CRP-Albumin-Lymphocyte index (CALLYI) as a risk-predicting biomarker in association with osteoarthritis.

doi:10.21203/rs.3.rs-4996358/v1

Purpose

The exact relationship between the C-reactive protein-Albumin-Lymphocyte Index (CALLYI) and osteoarthritis remains unclear. This study explores the potential link between CALLYI and osteoarthritis in U.S. adults.

Method

The study cohort consisted of 18,624 U.S. adults who participated in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2010. The CALLYI was calculated using the formula: albumin * lymphocytes / CRP * 10. For participants in this study, the CALLYI was quadrupled. We employed weighted multiple regression analysis and subgroup statistical analysis to investigate the association between CALLYI and osteoarthritis. Restricted cubic splines were utilized to assess the non-linear relationship between these two variables.

Results

The total sample size analyzed in this study was 18,624, of which 1,977 (10.62%) were diagnosed with osteoarthritis. CALLY is defined as Albumin (g/L) ⅹ Lymphocyte (10⁹/L) ¸ (CRP (mg/L) ⅹ 10), and the mean value of CALLYI was 5.13 (2.12,12.86). The multivariate logistic regression model revealed a negative correlation between elevated CALLYI and osteoarthritis. The fully adjusted Model 3 demonstrated a significant 28% reduction in OA risk in the Q4 compared to the Q1 of CALLYI (OR = 0.72 95% CI: 0.59–0.88, p = 0.001). Subgroup analyses did not reveal any significant interactions (p > 0.05). Additionally, a significant non-linear relationship between CALLYI and OA using restricted cubic splines (p < 0.0001).

Conclusion

The results indicate a negative correlation between CALLYI and osteoarthritis, suggesting that CALLYI may serve as a biomarker for predicting the risk of osteoarthritis in U.S. adults.

CRP-Albumin-Lymphocyte index

osteoarthritis

National Health and Nutrition Examination Survey (NHANES)

Osteoarthritis (OA) is one of the most prevalent orthopedic diseases worldwide, characterized by degenerative changes in articular cartilage that ultimately lead to chronic pain, joint deformity, and loss of function^1,2. Recent epidemiological studies indicate that by 2020, an estimated 590 million individuals globally were affected by OA, with 32 million patients in the United States alone. Furthermore, this number is projected to increase to 67 million by 2030^3,4. OA not only causes physical discomfort for patients but also leads to escalating financial losses annually⁵. Thus, early intervention and slowing the progression of OA, are crucial and are the focus of ongoing research.

The pathogenesis of OA remains unclear; however, recent studies indicate that its development is influenced by multiple factors. Obesity, inflammation, trauma, infection, and disorders related to immunity and metabolism all identified as risk factors for^6–9. The CRP-Albumin-Lymphocyte Index (CALLYI) proposed by Hiroya Iida et al, is a relatively novel nutritional indicator of inflammation¹⁰. CALLYI is calculated as AlbuminⅹLymphocyte ¸ (CRPⅹ10), incorporating assessments of the patient’s nutritional status, immune function, and inflammation level. The parameters of CALLYI are routinely utilized in clinical practice, are readily obtainable, and correlate closely various pathophysiological levels in patients, which significantly enhances its practicality and applicability. Subsequent clinical studies have affirmed the utility of CALLYI in predicting the correlation and prognosis of various diseases, particularly colorectal cancer, lung cancer, and other cancers^11–14.

The National Health and Nutrition Examination Survey (NHANES) is a national cross-sectional study designed to assess the nutritional status and health of the US population^15,16. Noteworthy for its extensive sample size, the database is weighted to accurately reflect the demographic makeup of the U.S. population across race, gender, and age. A review of existing literature indicates that the relationship between CALLYI and OA has not been previously explored using the NHANES database. Detailed information about NHANES is freely available on its official website (http://www.cdc.gov/nchs/nhanes/index.htm). This study aims to validate the association between CALLYI and OA by analyzing NHANES data from the years 1999 to 2010.

