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Current screening algorithms for type 2 diabetes (T2D) rely on fasting plasma glucose (FPG) and/or HbA1c. This fails to identify a sizeable subgroup of individuals in early stages of metabolic dysregulation who are at high risk for developing diabetes or cardiovascular disease. The Matsuda index, a combination of parameters derived from a fasting and postprandial insulin assay, is an early biomarker for metabolic dysregulation (i.e. insulin resistance/compensatory hyperinsulinemia). The aim of this analysis was to compare four widely available anthropometric and biochemical markers indicative of this condition [waist-to-height ratio (WHtR), hypertriglyceridemic-waist phenotype (HTW), triglycerides-to-HDL-C ratio (TG/HDL-C) and FPG] to the Matsuda index. This cross-sectional analysis included 2231 individuals with normal fasting glucose (NFG, n = 1333), impaired fasting glucose (IFG, n = 599) and T2D (n = 299) from an outpatient diabetes clinic in Germany and thus extended a prior analysis from our group done on the first two subgroups. We analyzed correlations of the Matsuda index with WHtR, HTW, TG/HDL-C and FPG and their predictive accuracies by correlation and logistic regression analyses and receiver operating characteristics. In the entire group and in NFG, IFG and T2D, the best associations were observed between the Matsuda index and the WHtR (r = - 0.458), followed by HTW phenotype (r = - 0.438). As for prediction accuracy, WHtR was superior to HTW, TG/HDL-C and FPG in the entire group (AUC 0.801) and NFG, IFG and T2D. A multivariable risk score for the prediction of insulin resistance was tested and demonstrated an area under the ROC curve of 0.765 for WHtR and its interaction with sex as predictor controlled by age and sex. The predictive power increased to 0.845 when FPG and TG/HDL-C were included. Using as a comparator the Matsuda index, WHtR, compared to HTW, TG/HDL-C and FPG, showed the best predictive value for detecting metabolic dysregulation. We conclude that WHtR, a widely available anthropometric index, could refine phenotypic screening for insulin resistance/hyperinsulinemia. This may ameliorate early identification of individuals who are candidates for appropriate therapeutic interventions aimed at addressing the twin epidemic of metabolic and cardiovascular disease in settings where more extended testing such as insulin assays are not feasible.

Background: Moderate intensity continuous training (MICT) ameliorates dysmetabolism in patients with metabolic syndrome (MetS). The impact of low- (1HIIT) versus high-volume high-intensity interval training (4HIIT) versus MICT on central adiposity, insulin resistance, and atherogenic dyslipidemia in patients with MetS has not yet been reported. Methods: Twenty-nine patients with MetS according to International Diabetes Federation criteria (nine females, age 61 ± 5 years, body mass index 31.1 ± 3.7 kg/m 2 , waist circumference (WC) ♀ 102.2 ± 10.6 cm, ♂ 108.5 ± 8.6 cm) were randomized (1:1:1) to 16 weeks of (1) MICT (5 × 30 min/week, 35%-50% heart rate reserve (HRR), (2) 1HIIT (3 × 17 min/week incl. 4 min @80%-90% HRR), and (3) 4HIIT (3 × 38 min/week incl. 4 × 4 min @80%-90% HRR). Peak oxygen uptake ( V ̇ O 2peak ), WC and anthropometric/metabolic indices indicative of MetS, fasting glucose/insulin, Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), dyslipidemia, and respiratory exchange ratio (RER) at warm-up were quantified at baseline and study completion. Analysis of variance and paired t tests were used for statistical analysis. Analyses were performed after checking for parametric distribution. Results: There were no significant differences between groups in waist-to-height ratio (♀: Δ -0.10 ± -0.05, ♂: Δ -0.08 ± -0.06, P  = 0.916), WC (♀: Δ -1.4 ± -0.1 cm, ♂: Δ 0.1 ± 0.9 cm, P  = 0.590), fasting glucose (Δ -1.18 ± 16.7 μU/mL, P  = 0.773), fasting insulin (Δ 0.76 ± 13.4 μU/mL, P  = 0.509), HOMA-IR (Δ 0.55 ± 4.1, P  = 0.158), atherogenic dyslipidemia [triglycerides (TAG) Δ -10.1 ± 46.9 mg/dL, P  = 0.468, high-density lipoprotein cholesterol (HDL-C) Δ 1.5 ± 5.4, P  = 0.665, TAG/HDL-C -0.19 ± 1.3, P  = 0.502], V ̇ O 2peak ( P  = 0.999), or RER ( P  = 0.842). In the entire group, waist-to-height-ratio and V ̇ O 2peak significantly improved by a clinically meaningful amount (Δ 2.7 ± 0.9 mL/min/kg; P  < 0.001) and RER at warm-up significantly decreased (Δ -0.03 ± 0.06, P  = 0.039). Conclusion: In patients with MetS, there was no significant difference between HIIT, irrespective of volume, to MICT for improving exercise capacity or metabolic health.

