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The Food Insulin Index (FII) is a novel algorithm for ranking foods on the basis of insulin responses in healthy subjects relative to an isoenergetic reference food. Our aim was to compare postprandial glycemic responses in adults with type 1 diabetes who used both carbohydrate counting and the FII algorithm to estimate the insulin dosage for a variety of protein-containing foods. A total of 11 adults on insulin pump therapy consumed six individual foods (steak, battered fish, poached eggs, low-fat yoghurt, baked beans and peanuts) on two occasions in random order, with the insulin dose determined once by the FII algorithm and once with carbohydrate counting. Postprandial glycemia was measured in capillary blood glucose samples at 15–30 min intervals over 3 h. Researchers and participants were blinded to treatment. Compared with carbohydrate counting, the FII algorithm significantly reduced the mean blood glucose level (5.7±0.2 vs 6.5±0.2 mmol/l, P=0.003) and the mean change in blood glucose level (−0.7±0.2 vs 0.1±0.2 mmol/l, P=0.001). Peak blood glucose was reached earlier using the FII algorithm than using carbohydrate counting (34±5 vs 56±7 min, P=0.007). The risk of hypoglycemia was similar in both treatments (48% vs 33% for FII vs carbohydrate counting, respectively, P=0.155). In adults with type 1 diabetes, compared with carbohydrate counting, the novel FII algorithm improved postprandial hyperglycemia after consumption of protein-containing foods.

BACKGROUND Eating carbohydrate foods with a high glycemic index (GI) has been postulated to result in fetoplacental overgrowth and higher infant body fat. A diet with a low glycemic index (LGI) has been shown to reduce birth percentiles and the ponderal index (PI). OBJECTIVES We investigated whether offering LGI dietary advice at the first antenatal visit would result in a lower fetal birth weight, birth percentile, and PI than providing healthy eating (HE) advice. This advice had to be presented within the resources of routine antenatal care. DESIGN The Pregnancy and Glycemic Index Outcomes study was a 2-arm, parallel-design, randomized, controlled trial that compared the effects of LGI dietary advice with HE advice on pregnancy outcomes. Eligible volunteers who attended for routine antenatal care at

Protein ingestion results in small but distinct changes in plasma glucose and insulin. We hypothesized that the glycemic and/or insulin response to protein might be related to the degree of insulin sensitivity. Our aim was to determine the relationships between insulin sensitivity (assessed by euglycemic-hyperinsulinemic clamp) and postprandial glucose, insulin, C-peptide, and glucagon responses to a 75-g protein meal and a 75-g glucose load. Sixteen lean healthy Caucasian subjects (mean +/- SD age, 25 +/- 6 years; body mass index [BMI], 23.1 +/- 1.7 kg/m2) participated in the study. After the protein meal, the mean plasma glucose declined gradually below fasting levels to a nadir of -0.36 +/- 0.46 mmol/L from 60 to 120 minutes, showing wide intraindividual variation. Insulin sensitivity (M value) was 1.1 to 3.9 mmol/L/m2 min in the subjects and correlated inversely with the plasma glucose response to the protein meal (r = -.58, P = .03), ie, the most insulin-sensitive subjects showed the greatest decline in plasma glucose. In contrast, there was no correlation between insulin sensitivity and the insulin or glucagon response to the protein load, or between the M value and the metabolic responses (glucose, insulin, C-peptide, and glucagon) to the glucose load. Our study suggests that the net effect of insulin and glucagon secretion on postprandial glucose levels after a protein meal might depend on the individual's degree of insulin sensitivity. Gluconeogenesis in the liver may be less susceptible to inhibition by insulin in the more highly resistant subjects, thereby counteracting a decline in plasma glucose.

OBJECTIVE—To systematically tabulate published and unpublished sources of reliable glycemic index (GI) values. RESEARCH DESIGN AND METHODS—A literature search identified 205 articles published between 1981 and 2007. Unpublished data were also included where the data quality could be verified. The data were separated into two lists: the first representing more precise data derived from testing healthy subjects and the second primarily from individuals with impaired glucose metabolism. RESULTS—The tables, which are available in the online-only appendix, list the GI of over 2,480 individual food items. Dairy products, legumes, and fruits were found to have a low GI. Breads, breakfast cereals, and rice, including whole grain, were available in both high and low GI versions. The correlation coefficient for 20 staple foods tested in both healthy and diabetic subjects was r = 0.94 (P < 0.001). CONCLUSIONS—These tables improve the quality and quantity of GI data available for research and clinical practice.

The role of glycemic index (GI) in the prevention of diabetes remains controversial. Some, but not all, prospective cohort studies have yielded positive relationships (1–8). A significant limitation of studies to date has been the fact that the food frequency questionnaires (FFQs) used were not validated for their ability to accurately assess GI. The purpose of this study was to assess the relationship of GI and fiber with incidence of type 2 diabetes in older Australians whose dietary intake was estimated by a fully validated FFQ. In 1991, 4,433 residents aged 49+ years were identified, of which 3,654 (82%) participated in detailed examinations during 1992–1994 (9,10). Of these, 2,335 (75% of survivors) returned for 5-year and 1,952 (76%) for 10-year examinations (2002–2004). Individuals were largely of Caucasian origin and broadly representative of the older Australian population (11). Diagnostic criteria were either self-reported diabetes and current use of diabetes medications or fasting glucose concentration ≥126 mg/dl (12). Diabetes incidence was defined in participants without diabetes at baseline who were diagnosed with diabetes before or at the 5- or 10-year follow-up. In total, 2,564 individuals were followed from baseline. After excluding 163 participants with existing diabetes and 52 with missing data, 2,349 were considered at risk of incident diabetes. An additional 226 participants did not have fasting blood tests at either the 5- or …

