Category Archives: Diet And Food

The results highlight both the need for routine blood bile acid level testing as well as caution when individuals with high bile acid levels consume fiber.

Fiber-enriched foodsare often consumed by many individuals to promote weight loss and fend against chronic diseases like cancer and diabetes.

Consuming highly refined fiber, however, may raise the risk of liver cancer in certain people, especially those with a silent vascular deformity, according to a recent study from The University of Toledo.

The finding, which is described in a report published in the journal Gastroenterology, adds to UToledos expanding body of knowledge about the undervalued role that our gut plays in the origin of disease.

We have worked for a long time on this idea that all diseases start from the gut, said Dr. Matam Vijay-Kumar, a professor in the Department of Physiology and Pharmacology in the College of Medicine and Life Sciences and the papers senior author. This study is a notable advancement of that concept. It also provides clues that may help identify individuals at a higher risk for liver cancer and potentially enable us to lower that risk with simple dietary modifications.

From left, Dr. Matam Vijay-Kumar, a professor in the Department of Physiology and Pharmacology, and Dr. Beng San Yeoh, a postdoctoral fellow. Credit: University of Toledo

Vijay-Kumars team published a major paper in the journal Cell in 2018 that revealed a large proportion of mice with immune system defects developed liver cancer after being given an inulin-fortified diet.

Inulin is a refined, plant-based fermentable fiber that is sold in supermarkets as a health-promoting prebiotic. Additionally, it is often found in processed foods.

Vijay-Kumar and colleagues found that around one in ten regular, otherwise healthy lab mice got liver cancer after consuming the inulin-containing diet, despite the fact that inulin promotes metabolic health in the majority of those who consume it.

That was very surprising, given how rarely liver cancer is observed in mice, said Vijay-Kumar, who is also director of the UToledo Microbiome Consortium. The findings raised real questions about the potential risks of certain refined fibers, but only now do we understand why the mice were developing such aggressive cancer.

The new study offers a clear explanation and may have implications that go beyond laboratory animals.

As the team furthered its investigation, the researchers discovered all mice that developed malignant tumors had high concentrations of bile acids in their blood caused by a previously unnoticed congenital defect called a portosystemic shunt.

Normally, blood leaving the intestines goes into the liver where it is filtered before returning to the rest of the body. When a portosystemic shunt is present, blood from the gut is detoured away from the liver and back into the bodys general blood supply.

The vascular defect also allows the liver to continuously synthesize bile acids. Those bile acids eventually spill over and enter circulation instead of going into the gut.

Blood thats diverted away from the liver contains high levels of microbial products that can stimulate the immune system and cause inflammation.

To check that inflammation, which can be damaging to the liver, the mice react by developing a compensatory anti-inflammatory response that dampens the immune response and reduces their ability to detect and kill cancer cells.

While all mice with excess bile acids in their blood were predisposed to liver injury, only those fed inulin progressed to hepatocellular carcinoma, a deadly primary liver cancer.

Remarkably, 100% of the mice with high bile acids in their blood went on to develop cancer when fed inulin. None of the mice with low bile acids developed cancer when fed the same diet.

Dietary inulin is good in subduing inflammation, but it can be subverted into causing immunosuppression, which is not good for the liver, said Dr. Beng San Yeoh, a postdoctoral fellow and the new papers first author.

Dr. Bina Joe, Distinguished University Professor and chair of the Department of Physiology and Pharmacology, and a co-author of the study said the high-impact publication demonstrates the pioneering research being done at UToledo.

The role of the gut and gut bacteria in health and disease is an exciting and important area of research, and our team is providing new insights on the leading edge of this field, she said.

Beyond the laboratory, UToledos research could provide insight that might help clinicians identify people who are at higher risk of liver cancer years in advance of any tumors forming.

Portosystemic shunts in humans are relatively rare the documented incidence is only one in 30,000 people at birth. However, given that they generally cause no noticeable symptoms, the true incidence may be many times greater. Portosystemic shunting also commonly develops following liver cirrhosis.

Theorizing that high bile acid levels might serve as a viable marker for liver cancer risk, Vijay-Kumars team tested bile acid levels in serum samples collected between 1985 and 1988 as part of a large-scale cancer prevention study.

In the 224 men who went on to develop liver cancer, their baseline blood bile acid levels were twice as high as men who did not develop liver cancer. Statistical analysis also found individuals with the highest blood bile acid levels had a more than four-fold increase in the risk of liver cancer.

