996 resultados para Glycemic index


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Background: Habitual consumption of diets with a high glycemic index (GI) and a high glycemic load (GL) may influence cancer risk via hyperinsulinemia and the insulin-like growth factor axis.
Objective: The objective was to conduct a systematic review to assess the association between GI, GL, and risk of digestive tract cancers.
Design: Medline and Embase were searched for relevant publications from inception to July 2008. When possible, adjusted results from a comparison of cancer risk of the highest compared with the lowest category of GI and GL intake were combined by using random-effects meta-analyses.
Results: Cohort and case-control studies that examined the risk between GI or GL intake and colorectal cancer (n = 12) and adenomas (n = 2), pancreatic cancer (n = 6), gastric cancer (n = 2), and squamous-cell esophageal carcinoma (n = 1) were retrieved. Most case-control studies observed positive associations between GI and GL intake and these cancers. However, pooled cohort study results showed no associations between colorectal cancer risk and GI intake [relative risk (RR): 1.04; 95% CI: 0.92, 1.12; n = 7 studies] or GL intake (RR: 1.06; 95% CI: 0.95, 1.17; n = 8 studies). Furthermore, no significant associations were observed in meta-analyses of cohort study results of colorectal cancer subsites and GI and GL intake. Similarly, no significant associations emerged between pancreatic cancer risk and GI intake (RR: 0.99; 95% CI: 0.83, 1.19; n = 5 studies) or GL intake (RR: 1.01; 95% CI: 0.86, 1.19; n = 6 studies) in combined cohort studies.
Conclusions: The findings from our meta-analyses indicate that GI and GL intakes are not associated with risk of colorectal or pancreatic cancers. There were insufficient data available regarding other digestive tract cancers to make any conclusions about GI or GL intake and risk.

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Objective: To examine the association between dietary glycemic index (GI), glycemic load (GL), total carbohydrate, sugars, starch, and fiber intakes and the risk of reflux esophagitis, Barrett’s esophagus, and esophageal adenocarcinoma.

Methods: In an all-Ireland study, dietary information was collected from patients with esophageal adenocarcinoma (n = 224), long-segment Barrett’s esophagus (n = 220), reflux esophagitis (n = 219), and population-based controls (n = 256). Multiple logistic regression analysis examined the association between dietary variables and disease risk by tertiles of intake and as continuous variables, while adjusting for potential confounders.

Results: Reflux esophagitis risk was positively associated with starch intake and negatively associated with sugar intake. Barrett’s esophagus risk was significantly reduced in people in the highest versus the lowest tertile of fiber intake (OR 0.44 95%CI 0.25–0.80). Fiber intake was also associated with a reduced risk of esophageal adenocarcinoma, as was total carbohydrate intake (OR 0.45 95%CI 0.33–0.61 per 50 g/d increase). However, an increased esophageal adenocarcinoma risk was detected per 10 unit increase in GI intake (OR 1.42 95%CI 1.07–1.89).

Conclusions: Our findings suggest that fiber intake is inversely associated with Barrett’s esophagus and esophageal adenocarcinoma risk. Esophageal adenocarcinoma risk is inversely associated with total carbohydrate consumption but positively associated with high GI intakes.

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Endometrial cancer risk has been directly associated with glycemic load. However, few studies have investigated this link, and the etiological role of specific dietary carbohydrate components remains unclear. Our aim was to investigate associations of carbohydrate intake, glycemic index, and glycemic load with endometrial cancer risk in the US Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Recruitment took place in 1993-2001. Over a median of 9.0 years of follow-up through 2009, 386 women developed endometrial cancer among 36,115 considered in the analysis. Dietary intakes were assessed using a 124-item diet history questionnaire. Cox proportional hazards models were applied to calculate hazard ratios and 95% confidence intervals. Significant inverse associations were detected between endometrial cancer risk and total available carbohydrate intake (hazard ratio (HR) = 0.66, 95% confidence interval (CI): 0.49, 0.90), total sugars intake (HR = 0.71, 95% CI: 0.52, 0.96), and glycemic load (HR = 0.63, 95% CI: 0.46, 0.84) when women in the highest quartile of intake were compared with those in the lowest. These inverse associations were strongest among overweight and obese women. No associations with endometrial cancer risk were observed for glycemic index or dietary fiber. Our findings contrast with previous evidence and suggest that high carbohydrate intakes and glycemic loads are protective against endometrial cancer development. Further clarification of these associations is warranted.

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Background: Epidemiologic evidence on the influence of dietary glycemic index (GI) and glycemic load (GL) on the development of obesity is limited.

Objective: This prospective study examined the associations between dietary GI and GL and changes in body composition measures during adolescence.

