Mika Kivimaki

University College London, Londinium, England, United Kingdom

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Publications (274)2335.43 Total impact

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    ABSTRACT: Aims: Among employees with diabetes, comorbidity may considerably deteriorate working capacity. We examined how socioeconomic status, lifestyle factors and job strain were related to work disability in individuals with diabetes with and without comorbidity. Methods: In this pooled analysis of individual-participant data from occupational cohorts from Finland (Finnish Public Sector Study FPS), UK (Whitehall II) and France (GAZEL), 1925 employees with diabetes were followed on average for 4 years. Participants were categorized into four groups, according to their baseline comorbidity status and subsequent work disability, as: non-comorbid diabetes with no/low work disability; comorbid diabetes with no/low work disability; non-comorbid diabetes with moderate/high work disability; and comorbid diabetes with moderate/high work disability. The risk of work disability was assessed with multinomial regression, using Group 1 (the first listed above) as the reference group. Results: Participants with low socioeconomic status had increased odds for higher work disability, irrespective of comorbidity (OR = 3.12; 95% CI 2.25-4.33, among participants with no comorbidity, and OR = 2.61; 95% CI 1.93-3.51 among those with comorbidity). Obesity was cross-sectionally associated with comorbidity (OR = 1.95; 95% CI 1.35-2.83 for comorbidity without disability), and this association was particularly pronounced among those whom became work disabled (OR = 2.61; 95% CI 1.89-3.60 for comorbidity with disability). Job strain was associated with high/moderate work disability, only among participants with comorbidity (OR = 1.63; 95% CI 1.14-2.34). Conclusions: Pooled data from three cohort studies showed that low socioeconomic status, obesity, and job strain are linked to both comorbidity and increased work disability in employees with diabetes.
    Scandinavian Journal of Public Health 09/2015; DOI:10.1177/1403494815605245 · 1.83 Impact Factor
  • Psychoneuroendocrinology 09/2015; 61:67. DOI:10.1016/j.psyneuen.2015.07.575 · 4.94 Impact Factor
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    ABSTRACT: Background The contribution of education and intelligence to resilience against age-related cognitive decline is not clear, particularly in the presence of 'normal for age' minor brain abnormalities.Method Participants (n = 208, mean age 69.2 years, s.d. = 5.4) in the Whitehall II imaging substudy attended for neuropsychological testing and multisequence 3T brain magnetic resonance imaging. Images were independently rated by three trained clinicians for global and hippocampal atrophy, periventricular and deep white matter changes.ResultsAlthough none of the participants qualified for a clinical diagnosis of dementia, a screen for cognitive impairment (Montreal Cognitive Assessment (MoCA) <26) was abnormal in 22%. Hippocampal atrophy, in contrast to other brain measures, was associated with a reduced MoCA score even after controlling for age, gender, socioeconomic status, years of education and premorbid IQ. Premorbid IQ and socioeconomic status were associated with resilience in the presence of hippocampal atrophy.Conclusions Independent contributions from a priori risk (age, hippocampal atrophy) and resilience (premorbid function, socioeconomic status) combine to predict measured cognitive impairment. © The Royal College of Psychiatrists 2015.