The research team also sought to examine the relationship between fiber consumption, bile acid levels, and liver cancer in humans.

While existing epidemiological studies dont differentiate between soluble and non-soluble fiber, researchers could look at fiber consumption in concert with blood bile acids.

There are two basic types of naturally occurring dietary fiber, soluble and insoluble. Soluble fibers are fermented by gut bacteria into short-chain fatty acids. Insoluble fibers pass through the digestive system unchanged.

Intriguingly, researchers found high total fiber intake reduced the risk of liver cancer by 29% in those whose serum bile acid levels were in the lowest quartile of their sample.

However, in men whose blood bile acid levels placed them in the top quarter of the sample, high fiber intake conferred a 40% increased risk of liver cancer.

Taken together, Yeoh and Vijay-Kumar say the findings suggest both the need for regular blood bile acid level testing and a cautious approach to fiber intake in individuals who know they have higher-than-normal levels of bile acids in their blood.

Serum bile acids can be measured by a simple blood test developed over 50 years ago. However, the test is usually only performed in some pregnant women, Vijay-Kumar said. Based on our findings, we believe this simple blood test should be incorporated into the screening measurements that are routinely performed to monitor health.

And while the researchers are not arguing broadly against the health-promoting benefits of fiber, they are urging attention to what kind of fiber certain individuals eat, underscoring the importance of personalized nutrition.

All fibers are not made equal, and all fibers are not universally beneficial for everyone. People with liver problems associated with increased bile acids should be cautious about refined, fermentable fiber, Yeoh said. If you have a leaky gut liver, you need to be careful of what you eat, because what you eat will be handled in a different way.

References: Enterohepatic Shunt-Driven Cholemia Predisposes to Liver Cancer by Beng San Yeoh, Piu Saha, Rachel M. Golonka, Jun Zou, Jessica L. Petrick, Ahmed A. Abokor, Xia Xiao, Venugopal R. Bovilla, Alexis C.A. Bretin, Jess Rivera-Esteban, Dominick Parisi, Andrea A. Florio, Stephanie J. Weinstein, Demetrius Albanes, Gordon J. Freeman, Amira F. Gohara, Andreea Ciudin, Juan M. Perics, Bina Joe, Robert F. Schwabe, Katherine A. McGlynn, Andrew T. Gewirtz and Matam Vijay-Kumar, 18 August 2022, Gastroenterology.DOI: 10.1053/j.gastro.2022.08.033

Dysregulated Microbial Fermentation of Soluble Fiber Induces Cholestatic Liver Cancer by Vishal Singh, Beng San Yeoh, Benoit Chassaing, Xia Xiao, Piu Saha, Rodrigo Aguilera Olvera, John D. Lapek Jr., Limin Zhang, Wei-Bei Wang, Sijie Hao, Michael D. Flythe, David J. Gonzalez, Patrice D. Cani, Jose R. Conejo-Garcia, Na Xiong, Mary J. Kennett, Bina Joe, Andrew D. Patterson, Andrew T. Gewirtz and Matam Vijay-Kumar, 18 October 2018, Cell.DOI: 10.1016/j.cell.2018.09.004

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Diets High in Processed Fiber May Increase Cancer Risk - SciTechDaily

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

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.

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

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

The post The great contributions given to the Mediterranean Diet by the rules stated in the Bible and the spread of early Christianity in that area appeared first on King Newswire.

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The great contributions given to the Mediterranean Diet by the rules stated in the Bible and the spread of early Christianity in that area - Digital...

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

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

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

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

Introduction

Obesity is one of the important challenges in public health worldwide. It may cause damage to the function of human organs and systems and ultimately lead to other chronic non-communicable diseases (NCDs) including cardiovascular disease, type 2 diabetes, dyslipidemia, chronic kidney disease, osteoarthritis, and cancer.18 Over the last decades, the global prevalence of obesity has increased rapidly, approximately 11% of men and 15% of women were obese in the world.9 In 2015, the prevalence of overweight and obesity among Chinese adults were 41.3% and 15.7%, respectively.10 Obesity-related NCDs brought a huge economic burden in China, and obesity and overweight accounted for 11.1% of deaths associated with NCDs in 2019.11

The root cause of obesity is that the bodys energy intake is greater than the bodys energy expenditure, resulting in excess energy being stored in the form of fat although lots of risk factors for obesity were explored and identified including genetics, diet, physical exercise, and psychological factors in previous studies.11 Thus, dietary factors still play a key role in the process of developing obesity even though some previous findings were controversial over countries or populations.12,13