This paper describes the compilation of a special edition of the AUSNUT2011-2013 food composition database that includes glycemic index (GI) values. A 6-step, systematic methodology was used to assign GI to 5644 foods included in AUSNUT2011-2013. A total of 1752 (31%) foods were assigned a GI of 0 owing to low carbohydrate content; 363 (6%) had a direct match in 1 of the 4 data tables used; 1738 (31%) were assigned the GI of a 'closely related' food item; 1526 (27%) were assigned the weighted mean GI of ingredients; 205 (4%) were assigned the median GI of their corresponding food subgroup; 49 (

and maintenance (RE-AIM) [3]. Progress is measured against contracted programme objectives and implementation against delivery objectives (number of visits over time, number of nutrition and physical activity sessions and review of six monthly centre driven action plans). Under 5 Energize has reached 93% of targeted centres, 88% have signed up to the programme and 61% have adopted a nutrition and physical activity action plan since August 2013. The population reached is representative of theWaikato population and matches programme objectives. Evidence for implementation includes 62 nutrition and physical activity education sessions involving 720 educators and parents. Monitoring is underway to record changes in the food and physical activity environment. ‘Energizer’ weekly progress reports already show changes occurring in policy and practice such as the replacement of sugary drinks with water.

High consumption of refined carbohydrate, in particular sugar, has been identified as a possible contributory factor in greater risk of excess weight gain. In spite of data limitations, one recent paper suggests that Australian sugar consumption has decreased over the same time period that obesity has increased, a so called 'Australian Paradox'. Given the significant public health focus on nutrition, we aimed to estimate Australian sugar supply and consumption over recent decades, to determine whether these data could be used to make any conclusions about sugar's role in obesity.Foods high in sugar were identified. Data relating to sugar supply and consumption from 1988 to 2010 were obtained from multiple sources. Using these data we attempted to generate a time series estimate of sugar in Australia's food supply.Australia produces and exports sugar from sugar cane and the sugar in imported foods has received little attention. We were unable to produce a reliable and robust estimate of total sugars in the Australian diet due to data limitations and a lack of current data sources. However, available Import data showed large increases in the volume and value of imported sweetened products between 1988 and 2010 to over 30 grams of sugar per person per day. Value estimates of local production of sweetened products also show substantial increases in this period.The Australian Paradox assertion is based on incomplete data, as it excludes sugar contained in imported processed foods, which have increased markedly. A major Australian public health target is to improve the quality of the food supply, and actions have been set in terms of achieving broader environmental changes. However, evaluation of progress is hampered by lack of high quality data relating to supply and consumption. We recommend the regular collection of comprehensive food supply statistics, which include both local production and imports. This would provide an inexpensive addition to survey data and could assist in monitoring sugar consumption trends in food supply. Such information would also help inform public health policy.

Powdered milk products for children (Growing Up Milk Powders or GUMPs) containing added carbohydrates such as glucose and sucrose are now well established in parts of Asia. We surveyed GUMPs in Malaysia and Indonesia to determine the content of added carbohydrates. The ingredient lists and nutrition information panels were used to calculate the percentage of declared carbohydrates contributed by added carbohydrates and a subset of seven products was tested for their glycemic index (GI) and insulin responses in healthy adults. The glycemic load for each product was calculated. In total, 58 products (n = 24 in Malaysia and n = 34 in Indonesia) were surveyed. Added carbohydrate content (excluding fibre) ranged from 0 to 21.5 g per serve. Milk powders without added sources of carbohydrate had similar GI values to standard liquid whole milk. Products containing maltodextrins, corn or glucose syrups increased the GI by more than 2-fold, and glycemic load (GL) by 7-fold compared to milk powders with no added carbohydrates. Insulin responses were significantly but not strongly correlated with glucose responses (r = 0.32, p < 0.006). Children’s milk powders containing higher levels of added carbohydrate ingredients elicit higher glucose and insulin responses than liquid or powdered whole milk.

Dietary patterns that induce excessive insulin secretion may contribute to worsening insulin resistance and beta-cell dysfunction. Our aim was to generate mathematical algorithms to improve the prediction of postprandial glycaemia and insulinaemia for foods of known nutrient composition, glycemic index (GI) and glycemic load (GL). We used an expanded database of food insulin index (FII) values generated by testing 1000 kJ portions of 147 common foods relative to a reference food in lean, young, healthy volunteers. Simple and multiple linear regression analyses were applied to validate previously generated equations for predicting insulinaemia, and develop improved predictive models. Large differences in insulinaemic responses within and between food groups were evident. GL, GI and available carbohydrate content were the strongest predictors of the FII, explaining 55%, 51% and 47% of variation respectively. Fat, protein and sugar were significant but relatively weak predictors, accounting for only 31%, 7% and 13% of the variation respectively. Nutritional composition alone explained only 50% of variability. The best algorithm included a measure of glycemic response, sugar and protein content and explained 78% of variation. Knowledge of the GI or glycaemic response to 1000 kJ portions together with nutrient composition therefore provides a good approximation for ranking of foods according to their "insulin demand".

