Abstract
Previous studies have demonstrated the connection between obesity and telomere length. A recently devised metric for determining obesity, the weight-adjusted-waist index (WWI), offers a distinct advantage in predicting fat and lean mass by depicting weight-independent abdominal adiposity. This article presents the results of the inaugural study on the relationship between WWI and telomere length in adult populations. The cross-sectional investigation analyzed data from 3479 participants from the National Health and Nutrition Examination Survey (NHANES) conducted from 1999 to 2000. To inspect linear and nonlinear correlations, we adopted weighted multiple logistic regression analysis and smooth curve fit, respectively. In addition, threshold effects and subgroup analyses were accomplished. In the fully adapted model, a significant adverse association of WWI with telomere length was detected [β = −0.02, 95% CI: (−0.03, −0.00), P value = 0.01]. The adverse correlation remained consistent across all subcategories. We also discovered an inverted U-shaped curve linking WWI and telomere length, with a conspicuous inflection point of 10.07 cm/√kg. For the first time, our research demonstrated strong links between WWI and telomere length. The inflection point suggests that controlling WWI within an optimum range might be essential for aging and health.
Keywords: aging, NHANES, obesity, telomere length, weight-adjusted-waist index, WWI
1. Introduction
Telomeres, which are also known as the “protective caps” of chromosomes, are DNA-protein complexes located at the tip of chromosomes consisting of TTAGGG-DNA repetitions and a small number of protective binding proteins.[1] They protect chromosomal ends from genomic damage and instability.[2] Telomeres shorten with each cell cycle, and when they become severely short, cells either enter senescence, cell cycle arrest, or undergo apoptosis.[3] Telomere attrition is widely recognized as a prominent hallmark of aging.[4] Telomere attrition has been linked to numerous ailments, including diabetes mellitus,[5] Alzheimer disease,[6] and major cardiovascular diseases (CVD) such as atherosclerosis, hypertension, and heart failure.[7] Moreover, shortened telomeres are connected with an elevated risk of all-cause mortality among the general population.[8]
Obesity poses a significant threat to public health worldwide. However, the most commonly used traditional metric to define being overweight, Body mass index (BMI), cannot distinguish between fat mass and lean mass, nor between central fat and peripheral fat.[9] It is worth noting that weight-adjusted-waist index (WWI) has the potential to compensate for both of these deficiencies. WWI, which is standardized by adjusting the waist circumference (WC) based on body weight, was first proposed by Park et al in 2018.[10] Their initial objective was to construct an obesity index that indicates WC, which exhibits a weak connection with BMI. The aim was to alleviate the obesity paradox of BMI versus mortality. Eventually, this index was produced by a sequence of mathematical deductions following the design methodology of the BMI. They considered WWI as the optimal obesity indicator for CVD mortality rather than BMI, WC, and waist-to-height ratio.[10] A follow-up study confirmed a positive prediction of WWI with fat mass and meanwhile a negative prediction with muscle mass.[11] Like telomere length, WWI was found to be directly proportional to age, suggesting its unique function to reflect the age-related alteration of body composition.[11]
Studies have found that adults with obesity have been discovered to suffer from increased telomere attrition.[12] There is a negative association between obesity and telomere length.[13,14] While a higher BMI has been observed to be associated with shorter telomere length,[15] there is currently no scientific literature on the potential connection between WWI and telomere length. To address this knowledge gap, this investigation aims to determine the relationships between WWI and telomere length among the American adult population, utilizing statistics collected through the National Health and Nutrition Examination Survey (NHANES).
2. Methods
2.1. Data origin and investigation subjects
The NHANES is conducted by professional staff as an elaborate cross-sectional survey with national representatives, whose fundamental purpose is to evaluate the nutrition and health conditions of the American populace.[16,17] Demographics, dietary, and health-related details are obtained through questionnaires and related examinations.[18,19] The Centers for Disease Control and Prevention Research Ethics Review Board has authorized this inquiry, and each respondent must submit written informed consent to participate.[20]
This study utilized NHANES data from 1999 to 2000. Among the initial 9965 subjects, 6398 who lacked telomere length data and 1 extreme deviation of telomere length were removed. After exclusion of 87 participants due to their inadequate WWI-related data, 3,479 individuals were ultimately included in the research (Fig. 1).
Figure 1.
Flow chat of the study participants.
