Objectives
This is a protocol for a Cochrane Review (prognosis). The objectives are as follows:
To assess the prognostic value of ambient heat exposures as a risk factor for diabetes and diabetes progression. This objective can be framed in two syntheses following the PICOTS scheme as demonstrated in Table 1.
| Table 1. PICOTS for the review questions | ||
| Synthesis 1: Heat as a prognostic factor for the development of diabetes | Synthesis 2: Heat as a prognostic factor for outcomes in people living with diabetes | |
| Population | Persons of any age without diabetes | Persons of any age with type 1 diabetes, type 2 diabetes, gestational diabetes mellitus, or other types of diabetes |
| Index prognostic factor | Different ambient heat exposures, including heatwaves, increasing temperatures, and seasonal variations of temperatures | |
| Comparator | Persons exposed to non‐extreme lower temperatures | |
| Outcomes | Incidence of diabetes |
Diabetes progression Including all‐cause mortality, hospital admissions, CVD incidence, nephropathy incidence, retinopathy incidence, neuropathy incidence, health‐related quality of life, glycaemic control, blood lipids, blood pressure, renal and inflammatory biomarkers, acute complications, progression of neuropathy, and mental and cognitive disorders |
| Timing | Ambient heat exposures, including heatwaves, increasing temperatures, and seasonal variations of temperatures as defined by the original studies, ranging from a few hours to several years depending on the prognostic factor and study design | |
| Setting | No restrictions on setting, including data from both high‐income countries and low‐ and middle‐income countries | |
Background
Description of the health condition and context
The World Health Organization (WHO) has reported that a large proportion of annual deaths can be attributed to non‐communicable diseases, with approximately 41 million deaths per year, particularly in low‐ and middle‐income countries [1]. In this context, diabetes mellitus represents a substantial public health challenge on a global scale, linked to numerous comorbidities and premature death. In 2021, it was estimated that approximately 537 million individuals aged 20 to 79 were living with diabetes, with 6.7 million deaths attributable to diabetes or its complications [2]. Projections indicate that by 2030, the number of individuals affected by diabetes will rise to approximately 643 million, escalating to about 783 million by 2045, thereby increasing the prevalence to 12.2% [2]. Diabetes has therefore emerged as one of the most rapidly escalating health emergencies of the 21st century, imposing a considerable medical, social, and economic burden on healthcare systems and governments globally, with total healthcare expenditures reaching USD 966,000 million in 2021 [2].
Concurrent with the growing number of people affected by diabetes, the frequency and intensity of heatwaves is increasing around the world as a consequence of the ongoing climate crisis [3, 4]. There is evidence that an increase in temperatures is associated with a higher risk of morbidity and mortality due to cardiovascular and respiratory diseases [5, 6]. Moreover, previous findings suggest that extreme temperatures, including heatwaves, may have a deleterious impact on glucose metabolism, resulting in an increased incidence of type 2 diabetes [7]. Beyond that, increasing temperatures may also have an enormous impact on individuals living with diabetes, affecting factors such as dehydration, changes in insulin sensitivity, medication efficacy and requirements, as well as the progression and manifestation of diabetes‐related complications and comorbidities [8, 9, 10].
Description of the prognostic factor
One of the main consequences of climate change is an increase in the mean global temperature. Since the inception of global records in 1850, the earth’s average temperature has risen by over 1° C, with the majority of this warming occurring in the last 50 years [11]. In addition, according to the National Oceanic and Atmospheric Administration’s (NOAA) 2024 Annual Global Climate Report, the 10 historically warmest years have occurred in the last decade, with 2024 being the warmest year since global record‐making began, at 1.3° C above the average temperature of the 20th century [12]. This indicates that global warming has been accelerating at a faster rate in recent years [11]. As a consequence, this increase in mean global temperature is leading to a higher frequency of regional and seasonal weather extremes, influencing the ecosystems of plants and animals, as well as biodiversity and agriculture [11, 13]. In addition, climate change is influencing the length of seasons, resulting in prolonged and elevated summer temperatures, whilst winters are becoming shorter and warmer [14].
