Assessing Diet and Musculoskeletal Pain in Adults: Results From a Cross-Sectional Analysis of the National Health and Nutrition Examination Survey (NHANES)

Abstract

Musculoskeletal (MSK) pain is highly prevalent worldwide and is associated with high levels of disability. Systemic inflammation may contribute to MSK pain, and diet is a crucial modulator of systemic inflammation. The Dietary Inflammatory Index (DII^®) assesses the tendency of diets to cause inflammation. Higher DII scores, indicating pro-inflammatory diets, were hypothesized to be associated with a higher prevalence of MSK pain symptoms and duration. Data from 4118 subjects from the 2003-2004 NHANES survey were included to assess the association between DII scores and the presence and duration of osteoarthritis symptoms and neck and low back pain. After adjusting for potential confounders, the multivariable analysis showed that DII scores were significantly associated (P < .05) with increased risk of osteoarthritis symptoms (joint pain, aching, and stiffness) in the past year (OR = 1.099; 95% CI 1.029,1.174), low back pain (OR = 1.070; 95% CI 1.003,1.142), and any pain lasting longer than 24 hours (OR = 1.128; 95% CI 1.046,1.216). Neck pain and pain duration were not significantly associated with DII scores. These findings suggest a significant association between a pro-inflammatory diet and the presence of different MSK pains (joint pain and low back pain) and with pain lasting longer than 24 hours.

Keywords

musculoskeletal pain dietary inflammatory index inflammation osteoarthritis low back pain

“The overall results offer promising perspectives for exploring potential pain mechanisms in individuals suffering from musculoskeletal pain”.

Introduction

Evidence has been building on the association between musculoskeletal (MSK) pain and inflammation.¹ Specifically, MSK pain presence, severity, duration, and the transition between acute and chronic pain, have been associated with heightened inflammatory status.² Both localized (tissue-specific) and systemic inflammation (involving circulatory markers) have been shown to play a role in pain experiences in MSK pain. They have also been found to have a bidirectional interaction, though the mechanisms involved are not currently known.^3-5

Diet is a known modulator of inflammation, as specific pro-inflammatory diets are associated with chronic systemic^6,7 and tissue-specific long-standing (also referred to as simmering) inflammation.^8,9 Overall, the evidence suggests a possible link between diet, inflammation, and musculoskeletal pain, though uncertainty exists on the strength, impact, and mechanisms involved in this interaction.¹⁰

The most prevalent MSK disorders include osteoarthritis (OA), neck pain, and low back pain (LBP).¹¹ OA is a major source of disability and socioeconomic burden and one of the main contributors to chronic pain, which is associated with even more severely decreased function and quality of life.¹² OA is characterized by joint pain, stiffness, aching, degeneration, and reduced range of motion. Inflammation, both local and systemic, is one of the main contributors to the development and progression of this disease.^13-17

LBP is the leading cause of disability worldwide,¹⁸ with an estimated lifetime prevalence between 65% and 80%.^19,20 Despite significant resources allocated to LBP research, there is still a lack of knowledge on properly preventing and treating this condition.^21,22 An estimated 15% to 20% of the people who experience acute LBP transition into chronic low back pain (CLBP).^23,24 CLBP has an even more significant burden on society by heavily affecting healthcare delivery systems and negatively impacting function and cost for individuals who suffer from it.²⁵ There is rapidly growing evidence of associations between inflammation and acute LBP, chronic LBP, and the transition between the 2.^26-34

Neck pain is the fourth cause of disability worldwide.^35,36 Though there has been attention to researching the contribution of inflammation to neck pain, the evidence has been largely inconclusive. A recent systematic review on systemic inflammatory cytokines and spinal pain concluded that CRP only was elevated in chronic whiplash disorders (moderate evidence).³⁷ The same review showed conflicting results for other cytokines. Another review showed no significantly elevated markers in acute neck pain and elevated IL-1β and TNFα when comparing chronic neck pain subjects vs controls.³⁸ Overall, neck pain remains a common musculoskeletal syndrome with significant impairments, especially when chronic.³⁹

The Dietary Inflammatory Index (DII^®) is a widely used measure for assessing the inflammatory potential of diet.^40-46 Diets vary significantly in their inflammatory and systemic impact on the body.^7,47-50 For example, dietary patterns are associated with heightened inflammatory status^51-53 and global genomic DNA methylation (a crucial epigenetic factor in regulating gene expression⁵⁴) in a cancer-free population.^55,56 Moreover, each food has different properties regarding the inflammatory response elicited in the body,⁵⁷ and individual responses may vary, exemplified in lactose intolerance⁵⁸ and non-celiac gluten sensitivity.⁵⁹ Last, the quantity and frequency of each food’s consumption also play an essential role in the overall inflammatory effect.^60-63 While the complex impact of diet on the human body is far from being understood, assessing the inflammatory tendencies of diets has provided encouraging results.^64-66

The primary aim of this study was to investigate the association between MSK pain (particularly OA, neck pain, and LBP) and diet-related inflammation, assessed through the DII, in a representative sample of the US population. The secondary aim of this study was to assess whether subjects with a pro-inflammatory diet experienced MSK pain more frequently and for a longer time, thus predisposing them to chronicity. We hypothesized that higher DII scores are associated with higher MSK pain prevalence and duration.

Methods

Study Design and Participants

The 2003-2004 National Health and Nutrition Examination Survey (NHANES) dataset is the only large dataset (and only NHANES cycle) that had collected specific MSK pain data and had all the dietary data necessary to calculate the DII. The NHANES represents the U.S. population and provides generalizable health and nutritional information. Since 1959, the NHANES has been an ongoing effort to survey the population’s nutritional status, other potentially relevant health determinants, and self-reported health outcomes such as LBP. More information on the NHANES can be found in Appendix 1 and the Centers for Disease Control and Prevention website.⁶⁷ Only individuals between 20 and 85 years of age were included in this cross-sectional study because the pain questionnaire was administered only to individuals above 20.

