Clostridia, mostly belonging to orders Lachnospirales and Oscillospirales. Our models were also predictive of the high FLI group in a different Finnish cohort, consisting of 258 participants, with an average AUC of 0.77 and AUPRC of 0.51 (baseline at 0.21). Pathway analysis of representative genomes of the positively FLI-associated taxa in (NCBI) Clostridium subclusters IV and XIVa indicated the presence of, e.g., ethanol fermentation pathways. These results support several findings from smaller case–control studies, such as the role of endogenous ethanol producers in the development of the fatty liver.Fatty liver disease is the most common liver disease in the world. Its connection with the gut microbiome has been known for at least 80 y, but this association remains mostly unstudied in the general population because of underdiagnosis and small sample sizes. To address this knowledge gap, we studied the link between the Fatty Liver Index (FLI), a well-established proxy for fatty liver disease, and gut microbiome composition in a representative, ethnically homogeneous population sample of 6,269 Finnish participants. We based our models on biometric covariates and gut microbiome compositions from shallow metagenome sequencing. Our classification models could discriminate between individuals with a high FLI  and low FLI (<60) in internal cross-region validation, consisting of 30% of the data not used in model training, with an average AUC of 0.75 and AUPRC of 0.56 (baseline at 0.30). In addition to age and sex, our models included differences in 11 microbial groups from class  Even though the rate of progressions and underlying causes of both diseases might be different, they can be broadly subdivided into those who have fat accumulation in the liver with no or minimal inflammation or those who have additional features of cellular injury and active inflammation with or without fibrosis typically seen in the peri-sinusoidal area.5 Patients with steatohepatitis may progress to cirrhosis and hepatocellular carcinoma and have an increased risk of liver-related morbidity and mortality, globally amounting to hundreds of thousands of deaths.6Fatty liver disease affects roughly a quarter of the world’s population.12 microbes per gram of content,7 and is intimately connected with the liver. Thus, it is no surprise that gut microbiome composition appears to have a strong connection with liver disease.8 Numerous studies over the past 80 y have reported associations between gut microbial composition and liver disease.9 For example, gut permeability and overgrowth of bacteria in the small intestine,10 changes in Gammaproteobacteria and Erysipelotrichi abundance during choline deficiency,11 an elevated abundance of ethanol-producing bacteria,13 metagenomic signatures of specific bacterial species,15 have all been linked to NAFLD in small case–control patient samples. However, the microbial signatures often overlap between NAFLD and metabolic diseases, while those of more serious liver disease, such as steatohepatitis and cirrhosis are more clear.16 For example, oral taxa appear to invade the gut in liver cirrhosis,17 and this phenotype can accurately be detected by analyzing the fecal microbiome composition .8 Furthermore, we recently demonstrated good prediction accuracy for incident liver disease diagnoses ,18 showing that the signatures of serious future liver disease are easy to detect.The human gut harbors up to 1019 Short-chain fatty acid production, conversion of choline into methylamines, modification of bile acids (BA) into secondary BA, and ethanol production, all of which are mediated by gut bacteria, are also known to be aggravating factors for NAFLD.19 Recent studies have also suggested that endogenous ethanol production by gut bacteria could lead to an increase in gut membrane permeability.13 This can facilitate the translocation of bacterial metabolites and cell components, such as lipopolysaccharides from the gut to the liver, leading to further inflammation and possible development of NAFLD.20The mechanisms underlying the contribution of gut microbiome content with fatty liver disease are thought to be primarily linked to gut bacterial metabolism. Bacterial metabolites can indeed be translocated from the gut through the intestinal barrier into the portal vein and transported to the liver, where they interact with liver cells, and can lead to inflammation and steatosis.21 but it is also impractical and unethical in a population-based setting. Ultrasound and MRI based assessment can help detect the presence of fatty liver, however, this data is not available in our cohort. Regardless, recent studies have shown that indices based on anthropometric measurements and standard blood tests can be a reliable tool for noninvasive diagnosis of fatty liver, particularly in population-based epidemiologic studies.23Liver biopsy assessment is the current gold standard for diagnosis of fatty liver disease and its severity,24 Because the fatty liver disease is generally underdiagnosed in the general population,25 we used population-wide measurements of BMI, waist circumference, blood triglycerides, and gamma-glutamyl transferase (GGT) to calculate a previously validated Fatty Liver Index (FLI) for each participant as a proxy for fatty liver.26 In parallel, we used shallow shotgun sequencing to analyze gut microbiome composition,27 which also enabled the use of phylogenetic and pathway prediction methods. In this work, we describe high-resolution associations between fatty liver and individual gut microbial taxa and clades, which are replicable in an external Finnish cohort, and thus generalizable in the Finnish population.Here, we designed and conducted computational analyses to examine the links between fatty liver and gut microbiome composition in a representative population sample of 7,211 extensively phenotyped Finnish individuals.28 This study mainly follows the GTDB taxonomy, unless otherwise noted. The Centrifuge/GTDB microbiome data used in our main analyses were based on archaeal and bacterial phylogenetic “balances”. This method was used to associate larger groups or clades of related organisms with fatty liver disease, and to avoid grouping of taxa on strict hierarchical taxonomic ranks featuring varying ranges of evolutionary divergence.28 Here, we used the PhILR transform, where each balance represents a single internal node in a phylogenetic tree, and its value is a log-ratio of the abundances of the two descending clades . Positive values of the balance signify that the clade in the numerator is more abundant, and negative values that the clade in the denominator is more abundant. Thus, each association of a balance with the target variable necessarily includes both microbial clades descending from the node, one of them positively and the other negatively associated with the target variable. The clades in the numerator and denominator can be also freely switched by changing the sign of the balance value to retain the equivalence. Notably, we used this feature to show all balance-FLI associations in the positive direction to facilitate the comparison of their effect sizes .In our main analyses, we classified our reads against the Genome Taxonomy Database (GTDB).Figures S1, S2). However, the proportion of taxa assigned to Firmicutes in Centrifuge/GTDB appeared to be slightly higher than in the SHOGUN/NCBI data. Furthermore, only 58% of the number of reads assigned in Centrifuge/GTDB to 6 main archaeal phyla were assigned to a single main archaeal phylum in SHOGUN/NCBI. Alpha diversity (as Shannon diversity) was significantly lower in the high FLI group, in both the SHOGUN/NCBI data  and the Centrifuge/GTDB  data while adjusting for age, sex, and self-reported alcohol use in both models.Because the combined approach of using the GTDB taxonomy and the recently introduced PhILR phylogenetic transform complicates the comparison of our results in previous studies, we also conducted more traditional statistical analyses with NCBI-annotated data to anchor our results in previous findings on the associations between fatty liver disease and gut microbiome composition. Overall, the Centrifuge/GTDB classification assigned 5.3 billion reads in the 6,269 samples (after exclusions in FINRISK 2002) to 23,457 bacterial and 1,248 archaeal taxa, and the SHOGUN/NCBI classification assigned 5.5 billion reads to 5,024 bacterial and 261 archaeal taxa. Starting from high level descriptions of the microbial communities in the high and low FLI groups  significantly correlated with all three bacterial PC axes, sex, age, and alcohol use in Centrifuge/GTDB data , and PC1, PC3, sex, age, and alcohol use in SHOGUN/NCBI data . Correlations between FLI and archaeal PC axes were not significant in Centrifuge/GTDB data , and between FLI and bacterial PC2 in SHOGUN/NCBI data (P > 0.001). In Centrifuge/GTDB data, the effect size estimate on log10(FLI) was a magnitude larger for PC1 (0.11 ± 0.008) than for PC2 (0.04 ± 0.008) and PC3 (−0.06 ± 0.008). The relationships between FLI and the bacterial PC components representing their beta diversity in Centrifuge/GTDB data are visualized for each of the three components in Figure S3).To further examine the high-level associations between FLI (as a proxy of fatty liver disease) and microbial community composition in FINRISK 2002, we fit a linear regression model on the three first principal component (PC) axes of the fecal bacterial beta diversity , sex, age, and alcohol. LogLachnospirales and Oscillospirales, class Bacilli, and the Ruminococcaceae, Bacteroidaceae, and Lachnospiraceae families . Several clades had a negative association with FLI, on the negative side of the balances contributing to PC1, such as order Christensenellales and genus Faecalibacterium. In addition, genus Bifidobacterium in PC2, and family Bifidobacteriaceae in PC3 had negative associations with continuous FLI.We also further assessed the phylogenetic balances contributing to the PC axes in the Centrifuge/GTDB data. Bacterial clades associated with higher FLI values, on the positive side of the balances contributing to PC1, included members of orders Q values <0.001; Table S7). In SHOGUN/NCBI data, 80 taxa (1.6%) had an increased abundance, and 44 (0.9%) had a decreased abundance in the high FLI group . While the number of associated taxa was higher in the Centrifuge/GTDB data than SHOGUN/NCBI data, the proportion of significantly associated taxa was similar between the two methods. In both data sets, family Lachnospiraceae comprised over 40% of taxa positively associated with the high FLI group and Bacterioidaceae were in the top 3 most common families. The negatively associated taxa were much more diverse, but Ruminococcaceae and Oscillospiraceae were among the top three most common families in both data sets .We also assessed significant differences in abundances of individual taxa between the high and low FLI groups in FINRISK 2002. In Centrifuge/GTDB data, we identified 244 taxa  with an increased abundance and 437 taxa (1.9%) with a decreased abundance in the high FLI group  were modeled with gradient boosting regression or classification