2.2. Definition of WWI and telomere length
In this study, the independent variable was WWI, with the dependent variant being mean telomere length (T/S ratio).
The WWI (cm/√kg) is a novel exponent for measuring body-compositional muscle and fat mass, which is calculated by dividing the WC (cm) by the square root of body weight (kg).[21] Qualified professionals measured weight and WC of partial respondents at the mobile examination facility.[22]
To assess telomere length relative to standard reference DNA, laboratory technicians used the quantitative polymerase chain reaction technique at Dr Elizabeth Blackburn lab at the University of California, San Francisco. The T/S ratio was generated throughout this process and was used throughout our research. We did not convert this outcome to base pairs due to the latter’s less accuracy, as mentioned in pertinent analytic notes of NHANES.
2.3. Assessment of covariates
In this study, continuous variables comprised age (year), poverty-to-income ratio, total cholesterol (mmol/L), triglyceride (mmol/L), low-density lipoprotein cholesterol (mmol/L), high-density lipoprotein cholesterol(mmol/L), serum uric acid levels (μmol/L) and C-reaction protein (mg/dL).
Demographic and behavioral attributes, categorized as discrete variables, encompassed gender, ethnicity, educational level, marital status, smoking habits (never = smoked <100 cigarettes in lifetime, former = smoked over 100 cigarettes during their life but ceased to smoke at present, now = smoked upwards of 100 cigarettes over the course of their life and now being smoking occasionally or daily) and alcohol consumption (yes = at least 12 drinks of any type of alcoholic beverage in any 1 year, no = <12). Furthermore, health indicators assessed the presence of hypertension, diabetes, and CVD. Hypertension was identified either through antihypertensive medication use, prior medical Acknowledgments, or consistent readings showcasing systolic pressure higher than or equal to 140 mm Hg, or diastolic pressure at or above 90 mm Hg.[23] During this survey cycle, diabetes was determined by having either a fasting plasma glucose level ≥ 7 mmol/L or a hemoglobin A1c level ≥ 6.5% from laboratory tests, taking hypoglycemic drugs, or having ever received a diagnosis from a doctor.[24] CVD was designated as having any medical history of coronary heart disease, angina, heart attack, congestive heart failure, or stroke from the questionnaire data.[1] Detailed methodologies for measuring WC, weight, telomere length, and all associated variables can be accessed at https://cdc.gov/nchs/nhanes.
For missing values of continuous variables, we used the mean or median to correspond to normally and nonnormally distributed data, respectively. Regarding categorical variables, all missing values were categorized in an additional set. Following the completion of each data population, data descriptions and statistical analyses were performed.
2.4. Statistical analysis
R (version 4.2) and Empowerstats (version 5.0) were used to conduct statistical analyses throughout our study. Sample weights were incorporated to ensure the outcomes reflected a statistically representative snapshot of the American non-hospitalization population.[25] The NHANES website offered recommendations using suitable sampling weights for statistical analyses. NHANES 1999 to 2000 overview mentioned most data analyses required either the interviewed sample weight (variable name: WTINT2YR) or examined sample weight (variable name: WTMEC2YR). We selected WTMEC2YR as the sample weight since our study involved many laboratory data from this only circle. At each stage of data description and analysis, the weight was taken into account for calculations. We initially employed multivariate logistic regression to elucidate the linear relation between WWI and telomere length. Moreover, we executed smoothing curve fitting and saturation effects evaluation to discern the correlation of WWI with telomere length nonlinearly. Ultimately, comprehensive subgroup analyses, augmented by pertinent interaction tests, were undertaken to verify the uniformity of associations across distinct subgroups.
3. Results
3.1. Baseline characteristics
This research encompassed 3479 individuals with an average (SD) age of 44.93 (16.61) years. The assembly comprised 48.45% males and 51.55% females. WWI and telomere length were calculated out mean (SD) numeric of 10.74 (0.81) cm/kg and 1.04 (0.26), respectively. Table 1 delineates the weighted attributes of the participants, with columns categorizing data according to WWI quartiles.
Table 1.
