Lagiou Low Carbohydrate Diets CaloriesLow carbohydrate- high protein diet and incidence of cardiovascular diseases in Swedish women: prospective cohort study. Participants. Women aged 3. Uppsala healthcare region in Sweden in 1. Swedish Women’s Lifestyle and Health Cohort. For this cohort, 9. Very-low-carbohydrate ketogenic diet v. Note: Order does not necessarily reflect citation order of authors. Citation: Lagiou, Pagona, Sven Sandin, Marie Lof, Dimitrios Trichopoulos, Hans-Olov Adami, and Elisabete Weiderpass. Low carbohydrate-high protein diet and incidence of cardiovascular diseases in swedish women: prospective. A total of 4. 9 2. Questionnaire and dietary assessment. The questionnaire used in the study was self administered and recorded information on several lifestyle variables (including detailed smoking and alcoholic drinking habits), anthropometry, and history of diagnoses of major diseases and conditions, including medical diagnosis of hypertension. For the assessment of physical activity, women rated their overall level of activity (that is, activities in the house and occupational and recreational physical activity) on a five point scale with examples attached to levels 1 (low), 3, and 5 (high). Dietary intakes were assessed with a validated food frequency questionnaire,1. We formed 1. 1 food groups from the food items: vegetables, legumes, fruits and nuts, dairy products, cereals, meat and meat products, fish and seafood, potatoes, eggs, sugars and sweets (all measured in g/day), and non- alcoholic beverages (measured in m. L/day). Alcoholic beverages were listed in a separate category, and we calculated the amount of ethanol. On the basis of the Swedish national food administration database,2. We estimated the energy adjusted intakes of protein and carbohydrates for each woman, using the “residual method.”2. Low carbohydrate-high protein diets and their combinations (such as the Atkins diet) have become popular worldwide and are frequently adopted for weight control by lay people. A solid understanding of the safety and potential adverse effects of high-fat, low-carbohydrate diet (HFLCD). Almost a decade ago, a comprehensive review of the efficacy and safety of low-carbohydrate diets found inconclusive evidence to recommend either for. This method allows evaluation of the “effect” of an energy generating nutrient, controlling for the energy generated by this nutrient, by using a simple regression of that nutrient on energy intake to calculate the residual. For each woman, we assigned a score from 1 (very low protein intake) to 1. We studied the scores for high protein and low carbohydrate intake both separately and after adding them together to create a composite low carbohydrate- high protein score (ranging from 2 to 2. Thus, a woman with a score of 2 was one with very high consumption of carbohydrates and very low consumption of proteins, whereas a woman with a score of 2. The use of energy adjusted residuals was necessary, because all energy generating nutrients are generally strongly positively associated with total energy intake. After controlling for energy intake, however, distinguishing the effects of a specific energy generating nutrient is all but impossible, as a decrease in the intake of one is unavoidably linked to an increase in the intake of one or several of the others. Nevertheless, in this context, a low carbohydrate- high protein score allows the assessment of most low carbohydrate diets, which are generally high protein diets,7 because it integrates opposite changes of two nutrients with equivalent energy values. Follow- up. The primary outcome under investigation in this study was a first diagnosis of cardiovascular disease, as defined by ICD- 9 (international classification of diseases, 9th revision) codes 4. ICD- 1. 0 codes I2. I2. 5, I6. 0- I6. I6. 9, I7. 0, and I7. We also studied ischaemic heart disease (ICD- 9 4. ICD- 1. 0 I2. 0- I2. ICD- 9 4. 33- 4. 38; ICD- 1. I6. 3- I6. 7, I6. I6. 9. 4, and I6. ICD- 9 4. 31- 4. 32. ICD- 1. 0 I6. 1- I6. I6. 9. 1- I6. 9. 2), subarachnoid haemorrhage (ICD- 9 4. ICD- 1. 0 I6. 0 and I6. ICD- 9 4. 40; ICD- 1. I7. 0 and I7. 4) separately. We used linkages with nationwide Swedish registers, by means of the unique national registration numbers, for the follow- up of the cohort with respect to hospital discharges, death, and emigration. Information on dates of death by cause, as well as on dates of emigration to 3. December 2. 00. 7, came from the Swedish Bureau of Statistics. Information on discharge diagnoses by date to 3. December 2. 00. 7 came from the Swedish in- patient registry. We defined the date of return of the questionnaire during 1. We calculated observation time from the date of entry into the cohort until the occurrence of a first diagnosis of the cardiovascular event under study, death from the cardiovascular disease under study without a previous diagnosis of this cardiovascular disease, or censoring. For overall cardiovascular diseases, censoring was on account of emigration, the end of the observation period, or death from any cause other than cardiovascular disease. For the specific cardiovascular outcomes, censoring was on account of emigration, the end of the observation period, death from any cause other than the specific cardiovascular disease studied, or occurrence of cardiovascular disease other than the one studied. Statistical analysis. Of the 4. 9 2. 61 Swedish women who returned the questionnaire, we excluded 1. J/day), and 5. 