Dietary Inflammatory Index (DII^®)

The DII is based on literature published through 2010 linking diet to inflammation assessed through the following blood markers: Interleukin (IL)-1β, IL-4, IL-6, IL-10, Tumor Necrosis Factor-alpha (TNFa) and C-reactive protein (CRP). Details regarding DII are described in detail elsewhere.⁵⁷ Higher DII scores represent more pro-inflammatory diets, while lower (i.e., more negative) DII scores indicate more anti-inflammatory diets.⁵⁷ The index has been validated in 48 studies with various inflammatory markers in different settings^57,68; for example, CRP levels were validated in another NHANES dataset.⁶⁹ Other relevant findings for DII calculated for NHANES datasets include positive association with depressive symptoms in women,⁷⁰ and leukocyte telomere length. (a biomarker of aging).⁷¹ It has also been associated with different measures of mortality (all-cause, cardiovascular disease, and digestive-tract cancer) in subjects diagnosed with pre-diabetes.⁷²

Study Variables

The data for this research were available on the Center for Disease Control website and were part of the National Health and Nutrition Examination Survey (NHANES; 2003-2004 cycle). The study assessed several dependent (outcome) variables. (1) Joint pain/aching/stiffness in the past year (yes/no) was assessed as descriptors of OA pain. The presence of symptoms for most days in the past month (yes/no) also was investigated for the subpopulation who reported having such pain. Moreover, the dichotomized presence of (2) neck pain and (3) LBP in the 3 months prior to the data collection was also studied, and if any pain lasting more than 24 hours had been experienced in the month prior to the data collection (yes/no) (4). For those who reported the presence of any pain longer than 24 hours, it was further asked for how long (dichotomized as less than 3 months and more than 3 months). The variable list and the exact wording for each question asked for each variable can be found in Supplemental Appendix Table 1. The duration of pain was dichotomized to meet the definition of chronic pain by the International Association for the Study of Pain.⁷³ Last, because CRP has been associated with DII,⁶⁹ we evaluated circulating CRP levels in this cohort (collected and available in the NHANES 2003-2004 dataset) to further assess the association between DII and validated circulating markers of inflammation.

Table 1.

Baseline Characteristics (unweighted).

		Joint Pain - Aching—Stiffness in the past year		Neck Pain in the past three months		Low Back Pain in the past three months
		Yes	No	Yes	No	Yes	No
		N = 1966	N = 2152	N = 866	N = 3251	N = 1568	N = 2548
Age	Mean	49.93	43.21	46.98	46.35	46.74	46.34
Age	95% CI	48.95 – 50.92	42.26 – 44.15	45.64 – 48.33	45.55 – 47.15	45.66 – 47.82	45.44 – 47.23
Body Mass Index	Mean	28.93	27.43	28.29	28.13	28.71	27.81
Body Mass Index	95% CI	28.50 – 29.36	27.09 – 27.79	27.63 – 28.94	27.83 – 28.43	28.23 – 29.19	27.48 – 28.15
Gender	Male	882	1069	362	1589	678	1273
Gender	Female	1084	1083	504	1662	890	1275
Education level	Less than High School	566	600	271	894	493	672
	High school diploma	509	517	220	806	416	610
	More than High School	890	1031	375	1546	659	1261
	Missing	1	4	0	5	0	5
Moderate level of physical activity	Yes	996	1127	443	1680	759	1364
	No	860	983	371	1472	731	1112
	Unable	109	42	52	99	78	72
Diabetes	Yes	265	176	123	318	199	242
	No	1667	1958	730	2894	1342	2281
	Borderline	34	18	13	39	27	25
Race - Ethnicity	Mexican American	1191	1079	151	668	279	540
	Other Hispanic	335	452	30	100	50	80
	Non-Hispanic White	343	477	511	1732	896	1346
	Non-Hispanic Black	47	64	143	632	288	487
	Other race	50	80	31	119	55	95
Annual income	Up to 45.000$/year	1241	1306	546	2000	1047	1498
	45.000 to 75.000$/year	365	424	165	624	261	528
	over 75.000$/year	360	422	155	627	260	522

The NHANES dietary data were collected using the 24 hour food recall interview (24HR) method. The 24HR has been widely used in literature to assess habitual dietary intake and calculate the DII. The data used to calculate the DII were collected as part of the 24HR administered on the first day (day 1) in the Mobile Examination Center (MEC, see Appendix 1 for more details) or at home for those who agreed to the interview only. A second 24HR was administered via phone in a time window between 3 to 10 days after the first one was completed.

The DII (independent variable) was calculated based on the method described by Shivappa et al⁵⁷ (see Appendix 2 for more details). The DII was calculated for each 24HR questionnaire, then averaged for each patient for statistical analysis; this approach better captures the intra-individual variability that typically occurs with diet. The DII calculation can include up to 45 items, though fewer dietary items were available and used for analysis here (n = 28); this reduced number of items does not compromise the DII calculation, as shown by previous research,^50,74 including a meta-analysis.⁷⁵

A literature review identified potential confounders related to pain symptoms and inflammation; they were determined a priori and examined in the adjusted models.^76-79 The following possible confounders were investigated: age, sex, Body Mass Index (BMI = weight (kg)/height(m)²), physical activity level, education level, diagnosis of diabetes, race/ethnicity, and total household annual. Age (calculated in years) and BMI (kg/m²) were treated as continuous variables, while the other variables were analyzed as categorical.

Statistical Analysis

The statistical analysis was performed following the guidelines provided on the Centers for Disease Control and Prevention (CDC) Website.⁸⁰ The official NHANES website suggests using SAS “SURVEY” procedures for statistical analysis because these survey-specific procedures are intended to account for the complex recruitment/stratified population sampling design employed in this survey. Moreover, missing data were treated with the “NOMCAR” option, which uses the Taylor series variance estimation to account for missing values as “not missing completely at random.” SAS^® 9.4 for Windows (SAS Institute Inc, Cary, NC, USA) was used for all analyses.

Because of the complex nature of the survey, the NHANES dataset comes with statistical weights to be included in the analysis to make the sample representative of the general US population at the time of data collection. The appropriate weight (“WTDR2D”) was used for every statistical calculation, thus making the results generalizable to the 2003-2004 US population. The weighted population numbers are reported in Appendix Table 2.

The statistical analysis was determined a priori. The independent variable (DII) was tested as a continuous variable against the 4 main outcome variables of OA symptoms, neck pain, LBP, and any pain lasting longer than 24 hours in the prior month (each treated as a dichotomous variable). The same statistical approach was used for the secondary aim variable of pain duration. A logistic regression model (“PROC SURVEYLOGISTIC”) was used to identify the significant factors at a P-value <.05. Odds Ratios (ORs) and 95% confidence intervals (CI) were calculated. Each model was run unadjusted and then adjusted for confounders, as often performed in analyses using complex population samples (such as the NHANES).^81-84 A linear regression model was performed to assess the relationship between DII scores and circulating CRP levels; for this analysis, CRP levels were log-transformed to normalize their distribution.

Secondary analyses were planned for anticipated potential effect modifiers such as rheumatoid arthritis (RA) diagnosis potentially driving OA symptoms and for participants experiencing only one type of pain (either OA symptoms, neck pain, or low back pain) compared to multiple types of pain. We also anticipated performing additional analyses for identifying potential effect modifiers and sensitivity analyses as suggested by the results; the intent was to explore associations and interactions that could not be determined a priori.

As anticipated, we ran additional analyses to explore why the unadjusted models of joint pain, aching, and stiffness, as well as the presence of the symptoms for the month, were both not significantly associated with DII, whereas they were in the adjusted models.

A sensitivity analysis showed that age was the only variable to affect both variables’ relationship (OA symptoms and their presence in the past month) with DII (Table 3). Because the interaction term with age was non-significant for both variables, the age distribution across the sample was explored and found to be non-normal, that is, skewed to the right (consistent with an overrepresented young population). A t-test analysis between the 2 groups was also performed to confirm the difference among the groups. We thus divided subjects into 2 groups, with 50 years old as a cutoff point. This age cutoff was chosen because it is often considered one of the OA risk factors.⁸⁵

Table 2.

