Aging and the risks of all-cause and cause-specific mortality among diabetics: a prospective cohort study

Aging and the risks of all-cause and cause-specific mortality among diabetics: a prospective cohort study | 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 Original investigation Aging and the risks of all-cause and cause-specific mortality among diabetics: a prospective cohort study Li Chen, Ying Zhao, Huimin Chen, Tianqi Tan, Ping Yao, Yuhan Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-961620/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 Aging is an important driver for age-related diseases and death, but the evidence regarding the effects of aging on diabetics is limited. This study aims to evaluate the associations of aging with all-cause and cause-specific mortality among diabetics. Methods A total of 5,278 diabetics from the National Health and Nutrition Examination Survey 1999-2014 were included. The aging status was measured from different perspectives, including Phenotypic Age, Biological Age, telomere length, and Klotho concentration. Cox proportional hazards models were used to examine the relationships between aging and all-cause, cardiovascular disease (CVD), and cancer mortality. Mediation analysis was performed to elucidate the role of aging in associations of metformin with mortality. Results Over median follow-up for 7.3 years, 1,355 diabetics died. There was a positive and linear association of mortality with Phenotypic Age (hazard ratio (HR) all-cause 1.05, 95% confidence interval (CI) 1.05-1.06; HR CVD 1.05, 95%CI 1.04-1.07; HR cancer 1.05, 95%CI 1.04-1.07; all P< 0.001) and Biological Age (HR all-cause 1.07, 95%CI 1.05-1.08; HR CVD 1.08, 95%CI 1.05-1.10 ; HR cancer 1.05, 95%CI 1.03-1.08; all P< 0.001). Telomere length was inversely associated with all-cause mortality (tertile (T)3 vs. T1: HR 0.67, 95%CI 0.45-0.98; P trend = 0.036). The concentration of Klotho had a U-shaped relationship with mortality (T2 vs. T1: HR all-cause 0.62, 95%CI 0.43-0.88; HR CVD 0.48, 95%CI 0.26-0.86; HR cancer 0.47 95%CI 0.25-0.88). Additionally, metformin users had a lower HR for mortality compared to those without use (HR all-cause 0.64, 95%CI 0.56-0.73; HR CVD 0.51, 95%CI 0.37-0.70; HR cancer 0.65, 95%CI 0.44-0.95; all P< 0.001), which was partly mediated by Phenotypic Age and Biological Age. Conclusions These findings suggested aging was a noteworthy risk factor of mortality for diabetics and therapies targeting anti-aging could be encouraged to halt the progression of diabetes. Cardiac & Cardiovascular Systems Endocrinology & Metabolism Internal Medicine Diabetes Mortality Aging Biological Age Phenotypic Age Telomere Klotho Figures Figure 1 Figure 2 Background Diabetes, as the ninth major cause of death, is a worldwide public health concern, with considerable healthcare and economic burden ( 1 ). The number of diabetics has quadrupled in the past 3 decades and is projected to exceed more than 642 million by 2040 ( 2 ). Diabetics often suffer from complications and premature death ( 3 ). Statistically, the risk of death and cardiovascular events is 2 to 4 times higher among diabetics than in the general population ( 4 ). Hence, the identification of risk factors affecting the survival of diabetics may contribute to delaying premature mortality and its development. Aging characterized by a progressive decline in homeostasis is generally accepted as the leading risk factor for age-related diseases and death ( 5 ). Nearly half of diabetics are older adults who are primarily responsible for the growing burden ( 6 , 7 ). Moreover, diabetics usually develop a premature aging status with dysfunction of multiple systems ( 8 ). Compelling animal studies and clinical trials also revealed drugs or gene editing targeting aging improved diabetic outcomes ( 9 , 10 ). Aging, though, was critical for diabetics, epidemiological evidence is limited. Previously, investigations explored the relationship between short telomere length and all-cause mortality among Chinese ( 11 ), Danish ( 12 ), and Italian diabetics ( 13 ), with the hazard ratio (HR) ranging from 1.87 to 3.45. However, some data showed that aging in the lung ( 14 ) and myocardium ( 15 ) was independent of telomere length, indicating the limited capacity of it to comprehensively reflect the aging status of the organ or organism. Recently, novel aging measures including multiple readily available clinical biomarkers were applied to capture body’s homeostasis, such as Phenotypic Age and Biological Age. These markers were reported to well predict adverse outcomes. For example, a prospective cohort study in the general population showed that higher Phenotypic Age was positively associated with increased death risk, especially diabetes-caused mortality (HR=1.20) ( 16 ). However, studies focused on diabetics are lacking. Therefore, we measured aging at multiple perspectives and conducted a prospective and representative cohort study to investigate the association between aging and all-cause and cause-specific mortality among U.S. diabetics based on the data from the National Health and Nutrition Examination Survey (NHANES) 1999-2014. Methods Study population The NHANES is an ongoing national cross-sectional survey to assess the health and nutritional status in the U.S. The survey was approved by National Center for Health Statistics (NCHS) Ethics Review Board and all of the participants provide their written informed consent. In this study, all individuals with diabetes were included based on the data of NHANES from 1999 to 2014. Diabetes was defined by self-reported diagnosis, use of insulin or oral hypoglycemic medication, glycated hemoglobin (HbA 1c ) level ≥6.5%, or fasting plasma glucose level ≥7.0 mmol/L ( 17 ). After excluding those who had missing information on any aging markers and/or mortality, and self-reported as pregnant, 5278 participants were enrolled in the current analysis. Aging Markers Measurement Phenotypic Age was calculated according to the method published previously ( 18 ). In brief, 9 aging-related variables, including chronological age, albumin, creatinine, glucose, Ln-C-reactive protein (CRP), lymphocyte percent, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count, were evaluated together based on the following equation. $$\text{P}\text{h}\text{e}\text{n}\text{o}\text{t}\text{y}\text{p}\text{i}\text{c} \text{A}\text{g}\text{e} =141.50 +\frac{\text{L}\text{n}[-0.00553 \times \text{L}\text{n}(\text{e}\text{x}\text{p}\left(\frac{-1.51714\times \text{e}\text{x}\text{p}\left(\text{x}\text{b}\right)}{0.0076927}\right)\left)\right]}{0.09165}$$ where: xb= -19.907།0.0336 × Albumin + 0.0095 × Creatinine + 0.1953 × Glucose + 0.0954 × LnCRP།0.0120 × Lymphocyte Percent + 0.0268 × Mean Cell Volume + 0.3306 × Red Cell Distribution Width + 0.00188 × Alkaline Phosphatase + 0.0554 × White Blood Cell Count + 0.0804× Chronological Age Biological Age was calculated using the methods proposed by Klemera and Doubal (19). In this study, 8 biomarkers (CRP, serum creatinine, HbA 1c , serum albumin, serum total cholesterol, serum urea nitrogen, serum alkaline phosphatase, and systolic blood pressure) were estimated based on the following formulas (1-4) (20). The values j and i represent the number of biomarkers and samples respectively. The values k , q , and s are the regression slope, intercept, and the root means squared error of a biomarker regressed on chronological age, respectively. The value r j 2 represents the variance explained by regression chronological age on biomarkers. $$\left(1\right) {\text{BA}} {E}=\frac{{\sum } {j=1}^{m}\left({x} {j}-{q} {j}\right)\left(\frac{{k} {j}}{{s} {j}^{2}}\right)}{{\sum } {j=1}^{m}{\left(\frac{{k} {j}}{{s} {j}}\right)}^{2}} \left(2\right) {r} {cℎar}=\frac{{\sum } {j=1}^{m}\frac{{r} {j}^{2}}{\sqrt{1-{r} {j}^{2}}}}{{\sum } {j=1}^{m}\frac{{r} {j}}{\sqrt{1-{r} {j}^{2}}}}$$ $${\left(3\right) s} {BA}^{2}={\frac{{\sum } {j=1}^{n}\left(\left({\text{BA}} {Ei}-\text{C}{\text{A}} {i}\right)-\frac{{\sum } {i=1}^{n}\left({\text{BA}} {Ei}-\text{C}{\text{A}} {i}\right)}{n}\right)}{n}}^{2}-\left(\frac{1-{r} {cℎar}^{2}}{{r} {cℎar}^{2}}\right)\times \left(\frac{{\left({\text{CA}} {max}-{CA}_{min}\right)}^{2}}{12m}\right)$$ $${\text{BA}} {EC}=\frac{{\sum } {j=1}^{m}\left({x} {j}-{q} {j}\right)\left(\frac{{k} {j}}{{s} {j}^{2}}\right)+\frac{\text{CA}}{{s} {BA}^{2}}}{{\sum } {j=1}^{m}{\left(\frac{{k} {j}}{{s} {j}}\right)}^{2}+\frac{1}{{s}_{\text{BA}}^{2}}}$$ (4) Additionally, markers of premature aging were defined as Phenotypic/Biological Age Acceleration and ΔPhenotypic/Biological Age (16). Phenotypic/Biological Age Acceleration was the residual resulting from a linear model when regressing Phenotypic/Biological Age on chronological age. ΔPhenotypic/Biological Age represented the value of Phenotypic/Biological Age minus chronological age. Measurement of telomere length (NHANES 1999-2002) has been reported elsewhere (21). Briefly, blood samples were collected and determined by quantitative polymerase chain reaction (qPCR) assays to assess the telomere length relative to standard reference DNA (the T/S ratio). Klotho (NHANES 2007-2014) was determined by a commercially available ELISA kit (IBL International, Japan). All sample analyses were performed in duplicate strictly following the manufacturer’s instruction and all results were checked to meet the laboratory’s standardized criteria for acceptability before being released for reporting. Mortality Ascertainment All participants from NHANES 1999-2014 were linked to death records in the National Death Index up to 31 December 2015. Follow-up duration is defined as the time from participating in the NHIS to death for decedents or to the censoring date for survivors. Mortality outcomes in the current study included all-cause, cardiovascular disease (CVD)-cause, and cancer-cause determined by ICD-10 codes recorded in NHANES. Statistical analysis All analyses were performed with SAS (version 9.4) or R (version 3.6.3) and accounted for the complex sampling design of the NHANES according to the analytic guidelines. We used χ 2 tests, nonparametric tests, and t -tests to assess the baseline characteristics of the participants by life or death status. Continuous variables were presented as mean ± standard error (SE) or median (interquartile range), and categorical variables were presented as n (%). Cox proportional hazards models were applied to estimate hazard ratio (HR) and 95% confidence interval (CI) for the associations of aging markers with all-cause and cause-specific mortality, with months of follow-up as the time scale. Aging markers were categorized into three tertiles (T1, T2, and T3) based on the distribution. The T/S ratio and Klotho were Ln-transformed (continuous). Model 1 was adjusted for age (<65 or ≥65 years), sex (male or female), race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or others), marital status (married/cohabiting, widowed/divorced/separated, or never married), BMI (<25.0, 25.0-29.9, or ≥30.0 kg/m 2 ), physical activity (moderate or vigorous), drinking alcohol status (ever or never), and Ln-cotinine concentration (continuous). Model 2 was further adjusted for triglyceride (continuous), medication use (insulin/pills or no), CVD (yes or no), cancer (yes or no), and hypertension (yes or no). The missing data of covariates were imputed with median values. The trend test across increasing exposure groups was calculated using integer values ( 1 , 2 , and 3 ). The dose-response relationships between aging markers and mortality were assessed by restricted cubic spline regression with 3 knots placed at the 25th, 50th, and 75th percentile. We also estimated the associations of metformin use (ever or never) with mortality by the Cox proportional hazards model, and subgroup analysis was stratified by HbA 1c level (<6.5% or ≥6.5%). Mediation analyses were conducted to explore whether the use of metformin reduced mortality by anti-aging with an R package (mediation). The mediated proportion referred to the average mediation effect between metformin use attributed to mortality changes relative to the total effect. P- value for mediated proportion was obtained from the quasi Bayesian Monte Carlo simulation 2000 times. Several sensitivity analyses were also performed. First, participants with a diagnosis of diabetes before the age of 20 years were excluded. Second, the deaths within 1 year of follow-up were excluded to reduce the potential reverse causation bias. Third, multiple imputations was used for covariates with missing values. Fourth, given the diabetes status, HbA 1c level (<6.5% or ≥6.5%) and duration of diabetes (10 years) were further adjusted. Fifth, considering other demographic confounding factors, we further adjusted for education levels (under high school, high school or equivalent, or above high school) and family income-poverty ratio (0–1.0, 1.0–3.0, or >3.0). Finally, some potential mediators were also further adjusted, including HDL-cholesterol, total cholesterol, and HOMA of insulin resistance. Results During median follow-up for 7.3 years, 1,355 deaths were documented among 5,278 diabetics. Based on the live or death status of the study population, baseline demographic was provided in Table 1 . The deaths were older, more likely to be Non-Hispanic White and widowed or divorced or separated, and were less likely to be obese, and had less frequently drinking alcohol. Those who died had a longer duration of diabetes, and a high percentage of anti-diabetic medication or insulin use but had a lower percentage of metformin use. The deaths had a lower median concentration of Klotho (0.76 vs. 0.82 pg/mL), a shorter median telomere length (0.88 vs. 0.97), and an older mean Phenotypic Age (74.55 vs. 57.88 years) and Biological Age (68.39 years vs. 55.05 years). Table 1 Baseline characteristics of diabetics by life or death status in NHANES 1999-2014. Characteristics Alive (3923) Dead (1355) P value Age, years <0.001 <65 2533 (70.55) 381 (35.84) ≥65 1390 (29.45) 974 (64.16) Gender 0.058 Male 1984 (51.17) 768 (55.08) Female 1939 (48.83) 587 (44.92) Race/ethnicity <0.001 Mexican American 912 (9.99) 264 (5.44) Other Hispanic 375 (6.39) 56 (4.51) Non-Hispanic White 1340 (60.60) 659 (70.89) Non-Hispanic Black 1012 (15.14) 326 (13.92) Other race/multiracial 284 (7.87) 50 (5.24) Marital status <0.001 Married/cohabiting 2428 (65.83) 664 (51.51) Widowed/divorced/separated 1092 (24.41) 582 (42.05) Never married 371 (9.76) 81 (6.44) BMI, kg/m 2 <0.001 <25.0 456 (10.75) 250 (19.70) 25-29.9 1107 (26.07) 430 (30.24) ≥30.0 2277 (63.19) 564 (50.07) Physical activity <0.001 Moderate 3180 (78.14) 1255 (91.80) Vigorous 743 (21.86) 100 (8.20) Drinking alcohol status 0.007 Ever 2295 (66.27) 726 (58.81) Never 1349 (33.73) 522 (41.19) Medication use <0.001 Insulin or pills 2006 (80.01) 806 (88.25) No 436 (19.99) 99 (11.74) Duration of diabetes, years 10 1162 (41.05) 497 (56.08) Serum cotinine, ng/mL 53.1 ± 3.16 49.67 ± 4.41 0.551 Triglyceride, mmol/L 2.12 ± 0.07 2.19 ± 0.06 0.488 CVD <0.001 Yes 785 (18.59) 596 (45.55) No 3091 (81.41) 742 (54.45) Cancer <0.001 Yes 428 (13.02) 269 (21.04) No 3467 (86.98) 1082 (78.96) Hypertension <0.001 Yes 2478 (61.58) 950 (70.13) No 1431 (38.42) 398 (29.87) Metformin use <0.001 Ever 1077 (37.40) 357 (26.99) Never 1709 (62.60) 989 (73.01) Klotho, ng/mL 0.82 (0.67, 1.02) 0.76 (0.60, 1.00) 0.027 Mean T/S ratio 0.97 (0.85, 1.13) 0.88 (0.77, 1.05) 0.001 Phenotypic Age, years 57.88 ± 0.38 74.55 ± 0.58 <0.001 Biological Age, years 55.05 ± 0.40 68.39 ± 0.54 <0.001 Continuous variables were presented as mean ± SE or median (interquartile range). Categorical variables were presented as numbers (percentages). All estimates accounted for complex survey designs. Table 2 presented the associations of 4 aging markers with all-cause, CVD, and cancer mortality. Each 1-year increase in Phenotypic Age increased the risk of all-cause, CVD, and cancer mortality by 5% (all P <0.001). Similarly, each 1-year increase in Biological Age was associated with an increase in all-cause, CVD, and cancer mortality (7%, 8%, and 5%, respectively, all P <0.001). Premature aging markers were also positively associated with all-cause, CVD, and cancer mortality (Supplementary Table 1). Additionally, for each unit increase in mean T/S ratio (a marker of cellular senescence), HR for all-cause mortality decreased by 47% (HR 0.53, 95%CI 0.29-0.97), consistent with the quantile analysis. The middle concentration of Klotho was associated with a lower risk of all-cause, CVD, and cancer mortality (T2 vs. T1: HR all−cause 0.62, 95%CI 0.43-0.88; HR CVD 0.48, 95%CI 0.26-0.86; HR cancer 0.47, 95%CI 0.25-0.88). Table 2 HR (95% CI) for all-cause and cause-specific mortality based on aging markers among diabetics. All-cause mortality CVD mortality Cancer mortality Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Phenotypic Age (years) Deaths/total 1251/4003 320/3072 208/2960 Continuous 1.05 (1.05, 1.06) 1.05 (1.05, 1.06) 1.06 (1.05, 1.07) 1.05 (1.04, 1.07) 1.05 (1.04, 1.07) 1.05 (1.04, 1.07) T1 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference) T2 2.28 (1.56, 3.33) 2.13 (1.43, 3.17) 1.59 (0.71, 3.55) 1.26 (0.53, 3.01) 1.39 (0.52, 3.70) 1.29 (0.49, 3.43) T3 6.28 (4.29, 9.20) 5.45 (3.62, 8.20) 3.88 (1.60, 9.40) 2.73 (1.02, 7.29) 4.02 (1.66, 9.72) 3.51 (1.43, 8.63) P trend <0.001 <0.001 <0.001 0.004 <0.001 0.001 Biological Age (years) Deaths/total 1204/3861 311/2968 198/2855 Continuous 1.07 (1.06, 1.08) 1.07 (1.05, 1.08) 1.09 (1.06, 1.11) 1.08 (1.05, 1.10) 1.06 (1.04, 1.09) 1.05 (1.03, 1.08) T1 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference) T2 2.20 (1.61, 3.01) 2.00 (1.46, 2.75) 1.93 (1.19, 3.14) 1.69 (0.98, 2.90) 2.11 (0.79, 5.65) 1.88 (0.71, 4.96) T3 4.47 (2.98, 6.71) 3.85 (2.62, 5.65) 6.40 (2.60, 15.72) 4.91 (1.87, 12.90) 2.97 (0.90, 9.83) 2.20 (0.66, 7.38) P trend <0.001 <0.001 <0.001 0.003 0.055 0.152 Mean T/S ratio Deaths/total 432/905 115/588 56/529 Continuous 0.49 (0.25, 0.95) 0.53 (0.29, 0.97) 0.32 (0.09, 1.11) 0.36 (0.11, 1.14) 0.31 (0.05, 1.90) 0.34 (0.04, 2.68) T1 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference) T2 0.68 (0.48, 0.95) 0.70 (0.50, 0.97) 0.59 (0.31, 1.13) 0.55 (0.26, 1.15) 0.70 (0.28, 1.72) 0.80 (0.32, 2.00) T3 0.63 (0.42, 0.95) 0.67 (0.45, 0.98) 0.70 (0.37, 1.33) 0.74 (0.42, 1.32) 0.48 (0.16, 1.48) 0.54 (0.15, 1.97) P trend 0.026 0.036 0.190 0.239 0.188 0.343 Klotho (ng/mL) Deaths/total 286/2548 60/2322 69/2331 Continuous 0.66 (0.43, 1.01) 0.69 (0.46, 1.04) 0.51 (0.16, 1.64) 0.53 (0.16, 1.70) 0.87 (0.29, 2.58) 0.87 (0.31, 2.47) T1 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference) T2 0.62 (0.45, 0.86) 0.62 (0.43, 0.88) 0.49 (0.28, 0.88) 0.48 (0.26, 0.86) 0.47 (0.24, 0.92) 0.47 (0.25, 0.88) T3 0.78 (0.55, 1.12) 0.81 (0.56, 1.17) 0.84 (0.55, 1.30) 0.84 (0.53, 1.34) 0.85 (0.40, 1.82) 0.85 (0.40, 1.83) P trend 0.152 0.219 0.435 0.798 0.640 0.651 Model 1: adjusted for age (<65 or ≥65), sex (male or female), race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or other), marital status (married/cohabiting, widowed/divorced/separated, or never married), BMI (<25.0, 25.0-29.9, or ≥30.0 kg/m 2 ), physical activity (moderate or vigorous), drinking alcohol status (ever or never), and Ln-cotinine concentration (continuous). Model 2: further adjusted (from Model 1) for triglyceride (continuous), medication use (insulin/pills or no), CVD (yes or no), cancer (yes or no), and hypertension (yes or no). Deaths/total, the ratio of the number of deaths to the total participants; T, tertile. A dose-response function showed Phenotypic and Biological Age had a linear relationship with all-cause, CVD, and cancer mortality (all P linearity <0.001). Telomere length also showed a linear relationship with all-cause ( P linearity 0.015), CVD ( P linearity 0.152), and cancer ( P linearity 0.037) mortality. A U-shaped correlation was observed between Klotho and all-cause ( P nonlinear 0.002), CVD ( P nonlinear 0.502), and cancer ( P nonlinear 0.014) mortality (Fig. 1 and Supplementary Fig. 1). In the sensitivity analyses, similar results were observed after excluding participants with a diagnosis of diabetes before the age of 20 (Supplementary Table 2), imputing the missing data of covariates with multiple imputations (Supplementary Table 4), or further adjusting for HbA 1c level and the duration of diabetes (Supplementary Table 5). When excluding the deaths within 1 year of follow-up, further adjusting for education levels and family income-poverty ratio, or further adjusting for HDL-cholesterol, total cholesterol, and HOMA of insulin resistance, the results did not materially change (Supplementary Table 3, 6, and 7). Furthermore, we investigated the relationship of metformin with mortality among diabetics. Metformin users had a lower HR for all-cause, CVD, and cancer mortality compared to non-use (HR all−cause 0.64, 95%CI 0.56-0.73; HR CVD 0.51, 95%CI 0.37-0.70; HR cancer 0.65, 95%CI 0.44-0.95) (Table 3 ). Consistently, the use duration of metformin was negatively associated with mortality (Supplementary Table 8). Such associations were also presented regardless of whether blood glucose was well controlled. Further, mediation analysis showed that through decreasing Phenotypic Age, metformin use reduced all-cause (mediated proportion=26.52%, P <0.001), and CVD mortality (mediated proportion=14.40%, P =0.018). Among people with HbA 1c greater than 6.5 percent, Phenotypic Age also mediated this association of metformin use with all-cause (mediated proportion=34.18%, P <0.001), and CVD mortality (mediated proportion=26.22%, P =0.048). Biological Age mediated the effect of metformin use on all-cause mortality in total people and people with HbA 1c greater than 6.5 percent, and the mediated proportion was 14.68% and 19.61%, respectively (Fig. 2 and Supplementary Fig. 2). Table 3 HR (95% CI) for all-cause and cause-specific mortality based on the use of metformin stratified by HbA1c level among diabetics in NHANES 1999-2014. Total population HbA 1c <6.5% HbA 1c ≥6.5% metformin vs. no metformin metformin vs. no metformin metformin vs. no metformin All-cause mortality Deaths/total 1346/5266 418/1643 928/3623 Model 1 0.70 (0.61, 0.80) 0.59 (0.44, 0.80) 0.74 (0.63, 0.87) Model 2 0.64 (0.56, 0.73) 0.57 (0.42, 0.77) 0.65 (0.56, 0.77) CVD mortality Deaths/total 340/4260 106/1331 234/2929 Model 1 0.57 (0.41, 0.78) 0.49 (0.27, 0.89) 0.60 (0.41, 0.88) Model 2 0.51 (0.37, 0.70) 0.43 (0.25, 0.75) 0.54 (0.36, 0.81) Cancer mortality Deaths/total 226/4146 80/1305 146/2841 Model 1 0.70 (0.48, 1.04) 0.49 (0.22, 1.12) 0.88 (0.55, 1.41) Model 2 0.65 (0.44, 0.95) 0.44 (0.20, 0.99) 0.81 (0.51, 1.27) Model 1: adjusted for age (<65, or ≥65 years), sex (male or female), race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or other), marital status (married/cohabiting, widowed/divorced/separated, or never married), BMI (<25.0, 25.0-29.9, or ≥30.0 kg/m 2 ), physical activity (moderate or vigorous), drinking alcohol status (ever or never), and Ln-cotinine concentration (continuous). Model 2: further adjusted (from Model 1) for triglyceride (continuous), medication use (insulin/pills or no), CVD (yes or no), cancer (yes or no), hypertension (yes or no). Deaths/total, the ratio of the number of deaths to the total participants. Discussion In the current prospective cohort study of the U.S. population with diabetes, we found that aging-related makers