Associations between serum amino acids and incident type 2 diabetes in Chinese rural adults

Gut microbiota and metabolomic profile changes play critical roles in tacrolimus-induced diabetes in rats

Aims

Hyperglycemia is one of the adverse effects of tacrolimus (TAC), but the underlying mechanism is not fully identified. We used multi-omics analysis to evaluate the changes in the gut microbiota and metabolic profile of rats with TAC-induced diabetes.

Methods

To establish a diabetic animal model, Sprague Dawley rats were divided randomly into two groups. Those in the TAC group received intraperitoneal injections of TAC (3 mg/kg) for 8 weeks, and those in the CON group served as the control. 16S rRNA sequencing was used to analyze fecal microbiota. The metabolites of the two groups were detected and analyzed by nontargeted and targeted metabolomics, including amino acids (AAs), bile acids (BAs), and short-chain fatty acids (SCFAs).

Results

The rats treated with TAC exhibited hyperglycemia as well as changes in the gut microbiota and metabolites. Specifically, their gut microbiota had significantly higher abundances of Escherichia-Shigella, Enterococcus, and Allobaculum, and significantly lower abundances of Ruminococcus, Akkermansia, and Roseburia. In addition, they had significantly reduced serum levels of AAs including asparagine, aspartic acid, glutamic acid, and methionine. With respect to BAs, they had significantly higher serum levels of taurocholic acid (TCA), and glycochenodeoxycholic acid (GCDCA), but significantly lower levels of taurodeoxycholic acid (TDCA) and tauroursodeoxycholic acid (TUDCA). There were no differences in the levels of SCFAs between the two groups. Correlations existed among glucose metabolism indexes (fasting blood glucose and fasting insulin), gut microbiota (Ruminococcus and Akkermansia), and metabolites (glutamic acid, hydroxyproline, GCDCA, TDCA, and TUDCA).

Conclusions

Both AAs and BAs may play crucial roles as signaling molecules in the regulation of TAC-induced diabetes.

Genetic risk score for insulin resistance based on gene variants associated to amino acid metabolism in young adults

Circulating concentration of arginine, alanine, aspartate, isoleucine, leucine, phenylalanine, proline, tyrosine, taurine and valine are increased in subjects with insulin resistance, which could in part be attributed to the presence of single nucleotide polymorphisms (SNPs) within genes associated with amino acid metabolism. Thus, the aim of this work was to develop a Genetic Risk Score (GRS) for insulin resistance in young adults based on SNPs present in genes related to amino acid metabolism. We performed a cross-sectional study that included 452 subjects over 18 years of age. Anthropometric, clinical, and biochemical parameters were assessed including measurement of serum amino acids by high performance liquid chromatography. Eighteen SNPs were genotyped by allelic discrimination. Of these, ten were found to be in Hardy-Weinberg equilibrium, and only four were used to construct the GRS through multiple linear regression modeling. The GRS was calculated using the number of risk alleles of the SNPs in HGD, PRODH, DLD and SLC7A9 genes. Subjects with high GRS (≥ 0.836) had higher levels of glucose, insulin, homeostatic model assessment- insulin resistance (HOMA-IR), total cholesterol and triglycerides, and lower levels of arginine than subjects with low GRS (p < 0.05). The application of a GRS based on variants within genes associated to amino acid metabolism may be useful for the early identification of subjects at increased risk of insulin resistance.

The association between dietary amino acid profile and the risk of type 2 diabetes: Ravansar non-communicable disease cohort study

BACKGROUND

Type 2 diabetes (T2D) is one of the most common chronic diseases and the main risk factors for T2D consist of a combination of lifestyle, unhealthy diet, and genetic factors. Amino acids are considered to be a major component of dietary sources for many of the associations between dietary protein and chronic disease. Therefore, this study amied to determine the association between dietary amino acid intakes and the incidence of T2D.

