Low Back Pain Research Study Renewed by NIH

Codirectors of the Ferguson Laboratory for Orthopaedic and Spine Research, Gwendolyn A. Sowa, MD, PhD, professor and chair of the Department of Physical Medicine and Rehabilitation at the University of Pittsburgh School of Medicine, and Nam Vo, PhD, professor of orthopaedic surgery and deputy vice chair of research in the Department of Orthopaedic Surgery at Pitt, have received a five-year, $20 million National Institutes of Health (NIH)/National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) award to continue and expand a multidisciplinary investigation into the phenotype evolution and clinical trajectories of chronic low back pain (CLBP).

The award supports the next phase of the original Low Back Pain Biological, Biomechanical, and Behavioral (LB³P) study, now called the Holistic Pain Phenotypes (H2P) LB³P study, building on work originally funded in 2020 through the NIH Helping to End Addiction Long-Term (HEAL) Initiative–funded Back Pain Consortium (BACPAC). The renewed study brings together the prior multidisciplinary team from orthopaedics, rehabilitation, bioengineering, pain medicine, psychology, molecular biology, biostatistics, and related fields, along with a strategic group of new researchers to examine CLBP as part of a broader whole-person pain experience.

“The first phase of LB³P focused on building the data foundation needed to understand how pain manifests across multiple domains and to determine whether distinct pain phenotypes exist,” Dr. Sowa says. “This next phase allows us to ask how those features relate to what actually happens to patients over time.”

Addressing Heterogeneity in CLBP

CLBP remains one of the most common and costly causes of disability. Patients with similar clinical presentations often experience very different outcomes when treated with the same interventions. Some improve, others remain stable, and others worsen, despite receiving similar types of care or treatments.

This variability reflects the heterogeneity in the biological, biomechanical, and behavioral factors that contribute to CLBP, which are not captured by traditional symptom-based classifications or imaging findings alone.

The original LB³P project was designed to address this problem directly. Rather than testing specific treatments, the study developed a comprehensively characterized cohort to examine how multiple domains (biological, biomechanical, and behavioral characteristics) interact within the same individuals and whether those interactions define distinct phenotypes of CLBP.

“The goal was not to prove that one factor matters more than another,” Dr. Vo says. “It was to collect the data needed to ask better questions about how and why patients with CLBP differ in the first place.”

Building the LB³P Dataset

The initial LB³P grant enrolled more than 1,000 participants with CLBP and followed them for one year. Data were collected from individuals without low back pain to provide a comparative control cohort for analysis

The LB³P study has collected a broad range of data capturing pain-related processes across domains, including clinical assessments, biomechanical and movement analyses, quantitative sensory testing, psychosocial and behavioral evaluations, and biospecimen collection for molecular and omics analyses.

Including control individuals without low back pain strengthens interpretation of the dataset by providing context for biological and biomechanical signals observed in the chronic pain cohort. This comparative framework is helping the study teams distinguish features specific to CLBP from those of general pain susceptibility, functional limitation, or aging-related changes.

“What LB³P gave us was the ability to study low back pain as a system rather than as isolated variables,” Dr. Vo says. “By collecting these data in the same patients, we can begin to see which combinations of factors are associated with different experiences of pain and disability.”

Analytic Progress and Early Findings

To date, the LB³P research team has published multiple peer-reviewed papers (see below) examining individual components of the dataset, including biological markers, biomechanical measures, behavioral characteristics, and sensory processing. These studies have provided initial insight into how specific domains relate to CLPB pain and function.

More recently, the analytic efforts have focused on integrative analyses to determine whether patients cluster into phenotypes.

“Our early analyses suggest that there are patient subgroups that differ in meaningful ways,” Dr. Sowa says. “For example, we see groups with similar reports of pain that tend to have different levels of function, movement patterns, or different medical comorbidities, as well as different biological profiles. These findings are preliminary and do not yet incorporate the entirety of the available data, but they suggest that distinct phenotypes may be present.”

Although the early analyses suggest candidate phenotypes, the initial LB³P project was not designed to determine how those phenotypes may evolve over time or how they are shaped by the presence of other chronic pain conditions. It is still unknown if phenotypic features represent stable characteristics, transitional states, or responses to changing biological and behavioral factors. Understanding these aspects of CLBP is the primary focus of the newly awarded grant.

Extending LB³P to Longitudinal Outcomes

The new grant extends the original LB³P project by allowing the team to follow the study’s cohort for up to five years. This longitudinal examination will provide data on how early phenotypic features relate to longer-term clinical trajectories, including improvement, persistence, or progression of pain and disability.

“What we want to understand now is what happens to these patients over time,” Dr. Vo says. “Do they get better, stay the same, or worsen, and which characteristics or biomarkers are associated with those paths.”

The longitudinal design of the new study also allows Dr. Vo, Dr. Sowa, and colleagues to examine how patient-specific features may change alongside their clinical outcomes. Rather than assuming the phenotypes identified at baseline remain fixed, the study will evaluate whether phenotypic profiles evolve as pain improves or persists and whether those changes are meaningful for prognostication.

