Diabesity: an old term, a modern challenge — multi-omics and precision anti-obesity pharmacotherapy in type 2 diabetes

Abstract

Type 2 diabetes mellitus (T2DM) and obesity are closely interconnected but heterogeneous conditions in which treatment response, safety, body weight, glycemic control, and long-term clinical outcomes vary substantially between individuals. This narrative review evaluates emerging anti-obesity pharmacotherapy in patients with coexisting T2DM and obesity and examines the translational role of multi-omics in future precision treatment selection. Distinct from reviews focused primarily on obesity pharmacotherapy or omics-based disease stratification, the present review emphasizes the interface between contemporary drug mechanisms, T2DM-associated metabolic heterogeneity, candidate molecular signatures, and the level of evidence required for clinically actionable prescribing. We summarize evidence for approved and investigational oral incretin therapies, dual and triple receptor agonists, amylin-based strategies, established antidiabetic drugs, and adjunctive approaches targeting body composition. Throughout the review, we distinguish established clinical evidence from mechanistic rationale, candidate biomarkers, exploratory associations, and hypothetical future precision applications. We also separate metabolic efficacy endpoints, such as body weight and HbA1c, from intermediate cardiometabolic risk markers and from cardiovascular or kidney outcomes demonstrated in dedicated outcome trials. At present, no genomic, metabolomic, lipidomic, proteomic, microbiome, or integrated multi-omics signature has been clinically validated to select a specific anti-obesity medication for patients with T2DM and obesity. Major unresolved issues include the need for prospective biomarker validation, direct comparative trials, standardized assays, externally validated and calibrated prediction models, preservation of skeletal muscle health and physical function, long-term safety, treatment durability, affordability, and equitable implementation. Multi-omics should therefore be viewed as an investigational framework for generating and testing precision-treatment hypotheses rather than as a basis for routine prescribing.

Introduction

Type 2 diabetes mellitus (T2DM) and obesity are closely interconnected conditions that reinforce one another through insulin resistance (IR), chronic low-grade inflammation, adipose tissue dysfunction, ectopic fat accumulation, and broader cardiometabolic dysregulation [1, 2]. The term “diabesity”, commonly attributed to Ethan A. H. Sims and colleagues in the 1970s, was introduced to emphasize the close metabolic relationship between excess adiposity and diabetes-related dysglycemia [1]. In this review, the term is used descriptively to refer to the coexistence of obesity and T2DM and should not be interpreted as a separate disease entity or independent clinical diagnosis. Coexisting T2DM and obesity are associated with a higher burden of cardiovascular (CV), kidney, hepatic, and functional complications, with important consequences for quality of life (QoL), mortality, and healthcare expenditure [3–5]. Their increasing prevalence reflects changes in lifestyle, nutrition, urbanization, population aging, and environmental exposures as well as substantial biological heterogeneity [6, 7]. Excess adiposity, particularly visceral and ectopic fat accumulation, promotes IR, inflammation, adipokine dysregulation, hepatic steatosis, and progressive impairment of glucose homeostasis. Obesity is therefore both a major risk factor for T2DM and an important modifier of its clinical course, complications, and response to pharmacological treatment [6–10].

The global burden of obesity has increased markedly over recent decades. According to the World Health Organization (WHO), approximately 2.5 billion adults were overweight in 2022, including about 890 million adults with obesity, corresponding to 43% and 16% of the adult population, respectively [6]. Large pooled analyses have shown that adult obesity prevalence more than doubled between 1990 and 2022, while obesity among children and adolescents increased even more rapidly [8]. Projections from the World Obesity Federation (WOF) suggest that the number of adults living with obesity may rise to 1.53 billion by 2035 if current trends continue [7]. This trajectory has major clinical and economic implications, with the annual global economic impact of overweight and obesity projected to exceed USD 4 trillion by 2035 [9].

In parallel, diabetes mellitus (DM) has become a major global health challenge. In 2021, approximately 536.6 million adults aged 20–79 years were estimated to be living with DM worldwide, corresponding to a prevalence of 10.5% [11], and this number was projected to increase to 783.2 million by 2045. More recent International Diabetes Federation estimates indicate that 589 million adults are living with DM and that this number may rise to approximately 853 million by 2050 [12]. Because more than 90% of DM cases are T2DM, the increasing burden of T2DM is closely linked to the global expansion of obesity and related cardiometabolic risk factors [12].

The clinical consequences of coexisting T2DM and obesity extend beyond hyperglycemia and excess body weight [13–15]. Both conditions increase the risk of cardiovascular disease (CVD), chronic kidney disease (CKD), metabolic dysfunction-associated steatotic liver disease (MASLD), obstructive sleep apnea (OSA), heart failure (HF), osteoarthritis, and several obesity-related cancers [10, 13–15]. Importantly, patients with T2DM and obesity do not represent a homogeneous population [16]. Individuals differ in the relative contribution of IR, beta-cell dysfunction, visceral adiposity, liver fat accumulation, inflammation, genetic susceptibility, gut microbiome composition, comorbidity burden, and environmental exposures. This heterogeneity contributes to substantial interindividual variability in therapeutic efficacy, tolerability, risk-factor modification, and treatment durability [10, 13–15].

Genome-wide association studies have identified multiple loci linked to T2DM and CVD [17], but genetic signals explain only part of the observed disease heterogeneity. Contemporary treatment therefore increasingly extends beyond glucose lowering to address body weight, cardiometabolic risk factors, liver disease, and, where supported by dedicated outcome trials, CV and kidney outcomes. These outcome domains should be interpreted separately: reductions in body weight or HbA1c and improvements in blood pressure (BP), lipid profile, liver fat, or albuminuria are clinically relevant but should not automatically be considered evidence of CV or kidney event reduction. Although incretin-based therapies have expanded treatment options, selection among available and emerging therapies remains largely empirical, and validated predictive biomarkers are limited [16, 17].

Previous reviews have examined precision medicine in obesity, multi-omics approaches to obesity management, heterogeneity-based precision medicine in T2DM, and the emerging field of pharmacomulti-omics in diabetes [18–21]. The distinctive contribution of the present review is not the introduction of multi-omics as a new concept, but the integration of the rapidly evolving anti-obesity pharmacotherapy pipeline with an explicit translational hierarchy of evidence in the specific context of coexisting T2DM and obesity. We distinguish population-level clinical efficacy from mechanistic rationale, candidate response biomarkers, exploratory associations, and hypothetical future treatment-selection strategies. Particular emphasis is placed on the gap between biomarker discovery and clinically actionable prescribing, including the validation requirements for treatment combinations, sequencing, body-composition outcomes, and organ-specific risk.

Accordingly, the aim of this narrative review is to summarize contemporary and emerging anti-obesity pharmacotherapies and to critically evaluate how, and at what current level of evidence, multi-omics could contribute to precision pharmacotherapy in patients with coexisting T2DM and obesity.

Methods and scope of the review

This narrative review was developed through a structured search of peer-reviewed literature complemented by searches of clinical trial registries and official regulatory or sponsor sources. PubMed/MEDLINE was used as the primary bibliographic database. ClinicalTrials.gov was searched to identify ongoing, recently completed, and late-stage development programs relevant to obesity pharmacotherapy and T2DM. Official regulatory documents and company communications were consulted when recent information on regulatory status, development programs, or preliminary results was not yet available in peer-reviewed publications. The search covered literature available through August 2026; no predefined lower publication-date limit was applied.

Search concepts included combinations of terms related to T2DM, obesity, diabesity, precision medicine, multi-omics, genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, microbiome, pharmacometabolomics, anti-obesity medications, glucagon-like peptide-1 (GLP-1) receptor agonists, glucose-dependent insulinotropic polypeptide (GIP), glucagon receptor agonism, dual receptor agonists, triple receptor agonists, amylin, body composition, treatment response, biomarkers, and the names of individual approved or investigational agents discussed in this review. Priority was given to peer-reviewed randomized controlled trials, particularly phase 2 and phase 3 studies, mechanistic studies, consensus or guideline documents, and human studies evaluating omics-based stratification, biomarkers, pharmacological response, or precision medicine in T2DM and obesity. Studies were considered when they provided clinically or mechanistically relevant information on obesity pharmacotherapy, T2DM, obesity-related metabolic complications, treatment-response heterogeneity, multi-omics, or precision-treatment strategies. Publications were excluded when they were outside the scope of the review, duplicated more complete or more recent evidence, or provided insufficient information on the population, intervention, or outcomes. Preclinical evidence was included selectively when required to explain the mechanism of an emerging pharmacological platform.

The pharmacological scope included established therapies used as contextual comparators, recently approved treatments, agents under regulatory review, and investigational compounds in phase 1, 2, or 3 development for obesity, T2DM, or obesity-related metabolic complications. Because this was a narrative review rather than a systematic review or meta-analysis, no formal protocol registration, duplicate study selection, risk-of-bias assessment, or pooled quantitative synthesis was performed. Clinical trial registry records, regulatory documents, and company communications were used primarily to describe current development or regulatory status and preliminary findings not yet available in peer-reviewed form. Such sources are explicitly distinguished from peer-reviewed randomized clinical evidence and are interpreted as provisional until full data are published. This approach improves transparency but does not eliminate the possibility of selection bias inherent to narrative reviews.

“Diabesity” as a heterogeneous metabolic phenotype

As it was indicated above, T2DM is not a single disease entity but a heterogeneous syndrome that may arise through different combinations of IR, beta-cell dysfunction, adipose tissue dysfunction, hepatic steatosis, inflammation, altered incretin signaling, gut microbiome changes, and neurobehavioral drivers of food intake. Data-driven clustering studies have identified subgroups of adult-onset DM with differing risks of complications and potentially different therapeutic needs [16]. Genetic and multi-trait analyses further support the presence of mechanistically distinct pathways leading to T2DM, including insulin secretion, insulin action, adiposity distribution, lipid metabolism, and hepatic metabolic traits [22].

In clinical practice, obesity-associated T2DM commonly coexists with visceral adiposity, hepatic IR, MASLD, dyslipidemia, arterial hypertension, OSA, and increased CV risk. Two patients with the same BMI and HbA1c may nevertheless differ substantially in beta-cell reserve, liver fat, inflammatory activity, appetite biology, skeletal muscle status, kidney function, CV vulnerability, and treatment barriers. These differences are clinically relevant, but most have not been validated as predictive biomarkers for selecting one anti-obesity pharmacotherapy over another [16, 17, 22].

