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Untargeted plasma metabolites in Dutch cohorts

In this study, we examined plasma metabolomes in 1,679 fasting plasma samples from 1,368 individuals from two LLD5 sub-cohorts (LLD1 and LLD2) and the GoNL6 cohort (Extended Data Fig. 1 and Supplementary Table 1). The LLD1 cohort was the discovery cohort, with information about genetics, diet and the gut microbiome available for 1,054 participants. Moreover, 311 LLD1 subjects were followed up 4years later (LLD1 follow-up). We also included two independent replication cohorts: 237 LLD2 participants for whom we had genetic and dietary data and 77 GoNL participants for whom only genetic data were available (Extended Data Fig. 1 and Supplementary Table 1). Untargeted metabolomics profiling was done using flow-injection time-of-flight mass spectrometry (FI-MS)10,11, which yielded plasma levels of 1,183 metabolites (Supplementary Table 2). These metabolites covered a wide range of lipids, organic acids, phenylpropanoids, benzenoids and other metabolites (Extended Data Fig. 2a). As we observed weak (absolute rSpearman<0.2) correlations among the 1,183 metabolites (Extended Data Fig. 2b), data reduction was not required and, consequently, all metabolites were subjected to subsequent analyses. We validated the identification and quantification of some metabolites (for example, bile acids, creatinine, lactate, phenylalanine and isoleucine) by comparing their abundance levels from FI-MS with those previously determined by liquid chromatography with tandem mass spectrometry (LC-MS/MS)12 or NMR13 (rSpearman>0.62; Extended Data Fig. 2c,d).

To compare the relative importance of diet, genetics and the gut microbiome in explaining inter-individual plasma metabolome variability, we calculated the proportion of variance explained by these three factors for the whole plasma metabolome profile and for the individual metabolites separately. We have detailed information on 78 dietary habits (Supplementary Table 3), 5.3million human genetic variants and the abundances of 156 species and 343 MetaCyc pathways for each individual of the LLD1 cohort. Diet, genetics and the gut microbiome could explain 9.3, 3.3 and 12.8%, respectively, of inter-individual variations in the whole plasma metabolome, without adjusting for covariates (see the Methods section Distance matrix-based variance estimation; false discovery rate (FDR)<0.05; Fig. 1a and Supplementary Table 4), whereas intrinsic factors (age, sex and body mass index (BMI)) and smoking collectively explained 4.9% of the variance. Together, these factors explain 25.1% of the variance in the plasma metabolome (Fig. 1a).

a, Inter-individual variation in the whole plasma metabolome explained by the indicated factors, estimated using the PERMANOVA method. All, all of the indicated factors combined; smk, smoking status. b, Venn diagram indicating the number of metabolites whose inter-individual variation was significantly explained by diet, genetics or the gut microbiome, as estimated using the linear regression method (FDRF-test<0.05). c, Inter-individual variations in metabolites explained by diet, genetics or the gut microbiome, as estimated using the linear regression method (the lasso regression method was applied for feature selection) with a significant estimated adjusted r2>5% (FDRF-test<0.05). The blue bars represent dietary contributions to metabolite variations, the yellow bars indicate genetic contributions and the orange bars indicate microbial contributions. The other colors indicate the metabolic categories of metabolites (see legend). The yaxis indicates the proportion of variation explained. TMAO, trimethylamine N-oxide.

Next, we tested for pairwise associations between each metabolite and the dietary variables, genetic variants and microbial taxa. We observed 2,854 associations with dietary habits (Supplementary Table 5), 48 associations with 40 unique genetic variants (metabolite quantitative trait loci (mQTLs); Supplementary Table 6), 1,373 associations with gut bacterial species (Supplementary Table 7) and 2,839 associations with bacterial MetaCyc pathways (Supplementary Table 8) (see the Methods sections Associations with dietary habits, QTL mapping and Microbiome-wide associations). In total, 769 metabolites were significantly associated with at least one factor (Fig. 1b and Supplementary Tables 58). We then performed interaction analysis to assess the role of dietmicrobiome, geneticsmicrobiome and dietgenetics interactions in regulating the human metabolome using an interaction term in the linear model (see the Methods section Interaction analysis). Among these, 185 metabolites were associated with multiple factors and seven were affected by either geneticsmicrobiome, geneticsdiet or dietmicrobiome interactions (Supplementary Table 9).

As interactions were limited, we further assessed the proportion of variance of each metabolite that was explained by these factors using an additive model with the least absolute shrinkage and selection operator (lasso) method (see the Methods section Estimating the variance of individual metabolites). In general, the inter-individual variations in 733 metabolites could be explained by at least one of the three factors (FDRF-test<0.05; Supplementary Table 10). In detail, dietary habits contributed 0.435% of the variance in 684 metabolites; microbial abundances contributed 0.725% of the variance in 193 metabolites; and genetic variants contributed 328% of the variance in 44 metabolites (adjusted r2; FDRF-test<0.05; Supplementary Table 10). We also estimated the explained variance of metabolites using Elastic Net14, which is designed for highly correlated features, and found that the estimated explained variances were comparable between linear regression and the Elastic Net regression (Supplementary Fig. 1).

We further compared the variance explained by each type of factor (diet, genetics or the microbiome) and assigned the dominant factor for each metabolite if one factor explained more variance than the other two. Inter-individual variations in 610 metabolites were mostly explained by diet, 85 were explained by the gut microbiome and 38 were explained by genetics (Supplementary Table 10). Hereafter, we refer to these as diet-dominant, microbiome-dominant and genetics-dominant metabolites, respectively. The dominant factors of metabolites highlight their origin. For instance, ten out of the 21 diet-dominant metabolites for which diet explained >20% of the variance (FDRF-test<0.05; Supplementary Table 10) were food components based on their annotation in the Human Metabolome Database (HMDB)15. Similarly, of the 85 microbiome-dominant metabolites, 23 were annotated in the HMDB as microbiome-related metabolites (including 15 uremic toxins). Furthermore, out of the 38 genetics-dominant metabolites, ten were lipid species and eight were amino acids. Taken together, our analysis highlights that one factoreither dietary, genetic or microbialcan have a dominant effect over the other two in explaining the variances of plasma metabolites, with diet or the microbiome being particularly dominant. However, we also found that the variances in 185 metabolites were significantly attributable to more than one factor (Supplementary Table 10), including six metabolites associated with both genetics and the microbiome and 153 metabolites associated with both diet and the microbiome. For example, genetics and the microbiome explained 4 and 5%, respectively, of the variance in plasma 5-carboxy--chromanol (Fig. 1c)a dehydrogenated carboxylate product of 5-hydroxy--tocopherol16 that may reduce cancer and cardiovascular risk17. Another example is hippuric acida uremic toxin that can be produced by bacterial conversion of dietary proteins18, with 13% of its variance explained by diet and 13% explained by the microbiome (Fig. 1c).

