Metformin: From Diabetes to Cancer and Prolongation of Life

Metformin: From diabetes to cancer to prolongation of life

The surprising findings of Metformin

The metformin molecule dates back over a century, but its clinical use started in the ‘50s. Since then, its use in diabetics has grown constantly, with over 150 million users today. The therapeutic profile also expanded, with an improved understanding of novel mechanisms. Metformin has a major activity on insulin resistance, acting on the insulin receptors and mitochondria, most likely by activating the adenosine monophosphate-activated kinase. These and associated mechanisms lead to significant lipid-lowering and body weight loss. An anti-cancer action has come up in recent years, with mechanisms partly dependent on mitochondrial activity and also on phosphatidylinositol 3-kinase resistance occurring in some malignant tumors. The potential of metformin to raise life length is the object of large ongoing studies and several basic and clinical investigations. The present review article will attempt to investigate the basic mechanisms behind these diverse activities and the potential clinical benefits. Metformin may act on transcriptional activity by histone modification, DNA methylation, and miRNAs. An activity on age-associated inflammation (inflammation) may occur via activation of the nuclear factor erythroid 2 related factor and changes in gut microbiota. A senolytic activity, leading to the reduction of cells with the senescent-associated secretory phenotype, may be crucial in lifespan prolongation as well as in ancillary properties in age-associated diseases, such as Parkinson’s disease. Telomere prolongation may be related to the activity of mitochondrial respiratory factor 1 and on peroxisome gamma proliferator coactivator 1-alpha. Very recent observations on the potential to act on the most severe neurological disorders, such as amyotrophic lateral sclerosis and frontotemporal dementia, have raised considerable hope.

Metformin is well known for control of type II Diabetes Mellitus. Metformin is taken by mouth, once a day and eliminates injection.

It is classified as a GLP 1 and exhibits other metabolic effects. It can also decrease lipids, and reduce the risks of cancer.

Metabolic Mechanism of Action of Metformin (graphic)

Metformin and Lipid-Lowering Effects

A cholesterol-lowering activity, specifically on the atherogenic low-density lipoprotein (LDL) fraction, has been reported for metformin from the early clinical studies, indicating a (-12 %) LDL-cholesterol reduction versus no change with the sulphonylurea

Mitochondrial activity of metformin

The major target of the pleiotropic effects of metformin and other biguanides is mitochondria. The early reports already indicated that the drug reduces cellular respiration by specific inhibition of the mitochondrial respiratory chain.

Metformin and cancer

An unexpected anti-cancer activity of metformin was the result of an early epidemiological study essentially indicating that metformin-treated diabetics had a significantly lower cancer burden versus diabetics treated with other agents. This observation was confirmed by several other reports. This potential benefit of metformin is in contrast with the apparently raised cancer risk following insulin-based therapies.  Although non-confirmatory data have been reported  two independent meta-analyses comparing metformin to other treatments reported 30–40 % reductions in cancer incidence in metformin-treated T2D 

Diabetes has a clear association with increased cancer risk, particularly in insulin-treated individuals 

Metformin use has been shown to reduce the frequency of specific cancers, in particular breast cancer, and to be an effective radiosensitizer in the treatment of this most frequent tumor 

Metformin appears to provide additional benefit for the treatment of e.g., cisplatin-treated cancers and the use of metformin has been associated with a clear reduction of cancer risk versus other antidiabetics 

Metformin and aging

Identification of the hallmarks of aging allowed us to identify those most sensitive to metformin. Among these, the activation of AMPK and SIRT1 and down-regulation of the insulin-IGF1 signaling and m-TORC1 are involved in the beneficial effects of metformin on energy metabolism[171]. Activation of AMPK via the liver kinase B1 (LKB1) mediates the prolonged lifespan in mice and C. elegans [172].

The inflammatory process is a major target of metformin. Suppression of the proinflammatory cytokines of the NF-kB pathway is associated with reduced mortality in older diabetics treated with metformin [173]. The activity of metformin on dysfunctional mitochondria with aging is well predictable from the drug’s mechanism on oxidative stress [174], possibly delaying mitochondrial biogenesis and senescence by AMPK-mediated H3K79 methylation acting through the SIRT1-DOT1L axis [175].

