Academic literature on the topic 'Glycaemic load'
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Journal articles on the topic "Glycaemic load"
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Emerson, Sam R., Mark D. Haub, Colby S. Teeman, Stephanie P. Kurti, and Sara K. Rosenkranz. "Summation of blood glucose and TAG to characterise the ‘metabolic load index’." British Journal of Nutrition 116, no. 9 (October 24, 2016): 1553–63. http://dx.doi.org/10.1017/s0007114516003585.
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AbstractResearch points to postprandial glucose and TAG measures as preferable assessments of cardiovascular risk as compared with fasting values. Although elevated postprandial glycaemic and lipaemic responses are thought to substantially increase chronic disease risk, postprandial glycaemia and lipaemia have historically only been considered separately. However, carbohydrates and fats can generally ‘compete’ for clearance from the stomach, small intestine, bloodstream and within the peripheral cell. Further, there are previous data demonstrating that the addition of carbohydrate to a high-fat meal blunts the postprandial lipaemic response, and the addition of fat to a high-carbohydrate meal blunts the postprandial glycaemic response. Thus, postprandial glycaemia and lipaemia are interrelated. The purpose of this brief review is 2-fold: first, to review the current evidence implicating postprandial glycaemia and lipaemia in chronic disease risk, and, second, to examine the possible utility of a single postprandial glycaemic and lipaemic summative value, which will be referred to as the metabolic load index. The potential benefits of the metabolic load index extend to the clinician, patient and researcher.
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OʼReilly, John, Stephen H. S. Wong, and Yajun Chen. "Glycaemic Index, Glycaemic Load and Exercise Performance." Sports Medicine 40, no. 1 (January 2010): 27–39. http://dx.doi.org/10.2165/11319660-000000000-00000.
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Jones, M. E., J. Louie, A. Barclay, and J. Brand-Miller. "Dietary glycaemic index and glycaemic load among Australians." Journal of Nutrition & Intermediary Metabolism 4 (June 2016): 9. http://dx.doi.org/10.1016/j.jnim.2015.12.180.
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Prasad, Madhrapakkam Pagadala Rajendra, Benhur Dayakar Rao, Kommi Kalpana, Mendu Vishuvardhana Rao, and Jagannath Vishnu Patil. "Glycaemic index and glycaemic load of sorghum products." Journal of the Science of Food and Agriculture 95, no. 8 (September 1, 2014): 1626–30. http://dx.doi.org/10.1002/jsfa.6861.
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YAMANOUCHI, Toshikazu, Tae INOUE, Eri OGATA, Akiko KASHIWABARA, Nobuyuki OGATA, Nori SEKINO, Tomoe YOSHIMURA, Kaoru ICHIYANAGI, and Takahiro KAWASAKI. "Post-load glucose measurements in oral glucose tolerance tests correlate well with 1,5-anhydroglucitol, an indicator of overall glycaemic state, in subjects with impaired glucose tolerance." Clinical Science 101, no. 3 (August 3, 2001): 227–33. http://dx.doi.org/10.1042/cs1010227.
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Using both cross-sectional and longitudinal methods, we investigated the relationship between post-load serum glucose concentration in a 75 g oral glucose tolerance test (OGTT) and overall glycaemic state in subjects with impaired glucose tolerance (IGT). Glycaemic state was assessed by measuring glycated haemoglobin (HbA1c) and the serum concentration of 1,5-anhydroglucitol (1,5-AG). In the cross-sectional study, the concentration of 1,5-AG, while remaining within a normal range, was reduced to a degree proportional to the post-load glycaemic level. Although the correlation between HbA1c and post-load plasma glucose was relatively weak (r = 0.281, P < 0.001), a significant inverse correlation (r =-0.824, P < 0.0001) was found between 1,5-AG and mean post-load plasma glucose concentration in 211 subjects with IGT. Fasting plasma glucose (r =-0.539, P < 0.0001) and 2 h plasma glucose (r =-0.621, P < 0.0001) were correlated with 1,5-AG less strongly than was post-load glycaemia. Both 1,5-AG and HbA1c were correlated weakly but significantly with the fasting insulin concentration. In the longitudinal study we measured 1,5-AG and mean post-load plasma glucose with an OGTT once yearly for 10 years in 15 subjects with IGT. Strong inverse correlations were seen between 1,5-AG and mean post-load plasma glucose in each subject (range of r values among subjects of -0.584 to -0.978). These findings suggest a close relationship between post-load plasma glucose concentration measured by OGTT and overall glycaemic state in subjects with IGT.
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Silvera, Stephanie AN, Thomas E. Rohan, Meera Jain, Paul D. Terry, Geoffrey R. Howe, and Anthony B. Miller. "Glycaemic index, glycaemic load and risk of endometrial cancer: a prospective cohort study." Public Health Nutrition 8, no. 7 (October 2005): 912–19. http://dx.doi.org/10.1079/phn2005741.
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AbstractObjectiveHigh-glycaemic-load diets may increase endometrial cancer risk by increasing circulating insulin levels and, as a consequence, circulating oestrogen levels. Given the paucity of epidemiological data regarding the relationship between dietary glycaemic index and glycaemic load and endometrial cancer risk, we sought to examine these associations using data from a prospective cohort study.Design, setting and subjectsWe examined the association between dietary glycaemic load and endometrial cancer risk in a cohort of 49 613 Canadian women aged between 40 and 59 years at baseline who completed self-administered food-frequency questionnaires between 1982 and 1985. Linkages to national mortality and cancer databases yielded data on deaths and cancer incidence, with follow-up ending between 1998 and 2000.ResultsDuring a mean of 16.4 years of follow-up, we observed 426 incident cases of endometrial cancer. Hazard ratios for the highest versus the lowest quartile level of overall glycaemic index and glycaemic load were 1.47 (95% confidence interval (CI) = 0.90–2.41; P for trend = 0.14) and 1.36 (95% CI = 1.01–1.84; P for trend = 0.21), respectively. No association was observed between total carbohydrate or total sugar consumption and endometrial cancer risk. Among obese women (body mass index > 30 kg m−2) the hazard ratio for the highest versus the lowest quartile level of glycaemic load was 1.88 (95% CI = 1.08–3.29; P for trend = 0.54) and there was a 55% increased risk for the highest versus the lowest quartile level of glycaemic load among premenopausal women. There was also evidence to support a positive association between glycaemic load and endometrial cancer risk among postmenopausal women who had used hormone replacement therapy.ConclusionsOur data suggest that diets with high glycaemic index or high glycaemic load may be associated with endometrial cancer risk overall, and particularly among obese women, premenopausal women and postmenopausal women who use hormone replacement therapy.
