This study was the first to examine molecular biomarkers and clinical metrics in patients with axial LBP randomized to manipulation versus standard medical care. As a secondary analysis of an RCT dataset, the goal was to examine and identify potential biomarkers of interest related to pain and functional disability for future validation.
The initial CRP levels were correlated with baseline pain values, with a |r| value of 0.37.; however, the correlation between CRP and baseline function showed a much smaller correlation. CRP also showed correlation coefficients of magnitude between |r| = 0.20 and |r| = 0.31 across the 3 treatment groups associated with changes in pain and in the manual manipulation and mechanical manipulation groups associated with changes in ODI. Previous literature has shown an association between CRP and LBP [15, 16, 26]. Interestingly, higher CRP levels in the acute phase have been associated with better recovery than initial lower CRP levels (when lower levels are associated with other comorbidities, such as depression or sleep disturbances) [26]; similar findings were replicated in the 12-months outcomes of a recent prospective cohort [27]. Our findings partially align with previous results and may eventually translate into clinical practice [28].
The current lack of understanding of the mechanisms by which these markers affect pain and treatment trajectory underscores the need for studying this potential marker in future studies. This could potentially provide further insight into the interaction between CRP levels and LBP and potential mechanisms of action of manipulation. Nonetheless, this result highlights the need to consider this biomarker for future research on its mechanistic role in spinal manipulation.
The second important finding was that baseline Vitamin D concentrations were inversely correlated with baseline pain (r = − 0.32). This baseline correlation is in line with growing evidence on the contributing role of vitamin D on musculoskeletal pain and, more specifically, on LBP [29]. Even though the evidence is not conclusive, there is growing consensus that decreased levels of vitamin D are associated with pain syndromes [30] and that vitamin D could be administered to people to help decrease pain [31], especially if their initial vitamin D levels are low [32]. Vitamin D levels were also correlated with pain changes and ODI changes in the manual manipulation group and with changes in ODI in the mechanical manipulation group, with coefficients ranging between |r| = 0.24 and |r| = 0.31.
The evidence from the current study suggests that vitamin D could be a potential predictive biomarker for physical treatment outcomes. Besides its effects on the skeletal system, vitamin D also influences nervous, immune, and cardiovascular systems through its vital role in calcium metabolism. Therefore, the explanation for the possible correlation between baseline vitamin D with the manipulation outcomes could be multifactorial and prompts further investigations. Unfortunately, and to the best of our knowledge, no research on this topic has been performed yet. However, a review article has pointed out that vitamin D presurgical levels could predict surgical outcomes [33]. Though manipulation is not an intervention as invasive as surgery, the outcomes of manipulation may similarly be impacted by the baseline vitamin D levels. We suggest that vitamin D should be included in future studies on pain, biomarkers, and treatment outcomes, with particular attention to manipulation treatment to investigate this relationship further.
Both CRP and vitamin D had a Spearman’s Rho above 0.30; while this value is generally acknowledged in research as low to moderate strength, further discussion is warranted within the context of biomarkers and LBP. Regarding biomarkers’ association with clinical features, previous literature has been published with a similar correlation coefficient when looking at clinical variables such as pain and change in functional score (also assessed with ODI) [9, 34]. It could be that the strength of correlation of a single blood marker should be considered within the context of how it correlates with clinical features and their application in the decision-making process for clinical treatment. In addition, it is implausible that any single biomarker alone would be able to determine the clinical course of action, but rather should be considered in the context of a complex clinical presentation. In this light, even a 0.20 correlation may contribute to making impactful decisions regarding clinically complex patients. This is particularly true when considering that the approach often used for clinical decision-making for LBP treatment relies heavily on diagnostic imaging, which has not been found to be correlated with pain and function [9, 35, 36]. While these associations are not sufficient independently to impact clinical guidelines, they represent an important contribution to potentially supplementing diagnostic and prognostic tools.
Furthermore, we found associations between baseline levels of NPY and E-Selectin, both of which had a correlation coefficient above 0.20. NPY levels have been found in previous literature to be correlated with chronic pain [37], though our finding in acute and subacute LBP subjects is novel, warranting further research involving this biomarker. The literature on E-Selectin and pain shows an unclear trend. There is evidence of a significant correlation between baseline E-Selectin and pain levels for patients with chronic LBP (but not acute) [19]. However, another study found no association between E-selectin levels and pain or pain-related function in an older adult cohort with disc degeneration [9]. Our study highlights that pain levels in acute and subacute LBP patients may be associated with heightened levels of E-selectin, thus adding evidence to an area of research that needs clarification [37,38,39,40].
This study allowed us to explore the potential association between biomarkers and treatment outcomes. This topic has been gaining increasing attention in the literature, having been explored across this population and by treatment groups. The examined biomarkers represent only a sampling of potential biomarkers that may show important associations. We chose to study well-known inflammatory and pain biomarkers in this initial effort to identify candidate predictive biomarkers. While clearly, additional validation and an examination of a broader array of biomarkers are needed, these findings support future research on the potential utility of circulating biomarkers in this population.
Considering the small numbers of patients in each treatment group, though no significant correlations were found, even a 0.20 correlation may contribute to making impactful decisions when choosing manipulation-related treatments over usual medical care for LBP. Interestingly, vitamin D showed a change in disability and/or change in pain (|r| ≥ 0.20) in manipulation-related treatment groups only. This pattern warrants further study with a larger study population. The correlation coefficient of the other analyzed biomarkers was at least 0.20 associated with pain and/or ODI changes at 4 weeks in one of three treatment groups. These results are difficult to interpret since they present across different treatment groups.
The small sample sizes for each group are clear limitations to these results. These limitations are even more relevant because we used this exploratory analysis for multiple comparisons. However, this limitation is tempered by the need for novel targets and the risk of missed findings. This study had some other limitations, including its small sample size and exploratory nature. The data distribution could not be considered normal, which was addressed using non-parametric statistical analysis. Furthermore, the average BMI value for the included population was elevated (29 ± 5.9), which has potential implications for the inflammatory markers. Nonetheless, numerous studies have presented similar average BMI values [38,39,40,41], which point to the complexity of controlling for such a variable when older age and higher BMI are closely confounded.
Another limitation of this study was the absence of a control group that received no treatment, which would have allowed for observation of changes in biomarker levels over time via natural history. This improvement could have been possible by having participants assigned to a wait-list control group and would have added a layer of information in comparing the various interventions. Additional work should be pursued in this area to gain further insight into the role of biomarkers and changes in clinical outcomes. Another limitation was the lack of a blood draw immediately after the first intervention session; this would have made the study more comparable to previous research in this article’s literature review section and could help form future research questions. However, on the contrary, the inclusion of later time points for clinical outcomes is a strength of this study since they support the association between baseline biomarkers and clinical outcomes. Last, confounders could have impacted the analysis results, such as medications, supplements, and other conditions which could contribute to the biomarker pool, even though the RCT design is generally accepted to address this potential issue.
In identifying relevant serum biomarkers, previous work suggests that panels of biomarkers may provide greater predictive power than any single biomarker alone [9]. Also, combining molecular biomarkers with clinical metrics may likely prove to be the most helpful approach in identifying those patients most expected to benefit from a given treatment. This combined approach represents a potential new avenue for sub-classifying patients and developing individualized treatment plans to address sub-group differences. Overall, the associations we observed do not point at robust, direct mechanisms but likely depend on a more comprehensive set of physiological and clinical interactions that are still not understood. More research is thus warranted in this area.