Methods: A systematic review was done to study the effects of naringin on the metabolic diseases using electronic databases which include Ovid and Scopus using specific descriptors published from the year 2010 till present to provide updated literature on this field. The articles were assessed and chosen based on the criteria in which the mechanisms and effects of naringin on different metabolic diseases were reported.
Results: Thirty-four articles were identified which referred to the studies that correspond to the previously stated criteria. Subsequently after screening for the articles that were published after the year 2010, finally, 19 articles were selected and assessed accordingly. Based on the assessment, naringin could alleviate MetS by reducing visceral obesity, blood glucose, blood pressure, and lipid profile and regulating cytokines.
Conclusions: Naringin is an antioxidant that appears to be efficacious in alleviating MetS by preventing oxidative damage and proinflammatory cytokine release. However, the dosage used in animal studies might not be achieved in human trials. Thus, adequate investigation needs to be conducted to confirm naringin's effects on humans.
OBJECTIVES: To assess the effects of low glycaemic index or low glycaemic load diets on weight loss in people with overweight or obesity.
SEARCH METHODS: We searched CENTRAL, MEDLINE, one other database, and two clinical trials registers from their inception to 25 May 2022. We did not apply any language restrictions.
SELECTION CRITERIA: We included RCTs with a minimum duration of eight weeks comparing low GI/GL diets to higher GI/GL diets or any other diets in people with overweight or obesity.
DATA COLLECTION AND ANALYSIS: We used standard Cochrane methods. We conducted two main comparisons: low GI/GL diets versus higher GI/GL diets and low GI/GL diets versus any other diet. Our main outcomes included change in body weight and body mass index, adverse events, health-related quality of life, and mortality. We used GRADE to assess the certainty of the evidence for each outcome.
MAIN RESULTS: In this updated review, we included 10 studies (1210 participants); nine were newly-identified studies. We included only one study from the previous version of this review, following a revision of inclusion criteria. We listed five studies as 'awaiting classification' and one study as 'ongoing'. Of the 10 included studies, seven compared low GI/GL diets (233 participants) with higher GI/GL diets (222 participants) and three studies compared low GI/GL diets (379 participants) with any other diet (376 participants). One study included children (50 participants); one study included adults aged over 65 years (24 participants); the remaining studies included adults (1136 participants). The duration of the interventions varied from eight weeks to 18 months. All trials had an unclear or high risk of bias across several domains. Low GI/GL diets versus higher GI/GL diets Low GI/GL diets probably result in little to no difference in change in body weight compared to higher GI/GL diets (mean difference (MD) -0.82 kg, 95% confidence interval (CI) -1.92 to 0.28; I2 = 52%; 7 studies, 403 participants; moderate-certainty evidence). Evidence from four studies reporting change in body mass index (BMI) indicated low GI/GL diets may result in little to no difference in change in BMI compared to higher GI/GL diets (MD -0.45 kg/m2, 95% CI -1.02 to 0.12; I2 = 22%; 186 participants; low-certainty evidence)at the end of the study periods. One study assessing participants' mood indicated that low GI/GL diets may improve mood compared to higher GI/GL diets, but the evidence is very uncertain (MD -3.5, 95% CI -9.33 to 2.33; 42 participants; very low-certainty evidence). Two studies assessing adverse events did not report any adverse events; we judged this outcome to have very low-certainty evidence. No studies reported on all-cause mortality. For the secondary outcomes, low GI/GL diets may result in little to no difference in fat mass compared to higher GI/GL diets (MD -0.86 kg, 95% CI -1.52 to -0.20; I2 = 6%; 6 studies, 295 participants; low certainty-evidence). Similarly, low GI/GL diets may result in little to no difference in fasting blood glucose level compared to higher GI/GL diets (MD 0.12 mmol/L, 95% CI 0.03 to 0.21; I2 = 0%; 6 studies, 344 participants; low-certainty evidence). Low GI/GL diets versus any other diet Low GI/GL diets probably result in little to no difference in change in body weight compared to other diets (MD -1.24 kg, 95% CI -2.82 to 0.34; I2 = 70%; 3 studies, 723 participants; moderate-certainty evidence). The evidence suggests that low GI/GL diets probably result in little to no difference in change in BMI compared to other diets (MD -0.30 kg in favour of low GI/GL diets, 95% CI -0.59 to -0.01; I2 = 0%; 2 studies, 650 participants; moderate-certainty evidence). Two adverse events were reported in one study: one was not related to the intervention, and the other, an eating disorder, may have been related to the intervention. Another study reported 11 adverse events, including hypoglycaemia following an oral glucose tolerance test. The same study reported seven serious adverse events, including kidney stones and diverticulitis. We judged this outcome to have low-certainty evidence. No studies reported on health-related quality of life or all-cause mortality. For the secondary outcomes, none of the studies reported on fat mass. Low GI/GL diets probably do not reduce fasting blood glucose level compared to other diets (MD 0.03 mmol/L, 95% CI -0.05 to 0.12; I2 = 0%; 3 studies, 732 participants; moderate-certainty evidence). AUTHORS' CONCLUSIONS: The current evidence indicates there may be little to no difference for all main outcomes between low GI/GL diets versus higher GI/GL diets or any other diet. There is insufficient information to draw firm conclusions about the effect of low GI/GL diets on people with overweight or obesity. Most studies had a small sample size, with only a few participants in each comparison group. We rated the certainty of the evidence as moderate to very low. More well-designed and adequately-powered studies are needed. They should follow a standardised intervention protocol, adopt objective outcome measurement since blinding may be difficult to achieve, and make efforts to minimise loss to follow-up. Furthermore, studies in people from a wide range of ethnicities and with a wide range of dietary habits, as well as studies in low- and middle-income countries, are needed.
