The role of different types and quantities of macronutrients on human health has been controversial, and the individual response to dietary macronutrient intake needs more investigation.
We aimed to ...use an ‘n-of-1’ study design to investigate the individual variability in postprandial glycemic response when eating diets with different macronutrient distributions among apparently healthy adults.
Thirty apparently healthy young Chinese adults (women, 68%) aged between 22 and 34 y, with BMI between 17.2 and 31.9 kg/m2, were provided with high-fat, low-carbohydrate (HF-LC, 60–70% fat, 15–25% carbohydrate, 15% protein, of total energy) and low-fat, high-carbohydrate (LF-HC, 10–20% fat, 65–75% carbohydrate, 15% protein) diets, for 6 d wearing continuous glucose monitoring systems, respectively, in a randomized sequence, interspersed by a 6-d wash-out period. Three cycles were conducted. The primary outcomes were the differences of maximum postprandial glucose (MPG), mean amplitude of glycemic excursions (MAGE), and AUC24 between intervention periods of LF-HC and HF-LC diets. A Bayesian model was used to predict responders with the posterior probability of any 1 of the 3 outcomes reaching a clinically meaningful difference.
Twenty-eight participants were included in the analysis. Posterior probability of reaching a clinically meaningful difference of MPG (0.167 mmol/L), MAGE (0.072 mmol/L), and AUC24 (13.889 mmol/L·h) between LF-HC and HF-LC diets varied among participants, and those with posterior probability >80% were identified as high-carbohydrate responders (n = 9) or high-fat responders (n = 6). Analyses of the Bayesian-aggregated n-of-1 trials among all participants showed a relatively low posterior probability of reaching a clinically meaningful difference of the 3 outcomes between LF-HC and HF-LC diets.
N-of-1 trials are feasible to characterize personal response to dietary intervention in young Chinese adults.
Furan fatty acid metabolite 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) is a strong biomarker of fish and n-3 polyunsaturated fatty acid (PUFA) intake. The relationship of CMPF with ...human health has been controversial, especially for type 2 diabetes and chronic kidney disease.
We performed a prospective cohort study to examine the association of serum CMPF with incident type 2 diabetes and chronic kidney disease.
In the Guangzhou Nutrition and Health Study, during a median follow-up of 8.8 y, we used a multivariable-adjusted Poisson regression model to investigate the association of baseline serum CMPF with the incidence of type 2 diabetes (1470 participants and 170 incident cases) and chronic kidney disease (1436 participants and 112 incident cases). We also examined the association of serial measures of serum CMPF with glycemic and renal function biomarkers. Mediation analysis was also performed to examine the contribution of CMPF in the association between marine n-3 PUFAs and risk of type 2 diabetes or chronic kidney disease.
Each standard deviation increase in baseline serum CMPF was associated with an 18% lower risk of type 2 diabetes (relative risk: 0.82, 95% confidence interval CI: 0.68, 0.99) but was not associated with chronic kidney disease (relative risk: 0.95; 95% CI: 0.77-1.16). Correlation analyses of CMPF with glycemic and renal function biomarkers showed similar results. Mediation analysis suggested that serum CMPF contributed to the inverse association between erythrocyte marine n-3 PUFAs and incident type 2 diabetes (proportion mediated 37%, P-mediation = 0.022).
Our findings suggest that serum CMPF was associated with a lower risk of type 2 diabetes but not chronic kidney disease. This study also suggests that CMPF may be a functional metabolite underlying the protective relationship between marine n-3 PUFA intake and type 2 diabetes.
Dietary diversity is essential for human health. The gut ecosystem provides a potential link between dietary diversity, host metabolism, and health, yet this mechanism is poorly understood.
Here, we ...aimed to investigate the relation between dietary diversity and the gut environment as well as host metabolism from a multiomics perspective.
Two independent longitudinal Chinese cohorts (a discovery and a validation cohort) were included in the present study. Dietary diversity was evaluated with FFQs. In the discovery cohort (n = 1916), we performed shotgun metagenomic and 16S ribosomal ribonucleic acid (rRNA) sequencing to profile the gut microbiome. We used targeted metabolomics to quantify fecal and serum metabolites. The associations between dietary diversity and the microbial composition were replicated in the validation cohort (n = 1320).