Study design and population

In this investigation, data from participants in the NHANES were utilized, covering six consecutive survey cycles from 1999 to 2010 (1999–2000,2001–2002,2003–2004,2005–2006,2007–2008,2009–2010), including demographic, laboratory, and questionnaire data, comprising a total of 62,160 participants. Through a review of prior relevant literature, additional confounders that might affect the CALLYI variable were included in our analysis, such as hypertension, diabetes, coronary heart disease, stroke, smoking, alcohol consumption, and BMI^17–20. The exclusion criteria for our study data were as follows: (1) Participants under the age of 20. (2) Missing CALLYI data. (3) Missing OA data. (4) Missing data on relevant confounding factors. The final sample size of this study was 18,624, with 1,977 participants diagnosed with OA. The design process of this study is depicted in Fig. 1.

Diagnosis of OA and definition of CALLYI

In this study, the formula used to calculate CALLYI is: Albumin (g/L)ⅹLymphocyte (10⁹/L) / (CRP (mg/L)ⅹ10). Diagnostic data for OA are obtained from the questionnaire component of the NHANES, where participants are asked whether a doctor or other health professional has ever diagnosed them with arthritis, specifically identifying OA or degenerative arthritis (excluding rheumatoid arthritis, psoriatic arthritis, and other types of arthritis). Numerous studies have demonstrated that this self-reported questionnaire method yields reliable results^21,22.

Covariates

In this study, we define the following variables as covariates: sex, age, race, educational level, and poverty impact ratio in demographic data. The questionnaire data include smoking, alcohol consumption, BMI, hypertension, diabetes, coronary heart disease, and stroke. For subsequent data analysis, we have redefined certain covariates: race (Non-Hispanic White: white, Non-Hispanic Black: black, Other Races Including Multi-Racial: multi-racial), educational attainment (9-11th Grade (Including 12th grade without a diploma): 9-12th Grade, High School Graduate/GED or Equivalent: 9-12th Grade, Less Than 9th Grade: <9th Grade), and BMI categorized into four groups: <18, 18–25, 25–30, and > = 30. The term “former” in alcohol and smoking data was redefined as “no”. And in the diagnosis of DM, IFG and IGT were classified as “NO”.

Statistical analyses

R software (version 4.3.3) (https://www.r-project.org/) was utilized for data analysis in this study. A p-value less than 0.05 was deemed statistically significant. Continuous variables, whether normally or skewed distributed, were presented as means (standard deviation), and categorical variables were expressed as weighted mean percentages. Given the goal of the NHANES to produce data representative of the U.S. population through a complex sampling design, all data were weighted according to NHANES data analysis guidelines, considering the categories and distribution characteristics of the included factors. This research employed a multivariate logistic regression model to examine the association between CALLYI and OA and to compute the odds ratio (OR) and its 95% confidence interval (CI) across three models. Model 1 involved no adjustments. Model 2 was adjusted for demographic characteristics including age, gender, race, education, and poverty impact ratio. Model 3 included adjustments for disease factors such as hypertension, diabetes mellitus, stroke, coronary heart disease, and lifestyle factors like smoking, drinking, and BMI. To ensure the robustness of the relationship between CALLYI and OA, subgroup analyses were conducted based on sex, age, race, education, and BMI. Restricted cubic spline (RCS) curves were used to explore the potential linear or nonlinear relationship between CALLYI and OA.

