The overall consumption of ultra-processed food (UPF) has previously been associated with type 2 diabetes. However, due to the substantial heterogeneity of this food category, in terms of their ...nutritional composition and product type, it remains unclear whether previous results apply to all underlying consumption patterns of UPF.
Of 70,421 participants (35-70 years, 58.6% women) from the Lifelines cohort study, dietary intake was assessed with a food frequency questionnaire. UPF was identified according to the NOVA classification. Principal component analysis (PCA) was performed to derive UPF consumption patterns. The associations of UPF and adherence to UPF consumption patterns with incidence of type 2 diabetes were studied with logistic regression analyses adjusted for age, sex, diet quality, energy intake, alcohol intake, physical activity, TV watching time, smoking status, and educational level.
During a median follow-up of 41 months, a 10% increment in UPF consumption was associated with a 25% higher risk of developing type 2 diabetes (1128 cases; OR 1.25 95% CI 1.16, 1.34). PCA revealed four habitual UPF consumption patterns. A pattern high in cold savory snacks (OR 1.16 95% CI 1.09, 1.22) and a pattern high in warm savory snacks (OR 1.15 95% CI 1.08, 1.21) were associated with an increased risk of incident type 2 diabetes; a pattern high in traditional Dutch cuisine was not associated with type 2 diabetes incidence (OR 1.05 95% CI 0.97, 1.14), while a pattern high in sweet snacks and pastries was inversely associated with type 2 diabetes incidence (OR 0.82 95% CI 0.76, 0.89).
The heterogeneity of UPF as a general food category is reflected by the discrepancy in associations between four distinct UPF consumption patterns and incident type 2 diabetes. For better public health prevention, research is encouraged to further clarify how different UPF consumption patterns are related to type 2 diabetes.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Sarcopenic obesity (SO) is defined by a relatively low muscle mass in combination with obesity. Sarcopenic obesity was first noted as a health risk in geriatric populations but has recently been ...recognized as a scientific and clinical priority that may extend beyond geriatric settings. Obesity is generally preceded by overweight, so the prevalence and health risks of sarcopenia in those with overweight (SOW) is of interest for preventive purposes. The aim of this study, therefore, was to assess the prevalence and determinants of SO and SOW in a general population.
Participants (n = 119,494), aged 18–90 years were included from the Dutch Lifelines cohort study. Muscle mass was assessed by 24-h urine creatinine excretion and stratified for gender for analysis, and obesity was defined as a Body Mass Index (BMI) ≥30 kg/m2 and overweight ≥25 kg/m2. Multivariate logistic regression models were applied to assess the relevant determinants of SO and SOW.
Respectively for men and women the prevalence of SO was 0.9% and 1.4%, and prevalence of SOW 6.5% and 6.0%. In subjects with sarcopenia, BMI was ≥25 kg/m2 in 45.5% and ≥30 kg/m2 in 6.1%. Overall females had a higher prevalence of SOW and SO in all age groups except for SOW in males between ages 40–59. Also, age was a significant determinant of SO and SOW, with a rise in prevalence as of age 50. Of all subjects with SO and SOW, respectively 82.5% and 80.4% were below the age of 70. Compared to those with no morbidities, the odds ratio of SO and SOW among participants with >3 comorbidities was 2.71 (95% CI: 1.62–4.54) and 1.33 (95% CI: 1.07–1.65) among males and 1.14 (95% CI: 0.79–1.65) and 1.28 (95% CI: 1.06–1.54) among females, independent of other determinants. Overall, an inverse association was found between SOW and SO and physical activity and macronutrient intake.
The results support the need for more awareness of SO beyond the field of geriatrics, in particular in subjects with comorbidities. SOW is more prevalent than SO and may provide opportunities for preventive strategies for the general population.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Multimorbidity is considered a major challenge for current health care. Lifestyle interventions, as a broad and generic approach, may have the potential to improve the management of care among ...patients with multimorbidity. The objective of this study was to evaluate the association of multimorbidity defined within the cardiometabolic disease domains with dietary patterns, representing habitual dietary intake.
We studied 129 369 participants from the Lifelines Cohort study (42% male, 45±13 years (range 18-93)) in which diet was assessed using a 110-item food frequency questionnaire. A composite morbidity score was applied in multivariable ordered logistic regression to test the association with dietary patterns derived by principal components analysis, based on sex-specific dietary pattern scores.
