Amaranthus palmeri, a fast adapting and highly competing plant. Here, we have examined the interactive effects of abiotic stress on mineral accumulation, chlorophyll a and b content, and the operating capacity of Photosystem II (PSII) in both male and female A. palmeri plants grown under three different intensities of white light, and under N, K or P deficiency. Mineral profiling of the leaves and stems (with inflorescence) highlighted intra- and intersexual differences in their accumulation pattern and mineral associations. Chlorophyll a and chlorophyll b were different between the male and the female plants, being slightly lower in the latter, at high light intensity towards maturity, or under K or P deficiency. Further, slight, although statistically significant differences were recorded in the chlorophyll a/b ratio, which was lower at the higher light intensity in the female, over that in the male, plants towards maturity. Chlorophyll fluorescence parameters, i.e., steady state and maximum fluorescence increased under high light intensity, whereas the PSII operating efficiency decreased in the female plants, indicating reduced PSII capacity. Sex-specific differences in A. palmeri showed a differential response to stressful conditions because of differences in their ontogeny and physiology, and possibly due to the cost of reproduction. We suggest that the breeding system of dioecious species has weaknesses that can be used for the ecological management of dioecious weeds without relying on the use of herbicides.The evolution of secondary sex-specific traits of dioecious species under abiotic stress conditions has received limited research, especially in the case of  Amaranthus palmeri, one of the most problematic weeds in many row and vegetable crops worldwide, exhibits remarkable biological and physiological characteristics such as rapid growth rate, high prolific capacity, and great adaptability under biotic  or abiotic  stressful conditions2. These characteristics enhance its invasive potential, competitive ability4 and its capability to develop multiple herbicide resistance to a range of herbicide families5 resulting in high infestation levels of natural habitats and agroecosystems4. Consequently, its presence leads to increases in food production costs3.A. palmeri grows well under both xerophytic and heliophytic conditions6. It prefers warm or moderate temperatures and thrives in soils rich in nitrogen content6. In addition, climate change imposes a strong selection pressure on weeds7 that eventually adapt rapidly to changes through alterations in their biology, physiology, and phenology8. This is particularly true for C4 weed species, as is the case for A. palmeri, which has already expanded its habitable range in the northern states of USA, as well as in the southern Canadian provinces8. Fitzpatrick et al.9 have emphasized the need for trait-based studies since phenotypic variations can affect plant-environment trade-offs, thus deepening our understanding of species interaction with its environment and consequently the species habituation. However, the habituation of a species, an important phenomenon per se, does not intrinsically expand the behavioral repertoire of an organism10. This is of a particular importance since the obligatory outcrossing of A. palmeri, due to its dioecious nature, results in high genetic diversity, broad adaptability, and enhanced evolutionary capacity12. Furthermore, A. palmeri is known to exhibit great plasticity to external stimuli and has distinguishable sex differentiation under a wide range of environments14. Research on the plasticity of plants has revealed morphological and allocation responses to environmental stimuli along with ontogenetic trajectories and offspring traits15. Further, dioecious species have evolved sex-specific secondary traits, i.e., secondary sexual dimorphism and functional differences16, some of them attributed to differences in their reproductive functions17, but also to physiological and ontogenetic functions during their vegetative growth20. For example, greater height and biomass production in the female than in the male A. palmeri plants grown under field conditions have been reported21. In addition, as reported by Delph et al.22 and Morales et al.23, sexual dimorphism in dioecious species is expressed through many ecophysiological traits including vegetative mass, leaf mass, leaf thickness, flower size, and CO2 exchange rate. This differential response is a fitness strategy by the female plants in their “effort” to reproduce24 and is related to physiological functions of the plant such as photosynthetic performance, water use efficiency or even phenology26. Another characteristic of dioecious species, “division of labor”27, is expressed through the manifestation of resource acquisition and allocation. In complex and ever-changing environment, resources are often scarce with unequal spatial and temporal distribution28. As plants are simultaneously exposed to diverse abiotic stresses, such as mineral deficiency, and/or inadequate light environment, they have developed specific mechanisms to tackle different types of abiotic stress they are exposed to during their growth29.As a summer ephemeral species, 30. In A. palmeri, light environment affects acclimation through adjustments in plant ontogeny, especially of morphological traits during both its vegetative and reproductive stages31. Investigating and characterizing the complex interactions between weeds and their environment in natural habitats and agroecosystems is vital for understanding the dynamics of species occurrence in both natural and agricultural-based habitats31. To the best of our knowledge, limited research has demonstrated the differential response of dioecious male and female A. palmeri plants to abiotic stress related to N, P and K deficient environment, as well as to varying light intensities14. The research described here clearly reveals key differences in sex-specific secondary traits related to biological and functional characteristics between the male and the female A. palmeri plants grown under different light intensities and NPK deficient environment. We suggest that the male and the female plants, grown under stress conditions, initiate secondary sex-specific traits related to mineral content accumulation, chlorophyll content and photosynthetic capacity. Specific research questions addressed here include: (i) Does A. palmeri mineral content accumulation differ between male and female plant organs (leaves vs. stems including inflorescences) when they are exposed to abiotic stress? (ii) Do male and female A. palmeri plants exhibit differences in chlorophyll content when grown under abiotic stress? (iii) Is the operating efficiency of PSII, as evaluated by measurements of chlorophyl fluorescence, of A. palmeri male plants less functionally plastic than that of the female plants under abiotic stress? Answers to these questions have revealed important information on the ontogenetic and physiological secondary traits that allow for greater competitive ability and invasive potential of dioecious species such as A. palmeri. However, the dioecious nature of the species conceals weaknesses that can be used for the ecological management of dioecious weeds without relying solely on the use of herbicides.Deficiency of any of the essential minerals, such as nitrogen (N), phosphorous (P) and potassium (K), restricts plant growth and development through reduction in photosynthetic efficiency. However, plants are concurrently exposed to various abiotic stresses such as mineral deficiency and varying regimes of light intensityA. palmeri grown under abiotic stress; effects where sex is not involved are not included here.We present below experimental results related to the similarities and differences between the male and female plants of A. palmeri plants showed no significant differences (P = 0.451), both in the leaves and the stems. However, mineral content between the leaves and the stems was significantly (P < 0.001) affected by NPK deficiency, which was different in male and female A. palmeri plants  between the stem and the leaf mineral content of  between ly Table  respectiA. palmeri plants , and Zn–Ca, B–Mg (in the stems) Fig. , S3 whernts Fig. , S3.FiguIn general, correlations between the micro-nutrients  were mostly observed in the leaves of female plants compared to that in the male plants Fig. A, B. We A. palmeri female plants. Further, the positive correlation, observed between Cu–K, Zn–Ca and Cu–P content in the stems, was not found in the leaves of female plants  and chlorophyll b (P = 0.006) content, averaged across the sampling times; this result was independent of the sex of the A. palmeri plants  of white light. Under K deficiency, at 450 µmol photons m−2 s−1, chlorophyll a and chlorophyll b increased significantly in the female plants compared to that in the male plants.  