Thank you to my patient Peter for introducing me to this lovely podcast by Dr. Attia.
The term Inflam-Aging and the benefit of Elysium Basis are new to me and likely most MDs.
I could not find a great deal of data yet on Elysium but NAD + looks interesting (references below).
The role of fasting on longevity is being investigated by more specialties of doctors.
It will take years to prove doctor-monitored-fasting is equivalent to or better than the myriad of drugs patients are on for diabetes, obesity, dry eye, etc.
The work of Dr. Longo is promising on proving how fasting strengthens our normal cells and kill abnormal or cancer cells.
SLC
https://peterattiamd.com/jasonfung/
J Immunol Res. 2016; 2016: 8426874.
Published online 2016 Jul 14. doi: 10.1155/2016/8426874
PMCID: PMC4963991
PMID: 27493973
An Update on Inflamm-Aging: Mechanisms, Prevention, and Treatment
This article has been cited by other articles in PMC.
1. Introduction
Inflamm-aging [1] was first named by Franceschi et al. in 2000, and it is a new addition to the types of aging studies. Inflamm-aging plays an increasingly important role in the rate of aging and age-related diseases. Research in this area has attracted attention of scholars in many fields and significant progress has been made in the last decade. Here, we review the concept of inflamm-aging and describe various research strategies that have led to insights into its inflammatory characteristics and mechanisms of action. We also discuss the relationship of inflamm-aging with diseases and novel interventions to delay or prevent inflamm-aging-related diseases.
2. The Concept of Inflamm-Aging
A main feature of the aging process is a chronic progressive increase in the proinflammatory status, which was originally called “inflamm-aging” [1]. Subsequently other similar terms were used such as “inflammaging” [2], “inflamm-ageing” [3], and “inflammageing” [4]. Inflamm-aging is the expansion of the network theory of aging [5] and the remodeling theory of aging [6, 7]. The network theory of aging posits that aging is indirectly controlled by the network of cellular and molecular defense mechanisms. The remodeling theory, which was put forward to explain immunosenescence, is the gradually adaptive net result of the process of the body fighting malignant damage and is a dynamic process of optimization of the trade-off in immunity [6, 7]. In the process of aging, some researchers pointed out that the phenomenon where adaptive immunity declines is called immunosenescence, while the phenomenon where innate immunity is activated, coupled with the rise of proinflammation, is called inflamm-aging [8]. Some regard the chronic inflammatory process with age as inflamm-aging [9], while others proposed the oxidation-inflammation theory of aging [10]. Despite the lack of agreement on definitions and terminology, there is consensus that the primary feature of inflamm-aging is an increase in the body’s proinflammatory status with advancing age. Furthermore, a new concept of “anti-inflammaging” was also proposed, which influences progressive pathophysiological changes, as well as lifespan, and acts along with inflamm-aging [11]. In the next section the characteristics of inflamm-aging are described in more detail.
3. The Inflammatory Characteristics of Inflamm-Aging
The five states of inflamm-aging are as follows [12]: low-grade, controlled, asymptomatic, chronic, and systemic. However, the inflammation during inflamm-aging is not in a controlled inflammatory state. We propose that inflammation in the process of inflamm-aging belongs to nonresolving inflammation [13]. Inflammation is a series of complex response events which are caused by the host system facing a pathogen infection or various types of tissue injury. These response events are characterized by interactions between the cells and factors in the microenvironment and by regulation of the balance between physiological and pathological signaling networks. In common conditions, inflammatory responses disappear when proinflammatory factors in infection and tissue injuries are eliminated and then change into a highly active and well regulated balanced state, which is called resolving inflammation [13]. However, in the presence of some as yet uncertain factors, such as persistent and low intensity stimulation and long-term and excessive response in target tissues, inflammation fails to move into a steady state of anti-infection and tissue injury repair; instead the inflammation continues and moves to a nonresolving inflammation state [13]. Given this background, inflammation in the process of inflamm-aging belongs to the state of nonresolving inflammation.
4. The Relationship between Inflamm-Aging and Diseases
Like the immune response, inflammation has a physiological function in the normal body. Moderate inflammatory response is beneficial to the body but when excessive, the response becomes harmful. Changes in the inflammatory cytokine network control the direction of the development of inflammation. The dynamic balance of the network of proinflammatory cytokines and anti-inflammatory cytokines maintains the physiologic function of inflammation in the normal body. Tipping the balance from anti-inflammation to proinflammation can lead to pathological changes. Persistent inflammation during the inflamm-aging process may cause inflammation-related diseases.
Inflamm-aging is a determinant of the speed of the aging process and of lifespan and is highly related to Alzheimer’s disease [2], Parkinson’s disease, acute lateral sclerosis, multiple sclerosis, atherosclerosis, heart disease, age-related macular degeneration [14], type II diabetes [15], osteoporosis and insulin resistance [16], cancer, and other diseases. Inflamm-aging also increases morbidity and mortality, significantly harming the health of patients, and causes a decline in the quality of life of patients [16]. Chronic, subclinical inflammation and immune disorders coexist in the process of inflamm-aging. Epidemiological studies show that with age there is an imbalance in the loss of old bone and the formation of new bone. Inflamm-aging may be one of the contributing factors to the imbalance and to the subsequent excessive loss of bone. Inflammatory markers of inflamm-aging provide clinicians with the necessary data for risk assessment of osteoporosis. Inflammatory cytokines may be therapeutic targets for improving the formation of bone in the elderly after bone operations [16]. Excessive inflammation during inflamm-aging increases the morbidity and mortality of patients after bone operations, even though the mechanism for this remains unclear [17]. In the process of inflamm-aging, the pathophysiological changes in the colon are revealed at the cellular and molecular levels, and these culminate in the inflammation that leads to injury of the gastric mucosa and epithelium as well as a decrease in the epithelium’s ability to regenerate [18] (Figure 1).
However, inflamm-aging seems to be a double-edged sword in that it decreases immune function but also increases the autoreactivity of the body [17]. Inflamm-aging is beneficial to the body by neutralizing the harmful cytokines in the early stage of the life but has a detrimental role in the later life [17].
Unfortunately, the strong correlation between inflamm-aging and disease development is complex and unclear. Because immunosenescence and inflamm-aging coexist, it is difficult to distinguish whether the inflammation-related diseases are caused by inflamm-aging or immunosenescence. Moreover, the crucial question is whether there is a causal relationship between inflamm-aging and diseases, which needs integrated biological and clinical research to resolve.
5. The Mechanisms of Inflamm-Aging
While the mechanism of inflamm-aging is not completely understood, the current theories in the field are summarized below.
5.1. The Theory of Stress
Generally, stress is either beneficial or harmful to the body. During inflamm-aging, the body is constantly in the stress environment, which is caused by different kinds of stressors that induce and maintain the chronic proinflammatory status in the body. Stress, as one of regulated factors of immunity, provokes the greatest immune response in the bodies of young persons, whereas it provokes that weakest response in elderly persons with signs of immunosenescence and inflamm-aging [19, 20]. According to a series of studies involving different species from invertebrates to humans, from an evolutionary perspective, inflammation is closely related to innate immunity and stress [20]. Based on evolutionary studies, immune response, stress response, and inflammation form a defensive network in the body. However, the compatibility between inflammatory status and longevity and the paradoxical proinflammatory character in healthy centenarians strongly suggest the existence of physiological inflammation [21]. Therefore, inflammation and the proinflammatory status in healthy persons and centenarians show a beneficial response that helps the elderly deal with the stimuli generated by chronic antigen stressors [19]. However, excessive stress response, as well as an accompanying increasingly high proinflammatory response, leads to human inflamm-aging.
5.2. The Theory of Oxidation-Inflammation
There are close relationships between oxidative stress and inflamm-aging [21]. Based on the close relationship between oxidative stress, inflammation, and aging, the oxidation-inflammatory theory of aging (oxi-inflamm-aging) was proposed [10]. In this theory, oxidative stress leads to inflamm-aging and influences the homeostasis and health of the body. The relationship between the redox state and the function of immune cells influences the speed of aging and lifespan [10]. According to this theory, sufficient antioxidants in food may improve immune function, decrease oxidative stress, and extend the lifespan [22].
5.3. The Theory of Cytokines
Proinflammatory cytokines play an important role in inflamm-aging caused by chronic inflammation [23]. Type I cytokines (such as IFN-γ and TNF-α) and type II cytokines (IL-4) in unactivated and memory CD4+ T lymphocytes participate in the proinflammatory process [24]. Further research shows that CD8+ and CD4+ T lymphocytes play a pivotal role in the developing cytokine network, and this can lead to the chronic proinflammatory state and inflamm-aging [25]. In animal experiments, increased expression of IL-1β, IL-15, IL-18, TNF-α mRNA, and TNF-α protein in the peripheral blood of elderly horses appears to be a distinctive feature of inflamm-aging [26].
Elevated levels of IL-6 and TNF-α in the serum of the elderly are associated with disease, disability, and mortality [3]. Studies employing large patient cohorts provide evidence that the level of serum IL-6 is a reliable marker, or a predictive index, of inflamm-aging [3]. Experiments with healthy elderly people show that aging relates to increased proinflammatory status. The cause of the increased proinflammatory status is elevated levels of proinflammatory cytokines in the circulation including IL-1, IL-6, TNF-α, and PGE2 [27, 28]. Although the identity of cells that secrete proinflammatory mediators in elderly persons remains controversial, the prevailing view is that, during inflamm-aging, high levels of proinflammatory cytokines in the circulation create an inflammatory environment for tissues and organs [29]. However, differences in levels of IL-10 and TNF-α in individuals may play an important role in the final outcome of inflammation [29]. IL-6 and TNF-α are upregulated, while growth hormone and IGF-1 are downregulated in the process of aging. The overall balance of cytokines, such as IL-6 and TNF-α, appears to play a decisive role in aging. As well, genetic variations in the promoter regions of proinflammatory and regulated cytokine genes have effects on inflamm-aging and susceptibility to age-related diseases [29].
Pes et al. [30] found that the frequency of the -174C single nucleotide polymorphism (SNP) in the promoter region of IL-6 gene is increased in Italian male centenarians and the frequency of the -1082G SNP at the 5′ flanking region of the IL-10 gene coding sequence is increased among male centenarians. These data indicate that different alleles in different cytokine gene coding regions for pro- (IL-6) or anti-inflammatory (IL-10) cytokines may influence immune-inflammatory responses and individual lifespan expectancy, suggesting that inflammatory cytokine gene polymorphisms for immune system genes may regulate immune-inflammatory responses. Gene polymorphisms of proinflammatory cytokines associated with high levels of IL-6 have decreased capacity to reach extremely old age, whereas genotypes associated with high levels of IL-10 were increased in centenarians [30]. Genetic polymorphism in proinflammatory cytokine genes is necessary and has important consequences in the body. On the one hand, moderate levels of proinflammatory cytokines contribute to inducing a protective response, when the body is invaded by pathogens. On the other hand, excessive proinflammatory cytokines may cause immune-inflammatory diseases and even death. Indeed, the process of evolution has shaped the ability of the body to fight and control pathogens. Therefore, the proinflammatory response may be beneficial to the body in fighting potentially fatal infections. Thus, high levels of IL-6 and low levels of IL-10 are associated with enhanced ability against pathogens [30].
The vicious cycle of reciprocal causation between the proinflammatory cytokines and cellular senescence aggravates inflamm-aging. On the one hand, proinflammatory cytokines induce cellular senescence. Proinflammatory cytokines, such as TNF-α, IFN-γ, and IFN-β, induce cellular senescence in epithelial cells by producing reactive oxygen species and activating the ATM/P53/P21 (WAF1/Cip1) signaling pathway [31]. CXCR2, a chemokine receptor, induces cellular senescence of fibroblasts [32]. DNA damage produces proinflammatory cytokines (such as IL-1, IL-6, and IL-8) by activating the NF-κB signaling pathway, blocking the cellular cycle and inducing and maintaining the phenotype of cellular senescence [32]. On the other hand, senescent cells secrete growth factors, proteases, chemokines, and cytokines such as IL-6 and IL-8 [33].
Most phenotypes of aging can be explained by an imbalance between proinflammation and anti-inflammation, which results in inflamm-aging with a low chronic proinflammatory status. However, centenarians have high levels of inflammatory mediators and more anti-inflammatory cytokines, suggesting that inflamm-aging can coexist with longevity, even though the underlying mechanisms have not been uncovered [34].
