At ProSciento, we approach metabolic disease as a continuum, not a set of isolated diagnoses.
Obesity, insulin resistance, prediabetes, type 2 diabetes, and steatotic liver disease exist along the same biological spectrum. They share mechanisms, progress over time, and frequently coexist long before a formal diagnosis is assigned.
For early-phase research to be meaningful, trial design must reflect that continuum from the outset.
What we mean by “the Metabolic Continuum”
The metabolic continuum reflects interconnected, progressive dysfunction. Individuals may present with excess visceral adiposity and insulin resistance, accumulate hepatic fat, and later meet criteria for prediabetes or type 2 diabetes, while cardiometabolic risk advances in parallel. Others enter through steatotic liver disease despite normoglycemia. Different entry points, shared pathophysiology.
In clinical research this framing has direct operational implications. It determines who we recruit, which phenotypes we enrich for, what endpoints we prioritize, and how we interpret early pharmacologic signals. In early-phase studies, precise phenotype selection is often the difference between interpretable data and inconclusive outcomes.
Quick definitions
- Obesity and visceral adiposity
While obesity is clinically defined as BMI ≥30 kg/m², visceral fat—not BMI alone—drives insulin resistance, dyslipidemia, and inflammation. BMI is practical but imprecise; it does not distinguish lean from fat mass or capture distribution. Early-phase trials benefit from pairing BMI with waist circumference and body composition tools such as DXA or MRI to improve phenotyping and signal detection. - Insulin resistance
Peripheral tissues respond inadequately to insulin, leading to compensatory hyperinsulinemia and eventual beta-cell failure. Insulin resistance often precedes overt hyperglycemia by years, making it one of the earliest detectable markers along the continuum. - Prediabetes
Defined by fasting glucose 100–125 mg/dL, 2-hour OGTT 140–199 mg/dL, or HbA1c 5.7–6.4%, prediabetes represents a high-risk state in which cardiometabolic and hepatic abnormalities are often already present. From a development perspective, it is a strategic window to demonstrate mechanistic and potentially disease-modifying effects. - Type 2 diabetes
Type 2 diabetes reflects progressive insulin resistance combined with beta-cell insufficiency, resulting in persistent hyperglycemia. Although it represents a later stage on the continuum, much of its underlying biology is established years earlier. - Steatotic liver disease (SLD)
Updated nomenclature—MASLD, MASH, ALD, and MetALD—better reflects underlying drivers. These distinctions matter in early-phase studies, particularly when mechanism-of-action readouts include hepatic fat or inflammatory markers.
Two practical corollaries follow:
- BMI thresholds do not equal risk thresholds. Metabolic risk emerges at lower BMI in several ethnic populations. Eligibility criteria based solely on standard BMI cut-points may miss high-risk phenotypes or over-enroll lower-risk participants.
- Glycemia lags underlying biology. By the time fasting glucose rises, insulin resistance and ectopic fat have often been present for years. Trials designed around glycemic thresholds alone may enter the continuum later than the mechanism intends.
Why BMI can mislead, and how we correct for it –
Different obesity phenotypes are real.
BMI is widely used because it is accessible and reproducible. However, it can misclassify metabolic risk. Individuals with high lean mass may meet BMI thresholds yet have low risk, while those with normal BMI but elevated visceral fat may carry substantial cardiometabolic burden.
In early-phase trials, pairing BMI with waist circumference, DXA or MRI-derived composition, and targeted metabolic laboratories (e.g., fasting insulin, lipids, ALT) reduces phenotype misclassification. More precise phenotyping improves screening efficiency, reduces variability, and strengthens early signal interpretability.
Clinical Reality and Trial Design
Consider two common profiles:
“Jane,” 52, BMI 37, normal glycemia but elevated ALT, high triglycerides, and low HDL. Despite not meeting prediabetes criteria, her hepatic and lipid profile reflects significant metabolic risk—appropriate for obesity or liver-fat–focused studies.
“Daniel,” 45, BMI 25.5 with central adiposity, prediabetes-range labs, low HDL, and elevated ALT. His near-normal BMI obscures visceral adiposity and probable steatosis.
Designing protocols that account for these patterns aligns eligibility with underlying biology, improves enrollment performance, reduces avoidable screen failures, and strengthens early pharmacologic signal interpretation.
Why it Matters
Metabolic dysfunction develops gradually and often silently. Insulin resistance and ectopic fat accumulation may precede overt disease by years. By the time hyperglycemia is diagnostic, underlying biology is well established.
For early-phase development, the implication is direct: targeting upstream mechanisms generates more interpretable data and better informs downstream strategy.
Implications for Early-Phase Trial Design
- Recruit to phenotypes, not labels.
Enrich for visceral adiposity or steatosis when mechanisms warrant it. Precision at screening improves cohort homogeneity and signal detection. - Match endpoints to mechanism.
Pair body composition tools (DXA, MRI-PDFF) with metabolic markers (fasting insulin, HOMA-IR, lipids). Prespecify dynamic testing when beta-cell effects are hypothesized. Endpoints should reflect biology, not convention. - Design for strategic windows.
Prediabetes offers an opportunity to demonstrate mechanistic impact before irreversible progression. - Account for population differences.
Risk thresholds vary by ethnicity and phenotype. Protocols should reflect appropriate BMI cut-points and consider bridging strategies when biology diverges. - Balance ambition with feasibility.
Advanced phenotyping adds value only if visit schedules, imaging capacity, and expected screen-pass rates support execution. Early-phase rigor requires operational discipline.
Why ProSciento Takes This View
ProSciento is purpose-built for early-phase metabolic research. Operationalizing the metabolic continuum means identifying relevant phenotypes, integrating advanced metabolic assessments, and aligning endpoints to mechanism so sponsors can make informed development decisions.
In early-phase metabolic development, designing to biology and phenotype—not isolated diseases—determines whether data are interpretable and development decisions are sound.