An increase in mean global temperature is not only influencing weather and ecosystems, but also threatening human health worldwide [15, 16]. Almost half of the global population is exposed to heat episodes, which can lead to negative health effects, including a higher risk of dehydration, incidence of cardiovascular diseases (CVD) and respiratory diseases, adverse pregnancy outcomes, worsened mental health, and mortality [17]. Moreover, there is an indication that higher local ambient temperatures and heatwaves are associated with an increased rate of diabetes‐related hospitalisations [18], as well as a higher risk of diabetes occurrence and diabetes‐related mortality [19, 20]. In addition, increasing temperatures and seasonal variations in temperatures appear to be associated with an increased risk of developing gestational diabetes [21, 22].
Health outcomes
Diabetes mellitus is one of the most prevalent chronic metabolic disorders worldwide and a risk factor for premature death [23]. It is therefore important to identify prognostic factors for the development of diabetes to improve future prevention strategies, especially under accelerating climate change.
Moreover, people living with diabetes are also at higher risk of developing CVD, nephropathy, and neuropathy [24, 25, 26]; experience an increased risk of comorbid depression and cognitive disorders; and report a lower quality of life [27, 28, 29]. Furthermore, laboratory measures, including glycaemic control, blood lipids, blood pressure, and renal and inflammatory biomarkers, are important parameters for the management of diabetes and its progression. Maintaining these parameters as close to normal ranges as possible has been associated with the prevention or delay of complications such as CVD [30, 31]. Additionally, in the context of climate change and rising temperatures, acute diabetes‐related complications, including dehydration, hospital admissions, hypoglycaemia, hyperglycaemia, injuries, and coma, may occur more frequently [9, 18]. It is thus essential to investigate whether increasing temperature or heat exposure affects the progression of the disease and the development of diabetes‐related complications.
Why it is important to do this review
Both diabetes mellitus and climate change represent significant global health challenges that are closely interrelated [8, 10, 32]. Further understanding of and assessing the links between diabetes and climate change is crucial for developing holistic and effective public health strategies for mitigation and adaptation to climate change. The exploration of the long‐term effects of increasing temperatures on the development of diabetes and the progression of diabetes‐related complications in people living with diabetes is particularly important for actions to be taken to prevent an additional increase in the global prevalence of diabetes and its associated comorbidities and public health expenditures. There is a first indication from systematic reviews and meta‐analysis that increasing temperatures and heatwaves are associated with an increased incidence of diabetes and diabetes‐related mortality [19, 20]. However, meanwhile, several recent studies have been published on this topic, and previous reviews did not consider any outcomes of diabetes progression other than diabetes‐related hospitalisation and mortality. Moreover, the methodological quality of existing reviews was low, as the retrieved articles were not screened by two researchers working independently; risk of bias was not assessed adequately; and the certainty of evidence was not evaluated. A high‐quality Cochrane review on this topic, adhering to guidance from the Cochrane Methods Group on Prognosis [33], is thus needed to synthesise the currently available evidence to better understand the impact of heat on diabetes incidence, as well as its impact on the progression of diabetes‐related complications, health‐related quality of life, and mortality in people living with diabetes. This review will help to inform evidence‐based prevention and treatment guidelines on diabetes and improve outcomes for people living with diabetes in the context of a changing climate.
Objectives
To assess the prognostic value of ambient heat exposures as a risk factor for diabetes and diabetes progression. This objective can be framed in two syntheses following the PICOTS scheme as demonstrated in Table 1.
