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Learn more: PMC Disclaimer | PMC Copyright Notice J Sci Food Agric . 2026 Feb 3;106(7):4060–4072. doi: 10.1002/jsfa.70494 Search in PMC Search in PubMed View in NLM Catalog Add to search Essential and toxic elements intake from botanical extracts: a probabilistic risk–benefit evaluation within the Italian dietary context Giovanni Tommaso Lanza Giovanni Tommaso Lanza 1 Department of Food and Drug, University of Parma, Parma, Italy Find articles by Giovanni Tommaso Lanza 1 , Maria Olga Varrà Maria Olga Varrà 1 Department of Food and Drug, University of Parma, Parma, Italy Find articles by Maria Olga Varrà 1, ✉ , Lenka Husáková Lenka Husáková 2 Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Pardubice, Czech Republic Find articles by Lenka Husáková 2 , Martina Piroutková Martina Piroutková 2 Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Pardubice, Czech Republic Find articles by Martina Piroutková 2 , Jan Patočka Jan Patočka 2 Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Pardubice, Czech Republic Find articles by Jan Patočka 2 , Emanuela Zanardi Emanuela Zanardi 1 Department of Food and Drug, University of Parma, Parma, Italy Find articles by Emanuela Zanardi 1 Author information Article notes Copyright and License information 1 Department of Food and Drug, University of Parma, Parma, Italy 2 Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Pardubice, Czech Republic * Correspondence to: M O Varrà, University of Parma, Department of Food and Drug, Strada del Taglio, 10, 43126 Parma, Italy. E‐mail: [email protected] ✉ Corresponding author. Revised 2026 Jan 9; Received 2025 Sep 29; Accepted 2026 Jan 19; Issue date 2026 May. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. PMC Copyright notice PMCID: PMC13067091 PMID: 41631476 Abstract BACKGROUND Botanical extracts are widely consumed for their claimed health benefits, yet their safety profile with respect to chronic consumption remains poorly characterized. Understanding the potential health risks associated with their inorganic content is a crucial issue for ensuring safe use, along with a characterization of the concurrent nutritional contribution of the mineral component. RESULTS The present study aimed to quantitatively assess exposure levels and potential health impacts of chronic intake of ten essential (Ca, K, P, Fe, Mg and Zn) and potentially toxic (Al, As, Ni and Pb) elements through the consumption of botanical extracts ( n = 25) among Italian adults. A probabilistic approach was employed to estimate exposure levels and both risk and benefit metrics. Results indicated that botanicals alone contributed only minimally to mineral intakes, with 5 th to 95 th percentile (P5–P95) ranges covering 0.01–16.80% of the dietary reference values. Exposure to inorganic As ( i As) raised health concerns because margin of exposure (MOE) values for skin cancer a ranged between 0.05 and 80.50 (P5–P95). When botanical extracts were considered alongside the baseline reference diet, Pb intake also raised concern because MOEs for nephrotoxic and cardiovascular effects fell below the critical threshold of 10. Similarly, cumulative exposure to Al, i As, Fe, Ni and Zn revealed potential non‐carcinogenic risks (mean hazard index > 1) only when considering the consumption of botanicals in addition to the baseline diet. CONCLUSION The findings of the present study underscore the importance of the cautious dietary use of botanical extracts because of potential risks that may outweigh the presumed benefits. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Keywords: food safety, essential elements, toxic metals, Monte Carlo simulation, risk–benefit assessment, cumulative risk INTRODUCTION Botanicals are plant materials that, when subjected to extraction, drying, pressing, fractionation, purification, concentration or fermentation processes, are referred to as ‘botanicals preparations’ 1 and represent concentrated sources of bioactive compounds used across the pharmaceutical, nutraceutical, cosmetic and food sectors. 2 , 3 In recent years, the consumption of botanicals has grown significantly, particularly in Western countries, driven by perceived health benefits, broad market availability as dietary supplements and food ingredients, and the misconception that natural products are inherently safe. 4 , 5 As a result, the simultaneous use of multiple botanicals is now common, 6 , 7 supported by claims of metabolic, cardiovascular, immune and anti‐inflammatory benefits. 8 The concentration of essential and non‐essential (hereunder ‘potentially toxic’) elements in commercial botanical products varies according to several factors, including plant species, geographical origin, climate, soil composition, water quality and agricultural practices. 9 , 10 Post‐harvest handling can further alter element concentrations as a result of oxidation, degradation or contamination from equipment. 11 In the European Union, several instances of contamination involving toxic metals and metalloids have been reported through the Rapid Alert System for Food and Feed, 12 with many cases linked to plants sourced from heavily industrialized regions. 13 , 14 Previous studies have already investigated the inorganic composition of botanical extracts, emphasizing their potential nutritional benefits from essential nutrients. 15 , 16 , 17 Conversely, many studies have reported toxic elements at levels that may pose health risks to consumers upon chronic exposure. 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 These contrasting findings reveal a complex and often contradictory scenario, leaving the overall risk–benefit profile of such products unclear. Risk–benefit assessment (RBA) is a systematic approach designed to weigh both the potential adverse effects (risks) and the beneficial health impacts (benefits) of consuming a food, one of its components, or a dietary intervention, with the aim of supporting policymakers in making informed decisions. 27 The methodology for conducting an RBA is not entirely standardized and can vary by context, 28 , 29 whereas limitations in toxicological data (especially for emerging contaminants and unassessed health effects) continue to constrain its wider application. 27 Given the widespread use of botanical extracts for their purported health benefits and considering the potential concurrent presence of toxic elements, there is the need for a systematic RBA to determine whether the associated risks outweigh the benefits, or vice versa, and to clarify their overall impact on consumers. Additionally, because simultaneous exposure to multiple toxic elements from botanicals is not well understood, the cumulative risk still remains uncertain. The main objectives of the present study were to quantitively assess the dietary exposure of Italian adults to essential (Ca, K, P, Fe, Mg and Zn) and potentially toxic (Al, i As, Ni and Pb) elements resulting from the inclusion of botanical extracts in the diet. The study also aimed to assess both the possible health benefits and adverse effects of these exposures in comparison with a reference diet that does not include regular botanical intake. Additionally, a cumulative risk assessment was conducted to evaluate the combined non‐carcinogenic effects of multiple toxic elements. To achieve these objectives, a probabilistic Monte Carlo‐based RBA approach was used, allowing a more robust and realistic estimation of potential health outcomes. MATERIALS AND METHODS Sample collection This study examined 25 commercial powdered botanical extracts, each from a different plant species and sold on the European market (Table 1 ). The declared country of origin for most samples was China, except for six products for which the origin was not clearly specified on the label (i.e. sea buckthorn extract, guarana extract, Cordyceps sinensis extract, skullcap extract and gardenia extract). Samples were deliberately selected to maximize taxonomic and compositional diversity, covering multiple plant families and representing botanical products commonly consumed in Europe. The inherent high degree of variability in their elemental composition was intentionally conceived and considered well‐suited for probabilistic analysis to capture a wide spectrum of real‐world exposure scenarios because the modeling approach adopted allowed