Association between Weight-Adjusted Waist Index and Chronic Pain in U.S. adults: a Cross- Sectional Study

doi:10.21203/rs.3.rs-4882222/v1

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Association between Weight-Adjusted Waist Index and Chronic Pain in U.S. adults: a Cross- Sectional Study

https://doi.org/10.21203/rs.3.rs-4882222/v1

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Background

Chronic pain is associated with significant levels of disability and is widely considered an important public health problem. Current evidence implicates a significant link between chronic pain and obesity, along with associated metabolic dysfunctions. The weight-adjusted waist index (WWI) is an innovative measure for obesity. This study aims to explore the association between WWI and chronic pain among American adults.

Methods

This study used data from the 1999–2004 National Health and Nutrition Examination Survey (NHANES). Chronic pain was defined as self-reported pain lasting for ≥ 3 months in the past year. Weighted multivariate linear regression and smoothed curve fitting were conducted to investigate the linear associations between WWI and chronic pain. Threshold effects were determined using a two-part linear regression model. Subgroup analyses were conducted to investigate factors influencing the relationship between WWI and chronic pain.

Results

Among the 12,694 participants, 1,856 (14.62%) experienced chronic pain. After complete adjustment, every unit increase in WWI correlated with a 10% higher odds of chronic pain prevalence (OR 1.10, 95% CI 1.01–1.19, P = 0.036). Individuals in the highest WWI quartile (11.54–15.52) cm/\(\:\sqrt{\text{k}\text{g}}\) had a 31% increased odds of chronic pain prevalence compared to those in the lowest quartile (7.90–10.36) cm/\(\:\sqrt{\text{k}\text{g}}\) (OR 1.31, 95% CI 1.08–1.60, P = 0.016). The positive association between WWI and chronic pain remained persisted across all subcategories except for race. The saturation effect between WWI and chronic pain was observed, with the inflection point at 11.88 cm/\(\:\sqrt{\text{k}\text{g}}\) for all participants and 11.79 cm/\(\:\sqrt{\text{k}\text{g}}\) for females.

Conclusions

Our research demonstrated a notable positive association between WWI and chronic pain. These findings help public health officials better understand of importance of controlling abdominal obesity in alleviating chronic pain, aid in the development and evaluation of pain management programs, and develop optimal interventions to diagnose and treat chronic pain.

chronic pain

weight-adjusted waist index

NHANES

obesity

Chronic pain is usually defined as pain that lasts longer than the typical healing time frame (usually 3 months) [1]. Epidemiological studies indicate that over 20% of adults in America experience chronic pain, which can lead to anxiety, depression, sleep disturbances, limitations in functional status, and deterioration in the quality of life for patients and their families [2, 3]. It requires pharmacologic treatment, complicates medical treatment for other conditions, and diminishes work capacity and daily living. The annual treatment costs associated with chronic pain are estimated to be nearly 30% higher than those of cancer and diabetes combined [4]. Chronic pain involves a complex interaction of physiological, psychological, and social factors [5]. Understanding these risk factors is essential to devise effective preventive and management strategies for chronic pain.

Obesity and psychological factors were identified as risk factors for chronic pain [6]. The excessive mechanical stress and the proinflammatory state induced by obesity may lower the body's pain threshold [7]. Conversely, individuals with chronic pain are more prone to overweight, obesity, and metabolic syndrome [8]. Furthermore, comprehensive interdisciplinary treatments addressing both obesity and chronic pain, such as dietary intervention for weight loss, have demonstrated effective outcomes, suggesting the clinical importance of monitoring and intervening in obesity in those with chronic pain [9, 10].

The most commonly used indicator for defining obesity in clinical and epidemiological studies is body mass index (BMI) [11]. However, BMI does not adequately reflect total body fat mass or central obesity, which has been shown to be independently associated with chronic pain [12, 13]. In response, the weight-adjusted waist index (WWI) has emerged as a new obesity index [14]. WWI primarily measures age-related changes to assess central obesity, which is closely associated with fat mass [15, 16]. Previous studies have demonstrated that WWI exhibits a strong association with depression, cognitive dysfunction, and cardiovascular disease [17–19]. However, its relationship with chronic pain status remains unknown.

Therefore, this study aimed to explore the associations between WWI and chronic pain based on data from the National Health and Nutrition Examination Survey (NHANES).