Background: Low–glycemic index (low-GI) diets may be less nutritious because of limited food choices. Alternately, high-GI diets could be less healthful because of a higher intake of refined carbohydrate. Objective: The objective was to investigate the association between dietary GI, intakes of carbohydrates from high-GI (CHOhigh GI) and low-GI (CHOlow GI) sources, and the risk of nutrient inadequacy in children and adolescents. Design: Children, aged 2–16 y, who provided 2 plausible 24-h recalls in a national survey were included (n = 4140). The ORs of not meeting the Australian Nutrient Reference Values (NRVs) were calculated by logistic regression. Results: Subjects with higher intakes of CHOhigh GI were found to be at risk of not meeting the NRVs for a wide range of nutrients, including calcium and iodine (both P-trend , 0.001). In comparison with subjects in the lowest quartile of CHOhigh GI, those in the highest quartile had 3 times (adjusted OR: 3.13; 95% CI: 2.47, 3.97; P-trend , 0.001) the risk of not meeting the Estimated Average Requirement for calcium. For iodine, the risk increased .5-fold (adjusted OR: 5.45; 95% CI: 3.97, 7.48; P-trend , 0.001). On the other hand, subjects with higher intakes of CHOlow GI were less likely to meet Adequate Intakes of unsaturated fatty acids (all Ptrend , 0.001), despite having lower risks of not meeting the NRVs for most nutrients. Conclusion: Children and adolescents who consume more CHOlow GI are more likely to meet most nutrient recommendations than those consuming higher GI diets. Am J Clin Nutr doi: 10.3945/ajcn. 111.015271.

Puberty is a so-called critical period for overweight development and is characterized by physiological insulin resistance during mid-puberty. This study addressed the hypothesis that habitual consumption of a diet inducing higher levels of postprandial glycemia or insulinemia during puberty may have an unfavorable effect on the body composition in young adulthood.Multivariate regression analysis was performed on 262 participants of the Dortmund Nutritional and Anthropometric Longitudinally Designed Study with at least two 3-day weighed dietary records during puberty (baseline: girls 9-14 years; boys 10-15 years) and anthropometric measurements in young adulthood (18-25 years). A published dietary glycemic index was assigned to each carbohydrate-containing food. Similarly, each food was assigned a food insulin index (insulinemic response to a 1 MJ portion of food relative to 1 MJ of glucose) using 121 values measured at Sydney University.Dietary glycemic index or glycemic load during puberty was not related to body composition in young adulthood. In contrast, a higher dietary insulin index and a higher dietary insulin load during puberty were associated with higher levels of percentage of body fat (%BF) in young adulthood, even after adjustment for early life, socioeconomic and nutritional factors; %BF in energy-adjusted tertiles of dietary insulin index were 22.9 (95% confidence intervals (CI): 21.6, 24.1), 24.5 (23.2, 25.7), 24.7 (23.5, 25.9) %, P (for trend)=0.01; %BF in energy-adjusted tertiles of dietary insulin load were 22.8 (95% CI: 21.5, 24.0), 24.5 (23.2, 25.7), 24.8 (23.6, 26.0) %, P (for trend)=0.01. Adjustment for baseline %BF attenuated these relationships (P (for trend)=0.1 and=0.08, respectively). Dietary insulin demand was not related to body mass index.This study suggests a prospective adverse influence of dietary insulin demand during puberty on %BF in young adulthood. Postprandial increases in insulinemia rather than increases in glycemia appear to be implicated in an unfavorable development of body composition.

To date there are 11 medium to long-term studies that have specifically used the glycemic index (GI) approach to determine the clinical gains in diabetes or lipid management. All but one study produced positive findings. On average, low-GI diets reduced glycosylated hemoglobin by 9%, fructosamine by 8%, urinary C-peptide by 20%, and day-long blood glucose by 16%. Cholesterol was reduced by an average of 6% and triglycerides by 9%. These are modest improvements but so too were the changes to the diet. Unlike high-fiber diets, low-GI diets are "user friendly." As part of studies on the GI of foods, we determined the glycemic and insulin responses to 44 foods containing simple sugars. Their mean (+/- SE) GI was 62 +/- 14, which compares favorably with bread (GI = 73, glucose = 100). There was often no difference in the GI between the sweetened and unsweetened product. The time has come to reassess the value of GI in planning meals for diabetics.

Ecological research from the USA has demonstrated a positive relationship between sugars consumption and prevalence of obesity; however, the relationship in other nations is not well described. The aim of this study was to analyze the trends in obesity and sugar consumption in Australia over the past 30 years and to compare and contrast obesity trends and sugar consumption patterns in Australia with the UK and USA. Data on consumption of sugar in Australia, the UK and USA were obtained from the Food and Agriculture Organization for the years 1980–2003. The prevalence of obesity has increased 3 fold in Australians since 1980. In Australia, the UK and USA, per capita consumption of refined sucrose decreased by 23%, 10% and 20% respectively from 1980 to 2003. When all sources of nutritive sweeteners, including high fructose corn syrups, were considered, per capita consumption decreased in Australia (−16%) and the UK (−5%), but increased in the USA (+23%). In Australia, there was a reduction in sales of nutritively sweetened beverages by 64 million liters from 2002 to 2006 and a reduction in percentage of children consuming sugar-sweetened beverages between 1995 and 2007. The findings confirm an “Australian Paradox”—a substantial decline in refined sugars intake over the same timeframe that obesity has increased. The implication is that efforts to reduce sugar intake may reduce consumption but may not reduce the prevalence of obesity.