incorporation and propagation of such variability. Table 1. List of the plant species and commercial product name of the botanical extracts analyzed in the present study Botanical species Product name Gardenia jasminoides Ellis Gardenia extract Garcinia mangostana L. Garcinia Mangostana fruit rind extract Hippophae rhamnoides Sea Buckthorn extract Crategus monogyna Hawthorn extract Capsicum frutescens L. Cayenne extract Ligustrum lucidum Ait. Lingustrin lucidum extract Allium sativum L. Black garlic extract Orthosiphon aristatus Java tea extract Gymnema Sylvestre Gymnema Sylvestre extract (75%) Rosmarinus officinalis L. Rosemary extract Ginkgo biloba L. Ginkgo Biloba extract Camellia sinensis L. Green Tea Extract Taraxacum officinale Dandelion extract Juglans reggia Walnut extract Vitis vininfera L. Grape Skin extract Punica granatum L. Pomegranate extract Cordyceps sinensis Cordyceps sinensis extract (4:1) Ganoderma lucidum Reishi mushroom extract Cordyceps militaris Cordyceps extract (beta glucan) Beta vulgaris L. Beetroot powder E2.6 Smilax china L. Sarsaparilla extract Scutellaria baicalensis Skullcap extract Urtica dioica L. Nettle Root extract Paullinia cupana Kunth. Guarana extract (10% caffeine) Monascus purpureus Red yeast rice extract Open in a new tab Quantification of essential and potentially toxic elements Ten elements, including essential nutrients (Ca, Fe, K, Mg, P and Zn) and toxicologically relevant metals and metalloids (Al, As, Ni and Pb), were considered. Selection of these elements was guided by three main criteria: (i) their potential to exert beneficial or adverse health effects; (ii) their consistently high yet variable concentrations across the analyzed samples; and (iii) the availability of literature data on their baseline dietary intake within the Italian population. Element concentrations were quantified using inductively coupled plasma‐mass spectrometry, following a previously described method. 30 Detailed analytical procedures are provided in the Supporting information (Section S1 and Tables S1 and S2 ). Risks and benefits evaluation: probabilistic modeling RBA was conducted in accordance with the general principles and recommendations of the ‘Guidance on risk‐benefit assessment on foods’ of the European Food Safety Authority (EFSA). 27 However, rather than evaluating the full chemical profile or the entire inorganic fraction of the botanicals, the assessment focused on specific nutritionally or toxicologically relevant elements for which analytical data were available. In line with EFSA guidance, 27 the assessment corresponds to a Tier 2 RBA because it does not use common or composite risk–benefit metrics, although it does account for uncertainty and variability through probabilistic modeling. To evaluate exposure levels of Italian adults to both essential nutrients and potentially toxic elements three distinct scenarios were formulated: (i) baseline dietary intake from the total diet excluding botanicals; (ii) chronic consumption of botanical extracts alone; and (iii) total exposure, reflecting the combined intake of toxic and essential elements from botanical extracts and the total diet. As a probabilistic approach was adopted, input parameters were modeled as probability distributions or retrieved as mean values with their standard deviations (SDs), assuming specific distribution shapes based on data characteristics. The main input parameters included dietary consumption patterns (retrieved as mean and SD values, with assumed distribution shapes) and the measured elemental concentrations (modeled as probability distributions). Probabilistic simulations combined all input parameters and were conducted using the Monte Carlo method with 100 000 iterations. This approach generated full probabilistic intake, risk and benefit profiles, capturing both inter‐individual variability and parameter uncertainty, thereby providing a robust and realistic assessment than conventional deterministic single‐point estimates. Simulations were performed using the R ‘stats’ package and all data processing, statistical analyses, and visualizations were conducted in RStudio, version 2024.09.0 + 375, using R, version 4.3.2 (R Development Core Team, Vienna, Austria). Formulation of a reference scenario for baseline dietary exposure As the reference dietary scenario, the average daily intake of the studied elements among Italian adults was considered. This scenario served to characterize baseline dietary status and evaluate the impact of additional chronic exposure from botanical extracts. Daily intakes of most elements were obtained from the 2012–2014 National Total Diet Study (TDS) carried out by the Italian National Institute of Health. 31 , 32 Specifically, data for i As 33 , Ni 34 , Ca, Fe, Zn, Al and Pb (Cubadda F, 2025, unpublished data) were retrieved as the mean ± SD and assumed to follow a lognormal distribution. Daily intakes of K, Mg and P were available from the ‘Italian National Food Consumption Survey, INRAN‐SCAI 2005‐06’, 35 retrieved and summarized as the weighted mean ± SD (i.e. to account for male and female proportions), and also assumed to follow a lognormal distribution, reflecting typical nutrient consumption patterns. 36 Intake levels of all elements under the dietary reference scenario are provided in Table 2 . Table 2. Estimated daily intakes (EDIs) or weekly intakes (EWIs) of essential and potentially toxic elements (mean ± SD) in Italian adults Element EDI/EWI Unit of measure Reference Essential K 3026 ± 875 mg day −1 Italian National Food Consumption Survey, INRAN‐SCAI 2005–06 35 P 1269 ± 366 mg day −1 Mg 279 ± 87 mg day −1 Ca 914 ± 317 mg day −1 2012–2014 National Total Diet Study, Cubadda F. (2025), unpublished data Fe 8.7 ± 2.9 mg day −1 Zn 10.7 ± 3.3 mg day −1 Potentially toxic iAs † 0.081 ± 0.030 mg kg BW −1 day −1 2012–2014 National Total Diet Study 33 Ni 1.55 ± 0.72 mg kg BW −1 day −1 2012–2014 National Total Diet Study 34 Al 58.2 ± 25.0 mg kg BW −1 day −1 2012–2014 National Total Diet Study, Cubadda F. (2025), unpublished data Pb 0.160 ± 0.081 mg kg BW −1 day −1 Open in a new tab Note: i As: inorganic arsenic. Retrieval of dietary consumption data of botanicals Mean ± SD values for acute and chronic daily consumption of ‘herbal formulations and plant extracts’ by Italian adults (18–64 years, consumers only) were obtained from the EFSA Comprehensive European Food Consumption Database, 37 which incorporates data from the latest Italian national dietary survey IV SCAI ADULT 2018–2020. 38 Data were extracted as overall consumption rates (8.1 ± 19.4 g day −1 , chronic) and as body weight–adjusted consumption rates [0.17 ± 0.35 g kg body weight (BW) −1 day −1 , acute; 0.11 ± 0.27 g kg BW −1 day −1 , chronic) and used to formulate the exposure scenario from botanical consumption. Because consumption data were available only as summary statistics, they were assumed to follow a lognormal distribution. 36 , 39 Distribution fitting of elemental concentrations in botanical extracts Element concentrations, determined experimentally, were fitted to statistical distributions using the Akaike information criterion (AIC) to identify the best‐fitting models. The delta AIC (ΔAIC) value was used to compare candidate distributions (normal, lognormal, exponential, Weibull and Pareto). According to established interpretation criteria, ΔAIC < 3 indicates minimal evidence favoring one model over another, values between 3 and 10 suggest moderate support, and ΔAIC < 10 provides evidence for the best‐fitting model. All calculations were performed in RStudio using the ‘fitdistrplus’ package. 40 Retrieval of dietary reference values and health‐based guidance values For Ca, K, Mg, Fe and Zn, benefit characterization was based on the comparison of dietary intakes with the following dietary reference values (DRVs) for adults, including adequate intake (AI), average requirement (AR), population reference intake (PRI) and upper intake level (UL). These values were obtained using the EFSA DRV Tool 41 and relevant EFSA scientific opinions on each element. 