Data source and study population

This cross-sectional study used data from NHANES, a program established by the National Center for Health Statistics (NCHS). NHANES is designed to assess the health and nutritional status of noninstitutionalized civilians in the United States, offering valuable insights into contemporary disease patterns and shaping public health policies.

All participants in the NHANES study protocols signed the informed consent form, approved by the Research Ethics Review Board of NCHS. More detailed information about the NHANES survey is available publicly at https://www.cdc.gov/nchs/nhanes/index.htm.

Data from 31,126 participants across three consecutive NHANES cycles (1999–2004) were collected for this analysis, as pain questionnaire data were available only during this period. Specific exclusion criteria were applied: (1) individuals without weight or waist circumference data necessary to calculate WWI (n = 5,502); (2) participants lacking chronic pain information (n = 12,200); (3) participants who self-reported as pregnant (n = 730). A total of 12,694 adults were ultimately chosen for subsequent analyses, as shown in the flow chart (Fig. 1).

Definition of chronic pain

According to the 11th version of the International Classification of Diseases (ICD-11), chronic pain persists for more than 3 months. In this study, chronic pain was identified based on the Miscellaneous Pain Questionnaire (MPQ100 and MPQ110). MPQ100 queried participants about their history of pain (whether they had a problem with pain lasting more than 24 hours during the past month), while MPQ110 asked about the duration of the pain (How long have you experienced this pain?). Participants who reported pain ‘lasting at least three months but less than one year’ or ‘greater than one year’ (MPQ100 = 1, MPQ110 = 3 or 4) were classified as experiencing chronic pain. Those who reported no pain during the past month (MPQ100 = 2) or who had pain problems for less than 3 months (MPQ100 = 1, MPQ110 = 1 or 2) were categorized as not having chronic pain. Non-responders or those who answered ‘I do not know’ were treated as missing values [20].

Calculation of weight-adjusted waist index (WWI)

The WWI score is an indicator positively correlated with central obesity, calculated as the participant's waist circumference in centimeters divided by the square root of their weight in kilograms, rounded to the nearest hundredth [14]. In this study, WWI was treated as a continuous variable and analyzed as the exposed variable. Individuals were subsequently categorized into groups based on WWI quartiles for further examination.

Covariables

In our study, the covariates included gender (male / female), age (20–39 years / 40–59 years / ≥ 60 years), race (non-Hispanic whites / non-Hispanic blacks / Mexican Americans / other racial backgrounds), education level (less than high school / high school / above high school), marital status (married or living with partner / widowed or divorced or separated / never married). Household income, based on the poverty-income ratio (PIR), was categorized into three groups: low (PIR ≤ 1.0), medium (PIR > 1.0 and PIR ≤ 3.0), and high (PIR > 3.0). Physical activity was classified into sedentary, mild physical activity, moderate physical activity, and severe physical activity. Smoking status included never-smokers (having smoked fewer than 100 cigarettes), former smokers (having quit after smoking more than 100 cigarettes), and current smokers. Alcohol consumption was categorized into three groups based on the number of glasses consumed daily: no alcohol consumption, 1–2 glasses per day, and > 2 glasses per day. Diabetes was identified based on participants' self-reporting of a doctor's diagnosis in the questionnaire.

Statistical analysis

Statistical analyses were carried out with proper NHANES sample weights and the missing data for baseline characteristics were imputed using the mean or mode. Categorical variables were presented as frequency and percentages, while continuous variables were presented as means (standard deviation, S.D.). Baseline characteristics of individuals with and without chronic pain were conducted using a t-test or Mann-Whitney test for continuous variables and a chi-square test for categorical variables. In three different models, multivariate logistic regression was employed to investigate the independent association between WWI (continuous and quartiles) and chronic pain (present or not). Crude model (Model 1) did not involve any adjustments. Adjusted model (Model 2) involved adjustments for age, sex, race, marital status, education level, and income. Fully adjusted model (Model 3) was further adjusted for lifestyle factors such as physical activity, alcohol consumption, smoking status, and diabetes status. Tests for linear trends were performed by entering the median value of each category of WWI as a continuous variable in the models. Subgroup analyses and interaction tests were conducted to test the stability of WWI and chronic pain within distinct groupings. Moreover, smoothing curve fitting and a two-piecewise linear regression model were carried out to calculate the threshold effect of WWI on chronic pain according to the resultant smoothing plot.

