MRAGDATASET/citation_with_pmcid · Datasets at Hugging Face

source_paper string	source_section string	premise string	target_doi string	target_paper string
PMC10013922	Introduction	There have been previous economic evaluations of maternal GBS vaccination in the United States [12–14], Europe [15–17], and sub-Saharan Africa [18–20].	10.1016/j.vaccine.2017.07.108	PMC5723707
PMC10013922	Introduction	Such an evaluation is central to a Full Value of Vaccines Assessment (FVVA), which WHO has identified as key to catalysing vaccine development and subsequent equitable access [21,22].	10.1016/j.vaccine.2017.09.048	PMC11369760
PMC10013922	Introduction	To inform the WHO GBS vaccine FVVA [23], we conducted the first global economic evaluation of maternal GBS vaccination in 183 countries, drawing on recently updated global disease burden estimates for GBS [1].	10.1016/S2214-109X(22)00093-6	PMC9090904
PMC10013922	Methods	The Bayesian disease model has been described in detail elsewhere; its structure is reflected in our methods below [1,25].	10.1016/S2214-109X(22)00093-6	PMC9090904
PMC10013922	Methods	The Bayesian disease model has been described in detail elsewhere; its structure is reflected in our methods below [1,25].	10.1371/journal.pcbi.1009001	PMC8202927
PMC10013922	Methods	We parameterised the probabilities of different GBS-related outcomes in our model using posterior samples of key epidemiological parameters from the global burden estimates reported by Gonçalves and colleagues [1].	10.1016/S2214-109X(22)00093-6	PMC9090904
PMC10013922	Methods	There are no data on CFRs for infants with EOGBS without access to care, so the authors considered 2 scenarios where they had either 90% CFR (following the approach of Seale and colleagues [3]) or the same CFR as other infants with EOGBS.	10.1093/cid/cix664	PMC5849940
PMC10013922	Methods	The excess risk attributable to iGBS exposure was calculated assuming a counterfactual risk of mild or moderate and severe NDI among unexposed children from a large Danish cohort study [27].	10.1016/S2352-4642(21)00022-5	PMC8131199
PMC10013922	Methods	Following the approach used in the burden estimation, our base case analysis included only the excess risk of moderate or severe NDI, which is likely to be more consistent across settings, but include mild NDI as a sensitivity analysis [1,4].	10.1016/S2214-109X(22)00093-6	PMC9090904
PMC10013922	Methods	Following the approach used in the burden estimation, our base case analysis included only the excess risk of moderate or severe NDI, which is likely to be more consistent across settings, but include mild NDI as a sensitivity analysis [1,4].	10.1016/j.eclinm.2022.101358	PMC9142788
PMC10013922	Methods	To estimate country-specific GBS-associated stillbirth risk, national stillbirth estimates from the WHO Global Health Observatory [28] were combined with regional estimates of the proportion of stillbirths caused by GBS [1].	10.1016/S2214-109X(22)00093-6	PMC9090904
PMC10013922	Methods	For the risk of GBS-associated prematurity, we used national data on the proportion of preterm births [29] together with the global odds ratio for the association between GBS maternal colonisation and preterm births [1].	10.1016/S2214-109X(18)30451-0	PMC6293055
PMC10013922	Methods	For the risk of GBS-associated prematurity, we used national data on the proportion of preterm births [29] together with the global odds ratio for the association between GBS maternal colonisation and preterm births [1].	10.1016/S2214-109X(22)00093-6	PMC9090904
PMC10013922	Methods	For the acute iGBS episode, we approximated QALY loss, assuming 29 days duration based on the average length of stay among studies in a recent systematic review of the acute costs of infant sepsis and meningitis [31], and applied health state utility decrements for hospitalisation with acute sepsis or meningitis from a US study in young children [32].	10.1016/j.vaccine.2020.07.050	PMC7482437
PMC10013922	Methods	For survivors with long-term sequelae, we applied utility decrements from birth for mild, moderate, and severe NDI to each year of life and, conservatively, given previous studies provide evidence of post-acute mortality after bacterial meningitis [33,34], assumed no change in life expectancy.	10.1371/journal.pone.0137095	PMC4564213
PMC10013922	Methods	For survivors with long-term sequelae, we applied utility decrements from birth for mild, moderate, and severe NDI to each year of life and, conservatively, given previous studies provide evidence of post-acute mortality after bacterial meningitis [33,34], assumed no change in life expectancy.	10.1371/journal.pone.0009662	PMC2837384
PMC10013922	Methods	Although clinical studies have demonstrated immunogenicity of candidate GBS vaccines, to date, there have been no Phase III efficacy trials [10].	10.2147/IDR.S203454	PMC7196769
PMC10013922	Methods	We also assumed no effect on GBS-associated prematurity because (i) the WHO PPC does not specify that GBS vaccines must reduce colonisation, which is most likely pathway for preventing GBS-associated prematurity, and (ii) the association between GBS maternal colonisation and higher risk of prematurity may be confounded [37].	10.1093/cid/cix661	PMC5850429
PMC10013922	Methods	For the latter scenario, we estimated the proportion of preterm births that are potentially protected through vaccination by combining the distribution of preterm births by gestational age [38] with the timing of vaccine visits based on country-specific ANC data [39] (S1 Appendix A2.5).	10.1186/1742-4755-10-S1-S2	PMC3828585
PMC10013922	Methods	For the latter scenario, we estimated the proportion of preterm births that are potentially protected through vaccination by combining the distribution of preterm births by gestational age [38] with the timing of vaccine visits based on country-specific ANC data [39] (S1 Appendix A2.5).	10.1371/journal.pone.0237718	PMC7446781
PMC10013922	Methods	To estimate acute healthcare costs, we combined one GBS-specific cost estimate from a study in the United Kingdom (UK) [42], with the findings from a systematic review on the acute costs of infant sepsis and meningitis [43], and result of a recent study reporting the acute costs of neonatal bacterial sepsis and meningitis in Mozambique and South Africa [44].	10.1093/cid/ciab815	PMC8776306
PMC10013922	Methods	Annual costs among survivors with moderate and severe NDI were parameterised as a fixed proportion of between 4% and 28% of the acute cost estimate in each country, based on the range between a UK study of costs in children with NDI [35] and a US study of costs in adults with disabilities [45].	10.1097/MLR.0000000000001371	PMC7505687
PMC10013922	Methods	For the vaccine programme costs, we extrapolated results from a systematic review of maternal vaccination delivery costs using regression against GDP per capita (S1 Appendix A2.7) [31].	10.1016/j.vaccine.2020.07.050	PMC7482437
PMC10013922	Methods	We used previously estimated vaccine prices by World Bank country income group, which were based on a combination of price benchmarking against other vaccines and cost of goods analysis: $50 for high-income countries; $15 for upper-middle-income countries; and $3.50 for lower-middle-income and low-income countries [46].	10.1093/cid/ciab782	PMC8775646
PMC10013922	Methods	Here, we use 2 commonly cited thresholds: (i) country gross domestic product per capita [47]; and (ii) published thresholds based on empirical estimates of the health opportunity cost of healthcare spending (S1 Appendix A2.8) [48,49].	10.12688/gatesopenres.13201.1	PMC7851575
PMC10013922	Methods	Here, we use 2 commonly cited thresholds: (i) country gross domestic product per capita [47]; and (ii) published thresholds based on empirical estimates of the health opportunity cost of healthcare spending (S1 Appendix A2.8) [48,49].	10.1016/j.jval.2016.02.017	PMC5193154
PMC10013922	Methods	Here, we use 2 commonly cited thresholds: (i) country gross domestic product per capita [47]; and (ii) published thresholds based on empirical estimates of the health opportunity cost of healthcare spending (S1 Appendix A2.8) [48,49].	10.1136/bmjgh-2018-000964	PMC6231096
PMC10013922	Methods	In many settings, these are not assigned any health or disability weight, but it has been argued that they should be assigned a QALY loss close or the same as that of the death of a newborn [50].	10.1111/bioe.12120	PMC4706157
