Conservar, y en la medida de lo posible incrementar, la riqueza natural de Sonora, definida esta por sus especies de animales, plantas y ecosistemas claves

The multivariate analysis, PCA and OPLS-DA, identified the metabolites with the greatest difference in concentration between the T2DM and OW/OB groups and

144 their healthy controls. The variance between the groups observed in the multivariate OPLS-DA were confirmed by the ANOVA, with the same metabolites appearing in both analysis methods in the same rank order. Although not necessarily biologically relevant, it was notable that almost twice as many metabolites had significant differences between the lean and OW/OB group (n = 90) than the OW/OB and T2DM (n = 56).

In the present study, the 24-h concentrations were used to compare differences between the groups, which is more representative of the changes in concentration over the whole day. The 24-h concentrations may not necessarily be comparable to the single time-point, fasting, samples analysed in most cohort studies. Much of the work regarding the effect of T2DM on the metabolome has related to the discovery of biomarkers for T2DM risk using case control and cohort data (Newgard et al., 2009, Suhre et al., 2010, Wang et al., 2005). Not all of the studies identified the same metabolites, but there were commonalities; higher BCAA, glutamate, tyrosine, alanine, carnitine and phenylalanine concentrations being associated with increased risk of T2DM and higher glycine and glutamine concentrations being associated with decreased risk of T2DM (Guasch-Ferre et al., 2016). Lower glycerophospholipid concentrations in obesity and T2DM and even newly discovered biomarkers of T2DM risk such as alpha-AAA (Wang et al., 2013) and t4-OH-Pro (Wang-Sattler et al., 2012) have also been observed. The associations between amino acids and obesity are well established in the literature. Felig et al (1969) reported higher valine, leucine, isoleucine, tyrosine and phenylalanine levels and lower glycine levels in obesity, concordant with the present study, with the exception of leucine where no significant differences were observed between any of the groups in the present study.

Intervention studies employing oral glucose tolerance tests (OGTT) have detected altered amino acids (Zhou et al., 2013), acylcarnitines (Mihalik et al., 2010) and lipid levels in T2DM (Mihalik et al., 2012, Yi et al., 2008, Zhou et al., 2013). Previous studies thus suggest that a T2DM metabolic phenotype could resemble lower glycogenic amino acids (alanine, glycine and serine), higher levels of BCAA (isoleucine, leucine and valine) and a higher Arg:Orn ratio (Altmaier et al.,

145 2008). In the present study tyrosine, proline, carnitine and AC-C3 had significantly higher levels in the OW/OB group that were even higher in T2DM. Although the elevated BCAA concentrations, seen as a T2DM signature in the literature, were significantly higher in the OW/OB group compared to the lean group and they were higher in the T2DM group compared to the OW/OB group, this difference was not statistically significant. The statistical significance between only the lean and OW/OB groups may be because the individuals in the OW/OB group were already in a pre-diabetic state therefore T2DM did not affect BCAA metabolism to the same degree as weight. Or that the T2DM diagnosis had less impact because the medication in 4/6 of the T2DM group may be having an effect although in the literature BCAA concentrations were not affected by metformin (Irving et al., 2015). It may also have been because of the smaller size of the T2DM group, with only 6 participants compared to the 9 in the OW/OB group. Other metabolites with elevated levels in the OW/OB group were tryptophan, kynurenine, and acylcarnitines. Of course there is no way of knowing whether these OW/OB individuals would go on to develop T2DM but these metabolites have also been associated with T2DM (Mihalik et al., 2010).

