Michel Zeitouni

MD Ibrahim Akin

MD Steffen Desch

Olivier Barthelemy

MD Delphine Brugier

PhD; Jean-Philippe Collet

MD, PhD Suzanne De Waha-Thiele

MD; John P Greenwood

PhD Paul Guedeney

MD Georges Hage

MD Olivier Barthélémy

MD Marie Hauguel-Moreau

MD Kurt Huber

MD Mathieu Kerneis

MD Marko Noc

PhD Keith G Oldroyd

MD Jan J Piek

PhD Stéphanie Rouanet

MS Stefano Savonitto

MD Pranas Serpytis

MD, PhD; Johanne Silvain

MD, PhD Janina Stepinska

MD, PhD Eric Vicaut

PhD ; Christiaan

J M Vrints

MD, PhD Stephan Windecker

PhD Uwe Zeymer

MD Holger Thiele C #

Professor Gilles Montalescot
email: gilles.montalescot@aphp.fr

Cjm Vrints

H Thiele

Clinical outcomes according to ECG presentations in infarct-related cardiogenic shock in CULPRIT-SHOCK

Keywords: cardiogenic shock, STE-MI, NSTE-MI, left bundle branch block, percutaneous coronary intervention

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Cardiogenic shock is the most frequent cause of mortality in patients admitted with acute myocardial infarction (AMI) [START_REF] Hollenberg | Cardiogenic shock[END_REF][START_REF] Christopher | Improving Care of STEMI in the United States 2008 to 2012[END_REF] . This life-threatening complication occurs in 3 to 5 % of non-STsegment myocardial infarction (NSTE-MI) patients and in 10 to 13 % of patients presenting with ST-segment elevation myocardial infarction (STE-MI) [START_REF] Holmes | Cardiogenic shock in patients with acute ischemic syndromes with and without ST-segment elevation[END_REF][START_REF] Monique | Differences in the Profile, Treatment, and Prognosis of Patients With Cardiogenic Shock by Myocardial Infarction Classification[END_REF] . Emergent percutaneous coronary intervention (PCI) is the only proven strategy that improves survival [START_REF] Berger | One-year survival among patients with acute myocardial infarction complicated by cardiogenic shock, and its relation to early revascularization: results from the GUSTO-I trial[END_REF][START_REF] Webb | Percutaneous coronary intervention for cardiogenic shock in the SHOCK Trial Registry[END_REF][START_REF] Hochman | Early Revascularization in Acute Myocardial Infarction Complicated by Cardiogenic Shock[END_REF] . Guidelines recommend an early invasive strategy for patients presenting with acute myocardial infarction (AMI) and hemodynamic instability, regardless of the ECG presentation [START_REF] Roffi | ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: Task Force for the Management of Acute Coronary Syndromes in Patients Presenting without Persistent ST-Segment Elevation of the European Society of Cardiology (ESC)[END_REF][START_REF] Ezra | AHA/ACC Guideline for the Management of Patients With Non-ST-Elevation Acute Coronary Syndromes[END_REF] .

Little is known about the profile of cardiogenic shock patients related to NSTE-MI or left bundle branch block (LBBB-MI). Because of the difficulty to quickly determine whether a LBBB is de novo or pre-existent, it is often considered as an equivalent of STE-MI. However, there is a considerable evolution in the appraisal of LBBB-MI patients in clinical practice and recent European Guidelines, as studies have demonstrated their specific characteristics and natural history [START_REF] Chang | Lack of association between left bundle-branch block and acute myocardial infarction in symptomatic ED patients[END_REF][START_REF] Neeland | Evolving considerations in the management of patients with left bundle branch block and suspected myocardial infarction[END_REF][START_REF] Wong | Patients with prolonged ischemic chest pain and presumed-new left bundle branch block have heterogeneous outcomes depending on the presence of ST-segment changes[END_REF][START_REF] Collet | ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevationThe Task Force for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC)[END_REF] . Whether the 3 ECG presentations of AMI have different outcomes and are associated with a variation in the effects of treatment strategies in cardiogenic shock is unknown [START_REF] Jacobs | Cardiogenic shock with non-ST-segment elevation myocardial infarction: a report from the SHOCK Trial Registry. SHould we emergently revascularize Occluded coronaries for Cardiogenic shocK?[END_REF][START_REF] Dhaval | Trends in Incidence, Management, and Outcomes of Cardiogenic Shock Complicating ST-Elevation Myocardial Infarction in the United States[END_REF] .

Thus, using the data of the CULPRIT-SHOCK trial, we aimed to compare the characteristics of patients admitted for cardiogenic shock with STE-MI, NSTE-MI or LBBB-MI as well as their short-and long-term outcomes and the effect of the revascularization strategy.

METHODS

Study design and population

The present post-hoc analysis included all the patients of the CULPRIT-SHOCK (Culprit Lesion Only PCI versus Multivessel PCI in Cardiogenic Shock) trial for whom the ECG presentation was collected. The investigation conforms to the principle's outlined in the Declaration of Helsinki and was approved by ethics committees involved in the CULPRIT-SHOCK trial. The design of the CULPRIT-SHOCK trial as well as the 30-day and 1-year results have previously been published [START_REF] Thiele | PCI Strategies in Patients with Acute Myocardial Infarction and Cardiogenic Shock[END_REF][START_REF] Thiele | Multivessel versus culprit lesion only percutaneous revascularization plus potential staged revascularization in patients with acute myocardial infarction complicated by cardiogenic shock: Design and rationale of CULPRIT-SHOCK trial[END_REF][START_REF] Thiele | One-Year Outcomes after PCI Strategies in Cardiogenic Shock[END_REF] . In brief, this multicenter and open-label trial randomized patients with cardiogenic shock related to AMI and multivessel coronary artery disease to a culprit-lesion-only PCI strategy versus an immediate multivessel PCI strategy. The patients assigned to a culpritlesion-only PCI strategy could be treated with complementary staged PCI. The primary endpoint of 30-day mortality or renal-replacement therapy occurred more often in patients treated with immediate multivessel PCI [START_REF] Thiele | PCI Strategies in Patients with Acute Myocardial Infarction and Cardiogenic Shock[END_REF] .

In the present post-hoc analysis, the baseline 12-lead ECG was recorded and analyzed for each patient of the trial, who was then classified as STE-MI, NSTE-MI or LBBB-MI. ST-segment elevation was defined by site investigators as a new ST-elevation ≥1 mm in ≥2 contiguous leads [START_REF] Thygesen | Fourth universal definition of myocardial infarction (2018)[END_REF] . LBBB-MI was described as the association of a supraventricular heart rhythm, a QRS duration ≥ 120 ms, a QS or rS complex in lead V1 and a R wave in lead V6 [START_REF] Sgarbossa | Electrocardiographic diagnosis of evolving acute myocardial infarction in the presence of left bundle-branch block. GUSTO-1 (Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded Coronary Arteries) Investigators[END_REF] . The new or old status of the LBBB was not collected in the trial. Patients with ECG alterations other than STsegment elevation or LBBB were considered as NSTE-MI.

Study endpoints

In the primary analysis, we compared the 30-day and 1-year all-cause death of patients admitted with cardiogenic shock related to NSTE-MI or LBBB-MI with those presenting with cardiogenic shock related to STE-MI (reference group). The secondary outcome included the composite of all-cause death or renal replacement therapy at 30 days. Then, we assessed whether the effect of revascularization strategies on outcomes of cardiogenic shock patients was consistent across the 3 ECG presentation groups.

Statistical analysis

Continuous variables are reported as median and interquartile range [Q1-Q3] and compared using the Kruskal-Wallis test. Categorical variables are reported as numbers and percentages and compared using the Chi-square or Fisher's exact test. Kaplan-Meier curves were also used to show event rates over time with classification according to ECG presentation and compared using the log-rank test. Patients without events were censored at 30 days or 1-year. NSTE-MI and LBBB-MI patients were respectively compared with patients presenting with STE-MI, which was the reference in this analysis. Univariate logistic regression was performed to assess the association between ECG presentation and outcomes at 30 days or 1 year, as previously published (14, 16). Multivariate logistic regression models were used to evaluate the independent association between ECG presentation and outcomes. In each model, ECG presentation was adjusted on baseline clinical and procedural characteristics (age, sex, body mass index, cardiovascular risk factors, history of MI, history of stroke, PCI, coronary artery bypass graft or peripheral artery disease, kidney dysfunction, arterial lactate level, fibrinolysis before randomization, resuscitation before randomization, femoral access, triple vessel disease, culprit lesion in the left main or left anterior descending artery, chronic total occlusion, stent in culprit lesion, mechanical circulatory support, therapeutic hypothermia, mechanical ventilation, catecholamine therapy, post-PCI Thrombolysis In Myocardial Infarction [TIMI] flow, randomization group) possibly associated with outcomes in univariate analysis (p<0.2) (e-table 1). The results are interpreted in terms of adjusted odd ratios (aOR) with their associated 95% confidence interval (CI).

The interaction between ECG presentation and revascularization strategy (randomization group) was evaluated for each outcome using logistic regression. A p-value <0.05 was considered significant. All statistical analyses were performed with SAS release 9.4 (SAS Institute Inc, Cary, NC) statistical software package.

RESULTS

Characteristics of STE-MI, NSTE-MI and LBBB-MI patients with cardiogenic shock

A description of the ECG presentation was available for 665 patients (96.9%) of the 686 cardiogenic shock patients related to AMI with multivessel coronary artery disease of the CULPRIT-SHOCK trial. There were 372 cardiogenic shock patients presenting with STE-MI (55.9%), 195 with NSTE-MI (29.3%) and 98 with LBBB-MI (14.7%). Patient characteristics were worse in patients with NSTE-MI and LBBB, as compared with those with STEMI, with regard to age (68.0 years in STE-MI, 71.0 years in NSTE-MI and 73.5 years in LBBB-MI, p=0.015), diabetes (25.1 % in STE-MI, 37.7 % in NSTE-MI and 50 % in LBBB-MI, p<0.001) and most of the cardiovascular risk factors and co-morbidities presented in Table 1.

Treatments and results of coronary angiograms according to ECG presentation are displayed in Table 2. The degree of severity of coronary artery disease also varied according to the ECG presentation, with triple vessel disease and chronic total occlusion being more frequent in LBBB-MI and NSTE-MI patients. The differences in the risk profile at admission are displayed in

Risk of short-term outcomes according to ECG presentation

The univariate and adjusted risk of 30-day all-cause death according to ECG presentation is presented in Table 3 and figure 2. There was no difference in the univariate and adjusted risk of mortality at 30 days between NSTE-MI and STE-MI patients (48.7% vs. 43.0%, aOR = 1.05, 95%CI [0.66 -1.67], p=0.85). Cardiogenic shock patients presenting with LBBB-MI had higher univariate risk of 30-day mortality than STE-MI, without significant difference after adjustment on confounding covariates (59.2 % vs. 43.0 %, aOR = 1.31, 95 %CI [0.73 -2.34], p=0.36).

There was no difference in the adjusted risk of all-cause death or renal-replacement therapy when NSTE-MI or LBBB were respectively compared with STE-MI.