Temporal changes in plasma metabolites can reflect changes in an individuals diet, gut microbiome and health status. When assessing the plasma metabolome in the 311 LLD1 follow-up samples, we indeed observed a significant shift in the plasma metabolome, with a significant difference in the second principal component (PPC1 paired Wilcoxon=0.1 and PPC2 paired Wilcoxon=1.3105; Fig. 2a). Baseline genetics, diet and microbiome, together with age, sex and BMI, could explain 59.4% of the variance in the follow-up plasma metabolome (PPERMANOVA=0.004) (Supplementary Fig. 2). We also observed that temporal stability can vary substantially between different metabolites (see the Methods section Temporal consistency of individual metabolites; Supplementary Table 11). Previously, we had assessed the changes in the gut microbiome in the LLD1 follow-up cohort and linked these to changes in the plasma metabolome7. Here, we further checked the temporal variability of the plasma metabolome and assessed the stability of diet-, microbiome- and genetics-dominant metabolites over time. Interestingly, the temporal correlation of the microbiome-dominant metabolites was similar to that of the genetics-dominant metabolites (PWilcoxon=0.51; Fig. 2b), whereas the temporal correlation between diet-dominant metabolites was significantly lower than between microbiome- and genetics-dominant metabolites (PWilcoxon<3.4105; Fig. 2b). However, the dominant dietary, microbial and genetic factors identified at baseline also explained similar variance in metabolic levels in the follow-up samples (Extended Data Fig. 3 and Supplementary Table 10). Our data also revealed a positive correlation between stability and the amount of variance that could be explained: the more variance explained, the more stable a metabolite is over time (Fig. 2c). For a few metabolites, we could not replicate the variance explained at baseline at the second time point, and these metabolites also showed weak or no correlation in their abundances between the two time points. For example, N-acetylgalactosamine showed very weak correlation between the two time points (r=0.13; P=0.02), and its genetic association was not replicated at the second time point.

a, Principal component analysis of metabolite levels at two time points (Euclidean dissimilarity). The green dots indicate baseline samples and the orange dots indicate follow-up samples (n=311 biologically independent samples). The KruskalWallis test (two sided) was used to check differences between baseline and follow-up. b, Temporal stability of metabolites stratified by the dominantly associated factor for each metabolite. The Wilcoxon test (two sided) was used to check the differences between groups. Each dot represents one metabolite. The yaxis indicates the Spearman correlation coefficient of abundances of each metabolite between two time points (n=311 biologically independent samples). In a and b, the box plots show the median and first and third quartiles (25th and 75th percentiles) of the first and second principal components (a) or correlation coefficients (b); the upper and lower whiskers extend to the largest and smallest value no further than 1.5 the interquartile range (IQR), respectively; and outliers are plotted individually. c, Correlation between metabolite stability and the metabolite variance explained by diet (left), genetics (middle) and the microbiome (right). The xaxis indicates the inter-individual variation explained by each factor and the yaxis indicates the Spearman correlation coefficient (two sided) of abundances of each metabolite between the two time points. The dashed white lines show the best fit and the gray shading represents the 95% confidence interval (CI) (n=311 biologically independent samples).

Having established the variances in metabolites explained by diet, genetics and the gut microbiome and the dominant factors that explained most of this variance, we focused on detailing specific associations and on the potential implications of our findings for assessing diet quality and improving our understanding of the genetic risk of complex diseases and the interaction and causality relationships among diet, the microbiome, genetics and metabolism.

We observed 2,854 significant associations (FDRSpearman<0.05) between 74 dietary factors and 726 metabolites (Fig. 3a and Supplementary Table 5; see the Methods section Lifelines diet quality score prediction). Associations with food-specific metabolites can, in theory, be used to verify food questionnaire data. For instance, the strongest association we observed was between quinic acid levels and coffee intake (rSpearman=0.54; P=1.61080; Fig. 3b). Quinic acid is found in a wide variety of different plants but has a particularly high concentration in coffee. Another example is 2,6-dimethoxy-4-propylphenol, which was strongly associated with fish intake (rSpearman=0.53; P=1.51076; Fig. 3c). This association is expected as this compound is particularly present in smoked fish according to HMDB annotation15. In addition, we also detected associations between dietary factors and metabolic biomarkers of some diseases. For example, 1-methylhistidine is a biomarker for cardiometabolic diseases including heart failure19 that is enriched in meat, and we observed significant associations between 1-methylhistidine and meat (rSpearman=0.12; P=7.2105) and fish intake (rSpearman=0.11; P=3.1104) as well as a lower level of 1-methylhistidine in vegetarians (rSpearman=0.15; P=9.7107; Fig. 3d).

a, Summary of the associations between diet and metabolites. The bars represent dietary habits, with the bar order sorted by the number of significant associations. Association directions are colored differently: orange indicates a positive association, whereas blue indicates a negative association. The length of each bar indicates the number of significant associations at FDR<0.05 (Spearman; two sided). b, Association between plasma quinic acid levels and coffee intake. The x and yaxes indicate residuals of coffee intake and the metabolic abundance after correcting for covariates, respectively (n=1,054 biologically independent samples). c, Association between plasma 2,6-dimethoxy-4-propylphenol levels and fish intake frequency (n=1,054 biologically independent samples). The x and yaxes refer to residuals of fish intake and metabolic abundance after correcting for covariates, respectively. d, Differential plasma levels of 1-methylhistidine between vegetarians and non-vegetarians (n=1,054 biologically independent samples). The yaxis indicates normalized residuals of metabolic abundance. The Pvalue from the Wilcoxon test (two sided) is shown. The box plots show the median and first and third quartiles (25th and 75th percentiles) of the metabolite levels. The upper and lower whiskers extend to the largest and smallest value no further than 1.5 the IQR, respectively. Outliers are plotted individually. e, Association between the diet quality score predicted by the plasma metabolome (yaxis) and the diet quality score assessed by the FFQ (xaxis) (n=237 biologically independent samples). In b, c and e, each gray dot represents one sample, the dark gray dashed line shows the linear regression line and the gray shading represents the 95% CI. In b and c, the association strength was assessed using Spearman correlation (two sided; the correlation coefficient and Pvalue are reported) and in e, the prediction performance was assessed with linear regression (F-test; two sided; the adjusted r2 value and Pvalue are reported).

Given the relationship between diet, metabolism and human health, we wondered whether the plasma metabolome could predict diet quality. For each of the Lifelines participants, we constructed a Lifelines Diet Score based on food frequency questionnaire (FFQ) data that reflected the relative diet quality based on dietdisease relationships8. To build a metabolic model to predict an individuals diet quality, we used LLD1 as the training set and LLD2 as the validation set. The resulting metabolic model included 76 metabolites, 51 of which were dominantly associated with diet. The diet score predicted by metabolites showed a significant association with the real diet score assessed by the FFQ in the validation set (r2adjusted=0.27; PF-test=3.5105; Fig. 3e). We also tested four other dietary scores (the Alternate Mediterranean Diet Score20, Healthy Eating Index (HEI)21, Protein Score22 and Modified Mediterranean Diet Score23) and found that the HEI predicted by plasma metabolites was also significantly associated with the FFQ-based HEI (r2adjusted=0.23; PF-test=6.5105; Supplementary Table 12).

