Blood-based obesity biomarkers and their relevance for disease risk.

Physical inactivity and poor diet are both commonly reported to be associated with a wide range of chronic diseases, including hypertension, type 2 diabetes, coronary heart disease (CHD) and stroke, and, together, contribute to substantial burden of disease.1 Although it is well known that those who follow a healthy diet also frequently have higher levels of physical activity, which may lead to confounding, diet and physical activity may also share and affect many common biological disease mechanisms, such as blood pressure, lipids, glucose, inflammation and adiposity. Therefore, some questions still remain whether dietary associations are (i) confounded by physical activity, (ii) causally independent of physical activity and/or (iii) obviated by physical activity superseding dietary effects in mutually shared intermediate disease pathways. In this issue of the IJE, Heroux et al.2 conducted a careful analysis to investigate the association of disease intermediate-derived dietary patterns and risk of mortality, and whether such a derived dietary pattern is associated with risk independent of physical activity. Conducted in a unique cohort comprising participants visiting an aerobic fitness centre, the authors used reduced rank regression (RRR) on a battery of biological intermediates—including body mass index, blood pressure, high-density lipoprotein and total cholesterol, triglyceride, fasting glucose, uric acid and white blood cell levels—to derive a dietary pattern score predictive of adverse levels of these intermediates. They found that adverse dietary pattern score associations were attenuated and the score was not predictive of mortality after controlling for self-reported physical activity and treadmill-assessed cardiorespiratory fitness levels in particular. The findings, at first glance, appear to suggest that dietary associations with mortality are either confounded or trumped by physical activity. However, such a conclusion should be interpreted in the context of numerous previous epidemiological studies, where dietary factors have been found to be associated with risk of type 2 diabetes, CHD, stroke, cancer and mortality, independent of lifestyle factors including physical activity levels.3 With a modest number of endpoints (136 cardiovascular deaths and 445 total deaths), statistical power of this analysis is somewhat limited, which may explain the lack of overall significant trends between the RRR dietary pattern and CVD and total mortality, even before adjustment for physical activity or fitness. Conceptually, because dietary factors may influence physical fitness directly or indirectly through body weight, it can be argued that dietary associations are mediated rather than simply confounded by physical fitness. Diet is a highly complex exposure, which can be analysed in many ways, including macronutrient composition, micronutrients, food items/groups, food indexes (e.g. glycemic index), certain eating behaviours (e.g. skipping breakfast) and overall dietary patterns.4 Characterization of overall dietary patterns is not straightforward either, but can be derived through a posteriori methods such as principle component analysis, cluster analysis or through construction of dietary quality indices (such as Mediterranean Diet Index or Healthy Eating Index) based on a priori knowledge. The RRR is a recently proposed method that takes into account the biological pathway from diet to outcome by identifying dietary patterns associated with specific intermediate biomarkers of a specific disease.5 One advantage of RRR over traditional methods is that it incorporates information on biological biomarkers into the analysis. However, this method is limited by the availability or selection of biomarkers that are used in the analysis. For example, useful blood biomarkers are lacking for most cancers. Because the RRR analysis is intended to explain maximum amount of variance in the selected biomarkers, the derived dietary pattern reflects only one aspect of dietary exposure that is related to the selected biomarkers. In this study, the selected biomarkers are mainly related to cardiovascular disease, although total mortality is the main focus of the analysis and discussion. Because diet influences cardiovascular disease through multiple pathways, beyond the biomarker intermediates included in the study, the derived dietary pattern cannot be interpreted as a complete global index of all dietary effects on cardiovascular or all-cause mortality. As complex human behaviours, there are many parallels between physical activity and diet in terms of health effects and assessment.6 The effects of physical activity go beyond energy expenditure, in the way that diet contributes more than just energy intake. No epidemiological methods are able to capture all dimensions of physical activity in free-living populations, including type, duration, frequency and intensity. Although physical fitness and physical activity are strongly correlated, they are not synonymous and should not be used interchangeably because physical activity consists of behaviour, whereas physical fitness is a functional attribute (like blood cholesterol) that can be influenced not only by physical activity behaviours, but also a wide range of other modifiable and non-modifiable factors including age, sex, genetics, body weight, existing and preclinical diseases and probably diet. Despite substantial measurement errors in assessing physical activity levels using questionnaires, numerous epidemiological studies have demonstrated that increasing physical activity is strongly and consistently associated with decreased risk of diabetes, CHD, stroke, several cancers and mortality.7 There is no epidemiological or clinical trial evidence, however, to suggest that the benefits of physical activity trump those of other lifestyle factors. Notably, in our recent analysis of a large cohort of women with 24 years of follow-up,8 poor diet, lack of physical activity, obesity and cigarette smoking were each independently associated with increased risk of mortality. Using repeated measures of physical activity assessed every 2–4 years during follow-up, we found that being physically inactive (<0.5 h/week of exercise vs ≥5.5 h/week) was associated with an ∼80% increased risk of cardiovascular mortality. The magnitude of this association is compatible with that observed for low cardiorespiratory fitness.9 A diet quality score remained strongly and significantly associated with mortality after adjustment for repeated measures of physical activity and other lifestyle factors. The estimated population attributable risks were 28% for cigarette smoking, 14% for being overweight, 17% for lack of physical activity (<30 min of physical activity per day), 13% for low diet quality (scored <40 percentile of the cohort for a Healthy Eating Index). Overall, 55% (95% confidence interval 47–62%) of total deaths were attributable to the combination of smoking, being overweight, lack of physical activity and a low diet quality. These results indicate that diet, exercise and other lifestyle factors have additive influences on the risk of premature mortality. Heroux et al.’s analysis reminds us of the complexity of teasing out independent associations of unhealthy diet and physical inactivity, two of underlying modifiable risk factors for chronic diseases and premature death. Similar challenges have arisen from assessing the relative importance of obesity vs physical activity. Because diet, physical activity and adiposity are all multidimensional variables that can influence each other, ranking the relative importance of them through statistical modelling oversimplifies these complex factors. From a public health point of view, promoting a healthy diet and encouraging physical activity are not mutually exclusive, but equally important factors for maintaining healthy weight and reducing the risk of chronic diseases and premature death.

