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Cited by 436Diet, Metabolism, and DiseaseObesity, Physical Activity, DietDiabetes, Cardiovascular Risks, and Lipoproteins

The relationship of sugar to population-level diabetes prevalence: an econometric analysis of repeated cross-sectional data

Open access
Basu, Sanjay, Yoffe, Paula, Hills, Nancy, Lustig, Robert H.·2013·PLoS ONE
DOI 10.1371/journal.pone.0057873PMC PMC3584048
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Abstract

While experimental and observational studies suggest that sugar intake is associated with the development of type 2 diabetes, independent of its role in obesity, it is unclear whether alterations in sugar intake can account for differences in diabetes prevalence among overall populations. Using econometric models of repeated cross-sectional data on diabetes and nutritional components of food from 175 countries, we found that every 150 kcal/person/day increase in sugar availability (about one can of soda/day) was associated with increased diabetes prevalence by 1.1% (p <0.001) after testing for potential selection biases and controlling for other food types (including fibers, meats, fruits, oils, cereals), total calories, overweight and obesity, period-effects, and several socioeconomic variables such as aging, urbanization and income. No other food types yielded significant individual associations with diabetes prevalence after controlling for obesity and other confounders. The impact of sugar on diabetes was independent of sedentary behavior and alcohol use, and the effect was modified but not confounded by obesity or overweight. Duration and degree of sugar exposure correlated significantly with diabetes prevalence in a dose-dependent manner, while declines in sugar exposure correlated with significant subsequent declines in diabetes rates independently of other socioeconomic, dietary and obesity prevalence changes. Differences in sugar availability statistically explain variations in diabetes prevalence rates at a population level that are not explained by physical activity, overweight or obesity.

AI snapshot

Written from the paper’s full text.

TL;DR

Using econometric models of repeated cross-sectional data from 175 countries, this study found that higher sugar availability was associated with higher population-level diabetes prevalence, independent of obesity and other food types. The author concludes that sugar availability statistically explains some population variation in diabetes rates not explained by physical activity, overweight, or obesity.

Key findings

  • Every 150 kcal/person/day increase in sugar availability (about one can of soda/day) was associated with a 1.1% increase in diabetes prevalence (95% CI: 0.48–1.7%; p<0.001) after controlling for other foods, total calories, overweight/obesity, period effects, and socioeconomic variables; total calorie availability was not significantly associated (0.1% rise per 150 kcal, not significant). No other food type had a significant individual association with diabetes prevalence after controls.
  • The fraction of total calories from sugar was the only significant food fraction: a 1% rise corresponded to a 0.167% rise in diabetes prevalence. Each extra year of high sugar availability (≥300 kcal/person/day) was associated with a 0.053% increase in diabetes prevalence (p<0.05), and periods after a country lowered sugar availability showed a 0.074% reduction in diabetes prevalence (p<0.05).
  • Diabetes prevalence rose 27% on average from 2000 to 2010, with just over one-fourth of the increase explained by rising sugar availability in the model. The sugar association remained after controlling for selection bias (magnified to 1.2% per 150 kcal/person/day, p<0.001), after removing outliers (1.2%, p<0.001), and in an autoregressive time-series model (0.4%, p<0.001).

Methods

  • Econometric analysis of repeated cross-sectional panel data from 175 countries, 2000–2010. Diabetes prevalence (ages 20–79) came from the International Diabetes Federation; sugar and other food availability (kcal/person/day) came from UN FAO food supply data.
  • Main models controlled for total calories, fibers, fruits, meats, cereals, oils, log GDP per capita, change in log GDP, urbanization, aging, obesity/overweight, period effects, and later physical inactivity, smoking, and heavy alcohol use. Food data were age-adjusted, regressions were population weighted, and robust standard errors were used.
  • Analyses included Generalized Estimating Equations for model selection, a Hausman test, a Heckman-type selection model for high sugar availability, Granger-causality tests, and robustness checks (outlier removal, autoregressive time-series, fixed effects, direct survey data, and excluding the U.S.).