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Monro, John. "Expressing the glycaemic potency of foods." Proceedings of the Nutrition Society 64, no. 1 (February 2005): 115–22. http://dx.doi.org/10.1079/pns2004401.
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The glycaemic index (GI) was introduced to guide food exchanges within equicarbohydrate food categories, and it expresses the glycaemic potency of the available carbohydrate component in a food relative to that of glucose. As GI is a relative value based on ‘available carbohydrate’ it cannot guide food choice for glycaemic control unless the foods are equal in available carbohydrate. Furthermore, GI cannot respond to food intake or to effects on food glycaemic potency of replacing glycaemic ingredients with non-glycaemic ingredients. The glycaemic glucose equivalent (GGE) overcomes these limitations of GI. The GGE content of an amount of food is the weight of glucose (g) that would induce a glycaemic response equal to that induced by the food. Few studies have compared GI and GGE as guides to food choice for glycaemic control, but in a direct test of the predictive validity of GGE in a group of foods of differing carbohydrate and GI, GGE predicted glycaemic potency well, whereas GI was unrelated to glycaemic effect. Furthermore, an information-processing model of the use of food information in food choice shows that GI has fundamental flaws when used outside the restriction of equicarbohydrate food exchange categories. As a general guide to food choices for the control of glycaemia GI does not satisfy the criteria predictive validity, accuracy, safety, ease of use, flexibility, sufficiency and compatability, whereas GGE does. GGE is also a scientifically precise and meaningful term with which to express glycaemic potency than is ‘glycaemic load’.
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Pardo-Buitimea, Naysin Yaheko, Montserrat Bacardí-Gascón, Lidia Castañeda-González, and Arturo Jiménez-Cruz. "Glycaemic index and glycaemic load of three traditional Mexican dishes." International Journal of Food Sciences and Nutrition 63, no. 1 (July 29, 2011): 114–16. http://dx.doi.org/10.3109/09637486.2011.604306.
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Kamchansuppasin, Achiraya, Prapaisri P. Sirichakwal, Luksana Bunprakong, Uruwan Yamborisut, Ratchanee Kongkachuichai, Wantanee Kriengsinyos, and Jureeporn Nounmusig. "Glycaemic index and glycaemic load of commonly consumed Thai fruits." International Food Research Journal 28, no. 4 (August 1, 2021): 788–94. http://dx.doi.org/10.47836/ifrj.28.4.15.
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The present work was aimed to determine the glycaemic index (GI) and glycaemic load (GL) of commonly consumed Thai fruits for the potential risk of chronic diseases. Healthy subjects consumed 25 g available carbohydrate (fruits and glucose) in random order. Eighteen fruits were classified as low GI (26.5 - 54.8%) including jujube, unripe mango, banana (Kluai-Namwa, Kluai-Khai, and Kluai-Leb-Mu-Nang varieties), guava, tamarind, jackfruit, durian (Monthong and Chanee varieties), tangerine, longan, starfruit, pomelo (Thong Dee variety), sapodilla, white dragon fruit, sala, and rambutan. Fruits with medium GI (55.4 - 69.6%) includes pomelo (Kao Nampheung variety), banana (Kluai Hom variety), red dragon fruit, watermelon, coconut, mangosteen, longkong, ripe mango, papaya, rose apple, and lychee. Pineapple has a high GI value. Most of the studied fruits were classified as low GL except for tamarind, red dragon fruit, mangosteen, lychee, and pineapple which were classified as medium GL. Various kinds of Thai fruits provided different GI and GL values. Therefore, low GI fruit with low GL regimen can be considered as alternative food sources to be used for diet manipulation in diabetic patients as well as in healthy population.
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Mayer-Davis, Elizabeth J., Ashish Dhawan, Angela D. Liese, Karen Teff, and Mandy Schulz. "Towards understanding of glycaemic index and glycaemic load in habitual diet: associations with measures of glycaemia in the Insulin Resistance Atherosclerosis Study." British Journal of Nutrition 95, no. 2 (February 2006): 397–405. http://dx.doi.org/10.1079/bjn20051636.
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Epidemiologic studies have applied the glycaemic index (GI) and glycaemic load (GL) to assessments of usual dietary intake. Results have been inconsistent particularly for the association of GI or GL with diabetes incidence. We aimed to advance understanding of the GI and GL as applied to food frequency questionnaires (FFQ) by evaluating GI and GL in relation to plasma measures of glycaemia. Included were 1255 adults at a baseline examination (1994–6) and 813 who returned for the 5-year follow-up examination. Usual diet, at both examinations, was assessed by a validated FFQ. GI and GL were evaluated in relation to average fasting glucose (two measures at each examination) and 2h post-75g glucose load plasma glucose (baseline and follow-up), and glycated haemoglobin (A1c; follow-up only); using generalized linear models. Correlation coefficients (r) for GI and GL related to measures of glycaemia, adjusted for total energy intake, ranged from −0·004 to 0·04 (all NS) for both examinations. Adjustment for potential confounders, for fasting glucose in models for 2h glucose (to model incremental glucose) and for average fasting glucose in models for A1c (to account, in part, for overnight endogenous glucose production) also did not materially alter findings, nor did inclusion of data from both examinations together in linear mixed models. The present results call into question the utility of GI and GL to reflect glycaemic response to food adequately, when used in the context of usual diet. Further work is needed to quantify usual dietary exposures relative to glucose excursion and associated chronic glycaemia and other metabolic parameters.
Dissertations / Theses on the topic "Glycaemic load"
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Mulholland, H. G. "Dietary glycaemic index, glycaemic load and carbohydrate intake and cancer risk." Thesis, Queen's University Belfast, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.517080.
