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Biomarkers for Aging Identified in Cross-sectional Studies Tend to Be Non-causative.

ABSTRACT: Biomarkers are important tools for diagnosis, prognosis, and identification of the causal factors of physiological conditions. Biomarkers are typically identified by correlating biological measurements with the status of a condition in a sample of subjects. Cross-sectional studies sample subjects at a single timepoint, whereas longitudinal studies follow a cohort through time. Identifying biomarkers of aging is subject to unique challenges. Individuals who age faster have intrinsically higher mortality rates and so are preferentially lost over time, in a phenomenon known as cohort selection. In this article, we use simulations to show that cohort selection biases cross-sectional analysis away from identifying causal loci of aging, to the point where cross-sectional studies are less likely to identify loci that cause aging than if loci had been chosen at random. We go on to show this bias can be corrected by incorporating correlates of mortality identified from longitudinal studies, allowing cross-sectional studies to effectively identify the causal factors of aging.

SUBMITTER: Nelson PG

PROVIDER: S-EPMC7457180 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Biomarkers for Aging Identified in Cross-sectional Studies Tend to Be Non-causative.

Nelson Paul G PG Promislow Daniel E L DEL Masel Joanna J

The journals of gerontology. Series A, Biological sciences and medical sciences 20200201 3

Biomarkers are important tools for diagnosis, prognosis, and identification of the causal factors of physiological conditions. Biomarkers are typically identified by correlating biological measurements with the status of a condition in a sample of subjects. Cross-sectional studies sample subjects at a single timepoint, whereas longitudinal studies follow a cohort through time. Identifying biomarkers of aging is subject to unique challenges. Individuals who age faster have intrinsically higher mo ...[more]

PMID: 31353411

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Project description:BackgroundIndividual differences in the rate of aging and susceptibility to disease are not accounted for by chronological age alone. These individual differences are better explained by biological age, which may be estimated by biomarker prediction models. In the light of the aging demographics of the global population and the increase in lifestyle-related morbidities, it is interesting to invent a new biological age model to be used for health promotion.ObjectiveThis study aims to develop a model that estimates biological age based on physiological biomarkers of healthy aging.MethodsCarefully selected physiological variables from a healthy study population of 100 women and men were used as biomarkers to establish an estimate of biological age. Principal component analysis was applied to the biomarkers and the first principal component was used to define the algorithm estimating biological age.ResultsThe first principal component accounted for 31% in women and 25% in men of the total variance in the biological age model combining mean arterial pressure, glycated hemoglobin, waist circumference, forced expiratory volume in 1 second, maximal oxygen consumption, adiponectin, high-density lipoprotein, total cholesterol, and soluble urokinase-type plasminogen activator receptor. The correlation between the corrected biological age and chronological age was r=0.86 (P<.001) and r=0.81 (P<.001) for women and men, respectively, and the agreement was high and unbiased. No difference was found between mean chronological age and mean biological age, and the slope of the regression line was near 1 for both sexes.ConclusionsEstimating biological age from these 9 biomarkers of aging can be used to assess general health compared with the healthy aging trajectory. This may be useful to evaluate health interventions and as an aid to enhance awareness of individual health risks and behavior when deviating from this trajectory.Trial registrationClinicalTrials.gov NCT03680768; https://clinicaltrials.gov/ct2/show/NCT03680768.International registered report identifier (irrid)RR2-10.2196/19209.

Project description:OBJECTIVE:Non-alcoholic fatty liver disease (NAFLD) is a major public health burden in China, and its prevalence is increasing. This study aimed to determine the risk factors and biomarkers of NAFLD. DESIGN:An observational cross-sectional primary survey. SETTING:Central China. PARTICIPANTS:The study included 1479 participants aged over 18 and below 80 years, not currently being treated for cancer or infectious disease or no surgery in the previous year, and no history of cancer or an infectious disease. Participants underwent clinical examination, metabolomic assay and anthropometric assessment. Univariate and logistic regression analyses were used to evaluate associations between covariates and NAFLD. MAIN OUTCOME MEASURES:Risk factors and metabolic biomarkers including sex, body mass index, hypertension, body fat ratio, blood triglycerides, blood fasting glucose, liver enzyme elevation, uric acid and oleic acid-hydroxy oleic acid (OAHOA). RESULTS:Data from the 447 participants (mean age 44.3±11.9 years) were analysed, and the prevalence of NAFLD was 24.7%. Male sex (OR 3.484, 95%?CI 2.028 to 5.988), body mass index ?24?kg/m2 (OR 8.494, 95%?CI 5.581 to 12.928), body fat ratio (?25 for women, ?20 for men) (OR 1.833, 95%?CI 1.286 to 2.756), triglycerides ?1.7?mmol/L (OR 1.340, 95%?CI 1.006 to 1.785), fasting glucose ?6.1?mmol/L (OR 3.324, 95%?CI 1.888 to 5.850), blood pressure ?140/90?mm Hg or antihypertensive drug treatment (OR 1.451, 95%?CI 1.069 to 1.970), uric acid (?357 ?mol/L for women, ?416 ?mol/L for men) (OR 2.755, 95%?CI 2.009 to 3.778) and OAHOA (<5?nmol/L) (OR 1.340, 95%?CI 1.006 to 1.785) were independent predictors of NAFLD (all P<0.05). These results were verified by all 1479 participants. CONCLUSIONS:NAFLD was common among the study participants. In particular, NAFLD was correlated with uric acid. We identified OAHOA as a novel marker of NAFLD prevalence. It provides a reference on the prevention of NAFLD and related metabolic diseases with the rapid urbanisation, technological advancement and population ageing in China over the recent decades.

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Biomarkers for Aging Identified in Cross-sectional Studies Tend to Be Non-causative.

Publications

Biomarkers for Aging Identified in Cross-sectional Studies Tend to Be Non-causative.

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