Nutrition & Metabolism最新文献

Background: Apoprotein A-I (ApoA-I) and Apoprotein B (ApoB) have emerged as novel cardiovascular risk biomarkers influenced by feeding behavior. Hypothalamic appetite peptides regulate feeding behavior and impact lipoprotein levels, which effects vary in different weight states. This study explores the intricate relationship between body mass index (BMI), hypothalamic appetite peptides, and apolipoproteins with emphasis on the moderating role of body weight in the association between neuropeptide Y (NPY), ghrelin, orexin A (OXA), oxytocin in cerebrospinal fluid (CSF) and peripheral ApoA-I and ApoB.

Methods: In this cross-sectional study, we included participants with a mean age of 31.77 ± 10.25 years, categorized into a normal weight (NW) (n = 73) and an overweight/obese (OW/OB) (n = 117) group based on BMI. NPY, ghrelin, OXA, and oxytocin levels in CSF were measured.

Results: In the NW group, peripheral ApoA-I levels were higher, while ApoB levels were lower than in the OW/OB group (all p < 0.05). CSF NPY exhibited a positive correlation with peripheral ApoA-I in the NW group (r = 0.39, p = 0.001). Notably, participants with higher CSF NPY levels had higher peripheral ApoA-I levels in the NW group and lower peripheral ApoA-I levels in the OW/OB group, showing the significant moderating effect of BMI on this association (R² = 0.144, β=-0.54, p < 0.001). The correlation between ghrelin, OXA and oxytocin in CSF and peripheral ApoB in both groups exhibited opposing trends (Ghrelin: r = -0.03 and r = 0.04; OXA: r = 0.23 and r=-0.01; Oxytocin: r=-0.09 and r = 0.04).

Conclusion: This study provides hitherto undocumented evidence that BMI moderates the relationship between CSF NPY and peripheral ApoA-I levels. It also reveals the protective role of NPY in the NW population, contrasting with its risk factor role in the OW/OB population, which was associated with the at-risk for cardiovascular disease.

Objective: This study was designed to evaluate the impact of VLCKD on cardiovascular risk factors in patients with T2DM.

Methods: Until March 2024, extensive searches were conducted on PubMed, Scopus, Web of Science, Embase, and other relevant databases. The purpose was to identify clinical trials examining the impact of VLCKD on glycemic control, lipid profile, and blood pressure. The GRADE (Grading of Recommendations Assessment, Development, and Evaluation) method was used to assess the evidence's degree of certainty.

Results: Our initial search found a total of 2568 records and finally 29 trials were included in final analysis. Our results showed that adherence from VLCKD led to significant reduction in fasting blood sugar (WMD= -11.68 mg/dl; 95% CI: -18.79, -4.56; P = 0.001), HbA1c (WMD= -0.29; 95% CI: -0.44, -0.14; P < 0.001), HOMA-IR(WMD= -0.71; 95% CI: -1.14, -0.29; P = 0.001), insulin (WMD= -1.45; 95% CI: -2.54, -0.36; P = 0.009), triglyceride (WMD= -17.95; 95% CI: -26.82, -9.07; P < 0.001), systolic blood pressure (WMD= -2.85, 95% CI: -4.99, -0.71; P = 0.009) and diastolic blood pressure (WMD= -1.40; 95% CI: -2.66, -0.13; P = 0.03). We also found a significant increase in high-density lipoprotein (HDL) level after adherence from VLCKD diet (WMD = 3.93, 95% CI: 2.03, 5.84; P = 0.000). We couldn't find any significant differences between groups in term of LDL and total cholesterol levels.

Conclusion: People following a VLCKD experience a more significant improvement in cardiovascular risk factors when compared to individuals on control diets.

Background: Natural compounds can positively impact health, and various studies suggest that they regulate glucose‒lipid metabolism by influencing short-chain fatty acids (SCFAs). This metabolism is key to maintaining energy balance and normal physiological functions in the body. This review explores how SCFAs regulate glucose and lipid metabolism and the natural compounds that can modulate these processes through SCFAs. This provides a healthier approach to treating glucose and lipid metabolism disorders in the future.

Methods: This article reviews relevant literature on SCFAs and glycolipid metabolism from PubMed and the Web of Science Core Collection (WoSCC). It also highlights a range of natural compounds, including polysaccharides, anthocyanins, quercetins, resveratrols, carotenoids, and betaines, that can regulate glycolipid metabolism through modulation of the SCFA pathway.

