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Junker D, Wu M, Reik A, Raspe J, Rupp S, Han J, Näbauer SM, Wiechert M, Somasundaram A, Burian E, Waschulzik B, Makowski MR, Hauner H, Holzapfel C, Karampinos DC. Impact of baseline adipose tissue characteristics on change in adipose tissue volume during a low calorie diet in people with obesity-results from the LION study. Int J Obes (Lond) 2024; 48:1332-1341. [PMID: 38926461 PMCID: PMC11347377 DOI: 10.1038/s41366-024-01568-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 06/04/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND/OBJECTIVES Weight loss outcomes vary individually. Magnetic resonance imaging (MRI)-based evaluation of adipose tissue (AT) might help to identify AT characteristics that predict AT loss. This study aimed to assess the impact of an 8-week low-calorie diet (LCD) on different AT depots and to identify predictors of short-term AT loss using MRI in adults with obesity. METHODS Eighty-one adults with obesity (mean BMI 34.08 ± 2.75 kg/m², mean age 46.3 ± 10.97 years, 49 females) prospectively underwent baseline MRI (liver dome to femoral head) and anthropometric measurements (BMI, waist-to-hip-ratio, body fat), followed by a post-LCD-examination. Visceral and subcutaneous AT (VAT and SAT) volumes and AT fat fraction were extracted from the MRI data. Apparent lipid volumes based on MRI were calculated as approximation for the lipid contained in the AT. SAT and VAT volumes were subdivided into equidistant thirds along the craniocaudal axis and normalized by length of the segmentation. T-tests compared baseline and follow-up measurements and sex differences. Effect sizes on subdivided AT volumes were compared. Spearman Rank correlation explored associations between baseline parameters and AT loss. Multiple regression analysis identified baseline predictors for AT loss. RESULTS Following the LCD, participants exhibited significant weight loss (11.61 ± 3.07 kg, p < 0.01) and reductions in all MRI-based AT parameters (p < 0.01). Absolute SAT loss exceeded VAT loss, while relative apparent lipid loss was higher in VAT (both p < 0.01). The lower abdominopelvic third showed the most significant SAT and VAT reduction. The predictor of most AT and apparent lipid losses was the normalized baseline SAT volume in the lower abdominopelvic third, with smaller volumes favoring greater AT loss (p < 0.01 for SAT and VAT loss and SAT apparent lipid volume loss). CONCLUSIONS The LCD primarily reduces lower abdominopelvic SAT and VAT. Furthermore, lower abdominopelvic SAT volume was detected as a potential predictor for short-term AT loss in persons with obesity.
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Affiliation(s)
- Daniela Junker
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany.
| | - Mingming Wu
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Anna Reik
- Institute for Nutritional Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Johannes Raspe
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Selina Rupp
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Jessie Han
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Stella M Näbauer
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Meike Wiechert
- Institute for Nutritional Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Arun Somasundaram
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Egon Burian
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Birgit Waschulzik
- Institute of AI and Informatics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Marcus R Makowski
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Hans Hauner
- Institute for Nutritional Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany
- Else Kroener-Fresenius-Center of Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Christina Holzapfel
- Institute for Nutritional Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany
- Department of Nutritional, Food and Consumer Sciences, Fulda University of Applied Sciences, Fulda, Germany
| | - Dimitrios C Karampinos
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
- Munich Institute of Biomedical Engineering, Technical University of Munich, Garching, Germany
- Munich Data Science Institute, Technical University of Munich, Garching, Germany
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3
