Evidence for protein leverage on total energy intake, but not body mass index, in a large cohort of older adults.

BACKGROUND: Protein leverage (PL) is the phenomenon of consuming food until absolute intake of protein approaches a 'target value', such that total energy intake (TEI) varies passively with the ratio of protein: non-protein energy (fat + carbohydrate) in the diet. The PL hypothesis (PLH) suggests that the dilution of protein in energy-dense foods, particularly those rich in carbohydrates and fats, combines with protein leverage to contribute to the global obesity epidemic. Evidence for PL has been reported in younger adults, children and adolescents. This study aimed to test for PL and the protein leverage hypothesis (PLH) in a cohort of older adults. METHODS: We conducted a retrospective analysis of dietary intake in a cohort of 1699 community-dwelling older adults aged 67-84 years from the NuAge cohort. We computed TEI and the energy contribution (EC) from each macronutrient. The strength of leverage of macronutrients was assessed through power functions ( TEI = µ * EC L ). Body mass index (BMI) was calculated, and mixture models were fitted to predict TEI and BMI from macronutrients' ECs. RESULTS: In this cohort of older adults, 53% of individuals had obesity and 1.5% had severe cases. The mean TEI was 7673 kJ and macronutrients' ECs were 50.4%, 33.2% and 16.4%, respectively for carbohydrates, fat, and protein. There was a strong negative association (L = -0.37; p < 0.001) between the protein EC and TEI. Each percent of energy intake from protein reduced TEI by 77 kJ on average, ceteris paribus. However, BMI was unassociated with TEI in this cohort. CONCLUSIONS: Findings indicate clear evidence for PL on TEI, but not on BMI, likely because of aging, body composition, sarcopenia, or protein wasting.

Application of early warning signs to physiological contexts: a comparison of multivariate indices in patients on long-term hemodialysis.

Early warnings signs (EWSs) can anticipate abrupt changes in system state, known as "critical transitions," by detecting dynamic variations, including increases in variance, autocorrelation (AC), and cross-correlation. Numerous EWSs have been proposed; yet no consensus on which perform best exists. Here, we compared 15 multivariate EWSs in time series of 763 hemodialyzed patients, previously shown to present relevant critical transition dynamics. We calculated five EWSs based on AC, six on variance, one on cross-correlation, and three on AC and variance. We assessed their pairwise correlations, trends before death, and mortality predictive power, alone and in combination. Variance-based EWSs showed stronger correlations (r = 0.663 ± 0.222 vs. 0.170 ± 0.205 for AC-based indices) and a steeper increase before death. Two variance-based EWSs yielded HR95 > 9 (HR95 standing for a scale-invariant metric of hazard ratio), but combining them did not improve the area under the receiver-operating curve (AUC) much compared to using them alone (AUC = 0.798 vs. 0.796 and 0.791). Nevertheless, the AUC reached 0.825 when combining 13 indices. While some indicators did not perform overly well alone, their addition to the best performing EWSs increased the predictive power, suggesting that indices combination captures a broader range of dynamic changes occurring within the system. It is unclear whether this added benefit reflects measurement error of a unified phenomenon or heterogeneity in the nature of signals preceding critical transitions. Finally, the modest predictive performance and weak correlations among some indices call into question their validity, at least in this context.

Rethinking the pros and cons of randomized controlled trials and observational studies in the era of big data and advanced methods: a panel discussion.

Randomized controlled trials (RCTs) have traditionally been considered the gold standard for medical evidence. However, in light of emerging methodologies in data science, many experts question the role of RCTs. Within this context, experts in the USA and Canada came together to debate whether the primacy of RCTs as the gold standard for medical evidence, still holds in light of recent methodological advances in data science and in the era of big data. The purpose of this manuscript, aims to raise awareness of the pros and cons of RCTs and observational studies in order to help guide clinicians, researchers, students, and decision-makers in making informed decisions on the quality of medical evidence to support their work. In particular, new and underappreciated advantages and disadvantages of both designs are contrasted. Innovations taking place in both of these research methodologies, which can blur the lines between the two, are also discussed. Finally, practical guidance for clinicians and future directions in assessing the quality of evidence is offered.

Validation of biomarkers of aging.

The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials.