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Key features: 

• The Northern Sweden Health and Disease Study (NSHDS) was initiated in the mid-1980s. The NSHDS is a population-based prospective longitudinal cohort comprising >140 000 participants in the two northernmost regions in Sweden, Norrbotten and Västerbotten, with >240 000 blood samples and 1.5 million person-years of follow-up.
• The NSHDS includes three sub-cohorts: the Västerbotten Intervention Programme (VIP), the expanded Northern Sweden Monitoring of Trends and Determinants of Cardiovascular Disease (MONICA) Study, and the Mammography Screening Project (MSP). The VIP is both a community-based cardiometabolic intervention programme encouraging healthy lifestyle (targeting individuals 40, 50, and 60 years of age), and a corresponding research cohort. The MONICA is an observational study focusing on cardiovascular disease and its associated risk factors, recruiting individuals aged 25–74 years. The MSP recruited women attending mammography during 1995–2006. The NSHDS median participation age is 50 years (53% women).
• Most participants contribute data on health, lifestyle, anthropometric measures, blood pressure, blood lipids, and glucose tolerance, along with research blood samples that are fractionated, frozen within an hour of collection, and stored at –80°C. Linkage to registries, clinical cohorts, and biological tissue archives facilitates studies of well-characterized participants (often combined with intervention studies).
• Collaborations are encouraged. Additional information can be found at: info.brs@umu.se; https://www.umu.se/en/biobank

The nutrient quality of 96 beverages in Sweden was assessed based on the Keyhole (KH), Nutri-Score (NS) and Nutrient Rich Food (NRF) index. Of milk- and plant-based beverages, 20% were similarly rated by all three as either high or low quality. Keyhole evaluated plain milk more coherently to the dietary guidelines, whilst NS assessed plant-based beverages more consistently than other indicators. Of water-based beverages, 61% were similarly rated by NS and NRF, with highest alignment in sugar-sweetened and non-nutritive sweeteners containing beverages. According to NS, water, unsweetened beverages, freshly squeezed fruit juices and vegetable juices qualified as nutritious choices. NRF index evaluated beverages less coherently to the guidelines and the rating of products largely mirrored fortification. Thus, agreement among indicators varies across beverage groups. NS is suitable to rate the nutrient quality of most beverages consistently with the dietary guidelines, except for milk where KH outperforms NS.

Background: Dietary diversity may affect type 2 diabetes (T2D) but no studies have examined protein diversity by source. We examined five diversity scores and the 10-year risk of T2D and effect modification.

Methods: A prospective study of 10 363 incident T2D cases and a representative sub-cohort of 13 937 individuals sampled from a cohort of 340 234 participants in eight European countries (1993-2007). Five diversity scores were derived from self-reported diet data (gr/day): diversity of food groups (range: 0-5); and diversity within subtype of vegetables (0-4); meat/alternatives (0-6); animal-protein (0-8); and plant-protein sources (0-5). Country-specific hazard ratios (HRs) and 95% confidence intervals (CIs) were obtained by using Prentice-weighted Cox regression and combined by using mixed-effects models. Models were stratified by sex (male/female) and obesity status (body mass index ≥ 30 kg/m2; waist circumference ≥ 88 cm for females and ≥102 cm for males).

Results: Daily intake of five food groups (versus up to three) was linked to lower T2D incidence overall [HR 0.86 (95% CI 0.75, 0.98)], in females [0.86 (0.77, 0.96)], and in people without central obesity [0.79 (0.70, 0.89)]. Three or more subtypes of plant protein were inversely associated with T2D overall [0.78 (0.65, 0.98)], in females [0.75 (0.62, 0.90)] and people without central obesity [0.82 (0.68, 1.00)]. Additionally, consuming three subtypes of vegetables was inversely associated with T2D overall [0.90 (0.83, 0.98)] and in males [0.85 (0.73, 0.99)].

Conclusion: Diabetes prevention may benefit not only from a diet consisting of five different food groups, but also from a diet that is diverse in plant-protein sources, with specific benefits for female Europeans and those without central obesity.

Objective: While associations of ultra-processed food (UPF) consumption with adverse health outcomes are accruing, its environmental and food biodiversity impacts remain underexplored. This study examines associations between UPF consumption and dietary greenhouse gas emissions (GHGe), land use, and food biodiversity.

Design: Prospective cohort study. Linear mixed models estimated associations between UPF intake (grams/day and kcal/day) and GHGe (kg CO₂-equivalents/day), land use (m²/day), and dietary species richness (DSR). Substitution analyses assessed the impact of replacing UPFs with unprocessed or minimally processed foods.

Participants: 368,733 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) study.

Setting: Europe

Results: Stronger associations were found for UPF consumption in relation with GHGe and land use compared to unprocessed or minimally processed food consumption. Substituting UPFs with unprocessed or minimally processed foods was associated with lower GHGe (8.9%; 95%CI: - 9.0; -8.9) and land use (9.3%; -9.5; -9.2) when considering consumption by gram per day and higher GHGe (2.6%; 95% CI: 2.5: 2.6) and land use (1.2%; 1.0; 1.3) when considering consumption in kilocalories per day. Substituting UPF by unprocessed or minimally processed foods led to negligible differences in DSR, both for consumption in grams (-0.1%; -0.2; -0.1) and kilocalories (1.0%; 1.0; 1.1).