PMC10013922	Discussion	Competitive and finely tiered vaccine prices could also be beneficial for manufacturers, with financial analyses suggesting that high global demand is needed to ensure the development costs of a GBS vaccine can be recouped [46].	10.1093/cid/ciab782	PMC8775646
PMC10013922	Discussion	Previous analyses have estimated cost-effectiveness in the US [12–14], the Netherlands [17], UK [15,16], South Africa [19], The Gambia [18], and 37 Gavi countries in Africa [20].	10.1136/bmj.39325.681806.AD	PMC1995477
PMC10013922	Discussion	Previous analyses have estimated cost-effectiveness in the US [12–14], the Netherlands [17], UK [15,16], South Africa [19], The Gambia [18], and 37 Gavi countries in Africa [20].	10.1016/j.vaccine.2017.07.108	PMC5723707
PMC10013922	Discussion	These prior estimates suggested cost-effectiveness of vaccination ranged from $320 to 573 per DALY averted in Gavi-eligible countries [20], to $3,550 per DALY averted in South Africa [19], to over $50,000 per QALY in the US [12,13], which is broadly consistent with our results.	10.1016/j.vaccine.2017.07.108	PMC5723707
PMC10013922	Supporting information	These estimates were provided by the authors of [39] and were calculated using input data on antenatal coverage that was available at the time of their analysis.	10.1371/journal.pone.0237718	PMC7446781
PMC10013922	Supporting information	Table H. Cost data from [31] used to extrapolate used to extrapolate country-specific estimates of vaccine delivery costs per dose.	10.1016/j.vaccine.2020.07.050	PMC7482437
PMC10015103	Discussion	Among a cohort of 51 children with history of MIS-C who received COVID-19 vaccination, the vaccine was found to be safe and well tolerated during short-term follow-up [11].	10.1097/INF.0000000000003803	PMC9935230
PMC10015776	Background	Considerable evidence has emerged indicating that the microbiome is an important contributor to an individual’s health [2].	10.1126/science.1208344	PMC3368382
PMC10015776	Background	This has been illustrated by links between the gut microbiome and numerous diseases, including irritable bowel syndrome [3], Crohn’s disease [4], type 2 diabetes [5], cardiovascular disease [6], and Parkinson’s disease (PD) [7].	10.1136/gutjnl-2016-313235	PMC5531220
PMC10015776	Background	This has been illustrated by links between the gut microbiome and numerous diseases, including irritable bowel syndrome [3], Crohn’s disease [4], type 2 diabetes [5], cardiovascular disease [6], and Parkinson’s disease (PD) [7].	10.1038/nm.3145	PMC3650111
PMC10015776	Background	Diet, being one of these factors, has the greatest known long-term interaction with the gut microbiome [8].	10.1093/ajcn/nqaa350	PMC7948864
PMC10015776	Background	Thus, a deep understanding of the relationship between diet and the gut microbiome and the consequential impact on disease processes holds promise for developing personalized dietary intervention strategies to modulate and maintain a healthy microbiome population [9, 10].	10.1017/S0007114514004127	PMC4405705
PMC10015776	Background	Thus, a deep understanding of the relationship between diet and the gut microbiome and the consequential impact on disease processes holds promise for developing personalized dietary intervention strategies to modulate and maintain a healthy microbiome population [9, 10].	10.1093/jn/nxz154	PMC6825832
PMC10015776	Background	Diet has a direct impact on the microbial community in the gut, which governs the activity of the intestinal ecosystem and can have considerable implications for an individual’s health [11, 12].	10.3389/fimmu.2017.00538	PMC5421151
PMC10015776	Background	These discoveries are generally based on an elaborate experimental design using model organisms [13] or dietary interventions [15–17].	10.1038/nature12820	PMC3957428
PMC10015776	Background	Recent observational studies suggest that long-term diets could be associated with the microbiome [18], and this can further affect overall health.	10.1038/s41591-020-01183-8	PMC8353542
PMC10015776	Background	Hence, using a data-driven approach, it is able to divide a population into multiple subcohorts with distinct microbiological signatures for health that can be best described by nutrient combinations, resulting in what we term “nutritional-ecotypes.” These subcohorts can be thought of as diet-based latent classes where they capture interaction between the constraints imposed by nutrient intake of individuals on the community dynamics of their microbiomes [22, 23].	10.3389/fnut.2019.00041	PMC6465639
PMC10015776	Background	Methods to discover such diet-based latent classes could be hypothesis-driven based on prior knowledge [24, 25] or guided by an unsupervised statistical learning method, such as clustering [26], followed by latent class analysis [27].	10.1017/S0007114518001800	PMC6137382
PMC10015776	Background	Methods to discover such diet-based latent classes could be hypothesis-driven based on prior knowledge [24, 25] or guided by an unsupervised statistical learning method, such as clustering [26], followed by latent class analysis [27].	10.1136/bmj.k2396	PMC5996879
PMC10015776	Background	Methods to discover such diet-based latent classes could be hypothesis-driven based on prior knowledge [24, 25] or guided by an unsupervised statistical learning method, such as clustering [26], followed by latent class analysis [27].	10.1093/advances/nmz049	PMC6855959
PMC10015776	Background	Methods to discover such diet-based latent classes could be hypothesis-driven based on prior knowledge [24, 25] or guided by an unsupervised statistical learning method, such as clustering [26], followed by latent class analysis [27].	10.3389/fimmu.2021.651709	PMC8111016
PMC10015776	Background	Consequently, classification models built within a subcohort defined just by diet (or microbiome) will not necessarily improve prediction of the health/disease state [28].	10.1186/s40168-021-01018-9	PMC8004395
PMC10015776	Background	Kim and colleagues [31] have used it for combining clinical data and genomics data.	10.1093/bioinformatics/btq107	PMC2859127
PMC10015776	Results	This approach has two distinct components: first, a gating network aimed at estimating latent classes shaped by nutritional intake, and second, an experts network aimed at modeling the relationship between the microbiota composition and the health state within each latent class [31, 32].	10.1093/bioinformatics/btq107	PMC2859127
PMC10015776	Results	We further assessed NEMoE’s capabilities on enterotype-separated subcohorts [35] within our PD dataset.	10.1038/s41564-017-0072-8	PMC5832044
PMC10015776	Results	Enterotype, a widely used concept in microbiome research, refers to the categorization of an individual’s microbiomes by the variance in composition [2, 36].	10.1126/science.1208344	PMC3368382
PMC10015776	Results	Enterotype, a widely used concept in microbiome research, refers to the categorization of an individual’s microbiomes by the variance in composition [2, 36].	10.1038/nature09944	PMC3728647
PMC10015776	Results	[19, 20, 38–42]Fig.	10.1038/s41531-020-0112-6	PMC7293233
PMC10015776	Results	[37]) all other datasets showed decreasing Fusicatenibacter in PD.	10.3389/fnins.2019.01184	PMC6883725
PMC10015776	Results	We processed the publicly available datasets using the dada2 pipeline [49](v1.16) and taxonomy reference “silva 138” [48, 50].	10.1038/nmeth.3869	PMC4927377
PMC10015776	Results	We processed the publicly available datasets using the dada2 pipeline [49](v1.16) and taxonomy reference “silva 138” [48, 50].	10.1093/nar/gks1219	PMC3531112
PMC10015776	Results	In all but one dataset [37], Fusicatenibacter had significantly lower relative abundance among PD individuals.	10.3389/fnins.2019.01184	PMC6883725