Newgard et al. observed higher BCAA concentrations in OW individuals compared to lean controls concordant with the present study. Their obese subjects were more insulin resistant than the lean subjects (Newgard et al., 2009), suggesting these changes may be due to insulin resistance rather than increased adiposity per se. In the present study, however, the lean and OW/OB group were matched for markers of insulin resistance. Short chain AC, AC-C3 and AC-C5 had significantly higher levels in the OW/OB group compared to the lean group with only AC-C3 having concentrations that were significantly higher in the T2DM group compared to the OW/OB group. These acylcarnitines have been linked to BCAA metabolism as leucine and isoleucine can be converted to AC-C5 and isoleucine and valine can be converted to AC-C3 (Menni et al., 2013). In BCAT2 gene knockout mice the first steps of BCAA conversion to AC-C3 and AC-C5 are blocked leading to higher BCAA concentrations, with no detrimental effect on glucose control, or insulin sensitivity and are even resistant to diet induced obesity. This suggests it is

146 the breakdown of BCAA rather than the higher concentrations per se that are associated with T2DM (She et al., 2007).

Many of the associations between metabolic risk factors (obesity, insulin resistance, high blood pressure, dyslipidaemia) centre on differences in amino acid concentrations in different metabolic states. Elevated BCAA, alanine, glutamate, phenylalanine, tyrosine and lower glutamine concentrations reported by Cheng et al. (2012) were also observed in both the OW/OB and T2DM groups compared to their control groups in the present study.

Most of the amino acids associated with the urea cycle were significantly lower in OW/OB and T2DM compared to their controls, with the exception of ornithine in the OW/OB group. Figure 4.30 illustrates the amino acids in the TCA and the urea cycles with significantly higher concentrations (as a positive number) or significantly lower concentrations (as a negative number) in OW/OB compared to lean (blue) and T2DM compared to OW/OB (black). The amino acid (alanine), associated with the TCA cycle, had higher levels in the T2DM and OW/OB groups compared to their control groups. Glutamate had higher concentrations in OW/OB and T2DM compared to their controls, while the amino acids associated with the urea cycle (citrulline and arginine) were lower in T2DM and OW/OB compared to their control groups with the exception of ornithine which had higher significantly concentrations in OW/OB compared to the lean group. As all amino acids are converted to glutamate to enter the urea cycle this could contribute to the higher circulating levels of other amino acids, such as the higher BCAA levels observed in the OW/OB and T2DM study groups of the present study (Section 4.4.8.5) and in the literature (Section 1.5.1.6).

147

Figure 4.30 Amino acids in the urea and TCA cycle. Percentage difference in OW/OB compared to lean (blue) and T2DM compared to OW/OB (black) amino acids related to the urea and TCA cycles.

-6.4% -7.7% -8.9% 13.6 % -6.1% 37.4 % 21.6 % 24.9 %

148 The significantly higher alpha-AAA concentrations in T2DM but not in the OW/OB group compared to their controls indicates the association is with T2DM only and not increased body mass. Our finding supports a previous prospective study showing that individuals in the top quartile of alpha-AAA concentrations had the strongest association with future diabetes over the 12-year follow-up period (Wang et al., 2013) The higher alpha-AAA concentrations were also associated with insulin resistance and β cell function (Wang et al., 2013) which would support the link with T2DM.

Long chain acylcarnitines accumulate in the circulation under conditions of ineffective β oxidation of fatty acids in the mitochondria. Thus Long chain acylcarnitines levels provide a good marker for insulin resistance (Chua et al., 2013).

Differences in AC concentrations in T2DM are well documented. Evidence from previous studies, in particular higher plasma long chain AC in obesity and T2DM, suggest more fatty acids can enter mitochondria (Section 1.5.3). Observing higher concentrations of long chain AC in the T2DM group during the day when the participants were awake and being fed was most likely due to reduced CPT-1 activity associated with T2DM, possibly slowing the entry of LC-AC into the mitochondria for ẞ-oxidation in the fed state. Our data were consistent with Mihalik et al who observed higher concentrations of short chain AC (C3, C4 and C5) in T2DM subjects. In T2DM, many shorter species AC accumulate, suggesting that they have a generalised complex oxidation defect (Adams et al., 2009). Consistent with Mihalik et al. (2010), in the present study the medium and long chain acylcarnitines (AC-C5–C18) levels were significantly higher in the OW/OB group but AC-(C18, C18:1 and C18:2) levels were significantly lower in the T2DM group, possibly a result of the medication in the T2DM group. Although, of the short chain acylcarnitines (AC-(C0, C2, C3, and C4)) with significantly higher concentrations in the OW/OB group, AC-C0 and AC-3 were higher still in the T2DM group. ACs, AC-(C2, C3 and C4), also displayed significant daily rhythms in all groups. These metabolites peaked at 04:00 h, 16:00 h and 18:00 h, respectively.

