常见谷类和薯类的血糖生成指数

目的 :　测定我国常见谷类和薯类食物血糖生成指数值 ( Glycemic Index,GI) ,探讨碳水化物种类、加工方法与食物消化吸收和血糖应答的关系。方法 :　根据食物碳水化物的含量计算含有 5 0 g碳水化物的食物量 ,用 5 0 g葡萄糖为对照物。每组 8～ 1 2人测空腹血糖后 ,服用实验物 ,测定 2 h内不同时间的血糖水平。根据 Wolver方法计算食物的 GI值。结果 :　测定了 9个糖类和 62个食物的 GI值。结论 :　相同量的碳水化物的食物有不同的 GI值 ;GI值的高低与食物中碳水化物的利用有关。食物的加工方式和碳水化物的种类对 GI水平有显著影响     

糖尿病病人膳食生糖效应与膳食纤维摄入的关系研究

目的通过对膳食血糖指数(DGI)和膳食血糖负荷(DGL)的评估,研究膳食生糖效应与膳食纤维的关系。方法采用食物称重记账法调查105名2型糖尿病病人的2个3日膳食摄入状况,根据食物血糖指数和膳食碳水化合物摄入量计算DGI和DGL,采用酶-重量法测定食物中总膳食纤维(TDF)、不可溶性膳食纤维(IDF)和可溶性膳食纤维(SDF)含量。结果调查对象的DGI值是62.9±6.8,DGL值是142.4±39.8,TDF、IDF和SDF的摄入量分别是(22.5±6.7)g/d,(16.1±5.5)g/d,(6.4±2.0)g/d。DGI与TDF的相关系数是-0.407(P<0.01)。与DGI最低五分位组的调查对象相比,DGI最高五分位组的调查对象蔬菜类食物摄入量减少了221.9g(P<0.01),TDF、IDF和SDF摄入量分别减少了6.5g、5.0g和1.4g(P值均<0.01),其中由蔬菜提供的膳食纤维减少量最多,谷类摄入量增加了68.0g(P<0.01)。结论DGI与膳食纤维摄入量呈负相关。富含蔬菜和膳食纤维的膳食可降低膳食生糖效应。     

大豆发酵制品唐可肽对血糖生成指数的影响

目的测定大豆发酵制品唐可肽的血糖生成指数,并探讨唐可肽对于血糖生成指数以及血糖应答的影响。方法根据唐可肽碳水化合物的含量计算出相当于25g碳水化合物的唐可肽量,用25g葡萄糖为对照量,将受试者分为唐可肽组和葡萄糖组,每组10人,测定他们的空腹血糖后分别服用唐可肽和葡萄糖,测定2h内不同时点的血糖水平。根据Wolver方法计算食物的血糖生成指数(GI)值。结果测定了唐可肽血糖生成指数为54.86。结论唐可肽为低血糖生成指数值的食物。具有很好的降血糖、平稳血糖的作用。     

燕麦和荞麦加工食品血糖生成指数与血糖负荷测定

<正>食物中碳水化合物的性质和含量均会对机体血糖造成影响,而食物血糖生成指数(glycemic index,GI)和血糖负荷(glycemic load,GL)值则是反映该影响的生理指标。大量研究均证实低GI和低GL膳食干预对防治肥胖、糖尿病、心血管疾病等慢性代谢性疾病具有重要价值[1]。本研究应用国际认可的食物GI值标准化测定方法,对20种燕麦、荞麦加工食品GI和GL值进行测定,旨在填补目前食物GI和GL值数据库中燕麦、荞     

复配式粗杂粮的营养成分特征及其对人体血糖生成的影响

目的评价复配式粗杂粮的营养成分及其血糖生成指数(GI),探讨其营养成分与生物功效的关系。方法按国家标准方法分别测定复配式粗杂粮及对照食物一般营养素含量。取含有50g碳水化物的复配式粗杂粮制成馒头,50g葡萄糖为对照物,10名志愿者测空腹血糖后,服用2种食品,测定3h内不同时间的血糖水平,依照Wolver方法计算食物的GI值。结果复配式粗杂粮每100g中含不溶性膳食纤维(粗纤维)13·7g、维生素B10·40mg、蛋白质13·5g、灰分2·0g、维生素B20·16mg、钙54mg、铁7·1mg、锌3·00mg。其餐后血糖绝对增加值和血糖反应曲线下增值面积显著低于葡萄糖(P<0·05),GI值为52·6。结论复配式粗杂粮中较高含量的蛋白质、矿物质、粗纤维和维生素B1、B2,特别是比较低的GI是复合式营养干预取得较好效果的重要原因。     

食物血糖指数与糖尿病防治

0引言 血糖指数(glycemic index,GI)是反映食物引起血糖应答的特性,是人体生理学对糖类食物进行分类的良好指标.血糖负荷(glycemic load,GL)的概念是在GI的基础上,将摄入糖类的质量和数量结合起来,以估价膳食总的血糖效应.近几年来,关于GI、GL的概念在糖尿病控制中的作用受到广泛关注.     