Accordingly, current treatment selection should continue to integrate established clinical factors, including glycemic status, obesity severity, cardiorenal and hepatic comorbidities, risk of hypoglycemia, gastrointestinal (GI) tolerability, frailty, nutritional status, patient preference, and access. Molecular or multi-omics profiles may eventually refine this process, but phenotype-treatment associations discussed in this review should be interpreted as hypothesis-generating unless they have been prospectively validated for differential treatment response.

Multi-omics approaches to precision pharmacotherapy in T2DM

Recent recommendations emphasize management of the cardiometabolic continuum beyond glucose-centered care, including body weight, comorbidities, and long-term patient-centered outcomes [3]. In parallel, the pharmacological landscape of T2DM and obesity has expanded rapidly. When interpreting this evidence, metabolic efficacy endpoints such as weight reduction and HbA1c should be distinguished from intermediate cardiometabolic risk markers and from CV or kidney event reduction demonstrated in dedicated outcome trials.

Multi-omics refers to the integrated analysis of multiple biological layers, including the genome, epigenome, transcriptome, proteome, metabolome, lipidome, and microbiome [23]. In T2DM, these technologies may help move from broad disease labels toward molecularly informed sub-phenotypes and treatment-response signatures [24–26]. Their relevance is particularly strong in obesity-associated T2DM, where metabolic dysfunction involves several interacting organs and pathways rather than isolated hyperglycemia [6, 10, 13, 14, 27].

Genomics can identify inherited variation associated with insulin secretion, adiposity distribution, hepatic lipid metabolism, drug metabolism, and susceptibility to adverse events. Although most common genetic variants individually have modest predictive value, pathway-level genetic information may improve subtype classification and reveal mechanisms that influence drug response [28]. For example, genetic evidence has identified clusters of T2DM risk loci linked to beta-cell dysfunction, proinsulin processing, obesity, lipodystrophy-like fat distribution, and liver-lipid traits [22].

Epigenomics may capture the influence of obesity, hyperglycemia, aging, inflammation, and environmental exposures on metabolic tissues [25, 29]. DNA methylation and chromatin accessibility patterns could eventually help explain metabolic memory, differential reversibility of IR, or long-term response to therapy for weight reduction [30]. Transcriptomics and proteomics may provide functional information on inflammatory pathways, adipose tissue remodeling, hepatic stress, beta-cell stress, endothelial dysfunction, and muscle metabolism [25, 29].

Metabolomics and lipidomics can provide direct information on small-molecule and lipid signatures that reflect the current metabolic state [31, 32]. Branched-chain amino acids, acylcarnitines, bile acids, triglyceride-rich lipoprotein patterns, ceramides, sphingolipids, ketone-related metabolites, and markers of hepatic lipid flux have been associated with IR, lipotoxicity, and MASLD-related phenotypes [33]. Treatment-related changes in these profiles may provide mechanistic or pharmacodynamic information; however, they should not be interpreted as validated pretreatment predictors of response or safety unless prospectively confirmed.

The gut microbiome represents another relevant omics layer because incretin secretion, bile acid metabolism, intestinal barrier function, immune activation, and energy harvest may be influenced by microbial ecology [34]. Although microbiome-guided prescribing is not yet ready for routine T2DM care, microbiome signatures may help explain interindividual variability in GI tolerability, appetite response, and metabolic improvement during incretin-based therapy [35, 36].

The ultimate value of multi-omics will depend on integration and validation rather than on the number of measured molecular layers. Artificial intelligence (AI) and multimodal computational models may support integration of clinical, imaging, digital, and multi-omics data, but their clinical utility depends on interpretability, reproducibility, calibration, external validation, standardized assays, and equitable implementation [37–39]. Throughout this review, three levels of evidence are distinguished: established clinical evidence from randomized trials, regulatory studies, or routine practice; mechanistic or biomarker evidence that may explain heterogeneity but does not constitute a prescribing criterion; and proposed future multi-omics-guided treatment selection, which remains investigational. At present, no validated multi-omics algorithm can reliably select among individual anti-obesity pharmacotherapies in patients with T2DM and obesity.

Systems biology of the gut-brain-liver-adipose-muscle axis

Energy balance and glucose homeostasis are regulated through a network linking the gut, central nervous system (CNS), liver, adipose tissue, skeletal muscle, pancreas, kidney, and CV system. Gut-derived hormones such as GLP-1, GIP, glucagon, peptide YY, and amylin modulate appetite, gastric emptying, insulin secretion, glucagon secretion, hepatic metabolism, and satiety signaling [40–46]. GLP-1 receptor activation reduces appetite, slows gastric emptying, improves glucose-dependent insulin secretion, and lowers glucagon secretion. GIP receptor signaling influences insulin secretion and adipose tissue biology, and its pharmacological role appears context-dependent. Glucagon receptor agonism may increase hepatic energy expenditure and lipid oxidation, which is relevant to MASLD/MASH phenotypes but may also require careful monitoring of heart rate (HR), tolerability, and glycemic effects [47–49]. Amylin contributes to meal termination, satiety, and potentially reward-related eating behavior [45, 46].

From a systems-biology perspective, emerging therapies modulate inter-organ pathways rather than acting solely through appetite suppression. Multi-omics profiling may help map gut-brain-liver-adipose tissue-skeletal muscle communication by identifying tissue-specific and circulating signatures of response. For example, high liver fat, inflammatory activity, or a lipotoxic metabolomic profile provides a mechanistic rationale for investigating whether therapies with substantial weight and hepatic effects produce differential benefit in particular subgroups. Similarly, sarcopenic obesity provides a rationale for prioritizing preservation of skeletal muscle health and resistance exercise. These examples are hypothesis-generating and have not been validated as treatment-selection criteria; prospective biomarker-enriched trials are required to test treatment-by-biomarker interactions.

Established and emerging incretin-based and multi-receptor therapies in T2DM with obesity

Liraglutide as an established GLP-1 receptor agonist and reference therapy

Liraglutide is an established GLP-1 receptor agonist that provides an important reference point for understanding the evolution of incretin-based pharmacotherapy in T2DM and obesity. In the SCALE Obesity and Prediabetes trial, once-daily subcutaneous liraglutide 3.0 mg, combined with lifestyle intervention, produced clinically significant weight reduction and improved metabolic control in adults with overweight or obesity without T2DM [50]. In adults with overweight or obesity and T2DM, the SCALE Diabetes trial showed that liraglutide 3.0 mg resulted in greater weight reduction than placebo [51], while the SCALE Insulin trial confirmed its efficacy in patients with T2DM and overweight or obesity treated with basal insulin [52]. At the lower antidiabetic dose, liraglutide 1.8 mg also demonstrated CV benefit in the LEADER trial, in which it reduced major adverse cardiovascular events (MACE) in patients with T2DM and high CV risk [53]. In addition, longer-term data from the SCALE Obesity and Prediabetes trial showed that liraglutide 3.0 mg reduced the risk of progression to T2DM in individuals with prediabetes and obesity or overweight [54]. Although the magnitude of weight reduction achieved with liraglutide is generally lower than that observed with newer agents such as semaglutide, tirzepatide, and investigational multi-receptor agonists, liraglutide remains clinically important because it established GLP-1 receptor activation as a therapeutic strategy capable of improving glycemic control, body weight, and cardiometabolic outcomes. In the context of precision pharmacotherapy for T2DM with obesity, liraglutide also illustrates the need to understand interindividual variability in response to GLP-1 receptor agonism and to identify biomarkers that may guide treatment selection.

Oral incretin therapies

The therapeutic landscape includes approved therapies, agents under regulatory review, and investigational compounds at different stages of development. Because this landscape changes rapidly, each therapy is identified below according to its regulatory or clinical-development status as of August 2026. Evidence from approved therapies, phase 3 programs, phase 2 trials, and early-phase studies should not be considered equivalent. Likewise, population-level efficacy and safety data should be distinguished from proposed precision-medicine applications, because molecular or multi-omics predictors have generally not been prospectively validated for treatment selection.

As of August 2026, oral semaglutide 25 mg had received FDA approval for chronic weight management. Peer-reviewed phase 3 evidence demonstrated clinically meaningful weight reduction in adults with overweight or obesity, while regulatory status was additionally documented by the manufacturer and regulatory sources [55, 56]. The oral formulation provides a non-injectable alternative to subcutaneous semaglutide, although administration requirements and GI tolerability may influence adherence. Weight reduction and glycemic effects should be interpreted separately from CV or kidney outcomes; organ-level benefit should only be inferred where supported by dedicated outcome evidence.

Orforglipron, a non-peptide oral GLP-1 receptor agonist, demonstrated weight reduction in peer-reviewed phase 2 and phase 3 trials before Food and Drug Administration approval in April 2026 [57–59]. The approval status is based on regulatory or manufacturer information, whereas efficacy estimates derive from the corresponding clinical trials. As a small-molecule agent, orforglipron can be administered without the fasting and water-intake restrictions required for oral peptide semaglutide.

Together, these agents provide clinical evidence that non-injectable incretin therapy is feasible for chronic weight management. Their use should be guided by approved indications, efficacy, tolerability, contraindications, patient preference, cost, and adherence considerations rather than by unvalidated molecular profiles.

GLP-1/GIP dual agonism

Tirzepatide is an approved dual GIP/GLP-1 receptor agonist with established weight and glycemic efficacy [60]. VK2735 and enicepatide (CT-388) remain investigational. VK2735 is in phase 2/3 development, with injectable and oral formulations under evaluation [61–63]. Enicepatide has progressed through early and phase 2 clinical development [64–66]. Preliminary publicly reported mid-stage results suggested substantial weight reduction, but non-peer-reviewed results should be regarded as provisional until complete trial data are published. Future precision-medicine studies should evaluate whether IR, beta-cell reserve, appetite burden, or metabolomic signatures modify response to dual agonism; these features are candidate response modifiers rather than validated treatment-selection biomarkers.

The clinically relevant question is therefore not whether emerging dual agonists can be assigned to molecular phenotypes today, but whether prospective comparative studies can demonstrate reproducible treatment-by-biomarker interactions. Direct comparator trials and biomarker-enriched studies are required before such profiles can be used for differential prescribing.

GLP-1/GIP/glucagon triple agonism

Retatrutide is an investigational phase 3 triple receptor agonist targeting GLP-1, GIP, and glucagon receptors. In a peer-reviewed phase 2 trial, retatrutide produced dose-dependent weight reduction approaching 24% at 48 weeks, with additional reductions in liver fat reported in a MASLD study [67, 68]. These findings provide clinical and mechanistic evidence but do not establish a biomarker-defined subgroup for preferential use. More recently, sponsor-reported topline results from the phase 3 TRIUMPH-1 trial indicated a mean weight reduction of 28.3% at 80 weeks with the 12-mg dose, and an extension subgroup reportedly reached 30.3% at 104 weeks [69, 70]. Because these phase 3 results were available primarily through sponsor communication at the time of manuscript preparation, they should be considered preliminary until full peer-reviewed data are available. The ongoing TRIUMPH-Outcomes trial is specifically evaluating CV and kidney outcomes [71]; until those data are available, weight reduction and changes in metabolic risk markers should not be interpreted as evidence of CV or kidney protection.