42 , 43 , 44 , 45 , 46 , 47 For nutrients with sex‐specific DRVs (Mg, Fe and Zn), an average value was calculated and employed. For Zn, PRI values were available for four phytate intake levels (300, 600, 900 and 1200 mg day −1 ) and the arithmetic mean across these levels was used. For Al, As, Ni and Pb, risk characterization was based on comparison with health‐based guidance values (HBGVs) and reference points (RPs), including tolerable daily intakes (TDIs), tolerable weekly intakes (TWIs), benchmark dose lower confidence limits (BMDLs) or the lowest‐observed‐adverse‐effect‐level (LOAEL). These were obtained from the EFSA Chemical Hazards Database (OpenFoodTox) 48 and from the EFSA scientific opinions on each element. 49 , 50 , 51 , 52 A summary of the benefit and risk metrics applied in this study is provided in Table 3 . Table 3. Dietary reference values (DRVs), health‐based guidance values (HBGVs) and reference points (RPs) used for the benefit and risk characterization of essential and potentially toxic elements Element Benefit or risk metric Value Unit of measure EFSA assessment: year Ref . Essential K AI ‡ 3500 mg day −1 2016 47 P AI ‡ 550 mg day −1 2015 46 UL § 3000 mg day −1 Ca PRI † 950 mg day −1 2015 43 UL 2500 mg day −1 Mg AI ‡ 325 mg day −1 2015 45 UL § 250 mg day −1 Fe PRI † 11 mg day −1 2015 44 Zn PRI † 11.5 mg day −1 2014 42 UL § 25 mg day −1 Potentially toxic Al TWI ɠ 1 mg kg BW −1 week −1 2008 49 Ni TDI ¶ 13 mg kg BW −1 day −1 2020 51 LOAEL ᴪ 4.3 mg kg BW −1 day −1 i As φ BMDL 05 ɸ (skin cancer) 0.06 mg kg BW −1 day −1 2024 52 Pb BMDL 01 ɸ (cardiovascular effects) 1.5 mg kg BW −1 day −1 2010 50 BMDL 10 ɸ (nephrotoxicity) 0.63 mg kg BW −1 day −1 Open in a new tab Note : ‡ AI, adequate intake; † PRI, population reference intake; § UL, upper intake level; ɠ TWI, tolerable weekly intake; ¶ TDI, tolerable daily intake; ᴪ LOAEL, lowest‐observed‐adverse‐effect‐level; φ i As, inorganic arsenic; ɸ BMDL, benchmark dose lower confidence limit. Exposure assessment and risk/benefit characterization The estimated daily intakes (EDIs) of Ca, K, Mg, Fe and Zn (mg day −1 ) from chronic consumption of botanicals were calculated using input Eqn ( 1 ) subjected to Monte Carlo simulations: EDIs mg day − 1 = Element concentration mg kg − 1 × Food consumption rate g day − 1 1000 (1) To characterize the associated beneficial effects of nutrient intakes under the three formulated scenarios, the probability distributions of EDIs were compared with the DRVs for each nutrient. The percentage contribution to the DRVs (% AI or % PRI) were calculated using Eqns ( 2 ) and ( 3 ), both subjected to Monte Carlo simulations: % DRV EDI a or EDI b DRV × 100 (2) % DRV tot = EDI a + EDI b DRV × 100 (3) where EDI a is the exposure deriving from the sole consumption of botanical extracts and EDI b is the exposure deriving from the sole baseline reference scenario. The EDIs of As, Ni, Al and Pb (μg kg BW −1 day −1 ) from chronic consumption of botanicals were calculated using input Eqn ( 4 ). The estimated weekly intake (EWI) of Al (μg kg BW −1 week −1 ) was then derived using Eqn ( 5 ) to allow comparison with its HBGV value (expressed on a weekly basis). EDIs of As, Ni and Pb and the EWI of Al were all subjected to Monte Carlo simulations. EDI μg kg BW − 1 day − 1 = Element concentration μg kg − 1 1000 × food consumption rate g da y − 1 (4) EWI Al = EDI Al × 7 (5) To characterize the risk associated with chronic toxic element intake under the three formulated scenarios, the probability distributions of EDIs or EWIs were compared with the HBGVs or RPs established for these elements. Percentage contributions to the TDI (% TDI, for Ni) or TWI (% TWI, for Al) and the margin of exposure (MOE, for i As and Pb), were calculated using Eqns ( 6 ) and ( 7 ), both subjected to Monte Carlo simulations: % TDI or % TWI = EDI or EWI HBGV × 100 (6) MOE = BMDL EDI (7) Because oral exposure to Ni may also induce acute adverse effects in sensitized individuals, acute exposure and risk characterization were also addressed using Eqns ( 8 ) and ( 9 ), respectively (both subjected to Monte Carlo simulations): Acute exposure μg kg b w − 1 = Ni concentration μg k g − 1 × Acute food consumption g kg b w − 1 1000 (8) MOE = LOAEL Acute exposure (9) where the lowest‐observed‐adverse‐effect level (LOAEL) equal to 4.3 μg kg BW −1 (associated with systemic contact dermatitis) was employed as the RP and an MOE < 30 was considered indicative of potential health concerns. 51 To determine which input parameters mostly influenced exposure, risk, or benefit outcomes, a sensitivity analysis was performed. This analysis focused only on estimates from chronic consumption of botanical extracts because the study aimed to assess botanicals specifically rather than total dietary intake. Spearman rank correlation coefficients ( ρ ), ranging from −1 to +1, were used to evaluate the relative influence of each modeled input variable, with higher absolute ρ values indicating greater contributions to variability in predicted exposure and identifying the main determinants of risks or benefits. Cumulative risk assessment The non‐carcinogenic cumulative risk from chronic intake of Al, As, Ni, Fe and Zn was estimated by calculating the target hazard quotient (THQ) of each element and the hazard index (HI) as the sum of individual THQs. For this purpose, the following reference doses (RfDs), as established by the United States Environmental Protection Agency (US EPA), were used: 1.00 mg kg BW −1 day −1 for Al; 0.0003 mg kg BW −1 day −1 for i As; 0.7 mg kg BW −1 day −1 for Fe; 0.02 mg kg BW −1 day −1 for Ni; and 0.30 mg kg BW −1 day −1 for Zn. 53 THQs were calculated using Eqns ( 10 ) and ( 11 ), both subjected to Monte Carlo simulations: THQ = EDI RfD × Exposure factor (10) Exposure factor = Ef × Ed At (11) where Ef is the exposure frequency to botanicals in days per year (modeled as a truncated normal distribution; mean = 182.5 days; SD = 70 days; range = 1–365 days), Ed is the exposure duration in years (modeled as a truncated normal distribution; mean = 23 years; SD = 10 years; range = 1–46 years) and At is the averaging time (assumed to be equal to Ed × 365 days), as supported by the US EPA and ATSDR guidance for non‐carcinogenic cumulative risk assessment. 54 , 55 Dose addition was used as the default model, following US EPA guidance for health risk assessment of chemical mixtures (including metals) when there is no clear evidence suggesting response addition is more appropriate. 55 Consequently, the HI parameter was calculated by the sum of all the individual THQ values. RESULTS AND DISCUSSION Elemental concentrations and distributions in botanical extracts Elemental concentrations measured in the botanical extracts are presented in Table 4 . The data exhibited substantial variability, with median values significantly lower than the means, indicating positively skewed distributions with long right tails. Major elements (K, P, Mg and Ca) spanned on average within the 1000–20 000 mg kg −1 range, whereas trace elements (Fe, Zn) were within 10–50 mg kg −1 (Table 4 ). Among potentially toxic elements, As was the most abundant, with a median concentration of 649 μg kg −1 (Table 4 ). Table 4. Concentrations of essential (K, P, Ca, Mg, Fe and Zn) and potentially toxic (Al, Ni, As and Pb) elements quantified across botanical extracts ( n = 25) Element Unit of measure Mean ± SD Median 5th to 95th percentile (P5–P95) Essential K mg kg −1 16 876 ± 31 000 8337 398–58 406 P mg kg −1 1605 ± 2000 986 76–5009 Ca mg kg −1 1400 ± 2300 756 42–4857 Mg mg kg −1 878 ± 1300 506 35–2820 Fe mg kg −1 55 ± 55 38 0–165 Zn mg kg −1 9.0 ± 9.0 6.2 0.0–26.9 Potentially toxic Al mg kg −1 28 ± 28 20 0–85 Ni μg kg −1 2433 ± 7400 719 13–9478 As μg kg −1 3026 ± 18 000 649 7–11 623 Pb μg kg −1 128 ± 220 66 3–438 Open in a new tab The results of the AIC test used to identify the best‐fitting distributions for elemental concentrations are provided in the Supporting information (Table S3 ). As, Ca, K, Mg, Ni, P and Pb concentrations were best described by lognormal distributions, whereas Fe, N, and Al concentrations comprised exponential distributions. These fitted models were subsequently used in the probabilistic exposure assessments. The observed variability in essential and toxic element concentrations reflects the heterogeneous composition of the 25 botanical extracts analyzed, which encompassed different plant species and parts (leaves, fruits, peels and seeds) from multiple producers or manufacturers. 