Using network science tools to identify novel diet patterns in prodromal dementia
Authors: Sophie Lefèvre-Arbogast
Catherine Helmer
Abhijeet Rajendra Sonawane
Cécilia Samieri
Francine Grodstein
Kimberly Glass
Journal: Neurology (2020)
Discipline: NEUROSCIENCE & BEHAVIOR
Keywords: network science; complex diet relationships; dementia prevention; food networks; mutual information
Affiliations: From the University of Bordeaux (C.S., S.L.-A., C.H.), Inserm, Bordeaux Population Health Research Center, UMR 1219, France; Channing Division of Network Medicine (A.R.S., F.G., K.G.), Department of Medicine, Brigham and Women's Hospital, Harvard Medical School; and Department of Epidemiology (F.G.), Harvard T.H. Chan School of Public Health, Boston, MA. ., From the University of Bordeaux (C.S., S.L.-A., C.H.), Inserm, Bordeaux Population Health Research Center, UMR 1219, France; Channing Division of Network Medicine (A.R.S., F.G., K.G.), Department of Medicine, Brigham and Women's Hospital, Harvard Medical School; and Department of Epidemiology (F.G.), Harvard T.H. Chan School of Public Health, Boston, MA.

Abstract:  Objective: To use network science to model complex diet relationships a decade before onset of dementia in a large French cohort, the 3-City Bordeaux study. Methods: We identified cases of dementia incident to the baseline food frequency questionnaire over 12 years of follow-up. For each case, we randomly selected 2 controls among individuals at risk at the age at case diagnosis and matched for age at diet assessment, sex, education, and season of the survey. We inferred food networks in both cases and controls using mutual information, a measure to detect nonlinear associations, and compared food consumption patterns between groups. Results: In the nested case-control study, the mean (SD) duration of follow-up and number of visits were 5.0 (2.5) vs 4.9 (2.6) years and 4.1 (1.0) vs 4.4 (0.9) for cases (n = 209) vs controls (n = 418), respectively. While there were few differences in simple, average food intakes, food networks differed substantially between cases and controls. The network in cases was focused and characterized by charcuterie as the main hub, with connections to foods typical of French southwestern diet and snack foods. In contrast, the network of controls included several disconnected subnetworks reflecting diverse and healthier food choices. Conclusion: How foods are consumed (and not only the quantity consumed) may be important for dementia prevention. Differences in predementia diet networks, suggesting worse eating habits toward charcuterie and snacking, were evident years before diagnosis in this cohort. Network methods, which are designed to model complex systems, may advance our understanding of risk factors for dementia. 