Risk of long-term outcome according to ECG presentation

At 1 year, both NSTE-MI and LBBB-MI patients had a higher univariate risk of mortality compared with STE-MI (Table 3 and figure 2). After adjustment for confounding covariates, NSTE-MI was not associated with a different risk of mortality at 1 year as compared with STE-MI (56.4% vs. 46.8%, aOR = 1.21, 95%CI [0.76 -1.34], p=0.42), nor was LBBB-MI (69.4% vs. 46.8%, aOR = 1.59, 95%CI [0.89 -2.84], p=0.12).

Effect of revascularization strategy STE-MI, NSTE-MI and LBBB patients

The effect of culprit-lesion-only PCI versus immediate multivessel PCI on all-cause death or renal therapy replacement at 30 days (p interaction = 0.76), all-cause death at 30 days (p interaction = 0.91) and 1-year (p interaction =0.97) was consistent across ECG presentations (Figure 3).

DISCUSSION

This post-hoc analysis of the CULPRIT-SHOCK trial analyzed the differences in risk profile and outcomes of patients admitted with STE-MI, NSTE-MI and LBBB-MI complicated by cardiogenic shock and multivessel coronary artery disease. ECG presentations reflect different risk profiles, with an ascending proportion of co-morbidities and severity of coronary artery disease from STE-MI to NSTE-MI and LBBB-MI [START_REF] Savonitto | Prognostic Value of the Admission Electrocardiogram in Acute Coronary Syndromes[END_REF] . Because of important differences in age and vascular severity, ECG presentations are associated with different shock evolutions with LBBB-MI patients suffering of the highest rate of mortality. The adjusted risk of mortality of NSTE-MI and LBBB-MI was not different to STE-MI patients, highlighting the contribution of each respective risk profile rather than the type of ECG presentation itself. The effect of revascularization strategies is consistent across groups supporting that an immediate culpritlesion-only PCI should be the preferred strategy irrespectively of the initial ECG presentation.

The high risk profile of NSTE-MI patients with older age, more risk factors and pre-existent heart disease than STE-MI patients was previously described in nationwide non-shock myocardial infarction registries [START_REF] Zeymer | Predictors of in-hospital mortality in 1333 patients with acute myocardial infarction complicated by cardiogenic shock treated with primary percutaneous coronary intervention (PCI); Results of the primary PCI registry of the Arbeitsgemeinschaft Leitende Kardiologische Krankenhausärzte (ALKK)[END_REF][START_REF] Polonski | A comparison of ST elevation versus non-ST elevation myocardial infarction outcomes in a large registry database: Are non-ST myocardial infarctions associated with worse long-term prognoses?[END_REF] . These differences underlie alternative physio-pathological pathways leading to myocardial oxygen deprivation, ischemia and cardiogenic shock [START_REF] Jacobs | Cardiogenic shock with non-ST-segment elevation myocardial infarction: a report from the SHOCK Trial Registry. SHould we emergently revascularize Occluded coronaries for Cardiogenic shocK?[END_REF] .

Whereas, in general, STE-MI results from an acute and complete thrombotic coronary occlusion, NSTE-MIs are the consequence of a more insidious and extensive atherosclerotic process involving transient unstable coronary stenosis and microcirculation dysfunction, though often with a persistent epicardial coronary blood flow [START_REF] Michel | Investigator Versus Core Laboratory Evaluation of Coronary Flow and Related Mortality in the CULPRIT-SHOCK Trial[END_REF] . As a result, the event of cardiogenic shock in NSTE-MI patients is less frequent, as compared with STEMI, and mostly results from more extensive coronary artery disease and prior MIs, in addition to more frequent and more severe co-morbidities [START_REF] Monique | Differences in the Profile, Treatment, and Prognosis of Patients With Cardiogenic Shock by Myocardial Infarction Classification[END_REF] .

Little is known about the characteristics of cardiogenic shock patients with AMI and LBBB. While these patients are historically considered and treated as ST-segment elevation presenters, recent findings have demonstrated that LBBB-MI patients are in fact more complex and heterogeneous, with more co-morbidities and less favorable outcomes than STE-MI presenters [START_REF] Chang | Lack of association between left bundle-branch block and acute myocardial infarction in symptomatic ED patients[END_REF][START_REF] Neeland | Evolving considerations in the management of patients with left bundle branch block and suspected myocardial infarction[END_REF][START_REF] Wong | Patients with prolonged ischemic chest pain and presumed-new left bundle branch block have heterogeneous outcomes depending on the presence of ST-segment changes[END_REF] . Although anterior MI can cause a new LBBB, presuming of de novo or pre-existing status of the LBBB is challenging -especially in the setting of cardiogenic shock. In our population, LBBB may more likely be a pre-existent marker of established chronic ischemic heart disease with left ventricular dysfunction and remodeling. This is well illustrated by the high rate of triple-vessel disease and chronic total occlusion.

The difference in mortality rates between groups is modest at 30 days but more important at 1year. STE-MI, NSTE-MI and LBBB-MI patients share a similar short-term high-risk period, involving hemodynamic instability or ventricular arrhythmia leading to cardiac arrest or deadly complications like cardiac rupture or acute mitral insufficiency. Beyond 30 days, STE-MI survivors have a low rate of incident death (+3.8%). In contrast, mortality kept in increasing between 30 days and 1 year in NSTE-MI (+7.7%) and LBBB-MI patients (+10.2 %). Overall, patients with LBBB-MI had worst clinical presentations at the acute phase, with more frequent resuscitation before randomization and more severe signs of shock. The very-high risk presentation of patient with LBBB-MI, on top of pre-existent co-morbidities is the major contribution of the worst short-term and long-term outcomes. This was also observed with a lower degree in patients with shock and NSTE-MI.

The effect of culprit-lesion-only PCI versus immediate multivessel PCI is consistent across each type of ECG presentation. Thus, as demonstrated by the results of the CULPRIT-SHOCK trial, an immediate culprit-lesion-only PCI strategy should be favored in patients with cardiogenic shock related to AMI and multivessel disease, irrespective of the ECG presentation. This is an important message for daily practice and revascularization decisions in these patients with multiple coronary stenoses potentially accessible to multivessel PCI. While facing the challenge of identifying the culprit lesion in NSTE-MI or LBBB-MI patients, interventional cardiologists should still favor an immediate culprit-lesion-only PCI strategy with staged complementary revascularization to avoid harmful peri-procedural complications and worse outcomes with immediate multivessel PCI.

LIMITATIONS

We acknowledge several limitations for this substudy of the CULPRIT-SHOCK trial. First, this is a post-hoc analysis from a randomized trial and should be interpreted accordingly. Of note, our results are concordant with previous findings concerning LBBB-MI and NSTE-MI without cardiogenic shock 25 . Secondly, ECG modifications were solely evaluated by site investigators without further review or adjudication. Thus, no specific criteria such as the modified Sgarbossa criteria could be applied for LBBB. Thirdly, the LBBB status -de novo or pre-existent -was unknown and concerned a low proportion of patients.

CONCLUSIONS

In patients with AMI and cardiogenic shock, NSTE-MI and LBBB-MI presentations reflect higher risk profiles contributing to worse evolutions but are not an independent risk factor of mortality. Importantly, there is no evidence of heterogeneity in the effect of revascularization strategies between cardiogenic patients presenting with STE-MI, NSTE-MI or LBBB-MI. Our results support culprit-lesion-only PCI as the standard immediate strategy irrespectively of the ECG presentation in AMI complicated by cardiogenic shock. revision. The other authors contributed to data gathering, biological measurements, and critical revision of the manuscript. All the authors vouch for the data and analyses reported.

TABLES

Figure 1 .

 1 Figure 1.

FIGURESFigure 1 .

 1 FIGURES

Figure 2 .

 2 Figure 2. All-cause mortality at 30 days and 1 year according to ECG presentation. OR stands

Figure 3 .

 3 Figure 3. Effect of revascularization strategy on outcomes according to ECG presentation

Table 1 .

 1 Presentations in STE-MI, NSTE-MI and LBBB-MI patients of the CULPRIT-SHOCK trial.

		Total	STE-MI	NSTE-MI	LBBB-MI	p-
		(N=665)	(N=372)	(N=195)	(n=98)	value
	Age (years), median [IQR]	70.0 [60.0 -78.0]	68.0 [58.0 -77.0]	71.0 [63.0 -78.0]	73.5 [63.0 -79.0] 0.02
	Female	156/664 (23.5%)	90/371 (24.3%)	39/195 (20.0%)	27/98 (27.6%)	0.31
	BMI (kg/m²)					0.74
	N	642	359	190	93

Table 2 .

 2 Treatments and angiographic findings in STEMI, NSTEMI and LBBB patients of the CULPRIT-SHOCK trial.

		Total	STE-MI	NSTE-MI	LBBB-MI	p-
		(N=665)	(N=372)	(N=195)	(n=98)	value
	Therapeutic strategies
	Mild hypothermia	217/664 (32.7%) 113/372 (30.4%) 70/194 (36.1%) 34/98 (34.7%)	0.35
	Fibrinolysis <24 hr before randomization	30/663 (4.5%)	20/370 (5.4%)	5/195 (2.6%)	5/98 (5.1%)	0.28
	Mechanical ventilation	536/662 (81.0%) 284/370 (76.8%)	168/195 (86.2%)	84/97 (86.6%) <0.01
	Mechanical circulatory support	185/665 (27.8%) 101/372 (27.2%) 50/195 (25.6%) 34/98 (34.7%)	0.24
	Number of vessels with disease
	Triple vessel disease	423/664 (63.7%) 219/371 (59.0%)	138/195 (70.8%)	66/98 (67.3%)	0.02
	Vessel related to the infarction*
	Right coronary artery	177/648 (27.3%) 108/367 (29.4%) 51/186 (27.4%) 18/95 (18.9%)
	Left main	58/648 (9.0%)	22/367 (6.0%)	25/186 (13.4%) 11/95 (11.6%)
	Left anterior descending	268/648 (41.4%) 156/367 (42.5%) 69/186 (37.1%) 43/95 (45.3%)
	Left circumflex	138/648 (21.3%) 79/367 (21.5%) 38/186 (20.4%) 21/95 (22.1%)
	Bypass graft	7/648 (1.1%)	2/367 (0.5%)	3/186 (1.6%)	2/95 (2.1%)
	At least 1 chronic total occlusion*	153/648 (23.6%) 72/367 (19.6%) 55/186 (29.6%) 26/95 (27.4%)	0.02
	Number of stents in culprit lesion				0.32
	No stent	35/664 (5.3%)	15/371 (4.0%)	11/195 (5.6%)	9/98 (9.2%)
	1 stent	323/664 (48.6%) 190/371 (51.2%) 92/195 (47.2%) 41/98 (41.8%)
	2 stents	182/664 (27.4%) 95/371 (25.6%) 59/195 (30.3%) 28/98 (28.6%)
	3 stents or more	124/664 (18.7%) 71/371 (19.1%) 33/195 (16.9%) 20/98 (20.4%)
	Drug eluting stent	592/629 (94.1%) 333/356 (93.5%)	175/184 (95.1%)	84/89 (94.4%)	0.76
	Aspiration thrombectomy of culprit lesion	98/664 (14.8%)	72/371 (19.4%)	12/195 (6.2%)	14/98 (14.3%) <0.01
	Antithrombotic therapy administered in the catheterization laboratory

Table 3 .