METHODS

The present nested case-control study was conducted using data from the Ravansar Non-Communicable Disease (RaNCD) Cohort Study. The information required for this study was collected from individuals who participated in the Adult Cohort Study from the start of the study until September 2023. Over a 6-year follow-up period, data from 113 new T2D cases were available. Four controls were then randomly selected for each case using density sampling. Cases and controls were matched for sex and age at the interview. Food frequency questionnaire (FFQ) was used to collect data related to all amino acids including tryptophan, threonine, isoleucine, leucine, lysine, methionine, cysteine, phenylalanine, tyrosine, valine, arginine, histidine, alanine, aspartic acid, glutamic acid, glycine, proline, and serine were also extracted. Binary logistic regression was used to estimate the crude and adjusted odds ratio for the risk of T2D.

RESULTS

Using the univariable model, a significant association was found between T2D risk and branched-chain, alkaline, sulfuric, and essential amino acids in the fourth quartile. Accordingly, individuals in the fourth quartile had a 1.81- to 1.87-fold higher risk of developing new T2D than individuals in the lowest quartile (P<0.05). After adjustment for several variables, the risk of developing a new T2D was 2.70 (95% CI: 1.16-6.31), 2.68 (95% CI: 1.16-6.21), 2.98 (95% CI: 1.27-6.96), 2.45 (95% CI: 1.02-5.90), and 2.66 (95% CI: 1.13-6.25) times higher, for individuals in the fourth quartile of branched-chain, alkaline, sulfuric, alcoholic, and essential amino acids compared with those in the lowest quartile, respectively.

CONCLUSIONS

The results showed that the risk of developing a new T2D was higher for individuals in the fourth quartile of branched-chain amino acids, alkaline, sulfate, and essential amino acids than in the lower quartile.

Incidence and temporal trends in type 2 diabetes by weight status: A systematic review and meta-analysis of prospective cohort studies

Background

Diabetes is more prevalent among overweight/obese individuals, but has become a significant public health challenge among normal weight populations. In this meta-analysis, we aimed to estimate diabetes/prediabetes incidence and its temporal trends by weight status.

Methods

PubMed, Embase, Web of Science, and Cochrane Library were searched until 8 December 2021. Prospective cohort studies reporting diabetes incidence by baseline body mass index (BMI) categories in adults were included. The median year of data collection was used to assess the temporal trends. Subgroup analyses and meta-regression were also performed.

Results

We included 94 studies involving 3.4 million adults from 22 countries. The pooled diabetes incidence in underweight, normal-weight, and overweight/obese adults was 4.5 (95% confidence interval (CI) = 2.8-7.3), 2.7 (95% CI = 2.2-3.3), and 10.5 (95% CI = 9.3-11.8) per 1000 person-years, respectively. The diabetes incidence in low- and middle-income countries (LMICs) was higher than in high-income countries among normal-weight (5.8 vs 2.0 per 1000 person-years) or overweight/obese (15.9 vs 8.9 per 1000 person-years) adults. European and American regions had a higher diabetes incidence than the non-Western areas, regardless of weight status. Underweight diabetes incidence decreased significantly from 1995-2000 to 2005-2010. Diabetes incidence in normal-weight populations has increased continuously since 1985 by an estimated 36% every five years. In overweight/obese adults, diabetes incidence increased between 1985-1990 and 1995-2000, stabilised between 2000 and 2010, and spiked suddenly after 2010.

Conclusions

Diabetes incidence and its temporal trends differed by weight status. The continuous upward trend of diabetes incidence among overweight/obese individuals requires urgent attention, particularly in LMICs. Furthermore, diabetes among normal-weight individuals is becoming a significant public health problem.

Registration

PROSPERO (CRD42020215957).

