1. Introduction
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease featuring persistent elevated blood glucose levels (hyperglycemia) resulting from impaired insulin secretion or resistance to peripheral actions of insulin in target tissues [1]. In this context, prediabetes is identified as a state of intermediate hyperglycemia, but not meeting the diagnosis criteria for diabetes [2]. Hyperglycemia is a phenomenon observed in essentially all individuals with diabetes and is associated with a hormonal balance dysregulation (including insulin, glucagon and cortisol impairments), increased hepatic glucose output and diverse metabolism dysfunctions, affecting multiple organs and body systems [3].
Because prediabetes is a highly heterogeneous metabolic state, phenotype stratification and mediation factor analyses are important for precision medicine to improve risk prediction, prognosis and disease management [4]. Thus, prediabetes characterization includes different biomarkers, such as impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and elevated glycated hemoglobin (HbA1c), under criteria formulated by several international committees [5], where other pathways may also be disturbed.
Indeed, quality of life (QoL), a subjective evaluation to capture global health, well-being and unspecific morbidities, has been explored as a potentially diagnostic tool in many chronic metabolic diseases including T2DM [6]. Indeed, QoL research has increased in recent years since it has been recognized as a health marker and an important endpoint in medical domains [7]. As in other chronic diseases, evaluating QoL is critically important since wellbeing is the ultimate goal of diabetes care, improving self-care management, adherence to prescribed medication lifestyle modification and interactions with other health markers [8].
Furthermore, the liver has a recognized role in the control of carbohydrate/lipid metabolism and other diverse metabolic functions [9]. In the presence of hepatic disease, the homeostasis of glucose and fatty acids is impaired as a consequence of disorders, including insulin resistance and gluconeogenesis, which are associated with hyperglycemia, compensatory hyperinsulinemia and hypertriglyceridemia [10]. In fact, the new consensus about steatosis liver disease [11] reinforces the importance to study the liver as a central axis in the development of metabolic diseases, whose understanding concerning physiopathological relationships with glycemia and health status based on QoL assessment will pave the way for precision medicine. Indeed, the feasibility of looking for new markers in the diagnosis of prediabetes in order to prevent liver damage has led to the proposal of potential new markers [12].
The aim of this research was to analyze the relationship between glycemia on the liver status and health-related markers, including QoL in a population with prediabetes, to understand the factors involved in such interrelationships and also the conjoint interpretation of independent glycemia determinants in prediabetics.
2. Materials and Methods
2.1. Population
The present ancillary study was based on the PREVIEW project (
Following the intervention design, after 8 weeks of weight loss, the main goal of the PREVIEW Study was to maintain the weight lost during the next 34 months, following a 2-factor (diet × physical activity) lifestyle intervention consisting of a diet high in protein or moderate in protein, and with moderate or intensive physical activity [13]. To achieve the final number of 2326 participants with prediabetes and presenting overweight or obesity, a total of 15,611 subjects were pre-screened, 5472 screened, and 2326 finally randomized and enrolled in this study. Of those randomized, 2223 subjects attended the baseline measures and 962 men and women finished the 3-year intervention. All centers had approval from their respective Research Ethics Boards before starting the trial [13], and all participants were required to give written informed consent in their mother tongue before enrollment. In this investigation, quality of life [14] and liver status [15] as global health surrogates, were specifically measured.
2.2. Study Variables and Data Management
Baseline variables of the adult population were reasonably requested to the PREVIEW project database administrator in Denmark. All data are stored in a central project database at the University of Copenhagen. The central database ensured standardized handling and storing of data, as well as the delivery of data both within and after the official project period (2013–2018).
The database received input from four data sources on a regular basis: (1) All immediate data measured (e.g., anthropometrics, blood glucose) and interviewed (e.g., use of medication) during the CIDs and entered into the OpenClinica server (electronic case report form). (2) Data on social-cognitive determinants of behavior, cultural and socio-demographics, and socio-economic components were collected by the questionnaire delivery platform (QDP) designed for PREVIEW by NetUnion. The participants entered their own data into the QDP. A paper version of the questionnaires was also available. (3) Physical activity was reported using the Baecke inventory, and an electronic physical activity log (PAL) was designed by Swansea University and the University of Stuttgart and implemented by NetUnion. (4) The Central Lab at the National Institute for Health and Welfare (THL) entered all the laboratory analyses into the data hub [13].
The data used within this ancillary study included anthropometrical, clinical and metabolic markers. In this context, weight was measured through calibrated scales to the nearest 0.1 kg, while height was measured with a stadiometer to the nearest 0.1 cm, and then the body mass index was calculated. Similarly, waist circumference was measured to the nearest 0.1 cm using an inelastic tap. For clinical markers, a digital sphygmomanometer was used to measure the resting systolic and diastolic blood pressures [13]. Metabolism markers included lipid, glucose, insulin and inflammatory determinations, which were collected and analyzed following standardized protocols specifically designed for this study [13].
The Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) index was calculated as fasting insulin (μU/L) × fasting glucose (nmol/L)/22.5 [16]. The triglyceride-glucose index (TyG index) was computed as ln[TG (mg/dL) × glucose (mg/dL)/2], as described elsewhere [17]. The hepatic steatosis index (HSI) was estimated according to the following formula: 8 × (ALT/AST) + BMI + 2 (if T2D) + 2 (if female), as previously reported [15]. A cutoff value of HSI > 36 was established for the screening of fatty liver, as defined elsewhere [18]. QoL information was collected using the SF-12 survey, as validated for a comparable population [19]. This survey offers a vision on the subjects’ self-perceived QoL, covering both the mental and physical score [19].
2.3. Statistical Analyses
Due to the large number of participants, continuous variables are presented as means ± standard deviations. Parametric tests were applied since the main variables (glycemia, lipid, insulin and liver metabolism markers) were normally distributed (p > 0.05). Moreover, 2 × 2 factorial ANOVA tests were accordingly used to compare the anthropometrical, clinical and biochemical variables by age (median) and sex. Univariate linear regression models were run to assess the age/sex statistical interactions regarding metabolic status, as well as explore putative interrelationships between fasting glucose, liver and several health estimators, including QoL. Bonferroni-adjusted multiple linear regression models were also computed for predicting the QoL and HSI variability, as well as the contributions of several health-related markers. Regional differences were introduced as a potential confounding variable but were discarded once the result was non-significant. All data were analyzed using the IBM SPSS software, version 20 (IBM Inc, Armonk, NY, USA), and STATA/SE software, version 12.0, 2011 (StataCorp LLC, College Station, TX, USA
3. Results
Clinical and metabolic characteristics of the total population categorized by age (median) and sex groups are shown, and statistical comparison and age/sex interactions are reported (Table 1). Additional analyses that separated the population by age decades are reported in Supplementary Table S1. These additional analyses did not vary in a relevant way from the median analysis performed (Table 1).