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George, Ramlah. "Dietary glycaemic index, glycaemic load and insulin resistance (HOMAIR) of healthy South Asians in Glasgow, UK." Thesis, University of Glasgow, 2015. http://theses.gla.ac.uk/6600/.
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High habitual dietary glycaemic index (GI) and glycaemic load (GL) may relate to elevated insulin resistance and therefore may be more important and relevant in South Asian populations known for high prevalence of insulin resistance. The main objective of this research was to investigate the dietary GI, GL and insulin resistance of a sample of healthy South Asians in Glasgow, UK (a total of 111 healthy individuals: 60 males, 30 South Asians and 30 Europeans; 51 females, 22 South Asians and 29 Europeans). Estimation of dietary GI and GL (from weighed food intake records) considered the GI values of single foods and mixed-meals from relevant publications and from laboratory food/mixed-meal GI measurements (Chapter 3). The GI of key staple South Asian foods alone (chapatti, rice, pilau rice) and as mixed meals with curried chicken was measured using standard methods on 13 healthy subjects. The key staples had medium GI (chapatti, 68; rice, 66 and pilau rice, 60) and glycaemic responses to the mixed-meal of staples with curried chicken were found to be lower than the staples eaten alone. GI of the mixed-meals fell in the low GI category (chapatti with curried chicken, 45 and pilau rice with curried chicken, 41). Weighed food intake records (WFR) (recorded for 3-7 days) and self-administered previously validated food frequency questionnaires (FFQ) (applied to habitual food intakes in the past 6 months) was assessed for agreement through correlation analyses, cross-classification analysis, weighted Kappa statistics and Bland and Altman statistics. The two methods mostly agreed in carbohydrate (CHO) food intakes implying that the WFR reflected habitual intakes (Chapter 4). In consideration of potential confounding effect of physical activity on the relationship between dietary variables and HOMAIR, physical activity level (PAL) and Metabolic equivalent score (METS) of main daily activities of study subjects were derived from self-reported physical activity records (Chapter 5). Mean PAL were similar between South Asian and European males (median PAL of 1.61 and 1.60, respectively) but South Asian females tended to be less physically active than European females (mean PAL of 1.57 and 1.66, respectively). South Asians were less physically active in structured exercise and sports activities, particularly South Asian females and South Asians (males and females combined) with reported family history of diabetes showed inverse relationship between daily energy expenditure and HOMAIR. South Asians were found to be more insulin resistant than Europeans (HOMAIR median (IQR) of 1.06 (0.58) and 0.91 (0.47), p-value= 0.024 respectively in males; mean (SD) of 1.57 (0.80) and 1.16 (0.58), p-value= 0.037, respectively in females) despite similarities in habitual diet including dietary GI and GL. The mean habitual dietary GI of South Asians was within the medium GI category and did not differ significantly from Europeans. South Asian and European males’ dietary GI (mean, SD) was: 56.20, 2.78 and 54.77, 3.53 respectively; p-value=0.086. South Asian and European females also did not differ in their dietary GI (median, IQR) was: 54, 4.25 and 54, 5.00; p-value=0.071). Top three staples ranked from highest to lowest intakes in the South Asian diet were: unleavened breads (chapatti, Naan/Pitta, Paratha), rice, bread (white, wholemeal, brown), and potatoes. After statistically controlling for energy intake, body mass index, age, physical activity level and socio-demographic status, an inverse relationship (Spearman partial correlation analyses) between dietary GI and HOMAIR was observed (r, -0.435; p-value, 0.030) in South Asian males. This may be explained by the observation that the lower the dietary GI, the lower also, the total carbohydrates and fibre intakes and the higher the fat intake. In South Asian females, dietary GI and GL respectively, did not relate to HOMAIR but sugars intake related positively with HOMAIR (r, 0.486; p-value, 0.048). South Asian females, compared to European females, reported higher intakes of dietary fat (38.5% and 34.2% energy from fat, respectively; p-value=0.035). Saturated fatty acid (SFA) intakes did not differ between ethnic groups but SFA intakes were above the recommended level of 10% of total dietary energy for the UK in all groups, the highest being in SA females. In conclusion, Ethnicity (South Asian), having family history of diabetes, the wider diet profile rather than habitual dietary glycaemic index and glycaemic load alone (low GI, low fibre and high fat diets in males for instance; and high fat, high sugar diets in females) as well as low physical activity particularly in structured exercise and sports may contribute to insulin resistance in South Asians. These observations should be confirmed in larger future studies.
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Price, Joanna McMillan. "The effect of four reduced-fat diets varying in glycaemic index, glycaemic load, carbohydrate and protein, on weight loss, body composition and cardiovascular disease risk factors." Thesis, The University of Sydney, 2006. http://hdl.handle.net/2123/1606.
