Associations and predictive power of dietary patterns on metabolic syndrome and its components.

BACKGROUND AND AIMS: Metabolic syndrome (MetS) defines important risk factors in the development of cardiovascular diseases and other serious health conditions. This study aims to investigate the influence of different dietary patterns on MetS and its components, examining both associations and predictive performance. METHODS AND RESULTS: The study sample included 10,750 participants from the seventh survey of the cross-sectional, population-based Tromsø Study in Norway. Diet intake scores were used as covariates in logistic regression models, controlling for age, educational level and other lifestyle variables, with MetS and its components as response variables. A diet high in meat and sweets was positively associated with increased odds of MetS and elevated waist circumference, while a plant-based diet was associated with decreased odds of hypertension in women and elevated levels of triglycerides in men. The predictive power of dietary patterns derived by different dimensionality reduction techniques was investigated by randomly partitioning the study sample into training and test sets. On average, the diet score variables demonstrated the highest predictive power in predicting MetS and elevated waist circumference. The predictive power was robust to the dimensionality reduction technique used and comparable to using a data-driven prediction method on individual food variables. CONCLUSIONS: The strongest associations and highest predictive power of dietary patterns were observed for MetS and its single component, elevated waist circumference.

Lifestyle factors as mediators of area-level socioeconomic differentials in mental health and cognitive function: the Tromsø Study.

INTRODUCTION: Low socioeconomic status (SES) is associated with poor mental health and cognitive function. Individual-level SES and area-level SES (ASES) may affect mental health and cognitive function through lifestyle. We aimed to quantify the associations of ASES with mental health and cognitive function and examine the mediating role of lifestyle behaviours independent of individual-level SES in a Norwegian population. METHODS: In this cross-sectional study, we included 7211 participants (54% women) from the seventh survey of the Tromsø Study (2015-2016) (Tromsø7). The exposure variable ASES was created by aggregating individual-level SES variables (education, income, housing ownership) from Statistics Norway at the geographical subdivision level. Tromsø7 data were used as mediators (smoking, snuff, alcohol, physical activity, diet) and outcomes (cognitive function, anxiety, depression, insomnia). Mediation and mediated moderation analysis were performed with age as a moderator, stratified by sex. RESULTS: Higher ASES was associated with better cognitive function and fewer depression and insomnia symptoms, independent of individual-level SES. These associations were mediated by smoking and physical activity. Alcohol was a mediator for depression and cognitive function in women. Age was a significant moderator of the association between ASES and global cognitive function in women. The largest total indirect effect of ASES was found for depression, with the joint effect of the mediators accounting for 36% of the total effect. CONCLUSIONS: People living in areas with lower ASES are at higher risk of poor mental health, such as depression and insomnia, and have lower cognitive function possibly due to unhealthy lifestyle (smoking, alcohol and physical inactivity).

Device-measured physical activity, sedentary time, and risk of all-cause mortality: an individual participant data analysis of four prospective cohort studies.

OBJECTIVES: To examine whether moderate-to-vigorous physical activity (MVPA) modifies the association between sedentary time and mortality and vice versa, and estimate the joint associations of MVPA and sedentary time on mortality risk. METHODS: This study involved individual participant data analysis of four prospective cohort studies (Norway, Sweden, USA, baseline: 2003-2016, 11 989 participants ≥50 years, 50.5% women) with hip-accelerometry-measured physical activity and sedentary time. Associations were examined using restricted cubic splines and fractional polynomials in Cox regressions adjusted for sex, education, body mass index, smoking, alcohol, study cohort, cardiovascular disease, cancer, and/or diabetes, accelerometry wear time and age. RESULTS: 6.7% (n=805) died during follow-up (median 5.2 years, IQR 4.2 years). More than 12 daily sedentary hours (reference 8 hours) was associated with mortality risk only among those accumulating <22 min of MVPA per day (HR 1.38, 95% CI 1.10 to 1.74). Higher MVPA levels were associated with lower mortality risk irrespective of sedentary time, for example, HR for 10 versus 0 daily min of MVPA was 0.85 (95% CI 0.74 to 0.96) in those accumulating <10.5 daily sedentary hours and 0.65 (95% CI 0.53 to 0.79) in those accumulating ≥10.5 daily sedentary hours. Joint association analyses confirmed that higher MVPA was superior to lower sedentary time in lowering mortality risk, for example, 10 versus 0 daily min of MVPA was associated with 28-55% lower mortality risk across the sedentary time spectrum (lowest risk, 10 daily sedentary hours: HR 0.45, 95% CI 0.31 to 0.65). CONCLUSIONS: Sedentary time was associated with higher mortality risk but only in individuals accumulating less than 22 min of MVPA per day. Higher MVPA levels were associated with lower mortality risk irrespective of the amount of sedentary time.