were associated with mortality. Specifically, Phenotypic and Biological Age showed positive associations with all-cause, CVD, and cancer mortality, higher telomere length was associated with lower all-cause mortality, and Klotho had a U-shaped relationship with all-cause, CVD, and cancer mortality. Further, metformin decreased all-cause, CVD, and cancer mortality risk. More importantly, such associations were partly mediated by Phenotypic Age and Biological Age. These findings suggested that the associations of aging with mortality were significant, and anti-aging was an important approach to reduce death among diabetics. Previously, a meta-analysis of 17 cohorts with 5575 diabetics and 6439 control subjects showed shorter telomere length in diabetes ( 22 ). An inverse association was noted between telomere length and mortality among Chinese, Danish, and Italian diabetics (HR=1.87-3.45) ( 23 ). Consistent findings were also pronounced in our analysis among the U.S. diabetics. Telomere length is accepted as an indicator of cellular aging. Interestingly, removing aging cells or delaying their formation in mice can improve diabetes progress and its complications ( 10 , 24 ). However, the aging process of mouse cardiomyocytes and human lungs is only linked to the telomere-associated DNA damage, without significant telomere shortening detected ( 14 , 15 ). The above results may be attributed to the limited role of telomere in interpretation at organs or the whole body level. Importantly, diabetics often develop multiple organ disorders and die by complications. Hence, more comprehensive indicators which can represent the body’s status were needed. Phenotypic and Biological Age incorporated composite clinical biomarkers, such as inflammation (e.g. CRP), immunity (e.g. number of immune cells), and organ function (e.g. albumin and serum urea nitrogen) ( 25 ). They are useful to identify the unhealthy condition of patients caused by multiple organ disorders. Herein, we found that Phenotypic Age and Biological Age were significantly associated with all-cause and cause-specific mortality among diabetics. Moreover, we defined premature aging status based on chronological age and found that diabetics who appeared older than expected physiologically had a higher death risk. The previous study also reported similar associations of Phenotypic and Biological Age with all-cause mortality, but in the general population (HR=1.09, 1.10, respectively). Notably, among the disease-specific mortality, the risk of diabetes-caused mortality increased mostly (HR=1.20) ( 16 ). These researches indicated the importance of the whole body’s aging for death among diabetics. On the other hand, as the aging-related molecule, Klotho has been reported to be involved in the aging process for over 30 years, where it regulates phosphate homeostasis, insulin, and Wnt signaling ( 26 ). Interestingly, in our study population, Klotho concentration had a U-shaped relationship with all-cause, CVD, and cancer mortality. Previous epidemiological studies in the old or chronic kidney disease population revealed that Klotho concentration was negatively associated with death ( 27 , 28 ). Indeed, overexpression of soluble Klotho up-regulated fibroblast growth factors-23 levels, which may lead to hypovitaminosis D ( 29 ). More importantly, a recent prospective cohort study including 6,329 diabetics reported that the level of serum 25-Hydroxyvitamin D was negatively associated with all-cause and cause-specific mortality ( 30 ). These studies supported our findings that circulating Klotho may have a U-shaped relationship with mortality risk in diabetics. Collectively, from many perspectives, we observed the effect of aging on promoting diabetics’ death. Metformin was first introduced to the world in 1957, as an anti-hyperglycemic agent. Consistent with previous studies, we found that metformin users had a lower risk of all-cause and cause-specific mortality, which were positively correlated with the number of days taken among diabetics. Recently, accumulating evidence has revealed the gerotherapeutic effect of metformin on lowering the incidence of multiple age-related diseases and all-cause mortality in diabetics ( 31 ). A meta-analysis indicated that the anti-aging function of metformin is possibly independent of its effect on diabetes control ( 32 ). Hence, we further investigated whether anti-aging was involved in the role of metformin in preventing mortality among diabetics. We found that metformin use decreased mortality risk among diabetics, regardless of whether HbA 1C was well controlled. Of note, our mediation analyses showed that metformin reduced mortality partly by decreasing Biological Age and Phenotypic Age, emphasizing the importance of anti-aging. Mechanism-related studies in multiple models have elucidated metformin participated in various pathways of aging, including deregulated nutrient sensing, altered intercellular communication, genomic instability, and loss of proteostasis ( 33 , 34 ). Therefore, treatments targeting anti-aging are greatly promising in improving the mortality of diabetics. Some interventions have been well validated in animal or cells models ( 35 ). For example, senolytic drugs (e.g. ABT263) improved glucose tolerance and insulin sensitivity in diabetes mice by reducing senescent cell burden ( 10 ). Another combination senolytic agents, dasatinib and quercetin (DQ), offered a good therapeutic effect on mice with age-related diseases ( 36 ). In particular, preliminary clinical trials presented the positive effects of DQ on patients with idiopathic pulmonary fibrosis and diabetic kidney disease ( 9 , 37 ). The current study provided evidence that anti-aging was effective to delay the progression of diabetes and mortality. This study has some strengths. Firstly, the study was a prospective cohort study based on data from a large nationally representative survey among U.S. diabetics. Secondly, the associations reported in this study were relatively robust by adjustment for a variety of confounders and several sensitivity analyses. Finally, we examined the relationship between aging and death outcomes among diabetics from multiple perspectives. There are also limitations. Firstly, considering the cross-sectional nature of the NHANES data, the time-varying changes in aging markers could not be investigated. Secondly, the study lacked further information on the severity of diabetes, though adjusting for diabetes medication use, duration of diabetes, HbA 1c levels, and some self-reported comorbidities. Thirdly, unmeasured confounders and measurement errors may bias our analyses. Finally, whether anti-aging is involved in the effect of metformin on mortality still needs further research. In addition, we only analyzed the role of one anti-aging drug in mortality, which limited the interpretation of our conclusions. Conclusions In summary, our results of nationally representative samples from a prospective cohort study showed significant associations of aging with total and cause-specific mortality among diabetics. Moreover, we observed the improvement in mortality by metformin use among diabetics and further highlighted the mediating effects of aging. These findings suggested that aging accelerated death among diabetics and anti-aging treatments as a promising approach for diabetics. Abbreviations CVD: cardiovascular disease; HR: hazard ratio; CI: confidence interval; T: tertile; HbA 1c : glycated hemoglobin; CRP: C-reactive protein; SE: standard error ; NCHS: National Center for Health Statistics. Declarations Acknowledgements The authors thank all participants and all investigators. Author’s contributions Li Chen and Tianqi Tan conducted analyses. Li Chen, Ying Zhao, and Huimin Chen wrote the draft of the article. Ping Yao and Yuhan Tang conceived of the study design. All authors contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. Yuhan Tang is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Funding This study was supported by the National Key Research and Development Project (2018YFC1604000). Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The survey was approved by National Center for Health Statistics (NCHS) Ethics Review Board. Consent for publication The authors have reviewed the manuscript and consent for publication. Competing interests No potential conflicts of interest relevant to this article were reported. 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Type 2 diabetes mellitus in older adults: clinical considerations and management. Nature Reviews Endocrinology. 2021;17(9):534–48. Huang ES, Laiteerapong N, Liu JY, John PM, Moffet HH, Karter AJ. Rates of Complications and Mortality in Older Patients With Diabetes Mellitus The Diabetes and Aging Study. Jama Internal Medicine. 2014;174(2):251–8. Palmer AK, Gustafson B, Kirkland JL, Smith U. Cellular senescence: at the nexus between ageing and diabetes. Diabetologia. 2019;62(10):1835–41. Hickson LJ, Prata LGPL, Bobart SA, Evans TK, Giorgadze N, Hashmi SK, et al. Senolytics decrease senescent cells in humans: Preliminary report from a clinical trial of Dasatinib plus Quercetin in individuals with diabetic kidney disease. Ebiomedicine. 2019;47:446–56. Aguayo-Mazzucato C, Andle J, Lee T, Midha A, Talemal L, Chipashvili V, et al. Acceleration of β Cell Aging Determines Diabetes and Senolysis Improves Disease Outcomes. Cell Metabol. 2019;30(1):129–42.e4. Cheng FF, Luk AO, Wu HJ, Lim CKP, Carroll L, Tam CHT, et al. Shortened relative leukocyte telomere length is associated with all-cause mortality in type 2 diabetes- analysis from the Hong Kong Diabetes Register. Diabetes Res Clin Pract. 2021;172:108649. Astrup AS, Tarnow L, Jorsal A, Lajer M, Nzietchueng R, Benetos A, et al. Telomere length predicts all-cause mortality in patients with type 1 diabetes. Diabetologia. 2010;53(1):45–8. Bonfigli AR, Spazzafumo L, Prattichizzo F, Bonafe M, Mensa E, Micolucci L, et al. Leukocyte telomere length and mortality risk in patients with type 2 diabetes. Oncotarget. 2016;7(32):50835–44. Birch J, Anderson RK, Correia-Melo C, Jurk D, Hewitt G, Marques FM, et al. DNA damage response at telomeres contributes to lung aging and chronic obstructive pulmonary disease. American Journal of Physiology-Lung Cellular Molecular Physiology. 2015;309(10):L1124-L37. Anderson R, Lagnado A, Maggiorani D, Walaszczyk A, Dookun E, Chapman J, et al. Length-independent telomere damage drives post-mitotic cardiomyocyte senescence. Embo Journal. 2019;38(5). Liu Z, Kuo P, Horvath S, Crimmins E, Ferrucci L, Levine M. A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study. PLoS Med. 2018;15(12):e1002718. Han T, Gao J, Wang L, Li C, Qi L, Sun C, et al. The Association of Energy and Macronutrient Intake at Dinner Versus Breakfast With Disease-Specific and All-Cause Mortality Among People With Diabetes: The U.S. National Health and Nutrition Examination Survey, 2003-2014. Diabetes Care. 2020;43(7):1442–8. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging-Us. 2018;10(4):573–91. Klemera P, Doubal S. A new approach to the concept and computation of biological age. Mech Ageing Dev. 2006;127(3):240–8. Levine M. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? The journals of gerontology Series A. Biological sciences. 2013;68(6):667–74. Banach M, Mazidi M, Mikhailidis DP, Toth PP, Jozwiak J, Rysz J, et al. Association between phenotypic familial hypercholesterolaemia and telomere length in US adults: results from a multi-ethnic survey. Eur Heart J. 2018;39(40):3635–40. Wang JF, Dong X, Cao L, Sun YY, Qiu Y, Zhang Y, et al. Association between telomere length and diabetes mellitus: A meta-analysis. J Int Med Res. 2016;44(6):1156–73. Cheng FF, Carroll L, Joglekar MV, Januszewski AS, Wong KK, Hardikar AA, et al. Diabetes, metabolic disease, and telomere length. Lancet Diabetes Endocrinology. 