BackgroundSaliva measures are generally more accessible than blood, especially in vulnerable populations. However, connections between aging biology biomarkers in different body tissues remain unknown.MethodsThe present study included individuals (N = 2406) who consented for saliva and blood draw in the Health and Retirement Telomere length study in 2008 and the Venous blood study in 2016 who had complete data for both tissues. We assessed biological aging based on telomere length in saliva and DNA methylation and physiology measures in blood. DNA methylation clocks combine information from CpGs to produce the aging measures representative of epigenetic aging in humans. We analyzed DNA methylation clocks proposed by Horvath (353 CpG sites), Hannum (71 CpG sites), Levine or PhenoAge, (513 CpG sites), GrimAge, (epigenetic surrogate markers for select plasma proteins), Horvath skin and blood (391 CpG sites), Lin (99 CpG sites), Weidner (3 CpG sites), and VidalBralo (8 CpG sites). Physiology measures (referred to as phenotypic age) included albumin, creatinine, glucose, [log] C-reactive protein, lymphocyte percent, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count. The phenotypic age algorithm is based on parametrization of Gompertz proportional hazard models. Average telomere length was assayed using quantitative PCR (qPCR) by comparing the telomere sequence copy number in each patient’s sample (T) to a single-copy gene copy number (S). The resulting T/S ratio was proportional to telomere length, mean. Within individual, relationships between aging biology measures in blood and saliva and variations according to sex were assessed.ResultsSaliva-based telomere length showed inverse associations with both physiology-based and DNA methylation-based aging biology biomarkers in blood. Longer saliva-based telomere length was associated with 1 to 4 years slower biological aging based on blood-based biomarkers with the highest magnitude being Weidner (β = − 3.97, P = 0.005), GrimAge (β = − 3.33, P < 0.001), and Lin (β = − 3.45, P = 0.008) biomarkers of DNA methylation.ConclusionsThere are strong connections between aging biology biomarkers in saliva and blood in older adults. Changes in telomere length vary with changes in DNA methylation and physiology biomarkers of aging biology. We observed variations in the relationship between each body system represented by physiology biomarkers and biological aging, particularly at the DNA methylation level. These observations provide novel opportunities for integration of both blood-based and saliva-based biomarkers in clinical care of vulnerable and clinically difficult to reach populations where either or both tissues would be accessible for clinical monitoring purposes.