Limitations

  • The paper states that cross-country ecological analysis risks ecological fallacy; food supply data measure availability, not actual consumption, and cannot account precisely for food wastage. It also cannot distinguish specific added sugars (e.g., HFCS vs. sucrose) or specific vehicles (e.g., soda vs. processed food).
  • The paper states that ecological data limit statistical power and lose age, sex, and racial predictions; IDF diabetes prevalence data come from surveys of varying quality, likely underestimate diabetes, and do not distinguish type 1 from type 2 diabetes. FAO, IDF, and WHO data are described as imperfect, making findings exploratory.
  • Evident from the text: the study is observational and country-level, so it cannot establish individual-level causation; despite extensive controls, unmeasured confounding remains possible, and sugar availability is a proxy for intake rather than a direct consumption measure.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1371/journal.pone.0057873

Cites

43 of 43 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 25 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    In countries like the Philippines, Romania, Sri Lanka, Georgia and Bangladesh, where high and rising diabetes rates were observed in the context of low obesity rates, sugar availability rose by over 20% during the study period.

    “In countries like the Philippines, Romania, Sri Lanka, Georgia and Bangladesh, where high and rising diabetes rates were observed in the context of low obesity rates, sugar availability rose by over 20% during the study period.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Food supply data on market availability accurately reflect population-level sugar intake or consumption.
    • Rests on an assumption: International Diabetes Federation diabetes prevalence estimates are valid and comparable across countries and years.
  • Findingown data

    The coefficient of log GDP per capita was 1.07, meaning that a 1% increase in GDP levels corresponded to a 1.07% rise in diabetes prevalence (p<0.05).

    “The coefficient of log Gross Domestic Product (GDP) per capita was 1.07, which means that a 1% increase in GDP levels corresponded to a 1.07% rise in diabetes prevalence (p<0.05), consistent with the notion that economic development is a powerful correlate to diabetes prevalence [35], [36].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [35] KH Yoon, 2006, Epidemic obesity and type 2 diabetes in Asia, Lancet source

      Not checked in the free run.

    • cites [36] A Ramachandran, 2010, Diabetes in Asia, Lancet source

      Not checked in the free run.

    • Rests on an assumption: Food supply data on market availability accurately reflect population-level sugar intake or consumption.
    • Rests on an assumption: International Diabetes Federation diabetes prevalence estimates are valid and comparable across countries and years.
  • Findingown data

    Combining fruits with vegetables and other fibers did not change the results.

    “We initially separated fruit from other vegetables/fibers given the potential glucose burden of fruit; when repeating the analysis combining fruits with vegetables and other fibers, the results did not change.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Food supply data on market availability accurately reflect population-level sugar intake or consumption.
    • Rests on an assumption: International Diabetes Federation diabetes prevalence estimates are valid and comparable across countries and years.
  • Findingown data

    Each 150 kcal/person/day rise in sugar availability (about one 12 oz. can of soft drink) was associated with a 1.1% rise in diabetes prevalence (95% CI: 0.48-1.7%; p<0.001) after controlling for other food types, total calories, overweight and obesity, period effects, and socioeconomic variables.

    “Using econometric models of repeated cross-sectional data on diabetes and nutritional components of food from 175 countries, we found that every 150 kcal/person/day increase in sugar availability (about one can of soda/day) was associated with increased diabetes prevalence by 1.1% (p <0.001) after testing for potential selection biases and controlling for other food types (including fibers, meats,”
    • Also stated in: § Correlates of diabetes prevalence · plain
      “Each 150 kilocalorie/person/day increase in total calorie availability related to a 0.1% rise in diabetes prevalence (not significant), whereas a 150 kilocalories/person/day rise in sugar availability (one 12 oz. can of soft drink) was associated with a 1.1% rise in diabetes prevalence (95% CI: 0.48–1.7%; p<0.001) after all control variables were incorporated into the model.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Food supply data on market availability accurately reflect population-level sugar intake or consumption.
    • Rests on an assumption: International Diabetes Federation diabetes prevalence estimates are valid and comparable across countries and years.
  • Findingcitation

    High diabetes yet low obesity prevalence are observed in countries with different ethnic compositions, such as the Philippines, Romania, France, Bangladesh and Georgia.

    “High diabetes yet low obesity prevalence are observed in countries with different ethnic compositions, such as the Philippines, Romania, France, Bangladesh and Georgia, although there are likely surveillance quality differences between nations [6], [7].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [6] International Diabetes Federation (2011) IDF Diabetes Atlas.

      Not checked in the free run.

    • cites [7] World Health Organization (2012) Global database on body mass index.

      Not checked in the free run.

