
==== Front
Metallomics
Metallomics
metallomics
Metallomics: Integrated Biometal Science
1756-5901
1756-591X
Oxford University Press

39217098
10.1093/mtomcs/mfae038
mfae038
Communication
AcademicSubjects/SCI00980
AcademicSubjects/SCI01180
AcademicSubjects/SCI00340
AcademicSubjects/SCI00840
Stable potassium isotope ratios in human blood serum towards biomarker development in Alzheimer's disease
Mahan Brandon Melbourne Analytical Geochemistry, School of Geography, Earth and Atmospheric Sciences, University of Melbourne, Melbourne, Australia
IsoTropics Geochemistry Lab, Earth and Environmental Science, James Cook University, Townsville, Queensland 4814, Australia

Hu Yan Institut de Physique du Globe de Paris, Université Paris Cité, CNRS, 75238 Paris, France
Department of Geoscience, University of Nevada, Las Vegas, Las Vegas, NV 89154, USA

https://orcid.org/0009-0007-3009-3235
Lahoud Esther Institut de Physique du Globe de Paris, Université Paris Cité, CNRS, 75238 Paris, France

Nestmeyer Mark IsoTropics Geochemistry Lab, Earth and Environmental Science, James Cook University, Townsville, Queensland 4814, Australia

McCoy-West Alex IsoTropics Geochemistry Lab, Earth and Environmental Science, James Cook University, Townsville, Queensland 4814, Australia

Manestar Grace Melbourne Analytical Geochemistry, School of Geography, Earth and Atmospheric Sciences, University of Melbourne, Melbourne, Australia

Fowler Christopher The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia

Bush Ashley I The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia

https://orcid.org/0000-0003-4321-5581
Moynier Frédéric Institut de Physique du Globe de Paris, Université Paris Cité, CNRS, 75238 Paris, France

Correspondence: Melbourne Analytical Geochemistry, School of Geography, Earth and Atmospheric Sciences, University of Melbourne, McCoy Building (#200), 253-283 Elgin Street, Parkville, Victoria 3010, Australia. E-mail: brandon.mahan@unimelb.edu.au
9 2024
31 8 2024
31 8 2024
16 9 mfae03819 5 2024
29 8 2024
19 9 2024
© The Author(s) 2024. Published by Oxford University Press.
2024
https://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The Alzheimer's disease (AD)-affected brain purges K with concurrently increasing serum K, suggesting brain–blood K transferal. Here, natural stable K isotope ratios—δ41K—of human serum samples were characterized in an AD biomarker pilot study (plus two paired Li-heparin and potassium ethylenediaminetetraacetic acid [K-EDTA] plasma samples). AD serum was found to have a significantly lower mean δ41K relative to controls. To mechanistically explore this change, novel ab initio calculations (density functional theory) of relative K isotope compositions between hydrated K+ and organically bound K were performed, identifying hydrated K+ as isotopically light (lower δ41K) compared to organically bound K. Taken together with literature, serum δ41K and density functional theory results are consistent with efflux of hydrated K+ from the brain to the bloodstream, manifesting a measurable decrease in serum δ41K. These data introduce serum δ41K for further investigation as a minimally invasive AD biomarker, with cost, scalability, and stability advantages over current techniques.

Graphical Abstract

Graphical Abstract Human blood serum stable K isotope compositions (δ41K) indicate statistically significant light K isotope enrichment in Alzheimer's disease (AD) relative to controls. This marker displays good performance in identifying AD (receiver operating characteristic curve analysis), introducing serum δ41K for further exploration as a minimally invasive AD biomarker.

potassium
isotope
Alzheimer's disease
metallomics
biomarker
==== Body
pmcIntroduction

Changes in brain biometals in Alzheimer's disease (AD) have led to their increasing exploration in biological systems.1–3 This has drawn attention from the field of isotope metallomics, which utilizes analytical (geo)chemistry techniques to characterize the abundance and distribution of biometal isotopes in biological systems.4–11 For biometals in relation to neurodegenerative diseases such as AD, it has been observed that metals including Ca, Fe, Cu, and Zn accumulate in the brain as a function of age and/or the development of neurodegenerative disorders (e.g. AD), where most metals are linked to the presence and/or aggregation of amyloid β (Aβ) fibrils and the development of senile plaques.1,12–26 Complementary research has indicated that the AD-affected brain also expresses deficits of certain metals, namely here, K,3 and that this may occur prodromally (e.g. midlife).27

Brain metal accumulation with neurodegeneration has led to the investigation of changes in total metal levels in the bloodstream as potential diagnostic metrics for AD, on the premise that metal dyshomeostasis in the brain might manifest a correlative change in blood fractions, thereby possibly serving as non-invasive biomarkers and diagnostic indicators. However, on the blood side of the blood–brain barrier, elemental abundances are more subject to exogenous and endogenous confounders such as sample processing/storage, environmental exposures, homeostatic transport mechanisms, genetics, and cultural differences, and therefore the use of blood metal abundances alone as indicators of disease can be variable and at times contradictory; see Acevedo et al.26 and Harris and Fahrenholz,28 and especially Babic Leko et al.29 and references therein. Furthermore, sample stability during storage and transport is a recognized and further emerging issue for many organic biomarkers, e.g. polypeptide-based approaches,30–34 beckoning for diagnostic tools that are less sensitive to these constraints, such as with inorganic mass spectrometry, where stability of blood fractions is of no consideration because samples are fully digested, atomized, and ionized prior to analysis.

The natural abundance of metal isotopes in biological systems can be altered by: (i) changes in their bonding environments during exchange reactions (equilibrium isotope fractionation, e.g. healthy vs diseased cells), where stronger bonds favour heavier isotopes8,35; and/or (ii) [non-equilibrium] kinetic effects during dominantly unidirectional processes such as diffusion.36,37 In both cases, isotope fractionation can be well-described and modelled through ab initio theoretical calculations, namely density functional theory38–40 for equilibrium isotope fractionation, allowing for mechanistic interpretations of empirical and experimental data.10,41,42 In brief, for equilibrium isotope fractionation, the isotopic composition of a given bodily reservoir, especially in relation to others (e.g. blood relative to brain tissue), is beholden to bonding environment, whereas for kinetic isotope fractionation, lighter isotopes tend to become enriched in reaction products along a chain of [unidirectional] chemical reactions.39,40 In AD, observations indicate that metals such as Ca, Fe, Cu, and Zn accumulate in the brain due to changed bonding environment (binding e.g. to Aβ), and these metals are hypothesized to play a mechanistic role in AD pathology; therefore, most elemental and isotopic studies to date have focused on these metals,17,43,44 with Cu and Zn showing promise for utility in AD diagnostics.8,35,45 Where available, results from ab initio calculations of equilibrium isotope fractionation agree with the direction and general magnitude of isotope fractionation in this context, even considering that such calculations simplify the bonding environment to that of amino acids as approximations for more complex protein binding sites.46

For K, previous research has reported a significant decrease in the brain with AD, with a correlative increase in serum K,3 indicating a linkage between the two and the possibility for developing a serum AD biomarker based on K and its isotopes. In Roberts et al.,3 total K concentrations in human AD brain homogenates decreased by >20% (24.4% decrease from ∼2 mg/g wet weight in the control group), with an average concomitant increase in serum K of 2.6% (from ∼145 mg/L). A key diagnostic pathology in AD is the formation of amyloid plaques by extracellular deposition of Aβ, but the soluble Aβ pool (i.e. that not yet aggregated into insoluble fibrils) is also indicative of disease severity.47 Increased K intake has been linked to reduced risk of dementia (especially vascular) in humans,48 and to reduced oxidative stress in an amyloid precursor protein/presenilin-1 (APP/PS1) murine model for AD (with decreased Aβ aggregation and reduced tau phosphorylation).49 Related work on the association between Aβ and K in humans reported a linkage between low K intake at midlife and low Aβ42 in cerebrospinal fluid in late life, further suggesting a biological and/or pathological link between K and AD (and Aβ), notably in the prodromal phase of the disease.27 The possibility that K systematics earlier in life are related to AD risk later in life is also supported by separate research linking increased serum K to mild cognitive impairment,50 corroborated by the independent observation that decreased K in the AD brain correlates with increased K in blood serum.3 Lastly, our recent work51 found a linkage between K brain concentrations and K isotopic compositions—41K/39K relative to a standard, denoted as δ41K in per mil (per thousand), ‰—in porcine AD models at midlife, wherein it was hypothesized that efflux of hydrated K+ from the brain with AD (due to the presence of Aβ) would manifest as a light K isotope excursion (lower δ41K) in the bloodstream. Taken together, these findings point to a connection between perturbed K metabolism and AD, likely present in the prodromal phase, indicating that serum K and its isotopes might serve as minimally invasive biomarker tools for understanding and diagnosing AD. It is noted that exogenous influences—diet in particular—on individual bodily reservoir isotopic compositions are yet not well constrained and will require future investigation (see Sullivan et al.52 and references therein).