| Table 1. PICOTS for the review questions | ||
| Synthesis 1: Heat as a prognostic factor for the development of diabetes | Synthesis 2: Heat as a prognostic factor for outcomes in people living with diabetes | |
| Population | Persons of any age without diabetes | Persons of any age with type 1 diabetes, type 2 diabetes, gestational diabetes mellitus, or other types of diabetes |
| Index prognostic factor | Different ambient heat exposures, including heatwaves, increasing temperatures, and seasonal variations of temperatures | |
| Comparator | Persons exposed to non‐extreme lower temperatures | |
| Outcomes | Incidence of diabetes |
Diabetes progression Including all‐cause mortality, hospital admissions, CVD incidence, nephropathy incidence, retinopathy incidence, neuropathy incidence, health‐related quality of life, glycaemic control, blood lipids, blood pressure, renal and inflammatory biomarkers, acute complications, progression of neuropathy, and mental and cognitive disorders |
| Timing | Ambient heat exposures, including heatwaves, increasing temperatures, and seasonal variations of temperatures as defined by the original studies, ranging from a few hours to several years depending on the prognostic factor and study design | |
| Setting | No restrictions on setting, including data from both high‐income countries and low‐ and middle‐income countries | |
Methods
Criteria for considering studies for this review
Types of studies
We will include longitudinal studies, including prospective and retrospective cohort studies, case‐crossover studies, and time series. We will exclude randomised controlled trials, modelling studies, reviews, case reports, case series, and comments.
Targeted population
Synthesis 1: Diabetes incidence: we will include studies including people of any age from the general population without diabetes at study start.
Synthesis 2: Diabetes progression: we will include studies including people of any age with a diagnosis of type 1 diabetes, type 2 diabetes, gestational diabetes mellitus, or other forms of diabetes (e.g. medication‐induced or infection‐induced diabetes).
For diabetes diagnosis, we will accept a broad definition of diabetes. We will include studies that defined a diabetes diagnosis using blood glucose measurements such as fasting plasma glucose, two‐hour postload glucose, or glycated haemoglobin in accordance with the criteria of the American Diabetes Association [34]. The diagnosis could have been a combination of physician‐diagnosed diabetes, information from a health insurance database, self‐reported diabetes, and use of antihyperglycaemic medications including hypoglycaemic drugs, insulin, or both.
Type of prognostic factors
The assessment of heat exposures will encompass various consequences of heat and increasing temperature conditions influenced by climate change. We will examine 1) the presence of heatwaves compared to the absence of heatwaves, 2) the impact of increasing ambient temperatures per ° C, and 3) the impact of seasonal variations in temperatures (e.g. summer versus winter). However, there is currently no universally accepted international definition of heatwaves [35, 36], and seasonal variations in temperatures depend on geographic location and climate zones [14], therefore we will include the temperature or heat exposures as defined by the original studies.
Types of outcomes to be predicted
In accordance with the Prognosis Research Strategy (PROGRESS) framework [37, 38], we will include diabetes incidence (synthesis 1), and diabetes progression (synthesis 2) as outcomes, with seven outcomes for diabetes progression selected as critical and further outcomes described as important.
Critical outcomes
We will consider the following critical outcomes:
Synthesis 1: Incidence of any type of diabetes diagnosed as previously defined in the targeted population section.
Synthesis 2: Progression of type 1 diabetes, type 2 diabetes, gestational diabetes mellitus, and other diabetes types, including the following.
All‐cause mortality: defined as death from any cause.
Hospital admissions: defined as diabetes‐related hospitalisation and emergency department visits.
CVD incidence: defined as incidence of total CVD, coronary heart disease, myocardial infarction, stroke (ischaemic stroke and haemorrhagic stroke), peripheral artery disease, assessed by physicians, information from a health insurance database, or self‐reports.
Nephropathy incidence: defined as the presence of albuminuria (urinary albumin‐creatinine ratio (UACR) ≥ 30 mg/g) in the absence of other renal diseases, assessed by physicians, information from a health insurance database, or self‐reports.
Retinopathy incidence: defined as damage to the retinal blood vessels, including microaneurysms, haemorrhages, vascular closure, increased vascular permeability, and diabetic macular oedema, assessed by physicians, information from a health insurance database, or self‐reports.
Neuropathy incidence: defined as predominantly distal symmetrical sensorimotor polyneuropathy of the limbs, assessed by physicians, information from a health insurance database, or self‐reports.