using Leave-One-Group-Out Cross-Validation (LOGOCV) between participants from different regions. Only the bacterial PhILR transformed Centrifuge/GTDB data were used here, to find robust associations between phylogenetically related bacterial clades and fatty liver disease (instead of single taxa).Continuous FLI and differences between FLI groups in the FINRISK 2002 cohort . After feature selection and optimization, the main classification models  averaged AUC = 0.75 (Table S2) and AUPRC = 0.56  on (unseen) test data from the omitted region. Models trained using only the covariates averaged AUC = 0.71 (AUPRC = 0.47) and using only the 11 bacterial balances they averaged AUC = 0.66 (AUPRC = 0.47) on test data. Alternative models were constructed by excluding participants with FLI between 30 and 60  and discerning between groups of FLI < 30  and FLI ≥ 60 . These models averaged AUC = 0.80  on their respective test data . They averaged AUC = 0.76 (AUPRC = 0.68) when using only the covariates, and AUC = 0.70 (AUPRC = 0.63) when using only the 20 bacterial balances.After feature selection and Bayesian hyperparameter optimization, the correlation between the predictions of the final regression models  and true values in unseen data from the omitted region averaged RTable S4). Using only covariates, they averaged AUC = 0.71 (AUPRC = 0.47), and AUC = 0.67 (AUPRC = 0.48) with only the bacterial balances.Because training data from all six regions were used to prevent overfitting in the selection of core features for all of the models, and similarly in searching for common hyperparameters, participants from the validation region of each model (in the training partition) partly influenced these parameters. Thus, we also constructed classification models discerning between the FLI < 60 and FLI ≥ 60 groups, where data of the validation region were completely excluded in the feature selection and hyperparameter optimization of each LOGOCV model. These models, using their individual feature sets and hyperparameters, averaged AUC = 0.75 and AUPRC = 0.57 (baseline at 0.30) on test data from their respective validation regions .30 The participants originate from North Karelia and Helsinki/Vantaa regions in Finland, and their samples were processed with the same methodology as was used for FINRISK 2002 (with Centrifuge/GTDB approach and PhILR). In this external validation, the six full models trained with covariates and the 11 bacterial balances in FINRISK 2002 averaged AUC = 0.77 . The covariate-only models averaged AUC = 0.72 (AUPRC = 0.40) and the balance-only models averaged AUC = 0.69 (AUPRC = 0.44). The receiver operating characteristic and precision–recall curves based on the averaged predictions of the models, tested on these external validation data, also display good predictive ability Our external validation data consisted of 258 participants after exclusion of pregnant participants or those on antibiotics in the past 6 months, in the FINRISK 2007 population cohort . All 11 associated balances were in phylum Firmicutes, class Clostridia, and largely in the NCBI Clostridium subclusters IV and XIVa (Negativibacillus (Clostridium), Clostridium M (Lachnoclostridium/Clostridium), CAG-81 (Clostridium), Dorea (Merdimonas/Mordavella/Dorea/Clostridium/Eubacterium), Faecalicatena (Blau-tia/Ruminococcus/Clostridium), Blautia (Blautia), Sellimonas (Sellimonas/Drancourtella), Clostridium Q (Lachnoclostridium [Clostridium]), and Tyzzerella (Tyzzerella/Coprococcus).(NCBI in brackets)  Notably, all but one of the features in the main classification models (n226) were identified in the feature selection for the alternative models , together with 10 additional balances . Only one of the balances in the alternative models was outside phylum Firmicutes (n1712 in Bacteroidota), and in addition, four balances were outside class Clostridia .To facilitate the interpretability of the results, we continued examining the main classification models using a common set of core features. In these models, the median effect sizes of the features on the model predictions at their minimum and maximum values were highest for age, followed by sex, and the 11 balances in the phylogenetic tree . A higher abundance of the clade including Angelakisella, D5, Anaerotruncus, and Phocea species  was also negatively associated with high FLI.Also, negative associations with the high FLI group were seen for  species , S8. A h31 To do so, we performed feature selection (similarly to continuous FLI) for GGT and triglyceride measurements in subsets of participants grouped by age, sex, and BMI. The feature selection identified two balances within the NCBI Clostridia XIVa subcluster (identified as n336 and n330) which were important for both GGT and triglyceride level prediction, and thus likely specific to liver function  Hungatella effluvii, H. hathewayi, and two new GTDB-defined species Clostridium M sp001517625 and C. M sp000431375.In addition to blood test results, FLI is based on two anthropometric markers linked to metabolic syndrome, waist circumference, and BMI. This prompted us to dissect the Fatty Liver Index and identify which of the covariates and associated microbial balances from the phylogenetic tree can be linked to blood GGT and triglyceride measurements see , and thefunction . BacteriFigure S8). These processes were chosen because they have been previously identified to have a mechanistic link to NAFLD .The values of predictive balances in the phylogenetic tree cannot be summarized for individual taxa, which means that only a qualitative investigation of the associations between their metabolism and fatty liver was possible in this study. We identified genetic pathways predicted to encode for SCFA  and ethanol production, BA metabolism, and choline degradation to trimethylamine (TMA) in representative genomes from the taxa we identified to be linked to liver function (Clostridum M clostridoforme A and Clostridum M sp000155435), and not in the negatively associated clade (Clostridium M sp001517625 and Clostridium M sp000431375). However, for most balances, such trends were not clear in the qualitative analysis. Furthermore, we did not detect any of these pathways in the representative genomes of two individual taxa positively associated with FLI, Negativibacillus sp000435195 and Phocea massiliensis .Acetate and ethanol production pathways appeared to be more common in the representative genomes of the taxa which had a positive association with FLI. In the liver function-specific clades, n336 and n330, MetaCyc pathways for pyruvate fermentation to ethanol III (PWY-6587) and L-glutamate degradation V  were present only in genomes positively associated with FLI. In balance n336, also heterolactic fermentation  was more often encoded in the clade positively associated with the high FLI group (3/5) than the opposing negatively associated clade (1/2). In representative genomes from the liver-specific balance n336, potential ethanol producers (PWY-6587) were seen in the positively associated clade ,30 to identify broad links between the overall gut microbiome composition and fatty liver disease, using FLI as a recognized proxy . Furthermore, we would like to emphasize that our results are not suitable for current clinical application, and should be validated by further, preferably mechanistic studies. We also do not know if our results generalize outside the Finnish population, as all participants in this study were exclusively from Finnish cohorts.The pathophysiology of fatty liver disease in general, and NAFLD in particular, is complex and its clinical diagnosis can be difficult.ed proxy , and ideed proxy . It shou23 our models were able to reasonably predict the FLI group with AUC = 0.75 , in our internal cross-region validation. Furthermore, the performance of our predictive models was highly similar in an external, Finnish validation cohort .Considering that the predictive ability of FLI for clinically diagnosed NAFLD ranges between AUC = 0.81–0.93, in populations of Caucasian ethnicity such as the Finnish population,35).Our additional analyses support these main results. While a thorough method comparison is beyond the scope of the current study, the results from the two taxa assignments were very similar despite their differences, such as the fourfold higher number of taxa in the Centrifuge/GTDB data. In the machine learning models (performed only with Centrifuge/GTDB data), excluding participants with intermediate FLI (between 30 and 60) increased the accuracy slightly in the internal cross-validation . However, discerning between participants with probable fatty liver disease (FLI ≥ 60) from others presents a clinically more relevant target for detecting changes in microbiome composition associated with the development of the disease. In another set of models, we negated the influence of validation region data in the individual models also for feature selection and hyperparameter optimization during training. This led to individualized sets of features and parameters in the models, but the average performance of the models was almost identical on validation region samples in the internal cross-validation . The aim of our study was to find patterns in microbiome composition which would be generalizable across the six sampled geographic regions in Finland and easy to interpret. Thus, we consider the use of all training data to define the common core feature set justified. This goal also guided our overall modeling architecture and likely led to a lower performance than if we instead performed interpolation within a smaller scale  have been used in Asian cohorts.37–39 Thus, it is likely that our high FLI groups include most participants with liver steatosis or fibrosis in both FINRISK cohorts, but the low FLI group also likely includes participants with low-grade steatosis.When interpreting our results, several levels of associations can be considered according to types of fatty liver disease and the gut microbiome composition. Because FLI has been mostly validated with simple steatosis and NAFLD,40 In this case–control study, the Shannon diversity of gut microbiomes in NASH patients without liver cirrhosis was on average 7% lower compared to controls, and in patients with cirrhosis, 14% lower. A significantly decreased gut microbiome alpha diversity of similar magnitude was also seen in cohort participants with persistent NAFLD compared to controls.41Among the significantly high level FLI-associated differences in the gut microbiomes of the participants in FINRISK 2002, we found a 14.7% lower Shannon alpha diversity in the high FLI group with SHOGUN/NCBI taxa assignments and 13.4% lower diversity with Centrifuge/GTDB assignments. These results are in good accordance with previous results of decreased gut bacterial diversity in patients with biopsy-proven nonalcoholic steatohepatitis (NASH), the most serious form of NAFLD., S3). The model fit was slightly better with Centrifuge/GTDB data, which might be due to the higher number of identified taxa, and thus increased taxonomic resolution . Our results support previous observations of differences in beta diversity in relation