The analytical challenges inherent to K stable isotope ratios measurements have only been operationally overcome in recent years.53–56 Innovative analytical methods such as new-generation collision–reaction cell, multi-collector inductively coupled plasma mass spectrometers (CRC-MC-ICP-MS) have opened new research avenues in the study of natural variations of K stable isotope ratios.54,55,57,58 Particularly relevant here, Moynier et al.54 showed that accurate and high-precision δ41K (<0.03‰ uncertainty) can be achieved with only 125 ng (or less) of K, opening up the possibility to isotopically characterize minute amounts of even low K concentration samples. To date, the only published data for K isotopes in blood fractions are in Moynier et al.,55 Cui et al.,58 Hobin et al.,59 Tacail et al.,60 Hobin et al.,61 and Higgins et al.62 While K isotope data are limited, K concentrations in most bodily reservoirs (e.g. plasma, organs, and brain) are two or more orders of magnitude higher than that of transition metals (e.g. Fe, Cu, and Zn) (e.g. Albarede et al.63 for human serum9; for porcine organs and blood fractions), marking K as an attractive tracer in both practical and analytical terms. That is, K isotope compositions in these reservoirs are more accessible due to much higher typical concentrations (thus also less susceptible to contamination), and far less sample is needed to generate statistically robust isotopic measurements, especially by CRC-MC-ICP-MS where much less analyte K is necessary for reliable measurements compared to conventional methods not using the collision–reaction cell.54,55,58

In the context of blood biomarker development, because typical K concentrations in the brain are generally over an order of magnitude higher than that in blood plasma/serum (e.g. ≈3,000 ppm compared to 100–200 ppm, respectively),9,63 the isotopic signal of K disruption in the brain (especially that which may purge K into extracellular space) plausibly could be detected in the bloodstream, supported by the size of the previously observed excursion in absolute brain K concentrations in AD,3 and previous observations that organ-biofluid differences in δ41K can be quite large under healthy conditions,58 meaning that a change in one reservoir can impart a measurable difference in another (especially moving from higher to lower concentration), the base logic in the application of K isotopes to human disease.

In the present study, we test the potential of human serum K isotope compositions for AD biomarker development (as hypothesized in Mahan et al.51) by characterizing serum K isotope compositions.

Methods

Sample collection and digestion

In total, 20 serum samples were analysed, from 10 unique AD subjects and 10 unique control (CN) subjects; Li-heparin and potassium ethylenediaminetetraacetic acid (K-EDTA) anticoagulated plasma samples from two AD cases were analysed to compare their paired serum values to determine whether plasma values differ to serum due to addition of anticoagulants. Cryogenically frozen blood serum and plasma samples (∼500 μl) were obtained from the Australian Biomarker & Lifestyle Flagship Study of Ageing (AIBL) through The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Australia), with ethics committee approval both by St Vincent's Health (HREC 028/06) and the James Cook University (JCU) Human Ethics Committees (HREC H8650). The AIBL cohort is 95% Northern European Caucasian (with remainder largely Southern European). Controls are unrelated to AD and were randomly selected from a CN pool who have remained longitudinally cognitively normal. All AD subjects had been clinically diagnosed via the Mini-Mental State Exam (MMSE) assessment and specialist panel review using National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria, with biomarker confirmation through positron emission tomography (PET) centiloid scores (Table 1); CN subjects were cognitively unaffected and had subdiagnostic brain amyloid on PET scan (null criteria fitting).64 Despite prior work indicating that age has no major effect on K isotope compositions in mammals,60 all subjects were age-matched to within 15 years and with identical average ages for both AD and CN (75 years old) (Table 1). Due to limited sample availability, it was not possible to match sex ratios for the samples; however, previous work has indicated that sex does not significantly affect K isotope systematics in mammals.60 There were serum samples from seven males and three females in the AD group, and from two males and eight females in the CN group.

Table 1. ID, clinical status, demographic, Mini-Mental State Exam (MMSE) scores, PET centiloid values, and δ41K for human serum samples in this pilot study

AIBL ID	Diagnosis	Sex	Age (year)	MMSE	PET centiloid	δ41K (‰)	2σ	n	
1994	AD	Male	83	17	166.4	−0.50	0.05	4	
1994dup						−0.50	0.04	4	
1984	AD	Male	72	21	122.2	−0.82	0.08	3	
2079	AD	Male	77	24	97.7	−0.26	0.05	4	
2064	AD	Male	76	28	107.8	−0.15	0.04	5	
2084	AD	Female	72	22	47.1	−0.55	0.04	3	
2086	AD	Male	84	21	116.5	−0.68	0.04	5	
2086dup						−0.71	0.06	4	
2526	AD	Female	70	27	110.6	−0.38	0.07	4	
2447	AD	Female	68	23	126.4	−0.45	0.04	4	
2486	AD	Male	72	25	142.1	−0.97	0.06	3	
2279	AD	Male	74	23	79.3	−0.67	0.08	3	
Average			75	23	112	−0.55			
Median			73	23	114	−0.53			
2SD			11	6	66	0.46			
2049	CN	Female	77	29	−1.2	−0.36	0.09	4	
2049dup						−0.33	0.08	4	
2021	CN	Male	87	27	−3.7	−0.32	0.02	4	
2021dup						−0.31	0.06	4	
2056	CN	Female	76	30	−3.7	−0.31	0.02	3	
2093	CN	Female	72	28	6.9	−0.37	0.07	3	
2058	CN	Female	71	28	−7.1	−0.18	0.03	3	
2066	CN	Female	75	29	−0.2	(−0.77)	0.05	3	
1912	CN	Male	76	29	6.1	−0.45	0.03	5	
1868	CN	Female	71	29	−7.3	−0.30	0.06	4	
1868dup						−0.28	0.05	4	
1869	CN	Female	72	28	−2.3	−0.47	0.03	3	
2090	CN	Female	68	29	1.9	−0.18	0.06	5	
Average			75	29	−1	−0.32			
Median			74	29	−2	−0.31			
2SD			10	2	10	0.18			
FBS1						−1.60	0.02	3	
FBS2						−1.63	0.06	4	
FBS3						−1.63	0.03	3	
Average					−1.62			
2SD						0.04			
Notes: () denotes statistical outlier, and ‘n’ denotes analytical replicates within the same analytical session.

The digestion protocol has been adapted and optimized from previous work.9,51,54,58,65 All samples were digested in an approximate 1:10 mixture of concentrated hydrogen peroxide (30% H2O2) and concentrated double-distilled nitric acid (70% HNO3) in 30 ml of ultra-clean polyfluoralkyl (PFA) vials, in three sequential and cumulative steps. First, serum samples were added to PFA vials without the use of pipettes (to avoid contamination), typically equating to ∼400 μl of serum; to this 100 μl of H2O2 and 2.0 ml of HNO3 were added and left loosely capped and unheated for 30 min (‘soft oxidation’), followed by heating at 120°C on a hotplate for 24 h tightly capped. A further 250 μl of H2O2 and 1.0 ml of HNO3 were added, samples tightly capped, and heated at 120°C for 24 h. A final 50 μl of H2O2 and 100μl of HNO3 were added, samples tightly capped, and heated at 120°C for 24 h. This equates to final volumes of 400 μl of H2O2 and 3.1 ml of HNO3, for a total digest volume of ∼3.5 ml (digestion is not fully conservative due to outgassing). After determination of average K concentrations by ICP-MS using a 500-μl aliquot of the final digests (see the ‘Results’ section and Supplementary Data), further aliquots of 400 μl were taken from the final digest (∼11%, equating to 35–40 μl of serum) and pipetted into ultra-clean 7-ml PFA vials for dispatch to Institut de Physique du Globe de Paris (IPGP) for further processing. Half of each aliquot (∼20 μl) was used for K chemistry and isotopic analyses using post-Cu extraction solutions, adopting a similar robust sample conservation approach as that taken for precious low-quantity cosmochemical samples (see Hu et al.56 and references therein). Assuming even low-end K concentrations in serum of ∼100 ppm K, ∼20-μl aliquots equate to ∼2 μg or more of K, i.e. enough for several repeat analyses in 100–150-ng/g analyte solutions (and four times this in 25-ng/g solutions). Aliquots were dried down at 80°C on a hotplate inside a sealed evaporation chamber and sent to IPGP for K separation chemistry and isotopic analysis by CRC-MC-ICP-MS (Nu Sapphire™).

All statistical analyses were conducted using GraphPad™ Prism™ (Mac v9.50) through licence at the University of Melbourne.