Genetic associations of plasma metabolites may provide functional insights into the etiologies of complex diseases. After correcting for the first two genetic principal components, age, sex, BMI, smoking, 78 dietary habits, 40 diseases and 44 medications, QTL mapping in LLD1 identified 48 study-wide, independent genetic associations between 44 metabolites and 40 single-nucleotide polymorphisms (SNPs) (PSpearman<4.21011; clumping r2=0.05; clumping window=500kilobases (kb); Fig. 4a and Supplementary Table 6). All 48 genetic associations were replicated in either LLD1 follow-up or the two independent replication datasets (LLD2 and GoNL; Supplementary Fig. 3 and Supplementary Table 6). We also assessed the impact of physical activity, as assessed by questionnaires24, on the genetics association of metabolism, but found its influence to be negligible (Supplementary Fig. 4). Functional mapping and annotation (FUMA) of genome-wide association studies (GWAS)25 analysis revealed that the identified mQTLs were enriched in genes expressed in the liver and kidney (Extended Data Fig. 4) and related to metabolic phenotypes (Supplementary Table 6).

a, Manhattan plot showing 48 independent mQTLs identified linking 44 metabolites and 40 genetic variants with P<4.21011 (Spearman; two sided). Representative genes for the SNPs with significant mQTLs are labeled. b, Association between a tag SNP (rs1495741) of the NAT2 gene and plasma AFMU levels. c, Association between a SNP (rs13100173) within the HYAL3 gene and plasma levels of N-acetylgalactosamine-4-sulfate. d, Association between a tag SNP (rs17789626) of the SCLT1 gene and plasma mizoribine levels. e, Differences in coffee intake between participants with different genotypes at rs1495741. f, Correlations between coffee intake and AFMU in participants with different genotypes at rs1495741. g, Differences in bacterial fatty acid -oxidation pathway abundance in participants with different genotypes at rs67981690. h, Correlations between bacterial fatty acid -oxidation pathway abundance and 5-carboxy--chromanol in participants with different genotypes at rs67981690. In be and g, the xaxis indicates the genotype of the corresponding SNP and the yaxis indicates normalized residuals of the corresponding metabolic abundance (n=927 biologically independent samples). Each dot represents one sample. The box plots show the median and first and third quartiles (25th and 75th percentiles) of the metabolite levels. The upper and lower whiskers extend to the largest and smallest value no further than 1.5 the IQR, respectively. Outliers are plotted individually. The association strength is shown by the Spearman correlation coefficient and corresponding Pvalue (two sided). In f and h, the xaxis indicates the normalized abundance of coffee intake (f) or the bacterial fatty acid -oxidation pathway (h) and the yaxis indicates the normalized residuals of the corresponding metabolic abundance. Each dot represents one sample (n=927 biologically independent samples). The lines indicate linear regressions for each genotype group separately. Areas with light gray shading indicate the 95% CI of the linear regression lines. The association strength per genotype is shown by the Spearman correlation and the corresponding Pvalue (two sided).

The strongest association we found was between the caffeine metabolite 5-acetylamino-6-formylamino-3-methyluracil (AFMU) and SNP rs1495741 near the N-acetyltransferase 2 (NAT2) gene (rSpearman=0.52; P=1.71066; Fig. 4b), which showed strong linkage disequilibrium (r2=0.98) with a SNP, rs35246381, that was recently reported to be associated with urinary AFMU26. AFMU is a direct product of NAT2 activity and has been associated with bladder cancer risk27. Interestingly, the plasma level of AFMU was associated not only with coffee intake (rSpearman=0.29; P=9.21022; Supplementary Table 5) and the genotype of rs1495741, but also with their interactions (Supplementary Table 9). Individuals with a homologous AA genotype had a similar level of coffee intake, but their correlation between coffee intake and plasma AFMU level was significantly lower compared with individuals with GG and GA genotypes (Fig. 4e,f).

Pleotropic mQTL effects were also observed at several loci, including SLCO1B1, FADS2, KLKB1 and PYROXD2 (Supplementary Table 6). For example, three associations (related to three metabolites, two of them lipids) were observed for two SNPs (rs67981690 and rs4149067; linkage disequilibrium r2=0.72 in Northern Europeans from Utah) in SLCO1B1, which encodes the solute carrier organic anion transporter family member 1B1. Expression of the SLCO1B1 protein is specific to the liver, where this transporter is involved in the transport of various endogenous compounds and drugs, including statins28, from blood into the liver. The SLCO1B1 locus has also been linked to plasma levels of fatty acids and to statin-induced myopathy29. Furthermore, we detected a geneticsmicrobiome interaction between rs67981690 and microbial fatty acid oxidation pathways in regulating plasma levels of 5-carboxy--chromanol (P=1.5103), where the association of the bacterial fatty acid oxidation pathway with plasma levels of 5-carboxy--chromanol was dependent on the genotype of rs67981690 (Fig. 4g,h).

To identify novel mQTLs, we performed a systematic search of all published mQTL studies from 2008 onwards (Supplementary Table 13). This approach identified three novel mQTLs in our datasets (Supplementary Table 13) that were either not located close to previously reported mQTLs (distance>1,000kb) or not in linkage disequilibrium (r2<0.05). The first two novel SNPsrs13100173 at HYAL3 and rs11741352 at ARSBwere associated with N-acetylgalactosamine-4-sulfate (Fig. 4c,d), which is associated with mucopolysaccharidosis30. Interestingly, N-acetylgalactosamine-4-sulfate can bind to HYAL proteins (HYAL1, HYAL2, HYAL3 and HYAL4), suggesting that mQTLs can also pinpoint potential metaboliteprotein interactions. The third novel mQTL was rs17789626 at SCLT1, which was associated with mizoribinea compound used to treat nephrotic syndrome31.

We established 4,212 associations between 208 metabolites and 314 microbial factors (114 species and 200 MetaCyc pathways) (FDRLLD1<0.05; PLLD1 follow-up<0.05; Supplementary Tables 7 and 8). Interestingly, many of the metabolites that were associated with microbial species and MetaCyc pathways are also known to be gut microbiome related based on their HMDB annotations15. For instance, we observed 919 associations with 25 uremic toxins, 142 associations with thiamine (vitamin B1) and 117 associations with five phytoestrogens (FDR<0.05; Supplementary Tables 7 and 8). Uremic toxins and thiamine have been shown to be related to various diseases, including chronic kidney disease and cardiovascular diseases32,33. Phytoestrogens are a class of plant-derived polyphenolic compounds that can be transformed by gut microbiota into metabolites that promote the hosts metabolism and immune system33,34.