GLP-1/glucagon dual agonism

Survodutide, pemvidutide, and mazdutide combine GLP-1 receptor agonism with glucagon receptor activation [47–49]. Survodutide has peer-reviewed phase 2 evidence in obesity and MASH and has progressed to phase 3 obesity development [47, 48]. Its hepatic effects provide a mechanistic rationale for investigation in metabolic liver disease, but liver fat or metabolic profiles are not validated biomarkers for preferential treatment selection. Long-term CV safety and dedicated clinical outcomes require separate evaluation.

Pemvidutide has peer-reviewed phase 2 data showing effects on body weight and hepatic outcomes in MASLD/MASH populations [49, 72]. These findings support further clinical development but should not be interpreted as proof that a hepatic molecular or lipidomic profile can select pemvidutide over other therapies. Phase 3 comparative and outcome data are required to establish durable efficacy, tolerability, and any organ-specific clinical benefit [47, 48, 73].

Mazdutide has phase 3 randomized clinical data in Chinese adults with overweight or obesity, with additional development in populations that include T2DM [74–76]. Reported effects include weight reduction and improvements in several metabolic and hepatic risk markers. These surrogate changes are clinically relevant but should remain distinct from demonstrated CV or kidney outcome benefits. GI adverse events, particularly nausea, vomiting, and diarrhea, remain important tolerability considerations [76].

Amylin-based and GLP-1/amylin therapies

CagriSema, amycretin, petrelintide, cagrilintide, and eloralintide incorporate amylin biology into obesity treatment [77–83]. Persistent hunger, large meal size, or appetite dysregulation provide a biological rationale for studying amylin-based strategies, but these clinical features have not been validated as predictive biomarkers of differential response. Current treatment selection should therefore be based on clinical evidence, regulatory status, safety, and patient factors rather than on presumed hyperphagic phenotypes.

CagriSema

CagriSema combines cagrilintide, a long-acting amylin analogue, with semaglutide. Phase 3 trials have demonstrated substantial weight reduction, including in patients with T2DM, with additional effects on HbA1c, waist circumference (WC), and BP [77, 78, 84]. As of August 2026, CagriSema remained under FDA regulatory review for weight management following submission of a new drug application [85, 86]. GI adverse events remain the principal tolerability concern. The complementary mechanisms of GLP-1 and amylin provide a rationale for studying appetite-related response modifiers, but no such biomarker has been prospectively validated for treatment selection.

Head-to-head evidence should be interpreted separately from indirect or network comparisons. CagriSema’s ultimate clinical position will depend on regulatory decisions, comparative effectiveness, tolerability, and the populations studied. Sponsor-reported head-to-head results available in 2026 should be regarded as preliminary until fully peer-reviewed [87].

Network meta-analysis suggests that several high-efficacy incretin and amylin-based strategies can produce large average reductions in body weight, with GI adverse events increasing as treatment intensity rises [88]. Such analyses are useful for contextualization but do not substitute for direct randomized comparisons and should not be used to infer biomarker-defined superiority for individual patients.

Amycretin

Amycretin is an investigational unimolecular GLP-1/amylin receptor agonist with oral and subcutaneous development programs [79, 80]. Early clinical data support continued evaluation of dose-dependent weight reduction, but the evidence remains early phase. Its dual mechanism provides a biological rationale for studying appetite and satiety phenotypes; however, longer randomized trials are required to define efficacy, tolerability, body-composition effects, and whether any pretreatment characteristic predicts differential benefit.

Petrelintide

Petrelintide is an investigational long-acting amylin analogue in phase 2 development [81]. Potential roles as monotherapy, an adjunct to incretin therapy, or part of maintenance strategies remain under investigation. Appetite-related phenotypes are biologically plausible research targets but should not be treated as validated indications for preferential selection. Longer studies are needed to determine efficacy, tolerability, durability, and functional body-composition outcomes.

Cagrilintide

Cagrilintide is a long-acting amylin analogue developed for once-weekly administration. A phase 2 dose-finding trial demonstrated dose-dependent weight reduction, with GI adverse events as the main tolerability issue [82]. Clinical studies of co-administration with semaglutide support the feasibility of combination therapy [89], and pharmacokinetic data suggest that renal or hepatic impairment does not substantially alter exposure or tolerability [90]. These findings support continued evaluation of cagrilintide in combination strategies; they do not establish an appetite phenotype or omics signature that should currently guide selection.

Eloralintide

Eloralintide (LY3841136) is an investigational selective, long-acting amylin receptor agonist administered once weekly. Phase 1 proof-of-concept and phase 2 data indicate clinically relevant weight reduction with a tolerability profile that requires continued evaluation [83, 91, 92]. The available evidence supports further development of this non-incretin mechanism, but its role as monotherapy or in combination with incretin-based agents and any predictive biomarker profile remain unvalidated.

Other anti-obesity medications under investigation

Several additional agents broaden the pharmacological landscape beyond conventional incretin agonism. Maridebart cafraglutide (MariTide) is an investigational once-monthly conjugate combining GLP-1 receptor agonism with GIP receptor antagonism and has phase 2 clinical data with phase 3 development underway [93–95]. CT-996 (RO7795081) is an investigational oral small-molecule GLP-1 receptor agonist in early and dose-ranging clinical development in overweight/obesity and T2DM [96, 97]. Development-stage evidence should be interpreted according to trial phase and publication status rather than grouped with approved therapies.

Setmelanotide is an FDA-approved melanocortin-4 R agonist for specific genetically defined or syndromic obesity indications and is not a treatment for common polygenic obesity [98]. Bimagrumab, an investigational activin type II R pathway blocker, represents an approach targeting body composition rather than appetite alone. In a phase 2 clinical trial in adults with T2DM and obesity, bimagrumab reduced fat mass and increased lean mass [99]. These therapies illustrate the importance of matching claims to the population and indication actually studied.

Collectively, these agents illustrate the diversity of contemporary obesity pharmacotherapy, including oral, less frequently administered, genetically targeted, and body-composition-oriented approaches. Their place in precision treatment will depend on regulatory status, comparative effectiveness, safety, population-specific evidence, and prospective validation of any proposed biomarkers.

Table 1 summarizes the regulatory and clinical-development status, studied populations, principal efficacy and safety findings, evidence maturity, and potential biomarker relevance of selected therapies. Table 2 provides omics domains and their current translational maturity.

Developmental uncertainty and the need for clinical differentiation

The expanding anti-obesity drug pipeline should be interpreted cautiously. Biological plausibility, receptor novelty, or short-term metabolic effects do not necessarily translate into durable clinical efficacy, acceptable tolerability, or practical long-term use. Danuglipron, for example, demonstrated dose-dependent weight reduction in a phase 2b trial but illustrates how tolerability, dosing practicality, and safety can affect development decisions [105, 106]. Similarly, proof-of-concept efficacy should not be equated with regulatory success or superiority over established therapy.

Body composition and functional outcomes

Substantial weight reduction may involve losses of both fat mass and lean mass; however, lean mass should not be considered synonymous with skeletal muscle mass [100, 107–113]. Depending on the assessment method, lean mass includes skeletal muscle as well as other non-fat tissues and body water. Therefore, changes in DXA-derived lean mass do not directly quantify loss of contractile skeletal muscle. More muscle-specific assessment may include appendicular lean mass and muscle cross-sectional area or volume assessed by computed tomography (CT) or magnetic resonance imaging (MRI). Muscle health should also be evaluated functionally: handgrip strength, chair-stand performance, gait speed, the Short Physical Performance Battery, and other validated measures provide information that cannot be inferred from body composition alone. This distinction is particularly important in older adults and in patients with frailty or sarcopenic obesity. A treatment-associated reduction in lean mass should not automatically be interpreted as clinically meaningful skeletal muscle deterioration unless supported by evidence of reduced skeletal muscle quantity, strength, or physical performance. Future trials should therefore combine body-composition and functional endpoints rather than relying on body weight or lean mass alone.

Omics-informed biomarkers of therapeutic response and safety in patients with T2DM and obesity

For clarity, biomarkers in this review are classified according to clinical maturity. Established clinical biomarkers or variables are routinely used in care, although none currently constitutes an omics-based tool for selecting a specific anti-obesity pharmacotherapy. Candidate biomarkers are molecular or metabolic features supported by human treatment-response data but not prospectively validated for treatment selection. Exploratory associations are signals identified mainly in cross-sectional, post hoc, or mechanistic analyses that require independent replication. Hypothetical future biomarkers are biologically plausible markers proposed from systems biology or pathway-level reasoning but currently lack sufficient clinical evidence. At present, no genomic, metabolomic, lipidomic, proteomic, microbiome, or integrated multi-omics signature is validated for selecting among GLP-1 receptor agonists, dual or triple receptor agonists, amylin-based therapies, or other emerging anti-obesity medications (AOM) in patients with T2DM and obesity.

Established clinical variables that inform treatment decisions include BMI, waist circumference (WC), duration of T2DM, HbA1c, beta-cell reserve, kidney function, CVD, MASLD status, eating behavior, medication history, and previous intolerance. Genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, and microbiome data may add biological resolution, but the available evidence is heterogeneous. Metabolomic and lipidomic signatures related to IR, lipotoxicity, hepatic dysfunction, or branched-chain amino acid metabolism are best regarded as candidate or exploratory markers unless prospective treatment-by-biomarker interactions have been demonstrated [22, 24–33, 102]. Microbiome signatures associated with bile-acid metabolism, incretin secretion, GI tolerability, or metabolic response remain exploratory or hypothetical and are not validated prescribing tools [34–36].

Pharmacometabolomics illustrates the distinction between association and clinical utility. Cross-sectional studies by Naja and colleagues identified metabolite patterns associated with good or poor response to metformin and sulfonylureas [24, 26], but these signatures have not been prospectively validated for choosing therapy. Pharmacogenetic evidence is similarly promising but incomplete. For example, SLC2A2 rs8192675 has been associated with differential HbA1c response to metformin [21], and GLP1R rs10305420 has been associated with glycemic response to liraglutide in one clinical population [101]. These findings are candidate biomarkers rather than established prescribing criteria.

