Conceived and designed the experiments: ECMM PW AA WHMS. Performed the experiments: PW FGB SvO WKWHW MRA. Analyzed the data: PW CH. Wrote the paper: PW ECMM. Clinical investigation and intervention: MvB TML SAJ AFHP JAM TH-D AK MK.
The authors have declared that no competing interests exist.
Weight regain after weight loss is common. In the Diogenes dietary intervention study, high protein and low glycemic index (GI) diet improved weight maintenance.
To identify blood predictors for weight change after weight loss following the dietary intervention within the Diogenes study.
Blood samples were collected at baseline and after 8-week low caloric diet-induced weight loss from 48 women who continued to lose weight and 48 women who regained weight during subsequent 6-month dietary intervention period with 4 diets varying in protein and GI levels. Thirty-one proteins and 3 steroid hormones were measured.
Angiotensin I converting enzyme (ACE) was the most important predictor. Its greater reduction during the 8-week weight loss was related to continued weight loss during the subsequent 6 months, identified by both Logistic Regression and Random Forests analyses. The prediction power of ACE was influenced by immunoproteins, particularly fibrinogen. Leptin, luteinizing hormone and some immunoproteins showed interactions with dietary protein level, while interleukin 8 showed interaction with GI level on the prediction of weight maintenance. A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%. A logistic regression model with independent variables from 9 blood analytes had a prediction accuracy of 92%.
A selected panel of blood proteins/steroids can predict the weight change after weight loss. ACE may play an important role in weight maintenance. The interactions of blood factors with dietary components are important for personalized dietary advice after weight loss.
ClinicalTrials.gov
The worldwide epidemic of obesity and related health problems like diabetes
It is well recognised that obesity has both a genetic and an environmental basis. An individual's susceptibility is determined in part by genetics, while the observed outcome is strongly influenced by environmental factors (diet, physical activity etc.). In addition to single-gene variants causing obesity
The development of obesity is a complex physiological process, so are weight loss and weight maintenance. Previous studies on the prediction of weight change after weight loss have mostly focussed on psychological and behavioural aspects
The participants were part of the pan-European, randomized and controlled dietary intervention study Diogenes (
On each CID, the anthropometrical and physiological parameters were measured, and blood, urine and fat biopsies were taken using the same standardized protocol at each centre
The sample size estimation was done based on the complete Diogenes study and has been described previously
The subjects beyond the 10–90 percentiles of the score of each diet group were considered as the extremes and excluded from this analysis. From the remaining subjects, the 12 with the lowest (negative) score were defined as the weight-losers and the 12 with highest (positive) score as the weight-regainers in each diet group. In total, 96 subjects were selected for our study.
We first composed a large list of blood proteins involved in obesity as reported in the literature, then searched for available assay methods. The targeted analytes were mainly determined by its relevance for obesity as described in
All the samples were blinded and randomly allocated with respect to dietary intervention and weight change prior to transport to the labs for analysis. The majority of the candidates were analyzed in plasma, unless otherwise stated in serum, by two multiplex biomarker testing laboratories with Clinical Laboratory Improvement Amendments (CLIA) certification: Rules Based Medicine (RBM; Austin, TX, USA) applying their Human Metabolic Map version 1.0, (
Interleukin (IL) 6, IL8 and tumor necrosis factor (TNF) α were analyzed with Sanquin Pelikine compact human ELISA kits (Amsterdam, the Netherlands). Amylin (IAPP) was analyzed with Linco human amylin (total) ELISA kit (St. Charles, MO, USA). Matrix metalloproteinase 9 (MMP9) was analyzed in serum by R&D systems Quantikine human MMP-9 (total) immunoassay kit (Minneapolis, MN, USA). Serum haptoglobin (HPT) was determined by a clinical immunoturbidimetric method using an LX-20 analyzer (Beckman-Coulter, Brea, CA, USA). Serum C-reactive protein (CRP) was quantified by an immunoturbidimetric assay with monocloncal antibodies (Roche Diagnostics, Hvidovre, Denmark) using a COBAS Integra 400 analyzer. Fibrinogen (FG) and coagulation factor VII (F7) concentrations were determined only at CID1 by measuring the clotting time of the diluted plasma with the STA-R Evolution Coagulation Analyzer (Diagnostica Stago, Asnieres Sur Seine, France).