10 , 30 , 56 Rather than being a limitation, this variability should be considered a strength because it enabled the simulation of a broad range of probabilistic exposure scenarios. Currently, Europe has not established maximum levels (MLs) for potentially toxic elements in botanical extracts themselves 57 . Where available, MLs set for the source raw materials or final food supplement products should be used as reference benchmarks. For Pb, a MLs of 3.0 mg kg −1 applies to food supplements 57 and none of the analyzed samples exceeded this threshold. For i As, existing MLs in plant‐based products range from 0.020 mg kg −1 (some fruit juices) to 0.30 mg kg −1 (certain rice products). 57 Notably, dandelion and walnut extracts were identified as the most contaminated, with mean As concentrations far above these limits (2.2 and 69 mg kg −1 , respectively). MLs for Ni have been recently established in specific food, with limits in products relevant for this study ranging from 0.25–0.40 mg kg −1 (fruit juices, fruiting and stem vegetables) to 10–15 10 mg kg −1 (nuts, dry beans, soybeans). 57 No MLs currently exist at the European level for Al in food products. 57 However, the high concentrations observed, the frequent Al occurrence in plant‐based products, 21 , 58 , 59 and the potential health risks from chronic exposure, underscore the need to consider these elements for inclusion in future regulatory frameworks. Exposure assessment to essential elements and benefit characterization Under the reference dietary scenario (Table 2 ), baseline EDIs of P (1269 ± 366 mg day −1 ) contributed 231 ± 67% of the AI of this element, although remaining below the tolerable upper intake level of 3000 mg day −1 . This may indicate a high likelihood of adequate P intake across the adult population. P is essential for many physiological processes, including bone mineralization, energy metabolism and cellular function. 46 The EDIs of the other essential nutrients were slightly below their respective DRVs but characterized by a high degree of variability (Table 2 ). Because nutrient intake distributions were modeled as lognormal, most of the population is expected to have intakes below the mean as a result of the right‐skewed nature of the distribution. 36 This pattern suggests that, except for P, a substantial proportion of individuals may be at risk of inadequate intake of all essential elements from the baseline diet alone. Fe and Zn are essential for oxygen transport, energy production, immune function and enzymatic activity, with deficiencies potentially causing anemia and impaired immune response. 42 , 44 Chronic inadequate Mg intake may lead to neurological or cardiac issues, whereas K intakes below the AI are associated with increased risk of stroke. 45 , 47 Prolonged insufficient Ca intake may reduce bone mineral density, increasing the risk of osteoporosis and bone fractures. 43 The EDIs of essential elements from the exclusive consumption of botanical extracts and from the scenario combining botanicals consumption plus baseline diet are reported in Table 5 , whereas the percentage contributions to DRVs deriving from sole consumption of botanicals are shown in Table 6 . As observed, nutrient intakes from the sole consumption of botanicals provided a minimal contribution to DRVs, with median values generally below the 1% and 95 th percentile (P95) values reaching a maximum of 17% (Table 6 ). Therefore, botanical extracts contribute only modestly to recommended nutrient intakes and, although they may help address minor nutritional shortfalls (especially in individuals with marginal deficiencies), 60 , 61 this effect is incidental because it is not the primary intended function of these products. Table 5. Estimated daily intakes (EDIs) or weekly intakes (EWIs) [mean ± SD, median and 5 th to 95 th percentile (P5–P95) values] of essential and potentially toxic elements in Italian adults through the sole chronic consumption of botanical extracts and though the whole diet supplemented with botanical extracts (for Ni, both the chronic and the acute § EDI were assessed) Element Unit of measure EDI/EWI (botanicals only) Total EDI/EWI (reference scenario + botanicals) Average contribution (%) of botanicals to total EDI/EWI of the reference scenario + botanicals Mean ± SD Median P5–P95 Mean ± SD Median P5–P95 Mean ± SD Essential Ca mg day −1 11 ± 45 2 0–44 926 ± 321 876 504–1522 1.2 ± 3.4 K mg day −1 135 ± 608 26 1–513 3160 ± 1069 3016 1891–4895 3.4 ± 7.5 P mg day −1 13 ± 49 3 0–51 1283 ± 370 1231 775–1962 1.0 ± 2.7 Mg mg day −1 7 ± 27 2 0–27 286 ± 91 273 166–449 2.2 ± 5.2 Fe mg day −1 0.4 ± 1.5 0.1 0.0–1.8 9.1 ± 3.2 8.6 5.1–14.7 4.2 ± 8.0 Zn mg day −1 0.07 ± 0.28 0.02 0.01–0.29 10.8 ± 3.3 10.29 6.3–16.8 0.7 ± 1.9 Potentially toxic Al mg kg BW −1 week −1 0.024 ± 0.092 0.006 0.000–0.091 0.43 ± 0.19 0.39 0.20–0.77 4.7 ± 8.8 Ni μg kg BW −1 day −1 0.3 ± 2.2 0.0 0.0–0.1 1.8 ± 2.0 1.6 0.1–3.5 9 ± 15 Ni (acute) § μg kg BW −1 0.4 ± 2.8 0.1 0.0 ± 1.5 / / / / i As † mg kg BW −1 day −1 0.4 ± 3.2 0.0 0.0–1.1 0.4 ± 3.3 0.12 0.0–1.2 37 ± 32 Pb mg kg BW −1 day −1 0.023 ± 0.082 0.003 0.000–0.056 0.18 ± 0.11 0.15 0.07–0.34 7 ± 12 Open in a new tab Note : § Acute exposure to Ni: calculated only for the scenario considering the sole consumption of botanicals; † i As: inorganic arsenic. Table 6. Percentage contributions [mean ± SD, median and 5 th –95 th percentile (P5–P95) values] of the of chronic consumption of botanical extracts by Italian adults to Ca, K, Mg, P, Fe and Zn benefit metrics and to Al and Ni risk metrics [margin of exposure (MOE) values are provided for the Ni (under the acute exposure scenario), inorganic As ( i As) and Pb (both under chronic exposure scenarios)] Element Mean ± SD Median P5–P95 Health metric Essential Ca 1.2 ± 4.9 0.3 0.0–4.7 %PRI K 4 ± 20 1.0 0–15 %AI Mg 2.2 ± 8.6 0.5 0.0–8.8 %AI P 2.4 ± 8.6 0.6 0.0–9.3 %AI Fe 4 ± 13 1.0 0–17 %PRI Zn 0.6 ± 2.0 0.2 0.0–2.6 %PRI Potentially toxic Al 2.3 ± 8.2 0.5 0.0–9.3 %TWI Ni 2 ± 15 0.2 0–7 %TDI Ni (acute scenario) ɸ 637 ± 7766 82 3–2231 MOE i As † 25 ± 207 2 0–81 MOE Pb § 1087 ± 4625 218 12–4225 MOE Pb ¶ 2588 ± 11 012 519 27–10 059 MOE Open in a new tab Note : ɸ MOE calculated using the LOAEL for eczematous flare‐up skin reactions in nickel‐sensitized individuals; † MOE calculated using the BMDL 05 for skin cancer; § MOE calculated using the BMDL 10 for nephrotoxicity; ¶ MOE calculated using the BMDL 01 for cardiovascular diseases. Botanical extracts, infusions and various herbal formulations are recognized sources of essential minerals and have been linked to a range of health benefits. 62 , 63 , 64 , 65 , 66 However, few studies have quantitatively estimated the chronic intake of such nutrients from these products. For example, Muhammad et al . 67 reported lower EDIs of K and Ca but higher EDIs of Fe (3.7 mg day −1 ) and Zn (7.5 mg day −1 ) from Viola betonicifolia compared to the botanicals analyzed in the present study. Ayurvedic extracts were also reported to be safe regarding heavy metals at the same time as providing adequate essential minerals, particularly Fe (EDIs up to 2.7 mg day −1 ). 24 Conversely, other herbal extracts such as Urtica dioica (also analyzed in the present study), were reported to provide negligible amounts of Ca, Mg, Fe, Zn and Cu for human nutrition. 68 Importantly, the bioavailability of essential elements from plant materials can vary widely. Therefore, high concentrations do not necessarily translate into high absorption in humans. Indeed, bioaccessibility and bioavailability depend on the chemical form of the element, as well as on interactions with other plant constituents (including phytates, oxalates and polyphenols), which can reduce mineral absorption. 69 From another perspective, despite limited bioavailability, essential elements can still help reduce the absorption and accumulation of toxic metals such as Pb and Cd, primarily through competitive interactions among divalent metals for the same intestinal transporters. 