Predictive biomarkers should also be distinguished from pharmacodynamic biomarkers. A predictive biomarker should identify, before treatment initiation, differential benefit from one therapy compared with another. By contrast, a pharmacodynamic marker reflects a biological change after treatment exposure and may clarify mechanism without supporting pretreatment drug selection. Exploratory metabolomic and lipidomic changes during tirzepatide therapy, including changes in branched-chain amino acid-related metabolites and lipid species, are examples of pharmacodynamic signatures rather than validated pretreatment predictors [103]. Similarly, preserved C-peptide secretion may predict some clinical responses to GLP-1 receptor agonists in insulin-treated T2DM, but the available evidence remains limited and does not constitute an omics-based selector [114]. Table 3 summarizes representative examples and their validation status.

Established drugs, repurposing, and rational combination therapy in T2DM with obesity

Combination and sequencing strategies differ substantially in evidentiary maturity. Some combinations are supported by clinical or real-world evidence, whereas phenotype-directed or omics-directed combinations remain hypothesis-generating. This distinction is important because a combination may be clinically reasonable on the basis of established cardiorenal or glycemic indications even when no biomarker-guided selection strategy has been validated.

Metformin remains foundational therapy in many treatment pathways. Sodium-glucose cotransporter 2 (SGLT2) inhibitors have demonstrated CV and kidney benefits in appropriate T2DM populations and may be selected according to established CKD, HF, or CV indications [115–122]. These dedicated outcome data should be distinguished from surrogate improvements in body weight, HbA1c, BP, lipids, or albuminuria. Thiazolidinediones may remain useful in selected insulin-resistant or MASLD-predominant patients because of effects on insulin sensitivity and hepatic steatosis, although weight gain, edema, and HF risk limit broader use [123, 124]. Incretin therapy combined with basal insulin and cagrilintide combined with semaglutide are additional examples supported by randomized clinical data [89, 125].

By contrast, preferential use of pioglitazone according to a multi-omics IR signature, glucagon-containing therapies according to a hepatic lipidomic profile, or amylin-based therapy according to a hyperphagic molecular phenotype remains investigational. These strategies are mechanistically plausible but require prospective comparative studies showing treatment-by-biomarker interactions.

Similarly, induction-maintenance strategies, treatment de-escalation, and sequencing after substantial weight reduction require dedicated trials. Future precision algorithms should only be implemented if they improve patient-relevant outcomes beyond high-quality conventional selection based on glycemic status, CV and kidney indications, hepatic disease, adverse events risk, functional status, patient preference, and access.

Controversies and unresolved issues in precision pharmacotherapy of T2DM with obesity

The pharmacological landscape of T2DM with obesity has changed rapidly, with therapies producing effects across several distinct domains. These domains should not be conflated: body weight reduction and glycemic improvement are metabolic efficacy endpoints; BP, lipid profile, liver fat, inflammation, and albuminuria are intermediate or surrogate measures; and CV death, MACE, HF events, CKD progression, and kidney failure are clinical outcomes requiring dedicated outcome evidence.

Is greater weight loss always better?

The field increasingly values large reductions in body weight, but the optimal balance among efficacy, tolerability, nutritional adequacy, glycemic control, CV benefit, preservation of skeletal muscle quantity, and maintenance of muscle strength and physical performance remains uncertain. In older adults and in patients with frailty, sarcopenic obesity, chronic illness, or advanced diabetic complications, functional outcomes may be more clinically consequential than an isolated reduction in DXA-derived lean mass [100, 107–113].

Durability and treatment discontinuation

Weight regain after discontinuation of incretin-based therapy indicates that long-term treatment may be required for many patients [126, 127]. In the STEP 1 extension, participants regained a substantial proportion of previously lost weight after withdrawal of semaglutide 2.4 mg [126]. Withdrawal of tirzepatide in SURMOUNT-4 was similarly associated with weight regain and partial reversal of several cardiometabolic risk-marker improvements [127]. Whether multi-omics can identify patients with durable remission or high relapse risk remains hypothetical and requires prospective validation.

Need for direct comparative and biomarker-enriched trials

Most development programs remain placebo-controlled rather than directly comparative. Head-to-head trials among established and emerging therapies are needed to determine comparative efficacy, tolerability, body-composition effects, and clinically relevant outcomes. Biomarker-enriched trials should prespecify candidate markers and formally test treatment-by-biomarker interactions; retrospective associations alone cannot establish predictive utility.

Safety, tolerability, and clinical differentiation

GI adverse events, gallbladder disease, pancreatitis concerns, heart-rate changes, dehydration, malnutrition, and changes in skeletal muscle quantity or function require appropriate long-term evaluation [47, 48, 67, 108–113, 128, 129]. For investigational multi-receptor therapies, improvements in weight, HbA1c, BP, lipids, or liver fat should not be described as CV or kidney protection unless confirmed in dedicated outcome trials. The same evidentiary distinction applies to emerging safety signals: early tolerability data are not equivalent to long-term safety characterization.

Equity and implementation

Multi-omics technologies and high-cost pharmacotherapies may widen disparities if implementation is limited to selected populations. Practical translation therefore requires affordable and standardized assays, interoperable data systems, transparent algorithms, population diversity, and reimbursement models that do not restrict access to clinically indicated therapy. Equity should be evaluated prospectively rather than assumed from model performance in selected research cohorts.

Discussion

The current evidence supports a clear distinction between population-level therapeutic efficacy and precision treatment selection. Randomized clinical trials have established that several incretin-based and emerging anti-obesity therapies can produce substantial average improvements in body weight and glycemic control, and selected established drug classes have dedicated CV or kidney outcome evidence. However, these findings do not establish that genomic, metabolomic, lipidomic, proteomic, or microbiome profiles can currently select one anti-obesity therapy over another in an individual patient.

This distinction is central to the interpretation of the proposed framework. Biological features such as hepatic steatosis, IR, appetite dysregulation, inflammation, or sarcopenic obesity may provide a mechanistic rationale for studying differential response to glucagon-containing, amylin-based, incretin-based, or body-composition-oriented strategies. Mechanistic plausibility, treatment-induced molecular changes, and retrospective biomarker associations are nevertheless insufficient to establish predictive utility. A further distinction is required between metabolic efficacy and organ-specific clinical benefit: improvements in body weight, HbA1c, BP, lipid profile, liver fat, or albuminuria may indicate favorable risk modification but cannot be assumed to reduce CV or kidney events without dedicated outcome evidence.

Predictive biomarkers must also be separated from pharmacodynamic signatures. Many omics studies identify molecular changes during treatment or correlate baseline features with response, but such findings do not necessarily predict, before treatment, which therapy will provide greater benefit than an alternative. Clinical precision prescribing requires prespecified treatment-by-biomarker interactions, replication in independent and diverse cohorts, standardized and reproducible assays, calibration and external validation of prediction models, and demonstration of incremental value beyond conventional clinical variables.

The principal contribution of this review is therefore not the introduction of a new omics technology or disease classification, but the integration of contemporary anti-obesity pharmacology with an explicit translational hierarchy of evidence. By separating established population-level effects from candidate molecular predictors, exploratory pharmacodynamic signatures, and hypothetical phenotype-treatment associations, the framework clarifies where precision pharmacotherapy currently stands and what evidence is still required before multi-omics can influence routine prescribing in T2DM with obesity.

Clinical translation will require more than statistically significant associations. Multi-omics models must resist overfitting, remain calibrated and transportable across populations and platforms, and be supported by standardized assays and transparent reporting. Most importantly, prospective studies must show that algorithm-guided decisions improve patient-relevant outcomes, safety, persistence, or efficiency compared with high-quality conventional care. Until these requirements are met, multi-omics should be considered a research framework for generating and testing precision-treatment hypotheses rather than an established component of routine prescribing. The major challenge is no longer simply to identify additional molecular associations, but to determine which signals are predictive, reproducible, clinically actionable, and sufficiently informative to change treatment decisions beyond established clinical assessment.

Current research gaps and future directions for precision pharmacotherapy in T2DM with obesity

First, future clinical trials should move beyond average treatment effects and incorporate prespecified clinical, metabolic, imaging, and multi-omics phenotyping. Candidate biomarkers should be defined before analysis and tested for treatment-by-biomarker interactions rather than merely correlated with outcome. Replication in independent cohorts is necessary to determine whether a marker is truly predictive of differential treatment response rather than simply prognostic or mechanistically plausible.

Second, trials should deliberately include clinically relevant T2DM subgroups, including early and long-duration disease, MASLD/MASH, CKD, HF, older adults, sarcopenic obesity, and participants from diverse ancestry and socioeconomic groups. External validity requires that predictive signatures be reproducible across populations, laboratories, platforms, and healthcare settings [17, 47, 130–135].

Third, future studies should prespecify distinct outcome domains. Metabolic efficacy should include body weight, HbA1c, time in range, insulin or sulfonylurea de-intensification, and beta-cell function. Intermediate organ or risk markers may include liver fat, MASH resolution, BP, lipids, or albuminuria. CV and kidney protection should be reserved for adjudicated clinical outcome evidence from appropriately designed trials. Body-composition endpoints should distinguish total lean mass from skeletal muscle quantity and should be complemented by muscle strength and physical performance, including validated measures such as handgrip strength, chair-stand performance, gait speed, or the Short Physical Performance Battery [100, 109, 110, 113].

Fourth, AI and computational modeling may facilitate integration of high-dimensional clinical, imaging, digital, and multi-omics data, but methodological rigor is essential before implementation. Because the number of molecular features may greatly exceed the number of participants, multi-omics models are particularly vulnerable to overfitting. Development should therefore include appropriate internal validation followed by independent external validation in temporally, geographically, and demographically distinct cohorts. Discrimination should be accompanied by calibration assessment, because accurate separation of responders from non-responders does not necessarily imply accurate estimation of individual treatment benefit. Reproducibility may also be compromised by batch effects, sample handling, storage conditions, platform differences, reagent lots, preprocessing, normalization, and analytical pipelines. Harmonized pre-analytical procedures, assay standardization, quality-control protocols, and transparent reporting are prerequisites for transportability [23, 38, 39, 104, 136, 137].

Fifth, statistical validity alone is insufficient to establish clinical utility. Candidate algorithms should be tested prospectively to determine whether biomarker- or multi-omics-guided treatment selection improves efficacy, safety, treatment persistence, patient-reported outcomes, or resource use compared with high-quality conventional assessment. Combination and sequencing strategies likewise require dedicated randomized trials; clinically supported combinations should be distinguished from phenotype-directed hypotheses that remain unvalidated.