PMC10015776	Results	5), verifying NEMoE’s ability to identify consensus microbial signatures of PD in multiple independent cohorts.Table 2Summary of eight publicly available Parkinson’s disease microbiome studies used for validation of the NEMoE modelStudyDesignCountrySample sizeSamplingDNA extraction16S regionENA Accession NumberLubomski_0 [19, 39]Lubomski_6Lubomski_12LongitudinalAustralia74PD, 74HCHome collection, stored at −80 °CMP Biomedicals FastDNATM SPIN KitV3-V4PRJNA808166Wallen_1 [36]Cross-sectionalUSA323PD, 184HCHome collection, swabs, stored at −20 °CMoBio PowerSoil DNA Isolation KitV4PRJNA601994Wallen_2 [36, 43]Cross-sectionalUSA197PD, 130HCSwabs, delivered at RTMoBio PowerMag Soil kitV4PRJNA601994Aho (baseline) [44]Aho (follow-up)LongitudinalFinland64PD, 64HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV3-V4PRJEB27564Weis [45]Cross-sectionalGermany34PD, 25HCMED AUXIL fecal collector setFastDNA Spin KitV4-V5PRJEB30615Pietrucci [46]Cross-sectionalItaly80PD, 72HCHome collection, DNA stabilizerPSP-Spin Stool KitV3-V4PRJNA510730Scheperjans [47]Cross-sectionalFinland72PD, 72HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV1-V3PRJEB4927Jin [48]Cross-sectionalChina72PD, 68HCNANAV3-V4PRJEB588834Studies Lubomski_0, Lubomski_6, and Lubomski_12 were part of the same longitudinal data set by Lubomski and colleagues [2] and they represent samples that were measured at 0, 6, and 12 months, respectivelyStudies Aho (baseline) and Aho (follow-up) were part of the same longitudinal data set by Aho and colleagues [44].	10.1038/nature09944	PMC3728647
PMC10015776	Results	5), verifying NEMoE’s ability to identify consensus microbial signatures of PD in multiple independent cohorts.Table 2Summary of eight publicly available Parkinson’s disease microbiome studies used for validation of the NEMoE modelStudyDesignCountrySample sizeSamplingDNA extraction16S regionENA Accession NumberLubomski_0 [19, 39]Lubomski_6Lubomski_12LongitudinalAustralia74PD, 74HCHome collection, stored at −80 °CMP Biomedicals FastDNATM SPIN KitV3-V4PRJNA808166Wallen_1 [36]Cross-sectionalUSA323PD, 184HCHome collection, swabs, stored at −20 °CMoBio PowerSoil DNA Isolation KitV4PRJNA601994Wallen_2 [36, 43]Cross-sectionalUSA197PD, 130HCSwabs, delivered at RTMoBio PowerMag Soil kitV4PRJNA601994Aho (baseline) [44]Aho (follow-up)LongitudinalFinland64PD, 64HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV3-V4PRJEB27564Weis [45]Cross-sectionalGermany34PD, 25HCMED AUXIL fecal collector setFastDNA Spin KitV4-V5PRJEB30615Pietrucci [46]Cross-sectionalItaly80PD, 72HCHome collection, DNA stabilizerPSP-Spin Stool KitV3-V4PRJNA510730Scheperjans [47]Cross-sectionalFinland72PD, 72HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV1-V3PRJEB4927Jin [48]Cross-sectionalChina72PD, 68HCNANAV3-V4PRJEB588834Studies Lubomski_0, Lubomski_6, and Lubomski_12 were part of the same longitudinal data set by Lubomski and colleagues [2] and they represent samples that were measured at 0, 6, and 12 months, respectivelyStudies Aho (baseline) and Aho (follow-up) were part of the same longitudinal data set by Aho and colleagues [44].	10.3389/fnut.2021.628845	PMC8138322
PMC10015776	Results	5), verifying NEMoE’s ability to identify consensus microbial signatures of PD in multiple independent cohorts.Table 2Summary of eight publicly available Parkinson’s disease microbiome studies used for validation of the NEMoE modelStudyDesignCountrySample sizeSamplingDNA extraction16S regionENA Accession NumberLubomski_0 [19, 39]Lubomski_6Lubomski_12LongitudinalAustralia74PD, 74HCHome collection, stored at −80 °CMP Biomedicals FastDNATM SPIN KitV3-V4PRJNA808166Wallen_1 [36]Cross-sectionalUSA323PD, 184HCHome collection, swabs, stored at −20 °CMoBio PowerSoil DNA Isolation KitV4PRJNA601994Wallen_2 [36, 43]Cross-sectionalUSA197PD, 130HCSwabs, delivered at RTMoBio PowerMag Soil kitV4PRJNA601994Aho (baseline) [44]Aho (follow-up)LongitudinalFinland64PD, 64HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV3-V4PRJEB27564Weis [45]Cross-sectionalGermany34PD, 25HCMED AUXIL fecal collector setFastDNA Spin KitV4-V5PRJEB30615Pietrucci [46]Cross-sectionalItaly80PD, 72HCHome collection, DNA stabilizerPSP-Spin Stool KitV3-V4PRJNA510730Scheperjans [47]Cross-sectionalFinland72PD, 72HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV1-V3PRJEB4927Jin [48]Cross-sectionalChina72PD, 68HCNANAV3-V4PRJEB588834Studies Lubomski_0, Lubomski_6, and Lubomski_12 were part of the same longitudinal data set by Lubomski and colleagues [2] and they represent samples that were measured at 0, 6, and 12 months, respectivelyStudies Aho (baseline) and Aho (follow-up) were part of the same longitudinal data set by Aho and colleagues [44].	10.1002/mds.26942	PMC5469442
PMC10015776	Results	5), verifying NEMoE’s ability to identify consensus microbial signatures of PD in multiple independent cohorts.Table 2Summary of eight publicly available Parkinson’s disease microbiome studies used for validation of the NEMoE modelStudyDesignCountrySample sizeSamplingDNA extraction16S regionENA Accession NumberLubomski_0 [19, 39]Lubomski_6Lubomski_12LongitudinalAustralia74PD, 74HCHome collection, stored at −80 °CMP Biomedicals FastDNATM SPIN KitV3-V4PRJNA808166Wallen_1 [36]Cross-sectionalUSA323PD, 184HCHome collection, swabs, stored at −20 °CMoBio PowerSoil DNA Isolation KitV4PRJNA601994Wallen_2 [36, 43]Cross-sectionalUSA197PD, 130HCSwabs, delivered at RTMoBio PowerMag Soil kitV4PRJNA601994Aho (baseline) [44]Aho (follow-up)LongitudinalFinland64PD, 64HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV3-V4PRJEB27564Weis [45]Cross-sectionalGermany34PD, 25HCMED AUXIL fecal collector setFastDNA Spin KitV4-V5PRJEB30615Pietrucci [46]Cross-sectionalItaly80PD, 72HCHome collection, DNA stabilizerPSP-Spin Stool KitV3-V4PRJNA510730Scheperjans [47]Cross-sectionalFinland72PD, 72HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV1-V3PRJEB4927Jin [48]Cross-sectionalChina72PD, 68HCNANAV3-V4PRJEB588834Studies Lubomski_0, Lubomski_6, and Lubomski_12 were part of the same longitudinal data set by Lubomski and colleagues [2] and they represent samples that were measured at 0, 6, and 12 months, respectivelyStudies Aho (baseline) and Aho (follow-up) were part of the same longitudinal data set by Aho and colleagues [44].	10.1371/journal.pone.0237779	PMC7446854
PMC10015776	Results	5), verifying NEMoE’s ability to identify consensus microbial signatures of PD in multiple independent cohorts.Table 2Summary of eight publicly available Parkinson’s disease microbiome studies used for validation of the NEMoE modelStudyDesignCountrySample sizeSamplingDNA extraction16S regionENA Accession NumberLubomski_0 [19, 39]Lubomski_6Lubomski_12LongitudinalAustralia74PD, 74HCHome collection, stored at −80 °CMP Biomedicals FastDNATM SPIN KitV3-V4PRJNA808166Wallen_1 [36]Cross-sectionalUSA323PD, 184HCHome collection, swabs, stored at −20 °CMoBio PowerSoil DNA Isolation KitV4PRJNA601994Wallen_2 [36, 43]Cross-sectionalUSA197PD, 130HCSwabs, delivered at RTMoBio PowerMag Soil kitV4PRJNA601994Aho (baseline) [44]Aho (follow-up)LongitudinalFinland64PD, 64HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV3-V4PRJEB27564Weis [45]Cross-sectionalGermany34PD, 25HCMED AUXIL fecal collector setFastDNA Spin KitV4-V5PRJEB30615Pietrucci [46]Cross-sectionalItaly80PD, 72HCHome collection, DNA stabilizerPSP-Spin Stool KitV3-V4PRJNA510730Scheperjans [47]Cross-sectionalFinland72PD, 72HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV1-V3PRJEB4927Jin [48]Cross-sectionalChina72PD, 68HCNANAV3-V4PRJEB588834Studies Lubomski_0, Lubomski_6, and Lubomski_12 were part of the same longitudinal data set by Lubomski and colleagues [2] and they represent samples that were measured at 0, 6, and 12 months, respectivelyStudies Aho (baseline) and Aho (follow-up) were part of the same longitudinal data set by Aho and colleagues [44].	10.1093/nar/gks1219	PMC3531112
PMC10015776	Results	5), verifying NEMoE’s ability to identify consensus microbial signatures of PD in multiple independent cohorts.Table 2Summary of eight publicly available Parkinson’s disease microbiome studies used for validation of the NEMoE modelStudyDesignCountrySample sizeSamplingDNA extraction16S regionENA Accession NumberLubomski_0 [19, 39]Lubomski_6Lubomski_12LongitudinalAustralia74PD, 74HCHome collection, stored at −80 °CMP Biomedicals FastDNATM SPIN KitV3-V4PRJNA808166Wallen_1 [36]Cross-sectionalUSA323PD, 184HCHome collection, swabs, stored at −20 °CMoBio PowerSoil DNA Isolation KitV4PRJNA601994Wallen_2 [36, 43]Cross-sectionalUSA197PD, 130HCSwabs, delivered at RTMoBio PowerMag Soil kitV4PRJNA601994Aho (baseline) [44]Aho (follow-up)LongitudinalFinland64PD, 64HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV3-V4PRJEB27564Weis [45]Cross-sectionalGermany34PD, 25HCMED AUXIL fecal collector setFastDNA Spin KitV4-V5PRJEB30615Pietrucci [46]Cross-sectionalItaly80PD, 72HCHome collection, DNA stabilizerPSP-Spin Stool KitV3-V4PRJNA510730Scheperjans [47]Cross-sectionalFinland72PD, 72HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV1-V3PRJEB4927Jin [48]Cross-sectionalChina72PD, 68HCNANAV3-V4PRJEB588834Studies Lubomski_0, Lubomski_6, and Lubomski_12 were part of the same longitudinal data set by Lubomski and colleagues [2] and they represent samples that were measured at 0, 6, and 12 months, respectivelyStudies Aho (baseline) and Aho (follow-up) were part of the same longitudinal data set by Aho and colleagues [44].	10.1126/science.1208344	PMC3368382
