Correlation of Alzheimer’s Disease Death Rates with Historical Per Capita Personal Income in the USA

Stȩpkowski, Dariusz; Wozniak, Grazyna; Studnicki, Marcin

doi:10.1371/journal.pone.0126139

Cited by 12 publications

(20 citation statements)

References 45 publications

(61 reference statements)

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“…However, the oscillations of correlation coefficients R observed earlier [9] are statistically valid and therefore, we proceeded with our analysis described in this paper assuming that, despite all the confounders, we previously observed real changes in the relationship between AADR and income over time so these changes can be a basis for the present analysis. Detailed discussion of the problem of confounding factors is contained in our previous paper and we refer interested readers to the appropriate fragments in Stępkowski et al 2015 [9]. …”

Section: Discussionmentioning

confidence: 97%

“…In this paper we try to explain the reason behind the oscillations of correlation strength (measured by R original ) between PCPI and AD AADR in 2005 that we have observed previously [9]. Since changes in the macronutrient availability n the period 1929–2005 resemble changes of R original in time [9] but usually with time shifts (precedence of peaks) (see Fig 1A, 1B, 1C and 1D), we have attempted to check whether they can explain the variability of R original that we have previously observed.…”

Section: Methodsmentioning

confidence: 96%

“…Here we propose a different strategy—we are looking for the relation between the proportions of macronutrients consumed by the studied population of the U.S.A. in the past and the future susceptibility to Alzheimer’s disease, measured as age-adjusted death rates (AADR). Since we have previously observed that the raw parameter of a lifestyle healthiness (per capita personal income, PCPI) correlates with AADR for AD [9] it seems that relating population statistical data describing social and lifestyle habits to the susceptibility to disease might be a promising strategy. In this paper we try to explain the reason behind the oscillations of correlation strength (measured by R original ) between PCPI and AD AADR in 2005 that we have observed previously [9].…”

Section: Methodsmentioning

confidence: 99%

“…Since we have previously observed that the raw parameter of a lifestyle healthiness (per capita personal income, PCPI) correlates with AADR for AD [9] it seems that relating population statistical data describing social and lifestyle habits to the susceptibility to disease might be a promising strategy. In this paper we try to explain the reason behind the oscillations of correlation strength (measured by R original ) between PCPI and AD AADR in 2005 that we have observed previously [9]. Since changes in the macronutrient availability n the period 1929–2005 resemble changes of R original in time [9] but usually with time shifts (precedence of peaks) (see Fig 1A, 1B, 1C and 1D), we have attempted to check whether they can explain the variability of R original that we have previously observed.…”

Section: Methodsmentioning

confidence: 99%

“…The flow chart describing the rationale for this study is presented in Fig 2. The correlation coefficient R original of correlations between PCPI (1925–2005) and AADR 2005 was taken from the previous paper[9]. R original was correlated by a multiple regression procedure implemented in the Statistica package (Statsoft) with macronutrient (carbohydrates, protein, fat total) per capita availability [data from USDA ERS service http://www.ers.usda.gov/data-products/food-availability-%28per-capita%29-data-system/.aspx#26715] and alcohol per capita consumption [10].…”

Section: Methodsmentioning

confidence: 99%

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The Calculator of Anti-Alzheimer’s Diet. Macronutrients

