Joint association of systemic immunue-inflammation index and phenotypic age acceleration with chronic respiratory disease: a cross-sectional study

doi:10.21203/rs.3.rs-4903734/v1

Download PDF

Research Article

Joint association of systemic immunue-inflammation index and phenotypic age acceleration with chronic respiratory disease: a cross-sectional study

https://doi.org/10.21203/rs.3.rs-4903734/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

Chronic respiratory diseases (CRD) represents a series of lung disorders and is posing a global health burden. Systemic inflammation and phenotypic ageing have been respectively reported to associate with certain CRD. However, little is known about the co-exposures and mutual associations of inflammation and ageing with CRD. Here, we aim to systematically elucidate the joint and mutual mediating associations of systemic immune-inflammation index (SII) and phenotypic age acceleration (PhenoAgeAccel) with CRD based on data from National Health and Nutrition Examination Survey (NHANES).

Methods

Data for this study was obtained from NHANES 2007–2010 and 2015–2018. The single and combined associations of SII and PhenoAgeAccel with CRD were analyzed using multivariable logistic regression models. The dose-response relationship between exposures and outcomes was determined by restricted cubic splines (RCS) regression. Subgroup and mediation analyses were further conducted.

Results

Totally, 15,075 participants were enrolled in this study including 3,587 CRD patients. Compare with controls, CRD patients tended to be older, females and present higher SII and PhenoAgeAccel values. Single-index analysis indicated that either SII or PhenoAgeAccel demonstrated a significantly positive association with CRD via logistic regressions and RCS curves. Furthermore, the joint-indexes analysis revealed that compared to individuals with lower SII and PhenoAgeAccel, those with higher SII and PhenoAgeAccel exhibited remarkably stronger associations with CRD (adjusted OR [aOR], 1.53; 95% CI, 1.28–1.81; P < 0.001), chronic obstructive pulmonary disease (aOR, 1.58; 95% CI, 1.23–2.03; P < 0.001) and asthma (aOR, 1.37; 95% CI, 1.14–1.66; P = 0.002), which were predominant among those aged above 40 years, females and smokers. Eventually, mediation analyses suggested the mutual mediating effects of SII and PhenoAgeAccel on CRD and PhenoAgeAccel mediated SII resulting in CRD more significantly.

Conclusion

This study confirmed the coexposure effect and mutual mediation between SII and PhenoAgeAccel on CRD. We recommend that the joint assessment may conduce to the accurate identification for populations susceptible to CRD and early prevention of chronic respiratory diseases.

Chronic respiratory diseases

Systemic immune-inflammation index

Phenotypic age acceleration

Co-exposure effect

Mediation analysis

Chronic respiratory diseases (CRD), taking chronic obstructive pulmonary disease (COPD) and asthma for instance, is a category of common disorder with high prevalence that accounts for an enormous global burden of disability and mortality[1]. Multiple factors can conduce to CRD risk, including smoking, air pollution, environmental allergens, occupational agents, etc[2, 3]. However, it’s difficult to predict the occurrence of CRD according to these factors, attributable to lacking quantifiable indicators. Thus, identifying viable and effective biomarkers is urgently required for CRD prediction and prevention.

As extensively acknowledged, CRD is not only characterized by the local inflammation of lung but also systemic inflammation. It’s reported that about 70% of COPD patients showed persistent systemic inflammation, which was associated with poor clinical outcomes[4, 5]. Moreover, some system-inflammation indexes like C-reactive protein and serum amyloid-A were also closely correlated to asthma prevalence[6]. Systemic immune-inflammation index (SII) is reported as an easily accessible evaluation index of systemic inflammation in recent years, which can be calculated based on the counting of platelets, neutrophils and lymphocytes in the blood[7]. Previous studies investigated the association between SII and various chronic diseases related to inflammation such as hepatocellular carcinoma, rheumatoid arthritis and diabetes[7–9]. As for CRD, the relatively comprehensive and concrete correlations with SII is not fully explored.

Recently, in-mounting evidence indicates that diversified CRDs are regarded as age-related disorders characterized by accelerated organic ageing[10]. Lung ageing can lead to declined lung function and damaged lung structure, which impairs the gas exchange and ability to defend exogenous pathogens[11]. Therefore, a valid evaluation of ageing may help to determine the susceptible individuals with CRD risk. Conventional biomarkers of biological ageing range from cellular and molecular hallmarks such as cellular senescence and mitochondrial dysfunction, to systemic hallmarks such as chronic inflammation[12]. However, most of above indexes are difficult to be detect in clinic. On this occasion, an innovative measure of biological ageing was recently proposed, and the quantized value namely phenotypic age acceleration (PhenoAgeAccel) can be acquired according to a linear combination of chronological age and 9 multi-system clinical chemistry biomarkers[13–15]. As reported, PhenoAgeAccel was positively associated with the incidence and mortality of various cardiovascular diseases[16, 17]. Previous studies also suggested that accelerated phenotypic ageing was correlated with worse lung function and elevated COVID-19 severity[18, 19]. It interests us if phenotypic ageing has a certain relationship with CRD prevalence.

Theoretically, there is a complicated and cooperative correlation between ageing and inflammation. Senescent cells secrete cytokines, chemokines and other inflammatory mediators, known as the senescence-associated secretory phenotype (SASP), to trigger a proinflammatory state in blood and solid tissues[20]. On the contrary, persistent inflammation can simultaneously cause DNA damage, mitochondrial dysfunction and cellular senescence[21]. Accordingly, we here explored the combined effect of SII and Phenotypic ageing on CRD. What’s more, a mediation analysis was furtherly performed to figure out the mutual mediating effects linking SII and Phenotypic ageing to CRD.