Potassium separation chemistry and isotopic analysis

Potassium separation chemistry and isotopic analyses follow previously established robust methods.54,58 At IPGP, dried sample aliquots were redissolved in 0.5-mole/litre (M) HNO3 in preparation for potassium isolation through cation exchange chromatography. In brief, BioRad® Poly-Prep™ columns were loaded with 2 ml of pre-cleaned Bio-Rad AG® 50W-X8 resin (200–400 mesh) and conditioned with 10 ml of 0.5 M HNO3. Sample solutions were then loaded on the resin in 1 ml of 0.5 M HNO3. Matrix elements are eluted with 13 ml of 0.5 M HNO3, with K isolates subsequently collected in another 22 ml of 0.5 M HNO3. In between column passes (3×), the resin was stripped of any remaining sample ions by eluting 10 ml of 6 M HCl through the column.

Potassium stable isotope measurements were carried out at IPGP using the Nu Sapphire™ CRC-MC-ICP-MS, where the collision cell pathway was used with H2 gas to neutralize Ar+ and ArH+ species, removing these as major mass interferences for K isotope measurements.54,55,66 Given the large amount of K present in our solutions (>2 μg), standard and sample solutions were introduced to the instrument as 100–150-ng/g (ppb) solutions with an ESI® Apex™ Omega desolvating system fitted with an integrated 100-μl/min PFA nebulizer/probe assembly (ESI® MicroFlow™ nebulizer), and standard Ni dry cones at the instrument interface. In line with convention, K isotope compositions (41K/39K, denoted as δ41K in per mil notation, or per thousand, ‰) were measured using standard–sample bracketing, with National Institute of Standards and Technology Standard Reference Material (NIST SRM)-3141a used as the natural abundance bracketing standard for direct comparison to other works.54,55 The K stable isotope compositions expressed in ‰ as δ41K are formulated as follows:

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{eqnarray*} {\mathrm{\delta }}{}_{}^{41}{\mathrm{K}} = \left( {\frac{{{}_{}^{41}{\mathrm{K}}/{}_{}^{39}{{{\mathrm{K}}}_{{\mathrm{sample}}}}}}{{{}_{}^{41}{\mathrm{K}}/{}_{}^{39}{{{\mathrm{K}}}_{{\mathrm{SRM}}3141{\mathrm{a}}}}}} - 1} \right){\mathrm{\ }} \times {\mathrm{\ }}1000, \end{eqnarray*}\end{document}

where 41K/39K refers to the measured abundance ratios. In this relativistic framework, sample-to-sample comparisons (both being defined relative to a standard composition) are discussed in terms of being isotopically lighter (or heavier) than one another (where lighter denotes relative enrichment in 39K, the lighter K isotope, and therefore lower δ41K). In general, sample analyte solutions were analysed four to six times (minimum three) to generate within-session reproducibility metrics. All uncertainties herein have been conventionally reported as two times the standard deviation (2σ).

Ab initio calculation of K isotope fractionation between relevant bonding environments

For further evidence-based interpretation of the current work, and to test hypotheses drawn out of previous work,51 we conducted a subset of ab initio calculations to predict K isotope fractionation between its hydrated form (with and without solvation effects) and when molecularly bound to aspartate and glutamate, such as is the case for K in Na/K-ATPase (the activity of which is altered in AD).67 Additional calculations were included for K-EDTA as a common additive in prepared biological samples, as well as for other biologically relevant forms of bound K.

Vibrational frequencies of metal complexes were calculated after successful geometry optimization in ORCA 5.0.3.68 Calculations were performed with density functional theory using the PBE0 functional69 and the def2-svp all-electron basis set70 for all elements. The numerical integration grid ‘defgrid3’ was used, convergence tolerance for self-consistent field was to set 1.0E9 Eh, and for geometry optimization to 2.0E−7 Eh. Calculations of amino acid metal complexes were performed in vacuo with K bound to carboxyl groups. Most recent experimental measurements show a hydration number of 6 for K+ in aqueous solution.71,72 Therefore, we modelled aqueous K+ with six water molecules in its first hydration shell. For vibrational frequency calculation, K was substituted by the isotopes 39K and 41K using the masses 38.9637069 and 40.96182597, respectively.73 Reduced partition function ratios (β-factors) were then calculated using the equation74:

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{eqnarray*} \beta = {{\left( {\mathop \prod \limits_i^{3N - 6} \frac{{{{u}_i}}}{{u{{^{\prime}}_i}}}\ \frac{{\frac{{{\mathrm{exp}} \left( { - \frac{{{{u}_i}}}{2}} \right)}}{{1 - {\mathrm{exp}}\left( { - {{u}_i}} \right)}}}}{{\frac{{\exp \left( {\frac{{ - {{{u^{\prime}}}_i}}}{2}} \right)}}{{1 - {\mathrm{exp}}\left( { - {{{u^{\prime}}}_i}} \right)}}}}} \right)}^{\frac{1}{n}}} \end{eqnarray*}\end{document}

with

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{eqnarray*} {{u}_i} = \frac{{hc{{\omega }_i}}}{{kT}}, \end{eqnarray*}\end{document}

where h is Planck's constant, c is speed of light, k is Boltzmann constant, ωi is ith of 3n − 6 vibrational frequencies, T is absolute temperature, n represents the number of atoms in the species, N represents the total number of atoms, and u′ refers to the lighter isotope. In complexes with multiple K atoms, all K atoms were substituted by the identical isotope and β was subsequently normalized by n number of K isotopes.75 Cartesian coordinates of optimized K-bearing species for ab initio calculations can be found in Supplementary Data (Tables S2–S9).

Results

Potassium isotope compositions, as δ41K relative to NIST-3141a, are reported in Tables 1 and 2 (serum and plasma data, respectively; uncertainties conventionally reported as two times the standard deviation, 2σ), along with neuropsychological testing (MMSE) and PET centiloid results and diagnosis. In total, 10 AD and 10 CN subjects were interrogated (Table 1 and Fig. 1). For reference, K concentrations determined by ICP-MS are reported in Supplementary Data alongside MMSE, PET centiloid, and δ41K (Table S1). As there are currently no certified reference materials (CRMs) with K isotope compositions (as certified values; however, see Moynier et al.54 for K isotope characterization of biological CRMs utilizing the same analytical approach herein), several full procedural replicates of a commercially available foetal bovine serum (FBS, Sigma Aldrich) were processed alongside sample batches as a matrix-matched control and to ensure no K isotope fractionation from ion exchange chromatography; all three FBS replicates returned δ41K values that are the same within analytical uncertainty (Table 1). Analytical duplicates (same K isolate, separate analytical session; five in total) were run across the analytical sessions to ensure there was no between-session analytical artefacts; all analytical duplicates returned δ41K values that are the same within analytical uncertainty (Table 1). Lastly, the overall mean for control samples of −0.32 ± 0.18‰ (2σ of all CN subjects, excluding one outlier, see below) is in excellent agreement with the previously determined value of −0.30 ± 0.04‰ for the pooled human blood serum CRM Seronorm™ Trace Elements Serum L-1,59 further validating the chemical and analytical techniques employed (see the ‘Discussion’ section). Typical analytical resolution within the current study is ±0.05‰.

Fig. 1 Boxplot of 41K/39K isotope ratios—δ41K in per mil, ‰—for Alzheimer's disease (AD) versus control (CN) subjects, with Welch's t-test P result reported. Outlier(s) indicated by grey circles.

Table 2. ID, clinical status, neuropsychological evaluation scores, PET centiloid values, and δ41K for paired serum–plasma samples; suffix ‘L’ denotes treatment with Li-heparin, and suffix ‘E’ denotes treatment with K-EDTA

AIBL ID	Diagnosis	Sex	Age (year)	MMSE	PET centiloid	δ41K	2σ	n	
2526	AD	Female	70	27	110.6	−0.38	0.07	4	
2526-L						0.01	0.08	3	
2526-E						−0.03	0.03	4	
2486	AD	Male	72	25	142.1	−0.97	0.06	3	
2486-L						−0.61	0.02	3	
2486-E						−0.13	0.04	3	

The initial dataset was screened for outlier rejection and effects of anticoagulants prior to further statistical analyses. Paired plasma samples treated with Li-heparin and K-EDTA returned markedly different δ41K values than their serum counterparts and trended towards heavier values (Table 2) and are thus excised from statistical analyses. An outlier, AIBL 2066 (CN) was identified and excised from statistical analyses, leaving 19 data (10 AD and 9 CN). AIBL 2066 (CN, δ41K = −0.77 ± 0.05‰) was first identified as a statistical outlier by falling outside the 2σ (95%) envelope for CN δ41K values, with statistical outlier status confirmed by both the Tukey method (1.5 × interquartile range [IQR]) and the Grubbs test (α = 0.05); it has been maintained elsewhere in the current work for transparency and further discourse (see the ‘Discussion’ section). Both AD and CN pools were assessed for a possible sex effect, with neither returning a statistically significant effect (P = 0.7489 and 0.3933, respectively). No statistically significant correlation was found between δ41K and MMSE or PET centiloid, with Pearson's R2 values of 0.21 and 0.18, respectively; for comparison, MMSE versus PET Pearson's R2 = 0.62.