DII and MSK Pain Analysis.

	N =	Model	P-value	Odds Ratio (95% CI)
Joint pain, aching, stiffness in the past year	4118	Unadjusted	.60	1.015 (.959 - 1.075)
Joint pain, aching, stiffness in the past year	4118	Adjusted	.005*	1.099 (1.029 - 1.174)
Presence of symptoms for most days in the past month	1966	Unadjusted	.17	1.059 (.976 - 1.148)
Presence of symptoms for most days in the past month	1966	Adjusted	.017*	1.118 (1.020 - 1.227)
Neck pain	4118	Unadjusted	.18	1.049 (.978 - 1.125)
Neck pain	4118	Adjusted	.16	1.058 (.978 - 1.145)
Low back pain	4118	Unadjusted	.018*	1.074 (1.012 - 1.139)
Low back pain	4118	Adjusted	.04*	1.070 (1.003 - 1.142)
Pain longer than 24 hours	4118	Unadjusted	.001*	1.117 (1.045 - 1.194)
Pain longer than 24 hours	4118	Adjusted	.002*	1.128 (1.046 - 1.216)
Pain longer than 3 months	1062	Unadjusted	.048*	1.124 (1.001 - 1.263)
Pain longer than 3 months	1062	Adjusted	.14	1.108 (.967 - 1.270)

All adjusted models include sex, age, BMI, household income, diabetes, race, physical activity levels, education levels.

Results

Baseline demographic characteristics of the included population are provided both unweighted (Table 1) and weighted (Supplemental Appendix Table 1).

OA Symptoms

In the unadjusted model, the variable of joint pain, aching, and stiffness in the past year was not significantly associated with DII values. Nonetheless, in the adjusted model for sex, age, BMI, household income, diabetes, race, physical activity level, and level of education, the same variable of joint pain, aching, and stiffness was significantly associated with DII (P = .005) (Table 2). When a model was run with only DII and age as predictors of the dependent variable, DII was significant (P = .004); this was not true for any of the other variables. This sensitivity analysis showed that age was the only variable to impact the association between joint pain, aching, and stiffness in the past year and DII (Table 3). The t-test analysis of mean age between the 2 groups (having or not having joint pain, aching, and stiffness variable in the past year) confirmed a statistically significant difference (P < .0001) (more info on the age distribution is presented in Appendix Figures 1-4). In the unadjusted model for the analysis stratified by age group, DII was found to be significant (P = .007, OR = 1.13) in those participants younger than 50 years old. However, it was not significant (P = .47) in the older group. These results were also found in the adjusted model, where DII was significant for the younger group and non-significant for the older group (P = .002 and P = .99, respectively).

Table 3.

DII and MSK Pain – Sensitivity Analysis by Age.

	N =	Model	P-value	Odds Ratio (95% CI)
Joint pain, aching, stiffness in the past year	4118	Adjusted by age only	.007*	1.090 (1.024 - 1.161)
		Fully adjusted: Age <50 years old	.002*	1.160 (1.056 - 1.275)
		Fully adjusted: Age >50 years old	.99	1.000 (.908 - 1.101)
Presence of symptoms for most days in the past month	1966	Adjusted by age only	.006*	1.126 (1.034 - 1.225)
		Fully adjusted: Age <50 years old	.24	1.092 (.942 - 1.267)
		Fully adjusted: Age >50 years old	.03*	1.140 (1.013 - 1.283)

All fully adjusted models include sex, age, BMI, household income, diabetes, race, physical activity levels, education.

OA symptoms for most days in the past month were also not significantly associated with DII in the unadjusted model. Nonetheless, DII was significant in the adjusted model (P = .017, OR = 1.12). Upon performing another sensitivity analysis (Table 3), DII was again significant in a model with age and DII only (P = .006), with no significant interaction between these two variables. No other variable showed changes in the significance level of the DII’s relationship to pain. In the unadjusted model stratified by age, DII was found to be non-significant (P = .14) in younger than 50 but significant (P = .039) in the older group. The adjusted model replicated the results highlighted in the unadjusted model, with DII being non-significant (P = .24) in the group younger than 50 years old and significant (P = .03) in the older group.

Neck Pain

Unadjusted and adjusted models showed no significant association between DII and neck pain (P = >.05).

Low Back Pain

DII was significantly associated with low back pain in the unadjusted model (P = .018; OR = 1.074) and in the adjusted model (P = .04; OR = 1.070).

Pain Duration

DII was associated with pain lasting longer than 24 hours in the month prior to data collection in the unadjusted (P = .001; OR = 1.117) and the adjusted model (P = .002; OR = 1.128). Regarding whether DII was associated with pain experienced more or less than 3 months, DII was found to be significantly associated in the unadjusted model (P = .047; OR = 1.124), but it was not significant in the adjusted model (P = .14).

DII and CRP Association in the Current Population

DII and CRP levels were significantly associated (P = <.0001) in both the unadjusted and the adjusted models.

Secondary Analyses

Since the symptoms of OA can overlap with symptoms from Rheumatoid Arthritis (RA), a secondary analysis was performed to assess if RA was driving the observed finding. A second fully adjusted model was run for this analysis, excluding the subjects with a reported RA diagnosis (n = 223). DII retained its significant association with OA symptoms in this second model, though the P-value decreased from .005 to .011.

A t-test was performed to assess the difference between the mean DII value of those who reported only one type of pain and multiple types of pain. This analysis aimed to explore if there is an effect of diet-related inflammation on the number of symptoms reported. For this analysis, the mean value of DII in those participants who reported pain consistent with either OA symptoms, neck pain, or low back pain (n = 2828) was compared to those who reported more than one of those pain (n = 1289) types. No statistical significance was found between these groups (P = >.05).

Discussion

The study results were largely consistent with the hypothesis that diet-associated inflammation increases the risk of musculoskeletal pain. This finding was true for the common OA symptoms of joint pain, aching, and stiffness and their presence in the fully adjusted models for most days. Furthermore, it also was significant in the final model for LBP and the presence of any pain lasting longer than 24 hours. The overall results offer promising perspectives for exploring potential pain mechanisms related to the associations found for individuals suffering from musculoskeletal pain. If replicated, these results could potentially be applied to developing anti-inflammatory dietary interventions for individuals affected by musculoskeletal pain.

An unexpected finding was age’s role in the presence of OA pain symptoms. The fact that joint pain, aching, and stiffness are associated with higher DII in younger people may be interpreted as a contributing role of diet in early symptom onset. It could also mean that the effect of a pro-inflammatory diet on OA pain diminishes when degenerative changes to the joint have already occurred. Furthermore, it could mean that the manifestation of diet-related inflammation in people over 50 years old (with more established OA degeneration of the joints) may present differently, for example with higher pain intensity or frequency. The literature on the topic is only present, scarcely, in animal models. A few studies have highlighted that systemic inflammation may have a role in the early onset of OA.^86,87 In line with those findings, the present study adds some evidence that will need further evaluation in future research. In any case, the overall adjusted model shows a significant association between DII and pain presence for most days in the past month, which points to a relevant contribution of DII in the frequency of pain and not only its presence.