The traditional nutritional epidemiology researches generally explore relationships between one or several foods or nutrients and health outcomes. Recently, dietary patterns of the overall diet were occupied to assess the comprehensive effects of food or nutrients on human health, and they showed more effectively and precisely than traditional those.14 However, different dietary patterns varied widely over countries, races, and research methods.15 Previous studies showed that western and junk food dietary patterns increased energy intake and risk of obesity,16 while Mediterranean dietary pattern was considered to reduce triglyceride levels.17 Also, an association between Chinese traditional dietary pattern and obesity was reported in one research.18 However, most of previous studies were cross-sectional studies between dietary patterns and obesity,1820 and it was rare to explore prospective associations between dietary patterns and obesity with community population cohorts in China.

There were huge differences in food culture and diet behaviors over different regions, even in China, due to the geographical features and ethnic diversity.21 Thus, based on a prospective community-based population cohort in Guizhou province, this study aimed to explore associations between dietary patterns and incident obesity in Southwest China.

Data for this study were from the Guizhou Population Health Cohort Study (GPHCS), a prospective community-based cohort in Guizhou province, China.22 The baseline survey was conducted between November 2010 and December 2012, and it was followed up between December 2016 and June 2020. The inclusion criteria for subjects in this study included followings: (1) aged 18 years or above; (2) lived in these communities and had no plan to move; (3) completed the questionnaire and blood sample collection; (4) signed written informed consent before data collection. A total of 9280 participants were recruited at the baseline. Those who had obesity at baseline (n = 644), who lost to follow-up (n = 1045), and who had missing data (n = 1634) or incomplete dietary survey (n = 215) were excluded. Finally, the remaining 5742 participants were eligible for the analysis (Figure 1). This study was approved by the Institutional Review Board of Guizhou Province Centre for Disease Control and Prevention (No. S2017-02).

Figure 1 Flow chart of participants in this cohort study.

A structured questionnaire was done through a face-to-face interview by local trained health professionals. The baseline and follow-up questionnaire included demographic characteristics (age, sex, ethnicity, educational level, marriage status, and occupation), lifestyle (smoking status, alcohol use, and physical activity), history of chronic diseases, and dietary factors. Current smokers referred to smoking tobacco products including manufactured or locally produced in a month.23 Alcohol drinkers referred to drinking alcohol more than once every month within the last 12 months.22 Physical activity was defined as meeting WHO recommendations on physical activity according to the global physical activity questionnaire (GPAQ).24

Dietary data including frequencies and quantities of 16 food items (fermented bean curd, bean paste, pickles, oil, legumes, meat, fruits, milk, eggs, fish, potatoes, grains, vegetables, beverages, desserts, and fried food) consumed during the recent 12 months before the study recruitment were collected by a simplified Food Frequency Questionnaire (FFQ). Anthropometric measurements including height, body weight, and blood pressure were measured. BMI was calculated as body weight in kilograms divided by height in meters squared (kg/m2). Obesity was defined as BMI 30kg/m2 based on the WHO BMI classification standard.25

In this study, factor analysis with eigenvalues >1 and varimax rotation was occupied to aggregate 16 food items into factors with food patterns. Four factors that explained most of the variances were determined based on scree plots and their loadings for the initial food items. The factor-loading matrix for the four dietary patterns and their food or food groups is shown in Table S1. Factor 1, named high-salt and high-oil pattern, was characterized by a high factor load of fermented bean curd, bean paste, pickles, and oil. Factor 2, named western pattern, was characterized by a high factor load of legumes, meat, fruits, milk, eggs, fish, and potatoes. Factor 3, named grain-vegetable pattern, was characterized by a high factor load of grains and vegetables. Factor 4, named junk food pattern, was characterized by a high factor load of beverages, desserts, and fried food. A summary score for each pattern was then derived and categorized into quartiles (Quartile 025th, Q1; 26th-50th, Q2; 51st-75th, Q3; 76th-100th, Q4) for further analysis.