to persistent NAFLD,41 and along the NAFLD-cirrhosis spectrum.8 Through the loadings of the phylogenetic balances on the PC axes in the Centrifuge/GTDB data, we detected several previously known connections between microbial clades and FLI . Among others, we observed a positive association between high FLI and family Lachnospiraceae and negative associations for order Christensenellales, genus Faecalibacterium, and genus Bifidobacterium. The positive association is supported by previous findings of their connection with obesity,42 and the negative associations by connections to lean individuals and healthy gut microbiome composition.43–45In both the SHOGUN/NCBI and Centrifuge/GTDB data, we found significant linear correlations between FLI and beta diversity or two or three main bacterial PC-axes of the samples, respectively , S3. TheLachnospiraceae, which supports their positive association with NAFLD reported previously in a number of studies,46 but also with obesity (Table S7).42 The increased abundance of genus Roseburia has also been highlighted as a characteristic change in the gut microbiome related to NAFLD.47 In the current study, two members of the genus Roseburia were in the top 10 taxa most strongly associated with high FLI. Furthermore, our results support previous findings on the positive associations of, for example, Collinsella,40Prevotella copri,48Dorea,47 with NAFLD. We also detected increases in Sutterella and Streptococcus, previously associated with cirrhosis.49 However, we did not find increases in families Kiloniellaceae and Pasteurellaceae, previously associated with NAFLD.46 Among the individual taxa negatively associated with high FLI, families Ruminococcaceae and Oscillospiraceae (such as genus Oscillibacter) were common, which supports previous findings on their connections with NAFLD.46 A high number of putative (GTDB) species were negatively associated with FLI in the Centrifuge/GTDB data, which were understandably not present in the SHOGUN/NCBI data. Many of these were classified in the recently described order Christensenellales,28 including families such as CAG-74, associated with healthy participants,50 and Christensenellaceae, which are widespread, highly heritable, and associated with health.51Our differential abundance analysis also detected a high number of taxa with significantly increased or decreased abundance in the high FLI group. All following results were observed both in the Centrifuge/GTDB and SHOGUN/NCBI data sets, unless otherwise noted. The majority of the taxa with increased abundance in the high FLI group was from family While our results from common statistical experiments mainly supported previous findings, we chose to leverage the phylogenetic information included in the GTDB data to find robust associations between larger bacterial clades and fatty liver disease in the Finnish population. This was accomplished by constructing predictive models to classify participants in the FLI groups based on the phylogenetic balances and covariates, subjected to feature selection and geographical cross-validation.Firmicutes phylum. A possible reason for this might be the higher relative abundance of phylum Firmicutes at high latitudes,52 where Finland is. Among the associations we identified, Faecalicatena gnavus (NCBI: Ruminococcus gnavus) was positively linked with FLI as part of three predictive balances and associated in previous studies with liver cirrhosis.17 In their study, oral Firmicutes, such as Veillonella, were suggested to invade the gut. While our balance-based approach did not detect these taxa, Megasphaera elsdenii was positively associated with the high FLI group in our differential abundance analyses (Table S7). This might be due to the strict feature selection employed before the predictive modeling.Strikingly, the strongest associations with FLI in our machine learning models were all inside the Negativibacillus sp000435195 and Phocea massiliensis, both had strong positive associations with the high FLI group (Negativibacillus sp000435195 (NCBI: Clostridium sp. CAG:169) and Phocea massiliensis (NCBI: Phocea massiliensis) were seen when the intake of meat and refined cereal was reduced isocalorically in favor of fruit, vegetables, wholegrain cereal, legumes, fish and nuts in overweight and obese subjects in Italy.53 While comparisons between these studies are difficult due to differences in taxa annotations, bacteria, such as Faecalicatena gnavus (NCBI: Ruminococcus gnavus) and Clostridium Q saccharolyticum (NCBI: Clostridium saccharolyticum), were also found to respond negatively to the Mediterranean diet. Thus, further study on the connections of these bacteria with gut health and diet is warranted.Two individual taxa, LI group , but theHungatella ,56 a metabolite associated with cardiovascular disease and NAFLD. In our study, on the positively associated side  opposite to genus Hungatella was a novel GTDB species, CAG-81 sp000435795, previously included in NCBI genus Clostridium. The CAG-81 genus was recently positively associated with TMAO levels in urine in a study using the GTDB classification.57 While we did not find the pathway for TMA (precursor to TMAO) production in its genome, this would explain the positive association of the CAG-81 species with high FLI. Furthermore, the previous contradictive results among these taxa could be explained by grouping of putatively TMA producing taxa in CAG-81 together with the closely related genus Hungatella.Among the taxa negatively associated with high FLI, Clostridium NCBI genus, which supports several previous observations.58 However, taxonomic standardization according to GTDB has identified the Clostridium genus as the most phylogenetically inconsistent of all bacterial genera in the NCBI taxonomy and divides it into a total of 121 monophyletic genera in 29 distinct families.28 The GTDB reassignment complicates comparisons to previous studies, but it is phylogenetically and biologically sensible, and can thus provide new insights into the microbiomes. Our results also strongly suggest that despite its higher cost compared to metabarcoding, the increased resolution of  shotgun metagenomic sequencing is highly useful in identifying specific taxon-disease associations .Most taxa in our study with a positive association with FLI belonged to the broadly defined Clostridia, which are obligate anaerobes. We observed that reference genomes from the bacterial taxa positively associated with high FLI in the liver-specific balances harbored several genetic pathways necessary for ethanol production. Specifically, genes predicted to enable the fermentation of pyruvate to ethanol (MetaCyc PWY-6587) appeared to be common. Endogenous production of ethanol has been known to both induce hepatic steatosis and increase intestinal permeability,60 and several of the taxa associated with the high FLI group have also been experimentally shown to produce ethanol, such as C. M asparagiforme, C. M bolteae, C. M clostridioforme/C. M clostridioforme A,61 and C. Q Saccharolyticum.62 The relative abundances of these putatively ethanol-producing taxa were also predictive of FLI groups in previously unseen data. However, the self-reported alcohol consumption from the participants was not among the best predictors for the FLI groups, as it was excluded in the feature selection step.We identified several plausible new associations between individual taxa and clades of bacteria and fatty liver. All taxa were from class Figure S8) pathway, which results in the production of acetate and butanoate. Glutamate fermentation could lead to increased microbial protein fermentation in the gut, which has been previously been linked with obesity, diabetes, and NAFLD.63 Recently, the combined intake of fructose and microbial acetate production in the gut was experimentally observed to contribute to lipogenesis in the liver in a mouse model.64 Interestingly, C. Q saccharolyticum  was experimentally shown to ferment various carbohydrates, including fructose, to acetate, hydrogen, carbon dioxide, and ethanol.62 Furthermore, while our own pathway analysis did not detect BA modification pathways in the reference genome of C. Q saccharolyticum, a strain of this species has been highlighted as a probable contributor to NAFLD development through the synthesis of secondary BA.15 The links between dietary intake and gene regulation, combined with microbial fermentation in the gut warrant further mechanistic experiments to elucidate their links with fatty liver and likely other metabolic diseases.All reference genomes from taxa positively associated with FLI in balance n330 harbored genes predicted to encode for the L-glutamate fermentation V  have been described as opportunistic pathogens,65 and are hypothesized to exacerbate fatty liver development similarly through endogenous ethanol production. This result suggests that geographical,35 and ethnic variability,66 might also strongly affect gut microbiome composition and its associations with disease. In addition to putative endogenous ethanol producers, we identified other taxa positively associated with high FLI in balance n330, for which reference genomes harbored a genetic pathway predicted to encode for the ability to ferment L-lysine to acetate and butyrate. While the production of these SCFAs is often considered beneficial for gut health, other metabolism of proteolytic bacteria might negatively contribute to fatty liver disease.67NAFLD-associated ethanol-producing bacteria in previous cohort studies have all been gram-negatives, such as (NCBI-defined) , S7). Their similar positive associations with fatty liver disease are known from previous studies.69 The associated microbial balances could be used to improve the predictions above the baseline of these covariates on 5/6 regions in Finland in the main cohort. For example, in the model cross-validated with Lapland, the balances were more predictive of the FLI group than the covariates by themselves, and their combination increased the AUC further. Yet, when testing the model where Turku/Loimaa region was used for internal cross-validation, the microbial balances were slightly predictive of the FLI group but failed to improve the AUC over the covariates (Table S2). This pattern might stem from the cultural and genetic west-east division in Finland,71 with a closer proximity of the Helsinki/Vantaa region to eastern regions than Turku/Loimaa, in both terms. It is thus likely that further incorporation and investigation on the use of spatial information in microbiome modeling would elucidate these geographical patterns in taxa-disease associations.Through modeling a previously validated index for fatty liver, FLI, we found replicable associations with specific microbial taxa and likely liver disease of the participants. In addition, the sex and age of participants were also strongly predictive of the FLI group in our models , S7. The35 might mean that associations reported in our study are specific to Finland and nearby regions. Notably, many of the positive associations between specific taxa and fatty liver disease have not been reported previously, but the functional