Study design and participants

The design of this study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cross-sectional studies. National Health and Nutrition Examination Survey (NHANES) is a national, cross-sectional survey conducted every two years to evaluate the health and nutritional status of the civilian, noninstitutionalized population in the United States. All NHANES studies were approved by the Ethics Review Committee of the National Center for Health Statistics (NCHS), and written consent was obtained from all participants. This study utilized data from four NHANES cycles spanning 2007–2010 and 2015–2018. Participants were excluded if they were under 20 years old (n = 16470), missing CRD-related data (n = 3199), lacking PhenoAgeAccel-related and SII related data (n = 1811), or missing covariates information (n = 3356). Ultimately, 15075 participants were included (Fig. 1).

Definition of exposure

In our study, SII were designed as the one of exposure variables. The complete blood count (CBC) parameters in the NHANES dataset were obtained by analyzing participants’ blood samples using Beckman Coulter equipment (Beckman Coulter, Brea, United States) at mobile examination centers. The formula of the SII calculation was based on the previous study as follows[7]:

$$\:SII\:=\frac{plateletcount\:\times\:neutrophil\:count}{lymphocyte\:count}$$

We calculated Phenotypic Age following the method described previously. Briefly, Phenotypic Age is calculated using chronological age and 9 biomarkers, including albumin (g/L), creatinine (µmol/L), glucose (mmol/L), ln-C-reactive protein (ln-CRP) (mg/d), lymphocyte percent (%), mean cell volume (fL), red blood cell distribution width (%), alkaline phosphatase (U/L), and white blood cell count (1000 cells/µL). The equation for calculating Phenotypic Age was as follows[15]:

$$\:Phenotypic\:Age=141.50+\:\frac{{ln}[-0.00553\:\times\:ln(1-xb\left)\right]}{0.09165}$$

In this formula,

Finally, we calculated the PhenoAgeAccel as below:

$$\:PhenoAgeAccel=Phenotypic\:Age-chronological\:age$$

This determines the extent to which a participant appears older (positive value, ≥ 0) or younger (negative value, < 0) than expected.

Definition of Outcome

For COPD, the participants were enrolled via questionnaire “Ever told you had COPD?” (answered “Yes”) in 2015–2018 cycles or postbronchodilator forced expiratory volume in 1s / forced vital capacity (FEV₁/FVC) < 70% according to spirometry examination in 2007–2010 cycles. For asthma, the participants were defined as an affirmative response to the question, “Ever been told you have asthma?”. Four common CRDs (asthma, emphysema, COPD, and chronic bronchitis) were selected for this study, and emphysema/chronic bronchitis were diagnosed according to the question, “has a doctor or other health professional ever told you that you have emphysema/chronic bronchitis?”.

Covariates

Baseline measurements of age, sex, race/ethnicity, education level, marital status, family poverty income ratio (PIR), body mass index (BMI), alcohol drinking status, smoking status, diabetes, hypertension, and survey cycle were included as covariates in this study. As used by NHANES, we divided race/ethnicity into Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black and Other (including multiracial). Education level was divided into two groups (high school or below and greater than high school). The marital status was classified as married, never married, living with a partner, and others (including divorced, widowed, and separated). Family income was categorized into 3 levels (< 1.3, 1.3–3.5, and ≥ 3.5) based on the family PIR. BMI was divided into 3 levels (< 25, 25–30, and ≥ 30 kg/m²). Alcohol drinking status was determined by the following survey question, “In any 1 year, have you had at least 12 drinks of any type of alcoholic beverage?” Participants who answered “yes” were defined as alcohol drinkers. Smoking status was determined by the following survey question, “Have you smoked at least 100 cigarettes in your entire life?” Participants who answered “yes” were defined as smokers. Diabetes was defined as self-reported diagnosed diabetes or blood tests HbA1c ≥ 6.5%. Hypertension was defined as self-reported diagnosed hypertension or using antihypertensive medication or average systolic blood pressure ≥ 140 mmHg and/or average diastolic blood pressure ≥ 90 mmHg.

Statistical analysis

In this study, we considered complex sampling design and sampling weights according to the NHANES analytic guidelines (https://wwwn.cdc.gov/nchs/nhanes/analyticguidelines.aspx). The characteristics of participants are described as means (95% CIs) for continuous variables and percentage frequencies (95% CIs) for categorical variables. Continuous data were compared using t-tests, and categorical data were compared by the χ2 test. These means and frequencies can be generalized to the US adult population. Due to lacking clinical cutoff point of SII, we used the median value of SII following the previous studies[22]. Furthermore, participants were divided into two groups according to the PhenoAgeAccel to determine whether they appeared older or younger. Then, we generated four groups by combining the two SII and PhenoAgeAccel groups in two-by-two combinations: Group 1, SII < median & PhenoAgeAccel < 0; Group 2, SII ≥ median & PhenoAgeAccel < 0; Group 3, SII < median & PhenoAgeAccel ≥ 0; Group 4 SII ≥ median & PhenoAgeAccel ≥ 0.

Odds ratios (ORs) and 95% CIs were calculated to assess the single and combined association between SII/PhenoAgeAccel and CRD/COPD/Asthma using multivariable logistic regression models. Three models were used in this study. Model 1 was the crude model with no covariates adjusted. Model 2 was adjusted for age, sex, and race/ethnicity. Model 3 was the fully adjusted model where sex, age, race/ethnicity, education level, marital status, family PIR, BMI, alcohol drinking status, smoking status, diabetes, hypertension, and survey cycle were included.

Subgroup analyses were conducted by age groups (20 ~ 39 years, 40 ~ 59 years, and 60 ~ years), sex groups (Male and female), and smoking status groups (No and Yes) to assess the sensitivity, interactions, and potential effect modification within different subgroups. Additionally, to further explore the dose-response relationship between single SII/PhenoAgeAccel and CRD/COPD/Asthma, the restricted cubic splines (RCS) regression was also performed[23].

