Abstract
Objective
To clarify the association between daily sitting time and accelerated aging in women, and explored that systemic immune-inflammation index and creatinine together play an important mediating effect throughout the process.
Methods
A total of 5578 women during 2007 to 2010 from the National Health and Nutrition Examination Survey was utilized in this study. Phenotypic age calculation, logistic regression analysis, restricted cubic spline model, and subgroup analysis were employed to investigate the association between daily sitting time and accelerated aging as well as its influencing factors.
Results
The threshold effect analysis identified 7 h as the inflection point. For every hour of sitting above the inflection point, the risk of accelerated aging will increase by 12.00%. Logistic regression analysis indicated that, compared to women who spent less than 4 h being sedentary during the day, the risk of accelerated aging increased 49.0%, 77%, 110% in the women spent 4 ~ 6 h (OR = 1.49, 95%CI 1.18,1.88, p = 0.0008), 6 ~ 8 h(OR = 1.77, 95%CI 1.36, 2.28, p < 0.001) and at least 8 h(OR = 2.10, 95%CI 1.70, 2.61, p < 0.001) respectively. Restricted cubic spline models validated nonlinear associations between the systemic immune-inflammation index, creatinine, and accelerated aging (p for nonlinearity < 0.001). Parallel mediation analysis found that although systemic immune-inflammation index and creatinine had significant mediating effects (p < 0.001) in the process of daily sitting time leading to accelerated aging, and they were a parallel double mediation effect.
Conclusions
Prolonged sitting time is significantly associated with accelerated aging, systemic immune-inflammation index and creatinine levels play important mediating roles in this process.
Keywords: Sedentary behavior, Daily sitting time, Accelerated aging, Systemic immune-inflammation index, Creatinine
Introduction
Accelerated aging is an important health issue that can have a significant impact on human’s quality of life and longevity. Phenotypic age is considered a more accurate reflection of a person’s aging process compared to chronological age, as the latter merely indicates the duration of time lived without any direct correlation to the individual’s health condition [1]. Phenotypic age is a biomarker calculated from clinical parameters that more accurately reflects biological aging compared to chronological age [2, 3]. This metric encapsulates the molecular and functional declines associated with aging, such as increased inflammation and renal impairment. The concept of biological aging is based on the notion that it emerges from the gradual accumulation of molecular alterations, often referred to as “hallmarks” which progressively impair the functionality and the ability of tissues and organs to recover from stress, ultimately resulting in the onset of various diseases and inevitably, death [4, 5]. Extensive research conducted on both animals and humans has demonstrated that biological aging is not an immutable process but rather one that can be influenced and potentially modified [3]. For instance, a composite index of a healthy lifestyle, which encompasses abstinence from alcohol and tobacco, adherence to a nutritious diet, regular physical activity, and maintaining a healthy body mass index (BMI), has been found to be significantly correlated with a measure of aging derived from biochemical markers [6, 7]. The prospect of decelerating biological aging holds the promise of not only preventing or postponing the emergence of numerous age-associated diseases but also potentially extending the lifespan of individuals [5].
The prevalence of prolonged sedentary behavior necessitates our attention due to the diverse range of contemporary women’s lifestyles.Sedentary behavior has been recognized as a significant risk factor for various diseases, including diabetes, obesity, cardiovascular disease, cancer, and dementia [8, 9].Physical inactivity is a well-documented risk factor for musculoskeletal decline, marked by reductions in muscle mass, strength, and mitochondrial function, which collectively contribute to accelerated aging [10, 11]. The 2020 Global guidelines on Physical activity and sedentary behavior by the World Health Organization strongly advocate individuals of all age groups to minimize their sedentary time and allocate more time to engage in physical activities for health benefits [12]. The impact of daily sitting time on human health is a growing concern.
Previous research has consistently demonstrated a strong association between sedentary behavior and the accelerated aging process, particularly through its impact on cardiovascular health [12–14] and metabolic status [15].Of particular concern is the association between prolonged sitting and an elevated risk of cardiovascular disease, which is notably more pronounced in women [16]. Furthermore, sedentary behavior appears to have a more detrimental impact on hormonal metabolism in women, increasing the risk of metabolic disorders such as overweight/obesity, type 2 diabetes, and hypertension [17, 18]. Given the unique physiological and hormonal characteristics of women, they may exhibit heightened sensitivity to sedentary behavior, rendering them more susceptible to these health issues. In women, estrogen is pivotal in preserving skeletal muscle health through its regulation of protein synthesis and attenuation of oxidative stress. The postmenopausal decline in estrogen exacerbates sarcopenia and metabolic dysfunction, thereby increasing the susceptibility of women to accelerated aging [19].Consequently, reducing sedentary time and promoting physical activity are crucial measures for enhancing women’s health.