    The British Journal of Psychiatry 09/2015; DOI:10.1192/bjp.bp.114.152363 · 7.99 Impact Factor
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    ABSTRACT: Metabolically healthy obesity possibly reflects a transitional stage before the onset of metabolic dysfunction, but few studies have characterised this transition. We examined the behavioural and biological characteristics of healthy obese adults that progressed to an unhealthy state over 8 years follow up. Participants were 2422 men and women (aged 63.3 ± 7.7 years, 44.2% men) from the English Longitudinal Study of Ageing. Obesity was defined as body mass index ≥ 30 kg/m2. Based on blood pressure, HDL-cholesterol, triglycerides, glycated haemoglobin, and C-reactive protein participants were classified as 'healthy' (0 or 1 metabolic abnormality) or 'unhealthy' (≥ 2 metabolic abnormalities). Over eight years follow-up, 44.5% of healthy obese had transitioned into an unhealthy state, compared to only 16.6% and 26.2% of healthy normal weight and overweight adults, respectively. Compared with healthy obese adults who remained stable, those who progressed to an unhealthy state were more likely to have high blood pressure (75.0% vs 37.0%, age- and sex-adjusted odds ratio [OR] 8.9, 95% confidence interval [CI] 4.7-17.0), high C-reactive protein (53.7% vs 17.0%, OR=8.6, 95% CI 4.1-18.0), high glycated haemoglobin (46.3% vs 5.9%, OR=13.8, 95% CI 6.1-31.2) and high triglycerides (45.4% vs 11.9%, OR=5.9, 95% CI 2.9-12.0) at follow-up, with excess risk remaining independent of lifestyle factors including self-reported physical activity. Progression to an unhealthy state was also linked with significant gains in waist circumference (B=2.7, 95% CI, 0.5 - 4.9 cm). These data show that a healthy obesity phenotype is relatively unstable. Transition to an unhealthy state is characterised by multiple biological changes which are not fully explained by lifestyle risk factors.
    European Journal of Endocrinology 08/2015; DOI:10.1530/EJE-15-0449 · 4.07 Impact Factor
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    ABSTRACT: There is growing evidence that obese adults without metabolic risk factor clustering (the so-called "healthy obese") progress to unhealthy obesity over time (1). However, the pathophysiological changes underlying the long-term transition into an unhealthy obese state have not been well characterized. To inform the clinical management of healthy obesity, we aimed to identify the metabolic risk factors responsible for this transition, as well as the timing of their onset.
    Journal of the American College of Cardiology 08/2015; 66(7):871-873. DOI:10.1016/j.jacc.2015.06.014 · 16.50 Impact Factor
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    ABSTRACT: Background: Plasma adiponectin levels have previously been inversely associated with carotid intima-media thickness (IMT), a marker of subclinical atherosclerosis. In this study, we used a sex-stratified Mendelian randomization approach to investigate whether adiponectin has a causal protective influence on IMT. Methods and results: Baseline plasma adiponectin concentration was tested for association with baseline IMT, IMT progression over 30 months, and occurrence of cardiovascular events within 3 years in 3430 participants (women, n=1777; men, n=1653) with high cardiovascular risk but no prevalent disease. Plasma adiponectin levels were inversely associated with baseline mean bifurcation IMT after adjustment for established risk factors (β=-0.018, P<0.001) in men but not in women (β=-0.006, P=0.185; P for interaction=0.061). Adiponectin levels were inversely associated with progression of mean common carotid IMT in men (β=-0.0022, P=0.047), whereas no association was seen in women (0.0007, P=0.475; P for interaction=0.018). Moreover, we observed that adiponectin levels were inversely associated with coronary events in women (hazard ratio 0.57, 95% CI 0.37 to 0.87) but not in men (hazard ratio 0.82, 95% CI 0.54 to 1.25). A gene score of adiponectin-raising alleles in 6 loci, reported recently in a large multi-ethnic meta-analysis, was inversely associated with baseline mean bifurcation IMT in men (β=-0.0008, P=0.004) but not in women (β=-0.0003, P=0.522; P for interaction=0.007). Conclusions: This report provides some evidence for adiponectin protecting against atherosclerosis, with effects being confined to men; however, compared with established cardiovascular risk factors, the effect of plasma adiponectin was modest. Further investigation involving mechanistic studies is warranted.