To assess whether gut microbiome composition causally contributes to plasma metabolite levels, we carried out bi-directional MR analyses (see the Methods section Bi-directional MR analysis). Here, we focused on the 37 microbial features that were associated with at least three independent genetic variants at P<1105 and with 45 metabolites (Supplementary Table 14). At FDR<0.05 (corresponding to P=2103 obtained from the inverse variance weighted (IVW) test)35, we observed four potential causal relationships at baseline that could also be found in the follow-up in the microbiomes to metabolites direction (Fig. 5ad and Supplementary Tables 15 and 16) but not in the opposite direction (Supplementary Table 17), and these outcomes were maintained following weighted median testing (P<0.03; Supplementary Fig. 5). To ensure that the data followed MR assumptions, we performed several sensitivity analyses, including checking for horizontal pleiotropy (MR-Egger36 intercept P>0.05; Supplementary Table 15) and heterogeneity (Cochrans Q test P>0.05; Supplementary Table 15) and leave-one-out analysis (Extended Data Fig. 5). We did not use causal estimates derived using the MR-Egger method to filter the results, as its power to detect causality is known to be low36. These sensitivity checks further confirmed the reliability of these four MR causal estimates.

a, Analysis of the association between adenosylcobalamin biosynthesis pathway abundance and 5-hydroxytryptophol levels. b, Glycogen biosynthesis pathway abundance versus 5-sulfo-1,3-benzenedicarboxylic acid levels. c, E. rectale abundance versus hydrogen sulfite levels. d, Veillonella parvula abundance versus 2,3-dehydrosilybin levels. In the top panels of ad, the xaxis shows the SNP exposure effect, and the yaxis shows the SNP outcome effect and each dot represents a SNP. Error bars represent the s.e. of each effect size. The bottom panels of ad, show the MR effect size (center dot) and 95% CI for the baseline (blue) and follow-up (green) datasets of the LLD1 cohort, estimated with the IVW MR approach (two sided) (n=927 biologically independent samples at baseline and n=311 biologically independent samples at follow-up).

We further found that increased abundance of microbial adenosylcobalamin biosynthesis (coenzyme B12) was associated with reduced plasma levels of 5-hydroxytryptophol (Fig. 5a)a uremic toxin related to Parkinsons disease37. We also found that plasma hydrogen sulfite levels were related to Eubacterium rectale (Fig. 5c)a core gut commensal species38 that is highly prevalent (presence rate=97%) and abundant (mean abundance=8.5%) in both our cohorts and in other populations39,40,41. As a strict anaerobe, E. rectale promotes the hosts intestinal health by producing butyrate and other short-chain fatty acids from non-digestible fibers42, and a reduced abundance of this species has been observed in subjects with inflammatory bowel disease39,43 and colorectal cancer44 compared with healthy controls. As a toxin, hydrogen sulfite interferes with the nervous system, cardiovascular functions, inflammatory processes and the gastrointestinal and renal system45. Our results thus reveal a potential new beneficial effect of E. rectale.

To further investigate the metabolic potential of individual bacterial species, we applied newly developed pipelines to identify microbial primary metabolic gene clusters (gutSMASH pathways)46 and microbial genomic structural variants (SVs)47. These two tools profile microbial genomic entities that are implicated in metabolic functions. By associating 1,183 metabolites with 3,075 gutSMASH pathways and 6,044 SVs (1,782 variable SVs (vSVs) and 4,262 deletion SVs (dSVs); see Methods), we observed 23,662 associations with gutSMASH pathways and 790 associations with bacterial SVs (FDRLLD1<0.05; PLLD1 follow-up<0.05; Supplementary Tables 1820). These associations connect the genetically encoded functions of microbes with metabolites, thereby providing putative mechanistic information underlying the functional output of the gut microbiome. In one example, we observed that the microbial uremic toxin biosynthesis pathways, including the glycine cleavage pathway (in Olsenella and Clostridium species) and the hydroxybenzoate-to-phenol pathway (in Clostridium species) responsible for hippuric acid and phenol sulfate biosynthesis, were associated with the hippuric acid (Olsenella species: rSpearman=0.15; P=9.3107; Clostridium species: rSpearman=0.18; P=5.9109) and phenol sulfate (rSpearman=0.17; P=4.2108; Extended Data Fig. 6a) levels measured in plasma, respectively (FDRLLD1<0.05 and PLLD1 follow-up<0.05; Extended Data Fig. 6b).

Next, we carried out a mediation analysis to investigate the links between diet, the microbiome and metabolites. For 675 microbial features that were associated with both dietary habits and metabolites (FDR<0.05), we applied bi-directional mediation analysis to evaluate the effects of microbiome and metabolites for diet (see the Methods section Bi-directional mediation analysis). This approach established 146 mediation linkages: 133 for the dietary impact on the microbiome through metabolites and 13 for the dietary impact on metabolites through the microbiome (FDRmediation<0.05 and Pinverse-mediation>0.05; Fig. 6a,b and Supplementary Table 21). Most of these linkages were related to the impact of coffee and alcohol on microbial metabolic functionalities (Fig. 6a).

a, Parallel coordinates chart showing the 133 mediation effects of plasma metabolites that were significant at FDR<0.05. Shown are dietary habits (left), plasma metabolites (middle) and microbial factors (right). The curved lines connecting the panels indicate the mediation effects, with colors corresponding to different metabolites. freq., frequency; PFOR, pyruvate:ferredoxin oxidoreductase; OD, oxidative decarboxylation; HGD, 2-hydroxyglutaryl-CoA dehydratase; TPP, thiamine pyrophosphate. b, Parallel coordinates chart showing the 13 mediation effects of the microbiome that were significant at FDR<0.05. Shown are dietary habits (left), microbial factors (middle) and plasma metabolites (right). For the microbial factors column, number ranges represent the genomic location of microbial structure variations (SVs) in kilobyte unit, and colons represent the detailed annotation of certain gutSMASH pathway. c, Analysis of the effect of coffee intake on the abundance of M. smithii as mediated by hippuric acid. d, Analysis of the effect of beer intake on the C. methylpentosum Rnf complex pathway as mediated by hulupinic acid. e, Analysis of the effect of fruit intake on urolithin B in plasma as mediated by a vSV in Ruminococcus species (300305kb). In ce, the gray lines indicate the associations between the two factors, with corresponding Spearman coefficients and Pvalues (two sided). Direct mediation is shown by a red arrow and reverse mediation is shown by a blue arrow. Corresponding Pvalues from mediation analysis (two sided) are shown. inv., inverse; mdei., mediation.

Coffee contains various phenolic compounds that can be converted to hippuric acid by colonic microflora48. Hippuric acid is an acyl glycine that is associated with phenylketonuria, propionic acidemia and tyrosinemia49. We observed that hippuric acid can mediate the impact of drinking coffee on Methanobrevibacter smithii abundance (Pmediation=2.21016; Fig. 6c). We also observed that hulupinic acid, which is commonly detected in alcoholic drinks, can mediate the impact of beer consumption on the Clostridium methylpentosum ferredoxin:NAD+ oxidoreductase (Rnf) complex (Pmediation=2.21016; Fig. 6d)an important membrane protein in driving the ATP synthesis essential for all bacterial metabolic activities50.