The Students t-test and the Chi-square test were used for continuous variables and categorical variables, respectively. Person-years (PYs) of follow-up were calculated from the date of enrolling the cohort until the date of diagnosis of obesity, death, or follow-up, whichever came first. Because physical activity violated the proportional hazards assumption, the multivariable Cox proportional hazards regression models stratified by physical activity were employed to determine the association between dietary patterns and incident obesity and to estimate hazard risk (HR), adjusted HR (aHR), and their 95% confidence intervals (CIs). Several variables were adjusted and controlled in the multivariable models: age (1829, 3064, 65 years), sex (male/female), Han Chinese (no/yes), education years (9/<9), current smokers (no/yes), alcohol drinkers (no/yes), diabetes mellitus (no/yes), hypertension (no/yes). Tests for linear trends across increasing quartiles of dietary pattern were performed by assigning median value to each quartile of dietary pattern. The sensitivity analysis was conducted after exclusion of participants with overweight at baseline. All statistical tests were two-sided and P < 0.05 was considered statistically significant. All analyses were performed in R software (Version 4.1.0; R Foundation for Statistical Computing, Vienna, Austria).

The baseline characteristics of participants are presented in Table 1. Of all subjects, the average age was 45.06 15.21 years old and more than half were women. Most of them were Han Chinese and had 9 education years or longer. The prevalence of current smoking and alcohol drinking was around one-third, while the proportion of physical activity was more than four-fifths. There were significant differences in education level, physical activity, current smokers, alcohol drinkers, hypertension, and diabetes between men and women (detailed in Table 1).

Table 1 Baseline Characteristics of Participants

As shown in Table 2, four dietary patterns statistically varied over different age groups and physical activity groups. Men (53.6%) had higher grain-vegetable pattern scores than women (46.4%). Han Chinese had more chances to have western pattern and junk food pattern. Participants with less than 9 education years had lower proportions of high-salt and high-oil pattern, western pattern, and junk food pattern. Those subjects with hypertension or diabetes tended to have high-salt and high-oil pattern and junk food pattern. There were also significant differences in high-salt and high-oil patterns and western pattern among participants who were current smokers or alcohol drinkers.

Table 2 Participants Characteristics According to Quartiles of Four Dietary Patterns

During the follow-up of 40,524.15 PYs, 427 new obesity cases were identified and the incidence rate of obesity was 10.54/1000PYs overall. There were significant sex differences in the incidence rate (9.36/1000PYs for men vs 11.64/1000PYs for women, p = 0.004). The incidence rate increased with age and the age-specific incidence rates of obesity are displayed over sex in Figure 2. Similar sex differences were observed among those aged 30 to 64 years old (p = 0.010) or elders (p = 0.031). Also, the highest incidence rate of obesity reached 12.27/1000PYs and 9.8/1000PYs in both women and men aged 30 to 64 years, respectively.

Figure 2 Age-specific Incidence rates of obesity for Chinese adults over sex.

Abbreviation: PYs, person years of follow-up.

Note: **P < 0.01.

In the Cox regression model stratified by physical activity, associations between dietary patterns and incident obesity are presented in Table 3. Participants in the higher quartile of junk food pattern score were more likely to develop obese with the HR (95% CI) of 1.54 (1.162.02) and 1.44 (1.091.89) for the third and fourth quartiles, respectively. After the adjustment for covariates, both aHRs in the Q3 and Q4 group of junk food pattern increased slightly and were still significant. Also, the risk of incident obesity significantly increased with the score of junk food pattern (p for trend = 0.040). In addition, subjects in the Q3 group of western pattern had a significantly higher risk of incident obesity (aHR: 1.33, 95% CI: 1.011.75) compared to those in the Q1 group, and there was a marginally raised trend in the risk of incident obesity as western pattern scores (p for trend = 0.087). It was not found that there were any significant associations between high-salt and high oil pattern or grain-vegetable pattern and incident obesity. No significant interactions were observed between dietary pattern and main covariates, either. In the sensitivity analysis, the main results remained robust after exclusion of participants with overweight at baseline (seen in Figure S1).

Table 3 Associations Between Baseline Dietary Patterns and Incident Obesity

The prevalence of obesity has been increasing dramatically worldwide. As a leading risk factor for obesity, unhealthy dietary has been prevalent in China. During the follow-up of 40,524.15 PYs, the incidence rate of obesity was estimated at 10.54/1000PYs in this study population overall with a significant sex difference. Also, the highest incidence rate of obesity reached at 12.27/1000PYs and 9.80/1000PYs in both women and men aged 3064 years, respectively. Those findings indicated that there was a high risk of developing obesity in this study population, especially for women, which called the development and implementation of specific intervention for the prevention and control of obesity.