The men younger than 55 years had higher central adiposity, glucose, insulin resistance, transaminases and blood pressure than the women, who, in turn, presented with more hepatic steatosis (measured by HSI) and a higher ALT/AST ratio (Table 1). Similar results were found within the group older than 55 years, but the women had a lower QoL than the men (Table 1). Interestingly, significant age and sex statistical interactions (effect modifications) were found for the values of insulin, HOMA-IR, C-peptide (C-Pep), aspartate aminotransferase (AST), alanine transaminase (ALT) and systolic blood pressure (SBP), with relevant roles in glucose homeostasis (Table 1). Regarding glycated hemoglobin (HbA1c), despite the differences regarding sex, the most significant were related to age (Table 1), although they were not too clinically relevant.
Simple and multiple linear regression models predicting the HSI and QoL are shown (Table 2 and Table 3, respectively).
Regarding the simple linear regression (Table 2), age, sex, glucose, waist circumference (WC) and QoL were significant predictors of HSI variability (Table 2, model 5).
Regarding QoL (Table 3), apparently the QoL status was explained by age, sex, HOMA-IR and HSI (Table 3, model 3).
The interrelationships between QoL, HSI and glycemia are illustrated (Figure 1). Of note, the QoL values declined with increased HSI scores, where a significant interaction was found (p = 0.011) when the data were analyzed by comparing lower glycemia vs. higher glycemia (based on median values) in individuals with prediabetes. Indeed, an effect modification was shown that depended on the glycemia levels concerning differential QoL and HSI worsening.
Furthermore, concerning the examination of the predictors of circulating glucose, the following parameters were found to anticipate higher levels: being older than 55 years; women; and individuals with higher WC, triglycerides, C-Pep, HSI and hs-CRP as independent contributors (Figure 2).
4. Discussion
Maintaining blood glucose homeostasis is important for optimal organ functioning, as well as analyzing the factors and interactions involved in glycemia regulation for health outcomes [21], which has been scarcely researched so far in prediabetes conditions [22]. Indeed, developing diagnostic markers related to prognostic outcomes are important for detecting metabolic pathways [12,23].
The role of QoL as a global indicator of health, as well as interactions with liver status concerning glucose utilization [15], has apparently never been addressed in subjects with prediabetes. The main finding of this study was the interaction (effect modification) found concerning the QoL and HSI depending on the glycemia levels, where the QoL declined more in individuals with higher glycemia and HSI scores compared with lower glycemia values, which suggests relevant interrelationships between these factors, with clinical implications for diabetes and liver disease management.
Age and sex are important variables in personalized precision medicine since they may contribute to optimize the effects of administered therapies and reduce adverse outcomes in many metabolic diseases, including diabetes and liver disease [24]. In this population of patients with prediabetes, relevant age and sex interactions concerning insulin, HOMA-IR, C-Pep and liver metabolic markers were found; these were also found when analyzed by decades of age, not only by the median, which are relevant confounding/mediating factors that should be considered in the clinical interpretation and the prescription of particular recommendations in prediabetic groups. In this context, previous reports from the PREVIEW trial revealed that the effects of a long-term lifestyle intervention on body weight and cardiometabolic health markers were dependent on age and sex in prediabetic adults [25]. Furthermore, adverse differences in cardiometabolic risk factors between subjects with prediabetes and normal glucose metabolism were found to be more pronounced in women than in men, as reported within the Maastricht Study [26].
The QoL score is a validated instrument to subjectively evaluate the perception about the influence of health and personal features on human well-being, which may be used as an integrative marker of health status [7]. In this investigation, QoL was sensitive to HSI and glycemia in this investigation, finding lower QoL scores in conditions of hyperglycemia and liver steatosis. In line with our results, prediabetes and T2DM were negatively associated with measures of QoL, which were similar between women and men [27]. Also, age, diabetes duration and fasting blood glucose level were inversely associated with QoL in adults [28]. Similarly, people with insulin resistance have worse QoL profiles in terms of social and physical functioning, emotional role limitations, pain and general health perception [29]. In this context, while the SF-12 QoL measure is validated cross-culturally, potential variations in self-reported QoL may arise due to cultural differences between the eight participating countries in the PREVIEW study.
Furthermore, it has been reported that patients with metabolic dysfunction-associated steatotic liver disease (MASLD) presented significantly poorer QoL in comparison with healthy controls, especially regarding the physical component of health [30]. Remarkably, it was postulated that the assessment of liver status by the HSI might have an impact on the health-related QoL response to lifestyle changes in subjects with metabolic syndrome [31], but never reported in subjects with prediabetes.
The HSI has been accepted as a simple, efficient and noninvasive tool to assess hepatic steatosis, being correlated with insulin resistance [15]. In this study, the HSI was predicted by age, sex, glucose, WC and QoL score, suggesting that liver steatosis, which is related to glucose/lipid metabolism, adiposity and insulin resistance, could be an early predictor of future metabolic complications. Consistently, a significant correlation between hepatic steatosis (as measured by the HSI) and dyslipidemia, overweight, obesity and hypertriglyceridemia has been reported in diabetic patients [32]. The HSI as a biomarker-based algorithm developed as a proxy for MASLD was also associated with cardiometabolic risk factors in a general Caucasian population [33]. Of note, estimated fatty liver disease using the HSI criteria, in conjunction with metabolic syndrome features and prediabetes, demonstrated a valuable clinical role to discriminate the patient risk of T2DM through the description of independent glucose metabolic remodeling phenotypes [34]. Additionally, hepatic steatosis scores (including HSIs) were used to estimate the response rate to lifestyle intervention in adults with obesity and/or diabetes [35]. Given the increased risk of adverse outcomes, the importance of regular prediabetes and diabetes screening in MASLD and the adoption of early lifestyle modifications to reduce disease progression should be highlighted [36]. Moreover, an interactive role of surrogate liver fibrosis with insulin resistance on the incidence of major cardiovascular events has been reported [36]. In addition to these issues, the psychological aspects affecting the adherence to a therapeutic approach must also be considered [37]. Regarding this point, the influence of psychological status, together with QoL, may positively or negatively influence the adherence to a prescribed treatment [38]. Indeed, a very important finding of this research was that glycemia was affected by age, sex, adiposity markers, inflammation, liver and insulin markers in people with prediabetes in an independent manner to that evidenced in diabetes [39].
On one hand, the strengths of this research included a high number of subjects with prediabetes analyzed with validated criteria that belonged to the multicenter PREVIEW study and facilitated investigating an intermittent/unstable stage before diabetes. Moreover, potential statistical interactions by age and sex were screened, as well as multiple comparison adjustments (under Bonferroni’s corrections) in the main outcome analysis. On the other hand, some limitations of this study encompassed the scarcity regarding exhaustive pharmacological therapies for metabolic alterations (i.e., dyslipidemia and blood pressure), as well as information concerning accurate alcohol consumption, smoking habit, and physical exercise as potential confounding factors in the predictive models.