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Somasundaram A, Wu M, Reik A, Rupp S, Han J, Naebauer S, Junker D, Patzelt L, Wiechert M, Zhao Y, Rueckert D, Hauner H, Holzapfel C, Karampinos DC. Evaluating Sex-specific Differences in Abdominal Fat Volume and Proton Density Fat Fraction at MRI Using Automated nnU-Net-based Segmentation. Radiol Artif Intell 2024; 6:e230471. [PMID: 38809148 PMCID: PMC11294970 DOI: 10.1148/ryai.230471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 03/19/2024] [Accepted: 04/24/2024] [Indexed: 05/30/2024]
Abstract
Sex-specific abdominal organ volume and proton density fat fraction (PDFF) in people with obesity during a weight loss intervention was assessed with automated multiorgan segmentation of quantitative water-fat MRI. An nnU-Net architecture was employed for automatic segmentation of abdominal organs, including visceral and subcutaneous adipose tissue, liver, and psoas and erector spinae muscle, based on quantitative chemical shift-encoded MRI and using ground truth labels generated from participants of the Lifestyle Intervention (LION) study. Each organ's volume and fat content were examined in 127 participants (73 female and 54 male participants; body mass index, 30-39.9 kg/m2) and in 81 (54 female and 32 male participants) of these participants after an 8-week formula-based low-calorie diet. Dice scores ranging from 0.91 to 0.97 were achieved for the automatic segmentation. PDFF was found to be lower in visceral adipose tissue compared with subcutaneous adipose tissue in both male and female participants. Before intervention, female participants exhibited higher PDFF in subcutaneous adipose tissue (90.6% vs 89.7%; P < .001) and lower PDFF in liver (8.6% vs 13.3%; P < .001) and visceral adipose tissue (76.4% vs 81.3%; P < .001) compared with male participants. This relation persisted after intervention. As a response to caloric restriction, male participants lost significantly more visceral adipose tissue volume (1.76 L vs 0.91 L; P < .001) and showed a higher decrease in subcutaneous adipose tissue PDFF (2.7% vs 1.5%; P < .001) than female participants. Automated body composition analysis on quantitative water-fat MRI data provides new insights for understanding sex-specific metabolic response to caloric restriction and weight loss in people with obesity. Keywords: Obesity, Chemical Shift-encoded MRI, Abdominal Fat Volume, Proton Density Fat Fraction, nnU-Net ClinicalTrials.gov registration no. NCT04023942 Supplemental material is available for this article. Published under a CC BY 4.0 license.
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Affiliation(s)
| | | | - Anna Reik
- From the Department of Diagnostic and Interventional Radiology,
Klinikum rechts der Isar (A.S., M. Wu, S.R., J.H., S.N., D.J., L.P., D.C.K.),
Institute of Nutritional Medicine, School of Medicine (A.R., M. Wiechert, H.H.,
C.H.), TUM School of Computation, Information, and Technology (Y.Z., D.R.), TUM
School of Medicine and Health (D.R.), and Else Kröner Fresenius Center
for Nutritional Medicine, School of Medicine (H.H.), Technical University of
Munich, Ismaninger Str 22, 81675 Munich, Germany; Department of Computing,
Imperial College London, London, UK (D.R.); Department of Nutritional, Food and
Consumer Sciences, Fulda University of Applied Sciences, Fulda, Germany (C.H.);
and Munich Institute of Biomedical Engineering and Munich Data Science
Institute, Technical University of Munich, Garching, Germany (D.C.K.)
| | - Selina Rupp
- From the Department of Diagnostic and Interventional Radiology,
Klinikum rechts der Isar (A.S., M. Wu, S.R., J.H., S.N., D.J., L.P., D.C.K.),
Institute of Nutritional Medicine, School of Medicine (A.R., M. Wiechert, H.H.,
C.H.), TUM School of Computation, Information, and Technology (Y.Z., D.R.), TUM
School of Medicine and Health (D.R.), and Else Kröner Fresenius Center
for Nutritional Medicine, School of Medicine (H.H.), Technical University of
Munich, Ismaninger Str 22, 81675 Munich, Germany; Department of Computing,
Imperial College London, London, UK (D.R.); Department of Nutritional, Food and
Consumer Sciences, Fulda University of Applied Sciences, Fulda, Germany (C.H.);
and Munich Institute of Biomedical Engineering and Munich Data Science
Institute, Technical University of Munich, Garching, Germany (D.C.K.)