In the present study, four major dietary patterns were identified and then associations between four dietary patterns and incident obesity were explored among adult residents in Southwest China. The junk food pattern consisted of high consumption of beverages, desserts, and fried food. Likewise, the western pattern was characterized by high consumption of legumes, meat, fruits, milk, eggs, fish, and potatoes. We found that junk food pattern and western pattern were positively associated with the increased risk of developing obesity, while no significant associations between high-oil and high-salt pattern, grain-vegetable pattern and incident obesity were observed in this study. The results were consistent with the South Asian consensus on Nutritional Medical Treatment of Diabesity, which advocated for a hypocaloric diet and reducing intake of carbohydrates and saturated fats.26 Meanwhile, among Iranian women, it was reported that a low-carbohydrate diet was not associated with overweight and obesity.27

In China, the consumption of junk food such as desserts, beverages, and fried food is on the rise since the 1980s.11 In this study, the contribution of junk food dietary pattern to a higher risk of obesity was demonstrated, which was consistent with a Mediterranean prospective cohort design with a median 6-year follow-up.28 Previous studies revealed that during the frying process, excessive fat and calories tended to increase, and trans-fatty acids related to the risk of weight gain29 were also prone to be generated.30 Furthermore, the junk food pattern has a high intake of beverages and sweets, and the positive associations of sugar-sweetened beverages (SSBs) to obesity were confirmed by Framingham Heart Study.31 A recent meta-analysis revealed that the consumption of SSBs increased waist circumference in adult populations.32 Also, a cross-sectional study33 indicated that fruit drink intake was significantly linked with a higher risk of obesity among women. In addition, added sweet or sugar foods were positively associated with BMI in the women.34 Excess sugar intake among sweets and desserts was a significant contributor to the development of overweight or obesity.35,36

Over the past decades, the socioeconomic level has changed dramatically in China, especially in the southwest region. The transition from the traditional dietary pattern characterized by a high intake of vegetables, grains, and legumes to the Western model had occurred.37,38 It was observed that western dietary pattern had a higher incident risk of obesity and there was a marginally raised trend in the incident risk of obesity as western pattern levels in this study. Several studies have demonstrated that Chinese who had a western dietary pattern were more likely to suffer from obesity.39,40 Some similar findings were also reported among children and adolescents.12,41,42 One of possible reasons might be that meat and meat products are rich in cholesterol and saturated fatty acids,43,44 which could increase the risk of suffering from obesity to a certain degree.45 However, Daneshzad et al46 demonstrated that there was no significant association between total meat consumption and obesity based on a meta-analysis of observational studies. Therefore, more prospective studies are needed to clarify the association between red meat and total meat, and obesity.

Moreover, given the topographical characteristics of the Guizhou region, a wide range of potato products, boiled, fried, or mashed, were widely consumed in the local area. As a staple food in the western world, potatoes, an energy-dense food, played a significant role in the western diet pattern, and contributed greater amounts of carbohydrates to the diet.47 Foods containing more starches and refined carbohydrates were positively associated with weight gain.48 A meta-analysis confirmed that weight change was positively associated with the consumption of potatoes (boiled or mashed potatoes, potato chips, and French fries).49 Halkjaer et al50 also reported that total potato intake was associated with the increase in waist circumstances in women. However, the evidence for a link between potato intake and the risk of obesity remains controversial.51,52

Based on this 10-year community population-based cohort in Southwest China,53 this study extended the evidence on the association between dietary patterns and incident obesity. Also, this study collected data through FFQ rather than 24h dietary recall to get long-term usual intake more accurately.41,54 However, there were some main limitations in the study. First, the outcome of obesity was only assessed by BMI and did not include those measures of central obesity such as waistline in this study, which may underestimate the incidence of obesity. Second, over several years of follow-up, the daily diet measured on baseline may be time-varying to bias our findings but we did not collect detailed diet information in the follow-up of this study. Third, Cox proportional hazards regression models were employed with the strata by physical activity to meet Proportional Hazards Assumption. In addition, some possible confounding factors such as medications, family history of obesity or genetic variants related to obesity were not collected in this study, which may bias the findings from this study. Our findings in this southwest Chinese population need to be confirmed or clarified by more prospective studies over different populations. For future studies, associations between diets and obesity measured by waistline or body composition should be explored, and genediet interactions on developing obesity should be considered, too.

In summary, there was a high risk of incident obesity among this Chinese community population of Southwest China. Also, four dietary patterns were identified in this community population of Southwest China, and junk food and western pattern increased risks of incident obesity. The findings provided new evidence for obesity prevention and control from the dietary perspective, especially for the Chinese population. Urgent intervention is called to be developed to promote a healthy dietary pattern and prevent the becoming obesity.