 3 Outcomes according to ECG presentation were included in the multivariate model for 30-day all cause death and 30-day all cause death or renal-replacement therapy. N=592 patients were included in the multivariate model for all-cause mortality. Covariates of adjustment of both models are detailed in e-table1. STE-MI stands for ST-segment elevation myocardial infarction, NSTE-MI for non-ST segment elevation myocardial infarction, LBBB-MI for left

	NSTE-MI vs. STE-MI	LBBB-MI vs. STE-MI

ACKNOWLEDGMENTS

The first author (M.Zeitouni) and the senior authors (G.Montalescot and H.Thiele) designed the study, gathered, analyzed the data, and drafted the manuscript. A lead statistician (S. Rouanet) and a senior statistician (E.Vicaut) provided an independent statistical analysis, results and

Funding

The CULPRIT-SHOCK trial was supported by a grant agreement (602202) from the European Union Seventh Framework Program and by the German Heart Research Foundation and the German Cardiac Society.

Conflicts of interest

M. Zeitouni has received research grants from Institut Servier, Federation Française de Cardiologie and lecture fees from BMS/Pfizer. JP. Collet has received research grants from AstraZeneca, Bayer, Bristol-Myers Squibb, Daiichi-Sankyo, Eli-Lilly, Fédération Française de Cardiologie, Lead-Up, Medtronic, MSD, Sanofi-Aventis, WebMD. Kurt Huber has received research grants form AstraZeneca and Sanofi as well as lecture fees form AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Daiichi-Sankyo, Sanofi and the Medicines Company M. Kerneis has received research grants from Féfération Francaise de Cardiologie and Institut Servier, consulting fees from Sanofi and Servier. KG. Oldroyd has received speaker and/or consultancy fees from Abbott Vascular, Boston Scientific, Biosensors and MedAlliance. JJ. Piek has received feed as consultant for Philips/Volcano S. Savonitto has received research grants (to his Institution) from Eli-Lilly, Daiichi Sankyo, Novartis; speaker or advisory board fees from Bayer, Astra-Zeneca and Abbott. J. Silvain has received research grants from Amed, Amgen, Algorythm, Astra-Zeneca, Bayer, Daiichi-Sankyo, Eli Lilly, Fondation de France, Gilead Science, Iroko Cardio, Sanofi-Aventis and Saint-Jude Medical. J. Stepinska has received research grants, from Bayer, Sanofi; consulting or lecture fees from, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Novartis , Pfizer, Sanofi, Servier E. Vicaut reports receiving personal fees from Eli Lilly; consultancy from Pfizer, Sanofi, LFB, Abbott, Fresenius, Medtronic, Hexacath; member of data safety monitoring board for CERC, lecture fees from Novartis, and grants from Boehringer and Sanofi S. Windecker has received research and educational grants from Abbott, Amgen, Bayer, BMS, CSL Behring, Boston Scientific, Biotronik, Edwards Lifesciences, Medtronic, Polares and Sinomed. U. Zeymer has received research grants or consulting fees from, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Daiichi-Sankyo, Ferrer, Idorsia, , WebMD. G. Montalescot has received research grants or consulting fees from Abbott, Amgen, Actelion,


L Jordan-Meille

J E Holland

S P Mcgrath

M J Glendining

C L Thomas

S M Haefele

The grain mineral composition of barley, oat and wheat on soils with pH and soil phosphorus gradients

Keywords: pH, Phosphorus, Grain, Liming, Macronutrients, Trace elements

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction 1.1 Grain nutrient concentrations: an emerging concern for humans and grain-fed animals

During the last 50 years, agricultural intensification has increased crop primary production at a higher rate than population growth (FAOSTAT 2018). However, parallel to this tremendous increase in available calories, and at a global scale, agriculture might not always provide adequate amounts of essential nutrients in the human diet (IFPRI 2014), especially because of a decrease in the concentrations of grain nutrients [START_REF] Fan | Evidence of decreasing mineral density in wheat grain over the last 160 years[END_REF][START_REF] Mcgrath | The effects of increasing yields on the macroelement and microelement concentrations and offtakes in the grain of winter wheat[END_REF]. This observation is particularly widespread in Asia and Western Africa [START_REF] Defries | Metrics for land-scarce agriculture[END_REF] but is also a real concern in developed countries [START_REF] Adams | Evidence of low selenium concentrations in UK bread-making wheat grain[END_REF].

The situation is likely to worsen in the medium term, in relation to climatic changes [START_REF] Smith | Impact of anthropogenic CO2 emissions on global human nutrition[END_REF]. The major deficiencies are in proteins, phytates, macronutrients (Ca, Mg) and micronutrients (Cu, Fe, I, Se, Zn) [START_REF] Joy | Dietary mineral supplies in Africa[END_REF]). Cereals constitute the major component of the human diet (44% of the daily intake of Fe, 27% of Mg, 25% of Zn and 31% of Cu, [START_REF] Henderson | The national diet & nutrition survey: adults, aged 19-64 years[END_REF][START_REF] Fan | Evidence of decreasing mineral density in wheat grain over the last 160 years[END_REF].

Therefore, the understanding of the determinants of grain mineral concentrations is of major importance to identify remediation techniques for soil and plant nutrition.

Overview of the factors determining the grain nutrient concentrations

The factors that determine the grain nutrient concentration can roughly be categorized between some "natural" variables (e.g. soil type) and some linked to agricultural practices. In the first category, the soil texture has a major influence, with clay soils generally associated to more mineral-enriched grains than sandy ones [START_REF] Manzeke | Fertilizer management and soil type influence grain zinc and iron concentration under contrasting smallholder cropping systems in Zimbabwe[END_REF]. In line with this property, [START_REF] Ivezic | Prediction of trace metal concentrations (Cd, Cu, Fe, Mn and Zn) in wheat grain from unpolluted agricultural soils[END_REF] states that total trace metal concentrations and, hence, their extractable forms, are a significant determinant of grain content, as long as soil organic matter is restricted in a small range of variations. Parallel to these physical and chemical factors, [START_REF] Ayoubi | Relationships between grain protein, Zn, Cu, Fe and Mn contents in wheat and soil and topographic attributes[END_REF] underline that the distribution of drier and wetter zones at landscape scale appears as the main factor in an attempt to predict the wheat grain micronutrients with a multiple linear regression. In the second category of factors, i.e. the agricultural practices, the impact of soil NPK fertilization and liming on grain mineral contents have been particularly studied. The special case of foliar nutrition by micronutrients (biofortification) is not reported here. As far as the role of fertilizers are concerned, several studies show that the NPK fertilization, taken as a whole, has only a weak influence on the grain of cereals of micronutrients and potentially toxic trace elements [START_REF] Hejcman | Effect of long-term fertilizer application on yield and concentrations of elements[END_REF], Moreno-Jimenez et al. 2016). The case of P-fertilization, alone, leads however to a different conclusion: it is known to drastically reduce root colonization by arbuscular mycorrhizal fugi (Giocoechea et al. 2004) which eventually leads to a detrimental effect on the uptake of micronutrients, as especially shown on barley [START_REF] Zhu | Phosphorus efficiencies and their effects on Zn, Cu, and Mn nutrition of different barley (Hordeum vulgare) cultivars grown in sand culture[END_REF]. Parallel to fertilization, liming, which neutralizes soil acidity [START_REF] Goulding | Soil acidification and the importance of liming agricultural soils with particular reference to the United Kingdom[END_REF], is one of the oldest and most widespread agricultural practices. Through its major impact on soil biogeochemistry, it influences the solubility of minerals, their potential uptake by crops and the mineral content of their grains and tissues.

Consequences of soil acidification on soil mineral solubility

In acid soils, chemical reactions are mainly driven by Al 3+ , Mn 2+ and Fe 2+/ 3+ , whereas in more alkaline soils, they are governed by Ca 2+ . This impacts the solubility of minerals, as shown by studies using the liming management technique to analyze the soil geochemistry responses to a large gradient of pH values [START_REF] Brallier | Liming effects on availability of Cd, Cu, Ni and Zn in a soil amended with sewage sludge 16 years previously[END_REF][START_REF] Rengel | Agronomic approaches for improving the micronutrient density in edible portions of field crops[END_REF]. With increasing pH of the soil, the solubility of most trace cations decreases asymptomatically to near-zero values (White 1970, Kabata-Pendias and[START_REF] Kabata-Pendias | Trace Elements in Soils and Plants[END_REF].

For divalent cations (Zn 2+ , Cu 2+ , Mn 2+ , Fe 2+ ) solubility is decreased by one hundred-fold for every unit increase in pH [START_REF] Verma | Zinc and Phosphorus interaction in a wheat-maize cropping system[END_REF]Minhas 1987, Rengel et al. 1999). This property makes liming an efficient tool to reduce the concentration of potentially toxic trace elements in contaminated soils [START_REF] Hooda | The effect of liming on heavy metal concentrations in wheat, carrots and spinach grown on previously sludge-applied soils[END_REF]. With macronutrients, the situation is more heterogeneous. For example, the solubility of potassium (K + ) shows minimal relation to pH, as stated by [START_REF] Holland | Yield responses of arable crops to liming -An evaluation of relationships between yields and soil pH from a long-term liming experiment[END_REF]. On the contrary, phosphorus (P) solubility is reduced by soluble Fe 2+ and Al 3+ at low pH values [START_REF] Evans | Extractable iron and aluminium and their relationship to phosphate retention in Irish soils[END_REF] and by soluble Ca 2+ at high pH values [START_REF] Marschner | Mineral Nutrition of Higher Plants[END_REF][START_REF] Ozturk | Variation in phosphorus efficiency among 73 bread and durum wheat genotypes grown in a phosphorus-deficient calcareous soil[END_REF]. Magnesium (Mg 2+ ) shows another pattern, linked to competition phenomena with Ca 2+ and Al 3+ [START_REF] Barraclough | Grass yield in relation to potassium supply and the concentration of cations in tissue water[END_REF]Leigh 1993, da Silva Domingues et al. 2016), thus giving non-linear responses to pH. In addition to pH, redox potential (rH) exhibits a strong influence on the solubility of some minerals; generally, anaerobic conditions are required to free some ions in the soil solution, as is the case for As, Mn, Fe and P [START_REF] Marschner | Mineral Nutrition of Higher Plants[END_REF]. The great seasonal variability of rH therefore makes it very difficult to correlate pH to the solubility of some minerals.

Consequences of soil acidification on mineral uptake and grain quality

The composition of plant minerals (concentrations, ratios…) is not a simple transposition of the soil solution composition. Soil mineral deficiencies, excess or imbalances, trigger specific plant reactions.