The study was approved by local ethical committees in the respective countries: 1. Medical Ethics Committee of the University Hospital Maastricht and Maastricht University, the Netherlands; 2. The Committees on Biomedical Research Ethics for the Capital region of Denmark, Denmark; 3. Suffolk Local Research Ethics Committee, United Kingdom; 4. University of Crete Ethics Committee, Greece; 5. the Ethics Commission of the University of Potsdam; 6. Research Ethics Committee at the University of Navarra, Spain; 7. Ethical Committee of the Institute of Endocrinology, Czech Republic; 8. Ethical Committee to the National Transport Multiprofile Hospital in Sofia, Bulgaria. All participants signed a written informed consent.
Analytes by multiplex assays were excluded if more than half of the samples were not measurable on the standard curve or if controls showed high variation. The final list of included analytes is shown in
Category | Symbol | Name | Executed |
Sex hormones | PRO | Progesterone | Rules Based Medicine |
TES | Testosterone | Rules Based Medicine | |
LH | Luteinizing Hormone | Rules Based Medicine | |
FSH | Follicle Stimulation Hormone | Rules Based Medicine | |
PRL | Prolactin | Rules Based Medicine | |
Other steroid hormone | COR | Cortisol | Rules Based Medicine |
Vascular factors | ACE | Angiotensin I converting enzyme 1 | Rules Based Medicine |
AGT | Angiotensinogen | Rules Based Medicine | |
PAI1 | Plasminogen activator inhibitor-1, active | Aushon SearchLight | |
FG | Fibrinogen | In-house | |
F7 | Coagulation factor VII | In-house | |
Adipokines | LEP | Leptin | Rules Based Medicine |
RETN | Resistin | Rules Based Medicine | |
ASP | Acylation stimulation protein | Rules Based Medicine | |
ADIPOQ | Adiponectin | Rules Based Medicine | |
RBP4 | Retinol binding protein 4 | Aushon SearchLight | |
Insulin and related hormones | INS | Insulin | Rules Based Medicine |
GCG | Glucagon | Rules Based Medicine | |
IAPP | Islet amyloid polypeptide, amylin, total | In-house | |
Immunoproteins | MIF | Macrophage migration inhibiting factor | Aushon SearchLight |
IL6 | Interleukin 6 | In-house | |
IL8 | Interleukin 8 | In-house | |
TNFα | Tumor necrosis factor alpha | In-house | |
MMP9 | Matrix metallopeptidase 9 | In-house |
|
HPT | Haptoglobin | In-house |
|
CRP | C-reactive protein | In-house |
|
Growth factors | GH | Growth hormone | Aushon SearchLight |
IGF1 | Insulin-like growth factor 1 | Rules Based Medicine | |
VEGFD | Vascular endothelial growth factor-D | Aushon SearchLight | |
PEDF | Pigment epithelium-derived factor | Aushon SearchLight | |
IGFBP1 | Insulin-like growth factor binding protein 1 | Aushon SearchLight | |
IGFBP3 | Insulin-like growth factor binding protein 3 | Aushon SearchLight | |
Satiety hormones | GLP1 | Glucagon-like Peptide-1, total | Rules Based Medicine |
PP | Pancreatic polypeptide | Rules Based Medicine |
Analyzed in serum, others in plasma.
The anthropometrical and physiological parameters were expressed as mean±SD. Student t-test or Mann-Whitney test was applied to compare the difference between weight-losers and -regainers.
Taking weight loss or regain during the 6-month maintenance period as the outcome, logistic regression with Logit function in Generalized Linear Model was used to examine blood analytes one by one with the concentrations at CID1, CID2, and the fold change during the weight loss period (CID2/CID1) (all Ln-transformed), with age and the fold change of weight as covariates. When the interactions of blood analytes with dietary components were examined, the dietary protein level and GI level were also included as factors in the regression model. Significant variables were further used to build multinomial logistic regression models by backward stepwise modelling, with age and the fold change of weight always as forced entry variables. Nagelkerke pseudo R2 was used to estimate the explained variance by the prediction model. The above analyses were done with SPSS version 15.0 (SPSS Inc., Chicago, IL, USA). A two-sided p-value<0.05 was taken as significant.