Interestingly, DII was not found to be associated with neck pain. These findings open the door for speculations on the reasons behind the difference in neck pain, LBP, and OA pain. One possible interpretation is that neck pain is less affected by systemic inflammation than OA pain and LBP. Another possible interpretation is that different cytokines characterize neck pain other than those considered in the DII calculation (IL-1β, IL-4, IL-6, IL-10, TNF-α, and C-reactive protein (CRP)). It could also mean that only specific phenotypes of neck pain are affected by systemic inflammation; thus, the association is not significant in a large general population sample. Though the study design and available data do not allow for testing these hypotheses, they also do not allow for ruling them out and thus require further research on the topic.

The DII showed a weak association with pain lasting more than 3 months in the unadjusted model; this association was not significant in the adjusted model. The fact that only a subset of the whole population (roughly 25%) was included in this analysis underlines the need for further clarification of these results in future studies. Nonetheless, the current evidence does not point to diet-related inflammation as a significant predictor of pain duration in excess of 3 months in this population. Also, the question in the survey was related to any pain lasting longer than 24 hours. This large category may include pains that are not musculoskeletal, thus potentially impairing the interpretation of such results. Future studies will need to address the role of diet-related systemic inflammation in transitioning from acute to chronic pain.

To better understand the significance of the DII results, it is important to remember that odds ratios measure a change in the unit of the variable in question. The DII range in the included population is between −5.19 (most anti-inflammatory diet) and +3.75 (most pro-inflammatory diet), thus having a range of almost 9 points. For example, the odd ratio calculated for an 8-point difference in DII among people with LBP would equal 1.720. This result highlights the full potential of the different impacts that a maximally anti-inflammatory diet can have compared to a maximally pro-inflammatory diet.

Even with the promising evidence of the advantages of an anti-inflammatory diet, translating the DII into individualized dietary suggestions may be challenging. For example, the “Western diet” is commonly acknowledged as pro-inflammatory.⁸⁸ This is due to its high intake of red and processed meat, high in saturated and trans fats, cholesterol, and protein,⁸⁹ all pro-inflammatory nutrients in the DII calculation. It is also important to note that the Western dietary pattern commonly lacks fruits, vegetables, nuts, whole grains, and spices, thus lacking many vitamins, mono- and poly-unsaturated fats, minerals, and fibers that have anti-inflammatory effects for the DII calculation. Additionally, a diet with a higher proportion of fruit and vegetable consumption, in contrast to higher consumption of meats, refined sugars, and processed foods, typically results in lower overall caloric intake, which is anti-inflammatory as per the DII calculation. Nonetheless, these broad guidelines based on nutrient consumption could be easily misinterpreted by the general public, thus removing the direct application of the DII from clinical practice and everyday use.

The main strength of this study is its contribution in showing that a pro-inflammatory diet is associated with increased OA pain symptoms and their daily presence. It also shows that, in this sample of US adults, LBP presence and the presence of pain lasting longer than 24 hours are associated with diet-related inflammation. Furthermore, the actual inflammatory status of the participants, as predicted by the DII, was consistent with the observed strong association between DII and CRP values. This finding is consistent with previously published data on a 2003-2008 NHANES cohort which also demonstrated an association between DII and CRP (dichotomized).⁶⁹ These findings align with growing evidence linking diet (e.g., specific dietary patterns or consumption of specific foods)⁹⁰ with chronic low-grade systemic inflammation and various chronic health conditions. More specifically, it supports the evidence that systemic inflammation may contribute to pain causation and duration, though this should be confirmed through more appropriate study designs (e.g., extensive longitudinal studies, intervention trials, and a more significant number of biomarkers included).

If confirmed, these findings could help reinforce the trend towards more comprehensive therapeutic approaches to prevent and manage pain. For example, diet could be integrated as part of a multidisciplinary approach to treating chronic pain, as seen in the DII-based IMAGINE intervention, which has shown to improve health and reduce inflammatory biomarkers.^91,92 In this intervention, participants attended 21 classes over a year where they were taught approaches known to help with inflammation reduction. These approaches included diet (with anti-inflammatory properties based on DII scores), exercise, and stress reduction.⁹³

Limitations

Despite its strengths, this study also presents some limitations. Many of those limitations were linked to the timeline and nature of the collected and coded data. For example, the dataset used was the only one identified that included all the data necessary for calculating DII and the data regarding non-arthritic and idiopathic pain. Because the dataset is from 2003-2004, it might not be a valid representation of the current US population or dietary habits. Generalizing the conclusions to other countries may also be limited.

Nonetheless, the interpretation of the DII does not change in light of potential differences with the US population or its current dietary choices. Even if the dietary intakes may have changed since the data were collected, the fact that there was an association between DII scores and pain outcomes remains relevant and highlights a significant association that should be investigated further. Moreover, DII scores are comparable across different large populations and diets, regardless of when the data were collected.

Some limitations existed related to the dataset itself. For example, the definition of OA was based on participants’ recollections of their symptoms rather than a clinical diagnosis. This limitation, though, offers a more inclusive approach to the patient experience since most individuals receive a diagnosis in the later stages of the disease.⁹⁴ Another limitation related to how the data were collected is that it was impossible to determine different pain groups and mechanisms. For example, it was not possible to differentiate subjects in acute, sub-acute, and chronic phases. Also, the question on pain lasting longer than 24 hours included any type of pain, which can include non-musculoskeletal pain. Likewise, it was not possible to differentiate between different pain mechanisms, so, for example, it was impossible to understand the degree of contribution from different types of pain, such as neuropathic (both central and peripheral), nociceptive, nociplastic, visceral, or other. Last, the information on the use of anti-inflammatory drugs did not provide enough details to be incorporated into the analysis. This limited the understanding of the impact of this obvious potential mediator on inflammation and pain.

Another study limitation is that the dietary information was collected solely from the 24HR. Even though this method has been validated and is a reliable tool for the data collection on dietary consumption, the 24HR depends on the ability of subjects to recall their dietary intake and is thus subject to inevitable recall bias.⁹⁵ Also, the recall period is limited to 24 hours before questionnaire completion; thus, it does not represent long-term habitual intake. The mean value of 2 different 24HR collections was used for statistical analysis to address this limitation.

Additionally, the DII does not consider the individual variability that plays a part in digestion. Growing evidence points to different factors that influence the individualized response of the digestive system to different foods.⁹⁶ The variability in the gut microbiome, genetics, and epigenetics plays a role in the individual response to food. In other words, while one specific food may be anti-inflammatory for an individual, it may cause an immune and inflammatory response in another. A classic example would be foods from the nightshade family that are generally anti-inflammatory but can have a pro-inflammatory effect in certain individuals.⁹⁷ The extent of this inter-individual variability is currently unknown, thus limiting the generalizability of results from DII.