Finally, pediatric and adolescent populations remain an important evidence gap. Trials should evaluate not only weight and glycemic outcomes but also growth, puberty, bone health, nutritional adequacy, psychological outcomes, body composition, muscle function, and durability after treatment discontinuation. Precision approaches in younger populations should not be extrapolated directly from adult omics or pharmacotherapy data.

Conclusion

Coexisting obesity and T2DM represent a heterogeneous metabolic phenotype requiring treatment strategies that extend beyond glycemic control and body weight reduction alone (Figure 1). Emerging incretin-based, amylin-based, multi-receptor, and oral therapies, together with approaches targeting body composition, have broadened the therapeutic landscape. Their selection, however, remains guided primarily by conventional clinical characteristics, approved indications, comorbidities, safety, and patient preferences rather than by validated molecular profiles.

FIGURE 1

Multi-omics may ultimately refine patient stratification and treatment selection, but its current role remains investigational. Translation into routine precision pharmacotherapy will require prospectively validated predictive biomarkers, standardized assays, externally validated and calibrated algorithms, direct comparative evidence, and demonstration that biomarker-guided decisions improve clinically relevant outcomes beyond standard care.

References

• 1.

  SimsEADanforthEJrHortonESBrayGAGlennonJASalansLB. Endocrine and metabolic effects of experimental obesity in man. Recent Prog Horm Res (1973) 29:457–96. 10.1016/b978-0-12-571129-6.50016-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 2.

  PappachanJMFernandezCJChackoEC. Diabesity and antidiabetic drugs. Mol Aspects Med (2019) 66:3–12. 10.1016/j.mam.2018.10.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 3.

  JanezAMuzurovicEBogdanskiPCzupryniakLFabryovaLFrasZet alModern management of cardiometabolic continuum: from overweight/obesity to prediabetes/type 2 diabetes mellitus. Recommendations from the eastern and southern Europe diabetes and obesity expert group. Diabetes Ther (2024) 15(9):1865–92. 10.1007/s13300-024-01615-5

  • CrossRef
  • Google Scholar
  • View reference in article

• 4.

  OkunogbeANugentRSpencerGPowisJRalstonJWildingJ. Economic impacts of overweight and obesity: current and future estimates for 161 countries. BMJ Glob Health (2022) 7(9):e009773. 10.1136/bmjgh-2022-009773

  • CrossRef
  • Google Scholar
  • View reference in article

• 5.

  GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet (2020) 396(10258):1223–49. 10.1016/S0140-6736(20)30752-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 6.

  World Health Organization. Obesity and Overweight. Geneva, Switzerland: World Health Organization (2025). Available online at: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (Accessed July 17, 2026).

  • Google Scholar
  • View reference in article

• 7.

  World Obesity Federation. World Obesity Atlas 2024. London: World Obesity Federation (2024). Available online at: https://data.worldobesity.org/publications/WOF-Obesity-Atlas-v7.pdf (Accessed July 17, 2026).

  • Google Scholar
  • View reference in article

• 8.

  NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. Lancet (2024) 403(10431):1027–50. 10.1016/S0140-6736(23)02750-2

  • CrossRef
  • Google Scholar
  • View reference in article

• 9.

  World Obesity Federation. Economic Impact of Overweight and Obesity to Surpass $4 Trillion by 2035 (2023). Available online at: https://www.worldobesity.org/news/economic-impact-of-overweight-and-obesity-to-surpass-4-trillion-by-2035 (Accessed July 17, 2026).

  • Google Scholar
  • View reference in article

• 10.

  Powell-WileyTMPoirierPBurkeLEDesprésJPGordon-LarsenPLavieCJet alObesity and cardiovascular disease: a scientific statement from the American heart association. Circulation (2021) 143(21):e984–e1010. 10.1161/CIR.0000000000000973

  • CrossRef
  • Google Scholar
  • View reference in article

• 11.

  SunHSaeediPKarurangaSPinkepankMOgurtsovaKDuncanBBet alIDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. (2022) 183:109119. 10.1016/j.diabres.2021.109119

  • CrossRef
  • Google Scholar
  • View reference in article

• 12.

  Diabetes FederationI. IDF Diabetes Atlas. 11th edBrussels: International Diabetes Federation (2025). Available online at: https://idf.org/news-and-resources/resources/idf-diabetes-atlas-11th-edition-2025/ (Accessed July 17, 2026).

  • Google Scholar
  • View reference in article

• 13.

  BlüherM. Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol (2019) 15(5):288–98. 10.1038/s41574-019-0176-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 14.

  DaviesMJArodaVRCollinsBSGabbayRAGreenJMaruthurNMet alManagement of hyperglycaemia in type 2 diabetes, 2022. A consensus report by the american diabetes association (ADA) and the european association for the study of diabetes (EASD). Diabetologia (2022) 65(12):1925–66. 10.1007/s00125-022-05787-2

  • CrossRef
  • Google Scholar
  • View reference in article

• 15.

  LingvayISumithranPCohenRVle RouxCW. Obesity management as a primary treatment goal for type 2 diabetes: time to reframe the conversation. Lancet (2022) 399(10322):394–405. 10.1016/S0140-6736(21)01919-X

  • CrossRef
  • Google Scholar
  • View reference in article

• 16.

  AhlqvistEStormPKarajamakiAMartinellMDorkhanMCarlssonAet alNovel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol (2018) 6(5):361–9. 10.1016/S2213-8587(18)30051-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 17.

  ChungWKErionKFlorezJCHattersleyATHivertMFLeeCGet alPrecision medicine in diabetes: a consensus report from the american diabetes association (ADA) and the european association for the study of diabetes (EASD). Diabetes Care (2020) 43(7):1617–35. 10.2337/dci20-0022

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 18.

  SzczerbinskiLFlorezJC. Precision medicine of obesity as an integral part of type 2 diabetes management - past, present, and future. Lancet Diabetes Endocrinol (2023) 11(11):861–78. 10.1016/S2213-8587(23)00232-2

  • CrossRef
  • Google Scholar
  • View reference in article

• 19.

  WoldemariamSDornerTEWiesingerTSteinKV. Multi-omics approaches for precision obesity management: potentials and limitations of omics in precision prevention, treatment and risk reduction of obesity. Wien Klin Wochenschr (2023) 135(5-6):113–24. 10.1007/s00508-022-02146-4

  • CrossRef
  • Google Scholar

• 20.

  AnazcoDAcostaA. Precision medicine for obesity: current evidence and insights for personalization of obesity pharmacotherapy. Int J Obes (Lond) (2025) 49(3):452–63. 10.1038/s41366-024-01599-z

  • CrossRef
  • Google Scholar
  • View reference in article

• 21.

  DhiebDMustafaDHassibaMAlasmarMElsayedMHMusaAet alHarnessing pharmacomultiomics for precision medicine in diabetes: a comprehensive review. Biomedicines (2025) 13(2):447. 10.3390/biomedicines13020447

  • CrossRef
  • Google Scholar
  • View reference in article

• 22.

  UdlerMSKimJvon GrotthussMBonas-GuarchSColeJBChiouJet alType 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. Nat Med (2018) 24(12):1828–32. 10.1371/journal.pmed.1002654

  • CrossRef
  • Google Scholar
  • View reference in article

• 23.

  HemmeCLAtoyanJCaiALiuC. Challenges and opportunities in multi-omics data acquisition and analysis: toward integrative solutions. Biomolecules (2026) 16(2):271. 10.3390/biom16020271

  • CrossRef
  • Google Scholar
  • View reference in article

• 24.

  NajaKAnwardeenNBashraheelSSElrayessMA. Pharmacometabolomics of sulfonylureas in patients with type 2 diabetes: a cross-sectional study. J Pharm Pharm Sci (2024) 27:13305. 10.3389/jpps.2024.13305

  • CrossRef
  • Google Scholar
  • View reference in article

• 25.

  LingCRönnT. Epigenetics in human obesity and type 2 diabetes. Cell Metab (2019) 29(5):1028–44. 10.1016/j.cmet.2019.03.009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 26.

  NajaKAnwardeenNAl-HaririMAl ThaniAAElrayessMA. Pharmacometabolomic approach to investigate the response to metformin in patients with type 2 diabetes: a cross-sectional study. Biomedicines (2023) 11(8):2164. 10.3390/biomedicines11082164

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 27.

  WongASunQLatifIIKarwiQG. Metabolic flux in macrophages in obesity and type-2 diabetes. J Pharm Pharm Sci (2024) 27:13210. 10.3389/jpps.2024.13210

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 28.

  FitipaldiHMcCarthyMIFlorezJCFranksPW. A global overview of precision medicine in type 2 diabetes. Diabetes (2018) 67(10):1911–22. 10.2337/dbi17-0045

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 29.

  DhawanSNatarajanR. Epigenetics and type 2 diabetes risk. Curr Diab Rep (2019) 19(8):47. 10.1007/s11892-019-1168-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 30.

  DongHSunYNieLCuiAZhaoPLeungWKet alMetabolic memory: mechanisms and diseases. Signal Transduct Target Ther (2024) 9(1):38. 10.1038/s41392-024-01755-x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 31.

  NewgardCB. Metabolomics and metabolic diseases: where do we stand?Cell Metab (2017) 25(1):43–56. 10.1016/j.cmet.2016.09.018

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 32.

  Rizo-RocaDHendersonJDZierathJR. Metabolomics in cardiometabolic diseases: key biomarkers and therapeutic implications for insulin resistance and diabetes. J Intern Med (2025) 297(6):584–607. 10.1111/joim.20090

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 33.

  MorzeJWittenbecherCSchwingshacklLDanielewiczARynkiewiczAHuFBet alMetabolomics and type 2 diabetes risk: an updated systematic review and meta-analysis of prospective cohort studies. Diabetes Care (2022) 45(4):1013–24. 10.2337/dc21-1705

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34.

  WangQLinHShenCZhangMWangXYuanMet alGut microbiota regulates postprandial GLP-1 response via ileal bile acid-TGR5 signaling. Gut Microbes (2023) 15(2):2274124. 10.1080/19490976.2023.2274124

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 35.

  ZengYWuYZhangQXiaoX. Crosstalk between glucagon-like peptide 1 and gut microbiota in metabolic diseases. mBio (2024) 15(1):e0203223. 10.1128/mbio.02032-23

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 36.

  GaoRMengXXueYMaoMLiuYTianXet alBile acids-gut microbiota crosstalk contributes to the improvement of type 2 diabetes mellitus. Front Pharmacol (2022) 13:1027212. 10.3389/fphar.2022.1027212

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 37.

  CollinsGSMoonsKGMDhimanPRileyRDBeamALVan CalsterBet alTRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. (2024) 385:q902. 10.1136/bmj-2023-078378

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 38.