However, there is currently a lack of scientific evidence to support whether daily sitting time leads to accelerated aging in women and the key influencing factors are unclear. But previous studies have confirmed that inflammation can not only lead to cell dysfunction, tissue degeneration, metabolic changes, and even contribute to the occurrence of aging-related diseases such as cancer, cardiovascular disease, and neurodegenerative diseases [20]. In addition, inflammatory response can also affect renal function and promote renal fibrosis and deterioration of renal function through a variety of pathways [21, 22]. At the same time, the decline of renal function is also closely related to the biological aging process, especially the mechanisms such as oxidative stress, DNA damage and epithelial-mesenchymal transition [23].In summary, there exists a significant interrelationship between renal function, inflammation, and biological aging. These findings provide a critical foundation for comprehending the intricate dynamics among renal function, inflammation, and biological aging.
And nowadays, systemic immune-inflammation index (SII) has been increasingly considered as a good, stable index, which could reflect the local immune response and systemic inflammation in the whole human body [24–26]. The primary independent variable, the SII, is a novel inflammation marker calculated by multiplying platelet and neutrophil counts and then dividing by the lymphocyte count, which was first developed by Hu et al. in 2014 and has been investigated widely [27]. However, whether SII mediates the association between daytime sedentary behavior and accelerated aging remains unclear.
National Health and Nutrition Examination Survey (NHANES) is a national investigation program conducted by the Centers for Disease Control and Prevention (CDC). The survey was designed to assess the health and nutritional status of the U.S. population. NHANES collects a wide range of data, including but not limited to: demographic information, dietary habits. Physical examination data, laboratory test results, health-related behavior information. The NHANES database provides a rich resource for researchers to analyze a variety of health issues and trends. Therefore, we want to explore the association between daily sitting time and accelerated aging in women by using NHANES data from 2007 to 2010. This study will be able to assess the potential association between daily sitting time and accelerated aging, identify key factors that may influence this association, providing new insights into the effects of sedentary behavior on women’s health. The results of this study may have important implications for public health policies and individual lifestyle choices, particularly in promoting healthy lifestyles and preventing accelerated ageing.
Materials and methods
Study design and participants
This cross-sectional study used data from NHANES, a survey conducted by the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC), to assess the health and nutrition status of a nationally representative sample of the United States through a multi-stage, stratified, cluster probability design. The survey included questionnaire interviews, laboratory tests and a physical examination. This study synthesizes NHANES continuous data sets (n = 20686) from 2007 to 2010. The NHANES study protocol has been approved by the NCHS Ethics Review Committee with the written informed consent of all participants [28, 29].
The study excluded 10,321 samples due to male. Meanwhile, we also excluded 4149 individuals who were missing the baseline data (education level, race, poverty income ratio, smoking status, BMI) and 638 individuals who lacked the data of daily sitting time and other required indicators. As a result, 5578 participants were included in the study (Fig. 1).
Fig. 1.
Flow chart for subject selection. NHANES: National Health and Nutrition Examination Survey. SII: Systemic Immune-Inflammation Index
Main variables
Sitting time
The data on daily sitting time were collected from the questionnaire Data of Physical Activity (PAQ). Daily sitting time was measured by the following question: “How much time do you usually spend sitting (or reclining) on a typical day?” This variable referred to the number of waking hours spent “sitting or reclining at work, at home, or at school, including time spent sitting at a desk, sitting with friends, travelling in a car, bus, or train, reading, playing cards, watching television, or using a computer.” The sequent analysis was converted into hours. We categorized daily sitting time into 4 groups [9, 30, 31]: G1 (less than 4 h per day), G2 (4 to 6 h per day, inclusive of 6 h), G3 (6 to 8 h per day, inclusive of 8 h), and G4 (8 h or more per day). G1 was defined as the reference in the subsequent analysis.
Phenotypic age
We utilized data from NHANES’s laboratory data, such as albumin, serum creatinine, glucose, C-reactive protein (CRP), percentage of lymphocytes, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count) to derive phenotypic age, and calculated phenotypic age in accordance with the method described previously [32, 33]. The resulting final equation for calculating Phenotypic Age is as follows: Phenotypic Age = 141.50+
, where xb= -19.907-0.0336 × albumin + 0.0095 × creatinine + 0.0195 × glucose + 0.0954 × ln(CRP)-0.0120 × lymphocyte percent + 0.0268 × mean cell volume + 0.3356 × red blood cell distribution width + 0.00188 × alkaline phosphatase + 0.0554 × white blood cell count + 0.0804 × chronological age. Aging acceleration was assessed by comparing the phenotypic age with the chronological age of participants. Acceleration in senescence was considered present if the difference between phenotypic age and chronological age was greater than zero (phenotypic age − chronological age > 0).