Baseline characteristics

Supplementary Table 1 presents the basic characteristics of the variables for the study participants. Table 1 further delineates these characteristics across the quartiles of the weighted CALLYI. The analysis included 18,624 U.S. adults, with an average age of 44.69 (standard deviation: 0.22), comprising 9,431 (50.64%) males and 9,193 (49.36%) females. The ethnic composition was predominantly non-Hispanic whites, accounting for 13,221 participants (72.39%). Supplementary Table 1 indicates that OA was more prevalent among older, well-educated, non-Hispanic white males compared to those without OA. Statistically significant differences were noted between the groups in various parameters including CRP, albumin, lymphocyte, hypertension, diabetes mellitus, stroke, coronary heart disease, smoking and drinking habits, and BMI (p < 0.05). The CALLY index quartiles ranged from 1 to 4, [0.017,1.917], (1.917,4.587], (4.587,11.459], (11.459,1706.4]. Additionally, the CALLY index was strongly associated with the wealth status of participants and exhibited a gradual decline with age. Higher CALLY index values correlated with fewer incidences of OA, hypertension, diabetes, coronary heart disease, and stroke (p < 0.05). Individuals in better health displaying higher CALLY index values.

Association between CALLYI and OA

Table 2 demonstrates that the prevalence of OA declines as the CALLYI increases, indicating a negative correlation between the CALLYI and OA prevalence. Initial unadjusted analysis (Model 1) revealed a reduction in OA prevalence in Q2, Q3, and Q4 relative to Q1 with ORs of 0.74 (95% CI: 0.65–0.85), 0.57 (95% CI: 0.49–0.67), and 0.39 (95% CI: 0.34–0.45), respectively. Model 2, incorporating demographic adjustments for gender, age, ethnicity, and education level, showed enhanced statistical significance for these groups while maintaining overall significance. Further adjustments in Model 3 for confounding factors such as smoking, alcohol consumption, BMI, hypertension, diabetes, coronary heart disease, and stroke revealed that the statistical difference for Q2 vanished. However, the results for Q3 (OR: 0.79, 95% CI: 0.67–0.93) and Q4 (OR: 0.72, 95% CI: 0.59–0.88) continued to align with the downward trend and remained statistically significant (p < 0.05).

Subgroup analysis between CALLYI and OA

The purpose of the subgroup analysis was to assess the reliability and stability of the association between CALLYI and OA. As presented in Table 3, variables such as sex, age, race, education level, and BMI were included in the analysis. The results indicated no significant differences in the association between CALLYI and OA across these subgroups, with all p-values for interaction exceeding 0.05. This suggests that, there is no interaction between these variables and the relationship between CALLYI and OA, confirming the robustness of the described negative association.

Nonlinear relationship between CALLYI and OA

We plotted five nodes as an RCS curve, as illustrated in Fig. 2. The RCS curve revealed a significant overall trend between CALLYI and the prevalence of OA (P < 0.05). Moreover, this relationship was found to exhibit a significant non-linear association (P < 0.05). Notably, the data indicate an inverse correlation between the prevalence of OA and the CALLYI index, with higher values of the CALLYI index associated with a reduced prevalence of OA.

This study analyzed national survey data from NHANES for the period 1999 to 2010 to explore the relationship between CALLYI and OA. The final sample included 18,624 participants, of whom 1,977 (10.62%) were diagnosed with OA. A statistically significant inverse association between CALLYI and OA was established using multivariate logistic regression models, suggesting that higher CALLYI correlate with a reduced likelihood of OA development. This relationship was consistent across both the unadjusted Model 1 and the adjusted Model 2 and 3. No significant interactions were observed in subgroup analyses, further supporting the validity of our findings and indicating the generalizability of this association across diverse populations.

CALLYI, a relatively novel biomarker derived by assessing patient systemic nutritional status, immune function, and inflammatory responses, was initially utilized more frequently to predict cancer patient outcomes. This study represents the first attempt to integrate CALLYI with OA metrics to explore their interrelation.

The etiology and pathophysiology of OA are highly complex, influenced by numerous factors such as genetics, aging, obesity, and traum^23,24. Concurrent research has identified several clinicopathological variables associated with OA. Variables like serum Klotho levels and low-density lipoprotein are negatively correlated with OA. In contrast, factors like the triglyceride glucose index, weight-adjusted waist index, and dietary selenium intake have been positively linked to OA and are considered risk factors^25–29. A groundbreaking study has suggested a connection between various of thyroid hormone indicators and OA, indicating a potential link between the thyroid endocrine system and cartilage differentiation and regeneration. This insight offers a novel clinical perspective for the managing and assessing OA³⁰.