2021;9(2):117–26. Chen L, Mei GB, Jiang CJ, Cheng XE, Li D, Zhao Y, et al. Carbon monoxide alleviates senescence in diabetic nephropathy by improving autophagy. Cell Prolif. 2021;54(6):e13052. Belsky D, Caspi A, Houts R, Cohen H, Corcoran D, Danese A, et al. Quantification of biological aging in young adults. Proc Natl Acad Sci USA. 2015;112(30):E4104-10. Xu YC, Sun ZJ. Molecular Basis of Klotho: From Gene to Function in Aging. Endocr Rev. 2015;36(2):174–93. Semba RD, Cappola AR, Sun K, Bandinelli S, Dalal M, Crasto C, et al. Plasma Klotho and Mortality Risk in Older Community-Dwelling Adults. Journals of Gerontology Series a-Biological Sciences and Medical Sciences. 2011;66(7):794–800. Charoenngam N, Ponvilawan B, Ungprasert P. Lower circulating soluble Klotho level is associated with increased risk of all-cause mortality in chronic kidney disease patients: a systematic review and meta-analysis. Int Urol Nephrol. 2020;52(8):1543–50. Torres PU, Prie D, Molina-Bletry V, Beck L, Silve C, Friedlander G. Klotho: An antiaging protein involved in mineral and vitamin D metabolism. Kidney Int. 2007;71(8):730–7. Wan ZZ, Guo JY, Pan A, Chen C, Liu LG, Liu G. Association of Serum 25-Hydroxyvitamin D Concentrations With All-Cause and Cause-Specific Mortality Among Individuals With Diabetes. Diabetes Care. 2021;44(2):350–7. Kulkarni AS, Gubbi S, Barzilai N. Benefits of Metformin in Attenuating the Hallmarks of Aging. Cell Metab. 2020;32(1):15–30. Campbell JM, Bellman SM, Stephenson MD, Lisy K. Metformin reduces all-cause mortality and diseases of ageing independent of its effect on diabetes control: A systematic review and meta-analysis. Ageing Res Rev. 2017;40:31–44. Barzilai N, Crandall JP, Kritchevsky SB, Espeland MA. Metformin as a Tool to Target Aging. Cell Metab. 2016;23(6):1060–5. Cabreiro F, Au C, Leung KY, Vergara-Irigaray N, Cocheme HM, Noori T, et al. Metformin Retards Aging in C. elegans by Altering Microbial Folate and Methionine Metabolism. Cell. 2013;153(1):228–39. Yang CB, Zhang W, Dong XD, Fu CJ, Yuan JM, Xu ML, et al. A natural product solution to aging and aging-associated diseases. Pharmacol Ther. 2020;216:107673. Novais EJ, Tran VA, Johnston SN, Darris KR, Roupas AJ, Sessions GA, et al. Long-term treatment with senolytic drugs Dasatinib and Quercetin ameliorates age-dependent intervertebral disc degeneration in mice. Nat Commun. 2021;12(1):5213. Justice J, Nambiar A, Tchkonia T, LeBrasseur N, Pascual R, Hashmi S, et al. Senolytics in idiopathic pulmonary fibrosis: Results from a first-in-human, open-label, pilot study. EBioMedicine. 2019;40:554–63. 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. We do this by developing innovative software and high quality services for the global research community. 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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-961620","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Original investigation","associatedPublications":[],"authors":[{"id":57134239,"identity":"dacdb933-6ed6-46dd-a24a-5e55652d48f3","order_by":0,"name":"Li Chen","email":"","orcid":"","institution":"Huazhong University of Science and Technology Tongji Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Chen","suffix":""},{"id":57134240,"identity":"1fd0a91f-a46e-4315-8dc2-21d403022109","order_by":1,"name":"Ying Zhao","email":"","orcid":"","institution":"Huazhong University of Science and Technology Tongji Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhao","suffix":""},{"id":57134241,"identity":"90d85728-a47b-41b3-abef-d319c8a6ff95","order_by":2,"name":"Huimin Chen","email":"","orcid":"","institution":"Huazhong University of Science and Technology Tongji Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huimin","middleName":"","lastName":"Chen","suffix":""},{"id":57134242,"identity":"9591aaa6-84e5-4c79-a614-b06fa1a484f2","order_by":3,"name":"Tianqi Tan","email":"","orcid":"","institution":"Huazhong University of Science and Technology Tongji Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tianqi","middleName":"","lastName":"Tan","suffix":""},{"id":57134243,"identity":"69438b7d-1858-46d4-8ee2-28ffeb25693d","order_by":4,"name":"Ping Yao","email":"","orcid":"","institution":"Huazhong University of Science and Technology Tongji Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Yao","suffix":""},{"id":57134244,"identity":"c68f4500-1358-4daa-9405-582a843e660c","order_by":5,"name":"Yuhan Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie3OsUrEQBCA4QkLSTMh7YTIPcPIwYkQyKskCKlSWFrmONg0pk8jPoONWuZYOJt9gCtPAqmuuEo4iGIO7DSJdhb7FwM78DELYDL9wzxhFYfDDWF032y+dvE48YuVtCodnjGkaf/macL6RQpXpiFDtvgdgW0id2grvAD91mDXgedkDMfnYWFVScGECi+X5dPclQz+7Z6tUg8TQUlOTApBuY+BmzPwNmNhyWFin0jMPdlgG2DHEE0RxLWkOk6RNdoB2v0VmiDkLFfneR2iX9kL/07OkXR7vS5HSKSc1+b9gyKPREv7bjbziquH3XGEfP/padR/ACaTyWT6oU8ty04yR/4KNQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-1290-2039","institution":"Huazhong University of Science and Technology Tongji Medical College","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yuhan","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2021-10-09 10:54:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-961620/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-961620/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":14673851,"identity":"c141682e-c274-405b-b8f7-d71e51f00838","added_by":"auto","created_at":"2021-10-19 15:00:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113407,"visible":true,"origin":"","legend":"The dose-response relationship between aging markers and all-cause mortality assessed by restricted cubic spline regression. Models were adjusted for age (\u003c65 or ≥65), sex (male or female), race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or other), marital status (married/cohabiting, widowed/divorced/separated, or never married), BMI (\u003c25.0, 25.0-29.9, or ≥30.0 kg/m2), physical activity (moderate or vigorous), drinking alcohol status (ever or never), Ln-cotinine concentration (continuous), triglyceride (continuous), medication use (insulin/pills or no), CVD (yes or no), cancer (yes or no), and hypertension (yes or no). ","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-961620/v1/caeb668a6b9bb263392b746e.png"},{"id":14673846,"identity":"5a6bc68a-d395-4c86-a780-86e544c3faa1","added_by":"auto","created_at":"2021-10-19 15:00:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":367078,"visible":true,"origin":"","legend":"The mediation effects of aging markers on associations of metformin use with all-cause mortality stratified by HbA1c level. Models were adjusted for age (\u003c65 or ≥65 years), sex (male or female), race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or other), marital status (married/cohabiting, widowed/divorced/separated, or never married), BMI (\u003c25.0, 25.0-29.9, or ≥30.0 kg/m2), physical activity (moderate or vigorous), drinking alcohol status (ever or never), Ln-cotinine concentration (continuous), triglyceride (continuous), medication use (insulin/pills or no), CVD (yes or no), cancer (yes or no), and hypertension (yes or no). ","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-961620/v1/94063b8bc76e05bd63a4e9d2.png"},{"id":14673854,"identity":"072b693a-8aea-4f4d-825a-570d2147f135","added_by":"auto","created_at":"2021-10-19 15:00:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":847609,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-961620/v1/710d1944-8aff-42fb-9fe5-b09c2d59d075.pdf"},{"id":14673853,"identity":"44d9bcc9-897a-4245-a37e-861583cff4aa","added_by":"auto","created_at":"2021-10-19 15:00:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":883410,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-961620/v1/1fb70283-6544-4e1a-8065-b7f972399524.pdf"},{"id":14673852,"identity":"fb2d6fe8-99e9-4a5f-890a-f6d6551f0a06","added_by":"auto","created_at":"2021-10-19 15:00:05","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":11234795,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-961620/v1/7262dabbfec16ede1aecab01.docx"}],"financialInterests":"","formattedTitle":"Aging and the risks of all-cause and cause-specific mortality among diabetics: a prospective cohort study","fulltext":[{"header":"Background","content":"\u003cp\u003eDiabetes, as the ninth major cause of death, is a worldwide public health concern, with considerable healthcare and economic burden (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The number of diabetics has quadrupled in the past 3 decades and is projected to exceed more than 642 million by 2040 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Diabetics often suffer from complications and premature death (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Statistically, the risk of death and cardiovascular events is 2 to 4 times higher among diabetics than in the general population (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Hence, the identification of risk factors affecting the survival of diabetics may contribute to delaying premature mortality and its development.\u003c/p\u003e \u003cp\u003eAging characterized by a progressive decline in homeostasis is generally accepted as the leading risk factor for age-related diseases and death (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Nearly half of diabetics are older adults who are primarily responsible for the growing burden (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Moreover, diabetics usually develop a premature aging status with dysfunction of multiple systems (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Compelling animal studies and clinical trials also revealed drugs or gene editing targeting aging improved diabetic outcomes (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Aging, though, was critical for diabetics, epidemiological evidence is limited. Previously, investigations explored the relationship between short telomere length and all-cause mortality among Chinese (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), Danish (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), and Italian diabetics (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), with the hazard ratio (HR) ranging from 1.87 to 3.45. However, some data showed that aging in the lung (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) and myocardium (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) was independent of telomere length, indicating the limited capacity of it to comprehensively reflect the aging status of the organ or organism. Recently, novel aging measures including multiple readily available clinical biomarkers were applied to capture body\u0026rsquo;s homeostasis, such as Phenotypic Age and Biological Age. These markers were reported to well predict adverse outcomes. For example, a prospective cohort study in the general population showed that higher Phenotypic Age was positively associated with increased death risk, especially diabetes-caused mortality (HR=1.20) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, studies focused on diabetics are lacking.\u003c/p\u003e \u003cp\u003eTherefore, we measured aging at multiple perspectives and conducted a prospective and representative cohort study to investigate the association between aging and all-cause and cause-specific mortality among U.S. diabetics based on the data from the National Health and Nutrition Examination Survey (NHANES) 1999-2014.