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(7) Breaking the rules of medical technology: OpenAI, ChatgGPT, DOUGALLMD, Medscape,Pubmed, MEDLINE and Electronic Medical Record will all contribute to Healthcare

 

1. Personalized Preventive Care

The traditional approach to preventive care uses broad, universal categories like age and sex to determine what medical screenings and preventive measures people should receive. While this approach works on a large—and mostly probabilistic—scale, it overlooks individual risk factors, such as genetic profiles and specific biomarkers. Humans can’t possibly retain and recall all the data that affects each individual patient, but they will be able to apply the totality of information using GenAI. These tools can almost instantaneously analyze vast amounts of data to identify those individuals at highest risk for serious conditions like cancer and heart disease and recommend targeted approaches to improve patient outcomes.

2. Reducing Diagnostic Errors

Traditionally, doctors rely on memory and experience to diagnose new symptoms, drawing from their medical training and past cases. However, this approach risks overlooking rare or complex conditions, and it is vulnerable to cognitive errors like confirmation and proximity bias. GenAI offers a powerful solution by integrating vast amounts of medical data—combining patient history, symptoms, and real-time imaging—and correlating it with comprehensive medical literature, including obscure case reports. In the future, combining a dedicated clinician with a generative AI application will produce more accurate diagnoses than either alone.

3. Enhancing Doctor-Patient Interactions

In today’s healthcare, doctors often spend more time inputting data into electronic health records (EHRs) than engaging with patients, leading to impersonal and transactional experiences. GenAI is changing this dynamic by automatically transcribing and organizing doctor-patient conversations into accurate, high-quality EHR entries. This technology not only frees up to two hours a day for clinicians but also improves the quality of care and helps reduce burnout.

A Fourth Opportunity: Accelerating Medical Research

In addition to Dr. Topol’s three points, I’d add a fourth: the ability of GenAI to accelerate research. In medical science today, it can take years to gather enough data to drive meaningful advances. GenAI can dramatically shorten this timeline by analyzing vast amounts of patient data quickly, leading to faster breakthroughs and more timely application of new treatments.

Clinical research conventionally starts with a question, followed by lengthy data collection and analysis. This approach is time-consuming and limited by the volume of data that researchers can analyze and manage. GenAI alters the calculus by enabling doctors to sift through enormous datasets. Today, U.S. hospitals produce up to 50 petabytes of data each year, 97% of which currently goes unused. By mining this data, GenAI will be able to uncover patterns and insights that would take years to find with traditional methods. One of the first practical applications will be identifying hospitalized patients who are likely to deteriorate over the next 24 hours, allowing clinicians to intervene earlier and potentially save lives.

Challenges and Risks

Of course, breaking the rules of medicine comes with challenges. Security is a major concern, especially when clinicians use generative AI for EHR data entry. However, the reality is that this danger already exists in the current EHRs, which can easily be hacked, but fail (alone) to offer the advantages that GenAI solutions will provide.

The evolution of medical technology always includes trade-offs. The advent of CT scans, MRIs and laparoscopic tools, for example, led doctors to lose their skill in physical exams, but the lives saved by these innovations are undeniable. No clinician would go back to the past.

Within the next five years, Dr. Topol predicts that GenAI will become a standard tool for creating electronic health records (EHRs). Other applications will follow soon after. I’m confident that the old rule “the doctor knows best” will be replaced by a new reality—one in which the best outcomes come from a collaboration between a dedicated clinician, an empowered patient and GenAI. Together, they will achieve more than any of the three could accomplish alone.

Neither Providers nor Patients will be able to avoid Chatbots and other AI assistants

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Dr. Robert Pearl is the author of “ChatGPT, MD: How AI-Empowered Patients & Doctors Can Take Back Control of American Medicine.” All profits from the book go to Doctors Without Borders. Fixing Healthcare is a co-production of Dr. Robert Pearl and Jeremy Corr. Subscribe to the show via Apple, Spotify or wherever you find podcasts. Join the conversation or suggest a guest by following the show on Twitter and LinkedIn.

Breaking the rules of medical technology: A conversation with Dr. Eric Topol | LinkedIn

How Snacks Took Over American Life - The Atlantic

The rhythms of our days may never be the same.