white light intensity. The content of chlorophyll a and chlorophyll b in female compared to male plants, under PK deficiency and high white light intensity (1300 µmol photons m−2 s−1) showed a significant decrease , in male compared to female plants  (Table a/b (Chl α/b) ratio was reduced (P = 0.0002) 28 DAT onwards in both male and female A. palmeri plants, compared to plants that were kept at lower intensities of white light (150 and 450 µmol photons m−2 s−1)  was slightly, although significantly greater (P = 0.0002) in the male compared to that in the female plants  Table . Consequ−1) Fig. . Furthernts Fig. .Figure 3A. palmeri plants throughout the experimental period showed no significant differences between the male and female plants  and chlorophyll b was 9.97 and 10.25 mg cm−2 (P = 0.712) for male and female plants, respectively. In addition, no statistically significant interaction between the untreated male and female plants was observed  when the sampling time was treated as a fixed variable. Likewise, no significant difference between the untreated male and female A. palmeri plants was observed for Chl a/b ratio (P = 0.351) when the sampling time was treated as a fixed variable.The total chlorophyll content in untreated nts Fig. . Chlorop34. Steady state fluorescence (FʹS), used in our research, is the fluorescence emission from a light-adapted leaf induced by a non-saturating irradiation35. On the other hand, maximum fluorescence (FʹM) describes the state of PSII when all its reaction centers are closed or reduced, i.e., the electron transport carrier QA (the first bound plastoquinone). Analysis of chlorophyll fluorescence parameters  and ΦPSII revealed an statistically significant interaction between the white light intensity, the sex of A. palmeri and the sampling time (P < 0.0001)  of white light, 28 DAT onwards in both steady state chlorophyll fluorescence (FʹS) (P = 0.09) and maximum fluorescence (FʹM) (P = 0.03)  Fig. .Figure 5PSII value (P = 0.04) at 1300 µmol photons m−2 s−1, 35 and 42 DAT compared to female plants. However, both male and female plants showed lower ΦPSII values at higher light intensity (1300 µmol photons m−2 s−1) in comparison to those that were kept at 150 and 450 µmol photons m−2 s−1 of white light. More specifically, ΦPSII values were 44.5 and 66.1% lower for male and female plants respectively at high light intensity compared to ΦPSII values at low white light intensity. Similarly, the ΦPSII values at 1300 µmol photons m−2 s−1 were 26 and 55.3% lower for male and female plants respectively at high light intensity compared to medium light intensity)  Φty) Fig. .Figure 6A. palmeri showed no differences between male and female plants for chlorophyll fluorescence parameter FʹS (P = 0.613) or between A. palmeri sex × sampling time (P = 0.056)  or when these parameters were analyzed against the sampling time (P = 0.586)  whereas FʹM measured were at 495.1 and 520.4 (P = 0.992). Likewise, no differences were obtained for ΦPSII between the male and female plants (P = 0.491), or when the values of ΦPSII of both male and female plants were analyzed against the sampling time (P = 0.109). ΦPSII of untreated A. palmeri male and female plants was 0.72 and 0.71 (P = 0.491) respectively.Analysis of untreated controls of SI, Fig. . LikewisSI, Fig. . FʹS valM, and ΦPSII values when they were under mineral deficient conditions  than in the leaves in contrast to the male plants which accumulate relatively equal amounts in both leaves and stems  and in the leaves of male and female A. palmeri; this is especially the case for the stems (including inflorescences) of female plants  Merr] plants, whereas Bouma44 has shown that when plants were grown at low white light intensities, P content was higher in all parts of the subterranean clover (Trifolium subterraneum L.) plant. Therefore, it is reasonable to suggest that the mineral content allocation and accumulation in plant organs, exposed to a range of light intensity, is a species-specific storage strategy to cope with light stress. Korres et al.14 have shown that female plants of A. palmeri respond to shading by stem elongation, whereas the male plants respond by increasing specific leaf area.Changes in the content of minerals in plant organs are related to dry matter accumulationms Table . The accts Table . Therefo45; however, such association is absent in A. palmeri female plants  and PK deficiencies, the male plants had greater chlorophyll a content compared to the female plants  and K deficiency  and N, P or K deficiency  at 35 and 42 DAT compared to that in the female plants, which showed greater chlorophyll a and b content at low light intensities up to 28 DAT  intensity, and under NPK mineral deficiency. Therefore, it is reasonable to suggest that increases of chlorophyll content in the male plants at high light intensity  towards maturity reflects intersexual differences in secondary sex-specific ontogenetic traits such as leaf area and specific leaf area. Chl a affects the photosynthetic activity in plants58; thus, the tendency towards a higher content of chlorophyll a and b in the male plants at high light intensity , is typically characterized by decrease of chlorophyll a/b ratio, a key parameter to judge the shade tolerance of a particular species, and other morpho-physiological traits61. Reduction of the Chl a/b ratio under low light indicates an increase in the relative abundance of Chl b62. However, Johnson et al.63 and Murchie and Horton64 showed only a weak association between Chl a/b ratio and shade tolerance. In addition, Kitajima65, and Beneragama and Goto66 reported an increase of Chl a/b ratio under low light in response to low light conditions. Similar results were obtained in this research as the Chl a/b ratio under low light intensity (150 and 450 μmol photons m−2 s−1) was higher for both Amaranthus palmeri sexes when they were exposed to high irradiance level . Consequently, changes in Chl a/b ratio is a characteristic of species themselves and not an “absolute” indicator of shade tolerance of species in general. Further, Grieco et al.67 have used the Chl a/b ratio as a parameter to monitor the acclimation processes of Arabidopsis thaliana. According to Anderson and Andersson68, differences in this ratio reflect modifications in the stoichiometry of the photosynthetic pigment protein complexes. Therefore, lower Chl a/b ratio in female plants at 42 DAT 74.Feng et al.iod Fig. . These e34. Female plants of A. palmeri show significantly higher steady state fluorescence (FʹS) values under high light intensity, especially 28 DAT onwards, compared to the male plants, indicating a lower photosynthetic performance of the former, given the competitive nature between the two processes i.e., chlorophyll fluorescence and photosynthetic efficiency75. On the other hand, the higher values of maximum fluorescence (FʹM) in female A. palmeri plants, under high light intensity, as time progresses    and total chlorophyll content are negatively correlated . This is an indication of a negative energy feedback that denotes a photoinhibition symptom, despite the positive correlation between ΦPSII and chlorophyll content, which causes reduction in the operating efficiency of A. palmeri PSII80, especially in the female plants. Further research is needed to find whether photoinhibition results from photodamage or it is a regulatory and protective adjustment to abiotic stress related to mineral deficiency and white light environment. It is also imperative, ultimately, to quantify these effects in relation to the mechanisms of non-photochemical quenching, including thermal dissipation and photosynthesis state transitions. Finally, investigating the dynamics between chlorophyll content and PSII operation efficiency will expand our knowledge in up- and down-stream regulatory processes in photosynthesis.Consequently, the lower Φity Fig.  seems toted Fig. , especiaPSII values of the female A. palmeri plants, compared to that of the male plants under P and to lesser extent under K and N deficiencies , where A. palmeri is a major problem. However, it has been hypothesized that reduction of chlorophyll content, in plants that overinvest in chlorophyll content such as soybean, can lead to an improved leaf and canopy photosynthesis83. A critical point that should be noted here is that the slight, although statistically significant, differences recorded for the chlorophyll a and chlorophyll b content and the Chl a/b ratio between the male and the female Amaranthus palmeri plants, under high white light intensity, were measured using a handheld SPAD-502 device. Chlorophyll content determination using spectrophotometry after chlorophyll extraction, using an organic solvent, could possibly have given a different value for this variable. Lichtenhaler and Babani84 investigating the differences between C4 grass species  grown under ambient light conditions and C3 plants , but not dioecious broadleaf weeds grown under abiotic stress as in our case, using a two-wavelength spectrophotometer after chlorophyll extraction with organic solvents, reported Chl a/b values between 3.4 and 4.5. However, a strong relationship has been reported between SPAD method and in vitro chlorophyll determination. Based on these observations future research on this topic is expected to enhance our understanding on the photochemical events in C4 plants.We suggest that the dioecious nature of the species has weaknesses based on which we can adjust our weed management approaches by manipulating the microclimate, e.g., light/dark conditions or mineral availability on a field scale, without relying solely on the herbicides for its control. In addition, the exogenous or endogenous stressful photodynamic treatments with the aim of decreasing the