OA a degenerative disease predominantly observed in the elderly, corroborated by the demographic data in this study (NO-OA: 42.81(0.21), OA: 61.49(0.38), p < 0.0001). OA features widespread inflammation of the synovium, cartilage, and subchondral bone, leading to the release of various inflammatory mediators³¹. Prolonged inflammation often coincides with chronic conditions in the elderly, such as respiratory and urinary system inflammations, which can elevate CRP levels. Elevated CRP is associated with pain and functional impairment of limbs ^32–34. Albumin, commonly used as a clinical indicator of nutritional status, is synthesized in the liver and has a prolonged half-life³⁵. While direct associations between albumin and OA have not been extensively documented, numerous studies have confirmed that albumin contributes to the synthesis of several compounds that directly or indirectly affect OA progression^36,37. Additionally, albumin is as a critical prognostic marker in inflammatory responses, with higher levels indicating a more favorable prognosis³⁸. This is relevant in clinical scenarios where exogenous albumin supplementation is necessary for patients with hypoalbuminemia. The baseline characteristics table of this study demonstrated that the NO-OA group exhibited significantly higher albumin levels compared to the OA group (p < 0.0001). Extensive research has focused on the role of lymphocytes in the pathogenesis of OA. Guney et al. explored the association between synovial CD30 + T lymphocyte counts and the extent of cartilage damage, highlighting a potential correlation between lymphocyte activity and joint deterioration³⁹. Furthermore, investigations into the impact of Lymphocyte Activation Gene-3 (LAG-3) on regulatory T cell functions in OA have revealed compromised function in populations with high LAG-3 expression ⁴⁰.Given the critical role of lymphocytes in the immune response, lymphocyte counts are frequently used as indicators of immunocompetence. Various lymphocyte- associated biomarkers and inflammatory predictors have been identified as linked to OA progression⁴¹. The neutrophil/lymphocyte ratio has been recognized as a marker of OA severity and activity⁴². Additionally, the diagnostic potential of the blood monocyte-lymphocyte ratio in knee OA has been examined, along with its prognostic value in predicting patient responses to conservative treatments, demonstrated also by the monocyte-to-lymphocyte ratio in knee synovial fluid^43,44.

This study offers several strengths: firstly, it utilizes nationally representative NHANES data, which were weighted and analyzed to ensure data authenticity and objectivity. Secondly, the sample size was sufficiently large to bolster the credibility of the findings. Additionally, multiple confounders were addressed in the subgroup analyses, with no interactions observed, thus confirming the robustness of the results. This research is pioneering in establishing a negative correlation between CALLYI and OA, illustrating a non-linear relationship between the two. However, there are limitations: as a cross-sectional study, the absence of longitudinal data limits exploration of the causal relationship between CALLYI and OA. Diagnosis based on participant questionnaires may introduce errors due to factors like memory loss, potentially skewing the diagnostic rates. Lastly, certain potential confounders were not considered in the analyses.

In conclusion, this study identified a correlation between higher levels of CALLYI and a reduced risk of OA in U.S. adults, demonstrating a nonlinear relationship. Based on these results, CALLYI may serve as a promising biomarker for OA prediction and could aid in the early screening and prevention of OA, thereby facilitating timely medical interventions. However, further research is necessary to confirm and clarify these findings, particularly through the conduct of l larger-scale prospective studies.

The outcomes of multiple logistic regression models revealed a negative correlation between CALLYI and OA. Elevated levels of CALLYI were associated with a decreased risk of OA. Additionally, RCS analysis confirmed the non-linear nature of this correlation. Subgroup analyses affirmed the robustness and reliability of the observed relationship. These findings are deemed statistically significant (p < 0.05).

