Metabolic and lifestyle factors accelerate disease onset and alter gut microbiome in inflammatory non-communicable diseases.

BACKGROUND: Biomedical and lifestyle factors in Western populations have significantly shifted in recent decades, influencing public health and contributing to the increasing prevalence of non-communicable diseases (NCDs) that share inflammation as common pathology. METHODS: We investigated the relationship between these factors and 11 NCDs in the cross-sectional FoCus cohort (n = 1220), using logistic regression models. Associations with age-at-disease-onset were specifically analyzed for type 2 diabetes (T2D, low-grade chronic inflammation) and inflammatory bowel disease (IBD, high-grade chronic inflammation) in disease-specific cohorts (FoCus-T2D, n = 514; IBD-KC, n = 1110). Important factors for disease risk were identified using Cox-PH-regression models and time-to-event analysis. We further explored the interaction between identified risk factors and gut microbiome composition using linear models. RESULTS: Lifestyle factors were clearly linked to disease phenotypes, particularly in T2D and IBD. Still, some factors affected only the age-at-onset, but not disease prevalence. High-quality nutrition significantly delayed onset for both IBD and T2D (IBD: HR = 0.81 [0.66; 0.98]; T2D: HR = 0.45 [0.28; 0.72]). Smoking accelerated T2D onset (HR = 1.82 [1.25; 2.65]) but delayed onset in ulcerative colitis (UC: HR = 0.47 [0.28; 0.79]). Higher microbiota diversity delayed IBD onset (Shannon: HR = 0.58 [0.49; 0.71]) but had no effect on T2D. The abundance of specific microbial genera was strongly associated with various biomedical and lifestyle factors in T2D and IBD. In unaffected controls, these effects were smaller or reversed, potentially indicating a greater susceptibility of the gut microbiome to negative influences in T2D and IBD. CONCLUSIONS: The dual insights into age-at-disease-onset and gut microbiota composition in disease emphasize the role of certain biomedical and lifestyle factors, e.g., nutrition quality, in disease prevention and management. Understanding these relationships provides a foundation for developing targeted strategies to mitigate the impact of metabolic and inflammatory diseases through lifestyle modifications and gut health management.

Changes in Periodontal Parameters and Microbiome Composition of Periodontal Pocket in Patients with Chronic Inflammatory Diseases Receiving Targeted Anti-Cytokine Therapy.

Periodontitis is associated with systemic chronic inflammatory diseases. There is limited evidence on the influence of anti-cytokine therapies on the periodontal condition and microbiome in the tooth pocket of such patients, so the aim of this study was to elucidate this issue. In this observational trial, the periodontal status and the gingival crevicular fluid of 13 patients with different chronic inflammatory diseases were obtained before the initiation of anti-cytokine treatment and 14 weeks after. Gingival crevicular fluid was collected for 16S rRNA gene sequencing from a clinically healthy tooth and the deepest measured pocket. The Shannon Diversity Index significantly increased in the deepest pockets of patients (p = 0.039). The data showed alterations in the diversity of the subgingival microbiome over the course of the study, implying a shift towards a healthier condition after starting anti-cytokine therapy. Additional investigations are needed to analyze whether the administration of selective biologicals can improve periodontal conditions in patients with or without chronic inflammatory diseases.

Microbial Features Linked to Medication Strategies in Cardiometabolic Disease Management.

Human gut microbiota are recognized as critical players in both metabolic disease and drug metabolism. However, medication-microbiota interactions in cardiometabolic diseases are not well understood. To gain a comprehensive understanding of how medication intake impacts the gut microbiota, we investigated the association of microbial structure with the use of single or multiple medications in a cohort of 134 middle-aged adults diagnosed with cardiometabolic disease, recruited from Alberta's Tomorrow Project. Predominant cardiometabolic prescription medication classes (12 total) were included in our analysis. Multivariate Association with Linear Model (MaAsLin2) was employed and results were corrected for age, BMI, sex, and diet to evaluate the relationship between microbial features and single- or multimedication use. Highly individualized microbiota profiles were observed across participants, and increasing medication use was negatively correlated with α-diversity. A total of 46 associations were identified between microbial composition and single medications, exemplified by the depletion of Akkermansia muciniphila by ß-blockers and statins, and the enrichment of Escherichia/Shigella and depletion of Bacteroides xylanisolvens by metformin. Metagenomics prediction further indicated alterations in microbial functions associated with single medications such as the depletion of enzymes involved in energy metabolism encoded by Eggerthella lenta due to ß-blocker use. Specific dual medication combinations also had profound impacts, including the depletion of Romboutsia and Butyriciocccus by statin plus metformin. Together, these results show reductions in bacterial diversity as well as species and microbial functional potential associated with both single- and multimedication use in cardiometabolic disease.