The recognized relationship between liver health and T2D suggest that screening with clinically available scores may be an important approach to reducing disease costs [40]. Thus, interventions to reduce liver fat have the potential to improve insulin regulation in prediabetes [41], while the normalization of glycemia has been associated with health benefits and reduced incidence in related comorbidities [42]. Therefore, deep phenotyping and additional pathophysiology information is giving support toward precision medicine [39]. As an example, prediabetes defined by FPG and/or HbA1c showed differences according to sex concerning high-density lipoprotein cholesterol [43]. Moreover, changes in liver markers may anticipate variations in insulin resistance and glycemia, given that transaminases provide a pathophysiological core in forecasting diabetes occurrence [44].
As a summary, our research was focused on subjects with prediabetes (a really difficult population to recruit), which is a major difference between currently published research and available information. Furthermore, the independent associations of liver status and quality of life with glycemia were evidenced after adjustments by appropriate variables and confounding factors, which have seemingly never been reported with a comparable approach to our experimental design, while the interaction outcome is a pioneering principal finding since it has been shown for the first time that the factorial relationship of glycemia and liver status with quality of life involved three directions and permitted reaching the following conclusions: quality of life was negatively affected by increased HSI values, where quality of life was lower in more advanced hyperglycemic conditions and a paramount finding was the statistically significant modification of quality of life that was found when both glycemia and HSI values were more deteriorated with a more sharpened decline in the slope of the predicted quality of life dimensions in the group with higher (over the median) circulating glucose levels and advanced liver status damage.
Another relevant attraction of this contribution is that the results demonstrated the independent involvement of adiposity, lipid metabolism, insulin synthesis, and inflammation measurements and markers that modulated glycemia levels before diabetes emergence.
5. Conclusions
In conclusion, this research concerning liver and health markers in people with prediabetes have translational and multidisciplinary implications for diagnosis, prognosis and treatment approaches. Indeed, relevant interrelationships between glycemia homeostasis and associated markers, QoL and liver status were specifically evidenced in subjects with prediabetes, which were demonstrated that adiposity (WC), lipid metabolism (TG), insulin and inflammation (C-reactive protein) together and independently are indicators of circulating glucose in a population with prediabetes. This fact led the authors to consider this translational research in applications for individualized precision management of glycemia dysfunctions. Moreover, the QoL was affected conjointly by the liver status and glycemia levels, including a modification of the effect depending on the glucose concentration, where the QoL declined more in individuals with higher glycemia with increased HSI deterioration than with lower glycemia values. Indeed, investigations relative to the liver health and quality of life of patients with prediabetes are of value for the diagnosis and predictors of their metabolic consequences, particularly when considering that higher liver steatosis assessed by the HSI was associated with a lower quality of life, as well as a higher glucose concentration, where a statistical interaction between these two factors was demonstrated.
Conceptualization, O.R.-L., D.M.-U. and J.A.M.; Data curation, S.N.-C., E.J., T.C.A., M.A.T., S.D.P., M.F., A.R. and J.A.M.; Formal analysis, O.R.-L., D.M.-U. and J.A.M.; Funding acquisition, I.M., S.D.P., J.B.-M., M.F. and A.R.; Investigation, O.R.-L., D.M.-U., S.N.-C., R.Z., M.H.-L., G.S., T.H.-D., J.E.S., M.P.S., E.J., T.C.A., E.S. and M.A.T.; Methodology, S.N.-C., M.H.-L., S.H., J.E.S., M.P.S., K.H.P., T.C.A., M.W.-P., I.M., S.D.P., W.S., J.B.-M., M.F. and A.R.; Project administration, G.S., S.H., M.W.-P., I.M., S.D.P., W.S., J.B.-M., A.R. and J.A.M.; Resources, G.S., T.H.-D., S.H., K.H.P., E.S., M.A.T., S.D.P., W.S., J.B.-M. and M.F.; Supervision, J.A.M.; Validation, O.R.-L., D.M.-U. and J.A.M.; Writing—original draft, O.R.-L. and D.M.-U.; Writing—review and editing, S.N.-C. and J.A.M. All authors have read and agreed to the published version of the manuscript.
The PREVIEW Study is registered at clinicaltrials.gov (identifier code: NCT01777893). PREVIEW for adults was conducted at eight intervention center in eight countries: University of Copenhagen (Denmark), University of Helsinki (Finland), University of Maastricht (the Netherlands), University of Nottingham (UK), University of Navarra (Spain), Medical University of Sofia (Bulgaria), University of Sydney (Australia) and University of Auckland (New Zealand). The study protocol and amendments were reviewed and approved by the local Human Ethics Committee at each of the eight intervention centers (Copenhagen: ref:H-1-2013-052, date: 31-7-2014; Helsinki: ref: 621/1999, date: 12-05-2014; Maastricht: ref: METC 13-3-008.23/ivb, date: 29-07-2015; Nottingham: ref: 13/EM/0259, date: 02-05-2014; Pamplona: ref: 71/13, date: 14-10-2015; Sofia: ref: 4303/13.06.2014, date: 02-07-2014; Sydney: ref: 2013/535, date: 22-01-2014; Auckland: ref: 13/NTB/41/AM06, date: 23-05-2014). The work of PREVIEW was carried out in full compliance with the relevant requirements of the latest version of the Declaration of Helsinki (59th WMA General Assembly, Seoul, Korea, October 2008) and The International Conference on Harmonisation for Good Clinical Practice to the extent possible and relevant.
All participants provided written informed consent prior to any screening procedures. All information obtained during the trial was handled according to the local regulations and the European Directive 95/46/CE (directive on protection of individuals with regard to the processing of personal data and on the free movement of such data).
Data used for this project are available from the corresponding author upon reasonable request.
The PREVIEW consortium wishes to thank all the study participants at every intervention center for their time and commitment to this study. In addition, we wish to thank all the research staff, scientists and students from each center for their dedication and contributions to the implementation of this study.
Anne Raben reports financial support was provided by European Commission Seventh Framework Programme for Research and Technological Development Health. Jennie Brand-Miller reports financial support was provided by National Health and Medical Research Council. Sally D Poppit reports financial support was provided by Health Research Council of New Zealand. Anne Raben reports financial support was provided by Danish Meat Research Institute. I.M. reports financial support was provided by National Institute for Health Research Maudsley Biomedical Research Centre. I.M. reports financial support was provided by Biotechnology and Biological Sciences Research Council. Mikael Fogelholm reports financial support was provided by Juho Vainio Fundation. Mikael Fogelholm reports financial support was provided by Finnish Medical Foundation. Anne Raben reports a relationship with Unilever Plc that includes consulting or advising. Anne Raben reports a relationship with International Sweeteners Association that includes consulting or advising. Jennie Brand-Miller reports a relationship with Glycemic Index Foundation that includes board membership. Jennie Brand-Miller reports a relationship with Novo Nordisk Foundation that includes board membership. Sally D Poppit was the Fonterra Chair in Human Nutrition during the PREVIEW intervention. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Footnotes
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Enlarge this image.Figure 1. Interrelationships between QoL, HSI and blood glucose in individuals with prediabetes.
