The global epidemic of obesity is a major public health problem today. Obesity increases the risk of many chronic diseases, such as type 2 diabetes, coronary heart disease, and certain types of ...cancer, and is associated with lower life expectancy. The body mass index (BMI), which is currently used to classify obesity, is only an imperfect measure of abnormal or excessive body fat accumulation. Studies have shown that waist circumference as a measure of fat distribution may improve disease prediction. More elaborate techniques such as magnetic resonance imaging are increasingly available to assess body fat distribution, but these measures are not readily available in routine clinical practice, and health-relevant cut-offs not yet been established. The measurement of biomarkers that reflect the underlying biological mechanisms for the increased disease risk may be an alternative approach to characterize the relevant obesity phenotype. The insulin/insulin-like growth factor (IGF) axis and chronic low-grade inflammation have been identified as major pathways. In addition, specific adipokines such as leptin, adiponectin and resistin have been related to obesity-associated health outcomes. This biomarker research, which is currently further developed with the application of high throughput methods, gives important insights in obesity-related disease etiology and pathophysiological pathways and may be used to better characterize obese persons at high risk of disease development and target disease-causing biomarkers in personalized prevention strategies.
•Obesity is associated with cardiovascular disease, cancer and lower life expectancy.•Obesity is traditionally classified based on the body mass index (BMI).•Taking body fat distribution into account may improve disease prediction.•Insulin/IGF axis and chronic inflammation are major pathophysiological pathways.•A biomarker-guided obesity definition may enable personalized prevention.
The worldwide rise of obesity has provoked intensified research to better understand its pathophysiology as a means for disease prevention. Several biomarkers that may reflect various ...pathophysiological pathways that link obesity and cardiometabolic diseases have been identified over the past decades.
We summarize research evidence regarding the role of established and novel obesity-related biomarkers, focusing on recent epidemiological evidence for detrimental associations with cardiometabolic diseases including obesity-related cancer. The reviewed biomarkers include biomarkers of glucose-insulin homeostasis (insulin, insulin-like growth factors, and C-peptide), adipose tissue biomarkers (adiponectin, omentin, apelin, leptin, resistin, and fatty-acid-binding protein-4), inflammatory biomarkers (C-reactive protein, interleukin 6, tumor necrosis factor α), and omics-based biomarkers (metabolites and microRNAs).
Although the evidence for many classical obesity biomarkers, including adiponectin and C-reactive protein (CRP), in disease etiology has been initially promising, the evidence for a causal role in humans remains limited. Further, there has been little demonstrated ability to improve disease prediction beyond classical risk factors. In the era of "precision medicine," there is an increasing interest in novel biomarkers, and the extended list of potentially promising biomarkers, such as adipokines, cytokines, metabolites, and microRNAs, implicated in obesity may bring new promise for improved, personalized prevention. To further evaluate the role of obesity-related biomarkers as etiological and early-disease-prediction targets, well-designed studies are needed to evaluate temporal associations, replicate findings, and test clinical utility of novel biomarkers. In particular, studies to determine the therapeutic implications of novel biomarkers beyond established metabolic risk factors are highly warranted.
The prevalence of obesity has increased substantially in the past in almost all countries of the world, and a further increase is expected for the future. Besides the well-established effects on type ...2 diabetes and cardiovascular disease, there is convincing evidence today that obesity also increases the risk of several types of cancer, including colorectal cancer, postmenopausal breast cancer, endometrial cancer, renal cell carcinoma, esophageal adenocarcinoma, pancreatic cancer, and liver cancer. Obesity probably also increases the risk of ovarian cancer, advanced prostate cancer, gallbladder cancer, and gastric cardia cancer. For some cancer types, there is also some evidence that weight gain during adulthood increases cancer risk, e.g., colorectal cancer, postmenopausal breast cancer, endometrial cancer, and liver cancer. However, for most cancers, it is an open question as to whether vulnerability to weight gain in relation to cancer risk depends on specific life periods. There are a number of plausible mechanisms that may explain the relationship between obesity and cancer risk, including pathways related to insulin resistance, inflammation, and sex hormones. For most cancers, there is only limited evidence that weight loss in adulthood decreases cancer risk, which is primarily due to the limited long-term success of weight loss strategies among obese individuals. There is limited evidence suggesting that obesity may also be associated with poor prognosis among patients with colorectal cancer, breast cancer, endometrial cancer, ovarian cancer, and pancreatic cancer. Taken together, these findings support efforts to prevent weight gain on an individual level as well as on a population level. Whether and to what extent overweight or obese cancer patients benefit from weight loss strategies is unclear and needs to be addressed in future studies.
