risk of developing severe MASLD, as well as liver cirrhosis and cancer. For example, when PhenoAgeAccel is lowest, the standardized 10-year absolute risk for severe MASLD was reduced by 5.78 (95%CI, 4.74–6.71), 5.48 (95%CI, 4.21–6.75), and 6.74 (95%CI, 5.37–7.98) per 1000 person years for low, intermediate, and high genetic risk categories, respectively (Fig. 3 ). Similar results were observed in the other two BA indicators (KDM-BAaccel and LTL) across different genetic risk groups (Tables S11 and S12). After conducting a series of sensitivity analyses, we found the robust associations between BA accelerationand three CLDs risk remained consistent even after excluding participants who were less than two years follow-up (Table S13), removing participants with absent or abnormal AST or ALT values of liver function at baseline (Table S14), and filling in missing covariates information (Table S15). Discussion In this large cohort of UK Biobank participants, we first identified KDM-BAacceland PhenoAgeAccel were significantly associated with prevalent severe MASLD, as well as liver cirrhosis and cancer. Particularly, we observed that the adverse effects of advanced biological aging on these three CLDs were mostly stronger in males than in females. KDM-BAaccel and PhenoAgeAccel showed better performance than CA in predicting MASLD. Moreover, there were joint effects but no interactions of BA acceleration and genetic risk in severe MASLD, liver cirrhosis and cancer incidence. In addition, participants who at a high genetic risk level had the greatest 10-year absolute risk reduction of severe MASLD (6.74 per 1000 person-years) if decreasing the PhenoAgeAccel. Our findings indicated that alleviating biological aging is important for preventing serious liver-related diseases and could offset the deleterious effect of inherent genetic risk. Most previous studies focused on the role of clinical biological aging indicators in the occurrence of several health outcomes. Mak et al. conducted a prospective cohort study to investigate the association between three clinical qualified biological aging indicators and the risk of five common cancers. They found that age-adjusted KDM-BA and PhenoAge were correlated with an increased risk of lung and colorectal cancers, while PhenoAge was additionally linked to increased risk of breast cancer [ 19 ]. Another prior study has suggested that PhenoAge was an independent risk factor for lung cancer and might serve as a potential biomarker for prediction of lung cancer [ 20 ]. In consistence with our findings, Xia et al. disclosed that participants with accelerated DNA methylation age were found to have a higher risk of incident MASLD than those without accelerated DNA methylation age [ 21 ]. Another research identified MASH patients exhibited accelerated epigenetic age that links with increased liver fibrosis [ 11 ]. A recent study also reported that long telomere length was associated with reduced risk of MASLD incidence and a positive addictive interaction between high genetic risk score and low telomere length [ 22 ]. Intriguingly, we found the deleterious effects of advanced biological aging on of CLDs in males were larger than in females, which was the first time reported by population-based study. Sex differences in MASLD prevalence was partly attributed to estrogens [ 23 ]. In both mice and humans, aging could enhance the susceptibility of alcohol-induced liver injury by modulating the SIRT1-C/EBPα-miR-223 axis in neutrophilic [ 24 ]. Generally, male drink more frequently than female, which might be a potential reason for our observed gender difference. There are several potential biological mechanisms might mediate the link between BA and severe liver-related diseases. In the process of aging, the liver cells may develop changes such as telomere shortening, nuclear area increase, mitochondrial DNA damage, and induce the secretion of pro-inflammatory cytokines, thereby resulting in liver damage [ 25 ]. Animal experiments have shown that aging could impact liver microcirculatory function and sinusoidal phenotype [ 26 ]. Moreover, the accumulation of senescent cells promotes hepatic steatosis and lipid accumulation, which lead to liver damage by inducing inflammation, cell death, fibrosis and promoting organ-specific toxicity [ 27 – 28 ]. Aging can not only directly lead to liver damage, but also induce the progression of MASLD to its more serious stage. Hepatic steatosis might cause mitochondrial dysfunction, hepatic stele cell activation, and hepatic fibrosis, that promote MASLD to the development of HCC from MASLD [ 1 , 29 ]. The exact biological mechanism underlying the aging and MASLD-associated liver cirrhosis and cancer remains to be further studied. Since a g ing is a multi-factorial process, a single BA predictor is not sufficient to monitor the risk of various age related disease phenotypes. Several existing BA predictors including epigenetic clocks, telomere length, transcriptomic, proteomic and metabolomic markers have been widely applied to predicted health outcomes [ 8 ]. Given each predictor reflects specific aspect of the aging process, a combination of these indicators appears to be an ideal predictor to predict MASLD risk. Aging rate is not the same in different tissues, and it is not realistic to get a comprehensive marker from various tissues. Compared to those epigenetic and omics biomarkers, routine clinical biomarkers with characteristics of convenient detection and economic advantage are more suitable for large scale population-based screening and dynamic monitoring. In this work, both PhenoAge and KDM-BA yielded better performance than CA in MASLD prediction. The accuracy of PRS for fat-related liver conditions seems not ideal compared with other reported parameters with a mean value exceeded 0.60 [ 30 – 32 ]. With regard to the numbers of known genetic components, more susceptibility loci for fat-related liver diseases are needed to be discovered in future. Additionally, we identified individuals at highest genetic level and BA had the highest standardized 10-year absolute risk of severe MASLD and its serious outcomes. Thus, individuals at a high genetic risk level should be more attention to the accelerated biological aging. A few limitations should be acknowledged in current study. First, the definition of the new-onset severe MASLD (including MASH) was according to the ICD-10 codes from hospital records, which was more accuracy but might underestimate the real prevalence and incidence. Indeed, it is unpractical to monitor the development of MASLD in a large scale cohort with almost half of million participants. On the other hand, we also confirmed BA acceleration predisposed to incident its serious outcome including liver cirrhosis and cancer. Second, the clinical parameters of each participant were detected at baseline, we could not evaluate the impact of biological aging trajectory on the risk of liver outcomes. Further longitudinal population-based study with repeated measurement of these clinical biomarkers at various times are warranted. Third, the majority of participants in UK Biobank cohort were middle-aged or elderly Europeans, the effect of accelerated BA on liver-related diseases needed to be evaluated in younger groups. Fourth, the unfavorable role of BA on CLD risk are warrant to be verified in different type of BA predictors in future. In conclusion, Accelerated BA quantified by clinical biomarkers was associated with an increased risk of severe MASLD, as well as its advanced outcomes. Implementing interventions that slow biological aging could serve as an effective preventive strategy for this growing public health problem. Abbreviations AUC The area under the receiver operating characteristic curve AP Attributable proportion of interaction BA Biological age CLD Chronic liver disease CLD Chronic liver disease CI Confidence interval HR Hazard ratio KDM-BA Klemera-Doubal method biological age LTL Leukocyte telomere length MASLD Metabolic dysfunction-associated steatotic liver disease PRS Polygenic risk score RCS Restricted cubic spline regression RERI Relative excess risk due to interaction SNP Single nucleotide polymorphism TDI Townsend deprivation index Declarations Acknowledgements The data of current study can be requested from the UK Biobank (https://www.ukbiobank.ac.uk/). This work was conducted under UK Biobank application number 80827. We thank all the UK Biobank participants and the management team for their participation and assistance. Data availability Data used in this study are availability via UK Biobank on request. Funding This study was supported by the National Natural Science Foundation of China (82103932, 82273710, 82100605), Research Fund of Anhui Institute of Translational Medicine (2023zhyx-C07), SJTU Trans-med Awards Research (20190104), Star Program of Shanghai Jiaotong University (YG2021QN54). Author contributions TT, JZ, SM and JN conceived and designed the study. TT and JZ cleaned the data. TT, JZ, SM and WX analysed the data. TT, JZ, XW and JN drafted the manuscript. TT, JZ, SZ and JN helped to interpret the results. JF, HP, and JN commented on the drafts of the manuscript. Approved the final version of the article, including the authorship list: All authors. Conflict of interest The authors disclose no conflicts. 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Characteristics of study participants Characteristic Total No. (n = 347,917 ) Severe MASLD Liver cirrhosis and cancer No.of case (n = 3222 ) No.of non- c ase (n = 3 44 , 695 ) P -value No.of case (n = 1 303 ) No.of non- c ase (n = 30 5 , 2 83 ) P -value Age (years), mean (SD) 56.47 (8.11) 57.25 (7.82) 56.46 (8.11) <0.001 59.45 (7.21) 56.46 (8.11) <0.001 Sex (Female), n (%) 184882 (53.14) 1596 (49.53) 183286 (53.17) <0.001 625 (35.53) 184257 (53.23) <0.001 Race (White), n (%) 331132 (95.18) 3032 (94.10) 328100 (95.19) 0.011 1692 (96.19) 329440 (95.17) 0.188 Townsend deprivation index, mean (SD) -1.41 (3.03) -0.44 (3.42) -1.42 (3.02) <0.001 -0.67 (3.40) -1.41 (3.03) <0.001 Smoking status, n (%) Never 191359 (55.00) 1472 (45.69) 189887 (55.09) <0.001 716 (40.7) 190643 (55.08) <0.001 Previous 121373 (34.89) 1296 (40.22) 120077 (34.83) 725 (41.22) 120648 (34.85) Current 35185 (10.11) 454 (14.09) 34731 (10.08) 318 (18.08) 34867 (10.07) Alcohol consumption, n (%) Never 13975 (4.02) 167 (5.18) 13808 (4.01) <0.001 61 (3.47) 13914 (4.02) <0.001 Previous drinker 11473 (3.30) 205 (6.36) 11268 (3.27) 92 (5.23) 11381 (3.29) Occasional drinker 322469 (92.68) 2850 (88.46) 319619 (92.72) 1606 (91.30) 320863 (92.69) Body mass index (BMI, kg/m 2 ), n (%) <18.5 1751 (0.50) 3 (0.09) 1748 (0.51) <0.001 7 (0.40) 1744 (0.50) =30 80832 (23.23) 1701 (52.79) 79131 (22.96) 700 (39.79) 80132 (23.15) Physical activity (MET-min/week) , n (%) Low (MET: <=600) 67528 (19.41) 815 (25.30) 66713 (19.36) <0.001 410 (23.31) 67118 (19.39) 3000) 102977 (29.60) 840 (26.07) 102137 (29.63) 520 (29.56) 102457 (29.60) Major diseases, n (%) a Diabetes 16815 (4.83) 558 (17.32) 16257 (4.72) <0.001 299 (17.00) 16516 (4.77) <0.001 Hypertension 81865 (23.53) 1213 (37.65) 80652 (23.40) <0.001 657 (37.35) 81208 (23.46) <0.001 Heart disease 18810 (5.41) 380 (11.79) 18430 (5.35) <0.001 210 (11.94) 18600 (5.37) <0.001 Biological ages, mean (SD) KDM-BA 52.04 (14.18) 55.75 (14.07) 52.00 (14.18) <0.001 56.81 (15.43) 52.01 (14.17) <0.001 KDM-BA acceleration -4.44 (11.77) -1.50 (12.58) -4.46 (11.76) <0.001 -2.65 (14.51) -4.44 (11.76) <0.001 PhenoAge 50.62 (9.33) 53.02 (9.51) 50.60 (9.33) <0.001 56.91 (9.35) 50.59 (9.32) <0.001 PhenoAge acceleration -5.85 (4.42) -4.23 (5.28) -5.87 (4.40) <0.001 -2.54 (6.48) -5.87 (4.40) <0.001 Leukocyte telomere length 0.83 (0.13) 0.82 (0.13) 0.83 (0.13) <0.001 0.80 (0.13) 0.83 (0.13) <0.001 Components of biological ages, mean (SD) FEV1 (L) * 2.84 (0.80) 2.67 (0.78) 2.85 (0.80) <0.001 2.72 (0.77) 2.85 (0.80) <0.001 SBP (mm Hg) * 139.70 (19.61) 142.63 (18.63) 139.67 (19.62) <0.001 143.92 (19.47) 139.68 (19.61) <0.001 Total cholesterol (mg/dL) * 220.43 (43.95) 213.00 (48.65) 220.50 (43.90) <0.001 208.61 (49.84) 220.49 (43.91) <0.001 Glycated hemoglobin (%) * 5.44 (0.59) 5.76 (0.93) 5.44 (0.59) <0.001 5.73 (0.93) 5.44 (0.59) <0.001 Blood urea nitrogenbin (%) * 15.13 (3.81) 15.26 (3.98) 15.13 (3.80) 0.059 15.19 (5.14) 15.13 (3.80) 0.512 Lymphocyte (%) # 28.93 (7.43) 28.97 (7.40) 28.92 (7.43) 0.755 27.92 (7.87) 28.93 (7.43) <0.001 Mean cell volume bin (%) * 82.87 (5.25) 82.59 (5.69) 82.87 (5.25) 0.003 85.34 (6.79) 82.85 (5.24) <0.001 Serum glucose (mg/bin (%) * 91.89 (21.48) 100.27 (36.43) 91.81 (21.27) <0.001 102.09 (38.62) 91.84 (21.34) <0.001 Red cell distribution width (%) * 13.47 (0.95) 13.58 (1.03) 13.47 (0.95) <0.001 13.75 (1.29) 13.47 (0.95) <0.001 White blood cell count (1000 cells/uL) # 6.78 (2.19) 7.12 (1.52) 6.78 (2.19) 0.124 6.52 (1.29) 6.78 (2.19) 0.368 Albumin (g/dL) *# 45.26 (2.60) 45.00 (2.78) 45.26 (2.60) <0.001 44.45 (3.08) 45.26 (2.60) <0.001 Creatinine (mg/dL) *# 72.38 (16.33) 73.08 (18.06) 72.37 (16.31) 0.015 74.98 (29.25) 72.37 (16.24) <0.001 C-reactive protein (mg/dL) *# 0.25 (0.42) 0.40 (0.52) 0.25 (0.42) <0.001 0.39 (0.51) 0.25 (0.42) <0.001 Alkaline phosphatase (U/L) *# 82.91 (25.10) 92.84 (36.95) 82.82 (24.95) <0.001 103.17 (67.98) 82.81 (24.65) <0.001 Genetic risk category , n (%) Low 83150 (23.90) 592 (18.37) 82558 (23.95) <0.001 319 (18.14) 82831 (23.93) <0.001 Intermediate 196828 (56.57) 1778 (55.18) 195050 (56.59) 986 (56.05) 195842 (56.58) High 67939 (19.53) 852 (26.44) 67087 (19.46) 454 (25.81) 67485 (19.50) P -values were calculated by ANOVA (continuous variables) or χ² test (categorical variables). a Major disease diagnosed by doctor. * Employed to construct KDM-BA. # Employed to construct PhenoAge. No., number; n, total number of observations; SD, standard deviation, MET, metabolic equivalent; MASLD, metabolic dysfunction-associated steatotic liver disease; KDM, Klemera-Doubal method; BA, biological age; FEV1, forced expiratory volume in 1 second; SBP, systolic blood pressure. Table 2. Association between biological age acceleration and the risk of incident severe MASLD, liver cirrhosis and cancer Exposures Total No. Severe MASLD Liver cirrhosis and cancer No.of case Model 1 Model 2 No.of case Model 1 Model 2 HR(95%CI) P value HR(95%CI) P value HR(95%CI) P value HR(95%CI) P value KDM-BA acceleration a 347917 3222 1.31(1.27-1.36) <0.001 1.10(1.07-1.14) <0.001 1759 1.25(1.20-1.31) <0.001 1.14(1.09-1.19) <0.001 KDM-BA acceleration b Q1 86980 628 Reference Reference 421 Reference Reference Q2 86979 633 1.07(0.96-1.19) 0.252 0.99(0.89-1.11) 0.864 368 1.01(0.87-1.16) 0.931 0.97(0.84-1.12) 0.659 Q3 86979 829 1.42(1.28-1.58) <0.001 1.15(1.04-1.28) 0.009 413 1.18(1.03-1.35) 0.020 1.06(0.92-1.22) 0.411 Q4 86979 1132 1.98(1.80-2.19) <0.001 1.28(1.15-1.41) <0.001 557 1.66(1.46-1.88) <0.001 1.31(1.15-1.50) <0.001 PhenoAge acceleration a 347917 3222 1.32(1.29-1.35) <0.001 1.12(1.09-1.16) <0.001 1579 1.45(1.42-1.49) <0.001 1.39(1.35-1.42) <0.001 PhenoAge acceleration b Q1 86980 547 Reference Reference 206 Reference Reference Q2 86979 656 1.20(1.07-1.35) 0.002 1.04(0.93-1.17) 0.464 299 1.37(1.14-1.63) <0.001 1.28(1.07-1.53) 0.006 Q3 86979 778 1.43(1.29-1.60) <0.001 1.11(0.99-1.24) 0.070 372 1.64(1.38-1.95) <0.001 1.45(1.22-1.72) <0.001 Q4 86979 1241 2.37(2.14-2.62) <0.001 1.36(1.22-1.51) <0.001 882 3.86(3.31-4.51) <0.001 2.81(2.40-3.29) <0.001 LTL a 347917 3222 0.91(0.87-0.94) <0.001 0.93(0.90-0.97) <0.001 1579 0.85(0.81-0.89) <0.001 0.87(0.83-0.91) <0.001 LTL b Q1 86980 947 Reference Reference 630 Reference Reference Q2 86979 820 0.87(0.80-0.96) 0.005 0.91(0.83-1.00) 0.044 450 0.77(0.69-0.87) <0.001 0.80(0.71-0.90) <0.001 Q3 86979 763 0.82(0.75-0.91) <0.001 0.87(0.79-0.96) 0.004 362 0.67(0.59-0.76) <0.001 0.70(0.61-0.80) <0.001 Q4 86979 692 0.76(0.69-0.84) <0.001 0.83(0.75-0.91) <0.001 317 0.65(0.56-0.74) <0.001 0.69(0.60-0.79) <0.001 Model 1 ： Adjusted for age at recruitment and sex. Model 2 ： Further adjusted for ethnicity, Townsend deprivation index, body mass index, alcohol status, smoking status, physical activity, history of hypertension, diabetes and heart disease. a Variables were continuous. b Variables were classified by quartile. quartile1, Q1; quartile2, Q2; quartile3, Q3; quartile4, Q4. Abbreviations: No., number; MASLD, metabolic dysfunction-associated steatotic liver disease; LTL, leukocyte telomere length; HR, hazard ratio; 95% CI, 95% confidence interval. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4170717","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288336166,"identity":"8b02624f-c207-4054-ba6f-5af65d0de77c","order_by":0,"name":"Tian Tian","email":"","orcid":"","institution":"Anhui Medical University School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Tian","middleName":"","lastName":"Tian","suffix":""},{"id":288336167,"identity":"42041251-9a61-4269-9ebb-e6d541f97c6d","order_by":1,"name":"Jing Zeng","email":"","orcid":"","institution":"Shanghai Jiaotong University School of Medicine Xinhua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zeng","suffix":""},{"id":288336168,"identity":"20171a53-0f18-477b-a48c-0fa806b8da34","order_by":2,"name":"Shi-Yin Meng","email":"","orcid":"","institution":"Anhui Medical University School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Shi-Yin","middleName":"","lastName":"Meng","suffix":""},{"id":288336169,"identity":"794f8d0e-5c5f-49eb-94d3-245b7edd8914","order_by":3,"name":"Xiang Wang","email":"","orcid":"","institution":"Anhui Medical University School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Wang","suffix":""},{"id":288336170,"identity":"07434520-572f-4a1b-a443-bda820b2a0a7","order_by":4,"name":"Shang-Xin Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shang-Xin","middleName":"","lastName":"Zhang","suffix":""},{"id":288336171,"identity":"8a511ad6-94cb-4cb0-b211-f786ee176079","order_by":5,"name":"Jian-Gao Fan","email":"","orcid":"","institution":"Shanghai Jiaotong University School of Medicine Xinhua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian-Gao","middleName":"","lastName":"Fan","suffix":""},{"id":288336172,"identity":"9154cf00-733b-4be7-a1f8-9e35f17bb5a2","order_by":6,"name":"Hai-Feng Pan","email":"","orcid":"","institution":"Anhui Medical University School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Hai-Feng","middleName":"","lastName":"Pan","suffix":""},{"id":288336173,"identity":"b7761c98-ace7-40e6-9b49-e26ec2e71c4d","order_by":7,"name":"Jing Ni","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYDACZoaEAwwMNgwGYB4b8VrSSNECAYdJ0GLOzvDwcMGv8/LmYmcMGD6UHWbgn92AX4tlM0PC4Zl9tw13zs4xYJxx7jCDxJ0D+LUYHAZq4e25zbjhdo4BM28b0IUSCURpOWcP1vKXaC08Pw4kgrUwEm9LQ3LyhttpBQd7zqXzSNwgpOX8meTPPH/sbDfcTt744EeZtRz/DAJaGBh4EhgY2yDMAyAuIfVAwA5U+IcIdaNgFIyCUTByAQB7IEf9eM9AkQAAAABJRU5ErkJggg==","orcid":"","institution":"Anhui Medical University School of Public Health","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Ni","suffix":""}],"badges":[],"createdAt":"2024-03-26 14:48:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4170717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4170717/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54450272,"identity":"6d2efa9b-9445-47cf-8528-af79446336e6","added_by":"auto","created_at":"2024-04-10 17:43:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":114291,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRisk of incident MASLD, liver cirrhosis and cancer according to biological age acceleration among different sex groups. \u003c/strong\u003e(A) Risk of incident MASLD, liver cirrhosis and cancer according to KDM-BA acceleration among different sex groups, (B) risk of incident MASLD, liver cirrhosis and canceraccording to PhenoAge acceleration among different sex groups.\u003c/p\u003e\n\u003cp\u003eHRs and 95% CIs were estimated with adjustment for for age, sex, ethnicity, Townsend deprivation index, body mass index, alcohol status, smoking status, physical activity, history of hypertension, history of diabetes, and history of heart disease.\u003c/p\u003e\n\u003cp\u003eMASLD, metabolic dysfunction-associated steatotic liver disease; KDM-BA, Klemera-Doubal method biological age; HR, hazard ratio; 95% CI, 95% confidence interval.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4170717/v1/af5cfb12589c1ffa0e84b3bf.png"},{"id":54450274,"identity":"17d7dea0-b036-4a29-8d0e-4c028317703a","added_by":"auto","created_at":"2024-04-10 17:43:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":229781,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe association effects of PRS with incident severe MASLD, liver cirrhosis and cancer in UK Biobank.\u003c/strong\u003e (A) the association effects of PRS with incident MASLD risk. (B) standardized cumulative MASLD rates in Q1 (lowest quintile), Q2 (2 to 4 quintile), and Q3 (highest quintile) genetic risk groups. (C) the association effects of PRS with incident liver cirrhosis and cancer risk. (D) standardized cumulative liver cirrhosis and cancer rates Q1 (lowest quintile), Q2 (2 to 4 quintile), and Q3 (highest quintile) genetic risk groups.\u003c/p\u003e\n\u003cp\u003eHRs and 95% CIs were estimated with adjustment for age, sex, ethnicity, Townsend deprivation index, body mass index, alcohol status, smoking status, physical activity, history of hypertension, history of diabetes, and history of heart disease.\u003c/p\u003e\n\u003cp\u003eMASLD, metabolic dysfunction-associated steatotic liver disease; 95% CI, 95% confidence interval; PRS, polygenic risk score.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4170717/v1/48c334b10982416078d8bd07.png"},{"id":54450273,"identity":"e5776490-9374-4c7b-b2e1-d4089a90bcf1","added_by":"auto","created_at":"2024-04-10 17:43:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102911,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAbsolute risk and risk reduction of incident severe MASLD according to PhenoAge acceleraction within each genetic risk category.\u003c/strong\u003eGenetic risk was categorized into low (the bottom quintile), intermediate (quintiles 2–4), and high (the top quintile). The 10-year absolute risks were standardized for age and sex in the UK Biobank. The HRs were estimated using Cox proportional hazards regression with adjustment for age, sex, ethnicity, Townsend deprivation index, body mass index, alcohol status, smoking status, physical activity, history of hypertension, history of diabetes, and history of heart disease. The 10-year absolute risk reduction and 95% CI were generated by drawing 1000 bootstrap samples from the estimation dataset.\u003c/p\u003e\n\u003cp\u003eMASLD, metabolic dysfunction-associated steatotic liver disease; 95% CI, 95% confidence interval; HR, hazard ratio; Ref, reference.