Precision Nutrition: Recent Advances in Obesity

Abstract

“Precision nutrition” is an emerging area of nutrition research that focuses on understanding metabolic variability within and between individuals and helps develop customized dietary plans and interventions to maintain optimal individual health. It encompasses nutritional genomic (gene-nutrient interactions), epigenetic, microbiome, and environmental factors. Obesity is a complex disease that is affected by genetic and environmental factors and thus a relevant target of precision nutrition-based approaches. Recent studies have shown significant associations between obesity phenotypes (body weight, body mass index, waist circumference, and central and regional adiposity) and genetic variants, epigenetic factors (DNA methylation and noncoding RNA), microbial species, and environment (sociodemographics and physical activity). Additionally, studies have also shown that the interactions between genetic variants, microbial metabolites, and epigenetic factors affect energy balance and adiposity. These include variants in FTO, MC4R, PPAR, APOA, and FADS genes, DNA methylation in CpG island regions, and specific miRNAs and microbial species such as Firmicutes, Bacteriodes, Clostridiales, etc. Similarly, studies have shown that microbial metabolites, folate, B-vitamins, and short-chain fatty acids interact with miRNAs to influence obesity phenotypes. With the advent of next-generation sequencing and analytical approaches, the advances in precision nutrition have the potential to lead to new paradigms, which can further lead to interventions or customized treatments specific to individuals or susceptible groups of individuals. This review highlights the recent advances in precision nutrition as applied to obesity and projects the importance of precision nutrition in obesity and weight management.

Introduction

People have different responses to and requirements for nutrients and other components of the diet, and this heterogeneity is an important source of variance in nutrition studies. Understanding this variation and its role in risk for diseases has gained more momentum with the advent of new tools and technologies (1–3). “Precision nutrition” is an emerging area of nutrition research that focuses on understanding metabolic variability within and between individuals and helps to develop customized dietary plans and interventions to maintain optimum individual health. It assumes that each person will respond differently and that their energy and dietary needs are different from each other (3, 4). Using one’s DNA (genetics) to predict their response to nutrient intake is only one of the various facets of precision nutrition. Other aspects that influence the variation in individual response to nutrients include epigenetic, microbiome, and environmental factors (1, 4, 5).

The US National Institutes of Health (NIH) has recognized and identified precision nutrition as a key area of research in the pursuit of optimal health (6). As an ancillary study to their “All of Us” study, the NIH made a substantial investment in precision nutrition research, in recognition that one-size-does-not-fit all with respect to dietary intake, metabolism, and disease risk (101). Precision nutrition can be categorized into personalized or individualized nutrition and stratified nutrition, the former being at an individual level whereas the latter one focusing on groups of susceptible individuals (4, 7). Precision nutrition is innovative in that it considers individual differences in people’s genes, environment, and lifestyles. This information is key for targeting treatments for specific group of individuals. Advances in precision nutrition have led to, and continue to lead to, new discoveries that can provide the foundations of interventions or treatments tailored to specific individuals (7, 8).

Obesity is a major health concern worldwide (9–11). It poses a considerable burden health-wise and cost-wise. It is also a precursor to various other metabolic diseases such as type 2 diabetes, cardiovascular diseases, metabolic syndrome, neurodegenerative diseases, and some types of cancers (12, 13). As per CDC 2017–2018 statistics, 42.4% and 9.2% of adults in the United States have obesity and severe obesity, respectively (102). The regulation of weight and metabolism including the physiological processes has been studied extensively. While the pathogenesis of obesity seems as simple as the difference between ingested and expended calories, scientific evidence shows that obesity is a complex disease and involves genetic, epigenetic, microbiome, psychosocial, and environmental factors, as well as the interactions between them. Additionally, a person’s social, cultural, and behavioral factors also impact a person’s weight and body composition (13, 14).