The cross-sectional study design limits causal inference. Further research should attempt to fill this gap. Future studies in this area should also be tailored to the MSK population’s specific characteristics and how to adequately stratify such a heterogeneous population. For example, studies have shown the importance of sub-classification in treating sub-acute and chronic pain populations.⁹⁸ Providing a specific therapeutic approach for different sub-groups within the LBP population has heightened response to treatment. This previous finding should be kept in mind—not only for the LBP population, which is known to have very different combinations of contributing factors—but for all MSK pain populations. Diet-related inflammation may be more relevant to specific sub-groups of MSK pain populations than others. A sub-classification approach should be considered when designing or analyzing the results of studies on MSK pain.

Conclusions

This study contributes to the challenging and multifactorial topic of MSK pain when considering the currently limited understanding of pain mechanisms and treatments. It demonstrated a significant positive association between diet-related inflammation and OA symptoms, duration, presence of LBP, and presence of pain lasting longer than 24 hours in a general sample of the adult US population. In conclusion, even though causality could not be established, this study highlights a promising path for future research assessing the role of diet in the presence of MSK pain, its severity, and its duration.

Supplemental Material

Supplemental Material - Assessing Diet and Musculoskeletal Pain in Adults: Results From a Cross-Sectional Analysis of the National Health and Nutrition Examination Survey (NHANES)

Supplemental Material for Assessing Diet and Musculoskeletal Pain in Adults: Results From a Cross-Sectional Analysis of the National Health and Nutrition Examination Survey (NHANES) by Valerio Tonelli Enrico, James R. Hebert, Gerald Mugford, Zhiwei Gao, Peter Wang, Nitin Shivappa, and Gwendolyn Sowa in American Journal of Lifestyle Medicine

Footnotes

Declaration of Conflicting Interests

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

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (U19AR076725-01S1).

Author’s Note

Dr James R. Hébert owns controlling interests in Connecting Health Innovations LLC (CHI). This company has licensed the right to his invention of the dietary inflammatory index (DII) from the University of South Carolina to develop computer and smartphone applications for patient counseling and dietary intervention in clinical settings. Dr Nitin Shivappa is an employee of CHI.

Disclosure

The first and corresponding author, Valerio Tonelli Enrico, had the time used in performing this research covered by a supplemental grant (U19AR076725-01S1) that is part of the Back Pain Consortium (BACPAC) consortium.

Ethical Considerations

The study was based on the public-use data files available to both American and international researchers via the NHANES database. The public-use data are completely de-identified and publicly available with necessary suppression methods to protect confidentiality; thus, according to the HREA, the ethics committee board for the province of Newfoundland and Labrador, the study was cleared from needing the actual IRB clearance.

ORCID iD

Valerio Tonelli Enrico

Supplemental Material

Supplemental material for this article is available online.

References

Koop

Lutke Schipholt

Scholten-Peeters

GGM

Coppieters

. Identifying the most important confounders when assessing the association between low-grade systemic inflammation and musculoskeletal pain: a modified delphi study. Pain Med. 2021;22(11):2661-2669. doi:10.1093/PM/PNAB243

Parisien

Lima

Dagostino

, et al. Acute inflammatory response via neutrophil activation protects against the development of chronic pain. Sci Transl Med. 2022;14(644). doi:10.1126/SCITRANSLMED.ABJ9954

Wood

Miller

Malfait

. The genesis of pain in osteoarthritis: inflammation as a mediator of osteoarthritis pain. Clin Geriatr Med. 2022;38(2):221-238. doi:10.1016/J.CGER.2021.11.013

Grigg

Sonnenberg

. Host-microbiota interactions shape local and systemic inflammatory diseases. J Immunol. 2017;198(2):564. doi:10.4049/JIMMUNOL.1601621

Klyne

Barbe

James

Hodges

. Does the interaction between local and systemic inflammation provide a link from psychology and lifestyle to tissue health in musculoskeletal conditions? Int J Mol Sci. 2021;22(14). doi:10.3390/IJMS22147299

Minihane

Russell

Baka

, et al. Low-grade inflammation, diet composition and health: current research evidence and its translation. Br J Nutr. 2015;114:999-1012. doi:10.1017/S0007114515002093

Gardener

Martins

Rainey-Smith

. Diet and inflammation in alzheimer’s disease and related chronic diseases: A review. J Alzheimers Dis. 2015;50(2):301-334. doi:10.3233/JAD-150765

Liu

Hebert

Shivappa

, et al. Inflammatory potential of diet and risk of incident knee osteoarthritis: A prospective cohort study. Arthritis Res Ther. 2020;22(1). doi:10.1186/s13075-020-02302-z

Lorne J. Hofseth, James R. Hébert,

Chapter 3 - Diet and acute and chronic, systemic, low-grade inflammation. In Hébert

Hofseth

(Eds.), Diet, Inflammation, and Health (pp. 85-111). https://doi.org/10.1016/B978-0-12-822130-3.00011-9

10.

Elma

Yilmaz

Deliens

, et al.

Chronic musculoskeletal pain and nutrition: Where are we and where are we heading?

PM&R. 2020;12(12):1268-1278. doi:10.1002/PMRJ.12346

11.

United States Bone and Joint Decade: The Burden of Musculoskeletal Diseases in the United States,

Second Edition. Rosemont, IL: American Academy of Orthopaedic Surgeons; 2010. https://www.boneandjointburden.org/docs/The Burden of Musculoskeletal Diseases in the United States (BMUS) 2nd Edition (2011).pdf

12.

Liechti

Tseli

Taeymans

Grooten

. Prognostic factors for quality of life after interdisciplinary pain rehabilitation in patients with chronic pain-a systematic review. Pain Med. 2022;23. doi:10.1093/PM/PNAC098

13.

Rahmati

Mobasheri

Mozafari

. Inflammatory mediators in osteoarthritis: A critical review of the state-of-the-art, current prospects, and future challenges. Bone. 2016;85:81-90. doi:10.1016/j.bone.2016.01.019

14.

Miller

Malfait

Miller

. The innate immune response as a mediator of osteoarthritis pain. Osteoarthritis Cartilage. 2020;28(5):562-571. doi:10.1016/j.joca.2019.11.006

15.

Roškar

Hafner-Bratkovič

. The role of inflammasomes in osteoarthritis and secondary joint degeneration diseases. Life. 2022;12(5). doi:10.3390/LIFE12050731

16.

Terkawi

Ebata

Yokota

, et al. Low-grade inflammation in the pathogenesis of osteoarthritis: cellular and molecular mechanisms and strategies for future therapeutic intervention. Biomedicines. 2022;10(5):1109. doi:10.3390/BIOMEDICINES10051109

17.