Random Forests (RF) is a supervised non-linear and non-parametric learning algorithm, which has been successfully applied to various, especially biological problems and with a good reputation on accuracy and robustness
From the Diogenes dietary intervention study, 96 overweight/obese but otherwise healthy women (29–49 years of age) who had most pronounced (but not extreme) continued weight loss or weight regain after weight loss according to the weight maintenance score, were selected evenly from 4 dietary groups. Weight-losers lost 3.3±2.2 kg weight, while weight-regainers regained 3.9±1.2 kg weight during the 6-month maintenance period.
Anthropometrical and physiological characteristics were not different at baseline (CID1) and post-weight loss (CID2), and only showed a trend (p = 0.057) for younger age in the weight-losers compared to the -regainers (
Parameters | Time | Pooled | Diet1 LP/LGI | Diet2 LP/HGI | Diet3 HP/LGI | Diet4 HP/HGI | |||||
WLn = 48 | WRn = 48 | WLn = 12 | WRn = 12 | WLn = 12 | WRn = 12 | WLn = 12 | WRn = 12 | WLn = 12 | WRn = 12 | ||
Weight maintenance score |
|
|
|
|
|
|
|
|
|
|
|
Age (year) | 39.3±5.3 | 41.3±5.0 | 43.0±5.8 | 41.1±3.8 |
|
|
38.4±4.4 | 39.3±6.0 | 37.4±5.6 | 41.8±6.0 | |
Weight (kg) | CID1 | 97.5±16.4 | 93.3±12.6 | 101.3±18.5 | 98.9±10.8 | 87.3±6.9 | 91.9±12.8 | 104.5±16.4 | 91.6±16.2 | 96.7±17.3 | 91.0±9.3 |
CID2 | 86.3±14.6 | 83.8±11.8 | 89.7±17.5 | 88.7±9.8 | 78.0±7.6 | 83.1±12.6 | 91.7±14.7 | 81.7±15.3 | 85.8±14.4 | 81.5±8.4 | |
CID3 | 83.0±13.6 | 87.7±12.2 | 86.1±16.9 | 93.2±10.4 |
|
|
87.1±13.5 | 85.4±15.9 | 82.4±13.6 | 84.7±8.2 | |
BMI (kg·m−2) | CID1 | 34.5±4.8 | 33.3±4.3 | 35.8±5.0 | 34.6±4.4 | 30.9±2.6 | 33.4±4.4 |
|
|
35.0±5.4 | 33.0±4.2 |
CID2 | 30.6±4.3 | 29.9±4.1 | 31.6±4.9 | 31.1±4.2 | 27.6±2.9 | 30.2±4.4 | 31.9±3.8 | 28.8±4.2 | 31.1±4.5 | 29.5±3.7 | |
CID3 |
|
|
30.4±4.8 | 32.6±4.3 |
|
|
30.3±3.3 | 30.0±4.3 | 29.8±4.2 | 30.6±3.7 | |
Waist (cm) | CID1 | 104±13 | 102±11 | 107±13 | 107±10 | 96±7 | 101±13 | 107±9 | 101±13 | 105±19 | 99±10 |
CID2 | 95±12 | 94±10 | 96±12 | 98±9 |
|
|
100±7 | 92±13 | 96±16 | 93±9 | |
Systolic blood pressure (mmHg) | CID1 | 121±13 | 120±12 | 128±12 | 122±13 | 114±10 | 116±13 | 116±12 | 119±11 | 124±12 | 124±13 |
CID2 | 116±12 | 114±13 | 123±12 | 115±12 | 110±11 | 110±15 | 113±10 | 118±11 | 119±14 | 114±13 | |
Diastolic blood pressure (mmHg) | CID1 | 75±10 | 73±10 | 80±11 | 75±9 | 71±8 | 70±12 | 74±6 | 73±11 | 75±13 | 75±8 |