![View Image - Figure 2. Independent relevant predictors of serum glucose. p-values were adjusted for multiple comparison using the Bonferroni test. LWC: low waist circumference; HWC: high waist circumference; LTG: low triglycerides; HTG: high triglycerides; LCP: low C-peptide; HCP: high C-peptide; LHSI: low hepatic steatosis index; HHSI: high hepatic steatosis index; LCRP: low C-reactive protein; HCRP: high C-reactive protein. * p [less than] 0.05.](https://media.proquest.com/media/hms/THUM/4/7lX2b?_a=ChgyMDI1MDcyNzAzNTc0Nzc3MToyNDk5NjgSBzEzODMzMjMaCk9ORV9TRUFSQ0giDjIxNi43My4yMTYuMTYzKgcyMDMyNDEwMgozMTQ5NTY4Mjc0OgtJbmxpbmVJbWFnZUIBMFIGT25saW5lWgNJTEliBFRIVU1qCjIwMjQvMDEvMDFyCjIwMjQvMTIvMzF6AIIBIFAtMTAwMDA4NC0yMDEzOTUtU1RBRkYtbnVsbC1udWxskgEGT25saW5lygFnTW96aWxsYS81LjAgQXBwbGVXZWJLaXQvNTM3LjM2IChLSFRNTCwgbGlrZSBHZWNrbzsgY29tcGF0aWJsZTsgQ2xhdWRlQm90LzEuMDsgK2NsYXVkZWJvdEBhbnRocm9waWMuY29tKdIBElNjaG9sYXJseSBKb3VybmFsc5oCB1ByZVBhaWSqAhtPUzpFTVMtRnVsbFRleHQtZ2V0RnVsbFRleHTKAg9BcnRpY2xlfEZlYXR1cmXiAgDyAgD6AgFZggMDV2ViigMcQ0lEOjIwMjUwNzI3MDM1NzQ3NzcxOjg4ODE0MA%3D%3D&_s=eOLZmR5AvZCt1PQ3muftRrqcvnE%3D "View Image - Figure 2. Independent relevant predictors of serum glucose. p-values were adjusted for multiple comparison using the Bonferroni test. LWC: low waist circumference; HWC: high waist circumference; LTG: low triglycerides; HTG: high triglycerides; LCP: low C-peptide; HCP: high C-peptide; LHSI: low hepatic steatosis index; HHSI: high hepatic steatosis index; LCRP: low C-reactive protein; HCRP: high C-reactive protein. * p [less than] 0.05.")
Enlarge this image.Figure 2. Independent relevant predictors of serum glucose. p-values were adjusted for multiple comparison using the Bonferroni test. LWC: low waist circumference; HWC: high waist circumference; LTG: low triglycerides; HTG: high triglycerides; LCP: low C-peptide; HCP: high C-peptide; LHSI: low hepatic steatosis index; HHSI: high hepatic steatosis index; LCRP: low C-reactive protein; HCRP: high C-reactive protein. * p [less than] 0.05.
Clinical characteristics of the total population evidencing interactions of age and sex with glucose homeostasis and hepatic markers in prediabetics.
| <55 Years | ≥55 Years | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Variable | Total | Women | Men | p | Women | Men | p | p Sex | p Age | p Int. |
| N | 2220 | 783 | 317 | - | 719 | 401 | - | - | - | - |
| BMI (kg/m2) | 36 ± 6 | 37 ± 6 | 36 ± 7 | 0.287 | 35 ± 6 | 34 ± 5 | 0.017 | 0.004 | <0.001 | 0.657 |
| WC (cm) | 110 ± 14 | 108 ± 15 | 117 ± 15 | <0.001 | 107 ± 13 | 116 ± 13 | <0.001 | <0.001 | 0.151 | 0.318 |
| Glucose (mg/dL) | 104 ± 13 | 103 ± 12 | 106 ± 13 | 0.012 | 103 ± 13 | 104 ± 15 | 0.308 | 0.016 | 0.246 | 0.108 |
| Insulin (μU/L) | 13 ± 8 | 14 ± 8 | 16 ± 11 | <0.001 | 12 ± 7 | 14 ± 7 | <0.001 | <0.001 | <0.001 | 0.027 |
| HOMA-IR | 3.4 ± 2.1 | 3.5 ± 2.2 | 4.5 ± 3.2 | <0.001 | 3.1 ± 1.8 | 3.5 ± 1.9 | 0.002 | <0.001 | <0.001 | 0.002 |
| C-Pep (pmol/L) | 913 ± 360 | 916 ± 373 | 956 ± 402 | 0.120 | 921 ± 352 | 860 ± 301 | 0.004 | 0.320 | 0.060 | 0.002 |
| HbA1c (%) | 5.4 ± 0.8 | 5.3 ± 1.1 | 5.4 ± 0.6 | 0.003 | 5.5 ± 0.5 | 5.4 ± 0.7 | <0.001 | 0.357 | <0.001 | 0.148 |
| TyG index | 8.6 ± 0.4 | 8.7 ± 0.5 | 8.8 ± 0.5 | 0.026 | 8.7 ± 0.5 | 8.8 ± 0.5 | 0.002 | <0.001 | 0.601 | 0.076 |
| TC (mg/dL) | 200 ± 39 | 201 ± 40 | 203 ± 38 | 0.559 | 198 ± 37 | 202 ± 41 | 0.086 | 0.113 | 0.135 | 0.677 |
| Triglycerides (mg/dL) | 133 ± 69 | 134 ± 71 | 140 ± 71 | 0.202 | 127 ± 68 | 138 ± 70 | 0.017 | 0.013 | 0.403 | 0.376 |
| HDL-c (mg/dL) | 48 ± 10 | 49 ± 11 | 49 ± 10 | 0.587 | 49 ± 11 | 50 ± 10 | 0.724 | 0.959 | 0.567 | 0.650 |
| LDL-c (mg/dL) | 125 ± 32 | 125 ± 33 | 127 ± 32 | 0.504 | 124 ± 31 | 126 ± 34 | 0.328 | 0.276 | 0.287 | 0.792 |
| hs-CRP (mg/L) | 5.3 ± 3.0 | 5.4 ± 8.3 | 5.2 ± 5.2 | 0.645 | 5.4 ± 6.5 | 5.0 ± 6.4 | 0.331 | 0.309 | 0.169 | 0.803 |
| AST (IU/L) | 27 ± 10 | 25 ± 9 | 32 ± 12 | <0.001 | 28 ± 10 | 30 ± 12 | 0.010 | <0.001 | 0.201 | <0.001 |
| ALT (IU/L) | 28 ± 15 | 24 ± 14 | 37 ± 17 | <0.001 | 26 ± 14 | 29 ± 16 | 0.002 | <0.001 | 0.007 | <0.001 |
| AST/ALT ratio | 1.2 ± 0.6 | 1.3 ± 0.7 | 1.0 ± 0.61 | <0.001 | 1.3 ± 0.7 | 1.2 ± 0.6 | 0.172 | <0.001 | 0.054 | 0.051 |
| SBP (mmHg) | 129 ± 16 | 122 ± 14 | 131 ± 13 | <0.001 | 132 ± 16 | 135 ± 16 | 0.016 | <0.001 | <0.001 | 0.008 |
| DBP (mmHg) | 78 ± 11 | 76 ± 11 | 80 ± 10 | <0.001 | 78 ± 11 | 82 ± 10 | <0.001 | <0.001 | <0.001 | 0.107 |
| HSI | 47 ± 8 | 50 ± 10 | 45 ± 9 | <0.001 | 47 ± 8 | 44 ± 7 | <0.001 | <0.001 | <0.001 | 0.243 |
| QoL score (0–100) | 60 ± 18 | 56 ± 19 | 57 ± 19 | 0.393 | 63 ± 19 | 67 ± 16 | <0.001 | <0.001 | <0.001 | 0.690 |
Values are presented as means ± standard deviations. Bold numbers indicate p < 0.05. BMI: body mass index; WC: waist circumference; SBP: systolic blood pressure; DBP: diastolic blood pressure; HOMA-IR: homeostatic model assessment of insulin resistance; C-Pep: C-peptide; TC: total cholesterol; TG: triglyceride; HDL-c: high-density lipoprotein cholesterol; LDL: low-density lipoprotein cholesterol; hs-CRP: high-sensitivity C-reactive protein; AST: aspartate aminotransferase; ALT: alanine transaminase: TyG: triglyceride glucose index; HSI: hepatic steatosis index.