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4170717/v1/610e39d9aab17cd167f0eb09.png"},{"id":55493072,"identity":"050aab0b-bf44-40b6-abe5-56aa7aedfaa2","added_by":"auto","created_at":"2024-04-29 07:46:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1187450,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4170717/v1/9dead0cd-8935-4870-b34a-ea9d703b3984.pdf"},{"id":54450275,"identity":"62b1f174-d268-45cf-b8b6-0857a98c19f4","added_by":"auto","created_at":"2024-04-10 17:43:48","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1185701,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4170717/v1/90fa248733a5de135fcb59a6.docx"}],"financialInterests":"","formattedTitle":"Accelerated biological aging, genetic susceptibility, and incident severe MASLD, liver cirrhosis and cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIt is well known aging is a complex biological process that can induce pathological changes in liver organ and potentiate the progression of age-related metabolic chronic liver disease (CLD) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Metabolic dysfunction-associated steatotic liver disease (MASLD), as the most prevalent CLD, affects more than 30% of the worldwide general population [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent decades, with the progressively aging of global, the incidence of MASLD and its serious form including cirrhosis and hepatocellular carcinoma (HCC) has been elevating. Although chronological age (CA) is a major risk factor for most CLDs, extensive heterogeneity is also existed among elderly individuals. Accumulating experimental evidence has shown that aging effects the liver volume, blood flow and liver regeneration [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Nie et al estimated biological age (BA) of several organs by utilizing multi-omics features, and they found liver ages has the most variance from CA [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Actually, BA reflects a decline in body function and considers as an ideal age predicted indicator than CA [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Multiple measurements have been proposed to estimate the BA, including telomere length, clinical biomarkers as well as epigenetic clocks [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For example, a longitudinal follow-up study has reported that short telomere length was significantly associated with a 1.39-fold increased risk of developing liver fibrosis and cirrhosis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Based on DNA methylation signatures, epigenetic age acceleration was observed among metabolic dysfunction associated steatohepatitis (MASH) patients compared with healthy controls [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Among them, clinical biomarkers are likely to be routinely applied and monitor liver health status in large population. However, there is limited evidence on the association of clinical biomarker-based age acceleration with the risk of incident CLDs.\u003c/p\u003e \u003cp\u003eIn a large-scale cohort, we quantified clinical biomarker-based BA using the Klemera-Doubal method biological age (KDM-BA) and PhenoAge algorithms and explored whether accelerated BA pose risks to severe MASLD and its progress form (liver cirrhosis and cancer). Additionally, we evaluated the potential interaction and joint effect of BA acceleration and genetic susceptibility on the incidence of severe MASLD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThe UK Biobank is an ongoing large-scale prospective cohort study that enrolled more than 500,000 participants aged 37\u0026ndash;73 years between 2006 and 2010, with multiple follow-ups. Participants\u0026rsquo; lifestyle, health information, and biological samples were collected at baseline. The UK Biobank research had approval from the North West Multicenter Research Ethical Committee. And all participants provided Written informed consent.\u003c/p\u003e \u003cp\u003eAs shown in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, a total of 502,411 participants were initially included in the study. After the initial exclusion of 5,224 participants with basic liver diseases at baseline (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), 497,187 participants remained. Next, participants who without trait data for BA algorithms (n\u0026thinsp;=\u0026thinsp;91,463) or without data for leukocyte telomere length (LTL) measurements (n\u0026thinsp;=\u0026thinsp;15,642) were removed. Finally, we excluded participants who without genetic data for polygenic risk score (PRS) algorithms (n\u0026thinsp;=\u0026thinsp;4,751) or who had other covariates missing (n\u0026thinsp;=\u0026thinsp;37,414). Ultimately, 347,917 participants were included in the preliminary analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCalculation of PRS\u003c/h2\u003e \u003cp\u003eThe detail of genotyping process, arrays and quality control used in the UK Biobank has been discussed elsewhere [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Brifely, the genotyping of participants was obtained using the Affymetrix UK BiLEVE Axiom or UK Biobank Axiom array. PRS was calculated used four single nucleotide polymorphisms (SNPs): \u003cem\u003ePNPLA3\u003c/em\u003e-rs738409, \u003cem\u003eTM6SF2\u003c/em\u003e-rs58542926, \u003cem\u003eMBOAT7\u003c/em\u003e-rs641738 and \u003cem\u003eGCKR\u003c/em\u003e-rs1260326, which have been shown to be closely associated with liver damage and the occurrence of severe liver disease (Table S2) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The calculation formula of PRS is shown in \u003cb\u003eSupplementary Method section 1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExposure assessment\u003c/h2\u003e \u003cp\u003eIn order to better assess individuals\u0026rsquo; degree of aging, three approaches (KDM-BA, PhenoAge acceleration and LTL) were adopted to calculate the BAs which are able to completely depict the whole landscape of the aging process of individuals as far as possible. According to the method originally described, both KDM-BA and PhenoAge were trained using data from the National Health and Nutrition Examination Survey (NHANES) with two sets of nine clinical traits [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. KDM-BA and PhenoAge were obtained based on different algorithms. The detailed calculation methods of KDM-BA, PhenoAge and LTL are shown in \u003cb\u003eSupplementary Method section 2\u003c/b\u003e .\u003c/p\u003e \u003cp\u003eTo quantify the difference between participants\u0026rsquo; CA and BA, we regress the computed BA values on their CA and calculate the residual values. These residuals are referred to as \"BA acceleration\" [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Meanwhile, age acceleration indicators and log-transformed LTL measurements (T/S ratio) were normalized by Z-scores to ensure comparability between different BA indicators in this analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of outcomes\u003c/h2\u003e \u003cp\u003eOutcome events were defined according to the 10th edition of the International Classification of Diseases (ICD) and obtained through electronic links to inpatient admission registries in England, Wales and Scotland. Severe MASLD was defined as MASLD or MASH, with codes K76.0 and K75.8, respectively. Definitions of liver cirrhosis and cancer included codes [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]: K70.2 (alcoholic fibrosis and cirrhosis), K70.3 (alcoholic cirrhosis), K70.4 (alcoholic liver failure), K74.0 (hepatic fibrosis), K74.1 (cirrhosis), K74.2 (hepatic fibrosis with cirrhosis), K74.6 (other nonspecific cirrhosis), K76.6 (portal hypertension), or I85.0 (esophageal varices, bleeding), I85.9 (esophageal varices, no bleeding) and C22 (liver cancer). The follow-up time was from recruitment until the data of first diagnosis, loss to follow-up, death or censoring data, whichever occurred first.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eWe adjusted age, sex, ethnicity, Townsend deprivation index, body mass index (BMI), alcohol status, smoking status, physical activity, history of hypertension, history of diabetes and history of heart disease as potential covariates. In model 1, we only adjusted for sex and age. In model 2, all potential covariates included in this study were adjusted. The definition of covariates see in \u003cb\u003eSupplementary Method section 3\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe used multivariable Cox proportional hazards model to estimate the hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). To investigate the dose-response associations between BA and risk of severe MASLD, liver cirrhosis and cancer, we performed restricted cubic spline regressions (RCS) fitted by Cox hazard regression with three knots (5th, 50th, and 95th). We assessed the \u003cem\u003eP\u003c/em\u003e values for trends by fitting categories as continuous variables in models and used Schoenfeld residuals to test the proportional hazards assumption. The joint analysis was used to investigate the combined effects of BAs and PRS on the risk of severe MASLD, liver cirrhosis and cancer, using individuals with youngest BA and the lowest PRS as a reference. The area under the receiver operating characteristic (ROC) curve (AUC) was used to test the predictive ability of different models for the risk of outcomes.\u003c/p\u003e \u003cp\u003eTo investigate the potential effect of the relationship between BA indicators and PRS on MASLD and its adverse results, we used additive and multiplicative interaction to evaluate the interaction between them. The computing method of interaction see in \u003cb\u003eSupplementary Method section 4\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe following sensitivity analyses were performed to better assess the robustness of this study. 1) Removing the individuals who with less than 2 years of follow-up, 2) filling in the miss covariates information by the chain inference (\u0026ldquo;mice\u0026rdquo; packet, all covariates\u0026thinsp;\u0026lt;\u0026thinsp;1% missing), 3) Excluding individuals with absent or abnormal aspartate transaminase (AST) or alanine aminotransferase (ALT) values of liver function at baseline.\u003c/p\u003e \u003cp\u003eAll analyses were performed using R software (version 4.3.0). A two-sided \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipants\u0026rsquo; characteristics\u003c/h2\u003e\n \u003cp\u003eThe baseline characteristics of 347,917 participants in the UK Biobank cohort were manifested in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. During 12.2 years of follow-up, there were a total of 3222 cases of MASLD and 1769 cases of liver cirrhosis and cancer, respectively. In general, participants were mostly aged 56\u0026thinsp;\u0026plusmn;\u0026thinsp;8-year-old, 95.18% were White, 53.14% were female. The distributions of BA and CA for all included participants were shown in Fig. S2. We observed that the individuals\u0026rsquo; BA was consistently younger than CA. Both KDM-BAaccel and PhenoAgeAccel were highly correlated with CA, with Pearson coefficients of 0.56 and 0.88, respectively (Fig. S3).\u003c/p\u003e\n \u003cp\u003eCompared to participants without severe MASLD, those with severe MASLD had lower TDI, higher BMI, lower physical activity levels, higher genetic risk, were more likely to smoke and drink, and had a higher prevalence of hypertension, heart disease and diabetes. Participants who developed liver cirrhosis and cancer also had similar characteristics.