PMC10015776	Results	The same subjects were measured twice, at baseline and then later at follow-up, which was on average 2.25 years apartStudies Wallen_1 and Wallen_2 were part of two large cohort studies set by Wallen and colleagues [38]	10.1038/s41531-020-0112-6	PMC7293233
PMC10015776	Results	Aho (baseline) [44]	10.1002/mds.26942	PMC5469442
PMC10015776	Results	Studies Lubomski_0, Lubomski_6, and Lubomski_12 were part of the same longitudinal data set by Lubomski and colleagues [2] and they represent samples that were measured at 0, 6, and 12 months, respectively	10.1126/science.1208344	PMC3368382
PMC10015776	Results	Studies Aho (baseline) and Aho (follow-up) were part of the same longitudinal data set by Aho and colleagues [44].	10.1002/mds.26942	PMC5469442
PMC10015776	Results	Studies Wallen_1 and Wallen_2 were part of two large cohort studies set by Wallen and colleagues [38]	10.1038/s41531-020-0112-6	PMC7293233
PMC10015776	Results	Recent studies found that Escherichia-Shigella is a pathogenic bacteria that potentially reduces short-chain fatty acid production and produces endotoxins and neurotoxins [51, 52].	10.1186/s13024-021-00427-6	PMC7869249
PMC10015776	Results	These bacteria are known to impact immune response and constipation, with many studies reporting an overrepresentation in PD [39, 40, 42, 53].	10.1002/mdc3.12840	PMC6856467
PMC10015776	Results	Most importantly, neither of these two genera (Escherichia-Shigella, Akkermansia) was discovered in our previous analysis using the ALDE model [54], where both classes were combined for microbiome biomarker identification (Escherichia-Shigella: p-value 0.14, Akkermansia: p-value 0.55) [20].	10.1186/2049-2618-2-15	PMC4030730
PMC10015776	Discussion	Finally, the downstream impact and practical importance of NEMoE is further demonstrated by the discovery of diet-specific PD microbiome markers, such as Escherichia-Shigella and Akkermansia, which are not identified by the ALDE model [54].	10.1186/2049-2618-2-15	PMC4030730
PMC10015776	Methods	Dietary information was collected by a comprehensive Food Frequency Questionnaire and resulted in a table of nutrient intake with 23 macronutrients, presented earlier [43].	10.3389/fnut.2021.628845	PMC8138322
PMC10015776	Methods	We curated a series of datasets from eight different publicly-available microbiome studies [37, 38, 44–46, 51] to further validate results from NEMoE.	10.3389/fnins.2019.01184	PMC6883725
PMC10015776	Methods	We curated a series of datasets from eight different publicly-available microbiome studies [37, 38, 44–46, 51] to further validate results from NEMoE.	10.1038/s41531-020-0112-6	PMC7293233
PMC10015776	Methods	We curated a series of datasets from eight different publicly-available microbiome studies [37, 38, 44–46, 51] to further validate results from NEMoE.	10.1002/mds.26942	PMC5469442
PMC10015776	Methods	We curated a series of datasets from eight different publicly-available microbiome studies [37, 38, 44–46, 51] to further validate results from NEMoE.	10.1186/s13024-021-00427-6	PMC7869249
PMC10015776	Methods	All the datasets were processed using the dada2 pipeline [49] (v1.16) and microbiome taxa were annotated using taxonomy reference “silva 138” [48, 50].	10.1038/nmeth.3869	PMC4927377
PMC10015776	Methods	All the datasets were processed using the dada2 pipeline [49] (v1.16) and microbiome taxa were annotated using taxonomy reference “silva 138” [48, 50].	10.1093/nar/gks1219	PMC3531112
PMC10015776	Methods	For the longitudinal datasets Aho [44], the data for baseline and follow-up, which were collected after 2.5 years, are denoted as Aho (baseline) and Aho (follow-up) respectively.	10.1002/mds.26942	PMC5469442
PMC10015776	Methods	an arcsin square root transformation, was performed on taxa proportion [47, 55]; the arcsin transformed data were further standardized to have mean zero and unit variance (z-score).	10.1371/journal.pone.0237779	PMC7446854
PMC10015776	Methods	These transformations of nutritional features are widely used in nutri-omics studies [56, 57].	10.3233/NHA-170027	PMC5734128
PMC10020069	Introduction	Healthcare workers (HCWs), particularly those involved in COVID-19-related patient care were at a heightened risk of infection [1, 2].	10.1016/j.ijid.2021.01.013	PMC7798435
PMC10020069	Introduction	Healthcare workers (HCWs), particularly those involved in COVID-19-related patient care were at a heightened risk of infection [1, 2].	10.1016/S2468-2667(20)30164-X	PMC7491202
PMC10020069	Introduction	For example, a meta-analysis, including 28 studies from seven countries, reported that the percentage of HCWs who tested positive for COVID-19 was as high as 51.7% [1].	10.1016/j.ijid.2021.01.013	PMC7798435
PMC10020069	Introduction	A prospective cohort study among community individuals and frontline HCWs reported that compared to the community individuals, frontline HCWs had 12-fold higher risk of reporting infection [2].	10.1016/S2468-2667(20)30164-X	PMC7491202
PMC10020069	Introduction	Since the beginning of the COVID-19 pandemic, the fear of transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from HCWs to the general population [3] provoked rapid stigma and discrimination towards HCWs, particularly against those involved in care of COVID-19 patients [4–6].	10.1016/S0140-6736(20)31191-0	PMC7239629
PMC10020069	Introduction	Since the beginning of the COVID-19 pandemic, the fear of transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from HCWs to the general population [3] provoked rapid stigma and discrimination towards HCWs, particularly against those involved in care of COVID-19 patients [4–6].	10.3389/fpubh.2020.570459	PMC7531183
PMC10020069	Introduction	It was reported that HCWs faced discrimination in the form of verbal attacks and threats [3], avoidance from family and community members [7], avoidance from community members towards their family [8], and stigmatization [9].	10.1016/S0140-6736(20)31191-0	PMC7239629
PMC10020069	Introduction	It was reported that HCWs faced discrimination in the form of verbal attacks and threats [3], avoidance from family and community members [7], avoidance from community members towards their family [8], and stigmatization [9].	10.1016/j.jiph.2020.12.028	PMC7778368
PMC10020069	Introduction	It was reported that HCWs faced discrimination in the form of verbal attacks and threats [3], avoidance from family and community members [7], avoidance from community members towards their family [8], and stigmatization [9].	10.1080/20008198.2021.1882781	PMC8075082
PMC10020069	Introduction	Although fewer numbers of infections and death from COVID-19 have been reported in Japan compared to many other countries [10], a few studies reported that frontline HCWs and their family members have experienced discrimination [11, 12].	10.15252/emmm.202012481	PMC7207161
PMC10020069	Introduction	Although fewer numbers of infections and death from COVID-19 have been reported in Japan compared to many other countries [10], a few studies reported that frontline HCWs and their family members have experienced discrimination [11, 12].	10.1093/phe/phab003	PMC7928580
PMC10020069	Introduction	For instance, children of HCWs were refused access to kindergartens, school, and childcare facilities [11, 12].	10.1093/phe/phab003	PMC7928580
PMC10020069	Introduction	Discrimination is an important determinant of negative mental health outcomes [13].	10.1037/a0016059	PMC2747726
PMC10020069	Introduction	Pathways that can link discrimination to mental health include the direct effect of discrimination, psychological stress response to decreased positive emotion and increased negative emotion, and the deterioration of health-related behaviors [13].	10.1037/a0016059	PMC2747726
PMC10020069	Introduction	For example, previous studies from the Philippines and Spain reported that those who perceived a higher level of discrimination during the current pandemic had poor mental health [14], and depression symptoms, psychological distress and death thoughts [15].	10.1111/inm.12920	PMC8447016