Linear regression models predicting HSI variability in prediabetic subjects.
| B Coefficient (95% CI) | p | ||
|---|---|---|---|
| HSI (model 1) | Adjusted R value = 0.1026 | <0.001 | |
| Age (years) | −0.1817 (−0.2269, −0.1364) | <0.001 | |
| Sex (women) | −5.2263 (−6.1386, −4.3141) | <0.001 | |
| Glucose (mg/dL) | 0.0590 (0.0183, 0.0996) | 0.004 | |
| HSI (model 2) | Adjusted R value = 0.0722 | <0.001 | |
| Age (years) | −0.1013 (−0.1348, −0.0677) | <0.001 | |
| Sex (women) | −4.1384 (−4.9891, −3.2878) | <0.001 | |
| WC (cm) | 0.0374 (0.0104, 0.0644) | 0.007 | |
| HSI (model 3) | Adjusted R value = 0.1042 | <0.001 | |
| Age (years) | −0.0651 (−0.0988, −0.0314) | <0.001 | |
| Sex (women) | −3.9255 (−4.7623, −3.0887) | <0.001 | |
| QoL score | −0.0902 (−0.1111, −0.0693) | <0.001 | |
| HSI (model 4) | Adjusted R value = 0.0925 | <0.001 | |
| Age (years) | −0.0385 (−0.0727, −0.0043) | 0.017 | |
| Sex (women) | −3.9823 (−4.8621, −3.1026) | <0.001 | |
| HOMA-IR | 0.7842 (0.6085, 0.9599) | <0.001 | |
| HSI (model 5) | Adjusted R value = 0.1393 | <0.001 | |
| Age (years) | −0.1410 (−0.1878, −0.0942) | <0.001 | |
| Sex (women) | −4.8923 (−5.8147, −3.9699) | <0.001 | |
| Glucose (mg/dL) | 0.0522 (0.0110, 0.0933) | 0.013 | |
| WC (cm) | 0.0493 (0.0185, 0.0801) | 0.002 | |
| QoL score | −0.0867 (−0.1106, −0.0629) | <0.001 |
Bold numbers indicate p < 0.05. WC: waist circumference; QoL: quality of life; C-Pep: C-peptide; HOMA-IR: homeostatic model assessment of insulin resistance; hs-CRP: high-sensitivity C-reactive protein; HSI: hepatic steatosis index.
Linear regression models predicting QoL variability in prediabetic subjects.
| B Coefficient (95% CI) | p | ||
|---|---|---|---|
| QoL (model 1) | Adj. R value = 0.0766 | <0.001 | |
| Age (years) | 0.2678 (0.1942, 0.3413) | <0.001 | |
| Sex (women) | 3.6912 (1.8207, 5.5617) | <0.001 | |
| HOMA-IR | −1.5522 (−1.9354, −1.1690) | <0.001 | |
| QoL (model 2) | Adj. R value = 0.0970 | <0.001 | |
| Age (years) | 0.3367 (0.2651, 0.4084) | <0.001 | |
| Sex (women) | 1.1810 (−0.6735, 3.0356) | 0.212 | |
| HSI | −0.4236 (−0.5219, −0.3254) | <0.001 | |
| QoL (model 3) | Adj. R value = 0.1130 | <0.001 | |
| Age (years) | 0.4249 (0.3265, 0.5233) | <0.001 | |
| Sex (women) | 2.4300 (0.3959, 4.4640) | 0.007 | |
| HOMA-IR | −1.3375 (−1.7570, −0.0180) | <0.001 | |
| HSI | −0.3940 (−0.5022, −0.2857) | <0.001 |
Bold numbers indicate p < 0.05. WC: waist circumference; QoL: quality of life; C-Pep: C-peptide; HOMA-IR: homeostatic model assessment of insulin resistance; hs-CRP: high-sensitivity C-reactive protein; HSI: hepatic steatosis index.