基于血糖负荷概念的食物交换份在糖尿病营养治疗中的应用

目的:建立以控制血糖负荷和总能量为特征,体现食物血糖应答差异的新型食物交换系统,探索其在糖尿病人饮食管理中的应用价值。方法:根据食物血糖指数(glycemicindex,GI)与碳水化物含量计算血糖负荷(glycemicload,GL),比较GI与GL的相关性以及对食物血糖效应的影响。融合GL和食物交换份方法的特点,提出基于GL的新型食物交换份。结果:粮谷类、豆类及制品的GI与GL%有较好的一致性,相关系数分别为0.64、0.55。而蔬菜、水果类食物的GI与GL%无明显相关性。基于GL的食物交换份既保留了传统交换方法简单易行的特点,又充分考虑了碳水化物在质和量方面的差异,有助于在控制总能量的同时,定量预测或调整混合膳食的血糖应答效应。结论:基于GL的食物交换份有较高的应用前景,值得实践和推广。     

维吾尔族常用传统食品和血糖生成指数测定

目的确定新疆维吾尔族常用传统食品的种类,测定其血糖生成指数（GI）。方法运用食物频率调查法进行问卷调查确定维吾尔族传统特色食品;根据食物碳水化合物的含量计算含有50克碳水化合物的食物量,用50克葡萄糖为对照量。受试者民族为维吾尔族,共分4个批次,每个批次为10人,测定他们的空腹血糖后服用试验物,测定2h内不同时点的血糖水平。根据Wolver方法计算食物的GI值。结果确定了15种新疆维吾尔族特色食品,并测定了其GI值。结论GI数据从一定程度上提示新疆维吾尔族糖尿病高发的原因。     

富碳水化合物食品血糖生成指数的体外回归模型

目的通过探讨富碳水化合物(CHO)食品营养组成及体外消化特性与体内血糖指数的关系,尝试建立体外回归模型。方法选择30种富CHO食品分析蛋白质、脂肪及CHO组成;在AOAC抗性淀粉测定的实验条件下,修改建立体外消化实验方法,测定淀粉在20min、120min、240min和16h的消化量以及不能消化的抗性淀粉含量;并采用国际标准方法完成食物血糖生成指数(GI)评估;通过逐步回归方法探讨各成分组成与GI关系。结果30种食品GI值分布为26～113,GI值高低与20min和120min内消化的淀粉量(S20、S120)呈明显正相关(P<0.05),而与抗性淀粉(RS)呈明显负相关(P<0.01)。将S120与葡萄糖含量相加定义为易利用糖(EAG),建立GI体外测试的回归方程为GI=39.65+1.008EAG-1.072RS。结论食品碳水化合物组成和体外消化实验有助于解释富碳水化合物食物体内血糖应答反应及预测GI值。     

糖尿病知识教育对糖调节受损患者膳食和营养结构的影响

目的探讨在糖调节受损患者中开展以血糖生成指数(GI)知识为基础的营养教育和膳食指导对患者的膳食和营养结构的影响。方法随机选取糖调节受损患者(IGR)76例,对照组和实验组各38例。对照组采取平衡饮食教育为主,实验组以GI知识宣传教育为主。营养教育和膳食指导9个月,观察患者的膳食结构、就餐GI值和营养摄入状况的变化。结果与教育前相比,实验组教育后低GI食物和鱼类的选择显著增加,瘦肉、肥肉、烹调油减少,全天总GI值降低,能量(EN)、碳水化合物(CHO)、钙(Ca)、镁(Mg)、维生素A(Vit A)、维生素B1(Vit B1)、维生素B2(Vit B2)、维生素C(Vit C)和膳食纤维(DF)摄入量增高,蛋白质(Pro)、脂肪(Fat)降低。与教育后对照组相比,实验组营养教育后,低GI食物和鱼类显著增多,瘦肉减少,全天总GI值降低,Ca、Mg、Vit A、Vit B1、Vit B2、Vit C和DF摄入量增高,Fat降低。结论在糖调节受损患者中开展基于GI知识的营养教育,可降低患者全天总GI值,有效地改善患者膳食和营养结构。     