There was a time, if you can believe it when a respectable person could not have a little treat whenever she wanted. This time was, roughly, from the dawn of the republic to the middle of the 1980s. The American workday, menu, and social clock were oriented around meals, and eating between them was discouraged: If you were a child, snacking gave you cavities and spoiled your appetite; if you were an adult, it was kind of unseemly. There were no elaborate treats after soccer practice, snack trays on strollers, or tubes of yogurt. Energy bars were for athletes, not accountants. National parks did not have vending machines. Grocery stores did not have aisles and aisles of portable abundance. The phrases girl dinner and new flavor drop were totally nonsensical, instead of just a bit nonsensical. Libraries, classrooms, cubicles, and theaters were, generally, where you read, learned, worked, and saw La bohème—but definitely did not eat.

Some 40 years later, we are not just eating between meals; we are abandoning them entirely. In the three decades leading up to 2008, the average American doubled their daily snack intake, and the percentage of adults snacking on any given day rose from 59 percent to 90 percent, according to a comprehensive government report. In the most recent iteration of the same study, which ended in 2020 before the pandemic, that number rose again, to 95 percent; more than half of respondents said they consumed at least three snacks a day. According to a survey released earlier this year by the international snack-food conglomerate Mondelēz International, in conjunction with the Harris Poll, six in 10 consumers prefer snacking over traditional meals. The trend will probably persist: Younger people are significantly more likely than older ones to report skipping meals, 

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How Snacks Took Over American Life - The Atlantic

The Sad, Sad, Sad Story about COVID vaccine injuries

COVID Archives • Children's Health Defense

‘Forest of the Fallen’: Tribute to Australians Killed or Injured by COVID Shots ‘Planted’ in Front of mRNA Factory Site

vaers logo and COVID-19 vaccine syringe

September 11, 2024

Their Vaccine Injury Reports Disappeared From VAERS — So They Developed a Tool Anyone Can Use to Track Their Own Reports

words "big pharma" and gavel

September 11, 2024

You’d Think Doctors Would Know About Big Pharma’s Rap Sheet — Many Don’t

September 09, 2024

Coming Sept. 18: ‘Vaxxed 3 Exposes Widespread Injuries, Deaths Following COVID Shots, Hospital Protocols

September 05, 2024

Rogan Rips Mainstream Media for Failure to Cover ‘Real Numbers’ of People Injured, Killed by COVID Shots

September 03, 2024

There Are No Licensed COVID Vaccines for Kids Under 12 — But CDC Wants Babies to Get 3 Pfizer Shots by Age 9 Months

All of these actions created serious concerns about regulators overstepping boundaries on freedoms.

COVID Archives • Children's Health Defense

30 Days of US Healthcare: Surprise Billing

How to Eliminate PFAs (microscopic plastic particle) from Drinking Water

How to Eliminate PFAs (microscopic plastic particles) from Drinking Water

MIT’s new silk-cellulose water filter blocks stubborn forever chemicals, metals

PFAs (Perfluoroalkoxy alkanes) have become the scourge as a result OF the ubiquitous use of plastics in many (most) toys, automobiles, and structural building materials. It is found in wastewater, rivers, and reservoirs.  

Analysis for finding PFAs

Practical application guide for the discovery of novel PFAS in environmental samples using high-resolution mass spectrometry

Are PFAs toxic to humans?

Polyfluoroalkyl substances (PFAS) are considered toxic to humans. These synthetic chemicals have been linked to various health issues, including:

Cancer: Certain PFAS have been associated with an increased risk of cancers, such as kidney and testicular cancer.

Hormonal Disruption: PFAS can interfere with hormone regulation, potentially affecting thyroid function and reproductive health.

Immune System Effects: Exposure to PFAS may weaken the immune response, making individuals more susceptible to infections.

Developmental Issues: Pregnant women exposed to PFAS may face risks of low birth weight and developmental delays in their children.

Cholesterol Levels: Some studies suggest a correlation between PFAS exposure and increased cholesterol levels.

 

The EPA has issued guidance and regulations concerning PFAS to address their potential health risks. Here are some key points of the EPA's guidance on PFAS:

Health Advisories: The EPA has established health advisories for certain PFAS, specifically PFOA and PFOS, recommending that levels in drinking water should be below 70 parts per trillion (ppt).

Monitoring and Testing: The EPA encourages regular monitoring of drinking water for PFAS, especially in areas near known sources of contamination, such as industrial sites and military bases.