Weighted characteristics of participants according to weight-adjusted-waist index quartile.
| Variables | Weight-adjusted-waist index (cm/√kg) | P value | |||
|---|---|---|---|---|---|
| Q1 (8.30–10.38) | Q2 (10.39–10.94) | Q3 (10.95–11.54) | Q4 (11.55–13.88) | ||
| N = 870 | N = 869 | N = 870 | N = 870 | ||
| Age (yr) | 38.69 ± 14.42 | 46.04 ± 17.27 | 53.64 ± 17.53 | 60.26 ± 17.94 | <.001 |
| Sex (%) | <.001 | ||||
| Male | 427 (49.08%) | 455 (52.36%) | 466 (53.66%) | 325 (37.36%) | |
| Female | 443 (50.92%) | 414 (47.64%) | 404 (46.44%) | 545 (62.64%) | |
| Race (%) | <.001 | ||||
| Mexican American | 148 (17.01%) | 239 (27.50%) | 263 (30.23%) | 305 (35.06%) | |
| Other Hispanic | 57 (6.55%) | 52 (5.98%) | 77 (8.85%) | 49 (5.63%) | |
| Non-Hispanic White | 449 (51.61%) | 409 (47.07%) | 388 (44.60%) | 386 (44.37%) | |
| Non-Hispanic Black | 195 (22.41%) | 151 (17.38%) | 114 (13.10%) | 93 (10.69%) | |
| Other Race | 21 (2.41%) | 18 (2.07%) | 28 (3.22%) | 37 (4.25%) | |
| Education level (%) | <.001 | ||||
| Less than high school | 190 (21.84%) | 305 (35.10%) | 366 (42.07%) | 457 (52.53%) | |
| High school or GED | 203 (23.33%) | 201 (23.13%) | 192 (22.07%) | 188 (21.61%) | |
| Above high school | 475 (54.60%) | 362 (41.66%) | 310 (35.63%) | 224 (25.75%) | |
| Others | 2 (0.23%) | 1 (0.12%) | 2 (0.23%) | 1 (0.11%) | |
| Marital status (%) | <.001 | ||||
| Married | 386 (44.37%) | 468 (53.86%) | 513 (58.97%) | 459 (52.76%) | |
| Single | 198 (22.76%) | 131 (15.07%) | 78 (8.97%) | 59 (6.78%) | |
| Others | 286 (32.87%) | 270 (31.07%) | 279 (32.07%) | 352 (40.46%) | |
| Smoking status (%) | <.001 | ||||
| Never | 476 (54.71%) | 451 (51.90%) | 436 (50.11%) | 438 (50.34%) | |
| Former | 152 (17.47%) | 230 (26.47%) | 276 (31.72%) | 293 (33.68%) | |
| Current | 237 (27.24%) | 188 (21.63%) | 156 (17.93%) | 138 (15.86%) | |
| Unknown | 5 (0.57%) | 0 (0.00%) | 2 (0.23%) | 1 (0.11%) | |
| Alcohol consumption (%) | <.001 | ||||
| Yes | 630 (72.41%) | 588 (67.66%) | 540 (62.07%) | 489 (56.21%) | |
| No | 213 (24.48%) | 245 (28.19%) | 285 (32.76%) | 348 (40.00%) | |
| Unknown | 27 (3.10%) | 36 (4.14%) | 45 (5.17%) | 33 (3.79%) | |
| Hypertension (%) | <.001 | ||||
| Yes | 142 (16.32%) | 272 (31.30%) | 396 (45.52%) | 475 (54.60%) | |
| No | 728 (83.68%) | 597 (68.70%) | 474 (54.48%) | 395 (45.40%) | |
| Diabetes (%) | <.001 | ||||
| Yes | 24 (2.76%) | 70 (8.06%) | 121 (13.91%) | 221 (25.40%) | |
| No | 846 (97.24%) | 799 (91.94%) | 749 (86.09%) | 649 (74.60%) | |
| CVD (%) | <.001 | ||||
| Yes | 28 (3.22%) | 75 (8.63%) | 119 (13.68%) | 143 (16.44%) | |
| No | 842 (96.78%) | 794 (91.37%) | 751 (86.32%) | 727 (83.56%) | |
| PIR | 2.86 ± 1.57 | 2.70 ± 1.52 | 2.53 ± 1.52 | 2.08 ± 1.29 | <.001 |
| Total cholesterol (mmol/L) | 4.95 ± 0.98 | 5.31 ± 1.01 | 5.42 ± 1.01 | 5.56 ± 1.13 | <.001 |
| Triglyceride (mmol/L) | 1.47 ± 0.62 | 1.64 ± 0.69 | 1.79 ± 0.82 | 1.90 ± 1.07 | <.001 |
| LDL-cholesterol (mmol/L) | 3.01 ± 0.67 | 3.12 ± 0.67 | 3.12 ± 0.69 | 3.13 ± 0.70 | <.001 |
| HDL-cholesterol (mmol/L) | 1.40 ± 0.41 | 1.30 ± 0.40 | 1.28 ± 0.39 | 1.27 ± 0.37 | <.001 |
| Serum uric acid (μmol/L) | 290.37 ± 81.05 | 318.10 ± 88.85 | 329.01 ± 93.80 | 327.53 ± 92.48 | <.001 |
| C-reaction protein (mg/dL) | 0.24 ± 0.40 | 0.37 ± 0.60 | 0.55 ± 0.84 | 0.74 ± 1.15 | <.001 |
| Telomere length (mean T/S ratio) | 1.08 ± 0.27 | 1.03 ± 0.27 | 0.95 ± 0.24 | 0.93 ± 0.23 | <.001 |
Mean ± SD for continuous variables: the P value was calculated by the weighted linear regression model.