  KhanSNDanishuddinKhanMWAGuarneraLAkhtarSMF. Multi-modal AI in precision medicine: integrating genomics, imaging, and EHR data for clinical insights. Front Artif Intell. (2026) 8:1743921. 10.3389/frai.2025.1743921

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 39.

  SerioVBDuPFFrullantiEPalmieriM. Editorial: refining precision medicine through AI and multi-omics integration. Front Genet (2026) 7:1851372. 10.3389/fgene.2026.1851372

  • CrossRef
  • Google Scholar
  • View reference in article

• 40.

  SchwartzMWWoodsSCPorteDJrSeeleyRJBaskinDG. Central nervous system control of food intake. Nature (2000) 404(6778):661–71. 10.1038/35007534

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 41.

  MortonGJMeekTHSchwartzMW. Neurobiology of food intake in health and disease. Nat Rev Neurosci (2014) 15(6):367–78. 10.1038/nrn3745

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 42.

  SecherAJelsingJBaqueroAFHecksher-SorensenJCowleyMADalbogeLSet alThe arcuate nucleus mediates GLP-1 receptor agonist liraglutide-dependent weight loss. J Clin Invest (2014) 124(10):4473–88. 10.1172/JCI75276

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 43.

  CampbellJEDruckerDJ. Pharmacology, physiology, and mechanisms of incretin hormone action. Cell Metab (2013) 17(6):819–37. 10.1016/j.cmet.2013.04.008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 44.

  DruckerDJ. Mechanisms of action and therapeutic application of glucagon-like Peptide-1. Cell Metab (2018) 27(4):740–56. 10.1016/j.cmet.2018.03.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 45.

  HolstJJ. The physiology of glucagon-like peptide 1. Physiol Rev (2007) 87(4):1409–39. 10.1152/physrev.00034.2006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 46.

  LutzTA. Control of energy homeostasis by amylin. Cell Mol Life Sci (2012) 69(12):1947–65. 10.1007/s00018-011-0905-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 47.

  le RouxCWSteenOLucasKJStartsevaEUnseldAHennigeAM. Glucagon and GLP-1 receptor dual agonist survodutide for obesity: a randomized phase 2 trial. Lancet Diabetes Endocrinol (2024) 12(3):162–73. 10.1016/S2213-8587(23)00356-X

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 48.

  SanyalAJBedossaPFraessdorfMNeffGWLawitzEBugianesiEet alA phase 2 randomized trial of survodutide in MASH and fibrosis. N Engl J Med (2024) 391(4):311–9. 10.1056/NEJMoa2401755

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 49.

  HarrisonSABrowneSKSuschakJJTomahSGutierrezJAYangJet alEffect of pemvidutide, a GLP-1/glucagon dual receptor agonist, on MASLD: a randomized, double-blind, placebo-controlled study. J Hepatol (2025) 82(1):7–17. 10.1016/j.jhep.2024.07.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 50.

  Pi-SunyerXAstrupAFujiokaKGreenwayFHalpernAKrempfMet alSCALE obesity and prediabetes NN8022-1839 study group. A randomized, controlled trial of 3.0 mg of liraglutide in weight management. N Engl J Med (2015) 373(1):11–22. 10.1056/NEJMoa1411892

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 51.

  DaviesMJBergenstalRBodeBKushnerRFLewinASkjøthTVet alNN8022-1922 study group. Efficacy of liraglutide for weight loss among patients with type 2 diabetes: the SCALE diabetes randomized clinical trial. JAMA. (2015) 314(7):687–99. 10.1001/jama.2015.9676

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 52.

  GarveyWTBirkenfeldALDickerDMingroneGPedersenSDSatylganovaAet alEfficacy and safety of liraglutide 3.0 mg in individuals with overweight or obesity and type 2 diabetes treated with basal insulin: the SCALE insulin randomized controlled trial. Diabetes Care (2020) 43(5):1085–93. 10.2337/dc19-1745

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 53.

  MarsoSPDanielsGHBrown-FrandsenKKristensenPMannJFNauckMAet alLEADER steering committee; LEADER trial investigators. Liraglutide and cardiovascular outcomes in type 2 diabetes. N Engl J Med (2016) 375(4):311–22. 10.1056/NEJMoa1603827

  • CrossRef
  • Google Scholar

• 54.

  le RouxCWAstrupAFujiokaKGreenwayFLauDCWVan GaalLet alSCALE obesity prediabetes NN8022-1839 study group. 3 years of liraglutide versus placebo for type 2 diabetes risk reduction and weight management in individuals with prediabetes: a randomised, double-blind trial. Lancet. (2017);389(10077):1399–409. 10.1016/S0140-6736(17)30069-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 55.

  Novo Nordisk. Wegovy® Pill Approved in the US as First Oral GLP-1 for Weight Management. Bagsværd, Denmark: Novo Nordisk A/S (2025). Available online at: https://www.novonordisk.com/content/nncorp/global/en/news-and-media/news-and-ir-materials/news-details.html?id=916472 (Accessed July 21, 2026).

  • Google Scholar

• 56.

  WhartonSLingvayIBogdanskiPDuque do ValeRJacobSKarlssonTet alOASIS 4 study group. Oral semaglutide at a dose of 25 mg in adults with overweight or obesity. N Engl J Med (2025) 393(11):1077–87. 10.1056/NEJMoa2500969

  • CrossRef
  • Google Scholar

• 57.

  WhartonSBlevinsTConneryLRosenstockJRahaSLiuRet alGZGI investigators. Daily oral GLP-1 receptor agonist orforglipron for adults with obesity. N Engl J Med (2023) 389(10):877–88. 10.1056/NEJMoa2302392

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 58.

  WhartonSAronneLJStefanskiAAlfarisNFCiudinAYokoteKet alATTAIN-1 trial investigators. Orforglipron, an oral small-molecule GLP-1 receptor agonist for obesity treatment. N Engl J Med (2025) 393(18):1796–806. 10.1056/NEJMoa2511774

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 59.

  Eli Lilly and Company. FDA Approves Lilly’s Foundayo™ (Orforglipron), the Only GLP-1 Pill for Weight Loss that Can Be Taken Any Time of Day Without Food or Water Restrictions. Indianapolis, IN: Eli Lilly and Company (2026). Available online at: https://investor.lilly.com/news-releases/news-release-details/fda-approves-lillys-foundayotm-orforglipron-only-glp-1-pill (Accessed July 22, 2026).

  • Google Scholar

• 60.

  JastreboffAMAronneLJAhmadNNWhartonSConneryLAlvesBet alSURMOUNT-1 investigators. Tirzepatide once weekly for the treatment of obesity. N Engl J Med (2022) 387(3):205–16. 10.1056/NEJMoa2206038

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 61.

  BaysHETothPAlkhouriNPullmanJFreilichBNeutelJet alWeekly subcutaneous VK2735, a GIP/GLP-1 receptor dual agonist, for weight management: phase 2, randomized, 13-Week VENTURE study. Obesity (Silver Spring) (2026) 34(3):537–49. 10.1002/oby.70106

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 62.

  ClinicalTrials.gov. VK2735 for Weight Management Phase 2 (Venture Oral Dosing). ID NCT06828055 Sponsor viking Therapeutics. Inc Bethesda, MD: U.S. National Library of Medicine (2026). Available online at: https://clinicaltrials.gov/study/NCT06828055 (Accessed July 22, 2026).

  • Google Scholar

• 63.

  ClinicalTrials.gov. VK2735 for Weight Management Type 2 Diabetes Phase 3 (VANQUISH 2). ID NCT07104383. Sponsor viking Therapeutics. Inc. Bethesda, MD: U.S. National Library of Medicine (2026). Available online at: https://clinicaltrials.gov/study/NCT07104383 (Accessed July 22, 2026).

  • Google Scholar

• 64.

  ChakravarthyMVRodriguezRHergardenAElliottMAFriasJPArgüelles-TelloFAet alEffects of CT-388, a once-weekly signaling-biased dual GLP-1/GIP receptor agonist, on weight loss and glycemic control in preclinical models and participants with obesity. Mol Metab (2026) 103:102291. 10.1016/j.molmet.2025.102291

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 65.

  ClinicalTrials.gov. A study of CT-388 in otherwise healthy overweight and Obese adults and patients with type 2 diabetes mellitus. In: ID NCT04838405. Sponsor Carmot Therapeutics. Inc. Bethesda, MD: U.S. National Library of Medicine (2026). Available online at: https://clinicaltrials.gov/study/NCT04838405 (Accessed July 25, 2026).

  • Google Scholar

• 66.

  Reuters.com. Roche obesity drug helps patients shed 22.7% of weight in mid-stage trial (2026). Available online at: https://www.reuters.com/business/healthcare-pharmaceuticals/roche-obesity-drug-helps-patients-shed-227-weight-mid-stage-trial-2026-06-05/(Accessed July 25, 2026).

  • Google Scholar

• 67.

  JastreboffAMKaplanLMFríasJPWuQDuYGurbuzSet alRetatrutide phase 2 obesity trial investigators. triple-hormone-receptor agonist retatrutide for obesity – a phase 2 trial. N Engl J Med (2023) 389(6):514–26. 10.1056/NEJMoa2301972

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 68.

  SanyalAJKaplanLMFriasJPBrouwersBWuQThomasMKet alTriple hormone receptor agonist retatrutide for metabolic dysfunction-associated steatotic liver disease: a randomized phase 2a trial. Nat Med (2024) 30(7):2037–48. 10.1038/s41591-024-03018-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 69.

  ClinicalTrials.gov. A Study of Retatrutide (LY3437943) in Participants who Have Obesity or Overweight (TRIUMPH-1). ID NCT05929066. Sponsor Eli Lilly and Company. Bethesda, MD: U.S. National Library of Medicine (2026). Available online at: https://clinicaltrials.gov/study/NCT05929066 (Accessed July 26, 2026).

  • Google Scholar

• 70.

  Eli Lilly and Company. Lilly’s triple agonist, retatrutide, delivered powerful weight loss in pivotal phase 3 obesity trial. News Release (2026) 1–6. Available online at: https://investor.lilly.com/news-releases/news-release-details/lillys-triple-agonist-retatrutide-delivered-powerful-weight-loss (Accessed July 06, 2026).

  • Google Scholar

• 71.

  ClinicalTrials.gov. The effect of retatrutide once weekly on cardiovascular outcomes and kidney outcomes in adults living with obesity (TRIUMPH-Outcomes). Sponsor Eli Lilly and Company. Bethesda, MD: U.S. National Library of Medicine (2026). Available online at: https://clinicaltrials.gov/study/NCT06383390 (Accessed July 26, 2026).