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Introduction: The conventional approach to weight loss, recommended by almost all health authorities around the world, has been to reduce the total amount of fat in the diet and replace with carbohydrates. However, research trials using this approach have produced only modest results at best, and despite the active promotion of low fat eating and an apparent decline in fat consumption, rates of overweight and obesity have continued to climb. More recently low glycaemic index (GI) and high protein diets have become popular and are widely used by the public. However, only a small number of randomised controlled trials have been conducted and none directly comparing the two. Both approaches effectively reduce glycaemic load (GL) and aim to reduce post-prandial glycaemia and insulinaemia. This study aimed to evaluate the ability of diets with reduced GL to enhance the weight loss effects of a reduced-fat diet, to compare the two approaches of reducing GL on metabolic and anthropometric changes, and to investigate any benefit of combining both approaches to produce the lowest GL. Methods: We conducted a 12-week intervention in 129 overweight or obese young adults who were assigned to one of four diets with varying GL, protein, carbohydrate and GI, but similar fat (30% energy), fat type and fibre content. DIET 1 (highest GL) contained 55% E as carbohydrate; DIET 2 was a low-GI version of DIET 1; DIET 3 was a high protein diet with 25% E as protein; DIET 4 (lowest GL) was a low-GI version of DIET 3. The increase in protein in DIETS 3 and 4 came primarily from lean red meat. All key foods and some pre-prepared frozen meals were provided to maximise dietary compliance. Outcome measures were body weight, body fat, lean mass, waist circumference and the following blood parameters: total cholesterol, LDL-cholesterol, HDL-cholesterol, triacylglycerols (TAG), free fatty acids, C-reactive protein, fasting insulin, fasting glucose and leptin. Insulin resistance and β-cell function were assessed using homeostatic model assessment (HOMA) and the newer computer models HOMA2-insulin sensitivity and HOMA2-β-cell function. Results: While all groups lost similar amounts of weight (4.2 to 6.2% of initial weight, p=0.09), the proportion who lost >5% of body weight varied significantly by diet: 31%, 56%, 66% and 33% in groups 1, 2, 3 and 4 respectively (p=0.011). Differences were strongest in women (76% of the total group) who showed significant differences among groups in percentage weight change (-3.7 ± 0.6%, -5.7 ± 0.6%, -6.5 ± 0.5%, -4.1 ± 0.7% respectively, p=0.005) and fat loss (-3.1 ± 0.4kg, -4.9 ± 0.6kg, -4.8 ± 0.4kg, -3.6 ± 0.7kg respectively, p=0.007). Total and LDL-cholesterol increased on DIET 3 (high protein) compared to a fall on diet 2 (high carbohydrate/low-GI, p=0.013). TAG, HDL-cholesterol and glucose homeostasis improved on all four diets, with no effect of diet composition. Goals for energy distribution were not achieved exactly: both carbohydrate groups ate less fat and the diet 2 group ate more fibre. Conclusions: Reducing GL, through either substituting low-GI foods or replacing some carbohydrate with protein, improved the efficacy of a reduced-fat diet in women and in those with high TAG. Combining both approaches to produce the lowest GL did not promote further weight or body fat loss. Although weight loss was similar in all four diets for the group as a whole, overall clinical outcomes were superior on the high carbohydrate, low-GI diet.
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Price, Joanna McMillan. "The effect of four reduced-fat diets varying in glycaemic index, glycaemic load, carbohydrate and protein, on weight loss, body composition and cardiovascular disease risk factors." University of Sydney, 2006. http://hdl.handle.net/2123/1606.
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Doctor of Philosophy (PhD)Introduction: The conventional approach to weight loss, recommended by almost all health authorities around the world, has been to reduce the total amount of fat in the diet and replace with carbohydrates. However, research trials using this approach have produced only modest results at best, and despite the active promotion of low fat eating and an apparent decline in fat consumption, rates of overweight and obesity have continued to climb. More recently low glycaemic index (GI) and high protein diets have become popular and are widely used by the public. However, only a small number of randomised controlled trials have been conducted and none directly comparing the two. Both approaches effectively reduce glycaemic load (GL) and aim to reduce post-prandial glycaemia and insulinaemia. This study aimed to evaluate the ability of diets with reduced GL to enhance the weight loss effects of a reduced-fat diet, to compare the two approaches of reducing GL on metabolic and anthropometric changes, and to investigate any benefit of combining both approaches to produce the lowest GL. Methods: We conducted a 12-week intervention in 129 overweight or obese young adults who were assigned to one of four diets with varying GL, protein, carbohydrate and GI, but similar fat (30% energy), fat type and fibre content. DIET 1 (highest GL) contained 55% E as carbohydrate; DIET 2 was a low-GI version of DIET 1; DIET 3 was a high protein diet with 25% E as protein; DIET 4 (lowest GL) was a low-GI version of DIET 3. The increase in protein in DIETS 3 and 4 came primarily from lean red meat. All key foods and some pre-prepared frozen meals were provided to maximise dietary compliance. Outcome measures were body weight, body fat, lean mass, waist circumference and the following blood parameters: total cholesterol, LDL-cholesterol, HDL-cholesterol, triacylglycerols (TAG), free fatty acids, C-reactive protein, fasting insulin, fasting glucose and leptin. Insulin resistance and β-cell function were assessed using homeostatic model assessment (HOMA) and the newer computer models HOMA2-insulin sensitivity and HOMA2-β-cell function. Results: While all groups lost similar amounts of weight (4.2 to 6.2% of initial weight, p=0.09), the proportion who lost >5% of body weight varied significantly by diet: 31%, 56%, 66% and 33% in groups 1, 2, 3 and 4 respectively (p=0.011). Differences were strongest in women (76% of the total group) who showed significant differences among groups in percentage weight change (-3.7 ± 0.6%, -5.7 ± 0.6%, -6.5 ± 0.5%, -4.1 ± 0.7% respectively, p=0.005) and fat loss (-3.1 ± 0.4kg, -4.9 ± 0.6kg, -4.8 ± 0.4kg, -3.6 ± 0.7kg respectively, p=0.007). Total and LDL-cholesterol increased on DIET 3 (high protein) compared to a fall on diet 2 (high carbohydrate/low-GI, p=0.013). TAG, HDL-cholesterol and glucose homeostasis improved on all four diets, with no effect of diet composition. Goals for energy distribution were not achieved exactly: both carbohydrate groups ate less fat and the diet 2 group ate more fibre. Conclusions: Reducing GL, through either substituting low-GI foods or replacing some carbohydrate with protein, improved the efficacy of a reduced-fat diet in women and in those with high TAG. Combining both approaches to produce the lowest GL did not promote further weight or body fat loss. Although weight loss was similar in all four diets for the group as a whole, overall clinical outcomes were superior on the high carbohydrate, low-GI diet.
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Negrini, Juliana de Almeida Egas. "Impacto do consumo de pães integrais na resposta glicêmica de voluntários saudáveis." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/9/9132/tde-28052015-090407/.