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the center of disease control and prevention of Guizhou Province (No. S2017-02).

Written informed consent was obtained from all subjects before the data collection.

This work was supported by the Guizhou Province Science and Technology Support Program (Qiankehe [2018]2819).

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

The authors declare no conflicts of interest in this work.

1. Lyall DM, Celis-Morales C, Ward J, et al. Association of body mass index with cardiometabolic disease in the UK biobank: a Mendelian randomization study. JAMA Cardiol. 2017;2(8):882889. doi:10.1001/jamacardio.2016.5804

2. Hansen L, Netterstrm MK, Johansen NB, et al. Metabolically healthy obesity and ischemic heart disease: a 10-year follow-up of the inter99 study. J Clin Endocrinol Metab. 2017;102(6):19341942. doi:10.1210/jc.2016-3346

3. Baena-Dez JM, Byram AO, Grau M, et al. Obesity is an independent risk factor for heart failure: Zona Franca Cohort study. Clin Cardiol. 2010;33(12):760764. doi:10.1002/clc.20837

4. Maggio CA, Pi-Sunyer FX. Obesity and type 2 diabetes. Endocrinol Metab Clin North Am. 2003;32(4):805822, viii. doi:10.1016/S0889-8529(03)00071-9

5. Riob Servn P. Obesity and diabetes. Nutr Hosp. 2013;28(Suppl 5):138143. doi:10.3305/nh.2013.28.sup5.6929

6. Zhang T, Chen J, Tang X, Luo Q, Xu D, Yu B. Interaction between adipocytes and high-density lipoprotein: new insights into the mechanism of obesity-induced dyslipidemia and atherosclerosis. Lipids Health Dis. 2019;18(1):223. doi:10.1186/s12944-019-1170-9

7. Di Angelantonio E, Bhupathiraju SN, Wormser D, et al.; Global BMIMC. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet. 2016;388(10046):776786. doi:10.1016/S0140-6736(16)30175-1

8. Saitta C, Pollicino T, Raimondo G. Obesity and liver cancer. Ann Hepatol. 2019;18(6):810815. doi:10.1016/j.aohep.2019.07.004

9. Wang Y, Xue H, Sun M, Zhu X, Zhao L, Yang Y. Prevention and control of obesity in China. Lancet Glob Health. 2019;7(9):e1166e1167. doi:10.1016/S2214-109X(19)30276-1

10. Ma S, Xi B, Yang L, Sun J, Zhao M, Bovet P. Trends in the prevalence of overweight, obesity, and abdominal obesity among Chinese adults between 1993 and 2015. Int J Obes. 2021;45(2):427437. doi:10.1038/s41366-020-00698-x

11. Pan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol. 2021;9(6):373392. doi:10.1016/S2213-8587(21)00045-0

12. Liu D, Zhao LY, Yu DM, et al. Dietary patterns and association with obesity of children aged 617 years in medium and small cities in China: findings from the CNHS 20102012. Nutrients. 2018;11(1):3. doi:10.3390/nu11010003

13. Shin KO, Oh SY, Park HS. Empirically derived major dietary patterns and their associations with overweight in Korean preschool children. Br J Nutr. 2007;98(2):416421. doi:10.1017/S0007114507720226

14. Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13(1):39. doi:10.1097/00041433-200202000-00002

15. Yu C, Shi Z, Lv J, et al. Major dietary patterns in relation to general and central obesity among Chinese adults. Nutrients. 2015;7(7):58345849. doi:10.3390/nu7075253

16. Gven Y, nc E. The relationship between junk food consumption, healthy nutrition, and obesity among children aged 7 to 8 years in Mersin, Turkey. Nutr Res. 2022;103:110. doi:10.1016/j.nutres.2022.03.004

17. Shively CA, Appt SE, Vitolins MZ, et al. Mediterranean versus western diet effects on caloric intake, obesity, metabolism, and hepatosteatosis in nonhuman primates. Obesity. 2019;27(5):777784. doi:10.1002/oby.22436

18. Zhang Q, Chen X, Liu Z, et al. Dietary patterns in relation to general and central obesity among adults in Southwest China. Int J Environ Res Public Health. 2016;13(11):1080. doi:10.3390/ijerph13111080

19. Zou Y, Zhang R, Xia S, et al. Dietary patterns and obesity among Chinese adults: results from a household-based cross-sectional study. Int J Environ Res Public Health. 2017;14(5):487. doi:10.3390/ijerph14050487