Blood-based biomarkers show great promise to revolutionize diagnosis and prognosis of Alzheimer's disease (AD) and other dementias. Cardiovascular disease (CVD) risk factors, such as hypertension and diabetes, contribute to risk of cognitive impairment (CI), vascular and Alzheimer's disease dementia. The relationship between CVD risk and blood-based biomarkers for AD remains unclear. We sought to explore whether CVD risk significantly moderates the relationship between blood-based AD biomarkers and CI. We included participants with mild cognitive impairment or probable AD (CI group), versus cognitively normal comparators (CN) from the Bio-Hermes study. Clinical presentation of CI was determined based on clinical screening procedures. CVD risk was calculated using the Atherosclerotic CVD (ASCVD) risk score calculator. We conducted a series of logistic regression analyses to evaluate the association of each of several AD biomarkers (plasma amyloid beta (Aβ) 42/Aβ40, phosphorylated tau (p-tau)181, p-tau217, apolipoprotein E ε4 allele (Apoε4) status) and CVD risk, with CI. We then tested moderation by CVD risk in each model. We included 661 participants (n=266 CN, n=395 CI; mean age 72.4 years; 55% male). In each of the models, blood-based biomarkers and CVD risk were significantly associated with CI. Strongest association was for p-tau217 (OR=2.34 [95% CI:1.89-2.9]). CVD risk moderated the relationship between p-tau181 and CI (OR=0.78 [95% CI: 0.64-0.95]) but no moderation effect was observed for any of the other biomarkers. In our study, blood-based dementia biomarkers and CVD risk significantly associated with CI, but CVD risk did not moderate the relationship between blood-based biomarkers and CI. Blood-based biomarkers and CVD risk might confer independent risk of CI. Given the multiple risk factors for dementia and large implications for social and healthcare systems globally, CVD risk assessment should complement other dementia biomarker assessments for accurate and expeditious prognosis.

Type 2 diabetes (T2D)2 is a heterogeneous condition that is characterized by increased insulin resistance and impaired insulin secretion. A progressive disorder with an insidious onset, T2D typically progresses from an early asymptomatic insulin resistance state to mild glucose intolerance and eventually to frank T2D that requires pharmacologic interventions. Whether insulin resistance or impaired insulin secretion is the primary defect in the pathogenesis of T2D remains a matter of debate. Interestingly, most genetic variants identified from recent genome-wide association studies are related to decreased β-cell function or impaired insulin secretion, which implicates a key role for β-cell dysfunction in the development of T2D. Still, obesity, with its fundamental influence on insulin resistance, is the single most important risk factor for T2D. Although T2D is largely predictable through anthropometric, lifestyle, and clinical factors and is preventable through diet and exercise, the metabolic pathways underlying the progression from normal glycemia to a prediabetes state and later to T2D are not completely understood. Classic epidemiology typically relates lifestyle and environmental exposures to chronic disease end points, such as T2D. This approach (sometimes referred to as “black-box epidemiology”) has identified many important lifestyle and environmental risk factors for chronic diseases, but it often does not illuminate biological mechanisms that underlie observed associations. Recent advances in “omics” technology, however, have enabled epidemiologists to incorporate novel biomarkers at multiple levels into human observational studies, with the potential to shift the research paradigm from the traditional black-box strategy to a systems approach (1, 2). This new model integrates a wide range of information—genetic predisposition (genome), epigenetic changes (epigenome), the expression of genes (transcriptome), proteins (proteome), metabolites (metabolome), and gut microbiota (microbiome)—into population-based studies to improve our understanding of the biological mechanisms that underlie disease pathophysiology in humans. Systems epidemiology is at the intersection of …

Obesity could be interpreted as a low grade inflammatory state. The role of cytokines for innate and acquired immune response and adipocytokines in pathogenesis of obesity is not completely understood. The aim of the study was to evaluate anthropometric parameters, adipocytokines and inflammatory cytokine levels as biomarkers of childhood obesity. This investigation was designed as a longitudinal observational study. Forty-seven obese children (19 males and 28 females) were enrolled by Pediatric Clinic of the Foundation IRCCS Policlinico San Matteo, Pavia, Italy. For each patients a blood sample, used for other biochemical evaluations, was collected. Cytokines and adipocytokines plasmatic levels were determined using an ELISA method. Plasma leptin levels are in correlation with age (r=0.5; P<0.001) and BMI-z score (r=0.36; P<0.001), particularly in girls; plasma resistin levels are in inverse correlation with age, particularly in boys (r=-0.67; P<0.001) and in correlation with BMI-z score (r=0.52; P=0.002). Plasma leptin and resistin levels show a good correlation with antrophometric parameters of child obesity (sex and BMI z score). This study suggests that leptin and resistin can be considered as biomarker of childhood obesity and its comorbility. We observed a statistically significant correlation between plasma leptin and resistin levels and antrophometric parameters of child obesity (sex and BMI z score). This study suggests that adipocytokines, such as leptin and resistin, can be considered as biomarkers of childhood obesity.