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Pães integrais são alimentos de consumo habitual da população brasileira, porém há poucas informações a respeito da resposta glicêmica pós-prandial, O presente trabalho teve como objetivo avaliar a resposta glicêmica produzida, em indivíduos saudáveis, após o consumo de pães de fôrma rotulados como integrais. Oito pães de fôrma integrais de três categorias (clássico, light e com grãos) foram avaliados, após o consumo de porcão equivalente a 25 g de carboidratos disponíveis, através do índice glicêmico (IG) e carga glicêmica (CG). Os voluntários (n=15) compareceram ao laboratório em jejum (10 a 12 horas), pela manhã, em onze ocasiões (três dias para o consumo do pão controle e um dia para cada tipo de pão de fôrma integral). A glicemia foi determinada em jejum (t=0) e após o consumo de cada pão nos tempos: 15; 30; 45; 60; 90 e 120 minutos. A curva de resposta glicêmica, a área sob a curva (ASC) e o cálculo do IG e CG para cada um dos pães foram realizados. Considerando a glicose como referência, os pães integrais clássicos (n=2) apresentaram alto IG (71 %); os light (n=2), IG baixo (50 %) e médio (58 %) e; os com grãos (n=4), IG baixo (44 e 49 %) e médio (57 e 60 %). Os pães de fôrma light e com grãos apresentaram IG menor que os do tipo clássico (p<0,05), os quais apresentaram IG igual ao pão francês (controle). Como foi consumida a mesma quantidade de carboidratos disponíveis, a menor proporção de açúcar solúvel na categoria light parece ter sido o fator que induziu ao menor IG observado. Em relação à CG, um pão de fôrma integral light (CG=10) e outro com grãos (CG=7) foram classificados como baixa CG; os demais pães integrais (n=6) foram classificados como média CG (11 a 16). Todos os pães integrais apresentaram CG inferior a do pão controle (CG=18) (p<0,05) e entre os integrais novamente os da categoria light e com grãos foram os que apresentaram menor CG. Assim, foi observada variação na resposta glicêmica após o consumo de pães de fôrma integrais, sendo que a redução no conteúdo de açúcares solúveis, para os pães light, e a adição de grãos integrais, nos pães com grãos, favoreceram menor elevação da resposta glicêmica pós-prandial.Whole meal breads are part of the habitual daily diet of the Brazilian population, but there is little information on the postprandial glycaemic response. The aim of this work was to evaluate the glycaemic response produced, in healthy volunteers, following the consumption of breads labeled as whole meal. Eight whole meal breads of three different categories (classic, light and grains) were evaluated, after the consumption of a portion containing approximately 25 g of available carbohydrates, using the glycaemic index (GI) and glycaemic load (GL). The subjects (n=15) attended to the laboratory after an overnight fasting (10 to 12 hours), in eleven different occasions (three days for the consumption of the control bread and a day for each whole meal bread). In every occasion, a portion of bread containing 25 g of available carbohydrate was consumed. Capillary blood samples were taken immediately before (t=0) and 15, 30, 45, 60, 90 and 120 minutes after the consumption of test breads. The glycaemic response curve, area under the curve (AUC), GI and GL for each bread were obtained. Considering glucose as reference, the classic breads (n=2) had high GI (71 %); the light (n=2), low (50 %) and medium (58 %) GI; and grains (n=4), low (44 and 49 %) and medium (57 and 60 %) GI. The light and grain breads had lower GI than the classic (p<0,05), which presented GI similar to white bread (control). As the same amount of available carbohydrates was consumed, the reduced proportion of soluble sugars in the light category breads seems to be a factor that induced the lower GI observed. In relation to the GL, one light bread (GL=10) and a grain bread (GL=7) were classified as low GL; the other whole meal breads (n=6) were classified as medium GL (11 to 16). All whole meal breads had lower GL than the control bread (GL=18) (p<0,05), and among the whole meal breads the ones in both light and grain categories presented the lower GL. Therefore, it was possible to observe variation on the glycaemic responses following the consumption of whole meal breads, the reduction in soluble sugar content, in the light breads, and the addition of whole grains, in the grain breads, favored lower elevation in the postprandial glycaemic response.
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Mukhtar, Rasha. "Metabolic syndrome, weight and cardiovascular co-morbidities : a randomised study comparing the effect of three dietary approaches on cardiovascular risk in subjects with the metabolic syndrome." Thesis, University of Bath, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.642020.
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The metabolic syndrome is a cluster of disorders (obesity, dyslipidaemia, hyperinsulinaemia and hypertension) which individually or collectively lead to an increase in the risk of cardiovascular disease. Over the years it has been associated with endothelial dysfunction, raised markers of chronic inflammation, insulin resistance and clotting dysregulation. Studies have shown that the prevalence of the metabolic syndrome in adults over the age of 20 years to be 24%, with approximately 12 million adults within the United Kingdom fulfilling the criteria for diagnosis. Numbers of individuals with the metabolic syndrome continue to rise following population trends of increasing sedentary lifestyle, high calorie intake, smoking, and stress. Associated is an increase in obesity, type 2 diabetes, cardiac disease, stroke and death. The increase is such that we can no longer be complacent about how we address the metabolic syndrome or its associated components. The management of the metabolic syndrome is varied and includes alterations in diet, physical exercise, and oral medication. It is well documented that a 10% reduction in weight leads to reductions in lipid abnormalities, diabetes and diabetes-related deaths, other total morbidity and deaths. Many dietary regimens have been postulated to benefit not only weight gain but improve cardiovascular risk. To address this we investigated the effect three different diets (low fat; low carbohydrate, high fat; and low glycaemic load) had on the metabolic syndrome to assess whether it is the changes in dietary caloric or macronutrient intake, or overall weight loss that had the greater influences on those aspects of metabolic syndrome which could potentially reduce cardiovascular risk.
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O'Sullivan, Therese Anne. "The relationship between glycemic intake and insulin resistance in older women." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/17814/1/Therese_Anne_O%27Sullivan_Thesis.pdf.