| | - Jessie Han
- From the Department of Diagnostic and Interventional Radiology,
Klinikum rechts der Isar (A.S., M. Wu, S.R., J.H., S.N., D.J., L.P., D.C.K.),
Institute of Nutritional Medicine, School of Medicine (A.R., M. Wiechert, H.H.,
C.H.), TUM School of Computation, Information, and Technology (Y.Z., D.R.), TUM
School of Medicine and Health (D.R.), and Else Kröner Fresenius Center
for Nutritional Medicine, School of Medicine (H.H.), Technical University of
Munich, Ismaninger Str 22, 81675 Munich, Germany; Department of Computing,
Imperial College London, London, UK (D.R.); Department of Nutritional, Food and
Consumer Sciences, Fulda University of Applied Sciences, Fulda, Germany (C.H.);
and Munich Institute of Biomedical Engineering and Munich Data Science
Institute, Technical University of Munich, Garching, Germany (D.C.K.)
| | - Stella Naebauer
- From the Department of Diagnostic and Interventional Radiology,
Klinikum rechts der Isar (A.S., M. Wu, S.R., J.H., S.N., D.J., L.P., D.C.K.),
Institute of Nutritional Medicine, School of Medicine (A.R., M. Wiechert, H.H.,
C.H.), TUM School of Computation, Information, and Technology (Y.Z., D.R.), TUM
School of Medicine and Health (D.R.), and Else Kröner Fresenius Center
for Nutritional Medicine, School of Medicine (H.H.), Technical University of
Munich, Ismaninger Str 22, 81675 Munich, Germany; Department of Computing,
Imperial College London, London, UK (D.R.); Department of Nutritional, Food and
Consumer Sciences, Fulda University of Applied Sciences, Fulda, Germany (C.H.);
and Munich Institute of Biomedical Engineering and Munich Data Science
Institute, Technical University of Munich, Garching, Germany (D.C.K.)
| | - Daniela Junker
- From the Department of Diagnostic and Interventional Radiology,
Klinikum rechts der Isar (A.S., M. Wu, S.R., J.H., S.N., D.J., L.P., D.C.K.),
Institute of Nutritional Medicine, School of Medicine (A.R., M. Wiechert, H.H.,
C.H.), TUM School of Computation, Information, and Technology (Y.Z., D.R.), TUM
School of Medicine and Health (D.R.), and Else Kröner Fresenius Center
for Nutritional Medicine, School of Medicine (H.H.), Technical University of
Munich, Ismaninger Str 22, 81675 Munich, Germany; Department of Computing,
Imperial College London, London, UK (D.R.); Department of Nutritional, Food and
Consumer Sciences, Fulda University of Applied Sciences, Fulda, Germany (C.H.);
and Munich Institute of Biomedical Engineering and Munich Data Science
Institute, Technical University of Munich, Garching, Germany (D.C.K.)
| | - Lisa Patzelt
- From the Department of Diagnostic and Interventional Radiology,
Klinikum rechts der Isar (A.S., M. Wu, S.R., J.H., S.N., D.J., L.P., D.C.K.),
Institute of Nutritional Medicine, School of Medicine (A.R., M. Wiechert, H.H.,
C.H.), TUM School of Computation, Information, and Technology (Y.Z., D.R.), TUM
School of Medicine and Health (D.R.), and Else Kröner Fresenius Center
for Nutritional Medicine, School of Medicine (H.H.), Technical University of
Munich, Ismaninger Str 22, 81675 Munich, Germany; Department of Computing,
Imperial College London, London, UK (D.R.); Department of Nutritional, Food and
Consumer Sciences, Fulda University of Applied Sciences, Fulda, Germany (C.H.);
and Munich Institute of Biomedical Engineering and Munich Data Science
Institute, Technical University of Munich, Garching, Germany (D.C.K.)