(%) for categorical variables: the P value was calculated by the weighted chi-square test.
CVD = cardiovascular disease, GED = general educational development, HDL-cholesterol = high-density lipoprotein cholesterol, LDL-cholesterol = low-density lipoprotein cholesterol, PIR = poverty income ratio, Q = quartile.
In comparison to the lowest quartile, individuals from the highest WWI quartile were observed to have a tendency to be older, female, nonsignalers, with lower education level and lower poverty income ratio, consumed less alcohol and tobacco, had a greater percentage of Mexican Americans, and displayed higher ratios of hypertension, diabetes, and CVD. Participants with higher WWI additionally exhibited increased levels of triglyceride, total cholesterol, low-density lipoprotein cholesterol, serum uric acid and C-reaction protein (mg/dL). In contrast, their standards in high-density lipoprotein cholesterol decreased (Table 1).
3.2. Linear association between WWI and telomere length
Table 2 presents the relationship between WWI and telomere length. Across raw, marginally adjusted, and fully refined models all verified an adverse relevance of WWI with telomere length. Specifically, each unit increment of WWI corresponded to a 0.02 ratio reduction of telomere length after adjustment for a total of 17 covariants (Model 3: β = −0.02, 95% CI: −0.03, −0.00, P value = 0.01). Segmenting WWI by quartiles, the significance of this correlation remains unyielding: each incremental unit of WWI resonates with a 0.04-unit further reduction in telomere length for those within the highest WWI quartile relative to their counterparts at the bottom (β = −0.04, 95% CI: −0.07, −0.01; P for trend = .0005).
Table 2.
Association between weight-adjusted-waist index (cm/√kg) and mean telomere length.
| Exposure | Model 1 [β (95% CI)] | Model 2 [β (95% CI)] | Model 3 [β (95% CI)] |
|---|---|---|---|
| WWI (continuous) | −0.07 (−0.08, −0.06) | −0.02 (−0.03, −0.01) | −0.02 (−0.03, −0.00) |
| <0.001 | 0.002 | 0.01 | |
| WWI (quartile) | |||
| Quartile 1 | 0 | 0 | 0 |
| Quartile 2 | −0.05 (−0.07, −0.03) <0.001 |
−0.01 (−0.03, −0.01) 0.23 |
−0.01 (−0.03, −0.01) 0.26 |
| Quartile 3 | −0.12 (−0.14, −0.10) <0.001 |
−0.05 (−0.07, −0.02) <0.001 |
−0.05 (−0.07, −0.02) <0.001 |
| Quartile 4 | −0.16 (−0.18, −0.13) <0.001 |
−0.05 (−0.07, −0.02) <0.001 |
−0.04 (−0.07, −0.01) 0.004 |
| P for trend | <.0001 | <.0001 | .0005 |
Model 1: No covariates were adjusted.
Model 2: Adjusted for age, gender, and race were adjusted.
Model 3: Adjusted for age, gender, race, education level, marital status, smoking status, alcohol consumption, hypertension, diabetes, CVD, PIR, triglyceride, triglyceride, LDL-cholesterol, HDL-cholesterol, serum uric acid, C-reaction protein.
CVD = cardiovascular disease, PIR = poverty income ratio, WWI = weight-adjusted-waist index.