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eBiological age and severe MASLD, liver cirrhosis and cancer incidence\u003c/h2\u003e\n \u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, after adjusting for CA and other potential confounders, higher values of BAs were observed to be associated with a higher risk of MASLD, liver cirrhosis and cancer incidence. Each 1 SD increment in KDM-BAaccel increment yielded fully adjusted HRs of 1.10 (95%CI, 1.07\u0026ndash;1.14) and 1.14 (95%CI, 1.07\u0026ndash;1.19) for MASLD, liver cirrhosis and cancer, respectively. For PhenoAgeAccel, per SD increase was related to a 12%, 39% increase in the risk of MASLD (HR, 1.12; 95%CI, 1.09\u0026ndash;1.16), liver cirrhosis and cancer (HR, 1.39; 95%CI, 1.35\u0026ndash;1.42), respectively. Compared to individuals in the lowest quartiles of KDM-BA, those in the highest quartiles had a 1.28-fold increased risk of MASLD (95%CI, 1.15\u0026ndash;1.41) and a 1.31-fold increased risk of liver cirrhosis and cancer (95%CI, 1.15\u0026ndash;1.50). The HRs for the highest quartiles of PhenoAgeAccel, compared with the lowest quartiles, were 1.36 for MASLD (95%CI, 1.22\u0026ndash;1.51) and 2.81 for liver cirrhosis and cancer (95%CI, 2.40\u0026ndash;3.29). Similarly, when BA was divided into two groups, the biologically older group was associated with a greater risk of MASLD (Table S3). As expected, LTL was closely correlated with the risk of MASLD, and the longer the LTL, the lower the risk of MASLD. Interestingly, the deleterious effects of advanced biological aging on three CLDs in males were mostly larger than in females (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Table 4).\u003c/p\u003e\n \u003cp\u003eThrough the changing trend of RCS curve, we observed that the incidence risk of severe MASLD increased monotonically with the increase of BA measures. However, the incidence risk of cirrhosis and liver cancer and the pattern of BA measures were relatively complex (Fig. S4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003ePRS and severe MASLD, liver cirrhosis and cancer incidence\u003c/h2\u003e\n \u003cp\u003eAfter adjusting for potential covariates, we observed that participants who develop severe MASLD, liver cirrhosis and cancer tend to have a higher PRS than those without these diseases. When the PRS score was divided into five groups by quintile, the risk of three CLDs occurrence among PRS groups showed a significantly gradient-increasing trend (all \u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u0026lt;0.001) (Table S5; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Participants in the highest genetic risk category had a 1.85- and 1.76-fold increased risk of incident severe MASLD (95%CI, 1.67\u0026ndash;2.06), liver cirrhosis and cancer (95%CI, 1.53\u0026ndash;2.03) compared to those in the lowest category (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, D\u003cstrong\u003e)\u003c/strong\u003e. The consistent dose-risk relationship was also observed in liver cancer, with a HR of 1.13 (95%CI, 1.07\u0026ndash;1.20) (Table S6; Fig. S5).\u003c/p\u003e\n \u003cp\u003eThen, we assessed the predictive performance of different age indicators in predicting severe MASLD. As depicted in Table S7, the AUC values were 0.526, 0.571 and 0.595 for CA, KDM-BAaccel and PhenoAgeAccel, respectively. However, in predicting liver cirrhosis and cancer, PhenoAgeAccel had the largest AUC among the three age indicators. When combining PRS with different age indicators, the AUCs ranged from 0.532 to 0.620 for MASLD, 0.566 to 0.684 for liver cirrhosis and cancer, respectively. These observations were similar in subgroup analysis based on gender.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eJoint impact and interaction of biological age and genetic susceptibility on incident severe MASLD, liver cirrhosis and cancer risk\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWhen combining BA measurements and genetic risk, significant joint effects were observed on the risk of severe MASLD, liver cirrhosis and cancer. Compared to participants with the lowest quintile PhenoAgeAccel and lowest genetic risk, those with simultaneously highest PhenoAgeAccel and genetic risk had a 2.50-fold and 4.58-fold risk of severe MASLD and liver cirrhosis and cancer, respectively (Table S8). Similar joint impact were also observed for KDM-BAaccel and PRS on the development of severe MASLD, liver cirrhosis and cancer (Table S9). While there was no additive and multiplicative interaction between BA measurements and PRS on the risk of severe MASLD, as well as liver cirrhosis and cancer (Table S10).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBenefits of adherence to a younger biological age with severe MASLD, liver cirrhosis and cancer prevention\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIndividuals with the highest genetic risk and BA had the highest standardized 10-year absolute risk of severe MASLD, liver cirrhosis and cancer. However, with the reduction of BAaccel, participants in each genetic risk category showed a decreased risk of developing severe MASLD, as well as liver cirrhosis and cancer. For example, when PhenoAgeAccel is lowest, the standardized 10-year absolute risk for severe MASLD was reduced by 5.78 (95%CI, 4.74\u0026ndash;6.71), 5.48 (95%CI, 4.21\u0026ndash;6.75), and 6.74 (95%CI, 5.37\u0026ndash;7.98) per 1000 person years for low, intermediate, and high genetic risk categories, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Similar results were observed in the other two BA indicators (KDM-BAaccel and LTL) across different genetic risk groups (Tables S11 and S12).\u003c/p\u003e\n \u003cp\u003eAfter conducting a series of sensitivity analyses, we found the robust associations between BA accelerationand three CLDs risk remained consistent even after excluding participants who were less than two years follow-up (Table S13), removing participants with absent or abnormal AST or ALT values of liver function at baseline (Table S14), and filling in missing covariates information (Table S15).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large cohort of UK Biobank participants, we first identified KDM-BAacceland PhenoAgeAccel were significantly associated with prevalent severe MASLD, as well as liver cirrhosis and cancer. Particularly, we observed that the adverse effects of advanced biological aging on these three CLDs were mostly stronger in males than in females. KDM-BAaccel and PhenoAgeAccel showed better performance than CA in predicting MASLD. Moreover, there were joint effects but no interactions of BA acceleration and genetic risk in severe MASLD, liver cirrhosis and cancer incidence. In addition, participants who at a high genetic risk level had the greatest 10-year absolute risk reduction of severe MASLD (6.74 per 1000 person-years) if decreasing the PhenoAgeAccel. Our findings indicated that alleviating biological aging is important for preventing serious liver-related diseases and could offset the deleterious effect of inherent genetic risk.\u003c/p\u003e \u003cp\u003eMost previous studies focused on the role of clinical biological aging indicators in the occurrence of several health outcomes. Mak et al. conducted a prospective cohort study to investigate the association between three clinical qualified biological aging indicators and the risk of five common cancers. They found that age-adjusted KDM-BA and PhenoAge were correlated with an increased risk of lung and colorectal cancers, while PhenoAge was additionally linked to increased risk of breast cancer [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Another prior study has suggested that PhenoAge was an independent risk factor for lung cancer and might serve as a potential biomarker for prediction of lung cancer [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In consistence with our findings, Xia et al. disclosed that participants with accelerated DNA methylation age were found to have a higher risk of incident MASLD than those without accelerated DNA methylation age [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Another research identified MASH patients exhibited accelerated epigenetic age that links with increased liver fibrosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A recent study also reported that long telomere length was associated with reduced risk of MASLD incidence and a positive addictive interaction between high genetic risk score and low telomere length [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Intriguingly, we found the deleterious effects of advanced biological aging on of CLDs in males were larger than in females, which was the first time reported by population-based study. Sex differences in MASLD prevalence was partly attributed to estrogens [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In both mice and humans, aging could enhance the susceptibility of alcohol-induced liver injury by modulating the SIRT1-C/EBPα-miR-223 axis in neutrophilic [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Generally, male drink more frequently than female, which might be a potential reason for our observed gender difference.\u003c/p\u003e \u003cp\u003eThere are several potential biological mechanisms might mediate the link between BA and severe liver-related diseases. In the process of aging, the liver cells may develop changes such as telomere shortening, nuclear area increase, mitochondrial DNA damage, and induce the secretion of pro-inflammatory cytokines, thereby resulting in liver damage [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Animal experiments have shown that aging could impact liver microcirculatory function and sinusoidal phenotype [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, the accumulation of senescent cells promotes hepatic steatosis and lipid accumulation, which lead to liver damage by inducing inflammation, cell death, fibrosis and promoting organ-specific toxicity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Aging can not only directly lead to liver damage, but also induce the progression of MASLD to its more serious stage. Hepatic steatosis might cause mitochondrial dysfunction, hepatic stele cell activation, and hepatic fibrosis, that promote MASLD to the development of HCC from MASLD [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The exact biological mechanism underlying the aging and MASLD-associated liver cirrhosis and cancer remains to be further studied.\u003c/p\u003e \u003cp\u003eSince a\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eg\u003c/span\u003eing is a multi-factorial process, a single BA predictor is not sufficient to monitor the risk of various age related disease phenotypes. Several existing BA predictors including epigenetic clocks, telomere length, transcriptomic, proteomic and metabolomic markers have been widely applied to predicted health outcomes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Given each predictor reflects specific aspect of the aging process, a combination of these indicators appears to be an ideal predictor to predict MASLD risk. Aging rate is not the same in different tissues, and it is not realistic to get a comprehensive marker from various tissues. Compared to those epigenetic and omics biomarkers, routine clinical biomarkers with characteristics of convenient detection and economic advantage are more suitable for large scale population-based screening and dynamic monitoring. In this work, both PhenoAge and KDM-BA yielded better performance than CA in MASLD prediction. The accuracy of PRS for fat-related liver conditions seems not ideal compared with other reported parameters with a mean value exceeded 0.60 [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. With regard to the numbers of known genetic components, more susceptibility loci for fat-related liver diseases are needed to be discovered in future. Additionally, we identified individuals at highest genetic level and BA had the highest standardized 10-year absolute risk of severe MASLD and its serious outcomes. Thus, individuals at a high genetic risk level should be more attention to the accelerated biological aging.