5.4. The Theory of DNA Damage
Sustained telomere DNA and mitochondrial DNA damage, caused by exogenous and endogenous factors, can induce errors of DNA replication or translation, which leads to point mutations or chromosomal rearrangements and stress reactions via various signaling pathways, which eventually contributes to cellular senescence. Researchers found that, in human senescent primary cells, the shortest telomeres lack most of the telomere repeat sequence, which leads to DNA damage accumulation and terminal cell cycle arrest and further induces replicative senescence [35]. A persistent DNA damage response (DDR) caused by telomere shortening is a key mechanism involved in replicative senescence and aging process [36]. New evidence indicates that DNA damage response (DDR) signaling is a major link between cell senescence and organism aging. DDR activation of senescent cells contributes to an increase in the proinflammatory secretory phenotype (PSP), which in turn triggers the activation of adjacent cell DDR and PSP. This local inflammatory environment eventually becomes systemic. The increasing number of cells with DDR activation may exacerbate inflamm-aging [37]. These results suggest that cells in an inflammatory environment induce aging at the systemic level. Stem cells and stromal fibroblasts differentiate into proinflammatory cytokine overexpressing cells and consequently the cytokine network breaks down, inducing inflamm-aging as a result of the accumulation of DNA damage [38]. Proinflammatory cytokines in the microenvironment of cells with DNA damage further induce inflamm-aging. Macrophages, which mediate the main effects of inflamm-aging, amplify inflamm-aging self-propagation via a cascade effect on the local and systemic proinflammatory response [38].
5.5. The Theory of Autophagy
Autophagy plays an important role in stress, removing harmful substances in cells to maintain homeostasis and normal metabolism [39, 40]. Autophagy transfers the abnormal substances of the cell to lysosomes for degradation and also plays a role in many pathophysiological processes [41]. For example, autophagy plays important roles in removing abnormal proteins, adapting to hunger, and cancer. More and more evidence shows that autophagy is important in increasing longevity. For example, knocking out the autophagy gene Atg7 leads to the accumulation of proteins and organelles in the cell, causing cellular senescence [42].
The process of aging accompanies disorder in homeostasis. However, autophagy plays an important role in maintaining homeostasis and delaying aging. In the process of aging, autophagic cleansing capacity declines gradually, which induces mitochondrion disordering and protein accumulation. This leads to increased reactive oxygen species (ROS) and consequently oxidative stress. Destabilized lysosomes release ROS, which activate Nod-like receptor 3 (NLRP3), and this initiates an inflammatory cascade reaction. During this process, inactive precursors of IL-1β and IL-18 are increased, and IL-1β and IL-18 release is stimulated, which causes an inflammatory reaction and accelerated aging [43].
5.6. The Theory of Stem Cell Aging
Stem cell aging is closely related to inflammation [44]. Stem cell aging is the cellular basis of aging and chronic inflammation is one of the main factors that induces stem cell aging. In the chronic inflammatory process, proinflammatory factors activate NF-κB/MAPKs, TOR, RIG-I, and JAK/STAT signaling pathways to induce cells to synthesize and to secrete large amounts of inflammatory cytokines, such as TNFα and IL-1β [45], which leads to a chronic low degree of inflammation in the environment of cells, thereby inhibiting the regenerative capacity of stem cells. This leads to dysfunctional differentiation of stem cells, damage of the stem cell microenvironment (i.e., that stem cell niche), homeostasis, and stem cell aging [44].
6. Regulatory Signaling Pathways of Inflamm-Aging
In principle, the pathways controlling inflammation are potential regulatory signaling pathways of inflamm-aging. The NF-κB and TOR signaling pathways, in particular, have been investigated.
6.1. NF-κB Signaling Pathway
NF-κB, a nuclear transcription factor, is regarded as the main molecular switch of inflammatory pathways. The NF-κB signaling pathway may also regulate inflamm-aging [8]. However, the longevity gene, SIRT1, can be combined with a subunit of NF-κB, Rel/p65, to deacetylate K310 and inhibit the transcriptional activity of NF-κB [46, 47]. NF-κB can regulate the occurrence of aging, whereas SIRT1 may regulate NF-κB to delay aging [48]. Thus, NF-κB can regulate both aging and inflammation [49]. NF-κB can also inhibit inflammatory reactions by regulating SIRT1 (Sir2 homolog) and FoxODAF-16 [8].
6.2. TOR Signaling Pathway
TOR, a highly conserved serine/threonine protein kinase, plays an important role in the regulating growth and proliferation of cells [50]. According to its different functions, TOR can be divided into TORC1 and TORC2. The former is sensitive to rapamycin, participating in the biological process of transcription and translation in cells. The latter is insensitive to rapamycin and mainly regulates remodeling the cytoskeleton [51]. The TOR signaling pathway regulates longevity. When TOR signaling is decreased or inactivated, the lifespan of wireworms and Drosophila is extended. Similarly, the lifespan of yeast can be increased by exposure to a low dose of rapamycin [52]. At present, it is believed that TORC1 participates in regulating aging. S6K is a positive regulation target of TORC1. In the mouse knockout of the S6K gene, lifespan is extended. 4E-BP is a gene that is necessary to lifecycle and is a negative regulator of TORC1. When 4E-BP is overexpressed, lifespan is prolonged [53].
In terms of the physiological function of TOR signal regulation, TOR regulates growth during embryonic development, and in maturity TOR regulates metabolism. However, in old age TOR signaling regulation is excessively activated and is associated with many age-related diseases. The excessive production of cytokines and inflammatory factors induces aging and the changes to the local microenvironment, causing age-related diseases. TOR regulates inflamm-aging by activating NF-κB [54].
6.3. RIG-I Signaling Pathway
Retinoic-acid-inducible gene-I (RIG-I) may be involved in inflamm-aging. RIG-I is induced via the ataxia telangiectasia mutated interferon regulatory factor-1 (ATM-IRF1) axis in senescent cells and interacts with increased levels of IL-6 and IL-8. The activation of RIG-I signaling pathway upregulates IL-6 expression [55]. RIG-I is a caspase recruitment domain- (CARD-) containing protein that functions as a cytoplasmic RNA sensor [55]. Liu et al. [55] showed that IL-6 and IL-8 levels increase in replicating senescent cells. They reported that senescent cells transfected with RIG-I show increased secretion of IL-6. However, knockdown of RIG-I in senescent cells leads to the extension of the lifespan of cells, which shows that RIG-I-induced inflammation plays a role in promoting and maintaining aging. Interfering with RIG-I expression significantly decreases the levels of inflammatory cytokines in senescent cells [55]. This imbalance in the inflammatory process may cause chronic inflammation during aging.
6.4. Notch Signaling Pathway
The Notch signaling pathway is a major intercellular communication pathway that is highly conserved through evolution. Notch signaling plays an essential role in aging [56]. At the cellular level, aging of vascular endothelial cells (EC) leads to senescence. Senescent EC secrete proinflammatory cytokines and this is often accompanied by a low-grade chronic upregulation of certain proinflammatory responses [56]. Constitutive activation of Notch signaling induces EC senescence. Consistent with these results, HeyL, a Notch downstream target, is elevated in aged compared to young EC. Notch activation also triggers EC inflammatory responses by upregulating expression of a panel of proinflammatory cytokines/chemokines and adhesion molecules in EC. This has revealed a novel function of Notch1 signaling in EC biology and may shed light on the mechanism whereby Notch signaling may contribute to some age-related vascular diseases characterized by chronic inflammation.
6.5. Sirtuin Signaling Pathway
Silent information regulator (Sir) proteins regulate lifespan in multiple model organisms [57]. Sir2 (SIRT1–7 in mammals) is a NAD-dependent deacetylase that has been implicated in aging and inflammation in yeast, worms, and flies. SIRT1, the most extensively studied in mammals, has a highly conserved NAD-dependent sirtuin core domain and is a good candidate lifespan regulator along with the other six homologs. Recent studies showed that SIRT1 is a potent anti-inflammatory protein and inhibits the COX-2/MMP pathway via suppression of the potent proinflammatory factor NF-κB. NF-κB signaling is limited by SIRT6, which is recruited to NF-κB target gene promoters by a physical interaction with the NF-κB subunit RelA. SIRT6 deacetylates histone H3 lysine 9 on target gene promoters, thereby altering the chromatin structure to facilitate NF-κB destabilization and signal termination. SIRT1 activation decreases the proinflammatory effects induced by TNF-α. In addition, treatment with SIRT1 activators such as resveratrol, or overexpression of SIRT1, inhibits the expression and activation of the main proinflammatory regulator NF-κB, which is increased by TNF-α. When SIRT1 is overexpressed, the anti-inflammatory action of SIRT1 is similar to that exerted by resveratrol. Resveratrol, as an SIRT1 activator, inhibits TNF-α-induced inflammatory factor release. Resveratrol effectively inhibits the activation of proinflammatory factors by activating SIRT1, leading to the deacetylation of NF-κB p-65 and subsequent downregulation of TNF-α-induced COX-2 and MMP expression [58].
6.6. TGF-β Signaling Pathway
Sequence variations in a variety of pro- or anti-inflammatory cytokine genes have been found to influence successful aging and longevity. TGF-β1 has been shown to have an essential role in inflammation and in the maintenance of immune response homeostasis. TGF-β1 belongs to the group of cytokines with anti-inflammatory effects and is a deactivating factor of macrophages with potent anti-inflammatory properties. Because of the role played by the transforming growth factor β1 (TGF-β1) in inflammation and the regulation of immune responses, variability of the TGF-β1 gene may affect longevity by playing a role in inflamm-aging. The potential role of TGF-β1 in aging and longevity has been suggested by many in vitro and in vivo studies. In particular, TGF-β1 gene overexpression has been observed in human fibroblasts that display a senescent-like phenotype following exposure to oxidative stress, which may help to seek a new proposal to treat disease-related aging [59].
6.7. Ras Signaling Pathway
Ras is an important signaling molecule involved in atherogenesis and is a proinflammatory molecule involved in inflammation and aging. Ras promotes aging in yeast and cellular senescence in primary human fibroblasts. Activation of Ras drastically increases the expression of proinflammatory cytokines, in part through extracellular signal-regulated kinase activation. Introduction of Ras into arteries enhances vascular inflammation and senescence [60].
Ang II promotes reactive oxygen species (ROS) production, cell growth, apoptosis, cell migration and differentiation, and extracellular matrix remodeling. Ang II regulates gene expression and can activate multiple intracellular signaling pathways leading to tissue injury. Ang II also mediates several key events in the inflammatory process. Blocking Ang II signaling protects against neurodegenerative processes and promotes longevity in rodents. Ang II-induced ROS production via the AT1 receptor promotes the onset of vascular senescence associated with functional and structural changes to blood vessels that contribute to age-related vascular diseases [60].
At present, the mechanisms of inflamm-aging remain unclear, because the methods and tools used for research into the mechanisms of inflamm-aging are not sufficiently sophisticated to explain inflammatory reactions caused by complex inflammatory cytokine cascades during inflamm-aging. Unfortunately, an adequate and reliable assessment system for aging has not yet been established. Furthermore, the causal relationships between inflammation and aging have not yet been elucidated. Moreover, the mechanisms referred to above need to be further verified. Inflamm-aging influences all levels of function, from cells to tissues, organs, and the whole body. Inflamm-aging also involves aberrant gene regulation and an imbalance in energy metabolism as well as interactions between these two factors. The mechanisms of inflamm-aging are very complicated and require multidisciplinary research to further investigate the interactions at multiple levels from cells to the whole body (Figure 2 and Table 1).
The mechanisms and regulated signaling pathways of inflamm-aging.
Table 1
Inflamm-aging-related mechanisms and regulated signaling pathways.
| Mechanisms | Effects | Signaling pathways | References |
|---|---|---|---|
| Stress | Chronic antigen stressors lead to excessive stress response and contribute to inflamm-aging | Ras | [19, 20] |
| Oxidation-inflammation | Oxidative stress and inflammation influences the homeostasis and health of body | NF-κB, Notch, TGF-β, sirtuin | [10] |
| Cytokines | High levels of proinflammatory cytokines result in inflamm-aging and age-related diseases | mTOR, RIG-I, Notch | [29, 30, 52, 53] |
| DNA damage | DNA damage response increases proinflammatory cytokines | NF-κB | [36] |
| Autophagy | Autophagic function dysfunction leads to increased oxidative products and oxidative stress | NF-κB | [40, 41] |
| Stem cell aging | Chronic inflammation induces stem cell aging and inhibits the regenerative capacity of stem cells | NF-κB, mTOR, RIG-I | [42] |
7. Potential Markers of Inflamm-Aging
One of the constraining factors of aging research is the lack of recognized, accurate, and reliable biological markers. The main biological markers of aging can be categorized as follows: (1) the marker is related to age; (2) the marker does not change with disease; (3) the marker does not change with metabolic and nutrient conditions; (4) the marker is influenced by the process of aging; (5) the marker does not change in immortalized cells. Unfortunately, the biological markers of aging have not yet been defined and need to be further investigated. This will facilitate the evaluation of the degree of inflamm-aging and assist in identifying the molecular mechanisms underlying inflamm-aging. The potential markers of inflamm-aging may include immune cell markers, serum cytokine markers, and microRNAs.