  • May combine two claims. 32
  • The quote only partly states this claim. 1
  • Classified as probably not a substantial claim. 11

  • May combine two claims.

    Findingown data

    The impact of sugar on diabetes was independent of sedentary behavior and alcohol use, and the effect was modified but not confounded by obesity or overweight.

    “The impact of sugar on diabetes was independent of sedentary behavior and alcohol use, and the effect was modified but not confounded by obesity or overweight.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Food supply data on market availability accurately reflect population-level sugar intake or consumption.
    • Rests on an assumption: International Diabetes Federation diabetes prevalence estimates are valid and comparable across countries and years.

  • May combine two claims.

    Findingnone given

    Sri Lanka's diabetes prevalence rate rose from 3% in 2000 to 11% in 2010 while its obesity rate remained at 0.1% during that period.

    “Trends in diabetes and obesity are also dyssynchronous within some nations; while Sri Lanka’s diabetes prevalence rate rose from 3% in the year 2000 to 11% in 2010, its obesity rate remained at 0.1% during that time period.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Incorporation of overweight (instead of obesity), physical inactivity, smoking, and heavy alcohol use did not affect the sugar variable and these factors did not reach statistical significance as independent correlates of diabetes when other control variables were included.

    “Incorporation of these factors (see Table S4) did not affect the sugar variable and did not themselves reach statistical significance as independent correlates of diabetes when the other control variables were included in the model.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Several countries with high diabetes prevalence rates have low obesity rates, and vice versa.

    “As shown in Figure 1 , several countries with high diabetes prevalence rates have low obesity rates, and vice versa.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Duration and degree of sugar exposure correlated significantly with diabetes prevalence in a dose-dependent manner, while declines in sugar exposure correlated with significant subsequent declines in diabetes rates independently of other socioeconomic, dietary and obesity prevalence changes.

    “Duration and degree of sugar exposure correlated significantly with diabetes prevalence in a dose-dependent manner, while declines in sugar exposure correlated with significant subsequent declines in diabetes rates independently of other socioeconomic, dietary and obesity prevalence changes.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Food supply data on market availability accurately reflect population-level sugar intake or consumption.
    • Rests on an assumption: International Diabetes Federation diabetes prevalence estimates are valid and comparable across countries and years.

  • May combine two claims.

    Findingown data

    Low-carbohydrate fibers (nuts and vegetables) individually included in the regression had no significant effect and did not change the impact of sugar on diabetes prevalence.

    “We tested the hypothesis that low-carbohydrate fibers (nuts and vegetables) might be protective against diabetes by individually including them in the regression (as opposed to all fiber-containing foods) but they had no significant effect, and did not change the impact of sugar on diabetes prevalence.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Food supply data on market availability accurately reflect population-level sugar intake or consumption.
    • Rests on an assumption: International Diabetes Federation diabetes prevalence estimates are valid and comparable across countries and years.

  • May combine two claims.

    Findingown data

    Each 150 kilocalorie/person/day increase in total calorie availability related to a 0.1% rise in diabetes prevalence, which was not significant.

    “Each 150 kilocalorie/person/day increase in total calorie availability related to a 0.1% rise in diabetes prevalence (not significant)”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Food supply data on market availability accurately reflect population-level sugar intake or consumption.
    • Rests on an assumption: International Diabetes Federation diabetes prevalence estimates are valid and comparable across countries and years.

  • The quote only partly states this claim.

    Findingown data

    In all robustness checks (fixed effects, without period effects, only direct diabetes survey data, excluding the U.S.), the sugar variable maintained a similar association with diabetes prevalence.

    “In all cases, the sugar variable maintained a similar association with diabetes prevalence.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: No direct measures of actual human consumption that account for food wastage and provide precise measures of food consumption internationally exist.

  • May combine two claims.

    Findingown data

    Diabetes prevalence rates rose 27% on average from 2000 to 2010, with just over one-fourth of the increase explained by a rise in sugar availability in this model.

    “Diabetes prevalence rates rose 27% on average from 2000 to 2010, with just over one-fourth of the increase explained by a rise in sugar availability in this model.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Food supply data on market availability accurately reflect population-level sugar intake or consumption.
    • Rests on an assumption: International Diabetes Federation diabetes prevalence estimates are valid and comparable across countries and years.

  • May combine two claims.