Systemic immune-inflammation index
To derive the SII, data from NHANES’s “Complete Blood Count (CBC)” segments were utilized, specifically employing lymphocyte, neutrophil, and platelet counts. Higher SII values are associated with elevated systemic inflammation. To account for potential nonlinear relationships, we categorized SII into quartiles (Q1 to Q4) for analysis: Q1 group (SII < 249); Q2 group (249 ≤ SII < 604.55); Q3 group (604.55 ≤ SII < 825.35); and Q4 group (SII ≥ 825.35) [34].
Others covariates
Data on other covariates were collected for NHANES cohorts. These covariates included age, race, education, poverty income ratio, smoking, BMI. educational level was distinguished as ‘Less than 9th grade’, ‘9-11th grade’, ‘High school graduate’, ‘Some college’, and ‘College graduate’ groups. Smoking status was categorized as ‘yes’ or ‘no’. According to BMI levels, they were divided into 3 groups as ‘under/normal weight’ (BMI < 25 kg/m2), ‘overweight’ (25 < BMI < 30 kg/m2), and ‘obesity’ (BMI > 30kg/m2).
Statistical analysis
Analyses were performed with Decision Linnc V1.0 software [DecisionLinnc Core Team. (2023). Decision Linnc. 1.0. https://www.statsape.com/]. Quantitative data were expressed as mean ± SD. To analyze the association between daily sitting time and accelerated aging, the crude model and adjusted models were applied in the logistic regression analysis: Model 1 was without any adjustments. Model 2 was additionally adjusted for age, race, education level, poverty, smoking status. Model 3 was additionally adjusted for BMI. The strength of the association was estimated by odds ratios (ORs) and associated 95% confidence intervals (CIs). To test for interaction effects between covariates and daily sitting time and assess the robustness of the results, we further performed stratified logistic regression analysis to identify variables that affect the association between sitting time and accelerated aging in the subgroup. The adjusted risk of accelerated aging among different daily sitting time groups was estimated by generalize additive models adjusted for age, race, education level, poverty, BMI, smoking status. The differences in accelerated aging risk between different daily sitting time groups were compared by Fisher’s exact test. p < 0.05 (two-sided) was considered as a significance.
Results
Population characteristics
From 2007 to 2010, a total of 5578 women aged ≥ 20 years with complete information were included in our study for further analysis (Fig. 1). The detailed baseline characteristics of the participants classified by quartile of sitting time per day are presented in Table 1.In the group of women who spent less than 4 h being sedentary during the day, SII was 562.11 ± 335.50 with a Cre level of 69.74 ± 25.64 µmol/L. In 4–6 h group, SII was 562.11 ± 335.50 and Cre was 69.74 ± 25.64 µmol/L. while in the 6–8 h group, the SII was 577.90 ± 345.08 with a Cre level of 70.64 ± 24.16 µmol/L. Finally, those women who spent more than 8 h being sedentary during the day, SII significantly increased to 597.27 ± 346.07 with a high Cre level of 72.31 ± 28.95 µmol/L. It can be seen that SII and Cre levels increased with the increase of sedentary time, and the differences between groups were significant (P < 0.01). In addition, there were significant differences in neutrophil count and C-reactive protein (CRP) among the three groups (P < 0.01). Among all participants,11.47% of the population had risk of accelerated aging by phenotypic age, and the percentages of patients with the risk of accelerated aging per group were 8.00%,11.49%,13.32%,and 15.47% for those sitting < 4 h, 4–6 h, 6–8 h, and > 8 h per day, respectively. The trend of a higher risk of accelerated aging in participants who sat for longer hours, and significant difference was observed among the groups (p < 0.001). These results suggest that people with more sedentary time have higher poverty income ratio, increased obesity rate, smoking rate, SII and Cre, which may lead to accelerated aging and greater health risk.
Table 1.