CID2 | 71±9 | 71±11 | 73±7 | 74±12 | 69±11 | 68±8 | 70±8 | 75±12 | 73±11 | 69±10 | |
Cholesterol (mmol/L) | CID1 | 4.6±0.8 | 4.6±1.0 | 5.0±0.8 | 4.3±1.4 | 4.6±0.8 | 4.9±0.9 | 4.5±0.9 | 4.6±0.7 | 4.5±0.7 | 4.7±1.0 |
CID2 | 4.1±0.7 | 4.2±0.8 | 4.3±0.8 | 4.0±1.0 |
|
|
4.1±0.4 | 4.0±0.9 | 4.0±0.9 | 4.2±0.8 | |
Triglycerides (mmol/L) | CID1 | 1.3±0.5 | 1.2±0.5 | 1.4±0.6 | 1.2±0.6 | 1.1±0.4 | 1.1±0.6 | 1.2±0.7 | 1.0±0.4 | 1.2±0.3 | 1.5±0.5 |
CID2 | 1.1±0.3 | 1.1±0.5 | 1.1±0.4 | 1.2±0.8 | 1.0±0.4 | 1.0±0.4 | 1.1±0.3 | 0.9±0.3 | 1.1±0.3 | 1.3±0.4 | |
HDL (mmol/L) | CID1 | 1.3±0.3 | 1.3±0.4 | 1.3±0.3 | 1.1±0.4 | 1.3±0.3 | 1.4±0.3 | 1.3±0.3 | 1.4±0.4 | 1.2±0.2 | 1.1±0.3 |
CID2 | 1.2±0.2 | 1.2±0.3 | 1.2±0.3 | 1.1±0.3 | 1.2±0.3 | 1.3±0.2 | 1.2±0.2 | 1.3±0.4 | 1.1±0.2 | 1.1±0.2 | |
LDL (mmol/L) | CID1 | 2.8±0.7 | 2.8±0.8 | 3.1±0.7 | 2.7±1.1 | 2.7±0.7 | 3.0±0.7 | 2.6±0.7 | 2.7±0.6 | 2.8±0.6 | 2.9±0.9 |
CID2 | 2.4±0.6 | 2.5±0.7 | 2.6±0.8 | 2.4±0.7 | 2.3±0.4 | 2.7±0.6 | 2.4±0.3 | 2.3±0.7 | 2.4±0.8 | 2.6±0.8 | |
Glucose (mmol/L) | CID1 | 4.9±0.8 | 5.0±0.6 | 5.2±0.7 | 5.4±0.9 | 4.7±0.4 | 4.8±0.4 | 4.8±0.5 | 5.0±0.5 | 4.8±1.2 | 5.0±0.6 |
CID2 | 4.7±0.7 | 4.8±0.4 | 4.9±1.0 | 4.9±0.6 | 4.5±0.4 | 4.7±0.4 | 4.6±0.5 | 4.7±0.4 | 4.8±0.5 | 4.7±0.3 | |
HOMA-IR |
CID1 | 2.0±1.3 | 2.4±1.8 | 2.4±1.7 | 3.8±3.0 | 1.8±0.7 | 1.7±0.8 | 1.6±0.7 | 1.9±0.8 | 2.3±1.8 | 2.1±0.7 |
CID2 | 1.2±0.9 | 1.4±0.8 | 1.3±0.9 | 1.8±1.2 | 1.0±0.6 | 1.1±0.4 | 1.1±0.6 | 1.1±0.5 | 1.5±1.3 | 1.5±0.5 |
Values are mean±SD from the fasted state. CID1: baseline, CID2: after 8-week weight loss; CID3: after 6-month weight maintenance/diet intervention. Bold values are different between continued weight-losers (WL) and weight-regainers (WR) by t-test (p<0.05), beside HOMR-IR by Mann-Whitney test.
Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated as fasting glucose (mM)×fasting insulin (µIU/mL)/22.5.
According to the post-intervention dietary record (n = 73), the protein content was 17.8±4.1 and 20.7±5.4 energy% for LP and HP diets, respectively, and the GI was 56.0±4.6 and 59.5±4.5 for LGI and HGI diets, respectively. There was a modest but significant difference in dietary protein (p = 0.012) and in GI (p = 0.002) between the assigned dietary groups. The difference were confirmed by the urinary nitrogen excretion as a marker of adherence to HP or LP diet (13.8±3.3 and 11.8±3.3 g/day, p = 0.023).
