Original Article | Published:


Low-carbohydrate, high-protein score and mortality in a northern Swedish population-based cohort

European Journal of Clinical Nutrition volume 66, pages 694700 (2012) | Download Citation



Long-term effects of carbohydrate-restricted diets are unclear. We examined a low-carbohydrate, high-protein (LCHP) score in relation to mortality.


This is a population-based cohort study on adults in the northern Swedish county of Västerbotten. In 37 639 men (1460 deaths) and 39 680 women (923 deaths) from the population-based Västerbotten Intervention Program, deciles of energy-adjusted carbohydrate (descending) and protein (ascending) intake were added to create an LCHP score (2–20 points). Sex-specific hazard ratios (HR) were calculated by Cox regression.


Median intakes of carbohydrates, protein and fat in subjects with LCHP scores 2–20 ranged from 61.0% to 38.6%, 11.3% to 19.2% and 26.6% to 41.5% of total energy intake, respectively. High LCHP score (14–20 points) did not predict all-cause mortality compared with low LCHP score (2–8 points), after accounting for saturated fat intake and established risk factors (men: HR for high vs low 1.03 (95% confidence interval (CI) 0.88–1.20), P for continuous=0.721; women: HR for high vs low 1.10 (95% CI 0.91–1.32), P for continuous=0.229). For cancer and cardiovascular disease, no clear associations were found. Carbohydrate intake was inversely associated with all-cause mortality, though only statistically significant in women (multivariate HR per decile increase 0.95 (95% CI 0.91–0.99), P=0.010).


Our results do not support a clear, general association between LCHP score and mortality. Studies encompassing a wider range of macronutrient consumption may be necessary to detect such an association.


In recent years, diet methods for weight control and their impact on health and survival have received much attention. Particularly, controversial are the carbohydrate-restricted diets that run counter to conventional dietary advice for weight loss, that is, caloric restriction.1

From a short-term perspective, low-carbohydrate, high-protein (LCHP) diets, or low-carbohydrate, high-fat diets, have been shown to be at least equivalent to conventional methods for weight loss and cardiovascular disease (CVD) risk reduction.2, 3 Many experimental studies indicate that carbohydrate-restricted diets may improve fasting glucose, triglycerides, HDL cholesterol and insulin resistance in patients with type 2 diabetes,4, 5, 6 though a high protein content is generally not recommended for people with diabetes because of the risk of nephropathy.7 In contrast to reported positive health effects of carbohydrate restriction, increased free fatty acids and LDL cholesterol were observed in a recent experimental study.8

From a long-term perspective, the health effects of carbohydrate-restricted diets are not well known. Increased all-cause, cardiovascular and cancer mortality demonstrated in cohort studies seem to contradict any sustainable benefit from a LCHP diet in the general population.9, 10, 11, 12 Elevated LDL cholesterol as a result of carbohydrate-restricted diet8 may contribute to atherosclerosis and CVD. Type of carbohydrates (for example, complex, simple) and proteins (for example, animal, vegetable) consumed may also affect risk associations. In cancer, a high consumption of carbohydrate-rich foods, such as fruit, vegetables and whole grain, has been associated with a reduced risk of many types of cancer,13 whereas high consumption of meat, especially red or processed meat, may increase the risk of colorectal cancer.13, 14 In a recent American observational study, an increased risk of diabetes was seen in men with a lower intake of carbohydrates and a higher intake of animal protein and fat over a period of 20 years,15 but a similar study in women showed no increased risk.16 More conclusive evidence is needed.

We hypothesised that variations in proportions of carbohydrate and protein intake are associated with all-cause, cancer and cardiovascular mortality in men and women in the northern Swedish county of Västerbotten, and that differences may exist in subjects with a low vs high metabolic risk profile, including subjects with diabetes, in this population.

Materials and methods

Study cohort

This study was based on the Västerbotten Intervention Program (VIP). During the study period, 1 January, 1990–31 December, 2008, residents of the county of Västerbotten turning 30 (1990–1996), 40, 50 and 60 years of age were invited to a health survey, with an average overall recruitment rate of 59% of target age-groups. Health risk measures were assessed and nearly all of those attending the health survey also completed a diet and lifestyle questionnaire. Little evidence of selection bias has been demonstrated in the VIP,17, 18 suggesting a truly population-based cohort.

