Glycemic load and weight loss -
Simple carbohydrates that have more glucose are digested rapidly, causing dramatic spikes and crashes in blood sugar levels. Complex carbohydrates with less glucose are digested more slowly and help maintain steadier blood sugar levels throughout the day.
Participants were also overweight or obese at the start of the trial, a risk factor for serious coronary artery disease complications like heart failure and heart attacks. During the three-month trial, researchers randomly assigned adults to either switch to a low-glycemic-index diet or join a control group following a diet typically recommended for people with heart disease.
In the control group, people were asked to limit fat and some proteins, such as whole milk, cheese, meat, egg yolks, and fried foods. No changes to fat or protein consumption were required for the low-glycemic-index diet group. In the control group, the average BMI declined by 1.
A BMI of 30 or more is considered obese, while a BMI of In addition, people on the low-glycemic-index diet reduced their waist circumference by an average of 9 centimeters 3. The results were presented at the ACNAP—EuroHeartCare Congress , a scientific congress of the European Society of Cardiology.
Low-glycemic-index foods have a score of 50 or less, while foods considered high on this index tend to have a score over 70, according to the Cleveland Clinic. Low-glycemic-index foods include:. Many heavily processed foods can be higher on the glycemic index, according to the Cleveland Clinic:.
Preparation also matters, since how foods are made can influence where they land on the glycemic index, the Cleveland Clinic notes. With oatmeal, for example, the instant packets you can make in the microwave could have a glycemic index score of 79, while the steel-cut oats cooked on the stove may have a glycemic index score of Previous research has linked a high-glycemic-index diet to an increased risk of premature death from all causes and from cardiovascular disease in particular.
One study published in April in The New England Journal of Medicine NEJM looked at eating habits for , people 35 to 70 years old, then followed them for about a decade. Compared with people with the lowest glycemic-index diet, participants with heart disease with the highest glycemic index diets were 51 percent more likely to experience or die from major cardiovascular disease events like heart attacks or strokes.
Subsequently, the glycemic load GL , which considers the GI and the amount of available carbohydrates eaten [ 10 ], was introduced and considered the primary measurement of carbohydrate quantity and quality. Reducing the GL may provide a modest additional benefit [ 11 , 12 ].
Several prospective observational studies [ 13 , 14 ] and meta-analyses [ 15 , 16 ] have shown that the diabetes risk increases with a higher dietary GL. In addition, a low-GL diet may have favorable effects in individuals with prediabetes who constitute a broad group with a high risk of developing diabetes [ 17 ].
However, traditional association analyses in the field of nutritional epidemiology typically examine disease in relation to a single GL, regardless of the rationality of the energy ratio of macronutrients and food intakes.
Although these analyses have been quite valuable, the results of the association between GL, health and disease may be affected. Whether the unsatisfactory effect of a low-GL diet should be attributed to its innate reasons or irrationality of the energy ratio of macronutrients and food intake is uncertain.
Previous studies were interested in indicating the benefit of reducing carbohydrate intake [ 18 ]. However, energy intake is derived from carbohydrates, fat and protein. The following question emerged: Can the excessive restriction of GL, mostly through an extreme reduction in the intake of carbohydrates, result in a relatively high intake of fat and protein and result in overweight and obesity [ 19 ]?
Indeed, China is facing an emerging obesity epidemic, and the prevalence of overweight and obesity has doubled over the past decade [ 20 ]. However, few previous studies examining the relationship between dietary GL and diabetes risk considered physiological endpoints, such as obesity.
In addition, definitive data warranting the establishment of evidence-based dietary GL recommendations are currently lacking. Several studies conducted outside of Asia have defined a low-GL diet as the maintenance of a GL less than 80 per day [ 21 ], the consumption of no more than 45 per kcal [ 22 ], or the consumption of no more than one serving of high-GL foods per day [ 23 , 24 , 25 ].
Such targets are difficult to achieve based on the Chinese Dietary Guidelines. Grains such as rice and noodles form the base of nearly every meal in the Chinese diet. The GL of the recommended diet would be more than per day or 80 per kcal.
Due to the existence of limited or no supporting evidence, advice pertaining to the GL can be misleading. This study, which involved middle-aged and elderly Chinese adults, was performed to examine the associations among GL, diabetes and obesity while considering the rationality of nutrient intake; in addition, this study initially aimed to determine the optimal GL range.
This study is an ongoing multiethnic, epidemiological study investigating lifestyle and the glucose metabolism state in China [ 26 , 27 , 28 ]. The data used in this cross-sectional analysis were obtained from a baseline survey conducted between July and December and focused on a subsample in Guangzhou, China.
