Common Diseases, Hidden Rarities: The 3% Logic That Challenges the 97%
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Executive Summary
A recent New England Journal of Medicine study (Rahimov et al., NEJM 2025;393:1589–1598) demonstrates that up to 3% of patients diagnosed with common diseases such as multiple sclerosis, inflammatory bowel disease, or atopic dermatitis in fact carry rare monogenic disorders. Using genome and exome sequencing in the UK Biobank and clinical trial cohorts, researchers revealed how hidden rare diseases confound clinical trials, distort therapeutic efficacy signals, and delay accurate patient care.
The paradox is clear: to capture a small minority (<3%), one must consider genetic testing across the entire 100% of patients. Yet clinical and economic logic suggests a more stratified approach—sequencing should be reserved for patients refractory to standard therapies.
Five Laws of Epistemic Integrity
1. Truthfulness of Information — High
The findings are grounded in large-scale genomic datasets (UK Biobank, n≈500,000) and replication cohorts from clinical trials. Variant calling, transcriptome integration, and molecular diagnosis thresholds are transparently reported.
2. Source Referencing — High
The study is published in The New England Journal of Medicine, with funding disclosed (AbbVie, NIHR Cambridge Biomedical Research Centre). Replication across multiple cohorts strengthens credibility.
3. Reliability & Accuracy — Moderate/High
The results show consistent prevalence of rare monogenic variants across MS (2.9%), IBD (1.1%), and dermatitis (2.5%). However, penetrance and clinical actionability vary, and generalizability to non-European populations remains limited.
4. Contextual Judgment — Moderate
The article emphasizes potential benefits of universal sequencing but underplays the cost and operational burden. The structural paradox—screening 97% to catch 3%—is not fully addressed, leaving a gap between scientific enthusiasm and healthcare pragmatism.
5. Inference Traceability — High
The logical chain is explicit: hidden rare diseases → distorted trial outcomes → inadequate patient care. What is less developed is the practical pathway of integrating sequencing selectively into clinical workflows.
Structured Opinion (BBIU Analysis)
Clinical Layer
Misdiagnosis of rare monogenic disorders as common diseases explains treatment refractoriness in a subset of patients. For these individuals, genetic diagnosis changes clinical management completely—redirecting them to orphan drugs, gene therapies, or bone marrow transplantation. For the majority (97%), sequencing confirms the existing diagnosis but does not alter treatment.
Economic Layer
Universal sequencing of common disease cohorts could redirect billions from therapeutic budgets to diagnostics. For industry, this expands the market for genomic platforms but compresses drug ROI, as trials risk dilution by hidden subgroups. A tiered diagnostic strategy—standard treatment first, sequencing only in non-responders—preserves economic balance.
Symbolic Layer
The distinction between “common” and “rare” diseases collapses. Every patient becomes a potential carrier of rarity. Medicine shifts from probabilistic categories to individualized molecular identities. Symbolically, this reframes disease not as population labels but as genomic mosaics.
Systemic Layer
If regulators adopt the logic of mandatory stratification, Phase 3 trial enrollment will require genomic pre-screening. This could delay trial initiation but increase precision. Health systems may resist universal sequencing, but payer incentives will converge toward covering sequencing in refractory or atypical patients.
Final Integrity Verdict
Integrity Level: Moderate–High
The study is methodologically sound and highlights a critical blind spot in clinical medicine. Yet the implied expansion to universal sequencing requires stronger cost–benefit justification. Selective deployment in refractory patients appears more rational.
Annex 1 — The Diagnostic Journey: From First Consultation to Genetic Reclassification
When a patient first arrives in a doctor’s office with troubling symptoms, the medical journey begins with the most traditional of tools: a conversation and a careful examination. The patient might complain of numbness, vision problems, or fatigue suggestive of multiple sclerosis; or perhaps of chronic abdominal pain, diarrhea, and weight loss hinting at inflammatory bowel disease; or instead of relentless itching, rashes, and skin thickening that resemble severe atopic dermatitis. These initial signs are nonspecific — they belong to the broad category of “common diseases,” and that is where the diagnostic process starts.