PMC10013922

Introduction

There have been previous economic evaluations of maternal GBS vaccination in the United States [12–14], Europe [15–17], and sub-Saharan Africa [18–20].

10.1016/j.vaccine.2017.07.108

PMC5723707

PMC10013922

Introduction

Such an evaluation is central to a Full Value of Vaccines Assessment (FVVA), which WHO has identified as key to catalysing vaccine development and subsequent equitable access [21,22].

10.1016/j.vaccine.2017.09.048

PMC11369760

PMC10013922

Introduction

To inform the WHO GBS vaccine FVVA [23], we conducted the first global economic evaluation of maternal GBS vaccination in 183 countries, drawing on recently updated global disease burden estimates for GBS [1].

10.1016/S2214-109X(22)00093-6

PMC9090904

PMC10013922

Methods

The Bayesian disease model has been described in detail elsewhere; its structure is reflected in our methods below [1,25].

10.1016/S2214-109X(22)00093-6

PMC9090904

PMC10013922

Methods

The Bayesian disease model has been described in detail elsewhere; its structure is reflected in our methods below [1,25].

10.1371/journal.pcbi.1009001

PMC8202927

PMC10013922

Methods

We parameterised the probabilities of different GBS-related outcomes in our model using posterior samples of key epidemiological parameters from the global burden estimates reported by Gonçalves and colleagues [1].

10.1016/S2214-109X(22)00093-6

PMC9090904

PMC10013922

Methods

There are no data on CFRs for infants with EOGBS without access to care, so the authors considered 2 scenarios where they had either 90% CFR (following the approach of Seale and colleagues [3]) or the same CFR as other infants with EOGBS.

10.1093/cid/cix664

PMC5849940

PMC10013922

Methods

The excess risk attributable to iGBS exposure was calculated assuming a counterfactual risk of mild or moderate and severe NDI among unexposed children from a large Danish cohort study [27].

10.1016/S2352-4642(21)00022-5

PMC8131199

PMC10013922

Methods

Following the approach used in the burden estimation, our base case analysis included only the excess risk of moderate or severe NDI, which is likely to be more consistent across settings, but include mild NDI as a sensitivity analysis [1,4].

10.1016/S2214-109X(22)00093-6

PMC9090904

PMC10013922

Methods

Following the approach used in the burden estimation, our base case analysis included only the excess risk of moderate or severe NDI, which is likely to be more consistent across settings, but include mild NDI as a sensitivity analysis [1,4].

10.1016/j.eclinm.2022.101358

PMC9142788

PMC10013922

Methods

To estimate country-specific GBS-associated stillbirth risk, national stillbirth estimates from the WHO Global Health Observatory [28] were combined with regional estimates of the proportion of stillbirths caused by GBS [1].

10.1016/S2214-109X(22)00093-6

PMC9090904

PMC10013922

Methods

For the risk of GBS-associated prematurity, we used national data on the proportion of preterm births [29] together with the global odds ratio for the association between GBS maternal colonisation and preterm births [1].

10.1016/S2214-109X(18)30451-0

PMC6293055

PMC10013922

Methods

For the risk of GBS-associated prematurity, we used national data on the proportion of preterm births [29] together with the global odds ratio for the association between GBS maternal colonisation and preterm births [1].

10.1016/S2214-109X(22)00093-6

PMC9090904

PMC10013922

Methods

For the acute iGBS episode, we approximated QALY loss, assuming 29 days duration based on the average length of stay among studies in a recent systematic review of the acute costs of infant sepsis and meningitis [31], and applied health state utility decrements for hospitalisation with acute sepsis or meningitis from a US study in young children [32].

10.1016/j.vaccine.2020.07.050

PMC7482437

PMC10013922

Methods

For survivors with long-term sequelae, we applied utility decrements from birth for mild, moderate, and severe NDI to each year of life and, conservatively, given previous studies provide evidence of post-acute mortality after bacterial meningitis [33,34], assumed no change in life expectancy.

10.1371/journal.pone.0137095

PMC4564213

PMC10013922

Methods

For survivors with long-term sequelae, we applied utility decrements from birth for mild, moderate, and severe NDI to each year of life and, conservatively, given previous studies provide evidence of post-acute mortality after bacterial meningitis [33,34], assumed no change in life expectancy.

10.1371/journal.pone.0009662

PMC2837384

PMC10013922

Methods

Although clinical studies have demonstrated immunogenicity of candidate GBS vaccines, to date, there have been no Phase III efficacy trials [10].

10.2147/IDR.S203454

PMC7196769

PMC10013922

Methods

We also assumed no effect on GBS-associated prematurity because (i) the WHO PPC does not specify that GBS vaccines must reduce colonisation, which is most likely pathway for preventing GBS-associated prematurity, and (ii) the association between GBS maternal colonisation and higher risk of prematurity may be confounded [37].

10.1093/cid/cix661

PMC5850429

PMC10013922

Methods

For the latter scenario, we estimated the proportion of preterm births that are potentially protected through vaccination by combining the distribution of preterm births by gestational age [38] with the timing of vaccine visits based on country-specific ANC data [39] (S1 Appendix A2.5).

10.1186/1742-4755-10-S1-S2

PMC3828585

PMC10013922

Methods

For the latter scenario, we estimated the proportion of preterm births that are potentially protected through vaccination by combining the distribution of preterm births by gestational age [38] with the timing of vaccine visits based on country-specific ANC data [39] (S1 Appendix A2.5).

10.1371/journal.pone.0237718

PMC7446781

PMC10013922

Methods

To estimate acute healthcare costs, we combined one GBS-specific cost estimate from a study in the United Kingdom (UK) [42], with the findings from a systematic review on the acute costs of infant sepsis and meningitis [43], and result of a recent study reporting the acute costs of neonatal bacterial sepsis and meningitis in Mozambique and South Africa [44].

10.1093/cid/ciab815

PMC8776306

PMC10013922

Methods

Annual costs among survivors with moderate and severe NDI were parameterised as a fixed proportion of between 4% and 28% of the acute cost estimate in each country, based on the range between a UK study of costs in children with NDI [35] and a US study of costs in adults with disabilities [45].

10.1097/MLR.0000000000001371

PMC7505687

PMC10013922

Methods

For the vaccine programme costs, we extrapolated results from a systematic review of maternal vaccination delivery costs using regression against GDP per capita (S1 Appendix A2.7) [31].

10.1016/j.vaccine.2020.07.050

PMC7482437

PMC10013922

Methods

We used previously estimated vaccine prices by World Bank country income group, which were based on a combination of price benchmarking against other vaccines and cost of goods analysis: $50 for high-income countries; $15 for upper-middle-income countries; and $3.50 for lower-middle-income and low-income countries [46].

10.1093/cid/ciab782

PMC8775646

PMC10013922

Methods

Here, we use 2 commonly cited thresholds: (i) country gross domestic product per capita [47]; and (ii) published thresholds based on empirical estimates of the health opportunity cost of healthcare spending (S1 Appendix A2.8) [48,49].

10.12688/gatesopenres.13201.1

PMC7851575

PMC10013922

Methods

Here, we use 2 commonly cited thresholds: (i) country gross domestic product per capita [47]; and (ii) published thresholds based on empirical estimates of the health opportunity cost of healthcare spending (S1 Appendix A2.8) [48,49].

10.1016/j.jval.2016.02.017

PMC5193154

PMC10013922

Methods

Here, we use 2 commonly cited thresholds: (i) country gross domestic product per capita [47]; and (ii) published thresholds based on empirical estimates of the health opportunity cost of healthcare spending (S1 Appendix A2.8) [48,49].

10.1136/bmjgh-2018-000964

PMC6231096

PMC10013922

Methods

In many settings, these are not assigned any health or disability weight, but it has been argued that they should be assigned a QALY loss close or the same as that of the death of a newborn [50].

10.1111/bioe.12120

PMC4706157

PMC10013922

Discussion

Competitive and finely tiered vaccine prices could also be beneficial for manufacturers, with financial analyses suggesting that high global demand is needed to ensure the development costs of a GBS vaccine can be recouped [46].

10.1093/cid/ciab782

PMC8775646

PMC10013922

Discussion

Previous analyses have estimated cost-effectiveness in the US [12–14], the Netherlands [17], UK [15,16], South Africa [19], The Gambia [18], and 37 Gavi countries in Africa [20].

10.1136/bmj.39325.681806.AD

PMC1995477

PMC10013922

Discussion

Previous analyses have estimated cost-effectiveness in the US [12–14], the Netherlands [17], UK [15,16], South Africa [19], The Gambia [18], and 37 Gavi countries in Africa [20].

