Association between fatty liver index and low back pain: A cross-sectional study from NHANES 1999

Abstract

Objective

The Fatty Liver Index (FLI) is a non-invasive algorithm that estimates the presence of hepatic steatosis based on body mass index (BMI), waist circumference, triglycerides, and gamma-glutamyl transferase (GGT). In recent years, FLI has been increasingly recognized for its associations with various metabolic disorders. Whether FLI is linked to low back pain (LBP) remains unclear. This study aims to explore the association between FLI and LBP.

Methods

Participants from the NHANES 1999–2004 survey were included. LBP was defined by self-reports. FLI was calculated from metabolic indicators using a standard formula. Logistic regression models assessed the association between FLI and LBP, adjusting for potential confounders, with restricted cubic splines to check for nonlinearity. Subgroup analyses and sensitivity analyses using multiple imputation for missing data were also conducted.

Results

A total of 5339 participants were included. Higher FLI levels were significantly associated with increased odds of LBP. For each 20-unit increase in FLI, the risk of LBP increased by 9% (OR = 1.09, 95% CI: 1.03, 1.14, P = 0.0017). The highest quartile of FLI had a 42% higher risk of LBP compared to the lowest quartile (OR = 1.42, 95% CI: 1.13, 1.79, P = 0.0029). Subgroup analysis showed a stronger association among individuals with lower physical activity levels. Sensitivity analyses confirmed robustness.

Conclusions

Higher FLI values corresponded to an increased risk of LBP, especially in individuals with lower physical activity. Further studies are needed to validate this relationship.

Keywords

fatty liver index low back pain metabolism NHANES

Introduction

Low back pain (LBP) is one of the most common health problems worldwide, with studies indicating that 80% of people will experience LBP at least once in their lifetime.¹ A 2008 review of global LBP prevalence, which included 165 studies from 54 countries, estimated the point prevalence at 18.3% and the one-month prevalence at 30.8%. LBP is most prevalent among individuals aged 40–69, with a higher prevalence in women than men and in high-income countries compared to middle- and low-income countries.² According to data from the 2021 Global Burden of Disease (GBD) study, although the global disability-adjusted life year (DALY) rate of LBP has declined, the total number of DALYs attributable to LBP has increased significantly by approximately 4,301,321 compared with 1990, further indicating that the burden of LBP remains substantial. In some low- and middle-SDI regions, the burden of LBP has shown little reduction and, in certain areas, has even increased.³ LBP imposes a significant economic burden on society, and for individuals, it forces workers into early retirement, impacting their subsequent wealth accumulation. For countries, the healthcare costs associated with LBP are inevitable.^4–6 The risk factors for LBP are diverse. Intervertebral disc degeneration is one of the major causes of LBP, with oxidative stress and mitochondrial dysfunction being key factors.^7,8 Poor lifestyle factors such as smoking, obesity, prolonged sitting and depression all increase the risk of LBP.^9–11

The Fatty Liver Index (FLI) is a composite measure that incorporates body mass index (BMI), waist circumference (WC), triglyceride levels, and gamma-glutamyl transferase (GGT). Initially used as a non-invasive biomarker to help diagnose non-alcoholic fatty liver disease (NAFLD),¹² FLI has gained widespread use as an important indicator for assessing the risk of various metabolic comorbidities.¹³ FLI has been shown to correlate with various metabolic syndrome parameters and adverse lipid profiles, confirming its suitability as a marker for evaluating the risk of metabolic syndrome.¹⁴ For example, elevated FLI is associated with an increased risk of cardiovascular diseases,¹⁵ diabetes,¹⁶ and dementia.¹⁷

Emerging evidence suggests that FLI, as a composite marker of hepatic steatosis and metabolic dysfunction, may also be linked to musculoskeletal conditions. Elevated FLI is associated with systemic low-grade inflammation, insulin resistance, and dysregulated adipokine profiles—all of which have been implicated in the pathogenesis of chronic pain and degenerative spine conditions. Furthermore, recent studies have reported associations between FLI and reduced bone mineral density, increased fracture risk, and sarcopenia. These shared metabolic and inflammatory pathways provide a plausible biological rationale for investigating the relationship between FLI and LBP. However, to date, no large-scale epidemiological study has examined this potential link. Furthermore, it is unclear whether there are potential factors that could modify the association between FLI and the risk of LBP. Therefore, further investigation and understanding of the relationship between the two are necessary to fill this gap in the field.