  • Google Scholar

• 72.

  NoureddinMHarrisonSALoombaRAlkhouriNChalasaniNSheikhMYet alSafety and efficacy of weekly pemvidutide versus placebo for metabolic dysfunction-associated steatohepatitis (IMPACT): 24-Week results from a multicentre, randomized, double-blind, phase 2b study. Lancet (2025) 406(10520):2644–55. 10.1016/S0140-6736(25)02114-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 73.

  Altimmune. Altimmune announces successful completion of end-of-phase 2 meeting with FDA for pemvidutide in the treatment of obesity. Altimmune Investor Relations (2026) 1–2. Available online at: https://ir.altimmune.com/news-releases/news-release-details/altimmune-announces-successful-completion-end-phase-2-meeting (Accessed July 27, 2026).

  • Google Scholar

• 74.

  BhattacharSNThamLSLiYChuaLNgSXTangYet alMazdutide reduces body weight in adults with overweight or obesity: a high-dose phase 1 trial. Diabetes Obes Metab (2025) 27(11):6460–9. 10.1111/dom.70040

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 75.

  JiLJiangHBiYLiHTianJLiuDet alGLORY-1 investigators. once-weekly mazdutide in Chinese adults with obesity or overweight. N Engl J Med (2025) 392(22):2215–25. 10.1056/NEJMoa2411528

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 76.

  GaoLJiangHCaiHTianJShiBQiuWet alGLORY-2 trial investigators. Treatment with 9-mg mazdutide for weight reduction in Chinese adults with obesity: the GLORY-2 randomized clinical trial. JAMA (2026) 336(5):377–88. 10.1001/jama.2026.8142

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 77.

  GarveyWTBlüherMOsorto ContrerasCKDaviesMJWinning LehmannEPietiläinenKHet alREDEFINE 1 Study GroupCoadministered cagrilintide and semaglutide in adults with overweight or obesity. N Engl J Med (2025) 393(7):635–47. 10.1056/NEJMoa2502081

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 78.

  DaviesMJBajajHSBroholmCEliasenAGarveyWTle RouxCWet alREDEFINE 2 study group. Cagrilintide-semaglutide in adults with overweight or obesity and type 2 diabetes. N Engl J Med (2025) 393(7):648–59. 10.1056/NEJMoa2502082

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 79.

  GasiorekAHeydornAGaberySHjerpstedJBKirkebyKKruseTet alSafety, tolerability, pharmacokinetics, and pharmacodynamics of the first-in-class GLP-1 and amylin receptor agonist, amycretin: a first-in-human, phase 1, double-blind, randomized, placebo-controlled trial. Lancet (2025) 406(10499):135–48. 10.1016/S0140-6736(25)01176-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 80.

  DahlKToubroSDeySDuque do ValeRFlintAGasiorekAet alAmycretin, a novel, unimolecular GLP-1 and amylin receptor agonist administered subcutaneously: results from a phase 1b/2a andomized controlled study. Lancet (2025) 406(10499):149–62. 10.1016/S0140-6736(25)01185-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 81.

  ClinicalTrials.gov. Once-weekly petrelintide versus placebo for obesity or overweight with Co-morbidities (ZUPREME) (2026). Available online at: https://clinicaltrials.gov/study/NCT06662539 (Accessed July 28, 2026).

  • Google Scholar

• 82.

  LauDCWErichsenLFranciscoAMSatylganovaAle RouxCWMcGowanBet alOnce-weekly cagrilintide for weight management in people with overweight and obesity: a phase 2 trial. Lancet (2021) 398(10317):2160–72. 10.1016/S0140-6736(21)01751-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 83.

  BriereDAQuHLansuKHeMMMoyersJSCoskunTet alEloralintide (LY3841136), a novel amylin receptor agonist for the treatment of obesity: from discovery to clinical proof of concept. Mol Metab (2025) 102:102271. 10.1016/j.molmet.2025.102271

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 84.

  YamauchiTBeckerNPHagemannCAHuangKCKiyosueALimSet alREDEFINE 5 study group. Efficacy and safety of co-administered cagrilintide and semaglutide versus semaglutide alone in adults with overweight or obesity with or without type 2 diabetes in Japan and Taiwan (REDEFINE 5): a multicentre, randomized, active-controlled, phase 3a trial. Lancet Diabetes Endocrinol (2026) 14(6):450–62. 10.1016/S2213-8587(25)00402-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 85.

  RosenstockJBillingsLKGajriaRGiorginoFJohansenNBKleinKRet alCagrilintide-semaglutide (CagriSema) as an add-on to basal insulin in adults with type 2 diabetes (REIMAGINE 3): a randomized, double-blind, placebo-controlled, multicentre, phase 3 study. Lancet (2026) 408(10549):38–51. 10.1016/S0140-6736(26)01022-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 86.

  ClinicalTrials.gov. A Research Study to See How Well Cagrisema Helps People with Excess Body Weight Lose Weight (REDEFINE 1). ID NCT05567796. Sponsor Novo Nordisk A/S. Bethesda, MD: U.S. National Library of Medicine (2026). Available online at: https://clinicaltrials.gov/study/NCT05567796 (Accessed July 29, 2026).

  • Google Scholar

• 87.

  NordiskN. Company announcement: cagrisema demonstrated 23% weight loss in an open-label head-to-head REDEFINE 4 trial in people with obesity, the primary endpoint was not achieved (2026). Available online at: https://ml-eu.globenewswire.com/Resource/Download/813a1be1-6fe0-4cf0-9f2e-337f8f2639e1 (Accessed July 29, 2026).

  • Google Scholar

• 88.

  HamarshehSJaberARAbu-KhaznehOAndonieCRMajadlehSZahranAet alComparative effectiveness of CagriSegma, semaglutide, cagrilintide and tirzepatide in the management of overweight and obesity: a network meta-analysis of randomized clinical trials. Endocrinol Diabetes Metab (2026) 9(4):e70248. 10.1002/edm2.70248

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 89.

  EneboLBBerthelsenKKKankamMLundMTRubinoDMSatylganovaAet alSafety, tolerability, pharmacokinetics, and pharmacodynamics of concomitant administration of multiple doses of cagrilintide with semaglutide 2·4 mg for weight management: a randomized, controlled, phase 1b trial. Lancet (2021) 397(10286):1736–48. 10.1016/S0140-6736(21)00845-X

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 90.

  NielsenMJFBeckerNPDuusHHHKirkebyKKupčováVLauenborgBWet alRenal or hepatic impairment does not affect pharmacokinetics, safety, or tolerability of subcutaneous cagrilintide. Clin Pharmacokinet (2026) 65(7):1087–99. 10.1007/s40262-026-01654-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 91.

  BhattacharSThamLSTidemann-MillerBIbrigaHQuHBriereDAet alEloralintide, a selective, long-acting amylin receptor agonist for treatment of obesity: phase 1 proof of concept. Diabetes Obes Metab (2026) 28(4):2651–60. 10.1111/dom.70439

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 92.

  BillingsLKHsiaSBaysHTidemann-MillerBO’HaganJThamLSet alEloralintide, a selective amylin receptor agonist for the treatment of obesity: a 48-week phase 2, multicentre, double-blind, randomized, placebo-controlled trial. Lancet (2025) 406(10520):2631–43. 10.1016/S0140-6736(25)02155-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 93.

  StrandeJLKurraVJeswaniROberoiRSharpstenLNeutelJet alA phase 1, randomized, double-blind, placebo-controlled single and multiple ascending dose study of AMG 133 in subjects with obesity. Metabolism (2023) 42(Suppl. l):155433. 10.1016/j.metabol.2023.155433

  • CrossRef
  • Google Scholar

• 94.

  VéniantMMLuSCAtanganLKomorowskiRStanislausSChengYet alA GIPR antagonist conjugated to GLP-1 analogues promotes weight loss with improved metabolic parameters in preclinical and phase 1 settings. Nat Metab (2024) 6(2):290–303. 10.1038/s42255-023-00966-w

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 95.

  JastreboffAMRyanDHBaysHEEbelingPRMackowskiMGPhiliposeNet alMariTide phase 2 obesity trial investigators. once-monthly maridebart cafraglutide for the treatment of obesity – a phase 2 trial. N Engl J Med (2025) 393(9):843–57. 10.1056/NEJMoa2504214

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 96.

  ClinicalTrials.gov. Dose-ranging study of RO7795081. Identifier: NCT07112872. A study to compare different doses of RO7795081 with a placebo or semaglutide in people with type 2 diabetes (2026). Available online at: https://clinicaltrials.gov/study/NCT07112872 (Accessed August 01, 2026).

  • Google Scholar

• 97.

  ClinicalTrials.gov. CT-996/RO7795081 in participants with overweight or obesity. Identifier: NCT05814107. Controlled phase 1 study evaluating the safety, tolerability, pharmacokinetics, and pharmacodynamics of single and multiple doses of CT-996 in overweight/obese participants and in patients with type 2 diabetes mellitus (2026). Available online at: https://clinicaltrial.be/en/details/264854?only_eligible=0&only_recruiting=0&per_page=100 (Accessed August 01, 2026).

  • Google Scholar

• 98.

  HaqqAMChungWKDollfusHHawsRMMartos-MorenoGÁPoitouCet alEfficacy and safety of setmelanotide, a melanocortin-4 receptor agonist, in patients with Bardet-Biedl syndrome and alström syndrome: a multicentre, andomized, double-blind, placebo-controlled, phase 3 trial with an open-label period. Lancet Diabetes Endocrinol. (2022) 10(12):859–68. 10.1016/S2213-8587(22)00277-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 99.

  HeymsfieldSBColemanLAMillerRRooksDSLaurentDPetricoulOet alEffect of bimagrumab vs placebo on body fat mass among adults with type 2 diabetes and obesity: a phase 2 randomized clinical trial. JAMA Netw Open. (2021) 4(1):e2033457. 10.1001/jamanetworkopen.2020.33457

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 100.

  LookMDunnJPKushnerRFCaoDHarrisCGibbleTHet alBody composition changes during weight reduction with tirzepatide in the SURMOUNT-1 study of adults with obesity or overweight. Diabetes Obes Metab. (2025) 27(5):2720–9. 10.1111/dom.16275

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 101.

  ZhouKYeeSWSeiserELvan LeeuwenNTavendaleRBennettAJet alVariation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin. Nat Genet (2016) 48(9):1055–9. 10.1038/ng.3632

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 102.