Mid-life alcohol consumption and survival to age 90 in men: The Tromsø Study 1979-1980 with follow-up to 2019.

AIMS: The Tromsø Study 1979-1980 collected information on alcohol (beer, wine and spirits) consumption frequency and inebriation frequency, and the oldest male participants (aged 50-54 years) were followed for all-cause mortality. This study aimed to identify the impact of habitual alcohol consumption in mid-life on reaching up to 90 years of age. RESULTS: Among the study sample of 778, a total of 120 (15.4%) men reached the age of 90. The most common reported alcohol consumption frequency was 'never or a few times a year', and 18.9% of those in this group reached 90 compared with 11.9% of those who reported a more frequent beer consumption. Fifty per cent survival in these groups was 80.5 and 76.9 years, respectively. The pattern was similar for spirits consumption and for inebriation but not for wine consumption. Number of deaths increased gradually with increasing beer and spirits consumption frequency and with inebriation frequency. We observed no J-shape or pattern that revealed a beneficial influence of light alcohol consumption. Daily smoking, physical inactivity, marital status, blood pressure and total cholesterol reduced the contribution of alcohol consumption to a small degree. CONCLUSIONS: This study shows that all beer and spirits consumption frequencies in mid-life affect later life and total lifespan. Refraining from alcohol consumption or drinking only a few times a year increases one's chances of living longer, and the chance of reaching 90 years of age is 1.6-fold higher than in those with more frequent alcohol consumption.

Lifestyle factors as mediators of area-level socio-economic differentials in cardiovascular disease risk factors. The Tromsø Study.

Introduction: Cardiovascular disease (CVD) is a leading cause of death and disability and living in areas with low socio-economic status (SES) is associated with increased risk of CVD. Lifestyle factors such as smoking, physical inactivity, an unhealthy diet and harmful alcohol use are main risk factors that contribute to other modifiable risk factors, such as hypertension, raised blood cholesterol, obesity, and diabetes. The potential impact of area-level socio-economic status (ASES) on metabolic CVD risk factors via lifestyle behaviors independent of individual SES has not been investigated previously. Aims: To estimate associations of ASES with CVD risk factors and the mediating role of lifestyle behaviors independent of individual-level SES. Methods: In this cross-sectional study, we included 19,415 participants (52% women) from the seventh survey of the Tromsø Study (2015-2016) (Tromsø7). The exposure variable ASES was created by aggregating individual-level SES variables (education, income, housing ownership) at the geographical subdivision level. Individual-level SES data and geographical subdivision of Tromsø municipality (36 areas) were obtained from Statistics Norway. Variables from questionnaires and clinical examinations obtained from Tromsø7 were used as mediators (smoking, snuff, alcohol, and physical activity), while the outcome variables were body mass index (BMI), total/high-density lipoprotein (HDL) cholesterol ratio, waist circumference, hypertension, diabetes. Mediation and mediated moderation analysis were performed with age as a moderator, stratified by sex. Results: ASES was significantly associated with all outcome variables. CVD risk factor level declined with an increase in ASES. These associations were mediated by differences in smoking habits, alcohol use and physical activity. The associations of ASES with total/HDL cholesterol ratio and waist circumference (women) were moderated by age, and the moderating effects were mediated by smoking and physical activity in both sexes. The largest mediated effects were seen in the associations of ASES with total/HDL cholesterol ratio, with the mediators accounting for 43% of the observed effects. Conclusions: Living in lower SES areas is associated with increased CVD risk due to unhealthy lifestyle behaviors, such as smoking, alcohol use and physical inactivity. These associations were stronger in women and among older participants.