Of the dietary impacts on metabolites through the microbiome (Fig. 6b and Supplementary Table 21), one interesting example is a Ruminococcus species vSV (300305kb) that encodes an ATPase responsible for transmembrane transport of various substrates51. This Ruminococcus species vSV mediated the effect of fruit consumption on plasma levels of urolithin B (Pmediation=2.21016; Fig. 6e). Urolithin B is a gut microbiota metabolite that protects against myocardial ischemia/reperfusion injury via the p62/Keap1/Nrf2 signaling pathway52. Taken together, our data provide potential mechanistic underpinnings for dietmetabolite and dietmicrobiome relationships.

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Influence of the microbiome, diet and genetics on inter-individual variation in the human plasma metabolome - Nature.com

A new study from scientists at MIT and Harvard University has delved into the complex relationship between nutrition, exercise and the human body, and turned up some fascinating insights. The research explores the cellular mechanics of high-fat diets and physical activity, and how they can guide cells and bodily systems in healthy or unhealthy directions.

The new study stems from prior research carried out by MIT researcher Manolis Kellis that focused on the FTO gene region, which is associated with fat mass and obesity risk. This earlier work demonstrated how genes in this region regulate a signaling pathway that turns some types of immature fat cells into either fat-burning cells or fat-storing cells.

Since, Kellis has turned an eye to exercise to explore what kind of role it might play in this process. Together with colleagues at MIT and Harvard Medical School, Kellis performed single-cell RNA sequencing on skeletal muscle tissue, the white fat tissue packed around internal organs and the subcutaneous white fat tissue found beneath the skin.

These tissues were sourced from mice in four different experimental groups. Two groups of mice were fed either a normal diet or a high-fat diet for three weeks, and then those groups were further split into a sedentary group or an exercise group with access to a treadmill, for another three weeks. The tissues were then analyzed from the four groups, enabling the scientists to determine which genes were activated or suppressed by exercise in 53 different cell types.

One of the general points that we found in our study, which is overwhelmingly clear, is how high-fat diets push all of these cells and systems in one way, and exercise seems to be pushing them nearly all in the opposite way, Kellis said. It says that exercise can really have a major effect throughout the body.

The analysis showed some interesting changes took place, with stem cells known as mesenchymal stem cells (MSCs) at the center of many of them. These cells can differentiate into other cells such fat cells or the fibroblasts that connect tissues and organs, and the scientists found a high-fat diet promoted their ability to differentiate into cells that store fat. Conversely, exercise was shown to reverse this effect.

Further, the high-fat diet caused the mesenchymal stem cells to secrete factors that altered the support structure around cells called the extracellular matrix. This reshaping of the matrix created a more inflammatory environment, and resulted in a new support structure more accommodating of fat-storing cells.

As the adipocytes (fat cells) become overloaded with lipids, theres an extreme amount of stress, and that causes low-grade inflammation, which is systemic and preserved for a long time, Kellis said. That is one of the factors that is contributing to many of the adverse effects of obesity.

Increasingly, we are seeing research that unravels the way our body clock, or circadian rhythm, can influence metabolism and the behavior of fat cells, and this new study also has relevance in this space. The authors found that high-fat diets suppressed the genes that govern circadian rhythms, while exercise had the opposite effect and boosted them. Two of these genes were matched with human genes linked to circadian rhythm and higher risk of obesity.

There have been a lot of studies showing that when you eat during the day is extremely important in how you absorb the calories, Kellis said. The circadian rhythm connection is a very important one, and shows how obesity and exercise are in fact directly impacting that circadian rhythm in peripheral organs, which could act systemically on distal clocks and regulate stem cell functions and immunity.

The scientists are now building on this work by analyzing samples of the mouse intestines, liver and brains to explore the changes on those tissues, and collecting blood and tissue samples from people to investigate the differences in human physiology. The authors consider the findings to be further evidence of how important a healthy diet and exercise are for our health.

But access to quality foods and being physically capable of regular exercise arent a given, and arent viable lifestyle interventions for everyone. For this reason, the scientists believe the findings are also important in that they point to new targets for drugs that might one day replicate the effects of exercise.

It is extremely important to understand the molecular mechanisms that are drivers of the beneficial effects of exercise and the detrimental effects of a high-fat diet, so that we can understand how we can intervene, and develop drugs that mimic the impact of exercise across multiple tissues, said Kellis.

The research was published in the journal Cell Metabolism.

Source: MIT

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MIT and Harvard study unpacks the push and pull of diet and exercise - New Atlas

There has always been a lot of focus and emphasis on a balanced diet, which is inclusive of proteins, vitamins, minerals, good fats, and carbohydrates in sufficient quantities. Many people believe if they switch to a healthy and balanced diet, they will be healthier, prevent unnecessary weight gain, and be free of diseases.But, no diet is fool-proof and even a balanced meal will not be able to help you meet your desired health and weight goals if you make mistakes along the way.

According to Nicky Sagar, a nutritionist, if one blindly follows a diet, it may not have a positive impact on the body.It is of utmost importance that we know the correct know-how for following a balanced diet, the expert says, busting some myths about a well-balanced diet that one must never believe.

Myth 1: Fruits are sources of sugar

While following a balanced diet in order to shed some kilos, many people ditch fruits. They feel fruits are loaded with sugar, which will lead to weight gain. Fruits contain natural fructose that provides a sweet taste to them and these are natural sugars that are important for the body. Also, fruits contain minerals, vitamins, and fibre, which are extremely beneficial. Consuming various fruits can help in reducing and maintaining weight, says Sagar.

Myth 2: Say no to carbs

Most people avoid carbs, thinking they are associated with weight gain and are unhealthy food. In reality, carbohydrates are extremely important for our body to function properly. They provide energy and make us more productive. Also, they are loaded with vitamins and minerals. Fruits and vegetables that are naturally sourced and unprocessed are great for the body. Avoid highly-processed foods like pastries, breads, etc. A complete no-carb diet is harmful to ones health, the nutritionist says.

Myth 3: Breakfast should never be skipped

You do not have to stuff yourself with food just because you have woken up. Having lunch is also totally fine for the body. Besides, many people follow intermittent fasting where they skip breakfast. Some studies have also shown skipping breakfast improves blood sugar levels. It is not mandatory to have breakfast every day.

Myth 4: Going for low-fat foods

Foods that are labelled low-fat are often detrimental for health. According to the expert, they are highly processed foods that contain added amounts of salt, sugar and other harmful ingredients.If you are on a balanced diet and you get provoked by such labels, stay away from them. These foods lead to weight gain, alter blood sugar levels and cause other long-term ill effects.

Myth 5: No more calories

A popular myth is that consuming calorie-inducing foods lead to weight gain. But, eating food with no or too few calories can lead to various health issues from fatigue to risking impact on the heart. Also, calories boost energy and keep the stomach fuller. If you choose to have no calories, you will feel hungry and end up eating more food than needed.

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Five myths about a balanced diet you should not believe - The Indian Express

Protein is found in every cell and tissue in the body. While it has many vital roles in the body, protein is crucial for muscle growth because it helps repair and maintain muscle tissue.

The current recommended dietary allowance (RDA) to prevent deficiency in minimally active adults is 0.8 grams (g) of protein per kilogram (kg) of body weight. However, newer research suggests individuals trying to build muscle need more than this.