Mineral deficiencies, on the one hand, lead to three kinds of adaptations: firstly, specific root ion channels can be activated (almost all macronutrients have specific transporters activated under adverse pH/rH soil conditions, [START_REF] Marschner | Mineral Nutrition of Higher Plants[END_REF]. Secondly, the specific chemistry of the rhizosphere may allow microbial or chemical solubilization (e.g. role played by specific phytosiderophores on the uptake of Fe of Poeceae, [START_REF] Marschner | Strategies of plants for acquisition of iron[END_REF]. Thirdly, the relative root to shoot growth ratio is generally enhanced [START_REF] Ericson | Growth and shoot: root ratio of seedlings in relation to nutrient availability[END_REF] under soil mineral deficiencies. On the other hand, plants protect themselves from potential toxic trace elements through binding induced by chelation, precipitation [START_REF] Morel | Measurement of Pb 2+ , Cu 2+ and Cd 2+ binding with mucilage exudates from maize (Zea mays L.)roots[END_REF], compartment storage processes (Ma et al. 2001, Harada and[START_REF] Harada | Investigation of metal exudates from tobacco glandular trichomes under heavy metal stresses using a variable pressure scanning electron microscopy system[END_REF]. In the extreme case of acid soils (pH < 4.5), mineral uptake is impaired by the deleterious toxicity of Al 3+ , causing inhibition of root elongation by the lysis of the cell structure of the root apex [START_REF] Foy | Physiological effects of hydrogen, aluminium, and manganese toxicities in acid soil[END_REF], which, in turn, affects water and nutrient uptake [START_REF] Zheng | Crop production on acidic soils: overcoming aluminium toxicity and phosphorus deficiency[END_REF]. At low pH values, Mn toxicity can also impair plant growth (Reid 1976, Schlichting and[START_REF] Schlichting | Distribution and amelioration of manganese toxic soils[END_REF], through physiological mechanisms still poorly understood [START_REF] Horst | Grain zinc concentration and its relation to soil nutrient availability in different wheat cropping regions of China[END_REF]. In summary, any change in soil pH modifies the solubility of minerals in soils, triggers uptake adaptation mechanisms and impacts the root system morphology. For these reasons, pH is likely to deeply influence the crop mineral composition; however, because of specific plant responses to mineral deficiencies / toxicities, predicting the tissue concentrations remains a great challenge, and even more so for grains [START_REF] Miner | Soil factors affecting plant concentrations of cadmium, copper, and zinc on sludge-amended soils[END_REF][START_REF] Wang | The influences of soil properties on Cu and Zn availability in soil and their transfer to wheat (Triticum aestivum L.) in the Yangtze River delta region, China[END_REF]. The multiple and complex physiological mechanisms between the soil solution composition and the grain mineral contents, described by [START_REF] Olsen | Many rivers to cross: the journey of zinc from soil to seed[END_REF] as "many rivers to cross", explains the success of empirical models [START_REF] Baize | Relationships between concentrations of trace metals in wheat grains and soil[END_REF][START_REF] Viala | Predictive statistical modelling of cadmium content in durum wheat grain based on soil parameters[END_REF], generally based on principal components analysis and multiple linear regression analysis [START_REF] Jan | Spatial patterns of essential trace element concentrations in Swedish soils and crops[END_REF].

Aim of the study and related hypothesis

This study aimed to analyze and assess the relative importance of the main drivers of the mineral concentrations in the grain of cereals. The soil-plant data was from a multi-location long term trial to study the effects of liming and P treatments on grain mineral concentrations of barley, oat and wheat.

The investigated minerals are those of concern for the diet and health of humans, and animals fed on grain: macronutrients (Ca, K, Mg, P, S) and micronutrients (Cu, Fe, Mn, Se, Zn). Although potentially toxic trace elements (As, Cd, Pb) are not a health issue for crops grown on non-polluted soils (Zhao et al. 2004, Kabata-Pendias and[START_REF] Kabata-Pendias | Trace Elements in Soils and Plants[END_REF], data from soil-grain transfer of those latter trace elements will also be presented.

It was hypothesized that, for a given crop, the grain mineral concentrations would result from a simple two-step process: 1) soil pH controls the minerals solubilization in the soil which, 2), in turn influences -to a certain extent-the grain mineral concentrations.

Step 1 derives from a substantive assertion that pH is the single most important factor controlling soil mineral solubility, especially for trace elements [START_REF] Brallier | Liming effects on availability of Cd, Cu, Ni and Zn in a soil amended with sewage sludge 16 years previously[END_REF][START_REF] Rengel | Agronomic approaches for improving the micronutrient density in edible portions of field crops[END_REF], Schöning and Bümmer 2008). Step 2 asserts that the chemical composition of plants generally mirrors the pool of phyto-available minerals, at least more than total trace element concentrations in the soil [START_REF] Mench | Cadmium availability to wheat in five soil series from the Yonne district, Burgundy, France[END_REF][START_REF] Kopittke | Changes in exchangeable cations and micronutrients in soils and grains of long-term, low input cropping systems of subtropical Australia[END_REF]. Therefore, we expected soil pH values to have an indirect influence on mineral concentrations in grains. Soil type and P treatments were hypothesized to have secondary roles in controlling grain quality. The main objectives of the study were:

(i) to quantify the effect of soil pH on the grain mineral concentrations of barley, oat and wheat, for two contrasting soils;

(ii) to analyze the influence of phosphorus fertilization on the grain mineral concentrations;

(iii) to provide insights for the interpretation of soil and grain testing.

Materials and methods

Experimental sites description

Two experimental sites were studied simultaneously: 1) Sawyers field, located at Rothamsted Research, Harpenden, Hertfordshire, UK (51.8157 N, 0.3752 W). The soil has a silty clay loam texture (20% clay, 52% silt, 28% sand) and is classified as Profundic Chromic Endostagnic Luvisol (WRB, 2006). The organic C content of the top horizon (0-23 cm) ranges between 0.85 and 1.12% [START_REF] Kemmitt | pH regulation of carbon and nitrogen dynamics in two agricultural soils[END_REF]. CEC, based on the method of [START_REF] Rhoades | Cation exchange capacity[END_REF], was 97.5 ± 18 meq kg -1 . This was calculated for the study period and for plots with pH between 6 and 7 (medium lime treatment, see below). 2) Stackyard field, located on the Section-C, at Woburn Experimental Farm, Husborne Crawley, Bedford, UK (52.0003 N, 0.6149 W). The soil is a sandy loam (12% clay, 17% silt, 71% sand) described as a Eutric Cambisol (WRB, 2006). The organic C content of the top horizon (0-23 cm) ranged between 0.68 and 0.77% [START_REF] Kemmitt | pH regulation of carbon and nitrogen dynamics in two agricultural soils[END_REF]) and CEC, averaged for the overall study period , was 71.1 ± 10 meq kg - 1 . Both sites are further described in [START_REF] Holland | Yield responses of arable crops to liming -An evaluation of relationships between yields and soil pH from a long-term liming experiment[END_REF], Glendining 2020aand Glendining 2020b.

The sites were cropped from 1962 until 1996. The same crops were grown at each site. The agronomy and management of the crops followed conventional practices over the course of the experiment and were the same at both sites. To avoid mineral deficiencies, N was applied to all plots, but P and K were split into two treatments (nil and an annual standard rate). All the information about the experiment is available in the electronic Rothamsted Archive (e-RA, 2021a,b).

Climatic recordings between 1962 and 1996 in Harpenden and Woburn are close enough to consider both locations as subject to the same climate: the mean annual temperature, rainfall and radiation are 9.2°C, 693 mm and 3344 MJ m -2 for Harpenden, and 9.4°C, 638mm and 3320 MJ m -2 for Woburn [START_REF] Holland | Yield responses of arable crops to liming -An evaluation of relationships between yields and soil pH from a long-term liming experiment[END_REF]. Therefore, soil properties constitute the main difference between both sites, especially the higher levels in clay and organic matter in Rothamsted, leading together to a 20% higher level of CEC in Rothamsted. In the following text and analysis, the influence of the two sites on the grain mineral contents will be expressed as a "soil type" effects.

Experimental design

A factorial experimental design was used at each site with two randomised blocks each of 16 plots. The size of each plot was 6×16m. There were four rates of limestone applied (as ground chalk, CaCO3) and these are described as control (C), low (L), medium (M) and high (H). The lime requirement was determined by the method of [START_REF] Shoemaker | Buffer methods for determining lime. requirement of soils with appreciable amounts of extractable Aluminium[END_REF] in order to reach soil pH target values from about 4.5 to 7.5. The total amounts of Ca brought by the lime over the course of the experiment (35 years duration) increased from 6 to 21 t Ca ha -1 and from 4 to 18 t Ca ha -1 for Rothamsted and Woburn, respectively. The lime treatments were combined with four P and K treatments, at the plot scale. Plots with no P fertilization during the overall period are further denoted P-contrary to P+ plots which received varied rates of P (from 25 to 100 kg P ha -1 y -1 ). The complexity of P treatments is reduced to two treatments in this article for clarity. The -K and+K treatments (1962-1978) were pooled, as there was no significant interaction with lime, nor with any other soil or grain cation. This study focused on three crops, cultivated in three different years: spring barley (var. Porthos, 1978), spring oat (var. Peniarth, 1981) and winter wheat (Var. Genesis, 1995). Sub-plots receiving short-term treatments of Mg (1974-1978), Mn (1987-1990) and S (1991-1995) were not included in this analysis.

Soil and plant analysis

Soil cation macro-nutrients (Ca, K, Mg), micro-nutrients (Cu, Fe, Mn, Zn) and toxic elements (Cd, Pb) were extracted by ammonium nitrate (NH4NO3), 1 mol.L -1 (DIN V 19730 1995, Zeien and[START_REF] Zeien | Chemische Extraktionen zur Bestimmung von chwermetallbindungsformen in Böden[END_REF]. The subsequent chemical analysis was conducted with inductively coupled plasma optical emission spectrometry (ICP OES). All values below detection limits were assigned that concentration (Ca: 3.2 µg g - 1 , K: 3.5 µg g -1 , Mg: 0.2 µg g -1 , Cd: 0.008 µg g -1 , Cu: 0.018 µg g -1 , Fe: 0.21 µg g -1 , Mn: 0.015 µg g -1 , Pb: 0.055 µg g -1 , Zn: 0.021 µg g -1 ). Extractable cation measurements were performed for the 0 -23 cm topsoil layer. Phyto-available P was estimated with Olsen extractant [START_REF] Olsen | Estimation of available phosphorus in soils by extraction with sodium bicarbonate[END_REF] followed by a SAN plus continuous colorimetric flow analysis from Skalar analytical BV (Breda, The Netherlands). Soil pH was measured in 1:2.5 water suspension using a standard electrode and pH-meter.

For crop grains, the following minerals were measured: macro-nutrients (Ca, K, Mg, S, P), micro-nutrients (Cu, Fe, Mn, Se, Zn) and potentially toxic trace elements (As, Cd, Pb). Sub-samples of grains from each plot were ground to powder <0.5 mm, using a Retsch 400 ultra-centrifugal mill (Retsch GmbH, Germany).

They were digested with a solution of hydrogen peroxide and nitric acid and the mineral concentrations were determined by inductively coupled plasma mass spectrometry (ICP-MS).

Ranges of pH and Olsen P values

The two P treatments led to contrasting Olsen P concentrations; the P-plots had significantly lower Olsen P values at both locations (twice as low). In Rothamsted, mean Olsen P values measured from 1978 to 1995 were 9.1 ± 2.2 µg g -1 and 20.5 ± 8 µg g -1 for the P-and P+ treatment, respectively. In Woburn, the contrast was about the same, with 16 ± 2.6 and 29 ± 9.5 µg g -1 for the P-and P+ treatment, respectively. At Woburn, the P content of plots receiving no P were considered as non-limiting throughout the duration of the experiment, as testified by the UK Nutrient Management Guide ("Index 2", AHDB 2020), and the same conclusion applied logically to the P fertilized plots ("Index 3"). At Rothamsted, the concentration of P (Olsen) for the control P treatment plots corresponded to the "Index 1", indicating a potential limiting effect. This relatively high P level may be explained by former Pfertilization history on both soils.