Identifying dietary patterns across age, educational level and physical activity level in a cross-sectional study: the Tromsø Study 2015 - 2016.

BACKGROUND: A healthy diet can decrease the risk of several lifestyle diseases. From studying the health effects of single foods, research now focuses on examining complete diets and dietary patterns reflecting the combined intake of different foods. The main goals of the current study were to identify dietary patterns and then investigate how these differ in terms of sex, age, educational level and physical activity level (PAL) in a general Nordic population. METHODS: We used data from the seventh survey of the population-based Tromsø Study in Norway, conducted in 2015-2016. The study included 21,083 participants aged [Formula: see text] years, of which [Formula: see text] completed a comprehensive food frequency questionnaire (FFQ). After exclusion, the study sample included 10,899 participants with valid FFQ data. First, to cluster food variables, the participants were partitioned in homogeneous cohorts according to sex, age, educational level and PAL. Non-overlapping diet groups were then identified using repeated hierarchical cluster analysis on the food variables. Second, average standardized diet intake scores were calculated for all individuals for each diet group. The individual diet (intake) scores were then modelled in terms of age, education and PAL using regression models. Differences in diet scores according to education and PAL were investigated by pairwise hypothesis tests, controlling the nominal significance level using Tukey's method. RESULTS: The cluster analysis revealed three dietary patterns, here named the Meat and Sweets diet, the Traditional diet, and the Plant-based- and Tea diet. Women had a lower intake of the Traditional diet and a higher preference for the Plant-based- and Tea diet compared to men. Preference for the Meat and Sweets diet and Traditional diet showed significant negative and positive trends as function of age, respectively. Adjusting for age, the group having high education and high PAL compared favourably with the group having low education and low PAL, having a significant lower intake of the Meat and Sweets and the Traditional diets and a significant higher intake of the Plant-based- and Tea diet. CONCLUSIONS: Three dietary patterns (Meat and Sweets, Traditional, and Plant-based- and Tea) were found by repeated clustering of randomly sampled homogeneous cohorts of individuals. Diet preferences depended significantly on sex, age, education and PAL, showing a more unhealthy dietary pattern with lower age, low education and low PAL.

Obesity Prevalence and Associated Socio-Demographic Characteristics and Health Behaviors in Russia and Norway.

Associations between obesity and socio-demographic and behavioral characteristics vary between populations. Exploring such differences should throw light on factors related to obesity. We examined associations between general obesity (GO, defined by body mass index) and abdominal obesity (AO, defined by waist-to-hip ratio) and sex, age, socio-economic characteristics (education, financial situation, marital status), smoking and alcohol consumption in women and men aged 40-69 years from the Know Your Heart study (KYH, Russia, N = 4121, 2015-2018) and the seventh Tromsø Study (Tromsø7, Norway, N = 17,646, 2015-2016). Age-standardized prevalence of GO and AO was higher in KYH compared to Tromsø7 women (36.7 vs. 22.0% and 44.2 vs. 18.4%, respectively) and similar among men (26.0 vs. 25.7% and 74.8 vs. 72.2%, respectively). The positive association of age with GO and AO was stronger in KYH vs. Tromsø7 women and for AO it was stronger in men in Tromsø7 vs. KYH. Associations between GO and socio-economic characteristics were similar in KYH and Tromsø7, except for a stronger association with living with spouse/partner in KYH men. Smoking had a positive association with AO in men in Tromsø7 and in women in both studies. Frequent drinking was negatively associated with GO and AO in Tromsø7 participants and positively associated with GO in KYH men. We found similar obesity prevalence in Russian and Norwegian men but higher obesity prevalence in Russian compared to Norwegian women. Other results suggest that the stronger association of obesity with age in Russian women is the major driver of the higher obesity prevalence among them compared to women in Norway.

Healthy Choices in Midlife Predict Survival to Age 85 in Women: The Tromsø Study 1979-2019.