The purpose of the present study was to determine the influence of the glycaemic index (GI) and glycaemic load (GL) on the ratio of tryptophan (TRP) relative to other large neutral amino acids (LNAA). Ten healthy men (age 22·9 (sd3·4) years; BMI 23·5 (sd1·6) kg/m2) underwent standard GI testing, and later consumed each of a mixed-macronutrient (1915 kJ; 66·5 % carbohydrate (CHO), 17 % protein and 16·5 % fat) high-GI (MHGI), an isoenergetic, mixed-macronutrient low-GI (MLGI) and a CHO-only (3212 kJ; 90 % CHO, 8 % protein, 2 % fat) high-GI (CHGI) meal on separate days. The GI, GL and insulin index values (e.g. area under the curve) were largest after the CHGI meal (117, 200, 158), followed by the MHGI (79, 59, 82) and MLGI (51, 38, 56) meals, respectively (all values were significantly different,P P P P P = 0·38), but it was threefold and nearly significantly greater than MLGI (8 %;P = 0·05). The present study demonstrates that the postprandial rise in TRP:LNAA was increased by additional CHO ingestion and higher GI. Therefore, the meal GL appears to be an important factor influencing the postprandial TRP:LNAA concentration.

The effect of bread consumption on overall food intake is poorly understood. The aim of this study was to measure postprandial food intake after a set breakfast containing three different breads. Ten males and 10 females aged 20.1–44.8 years, BMI 18.4–24.8 kg/m2, consumed two slices of White Bread, Bürgen® Wholemeal and Seeds Bread or Lupin Bread (all 1300 kJ) with 10 g margarine and 30 g strawberry jam. Fullness and hunger responses and were measured before and during the test breakfasts. Glucose and insulin responses (incremental area under each two-hour curve (iAUC)) were calculated. Food intake was measured and energy and nutrient intake determined at a buffet meal two hours later. Subjects consumed significantly less energy after the Bürgen® Bread meal compared to the White Bread meal (2548 ± 218 vs. 3040 ± 328 kJ, Bürgen® Bread vs. White Bread, P < 0.05). There were higher fullness responses for the Lupin Bread (P < 0.01), and the Bürgen® Bread (P < 0.05) compared with the White Bread. Lupin Bread and Bürgen® Bread produced smaller postprandial glucose responses (79 ± 7, 74 ± 4, 120 ± 10 mmol/L min iAUC, Lupin, Bürgen® and White Bread respectively, P < 0.01). Differences in insulin responses were also observed (6145 ± 1048, 6471 ± 976, 9674 ± 1431 pmol/L min iAUC, Lupin, Bürgen® and White Bread respectively, P < 0.01). Equal-energy portions of three different commercially available breads differed in their short-term satiation capacity. Further studies are needed to demonstrate any potential benefit for weight management. Refereed/Peer-reviewed

New US guidelines for the management of overweight and obesity have disregarded robust evidence from intervention trials demonstrating the importance of dietary factors for weight control and disease prevention. This omission implies that diet composition does not matter, a stance that is potentially counterproductive for prevention and treatment strategies.

Age-related hearing loss is a frequent disability in older adults and nutrition could play a role in the development of this condition. Carbohydrate nutrition [including dietary glycemic index (GI) and load (GL)] may be linked to hearing loss. We aimed to determine the association between carbohydrate nutrition (including mean dietary GI and GL, and the dietary intakes of carbohydrate and sugar), starch, cereal and total fiber, and age-related hearing loss. The Blue Mountains Hearing Study is a population-based survey of age-related hearing loss (1997-1999 to 2002-2004). Hearing loss was measured in 2956 participants (aged ≥50 y) and was defined as the pure-tone average of frequencies 0.5, 1.0, 2.0, and 4.0 kHz > 25 dB hearing level. Dietary data were collected in a semiquantitative FFQ. A purpose-built database based on Australian GI values was used to calculate the mean GI. A higher mean dietary GI was associated with an increased prevalence of any hearing loss, comparing quintiles 1 (lowest) and 5 (highest), [multivariable-adjusted odds ratio = 1.41 (95% CI = 1.01-1.97)]. Participants in the highest quartile of mean dietary GL intake compared with those in the lowest quartile had a 76% greater risk of developing incident hearing loss (P-trend = 0.04). Higher carbohydrate and sugar intakes were associated with incident hearing loss (P-trend = 0.03 and P-trend = 0.05, respectively). In summary, a high-GL diet was a predictor of incident hearing loss, as was higher intake of total carbohydrate. Hence, high postprandial glycemia might be a potential underlying biological mechanism in the development of age-related hearing loss.

Background: Circulating insulin levels have been positively associated with risk of colorectal cancer; however, it remains unclear whether a diet inducing an elevated insulin response influences colorectal cancer risk. On the basis of a novel insulin index for individual foods, we estimated insulin demand for overall diets and assessed its association with colorectal cancer in the Nurses' Health Study and Health Professionals Follow-up Study. Methods: We followed 86,740 women and 46,146 men who were free of cancer and diabetes at baseline and identified a total of 2,481 colorectal cancer cases during up to 26 years of follow-up. Dietary insulin load was calculated as a function of food insulin index and the energy content of individual foods was reported on food frequency questionnaires. Average dietary insulin index was calculated by dividing the dietary insulin load by the total energy intake. Results: Dietary insulin load and dietary insulin index were not associated with risk of colorectal cancer. Comparing the highest with the lowest quintiles, the pooled multivariate relative risks of colorectal cancer were 0.91 (95% CI = 0.79–1.05) for dietary insulin load and 0.93 (95% CI = 0.81–1.08) for dietary insulin index. Body mass index and physical activity did not modify the association of dietary insulin load or index with colorectal cancer. Conclusion: A diet high in foods that increase postprandial insulin levels did not increase the risk of colorectal cancer in this large prospective study. Impact: This study is the first to investigate insulin index and load in relation to colorectal cancer. Cancer Epidemiol Biomarkers Prev; 19(12); 3020–6. ©2010 AACR.