Step 1. Clinical Assessment
The physician listens, records the patient’s history, examines the body, and orders first-line tests. At this point, the diagnosis remains anchored in phenotype — that is, what the doctor sees and what the patient describes.
For multiple sclerosis, the neurologist will look at MRI scans of the brain and spinal cord, analyze spinal fluid for the telltale “oligoclonal bands,” and test nerve conduction.
For inflammatory bowel disease, a gastroenterologist will conduct endoscopy and colonoscopy, collect tissue samples, and check blood markers of inflammation.
For atopic dermatitis, the dermatologist examines the skin closely, checks IgE levels, and sometimes performs allergy tests.
From this combination of symptoms and initial data, the patient is classified under a familiar label: MS, IBD, or dermatitis. The physician proceeds with standard therapy.
Step 2. Treatment and Monitoring
The patient begins treatment: immune-modulating drugs in MS, steroids and biologics in IBD, topical or systemic immunomodulators in dermatitis. The hope is that symptoms improve and the course of disease follows the expected trajectory.
If the patient responds as anticipated, the diagnosis is confirmed by clinical behavior, and treatment continues.
If the patient does not respond, or if the disease behaves in ways that do not match the textbook description, the story becomes more complicated.
Step 3. When Treatment Fails
For the non-responders, doctors widen their investigation. Blood tests are ordered to search for other conditions that mimic the original diagnosis.
In suspected MS, additional tests may look for anti-MOG or anti-AQP4 antibodies, which indicate neuromyelitis optica rather than MS.
In IBD, doctors check for ANCA or ASCA antibodies, repeat inflammatory markers, and sometimes reconsider infections or other causes of colitis.
In atopic dermatitis, allergen-specific IgE, eosinophil counts, and detailed allergy panels are run.
This step functions as a confirmatory checkpoint: before deciding the patient is “unusual,” doctors systematically exclude other common explanations.
Step 4. The Role of Biopsy
If doubt remains, the physician moves to the histological level — studying the tissue itself.
For IBD, biopsies are routine from the very beginning, and in refractory cases they may be repeated to assess microscopic inflammation, dysplasia, or unusual tissue changes.
In dermatitis, skin biopsies are not routine, but in severe or non-responding cases, they are essential. Under the microscope, a dermatologist can distinguish eczema from psoriasis or even detect rare entities such as cutaneous lymphoma.
In MS, biopsies of brain or spinal tissue are exceedingly rare, reserved only for atypical cases where lymphoma or vasculitis cannot be excluded.
At this stage, the physician holds something invaluable: a tissue sample that objectively confirms the presence of disease at the microscopic level.
Step 5. The Logic of Sequencing at the Biopsy Stage
This is where the modern logic emerges. Once histology has proven that disease is present in the tissue — real, tangible, and documented — it makes sense to go deeper. Instead of relying only on clinical impressions, doctors can now apply genomic sequencing to the confirmed lesion.
A sample of blood or saliva is taken to establish the patient’s germline DNA.
The biopsy tissue is analyzed through whole-exome or whole-genome sequencing, sometimes complemented with RNA sequencing to capture active gene expression.
By comparing the patient’s inherited DNA with the actual genetic or transcriptional signals in the diseased tissue, scientists can detect whether the patient truly suffers from the common disease, or whether a rare monogenic disorder is hidden beneath the surface.
Step 6. Reclassification and Clinical Action
If a rare pathogenic variant is found, the patient’s story changes dramatically. What was once called “multiple sclerosis” may now be revealed as a leukodystrophy. What was thought to be “IBD” might turn out to be a monogenic immune deficiency. The treatment path is transformed: immunosuppressants and biologics are abandoned, replaced by targeted therapies, bone marrow transplantation, or experimental gene therapy.