The analyses were performed using R 3.6.1 software (R Foundation for Statistical Computing, Vienna, Austria; http://www.r-project.org/) and EmpowerStats (X&Y Solutions, Boston, MA; http://www.empowerstats.com/), with significance set at P < 0.05.

Baseline characteristics

This study involved 12,694 participants with an average age of (50.6 ± 18.7) years, of whom 6,585 (50.3%) were male and 6,309 (49.7%) were female. Among all participants, the prevalence of chronic pain was 14.62%, and it increased with the higher WWI quartiles. Table 1 shows the participants' clinical characteristics, displaying stratified groups based on chronic pain status. There are statistically significant differences between those with and without chronic pain in terms of demographic data, lifestyle habits, and diabetes status (all P < 0.001) (Table 1).

Table 1

Basic characteristics of participants by chronic pain status
Characteristics	No pain (N = 10,838)	Chronic pain (N = 1,856)	P - value
WWI (cm/\(\:\sqrt{kg}\))	10.75 ± 0.81	10.94 ± 0.82	< 0.001
Age(years)	45.2 ± 16.8	48.1 ± 15.3	< 0.001
Gender (%)			< 0.001
Male	50.13	43.26
Female	49.87	56.74
Race (%)			< 0.001
Non-Hispanic whites	71.28	79.07
Non-Hispanic blacks	11.09	9.35
Mexican Americans	7.59	3.84
Other racial backgrounds	10.04	7.74
Education level (%)			< 0.001
Less than high school	19.78	22.99
High school	25.68	28.73
Above high school	54.54	48.28
Marital status (%)			< 0.001
Married or living with a partner	63.30	67.65
Widowed or divorced or separated	17.58	21.35
Never married	19.12	11.00
PIR (%)			< 0.001
≤ 1	13.09	16.84
> 1, ≤ 3	36.15	38.81
> 3	50.76	44.35
Daily physical activity (%)			< 0.001
Sedentary	23.30	29.08
Mild physical activity	51.06	47.92
Moderate physical activity	17.96	15.66
Severe physical activity	7.68	7.34
Diabetes (%)			< 0.001
No	92.85	88.39
Yes	6.05	10.42
Borderline	1.10	1.19
Smoking (%)			< 0.001
Never smokers	52.00	38.65
Former smokers	23.69	33.75
Current smokers	24.31	27.60
Alcoholic drinks per day (%)			< 0.001
No	17.75	26.36
1–2 glasses	27.55	26.34
> 2 glasses	54.70	47.30
Note: Continuous variables are presented as Mean ± S.D. and categorical variables are presented as percentage.
Abbreviations: WWI, weight-adjusted waist index; PIR, poverty income ratio.
P values were calculated by using one-way ANOVA for continuous variables and the χ² test for categorical variables.

The mean WWI for all participants was (10.95 ± 0.85) cm/\(\:\sqrt{\text{k}\text{g}}\), with the values for the different quartiles as follows: Q1(7.90–10.36) cm/\(\:\sqrt{\text{k}\text{g}}\), Q2(10.37–10.94) cm/\(\:\sqrt{\text{k}\text{g}}\), Q3(10.95–11.53) cm/\(\:\sqrt{\text{k}\text{g}}\), and Q4 (11.54–15.52) cm/\(\:\sqrt{\text{k}\text{g}}\). Table 2 shows the participants' clinical characteristics and stratified groups by WWI quartiles. Compared to individuals with the lowest quartile of WWI, individuals in the highest quartile were more likely to be older, female, less educated, have lower family income, Non-Hispanic White, and smokers; they were less physically active and more likely to have diabetes (all P < 0.001).