Hartvigsen

Hancock

Kongsted

, et al. What low back pain is and why we need to pay attention. Lancet. 2018;391(10137):2356-2367. doi:10.1016/S0140-6736(18)30480-X

18.

Ibrahimi-Kaçuri

Murtezani

Rrecaj

Martinaj

Haxhiu

. Low back pain and obesity. Med Arch. 2015;69(2):114-116. doi:10.5455/medarh.2015.69.114-116

19.

Urits

Burshtein

Sharma

, et al. Low back pain, a comprehensive review: pathophysiology, diagnosis, and treatment. Curr Pain Headache Rep. 2019;23(3):23. doi:10.1007/s11916-019-0757-1

20.

Maher

Underwood

Buchbinder

. Non-specific low back pain. Lancet. 2017;389(10070):736-747. doi:10.1016/S0140-6736(16)30970-9

21.

Foster

Anema

Cherkin

, et al. Prevention and treatment of low back pain: evidence, challenges, and promising directions. Lancet. 2018;391(10137):2368-2383. doi:10.1016/S0140-6736(18)30489-6

22.

National Institute of Health - National Institute of Neurological Disorders and Stroke . Low Back Pain: Fact Sheets. https://www.ninds.nih.gov/sites/default/files/migrate-documents/low_back_pain_20-ns-5161_march_2020_508c.pdf

23.

Maas

Ostelo

Niemisto

, et al. Radiofrequency denervation for chronic low back pain. Cochrane Database Syst Rev. 2015;10. doi:10.1002/14651858.CD008572.pub2

24.

International Association for the Study of Pain (IASP) . Fact Sheets - The Global Burden of Low Back Pain. https://www.iasp-pain.org/resources/fact-sheets/the-global-burden-of-low-back-pain/

25.

Klyne

Barbe

van den Hoorn

Hodges

. ISSLS prize in clinical science 2018: longitudinal analysis of inflammatory, psychological, and sleep-related factors following an acute low back pain episode-the good, the bad, and the ugly. Eur Spine J. 2018;27(4):763-777. doi:10.1007/s00586-018-5490-7

26.

da Cruz Fernandes

Pinto

Ferreira

Lira

. Low back pain, obesity, and inflammatory markers: Exercise as potential treatment. J Exerc Rehabil. 2018;14(2):168-174. doi:10.12965/jer.1836070.035

27.

Gorth

Shapiro

Risbud

. Discovery of the drivers of inflammation induced chronic low back pain: from bacteria to diabetes. Discov Med. 2015;20(110):177-184.

28.

Liu

Duan

. Inflammation in low back pain may be detected from the peripheral blood: suggestions for biomarker. Biosci Rep. 2016;36(4). doi:10.1042/BSR20160187

29.

Hashem

Roffey

Alfasi

, et al. Exploration of the inter-relationships between obesity, physical inactivity, inflammation, and low back pain. Spine (Phila Pa 1976). 2018;43(17):1218-1224. doi:10.1097/BRS.0000000000002582.

30.

Lim

Wang

Cicuttini

, et al. Association between inflammatory biomarkers and nonspecific low back pain: A systematic review. Clin J Pain. 2020;36(5):379-389. doi:10.1097/AJP.0000000000000810

31.

Teodorczyk-Injeyan

Triano

Injeyan

. Nonspecific low back pain: Inflammatory profiles of patients with acute and chronic pain. Clin J Pain. 2019;35(10):818. doi:10.1097/AJP.0000000000000745

32.

Klyne

Hodges

. Circulating adipokines in predicting the transition from acute to persistent low back pain. Pain Med. 2020;21(11):2975-2985. doi:10.1093/pm/pnaa052

33.

Klyne

Barbe

Hodges

. Relationship between systemic inflammation and recovery over 12 months after an acute episode of low back pain. Spine J. 2022;22(2):214-225. doi:10.1016/J.SPINEE.2021.09.006

34.

Popescu

Lee

. Neck pain and lower back pain. Med Clin North Am. 2020;104(2):279-292. doi:10.1016/J.MCNA.2019.11.003

35.

Flynn

. Chronic musculoskeletal pain: Nonpharmacologic, noninvasive treatments. Am Fam Physician. 2020;102(8):465-477.

36.

Canli

Billens

Van Oosterwijck

Meeus

De Meulemeester

. Systemic cytokine level differences in patients with chronic musculoskeletal spinal pain compared to healthy controls and its association with pain severity: A systematic review. Pain Med. 2022;14. doi:10.1093/PM/PNAC091

37.

Farrell

de Zoete

RMJ

Cabot

Sterling

. Systemic inflammatory markers in neck pain: A systematic review with meta-analysis. Eur J Pain. 2020;24(9):1666-1686. doi:10.1002/EJP.1630

38.

Tatsios

Grammatopoulou

Dimitriadis

, et al. The effectiveness of spinal, diaphragmatic, and specific stabilization exercise manual therapy and respiratory-related interventions in patients with chronic nonspecific neck pain: systematic review and meta-analysis. Diagnostics. 2022;12(7):1598. doi:10.3390/DIAGNOSTICS12071598

39.

Asadi

Zirak

Khorasani

, et al. Dietary inflammatory index is associated with healthy eating index, alternative healthy eating index, and dietary patterns among Iranian adults. J Clin Lab Anal. 2020;34(12). doi:10.1002/JCLA.23523

40.

Wirth

Hurley

Hébert

Shivappa

. Association between previously diagnosed circulatory conditions and a dietary inflammatory index. Nutr Res. 2016;36:227-233. doi:10.1016/j.nutres.2015.11.016

41.

Hodge

Shivappa

Hébert

, et al. Dietary inflammatory index, Mediterranean diet score, and lung cancer: A prospective study. Cancer Causes Control. 2016;27:907-917. doi:10.1007/s10552-016-0770-1

42.

Sokol

Manczuk

Shivappa

, et al. Association between the dietary inflammatory index, waist-to-hip ratio and metabolic syndrome. Nutr Res. 2016;36:1298-1303. doi:10.1016/j.nutres.2016.04.004

43.

Sánchez-Villegas

de la Fuente-Arrillaga

Gea

, et al. Dietary inflammatory index, cardiometabolic conditions and depression in the Seguimiento Universidad de Navarra cohort study. Br J Nutr. 2015;114:1471-1479. doi:10.1017/S0007114515003074

44.

Shivappa

Hebert

Marcos

, et al. Association between dietary inflammatory index and inflammatory markers in the HELENA study. Mol Nutr Food Res. 2016;Dec(16):1600707. doi:10.1002/mnfr.201600707

45.

Wirth

Shivappa

Violanti

, et al. Association of a dietary inflammatory index with inflammatory indices and metabolic syndrome among police officers. J Occup Environ Med. 2014;56(9):986-989. doi:10.1097/JOM.0000000000000213

46.

Shivappa

Zucchetto

Serraino

LaVecchia

Hebert

Bosetti

. Dietary inflammatory index and risk of pancreatic cancer in an Italian case–control study. Br J Nutr. 2014;113:292-298. doi:10.1017/S0007114514003626

47.

