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Long-Term Fasting-Induced Ketosis in 1610 Subjects: Metabolic Regulation and Safety

Grundler, Franziska, Mesnage, Robin, Ruppert, Philip M. M., Kouretas, Demetrios, Wilhelmi de Toledo, Françoise·2024·Nutrients
DOI 10.3390/nu16121849PMC PMC11206495
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Abstract

Background: There is a growing consensus that fasting-induced ketosis has beneficial effects on human physiology. Despite these compelling benefits, fasting-induced ketosis raises concerns in some clinicians because it is often inappropriately compared with the pathologic uncontrolled ketone production in diabetic ketoacidosis. The determinants of the inter-individual differences in the intensity of ketosis during long-term fasting is unknown. Methods: We monitored daily variations in fasting ketonemia, as well as ketonuria, which is less invasive, in a large cohort of 1610 subjects, fasting between 4 and 21 days with the Buchinger Wilhelmi program, minimally supplemented with ~75–250 kcal (daily fruit juice, vegetable soup, and honey). Results: Ketonuria was detected in more than 95% of fasting subjects from day 4 onwards. Subjects consuming only soups, without fruit juice or honey, exhibited reduced caloric intake (72 kcal instead of 236 kcal) and carbohydrate intake (15.6 g instead of 56.5 g), leading to more intense ketonuria. Participants with high ketonuria were, in the majority, males, young, had a higher body weight, and had lower HDL-C and urea values. They had a larger decrease in blood glucose, glycated haemoglobin levels, body weight, and waist circumference. Furthermore, in the high-ketonuria group, a larger increase in blood uric acid concentration was observed. Conclusion: Our study showed that long-term fasting triggered ketosis, never reaching pathological levels, and that ketosis is influenced by age, gender, health, and the level of physical activity. Furthermore, it is modulated but not suppressed by minimal carbohydrate intake. Our study paves the way for better understanding how supplementation can modulate the therapeutic effects and tolerability of long-term fasting.

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Figure 1 Flowchart of the study populations. (A). Subjects were part of two observational studies including 1610 and 32 subjects. (B). Study timeline. (C). Distribution of the fasting duration.
Figure 1 Flowchart of the study populations. (A). Subjects were part of two observational studies including 1610 and 32 subjects. (B). Study timeline. (C). Distribution of the fasting duration.CC BY · PubMed Central
Figure 2 Correlation between ketonemia and ketonuria in 32 individuals during long-term fasting. This group of participants performed a 12-day fast (F1 up to F12) followed by 4 days of food reintroduction (REF1 to REF4). Ketonemia measured as the concentration in ß-hydroxybutyrate in blood (A) and ketonuria measured as the concentration in acetoacetate in urine were correlated (B). Cumulative calorie loss estimated from ketonuria and 24 h urine volume (C). An increased amount of physical exercise caused ketonemia to increase more rapidly (D). Red lines with shaded areas (95% confidence level interval) help in visualizing the general trends by fitting a smooth line (A,C), or a linear regression (B,D).
Figure 2 Correlation between ketonemia and ketonuria in 32 individuals during long-term fasting. This group of participants performed a 12-day fast (F1 up to F12) followed by 4 days of food reintroduction (REF1 to REF4). Ketonemia measured as the concentration in ß-hydroxybutyrate in blood (A) and ketonuria measured as the concentration in acetoacetate in urine were correlated (B). Cumulative calorie loss estimated from ketonuria and 24 h urine volume (C). An increased amount of physical exercise caused ketonemia to increase more rapidly (D). Red lines with shaded areas (95% confidence level interval) help in visualizing the general trends by fitting a smooth line (A,C), or a linear regression (B,D).CC BY · PubMed Central
Figure 3 Differences in the intensity of ketonuria during 20 days of fasting in the cohort of 1610 individuals. The heatmap represents individual ketonuria values as a colour scale (not available missing values in black) during 20 days of fasting (F01 up to F21). Hierarchical clustering of Euclidian distances performed using the Ward function highlights two groups separating according to the time of onset of ketonuria.
Figure 3 Differences in the intensity of ketonuria during 20 days of fasting in the cohort of 1610 individuals. The heatmap represents individual ketonuria values as a colour scale (not available missing values in black) during 20 days of fasting (F01 up to F21). Hierarchical clustering of Euclidian distances performed using the Ward function highlights two groups separating according to the time of onset of ketonuria.CC BY · PubMed Central
Figure 4 Influence of food consumption during fasting in the cohort of 1610 patients. Effects of the consumption of honey and juice during the second, third, or fourth day of fasting and its effects on next day ketonuria (0, none, XS, traces; S, small; M, medium; L, large). The mosaic plot shows an area-proportional visualization of observed frequencies. The p-value is computed from a Chi-squared distribution. Shadings for the contingency tables indicates the Pearson residuals, reflecting to which extent the observed values deviate from expected values.
Figure 4 Influence of food consumption during fasting in the cohort of 1610 patients. Effects of the consumption of honey and juice during the second, third, or fourth day of fasting and its effects on next day ketonuria (0, none, XS, traces; S, small; M, medium; L, large). The mosaic plot shows an area-proportional visualization of observed frequencies. The p-value is computed from a Chi-squared distribution. Shadings for the contingency tables indicates the Pearson residuals, reflecting to which extent the observed values deviate from expected values.CC BY · PubMed Central
Figure 5 Machine learning predicts differences in ketonuria during long-term fasting. (A). The most important variables for sample classification. (B–E). Differences for the levels of the variables predicting the separation according to the time of onset of ketonuria. The p-value of a t-test is displayed.
Figure 5 Machine learning predicts differences in ketonuria during long-term fasting. (A). The most important variables for sample classification. (B–E). Differences for the levels of the variables predicting the separation according to the time of onset of ketonuria. The p-value of a t-test is displayed.CC BY · PubMed Central
Figure 6 Uric acid levels correlate with ketonuria and ketonemia. (A). Uric acid levels during fasting correlated well with ketonuria in the group of 1610 patients between the start and the end of long-term fasting. (B). In the group of 32 patients where ketonemia was measured, the relationship between the retention of uric acid and the increase in blood ketone bodies level is even stronger. The red line with shaded area is a linear model with 95% confidence level interval.
Figure 6 Uric acid levels correlate with ketonuria and ketonemia. (A). Uric acid levels during fasting correlated well with ketonuria in the group of 1610 patients between the start and the end of long-term fasting. (B). In the group of 32 patients where ketonemia was measured, the relationship between the retention of uric acid and the increase in blood ketone bodies level is even stronger. The red line with shaded area is a linear model with 95% confidence level interval.CC BY · PubMed Central

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Citations

Cites

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Metadata

Authors
Grundler, F., Mesnage, R., Ruppert, P. M. M., Kouretas, D., & Wilhelmi de Toledo, F.
Affiliations
  • Buchinger Wilhelmi Clinic, Wilhelmi-Beck-Straße 27, 88662 Überlingen, Germany
  • Department for Biochemistry and Molecular Biology (BMB), University of Southern Denmark, 5230 Odense, Denmark
  • Department of Biochemistry-Biotechnology, School of Health Sciences, University of Thessaly, 41500 Larissa, Greece
ORCID
Ruppert, Philip M. M.
Venue
Nutrients
Publisher
MDPI AG
Published
2024-06-13
Volume
16
Issue
12
Pages
1849
ISSN
2072-6643
DOI
10.3390/nu16121849
PMC
PMC11206495
Cited by
29
References
5
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
Buchinger Wilhelmi Development and Holding GmbH

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