Our aim was to design a selection of foods with differing proportions of protein but equal palatability in two settings, Sydney Australia and Kingston Jamaica. The foods were manipulated to contain 10, 15 or 25% E as protein with reciprocal changes in carbohydrate to 60, 55 or 45% E and dietary fat was kept constant at 30%. Naive participants did not identify a difference in protein between the versions. On average, the versions were rated equal in pleasantness (Sydney-10%: 44 ± 2, 15%: 49 ± 2 and 25%: 49 ± 2 Kingston-10%: 41 ± 3, 15%: 41 ± 3 and 25%: 37 ± 3).

Background: Consumption of 3 g oat b-glucan/d is considered sufficient to lower serum LDL cholesterol, but some studies have shown no effect. LDL cholesterol lowering by oat b-glucan may depend on viscosity, which is controlled by the molecular weight (MW) and amount of oat b-glucan solubilized in the intestine (C). Objectives: Our 2 primary objectives were to determine whether consumption of 3 g high-MW oat b-glucan/d would reduce LDL cholesterol and whether LDL cholesterol lowering was related to the log(MW · C) of oat b-glucan. Design: In a double-blind, parallel-design, multicenter clinical trial, subjects with LDL cholesterol � 3.0 and � 5.0 mmol/L (n = 786 screened, n = 400 ineligible, n = 19 refused, n = 367 enrolled, and n = 345 completed) were randomly assigned to receive cereal containing wheat fiber (n = 87) or 3 g high-MW (2,210,000 g/mol, n = 86), 4 g medium-MW (850,000 g/mol, n = 67), 3 g mediumMW (530,000 g/mol, n = 64), or 4 g low-MW (210,000 g/mol, n = 63) oat b-glucan/d (divided doses, twice daily) for 4 wk. Results: LDL cholesterol was significantly less with 3 g high-MW, 4 g medium-MW, and 3 g medium-MW oat b-glucan cereals than with the wheat-fiber cereal by 0.21 (5.5%; 95% CI: 20.11, 20.30; P = 0.002), 0.26 (6.5%; 95% CI: 20.14, 20.37; P = 0.0007), and 0.19 (4.7%; 95% CI: 20.08, 20.30; P = 0.01) mmol/L, respectively. However, the effect of 4 g low-MW oat b-glucan/d (0.10 mmol/L) was not significant (2.3%; 95% CI: 0.02, 20.20). By analysis of covariance, log(MW · C) was a significant determinant of LDL cholesterol (P = 0.003). Treatment effects were not significantly influenced by age, sex, study center, or baseline LDL cholesterol. Conclusions: The physicochemical properties of oat b-glucan should be considered when assessing the cholesterol-lowering ability of oat-containing products; an extruded breakfast cereal containing 3 g oat b-glucan/d with a high-MW (2,210,000 g/mol) or a mediumMW (530,000 g/mol) lowered LDL cholesterol similarly by ’0.2 mmol/L (5%), but efficacy was reduced by 50% when MW was reduced to 210,000 g/mol. This trial was registered at www.clinicaltrials.gov as NCT00981981. Am J Clin Nutr 2010;92:723‐ 32.

Background Several studies suggest that carbohydrate nutrition is related to oxidative stress and inflammatory markers. Objective We examined whether dietary glycemic index (GI), dietary fiber, and carbohydrate-containing food groups were associated with the mortality attributable to noncardiovascular, noncancer inflammatory disease in an older Australian cohort. Design Analysis included 1490 postmenopausal women and 1245 men aged ge 49 y at baseline (1992-1994) from a population-based cohort who completed a validated food-frequency questionnaire. Cox proportional hazards ratios were calculated both for death from diseases in which inflammation or oxidative stress was a predominant contributor and for cardiovascular mortality. Results Over a 13-y period, 84 women and 86 men died of inflammatory diseases. Women in the highest GI tertile had a 2.9-fold increased risk of inflammatory death compared with women in the lowest GI tertile [multivariate hazard ratio in energy-adjusted tertile 3 (tertile 1 as reference): 2.89; 95% CI: 1.52, 5.51; P for trend: 0.0006, adjusted for age, smoking, diabetes, and alcohol and fiber consumption]. Increasing intakes of foods high in refined sugars or refined starches (P = 0.04) and decreasing intakes of bread and cereals (P = 0.008) or vegetables other than potatoes (P = 0.007) also independently predicted a greater risk, with subjects' GI partly explaining these associations. In men, only an increased consumption of fruit fiber (P = 0.005) and fruit (P = 0.04) conferred an independent decrease in risk of inflammatory death. No associations were observed with cardiovascular mortality. Conclusion These data provide new epidemiologic evidence of a potentially important link between GI and inflammatory disease mortality among older women.