Based on this background, we conducted a cross-sectional study using NHANES data to explore the association between FLI and LBP in a large sample.

Methods

Study design and population

NHANES is a research program conducted by the National Center for Health Statistics (NCHS) that collects comprehensive health and nutrition data on the U.S. population. To obtain a representative sample of study participants, the program employs a sampling method that includes stratification, multistage sampling, and probability-based clustering. More information about the data is available at https://www.cdc.gov/nchs/nhanes/. The website provides detailed descriptions of the continuous design of the NHANES survey, confirms that all study procedures were approved by the Ethics Review Board of the NCHS prior to data collection, and states that all participants provided informed consent. This study employed a cross-sectional design. To ensure consistency in the definition of variables, we selected data from NHANES 1999 to 2004. During the four survey cycles of NHANES 1999–2004, a total of 31,126 participants were included. Of these, 15,794 participants were excluded for being under 20 years of age, 10 participants were excluded due to missing low back pain data, 9103 participants were excluded due to missing key indicators for FLI calculation, and 880 participants were excluded due to missing any covariate data (543 participants had missing PIR data, 57 participants had missing hypertension data, 1 participant had missing diabetes data, 3 participants had missing smoking data, 265 participants had missing alcohol consumption data, 7 participants had missing education level data, and 4 participants had missing daily average physical activity data). Ultimately, 5339 participants were included in the study. The participant exclusion process is shown in Figure 1.

Figure 1.

The flowchart of this study. From 31,126 NHANES 1999–2004 participants, 15,794 aged <20, 10 with missing LBP, 9103 with missing FLI, and 880 with missing covariates were excluded, leaving 5339 for analysis—3275 without and 2064 with low back pain.

Study variables definitions and measurements

The outcome variable in this study is LBP, which was obtained through participant questionnaires. Participants were asked whether they had experienced LBP in the past three months. The FLI is the exposure variable, calculated based on BMI, waist circumference, triglyceride levels, and gamma-glutamyl transferase (GGT), with the specific calculation formula as follows:¹⁸

F L I = \frac{[e 0.953 \times l n (t r i g l y c e r i d e s) + 0.139 \times B M I + 0.718 \times l n (G G T) + 0.053 \times W C - 15.745]}{[1 + e 0.953 \times l n (t r i g l y c e r i d e s) + 0.139 \times B M I + 0.718 \times l n (G G T) + 0.053 \times W C - 15.745]} \times 100

Covariables

This study considered several covariates to control for potential confounders. The selection of these covariates was based on both biological plausibility and the results of univariate logistic regression analysis. These include age (recorded in years), gender (male/female), poverty income ratio (PIR), race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races), alcohol consumption (yes/no), education level (less than high school, high school, and more than high school), smoking status (yes/no), hypertension status (yes/no), diabetes status (yes/no), and daily average physical activity level (low/medium/high). These data were collected through standardized NHANES questionnaires and interviews. It should be clarified that physical activity level was classified into low, medium, and high according to the NHANES 1999–2004 question PAQ180 (“Average level of physical activity each day”), in which participants selected the single sentence that best described their usual daily activities: code 1 (“sit during the day and do not walk about very much”) was defined as low activity; codes 2 (“stand or walk about a lot during the day, but do not have to carry or lift things very often”) and 3 (“lift light loads or have to climb stairs or hills often”) were combined and defined as medium activity; and code 4 (“do heavy work or carry heavy loads”) was defined as high activity, whereas individuals with refused responses (code 7), “Don’t know” answers (code 9), or missing data were excluded from the activity-level analyses. The PIR is calculated by dividing household income by the poverty threshold and adjusting for household size. Smoking was defined as having smoked at least 100 cigarettes in a lifetime, while drinking was defined as having consumed at least 12 alcoholic drinks in a lifetime. Hypertension and diabetes were defined based on whether participants had ever been told by a doctor that they had the condition. Daily average physical activity level was self-reported by participants. All covariate data were collected following NHANES standardized procedures, with more detailed information available at https://www.cdc.gov/nchs/nhanes/.