We also conducted a mediation analysis to further investigate the direct and indirect associations between the SII and CRD/COPD/Asthma via PhenoAgeAccel. In brief, SII was used as a predictor variable (X), PhenoAgeAccel was used as a mediator (M) and the CRD/COPD/Asthma were used as outcome variable (Y). Meanwhile, the mediating effect of PhenoAgeAccel on CRD/COPD/Asthma through the SII was similarly evaluated.

The statistical analysis was performed with R (version 4.4.0, R Project for Statistical Computing, Vienna, Austria) and EmpowerStats (version 4.1, Boston, Massachusetts). In all tests, a two-sided of P < 0.05 was considered to indicate statistical significance.

Baseline characteristics of enrolled participants

As shown in Table 1, a total of 15,075 subjects were included in this study from the NHANES 2007–2010 and 2015–2018, of which 3,587 afflicted with CRD. Compared with participants without CRD, those with CRD were inclined to be older, females, non-Hispanic White, and High school or below. Meanwhile, significant differences could be observed between two groups in marital status, family PIR, BMI, and smoking status. Besides, CRD patients were more prone to comorbidity with diabetes and hypertension than those without CRD. Noteworthy, the mean PhenoAgeAccel value and SII value were respectively − 1.5 and 564.9 among individuals with CRD, markedly greater than those without CRD, which demonstrated that CRD patients were more likely to present biological ageing and systematic inflammation.

Table 1

Characteristic of participants in the NHANES 2007–2010 & 2015–2018 cycles
	Total participants^a (N = 15075)	Without CRD (N = 11488)	With CRD (N = 3587)	P value
Age, mean (95%CI), y	46.7 (46.1, 47.3)	45.9 (45.3, 46.6)	49.0 (48.2, 49.8)	< 0.001
Sex				< 0.001
Male	7520 (49.4) [48.5, 50.3]	5772 (50.5) [49.3, 51.6]	1748 (46.0) [43.9, 48.0]
Female	7555 (50.6) [49.7, 51.5]	5716 (49.5) [48.4, 50.7]	1839 (54.0) [52.0, 56.1]
Race/ethnicity^b				< 0.001
Mexican American	2546 (8.4) [4.7, 10.5]	2213 (9.7) [7.8, 12.1]	333 (4.2) [3.3, 5.3]
Other Hispanic	1610 (5.4) [4.4, 6.6]	1266 (5.6) [4.5, 6.9]	344 (4.7) [3.7, 6.0]
Non-Hispanic
White	6516 (68.9) [65.4, 72.1]	4610 (67.4) [63.8, 70.8]	1906 (73.8) [70.4, 77.0]
Black	2919 (9.9) [8.4, 11.7]	2201 (9.8) [8.3, 11.7]	718 (10.2) [8.4, 12.3]
Other race/multiracial	1484 (7.4) [6.4, 8.5]	1198 (7.5) [6.4, 8.6]	286 (7.1) [5.9, 8.6]
Education Level				0.002
High school or below	6984 (37.7) [35.4, 40.1]	5234 (36.7) [34.4, 39.0]	1750 (41.3) [37.8, 44.8]
Great than high school	8091 (62.3) [59.9, 64.6]	6254 (63.3) [61.0, 65.6]	1837 (58.7) [55.2, 62.2]
Marital status				< 0.001
Married	7863 (56.1) [54.2, 58.0]	6084 (56.4) [54.3, 58.5]	1779 (55.1) [52.5, 57.6]
Never married	2655 (17.6) [16.3, 19.1]	2065 (18.0) [16.6, 19.5]	590 (16.4) [14.4, 18.5]
Living with partner	1367 (8.9) [8.1, 9.7]	1060 (9.1) [8.2, 10.0]	307 (8.3) [7.3, 9.6]
Others^c	3190 (17.4) (16.3, 18.5)	2279 (16.5) [15.4, 17.6]	911 (20.2) [18.6, 22.0]
Family PIR				< 0.001
< 1.3	4479 (19.3) [17.9, 20.8]	3286 (18.2) [16.8, 19.7]	1193 (23.0) [20.8, 25.4]
1.3–3.5	5916 (35.4) [33.7, 37.1]	4537 (35.4) [33.6, 37.2]	1379 (35.4) [32.6, 38.2]
≥ 3.5	4680 (45.3) [42.8, 47.7]	3665 (46.4) [43.9, 48.9]	1015 (41.6) [38.1, 45.2]
BMI, kg/m²				< 0.001
< 25	4028 (28.4) [27.0, 29.9]	3075 (28.3) [26.7, 30.1]	953 (28.6) [26.7, 30.5]
25–30	4979 (32.6) [31.5, 33.6]	3940 (33.7) [32.4, 35.0]	1039 (28.8) [26.9, 30.7]
≥ 30	6068 (39.0) [37.5, 40.6]	4473 (37.9) [26.2, 39.7]	1595 (42.6) [40.7, 44.6]
Alcohol drinking status^d				0.31
No	4954 (28.0) [26.4, 29.6]	3855 (28.2) [26.6, 30.0]	1099 (27.2) [25.1, 29.4]
Yes	10121 (72.0) [70.4, 73.6]	7633 (71.8) [70.0, 73.4]	2488 (72.8) [70.6, 74.9]
Smoking status^e				< 0.001
No	8142 (54.6) [52.9, 56.4]	6694 (58.6) [57.0, 60.2]	1448 (41.6) [38.7, 44.5]
Yes	6933 (45.4) [43.6, 47.1]	4794 (41.4) [39.8, 43.0]	2139 (58.4) [55.5, 61.3]
Diabetes				< 0.001
No	12694 (88.6) [87.8, 89.4]	9832 (89.8) [88.9, 90.7]	2862 (84.7) [83.2, 86.0]
Yes	2381 (11.4) [10.6, 12.2]	1656 (10.2) [9.3, 11.1]	725 (15.3) [14.0, 16.8]
Hypertension				< 0.001
No	8877 (64.0) [62.5, 65.4]	7038 (66.1) [64.7, 67.6]	1839 (57.0) [54.3, 59.7]
Yes	6198 (36.0) [34.6, 37.5]	4450 (33.9) [32.4, 35.3]	1748 (43.0) [40.3, 45.7]
Survey cycle				< 0.001
2007–2008	3568 (22.3) [19.9, 25.0]	2555 (20.8) [18.7, 23.1]	1013 (27.2) [23.1, 31.7]
2009–2010	3798 (22.4) [20.1, 25.0]	2823 (21.8) [19.6, 24.1]	975 (24.5) [21.1, 28.4]
2015–2016	4131 (28.2) [25.5, 31.1]	3325 (29.4) [26.7, 32.3]	806 (24.2) [21.2, 27.6]
2017–2018	3578 (27.0) [25.2, 29.0]	2785 (28.0) [26.0, 30.1]	793 (24.0) [21.7, 26.6]
PhenoAgeAccel, mean (95%CI)	-2.7 (-2.9, -2.4)	-3.0 (-3.3, -2.7)	-1.5 (-1.9, -1.2)	< 0.001
SII, mean (95%CI)	532.8 (525.7,539.8)	522.9 (515.3, 530.5)	564.9 (550.6, 579.3)	< 0.001
Abbreviations: NHANES, National Health and Nutrition Examination Survey; CRD, Chronic Respiratory Disease; PIR, Poverty Income Ratio; BMI, Body Mass Index; CI, Confidence Interval; PhenoAgeAccel, Phenotypic Age Acceleration; SII, Systemic Immune-inflammation Index.
^a Data are presented as unweighted number (weighted percentage) [95% CI] unless otherwise specified.
^b Race and ethnicity were self-reported.
^c Included widowed, divorced or separated.
^d Determined by the survey question, “In any year, have you had at least 12 drinks of any type of alcoholic beverage?”.
^e Determined by the survey question, “Have you smoked at least 100 cigarettes in your entire life?”.

