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url: https://www.mdpi.com/1999-4923/16/11/1353
title: Research Progress on Peptide Drugs for Type 2 Diabetes and the Possibility of Oral Administration
scrape_date: 2025-02-28T10:41:52.146928
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[Pharmaceutics](/journal/pharmaceutics)

[Volume 16](/1999-4923/16)

[Issue 11](/1999-4923/16/11)

10.3390/pharmaceutics16111353

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Open AccessReview

#  Research Progress on Peptide Drugs for Type 2 Diabetes and the Possibility of Oral Administration 

by 

Xinxin Yang

![](/bundles/mdpisciprofileslink/img/unknown-user.png)Xinxin Yang

[ SciProfiles ](https://sciprofiles.com/profile/author/NUVoQnhIZkt4ZEtzdmhaQUFyQ2NIOU9UbjE1eGZjUkFWTlU3Z2lwTkxFND0=?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[ Scilit ](https://scilit.com/scholars?q=Xinxin%20Yang)[ Preprints.org ](https://www.preprints.org/search?search1=Xinxin%20Yang&field1=authors)[ Google Scholar ](https://scholar.google.com/scholar?q=Xinxin%20Yang)

1,†[ __](/cdn-cgi/l/email-protection#fbd498959fd6989c92d497d49e969a9297d68b89948f9e988f929495d8cbcbcac3ccc2caccca9ecbcacbcac8c2ca9aca9acb98ca9acacfceccca98ca9fcb98ceccca9acacc),

Ruiting Lin

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1,†[ __](/cdn-cgi/l/email-protection#250a464b410846424c0a490a4048444c490855574a514046514c4a4b0615151514161715151517151415171510151715151517154412171114111311121446101410141112101410431446101210131112144610141046),

Changzhuo Feng

![](/bundles/mdpisciprofileslink/img/unknown-user.png)Changzhuo Feng

[ SciProfiles ](https://sciprofiles.com/profile/author/aXYrZVdHdFdVTy9TVDc4WHUxNTBLTG9yRlBldkZRRkZNeVc2aEd4S3M2QT0=?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[ Scilit ](https://scilit.com/scholars?q=Changzhuo%20Feng)[ Preprints.org ](https://www.preprints.org/search?search1=Changzhuo%20Feng&field1=authors)[ Google Scholar ](https://scholar.google.com/scholar?q=Changzhuo%20Feng)

2[ __](/cdn-cgi/l/email-protection#7b5418151f56181c125417541e161a1217560b09140f1e180f121415584b4b4b4848494b4b4b494b4a4b494b4e4b494b4b4b494b484c494e4a4e4a4f4c4e4a4e1d4a184e4c4e4d4f4c4a184e4a4e18),

Qiyuan Kang

![](/bundles/mdpisciprofileslink/img/unknown-user.png)Qiyuan Kang

[ SciProfiles ](https://sciprofiles.com/profile/author/WjdKVkxCblBxbTFhSmpiMWV5SC9acUkrUWh3cTB1SFowUG00aGE4WTRaOD0=?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[ Scilit ](https://scilit.com/scholars?q=Qiyuan%20Kang)[ Preprints.org ](https://www.preprints.org/search?search1=Qiyuan%20Kang&field1=authors)[ Google Scholar ](https://scholar.google.com/scholar?q=Qiyuan%20Kang)

2[ __](/cdn-cgi/l/email-protection#220d414c460f41454b0d4e0d474f434b4e0f52504d564741564b4d4c01121212131110121012121210121412101211121212111510171317131615171317441341171517141615134117131741),

Peng Yu

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Yongzhi Deng

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Ye Jin

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1

School of Pharmacy, Changchun University of Chinese Medicine, Changchun 130117, China

2

College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130117, China

*

Authors to whom correspondence should be addressed. 

†

These authors contributed equally to this work.

_Pharmaceutics_ **2024** , _16_(11), 1353; <https://doi.org/10.3390/pharmaceutics16111353>

Submission received: 19 August 2024 / Revised: 10 October 2024 / Accepted: 21 October 2024 / Published: 23 October 2024

(This article belongs to the Special Issue [Advances in Oral Administration](        
    /journal/pharmaceutics/special_issues/T71C4OMV46
))  


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[ ](https://pub.mdpi-res.com/pharmaceutics/pharmaceutics-16-01353/article_deploy/html/images/pharmaceutics-16-01353-g001.png?1729687606 "
                        <strong>Figure 1</strong><br/>
                                                    <p>The mechanism/target of the combination of peptide drugs and nano-preparations on diabetic complications.</p>
                                                ") [ ](https://pub.mdpi-res.com/pharmaceutics/pharmaceutics-16-01353/article_deploy/html/images/pharmaceutics-16-01353-g002.png?1729687608 "
                        <strong>Figure 2</strong><br/>
                                                    <p>Glucose stimulates GLP-1 secretion from L-cells: Glucose closes ATP-sensitive K<sup>+</sup> <sub>ATP</sub> channels and opens Ca<sup>2+</sup> channel \(VDCC\) to induce membrane depolarization, and Ca<sup>2+</sup> inward flow triggers vesicular cytotoxicity and GLP-1 secretion.</p>
                                                ") [ ](https://pub.mdpi-res.com/pharmaceutics/pharmaceutics-16-01353/article_deploy/html/images/pharmaceutics-16-01353-g003.png?1729687610 "
                        <strong>Figure 3</strong><br/>
                                                    <p>Schematic representation of the transport mechanisms.</p>
                                                ")

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## Abstract

**:**

Diabetes is a global disease that can lead to a range of complications. Currently, the treatment of type 2 diabetes focuses on oral hypoglycemic drugs and insulin analogues. Studies have shown that drugs such as oral metformin are useful in the treatment of diabetes but can limit the liver's ability to release sugar. The development of glucose-lowering peptides has provided new options for the treatment of type 2 diabetes. Peptide drugs have low oral utilization due to their easy degradation, short half-life, and difficulty passing through the intestinal mucosa. Therefore, improving the oral utilization of peptide drugs remains an urgent problem. This paper reviews the research progress of peptide drugs in the treatment of diabetes mellitus and proposes that different types of nano-formulation carriers, such as liposomes, self-emulsifying drug delivery systems, and polymer particles, should be combined with peptide drugs for oral administration to improve their absorption in the gastrointestinal tract.

Keywords: 

[oral delivery](/search?q=oral+delivery); [nano-formulations](/search?q=nano-formulations); [peptide drugs](/search?q=peptide+drugs); [type 2 diabetes mellitus](/search?q=type+2+diabetes+mellitus)




##  1\. Introduction

Diabetes is one of the major causes of long-term ill health and a growing global disease. Often leading to severe metabolic disease and a range of serious complications, diabetes is a global disease burden [1]. Diabetes mellitus is classified as type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). T1DM is characterized by insufficient insulin secretion, which renders pancreatic islet cells incompetent against autoimmune [2]. T2DM is the most prevalent type, with a longer disease course, and is characterized by hyperglycemia due to a decrease in insulin secretion by the pancreas [3]. T2DM also causes complications such as cardiovascular disease and type 2 diabetic nephropathy, as well as an increased risk of hypertension [4,5]. T2DM is characterized by relative insulin deficiency or insulin resistance, in which the muscles and organs, such as the liver, are not sensitive enough to insulin so that blood glucose cannot be used effectively and remains at a high level [6]. In recent years, treatment for T2DM has focused on drugs that increase insulin secretion, sensitize, and reduce hepatic glucose production. Other non-modifiable risk factors include family history and ethnicity [7]. Obesity, disrupted sleep cycles or abnormal circadian rhythms, and a lack of sufficient regular physical activity may have compounding effects [8]. While current treatment strategies can help people with diabetes improve their symptoms, new and effective treatments are needed.

In recent years, peptide drugs synthesized using modern biotechnology have become one of the hotspots of drug research and development and are now widely used in the prevention, diagnosis, and treatment of tumors, hepatitis, diabetes, AIDS, and other diseases due to their wide indications, high safety, and remarkable efficacy, and have a broad development prospect. Some peptide drugs use less dosage, are more selective, have better specificity, and have fewer side effects than small-molecule drugs. Peptide therapeutics are usually administered by injection, but injections are invasive and reduce patient compliance. In addition, injectables increase the complexity and cost of manufacturing and storage. There is a growing interest in the oral delivery of peptides, a technology that could provide a safe and convenient alternative to peptide drug delivery [9]. However, direct delivery of peptides is not feasible. Oral peptides must overcome physiological barriers such as low solubility, permeability, and early degradation to achieve efficient and sustained drug delivery [10].

In exploring technological approaches to achieve efficient oral peptide delivery, drugs combined with nano-formulations are particularly promising due to their wide diversity, good biocompatibility, and ease of modification and thus functionalization [11]. As shown in Figure 1,this paper reviews the current research progress, therapeutic scope, and potential applications of peptide drugs for the treatment of diabetes (Table 1) and lists oral formulations that can be loaded with peptides. It is hoped that combining the two will provide new ideas for the oral delivery of peptide drugs for the treatment of diabetes.

##  2\. Glucagon-like Peptide 1 (GLP-1)

GLP-1 is a peptide derived from the glucagonogen (Gcg) sequence that shares the same precursor molecule as glucagon but has an opposite effect on blood glucose. GLP-1 is mainly secreted by intestinal L-cells and enters the peripheral circulation after entering the bloodstream, while glucagon preprogenitor neurons in the solitary tract nucleus of the brainstem also secrete GLP-1 and play a role in the central nervous system (CNS) [20,21]. Prohormone convertase 1 (PC1) cleaves Gcg in cells, thereby expressing GLP-1 [22,23,24]. GLP-1 promotes insulin secretion after binding to and activating the glucagon-like peptide-1 receptor (GLP-1R) on the surface of β-cells, and glucose dose-dependently increases the concentration of GLP-1 secreted by L cells. GLP-1 also inhibits β-cell apoptosis and induces β-cell proliferation by stimulating the expression of insulin receptor substrate 2, including mechanisms such as acute proinsulin action and stimulation of second messenger cAMP by activation of the response element binding protein (CREB) (Figure 2) [25,26]. Under conditions of insulin resistance and cellular stress, α-cells can regulate insulin secretion by producing GLP-1, thereby compensating for the increased functional demands of β-cells [27]. In addition, GLP-1 can stimulate growth inhibitor secretion and activate the protein kinase A system (PKA) to inhibit α-cell glucagon secretion [28]. Because GLP-1 is cleaved in vivo by dipeptidyl peptidase-4 (DPP-4) (EC number: 3.4.14.5) and the circulating half-life of GLP-1 in diabetic patients is only 1.5 to 2 min [29], GLP-1 receptor agonists (GLP-1RAs) for the treatment of T2DM are widely developed and used.

GLP-1R is expressed in the heart, brain, kidneys, stomach, and fat cells and ameliorates complications caused by diabetes. GLP-1R is mainly found in cardiac endothelium, coronary arteries, and smooth muscle cells [21,30]. GLP-1 is cardioprotective through the cAMP signaling pathway and DPP-4 degradation of GLP-1 (7–36 amide) to generate GLP-1 (9–36 amide) and its smaller degradation products. Thus, GLP-1RAs increase cardiomyocyte survival and improve endothelial dysfunction [31]. GLP-1 can improve blood pressure by stimulating urinary sodium excretion through epac2-dependent stimulation of atrial natriuretic peptide secretion [32]. Brain GLP-1R activation feeds through PKA/MAPK-induced AMPK inhibition. Peripheral GLP-1 is transmitted via the vagus nerve to the central nervous system to generate electrical signals, and brain-derived GLP-1 is released locally as a neurotransmitter, which together mediate inhibition of gastric motility and reduce the rate of glucose absorption [33,34]. In addition, GLP-1 inhibits gastric emptying through adrenergic signaling and promotes brown remodeling of white fat in a SIRT1-dependent manner, reducing body weight in T2DM patients [35,36,37]. GLP-1 inhibits angiotensin II and renin and reduces the risk of proteinuria in T2DM patients [38]. Although the exact details of renal regulation by GLP-1 are unknown, it has been found that GLP-1 ameliorates renal injury, fibrosis, and lipid accumulation in diabetic nephropathy rats via the Sirt1/AMPK/PGC1α pathway [39]. In addition, the researchers isolated a GLP-1RAs called Exendin-4 from lizard venom and optimized the structure of GLP-1 and Exendin-4 for the treatment of T2DM [40].

GLP-1 stimulates β cells to secrete insulin. GLP-1-induced elevation of cAMP leads to PKA activation and enhanced signaling via exchange proteins directly activated by cAMP (Epac). cAMP activation of PKA increases the sensitivity of the K+ATP channel to ATP, leading to the closure of the K+ATP channel, depolarization of the cell membrane, and opening of the VDCC channel. Subsequent Ca2+ influx promotes cytosolic action of insulin granules and acute secretion of insulin into the circulation. Simultaneously, GLP-1-activated PKA inhibits voltage-gated Kv channels, prevents membrane repolarization, and promotes Ca2+ influx by prolonging the opening of the VDCC channel. Epac stimulates the release of Ca2+ from the endoplasmic reticulum (ER), increasing insulin secretion by increasing the intracellular pool of Ca2+. cAMP activates Epac 2 by direct binding. Under high glucose conditions, the flow of Ca2+ through the VDCC channel into b-cells enhances Ca2+ influx, and Epac 2 opens ryanodine receptor Ca2+ channels in the ER, further increasing intracellular Ca2+ levels and enhancing insulin secretion.

####  2.1. Exenatide

In April 2005, Exenatide was approved by the US FDA as the first GLP-1RAs for the treatment of T2DM. Exenatide has 50% homology to human GLP-1 and similar affinity for the GLP-1 receptor. It exhibits glucose-regulatory activity similar to that of GLP-1, including enhanced glucose dependence of β-cells in response to insulin secretion, and is accompanied by progressive weight loss. Exenatide reduces the concentration of glucagon in the blood during periods of hyperglycemia. In patients with T2DM, glucagon levels are abnormally high during hyperglycemia, and lowering blood glucagon concentrations during hyperglycemia leads to reduced hepatic glucose output and reduced insulin requirements [41]. In addition, exenatide may also reduce renal dysfunction to a certain extent in patients with diabetic nephropathy, with exenatide significantly reducing proteinuria levels in 26.2% of the patients [42]. Exenatide mediates attenuation of diabetic nephropathy by SIRT1-dependent down-regulation of thioredoxin-interacting protein H3K9ac acetylation and reduces the recruitment of spliced X-box binding protein 1 to the Txnip promoter [43]. Weekly doses of exenatide have also been shown to modestly reduce body weight and systolic blood pressure [44]. Exenatide administered at a dose of 2 mg once a week (QW) showed greater improvements in glycemic control compared to the 10 µg dose taken twice a day (BID), with no increased risk of hypoglycemia and a corresponding reduction in body weight [45]. Compared with other hypoglycemic agents, exenatide significantly improved carotid intima-media thickening, which is associated with cardiovascular probability, and its cardioprotective effects were demonstrated by the inhibition of atrial fibrillation and reduced susceptibility to atrial fibrillation in rats [46,47]. Gastrointestinal adverse events (AEs), including nausea, vomiting, and diarrhea, are common with exenatide during treatment. In 2021, Bydureon, whose main ingredient is exenatide, became the first FDA-approved microsphere formulation for the treatment of T2DM, attenuating gastrointestinal AE. However, subcutaneous nodules can occur at the injection site when the microspheres produce an inflammatory foreign-body reaction in patients treated with exenatide QW [48]. Injection site reactions were more common in the exenatide QW group than in the exenatide BID or non-GLP-1RAs groups. In addition, after the initial release of surface-bound exenatide, the encapsulated peptide was released from the microspheres in approximately two weeks’ time [49].

####  2.2. Loxenatide

Loxenatide, also known as polyethylene glycol loxenatide (PEX-168). It is based on the structure of exenatide and modifies the drug molecule by adding polyethylene glycol, thus extending the half-life of the drug [40]. Clinical trials showed that loxenatide monotherapy significantly improved glycemic control in patients with T2DM, with a safety profile similar to other GLP-RA [14]. A short-term controlled trial with metformin showed that the addition of loxenatide to metformin was superior to the addition of glucagon in improving glycemic control and assessing glycemic variability with continuous glucose monitoring in patients with T2DM [14]. Weight loss was more pronounced in patients given loxenatide subcutaneously once a week compared to metformin, and loxenatide reduced body weight by no more than 20% in purely obese mice irrespective of dose, with some risk of hypoglycemia [50,51]. T2DM can lead to impaired endothelium-dependent vasodilatation and decreased NO expression, manifesting as cardiovascular and endothelial dysfunction. The T2DM patient population with loxenatide showed significant improvements in glycemic lowering indices and inflammation-related indices, as well as significant changes in vascular endothelial cell function-related indices [52]. A controlled trial also demonstrated that loxenatide improved endothelial cell function in patients with T2DM [53]. Loxenatide reduces high glucose-induced ROS production in a concentration-dependent mechanism and inhibits mitochondria-dependent apoptosis of endothelial progenitor cells (EPCs) and mitochondrial membrane depolarization via activation of the sirtuin 3/Foxo3 signaling pathway and enhances mitochondrial respiratory function [54].

####  2.3. Lixisenatide

Lixisenatide, an Exendin-4 analogue, was approved in the EMA in 2012 for the treatment of T2DM. In preclinical studies, lixisenatide demonstrated high affinity and selectivity for the GLP-1 receptor, binding approximately four times that of natural GLP-1 [55]. Clinical studies have evaluated the efficacy of lixisenatide in combination with oral hypoglycemic agents in the treatment of patients with T2DM, and changes in glycated hemoglobin (HbA1c) demonstrated that the combination of lixisenatide with oral hypoglycemic agents significantly improves the efficacy and safety profile [56]. In an analysis of the results of a phase 3 clinical trial, it was reported that the majority of patients receiving a fixed ratio of glycemic insulin/lixisenatide were no longer experiencing gastrointestinal AE at eight weeks, with a greater potential to achieve target blood glucose compared to those receiving lixisenatide alone [57]. Lixisenatide stimulates insulin secretion when blood glucose is elevated but not when blood glucose is normal, thereby circumventing the risk of hypoglycemia and glucose-dependently inhibiting glucagon secretion [58,59]. The major differences from other GLP-1 RAs are that lixisenatide significantly reduces postprandial glucose immediately after injection and that the delay in gastric emptying is more pronounced with lixisenatide. The increase in intragastric glucose residence time after lixisenatide treatment was associated with a decrease in the systemic oral glucose rate and a decrease in the area under the glycemic curve, and this effect was more pronounced as the duration of dosing increased [60]. Lixisenatide also improves endothelial cell function and inflammation-related markers in T2DM patients [61]. Lixisenatide carries out anti-inflammatory effects by inhibiting the activation of NF-kB signaling in HUVECs cells and inhibiting the adhesion of monocytes and HUVECs [62]. and promotes mitochondrial biogenesis in HUVECs by inducing phosphorylation at the Ser133 site of CREB [63]. Lixisenatide is filtered in the kidney and subsequently degraded in the renal tubules [64]. Lixisenatide dose adjustment is not required in patients with T2DM with mild or moderate renal impairment [65].

####  2.4. Liraglutide

Liraglutide is based on the natural GLP-1 structure and is 97% homologous to natural GLP-1 [40], chemically modified to extend its half-life to 13 h after subcutaneous injection and to resist DPP-4 degradation [66]. Liraglutide was approved by the FDA in 2010 to improve glycemic control in adult patients with T2DM, and liraglutide is the only GLP-RAs approved for weight loss [67]. Clinical Trial Demonstrates Effectiveness of liraglutide as Monotherapy and Add-on to First-Line Therapy [49]. Liraglutide inhibits oxidative stress and reduces cardiovascular disease while controlling blood glucose [68]. Liraglutide elevated SIRT1 expression, which increased Parkin expression, leading to mitochondrial autophagy activation. Protective mitochondrial autophagy reverses cytosolic adenosine 5′-triphosphate production, reduces cellular oxidative stress and balances redox reactions, maintains mitochondrial homeostasis, and repairs infarcted hearts [69]. Liraglutide inhibits oxidative stress and apoptosis by increasing the concentration of anti-apoptotic proteins and decreasing the levels of pro-apoptotic proteins. Liraglutide treatment is confirmed to reverse microvascular endothelial cell injury [70]. Whereas liraglutide alone reduced pro-inflammatory and pro-atherosclerotic chemokines [71], liraglutide effectively counteracts high glucose-induced dysfunction of HUVECs by inhibiting PINK1/Parkin-dependent mitochondrial autophagy [72]. Together, they validate the vaso-protective effects of liraglutide. In both T2DM and non-T2DM patients, liraglutide reduces the onset and progression of renal disease in addition to conventional therapy [73,74,75]. Liraglutide protects renal thylakoid cells from hyperglycemia-mediated mitochondrial apoptosis by upregulating SIRT3 expression and activating the ERK-Yap signaling pathway [76]. Liraglutide also inhibits renal cell apoptosis by increasing SIRT1 expression and down-regulating TXNIP expression [72,77]. In addition, liraglutide reduces higher circulating plasma concentrations of dihydroceramide and ceramide, which are positively associated with insulin resistance and hepatic steatosis, in patients with T2DM [13].