Lime treatments were used as factors in ANOVA, whereas continuous pH values were used for regression analysis. The four lime treatments led to a linear pH gradient from approximately 4.5 to 7.5 (Table 1).

The pH values of each location are not significantly different for the same lime treatment. Between 1978 and 1995, the pH values of each lime treatment slightly increased at both sites, as lime was applied in 1982 and 1986.

[insert Table 1 near here]

Statistical analysis

Analysis of variance (ANOVA, Jump software, version 5, SAS, 100 SAS Campus Drive, Cary, NC 27513-2414, USA) was used to explore the effects of liming (4 treatments), P fertilization (2 treatments), and soil type (Rothamsted, Woburn) on grain yield, soil pH, soil-extractable minerals (extractable cations, Olsen P) and grain mineral concentrations in all three crops (Fig. 1). Pairwise regression analyses were undertaken for estimating the relationships between the grain mineral concentrations (outcome variable) and their main determinants (pH, extractable cations, Olsen P). Extractable minerals, themselves, served as outcome variable towards pH (Fig. 1).

In order to synthesize the two-by-two regression analysis applied to the grain mineral concentrations, a Multiple Linear Regression Analysis (MLRA; GenStat software, 18 th edition, VSNI Product) was performed, activating the "all subset regression -linear model" function. The analyses were performed for each crop separately. The MLRA used one qualitative (soil type) and three quantitative (pH, Olsen P, extractable cations) variables. MLRA was only performed for minerals that were measured in both grains and in soils, thus excluding As, S and Se (not measured in soils).The "soil type" was therefore used as a factor for the ANOVA treatments, and as a qualitative variable in the MLRA. As stated before, the soil differences can be illustrated by their CEC, 97.5 and 71.1 meq kg -1 for Rothamsted and Woburn locations, respectively.

[insert Fig. 1 near here]

Results

Overview of the treatment effects on crop yields

The lime treatments had a very strong impact on yields (Table S1, p<0.0001). The treatment without lime, which corresponded to pH values between 4 and 4.5, led to a complete yield loss on 7 out of 8 plots of barley in Rothamsted (1978), on 6 out of 8 plots of barley in Woburn (1978), and on 6 out of 8 plots of wheat in Rothamsted (1995). The number of plots with no yield of the control lime treatment explains why the respective yield mean values are 5 to 10 times lower for those two crops. Wheat yields of the three higher lime treatments (L, M and H) were not found to be significantly different from each other, whereas for barley, yields from the first three lime treatments (C, L and M) significantly increased. Oat yield showed a completely different pattern, with a decrease with increasing soil pH values (although not significant). Moreover, if the control lime treatment is excluded, the mean yield of oat was half of that measured for barley and wheat, with mean values of 3.4 and 3.7 t ha -1 in Rothamsted and Woburn, respectively. This compares to 7.5 and 7.4 t ha -1 for barley and wheat yields in Rothamsted, and 4.5 and 7.4 t ha -1 in Woburn, respectively for barley and wheat yields. Conversely, the P treatments, which led to strongly contrasting Olsen P values, did not have any significant effect on yields when compared by crop and soil type (ANOVA, data not shown). Finally, the soil type effect had variable effects: yields of wheat were the same on both soils, whereas oat had significant higher yield at Woburn, and barley significant higher yield at Rothamsted (Table S1).

Grain mineral content: crop and lime effects

Independently of any factor (lime, P, soil type), macronutrient concentrations were always significantly different between crops (Fig. 2a). For micronutrients and non-essential elements (Fig. 2b, 2c, Table S2), grain mineral concentrations often overlapped between crops (e.g. Cu, Pb, Zn). Across all treatments and both soil types, wheat grain had lower mineral concentrations than barley and oat, as shown for eight minerals out of the thirteen measured (Fig. 2). Wheat always had the lowest concentrations of macronutrients compared to barley and oat (Fig. 2a), with a 20% difference overall. Average Ca concentrations were even lower (244 µg g -1 ), with values 2 to 3 fold smaller than in barley (555 µg g -1 ) and oat (890 µg g -1 ), respectively. Otherwise, maximum concentrations were found either in barley (K, Mg, P, Cu, As) or in oat (Ca, S, Fe, Mn, Zn and Pb) (Fig. 2). Oat almost never had the lowest mineral concentrations.

Out of the 78 grain mineral-crop combinations (13 minerals X 3 crops X 2 soil types), 14 (18%) showed a very strong effect of lime treatments on grain mineral concentrations (P value < 0.0001), whereas 46 (59%) showed no significant effect (P-value > 0.05). Detailed ANOVA results and mean mineral concentrations of grain are shown in Table S2. The mineral concentrations of barley were less dependent on lime treatments, with only 3 minerals out of 13 (P, Cd and Mn) being significantly affected in Rothamsted, and 4 out of 13 in Woburn (P, Cd, Mn plus Mg). In contrast, the grain mineral concentration of wheat was most sensitive to lime treatment, affecting half of the minerals. Macronutrients of wheat grains were all influenced by lime treatment on both soil types, with the exception of K (Table S2a). But of the micronutrients in wheat, only Se was significantly influenced by lime treatment. Oat exhibited an intermediate pattern, although more similar to wheat than to barley.

[insert Fig. 2 near here]

The highest relative grain mineral changes, calculated as the relative difference in grain mineral concentrations between the control and the high lime treatments, were observed for Cd and Mn, decreasing their concentrations by 70% and 60%, respectively, between the control and high lime treatments (Fig. 2b,c). To a lesser extent, Ca, Mg and P also exhibited relatively high variation (20%), A their concentrations being positively correlated with increasing lime treatment, except for Ca in wheat which did not show a clear trend. For micronutrients and other trace elements, only grain concentrations of Mn and Cd exhibited a regular trend across the three crops having a negative relationship with increasing lime treatment (Table S2b,c, Fig. 2b,c). Finally, three minerals exhibited no relationship at all with lime treatments: As, Fe and Pb.

Relationships between grain mineral concentrations and soil pH mirrored those with lime treatment (data not shown). The best regression coefficients between pH and grain concentrations were for Cd in wheat grain at Rothamsted (r² = 0.77, Fig. 3c), for Mn in oat (r² = 0.75) and barley (r² = 0.68) at Woburn (Fig. 3b), and for P on oat at Rothamsted (r² = 0.69, Fig. 3a). Cations-pH relationships exhibited different patterns for the same element, depending on the crop (Fig. 3b). Moreover, the existence of a relationship on one soil and for one crop did not necessarily mean that the same relationship existed on the other soil (e.g., for Cd, Mn, P, Zn and K in wheat, Fig. 2). Relationships can be linear with a positive slope as for P (Fig. 3a), exponential as for Mn (Fig. 3b) or linear with a negative slope as for Cd (Fig. 3c).

[insert Fig. 3 near here]

Lime effects on the pool of extractable nutrients in the soil

The lime treatments strongly affected the concentration of extractable cations in the soil (as defined by the ammonium nitrate extraction method) (Table S3). All soil nutrient extractabilities were significantly impacted by the lime treatment (p-values < 0.05) except K. In general, concentrations of extractable minerals in the soil were higher in Rothamsted soil due to its higher CEC, with the exception of Fe and P, naturally higher in Woburn (Table S3).

The extractable Ca and Olsen P showed pH-dependence on both soil types. The range of mean concentrations was 300% for Ca but only 24% for P, in both soil types (Table S3). The relationship between extractable Ca and pH was a linear increase with pH (and liming), even in the control treatment where no Ca was applied. Extractable P (Olsen P) was lowest (15 and 22 ppm for Rothamsted and Woburn, respectively) around pH 6, and increased up to 18 and 26 ppm for Rothamsted and Woburn, respectively, when lime treatment was applied.

For micronutrients, the relative variations were much higher, in most cases best modelled by exponential decreases of concentrations from low to high pH values (e.g. Extractable Zn, Fig. 4a), with the exception of extractable Mn which followed a decreasing linear relationship with pH (Fig. 4b). The highest relative variations of soil extractable cation concentrations were observed for Fe and Pb (> 3000%), whereas Cd, Cu and Mn varied around 500% (Table S3). As pH decreased, the following cations solubilised below different pH-threshold values (visually estimated, in the decreasing order): Mn (pH 7), Cd (pH 6), Zn (pH 5,5), Fe (pH 5), Pb (pH 4,8), Cu (pH 4,5). All of the equations describing extractable cation concentrations as a function of pH are shown in Table 2.

[insert Table 2 near here]

[insert Fig. 4 near here]

Relationship between the extractable minerals in the soil and the grain cation concentrations

The regression analysis performed between grain and soil mineral concentrations, on each soil type separately, identified only a limited number of significant relationships (p-value < 0.05) (table 3). In the case of extractable As, Se and S no relationship with the grain concentrations could be tested because their concentrations were not determined in the soil, explaining their absence in table 3.

For micronutrients and other trace elements, the strongest relationships were measured for Mn (except for wheat at Woburn) and Cd (except for oat and wheat in Woburn) (Table 3 and Fig. 5a,b). Cu and Zn showed statistically weaker relationships (generally p-value > 0.03) with poor r² values (<0.2), and the soil-grain relationships were only applicable for oat. For Fe and Pb, soil extractable and grain concentrations did not correlate at all.

For macronutrients, Ca and to a lesser extent P, were the only minerals showing significant soil-plant relationships (p-value < 0.05), mostly in wheat and oat. Barley showed only weak correlations (0.16 < r² < 0.18) (Table 3). The decreasing relationship between soil-and grain-P for barley at Woburn is surprising.

For wheat, the Ca relationship fits best with second order polynomial functions, with a minimum of grain concentrations of 1200 and 1800 µg g -1 of extractable Ca, in Woburn and Rothamsted, respectively (Fig. 5c). No relationship was found between the grain content of K and Mg and their respective extractable cations in soil.

[insert Table 3 near here]

[insert Fig. 5 near here]

Impact of soil type on the mineral concentrations in grains

The effect of soil type (Rothamsted vs Woburn) on the crop grain mineral concentration was tested through an ANOVA (Table S4). Results show that the soil of Rothamsted led to enriched grains for half of the minerals. Barley proved to be less sensitive to the soil type than oat and wheat, with only Mg, P, Cu, Zn and As being soil type-dependent. Soil type had no impact on the grain concentrations of Ca and Fe in all three crops. In contrast, soil type had such a strong impact on Cu and Zn concentrations in the grains of all three crops that the concentrations did not overlap between the two sites and for a given crop. For example, there was a great site (soil type) difference in the Zn concentration in oat grains (Fig. 6b), however in contrast the soil extractable Zn concentrations overlapped between the two sites (Fig. 6a).

The same observations applied to Cu.

[insert Fig. 6 near here] 3.6. The effect of P treatment on grain mineral concentrations The effects of the two P treatments on grain mineral concentrations, studied with an ANOVA (Table S5), only detected a few significant results, mostly in Woburn, where Olsen P concentrations were higher ([15-45 µg g -1 ], section 2.3 above). In terms of soil chemistry, there was no effect of the P treatments on the extractable cations. Moreover, lime × P interactions on grain concentrations were tested and were never significant.