149 Using the same targeted metabolomics kit (AbsoluteIDQ™ p180) as the present study, Wang et al. found lower lysoPC C18:2 and glycine, and higher AC- C2 concentrations were predictors of T2DM (Wang-Sattler et al., 2012). They also found that combining these three metabolites, as a metabolic indicator, created an even more robust candidate as an early T2DM biomarker (Wang-Sattler et al., 2012). Metabolic indicators are groups of interrelated metabolites that have been shown to be more powerful predictors of T2DM than when the same metabolites are analysed separately (Altmaier et al., 2008; Krug et al., 2012). Examples of metabolic indicators are subgroups of lipids classed according to structure (number and position of double bonds and carbon side-chain length) (Rhee et al., 2011; Wang-Sattler et al., 2012), BCAA (Wang et al., 2011). Ratios of metabolites joined in short metabolic pathways, such as Glu:Gln (Cheng et al., 2012) and Asp:Asn (Palmer et al., 2015) and Arg:Orn (Altmaier et al., 2008), have all been associated with T2DM. In the present study significant differences between the lean and OW/OB groups of the individual amino acids that form BCAA (Section 4.4.8.7) and the glucogenic amino acids between the OW/OB and T2DM groups (Section 4.4.8.8) were not all significant but when analysed collectively, as metabolic indicators, this difference was significant.

It is notable that all the lipid species levels were lower in OW/OB compared to the lean group. Between the OW/OB and the T2DM groups levels of 22 glycerophospholipids were elevated in T2DM. by contrast, lysoPC levels were lower in T2DM, which was also observed in another study of obese healthy and obese T2DM groups compared to lean individuals (Barber et al., 2012). Higher PC aa 36:1 levels have also been associated with T2DM risk in cohort studies (Floegel et al., 2013). Most of the lipid classes had significantly lower levels in the OW/OB group compared to the lean group, whereas there was no consistent pattern in T2DM compared OW/OB. PC aa C32:1 had the largest difference between the OW/OB and T2DM groups, which was 50% higher in the T2DM group. Lower total PC aa, total SFA and monounsaturated fatty acids (MUFA) levels were observed in OW/OB. However, MUFA levels were higher in T2DM whereas no difference was seen in the other subclasses. Guasch et al. also noted that a meta-analysis could

150 not be performed on the lipid class as there was not enough consistency between the metabolomics studies (Guasch-Ferre et al., 2016).

In summary, the cosinor analysis exposed significant daily rhythms in over a third of the metabolites in at least one study group, some displaying a daily rhythm in all study groups, with some metabolites losing and some gaining cosinor rhythmicity with increased body mass and T2DM. The concentrations of approximately a third of the metabolites were affected by both OW/OB and T2DM and 12% were affected by T2DM only. The metabolites with significant cosinor rhythms and group differences are shown in Figure 4.31.