Weight loss impacts risky decisions in obesity.

BACKGROUND & AIMS: Risky decision making is shaped by individual psychological and metabolic state. Individuals with obesity show not only altered risk behavior, but also metabolic and psychological abnormalities. The aim of the present study was to investigate whether a substantial weight loss in individuals with severe obesity will 1) normalize their metabolic and psychological state and 2) will change their pattern of decision guidance. METHODS: We assessed the effect of glycated hemoglobin (HbA1c) and mood on risk behavior in individuals with obesity (n = 62, 41 women; BMI, 46.5 ± 4.8 kg/m2; age, 44.9 ± 14.7 years) before and after 10-weeks weight loss intervention. RESULTS: Results showed that this intervention reduced participants' risk behavior, which was significantly predicted by their changes in BMI. Before intervention, mood, but not HbA1c significantly predicted decisions. After the weight loss, mood no longer, but HbA1c significantly predicted decisions. CONCLUSION: Our findings shed light on the psychological and metabolic mechanisms underlying altered risky decision making in severe obesity and can inform the development of strategies in the context of weight loss interventions.

Metabolic model predictions enable targeted microbiome manipulation through precision prebiotics.

While numerous health-beneficial interactions between host and microbiota have been identified, there is still a lack of targeted approaches for modulating these interactions. Thus, we here identify precision prebiotics that specifically modulate the abundance of a microbiome member species of interest. In the first step, we show that defining precision prebiotics by compounds that are only taken up by the target species but no other species in a community is usually not possible due to overlapping metabolic niches. Subsequently, we use metabolic modeling to identify precision prebiotics for a two-member Caenorhabditis elegans microbiome community comprising the immune-protective target species Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71. We experimentally confirm four of the predicted precision prebiotics, L-serine, L-threonine, D-mannitol, and γ-aminobutyric acid, to specifically increase the abundance of MYb11. L-serine was further assessed in vivo, leading to an increase in MYb11 abundance also in the worm host. Overall, our findings demonstrate that metabolic modeling is an effective tool for the design of precision prebiotics as an important cornerstone for future microbiome-targeted therapies.IMPORTANCEWhile various mechanisms through which the microbiome influences disease processes in the host have been identified, there are still only few approaches that allow for targeted manipulation of microbiome composition as a first step toward microbiome-based therapies. Here, we propose the concept of precision prebiotics that allow to boost the abundance of already resident health-beneficial microbial species in a microbiome. We present a constraint-based modeling pipeline to predict precision prebiotics for a minimal microbial community in the worm Caenorhabditis elegans comprising the host-beneficial Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71 with the aim to boost the growth of MYb11. Experimentally testing four of the predicted precision prebiotics, we confirm that they are specifically able to increase the abundance of MYb11 in vitro and in vivo. These results demonstrate that constraint-based modeling could be an important tool for the development of targeted microbiome-based therapies against human diseases.

MRI signs of intracranial hypertension in morbidly obese and normal-weight individuals.

