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Inflammatory and metabolic markers differ across systemic sclerosis lung phenotypes in retrospective studyScleroderma Lung Damage May Leave Hidden Clues in Blood

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Key Takeaway
Consider inflammatory and metabolic markers as associative signatures for SSc lung phenotypes in retrospective analysis.

This retrospective cross-sectional study analyzed 314 consecutive systemic sclerosis (SSc) inpatients (2018–2023) meeting 2013 ACR/EULAR criteria to examine integrated inflammatory-immune-nutritional signatures across SSc lung phenotypes. The comparator was ILD-/PAH- patients, with ILD-/PAH+ excluded due to small sample size (n = 12).

Main results showed higher inflammatory markers in ILD+/PAH+ versus ILD-/PAH-: ESR 38 [21–61.5] vs. 23 [10–45] mm/h (p = 0.008), hs-CRP 10.15 [3.34–28.18] vs. 5.9 [1.5–16.1] mg/L (p = 0.017), CRP/albumin ratio 0.33 [0.11–0.95] vs. 0.17 [0.04–0.49] (p = 0.019), and SII 1124.76 [700.8–2034.14] vs. 820.34 [461.33–1197.08] (p = 0.013). Anti-Scl-70 positivity was enriched in ILD-containing phenotypes (p < 0.001), and IgG and IgA varied across phenotypes (p = 0.032 and p = 0.028). Female sex (OR 3.48 for ILD+/PAH-; OR 3.49 for ILD+/PAH+), disease duration (OR 1.02–1.03 per month), triglycerides (OR 1.70 for ILD+/PAH-), and log(ESR) (OR 1.82 for ILD+/PAH+) were associated with specific phenotypes. A three-cluster structure driven by inflammation (ESR), mucosal immunity (IgA), and metabolic dysregulation (triglycerides) showed stable clustering (Adjusted Rand Index = 1.00), with internal validation accuracy ~0.62–0.65.

Safety and tolerability were not reported. Key limitations include exclusion of ILD-/PAH+ due to small sample size and sparse-data instability, and the observational design precludes causal inference. Practice relevance is restrained: a small set of markers (ESR, triglycerides, IgA, and autoantibodies) provides a reproducible framework for phenotype-oriented stratification, but this is associative and requires validation in prospective studies.

A quiet disease with loud consequences

Systemic sclerosis is rare but serious. It causes the body to make too much collagen, which stiffens skin and organs.

The lungs are often hit hardest. Two problems stand out: interstitial lung disease (ILD), which scars the lung tissue, and pulmonary arterial hypertension (PAH), which raises pressure in the lung's blood vessels.

Some patients get one. Some get both. And the "both" group tends to do the worst.

Until now, doctors have struggled to tell these groups apart early. Symptoms overlap. Imaging and specialist tests take time. And treatments work differently depending on which lung pattern a patient has.

The old way of looking

For years, doctors have treated ILD and PAH almost like separate problems that happen to show up in the same disease. They looked at lung scans and breathing tests, then matched treatment to the scan.

But here's the twist.

This new study suggests that each lung pattern in scleroderma may come with its own "fingerprint" in the blood. That means the body is sending signals long before lung damage becomes obvious on imaging.

Reading the body's signals

Think of the immune system like a city's traffic system. In healthy people, signals flow smoothly. In scleroderma, certain lights get stuck on red, causing jams.

Different jams show up in different places. Some patients have more inflammation, like a flashing alarm that won't turn off. Others have nutritional shifts, such as changes in fats and proteins, which hint at deeper metabolic stress.

The researchers asked a simple question: do these different "traffic jams" line up with different lung patterns? Their answer appears to be yes.

What the study looked at

Researchers in China reviewed records of 314 adults with confirmed systemic sclerosis, all admitted to the hospital between 2018 and 2023.

They sorted patients into four groups based on whether they had ILD, PAH, both, or neither. Then they compared routine lab results, including inflammation markers, cholesterol and triglycerides, albumin (a protein tied to nutrition), antibodies, and immune proteins.

No new drugs were tested. This was detective work with data the hospital already had.

Patients with both ILD and PAH carried the heaviest inflammatory load. Their ESR, a common blood test for inflammation, was nearly double that of patients with healthy lungs. Their hs-CRP, another inflammation marker, was also higher.

Put simply, their bodies looked like they were fighting a fire that would not go out.

Patients with only ILD had a different pattern. Their triglycerides, a type of blood fat, were more likely to be elevated. Women and people who had lived with the disease longer were more likely to fall into the ILD groups.

A specific antibody called anti-Scl-70 was far more common in anyone with lung scarring. And a form of immune protein called IgA, which guards the body's mucous surfaces like the airways, also shifted in telling ways.

This doesn't mean a blood test alone can diagnose lung disease in scleroderma today.

When the researchers used a statistical method called clustering, the patients sorted themselves into three natural groups driven by inflammation, mucosal immunity, and metabolic changes. The grouping was highly stable, meaning the pattern held up when tested again and again.

