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European Journal of Applied Sciences – Vol. 12, No. 1
Publication Date: February 25, 2024
DOI:10.14738/aivp.121.16300
Cornelli, U., Grossi, E., Recchia, M., Antonelli, C., Battaglia, L., Bonalume, G., Butti, R., Camurri, M., Carluccio, B., Clementi, C.,
Condoleo, F., D’Ambrosio, A., De Lucia, V., Giardinetti, R., Gusperti, G., Idonia, M., Idonia, L., Iftime, M. D., Malnati, S., Mandelli,
K., Masini, C., Messina, B., Nebbia, S., Piarulli, G., Piccinini, D., Pelucchi, F., Radici, A., Rattaggi, M., Testa, M., Volpi, V., & Zahra, M.
(2024). Effects of Biophotonic Treatment on Hematologic and Metabolic Parameters: Biophotonics, Hemoglobin A1c and SpO2.
European Journal of Applied Sciences, Vol - 12(1). 195-212.
Services for Science and Education – United Kingdom
Parkinson’s Disease and Food Expenditure in Italy: Stochastic
and Non-Stochastic Analyses of Food Elements
Cornelli, Umberto
Department of Molecular Pharmacology and Therapeutics, Loyola
University School of Medicine-Chicago Piazza Novelli 5, 20129 Milan Italy
Grossi, Enzo
Villa Santa Maria Foundation, Tavernerio CO Italy
Recchia, Martino
Department of Epidemiology and Clinics Biostatistics,
Mario Negri Institute Alumni Association, Via Salaino 8 20144 Milan Italy
Antonelli, Claudia
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Battaglia, Luca
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Bonalume, Giorgia
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Butti, Roberto
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Camurri, Matteo
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Carluccio, Beatrice
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Clementi, Camilla
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Condoleo, Federico
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
D’Ambrosio, Alessio
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
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European Journal of Applied Sciences (EJAS) Vol. 12, Issue 1, February-2024
De Lucia, Veronica
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Giardinetti, Rebecca
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Gusperti, Greta
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Idonia, Marco
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Idonia, Luca
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Iftime, Maria Daniela
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Malnati, Sofia
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Mandelli, Kevin
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Masini, Chiara
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Messina, Beatrice
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Nebbia, Stefano
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Piarulli, Gabriele
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Piccinini, Daniele
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Pelucchi, Francesca
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Radici Alessandro
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
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197
Cornelli, U., Grossi, E., Recchia, M., Antonelli, C., Battaglia, L., Bonalume, G., Butti, R., Camurri, M., Carluccio, B., Clementi, C., Condoleo, F.,
D’Ambrosio, A., De Lucia, V., Giardinetti, R., Gusperti, G., Idonia, M., Idonia, L., Iftime, M. D., Malnati, S., Mandelli, K., Masini, C., Messina, B., Nebbia,
S., Piarulli, G., Piccinini, D., Pelucchi, F., Radici, A., Rattaggi, M., Testa, M., Volpi, V., & Zahra, M. (2024). Effects of Biophotonic Treatment on
Hematologic and Metabolic Parameters: Biophotonics, Hemoglobin A1c and SpO2. European Journal of Applied Sciences, Vol - 12(1). 195-212.
URL: http://dx.doi.org/10.14738/aivp.121.16300
Rattaggi Matteo
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Testa Mattia
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Volpi Viviana
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
Zahra Meerab
ITS Nuove tecnologie della Vita, Viale Europa 15, 20145 Bergamo, Italy
ABSTRACT
Background: The correlation between food and Parkinson's disease (PD) shows
that the Mediterranean diet (MeD) brings positive benefits. Objective: To find the
correlation between PD and food components in the various regions of Italy in 2016.
