lunes, 27 de febrero de 2017

Low carbohydrate-high protein diet and incidence of cardiovascular diseases in Swedish women: prospective cohort study

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Overweight and obesity are risk factors for several common chronic diseases,1 and they have become endemic in most economically developed countries and beyond.2 3 Increased physical activity is one way of counteracting excessive energy intake, but reducing this intake is also important.4 Many dietary regimens have been proposed as conducive to weight control, invoking various mechanisms including increased satiety.5 6 The most popular among these diets emphasise reduction of carbohydrate intake, thereby encouraging high protein intake,7 as high fat diets are generally avoided in most Western societies. Low carbohydrate-high protein diets may have short term effects on weight control,8 9 but concerns have also been expressed, notably with respect to cardiovascular outcomes.10 11Although low carbohydrate-high protein diets may be nutritionally acceptable if the protein is mainly of plant origin and the reduction of carbohydrates applies mainly to simple and refined ones, the general public do not always recognise and act on these qualifications.
During the past few years, several cohort studies have evaluated the long term health effects of low carbohydrate-high protein diets, with emphasis on cardiovascular diseases. In the Nurses’ Health Study in the United States, diets lower in carbohydrate and higher in protein were not associated with increased incidence of ischaemic heart disease.12 Three smaller studies in Europe, however, indicated statistically significant increases in cardiovascular mortality in relation to low carbohydrate-high protein diets. One of these studies was from the same cohort of relatively young Swedish women as the investigation reported here (but presented data only on 75 cardiovascular deaths from shorter follow-up),13 the second study was based on middle aged Greek men and women,14 and the third study investigated elderly men in Sweden.15 Willett has considered possible reasons for the discrepant results of the European studies and the American one, including differences in the main dietary sources of protein (animal versus plant) and the prevalence of obesity in the studied populations.16
Given the importance of the topic and the widespread use of low carbohydrate diets for weight control, particularly among women, we followed up the women in the Swedish Women’s Lifestyle and Health Cohort,13 focusing on incidence of cardiovascular diseases and using the valuable registry resources available in Sweden.



Women aged 30-49 years, residing in the Uppsala healthcare region in Sweden in 1991-92, were the source population for the Swedish Women’s Lifestyle and Health Cohort. For this cohort, 96 000 women were randomly selected from four age strata (30-34, 35-39, 40-44, and 45-49), invited by mail to participate, and asked to fill in a questionnaire and return it in a pre-paid envelope.17 A total of 49 261 questionnaires were returned.

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,18 19 in which women recorded their frequency and quantities of consumption of about 80 food items and beverages, focusing on the six month period before their enrolment in the study. We formed 11 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 mL/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,20 we translated food consumption into macronutrient and energy intakes.
We estimated the energy adjusted intakes of protein and carbohydrates for each woman, using the “residual method.”21 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 10 (very high protein intake), according to her tenth of energy adjusted total protein intake, and an inverse score from 1 (very high carbohydrate intake) to 10 (very low carbohydrate intake), according to her tenth of energy adjusted total carbohydrate intake. 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 20), which simultaneously assessed the position of each woman in terms of protein and carbohydrate intake. 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 20 was one with very low consumption of carbohydrates and very high consumption of proteins. 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.22 Nevertheless, in this context, a low carbohydrate-high protein score allows the assessment of most low carbohydrate diets, which are generally high protein diets,7because it integrates opposite changes of two nutrients with equivalent energy values.


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 410-414, 430-438, and 440 and ICD-10 codes I20-I25, I60-I67, I69, I70, and I74. We also studied ischaemic heart disease (ICD-9 410-414; ICD-10 I20-I25), ischaemic stroke (ICD-9 433-438; ICD-10 I63-I67, I69.3, I69.4, and I69.8.), intracerebral haemorrhage (ICD-9 431-432.9; ICD-10 I61-I62 and I69.1-I69.2), subarachnoid haemorrhage (ICD-9 430; ICD-10 I60 and I69.0), and peripheral arterial disease (ICD-9 440; ICD-10 I70 and I74) 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 31 December 2007, came from the Swedish Bureau of Statistics. Information on discharge diagnoses by date to 31 December 2007 came from the Swedish in-patient registry. We defined the date of return of the questionnaire during 1991-92 as the start of follow-up. 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 49 261 Swedish women who returned the questionnaire, we excluded 1350 women from the analyses because of events before or at the cohort enrolment (10 emigrated, 136 had previous cardiovascular events, 621 had energy intake outside the extreme 1% centiles of the population energy intake (<1846.6 or >12 473.9 kJ/day), and 583 women had not filled in the food frequency questionnaire), leaving 47 911 women for the statistical analyses. After exclusion of women with missing data on any of the model covariates, 43 396 women remained for use in the statistical analyses. 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 680 000 person years of follow-up.
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 10 000 woman years.
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.23Specifically, 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).24 25 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 (<25, 25-29.99 and ≥30, categorically), smoking status (never smokers, former smokers of <10 cigarettes, former smokers of 10-14 cigarettes, former smokers of 15-19 cigarettes, former smokers of ≥20 cigarettes, current smokers of <10 cigarettes, current smokers of 10-14 cigarettes, current smokers of 15-19 cigarettes, and current smokers of ≥20 cigarettes, categorically), physical activity (from 1 (low) to 5 (high), categorically), education (≤10, 11-13, and ≥14 years in school, categorically), report of ever being diagnosed as having hypertension (yes versus no), energy intake (per 1000 kJ/day, continuously), unsaturated lipid intake (per 10 g/day, continuously), saturated lipid intake (per 10 g/day, continuously), and alcohol intake (<5 g/day, 5-25 g/day, and >25 g/day, categorically). 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.26 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,27 28 we also evaluated the association of the cardiovascular outcomes with the low carbohydrate-high protein score, as well as the low carbohydrate and high protein scores, separately among women with intake of animal protein equal to or higher than the cohort median and among women with intake of animal protein lower than the cohort median. 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 9.22 and 9.3 for all data management and statistical analyses (PROC GENMOD). We made all tests of statistical hypotheses on the two sided 5% level of significance corresponding to using two sided 95% confidence intervals. In addition, using R software 2.12.1, we calculated the variance inflation factor, to ensure that no problems were generated by co-linearity among the model covariates.29


Overall, the 43 396 women were followed up for an average of about 15.7 years and generated a total of 680 818 person years, with 1270 incident cardiovascular events (703 ischaemic heart disease, 294 ischaemic stroke, 70 haemorrhagic stroke, 121 subarachnoid haemorrhage, and 82 peripheral arterial disease). Table 1 shows the distribution of the women in the cohort and the incident cardiovascular cases (overall and by diagnostic category) by non-nutritional variables. The table also shows the corresponding age adjusted incidence rate ratios for descriptive purposes only, as confounding influences by factors other than age are not accounted for. Nevertheless, several well known patterns are evident, including the reduced risk of cardiovascular diseases with increasing level of education and physical activity and the increased risk with tobacco smoking and history of hypertension.

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