70 , 71 Fig. 1 shows the probability distributions of the total percentage contribution to the DRVs of Ca, K, Mg, P, Fe and Zn when combining intakes from the reference diet and botanical extracts. P intakes consistently exceeded nutritional requirements across the population, with most values between 150% and 250% of the DRV. This may suggest that, even without botanical supplementation, P intake from the diet is generally sufficient. In contrast, Ca, K, Mg and Zn to DRVs median intake contributed less than 100% of the DRVs, suggesting that a significant portion may be at risk of suboptimal intake. Figure 1. Open in a new tab Distribution of the calculated percentage contribution (%) of dietary intakes of essential nutrients to their respective dietary reference values (DRVs), resulting from the combined consumption of botanical extracts and the total (reference) diet. Overall, these results underscore that, although botanical extracts may provide beneficial minerals, they are insufficient on their own to fully address potential dietary inadequacies. Exposure assessment to potentially toxic elements and risk characterization The EDIs of As, Ni and Pb and EWIs of Al from the baseline reference scenario are presented in Table 2 . The highest value was observed for Al (58.2 ± 25.0 μg kg BW −1 day −1 ), whereas the lowest was observed for i As (0.081 ± 0.030 μg kg BW −1 day −1 ) (Table 2 ). These chronic intake levels contributed less than 100% of the TWI of Al (41%) and TDI of Ni (12%), indicating that adults are unlikely to face health risks such neurotoxicity, embryotoxicity, reproductive toxicity, or immunotoxicity from the baseline diet. 49 , 51 Chronic consumption of botanical extracts alone contributed less than 1% to the TWI of Al and the TDI of Ni at the median intake level, remaining below 10% even at the P95 of the entire simulated distribution values (Table 6 ). Therefore, the dietary consumption of botanicals appears safe regarding Al and Ni toxicity, although this contrasts with some findings reported in earlier studies. 14 , 21 , 64 Tables 5 and 6 present intake levels and associated risk estimates from the exclusive consumption of botanicals. Among the evaluated elements, i As emerged as the primary concern. As shown in Table 6 and further illustrated in Fig. 2 (displaying the entire probability distribution curve of simulated MOEs on a logarithmic scale), median MOEs were below 10, and fell below 1 when considering the P5 values of the simulations. These results align with previous studies that identify i As as a major contaminant in botanicals, 72 , 73 , 74 potentially exposing 54% of adults to carcinogenic risk. 26 , 75 Figure 2. Open in a new tab Probability distribution of margin of exposures (MOEs, logarithmic scale) to inorganic arsenic resulting from the consumption of botanical extracts and associated with a 5% relative increase of skin cancer incidence. Sensitivity analysis revealed that the MOE to i As had moderate‐to‐strong negative correlations with both the consumption rate of botanicals ( ρ = −0.59) and i As concentrations in the samples ( ρ = −0.77), consistent with the expectation that higher exposure results in lower MOEs. In this context, although i As was not directly measured in the samples, it is important to note that a large proportion of total As in botanicals is typically inorganic. 74 Therefore, these results indicate a potentially serious health risk and underscore the critical need to minimize exposure to this contaminant as much as reasonably achievable. 52 Regarding exposure to Pb, MOEs calculated under the reference scenario were substantially below 10 for both nephrotoxicity (95% of MOEs < 10) and cardiovascular toxicity (46% of MOEs < 10) endpoints, indicating that baseline dietary intake alone may pose health risks. 50 , 76 By contrast, exposure from botanicals alone did not raise concerns because all simulated MOEs exceeded the safety threshold of 10 (Table 6 ). However, when combined with background dietary intake, Pb exposure from botanicals resulted in median MOEs of 4.1 (nephrotoxicity) and 9.7 (cardiovascular effects), with even lower values of the P5 estimates (1.8 and 4.4, respectively). Hence, these results indicate that the already problematic baseline Pb exposure can be further worsened by botanical consumption, emphasizing the importance of limiting total dietary Pb intake. 77 This is consistent with previous studies identifying Pb as a common contaminant in plant‐derived materials. 19 , 74 , 78 , 79 , 80 Although the present assessment focused on adults, the implications for vulnerable subgroups are likely more concerning. Indeed, younger individuals have higher exposure relative to body weight and less mature detoxification systems, which increase their sensitivity to the harmful effects of i As and Pb. Pregnant and breastfeeding women may also face heightened risks because even small increases in exposure can negatively affect fetal and early‐life development. Therefore, although risks are already evident for adults, the findings emphasize the importance of careful consideration for these more susceptible populations. Acute exposure to Ni Acute exposure to Ni from the consumption of botanical extracts showed substantial variability, with intake estimates up to 1.5 μg kg BW −1 (Table 5 ). These exposure levels led to MOEs spanning from 3 (P5) to 2231 (P95), with a median value of 82 (Table 6 ; see also Supporting information, Fig. S1 ). Notably, 32% of the simulated MOEs fell below the threshold of 30, indicating that a substantial portion of Ni‐sensitized individuals could experience acute adverse effects from the Ni content of botanicals (see Supporting information, Fig. S1 ). This finding is of particular concern given that approximately 15% of the European population (especially women) is affected by Ni sensitization 51 and that a subset of these Ni‐sensitized individuals is affected by systemic contact dermatitis. Cumulative dietary risk assessment A probabilistic estimation of cumulative non‐carcinogenic risks from simultaneous exposure to Al, i As, Fe, Ni and Zn was further conducted using the HI as a composite risk metric, where values below 1 indicate low or unlikely risk. 54 , 55 The HI resulting from the consumption of botanicals alone ranged from 0.05 (median) to 1.95 (P95). The cumulative risk was mainly driven by the i As (THQ = 0.65), which contributed approximately 98% of the mean HI. Conversely, THQs of Al, Fe, Ni and Zn were all below 0.01, indicating minimal contribution to cumulative non‐carcinogenic risk. When botanical consumption was combined with background dietary intake, the HI increased substantially, reaching a mean value higher than the threshold of 1 and P95 value reaching 2.6 (Fig. 3 ). Even under this combined scenario, i As remained the dominant contributor, accounting for 65% of the HI. Fe and Zn also contributed significantly, with P5–P95 THQ ranges of 0.03–0.17 and 0.08–0.49, respectively, whereas Ni and Al contributed less (average THQs below 0.04). Although high exposure to Zn and Fe is partially regulated by adaptive mechanisms such as reduced absorption and increased excretion, prolonged intakes that exceed homeostatic capacity can still lead to adverse and potentially irreversible biological effects 81 , including immunological alterations, impaired copper absorption and neurological issues. 42 , 44 Figure 3. Open in a new tab Target hazard quotient (THQ) values for individual toxic elements and hazard index (HI), representing the cumulative non‐carcinogenic risk from exposure to all assessed elements from the combined consumption of botanical extracts and the total (reference) diet. Several studies have assessed the cumulative non‐carcinogenic risk from multiple elements in botanical and medicinal products. In some cases, HI values exceeded 1, with i As, Pb, Cd and Ni as main contributors. 19 , 72 , 74 , 82 , 83 In other cases, plants from non‐industrialized regions or certain herbal products at medicinal doses previously showed HI values below 1, suggesting minimal risk from chronic exposure. 