A key feature of obesity is the dysregulation of energy metabolism. The central nervous system plays a vital role in appetite, satiety, and energy regulation, with the hypothalamus as the master regulator. The arcuate nucleus (ARC), a part of the hypothalamus, is a vital center for the regulation of metabolism and feeding (15–17). It contains neurons that express two distinct, functionally antagonistic, peptides; orexigenic [appetite stimulating: neuropeptide Y (NPY) and agouti-related peptide (AgRP)] and anorexigenic [appetite suppressing: pro-opiomelanocortin (POMC) and cocaine and amphetamine-regulated transcript (CART)] peptides. The neuronal signals from the lateral hypothalamus (increase food intake) and parabrachial nucleus (decrease food intake) also contribute to the regulation of appetite (18, 19). The peripheral signals include hormonal signals such as leptin, insulin, and gut hormones [cholecystokinin (CCK), ghrelin, peptide YY (PYY), glucagon-like peptide1 (GLP1), oxyntomodulin, obestatin, and nesfatin] (19, 20). The integration of hormonal and nutritional signals from the circulation and peripheral and central neuronal (including astrocyte-neuron and microglia-neuron and gut-brain axis) signals happens in the ARC to generate a feeding response (15–17). Both components of energy metabolism, energy intake and expenditure, have been shown to be altered or modified by genetic variants and/or epigenetic modifications, microbial species and metabolites, lifestyle, and social and psychological factors, with serious implications for obesity (FIGURE 1) (15–17). This narrative review highlights and discusses the recent advances in the components of precision nutrition in relation to obesity.

FIGURE 1.

Individuality in metabolic response to diet/nutrition intake Components of energy metabolism, energy intake and expenditure, have been shown to be altered or modified by genetic variants and/or epigenetic modifications, microbial species and metabolites, lifestyle, and social and psychological factors, with serious implications for obesity.

Nutritional Genomics and Obesity

Nutritional genomics (NGx) is the study of how an individual’s genetics affect the way nutrients are metabolized (nutrigenetics) and how nutrients affect the way genes are expressed (nutrigenomics) (8, 21). The main aims of NGx are to identify genetic variants that are associated with diet-related diseases, to underlie the variability in responses to diet or nutrients, and to help develop nutritional strategies that can treat or prevent diseases (3, 8, 21). Among the metabolic diseases, obesity is the most heavily studied within the NGx paradigm. It is well known that lifestyle factors such as diet and exercise play a key role in the development and treatment of obesity and related comorbidities. However, it is also known that weight loss interventions, whether dietary plans or exercise regimens, do not elicit the same response from all individuals. Additionally, the equation of energy in and energy out is not as simple as it seems. The interindividual variability in response to diet or exercise is quite substantial, even after correcting for differences in age, gender, race/ethnicity, and sociodemographic characteristics (3, 22, 23).

The imbalance between energy intake and energy expenditure is a key determinant of obesity. Energy intake occurs through intake of macronutrients, carbohydrates, proteins, and fats. The amount and type of these macronutrients determine adiposity and energy equilibrium. A dynamic time-series analysis of three decades of US data and a decade of data from 164 other countries, after adjustment for total calories, has shown that carbohydrate, and not fat, consumption was associated with short- and long-term increases in weight (24). Meta-analysis of studies of low-carbohydrate and low-fat diets for weight loss showed that, irrespective of total calories and weight loss, reduction in carbohydrates may prove beneficial for improving metabolic syndrome markers, including obesity (25–27).

It is also known that simple or added sugars and saturated fatty acids can adversely affect adiposity. Simple sugars, such as glucose, fructose, lactose, and sucrose, are metabolized differently than complex carbohydrates. As per the American Heart Association, the recommended total sugar consumption for US adults is 150 and 100 calories per day for men and women, respectively (28). Although much decreased, the consumption of simple sugars and saturated fatty acid remains higher than the recommended intake. Both the monosaccharides glucose and fructose get metabolized and yield great amounts of fat in the liver. However, they differ in regional adipose deposition with glucose promoting subcutaneous adipose deposition while fructose favors visceral adipose tissue deposition (29). Other studies have shown the adverse effects of simple sugars, either fructose or glucose, on adiposity and weight gain (30–33). However, in a recent study investigating the association between macronutrient intakes and obesity and metabolic risk, the authors did not find any link between carbohydrates, fats, and weight (34). These studies and results show differences in the way nutrients affect obesity phenotypes and point to the involvement of other biological and environmental factors.

