The influence of six polymorphisms of uncoupling protein 3 (UCP3) gene and childhood obesity: a case-control study.

BACKGROUND: Obesity is defined as a multifactorial disease, marked by excessive accumulation of body fat, responsible for compromising the individual's health over the years. The energy balance is essential for the proper functioning of the body, as the individual needs to earn and spend energy in a compensatory way. Mitochondrial Uncoupling Proteins (UCP) help in energy expenditure through heat release and genetic polymorphisms could be responsible for reducing energy consumption to release heat and consequently generate an excessive accumulation of fat in the body. Thus, this study aimed to investigate the potential association between six UCP3 polymorphisms, that have not yet been represented in ClinVar®, and pediatric obesity susceptibility. METHODS: A case-control study was conducted with 225 children from Central Brazil. The groups were subdivided into obese (123) and eutrophic (102) individuals. The polymorphisms rs15763, rs1685354, rs1800849, rs11235972, rs647126, and rs3781907 were determined by real-time Polymerase Chain Reaction (qPCR). RESULTS: Biochemical and anthropometric evaluation of obese group showed higher levels of triglycerides, insulin resistance, and LDL-C and low level of HDL-C. Insulin resistance, age, sex, HDL-C, fasting glucose, triglyceride levels, and parents' BMI explained up to 50% of body mass deposition in the studied population. Additionally, obese mothers contribute 2 × more to the Z-BMI of their children than the fathers. The SNP rs647126 contributed to 20% to the risk of obesity in children and the SNP rs3781907 contribute to 10%. Mutant alleles of UCP3 increase the risk for triglycerides, total cholesterol, and HDL-C levels. The polymorphism rs3781907 is the only one that could not be a biomarker for obesity as the risk allele seem to be protective gains the increase in Z-BMI in our pediatric population. Haplotype analysis demonstrated two SNP blocks (rs15763, rs647126, and rs1685534) and (rs11235972 and rs1800849) that showed linkage disequilibrium, with LOD 76.3% and D' = 0.96 and LOD 57.4% and D' = 0.97, respectively. CONCLUSIONS: The causality between UCP3 polymorphism and obesity were not detected. On the other hand, the studied polymorphism contributes to Z-BMI, HOMA-IR, triglycerides, total cholesterol, and HDL-C levels. Haplotypes are concordant with the obese phenotype and contribute minimally to the risk of obesity.

Sandpile cascades on oscillator networks: The BTW model meets Kuramoto.

Cascading failures abound in complex systems and the Bak-Tang-Weisenfeld (BTW) sandpile model provides a theoretical underpinning for their analysis. Yet, it does not account for the possibility of nodes having oscillatory dynamics, such as in power grids and brain networks. Here, we consider a network of Kuramoto oscillators upon which the BTW model is unfolding, enabling us to study how the feedback between the oscillatory and cascading dynamics can lead to new emergent behaviors. We assume that the more out-of-sync a node is with its neighbors, the more vulnerable it is and lower its load-carrying capacity accordingly. Also, when a node topples and sheds load, its oscillatory phase is reset at random. This leads to novel cyclic behavior at an emergent, long timescale. The system spends the bulk of its time in a synchronized state where load builds up with minimal cascades. Yet, eventually, the system reaches a tipping point where a large cascade triggers a "cascade of larger cascades," which can be classified as a dragon king event. The system then undergoes a short transient back to the synchronous, buildup phase. The coupling between capacity and synchronization gives rise to endogenous cascade seeds in addition to the standard exogenous ones, and we show their respective roles. We establish the phenomena from numerical studies and develop the accompanying mean-field theory to locate the tipping point, calculate the load in the system, determine the frequency of the long-time oscillations, and find the distribution of cascade sizes during the buildup phase.

Unusual bilateral variation of the flexor digitorum accessorius longus muscle and its relation on tarsal tunnel syndrome.