####  2.5. Beinaglutide

Beinaglutide is an amine-free recombinant human GLP-1 (7–36) acid with 100% homology to human GLP-1. It is approved for the treatment of T2DM patients in China [78]. Beinaglutide shows significant weight loss benefits and effective glycemic control in the treatment of T2DM [79] and is more effective than metformin in reducing body weight and fat mass [80,81]. Although safe and effective in reducing visceral fat and body weight in obese T2DM patients, the dose of beinaglutide should be tailored to the individual [82]. Beinaglutide combats high-fat food-induced obesity by targeting the composition of major lipids in adipose tissue and the expression of lipid metabolism genes, e.g., by increasing Acrp30 levels and Akt phosphorylation in adipocytes, thereby ameliorating the associated fatty liver and dyslipidaemia [83]. In addition, serpin E1, leptin, c-reactive protein (CRP), and tumor necrosis factor-alpha (TNF-alpha) were significantly decreased in T2DM patients using beinaglutide, demonstrating that beinaglutide also has an anti-inflammatory effect in T2DM patients [84]. In a model of non-alcoholic steatohepatitis, long-term use of beinaglutide reduced liver weight and hepatic steatosis and improved insulin sensitivity, and increased Sirt1 gene expression levels improved hepatic insulin resistance [78].

####  2.6. Dulaglutide

Dulaglutide is produced by linking human immunoglobulin IgG-4 heavy chain to GLP-1 modification, which significantly increases its half-life [85]. In 2014, Dulaglutide became the first fragment crystallizable fusion protein to receive FDA approval for T2DM treatment [49]. Dulaglutide results in clinically dose-related reductions in HbA1c and body weight. In the six completed AWARD results, the majority of dulaglutide patients had HbA1c targets <7.0% (53.0 mmol/mol) and ≤6.5% (47.5 mmol/mol), which were superior to the active comparators (Exenatide, Glycine Insulin, Metformin, and Sitagliptin), with the most common adverse events being gastrointestinal AE, the lower rates of hypoglycemia [86,87,88]. In mouse control experiments, the expression levels of pancreatic beta-cell-related genes, insulin content, and insulin secretion were found to be significantly elevated in the dulaglutide group, and oxidative and endoplasmic reticulum stress, inflammation, fibrosis, and apoptosis were inhibited after treatment. Demonstration of a long-term protective effect of dulaglutide on β-cells [89]. In addition, dulaglutide can inhibit inflammation and oxidative stress by inhibiting the NF-κB signaling pathway and TGF-β/Smad2 signaling pathway, M1 macrophage polarization, and mitochondrial ROS release [90,91,92]. T2DM patients treated with dulaglutide improved systolic and pulse pressure, although one-third was due to weight loss effects [93]. However, the risk-related indices of urinary albumin/creatinine and HbA1c were reduced by 16.7% and 25.4%, respectively, in patients with adverse cardiovascular events with dulaglutide compared with placebo [94,95]. Dulaglutide may be considered for use in middle-aged and elderly T2DM patients with cardiovascular disease or cardiovascular risk factors [96]. Dulaglutide inhibits high glucose-induced activation of the NLRP3 inflammasome in HUVECs cells by increasing SIRT1 expression and suppressing NLRP3, ASC, and P10 expression. It also attenuated high glucose-induced cellular mitochondrial division in EPCs and significantly ameliorated vascular endothelial tissue damage and inflammation [97,98].

####  2.7. Semaglutide

Semaglutide is the first GLP-1RAs to have an oral dosage form. Semaglutide is prepared as a tablet in combination with the absorption enhancer sodium N-[8(2-hydroxybenzoyl)amino]octanoate (SNAC) to improve glycemic control in adults with T2DM [99,100]. SNAC binds to semaglutide, elevates local pH to decrease the efficacy of major digestive enzymes, increases the lipophilicity of semaglutide to promote gastric epithelial cell uptake, and facilitates the transit of semaglutide and its entry into the systemic circulation. When semaglutide and SNAC reach the bloodstream, the two molecules readily dissociate, allowing the oral formulation of semaglutide to interact with the body in the same manner as subcutaneous semaglutide [101]. However, the oral formulation of semaglutide has lower bioavailability compared to the subcutaneous formulation. Semaglutide is absorbed in the stomach with a rapid and dose-dependent increase in plasma concentrations, reaching peak concentrations within 15–35 min [102]. Semaglutide oral formulations at doses ≤ 10 mg are significantly less effective than subcutaneously administered forms, and therefore higher doses of semaglutide oral formulations need to be given to achieve the same clinical outcomes as subcutaneous formulations. Because SNAC protects semaglutide from pH-dependent degradation and because eating can lead to an increase in gastric pH, patients should fast prior to dosing and avoid eating for at least 30 min after dosing for optimal results [103,104].

Semaglutide is a favorable option for the treatment of T2DM, both subcutaneously and orally, and several clinical trials have demonstrated the safety and efficacy of semaglutide in the treatment of T2DM [105,106,107,108]. Semaglutide has shown significant efficacy in reducing HbA1c and body weight compared to other GLP-1RAs, although semaglutide can cause mild and transient gastrointestinal disturbances that decrease with continuation of treatment and increase the risk of cholelithiasis [109,110,111]. In controlled trials, 85% of participants taking oral semaglutide lost at least 5% of their body weight and had sustained improvements in obesity-related abnormalities such as hyperglycemia, hypertension, elevated lipids, and high-sensitivity c-reactive protein [112]. And semaglutide has been shown to be effective, safe, and well tolerated in T2DM patients with overweight and atherosclerotic cardiovascular disease [113]. Treatment with semaglutide Significantly Reduces Symptoms and Physical Limitations Associated with Obesity in Heart Failure Patients, Improves Exercise Function, and Reduces Weight [114,115]. Semaglutide ameliorates mitochondrial function and cardiac injury by modulating the PI3K-AKT signaling pathway [116]. Semaglutide avoids oxidative stress and apoptosis in the heart of diabetic mice by activating the Sirt1/AMPK pathway [117]. In a mouse model of HFpEF induced by obesity and T2DM, semaglutide improved cardiometabolic silhouette, cardiac structure, and cardiac function compared to weight loss induced by paired feeding. Transcriptomic and proteomic analyses showed that semaglutide improved left ventricular cytoskeletal function and endothelial function and restored protective immune responses in visceral adipose tissue. Exceeding the weight loss effect of paired feeding [118,119]. Semaglutide is Equally Effective in Glycemic Control and Weight in Patients with Chronic Kidney Disease [120]. Semaglutide directly ameliorates oxidative stress and inflammation-related metabolites such as NAD+ and adenosine in the kidneys of obese mice [121]. In a mouse model of hypertension-accelerated diabetic nephropathy, semaglutide significantly reduced hyperglycemia, hypertension, and proteinuria while significantly improving the severity of glomerulosclerosis and urine/kidney injury molecule-1 (KIM-1) levels [122].

##  3\. Tirzepatide

2022 FDA approval of dual agonists for GLP-1R and glucose-dependent insulinotropic polypeptide receptor (GIPR) tirzepatide to improve glycemic control in adults with T2DM as an adjunct to diet and exercise [123]. Bringing renewed attention to the potential of glucose-dependent insulinotropic polypeptide (GIP) in the treatment of T2DM. GIP is a polypeptide consisting of 42 amino acids, which, along with GLP-1, belongs to the same group of enteric insulin in the human body and is secreted by the K cells of the intestinal mucosa of the duodenum and jejunum in the small intestine. GIP is responsible for most of the body’s enteric insulin action; a normal human postprandial GIP level is about 4 times that of GLP-1, which is the most important enteric insulin [124]. Although both GIPR and GLP-1 R are present in β-cells, impaired β-cell function in patients with T2DM affects the regulatory functions of GIP and GLP-1 [125]. In T2DM, GIP has a limited ability to stimulate insulin secretion during hyperglycemia and does not significantly reduce blood glucose concentrations, whereas GLP-1 still significantly regulates blood glucose concentrations in patients with T2DM [126]. Unlike GLP-1, GIP has both glucagon and insulinotropic effects in a glucose-dependent manner, stimulating glucagon secretion under hypoglycemic conditions and insulin secretion under hyperglycemic conditions [127]. GIPRs were abundantly distributed in adipose tissue, and most of the distribution regions of GIPRs and GLP-1Rs in the CNS did not overlap. GIP is involved in the metabolism of adipose tissue carbohydrates and lipids, and it increases lipid storage in white adipose tissue and decreases ectopic storage of fat in the muscle [128]. In contrast, GLP-1 indirectly promotes lipolysis, works with GIP to maintain healthy adipocytes, and increases adipocyte production and secretion of lipocalin to improve insulin sensitivity [129]. GLP-1 synergizes with GIP to promote metabolic homeostasis, prevent hyperglycemia and hypoglycemia, attenuate dyslipidemia, and reduce the risk of cardiovascular disease in patients with T2DM and obesity.

Tirzepatide was injected once weekly, and the pharmacokinetics of tirzepatide were dose-proportional over the dose range studied [130]. In all SURPASS trials, tirzepatide showed a dose-dependent decrease in HbA 1c regardless of background glucose-lowering therapy [19]. The glycemic efficacy of tirzepatide in the treatment of T2DM was found to be due to simultaneous improvements in key components of diabetes pathophysiology, including beta-cell function, insulin sensitivity, and glucagon secretion, in Phase 1 clinical trials [131]. Tirzepatide synergistically activates both GIPR and GLP-1R in T2DM patients. In in vitro studies, tirzepatide elicited significantly higher cAMP responses in human pancreatic beta cell lines than those observed with GLP-1 or GIP alone, and in isolated islets, tirzepatide overcame the effects of GLP-1R to induce a robust increase in glucagon secretion [130,132]. However, the relative activity of tirzepatide at GLP-1 R and GIPR may vary in subjects with and without diabetes and with the degree of hyperglycemia. In SURPASS-2, tirzepatide was associated with dose-dependent weight loss from baseline, with up to 65%, 48%, and 31% of subjects treated with tirzepatide 15 mg/week losing ≥10%, ≥15%, and ≥20% of their body weight, respectively [133]. Tirzepatide provided more significant weight loss compared to selective GLP-1 RA treatment. Preclinical studies demonstrated greater weight loss in obese mice administered tirzepatide than in mice administered selective GLP-1 RA. GIP enhances GLP-1-induced food intake and weight loss by augmenting melanocortin expression and neuronal activation in different neuronal populations in the hypothalamus; thus, co-activation of GIPR and GLP-1R in the brain enhances weight loss efficacy [134,135]. T Therapeutic efficacy and side effects of tirzepatide are similar to those of selective GLP-1RA, with common AEs being gastrointestinal reactions. Tirzepatide effectively slowed the decline in glomerular filtration rate, lowered the urinary albumin/creatinine ratio, and reduced circulating levels of several biomarkers associated with cardiovascular risk, improving cardiovascular risk [136,137].

##  4\. Oral Medications for Type 2 Diabetes

Metformin is considered the best initial therapeutic agent for T2DM. Regarding the mechanism of action of this drug, it may be related to the reduction in hepatic glucose production [125]. In randomized trials of diabetes, metformin was most effective in young, obese subjects. The results showed a decreasing risk of diabetes and long-term weight loss in the subjects [126]. Glipizide, a second-generation sulfonylurea, is a gastrointestinal therapeutic system (GITS) extended-release formulation. Glipizide GITS provides more stable plasma drug concentrations than immediate-release formulations, and the once-daily dosing regimen optimizes patient compliance. In patients with T2DM, the effect of glipizide GITS on fasting blood glucose levels may be greater. Glipizide GITS was shown to reduce the incidence of hypoglycemic symptoms in a 3-year model of type 2 diabetes treatment [123]. Oral repaglinide is a rapid-acting insulinotropic agent that reduces postprandial glucose by targeting early insulin release, and lowering postprandial glucose is considered to be an important factor in the reduction in long-term cardiovascular complications of diabetes. It has a unique binding site on the beta cell membrane, and in in vitro and in vivo studies, alogliptin has been shown to be more potent and faster-acting in terms of insulinotropic effects, but may cause some weight gain [122]. DPP-4 inhibitors are widely used in the treatment of T2DM, which results in intra-insulin release when blood glucose is elevated. After evaluating the patients, the results suggest that DPP-4 inhibitors promote vascular endothelial cell regeneration, but when this effect occurs in the glomerulus, the proliferation of glomerular endothelial cells leads to tma-like lesions, which cause increased proteinuria and a rapid decline in renal function [124]. Miglitol is a second-generation alpha-glucosidase inhibitor that is generally well tolerated and does not cause weight gain or hypoglycemia with monotherapy. The drug is systemically absorbed but not metabolized and is rapidly excreted through the kidneys. Most of the adverse reactions associated with miglitol therapy involve gastrointestinal AE [127]. Pioglitazone is a thiazolidinedione drug that selectively agonizes the peroxisome growth factor-activated receptor PPARγ, which enhances insulin sensitivity in peripheral tissues and the liver and reduces insulin resistance. Pioglitazone significantly improved beta-cell function, liver function, and blood glucose indexes and slowed down the process of atherosclerosis. However, patients taking pioglitazone medication at high doses and for long periods of time may have an increased risk of bladder cancer [138]. Dagliflozin is a sodium-glucose cotransporter protein 2 (SGLT2) inhibitor that lowers blood glucose by inhibiting SGLT-2, reducing glucose reabsorption, and excreting sugar directly from the urine. In addition, dagliflozin can also reduce sodium reabsorption so that plasma volume decreases, achieving an antihypertensive effect and cardiovascular protection. And it reduces blood uric acid and proteinuria, relieves glomerular pressure, restores glomerular filtration rate, and controls the development of diabetic nephropathy [139] (Table 2).

Metformin is currently the first-line hypoglycemic agent of choice for the treatment of diabetes, either alone or as a base agent in various combination regimens, and almost half of patients with T2DM require additional hypoglycemic agents within 1 year of diagnosis. When considering second-line therapy, the central decisions facing providers and patients include safety, efficacy, tolerability, ease of administration, and cost. Several current second-line therapies, including sulfonylureas, glinides, DPP-4 inhibitors, alpha-glucosidase inhibitors, thiazolidinediones, SGLT-2 inhibitors, insulin, and GLP-1RA. Sulfonylureas, thiazolidinediones, and insulin can significantly improve glycemic control, but all three can cause weight gain, and sulfonylureas and insulin analogs can also cause a high risk of hypoglycemia. DPP-4 inhibitors, on the other hand, are less risky but lag behind insulin, sulfonylureas, and thiazolidinediones in glycemic control and are costly. Similar to DPP-4 inhibitors, SGLT-2 inhibitors have been associated with a low risk of hypoglycemia and high costs. However, unlike weight-neutral DPP-4 inhibitors, SGLT-2 inhibitors result in weight loss and lower blood pressure, and some members of this class have specific cardiovascular indications.

The advantage of GLP-1RA is that it has glucose-lowering efficacy similar to that of insulin but without the side effect of weight gain that accompanies insulin therapy. GLP-1RA also provides weight loss and a lower risk of hypoglycemia and has cardiovascular protective properties. Oral small-molecule drugs for GLP-1RA are currently under development and may further alter the use of glucose-lowering drugs.

Orforglipron is a non-peptide oral GLP-1RA biased toward G-protein activation. Orforglipron achieved similar blood glucose-lowering ability to exenatide at low blood concentrations in animal studies, and its G-protein bias may play a role in therapy [145]. Results from phase 2 clinical trials showed that orforglipron at doses of 12 mg or higher significantly reduced HbA1c and body weight compared to placebo or dulaglutide, with AEs similar to other GLP-1RAs [146]. Danuglipron, an oral small molecule GLP-1RA, demonstrated safety with twice-daily danuglipron in an 8-week randomized, double-blind trial, and 24-h mean daily glucose and HbA1c increased proportionally to the danuglipron dose [147]. In clinical trials, danuglipron demonstrated similar glucose-lowering effects to GLP-1RA and slightly reduced blood pressure in patients [148,149]. HRS-7535 is a novel oral small molecule GLP-1RA that significantly improves glucose tolerance, promotes insulin secretion, and reduces food intake without strict food restriction. In phase 1 trials, the pharmacokinetics of HRS-7535 were roughly proportional to the dose, significantly reducing patients’ body weight, with nausea and vomiting as common AEs, and its safety has been demonstrated [150]. There are other GLP-1RAs in other stages of development, e.g., Teng Ma et al. synthesized a small-molecule peptide that is a dual agonist of GLP-1R and GIP. It acts similarly to GIP and exendin-4 in a dose-dependent manner. When co-administered with sodium deoxycholate (SDC), SDC increased its bioavailability, and its oral hypoglycemic activity was enhanced. It also significantly improved glucose tolerance and hepatic fibrosis area in mice [151].

##  5\. Oral Strategies for Peptide Drugs

Oral administration holds better promise for the treatment of T2DM than injectable administration. The main limitations of oral delivery of peptides are how to avoid degradation in the digestive system and improve bioavailability. Both the PH environment and digestive enzymes in the gastrointestinal tract greatly reduce the bioactivity of peptides in the GI tract, and the mucus layer and intestinal epithelium in the intestine are major barriers to the entry of peptides into the somatic circulation. Although semaglutide has been successfully prepared for oral administration, this customized approach cannot be directly replicated by other GLP-1Rs. In contrast, nanocarriers can be prepared into oral formulations without altering the structure of GLP-1R, improving bioavailability and prolonging in vivo circulation time through different absorption routes (Figure 3).

####  5.1. Liposome

Liposomes are spherical vesicles composed of phospholipid bilayers with biological properties similar to those of cell membranes. Intestinal epithelial cells are tightly packed with protein complexes that limit intercellular transport of macromolecules, so liposomes with a structure similar to that of the cell membrane can mediate cellular transport via receptors [152]. In addition, by changing the physical or chemical properties of the liposome surface and thus the nature of the vesicles, different purposes of delivery can be achieved [153,154].

The mucus layer covering the surface of the intestinal epithelial cells is the first physical barrier that prevents drugs from entering the body circulation, rapidly trapping and eliminating larger molecules and pathogens through adhesion, protecting the intestinal tract from pathogen invasion whilst restricting the diffusion of drug absorption. The mucus layer is mainly composed of highly glycosylated mucins, which consist of a protein backbone and oligosaccharide side chains, and the mucus adheres to particles, especially those with positively charged or hydrophobic surfaces, mainly through multivalent adhesion interactions generated by the carboxyl groups of the oligosaccharides and the negative charge of sulphuric acid [155]. Wang and colleagues designed protein-crowned liposomes for insulin delivery by covering cationic liposomes with bovine serum albumin (BSA). Proteins can be adsorbed on the surface of nanoparticles in biological fluids to form a protein envelope, which is known as a protein crown. Hydrophilic and neutrally charged protein crowns promote mucus permeation of protein-crowned liposomes. As protein-crowned liposomes permeate the intestinal mucus, the protein crown is progressively degraded by enzymes, and cationic liposomes interact with anionic cell membranes to achieve trans-epithelial transport of insulin. The results showed that protein-crowned liposomes had an oral bioavailability of up to 11.9% and were effective in lowering blood glucose levels [156]. Ding and colleagues utilized electrostatic interaction forces to cause BSA to form protein crowns on the surface of liposomes and bind to cell-penetrating peptides to load liraglutide, which was used to improve the mucus permeability of the liposomes and enhance their absorption efficiency in the gut. In vitro experiments demonstrated the stability of liraglutide after liposome release. In vivo experiments have demonstrated that liposomes loaded with liraglutide have good intestinal endocytosis, a long intestinal absorption time, and good biosafety [157].

Recombinant microorganisms can be used as natural liposomes loaded with peptides and have unique advantages in biocompatibility. Recombinant microbial drug delivery avoids stimulation by environmental exogenous substances and controls the release of drugs or prodrugs in the body, and specific gene editing of recombinant microbial discourse specifically regulates homeostasis [158]. Fei et al. recombined and loaded Rhodotorula glutinis with peptides and insulin for oral administration to Type 2 diabetic mice. The cells of the recombinant strain accumulated more lecithin and fatty acids, which contributed to the loading of the peptide drug. Recombinant strain cells no longer proliferate in mice and slowly release the loaded drug; different levels of insulin were found in serum and tissues and significantly reduced blood glucose levels in mice [159]. Oral drug delivery based on recombinant microorganisms holds great promise, but some of the absorption mechanisms still require in-depth study.