Joint association of SII and Phenotypic age acceleration with CRD

The single association of SII or PhenoAgeAccel with CRD prevalence was firstly examined. As depicted in Table 2, SII showed a positive correlation to CRD (adjusted OR [aOR], 1.03; 95% CI, 1.01–1.04; P = 0.003) and the RCS curve illustrated a linear dose-response relationship between SII value and CRD risk (Fig. 2a). Likewise, PhenoAgeAccel demonstrated a positive association with CRD (aOR, 1.03; 95% CI, 1.02–1.03; P < 0.001) (Table 2), and RCS curve indicated a nonlinear relationship between Phenotypic age acceleration and CRD (Fig. 2b).

Table 2

Single and combined association of SII and PhenoAgeAccel with the prevalence of CRD.
	Model 1^a OR (95% CI)	P value	Model 2^b OR (95% CI)	P value	Model 3^c OR (95% CI)	P value
SII	1.04 (1.03, 1.06)	< 0.001	1.04 (1.02, 1.06)	< 0.001	1.03 (1.01, 1.04)	0.003
PhenoAgeAccel	1.03 (1.02, 1.03)	< 0.001	1.03 (1.02, 1.03)	< 0.001	1.03 (1.02, 1.03)	< 0.001
Combination
Group 1^d	1 [Reference]		1 [Reference]		1 [Reference]
Group 2^e	1.12 (0.99, 1.26)	0.090	1.08 (0.95, 1.22)	0.252	1.01 (0.89, 1.14)	0.869
Group 3^f	1.41 (1.20, 1.66)	< 0.001	1.41 (1.19, 1.68)	< 0.001	1.33 (1.13, 1.58)	0.002
Group 4^g	1.72 (1.49, 1.99)	< 0.001	1.68 (1.45, 1.94)	< 0.001	1.53 (1.28, 1.81)	< 0.001
Abbreviations: CRD, Chronic Respiratory Disease; PhenoAgeAccel, Phenotypic Age Acceleration; SII, Systemic Immune-inflammation Index; OR, Odds Ratio; CI, Confidence Interval.
^a Crude model.
^b Adjusted for sex, age, race/ethnicity.
^c Adjusted for sex, age, race/ethnicity, education level, marital status, family PIR, BMI, alcohol drinking status, smoking status, diabetes, hypertension, and survey cycle.
^d SII < median & PhenoAgeAccel < 0.
^e SII ≥ median & PhenoAgeAccel < 0.
^f SII < median & PhenoAgeAccel ≥ 0.
^g SII ≥ median & PhenoAgeAccel ≥ 0.

To seek more-effective biomarker for CRD identification, we afterwards investigated the combined association of SII and PhenoAgeAccel with CRD. It could be concluded in Table 2 that compared with group 1 (SII < median & PhenoAgeAccel < 0), group 3 (SII < median & PhenoAgeAccel ≥ 0) exhibited a significant association with CRD (aOR, 1.33; 95% CI, 1.13–1.58; P = 0.002), and even greater association was observed in group 4 (SII ≥ median & PhenoAgeAccel ≥ 0) (aOR, 1.53; 95% CI, 1.28–1.81; P < 0.001). Promisingly, joint evaluation of SII and PhenoAgeAccel might contribute to effective identification of CRD patients.