Consuming less protein than the body needs has been linked to decreased muscle mass. In contrast, increased protein intakes above the RDA may help increase strength and lean body mass when paired with resistance exercise.

Protein is made up of amino acids that act as building blocks for cells and tissues in the body. There are 20 amino acids that combine to form proteins.

While some can be synthesized by the human body, others cannot. The nine amino acids that the body cannot make are called essential amino acids. These must be obtained through diet.

When a person eats protein, it is digested and broken down into amino acids, which are involved in many processes in the body, including tissue growth and repair, immune function, and energy production.

Like other body tissues, muscle proteins are continuously broken down and rebuilt. In order to build muscle, a person must consume more protein than what is broken down. This is often referred to as a net positive nitrogen balance, as protein is high in nitrogen.

If a person is not consuming adequate amounts of protein, their body tends to break down muscle to provide the body with the amino acids needed to support body functions and preserve more important tissues. Over time, this can lead to decreased muscle mass and strength.

Lastly, the body uses amino acids for muscle protein synthesis (MPS), the primary driver of muscle repair, recovery, and growth after strenuous exercises.

According to the 2020-2025 Dietary Guidelines for Americans, most healthy adults over 19 years old should get between 10-35% of their daily calories from protein. One gram of protein provides 4 calories.

This means that a person who eats 2,000 calories per day would need to consume between 50 and 175 grams of protein per day.

The current RDA of 0.8 g per kg of body weight for protein is based on the amount required to maintain nitrogen balance and prevent muscle loss. However, extending these recommendations to active individuals who are looking to build muscle may not be appropriate.

When it comes to building muscle mass, the ideal amount of daily protein a person should consume varies depending on several factors, including age, gender, activity level, health, and other variables.

However, several studies have given us a good idea of how to calculate the amount of protein adults need for muscle gain based on body weight.

While most studies agree that higher protein intakes are associated with improvements in lean body mass and strength when combined with resistance training, the optimal amount of protein required to build muscle remains controversial.

Here is what the latest research says.

One 2020 meta-analysis published in the journal Nutrition Reviews found that protein intakes ranging from 0.5 to 3.5 g per kg of body weight can support increases in lean body mass. In particular, researchers noted that gradually increasing protein take, even by as little as 0.1 grams per kilogram of body weight per day, can help maintain or increase muscle mass.

The rate of increase in lean body mass from higher protein intakes rapidly decreased after 1.3 g per kg of body weight was exceeded. Strength training suppressed this decline. This suggests that increased protein intake paired with strength training is best for gaining lean body mass.

Another 2022 meta-analysis published in the journal Sports Medicine concluded that higher protein intakes of around 1.5 g per kg of body weight daily paired with resistance training are required for optimal effects on muscle strength. Researchers noted that the benefits of increased protein intake on strength and muscle mass appear to plateau at 1.5 to 1.6 g per kg of body weight per day.

Lastly, one 2022 systematic review and meta-analysis published in the Journal of Cachexia, Sarcopenia, and Muscle concluded that a protein intake of 1.6 g per kg of body weight per day or higher results in small increases in lean body mass in young, resistance-trained individuals. The results on older individuals were marginal.

Notably, 80% of studies examined in this review reported participants consuming a minimum of 1.2 g of protein per kg of body weight per day, which is still higher than the current RDA. This may be a potential contributor to the decreased effects of protein intervention in combination with resistance training in older adults.

While it is difficult to give exact figures due to varying study results, the optimum amount of protein for muscle-building appears to be between 1.2 and 1.6 g per kg of body weight.

This means a 180-pound (81.8 kg) male, for example, would need to consume between 98 and 131 g of protein daily, combined with resistance training, to support muscle growth.

A person can meet their daily protein needs by eating animal and plant-based protein sources.

Animal-based protein sources include:

Plant-based protein sources include:

Some nutritionists consider animal protein sources to be superior to plant-based protein sources when it comes to building muscle mass. This is because they are complete proteins and contain all the essential amino acids the body needs in sufficient amounts. They are also easy to digest.

Some experts consider most plant proteins to be incomplete proteins because they do not contain all essential amino acids. However, individuals can pair incomplete protein sources to form a complete protein. Examples include rice and beans, hummus and pita bread, or peanut butter on whole wheat bread.

Doctors generally agree that healthy adults can safely tolerate a long-term protein intake of up to 2 g per kg of body weight per day without any side effects. However, some groups of people, such as healthy, well-trained athletes, may tolerate up to 3.5 g per kg of body weight.

Most research suggests that eating more than 2 g of protein per kg of body weight per day can cause health issues over time.

Symptoms of excessive protein intake include:

More severe risks associated with chronic protein overconsumption include:

When combined with resistance training, protein intakes above the current RDA can support muscle building.

The best way to meet your daily protein needs is by consuming lean meat, fish, beans, nuts, and legumes.

Since the optimal amount of protein a person needs depends on age, health status, and activity level, consider speaking with a healthcare provider or a registered dietitian to discuss how much protein is suitable for you.

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Protein for muscle mass: What is the optimal intake? - Medical News Today

United States, 11th Oct 2022, King NewsWire, The Mediterranean Diet is a mainly green diet, based on the consumption of very large portions of vegetables, fruits, legumes, and cereals, having extra virgin olive oil as its only source of fat, and extensively using herbs. Other characteristics are its very little consumption of meat (almost exclusively white meat), fish as its predominant source of protein, and little consumption of milk and fermented dairy products. Moreover, various studies recognize its effectiveness in preventing Non Communicable Diseases (NCDs), particularly when combined with a healthy lifestyle, such as moderate daily physical activity, avoiding destructive behaviors (drugs, tobacco, or alcohol consupmtion), and nurturing good social relationships. Moreover, one can see the contributions given to the development of this diet by the spread of Christianity from the dietary rules given by the Bible; if you really want to learn about the Mediterranean diet, you need to delve into this aspect as well.

The Bible starts discussing nutrition as early as Genesis: Then God said, I give you every seed-bearing plant on the face of the whole earth and every tree that has fruit with seed in it. They will be yours for food. [] (Gn 1:29). From this first passage, we can already see some of the basic principles of the Mediterranean diet. In the Old Testament there are many references to the diet of the patriarchs: Isaac grew grain (Gn 26:12) and Jacob sent his sons to Egypt during the famine to buy some (Gn 42-44). Olive oil was used both as food and for cooking food. Moreover, as reported in the episode of the widow of Sarepta (1 Kings 17:12), oil was widely used and was the basic ingredient in bread and cakes (Ex 29:2). Also noteworthy are the various dietary requirements specified in Leviticus chapter 11. The exclusion of certain meatssuch as pork, hare, all fish without fins and scales, and many birds (mainly birds of prey)probably led to the dietary basis of the Mediterranean diet, given the enormous similarities between the two. There are also literary notations that claim that vegetarian and vegan diets have biblical origins: as stated in Genesis 1:30, And to [] everything that has the breath of life, I have given every green plant for food. And it was so. According to the Bible, in fact, in the beginning man was vegetarian, and began eating meat after the universal flood and will probably return to vegetarianism when the original harmony is rebuilt. Moreover, the hygienic standards stated in the Bible were also ahead of their time, and were very important for the wholesomeness and processing of food in the Mediterranean Diet.