Exosomes are external vesicles secreted by cells that can be released by all kinds of cells, such as human exosomes, plant exosomes, and animal exosomes. Exosomes have low immunogenicity and toxicity and offer advantages in the targeted delivery of therapeutic molecules because of the specific interactions between the surface proteins of these exosomes of different cellular origins and their target cells [160]. Currently the most promising of the oral exosomes are the milk-derived exosomes, which can promote the proliferation of intestinal epithelial cells, enhance intestinal permeability, and have the ability of immunomodulation [161]. Wu et al. prepared milk exosomes and loaded them with insulin for the treatment of Type 1 diabetic mice. Bovine milk exosomes were found to be actively multi-targeted to epithelial cells through active transport, membrane fusion, and niche-mediated transport. In addition, cow’s milk exosomes can regulate surface pH during transport to adapt to different environments, avoid endolysosomal restriction, and promote uptake and cytosolization. And subcutaneous insulin has a more sustained glycemic-lowering effect than oral milk exosome-loaded insulin [162]. Xiao and colleagues, on the other hand, utilized the flow and fusion properties of the phospholipid bilayer of the exosome membrane to fuse milk exosomes with modified liposomes. A hybridized vesicle with adaptive surface properties loaded with semaglutide for oral delivery was prepared. Hybridized vesicles contain about 85% of exosomal membrane proteins but encapsulate semaglutide at 2.4 times the rate of natural milk exosomes and are much more hydrophilic, while modified liposomes give hybridized vesicles a neutral surface and are pH-responsive and self-regulating. Hydrophilicity and neutral charge facilitate penetration of the intestinal mucus barrier, and upon reaching the surface of jejunal epithelial cells, the highly retained membrane proteins and positively charged mixed vesicle surfaces effectively overcome the apical barrier, the intracellular transit barrier, and the basolateral cytosolic barrier. Adaptive surface characterization of hybrid vesicles increased the oral bioavailability of semaglutide to 8.7%, significantly improving the pharmacological therapeutic effect [163]. Lactogenic exosomes are widely used in oral delivery, and exosome delivery studies of macromolecules are about to become hot research.

####  5.2. Self-Emulsifying Drug Delivery Systems (SEDDS)

SEDDS are liquid or solid formulations consisting of an oil phase, a nonionic surfactant, and a co-surfactant, which can spontaneously emulsify into emulsions with particle sizes around 100–500 nm by gentle agitation in the gastrointestinal tract or at room temperature. SEDDS are lipid-based formulations that pair hydrophilic molecules such as GLP-1R and proteins with hydrophobic ions of oppositely charged lipophilic auxiliaries to increase their hydrophobicity and improve lipid solubility and can be incorporated into SEDDS for oral administration.

Gastrointestinal hydrolytic enzymes such as pepsin, trypsin, and other peptidases and proteases are insoluble in oily SEDDS droplets but can degrade esters such as triglycerides in SEDDS; therefore, SEDDS loaded with GLP-1R should use a lipolytic stabilizing component to avoid enzymatic degradation. The addition of mucus pro-osmotic agents to SEDDS and a reduction in the size of SEDDS droplets can increase the rate of contact between SEDDS and epithelial cells and improve the amount of drug entering the corpuscular circulation [164,165]. Claudia and coworkers prepared ORAL SEDDS for Exenatide, where a solution of Exenatide was shock-mixed with a solution of the oppositely charged hydrophobic ion docusate, which was paired lipidated and added to the SEDDS. Exenatide interacts with docusate only by inter-ionic forces without structural changes, ensuring the stability and pharmacological effects of exenatide. SEDDS consists of Labrafil 1944, which lacks a triglyceride structure, and Capmul PG-8, whose high HLB value gives SEDDS a smaller droplet size and better mucus permeability. In healthy rats, the SEDDS bioavailability of loaded exenatide was approximately 9% and sustained release for at least 6 h. Healthy rats exhibit better mucus permeability and lower blood glucose levels in vivo, indicating the oral potential of peptides paired with hydrophobic ions in SEDDS [166]. Another hydrophobic ion pairing, n-octadecyl sulfate, was found to pair better with exenatide in SEDDS compared to docusate n-octadecyl sulfate in the study of Thi et al. The molar ratio of n-octadecyl sulfate paired with exenatide is higher than that of docusate paired with exenatide. This means that more n-octadecyl sulfate is required for the same exenatide, which increases the lipophilicity of exenatide and the log P value of exenatide-n-octadecyl sulfate. The ex vivo results showed that the intestinal permeability and bioavailability of n-octadecyl sulfate were 3.5 and 1.3 times higher than those of docusate, respectively [167].

An alternative to hydrophobic ion pairing is to load the peptide into a W/O emulsion, which is then dispersed into SEDDS. Lu et al. used soybean phospholipids to wrap exenatide to form reverse micelles, which were then incorporated into SEDDS. Subsequently, O/W emulsions were prepared on the basis of reverse micelles-SEDDS, the surface of which was positively charged, and finally hyaluronic acid was encapsulated in the outermost layer by electrostatic adsorption. The hyaluronic acid coating adheres to the intestinal mucosa and increases intestinal permeability. Animal experiments showed that exenatide-loaded reverse micelles-SEDDS was more significant in improving the indexes of T2DM rats compared to exenatide-loaded SEDDS. Long-term experiments also showed that long-term administration of exenatide-loaded reverse micelles-SEDDS protects pancreatic β-cells and improves insulin secretion to some extent in T2DM rats [168].

Converting liquid SEDDS to solid SEDDS (S-SEDDS) allows for controlled release of SEDDS. S-SEDDS improves the physicochemical stability and drug loading capacity of SEDDS and offers the advantages of controlled drug release, extended gastric retention time, and reduced production cost. S-SEDDS is prepared by solidifying SEDDS, which is commonly performed by solid carrier adsorption, spray drying, thermosolvent extrusion, and freeze drying [169]. Sun and colleagues prepared cabazitaxel S-SEDDS by solid carrier adsorption, which formed celiac particles into lymphatic transport to avoid the first pass effect, greatly improved the bioavailability of the BCS class IV drug cabazitaxel, and developed more potential formulations of cabazitaxel [170].

####  5.3. Polymer Nanoparticles (PNP)

Nanoparticles prepared from biocompatible and biodegradable polymers are widely used in oral delivery systems. Polymers are classified into natural and synthetic polymers; natural polymers such as polysaccharides, proteins, and polyamino acids have the advantage of being non-toxic and non-immunogenic in oral delivery. Synthetic polymers allow for the flexible production or modification of materials according to patient needs, such as synthetic polymers, silica, porous coordination polymers, etc. [171].

Chitosan is a cationic polysaccharide derived from naturally occurring chitin and therefore adheres to negatively charged mucus layers and cell surfaces. Intestinal epithelial intercellular channels are restricted by a combination of tightly linked protein complexes, cellular bridges, and adherens junctions, whereas chitosan transiently opens tight junctions to increase penetration of the drug into the paracellular pathway, and this process is reversible [172]. Sodium alginate is a PH-sensitive anionic polysaccharide and is adhesive, interacting with the mucus layer through hydrogen bonding and van der Waals forces [173]. Yang and colleagues prepared chitosan sodium alginate gel nanoparticles loaded with exenatide using sodium tripolyphosphate as a cross-linking agent. Due to the PH sensitivity of sodium alginate and the strong electrostatic interaction with chitosan, the nanoparticles shrink under acidic conditions, thereby protecting the exenatide. In the intestinal tract, sodium alginate adheres to the intestinal mucosa, increasing drug retention time, and chitosan increases the permeability of the nanoparticles. In vivo experiments showed that nanoparticles loaded with exenatide had an oral bioavailability of approximately 9% and significantly and consistently reduced blood glucose concentrations in diabetic rats [174].

Protein carriers are abundant and self-targeting, and the structure of the protein itself gives it the potential to deliver RNA, drugs, vaccines, and other biomolecules at specific sites in the body. However, it should be noted that high concentrations of proteins can be denatured by aggregation, and protein solubility can be improved by adding buffer components or low concentrations of surfactants [175]. Bao et al. used zeinolysin to protect the complex formed by the non-covalent interaction of liglutide with bile acids and added a surface-active glycolipid, rhamnolipid, to improve the stability of zeinolysin nanoparticles. Bile acids, as intestinal mucosal pro-osmotic agents, in combination with liraglutide may improve the oral absorption of liraglutide without affecting its potency. Corn Alcohol Soluble Protein, as a hydrophobic protein, can help its encapsulated material to resist protease degradation, while rhamnolipid prevents its hydrophobic aggregation, thereby reducing the particle size of the nanoparticles. Cellular experiments showed that in contrast to the ligraglutide bile acid complex alone, which is absorbed only via the bile acid transporter protein pathway, zeinolysin particles are absorbed via active transport, lattice proteins, vesicles, and microcellular drinking-mediated endocytosis. In vivo experiments revealed that oral administration of corn alcohol-soluble protein loaded with liraglutide had a bioavailability of 9.6% and significantly reduced blood glucose levels in T2DM mice [176].

The most interesting of the current polyamino acids are the amphoteric amino acid polymers, where the average distribution of oppositely charged groups makes them strongly hydrated, and thus the amphoteric amino acid polymers have the advantage of resisting protein adsorption, interfacial lubrication, facilitating mucus permeation, enhancing the internalization of the nanoparticles in the cell, and also preventing degradation of the proteopeptides in inflammatory and enzymatic environments [177]. The degradation products of poly(lactic-co-glycolic) acid, PLGA, are lactic acid and hydroxyacetic acid, which are also by-products of the human metabolic pathway, so when it is in the body, it is not toxic, except for those with lactose deficiency. PLGAs can also be modified in various ways and are therefore often used in the design and development of nanocarriers for controlled and targeted drug delivery systems. Zhao et al. selected amphoteric sulfobetaine for surface modification of PLGA nanoparticles for loading of liraglutide. Amphoteric sulfobetaine can self-assemble on the surface of PLGA nanoparticles to form hydrophilic and electroneutral coatings and to form protein coronas to resist adsorption to nonspecific proteins. The results showed that the hydrophilicity and electroneutrality of the surface of PLGA particles greatly improved their mucus permeability, and the relative bioavailability of the orally administered nanoparticles was 9.95% of that of the subcutaneously administered group, with a stable blood glucose-lowering effect [178].

Silica has the advantages of easy synthesis, large pore size, large specific surface area, good physical stability, non-toxicity, etc. Silica-skeleton-loaded drugs have better stability under temperature changes, organic solvents, or acidic conditions, making them suitable for use as a drug nanocarrier [179]. Silicon dioxide uptake in the GI tract is related to the size of the nanoparticles; e.g., 250 nm particles can be transported via cellular uptake and cytosis, while 100 nm particles result in a lower rate of cellular transport due to faster degradation. Silica particles smaller than 100 nm in size, on the other hand, improved intestinal osmotic penetration, but when penetrating the mucus layer, the penetration rate of 20 nm particles was much lower than that of 50 nm particles [180]. Therefore the relationship between the size of silica nanoparticles and their absorption in the GI tract still needs to be explored. Silica for the preparation of nanoparticles can be classified into mesoporous and non-porous silica, and mesoporous silica can be used for the delivery of peptide drugs. The ordered nanoscale porous structure in mesoporous silica nanoparticles allows drug molecules to be easily confined and stabilized within the mesopore in an amorphous form, which further enhances the drug dissolution rate [181]. Muhammad and colleagues tried macroporous dendritic silica nanoparticles loaded with exenatide, modified the nanoparticles using phosphonate, and covered the surface with a chitosan coating. The phosphonate enhanced the surface charge and porosity of the nanoparticles, resulting in a more homogeneous dispersion and increased exenatide loading. The stronger electronegativity of phosphonate can form electrostatic adsorption with chitosan, and the chitosan-coated nanoparticles can protect the exenatide leakage in the pores and improve the drug loading capacity. In addition, chitosan coating reduced the burst release of exenatide at acidic and alkaline pH, and increased the permeability of nanoparticles in intestinal epithelial cells [182].

Porous coordination polymers are materials with infinite network structures formed by self-assembly of metal nodes (metal ions or clusters) and organic linkers through coordination bonds, which have the advantages of diverse structures and compositions, designable structures, adjustable pore channels, and easy functionalization [183,184,185]. The nanocarriers commonly used in porous coordination polymers for loading drugs are metal-organic frameworks, and functional metal-organic framework nanoparticles can protect proteins from the gastric environment and overcome the intestinal barrier, offering the possibility of orally delivering biomolecules. Jun-Jie Zou et al. Encapsulation of insulin using acid-resistant metal-organic framework nanoparticles and modification of its surface with transferrin for efficient transport and targeted release of insulin [186]. The macroporous structure of metal-organic framework nanoparticles allows for up to 33% encapsulation of insulin and its environmental sensitivity for controlled release under phosphorus-rich physiological conditions. Transferrin achieves iron uptake and transport by binding to the transferrin receptor on epithelial cells, which increases the efficiency of insulin transport. The intracellular nanoparticles, due to their strong positive charge and unique membrane switching effect, interact electrostatically with the lysosomal membrane to achieve rapid escape from the lysosome and release from the cellular monolayer. Animal experiments showed that transferrin-modified metal-organic framework nanoparticles rapidly reduced in vivo blood glucose levels in rats to about 30% of the initial level and extended insulin efficacy up to 10 h, achieving a high oral bioavailability of 29.6%, while long-term experiments demonstrated that such orally-administered nanoparticles are well biocompatible. This demonstrates the potential of metal-organic frameworks for loading peptide drugs for oral treatment of diabetes.

##  6\. Conclusions and Outlook

Oral delivery of peptides is one of the major challenges in the field of drug delivery. Currently, the focus of peptide drug formulation research is mainly on protecting the biological activity of peptides and improving bioavailability. Nano-formulations can avoid the inactivation of peptides in the gastrointestinal environment, increase the transmembrane absorption in the small intestinal epithelium, change the distribution of the drug in the body, and improve bioavailability. Therapeutic effects of controlled release and targeted treatment of diseases are achieved by ligand modification, which prolongs retention time at specific absorption sites. In addition, nano-formulations activate cellular transport mechanisms to maximize drug activity, ensuring rapid and stable drug action [187]. This paper contributes to the field of therapeutic diabetes by reviewing the progress of research on peptide drugs for the treatment of diabetes, as well as exploring the possibility of combining peptide drugs with nano-formulations.

## Author Contributions

Conceptualization, X.Y. and R.L.; methodology, C.F.; software, Q.K.; validation, Y.D., P.Y. and Y.J.; formal analysis, X.Y.; investigation, R.L.; resources, X.Y.; data curation, C.F.; writing—original draft preparation, R.L.; writing—review and editing, Y.D.; visualization, P.Y.; supervision, X.Y.; project administration, R.L.; funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.

## Funding

This research was funded by the National Natural Science Foundation of China (Grant No. 82003985); Drug Administration project of Jilin province (Grant No. JLPZGF-2020-107), and the National College Students’ innovation and entrepreneurship training program (Grant No. 202310199001 and 202310199051X), Jilin Province Science and Technology Development Project in China (Grant No. 20210204013YY, 20230401062YY, 20230204019YY, 20230401075YY); Jilin Provincial Development and Reform Commission program (Grant No. 2023C027-3); Changchun University of Chinese Medicine postgraduate training innovation demonstration project (Grant No. 2022JP06).

## Conflicts of Interest

The authors declare no conflicts of interest.

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**Figure 1.** The mechanism/target of the combination of peptide drugs and nano-preparations on diabetic complications. 

**Figure 1.** The mechanism/target of the combination of peptide drugs and nano-preparations on diabetic complications.

**Figure 2.** Glucose stimulates GLP-1 secretion from L-cells: Glucose closes ATP-sensitive K+ ATP channels and opens Ca2+ channel (VDCC) to induce membrane depolarization, and Ca2+ inward flow triggers vesicular cytotoxicity and GLP-1 secretion. 

**Figure 2.** Glucose stimulates GLP-1 secretion from L-cells: Glucose closes ATP-sensitive K+ ATP channels and opens Ca2+ channel (VDCC) to induce membrane depolarization, and Ca2+ inward flow triggers vesicular cytotoxicity and GLP-1 secretion.

**Figure 3.** Schematic representation of the transport mechanisms. 

**Figure 3.** Schematic representation of the transport mechanisms.

**Table 1.** Application of polypeptide drugs in clinical research of type 2 diabetes mellitus. 

**Table 1.** Application of polypeptide drugs in clinical research of type 2 diabetes mellitus.

Type| Molecular Formula| CAS Number| Molecular Weight| Detection Indexes of Type 2 Diabetes Mellitus| Phase| Trial Number| Reference  
---|---|---|---|---|---|---|---  
Exenatide| C184H282N50O60S| 141758-74-9| 4186.57| Hepatic fat content| Phase 4| NCT01432405| [12]  
Lixisenatide| C215H347N61O65S| 320367-13-3| 4858.49| Blood lipid level| Phase 2| NCT02274740| [13]  
Loxenatide| C210H325N55O69S| 2420483-82-3| 44,212.65| Phase 1| kidney function| NCT02467790| [14]  
Liraglutide| C172H265N43O51| 204656-20-2| 3751.20| Cardiovascular| Phase 4| NCT01761318| [15]  
Beinaglutide| C149H225N39O46| 123475-27-4| 3298.61| Blood sugar| Not Applicable| NCT03829891| [16]  
Dulaglutide| C149H221N37O49| 923950-08-7| 3314.62| Blood pressure, heart rate| Phase 2| NCT01149421| [17]  
Semaglutide| C187H291N45O59| 910463-68-2| 4113.58| Heart disease, cardiovascular| Phase 3| NCT03914326| [18]  
Tirzepatide| C225H348N48O68 • XNa| 2023788-19-2| 4813.45| HbA1c| Phase 4| NCT05706506| [19]  
  
**Table 2.** Comparison of different kinds of oral drugs. 

**Table 2.** Comparison of different kinds of oral drugs.

Type| Representative Drug| Function| Advantages| Possible Side Effects| Reference  
---|---|---|---|---|---  
Polypeptide drugs| Semeglutide| When the blood glucose level rises, it causes the release of insulin  
It can be used with metformin, basal insulin or sulfonylureas.| It may reduce hunger.  
May lead to weight loss.| Disgusting.  
Vomiting.  
Diarrhoea.  
Abdominal pain.| [109]  
Meglitazone drugs| Reglinet| Triggers pancreas to release insulin| Quick results| Blood sugar levels drop too low, that is, hypoglycemia.  
Weight gain| [140]  
Sulfonylureas| Glipizide| Triggers pancreas to release insulin| low cost.  
Effectively reduce blood sugar| The blood sugar level dropped too low.  
Weight gain.  
Rash.  
If drinking, there will be nausea or vomiting.| [141]  
DPP-4 inhibitors| Saxagliptin| When blood sugar rises, it causes the release of insulin.  
Limit the ability of the liver to release glucose| Does not lead to weight gain.  
When used alone or in combination with metformin, it does not cause blood glucose levels to drop too low.| Upper respiratory tract infection.  
Throat pain.  
Headache.| [142]  
Metformin drug| Metformin| Limit the ability of the liver to release sugar.  
Improve the sensitivity of cells to insulin| Very effective.  
May cause a slight weight loss.  
Low cost.| Nausea.  
Stomachache.  
Diarrhoea.  
In rare cases, there is harmful accumulation of lactic acid in patients with renal or liver failure, that is, lactic acidosis.| [143]  
Alpha glucosidase inhibitors| Miglitol| Slow down the body’s ability to decompose starch and some sugars| Does not lead to weight gain.  
Unless taken with insulin or sulfonylureas, it will not cause blood glucose levels to drop too low.| Bloating  
Stomachache  
Diarrhoea| [144]  
PPARγ agonists | Pioglitazone| Agonizes peroxisome proliferator-activated receptor PPARγ and reduces insulin resistance| Does not stimulate insulin secretion, but exerts its hypoglycemic effect by enhancing tissue sensitivity to insulin. Used alone, it does not cause hypoglycemia.| Increased risk of heart failure. Increased incidence of bladder cancer| [138]  
SGLT-2 inhibitor| Dagliflozin| Inhibits SGLT-2, reduces glucose reabsorption, and excretes sugar directly from the urine.| SGLT-2 inhibitors are rarely hypoglycemic and have beneficial effects on improving body weight and blood pressure| May be associated with nasopharyngitis, fungal infections, and genitourinary infections| [139]  
  
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Yang, X.; Lin, R.; Feng, C.; Kang, Q.; Yu, P.; Deng, Y.; Jin, Y. Research Progress on Peptide Drugs for Type 2 Diabetes and the Possibility of Oral Administration. _Pharmaceutics_ **2024** , _16_ , 1353. https://doi.org/10.3390/pharmaceutics16111353 

**AMA Style**  


Yang X, Lin R, Feng C, Kang Q, Yu P, Deng Y, Jin Y. Research Progress on Peptide Drugs for Type 2 Diabetes and the Possibility of Oral Administration. _Pharmaceutics_. 2024; 16(11):1353. https://doi.org/10.3390/pharmaceutics16111353 

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Yang, Xinxin, Ruiting Lin, Changzhuo Feng, Qiyuan Kang, Peng Yu, Yongzhi Deng, and Ye Jin. 2024\. "Research Progress on Peptide Drugs for Type 2 Diabetes and the Possibility of Oral Administration" _Pharmaceutics_ 16, no. 11: 1353. https://doi.org/10.3390/pharmaceutics16111353 

**APA Style**  


Yang, X., Lin, R., Feng, C., Kang, Q., Yu, P., Deng, Y., & Jin, Y. (2024). Research Progress on Peptide Drugs for Type 2 Diabetes and the Possibility of Oral Administration. _Pharmaceutics_ , _16_(11), 1353. https://doi.org/10.3390/pharmaceutics16111353 

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Yang, X.; Lin, R.; Feng, C.; Kang, Q.; Yu, P.; Deng, Y.; Jin, Y. Research Progress on Peptide Drugs for Type 2 Diabetes and the Possibility of Oral Administration. _Pharmaceutics_ **2024** , _16_ , 1353. https://doi.org/10.3390/pharmaceutics16111353 

**AMA Style**  


Yang X, Lin R, Feng C, Kang Q, Yu P, Deng Y, Jin Y. Research Progress on Peptide Drugs for Type 2 Diabetes and the Possibility of Oral Administration. _Pharmaceutics_. 2024; 16(11):1353. https://doi.org/10.3390/pharmaceutics16111353 

**Chicago/Turabian Style**  


Yang, Xinxin, Ruiting Lin, Changzhuo Feng, Qiyuan Kang, Peng Yu, Yongzhi Deng, and Ye Jin. 2024\. "Research Progress on Peptide Drugs for Type 2 Diabetes and the Possibility of Oral Administration" _Pharmaceutics_ 16, no. 11: 1353. https://doi.org/10.3390/pharmaceutics16111353 

**APA Style**  


Yang, X., Lin, R., Feng, C., Kang, Q., Yu, P., Deng, Y., & Jin, Y. (2024). Research Progress on Peptide Drugs for Type 2 Diabetes and the Possibility of Oral Administration. _Pharmaceutics_ , _16_(11), 1353. https://doi.org/10.3390/pharmaceutics16111353 

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Obesity and Metabolic Disease Impair the Anabolic Response to Protein Supplementation and Resistance Exercise: A Retrospective Analysis of a Randomized Clinical Trial with Implications for Aging, Sarcopenic Obesity, and Weight Management

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#  Obesity and Metabolic Disease Impair the Anabolic Response to Protein Supplementation and Resistance Exercise: A Retrospective Analysis of a Randomized Clinical Trial with Implications for Aging, Sarcopenic Obesity, and Weight Management 

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Mats I. Nilsson

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Donald Xhuti

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Nicoletta Maria de Maat

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Bart P. Hettinga

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Mark A. Tarnopolsky

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1

Exerkine Corporation, McMaster University Medical Center, Hamilton, ON L8N 3Z5, Canada

2

Department of Pediatrics, McMaster University Medical Center, Hamilton, ON L8N 3Z5, Canada

*

Authors to whom correspondence should be addressed. 

_Nutrients_ **2024** , _16_(24), 4407; <https://doi.org/10.3390/nu16244407>

Submission received: 26 November 2024 / Revised: 16 December 2024 / Accepted: 18 December 2024 / Published: 23 December 2024

(This article belongs to the Special Issue [Diet and Nutrition Approaches in Obesity Treatment](        
    /journal/nutrients/special_issues/4W355M3A77
))  


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[ ](https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-ag.png?1735201185 "
                        <strong>Graphical abstract</strong><br/>
                                                    
                                                ") [ ](https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-g001.png?1735184389 "
                        <strong>Figure 1</strong><br/>
                                                    <p>Consort flow chart. Left panel \(1–4\): Enrollment, allocation, follow-up, and analysis of the original clinical trial \(Nilsson et al. \[<a href="#B22-nutrients-16-04407" class="html-bibr">22</a>\]\). Right panel \(5–6\): The retrospective data analyses conducted for the current manuscript.</p>
                                                ") [ ](https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-g002.png?1735184392 "
                        <strong>Figure 2</strong><br/>
                                                    <p>RCT timeline and clinical testing. X = time point of procedure \(weeks 0–1 and 13–14\).</p>
                                                ") [ ](https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-g003.png?1735184394 "
                        <strong>Figure 3</strong><br/>
                                                    <p>Overall best fit in prediction model 1. Baseline obesity \(BMI\) was the strongest individual predictor of % lean mass gains following three months of HBRE + PA + MIS polytherapy. The sample size for analyses <span class="html-italic">n</span> = 30–32. All correlations are shown in <a href="#nutrients-16-04407-t001" class="html-table">Table 1</a> and <a href="#app1-nutrients-16-04407" class="html-app">Table S1</a>. \(<b>A</b>\) Baseline BMI vs. Δ TLM \(% change\); \(<b>B</b>\) Baseline BMI vs Δ ASM \(% change\); \(<b>C</b>\) Baseline BMI vs Δ ASMI \(% change\).</p>
                                                ") [ ](https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-g004.png?1735184398 "
                        <strong>Figure 4</strong><br/>
                                                    <p>Overall best fit in prediction model 2. Compared to the individual metabolic risk factors \(i.e., obesity, dyslipidemia, dysglycemia, and systemic inflammation\), the Global MetS Risk Index \(i.e., total MetS risk factors <a href="#sec2dot11-nutrients-16-04407" class="html-sec">Section 2.11</a>\) was the overall strongest predictor of % lean mass gains following three months of HBRE + PA + MIS polytherapy. The sample size for analyses <span class="html-italic">n</span> = 30–32. All correlations are shown in <a href="#nutrients-16-04407-t002" class="html-table">Table 2</a> and <a href="#app1-nutrients-16-04407" class="html-app">Table S2</a>. \(<b>A</b>\) Baseline MetS Risk Index vs. Δ TLM \(% change\); \(<b>B</b>\) Baseline MetS Risk Index vs. Δ ASM \(% change\); \(<b>C</b>\) Baseline MetS Risk Index vs. Δ ASMI \(% change\).</p>
                                                ") [ ](https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-g005.png?1735184402 "
                        <strong>Figure 5</strong><br/>
                                                    <p>Δ LM, FM, and % BF. Between-group differences in the adaptive response \(i.e., Δ% changes\) were analyzed by independent <span class="html-italic">t</span>-tests \(** <span class="html-italic">p</span> ≤ 0.01; <sup>†</sup> <span class="html-italic">p</span> &gt; 0.05 &lt; 0.100\). Within-group differences in pre-post intervention results were analyzed by paired <span class="html-italic">t</span>-tests \(<sup>#</sup> <span class="html-italic">p</span> ≤ 0.05; <sup>‡</sup> <span class="html-italic">p</span> &gt; 0.05 &lt; 0.100\) \(<a href="#nutrients-16-04407-t008" class="html-table">Table 8</a>\). Sample size for analyses <span class="html-italic">n</span> = 20 \(PLA; <span class="html-italic">n</span> = 8, M5; <span class="html-italic">n</span> = 12\). \(<b>A</b>\) Δ TLM \(% change\); \(<b>B</b>\) Δ ASM \(% change\); \(<b>C</b>\) Δ FM \(% change\); \(<b>D</b>\) Δ% Body fat \(% change\).</p>
                                                ") [ ](https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-g006.png?1735184404 "
                        <strong>Figure 6</strong><br/>
                                                    <p>Δ Body Composition Indices. Between-group differences in the adaptive response \(i.e., Δ% changes\) were analyzed by independent <span class="html-italic">t</span>-tests \(* <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01\). Within-group differences in pre-post intervention results were analyzed by paired <span class="html-italic">t</span>-tests \(<sup>#</sup> <span class="html-italic">p</span> ≤ 0.05\) \(<a href="#nutrients-16-04407-t008" class="html-table">Table 8</a>\). Sample size for analyses <span class="html-italic">n</span> = 20 \(PLA; <span class="html-italic">n</span> = 8, M5; <span class="html-italic">n</span> = 12\). \(<b>A</b>\) Δ TLM/FM \(% change\); \(<b>B</b>\) Δ TLM/%BF \(% change\); \(<b>C</b>\) Δ ASM/FM \(% change\); \(<b>D</b>\) Δ ASM/%BF \(% change\).</p>
                                                ") [ ](https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-g007.png?1735184407 "
                        <strong>Figure 7</strong><br/>
                                                    <p>Δ Allometric LM. Between-group differences in the adaptive response \(i.e., Δ% changes\) were analyzed by independent <span class="html-italic">t</span>-tests \(* <span class="html-italic">p</span> ≤ 0.05\). Within-group differences in pre-post intervention results were analyzed by paired <span class="html-italic">t</span>-tests \(<sup>#</sup> <span class="html-italic">p</span> ≤ 0.05; <sup>‡</sup> <span class="html-italic">p</span> &gt; 0.05 &lt; 0.100\) \(<a href="#nutrients-16-04407-t008" class="html-table">Table 8</a>\). Sample size for analyses <span class="html-italic">n</span> = 20 \(PLA; <span class="html-italic">n</span> = 8, M5; <span class="html-italic">n</span> = 12\). \(<b>A</b>\) Δ TLM/BW \(% change\); \(<b>B</b>\) Δ TLM/BMI \(% change\); \(<b>C</b>\) Δ ASM/BW \(% change\); \(<b>D</b>\) Δ ASM/BMI \(% change\).</p>
                                                ") [ ](https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-g008.png?1735184410 "
                        <strong>Figure 8</strong><br/>
                                                    <p>Δ Strength and Muscle Quality. Between-group differences in the adaptive response \(i.e., Δ% changes\) were analyzed by independent <span class="html-italic">t</span>-tests \(<sup>†</sup> <span class="html-italic">p</span> &gt; 0.05 &lt; 0.100\). Within-group differences in pre-post intervention results were analyzed by paired <span class="html-italic">t</span>-tests \(<sup>#</sup> <span class="html-italic">p</span> ≤ 0.05; <sup>##</sup> <span class="html-italic">p</span> ≤ 0.01\) \(<a href="#nutrients-16-04407-t009" class="html-table">Table 9</a>\). Sample size for strength outcomes <span class="html-italic">n</span> = 19 \(PLA; <span class="html-italic">n</span> = 7, M5; <span class="html-italic">n</span> = 12\). \(<b>A</b>\) Δ Leg Press \(% change\); \(<b>B</b>\) Knee Extension \(% change\); \(<b>C</b>\) Grip Strength \(% change\); \(<b>D</b>\) Δ Muscle Quality \(% change\).</p>
                                                ") [ ](https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-g009.png?1735184412 "
                        <strong>Figure 9</strong><br/>
                                                    <p>Δ Performance. Between-group differences in the adaptive response \(i.e., Δ% changes\) were analyzed by independent <span class="html-italic">t</span>-tests \(no significance detected\). Within-group differences in pre-post intervention results were analyzed by paired <span class="html-italic">t</span>-tests \(<sup>‡</sup> <span class="html-italic">p</span> &gt; 0.05 &lt; 0.100\) \(<a href="#nutrients-16-04407-t010" class="html-table">Table 10</a>\). Sample size for performance outcomes <span class="html-italic">n</span> = 19 \(PLA; <span class="html-italic">n</span> = 7, M5; <span class="html-italic">n</span> = 12\). \(<b>A</b>\) Δ 4SSC \(% change\); \(<b>B</b>\) Δ SPPB Score \(% change\).</p>
                                                ") [ ](https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-g010.png?1735184414 "
                        <strong>Figure 10</strong><br/>
                                                    <p>Anabolic Ranks. Ranks \(low 1- high 20\) of anabolic responses \(Δ lean mass, Δ strength, Δ performance, and Δ overall\) in high-quality \(<span class="html-italic">n</span> = 12; M5 + fish oil\) vs. lower-quality \(<span class="html-italic">n</span> = 8; PLA; collagen + safflower oil\) supplement groups in obese/MetS older males following three months of home-based resistance exercise and daily walking \(HBRE + PA\). Between-group differences in the anabolic response ranks were analyzed by independent <span class="html-italic">t</span>-tests \(* <span class="html-italic">p</span> ≤ 0.05; **<span class="html-italic">p</span> ≤ 0.01; <sup>†</sup> <span class="html-italic">p</span> &gt; 0.05 &lt; 0.100\). Sample size for anabolic ranks <span class="html-italic">n</span> = 19–20 \(PLA; <span class="html-italic">n</span> = 7–8, M5; <span class="html-italic">n</span> = 12\). The rankings for lean mass, strength, performance and overall anabolic response are defined in <a href="#sec3dot9-nutrients-16-04407" class="html-sec">Section 3.9</a>. \(<b>A</b>\) Δ Lean Mass Rank \(1–20\); \(<b>B</b>\) Δ Strength Rank \(1–20\); \(<b>C</b>\) Δ Performance Rank \(1–20\); \(<b>D</b>\) Overall Anabolic Rank \(1–20\).</p>
                                                ") [ ](https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-g011.png?1735184416 "
                        <strong>Figure 11</strong><br/>
                                                    <p>Δ Sarcopenic Obesity Risk Rank. Between-group differences in the adaptive response \(i.e., Δ% changes\) were analyzed by independent <span class="html-italic">t</span>-tests \(** <span class="html-italic">p</span> ≤ 0.01\). Within-group differences in pre-post intervention results were analyzed by paired <span class="html-italic">t</span>-tests \(<sup>##</sup> <span class="html-italic">p</span> ≤ 0.01\) \(<a href="#nutrients-16-04407-t003" class="html-table">Table 3</a>\). The Sarcopenic Obesity Risk Rank is defined in <a href="#sec2dot11-nutrients-16-04407" class="html-sec">Section 2.11</a>. Sample size for anabolic ranks <span class="html-italic">n</span> = 19 \(PLA; <span class="html-italic">n</span> = 7, M5; <span class="html-italic">n</span> = 12\).</p>
                                                ") [ ](https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-g012.png?1735184418 "
                        <strong>Figure 12</strong><br/>
                                                    <p>Drivers and countermeasures of anabolic resistance in sarcopenic obesity.</p>
                                                ")

[Versions Notes](/2072-6643/16/24/4407/notes)

## Abstract

**:**

Background: Anabolic resistance accelerates muscle loss in aging and obesity, thus predisposing to sarcopenic obesity. Methods: In this retrospective analysis of a randomized clinical trial, we examined baseline predictors of the adaptive response to three months of home-based resistance exercise, daily physical activity, and protein-based, multi-ingredient supplementation (MIS) in a cohort of free-living, older males (n = 32). Results: Multiple linear regression analyses revealed that obesity and a Global Risk Index for metabolic syndrome (MetS) were the strongest predictors of Δ% gains in lean mass (TLM and ASM), LM/body fat ratios (TLM/%BF, ASM/FM, and ASM/%BF), and allometric LM (ASMI, TLM/BW, TLM/BMI, ASM/BW), with moderately strong, negative correlations to the adaptive response to polytherapy r = −0.36 to −0.68 (p < 0.05). Kidney function, PA level, and chronological age were only weakly associated with treatment outcomes (p > 0.05). Next, we performed a subgroup analysis in overweight/obese participants with at least one other MetS risk factor and examined their adaptive response to polytherapy with two types of protein-based MIS (PLA; collagen peptides and safflower oil, n = 8, M5; whey/casein, creatine, calcium, vitamin D3, and fish oil, n = 12). The M5 group showed greater improvements in LM (ASM; +2% vs. −0.8%), LM/body fat ratios (ASM/FM; +3.8% vs. −5.1%), allometric LM (ASM/BMI; +1.2% vs. −2.5%), strength (leg press; +17% vs. −1.4%), and performance (4-Step-Stair-Climb time; −10.5% vs. +1.1%) vs. the PLA group (p < 0.05). Bone turnover markers, indicative of bone accretion, were increased pre-to-post intervention in the M5 group only (P1NP; p = 0.036, P1NP/CTX ratio; p = 0.088). The overall anabolic response, as indicated by ranking low-to-high responders for Δ% LM (p = 0.0079), strength (p = 0.097), and performance (p = 0.19), was therefore significantly higher in the M5 vs. PLA group (p = 0.013). Conclusions: Our findings confirm that obesity/MetS is a key driver of anabolic resistance in old age and that a high-quality, whey/casein-based MIS is more effective than a collagen-based alternative for maintaining musculoskeletal health in individuals at risk for sarcopenic obesity, even when total daily protein intake exceeds current treatment guidelines.

Keywords: 

[obesity](/search?q=obesity); [aging](/search?q=aging); [sarcopenia](/search?q=sarcopenia); [sarcopenic obesity](/search?q=sarcopenic+obesity); [metabolic syndrome](/search?q=metabolic+syndrome); [anabolic resistance](/search?q=anabolic+resistance); [exercise](/search?q=exercise); [protein](/search?q=protein); [whey](/search?q=whey); [creatine](/search?q=creatine); [collagen](/search?q=collagen); [vitamin D](/search?q=vitamin+D); [omega-3](/search?q=omega-3); [calcium](/search?q=calcium); [weight management](/search?q=weight+management); [GLP-1](/search?q=GLP-1); [Ozempic](/search?q=Ozempic)

![](https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-ag-550.jpg?1735201185)

[](https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-ag.png?1735201185 "<strong>Graphical Abstract</strong>")

Graphical Abstract




##  1\. Introduction

Anabolic resistance of skeletal muscle (SM) is a key driver of muscle loss and dysfunction with aging, disuse, physical inactivity, catabolic disease states, and obesity [1]. Considering the rapid growth of the world’s aging population and soaring rates of obesity and sedentarism, it is essential to understand the complex physiologic and mechanistic basis of anabolic resistance and muscle deterioration in these conditions to develop targeted and effective interventions.

Both intrinsic and extrinsic mechanisms can drive anabolic resistance and impair SM basal protein turnover and the adaptive response to anabolic stimuli (i.e., growth factors, hormones, amino acids, and/or contractile activity), thereby accelerating muscle deterioration and dysfunction. Specifically, the biological aging process (i.e., hallmarks of aging [2]) and acquired risk factors in obesity/metabolic syndrome (MetS), such as excess body fat (BF), dyslipidemia, and dysglycemia, independently fuel a ‘vicious cycle’ of organellar damage, oxidative stress, and inflammation [3], which exacerbates peripheral insulin resistance, adipose tissue expansion, and ectopic lipid deposition (i.e., hepato- and myosteatosis). Furthermore, these independent pathoetiologies may converge into polymorbid conditions, such as sarcopenic obesity, which poses a major health risk for older adults and complicates both diagnosis and disease management [4].

Current management strategies for obesity and MetS broadly focus on either decreasing energy intake, increasing energy output or a combination of both. The first line of treatment is lifestyle management (e.g., daily physical activity (PA), structured exercise, and caloric restriction), while more radical solutions may include weight loss drugs, anti-diabetes medications, and/or bariatric surgery. Unfortunately, only 20% of obese individuals can preserve and stabilize weight loss in the long term, and more than half regain most of their weight loss within a year [5]. Two significant reasons for this are that weight loss is associated with an increased drive to eat and a reduction in resting and non-resting energy expenditure per kilogram of fat-free mass (FFM; water, organs, muscle, and bone) [6,7]. These changes predispose to significant weight regain and cycling [5], which are associated with progressive loss of lean body mass (LBM/LM), an increase in fat mass (FM), and a reduction in LM/FM ratios [8,9].

Based upon impressive weight loss outcomes (~−15 to −20% in the first year), the most popular weight loss methods that are currently being employed are glucagon-like peptide-1 receptor agonists (GLP-1RA; i.e., semaglutide) [10] and dual agonists of both GLP-1 and glucose-dependent insulinotropic polypeptide (GIP; i.e., tirzepatide) [11]. An increasingly recognized drawback of such medications is that ~20–40% of the total weight loss is derived from FFM, with the remainder being FM [12,13,14]. The magnitude of FFM loss is typically governed by the total reduction in body weight, making pharmacotherapy and bariatric surgery high-risk strategies in terms of maintaining musculoskeletal health, with both methods causing substantial SM loss, bone degradation, and a reduction in resting metabolic rate (RMR) [12,15,16]. Excessive muscle deterioration is obviously undesirable for all patients but may be more detrimental for older adults who are predisposed to sarcopenic obesity and SM anabolic resistance, potentially blunting the adaptive response to anabolic therapies and limiting FFM regain following weight loss [1,4]. At the same time, studies have confirmed that weight-bearing exercise and strength training attenuate SM loss [17], and the development and implementation of other low-risk therapies, besides exercise, that mitigate musculoskeletal deterioration are necessary to improve overall safety, efficacy, and acceptability of contemporary weight management programs.

Current strategies for treating sarcopenic obesity are centered around multimodal interventions with the aim of inducing a net negative energy balance for reducing FM, ectopic lipid deposition, and inflammation, with a concurrent increase in muscle mass and strength [4]. These interventions typically include hypocaloric diets (−200–700 kcal/day), increased protein intake (1.2–1.5 g/kg BW/day), and combined aerobic and resistance exercise [4,18], thus significantly overlapping with multimodal interventions for both sarcopenia [19] and obesity [20,21]. In a recent topical review, Prado and colleagues stressed the importance of consuming high-quality proteins that are rich in essential amino acids (i.e., animal > plant-based) for inducing satiety and anabolism and advocated for concurrent intake of polyunsaturated fatty acids (i.e., C20:5n-3 and C22:6n-3 PUFAs; EPA and DHA), calcium, vitamin D, and antioxidants for targeting other aspects of the underlying etiology (e.g., oxidative stress and inflammation) [4]. While the premise of multi-ingredient supplementation (MIS) for targeting multiple pathways in sarcopenia and obesity is not new [22,23], more research is needed to confirm the importance of high-quality, protein-based MIS for LBM preservation in polymorbid conditions, such as sarcopenic obesity.