    Findingown data

    Granger temporal causality tests identified a significant relationship between high sugar availability and subsequently higher diabetes prevalence rates, not vice versa.

    “We identified a significant relationship between high sugar availability and subsequently higher diabetes prevalence rates, not vice versa.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The fraction of total calories arising from sugar was the only significant food fraction correlated with diabetes, with a 1% rise in the fraction of total food calories as sugar corresponding to a 0.167% rise in diabetes prevalence.

    “As shown in Table 2 , the fraction of total calories arising from sugar was the only significant food fraction correlated with diabetes, with a 1% rise in the fraction of total food calories as sugar corresponding to a 0.167% rise in diabetes prevalence.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Food supply data on market availability accurately reflect population-level sugar intake or consumption.
    • Rests on an assumption: International Diabetes Federation diabetes prevalence estimates are valid and comparable across countries and years.

  • May combine two claims.

    Findingnone given

    New Zealand's diabetes prevalence declined from 8% in 2000 to 5% in 2010 while obesity rates rose from 23% to 34% during that decade.

    “Conversely, diabetes prevalence in New Zealand declined from 8% in 2000 to 5% in 2010 while obesity rates in the country rose from 23% to 34% during that decade.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    After removing potential outlying countries (standardized residuals greater than 2), a 150 kcal/person/day rise in sugar availability corresponded to a 1.2% rise in diabetes prevalence (p<0.001), compared with a 1.1% rise when outliers were included.

    “The results were strengthened: a 150 kcal/person/day rise in sugar availability corresponded to a 1.2% rise in diabetes prevalence (p<0.001) as opposed to a 1.1% rise when outliers were included.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: No direct measures of actual human consumption that account for food wastage and provide precise measures of food consumption internationally exist.

  • May combine two claims.

    Findingreasoning

    In our analysis, many foods did not have significant correlations to diabetes prevalence at the population level, even though they are associated with diabetes in cohort or clinical trial studies.

    “Similarly, in our analysis, many foods did not have significant correlations to diabetes prevalence at the population level, even though they are associated with diabetes in cohort or clinical trial studies.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Other societal factors associated with diabetes were income, urbanization, and aging.

    “Other societal factors associated with diabetes were those classically associated with metabolic syndrome; including income, urbanization and aging.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Sugar availability appears to be uniquely correlated to diabetes prevalence independent of overweight and obesity prevalence rates, unlike other food types and total consumption, and independent of other changes in economic and social change such as urbanization, aging, changes to household income, sedentary lifestyles and tobacco or alcohol use.

    “we identified that sugar availability appears to be uniquely correlated to diabetes prevalence independent of overweight and obesity prevalence rates, unlike other food types and total consumption, and independent of other changes in economic and social change such as urbanization, aging, changes to household income, sedentary lifestyles and tobacco or alcohol use.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: No direct measures of actual human consumption that account for food wastage and provide precise measures of food consumption internationally exist.

  • May combine two claims.

    Interpretationown data

    Urbanization and aging populations were associated with diabetes prevalence, but these variables fell from significance as total food availability and obesity were incorporated into the model, suggesting that calorie consumption and obesity are among the pathways by which these other factors may contribute to diabetes.

    “Similarly, variables capturing urbanization and aging populations were associated with diabetes prevalence; however these variables fell from significance as total food availability and obesity were incorporated into the model ( Table 1 ), suggesting that calorie consumption and obesity are among the pathways by which these other factors may contribute to diabetes, consistent with cross-sectional”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [37] KR Siegel, 2012, Societal correlates of diabetes prevalence: an analysis across 94 countries, Diabetes Res Clin Pract source

      Not checked in the free run.

    • Rests on an assumption: Food supply data on market availability accurately reflect population-level sugar intake or consumption.
    • Rests on an assumption: International Diabetes Federation diabetes prevalence estimates are valid and comparable across countries and years.

  • May combine two claims.

    Study design and conductcitation

    The analysis modeled the hazard of having high sugar availability rates in each country and used this constructed hazard variable in a Heckman selection model to explicitly control for potential unobserved selection bias.

    “We also modeled the hazard of having high sugar availability rates in each country, and used this constructed hazard variable to explicitly control for potential unobserved selection bias (a “Heckman selection model”, see Text S1) [29].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [29] J Heckman, 2004, Using matching, instrumental variables and control functions to estimate economic choice models, Rev Econ Stat source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    When obesity was removed from the model, the effect size of sugar was not significantly amplified (beta = 0.0081, p<0.001), suggesting that obesity does not appear to account for the major part of the impact of sugar on diabetes.