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe NHANES is an ongoing national cross-sectional survey to assess the health and nutritional status in the U.S. The survey was approved by National Center for Health Statistics (NCHS) Ethics Review Board and all of the participants provide their written informed consent. In this study, all individuals with diabetes were included based on the data of NHANES from 1999 to 2014. Diabetes was defined by self-reported diagnosis, use of insulin or oral hypoglycemic medication, glycated hemoglobin (HbA\u003csub\u003e1c\u003c/sub\u003e) level \u0026ge;6.5%, or fasting plasma glucose level \u0026ge;7.0 mmol/L (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). After excluding those who had missing information on any aging markers and/or mortality, and self-reported as pregnant, 5278 participants were enrolled in the current analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eAging Markers Measurement\u003c/h2\u003e\n\u003cp\u003ePhenotypic Age was calculated according to the method published previously (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In brief, 9 aging-related variables, including chronological age, albumin, creatinine, glucose, Ln-C-reactive protein (CRP), lymphocyte percent, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count, were evaluated together based on the following equation.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n\n$$\\text{P}\\text{h}\\text{e}\\text{n}\\text{o}\\text{t}\\text{y}\\text{p}\\text{i}\\text{c} \\text{A}\\text{g}\\text{e} =141.50 +\\frac{\\text{L}\\text{n}[-0.00553 \\times \\text{L}\\text{n}(\\text{e}\\text{x}\\text{p}\\left(\\frac{-1.51714\\times \\text{e}\\text{x}\\text{p}\\left(\\text{x}\\text{b}\\right)}{0.0076927}\\right)\\left)\\right]}{0.09165}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere: xb= -19.907།0.0336 \u0026times; Albumin + 0.0095 \u0026times; Creatinine + 0.1953 \u0026times; Glucose + 0.0954 \u0026times; LnCRP།0.0120 \u0026times; Lymphocyte Percent + 0.0268 \u0026times; Mean Cell Volume + 0.3306 \u0026times; Red Cell Distribution Width + 0.00188 \u0026times; Alkaline Phosphatase + 0.0554 \u0026times; White Blood Cell Count + 0.0804\u0026times; Chronological Age\u003c/p\u003e \u003cp\u003eBiological Age was calculated using the methods proposed by Klemera and Doubal\u0026nbsp;(19). In this study, 8 biomarkers (CRP, serum creatinine, HbA\u003csub\u003e1c\u003c/sub\u003e, serum albumin, serum total cholesterol, serum urea nitrogen, serum alkaline phosphatase, and systolic blood pressure) were estimated based on the following formulas (1-4)\u0026nbsp;(20). The values \u003cem\u003ej\u003c/em\u003e and \u003cem\u003ei\u0026nbsp;\u003c/em\u003erepresent the number of\u0026nbsp;biomarkers and samples respectively. The values \u003cem\u003ek\u003c/em\u003e, \u003cem\u003eq\u003c/em\u003e, and \u003cem\u003es\u003c/em\u003e are the regression slope, intercept, and the root means squared error of a biomarker regressed on\u0026nbsp;chronological age,\u0026nbsp;respectively. The value \u003cem\u003er\u003c/em\u003e\u003csub\u003ej\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e represents the variance explained by regression chronological age on biomarkers.\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n\n$$\\left(1\\right) {\\text{BA}}_{E}=\\frac{{\\sum }_{j=1}^{m}\\left({x}_{j}-{q}_{j}\\right)\\left(\\frac{{k}_{j}}{{s}_{j}^{2}}\\right)}{{\\sum }_{j=1}^{m}{\\left(\\frac{{k}_{j}}{{s}_{j}}\\right)}^{2}} \\left(2\\right) {r}_{cℎar}=\\frac{{\\sum }_{j=1}^{m}\\frac{{r}_{j}^{2}}{\\sqrt{1-{r}_{j}^{2}}}}{{\\sum }_{j=1}^{m}\\frac{{r}_{j}}{\\sqrt{1-{r}_{j}^{2}}}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n\n$${\\left(3\\right) s}_{BA}^{2}={\\frac{{\\sum }_{j=1}^{n}\\left(\\left({\\text{BA}}_{Ei}-\\text{C}{\\text{A}}_{i}\\right)-\\frac{{\\sum }_{i=1}^{n}\\left({\\text{BA}}_{Ei}-\\text{C}{\\text{A}}_{i}\\right)}{n}\\right)}{n}}^{2}-\\left(\\frac{1-{r}_{cℎar}^{2}}{{r}_{cℎar}^{2}}\\right)\\times \\left(\\frac{{\\left({\\text{CA}}_{max}-{CA}_{min}\\right)}^{2}}{12m}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n\n$${\\text{BA}}_{EC}=\\frac{{\\sum }_{j=1}^{m}\\left({x}_{j}-{q}_{j}\\right)\\left(\\frac{{k}_{j}}{{s}_{j}^{2}}\\right)+\\frac{\\text{CA}}{{s}_{BA}^{2}}}{{\\sum }_{j=1}^{m}{\\left(\\frac{{k}_{j}}{{s}_{j}}\\right)}^{2}+\\frac{1}{{s}_{\\text{BA}}^{2}}}$$\u003c/div\u003e\u003c/div\u003e(4) \u003c/p\u003e \u003cp\u003eAdditionally, markers of premature aging were defined as Phenotypic/Biological\u0026nbsp;Age Acceleration\u0026nbsp;and \u0026Delta;Phenotypic/Biological Age\u0026nbsp;(16). Phenotypic/Biological\u0026nbsp;Age Acceleration was the residual resulting from a linear model when regressing Phenotypic/Biological\u0026nbsp;Age on chronological age. \u0026Delta;Phenotypic/Biological Age represented the value of Phenotypic/Biological Age minus chronological age.\u003c/p\u003e\n\u003cp\u003eMeasurement of telomere length (NHANES 1999-2002) has been reported elsewhere (21). Briefly, blood samples were collected and determined by quantitative polymerase chain reaction (qPCR) assays to assess the telomere length relative to standard reference DNA (the T/S ratio). Klotho (NHANES 2007-2014) was determined by a commercially available ELISA kit (IBL International, Japan). All sample analyses were performed in duplicate strictly following the manufacturer\u0026rsquo;s instruction and all results were checked to meet the laboratory\u0026rsquo;s standardized criteria for acceptability before being released for reporting.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMortality Ascertainment\u003c/h2\u003e\n\u003cp\u003eAll participants from NHANES 1999-2014 were linked to death records in the National Death Index up to 31 December 2015. Follow-up duration is defined as the time from participating in the NHIS to death for decedents or to the censoring date for survivors. Mortality outcomes in the current study included all-cause, cardiovascular disease (CVD)-cause, and cancer-cause determined by ICD-10 codes recorded in NHANES.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e All analyses were performed with SAS (version 9.4) or R (version 3.6.3) and accounted for the complex sampling design of the NHANES according to the analytic guidelines. We used χ\u003csup\u003e2\u003c/sup\u003e tests, nonparametric tests, and \u003cem\u003et\u003c/em\u003e-tests to assess the baseline characteristics of the participants by life or death status. Continuous variables were presented as mean \u0026plusmn; standard error (SE) or median (interquartile range), and categorical variables were presented as n (%). Cox proportional hazards models were applied to estimate hazard ratio (HR) and 95% confidence interval (CI) for the associations of aging markers with all-cause and cause-specific mortality, with months of follow-up as the time scale. Aging markers were categorized into three tertiles (T1, T2, and T3) based on the distribution. The T/S ratio and Klotho were Ln-transformed (continuous). Model 1 was adjusted for age (\u0026lt;65 or \u0026ge;65 years), sex (male or female), race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or others), marital status (married/cohabiting, widowed/divorced/separated, or never married), BMI (\u0026lt;25.0, 25.0-29.9, or \u0026ge;30.0 kg/m\u003csup\u003e2\u003c/sup\u003e), physical activity (moderate or vigorous), drinking alcohol status (ever or never), and Ln-cotinine concentration (continuous). Model 2 was further adjusted for triglyceride (continuous), medication use (insulin/pills or no), CVD (yes or no), cancer (yes or no), and hypertension (yes or no). The missing data of covariates were imputed with median values. The trend test across increasing exposure groups was calculated using integer values (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, and \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The dose-response relationships between aging markers and mortality were assessed by restricted cubic spline regression with 3 knots placed at the 25th, 50th, and 75th percentile.\u003c/p\u003e \u003cp\u003eWe also estimated the associations of metformin use (ever or never) with mortality by the Cox proportional hazards model, and subgroup analysis was stratified by HbA\u003csub\u003e1c\u003c/sub\u003e level (\u0026lt;6.5% or \u0026ge;6.5%). Mediation analyses were conducted to explore whether the use of metformin reduced mortality by anti-aging with an R package (mediation). The mediated proportion referred to the average mediation effect between metformin use attributed to mortality changes relative to the total effect. \u003cem\u003eP-\u003c/em\u003evalue for mediated proportion was obtained from the quasi Bayesian Monte Carlo simulation 2000 times.\u003c/p\u003e \u003cp\u003eSeveral sensitivity analyses were also performed. First, participants with a diagnosis of diabetes before the age of 20 years were excluded. Second, the deaths within 1 year of follow-up were excluded to reduce the potential reverse causation bias. Third, multiple imputations was used for covariates with missing values. Fourth, given the diabetes status, HbA\u003csub\u003e1c\u003c/sub\u003e level (\u0026lt;6.5% or \u0026ge;6.5%) and duration of diabetes (\u0026lt;3, 3\u0026ndash;10, or \u0026gt;10 years) were further adjusted. Fifth, considering other demographic confounding factors, we further adjusted for education levels (under high school, high school or equivalent, or above high school) and family income-poverty ratio (0\u0026ndash;1.0, 1.0\u0026ndash;3.0, or \u0026gt;3.0). Finally, some potential mediators were also further adjusted, including HDL-cholesterol, total cholesterol, and HOMA of insulin resistance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDuring median follow-up for 7.3 years, 1,355 deaths were documented among 5,278 diabetics. Based on the live or death status of the study population, baseline demographic was provided in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The deaths were older, more likely to be Non-Hispanic White and widowed or divorced or separated, and were less likely to be obese, and had less frequently drinking alcohol. Those who died had a longer duration of diabetes, and a high percentage of anti-diabetic medication or insulin use but had a lower percentage of metformin use. The deaths had a lower median concentration of Klotho (0.76 vs. 0.82 pg/mL), a shorter median telomere length (0.88 vs. 0.97), and an older mean Phenotypic Age (74.55 vs. 57.88 years) and Biological Age (68.39 years vs. 55.05 years).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of diabetics by life or death status in NHANES 1999-2014.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlive (3923)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead (1355)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2533 (70.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e381 (35.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1390 (29.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e974 (64.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1984 (51.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e768 (55.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1939 (48.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e587 (44.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e912 (9.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e264 (5.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e375 (6.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (4.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1340 (60.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e659 (70.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1012 (15.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e326 (13.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther race/multiracial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e284 (7.