Results: Natural compounds enrich SCFA-producing bacteria, inhibit harmful bacteria, and regulate operational taxonomic unit (OTU) abundance and the intestinal transport rate in the gut microbiota to affect SCFA content in the intestine. However, most studies have been conducted in animals, lack clinical trials, and involve fewer natural compounds that target SCFAs. More research is needed to support the conclusions and to develop healthier interventions.

Conclusions: SCFAs are crucial for human health and are produced mainly by the gut microbiota via dietary fiber fermentation. Eating foods rich in natural compounds, including fruits, vegetables, tea, and coarse fiber foods, can hinder harmful intestinal bacterial growth and promote beneficial bacterial proliferation, thus increasing SCFA levels and regulating glucose and lipid metabolism. By investigating how these compounds impact glycolipid metabolism via the SCFA pathway, novel insights and directions for treating glucolipid metabolism disorders can be provided.

Background: Previous studies have reported a close association between the Geriatric Nutritional Risk Index (GNRI) and various conditions. However, the association between the GNRI and mortality remains unclear. To examine the correlation between the GNRI and all-cause, cancer-specific, and cardiovascular mortality, this study was performed.

Methods: We analyzed elderly participants in the National Health and Nutrition Examination Survey from 2005 to 2016. The GNRI was calculated using body mass index and serum albumin. Kaplan-Meier survival curves were drawn to compare the survival probability between the normal and decreased GNRI groups. Weighted multivariate Cox regression and restricted cubic spline (RCS) models were employed to determine the linear and non-linear associations of the GNRI with all-cause, cancer-specific, and cardiovascular mortality.

Results: A total of 3,276 participants were included in the analysis. The Kaplan-Meier survival curve showed that the decreased GNRI group had a lower survival probability for all-cause mortality and cancer-specific mortality (P < 0.001) but not for cardiovascular mortality (P > 0.05). In the full regression models, the decreased group had a higher risk of all-cause mortality (HR = 1.67, 95% CI = 1.21-2.30, P = 0.002), and cancer-specific mortality (HR = 2.20, 95% CI = 1.32-3.67, P = 0.003) than the normal group. For cardiovascular mortality, no significant association with GNRI (HR = 1.39, 95% CI = 0.60-3.22, P = 0.436) was detected. Notably, the RCS analysis identified a linear downward trend between the GNRI and all-cause, alongside cancer-specific mortalities (all P for overall < 0.05). The time-dependent Receiver Operating Characteristic (ROC) analysis unveiled the predictive power of the GNRI for 5-year all-cause mortality, cancer mortality, and cardiovascular mortality was 0.754, 0.757, and 0.836, respectively, after adjusting for covariates.

Conclusions: Individuals with a decreased GNRI had increased risks of all-cause, and cancer-specific mortality. There were linear associations of the GNRI with all-cause, and cancer-specific mortality. Nutritional status should be carefully monitored, which may improve the overall prognosis for the general population.

Background: Obesity is associated with alterations in the hypothalamic-pituitary-adrenal (HPA) axis. Effects of glucocorticoids on adipose tissues appear to depend on the specific adipose depot, in which they take place. In this study, we aimed to investigate the role of MRI-based adrenal gland volume as an imaging marker in association with different adipose tissue compartments.

Methods: The study cohort derives from the population-based research platform KORA (Cooperative Health Research in the Augsburg Region, Germany) MRI sub-study, a cross-sectional sub-study investigating the interactions between subclinical metabolic changes and cardiovascular disease in a study sample of 400 participants. Originally, eligible subjects underwent a whole-body MRI. MRI-based segmentations were performed manually and semi-automatically for adrenal gland volume, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), epi- and pericardial fat and renal sinus fat. Hepatic and pancreatic lipid content were measured as pancreatic proton density fraction (PDFF) and MR-spectroscopic hepatic fat fraction (HFF). Multivariable linear regression analyses were performed.

Results: A number of 307 participants (56.2 ± 9.1 years, 60.3% male, 14.3% with type 2 diabetes (T2DM), 30.6% with obesity, 34.2% with hypertension) were included. In multivariable analyses, strong positive associations between adrenal gland volume and VAT, total adipose tissue (TAT) as well as HFF persisted after extensive step-wise adjustment for possible metabolic confounders (VAT: beta = 0.31, 95%-CI [0.71, 0.81], p < 0.001; TAT: beta = 0.14, 95%-CI [0.06, 0.23], p < 0.001; HFF: beta = 1.17, 95%-CI [1.04, 1.31], p = 0.009). In contrast, associations between adrenal gland volume and SAT were attenuated in multivariate analysis after adjusting for BMI. Associations between pancreatic PDFF, epi- and pericardial fat and renal sinus fat were mediated to a great extent by VAT (pancreatic PDFF: 72%, epicardial adipose tissue: 100%, pericardial adipose tissue: 100%, renal sinus fat: 81.5%).