amount of chlorophyll through natural photosensitizers, for example porphyrins, merits further investigation. Rebeiz et al.Amaranthus palmeri seeds were collected from a field at the University of Arkansas, Fayetteville, AR. Cuttings from 50 different F1-generation plants of each sex, were placed under NPK deficient conditions and different white light intensities. Each NPK deficiency treatment contained only 10% N, P, or K of the standard N (91.4 g NH4NO3 L−1), P (40.3 g NaH2PO4 × 2H2O L−1) or K (71.4 g K2SO4 L−1) stock solutions85. For low, medium, and high light, we used 150, 450 and 1300 μmol photons m−2 s−1 of white light respectively. The experimental design was based on a randomized complete block arrangement with white light intensity (one growth chamber for each white light intensity) as blocking treatment and N, or P, or K deficiency (henceforth NPK deficiency) for both male and female A. palmeri cuttings as randomized treatments within each block. More specifically, for each white light-intensity, each of the 20 A. palmeri cuttings  were exposed to NPK deficient environment. Also, 10 plants from each A. palmeri sex, receiving full nutrition, were left to grow under greenhouse conditions, and used as controls. The above set of treatments was conducted twice. A detailed description of plant material preparation, experimental design and treatments is provided as Supplementary Information , leaves and stems (the latter including inflorescence) taken from five randomly selected plants were dried in a forced-draft oven at 70 °C for 48 h and mechanically ground prior to the determination of their mineral content.3/peroxide digest using hot block “digest” and inductively coupled Agron plasma (ICP) emission spectrometry, as described by Jones and Case86. Nitrogen content was determined by combustion, as described by Campbell87. To facilitate statistical analysis, mineral content was expressed in micrograms per g of leaf (or stem) dry weight.The determination of plant leaf and stem mineral content was made after digestion with a mixture of HNOa and b of both male and female A. palmeri plants was assessed by taking five measurements from five fully expanded, light-exposed leaves in the upper half of the main stem  from five randomly selected plants per treatment, using a handheld SPAD-502 . Using a leaf of 0.06 cm2 area, SPAD-502 was used to calculate an index in “SPAD units”, with an accuracy of ± 1.0 SPAD units. Chlorophyll a, chlorophyll b and total chlorophyll, in mg cm−2, based on the “SPAD units”, were estimated using the equations developed by Richardson et al.88 and Porra et al.89.Chlorophyll PSII) of Photosystem II (PSII) complex in the light-adapted state was estimated via chlorophyll fluorescence, as described by Baker79; here we used OS5p modulated fluorometer  for these measurements. For each treatment, five light-adapted, fully expanded young leaves, from the upper half of the main stem of 5 randomly selected A. palmeri plants, were chosen. These measurements were made weekly for both the experimental runs from the leaves of the same randomly chosen plants at the beginning of the experiment. Data was recorded 5 h after the initiation of the daily 14 h white light intensity treatment, i.e., in the photoperiod cycle, when the leaves were in a fully light-adapted state. Using exciting light of 1800 µmol photons m−2 s−1, we measured changes in chlorophyll fluorescence: Fʹq or ΔF = FʹM − FʹS, where F′S is the steady state fluorescence in the light-adapted state, and F′M is the maximum fluorescence induced by a saturating light pulse immediately after F′S; note that Fʹq is a measure of the photochemical quenching of chlorophyll fluorescence by open PSII centers77. In light-adapted leaves at high actinic light history, with all PSII reaction centers closed measurement of F′M is underestimated even with the highest amount of saturation light90. In addition, saturating pulses of ~ 2000 µmol photons m−2 s−1, while sufficient for measuring dark adapted FM, may be too low to provide a true FʹM under high actinic illumination. In our research we have used a method91 which involves the use of a single multiphase saturation flash of 7000 µmol photons m−2 s−1 for 0.3 s, with linearly decreasing light intensity by 20% over another 0.5 s; during this period chlorophyll fluorescence values were recorded. With this method, accurate values of F’M were recorded90. The operating  efficiency of PSII in light-adapted state of leaves (ΦPSII or ΔF/FʹM ratio) and the electron transport rate were also estimated. Particularly, an estimate of electron transport rate was calculated based on Eq.  and the leaves; for this, we measured the mineral content, chlorophyll a and chlorophyll b content, chlorophyll a/b ratio, as well as chlorophyll fluorescence parameters, namely the steady state fluorescence (F′S), maximum fluorescence (F′M), whereas the operating efficiency of PSII (ΦPSII) and the electron transport rate were estimated from these parameters. Before the analysis of stem (incl. inflorescences) and leaf mineral content, mineral concentrations were expressed as a fraction of stem and leaf dry weights  to standardize the units of mineral concentrations. The concentration of a mineral in a plant organ can affect the dynamics of another mineral in the same organ38. Therefore, an analysis of partial correlations between the leaf and the stem mineral content between A. palmeri sex was performed to examine whether the male and the female plants are different in their mineral trade-off strategies. Partial correlation analysis excludes the effects of confounding variables that are numerically related to the variables of interest. In addition, regressions were fitted between chlorophyll a, chlorophyll b, chlorophyll a/b ratio and fluorescence parameters F′S, F′M and the estimated ΦPSII against the sampling time throughout the experimental period using white light intensity as a grouping factor. Correlations were also fitted between total chlorophyll and the estimates of ΦPSII and ETR. JMP Pro v. 16.0 , and SigmaPlot v. 14.0  were used for statistical analyses and curve-fitting regressions. Means were separated by Least Significant Difference (LSD) test at a = 0.05 significance level.Analysis of variance was performed to obtain information on the effects of Plant material used in this research comply with relevant institutional, national and international guidelines and legislation.Supplementary Information.


Co-creating patient-facing educational materials (PEMs) can enhance person-centered care by responding to patient priorities and unmet needs. Little data exist on ‘best practices’ for co-creation. We followed the Arksey and O’Malley framework to conduct a systematic literature search of nine databases  to identify empirical studies published in English on PEM co-creation to distill ‘best practices’. Following an independent dual review of articles, data were collated into tables, and thematic analysis was employed to synthesize ‘best practices’ that were validated by a patient experienced in co-creating PEMs. Bias was not assessed, given the study heterogeneity. Of 6998 retrieved articles, 44 were included for data extraction/synthesis. Studies utilized heterogeneous methods spanning a range of health conditions/populations. Only 5/45 (11%) studies defined co-creation, 14 (32%) used a guiding framework, and 18 (41%) used validated evaluation tools. Six ‘best practices’ were identified: (1) begin with a review of the literature, (2) utilize a framework to inform the process, (3) involve clinical and patient experts from the beginning, (4) engage diverse perspectives, (5) ensure patients have the final decision, and (6) employ validated evaluation tools. This scoping review highlights the need for clear definitions and validated evaluation measures to guide and assess the co-creation process. Identified ‘best practices’ are relevant for use with diverse patient populations and health issues to enhance person-centered care. Co-creation is broadly conceptualized as the process of creation through interactions with others . In the A person-centered approach to care considers the whole individual and recognizes patients/families as full, active partners in the design and implementation of healthcare . Key tenCo-creation is an aspect of person-centered care representing a low-cost solution to help improve healthcare delivery, including educating and activating patients/families for self-management . Despitewww.covidence.org, (accessed on 23 December 2022)), and we report study findings using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for the reporting of scoping reviews (PRISMA-ScR).We conducted a scoping review guided by the Arksey and O’Malley framework . There iThe scoping review process was guided by a single primary question: “what is known about co-creating patient-facing materials with patients and/or families?”With the support of a research Librarian, we conducted literature searches (19 April 2022) in 9 databases . The structured search used the medical subject headings (MeSH) terms and keywords “co-creation” OR “co-production” OR “co-design” OR “co-construction” OR “co-innovation” OR “co-build” OR “codesign” OR “co-establish” OR “collaborative health” OR “patient-directed” OR “patient-centered” OR “patient focused” OR “community based participatory