Blood-based biomarkers can be very useful in formulating new diagnostic and treatment proposals in the field of dementia, especially in Alzheimer's disease (AD). However, due to the influence of several factors on the reproducibility and reliability of these markers, their clinical use is still very uncertain. Thus, up-to-date knowledge about the main blood biomarkers that are currently being studied is extremely important in order to discover clinically useful and applicable tools, which could also be used as novel pharmacological strategies for the AD treatment. A narrative review was performed based on the current candidates of blood-based biomarkers for AD to show the main results from different studies, focusing on their clinical applicability and association with AD pathogenesis. The aim of this paper was to carry out a literature review on the major blood-based biomarkers for AD, connecting them with the pathophysiology of the disease. Recent advances in the search of blood-based AD biomarkers were summarized in this review. The biomarkers were classified according to the topics related to the main hallmarks of the disease such as inflammation, amyloid, and tau deposition, synaptic degeneration and oxidative stress. Moreover, molecules involved in the regulation of proteins related to these hallmarks were described, such as non-coding RNAs, neurotrophins, growth factors and metabolites. Cells or cellular components with the potential to be considered as blood-based AD biomarkers were described in a separate topic. A series of limitations undermine new discoveries on blood-based AD biomarkers. The lack of reproducibility of findings due to the small size and heterogeneity of the study population, different analytical methods and other assay conditions make longitudinal studies necessary in this field to validate these structures, especially when considering a clinical evaluation that includes a broad panel of these potential and promising blood-based biomarkers.

Medical Hazards of Prolonged Sitting

To describe risk and protective factors for chronic diseases, in Brazilian capitals and the Federal District, collected by the National Health Survey (PNS) and by the Surveillance System for Protective and Risk Factors for Chronic Diseases by Telephone Survey (Vigitel) in 2013. Data analysis of the studies conducted by the PNS and Vigitel in 2013 was performed. Indicators analyzed were: smoking, alcohol consumption, diet, and physical activity, according to sex, with a 95% confidence interval. The prevalences found were: current cigarette smokers: PNS, 12.5% and Vigitel, 11.3%; abuse of alcoholic beverages: PNS, 14.9% and Vigitel, 16.4%; recommended intake of fruits and vegetables: PNS, 41.8% and Vigitel, 23.6%; and physical activity in leisure time: PNS, 26.6% and Vigitel, 35.8%. In the majority of indicators, the results were similar, especially when the questions and response options were equal. Surveys are useful for the monitoring of risk and protective factors of noncommunicable diseases and can support health promotion programs.

Systematic Review of Blood Biomarkers in Cystic Fibrosis Pulmonary Exacerbations

Dementia represents a growing public health burden with large social, racial, and ethnic disparities. The etiology of dementia is poorly understood, and the lack of robust biomarkers in diverse, population-representative samples is a barrier to moving dementia research forward. Existing biomarkers and other measures of pathology-derived from neuropathology, neuroimaging, and cerebrospinal fluid samples-are commonly collected from predominantly White and highly educated samples drawn from academic medical centers in urban settings. Blood-based biomarkers are noninvasive and less expensive, offering promise to expand our understanding of the pathophysiology of dementia, including in participants from historically excluded groups. Although largely not yet approved by the Food and Drug Administration or used in clinical settings, blood-based biomarkers are increasingly included in epidemiologic studies on dementia. Blood-based biomarkers in epidemiologic research may allow the field to more accurately understand the multifactorial etiology and sequence of events that characterize dementia-related pathophysiological changes. As blood-based dementia biomarkers continue to be developed and incorporated into research and practice, we outline considerations for using them in dementia epidemiology, and illustrate key concepts with Alzheimer's Disease Neuroimaging Initiative (2003-present) data. We focus on measurement, including both validity and reliability, and on the use of dementia blood-based biomarkers to promote equity in dementia research and cognitive aging. This article is part of a Special Collection on Mental Health.