Cleanup Standards: The EPA has developed guidelines for the cleanup of PFAS contamination in soil and water, promoting the use of best practices and technologies.

Risk Communication: The EPA advises communities on how to communicate risks related to PFAS exposure, emphasizing transparency and public health education.

Research and Regulation: The EPA is actively conducting research to better understand PFAS and is working on developing more comprehensive regulations to manage their use and prevent environmental contamination.

Should we be concerned about PFAs ?  YES

Health Care ‘Game-Changer’? The Homeless get care in the streets

 

Health Care ‘Game-Changer’? Feds Boost Care for Homeless Americans

Angela Hart October 19, 2023

The Biden administration is making it easier for doctors and nurses to treat homeless people wherever they find them, from creekside encampments to freeway underpasses. This marks a fundamental shift in how—and where—health care is delivered.

As of Oct. 1, the Centers for Medicare & Medicaid Services began allowing public and private insurers to pay “street medicine” providers for medical services they deliver anyplace homeless people might be staying.

Previously, these providers weren’t getting paid by most Medicaid programs, which serve low-income people, because the services weren’t delivered in traditional medical facilities, such as hospitals and clinics.

The change comes in response to the swelling number of homeless people across the country, and the skyrocketing number of people who need intensive addiction and mental health treatment — in addition to medical care for wounds, pregnancy, and chronic diseases like diabetes.

Peng searches for her homeless patients in West Hollywood

“It’s a game-changer. Before, this was really all done on a volunteer basis,” said Valerie Arkoosh, secretary of Pennsylvania’s Department of Human Services, which spearheaded a similar state-based billing change in July. “We are so excited. Instead of a doctor’s office, routine medical treatments and preventive care can now be done wherever unhoused people are.”

California led the nation when its state Medicaid director in late 2021 approved a new statewide billing mechanism for treating homeless people in the field, whether outdoors or indoors in a shelter or hotel. “Street medicine providers are our trusted partners on the ground, so their services should be paid for,” Jacey Cooper told KFF Health News.

Hawaii and Pennsylvania followed. And while street medicine teams already operate in cities like Boston and Fort Worth, Texas, the new government reimbursement rule will allow more healthcare providers and states to provide the services.

“It’s a bombshell,” said Dave Lettrich, executive director of the Pittsburgh-based nonprofit Bridge to the Mountains, which provides outreach services to street medicine teams in Pennsylvania. “Before, you could provide extensive primary care and even some specialty care under a bridge, but you couldn’t bill for it.”

Under the new rule, doctors, nurses, and other providers can get reimbursed to care for patients in a “non-permanent location on the street or found environment,” making it the first time the federal government has recognized the streets as a legitimate place to provide health care. This will primarily affect low-income, disabled, and older people on Medicaid and Medicare.

“The Biden-Harris Administration has been focused on expanding access to health care across the country,” said CMS spokesperson Sara Lonardo, explaining that federal officials created a new reimbursement code at the request of street medicine providers who weren’t consistently getting reimbursed.

The White House unveiled an ambitious strategy this year to reduce homelessness in America 25% by 2025, in part by plowing health care money into better care for those living on the streets.

Legislation pending in Congress would further expand reimbursement for street medicine, taking aim at the mental health and addiction crisis on the streets. The bipartisan bill, introduced earlier this year, has not yet had a committee hearing.

Nearly 600,000 people are homeless in America, based on federal estimates from 2022, and on average they die younger than those who have stable housing. The life expectancy for homeless people is 48, compared with the overall life expectancy of 76 years in the U.S.

More than 150 street medicine programs operate across the country, according to street medicine experts. At least 50 are in California, up from 25 in 2022, said Brett Feldman, director of street medicine at the University of Southern California’s Keck School of Medicine.

Feldman spearheaded the state and national efforts to help street medicine providers get paid, alongside the Street Medicine Institute. They submitted a formal request to the Biden administration in January 2022 to ask for a new street medicine billing code.

Isabelle Peng tries to talk with a homeless woman, Lisa Vernon, after receiving a call about someone in crisis on the streets

In the letter, they argued that street medicine saves lives — and money.

“This is done via walking rounds with backpacks, usually working out of a pick-up truck or car, but is also done via horseback, kayak, or any other means to reach hard-to-reach people,” they wrote. “The balance of power is shifted to the patient, with them as the lead of their medical team.”