Individual variability in weight phenotypes has a strong genetic basis. Twin, family-based, and other studies have estimated heritabilities of body composition and related phenotypes to be 40–70% (35–38). Genome-wide association and candidate gene studies have identified several genetic variants that affect various components of obesity. In a study of more than 300,000 European adults, Locke et al. (39) identified 97 loci associated with body mass index (BMI). Many of these loci, particularly a specific set of 32 loci, have been consistently reported to be associated with obesity across ethnicities. Some studies have generated polygenic risk scores (PRS) from these 32 loci and found stronger effects on anthropometric measures (40). The PRS is computed from an independent set of polymorphisms that are associated with the risk for a disease. The total number of risk alleles per individual is summed, weighted or unweighted by its effect size, and a score is calculated. A single score, the PRS, thus reflects the cumulative effect of the included polymorphisms and is a better predictor of the disease risk (41, 42). A study in the UK Biobank has generated a PRS with 2.1 million single nucleotide polymorphisms (SNPs) associated with obesity and assessed them in individuals ranging from birth to middle age (43).

Prominent among the genes that are associated with appetite or adiposity are fat mass and obesity related (FTO), melanocortin 4 receptor 4 (MC4R), apolipoprotein A5 (APOA5), neuropeptide Y (NPY), leptin (LEP), leptin receptor (LEPR), brain-derived neurotrophic factor (BDNF), single-minded homolog1 (SIM1), neurexin3 (NRXN3), NPC intracellular cholesterol transporter 1 (NPC1), neuronal growth regulator q (NEGR1), mitochondrial carrier homolog 2 (MTCH2), apolipoprotein E (APOE), tumor necrosis factor alpha (TNFα), plasminogen activator inhibitor 1 (PAI-1), transmembrane protein 18 (TMEM18), etc. (43–46). A study investigating genetic influence on extreme obesity found several loci that are linked to either the circadian rhythm of food consumption or hypothalamic signaling related to food intake (47, 48).

Studies have also shown differential effects of nutrients based on an individual’s genetic makeup. Genetic variants interact with nutrition and other environmental factors such as physical activity to affect obesity phenotypes. The most frequently studied nutrients have been simple sugars and saturated and unsaturated fatty acids. Simple sugars such as fructose and glucose, either in the form of sugar-sweetened beverages (SSB) or as part of high fructose/glucose diets, have been studied to understand their effects on obesity and related comorbidities (49, 50). In circulation, fructose and glucose have different metabolic fates, although fructose needs glucose for its metabolic effects. Fructose, more than glucose, favors hepatic lipogenesis (49). A large study including 6,934 women from the Nurses Health Study, 4,423 men from the Health Professionals Follow-up Study, and 21,740 women from the Women’s Genome Health Study generated a PRS, based on the aforementioned 32 obesity-associated SNPs. They found that with an increment per 10 risk alleles, the relative risk for BMI increased with every category of increasing SSB intake (49). Some other examples of gene by diet interactions with respect to obesity include carbohydrate intake and MC4R rs17782313 (51), low-protein diet and genetic risk score (GRS) of 15 SNPs (52), saturated fat intake and FTO (53), vitamin C intake and carboxy peptidase (CP) rs59465035 (54), fat and sugar intake and taste receptor (TAS1R2) (55), 5 SNP PRS and fried foods (56), high-fat hypocaloric diet and resistin (RETN) rs10401670 (57), etc. (Table 1).

TABLE 1.