Whether the gut microbiome in obesity is characterized by lower diversity and altered composition at the phylum or genus level may be more accurately investigated using high-throughput sequencing ...technologies. We conducted a systematic review in PubMed and Embase including 32 cross-sectional studies assessing the gut microbiome composition by high-throughput sequencing in obese and non-obese adults. A significantly lower alpha diversity (Shannon index) in obese versus non-obese adults was observed in nine out of 22 studies, and meta-analysis of seven studies revealed a non-significant mean difference (-0.06, 95% CI -0.24, 0.12,
= 81%). At the phylum level, significantly more Firmicutes and fewer Bacteroidetes in obese versus non-obese adults were observed in six out of seventeen, and in four out of eighteen studies, respectively. Meta-analyses of six studies revealed significantly higher Firmicutes (5.50, 95% 0.27, 10.73,
= 81%) and non-significantly lower Bacteroidetes (-4.79, 95% CI -10.77, 1.20,
= 86%). At the genus level, lower relative proportions of
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were found in obese versus non-obese adults. Although a proportion of studies found lower diversity and differences in gut microbiome composition in obese versus non-obese adults, the observed heterogeneity across studies precludes clear answers.
Body surface scanners (BS), which visualize a 3D image of the human body, facilitate the computation of numerous body measures, including height, waist circumference (WC) and hip circumference (HC). ...However, limited information is available regarding validity and reliability of these automated measurements (AM) and their correlation with parameters of the Metabolic Syndrome (MetS) compared to traditional manual measurements (MM).
As part of a cross-sectional feasibility study, AM of WC, HC and height were assessed twice in 60 participants using a 3D BS (VitussmartXXL). Additionally, MM were taken by trained personnel according to WHO guidelines. Participants underwent an interview, bioelectrical impedance analysis, and blood pressure measurement. Blood samples were taken to determine HbA1c, HDL-cholesterol, triglycerides, and uric acid. Validity was assessed based on the agreement between AM and MM, using Bland-Altman-plots, correlation analysis, and paired t-tests. Reliability was assessed using intraclass correlation coefficients (ICC) based on two repeated AM. Further, we calculated age-adjusted Pearson correlation for AM and MM with fat mass, systolic blood pressure, HbA1c, HDL-cholesterol, triglycerides, and uric acid.
Body measures were higher in AM compared to MM but both measurements were strongly correlated (WC, men, difference = 1.5 cm, r = 0.97; women, d = 4.7 cm, r = 0.96; HC, men, d = 2.3 cm, r = 0.97; women, d = 3.0 cm; r = 0.98). Reliability was high for all AM (nearly all ICC>0.98). Correlations of WC, HC, and the waist-to-hip ratio (WHR) with parameters of MetS were similar between AM and MM; for example the correlation of WC assessed by AM with HDL-cholesterol was r = 0.35 in men, and r = -0.48 in women, respectively whereas correlation of WC measured manually with HDL cholesterol was r = -0.41 in men, and r = -0.49 in women, respectively.
Although AM of WC, HC, and WHR are higher when compared to MM based on WHO guidelines, our data indicate good validity, excellent reliability, and similar correlations to parameters of the MetS.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
To date, the scientific literature on socioeconomic correlates and determinants of physical activity behaviours has been dispersed throughout a number of systematic reviews, often focusing on one ...factor (e.g. education or parental income) in one specific age group (e.g. pre-school children or adults). The aim of this umbrella review is to provide a comprehensive and systematic overview of the scientific literature from previously conducted research by summarising and synthesising the importance and strength of the evidence related to socioeconomic correlates and determinants of PA behaviours across the life course.
Medline, Embase, ISI Web of Science, Scopus and SPORTDiscus were searched for systematic literature reviews and meta-analyses of observational studies investigating the association between socioeconomic determinants of PA and PA itself (from January 2004 to September 2017). Data extraction evaluated the importance of determinants, strength of evidence, and methodological quality of the selected papers. The full protocol is available from PROSPERO (PROSPERO2014:CRD42015010616).
Nineteen reviews were included. Moderate methodological quality emerged. For adults, convincing evidence supports a relationship between PA and socioeconomic status (SES), especially in relation to leisure time (positive relationship) and occupational PA (negative relationship). Conversely, no association between PA and SES or parental SES was found for pre-school, school-aged children and adolescents.