    “When obesity was removed from the model, the effect size of sugar was not significantly amplified (beta = 0.0081, p<0.001), suggesting that obesity does not appear to account for the major part of the impact of sugar on diabetes.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Food supply data on market availability accurately reflect population-level sugar intake or consumption.
    • Rests on an assumption: International Diabetes Federation diabetes prevalence estimates are valid and comparable across countries and years.

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    A Hausman test was performed to test whether factors that differ across countries, such as differing strength of diabetes surveillance systems, would systematically affect the results, ensuring the available data were suitable to answer the research questions.

    “First, a Hausman test [28] was performed to test whether factors that differ across countries such as the differing strength of diabetes surveillance systems would systematically affect our results, ensuring the available data were suitable to answer our research questions.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [28] JA Hausman, 1978, Specification tests in econometrics, Econometrica source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Sugary foods have been significantly associated with the development of insulin resistance in laboratory-based studies.

    “Sugary foods have been significantly associated with the development of insulin resistance in laboratory-based studies [18], [19].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [18] KL Teff, 2009, Endocrine and metabolic effects of consuming fructose- and glucose-sweetened beverages with meals in obese men and women: influence of insulin resistance on plasma triglyceride responses, J Clin Endocrinol Metab source

      Not checked in the free run.

    • cites [19] AA Bremer, 2011, Fructose-fed rhesus monkeys: a nonhuman primate model of insulin resistance, metabolic syndrome, and type 2 diabetes, Clin Transl Sci source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    A high prevalence of smoking and heavy alcohol use have been associated with diabetes.

    “Lastly, a high prevalence of smoking and heavy alcohol use have been associated with diabetes [5].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [5] FB Hu, 2011, Globalization of diabetes: the role of diet, lifestyle, and genes, Diab Care source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Liver fructose metabolism in the fed state generates lipogenic substrates in an unregulated fashion, driving hepatic de novo lipogenesis and reduced fatty acid oxidation, forming excessive liver fat and inflammation that inactivates insulin signaling, leading to hepatic insulin resistance.

    “For example, liver fructose metabolism in the fed state generates lipogenic substrates in an unregulated fashion, which drives hepatic de novo lipogenesis and reduced fatty acid oxidation, forming excessive liver fat and inflammation that inactivates the insulin signaling pathway, leading to hepatic insulin resistance [16], [17].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [16] AA Bremer, 2012, Toward a unifying hypothesis of metabolic syndrome, Pediatrics source

      Not checked in the free run.

    • cites [17] Y Nagai, 2009, The role of peroxisome proliferator-activated receptor gamma coactivator-1 beta in the pathogenesis of fructose-induced insulin resistance, Cell Metab source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Sugars added to processed food, particularly fructose, can contribute to obesity but also appear to have properties that increase diabetes risk independently from obesity.

    “Sugars added to processed food, in particular the monosaccharide fructose, can contribute to obesity [14], but also appear to have properties that increase diabetes risk independently from obesity [15].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [14] RJ Johnson, 2007, Potential role of sugar (fructose) in the epidemic of hypertension, obesity and the metabolic syndrome, diabetes, kidney disease, and cardiovascular disease, Am J Clin Nutr source

      Not checked in the free run.

    • cites [15] Malik VS, Hu FB (2012) Sweeteners and risk of obesity and type 2 diabetes: the role of sugar-sweetened beverages. Curr Diab Rep epub Jan 31.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductcitation

    Granger-causality tests were performed to test whether high sugar availability preceded an increase in diabetes or whether high diabetes prevalence preceded high sugar availability.

    “We also performed Granger-causality tests, which use the temporal nature of the data to test whether high sugar availability preceded an increase in diabetes (“precedence”) or whether high diabetes prevalence preceded high sugar availability [34] (see Text S1).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [34] CWJ Granger, 1969, Investigating causal relations by econometric models and cross-spectral methods, Econometrica

      Not checked in the free run.

  • May combine two claims.

    Study design and conductown data

    Repeating the models with overweight prevalence rather than obesity and with measures of physical inactivity rather than BMI did not change the results.