potential of these taxa inferred from genomic data is similar to taxa positively associated with NAFLD in previous studies. Thus, the geographical limits of taxa-disease associations reported in studies, such as ours warrant further study. Unfortunately, the generalization of our own results outside of Finland also remains to be addressed.Our models were also able to accurately predict the FLI group of participants in the external validation cohort, which were from the North Karelia and Helsinki/Vantaa regions. The observed difficulty to geographically extrapolate taxa-disease associations,72 Indeed, the prevalences of diabetes and cardiovascular disease in both FINRISK 2002 and 2007 cohorts are elevated in the high FLI group, although the majority of the high FLI participants did not have either of these diagnoses at the time of sampling (Table S1). We also dissected the FLI by dividing participants into age/sex/BMI groups and detected microbial groups specific to the blood work measurements of liver damage, triglycerides, and GGT. These associated taxa can thus be thought of as most closely associated with liver function, if such a division is deemed practical.It is likely that not all associations in the current study are related solely to liver steatosis because FLI is based on measurements related to metabolic syndrome. However, our approach is supported by recent views of NAFLD as the integral liver component of the metabolic syndrome.Clostridia, representing several redefined GTDB genera previously included in the NCBI genus Clostridium. Many of the representative genomes of taxa positively associated with high FLI had the genomic potential for endogenous ethanol production. Our results support previous findings on the likely contribution of ethanol and increased gut permeability on the induction of hepatic steatosis. Further support was also found for the involvement of TMA and SCFAs, especially acetate, in the likely pathophysiology of fatty liver disease. Our models were able to predict the FLI group of participants in Finland across geographical regions and in an external Finnish cohort, showing that the associations are robust and generalizable in this population. Based on our results, mechanistic connections between specific microbes and fatty liver disease, and the geographical differences in such taxa-disease associations should be addressed in further studies.Modeling an established risk index for fatty liver enabled the detection of associations between the disease and gut microbiome composition, to the level of individual taxa. While utilizing FLI as a proxy, NCBI taxa identified with standard statistical methods were supportive of previously reported differences between NAFLD cases and healthy controls. In our machine learning framework, all clades robustly predictive of the FLI group were from the obligately anaerobic gram-positive class 30 In the FINRISK 2002 survey, a stratified random population sample was conducted on six geographical regions in Finland. These are North Karelia and Northern Savo in eastern Finland, Turku and Loimaa regions in southwestern Finland, the cities of Helsinki and Vantaa in the capital region, the provinces of Northern Ostrobothnia and Kainuu in northwestern Finland, and the province of Lapland in northern Finland.Cardiovascular disease risk factors have been monitored in Finland since 1972 by conducting a representative population survey every 5 y.Briefly, at baseline examination, the participants filled out a questionnaire form, and trained nurses carried out a physical examination and blood sampling in local health centers or other survey sites. Data were collected for physiological measures, biomarkers, and dietary, demographic, and lifestyle factors. Stool samples were collected by giving willing participants a stool sampling kit with detailed instructions. These samples were mailed overnight between Monday and Thursday under Finnish winter conditions to the laboratory of the Finnish Institute for Health and Welfare, where they were stored at −20°C. In 2017, the samples were shipped still unthawed to the University of California San Diego for microbiome sequencing.Table S1). Further details and sampling have also been extensively covered in previous publications (see refs.73). The Coordinating Ethics Committee of the Helsinki University Hospital District approved the study protocol for FINRISK 2002 (Ref. 558/E3/2001), and all participants have given their written informed consent.Details of the FINRISK cohorts analyzed in this study are included in the supplementary files (74 A miniaturized version of the Kapa HyperPlus Illumina-compatible library prep kit (Kapa Biosystems) was used for library generation, following the previously published protocol.75 DNA extracts were normalized to 5 ng total input per sample in an Echo 550 acoustic liquid handling robot (Labcyte Inc.). A Mosquito HV liquid-handling robot (TTP Labtech Inc.) was used for 1/10 scale enzymatic fragmentation, end-repair, and adapter-ligation reactions. Sequencing adapters were based on the iTru protocol,76 in which short universal adapter stubs are ligated first and then sample-specific barcoded sequences added in a subsequent PCR step. Amplified and barcoded libraries were then quantified by the PicoGreen assay and pooled in approximately equimolar ratios before being sequenced on an Illumina HiSeq 4000 instrument to an average read count of approximately 900,000 reads per sample.DNA extraction was performed according to the Earth Microbiome Project protocols, with the MagAttract PowerSoil DNA kit (Qiagen), as previously described.77 Host reads were removed by mapping the reads against the human genome assembly GRCh38 with Bowtie2.78 To improve the taxonomic assignments of our reads, we used a custom index,79 based on the Genome Taxonomy Database (GTDB) release 89 taxonomic redefinitions,80 for reading classification with default parameters in Centrifuge 1.0.4.81 Viral and eukaryotic sequences were removed in this step, as the database contains only bacterial and archaeal reference genomes. After read the classification, all following steps were performed with R version 3.5.2,82 using phyloseq 1.30.0,83 to manage the data. To reduce the number of spurious read assignments, and to facilitate more accurate phylogenetic transformations, only reads classified at the species level, matching individual GTDB reference genomes, were retained. Samples with less than 50,000 reads, from pregnant participants or recorded antibiotic use in the past 6 months were removed, resulting in a final number of 6,269 samples. We first filtered taxa not seen with more than 3 counts in at least 1% of the samples and those with a coefficient of variation ≤3 across all samples, following McMurdie and Holmes,83 with a slight adaption from 20% of the samples to 1% of the samples, because of our larger sample size. The complete bacterial and archaeal phylogenetic trees of the GTDB release 89 reference genomes, constructed from an alignment of 120 bacterial or 122 archaeal marker genes,28 were then combined with our taxa tables. The resulting trees were thus subset only to species that were observed in at least one sample in our data. The read counts were transformed to phylogenetic node balances in both trees with PhILR.29 The default method for PhILR inputs a pseudocount of 1 for taxa absent in an individual sample before the transform.We quality trimmed the sequences and removed the sequencing adapters with Atropos.29 introducing the concept of microbial “balances”. Indeed, evolutionary relationships of all species harbored in each microbiome sample can be represented on a phylogenetic tree, with species typically shown as external nodes that are related to each other by multiple branches connected by internal nodes. In this context, the value of a given microbial “balance” is defined as the log-ratio of the geometric mean abundance between two groups of microbes descending from the same corresponding internal node on a microbial phylogenetic tree. This phylogenetic transform was used because it (i) addresses the compositionality of the metagenomic read data,84 (ii) permits simultaneous comparison of all clades without merging the taxa by predefined taxonomic levels, and (iii) enables evolutionary insights into the microbial community. The links between microbes and their environment, such as the human gut, are mediated by their functions. Different functions are known to be conserved at different taxonomic resolutions, and most often at multiple different resolutions.85 Thus, associations between the microbes and the response variable are likely not best explained by predefined taxonomic levels. In the absence of functional data, concurrently analyzing all clades (partitioned by the nodes in the phylogenetic tree) would likely enable the detection of the associations at the appropriate resolution depending on the function and the local tree topography.In this study, we did not specifically and solely use relative abundances at various taxonomic levels, as is common practice for microbiome studies. Instead, we applied a PhILR transformation to our microbial composition data,59 was used for taxonomy assignments against the NCBI RefSeq version 82  database containing complete bacterial, archaeal, and viral genomes. To facilitate comparisons between different annotations, we subset the samples included in the SHOGUN/NCBI annotated data to those included in the Centrifuge/GTDB data .To further validate our approach, assess how the use of the GTDB taxonomic redefinitions and custom database affected our results, and to facilitate comparisons with previous results, we annotated our raw reads in FINRISK 2002 samples also with NCBI taxonomy and performed several additional analyses. For these comparison data, after quality trimming the FINRISK 2002 reads and removing host sequences as described above, SHOGUN v1.0.5,25 and our sampling did not have extensive coverage of liver fat measurements, we chose to use the Fatty Liver index as a proxy for fatty liver.26 Furthermore, the index performs well in cohorts of Caucasian ethnicity, such as ours, to diagnose the presence of NAFLD.23 We calculated FLI after Bedogni et al.26 (e0.953*loge(triglyceridesmg/dL) + 0.139*BMI + 0.718*loge(GGT) + 0.053*waist circumference–15.745)/ (1 + e0.953*loge(triglyceridesmg/dL) + 0.139*BMI + 0.718*loge(GGT) + 0.053*waist circumference–15.745) * 100. We chose the cutoff at FLI ≥ 60 to identify participants likely to be diagnosed with hepatic steatosis . Triglycerides, gamma-glutamyl transferase (GGT), BMI, and waist circumference measurements had near complete coverage for the participants in our data. Self-reported alcohol use was calculated as grams of pure ethanol per week. Cases with missing values were omitted in linear regression models. At least one feature used for FLI calculation was missing for 20 participants in FINRISK 2002 (0.3%) and the self-reported alcohol use was missing for 247 participants (3.9%). In the machine learning framework, missing values for FLI and self-reported alcohol use were mean imputed. However, for the feature selection to