10.1016/j.vaccine.2017.07.108

PMC5723707

PMC10013922

Discussion

These prior estimates suggested cost-effectiveness of vaccination ranged from $320 to 573 per DALY averted in Gavi-eligible countries [20], to $3,550 per DALY averted in South Africa [19], to over $50,000 per QALY in the US [12,13], which is broadly consistent with our results.

10.1016/j.vaccine.2017.07.108

PMC5723707

PMC10013922

Supporting information

These estimates were provided by the authors of [39] and were calculated using input data on antenatal coverage that was available at the time of their analysis.

10.1371/journal.pone.0237718

PMC7446781

PMC10013922

Supporting information

Table H. Cost data from [31] used to extrapolate used to extrapolate country-specific estimates of vaccine delivery costs per dose.

10.1016/j.vaccine.2020.07.050

PMC7482437

PMC10015103

Discussion

Among a cohort of 51 children with history of MIS-C who received COVID-19 vaccination, the vaccine was found to be safe and well tolerated during short-term follow-up [11].

10.1097/INF.0000000000003803

PMC9935230

PMC10015776

Background

Considerable evidence has emerged indicating that the microbiome is an important contributor to an individual’s health [2].

10.1126/science.1208344

PMC3368382

PMC10015776

Background

This has been illustrated by links between the gut microbiome and numerous diseases, including irritable bowel syndrome [3], Crohn’s disease [4], type 2 diabetes [5], cardiovascular disease [6], and Parkinson’s disease (PD) [7].

10.1136/gutjnl-2016-313235

PMC5531220

PMC10015776

Background

This has been illustrated by links between the gut microbiome and numerous diseases, including irritable bowel syndrome [3], Crohn’s disease [4], type 2 diabetes [5], cardiovascular disease [6], and Parkinson’s disease (PD) [7].

10.1038/nm.3145

PMC3650111

PMC10015776

Background

Diet, being one of these factors, has the greatest known long-term interaction with the gut microbiome [8].

10.1093/ajcn/nqaa350

PMC7948864

PMC10015776

Background

Thus, a deep understanding of the relationship between diet and the gut microbiome and the consequential impact on disease processes holds promise for developing personalized dietary intervention strategies to modulate and maintain a healthy microbiome population [9, 10].

10.1017/S0007114514004127

PMC4405705

PMC10015776

Background

Thus, a deep understanding of the relationship between diet and the gut microbiome and the consequential impact on disease processes holds promise for developing personalized dietary intervention strategies to modulate and maintain a healthy microbiome population [9, 10].

10.1093/jn/nxz154

PMC6825832

PMC10015776

Background

Diet has a direct impact on the microbial community in the gut, which governs the activity of the intestinal ecosystem and can have considerable implications for an individual’s health [11, 12].

10.3389/fimmu.2017.00538

PMC5421151

PMC10015776

Background

These discoveries are generally based on an elaborate experimental design using model organisms [13] or dietary interventions [15–17].

10.1038/nature12820

PMC3957428

PMC10015776

Background

Recent observational studies suggest that long-term diets could be associated with the microbiome [18], and this can further affect overall health.

10.1038/s41591-020-01183-8

PMC8353542

PMC10015776

Background

Hence, using a data-driven approach, it is able to divide a population into multiple subcohorts with distinct microbiological signatures for health that can be best described by nutrient combinations, resulting in what we term “nutritional-ecotypes.” These subcohorts can be thought of as diet-based latent classes where they capture interaction between the constraints imposed by nutrient intake of individuals on the community dynamics of their microbiomes [22, 23].

10.3389/fnut.2019.00041

PMC6465639

PMC10015776

Background

Methods to discover such diet-based latent classes could be hypothesis-driven based on prior knowledge [24, 25] or guided by an unsupervised statistical learning method, such as clustering [26], followed by latent class analysis [27].

10.1017/S0007114518001800

PMC6137382

PMC10015776

Background

Methods to discover such diet-based latent classes could be hypothesis-driven based on prior knowledge [24, 25] or guided by an unsupervised statistical learning method, such as clustering [26], followed by latent class analysis [27].

10.1136/bmj.k2396

PMC5996879

PMC10015776

Background

Methods to discover such diet-based latent classes could be hypothesis-driven based on prior knowledge [24, 25] or guided by an unsupervised statistical learning method, such as clustering [26], followed by latent class analysis [27].

10.1093/advances/nmz049

PMC6855959

PMC10015776

Background

Methods to discover such diet-based latent classes could be hypothesis-driven based on prior knowledge [24, 25] or guided by an unsupervised statistical learning method, such as clustering [26], followed by latent class analysis [27].

10.3389/fimmu.2021.651709

PMC8111016

PMC10015776

Background

Consequently, classification models built within a subcohort defined just by diet (or microbiome) will not necessarily improve prediction of the health/disease state [28].

10.1186/s40168-021-01018-9

PMC8004395

PMC10015776

Background

Kim and colleagues [31] have used it for combining clinical data and genomics data.

10.1093/bioinformatics/btq107

PMC2859127

PMC10015776

Results

This approach has two distinct components: first, a gating network aimed at estimating latent classes shaped by nutritional intake, and second, an experts network aimed at modeling the relationship between the microbiota composition and the health state within each latent class [31, 32].

10.1093/bioinformatics/btq107

PMC2859127

PMC10015776

Results

We further assessed NEMoE’s capabilities on enterotype-separated subcohorts [35] within our PD dataset.

10.1038/s41564-017-0072-8

PMC5832044

PMC10015776

Results

Enterotype, a widely used concept in microbiome research, refers to the categorization of an individual’s microbiomes by the variance in composition [2, 36].

10.1126/science.1208344

PMC3368382

PMC10015776

Results

Enterotype, a widely used concept in microbiome research, refers to the categorization of an individual’s microbiomes by the variance in composition [2, 36].

10.1038/nature09944

PMC3728647

PMC10015776

Results

[19, 20, 38–42]Fig.

10.1038/s41531-020-0112-6

PMC7293233

PMC10015776

Results

[37]) all other datasets showed decreasing Fusicatenibacter in PD.

10.3389/fnins.2019.01184

PMC6883725

PMC10015776

Results

We processed the publicly available datasets using the dada2 pipeline [49](v1.16) and taxonomy reference “silva 138” [48, 50].

10.1038/nmeth.3869

PMC4927377

PMC10015776

Results

We processed the publicly available datasets using the dada2 pipeline [49](v1.16) and taxonomy reference “silva 138” [48, 50].

10.1093/nar/gks1219

PMC3531112

PMC10015776

Results

In all but one dataset [37], Fusicatenibacter had significantly lower relative abundance among PD individuals.

10.3389/fnins.2019.01184

PMC6883725

PMC10015776

Results

5), verifying NEMoE’s ability to identify consensus microbial signatures of PD in multiple independent cohorts.Table 2Summary of eight publicly available Parkinson’s disease microbiome studies used for validation of the NEMoE modelStudyDesignCountrySample sizeSamplingDNA extraction16S regionENA Accession NumberLubomski_0 [19, 39]Lubomski_6Lubomski_12LongitudinalAustralia74PD, 74HCHome collection, stored at −80 °CMP Biomedicals FastDNATM SPIN KitV3-V4PRJNA808166Wallen_1 [36]Cross-sectionalUSA323PD, 184HCHome collection, swabs, stored at −20 °CMoBio PowerSoil DNA Isolation KitV4PRJNA601994Wallen_2 [36, 43]Cross-sectionalUSA197PD, 130HCSwabs, delivered at RTMoBio PowerMag Soil kitV4PRJNA601994Aho (baseline) [44]Aho (follow-up)LongitudinalFinland64PD, 64HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV3-V4PRJEB27564Weis [45]Cross-sectionalGermany34PD, 25HCMED AUXIL fecal collector setFastDNA Spin KitV4-V5PRJEB30615Pietrucci [46]Cross-sectionalItaly80PD, 72HCHome collection, DNA stabilizerPSP-Spin Stool KitV3-V4PRJNA510730Scheperjans [47]Cross-sectionalFinland72PD, 72HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV1-V3PRJEB4927Jin [48]Cross-sectionalChina72PD, 68HCNANAV3-V4PRJEB588834Studies Lubomski_0, Lubomski_6, and Lubomski_12 were part of the same longitudinal data set by Lubomski and colleagues [2] and they represent samples that were measured at 0, 6, and 12 months, respectivelyStudies Aho (baseline) and Aho (follow-up) were part of the same longitudinal data set by Aho and colleagues [44].