Street medicine experts argue that by dramatically expanding primary and specialty care on the streets, they can interrupt the cycle of homelessness and reduce costly ambulance rides, hospitalizations, and repeated trips to the emergency room. Street medicine could help California save 300,000 ER trips annually, Feldman projected, based on Medicaid data. Some street medicine teams are even placing people into permanent housing.

Arkoosh said there’s already interest bubbling up across Pennsylvania to expand street medicine because of the federal change. In Hawaii, teams are plotting to go into remote encampments, some in rainforests, to expand primary and behavioral health care.

“We’re seeing a lot of substance abuse and mental health issues and a lot of chronic diseases like HIV,” said Heather Lusk, executive director of the Hawai’i Health & Harm Reduction Center, which provides street medicine services. “We’re hoping this can help people transition from the streets into permanent housing.”

But the federal change, undertaken quietly by the Biden administration, needs a major public messaging campaign to get other states on board and to entice more providers to participate, said Jim Withers, a longtime street medicine provider in Pittsburgh who founded the Street Medicine Institute.

“This is just the beginning, and it’s a wake-up call because so many people are left out of health care,” he said.

This article was produced by KFF Health News, which publishes California Healthline, an editorially independent service of the California Health Care Foundation. 

KFF Health News is a national newsroom that produces in-depth journalism about health issues and is one of the core operating programs at KFF—an independent source of health policy research, polling, and journalism. Learn more about KFF.

Subscribe to KFF Health News' free Morning Briefing.

The Association between Gratitude and Depression: A Meta-Analysis

Many studies have explored the association between gratitude and depression, but no meta-analysis has been reported. The purpose of this meta-analysis was to fill that gap. The meta-analysis synthesized the association in 70 reported effect sizes from 62 published and unpublished articles, involving a total of 26,427 child, adolescent, and adult participants. The studies were completed by different research teams, using different samples, different measures, and various correlational research designs. The results showed a significant association between gratitude and depression, r = -0.39 (95% confidence intervals -0.44, -0.34), indicating that individuals who experience more gratitude have lower levels of depression. The results did not vary significantly with the measure of gratitude or depression used, whether the study was longitudinal or cross-sectional, the age of participants or the percentage of female participants, suggesting a robust connection between higher levels of gratitude and lower levels of depression. The findings show a substantial association between gratitude and depression. The association provides a reason to explore further the effects of gratitude-focused interventions as a method to alleviate depression and prevent the development of depression.

Depression is a mood disorder that often includes sadness or irritability, along with a lack of interest in daily activities, together with appetite change, sleep disturbance, and feelings of worthlessness and guilt [1]. Functional consequences of depression can be physical, social, and occupational, including impaired ability to concentrate and make decisions; in severe cases, individuals cannot take care of their basic needs and may be mute or catatonic [1]. Individuals experiencing depression show higher rates of unemployment, relationship breakdown, education dropout [2], and suicide [3].

Depression is a common health problem. Over 264 million people suffer from depression worldwide, and it is one of the leading causes of disability and a major contributor to the global burden of disease [4].

Depression is a major mental health challenge. This article attempts to use self-help using artificial intelligence.  It is not a substitute for professional therapy. 

The Science of Gratitude

Imagine yourself in this pose. The sky is blue, and flowers and grass surround you. You hear birds and other natural sounds. This picture is you in a natural state, a picture of humanity long ago, before the complexity of the 21st Century.  Our biological system was not designed for today's chaos.  Our genes were programmed for thousands of years ago. This a major stress on our behavior, one which requires our brain to adjust to, requiring a tremendous amount of energy and metabolism. This leads to depression.  Meditation and sleep can restore your psyche, as well as physical health.  It has been shown mental stress causes chronic illness, heart disease, and hypertension, and worsens other issues such as diabetes, and irritable bowel syndrome.