Design: In a representative sample of Northern Irish adolescents aged 12 years at baseline and 15 years at follow-up (n=426), dietary intake was assessed by a diet history interview. Body composition measures included body mass index (BMI; kg m(-2)), BMI z-score, sum of four skinfold thicknesses, percentage body fat, fat mass index (FMI; kg m(-2)) and fat-free mass index (kg m(-2)).

Results: After adjustment for potential confounding factors, baseline GI was associated with increased change in FMI. Mean (95% confidence interval) values of changes in FMI according to tertiles of baseline GI were 0.41 (0.25, 0.57), 0.42 (0.26, 0.58) and 0.67 (0.51, 0.83) kg m(-2), respectively (P for trend=0.03). There was no significant association of baseline GI with changes in other body composition measures (P for trend0.054). Conversely, baseline GL showed no association with changes in any of the measures (P for trend0.41). Furthermore, changes in GI or GL were not associated with changes in any of the measures (P for trend0.16).

Conclusion: Dietary GI at age 12 years was independently associated with increased change in FMI between ages 12 and 15 years in a representative sample from Northern Ireland, whereas dietary GL showed no association with changes in any of the body composition measures examined.

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BACKGROUND: Genetic polymorphisms of transcription factor 7-like 2 (TCF7L2) have been associated with type 2 diabetes and BMI. OBJECTIVE: The objective was to investigate whether TCF7L2 HapA is associated with weight development and whether such an association is modulated by protein intake or by the glycemic index (GI). DESIGN: The investigation was based on prospective data from 5 cohort studies nested within the European Prospective Investigation into Cancer and Nutrition. Weight change was followed up for a mean (±SD) of 6.8 ± 2.5 y. TCF7L2 rs7903146 and rs10885406 were successfully genotyped in 11,069 individuals and used to derive HapA. Multiple logistic and linear regression analysis was applied to test for the main effect of HapA and its interaction with dietary protein or GI. Analyses from the cohorts were combined by random-effects meta-analysis. RESULTS: HapA was associated neither with baseline BMI (0.03 ± 0.07 BMI units per allele; P = 0.6) nor with annual weight change (8.8 ± 11.7 g/y per allele; P = 0.5). However, a previously shown positive association between intake of protein, particularly of animal origin, and subsequent weight change in this population proved to be attenuated by TCF7L2 HapA (P-interaction = 0.01). We showed that weight gain becomes independent of protein intake with an increasing number of HapA alleles. Substitution of protein with either fat or carbohydrates showed the same effects. No interaction with GI was observed. CONCLUSION: TCF7L2 HapA attenuates the positive association between animal protein intake and long-term body weight change in middle-aged Europeans but does not interact with the GI of the diet.

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It is currently estimated that over 370 million individuals have diabetes, making diabetes a major public health issue contributing significantly to global morbidity and mortality. The steep rise in diabetes prevalence over the past decades is attributable, in a large part, to lifestyle changes, with dietary habits and behaviour significant contributors. Despite the relatively wide availability of anti-diabetic medicine, it is lifestyle approaches that still remain the cornerstone of diabetes prevention and treatment. Glycemic index (GI) is a nutritional tool, which represents the glycemic response to carbohydrate ingestion. In light of the major impact of nutrition on diabetes pathophysiology, with the rising need to combat the escalating diabetes epidemic, this review will focus on the role of GI in glycemic control, the primary target of diabetic treatment and beyond. The review will present the evidence relating GI and diabetes treatment and prevention, as well as weight loss, weight maintenance and CVD risk factors.

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Amaranth has attracted a great deal of interest in recent decades due to its valuable nutritional, functional, and agricultural characteristics. Amaranth seeds can be cooked, popped, roasted, flaked, or extruded for consumption. This study compared the in vitro starch digestibility of processed amaranth seeds to that of white bread. Raw seeds yielded rapidly digestible starch content (RDS) of 30.7% db and predicted glycemic index (pGI) of 87.2, the lowest among the studied products. Cooked, extruded, and popped amaranth seeds had starch digestibility similar to that of white bread (92.4, 91.2, and 101.3, respectively), while flaked and roasted seeds generated a slightly increased glycemic response (106.0 and 105.8, respectively). Cooking and extrusion did not alter the RDS contents of the seeds. No significant differences were observed among popped, flaked, and roasted RDS contents (38.0%,46.3%, and 42.9%, respectively), which were all lower than RDS content of bread (51.1%). Amaranth seed is a high glycemic food most likely because of its small starch granule size, low resistant starch content (< 1%), and tendency to completely lose its crystalline and granular starch structure during those heat treatments.