\u003c/p\u003e \u003cp\u003eA few limitations should be acknowledged in current study. First, the definition of the new-onset severe MASLD (including MASH) was according to the ICD-10 codes from hospital records, which was more accuracy but might underestimate the real prevalence and incidence. Indeed, it is unpractical to monitor the development of MASLD in a large scale cohort with almost half of million participants. On the other hand, we also confirmed BA acceleration predisposed to incident its serious outcome including liver cirrhosis and cancer. Second, the clinical parameters of each participant were detected at baseline, we could not evaluate the impact of biological aging trajectory on the risk of liver outcomes. Further longitudinal population-based study with repeated measurement of these clinical biomarkers at various times are warranted. Third, the majority of participants in UK Biobank cohort were middle-aged or elderly Europeans, the effect of accelerated BA on liver-related diseases needed to be evaluated in younger groups. Fourth, the unfavorable role of BA on CLD risk are warrant to be verified in different type of BA predictors in future.\u003c/p\u003e \u003cp\u003eIn conclusion, Accelerated BA quantified by clinical biomarkers was associated with an increased risk of severe MASLD, as well as its advanced outcomes. Implementing interventions that slow biological aging could serve as an effective preventive strategy for this growing public health problem.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe area under the receiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAttributable proportion of interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBiological age\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKDM-BA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKlemera-Doubal method biological age\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLTL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeukocyte telomere length\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMASLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetabolic dysfunction-associated steatotic liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolygenic risk score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRestricted cubic spline regression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRERI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRelative excess risk due to interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle nucleotide polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTDI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTownsend deprivation index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of current study can be requested from the UK Biobank (https://www.ukbiobank.ac.uk/). This work was conducted under UK Biobank application number 80827. We thank all the UK Biobank participants and the management team for their participation and assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used in this study are availability via UK Biobank on request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (82103932, 82273710, 82100605), Research Fund of Anhui Institute of\u0026nbsp;Translational\u0026nbsp;Medicine (2023zhyx-C07), SJTU Trans-med Awards Research (20190104), Star Program of Shanghai Jiaotong University (YG2021QN54).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTT, JZ, SM and JN conceived and designed the study. TT and JZ cleaned the data. TT, JZ, SM and WX analysed the data. TT, JZ, XW and JN drafted the manuscript. TT, JZ, SZ and JN helped to interpret the results. JF, HP, and JN commented on the drafts of the manuscript. Approved the final version of the article, including the authorship list: All authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors disclose no conflicts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe UK Biobank study received approval from the National Information Governance Board for Health and Social Care and the National Health Service Northwest Multi-Centre Research Ethics Committee (Ref: 11/NW/0382). And all participants provided written informed consent.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHe Y, Su Y, Duan C, et al. Emerging role of aging in the progression of NAFLD to HCC. Ageing Res Rev. 2023;84:101833.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiazi K, Azhari H, Charette JH, et al. The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2022;7(9):851\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevarbhavi H, Asrani SK, Arab JP, Nartey YA, Pose E, Kamath PS. Global burden of liver disease: 2023 update. J Hepatol. 2023;79(2):516\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWynne HA, Cope LH, Mutch E, Rawlins MD, Woodhouse KW, James OF. The effect of age upon liver volume and apparent liver blood flow in healthy man. Hepatology. 1989;9(2):297\u0026ndash;301.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Zhang W, Liu X, Kim M, Zhang K, Tsai RYL. Epigenome-wide analysis of aging effects on liver regeneration. BMC Biol. 2023;21(1):30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNie C, Li Y, Li R, et al. Distinct biological ages of organs and systems identified from a multi-omics study. Cell Rep. 2022;38(10):110459.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamczyk MR, Nevado RM, Barettino A, Fuster V, Andr\u0026eacute;s V. Biological versus chronological aging: JACC focus seminar. J Am Coll Cardiol. 2020;75(8):919\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJylh\u0026auml;v\u0026auml; J, Pedersen NL, H\u0026auml;gg S. Biological age predictors. EBioMedicine. 2017;21:29\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiebel LWM, Rockwood K. Determination of biological age: geriatric assessment vs biological biomarkers. Curr Oncol Rep. 2021;23(9):104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchneider CV, Schneider KM, Teumer A, et al. Association of telomere length with risk of disease and mortality. JAMA Intern Med. 2022;182(3):291\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoomba R, Gindin Y, Jiang Z et al. DNA methylation signatures reflect aging in patients with nonalcoholic steatohepatitis. JCI Insight. 2018;3(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSudlow C, Gallacher J, Allen N, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Vincentis A, Tavaglione F, Jamialahmadi O, et al. A polygenic risk score to refine risk stratification and prediction for severe liver disease by clinical fibrosis scores. Clin Gastroenterol Hepatol. 2022;20(3):658\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlemera P, Doubal S. A new approach to the concept and computation of biological age. Mech Ageing Dev. 2006;127(3):240\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevine ME. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? J Gerontol Biol Sci Med Sci. 2013;68(6):667\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevine ME, Lu AT, Quach A, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging. 2018;10(4):573\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcEwen LM, Jones MJ, Lin DTS, et al. Systematic evaluation of DNA methylation age estimation with common preprocessing methods and the Infinium MethylationEPIC BeadChip array. Clin Epigenetics. 2018;10(1):123.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmdin CA, Haas M, Ajmera V, et al. Association of genetic variation with cirrhosis: a multi-trait genome-wide association and gene-environment interaction study. Gastroenterology. 2021;160(5):1620\u0026ndash;e16331613.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMak JKL, McMurran CE, Kuja-Halkola R, et al. Clinical biomarker-based biological aging and risk of cancer in the UK Biobank. Br J Cancer. 2023;129(1):94\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa Z, Zhu C, Wang H, et al. Association between biological aging and lung cancer risk: cohort study and Mendelian randomization analysis. iScience. 2023;26(3):106018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia M, Li W, Lin H, et al. DNA methylation age acceleration contributes to the development and prediction of non-alcoholic fatty liver disease. Geroscience; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang L, Li D, Ma Y, Cui F, Wang J, Tian Y. The association between telomere length and non-alcoholic fatty liver disease: a prospective study. BMC Med. 2023;21(1):427.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMor\u0026aacute;n-Costoya A, Proenza AM, Gianotti M, Llad\u0026oacute; I, Valle A. Sex differences in nonalcoholic fatty liver disease: estrogen influence on the liver-adipose yissue crosstalk. Antioxid Redox Signal. 2021;35(9):753\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen R, He Y, Ding D, et al. Aging exaggerates acute-on-chronic alcohol-induced liver injury in mice and humans by inhibiting neutrophilic sirtuin 1-C/EBPα-miRNA-223 axis. Hepatology. 2022;75(3):646\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapatheodoridi AM, Chrysavgis L, Koutsilieris M, Chatzigeorgiou A. The role of senescence in the development of nonalcoholic fatty liver disease and progression to nonalcoholic steatohepatitis. Hepatology. 2020;71(1):363\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaeso-D\u0026iacute;az R, Ortega-Ribera M, Fern\u0026aacute;ndez-Iglesias A, et al. Effects of aging on liver microcirculatory function and sinusoidal phenotype. Aging Cell. 2018;17(6):e12829.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgrodnik M, Miwa S, Tchkonia T, et al. Cellular senescence drives age-dependent hepatic steatosis. Nat Commun. 2017;8:15691.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlawik M, Vidal-Puig AJ. Lipotoxicity, overnutrition and energy metabolism in aging. Ageing Res Rev. 2006;5(2):144\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDabravolski SA, Bezsonov EE, Orekhov AN. The role of mitochondria dysfunction and hepatic senescence in NAFLD development and progression. Biomed Pharmacother. 2021;142:112041.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma S, Tapper WJ, Collins A, Hamady ZZR. Predicting pancreatic cancer in the UK Biobank cohort using polygenic risk scores and diabetes mellitus. Gastroenterology. 2022;162(6):1665\u0026ndash;e16741662.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe YQ, Wang TM, Ji M, et al. A polygenic risk score for nasopharyngeal carcinoma shows potential for risk stratification and personalized screening. Nat Commun. 2022;13(1):1966.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBriggs SEW, Law P, East JE, et al. Integrating genome-wide polygenic risk scores and non-genetic risk to predict colorectal cancer diagnosis using UK Biobank data: population based cohort study. BMJ. 2022;379:e071707.