    Journal of the American Heart Association 08/2015; 4(8). DOI:10.1161/JAHA.115.001853 · 4.31 Impact Factor
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    ABSTRACT: To investigate, using a Mendelian randomisation approach, whether heavier smoking is associated with a range of regional adiposity phenotypes, in particular those related to abdominal adiposity. Mendelian randomisation meta-analyses using a genetic variant (rs16969968/rs1051730 in the CHRNA5-CHRNA3-CHRNB4 gene region) as a proxy for smoking heaviness, of the associations of smoking heaviness with a range of adiposity phenotypes. 148 731 current, former and never-smokers of European ancestry aged ≥16 years from 29 studies in the consortium for Causal Analysis Research in Tobacco and Alcohol (CARTA). Waist and hip circumferences, and waist-hip ratio. The data included up to 66 809 never-smokers, 43 009 former smokers and 38 913 current daily cigarette smokers. Among current smokers, for each extra minor allele, the geometric mean was lower for waist circumference by -0.40% (95% CI -0.57% to -0.22%), with effects on hip circumference, waist-hip ratio and body mass index (BMI) being -0.31% (95% CI -0.42% to -0.19), -0.08% (-0.19% to 0.03%) and -0.74% (-0.96% to -0.51%), respectively. In contrast, among never-smokers, these effects were higher by 0.23% (0.09% to 0.36%), 0.17% (0.08% to 0.26%), 0.07% (-0.01% to 0.15%) and 0.35% (0.18% to 0.52%), respectively. When adjusting the three central adiposity measures for BMI, the effects among current smokers changed direction and were higher by 0.14% (0.05% to 0.22%) for waist circumference, 0.02% (-0.05% to 0.08%) for hip circumference and 0.10% (0.02% to 0.19%) for waist-hip ratio, for each extra minor allele. For a given BMI, a gene variant associated with increased cigarette consumption was associated with increased waist circumference. Smoking in an effort to control weight may lead to accumulation of central adiposity. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
    BMJ Open 08/2015; 5(8):e008808. DOI:10.1136/bmjopen-2015-008808 · 2.27 Impact Factor
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    ABSTRACT: Participants' non adherence to protocol affects data quality. In longitudinal studies, this leads to outliers that can be present at the level of the population or the individual. The purpose of the present study is to elaborate a method for detection of outliers in a study of cognitive ageing. In the Whitehall II study, data on a cognitive test battery have been collected in 1997-99, 2002-04, 2007-09 and 2012-13. Outliers at the 2012-13 wave were identified using a 4-step procedure: (1) identify cognitive tests with potential non-adherence to protocol, (2) choose a prediction model between a simple model with socio-demographic covariates and one that also includes health behaviours and health measures, (3) define an outlier using a studentized residual, and (4) study the impact of exclusion of outliers by estimating the effect of age and diabetes on cognitive decline. 5516 participants provided cognitive data in 2012-13. Comparisons of rates of annual decline over the first three and all four waves of data suggested outliers in three of the 5 tests. Mean residuals for the 2012-13 wave were larger for the basic compared to the more complex prediction model (all p<0.001), leading us to use the latter for the identification of outliers. Residuals greater than two standard deviation of residuals identified approximately 7% of observations as being outliers. Removal of these observations from the analyses showed that both age and diabetes had associations with cognitive decline similar to that observed with the first three waves of data; these associations were weaker or absent in non-cleaned data. Identification of outliers is important as they obscure the effects of known risk factor and introduce bias in the estimates of cognitive decline. We showed that an informed approach, using the range of data collected in a longitudinal study, may be able to identify outliers.
    PLoS ONE 07/2015; 10(7):e0132110. DOI:10.1371/journal.pone.0132110 · 3.23 Impact Factor
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    ABSTRACT: Disease risk is lower in metabolically healthy obese adults than in their unhealthy obese counterparts. Studies considering physical activity as a modifiable determinant of healthy obesity have relied on self-reported measures, which are prone to inaccuracies and do not capture all movements that contribute to health. We aimed to examine differences in total and moderate-to-vigorous physical activity between healthy and unhealthy obese groups by using both self-report and wrist-worn accelerometer assessments. Cross-sectional analyses were based on 3457 adults aged 60-82 y (77% male) participating in the British Whitehall II cohort study in 2012-2013. Normal-weight, overweight, and obese adults were considered "healthy" if they had <2 of the following risk factors: low HDL cholesterol, hypertension, high blood glucose, high triacylglycerol, and insulin resistance. Differences across groups in total physical activity, based on questionnaire and wrist-worn triaxial accelerometer assessments (GENEActiv), were examined by using linear regression. The likelihood of meeting 2010 World Health Organization recommendations for moderate-to-vigorous activity (≥2.5 h/wk) was compared by using prevalence ratios. Of 3457 adults, 616 were obese [body mass index (in kg/m(2)) ≥30]; 161 (26%) of those were healthy obese. Obese adults were less physically active than were normal-weight adults, regardless of metabolic health status or method of physical activity assessment. Healthy obese adults had higher total physical activity than did unhealthy obese adults only when assessed by accelerometer (P = 0.002). Healthy obese adults were less likely to meet recommendations for moderate-to-vigorous physical activity than were healthy normal-weight adults based on accelerometer assessment (prevalence ratio: 0.59; 95% CI: 0.43, 0.79) but were not more likely to meet these recommendations than were unhealthy obese adults (prevalence ratio: 1.26; 95% CI: 0.89, 1.80). Higher total physical activity in healthy than in unhealthy obese adults is evident only when measured objectively, which suggests that physical activity has a greater role in promoting health among obese populations than previously thought.
