Kidney transplantation is the optimal treatment for eligible patients with end-stage kidney disease (ESKD), offering superior survival and quality of life compared to dialysis. Despite significant advances in transplant medicine, graft loss continues to be a major challenge. However, despite advances in transplant medicine, graft loss remains a significant challenge. Subclinical acute rejection (subAR)—defined as histologic evidence of rejection without overt clinical symptoms—occurs in approximately 20–27% of patients within the first two years post-transplant. As of 2025, post-transplant monitoring is largely reactive.

Clinicians typically detect complications only after inflammation or infection has progressed. While biopsy remains the gold standard for identifying subAR, it is invasive, costly, and impractical for routine surveillance. Emerging biomarkers such as donor-derived cell-free DNA (dd-cfDNA) and gene expression profiling (GEP) have enhanced detection capabilities, but they provide only static snapshots of immune activity.

To optimize long-term outcomes, clinicians need a dynamic, longitudinal view of immune status to personalize immunosuppression. Torque Teno Virus (TTV), a non-pathogenic virus that has an inverse correlation to T-cell replication, offers a promising solution. Serial TTV monitoring may enable earlier identification of over- or under-immunosuppression, supporting timely therapeutic adjustments and improved graft health.

What Is TTV and Why It Matters in Transplant Care

Recent advances in transplant biomarker research have positioned TTV as a promising tool for monitoring immunosuppression. Multiple studies demonstrate a consistent inverse correlation between TTV viral load and immune activity: higher viral loads of TTV are generally associated with stronger immunosuppression and lower immune activity, whereas lower viral loads may indicate higher immune activity (and a potential for inadequate immunosuppression). In essence, TTV behaves similarly to pathogenic viruses in its response to immunosuppressive therapy, reinforcing its potential as a surrogate marker for immune balance in transplant recipients.

While most studies have explored TTV in the first year post-transplant, they have primarily focused on population-level trends or comparisons with urine- and plasma-based biomarkers. These studies established foundational knowledge of TTV kinetics but lacked the granularity needed to predict individual risk of rejection or infection.

A pivotal study published in Journal of Clinical and Translational Research in September 2025—Using Torque Teno Virus as a Serial Monitoring Tool for the Net State of Immunosuppression of Kidney Transplant Recipients—is the first to evaluate serial TTV monitoring over a two-year post-transplant period. This longitudinal design highlights TTV’s unique ability to reflect directional changes in immune status, offering clinicians a dynamic tool to guide immunosuppressive management and achieve immune quiescence.

The remainder of this article explores the findings of the September 2025 study and describes how Eurofins TRAC ID, an evolutionary diagnostic tool from Eurofins Transplant Genomics (ETG), supports clinicians in creating TTV monitoring trends and working toward more proactive immunosuppression management.

Meet Eurofins TRAC ID: A New Framework for Interpreting Immune Risk Over Time

TRAC ID—Transplant Rejection and Infection Composite Immune Dynamics—is an analytics model developed by ETG to support proactive clinical decision-making in kidney transplant care. By integrating donor-derived cell-free DNA with TTV viral load data, TRACID enables clinicians to categorize patients into actionable immune risk profiles.

Unlike static assessments, TRAC ID employs a longitudinal framework to evaluate immune activity over time. It integrates three key metrics:

Current TTV viral load
Historical average TTV load
Rate of change (slope) in TTV load over time

This multidimensional approach enables clinicians to assess a patient’s current immune status, compare it to individualized baselines, and interpret the trajectory of immune modulation.. For example, stable TTV levels within a clinically appropriate range may indicate consistent immunosuppression, whereas abrupt changes—either increases or decreases—could indicate emerging immune activation, infection risk, or a response to therapeutic adjustments.

By combining these TTV dynamics with dd-cfDNA—a validated marker of graft injury—TRAC ID offers a more comprehensive picture of immune risk. This dual-modality strategy enhances the ability to distinguish between rejection and infection, reveals potential overlap between these processes, and supports earlier, more targeted clinical interventions

Key Study Findings: TTV Dynamics Predict Subclinical Rejection and Infection

The September 2025 study demonstrated that TTV trajectory is a powerful predictor of both rejection and infection risk in kidney transplant recipients.

Specifically, patients were significantly more likely to experience SubAR if they exhibited:

A past 1-year TTV slope below 0.006,
A current TTV level < 4.3 log₁₀ IU/mL, and
A historical average TTV level > 5.7 log₁₀ IU/mL.