20. Yuan YQ, Li F, Meng P, et al. Gender difference on the association between dietary patterns and obesity in Chinese middle-aged and elderly populations. Nutrients. 2016;8(8):448. doi:10.3390/nu8080448

21. Ruan Y, Huang Y, Zhang Q, Qin S, Du X, Sun Y. Association between dietary patterns and hypertension among Han and multi-ethnic population in southwest China. BMC Public Health. 2018;18(1):1106. doi:10.1186/s12889-018-6003-7

22. Cao L, Zhou J, Chen Y, et al. Effects of body mass index, waist circumference, waist-to-height ratio and their changes on risks of dyslipidemia among Chinese adults: the Guizhou population health cohort study. Int J Environ Res Public Health. 2021;19(1):341. doi:10.3390/ijerph19010341

23. Yu Y, Chen Y, Wang Y, Yu L, Liu T, Fu C. Is the efficiency score an indicator for incident hypertension in the community population of Western China? Int J Environ Res Public Health. 2021;18(19):10132. doi:10.3390/ijerph181910132

24. World Health Organization. Global Physical Activity Questionnaire (GPAQ). Available from: https://www.who.int/docs/default-source/ncds/ncd-surveillance/gpaq-analysis-guide.pdf. Accessed September 29, 2022.

25. World Health Organization. World Health Organization obesity: preventing and managing the global epidemic. Report of a WHO consultation WHO Technical Report Series; 2000:894.

26. Kapoor N, Sahay R, Kalra S, et al. Consensus on Medical Nutrition Therapy for Diabesity (CoMeND) in adults: a South Asian perspective. Diabetes Metab Syndr Obes. 2021;14:17031728. doi:10.2147/DMSO.S278928

27. Jafari-Maram S, Daneshzad E, Brett NR, Bellissimo N, Azadbakht L. Association of low-carbohydrate diet score with overweight, obesity and cardiovascular disease risk factors: a cross-sectional study in Iranian women. J Cardiovasc Thorac Res. 2019;11(3):216223. doi:10.15171/jcvtr.2019.36

28. Sayon-Orea C, Bes-Rastrollo M, Basterra-Gortari FJ, et al. Consumption of fried foods and weight gain in a Mediterranean cohort: the SUN project. Nutr Metab Cardiovasc Dis. 2013;23(2):144150. doi:10.1016/j.numecd.2011.03.014

29. Thompson AK, Minihane AM, Williams CM. Trans fatty acids and weight gain. Int J Obes. 2011;35(3):315324. doi:10.1038/ijo.2010.141

30. Bhardwaj S, Passi SJ, Misra A, et al. Effect of heating/reheating of fats/oils, as used by Asian Indians, on trans fatty acid formation. Food Chem. 2016;212:663670. doi:10.1016/j.foodchem.2016.06.021

31. Dhingra R, Sullivan L, Jacques PF, et al. Soft drink consumption and risk of developing cardiometabolic risk factors and the metabolic syndrome in middle-aged adults in the community. Circulation. 2007;116(5):480488. doi:10.1161/CIRCULATIONAHA.107.689935

32. Ardeshirlarijani E, Jalilpiran Y, Daneshzad E, Larijani B, Namazi N, Azadbakht L. Association between sugar-sweetened beverages and waist circumference in adult populations: a meta-analysis of prospective cohort studies. Clinical Nutrition ESPEN. 2021;41:118125. doi:10.1016/j.clnesp.2020.10.014

33. Nikpartow N, Danyliw AD, Whiting SJ, Lim H, Vatanparast H. Fruit drink consumption is associated with overweight and obesity in Canadian women. Can J Public Health. 2012;103(3):178182. doi:10.1007/BF03403809

34. Deglaire A, Mjean C, Castetbon K, Kesse-Guyot E, Hercberg S, Schlich P. Associations between weight status and liking scores for sweet, salt and fat according to the gender in adults (The Nutrinet-Sant study). Eur J Clin Nutr. 2015;69(1):4046. doi:10.1038/ejcn.2014.139

35. Lampur A, Castetbon K, Deglaire A, et al. Associations between liking for fat, sweet or salt and obesity risk in French adults: a prospective cohort study. Int J Behav Nutr Phys Act. 2016;13:74. doi:10.1186/s12966-016-0406-6

36. Andres-Hernando A, Kuwabara M, Orlicky DJ, et al. Sugar causes obesity and metabolic syndrome in mice independently of sweet taste. Am J Physiol Endocrinol Metab. 2020;319(2):E276E290. doi:10.1152/ajpendo.00529.2019