research” OR “patient participation” OR “patient partnership” OR “user-centered design” OR “patient partnerships” OR “patient supported” OR “patient engaged consultative” AND “learning health system” OR “learning healthcare system” OR “patient-facing materials” OR “education materials” OR “patient materials” OR “patient resources” OR “psychoeducational tool” OR “information sheet” OR “patient education materials” OR “patient education” AND “patients” OR “clients” OR “support groups” OR “family members” OR “caregivers” OR “spouses” OR “parents” OR “subjects” OR “participants” OR “patient collaborators”. No language restrictions were placed on retrieving published articles.Eligible studies were published in English, had no date restriction, and reported on the process of co-creating patient-facing educational materials  involving at least one healthcare professional and at least one patient or family member. Eligible studies could involve any healthcare discipline, disease entity, or research methodology . Studies that only included patients/families as a final validation step were excluded. Articles retrieved from the structured literature search were imported into Covidence™ for screening. After removal of duplicates, all titles and abstracts underwent independent dual review . Subsequently, the remaining articles underwent independent, dual, full-text review . Any discrepancies during the review process were resolved by group discussion (with AAD).Investigators  independently extracted data using a structured form, and findings were cross-checked by another investigator. The structured form developed for this scoping review captured the country the study was conducted in, the topic/health issue, framework/guidelines employed, whether co-creation was operationally defined, outcome measurements/tools, summary of key findings, and author-identified strengths/weaknesses. Risk of bias was not conducted due to the methodological variability of included studies.Extracted data from included articles were organized in a master table . FindingThematic categories of respective strengths (and respective weaknesses) were quantified to identify ‘best practices. The ‘best practices’ were organized along a timeline for the co-creation process to depict the natural sequencing of co-creation best practices.The final ‘best practices’ were reviewed by a patient leader (NS) who had previously participated in two projects co-creating PEMs. Discussion with the patient leader elicited feedback to refine and validate ‘best practices’.n = 17), not patient-facing educational materials (n = 14), not co-creation (n = 12), not English (n = 2)), leaving 44 articles for data extraction. There was “substantial agreement” [The literature search identified 9715 articles, of which 2717 were duplicates, leaving 6998 articles for eligibility screening . A dual tion n = , not Engials n = , not co-reement” ,49,50,51n = 17 [n = 7 [n = 4 [n = 2 [n = 1 [n = 11) [n = 4 each) and two publications each on asthma [Identified articles were published between 2007 and 2022, and 33/44 (75%) of papers were published since 2017, reflecting that co-creation is a relatively recent adoption in patient education  9,10,11,48,49,50n = 17 ,47,49,507 [n = 7 ,43,44,517 [n = 4 ,21,46,484 [n = 2 ,35, Unit2 [n = 1 , with fo2 [n = 1 ,15,38, t2 [n = 1 ,40, and 2 [n = 1 , France 2 [n = 1 , Iran [42 [n = 1 , Nigeria2 [n = 1 , Norway 2 [n = 1 , Spain [2 [n = 1 , and Swi2 [n = 1 . In tota2 [n = 1 ,41). Art[n = 11) ,23,27,29[n = 11)  and chro[n = 11) ,28,39,48n asthma , inflammn asthma ,49, raren asthma ,36, and n asthma ,20,25,49Most studies employed a framework or approach to guide the co-creation of PEM. However, nearly one-third 14/44, 31.8%) of studies .8% of st,48,49,51In terms of measuring outcomes of co-creating PEMs, almost a quarter  of studies ,39,40,43Author-reported strengths and weaknesses were captured in the data extraction form . Half ofThrough iterative discussion, the research team synthesized the findings relating to the use of a guiding theoretical framework, relative strengths/weaknesses of the studies, and outcome measures to identify the salient themes guiding the co-creation process and supporting the development of high-quality PEMs . Six key themes were identified and mapped in a temporal manner to reflect the process from planning through creation to evaluation preceding implementation. The ‘best practices’ include (i) conduct a literature review, (ii) adopt a guiding theoretical framework, (iii) involve patients and healthcare professionals from the beginning, (iv) engage diverse perspectives in the process, (v) empower patients to have the final say, and (vi) utilize validated assessment tools . As a fiOur scoping review on co-creation of PEMs identified 44 articles from the systematic review of nine databases. More than half  of studies were from groups in North America (United States and Canada). Two-thirds  of papers were published since 2017, indicating that co-creation of PEMs is a relatively recent adoption in healthcare, with growing interest. Identified studies spanned a range of health concerns and patient populations, suggesting that co-creation is an adaptable, flexible approach with broad applicability. Co-created PEMs support effective patient education, underpinning patient activation and self-management. As such, using ‘best practices’ for co-creating PEMs holds relevance for an array of stakeholders, including patients/families, healthcare providers, health systems, and payors.We identified six ‘best practices’ from our scoping review of co-creation of PEMs . First, Second, co-creating PEMs can be considered a complex intervention. Complex interventions are broadly considered as events that occur within systems . Conceptualizing interventions in the context of systems facilitates understanding of the interactions between the interventions and the context in which it is implemented. The United Kingdom Medical Research Council recommends utilizing a guiding theoretical framework when planning and implementing a complex intervention . Thus, uThird, involving both clinical and patient experts from the initial stages ensures that the process adheres to central tenets of patient-centered care  . Nearly The fourth key factor (engaging diverse perspectives) relates to involving an array of stakeholders with differing perspectives to help create value. Diversity reflects both the type of stakeholder  as well as diversity in identity . Ten (23%) articles cited the inclusion of diverse perspectives as a strength of the co-creation process ,32,48,51Fifth, the notion of empowering patients to have the final say on PEMs additionally supports the patient-centeredness of co-creation. It merits noting that among the identified ‘best practices’, empowering patients to have the final say was only noted as an author-identified strength in 3 of the 44 identified articles. The modest number of supporting articles may be an artifact of our selection process, as 12 studies were excluded  for not Last, utilizing validated instruments represents an important step in improving the rigor of work on co-creating PEMs. Many studies used evaluation methods that were not validated or evidence-based. An important caveat is that validated instruments may not be available for some co-created products . Mixed-methods approaches employing validated instruments combined with qualitative methodology can provide in-depth information. However, it is worthwhile to note there are several validated, widely used instruments measuring constructs highly relevant to PEMs. Examples include readability algorithms , Automated Readability Index, and the Linsear Write Formula), health literacy/numeracy instruments  , Rapid EThis project involved a leader of a patient organization who had experience in co-creating PEMs ,36 to vaRecent breakthroughs in natural language processing and large language models have helped generate powerful artificial intelligence and machine learning (AI/ML) tools . Such in silico tools have generated significant interest in healthcare. Recent provocative studies suggest that AI/ML chatbots can generate quality, empathetic responses to patient questions . While tAs co-creating PEMs is a relatively new phenomenon, we chose to conduct a systematic scoping review, as scoping studies are particularly relevant to areas with emerging evidence . A relatThere is growing interest in using co-creation as part of forming more person-centered approaches to care. Our synthesis of the existing literature identified six ‘best practices’ for co-creating PEMs: (i) begin by conducting a literature review; (ii) adopt a guiding theoretical framework; (iii) involve patients and healthcare professionals from the beginning; (iv) engage diverse perspectives in the co-creation process; (v) empower patients to have the final say; and (vi) utilize validated assessment tools. Our findings indicate that co-creation is a flexible, broadly applicable approach to enhancing person-centered care that is relevant to wide-ranging health conditions and patient populations. Future directions include clarifying terminology used to describe co-creation, more widespread use of validated and evidence-based evaluation tools, and establishing a structured reporting guideline  to facilitate comparability of co-creation projects in healthcare.