PURPOSE: During standard anatomical dissection for a medical anatomy course, we encountered an unusual bilateral variant of a unipennate flexor digitorum accessorius longus (FDAL) muscle, a supernumery muscle of the deep posterior leg and medial ankle. METHODS: We documented the muscles course and measured the diameter and length of the FDAL muscle belly, as well as the full length of its tendinous attachments. RESULTS: On both right and left legs, the FDAL originated from the proximal posterior fibula and distal one-third of the flexor hallucis longus muscle. The tendon had a distal attachment on the flexor digitorum longus (FDL) tendon and traveled with the FDL tendon as it inserted on the third distal phalanx. The left FDAL full length was 42.54 cm; the length of the muscle belly was 16.26 cm; and the circumference of the muscle belly was 4.44 cm. The right FDAL full length was 44.20 cm; the length of muscle belly was 12.06; and the circumference (belly) was 4.44 cm. Surrounding musculature and neurovasculature follow standard anatomical courses. CONCLUSION: This anatomical documentation provides opportunities for clinicians to consider mechanical influences of the FDAL on plantar foot function and further consider the accessory ankle muscles that have the potential to cause compressive neuropathies such as tarsal tunnel syndrome.

Homophily Based on Few Attributes Can Impede Structural Balance.

Homophily between agents and structural balance in connected triads of agents are complementary mechanisms thought to shape social groups leading to, for instance, consensus or polarization. To capture both processes in a unified manner, we propose a model of pair and triadic interactions. We consider N fully connected agents, where each agent has G underlying attributes, and the similarity between agents in attribute space (i.e., homophily) is used to determine the link weight between them. For structural balance we use a triad-updating rule where only one attribute of one agent is changed intentionally in each update, but this also leads to accidental changes in link weights and even link polarities. The link weight dynamics in the limit of large G is described by a Fokker-Planck equation from which the conditions for a phase transition to a fully balanced state with all links positive can be obtained. This "paradise state" of global cooperation is, however, difficult to achieve requiring G>O(N^{2}) and p>0.5, where the parameter p captures a willingness for consensus. Allowing edge weights to be a consequence of attributes naturally captures homophily and reveals that many real-world social systems would have a subcritical number of attributes necessary to achieve structural balance.

Why understanding multiplex social network structuring processes will help us better understand the evolution of human behavior.

Social scientists have long appreciated that relationships between individuals cannot be described from observing a single domain, and that the structure across domains of interaction can have important effects on outcomes of interest (e.g., cooperation; Durkheim, 1893). One debate explicitly about this surrounds food sharing. Some argue that failing to find reciprocal food sharing means that some process other than reciprocity must be occurring, whereas others argue for models that allow reciprocity to span domains in the form of trade (Kaplan and Hill, 1985.). Multilayer networks, high-dimensional networks that allow us to consider multiple sets of relationships at the same time, are ubiquitous and have consequences, so processes giving rise to them are important social phenomena. The analysis of multi-dimensional social networks has recently garnered the attention of the network science community (Kivelä et al., 2014). Recent models of these processes show how ignoring layer interdependencies can lead one to miss why a layer formed the way it did, and/or draw erroneous conclusions (Górski et al., 2018). Understanding the structuring processes that underlie multiplex networks will help understand increasingly rich data sets, giving more accurate and complete pictures of social interactions.

Interdependent Network Recovery Games.

Recovery of interdependent infrastructure networks in the presence of catastrophic failure is crucial to the economy and welfare of society. Recently, centralized methods have been developed to address optimal resource allocation in postdisaster recovery scenarios of interdependent infrastructure systems that minimize total cost. In real-world systems, however, multiple independent, possibly noncooperative, utility network controllers are responsible for making recovery decisions, resulting in suboptimal decentralized processes. With the goal of minimizing recovery cost, a best-case decentralized model allows controllers to develop a full recovery plan and negotiate until all parties are satisfied (an equilibrium is reached). Such a model is computationally intensive for planning and negotiating, and time is a crucial resource in postdisaster recovery scenarios. Furthermore, in this work, we prove this best-case decentralized negotiation process could continue indefinitely under certain conditions. Accounting for network controllers' urgency in repairing their system, we propose an ad hoc sequential game-theoretic model of interdependent infrastructure network recovery represented as a discrete time noncooperative game between network controllers that is guaranteed to converge to an equilibrium. We further reduce the computation time needed to find a solution by applying a best-response heuristic and prove bounds on ε-Nash equilibrium, where ε depends on problem inputs. We compare best-case and ad hoc models on an empirical interdependent infrastructure network in the presence of simulated earthquakes to demonstrate the extent of the tradeoff between optimality and computational efficiency. Our method provides a foundation for modeling sociotechnical systems in a way that mirrors restoration processes in practice.

Koopman operator and its approximations for systems with symmetries.

Nonlinear dynamical systems with symmetries exhibit a rich variety of behaviors, often described by complex attractor-basin portraits and enhanced and suppressed bifurcations. Symmetry arguments provide a way to study these collective behaviors and to simplify their analysis. The Koopman operator is an infinite dimensional linear operator that fully captures a system's nonlinear dynamics through the linear evolution of functions of the state space. Importantly, in contrast with local linearization, it preserves a system's global nonlinear features. We demonstrate how the presence of symmetries affects the Koopman operator structure and its spectral properties. In fact, we show that symmetry considerations can also simplify finding the Koopman operator approximations using the extended and kernel dynamic mode decomposition methods (EDMD and kernel DMD). Specifically, representation theory allows us to demonstrate that an isotypic component basis induces a block diagonal structure in operator approximations, revealing hidden organization. Practically, if the symmetries are known, the EDMD and kernel DMD methods can be modified to give more efficient computation of the Koopman operator approximation and its eigenvalues, eigenfunctions, and eigenmodes. Rounding out the development, we discuss the effect of measurement noise.

Maximizing synchronizability of duplex networks.

We study the synchronizability of duplex networks formed by two randomly generated network layers with different patterns of interlayer node connections. According to the master stability function, we use the smallest nonzero eigenvalue and the eigenratio between the largest and the second smallest eigenvalues of supra-Laplacian matrices to characterize synchronizability on various duplexes. We find that the interlayer linking weight and linking fraction have a profound impact on synchronizability of duplex networks. The increasingly large inter-layer coupling weight is found to cause either decreasing or constant synchronizability for different classes of network dynamics. In addition, negative node degree correlation across interlayer links outperforms positive degree correlation when most interlayer links are present. The reverse is true when a few interlayer links are present. The numerical results and understanding based on these representative duplex networks are illustrative and instructive for building insights into maximizing synchronizability of more realistic multiplex networks.

Complex Dynamical Networks Constructed with Fully Controllable Nonlinear Nanomechanical Oscillators.

Control of the global parameters of complex networks has been explored experimentally in a variety of contexts. Yet, the more difficult prospect of realizing arbitrary network architectures, especially analog physical networks that provide dynamical control of individual nodes and edges, has remained elusive. Given the vast hierarchy of time scales involved, it also proves challenging to measure a complex network's full internal dynamics. These span from the fastest nodal dynamics to very slow epochs over which emergent global phenomena, including network synchronization and the manifestation of exotic steady states, eventually emerge. Here, we demonstrate an experimental system that satisfies these requirements. It is based upon modular, fully controllable, nonlinear radio frequency nanomechanical oscillators, designed to form the nodes of complex dynamical networks with edges of arbitrary topology. The dynamics of these oscillators and their surrounding network are analog and continuous-valued and can be fully interrogated in real time. They comprise a piezoelectric nanomechanical membrane resonator, which serves as the frequency-determining element within an electrical feedback circuit. This embodiment permits network interconnections entirely within the electrical domain and provides unprecedented node and edge control over a vast region of parameter space. Continuous measurement of the instantaneous amplitudes and phases of every constituent oscillator node are enabled, yielding full and detailed network data without reliance upon statistical quantities. We demonstrate the operation of this platform through the real-time capture of the dynamics of a three-node ring network as it evolves from the uncoupled state to full synchronization.

Patterns of patterns of synchronization: Noise induced attractor switching in rings of coupled nonlinear oscillators.

Following the long-lived qualitative-dynamics tradition of explaining behavior in complex systems via the architecture of their attractors and basins, we investigate the patterns of switching between distinct trajectories in a network of synchronized oscillators. Our system, consisting of nonlinear amplitude-phase oscillators arranged in a ring topology with reactive nearest-neighbor coupling, is simple and connects directly to experimental realizations. We seek to understand how the multiple stable synchronized states connect to each other in state space by applying Gaussian white noise to each of the oscillators' phases. To do this, we first analytically identify a set of locally stable limit cycles at any given coupling strength. For each of these attracting states, we analyze the effect of weak noise via the covariance matrix of deviations around those attractors. We then explore the noise-induced attractor switching behavior via numerical investigations. For a ring of three oscillators, we find that an attractor-switching event is always accompanied by the crossing of two adjacent oscillators' phases. For larger numbers of oscillators, we find that the distribution of times required to stochastically leave a given state falls off exponentially, and we build an attractor switching network out of the destination states as a coarse-grained description of the high-dimensional attractor-basin architecture.

Suppressing cascades of load in interdependent networks.

Understanding how interdependence among systems affects cascading behaviors is increasingly important across many fields of science and engineering. Inspired by cascades of load shedding in coupled electric grids and other infrastructure, we study the Bak-Tang-Wiesenfeld sandpile model on modular random graphs and on graphs based on actual, interdependent power grids. Starting from two isolated networks, adding some connectivity between them is beneficial, for it suppresses the largest cascades in each system. Too much interconnectivity, however, becomes detrimental for two reasons. First, interconnections open pathways for neighboring networks to inflict large cascades. Second, as in real infrastructure, new interconnections increase capacity and total possible load, which fuels even larger cascades. Using a multitype branching process and simulations we show these effects and estimate the optimal level of interconnectivity that balances their trade-offs. Such equilibria could allow, for example, power grid owners to minimize the largest cascades in their grid. We also show that asymmetric capacity among interdependent networks affects the optimal connectivity that each prefers and may lead to an arms race for greater capacity. Our multitype branching process framework provides building blocks for better prediction of cascading processes on modular random graphs and on multitype networks in general.

Microtransition cascades to percolation.

We report the discovery of a discrete hierarchy of microtransitions occurring in models of continuous and discontinuous percolation. The precursory microtransitions allow us to target almost deterministically the location of the transition point to global connectivity. This extends to the class of intrinsically stochastic processes the possibility to use warning signals anticipating phase transitions in complex systems.

Evaluation of the Semmes-Weinstein filaments and a questionnaire to assess chemotherapy-induced peripheral neuropathy.

OBJECTIVE: This study aims to assess the use of Semmes-Weinstein monofilaments (SWMs) and of the Chemotherapy-Induced Neurotoxicity Questionnaire (CINQ) in the detection of chemotherapy-induced peripheral neuropathy (CIPN). METHOD: It is a comparative and cross-sectional study performed in a philanthropic general hospital, located in the state of Minas Gerais, Brazil. One hundred seventeen individuals have participated in this study; they were divided into two groups: patients (n = 87) treated with oxaliplatin, paclitaxel, or docetaxel and controls (n = 30) without malignant disease. RESULTS: There were statistically significant differences between groups for all symptoms assessed by means of the CINQ. Lower limbs were more severely affected. Patients had increased frequency and severity of changes in all points assessed with SWM compared with controls. In the analyses of concordance between CINQ and SWM, kappa = 0.320 (p < 0.001) was obtained, and there was a moderate and positive correlation (ρ = 0.357; p < 0.001). CONCLUSION: CINQ and SWM may be valid tools for diagnosing CIPN in oncology practice. SWM may identify subclinical CIPN.

Foundation model-driven distributed learning for enhanced retinal age prediction.

OBJECTIVES: The retinal age gap (RAG) is emerging as a potential biomarker for various diseases of the human body, yet its utility depends on machine learning models capable of accurately predicting biological retinal age from fundus images. However, training generalizable models is hindered by potential shortages of diverse training data. To overcome these obstacles, this work develops a novel and computationally efficient distributed learning framework for retinal age prediction. MATERIALS AND METHODS: The proposed framework employs a memory-efficient 8-bit quantized version of RETFound, a cutting-edge foundation model for retinal image analysis, to extract features from fundus images. These features are then used to train an efficient linear regression head model for predicting retinal age. The framework explores federated learning (FL) as well as traveling model (TM) approaches for distributed training of the linear regression head. To evaluate this framework, we simulate a client network using fundus image data from the UK Biobank. Additionally, data from patients with type 1 diabetes from the UK Biobank and the Brazilian Multilabel Ophthalmological Dataset (BRSET) were utilized to explore the clinical utility of the developed methods. RESULTS: Our findings reveal that the developed distributed learning framework achieves retinal age prediction performance on par with centralized methods, with FL and TM providing similar performance (mean absolute error of 3.57 ± 0.18 years for centralized learning, 3.60 ± 0.16 years for TM, and 3.63 ± 0.19 years for FL). Notably, the TM was found to converge with fewer local updates than FL. Moreover, patients with type 1 diabetes exhibited significantly higher RAG values than healthy controls in all models, for both the UK Biobank and BRSET datasets (P < .001). DISCUSSION: The high computational and memory efficiency of the developed distributed learning framework makes it well suited for resource-constrained environments. CONCLUSION: The capacity of this framework to integrate data from underrepresented populations for training of retinal age prediction models could significantly enhance the accessibility of the RAG as an important disease biomarker.

Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging.

OBJECTIVE: Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of subgroup performance disparities. However, since not all sources of bias in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess their impacts. In this article, we introduce an analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models. MATERIALS AND METHODS: Our framework utilizes synthetic neuroimages with known disease effects and sources of bias. We evaluated the impact of bias effects and the efficacy of 3 bias mitigation strategies in counterfactual data scenarios on a convolutional neural network (CNN) classifier. RESULTS: The analysis revealed that training a CNN model on the datasets containing bias effects resulted in expected subgroup performance disparities. Moreover, reweighing was the most successful bias mitigation strategy for this setup. Finally, we demonstrated that explainable AI methods can aid in investigating the manifestation of bias in the model using this framework. DISCUSSION: The value of this framework is showcased in our findings on the impact of bias scenarios and efficacy of bias mitigation in a deep learning model pipeline. This systematic analysis can be easily expanded to conduct further controlled in silico trials in other investigations of bias in medical imaging AI. CONCLUSION: Our novel methodology for objectively studying bias in medical imaging AI can help support the development of clinical decision-support tools that are robust and responsible.

A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm.

Distributed learning is a promising alternative to central learning for machine learning (ML) model training, overcoming data-sharing problems in healthcare. Previous studies exploring federated learning (FL) or the traveling model (TM) setup for medical image-based disease classification often relied on large databases with a limited number of centers or simulated artificial centers, raising doubts about real-world applicability. This study develops and evaluates a convolution neural network (CNN) for Parkinson's disease classification using data acquired by 83 diverse real centers around the world, mostly contributing small training samples. Our approach specifically makes use of the TM setup, which has proven effective in scenarios with limited data availability but has never been used for image-based disease classification. Our findings reveal that TM is effective for training CNN models, even in complex real-world scenarios with variable data distributions. After sufficient training cycles, the TM-trained CNN matches or slightly surpasses the performance of the centrally trained counterpart (AUROC of 83% vs. 80%). Our study highlights, for the first time, the effectiveness of TM in 3D medical image classification, especially in scenarios with limited training samples and heterogeneous distributed data. These insights are relevant for situations where ML models are supposed to be trained using data from small or remote medical centers, and rare diseases with sparse cases. The simplicity of this approach enables a broad application to many deep learning tasks, enhancing its clinical utility across various contexts and medical facilities.

The context of COVID-19 affected the long-term sleep quality of older adults more than SARS-CoV-2 infection.

Introduction: Sleep problems are one of the most persistent symptoms of post-COVID syndrome in adults. However, most recent research on sleep quality has relied on the impact of the pandemic, with scarcely any data for older adults on the long-term consequences of COVID infection. This study aims to understand whether older individuals present persistently impaired sleep quality after COVID-19 infection and possible moderators for this outcome. Methods: This is a cross-sectional analysis of a longitudinal cohort study with 70 elders with 6-month-previous SARS-CoV-2 infection and 153 controls. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality; Geriatric Depression Scale and Geriatric Anxiety Inventory for screening depression and anxiety. Demographics and comorbid conditions were collected. Results: The mean age of participants was 66,97 ± 4,64 years. There were no statistical differences in depression and anxiety between groups. Poor sleep quality was found in 52,9% and 43,8% of the COVID and control groups (p=.208). After controlling for multiple variables, all the following factors resulted in greater chances of poor sleep quality: female gender (OR, 2.12; p=.027), memory complaints (OR, 2.49; p=.074), insomnia (OR, 3.66; p=.032), anxiety (OR, 5.46; p<.001), depression (OR, 7.26; p=.001), joint disease (OR, 1.80; p=.050), glucose intolerance (OR, 2.20; p=.045), psychoactive drugs (OR, 8.36; p<.001), diuretics (OR, 2.46; p=.034), and polypharmacy (OR, 2.84; p=.016). Conclusion: Psychosocial burden in the context of the COVID-19 pandemic and pre-existing conditions seems to influence the sleep quality of older adults more than SARS-CoV-2 infection.

Identifying Biases in a Multicenter MRI Database for Parkinson's Disease Classification: Is the Disease Classifier a Secret Site Classifier?