Shivappa

Blair

Jacobs

Jr Steck

Hebert

Prizment

. Dietary inflammatory index (DII) and risk of colorectal cancer in Iowa Women’s Health Study. Cancer Epidemiol Biomarkers Prev. 2014;23(11):2383-2392.

48.

Wirth

Shivappa

Steck

, et al. Dietary inflammatory index scores differ by shift work status: NHANES 2005 to 2010. J Occup Environ Med. 2014;56(2):145-148. doi:10.1097/JOM.0000000000000088

49.

Wood

Berthon

Gibson

Hebert

Shivappa

. Dietary inflammatory index is related to asthma risk, lung function and systemic inflammation in asthma. Clin Exp Allergy. 2014;45(1):177-183.

50.

Poggio

Gutierrez

Irazola

Rubinstein

Danaei

Elorriaga

. Associations between dietary patterns and serum lipids, apo and C-reactive protein in an adult population: Evidence from a multi-city cohort in South America. Br J Nutr. 2017;117(4):548-555. doi:10.1017/S0007114517000514

51.

McGeoghegan

Almoosawi

Muirhead

. Association between an anti-inflammatory and anti-oxidant dietary pattern and diabetes in British adults: Results from the national diet and nutrition survey rolling programme years 1-4. Int J Food Sci Nutr. 2015;67:553-561. doi:10.1080/09637486.2016.1179268

52.

Ozawa

Kivimaki

Singh-Manoux

Brunner

Shipley

. Dietary pattern, inflammation and cognitive decline: The Whitehall II prospective cohort study. Clin Nutr. 2016;29:5-42. doi:10.1016/j.clnu.2016.01.013

53.

Moore

Fan

. DNA methylation and its basic function. Neuropsychopharmacology. 2013;38(1):23-38. doi:10.1038/NPP.2012.112

54.

Zhu

Wang

, et al. Dietary patterns and colorectal cancer recurrence and survival: a cohort study. BMJ Open. 2013;3:e002270. doi:10.1136/bmjopen-2012-002270

55.

Zhang

Carroll

Gonzalez

, et al. Dietary patterns are associated with levels of global genomic DNA methylation in a cancer-free population. J Nutr. 2011;141(6):1165-1171. doi:10.3945/jn.110.134536

56.

Shivappa

Steck

Hurley

, et al. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. 2014;17(8):1689-1696.

57.

Szilagyi

Ishayek

. Lactose intolerance, dairy avoidance, and treatment options. Nutrients. 2018; 10(12):1994. doi:10.3390/nu10121994

58.

Lebwohl

Ludvigsson

Green

PHR

. Celiac disease and non-celiac gluten sensitivity. doi:10.1136/bmj.h4347

59.

Esmaillzadeh

Mehrabi

Azadbakht

Willett

Kimiagar

. Dietary patterns and markers of systemic inflammation among Iranian women. J Nutr. 2007;137(4):992-998.

60.

Buscemi

Vasto

Massenti

, et al. Validation of a food frequency questionnaire for use in Italian adults living in Sicily. Int J Food Sci Nutr. 2015;66(4):426-438. doi:10.3109/09637486.2015.1025718

61.

Piyathilake

Kabagambe

Azuero

, et al. A dietary pattern associated with LINE-1 methylation alters the risk of developing cervical intraepithelial neoplasia. Cancer Prev Res. 2012;5(3):385-392. doi:10.1158/1940-6207.CAPR-11-0387

62.

Pou

del Pilar Diaz

Osella

. Applying multilevel model to the relationship of dietary patterns and colorectal cancer: An ongoing case-control study in Córdoba, Argentina. Eur J Nutr. 2012;51(6):755-764. doi:10.1007/s00394-011-0255-7

63.

Tsigalou

Konstantinidis

Paraschaki

Stavropoulou

Voidarou

Bezirtzoglou

. Mediterranean diet as a tool to combat inflammation and chronic diseases. An overview. Biomedicines. 2020;8(7). doi:10.3390/biomedicines8070201

64.

Strasser

Wolters

Weyh

Krüger

Ticinesi

. The effects of lifestyle and diet on gut microbiota composition, inflammation and muscle performance in our aging society. Nutrients. 2021;13(6). doi:10.3390/nu13062045

65.

Więckowska-Gacek

Mietelska-Porowska

Wydrych

Wojda

. Western diet as a trigger of Alzheimer’s disease: From metabolic syndrome and systemic inflammation to neuroinflammation and neurodegeneration. Ageing Res Rev. 2021;70:101397. doi:10.1016/j.arr.2021.101397

66.

Center for Disease Control and Prevention . National Health and Nutrition Examination Survey official website. https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2003

67.

Tabung

Zhang

, et al. Construct validation of the dietary inflammatory index among postmenopausal women. Ann Epidemiol. 2015;25:398-405.

68.

Shivappa

Wirth

Murphy

Hurley

Hébert

. Association between the dietary inflammatory index (DII) and urinary enterolignans and C-reactive protein from the national health and nutrition examination survey-2003-2008. Eur J Nutr. 2019;58(2):797-805. doi:10.1007/s00394-018-1690-5

69.

Wirth

Shivappa

Burch

Hurley

Hébert

. The dietary inflammatory index, shift work, and depression: results from NHANES. Health Psychol. 2017;36(8):760-769. doi:10.1037/hea0000514

70.

Shivappa

Wirth

Hurley

Hébert

. Association between the dietary inflammatory index (DII) and telomere length and C-reactive protein from the National Health and Nutrition Examination Survey-1999-2002. Mol Nutr Food Res. 2017;61(4):1600630. doi:10.1002/mnfr.201600630

71.

Deng

Shivappa

Tang

Mann

JRHJR

. Association between diet-related inflammation, all-cause, all-cancer, and cardiovascular disease mortality, with special focus on prediabetics: Findings from NHANES III. Eur J Nutr. 2017;56(3):1085-1093. doi:10.1007/s00394-016-1158-4

72.

Treede

Rief

Barke

, et al. Chronic pain as a symptom or a disease: The IASP classification of chronic pain for the international classification of diseases (ICD-11). Pain. 2019;160(1):19-27. doi:10.1097/J.PAIN.0000000000001384

73.

Shivappa

Blair

Prizment

Jacobs

Hébert

. Prospective study of the dietary inflammatory index and risk of breast cancer in postmenopausal women. Mol Nutr Food Res. 2017;61(5):1600592. doi:10.1002/mnfr.201600592

74.

Shivappa

Godos

Hébert

, et al. Dietary inflammatory index and colorectal cancer risk—a meta-analysis. Nutrients. 2017;9(9). doi:10.3390/NU9091043

75.

Wang

Rief

Gantz

Schiltenwolf

Richter

Ahrens

. Influence of depression symptoms on serum tumor necrosis factor-α of patients with chronic low back pain. Arthritis Res Ther. 2010;12(5):186. doi:10.1186/ar3156

76.