7.1. Immune Cell Markers
A main characteristic of the immune system in the elderly is antigenic T cell accumulation. The shortage of naive CD8+ T lymphocytes is regarded as a reliable biological marker related to the risk of death. The increase in CD8+ T cells, a decrease in CD4+ T cells and CD19+ B cells, and inhibition of mitogen-induced proliferation of T cells may be predictors of inflamm-aging [3].
7.2. Serum Cytokine Markers
The increase in serum IL-6 in the elderly is related to diseases, disability, and mortality. A study on a large cohort showed that IL-6 in the serum is a reliable marker of disability in the elderly and contributes to the predictive index of disability and mortality [3]. The functions of some cytokines, such as IL-10 and TNF-α, are complex and play opposing roles in the systemic inflammatory reaction. IL-10 inhibits inflammatory reactions, while TNF-α activates inflammatory reaction locally and systemically. Different regulation of IL-10 and TNF-α may be essential to the final outcome of the inflammatory reaction. Therefore, levels of IL-6, TNF-α, and IL-10 may be regarded as serum markers of inflamm-aging [3].
7.3. MicroRNA Markers
MicroRNAs (miRs) are a class of molecules involved in the regulation of gene expression and are emerging as modulators of biological pathways including NF-κB, mTOR, sirtuins, TGF-β, and Wnt. miRs may be associated with inflammation, cellular senescence, and age-related diseases and they can be classified as inflammation-associated (inflamm-miRs) and senescence-associated (SA-miRs) [61]. miR-based anti-inflammatory mechanisms may play a crucial role during aging, where a chronic, low-level proinflammatory status is likely sustained by the cell senescence secretome and by progressive activation of immune cells over time. Circulating miRs seem to be promising biomarkers of major age-related diseases [60]. Some miRs are found in plasma and leukocytes in centenarians. Some miRs, such as miR-21, miR-126, and miR-146a, which target mRNAs belonging to the NF-κB pathway can be considered as both SA-miRs and inflamm-miRs [61]. Thus specific inflamm-miRs may be regarded as biomarkers of inflamm-aging [62].
8. Intervention in Inflamm-Aging
A significant feature of inflamm-aging is that there is a chronic, low-grade, microinflammatory state in the body. Therefore, drugs used for the treatment of inflamm-aging must be effective, safe, nontoxic, and appropriate for long-term use. Calorie restriction, zinc (Zn), resveratrol, Epimedium total flavonoids, and icariin have these characteristics and may be candidate drugs to treat inflamm-aging.
8.1. Calorie Restriction
Calorie restriction (CR), also known as dietary restriction (DR), has been regarded as the gold standard of many aging interventions to counteract aging. CR together with adequate nutrient intake prolongs maximum lifespan possibly through beneficial metabolic, hormonal, and functional alterations [63]. The antiaging action of CR may be based largely on its ability to suppress oxidative stress related alterations and oxidative stress induced age-related diseases [64, 65]. CR can modulate many important inflammatory signaling pathways involved in aging and inflammation such as NF-κB, mTOR, and MAPK [66]. The age-related upregulation of NF-κB, IL-β, IL-6, and TNF-α in the proinflammatory states of the aging process is attenuated by CR [67].
8.2. Zn
Zn is an especially important, necessary microelement for the human body and has an important impact on regulating the balance between the genetic expression of metalloproteinases (MPs) and MPs inhibitors, on maintaining inducible nitric-oxide synthase (iNOS) activity, and on many biochemical functions. The interaction between Zn and IL-6, TNF-α, or heat shock protein 70 (Hsp70) regulates the immune-inflammatory reaction. The elderly frequently lack adequate levels of Zn. A moderate amount of Zn added to the diet may expand the lifespan of the elderly, which suggests that Zn may intervene in inflamm-aging [68].
8.3. Resveratrol
Some studies have found that resveratrol affects aging and lifespan in mammals [22]. Researchers reported that this potent natural compound is a SIRT1 activator and may help in preventing the aging-related decline in heart function and neuronal loss through stimulating SIRT1 activation [69]. Moreover, resveratrol decreases ovarian inflammation via inhibition of NF-κB by upregulation of PPAR-γ and SIRT1 expression [70]. Furthermore, resveratrol can suppress tumorigenesis at least in part by targeting Sirt1 and suppressing NF-κB activation [71]. Resveratrol suppresses the upregulation of proinflammatory molecules (such as IL-1β and IL-6) by TNF-α in 3T3 cells in a dose-dependent manner. However, knockdown of Sirt1 by RNA interference makes 3T3 cells susceptible to TNF-α stimulation and decreases the anti-inflammatory effect of resveratrol. The potential anti-inflammatory mechanisms of resveratrol involve a reduction in NF-κB subunit RelA/p65 acetylation, which is notably Sirt1 dependent. Resveratrol also attenuates the phosphorylation of the mammalian target of rapamycin (mTOR) and S6 ribosomal protein (S6RP) while ameliorating inflammation [72]. These data demonstrate that resveratrol may have inhibitory effect on inflamm-aging.
8.4. Epimedium Total Flavonoids and Icariin
In our previous studies, based on the neuroendocrine-immunological network, we used an inflammatory cytokine and genetic receptor chip-based assay to detect critical genes in the hippocampus, hypothalamus, hypophysis, adrenal gland, and spleen of elderly rats. We also detected the proteins corresponding to the genes referred to above. The findings showed that overexpression of some proinflammatory cytokines at the transcription and protein level may be involved in the highly proinflammatory reactive state during inflamm-aging. Additionally, our experiments showed that Epimedium total flavonoids (EF) and icariin (Ica) reduced the proinflammatory response, enhanced the anti-inflammatory response, and reestablished the equilibrium between proinflammatory and anti-inflammatory reactions in the process of inflamm-aging [73, 74].
8.5. Metformin
The biguanide drug metformin, a type of hypoglycemic drug, is widely prescribed to treat type 2 diabetes and metabolic syndrome. Researchers have also noted the effect of metformin on delaying aging, an effect validated in rodents [75] and in the nematode Caenorhabditis elegans [76]. Recently, Hall conducted a clinic trial called “Metformin, Anti-aging,” which was supported by the U.S. Food and Drug Administration (FDA) [77]. This was a landmark event in the history of aging research and showed that metformin may be used as an antiaging drug to improve the health span of humans. The mechanisms underlying the antiaging effects of metformin remain unclear. Several lines of evidence support that metformin may act by inducing metabolism associated with dietary restriction (DR) to increase lifespan and thereby limit the onset of age-associated diseases across species [78, 79]. A potential mediator of metformin benefits is the AMP-activated kinase (AMPK) and metformin can be viewed as DR-like compound [76]. Cabreiro et al. reported that metformin disrupts the bacterial folate cycle, leading to reduced levels of S-adenosylmethionine (SAMe) and decelerated aging in C. elegans [80]. Meanwhile, Moiseeva et al. showed that metformin inhibits the expression of genes coding for multiple inflammatory cytokines seen during cellular senescence, and metformin blocks the activity of NF-κB [81]. The effects of metformin on anti-inflammation and antiaging imply its potential on inflamm-aging.
9. Novel Research Strategies in Inflamm-Aging
Research into inflamm-aging is still at an early stage. The mechanisms, biomarkers, evaluation method, research models, and intervention methods of inflamm-aging have not been fully elucidated. Moreover, inflamm-aging involves cells, organs, and the whole body and this requires an extensive and varied range of research investigations.
Based on the essential effects and our understanding of inflammatory cytokine pathways in the process of inflamm-aging, we can begin to explore the inflammatory cytokine network and perform a quantitative evaluation of inflamm-aging. Inflammatory cytokines, including interleukins, tumor necrosis factor, and interferon, mediate their effects by binding to their receptors and competing in a complex cell-cell network. These cytokines act in both paracrine and autocrine ways to exert direct effects on the microenvironment. This plays an important regulatory role by activating inflammatory and immune cells and by releasing cytokines. In addition, inflammatory cytokines induce the systemic inflammatory response in the circulation. Interactions between many inflammatory cytokines comprise the inflammatory cytokine network, which features polyphyletism, pleiotropy, and overlapping effects. Inflammatory cytokines form a complex network which extends in all directions and throughout the whole body. The inflammatory cytokine network can be divided into the proinflammatory cytokine network and anti-inflammatory cytokine network. As with the immune reaction, the inflammatory reaction is also a normal defense function. A moderate inflammatory reaction is advantageous to the body, whereas a high reaction is harmful and the outcome of these reactions is determined by changes in the inflammatory cytokine network. The dynamic balance between the proinflammatory cytokine network and the anti-inflammatory cytokine network maintains the normal function of inflammation in body. Once the balance is broken, pathological inflammation occurs [82, 83]. Therefore, we infer that the cause of inflamm-aging is an imbalance in the proinflammatory cytokine and anti-inflammatory cytokine networks, which leads to a proinflammatory status with increasing age. This may be the mechanism of inflamm-aging.
The changes in the inflammatory cytokine network have three characteristics: parallelity, multilevel action, and nonlinearity. Unraveling the inflammatory cytokine network will require nonlinear research methods such as systems biology methods. The inflammatory cytokine network is a typical systems biology issue, which needs to replace the model involving individual genes with a systems biology model.
Systems biology is an interdisciplinary field integrating multiple disciplines such as biology, medicine, mathematics, physics, and chemistry. It integrates many kinds of experimental data and biological information to build a mathematical model tested and verified by experimental data and finally predicts the behavior of biological systems [84]. Systems biology provides an excellent opportunity to elucidate the essential features of the inflammatory cytokine network. It also provides a theoretical guide and new ways to illustrate the relevant mechanisms and build a quantitative evaluation system for the inflammatory cytokine network, which may provide a breakthrough in research on inflamm-aging.
Aging (including inflamm-aging) is also a systems biology issue involving a complex process that results from the combined effects of many factors. Cellular senescence is the basic unit of biological aging. Organ aging is not only the macropresentation of cellular senescence but also a bridge that connects cellular senescence and integral aging. Cellular senescence, organ aging, and integral aging form a chain of aging, and cellular senescence is the critical link in the chain of aging. In the aging process, at all levels (i.e., genes, proteins, metabolites, cells, and tissues), the organism undergoes varying degrees of change in these structures, such that the body’s systems (e.g., the nervous system, endocrine system, cardiovascular system, respiratory system, digestive system, urinary system, and motor system) undergo a significant functional decline. Aging is not the result of a unilateral factor, but a systemic decline in body function, and the distinctive features of aging are systemic [85]. The systemic features of aging strongly reflect the gradual changes in body function. Functional changes in the process of aging appear gradually with age, and all the changes are the results of progressive accumulation; time is the driving factor. The body is composed of various tissues and organs, so aging is a gradual process, not a point but an evolution of tissues and organs over time.
Multidisciplinary research including the fields of medicine, biology, mathematics, computer science, and systems biology should be applied and developed to investigate the mechanisms of inflamm-aging and the relationship between inflamm-aging and age-related diseases. Specifically, the important scientific problems that need to be addressed are the following: (1) the regulatory mechanisms responsible for the development of inflamm-aging and (2) the molecular mechanisms, the regulatory network, and the key role of the transformation from inflammation to age-related diseases in the process of inflamm-aging.
In summary, inflamm-aging and the inflammatory cytokine network are both classical systems biology issues. The inflammatory cytokine network is involved in the process of inflammation and senescence and may be the ideal breakthrough point of research into inflamm-aging. Omics, such as genomics, transcriptomics, proteomics, and metabolomics, are excellent methods to solve systemic biology problems. Therefore, under the guidance of systems biology, it would be novel strategy to conduct basic research into inflamm-aging using omics methods to identify characteristic inflammatory cytokine genes in the process of aging and to uncover new mechanisms to regulate inflammatory cytokines during inflamm-aging. This will also illustrate the mechanism of inflamm-aging and provide new ways to assess inflamm-aging.