Background: Women with polycystic ovarian syndrome (PCOS) are intrinsically insulin resistant and have a high risk of cardiovascular disease and type 2 diabetes. Weight loss improves risk factors, but the optimal diet composition is unknown. Low–glycemic index (low-GI) diets are recommended without evidence of their clinical effectiveness. Objective: We compared changes in insulin sensitivity and clinical outcomes after similar weight losses after consumption of a low-GI diet compared with a conventional healthy diet in women with PCOS. Design: We assigned overweight and obese premenopausal women with PCOS (n = 96) to consume either an ad libitum low-GI diet or a macronutrient-matched healthy diet and followed the women for 12 mo or until they achieved a 7% weight loss. We compared changes in whole-body insulin sensitivity, which we assessed using the insulin sensitivity index derived from the oral-glucose-tolerance test (ISIOGTT); glucose tolerance; body composition; plasma lipids; reproductive hormones; health-related quality of life; and menstrual cycle regularity. Results: The attrition rate was high in both groups (49%). Among completers, ISIOGTT improved more with the low-GI diet than with the conventional healthy diet (mean 6 SEM: 2.2 6 0.7 compared with 0.7 6 0.6, respectively; P = 0.03). There was a significant dietmetformin interaction (P = 0.048), with greater improvement in ISIOGTT among women prescribed both metformin and the low-GI diet. Compared with women who consumed the conventional healthy diet, more women who consumed the low-GI diet showed improved menstrual cyclicity (95% compared with 63%, respectively; P = 0.03). Among the biochemical measures, only serum fibrinogen concentrations showed significant differences between diets (P , 0.05). Conclusion: To the best of our knowledge, this study provides the first objective evidence to justify the use of low-GI diets in the management of PCOS. Am J Clin Nutr 2010;92:83–92.

Publisher Summary Although insulin has a number of metabolic effects, insulin resistance is usually defined as a state in which physiological levels of insulin have a decreased biological action on plasma glucose. Glucose uptake by skeletal muscle and adipose tissue and suppression of hepatic glucose production are affected. To maintain normoglycemia in the insulin-resistant state, excessive compensatory increases in insulin are required which may eventually lead to a decline in and exhaustion of insulin-producing pancreatic beta cells, the development of glucose intolerance, and, finally, type 2 diabetes. Insulin resistance is associated with a constellation of traits other than glucose intolerance, including visceral obesity, dyslipidemia, hypertension, and a prothrombotic state. Epidemiological studies consistently show an independent association between insulin resistance and risk of cardiovascular disease. The term insulin resistance is often used interchangeably with decreased insulin sensitivity or reduced insulin action. There are wide differences in the ability of insulin to mediate glucose disposal among individuals. Insulin sensitivity is a continuous variable, and distinguishing normal and abnormal insulin sensitivity in individuals is difficult because there is no uniform quantitative definition of what constitutes insulin resistance. Individuals are considered insulin resistant if they have normal glucose tolerance but lie in the most insulin-resistant quartile of a given population. In individuals with normal glucose tolerance, insulin resistance can vary by four- to tenfold, with some measures of resistance not differing substantially from those of people with impaired glucose tolerance (IGT) or type 2 diabetes.

change (3.66 kg [2.41, 4.93; 95% CI], P < 0.001), BMI (1.05 kgm−2 [0.62, 1.47; 95% CI], P < 0.001), waist circumference (4.98 cm [2.60, 7.35; 95% CI], P < 0.001), fat mass (2.99 kg [0.56, 5.41; 95% CI], P = 0.016), skeletal muscle mass (1.70 kg [0.35, 3.05; 95% CI], P = 0.014), predicted VO2max (2.37ml kg−1 min−1 [0.27, 4.47; 95% CI], P = 0.027) and insulin (2.77mIU/l [0.49, 5.05; 95% CI], P = 0.018). Conclusion: A home-based multi-component lifestyle program can provide significant health improvements for men at risk of T2D.

Prospective evidence on the extent to which serum lipid concentrations in older persons respond to dietary modification is scarce. It is not clear whether such behavioral changes are relevant in the context of more commonly initiated treatments with lipid-lowering drugs. We therefore examined whether individual changes in the consumption of dietary fatty acids or main food sources were associated with changes in the serum lipid profile of older Australians. A total of 903 participants (> or =49 y) in the Blue Mountains Eye Study had complete data on fasting lipids and dietary intake from a validated FFQ at baseline (1992-1994) and 5- and 10-y follow-up examinations. Decreasing consumption of SFA and butter during the 10-y period were associated with moderate decreases in serum total cholesterol independently of initiation of lipid-lowering drug treatment [adjusted estimates were 0.018 +/- 0.007 mmol/(L x % energy (%en) from SFA (P = 0.01) and 0.055 +/- 0.015 mmol/(L x 5 g butter) (P = 0.0003), respectively]. Increased consumption of (n-3) fatty acids and fish was independently related to modest increases in serum HDL-cholesterol [0.067 +/- 0.026 mmol/(L x %en from (n-3) fatty acids) (P = 0.01) and 0.010 +/- 0.004 mmol/(L x 20 g fish) (P = 0.02)] and decreases in log-transformed serum triglyceride concentrations [P = 0.02 for (n-3) fatty acids and P = 0.02 for fish intake]. Hence, 10-y changes in the intake of dietary fatty acids and their food sources appear to have contributed to concurrent improvements in the serum lipid profile of older Australians, independent of concomitantly initiated lipid-lowering drug treatment.