Amino acid analysis as a method of discovering biomarkers for diagnosis of diabetes and its complications

Diabetes mellitus (DM) is a severe chronic diseases with a global prevalence of 9%, leading to poor health and high health care costs, and is a direct cause of millions of deaths each year. The rising epidemic of diabetes and its complications, such as retinal and peripheral nerve disease, is a huge burden globally. A better understanding of the molecular pathways involved in the development and progression of diabetes and its complications can facilitate individualized prevention and treatment. High diabetes mellitus incidence rate is caused mainly by lack of non-invasive and reliable methods for early diagnosis, such as plasma biomarkers. The incidence of diabetes and its complications in the world still grows so it is crucial to develop a new, faster, high specificity and more sensitive diagnostic technologies. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner, and effective biomarkers can greatly improve the efficiency of diabetes and its complications. By providing information on potential metabolic pathways, metabolomics can further define the mechanisms underlying the progression of diabetes and its complications, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The application of amino acid metabolomics in epidemiological studies has identified new biomarkers of diabetes mellitus (DM) and its complications, such as branched-chain amino acids, phenylalanine and arginine metabolites. This study focused on the analysis of metabolic amino acid profiling as a method for identifying biomarkers for the detection and screening of diabetes and its complications. The results presented are all from recent studies, and in all cases analyzed, there were significant changes in the amino acid profile of patients in the experimental group compared to the control group. This study demonstrates the potential of amino acid profiles as a detection method for diabetes and its complications.

Low-dose radiation exaggerates HFD-induced metabolic dysfunction by gut microbiota through PA-PYCR1 axis

Co-exposure of High-fat-diet (HFD) behavior and environmental low-dose radiation (LDR) is common among majority occupational workers, but the synergism of this co-exposure in metabolic health is poorly understood. This study aimed to investigate the impact of gut microbiota and its metabolites on the regulation of HFD accompanied by LDR-associated with metabolic dysfunction and insulin resistance. Here, we reported that Parasutterella was markedly elevated in the gut microbiota of mice in co-exposure of HFD and LDR, accompanied by increased pyrrolidinecarboxylic acid (PA) level in both intestine and plasma. Transplantation of fecal microbiota from mice with co-exposure HFD and LDR with metabolic dysfunction resulted in increased disruption of metabolic dysfunction, insulin resistance and increased PYCR1 (Pyrroline-5-carboxylate reductase 1) expression. Mechanistically, intestinal barrier was damaged more serious in mice with co-exposure of HFD and LDR, leading high PA level in plasma, activating PYCR1 expression to inhibit insulin Akt/mTOR (AKT kinase-transforming protein/Serine threonine-protein kinase) signaling pathway to aggravate HFD-induced metabolic impairments. This study suggests a new avenue for interventions against western diet companied with low dose radiation exposure-driven metabolic impairments.

Novel Plasma Metabolomic Markers Associated with Diabetes Progression in Older Puerto Ricans

We assessed longitudinal associations between plasma metabolites, their network-derived clusters, and type 2 diabetes (T2D) progression in Puerto Rican adults, a high-risk Hispanic subgroup with established health disparities. We used data from 1221 participants free of T2D and aged 40-75 years at baseline in the Boston Puerto Rican Health and San Juan Overweight Adult Longitudinal Studies. We used multivariable Poisson regression models to examine associations between baseline concentrations of metabolites and incident T2D and prediabetes. Cohort-specific estimates were combined using inverse-variance weighted fixed-effects meta-analyses. A cluster of 13 metabolites of branched chain amino acids (BCAA), and aromatic amino acid metabolism (pooled IRR = 1.87, 95% CI: 1.28; 2.73), and a cell membrane component metabolite cluster (pooled IRR = 1.54, 95% CI: 1.04; 2.27) were associated with a higher risk of incident T2D. When the metabolites were tested individually, in combined analysis, 5 metabolites involved in BCAA metabolism were associated with incident T2D. These findings highlight potential prognostic biomarkers to identify Puerto Rican adults who may be at high risk for diabetes. Future studies should examine whether diet and lifestyle can modify the associations between these metabolites and progression to T2D.