10. A Novel Concept of Immuno-Inflamm-Aging
There is an essential relationship between inflammation and immunity. Both the inflammatory steady state and immune steady state have defense functions, protecting the body from injury [86]. However, when the inflammatory steady state and immune steady state are broken, excessive inflammation and pathological immunity ensue and the normal physiological function of the body is compromised, which causes immune-inflammatory diseases. Inflammation and immunity coexist in the same pathological process, the two sides of a whole, and are inseparable. In a sense, inflammatory cells are immune cells [87]; therefore, they have the same cellular foundation. Many inflammatory and immune cells have the same cytokine receptors [88], which mediate cell-cell and cell-cytokine interactions. The internal relationship between inflammation and immunity is not known, and the causes and pathological mechanisms of immune-inflammatory diseases are not understood and this limits effective treatments for immune-inflammatory diseases [86].
EF and Ica, anti-inflammatory immunomodulatory Chinese traditional medicines, not only reduce inflammation but also regulate immunity. As anti-inflammatory, immunomodulatory Chinese traditional medicines, EF and Ica have the double effects of intervening in immunosenescence and inflamm-aging, suggesting an intimate relationship between immunosenescence and inflamm-aging [73, 74].
Based on the integrated relationship between oxidative stress and inflammatory stress, De La Fuente and Miquel proposed an innovative oxidation-inflammation theory of aging (oxi-inflamm-aging) [10]. The oxi-inflamm-aging theory posits that chronic oxidative stress affects all immune cells, and particularly regulatory systems such as neural, endocrine, and immune systems, as well as the mutual interactions among these systems. These events lead to stable, internal environment disorders that are harmful to health. The relationship between the redox state and immune function affects the speed of aging and lifespan. A diet with sufficient antioxidants improves immune function, reduces oxidative stress, and prolongs lifespan, which supports the notion that the inflammatory response of immune cells and the immune system play an important role in oxi-inflamm-aging [10]. Inflamm-aging and immunosenescence are connected, and they cause and affect each other. This can result in a vicious cycle that further aggravates the occurrence and development of age-related diseases including atherosclerosis, metabolic syndrome, type 2 diabetes, insulin resistance, osteoporosis, bone arthritis, muscle mass and muscle weakness, cancer, and neurodegenerative diseases.
It is currently not possible to distinguish whether diseases are caused by inflamm-aging alone or immunosenescence alone [30, 89]. Therefore, we propose that inflamm-aging is accompanied by immunosenescence, and they occur together. We propose the novel concept of immune/inflammatory aging (immuno-inflamm-aging), instead of the individual concepts of inflamm-aging and immunosenescence.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant nos. 81460748, 31171129, and 81000859) and Shanghai Municipal Commission of Health (Grant no. 2013ZYJB0801).
Competing Interests
The authors declare that they have no competing interests regarding the publication of this paper.
Authors’ Contributions
Shijin Xia and Xinyan Zhang contributed equally to this work and should be considered the first authors.
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Sci Rep. 2019; 9: 9772.
Published online 2019 Jul 5. doi: 10.1038/s41598-019-46120-z
PMCID: PMC6611812
PMID: 31278280
Safety and Metabolism of Long-term Administration of NIAGEN (Nicotinamide Riboside Chloride) in a Randomized, Double-Blind, Placebo-controlled Clinical Trial of Healthy Overweight Adults
Introduction
The NAD+ co-enzymes NAD+, NADH, NADP+ and NADPH are the central regulators of metabolism. They are required for fuel oxidation, ATP generation, gluconeogenesis, ketogenesis, production of pentose phosphates, heme, lipids, steroid hormones and detoxification of free radical species1,2. NAD+ is also a consumed substrate of enzymes that polymerize and/or transfer ADPribose, form cyclic ADPribose (cyclic ADPribose synthetases) and deacylate protein lysine substrates (sirtuins) with production of acyl-ADPribosyl products. Poly(ADPribose) polymerases (PARPs) signal DNA damage in order to assemble repair machinery, while cyclic ADPribose synthetases produce second messengers that mobilize calcium ions from intracellular stores, and sirtuins influence gene expression and protein activities by virtue of reversing protein post-translational modifications3. In light of the important roles of NAD+ co-enzymes in metabolism and mediating some of the longevity benefits of calorie restriction via sirtuins, there is a renewed interest in the synthesis and maintenance of the NAD+ metabolome4.
All tissues produce NAD+ from nicotinamide (NAM) or the recently identified NAD+ precursor, nicotinamide riboside (NR)5 Some tissues can produce NAD+ from tryptophan de novo and nicotinic acid (NA)2, although the generation of NAD+ from tryptophan is much less efficient than from the vitamin precursors of NA, NAM, or NR, which are collectively termed vitamin B3. NAD+ can also be supported by dietary precursors6. For example, pellagra, a disease of deficiency of NAD+ precursors, can be prevented or treated with approximately 15 mg/day of NA or NAM or with 60-times as much tryptophan7. Importantly, despite homeostatic systems and dietary intake of NAD+ precursors, it is now known that the levels of NAD+ co-enzymes are continuously challenged by metabolic stress. In the overfed and type 2 diabetic mouse livers, levels of NADPH are strikingly depressed8, whereas in noise-induced hearing loss9, heart failure10, peripheral nerve damage11, central brain injury12 and the liver of a lactating mouse13, NAD+levels are compromised. Moreover, NAD+ levels have been reported to decline in response to DNA damage14, alcohol metabolism15, and aging16,17, and the expression of nicotinamide phosphoribosyltransferase (NAMPT), the enzyme required for NAM salvage, declines with aging18 and chronic inflammation19. Thus, considering the relationships between NAD+, metabolic stress and aging, nutritional scientists are now investigating whether the ingestion of higher levels of a B3 vitamin should be part of an evidence-based approach to optimize health2.
Although NA, NAM, and NR all produce NAD+ and NADP+2,7,20, it is important to note that each precursor has unique effects physiologically. NA can lower blood lipids and is used to treat dyslipidemia21. At doses of greater than 50 mg/day, NA can also induce flushing6,21. In contrast, NAM does not lower blood lipids or cause flushing, has been reported be a sirtuin inhibitor at high doses20,22, and appears to have a greater effect at elevating blood levels of homocysteine (HCY) in humans than NA via its metabolism to 1-methylnicotinamide (MeNAM)23. In yeast, NR activates SIR2 and extends replicative lifespan24. In mouse models, NR prevents high-fat diet-induced weight gain25, fatty liver and diabetic peripheral neuropathy8, noise-induced hearing loss9, heart failure10, and central brain injury12. In addition, oral NR greatly improves survival and hematopoietic stem cell regeneration after irradiation of mice—an activity that was not seen in NA or NAM supplemented mice26. In rats, oral NR promotes resistance to and reversal of chemotherapeutic neuropathy27. In mice, oral NR increases the hepatic levels of the NAD+metabolome with pharmacokinetics that are superior to that of NA and NAM20. In addition, postpartum female mice and rats who were administered NR exhibited increased lactation and produced offspring that are stronger, less anxious, have better memory, and have enhanced adult hippocampal neurogenesis and body composition as adults13. Because NR does not cause flushing or inhibit sirtuins25 and the genes (NRK1 and NRK2) required for the metabolism of NR to NAD+ are upregulated in conditions of metabolic stress10,28, NR has a particularly strong potential as a distinct vitamin B3 to support human wellness during metabolic stress and aging.
In a variety of animal models, nicotinamide mononucleotide (NMN), the 5′-phosphorylated form of NR, has also shown promise in conditions of metabolic stress and aging29. Moreover, the gut-expressed multispanning membrane protein Slc12a8, previously annotated as a Na+/K+ Cl− transporter, has been proposed to be a specific transporter of nicotinamide mononucleotide (NMN)30. However, the assignment of Slc12a8 as a transporter of NMN occurred without a reliable LC-tandem MS assay for the expected concentration of NMN31 and are inconsistent with genetic, cell biological, and pharmacological evidence from multiple studies demonstrating that NMN is extracellularly converted to NAM and NR prior to intracellular conversion to NMN and the rest of the NAD metabolome12,32–36. While it remains possible that data will emerge showing convincing NMN transport in one or more tissues, the consensus view is that NMN is a usefully circulating metabolite that makes NR available at plasma membranes, which express the 5′-nucleotidase activity of CD731,34. To our knowledge, tests of the safety and human oral availability of NMN are not yet available.
A crystalline form of NR chloride termed NIAGEN has been evaluated in a battery of preclinical studies including a bacterial reverse mutagenesis assay, an in vitro chromosome aberration assay, an in vivomicronucleus assay, and acute, 14-day and 90-day rat toxicology37. In the 90-day toxicology study, NR had a similar toxicity profile to NAM at equimolar doses, the lowest observed adverse effect level (LOAEL) for NR was 1000 mg/kg/day, and the no observed adverse effect level (NOAEL) was 300 mg/kg/day. NIAGEN is Generally Recognized as Safe (GRAS) in the United States for use in food products38 and the subject of two new dietary ingredient notifications39,40, which were filed with the United States Food and Drug Administration without objection.
To date, NR has also been tested in six clinical trials. The first clinical trial of NR established the safe oral availability of single doses and the timecourse by which NR elevates the human blood NAD metabolome20. The second trial provided additional safety data for healthy people taking NR for 8 days41. The third and fourth trials addressed NR safety in healthy people either taking 500 mg NR twice daily for 6 weeks or combination of up to 500 mg NR and 100 mg pterostilbene per day for 8 weeks42,43. Whereas Dellinger et al.42 found that the combination of NR and pterostilbene signficantly elevated low density protein cholesterol (LDL-C) in a dose and time-depended fashion42, no signficant increases in LDL-C were seen following the adminstration of NR alone43. A fifth clinical trial documented the safety and tolerance of ingesting 2 grams NR per day for 12 weeks in obese men and post hoc analyses suggested that there was an improvement in fatty liver in the NR-treated group44. In a sixth clinical trial, single 500 mg doses of NR depressed markers of oxidative damage while increasing NADPH and exercise performance in older individuals45.
To address the dose-dependent oral availability and safety of NR in overweight adults and the safety of daily NR without pterostilbene including effects on LDL-C and blood levels of HCY, we conducted a randomized, 8-week placebo-controlled trial with 3 doses of NR in overweight but otherwise healthy adults. Here we show that once a day doses of NR up to 1 gram per day are safe and orally available. Blood NAD+ was increased in study subjects in a dose-dependent manner with NAD+ levels achieving 14% to 114% increased levels within 2 weeks that were sustained. We also establish that daily high dose ingestion of NR does not elevate LDL-C or plasma HCY.
Methods
Study design
One hundred and forty healthy male and female participants were enrolled in a randomized, double-blind, placebo-controlled parallel study to investigate the safety and effect of NR (100 mg/day, 300 mg/day, and 1000 mg/day) on NAD+ metabolite concentrations in urine and blood over 8 weeks. The study consisted of a 2-week run-in and 8-week supplementation period (Fig. 1). To minimize the effect of dietary influences on NAD+ metabolite levels, subjects were instructed to avoid foods that contain high amounts of tryptophan and forms of vitamin B3 during the run-in and NR supplementation periods. After screening, all subjects attended the clinic prior to the run-in period to review their medical history and health status and receive counseling for the dietary restrictions. At the end of the run-in period (Day 0), the subjects visited the clinic for baseline safety assessments, blood and urine collection, randomization to one of four supplementation groups (placebo, 100 mg, 300 mg, 1000 mg NIAGEN per day groups; n = 35/group), and additional dietary restriction counseling. The subjects were then released to consume their study product for the subsequent 56 days, attending the clinic on Day 7, 14, 28, and 56 for safety assessments, and blood and urine collection. The study was conducted at KGK Science Inc. Suite 1440, One London Place, 255 Queens Ave, London, Ontario, following Good Clinical Practice (GCP) guidelines and in accordance with the ethical principles that have their origins in the Declaration of Helsinki and its subsequent amendments. The study was reviewed by the Natural Health Product Directorate (NHPD), Health Canada and a research ethics board. Notice of authorization was granted on December 9th, 2015 by the NHPD, Ottawa, Ontario and unconditional approval was granted on February 5th, 2016 by the Institutional Review Board (IRB Services, Aurora, Ontario). The study was registered on clinicaltrials.org on March 18, 2016 as NCT0271593 and posted to the WHO International Clinical Trial Registry Platform on January 3, 2016. External monitoring of source documents was conducted by ClynProject Consulting, LLC.