Background: Diets that provoke less insulin secretion may be helpful in the prevention and management of diabetes. A physiologic basis for ranking foods according to insulin “demand” could therefore assist further research. Objective: We assessed the utility of a food insulin index (FII) that was based on testing isoenergetic portions of single foods (1000 kJ) in predicting the insulin demand evoked by composite meals. Design: Healthy subjects (n = 10 or 11 for each meal) consumed 13 different isoenergetic (2000 kJ) mixed meals of varying macronutrient content. Insulin demand predicted by the FII of the component foods or by carbohydrate counting and glycemic load was compared with observed insulin responses. Results: Observed insulin responses (area under the curve relative to white bread: 100) varied over a 3-fold range (from 35 6 5t o 116 6 26) and were strongly correlated with insulin demand predicted by the FII of the component foods (r = 0.78, P = 0.0016). The calculated glycemic load (r = 0.68, P = 0.01) but not the carbohydrate content of the meals (r = 0.53, P = 0.064) also predicted insulin demand. Conclusions: The relative insulin demand evoked by mixed meals is best predicted by a physiologic index based on actual insulin responses to isoenergetic portions of single foods. In the context of composite meals of similar energy value, but varying macronutrient content, carbohydrate counting was of limited value. Am J Clin Nutr 2009;90:986–92.

Response: We thank Lim and colleagues for their interesting letter regarding potential dietary effects on the microvasculature in healthy children. Their approach is based on the ability to limit both socioeconomic and disease confounders by examining the retinal microcirculation in children, which has been useful to examine the early course of conditions like hypertension.1 For the reasons listed below, however, we believe that the findings by Lim et al are not directly comparable to our reported findings. We specifically examined the glycemic index of foods consumed and cereal fiber rather than separate intakes of carbohydrates, sugar and total fiber: food …

Background: Studies on obesity and glycemic index (GI) or glycemic load (GL) have had inconsistent results, perhaps in part because of underreporting or to heterogeneous dietary patterns across food cultures.

Udgivelsesdato: 2009-Mar Associations between the glycemic index (GI) or glycemic load (GL) and diseases are heterogeneous in epidemiological studies. Differences in assigning GI values to food items may contribute to this inconsistency. Our objective was to address methodological issues related to the use of current GI and GL values in epidemiological studies. We performed ecological comparison and correlation studies by calculating dietary GI and GL from country-specific dietary questionnaires (DQ) from 422,837 participants from 9 countries participating in the European Prospective Investigation into Cancer and Nutrition study and single standardized 24-h dietary recalls (24-HDR) obtained from a representative sample (n = 33,404) using mainly Foster Powell's international table as a reference source. Further, 2 inter-rater and 1 inter-method comparison were conducted, comparing DQ GI values assigned by independent groups with values linked by us. The ecological correlation between DQ and 24-HDR was good for GL (overall r = 0.76; P < 0.005) and moderate for GI (r = 0.57; P < 0.05). Mean GI/GL differences between DQ and 24-HDR were significant for most centers. GL but not GI from DQ was highly correlated with total carbohydrate (r = 0.98 and 0.15, respectively; P < 0.0001) and this was higher for starch (r = 0.72; P < 0.0001) than for sugars (r = 0.36; P < 0.0001). The inter-rater and inter-method variations were considerable for GI (weighted kappa coefficients of 0.49 and 0.65 for inter-rater and 0.25 for inter-method variation, respectively) but only mild for GL (weighted kappa coefficients > 0.80). A more consistent methodology to attribute GI values to foods and validated DQ is needed to derive meaningful GI/GL estimates for nutritional epidemiology.

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. 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). Subjects (n=12) 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. The high GI diet resulted in significantly higher glucose and insulin mean incremental areas under the curve (IAUC, P=0.027 and P=0.001 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, P=0.046), but there was no difference over 10 h (P=0.224). GHR concentration was inversely correlated with insulin concentration (Pearson correlation −0.48, P=0.007), but did not differ significantly between the low and high GI diets. 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.

BACKGROUND Dietary factors are known risk factors for age-related macular degeneration (AMD) -- the leading cause of visual loss among persons aged > or =65 y. High-glycemic-index diets have been hypothesized as a risk factor for AMD, but prospective data are unavailable. OBJECTIVE The objective was to examine the association between dietary glycemic index and the 10-y incidence of AMD in the Blue Mountain Eye Study population. DESIGN This was a population-based cohort study with 3,654 participants (> or =49 y) examined at baseline (1992-1994); 2,335 patients were reexamined after 5 y and 1952 after 10 y. The Wisconsin System was used to grade 10-y incident early and late AMD from retinal photographs. A food-frequency questionnaire was used to collect dietary information at baseline, and an Australian database was used to calculate the mean glycemic index. RESULTS Over 10 y, 208 of 1,810 persons (cumulative incidence: 14.1%) developed early AMD. After age, smoking, other risk factors, and dietary constituents were adjusted for, a higher mean dietary glycemic index was associated with an increased 10-y risk of early AMD in a comparison of quartiles 1 and 4 [relative risk (RR): 1.77; 95% CI: 1.13, 2.78; P for trend = 0.03]. Conversely, a greater consumption of cereal fiber (RR: 0.68; 95% CI: 0.44, 1.04; P for trend = 0.05) and breads and cereals (predominantly lower glycemic index foods such as oatmeal) (RR: 0.67; 95% CI: 0.44, 1.02; P for trend = 0.03) was associated with a reduced risk of incident early AMD. No relation was observed with late AMD. CONCLUSIONS A high-glycemic-index diet is a risk factor for early AMD -- the recognized precursor of sight-threatening late AMD. Low-glycemic-index foods such as oatmeal may protect against early AMD.

Recent reports have suggested that the problem of childhood and adolescent obesity has been exaggerated in Australia, and that community-wide obesity prevention initiatives are not warranted; we argue that this is not an accurate reflection of the situation. Available data indicate that obesity affects 6%-8% of Australian schoolchildren, and that the proportion has continued to increase in recent years. Childhood and adolescent obesity is associated with a wide range of immediate health concerns, as well as increasing the risk of disease in adulthood. Some weight-related health problems are also found in overweight children. A range of strategies, including whole-of-community obesity prevention programs, will be required to tackle this problem. Concerns about disordered eating in children and adolescents should not preclude appropriate action on childhood obesity.