The details of the study methodology have been previously published [ 29 ]. Briefly, all eligible adults who were a aged 40 years or older and b lived in Guangzhou for at least 3 years were recruited.
During the first face-to-face interview, all participants were trained in the level of detail required to adequately describe the foods and amounts consumed, including the name of the food brand name, if possible , preparation methods, recipes for food mixtures, and portion sizes.
Food models and measuring displays were used to ensure accurate portion sizes. Subsequently, the participants were instructed to record the amount and type of all foods and drinks they consumed during a continuous 3-day period, which ideally included 2 weekdays and 1 weekend day at home, highly suggesting that recording to be done at the time of the eating occasion in order to reliance on memory.
The foods eaten daily, the brand name, and the food preparation method were recorded in detail. The amounts consumed may be measured using a scale or household measures e. All records were received in real time, clarified entries and probed for forgotten foods by a dietitian prior to collection.
The Food Composition Table of China and international GI tables [ 30 , 31 ] were used to establish a software model to log, calculate and save the energy, nutrients, GI and GL values of the study subjects.
An appropriate GI value was chosen based on the cooking method used e. uncooked, boiled or fried. The mean GI value was calculated when multiple values were available. For foods without a published GI value, the GI value was estimated based on a standardized method [ 32 ]. The GL of each food was calculated by multiplying the carbohydrate content in each serving by the GI of that food, and the total GL was calculated as the sum of all GL values of each food consumed over the course of 1 day [ 32 ].
In addition, for each participant, the energy and nutrient intake were adjusted by the ideal body weight by converting the total value into the value per 1 kg of ideal body weight [ 33 ].
The mean daily indices of dietary intake were calculated. An NAR equal to 0 indicates a diet devoid of that micronutrient, whereas an NAR equal to 1 indicates a diet that achieved or exceeded the recommended nutrient intake of that micronutrient. To obtain an overall estimate of nutritional adequacy, a mean micronutrient adequacy ratio MAR was calculated based on the 14 NARs.
Each NAR was truncated at 1 to avoid the possibility that a micronutrient with a high NAR compensates for a micronutrient with a low NAR.
Therefore, the maximum possible MAR value was 1, and the minimum possible MAR value was 0. Reproducibility and validity tests of the 3-day food record were conducted using the answers obtained from 58 participants [ 34 ]. These participants completed the 3-day food record and food frequency questionnaire FFQ for the first time.
Then, after approximately 2 weeks, they completed a second 3-day food record. The Spearman correlation coefficients of the two food records were 0. Similarly, the Spearman correlation coefficients of the food records and FFQ were 0. The data regarding the sociodemographic characteristics and lifestyle information, including physical activity, educational history, smoking and alcohol drinking status were gathered by trained interviewers using a standard questionnaire.
In addition, the participants were invited to complete an oral glucose tolerance test OGTT. The MET-h of an activity was calculated by multiplying the time spent performing the activity by the MET value corresponding to that activity.
Regular exercise was defined as performing at least 7. Their body height and waist circumference were measured to the nearest 0. High-quality and accurate techniques and mean measurements were used. The body mass index BMI was calculated as the weight in kilograms divided by the square of the height in meters.
Overweight was defined as a BMI between Central obesity was defined as a waist circumference greater than or equal to The plasma glucose level was measured by a glucose oxidase assay AU; Beckman Coulter, Miami, FL, USA. Peripheral blood samples were collected in the morning after 8—12 h of fasting.
The fasting plasma glucose FPG and 2-h plasma glucose 2-hPG levels were measured at fasting and 2 h after the participants had ingested a standard g glucose solution, respectively.
Diabetes was defined as an FPG level greater than or equal to 7. The participants were considered fully eligible if it was verified that complete data were adequately recorded. In addition, to prevent the variables with larger ranges from having a greater contribution than the variables with smaller ranges, z-scores were calculated to standardize the data set before clustering.
All statistical tests were performed using PASW SPSS Statistics for Windows, Version The categorical variables are expressed as absolute values relative frequencies and were compared using the chi-squared test. A dominant component analysis was performed to identify the underlying dietary patterns.
The components were also orthogonally rotated the varimax option to enhance the difference between loadings, which allowed for easier interpretability.
A k-means cluster analysis was used to classify the participants into clearly distinct groups based on the dominant components. After excluding outliers and participants with incomplete data, participants men and women with a mean age of 56 years were included in the analysis.
Five principal components were extracted through a dominant component analysis of 16 variables, explaining Four identified clusters on dominant component loadings after varimax rotation.