The influence of Olsen P on grain P content has already been analysed (section 3.4). More interestingly, the grain concentrations of Cu, Zn and Cd appeared to be sensitive to the soil P content (Fig. 7, Table S5).

Grain Cu and Zn proved to be inversely linearly correlated with Olsen P, whereas grain Cd evolved proportionally to Olsen P. The best regression coefficient applied to Cu relationship (r² = 0.76), the worst to Cd (r² = 0.28). The results indicate an effect independent of the extractable cations in soils.

[insert Fig. 7 near here]

Synthesis: weighting of the different factors on grain mineral concentrations

Previous pairwise relationships were put together within a Multiple linear Regression Analysis (Fig. S1) in order to assign to each grain mineral of a given crop its main explanatory variable from pH, extractable soil minerals, soil type (2 soils) and soil Olsen P values derived from the two P treatments (control vs fertilized) (Fig. 8). The most significant results (r² > 0.3) of MLRA show that pH is the most common explanatory factor of grain mineral concentrations, particularly for major nutrients (Ca, Mg, P, Fig. 8), but also for Cd and Mn. The relative variation of grain concentrations caused by variations of pH was less for macronutrients (about 20%) than for micronutrients and trace elements (e.g. 66% for Cd, 45% for Mn, Fig. 2, Fig. 8). The importance of pH is followed closely by "soil type" as an explanatory factor of grain mineral concentrations. The grain mineral concentrations mainly affected by soil type were Cu and Zn, whose relative variations were 47% and 39%, respectively. As far as soil extractable minerals are concerned as explanatory factors (Olsen P, extractable cations) their influence was restricted to Cd and P (see dark dotted arrows on Fig. 8). Arsenic and Se, for which no soil measurements were performed, showed various relationships to pH and soil type, but no specific crop trend. Although P treatments proved to have a significant influence on a few grain minerals (Fig. 7), its influence was always less than that of other variables, which is the reason why the P treatment is not present in Fig. 8. In total, among the 13 elements studied, three of them (Fe, Pb and K) were not (p value > 0.05) or little (p value < 0.05 and r² < 0.3) correlated to any of the explanatory variables. In contrast, Cu, Zn, Cd and Ca exhibited the best correlation with one of the explanatory variables, regardless of the crop. About one third of the crop grain minerals are not explained by any of the variables considered (no significant contribution to the MLRA model, Fig. S1, Fig. 8). Whereas, one third of the crop grain mineral concentrations are accounted for by the MLR model with r² greater than 0.30. With the exception of Cu and Zn which both depend on the soil type variable in all three crops (Fig. 8), the way that grain composition is modified when pH, phyto-available nutrients and soil type change is species-dependent.

For example, barley and wheat Cd grain concentrations depend mostly on soil extractable Cd (r²=0.69), whereas oat Cd concentration is more directly linked to pH (r²=0.50). In detail (Fig. S1), barley grain mineral concentrations are more linked to the soil type (Cu, Zn, Mg and P), whereas oat grain mineral concentrations are more sensitive to pH (Cd, Mn, Ca, Mg and P). Wheat grain mineral concentrations are explained by soil type (Cu, Mn, Zn and K) and, to a lesser extent, by the level of phyto-available nutrients in the soil (for Cd and P). A change in pH would directly (light grey lines, Fig. 8) or indirectly (through mineral solubility, dark dotted lines) have a moderate impact on the grain mineral contents, for three minerals out of 13 for barley, four for oat and five for wheat. The minerals concerned are different from one crop to the others.

[insert Fig. 8 near here]

Discussion

Grain concentrations in crops: gross tendencies and comparisons with literature data

The impact of the liming practices and soil pH on soil extractable minerals, their uptake by plants and the resulting shoots mineral concentrations have been subject to several research studies, among which [START_REF] Bolan | Soil acidification and liming interactions with nutrient and heavy metal transformation and bioavailability[END_REF] and [START_REF] Tyler | Plant uptake of major and minor mineral elements as influenced by soil acidity and liming[END_REF] stand for the more extensive ones. However, by linking directly grain macro-and micro-mineral concentrations to varied pH conditions, our study provides original references for three major crops (Fig. 2, Table S2). The variability of grain concentrations between barley, oat and wheat proved to be larger than the variability due to the other studied factors (lime, P, extractable minerals, soil type, Fig. 2). This allowed the proposed crop mean concentrations to be evaluated as indicative of typical arable conditions. The mean measured concentrations (all lime treatments considered together, Table S2, Fig. 2) lie mainly within the range of those found in the scientific and technical literature (Table 4). The grain concentration of both macronutrients and micronutrients fits with values in the literature (Kabata-Pendias and Pendias 2001, Table 4), although including the low pH plots, concentrations measured in this study are generally at the lower range of those found in the literature [START_REF] Shi | Regional wheat grain manganese and its potential risks affected by soil pH and precipitation[END_REF][START_REF] Horst | Grain zinc concentration and its relation to soil nutrient availability in different wheat cropping regions of China[END_REF], Cellier and Niknahad-Gharmakhar 2017[START_REF] Kopittke | Changes in exchangeable cations and micronutrients in soils and grains of long-term, low input cropping systems of subtropical Australia[END_REF][START_REF] Cakmak | Biofortification and Localization of Zinc in Wheat Grain[END_REF][START_REF] Zhao | Factors affecting the concentrations of lead in British wheat and barley grain[END_REF][START_REF] Kabata-Pendias | Trace Elements in Soils and Plants[END_REF], Mench et al. 1997). One should however stress that, from a human-toxicity point of view, barley and oat had concentrations near or above the thresholds for As and Pb, irrespective of soil pH values (Fig. 2C).

[insert Table 4 near here]

pH largely accounted for the extractable minerals in soils

We tested to what extent grain mineral concentrations fitted with the following two-step rationale: 1) soil pH controls mineral solubility and, hence, phyto-availability, and, 2) the pools of phyto-available nutrients in the soil influence the grain mineral concentrations (Fig. 8).

Step one (i.e. pH -extractable minerals relationships) gave expected results. Most of the pH-soil extractable nutrient relationships proved to be significant, with the exception of K (Table 2). For extractable micronutrients and other trace elements, relationships were modelled by decreasing exponential equations, expressing a dramatic increase in soil micro-element concentrations (from several hundreds to thousand folds, Fig. 8, Table S3) with declining pH values, and a near-zero asymptote for higher pH values. These patterns were consistent with the literature [START_REF] Holland | Liming impacts barley yield over a wide concentration range of soil exchangeable cations[END_REF][START_REF] Ivezic | Prediction of trace metal concentrations (Cd, Cu, Fe, Mn and Zn) in wheat grain from unpolluted agricultural soils[END_REF] for Cd, Cu, Fe and Zn, [START_REF] Rengel | Agronomic approaches for improving the micronutrient density in edible portions of field crops[END_REF] for Cu, Fe and Zn, Verma and Minhas 1987 for Zn). The relationship between pH and soil extractable Pb is, however, hardly confirmed (Tyler and Olssen 2001) or investigated [START_REF] Bolan | Soil acidification and liming interactions with nutrient and heavy metal transformation and bioavailability[END_REF]. The response of extractable Mn to pH is an exception, being described by a simple decreasing linear model on both soils. This exception, already mentioned by others [START_REF] White | Effects of lime upon soil and plant manganese levels in an acid soil[END_REF][START_REF] Shi | Regional wheat grain manganese and its potential risks affected by soil pH and precipitation[END_REF]) can be explained by a strong interaction between Mn 2+ and Ca 2+ in the soil solution [START_REF] Reuter | Occurrence and correction of manganese Deficiency in plants[END_REF], where extractable Ca increased linearly with pH (Table 2). At higher pH values, most of the micronutrients were near zero (quantification limits). For macronutrients, the relationships between pH and soil extractable minerals exhibited more heterogeneous patterns, reflecting competition / chelation / sorption processes due to the excess of Al 3+ , Mn 2+ and Fe 2+ in acid soils, and with Ca 2+ in neutral to alkaline ones [START_REF] Marschner | Mineral Nutrition of Higher Plants[END_REF][START_REF] Ozturk | Variation in phosphorus efficiency among 73 bread and durum wheat genotypes grown in a phosphorus-deficient calcareous soil[END_REF][START_REF] Barraclough | Grass yield in relation to potassium supply and the concentration of cations in tissue water[END_REF], da Silva Domingues et al. 2016). This heterogeneity may explain why the extractable Mg, which is a poor competitor for the exchange sites [START_REF] Bolan | Soil acidification and liming interactions with nutrient and heavy metal transformation and bioavailability[END_REF]) is only submitted to the Ca competition in the soil exhibiting the lowest buffer (Table 2). As far as K is concerned, the independence of its extractable concentration towards pH may be surprising. One would have expected its liberation in the soil solution from the clay fraction and, hence, an increase under its extractable forms, with decreasing values of soil pH [START_REF] Sparks | Bioavailability of soil potassium[END_REF]. However, decreasing pH may also have reduced the soil variable charges, leading to a smaller stock of exchangeable K on the CEC and an increase in the K lixiviation [START_REF] Bolan | Soil acidification and liming interactions with nutrient and heavy metal transformation and bioavailability[END_REF]). In the case of P, the U-shape relationships involving pH and Olsen P, with a minimum concentration at around pH 6 (Table 2), do not fit with the popular "fixation" mechanism hypothesis between phosphate and aluminium at low pH and between phosphate and calcium at high pH [START_REF] Evans | Extractable iron and aluminium and their relationship to phosphate retention in Irish soils[END_REF]. Our results are in agreement with [START_REF] Barrow | The effects of pH on phosphate uptake from the soil[END_REF] who states that the optimal range of P availability is more likely at low pH values.

To what extent can pH be considered as an essential factor in explaining the grain mineral concentrations in barley, oat and wheat?

As far as soil-to grain-mineral relationships are concerned (second step of our rational), the results exhibited heterogeneous patterns, as summarized by the Multiple Linear Regression Analysis (Fig. 8, Fig. S1). Our analysis led us to split the minerals into four groups: firstly, the minerals exhibiting a relationship between their soil extractable form and their concentration in grains (i.e. the pattern expected in our rationale). Secondly, the minerals whose grain concentrations were more directly linked to pH than to their soil extractable species. Thirdly, the minerals whose grain concentrations were mostly explained by the soil type, and, finally, those grain minerals that did not correlate with any of the factors. This classification did not always apply to the three crops, but to a majority of them.

The first category was, surprisingly, the least representative one. In spite of the tremendous increases of phyto-available minerals under low pH values (Table 2, Fig. 4), the relative influence of soil phytoavailable minerals on grain concentration was restricted to two minerals (P, Cd) and in a reduced number of crops (Table 3). The well-known curvi-linear grain P concentration dependence to soil available P [START_REF] Colomb | Phosphorus management in low input stockless cropping systems: Crop and soil responses to contrasting P regimes in a 36-year experiment in southern France[END_REF][START_REF] Hejcman | Effect of long-term fertilizer application on yield and concentrations of elements[END_REF]) only applied to wheat. At the opposite, the strong link between soil and grain Cd for wheat and barley was more unlikely [START_REF] Viala | Predictive statistical modelling of cadmium content in durum wheat grain based on soil parameters[END_REF], Olivier et al. 1998).