| | - Meike Wiechert
- From the Department of Diagnostic and Interventional Radiology,
Klinikum rechts der Isar (A.S., M. Wu, S.R., J.H., S.N., D.J., L.P., D.C.K.),
Institute of Nutritional Medicine, School of Medicine (A.R., M. Wiechert, H.H.,
C.H.), TUM School of Computation, Information, and Technology (Y.Z., D.R.), TUM
School of Medicine and Health (D.R.), and Else Kröner Fresenius Center
for Nutritional Medicine, School of Medicine (H.H.), Technical University of
Munich, Ismaninger Str 22, 81675 Munich, Germany; Department of Computing,
Imperial College London, London, UK (D.R.); Department of Nutritional, Food and
Consumer Sciences, Fulda University of Applied Sciences, Fulda, Germany (C.H.);
and Munich Institute of Biomedical Engineering and Munich Data Science
Institute, Technical University of Munich, Garching, Germany (D.C.K.)
| | - Yu Zhao
- From the Department of Diagnostic and Interventional Radiology,
Klinikum rechts der Isar (A.S., M. Wu, S.R., J.H., S.N., D.J., L.P., D.C.K.),
Institute of Nutritional Medicine, School of Medicine (A.R., M. Wiechert, H.H.,
C.H.), TUM School of Computation, Information, and Technology (Y.Z., D.R.), TUM
School of Medicine and Health (D.R.), and Else Kröner Fresenius Center
for Nutritional Medicine, School of Medicine (H.H.), Technical University of
Munich, Ismaninger Str 22, 81675 Munich, Germany; Department of Computing,
Imperial College London, London, UK (D.R.); Department of Nutritional, Food and
Consumer Sciences, Fulda University of Applied Sciences, Fulda, Germany (C.H.);
and Munich Institute of Biomedical Engineering and Munich Data Science
Institute, Technical University of Munich, Garching, Germany (D.C.K.)
| | - Daniel Rueckert
- From the Department of Diagnostic and Interventional Radiology,
Klinikum rechts der Isar (A.S., M. Wu, S.R., J.H., S.N., D.J., L.P., D.C.K.),
Institute of Nutritional Medicine, School of Medicine (A.R., M. Wiechert, H.H.,
C.H.), TUM School of Computation, Information, and Technology (Y.Z., D.R.), TUM
School of Medicine and Health (D.R.), and Else Kröner Fresenius Center
for Nutritional Medicine, School of Medicine (H.H.), Technical University of
Munich, Ismaninger Str 22, 81675 Munich, Germany; Department of Computing,
Imperial College London, London, UK (D.R.); Department of Nutritional, Food and
Consumer Sciences, Fulda University of Applied Sciences, Fulda, Germany (C.H.);
and Munich Institute of Biomedical Engineering and Munich Data Science
Institute, Technical University of Munich, Garching, Germany (D.C.K.)
| | - Hans Hauner
- From the Department of Diagnostic and Interventional Radiology,
Klinikum rechts der Isar (A.S., M. Wu, S.R., J.H., S.N., D.J., L.P., D.C.K.),
Institute of Nutritional Medicine, School of Medicine (A.R., M. Wiechert, H.H.,
C.H.), TUM School of Computation, Information, and Technology (Y.Z., D.R.), TUM
School of Medicine and Health (D.R.), and Else Kröner Fresenius Center
for Nutritional Medicine, School of Medicine (H.H.), Technical University of
Munich, Ismaninger Str 22, 81675 Munich, Germany; Department of Computing,
Imperial College London, London, UK (D.R.); Department of Nutritional, Food and
Consumer Sciences, Fulda University of Applied Sciences, Fulda, Germany (C.H.);
and Munich Institute of Biomedical Engineering and Munich Data Science
Institute, Technical University of Munich, Garching, Germany (D.C.K.)