    “(It is possible that weight gain, rather than overt obesity, might account for some of the changes in diabetes, hence our models were repeated with overweight prevalence rather than obesity in Table S3, and with measures of physical inactivity rather than BMI in Table S4, but the results did not change).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Food supply data on market availability accurately reflect population-level sugar intake or consumption.
    • Rests on an assumption: International Diabetes Federation diabetes prevalence estimates are valid and comparable across countries and years.

  • May combine two claims.

    Interpretationown data

    Obesity appeared to exacerbate, but not confound, the impact of sugar availability on diabetes prevalence.

    “We found that obesity appeared to exacerbate, but not confound, the impact of sugar availability on diabetes prevalence”
    Quote in the paperSubstantial claim
    • Rests on an assumption: No direct measures of actual human consumption that account for food wastage and provide precise measures of food consumption internationally exist.

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductreasoning

    In all analyses, food availability data were age-adjusted, regressions were population weighted, and robust standard errors were computed to ensure stability of the results in the face of heteroskedasticity and intragroup correlations.

    “In all analyses, food availability data were age-adjusted, regressions were population weighted, and robust standard errors were computed to ensure stability of the results in the face of heteroskedasticity and intragroup correlations.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    Explicit model selection procedures using Generalized Estimating Equations were conducted to ensure the model was an optimal choice for the given data.

    “We conducted explicit model selection procedures using Generalized Estimating Equations (see results in Text S1) to ensure the model was an optimal choice for the given data [30].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [30] Hardin JW, Hilbe JM (2003) Generalized estimating equations. Boca Raton: CRC Press. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Reactive oxygen species produced by the Maillard reaction damage pancreatic beta cells and lead to a subcellular stress response that drives insulin inadequacy.

    “Reactive oxygen species are produced by the Maillard reaction [20], [21], damaging pancreatic beta cells, and leading to a subcellular stress response (the “unfolded protein response” in the endoplasmic reticulum) that drives insulin inadequacy [22], [23].”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [20] CG Schalkwijk, 2004, Fructose-mediated non-enzymatic glycation: sweet coupling or bad modification, Diabetes Metab Res source

      Not checked in the free run.

    • cites [21] WL Dills, 1993, Protein fructosylation: fructose and the Maillard reaction, Am J Clin Nutr source

      Not checked in the free run.

    • cites [22] CX Santos, 2009, Mechanisms and implications of reactive oxygen species generation during the unfolded protein response: roles of endoplasmic reticulum oxidoreductases, mitochondrial electron transport, and NADPH oxidase, Antioxid Redox Signal source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisecitation

    The observed associations are biologically plausible, given the numerous mechanisms by which sugar foments pathophysiologic processes leading to diabetes.

    “The observed associations are biologically plausible, given the numerous mechanisms by which sugar foments pathophysiologic processes leading to diabetes [19], [40].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [19] AA Bremer, 2011, Fructose-fed rhesus monkeys: a nonhuman primate model of insulin resistance, metabolic syndrome, and type 2 diabetes, Clin Transl Sci source

      Not checked in the free run.

    • cites [40] JS Lim, 2010, The role of fructose in the pathogenesis of NAFLD and the metabolic syndrome, Nat Rev Gastroenterol Hepatol source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Study design and conductnone given

    Much of the FAO data on foods and nutrients in the food supply have limits to their reliability, and IDF data and WHO data on obesity prevalence are difficult to validate independently.

    “It is thought that much of the FAO data on foods and nutrients in the food supply have limits to their reliability, and that IDF data and WHO data on obesity prevalence are difficult to validate independently.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductreasoning

    Our ecological approach limits statistical power as one makes inferences about individuals based on aggregates; age, sex, and racial predictions are lost.

    “our ecological approach limits statistical power as one makes inferences about individuals based on aggregates; age, sex, and racial predictions are lost.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    The International Diabetes Federation database contains diabetes prevalence data based on multiple surveys of varying quality; many diabetics go undiagnosed, so these are likely underestimates, and do not distinguish between Type 1 and Type 2 diabetes.

    “the International Diabetes Federation database contains diabetes prevalence data based on multiple surveys of varying quality; as many diabetics go undiagnosed, these are likely underestimates, and do not distinguish between Type 1 (approximately 10%) and Type 2 diabetes (90%)”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Study design and conductnone given

    As with all cross-country analyses, the potential exists for ecological fallacies.

