Nuts as a Part of Dietary Strategy to Improve Metabolic Biomarkers: A Narrative Review.

Background: Nuts are in the spotlight because of their association with improved health outcomes. We aimed to summarize the findings of previous studies to evaluate the impact of nuts consumption on glycaemic and lipid profile, inflammation, and oxidative stress. Methods: Electronic searches for observational and intervention studies were undertaken in PubMed, Embase, Web of Science, and Science Direct until 2022 for searching the studies aiming the application of different types of nuts and the beneficial effects of nuts in improving glycemia, dyslipidemia, inflammation, and oxidative stress. Results: Results from 56 interventional, 9 narrative and 3 systematic reviews, and 12 meta-analysis studies, aiming at the evaluating beneficial effects of different types of nuts on metabolic markers, showed that nut consumption could improve metabolic markers, including glycaemic factors, lipid profile, and inflammatory and oxidative stress parameters in both healthy and individuals with metabolic disorders in a type-, dose- and duration-dependent manner. According to their unique nutrient components, nuts can be known as a part of a healthy diet, resulting in improved metabolic biomarkers. Conclusion: Considering the efficacy of nuts in improving metabolic markers, incorporation of, incorporating nuts the effectiveness of nuts in improving metabolic markers, incorporating nuts in the diet may prevent the incidence or aggravation of chronic metabolic diseases. Considering the health benefits of the nuts' components, including essential micronutrients, if consumed in the appropriate dose and duration to provide the necessary amount of effective micronutrients to improve health, we will see an improvement in metabolic factors. At the same time, more research is required to determine the optimal type, dose, and duration of nut intervention with regards to metabolic control and reducing the risk of developing metabolic disorders.

Predictive Validity of Self-Reported Measures of Adherence to Noninsulin Antidiabetes Medication against Control of Glycated Hemoglobin Levels.

OBJECTIVE: To assess and compare the predictive validity of 4 self-reported adherence measures. METHODS: A convenience sample of 153 patients with type 2 diabetes completed a self-report with 4 items (SR-4) and a French version of the Morisky Medication Adherence Scale with 8 items (MMAS-8), reported the proportion of pills missed, and answered a single-item scale regarding their antidiabetes drug treatments. They also provided measures of glycated hemoglobin (A1C) taken between 3 and 6 months after the adherence measurements. We examined the relationship between self-reported adherence and glycemic control using the area under the receiver operating characteristics curve (AUC) and linear regression analyses. RESULTS: AUCs were 0.51, 0.52, 0.53 and 0.52 for the SR-4, MMAS-8, self-reported proportion of pills missed and single-item scale, respectively. AUCs stratified according to median duration of diabetes ranged from 0.55 to 0.63. Based on linear regression analyses adjusted for diabetes duration, the association measured in the total sample between adherence measures and A1C levels was not statistically significant. When regression analyses were performed among participants with A1C levels ≥7% only, SR-4, MMAS-8 and the single-item scale scores were significantly associated with A1C levels, and beta coefficients were associated with a 1-unit increase in adherence scores of -0.46, -0.20 and 0.38, respectively. CONCLUSION: The results support the predictive validity of all measures except the self-reported proportion of missed pills.