The aim of this study is to examine the association between single risk factors and multiple risk factors in midlife and older ages (up to 64 years) and survival to the age of 85 years in women. The study sample comprised 857 women who attended the second survey of the population-based Tromsø Study (Tromsø2, 1979-1980) at the ages of 45-49 years and were followed for all-cause mortality until 85 years of age. Daily smoking, physical inactivity, being unmarried, obesity, high blood pressure, and high cholesterol in midlife were used as explanatory variables in survival analyses. In total, 56% of the women reached the age of 85. Daily smoking, physical inactivity, being unmarried, and obesity were significant single risk factors for death before the age of 85. None of the women had all six risk factors, but survival to age 85 did decrease gradually with increasing number of risk factors: from 67% survival for those with no risk factors to 28% survival for those with four or five risk factors. A subset of the study sample also attended the third and fourth surveys of the Tromsø Study (Tromsø3, 1986-1987 and Tromsø4, 1994-1995, respectively). Women who quit smoking and those who became physically active between Tromsø3 and Tromsø4 had higher survival when compared to those who continued to smoke and remained physically inactive, respectively. This study demonstrates the importance of having no or few risk factors in midlife with respect to longevity. We observed a substantial increase in the risk of death before the age of 85 among women who were daily smokers, physically inactive, unmarried, or obese in midlife. This risk may be mitigated by lifestyle changes, such as quitting smoking and becoming physically active later in life.

The bidirectional associations between leisure time physical activity change and body mass index gain. The Tromsø Study 1974-2016.

OBJECTIVES: To examine whether leisure time physical activity changes predict subsequent body mass index (BMI) changes, and conversely, whether BMI changes predict subsequent leisure time physical activity changes. METHODS: This prospective cohort study included adults attending ≥3 consecutive Tromsø Study surveys (time: T1, T2, T3) during 1974-2016 (n = 10779). If participants attended >3 surveys, we used the three most recent surveys. We computed physical activity change (assessed by the Saltin-Grimby Physical Activity Level Scale) from T1 to T2, categorized as Persistently Inactive (n = 992), Persistently Active (n = 7314), Active to Inactive (n = 1167) and Inactive to Active (n = 1306). We computed BMI change from T2 to T3, which regressed on preceding physical activity changes using analyses of covariance. The reverse association (BMI change from T1 to T2 and physical activity change from T2 to T3; n = 4385) was assessed using multinomial regression. RESULTS: Average BMI increase was 0.86 kg/m2 (95% CI: 0.82-0.90) from T2 to T3. With adjustment for sex, birth year, education, smoking and BMI at T2, there was no association between physical activity change from T1 to T2 and BMI change from T2 to T3 (Persistently Inactive: 0.89 kg/m2 (95% CI: 0.77-1.00), Persistently Active: 0.85 kg/m2 (95% CI: 0.81-0.89), Active to Inactive: 0.90 kg/m2 (95% CI: 0.79-1.00), Inactive to Active 0.85 kg/m2 (95% CI: 0.75-0.95), p = 0.84). Conversely, increasing BMI was associated with Persistently Inactive (odds ratio (OR): 1.17, 95% CI: 1.08-1.27, p < 0.001) and changing from Active to Inactive (OR: 1.16, 95% CI: 1.07-1.25, p < 0.001) compared with being Persistently Active. CONCLUSIONS: We found no association between leisure time physical activity changes and subsequent BMI changes, whereas BMI change predicted subsequent physical activity change. These findings indicate that BMI change predicts subsequent physical activity change at population level and not vice versa.

Estimation of Excess Mortality and Years of Life Lost to COVID-19 in Norway and Sweden between March and November 2020.