PURPOSE: Idiopathic intracranial hypertension (IIH) is associated with intracranial abnormalities, although not obligatory, which can be detected with cranial MRI. Obesity is an important risk factor for IIH and a pathogenetic link is suspected but the extent to which these MRI signs are already related to obesity has not yet been examined. We investigated whether IIH-MRI signs are present more often in obese individuals with a BMI > 30 kg/m² than in individuals of normal weight. MATERIALS AND METHODS: Brain MRI of 32 obese and 53 normal-weight participants from the Food-Chain-Plus cohort were analyzed for three main signs of IIH: (I) stenosis of the transverse sinus, (II) increased width of the optic nerve sheath, and (III) reduction of pituitary height. In addition, a scoring system for the MRI signs was applied. Furthermore, tortuosity of the optic nerve and flattening of the posterior globe were considered as additional MRI signs. RESULTS: Obese participants had a significantly higher MRI score (p < 0.001) than those in the normal-weight cohort, with significant differences in quantitative measurements of cerebral venous sinus stenosis (p < 0.001), widening of the optic nerve sheath (p < 0.05), and flattening of the pituitary gland (p < 0.05) also always being observed more frequently and in a more pronounced manner in the obese group. Furthermore, our findings correlated significantly with BMI and proportionately with hip and waist circumference. CONCLUSION: Obese individuals show a significantly higher prevalence of cerebral MRI signs related to IIH. This supports the hypothesis that obesity is a risk factor for possible intracranial hypertension and reinforces the suspicion that obesity is involved in the pathomechanism of IIH. KEY POINTS: · Obesity is associated with changes such as those seen in IIH.. · BMI, weight, and body measurements correlate with the expression of MRI signs of IIH.. · To prevent IIH symptoms in obesity, the pathomechanism must be further clarified..

A genome-wide association study meta-analysis in a European sample of stage III/IV grade C periodontitis patients ≤35 years of age identifies new risk loci.

AIM: Few genome-wide association studies (GWAS) have been conducted for severe forms of periodontitis (stage III/IV grade C), and the number of known risk genes is scarce. To identify further genetic risk variants to improve the understanding of the disease aetiology, a GWAS meta-analysis in cases with a diagnosis at ≤35 years of age was performed. MATERIALS AND METHODS: Genotypes from German, Dutch and Spanish GWAS studies of III/IV-C periodontitis diagnosed at age ≤35 years were imputed using TopMed. After quality control, a meta-analysis was conducted on 8,666,460 variants in 1306 cases and 7817 controls with METAL. Variants were prioritized using FUMA for gene-based tests, functional annotation and a transcriptome-wide association study integrating eQTL data. RESULTS: The study identified a novel genome-wide significant association in the FCER1G gene (p = 1.0 × 10-9 ), which was previously suggestively associated with III/IV-C periodontitis. Six additional genes showed suggestive association with p < 10-5 , including the known risk gene SIGLEC5. HMCN2 showed the second strongest association in this study (p = 6.1 × 10-8 ). CONCLUSIONS: This study expands the set of known genetic loci for severe periodontitis with an age of onset ≤35 years. The putative functions ascribed to the associated genes highlight the significance of oral barrier tissue stability, wound healing and tissue regeneration in the aetiology of these periodontitis forms and suggest the importance of tissue regeneration in maintaining oral health.

Evaluating the Effect of Data Merging and Postacquisition Normalization on Statistical Analysis of Untargeted High-Resolution Mass Spectrometry Based Urinary Metabolomics Data.

Urine is one of the most widely used biofluids in metabolomic studies because it can be collected noninvasively and is available in large quantities. However, it shows large heterogeneity in sample concentration and consequently requires normalization to reduce unwanted variation and extract meaningful biological information. Biological samples like urine are commonly measured with electrospray ionization (ESI) coupled to a mass spectrometer, producing data sets for positive and negative modes. Combining these gives a more complete picture of the total metabolites present in a sample. However, the effect of this data merging on subsequent data analysis, especially in combination with normalization, has not yet been analyzed. To address this issue, we conducted a neutral comparison study to evaluate the performance of eight postacquisition normalization methods under different data merging procedures using 1029 urine samples from the Food Chain plus (FoCus) cohort. Samples were measured with a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR-MS). Normalization methods were evaluated by five criteria capturing the ability to remove sample concentration variation and preserve relevant biological information. Merging data after normalization was generally favorable for quality control (QC) sample similarity, sample classification, and feature selection for most of the tested normalization methods. Merging data after normalization and the usage of probabilistic quotient normalization (PQN) in a similar setting are generally recommended. Relying on a single analyte to capture sample concentration differences, like with postacquisition creatinine normalization, seems to be a less preferable approach, especially when data merging is applied.

Relapsing Syndrome of Inappropriate Antidiuretic Hormone Production Responding to Tolvaptan Treatment in a Patient With a Micronodular Formation of the Posterior Pituitary Gland.