If, on the other hand, sequencing confirms no rare variant of significance, the patient remains within the 97% majority, and treatment proceeds along the established course.
Step 7. The Broader Lesson
The journey shows why sequencing, when anchored to histological confirmation, is both logical and powerful. It avoids sequencing every patient at the outset, which would burden the system and deliver little new information for the majority. Instead, it reserves this expensive and complex tool for those who need it most — the non-responders, the atypical cases, the patients whose disease resists simple labels.
Annex 2 — Health Economics of Sequencing: From the Hospital Corridor to the Genomic Laboratory
When discussing the cost of modern medicine, numbers can appear deceptively small when expressed as percentages of a national budget. A fraction such as 0.0043% of NHS England’s annual expenditure may seem negligible. Yet in absolute terms, these sums are measured in tens of millions of dollars, enough to keep ambulances on the road, emergency rooms staffed, and vaccination campaigns running. The central dilemma of genomic screening is therefore not whether it is scientifically useful — it certainly is — but whether the method of deployment justifies the opportunity cost.
1. The Bedrock of Diagnosis: Why Classical Tools Cannot Be Replaced
Before discussing sequencing, it is essential to understand the role of classical diagnostic methods.
MRI in Multiple Sclerosis (USD 800–2,000): Magnetic resonance imaging provides the irreplaceable window into the central nervous system. It allows neurologists to visualize demyelinating lesions, to measure disease activity over time, and to determine whether powerful immunomodulating drugs should be started or escalated. Sequencing alone cannot show lesions or their progression.
Colonoscopy with Biopsy in Inflammatory Bowel Disease (USD 1,500–3,000): Endoscopy remains the gold standard. It confirms chronic inflammation, rules out malignancy, and provides tissue samples that guide the choice of biologic therapy. Sequencing cannot determine whether a patient has dysplasia or active ulceration — only histology can.
Skin Biopsy in Atopic Dermatitis (USD 300–500): Though not always required, biopsy becomes indispensable when disease is refractory or atypical. Under the microscope, eczema can be distinguished from psoriasis, cutaneous lymphoma, or chronic contact dermatitis. Again, genomic data cannot provide this morphological certainty.
These tools are not optional. They are the foundation upon which doctors prescribe disease-specific pharmacological interventions. To begin ocrelizumab without MRI, infliximab without biopsy, or dupilumab without histological clarity would not only be irresponsible but dangerous.
Molecular methods such as polymerase chain reaction (PCR) strengthen this foundation by ruling out infections that mimic chronic inflammatory diseases. Detecting tuberculosis in suspected Crohn’s, or JC virus reactivation in an MS patient on natalizumab, prevents catastrophic mis-prescribing.
2. The Economics of Universal Sequencing (Plan A)
If every patient with a “common disease” label were sequenced, the bill would rise rapidly. Consider a cohort of 10,000 patients:
Whole-Exome Sequencing (WES): USD 1,000 per patient → USD 10 million total.
Of these 10,000, fewer than 3% — roughly 300 — would actually be reclassified as rare disease cases.
Cost per detected rare case: ~USD 33,000.
For Whole-Genome Sequencing (WGS), the cost would double to USD 20 million, raising the per-case figure above USD 66,000.
This arithmetic reveals the paradox: a small minority requires sequencing, but the only way to find them under Plan A is to sequence everyone.
3. The Selective, Biopsy-Anchored Approach (Plan B)
Now consider a more rational strategy. Instead of sequencing all patients, doctors could sequence only those who:
Fail standard therapy,
Undergo biopsy because of refractoriness or atypical disease, and
Still show discordant clinical or histological features.
Under this Plan B, perhaps 20–25% of patients are non-responders, and among them ~80% undergo biopsy. Sequencing in this targeted group (≈2,000 patients in a cohort of 10,000) would cost:
Germline WES + tissue RNA-seq: USD 1,500 each → USD 3 million total.