Overall, potassium isotope compositions, δ41K, ranged from −0.97 to −0.15‰ (min/max both defined by AD results), for reference, a large range comparable in magnitude to that seen across Earth's major geological reservoirs.76 As illustrated in Fig. 1, mean AD serum is isotopically lighter for K (average δ41K = −0.55 ± 0.46‰, 2σ) relative to controls (δ41K = −0.32 ± 0.18‰, 2σ), with a mean difference of 0.23‰; it is noted that the offset towards lighter K isotope compositions in AD versus CN subjects is not significantly affected and is still >0.2‰ if median values are chosen as representative. To determine a nominally healthy range for δ41K, 1.5 × IQR was re-calculated after outlier removal, yielding a value of 0.10‰ and therefore a healthy δ41K range of −0.22 to −0.42‰ for δ41K. To confirm that AD and CN data herein do not deviate significantly from Gaussian populations, a D'Agostino & Pearson normality test was applied, yielding P = 0.9692 and 0.9361 for AD and CN (respectively). To interrogate the comparative variance between AD and CN, an F-test was applied, yielding an F value of 6.0 and P = 0.019 (*), indicating unequal variance. Due to unequal variance between AD and CN, a Welch's t-test approach was applied to compare means, yielding P = 0.0264 (*). Additionally, the sensitivity and specificity of δ41K as a method of detecting AD were interrogated using receiver operating characteristic (ROC) curve analysis, yielding an area under curve (AUC) of 0.8 (good/very good).77 Applying the lower threshold value of −0.42‰ for ‘healthy’ δ41K to ROC results yields a sensitivity of 70% and a specificity of 89% in identifying AD. In summary, blood serum from AD subjects has a significantly lighter potassium isotope composition (lower δ41K) than that of CN; additionally, CN data cluster tightly around −0.3‰, while AD data display greater dispersion. While not a focus of the current work, serum K concentrations were collected for most samples to constrain aliquoting approach (6 AD and 10 CN); AD serum K was slightly higher than that in CN (1.9%); however, this was not statistically significant (P = 0.6166, equal variance unpaired t-test).

Results of ab initio calculations to predict isotope fractionation of K between its hydrated form (six-coordinated hydrated K+; the conductor-like polarizable continuum model (CPCM) version includes solvation effects) and relevant molecularly bound environments are reported in Fig. 2. Calculation results are reported as 1000× the natural logarithm of the reduced partition function, or 1000 × lnβ, for δ41K (41K/39K); lower values indicate light isotope enrichment relative to higher values (i.e. lower δ41K refers to relative enrichment in 39K, and vice versa). In this reference frame, relative isotope compositions can be calculated by subtraction (e.g. 1.92–2.13 = −0.21‰; six-coordination hydrated K+ is 0.21 ‰ lighter than K-glutamate; Fig. 2). In line with the underlying quantum mechanical energy considerations governing equilibrium stable isotope fractionation, as well as previous ab initio calculations for other metals,7,46,78 theoretical predictions dictate that hydrated K+ is isotopically lighter than that more strongly bound in organic compounds. Hydrated K+ was the most isotopically light species of all ab initio calculations undertaken herein, meanwhile glutamate and aspartate (as in Na/K-ATPase) impel some of the heaviest K isotope compositions (0.2 and 0.4‰ higher, respectively).

Fig. 2 41K/39K reduced partition function for various biologically relevant species at 37°C. Relative isotopic enrichment can be predicted by subtraction; e.g. at normal body temperature, six-coordinated hydrated K+ is predicted to be 0.39‰ lighter than K2-glutamate (1.92–2.31 = −0.39).

Discussion

Acknowledging that this is a pilot study (20 unique δ41K values, 10 AD and 10 CN), the significant changes in serum δ41K that we observed in AD may encourage future biomarker development.

Summarizing from above, blood serum from AD subjects has a statistically significant lighter potassium isotope composition (lower δ41K) than that of CN, with ROC curve analysis indicating good performance of this tool in predicting AD within the cohort.

Previous work in porcine brain tissue samples has indicated heavy K isotope enrichment in the brain associated with Aβ accumulation, and with relative brain K depletion compared to brains with low Aβ accumulation.51 The magnitude of K isotopic change in these samples displayed further qualitative correlation with temporal onset of brain changes between brain regions, e.g. the brainstem displayed larger δ41K excursion and is affected earlier in AD progression, whereas the amygdala displayed no change in δ41K and is affected later (and non-bilaterally) in AD (Zanchi et al.79 and Ji et al.,80 respectively). Cumulatively, the observations of Mahan et al.51 led to the hypotheses therein that:

Aβ-induced efflux of hydrated ‘free’ K+ from the brain (see Roberts et al.3 and Yu et al.81) would be isotopically light relative to molecularly bound K, similar to that predicted by ab initio calculations for other metals (e.g. Moynier et al.7 for Zn isotopes within the context of AD); and

Because K is much more abundant in the brain than in blood, the isotopic signature of purged hydrated K+ from the brain due to AD may transfer a measurable signal to the bloodstream as a light K isotope excursion,51 in the serum fraction.82,83

Ab initio results (1000 × lnβ values; Fig. 2) confirm that hydrated K+ is isotopically light relative to K-glutamate and K-aspartate (by 0.2 to 0.4‰, respectively; only slightly less if including solvation effects). These results corroborate the hypotheses put forth by Mahan et al.,51 and agree with the direction and magnitude of difference in δ41K for AD serum in the current work, being on average ∼0.2‰ lighter than CN (Table 1 and Figs. 1 and 2). The linkage between K dysregulation in the precursor stage of AD (e.g. at midlife)27 indicates that a change in serum δ41K might be detectable prior to clinical or pathological changes.

δ41K of paired Li-heparin and K-EDTA-treated samples trended towards heavier values. While it was not possible to mechanistically constrain observations for Li-heparin-treated plasma, ab initio results for K-EDTA yielded some of the highest 41K enrichment (second only to K2-aspartate), likely explaining the heavier δ41K of plasma samples treated with K-EDTA relative to paired serum (Table 2). Additionally, the average CN human serum δ41K of −0.32 ± 0.18‰ agrees very well with the −0.30 ± 0.04‰ determined for Seronorm™ Trace Elements Serum L-1.59 While further baseline work is needed, this may indicate that −0.3‰ is nominally representative of serum δ41K for healthy individuals (at least for Northern European Caucasian demographics). Lastly, while the increase of ∼2% in AD serum K concentrations determined herein is in line with previous observations by Roberts et al.3 (2.6%), data interpretation is cautionary as there was no statistical significance herein (P = 0.6166), whereas the dataset of Roberts et al. 3 contained over 1000 serum data with concentrations being the sole focus of their analytical methodology.

While the above provides a consistent and useful interpretative framework, it is noted that two AD δ41K values are heavier than the average control value of −0.32‰. This indicates that hydrated efflux of K+ from the brain may not fully explain the data and there are other unknown endogenous or exogenous inputs influencing K isotope compositions in AD, and this should be a focus of future work. The work of Hobin et al.61 observed a possible sex effect (endogenous) on serum K isotope compositions in 10 healthy mice (5 male and 5 female)—where there was a trend towards lighter δ41K in healthy female mice—and hypothesized intra-/extracellular K balance disparities and/or the estrous cycle as possible causes. No statistical sex effect was observed within the current work. This lends to at least two possibilities for future work to resolve: (i) this effect is not present in humans; or (ii) the light δ41K excursion in AD observed herein is a minimum, given that the AD pool has a slight male bias. If human and murine reservoirs are directly comparable, this future work will also allow for further data integration to understand general K isotope distribution across mammalian bodily reservoirs, e.g. the collation of data herein with that of Cui et al.58

If dietary effects persist through the stochastic homogenization of larger datasets, future cumulative baseline work can account for dietary effects through relative normalization, e.g. normalizing serum δ41K values to another accessible bodily reservoir (e.g. erythrocytes, tissue, and/or urine), as previously suggested in Mahan et al.9 (for Zn isotopes), and recently applied to δ41K in Tacail et al.60 and Higgins et al.62

Finally, the ROC curve with an AUC of 0.8 and a sensitivity/specificity of 70%/89% (respectively) indicates promising performance of δ41K in identifying AD. As benchmark comparisons to proteomics-based diagnostics, modern plasma Aβ42/Aβ40 ratios yield sensitivities in the range of ∼65–90% and specificities of 80–95%,84,85 with plasma P-tau181 sensitivities ranging from 80% to 95% and specificities from 75% to 80% depending on the Braak stage (e.g. Janelidze et al.86; AUC 0.85–0.90) (ranges herein are indicative but non-exhaustive). In this context, ROC performance metrics of δ41K in identifying AD are within the range of these proteomics-based approach techniques.