Summary of nutrigenetics of obesity phenotypes

Variant of Interest	Gene	Type of Study	Sample Size	Nutrient/Dietary Intake	Outcome	Population	Conclusion	Reference(s)
rs9939609	FTO	Cohort	339,000	Total fat	BMI	European and Hispanic adults	TT allele of both SNPs associated with higher BMI with higher SFA intake	Corella et al. (53)
rs1121980
rs9939609	FTO	Intervention	86	Mediterranean diet	Weight loss	Italian	A allele was associated with less weight loss than TT carriers	Franzago et al. (58)
rs662799	APOA5	Cohort	3266	Red meat	Metabolic syndrome	Korean	G carriers and in highest tertile of red meat consumption had 1.7 HR as compared to those with A allele	Choi and Shin, (103)
GRS of 4 SNPs		Cohort	302	Total fat, SFA, MUFA, and PUFA	Waist circumference	Ghana	GRS >3 and high intake of fat, SFA, PUFA, and MUFA linked to increased waist circumference	Alsulami et al. (52)
				Fiber	Body fat		GRS >3 and high intake of fiber had lower total body fat as compared to low fiber intake
GRS of 94 SNPs		Cohort	362,496	Alcohol	BMI	UK adults (UK Biobank)	Increase in BMI per GRS is higher in infrequent drinkers as compared to frequent drinkers	Rask-Anderson et al. (104)
rs6722579	CAB39	Cohort	50,808	Fat intake	Abdominal obesity	Korea	A carriers were at higher risk for abdominal obesity in those who had fat intake >DRI	Kwon et al. (54)
rs59465035	CPQT						T carriers are at lower risk for abdominal obesity in those who had vitamin C >DRI
rs17782313	MC4R	Cohort	282	Carbohydrate intake	BMI	Iranian	Higher carbohydrate intake was associated with higher BMI and waist circumference in C carriers	Alizadeh et al. (51)
rs1801282	PPARG	Intervention	327	Diet therapy vs. diet therapy + metformin	Weight loss		CC carriers had a greater weight loss than others	Valeeva et al. (48)
rs59465035	CP	Cohort	50,808	Vitamin C	Waist circumference	Korean	T-allele carriers had lower risk of abdominal obesity as compared to others in those who consumed vitamin C higher than DRI	Kwon et al. (54)
rs10401670	RETN	Intervention	284	High-fat hypocaloric diet	BMI	Spain	T-allele carriers had greater weight loss than others	De Luis et al. (57)
5 SNP PRS		Cohort	>53,000	Fried foods	BMI	Korean	High PRS and menarche linked with higher BMI	Park et al. (56)
rs1761667,	CD36	Cohort	142	Fat and sugar intake	BMI percentiles	African American females	AA and altered fat perception linked to BMI increase	Primeaux et al. (55)
rs35874116	TAS1R2						TT and sugar intake linked to BMI increase
rs713598	TAS2R38						CC and energy intake linked to BMI increase

GRS, genetic risk score; PRS, polygenic risk score; BMI, body mass index; SNP, single nucleotide polymorphism; SFA, saturated fatty acid; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids; DRI, dietary reference intake.

Weight loss interventions have also shown better results when participant genetics were considered. For example, a randomized controlled clinical trial, The Nutrigenomics, Overweight/Obesity and Weight Management (NOW) study conducted a weight loss trial with two groups, one with a lifestyle balance program and the other one with a lifestyle balance program with genetic testing. The latter group showed better weight loss than the former one for as long as 6 months (59). Similar results were shown in another trial that had 284 individuals who underwent a high-fat, hypocaloric diet. They found greater weight loss in T carriers of rs10401670 of RETN as compared to others (57). Studies have also shown the effect of a few genetic variants on the extent of weight loss in intervention studies. A study by Franzago et al. (58) found that A-allele carriers of rs9939609 of the FTO gene lost less weight and decreased BMI less than TT carriers during a 12-mo diet/lifestyle intervention. A study by Garaulet et al. (60) found that eating late was linked to lower weight loss in AA genotype of PLIN1 14995 A > T carriers, thus pointing to the importance of meal timing along with genes in obesity. On the other hand, a 10-wk weight-loss intervention based on 35 SNPs did not find any difference between personalized dietary intervention based on 35 SNPs and a generic healthy diet with respect to either loss of body weight or fat mass (61).

In a similar vein, modified nutrient diets have been linked to changes in the expression of genes associated with adiposity. In a collaborative cross mice model, Yam et al (62) showed that genetic background and macronutrient alterations affected the expression of genes involved in adiposity. There is an increasing interest in intermittent fasting and its effect on metabolic diseases including obesity. A study of individuals with overweight/obesity showed a significant decrease in FTO gene expression after the month-long fasting period of Ramadan in contrast to normal weight individuals (63).

Nutritional Epigenetics and Obesity

Epigenetics refer to the genetic effects that are external to changes in DNA sequence. The various types of epigenetic changes include DNA methylation (addition of methyl groups to cysteine or adenine on the DNA), noncoding RNAs (short and long noncoding RNAs that modulate mRNAs to affect gene expression), and histone modification (posttranslational modification of histone proteins that can affect chromatin function and DNA accessibility) (64–66). A branch of epigenetics, nutritional epigenetics, is the study of the effects of food on the expression of genes (67). Human and animal studies have shown that epigenetic effects can transcend generations. Maternal nutrition, in particular, can have distinct epigenetic effects on the child’s risk for various metabolic diseases (68–70).