Statistical analysis

Given the complex sampling design of NHANES, our statistical analysis followed the NHANES weighting rules. First, we described the study population, grouping participants into four quartiles (Q1-Q4) based on the FLI. For continuous variables, we used the Kolmogorov-Smirnov test for normality. Due to the non-normal distribution of continuous variables, baseline characteristics were presented as weighted medians (interquartile range) for continuous variables, and as weighted percentages for categorical variables. Weighted chi-square tests and Kruskal-Wallis tests were performed to assess the statistical significance of categorical variables and non-normally distributed continuous variables. We conducted weighted univariate logistic regression analysis to examine the associations between each variable and LBP. To investigate the independent association between FLI and LBP, we performed weighted multivariate logistic regression analysis and constructed four models: Model 1 was unadjusted for any covariates; Model 2 adjusted for gender and age; Model 3 further adjusted for race, PIR, education status, smoking status, and alcohol consumption; and Model 4 additionally adjusted for hypertension, diabetes, and daily average physical activity level. The results are presented as odds ratio (OR) with 95% confidence interval (CI). To further explore potential nonlinear relationships between FLI and LBP, we employed a restricted cubic splines (RCS), controlling for confounding factors. To investigate how the association between FLI and LBP varies across subgroups and whether there are potential factors modifying the association, we conducted subgroup analyses and interaction tests based on gender, age, smoking status, alcohol consumption, hypertension, diabetes, and daily average physical activity level. Considering the impact of missing data on the study conclusions, we used multiple imputation with 5 repetitions and chain equation methods (R MI process) to handle missing data for sensitivity analysis. The same weighted multivariate logistic regression analysis was performed on the imputed data, constructing the same four models as in the non-imputed analysis to test the robustness of our findings. Statistical analyses were conducted using R Studio (version 4.2.0) and EmpowerStats (version 6.0). A two-tailed p-value of less than 0.05 was considered statistically significant.

Ethics approval and consent to participate

The present study analyzed data from the National Health and Nutrition Examination Survey (NHANES) 1999–2004, which is a publicly available dataset. All NHANES protocols were reviewed and approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board. Written informed consent was obtained from all participants in the 1999–2000, 2001–2002, and 2003–2004 NHANES cycles; copies of the consent forms are available at https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/documents.aspx?BeginYear = 1999, https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/documents.aspx?BeginYear = 2001 and https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/documents.aspx?BeginYear = 2003. The NHANES dataset accessed for this analysis was fully de-identified prior to public release. Therefore, this secondary analysis was exempt from additional IRB review or informed consent requirements.

Result

Weighted baseline characteristics of participants

Table 1 describes the baseline characteristics of the weighted study population grouped by FLI quartiles. The four groups showed statistically significant differences in age, gender, race, hypertension, diabetes, education level, smoking status, daily average physical activity level, and LBP prevalence. Participants in the fourth FLI quartile were more likely to be male, smokers, and have hypertension, diabetes, and lower levels of daily average physical activity. As FLI levels increased, the proportion of participants with LBP also increased, with significant intergroup differences (P = 0.0003).

Table 1.

Weighted characteristics of the study participants by the quartiles of FLI.