Regular walnut consumption is associated with better health. We have previously shown that eight weeks of walnut consumption (43 g/day) significantly improves lipids in healthy subjects. In the same study, gut microbiome was evaluated. We included 194 healthy subjects (134 females, 63 ± 7 years, BMI 25.1 ± 4.0 kg/m²) in a randomized, controlled, prospective, cross-over study. Following a nut-free run-in period, subjects were randomized to two diet phases (eight weeks each); 96 subjects first followed a walnut-enriched diet (43 g/day) and then switched to a nut-free diet, while 98 subjects followed the diets in reverse order. While consuming the walnut-enriched diet, subjects were advised to either reduce fat or carbohydrates or both to account for the additional calories. Fecal samples were collected from 135 subjects at the end of the walnut-diet and the control-diet period for microbiome analyses. The 16S rRNA gene sequencing data was clustered with a 97% similarity into Operational Taxonomic Units (OTUs). UniFrac distances were used to determine diversity between groups. Differential abundance was evaluated using the Kruskal-Wallis rank sum test. All analyses were performed using Rhea. Generalized UniFrac distance shows that walnut consumption significantly affects microbiome composition and diversity. Multidimensional scaling (metric and non-metric) indicates dissimilarities of approximately 5% between walnut and control ( p = 0.02). The abundance of Ruminococcaceae and Bifidobacteria increased significantly ( p < 0.02) while Clostridium sp. cluster XIVa species ( Blautia ; Anaerostipes ) decreased significantly ( p < 0.05) during walnut consumption. The effect of walnut consumption on the microbiome only marginally depended on whether subjects replaced fat, carbohydrates or both while on walnuts. Daily intake of 43 g walnuts over eight weeks significantly affects the gut microbiome by enhancing probiotic- and butyric acid-producing species in healthy individuals. Further evaluation is required to establish whether these changes are preserved during longer walnut consumption and how these are linked to the observed changes in lipid metabolism., Competing Interests: C.B., A.R., K.L., L.W., E.W., S.F., J.A. and K.H. have nothing to disclose. The research was supported by a grant from the California Walnut Commission (Folsom, CA, USA) to KGP. The Walnut Commission had no role in study performance, data analysis and or manuscript writing.

Studies indicate a positive association between walnut intake and improvements in plasma lipids. We evaluated the effect of an isocaloric replacement of macronutrients with walnuts and the time point of consumption on plasma lipids. We included 194 healthy subjects (134 females, age 63 ± 7 years, BMI 25.1 ± 4.0 kg/m²) in a randomized, controlled, prospective, cross-over study. Following a nut-free run-in period, subjects were randomized to two diet phases (8 weeks each). Ninety-six subjects first followed a walnut-enriched diet (43 g walnuts/day) and then switched to a nut-free diet. Ninety-eight subjects followed the diets in reverse order. Subjects were also randomized to either reduce carbohydrates ( n = 62), fat ( n = 65), or both ( n = 67) during the walnut diet, and instructed to consume walnuts either as a meal or as a snack. The walnut diet resulted in a significant reduction in fasting cholesterol (walnut vs., Control: -8.5 ± 37.2 vs. -1.1 ± 35.4 mg/dL; p = 0.002), non-HDL cholesterol (-10.3 ± 35.5 vs. -1.4 ± 33.1 mg/dL; p ≤ 0.001), LDL-cholesterol (-7.4 ± 32.4 vs. -1.7 ± 29.7 mg/dL; p = 0.029), triglycerides (-5.0 ± 47.5 vs. 3.7 ± 48.5 mg/dL; p = 0.015) and apoB (-6.7 ± 22.4 vs. -0.5 ± 37.7; p ≤ 0.001), while HDL-cholesterol and lipoprotein (a) did not change significantly. Neither macronutrient replacement nor time point of consumption significantly affected the effect of walnuts on lipids. Thus, 43 g walnuts/d improved the lipid profile independent of the recommended macronutrient replacement and the time point of consumption., Competing Interests: C.B., A.R., K.L., L.W., E.W., R.S., J.A. and K.H. declare no conflict of interest. The research was supported by a grant from the California Walnut Commission (Folsom, CA) to K.G.P.. The Walnut Commission had no role in study performance, data analysis and or manuscript writing.