Study design. Subjects were screened over a 4-week period. Eligible subjects were enrolled and instructed to avoid foods containing high amounts of tryptophan and forms of niacin for the duration of the study. Following a 2-week run-in period, the subjects visited the clinic on Day 0 for baseline safety assessments, blood and urine collection, and randomization to one of four supplementation groups (placebo, 100 mg, 300 mg, 1000 mg NIAGEN per day). The subjects then consumed either placebo or the NIAGEN treatments for 56 days and visited the clinic on Day 7, 14, 28, and 56 for safety assessments, and blood and urine collection. Dietary counseling and food records were dispensed and collected throughout the run-in and supplementation periods to ensure that the subjects adhered to the dietary restrictions.
The primary objective was to evaluate the difference in urinary MeNAM levels between placebo and NIAGEN (100 mg, 300 mg, and 1000 mg) after 8 weeks of supplementation. The secondary objectives were to evaluate the rate of increase in urinary MeNAM levels between placebo and NIAGEN (100 mg, 300 mg, and 1000 mg) after 8 weeks of supplementation, the difference and rate of increase in other NR metabolites levels in blood between placebo and NIAGEN (100 mg, 300 mg, and 1000 mg) after 8 weeks of supplementation, the difference and rate of increase in other NR metabolites levels in urine between placebo and NIAGEN (100 mg, 300 mg, and 1000 mg) after 8 weeks of supplementation, and the difference in other NR metabolites levels in muscle between placebo and NIAGEN (100 mg, 300 mg, and 1000 mg) after 8 weeks of supplementation. Exploratory outcomes included exploring the changes in Resting Energy Expenditure (REE) relative to placebo after 8 weeks of supplementation, the changes in blood levels of branched-chain amino acids relative to placebo after 8 weeks of supplementation, the changes in blood levels of high sensitivity C-reactive protein (hsCRP) relative to placebo after 8 weeks of supplementation. The safety objectives included the difference in vital signs, hematology and clinical chemistry parameters including high density lipoprotein cholesterol (HDL-C), LDL-C, triglycerides, and total cholesterol between the placebo- and NIAGEN-treated groups, and the difference in the incidence of adverse events between the placebo- and NIAGEN-treated groups. The effect of NIAGEN on plasma HCY levels was determined as a post hoc analysis. There were no changes to the trial outcomes or method during the trial and interim analyses were not conducted.
Subjects
Healthy men and non-pregnant, non-breastfeeding women (40–60 years of age) were eligible for the study if their body mass index was between 25–30, they were willing to avoid vitamin B3 supplements and limit ingestion of foods containing moderate amounts of tryptophan and vitamin B3, maintain current levels of physical activity throughout the study, and refrain from caffeine consumption on days when study visits included blood collection for metabolite measurement. Women of childbearing potential were eligible only if willing to use medically approved forms of birth control. Individuals with diabetes, active peptic ulcer disease, alcohol use >2 standard servings/day or history of drug or alcohol abuse in the past year, using medical marijuana, anti-hypertensives, or lipid lowering medications were excluded. Individuals with a history of renal disease, liver disease, or history of niacin deficiency were also excluded. Individuals were determined healthy by laboratory results, medical exam and physical exam. Informed consent was obtained from each participant at the screening prior to any study-related activities being performed.
Randomization
The participants were assigned to the different groups by simple randomization. Participants were identified by their initials and their date of birth and were assigned a participant number at their screening visit. If the potential participant met all the inclusion criteria and did not meet any of the exclusion criteria at baseline, a randomization number was assigned to the participant by a blinded investigator per the order of the randomization list generated by www.randomization.com.
Study product
The study consisted of a 2-week run-in and 8-week interventional period. Participants received either 100 mg, 300 mg, 1000 mg NR per day or placebo during the 8-week intervention. The NR capsule consisted of 100 mg or 250 mg of NR chloride (99% purity) as the active ingredient and microcrystalline cellulose and vegetarian capsule as non-active ingredients. The placebo capsule consisted of microcrystalline cellulose and a vegetarian capsule. No differences in size, color, taste, texture, or packaging were detectable between the two products. The investigational products and the placebo capsules were sealed in identically-appearing blister packets, which were labelled per ICH-GCP and applicable local regulatory guidelines. Unblinded personnel at KGK Science Inc., who were not involved in any study assessments, labelled the investigational product. A randomization schedule was created and provided to the investigator indicating the order of randomization. Investigators, other site personnel, and participants were blinded to the product.
Participants were instructed to take 4 capsules daily after breakfast beginning the day after their randomization visit (Day 1). The 4 capsules amounted to a single dose of either placebo (a total of 4 placebo capsules) or 100 mg NR (1 capsule containing 100 mg NR and three placebo capsules), 300 mg NR (3 capsules containing 100 mg NR and 1 capsule containing placebo) or 1000 mg NR (4 capsules containing 250 mg NR). Participants were instructed to save all unused and open packages and return them at each visit for a determination of compliance. Compliance to the protocol was also assessed by reviewing the 3-day food record and study diaries completed by each participant for adherence to the study’s dietary restrictions, ingestion of the investigational product, and maintenance of physical activity levels.
Laboratory measurements
Subjects fasted for 12 hours prior to study visits.
Anthropometric measures and vitals were assessed at screening, day 0, 7, 14, 28 and 56. Blood was collected for the assessment of laboratory parameters (CBC, electrolytes Na, K, Cl, HbA1c, creatinine, BUN, AST, ALT, GGT, and bilirubin) at screening, day 0, 7, 14, 28 and 56, blood lipids and NAD+metabolite analyses on day 0, 7, 14, 28, and 56. Urine was also collected for NAD+ metabolites analyses on day 0, 7, 14, 28, and 56. The assessments of laboratory parameters and blood lipids were conducted by LifeLabs (Etobicoke, Ontario, Canada) using standardized procedures. NAD+ metabolites in blood and urine were quantitated by LC-MS-MS at Keystone Bioanalytical, Inc. (North Wales, PA) using analytically validated methods in accordance with Good Laboratory Practices. Only metabolite data from participants who completed the study and had metabolite levels above the limit of quantitation were included in the analysis.
For whole blood NAD+ analysis, NAD+–13C5 was the internal standard. The lower limit of quantification (LLOQ) was 0.3 µg/ml, the upper limit of the quantification (ULOQ) was 50 µg/ml, and the inter-assay precision (% CV) was 1.10 to 11.83%. Plasma NAM was quantified against a NAM-d4 standard with a LLOQ = 5 ng/ml, ULOQ = 3000 ng/ml, and a % CV of 0.71 to 5.38%. Plasma MeNAM was quantified against an MeNAM-d3 standard with a LLOQ = 4 ng/ml, ULOQ = 2000 ng/ml, and a %CV of 0.34 to 13.31%. Urinary MeNAM and N1-methyl-2-pyridone-5-carboximide (Me2PY) were quantified against internal d3 standards with an LLOQ = 1 µg/ml and ULOQ = 256 µg/ml for both analytes. The %CV for urinary MeNAM and Me2PY were 1.25 to 4.60% and 1.10 to 3.22%, respectively.
Plasma HCY levels were quantified by LC-MS-MS at Keystone Bioanalytical. Sodium citrate-treated plasma was pretreated with 50 µL of 0.5 M DTT (1,4-dithiothreitol) and HCY and the internal standard (HCY-d4) were precipitated using 0.5% formic acid and 0.05% TFA in acetonitrile. After vortexing and centrifuging, 20 µL of the supernatant was diluted in 200 µL of nano-pure water in a clean HPLC vial, and 5–10 µL was injected into the liquid chromatography mass spectrometer. The standard curve range was 0.2–40 µg/mL with the LLOQ of 0.2 µg/mL.
Adverse events (AEs)
Subjects were instructed to record any AEs in a diary and were asked at each visit if they have experienced any difficulties or problems since the last visit.
Statistical analyses
Statistical analyses were completed using the R Statistical Software Package Version 3.2.1 (R Core Team, 2015) for Microsoft Windows. All statistical analyses were performed at a significance level of 5%. Although the primary outcome variable was the difference in urinary MeNAM levels between placebo and NR (100 mg, 300 mg, 1000 mg) treated subjects after 8 weeks of supplementation, the study was powered for a secondary outcome of elevation of blood NAD+. Statistical power was based on the estimated standard deviation of 10.1 µM for blood NAD+ levels46 and 80% power to detect an effect size of at least an 8.7 µM increase. With attrition estimated at 20% throughout the course of the study, a total of 140 subjects were enrolled. For reference, if the study had been powered to detect a significant increase in MeNAM levels, then a total of 128 subjects would have been required.
Statistical analyses were performed on a modified intent-to-treat population (ITT), which consisted of all subjects who received either product, and on whom any post-randomization efficacy information is available. Variables were tested for normality and log-normality where log-normality distributed variables were analyzed in the logarithmic domain. Appropriate non-parametric tests were used to analyze non-normal variables. All missing values were imputed with last observation carried forward (LCOF) imputation. No imputation was performed for missing values of safety variables.
Numerical endpoints were formally tested for significance between groups by analysis of covariance (ANCOVA). The dependent variable was the value at each visit, the factor was the treatment group, and the value at baseline (Day 0) was the covariate. When the effect of supplementation was significant (p-value ≤ 0.05), the pairwise Tukey-Kramer post hoc test was applied. Significant efficacy of the product, relative to placebo, was inferred if the coefficient of the treatment group in the ANCOVA model was significantly different from zero (p ≤ 0.05). Numerical endpoints that are intractably non-normal were assessed by the Mann-Whitney U test. A within group analysis on efficacy endpoints was done using the Student’s paired t-test or, in the case of intractable non-normality, the Wilcoxon sign rank test was performed.
Results
Compliance and completion of the clinical study
Two hundred and eighty-six subjects were screened against the eligibility requirements. One hundred and forty subjects with an age range of 40–60 years and a body mass index of 25.0–30.1 kg/m2 were deemed healthy per their screening laboratory values (complete blood panel, hematology, electrolytes, and liver and kidney function tests) and enrolled in the study (Fig. 2). After the 2-week run-in period, subjects were randomized to one of four treatment groups (placebo; 100 mg NR/day; 300 mg NR/day; 1000 mg NR/day). There were no significant differences in any of the screening laboratory values between the different groups. There were also no differences in demographics, anthropometric measurements or vital signs between groups (Table 1). The first potential participant was screened on March 1, 2016 and the last participant’s last visit was on March 17, 2017. The trial was ended after the last randomized subject completed the last visit.
Disposition of the study participants. Two hundred and eighty-six men and women were screened for eligibility. One hundred and forty subjects met the eligibility criteria and were enrolled in the study. After the 2-week run-in (Day 0), the subjects were randomized to one of four treatment groups (Placebo, 100 mg, 300 mg, or 1000 mg NIAGEN per day; n = 35/group). Over the course of the 56-day supplementation period, one subject withdrew from the placebo-treated group due to an adverse event, two subjects withdrew consent in the 100 mg NIAGEN treated group, one subject was withdrawn from the 300 mg NIAGEN-treated group and two subjects withdrew consent and one was lost to follow-up in the 1000 mg NIAGEN-treated group.