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (5.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/cohabiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2428 (65.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e664 (51.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/divorced/separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1092 (24.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e582 (42.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e371 (9.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (6.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e456 (10.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250 (19.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25-29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1107 (26.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e430 (30.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2277 (63.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e564 (50.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3180 (78.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1255 (91.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e743 (21.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (8.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking alcohol status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2295 (66.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e726 (58.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1349 (33.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e522 (41.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin or pills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006 (80.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e806 (88.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e436 (19.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (11.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of diabetes, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e735 (29.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158 (19.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e805 (29.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191 (24.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1162 (41.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e497 (56.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum cotinine, ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.1 \u0026plusmn; 3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.67 \u0026plusmn; 4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.12 \u0026plusmn; 0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.19 \u0026plusmn; 0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e785 (18.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e596 (45.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3091 (81.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e742 (54.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e428 (13.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269 (21.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3467 (86.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1082 (78.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2478 (61.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e950 (70.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1431 (38.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e398 (29.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetformin use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1077 (37.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e357 (26.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1709 (62.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e989 (73.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKlotho, ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.67, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.60, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean T/S ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.85, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.77, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenotypic Age, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.88 \u0026plusmn; 0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.55 \u0026plusmn; 0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological Age, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.05 \u0026plusmn; 0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.39 \u0026plusmn; 0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eContinuous variables were presented as mean \u0026plusmn; SE or median (interquartile range). Categorical variables were presented as numbers (percentages). All estimates accounted for complex survey designs.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presented the associations of 4 aging markers with all-cause, CVD, and cancer mortality. Each 1-year increase in Phenotypic Age increased the risk of all-cause, CVD, and cancer mortality by 5% (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Similarly, each 1-year increase in Biological Age was associated with an increase in all-cause, CVD, and cancer mortality (7%, 8%, and 5%, respectively, all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Premature aging markers were also positively associated with all-cause, CVD, and cancer mortality (Supplementary Table 1). Additionally, for each unit increase in mean T/S ratio (a marker of cellular senescence), HR for all-cause mortality decreased by 47% (HR 0.53, 95%CI 0.29-0.97), consistent with the quantile analysis. The middle concentration of Klotho was associated with a lower risk of all-cause, CVD, and cancer mortality (T2 vs. T1: HR\u003csub\u003eall\u0026minus;cause\u003c/sub\u003e 0.62, 95%CI 0.43-0.88; HR\u003csub\u003eCVD\u003c/sub\u003e 0.48, 95%CI 0.26-0.86; HR\u003csub\u003ecancer\u003c/sub\u003e 0.47, 95%CI 0.25-0.88).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHR (95% CI) for all-cause and cause-specific mortality based on aging markers among diabetics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCVD mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCancer mortality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ePhenotypic Age (years)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths/total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1251/4003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e320/3072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e208/2960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (1.05, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (1.05, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (1.05, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05 (1.04, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05 (1.04, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05 (1.04, 1.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.28 (1.56, 3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13 (1.43, 3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.59 (0.71, 3.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26 (0.53, 3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39 (0.52, 3.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.29 (0.49, 3.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.28 (4.29, 9.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.45 (3.62, 8.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.88 (1.60, 9.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.73 (1.02, 7.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.02 (1.66, 9.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.51 (1.43, 8.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eBiological Age (years)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths/total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1204/3861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e311/2968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e198/2855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.06, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 (1.05, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (1.06, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08 (1.05, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06 (1.04, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05 (1.03, 1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.20 (1.61, 3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00 (1.46, 2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.93 (1.19, 3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.69 (0.98, 2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.11 (0.79, 5.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.88 (0.71, 4.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.47 (2.98, 6.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.85 (2.62, 5.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.40 (2.60, 15.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.91 (1.87, 12.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.97 (0.90, 9.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.20 (0.66, 7.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eMean T/S ratio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths/total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e432/905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e115/588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e56/529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49 (0.25, 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53 (0.29, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32 (0.09, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36 (0.11, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31 (0.05, 1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34 (0.04, 2.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68 (0.48, 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70 (0.50, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59 (0.31, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55 (0.26, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70 (0.28, 1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80 (0.32, 2.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.63 (0.42, 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.45, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70 (0.37, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74 (0.42, 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48 (0.16, 1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54 (0.15, 1.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eKlotho (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths/total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e286/2548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e60/2322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e69/2331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66 (0.43, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69 (0.46, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51 (0.16, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53 (0.16, 1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87 (0.29, 2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87 (0.31, 2.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62 (0.45, 0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62 (0.43, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49 (0.28, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48 (0.26, 0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47 (0.24, 0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.47 (0.25, 0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78 (0.55, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81 (0.56, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84 (0.55, 1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84 (0.53, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85 (0.40, 1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85 (0.40, 1.