In this retrospective analysis of a three-month interventional RCT (Nilsson & Tarnopolsky [22]), we aimed to identify predictors of the adaptive response to home-based resistance exercise (HBRE; 3 days/week), daily physical activity (PA; walking), and protein-based MIS (Placebo; collagen peptides + safflower oil or Muscle 5; whey/casein + creatine + vitamin D3 \+ calcium + fish oil) in a cohort of free-living, older males, including a subgroup of obese, polymorbid individuals at risk for sarcopenic obesity (obese/MetS). Accordingly, we conducted backward stepwise regression on potential predictors of the adaptive response (e.g., baseline age, PA level, protein intake, kidney function, obesity, and MetS risk factors) and proceeded with an independent analysis in the obese/MetS subgroup to determine if the quality of protein-based MIS affected the adaptive response to the multimodal intervention. We hypothesized that obesity and other MetS risk factors would be negatively correlated to the treatment response (i.e., Δ% gains in LBM, strength, and performance) and that MIS with an incomplete protein source (i.e., collagen peptides) would be inferior to a higher-quality alternative (i.e., whey/casein) for mitigating sarcopenic obesity risk, even when daily protein intake meets or exceeds current treatment guidelines.

##  2\. Materials and Methods

####  2.1. Ethics, Clinical Trial Registration, and Funding

This is a retrospective analysis of a double-blind, placebo-controlled, randomized clinical trial conducted at the McMaster University Medical Centre (MUMC, Hamilton, ON) between June 2018 and April 2019 [22]. The deidentified and locked data set was re-analyzed between 1 October 2023 and 1 October 2024, and the results herein have not been published previously.

All methods and procedures were approved by the Hamilton Integrated Research Ethics Board (2018-4656-GRA; approval date: 12 June 2018), and the trial was registered at [clinicaltrials.gov](https://clinicaltrials.gov) (NCT03536871).

The funding for the multimodal intervention was provided by a CIHR grant awarded to Dr. Tarnopolsky (#143325). Some of the baseline samples were sent for use in an aging study funded by Buck Institute 2017-0471/Astellas Pharmaceuticals [24].

####  2.2. Consort Flow Chart, Participants, and Sample Size

As previously described [22], this 12-week trial was designed to assess the potential benefits of an exercise- and supplement-based, multimodal intervention under free-living conditions in older males representative of the North American aging community in general (i.e., sedentary/low active, overweight or obese, and with varying degrees of polymorbidity [25,26,27,28]).

General inclusion criteria were ≥65 years of age, male, sedentary or low active (≤150 min of PA per week), and of normal to class 1 obesity body mass index (BMI; 20–34.9). Individuals with class 2 and class 3 obesity were accepted into the study if fulfilling other criteria and were deemed safe for participation. For a detailed list of specific exclusion criteria, please refer to the original publication [22].

Between June 2018 and January 2019, 179 individuals from the general population in the Greater Hamilton Area were screened for participation (Figure 1). All volunteers were informed about the purpose of the research, the experimental procedures, and potential risks prior to providing their written informed consent for participation in the study. Forty-five individuals were then randomized into two intervention groups, with 32 participants completing pre- and post-clinical testing (Placebo/PLA; n = 16, Muscle 5/M5; n = 16). Withdrawals and dropouts were in line with other RCTs on unsupervised exercise and/or supplementation with limited subject interaction [29]. Specifically, three individuals withdrew prior to the start of the study, while ten dropped out during the intervention, with an even distribution across groups. Reasons for withdrawal/dropout included unrelated surgery (n = 2), unrelated health issues (n = 2), muscle soreness (n = 1), travel (n = 2), flavor of oil (n = 1), time constraints (n = 1), and other personal reasons (n = 4).

The original sample size calculations were based on a clinical trial on the effects of creatine supplementation and resistance exercise (RE) on muscle integrity in older adults (i.e., n = 8–11 per group) [30], setting power to 80% and alpha at 5%, yielding an estimate of 10 participants per group for detecting significant differences in body composition and strength. Previously, an authoritative meta-analysis by Morton et al. demonstrated that the average sample size in protein supplementation and RE studies is n = 19 [31]. Thus, accounting for an estimated 20–30% dropout rate for unsupervised training forms with limited subject interactions [29], we aimed for a final count of 15–20 participants per group [22].

For the current manuscript, the retrospective analyses were conducted on the full sample size (regression analyses; n = 32), followed by a subgroup analysis in obese/MetS individuals with body mass indices ≥27 plus one other risk factor for metabolic syndrome (n = 20) (Figure 1).

####  2.3. Study Design and Clinical Testing

Briefly, this 12-week multimodal intervention was designed to mimic a ‘real life situation’ in which the participants engaged in unsupervised HBRE (elastic band training, 3 d/week), daily PA (walking), and consumed either a higher- (M5; whey and casein + creatine + vitamin D3 \+ fish oil, n = 12) or a lower-quality (PLA; collagen peptides + safflower oil, n = 8), protein-based MIS daily. Baseline and post-study clinical tests were identical and consisted of vital signs, anthropometry, body composition, strength, and performance testing (Figure 2). Serum and plasma samples were obtained from venous blood draws to assess kidney and liver function, bone turnover markers, and MetS risk factors. Subjects arrived at the clinic in the fasted state (10–12 h) at the same time (AM) for all testing occasions.

####  2.4. Home-Based Resistance Exercise (HBRE)

As previously described [22], the HBRE program consisted of six upper-body and six lower-body resistance exercises, specifically, biceps curl, triceps extension, lateral raise, seated row, bench press, abdominal crunch, calf raises, chair squat, knee extension, knee flexion, hip flexion, and dorsi flexion. The program followed ACSM guidelines for strength training for older adults (i.e., full-body training 3 days/week on non-consecutive days, 3 × 10–15 repetitions per exercise, and progressive increases in exercise intensity) [32,33], with elastic band tension force (i.e., resistance) progressively increased throughout the study for continual adaptation (yellow, 1.32 kg; red, 1.77 kg; green, 2.27 kg; blue, 3.22 kg; and black, 4.40 kg). Compliance and elastic band resistance were recorded in an exercise log, which was collected post-intervention.

####  2.5. Physical Activity (PA)

Subjects were encouraged to maintain 10,000 steps on non-HBRE days and 5000 steps on HBRE days (i.e., 55,000 steps per week; 7857 steps per day) to improve health-related QoL and obesity/MetS status [34]. Daily PA levels were estimated by step counts (Omron HJ-321 Alvita Pedometer; Omron, Kyoto, Japan) and recorded in a step log.

####  2.6. Multi-Ingredient Supplementation (MIS), Randomization, and Blinding

Following enrollment, participants were coded/deidentified by the investigators and thereafter randomly assigned to the intervention by blocked randomization with an allocation ratio of 1:1 and block size of 4–8 participants by a third party. The allocation was not revealed to the investigators until pre- and post-intervention data had been cross-referenced, locked, and provided to an independent data safety and monitoring committee.

All supplements were manufactured by Gruppo Nutrition (Windsor, ON, Canada) and delivered to the subjects in a double-blind fashion with a coded alphanumerical system. Placebo (PLA) and active (M5) were isocaloric, matched for total protein, and identical in flavor, smell, and appearance. The PLA group received collagen peptides (40 g/d, Peptiplus®, Gelita, Eberbach, Germany) and safflower oil (two tsp./d), while the M5 group received two complete protein sources designed to mimic human breast milk (“humanized milk ratio”; 24 g/d whey and 16 g/d casein, i.e., 60:40 ratio), creatine monohydrate (3 g/d), vitamin D3 (1000 IU/d), calcium (416 mg/d as calcium caseinate), and fish oil (two tsp./d, EPA; 1.51 g/d, DHA; 0.95 g/d) (Muscle 5® and Omega 3®, Stayabove Nutrition, Hamilton, ON, Canada). Because M5 and PLA contained either complete or incomplete protein sources, they may be described as “higher-quality” and “lower-quality” protein-based MIS, respectively. Collagen lacks tryptophan and cysteine and has low levels of methionine, essential amino acids (EAAs) and branched-chain amino acids (BCAAs); thus, a Digestible Indispensable Amino Acid Score (DIAAS) of 0 and significantly lower anabolic potential vs. milk proteins [35,36,37]. For this study, PLA and M5 were matched for total protein (40 g) and contained 15.9% vs. 46.2% EAAs, 6.5% vs. 22.2% BCAAs, and 2.7% vs. 11.4% leucine, respectively (Table S5). All participants were instructed to consume the supplements in the morning with breakfast and to record their daily intake in a supplement log, which was collected at the end of the trial.

####  2.7. Dietary Records

To assess involuntary compensatory mechanisms from exercise participation and/or multi-ingredient supplementation (e.g., increased or decreased drive to eat), the participants were asked to maintain their normal food intake and kept a detailed dietary record for three non-consecutive days at the start and end of the trial, including two weekdays and one day of the weekend. Based on these 72-h recalls, an estimate of pre-to-post changes in energy and macronutrient intakes were obtained for each participant using the ASA-24 software NIH 2018a (National Cancer Institute, Rockville, MD, USA).

####  2.8. Anthropometry and Vital Measures

Basic anthropometry and vital measures were assessed pre- and post-intervention, including body weight, height, body mass index (BMI; kg/m2), heart rate, and arterial blood pressure.

####  2.9. Body Composition, Strength, and Performance Tests

A comprehensive test battery consisting of body composition, strength, and performance testing was completed pre- and post-intervention and is considered the reference standard for diagnosing, tracking disease progression, and assessing interventional efficacy in sarcopenia/sarcopenic obesity research [38,39,40,41].

Body composition was assessed by dual-energy X-ray absorptiometry (DXA; GE Lunar Prodigy, Madison, WI, USA), allowing for estimates of bone, lean and fat mass changes pre-to-post intervention. Primary outcomes were total and appendicular lean mass (TLM and ASM), muscle-to-body fat ratios (TLM/FM, TLM/%BF, ASM/FM, and ASM/%BF) and diagnostic criteria related to sarcopenia and sarcopenic obesity (i.e., allometric lean mass; ASMI, TLM/BW, TLM/BMI, ASM/BW, and ASM/BMI). Quality assurance testing with a QA-block was performed in the morning and as needed to ensure machine calibration. All subjects were scanned in the fasted state with body positions, bony landmarks, scan table references, and ROIs kept constant between pre- and post-scans.

Improvements in upper and lower body strength were assessed by 1-RM grip strength, 1-RM leg press, and 1-RM isometric leg extension (secondary outcomes). The muscle quality index (MQ), which is an indicator of strength per unit muscle [42], was then calculated by dividing upper and lower body strength (hand grip + ([leg press + leg extension]/2)) by appendicular lean mass (ASM).

Pre-to-post intervention changes in performance/functional mobility were tracked by the Short Physical Performance Battery (SPPB; 4-m gait speed, 5X sit-to-stand time (5XSTS), and balance testing) and an independent functional mobility test (4-step stair climb; 4SSC) (secondary outcomes).

####  2.10. Blood Collection and Analyses

Blood chemistry was performed on serum or plasma samples obtained from venous blood draws pre- and post-intervention. Diagnostic markers of metabolic syndrome (dyslipidemia, dysglycemia and systemic inflammation) and organ function (kidney and liver) were measured, including creatinine, bilirubin, alanine aminotransferase (ALT), gamma-glutamyltransferase (GGT), C-reactive protein (CRP), low-density lipoprotein (LDL), high-density lipoprotein (HDL), total cholesterol, triglycerides and glycosylated hemoglobin A1c (HbA1c) (Core Laboratory, Health Sciences Centre, Hamilton, UK).

Given the need for the development of adjunctive therapies that can maintain bone integrity during multimodal interventions, we also assessed the reference standard, bone turnover markers (BTM) type 1 procollagen N-terminal propeptide (P1NP, Novus, Toronto, ON, Canada) and beta-C-terminal telopeptide (β-CTX, Novus, Toronto, ON, Canada) using commercial ELISA kits. P1NP is a specific indicator of type 1 collagen deposition (i.e., bone formation), while β-CTX is a sensitive marker of bone resorption (i.e., bone degradation) and may be used to detect rapid treatment responses (i.e., ~weeks to months) [43]. Thus, P1NP and β-CTX are frequently used to monitor treatment effects before bone mineral density (BMD) changes can be detected [44]. These BTM were recently chosen as primary and secondary outcome variables in a recent 1-year weight management trial on the effects of semaglutide on bone health [45].

####  2.11. Metabolic Syndrome and Sarcopenic Obesity Risk Factors

As a part of this retrospective analysis, we assessed the adaptive response to polytherapy in overweight (BMI ≥ 27) or obese (BMI ≥ 30) participants with at least one other risk factor for metabolic syndrome (obese/MetS; n = 20). For each participant, a ‘Global MetS Risk Index’ was quantified based on their total number of diagnostic criteria for MetS and other metabolic risk factors (min 1 and max 9), including baseline obesity (≥27 BMI, ≥28% body fat, and ≥0.9 m waist-to-hip ratio), dyslipidemia (≥1.69 mmol/L triglycerides and ≤0.9 mmol/L HDL), dysglycemia (≥5.7% HbA1c), hypertension (≥140 systolic and ≥90 diastolic), as well as systemic inflammation (≥3 mg/L CRP).

Furthermore, the diagnostic criteria for sarcopenic obesity, as established in the ESPEN and EASO consensus statement [40], were used to generate a Sarcopenic Obesity Risk Rank for all study participants. Participants were ranked according to baseline obesity (% BF; low-to-high), allometric lean mass (ASM/BW; high-to-low), 5XSTS time (low-to-high), and the Global MetS Risk Index (low-to-high), with a rank of 1 representing the lowest and 20 the highest risk for each diagnostic criteria, respectively, which were then averaged to obtain an overall Sarcopenic Obesity Risk Rank.

####  2.12. Statistical Analyses

####  2.12.1. Retrospective Analysis 1

Multiple linear regression analyses were first run on putative predictors of the anabolic/treatment response to multimodal therapy using the full sample size of the original RCT (n = 32), including previously identified causes of anabolic resistance (i.e., chronological age, physical inactivity, and kidney dysfunction), metabolic risk factors (obesity, dyslipidemia, dysglycemia, hypertension, and systemic inflammation), a ‘global risk index’ for metabolic syndrome (MetS), and treatment compliance (MIS and HBRE). All independent variables, except MIS and HBRE compliances, were obtained pre-intervention.

Dependent variables were the improvements in primary and secondary outcomes, including lean mass (LM; TLM and ASM), allometric lean mass (TLM/BW, TLM/BMI, ASM/BW, and ASM/BMI), lean mass/body fat indices (TLM/FM, TLM/%BF, ASM/FM, and ASM/%BF), strength, and performance. Pre-to-post-intervention improvements were calculated as percent changes (Δ%) using the standard formula ((post-intervention test result − pre-intervention test result)/pre-intervention test result) * 100).

For all regression models, backward stepwise regression and collinearity testing were used to eliminate redundant variables and determine the best fit and strongest predictors of the adaptive response to multimodal therapy. Weakening of the overall model, negligible or weak correlation with the outcome (r < 0.2; p > 0.05), non-casual correlation with the outcome (nonsense), and redundancy/collinearity with other predictors were considered exclusion criteria for independent variables.

####  2.12.2. Retrospective Analysis 2

Secondly, to assess the difference between high- vs. lower-quality MIS on the adaptive response to multimodal therapy in individuals at risk for sarcopenic obesity, we compared improvements in lean mass, strength, and performance between placebo (n = 8) and M5 (n = 12) groups in a subgroup with obesity/MetS (n = 20). Between-group differences in improvement (Δ%) for each outcome variable were analyzed by independent t-tests (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; † p > 0.05 < 0.100). Within-group differences in pre vs. post-results for each outcome were analyzed by paired t-tests (# p ≤ 0.05; ## p ≤ 0.01; ### p ≤ 0.001; ‡ p > 0.05 < 0.100). STATISTICA Version 8.0 (StatSoft, Tulsa, OK, USA) was used for all statistical analyses.

Group means ± SE and/or improvement (Δ%) are presented in table and figure formats, and n-sizes for all outcomes are shown in figure and table captions.

##  3\. Results

####  3.1. Retrospective Analysis 1: Predictors of the Adaptive Response to HBRE + PA + MIS Polytherapy

In prediction model 1, known causes of anabolic resistance (chronological age, obesity, physical inactivity, and kidney dysfunction) and other putative predictors of the adaptive response (daily protein intake and treatment compliance) were included in the full model (Table 1 and Table S1). Independent variables were then gradually eliminated by backward stepwise regression and collinearity testing (Table S3). Overall, baseline obesity provided the best fit, with pre-intervention BMI being a moderate-to-strong negative predictor of the Δ% improvements in ASM (r = −0.36, p = 0.043), ASMI (r = −0.38, p = 0.030), TLM/BW (r = −0.67, p < 0.001), and TLM/BMI (r = −0.56, p = 0.001) (Figure 3). While baseline protein intake was a moderate-to-strong positive predictor of gains in TLM/BW (r = 0.57, p = 0.001) and TLM/BMI (r = 0.39, p = 0.025), it did not improve the overall fit of the model.

In the next model, diagnostic criteria for metabolic syndrome (MetS; obesity, dyslipidemia, dysglycemia, and hypertension), other metabolic risk factors (systemic inflammation), and a ‘global risk index’ for MetS (i.e., total risk factors) were included in the full model (Table 2 and Table S2). Redundant variables were removed by collinearity testing (Table S4), and the best fit was determined by backward stepwise regression. The Global MetS Risk Index provided the best overall fit being a moderate-to-strong negative predictor of the Δ% gains in lean mass (TLM, r = −0.45, p = 0.010; ASM, r = −0.40, p = 0.023), allometric lean mass (ASMI, r = −0.45, p = 0.011; TLM/BW, r = −0.53, p = 0.002; TLM/BMI, r = −0.37, p = 0.037) and lean mass/body fat indices (TLM/%BF, r = −0.40, p = 0.022; ASM/%BF, r = −0.42, p = 0.016) (Figure 4).

Among individual MetS diagnostic criteria, baseline obesity (BMI, %BF, and WH) and dyslipidemia (triglycerides and HDL) were moderate-to-very strong, negative predictors of Δ% gains in ASM, ASMI, TLM/BW and TLM/BMI (p < 0.05). Furthermore, HDL was positively correlated with % gains in lean mass, allometric lean mass, and lean mass/body fat indices, while other diagnostic criteria for MetS were negatively correlated with the adaptive response. 

####  3.2. Retrospective Analysis 2: Adaptive Response to HBRE + PA and High-Quality or Lower-Quality MIS in Free-Living Older Adults with Obesity/MetS

Next, we performed a subgroup analysis of participants who were overweight (BMI ≥ 27) or obese (BMI ≥ 30) with at least one other MetS risk factor (obese/MetS, n = 20). Our aim was to compare the adaptive response to polytherapy with either higher- (M5; whey + casein, creatine monohydrate, vitamin D3, calcium and fish oil, n = 12) or lower-quality (PLA; collagen + safflower oil, n = 8), protein-based MIS, with both groups either matching or exceeding current expert recommendations on total, daily protein intake (1.2–1.5 g/kg BW/d) [4,46].

####  3.3. Anthropometry and Vital Signs

At baseline, the obese/MetS subgroup exhibited several diagnostic criteria for MetS (“Global MetS Risk Index”) and were polymorbid and at risk for sarcopenic obesity. Thus, both PLA and M5 groups were representative of free-living older males in North America [25,26] and well-matched in terms of age, anthropometry and overall health status (Table 3).

Following the intervention, body weights and BMIs were moderately increased in both groups, with pre-to-post results borderline significant in the PLA group (p > 0.05 < 0.100). Vital signs were largely unaffected by HBRE + PA + MIS polytherapy, while diastolic BP significantly increased in the M5 group pre-to-post intervention (p < 0.05).

####  3.4. Compliance, PA Goals, and Dietary Intake

Compliance with the HBRE intervention was comparable to previously published unsupervised RCTs [29] and not significantly different between PLA and M5 groups (89% vs. 76%, respectively) (Table 4). Collectively, participants followed the ACSM guidelines for full-body strength training for older adults and increased elastic band resistance progressively over the 3-month intervention (p < 0.05).

The average daily step goal was chosen for improving health-related QoL and obesity/MetS status [34] but was not met by either cohort. While the M5 group marginally improved step counts over the duration of the study (the equivalent of ~400 m/day), the PLA group significantly decreased step counts pre-to-post intervention (the equivalent of ~1000 m/day) (p < 0.05). These PA levels were technically within the normal range for this population (2000–9000 steps), but both groups qualified as low-active/sedentary throughout the study [34].

Supplement adherences corresponded well to previously published RCTs [47] and were not significantly different between PLA and M5 groups (95% vs. 89%, respectively) (Table 5).

Macronutrient analyses of 72-h dietary recalls (food only) indicated that energy and carbohydrate intakes were significantly higher in the M5 as compared to the PLA group at baseline (p < 0.05) (Table 6), while there were no differences in protein or fat intakes. Participants were encouraged to maintain their dietary habits, but food consumption was significantly increased pre-to-post intervention in the PLA group, resulting in an overall higher energy intake (p < 0.05) (net positive energy balance). In contrast, food consumption was modestly decreased in the M5 group, decreasing overall energy intake (p < 0.05).