The selection of the final study population (37 639 men, 39 680 women) is shown in Figure 1. Food intake levels, applied in the selection, were calculated by dividing total energy intake by estimated basal metabolic rate.19

Figure 1
Figure 1

In 31 December 2008 the VIP cohort included 113 205 FFQs, of which 26 491 were repeated measures 10 years apart. Subjects with missing values for more than 10% of the items in the FFQ and/or portion size were excluded (n=6715). Subjects lacking data for BMI, or with BMI < 10 kg/m2 were excluded (n=60). Subjects with unrealistic food intake levels (FIL), defined as FIL in the lowest 5th percentile or the highest 2.5th percentile (specific to sex and FFQ version and based on the first sampling occasion for subjects with repeated measures), were excluded (n=7977). For remaining subjects with repeated health surveys (n=21 134), the most recent sampling occasion was excluded from all risk analyses. The final study population thus included 77 319 participants: 37 639 men and 39 680 women.

Food frequency questionnaire (FFQ)

In the VIP FFQ, intakes of food items are estimated on a 9-level fixed scale from never to 4 times per day. Portion sizes are estimated from four colour photographs of proportionally increasing amounts of main carbohydrate sources (potato/rice/pasta), main protein sources (meat/fish) and vegetables. In total, 33.5% of the subjects (n=25 886) had completed the original, 84-item VIP FFQ.20 The remaining subjects had either completed an older, nearly identical, 84-item FFQ (n=4083), or a more recent 65-item version (n=47 350). Validation studies have been published for the original 84-item FFQ and, for B vitamins, also the condensed 65-item version.20, 21, 22

Macronutrient intakes were calculated as described elsewhere.23 Intakes were energy adjusted by the residual method.24


A comparison of food intake level and physical activity level was used to define low-energy reporters, a method appropriate for large sample sizes.25 To estimate physical activity level, two questions on work-related and leisure-time physical activity were used.26 Cutoffs for the identification of low-energy reporters (64% of the subjects) were calculated according to Black/Goldberg,25 and applied to the food intake level/physical activity level ratio separately for FFQ version and sex.

Low-carbohydrate, high-protein score

Sex- and FFQ-specific deciles of carbohydrate intake (descending) and protein intake (ascending) as energy-adjusted residuals, were added, as described by Trichopoulou et al.10 LCHP scores thus ranged from 2 to 20, representing increasing proportions of protein and decreasing proportions of carbohydrates in the diet. The score was divided into three categories, low (2–8 points), medium (9–13 points) and high (14–20 points), with high scores representing the lowest carbohydrate and highest protein intakes. To allow for comparisons of temporal changes between sampling occasions, all calculations of LCHP score were done before the repeat measures were removed.

Identification of mortality

Mortality end-points were identified by linking the VIP database with the Swedish national cause-of-death registry. Cancer mortality was defined as underlying cause of death, ICD-9 codes 140–208 or ICD-10 codes C00-C97. Cardiovascular mortality was defined as the main cause of death and/or underlying cause of death, ICD-9 codes 390–438, or ICD-10 codes I00-I69.

Statistical analysis

Likely predictors of mortality and other macronutrient-related factors were examined for association with LCHP score categories by Kruskal–Wallis tests. Intra-individual, temporal changes in LCHP score and micronutrients were assessed by Wilcoxon's paired samples signed rank test.

Sex-specific hazard ratios (HR) for mortality were calculated by Cox regression. Only saturated fat met our criterion for a confounder: altering the HR for the LCHP score by 10% when included in a bivariate model. To facilitate comparison with other studies, common risk factors were kept in the multivariate Model 2, which thus included age, body mass index (BMI), current smoking, education, sedentary lifestyle, intake of alcohol, saturated fat, and total energy. Missing values were included as dummy variables.

Proportional hazard assumptions were confirmed by Schoenfeld's test. With age categorised into 10-year age groups, only two minor deviations from the proportional hazard assumption were found (BMI for cancer mortality in women, and energy intake for CVD mortality in women). Given the large number of covariates, this was not considered to violate the reliability of the results.