An individual’s alleles are determined by their maternal or paternal origins (genomic imprinting), and these are involved in a variety of metabolic functions. Alterations in genomic imprinting due to epigenetic mechanisms such as DNA methylation, histone modifications, or genetic events such as translocation, inversion, duplication, etc., can result in obesity (67). Animal studies have shown that paternal BMI at the time of conception can influence an infant’s birth weight and patterns of DNA methylation in infant cord blood (71). Several studies have evaluated the Dutch Famine Birth Cohort of the Hunger Winter. These studies showed that intrauterine exposure to maternal undernutrition results in long-term effects on a child’s metabolic health, including obesity, type 2 diabetes, and metabolic syndrome (72, 73). Interestingly, these exposures had long-term transgenerational effects. Similar results were shown in studies using data from the Chinese Famine that occurred between 1959 and 1961. Individuals born during this period reported higher cases of obesity, type 2 diabetes, and metabolic syndrome as compared to those born after the famine (74, 75). In another human study, DNA collected from infants born at term with 40 women of normal BMI and with obesity revealed genomic loci associated with differentially methylated regions (DMRs) in CpG-dense regions (70). When compared with another larger study, there was a significant overlap in DMRs, with key genes being PTPRN2, a gene involved in insulin secretion, and MAD1L1, a gene involved in cell cycle and tumor progression (70).

An intervention study that included 12 months of lifestyle modification (Mediterranean diet and physical activity) showed links between BMI and DNA methylation patterns. DNA methylation patterns in genes related to lipid metabolism and inflammation were the most modified by lifestyle interventions (65). In yet another intervention study that involved a very-low-calorie diet (VLCD), the investigators found that after a VLCD therapy in individuals with obesity, DNA methylation patterns changed and resembled more those of a normal weight person (76). In a cohort of normal-weight and overweight-obese individuals, higher BMI was associated with lower methylenetetrahydrofolate reductase (MTHFR) gene methylation (77). Obesity-associated alterations in methylation were also observed in individuals on either weight-loss diets or surgeries. In a group of individuals undergoing either a very-low-calorie ketogenic diet (VLCKD), a balanced hypocaloric diet (BHD), or bariatric surgery, DNA methylation in the angiotensin-converting enzyme (ACE2) was higher in individuals with obesity as compared to normal-weight individuals. VLCKD and BHDs helped reverse this hypermethylation, but bariatric surgery did not show a similar benefit (78).

Noncoding RNA, especially microRNAs (miRNA), is another class of epigenetic markers that have been linked to obesity and risk for other metabolic diseases (79). These miRNA can affect mRNA stability and degradation by binding anywhere along the length of the mRNA transcript (80, 81). Like genetic variants, several loci that correspond to miRNAs have been identified. Kunej et al. (37) identified 1,736 genomic loci associated with obesity, of which 221 correspond to miRNAs. Several studies have also found these miRNA to be correlated with diet and lifestyle (82, 83), key ones being miR-17/20/93, 21-590-5p, 200 b/c, 221/222, let-7/miR-98, and miR-203 families of miRNAs. In a children study, sex-specific association with obesity was found with miRNAs, 26 b-3p, hsa-576-5p, hsa-31-5p, hsa-10b-5p, and hsa-31-5p. (82). The understanding of the human epigenome and its interaction with environment, particularly diet, may provide greater insight into the pathogenesis of obesity and help develop personalized nutritional interventions.

Microbiome and Obesity

The human body contains millions of microbes that can have significant health effects. The microorganisms, collectively termed the microbiome, are from bacterial, fungal, and protozoal origins (84). The composition of gut microbiota is influenced, to a greater extent, by indigestible foods. The microbiota residing in GI tracts prefer fermentable carbohydrates as substrates whereas they use proteins and amino acids for their own enzymes. The distant colon thus gains energy from fermented residual peptides and proteins. The microorganisms residing in the gut have been associated with obesity risk (85, 86). Individuals with obesity have specific alterations in the composition and function of the gut microbiome (86–88), and the gut microbiome affects energy balance by affecting both energy utilization from the diet as well as energy expenditures and storage. The effects of gut microbiome on obesity phenotype and weight loss have been captured through population-based cohort studies as well as randomized controlled trials (89, 90).