Conclusions and outlook

A total of 20 human serum samples were characterized for their K isotope composition, δ41K in per mil (‰), from 20 subjects (10 AD and 10 CN) within the AIBL study (plus two paired Li-heparin and K-EDTA plasma samples). Anticoagulants Li-heparin and K-EDTA were observed to markedly alter δ41K compared to the paired serum value, and therefore it is concluded that data from such samples cannot be pooled with data from serum here or in the future.

The tight clustering of CN serum δ41K around −0.32‰, and its close agreement with data for pooled human serum determined elsewhere (−0.30‰), indicates that δ41K ≃ −0.3‰ is a current best estimate for nominally healthy human serum (at least for Northern European Caucasians). Statistical analyses returned a resolvable difference between AD and CN subjects, with the former having lighter (lower) δ41K values (*, P = 0.0264, Welch's t-test). An ROC curve with an AUC of 0.8, and a sensitivity of 70% and a specificity of 89%, indicates promising performance of δ41K in identifying AD. This is within reported performance metric ranges of modern proteomics-based diagnostics, and moreover the method described herein is based on inorganic mass spectrometry, and therefore is not susceptible to sample stability issues during transport and storage, e.g. organic breakdown/alteration. Together with novel ab initio predictions of K isotope fractionation, we hypothesize that the observed difference is due to enhanced hydrated K+ in AD serum, possibly reflecting a failure in brain Na/K-ATPase. This may reflect efflux of hydrated K+ from the brain because of Aβ, and given the early (midlife) link between K and Aβ, stable K isotopes might serve as an early biomarker for AD, and one which is robust with respect to sample stability, as well as being cost-effective and with great potential for translation and scalability.

These findings prompt the investigation of a much larger cohort of subjects for serum δ41K to enhance statistical resolving power and further interrogate any possible endogenous (e.g. age, sex, and genetics) or exogenous (e.g. diet and sampling) influences. Larger and more diverse subject cohorts in future work will also allow for combination of δ41K with other blood-based indicators, with plasma biomarkers—Aβ42/Aβ40, p-tau181, and p-tau217—being particularly useful (e.g. Fandos et al.,84 Doecke et al.,85 and Brickman et al87) given the known linkage between prodromal K dysregulation and Aβ.27

Supplementary Material

mfae038_Supplemental_File

Acknowledgements

B.M. thanks the JCU Early Career Researcher Funding Scheme, which funded parts of this work (B.M. travel to IPGP for chemistry/analytics), as well as the University of Melbourne School of Geography, Earth and Atmospheric Sciences (SGEAS), which funded parts of this work (travel, software licensing). F.M. thanks the European Research Council (ERC) for the POC Grant DAI (# 101081580) that partially funded B.M. travel to IPGP. Parts of this work were supported by IPGP multidisciplinary program PARI, by Region Île-de-France SESAME Grant No. 12015908, EX047016, and the IdEx Université de Paris grant, ANR-18-IDEX-0001 and the DIM ACAV + . AIBL is funded by the Australia Medical Research Future Fund (MRF2007656) and the National Health and Medical Research Council (GNT1194028). We thank Mark Walterfang and Ya Hui Huang for their participation in initial development of this cross-disciplinary work, and Brett Trounson from AIBL for access to these samples upon request. The Florey Institute of Neuroscience and Mental Health acknowledges the strong support from the Victorian Government, and particularly the funding from the Operational Infrastructure Support Grant.

The AIBL study and all authors herein would like to specifically thank all the participants who took part in the study and the clinicians who referred participants. The AIBL study (www.aibl.org.au) is a consortium between Austin Health, CSIRO, Edith Cowan University, the Florey Institute (the University of Melbourne), and the National Ageing Research Institute. Partial financial support provided by the Alzheimer's Association (US), the Alzheimer's Drug Discovery Foundation, an anonymous foundation, the Science and Industry Endowment Fund, the Dementia Collaborative Research Centres, the Victorian Government's Operational Infrastructure Support program, the McCusker Alzheimer's Research Foundation, the National Health and Medical Research Council, and the Yulgilbar Foundation. Numerous commercial interactions have supported data collection and analysis. In-kind support has also been provided by Sir Charles Gairdner Hospital, CogState Ltd, Hollywood Private Hospital, the University of Melbourne, and St Vincent's Hospital. A.I.B. is funded by the National Health & Medical Research Council of Australia (GNT1194028).

Author contributions

B.M., F.M., and A.I.B. conceived the study. B.M. completed all ethics documentation for sample access, transport, storage, and digestion/denaturing. C.F. and A.I.B. performed sample selection. B.M., Y.H., E.L., G.M., and F.M. performed sample preparation and isotopic measurements. M.N., B.M., and A.M.-W. designed and performed ab initio calculations. B.M. wrote the manuscript. All authors contributed to data interpretation and manuscript editing.

Funding

None declared.

Conflicts of interest

A.I.B. is a shareholder in Alterity Ltd. No further conflicts of interest exist.

Data availability

The data underlying this article are available in the article and in its online supplementary material.
==== Refs
References