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"915\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.24590163934426%\" rowspan=\"2\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.366120218579235%\" rowspan=\"2\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal No.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n =\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e347,917\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.256830601092897%\" colspan=\"3\" style=\"width: 42.1578%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere MASLD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.382513661202186%\" colspan=\"3\" style=\"width: 21.7025%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiver cirrhosis and cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.48013816925734%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.of case\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n =\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3222\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.825561312607945%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.of non-\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003ease\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 3\u003c/strong\u003e\u003cstrong\u003e44\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e\u003cstrong\u003e695\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.089810017271157%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.616580310880828%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.of case\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 1\u003c/strong\u003e\u003cstrong\u003e303\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.48013816925734%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.of non-\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003ease\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 30\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e, 2\u003c/strong\u003e\u003cstrong\u003e83\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.744386873920552%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years), mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e56.47 (8.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e57.25 (7.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e56.46 (8.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e59.45 (7.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e56.46 (8.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (Female), n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e184882 (53.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e1596 (49.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e183286 (53.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e625 (35.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e184257 (53.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace (White), n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e331132 (95.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e3032 (94.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e328100 (95.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e1692 (96.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e329440 (95.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTownsend deprivation index, mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e-1.41 (3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e-0.44 (3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e-1.42 (3.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e-0.67 (3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e-1.41 (3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e191359 (55.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e1472 (45.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e189887 (55.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e716 (40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e190643 (55.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003ePrevious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e121373 (34.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e1296 (40.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e120077 (34.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e725 (41.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e120648 (34.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e\u0026nbsp;35185 (10.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e454 (14.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e34731 (10.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e318 (18.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e34867 (10.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol consumption, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e13975 (4.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e167 (5.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e13808 (4.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e61 (3.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e13914 (4.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003ePrevious drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e11473 (3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e205 (6.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e11268 (3.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e92 (5.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e11381 (3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eOccasional drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e322469 (92.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e2850 (88.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e319619 (92.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e1606 (91.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e320863 (92.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody mass index (BMI, kg/m\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e), n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e\u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e1751 (0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e3 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e1748 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e7 (0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e1744 (0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e18.5-24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e115611 (33.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e325 (10.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e115286 (33.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e373 (21.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e115238 (33.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e25.0-29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e149723 (43.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e1193 (37.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e148530 (43.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e679 (38.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e149044 (43.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e\u0026gt;=30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e80832 (23.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e1701 (52.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e79131 (22.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e700 (39.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e80132 (23.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity (MET-min/week) , n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eLow (MET: \u0026lt;=600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e67528 (19.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e815 (25.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e66713 (19.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e410 (23.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e67118 (19.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eModerate (MET: 600-3000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e177412 (50.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e1567 (48.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e175845 (51.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e829 (47.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e176583 (51.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eHigh (MET: \u0026gt;3000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e102977 (29.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e840 (26.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e102137 (29.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e520 (29.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e102457 (29.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor diseases, n (%)\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e16815 (4.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e558 (17.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e16257 (4.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e299 (17.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e16516 (4.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e81865 (23.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e1213 (37.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e80652 (23.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e657 (37.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e81208 (23.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eHeart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e18810 (5.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e380 (11.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e18430 (5.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e210 (11.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e18600 (5.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiological ages, mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eKDM-BA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e52.04 (14.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e55.75 (14.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e52.00 (14.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e56.81 (15.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e52.01 (14.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eKDM-BA acceleration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e-4.44 (11.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e-1.50 (12.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e-4.46 (11.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e-2.65 (14.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e-4.44 (11.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003ePhenoAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e50.62 (9.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e53.02 (9.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e50.60 (9.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e56.91 (9.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e50.59 (9.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003ePhenoAge acceleration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e-5.85 (4.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e-4.23 (5.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e-5.87 (4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e-2.54 (6.