Variables	Quartiles of FLI				P-value
Variables	Q1(0.48, 23.56)	Q2(23.57–54.43)	Q3(54.44–81.99)	Q4(82.00–99.99)	P-value
Age(years)	38.00 [28.00,49.00]	45.00 [33.00,58.00]	48.00 [36.00,60.00]	47.00 [37.00,59.00]	<0.0001
PIR	3.14 [1.16,5.00]	3.16 [1.60,5.00]	3.20 [1.73,4.82]	2.97 [1.51,5.00]	0.7329
Gender(%)					<0.0001
Male	33.05	51.87	59.21	58.86
Female	66.95	48.13	40.79	41.14
Race(%)					0.0015
Mexican American	5.43	7.10	8.70	7.51
Other Hispanic	4.68	5.95	6.03	3.98
Non-Hispanic White	75.68	72.05	73.07	74.18
Non-Hispanic Black	9.73	9.95	8.53	11.20
Other Race - Including Multi-Racial	4.49	4.94	3.67	3.14
Hypertension(%)					<0.0001
Yes	13.16	22.09	33.00	45.64
No	86.84	77.91	67.00	54.36
Diabetes(%)					<0.0001
Yes	1.86	4.47	7.45	12.97
No	98.14	95.53	92.55	87.03
Smoking Status(%)					0.0037
Yes	46.89	51.92	51.50	55.50
No	53.11	48.08	48.50	44.50
Alcohol Status(%)YesNoEducation Status(%)	75.4124.59	73.5826.42	73.8926.11	71.5628.44	0.3021<0.0001
Under High School	14.34	19.71	22.77	19.34
High School Graduate	23.40	25.11	29.28	28.54
Above High School	62.26	55.18	47.95	52.12
Low Back Pain(%)					0.0003
Yes	35.50	38.52	40.27	45.34
No	64.50	61.48	59.73	54.66
Daily Average Physical Activity Level(%)					<0.0001
Low	21.87	20.06	23.66	32.42
Middle	69.84	72.08	68.33	60.74
High	8.29	7.86	8.01	6.84

Bold values indicate statistically significant differences.

Weighted univariate logistic regression analysis results

In the univariate regression analysis, FLI, hypertension, diabetes, race, smoking, education level, PIR, and daily average physical activity were significantly associated with LBP. Detailed information is provided in Supplementary Table S1.

Association between FLI and LBP

We performed weighted multivariate logistic regression analysis to investigate the independent association between the Fatty Liver Index (FLI) and low back pain (LBP), with detailed results presented in Table 2. To enhance the clinical interpretability of the results, the FLI variable was scaled in 20-point increments, an approach that yields more intuitive odds ratios than per-unit changes. This method mirrors strategies used in earlier epidemiologic studies, where continuous biomarkers with small per-unit effects were rescaled.¹⁹ The analysis results showed that for each 20-unit increase in FLI, the risk of LBP occurrence significantly increased. In the unadjusted crude model (Crude Model), for each 20-unit increase in FLI, the odds ratio (OR) was 1.10 (95% CI: 1.06–1.15), with a P value less than 0.0001. As the model adjustments progressed, the association between FLI and LBP remained stable. Specifically, after adjusting for basic variables such as age and gender (Model 1), and further adjusting for race, education level, PIR, smoking status, and alcohol consumption (Model 2), the ORs and 95% CIs were 1.12 (1.06–1.16) and 1.11 (1.06–1.16), respectively, with P values both less than 0.0001. Finally, in Model 3, after further adjustments for hypertension, diabetes, and physical activity level, the OR was 1.09 (95% CI: 1.03–1.14), with a P value of 0.0017, indicating that for each 20-unit increase in FLI, the risk of LBP increased by 9%. In the analysis of FLI quartiles, using the first quartile as the reference group, the ORs for the third and fourth quartiles were positively associated with LBP risk in the crude model, Model 1, and Model 2. In Model 3, compared to the first quartile, the OR for the fourth quartile was 1.42 (95% CI: 1.13–1.79), with a P value of 0.0029. In all models, the P value for the trend test between quartiles was significant. These analyses suggest a significant positive association between FLI and LBP, and this relationship remained robust after multivariable adjustments. The RCS shown in Figure 2 did not reveal any nonlinear relationship between FLI and LBP (P = 0.705).

Figure 2.

RCS results of FLI and LBP (knot = 4). Solid line = multivariable-adjusted odds ratio; shaded band = 95% CI; reference = FLI = 0. Poverall < 0.001; Pnon-linear = 0.705. Adjustment factors included gender, age, race, education level, PIR, smoking status, alcohol Status, hypertension, diabetes and daily average physical activity level.

Table 2.

Weighted multivariable-adjust ORs and 95% CI of the FLI associated with LBP.