In the second category, grain concentrations of Ca, Mg and Mn (Fig. 8, Fig. S2) were better described by soil pH than by the soil mineral concentrations defined as "phyto-available" (extractable cations). P, Se and Cd can be added to this category for one of the three crops studied. This counter-intuitive scheme has, however, already been pointed out by several authors. [START_REF] Tyler | Plant uptake of major and minor mineral elements as influenced by soil acidity and liming[END_REF] came to exactly the same conclusion, in a study based on more than 50 elements: "pH more often accounted for a higher share of the variability in biomass concentration of elements than did soil solution concentration of the same elements". This observation is particularly well illustrated in the case of Mn, for which better regression coefficients are calculated with pH than with extractable Mn, whatever the chemical extractant used (Shiu et al. 2020[START_REF] Jan | Spatial patterns of essential trace element concentrations in Swedish soils and crops[END_REF][START_REF] Baize | Relationships between concentrations of trace metals in wheat grains and soil[END_REF][START_REF] Reuter | Occurrence and correction of manganese Deficiency in plants[END_REF]. Moreover, [START_REF] Bolan | Soil acidification and liming interactions with nutrient and heavy metal transformation and bioavailability[END_REF] underlined that Mn uptake by plants is more closely related to the soil pH than is the uptake rate of any other micronutrients. A possible underlying reason for the absence of a relationship between grain minerals and the corresponding soil extractable mineral concentration may be linked to the type of chemical extraction method used. [START_REF] Abedin | An evaluation of extractants for assessment of metal phytoavailability to guide reclamation practices in acidic soilscapes in northern regions[END_REF] reported that it is highly unlikely that one single extractant can give biologically relevant results for all cations. This study used the NH4NO3 chemical extraction (DIN 19730 1995) which was justified by the generally good performance of the method to solubilize macro-and micro-nutrients [START_REF] Hall | Assessment of the 1 M NH4NO3 extraction protocol to identify mobile forms of Cd in soils[END_REF][START_REF] Sterckeman | Comparison of three chemical extraction methods for assessing the availability of Cd, Cu, Pb and Zn to winter wheat[END_REF], its easy implementation [START_REF] Stuanes | Ammonium-nitrate extractant for soil exchangeable cations, exchangeable acidity and aluminium[END_REF], its widespread use [START_REF] Baize | Relationships between concentrations of trace metals in wheat grains and soil[END_REF][START_REF] Abedin | An evaluation of extractants for assessment of metal phytoavailability to guide reclamation practices in acidic soilscapes in northern regions[END_REF], and because it proved to be a good predictor of plant tissue concentrations in several studies (Abedin et al. 2012, Schöning andBrümmer 2008).

In the third category, grain concentrations of As, Cu and Zn were mostly explained by the soil type (Table S4, Fig. 6). The grain concentration for these elements varied from several orders of magnitude within each crop, although no lime effects were detected (Fig. 8). The concentration of Cu in grains is known to be one of the hardest to predict amid all other micronutrients [START_REF] Ivezic | Prediction of trace metal concentrations (Cd, Cu, Fe, Mn and Zn) in wheat grain from unpolluted agricultural soils[END_REF]. For wheat, [START_REF] Soon | Content and uptake of phosphorus and copper by spring wheat: effect of environment, Genotype and management[END_REF] found that environmental, i.e., site-year effects were the most important explanatory factors on Cu grain concentration. Direct relations between grain Zn and soil properties remain unclear. Zn was expected to be responsive to soil extractable Zn and pH [START_REF] Verma | Zinc and Phosphorus interaction in a wheat-maize cropping system[END_REF]Minhas 1987, Alloway 2009). [START_REF] Moreno-Jiménez | Availability and transfer to grain of As, Cd, Cu, Ni, Pb and Zn in a barley agri-system: Impact of biochar, organic and mineral fertilizers[END_REF], however, failed to establish any relationship between the barley grain concentration and a gradient of Zn extractable fractions. In non-contaminated soils, factors such as soil macronutrients (P, K) or texture may have a more significant influence on the Zn grain concentrations (François et al. 2009[START_REF] Horst | Grain zinc concentration and its relation to soil nutrient availability in different wheat cropping regions of China[END_REF] than pH itself, which may explain why soil type proved to be the best factor in the MLRA. Soil type may look like a gross, ill-defined variable, integrating so many specific soil and climate properties that it could represent the adjustment variable of incomplete models.

However, Rothamsted and Woburn differ more by their soil characteristics than by their climate. The main differences between the two soil types (Fig. 8) was the texture and the CEC (97 and 71 µmol g -1 , for Rothamsted and Woburn, respectively). Soil type factor could therefore be defined as a soil buffer power factor, reinforcing the effect of soil extractable pools. The Rothamsted soil, with the highest CEC, produced crops with the highest concentrations of minerals in grain (Table S2, Fig. 2). This result is reinforced by [START_REF] Jan | Spatial patterns of essential trace element concentrations in Swedish soils and crops[END_REF] and Manzeke et al. (2018), who stated that for most trace elements, the risk of low concentrations in crop plants appeared to be greatest on coarse-textured soils.

Finally, for Fe, Pb and K, there were no explanations of their intra-crop variability amid the variables studied (Fig. 8). For K, the inability to correlate its soil concentration to the soil pH (Table 2, Fig. 8) may explain why its grain concentration is even harder to predict. In their large literature review, [START_REF] Bolan | Soil acidification and liming interactions with nutrient and heavy metal transformation and bioavailability[END_REF] already stressed that crop liming led to as many results supporting an increase in K uptake by plants as a decrease. [START_REF] Dudka | Transfer of cadmium, lead and zinc from industrially contaminated soil to crop plants: a field study[END_REF] found a relationship between Pb in the soil and grains of barley, but with such a low slope (0.0003) that the increase of Pb concentrations in plants was almost negligible.

In our soils, the natural low concentrations may explain the absence of response in grains. Besides, soilgrain minerals transfer can be dramatically lowered by chemical complexation (chelation in roots, [START_REF] Morel | Measurement of Pb 2+ , Cu 2+ and Cd 2+ binding with mucilage exudates from maize (Zea mays L.)roots[END_REF], detoxification of Al with organic acids in the rhizosphere, Ma et al. 2011) or physical binding (accelerated suberisation of fine roots cell walls, [START_REF] Lux | Root responses to cadmium in the rhizosphere: a review[END_REF]. Finally, Fe grain concentrations may be explained by active uptake (e.g. Fe phytosiderophores in cereals), which explains the shift between soil and grain composition. These chemical and physiological mechanisms, which are illustrated by [START_REF] Olsen | Many rivers to cross: the journey of zinc from soil to seed[END_REF] as "many rivers to cross", are species-dependent [START_REF] Goulding | Soil acidification and the importance of liming agricultural soils with particular reference to the United Kingdom[END_REF] stressed that oat was more tolerant of acidity than barley and wheat) and even vary within species [START_REF] Waters | Moving micronutrients from the soil to the seeds: Genes and physiological processes from a biofortification perspective[END_REF]Sankaran 2011, Clark 1983).

The effect of P on grain Zn, Cd and Cu was significant and due to plant-physiological mechanism.

There was a significant effect of P treatments on the barley grain concentrations of Cd (increasing with Olsen P), Cu and Zn (decreasing with Olsen P). They mostly applied to the soil type where Olsen P exhibited the highest range (sandy soil of Woburn, Fig. 7). It is therefore stated that such interactions need a certain amount of soluble P to occur. The effect of P on Zn was expected, as it is abundantly reported in the literature [START_REF] Horst | Grain zinc concentration and its relation to soil nutrient availability in different wheat cropping regions of China[END_REF][START_REF] Kabata-Pendias | Trace Elements in Soils and Plants[END_REF][START_REF] Grant | Cadmium and zinc concentrations and ratios in seed and tissue of solin (cv Linola TM 947) and flax (cvs McGregor and Vimy) as affected by nitrogen and phosphorus fertiliser and Provide (Penicillium bilaji)[END_REF][START_REF] Verma | Zinc and Phosphorus interaction in a wheat-maize cropping system[END_REF]. Cd-P and Cu-P interactions are more scarce, but have been reported previously [START_REF] Yu | Growth responses and cadmium accumulation of Mirabilis jalapa L. under interaction between cadmium and phosphorus[END_REF][START_REF] Kabata-Pendias | Trace Elements in Soils and Plants[END_REF][START_REF] Singh | Phosphorus induced zinc deficiency in wheat on residual phosphorus plots[END_REF]). There are three main explanations of how such interactions can modify the grain concentrations [START_REF] Rengel | Agronomic approaches for improving the micronutrient density in edible portions of field crops[END_REF]. Firstly, a modified grain concentration would be the consequence of soil geochemistry processes leading to a change in mineral solubility in the soil solution (e.g. co-precipitation). Secondly, the application of P fertilizers would dramatically reduce the arbuscular mycorrhizal fungi, which, in turn, would threaten the uptake of micronutrients [START_REF] Goicoechea | The association of barley with AM fungi can result in similar yield and grain quality as a long term application of P or P-K fertilizers by enhancing root phosphatase activity and sugars in leaves at tillering[END_REF]). Thirdly, the antagonism would only be accounted for by plant physiological characteristics. Our results clearly bring us to reject the first hypothesis, because P fertilization did not cause an effect in the extractability of Zn, Cu and Cd (data not shown). They would rather validate the third explanation, in agreement with [START_REF] Smilde | Crop response to phosphate and lime on acid sandy soils high in zinc[END_REF][START_REF] Zhu | Phosphorus efficiencies and their effects on Zn, Cu, and Mn nutrition of different barley (Hordeum vulgare) cultivars grown in sand culture[END_REF], who suggested that a higher rate of net P translocation from root to shoot may reduce Zn accumulation in shoots, without excluding additive dilution phenomenon, especially for Cu.

The lack of relation between soil pH and grain mineral concentrations limits the diagnosis of soil

mineral fertility and the attempt to modify the grain minerals.

Apart from P, Mn and Cd (plus S and Se for wheat, Ca and Mg for oat), the studied nutrients and nonessential trace elements present in the crop grains did not have any relation to pH and extractable nutrients (Table S2, Table 3, Fig. S2, Fig. 8). Moreover, the existence of a soil-grain mineral correlation for one crop (e.g. between pH and oat grain Mn) did not mean that such a relationship would apply for another crop (e.g. no effect of pH on wheat grain Mn). This points out the difficulty in relating grain mineral concentration to soil analysis and questions the utility of grain testing for a proper soil fertility diagnosis, as yet suggested by the latest UK Nutrient Management Guide (AHDB, 2020). Reciprocally, [START_REF] Abedin | An evaluation of extractants for assessment of metal phytoavailability to guide reclamation practices in acidic soilscapes in northern regions[END_REF] stressed that standard soil analysis failed globally in predicting the mineral concentrations for one given crop and, in turn, for useful fertilization recommendations.