Despite their involvement in many cognitive functions, β oscillations are among the least understood brain rhythms. Reports on whether the functional role of β is primarily inhibitory or excitatory have been contradictory. Our framework attempts to reconcile these findings and proposes that several β rhythms co-exist at different frequencies. β Frequency shifts and their potential influence on behavior have thus far received little attention. In this human magnetoencephalography (MEG) experiment, we asked whether changes in β power or frequency in auditory cortex and motor cortex influence behavior (reaction times) during an auditory sweep discrimination task. We found that in motor cortex, increased β power slowed down responses, while in auditory cortex, increased β frequency slowed down responses. We further characterized β as transient burst events with distinct spectro-temporal profiles influencing reaction times. Finally, we found that increased motor-to-auditory β connectivity also slowed down responses. In sum, β power, frequency, bursting properties, cortical focus, and connectivity profile all influenced behavioral outcomes. Our results imply that the study of β oscillations requires caution as β dynamics are multifaceted phenomena, and that several dynamics must be taken into account to reconcile mixed findings in the literature. Spontaneous changes in brain rhythms can bias performance on perceptual tasks. Here we focus on human beta band rhythms (∼13–30 Hz) and find that not only their power, but also their frequency are related to reaction times. We observe different effects in sensory and motor cortices, suggesting there could be multiple dynamics by which beta rhythms influence behavior.β Rhythms (∼13–30 Hz) are traditionally associated with the sensorimotor system where they are prominent . Beyond Current accounts of β mechanism and function have tried to reconcile these findings . One accβ Activity has been characterized and modeled as transient, high-amplitude events or “bursts,” which can be detected at the single-trial level . β BurstThe influence of β on behavioral outcomes might depend on several factors such as β power, frequency, bursting properties, cortical focus, and connectivity profile. In the current experiment we investigated the relationship between single-trial β activity and behavior, specifically reaction times. Since analyzing neural activity in a prestimulus or pretarget interval is a convenient method to uncover the influence of ongoing neural activity on subsequent behavior , we madeWe recorded MEG in 35 adult participants, 28 of which we included in our analyses . The study was approved by the local ethics committee. All participants gave informed consent before the experiment and were given monetary compensation for their participation.The auditory target discrimination task consisted of five rhythmic blocks and five nonrhythmic blocks . The order of the blocks was randomized. In the rhythmic blocks, four cue tones were presented, separated by 0.5 s. Following the rhythmic cue, a target tone was presented at 0.5, 1, 1.5, or 2 s  or at 0.75, 1.25, 1.75 s  after the onset of the last cue tone. In the nonrhythmic blocks, the cue tone was presented continuously for a period of 1.5 s, followed by a target that was presented with a flat probability distribution within a window of 0.5–2 s after cue offset. The task for the participant was to determine whether the target tone (a 40-ms chirp) went up or down in pitch. As we had previously shown the experimental cueing manipulation not to produce behaviorally different effects , here wechirp and was either increasing or decreasing in pitch. The sound had a 10-ms cosine ramp fading in and fading out to avoid onset and offset click perception. The resulting target tone had a sample rate of 44,100 Hz and a duration of 40 ms.The cue tones had a pitch frequency of 400 Hz, a sample rate of 44,100 Hz, and a duration of 40 ms (rhythmic blocks) or 1.5 s (nonrhythmic blocks). We used a Hanning taper to remove sharp edges. The target tone consisted of 30 different frequencies randomly drawn from within 500–1500 Hz. The target was a frequency-modulated sweep created with the MATLAB function We normalized all sounds  to the same sound pressure level. We individually adjusted target stimuli to participants’ discrimination threshold, using a custom adaptive-tracking procedure aiming for a discrimination performance between 65% and 85% correct responses. The threshold was the slope of the pitch increase and decrease, measured as the range from lowest to highest frequency (starting point to end). We presented each participant with a pair of sounds (“up” and “down”) consisting of the a priori randomly generated frequencies, modulated depending on their individual threshold, i.e., the 30 base-frequencies were the same for each participant, with the participant’s individual threshold changing the start and end frequencies of the sounds.Whole-head MEG data were recorded at a 1200-Hz sampling rate with a 275-channel CTF MEG system with axial gradiometers  in a magnetically shielded room. To monitor the participants’ head movements online and for offline co-registration of anatomic landmarks, three fiducial coils were placed at the nasion and both ear canals. Anatomical, T1-weighted MRI scans for source localization purposes were obtained in a separate session, using either a 1.5 or 3 T Siemens MRI system (Siemens). To co-register the MEG and MRI data, we additionally mapped the scalp with Polhemus 3D (Polhemus).We processed the MEG data offline with the Fieldtrip toolbox . First, We co-registered the MRI to the CTF coordinate system using the fiducial points and the mapped scalp surface, and segmented the MRI image with SPM8 (as implemented in Fieldtrip).https://gitlab.com/obob/obob_ownft). In order to model virtual sensors at the locations of maximum evoked activity in both the auditory and motor sources in the right and left hemispheres respectively, we used a linearly constrained minimum variance (LCMV) beamformer approach . We then normalized the difference of the sources of the pre and post windows and projected onto their co-registered anatomic scans b. For viWe performed all further data analysis using the Fieldtrip toolbox  and custd based on the average difference in the data within a cluster  activity, and to test the relationship between reaction times and β power, we extracted 700 ms of precue and pretarget data and computed single-trial Fourier spectra (0–30 Hz) with a fast Fourier approach and a Hanning taper, padded to 2 s for a frequency resolution of 0.5 Hz. We log-transformed the single-trial power data and extracted power in the β frequency range (13–30 Hz). For the reaction time contrast, we split the pretarget data along the median reaction time, and averaged the power spectra for faster and slower reaction times. We tested for group-level differences in both contrasts with a cluster-based permutation approach , cluster cluster .For time-resolved analyses, we performed time-frequency transformation based on multiplication in the frequency domain, using a sliding time window of 250 ms in steps of 20 ms from −750 to +250 ms relative to target onset, in steps of 0.5 Hz between 13 and 30 Hz. We then averaged power within this window to obtain a per-region normalization factor and divided each time-frequency point by that factor. Finally, we averaged across the frequency dimension within the β band and extracted single-trial β time-courses for the 700-ms pretarget window.d based on the average difference in the data within a cluster , relating each single-trial, pretarget time point of β power with the subsequent reaction time on that trial. This provided a time course of regression slopes per participant. We then generated time-courses of slopes obtained by randomly shuffling the correspondence between power values and reaction times, and tested for group-level differences between the real and shuffled data with a cluster-based permutation approach , cluster cluster . We alsoTo examine β burst properties in the source-reconstructed signals, we used the time-frequency representations as described above. We computed the mean and standard deviation of power within a trial for each frequency, and marked the time-frequency points that exceeded two standard deviations above the mean and that lasted at least the duration of one cycle (defined as 1/frequency). We zoomed in on the precue and pretarget delays, and based on temporal and spectral adjacency, we clustered the marked time-frequency points into burst events. We then extracted six parameters of interest from these burst events: for each trial, we counted the number of burst events. Focusing on the event that contained the time-frequency point with the highest power, we extracted the maximum power, the time point with maximum power, the frequency with maximum power, the frequency range, and the time range. This gave us single-trial estimates of burst properties during the precue and pretarget intervals.t tests, and estimated effect sizes by calculating Cohen’s d. To examine the relationship between reaction times and burst properties, we z-scored the burst properties in the pretarget interval and reaction times, and used linear regression analysis to relate them. To test for group-level relationships, we used paired t tests contrasting the regression slopes against a shuffled distribution.To contrast precue and pretarget burst properties at the group level, we used paired To investigate the time course of the peak β frequency in the source-reconstructed signals, we analyzed instantaneous frequency as detailed by Cohen . Brieflyd based on the average difference in the data within a cluster , we used a cluster-based permutation approach , and est cluster . To rela cluster .t test, and estimated effect sizes by calculating Cohen’s d.To test the relationship between reaction time and peak frequencies in the power spectra, we averaged power spectra separately for slower and faster trials (based on median split), and detected the peaks of maximum power within the