Full textAbstract:
Glycemic intake influences the rise in blood glucose concentration following consumption of a carbohydrate containing meal, known as the postprandial glycemic response. The glycemic response is a result of both the type and amount of carbohydrate foods consumed and is commonly measured as the glycemic index (GI) or glycemic load (GL), where the GI is a ranking in comparison to glucose and the GL is an absolute value encompassing both the GI and amount of carbohydrate consumed. Evidence from controlled trials in rat models suggests that glycemic intake has a role in development of insulin resistance, however trials and observational studies of humans have produced conflicting results. As insulin resistance is a precursor to type 2 diabetes mellitus, lifestyle factors that could prevent development of this condition have important public health implications. Previous observational studies have used food frequency questionnaires to assess usual diet, which could have resulted in a lack of precision in assessment of individual serve sizes, and have been limited to daily measures of glycemic intake. Daily measures do not take fluctuations in glycemic intake on a per meal basis into account, which may be a more relevant measure for investigation in relation to disease outcomes. This PhD research was conducted in a group of Brisbane women aged 42 to 81 years participating in the multidisciplinary Brisbane Longitudinal Assessment of Ageing in Women (LAW study). Older women may be at particular risk of insulin resistance due to age, hormonal changes, and increases in abdominal obesity associated with menopause, and the LAW study provided an ideal opportunity to study the relationship between diet and insulin resistance. Using the diet history tool, we aimed to assess the glycemic intake of the population and hypothesised that daily GI and daily GL would be significantly positively associated with increased odds of insulin resistant status. We also hypothesised that a new glycemic measure representing peaks in GL at different meals would be a stronger predictor of insulin resistant status than daily measures, and that a specially designed questionnaire would be an accurate and repeatable dietary tool for assessment of glycemic intake. To address these hypotheses, we conducted a series of studies. To assess glycemic intake, information on usual diet was obtained by detailed diet history interview and analysed using Foodworks and the Australian Food and Nutrient (AUSNUT) database, combined with a customised GI database. Mean ± SD intakes were 55.6 ± 4.4% for daily GI and 115 ± 25 for daily GL (n=470), with intake higher amoung younger participants. Bread was the largest contributor to intakes of daily GI and GL (17.1% and 20.8%, respectively), followed by fruit (15.5% and 14.2%, respectively). To determine whether daily GI and GL were significantly associated with insulin resistance, the homeostasis model assessment of insulin resistance (HOMA) was used to assess insulin resistant status. Daily GL was significantly higher in subjects who were insulin resistant compared to those who were not (134 ± 33 versus 114 ± 24 respectively, P<0.001) (n=329); the odds of subjects in the highest tertile of GL intake being insulin resistant were 12.7 times higher when compared with the lowest tertile of GL (95% CI 1.6-100.1, P=0.02). Daily GI was not significantly different in subjects who were insulin resistant compared to those who were not (56.0 ± 3.3% versus 55.7 ± 4.5%, P=0.69). To evaluate whether a new glycemic measure representing fluctuations in daily glycemic intake would be a stronger predictor of insulin resistant status than other glycemic intake measures, the GL peak score was developed to express in a single value the magnitude of GL peaks during an average day. Although a significant relationship was seen between insulin resistant status and GL peak score (Nagelkerke’s R2=0.568, P=0.039), other glycemic intake measures of daily GL (R2=0.671, P<0.001) and daily GL per megajoule (R2=0.674, P<0.001) were stronger predictors of insulin resistant status. To develop an accurate and repeatable self-administered tool for assessment of glycemic intake, two sub-samples of women (n=44 for the validation study and n=52 for the reproducibility study) completed a semi-quantitative questionnaire that contained 23 food groupings selected to include the top 100 carbohydrate foods consumed by the study population. While there were significant correlations between the glycemic intake questionnaire and the diet history for GL (r=0.54, P<0.01), carbohydrate (r=0.57, P<0.01) and GI (r=0.40, P<0.01), Bland-Altman plots showed an unacceptable difference between individual intakes in 34% of subjects for daily GL and carbohydrate, and 41% for daily GI. Reproducibility results showed significant correlations for daily GL (r=0.73, P<0.001), carbohydrate (r=0.76, P<0.001) and daily GI (r=0.64, P<0.001), but an unacceptable difference between individual intakes in 25% of subjects for daily GL and carbohydrate, and 27% for daily GI. In summary, our findings show that a significant association was observed between daily glycemic load and insulin resistant status in a group of older women, using a diet history interview to obtain precise estimation of individual carbohydrate intake. Both the type and quantity of carbohydrate are important to consider when investigating relationships between diet and insulin resistance, although our results suggest the association is more closely related to overall daily glycemic intake than individual meal intake variations. A dietary tool that permits precise estimation of carbohydrate intake is essential when evaluating possible associations between glycemic intake and individual risk of chronic diseases such as insulin resistance. Our results also suggest that studies using questionnaires to estimate glycemic intake should state degree of agreement as well as correlation coefficients when evaluating validity, as imprecise estimates of carbohydrate at an individual level may have contributed to the conflicting findings reported in previous studies.
8
O'Sullivan, Therese Anne. "The relationship between glycemic intake and insulin resistance in older women." Queensland University of Technology, 2008. http://eprints.qut.edu.au/17814/.