As designed, total protein consumption (food + supplement) was significantly increased in both groups pre-to-post intervention (PLA, 1.43 ± 0.09 g/kg BW; M5, 1.26 ± 0.09 g/kg BW) and exceeded the current RDA (0.8 g/kg BW/d) and expert recommendations on optimal protein intake for older adults (≥1.2 g/kg BW/d) [46,48,49,50], thus, meeting the proposed guidelines for sarcopenic obesity (1.2–1.5 g/kg/BW/day) [4] and weight management (1.2–1.6 g/kg BW/d) [46,51]. Although total protein intake was higher in PLA vs. M5, it was not statistically different between groups (p = 0.22).

####  3.5. Blood Chemistry

Markers of metabolic disease (i.e., dyslipidemia, dysglycemia and systemic inflammation) were measured pre and post intervention, including bloods lipids, hemoglobin A1c, and C-reactive protein. While not statistically significant, these risk markers were consistently reduced and increased, respectively, in the M5 vs. PLA group (Table 7). These diametrically opposing effects were confirmed directionally by the Global Risk Index for MetS (i.e., total MetS risk factors; Table 3) which was determined to be a key driver of anabolic resistance at old age (Section 3.1 and Section 3.2).

Furthermore, P1NP levels and the P1NP/CTX ratio were increased in the M5 group only, indicative of improved bone turnover and bone formation following three months of HBRE + PA with high-quality, protein-based MIS. While a longer duration trial is necessary to confirm BMD changes (i.e., 6 months–2 years), P1NP is a specific marker of type 1 collagen deposition and a sensitive indicator of bone formation that is used for tracking treatment efficacy in short-duration, interventional RCTs (weeks-months) [43].

Clinical markers of liver (bilirubin, ALT, and GGT) and kidney (creatinine and eGFR) function indicated no toxic effects of MIS in either group (Table S6). Notably, an increase in creatinine excretion is to be expected following significant LBM gain and creatine supplementation, such as in the M5 group. Creatinine is a natural byproduct of creatine breakdown and in line with muscle levels of creatine and phosphocreatine [52]. Because eGFR is calculated using serum creatinine levels, a reduction in eGFR is not an indication of decreased kidney function in this case, but rather creatine loading and muscle anabolism.

####  3.6. Δ Lean Mass, Lean Mass/Body Fat Indices, and Allometric Lean Mass

The M5 group showed moderate Δ gains in TLM and ASM, a marginal loss of fat mass, and significantly improved % BF (p < 0.05) (Table 8 and Figure 5A–D). Concurrently, the PLA group exhibited marginal losses of TLM and ASM, combined with significant gains in FM (p < 0.05) and %BF (p < 0.05). Predictably, these opposing outcomes between-groups yielded significantly different Δ changes in lean mass/body fat ratios, including TLM/FM (p < 0.05), TLM/%BF (p < 0.05), ASM/FM (p < 0.05), and ASM/%BF (p < 0.05) (Figure 6A–D).

The appendicular skeletal muscle mass index (ASMI; ASM/h2) is a key diagnostic measure for sarcopenia diagnosis, associated with lower mortality risk in obese individuals, and positively correlated to leukocyte telomere length [38,39,53,54]. Because of a modest gain in appendicular lean mass, ASMI was improved in the M5 group (p > 0.05 < 0.100), while it was marginally reduced in the PLA group following the intervention.

The DXA outcomes of most clinical relevance for sarcopenic obesity, i.e., allometric lean mass, significantly worsened pre-to-post intervention in the PLA group, including TLM/BW (p < 0.05), TLM/BMI (p < 0.05), ASM/BW (p < 0.05), and ASM/BMI (p > 0.05 < 0.10) (Figure 7A–D). Meanwhile, allometric lean mass was moderately improved in the M5 group; thus, the Δ% changes were significantly different between-groups (p < 0.05).

####  3.7. Δ Strength

Both maximal grip and leg press results were significantly improved in the M5 group pre-to-post intervention (p < 0.05), combined with a moderate improvement in isometric knee extension (p = 0.105) (Table 9 and Figure 8A–C). As expected, the PLA group also exhibited some improvement in strength (grip) following three months of HBRE, but this did not reach statistical significance because of interindividual variance within the group. Collectively, the strength improvements were more robust and uniform in the M5 vs. PLA group, which was also indicated in the muscle quality (MQ) index (Figure 8D).

####  3.8. Δ Performance

Albeit not reaching statistical significance per se, the time to complete the functional mobility tests was modestly improved pre-to-post intervention in the M5 group (4SSC; p = 0.24 and 5XSTS; p > 0.05 < 0.10), but not in the PLA group (Table 10 and Figure 9). Other components of the SPPB, such as balance testing and walk speed, were largely unchanged in both groups.

####  3.9. Ranks of Anabolic Response

The adaptive response (Δ%) to the multimodal intervention for each participant was ranked from 1 (lowest responder) to 20 (highest responder) for lean mass (LM, allometric LM, and LM/BF ratios), strength (isometric knee extension and leg press) and performance (4SSC and SPPB score). An overall rank of the anabolic response could then be generated from the average ranks of Δ lean mass, Δ strength, and Δ performance.

For each area of assessment, the M5 group consistently ranked higher than the PLA group (rank Δ lean mass, p = 0.0079; rank Δ strength, p = 0.097; rank Δ performance, p = 0.186) (Figure 10A–C), suggestive of a more robust overall anabolic response in the high-quality protein group (p < 0.013) (Figure 10D).

####  3.10. Sarcopenic Obesity Risk

The differential adaptive response to HBRE + PA + MIS polytherapy between high- (M5 + fish oil) vs. lower-quality (collagen + safflower oil) supplement groups was also reflected in the Sarcopenic Obesity Risk Rank, with a significant increase vs. modest decrease in PLA (p < 0.01) vs. M5 (p > 0.05), respectively (Δ% between-groups p < 0.01) (Figure 11 and Table 3).

Taken all together, these results suggest that the combined benefits of resistance exercise, higher-quality MIS (i.e., whey/casein > collagen peptides), and lifestyle modification (PA and diet) are necessary to mitigate muscle deterioration and sarcopenic obesity risk in older, polymorbid individuals (Figure 12).

##  4\. Discussion

In this retrospective analysis, we demonstrated that the long-term adaptive response to home-based resistance exercise (HBRE), daily physical activity (PA), and multi-ingredient supplementation (MIS) was negatively correlated with pre-intervention obesity and total MetS risk factors (Global MetS Risk Index) in a free-living cohort of older men. Specifically, obesity and the Global MetS Risk Index were moderate-to-strong, negative predictors of the adaptive response to polytherapy, while baseline chronological age, kidney function, and physical activity levels were only weakly associated with outcomes, suggesting that obesity and metabolic disease are key drivers of anabolic resistance at old age. Furthermore, we showed that a higher-quality, protein-based MIS (M5; whey/casein, creatine, calcium, vitamin D3, and fish oil) provided superior lean mass, strength and performance adaptations as compared to a lower-quality alternative (PLA; collagen peptides and safflower oil) in obese/MetS individuals undergoing HBRE + PA + MIS polytherapy.

The main findings from the regression analyses are largely consistent with those from tracer studies assessing the acute anabolic response to protein feeding and/or contractile activity in obese humans and animal models [55,56,57,58,59]. Work by Nilsson and Fluckey demonstrated a blunted 24-h protein synthetic response (MPS) to resistance exercise in various subfractions of fast-twitch muscles from sarcopenic obese rats [58]. Similar results have been reported in both young and old obese humans, including the recent works of Murton and Greenhaff [59], Beals and Burd [55,56], and Smeuninx and Breen [57]. While chronological age, PA levels, and kidney function were only weakly correlated to the long-term adaptations to HBRE + PA + MIS polytherapy, there are plausible explanations for this. Compared to end-stage renal disease (eGFR < 15 mL/min) (reviewed in [1]), age-associated kidney impairment is likely too mild to affect the long-term adaptive response. Secondly, we are not aware of any studies demonstrating that anabolic resistance becomes progressively worse across older age categories, such as septuagenarians (70–79 years old), octogenarians (80–89 years old), and nonagenarians (90–99 years old). Our cohort ranged from 66 to 91 years of age, and our findings do not support the notion that chronological age is a significant driver of anabolic resistance across these age categories. Third, it is well known that PA protects against age-related muscle loss, and Smeuninx and Breen recently reported a positive correlation between daily step counts and the MPS response to protein feeding in older adults [57]. Surprisingly, we did not find that baseline PA was significantly associated with the adaptive response, although our trial was designed to assess long-term adaptations rather than acute signaling events or MPS. Taken all together, we surmise that obesity and associated metabolic risk factors are the strongest contributors to SM anabolic resistance in old age and impair both the acute and long-term response to exercise- and supplement-based therapies.

Next, we demonstrated that the adaptive response to HBRE + PA + MIS was dependent on the quality of the multi-ingredient supplement in a subgroup of overweight/obese participants at risk for sarcopenic obesity (obese/MetS). Consistently, the higher-quality, protein-based MIS group (M5) exhibited greater Δ% gains in all clinically relevant outcomes, including body composition, strength, and performance vs. the PLA group (i.e., lower-quality MIS), although both cohorts exceeded current recommendations for daily protein intake in older adults. Furthermore, obesity-associated metabolic risk factors were modestly improved in the M5 group only, resulting in a significant decrease in the Sarcopenic Obesity Risk Rank vs. the PLA group. While the PLA-treated participants also improved strength and muscle quality, their overall adaptive response was significantly attenuated despite being compliant with HBRE + MIS and with an overall daily protein consumption that exceeded the M5 group (PLA, 1.43 ± 0.09 g/kg BW; M5, 1.26 ± 0.09 g/kg BW), and in line with expert recommendations on optimal protein intake for sarcopenic obesity (1.2–1.5 g/kg/BW/day). These results are consistent with those studies showing a superior MPS response [36,37], greater LBM regain during recovery from energy restriction and physical inactivity [60], and more robust adaptations to exercise interventions in whey- (or whey/casein) vs. collagen-supplemented groups [22,47]. Notably, hydrolyzed collagen has been used as an isocaloric, protein-matched placebo by our research team because of its inferior anabolic potential, including low levels of methionine (i.e., rate limiting for translation initiation), EAAs, BCAAs, and poor amino acid digestibility score (DIAAS of 0). Thus, our results are in contrast to those of Jendricke, Zdzieblik and König, who have reported generally superior results for collagen peptide vs. whey protein supplementation upon body composition, bone health, and knee joint discomfort in a series of studies [61,62,63,64,65]. Currently, we, and others [66], are unable to explain this discrepancy, and we refer the readers to the excellent reviews performed by Deane and Atherton and Holwerda and van Loon for more information on collagen and its therapeutic potential on muscle, bone, and connective tissue [35,67].

A limitation of the current study with respect to exercise-induced energy expenditure is that the daily step goals were not met by the participants and that the collagen-supplemented group increased their daily energy intake during the intervention (i.e., net positive energy balance). While speculative, an increased food intake may be a compensatory response to engaging in a chronic exercise program in low-active/sedentary individuals. Specifically, various forms of strength training have been shown to increase the drive to eat and alter appetite-regulatory hormones in obese males [68]. Conversely, daily energy intake was modestly reduced in the M5 group, potentially indicative of a greater satiety-inducing effect from daily consumption of humanized milk proteins (whey/casein; 60:40 ratio) vs. collagen peptides. In support of our data, Zdzieblik and König’s RCT also showed that collagen peptides increased daily energy intake in middle-aged, untrained males undergoing 12 weeks of resistance training [63], yet it was decreased in the control and whey-supplemented groups. Currently, it is well-accepted that proteins are beneficial for regulating appetite and energy expenditure, but it is unclear which protein source is more satiating in the long term [69]. Interestingly, reversing the whey/casein ratio in cowmilk (20:80) to resemble human breast milk (70:30) has been shown to augment plasma AUCs of insulin, C-peptide, incretins (GLP-1 and GIP), total amino acids (AAs), and branched-chain AAs following ingestion [70]. Evidence also points to the co-ingestion of protein or specific AAs with calcium, providing a potent synergy on GLP-1 release and appetite suppression [71]. Thus, these observations may suggest that the whey/casein ratio in the M5 supplement (“humanized milk ratio”), combined with the calcium caseinate, is optimized for inducing satiety and anabolism compared to an incomplete protein source. Notwithstanding, we surmise that the home-based intervention affected overall compliance negatively (specifically, daily step goals and food intake) and that involuntary compensatory mechanisms, such as satiety-signaling, may have contributed to the observed outcomes in the obese/MetS cohort.

While not designed as a weight loss trial per se, the current study may also be of interest for LBM maintenance in weight management, with specific relevance to individuals at risk for sarcopenic obesity. All types of weight management, such as bariatric surgery, pharmacotherapy, and low-calorie diets, lead to substantial FFM loss [10,12,16,17,72], typically exceeding the widely cited ‘Quarter FFM Rule’ [73] and accounting for ≥20–40% of total weight loss [17]. With the meteoric rise in the use of GLP-1RA and GLP-1RA/GIP dual agonists, there has been a renewed interest in developing countermeasures for maintaining LBM in the face of rapid weight loss. For example, it has been demonstrated that even combining aerobic and resistance exercise (or each on their own), protein intake of 1.0 g/kg BW/d, and daily supplementation with vitamin D (1000 IU/d) and calcium (1500 mg/d) is not sufficient to prevent LBM loss during caloric restriction (−500 to −750 kcal/d) in obese, older adults [74,75]. Considering that weight loss is more rapid and extreme in bariatric surgery and pharmacotherapy, it cannot be expected that just an ‘adequate’ daily protein intake (i.e., 0.8–1 g/kg BW/d) without provision of extra high-quality proteins (e.g., whey and casein) to match or exceed recommended daily protein intake (1.2–1.6 g/kg BW/d), would prevent muscle deterioration with GLP-1RA and GLP-1RA/GIP dual agonist use. Our study also suggests that increasing the protein intake from mixed sources (0.84 g/kg/d) by over 50% (to 1.43 g/kg/d) using a lower quality protein source (collagen peptides) does not yield positive effects upon satiety/food intake vs. a high-quality protein source. Thus, when applying multimodal interventions for weight management, it is important to consider both protein quality (whey/casein > collagen > plant) and quantity (1.2–1.6 g/kg BW/d).

Concurrent with LBM loss, bone deterioration is a significant issue in weight management, which is particularly relevant for older adults who have difficulty regaining bone mass and are at higher risk for fractures, falls, and serious injuries [76,77]. Large-scale epidemiological studies have consistently shown that weight loss over 6–12 months is associated with a ~2% reduction in bone mineral density (BMD) and higher fracture risk in older adults, including overweight and obese individuals [77]. Studies also indicate that potential countermeasures, such as weight-bearing exercise and resistance training, do not fully protect against LBM and bone deterioration, as exemplified by the Villareal studies [74,75]. Furthermore, when bone mass is lost, it does not seem to be regained in full even after weight regain [77]. In terms of robust weight loss solutions, GLP1-RA therapy (i.e., semaglutide and liraglutide) and bariatric surgery significantly reduce spine and hip BMDs [15,45,77,78]. Specifically, bariatric surgery is associated with substantial biochemical, hormonal, and mechanical changes and reduces BMD by ~5–10% at the spine and hip, thus increasing fracture risk [78,79,80]. While the mechanisms of bone loss are multiple, bone resorption, as measured by CTX levels, is increased 6 months onward during pharmacotherapy (semaglutide [45]) and persists even 2–3 years following bariatric surgery [78], further stressing the importance of developing new countermeasures for LBM and bone loss during weight management. AACE/TOS/ASMBS clinical practice guidelines currently stipulate supplementation with calcium citrate and vitamin D3 to attenuate bone loss following surgical weight loss [78]. Muschitz et al. found that calcium and vitamin D3 significantly attenuated bone loss following bariatric surgery when combined with protein supplementation and exercise [81]. Our study also suggests that a whey/casein-based MIS that contains creatine, vitamin D3, calcium and fish oil may be ideal for maintaining bone health in overweight and obese individuals who undergo exercise-based therapy, although a longer duration, dedicated weight-loss trial is necessary to confirm structural bone benefits.

While there are many benefits to using an unsupervised study design and multi- vs. single-ingredient supplementation, we certainly acknowledge that the current trial has several limitations. First, it is not possible to determine the individual contributions of each component of M5 to the observed outcomes; however, the ingredients were carefully chosen for their independent benefits on LBM, body composition (muscle-to-fat ratios), bone accretion, and/or metabolic risk factors. We have now also shown that this combination of ingredients improves LBM, strength/function, bone formation, and metabolic risk factors under both controlled and free-living conditions [22,47]. Secondly, the sample size for the current analysis was modest (n = 20), increasing overall variance and the likelihood of type 2 error. However, the results consistently favored M5 over the PLA group, which was clearly indicated in the individual and overall anabolic response ranks (Figure 10). While the observed outcomes may have been partially driven by involuntary compensatory mechanisms affecting daily PA and food intake levels, this is an inherent risk of unsupervised trials. We also surmise that the long-term, satiety-inducing effect of consuming whey/casein over collagen is a true phenomenon grounded in evolutionary biology that deserves to be studied in more detail, especially in the context of body re-composition and weight management. For millions of years, mammalian species have consumed milk for satiety, growth and development [82], and modern humans have also consumed milk from other species, such as cows, buffaloes, goats, and camels, since the Neolithic revolution (~10,000 years ago) [83]. Thus, casein and whey are outstanding functional components of milk that have co-evolved to optimize satiety and growth. Lastly, there is a definite need for more RCTs on the role of protein quality (milk vs. plant vs. collagen vs. protein blends) on musculoskeletal health, training adaptations, and recovery, particularly in females. Future research should aim to optimize exercise and nutritional interventions, such as whey/casein-based MIS, for improving body composition, health, and overall well-being in this population, specifically.

##  5\. Conclusions

In conclusion, our study confirms that obesity and associated metabolic risk factors are the main drivers of anabolic resistance in old age and impair the long-term adaptive response to exercise- and supplement-based interventions. We also demonstrate that a higher-quality, protein-based multi-ingredient supplement (Muscle 5; whey/casein + creatine + calcium + vitamin D3 \+ fish oil) confers superior lean mass, strength, and performance adaptations vs. a lower-quality alternative (collagen peptides + safflower oil) and attenuates sarcopenic obesity risk in polymorbid, older adults. Based on these and other findings, management strategies for mitigating concurrent LBM loss and body fat gain in aging, obesity, and polymorbid disease states (e.g., sarcopenic obesity) should ideally incorporate a higher-quality, protein-based MIS for optimizing body composition and overall health.

##  Supplementary Materials

The following supporting information can be downloaded at: <https://www.mdpi.com/article/10.3390/nu16244407/s1>, Table S1: Prediction Model 1 (strength and function). Table S2: Prediction Model 2 (strength and function). Table S3: Collinearity Model 1. Table S4: Collinearity Model 2. Table S5: Supplement Amino Acid Profiles. Table S6: Kidney and Liver Markers. Between-group differences in the adaptive response (i.e., Δ% changes) were analyzed by independent t-tests (* p ≤ 0.05). Within-group differences in pre-post intervention results were analyzed by paired t-tests (# p ≤ 0.05). Sample size for kidney and liver outcomes n = 19 (PLA; n = 8, M5; n = 11).

## Author Contributions

Conceptualization, B.P.H. and M.A.T.; Methodology, M.I.N., B.P.H. and M.A.T.; Validation, M.I.N.; Formal analysis, M.I.N. and N.M.d.M.; Investigation, M.I.N.; Resources, M.A.T.; Data curation, D.X.; Writing—original draft, M.I.N. and M.A.T.; Writing—review & editing, M.I.N. and M.A.T.; Supervision, M.I.N., B.P.H. and M.A.T.; Project administration, M.A.T.; Funding acquisition, M.A.T. All authors have read and agreed to the published version of the manuscript.

## Funding

This research was funded by a CIHR grant to M.A.T (grant number 143325). 

## Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Hamilton Integrated Research Ethics Board (2018-4656-GRA; approval date: 12 June 2018).

## Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. 

## Data Availability Statement

The data presented in this study are available on request from the corresponding author due to privacy.

## Acknowledgments

We would like to thank Joshua Nederveen (McMaster University) for proofreading the manuscript.

## Conflicts of Interest

Authors Mats I. Nilsson and Bart P. Hettinga were employed by the Exerkine Corporation, McMaster University Medical Center, and Mark A. Tarnopolsky is the CEO of the Exerkine Corporation, McMaster University Medical Center. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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**Figure 1.** Consort flow chart. Left panel (1–4): Enrollment, allocation, follow-up, and analysis of the original clinical trial (Nilsson et al. [22]). Right panel (5–6): The retrospective data analyses conducted for the current manuscript. 

**Figure 1.** Consort flow chart. Left panel (1–4): Enrollment, allocation, follow-up, and analysis of the original clinical trial (Nilsson et al. [22]). Right panel (5–6): The retrospective data analyses conducted for the current manuscript.