Table 2

Baseline characteristics of participants stratified by quartiles of weight-adjusted waist index (WWI)
Characteristics	Weight-adjusted waist index (WWI) (cm/\(\:\sqrt{\mathbf{k}\mathbf{g}}\))				P - value
Characteristics	Q1 (7.90–10.36 ) N = 3,174	Q2 (10.37–10.94) N = 3,173	Q3 (10.95–11.53) N = 3,173	Q4 ( 11.54–15.52) N = 3,174	P - value
Pain (%)					< 0.001
No pain	87.85	83.79	84.66	78.74
Chronic pain	12.15	16.21	15.34	21.26
Age	36.6 ± 12.5	43.6 ± 14.6	50.5 ± 15.7	58.3 ± 16.7	< 0.001
Gender (%)					< 0.001
Male	50.14	54.01	50.91	37.45
Female	49.86	45.99	49.09	62.55
Race (%)					< 0.001
Non-Hispanic whites	72.38	72.76	71.17	73.94
Non-Hispanic blacks	14.18	9.73	9.89	7.80
Mexican Americans	4.70	7.74	8.88	7.54
Other racial backgrounds	8.74	9.77	10.05	10.72
Education level (%)					< 0.001
Less than high school	13.87	17.26	23.93	31.37
High school	23.12	27.11	27.03	28.90
Above high school	63.01	55.63	49.04	39.73
Marital status (%)					< 0.001
Married or living with a partner	59.29	67.44	68.76	60.90
Widowed or divorced or Separated	12.79	15.45	19.35	29.78
Never married	27.92	17.11	11.89	9.32
PIR (%)					< 0.001
≤ 1	12.45	11.78	14.20	18.11
> 1, ≤ 3	33.03	33.49	37.50	46.31
> 3	54.52	54.73	48.30	35.58
Daily physical activity (%)					< 0.001
Sedentary	20.31	23.80	23.54	32.34
Mild physical activity	48.93	49.49	52.93	52.12
Moderate physical activity	21.30	17.76	16.60	12.21
Severe physical activity	9.46	8.95	6.93	3.33
Diabetes (%)					< 0.001
No	97.81	96.05	90.29	78.84
Yes	1.63	3.30	8.03	19.08
Borderline	0.56	0.65	1.68	2.08
Smoking (%)					< 0.001
Never smokers	54.72	49.20	48.21	44.78
Former smokers	27.88	26.13	23.36	21.83
Current smokers	17.40	24.67	28.43	33.39
Alcoholic drinks per day (%)					< 0.001
No	10.63	15.08	24.46	34.66
1–2 glasses	27.62	26.82	27.38	27.71
> 2 glasses	61.75	58.10	48.16	37.63
Note: Continuous variables are presented as Mean ± S.D. and categorical variables are presented as percentage.
Abbreviations: Q, quartile; PIR, poverty income ratio.
P values were calculated by using one-way ANOVA for continuous variables and the χ² test for categorical variables.

Association between WWI and chronic pain

Table 3 shows the correlation between WWI and chronic pain in the crude (OR 1.21, 95% CI 1.14–1.28, P < 0.001) and partially adjusted (OR 1.14, 95% CI 1.06–1.23, P = 0.001) models. The WWI and chronic pain showed a significant positive association (all P < 0.05). After complete adjustment, this positive association remained robust, with a 10% increase in odds of chronic pain prevalence for every unit increase in WWI (OR 1.10, 95% CI 1.01–1.19, P = 0.036). After transforming WWI into quartiles, this positive relationship maintained stable (all P for trend < 0.05). Individuals in the highest WWI quartile had a 31% increased odds of chronic pain prevalence compared to those in the lowest quartile (OR 1.31, 95% CI 1.08–1.60, P = 0.016).

Table 3

Associations between weight-adjusted waist index (WWI) and chronic pain in the unadjusted and adjusted logistic regression models
Exposure	Crude model OR (95% CI) P	Adjusted model OR (95% CI) P	Fully adjusted model OR (95% CI) P
WWI (continuous)	1.21 (1.14, 1.28) < 0.001	1.14 (1.06, 1.23) 0.001	1.10 (1.01, 1.19) 0.036
WWI (quartile)
Q1 (7.90–10.36)	Reference	Reference	Reference
Q2 (10.37–10.94)	1.27 (1.10, 1.47) 0.001	1.22 (1.04, 1.43) 0.013	1.18 (1.00, 1.40) 0.056
Q3 (10.95–11.53)	1.20 (1.04, 1.39) 0.015	1.14 (0.96, 1.34) 0.133	1.10 (0.92, 1.32) 0.297
Q4 (11.54–15.52)	1.59 (1.38, 1.83) < 0.001	1.40 (1.18, 1.68) < 0.001	1.31 (1.08, 1.60) 0.007
P for trend	1.24 (1.15, 1.32) < 0.001	1.16 (1.07, 1.27) 0.001	1.13 (1.02, 1.24) 0.016
Note: OR (95% CI) was calculated by using linear regression models. Covariables include the following: Crude model: Model 1, No covariates were adjusted. Adjusted model: Model 2, adjusted for age, sex, race, education level, marital status, and PIR. Fully adjusted model: Model 3, adjusted for age, sex, race, education level, marital status, PIR, alcohol consumption, smoking, diabetes, and physical activity.
Abbreviations: Q, quartile, PIR, poverty income ratio; OR, Odds Ratio; CI, confidence interval.