Full textAbstract:
Glycemic intake influences the rise in blood glucose concentration following consumption of a carbohydrate containing meal, known as the postprandial glycemic response. The glycemic response is a result of both the type and amount of carbohydrate foods consumed and is commonly measured as the glycemic index (GI) or glycemic load (GL), where the GI is a ranking in comparison to glucose and the GL is an absolute value encompassing both the GI and amount of carbohydrate consumed. Evidence from controlled trials in rat models suggests that glycemic intake has a role in development of insulin resistance, however trials and observational studies of humans have produced conflicting results. As insulin resistance is a precursor to type 2 diabetes mellitus, lifestyle factors that could prevent development of this condition have important public health implications. Previous observational studies have used food frequency questionnaires to assess usual diet, which could have resulted in a lack of precision in assessment of individual serve sizes, and have been limited to daily measures of glycemic intake. Daily measures do not take fluctuations in glycemic intake on a per meal basis into account, which may be a more relevant measure for investigation in relation to disease outcomes. This PhD research was conducted in a group of Brisbane women aged 42 to 81 years participating in the multidisciplinary Brisbane Longitudinal Assessment of Ageing in Women (LAW study). Older women may be at particular risk of insulin resistance due to age, hormonal changes, and increases in abdominal obesity associated with menopause, and the LAW study provided an ideal opportunity to study the relationship between diet and insulin resistance. Using the diet history tool, we aimed to assess the glycemic intake of the population and hypothesised that daily GI and daily GL would be significantly positively associated with increased odds of insulin resistant status. We also hypothesised that a new glycemic measure representing peaks in GL at different meals would be a stronger predictor of insulin resistant status than daily measures, and that a specially designed questionnaire would be an accurate and repeatable dietary tool for assessment of glycemic intake. To address these hypotheses, we conducted a series of studies. To assess glycemic intake, information on usual diet was obtained by detailed diet history interview and analysed using Foodworks and the Australian Food and Nutrient (AUSNUT) database, combined with a customised GI database. Mean ± SD intakes were 55.6 ± 4.4% for daily GI and 115 ± 25 for daily GL (n=470), with intake higher amoung younger participants. Bread was the largest contributor to intakes of daily GI and GL (17.1% and 20.8%, respectively), followed by fruit (15.5% and 14.2%, respectively). To determine whether daily GI and GL were significantly associated with insulin resistance, the homeostasis model assessment of insulin resistance (HOMA) was used to assess insulin resistant status. Daily GL was significantly higher in subjects who were insulin resistant compared to those who were not (134 ± 33 versus 114 ± 24 respectively, P<0.001) (n=329); the odds of subjects in the highest tertile of GL intake being insulin resistant were 12.7 times higher when compared with the lowest tertile of GL (95% CI 1.6-100.1, P=0.02). Daily GI was not significantly different in subjects who were insulin resistant compared to those who were not (56.0 ± 3.3% versus 55.7 ± 4.5%, P=0.69). To evaluate whether a new glycemic measure representing fluctuations in daily glycemic intake would be a stronger predictor of insulin resistant status than other glycemic intake measures, the GL peak score was developed to express in a single value the magnitude of GL peaks during an average day. Although a significant relationship was seen between insulin resistant status and GL peak score (Nagelkerke’s R2=0.568, P=0.039), other glycemic intake measures of daily GL (R2=0.671, P<0.001) and daily GL per megajoule (R2=0.674, P<0.001) were stronger predictors of insulin resistant status. To develop an accurate and repeatable self-administered tool for assessment of glycemic intake, two sub-samples of women (n=44 for the validation study and n=52 for the reproducibility study) completed a semi-quantitative questionnaire that contained 23 food groupings selected to include the top 100 carbohydrate foods consumed by the study population. While there were significant correlations between the glycemic intake questionnaire and the diet history for GL (r=0.54, P<0.01), carbohydrate (r=0.57, P<0.01) and GI (r=0.40, P<0.01), Bland-Altman plots showed an unacceptable difference between individual intakes in 34% of subjects for daily GL and carbohydrate, and 41% for daily GI. Reproducibility results showed significant correlations for daily GL (r=0.73, P<0.001), carbohydrate (r=0.76, P<0.001) and daily GI (r=0.64, P<0.001), but an unacceptable difference between individual intakes in 25% of subjects for daily GL and carbohydrate, and 27% for daily GI. In summary, our findings show that a significant association was observed between daily glycemic load and insulin resistant status in a group of older women, using a diet history interview to obtain precise estimation of individual carbohydrate intake. Both the type and quantity of carbohydrate are important to consider when investigating relationships between diet and insulin resistance, although our results suggest the association is more closely related to overall daily glycemic intake than individual meal intake variations. A dietary tool that permits precise estimation of carbohydrate intake is essential when evaluating possible associations between glycemic intake and individual risk of chronic diseases such as insulin resistance. Our results also suggest that studies using questionnaires to estimate glycemic intake should state degree of agreement as well as correlation coefficients when evaluating validity, as imprecise estimates of carbohydrate at an individual level may have contributed to the conflicting findings reported in previous studies.
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Warner, Rich S. "The nutritional profile of meals and the association between their glycaemic load and the mood of older adults with and without dementia residing in care homes." Thesis, 2019. https://arro.anglia.ac.uk/id/eprint/705886/6/Warner_2019.pdf.
Full textAbstract:
Low glycaemic index/glycaemic load (GI/GL) foods offer several health benefits. They avoid large fluctuations of blood glucose levels resulting in improved cognitive function and mood. Currently, most studies examining the association between GL and mood have focused almost exclusively on children and young adults. Both groups present different physiological and lifestyle characteristics to older adults. This observational study examined the relationship between the glycaemic load (GL) and the mood of older adults in care homes, focusing on those with and without dementia, the nutrient offerings as well as the nutrient density and GL relationship.
The nutrient content of all meals offered in each care home was analysed using Nutritics Software. The Profile of Mood States (POMS-short form) was used to assess mood after meal consumption. Nutrient density was determined using the UK Ofcom Nutrient Profiling Model. Participants included 147 older adults from four care homes. Descriptive statistics, paired t-test, linear regression, Pearson’s correlation and Cronbach’s alpha, were employed to analyse data.
The POMS Total Mood Disturbance (TMD) for the high glycaemic load (HGL) meals=+4.21 and low glycaemic load (LGL) meals= +0.67. t(146)= 4.21 P<.001. Dementia group TMD for HGL (+6.71) and LGL (+2.13). t(74)= 4.79 P<.001. Non-Dementia group TMD for HGL (+1.62) and LGL (-0.9). t(71)= 1.92 P> 0.05 (0.059).The relationship between nutrient density and GL was statistically significant.
Overall macronutrient offerings were satisfactory. Fibre and micronutrient offerings for vitamin D, iodine and folates were below the recommended targets. The GL of a meal appears to be associated with the mood outcomes of older adults. This association is more pronounced in those with dementia. Nutrient density and GL of meals appear to be positively associated. GL should be considered in the creation of menus and nutritional guidelines for older residents of care homes as it does appear to impact mood outcomes. More definitive interventional evidence required.
Books on the topic "Glycaemic load"
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The Greek doctor's diet: Beyond GI : understanding glycaemic load. London: Rodale, 2006.
Find full textBook chapters on the topic "Glycaemic load"
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Henry, C. Jeya, and P. Sangeetha Thondre. "Glycaemic index and glycaemic load in diabetes." In Advanced Nutrition and Dietetics in Diabetes, 41–49. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781119121725.ch6.
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Müller-Wieland, D., J. Brandts, M. Verket, N. Marx, and K. Schütt. "Glycaemic Control in Diabetes." In Prevention and Treatment of Atherosclerosis, 47–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/164_2021_537.
