Objectives: Accumulating epidemiological and intervention evidence suggest that nut consumption is associated with reduced incidence of some cardiometabolic diseases. However, to date no review of meta-analyses of epidemiological and intervention studies has evaluated the effects of nut consumption on cardiometabolic disease. Design/Results: Electronic searches for meta-analyses of epidemiological and intervention studies were undertaken in PubMed^®/MEDLINE^®. Meta-analyses of prospective studies show that nut consumption appears to be associated with reduced all-cause mortality by 19–20% (n = 6), cardiovascular disease (CVD) incidence (19%; n = 3) and mortality (25%; n = 3), coronary heart disease (CHD) incidence (20–34%; n = 2) and mortality (27–30%; n = 2) and stroke incidence (10–11%; n = 7) and mortality (18%; n = 2). No association between nut consumption and the risk of type 2 diabetes mellitus (T2DM) was observed in meta-analyses of prospective studies, whereas a decrease in fasting blood glucose ranging from 0.08 to 0.15 mmol/L was observed in 3 meta-analyses of intervention studies. In the interventions, nut consumption also had favorable effects on total cholesterol (0.021 to 0.28 mmol/L reduction from 8 meta-analyses of interventions) and low-density lipoprotein cholesterol (0.017 to 0.26 mmol/L reduction from 8 meta-analyses of interventions) and endothelial function (0.79 to 1.03% increase in flow-mediated dilation from 4 meta-analyses of interventions). Nut consumption did not significantly affect body weight. Nut consumption had no effect on inflammatory markers in intervention studies. The effect on blood pressure was inconsistent. A higher nut consumption was associated with a lower incidence of hypertension in prospective studies, while nut consumption did not improve blood pressure in intervention studies. Conclusions: Nut consumption appeared to be associated with lower all-cause mortality and CVD and CHD mortality. There was no association between nut consumption and the incidence of T2DM although fasting blood glucose is decreased in intervention studies. In intervention studies nuts lower total cholesterol and low-density lipoprotein cholesterol (LDL-C).

Current research indicates curcumin [diferuloylmethane; a polyphenolic compound isolated from the rhizomes of the dietary spice turmeric (Curcuma longa)] exerts a beneficial effect on health which may be partly attributable to its anti-oxidative and anti-inflammatory properties. The aim of this review is to examine potential mechanisms of the actions of curcumin in both animal and human studies. Curcumin modulates relevant molecular target pathways to improve glucose and lipid metabolism, suppress inflammation, stimulate antioxidant enzymes, facilitate insulin signalling and reduce gut permeability. Curcumin also inhibits Aβ and tau accumulation in animal models and enhances mitochondria and synaptic function. In conclusion, in high-dose animal studies and in vitro, curcumin exerts a potential beneficial effect on cardiometabolic disease. However, human studies are relatively unconvincing. More intervention studies should be conducted with the new curcumin formulation with improved oral bioavailability.

Few studies have been conducted to classify and predict the influence of nutritional intake on overweight/obesity, dyslipidemia, hypertension and type 2 diabetes mellitus (T2DM) based on deep learning such as deep neural network (DNN). The present study aims to classify and predict associations between nutritional intake and risk of overweight/obesity, dyslipidemia, hypertension and T2DM by developing a DNN model, and to compare a DNN model with the most popular machine learning models such as logistic regression and decision tree. Subjects aged from 40 to 69 years in the 4–7th (from 2007 through 2018) Korea National Health and Nutrition Examination Survey (KNHANES) were included. Diagnostic criteria of dyslipidemia (n = 10,731), hypertension (n = 10,991), T2DM (n = 3889) and overweight/obesity (n = 10,980) were set as dependent variables. Nutritional intakes were set as independent variables. A DNN model comprising one input layer with 7 nodes, three hidden layers with 30 nodes, 12 nodes, 8 nodes in each layer and one output layer with one node were implemented in Python programming language using Keras with tensorflow backend. In DNN, binary cross-entropy loss function for binary classification was used with Adam optimizer. For avoiding overfitting, dropout was applied to each hidden layer. Structural equation modelling (SEM) was also performed to simultaneously estimate multivariate causal association between nutritional intake and overweight/obesity, dyslipidemia, hypertension and T2DM. The DNN model showed the higher prediction accuracy with 0.58654 for dyslipidemia, 0.79958 for hypertension, 0.80896 for T2DM and 0.62496 for overweight/obesity compared with two other machine leaning models with five-folds cross-validation. Prediction accuracy for dyslipidemia, hypertension, T2DM and overweight/obesity were 0.58448, 0.79929, 0.80818 and 0.62486, respectively, when analyzed by a logistic regression, also were 0.52148, 0.66773, 0.71587 and 0.54026, respectively, when analyzed by a decision tree. This study observed a DNN model with three hidden layers with 30 nodes, 12 nodes, 8 nodes in each layer had better prediction accuracy than two conventional machine learning models of a logistic regression and decision tree.