Characteristics of participants by categories of sitting time per day
| Variable Names | Overall | daily sitting time (h) | p-value | |||
|---|---|---|---|---|---|---|
| < 4 | 4 ~ 6 | 6 ~ 8 | >=8 | |||
| n | 5578 | 2074 | 1271 | 811 | 1422 | |
| Age (year) | 49.65 ± 17.81 | 48.22 ± 16.85 | 51.12 ± 18.13 | 51.48 ± 19.22 | 49.37 ± 17.86 | < 0.01 |
| Race (%) | ||||||
| Mexican American | 1024 (18.36) | 556 (26.81) | 186 (14.63) | 110 (13.56) | 172 (12.10) | < 0.001 |
| Other Hispanic | 653 (11.71) | 334 (16.10) | 138 (10.86) | 67 (8.26) | 114 (8.02) | |
| Non-Hispanic White | 2637 (47.28) | 774 (37.32) | 643 (50.59) | 456 (56.23) | 764 (53.73) | |
| Non-Hispanic Black | 1002 (17.96) | 335 (16.15) | 237 (18.65) | 130 (16.03) | 300 (21.10) | |
| Other Race | 262 (4.70) | 75 (3.62) | 67 (5.27) | 48 (5.92) | 72 (5.06) | |
| Poverty income ratio | 2.38 ± 1.53 | 2.11 ± 1.39 | 2.38 ± 1.55 | 2.42 ± 1.55 | 2.76 ± 1.6 | < 0.01 |
| Education level (%) | ||||||
| Less than 9th grade | 700 (12.55) | 417 (20.11) | 140 (11.01) | 61 (7.52) | 82 (5.77) | < 0.001 |
| 9-11th grade | 948 (17.00) | 422 (20.35) | 214 (16.84) | 141 (17.39) | 171 (12.03) | |
| High school graduate | 1263 (22.64) | 461 (22.23) | 311 (24.47) | 198 (24.41) | 293 (20.60) | |
| Some college | 1615 (28.95) | 512 (24.69) | 362 (28.48) | 257 (31.69) | 484 (34.04) | |
| College graduate or above | 1052 (18.86) | 262 (12.63) | 244 (19.20) | 154 (18.99) | 392 (27.57) | |
| BMI (kg/m2) | 29.36 ± 7.32 | 28.67 ± 6.46 | 29.32 ± 7.26 | 29.31 ± 7.83 | 30.41 ± 8.11 | < 0.01 |
| Normal weight (%) | 1669 (29.92) | 642 (30.95) | 362 (28.48) | 266 (32.80) | 399 (28.06) | < 0.001 |
| Overweight (%) | 1714 (30.73) | 679 (32.74) | 426 (33.52) | 219 (27.00) | 390 (27.43) | |
| Obesity (%) | 2195 (39.35) | 753 (36.31) | 483 (38.00) | 326 (40.20) | 633 (44.51) | |
| Smoking status (%) | ||||||
| Yes | 2138 (38.33) | 663 (31.97) | 520 (40.91) | 349 (43.03) | 606 (42.62) | < 0.001 |
| no | 3440 (61.67) | 1411 (68.03) | 751 (59.09) | 462 (56.97) | 816 (57.38) | |
| SII | 567.05 ± 331.32 | 545.13 ± 310.46 | 562.11 ± 335.5 | 577.9 ± 345.08 | 597.27 ± 346.45 | < 0.01 |
| Q1 | 527 (9.45) | 223 (10.75) | 114 (8.97) | 75 (9.25) | 115 (8.09) | < 0.001 |
| Q2 | 3101 (55.59) | 1183 (57.04) | 731 (57.51) | 456 (56.23) | 731 (51.41) | |
| Q3 | 1067 (19.13) | 370 (17.84) | 230 (18.10) | 151 (18.62) | 316 (22.22) | |
| Q4 | 883 (15.83) | 298 (14.37) | 196 (15.42) | 129 (15.91) | 260 (18.28) | |
| WBC (1000 cells/uL) | 7.24 ± 2.16 | 7.13 ± 2.02 | 7.23 ± 2.28 | 7.31 ± 2.19 | 7.36 ± 2.22 | 0.01 |
| Neno (1000 cells/uL) | 4.28 ± 1.67 | 4.18 ± 1.61 | 4.25 ± 1.63 | 4.36 ± 1.73 | 4.41 ± 1.76 | < 0.01 |
| Lym (1000 cells/uL) | 2.2 ± 0.85 | 2.21 ± 0.74 | 2.22 ± 1.12 | 2.18 ± 0.73 | 2.18 ± 0.77 | 0.50 |
| RBC (million cells/uL) | 4.41 ± 0.41 | 4.4 ± 0.39 | 4.42 ± 0.42 | 4.39 ± 0.42 | 4.43 ± 0.43 | 0.05 |
| PLT (1000 cells/uL) | 267.91 ± 70.8 | 265.97 ± 69.88 | 267.46 ± 67.58 | 266.8 ± 74.73 | 271.77 ± 72.53 | 0.11 |
| CRP(mg/dL) | 0.48 ± 0.8 | 0.41 ± 0.7 | 0.48 ± 0.75 | 0.5 ± 0.82 | 0.55 ± 0.95 | < 0.01 |
| Cre(µmol/L) | 68.55 ± 23.8 | 64.43 ± 17.03 | 69.74 ± 25.64 | 70.64 ± 24.16 | 72.31 ± 28.95 | < 0.01 |
| PhenoAge | ||||||
| Accelerated aging | 640 (11.47) | 166 (8.00) | 146 (11.49) | 108 (13.32) | 220 (15.47) | < 0.001 |
| Delayed aging | 4938 (88.53) | 1908 (92.00) | 1125 (88.51) | 703 (86.68) | 1202 (84.53) | |
Association between sitting and accelerated aging