β range. We then contrasted the peaks at the group level with a paired d based on the average difference in the data within a cluster  interval . We foun10.1523/ENEURO.0473-22.2023.f2-1Extended Data Figure 2-1Figure 2-1, EPS file.Time-frequency analysis. Top, Pretarget (vs precue) β power was significantly suppressed throughout the entire tested time-frequency range. Bottom, No significant differences in β power between pretarget and precue intervals. Download p = 1e-6, d = −0.78; see also Extended Data p = 2e-4, d = 0.85). Consistently, there were fewer bursts (t(27) = −5.6, p = 6e-6, d = −1.06; Wilcoxon signed rank z-value = −3.9, p = 9e-5), with narrower time spans (t(27) = −9.0, p = 1.3e-9, d = −1.70) and narrower frequency spans (t(27) = −12.4, p = 1.4e-12, d = −2.35), and the peak burst frequency was also increased (t(27) = 5.4, p = 1.3e-5, d = 1.01) during the pretarget delay as compared with baseline (t(27) = 3.6, p = 0.001, d = 0.69). There were no differences in the maximum power of the bursts.In motor cortex, we observed the pretarget power decrease across the whole range of β frequencies .In addition, there were more bursts (t(27) = 5.3, p = 1.5e-5, d = 0.99; Wilcoxon signed rank z-value = 3.7, p = 2e-4) with wider time spans (t(27) = 4.6, p = 1e-4, d = 0.86) and wider frequency spans (t(27) = 5.1, p = 2.5e-5, d = 0.96) during the pretarget delay as compared with baseline . This effect was present throughout the whole pretarget interval when looking at the time-resolved power envelopes . A time-resolved single-trial regression approach confirmed this effect as well . To further characterize this difference, we zoomed in on the β bursting profile and found that slower reaction times were preceded by more bursts  with wider time spans  and wider frequency spans .In motor cortex a–c, slowp = 0.016, d = −0.63), an effect driven by differences in the 17- to 19-Hz frequency range. However, pretarget β power was not related to reaction times when using the time-resolved regression approach . Given the discrepancy in results between the two approaches, we further investigated the observed difference in auditory cortex as a possible shift in peak frequency.In auditory cortex, when splitting the data along the median reaction time and contrasting the power spectra, we found an effect opposite to that observed in motor cortex, such that faster reaction times were preceded by higher β power (t(24) = 2.4, p = 0.026, d = 0.48). This effect was most pronounced around 660–450 ms before target onset when looking at instantaneous frequency . A time-resolved single-trial regression approach confirmed the same result . When zooming in on the peak burst frequencies, we found the same relationship again . In motor cortex, β frequency was not related to reaction times .In auditory cortex d–f, slowp = 0.027, d = 0.57). The difference was most prominent at frequencies from 19 to 20 Hz , most prominently at frequencies from 20 to 21 Hz (We then asked whether auditory-motor β connectivity was related to reaction times (using the median-split approach). Slower reaction times were preceded by increased β connectivity between auditory and motor cortices, as quantified with pairwise phase consistency  reaction times were preceded by increased β power in motor cortex, increased β frequency in auditory cortex, and increased motor-to-auditory connectivity in the β range. The results were robust across our analysis approaches. We used a regression approach to relate single-trial reaction times to β measures, as well as a median-split approach to relate slower versus faster trials to changes in β measures, with both approaches yielding the same pattern of results. We further analyzed β activity separately in a time-resolved manner, a frequency-resolved manner, and by characterizing its burst profile, with all approaches yielding the same pattern of results.β Activity over somato-motor cortex is traditionally viewed as a component of the μ rhythm . This β rhythm has been associated with slower movement (or lack of movement) and therefore thought to reflect cortical inhibition . More reIn auditory cortex, β activity has been observed in tandem with alpha activity, and assumed to serve a similarly inhibitory function . HoweverBased on this set of results, we speculate that the β rhythm potentially serves different functions and operates via different mechanisms in different cortical locations. On the one hand, a local change in β power could relate to the local excitability of a brain region in an “α-like” way , meaningThe nonstationarity of β rhythm  frequencies across time has so far received little attention. Models of β function account for the potential of different β rhythms occurring at different frequencies, assuming different cortical locations or different generators within a location. But it is so far under-appreciated that a single rhythm can shift in frequency over time . FrequenBeyond local β dynamics, β has also been shown to be involved in long-range communication between cortical sites . Here, wFinally, our results imply that the analysis of β oscillations requires caution as β dynamics are multifaceted phenomena. For example, it is possible that observed power modulations are better explained as frequency shifts (as is the case for our results). It is also possible that the β rhythm serves different functions  depending on the cortical region where it is found or depending on whether it is local or interareal. Future investigations could focus on interareal variability in β peak frequency, for example in intracranial human electrophysiological recordings.


APOE signal on chr19q.13.32 , driven by APOE ɛ4, and five additional novel associations  independent of APOE ɛ4. APOE ɛ4 and ε2 showed race specific effect with stronger association in Non-Hispanic Whites, with the lowest association in Asians. Besides the APOE, we also identified three other genome-wide loci: ABCA7 , CR1  and FERMT2 locus  that all colocalized with AD risk. Sex-stratified analyses identified two novel female-specific signals on chr5p.14.1  and chr11p.15.2 . We also demonstrated that the overall genetic architecture of brain amyloidosis overlaps with that of AD, Frontotemporal Dementia, stroke, and brain structure-related complex human traits. Overall, our results have important implications when estimating the individual risk to a population level, as race and sex will needed to be taken into account. This may affect participant selection for future clinical trials and therapies.Amyloid PET imaging has been crucial for detecting the accumulation of amyloid beta (Aβ) deposits in the brain and to study Alzheimer’s disease (AD). We performed a genome-wide association study on the largest collection of amyloid imaging data  to date, across multiple ethnicities from multicenter cohorts to identify variants associated with brain amyloidosis and AD risk. We found a strong The online version contains supplementary material available at 10.1186/s40478-023-01563-4. APOE ɛ4 locus, implicating various biological process e.g., amyloid processing and innate immunity in the development of AD [Alzheimer’s disease (AD) is a complex polygenic disease with a genetic heritability estimated to be 58–79% . This hint of AD –5. Howevnt of AD  and a lain-vivo detection of Aβ accumulation in the brain, as measured by positron emission tomographic (PET) imaging tracers such as 11C-labeled Pittsburgh Compound-B (PiB) [Although most of the genetic studies on AD are focused on clinical diagnosis as the primary outcome, using quantitative endophenotypes can also be helpful in identifying additional AD-related genes. Two such AD-related endophenotypes are accumulation of amyloid-beta (Aβ) in the brain and the formation of tau deposits in the form of neurofibrillary tangles and dystrophic neurites (tau pathology) . Differe-B (PiB)  has provAPOE locus [RBFOX1 gene. However, clinical heterogeneity across these studies and their limited sample size demands further investigations to replicate and expand on these findings. Here, we systematically analyzed the largest collection of amyloid imaging data , across multiple ethnicities from multicenter cohorts  as a quantitative trait to identify the functional variants and genes driving the association of AD. Furthermore, we have conducted gender-, and APOE-stratified analyses to investigate the effect of these variables on brain amyloidosis.Previous genetic investigations leveraging amyloid PET imaging data as AD endophenotype have established its association with OE locus –14. In aOE locus , a novelFor this study, we collected data from 14 different cohorts with a total sample size of 13,409 participants  or clinical AD (cases), based on their Clinical Dementia Rating® (CDR®) that was available for 86% of the complete dataset. The CDR is a scaling system that categorizes the overall dementia severity for each participant into five classes . For this study, individuals with CDR = 0 were considered as controls and the remaining were classified as cases. Participants were included if they had measurements of raw amyloid-PET levels and corresponding genotype data. Any participant that was missing information about the sex, age, and genetic principal components (PCs), was excluded from the study.For standardization, amyloid PET data from each cohort was normalized to their reference cerebellar regions in order to obtain standardized uptake value ratios (SUVR) in a composite of cortical brain areas. As different cohorts obtained quantitative amyloid PET data using different tracers , the available raw phenotypic data cannot combine unless it is normalized in advance. To normalize amyloid PET endophenotype across different cohorts, we converted different amyloid imaging measures into log-normalized z-score using “scale” function in base R. Briefly, z-scores were calculated by using the mean and standard deviation (SD) units across each cohort and applied to the entire endophenotype in order to account for within cohort variation. Samples having normalized z-score 3-SD away from the mean of the population were considered