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  • 1. Berg, Noora
    et al.
    Nummi, Tapio
    Bean, Christopher G.
    Westerlund, Hugo
    Virtanen, Pekka
    Hammarström, Anne
    Umeå University, Faculty of Medicine, Department of Epidemiology and Global Health. Institute of Environmental Medicine, Unit of Occupational Medicine, Karolinska Institutet Stockholm, Sweden.
    Risk factors in adolescence as predictors of trajectories of somatic symptoms over 27 years2022In: European Journal of Public Health, ISSN 1101-1262, E-ISSN 1464-360X, Vol. 32, no 5, p. 696-702Article in journal (Refereed)
    Abstract [en]

    Background: Somatic symptoms among adolescents are common, yet little is known about long-term trajectories of somatic symptoms and the factors in adolescence that shape them. We examined individual, family and school-based factors at age 16 as predictors of trajectories of somatic symptoms over 27 years.

    Methods: Participants from the Northern Swedish Cohort (n = 1001) responded to questions about individual factors (e.g. health behaviours), family factors (e.g. contact with parents, social and material adversity) and school satisfaction at age 16; as well as 10 somatic symptoms at ages 16, 18, 21, 30 and 43. Teacher assessments at age 16 included overall ability at school and peer relations. Age 16 predictors of somatic symptom trajectory group membership were analysed using multinomial logistic regression.

    Results: Poor contact with mother and poor school satisfaction were significant predictors of adverse symptom trajectories among both men and women. Low birth weight and low parental academic involvement were contributing factors for women, while smoking and social adversity were more relevant factors for men.

    Conclusions: Our findings emphasize the importance of a holistic approach that considers the unique contributions of individual, family and school-based factors in the development of trajectories of somatic symptoms from adolescence to middle age.

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  • 2. Kivimaki, Mika
    et al.
    Kuosma, Eeva
    Ferrie, Jane E.
    Luukkonen, Ritva
    Nyberg, Solja T.
    Alfredsson, Lars
    Batty, G. David
    Brunner, Eric J.
    Fransson, Eleonor
    Goldberg, Marcel
    Knutsson, Anders
    Koskenvuo, Markku
    Nordin, Maria
    Umeå University, Faculty of Social Sciences, Department of Psychology.
    Oksanen, Tuula
    Pentti, Jaana
    Rugulies, Reiner
    Shipley, Martin J.
    Singh-Manoux, Archana
    Steptoe, Andrew
    Suominen, Sakari B.
    Theorell, Tores
    Vahtera, Jussi
    Virtanen, Marianna
    Westerholm, Peter
    Westerlund, Hugo
    Zins, Marie
    Hamer, Mark
    Bell, Joshua A.
    Tabak, Adam G.
    Jokela, Markus
    Overweight, obesity, and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120 813 adults from 16 cohort studies from the USA and Europe2017In: LANCET PUBLIC HEALTH, ISSN 2468-2667, Vol. 2, no 6, p. E277-E285Article in journal (Refereed)
    Abstract [en]