    American Journal of Clinical Nutrition 07/2015; DOI:10.3945/ajcn.115.110924 · 6.77 Impact Factor
  • M Hamer · G D Batty · M Kivimaki
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    ABSTRACT: We examined the role of sarcopenic obesity as a risk factor for new-onset depressive symptoms over 6 years follow up in a large sample of older adults. The sample comprised 3862 community dwelling participants (1779 men, 2083 women; mean age 64.6±8.3 yrs) without depressive symptoms at baseline, recruited from the English Longitudinal Study of Ageing (ELSA). At baseline and 4 years follow up, handgrip strength (kg) of the dominant hand was assessed using a hand-held dynamometer, as a measure of sarcopenia. The outcome was new onset depressive symptoms at 6 years follow-up, defined as a score of ≥4 on the 8-item Centre of Epidemiological Studies Depression (CES-D) scale. Sarcopenic obesity was defined as obese individuals (BMI≥30 kg/m(2)) in the lowest tertile of sex specific grip strength (<35.3 kg men;<19.6 kg women). Using a multivariable logistic regression model, the risk of depressive symptoms was greatest in obese adults in the lowest tertile of handgrip strength (Odds ratio, 1.79, 95% CI, 1.10, 2.89) compared to non-obese individuals with high handgrip strength. Participants who were obese at baseline and had a decrease of more than 1 standard deviation in grip strength over 4 years follow up were at greatest risk of depressive symptoms (OR=1.97, 95% CI, 1.22, 3.17) compared to non-obese with stable grip strength. A reduction in grip strength was associated with higher risk of depressive symptoms in obese participants only, suggesting that sarcopenic obesity is a risk factor for depressive symptoms.International Journal of Obesity accepted article preview online, 30 June 2015. doi:10.1038/ijo.2015.124.
    International journal of obesity (2005) 06/2015; DOI:10.1038/ijo.2015.124 · 5.00 Impact Factor
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    ABSTRACT: We sought to determine whether adiposity in later midlife is an independent predictor of accelerated stiffening of the aorta. Whitehall II study participants (3789 men; 1383 women) underwent carotid-femoral applanation tonometry at the mean age of 66 and again 4 years later. General adiposity by body mass index, central adiposity by waist circumference and waist:hip ratio, and fat mass percent by body impedance were assessed 5 years before and at baseline. In linear mixed models adjusted for age, sex, ethnicity, and mean arterial pressure, all adiposity measures were associated with aortic stiffening measured as increase in pulse wave velocity (PWV) between baseline and follow-up. The associations were similar in the metabolically healthy and unhealthy, according to Adult Treatment Panel-III criteria excluding waist circumference. C-reactive protein and interleukin-6 levels accounted for part of the longitudinal association between adiposity and PWV change. Adjusting for chronic disease, antihypertensive medication and risk factors, standardized effects of general and central adiposity and fat mass percent on PWV increase (m/s) were similar (0.14, 95% confidence interval: 0.05-0.24, P=0.003; 0.17, 0.08-0.27, P<0.001; 0.14, 0.05-0.22, P=0.002, respectively). Previous adiposity was associated with aortic stiffening independent of change in adiposity, glycaemia, and lipid levels across PWV assessments. We estimated that the body mass index-linked PWV increase will account for 12% of the projected increase in cardiovascular risk because of high body mass index. General and central adiposity in later midlife were strong independent predictors of aortic stiffening. Our findings suggest that adiposity is an important and potentially modifiable determinant of arterial aging. © 2015 American Heart Association, Inc.