Methods: The protein, fat, mineral and vitamin content of 275 foods belonging to 56
distinct food categories were correlated with PD in terms of standardised mortality
ratio (SMR). Data were computed for 19,500 families in 540 Italian municipalities
for 2016. Life expectancy, demographic data and level of well-being were also
analysed. Stochastic and non-stochastic analyses (neural network mapping) were
used to compute the associations with PD. Results: The following results were
obtained by focusing on the food components deemed significant in both stochastic
and non-stochastic analyses: Alcohol, saturated and monounsaturated fats, calcium
and sodium were found to be causative or partially causative factors. Soluble
sugars, carbohydrates, starch, selenium and vitamin D were seen to be protective
or partially protective. The SMR of PD was significantly lower in Southern Italy than
in the North due to a lower consumption of causative foods and higher consumption
of protective ones. Furthermore, the lower gross domestic product (GDP) in the
South may also have a significant effect. Conclusions: In 2016, the PD death rate in
Southern Italy was significantly lower than in the North. The food component
pattern that emerged in Southern regions was also significantly different: a lower
consumption of causative food components and higher consumption of protective
ones together with a lower GDP and life expectancy. Using data on food expenditure
and quantities enable us to track the correlation with PD SMR on an annual basis.
INTRODUCTION
Between 1990 and 2016, PD was the fastest growing neurological disorder in the world [1]. Age
was the most significant risk factor, but pollutants (metals and pesticides) and industrial and
chemical factors were also found to be linked with the development of the disease [2, 3].
Dietary factors have also been considered, and some research indicates that the Mediterranean
diet (MeD) can effectively reduce the risk of PD [4-8].
The differences in PD age standardized death rate (ASDR) were calculated for 199 countries
between 1990 and 2016. Nine nations have lower values than the rest: American Samoa,
Bangladesh, Bulgaria, France, Israel, Italy, the Marshall Islands, the Netherlands and Taiwan [1].
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European Journal of Applied Sciences (EJAS) Vol. 12, Issue 1, February-2024
However, based on the World Health Organization's (WHO) 2016 statement, only France, Italy
and the Netherlands among those nine countries were acknowledged for high completeness
and quality of cause of death assignment (WHO 206 definitions [9]: referred to in this paper as
reliable countries or RCs).
Out of the 199 countries examined, a mere 47 were designated as RCs.
The aims of our study are as follows:
1. To compute correlations between the common LEEDELS variables (life expectancy,
economic, demographic, ecological and lifestyle) and changes in PD ASDR between 1990
and 2016 in the 47 RCs.
2. To establish correlations between the main food components (on the basis of food
expenditure) and PD SMR across 19 Italian regions in 2016.
3. To discern and compare the differences between Northern and Southern Italy.
MATERIAL AND METHODS
According to WHO reports, the following 49 countries may be considered RCs: Armenia,
Australia, Austria, Belgium, Brazil, Brunei, Canada, Chile, Croatia, Cuba, Czechia, Denmark,
Estonia, Finland, France, Germany, Grenada, Guatemala, Hungary, Iceland, Ireland, Israel, Italy,
Japan, Kyrgyzstan, Latvia, Lithuania, Luxembourg, Malta, Mauritius, Mexico, Moldova, New
Zealand, North Macedonia, Norway, Romania, Saint Vincent and the Grenadines, Slovakia,
Slovenia, South Korea, Spain, Sweden, Switzerland, Trinidad and Tobago, the Bahamas, the
Netherlands, the United Kingdom, the United States and Uzbekistan.
The LEEDELS data for the countries were sourced from the Atlante Geografico De Agostini [10]
and consisted of:
life expectancy, population density, gross domestic product (GDP), number of cars per 1000
inhabitants, number of mobile phones per 1000 inhabitants and number of people connected
to the internet per 1000 inhabitants.
For the Italian regions, the LEEDELS data were retrieved from public data [11] and consisted
of life expectancy, population density, GDP, cars, mobile phone numbers and internet
connections.
The increase in PD ASDRs (x105) from 1990 to 2016 in the 47 RCs was taken from Lancet Neurol
2018 [1].
In the case of Italy, PD SMRs were used instead of ASDRs since the comparison involved regions
within the same country. The data were retrieved from the Italian National Institute of Statistics
(ISTAT) for 2016 [12].