Carbohydrate and the Regulation of Blood Glucose and Metabolism

Classifying the glycemic responses of carbohydrate foods using the glycemic index (GI) requires standardized methodology for valid results. Dietary carbohydrates influence metabolism by at least four mechanisms: nature of the monosaccharides absorbed, amount of carbohydrate consumed, rate of absorption, and colonic fermentation. Reducing glycemic responses by reducing carbohydrate intake increases postprandial serum free-fatty acids (FFA) and does not improve overall glycemic control in diabetic subjects. By contrast, low-GI diets reduce serum FFA and improve glycemic control. Thus, current evidence supports FAO/WHO recommendations to maintain a high-carbohydrate diet and choose low-GI starchy foods.     

Dietary glycemic index and liver steatosis

BACKGROUND: Insulin resistance (IR) and liver steatosis (LS) are interlinked metabolic derangements whose prevalence is rapidly increasing, but the effect of dietary carbohydrate quality on LS is unknown. OBJECTIVE: The objective was to describe the relation of IR and LS to total carbohydrate, total dietary fiber, and the glycemic index (GI) and glycemic load of the diet. DESIGN: The study was a cross-sectional evaluation of 247 apparently healthy subjects who had no evidence of viral, toxic, or autoimmune hepatitis and who were unselected for alcohol intake. The homeostasis model assessment index was used as a surrogate measure of IR, and a liver echography was used as a proxy for LS grading. Dietary data were collected by using 3-d food records. Total carbohydrate intake, total dietary fiber, GI, and glycemic load were calculated by using a semiquantitative food-frequency questionnaire concerning the dietary sources of carbohydrates. RESULTS: The prevalence of high-grade LS (HG-LS) increased significantly across quartiles of dietary GI (P for trend < 0.034): HG-LS in the 4th quartile (high GI) was twice that in the first 3 quartiles (low to medium GIs), whereas no relation was observed with total carbohydrates, total dietary fiber, or glycemic load. In insulin-sensitive subjects (first 3 quartiles of homeostasis model assessment index of IR), the prevalence of HG-LS did not differ significantly between GI groups, but, in insulin-resistant subjects (4th quartile of homeostasis model assessment index of IR), it was twice as high in those with high GI as in those with low to medium GIs (P = 0.005). CONCLUSIONS: High-GI dietary habits are associated with HG-LS, particularly in insulin-resistant subjects. Dietary advice on the quality of carbohydrate sources therefore may be a complementary tool for preventing or treating LS of metabolic origin.     

Dietary glycemic index is associated with decreased premenstrual symptoms in young Japanese women

OBJECTIVES: High glycemic index (GI) carbohydrates may increase brain serotonin, which in turn acts to alleviate premenstrual symptoms, because, although the main determinant of brain serotonin concentration is a high plasma ratio of tryptophan to other large neutral amino acids, a high-GI diet has been shown to increase this ratio. In this observational cross-sectional study, we investigated associations between dietary GI and other dietary carbohydrates and premenstrual symptoms. METHODS: Subjects were 640 female Japanese dietetic students 18-22 y of age. Dietary carbohydrates were assessed using a validated, self-administered, comprehensive diet history questionnaire. Menstrual cycle symptoms were assessed using the retrospective version of the Moos Menstrual Distress Questionnaire (MDQ). Independent associations of dietary GI and glycemic load and intake of available carbohydrate and dietary fiber with the MDQ total score and subscale scores (pain, concentration, behavioral change, autonomic reactions, water retention, and negative affect) in the premenstrual phase (expressed as percentages relative to those in the intermenstrual phase) were examined. RESULTS: Dietary GI was independently inversely associated with total MDQ score in the premenstrual phase (P for trend = 0.02). Dietary GI also showed independent and inverse associations with several MDQ subscale scores in the premenstrual phase, including concentration, autonomic reactions, and water retention (P for trend < 0.05). Conversely, dietary glycemic load and intake of available carbohydrate and dietary fiber were not associated with any of the MDQ scores in the premenstrual phase. CONCLUSION: Dietary GI was independently associated with decreased premenstrual symptoms in a group of young Japanese women.     