identify liver function-specific balances, GGT, triglycerides, and BMI were not imputed but observations where any of these were missing were simply removed.Because the fatty liver disease is underdiagnosed at the population level,Figure S1), and with SHOGUN/NCBI data in FINRISK 2002  by total sum scaling and merging taxa at the phylum level, separately for bacteria and archaea.The baseline compositions of the microbial communities in the samples were summarized at phylum level in the different FLI groups (<60 and ≥60 FLI) with the Centrifuge/GTDB data in FINRISK 2002 and 2007 (86), separately in both the Centrifuge/GTDB and SHOGUN/NCBI data sets. Associations between the FLI group (<60 or ≥60 FLI) and Shannon diversity in the data sets were modeled using binomial regression and adjusted for age, sex, and self-reported alcohol use, using “glm” in base R.82Bacterial alpha diversity of each individual sample in FINRISK 2002 was estimated through Shannon diversity as the mean of 10 random rarefactions of raw annotated read counts  transformed with “transform” in microbiome 1.8.0,88 and their Euclidian distances were obtained similarly through PCA. Linear regression models were constructed for FLI with “lm” in base R,82 with Centrifuge/GTDB data, and separately with SHOGUN/NCBI. Log10(FLI) was used as the dependent variable and the first three bacterial PCs, sex, age, and self-reported alcohol were used as the independent variables. Archaeal PCs were not included in the models because none of them was significantly correlated with FLI in Centrifuge/GTDB data . To visualize the association between beta diversity and FLI, the FLI of each participant was plotted against its quantiles along the three bacterial PC axes in Centrifuge/GTDB data .In the Centrifuge/GTDB data, beta diversity was calculated as Euclidian distance of the PhILR balances through Principal Component Analysis (PCA) on bacterial and archaeal balances separately with “rda” in vegan 2.5.6.TDB data . A compa89 Briefly, the significance of the abundance differences between the groups was estimated with a Welch’s t-test, and only taxa with (Benjamini Hochberg) false discovery rate-adjusted P values  <0.001 were retained. The associated taxa were then divided in each data set to those positively or negatively associated with the high FLI group and sorted based on effect sizes estimated from the median CLR differences between the groups.To facilitate comparisons to previous studies, we assessed the associations between the FLI group (<60 or ≥60 FLI) of the participants and Centrifuge/GTDB and SHOGUN/NCBI annotated individual taxa present in the samples. With both data sets, the differential abundance of the bacterial taxa between the FLI groups was assessed with the ALDEx2 compositional data analysis tool.90 by using both bacterial and archaeal balances, sex, age, and self-reported alcohol use as preliminary predictor features. We used FLI 60 as the cutoff for ruling in fatty liver (steatosis) for the classification, after Bedogni et al.26 The data were first split into 70% train and 30% test sets while preserving sex and region balance. To take into account geographical differences  and to find robust patterns across all six sampled regions in Finland between the features and FLI group, we used Leave-One-Group-Out Cross-Validation (LOGOCV) inside the 70% train set to construct 6 separate models in each optimization step. Because of the high dimensionality of the data , feature selection by filtering was first performed inside the training data, based on random forest permutation as recommended by Bommert et al.91 Briefly, permutation importance is based on accuracy, or specifically, the difference in accuracy between real and permuted (random) values of the specific variable, averaged in all trees across the whole random forest. The permutation importance in models based on the six LOGOCV subsets of the training data was calculated with mlr 2.16.0,92 and the simple intersect between the top 50 features in all LOGOCV subsets was retained as the final set of features. Thus, the feature selection was influenced by the training data from all six geographical regions, but this only serves to limit the number of chosen features because of the required simple intersect. This approach was used to obtain a set of core predictive features that would have the potential for generalizability across the regions. The number of features included in the models by this approach was deemed appropriate, since the relative effect size of the last included predictor was very small (<0.1 change in classification probability across its range).In the machine learning framework, both regression and categorical models were constructed for FLI, using only the Centrifuge/GTDB data. The feature selection, hyperparameter optimization, and internal cross-validation methods were identical for both approaches, unless otherwise stated. The continuous or categorical FLI (groups of FLI < 60 and FLI ≥ 60) were modeled with xgboost 0.90.0.2,93 using 30 preliminary rounds with randomized parameters, followed by 100 optimization rounds. Parameters in the xgboost models and their considered ranges were learning rate (eta) , gamma , maximum depth of a tree , minimum child weight , fraction of data subsampled per each iteration , fraction of columns subsampled per tree , and maximum number of iterations (nrounds) . The parameters recommended by these searchers were as following for regression: eta = 0.00889; gamma = 2.08; max_depth = 2; min_child_weight = 8; subsample = 0.783; colsample_bytree = 0.672; nrounds = 1,810, and for classification: eta = 0.00107; gamma = 0.137; max_depth = 5; min_child_weight = 9; subsample = 0.207; colsample_bytree = 0.793; nrounds = 4,328. We used Root-Mean-Square Error (RMSE) for the regression models and Area Under the ROC Curve (AUC) for the classification models to measure model fit on the left-out data (region) in each LOGOCV subset. Receiver operating characteristic and precision–recall curves for these validation metrics were calculated with “evalmod” in precrec v0.11.2.94 The final models were trained on the LOGOCV subset training data, the data from one region thus omitted per model, and using the selected features and optimized hyperparameters. Internal validation of these models was conducted against participants only from the region omitted from each model, in the 30% test data which was not used in model training or optimization. Sensitivity analysis was conducted by using only the predictive covariates (sex and age) or balances separately, with the same hyperparameters, data partitions, and cross-region internal validation as for the full models.Bayesian hyperparameter optimization of the xgboost models was then performed with only the selected features. An optimal set of parameters for the xgboost models was searched over all LOGOCV subsets with “mbo” in mlrMBO 1.1.3,26 we further interpreted the partial dependence of their predictions. Partial dependence of the classification model predictions on individual features was calculated with “partial” in pdp 0.7.0.95 The partial dependence of the features on the model predictions was also plotted, overlaying the results from each of the six models. For each feature, its relative effect on the model prediction was estimated as medians of the minimum and maximum that (output probability of the model for the FLI ≥ 60 class), calculated at the minimum and maximum values of the feature separately in each of the six models. The relative effects of the balances were then overlaid as a heatmap on a genome cladogram which covers all balances in the model with ggtree 2.1.1.96Because the classification models have a more clinically relevant modeling target for the difference between FLI < 60 and FLI ≥ 60, the latter used to rule in fatty liver,To assess the robustness of the models and how removing the participants with intermediate FLI (between 30 and 60) affects model performance, we removed this group  and constructed alternative classification models to discern between the FLI < 30 and FLI ≥ 60 groups. Other than removing the intermediate FLI participants and resulting new random split to the train (70%) and test (30%) sets, these models were constructed identically to the main models, including LOGOCV design, feature selection, and hyperparameter optimization. The recommended parameters for this classification task were eta = 0.00102; gamma = 3.7; max_depth = 2; min_child_weight = 5; subsample = 0.49; colsample_bytree = 0.631; nrounds = 3,119. Interpretation of partial dependence was also performed identically, but only the relative effects of the model features were plotted without a cladogram.Because training data from all six regions are used to inform the selection of optimal features and hyperparameters, the validation region data cannot be considered completely independent of the training of the LOGOCV models. Thus, we constructed a set of classification models for the FLI ≥ 60 and FLI < 60 groups, where all validation region samples also in the training data were excluded from the simple intercept of top 50 features in each LOGOCV set and from the subsequent hyperparameter optimization. These models with individualized features and hyperparameters were then tested on the validation region samples in the unseen test data to estimate how model performance was affected. The main test (70%) and train (30%) sets were identical to the main models, but additionally, 6 randomized 70/30 splits nested inside the test set  were used in hyperparameter optimization to reduce overfitting. Average optimal hyperparameters in the six models were eta = 0.00106; gamma = 4.3; max_depth = 2; min_child_weight = 7; subsample = 0.36; colsample_bytree = 0.613; nrounds = 1,772.Table S1). In this cohort, the choice of participants, sample collection, and related methods for the data used in the current study were similar to FINRISK 2002 to facilitate inter-cohort comparisons, and are reported elsewhere.30 The study protocol of FINRISK 2007 was approved by the Coordinating Ethical Committee of the Hospital District of Helsinki and Uusimaa (Ref. 229/EO/2006). All participants have signed an informed consent.To further validate our models and results, we leveraged the data from a more recent population cohort in Finland, FINRISK 2007 (see Table S5). Receiver operating characteristic and precision–recall curves for the external validation were calculated similarly to the main models for the AUC and AUPRC metrics and plotted after averaging the predictions of the six models to obtain single curves .Briefly, compared to FINRISK 2002, FINRISK 2007 features a smaller number of participants who donated fecal samples , they were younger on average, and a smaller proportion of them were in the high FLI group. To produce data for the validation, methods and quality control related to DNA extraction, sequencing, taxonomic assignments, and calculation of FLI values were identical to FINRISK 2002 data, as described above. For the phylogenetic transform , only taxa passing the filtering in FINRISK 2002 bacterial data set were retained in FINRISK 2007 and a pseudo-count