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e-5.87 (4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eLeukocyte telomere length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e0.83 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e0.82 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e0.83 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e0.80 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e0.83 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.65207877461707%\" colspan=\"2\" style=\"width: 32.928%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponents of biological ages, mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eFEV1 (L)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e2.84 (0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e2.67 (0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e2.85 (0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e2.72 (0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e2.85 (0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eSBP (mm Hg)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e139.70 (19.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e142.63 (18.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e139.67 (19.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e143.92 (19.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e139.68 (19.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eTotal cholesterol (mg/dL)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e220.43 (43.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e213.00 (48.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e220.50 (43.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e208.61 (49.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e220.49 (43.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eGlycated hemoglobin (%)\u003csup\u003e*\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e5.44 (0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e5.76 (0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e5.44 (0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e5.73 (0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e5.44 (0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eBlood urea nitrogenbin (%)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e15.13 (3.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e15.26 (3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e15.13 (3.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e15.19 (5.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e15.13 (3.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eLymphocyte (%)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e28.93 (7.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e28.97 (7.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e28.92 (7.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e27.92 (7.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e28.93 (7.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eMean cell volume bin (%)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e82.87 (5.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e82.59 (5.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e82.87 (5.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e85.34 (6.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e82.85 (5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eSerum glucose (mg/bin (%)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e91.89 (21.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e100.27 (36.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e91.81 (21.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e102.09 (38.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e91.84 (21.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eRed cell distribution width (%)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e13.47 (0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e13.58 (1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e13.47 (0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e13.75 (1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e13.47 (0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eWhite blood cell count (1000 cells/uL)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e6.78 (2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e7.12 (1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e6.78 (2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e6.52 (1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e6.78 (2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eAlbumin (g/dL)\u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e45.26 (2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e45.00 (2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e45.26 (2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e44.45 (3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e45.26 (2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e72.38 (16.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e73.08 (18.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e72.37 (16.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e74.98 (29.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e72.37 (16.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eC-reactive protein (mg/dL)\u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e0.25 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e0.40 (0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e0.25 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e0.39 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e0.25 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eAlkaline phosphatase (U/L)\u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e82.91 (25.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e92.84 (36.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e82.82 (24.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e103.17 (67.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e82.81 (24.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic risk category , n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e83150 (23.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e592 (18.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e82558 (23.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e319 (18.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e82831 (23.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e196828 (56.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e1778 (55.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e195050 (56.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e986 (56.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e195842 (56.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.27352297592998%\" style=\"width: 22.2014%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.37855579868709%\" style=\"width: 10.7265%;\"\u003e\n \u003cp\u003e67939 (19.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 15.5909%;\"\u003e\n \u003cp\u003e852 (26.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.925601750547045%\" style=\"width: 16.0898%;\"\u003e\n \u003cp\u003e67087 (19.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.658643326039387%\" style=\"width: 10.6018%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.159737417943107%\" style=\"width: 9.8534%;\"\u003e\n \u003cp\u003e454 (25.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.706783369803064%\" style=\"width: 6.1116%;\"\u003e\n \u003cp\u003e67485 (19.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.439824945295404%\" style=\"width: 5.7374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-values were calculated by\u0026nbsp;ANOVA\u0026nbsp;(continuous\u0026nbsp;variables)\u0026nbsp;or\u0026nbsp;\u0026chi;\u0026sup2; test (categorical variables).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eMajor disease diagnosed by doctor.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eEmployed to construct KDM-BA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e#\u003c/sup\u003eEmployed to construct PhenoAge.\u003c/p\u003e\n\u003cp\u003eNo., number; n, total number of observations; SD, standard deviation, MET, metabolic equivalent; MASLD, metabolic dysfunction-associated steatotic liver disease; KDM, Klemera-Doubal method; BA, biological age; FEV1, forced expiratory volume in 1 second; SBP, systolic blood pressure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2. Association between biological age acceleration and the risk of incident severe MASLD, liver cirrhosis and cancer\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"928\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.854682454251884%\" rowspan=\"3\" style=\"width: 10.3511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3509149623250805%\" rowspan=\"3\" style=\"width: 5.3356%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.78256189451022%\" colspan=\"5\" style=\"width: 34.0414%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMASLD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.28955866523143%\" colspan=\"5\" style=\"width: 29.8049%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiver cirrhosis and cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.019151846785226%\" rowspan=\"2\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.of case\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.835841313269494%\" colspan=\"2\" style=\"width: 14.9398%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.972640218878247%\" colspan=\"2\" style=\"width: 14.9398%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.429548563611491%\" rowspan=\"2\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.of case\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.79343365253078%\" colspan=\"2\" style=\"width: 15.1532%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.383036935704514%\" colspan=\"2\" style=\"width: 14.9398%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.330218068535826%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.411214953271028%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.485981308411215%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190031152647975%\" style=\"width: 5.7625%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.797507788161994%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.566978193146417%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.838709677419354%\" style=\"width: 10.3511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKDM-BA acceleration\u003c/strong\u003e\u003csup\u003ea\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.3356%;\"\u003e\n \u003cp\u003e347917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.731182795698925%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e3222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89247311827957%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e1.31(1.27-1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.806451612903226%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e1.10(1.07-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.591397849462366%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.053763440860215%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e1759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10752688172043%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e1.25(1.20-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.7625%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e1.14(1.09-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.913978494623656%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.838709677419354%\" style=\"width: 10.3511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKDM-BA