Sharing multicenter imaging datasets can be advantageous to increase data diversity and size but may lead to spurious correlations between site-related biological and non-biological image features and target labels, which machine learning (ML) models may exploit as shortcuts. To date, studies analyzing how and if deep learning models may use such effects as a shortcut are scarce. Thus, the aim of this work was to investigate if site-related effects are encoded in the feature space of an established deep learning model designed for Parkinson's disease (PD) classification based on T1-weighted MRI datasets. Therefore, all layers of the PD classifier were frozen, except for the last layer of the network, which was replaced by a linear layer that was exclusively re-trained to predict three potential bias types (biological sex, scanner type, and originating site). Our findings based on a large database consisting of 1880 MRI scans collected across 41 centers show that the feature space of the established PD model (74% accuracy) can be used to classify sex (75% accuracy), scanner type (79% accuracy), and site location (71% accuracy) with high accuracies despite this information never being explicitly provided to the PD model during original training. Overall, the results of this study suggest that trained image-based classifiers may use unwanted shortcuts that are not meaningful for the actual clinical task at hand. This finding may explain why many image-based deep learning models do not perform well when applied to data from centers not contributing to the training set.

HarmonyTM: multi-center data harmonization applied to distributed learning for Parkinson's disease classification.

Purpose: Distributed learning is widely used to comply with data-sharing regulations and access diverse datasets for training machine learning (ML) models. The traveling model (TM) is a distributed learning approach that sequentially trains with data from one center at a time, which is especially advantageous when dealing with limited local datasets. However, a critical concern emerges when centers utilize different scanners for data acquisition, which could potentially lead models to exploit these differences as shortcuts. Although data harmonization can mitigate this issue, current methods typically rely on large or paired datasets, which can be impractical to obtain in distributed setups. Approach: We introduced HarmonyTM, a data harmonization method tailored for the TM. HarmonyTM effectively mitigates bias in the model's feature representation while retaining crucial disease-related information, all without requiring extensive datasets. Specifically, we employed adversarial training to "unlearn" bias from the features used in the model for classifying Parkinson's disease (PD). We evaluated HarmonyTM using multi-center three-dimensional (3D) neuroimaging datasets from 83 centers using 23 different scanners. Results: Our results show that HarmonyTM improved PD classification accuracy from 72% to 76% and reduced (unwanted) scanner classification accuracy from 53% to 30% in the TM setup. Conclusion: HarmonyTM is a method tailored for harmonizing 3D neuroimaging data within the TM approach, aiming to minimize shortcut learning in distributed setups. This prevents the disease classifier from leveraging scanner-specific details to classify patients with or without PD-a key aspect for deploying ML models for clinical applications.

Factors associated with older adults' cognitive decline 6 months after gamma-variant SARS-CoV-2 infection.

Background: Cognitive deficits are commonly reported after COVID-19 recovery, but little is known in the older population. This study aims to investigate possible cognitive damage in older adults 6 months after contracting COVID-19, as well as individual risk factors. Methods: This cross-sectional study involved 70 participants aged 60-78 with COVID-19 6 months prior and 153 healthy controls. Montreal Cognitive Assessment-Basic (MoCA-B) screened for cognitive impairment; Geriatric Depression Scale and Geriatric Anxiety Inventory screened for depression and anxiety. Data were collected on demographics and self-reports of comorbid conditions. Results: The mean age of participants was 66.97 ± 4.64 years. A higher proportion of individuals in the COVID group complained about cognitive deficits (χ2 = 3.574; p = 0.029) and presented with deficient MoCA-B scores (χ2 = 6.098, p = 0.014) compared to controls. After controlling for multiple variables, all the following factors resulted in greater odds of a deficient MoCA-B: COVID-19 6-months prior (OR, 2.44; p = 0.018), age (OR, 1.15; p < 0.001), lower income (OR, 0.36; p = 0.070), and overweight (OR, 2.83; p = 0.013). Further analysis pointed to individual characteristics in COVID-19-affected patients that could explain the severity of the cognitive decline: age (p = 0.015), lower income (p < 0.001), anxiety (p = 0.049), ageusia (p = 0.054), overweight (p < 0.001), and absence of cognitively stimulating activities (p = 0.062). Conclusion: Our study highlights a profile of cognitive risk aggravation over aging after COVID-19 infection, which is likely mitigated by wealth but worsened in the presence of overweight. Ageusia at the time of acute COVID-19, anxiety, being overweight, and absence of routine intellectual activities are risk factors for more prominent cognitive decline among those infected by COVID-19.