3.3. Nonlinear association between WWI with telomere length
After adjusting for the complete set of covariates, we uncovered an inverted U-shaped curve of WWI with telomere length ratio (Fig. 2), with a notable inflection point of statistical significance at 10.07 cm/√kg (Table 3). Specifically, a positive correlation between these 2 variables as a numerical range of WWI was <10.07 [β = 0.06, 95% CI (0.02, 0.10), P = 0.007], whereas negatively correlated when WWI > 10.07 [β = −0.03, 95% CI (−0.05, −0.02), P < 0.001]. Considering P values, the relationships delineated postinflection appear more robust.
Figure 2.
Smooth curve fitting for WWI and telomere length. WWI = weight-adjusted-waist index.
Table 3.
Threshold effect analysis of weight-adjusted-waist index on telomere length using a 2-piecewise linear regression model before and after adjustment of covariates.
| Before adjustment* β (95% CI) P value |
After adjustment† β (95% CI) P value |
|
|---|---|---|
| Fitting by the standard linear model | −0.07 (−0.08, −0.06) | 0.02 (−0.03, −0.00) |
| <0.001 | 0.01 | |
| Fitting by the 2-piecewise linear model | ||
| Inflection point | 10.08 | 10.07 |
| <K segment effect | 0.01 (−0.03, 0.05) | 0.06 (0.02, 0.10) |
| 0.67 | 0.007 | |
| >K segment effect | −0.09 (−0.10, −0.07) | −0.03 (−0.05, −0.02) |
| <0.001 | <0.001 | |
| Log likelihood ratio | <0.001 | <0.001 |
No covariates were adjusted.
Adjusted for age, gender, race, education level, marital status, smoking status, alcohol consumption, hypertension, diabetes, CVD, PIR, triglyceride, triglyceride, LDL-cholesterol, HDL-cholesterol, serum uric acid, C-reaction protein.
CVD = cardiovascular disease, PIR = poverty income ratio.
3.4. Subgroup analysis
Subgroup analysis was undertaken to evaluate the uniformity of WWI’s associations with telomere length across diverse demographic segments. None of the analyzed subgroups, encompassing gender, age, smoking and drinking habits, hypertension, glycolysis, and CVD status, had a significant impact on the adverse relationship of WWI with telomere length (each P for interaction > .05), as demonstrated in Figure 3. Based on our analysis, the relationship between WWI and telomere length remained independent.
Figure 3.
Subgroup analysis for the association between WWI and telomere length. WWI = weight-adjusted-waist index.
4. Discussion
As far as we know, our study is the pioneering exploration elucidating the link between WWI and telomere length. We discerned a pronounced inverse association between WWI and telomere length in adults. This negative relationship persisted consistently across all scrutinized subcategories, as evidenced by our subgroup and interaction analyses. Notably, we identified an inverted U-shaped curve of WWI with telomere length, with a major turning point at 10.07 cm/√kg.
The escalating prevalence of obesity poses a grave global health challenge.[26] To address this, it’s imperative to first accurately identify obesity. Consequently, several anthropometric indices delineating adiposity with or without its distribution have been developed, including the widely recognized BMI, waist-to-hip ratio (WHR), and body fat percentage (BFP).[27] Numerous studies have posited that BMI,[28–31] WHR,[32] and BFP[33] are inversely correlated with telomere length. However, some academics dispute this, particularly in relation to WHR.[31,33] Regrettably, these studies were unable to identify the ideal inflection point to act as a guide. Intriguingly, it is the distribution, not merely the quantity, of fat that signifies obesity risk.[27] Abdominal adiposity, a notable contributor to metabolic syndrome,[34] concurrently elevates the likelihood of cardiovascular ailments,[35] diabetes,[36] and cancer,[37] etc. However, BMI predominantly gauges holistic weight, neglecting nuanced facets of body composition like visceral fat.[38,39] BFP evaluation demands specialized apparatus, rendering it less practical. WHR, despite its contentious results, induces information loss in many circumstances and is not optimal for assessing visceral fat.[40] Given these limitations, the pursuit of refined metrics persists. WWI is a developing indicator that highlights the importance of WC and essentially reflects centripetal obesity irrespective of weight.[41] Moreover, WWI is extremely easy to access, which can be measured and calculated by individuals at home instantly.