10.1038/nature09944

PMC3728647

PMC10015776

Results

5), verifying NEMoE’s ability to identify consensus microbial signatures of PD in multiple independent cohorts.Table 2Summary of eight publicly available Parkinson’s disease microbiome studies used for validation of the NEMoE modelStudyDesignCountrySample sizeSamplingDNA extraction16S regionENA Accession NumberLubomski_0 [19, 39]Lubomski_6Lubomski_12LongitudinalAustralia74PD, 74HCHome collection, stored at −80 °CMP Biomedicals FastDNATM SPIN KitV3-V4PRJNA808166Wallen_1 [36]Cross-sectionalUSA323PD, 184HCHome collection, swabs, stored at −20 °CMoBio PowerSoil DNA Isolation KitV4PRJNA601994Wallen_2 [36, 43]Cross-sectionalUSA197PD, 130HCSwabs, delivered at RTMoBio PowerMag Soil kitV4PRJNA601994Aho (baseline) [44]Aho (follow-up)LongitudinalFinland64PD, 64HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV3-V4PRJEB27564Weis [45]Cross-sectionalGermany34PD, 25HCMED AUXIL fecal collector setFastDNA Spin KitV4-V5PRJEB30615Pietrucci [46]Cross-sectionalItaly80PD, 72HCHome collection, DNA stabilizerPSP-Spin Stool KitV3-V4PRJNA510730Scheperjans [47]Cross-sectionalFinland72PD, 72HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV1-V3PRJEB4927Jin [48]Cross-sectionalChina72PD, 68HCNANAV3-V4PRJEB588834Studies Lubomski_0, Lubomski_6, and Lubomski_12 were part of the same longitudinal data set by Lubomski and colleagues [2] and they represent samples that were measured at 0, 6, and 12 months, respectivelyStudies Aho (baseline) and Aho (follow-up) were part of the same longitudinal data set by Aho and colleagues [44].

10.3389/fnut.2021.628845

PMC8138322

PMC10015776

Results

5), verifying NEMoE’s ability to identify consensus microbial signatures of PD in multiple independent cohorts.Table 2Summary of eight publicly available Parkinson’s disease microbiome studies used for validation of the NEMoE modelStudyDesignCountrySample sizeSamplingDNA extraction16S regionENA Accession NumberLubomski_0 [19, 39]Lubomski_6Lubomski_12LongitudinalAustralia74PD, 74HCHome collection, stored at −80 °CMP Biomedicals FastDNATM SPIN KitV3-V4PRJNA808166Wallen_1 [36]Cross-sectionalUSA323PD, 184HCHome collection, swabs, stored at −20 °CMoBio PowerSoil DNA Isolation KitV4PRJNA601994Wallen_2 [36, 43]Cross-sectionalUSA197PD, 130HCSwabs, delivered at RTMoBio PowerMag Soil kitV4PRJNA601994Aho (baseline) [44]Aho (follow-up)LongitudinalFinland64PD, 64HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV3-V4PRJEB27564Weis [45]Cross-sectionalGermany34PD, 25HCMED AUXIL fecal collector setFastDNA Spin KitV4-V5PRJEB30615Pietrucci [46]Cross-sectionalItaly80PD, 72HCHome collection, DNA stabilizerPSP-Spin Stool KitV3-V4PRJNA510730Scheperjans [47]Cross-sectionalFinland72PD, 72HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV1-V3PRJEB4927Jin [48]Cross-sectionalChina72PD, 68HCNANAV3-V4PRJEB588834Studies Lubomski_0, Lubomski_6, and Lubomski_12 were part of the same longitudinal data set by Lubomski and colleagues [2] and they represent samples that were measured at 0, 6, and 12 months, respectivelyStudies Aho (baseline) and Aho (follow-up) were part of the same longitudinal data set by Aho and colleagues [44].

10.1002/mds.26942

PMC5469442

PMC10015776

Results

5), verifying NEMoE’s ability to identify consensus microbial signatures of PD in multiple independent cohorts.Table 2Summary of eight publicly available Parkinson’s disease microbiome studies used for validation of the NEMoE modelStudyDesignCountrySample sizeSamplingDNA extraction16S regionENA Accession NumberLubomski_0 [19, 39]Lubomski_6Lubomski_12LongitudinalAustralia74PD, 74HCHome collection, stored at −80 °CMP Biomedicals FastDNATM SPIN KitV3-V4PRJNA808166Wallen_1 [36]Cross-sectionalUSA323PD, 184HCHome collection, swabs, stored at −20 °CMoBio PowerSoil DNA Isolation KitV4PRJNA601994Wallen_2 [36, 43]Cross-sectionalUSA197PD, 130HCSwabs, delivered at RTMoBio PowerMag Soil kitV4PRJNA601994Aho (baseline) [44]Aho (follow-up)LongitudinalFinland64PD, 64HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV3-V4PRJEB27564Weis [45]Cross-sectionalGermany34PD, 25HCMED AUXIL fecal collector setFastDNA Spin KitV4-V5PRJEB30615Pietrucci [46]Cross-sectionalItaly80PD, 72HCHome collection, DNA stabilizerPSP-Spin Stool KitV3-V4PRJNA510730Scheperjans [47]Cross-sectionalFinland72PD, 72HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV1-V3PRJEB4927Jin [48]Cross-sectionalChina72PD, 68HCNANAV3-V4PRJEB588834Studies Lubomski_0, Lubomski_6, and Lubomski_12 were part of the same longitudinal data set by Lubomski and colleagues [2] and they represent samples that were measured at 0, 6, and 12 months, respectivelyStudies Aho (baseline) and Aho (follow-up) were part of the same longitudinal data set by Aho and colleagues [44].

10.1371/journal.pone.0237779

PMC7446854

PMC10015776

Results

5), verifying NEMoE’s ability to identify consensus microbial signatures of PD in multiple independent cohorts.Table 2Summary of eight publicly available Parkinson’s disease microbiome studies used for validation of the NEMoE modelStudyDesignCountrySample sizeSamplingDNA extraction16S regionENA Accession NumberLubomski_0 [19, 39]Lubomski_6Lubomski_12LongitudinalAustralia74PD, 74HCHome collection, stored at −80 °CMP Biomedicals FastDNATM SPIN KitV3-V4PRJNA808166Wallen_1 [36]Cross-sectionalUSA323PD, 184HCHome collection, swabs, stored at −20 °CMoBio PowerSoil DNA Isolation KitV4PRJNA601994Wallen_2 [36, 43]Cross-sectionalUSA197PD, 130HCSwabs, delivered at RTMoBio PowerMag Soil kitV4PRJNA601994Aho (baseline) [44]Aho (follow-up)LongitudinalFinland64PD, 64HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV3-V4PRJEB27564Weis [45]Cross-sectionalGermany34PD, 25HCMED AUXIL fecal collector setFastDNA Spin KitV4-V5PRJEB30615Pietrucci [46]Cross-sectionalItaly80PD, 72HCHome collection, DNA stabilizerPSP-Spin Stool KitV3-V4PRJNA510730Scheperjans [47]Cross-sectionalFinland72PD, 72HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV1-V3PRJEB4927Jin [48]Cross-sectionalChina72PD, 68HCNANAV3-V4PRJEB588834Studies Lubomski_0, Lubomski_6, and Lubomski_12 were part of the same longitudinal data set by Lubomski and colleagues [2] and they represent samples that were measured at 0, 6, and 12 months, respectivelyStudies Aho (baseline) and Aho (follow-up) were part of the same longitudinal data set by Aho and colleagues [44].

10.1093/nar/gks1219

PMC3531112

PMC10015776

Results

5), verifying NEMoE’s ability to identify consensus microbial signatures of PD in multiple independent cohorts.Table 2Summary of eight publicly available Parkinson’s disease microbiome studies used for validation of the NEMoE modelStudyDesignCountrySample sizeSamplingDNA extraction16S regionENA Accession NumberLubomski_0 [19, 39]Lubomski_6Lubomski_12LongitudinalAustralia74PD, 74HCHome collection, stored at −80 °CMP Biomedicals FastDNATM SPIN KitV3-V4PRJNA808166Wallen_1 [36]Cross-sectionalUSA323PD, 184HCHome collection, swabs, stored at −20 °CMoBio PowerSoil DNA Isolation KitV4PRJNA601994Wallen_2 [36, 43]Cross-sectionalUSA197PD, 130HCSwabs, delivered at RTMoBio PowerMag Soil kitV4PRJNA601994Aho (baseline) [44]Aho (follow-up)LongitudinalFinland64PD, 64HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV3-V4PRJEB27564Weis [45]Cross-sectionalGermany34PD, 25HCMED AUXIL fecal collector setFastDNA Spin KitV4-V5PRJEB30615Pietrucci [46]Cross-sectionalItaly80PD, 72HCHome collection, DNA stabilizerPSP-Spin Stool KitV3-V4PRJNA510730Scheperjans [47]Cross-sectionalFinland72PD, 72HCHome collection, DNA stabilizer, stored in fridgePSP-Spin Stool KitV1-V3PRJEB4927Jin [48]Cross-sectionalChina72PD, 68HCNANAV3-V4PRJEB588834Studies Lubomski_0, Lubomski_6, and Lubomski_12 were part of the same longitudinal data set by Lubomski and colleagues [2] and they represent samples that were measured at 0, 6, and 12 months, respectivelyStudies Aho (baseline) and Aho (follow-up) were part of the same longitudinal data set by Aho and colleagues [44].