AIM: To investigate the effects of food order on postprandial glucose (PPG) excursion, in Indian adults with normal (NL) and overweight/obese (OW) Body Mass Index.
METHODS: This randomised crossover study was conducted at a Malaysian university among Indian adults without diabetes. The participants consumed isocaloric test meals at three study visits based on randomised food orders: carbohydrate first/protein last (CF); protein first/carbohydrate last (CL); and a composite meal containing carbohydrate and protein (CM). Capillary blood glucose was measured at baseline, 30, 60, 90 and 120 minutes after starting the meal.
RESULTS: The CL food order had a blunting effect on PPG excursion at 30 and 60 minutes (p < 0.01). The CL food order resulted in lower glucose peak when compared with the CF and CM food order (p < 0.001). The CL food order resulted in lower incremental glucose peak (mmol/L) (NL: CF 3.9 ± 0.3, CM 3.0 ± 0.3, CL 2.0 ± 0.2; OW: CF 2.9 ± 0.3, CM 2.5 ± 0.3, CL 1.8 ± 0.2) and iAUC 0-120 min (mmol/Lxmin) (NL: CF 272.4 ± 26.7, CM 206.2 ± 30.3, CL 122.0 ± 14.8; OW: CF 193.2 ± 23.1, CM 160.1 ± 21.7, CL 113.6 ± 15.3) when compared with the CF food order (p < 0.001). The effect of food order on postprandial excursion did not differ between the NL (n = 14) and the OW (n = 17) groups.
CONCLUSION: In participants with normal and overweight/obese BMI, consuming food in the protein first/carbohydrate last order had the biggest effect in reducing PPG excursion.
Methods: This cross-sectional study involved 227 adults aged 40 to 59 years at low-cost housing flats in suburban area of Cheras, Kuala Lumpur. Data collection involved food frequency questionnaire (FFQ) for polyphenols and international physical activity questionnaire (IPAQ). Subjects were measured for anthropometric parameters including height, weight, waist and neck circumferences (NC), and body fat percentage. The polyphenol intake from the diet was estimated using local polyphenol database built according to PHENOL-EXPLORER.
Results: The average intake of polyphenol of subjects was 1815 (672) mg/day. The main food sources of polyphenol were coffee with milk, followed by chocolate milk and red beans. A higher polyphenol intake according to quartile was significantly associated with a lower neck circumference (χ2 = 8.30, P = 0.040), waist circumference (χ2 = 8.45, P = 0.038) and body fat percentage (χ2 = 8.06, P = 0.045). Binomial logistic regression analysis showed that the association remained significant for the neck circumference (P = 0.032), after controlling for age, household income, energy intake and physical activity level. More subjects with normal NC had higher intake of polyphenols (50th percentile and above). In contrast, subjects with high NC showed lower percentiles of polyphenols intake (50th percentile and below).
Conclusion: The result showed that polyphenol intake was associated with neck circumference and thus it can be suggested that polyphenol intake is associated with obesity.
METHODS: We studied the health of 636 OA from seven sub-tribes in the Peninsular. Parameters that were assessed included height, weight, BMI and waist circumference whilst blood pressure, cholesterols, fasting blood glucose and HbA1c levels were recorded. We then analysed cardio-metabolic risk factor prevalences and performed multiple pair-wise comparisons among different sub-tribes and socio-economic clusters.
RESULTS: Cardio-metabolic risk factors were recorded in the seven sub-tribes.. Prevalence for general and abdominal obesity were highest in the urbanized Orang Seletar (31 · 6 ± 5 · 7%; 66 · 1 ± 5 · 9%). Notably, hunter gatherer Jehai and Batek tribes displayed the highest prevalence for hypertension (43 · 8 ± 9 · 29% and 51 · 2 ± 15 · 3%) despite being the leanest and most remote, while the Mendriq sub-tribe, living in the same jungle area with access to similar resources as the Batek were less hypertensive (16.3 ± 11.0%), but displayed higher prevalence of abdominal obesity (27.30 ± 13.16%).
CONCLUSIONS: We describe the cardio-metabolic risk factors of seven indigenous communities in Malaysia. We report variable prevalence of obesity, cholesterol, hypertension and diabetes in the OA in contrast to the larger ethnic majorities such as Malays, Chinese and Indians in Malaysia These differences are likely to be due to socio-economic effects and lifestyle changes. In some sub-tribes, other factors including genetic predisposition may also play a role. It is expected that the cardio-metabolic risk factors may worsen with further urbanization, increase the health burden of these communities and strain the government's resources.