To further investigate the association between sedentary behavior and accelerated aging, we conducted curve smoothing fitting analysis. The resulting curve smoothing plot depicted an “S” shape pattern (Fig. 2), indicating that the risk of accelerated aging gradually increased with longer daytime sedentary periods. Notably, this risk appeared to be more pronounced within the range of 5 to10 hours of sedentary time, suggesting a greater impact on accelerated aging during this interval. Furthermore, threshold effect analysis revealed that the critical inflection point for daytime sedentary time was at approximately 7 h; beyond this threshold, there was a sharp increase in the risk of accelerated aging. Additionally, each additional hour spent in sedentary activities corresponded to 12% increase in women’s risk for accelerated aging (P < 0.001). As shown in Table 2, three models established a statistically significant association between daily sitting time and accelerated aging. Table 2 showed the effect size, ORs, and 95% CIs for the three multivariate logistic regression models. In the unadjusted model (Model 1), compared to women who spent less than 4 h being sedentary during the day, the risk of accelerated aging significantly increased 49.0%, 77%, 110% in the women spent 4 ~ 6 h (OR = 1.49, 95%CI 1.18, 1.88, p = 0.0008), 6 ~ 8 h(OR = 1.77, 95%CI 1.36, 2.28, p < 0.001) and at least 8 h(OR = 2.10, 95%CI 1.70, 2.61, p < 0.001) respectively, as shown in Table 2. After adjusting for general data confounding factors such as age, race, education, poverty income ratio, smoking status (Model 2), daily sitting time was still associated with an increased risk of accelerated aging. The association between daytime sedentary time and aging remained significant even after adjusting for BMI (Model 3).
Fig. 2.
Association of daily sitting time with accelerated aging among participants by curve smoothing plot
Table 2.
Multivariable-adjust ORs and 95%CI of daily sitting time and accelerated aging
| Daily sitting time | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| OR (95%CI) | p-value | OR (95%CI) | p-value | OR (95%CI) | p-value | |
| < 4 h | Reference | Reference | Reference | |||
| 4 ~ 6 h | 1.49(1.18, 1.88) | 0.0008 | 1.50(1.18, 1.91) | 0.0010 | 1.42(1.11, 1.82) | 0.0049 |
| 6 ~ 8 h | 1.77(1.36, 2.28) | 0.0000 | 1.82(1.39, 2.37) | 0.0000 | 1.67(1.27, 2.19) | 0.0002 |
| >=8 h | 2.10(1.70, 2.61) | 0.0000 | 2.37(1.89, 3.00) | 0.0000 | 2.07(1.65, 2.63) | 0.0000 |
Model 1 without adjustments
Model 2 was additionally adjusted for age, race, education level, poverty, smoking status
Model 3 was additionally adjusted for BMI
Double mediation effects of SII and Cre
Analyses using restricted cubic spline (RCS) showed that SII and Cre were significantly associated with risk for accelerated aging from daytime sedentary behavior, even after adjustment for age, race, education level, poverty income ratio, and smoking status(p for overall < 0.001). As shown in Fig. 3a, Cre was positively and nonlinear association with the risk of accelerated aging due to daytime sedentary behavior (p for nonlinear < 0.001). As Cre increases, the risk of accelerated aging increases. As in Fig. 3b, it was suggested that SII level also had a positive, nonlinear association with the risk of accelerated aging (p for nonlinear < 0.001). Although parallel mediation analysis showed that SII and Cre had a significant mediation effect (p < 0.001), but their respective effect sizes were low. Next, we conducted dual mediation effect analysis and found that SII and Cre had concurrent dual mediation effects on daytime sedentary time and accelerated aging(Fig. 3c). The results of dual mediation effect analysis showed that the indirect effect of SII and Cre was 0.0416 (p = 0.0001), the direct effect was 0.0128 (p = 0.2135), and the total effect was 0.0544 (p = 0.0001). We observed double mediation effects between SII and Cre and daytime sedentary time leading to accelerated aging.
Fig. 3.
