> ## Documentation Index > Fetch the complete documentation index at: https://docs.consensus.app/llms.txt > Use this file to discover all available pages before exploring further. # Create a biomarker evidence report > Assess the evidence for a biomarker's optimal range across Mendelian randomization, large cohorts, and trials — or establish that no evidence-based range exists. ## What you get A tiered evidence report on one biomarker: what range the published evidence actually supports for the outcomes you care about, which evidence tiers support it, where the tiers disagree, and — when the evidence cannot bound a range — an explicit statement that it cannot, instead of a plausible-sounding number. ## Who it's for Biotech and pharma teams, longevity clinicians, and translational researchers who need more than a lab reference interval. This is one of the heaviest real usage patterns on Consensus. ## The prompt The structure below is what makes this work: the definitional constraints and the per-tier instructions do more for output quality than any amount of prompt polish. Replace `[BIOMARKER]` and the outcome set. ```text theme={null} You are a clinical epidemiologist, longevity medicine researcher, and evidence synthesis expert. Task: Assess all available evidence to determine the evidence-based optimal range of the biomarker [BIOMARKER] in adults, optimized for longevity and disease-risk reduction, not merely the conventional lab reference interval. Population: Adults, separated by sex where biologically or clinically relevant. Note differences by age, menopausal status, pregnancy status, ethnicity, kidney function, body composition, medication use, assay type, and disease state when relevant. Core question: What biomarker range is most consistent with the lowest risk of all-cause mortality, disease-specific mortality, major age-related disease, frailty, and biological aging, where both the lower and upper bounds of the range are supported by evidence that values outside the range are less favorable? If such a true optimal range cannot be established, report the best-supported risk thresholds and explicitly state that no evidence-based optimal range is known. Critical definition of "optimal range": Do not define the optimal range as merely: - within the standard reference interval; - not clinically deficient; - not clinically excessive; - a range where higher or lower values have not been proven harmful; - a range used as a treatment target in a clinical trial; - a common clinical target; - the middle of the reference interval. Only call a range "optimal" if the evidence supports both its lower and upper bounds. If proposing a bound that lies inside the conventional reference interval, justify it with evidence that values beyond it are less favorable. Explicitly report whether risk rises on both sides of the proposed range. If only one side of the curve shows increased risk, do not claim a bounded optimal range unless other evidence supports the opposite boundary. Search each evidence tier separately using Consensus, and report them separately: A. Randomized controlled trials - Interventions that moved the biomarker and measured hard outcomes. - Note whether the trial targeted a range or a threshold. B. Systematic reviews and meta-analyses - Pooled dose-response relationships where available. C. Genetic and Mendelian randomization studies - Assess whether genetically predicted differences in the biomarker are associated with longevity, mortality, or disease outcomes. - Discuss pleiotropy, instrument strength, directionality, and whether MR supports causality. - State which MR supports: lower is better, higher is better, mid-range optimal, harm at high levels, harm at low levels, or no causal effect. - Distinguish causal evidence for the biomarker itself from causal evidence for related pathways or binding proteins. D. Large observational cohorts - Include dose-response, nonlinear, U-shaped, J-shaped, reverse J-shaped, or threshold analyses. - Extract nadir risk ranges where reported. - Note adjustment covariates, reverse causation risk, and residual confounding. - Report whether results differ after excluding early deaths, current smokers, people with prevalent disease, and people with frailty, inflammation, kidney disease, malnutrition, or major comorbidity. - Do not interpret absence of additional benefit at high or low values as evidence of harm. Output: 1. Proposed optimal range with both bounds, or an explicit statement that none can be established. 2. A table by evidence tier: what it shows, its direction, and its strength. 3. Where the tiers disagree, and the most likely reason. 4. Stratification notes where the answer differs by subgroup. 5. What evidence would most change this conclusion. Cite every claim with journal, year, and DOI. ``` ## How it works Without the "do not define optimal range as…" list, models converge on the reference interval and call it an answer. Enumerating the wrong definitions is what forces a real evidence assessment. Mendelian randomization and observational cohorts fail in different ways, so they need different critical questions. Searching them separately also makes tier disagreement visible instead of averaging it away. This is the constraint that prevents a one-sided risk curve being reported as a bounded range — the most common error in biomarker summaries. "No evidence-based optimal range is known" is frequently the correct answer. Naming it up front stops the model manufacturing a range to satisfy the request. ## The API equivalent Run one call per tier and keep the results separate: ```bash theme={null} # Tier C — Mendelian randomization curl -G "https://api.consensus.app/v1/search" \ -H "x-api-key: $CONSENSUS_API_KEY" \ --data-urlencode "query=homocysteine Mendelian randomization all-cause mortality causal" \ --data-urlencode "human=true" \ --data-urlencode "domain=med,bio" \ --data-urlencode "page_size=50" # Tier D — large cohorts, nonlinear dose-response curl -G "https://api.consensus.app/v1/search" \ -H "x-api-key: $CONSENSUS_API_KEY" \ --data-urlencode "query=homocysteine U-shaped association all-cause mortality cohort nadir" \ --data-urlencode "study_types=cohort study,longitudinal / panel data study" \ --data-urlencode "human=true" \ --data-urlencode "sample_size_min=1000" \ --data-urlencode "page_size=50" ``` `medical_mode=true` narrows to top medical journals and guidelines (about 8M documents) when a tier returns too much general literature. ## What to check before you trust it This is a research summary for qualified human review, not clinical guidance. Nothing here should drive a treatment decision on its own. Keep the citation trail intact so a reviewer can check each statement against its source. * **Check that both bounds are actually evidenced.** The most common failure is a lower bound with real support and an upper bound asserted from convention. Read the justification for each separately. * **Reverse causation is the main confounder.** Low values of many biomarkers mark existing illness. If the report does not discuss the sensitivity analyses excluding early deaths and prevalent disease, the U-shape may be an artefact. * **Check assay comparability.** Ranges from different assays are often not interchangeable — the prompt asks for this, but verify it was answered. * **Watch for tier disagreement being smoothed over.** If MR says no causal effect and cohorts show a strong association, that conflict is the finding. ## Related The same tiered method, applied to a target rather than a biomarker. Pull the nadir ranges and effect sizes into rows you can compute on. Fan-out, the filter ladder, and the rest of the primitives. Browse the gallery by persona.