Table 1
Demographics of All Participants Enrolled in the Study at Screening.
| Placebo (n = 35) | 100 mg NIAGEN (n = 35) | 300 mg NIAGEN (n = 35) | 1000 mg NIAGEN (n = 35) | |
|---|---|---|---|---|
| Age (years) | ||||
| Mean ± SD | 50.7 ± 5.6 | 52.3 ± 5.9 | 50.2 ± 5.8 | 50.9 ± 5.6 |
| Gender [n (%)] | ||||
| Male | 12 (34%) | 12 (34%) | 16 (46%) | 15 (43%) |
| Female | 23 (66%) | 23 (66%) | 19 (54%) | 20 (57%) |
| Alcohol Use [n (%)] | ||||
| None | 0 (0%) | 1 (3%) | 1 (3%) | 0 (0%) |
| Occasionally | 12 (34%) | 12 (34%) | 12 (34%) | 19 (54%) |
| Weekly | 17 (49%) | 17 (49%) | 15 (43%) | 10 (29%) |
| Daily | 6 (17%) | 5 (14%) | 7 (20%) | 6 (17%) |
| Smoking Status [n (%)] | ||||
| Current Smoker | 4 (11%) | 3 (9%) | 3 (9%) | 4 (11%) |
| Non-Smoker | 26 (74%) | 19 (54%) | 26 (74%) | 28 (80%) |
| Ex-Smoker | 5 (14%) | 13 (37%) | 6 (17%) | 3 (9%) |
| Race [n (%)] | ||||
| Western European White | 28 (80%) | 28 (80%) | 29 (83%) | 29 (83%) |
| Eastern European White | 1 (3%) | 1 (3%) | 2 (6%) | 3 (9%) |
| Black African American | 0 (0%) | 1 (3%) | 1 (3%) | 1 (3%) |
| East Asian | 1 (3%) | 1 (3%) | 0 (0%) | 0 (0%) |
| South East Asian | 0 (0%) | 1 (3%) | 0 (0%) | 0 (0%) |
| Middle Eastern | 1 (3%) | 1 (3%) | 1 (3%) | 0 (0%) |
| Central American | 1 (3%) | 0 (0%) | 0 (0%) | 0 (0%) |
| South American | 3 (9%) | 2 (6%) | 2 (6%) | 2 (6%) |
| Ethnicity [n (%)] | ||||
| Hispanic or Latino | 5 (14%) | 2 (6%) | 3 (9%) | 2 (6%) |
| Not Hispanic or Latino | 30 (86%) | 33 (94%) | 32 (91%) | 33 (94%) |
| Male Serum Creatinine [μmol/L] | ||||
| Mean ± SD (n) | 76.4 ± 8.2 (12) | 82.5 ± 13.1 (12) | 81.0 ± 11.4 (16) | 82.7 ± 14.5 (15) |
| Female Serum Creatinine [μmol/L] | ||||
| Mean ± SD (n) | 64.1 ± 10.6 (23) | 62.8 ± 12.5 (23) | 65.1 ± 12.5 (19) | 61.7 ± 10.4 (20) |
| Systolic Blood Pressure (mmHg) | ||||
| Mean ± SD | 118.9 ± 10.5 | 121.6 ± 13.3 | 123.2 ± 11.5 | 122.5 ± 14.2 |
| Diastolic Blood Pressure (mmHg) | ||||
| Mean ± SD | 74.5 ± 7.0 | 75.2 ± 9.1 | 78.0 ± 8.7 | 78.4 ± 11.1 |
| Heart Rate (BPM) | ||||
| Mean ± SD | 67.7 ± 9.4 | 70.2 ± 9.3 | 66.0 ± 8.2 | 67.9 ± 8.3 |
| Weight (kg) | ||||
|
Mean ± SD
Median (Min − Max)
|
76.1 ± 7.8 | 79.5 ± 9.2 | 79.6 ± 9.8 | 79.7 ± 8.2 |
| BMI (kg/m 2 ) | ||||
| Mean ± SD | 28 ± 2 | 28 ± 2 | 28 ± 1 | 28 ± 2 |
Kg, kilogram; L, liter; m, meter; Max, maximum; Min, minimum; μmol, micromole; n, number; %, percentage; SD, standard deviation.
BPM, beat per minute; kg, kilogram; m, meter; Max, maximum; Min, minimum; mmHg, millimeter of mercury; N/n, number; SD, standard deviation.
Seven participants failed to complete the study (Fig. 2). One subject dropped out of the placebo group due to nausea, one subject was withdrawn from the 300 mg NR-treated group due to non-compliance with the study product, four subjects in the 100 and 1000 mg NR-treated groups withdrew consent (100 mg NR, n = 2; 1000 mg NR n = 2), and one subject in the 1000 mg NR group was lost to follow-up.
Compliance to NR, measured by counting unused capsules returned to the study site, was 98% with a mean compliance of 97.5% in the 100 mg/day NR group, 98.6% in the 300 mg/d group, 97.1% in the 1000 mg/d group, and 99% for participants in the placebo group. Based on dietary records maintained by the subjects, there were no significant between-group differences in total caloric intake or intake of forms of vitamin B3 during the course of the trial.
NR produces dose-dependent increases in blood and urinary NAD+ metabolites
Blood NAD+
NAD+ levels in peripheral blood mononuclear cells (PBMCs) peak 8 hours after the administration 300 and 1000 mg of NR20. However, the time course and dose-dependency by which oral NR increases steady-state NAD+ levels in whole blood is not known. Relative to baseline, small but significant decreases in blood NAD+ levels occurred in the placebo group over the 56-day supplementation period (p < 0.05). In contrast, daily doses of 300 mg and 1000 mg NIAGEN significantly (p < 0.05) increased NAD+ within seven days relative to baseline and placebo (Fig. 3A) and were sustained for the remainder of the study. Blood NAD+ levels in the 100 mg-treated group were significantly increased at day 14 relative to baseline and similar to the placebo group at all time points. The day 56 whole blood NAD+level and the rate of change effect sizes also increased dose-dependently to 1.74 and 1.98, respectively (Supplemental Tables 3 and 4). At day 14, the blood NAD+ levels of the 100 mg, 300 mg and 1000 mg participants were increased by 22 ± 9%, 51 ± 7% and 142 ± 14% with respect to their baseline blood NAD+levels. At day 56, the blood NAD+ levels of the same 100 mg, 300 mg and 1000 mg participants were sustained at increases of 10% ± 4%, 48 ± 8% and 139 ± 19% with respect to their baseline blood NAD+levels.
NIAGEN supplementation significantly increases NAD+ and other NAD+ metabolites. (A) Whole blood levels of NAD+ in the intent-to-treat (ITT) population over the course of 56 days of placebo, 100, 300, or 1000 mg of NIAGEN per day supplementation. (B) Plasma nicotinamide (NAM); (C) Plasma 1-methylnicotinamide MeNAM; (D) urinary (MeNAM); and (E) urinary N-methyl-2-pyridone-3/5-carboximide (Me2PY) levels in the ITT population before and after 56 days of supplementation with placebo, 100, 300, or 1000 mg of NIAGEN per day. Urinary MeNAM and Me2PY levels were normalized to urinary creatinine concentrations. Asterisks denote significant (p < 0.05) between group differences versus placebo. Number signs denote significant (p < 0.05) within group differences relative to Day 0. Error bars represent standard error of the mean. Only data from participants who completed the study and had metabolite levels above the limit of quantitation were included in the analysis. Data for within group differences in panels A, B, C and E were transformed logarithmically to achieve normality.
Plasma and urinary metabolites
NAD+-consuming enzymes such as the sirtuins, PARP, and cyclic ADPribose synthases hydrolyze the linkage between the NAM and the ADPribosyl moieties of NAD+, producing NAM and ADPribosyl products14,47,48. NAM then circulates and is methylated in the liver and other tissues to MeNAM49–51. Both plasma and urinary blood MeNAM and its oxidation products Me2PY and Me4PY are considered to be biomarkers of increased NAD+ metabolism52. Fifty-six days of supplementation with NR resulted a significant (p < 0.05) increase in plasma NAM in the 1000 mg group compared to placebo (Fig. 3B) with an effect size of 1.21 (Supplemental Table 5). Relative to baseline, significant (p < 0.05) increases in plasma NAM were also detected in the 100, 300 and 1000 mg-treated groups. Correspondingly, plasma and urinary levels of MeNAM and Me2PY were also significantly (p < 0.05) and dose-dependently increased in the 300 and 1000 mg-treated groups compared to placebo (Fig. 3C–E), resulting in day 56 metabolite level and rate of change effect sizes that ranged from 0.49 to 2.85 and increased with the amount of NIAGEN ingested (Supplemental Tables 1, 2, 7, 8, 9 and 10). Significant (p < 0.05) and dose-dependent increases plasma and urinary levels of MeNAM and Me2PY relative with baseline were also noted in the 100, 300 and 1000 mg groups (Fig. 3C–E).
Oral NIAGEN is safe and well-tolerated up to 1000 mg/day for 8 weeks
No dose-dependent AEs
AEs were coded with Medical Dictionary for Regulatory Activities version 17.0. According to this coding system, flushing (flushing, feeling of warmth transient, hot flush) would be reported under the general disorders and administration site conditions. Ninety-five AEs were reported by 61 participants (Table 2). There were no serious AEs or reports of flushing. Moreover, the type, incidence and severity of the AEs were similar across the different groups.
Table 2
Adverse Events and Number of Participants Experiencing at Least One Adverse Event in the ITT Population Separated by Organ Class Category.
| Adverse Event | Placebo (n = 35) | 100 mg NIAGEN (n = 35) | 300 mg NIAGEN (n = 35) | 1000 mg NIAGEN (n = 35) | ||||
|---|---|---|---|---|---|---|---|---|
| Number of AEs | Participants Experiencing AEs | Number of AEs | Participants Experiencing AEs | Number of AEs | Participants Experiencing AEs | Number of AEs | Participants Experiencing AEs | |
| n | n (%) | n | n (%) | n | n (%) | n | n (%) | |
| Cardiac disorders | 0 | 0 (0.0%) | 1 | 1 (2.9%) | 1 | 1 (2.9%) | 1 | 1 (2.9%) |
| Gastrointestinal disorders | 5 | 5 (14.3%) | 8 | 7 (20.0%) | 8 | 5 (14.3%) | 4 | 4 (11.4%) |
| General disorders and administration site conditions | 2 | 2 (5.7%) | 6 | 6 (17.1%) | 5 | 5 (14.3%) | 3 | 2 (5.7%) |
| Immune system disorders | 0 | 0 (0.0%) | 1 | 1 (2.9%) | 0 | 0 (0.0%) | 0 | 0 (0.0%) |
| Infections and infestations | 6 | 6 (17.1%) | 4 | 4 (11.4%) | 4 | 4 (11.4%) | 5 | 4 (11.4%) |
| Injury, poisoning and procedural complications | 0 | 0 (0.0%) | 1 | 1 (2.9%) | 0 | 0 (0.0%) | 0 | 0 (0.0%) |
| Investigations | 1 | 1 (2.9%) | 0 | 0 (0.0%) | 0 | 0 (0.0%) | 0 | 0 (0.0%) |
| Metabolism and nutrition disorders | 0 | 0 (0.0%) | 1 | 1 (2.9%) | 0 | 0 (0.0%) | 0 | 0 (0.0%) |
| Musculoskeletal and connective tissue disorders | 1 | 1 (2.9%) | 3 | 3 (8.6%) | 6 | 5 (14.3%) | 5 | 3 (8.6%) |
| Nervous system disorders | 3 | 3 (8.6%) | 0 | 0 (0.0%) | 3 | 2 (5.7%) | 2 | 2 (5.7%) |
| Renal and urinary disorders | 1 | 1 (2.9%) | 0 | 0 (0.0%) | 0 | 0 (0.0%) | 0 | 0 (0.0%) |
| Respiratory, thoracic and mediastinal disorders | 0 | 0 (0.0%) | 0 | 0 (0.0%) | 0 | 0 (0.0%) | 1 | 1 (2.9%) |
| Skin and subcutaneous tissue disorders | 1 | 1 (2.9%) | 0 | 0 (0.0%) | 0 | 0 (0.0%) | 1 | 1 (2.9%) |
| Vascular disorders | 0 | 0 (0.0%) | 1 | 1 (2.9%) | 0 | 0 (0.0%) | 0 | 0 (0.0%) |
| Overall Adverse Events | 20 | 17 (48.6%) | 26 | 16 (45.7%) | 27 | 14 (40.0%) | 22 | 14 (40.0%) |
AE, adverse event; n, number.
Of the 26 AEs reported in the 100 mg NR group, 24 were reported as being unlikely or not related to the study product. The 2 AEs reported as being possibly related were leg pain and high blood pressure and were mild in intensity. Of the 27 AEs reported in the 300 mg NR group, 25 were reported as being unlikely or not related to the study product. The 2 AEs reported as being possibly related were nausea and muscle pain and were mild in intensity. Of the 22 AE reported in the 1000 mg NR group, 19 were reported as being unlikely or not related to the study product. The 3 AEs reported as being possibly related were sore back, muscle soreness and nausea and were all mild in intensity. Of the 20 AEs reported in the placebo group, 16 were reported as being unlikely to the study product. Of the 4 AEs reported as being possible related, 3 were mild in intensity (rash, raised liver function tests, nausea) and 1 was moderate in intensity (upset stomach). Importantly, all AEs were resolved by the end-of-study.