of 1 was used for taxa unobserved in the new data, to exactly match the node balance names. The FINRISK 2007 data were then subset to the model features of the main classification models , and input in each of the 6 LOGOCV classification models. The results of these predictions were then compared against the true FLI groups (FLI ≥ 60 and FLI < 60) of the participants , sex  and BMI  into 18 categories (N = 105–711 per category). We performed feature selection similarly to the FLI models by fitting random forest regressors for GGT and triglycerides with mlr 2.16.0.92 This was done separately in each of the 18 categories, and in each category, we again used LOGOCV with the regions to obtain 6 runs per category. Finally, the features predictive of GGT or triglycerides in each category were selected as the intersect of top 50 features in the 6 LOGOCV iterations by permutation importance. The intersect of features predictive of GGT or triglycerides in any of the categories and the features predictive of categorical FLI were identified as specific to liver function.Because the FLI also incorporates BMI and waist circumference, and they strongly contribute to the index,28 which are all complete or nearly complete and available in online databases. This enables us to estimate the likely genetic content, and thus, the metabolic potential of the microbes associated with FLI. We use this approach because the sequencing depth of our samples does not allow assembling contigs and (metagenome-assembled) genomes, required for pathway predictions. Because of the compositional phylogenetic transform, among other features of our data, previously developed approaches such as PICRUSt,97 could not be used here.Our taxonomic matching of the reads is based on the genomes of GTDB (release 89),98 was used to annotate the 119 unannotated genomes as a preliminary step. Pathway predictions were then performed for all 336 genomes with mpwt v0.5.3 multiprocessing tool,99 for the PathoLogic pipeline of Pathway Tools 23.0.100 Pathways for ethanol and short chain fatty acid  production, bile acid metabolism, and choline degradation to trimethylamine were identified from MetaCyc pathway classifications (see ref.101 and Table S4). The prevalence of these processes was then assessed in the analyzed genomes and summarized per process to consider the possible links of the taxa with fatty liver pathophysiology. Finally, the presence of individual pathways for acetate and ethanol production was also outlined for each genome.The genomes of all 336 bacteria under at least one of the predictive balances were downloaded from NCBI. One hundred and nineteen of these genomes were originally not annotated, which is a requirement for pathway prediction. Therefore, Prokka v1.14.6,Click here for additional data file.


Allium cepa L.) is one of the main agricultural commodities produced and consumed around the world. In the present work, for the first time, the volatile signature of onions from different geographical regions of Madeira Island  was tested with headspace solid-phase microextraction (HS-SPME/GC-qMS) and chemometric tools, showing that the volatile signature was affected by the geographical region of cultivation. Sulfur compounds, furanic compounds, and aldehydes are the most dominant chemical groups. Some of the identified volatile organic metabolites (VOMs) were detected only in onions cultivated in specific regions; 17 VOMs were only identified in onions cultivated at Caniço, eight in Porto Moniz, two in Santa Cruz, two in Ribeira Brava, while 12 VOMs are common to all samples from the four regions. Moreover, some VOMs belonging to sulfur compounds -2-methylfuran), furanic compounds , and lactones (whiskey lactone isomer), could be applied as potential geographical markers of onions, providing a useful tool to authenticate onions by farming regions where the influence of latitude seems to be an important factor for yielding the chemical profile and may contribute to geographical protection of food and simultaneously benefiting both consumers and farmers.Onion ( Allium cepa L.) is one of the main agricultural commodities produced and consumed around the world [Onion  that are a part of their metabolome and contribute to the sensory characteristic of foods, often determining their acceptability and aroma quality. Among the VOMs in onions, sulfur compounds are the most dominant volatiles in fresh onions as disulfides and trisulfides, in addition to aldehydes and terpene compounds in a lower extent . PreviouIn the last years, several techniques as solid–liquid extraction (SLE), heat extraction, enzyme-assisted extraction, solid-phase extraction (SPE), and solid-phase microextraction (SPME) have been proposed to establish the volatile profile in food matrices . SPME teFor the authors’ knowledge, no studies have been carried out showing the volatile signature of onions from Spanish variety cultivated at different geographical origins, in order to evaluate the effect of growing region in its volatile pattern. This issue is worthy to be assessed since the volatile signature may be affected by agronomic and edaphoclimatic factors, representing the geographical origin one of the most significant determinants because of climate and soil particularities. In this context, the main purpose of this work was to establish the volatile signature of onions from Spanish variety cultivated at different geographical regions of Madeira Island  as a powerful strategy to identify potential geographical authenticity markers and as a valuable way toward product protection and valorization.8 to C20 straight-chain n-alkanes (concentration of 40 mg/L in n-hexane) was purchased from Fluka  in order to calculate the Kovats index (KI).All chemicals used in this trial were of analytical grade. Sodium chloride  for adjusting the ionic strength was acquired from Panreac . Volatile organic metabolites standards used for identification were obtained from Sigma-Aldrich , Acros Organics , and Fluka  with purity >98%. The individual stock solutions were prepared in ethanol (concentration of 500 mg/L) and stored at 4 °C. Helium  was used as the GC carrier gas. The glass vials, SPME fiber, and SPME holder for manual sampling were purchased from Supelco . A CAllium cepa L.) from four different geographical regions of Madeira Island , belonging to the same variety (Spanish variety) and harvested under the same traditional agricultural practices, were used in the analysis. These onions were purchased directly from producers. Fresh onions (Five sets of onions (250 g/each) were obtained from four different areas in order to achieve a sample as representative of the region as possible, in as much as the reliability of the discrimination models will depend on the set of samples tested. All samples were transported in a refrigerated (2–5 °C) dark box to the laboratory and stored at −20 °C until analysis.Allium cepa L.) were cut into smaller pieces using a knife in a short time interval of 30 s and homogenized by dispersant Ultra Turrax DI 25 basic . Then, 10 g onion sample was transferred to a 50 mL amber glass vial containing 1.5 g of NaCl and 5 mL of deionized water with a magnetic stir bar. The vial, tightly capped with a PTFE-faced silicone septum, was placed in a thermostatic block with constant magnetic stirring (750 rpm). The divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber was exposed to the headspace for 30 min at 40 ± 1 °C. After SPME, the fiber was withdrawn into the holder needle, removed from the vial and immediately introduced into the GC injector port for 6 min at 250 °C for thermal desorption of the VOMs. These analytical conditions were chosen taking into account a previous work that reported the analysis obtained through absorption in the SPME fiber at the higher temperature (40 °C) compared to 20 °C isolated new compounds in onion samples [For each HS-SPME assay, onions Comparison the GC retention times (RT) and mass spectra with those of the standard, when available;(ii)All mass spectra were also compared with the data system library . Single VOM peak was considered as identified compound when its experimental spectrum corresponded with a score of over 80% that present in the library;(iii)8–C20n-alkanes series were used and the values were compared, when available, with values reported in the scientific literature for similar columns [Kovats’s retention index (KI) values were determined according to the van den Dool and Kratz equation . For the columns ,19,20.An Agilent 6890N  gas chromatography system combined with an Agilent 5975 quadrupole mass selective detector and splitless injector were used. Chromatographic separation was carried out by a BP-20 (30 m × 0.25 mm i.d. × 0.25 µm film thickness) fused silica capillary column provided by SGE  with helium  as carrier gas at a flow rate of 1 mL/min (column-head pressure: 13 psi). An insert of 0.75 mm i.d. was used and the injector temperature was fixed at 250 °C. The GC oven temperature was held for 5 min at 45 °C, ramped to 150 °C at 2 °C/min (held for 10 min) and increased from 150 to 220 °C (held for 10 min) at 2 °C/min. The manifold, GC-qMS interface, and quadrupole temperatures were held at 180, 220, and 180 °C, respectively. In addition, MS detection was set in full scan, the ion energy used for the electron impact (EI) was 70 eV and the source temperature was 180 °C. The electron multiplier was set to the auto-tune procedure. The mass acquisition range, made in full scan mode, was 30–300 The differentiation of onion samples from the different geographic origins based on VOMs profile was performed by principal component analysis (PCA) applied to the auto scaled areas of VOMs using SPSS statistical package version 25 . Auto-scaling is a process of prior data processing, which allows the variables of different scales comparable. Each variable is automatically scaled separately by subtracting its average value and dividing by its standard deviation. In addition, the hierarchical clustering analysis (HCA) was processed employing the ward’s minimum variance algorithm method and the distance was determined using squared Euclidean distances by MetaboAnalyst 3.0. Also, HCA results are shown as a dendrogram. The objective of these approaches was to extract the main sources of variability and, therefore, to contribute in the characterization of the dataset.The volatile signature of Spanish onions from four distinct areas of Madeira Island  was investigated using headspace solid-phase microextraction (HS-SPME/GC-qMS). A total of 86 VOMs, 75 of them belong to ten different chemical groups  were identified . In addi5), and relative standard deviation (RSD) (n = 3). The Kovats’s index (KIs) for each VOM was calculated under identical chromatographic conditions as the samples and then compared with the scientific literature to ensure the unequivocal identification of the VOMs. A representative typical GC-qMS chromatogram of the volatile signature of Spanish onions cultivated at Caniço, Santa Cruz, Ribeira Brava, and Porto Moniz is shown in The results revealed that the volatile signature of Spanish onions from Porto Moniz was much richer  