Health‐related quality of life: defined as mental and physical health‐related quality of life and evaluated by a validated instrument such as 36‐item Short Form Health Survey (SF‐36), EQ‐5D, World Health Organization Quality of Life Brief Version (WHOQOL‐BREF).
Important outcomes
We will consider the following important outcomes (Synthesis 2).
Glycaemic control: defined as the following markers: time in range (TIR), time above range (TAR), time below range (TBR), average glucose measured by a continuous glucose monitor (CGM), fasting plasma glucose, two‐hour glucose, glycated haemoglobin, fasting insulin, and insulin resistance (Homeostatic Model Assessment for Insulin Resistance (HOMA‐IR)).
Blood lipids: defined as total cholesterol, low‐density lipoprotein (LDL) cholesterol, high‐density lipoprotein (HDL) cholesterol, and triglycerides.
Systolic and diastolic blood pressure.
Renal function: defined as the following markers: estimated glomerular filtration rate (eGFR) and albumin (UACR).
Inflammation: defined as the following marker: C‐reactive protein (CRP).
Acute complications: defined as medical consultation, dehydration, hypoglycaemia, severe hyperglycaemia, diabetic foot ulcer and wound healing, injuries and amputations, diabetic coma (all as defined by the original study).
Progression of neuropathy: defined as worsening of nerve function (10‐gram monofilament, 128‐hertz tuning fork, reflexes, temperature reception, electrophysical testing) or symptoms related to neuropathy including pain, tingling, numbness, and weakness assessed by pain assessment scores, such as numerical rating scale (NRS), visual analogue scale (VAS), total symptom score (TSS).
Mental and cognitive disorders: defined as anxiety, depression, and dementia (all as defined by the original study).
Time points for outcome assessments
For all outcomes, we will consider ambient heat exposures, including heatwaves, increasing temperatures, or seasonal variations of temperatures as defined by the original studies. We will therefore include studies with any duration of follow‐up, including short‐term follow‐ups of only a few hours to several years, depending on the prognostic factor and study design.
Search methods for identification of studies
Electronic searches
To inform the design of the search strategy, we identified relevant studies from 10 known systematic reviews [10, 18, 19, 20, 21, 22, 39, 40, 41, 42], which we obtained through exploratory searches in several databases. This yielded a set of 65 relevant references as a study pool (year range = 1995 to 2023), which were all indexed in MEDLINE.
We analysed the titles, abstracts, and MeSH of this study pool and developed a search strategy that comprises the following three concepts: 1) diabetes, 2) other topic‐relevant terms, and 3) heat, which were then combined as a two‐tier search strategy: (1 OR 2) AND 3. Concept 1 (diabetes) was developed conceptually, but did not suffice to identify all relevant studies, as some of them do not mention diabetes as an outcome in the title, abstract, or MeSH. We therefore developed concept 2 (other topic‐relevant terms) that enhances the identification of the known study pool. Both of these concepts are then combined each with concept 3 (heat), using a validated search filter for 'heat stress' [43].
Our final MEDLINE search strategy (for details see Supplementary material 1) retrieves 62 out of 65 relevant references. We will adapt this search strategy to other sources, thus establishing a comprehensive search with no restrictions on language of publication, publication status, or study design.
We will conduct the literature search in the following sources from inception to the date of search:
MEDLINE (Ovid MEDLINE ALL 1946 to Daily Update);
Science Citation Index Expanded (SCI‐EXPANDED 1945 to present) and Emerging Sources Citation Index (ESCI 2020 to present) (Clarivate Web of Science);
Scopus (Elsevier);
Centre for Agriculture and Bioscience International (CABI) Global Health database (Ovid Global Health 1973 to Weekly Update);
World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) (trialsearch.who.int);
ClinicalTrials.gov (www.clinicaltrials.gov);
Searching other resources
As an important complementary search method, we will screen the reference lists of relevant studies identified by extracting studies from known systematic reviews on the topic and the exploratory searches described in the previous section. We will also screen the reference lists of other potentially eligible studies or publications identified by our search.