Risk estimates were determined for subgroups based on: (a) subjects with a low metabolic risk profile, that is, free from hypertension, diabetes and obesity; (b) subjects with a high metabolic risk profile, that is, with hypertension and/or diabetes and/or obesity; and (c) subjects with diabetes. Hypertension was defined as systolic blood pressure 140 mm Hg and/or diastolic blood pressure 90 mm Hg and/or use of medication to lower blood pressure; diabetes as self-reported diabetes and/or fasting plasma glucose 7.0 mmol/l and/or post-load glucose 12.2 mmol/l, measured in capillary plasma; and obesity as BMI 30 kg/m2. Analyses were also performed after stratification by age and intake of saturated fat, and for intake of protein, carbohydrate, and whole grain. LCHP scores using only plant or animal protein intake and only whole grain or sucrose intake were tested.

Statistical analyses were performed with IBM SPSS software, version 19.0 (IBM, Somers, NY, USA). All tests were two-sided, and P-values <0.05 were considered statistically significant.


The study was approved by the Regional Ethical Review Board of Northern Sweden (Dnr 07–165 M). All study subjects provided written informed consent, and the study was conducted in accordance with the Declaration of Helsinki.


The median age at recruitment was 49 years. Follow-up times ranged from 1 day to 19 years, with a median of 10 years. Almost all participants, 97.2%, reported a protein intake in line with Swedish national food recommendations (10–20% of total energy). However, 80.1% of the participants reported a lower carbohydrate intake than recommended (55–60% of energy), and 76.8% exceeded the recommendation for fat (<30% of energy). In the 1640 subjects with LCHP score 2 points, median intakes (1st–3rd quartile) of carbohydrates, protein and fat were 61.0 (58.9–63.6)%, 11.3 (10.5–11.8)% and 26.6 (23.8–29.2)% of energy, respectively. In the 1639 subjects with LCHP score 20 points, median intakes (1st–3rd quartile) of carbohydrates, protein and fat were 38.6 (35.7–41.3)%, 19.2 (18.3–20.4)% and 41.5 (38.8–44.5)% of energy, respectively. High LCHP scores were particularly associated with lower age, higher prevalence of obesity, current smoking, diabetes, lower prevalence of hypertension and a higher intake of alcohol (Tables 1 and 2). The increase in protein intake with increasing LCHP score was entirely attributable to animal protein, whereas for fat, both saturated and unsaturated fats increased with increasing LCHP score (Table 2).

Table 1: Baseline characteristics of Västerbotten Intervention Program participants according to low-carbohydrate, high-protein (LCHP) score
Table 2: Baseline dietary characteristics of Västerbotten Intervention Program participants according to low-carbohydrate, high-protein (LCHP) score

HRs for all-cause, cancer, and CVD mortality, in relation to increased LCHP score, are presented in Table 3. In the crude Model 1, adjusted only for age, an increased risk of all-cause mortality was found for high LCHP scores (14–20 points) compared with low LCHP scores (2–8 points) in both men (Crude HR for high vs low 1.18 (95% CI 1.03–1.35), P for continuous=0.011) and women (Crude HR for high vs low 1.23 (95% CI 1.04–1.44), P for continuous=0.011). In the multivariate Model 2, adjusted for age, BMI, current smoking, education, sedentary lifestyle, intake of alcohol, total energy and saturated fat (Table 3), risk associations were attenuated and not statistically significant (Multivariate HR for men 1.03 (95% CI 0.88–1.20), P for continuous=0.721; Multivariate HR for women 1.10 (95% CI 0.91–1.32), P for continuous=0.229). In men, the result of the crude analysis was stronger in the low metabolic risk subgroup (Crude HR for high vs low 1.30 (95% CI 1.07–1.58), P for continuous=0.015). This finding was slightly weaker in a multivariate model adjusted for age, BMI, current smoking, education, sedentary lifestyle, intake of alcohol and total energy (HR for high vs low 1.22 (95% CI 1.00–1.49), P for continuous=0.082), and was lost when saturated fat was added to the model (Table 3).

