- Liittynyt
- 27.4.2003
- Viestejä
- 417
Fatsolle ym. kokonaisartikkelista:
"
Questionnaire and dietary assessment
The self-administered questionnaire recorded information on lifestyle variables (including detailed smoking and alcoholic drinking habits), anthropometry and history of diagnoses of major diseases. Women were also asked to rate their overall level of physical activity (i.e. activities in the house, occupational and recreational physical activity) on a 5-point scale with examples attached to levels 1, 3 and 5. A validated food frequency questionnaire [22] was used to assess the frequency of consumption and quantity of about 80 food items and beverages, focusing on the 6-month period prior to the woman's enrolment in the study. Eleven food groups were formed (measured in g day 1), namely vegetables, legumes, fruits and nuts, dairy products, cereals, meat and meat products, fish and seafood, potatoes, eggs, sugars and sweets, and nonalcoholic beverages (measured in mL day 1). Food consumption was translated into macronutrient and energy intakes on the basis of the Swedish National Food Administration database [23].
We used residuals from the regressions of, alternatively, protein and carbohydrate intake on total energy intake to estimate the energy-adjusted intakes of protein and carbohydrates for each woman [24]. Women were then assigned a score from 1 (very low protein intake) to 10 (very high protein intake), according to their decile of energy-adjusted total protein intake. An inverse score, from 1 (very high carbohydrate intake) to 10 (very low carbohydrate intake) was also assigned according to the woman's decile of energy-adjusted total carbohydrate intake. The scores were studied both separately and after being added creating a composite additive score simultaneously assessing the position of each subject in terms of protein and carbohydrate intake. Thus, a woman with a score of 2 is one with very high consumption of carbohydrates and very low consumption of proteins, whereas a woman with a score of 20 is one with very low consumption of carbohydrates and very high consumption of proteins.
Follow up
Linkages with the Swedish nationwide health registers, by means of the unique per individual Swedish national registration number, were used for the follow up of the cohort with respect to death and emigration. Information on dates of death for women who died during the follow-up period until 31 December 2003 was retrieved from the Register of Total Population. Additional information on cause of death, updated till 31 December 2002, was derived from the Swedish Cause of Death Register. Dates of emigration for women who moved out of Sweden were provided by the Register of Total Population. The date of return of the questionnaire during 1991–1992 was defined as the start of follow up. Observation time was calculated from date of entry into the cohort until the occurrence of death, or censoring. For overall mortality, censoring was on account of emigration or end of the observation period, whilst for cardiovascular or cancer mortality, it was also on account of death from any cause other than the one under study.
Statistical analysis
Of the original 49 261 Swedish women, the following were sequentially excluded: those who had emigrated without re-immigration prior to start of study (16 women), those who had not filled in the dietary questionnaire (583 women), those with prevalent cancer (excluding nonmelanoma skin cancer), coronary heart disease or diabetes at enrolment (1418 women), and those with missing information on any of the covariates studied (4403 women), as well as those with energy intake outside the first (1847 kJ day1) and 99th (12 474 kJ day1) centiles (604 women). Thus, a total of 42 237 women were available for the analysis.
The participating women and the deaths that occurred amongst them were distributed by non-nutritional covariates, and age- and multivariate- adjusted mortality ratios were calculated. Hazard ratios for overall mortality and mortality from cancer, as well as cardiovascular diseases, were estimated through Cox proportional hazards regression using, alternatively, the high protein score, the low carbohydrate score and the composite additive score as the principal exposure variables. To accommodate secular trends the models were stratified by 1-year birth cohorts with attained age as time scale. In a stratified Cox model the baseline hazard is allowed to vary across strata. The models were adjusted for the following variables as reported at enrolment: height (cm, continuously), body mass index (BMI; <25, 25–29.99 and ≥30 kg m2, 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 or more cigarettes, current smokers of <10 cigarettes, current smokers of 10–14 cigarettes, current smokers of 15–19 cigarettes, current smokers of 20 or more cigarettes, categorically), physical activity [from 1 (low) to 5 (high), categorically], education (0–10, 11–13 and 14 or more years in school, categorically), energy intake (per 1000 kJ day1, continuously), saturated lipid intake (per 10 g, continuously) and alcohol intake (<5, 5–25 or >25 g day1, categorically). Unsaturated lipids should not be and were not controlled for in these models to avoid overdetermination generated by inclusion of all energy-generating nutrients as well as total energy intake in the same models. Fine control for tobacco smoking was necessary because of the powerful influence of smoking on mortality and the possibility that smoking may be associated with some dietary intakes. All analyses were conducted for all women, as well as separately for women <40 years old at enrolment and for women 40 years or older at enrolment, the rationale being that genetic and early life factors are likely to have a stronger influence amongst younger than amongst older adults.
Tuloksista mm.:
In this cohort of women, median intake of energy was 6396 kJ day1 with 10th and 90th centiles 4246 and 9060 kJ day1, respectively. Percentage of energy intake from carbohydrates ranged from 72.0% (10th centile) to 32.4% (90th centile) and for proteins from 10.4% (10th centile) to 23.0% (90th centile). The additive score was significantly correlated positively with protein intake (Spearman r = +0.35), inversely with carbohydrate intake (Spearman r = −0.28), positively with lipid intake (for saturated lipids Spearman r = +0.26; for unsaturated lipids Spearman r = +0.16), but, importantly, it was not correlated with energy intake (Spearman r = −0.006).
...
The additive low carbohydrate–high protein score is positively associated with overall mortality, a 5 units increment corresponding to an increase in mortality by 11% [95% confidence interval (CI): 0–23%]. This increase in overall mortality is mostly accounted for by an increase of 37% in cardiovascular mortality (95% CI: 2–84%).