as outliers and removed from the subsequent analyses.For seven additional cohorts , the raw phenotype and genetic data was not accessible due to the strict patient data sharing policies Table . In thoshttps://imputation.biodatacatalyst.nhlbi.nih.gov/#!). Phasing was performed using eagle v2.4 [2) of 0.3 or greater were retained [−6) were also removed. Subject duplication and relatedness were estimated from identity-by-descent (IBD) analysis carried out in Plink version 2.0 [The genotyping platforms used by each cohort are listed in the respective studies , 27–29. gle v2.4  and onlyretained . Genotypsion 2.0 . In caseAPOE ε4 carriers. The number of cases and controls samples were 23% and 63%, respectively, and 14% of the individuals were missing the clinical diagnosis. There was an even distribution of gender across all cohorts with a total of 48% female and 52% male participants.After QC of genotype and phenotype data, a total of 7557 participants remained available from eight different cohorts for the subsequent analyses. According to the genetic PCA, participants were determined to be Non-Hispanic Whites , American Africans , and Asians ASN; n = 162). Additionally, summary statistics data was leveraged from six external amyloid PET cohorts to further increase the study sample size for NHW  and ASN (n = 1494) ancestries  and the meta-analysis for all cohorts for those having raw phenotypic data available . Briefly, (1) the raw phenotype values were converted to log10 scale for each cohort and each cohort was analyzed independently. Then the cohort-specific results were combined by meta-analyses. For meta-analysis, we used METAL software and standard-error (SE)-based meta-analysis approach. (2) In parallel, the raw phenotypes were also converted to z-score, separately for each cohort, combined by performing a single joint analyses. (3) Finally, the p-values and effect sizes of associations were compared between these two analyses . We assessed the correlation of effect sizes and p-values at different p-values thresholds of associations  and found very strong correlation (R > 0.94) in all comparisons  across different ethnicities, we used two-sample t-test that takes into account the BETA and standard error (SE) for performing a pair-wise comparison and provide the Multiple post-GWAS analyses were conducted for the functional annotation of the identified hits. A schematic overview of study design and conducted analyses is provided in Fig. We used Genetic Covariance Analyzer (GNOVA) program  for asseWe performed co-localization analysis using COLOC  tool -clumped SNPs by retaining the SNP with the smallest p-value and excluding the variants with r2 > 0.1 in a 250-kb window. We also calculated PRS by excluding the APOE region  because of the high LD in this region.PRSice-2 software  was usedAPOE-, sex-, and diagnosis-stratified analyses were performed including those datasets with individual-level data to identify additional loci and genes to further explore the genetic architecture of brain amyloidosis. Functional annotation and expression and protein Quantitative Trait Loci (pQTL) mapping was performed to identify the most likely functional genes driving the associations for the GWAS signals. Finally, the overlap between the genetic architecture of brain amyloid imaging with AD risk and other traits were analyzed using Polygenic Risk Scores (PRS) and genetic covariance analysis.In this study, we harmonized and integrated brain amyloid imaging and genetic data from 14 different cohorts with a total sample size of 13,409  on the FERMT2 locus and chr1q.32.2  on the CR1 locus , the chr14q.22.1, rs117834516, with STYX mRNA levels (PP.H4 = 0.93), but not FERMT2 (PP.H4 = 0.08)  from 13 different cohorts yielded a very strong signal in the 1,556 front of AD . Specifispho-Tau .APOE locus  that passed the genome-wide significance. This same SNP has a MAF of 1.3% in ASN  and 0.21% in NHW  but it was not even nominally associated in these two ancestries, suggesting it to be an AFR-specific signal, or a false positive. Additional studies having larger dataset, focused on individuals with AFR background, will be needed to further replicate this association. This SNP is located in the PTDSS1 gene-region that also includes GDF6, UQCRB, MTERF3, and SDC2 genes  with a consistent negative log2 fold change [PTDSS1 as the functional gene in this locus.In case of AFR-specific analyses (n = 359), rs429358 was found to be the most significant SNP in the d change , nominatAPOE locus , no additional genome-wide signals were found besides ,494, no –09, β = 0.07, SE = 0.01, MAF = 0.32, I2 = 0; Fig. ABCA7 gene , and colocalizes (PP.H4 = 0.99) with AD risk [ABCA7 mRNA levels (PP.H4 < 0.05) based on GTEx [RNU6-2 and GRIN3B were colocalized with rs12151021 in brain frontal cortex (PP.H4 = 0.93) and brain cortex (PP.H4 = 0.94), respectively  further validated the results from individual ancestry GWAS and revealed one additional locus passing the genome-wide significance threshold on chr19p.13.3  is a strong genetic risk factor for AD. Previous studies have shown that APOE ε4 individuals have higher amyloid burden [APOE ε2 carriers, showing reduced Aβ deposition [The d burden , 52. Conposition , have beposition .APOE ε2/ε3/ε4, we performed APOE conditional analyses using the summary statistics data from the multi-ethnic meta-analysis , using COJO [APOE ε4, was found to be the most significant SNP from chr19 . Therefore, a conditional analysis was performed by adjusting for rs429358 (APOE ε4) and rs7412 (APOE ε2). A total of 50 SNPs on the APOE locus remained genome-wide significant at P < 5 × 10–08  that remained genome-wide significant in the conditional analyses, indicating that there are up to six independent signals in the APOE locus  with AD risk GWAS [In order to determine if the signals observed in the current analysis are driven by ing COJO . In the ysis N = 3,409, usysis N = 3,409, usysis N = 3,409, usysis N = 3,409, usAPOE ε4, APOE ε2, we conditioned on the full APOE genotypes: ε22, ε32, ε33, ε24, ε34 and ε44, codified as 0, 1, 2, 3, 4 and 5 respectively , after condition for full APOE genotype. This SNP is in high LD with rs73052335 , but instead of conditioning on the SNPs that codified for APOE exhibits a race-specific effect, we analyzed the association between quantitative amyloid PET levels and APOE-associated variants on chr19q.13.32 . APOE ε4 SNP showed a consistent and positive association with amyloid PET levels  and AFR  ethnicities.In order to assess whether APOE ε2 variant across all ethnicities with a consistent negative effect size , AFR had almost half the effect size of NHW, indicating a race-specific effect of APOE ε2.We observed a protective role of APOE locus, rs5117, consistently positively associated with brain amyloidosis  and ASN  ethnicities. The fourth independent signal in the APOE locus, rs73052335, also showed consistently positive association across all races , but the observed effect size was not race-specific. Overall, this data indicates that APOE is a complex locus in which multiple race-specific signals influence brain amyloidosis.The third independent signal in the APOE ε4 and protective effect of APOE ε2 alleles in AD, we asked if amyloid positivity also shows any APOE ε4-dependent difference. Therefore, we performed an APOE ε4-stratified analysis and observed genome-wide significant SNPs in both APOE ɛ4+  and APOE ɛ4-  strata , which is capturing the association of one vs two APOE ɛ4 alleles. The signal in the CR1 locus, chr1q.32.2  was in high LD (R2 = 0.97) with previously observed signal in the NHW and multi-ethnic meta-analysis (chr1q.32.2), also remained significant in the APOE ɛ4+ strata, but only nominally significant in the APOE ɛ4- . We found that this SNP also has a significant interaction with APOE ɛ4+ (P = 0.008) and the effect size in the APOE ɛ4+ vs ɛ4- is also significantly different  passed the genome-wide significance threshold . We did not find any significant interaction between this SNP and APOE ɛ4, nor was the effect size in ɛ4+ vs APOE ɛ4- strata significantly different  compared to APOE ɛ4+  but without interaction with APOE ɛ4.In case of the locus chr9p.13.3 pAPOE ɛ4- strata we identified rs1065853 as the most significant SNP on the APOE locus . This SNP is in high LD with APOE ɛ2 . In addition, we identified a novel signal on chr2q.12.2 . Functional annotation by VEP [UXS1 protein coding gene, however, another nominally significant SNP, chr2q.12.2  within high LD , is an eQTL for NCK2, which is a known AD risk gene [Notably, in the About two thirds of people diagnosed with AD are women, however, the life expectancy for women is longer than for men . Further–08, sex-interaction P = 9.8 × 10–07, MAF = 0.006, I2 = 0; Fig. –08, sex-interaction P = 1.3 × 10–03, MAF = 0.004, I2 = 0). We looked for SNPs in high LD with these signals and found several SNPs in high LD that were also highly significant, indicating them to be potential new signals and not an imputation error or artifact  and chr11p.15.2  variants. Functional annotation of these rare genetic variants indicates that the intergenic upstream genes are MSNP1 and INSC/EHF for rs529007143 and rs192346166, respectively , we conducted a case–control stratified association analysis  and identified multiple variants from the APOE locus passed the genome-wide significance, with APOE ɛ4 (rs429358) being the most significant SNP. Even, the effect size in cases and controls  were in the same direction, the statistical analyses indicate that they are significantly different , with cases showing significantly higher effect size than controls , indicating that both strata contribute to the association.Briefly, in these analyses only the SLC24A4, P = 0.0010, β =  − 0.14, SE = 0.04, MAF = 0.37; Additional file UMAD1, p = 0.0010, β = 0.05, SE = 0.02, MAF = 0.42; Additional file We also checked the overlap between known AD risk loci  and the APOE, ABCA7, FERMT2, and CR1 loci that all colocalized with AD risk . The identified SNPs included key AD associated genes, such as ANK3, APP, BIN1, CLU, FERMT2, and