Evidence map›Paper›PMID 26441829›Full record

ArticleFrontiers in endocrinology2015

Effects of a Macro-Nutrient Preload on Type 2 Diabetic Patients.

Chun-Jun Li, Gunnar Norstedt, Zhao-Gian Hu, Pei Yu, Dai-Qing Li, Jing Li, Qian Yu, Magnus Sederholm, De-Min Yu

Abstract read
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Article in Frontiers in endocrinology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Chun-Jun LiKey Laboratory of Hormone and Development (Ministry of Health), Department of Endocrinology, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Institute of Endocrinology, Metabolic Disease Hospital, Tianjin Medical University , Tianjin , China.
Gunnar NorstedtCenter for Molecular Medicine, Karolinska Institutet , Stockholm , Sweden.
Zhao-Gian HuKey Laboratory of Hormone and Development (Ministry of Health), Department of Endocrinology, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Institute of Endocrinology, Metabolic Disease Hospital, Tianjin Medical University , Tianjin , China.
Pei YuKey Laboratory of Hormone and Development (Ministry of Health), Department of Endocrinology, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Institute of Endocrinology, Metabolic Disease Hospital, Tianjin Medical University , Tianjin , China.
Dai-Qing LiKey Laboratory of Hormone and Development (Ministry of Health), Department of Endocrinology, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Institute of Endocrinology, Metabolic Disease Hospital, Tianjin Medical University , Tianjin , China.
Jing LiKey Laboratory of Hormone and Development (Ministry of Health), Department of Endocrinology, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Institute of Endocrinology, Metabolic Disease Hospital, Tianjin Medical University , Tianjin , China.
Qian YuKey Laboratory of Hormone and Development (Ministry of Health), Department of Endocrinology, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Institute of Endocrinology, Metabolic Disease Hospital, Tianjin Medical University , Tianjin , China.
Magnus SederholmDepartment of Women's and Children's Health, Karolinska Institutet , Stockholm , Sweden.
De-Min YuKey Laboratory of Hormone and Development (Ministry of Health), Department of Endocrinology, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Institute of Endocrinology, Metabolic Disease Hospital, Tianjin Medical University , Tianjin , China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsMacro-nutrient preloads given 30 min before regular meals may improve metabolism. The aim was to investigate how type 2 diabetic patients react to a preload consisting of a blend of macro-nutrients with a low-glycemic index (Inzone Preload(®)).

methodsIn a before-after study design, 30 subjects with type 2 diabetes mellitus (T2DM) were enrolled in a 12-week program. All subjects were given Inzone Preload (43% proteins, 29% carbohydrates, 10% lipids, and 9% fibers, 71 kcal), 30 min before each meal during 12 weeks. Fasting glucose and postprandial 2 h glucose were monitored every second week. Body weight (BW) and waist circumference were measured each month. Fasting plasma glucose, glycosylated hemoglobin, serum lipids, fasting insulin, C-reactive protein, and homeostasis model assessment were evaluated before and after the intervention. Subjective appetite was monitored using visual analogue scales after the Inzone Preload.

resultsThe dietary intervention significantly influenced several metabolic parameters compared to base line. Inzone Preload treatment reduced mean postprandial plasma glucose levels (12.2 ± 1.2 vs. 10.5 ± 2.0 mmol/L), HbA1c (7.4 ± 0.3 vs. 7.1 ± 0.2%), mean total cholesterol (4.8 ± 0.9 vs. 4.3 ± 0.8 mmol/L), low-density lipoprotein cholesterol (2.8 ± 0.6 vs. 2.5 ± 0.4 mmol/L), and CRP (1.5 ± 1.4 vs. 0.7 ± 0.7 mg/L). BW loss of more than 3% was seen in 13 participants (43%). Feelings of satiety were significantly higher after Inzone Preload than after habitual breakfast (p < 0.05). No significant changes in fasting blood glucose, high-density lipoprotein and total triacylglycerol, HOMA-IR, and HOMA-β were observed.

conclusionA macro-nutrient preload treatment reduces postprandial glucose, inflammatory markers, and serum lipids in patients with T2DM. Approximately half of the study group also displayed reduced BW.

Indexed as

diabetes type IIglucose tolerancehumannutritionpreload

Identifiers

PMID26441829
PMCPMC4584965

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.