CRP: C-reactive protein

CALLYI: CRP- Albumin-Lymphocyte index

NHANES: National Health and Nutrition Examination Survey

OA: osteoarthritis

BMI: body measure index

DM: diabetes mellitus

GED: general educational development

OR: odds ratio

CI: confidence interval

RCS: restricted cubic spline

LAG-3: Lymphocyte Activation Gene-3

Acknowledgements

We sincerely thank all the designers and participants who contributed to the NHANES database.

Author contributions

KZ and MSG designed this study and contributed to data collection and analysis, KZ drafted this manuscript, and all authors reviewed, revised, and approved this manuscript for publication.

Funding

All authors declare that no funding was received for this study.

Data availability

We declare that no datasets were generated or analyzed for this study

Ethics approval and consent to participate

The data used in this study were obtained from publicly available data in the NHANES database and therefore no ethical approval was required.

Consent for publication

All authors approved the final manuscript and the submission to this journal.

Competing interests

The authors declare no competing interests.

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Table 1. Basic characteristics of participants by weighted CALLY index quartiles

variable	total	Quartiles of CALLYI				Pvalue
variable	total	Q1	Q2	Q3	Q4	Pvalue
Age	44.69 (0.22)	48.30 (0.33)	46.72 (0.33)	44.61 (0.32)	40.05 (0.28)	< 0.0001
Ethnic data						< 0.0001
black	9.75	12.73	9.80	8.61	8.33
mexican American	7.96	8.26	7.67	8.43	7.53
multi_racial	5.02	3.27	4.46	5.51	6.49
other Hispanic	4.88	4.55	5.27	4.61	5.06
white	72.39	71.19	72.81	72.85	72.58
Educational level						< 0.0001
<9th Grade	5.28	6.01	5.53	5.60	4.20
9-12th Grade	35.77	38.88	37.25	35.09	32.58
College Graduate or above	58.95	55.11	57.22	59.31	63.23
Sex						< 0.0001
Female	49.36	61.94	50.86	42.73	43.93
Male	50.64	38.06	49.14	57.27	56.07
Poverty Impact Ratio						< 0.0001
<1.3	18.64	21.82	18.79	16.69	17.72
>=3.5	45.81	41.34	44.94	47.23	48.90
1.3-3.49	35.55	36.84	36.27	36.08	33.38
Hypertension						< 0.0001
no	67.71	55.70	62.89	69.87	79.66
yes	32.29	44.30	37.11	30.13	20.34
DM						< 0.0001
no	90.83	85.01	89.41	92.62	95.15
yes	9.17	14.99	10.59	7.38	4.85
Coronary heart disease						< 0.0001
no	97.21	96.06	96.48	97.72	98.31
yes	2.79	3.94	3.52	2.28	1.69
Alcohol user						< 0.0001
no	26.02	33.30	27.47	23.59	21.07
yes	73.98	66.70	72.53	76.41	78.93
BMI						< 0.0001
<18	1.07	0.48	0.35	0.91	2.33
>=30	30.80	53.37	40.01	24.30	10.44
18-25	33.38	17.35	22.70	34.09	55.02
25-30	34.75	28.81	36.94	40.70	32.20
Stroke						< 0.0001
no	98.11	96.88	98.05	98.67	98.67
yes	1.89	3.12	1.95	1.33	1.33
Smoke						0.25
no	76.62	77.99	76.00	76.10	76.52
yes	23.38	22.01	24.00	23.90	23.48
Osteoarthritis or degenerative arthritis						< 0.0001
NO-OA	89.95	85.33	88.73	91.07	93.72
OA	10.05	14.67	11.27	8.93	6.28

Continuous variables are expressed as means (standard deviation): p value was calculated using a weighted linear regression model. Categorical variables are expressed as %: p value was calculated by a weighted chi-square test.