Dietary diversity was positively associated with α diversity of the gut microbiota. We identified dietary diversity–related gut environment features, including the microbial structure (β diversity), 68 microbial genera, 18 microbial species, 8 functional pathways, and 13 fecal metabolites. We further found 332 associations of dietary diversity and related gut environment features with circulating metabolites. Both the dietary diversity and diversity-related features were inversely correlated with 4 circulating secondary bile acids. Moreover, 16 mediation associations were observed among dietary diversity, diversity-related features, and the 4 secondary bile acids.
These results suggest that high dietary diversity is associated with the gut microbial environment. The identified key microbes and metabolites may serve as hypotheses to test for preventing metabolic diseases.
The relationship between PM2.5 and metabolic diseases, including type 2 diabetes (T2D), has become increasingly prominent, but the molecular mechanism needs to be further clarified. To help ...understand the mechanistic association between PM2.5 exposure and human health, we investigated short-term PM2.5 exposure trajectory-related multi-omics characteristics from stool metagenome and metabolome and serum proteome and metabolome in a cohort of 3267 participants (age: 64.4 ± 5.8 years) living in Southern China. And then integrate these features to examine their relationship with T2D. We observed significant differences in overall structure in each omics and 193 individual biomarkers between the high- and low-PM2.5 groups. PM2.5-related features included the disturbance of microbes (carbohydrate metabolism-associated Bacteroides thetaiotaomicron), gut metabolites of amino acids and carbohydrates, serum biomarkers related to lipid metabolism and reducing n-3 fatty acids. The patterns of overall network relationships among the biomarkers differed between T2D and normal participants. The subnetwork membership centered on the hub nodes (fecal rhamnose and glycylproline, serum hippuric acid, and protein TB182) related to high-PM2.5, which well predicted higher T2D prevalence and incidence and a higher level of fasting blood glucose, HbA1C, insulin, and HOMA-IR. Our findings underline crucial PM2.5-related multi-omics biomarkers linking PM2.5 exposure and T2D in humans.
Display omitted
•Trajectory grouping in short-term PM2.5 exposures effectively capture variations in microbiota composition.•Novel PM2.5-related multi-omics biomarkers well predicted higher T2D prevalence and incidence.•The crucial PM2.5-related biomarkers include fecal rhamnose and glycylproline, serum hippuric acid, and protein TB182.•The subnetwork correlation provides critical insights into the molecular mechanisms underlying PM2.5-related T2D impacts.
The role of circulatory proteomics in osteoporosis is unclear. Proteome‐wide profiling holds the potential to offer mechanistic insights into osteoporosis. Serum proteome with 413 proteins was ...profiled by liquid chromatography–tandem mass spectrometry (LC–MS/MS) at baseline, and the 2nd, and 3rd follow‐ups (7704 person‐tests) in the prospective Chinese cohorts with 9.8 follow‐up years: discovery cohort (n = 1785) and internal validation cohort (n = 1630). Bone mineral density (BMD) was measured using dual‐energy X‐ray absorptiometry (DXA) at follow‐ups 1 through 3 at lumbar spine (LS) and femoral neck (FN). We used the Light Gradient Boosting Machine (LightGBM) to identify the osteoporosis (OP)‐related proteomic features. The relationships between serum proteins and BMD in the two cohorts were estimated by linear mixed‐effects model (LMM). Meta‐analysis was then performed to explore the combined associations. We identified 53 proteins associated with osteoporosis using LightGBM, and a meta‐analysis showed that 22 of these proteins illuminated a significant correlation with BMD (p < 0.05). The most common proteins among them were PHLD, SAMP, PEDF, HPTR, APOA1, SHBG, CO6, A2MG, CBPN, RAIN APOD, and THBG. The identified proteins were used to generate the biological age (BA) of bone. Each 1 SD‐year increase in KDM‐Proage was associated with higher risk of LS‐OP (hazard ratio HR, 1.25; 95% CI, 1.14–1.36, p = 4.96 × 10−06), and FN‐OP (HR, 1.13; 95% CI, 1.02–1.23, p = 9.71 × 10−03). The findings uncovered that the apolipoproteins, zymoproteins, complements, and binding proteins presented new mechanistic insights into osteoporosis. Serum proteomics could be a crucial indicator for evaluating bone aging.
