Create a biomarker evidence report

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.

# 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"