Another consequence of the relative independence of grain mineral contents towards soil pH and extractable minerals is to limit the grain concentrations manipulation by changing soil pH, at least in the range of the pH values studied encountered in this study. This is particularly a concern for two types of minerals: firstly, potential toxic trace elements (As, Pb) which proved to be close to the thresholds given by the Codex standards (1995) for Barley and Oat, on the whole range of pH values. By limiting their toxicities, liming increases yields (Table S1, [START_REF] Brallier | Liming effects on availability of Cd, Cu, Ni and Zn in a soil amended with sewage sludge 16 years previously[END_REF][START_REF] Holland | Yield responses of arable crops to liming -An evaluation of relationships between yields and soil pH from a long-term liming experiment[END_REF]) but this might lead to adverse effects on grain mineral concentration [START_REF] Fan | Evidence of decreasing mineral density in wheat grain over the last 160 years[END_REF]. Secondly, the micronutrients of concern in the diet (Zn, Fe, Cu, Se), whose concentrations in grains have been decreasing in the last decades [START_REF] Fan | Evidence of decreasing mineral density in wheat grain over the last 160 years[END_REF][START_REF] Gooding | Contrasting effects of dwarfing alleles and nitrogen availability on mineral concentrations in wheat grain[END_REF], McGrath et al. 2012). Our results, supported by [START_REF] Poblaciones | Selenium accumulation and speciation in biofortified chickpea (Cicer arietinum L.) under Mediterranean conditions[END_REF] and [START_REF] White | Biofortification of crops with seven mineral elements often lacking in human diets -iron, zinc, copper, calcium, magnesium, selenium and iodine[END_REF], show that grain bio-fortification is unlikely to be solved with conventional fertilization practices. Besides, it should be underlined that attempting to increase the level of some micronutrients in the grains by withholding liming would increase the risks of Al and Mn crop toxicity, as seen on the yields of barley [START_REF] Holland | Liming impacts barley yield over a wide concentration range of soil exchangeable cations[END_REF]) and wheat (Table S1).

Finally, for the few grain minerals sensitive to soil pH, providing lime generally decreased the levels of grain micronutrients (Mn, Cd) and showed contrasting trends for Zn and Se. The responses of those latter minerals to pH were not particularly expected, as their response to pH had not been studied before, to our knowledge (S.P. Mc-Grath, pers. comm.). For macronutrients, the results suggest that maintaining pH values above 6.5 only led to an increase of P in barley, oat and wheat grains, without any significant change on Ca, Mg, K and S content of grains.

Conclusion

The greatest systematic effect of increasing pH was to decrease the soil solubility of minerals, defined as extractable nutrients, with the exception of extractable Ca, whose increase was related to the lime rate.

However, the grain mineral concentrations did not mirror the dramatic gradients of soil mineral solubility, ranging from potentially toxic levels (Extractable Mn) to near-zero concentrations of extractable minerals (Cu, Fe, Mn, Pb, Zn), with the exception of grain Cd which was closely correlated with extractable Cd. Grain macro-nutrients generally increased with pH (Ca, Mg, P), decreased (S) or were insensitive (K), these trends being however species-dependent. Grain Fe, Zn and Cu concentrations, which are often insufficient for nutrition, showed no relationship with pH, thus potentially compromising attempts to use pH manipulation as a tool for biofortification. The soil type effect showed a significant effect on Cu and Zn grain concentrations, with the highest concentrations being linked to the soil exhibiting the highest CEC. Soil P content significantly decreased grain Cu and Zn, although it was a weaker effect than other explanatory factors. The effect of P on those micronutrients was more likely to act through plant physiological mechanisms rather than through soil geochemistry processes. Finally, the three crops behaved identically to the tested variables only for Cd, Cu and Zn, and their grain concentration of Fe, Pb and K proved to be insensitive to any of the tested variables. For all the other minerals (Ca, Mg, P, Mn, As and Se), there was no obvious trend between crops, with various responses to environmental variables. explain the mineral concentrations in grains, for the three crops (barley, oat, wheat), according to the Multi Linear Regression Analysis (MLRA). Results only show the relationships which accounted for more than 30% of the variability (r² > 0.3). This explains why Olsen P values, which proved to have a weak influence, do not appear on this synthesis. The number following the "±" symbol in the columns represents half of the relative variation of the soil mineral concentrations due to pH variation (second column), or of the grain content (mean of the three crops, third column). Black arrows between pH and soil extractable minerals symbolise all significant relationships (P values < 0.05) obtained from Table 2 and Table S3, and the numbers are the r² of the models (generally exponential). For each grain mineral (dark grey column), the incoming arrows represent the variable which had the highest r² among pH (light grey arrows), soil extractable minerals (dark dotted arrows) or soil type (dark grey arrows), according to the MLRA. Letters represent the effect on barley (B), Oat (O) and wheat (W), and their adjacent number is the r² value calculated through the MLRA (Fig. S1).

Figure captions

Arsenic, Sulfur and Selenium have no r² because no soil analysis was made. The thicker the arrows, the larger the r². 1964, 1967, 1974, 1979, 1983 and 1989 (n = 192) were used to perform the regression analysis. For P relationships, data from fertilized treatments were not included. Extractions were performed with NH4NO3 and Olsen methods for cations and P, respectively.

Tables

Grain

Figure 1 .

 1 Figure 1. Statistical methods applied between treatment factors (italics), intermediate variables (grey highlight) and grain mineral concentrations, for a given crop. ANOVA were performed between factors and variables linked with a solid line. Pairwise regression analyses were performed between variables linked with a dotted line. Variables indicated by a star were used for Multiple Linear Regression Analyses.

Figure 2 :

 2 Figure 2: Means ± standard deviation of mineral concentrations of barley, oat and wheat grains (ppm), at different lime treatments (C: Control, L: Low, M: Medium, H: High) and soils (Rothamsted, Woburn). (a): macronutrients (b): Micronutrients (c): Other trace-elements. Dotted bold line show the concentration threshold values of contaminants (Codex General Standards). Lower case letters indicate significant differences in means (α = 0.05) across crops.

Figure 3 :

 3 Figure 3: Examples of relationships between soil pH and grain macro-nutrients (a) micronutrients (b) and other trace element (Cd) in grains, corresponding to P values < 0.05. (a): positive linear P-pH relationship for wheat (circles) and oat (triangle). (b): negative exponential Mn-pH relationship for oat (triangles) and barley (squares). (c): negative linear Cd-pH relationship for barley (squares) and wheat (circles). Open symbols: Rothamsted. Filled symbols: Woburn.

Figure 4 :

 4 Figure 4: Examples of relationships between pH and soil extractable cations exhibiting different patterns: negative exponential functions for extractable Zn (a) and negative linear function for extractable Mn (b). Open symbols: Rothamsted -Filled symbols: Woburn. Equations are described in theTable 2.

Figure 5 .

 5 Figure 5. Example of relationships between soil-extractable cation and grain mineral concentrations in oat and wheat at Rothamsted (a), oat and barley in Woburn (b) and wheat in Rothamsted and Woburn (c). Triangles: oat -Squares: barley -Circles: wheat. Open symbols: Rothamsted -Filled symbols: Woburn. Equations and correlation coefficients are shown in the Table3.

Figure 6 .

 6 Figure 6. Mean ± standard deviation of extractable Zinc in the soil in Rothamsted (open symbols) and Woburn (filled symbols) (a), and corresponding grain Zn concentration in oat (b). C, L, M and H letters stand for Control-, Low-, Medium-and High-lime treatments, respectively.

Figure 7 .

 7 Figure 7. Relations between soil Olsen P concentration and barley grain concentrations of Cd, Cu and Zn. Data from Woburn. Legend of lime treatments: ○: Control, •: Low, X: Medium, +: High.

Figure 8 .

 8 Figure 8. Simplified representation of the main factors (pH, soil mineral solubility, soil type) that

  Figure1

Table 1 :

 1 Effect of lime treatment (C: Control, L: Low, M: Medium, H: High) on the mean and standard deviation (s.d.) pH values of the soils in Rothamsted and Woburn for the three selected years(1978, 1981, 1995). n=8

		Lime	Rothamsted	Woburn
		treatment	Mean	s.d.	Mean	s.d.
		C	4.17	0.12	4.33	0.10
	Barley	L	4.91	0.19	5.14	0.13
	1978	M	6.05	0.51	5.98	0.23
		H	6.96	0.12	6.76	0.20
		C	4.26	0.12	4.59	0.10
	Oat	L	5.25	0.22	5.51	0.11
	1981	M	6.36	0.37	6.54	0.17
		H	7.19	0.17	7.21	0.14
		C	4.79	0.26	4.53	0.18
	Wheat	L	5.69	0.27	5.44	0.25
	1995	M	6.42	0.28	6.53	0.15
		H	7.99	0.17	7.40	0.10

Table 2 :

 2 Models for extractable minerals (Ex.) as a function of pH. Data from the years

Table 3 .

 3 Regression relationships between soil-extractable cation and grain mineral concentrations, as a function of crop species (barley, oat, wheat) and soil type (Rothamsted vs Woburn). Mg, Fe and Pb, which did not show any kind of relationships, are not mentioned in the table. ns: P > 0.05; *: 0.05 < P < 0.005; **: 0.005 < P < 0.0001; ***: P < 0.0001

		Soil			Barley			Oat			Wheat
	min.	type	r²	P	Equation	r²	P	Equation	r²	P	Equation
	Ca	Roth. Wob. 0.16	ns *	474.7 + 0.07 Ex. Ca	0.69 0.46	*** 763.7 + 0.08 Ex. Ca ** 811.5 + 0.10 Ex. Ca	0.76 *** 401 -0.2 Ex. Ca + 5 10 -5 (Ex. Ca)² 0.57 *** 432 -0.4 Ex. Ca + 2 10 -4 (Ex. Ca)²
	K	Roth. Wob. 0.25	ns *	4887 + 4.9 Ex. K		ns ns			ns ns
	P	Roth. Wob. 0.18	ns *	3464 -9.8 POlsen	0.34	ns **	2857 + 10.6 POlsen	0.18 0.36 ** 1915 + 29.40 POlsen * 2078.7 + 20.20 POlsen
	Cu	Roth. Wob. 0.17	ns *	3.9 -13.01 Ex. Cu	0.18 0.15	* *	4.7 + 1.32 Ex. Cu 2.8 + 2.80 Ex. Cu		ns ns
	Mn	Roth. 0.24 Wob. 0.61	* *** 8.00 + 0.25 Ex. Mn 17.6 + 0.03 Ex. Mn	0.63 0.64	** *** 41.94 + 0.43 Ex. Mn 44.9 + 0.058 Ex. Mn	0.6	*** 37.23 + 0.032 Ex. Mn ns
	Cd	Roth. Wob. 0.58 0.5	** *** 0.112 + 0.02 Log (Ex. Cd) 0.083 + 0.014 Log (Ex. Cd)	0.66	*** 0.107 + 0.02 Log (Ex. Cd) 0.75 *** 0.236 + 0.039 Log (Ex. Cd) ns ns
	Zn	Roth. Wob.		ns ns		0.23 0.24	** **	27.2 + 0.79 Ex. Zn 20.0 + 2.1 Ex. Zn		ns ns

Acknowledgements

SPM, SMH and CLT were partly funded by Rothamsted Research's Institute Strategic Programme -Soil to Nutrition (BBS/E/C/000I0310) by the UK Biotechnology and Biological Sciences Research Council (BBSRC). MJG was funded by the Rothamsted Long-term Experiments National Capability (LTE-NC), supported by the UK BBSRC (BBS/E/C/000J0300) and the Lawes Agricultural Trust. LJM was funded by the French Ministry of Agriculture. We are grateful to Chloe Garwood & Ruth Skilton for the grain digestion and to Mark Durenkamp and Chloe Garwood for the ICP-OES Analysis.