Vital signs
There were no between-group differences in mean systolic blood pressure, mean diastolic blood pressure, mean heart rate or weight. Further, all within-group changes were within normal clinical ranges and were not of clinical significance for this population.
Hematology and clinical chemistry
Some differences were observed in the hematology parameters at day 56 (Table 3, Supplemental Figure). Specifically, decreases occurred in the white blood cell count and monocyte count in the placebo-treated group, white blood cell, neutrophil, and lymphocyte counts in the 100 mg-treated group, white blood cell, neutrophil, lymphocyte, monocyte, and basophil counts in the 300 mg-treated group, and the white blood cell, neutrophil, and lymphocyte counts in the 1000 mg-treated group. In contrast, increases in mean corpuscular volume, mean corpuscular hemoglobin, and red cell distribution width occurred only in the 1000 mg-treated group. Statistically significant differences also occurred in the white blood cell count in the 300 mg group compared to the placebo-, 100 mg-, and 1000 mg-treated groups and the red cell distribution width in 1000 mg-treated group compared to placebo-, 100 mg-, and 300 mg-treated groups. Importantly, the differences were not dose-dependent, within the healthy clinical reference ranges for the laboratory and clinic location, and deemed to be not clinically meaningful or an AE.
Table 3
Hematology After 56 Days of NIAGEN.
| Parameter | Value | Results (Mean ± St. Dev. (n)) | |||
|---|---|---|---|---|---|
| Placeboδ | 100 mg NIAGENδ | 300 mg NIAGENδ | 1000 mg NIAGENδ | ||
| Hemoglobin (g/L)*,§,Δ | Screening | 137.8 ± 9.6 (35) | 140.3 ± 12.3 (35) | 140.1 ± 14.1 (35) | 140.1 ± 13.4 (35) |
| Day 56 | 137.6 ± 9.3 (34) | 138.9 ± 12.8 (33) | 137.0 ± 11.7 (34) | 138.8 ± 13.8 (32) | |
| Change from screening | −0.6 ± 6.3 (34) | −1.1 ± 6.1 (33) | −2.0 ± 5.7 (34) | −1.2 ± 6.5 (32) | |
| Hematocrit (L/L)*,§,Δ | Screening | 0.409 ± 0.026 (35) | 0.415 ± 0.032 (35) | 0.414 ± 0.036 (35) | 0.414 ± 0.035 (35) |
| Day 56 | 0.409 ± 0.027 (34) | 0.409 ± 0.032 (33) | 0.406 ± 0.031 (34) | 0.410 ± 0.035 (32) | |
| Change from screening | −0.0009 ± 0.0175 (34) | −0.0048 ± 0.0177 (33) | −0.0050 ± 0.0162 (34) | −0.0037 ± 0.0170 (32) | |
| White Blood Cell Count (×109/L)§,Δ | Screening | 6.31 ± 1.21 (35) | 6.29 ± 1.63 (35) | 6.17 ± 1.45 (35) | 6.54 ± 1.90 (35) |
| Day 56 | 5.83 ± 1.25 (34)a | 5.65 ± 1.68 (33)a.b | 4.96 ± 1.01 (34)b | 5.69 ± 1.41 (32)a.b | |
| Change from screening | −0.49 ± 1.13 (34)a,∞ | −0.59 ± 0.84 (33)a,∞ | −1.10 ± 1.29 (34)a,∞ | −0.99 ± 1.23 (32)a,∞ | |
| Red Blood Cell Count (×1012/L)§,Δ | Screening | 4.59 ± 0.42 (35) | 4.64 ± 0.41 (35) | 4.74 ± 0.44 (35) | 4.71 ± 0.46 (35) |
| Day 56 | 4.60 ± 0.39 (34) | 4.60 ± 0.41 (33) | 4.68 ± 0.39 (34) | 4.63 ± 0.47 (32) | |
| Change from screening | −0.001 ± 0.197 (34) | −0.045 ± 0.181 (33) | −0.044 ± 0.198 (34) | −0.069 ± 0.202 (32) | |
| Mean Corpuscular Volume (fL)§,Δ | Screening | 89.4 ± 4.1 (35) | 89.6 ± 4.7 (35) | 87.7 ± 4.0 (35) | 88.2 ± 3.4 (35) |
| Day 56 | 89.3 ± 4.2 (34) | 89.5 ± 5.2 (33) | 87.1 ± 4.0 (34) | 88.7 ± 3.7 (32) | |
| Change from screening | −0.12 ± 1.98 (34) | 0.09 ± 1.42 (33) | −0.32 ± 1.53 (34) | 0.50 ± 1.08 (32)∞ | |
| Mean Corpuscular Hemoglobin (pg)§,Δ | Screening | 30.10 ± 1.60 (35) | 30.28 ± 1.96 (35) | 29.57 ± 1.52 (35) | 29.80 ± 1.13 (35) |
| Day 56 | 30.00 ± 1.50 (34) | 30.23 ± 1.91 (33) | 29.31 ± 1.51 (34) | 29.97 ± 1.23 (32) | |
| Change from screening | −0.14 ± 0.67 (34) | 0.04 ± 0.58 (33) | −0.14 ± 0.62 (34) | 0.19 ± 0.51 (32)∞ | |
| Mean Corpuscular Hemoglobin Concentration (g/L)§,Δ | Screening | 336.7 ± 6.7 (35) | 338.0 ± 6.9 (35) | 337.4 ± 7.5 (35) | 338.2 ± 7.3 (35) |
| Day 56 | 336.0 ± 5.7 (34) | 338.0 ± 7.0 (33) | 336.4 ± 6.7 (34) | 337.7 ± 6.6 (32) | |
| Change from screening | −0.9 ± 6.1 (34) | 0.3 ± 5.5 (33) | −0.6 ± 5.6 (34) | −0.1 ± 5.3 (32) | |
| Red Cell Distribution Width (%)§,Δ | Screening | 13.69 ± 0.71 (35) | 13.48 ± 0.54 (35) | 13.84 ± 0.81 (35) | 13.56 ± 0.55 (35) |
| Day 56 | 13.58 ± 0.74 (34) | 13.51 ± 0.52 (33) | 13.76 ± 0.69 (34) | 13.84 ± 0.73 (32) | |
| Change from screening | −0.10 ± 0.45 (34)a | 0.03 ± 0.55 (33)a.b | −0.08 ± 0.57 (34)a.b | 0.25 ± 0.50 (32)b,∞ | |
| Platelet Count (×109/L)*, §,Δ | Screening | 265 ± 48 (35) | 265 ± 54 (35) | 263 ± 43 (35) | 276 ± 55 (35) |
| Day 56 | 265 ± 51 (34) | 265 ± 63 (33) | 252 ± 36 (34) | 269 ± 71 (32) | |
| Change from screening | −2.4 ± 29.4 (34) | −3.2 ± 27.8 (33) | −11.2 ± 27.5 (34) | −9.3 ± 33.0 (32) | |
| Neutrophil Count (×109/L)*,§,Δ | Screening | 3.63 ± 0.96 (35) | 3.52 ± 1.12 (35) | 3.68 ± 1.23 (35) | 3.83 ± 1.26 (35) |
| Day 56 | 3.34 ± 1.00 (34) | 3.11 ± 1.12 (33) | 2.78 ± 0.96 (34) | 3.17 ± 0.93 (32) | |
| Change from screening | −0.31 ± 0.96 (34) | −0.41 ± 0.74 (33)∞ | −0.82 ± 1.22 (34)∞ | −0.76 ± 0.96 (32)∞ | |
| Lymphocyte Count (×109/L)*,§,Δ | Screening | 1.96 ± 0.62 (35) | 2.09 ± 0.58 (35) | 1.77 ± 0.32 (35) | 1.93 ± 0.73 (35) |
| Day 56 | 1.80 ± 0.46 (34) | 1.87 ± 0.57 (33) | 1.56 ± 0.31 (34) | 1.77 ± 0.54 (32) | |
| Change from screening | −0.14 ± 0.40 (34) | −0.18 ± 0.35 (33)∞ | −0.19 ± 0.31 (34)∞ | −0.18 ± 0.38 (32)∞ | |
| Monocyte Count (×109/L)†,‡ | Screening | 0.523 ± 0.135 (35) | 0.483 ± 0.150 (35) | 0.511 ± 0.164 (35) | 0.523 ± 0.165 (35) |
| Day 56 | 0.485 ± 0.102 (34) | 0.545 ± 0.460 (33) | 0.441 ± 0.146 (34) | 0.491 ± 0.147 (32) | |
| Change from screening | −0.038 ± 0.107 (34)∞ | 0.067 ± 0.463 (33) | −0.062 ± 0.126 (34)∞ | −0.038 ± 0.139 (32) | |
| Eosinophil Count (×109/L)†,‡ | Screening | 0.186 ± 0.119 (35) | 0.174 ± 0.117 (35) | 0.169 ± 0.141 (35) | 0.226 ± 0.174 (35) |
| Day 56 | 0.174 ± 0.083 (34) | 0.188 ± 0.124 (33) | 0.171 ± 0.147 (34) | 0.244 ± 0.164 (32) | |
| Change from screening | −0.018 ± 0.090 (34) | 0.024 ± 0.083 (33) | 0.000 ± 0.115 (34) | 0.006 ± 0.105 (32) | |
| Basophil Count (×109/L)†,‡ | Screening | 0.009 ± 0.028 (35) | 0.017 ± 0.038 (35) | 0.023 ± 0.043 (35) | 0.023 ± 0.043 (35) |
| Day 56 | 0.009 ± 0.029 (34) | 0.012 ± 0.033 (33) | 0.009 ± 0.029 (34) | 0.012 ± 0.034 (32) | |
| Change from screening | 0.000 ± 0.025 (34) | −0.006 ± 0.035 (33) | −0.015 ± 0.036 (34)∞ | −0.012 ± 0.034 (32) | |
fL, femtoliter; g, gram; L, liter; Max, maximum; m, meters; μg, microgram; μmol, micromoles; mL, milliliter; mmol, millimoles; Min, minimum; min, minutes; nmol, nanomoles; N, number; % Percent; pg, picogram; SD, standard deviation; U, units.
§Between group comparisons were made using ANOVA.
†Between group comparisons were made using the Kruskall-Wallis test.
ΔBetween group comparisons were made using ANCOVA adjusting for screening.
δWithin group comparisons were made using the paired Student t-test.
‡Within group comparisons were made using the non-parametric signed-rank test.
*Logarithmic transformation was required to achieve normality.
∞Denotes statistically significant (p < 0.05) within group differences.
Endpoints with different superscript letters denotes statistically significant (p < 0.05) between group differences via Tukey-Kramer pairwise test.
Recently, dose-dependent, statistically significant increases in total cholesterol and LDL-C were observed in a clinical study in which participants received a combination of 250 mg NR plus 50 mg pterostilbene or a combination of 500 mg NR plus 100 mg pterostilbene for eight weeks42. As shown in Table 4, there were no statistically significant differences in the NIAGEN and placebo-groups with respect to any clinical chemistry parameter. Clinical testing of pterostilbene alone indicates that it produces time and dose-dependent increases in human LDL-C53 of a magnitude that are a public health concern54,55 and are inconsistent with pterostilbene being a sirtuin 1 activator or included as part of a consumer wellness product56.