Supplementary Materials
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References
1. Galicia-Garcia, U.; Benito-Vicente, A.; Jebari, S.; Larrea-Sebal, A.; Siddiqi, H.; Uribe, K.B.; Ostolaza, H.; Martín, C. Pathophysiology of Type 2 Diabetes Mellitus. Int. J. Mol. Sci.; 2020; 21, 6275. [DOI: https://dx.doi.org/10.3390/ijms21176275] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32872570]
2. Bell, K.; Shaw, J.E.; Maple-Brown, L.; Ferris, W.; Gray, S.; Murfet, G.; Flavel, R.; Maynard, B.; Ryrie, H.; Pritchard, B. et al. A position statement on screening and management of prediabetes in adults in primary care in Australia. Diabetes Res. Clin. Pract.; 2020; 164, 108188. [DOI: https://dx.doi.org/10.1016/j.diabres.2020.108188] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32360708]
3. Sameer, A.S.; Banday, M.Z.; Nissar, S. Pathophysiology of diabetes: An overview. Avicenna J. Med.; 2020; 10, pp. 174-188. [DOI: https://dx.doi.org/10.4103/ajm.ajm_53_20]
4. Stefan, N.; Fritsche, A.; Schick, F.; Häring, H.-U. Phenotypes of prediabetes and stratification of cardiometabolic risk. Lancet Diabetes Endocrinol.; 2016; 4, pp. 789-798. [DOI: https://dx.doi.org/10.1016/S2213-8587(16)00082-6]
5. Echouffo-Tcheugui, J.B.; Perreault, L.; Ji, L.; Dagogo-Jack, S. Diagnosis and Management of Prediabetes: A Review. JAMA; 2023; 329, pp. 1206-1216. [DOI: https://dx.doi.org/10.1001/jama.2023.4063]
6. Post, M. Definitions of Quality of Life: What Has Happened and How to Move On. Top. Spinal Cord Inj. Rehabil.; 2014; 20, pp. 167-180. [DOI: https://dx.doi.org/10.1310/sci2003-167]
7. Haraldstad, K.; Wahl, A.; Andenæs, R.; Andersen, J.R.; Andersen, M.H.; Beisland, E.; Borge, C.R.; Engebretsen, E.; Eisemann, M.; Halvorsrud, L. et al. A systematic review of quality of life research in medicine and health sciences. Qual. Life Res.; 2019; 28, pp. 2641-2650. [DOI: https://dx.doi.org/10.1007/s11136-019-02214-9]
8. Tran, B.X.; Nguyen, L.H.; Pham, N.M.; Vu, H.T.T.; Nguyen, H.T.; Phan, D.H.; Ha, G.H.; Pham, H.Q.; Nguyen, T.P.; Latkin, C.A. et al. Global Mapping of Interventions to Improve Quality of Life of People with Diabetes in 1990–2018. Int. J. Environ. Res. Public Health; 2020; 17, 1597. [DOI: https://dx.doi.org/10.3390/ijerph17051597]
9. Trefts, E.; Gannon, M.; Wasserman, D.H. The liver. Curr. Biol.; 2017; 27, pp. R1147-R1151. [DOI: https://dx.doi.org/10.1016/j.cub.2017.09.019]
10. Chao, H.-W.; Chao, S.-W.; Lin, H.; Ku, H.-C.; Cheng, C.-F. Homeostasis of Glucose and Lipid in Non-Alcoholic Fatty Liver Disease. Int. J. Mol. Sci.; 2019; 20, 298. [DOI: https://dx.doi.org/10.3390/ijms20020298]
11. European Association for the Study of the LiverAmerican Association for the Study of Liver Diseases, Latin American Association for the Study of the Liver. A call for unity: The path towards a more precise and patient-centric nomenclature for NAFLD. J. Hepatol.; 2023; 79, pp. 4-5. [DOI: https://dx.doi.org/10.1016/j.jhep.2023.05.003] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37224924]
12. Galarregui, C.; Marin-Alejandre, B.A.; Perez-Diaz-Del-Campo, N.; Cantero, I.; Monreal, J.I.; Elorz, M.; Benito-Boillos, A.; Herrero, J.I.; Tur, J.A.; Martínez, J.A. et al. Predictive Value of Serum Ferritin in Combination with Alanine Aminotransferase and Glucose Levels for Noninvasive Assessment of NAFLD: Fatty Liver in Obesity (FLiO) Study. Diagnostics; 2020; 10, 917. [DOI: https://dx.doi.org/10.3390/diagnostics10110917] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33171699]
13. Fogelholm, M.; Larsen, T.M.; Westerterp-Plantenga, M.; Macdonald, I.; Martinez, J.A.; Boyadjieva, N.; Poppitt, S.; Schlicht, W.; Stratton, G.; Sundvall, J. et al. PREVIEW: Prevention of Diabetes through Lifestyle Intervention and Population Studies in Europe and around the World. Design, Methods, and Baseline Participant Description of an Adult Cohort Enrolled into a Three-Year Randomised Clinical Trial. Nutrients; 2017; 9, 632. [DOI: https://dx.doi.org/10.3390/nu9060632] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28632180]
14. Hanmer, J.; Hess, R.; Sullivan, S.; Yu, L.; Teuteberg, W.; Teuteberg, J.; Kavalieratos, D. Benchmarking health-related quality-of-life data from a clinical setting. Am. J. Manag. Care; 2016; 22, pp. 669-675.
15. Perez-Diaz-Del-Campo, N.; Martínez-Urbistondo, D.; Bugianesi, E.; Martínez, J.A. Diagnostic scores and scales for appraising Nonalcoholic fatty liver disease and omics perspectives for precision medicine. Curr. Opin. Clin. Nutr. Metab. Care; 2022; 25, pp. 285-291. [DOI: https://dx.doi.org/10.1097/MCO.0000000000000849]
16. Salgado, A.L.F.d.A.; de Carvalho, L.; Oliveira, A.C.; dos Santos, V.N.; Vieira, J.G.; Parise, E.R. Insulin resistance index (HOMA-IR) in the differentiation of patients with non-alcoholic fatty liver disease and healthy individuals. Arq. Gastroenterol.; 2010; 47, pp. 165-169. [DOI: https://dx.doi.org/10.1590/S0004-28032010000200009]
17. Vidal-Ostos, F.; Ramos-Lopez, O.; Blaak, E.E.; Astrup, A.; Martinez, J.A. The triglyceride-glucose index as an adiposity marker and a predictor of fat loss induced by a low-calorie diet. Eur. J. Clin. Investig.; 2021; 52, e13674. [DOI: https://dx.doi.org/10.1111/eci.13674]
18. Lee, J.-H.; Kim, D.; Kim, H.J.; Lee, C.-H.; Yang, J.I.; Kim, W.; Kim, Y.J.; Yoon, J.-H.; Cho, S.-H.; Sung, M.-W. et al. Hepatic steatosis index: A simple screening tool reflecting nonalcoholic fatty liver disease. Dig. Liver Dis.; 2010; 42, pp. 503-508. [DOI: https://dx.doi.org/10.1016/j.dld.2009.08.002]
19. Huo, T.; Guo, Y.; Shenkman, E.; Muller, K. Assessing the reliability of the short form 12 (SF-12) health survey in adults with mental health conditions: A report from the wellness incentive and navigation (WIN) study. Health Qual. Life Outcomes; 2018; 16, pp. 1-8. [DOI: https://dx.doi.org/10.1186/s12955-018-0858-2]
20. Pérusse, L.; Jacob, R.; Drapeau, V.; Llewellyn, C.; Arsenault, B.J.; Bureau, A.; Labonté, M.; Tremblay, A.; Vohl, M.-C. Understanding Gene-Lifestyle Interaction in Obesity: The Role of Mediation versus Moderation. Lifestyle Genom.; 2022; 15, pp. 67-76. [DOI: https://dx.doi.org/10.1159/000523813]