Measures of gratitude

There are currently two main measures of gratitude used in research. The most commonly used is the Gratitude Questionnaire-6 (GQ-6; [5]). The GQ-6 is a six-item self-report measure with two reverse-scored items. Participants are asked to rate their answers on a scale from one to seven (1 = "strongly disagree", 7 = "strongly agree"). Examples of the items include "I have so much in life to be thankful for" and "I am grateful to a wide variety of people". Although the GQ-6 is a unifactorial measure, the items capture both gratitude toward the good deeds of others and a habitual focus on the positive aspects of life [6]. Scores on the GQ-6 correlate substantially with measures of related constructs such as hope, optimism, and life satisfaction [5], thus providing evidence of validity. Also, the GQ-6 has good internal reliability with Cronbach's alpha between 0.82 and 0.87 [5].

The other popular measure of gratitude is the Gratitude Resentment and Appreciation Test (GRAT; [16]). This measure is based on a multifactor model. The GRAT is a 44-item self-report measure with five reverse-scored items. Participants are asked to rate their answers on a scale from one to nine (1 = "I strongly disagree"; 9 = "I strongly agree"). There are three subscales: Appreciation for life's simple pleasures, sense of abundance, and social appreciation. An example item is "Life has been good to me".

The GRAT and the GRAT short form, with 16 items, have both been shown to have good validity and reliability [16]. A meta-analysis of the reliabilities of gratitude measures showed GRAT has good internal reliability with Cronbach's alpha 0.92 [17].

Measures of depression

There are many measures of depression. A commonly used measure for depression is the Center for Epidemiologic Studies Depression Scale (CES-D; [18]). The CES-D is a 20-item self-report measure used to assess the frequency of depressive symptoms in the past week. Participants rate themselves on a scale ranging from zero (rarely or none of the time) to three (most or all of the time). Example items include "I felt sad" and "My sleep was restless" [18]. Reliability, validity and factor structure are shown to be similar across different populations and different types of depressive symptoms [18]. Cronbach's alpha has been reported as 0.90 [19].

Three other measures of depression have been commonly used in studies of the relationship between gratitude and depression. The Depression Anxiety Stress Scale (DASS) is a 42-item self-report measure of depression, anxiety and stress developed by Lovibond and Lovibond [20]. Participants are asked to rate how much a statement applies to them over the past week on a scale ranging from zero (never) to three (almost always). An example item is "I felt that life was meaningless". Evidence shows the DASS to be a valid and reliable measure of depression in clinical and non-clinical samples [21]. Cronbach's alpha of the depression subscale of DASS has been reported as 0.96 in clinical samples [22] and 0.95 in non-clinical samples [21].

The Beck Depression Inventory, 2nd edition (BDI-II; [23]) is a 21-item self-report scale that measures the severity of depressive symptoms over the past two weeks. Each item is rated on a four-point Likert scale, ranging from zero to three. The BDI-II shows good validity and reliability; Cronbach's alpha is reported to be 0.92 in psychiatric outpatient samples and 0.93 in non-clinical samples [23].

The final measure common to the relevant studies is the Hospital Anxiety and Depression Scale (HADS; [24]). It is a 14-item measure of anxiety and depressive symptoms. An example item is "I feel as if I am slowed down". Participants rate themselves in the past week on each item using a scale that goes from zero (not at all) to three (nearly all the time) Reliability and validity of the depression scale has been demonstrated in numerous studies with medical patients; Cronbach's alpha for the depression subscale is reported as 0.82 [24].

Although many studies have examined the relationship between gratitude and depression, no meta-analysis has been reported on the association between these two variables. The aim of this study was to use meta-analysis to combine results from studies on the association between gratitude and depression and find an overall weighted effect size. The main research hypothesis was that a higher level of gratitude would be associated with fewer symptoms of depression. We had no specific hypotheses regarding moderators of the size of the association, so we conducted exploratory moderator analyses, using meta-regression or subgroup analyses, to test as moderators: a) Mean sample age; b) Percentage of female participants; c) The gratitude measure used; d) The depression measure used, and e) Whether the study was cross-sectional or longitudinal.

Method

Protocol and registration

This meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA; [25]). We developed a protocol for this meta-analysis and registered it at Prospero [26]. A record of the registered protocol for this meta-analysis can be found at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=CRD42020193842.

Literature search

We conducted a systematic literature search to locate reports of relevant studies, including published and unpublished studies. Online databases searched included PsychINFO, PubMed, Google Scholar, and Web of Science. Search terms included gratitude, depress*, associate*, correlate*, and predict*. Boolean operators AND and OR were used to refine the search. We screened the results from these searches by title and abstract initially and then focused on relevant full articles.