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eModel 1: adjusted for age (\u0026lt;65 or \u0026ge;65), sex (male or female), race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or other), marital status (married/cohabiting, widowed/divorced/separated, or never married), BMI (\u0026lt;25.0, 25.0-29.9, or \u0026ge;30.0 kg/m\u003csup\u003e2\u003c/sup\u003e), physical activity (moderate or vigorous), drinking alcohol status (ever or never), and Ln-cotinine concentration (continuous). Model 2: further adjusted (from Model 1) for triglyceride (continuous), medication use (insulin/pills or no), CVD (yes or no), cancer (yes or no), and hypertension (yes or no). Deaths/total, the ratio of the number of deaths to the total participants; T, tertile.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA dose-response function showed Phenotypic and Biological Age had a linear relationship with all-cause, CVD, and cancer mortality (all \u003cem\u003eP\u003c/em\u003e linearity \u0026lt;0.001). Telomere length also showed a linear relationship with all-cause (\u003cem\u003eP\u003c/em\u003e linearity 0.015), CVD (\u003cem\u003eP\u003c/em\u003e linearity 0.152), and cancer (\u003cem\u003eP\u003c/em\u003e linearity 0.037) mortality. A U-shaped correlation was observed between Klotho and all-cause (\u003cem\u003eP\u003c/em\u003e nonlinear 0.002), CVD (\u003cem\u003eP\u003c/em\u003e nonlinear 0.502), and cancer (\u003cem\u003eP\u003c/em\u003e nonlinear 0.014) mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the sensitivity analyses, similar results were observed after excluding participants with a diagnosis of diabetes before the age of 20 (Supplementary Table 2), imputing the missing data of covariates with multiple imputations (Supplementary Table 4), or further adjusting for HbA\u003csub\u003e1c\u003c/sub\u003e level and the duration of diabetes (Supplementary Table 5). When excluding the deaths within 1 year of follow-up, further adjusting for education levels and family income-poverty ratio, or further adjusting for HDL-cholesterol, total cholesterol, and HOMA of insulin resistance, the results did not materially change (Supplementary Table 3, 6, and 7).\u003c/p\u003e \u003cp\u003eFurthermore, we investigated the relationship of metformin with mortality among diabetics. Metformin users had a lower HR for all-cause, CVD, and cancer mortality compared to non-use (HR\u003csub\u003eall\u0026minus;cause\u003c/sub\u003e 0.64, 95%CI 0.56-0.73; HR\u003csub\u003eCVD\u003c/sub\u003e 0.51, 95%CI 0.37-0.70; HR\u003csub\u003ecancer\u003c/sub\u003e 0.65, 95%CI 0.44-0.95) (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Consistently, the use duration of metformin was negatively associated with mortality (Supplementary Table 8). Such associations were also presented regardless of whether blood glucose was well controlled. Further, mediation analysis showed that through decreasing Phenotypic Age, metformin use reduced all-cause (mediated proportion=26.52%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), and CVD mortality (mediated proportion=14.40%, \u003cem\u003eP\u003c/em\u003e=0.018). Among people with HbA\u003csub\u003e1c\u003c/sub\u003e greater than 6.5 percent, Phenotypic Age also mediated this association of metformin use with all-cause (mediated proportion=34.18%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), and CVD mortality (mediated proportion=26.22%, \u003cem\u003eP\u003c/em\u003e=0.048). Biological Age mediated the effect of metformin use on all-cause mortality in total people and people with HbA\u003csub\u003e1c\u003c/sub\u003e greater than 6.5 percent, and the mediated proportion was 14.68% and 19.61%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHR (95% CI) for all-cause and cause-specific mortality based on the use of metformin stratified by HbA1c level among diabetics in NHANES 1999-2014.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHbA\u003csub\u003e1c\u003c/sub\u003e\u0026lt;6.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHbA\u003csub\u003e1c\u003c/sub\u003e\u0026ge;6.5%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emetformin vs. no metformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emetformin vs. no metformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emetformin vs. no metformin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths/total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1346/5266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e418/1643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e928/3623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.61, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59 (0.44, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74 (0.63, 0.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64 (0.56, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57 (0.42, 0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65 (0.56, 0.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths/total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e340/4260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106/1331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234/2929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57 (0.41, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49 (0.27, 0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60 (0.41, 0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51 (0.37, 0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43 (0.25, 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54 (0.36, 0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths/total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e226/4146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80/1305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146/2841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.48, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49 (0.22, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88 (0.55, 1.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65 (0.44, 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44 (0.20, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81 (0.51, 1.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eModel 1: adjusted for age (\u0026lt;65, or \u0026ge;65 years), sex (male or female), race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or other), marital status (married/cohabiting, widowed/divorced/separated, or never married), BMI (\u0026lt;25.0, 25.0-29.9, or \u0026ge;30.0 kg/m\u003csup\u003e2\u003c/sup\u003e), physical activity (moderate or vigorous), drinking alcohol status (ever or never), and Ln-cotinine concentration (continuous). Model 2: further adjusted (from Model 1) for triglyceride (continuous), medication use (insulin/pills or no), CVD (yes or no), cancer (yes or no), hypertension (yes or no). Deaths/total, the ratio of the number of deaths to the total participants.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the current prospective cohort study of the U.S. population with diabetes, we found that aging-related makers were associated with mortality. Specifically, Phenotypic and Biological Age showed positive associations with all-cause, CVD, and cancer mortality, higher telomere length was associated with lower all-cause mortality, and Klotho had a U-shaped relationship with all-cause, CVD, and cancer mortality. Further, metformin decreased all-cause, CVD, and cancer mortality risk. More importantly, such associations were partly mediated by Phenotypic Age and Biological Age. These findings suggested that the associations of aging with mortality were significant, and anti-aging was an important approach to reduce death among diabetics.\u003c/p\u003e \u003cp\u003ePreviously, a meta-analysis of 17 cohorts with 5575 diabetics and 6439 control subjects showed shorter telomere length in diabetes (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). An inverse association was noted between telomere length and mortality among Chinese, Danish, and Italian diabetics (HR=1.87-3.45) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Consistent findings were also pronounced in our analysis among the U.S. diabetics. Telomere length is accepted as an indicator of cellular aging. Interestingly, removing aging cells or delaying their formation in mice can improve diabetes progress and its complications (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, the aging process of mouse cardiomyocytes and human lungs is only linked to the telomere-associated DNA damage, without significant telomere shortening detected (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The above results may be attributed to the limited role of telomere in interpretation at organs or the whole body level. Importantly, diabetics often develop multiple organ disorders and die by complications. Hence, more comprehensive indicators which can represent the body\u0026rsquo;s status were needed.\u003c/p\u003e \u003cp\u003ePhenotypic and Biological Age incorporated composite clinical biomarkers, such as inflammation (e.g. CRP), immunity (e.g. number of immune cells), and organ function (e.g. albumin and serum urea nitrogen) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). They are useful to identify the unhealthy condition of patients caused by multiple organ disorders. Herein, we found that Phenotypic Age and Biological Age were significantly associated with all-cause and cause-specific mortality among diabetics. Moreover, we defined premature aging status based on chronological age and found that diabetics who appeared older than expected physiologically had a higher death risk. The previous study also reported similar associations of Phenotypic and Biological Age with all-cause mortality, but in the general population (HR=1.09, 1.10, respectively). Notably, among the disease-specific mortality, the risk of diabetes-caused mortality increased mostly (HR=1.20) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). These researches indicated the importance of the whole body\u0026rsquo;s aging for death among diabetics.\u003c/p\u003e \u003cp\u003eOn the other hand, as the aging-related molecule, Klotho has been reported to be involved in the aging process for over 30 years, where it regulates phosphate homeostasis, insulin, and Wnt signaling (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Interestingly, in our study population, Klotho concentration had a U-shaped relationship with all-cause, CVD, and cancer mortality. Previous epidemiological studies in the old or chronic kidney disease population revealed that Klotho concentration was negatively associated with death (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Indeed, overexpression of soluble Klotho up-regulated fibroblast growth factors-23 levels, which may lead to hypovitaminosis D (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). More importantly, a recent prospective cohort study including 6,329 diabetics reported that the level of serum 25-Hydroxyvitamin D was negatively associated with all-cause and cause-specific mortality (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). These studies supported our findings that circulating Klotho may have a U-shaped relationship with mortality risk in diabetics. Collectively, from many perspectives, we observed the effect of aging on promoting diabetics\u0026rsquo; death.