Expanding Beyond the Spine

An important part of the new study is designed to understand how CLBP phenotypes relate to other chronic overlapping pain conditions and common musculoskeletal disorders. These conditions often coexist with low back pain and can substantially influence symptom severity, disability, and treatment outcomes.

Without accounting for overlapping pain conditions, it is difficult to determine whether observed phenotypic features are specific to low back pain or reflect broader pain processing mechanisms.

“What became clear is that low back pain does not occur in isolation for many patients,” Dr. Sowa says. “If we ignore other pain sites, we risk misinterpreting what we are seeing in the data.”

New and Continuing Collaborators

The study continues to involve many investigators who contributed to the original project, while adding new collaborators to address scientific gaps identified during the first funding period. The new investigators are:

Ajay D. Wassan, MD, who leads an NIH HEAL–funded study focused on myofascial pain, is now more deeply engaged in the project to support interpretation of widespread and nonregion-specific pain patterns that may influence low back pain trajectories.

To address co-occurring craniofacial pain, the team has added Alex Almarza, PhD, from the University of Pittsburgh School of Dental Medicine. Dr. Almarza is an investigator in the NIH HEAL–funded RE-JOIN consortium and leads temporomandibular joint assessments within the study.

“These additions allow us to ask whether features we observe in low back pain reflect spine-specific mechanisms or broader pain processes,” Dr. Vo says.

At the mechanistic level, Michael Gold, PhD, from the Department of Neurobiology, and Hang Lin, PhD, from the Department of Orthopaedic Surgery, both investigators on NIH HEAL–funded projects, are contributing expertise in interactions between musculoskeletal and nerve cells that may underlie pain persistence across tissue types.

The project also has expanded its collaboration with the Department of Biostatistics. George Tseng, PhD, and Rebecca Deek, PhD, bring expertise in multi-omics, machine learning and high-dimensional data analysis.

“These analytic approaches are essential once you start working with the level of data complexity we have and will continue to collect from our study participants” Dr. Sowa says.

Preparing for Phenotype-Informed Clinical Trials

Using longitudinal outcome data and refined phenotypic profiles, the research team plans to design a clinical trial to test whether treatments can be matched to patient subgroups defined by underlying contributors.

“What this study will allow us to do in the future is design a clinical trial that is informed by the data we have generated, rather than assumptions,” Dr. Sowa says. “The goal is to determine whether patients who share certain characteristics respond differently to specific interventions, and whether matching treatments to those characteristics actually improves outcomes.”

This approach is part of a deliberate progression from data generation to hypothesis testing and eventual translation into efficacious therapy in the clinic, ensuring that future trials are grounded in a clear understanding of patient heterogeneity rather than treating CLBP as a single entity.

H2P LB³P Data’s Long-term Value to the Ferguson Lab Research Community

The continuation of the LB³P study will remain a major data resource for the Ferguson Laboratory. The dataset has the ability to support secondary analyses, methodological innovation, and trainee-driven research, with longitudinal follow-up expected to generate new scientific questions over time.

“Following patients over time allows us to see which early characteristics actually matter for outcomes,” Dr. Vo says. “That is information we simply could not obtain without this kind of sustained investment.”

NIH RePORT Abstract

Whole Person Experience of Pain: Novel Integration With Low Back Pain Phenotypes. Project Number1UC2AR086243.