Subgroup analyses

Subgroup analyses and interaction tests were carried out to evaluate the uniformity of the association between WWI and chronic pain across various demographic groups. As illustrated in Table 4, significant disparities were observed in the association between WWI and chronic pain among individuals of different races or other Hispanics. Specifically, participants categorized as 'other races or other Hispanics' showed a stronger positive correlation between WWI and chronic pain (OR 1.97, 95% CI 1.29–3.01, P = 0.002) compared to non-Hispanic whites (OR 1.14, 95% CI 1.02–1.27, P = 0.026) and Mexican Americans (OR 0.85, 95% CI 0.67–1.07, P = 0.162).

Table 4

Subgroup analysis of the association between weight-adjusted waist index (WWI) and chronic pain
Subgroups	Chronic pain OR (95% CI) P	P for interaction
Gender		0.169
Male	1.17(1.02, 1.34) 0.025
Female	1.03(0.92, 1.15) 0.568
Age		0.932
20–39 years	1.08(0.92, 1.28) 0.339
40–59 years	1.04(0.91, 1.20) 0.572
≥ 60 years	1.06(0.92, 1.22) 0.432
Race		0.005
Non-Hispanic whites	1.14 (1.02, 1.27) 0.026
Non-Hispanic blacks	1.05(0.87, 1.28) 0.595
Mexican Americans	0.85(0.67, 1.07) 0.162
Other racial backgrounds	1.97 (1.29, 3.01) 0.002
Education		0.826
Less than high school	1.08(0.92, 1.27) 0.331
High school	1.05(0.88, 1.25) 0.583
Above high school	1.12(0.99, 1.27) 0.080
Marital status		0.418
Married or living with a partner	1.05(0.94, 1.17) 0.408
Widowed or divorced or separated	1.19(1.02, 1.40) 0.030
Never married	1.10(0.87, 1.39) 0.426
PIR		0.388
≤ 1	1.09(0.89, 1.32) 0.400
> 1, ≤ 3	1.18 (1.03, 1.34) 0.014
> 3	1.03 (0.89, 1.19) 0.703
Daily physical activity		0.204
Sedentary	1.26(1.08, 1.48) 0.004
Mild physical activity	1.02(0.90, 1.15) 0.741
Moderate physical activity	1.05 0.84, 1.32) 0.666
Severe physical activity	1.06(0.72, 1.56) 0.766
Diabetes		0.663
No	1.09(1.00, 1.20) 0.057
Yes	1.06(0.81, 1.38) 0.690
Borderline	0.74(0.31, 1.76) 0.493
Smoking		0.098
Never smokers	1.14(0.99, 1.31) 0.070
Former smokers	0.96(0.82, 1.12) 0.583
Current smokers	1.21(1.03, 1.41) 0.017
Alcoholic drinks per day		0.475
No	1.14(0.97, 1.33) 0.105
1–2 glasses	1.00(0.84, 1.18) 0.956
> 2 glasses	1.11(0.97, 1.26) 0.123
Note: P for interaction was calculated using the likelihood ratio test comparing models with and without the interaction term.
Abbreviations: PIR, poverty income ratio; OR, Odds Ratio; CI, confidence interval.

Saturation effect analysis between WWI and chronic pain

The findings from smoothed curve fitting analysis revealed the associations and saturation phenomenon between WWI and chronic pain (after adjusting age, gender, race, education level, marital status, income, smoking and exercise status, alcohol consumption and diabetes) (Fig. 2). The analysis identified a saturation effect value of 11.88 cm/\(\:\sqrt{\text{k}\text{g}}\) for all participants (Table 5). Below this threshold, a 1-unit increase in the WWI level was associated with a 16% increased odds of chronic pain prevalence (OR 1.16, 95% CI 1.05–1.29, P = 0.004). However, above 11.88 cm/\(\:\sqrt{\text{k}\text{g}}\), no significant association with chronic pain was observed (OR 0.78, 95% CI 0.56–1.08, P = 0.129).

