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A diagnosis may appear as a single event in a medical record, but the path leading to it is rarely a single event. It is often shaped by years of symptoms, healthcare visits, diagnoses, investigations, and treatments. When these events are analysed separately, much of their temporal and clinical context is lost. Network modelling offers another perspective: it connects these events and represents disease as an evolving trajectory rather than a collection of isolated observations.

This is particularly important for complex diseases such as chronic inflammatory diseases such as multiple sclerosis (MS), where early symptoms including fatigue, pain, anxiety, or sensory disturbances are common, non-specific and rarely informative on their own. Conventional analyses typically examine whether each condition on its own occurs more frequently among future MS patients. Network modelling asks broader questions: Which conditions occur together? In what order? Which diagnoses form disease communities (known as clusters), become central hubs and essential for MS development, or connect otherwise separate clinical domains? A diagnosis may become important not because it occurs more frequently, but because its relationships with other conditions change over time. By capturing these connections, communities, and patterns of network rewiring, we can examine disease development as a changing system rather than as a list of independent risk factors.

In our recently published nationwide cohort study, at the first step, we reconstructed longitudinal disease networks from Swedish registry data across the 15 years preceding MS onset (the first time MS manifested itself clinically). The analysis showed that at the time before MS, there is a gradual reorganisation of common diagnoses into distinct multisystem patterns spanning psychiatric, metabolic, immune-related, neurological, and general symptom domains (Figure 1). Building on these findings, the second step was to develop a heterogeneous, multilayer temporal clinical graph connecting patients, visits, diagnoses, and treatments in chronological order. Covering 56,548 individuals, the graph contained more than 1.19 million nodes and 4.97 million connections, with additional layers translating fragmented medical codes into clinical concepts and identifying recurrent patterns associated with MS trajectories (Figure 2).

Figure 1. The 50 most connected diagnoses in the MS network (left) and control network (right). Nodes represent ICD-10 diagnoses, while directed edges capture their temporal sequence. Source: Ebrahimi et al. (2026), Multiple Sclerosis Journal.

Figure 2. Example of the patient-level layer of the heterogeneous temporal clinical graph. The graph connects the patient to chronologically ordered healthcare visits and links each visit to recorded diagnoses (ICD) and treatments (ATC).

This project demonstrate the value of network modelling at two complementary levels: revealing how disease patterns reorganise across a population when the onset of MS approaches, while preserving the structure of individual patient journeys. Because graph-based patterns can be traced back to the underlying visits, diagnoses, treatments, and temporal paths, they also offer a more interpretable foundation for AI than purely black-box predictions. As part of WISDOM, this work provides a basis for developing and validating trajectory-aware methods that may eventually support earlier recognition and a better understanding of complex diseases such as MS.

Ali Ebrahimi, Ali Manouchehrinia, Narsis Kiani

In a recent episode of the MS-Perspektive podcast, host Nele von Horsten speaks with neurologist Prof. Bernhard Hemmer about one of the most exciting questions in MS research: can multiple sclerosis be prevented?

Prof. Hemmer, who is involved in the WISDOM project, an international initiative focused on improving MS risk prediction and prevention, is a guest in episode #366 of the podcast. The interview is available via the MS-Perspektive website and podcast feed.

Some key highlights:

MS starts long before symptoms

• MS likely starts years before the first clinical symptoms appear
• By the time of diagnosis, MRI scans often already show earlier inflammatory activity
• This suggests MS has a long silent phase before it becomes clinically noticeable
• The disease process is therefore often well underway before the first relapse or symptoms occur

Moving toward early detection and prevention

Researchers are increasingly focusing on identifying people at higher risk earlier. Important risk factors include Epstein–Barr virus (EBV), vitamin D deficiency, smoking, obesity, and genetic predisposition. Biomarkers such as neurofilament light chain and early MRI findings may also help detect disease activity before symptoms occur.

EBV is a major focus of current research, as MS appears to be extremely rare in people who have never been infected. This has led to interest in vaccine-based prevention strategies, although these would require long-term studies to confirm effectiveness.

Who is at risk?

First-degree relatives of people with MS have a higher risk, but most will never develop the disease. Another important group is people with Radiologically Isolated Syndrome (RIS), where MRI scans show MS-like lesions without symptoms.

The role of WISDOM and future challenges

Despite progress, major challenges remain: prediction models are still imperfect, most high-risk individuals will never develop MS, and prevention studies require large, long-term international collaboration.

Initiatives like the WISDOM project, in which Prof. Hemmer participates, aim to improve risk prediction and support future prevention strategies.

Looking ahead

Over the next 5–10 years, researchers expect advances in blood-based diagnostics, MRI techniques, and large cohort studies of at-risk individuals. While true MS prevention is not yet possible, the field is steadily moving from treatment toward earlier intervention, and potentially prevention.

👉 You can listen to the full conversation with Prof. Hemmer here: https://ms-perspektive.de/366-prof-hemmer/