    Background Although overweight and obesity have been studied in relation to individual cardiometabolic diseases, their association with risk of cardiometabolic multimorbidity is poorly understood. Here we aimed to establish the risk of incident cardiometabolic multimorbidity (ie, at least two from: type 2 diabetes, coronary heart disease, and stroke) in adults who are overweight and obese compared with those who are a healthy weight. Methods We pooled individual-participant data for BMI and incident cardiometabolic multimorbidity from 16 prospective cohort studies from the USA and Europe. Participants included in the analyses were 35 years or older and had data available for BMI at baseline and for type 2 diabetes, coronary heart disease, and stroke at baseline and follow-up. We excluded participants with a diagnosis of diabetes, coronary heart disease, or stroke at or before study baseline. According to WHO recommendations, we classified BMI into categories of healthy (20.0-24.9 kg/m(2)), overweight (25.0-29.9 kg/m(2)), class I (mild) obesity (30.0-34.9 kg/m(2)), and class II and III (severe) obesity (>= 35.0 kg/m(2)). We used an inclusive definition of underweight (<20 kg/m(2)) to achieve sufficient case numbers for analysis. The main outcome was cardiometabolic multimorbidity (ie, developing at least two from: type 2 diabetes, coronary heart disease, and stroke). Incident cardiometabolic multimorbidity was ascertained via resurvey or linkage to electronic medical records (including hospital admissions and death). We analysed data from each cohort separately using logistic regression and then pooled cohort-specific estimates using random-effects meta-analysis. Findings Participants were 120 813 adults (mean age 51.4 years, range 35-103; 71445 women) who did not have diabetes, coronary heart disease, or stroke at study baseline (1973-2012). During a mean follow-up of 10.7 years (1995-2014), we identified 1627 cases of multimorbidity. After adjustment for sociodemographic and lifestyle factors, compared with individuals with a healthy weight, the risk of developing cardiometabolic multimorbidity in overweight individuals was twice as high (odds ratio [OR] 2.0, 95% CI 1.7-2.4; p<0.0001), almost five times higher for individuals with class I obesity (4.5, 3.5- 5.8; p<0.0001), and almost 15 times higher for individuals with classes II and III obesity combined (14.5, 10.1-21.0; p<0.0001). This association was noted in men and women, young and old, and white and non-white participants, and was not dependent on the method of exposure assessment or outcome ascertainment. In analyses of different combinations of cardiometabolic conditions, odds ratios associated with classes II and III obesity were 2.2 (95% CI 1.9-2.6) for vascular disease only (coronary heart disease or stroke), 12.0 (8.1-17.9) for vascular disease followed by diabetes, 18.6 (16.6-20.9) for diabetes only, and 29.8 (21.7-40.8) for diabetes followed by vascular disease. Interpretation The risk of cardiometabolic multimorbidity increases as BMI increases; from double in overweight people to more than ten times in severely obese people compared with individuals with a healthy BMI. Our findings highlight the need for clinicians to actively screen for diabetes in overweight and obese patients with vascular disease, and pay increased attention to prevention of vascular disease in obese individuals with diabetes. Copyright (C) The Author(s). 

  • 3. Kivimäki, Mika
    et al.
    Luukkonen, Ritva
    Batty, G. David
    Ferrie, Jane E.
    Pentti, Jaana
    Nyberg, Solja T.
    Shipley, Martin J.
    Alfredsson, Lars
    Fransson, Eleonor I.
    Goldberg, Marcel
    Knutsson, Anders
    Koskenvuo, Markku
    Kuosma, Eeva
    Nordin, Maria
    Umeå University, Faculty of Social Sciences, Department of Psychology. Division of Epidemiology, Stress Research Institute, Stockholm University, Stockholm, Sweden.
    Suominen, Sakari B.
    Theorell, Töres
    Vuoksimaa, Eero
    Westerholm, Peter
    Westerlund, Hugo
    Zins, Marie
    Kivipelto, Miia
    Vahtera, Jussi
    Kaprio, Jaakko
    Singh-Manoux, Archana
    Umeå University, Faculty of Social Sciences, Department of Psychology. Department of Epidemiology and Public Health, University College London, London, UK; Population-based Epidemiologic Cohort Unit, UMS 011, Inserm, Villejuif, France.
    Jokela, Markus
    Umeå University, Faculty of Social Sciences, Department of Psychology. Institute of Behavioral Sciences, University of Helsinki, Helsinki, Finland.
    Body mass index and risk of dementia: Analysis of individual-level data from 1.3 million individuals2018In: Alzheimer's & Dementia: Journal of the Alzheimer's Association, ISSN 1552-5260, E-ISSN 1552-5279, Vol. 14, no 5, p. 601-609Article in journal (Refereed)
    Abstract [en]

    Introduction: Higher midlife body mass index (BMI) is suggested to increase the risk of dementia, but weight loss during the preclinical dementia phase may mask such effects. Methods: We examined this hypothesis in 1,349,857 dementia-free participants from 39 cohort studies. BMI was assessed at baseline. Dementia was ascertained at follow-up using linkage to electronic health records (N = 6894). We assumed BMI is little affected by preclinical dementia when assessed decades before dementia onset and much affected when assessed nearer diagnosis. Results: Hazard ratios per 5-kg/m(2) increase in BMI for dementia were 0.71 (95% confidence interval = 0.66-0.77), 0.94 (0.89-0.99), and 1.16 (1.05-1.27) when BMI was assessed 10 years, 10-20 years, and >20 years before dementia diagnosis. Conclusions: The association between BMI and dementia is likely to be attributable to two different processes: a harmful effect of higher BMI, which is observable in long follow-up, and a reverse-causation effect that makes a higher BMI to appear protective when the follow-up is short. 

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