Weight loss can be achieved by any means of energy restriction, but the challenge is to achieve sustainable weight loss and prevent weight 'creep' without increasing the risk of chronic disease. The modest success of low fat diets has prompted research on alternative dietary strategies including high protein diets and low glycemic index (GI) diets. Conventional high carbohydrate diets, even when based on whole grain foods, increase postprandial glycaemia and insulinemia and may compromise weight control via mechanisms relating to appetite stimulation, fuel partitioning and metabolic rate. This paper makes the case for the benefits of low glycemic index diets over higher protein diets. Both strategies are associated with lower postprandial glycemia and both are commonly labelled as 'low glycemic load' but the long-term health effects are likely to be different. There is now a large body of evidence comprising observational prospective cohort studies, randomised controlled trials and mechanistic experiments in animal models, that provides robust support for low GI carbohydrate diets in the prevention of obesity, diabetes and cardiovascular disease. While lower carbohydrate, higher protein diets also increase the rate of weight loss, cohort studies and meta-analyses of clinical trials suggest the potential for increased mortality.

High-glycemic index diets have been linked to greater risk of cardiovascular disease and type 2 diabetes. Postprandial glycemia within the normal range may promote oxidative stress and inflammatory processes underlying the development of disease.We explored acute differences in the activation of the inflammatory marker nuclear factor-kappaB after consumption of 2 carbohydrate meals matched for macronutrient and micronutrient composition but differing in glycemic index.After an overnight fast, 10 young, lean healthy subjects were fed in random order 3 meals providing 50 g of available carbohydrate as glucose, white bread, or pasta. Venous blood samples were collected at 0, 1, 2, and 3 h, and nuclear proteins were extracted from mononuclear cells. Changes in nuclear factor-kappaB-p65 proteins were detected by Western blotting. Acute changes in other markers of oxidative stress (nitrotyrosine and soluble intercellular adhesion molecule-1) were also assessed.The maximum increase in nuclear factor-kappaB activation was similar after the bread meal [mean (+/-SEM) area under the curve: 69 +/- 16% optical density x h] and the glucose challenge (75 +/- 9% optical density x h), but was 3 times higher than after the pasta meal (23 +/- 5% optical density x h) (P0.05). Similarly, changes in nitrotyrosine, but not soluble intercellular adhesion molecule-1, were higher after glucose and bread than after pasta (P = 0.01 at 2 h).The findings suggest that high-normal physiologic increases in blood glucose after meals aggravate inflammatory processes in lean, young adults. This mechanism may help to explain relations between carbohydrates, glycemic index, and the risk of chronic disease.

Postprandial hyperglycemia is increasingly recognized as an independent risk factor for cardiovascular disease. Glycemic "spikes" may adversely affect vascular structure and function via multiple mechanisms, including (acutely and/or chronically) oxidative stress, inflammation, low-density lipoprotein oxidation, protein glycation, and procoagulant activity. Postprandial glycemia can be reliably predicted by considering both the amount and type of carbohydrate. In particular, the glycemic index (GI), a measure of postprandial glycemic potency weight for weight of carbohydrate, has provided insights that knowledge of the sugar or starch content has not. In prospective observational studies, dietary GI and/or glycemic load independently predict cardiovascular disease, with relative risk ratios of 1.2 to 1.7 comparing highest and lowest quintiles. In randomized controlled trials in overweight subjects, diets based on low-GI carbohydrates have produced better cardiovascular-related outcomes than conventional low-fat diets. Taken together, the findings suggest that health professionals may be able to improve cardiovascular outcomes by recommending the judicious use of low- GI/glycemic load foods.

Background: Inconsistent findings from observational studies have prolonged the controversy over the effects of dietary glycemic index (GI) and glycemic load (GL) on the risk of certain chronic diseases. Objective: The objective was to evaluate the association between GI, GL, and chronic disease risk with the use of meta-analysis techniques. Design: A systematic review of published reports identified a total of 37 prospective cohort studies of GI and GL and chronic disease risk. Studies were stratified further according to the validity of the tools used to assess dietary intake. Rate ratios (RRs) were estimated in a Cox proportional hazards model and combined by using a random-effects model. Results: From 4 to 20 y of follow-up across studies, a total of 40 129 incident cases were identified. For the comparison between the highest and lowest quantiles of GI and GL, significant positive associations were found in fully adjusted models of validated studies for type 2 diabetes (GI RR 1.40, 95% CI: 1.23, 1.59; GL RR 1.27, 95% CI: 1.12, 1.45), coronary heart disease (GI RR 1.25, 95% CI: 1.00, 1.56), gallbladder disease (GI RR 1.26, 95% CI: 1.13, 1.40; GL RR 1.41, 95% CI: 1.25, 1.60), breast cancer (GI RR 1.08, 95% CI: 1.02, 1.16), and all diseases combined (GI RR 1.14, 95% CI: 1.09, 1.19; GL RR 1.09, 95% CI: 1.04, 1.15). Conclusions: Low-GI and/or low-GL diets are independently associated with a reduced risk of certain chronic diseases. In diabetes and heart disease, the protection is comparable with that seen for whole grain and high fiber intakes. The findings support the hypothesis that higher postprandial glycemia is a universal mechanism for disease progression. Am J Clin Nutr 2008;87:627–37.