Conclusion: UPF consumption was strongly associated with GHGe and land use as compared to unprocessed or minimally processed food consumption, while associations with food biodiversity were marginal. Substituting UPFs with unprocessed or minimally processed foods resulted in differing directions of associations with environmental impacts, depending on whether substitutions were weight- or calorie-based.

Purpose: Investigate the association between choline and betaine intake and all-cause mortality in a large Swedish cohort.

Methods: Women (52,246) and men (50,485) attending the Västerbotten Intervention Programme 1990–2016 were included. Cox proportional hazard regression models adjusted for energy intake, age, BMI, smoking, education, and physical activity were used to estimate mortality risk according to betaine, total choline, phosphatidylcholine, glycerophosphocholine, phosphocholine, sphingomyelin, and free choline intakes [continuous (per 50 mg increase) and in quintiles].

Results: During a median follow-up of 16 years, 3088 and 4214 deaths were registered in women and men, respectively. Total choline intake was not associated with all-cause mortality in women (HR 1.01; 95% CI 0.97, 1.06; P = 0.61) or men (HR 1.01; 95% CI 0.98, 1.04; P = 0.54). Betaine intake was associated with decreased risk of all-cause mortality in women (HR 0.95; 95% CI 0.91, 0.98; P < 0.01) but not in men. Intake of free choline was negatively associated with risk of all-cause mortality in women (HR 0.98; 95% CI 0.96, 1.00; P = 0.01). No other associations were found between intake of the different choline compounds and all-cause mortality. In women aged ≥ 55 years, phosphatidylcholine intake was positively associated with all-cause mortality. In men with higher folate intake, total choline intake was positively associated with all-cause mortality.

Conclusion: Overall, our results do not support that intake of total choline is associated with all-cause mortality. However, some associations were modified by age and with higher folate intake dependent on sex. Higher intake of betaine was associated with lower risk of all-cause mortality in women.

We investigated data-driven and hypothesis-driven dietary patterns and their association to plasma metabolite profiles and subsequent colorectal cancer (CRC) risk in 680 CRC cases and individually matched controls. Dietary patterns were identified from combined exploratory/confirmatory factor analysis. We assessed association to LC–MS metabolic profiles by random forest regression and to CRC risk by multivariable conditional logistic regression. Principal component analysis was used on metabolite features selected to reflect dietary exposures. Component scores were associated to CRC risk and dietary exposures using partial Spearman correlation. We identified 12 data-driven dietary patterns, of which a breakfast food pattern showed an inverse association with CRC risk (OR per standard deviation increase 0.89, 95% CI 0.80–1.00, p = 0.04). This pattern was also inversely associated with risk of distal colon cancer (0.75, 0.61–0.96, p = 0.01) and was more pronounced in women (0.69, 0.49–0.96, p = 0.03). Associations between meat, fast-food, fruit soup/rice patterns and CRC risk were modified by tumor location in women. Alcohol as well as fruit and vegetables associated with metabolite profiles (Q² 0.22 and 0.26, respectively). One metabolite reflecting alcohol intake associated with increased CRC risk, whereas three metabolites reflecting fiber, wholegrain, and fruit and vegetables associated with decreased CRC risk.

The healthy lifestyle index (HLI), defined as the unweighted sum of individual lifestyle components, was used to investigate the combined role of lifestyle factors on health-related outcomes. We introduced weighted outcome-specific versions of the HLI, where individual lifestyle components were weighted according to their associations with disease outcomes. Within the European Prospective Investigation into Cancer and Nutrition (EPIC), we examined the association between the standard and the outcome-specific HLIs and the risk of T2D, CVD, cancer, and all-cause premature mortality. Estimates of the hazard ratios (HRs), the Harrell’s C-index and the population attributable fractions (PAFs) were compared. For T2D, the HR for 1-SD increase of the standard and T2D-specific HLI were 0.66 (95% CI: 0.64, 0.67) and 0.43 (0.42, 0.44), respectively, and the C-index were 0.63 (0.62, 0.64) and 0.72 (0.72, 0.73). Similar, yet less pronounced differences in HR and C-index were observed for standard and outcome-specific estimates for cancer, CVD and all-cause mortality. PAF estimates for mortality before age 80 were 57% (55%, 58%) and 33% (32%, 34%) for standard and mortality-specific HLI, respectively. The use of outcome-specific HLI could improve the assessment of the role of lifestyle factors on disease outcomes, thus enhancing the definition of public health recommendations.

Background: Low-quality, non-diverse diet is a main risk factor for premature death. Accurate measurement of habitual diet is challenging and there is a need for validated objective methods. Blood metabolite patterns reflect direct or enzymatically diet-induced metabolites. Here, we aimed to evaluate associations between blood metabolite patterns and a priori and data-driven food intake patterns.