Variables	Crude Model	Model1	Model2	Model3
FLI per 20	1.10 (1.06,1.15)<0.0001	1.12 (1.06,1.16)<0.0001	1.11 (1.06,1.16)<0.0001	1.09 (1.03,1.14)0.0017
Quartile of FLI
Q1 (0.48-23.56)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
Q2 (23.57–54.43)	1.14 (0.92,1.41)0.2424	1.17 (0.95,1.44)0.1592	1.17 (0.94,1.44)0.1260	1.16 (0.93,1.46)0.1294
Q3 (54.44–81.99)	1.23 (1.02,1.48) 0.0402	1.27 (1.05,1.53) 0.0193	1.25 (1.03,1.51) ＜0.0001	1.21 (0.98,1.49)0.0696
Q4 (82.00,99.00)	1.51 (1.23,1.84) 0.0003	1.56 (1.27,1.91) ＜0.0001	1.54 (1.26,1.87) 0.0313	1.42 (1.13,1.79) 0.0029
P for trend	＜0.0002	＜0.0001	＜0.0001	0.0024

Crude Model was adjusted for no covariates;

Model 1 was adjusted for gender and age;

Model 2 was adjusted for Model 1 + race, education level, PIR, smoking Status and alcohol status;

Model 3 was adjusted for Model 2 + hypertension, diabetes and daily average physical activity level.

Bold values indicate statistically significant differences.

Subgroup analysis

The results of the subgroup analysis and interaction test for the relationship between a 20-unit increase in FLI and LBP are shown in Figure 3. Our findings suggest an interaction between the average daily physical activity level and FLI (interaction P = 0.0062). Specifically, after adjusting for covariates, the positive association between FLI and LBP was particularly evident in the group with low average daily physical activity levels. In the other subgroups analyzed, no significant differences were observed in the relationship between FLI and LBP (P > 0.05). We then analyzed the relationship between FLI quartiles and LBP within subgroups based on average daily physical activity levels, with the interaction test again indicating an interaction between daily physical activity level and FLI (interaction P = 0.0354), as detailed in Supplementary Figure S1.

Figure 3.

The results of the subgroup analysis and interaction test for the relationship between a 20-unit increase in FLI and LBP. Forest plot showing the ORs with 95% CIs for the association between FLI and LBP across different models. Each square represents the point estimate, with horizontal lines indicating the 95%CIs. The plot visually summarizes the effect sizes and precision of the estimates in the analysis.

Multiple imputation results

In the dataset after multiple imputation, a multivariable logistic regression model was constructed to explore the relationship between FLI and LBP. The results indicated that in Model 3, for every 20-unit increase in FLI, the OR (95% CI) was 1.09 (1.04, 1.15). When FLI was categorized into quartiles, compared to the first quartile, the OR (95% CI) for the fourth quartile was 1.47 (1.18, 1.82).

These findings further suggest a significant association between increased FLI and a higher risk of LBP. Detailed data can be found in Supplementary Table S2.

Discussion

In this study, we utilized data from NHANES 1999–2004 to explore the relationship between FLI and LBP. Despite plausible biological links between metabolic dysfunction and musculoskeletal disorders, including LBP, no previous large-scale epidemiologic investigation has specifically examined the relationship between FLI and LBP. Using nationally representative NHANES data, our study addresses this gap by quantifying the association in a sample of over 5000 US adults and by assessing potential effect modification by physical activity and cardiometabolic status. To the best of our knowledge, this is the first large-scale, weighted data study investigating the association between FLI and LBP. Our findings indicate that an increase in FLI is significantly associated with an elevated risk of LBP, and this association remains robust even after adjusting for potential confounders. Specifically, for every 20-unit increase in FLI, the risk of LBP increases by 9%. Moreover, when FLI is treated as a categorical variable, individuals in the highest FLI quartile show a 42% higher risk of LBP compared to those in the lowest quartile. Our study provides novel epidemiological evidence that higher FLI is linearly associated with increased risk of LBP in a nationally representative sample. By identifying physical activity as a significant effect modifier, we highlight the potential of lifestyle factors to mitigate the musculoskeletal consequences of metabolic–hepatic dysfunction. The robustness of our findings, preserved after extensive adjustment and multiple imputation, underscores the validity of this association. Clinically, FLI may serve as a simple and accessible tool for early identification of individuals at elevated risk of LBP, supporting timely preventive strategies and offering new insights into the role of metabolic health in musculoskeletal disorders.