**Figure 2.** RCT timeline and clinical testing. X = time point of procedure (weeks 0–1 and 13–14). 

**Figure 2.** RCT timeline and clinical testing. X = time point of procedure (weeks 0–1 and 13–14).

**Figure 3.** Overall best fit in prediction model 1. Baseline obesity (BMI) was the strongest individual predictor of % lean mass gains following three months of HBRE + PA + MIS polytherapy. The sample size for analyses n = 30–32. All correlations are shown in Table 1 and Table S1. (**A**) Baseline BMI vs. Δ TLM (% change); (**B**) Baseline BMI vs Δ ASM (% change); (**C**) Baseline BMI vs Δ ASMI (% change). 

**Figure 3.** Overall best fit in prediction model 1. Baseline obesity (BMI) was the strongest individual predictor of % lean mass gains following three months of HBRE + PA + MIS polytherapy. The sample size for analyses n = 30–32. All correlations are shown in Table 1 and Table S1. (**A**) Baseline BMI vs. Δ TLM (% change); (**B**) Baseline BMI vs Δ ASM (% change); (**C**) Baseline BMI vs Δ ASMI (% change).

**Figure 4.** Overall best fit in prediction model 2. Compared to the individual metabolic risk factors (i.e., obesity, dyslipidemia, dysglycemia, and systemic inflammation), the Global MetS Risk Index (i.e., total MetS risk factors Section 2.11) was the overall strongest predictor of % lean mass gains following three months of HBRE + PA + MIS polytherapy. The sample size for analyses n = 30–32. All correlations are shown in Table 2 and Table S2. (**A**) Baseline MetS Risk Index vs. Δ TLM (% change); (**B**) Baseline MetS Risk Index vs. Δ ASM (% change); (**C**) Baseline MetS Risk Index vs. Δ ASMI (% change). 

**Figure 4.** Overall best fit in prediction model 2. Compared to the individual metabolic risk factors (i.e., obesity, dyslipidemia, dysglycemia, and systemic inflammation), the Global MetS Risk Index (i.e., total MetS risk factors Section 2.11) was the overall strongest predictor of % lean mass gains following three months of HBRE + PA + MIS polytherapy. The sample size for analyses n = 30–32. All correlations are shown in Table 2 and Table S2. (**A**) Baseline MetS Risk Index vs. Δ TLM (% change); (**B**) Baseline MetS Risk Index vs. Δ ASM (% change); (**C**) Baseline MetS Risk Index vs. Δ ASMI (% change).

**Figure 5.** Δ LM, FM, and % BF. Between-group differences in the adaptive response (i.e., Δ% changes) were analyzed by independent t-tests (** p ≤ 0.01; † p > 0.05 < 0.100). Within-group differences in pre-post intervention results were analyzed by paired t-tests (# p ≤ 0.05; ‡ p > 0.05 < 0.100) (Table 8). Sample size for analyses n = 20 (PLA; n = 8, M5; n = 12). (**A**) Δ TLM (% change); (**B**) Δ ASM (% change); (**C**) Δ FM (% change); (**D**) Δ% Body fat (% change). 

**Figure 5.** Δ LM, FM, and % BF. Between-group differences in the adaptive response (i.e., Δ% changes) were analyzed by independent t-tests (** p ≤ 0.01; † p > 0.05 < 0.100). Within-group differences in pre-post intervention results were analyzed by paired t-tests (# p ≤ 0.05; ‡ p > 0.05 < 0.100) (Table 8). Sample size for analyses n = 20 (PLA; n = 8, M5; n = 12). (**A**) Δ TLM (% change); (**B**) Δ ASM (% change); (**C**) Δ FM (% change); (**D**) Δ% Body fat (% change).

**Figure 6.** Δ Body Composition Indices. Between-group differences in the adaptive response (i.e., Δ% changes) were analyzed by independent t-tests (* p ≤ 0.05; ** p ≤ 0.01). Within-group differences in pre-post intervention results were analyzed by paired t-tests (# p ≤ 0.05) (Table 8). Sample size for analyses n = 20 (PLA; n = 8, M5; n = 12). (**A**) Δ TLM/FM (% change); (**B**) Δ TLM/%BF (% change); (**C**) Δ ASM/FM (% change); (**D**) Δ ASM/%BF (% change). 

**Figure 6.** Δ Body Composition Indices. Between-group differences in the adaptive response (i.e., Δ% changes) were analyzed by independent t-tests (* p ≤ 0.05; ** p ≤ 0.01). Within-group differences in pre-post intervention results were analyzed by paired t-tests (# p ≤ 0.05) (Table 8). Sample size for analyses n = 20 (PLA; n = 8, M5; n = 12). (**A**) Δ TLM/FM (% change); (**B**) Δ TLM/%BF (% change); (**C**) Δ ASM/FM (% change); (**D**) Δ ASM/%BF (% change).

**Figure 7.** Δ Allometric LM. Between-group differences in the adaptive response (i.e., Δ% changes) were analyzed by independent t-tests (* p ≤ 0.05). Within-group differences in pre-post intervention results were analyzed by paired t-tests (# p ≤ 0.05; ‡ p > 0.05 < 0.100) (Table 8). Sample size for analyses n = 20 (PLA; n = 8, M5; n = 12). (**A**) Δ TLM/BW (% change); (**B**) Δ TLM/BMI (% change); (**C**) Δ ASM/BW (% change); (**D**) Δ ASM/BMI (% change). 

**Figure 7.** Δ Allometric LM. Between-group differences in the adaptive response (i.e., Δ% changes) were analyzed by independent t-tests (* p ≤ 0.05). Within-group differences in pre-post intervention results were analyzed by paired t-tests (# p ≤ 0.05; ‡ p > 0.05 < 0.100) (Table 8). Sample size for analyses n = 20 (PLA; n = 8, M5; n = 12). (**A**) Δ TLM/BW (% change); (**B**) Δ TLM/BMI (% change); (**C**) Δ ASM/BW (% change); (**D**) Δ ASM/BMI (% change).

**Figure 8.** Δ Strength and Muscle Quality. Between-group differences in the adaptive response (i.e., Δ% changes) were analyzed by independent t-tests († p > 0.05 < 0.100). Within-group differences in pre-post intervention results were analyzed by paired t-tests (# p ≤ 0.05; ## p ≤ 0.01) (Table 9). Sample size for strength outcomes n = 19 (PLA; n = 7, M5; n = 12). (**A**) Δ Leg Press (% change); (**B**) Knee Extension (% change); (**C**) Grip Strength (% change); (**D**) Δ Muscle Quality (% change). 

**Figure 8.** Δ Strength and Muscle Quality. Between-group differences in the adaptive response (i.e., Δ% changes) were analyzed by independent t-tests († p > 0.05 < 0.100). Within-group differences in pre-post intervention results were analyzed by paired t-tests (# p ≤ 0.05; ## p ≤ 0.01) (Table 9). Sample size for strength outcomes n = 19 (PLA; n = 7, M5; n = 12). (**A**) Δ Leg Press (% change); (**B**) Knee Extension (% change); (**C**) Grip Strength (% change); (**D**) Δ Muscle Quality (% change).

**Figure 9.** Δ Performance. Between-group differences in the adaptive response (i.e., Δ% changes) were analyzed by independent t-tests (no significance detected). Within-group differences in pre-post intervention results were analyzed by paired t-tests (‡ p > 0.05 < 0.100) (Table 10). Sample size for performance outcomes n = 19 (PLA; n = 7, M5; n = 12). (**A**) Δ 4SSC (% change); (**B**) Δ SPPB Score (% change). 

**Figure 9.** Δ Performance. Between-group differences in the adaptive response (i.e., Δ% changes) were analyzed by independent t-tests (no significance detected). Within-group differences in pre-post intervention results were analyzed by paired t-tests (‡ p > 0.05 < 0.100) (Table 10). Sample size for performance outcomes n = 19 (PLA; n = 7, M5; n = 12). (**A**) Δ 4SSC (% change); (**B**) Δ SPPB Score (% change).

**Figure 10.** Anabolic Ranks. Ranks (low 1- high 20) of anabolic responses (Δ lean mass, Δ strength, Δ performance, and Δ overall) in high-quality (n = 12; M5 + fish oil) vs. lower-quality (n = 8; PLA; collagen + safflower oil) supplement groups in obese/MetS older males following three months of home-based resistance exercise and daily walking (HBRE + PA). Between-group differences in the anabolic response ranks were analyzed by independent t-tests (* p ≤ 0.05; **p ≤ 0.01; † p > 0.05 < 0.100). Sample size for anabolic ranks n = 19–20 (PLA; n = 7–8, M5; n = 12). The rankings for lean mass, strength, performance and overall anabolic response are defined in Section 3.9. (**A**) Δ Lean Mass Rank (1–20); (**B**) Δ Strength Rank (1–20); (**C**) Δ Performance Rank (1–20); (**D**) Overall Anabolic Rank (1–20). 

**Figure 10.** Anabolic Ranks. Ranks (low 1- high 20) of anabolic responses (Δ lean mass, Δ strength, Δ performance, and Δ overall) in high-quality (n = 12; M5 + fish oil) vs. lower-quality (n = 8; PLA; collagen + safflower oil) supplement groups in obese/MetS older males following three months of home-based resistance exercise and daily walking (HBRE + PA). Between-group differences in the anabolic response ranks were analyzed by independent t-tests (* p ≤ 0.05; **p ≤ 0.01; † p > 0.05 < 0.100). Sample size for anabolic ranks n = 19–20 (PLA; n = 7–8, M5; n = 12). The rankings for lean mass, strength, performance and overall anabolic response are defined in Section 3.9. (**A**) Δ Lean Mass Rank (1–20); (**B**) Δ Strength Rank (1–20); (**C**) Δ Performance Rank (1–20); (**D**) Overall Anabolic Rank (1–20).

**Figure 11.** Δ Sarcopenic Obesity Risk Rank. Between-group differences in the adaptive response (i.e., Δ% changes) were analyzed by independent t-tests (** p ≤ 0.01). Within-group differences in pre-post intervention results were analyzed by paired t-tests (## p ≤ 0.01) (Table 3). The Sarcopenic Obesity Risk Rank is defined in Section 2.11. Sample size for anabolic ranks n = 19 (PLA; n = 7, M5; n = 12). 

**Figure 11.** Δ Sarcopenic Obesity Risk Rank. Between-group differences in the adaptive response (i.e., Δ% changes) were analyzed by independent t-tests (** p ≤ 0.01). Within-group differences in pre-post intervention results were analyzed by paired t-tests (## p ≤ 0.01) (Table 3). The Sarcopenic Obesity Risk Rank is defined in Section 2.11. Sample size for anabolic ranks n = 19 (PLA; n = 7, M5; n = 12).

**Figure 12.** Drivers and countermeasures of anabolic resistance in sarcopenic obesity. 

**Figure 12.** Drivers and countermeasures of anabolic resistance in sarcopenic obesity.

**Table 1.** Full prediction model 1: Baseline predictors and correlations to the adaptive response to HBRE + PA + MIS polytherapy. Correlation coefficients (r) in bold are statistically significant (p ≤ 0.05). 

**Table 1.** Full prediction model 1: Baseline predictors and correlations to the adaptive response to HBRE + PA + MIS polytherapy. Correlation coefficients (r) in bold are statistically significant (p ≤ 0.05).

| PREDICTION MODEL 1  
---|---  
Δ LEAN MASS   
(% Pre-Post)| Δ ALLOMETRIC LEAN MASS   
(% Pre-Post)| Δ LEAN MASS/BODY FAT INDICES   
(% Pre-Post)  
BASELINE PREDICTORS| ∆ TLM| ∆ ASM| ∆ ASMI| ∆ TLM/BW| ∆ TLM/BMI| ∆ ASM/BW| ∆ ASM/BMI| ∆ TLM/FM| ∆ TLM/% FM| ∆ ASM/FM| ∆ ASM/% FM  
**1\. Age (years)**|  r = 0.06| r = 0.18| r = 0.20| r = 0.32| r = 0.13| r = 0.18| r = 0.21| r = 0.13| r = 0.15| r = 0.20| r = 0.23  
p = 0.745| p = 0.330| p = 0.270| p = 0.071| p = 0.467| p = 0.317| p = 0.238| p = 0.480| p = 0.419| p = 0.275| p = 0.208  
**2\. Obesity (BMI)**|  r = −0.23| **r = −0.36**| **r = −0.38**| **r = −0.67**| **r = −0.56**|  r = −0.33| r = −0.29| r = −0.06| r = −0.11| r = −0.15| r = −0.22  
p = 0.212| **p = 0.043**| **p = 0.030**| **p = 0.000**| **p = 0.001**| p = 0.067| p = 0.106| p = 0.747| p = 0.536| p = 0.407| p = 0.234  
**3\. PA (steps/day)**|  r = 0.03| r = −0.14| r = −0.10| r = 0.15| r = 0.22| r = −0.12| r = −0.21| r = 0.03| r = 0.06| r = −0.05| r = −0.03  
p = 0.893| p = 0.450| p = 0.597| p = 0.442| p = 0.204| p = 0.530| p = 0.258| p = 0.894| p = 0.754| p = 0.781| p = 0.892  
**4\. Kidney Function**|  |  |  |  |  |  |  |  |  |  |   
| **A. eGFR (mL/min/1.73 m 2)**| r = 0.16| r = 0.11| r = 0.12| r = −0.05| r = 0.04| r = 0.01| r = −0.07| r = −0.23| r = −0.08| r = −0.24| r = −0.09  
p = 0.399| p = 0.567| p = 0.535| p = 0.794| p = 0.810| p = 0.961| p = 0.704| p = 0.204| p = 0.684| p = 0.193| p = 0.649  
| **B. Creatinine (μmol/L)**|  r = −0.19| r = −0.13| r = −0.15| r = −0.01| r = −0.07| r = −0.02| r = 0.06| r = 0.20| r = 0.03| r = 0.20| r = 0.04  
p = 0.316| p = 0.476| p = 0.413| p = 0.975| p = 0.705| p = 0.915| p = 0.765| p = 0.290| p = 0.882| p = 0.277| p = 0.848  
**6\. Protein Intake (g/kgBW/d)**|  r = −0.05| r = 0.10| r = 0.10| **r = 0.57**| **r = 0.39**|  r = 0.15| r = 0.19| r = −0.02| r = −0.04| r = 0.05| r = 0.04  
p = 0.765| p = 0.597| p = 0.581| **p = 0.001**| **p = 0.025**| p = 0.403| p = 0.295| p = 0.897| p = 0.835| p = 0.770| p = 0.793  
**7\. Exercise Compliance (%)**|  r = 0.20| r = 0.23| r = 0.24| r = 0.19| r = 0.08| r = 0.20| r = 0.12| r = 0.09| r = 0.18| r = 0.12| r = 0.21  
p = 0.291| p = 0.229| p = 0.201| p = 0.322| p = 0.659| p = 0.282| p = 0.511| p = 0.618| p = 0.354| p = 0.513| p = 0.263  
**8\. Supplement Compliance (%)**|  r = −0.02| r = −0.01| r = 0.03| r = 0.02| r = 0.04| r = −0.18| r = −0.17| r = −0.14| r = −0.11| r = −0.14| r = −0.11  
p = 0.931| p = 0.979| p = 0.866| p = 0.922| p = 0.833| p = 0.349| p = 0.382| p = 0.465| p = 0.572| p = 0.460| p = 0.573  
  
**Table 2.** Full prediction model 2: Baseline predictors and correlations to the adaptive response to HBRE + PA + MIS polytherapy. Correlation coefficients (r) in bold are statistically significant (p ≤ 0.05). 

**Table 2.** Full prediction model 2: Baseline predictors and correlations to the adaptive response to HBRE + PA + MIS polytherapy. Correlation coefficients (r) in bold are statistically significant (p ≤ 0.05).

| PREDICTON MODEL 2   
---|---  
Δ LEAN MASS   
(% Pre-Post)| Δ ALLOMETRIC LEAN MASS   
(% Pre-Post)| Δ LEAN MASS /BODY FAT INDICES   
(% Pre-Post)  
BASELINE PREDICTORS| ∆ TLM| ∆ ASM| ∆ ASMI | ∆ TLM/BW| ∆ TLM/BMI| ∆ ASM/BW| ∆ ASM/BMI| ∆ TLM/FM| ∆ TLM/% FM| ∆ ASM/FM| ∆ ASM/% FM  
**Obesity**|  |  |  |  |  |  |  |  |  |  |   
| **1\. BMI**|  r = −0.23| **r = −0.36**| **r = −0.38**| **r = −0.67**| **r = −0.56**|  r = −0.33| r = −0.29| r = −0.06| r = −0.11| r = −0.15| r = −0.22  
p = 0.212| **p = 0.043**| **p = 0.030**| **p = 0.000**| **p = 0.001**| p = 0.067| p = 0.106| p = 0.747| p = 0.536| p = 0.407| p = 0.234  
| **2\. % Body Fat**|  r = −0.11| r = −0.32| **r = −0.359**| **r = −0.99**| **r = −0.82**|  r = −0.27| r = −0.25| r = 0.04| r = 0.01| r = −0.08| r = −0.13  
p = 0.555| p = 0.076| **p = 0.044**| **p = 0.000**| **p = 0.000**| p = 0.136| p = 0.171| p = 0.812| p = 0.965| p = 0.682| p = 0.489  
| **3\. Waist-to-Hip Ratio**|  r = −0.19| r = −0.31| **r = −0.35**| **r = −0.48**|  r = −0.30| **r = −0.42**|  r = −0.27| r = −0.06| r = −0.11| r = −0.14| r = −0.20  
p = 0.291| p = 0.085| **p = 0.050**| **p = 0.005**| p = 0.100| **p = 0.016**| p = 0.133| p = 0.759| p = 0.554| p = 0.461| p = 0.281  
**CVD Risk**|  |  |  |  |  |  |  |  |  |  |   
| **4\. Systolic BP**|  r = 0.09| r = 0.06| r = 0.06| r = −0.04| r = 0.10| r = 0.08| r = 0.04| r = −0.08| r = −0.05| r = −0.08| r = −0.07  
p = 0.635| p = 0.738| p = 0.753| p = 0.841| p = 0.573| p = 0.663| p = 0.806| p = 0.680| p = 0.765| p = 0.648| p = 0.721  
| **5\. Diastolic BP**|  r = −0.03| r = −0.18| r = −0.20| r = −0.04| r = 0.16| r = −0.17| r = −0.19| r = −0.12| r = −0.11| r = −0.20| r = −0.21  
p = 0.891| p = 0.329| p = 0.281| p = 0.810| p = 0.374| p = 0.362| p = 0.294| p = 0.522| p = 0.549| p = 0.261| p = 0.252  
**Dyslipidemia**|  |  |  |  |  |  |  |  |  |  |   
| **6\. Triglycerides**|  r = −0.33| r = −0.25| r = −0.30| **r = −0.39**| **r = −0.46**|  r = −0.10| r = −0.09| r = −0.14| r = −0.28| r = −0.13| r = −0.26  
p = 0.72| p = 0.180| p = 0.098| **p = 0.029**| **p = 0.009**| p = 0.580| p = 0.637| p = 0.458| p = 0.127| p = 0.487| p = 0.151  
| **7\. HDL**|  r = 0.33| r = 0.27| r = 0.33| **r = 0.72**| **r = 0.55**|  r = 0.20| r = 0.12| r = 0.16| r = 0.24| r = 0.16| r = 0.24  
p = 0.067| p = 0.144| p = 0.069| **p = 0.000**| **p = 0.001**| p = 0.284| p = 0.505| p = 0.398| p = 0.186| p = 0.404| p = 0.194  
**Dysglycemia**|  |  |  |  |  |  |  |  |  |  |   
| **8\. HbA1c**|  r = −0.28| r = −0.17| r = −0.20| r = −0.23| r = 0.03| r = −0.13| r = −0.11| r = −0.06| r = −0.19| r = −0.03| r = −0.16  
p = 0.125| p = 0.348| p = 0.289| p = 0.206| p = 0.869| p = 0.495| p = 0.550| p = 0.732| p = 0.305| p = 0.861| p = 0.400  
**Inflammation**|  |  |  |  |  |  |  |  |  |  |   
| **9\. CRP**|  r = −0.23| r = −0.21| r = −0.19| r = 0.07| r = 0.03| r = −0.25| r = −0.21| r = −0.19| r = −0.25| r = −0.19| r = −0.26  
p = 0.214| p = 0.259| p = 0.311| p = 0.714| p = 0.869| p = 0.178| p = 0.263| p = 0.327| p = 0.175| p = 0.311| p = 0.165  
**Global MetS Risk Index**|  |  |  |  |  |  |  |  |  |  |   
| **10\. # Risk Factors**| **r = −0.45**| **r = −0.40**| **r = −0.45**| **r = −0.53**| **r = −0.37**|  r = −0.30| r = −0.25| r = −0.26| **r = −0.40**|  r = −0.27| **r = −0.42**  
**p = 0.010**| **p = 0.023**| **p = 0.011**| **p = 0.002**| **p = 0.037**| p = 0.097| p = 0.159| p = 0.155| **p = 0.022**| p = 0.134| **p = 0.016**  
  
**Table 3.** Participant descriptives. 

**Table 3.** Participant descriptives.

| Obese/MetS   
---|---  
PLA| M5  
Descriptive Data| Pre| Post| Pre| Post  
**Chronological**|  |  |  |   
Age (years)| 75 ± 1.3| -| 75 ± 1.8| -  
**Anthropometric**|  |  |  |   
Height (cm)| 174.2 ± 2.3| -| 174.3 ± 1.1| -  
BW (kg)| **91.9 ± 3.5**| **93.9 ± 3.9 ‡**| 93.8 ± 3.4| 94.6 ± 3.6  
∆%| **2.2%**|  0.9%  
BMI (kg/m2)| **30.2 ± 0.71**| **30.8 ± 0.64 ‡**| 30.8 ± 1.06| 31.1 ± 1.16  
∆%| **2.0%**|  1.0%  
WHR (waist/hip; cm/cm)| 1.07 ± 0.03| 1.05 ± 0.03| 1.04 ± 0.01| 1.02 ± 0.01  
∆%| −1.9%| −1.9%  
**Vital Signs**|  |  |  |   
HR (bpm)| **69.8 ± 1.4**| **74.9 ± 3.0 ‡**| 69.5 ± 3.9| 69.5 ± 4.8  
∆%| **7.3%**|  0.0%  
SBP (mmHg)| 132 ± 3| 137 ± 5| 135 ± 5| 140 ± 6  
∆%| 3.8%| 3.7%  
DPB (mmHg)| **78.3 ± 1.5**| **76.9 ± 2.6**| **73.7 ± 2.1**| **78.8 ± 2.8 #**  
∆%| **−1.8% †**| **6.9% †**  
**Metabolic Syndrome and Sarcopenic Obesity**|  |  |  |   
Global MetS Risk Index (0 min–9 max)| 4.5 ± 0.38| 4.88 ± 0.35| 5.00 ± 0.21| 4.83 ± 0.34  
∆%| 8.4%| −3.4%  
Sarcopenic Obesity Risk Rank (1 min–20 max)| **7.43 ± 1.49**| **9.14 ± 1.53 ##**| **10.33 ± 1.23**| **9.44 ± 1.22**  
∆%| **31.1% ****|  **−7.4% ****  
 **Medical History**|  |  |  |   
Diagnosed comorbidities (#)| 3.25 ± 0.31| -| 3.17 ± 0.51| -  
Prescription medications (#)| 3.63 ± 0.8| -| 5.33 ± 1.0| -  
  
Between-group differences in the treatment response (i.e., Δ% changes) were analyzed by independent t-tests (** p ≤ 0.01; † p > 0.05 < 0.100). Within-group differences in pre-post intervention results were analyzed by paired t-tests (# p ≤ 0.05; ## p ≤ 0.01; ‡ p > 0.05 < 0.100). For clarity, outcomes and p-values that are borderline or statistically significant are in bold. The Global MetS Risk Index and Sarcopenic Obesity Risk Rank are defined in Section 2.11. Sample size for descriptive data n = 18–20 (PLA; n = 7–8, M5; n = 11–12).