The intricate mechanisms underlying the noted reverse relationship of WWI with telomere length remain not fully elucidated. Augmented levels of inflammatory milieu and oxidative stress have been postulated as potential catalysts for DNA degradation and ensuing telomere diminution in overweight and obese individuals.[42] Obesity might amplify inflammation through adipocyte hyperplasia and hypertrophy, potentially linked to adipose tissue hypoxia.[43] Inflammation can be exacerbated by alterations in cytokine release, oxygen deprivation, necrosis, enhanced immunological cell aggregation, and lipid metabolism disorders following adipose tissue hyperplasia as well as hypertrophy.[44] Richter and von Zglinicki[45] ascertained telomere attrition rates in both mankind and sheep fibroblast cells, proved their continuously exponential association to cellular oxidative stress degrees, which could demonstrate oxidative stress-mediated telomere impairment as a key element in telomere shortening. Nutritionally speaking, heightened plasma concentrations of lutein, zeaxanthin, and vitamin C had been linked to longer leukocyte telomere length among the healthy aged, underscoring the safeguarding role these antioxidants play in telomere conservation.[46] As for the generally neglected micronutrients, higher dietary consumption of copper, for instance, was related to more telomere base pairs for hypertensive sufferers.[47]
Given the authoritative and representative nature of the NHANES database, these insights could be expanded to cover millions of people. Undoubtedly, the inferences drawn from this investigation bear inherent limitations warranting recognition. Primarily, we were precluded from establishing a definitive causality between WWI and telomere length, owing to the intrinsic property of the cross-sectional study modality. Additionally, the covariates might lack exhaustive consideration, for example, the administration of certain medications like steroids was not taken into account. Finally, our opinions depended on an individual nation with specific ethnics, therefore whether the outcomings can be applied to various races or nations still needs further verification. We thus recommend undertaking and integrating larger-scale longitudinal research including a wider variety of variables that are not limited to certain countries or racial categories in order to validate our findings.
5. Conclusion
For the first time, we unearthed pronounced correlations between WWI and telomere length. The conclusions implied that pursuing and preserving WWI within the ideal range could be crucial for aging and well-being.
Acknowledgments
We would like to express our gratitude to all participants and researchers of NHANES. The authors were particularly grateful for the technical assistance offered by Xu Lu.
Author contributions
Conceptualization: Jiaying Xia.
Data curation: Jiaying Xia.
Formal analysis: Jiaying Xia.
Funding acquisition: Jiaying Xia.
Investigation: Jiaying Xia.
Methodology: Jiaying Xia.
Project administration: Jiaying Xia.
Resources: Jiaying Xia.
Software: Jiaying Xia.
Supervision: Jiaying Xia.
Validation: Jiaying Xia.
Visualization: Jiaying Xia.
Writing—original draft: Jiaying Xia, Lu Xu, Yihua Yu, Min Wu, Xiao Wang, Yangyi Wang, Chaoxi Li, Jiemin Sun, Xin Lv, Jing Zhao.
Writing—review & editing: Yue Zhang.
Abbreviations:
- BFP
- body fat percentage
- BMI
- body mass index
- CVD
- cardiovascular disease
- NHANES
- National Health and Nutrition Examination Survey
- WC
- waist circumference
- WHR
- waist-to-hip ratio
- WWI
- weight-adjusted-waist index
The research was funded by the Medical Science and Technology Project of Zhejiang Province (2024KY619) and the Medical Science and Technology Project of Zhejiang Province (2022ZH002).
The participants provided their written informed consent to participate in this study.
The National Center for Health Statistics (NCHS) Ethics Review Board reviewed and approved the studies involving human participants.
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
How to cite this article: Xia J, Xu L, Yu Y, Wu M, Wang X, Wang Y, Li C, Sun J, Lv X, Zhao J, Zhang Y. Associations between weight-adjusted-waist index and telomere length: Results from NHANES: An observational study. Medicine 2024;103:17(e37905).
Contributor Information
Jiaying Xia, Email: xiajy2023@163.com.
Lu Xu, Email: 15068757051@163.com.
Yihua Yu, Email: yuyihua2023@163.com.
Min Wu, Email: wumin2023@163.com.
Xiao Wang, Email: wangyangyi2023@163.com.
Yangyi Wang, Email: wangyangyi2023@163.com.
Chaoxi Li, Email: lichaoxi@163.com.
Jiemin Sun, Email: sunjiemin@163.com.
Xin Lv, Email: Lvxin1988@163.com.
Jing Zhao, Email: Zhaojing1988@163.com.
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