  AbdualkaderAMKarwiQGLopaschukGDAl BatranR. The role of branched-chain amino acids and their downstream metabolites in mediating insulin resistance. J Pharm Pharm Sci (2024) 27:13040. 10.3389/jpps.2024.13040

  • CrossRef
  • Google Scholar

• 103.

  EghbaliMAlaei-ShahmiriFHashemi-MadaniNEmamiZMostafaviLMalekMet alGlucagon-like peptide 1 receptor variants and glycemic response to liraglutide: a pharmacogenetics study in Iranian people with type 2 diabetes mellitus. Adv Ther (2024) 41(2):826–36. 10.1007/s12325-023-02761-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 104.

  RinellaMENeuschwander-TetriBASiddiquiMSAbdelmalekMFCaldwellSBarbDet alAASLD practice guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology (2023) 77(5):1797–835. 10.1097/HEP.0000000000000323

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 105.

  BuckeridgeCCobainSBaysHEMatsuokaOFukushimaYHalsteadPet alEfficacy and safety of danuglipron (PF-06882961) in adults with obesity: a randomized, placebo-controlled, dose-ranging phase 2b study. Diabetes Obes Metab (2025) 27(9):4915–26. 10.1111/dom.16534

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 106.

  Pfizer. Pfizer Provides Update on Oral GLP-1 Receptor Agonist Danuglipron. New York, NY: Pfizer Inc. (2025). Available online at: https://www.pfizer.com/news/press-release/press-release-detail/pfizer-provides-update-oral-glp-1-receptor-agonist (Accessed on August 05, 2026).

  • Google Scholar

• 107.

  TzoulisPBaldewegSE. Semaglutide for weight loss: unanswered questions. Front Endocrinol (Lausanne) (2024) 15:1382814. 10.3389/fendo.2024.1382814

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 108.

  RossiGBucciarelliLMananguiteCLGiovarelliMFiorinaP. Muscle loss and GLP-1R agonists use. Acta Diabetol (2026) 63(2):333–42. 10.1007/s00592-025-02611-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 109.

  AlissouMDemangeatTFolopeVVan ElslandeHLelandaisHBlanchemaisonJet alImpact of semaglutide on fat mass, lean mass and muscle function in patients with obesity: the SEMALEAN study. Diabetes Obes Metab (2026) 28(1):112–21. 10.1111/dom.70141

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 110.

  von HaehlingSSatoRLangerHKhanMSCoatsAJSEvansWet alMuscle loss in obesity therapy as a therapeutic target: trial design and endpoints for regulatory discussions. J Cachexia Sarcopenia Muscle (2025) 16(6):e70147. 10.1002/jcsm.70147

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 111.

  ProkopidisKDalyRMSuettaC. Weighing the risk of GLP-1 treatment in older adults: should we be concerned about sarcopenic obesity?J Nutr Health Aging (2025) 29(10):100652. 10.1016/j.jnha.2025.100652

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 112.

  SimsekHUcarA. GLP-1 receptor agonists for obesity management in older adults: a scoping review on the risk of sarcopenia and sarcopenic obesity. Curr Nutr Rep (2026) 15(1):55. 10.1007/s13668-026-00777-x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 113.

  NeelandIJLingeJBirkenfeldAL. Changes in lean body mass with glucagon-like peptide-1-based therapies and mitigation strategies. Diabetes Obes Metab (2024) 26(Suppl. 4):16–27. 10.1111/dom.15728

  • CrossRef
  • Google Scholar

• 114.

  GaviganCDonnerT. Predictors of responsiveness to GLP-1 receptor agonists in insulin-treated patients with type 2 diabetes. J Diabetes Res (2023) 2023:9972132. 10.1155/2023/9972132

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 115.

  AnwardeenNRNajaKElrayessMA. Advancements in precision medicine: multi-omics approach for tailored metformin treatment in type 2 diabetes. Front Pharmacol (2024) 15:1506767. 10.3389/fphar.2024.1506767

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 116.

  DamanhouriZAAlkreathyHMAlharbiFAAbualhamailHAhmadMS. A review of the impact of pharmacogenetics and metabolomics on the efficacy of metformin in type 2 diabetes. Int J Med Sci (2023) 20(1):142–50. 10.7150/ijms.77206

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 117.

  CaturanoANiloDNiloRSircanaMCErulEZielińskaKet alOld drug, new science: Metformin and the future of pharmaceutics. Pharmaceutics (2026) 18(1):77. 10.3390/pharmaceutics18010077

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 118.

  American Diabetes Association Professional Practice Committee for Diabetes*. 9. Pharmacologic approaches to glycemic treatment: standards of care in Diabetes-2026. Diabetes Care (2026) 49(Suppl. 1):S183–S215. 10.2337/dc26-S009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 119.

  ScheenAJ. Cardiovascular and renal effects of the combination therapy of a GLP-1 receptor agonist and an SGLT2 inhibitor in observational real-life studies. Diabetes Metab (2025) 51(1):101594. 10.1016/j.diabet.2024.101594

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 120.

  NeuenBLFletcherRAHeathLPerkovicAVaduganathanMBadveSVet alCardiovascular, kidney, and safety outcomes with GLP-1 receptor agonists alone and in combination with SGLT2 inhibitors in type 2 diabetes: a systematic review and meta-analysis. Circulation (2024) 150(22):1781–90. 10.1161/CIRCULATIONAHA.124.071689

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 121.

  NeuenBLHeerspinkHJLVartPClaggettBLFletcherRAArnottCet alEstimated lifetime cardiovascular, kidney, and mortality benefits of combination treatment with SGLT2 inhibitors, GLP-1 receptor agonists, and nonsteroidal MRA compared with conventional care in patients with type 2 diabetes and albuminuria. Circulation (2024) 149(6):450–62. 10.1161/CIRCULATIONAHA.123.067584

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 122.

  Simms-WilliamsNTrevesNYinHLuSYuOPradhanRet alEffect of combination treatment with glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors on incidence of cardiovascular and serious renal events: population based cohort study. BMJ (2024) 385:e078242. 10.1136/bmj-2023-078242

  • CrossRef
  • Google Scholar

• 123.

  European Association for the Study of the Liver (EASL); European Association for the Study of Diabetes (EASD); European Association for the Study of Obesity (EASO). EASL-EASD-EASO clinical practice guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). J Hepatol (2024) 81(3):492–542. 10.1016/j.jhep.2024.04.031

  • CrossRef
  • Google Scholar

• 124.

  PapaetisGSPicolosMKSacharidouA. Pioglitazone with SGLT2 inhibitors or GLP-1 receptor agonists in patients with type 2 diabetes and non-alcoholic fatty liver disease: could the combinations of an old friend with new players yield better outcomes?Arch Med Sci Atheroscler Dis (2025) 10:e1–e15. 10.5114/amsad/202298

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 125.

  DahlDOnishiYNorwoodPHuhRBrayRPatelHet alEffect of subcutaneous tirzepatide vs placebo added to titrated insulin glargine on glycemic control in patients with type 2 diabetes: the SURPASS-5 randomized clinical trial. JAMA (2022) 327(6):534–45. 10.1001/jama.2022.0078

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 126.

  WildingJPHBatterhamRLDaviesMVan GaalLFKandlerKKonakliKet alSTEP 1 study group. Weight regain and cardiometabolic effects after withdrawal of semaglutide: the STEP 1 trial extension. Diabetes Obes Metab (2022) 24(8):1553–64. 10.1111/dom.14725

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 127.

  HornDBLinetzkyBDaviesMJLaffinLJWangHMurphyMAet alCardiometabolic parameter change by weight regain on tirzepatide withdrawal in adults with obesity: a post hoc analysis of the SURMOUNT-4 trial. JAMA Intern Med (2026) 186(2):157–67. 10.1001/jamainternmed.2025.6112

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 128.

  BrowneSKSuschakJJTomahSGutierrezJAYangJGeorgesBet alSafety and efficacy of 24 weeks of pemvidutide in metabolic dysfunction-associated steatotic liver disease: a randomized, controlled clinical trial. JHEP Rep (2025) 7(11):101483. 10.1016/j.jhepr.2025.101483

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 129.

  VermaSBöttcherMBrownPDickerDRubinoDSbracciaPet alCagriSema reduces blood pressure in adults with overweight or obesity: REDEFINE 1. Hypertension (2026) 83(2):e26055. 10.1161/HYPERTENSIONAHA.125.26055

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 130.

  TobiasDKMerinoJAhmadAAikenCBenhamJLBodhiniDet alSecond international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine. Nat Med (2023) 29(10):2438–57. 10.1038/s41591-023-02502-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 131.

  LoombaRHartmanMLLawitzEJVuppalanchiRBoursierJBugianesiEet alSYNERGY-NASH investigators. Tirzepatide for metabolic dysfunction-associated steatohepatitis with liver fibrosis. N Engl J Med (2024) 391(4):299–310. 10.1056/NEJMoa2401943

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 132.

  NewsomePNSanyalAJEngebretsenKAKliersIØstergaardLVanniDet alSemaglutide 2.4 mg in participants with metabolic dysfunction-associated steatohepatitis: Baseline characteristics and design of the phase 3 ESSENCE trial. Aliment Pharmacol Ther (2024) 60(11-12):1525–33. 10.1111/apt.18331

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 133.

  SanyalAJNewsomePNKliersIØstergaardLHLongMTKjærMSet alESSENCE study group. Phase 3 trial of semaglutide in metabolic dysfunction-associated steatohepatitis. N Engl J Med (2025) 392(21):2089–99. 10.1056/NEJMoa2413258

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 134.

  MantovaniAMorandinRFiorioVLandoMGStefanNTilgHet alGlucagon-like Peptide-1 receptor agonists improve MASH and liver fibrosis: a meta-analysis of randomised controlled trials. Liver Int (2025) 45(9):e70256. 10.1111/liv.70256

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 135.

  VolpeSLiscoGFanelliMRacanielloDColaianniVLavarraVet alOral semaglutide improves body composition and preserves lean mass in patients with type 2 diabetes: a 26-week prospective real-life study. Front Endocrinol (Lausanne) (2023) 14:1240263. 10.3389/fendo.2023.1240263

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 136.

  BaiãoARCaiZPoulosRCRobinsonPJReddelRRZhongQet alA technical review of multi-omics data integration methods: from classical statistical to deep generative approaches. Brief Bioinform (2025) 26(4):bbaf355. 10.1093/bib/bbaf355

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 137.

  WuYXieL. AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships. Comput Struct Biotechnol J (2025) 27:265–77. 10.1016/j.csbj.2024.12.030

  • Pubmed Abstract
  • CrossRef
  • Google Scholar