We measured 31 blood proteins and 3 steroid hormones of 96 subjects at two time points before the dietary intervention/weight maintenance, namely at CID1 and CID2 (
In the pooled subjects, the fold change of angiotensin I converting enzyme 1 (ACE, p = 0.007), progesterone (PRO, p = 0.024), IGF binding protein 1 (IGFBP1, p = 0.032), the baseline concentrations of MMP9 (p = 0.029) and IGFBP1 (p = 0.033), and the concentration of testosterone (TES, p = 0.048) at CID2 were significant to predict the outcome of weight maintenance. In addition, the baseline concentrations of IL8 (p = 0.090) and IAPP (p = 0.093), the concentrations of CRP (p = 0.059), macrophage migration inhibiting factor (MIF, p = 0.067), glucagon-like peptide-1 (GLP1, p = 0.086) and glucagon (GCG, p = 0.086) at CID2, and the fold change of GLP1 (p = 0.053), TES (p = 0.063) and plasminogen activator inhibitor-1 (PAI1, p = 0.074) had a trend towards significance (
Volcano plot of the significance P-value versus odd ratio exp(B) of blood proteins/steroids for predicting continued weight loss during the 6-month maintenance period by logistic regression controlled for age and the fold change of weight. The symbols of the analytes are listed in
When these 15 variables were analyzed by multinomial logistic regression, 8 were selected as most important independent variables to build up a prediction model (
|
|
|||
pseudo R2 (Nagelkerke) | model fitting p-value by Likelihood Ratio Tests | Variable | p-value by Wald Tests | Exp(B) |
|
Intercept |
|
||
0.108 | 0.017 | age | 0.111 | 0.93 |
weight_12 |
|
5.3E-09 | ||
|
Intercept | 0.059 | ||
0.653 | 1.5E-09 | age | 0.528 | 0.95 |
weight_12 | 0.375 | 3.1E-06 | ||
ACE_12 |
|
3.0E-05 | ||
MMP9_1 |
|
0.07 | ||
TES_2 |
|
22.6 | ||
IGFBP1_12 |
|
0.25 | ||
MIF_2 |
|
2.40 | ||
PAI1_12 |
|
0.40 | ||
CRP_2 |
|
2.10 | ||
IAPP_1 | 0.052 | 0.50 | ||
|
Intercept |
|
||
0.468 | 1.7E-04 | [dietprotein = low] |
|
1.6E+06 |
[dietGI = low] | 0.269 | 1.94 | ||
age |
|
0.85 | ||
R_Weight_12 | 0.055 | 1.8E-11 | ||
[dietGI = low] * IL8_12 |
|
0.03 | ||
IL8_12 | 0.133 | 3.08 | ||
[dietprotein = low] * LEP_1 |
|
0.02 | ||
LEP_1 | 0.232 | 2.68 | ||
[dietprotein = low] * LH_1 |
|
11.3 | ||
LH_1 | 0.090 | 0.23 | ||
[dietGI = low] * F7_1 | 0.061 | 115 | ||
F7_1 | 0.218 | 0.13 | ||
[dietGI = low] * MMP9_12 | 0.068 | 12.4 | ||
MMP9_12 | 0.543 | 0.56 | ||
|
Intercept |
|
||
0.835 | 3.2E-12 | [dietprotein = low] | 0.129 | 3.6E+07 |
[dietGI = low] | 0.483 | 2.46 | ||
age | 0.051 | 0.80 | ||
Weight_12 | 0.122 | 4.2E-20 | ||
ACE_12 |
|
3.6E-11 | ||
MMP9_1 |
|
0.03 | ||
CRP_2 |
|
6.69 | ||
PAI1_12 | 0.054 | 0.33 | ||
TES_2 | 0.072 | 49.4 | ||
IAPP_1 | 0.079 | 0.29 | ||
[dietGI = low] *IL8_12 |
|
9.6E-06 | ||
IL8_12 |
|
24.8 | ||
[dietprotein = low] * LH_1 |
|
5.5E+03 | ||
LH_1 |
|
2.1E-03 | ||
[dietprotein = low] * LEP_1 | 0.145 | 0.01 | ||
LEP_1 | 0.190 | 0.12 |
Dependent Variable is weight loss vs. weight regain during dietary intervention/maintenance.
The variables of measured blood analytes were Ln-transformed in the model. Suffix “_1”: concentration at CID1, “_2”: concentration at CID2, “_12”: fold change of the concentration (CID2/CID1). The symbol of blood analytes are listed in
All 98 blood protein/steroid variables were ranked by their MDG and MDA for the importance to classify the subjects into weight-losers or weight-regainers. With the pooled subjects the top 15 most important variables were identified based on MDG, which is very constant during classification permutation (
Overall, there is a strong correlation between the p-value from the logistic regression assay and the MDG from RF assay (r = 0.611, p<0.001 in Pearson correlation on Ln-transformed values). Nine of the top 15 most important variables from RF overlapped with significant or tending to be significant variables from logistic regression. The fold change of ACE was identified as the most important variable by both methods. For the top 5, only baseline FG is different from the logistic regression outcome. Its importance is even clearer when MDA is used for ranking.