acceleration\u003c/strong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.3356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.731182795698925%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89247311827957%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.806451612903226%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.591397849462366%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.053763440860215%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10752688172043%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.7625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.913978494623656%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.838709677419354%\" style=\"width: 10.3511%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.3356%;\"\u003e\n \u003cp\u003e86980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.731182795698925%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89247311827957%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.806451612903226%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.591397849462366%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.053763440860215%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10752688172043%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.7625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.913978494623656%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n 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width=\"9.89247311827957%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e1.42(1.28-1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.806451612903226%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e1.15(1.04-1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.591397849462366%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.053763440860215%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10752688172043%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e1.18(1.03-1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.7625%;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3907%;\"\u003e\n 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width=\"5.591397849462366%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.053763440860215%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10752688172043%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e1.66(1.46-1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.7625%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e1.31(1.15-1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.913978494623656%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.838709677419354%\" style=\"width: 10.3511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhenoAge acceleration\u003c/strong\u003e\u003csup\u003ea\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.3356%;\"\u003e\n \u003cp\u003e347917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.731182795698925%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e3222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89247311827957%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e1.32(1.29-1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.806451612903226%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e1.12(1.09-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.591397849462366%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.053763440860215%\" style=\"width: 4.1618%;\"\u003e\n 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width=\"10%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.591397849462366%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.053763440860215%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10752688172043%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.7625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.913978494623656%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.838709677419354%\" style=\"width: 10.3511%;\"\u003e\n 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width=\"5.053763440860215%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10752688172043%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e3.86(3.31-4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.7625%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e2.81(2.40-3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.913978494623656%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.838709677419354%\" style=\"width: 10.3511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLTL\u003c/strong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.3356%;\"\u003e\n \u003cp\u003e347917\u003c/p\u003e\n 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style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.591397849462366%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.053763440860215%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10752688172043%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.7625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.913978494623656%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n 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width=\"9.89247311827957%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e0.87(0.80-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.806451612903226%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e0.91(0.83-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.591397849462366%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.053763440860215%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10752688172043%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e0.77(0.69-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.7625%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e0.80(0.71-0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.913978494623656%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.838709677419354%\" style=\"width: 10.3511%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.3356%;\"\u003e\n \u003cp\u003e86979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.731182795698925%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89247311827957%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e0.82(0.75-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.806451612903226%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e0.87(0.79-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.591397849462366%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.053763440860215%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10752688172043%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e0.67(0.59-0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.7625%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e0.70(0.61-0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.913978494623656%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.838709677419354%\" style=\"width: 10.3511%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.3356%;\"\u003e\n \u003cp\u003e86979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.731182795698925%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89247311827957%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e0.76(0.69-0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.806451612903226%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e0.83(0.75-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.591397849462366%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.053763440860215%\" style=\"width: 4.1618%;\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10752688172043%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e0.65(0.56-0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.344086021505376%\" style=\"width: 5.7625%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3907%;\"\u003e\n \u003cp\u003e0.69(0.60-0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.913978494623656%\" style=\"width: 5.5491%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eAdjusted for age at recruitment and sex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eFurther adjusted for ethnicity, Townsend deprivation index, body mass index, alcohol status, smoking status, physical activity, history of hypertension, diabetes and heart disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eVariables were continuous.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eVariables were classified by quartile. quartile1, Q1; quartile2, Q2; quartile3, Q3; quartile4, Q4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e No., number; MASLD, metabolic dysfunction-associated steatotic liver disease; LTL, leukocyte telomere length; HR, hazard ratio; 95% CI, 95% confidence interval.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Biological aging, Genetic risk score, Chronic liver diseases, Cohort study","lastPublishedDoi":"10.21203/rs.3.rs-4170717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4170717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThere is an explicit link between biological age (BA) and chronic liver disease (CLD). This study aimed to explore the association between clinical biomarker-based BA and potential interaction with genetic risk on incident CLD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospectively cohort study was conducted in UK Biobank included 347,917 participants. We quantified clinical biomarker-based BAs using the KDM-BA and PhenoAge algorithms and constructed the polygenic risk score (PRS) to examine its interactions with BAs on CLD risk.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe first identified acceleration for KDM-BA (KDM-BAaccel) and PhenoAge (PhenoAgeAccel) were significantly associated with prevalent severe metabolic dysfunction-associated steatotic liver disease (MASLD), as well as liver cirrhosis and cancer. Each SD increase in KDM-BAaccel and PhenoAgeAccel was correlated with an 10% elevated risk of MASLD. Particularly, we observed the deleterious effects of advanced biological aging on three CLDs in males were mostly stronger than in females. In predicting MASLD, the two BA indicators showed better performance than chronological age, with AUC values of 0.526, 0.571 and 0.595 for chronological age, KDM-BAaccel and PhenoAgeAccel, respectively. Moreover, individuals with the highest BA acceleration and PRS had the highest risk of developing severe MASLD, although no significant additive and multiplicative interactions were found. Additionally, participants who at a high genetic risk level had the greatest 10-year absolute risk reduction of severe MASLD (6.74 per 1000 person-years) if their PhenoAgeAccel decreased.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings elucidate that relieving biological aging is important for preventing serious fatty liver-related diseases and could offset the adverse effects of inherent genetic risk.\u003c/p\u003e","manuscriptTitle":"Accelerated biological aging, genetic susceptibility, and incident severe MASLD, liver cirrhosis and cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 17:43:43","doi":"10.21203/rs.3.rs-4170717/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bce5b908-171b-4f4d-8894-1f9aadff27e0","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-29T07:38:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-10 17:43:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4170717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4170717","identity":"rs-4170717","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}