The syndrome of inappropriate ADH-secretion (SIADH) is a common cause of low sodium levels with diverse aetiology. Here, we report a case of a 41 years old male patient diagnosed with SIADH and a good response to Tolvaptan therapy. Of interest, as a potential unique cause, magnetic resonance imaging revealed a micronodular structure in the posterior pituitary, while no other common cause of SIADH could be identified. Hence, to the best of our knowledge, this is the first case of a Tolvaptan-responsive SIADH associated with a pituitary micronodular structure.

Effects of lifestyle and associated diseases on serum CC16 suggest complex interactions among metabolism, heart and lungs.

INTRODUCTION: Clara cell 16-kDa protein (CC16) is an anti-inflammatory, immunomodulatory secreted pulmonary protein with reduced serum concentrations in obesity according to recent data. OBJECTIVE: Studies focused solely on bodyweight, which does not properly reflect obesity-associated implications of the metabolic and reno-cardio-vascular system. The purpose of this study was therefore to examine CC16 in a broad physiological context considering cardio-metabolic comorbidities of primary pulmonary diseases. METHODS: CC16 was quantified in serum samples in a subset of the FoCus (N = 497) and two weight loss intervention cohorts (N = 99) using ELISA. Correlation and general linear regression analyses were applied to assess CC16 effects of lifestyle, gut microbiota, disease occurrence and treatment strategies. Importance and intercorrelation of determinants were validated using random forest algorithms. RESULTS: CC16 A38G gene mutation, smoking and low microbial diversity significantly decreased CC16. Pre-menopausal female displayed lower CC16 compared to post-menopausal female and male participants. Biological age and uricosuric medications increased CC16 (all p < 0.01). Adjusted linear regression revealed CC16 lowering effects of high waist-to-hip ratio (est. -11.19 [-19.4; -2.97], p = 7.99 × 10-3), severe obesity (est. -2.58 [-4.33; -0.82], p = 4.14 × 10-3) and hypertension (est. -4.31 [-7.5; -1.12], p = 8.48 × 10-3). ACEi/ARB medication (p = 2.5 × 10-2) and chronic heart failure (est. 4.69 [1.37; 8.02], p = 5.91 × 10-3) presented increasing effects on CC16. Mild associations of CC16 were observed with blood pressure, HOMA-IR and NT-proBNP, but not manifest hyperlipidemia, type 2 diabetes, diet quality and dietary weight loss intervention. CONCLUSION: A role of metabolic and cardiovascular abnormalities in the regulation of CC16 and its modifiability by behavioral and pharmacological interventions is indicated. Alterations by ACEi/ARB and uricosurics could point towards regulatory axes comprising the renin-angiotensin-aldosterone system and purine metabolism. Findings altogether strengthen the importance of interactions among metabolism, heart and lungs.

Physical activity-induced alterations of the gut microbiota are BMI dependent.

Physical inactivity is one of the leading causes of chronic metabolic disease including obesity. Increasing physical activity (PA) has been shown to improve cardiometabolic and musculoskeletal health and to be associated with a distinct gut microbiota composition in trained athletes. However, the impact of PA on the gut microbiota is inconclusive for individuals performing PA in their day-to-day life. This study examined the role of PA and hand-grip strength on gut microbiome composition in middle-aged adults (40-65 years, n = 350) with normal (18.5-24.9 kg/m2 ) and overweight (25-29.9 kg/m2 ) body mass index (BMI). PA was recorded using the International Physical Activity Questionnaire, and hand-grip strength was measured using a dynamometer. Serum samples were assessed for lipidomics while DNA was extracted from fecal samples for microbiome analysis. Overweight participants showed a higher concentration of triacylglycerols, and lower concentrations of cholesteryl esters, sphingomyelin, and lyso-phosphotidylcholine lipids (p < .05) compared with those with normal BMI. Additionally, overweight participants had a lower abundance of the Oscillibacter genus (p < .05). The impact of PA duration on the gut microbiome was BMI dependent. In normal but not overweight participants, high PA duration showed greater relative abundance of commensal taxa such as Actinobacteria and Proteobacteria phyla, as well as Collinsella and Prevotella genera (p < .05). Furthermore, in males with normal BMI, a stronger grip strength was associated with a higher relative abundance of Faecalibacterium and F. prausnitzii (p < .05) compared with lower grip strength. Taken together, data suggest that BMI plays a significant role in modeling PA-induced changes in gut microbiota.

Is human obesity an inflammatory disease of the hypothalamus?

