Considering the lack of evidence in humans of a progressive increase of TMAO levels across BMI classes and to gain further insight into the levels of TMAO in the setting of obesity, in this study we aimed to investigate the circulating levels of TMAO in a sample of the adult population stratified according to BMI. In addition, considering the still controversial role of TMAO in the pathogenesis of MetS and the predictive value of VAI and FLI as easy and early indicators of MetS, we investigated the association of circulating levels of TMAO with VAI and FLI and hypothesized that this association could serve as a biological marker of early metabolic derangement in subjects with overweight and obesity.

The VAI, a gender-specific indicator of adipose distribution and dysfunction [ 20 ], and the FLI, an accurate predictor of the NAFLD [ 21 ], are two surrogate indices of cardio-metabolic risk and are linked to the inflammatory pathways [ 22 23 ]. Both indices are based on simple anthropometric and metabolic parameters, and are strictly correlated with MetS, representing early predictors of MetS [ 20 24 ]. VAI has been counted as an effective marker to assess insulin resistance and metabolic disturbances that contribute to CVD in primary-care non-obese subjects, with specific cut-off values depending on the population of interest [ 20 ]. Also, FLI has been proposed as a marker of insulin resistance [ 21 ] and recently the clinical utility of FLI as a predictor of incident T2DM has been reported [ 25 ]. As three of the variables making up VAI (waist circumference (WC), plasma triglycerides (TG), and HDL cholesterol) and FLI (BMI, WC, and TG) are used as continuous variables, and while they are dichotomically expressed in the criteria for MetS, VAI and FLI might represent useful indicators of early cardio-metabolic risk in all borderline conditions in which overt MetS is not present [ 22 ].

Nevertheless, controversy remains over the exact role of TMAO in the pathogenesis of MetS, a constellation of cardio-metabolic risk factors, including central obesity, hypertriglyceridemia, low high-density lipoprotein (HDL) cholesterol, hyperglycemia, and hypertension [ 15 ], which predispose T2DM and CVD [ 16 ], according to the definition of nascent MetS [ 17 ]. Very recently, a clinical study investigating several biogenic amines in urine, including TMAO in a sample of patients with MetS showed that these subjects presented higher levels of TMAO compared with their counterparts without MetS [ 18 ]. In addition, a further study investigating unselected white patients undergoing coronary angiography for the evaluation of suspected coronary artery disease showed a positive correlation between TMAO and MetS [ 19 ]. However, this association was lost after adjusting for impaired kidney function and poor metabolic control in this population sample.

Several studies have shown a mechanistic link between TMAO, inflammatory pathways [ 9 ] atherosclerosis, type 2 diabetes mellitus (T2DM), and cardiovascular diseases (CVD) [ 1 11 ]. Namely, circulating levels of TMAO and its metabolites (choline and betaine) are associated with atherosclerosis risks in both humans and mice [ 1 ]. Among the pro-atherosclerotic mechanisms proposed for TMAO there are the inhibition of the reverse cholesterol transport, although this effect was reported in animal studies only [ 6 ], and the enhancement of human platelet hyperresponsiveness and thrombosis potential [ 10 ]. A systematic review and meta-analysis by Schiattarella et al. [ 2 ] demonstrated that in humans there is a positive dose-dependent association between circulating levels of TMAO and increased cardiovascular risk and mortality [ 2 ]. Nevertheless, in this metanalysis, the population samples were not divided according the BMI classes. More recently, Kanitsoraphan et al. [ 3 ] reported that in patients with T2DM higher circulating levels of TMAO were significantly associated with higher overall mortality by 2.07- to 2.7-fold, also after adjustment for BMI [ 3 ]. Recently, the strong association between gut microbiota and either obesity and obesity-related diseases in humans on the one hand, and the association between the TMAO pathway and cardio-metabolic diseases on the other, suggested that the TMAO pathway may be also mechanistically linked to the pathogenesis of obesity. Schugar et al. [ 11 ] reported that plasma TMAO levels were linked to with obesity traits in the different inbred strains of mice receiving a high-fat diet and suggested that the TMAO-generating pathway is linked to obesity and energy metabolism [ 11 ], although scientific evidence to support this association in humans has not yet been provided. Only Randrianarisoa et al. [ 12 ] reported that TMAO correlated positively with BMI, insulin resistance, visceral fat mass, and liver fat content [ 12 ]. In addition, Chen et al. [ 13 ] showed positive associations of the circulating TMAO levels and two of its nutrient precursors, choline, and betaine, with the presence and severity of NAFLD, the hepatic manifestation of the MetS [ 14 ], in a large sample of hospital- and community-based Chinese adults [ 13 ].