Methods: 1, 895 participants in the Northern Sweden Health and Disease Study, a population-based prospective cohort study, were included. Fasting plasma samples were analyzed with 1H Nuclear Magnetic Resonance. Food intake data from a 64-item validated food frequency questionnaire were summarized into a priori Healthy Diet Score (HDS), relative Mediterranean Diet Score (rMDS) and a set of plant-based diet indices (PDI) as well as data driven clusters from latent class analyses (LCA). Orthogonal projections to latent structures (OPLS) were used to explore clustering patterns of metabolites and their relation to reported dietary intake patterns.

Results: Age, sex, body mass index, education and year of study participation had significant influence on OPLS metabolite models. OPLS models for healthful PDI and LCA-clusters were not significant, whereas for HDS, rMDS, PDI and unhealthful PDI significant models were obtained (CV-ANOVA p < 0.001). Still, model statistics were weak and the ability of the models to correctly classify participants into highest and lowest quartiles of rMDS, PDI and unhealthful PDI was poor (50%/78%, 42%/75% and 59%/70%, respectively).

Conclusion: Associations between blood metabolite patterns and a priori as well as data-driven food intake patterns were poor. NMR metabolomics may not be sufficiently sensitive to small metabolites that distinguish between complex dietary intake patterns, like lipids.

Background: Dietary habits strongly influence health, with poor diets contributing to numerous deaths annually. Addressing this requires improved dietary habits and consistent monitoring thereof. In northern Sweden, a validated food frequency questionnaire (FFQ) has been used for decades, but trends show that its ability to accurately measure intake has diminished. With changing eating habits and food supply, updating the FFQ was crucial, leading to the development of FFQ2020. This study assessed FFQ2020’s relative validity using 24-hour recalls and evaluated its reproducibility.

Methods: Participants were recruited from one of the northern-Sweden population-based health screenings and by advertising. Food intake was registered in an electronic food frequency questionnaire (FFQ2020) (test instrument) and reference data were obtained by six repeated electronic 24-hour dietary recalls (24HDR). Intakes of single foods were aggregated into food groups and healthy diet index scores, and daily energy and nutrient intakes were estimated. Results from the two methods were described and tested in univariate analyses and correlation tests, Bland Altman plots, cross-classification validity, and intra-class correlation analyses.

Results: Totally, 628 adults were invited to participate in the study. Of these, 320 joined, and 244 completed at least four 24HDRs. The median intakes in food groups, as well as the mean index scores and estimated nutrient intakes, were largely similar between the FFQ2020 and 24HDR recordings. The correlation coefficients between the two assessments ranged from 0.253 to 0.693 for food groups, 0.520 to 0.614 for diet indices, and 0.340 to 0.629 for energy and nutrients. Intra-class correlation coefficients indicated at least good reproducibility for intakes of food groups, diet index scores, and nutrients. Generally, Bland-Altman plots did not reveal any gross systematic disagreement between the two methods for any of the assessments. However, there were single observations located outside the upper or lower 95% confidence interval (CI) limits for the difference between FFQ2020 and the 24HDR recordings.

Conclusion: In concert, the results suggest that the relative validity and reproducibility of FFQ2020 are acceptable for trend analyses and group comparisons in large-scale studies but also that extended reference periods would improve the precision of less frequently consumed foods.

Objective: To assess the associations between adherence to the Swedish dietary guidelines and all-cause mortality and thus assessing the index' ability to predict health outcomes, as well as levels of dietary greenhouse gas emissions (GHGEs).

Design: A longitudinal study 1990-2016 within the population-based cohort Västerbotten Intervention Programme. Dietary data were based on food frequency questionnaires. Diet quality was assessed by the Swedish Healthy Eating Index for Adults 2015 (SHEIA15), based on the 2015 Swedish dietary guidelines. Dietary GHGEs were estimated from life cycle assessment data including emissions from farm to industry gate. Hazard ratios (HRs) and 95% confidence intervals (CIs) of all-cause mortality were evaluated with Cox proportional hazards regression, and differences in median GHGEs were tested using the Kruskal-Wallis one-way ANOVA test, between quintiles of SHEIA15 score.

Setting: Northern Sweden.

Participants: In total, 49,124 women and 47,651 men, aged 35-65 years.

Results: Median follow-up times were 16.0 years for women and 14.7 years for men, during which time 3074 women and 4212 men died. A consistent trend of lower all-cause mortality HRs for both sexes with higher SHEIA15 scores was demonstrated. For women, the all-cause mortality HR was 0.81 [(95% CI 0.71-0.92); p=0.001] and for men 0.90 [(95% CI 0.81-0.996); p=0.041] between the quintile with the highest SHEIA15 score compared with the quintile with the lowest SHEIA15 score. A consistent trend of lower estimated dietary GHGEs among both sexes with higher SHEIA15 scores was also found.

Conclusions: Adherence to Swedish dietary guidelines, estimated by SHEIA15, seems to promote longevity and reduce dietary climate impact.