Association Between Sugar Intake, Body Mass Index, Income, and Life Expectancy: A Global Analysis Article Swipe

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Kalki Rajamanickam Chandrasekaran , Arpit Srivastava , S. Ramakrishnan , Rajneesh Kumar ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.20944/preprints202507.0086.v2

Background: Life expectancy is shaped by a combination of lifestyle and socioeconomic factors. While sugar consumption, body mass index (BMI), and income have been individually linked to health outcomes, their combined effects on longevity—particularly across countries with differing development levels—are less well understood. Objective: This study investigates how sugar intake, BMI, and per capita income interact to influence life expectancy across 187 countries. Methods: We conducted a cross-sectional analysis using national-level data from FAOSTAT, the World Bank, and the World Health Organization for the years 2022–2023. Pearson correlation was used to assess bivariate relationships, and multiple linear regression was employed to estimate the collective impact of BMI, sugar consumption (kg per capita/year), and income (USD) on life expectancy (years). The model was adjusted for confounding variables such as healthcare expenditure, education, and smoking prevalence. Results: Sugar intake was moderately associated with BMI (r = 0.52) and life expectancy (r = 0.50), with p value <0.05 for both with stronger relationships observed in countries with per capita income below USD 10,000. BMI also showed a moderate positive correlation with life expectancy (r = 0.49), but this association weakened—and slightly reversed—for countries with BMI greater than 27 kg/m². The regression model explained 33.9% of the variance in life expectancy (adjusted R² = 0.325), increasing to 61.6% after accounting for structural confounders. Conclusion: BMI and sugar intake are important predictors of life expectancy, with their impact shaped by income level and healthcare infrastructure. The decline in longevity above a BMI threshold of 27 kg/m² highlights a critical point for public health intervention. Integrated strategies targeting obesity, sugar intake, and structural inequities are essential for improving global health outcomes.

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Life expectancy Body mass index Index (typography) Sugar Association (psychology) Demographic economics Economics Environmental health Demography Gerontology Psychology Food science Medicine Chemistry Endocrinology Sociology Computer science Population World Wide Web Psychotherapist

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Type: preprint
Language: en
Landing Page: https://doi.org/10.20944/preprints202507.0086.v2
PDF: https://www.preprints.org/frontend/manuscript/912517ebc4223c0373bb9738e6a91b8c/download_pub
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4412095729

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DOI: https://doi.org/10.20944/preprints202507.0086.v2

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Title: Association Between Sugar Intake, Body Mass Index, Income, and Life Expectancy: A Global Analysis

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-07-03

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Authors: Kalki Rajamanickam Chandrasekaran, Arpit Srivastava, S. Ramakrishnan, Rajneesh Kumar

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Landing page: https://doi.org/10.20944/preprints202507.0086.v2

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PDF URL: https://www.preprints.org/frontend/manuscript/912517ebc4223c0373bb9738e6a91b8c/download_pub

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Concepts: Life expectancy, Body mass index, Index (typography), Sugar, Association (psychology), Demographic economics, Economics, Environmental health, Demography, Gerontology, Psychology, Food science, Medicine, Chemistry, Endocrinology, Sociology, Computer science, Population, World Wide Web, Psychotherapist

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abstract_inverted_index.explained	199
abstract_inverted_index.important	225
abstract_inverted_index.improving	271
abstract_inverted_index.influence	57
abstract_inverted_index.lifestyle	9
abstract_inverted_index.longevity	243
abstract_inverted_index.outcomes,	28
abstract_inverted_index.outcomes.	274
abstract_inverted_index.targeting	261
abstract_inverted_index.threshold	247
abstract_inverted_index.variables	125
abstract_inverted_index.Integrated	259
abstract_inverted_index.Objective:	43
abstract_inverted_index.accounting	215
abstract_inverted_index.associated	139
abstract_inverted_index.collective	103
abstract_inverted_index.countries.	62
abstract_inverted_index.education,	130
abstract_inverted_index.expectancy	2, 59, 117, 147, 179, 206
abstract_inverted_index.healthcare	128, 238
abstract_inverted_index.highlights	251
abstract_inverted_index.increasing	211
abstract_inverted_index.inequities	267
abstract_inverted_index.moderately	138
abstract_inverted_index.predictors	226
abstract_inverted_index.regression	97, 197
abstract_inverted_index.strategies	260
abstract_inverted_index.structural	217, 266
abstract_inverted_index.Background:	0
abstract_inverted_index.Conclusion:	219
abstract_inverted_index.association	185
abstract_inverted_index.combination	7
abstract_inverted_index.confounding	124
abstract_inverted_index.consumption	108
abstract_inverted_index.correlation	87, 176
abstract_inverted_index.development	38
abstract_inverted_index.expectancy,	229
abstract_inverted_index.prevalence.	133
abstract_inverted_index.understood.	42
abstract_inverted_index.&lt;0.05	154
abstract_inverted_index.2022–2023.	85
abstract_inverted_index.Organization	81
abstract_inverted_index.confounders.	218
abstract_inverted_index.consumption,	15
abstract_inverted_index.expenditure,	129
abstract_inverted_index.individually	24
abstract_inverted_index.investigates	46
abstract_inverted_index.levels—are	39
abstract_inverted_index.capita/year),	111
abstract_inverted_index.intervention.	258
abstract_inverted_index.relationships	159
abstract_inverted_index.socioeconomic	11
abstract_inverted_index.national-level	70
abstract_inverted_index.relationships,	93
abstract_inverted_index.reversed—for	188
abstract_inverted_index.weakened—and	186
abstract_inverted_index.cross-sectional	67
abstract_inverted_index.infrastructure.	239
abstract_inverted_index.longevity—particularly	33
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	4
citation_normalized_percentile.value	0.35369053
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	True