Possible explanation

An increase in FLI is closely associated with abnormalities in fat metabolism, insulin resistance, and heightened systemic inflammatory responses, all of which have been shown to play critical roles in various metabolic diseases. We hypothesize that elevated FLI may increase the risk of LBP through several mechanisms. Firstly, systemic inflammation plays a significant role. The elevation of FLI is often accompanied by low-grade chronic inflammation throughout the body, such as C-reactive protein,²⁰ proinflammatory cytokines (tumour necrosis factor, interleukin-6) and anti-inflammatory cytokines (interleukin-4, interleukin-10).²¹ The elevation of FLI is also associated with some novel inflammatory markers such as the lymphocyte-to-monocyte ratio,²² CD36,²³ and the adipokine Chemerin, which are closely linked to inflammation and lipid metabolism.^24,25 Previous studies have indicated that inflammatory factors can promote the degenerative processes of the spine and joints, thereby increasing the risk of LBP. Specifically, in the context of long-term chronic inflammation, these mediators may induce damage to cartilage and bones, exacerbating degenerative changes and contributing to the onset of LBP.²⁶ Secondly, metabolic abnormalities are also a key factor. FLI is strongly associated with metabolic syndrome, and components of metabolic syndrome, such as obesity, diabetes, and dyslipidemia, are well-established risk factors for LBP. Obesity not only increases the load on the spine but is also linked to the distribution of fat tissue and the accumulation of visceral fat, which can exacerbate degenerative changes in the spine and joints.²⁷ Diabetes and hyperglycemia, on the other hand, can impair nerve and muscle function, further contributing to the development of LBP.^28,29 We also consider a potential link to bone mineral density (BMD). Several studies have shown an association between elevated FLI and decreased BMD.³⁰ The increase in FLI may disrupt bone metabolism, accelerating bone loss and leading to osteoporosis, which in turn raises the risk of LBP. Furthermore, a reduction in BMD could affect the structural integrity and mechanical properties of the spine, increasing the likelihood of degenerative changes.³¹ Additionally, previous literature has reported that elevated FLI is linked to an increased risk of fractures,³² osteoarthritis,³³ and sarcopenia,^34,35 suggesting that FLI may have detrimental effects on the musculoskeletal system through mechanisms that have not yet been fully elucidated. These effects are likely unavoidable and may contribute to the development of LBP. Our subgroup analysis further revealed that physical activity levels modulate the relationship between FLI and LBP. In individuals with low levels of physical activity, higher FLI was significantly associated with an increased risk of LBP. This finding may be related to factors such as muscle atrophy, decreased metabolic function, and weight gain observed in individuals with low activity levels. Furthermore, atrophy of the paravertebral muscles can further exacerbate intervertebral disc degeneration, creating a vicious cycle.^36–38 In contrast, individuals with higher levels of physical activity may experience a reduced impact of elevated FLI on LBP, likely due to better muscle support and metabolic health.^39,40 This suggests that, in clinical practice, in addition to monitoring FLI levels, increasing physical activity and improving lifestyle may be important strategies for preventing LBP.

In this study, although we did not include BMI, obesity, and metabolic syndrome (MetS) as independent covariates in the regression models, this methodological choice is well-supported. First, the FLI, as a composite metabolic risk indicator, already includes BMI, waist circumference, and other key components in its calculation formula. Therefore, including BMI and MetS separately in the model could lead to multicollinearity, making the estimates unstable and potentially distorting the interpretation of the relationship between FLI and low back pain LBP. Additionally, over-adjusting for these variables may obscure the independent role of FLI in predicting LBP. We believe that excluding BMI and MetS allows for a clearer reflection of the true association between FLI and LBP, while avoiding statistical biases. Future research could further explore subgroup analyses of these variables to examine their potential impact on the relationship between FLI and LBP, thereby providing a more comprehensive understanding.

Limitations and future research directions

Although this study reveals a significant association between FLI and LBP, several limitations must be acknowledged. First, the cross-sectional design precludes causal inference. While we observed an association between higher FLI and increased risk of LBP, it remains unclear whether elevated FLI precedes LBP, or whether LBP leads to reduced physical activity and metabolic dysfunction, subsequently elevating FLI. This potential reverse causality could exaggerate the strength of the observed association.

Second, the outcome of LBP was self-reported and lacked objective clinical or imaging confirmation. Participants may have under- or over-reported their symptoms due to recall bias or individual differences in pain perception. If misclassification was nondifferential, the true association may have been attenuated; if differential, the risk may have been over- or underestimated.