This strategy would capture ≈90% of the hidden rare cases (≈270 out of 300).
Cost per detected rare case: ~USD 11,000.
The efficiency gain is dramatic: Plan B cuts the overall expenditure by two-thirds and triples the cost-effectiveness per true diagnosis.
4. The National Budget Context
At the level of NHS England, which commands an annual budget of ~USD 232 billion, the USD 10 million for universal sequencing represents just 0.0043%. On paper, the figure seems trivial. But public budgets are not abstract spreadsheets — they are the scaffolding of daily services.
USD 10 M is enough to pay the annual salaries and support infrastructure for ~100 general practitioners (GPs).
It can purchase 2 million doses of influenza vaccine, enough for a large-scale prevention campaign.
It could fund the screening of over 300,000 citizens for colorectal cancer.
Thus, while 0.0043% is small at the macro level, it is large enough to displace visible, tangible programs.
5. The Emergency Medicine Trade-off
The sharpest contrast emerges in emergency care. If USD 10 million (≈ £7.9 M) were redirected from urgent care budgets to universal sequencing, the losses would be concrete and immediate:
Accident & Emergency (A&E) attendances lost:
14,000 (high-cost band) to 45,000 (low-cost band).
Ambulance responses lost:
17,000–24,000 incidents, depending on case mix.
Unlike genomic screening, where a delay does not threaten life in the immediate term, every missed emergency response risks mortality or permanent disability. Patients in emergency corridors do not survive on the promise of long-term diagnostic refinement. They survive on ambulances that arrive on time and doctors who treat them within the critical hour.
6. Symbolic and Political Implications
Genomic sequencing has undeniable value. It prevents years of ineffective treatment, spares patients unnecessary toxicity, and identifies rare disorders hidden inside the fabric of common diseases. But it is not an emergency. It is a tool of precision and foresight, not of life and death at midnight in an emergency ward.
To present universal sequencing as harmless because it represents “only 0.0043%” of the budget is misleading. Each pound diverted must come from somewhere, and in practice it means fewer ambulances, fewer emergency doctors, fewer vaccinations.
The responsible compromise is therefore Plan B: selective, biopsy-anchored sequencing. It ensures that patients most likely to harbor rare diseases are identified, while preserving the backbone of the health system — emergency medicine.
BBIU Final Assessment
The health economics of sequencing do not support a universal approach. For <3% of patients, universal sequencing is an inefficient luxury; for the 97% it adds little clinical value. By contrast, a targeted, biopsy-anchored strategy offers precision without sacrificing emergency capacity. The narrative is straightforward:
Genomic screening is not an emergency.
Emergency services are.
When funds are finite, prioritization is not optional — it is survival.
Annex 3 — Genetic Screening as an Inclusion Criterion: Ethics, Feasibility, and the New Rules of Drug Approval
Opening narrative: Why finding the right patient matters
Clinical trials live and die by one deceptively simple question: are we testing the right drug in the right people? When a study aims to treat a “common” disease—multiple sclerosis (MS), inflammatory bowel disease (IBD), or atopic dermatitis—it quietly assumes that every enrolled participant truly has that disease. Recent evidence shows that a small, stubborn fraction (often <3%) are misclassified: they carry rare, monogenic disorders that mimic the common condition. They look similar in the clinic, but they do not respond to the same drugs.
From an ethical standpoint, genetic screening prior to randomization is compelling: it protects patients from futile exposure and protects trials from corrupted evidence. Yet ethics has a twin: feasibility. Screening tightens eligibility, slows recruitment, raises costs, and shifts regulatory expectations. This annex explains the trade-offs—and how to navigate them.
1) Ethics first: purity of cohorts and patient protection
Beneficence and non-maleficence. Sequencing ensures that participants really have the target biology. Patients with rare, look-alike diseases are spared months of ineffective therapy and potential toxicity.