83 women had not filled in the food frequency questionnaire), leaving 4. After exclusion of women with missing data on any of the model covariates, 4. No major differences existed between the women excluded on account of missing data and the women who contributed to the study. For each cardiovascular diagnostic category, we had about 6. The participating women and the number of incident cases of cardiovascular disease (overall and by specific diagnostic categories) that occurred among them were distributed by non- nutritional covariates, and we calculated age adjusted incidence rate ratios by using Poisson regression models. We also calculated summary statistics of daily dietary intakes of energy generating nutrients. Subsequently, we tabulated the distribution by low carbohydrate- high protein score of the person time, the number of incident cases of cardiovascular disease (overall and by diagnostic categories), and the corresponding crude incidence rates per 1. We calculated incidence rate ratios, for all cardiovascular diseases combined and for each category of cardiovascular disease, by using survival analysis with attained age as the primary underlying timescale. Specifically, we fitted Poisson regression models by splitting the follow- up- time (that is, attained age between cohort entry and exit) into two year intervals and then allowing for one parameter for each such interval and an offset parameter equal to the logarithm of the observed risk time in each interval. For each participant, we considered only the first cardiovascular event. Poisson regression is commonly used in survival analysis and gives approximately the same parameter estimates and likelihood ratios as Cox proportional hazards regression when the length of follow- up is split into finer intervals (here we used two year intervals). We used, alternatively, the high protein score, the low carbohydrate score, and the additive low carbohydrate- high protein score as the primary exposure variable, introducing the scores, alternatively, as ordered or categorical variables. We ensured that the parameter for each of high protein, low carbohydrate, and low carbohydrate- high protein score was approximately constant across time by visual inspection of the estimated log rates and by calculating likelihood ratio tests for parameters capturing the high protein, low carbohydrate, and low carbohydrate- high protein exposure by time (attained age) interactions. We adjusted the models for the following variables as reported at enrolment: height (cm, continuously), body mass index (< 2. We used categories to accommodate trends that may not be log linear and also to take into account biological considerations or guidelines, as for body mass index and alcohol intake. In models evaluating the associations of the outcome variables with the low carbohydrate and the high protein scores, we repeated the analysis by also introducing the complementary score (that is, high protein in the models evaluating low carbohydrate and vice versa) without energy intake in the model. To examine whether the dietary exposure variables were differentially associated with the five distinct cardiovascular outcomes, we also fitted Poisson regression models including all five cardiovascular conditions simultaneously. The joint model, stratifying on cardiovascular outcome and under the proportional hazards assumption, allows the evaluation of separate associations between exposure and each outcome. This approach enabled us to test directly for heterogeneity in incidence rate ratios across the cardiovascular conditions by using likelihood ratio tests. We did this for the high protein, low carbohydrate, and low carbohydrate- high protein scores in separate models. Because the origin of proteins (animal versus plant sources) may have differential effects on the occurrence of cardiovascular events,2. We calculated P values for interaction by likelihood ratio tests after the introduction of product terms of the respective scores (as continuous variables) with protein intake (as a two level categorical variable: above or equal to versus below the median). We used SAS versions 9. PROC GENMOD). We made all tests of statistical hypotheses on the two sided 5% level of significance corresponding to using two sided 9. In addition, using R software 2. Beyond that, after controlling for energy intake and P. Low carbohydrate diets, recommended for weight control, typically contain <15% of energy intake from carbohydrates and about 30% of. This article is from BMJ : British Medical Journal, volume 344. Abstract Objective To study the long term consequences of low carbohydrate diets, generally characterised by concomitant increases in protein intake, on cardiovascular health.Design Prospective cohort. Low-carbohydrate diets are based on the premise that weight gain and obesity are tied to inefficient or unhealthy insulin cycles. Low carbohydrate diets are not more effective for weight loss than balanced diets, so what does this mean? Issued by: Association for Dietetics in South Africa, Chronic Disease Initiative for Africa, Heart and Stroke Foundation South Africa, Nutrition Society of. Dc.description.abstract Objective: To study the long term consequences of low carbohydrate diets, generally characterised by concomitant increases in protein intake, on cardiovascular health. Design Prospective cohort study. Setting Uppsala, Sweden. Participants From a random population sample, 43. Low-carbohydrate diets and cardiovascular and total mortality in Japanese: a 29-year follow-up of NIPPON DATA80 Volume 112, Issue 6. 9 P Lagiou, S Sandin, M Lof, et al.
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