Third, while FLI is a validated surrogate marker for hepatic steatosis in epidemiological studies, it is not a definitive diagnostic tool. FLI levels may be influenced by dietary status, medication use, or transient fluctuations in liver function, introducing potential measurement error. Such error is likely to bias the results toward the null, suggesting that the true effect may be stronger than observed.

Furthermore, although we adjusted for several potential confounders, there may still be unmeasured factors influencing the results. Psychosocial factors (such as depression, anxiety), sleep quality, dietary patterns, and analgesic use may all influence the relationship between FLI and LBP to varying degrees, though the direction and magnitude of their effects remain uncertain.

The data used in this study are from 1999–2004, which may limit the generalizability of the results. With changing disease prevalence and updates in medical and lifestyle interventions, the strength of the association may vary in more recent populations. Therefore, further validation using more recent data is warranted.

Future research should explore the causal relationship between FLI and LBP and evaluate the potential role of improving metabolic status and increasing physical activity in reducing the risk of LBP. Prospective studies and long-term follow-up would help validate whether FLI is an independent predictor of LBP occurrence and elucidate the potential mechanisms through which fatty liver affects the musculoskeletal system. Additionally, clinical intervention studies should focus on early interventions for individuals with high FLI to reduce LBP incidence and improve quality of life.

Conclusions

In conclusion, this cross-sectional study demonstrates an association between elevated FLI levels and an increased risk of LBP, with the relationship being more pronounced in individuals with low levels of daily average physical activity. In public health strategies, FLI, as a modifiable factor, should be targeted for early intervention in patients with elevated FLI to prevent the occurrence of LBP. Physical activity levels modify the relationship between the two, further highlighting the complexity of the relationship between exercise, liver health, and musculoskeletal health. Future prospective research strategies are necessary to determine causal relationships and further explore the mechanisms underlying these associations.

Supplemental Material

sj-docx-1-bmr-10.1177_10538127251392826 - Supplemental material for Association between fatty liver index and low back pain: A cross-sectional study from NHANES 1999–2004

Supplemental material, sj-docx-1-bmr-10.1177_10538127251392826 for Association between fatty liver index and low back pain: A cross-sectional study from NHANES 1999–2004 by Jiangtao Liao, Zhenyu Song, Hao Xu, Yi Li, Dongdong Xu, Hongtao Tian, Zhipeng Dai and Wei Tong in Journal of Back and Musculoskeletal Rehabilitation

Supplemental Material

sj-tif-2-bmr-10.1177_10538127251392826 - Supplemental material for Association between fatty liver index and low back pain: A cross-sectional study from NHANES 1999–2004

Supplemental material, sj-tif-2-bmr-10.1177_10538127251392826 for Association between fatty liver index and low back pain: A cross-sectional study from NHANES 1999–2004 by Jiangtao Liao, Zhenyu Song, Hao Xu, Yi Li, Dongdong Xu, Hongtao Tian, Zhipeng Dai and Wei Tong in Journal of Back and Musculoskeletal Rehabilitation

Footnotes

Acknowledgements

The authors would like to express their sincere gratitude to Wei Yu for his valuable support during the revision process, including assistance with data checking, language polishing, and improvements in detailed content. His contribution was important for the improvement of this study, although it does not meet the criteria for authorship.

Author contributions

Contributors Jiangtao Liao was responsible for the investigation and writing original draft, organization and coordination of the trial. Zhenyu Song and Dongdong Xu also was one of the chief investigators. Hao Xu and Yi Li was responsible for the methodologies. Jiangtao Liao and Zhenyu Song was responsible for the data validation, while Hongtao Tian and Zhipeng Dai mainly conduct software practical operations. Hongtao Tian, Zhipeng Dai and Wei Tong supervised this project. All authors contributed to the writing of the final manuscript. All authors reviewed the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Department of Science and Technology of Hubei Province, China (2023BCB089) and the National Natural Science Foundation of China (82372465).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

Publicly available datasets were analyzed in this study. Those data can be found here: .

ORCID iD

Jiangtao Liao

Supplemental material

Supplemental material for this article is available online.

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