TMAO is a small, organic, gut microbiota-derived metabolite, which is emerging as a new potentially important cause of increased atherosclerosis and cardiovascular risk [ 1 3 ]. Circulating levels of TMAO increase after the gut microbial metabolism of dietary L-carnitine and phosphatidylcholine-rich foods, including red meat, eggs, dairy products, which are common nutrients of the Western diet. Very recently we reported a novel association between circulating levels of TMAO and the Mediterranean diet in healthy normal-weight adults, with a clear gender difference in this association [ 4 ]. The metabolic pathway of TMAO includes the digestion of these amines from gut microbiota with the production of trimethylamine (TMA), which is then converted to TMAO via flavin-monooxygenase-3 (FMO3) in the liver [ 5 6 ]. The interplay between dietary composition, gut microbiota and microbe-generated metabolites has been intensely investigated [ 7 8 ].

The data distribution was evaluated by Kolmogorov-Smirnov test and the abnormal data were normalized by logarithm. Skewed variables were back-transformed for presentation in tables and figures. Results are expressed as mean ± standard deviation (SD). The chi square (χ 2 ) test was used to determine the significance of differences in frequency distribution of smoking habit, physical activity, and presence/absence of MetS. Differences according to gender, lifestyle habits, cardio-metabolic indices, and MetS were analyzed by Student’s unpaired t -test, while the differences among the classes of BMI were analyzed by ANOVA followed by the Bonferroni post-hoc test. The correlations between study variables were performed using Pearson r correlation coefficients and were estimated after adjusting for gender, BMI, smoking, physical activity, and total energy intake. Proportional Odds Ratio (OR) models, 95% Interval Confidence (IC), and R 2 were performed to assess the association between gender, lifestyle habits, classes of BMI, cardio-metabolic indices, and MetS. In addition, two multiple linear regression analysis models (stepwise method), expressed as R 2 , Beta (β), and t , with circulating levels of TMAO as dependent variables were used to estimate the predictive value of: (a) cardio-metabolic indices; and (b) FLI and MetS. Receiver operator characteristic (ROC) curve analysis was performed to determine sensitivity and specificity, area under the curve (AUC), and IC, as well as cut-off values of circulating levels of TMAO in detecting FLI and MetS. Test AUC for ROC analysis was also performed. We wanted to show that AUC being 0.943 for a particular test is significant from the null hypothesis value 0.5 (meaning no discriminating power), so we entered 0.943 for AUC ROC and 0.5 for null hypothesis values. For α level we selected 0.05 type I error and for β level we selected 0.20 type II error. In these analyses, we entered only those variables that had a p -value < 0.05 in the univariate analysis (partial correlation). To avoid multicollinearity, variables with a variance inflation factor (VIF) >10 were excluded. Values ≤0.05% were considered statistically significant. Data were stored and analyzed using the MedCalc ® package (Version 12.3.0 1993–2012 MedCalc Software bvba—MedCalc Software, Mariakerke, Belgium).

As has been already widely reported in the literature [ 34 40 ], data were obtained during a face-to-face interview between the patient and a qualified nutritionist. Specifically, the dietary interview allowed the quantification of foods and drinks by using a photographic food atlas (≈1000 photographs) of known portion sizes to ensure accurate completion of the records [ 41 ]. On day 1, the diary nutritionists were trained to standardized protocols and provided participants with instructions on how to complete the diary at the health check, and asked participants to recall the previous day’s intake. Participants prospectively completed the remaining 6 days and returned the records to the nutritionist [ 42 ]. Data were processed using a commercial software (Terapia Alimentare DietosystemDS-Medica, http://www.dsmedica.info ). Considering quantities of foods consumed, the software can calculate the total energy intake, expressed in kilocalories (kcal).

Samples were collected in the morning between 8 and 10 a.m., after an overnight fast of at least 8 h and stored at −80 °C until being processed. All biochemical analyses including fasting plasma glucose, total cholesterol, fasting plasma TG, Alanine Transaminase (ALT), Aspartate Aminotransferase (AST), and γ-Glutamyltransferase (γGT) were performed with a Roche Modular Analytics System in the Central Biochemistry Laboratory of our Institution. Low-Density Lipoprotein (LDL) cholesterol and HDL cholesterol were determined by a direct method (homogeneous enzymatic assay for the direct quantitative determination of LDL and HDL cholesterol). Fasting insulin levels were measured by a solid-phase chemiluminescent enzyme immunoassay using commercially available kits (Immunolite Diagnostic Products Co., Los Angeles, CA, USA). The intra-assay coefficients of variations (CV) was <5.5%, as already widely reported in our previous studies [ 32 36 ].