Table 4
Clinical Chemistry After 56 Days of NIAGEN.
| Parameter | Value | Results (Mean ± St. Dev. (n)) | |||
|---|---|---|---|---|---|
| Placeboδ | 100 mg NIAGENδ | 300 mg NIAGENδ | 1000 mg NIAGENδ | ||
| Sodium Concentration (mmol/L)†,‡ | Screening | 142.11 ± 1.97 (35) | 141.26 ± 2.48 (35) | 141.80 ± 2.06 (35) | 141.00 ± 1.81 (35) |
| Day 56 | 140.97 ± 1.59 (34) | 140.52 ± 2.43 (33) | 140.74 ± 2.30 (34) | 140.91 ± 1.63 (32) | |
| Change from screening | −1.24 ± 2.09 (34)∞ | −0.64 ± 2.82 (33) | −1.09 ± 2.30 (34)∞ | −0.16 ± 2.34 (32) | |
| Potassium (mmol/L)*,§,Δ | Screening | 4.48 ± 0.42 (35) | 4.53 ± 0.51 (35) | 4.75 ± 0.51 (35) | 4.64 ± 0.39 (35) |
| Day 56 | 4.35 ± 0.32 (34) | 4.35 ± 0.34 (33) | 4.32 ± 0.27 (34) | 4.48 ± 0.37 (32) | |
| Change from screening | −0.14 ± 0.49 (34) | −0.20 ± 0.60 (33) | −0.42 ± 0.59 (34)∞ | −0.16 ± 0.48 (32) | |
| Chloride (mmol/L)*,§,Δ | Screening | 105.51 ± 2.25 (35) | 105.23 ± 2.85 (35) | 106.06 ± 1.70 (35) | 104.97 ± 2.29 (35) |
| Day 56 | 103.76 ± 2.55 (34) | 104.06 ± 3.69 (33) | 104.44 ± 2.34 (34) | 104.12 ± 2.43 (32) | |
| Change from screening | −1.76 ± 2.85 (34)∞ | −1.00 ± 3.54 (33) | −1.62 ± 2.80 (34)∞ | −0.91 ± 2.44 (32)∞ | |
| Creatinine (μmol/L)*,§,Δ | Screening | 68.3 ± 11.4 (35) | 69.5 ± 15.7 (35) | 72.4 ± 14.3 (35) | 70.7 ± 16.1 (35) |
| Day 56 | 68.6 ± 10.6 (34) | 69.2 ± 13.8 (33) | 72.8 ± 14.1 (34) | 73.3 ± 17.0 (32) | |
| Change from screening | −0.2 ± 5.7 (34) | 1.0 ± 8.3 (33) | 0.9 ± 8.4 (34) | 3.2 ± 7.9 (32)∞ | |
| Estimated Glomerular Filtration Rate (mL/min/1.73 m2)§,Δ | Screening | 96.3 ± 12.3 (35) | 94.5 ± 14.3 (35) | 93.5 ± 12.4 (35) | 95.9 ± 13.2 (35) |
| Day 56 | 95.7 ± 12.5 (34) | 94.1 ± 12.8 (33) | 93.7 ± 13.1 (34) | 92.9 ± 12.6 (32) | |
| Change from screening | −0.0 ± 6.5 (34) | −1.2 ± 9.9 (33) | 0.3 ± 7.9 (34) | −2.6 ± 8.6 (32) | |
| Bilirubin (μmol/L)*,§,Δ | Screening | 8.0 ± 3.5 (35) | 8.9 ± 3.4 (35) | 8.3 ± 3.2 (35) | 8.9 ± 4.1 (35) |
| Day 56 | 10.2 ± 4.0 (34) | 10.5 ± 3.5 (33) | 9.8 ± 3.9 (34) | 9.2 ± 3.2 (32) | |
| Change from screening | 2.3 ± 3.8 (34)∞ | 1.8 ± 2.8 (33)∞ | 1.5 ± 3.5 (34)∞ | 0.3 ± 3.1 (32) | |
| Blood Urea (mmol/L)*,§,Δ | Baseline | 5.03 ± 1.13 (35) | 4.77 ± 1.26 (35) | 5.14 ± 1.24 (35) | 5.21 ± 1.00 (35) |
| Day 56 | 4.80 ± 1.07 (34) | 4.85 ± 1.20 (33) | 4.78 ± 0.89 (34) | 5.06 ± 1.34 (32) | |
| Change from baseline | −0.21 ± 0.90 (34) | 0.20 ± 1.24 (33) | −0.41 ± 0.91 (34)∞ | −0.14 ± 1.25 (32) | |
| Aspartate Transaminase (U/L)*,§,Δ | Baseline | 23.6 ± 6.3 (35) | 21.6 ± 4.7 (35) | 21.5 ± 3.9 (35) | 21.5 ± 4.8 (35) |
| Day 56 | 22.2 ± 6.6 (34) | 20.5 ± 4.6 (33) | 21.1 ± 3.6 (34) | 20.8 ± 5.5 (32) | |
| Change from baseline | −1.5 ± 4.4 (34)∞ | −0.7 ± 4.0 (33) | −0.5 ± 3.8 (34) | −1.1 ± 5.2 (32) | |
| Alanine Transaminase (U/L)*,§,Δ | Baseline | 23.6 ± 10.3 (35) | 20.8 ± 6.6 (35) | 21.2 ± 7.9 (35) | 23.0 ± 10.4 (35) |
| Day 56 | 24.7 ± 11.9 (34) | 20.2 ± 7.7 (33) | 20.4 ± 6.7 (34) | 21.2 ± 8.9 (32) | |
| Change from baseline | 0.9 ± 7.2 (34) | −0.3 ± 5.5 (33) | −0.8 ± 6.6 (34) | −2.6 ± 7.9 (32)∞ | |
| Gamma-glutamyl transferase (U/L)†, ‡ | Baseline | 24.3 ± 24.6 (35) | 18.9 ± 12.6 (35) | 21.0 ± 15.3 (35) | 21.2 ± 16.5 (35) |
| Day 56 | 26.4 ± 31.0 (34) | 17.7 ± 7.5 (33) | 19.9 ± 13.0 (34) | 28.0 ± 40.3 (32) | |
| Change from baseline | 2.4 ± 7.8 (34)∞ | 0.2 ± 6.0 (33) | −0.9 ± 5.3 (34) | 6.3 ± 26.5 (32) | |
| Total Cholesterol (mmol/L)§,Δ | Baseline | 5.46 ± 0.86 (35) | 5.21 ± 0.82 (35) | 5.10 ± 0.84 (35) | 5.17 ± 0.98 (35) |
| Day 56 | 5.55 ± 0.77 (34) | 5.26 ± 0.90 (33) | 5.11 ± 0.74 (34) | 5.19 ± 0.92 (32) | |
| Change from baseline | 0.07 ± 0.47 (34) | 0.06 ± 0.52 (33) | −0.04 ± 0.46 (34) | −0.07 ± 0.42 (32) | |
| Low-density Lipoprotein Cholesterol (mmol/L)§,Δ | Baseline | 3.33 ± 0.65 (35) | 3.16 ± 0.77 (35) | 3.12 ± 0.72 (35) | 3.18 ± 0.82 (35) |
| Day 56 | 3.37 ± 0.59 (33)1 | 3.08 ± 0.86 (33) | 3.18 ± 0.63 (34) | 3.15 ± 0.81 (32) | |
| Change from baseline | 0.04 ± 0.40 (33) | −0.06 ± 0.56 (33) | 0.02 ± 0.37 (34) | −0.11 ± 0.34 (32) | |
| High-density Lipoprotein Cholesterol (mmol/L)*,§,Δ | Baseline | 1.46 ± 0.37 (35) | 1.45 ± 0.35 (35) | 1.39 ± 0.36 (35) | 1.38 ± 0.42 (35) |
| Day 56 | 1.48 ± 0.35 (34) | 1.56 ± 0.48 (33) | 1.40 ± 0.36 (34) | 1.42 ± 0.45 (32) | |
| Change from baseline | 0.013 ± 0.167 (34) | 0.087 ± 0.254 (33) | 0.005 ± 0.160 (34) | 0.031 ± 0.199 (32) | |
| Triglycerides (mmol/L)*,§,Δ | Baseline | 1.45 ± 0.80 (35) | 1.33 ± 0.67 (35) | 1.29 ± 0.79 (35) | 1.33 ± 0.65 (35) |
| Day 56 | 1.56 ± 0.95 (34) | 1.37 ± 0.67 (33) | 1.17 ± 0.51 (34) | 1.38 ± 0.69 (32) | |
| Change from baseline | 0.09 ± 0.57 (34) | 0.07 ± 0.31 (33) | −0.13 ± 0.45 (34) | 0.03 ± 0.32 (32) | |
fL, femtoliter; g, gram; L, liter; Max, maximum; m, meters; μg, microgram; μmol, micromoles; mL, milliliter; mmol, millimoles; Min, minimum; min, minutes; nmol, nanomoles; N, number; % Percent; pg, picogram; SD, standard deviation; U, units.
1Low-density lipoprotein cholesterol could not be calculated for one participant in this group because their triglyceride level was greater than 4.52 mmol/L.
§Between group comparisons were made using ANOVA.
†Between group comparisons were made using the Kruskall-Wallis test.
ΔBetween group comparisons were made using ANCOVA adjusting for screening.
δWithin group comparisons were made using the paired Student t-test.
‡Within group comparisons were made using the non-parametric signed-rank test.
*Logarithmic transformation was required to achieve normality.
∞Denotes statistically significant (p < 0.05) within group differences.
NR and plasma homocysteine
Nicotinamide N-methyltransferase catalyzes the transfer of a methyl group from S-adenosylmethionine (SAM) to NAM, generating to MeNAM and S-adenosylhomocysteine49–51. S-adenosylhomocysteine is then subsequently cleaved to homocysteine (HCY) and adenosine. It has been reported that single 300 mg oral doses of NA and NAM increase plasma HCY levels23, indicating a potential shortage of methyl groups that could be needed for formation of molecules such as dopamine and creatine. Moreover, increased plasma HCY is an independent risk factor for the development of vascular disease57–59. To determine whether prolonged ingestion of NR increases plasma HCY levels, a post hocanalysis was conducted using sodium citrate-treated plasma samples collected during the study. Compared to baseline or the placebo-treated group, NR ingestion had no effect on plasma HCY levels (Fig. 4).
NIAGEN supplementation does not disturb plasma homocysteine. Plasma HCY levels in the intent-to-treat population before and after 56 days of supplementation with placebo, 100, 300, or 1000 mg of NIAGEN per day. Error bars represent standard error of the mean. Only data from participants who completed the study and had metabolite levels above the limit of quantitation were included in the analysis.
Exploratory endpoints
No significant differences between the any of the NR- and placebo-treated groups were seen in either the REE, blood levels of branched-chain amino acids, or hsCRP after 8 weeks of supplementation.
Discussion
Because NAD+ is the most abundant NAD+ metabolite in any cellular sample60, it is the breakdown of NAD+ and NAD+-related coenzymes in food that produces the three salvageable NAD+ precursor vitamins: NR, NAM and NA. In addition to the existence of NR in milk5,61 and apart from the availability of NR as a supplement, mammals are exposed to NR from the digestive breakdown of dietary NAD+ and endogenous NR circulation. Endogenous NR has been shown to be a critical nutrient in maintaining health as mice lacking the major NR kinase gene have depressed hepatic NAD+ and depressed liver function62. In addition, people undergoing heart failure increase their cardiac expression of the NR kinase 2 gene10. This only makes sense if NR is an endogenous form of B3.
NR has been demonstrated to be safe and GRAS, supported by a rigorous battery of animal toxicology studies37. Additionally, NR was well-tolerated in all published clinical studies20,41,43,44. Because NA use is limited by flushing, it was of particular interest to assess whether there would be reports of flushing or other treatment related AEs that are associated with ingestion of NR. Here we show in a randomized, placebo-controlled, double-blind, parallel-group study involving 140 overweight, otherwise healthy adults that the ingestion of up 1000 mg of NR is not associated with flushing. Limitations of the study were that it was conducted in predominantly white, middle-aged adults who consumed a diet limited in niacin equivalents.
The concept of niacin equivalence among the NAD+ precursors is clearly useful when defining reference intakes because adequate amounts of tryptophan, NAM or NA can prevent pellagra7. However, niacin equivalency does not apply at the higher doses used to support other health endpoints as evidenced by the independent ULs for NAM and NA derived by the European Commission and UK Expert Group on Vitamins and Minerals. The UL for NA was established at 10 mg/day based on flushing63 and the UL for NAM is 900 mg/day based on the NOAELs established in clinical studies administering doses up to 3 g NAM per day64. Additionally, on the basis of elevating HCY, a sensitive biomarker of methylation status, NAM and NA differ in terms of their potential to dysregulate 1-carbon metabolism. While both of the classical forms of B3 elevated plasma HCY after single doses of 300 mg, NAM elevated HCY substantially more than NA23. On a molar basis, 300 mg of NAM (MW = 122 Da) is equivalent to 716 mg of NR Cl (MW = 291 Da) and our data show that NR does not elevate HCY at daily doses up to 1000 mg for 8 weeks.
NR, NAM and NA are converted to NAD+ through three different gene-encoded pathways that are tissue-restricted in the case of NA2. Because NA uniquely produces flushing, there is a reason for a lower UL for NA. Additionally, although NAM does not appear to produce AEs, there is some concern around its use as a vitamin due to its ability to dysregulate 1-carbon metabolism23 and inhibit sirtuins at high doses20,22. The safe oral availability of NR and its lack of adverse effects on HCY and LDL-C at doses up to 1000 mg/day support the establishment of a UL for NR that is equal to or greater than that of NAM.