Dietary advanced glycation end products (AGEs) are believed to contribute to pathogenesis of diabetes and cardiovascular disease. The objective of this study was to determine if a diet high in red and processed meat and refined grains (HMD) would elevate plasma concentrations of protein-bound AGEs compared with an energy-matched diet high in whole grain, dairy, nuts and legumes (HWD). We conducted a randomized crossover trial with two 4-week weight-stable dietary interventions in 51 participants without type 2 diabetes (15 men and 36 women aged 35.1 ± 15.6 y; body mass index (BMI), 27.7 ± 6.9 kg/m²). Plasma concentrations of protein-bound Nε-(carboxymethyl) lysine (CML), Nε-(1-carboxyethyl) lysine (CEL) and Nδ-(5-hydro-5-methyl-4-imidazolon-2-yl)-ornithine (MG-H1) were measured by liquid chromatography–tandem mass spectrometry (LC-MS/MS). The HMD significantly increased plasma concentrations (nmol/mL) of CEL (1.367, 0.78 vs. 1.096, 0.65; p < 0.01; n = 48) compared with the HWD. No differences in CML and MG-H1 between HMD and HWD were observed. HMD increased plasma CEL concentrations compared with HWD in individuals without type 2 diabetes.

Aim We aimed to determine if nut consumption decreases mortality and/or the risk of cardiometabolic diseases based on updated meta-analyses of epidemiological and intervention studies. Methods. An updated electronic search was conducted in PubMed/MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and the Cochrane Library databases for original meta-analyses to investigate the effects of nut consumption on cardiometabolic disease in humans. Results. Seven new meta-analyses were included in this updated review. Findings similar to our previous review were observed, showing that nut consumption significantly decreased cardiovascular disease (CVD) mortality (−19% to −25%; n = 4), coronary heart disease (CHD) mortality (−24% to −30%; n = 3), stroke mortality (−17% to −18%; n = 3), CVD incidence (−15% to −19 %; n = 4), CHD [or coronary artery disease (CAD)] incidence (−17% to −34%; n = 8), and stroke incidence (−10% to −11%; n = 6) comparing high with low categories of nut consumption. Fasting glucose levels (0.08 to 0.15 mmol/L; n = 6), total cholesterol (TC; 0.021 to 0.30 mmol/L; n = 10), and low-density lipoprotein cholesterol (LDL-C; 0.017 to 0.26 mmol/L; n = 10) were significantly decreased with nut consumption compared with control diets. Body weight and blood pressure were not significantly affected by nut consumption. Conclusion. Nut consumption appears to exert a protective effect on cardiometabolic disease, possibly through improved concentrations of fasting glucose, total cholesterol, and LDL-C.

This review aimed to examine the effects of curcumin on chronic inflammatory metabolic disease by extensively evaluating meta-analyses of randomized controlled trials (RCTs). We performed a literature search of meta-analyses of RCTs published in English in PubMed^®/MEDLINE up to 31 July 2023. We identified 54 meta-analyses of curcumin RCTs for inflammation, antioxidant, glucose control, lipids, anthropometric parameters, blood pressure, endothelial function, depression, and cognitive function. A reduction in C-reactive protein (CRP) levels was observed in seven of ten meta-analyses of RCTs. In five of eight meta-analyses, curcumin intake significantly lowered interleukin 6 (IL-6) levels. In six of nine meta-analyses, curcumin intake significantly lowered tumor necrosis factor α (TNF-α) levels. In five of six meta-analyses, curcumin intake significantly lowered malondialdehyde (MDA) levels. In 14 of 15 meta-analyses, curcumin intake significantly reduced fasting blood glucose (FBG) levels. In 12 of 12 meta-analyses, curcumin intake significantly reduced homeostasis model assessment of insulin resistance (HOMA-IR). In seven of eight meta-analyses, curcumin intake significantly reduced glycated hemoglobin (HbA1c) levels. In eight of ten meta-analyses, curcumin intake significantly reduced insulin levels. In 14 of 19 meta-analyses, curcumin intake significantly reduced total cholesterol (TC) levels. Curcumin intake plays a protective effect on chronic inflammatory metabolic disease, possibly via improved levels of glucose homeostasis, MDA, TC, and inflammation (CRP, IL-6, TNF-α, and adiponectin). The safety and efficacy of curcumin as a natural product support the potential for the prevention and treatment of chronic inflammatory metabolic diseases.