\u003c/p\u003e \u003cp\u003eMetformin was first introduced to the world in 1957, as an anti-hyperglycemic agent. Consistent with previous studies, we found that metformin users had a lower risk of all-cause and cause-specific mortality, which were positively correlated with the number of days taken among diabetics. Recently, accumulating evidence has revealed the gerotherapeutic effect of metformin on lowering the incidence of multiple age-related diseases and all-cause mortality in diabetics (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). A meta-analysis indicated that the anti-aging function of metformin is possibly independent of its effect on diabetes control (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Hence, we further investigated whether anti-aging was involved in the role of metformin in preventing mortality among diabetics. We found that metformin use decreased mortality risk among diabetics, regardless of whether HbA\u003csub\u003e1C\u003c/sub\u003e was well controlled. Of note, our mediation analyses showed that metformin reduced mortality partly by decreasing Biological Age and Phenotypic Age, emphasizing the importance of anti-aging. Mechanism-related studies in multiple models have elucidated metformin participated in various pathways of aging, including deregulated nutrient sensing, altered intercellular communication, genomic instability, and loss of proteostasis (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Therefore, treatments targeting anti-aging are greatly promising in improving the mortality of diabetics. Some interventions have been well validated in animal or cells models (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). For example, senolytic drugs (e.g. ABT263) improved glucose tolerance and insulin sensitivity in diabetes mice by reducing senescent cell burden (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Another combination senolytic agents, dasatinib and quercetin (DQ), offered a good therapeutic effect on mice with age-related diseases (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In particular, preliminary clinical trials presented the positive effects of DQ on patients with idiopathic pulmonary fibrosis and diabetic kidney disease (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The current study provided evidence that anti-aging was effective to delay the progression of diabetes and mortality.\u003c/p\u003e \u003cp\u003eThis study has some strengths. Firstly, the study was a prospective cohort study based on data from a large nationally representative survey among U.S. diabetics. Secondly, the associations reported in this study were relatively robust by adjustment for a variety of confounders and several sensitivity analyses. Finally, we examined the relationship between aging and death outcomes among diabetics from multiple perspectives. There are also limitations. Firstly, considering the cross-sectional nature of the NHANES data, the time-varying changes in aging markers could not be investigated. Secondly, the study lacked further information on the severity of diabetes, though adjusting for diabetes medication use, duration of diabetes, HbA\u003csub\u003e1c\u003c/sub\u003e levels, and some self-reported comorbidities. Thirdly, unmeasured confounders and measurement errors may bias our analyses. Finally, whether anti-aging is involved in the effect of metformin on mortality still needs further research. In addition, we only analyzed the role of one anti-aging drug in mortality, which limited the interpretation of our conclusions.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our results of nationally representative samples from a prospective cohort study showed significant associations of aging with total and cause-specific mortality among diabetics. Moreover, we observed the improvement in mortality by metformin use among diabetics and further highlighted the mediating effects of aging. These findings suggested that aging accelerated death among diabetics and anti-aging treatments as a promising approach for diabetics.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCVD: cardiovascular disease; HR: hazard ratio; CI: confidence interval; T: tertile; HbA\u003csub\u003e1c\u003c/sub\u003e: glycated hemoglobin; CRP: C-reactive protein; SE:\u0026nbsp;\u003ca href=\"file:///D%3A/Dict/8.9.9.0/resultui/html/index.html#/javascript%3A;\"\u003estandard\u003c/a\u003e \u003ca href=\"file:///D%3A/Dict/8.9.9.0/resultui/html/index.html#/javascript%3A;\"\u003eerror\u003c/a\u003e; NCHS: National Center for Health Statistics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all participants and all investigators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLi Chen\u0026nbsp;and\u0026nbsp;Tianqi Tan\u0026nbsp;conducted analyses. Li Chen,\u0026nbsp;Ying Zhao, and\u0026nbsp;Huimin Chen\u0026nbsp;wrote the draft of the article.\u0026nbsp;Ping Yao and\u0026nbsp;Yuhan Tang conceived of the study design. All authors contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. Yuhan Tang is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Key Research and Development Project (2018YFC1604000).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey was approved by National Center for Health Statistics (NCHS) Ethics Review Board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have reviewed the manuscript and consent for publication.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflicts of interest relevant to this article were reported.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRawshani A, Rawshani A, Franzen S, Sattar N, Eliasson B, Svensson AM, et al. 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Cell Prolif. 2021;54(6):e13052.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelsky D, Caspi A, Houts R, Cohen H, Corcoran D, Danese A, et al. Quantification of biological aging in young adults. Proc Natl Acad Sci USA. 2015;112(30):E4104-10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu YC, Sun ZJ. Molecular Basis of Klotho: From Gene to Function in Aging. Endocr Rev. 2015;36(2):174\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSemba RD, Cappola AR, Sun K, Bandinelli S, Dalal M, Crasto C, et al. Plasma Klotho and Mortality Risk in Older Community-Dwelling Adults. Journals of Gerontology Series a-Biological Sciences and Medical Sciences. 2011;66(7):794\u0026ndash;800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharoenngam N, Ponvilawan B, Ungprasert P. 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Cell Metab. 2020;32(1):15\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell JM, Bellman SM, Stephenson MD, Lisy K. Metformin reduces all-cause mortality and diseases of ageing independent of its effect on diabetes control: A systematic review and meta-analysis. Ageing Res Rev. 2017;40:31\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarzilai N, Crandall JP, Kritchevsky SB, Espeland MA. Metformin as a Tool to Target Aging. Cell Metab. 2016;23(6):1060\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCabreiro F, Au C, Leung KY, Vergara-Irigaray N, Cocheme HM, Noori T, et al. Metformin Retards Aging in C. elegans by Altering Microbial Folate and Methionine Metabolism. Cell. 2013;153(1):228\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang CB, Zhang W, Dong XD, Fu CJ, Yuan JM, Xu ML, et al. A natural product solution to aging and aging-associated diseases. Pharmacol Ther. 2020;216:107673.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNovais EJ, Tran VA, Johnston SN, Darris KR, Roupas AJ, Sessions GA, et al. Long-term treatment with senolytic drugs Dasatinib and Quercetin ameliorates age-dependent intervertebral disc degeneration in mice. Nat Commun. 2021;12(1):5213.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJustice J, Nambiar A, Tchkonia T, LeBrasseur N, Pascual R, Hashmi S, et al. Senolytics in idiopathic pulmonary fibrosis: Results from a first-in-human, open-label, pilot study. EBioMedicine. 2019;40:554\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Diabetes, Mortality, Aging, Biological Age, Phenotypic Age, Telomere, Klotho","lastPublishedDoi":"10.21203/rs.3.rs-961620/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-961620/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAging is an important driver for age-related diseases and death, but the evidence regarding the effects of aging on diabetics is limited. This study aims to evaluate the associations of aging with all-cause and cause-specific mortality among diabetics.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA total of 5,278 diabetics from the National Health and Nutrition Examination Survey 1999-2014 were included. The aging status was measured from different perspectives, including Phenotypic Age, Biological Age, telomere length, and Klotho concentration. Cox proportional hazards models were used to examine the relationships between aging and all-cause, cardiovascular disease (CVD), and cancer mortality. Mediation analysis was performed to elucidate the role of aging in associations of metformin with mortality.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\u003cp\u003eOver median follow-up for 7.3 years, 1,355 diabetics died. There was a positive and linear association of mortality with Phenotypic Age (hazard ratio (HR)\u003csub\u003eall-cause \u003c/sub\u003e1.05, 95% confidence interval (CI) 1.05-1.06; HR\u003csub\u003eCVD \u003c/sub\u003e1.05, 95%CI 1.04-1.07; HR\u003csub\u003ecancer\u003c/sub\u003e 1.05, 95%CI 1.04-1.07; all\u003cem\u003e P\u0026lt;\u003c/em\u003e0.001) and Biological Age (HR\u003csub\u003eall-cause \u003c/sub\u003e1.07, 95%CI 1.05-1.08; HR\u003csub\u003eCVD\u003c/sub\u003e 1.08, 95%CI 1.05-1.10\u003csub\u003e; \u003c/sub\u003eHR\u003csub\u003ecancer\u003c/sub\u003e 1.05, 95%CI 1.03-1.08; all\u003cem\u003e P\u0026lt;\u003c/em\u003e0.001). Telomere length was inversely associated with all-cause mortality (tertile (T)3 vs. T1: HR 0.67, 95%CI 0.45-0.98; \u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003cem\u003e=\u003c/em\u003e0.036). The concentration of Klotho had a U-shaped relationship with mortality (T2 vs. T1: HR\u003csub\u003eall-cause \u003c/sub\u003e0.62, 95%CI 0.43-0.88; HR\u003csub\u003eCVD\u003c/sub\u003e 0.48, 95%CI 0.26-0.86; HR\u003csub\u003ecancer\u003c/sub\u003e 0.47 95%CI 0.25-0.88). Additionally, metformin users had a lower HR for mortality compared to those without use (HR\u003csub\u003eall-cause\u003c/sub\u003e 0.64, 95%CI 0.56-0.73; HR\u003csub\u003eCVD\u003c/sub\u003e 0.51, 95%CI 0.37-0.70; HR\u003csub\u003ecancer\u003c/sub\u003e 0.65, 95%CI 0.44-0.95; all\u003cem\u003e P\u0026lt;\u003c/em\u003e0.001), which was partly mediated by Phenotypic Age and Biological Age.\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThese findings suggested aging was a noteworthy risk factor of mortality for diabetics and therapies targeting anti-aging could be encouraged to halt the progression of diabetes.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Aging and the risks of all-cause and cause-specific mortality among diabetics: a prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-10-19 15:00:03","doi":"10.21203/rs.3.rs-961620/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":"3b6757fb-43e9-4670-a0ea-19ee09140619","owner":[],"postedDate":"October 19th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":7949155,"name":"Cardiac \u0026 Cardiovascular Systems"},{"id":7949156,"name":"Endocrinology \u0026 Metabolism"},{"id":7949157,"name":"Internal Medicine"}],"tags":[],"updatedAt":"2021-10-19T15:00:05+00:00","versionOfRecord":[],"versionCreatedAt":"2021-10-19 15:00:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-961620","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-961620","identity":"rs-961620","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}