2016

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The opinions about optimal proportions of macronutrients in a healthy diet have changed significantly over the last century. At the same time nutritional sciences failed to provide strong evidence backing up any of the variety of views on macronutrient proportions. Herein we present an idea how these proportions can be calculated to find an optimal balance of macronutrients with respect to prevention of Alzheimer’s Disease (AD) and dementia. These calculations are based on our published observation that per capita personal income (PCPI) in the USA correlates with age-adjusted death rates for AD (AADR). We have previously reported that PCPI through the period 1925–2005 correlated with AADR in 2005 in a remarkable, statistically significant oscillatory manner, as shown by changes in the correlation coefficient R (Roriginal). A question thus arises what caused the oscillatory behavior of Roriginal? What historical events in the life of 2005 AD victims had shaped their future with AD? Looking for the answers we found that, considering changes in the per capita availability of macronutrients in the USA in the period 1929–2005, we can mathematically explain the variability of Roriginal for each quarter of a human life. On the basis of multiple regression of Roriginal with regard to the availability of three macronutrients: carbohydrates, total fat, and protein, with or without alcohol, we propose seven equations (referred to as “the calculator” throughout the text) which allow calculating optimal changes in the proportions of macronutrients to reduce the risk of AD for each age group: youth, early middle age, late middle age and late age. The results obtained with the use of “the calculator” are grouped in a table (Table 4) of macronutrient proportions optimal for reducing the risk of AD in each age group through minimizing Rpredicted−i.e., minimizing the strength of correlation between PCPI and future AADR.

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Section: Discussionmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 96%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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The Calculator of Anti-Alzheimer’s Diet. Macronutrients

2016

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β‐sheet breakers with consecutive phenylalanines: Insights into mechanism of dissolution of β‐amyloid fibrils

2021

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β‐sheet breakers (BSB) constitute a class of peptide inhibitors of amyloidogenesis, a process which is a hallmark of many diseases called amyloidoses, including Alzheimer's disease (AD); however, the molecular details of their action are still not fully understood. Here we describe the results of the computational investigation of the three BSBs, iaβ6 (LPFFFD), iaβ5 (LPFFD), and iaβ6_Gly (LPFGFD), in complex with the fibril model of Aβ42 and propose the kinetically probable mechanism of their action. The mechanism involves the binding of BSB to the central hydrophobic core (CHC) region (LVFFA) of Aβ fibril and the π‐stacking of its Phe rings both internally and with the Aβ fibril. In the process, the Aβ fibril undergoes distortion accumulating on the side of chain A (located on the odd tip of the fibril). In a single replica of extended molecular dynamics run of one of the iaβ6 poses, the distortion concludes in a dissociation of chain A from the fibril model of Aβ42. Altogether, we postulate that including consecutive Phe residues into BSBs docked around Phe 20 in the CHC region of Aβ42 improve their potency for dissolution of fibrils.

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Proportions of macronutrients, including specific dietary fats, in prospective anti-Alzheimer’s diet

Studnicki

Dębski²,

Stȩpkowski

2019

Sci Rep

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Here we present a novel life-long whole-population study, which enabled us to predict a diet that, in terms of macronutrient proportions, may be prophylactic against Alzheimer’s Disease (AD). The method is based on the existence of oscillations in the correlation between historical per capita personal income (PCPI) and age-adjusted death rates (AADR) for AD for each state of the USA in 2005. These oscillations can be explained by changing proportions of macronutrients in the average American diet between 1929 and 2005. We assumed that reducing future correlation of PCPI with AADR will reduce the population’s susceptibility to AD. Based on the results of fitting macronutrient availabilities to the variability of Roriginal, using Generalized Additive Models (GAM) analysis, we constructed four “Calculator” equations. The Calculator allowed for prediction of an optimal diet characterized by low correlation of PCPI with AADR (Rpredicted) and minimum energy difference from the historical average macronutrient consumption for each corresponding period of life. We predict that protein consumption should be reduced by half in early middle age and late middle age, whereas in late age it should increase. Our predictions are in line with results on humans and simpler organisms in the context of prolonging life.

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Correlation of Alzheimer’s Disease Death Rates with Historical Per Capita Personal Income in the USA

Cited by 12 publications

References 45 publications

The Calculator of Anti-Alzheimer’s Diet. Macronutrients

The Calculator of Anti-Alzheimer’s Diet. Macronutrients

β‐sheet breakers with consecutive phenylalanines: Insights into mechanism of dissolution of β‐amyloid fibrils

Proportions of macronutrients, including specific dietary fats, in prospective anti-Alzheimer’s diet

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