Schopflocher

Jovey

Taenzer

. The prevalence of chronic pain in Canada. Pain Res Manag. 2011;16(6):445-450. doi:10.1155/2011/876306

77.

Jackson

Thomas

Stabile

Shotwell

Han

XMK

. A systematic review and meta-analysis of the global burden of chronic pain without clear etiology in low- and middle-income countries: trends in heterogeneous data and a proposal for new assessment methods. Anesth Analg. 2016;123(3):739-748.

78.

Attal

Martinez

Jayr

, et al. Does cognitive functioning predict chronic pain? Results from a prospective surgical cohort. Brain. 2014;137(Mar):904-917.

79.

Center for Disease Control and Prevention . National Health and Nutrition Examination Survey official website. NHANES Tutorials - Software Tips. Accessed December 13, 2022. https://wwwn.cdc.gov/nchs/nhanes/tutorials/SoftwareTips.aspx

80.

Chu

Liu

Wang

Lin

. Association between acrylamide exposure and sex hormones in males: NHANES, 2003–2004. PLoS One. 2020;15(6). doi:10.1371/JOURNAL.PONE.0234622

81.

Marriott

Hunt

Malek

Newman

. Trends in intake of energy and total sugar from sugar-sweetened beverages in the United States among children and adults, NHANES 2003–2016. Nutrients. 2019;11(9). doi:10.3390/NU11092004

82.

Liu

Wang

Huang

, et al. Associations of exposure to melamine, cyanuric acid, phthalates with markers of early kidney impairment, and their interactions in US adults: Analyses of NHANES 2003–2004 data. Environ Sci Pollut Control Ser. 2022;29(52):79516-79528. doi:10.1007/S11356-022-21455-9/FIGURES/2

83.

Jin

Qiu

, et al. Phthalate exposure enhances incidence of urinary incontinence: US NHANES, 2003–2004 and 2005–2006. Environ Sci Pollut Control Ser. 2022;29(43):64692-64703. doi:10.1007/S11356-022-20307-W/TABLES/5

84.

Taruc-Uy

Lynch

. Diagnosis and treatment of osteoarthritis. Prim Care. 2013;40(4):821-836. doi:10.1016/J.POP.2013.08.003

85.

Jurynec

Sawitzke

Beals

, et al. A hyperactivating proinflammatory RIPK2 allele associated with early-onset osteoarthritis. Hum Mol Genet. 2018;27(13):2383-2391. doi:10.1093/HMG/DDY132

86.

Morais

Chemerka

Masson

, et al. Excessive downhill training leads to early onset of knee osteoarthritis. Osteoarthritis Cartilage. 2021;29(6):870-881. doi:10.1016/J.JOCA.2021.03.016

87.

Strate

Keeley

Cao

Giovannucci

Chan

. Western Dietary Pattern Increases, and Prudent Dietary Pattern Decreases, Risk of Incident Diverticulitis in a Prospective Cohort Study. W.B. Saunders; 2017:1023-1030. doi:10.1053/j.gastro.2016.12.038

88.

Halton

Liu

Manson

Stampfer

Willett

. Potato and French fry consumption and risk of type 2 diabetes in women. Am J Clin Nutr. 2006;83(2):284-290.

89.

de Vos

Steenbergen

van der Aa

Buschman

Rajan

. Effect and safety of spinal cord stimulation for treatment of chronic pain caused by diabetic neuropathy. J Diabetes Complications. 2009;23(1):40-45. doi:10.1016/j.jdiacomp.2007.08.002

90.

Turner-McGrievy

Wirth

Shivappa

, et al. Impact of a 12-month inflammation management intervention on the dietary inflammatory index, inflammation, and lipids. Clin Nutr ESPEN. 2019;30:42-51.

91.

Wirth

Jessup

Turner-McGrievy

Shivappa

Hurley

Hébert

. Changes in dietary inflammatory potential predict changes in sleep quality metrics, but not sleep duration. Sleep. 2020;43(11). doi:10.1093/sleep/zsaa093

92.

Turner-McGrievy

Wirth

Shivappa

, et al. Impact of a 12-month inflammation management intervention on the dietary inflammatory index, inflammation, and lipids. Clin Nutr ESPEN. 2019;30:42. doi:10.1016/J.CLNESP.2019.02.008

93.

Yip

Badley

Canizares

Power

Perruccio

. Risk factor profiles for individuals with diagnosed OA and with symptoms indicative of OA: Findings from the Canadian longitudinal study on aging. ACR Open Rheumatol. 2020;2(3):174. doi:10.1002/ACR2.11120

94.

Hebert

Ebbeling

Matthews

Clemow

Hurley

TGDS

. Systematic errors in middle-aged women’s estimates of energy intake: Comparing three self-report measures to total energy expenditure from doubly labeled water. Ann Epidemiol. 2002;12:577-586.

95.

Comerford

Pasin

. Gene-dairy food interactions and health outcomes: A review of nutrigenetic studies. Nutrients. 2017;9(7):710. doi:10.3390/nu9070710

96.

Cárdenas

Sonawane

Heinig

Bocobza

Burdman

Aharoni

. The bitter side of the nightshades: Genomics drives discovery in Solanaceae steroidal alkaloid metabolism. Phytochemistry. 2015;113:24-32. doi:10.1016/j.phytochem.2014.12.010

97.

Astfalck

Smith

Straker

Burnett

O’Sullivan

. Lumbar spine repositioning sense in adolescents with and without non-specific chronic low back pain--an analysis based on sub-classification and spinal regions. Man Ther. 2013;18(5):410-417. doi:10.1016/j.math.2013.02.005

98.

Lehtola

Luomajoki

Leinonen

Gibbons

Airaksinen

. Sub-classification based specific movement control exercises are superior to general exercise in sub-acute low back pain when both are combined with manual therapy: A randomized controlled trial. BMC Musculoskelet Disord. 2016;17(1):135. doi:10.1186/s12891-016-0986-y

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.29 MB

Assessing Diet and Musculoskeletal Pain in Adults: Results From a Cross-Sectional Analysis of the National Health and Nutrition Examination Survey (NHANES)

Abstract

Keywords

Introduction

Methods

Study Design and Participants

Dietary Inflammatory Index (DII®)

Study Variables

Statistical Analysis

Results

OA Symptoms

Neck Pain

Low Back Pain

Pain Duration

DII and CRP Association in the Current Population

Secondary Analyses

Discussion

Limitations

Conclusions

Supplemental Material

Supplemental Material - Assessing Diet and Musculoskeletal Pain in Adults: Results From a Cross-Sectional Analysis of the National Health and Nutrition Examination Survey (NHANES)

Footnotes

Declaration of Conflicting Interests

Funding

Author’s Note

Disclosure

Ethical Considerations

ORCID iD

Supplemental Material

References

Supplementary Material

Dietary Inflammatory Index (DII^®)