We estimate the weekly excess all-cause mortality in Norway and Sweden, the years of life lost (YLL) attributed to COVID-19 in Sweden, and the significance of mortality displacement. We computed the expected mortality by taking into account the declining trend and the seasonality in mortality in the two countries over the past 20 years. From the excess mortality in Sweden in 2019/20, we estimated the YLL attributed to COVID-19 using the life expectancy in different age groups. We adjusted this estimate for possible displacement using an auto-regressive model for the year-to-year variations in excess mortality. We found that excess all-cause mortality over the epidemic year, July 2019 to July 2020, was 517 (95%CI = (12, 1074)) in Norway and 4329 [3331, 5325] in Sweden. There were 255 COVID-19 related deaths reported in Norway, and 5741 in Sweden, that year. During the epidemic period of 11 March-11 November, there were 6247 reported COVID-19 deaths and 5517 (4701, 6330) excess deaths in Sweden. We estimated that the number of YLL attributed to COVID-19 in Sweden was 45,850 [13,915, 80,276] without adjusting for mortality displacement and 43,073 (12,160, 85,451) after adjusting for the displacement accounted for by the auto-regressive model. In conclusion, we find good agreement between officially recorded COVID-19 related deaths and all-cause excess deaths in both countries during the first epidemic wave and no significant mortality displacement that can explain those deaths.

Is the ongoing obesity epidemic partly explained by concurrent decline in cigarette smoking? Insights from a longitudinal population study. The Tromsø Study 1994-2016.

The increase of obesity coincides with a substantial decrease in cigarette smoking. We assessed post-cessation weight change and its contribution to the obesity epidemic in a general population in Norway. A total of 14,453 participants (52.6% women), aged 25-54 years in 1994, who attended at least two of four surveys in the Tromsø Study between 1994 and 2016, were included in the analysis. Hereof 77% participated in both the first and the last survey. Temporal trends in mean body mass index (BMI), prevalence of obesity (BMI ≥ 30 kg/m2) and daily smoking were estimated with generalized estimation equations. We assessed BMI change by smoking status (ex-smoker, quitter, never smoker, daily smoker), and also under a scenario where none quit smoking. In total, the prevalence of daily smoking was reduced over the 21 years between Tromsø 4 (1994-1995) and Tromsø 7 (2015-2016) by 22 percentage points. Prevalence of obesity increased from 5 - 12% in 1994-1995 to 21-26% in 2015-2016, where obesity in the youngest (age 25-44 in 1994) increased more than in the oldest (p < 0.0001). Those who quit smoking had a larger BMI gain compared to the other three smoking subgroups over the 21 years (p < 0.0001). The scenario where none quit smoking would imply a 13% reduction in BMI gain in the population, though substantial age-related differences were noted. We conclude that smoking cessation contributed to the increase in obesity in the population, but was probably not the most important factor. Public health interventions should continue to target smoking cessation, and also target obesity prevention.

Modelling suggests limited change in the reproduction number from reopening Norwegian kindergartens and schools during the COVID-19 pandemic.

BACKGROUND: To suppress the COVID-19 outbreak, the Norwegian government closed all schools on March 13, 2020. The kindergartens reopened on April 20, and the schools on April 27 and May 11 of 2020. The effect of these measures is largely unknown since the role of children in the spread of the SARS-CoV-2 virus is still unclear. There are only a few studies of school closures as a separate intervention to other social distancing measures, and little research exists on the effect of school opening during a pandemic. OBJECTIVE: This study aimed to model the effect of opening kindergartens and the schools in Norway in terms of a change in the reproduction number (R). A secondary objective was to assess if we can use the estimated R after school openings to infer the rates of transmission between children in schools. METHODS: We used an individual-based model (IBM) to assess the reopening of kindergartens and schools in two Norwegian cities, Oslo, the Norwegian capital, with a population of approximately 680 000, and Tromsø, which is the largest city in Northern Norway, with a population of approximately 75 000. The model uses demographic information and detailed data about the schools in both cities. We carried out an ensemble study to obtain robust results in spite of the considerable uncertainty that remains about the transmission of SARS-CoV-2. RESULTS: We found that reopening of Norwegian kindergartens and schools are associated with a change in R of 0.10 (95%CI 0.04-0.16) and 0.14 (95%CI 0.01-0.25) in the two cities under investigation if the in-school transmission rates for the SARS-CoV-2 virus are equal to what has previously been estimated for influenza pandemics. CONCLUSION: We found only a limited effect of reopening schools on the reproduction number, and we expect the same to hold true in other countries where nonpharmaceutical interventions have suppressed the pandemic. Consequently, current R-estimates are insufficiently accurate for determining the transmission rates in schools. For countries that have closed schools, planned interventions, such as the opening of selected schools, can be useful to infer general knowledge about children-to-children transmission of SARS-CoV-2.