Why this might matter

This is where things get interesting. If doctors can spot these patterns early, they may act sooner.

Right now, many scleroderma patients find out about lung involvement only after coughing, shortness of breath, or fatigue pushes them into testing. By then, damage may already be done. A blood-based early warning system could flip that timeline.

It could also help match the right treatment to the right patient. Someone with a high-inflammation pattern might benefit from stronger anti-inflammatory therapy. Someone with a metabolic pattern might need nutrition support along with lung care.

If you or a loved one has scleroderma, this research is not ready to change your care yet. There is no approved blood panel that predicts lung outcomes.

But it is a reason to keep up with regular check-ins. Ask your rheumatologist whether routine inflammation markers, antibody tests, and lipid panels are being tracked over time. Small changes, watched carefully, may matter.

And if breathing feels different, speak up early. Do not wait.

Honest limits

The study was retrospective, meaning it looked backward at records rather than following patients forward in time. That makes it harder to prove cause and effect.

It was also done at a single center in one country, with a small number of patients in the PAH-only group. The findings need to be repeated in larger, more diverse populations before they can guide treatment.

The next step is clear. Researchers need to test these blood patterns in new groups of scleroderma patients and follow them over time to see if the patterns predict who gets worse and who stays stable.

If that works, the patterns could be built into a simple scoring tool that doctors use at the bedside. Bringing a new tool from early data to daily practice usually takes years of careful study, so patience will be needed. But the path forward, for the first time, looks mapped.

Study Details

Study typeCohort
EvidenceLevel 3
PublishedApr 2026
View Original Abstract ↓
BackgroundSystemic sclerosis (SSc) exhibits heterogeneous pulmonary involvement, most commonly interstitial lung disease (ILD) and pulmonary arterial hypertension (PAH). How systemic inflammatory, immune, and nutritional states differ across these lung phenotypes remains incompletely understood.ObjectiveTo characterize integrated inflammatory-immune-nutritional signatures across SSc lung phenotypes and validate their robustness using multivariable modeling and unsupervised clustering.MethodsWe conducted a retrospective cross-sectional study of 314 consecutive SSc inpatients fulfilling the 2013 ACR/EULAR criteria (2018–2023). Patients were stratified into four lung phenotypes: ILD-/PAH- (n = 45), ILD + /PAH- (n = 183), ILD-/PAH + (n = 12), and ILD + /PAH + (n = 74). Routine laboratory data were used to derive inflammatory markers (hs-CRP, ESR, NLR, PLR, MLR, SII, SIRI, SCI), nutritional parameters (BMI, albumin, lipid profiles), and immune markers (autoantibodies, immunoglobulins, complement). We performed (1) group comparisons; (2) multinomial logistic regression focusing on the three predominant phenotypes (ILD-/PAH- as reference; ILD + /PAH-; ILD + /PAH +), excluding ILD-/PAH + due to small sample size (n = 12) and sparse-data instability; and (3) k-means clustering of standardized biomarker profiles. Internal validation and clustering stability analyses were performed.ResultsILD + /PAH + patients showed a higher systemic inflammatory burden, including higher ESR (38 [21–61.5] vs. 23 [10–45] mm/h, p = 0.008), hs-CRP (10.15 [3.34–28.18] vs. 5.9 [1.5–16.1] mg/L, p = 0.017), CRP/albumin ratio (0.33 [0.11–0.95] vs. 0.17 [0.04–0.49], p = 0.019), and SII (1124.76 [700.8–2034.14] vs. 820.34 [461.33–1197.08], p = 0.013) compared with ILD-/PAH-. Anti-Scl-70 positivity was enriched in ILD-containing phenotypes (p < 0.001), and immunoglobulins varied (IgG p = 0.032; IgA p = 0.028). In multinomial models, female sex and longer disease duration were associated with both ILD + /PAH- (OR 3.48, 95%CI 1.51–8.04; OR 1.02 per month, 95%CI 1.01–1.04) and ILD + /PAH + (OR 3.49, 95%CI 1.31–9.33; OR 1.03 per month, 95%CI 1.01–1.05). Triglycerides (Z-score) were associated with ILD + /PAH- (OR 1.70, 95%CI 1.05–2.77), while log(ESR) (Z-score) was associated with ILD + /PAH + (OR 1.82, 95%CI 1.01–3.27). Clustering supported a three-cluster structure driven by inflammation (ESR), mucosal immunity (IgA), and metabolic dysregulation (triglycerides), with strong stability (Adjusted Rand Index = 1.00). Internal validation showed stable within-cohort performance (accuracy ∼0.62–0.65).ConclusionSSc lung phenotypes defined by ILD/PAH co-occurrence display distinct integrated inflammatory-immune-nutritional signatures. The ILD + /PAH + phenotype reflects a high-burden systemic inflammatory state, and a small set of coherent markers (ESR, triglycerides, IgA, and autoantibodies) provides a reproducible framework for phenotype-oriented stratification.
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