Serum proteomics offers potential insight into accelerated aging in osteoporosis. Osteoporosis is commonly referred to as an aging disorder characterized by decreased bone mineral density (BMD) and an elevated risk of fractures. Using longitudinal serum proteomics analyses with 413 protein species covering various protein classes in a population‐based study, we found that the proteins of apolipoproteins, zymoprotein, coagulation, immunoglobulins, complement, and binding proteins may be the potential therapeutic targets for osteoporosis. Serum proteomics could be a crucial indicator for evaluating bone aging.
Previous studies suggest an interaction of CD36 genetic variant rs1527483 with n-3 polyunsaturated fatty acids (PUFAs) to modulate blood lipids. However, successful replication is lacking and the ...role of gut microbiome remains unclear. Here, we aimed to replicate these gene–diet interactions on blood lipids and investigate their possible associations with gut microbiome.
We evaluated the n-3 PUFA-rs1527483 interaction on blood lipids in two population-based cohorts (n = 4,786). We profiled fecal microbiome and short-chain fatty acids among 1,368 participants. The associations between n-3 PUFAs and bacterial alpha-diversity, taxonomies and short-chain fatty acids by rs1527483 genotypes were analyzed using regression models.
CD36 rs1527483-GG carriers responded better to high n-3 PUFA exposure; higher blood HDL-C (beta (95% CI): 0.05 (0.01, 0.08) mmol/L) and lower TG (log-transformed, beta (95% CI): −0.08 (−0.14, −0.02)) were observed among participants whose n-3 PUFA exposure ranked in the top quartile comparing with those in the bottom quartile. We identified docosahexaenoic acid (DHA) as the driven individual n-3 PUFA biomarker, which showed interaction with rs1527483. Among the rs1527483-GG carriers, but not other genotype groups, DHA exposure was positively associated with bacterial Faith's phylogenetic diversity, Observed OTUs, Shannon’s diversity index, Dorea, Coriobacteriales Incertae Sedis spp, and fecal propionic acid levels. Another independent longitudinal cohort validated the DHA-rs1527483 interaction on gut microbiome. The identified microbial features were correlated with blood lipids, and the host biosynthesis and metabolism pathways of bile acids and aromatic amino acids.
The present study found that higher n-3 PUFAs were associated with improved blood lipids and gut microbial features only among rs1527483-GG carriers. These findings highlight a potential role of gut microbiome to link the CD36 genetic variant, n-3 PUFAs and blood lipids, revealing a new research direction to interpret the gene–diet interaction for cardiometabolic health.
Scope
Little is known about the effect of blood vitamin D status on the gut mycobiota (i.e., fungi), a crucial component of the gut microbial ecosystem. The study aims to explore the association ...between 25‐hydroxyvitamin D 25(OH)D and gut mycobiota and to investigate the link between the identified mycobial features and blood glycemic traits.
Methods and results
The study examines the association between serum 25(OH)D levels and the gut mycobiota in the Westlake Precision Birth Cohort, which includes pregnant women with gestational diabetes mellitus (GDM). The study develops a genetic risk score (GRS) for 25(OH)D to validate the observational results. In both the prospective and cross‐sectional analyses, the vitamin D is associated with gut mycobiota diversity. Specifically, the abundance of Saccharomyces is significantly lower in the vitamin D‐sufficient group than in the vitamin D‐deficient group. The GRS of 25(OH)D is inversely associated with the abundance of Saccharomyces. Moreover, the Saccharomyces is positively associated with blood glucose levels.
Conclusion
Blood vitamin D status is associated with the diversity and composition of gut mycobiota in women with GDM, which may provide new insights into the mechanistic understanding of the relationship between vitamin D levels and metabolic health.
There is limited research conducted on the relationship between vitamin D and gut mycobiota. This study reveals that individuals with sufficient serum vitamin D exhibit a decreased abundance of fecal Saccharomyces. The results obtain from the vitamin D genetic risk score analysessupport this relationship. The study also identifies the abundance of Saccharomyces is positively associated with fasting glucose.
PDF