Obesity and its comorbidities are long-standing, challenging global health problems. Lack of exercise, overnutrition, and especially the consumption of fat-rich foods are some of the most important factors leading to an increase in prevalence in modern society. The pathophysiology of obesity as a metabolic inflammatory disease has moved into focus since new therapeutic approaches are required. The hypothalamus, a brain area responsible for energy homeostasis, has recently received special attention in this regard. Hypothalamic inflammation was identified to be associated with diet-induced obesity and new evidence suggests that it may be, beyond that, a pathological mechanism of the disease. This inflammation impairs the local signaling of insulin and leptin leading to dysfunction of the regulation of energy balance and thus, weight gain. After a high-fat diet consumption, activation of inflammatory mediators such as the nuclear factor κB or c-Jun N-terminal kinase pathway can be observed, accompanied by elevated secretion of pro-inflammatory interleukins and cytokines. Brain resident glia cells, especially microglia and astrocytes, initiate this release in response to the flux of fatty acids. The gliosis occurs rapidly before the actual weight gain. Dysregulated hypothalamic circuits change the interaction between neuronal and non-neuronal cells, contributing to the establishment of inflammatory processes. Several studies have reported reactive gliosis in obese humans. Although there is evidence for a causative role of hypothalamic inflammation in the obesity development, data on underlying molecular pathways in humans are limited. This review discusses the current state of knowledge on the relationship between hypothalamic inflammation and obesity in humans.

Network-based quantitative trait linkage analysis of microbiome composition in inflammatory bowel disease families.

Introduction: Inflammatory bowel disease (IBD) is characterized by a dysbiosis of the gut microbiome that results from the interaction of the constituting taxa with one another, and with the host. At the same time, host genetic variation is associated with both IBD risk and microbiome composition. Methods: In the present study, we defined quantitative traits (QTs) from modules identified in microbial co-occurrence networks to measure the inter-individual consistency of microbial abundance and subjected these QTs to a genome-wide quantitative trait locus (QTL) linkage analysis. Results: Four microbial network modules were consistently identified in two cohorts of healthy individuals, but three of the corresponding QTs differed significantly between IBD patients and unaffected individuals. The QTL linkage analysis was performed in a sub-sample of the Kiel IBD family cohort (IBD-KC), an ongoing study of 256 German families comprising 455 IBD patients and 575 first- and second-degree, non-affected relatives. The analysis revealed five chromosomal regions linked to one of three microbial module QTs, namely on chromosomes 3 (spanning 10.79 cM) and 11 (6.69 cM) for the first module, chr9 (0.13 cM) and chr16 (1.20 cM) for the second module, and chr13 (19.98 cM) for the third module. None of these loci have been implicated in a microbial phenotype before. Discussion: Our study illustrates the benefit of combining network and family-based linkage analysis to identify novel genetic drivers of microbiome composition in a specific disease context.

Metabolic model predictions enable targeted microbiome manipulation through precision prebiotics.

The microbiome is increasingly receiving attention as an important modulator of host health and disease. However, while numerous mechanisms through which the microbiome influences its host have been identified, there is still a lack of approaches that allow to specifically modulate the abundance of individual microbes or microbial functions of interest. Moreover, current approaches for microbiome manipulation such as fecal transfers often entail a non-specific transfer of entire microbial communities with potentially unwanted side effects. To overcome this limitation, we here propose the concept of precision prebiotics that specifically modulate the abundance of a microbiome member species of interest. In a first step, we show that defining precision prebiotics by compounds that are only taken up by the target species but no other species in a community is usually not possible due to overlapping metabolic niches. Subsequently, we present a metabolic modeling network framework that allows us to define precision prebiotics for a two-member C. elegans microbiome model community comprising the immune-protective Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71. Thus, we predicted compounds that specifically boost the abundance of the host-beneficial MYb11, four of which were experimentally validated in vitro (L-serine, L-threonine, D-mannitol, and γ-aminobutyric acid). L-serine was further assessed in vivo, leading to an increase in MYb11 abundance also in the worm host. Overall, our findings demonstrate that constraint-based metabolic modeling is an effective tool for the design of precision prebiotics as an important cornerstone for future microbiome-targeted therapies.

A Prospective Analysis of the Metyrapone Short Test Using Targeted and Untargeted Metabolomics.