Following these rules, Atlanta Tech Park-based spin-off MAGISNAT (www.magisnat.com) has decided to market its dietary supplements, GARLIVE RECOVERY (https://www.amazon.com/dp/B0B4T82ZLV) and GARLIVE ORAL SPRAY (https://www.amazon.com/dp/B0B4T7YZ9Z), currently available only on Amazon.

Our philosophy is to study the plants that are typically employed in the Mediterranean diet, looking for molecules with beneficial effects from which they formulate their dietary supplements: the first ones are based on polyphenols from olive trees, titrated in hydroxytyrosol, with the following characteristics:

Highly concentrated: one daily dose of GARLIVE Dietary Supplements contains more polyphenols than two cups of extra virgin olive oil; also vitamins are in high dosages; for example, GARLIVE Recovery contains high concentrations of vitamins from the B, C, D groups (one tablet contains more vitamins than 14 oz of fruits);

At MAGISNAT, we are sure that rediscovering the uses of the plants we were gifted by God Is a Way Forward

Disclaimer: None of the reported information can be used to claim the properties of dietary supplements. Dietary supplements do not possess any therapeutic or preventive properties.

Organization: MAGISNAT

Contact Person: Matteo Bertelli MD, PhD

Email: [emailprotected]

Website: https://magisnat.com/

Address 1: Atlanta Tech Park 107 Technology Parkway Suite 801 PEACHTREE CORNERS, GA 30092

Country: United States

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Leaner individuals, who attempt weight loss by exercise, dieting, or commercial programmes and pills, ended up gaining weight in the long run, with their 24-year risk of Type-2 diabetes also going up. In contrast, intentional weight loss in obese persons was found to be overall beneficial, according to a recent study by the Harvard TH Chan School of Public Health.

Obesity is one of the biggest risk factors for developing Type-2 diabetes.

The researchers found exercise to be the most effective weight-loss strategy during a four-year follow-up with the average weight being 4.2 per cent less in obese individuals, 2.5 per cent in overweight individuals and 0.4 per cent in lean individuals as compared to their counterparts who did not attempt weight loss. Among those who tried commercial programmes or diet pills, the obese weighed 0.3 per cent less, the overweight individuals weighed two per cent more, and the leaner individuals 3.7 per cent more than their counterparts.

What was the impact of weight loss on diabetes?

The researchers looked at the risk of Type-2 diabetes 24 years later and found that it went down in obese individuals irrespective of the weight-loss method attempted. The risk of diabetes went down by 21 per cent in obese individuals who exercised and 13 per cent in those who took diet pills.

As for overweight people, the risk of diabetes went down by nine per cent with exercise but shot up by 42 per cent in those who took the pills.

In lean individuals, all weight loss strategies led to an increase in the risk of Type-2 diabetes. The risk increased by nine per cent in those who lost weight through exercise and 54 per cent for those who took pills, according to the study.

We were a bit surprised when we first saw the positive associations of weight loss attempts with faster weight gain and higher Type 2 diabetes risk among lean individuals. However, we now know that such observations are supported by biology that unfortunately entails adverse health outcomes when lean individuals try to lose weight intentionally. Good news is that individuals with obesity will clearly benefit from losing a few pounds and the health benefits last even when the weight loss is temporary, said Qi Sun from the department of nutrition at Harvard TH Chan School of Public Health in a release.

What does this study mean for India?

With around 77 million people in India living with diabetes with the numbers projected to grow several fold in the coming years should leaner individuals stop exercising? No, says Dr Ambrish Mithal, Chairman and Head of Diabetes and Endocrinology at Max Healthcare.

Everyone, including those with lower BMI, should continue to do their regular exercise to maintain a healthy weight we tend to put on weight as we age and be physically fit. What they are not supposed to do is try and lose more weight, he said.

With over 80 per cent of Type-2 diabetes in people who are overweight and obese, when we talk of diabetes remission now, weight loss is a very important strategy. But we have always maintained that it cannot be the only strategy for everyone. When it comes to diabetes in leaner people, it may be because of less production of insulin rather than the cells being resistant to it, and then weight loss will not be of help, said Dr Mithal.

He said that sometimes hyper-aware persons, someone who is lean and has been maintaining their HbA1c for years but now wants to get off all medications, attempt to lose weight. Reversal is not possible for everybody, he said.

But who should be considered overweight in India? Dr Mithal as well as Dr Anoop Misra, Chairman of Fortis CDOC Center for Diabetes agree that a BMI cut-off of 25 for being overweight does not work in India.

Even leaner Indians have a lot of fat around their belly, so the international cut-off for BMI 25 does not work here. I say, people should try to bring their BMI to around 21.5 in order to lose the fat stored in the liver, said Dr Misra.

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What is the best way for long-term weight loss: exercise, diet, or pills? This new study has the answers. - The Indian Express

If you have dry skin, you may find that your skin feels tight, rough, and flaky. To help hydrate and protect your dry skin, be sure to include plenty of fatty fish, avocados, olive oil, nuts, and seeds in your diet (via Medical News Today). Fatty fish, like salmon and yellowfin tuna, are a great source of omega-3 fatty acids, which help to keep your skin hydrated. Avocados and olive oil are also rich in healthy fats that help to nourish and moisturize your skin. Nuts and seeds are another good option for dry skin, as they're packed with vitamins and minerals that can help to keep your skin healthy.

Foods that are high in vitamin A can also help with dry skin. These include foods like sweet potato, carrots, kale, and spinach. Vitamin A helps to protect your skin from damage and can also help to reduce the appearance of wrinkles. You should also try to eat plenty of fruits and vegetables that contain a lot of water, like watermelon, cucumber, and strawberries. These foods can help to keep your skin hydrated and looking healthy.

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The Diet You Should Eat To Benefit Your Skin Type - Health Digest

"Diet ID is on a mission to make diet qualitythe single leading predictor of mortality and morbidity in the modern worlda vital sign."

ST. LOUIS (PRWEB) October 11, 2022

The American College of Lifestyle Medicine (ACLM) has announced the addition of nutrition measurement, management and behavior change platform Diet ID to its Lifestyle Medicine Corporate Roundtable, a group of thought leaders and industry professionals who explore effective clinical innovations, activate marketing strategies, accelerate reimbursement and policy adoption, and pursue research and demonstrations of lifestyle medicine in practice. ACLM launched its Corporate Roundtable in 2016 and it now includes nearly 50 active member organizations in the lifestyle medicine ecosystem.

Lifestyle medicine is a medical specialty that uses therapeutic lifestyle interventions as a primary modality to treat chronic conditions including, but not limited to, cardiovascular diseases, type 2 diabetes, and obesity. Lifestyle medicine certified clinicians are trained to apply evidence-based, whole-person, prescriptive lifestyle change to treat and, when used intensively, often reverse such conditions. Applying the six pillars of lifestyle medicinea whole-food, plant-predominant eating pattern, physical activity, restorative sleep, stress management, avoidance of risky substances and positive social connectionsalso provides effective prevention for these conditions.