Table 3: Hazard ratios for all-cause, cancer and cardiovascular disease mortality by low-carbohydrate, high-protein (LCHP) score

No stable risk associations were found between increasing LCHP score and either all-cancer mortality (n=975, Table 3), or the most common cancer sites leading to death: colorectum (n=127), lymphatic tissue (n=99), respiratory tract (n=122), pancreas (n=93), breast (n=81), prostate (n=60) and stomach (n=52) (data not shown).

For CVD mortality, no statistically significant risk associations were found in men or women in the full study group. In men with diabetes, increasing LCHP scores were significantly associated with a reduced CVD mortality (Multivariate HR for high vs low 0.30 (95% CI 0.14–0.68), P for continuous=0.002). The opposite was seen in women with diabetes (Multivariate HR for high vs low 5.3 (95% CI 1.02–27.7), P for continuous=0.090).

Using absolute macronutrient intakes without energy adjustment to calculate LCHP score did not affect the risk patterns materially (data not shown). Neither did excluding subjects with 2 years of follow-up. An LCHP score based solely on animal protein resulted in stronger, positive risk associations, but only in Model 1, whereas an LCHP score based solely on plant protein retained the null results, even in Model 1. Similarly, there were stronger, positive risk associations for an LCHP score based on whole grain instead of carbohydrates in Model 1 and opposite tendencies for an LCHP score based on sucrose, but neither of these, was statistically significant in Model 2. There were no material differences in results with respect to energy reporting.

Results for all-cause, cancer, and CVD mortality were generally similar in subgroups based on age and saturated fat intake (Table 4). In women 50 years of age, increasing LCHP scores were associated with an increased all-cause mortality (Multivariate HR for high vs low 1.27 (95% CI 0.90–1.77), P for continuous=0.035). An Increased all-cause mortality with increasing LCHP scores was also found in women with saturated fat intakes above the median (Multivariate HR for high vs low 1.34 (95% CI 1.00–1.80), P for continuous 0.020).

Table 4: Hazard ratios for all-cause, cancer and cardiovascular disease mortality by low-carbohydrate, high-protein (LCHP) score in subgroups of participants in the Västerbotten Intervention Program, based on age and saturated fat intake

HRs for all-cause, cancer and CVD mortality for increasing deciles of protein, carbohydrate and whole grain are shown in Table 5. For protein, null results were found. Carbohydrate intake was significantly associated with a decreased all-cause mortality in women (Multivariate HR per decile increase 0.95 (95% CI 0.92–0.99), P=0.010). In men, results were similar, but not statistically significant in the multivariate model. Carbohydrate intake was not statistically significantly related to cancer or CVD mortality in either sex in the multivariate Model 2, though a significant inverse association was observed in the crude Model 1 for cancer mortality in men and CVD mortality in women. The risk patterns for whole grain intake were similar to those for carbohydrate in men, whereas the finding for carbohydrate in women was not replicated for whole grain.

Table 5: Hazard ratios for all-cause, cancer and cardiovascular disease mortality by ascending deciles of energy-adjusted protein, carbohydrate and whole grain intake

An intraindividual, temporal increase in LCHP score was seen in subjects with repeated 65-item FFQs 10 years apart (n=5780). LCHP score increased from a median (1st–3rd quartile) of 10 (7–13) to 12 (9–15), with no substantial differences between women and men. This corresponded to a decreased carbohydrate intake from 49.8 (45.8–53.7) to 48.7 (44.2–52.9)% of energy, and an increased protein intake from 14.2 (12.9–15.6) to 15.4 (13.9–17.0)% of energy. BMI rose from 24.5 (22.5–26.8) to 25.6 (23.4–28.2) kg/m2 in the same subjects. The increase in LCHP score was similar in metabolic low and high-risk groups. All P for temporal changes were 0.001.


In this large, population-based, cohort study we examined all-cause, cancer and CVD mortality in relation to proportions of carbohydrate and protein in the diet. A diet relatively low in carbohydrate and high in protein (high LCHP score, 14–20 points) did not significantly predict all-cause mortality compared with a low LCHP score (2–8 points), when established risk factors and intake of saturated fat were taken into account.