We will not run a specific search for grey literature, but the CABI Global Health database, which we will search for as a source, includes grey literature as a publication type.
We will search for expressions of concern, errata, or retractions for included studies on PubMed and Retraction Watch (retractiondatabase.org/RetractionSearch.aspx?).
Data collection and analysis
Selection of studies
We will de‐duplicate all identified references using Deduklick [44], and upload the resulting references into Covidence [45]. Two review authors will independently screen the titles and abstracts according to the pre‐established inclusion and exclusion criteria to determine which studies should be assessed further. Then, we will screen the full texts of identified articles for eligibility. Any disagreements between review authors will be resolved through consensus or by discussion with a third review author. We will provide the reasons for the exclusion of each excluded full‐text study in a list ('excluded studies'). In addition, we will present a PRISMA flow diagram showing the full process of study selection [46].
Data extraction and management
We will develop and pilot test a data extraction form based on the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist adapted for prognostic factor studies before implementation [33, 47]. For studies that meet the aforementioned inclusion criteria, one review author will extract the key characteristics, which will be presented in the 'Characteristics of included studies' table. Another review author will check these data for accuracy. Any disagreements will be resolved by discussion or in consultation with a third review author. We will contact authors of the included studies via email to obtain key missing data as required. We will extract the following information from each study.
Surname of the first author, publication date of the article.
Study design.
Recruiting time or years, settings, and country.
Participants: characteristics at baseline including age, sex, body mass index (BMI), diabetes status, diabetes type, diabetes duration, and pharmacological diabetes treatment use.
Sample size: number of participants and number of events (if applicable).
Prognostic factors: definition and method of measurement of high temperatures, heatwaves, and seasonal variations, duration of the prognostic factor.
Outcome(s) to be predicted: definition and method for measurement of outcome(s), duration of follow‐up.
Missing data: number of participants with missing exposure or outcome, details of attrition (loss to follow‐up).
Analysis: modelling method, assumptions, most adjusted prognostic effect estimates, and adjustment factors.
Results: interpretation of presented results, comparison with other studies, discussion of generalisability, strengths, and limitations.
Study funding sources and declarations of interest by the primary investigators.
Dealing with duplicates and companion publications
In the case of duplicate publications and companion documents, or multiple reports associated with a primary study, our objective is to derive the maximum possible information from the available data. In cases where uncertainty exists, we will give priority to publications reporting the largest number of included participants or longest follow‐up period associated with the investigated outcomes.
Assessment of risk of bias in included studies
Two review authors will independently evaluate risk of bias in the included studies using the established Cochrane tool Quality In Prognosis Studies (QUIPS) for observational studies [48]. Any disagreements will be resolved by discussion or in consultation with a third review author. Authors involved in the risk of bias assessment will pilot‐test the QUIPS tool.
Each domain will be evaluated by the two review authors according to the signalling items and rated as low, moderate, high, or unclear risk of bias. The signalling items and the explanations for the classification as low risk of bias are shown in Table 2. We will evaluate the overall risk of bias for each primary study as follows: low if all domains are rated as low; moderate if at least one domain is rated as moderate, but none as high; and high if at least one domain is rated as high.
Table 2.QUIPS tool including the signalling items and the rating for low risk of bias across all domains
| Domains | Signalling items | Low risk rating |
| Study participation (selection bias) | Adequate description of the source of target population | Source of the population cohort with participants free of diabetes (for incidence of diabetes) or with diabetes (for outcomes of diabetes progression and management) is clearly described. |
| Method used to identify problem | The sampling frame and recruitment are adequately described, possibly including methods to identify the sample (e.g. consecutive participants).