    “as with all cross-country analyses, the potential exists for ecological fallacies.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    The sugar-diabetes relationship appeared to meet criteria for temporal causality without being the result of selection biases or the effect of secular trends that may be artifacts of economic development or changes in surveillance.

    “the sugar-diabetes relationship appeared to meet criteria for temporal causality without being the result of selection biases or the effect of secular trends that may be artifacts of economic development or changes in surveillance.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductown data

    Our analysis cannot distinguish between specific added sugars, such as HFCS or sucrose, or between specific vehicles, such as soda or processed food.

    “our analysis cannot distinguish between any specific added sugars, such as HFCS or sucrose, or between any specific vehicle, such as soda or processed food.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    In other countries, supply and consumption are more closely aligned, and differential wastage among foodstuffs does not appear to occur.

    “In other countries, supply and consumption are more closely aligned [41], and differential wastage among foodstuffs does not appear to occur [42].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [41] Gustavsson J, Cederberg C, Sonesson U, van Otterdijk R, Meybeck A (2011) Global food losses and food waste. Rome: Food and Agriculture Organization.

      Not checked in the free run.

    • cites [42] Muth MK, Karns SA, Nielsen SJ, Buzby JC, Wells HF (2011) Consumer-level food loss estimates and their use in the ERS loss-adjusted food availability data. Washngton, D.C.: U.S. Dept. of Agriculture.

      Not checked in the free run.

    • Rests on an assumption: No direct measures of actual human consumption that account for food wastage and provide precise measures of food consumption internationally exist.

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The data used are known to be highly imperfect.

    “these data are known to be highly imperfect.”
    Quote in the paperSubstantial claim
39 of 39

  • The paper relies on this without support.

    Food supply data on market availability accurately reflect population-level sugar intake or consumption.

    “We used United Nations Food and Agricultural Organization food supply data [26] to capture market availability of different food items (sugars, fibers, fruits, meats, cereals, oils, and total food) in kilocalories per person per day in each country for each year of the analysis.”

  • The paper relies on this without support.

    International Diabetes Federation diabetes prevalence estimates are valid and comparable across countries and years.

    “The dependent variables in the analysis were International Diabetes Federation estimates of diabetes prevalence among persons aged 20 to 79 years old from 2000 through 2010 [6].”

  • The paper relies on this without support.

    No direct measures of actual human consumption that account for food wastage and provide precise measures of food consumption internationally exist.

    “as there are no direct measures of actual human consumption that can account for food wastage and provide precise measures of food consumption internationally.”
3 of 3

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In the 1985 to 2005 period extensive added sugar intake occurred across HIC, but more recently large increases have occurred in LMIC, particularly in consumption of sugar-sweetened beverages and processed foods.

    “In the 1985 to 2005 period extensive added sugar intake occurred across HIC (55) but more recently large increases have occurred in LMIC, particularly in consumption of sugar-sweetened beverages and processed foods (56–59).”

    From Food Consumption and its Impact on Cardiovascular Disease: Importance of Solutions Focused on the Globalized Food System (2015) · cites this paper as [56]

Cites

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Related

Authors
Basu, S., Yoffe, P., Hills, N., & Lustig, R. H.
Affiliations
  • Stanford University (US)
  • University of California, Berkeley (US)
  • University of California, San Francisco (US)
Corresponding author
Basu, Sanjay
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2013-02-27
Volume
8
Issue
2
Pages
e57873
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0057873
PMC
PMC3584048
OpenAlex
W2014750665
Cited by
435
References
43
Citations by year
2026: 11 · 2025: 10 · 2024: 16 · 2023: 21 · 2022: 29 · 2021: 66 · 2020: 37 · 2019: 31 · 2018: 34 · 2017: 34 · 2016: 48 · 2015: 41 · 2014: 31 · 2013: 23
FWCI
37.57
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diet, Metabolism, and Disease
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
Keywords
sugar consumption, type 2 diabetes, diabetes prevalence, sugar availability, soft drink consumption, obesity, dietary exposure, dose-response relationship, cross-national comparison, socioeconomic determinants of health, urbanization, income
MeSH terms
Adult, Aged, Energy Intake, Carbohydrates, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Humans, Middle Aged, Obesity, Selection Bias, Prevalence, Models, Econometric, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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