Do declines in occupational physical activity contribute to population gains in body mass index? Tromsø Study 1974-2016.

OBJECTIVE: To examine whether occupational physical activity changes predict future body mass index (BMI) changes. METHODS: This longitudinal cohort study included adult participants attending ≥3 consecutive Tromsø Study surveys (examinations 1, 2 and 3) from 1974 to 2016 (N=11 308). If a participant attended >3 surveys, the three most recent surveys were included. Occupational physical activity change (assessed by the Saltin-Grimby Physical Activity Level Scale) was computed from the first to the second examination, categorised into persistently inactive (n=3692), persistently active (n=5560), active to inactive (n=741) and inactive to active (n=1315). BMI change was calculated from the second to the third examination (height being fixed at the second examination) and regressed on preceding occupational physical activity changes using analysis of covariance adjusted for sex, birth year, smoking, education and BMI at examination 2. RESULTS: Overall, BMI increased by 0.84 kg/m2 (95% CI 0.82 to 0.89). Following adjustments as described previously, we observed no differences in BMI increase between the occupational physical activity change groups (Persistently Inactive: 0.81 kg/m2, 95% CI 0.75 to 0.87; Persistently Active: 0.87 kg/m2, 95% CI 0.82 to 0.92; Active to Inactive: 0.81 kg/m2, 95% CI 0.67 to 0.94; Inactive to Active: 0.91 kg/m2, 95% CI 0.81 to 1.01; p=0.25). CONCLUSION: We observed no prospective association between occupational physical activity changes and subsequent BMI changes. Our findings do not support the hypothesis that occupational physical activity declines contributed to population BMI gains over the past decades. Public health initiatives aimed at weight gain prevention may have greater success if focusing on other aspects than occupational physical activity.

Prevalence of general and abdominal obesity in 2015-2016 and 8-year longitudinal weight and waist circumference changes in adults and elderly: the Tromsø Study.

OBJECTIVES: To describe the prevalence of general (body mass index (BMI) ≥30 kg/m2) and abdominal (waist circumference women >88 cm, men >102 cm) obesity in Tromsø 7 (2015-2016), and the secular change from Tromsø 6 (2007-2008). Furthermore, to study longitudinal changes in body weight and waist circumference from Tromsø 6 to Tromsø 7. SETTING: A population study in Tromsø, Norway. PARTICIPANTS: The cross-sectional analyses included 20 855 participants in Tromsø 7 (aged ≥40 years) and 12 868 in Tromsø 6 (aged ≥30 years). The longitudinal analyses included 8592 participants with repeated measurements, aged 35-79 in Tromsø 6. OUTCOME MEASURES: Mean age-specific and sex-specific BMI, waist circumference, prevalence of general and abdominal overweight and obesity, as well as longitudinal changes in body weight and waist circumference according to sex and birth cohort. RESULTS: Over 8 years, the age-adjusted prevalence of general obesity increased (p<0.0001) from 20.1% to 23.0% in women and from 20.7% to 25.2% in men. The age-adjusted prevalence of abdominal obesity did not increase in women (from 54.7% to 53.4%), and the increase in men was modest (from 36.8% to 38.6%, p=0.003). Longitudinal analyses showed an increase in body weight, by 1.1 kg (95% CI 0.9 to 1.2) in women and 0.7 kg (95% CI 0.6 to 0.9) in men, and also waist circumference, by 1.3 cm (95% CI 1.0 to 1.5) in women and 1.4 cm (95% CI 1.2 to 1.6) in men. There were inverse relationships (p<0.001) between age at baseline and change in weight and waist circumference. CONCLUSIONS: Repeated cross-sectional analyses showed that the prevalence of general obesity increased, whereas the increase in abdominal obesity was less marked. Longitudinal analyses showed increases in both body weight and waist circumference. The youngest age groups have the largest increase.