1. Adlard  P. A., Bush  A. I., Metals and Alzheimer's disease, J. Alzheimers Dis., 2006, 10 (2–3 ), 145–163. 10.3233/JAD-2006-102-303 17119284
2. Li  Y., Jiao  Q., Xu  H., Du  X., Shi  L., Jia  F., Jiang  H., Biometal dyshomeostasis and toxic metal accumulations in the development of Alzheimer's disease, Front. Mol. Neurosci., 2017, 10 , 339. 10.3389/fnmol.2017.00339 29114205
3. Roberts  B. R., Doecke  J. D., Rembach  A., Yevenes  L. F., Fowler  C. J., McLean  C. A., Lind  M., Volitakis  I., Masters  C. L., Bush  A. I., Hare  D. J.; AIBL research group, Rubidium and potassium levels are altered in Alzheimer's disease brain and blood but not in cerebrospinal fluid, Acta Neuropathol. Commun., 2016, 4 (1 ), 119. 10.1186/s40478-016-0390-8 27842602
4. Balter  V., Zazzo  A., Moloney  A. P., Moynier  F., Schmidt  O., Monahan  F. J., Albarede  F., Bodily variability of zinc natural isotope abundances in sheep, Rapid Commun. Mass Spectrom., 2010, 24 (5 ), 605–612. 10.1002/rcm.4425 20155761
5. Costas-Rodríguez  M., Delanghe  J., Vanhaecke  F., High-precision isotopic analysis of essential mineral elements in biomedicine: natural isotope ratio variations as potential diagnostic and/or prognostic markers, TrAC Trends Anal. Chem., 2016, 76 , 182–193. 10.1016/j.trac.2015.10.008
6. Albarede  F., Telouk  P., Balter  V., Bondanese  V. P., Albalat  E., Oger  P., Bonaventura  P., Miossec  P., Fujii  T., Medical applications of Cu, Zn, and S isotope effects, Metallomics, 2016, 8 (10 ), 1056–1070. 10.1039/c5mt00316d 27513195
7. Moynier  F., Fujii  T., Shaw  A. S., Le Borgne  M., Heterogeneous distribution of natural zinc isotopes in mice, Metallomics, 2013, 5 (6 ), 693–699. 10.1039/c3mt00008g 23589059
8. Moynier  F., Foriel  J., Shaw  A. S., Le Borgne  M., Distribution of Zn isotopes during Alzheimer's disease, Geochem. Perspect. Lett., 2017, 3 (2 ), 142–150. 10.7185/geochemlet.1717
9. Mahan  B., Moynier  F., Jorgensen  A. L., Habekost  M., Siebert  J., Examining the homeostatic distribution of metals and Zn isotopes in Gottingen minipigs, Metallomics, 2018, 10 (9 ), 1264–1281. 10.1039/c8mt00179k 30128473
10. Mahan  B., Chung  R. S., Pountney  D. L., Moynier  F., Turner  S., Isotope metallomics approaches for medical research, Cell. Mol. Life Sci., 2020, 77 (17 ), 3293–3309. 10.1007/s00018-020-03484-0 32130428
11. Eisenhauer  A., Muller  M., Heuser  A., Kolevica  A., Gluer  C. C., Both  M., Laue  C., Hehn  U. V., Kloth  S., Shroff  R., Schrezenmeir  J., Calcium isotope ratios in blood and urine: a new biomarker for the diagnosis of osteoporosis, Bone Rep., 2019, 10 , 100200. 10.1016/j.bonr.2019.100200 30997369
12. Bush  A. I., Pettingell  W. H., Multhaup  G., Paradis  M., Vonsattel  J. P., Gusella  J. F., Beyreuther  K., Masters  C. L., Tanzi  R. E., Rapid induction of Alzheimer A beta amyloid formation by zinc, Science, 1994, 265 (5177 ), 1464–1467. 10.1126/science.8073293 8073293
13. Lovell  M. A., Robertson  J. D., Teesdale  W. J., Campbell  J. L., Markesbery  W. R., Copper, iron and zinc in Alzheimer's disease senile plaques, J. Neurol. Sci., 1998, 158 (1 ), 47–52. 10.1016/S0022-510X(98)00092-6 9667777
14. Bush  A. I. , The metallobiology of Alzheimer's disease, Trends Neurosci., 2003, 26 (4 ), 207–214. 10.1016/s0166-2236(03)00067-5 12689772
15. Maynard  C. J., Bush  A. I., Masters  C. L., Cappai  R., Li  Q.-X., Metals and amyloid-B in Alzheimer's disease, Int. J. Exp. Pathol., 2005, 86 (3 ), 147–159. 10.1111/j.0959-9673.2005.00434.x 15910549
16. Religa  D., Strozyk  D., Cherny  R. A., Volitakis  I., Haroutunian  V., Winblad  B., Naslund  J., Bush  A. I., Elevants cortical zinc in Alzheimer's disease, Neurology, 2006, 67 (1 ), 69–75. 10.1212/01.wnl.0000223644.08653.b5 16832080
17. Mattson  M. P. , Calcium and neurodegeneration, Aging Cell, 2007, 6 (3 ), 337–350. 10.1111/j.1474-9726.2007.00275.x 17328689
18. Jomova  K., Vondrakova  D., Lawson  M., Valko  M., Metals, oxidative stress and neurodegenerative disorders, Mol. Cell. Biochem., 2010, 345 (1–2 ), 91–104. 10.1007/s11010-010-0563-x 20730621
19. Roberts  B. R., Ryan  T. M., Bush  A. I., Masters  C. L., Duce  J. A., The role of metallobiology and amyloid-beta peptides in Alzheimer's disease, J. Neurochem., 2012, 120 (s1 ), 149–166. 10.1111/j.1471-4159.2011.07500.x 22121980
20. Bush  A. I. , The metal theory of Alzheimer's disease, J. Alzheimers Dis., 2012, 33 (1 ), S277–SS81. 10.3233/JAD-2012-129011
21. Ayton  S., Lei  P., Bush  A. I., Metallostasis in Alzheimer's disease, Free Radic. Biol. Med., 2013, 62 , 76–89. 10.1016/j.freeradbiomed.2012.10.558 23142767
22. Tao  Y., Wang  F., Rogers  J., Wang  F., Perturbed iron distribution in Alzheimer's disease serum, cerebrospinal fluid, and selected brain regions—a systematic review and mata-analysis, J. Alzheimers Dis., 2014, 42 (2 ), 679–690. 10.3233/JAD-140396 24916541
23. Sastre  M., Ritchie  C. W., Hajji  N., Metal ions in Alzheimer's disease brain, JSM Alzheimers Dis. Relat. Dement., 2015, 2 (1 ), 1014.
24. Cristovao  J. S., Santos  R., Gomes  C. M., Metals and neuronal metal binding proteins implicated in Alzheimer's disease, Oxid. Med. Cell Longev., 2016, 2016 (1 ), 9812178. 10.1155/2016/9812178 26881049
25. Mot  A. I., Crouch  P. J., Biometals and Alzheimer's disease. In: AR  White, M  Aschner, LG  Costa, AI  Bus (eds), Biometals in Neurodegenerative Diseases. Amsterdam, Netherlands: Academic Press, 2017, pp. 1–17.
26. Acevedo  K., Masaldan  S., Opazo  C. M., Bush  A. I., Redox active metals in neurodegenerative diseases, J. Biol. Inorg. Chem., 2019, 24 (8 ), 1141–1157. 10.1007/s00775-019-01731-9 31650248
27. Mielke  M. M., Zandi  P. P., Blennow  K., Gustafson  D., Sjögren  M., Rosengren  L., Skoog  I., Low serum potassium in mid life associated with decreased cerebrospinal fluid Ab42 in late life, Alzheimer Dis. Assoc. Disord., 2006, 20 (1 ), 30–36. 10.1097/01.wad.0000201848.67954.7d 16493233
28. J. R. Harris and F. Fahrenholz, Alzheimer's Disease—Cellular and Molecular Aspects of Amyloid B. New York: Springer Science, 2005.
29. Babic Leko  M., Langer Horvat  L., Spanic Popovacki  E., Zubcic  K., Hof  P. R., Simic  G., Metals in Alzheimer's disease, Biomedicines, 2023, 11 (4 ), 1161. 10.3390/biomedicines11041161 37189779
30. Geyer  P. E., Voytik  E., Treit  P. V., Doll  S., Kleinhempel  A., Niu  L., Muller  J. B., Buchholtz  M. L., Bader  J. M., Teupser  D., Holdt  L. M., Mann  M., Plasma proteome profiling to detect and avoid sample-related biases in biomarker studies, EMBO Mol. Med., 2019, 11 (11 ), e10427. 10.15252/emmm.201910427 31566909
31. Haslam  D. E., Li  J., Dillon  S. T., Gu  X., Cao  Y., Zeleznik  O. A., Sasamoto  N., Zhang  X., Eliassen  A. H., Liang  L., Stampfer  M. J., Mora  S., Chen  Z. Z., Terry  K. L., Gerszten  R. E., Hu  F. B., Chan  A. T., Libermann  T. A., Bhupathiraju  S. N., Stability and reproducibility of proteomic profiles in epidemiological studies: comparing the Olink and SOMAscan platforms, Proteomics, 2022, 22 (13–14 ), e2100170. 10.1002/pmic.202100170 35598103
32. Gegner  H. M., Naake  T., Dugourd  A., Muller  T., Czernilofsky  F., Kliewer  G., Jager  E., Helm  B., Kunze-Rohrbach  N., Klingmuller  U., Hopf  C., Muller-Tidow  C., Dietrich  S., Saez-Rodriguez  J., Huber  W., Hell  R., Poschet  G., Krijgsveld  J., Pre-analytical processing of plasma and serum samples for combined proteome and metabolome analysis, Front. Mol. Biosci., 2022, 9 , 961448. 10.3389/fmolb.2022.961448 36605986
33. Valo  E., Colombo  M., Sandholm  N., McGurnaghan  S. J., Blackbourn  L. A. K., Dunger  D. B., McKeigue  P. M., Forsblom  C., Groop  P. H., Colhoun  H. M., Turner  C., Dalton  R. N., Effect of serum sample storage temperature on metabolomic and proteomic biomarkers, Sci. Rep., 2022, 12 (1 ), 4571. 10.1038/s41598-022-08429-0 35301383