| | - Christina Holzapfel
- From the Department of Diagnostic and Interventional Radiology,
Klinikum rechts der Isar (A.S., M. Wu, S.R., J.H., S.N., D.J., L.P., D.C.K.),
Institute of Nutritional Medicine, School of Medicine (A.R., M. Wiechert, H.H.,
C.H.), TUM School of Computation, Information, and Technology (Y.Z., D.R.), TUM
School of Medicine and Health (D.R.), and Else Kröner Fresenius Center
for Nutritional Medicine, School of Medicine (H.H.), Technical University of
Munich, Ismaninger Str 22, 81675 Munich, Germany; Department of Computing,
Imperial College London, London, UK (D.R.); Department of Nutritional, Food and
Consumer Sciences, Fulda University of Applied Sciences, Fulda, Germany (C.H.);
and Munich Institute of Biomedical Engineering and Munich Data Science
Institute, Technical University of Munich, Garching, Germany (D.C.K.)
| | - Dimitrios C. Karampinos
- From the Department of Diagnostic and Interventional Radiology,
Klinikum rechts der Isar (A.S., M. Wu, S.R., J.H., S.N., D.J., L.P., D.C.K.),
Institute of Nutritional Medicine, School of Medicine (A.R., M. Wiechert, H.H.,
C.H.), TUM School of Computation, Information, and Technology (Y.Z., D.R.), TUM
School of Medicine and Health (D.R.), and Else Kröner Fresenius Center
for Nutritional Medicine, School of Medicine (H.H.), Technical University of
Munich, Ismaninger Str 22, 81675 Munich, Germany; Department of Computing,
Imperial College London, London, UK (D.R.); Department of Nutritional, Food and
Consumer Sciences, Fulda University of Applied Sciences, Fulda, Germany (C.H.);
and Munich Institute of Biomedical Engineering and Munich Data Science
Institute, Technical University of Munich, Garching, Germany (D.C.K.)
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5
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Metzendorf MI, Wieland LS, Richter B. Mobile health (m-health) smartphone interventions for adolescents and adults with overweight or obesity. Cochrane Database Syst Rev 2024; 2:CD013591. [PMID: 38375882 PMCID: PMC10877670 DOI: 10.1002/14651858.cd013591.pub2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
BACKGROUND Obesity is considered to be a risk factor for various diseases, and its incidence has tripled worldwide since 1975. In addition to potentially being at risk for adverse health outcomes, people with overweight or obesity are often stigmatised. Behaviour change interventions are increasingly delivered as mobile health (m-health) interventions, using smartphone apps and wearables. They are believed to support healthy behaviours at the individual level in a low-threshold manner. OBJECTIVES To assess the effects of integrated smartphone applications for adolescents and adults with overweight or obesity. SEARCH METHODS We searched CENTRAL, MEDLINE, PsycINFO, CINAHL, and LILACS, as well as the trials registers ClinicalTrials.gov and World Health Organization International Clinical Trials Registry Platform on 2 October 2023 (date of last search for all databases). We placed no restrictions on the language of publication. SELECTION CRITERIA Participants were adolescents and adults with overweight or obesity. Eligible interventions were integrated smartphone apps using at least two behaviour change techniques. The intervention could target physical activity, cardiorespiratory fitness, weight loss, healthy diet, or self-efficacy. Comparators included no or minimal intervention (NMI), a different smartphone app, personal coaching, or usual care. Eligible studies were randomised controlled trials of any duration with a follow-up of at least three months. DATA COLLECTION AND ANALYSIS We used standard Cochrane methodology and the RoB 2 tool. Important outcomes were physical activity, body mass index (BMI) and weight, health-related quality of life, self-efficacy, well-being, change in dietary behaviour, and adverse events. We focused on presenting studies with medium- (6 to < 12 months) and long-term (≥ 12 months) outcomes in our summary of findings table, following recommendations in the core outcome set for behavioural weight management interventions. MAIN RESULTS We included 18 studies with 2703 participants. Interventions lasted from 2 to 24 months. The mean BMI in adults ranged from 27 to 50, and the median BMI z-score in adolescents ranged from 2.2 to 2.5. Smartphone app versus no or minimal intervention Thirteen