Furthermore, the single and combined association of SII and PhenoAgeAccel with COPD or asthma was explored. As shown in Table S1 and Fig. 2c-d, either SII or PhenoAgeAccel demonstrated a positive association with COPD, while the joint effect exhibited a stronger association for group 4 (aOR, 1.58; 95% CI, 1.23–2.03; P < 0.001). Analogously, the single association of two indexes above was significantly different (Table S2), but RCS curve showed no marked dose-response relationship between SII and asthma prevalence (Fig. 2e-f). Of note, the combined association in group 4 with asthma was particularly remarkable (aOR, 1.37; 95% CI, 1.14–1.66; P = 0.002) (Table S2).

Subgroup analysis

We subsequently conducted a subgroup analysis by age, sex and smoking status, to further explore the joint effect of SII or PhenoAgeAccel on CRD. As shown in Fig. 3a, the joint effect of SII and PhenoAgeAccel on CRD or COPD or Asthma presented to be predominant among individuals aged above 40 years. In subjects aged over 60 years, group 4 showed remarkably positive associations with CRD (aOR, 1.57; 95% CI, 1.17–2.12; P = 0.005), COPD (aOR, 1.72; 95% CI, 1.23–2.42; P = 0.004), and Asthma (aOR, 1.43; 95% CI, 1.03–1.99; P = 0.042) (Table S3). As for sex, preferable joint association could be observed in females, especially for Asthma (Fig. 3b). In terms of smoking status, the joint association of SII and PhenoAgeAccel with CRD was highly significant among participants with smoking history (Fig. 3c). Among those with smoking history, people with both higher SII and PhenoAgeAccel levels had a greatest association with COPD (aOR, 1.92; 95% CI, 1.40–2.62; P < 0.001) (Table S3).

Mediation analysis

The mutual mediating effects linking SII and PhenoAgeAccel to CRD were summarized in Fig. 4a-b. A higher PhenoAgeAccel level mediated 45.7% of the association between a high SII index and CRD (P < 0.001). At meanwhile, a higher SII level mediated 12.6% of the association between a high PhenoAgeAccel value and CRD (P = 0.008). For specific disease, a mutual mediating effect of SII and PhenoAgeAccel conducing to COPD was remarkably significant (Fig. 4c-d). Nevertheless, a higher PhenoAgeAccel mediated 60.4% of the association between a high SII index and asthma (P = 0.042), but not vice versa (Fig. 4e-f). The mediation analysis furtherly indicated that inflammation and aging mutually reinforced with each other resulting in the development of various chronic lung diseases like COPD, partially consistent with our previous research[24].

Based on the analysis from 15,075 American participants, we concluded in this study that joint associations of SII and PhenoAgeAccel with CRD (including COPD and asthma) were remarkably superior to the single associations, especially for those with age above 40 years, females and smoking history. Furthermore, we highlighted the mutual mediation relationship between SII and PhenoAgeAccel contributing to CRD, particularly for COPD. The findings may provide new insight into judgement or prediction for CRD risk via the combined evaluation of SII and PhenoAgeAccel levels.

As extensively acknowledged, chronic inflammation is a key component for CRD prevalence. For COPD, most patients have predominantly neutrophilic inflammation accompanied by increased number of neutrophils, T lymphocytes and alveolar macrophages in the airways[4]. While for asthma, an important pathogenic mechanism is type 2 inflammation characterized by increased eosinophils, mast cells and basophils in the lungs[25]. In addition to local inflammation within the lung, systemic inflammation is commonly found in both diseases and often correlated with worse prognosis[26, 27]. There have been many studies reporting and discussing the associations between systemic inflammation markers and CRD. A prospective cohort study in 2013 have concluded that simultaneously elevated levels of C-reactive protein (CRP) and fibrinogen and leukocyte count with COPD were associated with increased risk of exacerbations[28]. A mendelian randomization analysis also suggested that higher CRP genetically predicted an increased risk of developing asthma[29]. SII, a novel systemic inflammation marker, is based on three easily accessible blood biomarkers[30]. Previous studies have investigated that SII could be utilized to predict the risk of COPD among adults aged 40 and above in the United States, which got the same conclusion with us[31, 32]. Whereas our study also found a positive association between SII and COPD, even for people age below 40. There are also several studies suggesting that higher SII values increased persistence and mortality of asthma patients[33, 34]. However, no one explored the correlation of SII and asthma prevalence, which was shown to have a strong positive association in our study. In summary, our study provides a strongly supportive evidence for the relationship between systemic inflammation and CRD prevalence. That’s to say, we need pay more attention to preventing CRD occurrence for people with high SII values.

It's well established that ageing-associated changes such as depletion of stem cell reservoirs, mitochondrial dysfunction, increased oxidative stress and telomere shortening contributed to an inability of lung cells to maintain baseline homeostasis[10]. In macroscopic view, ageing is accompanied by the decline of lung function and airway structural changes[35]. In microscopic view, cigarette smoking could dysregulate aging-related gene expression of lung cells[36]. Moreover, countless researches have shown the involvement of ageing-related markers in the development of CRD. For example, a mendelian randomization study has identified the positive association between shorter leukocyte telomere length and IPF risk[37]. A similar result has also been found in COPD[38]. PhenoAgeAccel, a new ageing measure, has got lots of attention because it could more directly reflect the rate of ageing among peers[39]. Recently, a prospective cohort study has confirmed a strong association of PhenoAgeAccel with increased risk of CRD based on the data from UK Biobank[40]. In our study, we got an analogous conclusion according to the data from NHANES. In addition, the correlation between positive PhenoAgeAccel and impaired lung function was investigated in another study[41]. All of the above studies demonstrated the importance and value of assessing PhenoAgeAccel in the development of CRD.