Double mediation effects of SII and Cre. a: Prediction plot showing association of SII with daily sitting time among participants by by restricted cubic splines. b: Prediction plot showing association of Cre with daily sitting time among participants by by restricted cubic splines. c: Double mediation effects of SII and Cre on daily sitting time and accelerated aging. RSC: restricted cubic spline
Subgroup analysis
Subgroup analyses were performed to assess the association between daytime sedentary time and accelerated aging. Stratification was based on age, race, poverty income ratio, smoking status, BMI. Sedentary daytime time was significantly associated with accelerated aging in the overall participants (OR = 1.01, 95%CI 1.00, 1.02, p = 0.0221). There was a significant interaction between daytime sedentary time and age. In group over 40 years old (OR = 1.01, 95%CI 1.01, 1.02, p = 0.025), indicating that daytime sedentary time was significantly associated with accelerated aging in this group. No significant associations were shown in the group 40 years of age or younger. The interaction effect of race was significant. There was a significant positive correlation between sedentary time and the risk of accelerated aging in the “Other Hispanics”. Specifically, each additional hour of sedentary behavior was associated with a 15% increase in the risk of accelerated aging (OR = 1.15, 95% CI: 1.07, 1.24, p < 0.001). This finding indicates that the impact of sedentary behavior on accelerated aging may be more pronounced among individuals categorized as “Other Hispanics.” For women with poverty income ratio below 5.0 (OR = 1.01, 95%CI 1.00, 1.02, p = 0.016), indicating a significant association between daytime sedentary time and accelerated aging in poorer women with poverty income ratio above 5.0. Significant association was found in non-smokers (OR = 1.01, 95%CI 1.01, 1.02, p = 0.038), but not in smokers. There was no significant association between different BMI groups (p > 0.05). No significant association was found in the quartiles of SII (p > 0.05). In summary, the results of the subgroup analyses indicated that daytime sedentary time and the risk of accelerated aging were significantly associated with age, race, and poverty income ratio. Older women over 40 years of age and poorer women have a significantly higher risk as shown in Table 3; Fig. 4.
Table 3.
Subgroup analysis of associations between daily sitting time and accelerated aging
| Subgroups | n | OR (95%CI) | p-value | p-value for interaction |
|---|---|---|---|---|
| Overall | 5578 | 1.01(1,1.02) | 0.022 | |
| Age | 0.951 | |||
| <30 | 911 | 1(0.97,1.04) | 0.936 | |
| >40 | 3703 | 1.01(1,1.02) | 0.025 | |
| 30 ~ 34 | 473 | 0.99(0.91,1.08) | 0.88 | |
| 35 ~ 40 | 491 | 1.02(0.93,1.11) | 0.727 | |
| Race | 0.006 | |||
| Mexican American | 1024 | 0.99(0.96,1.03) | 0.753 | |
| Non-Hispanic Black | 1002 | 1.01(0.99.1.03) | 0.401 | |
| Non-Hispanic White | 2637 | 1.01(1,1.02) | 0.226 | |
| Other Hispanic | 653 | 1.15(1.07,1.24) | < 0.001 | |
| Other Race | 262 | 1.03(0.99,1.08) | 0.181 | |
| Education level | 0.809 | |||
| 9-11th grade | 948 | 1.01(0.99,1.02) | 0.357 | |
| College graduate or above | 1052 | 1(0.96,1.04) | 0.997 | |
| High school graduate | 1263 | 1.01(0.991,1.02) | 0.282 | |
| Less than 9th grade | 700 | 1.02(1,1.04) | 0.052 | |
| Some college | 1615 | 1.01(0.99,1.03) | 0.269 | |
| Poverty ratio | 0.77 | |||
| <5.0 | 4808 | 1.01(1,1.02) | 0.016 | |
| >5.0 | 770 | 1(0.92,1.08) | 0.941 | |
| Smoking status | 0.478 | |||
| 0 | 3440 | 1.01(1,1.02) | 0.038 | |
| 1 | 2138 | 1(0.99,1.02) | 0.399 | |
| BMI (kg/m2) | 0.973 | |||
| Normal weight | 1669 | 1.01(0.99,1.03) | 0.4 | |
| Obesity | 2195 | 1.01(1,1.02) | 0.202 | |
| Overweight | 1714 | 1.01(0.99,1.02) | 0.417 | |
| SII | 0.551 | |||
| Q1 | 527 | 1.01(0.98,1.04) | 0.444 | |
| Q2 | 3101 | 1.01(1,1.02) | 0.031 | |
| Q3 | 1067 | 1.01(0.98,1.03) | 0.595 | |
| Q4 | 883 | 1(0.99,1.01) | 0.981 |
Fig. 4.
Forest plots of subgroups analyses of the effect of daily sitting time and accelerated aging
Discussion
To the best of our knowledge, this cohort represents a innovative cross-sectional study investigating the impact of blood cell-based inflammatory biomarkers on sedentary behavior and accelerated aging. Initially, a representative large sample population from NHANES was utilized. In this study, we have discovered an independent and substantial association between sedentary behavior during daytime and accelerated aging. Even after accounting for potential confounding variables, daily sitting time remains significantly linked to an increased risk of accelerated aging. This study represents the first attempt to evaluate the combined impact of daytime sedentary behavior with SII and Cre on accelerated aging. Parallel mediation analysis showed that SII and Cre had a significant mediation effect, but their respective effect sizes were low. But, we observed double mediation effects between SII and Cre and daytime sedentary time leading to accelerated aging.