Jos halajaa kokonaisartsua, pyydä vaikka privalla.
"
Questionnaire and dietary assessment
The self-administered questionnaire recorded information on lifestyle variables (including detailed smoking and alcoholic drinking habits), anthropometry and history of diagnoses of major diseases. Women were also asked to rate their overall level of physical activity (i.e. activities in the house, occupational and recreational physical activity) on a 5-point scale with examples attached to levels 1, 3 and 5. A validated food frequency questionnaire [22] was used to assess the frequency of consumption and quantity of about 80 food items and beverages, focusing on the 6-month period prior to the woman's enrolment in the study. Eleven food groups were formed (measured in g day 1), namely vegetables, legumes, fruits and nuts, dairy products, cereals, meat and meat products, fish and seafood, potatoes, eggs, sugars and sweets, and nonalcoholic beverages (measured in mL day 1). Food consumption was translated into macronutrient and energy intakes on the basis of the Swedish National Food Administration database [23].
We used residuals from the regressions of, alternatively, protein and carbohydrate intake on total energy intake to estimate the energy-adjusted intakes of protein and carbohydrates for each woman [24]. Women were then assigned a score from 1 (very low protein intake) to 10 (very high protein intake), according to their decile of energy-adjusted total protein intake. An inverse score, from 1 (very high carbohydrate intake) to 10 (very low carbohydrate intake) was also assigned according to the woman's decile of energy-adjusted total carbohydrate intake. The scores were studied both separately and after being added creating a composite additive score simultaneously assessing the position of each subject in terms of protein and carbohydrate intake. Thus, a woman with a score of 2 is one with very high consumption of carbohydrates and very low consumption of proteins, whereas a woman with a score of 20 is one with very low consumption of carbohydrates and very high consumption of proteins.
Follow up
Linkages with the Swedish nationwide health registers, by means of the unique per individual Swedish national registration number, were used for the follow up of the cohort with respect to death and emigration. Information on dates of death for women who died during the follow-up period until 31 December 2003 was retrieved from the Register of Total Population. Additional information on cause of death, updated till 31 December 2002, was derived from the Swedish Cause of Death Register. Dates of emigration for women who moved out of Sweden were provided by the Register of Total Population. The date of return of the questionnaire during 1991–1992 was defined as the start of follow up. Observation time was calculated from date of entry into the cohort until the occurrence of death, or censoring. For overall mortality, censoring was on account of emigration or end of the observation period, whilst for cardiovascular or cancer mortality, it was also on account of death from any cause other than the one under study.
Statistical analysis
Of the original 49 261 Swedish women, the following were sequentially excluded: those who had emigrated without re-immigration prior to start of study (16 women), those who had not filled in the dietary questionnaire (583 women), those with prevalent cancer (excluding nonmelanoma skin cancer), coronary heart disease or diabetes at enrolment (1418 women), and those with missing information on any of the covariates studied (4403 women), as well as those with energy intake outside the first (1847 kJ day1) and 99th (12 474 kJ day1) centiles (604 women). Thus, a total of 42 237 women were available for the analysis.
The participating women and the deaths that occurred amongst them were distributed by non-nutritional covariates, and age- and multivariate- adjusted mortality ratios were calculated. Hazard ratios for overall mortality and mortality from cancer, as well as cardiovascular diseases, were estimated through Cox proportional hazards regression using, alternatively, the high protein score, the low carbohydrate score and the composite additive score as the principal exposure variables. To accommodate secular trends the models were stratified by 1-year birth cohorts with attained age as time scale. In a stratified Cox model the baseline hazard is allowed to vary across strata. The models were adjusted for the following variables as reported at enrolment: height (cm, continuously), body mass index (BMI; <25, 25–29.99 and ≥30 kg m2, 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 or more cigarettes, current smokers of <10 cigarettes, current smokers of 10–14 cigarettes, current smokers of 15–19 cigarettes, current smokers of 20 or more cigarettes, categorically), physical activity [from 1 (low) to 5 (high), categorically], education (0–10, 11–13 and 14 or more years in school, categorically), energy intake (per 1000 kJ day1, continuously), saturated lipid intake (per 10 g, continuously) and alcohol intake (<5, 5–25 or >25 g day1, categorically). Unsaturated lipids should not be and were not controlled for in these models to avoid overdetermination generated by inclusion of all energy-generating nutrients as well as total energy intake in the same models. Fine control for tobacco smoking was necessary because of the powerful influence of smoking on mortality and the possibility that smoking may be associated with some dietary intakes. All analyses were conducted for all women, as well as separately for women <40 years old at enrolment and for women 40 years or older at enrolment, the rationale being that genetic and early life factors are likely to have a stronger influence amongst younger than amongst older adults.
Tuloksista mm.:
In this cohort of women, median intake of energy was 6396 kJ day1 with 10th and 90th centiles 4246 and 9060 kJ day1, respectively. Percentage of energy intake from carbohydrates ranged from 72.0% (10th centile) to 32.4% (90th centile) and for proteins from 10.4% (10th centile) to 23.0% (90th centile). The additive score was significantly correlated positively with protein intake (Spearman r = +0.35), inversely with carbohydrate intake (Spearman r = −0.28), positively with lipid intake (for saturated lipids Spearman r = +0.26; for unsaturated lipids Spearman r = +0.16), but, importantly, it was not correlated with energy intake (Spearman r = −0.006).
...
The additive low carbohydrate–high protein score is positively associated with overall mortality, a 5 units increment corresponding to an increase in mortality by 11% [95% confidence interval (CI): 0–23%]. This increase in overall mortality is mostly accounted for by an increase of 37% in cardiovascular mortality (95% CI: 2–84%).
Jos halajaa kokonaisartsua, pyydä vaikka privalla.