Since the discovery of SASP in 2008, it perpetuated a heated focus about interactions of ageing and inflammation, which has been convinced of presence of a vicious cycle between them[42]. SASP promotes chronic inflammation and induces senescence in normal cells. At meanwhile, chronic inflammation accelerates the senescence of various cells like immune cells, which impairs immune function and the ability to clear senescent cells and inflammatory factors[43]. Inflammageing, it means that organisms tend to develop a characteristic inflammatory state with high levels of proinflammatory markers during ageing. This chronic inflammation is considered a major risk factor for multiple age-related diseases[44]. In a word, inflammation and ageing can jointly promote the progression of age-related disease[45]. A recent study showed that the joint assessment of triglyceride-glucose (TyG) index and high sensitivity CRP, rather than TyG or CRP alone, was better for the prediction of cardiovascular diseases[46]. It reminded us if the joint evaluation of inflammation and ageing markers had a greater effect on the identification of CRD. As we expected, a more significant association was observed after the joint analysis was performed. Subsequently, we performed the mediation analysis and the result revealed the mutual mediating effects linking SII and PhenoAgeAccel to CRD, which furtherly proved the vicious cycle of inflammation and ageing. Whereas we didn’t observe the mediation of SII between a higher PhenoAgeAceel and asthma, which indicated that ageing contributed to asthma possibly independent of inflammation. Actually, a previous study yielded a similar conclusion, where an elevated p21 expression was observed in asthmatic epithelium but the elevation was not decreased with anti-inflammatory corticosteroid treatment[47]. Moreover, Asthma in the aged populations is a type of atypical asthma. The underlying airway inflammation in the elder patients likely differs from younger patients and they were less effective in respond to traditional therapies such as corticosteroid treatment[48]. All of the above studies suggested a complicated relationship between inflammation and ageing in asthma. Whatever there is an urgent need to unravel the mystery behind it.

Lastly, the result of subgroup analysis demonstrated that the joint effect of SII and PhenoAgeAccel on CRD was predominant among individuals who are aged above 40, females and smokers. It’s easy to understand the presence of higher prediction power in middle aged and elderly patients owing to the population ageing worldwide accompanied by increased prevalence of various chronic diseases, such as cardiovascular disease, chronic respiratory disease and diabetes[49]. During ageing, the immune system undergoes remarkable changes that collectively known as immunosenescence. It could lead to a low grade, chronic inflammation and a progressive reduction in cellular responses against infections[50]. That’s to say, there’s a more sensitive and easily detectable inflammation in elderly individuals, which may partially explain the higher association among individuals aged above 40. For preferable joint association in females, especially for asthma, it could be attributed to the influence of estrogen. It reported that sex hormones could regulate many autoimmune and inflammatory diseases, such as asthma, by modulation of various aspects of innate and adaptive immune response[51]. Multiple cells and tissues express estrogen receptors on their surface, such as eosinophils, mast cells and macrophages. Estrogens are mainly considered immune-enhancers in asthma by enhancing M2 polarization, eosinophil degranulation and inflammatory cell adhesion[52]. It seems to be supportive for the theory from the result of our subgroup analysis. Eventually, as for the predominance of the joint assessment in smokers, it could be easily attributed to that smoking itself could significantly contribute to inflammation and senescence within the lungs, thereby facilitating the incidence and development of various CRD[24, 53].

There remain some limitations. First, NHANES is a cross-sectional study, which cannot provide the temporal relationship between exposures and outcomes, with relatively weakened evidence for causal association. Second, other CRDs like interstitial lung disease and bronchiectasis were not analyzed in specific due to the unsatisfying case size in the database. Third, it’s unclear that our findings can be generalizable to other countries and regions. Thus, a large-scale, multinational, prospective design is warranted to validate these conclusions.

In summary, we demonstrated the joint associations of SII and PhenoAgeAccel with CRD and mutual mediating effects linking these two parameters to CRD via the analyses based on NHANES database. We highlighted that the combined assessment, rather than SII or PhenoAgeAccel alone, was of better power to associate CRD prevalence and facilitated to further stratify high-risk individuals for CRD.

BMI

body mass index

COPD

chronic obstructive pulmonary disease

CRD

chronic respiratory diseases

CRP

C-reactive protein

NHANES

National Health and Nutrition Examination Survey

PhenoAgeAccel

phenotypic age acceleration

PIR

poverty income ratio

RCS

restricted cubic splines

SASP

senescence-associated secretory phenotype

SII

systemic immune-inflammation index

TyG

triglyceride-glucose.

Ethics approval and consent to participate

NHANES study was approved by the Ethics Review Committee of the National Center for Health Statistics (NCHS), and written consent was obtained from all participants.

Consent for publication

Not applicable.

Availability of data and materials

The data utilized in this study can be available at https://wwwn.cdc.gov/nchs/nhanes/.

Competing interests

The authors have no competing interests to declare.

Funding

None.

Authors’ Contributions

Yuan Zhan: Conceptualization, Methodology, Writing-original draft, Visualization. Ruonan Yang: Writing-original draft. Jie Feng: Writing-review and editing. Genlong Bai: Writing-review and editing. Xiangyun Shi: Software, Visualization. Jiaheng Zhang: Writing-review and editing, Supervision. Jingbo Zhang: Conceptualization, Methodology, Software, Formal analysis, Supervision.

Acknowledgements

We are grateful to the participants and workers who contributed to the NHANES data.