Reference List of Publications to Date Based on LB3P Study Data

1. Alfikri ZF, Johnson ME, Dicianno BE, Greco CM, Parmanto B, Piva SR, Roos RE, Saptono A, Sowa GA, Zhou L, Bell KM. Mobile health for chronic low back pain assessment: design, development, and usability evaluation. JOR Spine. 2025; 8(4): e70118.
2. Anderst W, Kim CJ, Bell KM, Gale T, Gray C, Greco CM, LeVasseur C, McKernan G, Megherhi S, Patterson CG, Piva SR, Pellegrini C, Schneider MJ, Shoemaker J, Smith P, Vo NV, Sowa GA. Intervertebral lumbar spine kinematics in chronic low back pain patients measured using biplane radiography. JOR Spine. 2025; 8(2): e70069.
3. Bailes AH, Johnson M, Roos R, Clark W, Cook H, McKernan G, Sowa GA, Cham R, Bell KM. Assessing the reliability and validity of inertial measurement units to measure three-dimensional spine and hip kinematics during clinical movement tasks. Sensors (Basel). 2024; 24(20).
4. Bailes AH, McKernan GP, Redfern MS, Cham R, Greco CM, Brach JS, Piva SR, Vo NV, Sowa G. Associations between fear-avoidance or pain catastrophizing and gait quality in chronic low back pain: a cross-sectional study. Phys Ther. 2025; 105(8): pzaf089.
5. Bailes AH, Redfern MS, Sowa G, Perera S, Greco CM, Brach JS, Cham R. Gait dual-task cost in individuals with chronic low back pain and high avoidance or catastrophizing. Gait Posture. 2025; 122: 312-319.
6. Batorsky A, Bowden AE, Darwin J, et al. The Back Pain Consortium (BACPAC) research program data harmonization: rationale for data elements and standards. Pain Med. 2023; 24(suppl 1): S95-S104.
7. Bell KM, Roos RE, Alfikri ZF, et al. Lumbopelvic kinematics during functional tasks in a chronic low back pain observational cohort. JOR Spine. 2025; 8(4): e70117.
8. Bell KM, Alfikri ZF, Anderst W, et al. In-field ecological momentary assessment from wearable motion sensors and self-report in a chronic low back pain cohort. JOR Spine. 2025. Accepted.
9. Carlesso C, Anderst W, Schneider MJ, Johnson B, Piva SR. Relationships between lumbar paraspinal muscle size and quality, physical function, disability, and pain in adults with chronic low back pain. Spine J. 2025; 25(11 suppl): S29-S30.
10. Enrico VT, Anderst W, Bell KM, Coelho JP, Darwin J, Delitto A, Greco CM, Lee JY, McKernan GP, Patterson CG, Piva SR, Schneider MJ, Wilcox L, Vo NV, Sowa GA. Plasma pro- and anti-inflammatory cytokines in an observational chronic low back pain cohort. JOR Spine. 2025; 8(3): e70095.
11. Fields AJ, Dudli S, Schrepf A, et al. Protocol for biospecimen collection and analysis within the BACPAC research program. Pain Med. 2023; 24(suppl 1): S71-S80.
12. Greco CM, Wasan AD, Schneider MJ, et al. Biobehavioral assessments in BACPAC: recommendations, rationale, and methods. Pain Med. 2023; 24(suppl 1): S61-S70.
13. Greco CM, Dodds NE, Acevedo AM, et al. Patient-reported outcomes among an observational cohort of individuals with chronic low back pain. JOR Spine. 2025; 8(3): e70097.
14. Hoydick J, Johnson ME, Cook HA, Alfikri ZF, Jakicic JM, Piva SR, Chambers AJ, Bell KM. Algorithm validation for quantifying ActiGraph physical activity metrics in individuals with chronic low back pain and healthy controls. Sensors (Basel). 2024; 24(16): 5323.
15. Johnson M, LeVasseur C, Gale T, Megherhi S, Shoemaker J, Pellegrini C, Gray E, Smith P, Anderst W. Lumbar spine marker placement errors and soft tissue artifact during dynamic flexion-extension and lateral bending in individuals with chronic low back pain. J Biomech. 2024; 176: 112356.
16. Mauck MC, Lotz J, Psioda MA, et al. The Back Pain Consortium (BACPAC) research program: structure, research priorities, and methods. Pain Med. 2023; 24: pnac202.
17. Mauck MC, Barth KS, Bell KM, Brooks AK, et al. The design and rationale of the Biomarkers for Evaluating Spine Treatments (BEST) trial: a sequential multiple assignment randomized trial. Pain Med. 2025; 26(9): 538-553.
18. Nagar H, Morikone M, Baldoni P, et al. Identification of genetic variations in a chronic low back pain population: a descriptive analysis. JOR Spine. 2025. Accepted.
19. Piva SR, Smith C, Anderst W, et al. Demographic and biomedical characteristics of an observational cohort with chronic low back pain: a descriptive analysis. JOR Spine. 2025; 8(3): e70094.
20. Piva SR, Alfikri ZF, Anderst W, et al. Feasibility of physical examination and performance-based tests in individuals with chronic low back pain: a descriptive study. JOR Spine. 2025; 8(3): e70096.
21. Quirk DA, Johnson ME, Anderson DE, et al. Biomechanical phenotyping of chronic low back pain: protocol for BACPAC. Pain Med. 2023; 24(suppl 1): S48-S60.
22. Rowland B, Barth KS, Bell KM, et al. Baseline characteristics of participants in the Biomarkers for Evaluating Spine Treatments clinical trial: a sequential multiple assignment randomized trial for chronic low back pain. Pain Med. 2025. Epub ahead of print.
23. Schneider MJ, Greco CM, Acevedo AM, et al. Quantitative sensory testing in an observational cohort of adults with chronic low back pain. JOR Spine. 2025; 8(3): e70103.
24. Sollmann N, Fields AJ, O’Neill C, et al. Magnetic resonance imaging of the lumbar spine: recommendations for acquisition and image evaluation from the BACPAC Spine Imaging Working Group. Pain Med. 2023; 24(suppl 1): S81-S94.
25. Vo NV, Piva SR, Patterson CG, et al. Toward the identification of distinct phenotypes: research protocol for the Low Back Pain Biological, Biomechanical, and Behavioral (LB3P) cohort study and the BACPAC Mechanistic Research Center at the University of Pittsburgh. Pain Med. 2023; 24: pnad009.
26. Vo NV, Sowa G. Introduction to a special issue describing an observational cohort of adults with chronic low back pain. JOR Spine. 2025. Accepted.
27. Carlesso C, Sions J, Anderst W, Patterson CG, Johnson B, Voss A, Weissmann L, Schneider MJ, Piva SR. Development and reliability of a systematic method to evaluate lumbar paraspinal muscle size and quality in computed tomography images. NASSJ. 2025. Accepted.