RCM rotated component matrix, GL glycemic load, MAR micronutrient adequacy ratio. The general, anthropometric, and laboratory characteristics of the participants classified in different clusters are shown in Table 1.
Cluster 1 included more male subjects and tended to have an unhealthier lifestyle pattern, such as smoking and less regular exercise. Clusters 2 and 3 included more female subjects and tended to have a healthier lifestyle pattern. Cluster 4 tended to include more younger subjects.
Among the individuals, were diagnosed with overweight and obesity by BMI, and were diagnosed with central obesity by waist circumference, resulting in a prevalence of The lowest prevalence of overweight and obesity was observed in cluster 3 A similar trend of the prevalence of central obesity was observed across the four clusters.
Among the individuals, were diagnosed with abnormal glucose metabolism, and were diagnosed with diabetes by OGTT, resulting in a prevalence of The prevalence of both abnormal glucose metabolism and diabetes was relatively lower in clusters 2 and 3. The total GL was similar between clusters 2 and 3; however, the food composition differed.
Cluster 2 consumed the highest GL intake from fruit and nuts, while cluster 3 consumed the highest GL intake from whole grains, mixed beans, dairy, beans and nuts.
The MARs were higher in clusters 2 and 3 Fig. Dietary characteristics of the four identified clusters. Whisker-box plot with boxes indicating the median and 25th and 75th percentiles and whiskers indicating the 10th and 90th percentiles. The shadow indicates the Chinese dietary reference intakes a — e or the interquartile range of cluster 3 f.
GL glycemic load, MAR micronutrient adequacy ratio. Consistent with several previous cross-sectional studies [ 36 , 37 ], our results suggest that a low GL is associated with better glucose homeostasis. Nevertheless, our results contributed to the debate regarding whether excessive GL restriction may increase the risk of obesity.
In this study, participants with moderate GL intake clusters 2 and 3 had a lower prevalence of overweight and obesity, while both those with the highest GL intake cluster 1 and the lowest GL intake cluster 4 showed an increased risk of overweight and obesity.
Only one previous study suggested a negative association between GL and BMI [ 38 ], while other studies have indicated that GL is not associated with the BMI [ 37 , 39 , 40 ].
However, in addition to BMI, associations with waist circumference have been examined, and both a positive association [ 41 ] and no association [ 39 , 40 ] between GL and waist circumference have been reported.
In the present study, dietary GI and GL were assessed using a previously validated 3-day food record instead of an FFQ. This methodology was selected for three reasons. First, FFQ usually overestimates food intake compared to other nutritional assessment methods, which leads to an overestimation of the energy and nutritional values of diets [ 42 ].
Second, possible errors include the omission or addition of food, as well as an inadequate assessment of the frequency and amount of consumed products [ 43 ].
Third, with a 3-day food record, details about the sources, preparation, and processing of foods and timing and location of meals together with quantitative data on all food sources of energy and nutrients can be captured.
Last, a 3-day food record can be designed to be culturally sensitive and cognitively easy, making it especially suitable for respondents with limited education, such as elderly adults [ 44 ].
Therefore, the food record provides relatively accurate data concerning the intake of food and nutrients. In our reproducibility and validity test, the intake of certain foods was sometimes underestimated using 3-day food records.
However, the intake of cereal, which is the dominant source of GL, rarely changed. Dietary pattern analyses using component [ 45 , 46 ] or cluster analyses [ 47 , 48 ] reflecting the complexity of dietary intake have recently received greater attention from nutritional epidemiologists [ 49 , 50 , 51 ].
Component analyses reduce the number of variables by identifying independent vectors that are combinations of original correlated variables; cluster analyses create groups or clusters of subjects with similar profiles and are very useful for descriptive purposes.
In this study, we preliminarily used a cluster analysis to identify the GL intake patterns, and nonoverlapping groups of individuals who exhibited similar patterns of GL intake were created based on the dominant pattern of GL intake. To the best of our knowledge, there are no comparable studies investigating GL clusters in terms of overweight and obesity or diabetes.
Traditionally, studies investigating dietary GL intake and chronic metabolic disease have focused on the total GL. However, food is typically consumed in combination, not in isolation, and therefore, comprehensive investigations are needed to understand the dietary patterns associated with a lower risk of diabetes.
Dietary GL decreased from cluster 1 to cluster 4. However, the lowest risks for overweight and obesity, central obesity, abnormal glucose metabolism, and diabetes were observed in the middle clusters cluster 2 or 3 rather than either the highest cluster 1 or the lowest cluster 4 cluster.