    Hypertension 06/2015; 66(2). DOI:10.1161/HYPERTENSIONAHA.115.05494 · 6.48 Impact Factor
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    ABSTRACT: The direction of the association between mental health and adiposity is poorly understood. Our objective was to empirically examine this link in a UK study. This is a prospective cohort study of 3 388 people (men) aged ≥ 18 years at study induction who participated in both the UK Health and Lifestyle Survey at baseline (HALS-1, 1984/1985) and the re-survey (HALS-2, 1991/1992). At both survey examinations, body mass index, waist circumference and self-reported common mental disorder (the 30-item General Health Questionnaire, GHQ) were measured. Logistic regression models were used to compute odds ratios (OR) and accompanying 95% confidence intervals (CI) for the associations between (1) baseline common mental disorder (QHQ score > 4) and subsequent general and abdominal obesity and (2) baseline general and abdominal obesity and re-survey common mental disorders. After controlling for a range of covariates, participants with common mental disorder at baseline experienced greater odds of subsequently becoming overweight (women, OR: 1.30, 1.03 - 1.64; men, 1.05, 0.81 - 1.38) and obese (women, 1.26, 0.82 - 1.94; men, OR: 2.10, 1.23 - 3.55) than those who were free of common mental disorder. Similarly, having baseline common mental health disorder was also related to a greater risk of developing moderate (1.57, 1.21 - 2.04) and severe (1.48, 1.09 - 2.01) abdominal obesity (women only). Baseline general or abdominal obesity was not associated with the risk of future common mental disorder. These findings of the present study suggest that the direction of association between common mental disorders and adiposity is from common mental disorder to increased future risk of adiposity as opposed to the converse.
    PLoS ONE 05/2015; 10(5):e0119970. DOI:10.1371/journal.pone.0119970 · 3.23 Impact Factor
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    ABSTRACT: We investigated causal effect of completed growth, measured by adult height, on coronary heart disease (CHD), stroke and cardiovascular traits, using instrumental variable (IV) Mendelian randomization meta-analysis. We developed an allele score based on 69 single nucleotide polymorphisms (SNPs) associated with adult height, identified by the IBCCardioChip, and used it for IV analysis against cardiovascular risk factors and events in 21 studies and 60 028 participants. IV analysis on CHD was supplemented by summary data from 180 height-SNPs from the GIANT consortium and their corresponding CHD estimates derived from CARDIoGRAMplusC4D. IV estimates from IBCCardioChip and GIANT-CARDIoGRAMplusC4D showed that a 6.5-cm increase in height reduced the odds of CHD by 10% [odds ratios 0.90; 95% confidence intervals (CIs): 0.78 to 1.03 and 0.85 to 0.95, respectively],which agrees with the estimate from the Emerging Risk Factors Collaboration (hazard ratio 0.93; 95% CI: 0.91 to 0.94). IV analysis revealed no association with stroke (odds ratio 0.97; 95% CI: 0.79 to 1.19). IV analysis showed that a 6.5-cm increase in height resulted in lower levels of body mass index (P < 0.001), triglycerides (P < 0.001), non high-density (non-HDL) cholesterol (P < 0.001), C-reactive protein (P = 0.042), and systolic blood pressure (P = 0.064) and higher levels of forced expiratory volume in 1 s and forced vital capacity (P < 0.001 for both). Taller individuals have a lower risk of CHD with potential explanations being that taller people have a better lung function and lower levels of body mass index, cholesterol and blood pressure. © The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.