Justice. The same logic that improves safety can unintentionally limit access to trials if sequencing capacity is scarce or slow. Ethics requires not only purity, but fair logistics: timely access to testing, clear consent for genetic analyses, and counseling for unexpected findings.
Truthfulness. A trial that silently enrolls misclassified patients risks producing ambiguous or negative results that mislead clinicians and deny future patients effective medicines. Genetic inclusion criteria are, ultimately, a truth-seeking device.
Bottom line: Ethically sound trials enroll confirmed target patients. But ethics must be matched with an operational plan that keeps the doors open.
2) Feasibility shock: recruitment shrinks, timelines stretch
The funnel narrows. Phenotypic diagnosis may identify 100 candidates; genetic confirmation disqualifies ~2–3. That sounds minor, but trials already apply many other filters (age, severity, comorbidities, prior treatments). Each extra criterion compounds the drop-off.
Screen more to enroll the same. To randomize 1,000 genetically confirmed participants, sponsors might need to pre-screen 1,300–1,500 candidates, accounting for: failed sequencing runs, variants of uncertain significance (VUS), logistics failures, and patient dropouts after waiting for results.
Timeline drag. Even in mature systems, sequencing plus interpretation can add weeks to screening. Across dozens of sites, those weeks expand into months of recruitment delay—especially if samples cross borders or if re-biopsies are needed.
Mitigations that work
Biopsy-anchored, selective screening: sequence non-responders or atypical cases (our Plan B) in disease-area trials; or, in early-phase precision programs, sequence everyone upfront but with rapid local labs and pre-approved logistics.
Decentralized pre-screening networks: use high-throughput regional labs with harmonized pipelines; return “genetically eligible” flags to trial sites within 7–10 days.
Adaptive enrollment windows: allow a run-in period to start standard care while sequencing completes; avoid idle time that leads to dropout.
3) The money problem: cost per participant and total trial budget
Added screening cost: USD 1,000–1,500 per candidate (WES ± RNA-seq tissue in biopsy-stage designs). Multiply by the pre-screened, not just the enrolled.
Operational overhead: more screen fails → more site time, monitoring, CRO labor, couriering, data reconciliation.
Budget inflation: a Phase 3 program that might have cost USD 500M can drift toward USD 700–800M with genetic screening, extended timelines, and tighter safety oversight.
But also: the cost of not screening can be catastrophic if misclassified patients dilute efficacy below statistical thresholds—leading to trial failure and the loss of a multi-billion-dollar program. In that sense, screening is an insurance premium on the evidentiary core of the trial.
4) Regulators and the “n”: fewer patients, stronger evidence?
Negotiating sample size. Regulators will still demand adequate power (e.g., 80–90%). But if cohorts are genetically homogeneous, the variance falls, effect sizes become clearer, and the required “n” may be lower for the same power.
Quality over quantity. Agencies can accept smaller “n” when inclusion criteria pin down the mechanism and preclinical data are strong. This is especially persuasive if pharmacodynamic markers (e.g., pathway suppression on RNA-seq) demonstrate on-target activity.
Documentation burden. Sponsors should expect to submit:
A genetic testing plan (assays, thresholds, re-testing rules),
Companion diagnostic (CDx) co-development or bridging plan,
Data integrity measures (chain of custody, bioinformatics validation),
DSMB charters that reflect the higher weight of each SAE in smaller cohorts.
5) Safety gets louder: SAEs, DSMBs, and real-time oversight
Each SAE counts more. In genetically tight cohorts, an unexpected severe event immediately re-weights the risk–benefit curve. DSMBs will be quicker to pause, request unblinded analyses, or demand protocol changes.
Stricter adjudication. Expect centralized SAE adjudication, predefined “stopping rules,” and mandatory signal detection analytics (e.g., sequential monitoring).