To reduce the risk of the file drawer problem, we sent emails to the corresponding authors of all included articles to request any unpublished or in-press research relevant to the relationship between gratitude and depression. Also, we reviewed the reference list of each included article to identify any other studies of interest.

Eligibility criteria

To be included in this meta-analysis, the report or data set had to provide the association between gratitude and depression. Studies using either cross-sectional design or longitudinal design were eligible for inclusion the titles and abstracts of identified articles were reviewed, and irrelevant articles were excluded. The full texts of the remaining articles were reviewed to determine if they met the eligibility criteria. Figure 1 shows the PRISMA study flow diagram.

Figure 1: PRISMA flow diagram showing identification, screening, eligibility, and inclusion of articles. View Figure 1

Collecting and managing data

The data recorded for each study included effect size or data necessary to calculate effect size, along with moderator information. Potential moderators were the percentage of female participants, mean participant age, gratitude measure used, and depression measure used. Gratitude measures used were coded as GQ, GRAT, or other. Depression measures used were coded as CESD, BDI, DASS-Depression, or other.

When there were multiple measures of gratitude reported in one study, to avoid bias in the results by reporting too many effect sizes from one sample, we used an average effect size. Each study was coded as either cross-sectional or longitudinal. For longitudinal studies, the number of weeks between measuring gratitude and measuring depression was recorded. The effect size reported for longitudinal studies was chosen as the relationship between baseline gratitude and the longest follow-up of depression.

In some studies, there was missing data for moderator analysis. Corresponding authors were contacted in an attempt to gain access to the missing data. Where mean age was not provided for the study, the median was used as a substitute. Where other data were missing, the study was excluded from the related moderator analysis.

One of us entered data, another one then checked the entries, and the third one of us independently checked data from 20% of articles. Using the standard of a disagreement of less than 5% as an agreement, we found an inter-rater agreement of 94%. Follow-up discussion led to all authors agreeing on the final data set.

Data analysis

We used r for the effect size and meta-analysis software Comprehensive Meta-Analysis by Borenstein, et al. [27]. To generalize beyond the data set, we used the random effects model, as suggested by Borenstein, et al. [28]. Under the random effects model, it is assumed that the true effect size varies from study to study. Random effects models produce larger confidence intervals compared to fixed effects models and lead to more conservative conclusions [29].

We used meta-regression to conduct an exploratory analysis of continuous-data moderators. We used the Q statistic to test the significance in moderator analyses. Publication bias was evaluated using observation of a funnel plot and the Duval and Tweedie [30] trim and fill procedure.

Results

This meta-analysis synthesized 70 effect sizes from 62 published and unpublished studies, with a total of 26,427 participants. Table 1 shows the key characteristics of studies included in the meta-analysis. Figure 2 shows a forest plot of correlation coefficients and confidence intervals for studies included in the meta-analysis. A fixed effects analysis of homogeneity indicated that variance between studies was significant Q (69) = 2227.81, p < 0.001 and I2 was 96.9. These results suggest the presence of heterogeneity, so we used the random-effects model for the meta-analysis. The overall weighted correlation coefficient was significant, r = -0.39, 95% confidence interval (-0.44, -0.34), p < 0.001. The data file for the meta-analysis is available at The Association between Gratitude and Depression [34].

Figure 2: Forest plot and effect size for each study. View Figure 2

 Table 1: Key characteristics of the studies included in this meta-analysis. View Table 1

The classic fail-safe N test indicated that 3571 correlation coefficients averaging null results would need to be added to the meta-analysis to reduce the current overall correlation coefficient to a non-significant level. The funnel plot, presented in Figure 3, shows that the distribution of correlational coefficients is not symmetrical, indicating some publication bias may be present. The Duval and Tweedie trim and fill analysis suggested that 13 studies be removed from the funnel plot, leaving an adjusted effect size of r = -0.36, 95% CIs (-0.40, -0.32), down slightly from the unadjusted r of -0.39.