Numerous studies have demonstrated that PhenoAgeAccel serves as a critical biomarker for predicting the risk of various age-related diseases, such as chronic respiratory diseases [35]dementia [36]and metabolic disorders [37]. Its user-friendly nature and high efficacy render PhenoAgeAccel an indispensable tool for evaluating individual health status and guiding precision interventions. Our study underscores the utility of phenotypic age as a robust biomarker for biological aging, expanding its application from disease prediction to quantifying lifestyle-related aging acceleration. Physiological and psychological changes unique to menopausal and midlife women have a profound impact on their overall health.
Estrogen plays a pivotal role in maintaining overall health in women, particularly in the musculoskeletal system. Extensive research has demonstrated that estrogen is instrumental in regulating muscle mass, strength, and mitochondrial function, all of which are critical for sustaining physical performance and metabolic health. The decline in estrogen levels, especially during menopause, has been strongly linked to significant alterations in muscle physiology, including diminished muscle mass, reduced strength, and increased fat infiltration. This hormonal reduction can heighten the risk of sarcopenia, a condition marked by progressive muscle loss and weakness, which disproportionately affects postmenopausal women [19].
Moreover, estrogen exhibits potent anti-inflammatory properties that help counteract oxidative stress and inflammation, key contributors to the aging process. The decrease in estrogen levels can result in heightened systemic inflammation, thereby impairing muscle health and accelerating aging. Clinical studies have indicated that estrogen replacement therapy (ERT) may effectively alleviate some of these adverse effects by enhancing muscle mass and strength, as well as reducing inflammation [38].
Menopausal symptoms are also significantly associated with an elevated risk of chronic conditions such as cardiovascular disease, mental health disorders, diabetes, and decreased bone mineral density [39]and contribute to poor sleep quality and cognitive decline [40, 41]. Furthermore, health issues among middle-aged women vary considerably across racial and ethnic groups, with black women exhibiting notably higher incidences of hypertension and esophageal disease [42]. These findings from the literature highlight the importance of addressing menopausal symptoms and preventing chronic diseases in the health management of middle-aged women, particularly those from low socioeconomic backgrounds.
There are multiple potential mechanisms underlying the current association between daily sitting time and health and aging. Firstly, physical inactivity has been identified as a significant risk factor for musculoskeletal decline, particularly in the context of aging. Sedentary behavior is associated with reduced muscle mass, strength, and mitochondrial function, all of which contribute to accelerated aging and increased frailty [43]. Moreover, prolonged periods of inactivity can lead to elevated levels of inflammation and oxidative stress, which are key drivers of the aging process. Inflammatory markers such as CRP and interleukin-6 are often elevated in sedentary individuals, contributing to a chronic low-grade inflammatory state that impairs muscle repair and regeneration [44]. This inflammatory state further exacerbates muscle loss and weakness, creating a vicious cycle of declining physical function and accelerated aging. Within the context of our study, the strong association between sedentary behavior and accelerated aging highlights the critical importance of maintaining physical activity to preserve musculoskeletal health. Regular physical activity, including both aerobic exercise and resistance training, has been shown to effectively counteract the negative effects of aging on muscle mass and function [45]. Exercise interventions not only improve muscle strength but also reduce inflammation and enhance overall physical performance, thereby delaying the onset of age-related musculoskeletal decline.
Simultaneously, prolonged sedentary behavior may decrease insulin sensitivity and disrupt postprandial feeding, consequently affecting chronic glucose and lipid metabolism [46] while also promoting inflammation. Secondly, prolonged sedentary behavior can have detrimental effects on peripheral and central vascular markers [47]including cerebral blood flow [48].
But, what mediates the relationship between sitting time and accelerated aging is not known. By regression analysis, we found that SII and Cre played an important mediating role together. SII is an innovative and comprehensive biomarker that combines neutrophil, lymphocyte, and platelet counts to provide valuable insights into accurately reflecting the inflammatory state of various diseases. In this study, we found SII to explore the mediation effect of inflammation in the association between sedentary behavior and accelerated aging. Based on previous studies, the relationship between inflammation and aging is mainly manifested in many aspects. Such as persistent inflammatory responses can lead to increased oxidative stress in cells and tissues, accelerating cellular senescence and death [49]. Inflammation may accelerate telomere shortening, one of the hallmarks of cellular aging [50]. Chronic inflammation may increase the risk of DNA damage, affecting normal cell function and replication [51]. Inflammation may affect the regeneration and repair ability of stem cells and weaken the self-repair ability of the body [52]. In addition, chronic inflammation is associated with metabolic problems such as insulin resistance and abnormal lipid metabolism, which may accelerate the aging process. Long-term chronic inflammation is associated with a variety of age-related diseases, including: cardiovascular disease [53]diabetes [54]neurodegenerative diseases [55] and other diseases, which may accelerate the overall aging process.