Because the interactions among variables increase their importance during making the decision trees, we checked possible interactions of baseline FG with other variables by logistic regression and found that it significantly interacts with the fold change of ACE on the prediction (p = 0.023). This interaction was still significant after controlling for the fold change of body fat mass (p = 0.014) as tested in a subset of subjects who had fat mass measured (n = 59). Based on the median baseline FG values we split the subjects into a low and a high group. Only in the high FG group was the fold change of ACE significantly associated with the outcome of weight maintenance, with a greater reduction in ACE predicting a greater chance for continued weight loss. In the low FG group, no difference was observed (
Boxplot shows the quartile range of weight maintenance score with outliers (in circle) across tertile of the fold change of ACE during weight loss, for subjects with low (≤9.6µmol/L, n = 48, blank bar) and with high (>9.6 µmol/L n = 47, grey bar) baseline fibrinogen level. The variation of weight maintenance score attributed to the fold change of ACE, p = 0.478 in low group and p = 0.014 in high group, was tested by one-way ANOVA controlled for age and the fold change of weight, and Bonferroni test for multiple comparisons. *T3 significantly different from T1 in high fibrinogen group, p = 0.013.
By logistic regression, luteinizing hormone (LH), CRP, IL6, HPT, leptin (LEP), vascular endothelial growth factor-D (VEGFD) and IGFBP3 showed significant interaction with dietary protein level for the prediction of the weight change during maintenance (
Boxplots show the quartile range of the blood analytes without outliers for continued weight-losers (blank bar) and weight-regainers (grey bar) in each dietary group. The p-value above the chart is the significance of the interaction between dietary protein/GI and the concentration/change of the blood analyte with respect to the outcome of weight maintenance (weight-loss or -regain). The p-values under the chart is the significance of the prediction of the variable inside the subgroups. All were obtained by logistic regression (controlled for age and the fold change of weight).
IL8, MIF, MMP9 and F7 showed significant interaction with dietary GI level for the prediction (
A model using the independent interactions with dietary protein or GI (
We also tried to use RF to search for the interactions between blood analytes and diet components on the prediction. However, there was no difference for the MDG of variables after taking the dietary components into the classification forests (p>0.99). This might be due to the fact that category variables of diet protein and GI were underrepresented by RF as compared to continuous variables
Because ACE was identified as the most important predictor among all candidates, we checked for its relation with measures of obesity and other blood analytes (
Length |
CID1 | CID2 | CID2/CID1 | |||||
Category | Analyte | (AA) |
|
p-value |
|
p-value |
|
p-value |
Body adiposity | BMI | - | 0.231 |
|
0.131 | 0.203 | 0.186 | 0.070 |
weight | - | 0.215 |
|
0.099 | 0.337 | 0.186 | 0.070 | |
Fat mass (%) |
- | 0.304 |
|
0.202 | 0.086 | −0.116 | 0.376 | |
Sex hormones | FSH | 92/111 | 0.198 | 0.053 | 0.024 | 0.813 | 0.463 |
|
LH | 92/121 | 0.282 |
|
0.198 | 0.053 | 0.348 |
|
|
PRL | 199 | 0.217 |
|
0.192 | 0.063 | 0.396 |
|
|
Satiety factors | PP | 36 | 0.220 |
|
0.251 |
|
0.263 |
|
GLP1 | 37 | 0.196 | 0.056 | 0.135 | 0.189 | 0.329 |
|
|
Insulin related hormones | GCG | 29 | 0.242 |
|
0.184 | 0.073 | 0.320 |
|
IAPP | 37 | 0.156 | 0.136 | 0.034 | 0.750 | 0.403 |
|
|
Others | LEP | 146 | 0.219 |
|
0.055 | 0.592 | 0.377 |
|
IL6 | 183 | 0.156 | 0.129 | −0.024 | 0.818 | 0.256 |
|
|
VEGFD | 117 | −0.134 | 0.192 | −0.115 | 0.266 | −0.289 |
|
|
PAI1 | 379 | 0.242 |
|
0.017 | 0.870 | 0.130 | 0.205 |
Analyzed by Pearson correlation. Data of blood analytes were Ln transformed before analysis.