Glycemic response and health--a systematic review and meta-analysis: the database, study characteristics, and macronutrient intakes

BACKGROUND: Reduction of dietary glycemic response has been proposed as a means of reducing the risk of diabetes and coronary heart disease. Its role in health maintenance and management, alongside unavailable carbohydrate (eg, fiber), is incompletely understood. OBJECTIVE: We aimed to assess the evidence relating the glycemic impact of foods to a role in health maintenance and management of disease. DESIGN: We searched the literature for relevant controlled dietary intervention trials on glycemic index (GI) according to inclusion and exclusion criteria, extracted the data to a database, and synthesized the evidence via meta-analyses and meta-regression models. RESULTS: Among literature to January 2005, 45 relevant publications were identified involving 972 subjects with good health or metabolic disease. With small reductions in GI (<10 units), increases in available carbohydrate, energy, and protein intakes were found in all studies combined. Falling trends in energy, available carbohydrate, and protein intakes then occurred with progressive reductions in GI. Fat intake was essentially unchanged. Unavailable carbohydrate intake was generally higher for intervention diets but showed no trend with GI (falling or rising). Among studies reporting on GI, variation in glycemic load was approximately equally explained by variation in GI and variation in available carbohydrate intake. An exchange of available and unavailable carbohydrate (approximately 1 g/g) was evident in these studies. CONCLUSIONS: Among GI studies, observed reductions in glycemic load are most often not solely due to substitution of high for low glycemic carbohydrate foods. Available carbohydrate intake is a confounding factor. The role of unavailable carbohydrate remains to be accounted for.     

Glycemic, insulinemic index, glycemic load of soy beverage with low and high content of carbohydrates

Consumption of soy has increased in Western countries due to the benefits on health and the attitude of the people to consume natural products as alternative to the use of pharmacological therapies. However, there is no evidence whether the consumption of 25 g of soy protein as recommended by the Food and Drug Administration has some effect on glucose absorption and consequently on insulin secretion. The aim of the present study was to determine glycemic index (GI), insulinemic index (InIn), and glycemic load (GL) of several soy beverages containing low or high concentration of carbohydrates, and compare them with other foods such as peanuts, whole milk, soluble fiber and a mixed meal on GI and InIn. The results showed that soy beverages had low or moderate GI, depending of the presence of other compounds like carbohydrates and fiber. Consumption of soy beverages with low concentration of carbohydrates produced the lowest insulin secretion. Therefore, these products can be recommended in obese and diabetic patients. Finally soy beverages should contain low maltodextrins concentration and be added of soluble fiber.     

Second-meal effect: low-glycemic-index foods eaten at dinner improve subsequent breakfast glycemic response

The effects of the glycemic index (GI) of carbohydrate eaten the previous night on the glycemic response to a standard test meal eaten subsequently in the morning (breakfast) was studied. On separate evenings normal subjects ate low- or high-GI test meals of the same nutrient composition. The dinners consisted of single foods in two experiments and mixed meals containing several foods in the third. The differences between the observed glycemic responses to low- and high-GI dinners were predicted by their GIs. The glycemic responses to breakfast were significantly lower on mornings after low-GI dinners than after high-GI dinners. Eating, at dinner, foods with different fiber contents but the same GI had no effect on postbreakfast glycemia. We conclude that the GI predicts the difference between glycemic responses of mixed dinner meals; breakfast carbohydrate tolerance is improved when low-GI foods are eaten the previous evening.     

Glycemic index and glycemic load: measurement issues and their effect on diet-disease relationships

Glycemic index (GI) describes the blood glucose response after consumption of a carbohydrate containing test food relative to a carbohydrate containing reference food, typically glucose or white bread. GI was originally designed for people with diabetes as a guide to food selection, advice being given to select foods with a low GI. The amount of food consumed is a major determinant of postprandial hyperglycemia, and the concept of glycemic load (GL) takes account of the GI of a food and the amount eaten. More recent recommendations regarding the potential of low GI and GL diets to reduce the risk of chronic diseases and to treat conditions other than diabetes, should be interpreted in the light of the individual variation in blood glucose levels and other methodological issues relating to measurement of GI and GL. Several factors explain the large inter- and intra-individual variation in glycemic response to foods. More reliable measurements of GI and GL of individual foods than are currently available can be obtained by studying, under standard conditions, a larger number of subjects than has typically been the case in the past. Meta-analyses suggest that foods with a low GI or GL may confer benefit in terms of glycemic control in diabetes and lipid management. However, low GI and GL foods can be energy dense and contain substantial amounts of sugars or undesirable fats that contribute to a diminished glycemic response. Therefore, functionality in terms of a low glycemic response alone does not necessarily justify a health claim. Most studies, which have demonstrated health benefits of low GI or GL involved naturally occurring and minimally processed carbohydrate containing cereals, vegetables and fruit. These foods have qualities other than their immediate impact on postprandial glycemia as a basis to recommend their consumption. When the GI or GL concepts are used to guide food choice, this should be done in the context of other nutritional indicators and when values have been reliably measured in a large group of individuals.