TMAO serum levels were measured in samples stored at −80 °C. A previous study indicated that, under these conditions, TMAO is stable for several years [ 29 ]. The quantification of circulating TMAO levels has been performed using the method described by Beale and Airs [ 30 ], and reported in our previous study [ 4 ], with slight modifications. Briefly, serum proteins were precipitated with methanol (serum:methanol, 1:2,); samples were vortex-mixed for 2 min, centrifuged at 14,000 g for 10 min (4 °C) [ 31 ] and supernatants were collected and analyzed by High-Performance Liquid Chromatography-Mass Spectrometry (HPLC-MS) method. Both HPLC-MS conditions and method optimization were performed in accordance with Beale and Airs [ 30 ]. The HPLC system Jasco Extrema LC-4000 system (Jasco Inc., Easton, MD, USA) was coupled to a single quadrupole mass spectrometer (Advion ExpressIonL CMS, Advion Inc., Ithaca, NY, USA) equipped with an Electrospray ionization (ESI) source, operating in positive ion mode. The chromatographic separation was performed with a Luna HILIC column (150 × 3 mm, 5 µm particles) in combination with a guard column (HILIC), both supplied by Phenomenex (Torrance, CA, USA).

Measurements were performed between 8 and 12 a.m. All subjects were measured after an overnight fast. The measurements were made in a standard way by the same operator (a nutritionist experienced in providing nutritional assessment and body composition). At the beginning of the study, all anthropometric measurements were taken with subjects wearing only light clothes and without shoes. In each subject, weight and height were measured to calculate the BMI [weight (kg) divided by height squared (m), kg/m]. Height was measured to the nearest 0.5 cm using a wall-mounted stadiometer (Seca 711; Seca, Hamburg, Germany). Body weight was determined to the nearest 0.1 kg using a calibrated balance beam scale (Seca 711; Seca, Hamburg, Germany). BMI was classified according to the World Health Organization (WHO)’s criteria with normal weight: 18.5–24.9 kg/m; overweight, 25.0–29.9 kg/m; grade I obesity, 30.0–34.9 kg/m; grade II obesity, 35.0–39.9 kg/m; grade III obesity ≥ 40.0 kg/m 27 ]. WC was measured to the closest 0.1 cm using a non-stretchable measuring tape at the natural indentation or at a midway level between the lower edge of the rib cage and the iliac crest if no natural indentation was visible, as per the National Center for Health Statistics (NCHS) [ 28 ]. In all subjects Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) were measured three times, two min apart, with a random zero sphygmomanometer (Gelman Hawksley Ltd., Sussex, UK) after the subject had been sitting for at least 10 min. The average of the second and third reading was recorded.

This is a cross-sectional observational study carried out at the Department of Clinical Medicine and Surgery, Unit of Endocrinology, University Federico II, Naples (Italy), from January 2017 to August 2018. The work has been carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans, and it has been approved by the Ethical Committee of the University of Naples “Federico II” Medical School (n. 173/16). The purpose of the protocol was explained to all the study participants, and written informed consent was obtained. This trial was registered at http://register.clinicaltrials.gov/ . Unique identifier: NCT03060811.

To compare the relative predictive power of the cardio-metabolic indices associated with the circulating levels of TMAO, we performed a multiple linear regression analysis using a model that included as HoMA-IR, VAI, and FLI. Using this model, FLI entered at the first step (< 0.001), while HoMA-IR, VAI were excluded. To compare the relative predictive power of FLI and number of components of MetS associated with the circulating levels of TMAO, we performed a second multiple linear regression analysis model. Using this second model, FLI entered at the first step (< 0.001), while the number of components of MetS were excluded. Results were reported in Table 5

The results of bivariate proportional OR model performed to assess the association of circulating levels of TMAO with quantitative variables are reported in Table 4 . The highest circulating levels of TMAO are significantly associated with the severity of obesity (OR 9.59;< 0.001), and insulin resistance (OR 2.82;< 0.001). Moreover, the highest levels of TMAO are associated with the highest levels of VAI (OR 1.58;< 0.001) and FLI (OR 4.31;< 0.001), presence of MetS (OR 2.36;< 0.001) and of all components of the MetS.

4. Discussion

In this cross-sectional observational study, we evaluated the circulating levels of TMAO in a sample of adult population stratified according to categories of BMI. In addition, we investigated the association among circulating levels of TMAO and cardio-metabolic indices. The classification of the study population according to the BMI demonstrated that circulating levels of TMAO increased along with BMI. To the best of our knowledge, this is the first study that reported statistical differences in the circulating levels of TMAO across classes of BMI. Moreover, we confirmed the presence of a positive association of circulating levels of TMAO with MetS, the increasing number of its components, and HoMA-IR. Finally, a novel association was also reported among the circulating levels of TMAO and VAI and FLI, two surrogate indices of cardio-metabolic risk and early indicators of MetS, independently on common potential covariates.