Diet ID is a business-to-business, software-as-a-service (SAAS) company that has invented, patented, scientifically validated and widely commercialized a revolutionary advance in dietary assessment employing a visual approach to measuring and optimizing diet quality. Compared to other dietary assessment tools, this personalized, customizable platform saves time, effort and cost while generating a personalized route to wellness in just minutes. The first fundamentally new way to measure dietary intake in half a century, Diet ID is on a mission to make diet qualitythe single leading predictor of mortality and morbidity in the modern worlda vital sign.

According to multiple studies, diet is the leading predictor of chronic disease risk more than genetics, physical activity, or smoking. A critical element in health optimization, managing diet is extremely challenging, as its tedious, costly to measure and track, and difficult to personalize. Diet ID's digital nutrition tools quickly help providers understand their populations nutrition intake, as well as provide a personalized blueprint for changes to improve and optimize health. In addition, used as a dietary intervention, the platform offers the unique ability to impact downstream cost savings achieved through upstream improvement in diet quality.

Rooted in decades of research and validation, Diet ID offers transparency with the fastest way to measure diet quality, food intake, and estimated nutrient intake. Diet ID allows effortless diet quality assessment with its digital one-minute, validated visual dietary assessment. A clinician can generate results and individual plans in real time. For the patient, the Daily Actions Module swaps out burdensome food logging for joyful social challenges, meeting people wherever they are on their health improvement journeys.

The advancement of lifestyle as medicine in all of its luminous promise to add years to lives, and life to years, is dependent upon pearls of innovation, said Diet ID Founder and CEO David L. Katz, MD, MPH, FACPM, FACP, FACLM. Diet ID is to diet as a vital sign as the blood pressure cuff is to blood pressure and should be applied as such universally. The routine inclusion of dietary assessment, and management, is the next evolutionary advance in the standard of food as medicine in clinical care. ACLMs Corporate Roundtable provides us all the opportunity to explore our synergies, be more than a sum of parts, and make together a greater difference.

Nutrition is the foundation of lifestyle medicine, with the power to prevent, treat, and reverse chronic disease, said ACLM President Cate Collings, MD, FACC, MS, DipABLM. Conventional dietary assessments are cumbersome and neither user nor provider friendly. Diet ID makes it possible to more easily standardize and streamline dietary measurement so that providers can spend their time and target the resources to best help patients. We welcome Diet ID to our Corporate Roundtable.

ABOUT DIET ID Founded in 2016 and headquartered in in Detroit, MI, with a team around the U.S., Diet ID is focused on making dietary assessment a vital sign. Founded by Dr. David L. Katz, lifestyle medicine and nutrition leader and founder of the Yale-Griffin Prevention Research Center, Diet ID provides a scientifically valid approach to help people improve what and how they eat, one bite at a time. The result is permanent habit change, with a preference for healthful foods. Learn more about Diet ID at dietID.com.

ABOUT ACLM--The American College of Lifestyle Medicine is the nations only medical professional society advancing lifestyle medicine as the foundation for a redesigned, value-based and equitable healthcare delivery system, leading to whole person health. ACLM educates, equips, empowers and supports its members through quality, evidence-based education, certification and research to identify and eradicate the root cause of chronic disease, with a clinical outcome goal of health restoration as opposed to disease management.

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Inflammation occurs when cells travel to the place of an injury or foreign body like bacteria, but if these cells stay in the body for too long, it may lead to chronic inflammation

New Delhi: If you suffer from the issue of inflammation, according to doctors, before taking any medicine try and follow the natural route.

What is inflammation and how does it affect the body?

Health experts believe that chronic inflammation is a symptom of many underlying health conditions like arthritis or even stress.

How to reduce inflammation naturally

The best way to reduce chronic inflammation is to adopt an anti-inflammatory diet and lifestyle that may help you stay healthy and slow down aging. The diet would also help reduce the risk of heart disease, diabetes, dementia, and autoimmune diseases like joint pain, and cancer.

Doctors believe that an anti-inflammatory diet provides a healthy balance of protein, carbs, and fat at each meal. Make sure you also meet your bodys needs for vitamins, minerals, fiber, and water.

A low-carb diet also reduces inflammation, particularly for people with obesity or metabolic syndrome.

Some of the foods that help reduce inflammation are:

Disclaimer: Tips and suggestions mentioned in the article are for general information purposes only and should not be construed as professional medical advice. Always consult your doctor or a dietician before starting any fitness programme or making any changes to your diet.

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Anti-inflammatory diet: How to reduce inflammation through eating right - Times Now

STRONGMAN Eddie Hall is bulking up as he heads back into competition.

Brit star Hall, 34, slimmed down from a whopping top weight of 434lbs for his heavyweight boxing fight with Hafthor Bjornson earlier this year.

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He tipped the scales at 310lbs for the exhibition bout which saw Game of Thrones actor Thor come out on top.

Now Hall - who has 3.4m Instagram followers - is ditching his foray into the squared circle and returning to his strongman roots.

Later this fall, the strongman officially returns to the sport at the 2022Giants Live Worlds Strongest Nation competition.

It'll be the UK taking on the USA in the team-led event, with Hall a team caption for the Brits andRobert Oberstfor Team USA.

The event is all set for November in Liverpool, England and the 2017 World's Strongest Man is taking his prep seriously.

Although he's not looking to return to his 400lbs-plus days, Eddie is having to fuel his intense workouts with a new diet.

And that involves a lot of effort - as shown by a viral video he uploaded of his daily food routine.

With wife and usual chef Alexandra out on errands, it was up to the strongman to don the apron and cook his own meals.

He kicks off his day with a hefty breakfast shake, which provides around 700 calories of crucial early morning fuel.

Packed full of protein, the shake includes two hefty scoops of whey protein, peanut butter, one banana, chocolate spread, milk and a hearty helping of ice.

Training for Hall doesn't start until after lunch, which is when he really starts to chow down.

All about the protein again, the man mountain demolishes five chicken-filled wraps before his afternoon workout.

They provide him with around 1,500 calories and 80 grams of protein, with a further two wraps held back for after his training session.

Following an intense couple of hours in the gym, Eddie concludes his food marathon with two humungous burgers.

He packs two massive patties into buns along with sauce, tomatoes, cheese and bacon - before adding a whopping amount of home-cooked potato wedges.

In total, he guzzles 4,600 calories and 385 grams of protein during a typical day.

Eddie is looking forward after his boxing defeat to Thor in March, where he was dropped twice by the giant Icelandic star.

He told Men's Health: "Obviously, losing the fight is hard to take, but I think losing is a big part of life.

"I didn't win World Strongest Man first time around.

"You've got to take those losses, learn, go away, recoup and come back bigger and stronger.

"Sometimes, losses are better than the wins, because they really do shape you, and who likes somebody that wins everything?"

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Eddie Hall reveals his insane new diet as strongman piles on the pounds for return at Worlds Strongest N... - The US Sun

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