INTRODUCTION: The present study aimed to prove the metyrapone short test in a day clinic to be suitable for examining the integrity of the hypothalamic-pituitary-adrenal (HPA) axis in patients with suspected secondary and tertiary adrenal insufficiency and to identify novel effector molecules in acute stress response. METHODS: 44 patients were prospectively enrolled. Based on stimulated 11-deoxycortisol levels, patients were divided into a physiological (11-deoxycortisol ≥70 µg/L) and a pathological (11-deoxycortisol <70 µg/L) response group. Clinical follow-up examination was performed for validation. Ultraperformance liquid chromatography tandem mass spectrometry and a Fourier-transform-ion-cyclotron-resonance-mass-spectrometry were used for targeted and untargeted steroid metabolomics. RESULTS: At baseline, lower levels of cortisone (42 vs. 50 nmol/L, p = 0.048) and 17-OH-progesterone (0.6 vs. 1.2 nmol/L, p = 0.041) were noted in the pathological response group. After metyrapone administration, the pathological response group exhibited significantly lower 11-deoxycortisol (39.0 vs. 94.2 µg/L, p < 0.001) and ACTH (49 vs. 113 pg/mL, p < 0.001) concentrations as well as altered upstream metabolites. Untargeted metabolomics identified a total of 76 metabolites to be significantly up- or downregulated by metyrapone. A significant increase of the bile acid glycochenodeoxycholic acid (GCDC, p < 0.01) was detected in both groups with an even stronger increase in the physiological response group. After a mean follow-up of 17.2 months, an 11-deoxycortisol cut-off of 70 µg/L showed a high diagnostic performance (sensitivity 100%, specificity 96%). CONCLUSION: The metyrapone short test is safe and feasible in a day clinic setting. The alterations of the bile acid GCDC indicate that the liver might be involved in the acute stress response of the HPA axis.

Differential Diagnosis in Hypophysitis: First Report on a Spindle Cell Rhabdomyosarcoma of the Pituitary Gland.

Pituitary gland metastases are very rare. Most patients with pituitary gland metastases are asymptomatic; therefore, most cases of this disease are diagnosed during autopsies. Moreover, the four most common primary tumors that metastasize to the pituitary gland are breast, lung, thyroid, and renal carcinomas. We present a very rare case of pituitary metastasis of spindle cell rhabdomyosarcoma (RMS). Our patient presented with headache, visual disorder, panhypopituitarism, and diabetes insipidus. Due to tumor expansion, resection was not possible, so diagnosis was confirmed by biopsy, and chemotherapy and irradiation were administered. Our patient showed widespread spindle cell RMS, which harbors a mutation of myogenic differentiation 1 (MYOD1) and is associated with a poor prognosis. Even high-risk patients can show a remission after chemotherapy and irradiation. In the cases with indistinct lesions in the sella region, pituitary metastasis should always be considered.

Cohort profile: the Food Chain Plus (FoCus) cohort.

The Food Chain Plus (FoCus) cohort was launched in 2011 for population-based research related to metabolic inflammation. To characterize this novel pathology in a comprehensive manner, data collection included multiple omics layers such as phenomics, microbiomics, metabolomics, genomics, and metagenomics as well as nutrition profiling, taste perception phenotyping and social network analysis. The cohort was set-up to represent a Northern German population of the Kiel region. Two-step recruitment included the randomised enrolment of participants via residents' registration offices and via the Obesity Outpatient Centre of the University Medical Center Schleswig-Holstein (UKSH). Hence, both a population- and metabolic inflammation- based cohort was created. In total, 1795 individuals were analysed at baseline. Baseline data collection took place between 2011 and 2014, including 63% females and 37% males with an age range of 18-83 years. The median age of all participants was 52.0 years [IQR: 42.5; 63.0 years] and the median baseline BMI in the study population was 27.7 kg/m2 [IQR: 23.7; 35.9 kg/m2]. In the baseline cohort, 14.1% of participants had type 2 diabetes mellitus, which was more prevalent in the subjects of the metabolic inflammation group (MIG; 31.8%). Follow-up for the assessment of disease progression, as well as the onset of new diseases with changes in subject's phenotype, diet or lifestyle factors is planned every 5 years. The first follow-up period was finished in 2020 and included 820 subjects.