Four dietary patterns were retained, accounting for 26.6% of the variation in overall diet. After control for potential confounders, men and women in the highest quintile of "meat, alcohol and potato pattern" and "snack pattern" had a higher likelihood of having higher morbidity scores than those in the lowest quintile (e.g. men: OR = 1.83(95% CI:1.71-1.97), OR = 1.18(95% CI 1.11-1.27 respectively). The opposite was observed with respect to the "bread and sweets pattern" and "vegetable, fish and fruit pattern" (e.g. women: OR = 0.88(95% CI: 0.81-0.96), OR = 0.86(95% CI 0.81-0.92 respectively). The association partially attenuated after adjusting for BMI, but the associations remained significant among men.
Robust associations between dietary patterns and multimorbidity within the cardiometabolic domain, in particular a "meat, alcohol and potato pattern", suggest an important opportunity of dietary interventions in multimobidity prevention. Generic prevention strategies based on population derived dietary patterns may have the potential to enhance lifestyle management among people with multimorbidity.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Ultraprocessing makes food products more convenient, appealing, and profitable. Recent studies show that high ultraprocessed food (UPF) intake is associated with cardiometabolic diseases.
The aim of ...this study is to investigate the association between UPF consumption and risks of kidney function decline in the general population.
In a prospective, general population–based Lifelines cohort from Northern Netherlands, 78,346 participants free of chronic kidney disease (CKD) at baseline responded to a 110-item FFQ. We used a multivariable regression analysis to study the associations of the proportion (in grams/day) of UPFs in the total diet with a composite kidney outcome incident CKD or a ≥30% estimated glomerular filtration rate (eGFR) decline relative to baseline and annual change in eGFR.
On average, 37.7% of total food intake came from UPFs. After 3.6 ± 0.9 years of follow-up, 2470 participants (3.2%) reached the composite kidney outcome. Participants in the highest quartile of UPF consumption were associated with a higher risk of the composite kidney outcome (OR, 1.27; 95% CI, 1.09–1.47; P = 0.003) compared with those in the lowest quartile, regardless of their macro- or micronutrient intake or diet quality. Participants in the highest quartile had a more rapid eGFR decline (β, –0.17; 95% CI, –0.23 to –0.11; P < 0.001) compared with those in the lowest quartile. Associations were generally consistent across different subgroups.
Higher UPF consumption was associated with a higher risk of a composite kidney outcome (incident CKD or ≥30% eGFR decline) and a more rapid eGFR decline in the general population, independent of confounders and other dietary indices.
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CMK, GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Diet is an important modifiable risk factor for chronic diseases. In the search for effective strategies to improve dietary patterns in order to promote healthy ageing, new approaches considering ...contextual factors in public health medicine are warranted. The aim of this study is to examine the spatial clustering of dietary patterns in a large representative sample of adults.
Dietary patterns were defined on the basis of a 111 item Food Frequency Questionnaire among n = 117,570 adults using principal components analysis. We quantified the spatial clustering of dietary pattern scores at the neighborhood level using the Global Moran's I spatial statistic, taking into consideration individual demographic and (neighborhood) socioeconomic indicators.
Four dietary patterns explaining 27% of the variance in dietary data were extracted in this population and named the "bread and cookies" pattern, the "snack" pattern, the "meat and alcohol" pattern and the "vegetable, fruit and fish" pattern. Significant spatial clustering of high (hot spot) and low (cold spot) dietary pattern scores was found for all four dietary patterns irrespective of age and gender differences. Educational attainment and neighborhood income explained the global clustering to some extent, although clustering at smaller regional scales persisted.
The significant region-specific hot and cold spots of the four dietary patterns illustrate the existence of regional "food cultures" and underscore the need for interventions targeted at the sub-national level in order to tackle unhealthy dietary behavior and to stimulate people to make healthy dietary choices.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Many diet quality scores exist, but fully food-based scores based on contemporary evidence are scarce. Our aim was to develop a food-based diet score based on international literature and examine its ...discriminative capacity and socio-demographic determinants.