Full textAbstract:
AbstractReduction of glucose is the hallmark of diabetes therapy proven to reduce micro- and macro-vascular risk in patients with type 1 diabetes. However glucose-lowering efficacy trials in type 2 diabetes didn’t show major cardiovascular benefit. Then, a paradigm change in the treatment of patients with type 2 diabetes has emerged due to the introduction of new blood glucose-lowering agents. Cardiovascular endpoint studies have proven HbA1c-independent cardioprotective effects for GLP-1 receptor agonists and SGLT-2 inhibitors. Furthermore, SGLT-2 inhibitors reduce the risk for heart failure and chronic kidney disease. Mechanisms for these blood glucose independent drug target-related effects are still an enigma. Recent research has shown that GLP-1 receptor agonists might have anti-inflammatory and plaque stabilising effects whereas SGLT-2 inhibitors primarily reduce pre- and after-load of the heart and increase work load efficiency of the heart. In addition, reduction of intraglomerular pressure, improved energy supply chains and water regulation appear to be major mechanisms for renoprotection by SGLT-2 inhibitors. These studies and observations have led to recent changes in clinical recommendations and treatment guidelines for type 2 diabetes. In patients with high or very high cardio-renal risk, SGLT-2 inhibitors or GLP-1 receptor agonists have a preferred recommendation independent of baseline HbA1c levels due to cardioprotection. In patients with chronic heart failure, chronic kidney disease or at respective risks SGLT-2 inhibitors are the preferred choice. Therefore, the treatment paradigm of glucose control in diabetes has changed towards using diabetes drugs with evidence-based organ protection improving clinical prognosis.
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Lim, Siew, Aya Mousa, Soulmaz Shorakae, and Lisa Moran. "Exogenous Factors and Female Reproductive Health." In Oxford Textbook of Endocrinology and Diabetes 3e, edited by John A. H. Wass, Wiebke Arlt, and Robert K. Semple, 1401–9. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780198870197.003.0168.
Full textAbstract:
Undernutrition adversely affects fertility. A low body weight is associated with delayed conception. When conception does occur, undernutrition could also adversely affect pregnancy outcomes. Low prepregnancy BMI (<18.5 kg/m2) is associated with increased risk of early miscarriage, preterm labour, anaemia, insufficient weight gain, and impaired intrauterine fetal growth. On the other hand, overweight and obesity are associated with increased risk of gestational diabetes, pre-eclampsia, and other complications during pregnancy and delivery. Weight loss through energy restriction, with or without exercise, improves reproductive function in overweight or obese women. Aside from body weight and energy status, maternal macronutrient, and micronutrient intakes before and during pregnancy would also influence pregnancy outcomes. Studies in mostly nutritionally at-risk women reported that balanced energy/protein supplementation (<25% energy from protein) is associated with higher birth weights but high protein supplementation (> 25% energy from protein) may increase the risk of small-for-gestational-age (SGA) infants. Reducing glycaemic index or glycaemic load of maternal diet may reduce the risk of large-for-gestational-age (LGA) births or gestational diabetes. In terms of micronutrients, current evidence supports folic acid supplementation (at least 400 µg/day) to reduce the risk of fetal abnormalities, iodine supplementation for women at risk of iodine deficiency to prevent complications in fetal physical and mental development, and iron supplementation to reduce the risk of maternal anaemia where required.
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Bhopal, Raj S. "Established CVD and DM2 risk factors: reappraisal in relation to South Asians." In Epidemic of Cardiovascular Disease and Diabetes, 139–72. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198833246.003.0007.
Full textAbstract:
The causal basis of the established CVD and type 2 diabetes risk factors rests mainly on cohort studies, sometimes with supplementary data from trials, Mendelian randomization studies, and experiments in animals. In South Asian populations, specifically, the direct evidence is limited but the associations between risk factors and disease outcomes are generally as expected. The lifestyle-related risk factors can be grouped into those where an excess is a problem (e.g. diets leading to adiposity or a high glycaemic load) and those where a deficit is a problem (e.g. insufficient physical activity). These kinds of risk factors, particularly in the context of adverse socio-economic circumstances, provide an excellent basis for causal thinking. So far, even combined with a wide range of biochemical and physiological risk factors, however, such factors are insufficient, though necessary, parts of a convincing explanation for the excess of DM2 and CVD in South Asians.
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Prakash Sah, Om. "Medical Nutrition Therapy for Type I Diabetes Mellitus." In Type 1 Diabetes Mellitus [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.108619.
Full textAbstract:
Diabetes mellitus is described by high blood glucose level resulting from deficiencies in insulin secretion, insulin action, or both. Type 1 diabetes is a condition in which pancreatic beta-cell get destructed and leads to absolute insulin deficiency. Lack of insulin causes hyperglycemia, polyuria, polydipsia, polyphagia, body mass loss, dehydration, electrolyte disturbance, and ketoacidosis. MNT necessitates an individualized tactic and effective nutrition self-management education, recommendation, and support. A key component of MNT is the provision of adequate calories for normal growth and development for children and adolescents with T1DM. The patient should monitor their saccharide intake either through saccharide counting or meal planning exchange lists for flexibility and variety in meals. Saccharide intake from whole grains, vegetables, fruits, legumes, and dairy products, with an emphasis on foods higher in fiber and lower in glycaemic load, should be advised over other sources, especially those containing sugars. Saccharide counting is helpful for people with diabetes in managing blood glucose level by tracking the grams of saccharide consumed at meals. All persons with T1DM need a substitute of insulin that mimics normal insulin action. An insulin-to-saccharide ratio can be established for an individual that will guide determinations on the amount of mealtime insulin to infuse.
Conference papers on the topic "Glycaemic load"
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Inskip, H., H. Okubo, S. Crozier, C. Cooper, K. Godfrey, S. Robinson, and J. Baird. "OP42 Glycaemic load and index in pregnancy are associated with postnatal, but not pre-pregnancy, depressive symptoms; longitudinal data from the southampton women’s survey." In Society for Social Medicine, 61st Annual Scientific Meeting, University of Manchester, 5–8 September 2017. BMJ Publishing Group Ltd, 2017. http://dx.doi.org/10.1136/jech-2017-ssmabstracts.42.
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