The study, published in the Journal of Cellular and Molecular Medicine, followed 462 adults with sepsis admitted to the intensive care unit of Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, between June 2014 and March 2025. All patients met the Sepsis-3 criteria, the internationally accepted definition of life-threatening organ dysfunction caused by a dysregulated host response to infection. Rather than relying on a single blood draw at admission, the investigators tracked serum levels of C-reactive protein, or CRP, and albumin on days 1, 3, 5, 7 and 14 after ICU admission, capturing the dynamic choreography of the body’s inflammatory and metabolic responses.

CRP and albumin are two of the most familiar numbers on any critical care chart, and both are manufactured in the liver. They are biological mirror images. CRP is a prototypic positive acute-phase reactant, ramped up by pro-inflammatory cytokines, particularly interleukin-6, and serves as a robust readout of systemic inflammatory activity. Albumin, by contrast, is a negative acute-phase protein: as inflammation flares, the liver suppresses albumin synthesis, and serum levels fall. The degree of hypoalbuminemia in critically ill patients therefore tracks the intensity of the inflammatory storm, while also reflecting nutritional reserve, hepatic synthetic function and vascular permeability. Previous work has shown that composite indices combining the two, such as the CRP-to-albumin ratio and the CALLY index, can outperform either marker alone in diagnosing and prognosticating sepsis.

What has been missing, the researchers argue, is a way to capture the coordinated temporal behaviour of the two proteins rather than their static values. Most prior studies examined single time points, ratios, or separate trajectories of one marker at a time. To fill that gap, the team applied group-based dual-trajectory modelling, or GBDTM, a statistical technique that extends conventional group-based trajectory modelling by jointly modelling correlated longitudinal markers and identifying clinically interpretable subphenotypes. Using the lcmm package in R, they tested linear, quadratic and cubic time functions across solutions containing one to six latent classes, judging the models by log likelihood, the Akaike and Bayesian information criteria, entropy, class-specific average posterior probabilities, minimum class prevalence and clinical interpretability.

The winning model was a three-class solution with an entropy of 0.80 and average posterior probabilities of 0.84, 0.87 and 0.92, indicating clean separation between groups. It carved the cohort into three distinct CRP-albumin phenotypes. The vast majority, 368 patients or 79.65 percent, followed a slow-decreasing pattern in which inflammation gradually subsided. A second group of 48 patients, 10.39 percent, showed a rapid-decreasing pattern. The third and smallest group, 46 patients or 9.96 percent, displayed the ominous signature: CRP and albumin trajectories that increased over the observation window, a pattern suggesting persistent or worsening inflammatory activity coupled with deteriorating metabolic reserve.

The clinical stakes became clear in the survival analysis. The overall 28-day mortality rate in the cohort was 34.6 percent, but it was far from evenly distributed. Patients in the increasing group died within 28 days at a rate of 52.2 percent, compared with 32.6 percent in the slow-decreasing group and 33.3 percent in the rapid-decreasing group. Kaplan-Meier curves diverged significantly between the trajectory groups, with log-rank p values of 0.034 for 28-day mortality and 0.049 for 60-day mortality. The baseline characteristics of the groups also differed in telling ways: the increasing group had a lower prevalence of skin and soft-tissue infection, different distributions of acute gastrointestinal injury grade, blood pH and hemofiltration duration.

To determine whether the increasing phenotype carried independent prognostic weight, the researchers turned to least absolute shrinkage and selection operator, or LASSO, Cox regression, which selected 27 adjustment covariates from clinically relevant demographic, physiological, infection-related, organ-function, treatment and laboratory domains. In the fully adjusted model, membership in the increasing trajectory group remained associated with 28-day mortality with a hazard ratio of 1.943, a 95 percent confidence interval of 1.202 to 3.140 and a p value of 0.007, and with 60-day mortality with a hazard ratio of 1.946, a 95 percent confidence interval of 1.218 to 3.108 and a p value of 0.005, relative to the slow-decreasing reference group. The rapid-decreasing group showed no significant excess risk. Scaled Schoenfeld-residual tests found no violation of the proportional hazards assumption, and subgroup analyses detected no evidence that the association differed by sex, smoking, alcohol use or infection site.

Sensitivity analyses reinforced the signal while adding important nuance. Parsimonious models adjusted only for age, sex and either the Sequential Organ Failure Assessment or the Acute Physiology and Chronic Health Evaluation II score yielded similar estimates, with hazard ratios for the increasing group ranging from 1.564 to 1.808. Excluding red blood cell count to resolve multicollinearity left the association essentially unchanged. Yet the incremental value over established severity scores proved modest: adding trajectory membership to a SOFA-based model raised the C-index from 0.689 to 0.702 for 28-day mortality, a change of 0.013 that fell short of statistical significance, and the corresponding gains in APACHE II-based models were even smaller. The joint model also did not consistently outperform a CRP-only trajectory model, though it did edge out an albumin-only model in both Akaike information criterion and discrimination.

The authors are candid about these limits. The value of the joint trajectory, they conclude, lies in dynamic phenotyping rather than in replacing established severity scores. The increasing pattern describes a biologically coherent subgroup whose inflammatory and metabolic systems are moving in the wrong direction together, a picture that admission values alone cannot capture. But the high-risk class proved sensitive to the number of latent classes specified: a four-class solution split the adverse phenotype into a 30-patient high-increasing group with an attenuated association and an 18-patient moderate inverted-U group with a strong association but a prevalence below the 5 percent reliability threshold, hinting at heterogeneity that the present sample cannot stably resolve.

Limitations temper the excitement. The study was single-center and retrospective, so trajectory prevalence and effect sizes may shift across different health-care systems, sepsis etiologies and measurement schedules, and external validation in independent multicenter cohorts or public databases such as MIMIC-IV and eICU remains to be done. Imputation of missing baseline covariates introduces uncertainty, and the small size of the increasing group yields imprecise estimates. The researchers also did not establish the earliest time point at which class membership can be assigned reliably, nor did they test whether trajectory-guided treatment improves outcomes. For now, the message is hypothesis-generating rather than practice-changing: in the first two weeks of sepsis, the coupled dance of CRP rising while albumin fails to recover may identify a small but distinctly vulnerable population, one that prospective multicenter studies must now confirm before this dynamic signature can inform bedside decisions.

Subject of Research: Joint longitudinal trajectories of C-reactive protein and albumin as prognostic phenotypes in early sepsis

Article Title: Dynamic Co‐Evolution of C‐Reactive Protein and Albumin in Early Sepsis: A Group‐Based Dual‐Trajectory Analysis for Risk Stratification

Article References: Chen, T., Zhang, S., Ding, M., Yang, Y., Yang, Q., Guo, W., Xu, X., & Liu, Q. (2026). Dynamic Co‐Evolution of C‐Reactive Protein and Albumin in Early Sepsis: A Group‐Based Dual‐Trajectory Analysis for Risk Stratification. Journal of Cellular and Molecular Medicine, 30(20), Article e71402. https://doi.org/10.1111/jcmm.71402

Image Credits: AI Generated

DOI: 10.1111/jcmm.71402

Keywords: sepsis, C-reactive protein, albumin, group-based dual-trajectory modelling, ICU mortality, risk stratification, acute-phase response, prognosis, critical care, biomarkers, Cox regression, SOFA score