Conclusion: Our results found MRI-based adrenal gland volume as a possible imaging biomarker of unfavorable adipose tissue distribution, irrespective of metabolic risk factors. Thus, adrenal gland volume may serve as a potential MRI-based biomarker of metabolic changes and contributes to an individual characterization of metabolic states and individual risk stratification. Future studies should elucidate in a longitudinal study design, if and how HPA axis activation may trigger unfavorable adipose tissue distribution and whether and to which extent this is involved in the pathogenesis of manifest metabolic syndrome.

Background: Although several studies have found the relationship between essential elements and diabetes, the studies about the association of essential elements with diabetes diagnosed according to an oral glucose tolerance test (OGTT) and glycated hemoglobin (HbA1c) in a sex- and age-specific manner were limited. To investigate the linear and nonlinear relationship of five essential elements including iron (Fe), copper (Cu), Zinc (Zn), magnesium (Mg), and calcium (Ca) with diabetes, fasting plasma glucose (FPG), 2-h postprandial plasma glucose (PPG), and HbA1c and to evaluate the sex- and age-specific heterogeneities in these relationships.

Methods: A total of 8392 community-dwelling adults were recruited to complete a questionnaire and undergo checkups of anthropometric parameters and serum levels of five metals (Fe, Cu, Zn, Mg, and Ca). The multivariable logistic and linear regression, the restricted cubic spline (RCS) analysis, and subgroup analysis were applied to find the associations between the essential elements and the prevalence of diabetes as well as FPG, PPG, and HbA1c.

Results: In the multivariable logistic regression and multivariable linear regression, serum Cu was positively associated with FPG, PPG, and HbA1c while serum Mg was significantly inversely correlated with FPG, PPG, HbA1c, and diabetes (all P < 0.001). In the RCS analysis, the non-linear relationship of Cu and diabetes (P < 0.001) was found. In the subgroup analysis, stronger positive associations of Cu with diabetes (P for interaction = 0.027) and PPG (P for interaction = 0.002) were found in younger women.

Conclusions: These findings may lead to more appropriate approaches to essential elements supplementation in people with diabetes of different ages and sexes. However, more prospective cohort and experimental studies are needed to probe the possible mechanism of sex- and age-specific associations between serum essential elements and diabetes.

Background: Previous studies have linked sports-related concussions and repeated subconcussive head impacts in contact sport athletes to elevated brain injury biomarkers. Docosahexaenoic acid (DHA), the primary omega-3 (n-3) highly unsaturated fatty acid (HUFA) in the brain, has shown neuroprotective effects in animal models after brain injury, but clinical research has shown mixed results.

Methods: We conducted a randomized, double-blind, placebo-controlled study on 29 Division 1 collegiate American football players, exploring the impact of DHA (2.5 g) and eicosapentaenoic acid (EPA) (1.0 g) supplied as ethyl esters, on levels of plasma lipids shown to cross the blood-brain barrier. Dietary intake data was collected using food frequency questionnaires (FFQ). Complex lipids and unesterified fatty acids were isolated from plasma, separated via reversed-phase liquid chromatography and analyzed by targeted lipidomics analysis.

Results: FFQ results indicated that participants had low dietary n-3 HUFA intake and high omega-6 (n-6):n-3 polyunsaturated fatty acids (PUFA) and HUFA ratios at baseline. After DHA + EPA supplementation, plasma lysophosphatidylcholine (LPC) containing DHA and EPA significantly increased at all timepoints (weeks 17, 21, and 26; p < 0.0001), surpassing placebo at Weeks 17 (p < 0.05) and 21 (p < 0.05). Phosphatidylcholine (PC) molecular species containing DHA or EPA, PC38:6 PC36:6, PC38:7, PC40:6, and PC40:8, increased significantly in the DHA + EPA treatment group at Weeks 17 (and 21. Plasma concentrations of non-esterified DHA and EPA rose post-supplementation in Weeks 17 and 21.

Conclusions: This study demonstrates that n-3 HUFA supplementation, in the form of ethyl esters, increased the DHA and EPA containing plasma lipid pools the have the capacity to enrich brain lipids and the potential to mitigate the effects of sports-related concussions and repeated subconcussive head impacts.

Trial registration: All deidentified data are available at ClinicalTrials.gov #NCT0479207.