Table 5

Saturation effect analysis of weight-adjusted waist index (WWI) on chronic pain
Chronic pain	Model: Saturation effect analysis OR (95% CI) P
WWI turning point (K)	11.88
< K effect 1	1.16(1.05, 1.29) 0.003
> K effect 2	0.78 (0.56, 1.08) 0.129
Log-likelihood ratio	0.028
Subgroup analysis stratified by gender
Male
Turning point (K)	11.41
< K effect 1	1.08 (0.90, 1.29) 0.427
> K effect 2	1.42 (1.03, 1.95) 0.031
Log-likelihood ratio	0.188
Female
Turning point (K)	11.79
< K effect 1	1.18 (1.03, 1.37) 0.020
> K effect 2	0.61 (0.42, 0.88) 0.009
Log-likelihood ratio	0.002

Note: Saturation effect analysis adjusted for age, sex, race, education level, marital status, PIR, alcohol consumption, smoking, diabetes, and physical activity.

Abbreviations: PIR, poverty income ratio; OR, Odds Ratio; CI, confidence interval.

The solid red line represents the smooth curve fit between variables. Blue bands represent the 95% confidence interval from the fit

Additionally, a saturation effect was found at 11.79 cm/\(\:\sqrt{\text{k}\text{g}}\), specifically for females. Below this threshold, the effect value registered at 1.18; above it, the association reversed, with a 1-unit increase in WWI was associated with a 39% decreased odds of chronic pain prevalence (OR 0.61, 95% CI 0.42–0.88, P = 0.009) (Table 5; Fig. 3).

This cross-sectional study investigated the association between WWI and chronic pain, revealing a significant positive linear relationship. Subgroup analyses and interaction evaluations showed that this positive correlation remained consistent across most subgroups, including age, gender, physical activity, alcohol consumption, smoking status, and diabetes status, except for race. Furthermore, saturation effect analysis identified an inflection point at WWI 11.88 cm/\(\:\sqrt{\text{k}\text{g}}\). Above this threshold, no significant association with chronic pain was observed.

Chronic pain presents a substantial challenge for individuals with obesity, with reports indicating that obese individuals experience clinically significant pain three times higher than the general population [21]. Studies have shown that across increasing BMI categories, there is an elevated likelihood of reporting daily pain [22]. A meta-analysis suggested that obesity (BMI ≥ 30.0) correlates with higher self-reported pain intensity and pain interference in daily life among adults [23, 24]. Conversely, experiencing pain can exacerbate obesity by promoting sedentary behavior and increasing food intake due to fear of exercise or physical limitations [25].

Obesity contributes to chronic pain through various mechanisms, primarily due to its proinflammatory status. Adipose, metabolically active tissue releases proinflammatory cytokine such as IL-6, TNF, and prostaglandins [26]. These cytokines play a crucial role in sensitizing nociceptors, increasing nociceptive input to the central nervous system, and sustaining sensitization of the nociceptive system [27, 28]. This inflammatory response likely intensifies pain symptoms experienced by individuals with obesity. The second mechanism involves excessive mechanical stress on the skeletal system and joints [8, 29]. Excessive stress on joint tissue may also lead to joint cartilage rupture, triggering local inflammation and pain [7]. Thirdly, obesity can also have psychological effects. Contributing to increased rates of depression, decreased self-esteem, and reduced physical activity levels, all of which can worsen chronic pain [30].

In terms of dietary habits, obesity-related dietary patterns can exacerbate nociceptive stimuli by promoting inflammation and recruiting M1 macrophages that trigger immune responses [31]. Among chronic pain patients receiving long-term opioid therapy, there is a tendency to consume diets high in sugar, fat, sodium, and caffeine, which further aggravates obesity [32]. Therefore, various treatment methods for obesity offer benefits for improving pain outcomes. For example, physical exercise has a positive impact on both weight loss and pain relief [33]. In addition, pain experiences and syndromes ameliorate significantly after weight loss surgery [34].