Transparency. Genetic misclassification ceases to be a plausible excuse; sponsors must prove whether an event is on-target biology, off-target toxicity, or independent comorbidity.
6) After approval: 3–5 years of enhanced pharmacovigilance
Reality check. Approval cohorts are clean; real-world patients are messy. To bridge the gap, agencies often require enhanced pharmacovigilance (PV) for 3–5 years: registries, post-authorization safety studies, periodic safety update reports with genetic sub-analyses, and risk management plans (e.g., testing requirements, restricted distribution if needed).
Learning system. Real-world data can refine the label: expand to adjacent genotypes, or, conversely, tighten the indication if certain variants exhibit higher risk.
7) Market math and ROI: precision shrinks the denominator
Indication narrows. Limiting use to genetically confirmed patients protects effectiveness—but reduces market size. If 100,000 phenotypic patients become 3,000–5,000 genetically eligible, revenue falls unless price or duration compensate.
Two-sided squeeze. Development costs rise (screening, logistics, PV), while addressable population falls. Without policy adjustments, ROI can drop below investable thresholds for many firms.
Survival strategies for sponsors
Price/value alignment: demonstrate that genetic selection delivers higher absolute benefit per patient (e.g., larger effect sizes, fewer failures), justifying premium pricing.
Label architecture: start narrow (genetically confirmed core) with pre-planned expansions contingent on post-market evidence.
CDx integration: co-develop and subsidize testing to reduce friction at the point of care.
8) Patents and exclusivity: resetting the clock
Your proposal is on point: if precision shrinks markets and lengthens development, the effective exclusivity for recouping investment erodes.
The problem today: patent life starts near discovery/filing. By the time a precision therapy is approved—after multi-year screening-heavy trials—a big chunk of the patent term is gone.
Policy lever: tie market exclusivity to the date of approval, not discovery. Variants exist (orphan exclusivity, data exclusivity), but a broader shift would realign incentives with the realities of genomic fragmentation.
Public interest test: society pays for exclusivity with temporary higher prices; in return, it gets credible, effective drugs for clearly defined patients—and avoids wasting billions on the wrong biology.
9) A concrete scenario (illustrative)
A Phase 3 IBD trial plans to enroll 1,200 patients. With genetic screening, 8–10% of phenotypic candidates fail screening/logistics; ~2–3% are reclassified as monogenic disorders; the rest pass. To maintain power, the sponsor pre-screens 1,600–1,800 candidates across 120 sites. Sequencing adds USD 1,200 per candidate (combined germline + tissue transcriptome in biopsy-stage cases) and 6–10 weeks to aggregate recruitment. Budget rises by USD 120–150M.
Regulators accept n=1,000 (instead of 1,200) due to lower variance and stronger mechanistic data, but impose a 5-year PV registry with genotype capture. The label at approval is genotype-confirmed Crohn’s disease, pathway-positive; expansions to adjacent variants are contingent on year-3 registry data. The sponsor seeks approval-tied exclusivity to ensure ROI in a 5,000–8,000-patient annual market.
10) Strategic synthesis (what to actually do)
Adopt screening where misclassification risk is non-trivial or mechanism-specific efficacy is essential.
Engineer feasibility: decentralized pre-screening, rapid labs, run-ins, adaptive enrollment, and biopsy-anchored selectivity in diseases where that path is logical.
Pre-negotiate with regulators: sample-size logic, CDx, SAE rules, and PV commitments before Phase 3.
Plan the business case: pricing justified by effect size, CDx support, and a policy ask on approval-anchored exclusivity.
BBIU Final Assessment
Genetic screening as an inclusion criterion is ethically superior and scientifically cleaner. It makes trials smaller, slower, and more expensive—but also more truthful. Regulators can accept fewer, better-defined patients if sponsors shoulder strict safety oversight and longer post-market vigilance. On the economic front, restricting use to genetically confirmed patients compresses ROI; aligning exclusivity to approval rather than discovery is the rational fix.