Sayers I, John C, Chen J, Hall IP. Genetics of chronic respiratory disease. Nat Rev Genet. 2024;25(8):534–47.
Collaborators GBDCRD. Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2020;8(6):585–96.
Fallahzadeh A, Sharifnejad Tehrani Y, Sheikhy A, Ghamari SH, Mohammadi E, Saeedi Moghaddam S, Esfahani Z, Nasserinejad M, Shobeiri P, Rashidi MM, et al. The burden of chronic respiratory disease and attributable risk factors in North Africa and Middle East: findings from global burden of disease study (GBD) 2019. Respir Res. 2022;23(1):268.
Barnes PJ. Inflammatory mechanisms in patients with chronic obstructive pulmonary disease. J Allergy Clin Immunol. 2016;138(1):16–27.
Gan WQ, Man SF, Senthilselvan A, Sin DD. Association between chronic obstructive pulmonary disease and systemic inflammation: a systematic review and a meta-analysis. Thorax. 2004;59(7):574–80.
Jousilahti P, Salomaa V, Hakala K, Rasi V, Vahtera E, Palosuo T. The association of sensitive systemic inflammation markers with bronchial asthma. Ann Allergy Asthma Immunol. 2002;89(4):381–5.
Hu B, Yang XR, Xu Y, Sun YF, Sun C, Guo W, Zhang X, Wang WM, Qiu SJ, Zhou J, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res. 2014;20(23):6212–22.
Liu B, Wang J, Li YY, Li KP, Zhang Q. The association between systemic immune-inflammation index and rheumatoid arthritis: evidence from NHANES 1999–2018. Arthritis Res Ther. 2023;25(1):34.
Guo W, Song Y, Sun Y, Du H, Cai Y, You Q, Fu H, Shao L. Systemic immune-inflammation index is associated with diabetic kidney disease in Type 2 diabetes mellitus patients: Evidence from NHANES 2011–2018. Front Endocrinol (Lausanne). 2022; 13:1071465.
Cho SJ, Stout-Delgado HW. Aging and Lung Disease. Annu Rev Physiol. 2020;82:433–59.
Faner R, Rojas M, Macnee W, Agusti A. Abnormal lung aging in chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2012;186(4):306–13.
Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. Hallmarks of aging: An expanding universe. Cell. 2023;186(2):243–78.
Kuo PL, Schrack JA, Shardell MD, Levine M, Moore AZ, An Y, Elango P, Karikkineth A, Tanaka T, de Cabo R, et al. A roadmap to build a phenotypic metric of ageing: insights from the Baltimore Longitudinal Study of Aging. J Intern Med. 2020;287(4):373–94.
Liu Z, Kuo PL, 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.
Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging. 2018;10(4):573–91.
Duan S, Wu Y, Zhu J, Wang X, Fang Y. Associations of polycyclic aromatic hydrocarbons mixtures with cardiovascular diseases mortality and all-cause mortality and the mediation role of phenotypic ageing: A time-to-event analysis. Environ Int. 2024;186:108616.
Kresovich JK, Sandler DP, Taylor JA. Methylation-Based Biological Age and Hypertension Prevalence and Incidence. Hypertension. 2023;80(6):1213–22.
Wang C, Just A, Heiss J, Coull BA, Hou L, Zheng Y, Sparrow D, Vokonas PS, Baccarelli A, Schwartz J. Biomarkers of aging and lung function in the normative aging study. Aging. 2020;12(12):11942–66.
Reeves J, Kooner JS, Zhang W. Accelerated ageing is associated with increased COVID-19 severity and differences across ethnic groups may exist. Front Public Health. 2022;10:1034227.
Walker KA, Basisty N, Wilson DM 3rd, Ferrucci L. Connecting aging biology and inflammation in the omics era. J Clin Invest. 2022; 132(14).
Singh A, Schurman SH, Bektas A, Kaileh M, Roy R, Wilson DM 3rd, Sen R, Ferrucci L. Aging and Inflammation. Cold Spring Harb Perspect Med. 2024; 14(6).
Yin X, Zhang Y, Zou J, Yang J. Association of the systemic immune-inflammation index with all-cause and cardiovascular mortality in individuals with rheumatoid arthritis. Sci Rep. 2024;14(1):15129.
Desquilbet L, Mariotti F. Dose-response analyses using restricted cubic spline functions in public health research. Stat Med. 2010;29(9):1037–57.
Zhan Y, Huang Q, Deng Z, Chen S, Yang R, Zhang J, Zhang Y, Peng M, Wu J, Gu Y et al. DNA hypomethylation-mediated upregulation of GADD45B facilitates airway inflammation and epithelial cell senescence in COPD. J Adv Res. 2024.
Fahy JV. Type 2 inflammation in asthma–present in most, absent in many. Nat Rev Immunol. 2015;15(1):57–65.
Sinden NJ, Stockley RA. Systemic inflammation and comorbidity in COPD: a result of 'overspill' of inflammatory mediators from the lungs? Review of the evidence. Thorax. 2010;65(10):930–6.
Tattersall MC, Jarjour NN, Busse PJ. Systemic Inflammation in Asthma: What Are the Risks and Impacts Outside the Airway? J Allergy Clin Immunol Pract. 2024;12(4):849–62.
Thomsen M, Ingebrigtsen TS, Marott JL, Dahl M, Lange P, Vestbo J, Nordestgaard BG. Inflammatory biomarkers and exacerbations in chronic obstructive pulmonary disease. JAMA. 2013;309(22):2353–61.
Mou Y, Cao W, Wang R, Liu X, Yang X, Zhu J. The causality between C-reactive protein and asthma: a two-sample Mendelian randomization analysis. Postgrad Med J. 2024;100(1186):555–61.
Shi L, Zhang L, Zhang D, Chen Z. Association between systemic immune-inflammation index and low muscle mass in US adults: a cross-sectional study. BMC Public Health. 2023;23(1):1416.
Xu Y, Yan Z, Li K, Liu L. The association between systemic immune-inflammation index and chronic obstructive pulmonary disease in adults aged 40 years and above in the United States: a cross-sectional study based on the NHANES 2013–2020. Front Med (Lausanne). 2023; 10:1270368.
