Heavy Metal Index

Probabilistic assessment of the cumulative risk from dietary heavy metal exposure in Chongqing, China using a hazard-driven approach

5 min read

Source access

Publication
Scientific Reports 15:2229
Year
2025
Authors
Chen J; Chen J; Li M; Feng P
Source type
peer reviewed
License
CC BY

Chen et al. 2025 — Cumulative dietary Pb/Cd/iAs/MeHg risk in Chongqing, China (hazard-driven mRPI + Monte Carlo)

This study applies the EFSA-recommended hazard-driven cumulative risk assessment framework to dietary Pb, Cd, iAs, and MeHg exposure in Chongqing residents (n=969 across four age groups), using the modified Reference Point Index (mRPI) method combined with @RISK Monte Carlo simulation (100,000 iterations, Latin hypercube). The four metals are grouped by mode-of-action/AOP for two toxicity endpoints: neurotoxicity (mRPI 0.922 to 4.835 across scenarios) and nephrotoxicity (mRPI 1.306 to 7.031). Both endpoint mRPIs exceed the 1.0 acceptable-risk threshold across scenarios, indicating unacceptable cumulative risk for the Chongqing population’s typical and high-exposure diets. Pb is the dominant contributor to neurotoxicity; Cd and Pb together dominate nephrotoxicity. The paper provides established conversion factors for total-to-speciated metal interpretation (iAs/tAs = 2% in fish, 70% in non-fish; MeHg/tHg = 100% in fish, 0% in non-fish), which are useful for the wiki’s speciation methodology.

Key numbers

Participant demographics (n=969 from 2018 Chongqing China Health and Nutrition Survey):

Age groupn%
Preschoolers (3-6 yr)313.2%
Adolescents (7-17 yr)11311.7%
Adults (18-59 yr)39941.2%
Elderly (≥60 yr)42643.9%
Male43244.6%
Female53755.4%

Cumulative risk results (modified Reference Point Index mRPI; >1 = unacceptable):

EndpointmRPI range (mean to 95th percentile high-exposure)Primary contributors
Neurotoxicity0.922 to 4.835Pb (dominant)
Nephrotoxicity1.306 to 7.031Cd + Pb (combined dominant)

Speciation conversion factors used (cited from prior literature):

  • For Hg in food: MeHg/total Hg = 100% in fish; 0% in non-fish food (per EFSA 2013 default)
  • For As in food: iAs/total As = 2% in fish; 70% in non-fish food (per Suomi et al.)
  • For Hg in food, inorganic Hg / total Hg: 20% in fish; 100% in non-fish food

These are useful generic conversion factors for the wiki’s speciation methodology when source studies report only total Hg or total As.

Food categories covered (10):

  1. Cereals + cereal products (rice, rice products, wheat grains, wheat flour, wheat products, other cereal products)
  2. Vegetables + vegetable products (leafy, root/tuber, stem, brassica, legumes, other fresh, vegetable products)
  3. Nuts, fruits, beans, bean products
  4. Meat + meat products (livestock meat, poultry meat, meat products, edible livestock + poultry offal)
  5. Fish + fish products
  6. Eggs + egg products
  7. Milk + dairy + dairy products
  8. Foods for special dietary uses
  9. Beverages
  10. (additional minor categories)

Methods

Data sources:

  • Contamination data: 2017-2021 China National Food Contamination Monitoring Program (5-year average), Chongqing Municipality, multi-stage stratified random sampling from retail stores, supermarkets, grocery stores, farmers’ markets, convenience stores in 38 districts and counties.
  • Consumption data: 2018 China Health and Nutrition Survey of Chongqing, 969 participants from 6 survey sites (Shapingba, Fengjie, Dazu, Jiangjin, Qijiang, Nanan), three-day 24-h dietary recall.

Sample analysis: AAS or AFS (atomic fluorescence spectrometry) or ICP-MS. Standard sample prep per Chinese national standards GB 5009.11-2014 (Pb), GB 5009.12-2017 (lead), GB 5009.15-2014 (Cd), GB 5009.17-2014 (Hg), GB 5009.123-2014, GB 5009.268-2016 (multi-element). Microwave digestion with HNO3 (5-10 mL) on 0.2-0.5 g solid or 1.0-3.0 mL liquid samples. Left-censored data treated per EFSA: LB (set to 0), MB (½ LOD), UB (LOD). High-exposure scenario substitutes 95th percentile for individual exposure estimates.

Risk modeling: modified Reference Point Index (mRPI) — for each element, RPQ_i = (EDI_i × UF_i) / RP_i where RP is the toxicological reference point for the specific endpoint and UF is the uncertainty factor. mRPI = sum of RPQ across the four metals for a given endpoint. Monte Carlo with @RISK v8.2 (Palisade), 100,000 iterations, Latin hypercube sampling. AIC selected optimal distribution fit for contamination data.

Speciation: Total Hg and total As measured. iAs and MeHg estimated via conversion factors (above). This is the standard approach when sample-level speciation is not feasible.

Implications

Certification: For HMTc Cat 1 cumulative-risk framework + Chinese-market food sourcing:

  1. The mRPI framework is the EFSA-recommended approach for cumulative risk assessment from multiple heavy metals with shared/compatible MOA/AOP. HMTc Cat 1 standards-setting can use mRPI alongside per-analyte HQ to evaluate combined Pb+Cd+iAs+MeHg exposure in product formulations.

  2. The Chongqing finding (mRPI >1 for both neurotoxicity and nephrotoxicity) establishes that even in a Chinese provincial population with relatively typical exposure, combined heavy-metal exposure exceeds acceptable cumulative risk thresholds. HMTc certification of Chinese-sourced food ingredients (rice, vegetables, meat, fish) needs both per-analyte certification AND cumulative-risk consideration.

  3. Speciation conversion factors documented in this paper (iAs/tAs and MeHg/tHg ratios for fish vs non-fish) are useful for the wiki’s CLAUDE.md Part 14 speciation methodology when source studies report only total metals. The 2% iAs/tAs in fish vs 70% in non-fish is particularly important for converting total-As surveys into iAs estimates.

  4. Pb dominates neurotoxicity; Cd+Pb dominate nephrotoxicity. HMTc Cat 1 standards-prioritization should reflect this — Pb thresholds for child-targeted products are the highest leverage; Cd thresholds matter most for kidney-burden control in adult products.

Courses: Excellent reference for an HMTc course module on cumulative risk assessment methodology. The EFSA mRPI + Monte Carlo framework is the current standard practice for combined-chemical-mixture risk.

App: For the consumer app’s cumulative-risk-per-meal estimation, the mRPI structure (RPQ sum across metals for a given endpoint) is directly implementable. The app should support neurotoxicity-endpoint and nephrotoxicity-endpoint mRPI as separate scoring outputs.

Microbiome: Not addressed.

Wiki pages updated on ingest