Association Between Depression and Risk of Incident Cardiovascular Diseases and Its Sex and Age Modifications: A Prospective Cohort Study in Southwest China

To examine possible associations between depression and cardiovascular disease (CVD) incidence and whether demographic factors modified those associations in the Chinese population. This prospective cohort study comprised 7,735 adults aged 18 years or older in Guizhou, China from 2010 to 2020. The Patient Health Questionnaire-9 (PHQ-9) was used to measure the prevalence of depression. Cox proportional hazard models were used to estimated hazard ratios (HRs) and 95% confidence intervals (95%CIs) of depression and incident CVD. We identified 215 CVD cases (including 28 acute myocardial infarction (AMI) and 197 stroke cases) during an average follow-up of 7.07 years. In the multivariable-adjusted model, baseline PHQ-9 score was associated with incident CVD, AMI, and stroke. The HR per 1-SD increase for PHQ-9 score was 1.14 (95%CI: 1.03, 1.26) for CVD, 1.26 (95%CI: 1.01, 1.57) for AMI, and 1.12 (95%CI: 1.01, 1.25) for stroke. Compared with participants without depression, those with any mild or more advanced depression had a higher risk of incident CVD (HR: 1.69, 95%CI: 1.08, 2.64) and AMI (HR: 3.36, 95%CI: 1.17, 10.56). Associations between depression with CVD and stroke were suggested to be even stronger among women and participants aged <65 years (P for interaction <0.05). The effect of depression on stroke tended to be preserved in non-smokers. Depression was associated with a higher risk of incident CVD, AMI, and stroke in adults of Southwest, China, particularly in women, participants aged <65 years, and non-smokers. These findings highlighted the importance and urgency of depression healthcare.

Metabolomic Analysis of Serum and Tear Samples from Patients with Obesity and Type 2 Diabetes Mellitus

Metabolomics strategies are widely used to examine obesity and type 2 diabetes (T2D). Patients with obesity (n = 31) or T2D (n = 26) and sex- and age-matched controls (n = 28) were recruited, and serum and tear samples were collected. The concentration of 23 amino acids and 10 biogenic amines in serum and tear samples was analyzed. Statistical analysis and Pearson correlation analysis along with network analysis were carried out. Compared to controls, changes in the level of 6 analytes in the obese group and of 10 analytes in the T2D group were statistically significant. For obesity, the energy generation, while for T2D, the involvement of NO synthesis and its relation to insulin signaling and inflammation, were characteristic. We found that BCAA and glutamine metabolism, urea cycle, and beta-oxidation make up crucial parts of the metabolic changes in T2D. According to our data, the retromer-mediated retrograde transport, the ethanolamine metabolism, and, consequently, the endocannabinoid signaling and phospholipid metabolism were characteristic of both conditions and can be relevant pathways to understanding and treating insulin resistance. By providing potential therapeutic targets and new starting points for mechanistic studies, our results emphasize the importance of complex data analysis procedures to better understand the pathomechanism of obesity and diabetes.

Adiposity and Long-Term Adiposity Change Are Associated with Incident Diabetes: A Prospective Cohort Study in Southwest China

In order to estimate the associations of different adiposity indicators and long-term adiposity changes with risk of incident type 2 diabetes (T2DM), we conducted a 10-year prospective cohort study of 7441 adults in Guizhou, China, from 2010 to 2020. Adiposity was measured at baseline and follow-up. Cox proportional hazard models were used to estimated hazard ratios (HRs) and 95% confidence intervals (95% CIs). A total of 764 new diabetes cases were identified over an average follow-up of 7.06 years. Adiposity indicators, body mass index (BMI), waist circumference (WC), waist-height ratio (WHtR), and long-term adiposity changes (both weight change and WC change) were significantly associated with an increased risk of T2DM (adjusted HRs: 1.16–1.48). Significant non-linear relationships were found between weight/WC change and incident T2DM. Compared with subjects with stable WC from baseline to follow-up visit, the subjects with WC gain ≥9 cm had a 1.61-fold greater risk of T2DM; those with WC loss had a 30% lower risk. Furthermore, the associations were stronger among participants aged 40 years or older, women, and Han Chinese. Preventing weight or WC gain and promoting maintenance of normal body weight or WC are important approaches for diabetes prevention, especially for the elderly, women, and Han Chinese.