    International Journal of Epidemiology 05/2015; DOI:10.1093/ije/dyv074 · 9.18 Impact Factor
  • Médecine du Sommeil 03/2015; 12(1). DOI:10.1016/j.msom.2015.01.112
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    ABSTRACT: Physical activity is critically important for successful aging, but its effect on adiposity markers at older ages is unclear as much of the evidence comes from self-reported data on physical activity. We assessed the associations of questionnaire-assessed and accelerometer-assessed physical activity with adiposity markers in older adults. This was a cross-sectional study on 3940 participants (age range 60-83 years) of the Whitehall II study who completed a 20-item physical activity questionnaire and wore a wrist-mounted accelerometer for 9 days in 2012 and 2013. Total physical activity was estimated using metabolic equivalent hours/week for the questionnaire and mean acceleration for the accelerometer. Time spent in moderate-and-vigorous physical activity (MVPA) was also assessed by questionnaire and accelerometer. Adiposity assessment included body mass index, waist circumference, and fat mass index. Fat mass index was calculated as fat mass/height² (kg/m²), with fat mass estimated using bioimpedance. Greater total physical activity was associated with lower adiposity for all adiposity markers in a dose-response manner. In men, the strength of this association was 2.4 to 2.8 times stronger with the accelerometer than with questionnaire data. In women, it was 1.9 to 2.3 times stronger. For MVPA, questionnaire data in men suggested no further benefit for adiposity markers past 1 hour/week of activity. This was not the case for accelerometer-assessed MVPA where, for example, compared with men undertaking <1 hour/week of accelerometer-assessed MVPA, waist circumference was 3.06 (95% confidence interval 2.06-4.06) cm lower in those performing MVPA 1-2.5 hours/week, 4.69 (3.47-5.91) cm lower in those undertaking 2.5-4 hours/week, and 7.11 (5.93-8.29) cm lower in those performing ≥4 hours/week. The association of physical activity with adiposity markers in older adults was stronger when physical activity was assessed by accelerometer compared with questionnaire, suggesting that physical activity might be more important for adiposity than previously estimated. Copyright © 2015 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.
    Journal of the American Medical Directors Association 03/2015; 36(5). DOI:10.1016/j.jamda.2015.01.086 · 4.94 Impact Factor
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    ABSTRACT: Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis
    Nature 02/2015; 518(7538). DOI:10.1038/nature14177 · 41.46 Impact Factor
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    ABSTRACT: Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 x 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.
    Nature 02/2015; 518(7538-7538):187-96. DOI:10.1038/nature14132 · 41.46 Impact Factor

Publication Stats

7k Citations
2,335.43 Total Impact Points


  • 2008–2015
    • University College London
      • Department of Epidemiology and Public Health
      Londinium, England, United Kingdom
  • 2006–2015
    • Finnish Institute of Occupational Health
      • Centre of Expertise for Work Organizations
      Helsinki, Uusimaa, Finland
  • 2004–2014
    • University of Helsinki
      • Department of Psychology
      Helsinki, Uusimaa, Finland
  • 2012
    • McGill University
      • Department of Epidemiology, Biostatistics and Occupational Health
      Montréal, Quebec, Canada
  • 2011
    • University Hospital of Lausanne
      Lausanne, Vaud, Switzerland
  • 2010–2011
    • Research Institute of the Finnish Economy, Finland, Helsinki
      Helsinki, Uusimaa, Finland
    • French Institute of Health and Medical Research
      • Centre de Recherche en Épidémiologie et Santé des Populations CESP U1018
      Paris, Ile-de-France, France
  • 2007–2011
    • London School of Hygiene and Tropical Medicine
      • Faculty of Epidemiology and Population Health
      Londinium, England, United Kingdom
    • University of Turku
      Turku, Varsinais-Suomi, Finland
    • University of Glasgow
      • MRC/CSO Social and Public Health Sciences Unit
      Glasgow, SCT, United Kingdom
    • University of Nottingham
      • Institute of Work, Health and Organisations
      Nottigham, England, United Kingdom
  • 1999
    • University of Jyväskylä
      Jyväskylä, Province of Western Finland, Finland