There is a paucity of literature that has studied the relationship between TMAO and adiposity. A recent experimental study reported that both antisense oligonucleotide-mediated knockdown and genetic deletion of the TMAO-producing enzyme FMO3 protected mice against high-fat diet-induced obesity, thus highlighting a role of the gut microbe-driven TMA/FMO3/TMAO pathway in affecting specific transcriptional reprogramming in white adipocytes [ 11 ]. Of interest, in this study circulating levels of TMAO were positively associated with body weight, fat mass, mesenteric adiposity, and subcutaneous adiposity across the different mice-inbred strains; in addition, in cohorts of overweight or obese subjects with metabolic traits and different ethnicity the expression of FMO3 in liver biopsies was positively correlated with BMI and waist-to-hip ratio, and negatively correlated with the Matsuda Index, a measure of insulin sensitivity [ 11 ]. Consistent with these data, the findings of the present study show that there is a clear positive association of circulating TMAO levels and classes of BMI. Thus, besides its role as a risk factor for CVD and adverse event in risks subjects, emerging evidence suggests that gut microbiota-derived TMAO might represent per se a key environmental factor contributing to obesity and obesity-associated disorders.

VAI is a surrogate of adipocyte dysfunction independently correlated with insulin sensitivity, and cardio-metabolic risk in primary-care non-obese subjects [ 20 ]. In particular, VAI was proposed as a useful tool for the early detection of a condition of insulin resistance and cardio-metabolic risk before it develops into an overt MetS [ 38 ]. FLI is a surrogate marker of a fatty liver considered a screening tool to identify NAFLD, recognized as the liver manifestation of MetS, in subjects with insulin resistance and cardio-metabolic risk factors where ultrasound is unavailable [ 43 ]. In our population sample there was a positive association between circulating levels of TMAO and both indices. Of interest, the association of the circulating levels of TMAO with VAI and FLI was also shown independently on potential covariates, such as gender, BMI, smoking status, physical activity, and energy intake. In addition, among the cardio-metabolic indices included in this study, FLI, which incorporates BMI, WC, TG, and liver function, was a better predictor of TMAO variability than MetS per se. This finding was in line with the putative role of FLI as early predictor of MetS and likely reflects that the main site of expression of FMO3, the enzyme that metabolizes gut microbe-derived TMA to produce TMAO, is the liver. Based on ROC curve analysis, the most sensitive and specific cut-offs for circulating levels of TMAO to predict the presence of NAFLD-FLI and MetS were ≥8.02 µM and ≥8.74 µM, respectively. Experimental studies in mice fed with a high-fat diet showed that a high urinary excretion of TMAO was associated with insulin resistance and NAFLD in mice (129S6) prone to these diseases [ 44 ]. Miao et al. [ 45 ] found that liver insulin receptor knockout mice, characterized by a selective hepatic insulin resistance, have increased circulating TMAO levels associated with a strong up-regulation of the TMAO-producing enzyme FMO3 in the liver [ 45 ]. According to the proposed mechanism, TMAO may block the hepatic insulin signaling pathway, thereby exacerbating the impaired glucose tolerance, and promoting the development of fatty liver [ 46 ]. Turning to human studies, the above-mentioned study by Randrianarisoa et al. [ 12 ] reported a positive correlation between TMAO and liver fat content [ 12 ]. In addition, in a large sample of hospital and community-based Chinese adults, Chen et al. [ 13 ] showed positive associations of the circulating TMAO levels and two of its nutrient precursors, choline and betaine, with the presence and severity of NAFLD [ 13 ]. Choline can be oxidized to betaine, and betaine is generally regarded as safe for dietary ingestion due to its sparing effects on choline, a basic component for the synthesis of phosphatidylcholine, which in turn is necessary for promoting lipid exportation from the liver [ 47 ]. In this study, the authors suggested that TMAO may represent a possible risk factor for NAFLD due to its effect in altering the synthesis and transport of bile acids, with subsequent effects on lipid metabolism, intrahepatic triglycerides levels and glucose homeostasis [ 6 ]. More recently, Ntzouvani et al. [ 18 ] reported a strong positive association between liver function and a pattern of amino acids, which included TMAO, in a sample of Greek adult males with MetS [ 18 ].

The results of this study lend support to the evidence that circulating levels of TMAO were positively associated with body weight, thus concurring with an increased risk of developing MetS, through insulin resistance, adipocyte dysfunction, and fatty liver. In this context, it is tempting to hypothesize that circulating levels of TMAO could have a role of a biological marker of early metabolic derangement in subjects with overweight and obesity. Thus, it allows speculation on the possible beneficial and cost-effective effects to early address specific nutrition interventions aimed to reduce the excessive intake of TMAO precursors in subjects at risk of MetS and NAFLD.