studies compared a smartphone app versus NMI in adults; no studies were available for adolescents. The comparator comprised minimal health advice, handouts, food diaries, smartphone apps unrelated to weight loss, and waiting list. Measures of physical activity: at 12 months' follow-up, a smartphone app compared to NMI probably reduces moderate to vigorous physical activity (MVPA) slightly (mean difference (MD) -28.9 min/week (95% confidence interval (CI) -85.9 to 28; 1 study, 650 participants; moderate-certainty evidence)). We are very uncertain about the results of estimated energy expenditure and cardiorespiratory fitness at eight months' follow-up. A smartphone app compared with NMI probably results in little to no difference in changes in total activity time at 12 months' follow-up and leisure time physical activity at 24 months' follow-up. Anthropometric measures: a smartphone app compared with NMI may reduce BMI (MD of BMI change -2.6 kg/m2, 95% CI -6 to 0.8; 2 studies, 146 participants; very low-certainty evidence) at six to eight months' follow-up, but the evidence is very uncertain. At 12 months' follow-up, a smartphone app probably resulted in little to no difference in BMI change (MD -0.1 kg/m2, 95% CI -0.4 to 0.3; 1 study; 650 participants; moderate-certainty evidence). A smartphone app compared with NMI may result in little to no difference in body weight change (MD -2.5 kg, 95% CI -6.8 to 1.7; 3 studies, 1044 participants; low-certainty evidence) at 12 months' follow-up. At 24 months' follow-up, a smartphone app probably resulted in little to no difference in body weight change (MD 0.7 kg, 95% CI -1.2 to 2.6; 1 study, 245 participants; moderate-certainty evidence). A smartphone app compared with NMI may result in little to no difference in self-efficacy for a physical activity score at eight months' follow-up, but the results are very uncertain. A smartphone app probably results in little to no difference in quality of life and well-being at 12 months (moderate-certainty evidence) and in little to no difference in various measures used to inform dietary behaviour at 12 and 24 months' follow-up. We are very uncertain about adverse events, which were only reported narratively in two studies (very low-certainty evidence). Smartphone app versus another smartphone app Two studies compared different versions of the same app in adults, showing no or minimal differences in outcomes. One study in adults compared two different apps (calorie counting versus ketogenic diet) and suggested a slight reduction in body weight at six months in favour of the ketogenic diet app. No studies were available for adolescents. Smartphone app versus personal coaching Only one study compared a smartphone app with personal coaching in adults, presenting data at three months. Two studies compared these interventions in adolescents. A smartphone app resulted in little to no difference in BMI z-score compared to personal coaching at six months' follow-up (MD 0, 95% CI -0.2 to 0.2; 1 study; 107 participants). Smartphone app versus usual care Only one study compared an app with usual care in adults but only reported data at three months on participant satisfaction. No studies were available for adolescents. We identified 34 ongoing studies. AUTHORS' CONCLUSIONS The available evidence is limited and does not demonstrate a clear benefit of smartphone applications as interventions for adolescents or adults with overweight or obesity. While the number of studies is growing, the evidence remains incomplete due to the high variability of the apps' features, content and components, which complicates direct comparisons and assessment of their effectiveness. Comparisons with either no or minimal intervention or personal coaching show minor effects, which are mostly not clinically significant. Minimal data for adolescents also warrants further research. Evidence is also scarce for low- and middle-income countries as well as for people with different socio-economic and cultural backgrounds. The 34 ongoing studies suggest sustained interest in the topic, with new evidence expected to emerge within the next two years. In practice, clinicians and healthcare practitioners should carefully consider the potential benefits, limitations, and evolving research when recommending smartphone apps to adolescents and adults with overweight or obesity.