34. Sunde  A. L., Alsnes  I. V., Aarsland  D., Ashton  N. J., Tovar-Rios  D. A., De Santis  G., Blennow  K., Zetterberg  H., Kjosavik  S. R., Preanalytical stability of plasma biomarkers for Alzheimer's disease pathology, Alzheimers Dement. (Amst.), 2023, 15 (2 ), e12439. 10.1002/dad2.12439 37192842
35. Moynier  F., Creech  J., Dallas  J., Le Borgne  M., Serum and brain natural copper stable isotopes in a mouse model of Alzheimer's disease, Sci. Rep., 2019, 9 (1 ), 11894. 10.1038/s41598-019-47790-5 31417103
36. Skulan  J., DePaolo  D. J., Calcium isotope fractionation between soft and mineralized tissues as a monitor of calcium use in vertebrates, Proc. Natl. Acad. Sci. USA, 1999, 96 (24 ), 13709–13713. 10.1073/pnas.96.24.13709 10570137
37. DePaolo  D. J. , Calcium isotopic variations produced by biological, kinetic, radiogenic nad nucleosynthetic processes, Rev. Mineral. Geochem., 2004, 55 (1 ), 255–288. 10.2138/gsrmg.55.1.255
38. Urey  H. C. , The thermodynamic properties of isotopic substances, J. Chem. Soc., 1947, 562–581. 10.1039/jr9470000562 20249764
39. Young  E. D., Galy  A., Nagahara  H., Kinetic and equilibrium mass-dependent isotope fractionation laws in nature and their geochemical and cosmochemical significance, Geochim. Cosmochim. Acta, 2002, 66 (6 ), 1095–1104. 10.1016/S0016-7037(01)00832-8
40. Young  E. D., Manning  C. E., Schauble  E. A., Shahar  A., Macris  C. A., Lazar  C., Jordan  M., High-temperature equilibrium isotope fractionation of non-traditional stable isotopes: experiments, theory, and applications, Chem. Geol., 2015, 395 , 176–195. 10.1016/j.chemgeo.2014.12.013
41. Fujii  T., Kato  C., Mahan  B., Moynier  F., Study on the isotope fractionation of zinc in complexation with macrocyclic polyethers, Z. Anorg. Allg. Chem ., 2021, 647 (6 ), 599–605. 10.1002/zaac.202000413
42. Selden  C. R., Schilling  K., Godfrey  L., Yee  N., Metal-binding amino acid ligands commonly found in metalloproteins differentially fractionate copper isotopes, Sci. Rep., 2024, 14 (1 ), 1902. 10.1038/s41598-024-52091-7 38253574
43. Mattson  M. P., Magnus  T., Ageing and neuronal vulnerability, Nat. Rev. Neurosci., 2006, 7 (4 ), 278–294. 10.1038/nrn1886 16552414
44. Stutzmann  G. E. , The pathogenesis of Alzheimers disease: is it a lifelong “calciumopathy”?, Neuroscientist, 2007, 13 (5 ), 546–559. 10.1177/1073858407299730 17901262
45. Moynier  F., Borgne  M. L., Lahoud  E., Mahan  B., Mouton-Liger  F., Hugon  J., Paquet  C., Copper and zinc isotopic excursions in the human brain affected by Alzheimer's disease, Alzheimers Dement. (Amst.), 2020, 12 (1 ), e12112. 10.1002/dad2.12112 33102682
46. Fujii  T., Moynier  F., Blichert-Toft  J., Albarède  F., Density functional theory estimation of isotope fractionation of Fe, Ni, Cu, and Zn among species relevant to geochemical and biological environments, Geochim. Cosmochim. Acta, 2014, 140 , 553–576. 10.1016/j.gca.2014.05.051
47. McLean  C. A., Cherny  R. A., Fraser  F. W., Fuller  S. J., Smith  M. J., Vbeyreuther  K., Bush  A. I., Masters  C. L., Soluble pool of aβ amyloid as a determinant of severity of neurodegeneration in Alzheimer's disease, Ann. Neurol., 1999, 46 (6 ), 860–866. 10.1002/1531-8249(199912)46:6<860::AID-ANA8>3.0.CO;2-M 10589538
48. Ozawa  M., Ninomiya  T., Ohara  T., Hirakawa  Y., Doi  Y., Hata  J., Uchida  K., Shirota  T., Kitazono  T., Kiyohara  Y., Self-reported dietary intake of potassium, calcium, and magnesium and risk of dementia in the Japanese: the Hisayama Study, J. Am. Geriatr. Soc., 2012, 60 (8 ), 1515–1520. 10.1111/j.1532-5415.2012.04061.x 22860881
49. Cisternas  P., Lindsay  C. B., Salazar  P., Silva-Alvarez  C., Retamales  R. M., Serrano  F. G., Vio  C. P., Inestrosa  N. C., The increased potassium intake improves cognitive performance and attenuates histopathological markers in a model of Alzheimer's disease, Biochim. Biophys. Acta, 2015, 1852 (12 ), 2630–2644. 10.1016/j.bbadis.2015.09.009 26391254
50. Vintimilla  R. M., Large  S. E., Gamboa  A., Rohlfing  G. D., O'Jile  J. R., Hall  J. R., O'Bryant  S. E., Johnson  L. A., The link between potassium and mild cognitive impairment in Mexican-Americans, Dement. Geriatr. Cogn. Dis. Extra, 2018, 8 (1 ), 151–157. 10.1159/000488483 29805381
51. Mahan  B., Tacail  T., Lewis  J., Elliott  T., Habekost  M., Turner  S., Chung  R., Moynier  F., Exploring the K isotope composition of Gottingen minipig brain regions, and implications for Alzheimer's disease, Metallomics, 2022, 14 (12 ), mfac090. 10.1093/mtomcs/mfac090
52. Sullivan  K. V., Moore  R. E. T., Vanhaecke  F., The influence of physiological and lifestyle factors on essential mineral element isotopic compositions in the human body: implications for the design of isotope metallomics research, Metallomics, 2023, 15 (3 ), mfad012. 10.1093/mtomcs/mfad012 36881726
53. Telouk  P., Albalat  E., Tacail  T., Arnaud-godet  F., Balter  V., Steady analyses of potassium stable isotopes using the Thermo Scientific Neoma MC-ICP-MS, J. Anal. At. Spectrom., 2022, 37 (6 ), 1259–1264. 10.1039/d2ja00050d
54. Moynier  F., Hu  Y., Dai  W., Kubik  E., Mahan  B., Moureau  J., Potassium isotopic composition of seven widely available biological standards using collision cell (CC)-MC-ICP-MS, J. Anal. At. Spectrom., 2021, 36 (11 ), 2444–2448. 10.1039/d1ja00294e
55. Moynier  F., Hu  Y., Wang  K., Zhao  Y., Gérard  Y., Deng  Z., Moureau  J., Li  W., Simon  J. I., Teng  F.-Z., Potassium isotopic composition of various samples using a dual-path collision cell-capable multiple-collector inductively coupled plasma mass spectrometer, Nu instruments Sapphire, Chem. Geol., 2021, 571 , 120144. 10.1016/j.chemgeo.2021.120144
56. Hu  Y., Moynier  F., Dai  W., Paquet  M., Yokoyama  T., Abe  Y., Aléon  J., Alexander  C., Amari  S., Amelin  Y., Bajo  K.-i, Bizzarro  M., Bouvier  A., Carlson  R. W., Chaussidon  M., Choi  B.-G., Dauphas  N., Davis  A. M., Di Rocco  T., Fujiya  W., Fukai  R., Gautam  I., Haba  M. K., Hibiya  Y., Hidaka  H., Homma  H., Hoppe  P., Huss  G. R., Ichida  K., Iizuka  T., Ireland  T. R., Ishikawa  A., Itoh  S., Kawasaki  N., Kita  N. T., Kitajima  K., Kleine  T., Komatani  S., Krot  A. N., Liu  M.-C., Masuda  Y., Morita  M., Motomura  K., Nakai  I., Nagashima  K., Nesvorný  D., Nguyen  A., Nittler  L., Onose  M., Pack  A., Park  C., Piani  L., Qin  L., Russell  S. S., Sakamoto  N., Schönbächler  M., Tafla  L., Tang  H., Terada  K., Terada  Y., Usui  T., Wada  S., Wadhwa  M., Walker  R. J., Yamashita  K., Yin  Q.-Z., Yoneda  S., Young  E. D., Yui  H., Zhang  A.-C., Nakamura  T., Naraoka  H., Noguchi  T., Okazaki  R., Sakamoto  K., Yabuta  H., Abe  M., Miyazaki  A., Nakato  A., Nishimura  M., Okada  T., Yada  T., Yogata  K., Nakazawa  S., Saiki  T., Tanaka  S., Terui  F., Tsuda  Y., Watanabe  S.-i, Yoshikawa  M., Tachibana  S., Yurimoto  H., Pervasive aqueous alteration in the early Solar System revealed by potassium isotopic variations in Ryugu samples and carbonaceous chondrites, Icarus, 2024, 409 , 115884. 10.1016/j.icarus.2023.115884
57. Chen  H., Saunders  N., Jerram  M., Halliday  A. N., High-precision potassium isotopic measurements by collision cell equipped MC-ICPMS, Chem. Geol., 2021, 578 , 120281. 10.1016/j.chemgeo.2021.120281
58. Cui  M. M., Moynier  F., Su  B. X., Dai  W., Hu  Y., Rigoussen  D., Mahan  B., Le Borgne  M., Stable potassium isotope distribution in mouse organs and red blood cells: implication for biomarker development, Metallomics, 2023, 15 (7 ), mfad033. 10.1093/mtomcs/mfad033
59. Hobin  K., Costas Rodriguez  M., Vanhaecke  F., Robust potassium isotopic analysis of geological and biological samples via multicollector ICP-mass spectrometry using the “extra-high resolution mode,” Anal. Chem., 2021, 93 (25 ), 8881–8888. 10.1021/acs.analchem.1c01087