Between 2006 and 2013, dietary intake of 129,369 participants of the Lifelines Cohort (42% male, 45 ± 13 years (range 18-93)) was assessed with a 110-item food frequency questionnaire. Based on the 2015 Dutch Dietary Guidelines and underlying literature, nine food groups with positive (vegetables, fruit, whole grain products, legumes&nuts, fish, oils&soft margarines, unsweetened dairy, coffee and tea) and three food groups with negative health effects (red&processed meat, butter&hard margarines and sugar-sweetened beverages) were identified. Per food group, the intake in grams per 1000 kcal was categorized into quintiles, awarded 0 to 4 points (negative groups scored inversely) and summed. Food groups with neutral, unknown or inconclusive evidence are described but not included.
The Lifelines Diet Score (LLDS) discriminated well between high and low consumers of included food groups. This is illustrated by e.g. a 2-fold higher vegetable intake in the highest, compared to the lowest LLDS quintile. Differences were 5.5-fold for fruit, 3.5-fold for fish, 3-fold for dairy and 8-fold for sugar-sweetened beverages. The LLDS was higher in females and positively associated with age and educational level.
The LLDS is based on the latest international evidence for diet-disease relations at the food group level and has high capacity to discriminate people with widely different intakes. Together with the population-based quintile approach, this makes the LLDS a flexible, widely applicable tool for diet quality assessment.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
Although the interregional disparity in chronic kidney disease (CKD) prevalence has been reported globally, little is known about differences in CKD prevalence within a region. We aimed to study the ...intraregional distribution of renal function in the Northern Netherlands and identify determinants of geographical differences in renal function.
We included 143,735 participants from the Lifelines population-based cohort in the Northern Netherlands. Spatial analysis was performed to identify regional clusters of lower eGFR (cold spots) and higher eGFR (hot spots) at the postal code level, without and with adjustment for clinical risk factors. Multivariate logistic regression was used to identify the contribution of neighborhood-level health-related behaviors, socioeconomic status, and environmental factors (air pollution parameters, urbanity) to regional clustering of lower eGFR.
Significant spatial clustering of renal function was found for eGFR as well as for early stage renal function impairment (eGFR<90 ml/min/1.73 m2), (p<0.001). Spatial clustering persisted after adjustment of eGFR for clinical risk factors. In adjusted cold spots, the aggregate eGFR was lower (mean ± SD: 96.5±4.8 vs. 98.5±4.0 ml/min/1.73 m2, p = 0.001), and the prevalence of early stage renal function impairment (35.8±10.9 vs. 28.7±9.8%, p<0.001) and CKD stages 3-5 was higher (median (interquartile range): 1.2(0.1-2.4) vs 0(0-1.4)%, p<0.001) than in hot spots. In multivariable logistic regression, exposure to NO2 (Odd ratio OR, 1.45; 95% confidence interval 95% CI, 1.19 to 1.75, p<0.001) was associated with cold spots (lower renal function), whereas proportion of fat intake in the diet (OR, 0.68; 95%CI, 0.48-0.97, P = 0.031) and income (OR, 0.91; 95%CI, 0.86-0.96, p<0.001) for median level income) were inversely related.
Significant intraregional clustering of renal function, early renal function impairment and CKD were observed in the Northern Netherlands even after adjustment for renal function-related clinical risk factors. Environmental (air pollution), neighborhood-level socioeconomic factors and diet are determinants of intraregional renal function distribution. Spatial analysis might be a useful adjunct to guide public health strategies for the prevention of CKD.
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PurposeThe Lifelines COVID-19 cohort was set up to assess the psychological and societal impacts of the COVID-19 pandemic and investigate potential risk factors for COVID-19 within the Lifelines ...prospective population cohort.ParticipantsParticipants were recruited from the 140 000 eligible participants of Lifelines and the Lifelines NEXT birth cohort, who are all residents of the three northern provinces of the Netherlands. Participants filled out detailed questionnaires about their physical and mental health and experiences on a weekly basis starting in late March 2020, and the cohort consists of everyone who filled in at least one questionnaire in the first 8 weeks of the project.Findings to date>71 000 unique participants responded to the questionnaires at least once during the first 8 weeks, with >22 000 participants responding to seven questionnaires. Compiled questionnaire results are continuously updated and shared with the public through the Corona Barometer website. Early results included a clear signal that younger people living alone were experiencing greater levels of loneliness due to lockdown, and subsequent results showed the easing of anxiety as lockdown was eased in June 2020.Future plansQuestionnaires were sent on a (bi)weekly basis starting in March 2020 and on a monthly basis starting July 2020, with plans for new questionnaire rounds to continue through 2020 and early 2021. Questionnaire frequency can be increased again for subsequent waves of infections. Cohort data will be used to address how the COVID-19 pandemic developed in the northern provinces of the Netherlands, which environmental and genetic risk factors predict disease susceptibility and severity and the psychological and societal impacts of the crisis. Cohort data are linked to the extensive health, lifestyle and sociodemographic data held for these participants by Lifelines, a 30-year project that started in 2006, and to data about participants held in national databases.