21. Sohn, J.-W.; Ho, W.-K. Cellular and systemic mechanisms for glucose sensing and homeostasis. Pflugers Arch.; 2020; 472, pp. 1547-1561. [DOI: https://dx.doi.org/10.1007/s00424-020-02466-2] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32960363]
22. Chianelli, M.; Busetto, L.; Vettor, R.; Annibale, B.; Paoletta, A.; Papini, E.; Albanese, A.; Carabotti, M.; Casarotto, D.; De Pergola, G. et al. Italian guidelines for the management of adult individuals with overweight and obesity and metabolic comorbidities that are resistant to behavioral treatment. J. Endocrinol. Investig.; 2024; 47, pp. 1361-1371. [DOI: https://dx.doi.org/10.1007/s40618-024-02361-y] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38630213]
23. Perez-Diaz-Del-Campo, N.; Riezu-Boj, J.I.; Marin-Alejandre, B.A.; Monreal, J.I.; Elorz, M.; Herrero, J.I.; Benito-Boillos, A.; Milagro, F.I.; Tur, J.A.; Abete, I. et al. Three Different Genetic Risk Scores Based on Fatty Liver Index, Magnetic Resonance Imaging and Lipidomic for a Nutrigenetic Personalized Management of NAFLD: The Fatty Liver in Obesity Study. Diagnostics; 2021; 11, 1083. [DOI: https://dx.doi.org/10.3390/diagnostics11061083] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34199237]
24. Labenz, C.; Kostev, K.; Alqahtani, S.A.; Galle, P.R.; Schattenberg, J.M. Impact of Non-Alcoholic Fatty Liver Disease on Metabolic Comorbidities in Type 2 Diabetes Mellitus. Exp. Clin. Endocrinol. Diabetes; 2021; 130, pp. 172-177. [DOI: https://dx.doi.org/10.1055/a-1378-4679]
25. Zhu, R.; Craciun, I.; Bernhards-Werge, J.; Jalo, E.; Poppitt, S.D.; Silvestre, M.P.; Huttunen-Lenz, M.; McNarry, M.A.; Stratton, G.; Handjiev, S. et al. Age- and sex-specific effects of a long-term lifestyle intervention on body weight and cardiometabolic health markers in adults with prediabetes: Results from the diabetes prevention study PREVIEW. Diabetologia; 2022; 65, pp. 1262-1277. [DOI: https://dx.doi.org/10.1007/s00125-022-05716-3]
26. de Ritter, R.; Sep, S.J.S.; van der Kallen, C.J.H.; Schram, M.T.; Koster, A.; Kroon, A.A.; van Greevenbroek, M.M.J.; Eussen, S.J.P.M.; Dagnelie, P.C.; de Jong, M. et al. Adverse differences in cardiometabolic risk factor levels between individuals with pre-diabetes and normal glucose metabolism are more pronounced in women than in men: The Maastricht Study. BMJ Open Diabetes Res. Care; 2019; 7, e000787. [DOI: https://dx.doi.org/10.1136/bmjdrc-2019-000787]
27. de Ritter, R.; Sep, S.J.S.; van der Kallen, C.J.H.; van Greevenbroek, M.M.J.; Koster, A.; Eussen, S.J.P.M.; Dagnelie, P.C.; van Boxtel, M.; Schram, M.T.; Köhler, S. et al. Sex comparisons in the association of prediabetes and type 2 diabetes with cognitive function, depression, and quality of life: The Maastricht study. Diabet. Med.; 2023; 40, e15115. [DOI: https://dx.doi.org/10.1111/dme.15115]
28. Gebremedhin, T.; Workicho, A.; Angaw, D.A. Health-related quality of life and its associated factors among adult patients with type II diabetes attending Mizan Tepi University Teaching Hospital, Southwest Ethiopia. BMJ Open Diabetes Res. Care; 2019; 7, e000577. [DOI: https://dx.doi.org/10.1136/bmjdrc-2018-000577]
29. Kazukauskiene, N.; Podlipskyte, A.; Varoneckas, G.; Mickuviene, N. Health-related quality of life and insulin resistance over a 10-year follow-up. Sci. Rep.; 2021; 11, 24294. [DOI: https://dx.doi.org/10.1038/s41598-021-03791-x]
30. Golubeva, J.A.; Sheptulina, A.F.; Yafarova, A.A.; Mamutova, E.M.; Kiselev, A.R.; Drapkina, O.M. Reduced Quality of Life in Patients with Non-Alcoholic Fatty Liver Disease May Be Associated with Depression and Fatigue. Healthcare; 2022; 10, 1699. [DOI: https://dx.doi.org/10.3390/healthcare10091699]
31. Martínez-Urbistondo, D.; San-Cristóbal, R.; Villares, P.; Martínez-González, M.; Babio, N.; Corella, D.; del Val, J.L.; Ordovás, J.M.; Alonso-Gómez, M.; Wärnberg, J. et al. Role of NAFLD on the Health Related QoL Response to Lifestyle in Patients with Metabolic Syndrome: The PREDIMED Plus Cohort. Front. Endocrinol.; 2022; 13, 868795. [DOI: https://dx.doi.org/10.3389/fendo.2022.868795] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35846291]
32. Fennoun, H.; El Mansouri, S.; Tahiri, M.; Haraj, N.E.; El Aziz, S.; Hadad, F.; Hliwa, W.; Badr, W.; Chadli, A. Interest of hepatic steatosis index (HSI) in screening for metabolic steatopathy in patients with type 2 diabetes. Pan Afr. Med. J.; 2020; 37, 270. [DOI: https://dx.doi.org/10.11604/pamj.2020.37.270.9087] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33598084]
33. Kunutsor, S.K.; Bakker, S.J.L.; Blokzijl, H.; Dullaart, R.P.F. Associations of the fatty liver and hepatic steatosis indices with risk of cardiovascular disease: Interrelationship with age. Clin. Chim. Acta; 2017; 466, pp. 54-60. [DOI: https://dx.doi.org/10.1016/j.cca.2017.01.008]
34. Martinez-Urbistondo, D.; Huerta, A.; Navarro-González, D.; Sánchez-Iñigo, L.; Fernandez-Montero, A.; Landecho, M.F.; Martinez, J.A.; Pastrana-Delgado, J.C. Estimation of fatty liver disease clinical role on glucose metabolic remodelling phenotypes and T2DM onset. Eur. J. Clin. Investig.; 2023; 53, e14036. [DOI: https://dx.doi.org/10.1111/eci.14036]
35. Reginato, E.; Pippi, R.; Aiello, C.; Tomaro, E.S.; Ranucci, C.; Buratta, L.; Bini, V.; Marchesini, G.; De Feo, P.; Fanelli, C. Effect of Short Term Intensive Lifestyle Intervention on Hepatic Steatosis Indexes in Adults with Obesity and/or Type 2 Diabetes. J. Clin. Med.; 2019; 8, 851. [DOI: https://dx.doi.org/10.3390/jcm8060851]
36. Ng, C.H.; Chan, K.E.; Chin, Y.H.; Zeng, R.W.; Tsai, P.C.; Lim, W.H.; Tan, D.J.H.; Khoo, C.M.; Goh, L.H.; Ling, Z.J. et al. The effect of diabetes and prediabetes on the prevalence, complications and mortality in nonalcoholic fatty liver disease. Clin. Mol. Hepatol.; 2022; 28, pp. 565-574. [DOI: https://dx.doi.org/10.3350/cmh.2022.0096]