This main finding does not support previous cohort studies, which may in part reflect the apparent lack of adjustment for saturated fat intake in some reports, in the analyses most comparable to ours.9, 10 Saturated fat was the most important confounder in our study. However, other studies have reported a positive association between a carbohydrate-restricted diet and increased mortality after adjusting for saturated fat,11 or with fat intake taken into account by other means.12 Stratifying by saturated fat intake had little effect on the results, though a significant increase in mortality at higher LCHP scores was apparent in women with saturated fat intakes above the median. Protein and carbohydrate quality may also be relevant in assessing the role of macronutrient distribution for health, as suggested by the results of the present, and previous, studies.12, 15, 27

The intake levels of carbohydrates and protein in the VIP cohort were by no means extreme, which may have prevented the detection of possible effects of stricter carbohydrate restriction. However, compared with other cohort studies, intakes of carbohydrates and protein in the VIP cohort, calculated as percent of total energy intake, were intermediate.9, 10, 11, 12 A difference in the macronutrient proportions of the VIP population compared with other populations is thus not likely to explain the discrepancies in risk associations observed, though the temporal, intraindividual increase in LCHP over 10 years likely diluted our results.

More in line with previous cohort studies was the increased all-cause mortality for a high LCHP score in women 50 years of age. This finding may reflect sex and age differences in the relationship between dietary factors and mortality. For example, a recent meta-analysis found that intake of red meat was associated with premenopausal breast cancer in specific.28 However, our result was attenuated and not statistically significant for all-cancer mortality in women, suggesting the possibility of a chance finding.

In previous cohort studies, subjects with diabetes and other diseases have generally been excluded.9, 11, 12 In one study, an increased mortality at high LCHP scores was found in subjects with coronary artery disease and diabetes.10 The reduced CVD mortality at high LCHP scores in men with diabetes in our study is thus a novel finding. However, the opposite result observed in women and the low numbers of cases in these subgroups necessitate a conservative interpretation. In the VIP-cohort, participants with diabetes have been shown to maintain their body weight over a 10-year period to a greater extent than subjects without.29 Reversed causality related to changes in lifestyle might thus also explain our finding.

The population-based cohort design and the nearly 20 years of follow-up are the main strengths of this study. The large sample size allowed for stratified analyses, which enabled a broader perspective. False positives owing to multiple testing may be present, but are not adjusted for, as the study is exploratory rather than explanatory. Residual confounding due to risk factors not, or not adequately, assessed by the VIP questionnaire and health survey, such as smoking history, may have occurred. Some misclassification due to the interventions built into the VIP, such as treating diabetes, hypertension or dyslipidemia diagnosed through the health survey, cannot be excluded, with an expected effect of diluting the study results.

In conclusion, this population-based cohort study does not support a clear, general association between a diet relatively low in carbohydrates and high in protein and increased mortality, when intake of saturated fat is taken into account. Studies encompassing a wider range of macronutrient consumption may be necessary to detect such an association. In studies of carbohydrate-restricted diet in health and disease, factors such as sex, age, metabolic risk profile and macronutrient quality should also be considered.


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This study was supported by Nordic Health Whole Grain Food (HELGA)/NordForsk and Visare Norr, Northern County Councils. We thank the participants in the Västerbotten Intervention Program for their valuable contribution to medical research. We also acknowledge Professor Göran Broström of the Department of Statistics, Umeå University, for excellent statistical advice.

Author information


  1. Department of Public Health and Clinical Medicine, Nutritional Research, Umeå University, Umeå, Sweden

    • L M Nilsson
    •  & G Hallmans
  2. Department of Clinical Nutrition, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

    • A Winkvist
  3. Department of Medicine, Sunderby Hospital, Luleå, Sweden

    • M Eliasson
  4. Department of Public Health and Clinical Medicine, Medicine, Umeå University, Umeå, Sweden

    • M Eliasson
    •  & J-H Jansson
  5. Department of Medicine, Skellefteå Hospital, Skellefteå, Sweden

    • J-H Jansson
  6. Department of Odontology, Umeå University, Umeå, Sweden

    • I Johansson
  7. Department of Public Health and Clinical Medicine, Occupational and Environmental Medicine, Umeå University, Umeå, Sweden

    • B Lindahl
  8. Department of Oncology and Radiation Sciences, Oncological Center, Umeå University, Umeå, Sweden

    • P Lenner
  9. Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden

    • B Van Guelpen


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The authors declare no conflict of interest.

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Correspondence to L M Nilsson.

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