The length of the (main) active processed chain is resourced from the UniProt Knowledgebase (
Subjects used in the analysis with respect to fat mass measured at CID1 n = 78, at CID1 n = 73, and the fold change CID2/CID1 n = 60. For other analytes subjects n = 96.
All measured peptide sex hormones, satiety hormones and insulin related hormones, but not insulin itself, were positively correlated with ACE. For most proteins this correlation concerned their fold changes. Except for PAI1, all those factors are low molecular weight proteins/peptides.
ACE is a zinc metallopeptidase and catalyses the hydrolysis of dipeptides or tripeptides from the carboxyl terminus of oligopeptides
In the present study we showed correlations between ACE and sex hormones, satiety hormones, insulin related hormones, LEP and other blood metabolic proteins, suggesting its broad range of substrates and involved pathways. Indeed, ACE was found to be able to process gonadotropin-releasing hormone (LHRH) in vitro, thus possibly regulating both LH and follicle-stimulating hormone levels
Here for the first time, we found that neither the baseline level, nor the post-weight-loss level, but the extent of the reduction of ACE during weight loss discriminates between subjects who will continue to lose weight and those who will regain weight during the 6-month weight maintenance period. A greater reduction in ACE predicts a greater chance for continued weight loss. However, the predicting power of the fold change of ACE alone is limited due to a large overlap between weight-losers and -regainers. Apparently, more processes in the body are involved in weight regain/maintenance than the range of pathways that ACE may cover.
We also reported a novel interaction between ACE and FG, namely the prediction power of ACE is only observed in subjects with high baseline FG. FG is the key component of blood coagulation, but it is also an anti-inflammatory acute phase protein
Our findings show that the prediction can be manipulated by the dietary protein and GI intake during the weight maintenance period. We did not perform multiple testing corrections, but the repeatedly detected similar interaction of analytes from the same functional group may secure the finding. This is the case for the interaction between dietary protein level and immunoproteins (IL6, CRP and HPT). The interactions between GI level and other immunoproteins IL8, MIF and MMP9 were not consistent. Therefore we only discuss the role of dietary protein in weight maintenance. Our results suggest that in order to prevent weight regain, subjects with a high baseline level of LEP, IL6, CRP and HPT should follow a HP diet, and subjects with a low baseline level are most likely to succeed with a LP diet.
In the Diogenes study, the fat content was kept relatively constant among diets. As a consequence HP diets also mean low carbohydrate diets
LEP represents the amount/size of adipocytes and/or activity of adipose tissue, confirmed by the strong correlation between LEP and fat mass at baseline in our study (r = 0.595, p<0.001). As expected, the interaction between LEP and dietary protein level lost significance (p = 0.190) if we add the interaction between fat mass and dietary protein level (p = 0.418) in the model. Thus subjects with high LEP level taking LP diet will easily recover energy storage without fuel flow restriction. The relation between immunoproteins and weight regain may also be a secondary effect, because immune system and adipocytes/adipose tissue are positively associated, confirmed by strong correlations between LEP and immunoproteins in our study. Also their interactions with dietary protein were not independent from each other. For subjects with profound/active fat mass, a HP diet is preferred to prevent weight regain.
Because obesity and weight regulation is complex, a panel of predictors covering various processes performs better than one or two predictors on the prediction of weight change after weight loss. A logistic regression model with 9 independent predictors has an accuracy of prediction of >90%. However, such self evaluation is too optimistic. RF gave a more realistic evaluation with a moderate accuracy of 76% by a 15-predictor panel. The present study was conducted on a limited number of adult females extracted from a pan-European project. Therefore, our findings should be validated in other cohorts and also in males. Nevertheless, the information about 34 blood proteins/steroids, particularly the importance of ACE, and the interaction between dietary protein/carbohydrate level and LEP and immunoproteins, may help to develop personalized programmes to improve weight maintenance.
Selection of analytes as predictor.
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Measured blood proteins and steroids by diet and the outcome of weight maintenance at baseline (CID1) and after 8-week weight loss (CID2).
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