Blood lipids play a critical role in the pathogenesis of type 2 diabetes, and they are closely related to dietary factors. However, the associations between blood lipids-related dietary patterns and ...risk of type 2 diabetes are controversial and not fully clear. In this study, we aimed to derive dietary patterns that explained variation in blood lipids and to investigate their associations with incident type 2 diabetes.
The analysis was based on 39,000 women and 25,777 men participating in the Lifelines cohort study (aged 18–65 years, mean 43.2 years for women and 43.5 years for men). Dietary intake was measured using a 110-item semi-quantitative food frequency questionnaire. Reduced rank regression was used to derive dietary patterns with blood lipids (HDL-cholesterol, LDL-cholesterol, triglycerides, total cholesterol, and total cholesterol:HDL-cholesterol ratio) as response variables for women and men separately. The first dietary pattern identified for each sex was selected because they explained the largest variance in blood lipids. The associations between the identified dietary patterns and incident type 2 diabetes were subsequently investigated using multivariate logistic regression models. All analyses were performed separately for women and men.
During an average follow-up of 43 months, 479 new cases (incidence 0.74%) of type 2 diabetes were identified. Using reduced rank regression, we identified two sex-specific blood lipids-associated dietary patterns characterized by high intake of sugary beverages, added sugar, and low intake of vegetables, fruits, tea, and nuts/seeds. These two sex-specific dietary patterns were similar in food groups but differed in factor loadings. High dietary pattern scores were associated with increased risk of type 2 diabetes after adjustment for age, total energy intake, body mass index, waist-hip ratio, and blood pressure (ORs for the fifth quintile Q5 using the first quintile Q1 as reference, 1.87 95% CI 1.23, 2.83 for women P-trend < 0.001, and 1.72 95% CI 1.11, 2.66 for men P-trend = 0.018). The associations were attenuated but remained significant after further adjustment for lifestyle and socio-economic factors.
Dietary patterns associated with adverse blood lipids are associated with incidence of type 2 diabetes. The present study provides new insights in optimizing blood lipids for the prevention of type 2 diabetes through dietary approaches.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Health inequalities are already present at young age and tend to vary with ethnicity and socioeconomic status (SES). Diet is a major determinant of overweight, and studying dietary patterns as a ...whole in relation to overweight rather than single nutrients or foods has been suggested. We derived dietary patterns at age 5 and determined whether ethnicity and SES were both related to these dietary patterns.
We analysed 2769 validated Food Frequency Questionnaires filled in by mothers of children (5.7 ± 0.5y) in the Amsterdam Born Children and their Development (ABCD) cohort. Food items were reduced to 41 food groups. Energy adjusted intake per food group (g/d) was used to derive dietary patterns using Principal Component Analysis and children were given a pattern score for each dietary pattern. We defined 5 ethnic groups (Dutch, Surinamese, Turkish, Moroccan, other ethnicities) and 3 SES groups (low, middle, high, based on maternal education). Multivariate ANOVA, with adjustment for age, gender and maternal age, was used to test potential associations between ethnicity or SES and dietary pattern scores. Post-hoc analyses with Bonferroni adjustment were used to examine differences between groups.
Principal Component Analysis identified 4 dietary patterns: a snacking, full-fat, meat and healthy dietary pattern, explaining 21% of the variation in dietary intake. Ethnicity was related to the dietary pattern scores (p < 0.01): non-Dutch children scored high on snacking and healthy pattern, whereas Turkish children scored high on full-fat and Surinamese children on the meat pattern. SES was related to the snacking, full-fat and meat patterns (p < 0.01): low SES children scored high on the snacking and meat pattern and low on the full-fat pattern.
This study indicates that both ethnicity and SES are relevant for dietary patterns at age 5 and may enable more specific nutrition education to specific ethnic and low socioeconomic status target groups.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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