Epidemiology of diabetes Forouhi, Nita Gandhi; Wareham, Nicholas J
Medicine,
12/2014, Letnik:
42, Številka:
12
Journal Article, Book Review
Recenzirano
Odprti dostop
Abstract The disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles. The latest estimates show a ...global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035. The aetiological classification of diabetes has now been widely accepted. Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence. Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease. The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition.
Objective:
The authors investigated the association between major depressive disorder, including its clinical course, and mortality from ischemic heart disease.
Method:
This was a prospective cohort ...study of 8,261 men and 11,388 women 41-80 years of age who were free of clinical manifestations of heart disease and participated in the Norfolk, U.K., cohort of the European Prospective Investigation Into Cancer. The authors conducted a cross-sectional assessment of major depressive disorder during the period 1996-2000 and ascertained subsequent deaths from ischemic heart disease through linkage with data from the U.K. Office for National Statistics.
Results:
As of July 31, 2006, 274 deaths from ischemic heart disease were recorded over a total follow-up of 162,974 person-years (the median follow-up period was 8.5 years). Participants who had major depression during the year preceding baseline assessment were 2.7 times more likely to die from ischemic heart disease over the follow-up period than those who did not, independently of age, sex, smoking, systolic blood pressure, cholesterol, physical activity, body mass index, diabetes, social class, heavy alcohol use, and antidepressant medication use. This association remained after exclusion of the first 6 years of follow-up data. Consideration of measures of major depression history (including recency of onset, recurrence, chronicity, and age at first onset) revealed recency of onset to be associated most strongly with ischemic heart disease mortality.
Conclusions:
Major depression was associated with an increased risk of ischemic heart disease mortality. The association was independent of established risk factors for ischemic heart disease and remained undiminished several years after the original assessment.
Accurate assessment of energy expenditure (EE) is important for the study of energy balance and metabolic disorders. Combined heart rate (HR) and acceleration (ACC) sensing may increase precision of ...physical activity EE (PAEE) which is the most variable component of total EE (TEE).
To evaluate estimates of EE using ACC and HR data with or without individual calibration against doubly-labelled water (DLW) estimates of EE.
23 women and 23 men (22-55 yrs, 48-104 kg, 8-46%body fat) underwent 45-min resting EE (REE) measurement and completed a 20-min treadmill test, an 8-min step test, and a 3-min walk test for individual calibration. ACC and HR were monitored and TEE measured over 14 days using DLW. Diet-induced thermogenesis (DIT) was calculated from food-frequency questionnaire. PAEE (TEE ÷ REE ÷ DIT) and TEE were compared to estimates from ACC and HR using bias, root mean square error (RMSE), and correlation statistics.
Mean(SD) measured PAEE and TEE were 66(25) kJ·day(-1)·kg(-1), and 12(2.6) MJ·day(-1), respectively. Estimated PAEE from ACC was 54(15) kJ·day(-1)·kg(-1) (p<0.001), with RMSE 24 kJ·day(-1)·kg(-1) and correlation r = 0.52. PAEE estimated from HR and ACC+HR with treadmill calibration were 67(42) and 69(25) kJ·day(-1)·kg(-1) (bias non-significant), with RMSE 34 and 20 kJ·day(-1)·kg(-1) and correlations r = 0.58 and r = 0.67, respectively. Similar results were obtained with step-calibrated and walk-calibrated models, whereas non-calibrated models were less precise (RMSE: 37 and 24 kJ·day(-1)·kg(-1), r = 0.40 and r = 0.55). TEE models also had high validity, with biases <5%, and correlations r = 0.71 (ACC), r = 0.66-0.76 (HR), and r = 0.76-0.83 (ACC+HR).
Both accelerometry and heart rate may be used to estimate EE in adult European men and women, with improved precision if combined and if heart rate is individually calibrated.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Blood-based or urinary biomarkers may play a role in quantifying the future risk of type 2 diabetes (T2D) and in understanding possible aetiological pathways to disease. However, no systematic review ...has been conducted that has identified and provided an overview of available biomarkers for incident T2D. We aimed to systematically review the associations of biomarkers with risk of developing T2D and to highlight evidence gaps in the existing literature regarding the predictive and aetiological value of these biomarkers and to direct future research in this field.
We systematically searched PubMed MEDLINE (January 2000 until March 2015) and Embase (until January 2016) databases for observational studies of biomarkers and incident T2D according to the 2009 PRISMA guidelines. We also searched availability of meta-analyses, Mendelian randomisation and prediction research for the identified biomarkers. We reviewed 3910 titles (705 abstracts) and 164 full papers and included 139 papers from 69 cohort studies that described the prospective relationships between 167 blood-based or urinary biomarkers and incident T2D. Only 35 biomarkers were reported in large scale studies with more than 1000 T2D cases, and thus the evidence for association was inconclusive for the majority of biomarkers. Fourteen biomarkers have been investigated using Mendelian randomisation approaches. Only for one biomarker was there strong observational evidence of association and evidence from genetic association studies that was compatible with an underlying causal association. In additional search for T2D prediction, we found only half of biomarkers were examined with formal evidence of predictive value for a minority of these biomarkers. Most biomarkers did not enhance the strength of prediction, but the strongest evidence for prediction was for biomarkers that quantify measures of glycaemia.