The data for the food analysis were derived from the CAPI (computer assisted personal
interview) system for 2016 [13]. This system is acknowledged as the most reliable,
comprehensive, and meticulous questionnaire, assisted with technical expertise, for recording
food expenditure.
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199
Cornelli, U., Grossi, E., Recchia, M., Antonelli, C., Battaglia, L., Bonalume, G., Butti, R., Camurri, M., Carluccio, B., Clementi, C., Condoleo, F.,
D’Ambrosio, A., De Lucia, V., Giardinetti, R., Gusperti, G., Idonia, M., Idonia, L., Iftime, M. D., Malnati, S., Mandelli, K., Masini, C., Messina, B., Nebbia,
S., Piarulli, G., Piccinini, D., Pelucchi, F., Radici, A., Rattaggi, M., Testa, M., Volpi, V., & Zahra, M. (2024). Effects of Biophotonic Treatment on
Hematologic and Metabolic Parameters: Biophotonics, Hemoglobin A1c and SpO2. European Journal of Applied Sciences, Vol - 12(1). 195-212.
URL: http://dx.doi.org/10.14738/aivp.121.16300
The food expenditure data were calculated based on 19,500 families across 540 municipalities
in the 20 Italian regions. The average number of family members was 2.32 ± 0.153. This sample
represents 6.3% of all Italian municipalities and is considered representative of the entire
country.
For the purposes of this study, Northern Italy comprised Piedmont + Val d’Aosta, Liguria,
Lombardy, Veneto, Friuli Venezia Giulia (FVG), Trentino Alto Adige (TAA), Tuscany and Emilia
Romagna. The eleven regions of Southern Italy were Lazio, Umbria, Marche, Abruzzo, Molise,
Basilicata, Campania, Puglia, Calabria, Sicily and Sardinia.
Piedmont and Val d’Aosta were treated as a single region. So, the total number of regions
analysed was nineteen.
The annual expenditure (€) for 56 of the highest-selling food categories was considered.
The expenditures were converted into quantities using cost per kilogram. Since costs are not
the same throughout Italy, the values in kilograms were calculated based on regional costs.
The food components of the most common 275 foods taken into consideration were water,
proteins, lipids, starch, soluble sugars, fibre, energy (kcal), Na, K, Mg, Fe, Ca, P, Zn, Cu, Se,
thiamine, riboflavin, niacin, vitamin A, vitamin D, folate and vitamin B6 The data aligned with
those reported by INRAN (Istituto Nazionale Ricerca Alimenti Nutrizione [14], red edition [15]).
The statistical analysis was conducted in six steps:
• Firstly, the Spearman’s ρ correlation coefficient was computed between the LEEDELS
data and the difference in ASDRs for PD in the 49 RCs during the 1990-2016 period.
For Italy, the 2016 standardized mortality ratio (SMR) in the 19 regions was used to
examine correlation with the LEEDELS data.
• The second step involved calculating the SMR (and MR) by comparing the 2016 data for
Northern and Southern Italy.
• The third step involved correlating food components and PD SMR in the 19 Italian
regions analysed based on Spearman’s ρ (stochastic analysis).
• The fourth step was the analysis of the food components of the 275 most used foods
according to the CAPI records for the 56 food categories. For this analysis, the food
expenditure was transformed into quantities in kilograms by considering the cost
differences between the Italian regions (an error of < 2% is possible).
• The fifth step was non-stochastic analysis using a minimum spanning tree (MST) map
calculated according to Euclidean distance. This map considered the data relating to
“high” and “low” MRs (mortality ratios). Food categories close to “high” mortality were
deemed causative, while those close to low mortality were considered protective. Only
raw data without any previous standardization can be used for an MST [16]. For this
reason, MR was used instead of SMR.
• The last step consisted of analysing the food categories classified as causative or
protective through the MST (non-stochastic analysis) and those identified through
statistical differences (stochastic analysis) and comparing Northern and Southern Italy.