TREM2. Importantly, for most of the identified SNPs, the direction of effect size (β) was consistent between Bellenguez et al. [Next, we assessed if there is an overlap between the genetic architecture of amyloid imaging and AD risk beyond the identified SNPs in the z et al.  and the APOE region  from the most recent AD case–control GWAS [–85) at P-value threshold (PT) equal to genome-wide significance  even when the APOE region was removed from the reference GWAS. Overall, age, sex, cohort, first ten genetic principal components, and the polygenic score explained 8.7% (Full.R2 = 0.087) and 4.7% (Full.R2 = 0.047) of the variance in amyloid levels with and without APOE, respectively. These results further strengthen our hypothesis that there are loci besides APOE that are associated with amyloid burden in the brain and they also contribute to the observed association with AD risk PRS.In order to further determine the association of brain amyloidosis and AD risk, we determined if AD Polygenic risk score (PRS) were associated with amyloid PET. PRS were calculated with and without To assess whether amyloid PET endophenotype shares its genetic basis with other complex human traits, we performed genetic covariance analyses using summary statistics from amyloid PET GWAS from NHW ancestry N = 11,556) and 63 human health-related phenotypes  , Total cholesterol , and Asthma , among others. Moreover, we observed significantly negative correlations with Epilepsy  and Celiac disease . Removing the APOE region had minimal effect on the pair-wise genetic correlations and their significance, with the exception of identifying a new significant correlation for Multiple Sclerosis . Taken together, these results provide evidence of consistency in the observed polygenic trends across comparable measures from independent datasets.We observed a strongly positive correlation with high-density lipoprotein . Further, the summary statistics data from 6 additional cohorts were included to perform multi-ethnic meta-analysis for identifying novel genetic associations with brain amyloidosis .APOE gene region, validating previous findings [APOE in brain amyloidosis and AD risk. It is known, and well accepted that there are at least two independent signals in the APOE gene: ɛ4 and ɛ2 [APOE ɛ2, with strong protective effect of this variant in NHW , but almost nothing at all in ASN , and AFR showing an intermediate but not significant association of this variant with brain amyloidosis . As our population principal component factor analyses indicate that our AFR population includes samples with some NHW contribution /rs5117 , rs1081105 , rs438811 , and rs4420638 , that have also been reported to be associated with AD risk with positive colocalization  has also been reported to be associated with LOAD and multiple LOAD-related neuroimaging phenotypes in the 19q13 region [TOMM40 has been proposed to modify AD risk independently of APOE in multiple studies, but with contradictory results [NECTIN2), an important mediator of immune system, has already been shown to be downregulated in the neurons of AD cases [NECTIN2, have shown a strong association with CSF sTREM2 [APOE ɛ4 and ɛ2, various other variants have also been shown to exhibit protective or risk effect on AD. For example, a recent report suggest that the individuals having two copies of APOE ɛ3 Christchurch (p.R136S) mutation may slow down the progression of AD by preventing the accumulation of tau tangles and associated cell death [APOE locus for CSF Aβ1-42 after controlling for APOE genotype and adjusting for multiple comparisons based on Bonferroni threshold [In addition to the known ɛ4 and ɛ2 independent variants, our 3 region –61. A po results –62. NectAD cases , 65. In F sTREM2 . Besidesll death . Furtherhreshold .APOE ɛ4 and ɛ2 with AD risk and AD endophenotypes is so strong that identifying, validating, and characterizing additional independent signals in this region remains challenging and additional studies are needed. In any case, these results strength the notion that multiple independent risk and protective variants are found in the APOE locus, and more importantly that these signals show a very clear race-specific effect. Other studies have also reported a race-specific effect of APOE on AD risk [APOE ɛ4. Here we demonstrate that this observation can be extended to amyloid imaging but more importantly, we demonstrate that this differential effect is more pronounced to APOE ɛ2 and for the new additional signal we identified. This has important implications when estimating the individual risk at a population level that may affect future clinical trial strategies and therapies. The new results from the Lecanemab study [APOE genotype. The results from this study could help to further understand the result from this trial.The effect of  AD risk , mainly ab study  indicateAPOE locus, we also found two additional NHW signals in the CR1  and FERMT2  loci, and a multi-ethnic signal in ABCA7  locus. All these signals have already been shown to be significantly associated  with AD risk [APOE and CR1 signal . This signal was mainly driven by APOE ɛ4+ stratum  but also nominally significant in the APOE ɛ4− stratum , and exhibited significant difference in the effect size between both these strata (two-sample t-test p = 0.001). We did not observe an interaction of APOE with FERMT2 and ABCA7 signal (p > 0.20), also their effect sizes were not significantly different (p > 0.10) between ɛ4+ and ɛ4- strata.Besides identifying multiple independent signals in the –85) and without (P = 1.4 × 10–19) APOE showed a strong association with amyloid imaging. Additionally, 21 of the sentinel SNPs for the latest GWAS for AD risk [p < 0.05) for brain amyloidosis. For most of the overlapping SNPs, the effect size directions were consistent across both analyses. Some of the most important genes that have been associated with these SNPs included, CR1 (Complement Receptor Type 1), ABCA7 (ATP-Binding Cassette Sub-Family A Member 7), BIN1 (Bridging Integrator 1), ANK3 (Ankyrin 3), TREM2 (Triggering Receptor Expressed On Myeloid Cells 2), and CLU (Clusterin). All these genes have been shown to be implicated in neuropathology and AD [ABCA7 has been shown to mediate the generation of high-density lipoprotein (HDL) with apolipoproteins [APOE, CLU is also known as a component of lipoprotein and implicated in a wide range of biological functions including cholesterol and lipid transport [We found a strong overlap in the genetic architecture of AD risk and brain amyloid imaging. Alzheimer disease PRS with  [p = 0.75, β = − 0.01, SE = 0.03; Additional file p = 0.67, β = − 0.01, SE = 0.03). As the initial association of this SNP was identified on controls only, we analyzed if this SNP showed an association in controls-only . We found rs56081887  and rs34860942  to be nominally associated with brain amyloidosis.Besides the robust association of brain amyloidosis with  = 0.09) . We did MSNP1 gene on chr5.p14.1  and EHF gene on chr11.p15.2 . Our analyses indicate that expression level of this gene is altered in AD brains, specifically in women, supporting the race-specific association. EHF (ETS Homologous Factor) is a DNA-binding transcription factor activity and RNA polymerase II cis-regulatory region sequence-specific DNA binding protein that play a crucial role in regulating epithelial cell differentiation and proliferation [EHF on AD risk in females.Additional interactions were found on our sex-specific analyses. Two new female-specific loci were detected in the sex-stratified multi-ethnic GWAS: feration , however–09, MAF = 0.028) that, based on our functional mapping, is driven by PTDSS1. This gene encodes the enzyme phosphatidylserine synthase 1 (PSS1), which is involved in the transport of phospholipids between endoplasmic reticulum and mitochondria [p = 0.84, β = − 0.04, SE = 0.19, MAF = 0.013) and 0.21% in NHW  but it was not even nominally associated in these two ancestries, suggesting it to be an AFR-specific signal or be a false-positive signal. Additional studies with an even larger AFR-specific sample size will be needed to further confirm this finding.Another novel signal identified in this study is the AFR-specific signal on chr8q.22.1  and Multiple Sclerosis . These results highlight the shared genetic architecture underlying central mechanism in AD and other neuroinflammatory disorders [CD33 inhibition [TREM2 activation [Besides the positive genetic overlap of amyloid PET AD endophenotype with different neurological disorders such as AD, Amyotrophic Lateral Sclerosis (ALS), and Frontotemporal dementia (FTD), we also found a significant genetic correlation with a number of different human traits. We observed strong genetic correlations with HDL and total cholesterol levels  that has confirmed the previously known association of the Additional file 1. Supplementary tables and figures


Author list, and author “Jia Yin” was erroneously excluded. The corrected Author list appears below.In the published article, there was an error in the 1, Huimin Shan2, Jianqiao Li2, Lijie Pan2, Shudan Wang3, Jing Zhu2, Hui Guo2, Fenghua Mi2, Xinyi Wu2, Jia Yin3*, Kunpeng Pang2*.”“Shuyan ZhuCorrespondence information is “Jia Yin, jia_yin@meei.harvard.edu; Kunpeng Pang, k.pang@sdu.edu.cn.”In the published article, Jia Yin’s information was not included in the correspondence information. The corrected Author Contributions statement did not mention Jia Yin’s contributions. The correct statement is “JY and KP contributed to conception and design of the study. KP supervised the study. SZ, HS, SW, and KP conducted experiments and acquired data; HS and KP wrote the manuscript; LP analyzed data; FM assisted in conducting the experiments; JZ and HG assisted in analyzing data; JL and XW provided the reagents; KP designed research studies and revised the manuscript.”In the published article, the Acknowledgements statement was displayed as “We thank Dr. Jia Yin as an advisor for the hypoxia study.” The correct statement is “None.”In the published article, the The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.