Alzheimer's disease (AD) trial participants are often screened for eligibility by brain amyloid positron emission tomography/cerebrospinal fluid (PET/CSF), which is inefficient as many are not amyloid positive. Use of blood-based biomarkers may reduce screen failures. We recruited 755 non-Hispanic White, 115 Hispanic, 112 non-Hispanic Black, and 19 other minority participants across groups of cognitively normal (n=417), mild cognitive impairment (n=312), or mild AD (n=272) participants. Plasma amyloid beta (Aβ)40, Aβ42, Aβ42/Aβ40, total tau, phosphorylated tau (p-tau)181, and p-tau217 were measured; amyloid PET/CSF (n=956) determined amyloid positivity. Clinical, blood biomarker, and ethnicity/race differences associated with amyloid status were evaluated. Greater impairment, older age, and carrying an apolipoprotein E (apoE) ε4 allele were associated with greater amyloid burden. Areas under the receiver operating characteristic curve for amyloid status of plasma Aβ42/Aβ40, p-tau181, and p-tau217 with amyloid positivity were ≥ 0.7117 for all ethnoracial groups (p-tau217, ≥0.8128). Age and apoE ε4 adjustments and imputation of biomarker values outside limit of quantitation provided small improvement in predictive power. Blood-based biomarkers are highly associated with amyloid PET/CSF results in diverse populations enrolled at clinical trial sites. Amyloid beta (Aβ)42/Aβ40, phosphorylated tau (p-tau)181, and p-tau 217 blood-based biomarkers predicted brain amyloid positivity. P-tau 217 was the strongest predictor of brain amyloid positivity. Biomarkers from diverse ethnic, racial, and clinical cohorts predicted brain amyloid positivity. Community-based populations have similar Alzheimer's disease (AD) biomarker levels as other populations. A prescreen process with blood-based assays may reduce the number of AD trial screen failures.

Alzheimer’s disease (AD), the most common form of dementia, remains challenging to understand and treat despite decades of research and clinical investigation. This might be partly due to a lack of widely available and cost-effective modalities for diagnosis and prognosis. Recently, the blood-based AD biomarker field has seen significant progress driven by technological advances, mainly improved analytical sensitivity and precision of the assays and measurement platforms. Several blood-based biomarkers have shown high potential for accurately detecting AD pathophysiology. As a result, there has been considerable interest in applying these biomarkers for diagnosis and prognosis, as surrogate metrics to investigate the impact of various covariates on AD pathophysiology and to accelerate AD therapeutic trials and monitor treatment effects. However, the lack of standardization of how blood samples and collected, processed, stored analyzed and reported can affect the reproducibility of these biomarker measurements, potentially hindering progress toward their widespread use in clinical and research settings. To help address these issues, we provide fundamental guidelines developed according to recent research findings on the impact of sample handling on blood biomarker measurements. These guidelines cover important considerations including study design, blood collection, blood processing, biobanking, biomarker measurement, and result reporting. Furthermore, the proposed guidelines include best practices for appropriate blood handling procedures for genetic and ribonucleic acid analyses. While we focus on the key blood-based AD biomarkers for the AT(N) criteria (e.g., amyloid-beta [Aβ]40, Aβ42, Aβ42/40 ratio, total-tau, phosphorylated-tau, neurofilament light chain, brain-derived tau and glial fibrillary acidic protein), we anticipate that these guidelines will generally be applicable to other types of blood biomarkers. We also anticipate that these guidelines will assist investigators in planning and executing biomarker research, enabling harmonization of sample handling to improve comparability across studies.

Diet plays an important role in health and is a modifiable risk factor for chronic diseases. In men, sex steroid hormones influence, and are influenced by, a number of health states. Specific dietary patterns have been found to alter sex steroid hormone levels in observational and intervention studies. Thus, we hypothesized that dietary patterns captured by the Healthy Eating Index (HEI) are associated with serum concentrations of sex steroid hormones and sex hormone-binding globulin (SHBG). The objective is investigating the association between HEI and sex steroid hormones and SHBG in a general US population of men. We used data on serum sex steroid hormones and SHBG levels, HEI, and other variables collected in the National Health and Nutrition Examination Survey (NHANES), 1999-2002. A total of 550 men >20years old were included in the analysis. The cross-sectional associations between HEI (from 0 to 100 points, higher score equates to a healthier diet) with natural logarithm transformed concentrations of total and free testosterone, total and free estradiol, and SHBG were evaluated with multivariable linear regression models and adjusted for potential confounders. We also stratified by the body mass index (BMI) and race/ethnicity and tested for interactions. HEI showed a significant inverse association with free estradiol (p=0.03), but was not associated with total or free testosterone, total estradiol, or SHBG concentrations. Neither BMI nor race/ethnicity statistically significantly modified the association between HEI and sex steroid hormone levels. The present cross-sectional analysis in a representative sample of US men showed no consistent association between eating habits, sex steroid hormones, and SHBG. Longitudinal studies are needed to further investigate potential associations.

Early cancer-related death after resection of hepatocellular carcinoma

Tracking the potential involvement of metabolic disease in Alzheimer's disease—Biomarkers and beyond