84 , 85 Overall, although exposure to individual elements may appear safe, combined chronic exposure can pose significant health risks. The substantial uncertainty observed in the results (particularly for i As) underscores many challenges in accurately assessing the risks more than the benefits associated with botanical consumption. CONCLUSIONS The results of the present study reveal a complex interplay between modest nutritional benefits and non‐negligible toxicological risks from regular consumption of botanical extracts by Italian adults. Overall, botanicals contributed minimally to essential nutrient intake and their use should not be considered as a means to address potential nutritional deficiencies because they are neither intended nor marketed for this purpose. From a risk perspective, health concerns were evident from exposure to individual toxic elements ( i As and Pb) and from cumulative exposure to Al, i As, Fe, Ni and Zn. By providing a comprehensive assessment of both essential and toxic elements in botanicals, the present study aligns with several United Nations Sustainable Development Goals (SDGs) by informing nutritional adequacy (SDG 2), characterizing chronic exposure risks (SDG 3), and promoting responsible evaluation and management of chemical components in botanicals (SDG 12). However, some limitations should be acknowledged, including the exclusion of some toxic elements with similar endpoints and the exclusive focus on healthy adults, which may have led to an underestimation of the risks for more vulnerable populations. Therefore, future research should address these gaps and explore mitigation strategies to reduce contamination during production, processing, and formulation. From a policy perspective, there is a clear need for a harmonized and enforced regulatory framework for botanical extracts. Priorities should include establishing clear quality requirements, standardized composition guidelines and maximum allowable limits for multiple inorganic contaminants. Equally important is the development of public guidance to promote informed and safe use of these products, which should be effectively communicated through standardized and transparent labeling. Together, these measures are crucial to ensure that the nutritional advantages of these botanical extracts are safely exploited without compromising consumer health. FUNDING This work was supported by Univerzita Pardubice (Grant Number SGS_2025_001) and Università degli Studi di Parma, Project funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 13 – Call for tender No 341 of 15/03/2022 of Ministero dell'Università e della Ricerca – NextGenerationEU Award Number: Project code PE0000003, Project title ‘Research and innovation network on food and nutrition Sustainability, Safety and Security – Working ON Foods’ (ONFoods). This research was also undertaken within the framework of the ALIFAR project, supported by Ministero dell'Università e della Ricerca through the ‘Dipartimenti di Eccellenza 2023–2027’ program. CONFLICTS OF INTEREST The authors declare that they have no conflicts of interest. Supporting information Table S1. Accuracy and precision of ICP‐MS quantification of essential and potentially toxic elements in certified reference materials (CRMs). Table S2 . Calculated detection limits of the method (MLOD, μg kg −1 ) and quantification limits of the method (MLOQs, μg kg −1 ) of essential and potentially toxic elements with the use of Rh as internal standard. Table S3 . Results of the Akaike Information Criterion (AIC) test used to identify the best‐fitting probability distributions for concentration values of each element measured in botanical extracts. Figure S1 . Probability distribution of margin of exposure (MOE, logarithmic scale) values to nickel based on acute dietary consumption of botanical extracts associated with systemic contact dermatitis in nickel‐sensitized individuals. JSFA-106-4060-s001.docx (2.4MB, docx) ACKNOWLEDGMENTS We gratefully acknowledge the Italian National Institute of Health for providing intake data of the Italian population for most of the elements considered in this study. We also thank Professor Maria João dos Ramos Fraqueza and Professor Telmo Pina Nunes of the Faculty of Veterinary Medicine, Department of Animal Production and Food Safety, University of Lisbon (Lisboa, Portugal), for their guidance in the use of statistical tools. Open access publishing facilitated by Universita degli Studi di Parma, as part of the Wiley ‐ CRUI‐CARE agreement. DATA AVAILABILITY The data that support the findings of this study are available from the corresponding author upon reasonable request. REFERENCES 1. EFSA , Guidance on safety assessment of botanicals and botanical preparations intended for use as ingredients in food supplements. EFSA J 7:1249 (2009). 10.2903/j.efsa.2009.1249. [ DOI ] [ Google Scholar ] 2. Pashazadeh H, Zannou O, Ghellam M, Koca I, Galanakis CM and Aldawoud TMS, Optimization and encapsulation of phenolic compounds extracted from maize waste by freeze‐drying, spray‐drying, and microwave‐drying using maltodextrin. Foods 10:1396 (2021). 10.3390/foods10061396. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Monagas M, Brendler T, Brinckmann J, Dentali S, Gafner S, Giancaspro G et al ., Understanding plant to extract ratios in botanical extracts. Front Pharmacol 13:981978 (2022). 10.3389/fphar.2022.981978. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Filipiak‐Szok A, Kurzawa M, Cichosz M and Szłyk E, Elemental analysis of medicinal herbs and dietary supplements. Anal Lett 48:2626–2638 (2015). 10.1080/00032719.2015.1041031. [ DOI ] [ Google Scholar ] 5. Egan B, Hodgkins C, Shepherd R, Timotijevic L and Raats M, An overview of consumer attitudes and beliefs about plant food supplements. Food Funct 2:747–752 (2011). 10.1039/C1FO10109A. [ DOI ] [ PubMed ] [ Google Scholar ] 6. Barnes K, Ball L, Desbrow B, Alsharair N and Ahmed F, Consumption and reasons for use of dietary supplements in an Australian university population. Nutrition 32:524–530 (2016). 10.1016/j.nut.2015.10.022. [ DOI ] [ PubMed ] [ Google Scholar ] 7. Binns CW, Lee MK and Lee AH, Problems and prospects: public health regulation of dietary supplements. Annu Rev Publ Health 39:403–420 (2018). 10.1146/annurev-publhealth-040617-013638. [ DOI ] [ PubMed ] [ Google Scholar ] 8. Ghani U, Batool A, Rafeeq H, Naeem M, Hassan Bukhari SS, Ahsan H et al ., Advancement and future directions towards herbal treatment for various diseases. Saudi J Med Pharm Sci 5:931–941 (2019). 10.36348/sjmps.2019.v05i11.003. [ DOI ] [ Google Scholar ] 9. Mleczek M, Rzymski P, Budka A, Siwulski M, Jasi A, Kala P et al ., Elemental characteristics of mushroom species cultivated in China and Poland. J Food Compos Anal 66:168–178 (2018). 10.1016/j.jfca.2017.12.018. [ DOI ] [ Google Scholar ] 10. Rempelos L, Barański M, Sufar EK, Gilroy J, Shotton P, Leifert H et al ., Effect of climatic conditions, and agronomic practices used in organic and conventional crop production on yield and nutritional composition parameters in potato, cabbage, lettuce and onion; results from the long‐term NFSC‐trials. Agronomy 13:1225 (2023). 10.3390/agronomy13051225. [ DOI ] [ Google Scholar ] 11. Mahlangeni NT, Moodley R and Babu S, Elemental analysis of edible mountain nettle (Obetia Tenax) and the influence of soil on its chemical composition. Anal Lett 50:1531–1551 (2017). 10.1080/00032719.2016.1236263. [ DOI ] [ Google Scholar ] 12. RASFF , RASFF Window European commission [Online] (2025) Available: https://webgate.ec.europa.eu/rasff-window/screen/search [June 2025]. 13. Li Q, Zhou J, Chen B, Huang B, Zeng X, Zhan J et al ., Toxic metal contamination and distribution in soils and plants of a typical metallurgical industrial area in southwest of China. Environ Earth Sci 72:2101–2109 (2014). 10.1007/s12665-014-3118-8. [ DOI ] [ Google Scholar ] 14. Alagić SČ, Tošić SB, Dimitrijević MD, Antonijević MM and Nujkić MM, Assessment of the quality of polluted areas based on the content of heavy metals in different organs of the grapevine ( Vitis vinifera ) cv Tamjanika. Environ Sci Pollut Res Int 22:7155–7175 (2015). 10.1007/s11356-014-3933-1. [ DOI ] [ PubMed ] [ Google Scholar ] 15. Polyakov A, Alekseeva T and Muravieva I, The elemental composition of garlic (Allium sativum L) and its variability. E3S Web Conf 175:1016 (2020). 10.1051/e3sconf/202017501016. [ DOI ] [ Google Scholar ] 16. Proestos C, The benefits of plant extracts for human health. Foods 9:1653 (2020). 10.3390/foods9111653. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Abibu MA, Taleat AAT, Olaoluwa DT, Muraina TA, Adenipekun OD and Adeniyi BO, Elemental assessment of four popular African immunity boosting medicinal plants marketed in Ede local market, Nigeria. Int J Innov Sci Res Technol 10:144–150 (2025). 10.5281/zenodo.14621451. [ DOI ] [ Google Scholar ] 18. Briffa J, Sinagra E and Blundell R, Heavy metal pollution in the environment and their toxicological effects on humans. Heliyon 6:e04691 (2020). 10.1016/j.heliyon.2020.e04691. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Ćwielag‐Drabek M, Piekut A, Szymala I, Oleksiuk K, Razzaghi M, Osmala W et al ., Health risks from consumption of medicinal plant dietary supplements. Food Sci Nutr 8:3535–3544 (2020). 10.1002/fsn3.1636. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 20. Kong D, Li X, Yao J, He Y, Luo J and Yang M, Health risk assessment and bioaccessibility of toxic elements in edible and medicinal plants under different consumption methods. Microchem J 159:105577 (2020). 10.1016/j.microc.2020.105577. [ DOI ] [ Google Scholar ] 21. Augustsson A, Qvarforth A, Engström E, Paulukat C and Rodushkin I, Trace and major elements in food supplements of different origin: implications for daily intake levels and health risks. Toxicol Rep 8:1067–1080 (2021). 10.1016/j.toxrep.2021.04.012. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Karami H, Shariatifar N, Nazmara S, Moazzen M, Mahmoodi B and Mousavi KA, The concentration and probabilistic health risk of potentially toxic elements (PTEs) in edible mushrooms (wild and cultivated) samples collected from different cities of Iran. Biol Trace Elem Res 199:389–400 (2021). 10.1007/s12011-020-02130-x. [ DOI ] [ PubMed ] [ Google Scholar ] 23. Rubio C, Dominik‐Jakubiec M, Paz S, Gutiérrez AJ, González‐Weller D and Hardisson A, Dietary exposure to trace elements (B, Ba, Li, Ni, Sr, and V) and toxic metals (Al, Cd, and Pb) from the consumption of commercial preparations of Spirulina platensis. Environ Sci Pollut Control Ser 28:22146–22155 (2021). 10.1007/s11356-020-12260-3. [ DOI ] [ PubMed ] [ Google Scholar ] 24. Deswal M, Deswal P and Laura JS, Risk assessment of some herbal powders and herbal formulations contaminated with heavy metals. Int J Pharm Sci Res 10:393 (2019). 10.13040/IJPSR.0975-8232.10(1).393–00. [ DOI ] [ Google Scholar ] 25. Huang Y, Wang X, Zhou Y, Li B, Du H, Tie B et al ., The ignored risk: heavy metal pollution of medicine and food homologous substances. Environ Sci Pollut Res 30:18577–18587 (2023). 10.1007/s11356-022-23372-3. [ DOI ] [ PubMed ] [ Google Scholar ] 26. Torović L, Vojvodić S, Lukić D, Srđenović Čonić B and Bijelović S, Safety assessment of herbal food supplements: elemental profiling and associated risk. Foods 12:2746 (2023). 10.3390/foods12142746. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. EFSA , Guidance on risk–benefit assessment of foods. EFSA J 22:e8875 (2024). 10.2903/j.efsa.2024.8875. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Assunção R, Alvito P, Brazão R, Carmona P, Fernandes P, Jakobsen LS et al ., Building capacity in risk‐benefit assessment of foods: lessons learned from the RB4EU project. Trends Food Sci Technol 91:541–548 (2019). 10.1016/j.tifs.2019.07.028. [ DOI ] [ Google Scholar ] 29. EFSA , EFSA scientific colloquium 26: risk‐benefit assessment of combined exposure to nutrients and contaminants through food. EFSA Support Publ 19:7772E (2022). 10.2903/sp.efsa.2022.EN-7772. [ DOI ] [ Google Scholar ] 30. Varrà MO, Husáková L, Lanza GT, Piroutková M, Patočka J, Ghidini S et al ., Multi‐elemental composition of botanical preparations and probabilistic evaluation of toxic metals and metalloids intake upon dietary exposure. Food Chem Toxicol 188:114664 (2024). 10.1016/j.fct.2024.114664. [ DOI ] [ PubMed ] [ Google Scholar ] 31. D'Amato M, Turrini A, Aureli F, Moracci G, Raggi A, Chiaravalle E et al ., Dietary exposure to trace elements and radionuclides: the methodology of the Italian Total diet study 2012‐2014. Ann Ist Super Sanità 49:272–280 (2013). 10.4415/ANN_13_03_07cubadda. [ DOI ] [ PubMed ] [ Google Scholar ] 32. Cubadda F, Aureli A, D'Amato M, Raggi A, Mantovani A, Silano M et al ., The Italian Total Diet Study: intake of nutrients and exposure to contaminants in the Italian population. Notiziario dell'Istituto Superiore di Sanità 31:3–8 (2018). [ Google Scholar ] 33. Cubadda F, D'Amato M, Aureli F, Raggi A and Mantovani A, Dietary exposure of the Italian population to inorganic arsenic: the 2012‐2014 Total diet study. Food Chem Toxicol 98:148–158 (2016). 10.1016/j.fct.2016.10.015. [ DOI ] [ PubMed ] [ Google Scholar ] 34. Cubadda F, Iacoponi F, Ferraris F, D'Amato M, Aureli F, Raggi A et al ., Dietary exposure of the Italian population to nickel: the national Total diet study. Food Chem Toxicol 146:111813 (2020). 10.1016/j.fct.2020.111813. [ DOI ] [ PubMed ] [ Google Scholar ] 35. Sette S, Le Donne C, Piccinelli R, Arcella D, Turrini A and Leclercq C, The third Italian National Food Consumption Survey, INRAN‐SCAI 2005‐06 ‐ part 1: nutrient intakes in Italy. Nutr Metab Cardiovasc Dis 21:922–932 (2011). 10.1016/j.numecd.2011.03.001. [ DOI ] [ PubMed ] [ Google Scholar ] 36. Passarelli S, Free CM, Allen LH, Batis C, Beal T, Biltoft‐Jensen AP et al ., Estimating national and subnational nutrient intake distributions of global diets. Am J Clin Nutr 116:551–560 (2022). 10.1093/ajcn/nqac108. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 37. EFSA , The EFSA Comprehensive European Food Consumption Database [Online] (2025) Available: https://www.efsa.europa.eu/en/data-report/food-consumption-data [June 2025]. 38. Turrini A, Le Donne C, Piccinelli R, Di Leo L, Minardi L, Scatigna S et al ., Italian national dietary survey on adult population from 10 up to 74 years old – IV SCAI ADULT. EFSA Support Publ 19:1–32 (2022). 10.2903/sp.efsa.2022.en-7559. [ DOI ] [ Google Scholar ] 39. EFSA , Software for Monte Carlo simulation of a simple risk assessment. EFSA J 14:1316E (2017). 10.2903/sp.efsa.2017.EN-1316. [ DOI ] [ Google Scholar ] 40. Delignette‐Muller ML and Dutang C, Fitdistrplus: an R package for fitting distributions. J Stat Softw 64:1–34 (2015). 10.18637/jss.v064.i04. [ DOI ] [ Google Scholar ] 41. EFSA , DRV Finder – Dietary Reference Values EFSA [Online] (2025) Available: https://www.efsa.europa.eu/en/interactive-pages/drvs [December 2025]. 42. EFSA , Scientific opinion on dietary reference values for zinc. EFSA J 12:1–57 (2014). 10.2903/j.efsa.2014.3844. [ DOI ] [ Google Scholar ] 43. EFSA , Scientific opinion on dietary reference values for calcium. EFSA J 13:1–78 (2015). 10.2903/j.efsa.2015.4101. [ DOI ] [ Google Scholar ] 44. EFSA , Scientific opinion on dietary reference values for iron. EFSA J 13:4254 (2015). 10.2903/j.efsa.2015.4254. [ DOI ] [ Google Scholar ] 45. EFSA , Scientific opinion on dietary reference values for magnesium. EFSA J 13:1–63 (2015). 10.2903/j.efsa.2015.4186. [ DOI ] [ Google Scholar ] 46. EFSA , Scientific opinion on dietary reference values for phosphorus. EFSA J 13:1–54 (2015). 10.2903/j.efsa.2015.4185. [ DOI ] [ Google Scholar ] 47. EFSA , Scientific opinion on dietary reference values for potassium. EFSA J 14:e04592 (2016). 10.2903/j.efsa.2016.4592. [ DOI ] [ Google Scholar ] 48. EFSA , Chemical Hazards Database (OpenFoodTox) [Online] (2025) Available: https://www.efsa.europa.eu/en/microstrategy/openfoodtox [December 2025]. 49. EFSA , Safety of aluminium from dietary intake ‐ scientific opinion of the panel on food additives, flavourings, processing aids and food contact materials (AFC). EFSA J 6:1–34 (2008). 10.2903/j.efsa.2008.754. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 50. EFSA , Scientific opinion on lead in food. EFSA J 8:1–151 (2010). 10.2903/j.efsa.2010.1570. [ DOI ] [ Google Scholar ] 51. EFSA , Update of the risk assessment of nickel in food and drinking water. EFSA J 18:e06268 (2020). 10.2903/j.efsa.2020.6268. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 52. EFSA , Update of the risk assessment of inorganic arsenic in food. EFSA J 22:e8488 (2024). 10.2903/j.efsa.2024.8488. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 53. US EPA , Integrated Risk Information System (IRIS): Chemical Assessments and RfD values [Online] (2025) Available: https://iris.epa.gov [December 2025]. 54. ATSDR , Calculating Hazard Quotients and Cancer Risk Estimates [Online] (2025) Available: https://www.atsdr.cdc.gov/pha‐guidance/conducting_scientific_evaluations/epcs_and_exposure_calculations/hazardquotients_cancerrisk.html#CommonNonCancerGuidelines [December 2025]. 55. US EPA , Supplementary guidance for conducting health risk assessment of chemical mixtures EPA/630/R‐00/002 [Online] (2000) Available: https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=20533 [December 2025]. 56. Kabata‐Pendias A, Soil–plant transfer of trace elements—an environmental issue. Geoderma 122:143–149 (2004). 