This study presents an extensive review of the current state of the literature to inform the strategy for future interrogation of existing and newly described biomarkers for T2D. Many biomarkers have been reported to be associated with the risk of developing T2D. The evidence of their value in adding to understanding of causal pathways to disease is very limited so far. The utility of most biomarkers remains largely unknown in clinical prediction. Future research should focus on providing good genetic instruments across consortia for possible biomarkers in Mendelian randomisation, prioritising biomarkers for measurement in large-scale cohort studies and examining predictive utility of biomarkers for a given context.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Evidence for a major role of genetic factors in the determination of body mass index (BMI) comes from studies of related individuals. Despite consistent evidence for a heritable component of BMI, ...estimates of BMI heritability vary widely between studies and the reasons for this remain unclear. While some variation is natural due to differences between populations and settings, study design factors may also explain some of the heterogeneity. We performed a systematic review that identified 88 independent estimates of BMI heritability from twin studies (total 140,525 twins) and 27 estimates from family studies (42,968 family members). BMI heritability estimates from twin studies ranged from 0.47 to 0.90 (5th/50th/95th centiles: 0.58/0.75/0.87) and were generally higher than those from family studies (range: 0.24-0.81; 5th/50th/95th centiles: 0.25/0.46/0.68). Meta-regression of the results from twin studies showed that BMI heritability estimates were 0.07 (P = 0.001) higher in children than in adults; estimates increased with mean age among childhood studies (+0.012/year, P = 0.002), but decreased with mean age in adult studies (-0.002/year, P = 0.002). Heritability estimates derived from AE twin models (which assume no contribution of shared environment) were 0.12 higher than those from ACE models (P < 0.001), whilst lower estimates were associated with self reported versus DNA-based determination of zygosity (-0.04, P = 0.02), and with self reported versus measured BMI (-0.05, P = 0.03). Although the observed differences in heritability according to aspects of study design are relatively small, together, the above factors explained 47% of the heterogeneity in estimates of BMI heritability from twin studies. In summary, while some variation in BMI heritability is expected due to population-level differences, study design factors explained nearly half the heterogeneity reported in twin studies. The genetic contribution to BMI appears to vary with age and may have a greater influence during childhood than adult life.
Although television viewing time is detrimentally associated with intermediate cardiovascular risk factors, the relationship with incident total (i.e. combined fatal and non-fatal) cardiovascular ...disease (CVD), non-fatal CVD and coronary heart disease is largely unknown. This study examined whether television viewing time is associated with these three outcomes, independently of physical activity energy expenditure and other confounding variables.
A population-based cohort of 12,608 men and women (aged 61.4±9.0), free from stroke, myocardial infarction and cancer at baseline in 1998-2000 were followed up until 2007 (6.9±1.9 years). Participants self-reported education, smoking, alcohol use, antihypertensive, lipid lowering and antidepressant medication, disease history, total energy intake, sleep duration, physical activity and television viewing. BMI, waist circumference, blood pressure, triglycerides, HDL cholesterol and glycated haemoglobin (HbA(1c)) were measured by standardized procedures; a clustered metabolic risk score was constructed. Every one hour/day increase in television viewing was associated with an increased hazard for total (HR = 1.06, 95%CI = 1.03-1.08; 2,620 cases), non-fatal CVD (HR = 1.06, 95%CI = 1.03-1.09; 2,134 cases), and coronary heart disease (HR = 1.08, 95%CI = 1.03-1.13; 940 cases), independent of gender, age, education, smoking, alcohol, medication, diabetes status, CVD family history, sleep duration and physical activity energy expenditure. Energy intake, BMI, waist circumference, blood pressure, triglycerides, HDL cholesterol, HbA(1c) and the clustered metabolic risk score only partially mediated these associations.
These results indicate that the most prevalent leisure time (sedentary) behaviour, television viewing, independently contributes to increased CVD risk. Recommendations on reducing television viewing time should be considered.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background: Detailed associations between physical activity (PA) subcomponents, sedentary time, and body composition in preschoolers remain unclear.Objective: We examined the magnitude of ...associations between objectively measured PA subcomponents and sedentary time with body composition in 4-y-old children.Design: We conducted a cross-sectional study in 398 preschool children recruited from the Southampton Women's Survey. PA was measured by using accelerometry, and body composition was measured by using dual-energy X-ray absorptiometry. Associations between light physical activity, moderate physical activity (MPA), vigorous physical activity (VPA), and moderate-to-vigorous physical activity (MVPA) intensity; sedentary time; and body composition were analyzed by using repeated-measures linear regression with adjustment for age, sex, birth weight, maternal education, maternal BMI, smoking during pregnancy, and sleep duration. Sedentary time and PA were also mutually adjusted for one another to determine whether they were independently related to adiposity.Results: VPA was the only intensity of PA to exhibit strong inverse associations with both total adiposity P < 0.001 for percentage of body fat and fat mass index (FMI) and abdominal adiposity (P = 0.002 for trunk FMI). MVPA was inversely associated with total adiposity (P = 0.018 for percentage of body fat; P = 0.022 for FMI) but only because of the contribution of VPA, because MPA was unrelated to fatness (P ≥ 0.077). No associations were shown between the time spent sedentary and body composition (P ≥ 0.11).Conclusions: In preschoolers, the time spent in VPA is strongly and independently associated with lower adiposity. In contrast, the time spent sedentary and in low-to-moderate–intensity PA was unrelated to adiposity. These results indicate that efforts to challenge pediatric obesity may benefit from prioritizing VPA.