10.1126/science.1208344

PMC3368382

PMC10015776

Results

The same subjects were measured twice, at baseline and then later at follow-up, which was on average 2.25 years apartStudies Wallen_1 and Wallen_2 were part of two large cohort studies set by Wallen and colleagues [38]

10.1038/s41531-020-0112-6

PMC7293233

PMC10015776

Results

Aho (baseline) [44]

10.1002/mds.26942

PMC5469442

PMC10015776

Results

Studies Lubomski_0, Lubomski_6, and Lubomski_12 were part of the same longitudinal data set by Lubomski and colleagues [2] and they represent samples that were measured at 0, 6, and 12 months, respectively

10.1126/science.1208344

PMC3368382

PMC10015776

Results

Studies Aho (baseline) and Aho (follow-up) were part of the same longitudinal data set by Aho and colleagues [44].

10.1002/mds.26942

PMC5469442

PMC10015776

Results

Studies Wallen_1 and Wallen_2 were part of two large cohort studies set by Wallen and colleagues [38]

10.1038/s41531-020-0112-6

PMC7293233

PMC10015776

Results

Recent studies found that Escherichia-Shigella is a pathogenic bacteria that potentially reduces short-chain fatty acid production and produces endotoxins and neurotoxins [51, 52].

10.1186/s13024-021-00427-6

PMC7869249

PMC10015776

Results

These bacteria are known to impact immune response and constipation, with many studies reporting an overrepresentation in PD [39, 40, 42, 53].

10.1002/mdc3.12840

PMC6856467

PMC10015776

Results

Most importantly, neither of these two genera (Escherichia-Shigella, Akkermansia) was discovered in our previous analysis using the ALDE model [54], where both classes were combined for microbiome biomarker identification (Escherichia-Shigella: p-value 0.14, Akkermansia: p-value 0.55) [20].

10.1186/2049-2618-2-15

PMC4030730

PMC10015776

Discussion

Finally, the downstream impact and practical importance of NEMoE is further demonstrated by the discovery of diet-specific PD microbiome markers, such as Escherichia-Shigella and Akkermansia, which are not identified by the ALDE model [54].

10.1186/2049-2618-2-15

PMC4030730

PMC10015776

Methods

Dietary information was collected by a comprehensive Food Frequency Questionnaire and resulted in a table of nutrient intake with 23 macronutrients, presented earlier [43].

10.3389/fnut.2021.628845

PMC8138322

PMC10015776

Methods

We curated a series of datasets from eight different publicly-available microbiome studies [37, 38, 44–46, 51] to further validate results from NEMoE.

10.3389/fnins.2019.01184

PMC6883725

PMC10015776

Methods

We curated a series of datasets from eight different publicly-available microbiome studies [37, 38, 44–46, 51] to further validate results from NEMoE.

10.1038/s41531-020-0112-6

PMC7293233

PMC10015776

Methods

We curated a series of datasets from eight different publicly-available microbiome studies [37, 38, 44–46, 51] to further validate results from NEMoE.

10.1002/mds.26942

PMC5469442

PMC10015776

Methods

We curated a series of datasets from eight different publicly-available microbiome studies [37, 38, 44–46, 51] to further validate results from NEMoE.

10.1186/s13024-021-00427-6

PMC7869249

PMC10015776

Methods

All the datasets were processed using the dada2 pipeline [49] (v1.16) and microbiome taxa were annotated using taxonomy reference “silva 138” [48, 50].

10.1038/nmeth.3869

PMC4927377

PMC10015776

Methods

All the datasets were processed using the dada2 pipeline [49] (v1.16) and microbiome taxa were annotated using taxonomy reference “silva 138” [48, 50].

10.1093/nar/gks1219

PMC3531112

PMC10015776

Methods

For the longitudinal datasets Aho [44], the data for baseline and follow-up, which were collected after 2.5 years, are denoted as Aho (baseline) and Aho (follow-up) respectively.

10.1002/mds.26942

PMC5469442

PMC10015776

Methods

an arcsin square root transformation, was performed on taxa proportion [47, 55]; the arcsin transformed data were further standardized to have mean zero and unit variance (z-score).

10.1371/journal.pone.0237779

PMC7446854

PMC10015776

Methods

These transformations of nutritional features are widely used in nutri-omics studies [56, 57].

10.3233/NHA-170027

PMC5734128

PMC10020069

Introduction

Healthcare workers (HCWs), particularly those involved in COVID-19-related patient care were at a heightened risk of infection [1, 2].

10.1016/j.ijid.2021.01.013

PMC7798435

PMC10020069

Introduction

Healthcare workers (HCWs), particularly those involved in COVID-19-related patient care were at a heightened risk of infection [1, 2].

10.1016/S2468-2667(20)30164-X

PMC7491202

PMC10020069

Introduction

For example, a meta-analysis, including 28 studies from seven countries, reported that the percentage of HCWs who tested positive for COVID-19 was as high as 51.7% [1].

10.1016/j.ijid.2021.01.013

PMC7798435

PMC10020069

Introduction

A prospective cohort study among community individuals and frontline HCWs reported that compared to the community individuals, frontline HCWs had 12-fold higher risk of reporting infection [2].

10.1016/S2468-2667(20)30164-X

PMC7491202

PMC10020069

Introduction

Since the beginning of the COVID-19 pandemic, the fear of transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from HCWs to the general population [3] provoked rapid stigma and discrimination towards HCWs, particularly against those involved in care of COVID-19 patients [4–6].

10.1016/S0140-6736(20)31191-0

PMC7239629

PMC10020069

Introduction

Since the beginning of the COVID-19 pandemic, the fear of transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from HCWs to the general population [3] provoked rapid stigma and discrimination towards HCWs, particularly against those involved in care of COVID-19 patients [4–6].

10.3389/fpubh.2020.570459

PMC7531183

PMC10020069

Introduction

It was reported that HCWs faced discrimination in the form of verbal attacks and threats [3], avoidance from family and community members [7], avoidance from community members towards their family [8], and stigmatization [9].

10.1016/S0140-6736(20)31191-0

PMC7239629

PMC10020069

Introduction

It was reported that HCWs faced discrimination in the form of verbal attacks and threats [3], avoidance from family and community members [7], avoidance from community members towards their family [8], and stigmatization [9].

10.1016/j.jiph.2020.12.028

PMC7778368

PMC10020069

Introduction

It was reported that HCWs faced discrimination in the form of verbal attacks and threats [3], avoidance from family and community members [7], avoidance from community members towards their family [8], and stigmatization [9].

10.1080/20008198.2021.1882781

PMC8075082

PMC10020069

Introduction

Although fewer numbers of infections and death from COVID-19 have been reported in Japan compared to many other countries [10], a few studies reported that frontline HCWs and their family members have experienced discrimination [11, 12].

10.15252/emmm.202012481

PMC7207161

PMC10020069

Introduction

Although fewer numbers of infections and death from COVID-19 have been reported in Japan compared to many other countries [10], a few studies reported that frontline HCWs and their family members have experienced discrimination [11, 12].

10.1093/phe/phab003

PMC7928580

PMC10020069

Introduction

For instance, children of HCWs were refused access to kindergartens, school, and childcare facilities [11, 12].

10.1093/phe/phab003

PMC7928580

PMC10020069

Introduction

Discrimination is an important determinant of negative mental health outcomes [13].

10.1037/a0016059

PMC2747726

PMC10020069

Introduction

Pathways that can link discrimination to mental health include the direct effect of discrimination, psychological stress response to decreased positive emotion and increased negative emotion, and the deterioration of health-related behaviors [13].

10.1037/a0016059

PMC2747726

PMC10020069

Introduction

For example, previous studies from the Philippines and Spain reported that those who perceived a higher level of discrimination during the current pandemic had poor mental health [14], and depression symptoms, psychological distress and death thoughts [15].

10.1111/inm.12920

PMC8447016