compared to the volatile pattern of Spanish onions cultivated in the other investigated regions. A total of 58 VOMs were identified in Spanish onions cultivated in Caniço, whereas in Spanish onions from Porto Moniz, Santa Cruz, and Ribeira Brava were identified 56, 40, and 28 VOMs, respectively. Among all detected VOMs, 17 were only found in Spanish onions cultivated in Caniço, eight in Porto Moniz, two in Santa Cruz, two in Ribeira Brava, while 12 VOMs were common to all samples from four target regions (none of them belonging to sulfur compounds) .It was well-known that sulfur compounds are the main volatiles in fresh onions. In this regard, dipropyl disulfide was the major sulfur-identified compound in onions of Spanish variety cultivated in Ribeira Brava and Porto Moniz, followed by phenylethylthiol. Within the aldehydes, the highest VOM was 2-methyl-2-pentenal isomer (onions from the Porto Moniz region showed higher GC peak area compared to other ones). Related to furanic compounds, it can be mentioned that dimethylmethoxyfuranone and ethyl furanone presented the highest GC peak area (both in Porto Moniz samples), conditioning the sensory attributes of the Spanish onions. Based on its chemical structure, the identified VOMs were grouped into different chemical families, as is shown in p-value < 0.05) than the onions grown in the other regions under study. Regarding lactones, whiskey lactone isomer (72) and γ-nonalactone (80) were detected at the highest levels in Spanish onions from Porto Moniz. In addition, undecanol (58) was only identified in Spanish onions from Porto Moniz suggesting that it could be a good candidate as a possible geographical authenticity marker.This study revealed that sulfur compounds, aldehydes, furanic compounds, and lactones were the main chemical groups of volatile composition in onions from Spanish variety from all investigated geographical regions. Nevertheless, it should be mentioned that there was an increase of specific chemical groups content , highlighting a rise of furanic compounds, lactones, and alcohols in onions from Spanish variety cultivated at Porto Moniz and ketones and fatty acids in Caniço Spanish onions. This increasing trend is determined by the high levels of some individual VOMs . DimethyBy application of PCA to the Spanish onions dataset, the first two principal components (PCs) accounted for 92% of the total variability. A score plot for the onions of Spanish variety according to the geographical region of cultivation is shown in The first principal component (PC1) explains 60% of the variability in the initial dataset and the second (PC2) explains 32%, allowing us to observe four clusters clearly defined: a group constituted by onions from Porto Moniz, another group by Ribeira Brava onion samples, and two more groups constituted by onions from Caniço and Santa Cruz, which are located close to each other A. This fThe PCA loading plot B displayterroir). To assess the differences in VOMs among onions from Spanish variety cultivated in different regions of Madeira Island, hierarchical cluster analysis (HCA) was performed. A dendrogram based on Euclidean distance and Ward’s linkage is shown in The phytochemical composition is affected by factors such as genetic diversity, agricultural practices, and pedo-climatic factors based on a combination of land, soil and climate aspects  in conjunction with chemometrics analysis for the discrimination of foodstuffs according to geographical origin, an issue very relevant in the food authenticity and traceability. In this context, onions origin recognition has received great attention. In fact, the correlation between the geographic origin of onion and their trace element concentration has been investigated . The modOn the other hand, latitude has a strong impact on the local climate environment and the latitude gradients of cultivation areas could influence on the chemical profile of agro-based foods, also lower and higher latitudes samples seemed to be clearly distinguishable . This fa6 aldehydes and alcohols) from virgin olive oil samples from different regions suggests that environmental growth conditions may influence the activity of ADH, which could explain the differences in the levels of specific VOMs [Furthermore, and in connection with the biosynthesis pathways implicated in the formation of VOMs, it might be directly affected by the different expression of the enzyme pool. The availability of these enzymes, such as LOX (lipoxygenase), HPL (hydroperoxide lyase) or ADH , among others, could be dependent on the climatic conditions and therefore, so and we have mentioned above, the VOMs profile could be employed as “potential markers” for the identification of geographical scope . A studyfic VOMs .To the best of our knowledge, this study represents the first characterization of the volatile signature of onions from Spanish variety cultivated in the different geographical areas of Madeira Island. HS-SPME/GC-qMS methodology combined with multivariate statistical analysis revealed a useful approach to discriminate the geographical origin of Spanish onions crops. The volatile composition of the Spanish onions is affected by the geographic region of harvest, both qualitative and quantitative expression. VOMs belonging to sulfur compounds (dipropyl disulfide (26), 3-(acetylthio)-2-methylfuran (59)), furanic compounds (dimethylmethoxyfuranone (50), ethyl furanone (65) and acetyloxy-dimethylfuranone (74)), and lactones (whiskey lactone isomer (72)), could be applied as authenticity markers of Spanish onions cultivated in Madeira Island, contributing within regulatory framework to assessment of the samples with claims such as “protected geographical indication” or “product of island farming.” However, authenticity markers validation in the food science field is a slow process and further investigations in this sense (with multi-year assay) are required.


Ducks, like other poultry species, have the ability to see in the ultraviolet (UV) portion of the light spectrum; however, very little research has explored how UV lighting affects Pekin duck welfare. Therefore, the objective of this study was to determine how providing supplementary UV light in a UV-deficient environment could affect production parameters and behavior. Pekin ducks were reared under either light-emitting diode (LED) bulbs with supplemental UV light (UV) or just LED bulbs (control). The results from this study indicate that Pekin ducks reared under UV light have both lower acute and chronic stress susceptibility and lower fear responses. It was also found that eye morphology can be manipulated with UV light exposure. There were no treatment differences in production parameters such as growth, feed efficiency, tibial mineral composition, visual gait score, or stride length. The results of this study demonstrate the necessity of UV light exposure in duck growout settings and emphasize the need for correct artificial lighting in the poultry industry.p = 0.32), feed conversion ratio (p = 0.38), or gait score (p = 0.89). Differences in eye morphology were observed, with ducks reared under UV light having narrower  and lighter  eyes than the control . Ducks reared in UV environments had lower acute and chronic stress susceptibility with lower plasma corticosterone , heterophil to lymphocyte ratios , and composite asymmetry  than control ducks . Ultraviolet ducks had a faster latency for the first head movement during tonic immobility  and required more attempts to induce tonic immobility  than control ducks . There were no differences in inversion testing (p = 0.91). These results indicate that UV lighting can lower stress and fear responses in Pekin ducks and can therefore increase welfare. Additionally, this study emphasizes the importance of choosing correct artificial lighting for all poultry species.Ducks, like other domestic poultry species, can visualize the ultraviolet (UV) portion of the light spectrum; however, the importance of UV light radiation in artificially lit duck growout facilities remains unknown. The objective of this study was to determine the effects of UV light supplementation on Pekin duck production parameters, eye development, stress, and fear. Pekin ducks were reared with light-emitting diode (LED) lights supplemented with UV light or just LED lights (control). There were no differences in body weight ( Avian species, including ducks, have very sophisticated visual systems that allow discrimination of a broad area of the visible light spectrum . SeveralA wavebands than ducks. There is evidence that mating frequency of cockerels increases when birds are in an environment supplemented with UV light, possibly due to increased cockerel locomotion, which then leads to more encounters with hens and the birds’ ability to visualize the fluorescent plumage patches of their conspecifics under UV light supplementation [Poultry use UV light to perceive and interact with their environment and conspecifics in the form of visually mediated behaviors including foraging, signaling, and social interactions ,6,7,8. PStress occurs as a physiological response to environmental changes in order to reestablish homeostasis within the body ,18. SeveLimited research has been conducted to investigate the effects of artificial UV light supplementation on Pekin duck production and welfare. The aim of this study was to determine the effects of UV supplementation on Pekin duck production parameters, stress susceptibility, and fear response. The working hypothesis was that UV supplementation would increase production parameters such as feed conversion ratio (FCR) and would improve duck welfare by decreasing stress and fear responses.Ducks were managed according to the Guide for the Care and Use of Agricultural Animals in Research and Teaching  guidelinN = 384) obtained from Maple Leaf Farms, Inc.  for 35 days. This experiment consisted of two treatments: control LEDs  and control LEDs plus ultraviolet (UV) LEDs . A spectral comparison of the control LED and UV bulbs are shown in The study was conducted using straight run Pekin ducklings  per room, for a total of 16 pens per trial. All pens were lined with approximately 3 inches of fresh pine shavings. Each pen also contained a nipple drinking system with 3 nipples per pen and one tube feeder, both of which could be adjusted in height during the growout period.One of the two lighting treatments described above were used in each room. The first treatment consisted of 6 control LED light fixtures per room, which were installed directly over the pens, 3 m above the floor, and were controlled by a single dimmer and timer. The second lighting treatment consisted of (i) two ultraviolet fixtures hung 2.5 m above the floor, 3 m apart, and (ii) 6 control LED light fixtures installed as described in the first lighting treatment. To avoid “room affects”, the two lighting treatments were rotated between the two rooms upon conclusion of the first trial.On the day of hatch, ducklings were randomly selected, weighed, and allocated to pens, where 12 ducklings were stocked in each of the 16 pens used per trial. For the first 24 h after placement, the