37. Du SF, Wang HJ, Zhang B, Zhai FY, Popkin BM. China in the period of transition from scarcity and extensive undernutrition to emerging nutrition-related non-communicable diseases, 19491992. Obes Rev. 2014;15:815. doi:10.1111/obr.12122

38. Wilson AS, Koller KR, Ramaboli MC, et al. Diet and the human gut microbiome: an international review. Dig Dis Sci. 2020;65(3):723740. doi:10.1007/s10620-020-06112-w

39. Cao Y, Xu X, Shi Z. Trajectories of dietary patterns, sleep duration, and body mass index in China: a population-based longitudinal study from China Nutrition and Health Survey, 19912009. Nutrients. 2020;12(8):2245. doi:10.3390/nu12082245

40. Xu X, Byles J, Shi Z, McElduff P, Hall J. Dietary pattern transitions, and the associations with BMI, waist circumference, weight and hypertension in a 7-year follow-up among the older Chinese population: a longitudinal study. BMC Public Health. 2016;16:743. doi:10.1186/s12889-016-3425-y

41. Zhen S, Ma Y, Zhao Z, Yang X, Wen D. Dietary pattern is associated with obesity in Chinese children and adolescents: data from China Health and Nutrition Survey (CHNS). Nutr J. 2018;17(1):68. doi:10.1186/s12937-018-0372-8

42. Zhang J, Wang H, Wang Y, et al. Dietary patterns and their associations with childhood obesity in China. Br J Nutr. 2015;113(12):19781984. doi:10.1017/S0007114515001154

43. Laskowski W, Grska-Warsewicz H, Kulykovets O. Meat, meat products and seafood as sources of energy and nutrients in the average Polish diet. Nutrients. 2018;10(10):1412. doi:10.3390/nu10101412

44. Larsson SC, Virtamo J, Wolk A. Red meat consumption and risk of stroke in Swedish women. Stroke. 2011;42(2):324329. doi:10.1161/STROKEAHA.110.596510

45. Rouhani MH, Salehi-Abargouei A, Surkan PJ, Azadbakht L. Is there a relationship between red or processed meat intake and obesity? A systematic review and meta-analysis of observational studies. Obes Rev. 2014;15(9):740748. doi:10.1111/obr.12172

46. Daneshzad E, Askari M, Moradi M, et al. Red meat, overweight and obesity: a systematic review and meta-analysis of observational studies. Clinical Nutrition ESPEN. 2021;45:6674. doi:10.1016/j.clnesp.2021.07.028

47. King JC, Slavin JL. White potatoes, human health, and dietary guidance. Adv Nutr. 2013;4(3):393s401s. doi:10.3945/an.112.003525

48. Robertson TM, Alzaabi AZ, Robertson MD, Fielding BA. Starchy carbohydrates in a healthy diet: the role of the humble potato. Nutrients. 2018;10(11):1764. doi:10.3390/nu10111764

49. Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. Changes in diet and lifestyle and long-term weight gain in women and men. N Engl J Med. 2011;364(25):23922404. doi:10.1056/NEJMoa1014296

50. Halkjaer J, Tjnneland A, Overvad K, Srensen TI. Dietary predictors of 5-year changes in waist circumference. J Am Diet Assoc. 2009;109(8):13561366. doi:10.1016/j.jada.2009.05.015

51. Aljuraiban GS, Pertiwi K, Stamler J, et al. Potato consumption, by preparation method and meal quality, with blood pressure and body mass index: the INTERMAP study. Clin Nutr. 2020;39(10):30423048. doi:10.1016/j.clnu.2020.01.007

52. Linde JA, Utter J, Jeffery RW, Sherwood NE, Pronk NP, Boyle RG. Specific food intake, fat and fiber intake, and behavioral correlates of BMI among overweight and obese members of a managed care organization. Int J Behav Nutr Phys Act. 2006;3:42. doi:10.1186/1479-5868-3-42

53. Chen Y, Wang Y, Xu K, et al. Adiposity and long-term adiposity change are associated with incident diabetes: a prospective cohort study in Southwest China. Int J Environ Res Public Health. 2021;18(21):11481.

54. Moghames P, Hammami N, Hwalla N, et al. Validity and reliability of a food frequency questionnaire to estimate dietary intake among Lebanese children. Nutr J. 2016;15:4. doi:10.1186/s12937-015-0121-1

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