|
|
| Inclusion and exclusion criteria | Clear inclusion and exclusion criteria described (e.g. including diabetes status). | |
| Adequate study participation | Sample size calculation to identify that there is adequate participation in the study. | |
| Baseline characteristics | Reporting on key participant characteristics (e.g. age, sex, BMI, diabetes status, diabetes type, diabetes duration, and pharmacological diabetes treatment use) | |
| Study attrition (attrition bias) | Proportion of baseline sample available for analysis | Response rate (proportion of the study sample providing outcome data) is adequate, with a response > 80%. |
| Attempts to collect information on participants who dropped out | Attempts to collect information on participants who dropped out of the study are described. | |
| Reasons for loss to follow‐up are described | Reasons for loss to follow‐up are described for all dropouts. | |
| Outcome and prognostic factor information on those lost to follow‐up | There are no important differences between key characteristics and outcomes in participants who completed the study and those who did not. | |
|
Prognostic factor (PF) measurement (measurement bias) |
Definition of the PF | Definition of ambient heat exposure (heatwaves, ambient temperatures, seasonal variations) is provided. |
| Valid and reliable measurement of PF | The method for measuring ambient heat exposure (heatwaves, ambient temperatures, seasonal variations) is sufficiently valid and reliable. | |
| Method and setting of PF measurement | The method for measuring ambient heat exposure (heatwaves, ambient temperatures, seasonal variations) is the same for all study participants. | |
| Proportion of data on PF available for analysis | More than 80% of the study sample have completed data for ambient heat exposure (heatwaves, ambient temperatures, seasonal variations). | |
| Method used for missing data | Appropriate methods of imputation are used for missing data on ambient heat exposure (heatwaves, ambient temperatures, seasonal variations). | |
| Outcome measurement (measurement bias) | Definition of the outcome | A clear definition of the outcome is provided. |
| Valid and reliable measurement of outcome | The method of outcome measurement used is valid and reliable to limit misclassification bias. | |
| Method and setting of outcome measures | The method and setting of outcome measurement is the same for all study participants. | |
| Study confounding (confounder bias) | Important confounders adjusted for | All important confounders are measured (e.g. air pollution) and/or effect modifiers are considered (e.g. age, sex, obesity, smoking, comorbid condition(s)). |
| Definition of the confounding factor | Clear definitions of the important confounders measured are provided. | |
| Method and setting of confounding | The method and setting of confounding measurement are the same for all study participants. | |
| Appropriate accounting for confounders | Important potential confounders (e.g. air pollution) and/or effect modifiers (e.g. age, sex, obesity, smoking, comorbid condition(s)) are accounted for in the study design. | |
| Statistical analysis and reporting (analysis and reporting bias) | Presentation of analytical strategy | There is sufficient presentation of data to assess the adequacy of the analysis. |
| Model development strategy | The selected statistical model is adequate for the study design. | |
| Reporting of results | There is no selective reporting of results. | |
| Overall risk of bias |
Low: all domains are classified as low risk of bias. Moderate: at least one domain is classified as moderate, but none as high risk of bias. High: at least one domain is rated as high risk of bias. |
|
Measures of associations to be extracted
For dichotomous outcome data, we will extract odds ratios (OR), risk ratios (RR), hazard ratios (HR), absolute risk reduction (ARR), or incidence rate ratios (IRR) with corresponding 95% confidence intervals (CI). For continuous outcomes, we will extract mean differences (MD) with corresponding 95% CIs or standard deviations (SD) or standard errors (SE). We will extract adjusted effect estimates when available; only in the case that no adjusted effect estimate is available will we extract unadjusted estimates.
Dealing with missing data
In cases where information is not available from the published articles, or additional information is required for the purposes of the review, we will contact the authors of the included studies via email to request the missing information. Where measures from visualisations have not been reported, we will extract estimates using WebPlotDigitizer [49].
Reporting bias assessment
If at least 10 studies are available for meta‐analysis, we will assess small‐study effects by using graphical and statistical tests, such as the funnel plot and Egger’s test [50].
Data synthesis and meta‐analysis approach
In the case of sufficient data, we will conduct meta‐analyses for each respective ambient heat exposure (heatwaves, increasing temperatures, or seasonal variations in temperatures) and the respective outcome measures, namely the incidence of diabetes, as well as the listed outcomes of diabetes progression in individuals with diabetes separately. We will calculate the summary risk ratio (SRR) for each effect estimate for dichotomous outcome data (OR, RR, HR, ARR, IRR) separately, as well as MDs, or SMDs (for outcomes measured using different scales) for continuous outcomes along with corresponding 95% CIs using random‐effects models by Hartung‐Knapp‐Sidik‐Jonkman (HKSJ) [51]. We will use RevMan for the data analysis [52].