Ye C, Yuan L, Wu K, Shen B, Zhu C. Association between systemic immune-inflammation index and chronic obstructive pulmonary disease: a population-based study. BMC Pulm Med. 2023;23(1):295.
Benz E, Wijnant SRA, Trajanoska K, Arinze JT, de Roos EW, de Ridder M, Williams R, van Rooij F, Verhamme KMC, Ikram MA et al. Sarcopenia, systemic immune-inflammation index and all-cause mortality in middle-aged and older people with COPD and asthma: a population-based study. ERJ Open Res. 2022; 8(1).
Tian T, Xie M, Sun G. Association of systemic immune-inflammation index with asthma and asthma-related events: a cross-sectional NHANES-based study. Front Med (Lausanne). 2024;11:1400484.
Quirk JD, Sukstanskii AL, Woods JC, Lutey BA, Conradi MS, Gierada DS, Yusen RD, Castro M, Yablonskiy DA. Experimental evidence of age-related adaptive changes in human acinar airways. J Appl Physiol (1985). 2016; 120(2):159–165.
Barnes PJ, Baker J, Donnelly LE. Cellular Senescence as a Mechanism and Target in Chronic Lung Diseases. Am J Respir Crit Care Med. 2019;200(5):556–64.
Duckworth A, Gibbons MA, Allen RJ, Almond H, Beaumont RN, Wood AR, Lunnon K, Lindsay MA, Wain LV, Tyrrell J, et al. Telomere length and risk of idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease: a mendelian randomisation study. Lancet Respir Med. 2021;9(3):285–94.
Andujar P, Courbon D, Bizard E, Marcos E, Adnot S, Boyer L, Demoly P, Jarvis D, Neukirch C, Pin I, et al. Smoking, telomere length and lung function decline: a longitudinal population-based study. Thorax. 2018;73(3):283–5.
Kuo CL, Pilling LC, Liu Z, Atkins JL, Levine ME. Genetic associations for two biological age measures point to distinct aging phenotypes. Aging Cell. 2021;20(6):e13376.
Wang T, Duan W, Jia X, Huang X, Liu Y, Meng F, Ni C. Associations of combined phenotypic ageing and genetic risk with incidence of chronic respiratory diseases in the UK Biobank: a prospective cohort study. Eur Respir J. 2024; 63(2).
Ruan Z, Li D, Huang D, Liang M, Xu Y, Qiu Z, Chen X. Relationship between an ageing measure and chronic obstructive pulmonary disease, lung function: a cross-sectional study of NHANES, 2007–2010. BMJ Open. 2023;13(11):e076746.
Coppe JP, Desprez PY, Krtolica A, Campisi J. The senescence-associated secretory phenotype: the dark side of tumor suppression. Annu Rev Pathol. 2010;5:99–118.
Li X, Li C, Zhang W, Wang Y, Qian P, Huang H. Inflammation and aging: signaling pathways and intervention therapies. Signal Transduct Target Ther. 2023;8(1):239.
Ferrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol. 2018;15(9):505–22.
Franceschi C, Garagnani P, Parini P, Giuliani C, Santoro A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat Rev Endocrinol. 2018;14(10):576–90.
Cui C, Liu L, Qi Y, Han N, Xu H, Wang Z, Shang X, Han T, Zha Y, Wei X, et al. Joint association of TyG index and high sensitivity C-reactive protein with cardiovascular disease: a national cohort study. Cardiovasc Diabetol. 2024;23(1):156.
Parikh P, Wicher S, Khandalavala K, Pabelick CM, Britt RD Jr., Prakash YS. Cellular senescence in the lung across the age spectrum. Am J Physiol Lung Cell Mol Physiol. 2019;316(5):L826–42.
Dunn RM, Busse PJ, Wechsler ME. Asthma in the elderly and late-onset adult asthma. Allergy. 2018;73(2):284–94.
Tourlouki E, Matalas AL, Panagiotakos DB. Dietary habits and cardiovascular disease risk in middle-aged and elderly populations: a review of evidence. Clin Interv Aging. 2009;4:319–30.
Santoro A, Bientinesi E, Monti D. Immunosenescence and inflammaging in the aging process: age-related diseases or longevity? Ageing Res Rev. 2021;71:101422.
Radzikowska U, Golebski K. Sex hormones and asthma: The role of estrogen in asthma development and severity. Allergy. 2023;78(3):620–2.
Keselman A, Heller N. Estrogen Signaling Modulates Allergic Inflammation and Contributes to Sex Differences in Asthma. Front Immunol. 2015;6:568.
Lee HS, Park HW. Role of mTOR in the Development of Asthma in Mice With Cigarette Smoke-Induced Cellular Senescence. J Gerontol Biol Sci Med Sci. 2022;77(3):433–42.

No competing interests reported.

Supplementarytables.docx
Table S1. Single and Combined association of SII and PhenoAgeAccel with the prevalence of COPD. Table S2.Single and Combined association of SII and PhenoAgeAccel with the prevalence of Asthma. Table S3. The data used in the subgroup analysis.

Download PDF

Reviews received at journal
05 Sep, 2024
Reviewers agreed at journal
26 Aug, 2024
Reviewers invited by journal
26 Aug, 2024
Editor invited by journal
14 Aug, 2024
Editor assigned by journal
14 Aug, 2024
Submission checks completed at journal
14 Aug, 2024
First submitted to journal
12 Aug, 2024

You are reading this latest preprint version

Joint association of systemic immunue-inflammation index and phenotypic age acceleration with chronic respiratory disease: a cross-sectional study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Background

Methods

Study design and participants

Definition of exposure

Definition of Outcome

Covariates

Statistical analysis

Results

Baseline characteristics of enrolled participants

Joint association of SII and Phenotypic age acceleration with CRD

Subgroup analysis

Mediation analysis

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1