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Background: Low glycemic index (GI) carbohydrates have been linked to increased satiety. The drive to eat may be mediated by postprandial changes in glucose, insulin and gut peptides.
Objective
: To investigate the effect of a low and a high GI diet on day-long (10 h) blood concentrations of glucose, insulin, cholecystokinin (CCK) and ghrelin (GHR).
Design: Subjects (n¼12) consumed a high and a low GI diet in a randomized, crossover design, consisting of four meals that were matched for macronutrients and fibre, and differed only in carbohydrate quality (GI). Blood was sampled every 30–60 min and assayed for glucose, insulin, CCK and GHR.
Results: The high GI diet resulted in significantly higher glucose and insulin mean incremental areas under the curve (IAUC, P¼0.027 and P¼0.001 respectively). CCK concentration was 59% higher during the first 7 h of the low GI diet (394±95 pmol/l min) vs the high GI diet (163±38 pmol/l min, P¼0.046), but there was no difference over 10 h (P¼0.224). GHR concentration was inversely correlated with insulin concentration (Pearson correlation 0.48, P¼0.007), but did not differ significantly between the low and high GI diets.
Conclusions: Mixed meals of lower GI are associated with lower day-long concentrations of glucose and insulin, and higher CCK after breakfast, morning tea and lunch. This metabolic profile could mediate differences in satiety and hunger seen in some, but not all, studies.

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INTRODUCTION : Fatigue in sports is often associated with depletion of muscle glycogen storage. Obesity is considered to be a major barrier against physical activity in sports. In order to bring the glycogen storage to a satisfactory level sports persons tend to increase consumption carbohydrates, preferred consumption of high glycemic index (HGI) than low glycemic index (LGI) diets. But HGI foods may promote postprandial carbohydrate oxidation at the expense of fat oxidation and increase body fat gain. LGI diets that produce a low and slow glycemic response may enhance higher glycogen storage instead of fat deposition.

METHODOLOGY :
To test this hypothesis, 30 male Wistar rats after weaning were given either a high glycemic index (HGI) or low glycemic index (LGI) diet for until their age of 12 weeks. Then the subjects were scarified and their plasma, serum, and muscle samples were collected. RESULTS-The study revealed that HGI diets fed rats had higher plasma cholesterol and Leptin (LGI Leptin 1.34 +/- 0.13ng/ml, HGI Leptin 2.12 +/- .20ng/ml) concentrations. It also found the liver and muscle glycogen storage in LGI diets showed higher level (LGI-liver 108 +/-3.0 mg/100g, LGI-muscle 22.6+/- 2.3g/100g) than that of HGI (HGI-liver 96 +/- 2.0mg/100g, HGI-muscle 18+/- 1.5g/100g) diets.

CONCLUSION :
the long term feeding of LGI carbohydrate encourages more glycogen storage while HGI increases fat deposition. Consumption of LGI diets has an advantage over HGI diets of higher physical activity while elevating glycogen storage and reducing chances of obesity.

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Purpose: This study aimed to examine the effect of glycemic index of pre-exercise carbohydrate (CHO) ingestion on exercise metabolism and performance.

Methods: Eight endurance trained men ingested a high glycemic index (HGI), low glycemic index (LGI), or a placebo (CON) meal 45 min before exercise and then cycled for 50 min at 67% V·O2max. Subjects subsequently performed a 15-min self-paced performance ride in which total work (kJ) was recorded.

Results: Plasma glucose concentrations were higher (P < 0.01) after ingestion in HGI compared with LGI and CON (7.53 ± 0.64 vs 5.55 ± 0.21 and 4.65 ± 0.14 mmol·L-1 for HGI, LGI, and CON, respectively, 30 min postprandial; mean ± SE) but declined at the onset of exercise and were lower (P < 0.01) compared with LGI and CON (4.03 ± 0.31 vs 4.64 ± 0.24 and 5.09 ± 0.16 mmol·L-1 for HGI, LGI, and CON respectively; mean ± SE) at 10 min of exercise. Plasma glucose remained depressed (P < 0.01) until 30 min into exercise in HGI compared with other trials. Plasma insulin concentrations were higher (P < 0.01) following ingestion during rest and exercise in HGI compared with LGI and CON. Plasma FFA concentrations were lower (P < 0.05) following ingestion in HGI and LGI compared with CON and higher (P < 0.05) in LGI compared with HGI at the start and end of exercise. RER and CHO oxidation was higher (P < 0.01) in HGI compared with LGI and CON during submaximal exercise. There were no differences in work output during the performance cycle.


Conclusions: These data indicate that pre-exercise CHO feedings with varying glycemic indexes do not affect exercise performance following short term submaximal exercise despite alterations in metabolism.

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To investigate the effect of low glycemic index (LGI) carbohydrate meal on subjective, metabolic and physiological responses, and endurance performance in the Ramadan fasted state.