37. Martinez-Urbistondo, D.; D’avola, D.; Navarro-González, D.; Sanchez-Iñigo, L.; Fernandez-Montero, A.; Perez-Diaz-Del-Campo, N.; Bugianesi, E.; Martinez, J.A.; Pastrana, J.C. Interactive Role of Surrogate Liver Fibrosis Assessment and Insulin Resistance on the Incidence of Major Cardiovascular Events. J. Clin. Med.; 2022; 11, 5190. [DOI: https://dx.doi.org/10.3390/jcm11175190]
38. Funuyet-Salas, J.; Martín-Rodríguez, A.; Pérez-San-Gregorio, M.; Romero-Gómez, M. Influence of Psychological Biomarkers on Therapeutic Adherence by Patients with Non-Alcoholic Fatty Liver Disease: A Moderated Mediation Model. J. Clin. Med.; 2021; 10, 2208. [DOI: https://dx.doi.org/10.3390/jcm10102208]
39. Pina, A.F.; Patarrão, R.S.; Ribeiro, R.T.; Penha-Gonçalves, C.; Raposo, J.F.; Gardete-Correia, L.; Duarte, R.; Boavida, J.M.; Medina, J.L.; Henriques, R. et al. Metabolic Footprint, towards Understanding Type 2 Diabetes beyond Glycemia. J. Clin. Med.; 2020; 9, 2588. [DOI: https://dx.doi.org/10.3390/jcm9082588]
40. Corbin, K.D.; Pittas, A.G.; Desouza, C.; Grdinovac, K.K.; Herzig, K.-H.; Kashyap, S.R.; Kim, S.H.; Nelson, J.; Rasouli, N.; Vickery, E.M. et al. Indices of hepatic steatosis and fibrosis in prediabetes and association with diabetes development in the vitamin D and type 2 diabetes study. J. Diabetes Its Complicat.; 2023; 37, 108475. [DOI: https://dx.doi.org/10.1016/j.jdiacomp.2023.108475]
41. Wagner, R.; Heni, M.; Kantartzis, K.; Sandforth, A.; Machann, J.; Schick, F.; Peter, A.; Fritsche, L.; Szendrödi, J.; Pfeiffer, A.F. et al. Lower Hepatic Fat Is Associated with Improved Insulin Secretion in a High-Risk Prediabetes Subphenotype During Lifestyle Intervention. Diabetes; 2022; 72, pp. 362-366. [DOI: https://dx.doi.org/10.2337/db22-0441] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36525512]
42. McKenzie, A.L.; Athinarayanan, S.J.; McCue, J.J.; Adams, R.N.; Keyes, M.; McCarter, J.P.; Volek, J.S.; Phinney, S.D.; Hallberg, S.J. Type 2 Diabetes Prevention Focused on Normalization of Glycemia: A Two-Year Pilot Study. Nutrients; 2021; 13, 749. [DOI: https://dx.doi.org/10.3390/nu13030749] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33652715]
43. Srivanichakorn, W.; Godsland, I.F.; Washirasaksiri, C.; Phisalprapa, P.; Charatcharoenwitthaya, P.; Pramyothin, P.; Sitasuwan, T.; Preechasuk, L.; Elkeles, R.; Alberti, K.G.M.M. et al. Cardiometabolic risk factors in Thai individuals with prediabetes treated in a high-risk, prevention clinic: Unexpected relationship between high-density lipoprotein cholesterol and glycemia in men. J. Diabetes Investig.; 2018; 10, pp. 771-779. [DOI: https://dx.doi.org/10.1111/jdi.12967] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30387292]
44. Pinnaduwage, L.; Ye, C.; Hanley, A.J.; Connelly, P.W.; Sermer, M.; Zinman, B.; Retnakaran, R. Changes Over Time in Hepatic Markers Predict Changes in Insulin Sensitivity, β-Cell Function, and Glycemia. J. Clin. Endocrinol. Metab.; 2018; 103, pp. 2651-2659. [DOI: https://dx.doi.org/10.1210/jc.2018-00306]
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Details
; Martinez-Urbistondo, Diego 2
; Navas-Carretero, Santiago 3
; Zhu, Ruixin 4 ; Huttunen-Lenz, Maija 5
; Stratton, Gareth 6
; Handjieva-Darlenska, Teodora 7 ; Handjiev, Svetoslav 7 ; Sundvall, Jouko Ensio 8 ; Silvestre, Marta P 9
; Jalo, Elli 10
; Pietiläinen, Kirsi H 11
; Adam, Tanja C 12 ; Westerterp-Plantenga, Margriet 12
; Simpson, Elizabeth 13 ; MacDonald, Ian 14 ; Taylor, Moira A 15
; Poppitt, Sally D 16
; Schlicht, Wolfgang 17
; Brand-Miller, Jennie 18
; Fogelholm, Mikael 10
; Raben, Anne 4
; Martinez, J Alfredo 19 1 Medicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Baja California, Mexico;
2 Internal Medicine Department, Clinica Universidad de Navarra, 28027 Madrid, Spain;
3 Centre for Nutrition Research, Department of Nutrition, Food Science, Physiology and Toxicology, University of Navarra, 31009 Pamplona, Spain;
4 Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, DK-2200 Copenhagen, Denmark;
5 Institute for Nursing Science, University of Education Schwäbisch Gmünd, 73525 Schwäbisch Gmünd, Germany;
6 Applied Sports, Technology, Exercise and Medicine (A-STEM) Research Centre, Swansea University, Swansea SA1 8EN, UK;
7 Department of Pharmacology and Toxicology, Medical University of Sofia, 1000 Sofia, Bulgaria;
8 Finnish Institute for Health and Welfare, 00271 Helsinki, Finland;
9 Human Nutrition Unit, Department of Medicine, School of Biological Sciences, University of Auckland, Auckland 1024, New Zealand;
10 Department of Food and Nutrition, University of Helsinki, 00014 Helsinki, Finland;
11 Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland;
12 Department of Nutrition and Movement Sciences, NUTRIM, School of Nutrition and Translational Research in Metabolism, Maastricht University, 6200 Maastricht, The Netherlands;
13 MRC/ARUK Centre for Musculoskeletal Ageing Research, ARUK Centre for Sport, Exercise and Osteoarthritis, National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham DE22 3DT, UK;
14 MRC/ARUK Centre for Musculoskeletal Ageing Research, ARUK Centre for Sport, Exercise and Osteoarthritis, National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham DE22 3DT, UK;
15 NIHR Nottingham Biomedical Research Centre at Nottingham University Hospitals NHS Trust and University of Nottingham, The David Greenfield Human Physiology Unit, Division of Physiology, Pharmacology and Neuroscience, School of Life Sciences, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham NG1 5DU, UK;
16 Human Nutrition Unit, Department of Medicine, School of Biological Sciences, University of Auckland, Auckland 1024, New Zealand;
17 Exercise and Health Sciences, University of Stuttgart, 70569 Stuttgart, Germany;
18 School of Life and Environmental Sciences and Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia;
19 Centre for Nutrition Research, Department of Nutrition, Food Science, Physiology and Toxicology, University of Navarra, 31009 Pamplona, Spain;