Figure 3: Funnel plot of standard error by Fisher's Z. View Figure 3

We used meta-regression to examine whether sample mean age or percentage of female participants in samples had a moderating effect on the relationship between gratitude and depression. Mean age did not have a significant moderating effect, r = 0.001 (-0.001, 0.002), p = 0.31, with study means ranging from 10 years old to 74. The percentage of female participants did not have a significant moderating effect, r = 0.000 (-0.002, 0.002) p = 0.48. Type of gratitude measure, depression measure, and whether the study was longitudinal or cross-sectional did not explain a significant proportion of between-study heterogeneity. Table 2 shows the results of these subgroup analyses.

 Table 2: Moderator results. View Table 2

Discussion

This scientific paper was from a credible academic author. The methodology is a standard for peer-reviewed articles.  ref:  Iodice et al. Int J Depress Anxiety 2021, 4:024

A meta-analysis of 70 effect sizes based on the responses of 26,427 participants found that higher gratitude was significantly associated with lower depression. The weighted average association between gratitude and depression was r = -0.39. An adjustment suggested by the Duval and Tweedie assessment for publication bias caused by Small N studies reduced the meta-analytic r slightly to -0.36. The results, which indicate a medium negative relationship between the two variables according to guidelines from Cohen [35], support the hypothesis that higher levels of gratitude would be associated with lower levels of depression.

As the Q statistic test and I squared percentage showed significant heterogeneity in effect sizes, we examined several possible moderators in the meta-analysis. There was no evidence that the degree of association varies with the sample mean age or the percentage of female participants. Subgroup analysis provided no evidence to show that the specific measures used for gratitude and depression had a moderating effect on the association between the two variables. There was a marginally significant trend in the direction of cross-sectional studies showing a higher effect size than longitudinal studies. All moderator subcategories showed a significant negative association between gratitude and depression.

The current findings are consistent with the positive psychology model [10]. Individual studies have shown that positive psychology constructs such as optimism and hope have shown similar correlations with depression [36,37].

The results from this meta-analysis are consistent with research focusing on gratitude interventions as a treatment for depression. Several studies have examined the effects of gratitude interventions on depression; reviews and meta-analyses have found that the effects were significant but small, especially when the comparison group received an active placebo [6,38,39]. Hence, some or all of the intervention effects may be due to placebo or nonspecific aspects of the interventions, and the connection between higher gratitude and lower depression may be due to various factors. It may be that gratitude reduces depression [9], as suggested by the results of some intervention studies (e.g., Cregg & Cheavans [38]), or depression may reduce gratitude. It is also possible that a third variable, such as specific genes [40], could lead to both high gratitude and low depression. Finally, there might be reciprocal and continuous relationships between gratitude and depression such that increases in the experience of gratitude lead to alleviation of symptoms of depression, and alleviation of depression in turn allows individuals to more fully experience and be grateful for positive elements of life.

A strength of the present meta-analysis is that it quantified the extent of the association between gratitude and depression across many studies, with a large total number of participants and diverse samples and research groups. While an asymmetrical distribution of correlation coefficients in the funnel plot displayed in Figure 3 suggests the possibility of publication bias, the Duval and Tweedie trim and fill analysis suggests that an adjustment for publication bias would only have a minor impact on the overall effect size.

Another strength of the meta-analysis is that most included studies used psychometrically sound measures of gratitude and depression. The reliability and validity of the measures help ensure that the meta-analytic results are meaningful. A final strength of the meta-analysis was that the large number of included studies provided reasonable power to search for moderators that might be associated with the effect size.

One of the limitations of this meta-analysis is that all included studies used measures of self-report for both variables. The exclusive use of self-report measures creates the possibility of inflated correlations due to same-method and same-source response bias [41].

Future research could examine whether gratitude interventions help prevent the development of depression. Also, future research could use longitudinal analyses to test for reciprocal relationships between gratitude and depression.

High heterogeneity of effect sizes in the present meta-analysis suggests that there might be moderators of effect size. However, none of the moderators examined in this meta-analysis showed significant evidence of an effect. One interesting potential moderator would be whether individuals feel grateful (the cognitive and emotional elements of gratitude) or express gratitude to others (the behavioral aspect). Any differences in association with depression might provide valuable clues about how best to structure gratitude interventions.

In conclusion, the significant association between gratitude and depression found in the present meta-analysis, together with previous research focusing on the effect gratitude interventions have on lessening depression, suggests that more research is appropriate to determine the causal relationship between gratitude and depression.

The Association between Gratitude and Depression: A Meta-Analysis