In cross-platform analyses of 174 metabolites, we identify 499 associations (P < 4.9 × 10
) characterized by pleiotropy, allelic heterogeneity, large and nonlinear effects and enrichment for ...nonsynonymous variation. We identify a signal at GLP2R (p.Asp470Asn) shared among higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes, with β-arrestin signaling as the underlying mechanism. Genetically higher serine levels are shown to reduce the likelihood (by 95%) and predict development of macular telangiectasia type 2, a rare degenerative retinal disease. Integration of genomic and small molecule data across platforms enables the discovery of regulators of human metabolism and translation into clinical insights.
Diet is critical to health and social relationships are an important determinant of diet. We report the association between transitions in marital status and healthy eating behaviours in a UK ...population.
Longitudinal study of middle-age and older adults 39−78y (n = 11 577) in EPIC-Norfolk, a population-based cohort, who completed food frequency questionnaires in 1993–97 and 1998–2002. Multivariable linear regression analyses assessed gender-specific associations between five categories of marital transitions and changes in quantity (g/d), and variety (no/month) of fruits or vegetables.
In 3.6 years of follow-up and relative to men who stayed married, widowed men showed significant declines (mean difference, 95% CI) in all four indicators of healthy eating including fruit quantity (−47.7, −80.6 to −14.9 g/d), fruit variety (−0.6, −1.1 to −0.2 no/month), vegetable quantity (−27.7, −50.5 to −4.9 g/d), and vegetable variety (−1.6, −2.2 to −0.9 no/month). Men who were separated or divorced or who remained single also showed significant declines in three of the indicators. Among women, only those who became separated/divorced or stayed single showed declines in one indicator, vegetable variety.
Unhealthy changes to diet accompanying divorce, separation and becoming widowed may be more common among men than women. Moreover, deterioration in fruit and vegetable intakes was more apparent for variety rather than quantity consumed. Programmes to promote healthy eating among older adults need to recognise these social determinants of diet and consider prioritising people who live alone and in particular men who have recently left relationships or who have been widowed.
•Both quantity and variety of fruit and vegetable (F&V) intake are associated with lower risk of chronic disease.•F&V intake is associated with marital status, but less is known about marital transitions and diet, particularly by gender.•We studied marital transitions and changes in quantity and variety of F&V in middle-age and older UK men and women.•For men more than women, becoming widowed or separated was linked to declines in F&V quantity and variety.•Interventions to promote healthy eating may be needed for men experiencing separation, divorce or death of a spouse.
Assessment of physical activity in youth Corder, Kirsten; Ekelund, Ulf; Steele, Rebekah M ...
Journal of applied physiology (1985),
09/2008, Letnik:
105, Številka:
3
Journal Article
Recenzirano
Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Cambridge, United Kingdom
Submitted 29 January 2008
; accepted in final form 17 July 2008
ABSTRACT
Despite much progress ...with physical activity assessment, the limitations concerning the accurate measurement of physical activity are often amplified in young people due to the cognitive, physiological, and biomechanical changes that occur during natural growth as well as a more intermittent pattern of habitual physical activity in youth compared with adults. This mini-review describes and compares methods to assess habitual physical activity in youth and discusses main issues regarding the use and interpretation of data collected with these techniques. Self-report instruments and movement sensing are currently the most frequently used methods for the assessment of physical activity in epidemiological research; others include heart rate monitoring and multisensor systems. Habitual energy expenditure can be estimated from these input measures with varying degree of uncertainty. Nonlinear modeling techniques, using accelerometry perhaps in combination with physiological parameters like heart rate or temperature, have the greatest potential for increasing the prediction accuracy of habitual physical activity energy expenditure. Although multisensor systems may be more accurate, this must be balanced against feasibility, a balance that shifts with technological and scientific advances and should be considered at the beginning of every new study.
measurement; self-report; pedometry; accelerometry; heart rate
Address for reprint requests and other correspondence: S. Brage, MRC Epidemiology Unit, Institute of Metabolic Science, Box 285, Addenbrooke's Hospital CB2 0QQ Cambridge, UK (e-mail: soren.brage{at}mrc-epid.cam.ac.uk )
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