60. Tacail  T., Lewis  J., Clauss  M., Coath  C. D., Evershed  R., Albalat  E., Elliott  T. R., Tutken  T., Diet, cellular, and systemic homeostasis control the cycling of potassium stable isotopes in endothermic vertebrates, Metallomics, 2023, 15 (11 ), mfad065. 10.1093/mtomcs/mfad065
61. Hobin  K., Costas Rodriguez  M., Van Wonterghem  E., Vandenbroucke  R. E., Vanhaecke  F., High-precision K isotopic analysis of cerebrospinal fluid and blood serum microsamples via multicollector inductively coupled plasma-mass spectrometry equipped with 10(13) omega faraday cup amplifier resistors, Anal. Chim. Acta, 2024, 1315 , 342812. 10.1016/j.aca.2024.342812 38879212
62. Higgins  J. A., Ramos  D. S., Gili  S., Spetea  C., Kanoski  S., Ha  D., McDonough  A. A., Youn  J. H., Stable potassium isotopes (41K/39K) track transcellular and paracellular potassium transport in biological systems, Front. Physiol., 2022, 13 , 1016242. 10.3389/fphys.2022.1016242 36388124
63. Albarede  F., Telouk  P., Lamboux  A., Jaouen  K., Balter  V., Isotopic evidence of unaccounted for Fe and Cu erythropoietic pathways, Metallomics, 2011, 3 (9 ), 926–933. 10.1039/c1mt00025j 21789323
64. Fowler  C., Rainey-Smith  S. R., Bird  S., Bomke  J., Bourgeat  P., Brown  B. M., Burnham  S. C., Bush  A. I., Chadunow  C., Collins  S., Doecke  J., Dore  V., Ellis  K. A., Evered  L., Fazlollahi  A., Fripp  J., Gardener  S. L., Gibson  S., Grenfell  R., Harrison  E., Head  R., Jin  L., Kamer  A., Lamb  F., Lautenschlager  N. T., Laws  S. M., Li  Q. X., Lim  L., Lim  Y. Y., Louey  A., Macaulay  S. L., Mackintosh  L., Martins  R. N., Maruff  P., Masters  C. L., McBride  S., Milicic  L., Peretti  M., Pertile  K., Porter  T., Radler  M., Rembach  A., Robertson  J., Rodrigues  M., Rowe  C. C., Rumble  R., Salvado  O., Savage  G., Silbert  B., Soh  M., Sohrabi  H. R., Taddei  K., Taddei  T., Thai  C., Trounson  B., Tyrrell  R., Vacher  M., Varghese  S., Villemagne  V. L., Weinborn  M., Woodward  M., Xia  Y., Ames  D., Fifteen years of the Australian imaging, biomarkers and lifestyle (AIBL) study: progress and observations from 2,359 older adults spanning the spectrum from cognitive normality to Alzheimer's disease, J. Alzheimers Dis. Rep., 2021, 5 (1 ), 443–468. 10.3233/ADR-210005 34368630
65. Moynier  F., Le Borgne  M., High precision zinc isotopic measurements applied to mouse organs, J. Vis. Exp., 2015, (99 ), e52479. 10.3791/52479 26065372
66. Li  W., Cui  M., Pan  Q., Wang  J., Gao  B., Liu  S., Yuan  M., Su  B., Zhao  Y., Teng  F.-Z., Han  G., High-precision potassium isotope analysis using the Nu Sapphire collision cell (CC)-MC-ICP-MS, Sci. China Earth Sci., 2022, 65 (8 ), 1510–1521. 10.1007/s11430-022-9948-6
67. Clausen  M. V., Hilbers  F., Poulsen  H., The structure and function of the Na,K-ATPase isoforms in health and disease, Front. Physiol., 2017, 8 , 371. 10.3389/fphys.2017.00371 28634454
68. Neese  F. , The ORCA program system, WIREs Comput. Molec. Sci., 2011, 2 (1 ), 73–78. 10.1002/wcms.81
69. Adamo  C., Barone  V., Toward reliable density functional methods without adjustable parameters: the PBE0 model, J. Chem. Phys., 1999, 110 (13 ), 6158–6170. 10.1063/1.478522
70. Weigend  F., Ahlrichs  R., Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: design and assessment of accuracy, Phys. Chem. Chem. Phys., 2005, 7 (18 ), 3297–3305. 10.1039/b508541a 16240044
71. Mancinelli  R., Botti  A., Bruni  F., Ricci  M. A., Soper  A. K., Hydration of sodium, potassium, and chloride ions in solution and the concept of structure maker/breaker, J. Phys. Chem. B, 2007, 111 (48 ), 13570–13577. 10.1021/jp075913v 17988114
72. Varma  S., Rempe  S. B., Coordination numbers of alkali metal ions in aqueous solutions, Biophys. Chem., 2006, 124 (3 ), 192–199. 10.1016/j.bpc.2006.07.002 16875774
73. Meija  J., Coplen  T. B., Berglund  M., Brand  W. A., De Bièvre  P., Gröning  M., Holden  N. E., Irrgeher  J., Loss  R. D., Walczyk  T., Prohaska  T., Isotopic compositions of the elements 2013 (IUPAC Technical Report), Pure Appl. Chem., 2016, 88 (3 ), 293–306. 10.1515/pac-2015-0503
74. Bigeleisen  J., Mayer  M. G., Calculation of equilibrium constants for isotopic exchange reactions, J. Chem. Phys., 1947, 15 (5 ), 261–267. 10.1063/1.1746492
75. Schauble  E. A. , Applying stable isotope fractionation theory to new systems, Rev. Mineral. Geochem., 2004, 55 (1 ), 65–111. 10.2138/gsrmg.55.1.65
76. Wang  K., Li  W., Tian  Z., Koefoed  P., Zheng  X.-Y., Geochemistry and cosmochemistry of potassium stable isotopes, Geochemistry, 2021, 81 (3 ), 125786. 10.1016/j.chemer.2021.125786
77. Hanley  J. A., McNeil  B. J., The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 1982, 143 (1 ), 29–36. 10.1148/radiology.143.1.7063747 7063747
78. Moynier  F., Fujii  T., Calcium isotope fractionation between aqueous compounds relevant to low-temperature geochemistry, biology and medicine, Sci. Rep., 2017, 7 , 44255. 10.1038/srep44255 28276502
79. Zanchi  D., Giannakopoulos  P., Borgwardt  S., Rodriguez  C., Haller  S., Hippocampal and amygdala gray matter loss in elderly controls with subtle cognitive decline, Front. Aging Neurosci., 2017, 9 , 50. 10.3389/fnagi.2017.00050 28326035
80. Ji  X., Wang  H., Zhu  M., He  Y., Zhang  H., Chen  X., Gao  W., Fu  Y., Alzheimer's disease neuroimaging I. Brainstem atrophy in the early stage of Alzheimer's disease: a voxel-based morphometry study, Brain Imaging Behav., 2021, 15 (1 ), 49–59. 10.1007/s11682-019-00231-3 31898091
81. Yu  S. P., Farhangrazi  Z. S., Ying  H. S., Yeh  C.-H., Choi  D. W., Enhancement of outward potassium current may participate in β-amyloid peptide-induced cortical neuronal death, Neurobiol. Dis., 1998, 5 (2 ), 81–888. 10.1006/nbdi.1998.0186 9746905
82. Asirvatham  J. R., Moses  V., Bjornson  L., Errors in potassium measurement: a laboratory perspective for the clinician, N Am J Med Sci, 2013, 5 (4 ), 255–259. 10.4103/1947-2714.110426 23724399
83. Udensi  U. K., Tchounwou  P. B., Potassium homeostasis, oxidative stress, and Human disease, Int J Clin Exp Physiol, 2017, 4 (3 ), 111–122. 10.4103/ijcep.ijcep_43_17 29218312
84. Fandos  N., Perez-Grijalba  V., Pesini  P., Olmos  S., Bossa  M., Villemagne  V. L., Doecke  J., Fowler  C., Masters  C. L., Sarasa  M., Group  A. R., Plasma amyloid beta 42/40 ratios as biomarkers for amyloid beta cerebral deposition in cognitively normal individuals, Alzheimers Dement. (Amst.), 2017, 8 (1 ), 179–187. 10.1016/j.dadm.2017.07.004 28948206
85. Doecke  J. D., Perez-Grijalba  V., Fandos  N., Fowler  C., Villemagne  V. L., Masters  C. L., Pesini  P., Sarasa  M., Group  A. R., Total abeta(42)/abeta(40) ratio in plasma predicts amyloid-PET status, independent of clinical AD diagnosis, Neurology, 2020, 94 (15 ), e1580–e1e91. 10.1212/WNL.0000000000009240 32179698
86. Janelidze  S., Mattsson  N., Palmqvist  S., Smith  R., Beach  T. G., Serrano  G. E., Chai  X., Proctor  N. K., Eichenlaub  U., Zetterberg  H., Blennow  K., Reiman  E. M., Stomrud  E., Dage  J. L., Hansson  O., Plasma P-tau181 in Alzheimer's disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer's dementia, Nat. Med., 2020, 26 (3 ), 379–386. 10.1038/s41591-020-0755-1 32123385
87. Brickman  A. M., Manly  J. J., Honig  L. S., Sanchez  D., Reyes-Dumeyer  D., Lantigua  R. A., Lao  P. J., Stern  Y., Vonsattel  J. P., Teich  A. F., Airey  D. C., Proctor  N. K., Dage  J. L., Mayeux  R., Plasma p-tau181, p-tau217, and other blood-based Alzheimer's disease biomarkers in a multi-ethnic, community study, Alzheimers Dement., 2021, 17 (8 ), 1353–1364. 10.1002/alz.12301 33580742