DM: diabetes mellitus; BMI: body mass index; OA: osteoarthritis

Table 2. Association between CALLYI and osteoarthritis

	Model 1		Model 2		Model 3
character	Pr(>\|t\|)	95% CI	Pr(>\|t\|)	95% CI	Pr(>\|t\|)	95% CI
CALLYI Q1	ref	ref	ref	ref	ref	ref
CALLYI Q2	<0.0001	0.74(0.65,0.85)	0.02	0.83(0.71,0.98)	0.06	0.86(0.73,1.01)
CALLYI Q3	<0.0001	0.57(0.49,0.67)	<0.001	0.74(0.63,0.88)	0.01	0.79(0.67,0.93)
CALLYI Q4	<0.0001	0.39(0.34,0.45)	<0.0001	0.67(0.55,0.81)	0.001	0.72(0.59,0.88)

Model 1 involved no adjustments.

Model 2 was adjusted for demographic characteristics including age, gender, race, education, and the poverty impact ratio.

Model 3 included adjustments for disease factors such as hypertension, diabetes mellitus, stroke, coronary heart disease, and lifestyle factors like smoking, drinking, and BMI.

Table 3. Subgroup analysis between CALLYI quartiles and osteoarthritis.

character	Q1	Q2	Q3	Q4	p for interaction
Sex					0.4
Male	ref	0.76 (0.59,0.96)	0.70 (0.54,0.91)	0.49 (0.37,0.65)
Female	ref	0.96 (0.80,1.14)	0.87 (0.73,1.05)	0.74 (0.59,0.92)
Ethnic data					0.95
white	ref	0.82 (0.70,0.96)	0.72 (0.61,0.84)	0.57 (0.47,0.70)
black	ref	0.65 (0.45,0.94)	0.82 (0.55,1.23)	0.63 (0.41,0.98)
mexican American	ref	0.85 (0.58,1.27)	0.64 (0.38,1.08)	0.54 (0.31,0.93)
other Hispanic	ref	0.75 (0.31,1.77)	0.49 (0.22,1.12)	0.24 (0.07,0.79)
multi racial	ref	0.66 (0.31,1.43)	0.50 (0.21,1.19)	0.40 (0.17,0.94)
Age					0.91
20-55years old	ref	0.77 (0.56,1.05)	0.72 (0.54,0.96)	0.65 (0.47,0.90)
>55years old	ref	0.82 (0.69,0.97)	0.74 (0.61,0.89)	0.64 (0.51,0.79)
Educational level					0.65
College Graduate or above	ref	0.90 (0.74,1.10)	0.75 (0.61,0.92)	0.64 (0.51,0.80)
9-12th Grade	ref	0.74 (0.59,0.92)	0.70 (0.55,0.88)	0.51 (0.36,0.72)
<9th Grade	ref	0.69 (0.43,1.09)	0.57 (0.32,1.02)	0.35 (0.17,0.71)
BMI					0.31
25-30	ref	0.87 (0.69,1.10)	0.71 (0.54,0.94)	0.61 (0.46,0.82)
>=30	ref	0.78 (0.64,0.96)	0.82 (0.66,1.04)	0.77 (0.53,1.11)
18-25	ref	0.82 (0.51,1.31)	0.62 (0.42,0.92)	0.49 (0.35,0.69)
<18	ref	0.26 (0.00, 16.15)	0.10 (0.01, 0.64)	0.34 (0.08, 1.36)

DM: diabetes mellitus; BMI: body mass index;

No competing interests reported.

SupplementaryTable1.docx

CRP-Albumin-Lymphocyte index (CALLYI) as a risk-predicting biomarker in association with osteoarthritis.

Status:

Version 1

Abstract

Purpose

Method

Results

Conclusion

Figures

Introduction

Methods

Study design and population

Diagnosis of OA and definition of CALLYI

Covariates

Statistical analyses

Results

Baseline characteristics

Association between CALLYI and OA

Subgroup analysis between CALLYI and OA

Nonlinear relationship between CALLYI and OA

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1