Investigation of heterogeneity and subgroup analysis
We will evaluate heterogeneity between studies using the I² and Tau² statistics. In addition, we will calculate 95% prediction intervals (PI) if at least five studies are available for meta‐analysis, to also account for heterogeneity and show the range in which the underlying true effect size of future studies will lie with 95% certainty [53].
There is evidence that heat may affect different regions differently [54], and that the populations of low‐income countries in particular may face adverse consequences of increased temperatures [55]. Consequently, we plan to conduct subgroup analyses by:
climate zones according to the Köppen‐Geiger classification [56];
national income level classified by World Bank data [57].
Furthermore, we expect the following characteristics to introduce clinical heterogeneity. We will therefore conduct subgroup analyses if at least 10 studies are available for meta‐analysis on:
age (< 65 and ≥ 65 years);
sex;
diabetes type;
study design.
Equity‐related assessment
We will explore health inequities through three characteristics defined by PROGRESS‐Plus: age, sex, and national income [50, 58]. These factors influence the incidence of diabetes and its complications due to multiple factors, including biological, clinical, social, and socioeconomic differences [59]. Thus, as described in the previous section, we plan to conduct subgroup analyses for national income level and if at least 10 studies are available for meta‐analysis, as well as for age and sex, to account for these potential inequities.
Sensitivity analysis
In order to assess the robustness of the meta‐analyses, we will conduct sensitivity analyses excluding studies with a high risk of bias.
Certainty of the evidence assessment and summary of findings table
We will present separate summary of findings tables for heat as a prognostic factor for the incidence of diabetes and for the critical outcomes of diabetes progression in people living with diabetes. Moreover, we will present separate summary of findings tables for each heat exposure category (heatwaves, increasing temperatures, and seasonal variations of temperatures) in combination with these outcomes, where applicable. Two review authors will independently use the validated GRADE approach for prognostic reviews to assess the certainty of evidence of the meta‐findings [60, 61]. The certainty of evidence can be downgraded by one or two levels for the following reasons: risk of bias, inconsistency, indirectness, imprecision, and publication bias. Conversely, it may also be upgraded by one level in the event of a large summary effect, plausible confounding, or dose‐response analysis. The GRADE system employs a four‐point classification system to categorise the certainty of evidence, as follows.
High: we are very confident that the variation in risk associated with the prognostic factor (probability of future events in those with/without the prognostic factor) is similar to that of the estimate.
Moderate: we have moderate confidence that the variation in risk associated with the prognostic factor (probability of future events in those with/without the prognostic factor) is likely to be close to the estimate; however, there is a possibility that it is substantially different.
Low: our confidence in the estimate is limited, and there is a possibility that the variation in risk associated with the prognostic factor (probability of future events in those with/without the prognostic factor) may be substantially different from the estimate.
Very low: we have very little confidence in the estimate, with a high probability that the variation in risk associated with the prognostic factor (probability of future events in those with/without the prognostic factor) is likely to be substantially different from the estimate.
Consumer involvement
No consumer has been involved in the protocol, but the review authors may reconsider this for the review.
Supporting Information
Supplementary materials are available with the online version of this article: 10.1002/14651858.CD016289.
Supplementary materials are published alongside the article and contain additional data and information that support or enhance the article. Supplementary materials may not be subject to the same editorial scrutiny as the content of the article and Cochrane has not copyedited, typeset or proofread these materials. The material in these sections has been supplied by the author(s) for publication under a Licence for Publication and the author(s) are solely responsible for the material. Cochrane accordingly gives no representations or warranties of any kind in relation to, and accepts no liability for any reliance on or use of, such material.