Available evidence on the socioeconomic determinants of PA behaviour across the life course is probable (shows fairly consistent associations) at best. While some evidence is available for adults, less was available for youth. This is mainly due to a limited quantity of primary studies, weak research designs and lack of accuracy in the PA and SES assessment methods employed. Further PA domain specific studies using longitudinal design and clear measures of SES and PA assessment are required.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
To investigate abdominal volume determined by a new body scanner algorithm as anthropometric marker for Metabolic Syndrome (MetS) and its parameters compared to manually measured waist circumference ...(WC), we performed body scans in 411 participants (38% men, 20-81 years). WC and triglyceride, HDL-cholesterol, and fasting glucose concentrations, and blood pressure were assessed as MetS parameters. We used Spearman correlations and linear regression to investigate associations and goodness-of-fit (R², BIC) of abdominal volume and WC with MetS parameters, and logistic regression to analyse the discriminative power of WC and abdominal volume to assess likelihoods of MetS components and MetS. Correlations with triglyceride, HDL-cholesterol, and glucose concentration were slightly stronger for abdominal volume (r; 0.32, -0.32, and 0.34, respectively) than for WC (0.28, -0.28, and 0.29, respectively). Explained variances in MetS parameters were slightly higher and goodness-of-fit slightly better for abdominal volume than for WC, but differences were small. Exemplarily, glucose levels were 0.28 mmol/L higher (R² = 0.25; BIC = 945.5) per 1-SD higher WC, and 0.35 mmol/L higher (R² = 0.28; BIC = 929.1) per 1-SD higher abdominal volume. The discriminative power to estimate MetS components was similar for WC and abdominal volume. Our data show that abdominal volume allows metabolic characterization comparable to established WC.
Low levels of physical activity (PA) are reported to contribute to the occurrence of non-communicable diseases over the life course. Although psychological factors have been identified as an ...important category concerning PA behavior, knowledge on psychological determinants of PA is still inconclusive. Therefore, the aim of this umbrella systematic literature review (SLR) was to summarize and synthesize the scientific evidence on psychological determinants of PA behavior across the life course. A systematic online search was conducted on MEDLINE, ISI Web of Science, Scopus, and SPORTDiscus databases. The search was limited to studies published in English from January 2004 to April 2016. SLRs and meta-analyses (MAs) of observational studies investigating the association of psychological variables and PA were considered eligible. Extracted data were evaluated based on importance of determinants, strength of evidence, and methodological quality. The full protocol is available from PROSPERO (Record ID: CRD42015010616). Twenty reviews (14 SLRs and 6 MAs), mostly of moderate methodological quality, were found eligible. Convincing evidence was found for self-efficacy (positive association with PA) in children and adolescents, and stress (negative association with PA) regardless of age. Most of the evidence revealing an association between psychological determinants and PA is probable and limited, mainly due to differences in the definition of PA and of psychological determinants across reviews. Thus, scholars are urged to reach a consensus on clear definitions of relevant psychological determinants of PA, subsuming cultural biases and allowing the possibility to obtain clear interpretations and generalizability of findings. Finally, most psychological determinants should be considered within a larger framework of other multi-level determinants that may interact or mediate some of the effects.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
24 h-accelerometry is now used to objectively assess physical activity (PA) in many observational studies like the German National Cohort; however, PA variability, observational time needed to ...estimate habitual PA, and reliability are unclear.
We assessed 24 h-PA of 50 participants using triaxial accelerometers (ActiGraph GT3X+) over 2 weeks. Variability of overall PA and different PA intensities (time in inactivity and in low intensity, moderate, vigorous, and very vigorous PA) between days of assessment or days of the week was quantified using linear mixed-effects and random effects models. We calculated the required number of days to estimate PA, and calculated PA reliability using intraclass correlation coefficients.
Between- and within-person variance accounted for 34.4-45.5% and 54.5-65.6%, respectively, of total variance in overall PA and PA intensities over the 2 weeks. Overall PA and times in low intensity, moderate, and vigorous PA decreased slightly over the first 3 days of assessment. Overall PA (p = 0.03), time in inactivity (p = 0.003), in low intensity PA (p = 0.001), in moderate PA (p = 0.02), and in vigorous PA (p = 0.04) slightly differed between days of the week, being highest on Wednesday and Friday and lowest on Sunday and Monday, with apparent differences between Saturday and Sunday. In nested random models, the day of the week accounted for < 19% of total variance in the PA parameters. On average, the required number of days to estimate habitual PA was around 1 week, being 7 for overall PA and ranging from 6 to 9 for the PA intensities. Week-to-week reliability was good (intraclass correlation coefficients, range, 0.68-0.82).
Individual PA, as assessed using 24 h-accelerometry, is highly variable between days, but the day of assessment or the day of the week explain only small parts of this variance. Our data indicate that 1 week of assessment is necessary for reliable estimation of habitual PA.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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