In 484 adults (the OPEN study), compares FFQ- and 24-hour-recall-reported energy and protein intake against unbiased biomarkers (doubly labeled water for energy, urinary nitrogen for protein). Finds substantial systematic underreporting: energy underreported by ~12-14% (men) / 16-20% (women) on 24-hour recall and ~31-36% / 34-38% on FFQ, with misreporting magnitude correlated with BMI. Provides a direct empirical measurement-error base rate for any FFQ-derived exposure variable, including egg-intake frequency in the cohorts underlying slices 1-3.
relevance_note: The primary empirical FFQ/24HR measurement-error data that FFQ-invalidity critiques (e.g. Archer 2018) are built on.
Extracted (structured summary)
Established fact: self-report under-reports energy vs biomarker
O-90 - Self-reported dietary intake systematically under-reports true energy intake relative to the doubly-labeled-water biomarker
The firmly-established, uncontested directional fact that memory-based dietary self-report under-reports energy versus an unbiased recovery biomarker. It rests on the broad doubly-labeled-water validation literature, not on OPEN’s dataset alone; OPEN (see the paper-derived observations) is one primary quantification of it. Evidence-like because it constrains how much confidence any FFQ-based egg-intake exposure variable deserves.
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Energy under-reporting magnitudes (FFQ vs 24HR)
O-91 - In OPEN (n=484) the FFQ under-reported energy by ~31-38% and the 24-hour recall by ~12-20%, FFQ roughly twice as biased
The direct empirical measurement-error base rate for FFQ-derived energy in the OPEN validation sample. Because egg-intake frequency in the major cohorts is FFQ-derived, this quantifies the magnitude of self-report error underlying those exposure variables. The FFQ-vs-24HR gap shows the instrument, not just the respondents, drives much of the bias.
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Protein under-reporting and protein density
O-92 - OPEN protein under-reporting was ~27-34% on FFQ and ~11-15% on 24HR, but protein density was barely biased
Protein absolute-intake under-reporting parallels energy (FFQ worse than 24HR). The near-absence of bias in protein DENSITY is important: much of the absolute under-reporting is a scaling/energy effect that partly cancels in ratios, which is why energy-adjusted or density exposure measures are less biased than absolute intakes.
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Person-specific bias correlated with BMI
O-93 - In OPEN the magnitude of dietary under-reporting increased with body mass index (person-specific systematic bias)
The load-bearing structural finding: under-reporting is not just large but person-specific and correlated with BMI, a determinant of many disease outcomes. This is the property that distinguishes OPEN’s error from the classical additive, non-differential model assumed by standard measurement-error correction (see the attached argument).
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Implication: classical error-correction assumptions fail
A-29 - FFQ under-reporting is large and BMI-correlated, violating the classical measurement-error model that standard attenuation correction assumes
Reasoning
Standard measurement-error correction (regression calibration / attenuation correction) assumes the reported value equals true intake plus error that is (i) additive on the intake scale, (ii) independent of true intake (or at most linearly related), and (iii) non-differential with respect to the outcome. Under those assumptions an observed diet–disease association is attenuated toward the null by a factor recoverable from a validity coefficient, and de-attenuation restores the true estimate. OPEN’s biomarker validation contradicts assumptions (i)–(iii) for the FFQ: the under-reporting is large (~30–38% for energy) AND its magnitude varies systematically with the person’s BMI, i.e. the error has a person-specific bias term correlated with a characteristic that itself predicts many disease outcomes. Consequences: the error is not a single multiplicative attenuation factor (it differs across people by BMI), so one validity coefficient cannot describe it; and because BMI is a determinant of CVD/T2D/mortality, error correlated with BMI is potentially differential with respect to the outcome, which can bias associations in either direction, not merely toward the null. Therefore FFQ-based diet–disease hazard ratios cannot be assumed to be simply attenuated and safely de-attenuated by a standard factor — the correction machinery’s own preconditions fail. The inference is valid: it derives the failure of the correction directly from the empirically-observed violation of the assumptions that the correction requires.
Validity verdict — approved (checked)
Reconstruction. Premises: FFQ under-reporting is large (~30-38% energy, O-91) AND its magnitude rises systematically with BMI (person-specific bias, O-93); the classical error model that a single attenuation/regression-calibration factor presumes requires additive error, independent of true intake, non-differential w.r.t. outcome; BMI is itself a determinant of CVD/T2D/mortality. Conclusion: a single validity coefficient cannot fully remove the bias, and the residual error can bias associations in either direction (not merely toward the null).
Traced step. This is close to analytic: if a correction is licensed only under assumptions X, and X are empirically violated, the correction’s preconditions fail. The two consequences follow directly — (i) a BMI-varying error is not a single multiplicative factor, so one validity coefficient is misspecified; (ii) error correlated with an outcome determinant (BMI) is potentially differential, and differential error can bias in either direction. Both are standard measurement-error results. Probed defeater: “modern regression calibration with a proper biomarker reference can accommodate person-specific bias.” This does not undercut, because the statement is explicitly scoped to the standard/single-factor attenuation correction, whose classical assumptions are exactly what OPEN’s data violate. No defeater survives at the scope claimed. Step holds as stated.
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