Cluster 2 consumed the highest intake of GL from fruit and nuts, and cluster 3 consumed the highest intake of GL from whole grains and mixed beans, dairy and beans. Numerous previous studies have suggested the favorable effects of such foods on obesity and diabetes [ 7 ].
Dietary patterns i. Compared to the Chinese dietary reference intakes of macronutrients, participants with moderate GL intake clusters 2 and 3 were more consistent with the macronutrient intake reference. In contrast, participants with the lowest GL intake cluster 4 consumed relatively higher fat and protein.
Generally, accepted that consuming a high fat diet increases the likelihood of obesity, which is one of the identified significant risk factors for diabetes. However, the role of proteins in diabetes prevention is conflicting.
Dietary proteins have an insulinotropic effect and promote insulin secretion, which leads to an increased rate of glucose clearance from the blood [ 52 ]. However, the results from clinical trials and observational studies have been mixed. A meta-analysis showed beneficial effects of a high-protein diet on several obesity and cardiometabolic parameters, including weight loss and fasting insulin [ 53 ].
Conversely, several large prospective cohort studies have shown detrimental associations between protein intake and diabetes risk [ 54 , 55 ]. A meta-analysis suggested that high total protein and animal protein intake were associated with an increased risk of diabetes while high plant protein intake was associated with a decreased risk [ 56 ].
Therefore, the efficacy and safety of high-protein, low-carbohydrate diets have to be studied more extensively. The relationship between individual micronutrients and a low-GL diet is still uncertain.
Low-GI foods are by definition moderate to high sources of carbohydrates, yet some are also particularly rich in micronutrients, such as fruits, whole grains and dairy products. Several studies have reported that a low-GL diet is associated with higher intakes of micronutrients [ 57 ], whereas a diet with low or no gluten may lead to micronutrient deficiencies [ 58 ].
Combined with our results, a low GL with a proper food intake diet, which ideally contains many whole grains, mixed beans, vegetables, fruits, dairy, nuts and beans, should be fundamental for the adequate intake of micronutrients. A reasonable collocation dietary pattern could be better than a dietary pattern that excessively restricts the GL.
Our study has the following limitations. Therefore, sex male or female was adjusted when analyzing the association between dietary GL and the prevalence of abnormal glucose metabolism.
In addition, the data analyzed in this cross-sectional analysis were derived from a baseline survey of an ongoing multiethnic, epidemiological study. Therefore, the results could be further studied based on the following prospective observations. Second, all participants were Chinese with traditional high-GL dietary habits.
The generalization of the results to other ethnic groups should be performed with caution. Third, measurements of dietary intake were secured by self-reported dietary records, as known recovery biomarkers of GL are limited. To secure a securing more accurate measurement of diet, all participants were trained on how to record the diet intake and were suggested to record at the time of the eating occasion.
Despite these limitations, this study was the first to evaluate the associations among the GL, macro- and micronutrient intake and the risk of obesity and diabetes. In addition, the optimal range of the GL for lowering both obesity and diabetes risk was preliminarily explored.
Our results demonstrate that reducing GL to prevent diabetes deserves more attention based on dietary patterns. An appropriate GL is better for reducing the risk of obesity and diabetes than excessive GL restriction.
This study underscores that required educational interventions should not only promote a specific GL limitation but also promote a more general healthy eating pattern. The datasets used in the present study are available from the corresponding author upon reasonable request.
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B vitamins for hair growth Post-workout recovery B vitamins for hair growth wsight smidge more useful than glycemic index when it comes to choosing healthy, diabetes-friendly foods. Have you ever eaten a Hydration habits for aspiring young athletes in hopes of curing your afternoon slump only to feel up and then down again? For people with diabetes, this kind of fluctuation can be pronounced and dangerous. For everyone else, as the University of Texas MD Anderson Glycekic Center notes, it can be the ultimate downer and productivity killer. By using an easy formula no major arithmetic required! In a small clinical Glycemci, eating low-glycemic-index Glycemicc helped people with coronary artery disease lose Endurance speed training and trim down their liad. People with heart disease who eat Promoting digestive wellness low-glycemic-index diet with B vitamins for hair growth of leafy greens, whole grains, legumes, weighy fiber-rich koss and vegetables may find it easier to lose excess weight and slim down around their midsection, a small clinical trial suggests. The glycemic index ranks carbohydrates from zero foods with no glucose at all to sweets and drinks that contain nothing but glucose. Simple carbohydrates that have more glucose are digested rapidly, causing dramatic spikes and crashes in blood sugar levels. Complex carbohydrates with less glucose are digested more slowly and help maintain steadier blood sugar levels throughout the day.
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