Studies have shown that individuals with obesity have a greater amount of Firmicutes and higher Firmicutes to Bacteriodes ratios (91). Other studies have shown Firmicutes and Bacteriodes to be inversely and positively associated with energy expenditure and percent body fat, respectively (63). A recent study by Depommier et al. (92) showed that pasteurized Akkermansia muciniphila increased energy expenditure and reduced body weight and fat mass in diet-induced obesity in mice models. The same groups showed that Akkermansia also improved gut barrier integrity, insulin resistance, and dyslipidemia in mice. They also translated these findings to humans in an exploratory study where they found a reduction in markers of liver dysfunction and inflammation after supplementing the diet with Akkermansia (93). Similar results have been shown in humans using other forms of bacteria (88, 90, 93). In a study in normal weight individuals with high and low visceral fat, the investigators found that a total of 16 species of microbes were significantly correlated with visceral fat accumulation as measured by quantitative computed tomography but not with BMI or waist circumference (90). Of these Bacteriodes were associated with low visceral fat. A longitudinal study of obesity conducted in individuals undergoing laparoscopic sleeve gastrectomy (LSG), found a negative correlation between visceral fat and few microbial species, the strongest being with Eubacterium eligens. Before and after LSG analysis showed a significant increase in specific species such as C. symbiosum, C. hathewayi, C. citroniae, and other Clostridiales after LSG (88). This is significant as Clostridiales are considered to be “good” microbes and are negatively associated with metabolic diseases (94).

The gut microbiota, whose genome is more than 100 times the human genome, are also known to pass from the mother to the neonate through exposure during delivery and through the breastfeeding (95). In addition, the microbial metabolites such as short-chain fatty acids, trimethylamine-N-oxide (TMAO), folate, and other B vitamins, etc., are known to modulate epigenetic mechanisms including DNA methylation, histone modifications, and noncoding RNAs (96, 97). The microbiota is increasingly being recognized as a potential synthesizer of biological compounds that can be used as epigenetic substrates or regulators of epigenetic enzyme activity (98).

Conclusions

Precision nutrition is critical to formulating effective dietary plans customized for individuals or a group of susceptible individuals of diverse ethnicities and populations. To develop personalized or stratified dietary plans, either to maintain optimal health or to treat metabolic diseases, there is a need to understand how an individual responds to diet or nutrients and the factors responsible for this variation in response. These areas of nutrition fall under the purview of precision nutrition. Knowledge of genetic variants affecting nutrient metabolic pathways is not new but dates to the recognition of phenylketonuria, lactase persistence, glucose phosphate dehydrogenase deficiency, etc. What is new is that with the advent of next-generation sequencing technologies and the latest analytical approaches, we are now able to conduct a comprehensive investigation using ‘Omics’ approaches, which are revolutionizing the field. However, there are some issues that need to be resolved before precision nutrition can be put into practice. First, self-reporting approaches continue to be the primary methods for collecting dietary data. Unless there is an objective approach to measuring dietary intake, accurate estimates of dietary intake (especially long term) will be difficult. Second, there is a need to better integrate multiple sources of data. Usage of machine learning approaches is helping address the integration of multiple sources of data. Third, proper flow of data among researchers, practitioners, and educators is needed for dissemination or translation of these results. For example, physicians and dietitian need proper training to be able to prescribe genotype-guided diet plans to patients. Fourth, proper counseling of patients is needed for them to be able to comply with the personalized diet. Despite all these caveats, incorporation of precision nutrition approaches has started showing noteworthy results.

In a study conducted by Connell et al. (99), researchers provided participants with personalized data-driven dietary recommendations, using artificial intelligence, and found significant improvement in clinical outcomes in all conditions and disease activities. Using dietary, genetic, and epigenetic data and machine learning approaches, Lee et al. (100) identified 21 SNPs, 230 DNA methylation sites in lipid and carbohydrate metabolism-related genes, and 26 dietary factors that predicted obesity with 70% accuracy. The main goal of precision nutrition is to understand the differences in the metabolic responses to diet/nutrient intake between individuals and develop tailored or customized nutritional plans for optimal health. With the latest Omics and machine-learning-based approaches, the future of precision nutrition, individualized or stratified, is bright and can be an effective adjunct therapy to existing treatment strategies.