ducks were reared under a 24L:0D photoperiod at an intensity of 20 lux as measured by light meter  held parallel to the floor at duck head height. From days 1–10, ducks were reared under a 16L:8D photoperiod at an intensity of 20 lux. A spectral flickering irradiance meter  was used to determine the spectral flickering irradiance of each bulb type. At 20 lux, both bulbs had a flicker index of 0.04 and 150 Hz. Beginning on day 11, lights in both treatments were dimmed to an intensity of 5 lux as per industry standards . Both bu2 gas upon conclusion of the study on day 35 of growout.Feed was weighed and recorded , and any remaining feed at the end of each trial was subtracted from the total amount fed. Standard commercial duck starter (d0–14) and grower (d15–35) diets were provided during the course of the study. All administered feed was produced by the Texas A&M feed mill. All ducks were euthanized with a mixture of air and COTo determine body weight gain, day 0 weights were subtracted from day 35 weights. Feed was weighed before being added to feeders in each pen, and residual feed was weighed back on day 15 at the end of the starter phase and again on day 35 upon conclusion of the study to allow feed intake calculations. FCR was calculated by dividing the total feed intake per pen by the total body weight gain per pen and was corrected for mortality. Mortalities were weighed and recorded daily.N = 192) were randomly selected and placed in an observation pen with a flat concrete floor in a well-lit observation room. The gait of each duck was assessed by two trained observers with a clear view of the duck’s legs, and a single score was determined between the two observers for every duck assessed at the time of observation. A gait score was measured based on a 3-point rubric ranging from 0 to 2. A score of “0” indicated no visible waddle impediments, a score of “1” indicated slightly labored walking or limping, and a score of “2” indicated a poor gait or a reluctance to walk.Gait scoring was conducted at 5 weeks of age using methods described in Makagon et al. . Six ducN = 192) and then by allowing each duck to walk in a straight line across brown paper [Stride length was obtained during week 5 of the study by applying black ink to the feet of all ducks assessed for gait scoring (wn paper . If a duN = 80). Twenty birds per treatment (N = 80) were randomly selected and euthanized via a mixture of air and CO2 gas on d 35. The heads of all euthanized ducks were removed and placed in bags of deionized water to be stored overnight. The muscle, connective tissue, and fibula were removed from each tibia, and the bones were dried using a Forced Air Oven  at 100 °C for 12 h. Left tibias were used to measure bone mineral content. Right tibias were used to measure bone breaking strength. Left tibias were defatted using diethyl ether for 6–8 h and air dried under a chemical hood, allowing all remaining ether to evaporate. Defatted tibias were then dried again at 100 °C for 12 h and then ashed at 600 °C in ceramic crucibles for 24 h. To determine bone mineral content, all tibias and crucibles were weighed before and after ashing. To minimize moisture content, crucibles were kept at 100 °C for 12 h prior to ashing. Right tibia breaking strength (g) was determined using a QC-SPA system  to break each tibia bone at the center point of the tibial shaft.On day 35, both the left and right tibia were removed from 20 randomly selected birds per treatment (N = 80) used for tibia bone ash mineral content and breaking strength were used to determine all eye development parameters. After euthanasia on d 35 (described above), the heads of all euthanized ducks were removed and placed in bags of deionized water to be stored overnight. The following day, the left and right eyes from each duck were extracted and measured. A calibrated Craftsman IP54 Digital Caliper  was used to measure the side-to-side diameter (mm) and back-to-front diameter (mm) of each eye. Additionally, the weight of each eye (g) was recorded. The average eye length, width, weight, and the differences between the left and right eyes for each of these respective measurements was recorded for each treatment.The physical differences in eye development between treatments were evaluated during the fifth week of growout. The same 20 birds per treatment (N = 80) were randomly selected for plasma corticosterone (CORT) and heterophil to lymphocyte ratio (HL) analysis. Approximately 1–2 mL of blood was collected from the brachial wing vein of each duck. A small drop of blood from each bird sampled was smeared on a glass plate for heterophil to lymphocyte ratio (HL) analysis (described below). The remaining blood collected was injected into a plasma separation gel and lithium heparin vacutainer  and temporarily stored on an ice bath. Once all blood samples had been collected, all vacutainers were spun down using a centrifuge  at 4000 RPM for 15 min to separate the plasma and blood cells. Each blood plasma sample was then poured into a 2-mL microcentrifuge tube and stored at −19 °C until further analysis was performed. A hematology staining kit  was used for staining blood smear slides used for HL.At 35 days of age, 20 ducks per treatment . The inter- and intra-assay %CV were both under 5%.To determine the heterophil to lymphocyte ratio of collected samples, one layer of stained blood cells on glass slides were observed under 40× magnification using an oil immersion lens on a microscope . The number of heterophils and lymphocytes observed in an area of the blood smear slide without overlapping cells was counted using a keystroke counter  until a total of 100 cells had been recorded . IncreasN = 240) were randomly selected to be measured for physical asymmetry of 3 bilateral traits (ASYM) as described in Archer et al. [On day 35 of the current study, 60 live ducks per treatment  were randomly selected and were gently placed on their back in a wooden U-shaped cradle lined with black cloth. Slight pressure was placed on the thoracic cavity of the duck for approximately 25 s, until tonic immobility was induced. Contact was then removed, and a timer was started. Each duck must be immobile for at least 10 s in order for its latency to right from TI to be recorded. If the duck righted itself in under 10 s, it was recorded as a time of 0 s. Otherwise the first head movement and the duck’s latency to right itself was recorded, with a maximum time of 10 min. Each duck was allowed a maximum of 3 attempts to be successfully tested. If all 3 attempts were unsuccessful, the duck’s final time was recorded as 0 s. A longer latency to right during tonic immobility is indicative of greater fear responses in poultry [Tonic immobility measurements were collected during the fifth week of growout using methods described in Archer  and HousN = 240) were randomly selected and caught and then inverted while holding each duck by its legs until the duck ceased to wing flap or for 30 s [Inversion measurements were also collected during the fifth week of growout. Sixty ducks per treatment  were used to investigate the treatment, trial, and treatment × trial effects on feed conversion, weight gain, eye parameters, plasma corticosterone concentration, heterophil to lymphocyte ratio, composite asymmetry scores of bilateral traits, tonic immobility, and inversion. Levene’s test for homogeneity of variance and the Shapiro–Wilk test for normality were used to test all GLM assumptions. All assumptions were met without transformations, and all planned comparisons were tested using the least significant difference test. Windows SAS 9.3  was used to perform all analyses. p > 0.05). Additionally, no differences in gait score or stride length (p > 0.05) were observed between the two treatments, as shown in p > 0.05).All results for FCR, d 35 body weight, gait parameters, tibia bone ash mineral content, and tibia bone breaking strength are shown in p = 0.025) and narrower eyes  than the control treatment . However, there was no difference in average eye length between the two treatments (p > 0.05). Additionally, the average differences in weight, width, and length between the left and right eyes of each duck did not differ between the two treatments (p > 0.05). All data for eye development parameters are shown in The UV treatment had lighter eyes  and lower heterophil to lymphocyte ratios  compared to control ducks , indicating lower stress levels in ducks reared under supplemental UV light. The UV ducks had lower composite asymmetry scores  than control ducks (0.76 ± 0.03726 mm), indicating lower long-term stress in the UV treatment compared to the control treatment.All results for stress susceptibility and fear response parameters are shown in p = 0.026) and required more attempts to induce tonic immobility  than control ducks . There were no differences in inversion intensity or the latency to right during tonic immobility between the two treatments.Ultraviolet ducks had a faster latency for the first head movement during tonic immobility (61.28 ± 9.4863 s, In the current study, there were no differences in duck growth performance, feed conversion, gait score, stride length, tibia bone ash mineral content, or tibia bone breaking strength. A study previously conducted by Zhang et al.  found thIn the current study, ducks reared under supplementary UV light had lighter and narrower eyes than control ducks. No differences were observed in eye width or average differences between the length, width, or weight of the left and right eyes of each duck. Very few studies have examined the effects of ultraviolet light on eye development in poultry. A study conducted by Hogsette et al.  comparedDucks reared under UV supplementation had lower plasma corticosterone concentrations, lower heterophil to lymphocyte ratios, and grew less asymmetrically than those reared in UV-deficient environments. It has been previously demonstrated that stress parameters such as CORT, H:L, and ASYM can be affected by light ,40,41,42No differences were found in the latency to right from tonic immobility or in wing flap intensity between UV and control ducks in this study, although UV ducks had a faster latency for first head movements during tonic immobility and also required more attempts to induce tonic immobility. Tonic immobility can be divided into two stages, with the first stage being complete immobility without head movements and the second stage involving head movements , meaningAlthough there is very little knowledge concerning the effects of lighting on Pekin ducks in growout settings, as the meat duck industry grows within the United States and abroad, there will continue to be an increasing and persistent need for better welfare in Pekin duck growout facilities. Rearing calmer birds will ultimately result in fewer mortalities during transport and a fewer damaged carcasses during processing . Based oIn comparison to other aspects of poultry production such as rearing parameters, biosecurity, and nutritional requirements, knowledge of poultry light perception and the effects of lighting on poultry behavior and development is relatively limited . Similar