**Table 4.** Physical activity (PA; steps), home-based resistance exercise (HBRE) progression, and exercise/PA compliance. 

**Table 4.** Physical activity (PA; steps), home-based resistance exercise (HBRE) progression, and exercise/PA compliance.

| Obese/MetS  
---|---  
PLA| M5  
Physical Activity and Exercise| Pre| Post| Pre| Post  
**PA**|  |  |  |   
Daily activity (steps/day)| **6149 ± 1078**| **4735 ± 668 #**| **4369 ± 484**| **4921 ± 568**  
∆%| **−23% ***|  **13% ***  
 **HBRE**|  |  |  |   
Elastic band resistance (kg)| **2.19 ± 0.22**| **2.98 ± 0.17 #**| **1.96 ± 0.14**| **2.55 ± 0.14 ##**  
∆%| **36%**| **30%**  
**Compliance**|  |  |  |   
Step goals| -| 7857/d (60%)| -| 7857/d (63%)  
HBRE (%)| -| 89 ± 12 | -.| 76 ± 7  
  
Between-group differences (i.e., Δ% changes) were analyzed by independent t-tests (* p ≤ 0.05). Within-group differences in pre-post intervention results were analyzed by paired t-tests (# p ≤ 0.05; ## p ≤ 0.01). For clarity, outcomes and p-values that are borderline or statistically significant are in bold. Sample size for physical activity and exercise outcomes n = 19 (PLA; n = 8, M5; n = 11).

**Table 5.** Supplement ingredient list and compliance. 

**Table 5.** Supplement ingredient list and compliance.

| PLA| M5  
---|---|---  
Ingredients| Per Serving| Per Serving  
**Macronutrients**| **Collagen peptides**| **Muscle 5**  
Calories (kcal)| 272| 272  
Protein (g)| 40| 40  
Sucrose (g)| 6.4| 6.4  
Fat (g)| 0.5 | 0.5  
**Actives**| **Collagen peptides**| **Muscle 5**  
Whey Protein Isolate (WPI 895; g)| -| 24  
Milk Protein Isolate (MPI 4900; g)| -| 16  
Bovine collagen (Peptiplus; g)| 40 | -  
Creatine monohydrate (g)| -| 3  
Vitamin D3 (UI)| 0.0| 1000  
Calcium (mg)| 0.0| 416  
**Oils and fatty acids (FAs)**| **Safflower oil (two tsp.)**| **Fish oil (two tsp.)**  
Total oil contents (mono-, di-, triglycerides etc.) (g)| 8.6| 8.6   
Long-chain omega-3 PUFAs (C20–C22; g)| 0.0| 2.46   
Eicosapentaenoic acid (C20; EPA) (g)| 0.0| 1.51   
Docosahexaenoic acid (C22; DHA) (g)| 0.0| 0.95  
Other FAs (%)| 100% (linoleic > oleic > palmitic > stearic)| 71%  
Vitamin E (d-alpha tocopherol) (g)| 0.1| 0.08  
**Compliance**| **PLA**| **M5**  
Supplement intake (%)| 95.40 ± 1.9| 89.29 ± 5.0  
  
**Table 6.** Macronutrient analyses 72-h dietary recall (food only). 

**Table 6.** Macronutrient analyses 72-h dietary recall (food only).

| Obese/MetS  
---|---  
PLA| M5  
Dietary Intake| Pre| Post| Pre| Post  
**Macronutrients**|  |  |  |   
Calories (kcal/d)| **1602 ± 119**| **1928 ± 73 #**| **2027 ± 169**| **1751 ± 140 #**  
∆%| **20% ****|  **−14% ****  
Protein (g/d)| **77 ± 10**| **92 ± 5 ‡**| **84 ± 8**| **80 ± 6**  
∆%| **19% ***|  **−5% ***  
Protein (g/kg BW/d)| **0.84 ± 0.08**| **1.00 ± 0.08 ‡**| **0.92 ± 0.10**| **0.87 ± 0.08**  
∆%| **19% ***|  **−5% ***  
Fat (g/d)| **71 ± 9**| **80 ± 6**| **79 ± 8**| **73 ± 11**  
∆%| **13% †**| **−8% †**  
Carbohydrates (g/d)| **154 ± 12**| **205 ± 11 ##**| **221 ± 27**| **184 ± 23 #**  
∆%| **33% ****|  **−17% ****  
  
Between-group differences (i.e., Δ% changes) were analyzed by independent t-tests (* p ≤ 0.05; ** p ≤ 0.01; † p > 0.05 < 0.100). Within-group differences in pre-post intervention results were analyzed by paired t-tests (# p ≤ 0.05; ## p ≤ 0.01; ‡ p > 0.05 < 0.100). For clarity, outcomes and p-values that are borderline or statistically significant are in bold. Sample size for macronutrient outcomes n = 18 (PLA; n = 8, M5; n = 10).

**Table 7.** Blood chemistry. 

**Table 7.** Blood chemistry.

| Obese/MetS  
---|---  
PLA| M5  
Blood Chemistry| Pre| Post| Pre| Post  
**Lipid Profile**|  |  |  |   
Cholesterol (mmol/L)| 3.39 ± 0.31| 3.53 ± 0.36| 4.38 ± 0.38| 4.17 ± 0.41  
∆%| 4.1%| −4.8%  
Triglycerides (mmol/L)| 1.26 ± 0.20| 1.27 ± 0.15| 1.74 ± 0.24| 1.60 ± 0.19  
∆%| 0.8%| −8.0%  
HDL-Cholesterol (mmol/L)| 1.09 ± 0.10| 1.12 ± 0.11| 1.01 ± 0.05| 1.00 ± 0.05  
∆%| 2.8%| −1.0%  
LDL-Cholesterol (mmol/L)| 1.72 ± 0.26| 1.83 ± 0.32| 2.57 ± 0.38| 2.43 ± 0.39  
∆%| 6.4%| −5.4%  
Non-HDL-Cholesterol (mmol/L)| 2.30 ± 0.26| 2.42 ± 0.28| 3.36 ± 0.36| 3.17 ± 0.37  
∆%| 5.2%| −5.7%  
TC/HDL-C| 3.15 ± 0.28| 3.19 ± 0.22| 4.33 ± 0.31| 4.13 ± 0.28  
∆%| 1.3%| −4.6%  
**Hemoglobin A1C Test**|  |  |  |   
HbA1c (%)| 6.09 ± 0.38| 6.14 ± 0.32| 6.12 ± 0.24| 6.17 ± 0.25  
∆%| 0.8%| 0.8%  
**C-reactive Protein Test**|  |  |  |   
CRP (mg/L)| 1.66 ± 0.50| 1.84 ± 0.46| 2.34 ± 0.72| 2.44 ± 0.74  
∆%| 10.8%| 4.3%  
**Bone Markers**|  |   
P1NP (pg/mL)| 283 ± 108| 267 ± 87| **163 ± 28**| **228 ± 40 #**  
∆%| −5.6%| **39.9%**  
CTX (pg/mL)| 193 ± 14| 202 ± 7.5| **197 ± 11**| **206 ± 11 ‡**  
∆%| 4.6%| **4.5%**  
P1NP/CTX ratio| 1.56 ± 0.65| 1.31 ± 0.39| **0.85 ± 0.15**| **1.26 ± 0.25 ‡**  
∆%| −16%| **48.2%**  
  
Between-group differences in the treatment response (i.e., Δ% changes) were analyzed by independent t-tests (no significances detected). Within-group differences in pre-post intervention results were analyzed by paired t-tests (# p ≤ 0.05; ‡ p > 0.05 < 0.100). For clarity, outcomes and p-values that are borderline or statistically significant are in bold. Sample size for metabolic risk markers n = 15–19 (PLA; n = 5–8, M5; n = 10–11) and bone turnover markers n = 9–11 (PLA; n = 4, M5; n = 5–7).

**Table 8.** Body composition. 

**Table 8.** Body composition.

| Obese/MetS  
---|---  
PLA| M5  
DEXA| Pre| Post| Pre| Post  
**Lean Mass**|  |  |  |   
TLM (kg)| 56.6 ± 2.2| 56.3 ± 2.2| 57.1 ± 2.1| 57.8 ± 2.0  
∆%| −0.5%| 1.2%  
ASM (kg)| 24.5 ± 0.8| 24.3 ± 0.8| **25.1 ± 1.0**| **25.6 ± 1.0 ‡**  
∆%| −0.8%| **2.0%**  
**Fat Mass**|  |  |  |   
Total Fat Mass (FM; kg)| **32.1 ± 1.9**| **33.6 ± 2.1 #**| **33.3 ± 2.3**| **33.0 ± 2.5**  
∆%| **4.7% †**| **−0.9% †**  
Body Fat (BF; %)| **36.1 ± 1.1**| **37.2 ± 1.1 #**| **36.6 ± 1.7**| **35.8 ± 1.6 #**  
∆%| **3.0% ****|  **−2.2% ****  
 **Body Composition Indices**|  |  |  |   
TLM/FM| **1.79 ± 0.08**| **1.70 ± 0.07 #**| **1.81 ± 0.15**| **1.86 ± 0.15**  
∆%| **−5.0% ***|  **2.8% ***  
ASM/FM| **0.78 ± 0.04**| **0.74 ± 0.04 #**| **0.79 ± 0.06**| **0.82 ± 0.07**  
∆%| **−5.1% ****|  **3.8% ****  
TLM/%BF| **1.58 ± 0.08**| **1.52 ± 0.07**| **1.62 ± 0.11**| **1.67 ± 0.11 #**  
∆%| **−3.8% ***|  **3.1% ***  
ASM/%BF| **0.68 ± 0.03**| **0.66 ± 0.03**| **0.71 ± 0.05**| **0.74 ± 0.05 #**  
∆%| **−2.9% ****|  **4.2% ****  
 **Allometric Lean Mass**|  |  |  |   
TLM/BMI| **1.87 ± 0.05**| **1.82 ± 0.05 #**| **1.86 ± 0.06**| **1.87 ± 0.06**  
∆%| **−2.7% ***|  **0.5% ***  
TLM/BW| **0.62 ± 0.01**| **0.60 ± 0.01 #**| **0.61 ± 0.02**| **0.61 ± 0.02**  
∆%| **−3.2% ***|  **0.0% ***  
ASM/h2 (ASMI; kg/m2)| 8.08 ± 0.25| 8.02 ± 0.21| **8.25 ± 0.30**| **8.41 ± 0.32 ‡**  
∆%| −0.7%| **1.9%**  
ASM/BMI (kg/[kg/m2])| **0.81 ± 0.02**| **0.79 ± 0.03 ‡**| **0.82 ± 0.02**| **0.83 ± 0.03**  
∆%| **−2.5% ***|  **1.2% ***  
ASM/BW| **0.27 ± 0.01**| **0.26 ± 0.01 #**| **0.27 ± 0.01**| **0.27 ± 0.01**  
∆%| **−3.7% ***|  **0.0% ***  
 **Bone Mass**|  |  |  |   
Bone Mineral Density (g/cm2)| 1.23 ± 0.03| 1.24 ± 0.03| 1.21 ± 0.03| 1.21 ± 0.03  
∆%| 0.8%| 0.0%  
  
Between-group differences in the treatment response (i.e., Δ% changes) were analyzed by independent t-tests (* p ≤ 0.05; ** p ≤ 0.01; † p > 0.05 < 0.100). Within-group differences in pre-post intervention results were analyzed by paired t-tests (# p ≤ 0.05; ‡ p > 0.05 < 0.100). For clarity, outcomes and p-values that are borderline or statistically significant are in bold. Sample size for analyses n = 20 (PLA; n = 8, M5; n = 12). Sample size for body composition outcomes n = 20 (PLA; n = 8, M5; n = 12).

**Table 9.** Strength and Muscle Quality. 

**Table 9.** Strength and Muscle Quality.

| Obese/MetS  
---|---  
PLA| M5  
Strength| Pre| Post| Pre| Post  
**Lower Body**|  |  |  |   
Leg Press 1RM (kg)| 144 ± 16| 142 ± 16| **120 ± 9**| **140 ± 9 #**  
∆%| −1.4%| **17% †**  
Isometric Knee Extension (Nm)| 189 ± 13| 194 ± 16| 170 ± 11| 183 ± 11  
∆%| 2.6%| 7.6%  
**Upper Body**|  |  |  |   
Hand Grip (kg)| 40.7 ± 2.8| 45.3 ± 3.0| **39.1 ± 1.8**| **42.7 ± 1.7 #**  
∆%| 11.3%| **9.2%**  
**Muscle Quality**|  |  |  |   
MQ Index| 47.4 ± 2.7| 52.0 ± 3.3| **44.9 ± 2.0**| **49.0 ± 1.8 ##**  
∆%| 9.7%| **9.1%**  
  
Between-group differences in the treatment response (i.e., Δ% changes) were analyzed by independent t-tests († p > 0.05 < 0.100). Within-group differences in pre-post intervention results were analyzed by paired t-tests (# p ≤ 0.05; ## p ≤ 0.01). For clarity, outcomes and p-values that are borderline or statistically significant are in bold. Sample size for strength outcomes n = 19 (PLA; n = 7, M5; n = 12).

**Table 10.** Performance. 

**Table 10.** Performance.

| Obese/MetS  
---|---  
PLA| M5   
Performance| Pre| Post| Pre| Post  
**Walk Speed**|  |  |  |   
4-M (m/s)| 1.01 ± 0.06| 1.01 ± 0.05| 0.87 ± 0.06| 0.90 ± 0.06  
∆%| 0.0%| 3.4%  
**Functional Mobility**|  |  |  |   
Timed 5x Sit to Stand (5XSTS; s)| 11.0 ± 1.3| 10.8 ± 1.2| 12.5 ± 0.5| 11.8 ± 0.9  
∆%| −1.8%| −5.6%  
Timed 4-Step Stair Climb (4SSC; s)| 2.64 ± 0.18| 2.67 ± 0.12| **3.25 ± 0.20**| **2.91 ± 0.13 ‡**  
∆%| 1.1%| **−10.5%**  
**Short Physical Performance Battery**|  |  |  |   
SPPB Score (max 12)| 11.0 ± 0.69| 10.9 ± 0.70| 10.3 ± 0.35| 10.4 ± 0.53  
∆%| −0.9%| 1.0%  
  
Between-group differences in the treatment response (i.e., Δ% changes) were analyzed by independent t-tests (no significance detected). Within-group differences in pre-post intervention results were analyzed by paired t-tests (‡ p > 0.05 < 0.100). For clarity, outcomes and p-values that are borderline or statistically significant are in bold. Sample size for performance outcomes n = 19 (PLA; n = 7, M5; n = 12).

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**MDPI and ACS Style**

Nilsson, M.I.; Xhuti, D.; de Maat, N.M.; Hettinga, B.P.; Tarnopolsky, M.A. Obesity and Metabolic Disease Impair the Anabolic Response to Protein Supplementation and Resistance Exercise: A Retrospective Analysis of a Randomized Clinical Trial with Implications for Aging, Sarcopenic Obesity, and Weight Management. _Nutrients_ **2024** , _16_ , 4407. https://doi.org/10.3390/nu16244407 

**AMA Style**  


Nilsson MI, Xhuti D, de Maat NM, Hettinga BP, Tarnopolsky MA. Obesity and Metabolic Disease Impair the Anabolic Response to Protein Supplementation and Resistance Exercise: A Retrospective Analysis of a Randomized Clinical Trial with Implications for Aging, Sarcopenic Obesity, and Weight Management. _Nutrients_. 2024; 16(24):4407. https://doi.org/10.3390/nu16244407 

**Chicago/Turabian Style**  


Nilsson, Mats I., Donald Xhuti, Nicoletta Maria de Maat, Bart P. Hettinga, and Mark A. Tarnopolsky. 2024\. "Obesity and Metabolic Disease Impair the Anabolic Response to Protein Supplementation and Resistance Exercise: A Retrospective Analysis of a Randomized Clinical Trial with Implications for Aging, Sarcopenic Obesity, and Weight Management" _Nutrients_ 16, no. 24: 4407. https://doi.org/10.3390/nu16244407 

**APA Style**  


Nilsson, M. I., Xhuti, D., de Maat, N. M., Hettinga, B. P., & Tarnopolsky, M. A. (2024). Obesity and Metabolic Disease Impair the Anabolic Response to Protein Supplementation and Resistance Exercise: A Retrospective Analysis of a Randomized Clinical Trial with Implications for Aging, Sarcopenic Obesity, and Weight Management. _Nutrients_ , _16_(24), 4407. https://doi.org/10.3390/nu16244407 

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**MDPI and ACS Style**

Nilsson, M.I.; Xhuti, D.; de Maat, N.M.; Hettinga, B.P.; Tarnopolsky, M.A. Obesity and Metabolic Disease Impair the Anabolic Response to Protein Supplementation and Resistance Exercise: A Retrospective Analysis of a Randomized Clinical Trial with Implications for Aging, Sarcopenic Obesity, and Weight Management. _Nutrients_ **2024** , _16_ , 4407. https://doi.org/10.3390/nu16244407 

**AMA Style**  


Nilsson MI, Xhuti D, de Maat NM, Hettinga BP, Tarnopolsky MA. Obesity and Metabolic Disease Impair the Anabolic Response to Protein Supplementation and Resistance Exercise: A Retrospective Analysis of a Randomized Clinical Trial with Implications for Aging, Sarcopenic Obesity, and Weight Management. _Nutrients_. 2024; 16(24):4407. https://doi.org/10.3390/nu16244407 

**Chicago/Turabian Style**  


Nilsson, Mats I., Donald Xhuti, Nicoletta Maria de Maat, Bart P. Hettinga, and Mark A. Tarnopolsky. 2024\. "Obesity and Metabolic Disease Impair the Anabolic Response to Protein Supplementation and Resistance Exercise: A Retrospective Analysis of a Randomized Clinical Trial with Implications for Aging, Sarcopenic Obesity, and Weight Management" _Nutrients_ 16, no. 24: 4407. https://doi.org/10.3390/nu16244407 

**APA Style**  


Nilsson, M. I., Xhuti, D., de Maat, N. M., Hettinga, B. P., & Tarnopolsky, M. A. (2024). Obesity and Metabolic Disease Impair the Anabolic Response to Protein Supplementation and Resistance Exercise: A Retrospective Analysis of a Randomized Clinical Trial with Implications for Aging, Sarcopenic Obesity, and Weight Management. _Nutrients_ , _16_(24), 4407. https://doi.org/10.3390/nu16244407 

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details [here](https://www.mdpi.com/about/announcements/784). 

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