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Affiliation(s)
- Maria-Inti Metzendorf
- Institute of General Practice, Medical Faculty of the Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - L Susan Wieland
- Center for Integrative Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Bernd Richter
- Institute of General Practice, Medical Faculty of the Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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6
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Zhang S, Wu P, Tian Y, Liu B, Huang L, Liu Z, Lin N, Xu N, Ruan Y, Zhang Z, Wang M, Cui Z, Zhou H, Xie L, Chen H, Sun J. Gut Microbiota Serves a Predictable Outcome of Short-Term Low-Carbohydrate Diet (LCD) Intervention for Patients with Obesity. Microbiol Spectr 2021; 9:e0022321. [PMID: 34523948 PMCID: PMC8557869 DOI: 10.1128/spectrum.00223-21] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 08/15/2021] [Indexed: 12/25/2022] Open
Abstract
To date, much progress has been made in dietary therapy for obese patients. A low-carbohydrate diet (LCD) has reached a revival in its clinical use during the past decade with undefined mechanisms and debatable efficacy. The gut microbiota has been suggested to promote energy harvesting. Here, we propose that the gut microbiota contributes to the inconsistent outcome under an LCD. To test this hypothesis, patients with obesity or patients who were overweight were randomly assigned to a normal diet (ND) or an LCD group with ad libitum energy intake for 12 weeks. Using matched sampling, the microbiome profile at baseline and end stage was examined. The relative abundance of butyrate-producing bacteria, including Porphyromonadaceae Parabacteroides and Ruminococcaceae Oscillospira, was markedly increased after LCD intervention for 12 weeks. Moreover, within the LCD group, participants with a higher relative abundance of Bacteroidaceae Bacteroides at baseline exhibited a better response to LCD intervention and achieved greater weight loss outcomes. Nevertheless, the adoption of an artificial neural network (ANN)-based prediction model greatly surpasses a general linear model in predicting weight loss outcomes after LCD intervention. Therefore, the gut microbiota served as a positive outcome predictor and has the potential to predict weight loss outcomes after short-term LCD intervention. Gut microbiota may help to guide the clinical application of short-term LCD intervention to develop effective weight loss strategies. (This study has been registered at the China Clinical Trial Registry under approval no. ChiCTR1800015156). IMPORTANCE Obesity and its related complications pose a serious threat to human health. Short-term low-carbohydrate diet (LCD) intervention without calorie restriction has a significant weight loss effect for overweight/obese people. Furthermore, the relative abundance of Bacteroidaceae Bacteroides is a positive outcome predictor of individual weight loss after short-term LCD intervention. Moreover, leveraging on these distinct gut microbial structures at baseline, we have established a prediction model based on the artificial neural network (ANN) algorithm that could be used to estimate weight loss potential before each clinical trial (with Chinese patent number 2021104655623). This will help to guide the clinical application of short-term LCD intervention to improve weight loss strategies.
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Affiliation(s)
- Susu Zhang
- Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - Peili Wu
- Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Department of Endocrinology and Metabolism, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Ye Tian
- Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - Bingdong Liu
- State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
- The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Liujing Huang
- State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - Zhihong Liu
- State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - Nie Lin
- Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Department of Endocrinology and Metabolism, Shantou Central Hospital, Shantou, China
| | - Ningning Xu
- Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yuting Ruan
- Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zhen Zhang
- Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Ming Wang
- Nephrology Center of Integrated Traditional Chinese and Western Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zongbing Cui
- State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - HongWei Zhou
- State Key Laboratory of Organ Failure Research, Microbiome Medicine Center, Division of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Liwei Xie
- Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Hong Chen
- Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jia Sun
- Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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