This combination was associated with a 13.9-fold increase in the odds of subAR compared to patients without this pattern. A declining slope following previously high values may indicate a rising immune response—potentially preceding graft inflammation and rejection.

Patients were significantly more likely to experience viral infection if they exhibited a past 1-year TTV slope above 0.076, which conferred a 12.15-fold increase in infection risk.

This reflects excessive immunosuppression and vulnerability to opportunistic infection.

By leveraging serial measurements rather than single time-point data, clinicians can move from reactive detection to predictive insight. Monitoring slope alongside absolute TTV values enables earlier recognition of trends toward under- or over-immunosuppression, allowing timely therapeutic adjustments before clinical complications occur.

How Eurofins TRAC ID Supports Safer Immunosuppression Decisions

Managing immunosuppression after kidney transplantation requires continuous calibration. Clinicians must decide when to taper therapy, when to intervene, and when to maintain current regimens—while balancing the competing risks of rejection and infection. Historically, these decisions have relied on isolated data points or clinical events that often emerge only after an immune imbalance has caused harm.

TRAC ID introduces a layer of foresight into this decision-making process. By integrating longitudinal TTV trends with dd-cfDNA signals, TRAC ID reveals the direction of a patient’s immune response, enabling more confident and timely therapeutic adjustments. This approach may reduce reliance on for-cause biopsies and offer a more complete picture of graft health.

TRACID helps address practical questions commonly encountered in post-transplant care, such as:

Can we intervene earlier—before rejection becomes clinically apparent?
Is it safe to reduce this patient’s immunosuppressive medication?
Is the current regimen achieving immune stability—balancing rejection and infection risk?

While other biomarkers offer point-in-time insights or slow trend accumulation, TRAC ID uses regression-based slope analysis and statistical modeling to quantify the rate and direction of TTV change. This converts TTV from a static value into a dynamic indicator of immune trajectory, enabling clinicians to anticipate shifts toward under- or over-immunosuppression and adjust therapy proactively.

Validated Findings: TTV Dynamics Inform Rejection and Infection Risk

This study evaluated 252 kidney transplant recipients from the CTOT-08 cohort, monitored over a two-year period, and is one of only a few current research efforts looking at TTV trends beyond the one-year mark.. Researchers collected 1,967 plasma samples, with patients averaging 7–8 follow-up visits. Surveillance biopsies were performed at 2, 6, 12, and 24 months post-transplant to assess graft health. Patients were seen monthly for the first six months and quarterly thereafter.

TTV was quantified using metagenomic next-generation sequencing (mNGS), enabling high-resolution detection of viral load shifts. These thresholds and predictive models were validated in an independent cohort from the Northwestern Biorepository, which included 162 patients and 173 samples.

The study revealed that changes in TTV levels over time can help predict whether a patient is at risk for rejection or infection:

A past 1-year logTTV slope < 0.0066, combined with current logTTV < 4.3 and historical average logTTV > 5.7, was associated with a 13.88-fold increase in odds of subclinical acute rejection (95% CI: 5.49–37.42) compared to patients whose slope exceeded 0.0066.
A past 1-year logTTV slope > 0.076 conferred a 12.15-fold increase in odds of infection over stable transplant (TX) (95% CI: 2.99–81.42).
Two-sided logTTV thresholds (4.5, 7.8) stratified patients into immunosuppression states:
- Under-IS (logTTV < 4.5) increased subclinical AR odds over TX by 2.39-fold (95% CI: 1.53–3.83).
- Over-IS (logTTV > 7.8) increased infection odds over subclinical AR by 2.5-fold (95% CI: 1.03–6.42).
Decision trees incorporating TTV trajectories achieved AUCs of 0.67 for both subclinical AR and infection—outperforming models using a single TTV measurement.

What This Means for Your Practice

Serial TTV monitoring—interpreted through the TRAC ID model and integrated with dd-cfDNA—adds a predictive layer to post-transplant care. Unlike single-point measurements, TRAC ID uses slope-based analysis to quantify the rate and direction of immune change, helping distinguish patients who appear stable today but are trending toward under- or over-immunosuppression.

In clinical practice, TRACID supports:

Earlier intervention before rejection or infection becomes clinically apparent
Safer, data-driven immunosuppressive adjustments
Reduced reliance on for-cause biopsies through proactive monitoring

By converting TTV from a static marker into a dynamic trajectory, TRAC ID enables clinicians to anticipate immune shifts and personalize therapy—making long-term transplant care more predictable and reducing complications.