Figure 4.31 Metabolites with significant daily rhythms and group differences. Venn diagram of the metabolites with significant daily rhythms and the peak time of metabolites with daily rhythms in more than one study groups. The arrows illustrate the metabolites, which also showed significant differences in the 24-h concentration between the T2DM and OW/OB groups and their controls. ↑↑ = higher concentrations in both OW/OB and T2DM, ↓↓ = lower concentrations in both OW/OB and T2DM, ↑↓ = higher concentrations in OW/OB and lower concentrations in T2DM, ↓↑ = lower concentrations in OW/OB and higher concentrations in T2DM. ↑ = higher concentrations in T2DM and ↓ = lower concentrations in T2DM compared to OW/OB only.

citrulline  proline  sarcosine SDMA AC-C2  AC-C3 AC-C4 AC-C16  lysoPC a C18:1  lysoPC a C18:2 lysoPC a C20:3 lysoPC a C20:4  PC aa C32:1  PC aa C36:5  alanine glycine isoleucine  tyrosine valine  alpha-AAA  AC-C18:1  PC aa C36:1  PC aa C36:3  PC aa C40:2  PC ae C36:2 AC-C14:1  AC-C18  lysoPC a C16:0  lysoPC a C18:0 PC aa C36:2 time of day (h) Peak time 7 _{15 23} 7 1 4 5 0 10 1 9 80 ₁₁ Non significant Lean T2DM OW/OB kynurenine, AC-C5 PC aa (↑C34:4, C36:0, ↓↑C36:6 and C42:2) PC ae (C30:0, ↓↑C34:0 and C34:1) ↑SM C20:2

↑↓ornithine, threonine, ↑↑AC-C0, lysoPC a (C16:1, ↓↓C17:0, C26:0) ↑PC aa C36:4, PC ae (C40:1, C44:3) taurine

151

5 Circulating metabolites after a 5-h shift in meals

5.1 Introduction

It is clear from the previous chapters that the circadian system and metabolism are intrinsically linked (Section 1.2.1). Desynchrony between the endogenous timing system in the SCN and the peripheral clocks that are involved in the control of metabolism creates an internal desynchrony that may result in metabolic disturbances such as increased incidence of obesity and T2DM risk (Section 1.3.2). Desynchrony can occur with acute changes in externally imposed cycles such as people who cross time zones or shift workers, (e.g. they may be awake and eat at night when the body is programmed to sleep and fast), leading to feelings of jet lag and disturbances in metabolism (Arendt, 2009, Knutsson and Boggild, 2000).

Light is the primary zeitgeber (Section 1.1), the weaker, non-photic zeitgebers, exercise and food, need more research (Section 1.1.2). Much of the evidence to support the hypothesis that timed feeding can influence endogenous rhythms comes from animal studies. Meal timing has been shown to act as a weak zeitgeber on the circadian system (Damiola et al., 2000, Stephan, 2002b). The food anticipatory activity observed when food was only available for a limited period of time, not seen it in ad libitum fed rodents, persisted for a few days, even when the rodents were deprived of food (Mistlberger, 2009, Stephan, 2002a). Other restricted feeding protocols have demonstrated the impact of food timing on physiology. In restricted feeding schedules leptin, TAG, FFA, ketone bodies and corticosterone levels are linked to meal time compared to ad libitum feeding, indicating entrainment of metabolism by food in these rodent models (Diaz- Munoz et al., 2000, Escobar et al., 1998, Martinez-Merlos et al., 2004). Timed feeding affects peripheral clocks in rodents (Pezuk et al., 2010, Stokkan et al., 2001). There is also evidence that clock genes, BMAL and CLOCK, play a role in the regulation of glucose homeostasis (Rudic et al., 2004).

Only a few studies have investigated the effects of timed feeding in humans (Section 1.2.3). A CHO rich evening meal led to an acute increase and subsequent

152 phase delay in core body temperature (CBT) and heart rate rhythms compared to a morning meal (Kräuchi et al., 2002).

The animal and human data indicate that timed feeding shifts the clocks in peripheral tissues (Section 1.2.1) but not the central SCN clock suggesting there may be separate light and food entrainable oscillators (FEO). The study in this chapter examines the metabolic effects when a typical daily eating routine of three meals (breakfast, lunch and dinner) was delayed by 5-h. The protocol enabled the comparison of 5-h delay in ‘normal’ meal times without a shift in the light-dark or sleep-wake cycle.