10.1016/j.geoderma.2004.01.004. [ DOI ] [ Google Scholar ] 57. European Commission , Commission regulation (EU) 2023/915 of 25 April 2023 on maximum levels for certain contaminants in food and repealing regulation (EC) No 1881/2006. Off J Eur Union L 119:1–72 (2023) Available: http://data.europa.eu/eli/reg/2023/915/2023-08-10 . [ Google Scholar ] 58. Filippini T, Tancredi S, Malagoli C, Cilloni S, Malavolti M, Violi F et al ., Aluminum and tin: food contamination and dietary intake in an Italian population. J Trace Elem Med Biol 52:293–301 (2019). 10.1016/j.jtemb.2019.01.012. [ DOI ] [ PubMed ] [ Google Scholar ] 59. Rzymski P, Budzulak J, Niedzielski P, Klimaszyk P, Proch J, Kozak L et al ., Essential and toxic elements in commercial microalgal food supplements. J Appl Phycol 31:3567–3579 (2019). 10.1007/s10811-018-1681-1. [ DOI ] [ Google Scholar ] 60. RIVM , Our Food, our Health Healthy Diet and Safe Food in The Netherlands. National Institute for Public Health and the Environment, Bilthoven: (2006). [ Google Scholar ] 61. Blumberg JB, Frei B, Fulgoni VL, Weaver CM and Zeisel SH, Contribution of dietary supplements to nutritional adequacy in various adult age groups. Nutrients 9:1–10 (2017). 10.3390/nu9121325. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 62. Lesniewicz A, Jaworska K and Zyrnicki W, Macro‐ and micro‐nutrients and their bioavailability in polish herbal medicaments. Food Chem 99:670–679 (2006). 10.5219/1553. [ DOI ] [ Google Scholar ] 63. Garg AN, Availability of essential elements in nutrient supplements used as antidiabetic herbal formulations. Biol Trace Elem Res 120:148–162 (2007). 10.1007/S12011-007-8022-6. [ DOI ] [ PubMed ] [ Google Scholar ] 64. Bhat R, Kiran K, Arun AB and Karim AA, Determination of mineral composition and heavy metal content of some nutraceutically valued plant products. Food Anal Methods 3:181–187 (2010). 10.1007/s12161-009-9107-y. [ DOI ] [ Google Scholar ] 65. Uraku AJ, Nutritional potential of Citrus sinensis and Vitis vinifera peels. J Adv Med Life Sci 3:3–4 (2015). 10.5281/zenodo.893564. [ DOI ] [ Google Scholar ] 66. Zheplinska M, Mushtruk M, Vasyliv V, Kuts A, Slobodyanyuk N, Bal‐Prylypko L et al ., The micronutrient profile of medicinal plant extracts. Potravin Slovak J Food Sci 15:528–535 (2021). 10.5219/1553. [ DOI ] [ Google Scholar ] 67. Muhammad N, Saeed M, Khan H, Hassan S and Gul F, Evaluation of Viola betonicifolia for its nutrition value. Pak J Pharm Sci 25:639–644 (2012). [ PubMed ] [ Google Scholar ] 68. Suliburska J and Kaczmarek K, Herbal infusions as a source of calcium, magnesium, iron, zinc and copper in human nutrition. Int J Food Sci Nutr 63:194–198 (2011). 10.3109/09637486.2011.617359. [ DOI ] [ PubMed ] [ Google Scholar ] 69. Frossard E, Bucher M, Mächler F, Mozafar A and Hurrell R, Potential for increasing the content and bioavailability of Fe, Zn and Ca in plants for human nutrition. J Sci Food Agric 80:861–879 (2000). [ Google Scholar ] 70. Hudson LG, Dashner‐Titus EJ and MacKenzie D, Zinc as a mechanism‐based strategy for mitigation of metals toxicity. Curr Environ Health Rep 12:5 (2025). 10.1007/s40572-025-00474-x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 71. Vesey DA, Transport pathways for cadmium in the intestine and kidney proximal tubule: focus on the interaction with essential metals. Toxicol Lett 198:13–19. (2010). ATSDR. Calculating Hazard Quotients and Cancer Risk Estimates [Online] (2025) Available: https://www.atsdr.cdc.gov/pha‐guidance/conducting_scientific_evaluations/epcs_and_exposure_calculations/hazardquotients_cancerrisk.html#CommonNonCancerGuidelines [December 2025]. [ DOI ] [ PubMed ] 72. Luo L, Wang B, Jiang J, Fitzgerald M, Huang Q, Yu Z et al ., Heavy metal contaminations in herbal medicines: determination, comprehensive risk assessments, and solutions. Front Pharmacol 11:595335 (2021). 10.3389/fphar.2020.595335. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 73. Kenny CR, Ring G, Sheehan M, Auliffe MAP, Lucey B and Furey A, Novel metallomic profiling and non‐carcinogenic risk assessment of botanical ingredients for use in herbal, phytopharmaceutical and dietary products using HR‐ICP‐SFMS. Sci Rep 12:17582 (2022). 10.1038/s41598-022-16873-1. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 74. Rojas P, Ruiz‐Sánchez E, Rojas C, García‐Martínez BA, López‐Ramírez AM, Osorio‐Rico L et al ., Human health risk assessment of arsenic and other metals in herbal products containing St John's wort in the metropolitan area of Mexico City. Toxics 11:801 (2023). 10.3390/toxics11090801. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 75. Van den Berg SJPL, Restani P, Boersma MG, Delmulle L and Rietjens IMCM, Levels of genotoxic and carcinogenic compounds in plant food supplements and associated risk assessment. Food Nutr Sci 2:989–1010 (2011). 10.4236/fns.2011.29134. [ DOI ] [ Google Scholar ] 76. IARC , IARC (International Agency for Research on Cancer) monographs on the identification of carcinogenic hazards to humans [Online] (2025) Available: https://monographs.iarc.who.int/agents‐classified‐by‐the‐iarc/ [June 2025]. 77. Malavolti M, Fairweather‐Tait SJ, Malagoli C, Vescovi L, Vinceti M and Filippini T, Lead exposure in an Italian population: food content, dietary intake and risk assessment. Food Res Int 137:109370 (2020). 10.1016/j.foodres.2020.109370. [ DOI ] [ PubMed ] [ Google Scholar ] 78. Ezeabara CA, Okanume OE, Emeka AN, Okeke CU and Mbaekwe EI, Heavy metal contamination of herbal drugs: implication for human health‐a review. Int J Trop Dis Health 4:1044–1058 (2014). 10.9734/IJTDH/2014/11481. [ DOI ] [ Google Scholar ] 79. Mustatea G, Ungureanu EL, Iorga SC, Ciotea D and Popa ME, Risk assessment of lead and cadmium in some food supplements available on the Romanian market. Foods 10:581 (2021). 10.3390/foods10030581. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 80. Zukowska G, Chomczyńska M and Myszura M, Influence of diversified anthropogenic pressure on heavy metals contents in soils and plants of garden allotments. J Phys Conf Ser 1736:012055 (2021). 10.1088/1742-6596/1736/1/012055. [ DOI ] [ Google Scholar ] 81. EFSA , Statement on the derivation of health‐based guidance values (HBGV s) for regulated products that are also nutrients. EFSA J 19:e06479 (2021). 10.2903/sp.efsa.2021.EN-6480. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 82. Yang CM, Chien MY, Chao PC, Huang CM and Chen CH, Investigation of toxic heavy metals content and estimation of potential health risks in Chinese herbal medicine. J Hazard Mater 412:125142 (2021). 10.1016/j.jhazmat.2021.125142. [ DOI ] [ PubMed ] [ Google Scholar ] 83. Islam N, Zamir R and Faruque O, Health risk assessment of toxic metal(loids) consumed through plant‐based anti‐diabetic therapeutics collected in the northern divisional city of Rajshahi, Bangladesh. Biol Trace Elem Res 203:2149–2158 (2025). 10.1007/s12011-024-04338-7. [ DOI ] [ PubMed ] [ Google Scholar ] 84. Deswal M, Deswal P and Laura JS, Study on heavy metals in some ayurvedic herbal products and risk assessment of their calculated average daily dose intake in humans. J Herb Med 34:100569 (2022). 10.1016/j.hermed.2022.100569. [ DOI ] [ Google Scholar ] 85. Ebrahimzadeh G, Omer AK, Naderi M and Sharafi K, Human health risk assessment of potentially toxic and essential elements in medicinal plants consumed in Zabol, Iran, using the Monte Carlo simulation method. Sci Rep 14:23756 (2024). 10.1038/s41598-024-75159-w. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Table S1. Accuracy and precision of ICP‐MS quantification of essential and potentially toxic elements in certified reference materials (CRMs). Table S2 . Calculated detection limits of the method (MLOD, μg kg −1 ) and quantification limits of the method (MLOQs, μg kg −1 ) of essential and potentially toxic elements with the use of Rh as internal standard. Table S3 . Results of the Akaike Information Criterion (AIC) test used to identify the best‐fitting probability distributions for concentration values of each element measured in botanical extracts. Figure S1 . Probability distribution of margin of exposure (MOE, logarithmic scale) values to nickel based on acute dietary consumption of botanical extracts associated with systemic contact dermatitis in nickel‐sensitized individuals. JSFA-106-4060-s001.docx (2.4MB, docx) Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. 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