Traditionally, obesity has been assessed using BMI, but BMI fails to differentiate between fat and muscle mass or distinguish between fat and muscle mass or abdominal visceral fat and peripheral fat [35]. Abdominal visceral fat has distinct metabolic properties and is increasingly recognized as an independent risk factor for medical complications and chronic pain [36, 37]. In chronic pain conditions, abdominal obesity may be critical as visceral fat releases various systemic inflammatory markers implicated in pain pathophysiology [38].

To address this issue, WWI was used to evaluate the relationship between obesity and chronic pain. WWI was designed to reflect the composition of abdominal tissue, significantly positively correlated with abdominal fat mass and negatively correlated with muscle mass and bone mass [39]. It is found that metabolic syndrome is physiologically associated with chronic pain, with abdominal obesity being the strongest predictor of pain over the past three months [40]. Another research suggested that body fat, rather than lean weight, is associated with lower back pain and disability [41]. Our study findings also suggested a positive association between WWI, which better represents abdominal obesity and chronic pain. Meanwhile, evidence has shown a positive correlation between WWI and depression, diabetes, and cognitive function, which were important factors in the weight-pain relationship [42–45].

Subgroup analysis revealed notable differences in the relationship between WWI and chronic pain across racial and gender groups. Non-Hispanic whites and individuals of other racial backgrounds showed a stronger association between WWI and chronic pain compared to non-Hispanic blacks or Mexican Americans, possibly influenced by body size and fat composition differences [46]. Additionally, our analysis highlighted gender disparities in the relationship between WWI and chronic pain, particularly noting a significant threshold effect among females. Studies suggested that gender differences in BMI and pain may be attributed to metabolic factors associated with obesity [47, 48, 40]. Variations in body composition and fat mass distribution likely contribute to these gender differences in the association between WWI and chronic pain.

Our study has several limitations. Firstly, cross-sectional design prevents establishing a causal relationship between WWI and chronic pain. Secondly, chronic pain status was based on self-reported recall, which may introduce recall bias. Further prospective research is necessary to validate our findings and explore causality.

In conclusion, this study demonstrates that a higher WWI is associated with increased odds of chronic pain prevalence. WWI appears to be a promising indicator for assessing adiposity in relation to chronic pain. However, further prospective studies are necessary to establish a causal relationship.

Weight-adjusted waist index (WWI)

National Health and Nutrition Examination Survey (NHANES)

National Center for Health Statistics (NCHS)

Body mass index (BMI)

Income-to-Poverty Ratio (PIR)

Acknowledgements

We thank the participants and investigators of the NHANES.

Funding

The study was supported by Extensive Collection & Harmony Integration Projects of Peking University Third Hospital (2404-01-09) and the National Science Foundation of China (82201336).

Author information

Huili Liu, Ya Gao, Xue Bai, Mingan Chen, Ruijie Xie, Yanan Song and Min Li

Huili Liu and Ya Gao contributed equally as first authors.

Affiliations

Department of Anesthesiology, Peking University Third Hospital, Beijing, 100191, China. Huili Liu, Ya Gao, Yanan Song and Min Li
Department of Anesthesiology, Yanan Hospital of Traditional Chinese Medicine, Yanan, 716000, China. Xue Bai, Mingan Chen
Department of Hand & Microsurgery, The Affiliated Nanhua Hospital, Hengyang Medical School, University of South China, Hengyang, 421002, China. Ruijie Xie

Contributions

All authors contributed to the study's conception and design. HL and YG designed the research. XB, MC, and RX collected and analyzed the data. HL and YG drafted the manuscript. YS and ML revised the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Min Li and Yanan Song.

Min Li, Email: [email protected]

Yanan Song, Email: [email protected]

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

The authors give consent for publication of this paper in BMC Public Health.

Competing interests

The authors declare no competing interests.

Data availability

The survey data are publicly available online for data users and researchers worldwide ( www.cdc.gov/nchs/nhanes/).

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No competing interests reported.

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Association between Weight-Adjusted Waist Index and Chronic Pain in U.S. adults: a Cross- Sectional Study

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Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

Data source and study population

Definition of chronic pain

Calculation of weight-adjusted waist index (WWI)

Covariables

Statistical analysis

Results

Baseline characteristics

Association between WWI and chronic pain

Subgroup analyses

Saturation effect analysis between WWI and chronic pain

Discussion

Conclusions

Abbreviations

Declarations

Data availability

References

Additional Declarations

Status:

Version 1