Cre, a byproduct of muscle metabolism, is commonly used as an indicator of renal excretory function and can serve as a marker for kidney health. Several studies have investigated the association between sedentary behavior and Cre levels, revealing that prolonged sedentary time may be associated with alterations in Cre levels [56, 57]. Increased sedentary behavior could potentially elevate Cre levels, indicating its potential impact on renal function [58, 59]. Furthermore, experts have highlighted a significant correlation between sedentary behavior and various metabolic markers such as blood glucose, urea nitrogen, Cre, and uric acid [57, 60]. Particularly for individuals with type II diabetes, long-term sitting may contribute to a decline in glomerular filtration rate, subsequently affecting an increase in Cre level [59, 60]. Occupational sitting has been identified as an independent risk factor associated with overall mortality; research suggests that prolonged sitting in occupational settings can have detrimental effects on health [61]. Without intervention, the health risks associated with prolonged sitting can lead to elevated Cre levels and other metabolic abnormalities [62]. Studies indicate that among patients with chronic kidney disease, extended periods of sitting are correlated with increased urinary protein content and higher levels of Cre. This suggests that prolonged sitting may impose an additional burden on the kidneys [63, 64]. Therefore, reducing sedentary time may help improve the condition of these patients and lower their Cre levels.
Collectively, it can be inferred that the SII and Cre play pivotal mediating roles in the association between sedentary behavior during daytime and accelerated aging: prolonged daily sitting time leads to heightened levels of systemic inflammation, which may contribute to renal impairment. This effect potentially expedites the aging process and enhances susceptibility to age-related diseases. Furthermore, exacerbation of systemic inflammation further reinforces the acceleration of aging, thereby establishing a detrimental cycle.
Aging is a multifactorial process that can be influenced by lifestyle interventions. Reducing sedentary behavior and ERT have shown promise in mitigating age-related declines. Public health strategies should prioritize these approaches, particularly for high-risk populations, such as postmenopausal women and individuals from low-income backgrounds.
However, our study has several limitations. Firstly, the absence of a universally accepted gold standard for measuring biological aging poses a challenge. Nevertheless, phenotypic age has been widely recognized as a reliable predictor of age-related diseases across diverse populations. Secondly, due to the cross-sectional design employed in this study and the potential temporal discrepancy between blood sample collection and survey information in the NHANES setting, establishing causality between variables was not feasible; thus, associations could only be assessed at a single time point. Thirdly, reliance on self-reported data introduces inherent biases such as recall bias despite efforts made to mitigate them through large-scale population sampling and complex multistage sampling techniques.
Conclusion
In conclusion, our findings suggest that an increase in daily sitting time is associated with an increased risk of accelerated aging. Furthermore, we confirmed that SII and Cre play important dual mediating roles in driving the aging process. We hope that in follow-up studies, we can further explore the mechanism of female sedentary and accelerated aging, so as to provide a new idea for protecting women health and delaying aging.
Acknowledgements
First of all, We would like to give my heartfelt thanks to all the people who have ever helped us in this work. Finally, we would like to thank all the patients for their participation in this study.
Author contributions
J.X.: Data curation, Formal analysis, Writing-Original draft, Funding acquisition. L.Y.: Data collection, Data curation, Writing-Original draft. X.L.: Data collection, Data curation, Writing-Original draft. H. W.: Study design, Formal analysis, Data interpretation, Methodology guidance, Writing-Review & editing, Funding acquisition. B.Y.: Study conceptualization, Data collection oversight, Data analysis direction, Result interpretation, Writing-Review & editing, Funding acquisition.
Funding
This study was supported by Project funding for the training of high-level health professionals in Changzhou (2022CZZY007), Clinical frontier technology of Changzhou Health Commission (QY202308), Changzhou Medical Center Project of Nanjing Medical University (CZKYCMCB202203, CMCC202410). the Leading Talent of Changzhou “The 14th Five-Year Plan” High-Level Health Talents Training Project (2022CZLJ023), Applied Basic Research General Program of Changzhou Science and Technology Bureau(CJ20220117), Research project of Changzhou Maternal and Child Health Care Hospital (YJ202402).
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethical approval
NHANES data were collected in accordance with ethical guidelines and were approved by the NCHS Research Ethics Review Board (Protocol #2005-06). This study will be conducted within the framework of these ethical guidelines.
Consent to publish
All authors approved the final manuscript and the submission to this journal.
Consent to participate
Informed consent was obtained from all individual participants included in the study.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Huiyan Wang, Email: huiyanwang@njmu.edu.cn.
Bin Yu, Email: binyu@njmu.edu.cn.
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Associated Data
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Data Availability Statement
No datasets were generated or analysed during the current study.