Supplementary material 1 Search strategies
These authors should be considered joint last author
New
Additional information
Acknowledgements
Editorial and peer‐reviewer contributions
The Cochrane Planetary Health Thematic Group supported the authors in the development of this prognosis review.
The following people conducted the editorial process for this article:
Sign‐off Editor (final editorial decision): Toby Lasserson, Acting Editor in Chief, Cochrane;
Managing Editor (selected peer reviewers, provided editorial guidance to authors, edited the article): Anupa Shah, Cochrane Central Editorial Service;
Editorial Assistant (conducted editorial policy checks, collated peer‐reviewer comments, and supported the editorial team): Andrew Savage, Cochrane Central Editorial Service;
Copy Editor (copy editing and production): Lisa Winer, Cochrane Central Production Service;
Peer reviewers (provided comments and recommended an editorial decision): James Flory, Memorial Sloan Kettering Cancer Center (clinical/content review), Jeremy Dearling (consumer review), Rachel Richardson, Cochrane (methods review), Jo Platt, Central Editorial Information Specialist (search review).
Contributions of authors
AL drafted the protocol.
TT revised the protocol and provided methodological input.
JB revised the protocol and provided methodological input.
SFP developed the review idea, revised the protocol, and provided clinical input.
MK developed the review idea, revised the protocol, and provided clinical input.
LH developed the review idea, revised the protocol, and provided clinical input.
RL developed the review idea, revised the protocol, and provided clinical input.
JF revised the protocol and provided methodological input.
BB revised the protocol and provided methodological input.
MIM developed the search methods, drafted the Search methods section of the protocol, revised the protocol, provided methodological input, and oversaw the project.
SS developed the review idea, revised the protocol, provided methodological input, and oversaw the project.
Declarations of interest
AL received research grants from the German Diabetes Foundation (Deutsche Diabetes Stiftung (DDS)).
TT declares no conflicts of interest.
JB declares no conflicts of interest.
SFP received honoraria from Boehringer Ingelheim and Sanofi‐Aventis Germany as a speaker for a sponsored medical education and financial support for congress travels from Lilly Germany and Novo Nordisk.
MK declares no conflicts of interest.
LH is member of the board of directors of Lifecare Norway; received consulting fees, honoraria, or payment for expert testimony from Lifecare, Roche Diagnostics, and Indigo; and owns stocks in Profil and Science Consulting in Diabetes GmbH.
RL is part of the advisory board of the Novo Academy and Lilly Germany, and received honoraria for seminars and presentations for the Novo Academy and Lilly Germany, as well as travel expenses from Novo Nordisk and Lilly Germany. He was on the advisory board of AbbVie until the end of 2022.
JF declares no conflicts of interest.
BB declares no conflicts of interest.
MIM declares no conflicts of interest.
SS received research grants from the Alpro Foundation and the German Diabetes Foundation (Deutsche Diabetes Stiftung (DDS)) for employees and honoraria from Novo Nordisk for a lecture.
Sources of support
Internal sources
-
Institute of General Practice, Heinrich‐Heine‐University Düsseldorf, Germany
JF, BB, and MIM provide in‐kind support for the development of Cochrane reviews.
-
AL, TT, JB, and SS work at the German Diabetes Center (DDZ), Germany
The DDZ is funded by the German Federal Ministry of Health (BMG, Berlin, Germany) and the Ministry of Culture and Science of Northrhine‐Westphalia (MKW‐NRW, Düsseldorf, Germany) and receives additional funding by the German Federal Ministry of Education and Research (BMBF, Berlin, Germany) through the German Center for Diabetes Research (DZD e.V.).
External sources
This work is partly supported by a research grant from the German Diabetes Foundation (Deutsche Diabetes Stiftung (DDS)), Germany
Registration and protocol
Cochrane approved the proposal for this review in January 2025.
Data, code and other materials
Data sharing not applicable to this article as it is a protocol, so no datasets were generated or analysed.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary material 1 Search strategies
Data Availability Statement
Data sharing not applicable to this article as it is a protocol, so no datasets were generated or analysed.