Retracted: No

PubPeer comments:
 This study sets out to examine whether facemasks can be used to stop the spread of Covid-19. It includes a review of the strengths and weaknesses of different types if masks and develops a mathematical model of the spread of Coronavirus through the air. There are a few irregularities in this paper that could be addressed by the authors. First, the authors suggest that the inhalation of carbon dioxide can have negative consequences for human health and imply that masks can exacerbate these consequences: * Despite the necessity to ensure the adequate provision of facemasks to comply with WHO recommendations; the consequences of reduced intake of oxygen and forceful inhaling of exhaled carbon dioxide due to wearing facemask has not yet been considered. The COVID-19 infected people are already suffering from symptoms such as fatigue, headache or shortness of breath; how much more does the inhaling of CO2 can influence the health of the general public? * However, nowhere in the manuscript do the authors provide any evidence that masks are impermeable to carbon dioxide. In other words, exhaled carbon dioxide can simply pass through or around most of the masks, so the risk or re-inhalation is negligible (for example, surgeons wear masks for 5 hour surgeries without ill-effects). Similarly, the authors accept that inhalation is unencumbered by masks, but for some reason assume exhalation will be inhibited (if masks are truly impermeable, why don t wearers suffer from asphyxiation?). Second, the authors review a variety of masks in Table 2. Many of the masks being reviewed are not designed for medical use, so reviewing their merits for preventing the spread of Covid-19 has a comical effect. For example, the authors review a diving mask, which is also highlighted in the conclusion, and write: * Suitable facemasks that will help release into the atmospheric environment exhaled CO 2 should be recommended, for example the full mask respirator described in Table 2i could be suggested due to the following: No jaw fatigue, **no snorkel clearing**, less fogging due to better air circulation, suitable for colder climates, **no risk losing your regulator**, can be used when u [sic] have a beard, **optional under water communication** * They could just as well have listed its 100% effectiveness because Coronavirus cannot be transmitted underwater! Third, the authors present a complex mathematical model that is described over 24 equations and 20 figure panels (Figs 2 - 7). This model is, however, useless for addressing the title and objectives of this study ( *Facemasks simple but powerful weapons to protect against COVID-19 spread* ) because the model setup does not include the resistance of various masks or viral loads (modelling, I assume, respiratory particles through air). What is the point of the complex model if it doesn t actually include the single independent variable that is the centerpiece of this study: the mask? Fourth, based on the model, the authors conclude: * The following parameters were used to performed the simulation, we assumed that the **speed of wing**[sic] **is v = 100 km h**, the **initial concentration** released by an infected person is considered to be **100 mol litre**, we assume that the retardation factor could varies from 0 to 0.4. * This wind velocity is 10 on the [Beaufort scale] , which is associated with uprooted trees and infrastructure damage. It is very unrealistic that a Covid-positive person will be walking around in winds of this speed. Similarly, it is unclear what is meant by *initial concentration* here. Is it the viral load, the concentration of CO2, or the concentration of respiratory aerosols? Without defining this, the units of 100 mol litre are useless, because molar concentration adds no value if the reader does not know which molecules are being referred to (i.e. the molar weight is unknown). Fifth, in addition to these fundamental conceptual shortcomings, this manuscript is littered with spelling mistakes and poor grammar, which makes me wonder how thorough the peer review process was. In the conclusions alone, there are several mistakes (in bold): * * The use of beaches in coastal areas should be prohibited as the wave **spry** of the ocean could help spread droplets containing COVID-19 on beachgoers.* * * In all **winding** regions around the world the **port** of facemasks **outdoor** should be imposed even when not in public, as droplets containing COVID-19 could be transported by wind and make a **non-facemasks** wearer a victim *. (I assume that * port * is perhaps misused from the French for * wearing *? ) In the acknowledgement, the authors write: * The authors also express their sincere gratitude to the unknown referee for critically reviewing the manuscript and suggesting useful changes *. This suggest that this manuscript was only reviewed by a single peer-reviewer. My concerns are amplified by the short duration between submission and acceptance (20 days, including revision, from received on 28 August to accepted on 17 September, as reported on the PDF). The second author is on [the editorial board of the journal] , so I hope the Editors of *Results in Physics* can confirm the veracity of the independent peer-review process and how they handled the potential conflict of interest. Hopefully the editors can also explain why a physics journal is publishing papers on public health in the first place. , 
 There are at least two other serious weaknesses in the model presented in this paper. 1) There are no physical, or even mathematical, considerations supporting the use of the operators introduced in Eqs (9) and (11) for the derivative with respect to time. The authors vaguely refer to non-locality due to heterogeneity of the air, but this property would signify the use a nonlocal operator with respect to the spatial variable and not to the time variable as in this paper. They also refer to the resistance of the air which could be considered as a retardation factor, but this consideration should lead to use a differential equation with delay. In any case, the operators seem to be accidentally chosen without a physical discussion about their suitability for describing the phenomenon under investigation. 2) There is no validation of the model. Basically, the paper just proposes some equations with some operators and there are showed the numerical approximations of the solutions of these equations. Due to the absence of comparisons with observed data, it is not possible to establish if the results provided by this model are useful and or meaningful. One should blindly trust the goodness of the model which, however, is built in a very empirical way, without solid physical foundations. It is truly a pity that no reviewer pointed out these several weaknesses that deserved a detailed investigation. , 
 There are some serious mathematical fallacies in this paper. For instance, it is immediate to observe that the left-hand side of Equation (16) vanishes when evaluated at 0, i.e. $$ frac{AB(alpha)}{1-alpha} int_0^t frac{partial c(x,l)}{partial l} E_{alpha}Bigl[ frac{alpha}{1-alpha}(t-l)^{alpha} Bigr] dl Biggl|_{t=0} = 0 , $$ and therefore, also its right-hand side must be 0 when $t=0$, i.e. $$ v frac{partial c(x,0)}{partial{x}} = 0 . $$ As a consequence, the only admissible initial condition $c(x,0)=u_0(x)$ is a function $u_0(x)$ such that $u _0(x)=0$ (unless $v=0$ but it would lead to a meaningless equation). The same happens with Equation (17) and, similarly with others equations. Thus, Eq. (16) and Eq. (17) are allowed to admit just constant initial concentrations $u_0(x)=c_0$ and when the initial concentration is no longer constant the model does not make sense since the corresponding equation does not have solution. Models working under such very restrictive assumptions are unreliable and useless. The values of the initial data should come from observations of real data and must not be imposed by the operator selected for the model. We are in the presence of a paradox: it is the equation which establishes the initial data while instead it should be data to drive in the formulation of the equation and the model. Moreover, disregarding the mathematics of the operators not only leads to meaningless models but it can produce serious errors too. It is the case of the models described by Equations (9) and (11). Based on the previous reasoning, in these cases one must impose $$ v(0,t) frac{partial c(x,0)}{partial{x}} + R = 0 $$ In the several plots presented in the paper constant values are considered for $v$ and $c(x,0)$ but in Figures 5, 6, and 7 it is $R  e 0$ and therefore the above condition is not satisfied. The conclusion is that those presented in Figures 5, 6 and 7 cannot be the solutions of Equations (9) and (11) since these equations do not have solution! This is a further paradox of this paper: there are presented plots of solutions which do not exist! , 
 I think it should be not surprising that this paper has been published despite its serious weaknesses. Indeed, one of the authors is one of the main editors of the journal https: www.sciencedirect.com journal results-in-physics about editorial-board , 
 Agalinus Laxa writes  Similarly, it is unclear what is meant by initial concentration here. Is it the viral load, the concentration of CO2, or the concentration of respiratory aerosols? Without defining this, the units of 100 mol litre are useless, because molar concentration adds no value if the reader does not know which molecules are being referred to (i.e. the molar weight is unknown). It s worse than what Agalinus Laxa concludes, because a concentration of 100 moles Liter is orders of magnitude larger than that of pure air at 1 atm and 298 L (0.041 moles Liter). So the numbers used in the calculation make no sense. As a point of comparison, pure liquid water has a concentration of 55 moles Liter. , 
 The way by which the numerical approximations are obtained (see Eq. (21) and following) cannot be correct. The conditions for existence of solutions, pointed out by Candelariella Citrina in #3, are also necessary to allows the reformulation of the differential equations (19) and (20) in the integral equations (21) and (22). Without these conditions, which are not always satisfied in this paper as timely described in #3, differential and integral formulations are no longer equivalent. That explains the paradox pointed out in #3: it is true that there are evaluated solutions which do not exist, indeed there are instead evaluated solutions of different problems. Basically, each model is described by means of an equation, but it is provided the solution of a different problem. I think that this paper provides very misleading information and could generate lot of confusion. Readers should be warned. ,